A comparative assessment of resource efﬁciency in petroleum reﬁning
Jeongwoo Han a,⇑, Grant S. Forman b, Amgad Elgowainy a, Hao Cai a, Michael Wang a, Vincent B. DiVita c
a Systems Assessment Group, Energy Systems Division, Argonne National Laboratory, 9700 S. Cass Avenue, Argonne, IL 60439, United states
b Sasol Synfuels International, 900 Threadneedle, Suite 100, Houston, TX 77079, United States
c Jacobs Consultancy Inc., 5995 Rogerdale Road, Houston, TX 77072, United States
h i g h l i g h t s
 Investigate reﬁneries with various complexities and operational ﬂexibilities.
 Categorize reﬁneries into three groups by crude density and heavy products yield.
 Estimate GHG emissions cost to produce more of the desirable fuels.
 Complex reﬁneries can process heavier crude into more gasoline and distillate.
 Complex reﬁneries are more resource efﬁcient, but more energy and GHG intensive.
a r t i c l e
i n f o
Article history:
Received 12 January 2015
Received in revised form 4 March 2015
Accepted 10 March 2015
Available online 25 March 2015
Keywords:
Petroleum reﬁnery
Life-cycle analysis
Energy efﬁciency
Resource efﬁciency
Greenhouse gas emissions
a b s t r a c t
Because of increasing environmental and energy security concerns, a detailed understanding of energy
efﬁciency and greenhouse gas (GHG) emissions in the petroleum reﬁning industry is critical for fair
and equitable energy and environmental policies. To date, this has proved challenging due in part to
the complex nature and variability within reﬁneries. In an effort to simplify energy and emissions reﬁn-
ery analysis, we delineated LP modeling results from 60 large reﬁneries from the US and EU into broad
categories based on crude density (API gravity) and heavy product (HP) yields. Product-speciﬁc efﬁcien-
cies and process fuel shares derived from this study were incorporated in Argonne National Laboratory’s
GREET life-cycle model, along with regional upstream GHG intensities of crude, natural gas and electricity
speciﬁc to the US and EU regions. The modeling results suggest that reﬁneries that process relatively
heavier crude inputs and have lower yields of HPs generally have lower energy efﬁciencies and higher
GHG emissions than reﬁneries that run lighter crudes with lower yields of HPs. The former types of
reﬁneries tend to utilize energy-intensive units which are signiﬁcant consumers of utilities (heat and
electricity) and hydrogen. Among the three groups of reﬁneries studied, the major difference in the
energy intensities is due to the amount of purchased natural gas for utilities and hydrogen, while the
sum of reﬁnery feed inputs are generally constant. These results highlight the GHG emissions cost a reﬁ-
ner pays to process deep into the barrel to produce more of the desirable fuels with low carbon to hydro-
gen ratio.
 2015 Argonne National Laboratory. Published by Elsevier Ltd. This is an open access article under the CC
BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
1. Introduction
Increasing concerns with the consequences of climate change
turns scrutiny towards the source and efﬁciency of energy produc-
tion and consumption. Within this context, petroleum is a major
source of global energy demand and a primary component of
transportation fuels. In 2011, petroleum accounted for 34% of
global energy consumption and 36% of global greenhouse gas
(GHG) emissions [1], while the transportation sector in the US
and the EU consumed 71% and 62% of total petroleum products,
respectively, as shown in Fig. S1 [2,3].
Regulations are being developed in the US and EU to reduce pet-
roleum consumption, encourage use of alternative fuels and pro-
mote energy efﬁciency. In the US, the Renewable Fuel Standard
(RFS) mandates the production of 36 billion gallons of renewable
fuels with various GHG emissions reduction thresholds relative
to conventional gasoline and diesel [4]. California implemented
the Low Carbon Fuel Standard (LCFS) in 2009 to reduce the GHG
intensity of transportation fuels [5]. The Renewable Energy
http://dx.doi.org/10.1016/j.fuel.2015.03.038
0016-2361/ 2015 Argonne National Laboratory. Published by Elsevier Ltd.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
⇑Corresponding author. Tel.: +1 630 262 6519.
E-mail
addresses:
jhan@anl.gov
(J.
Han),
grant.forman@us.sasol.com
(G.S.
Forman),
aelgowainy@anl.gov
(A.
Elgowainy),
hcai@anl.gov
(H.
Cai),
mqwang@anl.gov (M. Wang), Vince.Divita@jacobs.com (V.B. DiVita).
Fuel 157 (2015) 292–298
Contents lists available at ScienceDirect
Fuel
journal homepage: www.elsevier.com/locate/fuel
Directive (RED) in the EU requires 10% of transportation energy
consumption to be produced from renewable sources by 2020
[6]. The production of energy from these renewable sources must
achieve a minimum 35% reduction in life-cycle GHG emissions
against conventional, petroleum-derived baseline fuels, with the
threshold being elevated to 50% in 2018 [7].
Notably, all of these regulations require a reliable estimation of
life-cycle
GHG
emissions
of
alternative
transportation
fuels,
including petroleum-derived gasoline and diesel baseline fuels.
Among the major stages in the life-cycle of a petroleum fuel (crude
recovery, transportation, reﬁning and fuel transportation, dis-
tribution and combustion), the largest GHG emissions source is
fuel combustion, which can be accurately estimated from the car-
bon content of the fuel. The next largest GHG emissions source for
desirable fuels (e.g., gasoline, diesel and jet) is the petroleum reﬁn-
ing stage. Oil reﬁneries process a slate of crude oils of different
qualities into multiple fuel products for various applications. In
order to accurately estimate variations in petroleum reﬁnery efﬁ-
ciency and GHG emissions, reliable information relating to overall
energy inputs and outputs is required for different crude types,
reﬁnery conﬁgurations and product outputs. In addition, energy
inputs and GHG emissions at the reﬁnery level need to be allocated
systematically among petroleum products in order to develop
accurate product-speciﬁc GHG emissions intensities.
Both crude quality and ﬁnal production speciﬁcation are key
drivers for reﬁnery conﬁguration, operations and ultimately, reﬁn-
ing energy efﬁciency. For example, historically, crude inputs into
US reﬁneries have typically been heavier (average API gravity of
30–32) than EU reﬁneries (average API gravity of 36–37) [1,8].
In the former case, because crude inputs are heavier, more inten-
sive processing is required to produce gasoline and distillate. In
terms of market demands, non-transportation fuel oil demands
in the US (Fig. S1) are smaller than in the EU. Consequently, US
reﬁneries produce a smaller share of residual fuel oil (RFO) than
EU reﬁneries do, and thus are considered to be more resource efﬁ-
cient. Since gasoline and diesel require signiﬁcantly more process-
ing than heavy products, US reﬁneries in general are more complex
and energy-intensive than EU reﬁneries. On this basis, it is unsur-
prising that US reﬁneries have larger deep conversion units such as
cokers and ﬂuidized catalytic crackers (FCC) relative to other
regions
(Fig.
S3).
These
process
units
are
instrumental
in
converting heavy reﬁnery intermediate streams into gasoline and
diesel and are typically energy-intensive; hence their impacts on
reﬁning efﬁciency and life-cycle analysis GHG emissions can be
substantial [9].
Other studies have examined product-speciﬁc efﬁciencies and
GHG intensities of reﬁned products and there is a wide variation
in the potential emissions due to differences in modeling metho-
dology and input data. Furuholt used data of eight general reﬁning
processes in Norwegian reﬁneries to allocate reﬁning energy use
and emissions to gasoline and diesel [10]. Similarly, Wang et al.
used a detailed process-level approach for a notional reﬁnery and
demonstrated the difference between various allocation metrics
(energy, market-value and mass) [11]. Recently, Elgowainy and
Forman et al. used a reﬁnery Linear Programming (LP) model to
simulate operation of 43 large US reﬁneries in order to estimate
life-cycle GHG emissions of major petroleum products such as
gasoline, diesel and jet fuels [9,12]. By covering 70% of the total
US reﬁning capacity, they: (1) developed a correlation between
the overall efﬁciency of US reﬁneries and the corresponding crude
quality, reﬁnery complexity and product slate; (2) provided aver-
age and variations of product-speciﬁc efﬁciency and process fuel
shares for each reﬁned product; and (3) examined the possible
impacts relating to changing crude slates, regional and seasonal
variation, changing gasoline-to-diesel (G/D) ratios and Gas to
Liquid (GTL) diesel blending on reﬁnery and product-speciﬁc
efﬁciencies. A recent well-to-wheels study by the Joint Research
Centre (JRC) of the European Commission evaluated energy and
GHG emissions performance associated with various automotive
fuels and powertrains, including petroleum gasoline and diesel.
By considering a marginal approach (future reduction in gasoline
and diesel demand), JRC concluded that marginal diesel in the EU
is more energy- and GHG emission-intensive than marginal gaso-
line [13]. Other authors have performed individual reﬁnery analy-
ses and incorporated these results into life-cycle analysis (LCA) for
multiple notional reﬁnery conﬁgurations [14–18].
These studies only focused on a speciﬁc region or conﬁgurations
and considered only a limited range of crude quality and product
slates, which is not sufﬁcient to fully understand the complex
interaction between crude quality, reﬁnery conﬁguration and yield
of gasoline and distillate on one hand, and the consequent life-
cycle GHG emissions on the other hand. These disparities between
previous studies suggest a need to use a large pool of reﬁnery data
to potentially simplify general understanding of reﬁnery GHG
emissions. Noting the impact of key reﬁnery metrics such as API
gravity and heavy product (HP) yield (e.g., RFO, pet coke and
asphalt) could have on reﬁnery efﬁciency and GHG emissions, we
analyzed results from LP modeling of 17 large EU reﬁneries in addi-
tion to recently reported 43 US reﬁneries [9,12] and grouped them
according to their crude API gravity and HP yields. Note that these
two parameters (API gravity and HP yield) were recently identiﬁed
by Elgowainy et al. [12] as the key parameters that determine a US
reﬁnery’s overall energy efﬁciency [9,12]. In this study, API gravity
and HP are used to represent resource efﬁciency. By analyzing data
at the sub-process level in these 60 reﬁneries, this study correlates
the crude API gravity and HP yields of different groups of reﬁneries
with the product-speciﬁc energy efﬁciency for each reﬁnery pro-
duct and presents previously unavailable life-cycle impacts of
reﬁnery resource efﬁciency on product-speciﬁc and reﬁnery-level
GHG emissions. The life-cycle analysis of petroleum fuels from
various
reﬁneries
was
facilitated
using
Argonne
National
Laboratory’s Greenhouse gases, Regulated Emissions, and Energy
use in Transportation (GREET™) model [19]. The GHG emissions
calculation combines carbon dioxide, methane and nitrous oxide
with their global warming potentials, which are 1, 25 and 298,
respectively, as recommended by the latest Intergovernmental
Panel on Climate Change for a 100-year time horizon [20].
2. Reﬁnery modeling and analysis approach
In the current study, reﬁnery LP modeling was employed to
simulate and compare the operations of 43 US and 17 EU reﬁneries
with individual processing capacity of over 100,000 bbl/day crude
oil. Note that although the 17 EU reﬁneries account for only 25% of
the total EU reﬁning capacity, their operational characteristics
appear to be quite consistent with aggregate average EU reﬁnery
operations (see Table S1).
The
selected
US
reﬁneries
were
located
in
Petroleum
Administration for Defense Districts (PADDs) 1, 2, 3 and 5, while
the selected EU reﬁneries were located in the coastal regions of
Europe. Reﬁnery LP models typically maximize proﬁt by determin-
ing the optimal volumetric throughput and utility balance among
various process units within a reﬁnery under speciﬁc market and
operation conditions [21]. The output ﬁles from LP model sim-
ulations contain volumetric and mass ﬂow rates associated with
inputs and outputs of process units. Using this information, energy
inputs and outputs can be calculated by using known heating val-
ues of various stream components.
In this study, we grouped the U.S and EU reﬁneries described
above into three different groups according to their average crude
API gravity and HP yield. As shown in Fig. 1, reﬁneries were
J. Han et al. / Fuel 157 (2015) 292–298
293
categorized in the following manner: (1) Low API (API grav-
ity < 29), (2) High API/Low HP (API gravity > 29 and HP < 0.22)
and (3) High API/High HP (API gravity > 29 and HP > 0.22).
Table S2 also shows the operational characteristics of reﬁneries
in each reﬁnery group. Note the almost no overlaps in the key
parameters between the Low API and High API/High HP group.
Among the two High API groups, the Low HP group is clearly more
resource-efﬁcient than the High HP group. It also needs to be noted
that assigning reﬁneries to any of the three reﬁnery groups is not
intended to provide a statistical or physical classiﬁcation among
reﬁneries; rather it is intended to examine the impacts of resource
and energy efﬁciencies on life-cycle GHG emissions. Within each
reﬁnery group, three major metrics were evaluated for each reﬁn-
ery: overall reﬁnery efﬁciency, product-speciﬁc reﬁning efﬁciency
and life-cycle GHG emissions intensity. Based on the volumetric
amounts of reﬁnery inputs and outputs, and purchased electricity
energy
estimated
by
the
LP
modeling,
the
overall
reﬁnery
efﬁciency was estimated by dividing the total energy output by
the total energy input on a lower heating value (LHV) basis
(see Eq. (1)).
where gLHV is the LHV-based overall efﬁciency of a reﬁnery. Pn, Cm
and OIoare the amounts of reﬁning product n (e.g., gasoline, jet fuel,
diesel, liqueﬁed petroleum gas [LPG], RFO, pet coke), crude input
m, and other input material o (e.g., normal butane, iso-butane,
reformate, alkylate and natural gasoline) in barrels for liquid prod-
ucts
and
tons
for
pet
coke,
respectively.
NGpurchased;LHV
and
H2;purchased;LHV are the LHV-based energy of purchased natural gas
(NG) and purchased H2, respectively. Electricitypurchased is the energy
in purchased electricity. LHVm, LHVn, and LHVo are the LHVs of
crude input m, reﬁned product n, and other input material o,
respectively, in MJ/barrel for liquid products and MJ/ton for pet
coke.
In order to calculate the GHG emissions intensity for each
reﬁned product, the product-speciﬁc efﬁciency and process fuel
shares need to be determined. This determination is essential as
each product pool is supplied from a different set of process units,
each with different energy and emissions burdens (see Fig. S2).
Since reﬁnery inputs propagate through individual process units
to ﬁnal products via intermediate products, each intermediate or
ﬁnal product carries with it certain energy and emissions burdens
of the total reﬁnery inputs, such as crude, natural gas, electricity,
etc. [12].
The ratio of the sum of energy burdens of a particular product to
its energy content is deﬁned as the energy intensity of that pro-
duct. Note that the inverse of energy intensity of a product repre-
sents its product-speciﬁc efﬁciency. In the current study, a process-
based energy allocation was employed, which was reported in
Elgowainy et al. [12].
LCA of petroleum products accounts for energy use and emis-
sions associated with all stages in the fuel cycle, including crude
recovery and transportation, fuel production, transportation, dis-
tribution and combustion of the fuel by end-use application
[9,12]. Furthermore, allocation of energy use and emissions bur-
dens among co-products was performed by utilizing product-
speciﬁc efﬁciencies and process fuel shares [9,12]. This protocol
was followed along each stage of the product life-cycle. Key
parameters for upstream energy efﬁciencies and emissions associ-
ated with recovery, processing and transportation of various crude
inputs, NG and electricity generation are presented in Table S4, as
well as the references of the parameters. Crude oil, NG, and elec-
tricity generation mixes for US reﬁneries are based on 2010 data
to match reﬁnery LP modeling data inputs. The EU parameters in
Table S4 are based on data reported by JRC and Eurostat of the
European Commission [3,13]. GREET was populated with these
US and EU parameters (Table S4) to compare life-cycle energy
and GHG intensities of petroleum products from US and EU
reﬁneries.
A notable difference between US and EU crude recovery GHG
emissions estimates is the magnitude of associated methane
(CH4) emissions. This is attributed to the difference in methodolo-
gies used to estimate CH4 emissions. For the US, the GREET model
estimates CH4 emissions based on the ﬂaring emissions from satel-
lite data using a 5:1 ratio of ﬂared to vented associated gas [22]. On
the other hand, the JRC study relies on a report by the International
Association of Oil and Gas Producers (OGP), which collected emis-
sions data from OGP members [23]. Another key difference is the
share of oil sands in crude feed to US reﬁneries since GHG intensi-
ties of oil sands crude are typically higher compared to conven-
tional crude (see Fig. S4). Electricity GHG intensity is decided
primarily by the electricity generation mix. Compared to the US,
the GHG emission intensity of EU-generated electricity is signiﬁ-
cantly lower, mostly due to the lower share of coal power genera-
tion and higher share of nuclear and renewable power generation
in the EU mix. GHG emission factors for fuel combustion are fairly
consistent between the US and EU, except for diesel. This differ-
ence is due to the lower carbon content (on a mass basis) of EU die-
sel compared to US diesel (Table S5).
3. Results
3.1. Overall reﬁnery efﬁciency
Fig. 1 presents the grouping of US and EU reﬁneries using the
parametric assumptions described above. HP yields and crude
input
API
gravity
are
plotted
to
show
their
relevance
in
gLHV ¼
P
nðPn  LHVnÞ
P
mðCm  LHVmÞ þ P
0ðOIo  LHVoÞ þ NGpurchased;LHV þ H2;purchased;LHV þ Electricitypurchased
ð1Þ
Fig. 1. Crude API gravity and heavy product yield of the studied US and EU
reﬁneries (The yield of heavy products, such as residual fuel oil, pet coke, asphalt,
slurry oil and reduced crude, is calculated as a share of all energy products by
energy value).
294
J. Han et al. / Fuel 157 (2015) 292–298
categorizing reﬁneries. Filled shapes represent US reﬁneries, while
unﬁlled shapes represent EU reﬁneries. These results show that
almost all Low API and High API/Low HP reﬁneries are present
within the US, rather than the EU Conversely, almost all EU reﬁner-
ies form part of the High API/High HP group. For all subsequent
results, comparing the Low API group with the High API/Low HP
highlights the impact of crude API gravity, while comparing the
High API/Low HP group with the High API/High HP group highlights
the impact of heavy product yield.
Fig. 2 illustrates the overall reﬁnery efﬁciency in each of the
three reﬁnery groups. The bottom, mid and top of the boxes in
Fig. 2 represent the 25th percentile, production-weighted average
and 75th percentile, respectively, while the ends of the error bars
represent the 10th and 90th percentiles. These results suggest
strong impacts of API gravity and HP yield on overall reﬁnery efﬁ-
ciency. This can be rationalized by the installed capacity (MJ
throughput/MJ crude inputs) of deep conversion units, such as cok-
ers and catalytic crackers, in each group (see Table S3). The Low API
group has a much larger installed capacity of deep conversion units
then the other groups. On the other hand, the High API/High HP
group has a negligible capacity of cokers and hydrocrackers.
These conversion units are more energy-intensive than other pro-
cess units within reﬁneries, and thus consume signiﬁcant amount
of utilities (heat and electricity) and hydrogen.
Hydrogen is highly GHG-intensive, depending on the source.
Thus, the amount and source of hydrogen consumption are key
LCA parameters. Fig. 3 illustrates that on a MJ/MJ crude basis, the
total hydrogen consumption decreases signiﬁcantly as API gravity
and HP increase. As discussed above, Low API reﬁneries have much
larger hydrocrackers (HYK), one of the largest consumers of hydro-
gen in reﬁneries [12]. Removal of crude sulfur content also con-
sumes a large amount of hydrogen. Table S2 shows that average
crude sulfur content decreases monotonically from Low API to
High API reﬁneries, which drives the reduction in hydrogen con-
sumption. Hydrogen demand in the High API/High HP group of
reﬁneries is reduced further because the sulfur removal require-
ment via hydroprocessing in HP is low relative to gasoline and
distillate.
Fig. 3 also shows that the amount of hydrogen from SMR
decreases signiﬁcantly as API gravity and HP yield increase. A con-
sequence of this lower share of hydrogen from the SMR results in a
higher overall energy efﬁciency for the High API/High HP reﬁnery
group because hydrogen consumption via SMR is relatively inefﬁ-
cient (70% efﬁciency) compared to other reﬁnery units, resulting
in signiﬁcant energy burdens for products of hydrocracking and
hydrotreating units. Hydrogen is also a co-product of catalytic
reforming, which produces high-octane reformate that contributes
to the gasoline pool. Thus, hydrogen originating from catalytic
reformers has a signiﬁcantly lower energy burden relative to
hydrogen produced from the SMR.
3.2. Product-speciﬁc efﬁciency
Fig. 4 shows the calculated average and variation of product-
speciﬁc efﬁciencies for each group of reﬁneries using the energy
allocation method. The product-speciﬁc efﬁciency for all products
in the High API/High HP group are consistently higher than the
other two reﬁnery groups, mainly due to more favorable crude
quality, higher HP yields and lower complexity. These results are
consistent with those recently reported by Elgowainy et al. [12],
which showed (1) among reﬁnery products, gasoline has the low-
est efﬁciency, (2) RFO has the highest efﬁciency, and (3) diesel can
display a wide range of efﬁciencies. In the latter case, Forman et al.
[9] showed that tighter regulation of aromatics in CARB diesel
combined with reﬁneries that utilize multiple inefﬁcient units via
deep-conversion pathways can result in relatively low diesel efﬁ-
ciency. Although noted only for California reﬁneries, its impact
can exacerbate the already wide range of diesel efﬁciencies in
reﬁneries outside California [9], in general due to the relatively
inefﬁcient diesel reﬁning pathways. Interestingly, HP yield has a
much larger impact on the reﬁning efﬁciency of RFO compared to
the impact of API gravity. The lower reﬁning efﬁciency of RFO with
lower HP yield is likely due to the larger share of HP components
from downstream processes (e.g., HYK and coker), which carry lar-
ger energy and emission burdens.
It is important to note that the estimation of product-speciﬁc
efﬁciencies (as well as energy intensities) depends on allocation
approaches. As mentioned earlier, a marginal approach employed
in the JRC study results in a lower reﬁning efﬁciency (or higher
energy intensity) of diesel than of gasoline in the EU reﬁneries
because the EU reﬁneries operate at the diesel limit, while the US
reﬁneries operate at the gasoline limit. In this study, on the other
hand, an attributional approach is applied where process energy
in each process unit is allocated to its products based on the prod-
ucts’ energy content. One could argue that, on the other hand, a
market-value-based allocation could in principle be more consis-
tent with the LP modeling approach since reﬁneries operate to
maximize proﬁt rather than energy efﬁciency. Elgowainy et al.
(2014) compared the product-speciﬁc efﬁciencies by a market-
value-based allocation with those by an energy-based allocation,
and observed no statistically signiﬁcant differences between them
for all reﬁned products (except for coke). This study also conducted
a process level market-value allocation, and found a similar trend
as shown in Fig. S5.
Fig. 2. Overall reﬁnery efﬁciency.
Fig. 3. Hydrogen consumption in kJ of hydrogen/MJ crude (Each box represent the
hydrogen from each source. ‘‘Purchase’’ refers to hydrogen produced outside of the
gate of the reﬁnery, typically external steam methane reformers [SMR]) while
‘‘SMR’’ refers to internal production of H2 through SMR of NG within the reﬁnery.
‘‘Reformer’’ refers to H2 from catalytic reformers).
J. Han et al. / Fuel 157 (2015) 292–298
295
Fig. 5 illustrates the energy intensities of petroleum products
for each group. Each bar denotes the contribution of each input
into the particular petroleum product. The energy intensity of a
given product is simply the aggregation of energy burdens (allo-
cated at the processing unit level) along the pathways that lead
to that product pool. The derivatives of crude and purchased HP,
as well as purchase butane and purchased blendstocks, comprise
the product pool. For example, HP, purchased in the form of heavy
gas oil or vacuum gas oil as a feed for the FCC, is processed into the
components of gasoline, distillate and residual oil, while purchased
butane and other blendstocks, such as reformate, alkylate and iso-
merate, are blended directly into the gasoline pool. Therefore, we
noticed that the sum of crude inputs—purchased HP, purchased
butane and blendstocks—are generally consistent, although the
compositions of the individual product pools are different. The dif-
ferent inputs that contribute to the individual product pools are
likely driven by the reﬁnery complexity and installed capacity of
process units and determined through reﬁnery optimization. For
example, the relatively smaller FCC reﬁning capacities in the High
API/High HP group result in a smaller contribution from purchased
HP relative to other groups throughout all products (see Table S3).
Notably, the FCC capacities in the Low API and High API/Low HP
reﬁnery groups are similar, affording similar contributions of pur-
chased HP in each reﬁnery group.
Consistent with the discussion above related to hydrogen con-
sumption, higher product-speciﬁc efﬁciencies and lower energy
intensities are observed in the High API/High HP reﬁnery groups,
mainly due to the smaller consumption of purchased NG. In
particular, relative to other reﬁnery groups, diesel-associated NG
consumption is signiﬁcantly lower in the High API/High HP group.
This can be rationalized by diesel production processes requiring
hydrogen that is mainly derived from catalytic reforming rather
than NG SMR in the High API/High HP group.
3.3. Life-cycle GHG emissions
Fig. 6 and Table S6 show the life-cycle GHG emissions of various
petroleum products for each reﬁnery group, as well as the overall
GHG emissions, which combine the GHG emissions from all reﬁn-
ery products weighted by their energy values. Although fuel com-
bustion accounts for a large portion of the life-cycle GHG emissions
for all products, the emissions from the combustion phase are dif-
ferent for each product due to its carbon content (i.e., grams of car-
bon per MJ in fuel). In general, RFO has higher carbon content
compared to diesel, which has higher carbon content than gasoline.
Despite this, the major difference in life-cycle GHG emissions
among each reﬁnery group is driven mainly by the reﬁning stage
emissions. In general, High API/High HP reﬁneries emit a smaller
amount of GHGs during the reﬁning stage compared to reﬁneries
with low API gravity and low HP yield. Most of the reﬁneries in this
former group are located in the EU. For example, the difference in
the reﬁning GHG emissions between the High API/High HP and the
High API/Low HP groups, mainly driven by HP yields, are 2.4, 2.5, 2.5
and 3.6 g CO2e/MJ for gasoline, diesel, RFO and overall (i.e., aggre-
gate of all) petroleum products, respectively. Moreover, the differ-
ence in the reﬁning GHG emissions between the High API/Low HP
Fig. 5. Energy intensities of gasoline, diesel and residual fuel oil in each group of reﬁneries.
Fig. 4. Product-speciﬁc efﬁciency of gasoline, diesel and residual fuel oil in each group of reﬁneries (diesel does not include kerosene).
296
J. Han et al. / Fuel 157 (2015) 292–298
and Low API groups, mainly driven by API gravity, are 1.6, 2.1, 0.5
and 1.2 g CO2e/MJ for gasoline, diesel, RFO and overall petroleum
products, respectively.
API gravity and HP yield appear to impact the direct and indi-
rect reﬁning GHG emissions of each product to different extents.
For gasoline and diesel, API gravity inﬂuences direct reﬁning emis-
sions because API gravity directly affects the intensity of internal
reﬁnery processing for a given HP yield. Meanwhile, the inﬂuence
of HP yield on indirect reﬁning emissions associated with gasoline
and diesel production is also signiﬁcant. In this context, reﬁneries
with deep conversion units (such as coker, FCC and HYK) have a
greater demand for purchased heavy products (see Fig. 5). HP yield
also inﬂuences the direct emissions of diesel reﬁning through
hydrogen consumption, as discussed above. As shown in Fig. 3,
the total hydrogen consumption decreases signiﬁcantly with high
HP yield.
Some of the life cycle impacts discussed above are attributed to
differences in region and fuel speciﬁcations. For example, because
of the large share of EU diesel in the High API/High HP group, com-
bustion GHG emissions of diesel are about 1.5 g CO2e/MJ lower
than in other groups (because of the lower carbon content of EU
diesel compared to US diesel), while those for gasoline and RFO
are consistent among the three reﬁnery groups. A consequence of
the relatively large share of EU reﬁneries in the High API/High HP
group is the lower GHG emissions in crude recovery for all prod-
ucts. Note that in this study, it is assumed that the crude input into
EU reﬁneries is less GHG-intensive by about 1.8 g CO2e/MJ com-
pared to US reﬁneries. As mentioned earlier, these differences are
primarily due to higher associated methane emissions estimates
for US crude and the contribution of oil sands to crude utilized in
US reﬁneries. The difference in associated methane emissions
estimates results partly from the difference in estimation methods
rather
than
physical
differences,
which
warrants
further
investigation.
4. Discussion and conclusions
This study combined comprehensive LP modeling data, unit-
wide energy analysis and allocation, and a reﬁnery categorization
framework to derive fundamental information of reﬁnery energy
consumption. We analyzed the LP results of selected 43 US and
17 EU reﬁneries with various operational characteristics (e.g., API
gravity, HP, sulfur contents and complexity index), and categorized
them into three groups (Low API, High API/Low HP and High API/
High HP). The results of this study show that reﬁneries that process
heavier crudes and process deep into the barrel to produce lower
yields of heavy products have lower energy efﬁciencies and higher
GHG emissions compared to reﬁneries that process lighter crudes
and produce higher yields of heavy products. The reﬁning energy
intensities (the inverse of energy efﬁciencies) of gasoline and diesel
in the Low API group are 22 and 26 kJ/MJ higher compared to the
High API/Low HP group, mainly owning to API gravity. Moreover,
the reﬁning energy intensities of gasoline and diesel in the High
API/Low HP group are 14 and 26 kJ/MJ higher than the High API/
High HP group, mainly owning to HP yields. Consequently, the
GHG emissions of gasoline and diesel in the Low API group are
1.7 and 3.1 g CO2e/MJ higher compared to those in the High API/
Low HP group and the GHG emissions of gasoline and diesel in
the High API/Low HP group are 3.1 and 5.2 g CO2e/MJ higher com-
pared to those in the High API/High HP group. The higher energy
intensity and higher GHG emissions described above are attributed
to the larger energy-intensive process units (e.g., FCC, cokers and
HYK) used in the more complex reﬁneries. These types of reﬁneries
tend to use energy-intensive units, which are signiﬁcant con-
sumers of utilities (heat and electricity) and hydrogen. Between
the three groups of reﬁneries described here, the major difference
in the energy intensity is the amount of purchased natural gas for
utilities and hydrogen production, while the sum of feed reﬁnery
inputs are generally constant. Thus, in principle, GHG intensive
reﬁneries have the capacity to reduce life-cycle GHG emissions if
process fuels can be derived from renewable sources (e.g., renew-
able natural gas from anaerobic digestion of organic waste) instead
of fossil-fuel based natural gas, even though supplying a noticeable
amount of renewable process fuels to reﬁneries is challenging and
may affect the economics of reﬁneries adversely.
Systematic disaggregation of GHG emissions by each fuel-cycle
stage revealed the impacts of technical variations in reﬁneries in
the reﬁning life-cycle stage. Reﬁneries with higher resource efﬁ-
ciency tend to process heavier crude and yield more of the gasoline
and distillate, but are generally less energy-efﬁcient and produce
more GHG emissions compared to reﬁneries with higher HP yield,
i.e., less resource-efﬁcient.
Although the results of this study are limited to assessment of
the investigated group of reﬁneries, this work has shown that by
grouping reﬁneries into different groups it is possible to simplify
the understanding of reﬁnery energy and GHG intensities. These
results highlight the GHG emissions cost a reﬁner pays to process
deep into the barrel to produce more of the desired fuels (gasoline
and distillate). Within the context of possible future policy scenar-
ios, these results would likely be very different if reﬁners opti-
mized for GHG emissions in addition to proﬁt. Despite this, it is
clear that even if a reﬁner produced more HP for export at the
expense of gasoline and distillate, these HP (with higher carbon
Fig. 6. Life-cycle GHG emissions of gasoline, diesel, and residual fuel oil, as well as overall petroleum products for each group of reﬁneries.
J. Han et al. / Fuel 157 (2015) 292–298
297
content) will ultimately be consumed in the wider economy, pro-
ducing additional GHG emissions. Further work can complement
this study to better understand the environmental implications
of crude sourcing and reﬁnery yields in various markets.
Acknowledgment
We gratefully acknowledge the support of Sasol Synfuels
International and Jacobs Consultancy by providing data and giving
permission to publish this manuscript. This research effort by
Argonne National Laboratory was supported by the Bioenergy
Technology Ofﬁce and the Vehicle Technology Ofﬁce of the US
Department
of
Energy’s
Ofﬁce
of
Energy
Efﬁciency
and
Renewable Energy under Contract Number DE-AC02-06CH11357.
Appendix A. Supplementary material
Supplementary data associated with this article can be found, in
the online version, at http://dx.doi.org/10.1016/j.fuel.2015.03.038.
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Journal of Environmental Management 272 (2020) 111057
Available online 21 July 2020
0301-4797/© 2020 Elsevier Ltd. All rights reserved.
Review 
A review on treatment of petroleum refinery and petrochemical plant 
wastewater: A special emphasis on constructed wetlands 
Mahak Jain a, Abhradeep Majumder b, Partha Sarathi Ghosal a, Ashok Kumar Gupta c,* 
a School of Water Resources, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India 
b School of Environmental Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India 
c Environmental Engineering Division, Department of Civil Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India   
A R T I C L E  I N F O   
Keywords: 
PRPP wastewater 
Constructed wetlands 
Environmental impacts 
Phytoremediation 
Biological treatment 
Persistent organic pollutants 
A B S T R A C T   
Petroleum refinery and petrochemical plants (PRPP) are one of the major contributors to toxic and recalcitrant 
organic polluted water, which has become a significant concern in the field of environmental engineering. 
Several contaminants of PRPP wastewater are genotoxic, phytotoxic, and carcinogenic, thereby imposing 
detrimental effects on the environment. Many biological processes were able to achieve chemical oxygen demand 
(COD) removal ranging from 60% to 90%, and their retention time usually ranged from 10 to 100 days. These 
methods were not efficient in removing the petroleum hydrocarbons present in PRPP wastewater and produced a 
significant amount of oily sludge. Advanced oxidation processes achieved the same COD removal efficiency in a 
few hours and were able to break down recalcitrant organic compounds. However, the associated high cost is a 
significant drawback concerning PRPP wastewater treatment. In this context, constructed wetlands (CWs) could 
effectively remove the recalcitrant organic fraction of the wastewater because of the various inherent mecha­
nisms involved, such as phytodegradation, rhizofiltration, microbial degradation, sorption, etc. In this review, we 
found that CWs were efficient in handling large quantities of high strength PRPP wastewater exhibiting average 
COD removal of around 80%. Horizontal subsurface flow CWs exhibited better performance than the free surface 
and floating CWs. These systems could also effectively remove heavy oil and recalcitrant organic compounds, 
with an average removal efficiency exceeding 80% and 90%, respectively. Furthermore, modifications by 
varying the aeration system, purposeful hybridization, and identifying the suitable substrate led to the enhanced 
performance of the systems.   
1. Introduction 
Petroleum refinery and petrochemical plants (PRPP) are a group of 
industries that deal with the production of fuels, lubricants, petro­
chemicals, and their intermediates. The global economic development 
and increase in population have created a considerable demand for 
PRPP products. The steps involved in crude-oil extraction and process­
ing involve large quantities of water, resulting in the generation of a 
significant volume of wastewater. The amount of wastewater generated 
by PRPP is almost around 0.4 to 1.6 times the amount of crude oil 
produced (Coelho et al., 2006). As per Energy Information Administra­
tion (EIA), 2019 report world oil consumption was 99.93 million barrels 
per day (mBPD) in 2018, indicating generation of about 6500 million 
liters of PRPP wastewater per day (U.S. Energy Information 
Administration, 2020). Furthermore, the world oil demand is expected 
to rise to 102.22 mBPD and 107 mBPD in 2020 and 2030, respectively 
(Diya’Uddeen et al., 2011; U.S. Energy Information Administration, 
2020). This surge in the demand for PRPP products is making the sci­
entists apprehensive about the safety of the environment. The PRPP 
wastewater is composed of various toxic organic compounds, which 
impose a significant threat to the aquatic environment. As a result, the 
development of advanced strategies for PRPP wastewater remediation is 
of utmost priority. 
Large quantities of aromatic and aliphatic hydrocarbon compounds 
are present in PRPP wastewater, which can significantly affect the 
aquatic ecosystem. Furthermore, oil being an immiscible liquid forms a 
layer on the surface of water bodies and inhibits the entry of sunlight 
and oxygen, leading to less dissolved oxygen (DO) and increased mor­
tality rate of the aquatic species. Onwumere and Oladimeji (1990) 
* Corresponding author. 
E-mail addresses: mahakjain140@iitkgp.ac.in (M. Jain), abhradeep.majumder@iitkgp.ac.in (A. Majumder), psghosal@swr.iitkgp.ac.in (P.S. Ghosal), agupta@ 
civil.iitkgp.ac.in (A.K. Gupta).  
Contents lists available at ScienceDirect 
Journal of Environmental Management 
journal homepage: http://www.elsevier.com/locate/jenvman 
https://doi.org/10.1016/j.jenvman.2020.111057 
Received 2 May 2020; Received in revised form 29 June 2020; Accepted 3 July 2020   
Journal of Environmental Management 272 (2020) 111057
2
showed that there was an accumulation of metals in Oreochromis nilo­
ticus when the fish was exposed to treated petroleum refinery effluent 
from the Nigerian National Petroleum Corporation, Kaduna. Uzoekwe 
and Oghosanine (2011) studied the effect of petrochemical effluent on 
the water quality of Ubeji Creek in the Niger Delta of Nigeria and sug­
gested that the mixing of petrochemical effluent with brackish waters at 
the lower reaches of the river was detrimental to aquatic life. Also, it has 
been reported that exposure to these toxic hydrocarbons over a pro­
longed period can also severely affect human beings (Zhang et al., 
2016). Furthermore, they are highly soluble and persistent and may 
migrate into groundwater. As a result, PRPP wastewater should be 
treated to meet the effluent standards before it can become detrimental 
to the environment. 
Numerous processes, such as membrane bio-reactor (MBR), moving 
bed bio-reactor (MBBR), activated sludge process (ASP), up-flow 
anaerobic sludge blanket (UASB), anaerobic membrane bioreactors 
(AMBR), hybrid anaerobic reactor (HAR), up-flow anaerobic fixed bed 
(UAFB) reactor, anaerobic-aerobic-biofilm reactor (A/O-BR), micro­
aerobic hydrolysis acidification (MHA), membrane sequencing batch 
reactors (MSBR) photocatalysis, electro-Fenton (EF), catalytic ozona­
tion, membrane filtration, etc. have been generally used for the treat­
ment of PRPP wastewater (Jafarinejad and Jiang, 2019; Tian et al., 
2019). However, such established technologies are characterized by 
inherent limitations, such as high capital and operation/maintenance 
cost, technical complexity, etc. These limitations reduce the technical 
feasibility and economic viability of the treatment processes, especially 
for developing countries (Ahmad et al., 2019). Also, the disposal of a 
considerable amount of oily sludge generated after the conventional 
treatment processes is a significant concern. PRPP wastewater comprises 
of non-biodegradable, refractory, recalcitrant organic matters, which 
are resistant to the existing technologies. Hence eco-friendly, 
cost-efficient, easy-to-operate treatment technologies are required, 
which can efficiently treat the various components of PRPP wastewater 
and also not produce any harmful metabolites and sludge. Constructed 
wetlands (CWs) have shown considerable viability for the treatment of 
such contaminants due to the presence of multiple removal mechanisms, 
such as phytoremediation, microbial degradation, substrate intercep­
tion, etc. They do not require skilled labor, regular monitoring, high 
initial, and operation cost, which add on to the numerous advantages of 
these systems. Additionally, since PRPP wastewater comprises of 
various organic hydrocarbons, it may act as a source of nutrients for the 
plants and microbes (Martin et al., 2014). 
Over the past few decades, numerous studies have been carried out 
involving constructed wetlands and PRPP wastewater separately. Fig. 1 
depicts that research involving PRPP wastewater treatment started back 
in the late 1970s, and substantial work on constructed wetlands started 
from the early 1990s. However, only a handful of studies have been 
carried out involving the treatment of PRPP wastewater using CWs. Tian 
et al. (2019) and Jafarinejad and Jiang (2019) reviewed the efficiency of 
various biological methods and advanced oxidation methods in terms of 
PRPP wastewater. However, constructed wetlands were not considered 
in their study. On the other hand, various researchers reviewed the 
performance of constructed wetlands in terms of removal of nitrogen, 
phosphorous, COD and other nutrients from various types of wastewater 
but did not focus on PRPP wastewater (Healy and O’ Flynn, 2011; 
Lakatos et al., 2014; Valipour and Ahn, 2016; Vymazal, 2014, 2013, 
2007). Mustapha and Lens (2018) addressed the role of various oper­
ating conditions and performance of CWs in treating PRPP wastewater 
but comparison of the performance of CWs with other treatment 
methods was not addressed. Moreover, there is a lack of compiled 
literature addressing optimum operating conditions, plants, and mi­
croorganisms capable of degrading phenolic compounds, etc. 
List of abbreviations 
A/O-BR Anaerobic-aerobic-biofilm reactor 
AASP 
Aerobic activated sludge process 
ABR 
Anaerobic baffled reactor 
AMBR 
Anaerobic membrane bioreactors 
AOP 
Advanced oxidation processes 
API 
American petroleum institute 
ASP 
Activated sludge process 
BOD 
Biochemical oxygen demand 
BTEX 
Benzene, toluene, ethylbenzene, xylene 
BPA 
Bisphenol A 
COD 
Chemical oxygen demand 
CW 
Constructed wetlands 
DO 
Dissolved oxygen 
EF 
Electro-Fenton 
EGSB-BR Expanded granular sludge bed bioreactor 
EIA 
Energy information administration 
EU 
European Union 
FSF-CW Free surface flow constructed wetlands 
FW 
Floating wetland 
HAR 
Hybrid anaerobic reactor 
HF 
Horizontal flow 
HF-MBR Hollow fiber membrane bioreactor 
HLR 
Hydraulic loading rate 
HRT 
Hydraulic retention time 
HSSF-CW Horizontal subsurface flow constructed wetlands 
KH 
Henry’s constant 
mBPD 
Million barrel per day 
MBR 
Membrane bio-reactor 
MBBR 
Moving bed bio-reactor 
MFC 
Microbial fuel cell 
MHA 
Microaerobic hydrolysis–acidification 
MSBR 
Membrane sequencing batch reactors 
MTBE 
Methyl tertiary butyl ether 
NP 
Nonylphenols 
O&G 
Oil and grease 
OCP 
Organochlorine pesticides 
PACT 
Packed activated carbon treatment 
PAH 
Polycyclic aromatic hydrocarbons 
PBR 
Photo-bioreactors 
PCB 
Polychlorinated biphenyls 
POP 
Persistent organic pollutant 
PRPP 
Petroleum refinery and petrochemical plants 
SBBR 
Spouted bed bioreactor 
SBR 
Sequential bioreactor 
SFCW 
Surface flow constructed wetlands 
SSF-CW Subsurface flow constructed wetlands 
TDS 
Total dissolved solids 
TN 
Total nitrogen 
TOC 
Total organic carbon 
TPH 
Total petroleum hydrocarbon 
TSS 
Total suspended solids 
UAFB 
Up-flow anaerobic fixed bed 
UASB 
Up-flow anaerobic sludge blanket 
USEPA 
United States Environmental Protection Agency 
VF 
Vertical flow 
VSSF-CW Vertical subsurface flow constructed wetlands 
WAO 
Wet air oxidation 
WBG 
World bank group 
WWTP 
Wastewater treatment plant  
M. Jain et al.                                                                                                                                                                                                                                    
Journal of Environmental Management 272 (2020) 111057
3
This paper presents a qualitative and quantitative analysis of the 
major components of PRPP wastewater. It also highlights the various 
environmental impacts concerning the harmful contaminants of this 
wastewater. This article not only focuses on CWs in treating PRPP 
wastewater, but also compares its treatment efficiency with other 
established wastewater treatment technologies in this field. Due to the 
lack of compiled literature on CWs in treating PRPP wastewater, a 
special emphasis has been given on this technology. Various types of 
CWs have been addressed along with the role of operational parameters 
(macrophytes, flow rate, microorganisms, substrate, and aeration) on 
contaminant removal. An insight has been provided on the various 
mechanisms involved in PRPP wastewater treatment by CWs. Modifi­
cation of conventional CWs by varying operating conditions to attain 
enhanced performance have also been discussed. 
2. Sources of water pollution from PRPPs 
PRPPs produce several valuable and useful products, such as poly­
mers, fertilizers, synthetic rubber, pharmaceuticals, additives, adhe­
sives, etc. They involve various processes, such as drilling, exploration, 
cracking, crude desalting, fractional distillation, polymerization, isom­
erization, catalytic reforming, hydro-treating, alkylation, trans­
portation, etc. (Cholakov, 2009). At each step, a wide range of 
contaminants is discharged into the environment (Fig. 2). A large 
quantum of water is required in the production process, which makes 
the petroleum industry as one of the most water demanding establish­
ment. Consequently, a large volume of wastewater is also generated in 
the production stages. Exploration and production of crude oil deterio­
rate the quality of the water and land. Exploration is carried out to 
outline the reservoirs of oil and gas (Cholakov, 2009; Jafarinejad and 
Jiang, 2019). This process involves geological surveys using methods, 
such as magnetic and seismic geophysical methods. Improper applica­
tion of seismic methods can severely affect the aquatic ecosystem and 
harm marine species. Drilling requires gel-like water or oil-based 
emulsions, which include various polymers, ionic and nonionic emul­
sifiers, etc. (Cholakov, 2009). These chemicals come in contact with the 
ground creating soil and water pollution. Enhanced oil recovery 
methods and on-site processing of crude oil and natural gas may release 
hydrocarbons and sulfur into the atmosphere. Processes, such as 
cracking, distillation, catalytic reforming, alkylation, are aimed at pro­
ducing high-quality end-products, such as petrol (or gasoline), kerosene, 
diesel, lubricating oil, fuel oil, grease, wax, etc. However, large quan­
tities of sulfides, ammonia, cyanide, hydrocarbons, etc. are released with 
effluent (Cholakov, 2009; Tian et al., 2019). Propylene and benzene are 
also discharged into the water after thermal or catalytic cracking. 
Transport networks, such as pipelines, tankers, rail-tracks, suffer from 
oil spills resulting in further contamination of the environment 
(Alva-Arg
aez et al., 2007; Cholakov, 2009). The contaminants contrib­
uting to PRPP effluents from various production processes are given in 
Fig. 2. 
Fig. 1. Frequency of publications on petroleum refinery and petrochemical 
plants wastewater treatment and constructed wetlands with selected keywords 
(Details are given in Section A of supplementary materials). 
Fig. 2. Discharge of different contaminants resulting from various processes in petroleum refinery and petrochemical plants.  
M. Jain et al.                                                                                                                                                                                                                                    
Journal of Environmental Management 272 (2020) 111057
4
2.1. PRPP wastewater characteristics and disposal standards 
The components of PRPP effluent varies widely depending on their 
origin. The quality and quantity of wastewater produced depend on the 
type of crude oil used, the final product generated, and the operation 
processes involved. Several aliphatic and aromatic hydrocarbons of 
toxic nature are found in PRPP effluent, such as benzene, toluene, eth­
ylbenzene,t, xylene (BTEX), methyl tertiary butyl ether (MTBE), poly­
cyclic aromatic hydrocarbons (PAHs), phenols, naphthalenic acid, 
sulfides, metals derivatives, etc. (Liu et al., 2014; Yavari et al., 2015). 
The characteristics of major PRPP compounds have been mentioned in 
Table 1. These components are persistent in nature and are responsible 
for the high Chemical oxygen demand (COD) and toxicity of the PRPP 
wastewater. PAHs are a group of polycyclic aromatic hydrocarbons with 
two or more benzene rings. These compounds are nearly colorless, hy­
drophobic, and has less vapor pressure, with high melting and boiling 
points (Haritash and Kaushik, 2009). Furthermore, they have very high 
toxicity levels and can undergo bio-accumulation leading to subsequent 
bio-magnification. Oil and grease (O&G) or heavy oil, is another primary 
component of PRPP effluent, which comes in contact with water bodies 
through oil spills and discharge. Heavy oils are large hydrocarbons 
having a higher number of carbon atoms, having high viscosity, heat 
resistance, and chemical stability along with low biodegradability and 
water solubility (Kuo et al., 2014). The treated effluents from various 
conventional treatment systems of PRPP wastewater have a consider­
able amount of COD, Biochemical Oxygen Demand (BOD), Total 
Organic Carbon (TOC), turbidity, phenolic compounds, etc., which 
highlight the incompetence of these treatment systems (Banerjee and 
Ghoshal, 2017; Majone et al., 2010). The variation of concentration of 
these contaminants in PRPP effluent has been presented in Fig. 3. The 
concentration of contaminants varies with the location and the type of 
petrochemical plant. A maximum COD concentration of 266,000 mg/L 
in the petrochemical wastewater of Taiwan was reported by Chang et al. 
(2005). Bayat et al. (2015) reported a BOD concentration of 1266 mg/L 
in petrochemical wastewater in Iran. High levels of phenol, BTEX, Total 
Suspended Solids (TSS), Total Dissolved Solids (TDS), O&G were also 
reported by various authors in petrochemical wastewater (Al Zarooni 
and Elshorbagy, 2006; Jafarinejad and Jiang, 2019; Mustapha and Lens, 
2018; Rehman et al., 2019). Due to the high concentrations of the 
various contaminants in PRPP wastewater and their subsequent toxi­
cological impacts, various environmental agencies have set standard 
limits for disposal of treated PRPP effluent to surface water, marine 
water, and reuse in agricultural fields. Table S2 shows the standards set 
by United States Environmental Protection Agency (USEPA) and the 
World Bank Group (WBG) for different contaminants in oil refinery 
effluent. However, conventional treatment systems are often unable to 
meet these standards. These circumstances pave the way for a new 
domain of proficient technologies. 
2.2. Impacts of PPRP wastewater on environmental health 
The PRPP contaminants are not only toxic to microorganisms 
and aquatic life but also pose a severe health hazard to human beings. 
Prolonged exposure to these PRPP contaminants is associated with 
several kinds of anomalies among all kinds of lifeforms. Various 
environmental impacts of some major components of PRPP wastewater 
have been mentioned in Table 1. O&G form a layer on the water surface 
and create an anoxic condition for the aquatic organisms. Researchers 
observed a delay in the development process, late head formation, and 
abnormal neural development in the embryo of a fish (Verasper 
variegatus) due to the presence of heavy oil on the seawater surface 
(Murakami et al., 2008). It also increased larval mortality and showed a 
Table 1 
PRPP wastewater major components and their environmental impacts.  
Wastewater 
components 
Characteristics 
Environmental Impact 
References 
Heavy oil 
Large no. of C- atoms, viscous, heat resistant, stable chemical 
structure, less biodegradable 
Growth retardation, Toxic 
Murakami et al. (2008) 
PAH 
Colorless, hydrophobic, volatile, high melting, high boiling points. 
Most common PAHs in PRPP wastewater are naphthalene, 
phenanthrene, anthracene, fluorine, pyrene, benzo(e)pyrene and 
benzo(k)fluoranthene 
Genotoxic, Carcinogenic, 
mutagenic in nature 
Haritash and Kaushik (2009), Xiu et al. (2014) 
Phenolic 
compounds 
Colorless to pale color liquid or crystals, volatile in nature. 
Common phenolic compounds in PRPP wastewater are phenol, 2- 
methylphenol, 1-napthol, 2-napthol 
Phytotoxic, ecotoxic, carcinogenic 
in nature 
Khairy (2013), Kottuparambil et al. (2014),  
MacCrehan and Brown-Thomas (1987);  
Pugazhendi et al. (2017); Vosoughi et al. (2017) 
Benzene 
Colorless liquid with distinct gasoline like smell 
Flash point: 11.07 F 
Density: 876 kg/m3 
Carcinogenic in nature 
Reduces the production of both 
red and white blood cells 
Affects the lymphatic system and 
central nervous system 
Carvajal et al. (2018), Dehghani et al. (2018),  
Pubchem (2020), Rahul et al. (2013) 
Toluene 
Clear colorless liquid with aromatic odor 
Flash point: 40F 
Density: 867 kg/m3 
Affects the central nervous system, 
eyes, respiratory system, liver and 
kidney. 
Non-carcinogenic 
Causes drowsiness, fatigue, ataxia, 
tremors, cerebral atrophy, 
nystagmus 
Carvajal et al. (2018), Pubchem (2020), Rahul et al. 
(2013), Tham et al. (2011) 
Xylene 
Colorless liquid with sweet color 
Flash point: 86 F 
Density: 864 kg/m3 
Non- carcinogenic 
Alters enzymatic activity, causes 
skin inflammation and can affect 
the kidney 
Carvajal et al. (2018), Pubchem (2020), Rahul et al. 
(2013) 
Ethylbenzene 
Colorless, volatile and highly inflammable liquid with gasoline like 
odor 
Flash Point: 59 F 
Density: 866 kg/m3 
Affects the respiratory system and 
nervous system 
Carvajal et al. (2018), Pubchem (2020), Rahul et al. 
(2013) 
MTBE 
High water solubility, small molecular size and relatively low 
Henry’s law constant, non- degradable 
Flash point:  27.4 F 
Density: 740 kg/m3 
Carcinogenic, Allergic, affects the 
central nervous system 
Causes mucous membrane 
irritation, labored breathing and 
ataxia 
Pubchem (2020); Yu and Gu (2006), Zadaka-Amir 
et al. (2012)  
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Journal of Environmental Management 272 (2020) 111057
5
detrimental impact on the embryo stage of the fish due to its highly toxic 
nature (Murakami et al., 2008). The adverse effects of BTEX include 
genotoxicity, carcinogenicity, nervous system, and respiratory system 
disorders (Carvajal et al., 2018; Thullner et al., 2018). Moreover, 
toluene, ethylbenzene, and xylene may act as a mutagen. USEPA 
recognized benzene as a class-A carcinogenic pollutant because of its 
highly carcinogenic nature (Carvajal et al., 2018; Rahul et al., 2013). 
BTEX is readily absorbed through the gastrointestinal tract and easily 
reach the nervous tissues just after ingestion, which causes subsequent 
damage to the nervous system (Bustillo-Lecompte et al., 2018). MTBE is 
a highly volatile compound, which may lead to irritation, nausea, or 
headache. It not only affects the respiratory, nervous, and cardiac 
systems but also exhibits carcinogenic properties (Yu and Gu, 2006). 
Apart from that, these compounds show toxic effects on soil 
invertebrates, namely earthworms, springtail, etc., and many strains of 
bacteria and yeast (Hentati et al., 2013; Roslev et al., 2015). However, 
microorganisms are more sensitive towards MTBE as compared to 
invertebrates in the water, exhibiting a negative impact on ecological 
processes, such as biodegradation and nutrient cycles (Roslev et al., 
2015; Werner et al., 2001). Phenolic compounds generally occur in 
the PRPP wastewater and can affect the metabolic and enzymatic 
mechanisms of aquatic microorganisms even at low doses. Since the 
degradation of phenolic compounds is a prolonged process, these 
persistent compounds accumulate in the tissues of aquatic organisms 
and subsequently cause bio-magnification. Phenols have been known to 
be immunotoxic, genotoxic, carcinogenic, teratotoxic, as well as hae­
matologically and physiologically toxic (Mustapha and Lens, 2018). 
Therefore, USEPA has designated phenolic compounds as priority 
pollutant (Saleem et al., 2018). Kottuparambil et al. (2014) experi­
mentally confirmed the phytotoxicity of phenols on Eucyclops agilis, 
where phenol reduced photosynthetic activity and motility of euglena. 
Apart from that, phenol can infiltrate into the cell and rupture the in­
ternal membranous structure. Exposure to PAH compounds present in 
the PRPP wastewater may also cause genotoxicity among the aquatic 
organisms. Xiu et al. (2014) reported that the exposure to some of PAHs, 
such as benzo[a]pyrene (BaP), benzo[b]fluoranthene (BbF) and 
chrysene (CHR) compounds in juvenile scallop Chlamys farreri resulted 
in lipid peroxidation, protein oxidation, DNA damage, etc. and 
their toxicity sequence was reported as BaP > BbF > CHR. If PAH con­
centrations increase in the different exposure routes, such as drinking, 
inhalation, etc., then its overall concentration may exceed Excess 
Lifetime Cancer Risk standards set by USEPA for carcinogenic chemicals, 
thereby adversely affecting human life (Li and Li, 2017). 
3. Treatment technologies 
Treatment of PRPP wastewater targets a multifaceted approach for 
the removal of oil, hydrocarbons, sulfates, and trace metals simulta­
neously. Over the past few decades, numerous treatment techniques 
have been employed to treat PRPP wastewater (Fig. 4). Firstly, oil is 
separated from water in the primary stage of treatment by several in­
terceptors, floatation chambers, and equalization tanks. Gravity sepa­
ration, such as the American petroleum institute (API) separator, can 
also be used with less energy consumption (Jafarinejad and Jiang, 
2019). The dissolved air flotation method was found to remove oil in the 
range of 70%–90% from oily wastewater (Al-Shamrani et al., 2002; 
Hanafy and Nabih, 2007). The primary system reduces the suspended 
solids, O&G, and turbidity, which may otherwise influence the func­
tioning of microbes used in the secondary treatment (Aljuboury et al., 
2017). 
Biological treatment aims at the decomposition of any remaining oil, 
degradable organic compounds, a fraction of recalcitrant organic pol­
lutants, trace metals, nutrients, etc. (Gümüs
¸ and Akbal, 2016; Jafar­
inejad and Jiang, 2019; Khatri et al., 2018). Many biological treatment 
processes have been efficient in the treatment of PRPP wastewater. The 
salient features of the biological treatment systems have been depicted 
in Fig. S1. Amongst, the ASP is the most commonly used treatment 
process Ahmadi et al. (2017a). Ma et al. (2009) and Ebrahimi et al. 
(2016) observed a COD removal efficiency in the range of 70%–80% 
while treating PRPP wastewater using ASP. A relatively higher COD 
removal efficiency of 96% was obtained by Mirbagheri et al. (2014), 
which may be accounted for the high artificial aeration induced in the 
process. The combination of two or more biological processes or the 
combination of the biological process with membrane-based technolo­
gies has been effective in the treatment of wastewater. Liang et al. 
(2019) combined an expanded granular sludge bed bioreactor 
(EGSB-BR) with ASP to treat petroleum wastewater, having a COD of 
4600–5300 mg/L and obtained a COD reduction of 85% after 62.8 h 
retention time. Razavi and Miri (2015) used hollow fiber membrane 
bioreactors (HF-MBR) to treat refinery effluent and achieved a COD 
removal of 82% after providing 36 h retention time. Huo et al. (2018a) 
combined photo-bioreactors (PBR) with traditional oxic/anoxic process 
to treat low concentration petrochemical wastewater (COD: 312.8 
mg/L) and achieved a removal efficiency of 71%. El-Naas et al. (2014) 
augmented a spouted bed bioreactor (SBBR) with packed activated 
carbon treatment (PACT) and electrocoagulation cell (EC) to treat high 
concentration petrochemical water (COD: 3600–5300 mg/L) and ach­
ieved a removal efficiency of 97%. Ji et al. (2009) used the anaerobic 
baffled reactor (ABR) to treat heavy oil produced water and achieved a 
removal efficiency of 65% and O&G removal of 88%. Ruwais refinery 
wastewater treatment plant (WWTP) successfully used a corrugated 
plate interceptor for the separation of immiscible oil and water 
(Benyahia et al., 2006). However, most of these processes produce a 
tremendous amount of sludge, requires skilled labor and regular main­
tenance. Some of the processes also require very high hydraulic reten­
tion time (HRT), which is an inherent disadvantage to these methods. 
Various biological treatment processes employed to treat petrochemical 
wastewater have been given in Table 2. Fig. 5 depicts the HRT and 
performance of various biological processes in terms of treating petro­
chemical wastewater. The operating conditions and performance level 
of these processes have been listed in Table 2. 
Tertiary treatment is provided to bring down the concentration of the 
contaminant in secondary effluent to meet the reuse or discharge stan­
dards set by environmental agencies (USEPA, WHO, etc.). Various 
advanced oxidation processes (AOPs), such as photocatalysis, wet air 
oxidation (WAO), Fenton process, EF oxidation, etc. have been 
Fig. 3. Box-and-whisker plots (along with data points) showing variation in the 
concentration of selected contaminants in petroleum refinery and petrochem­
ical plants wastewater as obtained from various literature. Data from: Table S1. 
M. Jain et al.                                                                                                                                                                                                                                    
Journal of Environmental Management 272 (2020) 111057
6
employed for tertiary treatment of PRPP wastewater. AOPs generate 
various oxidizing radicals that react with the complex organics to form 
simpler compounds (Majumder et al., 2019). AOPs may be considered as 
a better option than adsorption as they can breakdown the parent 
compound into their non-toxic forms, whereas the organics are only 
transferred from aqueous medium to a solid medium by adsorption 
(Majumder et al., 2019; Raza et al., 2019). Zhao et al. (2018) used a 
two-stage WAO method to treat oily sludge from petrochemical waste­
water and achieved a removal efficiency of 85.4%. Mohanakrishna et al. 
(2018) used a bioelectrochemical treatment system to treat refinery 
wastewater and reduced COD by 75%. Several photocatalysts were used 
for degradation of petrochemical wastewater, and a COD removal effi­
ciency ranging from 65% to 85% was achieved (Bustillo-Lecompte et al., 
2018; Meng et al., 2018; Shokri et al., 2016). Keramati and Ayati (2019) 
combined photocatalysis with electrocoagulation to treat oil refinery 
wastewater and achieved a COD removal of 95%. The EF process has 
also been used to treat petroleum wastewater, and their COD removal 
efficiency has varied from 54% to 84% (Davarnejad et al., 2014; Gümüs
¸ 
and Akbal, 2016; Khatri et al., 2018). The catalytic ozonation process 
exhibited more efficiency compared to photocatalysis and the EF process 
in the treatment of petrochemical wastewater, showing a COD reduction 
of up to 96% (Ahmadi et al., 2017b; Huang et al., 2019). Studies 
reporting different AOPs and other tertiary treatment methods used in 
the treatment of petrochemical wastewater are listed in Table 3, and 
their effectiveness and reaction time have been provided in Fig. 5. 
However, AOPs and other processes involved in tertiary treatment 
require very expensive reactants, which limit their application at 
full-scale levels. Furthermore, the efficiency of these AOPs decrease with 
the increasing COD concentration (Levchuk et al., 2014). Hence, various 
combinations of mechanical, physiochemical, and biological treatments 
have been used in PRPPs for achieving higher removal efficiency. High 
cost, low operating pH, and a large volume of sludge production are the 
major drawbacks of PRPP wastewater treatment, which demands 
extensive research and development of advanced and cost-effective 
treatment systems. 
4. Constructed wetlands as a plausible solution to remediate 
PRPP wastewater 
CWs have been recognized as an efficient water treatment technol­
ogy for the last five decades. These artificial wetlands have been used to 
treat sewage, greywater, effluent from dairy, pulp, glass, petrochemical, 
pharmaceutical, paper mill, and other industries (Aalam and Khalil, 
2019; Avery et al., 2007; Chen et al., 2019; Gholipour et al., 2020; 
Newman et al., 1999; Rühmland et al., 2015; Yusoff et al., 2019). CWs 
are environment-friendly and economically sustainable systems having 
multiple social advantages, such as habitat enrichment, raising the 
aesthetic value of a wastewater treatment site, etc. (Bedessem et al., 
2007; Thullner et al., 2018). Furthermore, they are easy to operate, do 
not require skilled labor and daily maintenance. Hence, it can be a better 
treatment option for both developing and developed countries. Studies 
have shown that this robust process can deal with varying contaminants 
in wastewater, and its performance is not significantly affected by the 
type and strength of the wastewater (Stefanakis et al., 2016). Moreover, 
the substrate and macrophytes used in wetlands are not strictly specific, 
very cheap, and are conveniently available. Plants used in wetlands can 
be selected depending upon the environmental conditions and avail­
ability in close vicinity. Low sludge production is a fascinating feature of 
CWs, which makes it a more desirable option over conventional treat­
ment systems associated with the production of a large amount of bulky 
sludge (Jafarinejad and Jiang, 2019). Flores et al. (2019) carried out the 
life cycle assessment of CW and ASP, and concluded that engineered 
wetlands provide a natural way of treating contaminated wastewater 
with minimum harm to the surrounding. 
4.1. Conventional types of constructed wetlands 
The efficiency of CW depends on its size, flow rates, retention time, 
substrate, and macrophytes used (Aalam and Khalil, 2019; Al-Baldawi 
et al., 2017; Ji et al., 2002, 2007; Stefanakis et al., 2016; Sudarsan et al., 
2016). The different types of conventional CWs have been depicted in 
Fig. 6. Based on the macrophytes, CWs can be classified into three major 
categories. The plants can be free-floating, floating-leaved, emerged, or 
completely submerged. In floating wetlands (FW), the plants are not 
Fig. 4. Various treatment processes and their target contaminants during the treatment of petroleum refinery and petrochemical plants wastewater.  
M. Jain et al.                                                                                                                                                                                                                                    
Journal of Environmental Management 272 (2020) 111057
7
Table 2 
Performance of biological treatment methods in treating PRPP wastewater.  
Treatment method 
Wastewater type 
Wastewater characteristics 
HRT 
Operating conditions 
Removal Efficiency 
References 
HF-MBR 
Refinery effluent 
TSS ¼ 110 mg/l, BOD ¼ 203 mg/l, 
COD ¼ 580 mg/l, Turbidity-40 NTU 
25–36 h 
mixed liquor suspended solids (MLSS)- 3–6.6 g/ 
L, temperature- 20 C 
COD-82%, BOD-89%, TSS-98%, VSS-99%, 
Turbidity-98% 
Razavi and Miri 
(2015) 
ASP 
Petrochemical 
wastewater 
COD-300–600 mg/l, NH4
þ-N- 10–30 
mg/l 
30 h 
Temperature- 27–32 C, pH- 7–9 
COD-74.4%, NH4
þ-N-19.6% 
Ma et al. (2009a) 
MSBR 
Refining wastewater 
COD – 310 mg/l 
24 h 
MLSS-8.5 mg/l, temperature- 21  2 C, DO- 2.5 
 0.2 mg/l 
COD- 97% 
Ahmadi et al. (2019) 
EGSB-BR and AAS 
Petrochemical 
wastewater 
COD- 4600–5300 mg/l, 
petrochemicals- 50–80 mg/l 
EGSB- 62.8 h, 
AAS- 133.3 h 
Annual treated Volume ¼ 657,000 m3, 
temperature- 35 C 
COD - 85.6  2.5%, petrochemicals - 81.5 
4.8% 
Liang et al. (2019) 
ABR 
heavy oil produced water 
heavy oil ¼ 1.01 g/cm 3, loading rate 
0.070–0.5 kg COD/m3/d 
60.0–144 h 
Low nutrient ratio COD:TN:TP ratio ¼
1200:15:1 
COD- 65% and oil- 88% 
Ji et al. (2009) 
ASP 
Petrochemical 
wastewater 
COD- 1000–1500 mg/l, OLR- 
0.33–1.21 kg COD/m3/d 
24–96 h 
MLSS- 3000 mg/l, flow rate- 3–12 l/h, SRT- 
13.5–5 d 
COD- 78.7-61.5% 
Ahmadi et al. (2017a) 
UASB 
Synthetic phenolic 
wastewater 
COD- 530–2240 mg/l, phenolics- 226 
to 752 mg/l 
18–7.92 h 
Temperature-27-35 C, working volume-13.3 L, 
SRT- 32–68 d 
COD- 82.8%–91% 
Phenolics- 68%–95% 
Ramakrishnan and 
Surampalli (2012) 
ARH 
Synthetic phenolic 
wastewater 
COD- 530–2240 mg/l, phenolics- 226 
to 752 mg/l 
18–7.92 h 
Temperature-27-35 C, filter media- PVC rings, 
SRT- 32–68 d 
COD- 84%–90% 
Phenolics- 77%–99% 
Ramakrishnan and 
Surampalli (2012) 
UASB and biological 
aerated filter 
Heavy oil wastewater 
COD- 129.8–1238 mg/l, NH4
þ-N- 
38.21–83.64 mg/l, SS- 23.80–777.0 
mg/l 
12 h 
Temperature- 25–35 C, carrier-FPU (Function 
Polycin Urepan) 
COD- 74%, NH4
þ-N- 94% SS- 98% 
(Liu et al. (2013a)) 
AMBR 
synthetic wastewater 
OLR- 0.5–0.57 kg COD/m3/d and 
nitrobenzene (NB)- 20–400 mg/l 
144–216 h 
Temperature- 37  1 C, flow rate- 2.25 l/day, 
NB-100%, COD- 85–94% 
Kuscu and Sponza 
(2009a) 
ASP 
Refinery wastewater 
COD- 377–422 mg/l, MLSS- 
1865–2389 mg/l 
4.19 h 
DO- 3.7 mg/l, flow rate- 700 l/h, contact 
stabilization method 
COD- 78.65% 
Ebrahimi et al. (2016) 
ABR 
synthetic wastewater 
NB- 30–700 mg/l, OLR- 0.29–3 kg 
COD/m3/d, 
24–250 h 
Flow rate- 2.77–28.8 l/day 
NB- 100%, COD- 79–92% 
Kuscu and Sponza 
(2009b) 
HAR 
synthetic TA wastewater 
Terephthalate (TA) 600 mg/L,1000 
mg/L, OLR ¼ 1.6–4.5 kg COD m3/d 
18.9–24 h 
Temperature- 33-52 C 
COD- 66.1–91.9%, TA- 60.8–94.0% 
(Li et al. (2014b)) 
UASB 
synthetic petroleum 
wastewater 
OLR ¼ 0 - 11 kg-COD/m3/d 
240–480 h 
Temperature- 36  2 C 
COD- 93% at OLR- 11 kg-COD/m3/d 
Chen et al. (2017) 
ABR 
synthetic wastewater 
OLR- 2.67–5.4 kg COD m3/d 
40–60 h 
Temperature- 40  1 C, COD: N:P – 
200–300:5:1 
COD- 88.2- 84.9% 
Zhang et al. (2011) 
Packed-bed biofilm 
reactor (PBBR) 
synthetic wastewater 
from fisher tropsch 
process 
COD- 14–28 g/L, OLR- 3.4–20 g COD/ 
L/d 
33–100 h 
Temperature- 35 C, 
COD- 78–97% 
Majone et al. (2010) 
UAFB 
FT wastewater 
COD- 32.855–38.461 g/L 
40–144 h 
Temperature- 34  1 C 
COD- 43–72% 
Wang et al. (2017) 
hydrolytic 
acidification with 
algal microcosms 
Petrochemical 
wastewater 
COD- 856  11 mg/l, NH4
þ-N- 40  1 
mg/l, TP- 1.83  0.03 mg/l 
– 
pH 6.47–7.45, ORP-110  25.0 mV and 
temperature- 35.0 
C 
NH4
þ-N- 100.0%, 
TP- 89.0%, COD- 83% 
Huo et al. (2018b) 
MFC 
Petrochemical 
wastewater from acrylic 
acid plants 
COD- 5000 mg/l 
264 h 
Electrode- Carbon felt, biocatalyst -anaerobic 
sludge 
COD- 82% 
Sarmin et al. (2019) 
ASP 
Refinery wastewater 
COD- 1060 mg/l  
F/M- 0.38, Aeration- 120 L/min, F/M ratio- 
0.38, Return sludge- 110%, 
COD- 96% 
Mirbagheri et al. 
(2014) 
MSBR 
Refinery wastewater 
O&G- 21.5 mg/l, TOC- 123.4 mg/l, 
COD- 191.8 mg/l, Turbidity- 42 NTU 
SRT- 480 h, 
HRT- 8 h 
total cycle time- 4 h and volume exchange ratio 
(VER)  0.5, temperature- 27  1 C 
COD-80%, TOC-81.5%, O&G-82% 
Turbidity- 99.3% 
Pajoumshariati et al. 
(2017) 
UASB and CSTR 
synthetic wastewater 
2,4 dichlorophenol (DCP)- 5–120 mg/ 
l, COD- 3000–3193 mg/l 
20 h 
flow rate – 3–3.48 l/d, temperature – 20 C 
2,4 DCP- 91.08–99.90%, COD- 
86.67–95.14% 
Sponza and Uluk€
oy 
(2006) 
UASB 
Synthetic wastewater 
phenol-0-840 mg/l 
48–72 h 
Temperature- 35  1 C 
Phenol- 90% 
Guo et al. (2015) 
UASB 
phenolic wastewater 
OLR of 1–2.5 kg-COD/m3/day 
– 
Temperature- 36  2 C, Turf soil- 1.12 mg/g, 
pH- 6.5- 8 
COD and phenols- 97% 
Chen et al. (2018) 
MHA - A/O process 
petrochemical 
wastewater 
COD- 348  59 mg/l - 529  30 mg/l, 
Ammonium-N- 25.8  5.8 mg/L - 
28.5  5.4 mg/L 
20.0 h 
Dominant bacteria- Proteobacteria, DO- 4–6 mg/ 
L, air flow rate- 0.3 L/h, temperature- 22–28 C, 
SRT- 30 dli 
Ammonium ->94.0%, COD-72–79% 
Yang et al. (2015) 
A/O-BR 
Heavy oil refinery 
wastewater 
COD- 650–1150 mg/L, Petroleum- 
70.5–95.8 mg/L, TN- 35–70 mg/L 
36.0–50.0 h 
Temperature- 30–35 C, flow rate- 6.9 mL/h and 
184.7 mL/h 
COD- 93.2% and TN - 82.8% 
Li et al. (2017) 
(continued on next page) 
M. Jain et al.                                                                                                                                                                                                                                    
Journal of Environmental Management 272 (2020) 111057
8
rooted in the soil media; rather, they are entangled with each other in 
the form of a mat, facilitating the plant to float on the water surface 
freely and promoting the hydroponic growth of emergent macrophytes 
(Healy and O’ Flynn, 2011; Vymazal, 2007; Zhang et al., 2014). FWs 
allow nutrient removal directly via root uptake, although the filtering 
action of the media is absent (McAndrew and Ahn, 2017). CWs having 
emergent macrophytes can be further classified based on the flow 
pattern of wastewater. In free surface flow CW (FSF-CW), water flows 
over the surface of the soil, having direct contact with atmospheric ox­
ygen, while in subsurface flow (SSF) treatment systems, the flow path of 
water is beneath the media surface. This flow path may be horizontal 
(HF) or vertical (VF), depending upon the availability of land and media 
(Vymazal, 2013). Furthermore, the VF can be upward with the help of a 
pump or can be downward under the influence of gravity. Often, a 
combination of two or more CWs, is used to achieve better removal ef­
ficiencies. Such combinations of VF-CW and HF-CW, multistage CWs are 
classified as hybrid CWs. Many studies compare the performance of 
wetlands depending on their flow patterns. Substrate selection is one of 
the essential criteria for better working of a CW. Flow pattern, hydraulic 
loading rate (HLR), and HRT are some of the significant parameters, 
which are critically considered while designing the CWs. 
4.2. Use of constructed wetlands for PRPP treatment 
In the past two decades, various pilot-scale and full-scale CWs have 
been used for the treatment of PRPP wastewater. Full-scale CW treat­
ment systems were adopted in Hungary for the treatment of Tisza Oil 
Refinery Plant (TIFO) and Nyirbogd
any Petrochemical Plant (NYKV) 
wastewater (Lakatos et al., 2014). The pre-treatment system in TIFO 
consisted of interceptors, flotation chambers, and an aeration basin, 
while in NYKV, the wastewater was subjected to ASP after mechanical 
and chemical treatment. This pre-treated effluent was transferred to a 
CW system. NYKC consisted of a pre-settling pond unit, an oxidation 
pond unit, and a post-settling reed pond, while TIFO consisted of algal 
pond, fish pond, and reed pond units. Lakatos et al. (2014) found that 
the TIFO wetland pond system contributed around 40% TP and 35% TN 
removal to the system over a period of 13 years. Likewise, the NYKV CW 
system removed around 30% oil, and the whole composite treatment 
system could remove up to 90% COD and 72–100% oil from wastewater. 
Fig. 5. Box-and-whisker plots showing variation in the performance of various 
treatment processes in terms of COD removal and time taken to achieve the 
removal efficiency as obtained from diverse literature. Data from: Tables 2 
and 3. 
Table 2 (continued) 
Treatment method 
Wastewater type 
Wastewater characteristics 
HRT 
Operating conditions 
Removal Efficiency 
References 
PBR 
Low concentration 
petrochemical 
wastewater 
COD-80.8 mg/l- 312.8 mg/l, NH4
þ-N- 
18.1 mg/L- 7.9 mg/L, P-0.87 mg/L- 
0.12 mg/L 
216 h 
Used filamentous microalgae Tribonema sp., 
COD- 71%–97.8%, NH4
þ-N- >99%, P- >99% 
Huo et al. (2018a) 
EC, SBBR and PACT 
Refinery wastewater 
COD- 3600 to 5300 mg/l and Phenol- 
11 to 14 mg/l, m, p-Cresol - 72–75 
mg/l 
24 h 
Electrodes type: aluminum, current density of 3 
mA/cm2, air flow rate- 3 L/min, temperature- 
30 C, Liquid flow rate-10 mL/min 
COD- 97%, Phenol-100%, m, p-cresol- 
100%, while the activated carbon was 
saturated after only 8 h of operation 
El-Naas et al. (2014) 
SBR and BET 
Petrochemical 
wastewater 
OLR- 9.68 kg COD/m3- day 
48 h 
operated in suspended growth configuration at 
ambient room temperature, DO- 0.75  0.25 
mg/l 
COD removal was 54.45% 
Yeruva et al. (2015) 
MBBR 
Petrochemical 
wastewater 
OLR- 1.0 – 2.0 kg COD/m3- day 
8 h 
Air flow rate ¼ 1.25 L/min 
COD-70 to 80% 
Cao and Zhao (2012) 
MBBR 
Petroleum contaminated 
wastewater 
COD- 1568 mg/L 
Phenol- 0.66 mg/L 
4 h 
Volume ¼ 550 L, 85% of the reactor was filled 
with Polyurethane elements and MLSS ¼
1400–1700 mg/L 
Phenol- 55 to 90% 
COD- 62 to 63% 
Mahmoudkhani et al. 
(2012)  
M. Jain et al.                                                                                                                                                                                                                                    
Journal of Environmental Management 272 (2020) 111057
9
Table 3 
Performance of tertiary treatment methods in treating PRPP wastewater.  
Treatment method 
Wastewater type 
Wastewater characteristics 
Reaction time 
Operating Conditions 
Removal Efficiency 
References 
Adsorption 
Super heavy oil 
wastewater 
Oil- 0.754 mg/l and COD- 
74.84 mg/l  
Temperature-293, 303, 313 and 323  0.2 K, pH- 8.483, 
adsorbent- Lignite activated coke 
COD- 50.3%, Oil- 25.83% 
Tong et al. (2014) 
Electrochemical catalytic 
treatment 
Synthetic wastewater 
Phenol-520 mg/L, COD 1214 
mg/L 
40 min 
Catalyst- ferric sulfate and potassium permanganate, adsorbent- 
active bentonite, electrode- graphite 
COD- up to 99% 
Ma et al., (2009b) 
Two stage WAO 
Oily sludge from 
petrochemical plant 
Oil content- 15% (mass 
percentage), COD-419.9 mg/l 
150 min 
Emulsion splitter- Sodium carbonate, oxidant- H2O2, 
Temperature- 240 C. 
First stage- reaction time- 90 min, oxidant dose- 80 ml 
Second stage- reaction time- 60 min, oxidant dose- 40 ml 
Overall oil content- 93.1% 
and oil sludge volume- 
85.4% 
Zhao et al. (2018) 
Bioelectrochemical 
treatment 
Refinery wastewater 
COD- 2150 mg/L, TPH- 15 mg/ 
L, pH- 7.45 
96 h 
Electrode- Graphite, current density- 278 mA/m2, power density- 
222 mW/m2, potential- 800 mV 
COD- 75%, diesel range 
organics- 90% 
Mohanakrishna et al. 
(2018) 
Electrocoagulation cell and 
Photo-catalysis 
Oil refinery effluent 
COD- 1000 mg/l 
EC- 8.7 min, 
Photo-catalysis- 
180 min 
Electrode- Stainless steel plates, Power- UV-C lamps of 8 W 
COD-95.8% 
Keramati and Ayati 
(2019) 
Photochemical treatment 
BTEX wastewater 
TOC ¼ 65–135 mg/l 
4h 
UV-254nm/H2O2 and UV-185nm/H2O2, pH- 3 (acidic condition) 
UV-254/H2O2-TOC-62%, 
UV-185/H2O2- TOC-80% 
Bustillo-Lecompte 
et al. (2018) 
Fenton process 
Petroleum refinery 
effluent 
COD- 1259 mg/l, TOC-186 mg/ 
l, phenol-14.7 mg/l, O&G- 233 
mg/l 
10 h 
pH ¼ 3.0, molar ratio-[H2O2]:[COD]0- 6, [H2O2]:[Fe0]- 10 
COD- 76.5%, TOC- 45% 
and phenol- 96%, O&G- 
100% 
Diya’uddeen et al. 
(2015) 
Photocatalysis 
Petrochemical 
wastewater 
COD ¼ 300 mg/l, 4 Nitro phenol 
(4-NP) ¼ 25 mg/l 
180 min 
Catalyst (dose- 1 g/l, 100 W tungsten lamp visible light) 
4-NP: 
B–GO–TiO2-100%, 
B–TiO2- 85%, 
GO–TiO2- 80% 
COD: 
B–GO–TiO2-85%, 
B–TiO2-70%, 
GO–TiO2- 65% 
Shokri et al. (2016) 
Photocatalysis 
Phenolic water 
– 
80 min 
Catalysts- Bi4O5BrxI2-x, 
Catalyst dosage- 1.00 mg/ml, light intensity- 500 W halogen lamp 
visible light 
Phenols- 92%, TOC- 79% 
Meng et al. (2018) 
Catalytic ozonation 
Petrochemical 
wastewater 
nitrobenzene-300 mg/L, pH- 
6.53, and COD- 538.4 mg/L 
30 min 
Temperature- 25.0–50.0 C, catalyst- Spent fluid catalytic cracking 
catalysts (dose ¼ 0.250–1.00 g), Oxygen dose ¼ 0.62–2.25 mg/ 
min 
COD- 55.6–87.2 
Chen et al. (2015) 
Catalytic ozonation 
Petrochemical 
wastewater 
COD- 144 mg/L 
120 min 
Temperature-20.0–25.0 C, catalyst- Fe–Ni foam (dose-110 g/L), 
Ozone dose ¼ 10.2 mg/L O3 
COD- 96%, DOC- 61% 
Huang et al. (2019) 
Catalytic ozonation 
Petrochemical 
wastewater 
COD- 362  36.0 mg/L 
120 min 
Temperature-20.0–25.0 C, catalyst- PAC@Fe3O4 (dose- 
0.150–0.750 g/L), Ozone dose ¼ 0.050–0.300 g/h O3, pH- 
3.00–11.0 
COD- 75.3%, TOC- 50.3% 
Ahmadi et al. (2017a) 
EF 
Synthetic wastewater 
Phenol ¼ 250 mg/L 
30 min 
Electrode- iron, electrolyte- NaCl, H2O2- 37.2 mM, pH- 5.20, EC- 
125 μS/cm, stirring speed-100 rpm, inter-electrode gap 4.00 cm, 
current density- 0.800 mA/cm2 
COD- 84%, TOC- 52%, 
phenol- 100% 
Khatri et al. (2018) 
EF 
Synthetic wastewater 
Phenol- 250 mg/L, COD- 800 
mg/L 
5 min 
Electrode- iron, electrolyte- raw effluent, pH- 3.00, H2O2-500 mg/ 
L, current density- 1.00 mA/cm2, EC- 1000 μS/cm 
COD- 87.5%, Phenol- 
93.3% 
Gümüs
¸ and Akbal 
(2016) 
Electrocoagulation cell 
Petrochemical 
industry 
COD- 2,746 mg/L 
480 min 
Electrode- Ti/Pt, boron-doped diamond (BDD), electrolyte- raw 
effluent, pH- 3.00, electrode gap- 10 mm, current density- 40.00 
mA/cm2, temperature- 60
COD- 98.7% 
(dos Santos et al., 
2014) 
EF 
Petrochemical 
wastewater (PW) 
COD- 1400–1700 mg/l, BOD/ 
COD- 0.4–0.6 
78.97 min 
Electrode- Aluminum, electrolyte-Feþ2 solution, Inter-electrode 
gap- 3.00 cm, current density 68.7 mA/m2, pH- 3.06, H2O2/PW - 
2.14 ml/l, and H2O2/Feþ2–4.99 M ratio. 
COD- 51.23% 
Davarnejad et al. 
(2014) 
EF 
Petrochemical 
wastewater (PW) 
COD- 1400–1700 mg/l, BOD/ 
COD- 0.4–0.6 
73.19 min 
Electrode- Iron, electrolyte-Feþ2 solution, Inter-electrode gap 3.00 
cm, current density 59.7 mA/m2, pH- 2.67, H2O2/PW - 1.23 ml/l, 
and H2O2/Feþ2–3.65 M ratio. 
COD- 66.85% 
Davarnejad et al. 
(2014)  
M. Jain et al.                                                                                                                                                                                                                                    
Journal of Environmental Management 272 (2020) 111057
10
It was observed that the presence of phytoplankton and zooplankton in 
both CW systems did not allow oxygen deficiency or toxic conditions to 
prevail. 
Wallace et al. (2011) carried out the study of two full-scale CWs and 
demonstrated a very high potential of SSF-CW for organic compounds 
removal from oil refinery wastewater in cold climates. In order to free 
the wastewater from iron, a cascade aerator was used before the CW. 
The first CW system located in Casper, Wyoming, was operated at 
temperatures as low as  35 C and showed a 100% removal efficiency 
for BTEX and gasoline-range organics. The second CW located in 
Wellsville, New York, was operated at temperatures below  20 C, and 
it was found to remove 94% of the aniline and 93% of the nitrobenzene 
with a high percentage of BTEX removal. This system also included 
artificial aeration to prevent the freezing of water in VSSF-CW. 
Stefanakis et al. (2016) used horizontal subsurface flow CWs 
(HSSF-CWs) having a surface area of 6.5 m2 and a bed depth of 1 m filled 
with gravel (grain size 2–3.2 mm) to treat groundwater contaminated 
with phenols, benzene, m-cresol, etc. The flow rate was maintained 
around 11 L/h, and Phragmites australis was planted to enhance the 
performance of the CW. High removal efficiency of 99.8% and 97.7% 
were observed for phenol and m-cresol, respectively, after ten days of 
HRT. The removal of phenolic compounds did not affect the removal 
efficiency of MTBE and benzene, which ascertained the versatility of 
CWs. It was also found that the presence of phenolic compounds did not 
have any toxic effects on the plants. Aerobic conditions in the top layer 
of the bed facilitated the growth of microorganisms leading to biodeg­
radation, which was found to be the primary mechanism for the treat­
ment system. 
Pardue et al. (2014) developed a pilot-scale CW system comprising of 
three series, out of which two series consisted of four vertical subsurface 
flow CWs (VSSF-CWs) and the other one consisted of four free water 
surface CW. An oil-water separator was also attached as a preliminary 
treatment. Pea gravel of size 5–10 mm and granitic gravel of size 20–30 
mm was used as the substrate while Phragmites australis was the plant 
used. Pardue et al. (2014) observed an O&G reduction ranging from 
69.3% to 98.4% after a retention time of 2–4 days. Additionally, a high 
average removal efficiency (>85%) of metals, such as Zn, Ni, Fe, and 
Mn, was also observed. 
Seeger et al. (2011) used Phragmites australis planted HSSF-CWs (L 
B  H ¼ 5 m  1.1 m  0.6 m) with gravel substrate to treat benzene, 
MTBE contaminated water. Temperature above 15 C was found to be 
favorable for the performance of the CWs. Maximum removal efficiency 
for benzene, MTBE, and ammonia was found to be 99%, 82%, and 41%, 
respectively. Microbial degradation was found to be the primary driving 
force in contaminant removal. 
Ranieri et al. (2013) used CWs (L  B  H ¼ 7.5 m  4.8 m  0.6 m) 
having a surface area of 35 m2 and planted area of 15 m2 (Phragmites 
australis and Typha latifolia) for BTEX (0.5 mg/L) removal at a flow rate 
of 1 m3/day. The substrate comprised 0.1 m of clay soil (0.05 mm), 0.2 
m of stones (5 mm), and 0.30–0.35 m of gravel (1.5 mm). The volume of 
voids was calculated to be 1.56 m3, and the porosity of the overall 
substrate was found to be 0.17. After 100 h of HRT, the overall BTEX 
removal in the unplanted CW was found to be 46%, while the removal 
efficiency increased up to 57% in the presence of Phragmites australis. 
Al-Baldawi et al. (2013a) used Scirpus grossus planted HSSF-CWs (L 
 B  H ¼ 1.8 m  0.9 m  0.9 m) to treat diesel contaminated 
wastewater. Gravels of sizes ranging from 1 to 2 mm, 10–20 mm, and 
20–50 mm were used as the substrate. The TPH removal efficiencies 
obtained after 72 days was found to vary from 67% to 82%. Further­
more, a high removal efficiency of 100% and 75% were obtained for TSS 
and COD, respectively. In another study, Al-Baldawi et al. (2014) 
attained a removal efficiency of 72.5% after 63 days of HRT. Al-Baldawi 
et al. (2013c) used Scirpus grossus planted SSF-CWs (L  B  H ¼ 0.3 m 
 0.3 m  0.3 m) for treating synthetic diesel contaminated wastewater 
and attained a TPH removal efficiency of 91.5%. 
A detailed list of studies regarding the treatment of PRPP wastewater 
using CW and their contaminant removal efficacy, including the 
wastewater characteristics, operation conditions, flow types, and mac­
rophytes used, have been presented in Table 4. Based on various liter­
ature, the performance of CWs in terms of removal of BOD, COD, O&G, 
Fig. 6. Classification of constructed wetlands for wastewater treatment and their features.  
M. Jain et al.                                                                                                                                                                                                                                    
Journal of Environmental Management 272 (2020) 111057
11
Table 4 
Performance of constructed wetlands in treating PRPP wastewater.  
Wastewater type 
Wastewater characteristics 
CW type 
CW Scale & Size 
HRT 
Flow rate 
Plant 
Removal efficiency 
References 
Refinery effluent 
– 
SSF 
Full scale 
– 
– 
Common reed Phragmites australis, 
cattail (Typha angustifolia L.) 
COD-90%, O&G-72-100%, TN- 
35%, TP- 40% 
Lakatos et al. 
(2014) 
Refinery wastewater 
Turbidity- 18.3 NTU, BOD-20.4 mg/L, 
COD- 86 mg/L, O&G- 18.4 mg/L, TPH- 
16.6 mg/L 
VSSF 
Lab scale-50 L Batch 
fill 
10 d 
Batch 
Eichhornia crassipes (water hyacinth) 
Turbidity-91.5%, BOD5-94.6%, 
COD-80.2%, O&G- 90.4%, TPH- 
92.6% 
Agarry et al. 
(2018) 
Petrochemical 
effluent  
HSSF 
Lab scale-70x40  30 
cm and a slight slope of 
<1% 
90 d 
6 L/day 
a-Typha latifolia (Cattail) b-Pragmites 
australis(Reed) 
BOD, COD, TSS-90-95% 
Phenolic compounds- 65–90% 
Sudarsan et al. 
(2016) 
Oil produced water 
COD- 401 mg/L/BOD- 31.9 mg/L/ 
O&G- 24.7 mg/L, TKN- 11.7 mg/L. 
SSF 
Pilot scale- 60x15 
0.6 m slope ¼ 2% 
3 d 
30,000 L/day 
and 18,000 L/ 
day 
Common reed 
Reed1 (30,000 L/day): BOD- 
80%, COD- 67%, O&G: 78%, 
TKN: 75% 
Reed2 (18,000 L/day): BOD- 
89%, COD- 81%, O&G: 89%, 
TKN: 81% 
Ji et al. (2002) 
Refinery wastewater 
TN-15.6 mg/l,TP-0.6 mg/l,COD-142.8 
mg/l,TPH-1720 mg/l 
FW 
Lab scale-75 L 
container D ¼ 80 cm, 
H ¼ 100 cm 
35 d 
Batch fill 
Perennial grasses [Geophila herbacea 
O Kumtze (GHK), Lolium perenne CV. 
Caddieshack (LCC), Lolium perenne 
Topone (LPT) and Lolium perenne L. 
(LPL)] 
COD-52-67%, TPH-40-55%, TP- 
up to 56%, TN-up to 60% 
Li et al. (2012) 
Secondary refinery 
wastewater 
Phenol- 0.053 μg/L, O&G- 6.78 mg/L, 
TPH- 6.2 mg/L, COD- 200–600 mg/L, 
TSS- 70–110 mg/L 
VSSF 
Lab scale H ¼ 0.88m, 
D ¼ 0.22m 
180 d 
11,520 L/day 
Typha latifolia and unplanted 
microcosm CW 
Unplanted: COD- 66%, TPH- 
58%, O&G- 44%, Phenol- 91%, 
TSS- 55% 
Planted: COD- 91%, TPH- 99%, 
O&G- 80%, Phenol- 100%, TSS- 
88% 
Mustapha 
et al. (2018b) 
Petroleum Refinery 
Wastewater 
TSS-60.8 mg/L, BOD- 95.2 mg/L, COD- 
164 mg/L, NH4
þ-N - 1.8 mg/L, NO3
 N- 
1.6 mg/L, TP-4 mg/L 
VSSF þ
HSSF 
Lab scale VSSF- 
circular D ¼ 47 cm, H 
¼ 55 cm HSSF- LxBxH 
¼ 110 cm  70 cm 
40 cm 
VSSF-48 h, 
and HSSF- 
148 h 
19.92 L/day 
Typha latifolia 
(HSSF þ VSSF) Turbidity-97%, 
BOD5-94%, COD-88%, TP-78%, 
TN-85%, NH4
þ-N-84%, NO3
 N- 
89% 
Mustapha and 
Bruggen 
(2018) 
Phenol and 
petroleum 
derivatives in 
groundwater 
Phenol- 6.39 mg/L, m-cresol-0.895 mg/ 
L 
HSSF 
Pilot-scale 3 units 
LxBxH ¼ 5.9 m  1.1 
m  1.2 m 
10 d 
264 L/day 
2 common reeds (Phragmites 
australis) þ unplanted 
Phenol- 99.8%, m-cresol- 97.7% 
Stefanakis 
et al. (2016) 
TPH effluents 
0.25% diesel 
HSSF 
Pilot scale LxBxH ¼
1.8 m  0.9 m x0.9m 
vol. ¼ 500 L 
63 d 
Batch 
Scirpus grossus three rhizobacteria 
strains (Bacillus aquimaris, Bacillus 
anthracis, and Bacillus cereus) 
Without rhizobacteria- TPH-72%, 
With rhizobacteria- TPH-84% 
Al-Baldawi 
et al. (2017) 
Synthetic diesel oil 
refinery 
wastewater 
COD-31.7 mg/L, Diesel range organics- 
246.4 mg/L, benzene- 0.2 mg/L, 
toluene- 0.06 mg/L, ethyl benzene- 
0.09 mg/L, m/p- xylene-0.06 mg/L, o- 
xylene- 0.06 mg/L 
VSSF þ
HSSF 
Lab Scale- VSSF- 
LxBxH ¼ 0.6 m  0.4 
m x0.8m, HSSF- 
LxBxH ¼ 0.6 m  0.4 
m x 0.35m 
VSSF-4.85 
d HSSF- 
2.1 d  
VSSF- Phragmites australis HSSF- 
unplanted 
COD- >32%, Diesel range 
organics->97%, Benzene->96%, 
Toluene->93%, Ethyl benzene- 
>96%, m/p-xylene->86%, 0- 
xylene->98% 
Mustapha 
et al. (2018a) 
Synthetic 
wastewater with 
diesel 
Diesel concentration 17400 mg/L 
SSF 
Lab scale-L ¼
B–
–H¼30 cm  7 L 
72 d 
Batch 
Scirpus grossus 
TPH-91.5% 
Al-Baldawi 
et al. (2013b) 
Synthetic 
wastewater 
Diesel concentration of 0.25%. 
HSSF 
Pilot-scale LxBxH ¼
180 cm  90 cm x 90 
cm 
63 d  
Scirpus grossus 
After 63 days-TPH from water- 
72.5%, TPH from sand- 59% 
Al-Baldawi 
et al. (2014) 
Synthetic 
wastewater 
Diesel conc. 0.1%(a), 0.2%(b), 0.25% 
(c) 
HSSF 
Pilot scale LxBxH ¼
180 cm  90 cm 90 
cm 
72 d  
Scirpus grossus 
a-82%, b-71%, c-67% 
Al-Baldawi 
et al. (2013a) 
Synthetic water 
Diesel concentrations (1%, 2%, and 3%) 
SSF, FSF 
Pilot scale L ¼
B–
–H¼30 cm 
72 d  
Scirpus grossus 
Avg TPH removal- SSF-91.5%, 
FSF-80.2% 
Al-Baldawi 
et al. (2013c) 
Crude oil 
Cd- 3.21 mg/L, Cr- 11.48 mg/L, Pb, 
0.39 mg/L, V- 3.64 mg/L. 
FW 
Lab Scale-LxBxH ¼ 50 
cm  50 cm x 10 cm 
60 d  
Duckweeds (L. paucicostata) 
After 60 days- Cd-32.45%, Cr- 
13.76%, Pb-41.03%, V-26.37% 
Ekperusi et al. 
(2019) 
(continued on next page) 
M. Jain et al.                                                                                                                                                                                                                                    
Journal of Environmental Management 272 (2020) 111057
12
Table 4 (continued) 
Wastewater type 
Wastewater characteristics 
CW type 
CW Scale & Size 
HRT 
Flow rate 
Plant 
Removal efficiency 
References 
Crude oil- 
contaminated 
wastewater 
COD- 1324 mg/L, BOD- 475 mg/L, 
Total hydrocarbon- 304 mg/L 
FW 
Lab Scale- 20 L tank 
42 d 
Batch 
Plant-T.domingensis, Leptochloa 
fusca, bacteria- Bacillus subtilis, 
Klebsiella sp., Acinetobacter Junii, 
Acinetobacter sp. 
T. domingensis þ bacteria- Total 
hydrocarbon (95%), COD (90%), 
and BOD (93%) 
Rehman et al. 
(2019) 
Groundwater 
contaminated with 
benzene and 
MTBE 
Benzene- 13 mg/L, MTBE- 2.2 mg/L 
HSSF 
combined 
with FW 
Pilot-scale LxBxH ¼
5.0 m  1.1 m  0.6 
6 d 
144 L/day 
Common reed (Phragmites australis) 
þ Unplanted 
Unplanted (HSSF): Benzene-33%, 
MTBE: 33% 
Reed (HSSF): Benzene- 24%– 
100%, MTBE- 16%–93% 
Reed (FW): Benzene- 22%–100%, 
MTBE- 8%–93% 
(Z. Chen et al., 
2012) 
Municipal 
wastewater 
Bisphenol A (8.8 μg/L) (BPA) and 
nonylphenols (1671 μg/L) (NP) 
HSSF 
Pilot scale L ¼ 9 m, B 
¼ 3 m, H ¼ 0.25m 
1.8 d 
3500 L/day 
Phragmites australis(a); Heliconia 
psitacorum(b); unplanted(c). 
Plant a: BPA- 70.2%, NP- 52.1% 
Plant b: BPA- 73.3%, NP- 62.8% 
Plant c: BPA- 62.2%, NP- 25.3% 
Toro-V
elez 
et al. (2016) 
Heavy oil-produced 
water 
COD- 390 mg/L, BOD- 32 mg/L, O&G- 
20 mg/L, TKN- 11.6 mg/L 
FSF 
Pilot scale L ¼ 75m, B 
¼ 7.5m, H ¼ 0.25m 
Bed 1–15 
d Bed 
2–7.5 d 
Bed 1–18,750 
L/day, Bed 
2–37,500 L/ 
day 
Common reed (Phragmites australis) 
Reed 1: BOD5- 88%, COD- 80%, 
O&G- 93%, TKN:86% 
Reed 2: BOD5- 77%, COD- 71%, 
O&G- 92%, TKN- 81% 
Ji et al. (2007) 
Benzene, MTBE, 
NH4
þ-N 
contaminated 
water 
Benzene- 20 mg/L, MTBE- 3.7 mg/L, 
NH4
þ-N- 57 mg/L 
HSSF and 
FW 
Pilot scale- 5 m (L) 
1.1 m (W)  0.6 m (H) 
3 d 
287 L/day 
Phragmites australis 
HSSF: Benzene- 53%, MTBE- 
33%, NH4
þ- N-39% 
FW: Benzene- 98%, MTBE- 78%, 
NH4
þ- N-74% 
Seeger et al. 
(2013) 
Benzene, MTBE, 
NH4
þ-N 
contaminated 
water 
Benzene- 20 mg/L, MTBE- 3.7 mg/L, 
NH4
þ-N- 45 mg/L 
HSSF and 
FW 
Pilot scale- 5 m (L) 
1.1 m (W)  0.6 m (H)  
144 L/day 
Phragmites australis 
HSSF: Benzene-81%, MTBE -17%, 
NH4
þ-N  54% 
FW: Benzene-99%, MTBE -82%, 
BTEX-41% 
Seeger et al. 
(2011) 
Synthetic 
wastewater 
BTEX- 0.5 mg/L 
HSSF 
Pilot Scale- 3 m (L)  5 
m (W)  0.6 m (H) 
100 h  
Phragmites australis, Typha latifolia, 
unplanted 
46% (unplanted field) - 57% 
(Phragmites field) 
Ranieri et al. 
(2015) 
Petroleum- 
Contaminated 
Groundwater  
HSSF and 
VSSF 
Pilot scale  
5472 L/day 
willows (Salix), reed (Phragmites), 
bulrush (Scirpus), rush (Juncus), and 
dogwood (Corn 
Benzene-80%, total BTEX- 88% 
Bedessem 
et al. (2007) 
Synthetic diesel- 
contaminated 
water 
total petroleum hydrocarbons TPH- 
0.4–1.18 mg/L 
HSSF 
Pilot Scale- 1.8 m (L) 
 0.9 m (W)  0.9 m 
(H) 
72 d  
Scirpus grossus 
TPH- 81.5%–66.6% 
Al-Baldawi 
et al. (2015)  
M. Jain et al.                                                                                                                                                                                                                                    
Journal of Environmental Management 272 (2020) 111057
13
total phosphorus (TP), total nitrogen (TN), and other organic com­
pounds (MTBE, BTEX, Phenol, etc.) have been depicted in Fig. 7a. 
4.2.1. Role of flow pattern and type of CW 
The flow pattern of wastewater is one of the determining factors for 
the performance of wetlands. Among the various single CW systems used 
for the treatment of PRPP wastewater, it was found that HSSF-CWs 
exhibited higher removal efficiency (Fig. 7b). HSSF-CWs have shown a 
slightly better removal efficiency when compared to VSSF-CWs because 
of the large surface area and more exposure to atmospheric oxygen. 
Furthermore, a continuous flow with a low gradient can efficiently 
remove BOD, COD, and TSS (Vymazal, 2013). Also, the intermittent 
supply of wastewater, which is better suited for the proper functioning 
of VSSFs, accounted for the slightly less COD removal. VSSF-CWs are 
more efficient than HSSF-CWs, in terms of nitrogen removal because 
conditions favoring nitrification prevail in this system. However, these 
systems are subjected to clogging and are inefficient in the removal of 
solids. It was observed that when HSSF-CWs were combined with 
VSSF-CWs, the performance of the hybrid system was better as 
compared to the single system CWs (Fig. 7b). Such enhancement in 
performance of the hybrid CWs may be because the advantages of both 
HSSF-CWs and VSSF-CWs nullify their individual drawbacks. Vymazal 
(2013) reported an increased COD removal efficiency ranging from 10 to 
30% when the two systems were combined. The COD reduction ach­
ieved was greater than 90%, which indicated the higher efficiency of 
these processes over other CWs (Fig. 7b). FSF-CWs provide an average 
removal efficiency of 80% (Al-Baldawi et al., 2013c; Ji et al., 2007). Li 
et al. (2012) reported lower COD removal efficiency for FWs, which can 
be attributed to the lack of substrate. The flow rate in CWs also played a 
significant role in the removal of COD, as observed in Fig. 7c. It is 
evident that the performance of the CW deteriorates with an increased 
flow rate. When the flow rate was kept below 20 L/day, COD removal 
efficiency of around 90% was achieved. However, when the flow rate 
was increased, the COD removal kept on decreasing (Ji et al., 2002, 
2007; Mustapha et al., 2018b; Mustapha and Bruggen, 2018; Sudarsan 
et al., 2016). It may be attributed to the less contact time available to 
wastewater at higher flow rates. 
4.2.2. Types and role of substrate 
Apart from microbes and plants, the substrate of the wetland is also 
an integral factor for the removal of contaminants from wastewater. The 
most commonly used natural substrates used in CWs are gravel, sand, 
zeolite, minerals, limestone, and volcanic rocks (Wang et al., 2020). The 
substrates act as the base for all biotic (phytoremediation, biodegrada­
tion) and abiotic (sorption, volatilization, hydrolysis, redox reactions) 
functions of the wetlands. The substrate is decided according to its 
capability to support the natural growth conditions required for the 
biological species (bacteria, plants, fungi, etc.) and also its ability for 
adsorption and retention of pollutants in the small pores (filter media). 
The sand was found to be more efficient in the removal of TP and TN as 
Fig. 7. Plots showing variation in removal efficiency of a) CWs in removing various contaminants, b) different types of CWs in removing COD, the effect of c) flow 
rate on COD removal, and d) plants on organics removal from petroleum refinery and petrochemical plants wastewater. Data from Table 4. 
M. Jain et al.                                                                                                                                                                                                                                    
Journal of Environmental Management 272 (2020) 111057
14
compared to gravel (Wang et al., 2020). Sandy soil exhibited better 
performance as compared to sandy clay. Zeolite has shown excellent 
efficiency in terms of the removal of TP and ammonia. It is also rich in 
micro-pores and macro-pores, which facilitates the adsorption of various 
pollutants (Wang et al., 2020). Sometimes an upper layer of soil is also 
provided for plant root stabilization. Various substrates have different 
interactions with the wastewater due to their diverse chemical compo­
sitions, surface/charge ratio, porosity, hydraulic properties, mineral­
ogical compositions, and sorptive properties. Large pore spaces provide 
more surface area for microbial growth, and higher hydraulic conduc­
tivity, while the pH of media affects the plant and biofilm growth 
(Dordio and Carvalho, 2013). Ranieri et al. (2013) used an HSSF-CW 
with a substrate layer of clay soil, stones, and gravels for the treat­
ment of BTEX solution and obtained a removal efficiency of around 46% 
in the absence of macrophytes. The use of medium size gravel (10–20 
mm), fine gravel (1–5 mm), and fine sand (1–2 mm) in a CW planted 
with Scirpus grossus resulted in 66.6%–81.5% removal of total petroleum 
hydrocarbon (TPH) from water contaminated with different concen­
tration of diesel (Al-Baldawi et al., 2015). 
4.2.3. Role of various species of macrophytes and microorganisms 
The removal of nutrients and organic contaminants in wetlands de­
pends mostly on the type of plant and microbial species. Many re­
searchers studied the effect of different plant and microbial species on 
treatment efficiencies of CWs. The efficiency of CW in the presence and 
absence of plants was compared by Mustapha et al. (2018b). At the same 
time, Mustapha and Bruggen (2018) observed the alteration of the 
performance of the CW using different plant species. The presence of 
plants facilitated the phytoremediation process and enhanced the 
removal efficiencies of various contaminants (Fig. 7d). Sudarsan et al. 
(2016) performed multiple trials for the treatment of petrochemical 
industry wastewater using Typha latifolia and Phragmites australis and 
achieved removal efficiencies in the range of 90–95% for BOD, COD, and 
TSS, while the removal efficiency of phenolic compounds was found to 
be in the range of 65–90%. It was found that Typha latifolia performed 
better than Phragmites australis for the removal of phenolic compounds. 
Additionaly, Sudarsan et al. (2018) had also reported that the CW 
containing Typha latifolia exhibited better removal of BOD and COD as 
compared to a CW containing Phragmites australis The CW containing 
Phragmites australis exhibited around 85% BOD removal and 89% COD 
removal, whereas the CW containing Typha latifolia exhibited around 
90% BOD removal and 93% COD removal after 24 h of the study 
(Sudarsan et al., 2018). Ranieri et al. (2013) observed BTEX removal 
from HSSF-CWs planted with Typha latifolia, Phragmites australis, and 
one unplanted CW at a flow rate of 1 m3/d. Phragmites australis planted 
CW field removal was 5% higher than the Typha latifolia planted field 
and 23% higher than the unplanted field. Phytoremediation is not only 
confined to the use of grasses or weeds, but different kinds of cereals are 
also capable of removing contaminants from wastewater. Baoune et al. 
(2019) used Zea mays inoculated with Streptomyces sp. for removing 
petroleum hydrocarbons, i.e., phenanthrene, pyrene, and anthracene 
from petroleum crude contaminated soil, and achieved the removal ef­
ficiency of 61%, 59%, and 46%, respectively. Hairy tomato roots can 
also be used for phytoremediation of phenols by exploiting their basic 
peroxidase isoenzymes (Gonz
alez et al., 2006). However, the tolerance 
capacity of the plant for different contaminants should be explored for 
selecting the appropriate species (Zhang et al., 2010). Guittonny-Phi­
lippe et al. (2015) assessed the tolerance of five helophyte species in 
water containing a mixture of metals and organic pollutants and sug­
gested the use of species growing at close vicinity of the industrial dis­
charges for phytoremediation. In the case of the submerged 
macrophytes, the biofilms present on their roots also affect the perfor­
mance of wetlands. Biofilms attached to the submerged macrophytes are 
positively correlated to PAH (pyrene & phenanthrene) and TN removal, 
and also help to regulate the nitrogen cycle, thereby reducing their risk 
of contamination in other organisms and environment (Qin et al., 2019). 
Recently, FWs have gained importance in the PRPP wastewater treat­
ment due to their low complexity. Free-floating plants, such as Lemna 
paucicostata (duckweeds), Eichhornia crassipes, Typha domingensis, 
perennial grass species, such as Geophila herbacea, Lolium perenne, etc. 
are commonly used for removal of contaminants from oily wastewater. 
Ekperusi et al. (2019) used duckweed for simulating crude oil spill sites 
for the remediation of heavy metal by using an artificial FW and 
observed an increase in the heavy metal removal potential of plants. 
Plant configuration can be decided based on the local climate, nutri­
tional level of the wastewater, etc. The various plants used for treating 
petrochemical wastewater in CWs have been shown in Table 4. 
Microorganisms also play a vital role in the removal of organics as 
they degrade the complex compounds into simpler non-toxic forms. 
Different types of bacteria and fungi are present in the rhizosphere of the 
plant root system in CWs. Fester (2013) observed substantial arbuscular 
mycorrhizal fungi colonization in plant roots of Phragmites australis 
treating groundwater contaminated with benzene, MTBE, and ammonia. 
Bioaugmentation can assist wetlands in improving their removal effi­
ciency by aiding the synergistic effect on plants. Bioaugmentation using 
Typha domingensis with three phenol-degrading bacterial strains, Aci­
netobacter lwofii, Bacillus cereus, and Pseudomonas sp., can provide 
enhanced phenol removal varying from 0.146 g/m2/day to 0.166 
g/m2/day (Saleem et al., 2018). Wang et al. (2019) reported the increase 
in the population of oil-degrading bacteria and soil respiration with the 
rise in oil dosage in soil. Furthermore, PAH degrading bacteria were 
found to be more effective for oil degradation than alkane degrading 
bacteria (Wang et al., 2019). El-Naas et al. (2014) and Fahid et al. (2020) 
investigated the activity of bacteria, such as Acinetobacter sp. BRRH61, 
Bacillus megaterium RGR14, Acinetobacter iwoffii AKR1, and Pseudony­
mous putida in CWs to treat diesel contaminated wastewater and pe­
troleum refinery wastewater. The presence of bacteria not only 
improved the efficiency of the system but also enhanced plant growth. 
Supplementing the CWs with dephosphorization and nitrogen removing 
bacteria can further enhance the removal of TN and TP from the 
wastewater (Ji et al., 2020). 
4.2.4. Role of dissolved oxygen 
DO in the wastewater plays a pivotal role in the performance of the 
CWs. The DO primarily influences the microbial degradation of organic 
contaminants in the CW. Although microbial degradation can be both 
aerobic and anaerobic, aerobic degradation is preferred in the treatment 
of PRPP wastewater (Al-Baldawi et al., 2013d). It has been reported that 
higher oxygen content facilitates the degradation of organic compounds 
(Liu et al., 2016). Since the organic compounds tend to use up most of 
the available DO, there is little DO left for removal of ammonia and TN. 
Establishing aerobic condition can significantly improve the COD, 
ammonia, and TN removal (Li et al., 2014a). However, denitrification is 
also essential for complete nitrogen removal, and denitrification re­
quires anaerobic conditions. As a result, proper distribution of DO is 
required so that aerobic and anaerobic zones are created, which can 
facilitate the growth of different bacteria (Li et al., 2014a). In order to 
induce both aerobic and anaerobic conditions and achieve a proper 
distribution of DO, artificial aeration is carried out in the CWs. It en­
hances both nitrification and denitrification, leading to the improved 
overall performance of the system. Artificial aeration is usually achieved 
by air pumps and air blowers. In VSSF-CWs and HSSF-CWs increased 
removal of organic matter, TN and ammonia were obtained when arti­
ficial aeration was provided (Al-Baldawi et al., 2013d; Dong et al., 
2012). 
4.3. Removal of contaminants present in PRPP wastewater and their 
mechanism 
The main concern associated with the effluent coming out of the 
PRPPs is the complex organic compounds that need to be removed for 
preventing toxicity of wastewater. CWs are believed to be an effective 
M. Jain et al.                                                                                                                                                                                                                                    
Journal of Environmental Management 272 (2020) 111057
15
technique to get rid of these recalcitrant components. They are efficient 
in transforming complex organic compounds into simple non-toxic 
products. The main feature of the CW system is that the pollutants are 
removed via multiple pathways/mechanisms. Physical removal, such as 
adsorption, sedimentation, volatilization, and chemical processes, such 
as oxidation, reduction, photolysis (in FWS), etc. are essential pathways. 
Biological processes, namely nitrification, denitrification, microbial 
degradation, rhizofiltration, and also phytoremediation, lead to com­
plete bioremediation of contaminants (Fig. 8). Rhizofiltration involves 
the filtration of toxic components through the roots (Rahman and 
Hasegawa, 2011). Phytoremediation consists of processes, such as 
phytoextraction, 
phytostabilization, 
phytovolatilization, 
phytode­
gradation, and phytodesalination (Ali et al., 2013). In phytoextraction, 
pollutants are absorbed by roots, transported, and stored in the upper 
parts of the plants. Pollutants are stabilized into the rhizosphere by 
reducing its mobility through phytostabilization. The organic hydro­
carbons are absorbed from the soil and converted to a less toxic, volatile 
form in plants and are subsequently released into the atmosphere 
through phytovolatilization. However, during phytovolatilization, con­
taminants are transferred from the wastewater to the atmosphere 
instead of degrading them. Hence, it should be avoided in highly 
populated areas (Padmavathiamma and Li, 2007). Phytodegradation 
reduces the toxic pollutants into less toxic components by using the 
plant enzymes. 
Literature suggests that aerobic microbial degradation is a useful 
technique for MTBE removal (Thullner et al., 2018). Studies have shown 
that MTBE is primarily volatilized from the plant with a minimal amount 
detected in plant tissues (Yu and Gu, 2006). Furthermore, MTBE is 
sensitive to photo-oxidation in the atmosphere (Reiche et al., 2010). 
Stefanakis et al. (2016) compared two planted HSSF-CW (both with reed 
grass) and one unplanted HSSF-CW and found higher removal rates for 
MTBE and Benzene in planted CW and around complete removal of 
phenols in all CWs. Seeger et al. (2013) investigated the efficiency of two 
CWs and one plant root mat (FW) and observed that the FW achieved the 
highest removal for benzene, MTBE, and ammonium-nitrogen. The 
removal of MTBE was found to be lower than that of BTEX in 
petroleum-contaminated groundwater, which may be due to lower 
biodegradability of MTBE as compared to BTEX constituents (Bedessem 
et al., 2007). In another study, similar results were obtained for BTEX 
removal, and also around 5–10% higher removal of toluene was 
observed compared to other BTEX compounds, which may be due to the 
higher volatile nature of toluene (Ranieri et al., 2015). 
For most of the hydrocarbons, degradation is the main pathway that 
predominantly occurs inside the plant or in the rhizosphere (Yin et al., 
2011). Additionally, for volatile organic compounds, such as BTEX, 
trichloroethene, vinyl chloride, and carbon tetrachloride, having Hen­
ry’s constant (KH) > 10 atm m3 water m 3 air, phytovolatilization also 
acts as a vital reduction pathway (Rozaimah et al., 2020). Phenols and 
PAH can also be removed by sorption through plant roots (Poerschmann 
and Schultze-Nobre, 2014). Al-Baldawi et al. (2015) showed that plants 
S. grossus could uptake TPH (Phytoextraction), and the rhizobacteria 
degraded it in sub-surface batch CW (biodegradation). Salmon et al. 
(1998) found that hydrocarbon removal in the artificial wetland was 
about 90%, in which biodegradation accounted for 60%, phytovolatili­
zation accounted for more than 25%, while the remaining 10% was 
removed by sorption. Ranieri et al. (2013) found that the BTEX removal 
in pilot-scale HSSF-CW was primarily through volatilization and 
biodegradation played a secondary role. TPH, phenol, O&G, and TN 
have good translocation ability in plants and were removed mainly by 
phytoextraction (Mustapha et al., 2018b). Other organic matters, which 
are represented in terms of COD and BOD in the PRPP wastewater, are 
removed by a combination of physical, chemical, and biological pro­
cesses. In FSF-CWs, photochemical reactions, sorption, reduction, 
oxidation reactions, and biodegradation are the main pathways for 
organic matter decomposition. Mishra and Maiti (2017) found that 
water hyacinth (Eichhornia crassipes) can be instrumental in the phy­
toextraction of organics and heavy metals from wastewater. 
Metals removal from PRPP wastewater is also a major concern in 
conventional treatment systems. However, the removal of metals is 
easily attained in CWs due to the presence of multiple mechanisms. The 
media adsorbs and filters the pollutants from the wastewater stream and 
plants help in the translocation of contaminants from the media leading 
to subsequent contaminant removal from the treatment system. 
Furthermore, the elemental composition of the media, such as Ca, Al, Si 
oxides, etc. may act as a catalyst for the removal processes. Additionally, 
processes, such as reduction, oxidation, sedimentation, flocculation, 
Fig. 8. Mechanism of contaminant removal from petroleum refinery and petrochemical plants wastewater using constructed wetlands.  
M. Jain et al.                                                                                                                                                                                                                                    
Journal of Environmental Management 272 (2020) 111057
16
precipitation, and ion exchange, also occur simultaneously in CWs 
(Batool and Saleh, 2019). Mustapha et al. (2018b) used VSSF-CW for 
removing heavy metals from secondary treated refinery wastewater and 
found that Cu, Cr, Zn, Pb, Cd, and Fe were accumulated in the stem, 
leaves, and roots of the plants with maximum accumulation in roots. 
Additionally, the substrate helps to reduce the mobility of metals in the 
rhizosphere (Lesage, 2007). The floating aquatic plants uptake metals 
through their roots, while the whole plant body acts as the uptake 
pathway for submerged plants (Rahman and Hasegawa, 2011). 
TSS is primarily removed by interception and sedimentation in the 
wetland media. In HSSF-CW, its maximum removal is achieved near the 
inlet zone, and gradually the removal rate decreases. In FSF-CWs, 
interception plays a minor role, while the major part of the TSS is 
adsorbed on the macrophyte surface. Ammonia can be removed by 
physical processes, such as filtration, adsorption, and also by microbes 
involving aerobic degradation. Since the removal of TP primarily de­
pends on the adsorption by the media, a media with higher sorption 
capacity is preferred (Zhaia et al., 2011). Furthermore, phosphorus can 
be retained in the roots of the macrophyte population (Mustapha et al., 
2018b). 
4.4. Modification of CW systems for enhanced performance 
The salient features in the modification of single wetland systems 
include modification of flow, implementation of artificial aeration, and 
use of substrate supplements (Ji et al., 2020; Valipour and Ahn, 2016). 
Tidal flow works on the batch principle, where the CW is flooded peri­
odically multiple times. The primary attribute for this process in the high 
oxygen transfer efficiency. In order to increase the flow path and to 
create sequential aerobic, anoxic, and anaerobic conditions, vertical 
baffles along the width of the CW are provided. Step feeding of waste­
water in a CW is carried out by providing multiple inlet points to give the 
entire area of the CW a uniform loading (Valipour and Ahn, 2016). 
Artificial aeration is predominately carried out to induce aerobic 
conditions. The position of the aerators has been reported to influence 
the performance of the system. Researchers achieved better removal of 
COD and TN when they employed middle aeration to achieve uniform 
oxygen distribution (Dong et al., 2012; Liu et al., 2013b). Wang et al. 
(2015) observed better performance in the case of middle aeration as 
compared to surface aeration and bottom aeration in a VSSF-CW. Re­
searchers reported that intermittent aeration facilitated better removal 
of TN in HSSF-CW and VSSF-CW because it provides both aerobic and 
anaerobic conditions for efficient nitrification and denitrification. 
Continuous aeration facilitated better removal of ammonia, but the 
oversaturation of the CW with DO inhibited the removal of nitrate and 
TN (Ji et al., 2020). Artificial aeration stimulates the activity of het­
erotrophic bacteria even in a cold climate to enhance the performance of 
the CWs. Fan et al. (2013) found that a combination of intermittent 
aeration combined with step feeding resulted in the best removal of both 
nitrate and ammonia. Artificial aeration has also been instrumental in 
treating PRPP wastewater in cold climates, where the low temperature 
severely affects the working of the CWs. Wallace et al. (2011) incorpo­
rated artificial aeration in CWs and achieved a removal efficiency of 
100%, 94%, and 93% for BTEX, aniline, and nitrobenzene at tempera­
tures below  20 C. It can be observed that if proper aeration is sup­
plied, then the performance of the single system CWs can be significantly 
enhanced. 
Apart from the regular substrates that are used in CWs, as mentioned 
in Sect. 4.2.4, various agricultural or industrial wastes, such as oyster 
shells, alum sludge, woodchip, plant waste, fly ash, slag, etc. can also be 
used as substrates. Among the plant wastes, various plant by-products 
generated during plant processing, such as coconut dust, rice husk, 
reed leaves, etc. were used. Artificial materials, such as ceramics, acti­
vated carbon, cement clicker, synthetic fibers, modified clays, and 
recycled concrete, have also been used in CWs to evaluate the perfor­
mance of the system (Wang et al., 2020). Activated carbon showed 
promising removal efficiency when it comes to the removal of TP, TN, 
and ammonia (Fu et al., 2017). However, synthetic fibers showed poor 
removal efficiency of ammonia (Y. Chen et al., 2012). The effectiveness 
of these systems primarily depends on the adsorption capacity of these 
substrates. Adequate research needs to be done to identify the best 
substrate for the treatment of petrochemical wastewater in CWs. 
Hybrid CWs are improvised in many places by using the combination 
of different CWs for an enhanced level of performance. Different 
multistage CW, such as VF-VF-HF-CWs, VF-HF-VF-CWs, HF-VF-FH-CWs, 
and HF-HF-VF-CWs, have been used to improve the efficiency of the 
process. Often two VSSF-CWs are placed atop each other to create 
anaerobic conditions. These systems usually employ one down-flow 
VSSF and one up-flow VSSF connected at the bottom. Since the final 
outflow of the water is from the top of the second bed, both the beds are 
saturated, leading to anoxic conditions. The FSF-CW has also been 
augmented with HSSF-CWs and VSSF-CWs. This process may not be as 
efficient as a combination of VSSF-CWs and HSSF-CWs or multistage 
CWs, but it increases the overall performance of the single wetland 
system (Vymazal, 2013). These modifications were not only instru­
mental in the removal of COD, but also enhanced the removal efficiency 
of ammonia, TN, and TP. However, most of the applications of these 
CWs have been limited to the treatment of municipal sewage, waste­
water from food industries, aquaculture, and tannery. Their perfor­
mance in the remediation of petrochemical wastewater has not been 
thoroughly studied. 
5. Issues and concerns 
Most of the studies in this field involving CWs have been carried out 
for a selected number of organic contaminants or with synthetic 
wastewater, while real-life PRPP wastewater contains numerous organic 
contaminants with pronounced toxicity. Furthermore, there exist many 
ambiguities regarding the shock loading capacity of wetlands concern­
ing the variability of the PRPP wastewater. In order to be implemented 
at an industrial level, CWs must be able to bear continuous variations in 
influent. As a result, proper designing and planning are required to cater 
to the influent water quality and quantity from various industries. 
Denitrification and nitrification are essential steps for TN removal, and it 
is greatly influenced by the presence of DO. Proper TN removal is not 
achieved due to the poor control of DO. This drawback can be addressed 
by providing proper aeration or distribution of DO in the system (Jiz­
heng et al., 2019). The performance of CWs in a cold climate is another 
concern because of the freezing of water, and the low temperature may 
not be suitable for the proper growth and performance of the microor­
ganisms. Concerning these problems, various aeration techniques have 
been used. However, further research is needed in this area to 
completely eradicate this drawback (Rozema et al., 2016). Additionally, 
media porosity and clogging are other factors influencing the perfor­
mance of the CW for a long-term period. Porosity is affected by the 
solids, root elongation, and attached biofilm growth. Zidan et al. (2015) 
studied the effect of different types of media for CW and found that after 
218 days of operation of CW, porosity decreased by 16.94% for gravel 
media, 12.33% for rubber media, and by 9.01% for plastic media. It was 
found that plastic media performed better than the other two media in 
the removal of COD, BOD, and TSS and also showed more resistance to 
clogging. However, Al-Isawi et al. (2015) observed that clogging of 
media due to diesel spills did not severely affect the contaminant 
removal. As a result, a long term assessment of porosity of the soil and 
contaminant removal is required. 
Phytotoxicity is a matter of concern, that is required for examining 
the tolerance levels of plant species for different concentrations of 
contaminants. Al-Baldawi et al. (2013a) used an HSSF-CW planted with 
Scirpus grossus species for the treatment of diesel in water and found that 
this plant can remove diesel, only up to a certain concentration level, 
above which the removal efficiency of wetlands decreases. In a similar 
study, Wang et al. (2014) found that Polygonum orientale was intolerant 
M. Jain et al.                                                                                                                                                                                                                                    
Journal of Environmental Management 272 (2020) 111057
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for phenol above a concentration of 100 mg/L. These issues have a 
negative impact on the performance of CWs and need to be addressed 
while designing. Generally, a single bacterial strain is not sufficient for 
the complete remediation of petrochemical contaminants (Singh and 
Borthakur, 2018). As a result, a microbial consortium or coupling of 
bacteria and fungi is required for treatment (Singh and Borthakur, 
2018). Microbial processes are affected by root exudation of oxygen and 
organics, which may be affected by photosynthesis (Lünsmann et al., 
2016). The diurnal variations in photosynthesis and the release of oxy­
gen in CWs need to be explored more to bring out the maximum effi­
ciency of CWs. Furthermore, the reuse of CW effluent for irrigation is 
still a topic of research, and analysis of contamination in the soil 
resulting from the reuse of CW effluent needs to be carried out carefully. 
However, the most challenging attribute about the CWs is the require­
ment of a vast area for their establishment. Many developing countries 
do not have large spaces available for the accommodation of these 
wetlands, which is a significant challenge for this efficient technique. 
6. Summary of findings 
In the present review, we have demonstrated the global challenges 
concerning the management of PRPP wastewater, in emphasizing 
environmental impact and its remediation technologies. It was found 
that various components of PRPP wastewater exceeded the permissible 
limits set by USEPA and the WBG. The PRPP wastewater of multiple 
regions was found to be characterized by high concentrations of BTEX, 
Phenol, MTBE, etc. which may pose a threat to all kinds of living or­
ganisms. Subsequently, tremendous stress on research concerning the 
remediation of PRPP wastewater is prioritized in the arena of environ­
mental engineering. Various studies of remediation of PRPP wastewater 
by conventional treatment technologies focused on the reduction of 
COD; however, the remediation of toxic organic compounds have not 
been provided significant attention. Furthermore, a considerable 
amount of oily sludge is produced, which is an added drawback to many 
conventional biological treatment processes. Among the established 
advanced treatment techniques, AOPs were efficient in terms of the 
removal of recalcitrant organic compounds, but they are not as 
economical as that of the CWs. CWs were found to overcome the limi­
tations of other processes in terms of treating PRPP wastewater, since 
they were not only able to remove COD, ammonia, phosphate, nitrate 
but also were quite efficient in the removal of PAHs, BTEX, phenols, and 
other recalcitrant organic compounds without producing any significant 
amount of sludge. Such efficiency of CWs is attributed to the various 
mechanisms, such as phytodegradation, phytovolatilization, phytosta­
bilization, microbial degradation, rhizofiltration, sorption, substrate 
filtration, etc., which occur simultaneously. The type of flow was found 
to vary the performance of the constructed wetlands. HSSF-CWs pro­
vided better performance when compared to other single system CWs. 
Often the CW systems were provided with aeration or arranged in a way 
to provide both aerobic and anaerobic conditions for efficient removal of 
nutrients. Middle aeration was found to be the most effective in VSSF- 
CWs. Planted CW systems showed considerable improvement in treat­
ment when compared to unplanted CW systems. Typha latifolia and 
Phragmites australis were found to be the most efficient in terms of 
treating PRPP wastewater. The performance of the single CW systems 
can be further enhanced by providing different flow patterns, artificial 
aeration, and modification of the substrate. Hybrid CWs further negate 
few drawbacks of single CW systems and provide excellent treatment. 
This paper opens various avenues for the use of CWs for the treatment of 
PRPP wastewater by providing insights into various modifications of the 
existing CWs and the role of operating parameters. This review can also 
catalyze the research involving proper modifications of CW and opti­
mization of the operating conditions, thereby providing a breakthrough 
in the field of PRPP wastewater treatment. 
Declaration of competing interest 
The authors declare that they have no known competing financial 
interests or personal relationships that could have appeared to influence 
the work reported in this paper. 
Acknowledgments 
M.Jain and A. Majumder are grateful to the Indian Institute of 
Technology Kharagpur, India, for financial support. 
Appendix A. Supplementary data 
Supplementary data to this article can be found online at https://doi. 
org/10.1016/j.jenvman.2020.111057. 
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Computers and Chemical Engineering 153 (2021) 107455 
Contents lists available at ScienceDirect 
Computers and Chemical Engineering 
journal homepage: www.elsevier.com/locate/compchemeng 
Real-time reﬁnery optimization with re 
duce 
d-order ﬂuidize 
d catalytic 
cracker model and surrogate-based trust region ﬁlter method 
Xinhe Chen 
a 
, Kai Wu 
a 
, Andrew Bai a 
, Cornelius M. Masuku a 
, Jacques Niederberger 
b 
, 
Fábio S. Liporace 
b 
, Lorenz T. Biegler 
a 
, 
∗
a 
Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA 
b 
Petrobras Research & Development Center (CENPES/PETROBRAS), Cidade Universitária, Avenida Horácio Macedo, 950, Rio de Janeiro, RJ CEP 21949-900, 
Brazil 
a r t i c l e 
i n f o 
Article history: 
Received 26 January 2021 
Revised 25 June 2021 
Accepted 17 July 2021 
Available online 21 July 2021 
Keywords: 
Real-time optimization 
Nonlinear programming 
Trust region methods 
Surrogate modeling 
a b s t r a c t 
Since its initial development in the 1980 
′ 
s, Real-Time Optimization (RTO) has been widely appreciated as 
an eﬃcient way to optimize process decision variables and improve economic performance of reﬁneries. 
RTOs consist of nonlinear optimization models with hundreds of thousands of equations, which are built 
within equation-oriented (EO) modeling platforms. With increasing size and complexity of RTO applica- 
tions, there is increased demand for improved optimization strategies. To address this demand, surrogate 
models for complex reﬁnery units have been embedded within the general EO framework for RTO. More- 
over, the recent trust region ﬁlter (TRF) optimization strategy allows great ﬂexibility in the choice of 
surrogates, while ensuring convergence to the optimum of the rigorous RTO model. This study considers 
this approach for a real-world reﬁnery. The Petrobras S.A. RECAP unit in Mauá, Brazil runs an RTO reﬁn- 
ery model with an Aspen RTO optimizer to maximize the proﬁt within two hour cycles. To reduce the 
computational burden, we embed a reduced model (RM) to replace the detailed (truth) model for the 
residue ﬂuid catalytic cracking (RFCC) unit, and implement a TRF optimization strategy. The TRF driver 
is written in Python and integrates with the RFCC truth model, the Aspen-EO RECAP model, and the As- 
pen RTO optimizer. The approach is illustrated on three real-world scenarios in order to demonstrate the 
effectiveness and eﬃciency of this RM-based optimization strategy. 
© 2021 Published by Elsevier Ltd. 
1. Introduction 
Advances in open equation process modeling and nonlinear 
programming (NLP) algorithms have led to rigorous steady state 
process optimization formulation and reliable solution strategies 
for real-time optimization (RTO) problems ( 
Cameron et al., 2019 
; 
Marlin and Hrymak, 1997 
). Real-time optimization (RTO) is a ro- 
bust method which can monitor the state of the process and make 
decisions to optimize the objective function (like proﬁt or cost) 
in real time systems. RTO systems are model-based, closed-loop 
process systems. The optimization models consist of hundreds of 
thousands of variables and nonlinear equations and are routinely 
solved in 2–4 h cycles. Real-time optimization (RTO) models have 
been developed since the early 1980s ( 
Cutler and Perry, 1983 
) and 
are abundant in petrochemical and chemical applications, espe- 
cially in the production of ethylene. With over 300 applications 
∗Corresponding author. 
E-mail address: lb01@andrew.cmu.edu (L.T. Biegler). 
worldwide, the real-time optimization market is estimated at over 
a billion dollars per year ( 
http://www.arcweb.com 
). This growth 
leads to increased agility in plants to execute production plans, 
eliminates ineﬃciencies and proﬁtably captures market opportu- 
nities. In particular, manufacturers use RTO to tune existing pro- 
cesses to changes in product demand and price as well as cost 
and availability of feedstocks. Moreover, RTO is essential to mit- 
igate and reject long-term disturbances and equipment perfor- 
mance losses (for example, through fouling of heat exchangers or 
deactivation of catalysts ( 
Darby et al., 2011 
; Camara et al., 2016 
)). 
Today, dozens of suppliers populate the Advanced Process Control 
and RTO market. These include global suppliers like ABB, Aspen- 
Tech, Emerson/Ovation, Honeywell, Rockwell Automation/Pavilion, 
AVEVA, and Siemens. Along with the beneﬁts of RTO ( 
Ansari et al., 
2020 
; Aspen Custom Modeler User’s Guide 2002 
; Darby et al., 
2011 
; Marlin and Hrymak, 1997 
; Quelhas et al., 2013 
; Ruiz, 2009 
; 
Shokri et al., 20 
09 
; SimSci-Esscor 20 
04 
; Yip and Marlin, 2004 
; 
Zhang et al., 2001 
), there are a number of challenges to its imple- 
mentation including formulating models that are consistent with 
https://doi.org/10.1016/j.compchemeng.2021.107455 
0098-1354/© 2021 Published by Elsevier Ltd. 
X. Chen, K. Wu, A. Bai et al. 
Computers and Chemical Engineering 153 (2021) 107455 
Fig. 1. Plant decision hierarchy (source: Darby et al., 2011). 
the plant, maintaining stability of the RTO cycle, ensuring useful 
solutions in the face of disturbances, and solving the NLP problems 
quickly and reliably. 
As shown in Fig. 1 
, Planning is at the top of the decision hi- 
erarchy and it determines the production goals over an extended 
time period, by making the plan for different orders. The lay- 
ers above RTO also include Scheduling, which decides the tim- 
ing to execute the plan. Planning and Scheduling are both related 
with supply chain management. They perform at the macroscopic 
decision-making level and are executed off-line (with only inter- 
mittent feedback) and provide the information framework for real- 
time optimization. The RTO and model predictive control (MPC) 
layers execute in real-time, assisted by automatic, continuous feed- 
back from the process (note the solid lines in Fig. 1 
). Finally, the 
distributed control system (DCS) is at the lowest level of this hi- 
erarchy with the shortest time scale, usually within seconds. The 
DCS layer rejects short-term disturbances and keeps the operation 
at the set conditions. 
In the process hierarchy, RTO provides the connection between 
plant scheduling (medium-term planning) and the control system 
(short-term process performance) ( 
Ansari et al., 2020 
; Darby et al., 
2011 
; Ruiz, 2009 
). Here, the time scale of RTO is in hours and 
the RTO system provides optimal steady state operating conditions 
of the process (temperature, pressure, and ﬂow rate etc.) based 
on inputs from the process measurements as well as the product 
scheduling plan ( 
Trierweiler, 2014 
). 
The RTO information ﬂow is shown in Fig. 2 
. Sensor data 
are ﬁrst analyzed to detect steady state operation from the DCS. 
These plant data are matched to a plant model in a data rec- 
onciliation/parameter estimation step and the resulting optimiza- 
tion model is solved to determine the process states and operat- 
ing conditions. For the Reconciliation step, the nonlinear optimiza- 
tion model contains information about mass and energy balances 
as well as thermodynamic and kinetic parameters. Once the real- 
time data from the plant are reconciled in the Model Update step, 
an economic based optimization problem is formulated and solved 
in order to determine the new operating conditions. The solution 
of the Model-based Optimizer provides the changes to the oper- 
ating conditions. These conditions are then passed to the DCS as 
setpoints, so that the process control system steers the plant to- 
ward the desired operating point. A key assumption is that the 
steady-state model is suﬃcient to describe the plant, and that fast 
dynamics and disturbances can be handled by the process control 
system. As a result, RTO coordinates the on-line decision process; 
Fig. 2. RTO functional diagram. 
it not only receives commands from the decision plan but also ad- 
justs itself according to the plant feedback. 
In this study, we consider a real-world RTO model from Petro- 
bras’ reﬁnery process, Reﬁnaria de Capuava (Recap), located in 
Mauá, Brazil. The RTO system was implemented over eight years 
ago and runs in a two hour cycle. The reﬁnery’s main products are: 
diesel, gasoline, LPG, solvents and propylene. This reﬁnery con- 
tains 6 major sub processes, including crude distillation unit, naph- 
tha distillation, residue ﬂuid catalytic cracking (RFCC plus main air 
blower, fractionator, wet gas compressor and gas recovery section), 
propylene production, sulfur recovery and solvent recovery units. 
Fig. 3 shows how the Reﬁnery was built in Aspen RTO hierar- 
chies. FEED contains information about the tanks, their composi- 
tion over time and which ones are sending feedstock to be pro- 
cessed by the Reﬁnery. Its purpose is to deﬁne the crude oil qual- 
ity and composition that will be pumped to the crude distillation 
unit. The main objective of crude distillation units is to separate 
petroleum into several fractions, such as diesel, kerosene, naphtha 
and LPG. It contains 5 hierarchies: PHT1, PHT2, PHT3, PRF and ATM. 
PHT1, PHT2 and PHT3 are three preheat trains. There is a pre-ﬂash 
tower in the PRF unit which separates the lighter fractions present 
in the crude oil. The light component, non-stabilized Naphtha, is 
cooled down and sent to solvent recovery units or stored. The 
heavy component, reduced crude, will be processed in the atmo- 
spheric distillation column (ATM hierarchy). The Atmospheric Dis- 
tillation Column is responsible for fractionating the reduced crude 
oil into Heavy Naphtha, Kerosene, Diesel and Atmospheric Residue 
(RAT). Heavy naphtha may be sent to the RFCC riser, Gas Recov- 
ery Unit and storage. Kerosene side-draw, after passing through the 
side-stripper, is incorporated into the Diesel stream. There is also 
an intermediate pump-around (RCI) which, after passing through 
several heat exchangers, is used to control the temperature in the 
intermediate region of the column. The Diesel stream is cooled be- 
fore going to storage. The Atmospheric Residue (RAT) from the bot- 
tom of Atmospheric Distillation Column is cooled and routed to the 
RFCC. 
The RFCC process consists of 9 hierarchies: MAB, SIMCRAQ, PRT, 
MFC, WGC, DC2, DC4, C3S and ABS. Atmospheric Residue (RAT) 
from previous units is fed into the riser through eight feed injec- 
tion nozzles provided with dispersion steam. After injection into 
the riser, the feed contacts with hot regenerated catalyst, coming 
from the regenerator. Cracking reactions take place along the riser 
and are interrupted after the catalyst is separated from the gas 
2 
X. Chen, K. Wu, A. Bai et al. 
Computers and Chemical Engineering 153 (2021) 107455 
Fig. 3. Aspen Reﬁnery Flowsheet. 
stream by a group of cyclones located at the top of the reactor ves- 
sel. The gas stream, which contains the cracking reaction products, 
is sent to the main fractionator (MFC). The catalyst, impregnated 
with coke and hydrocarbon vapors after the reaction, is sent to the 
Regenerator, where there is a combustion reaction, air being sup- 
plied by a blower (MAB), and the catalyst is regenerated, ﬂowing 
back to the riser. The stream leaving the Regenerator is used to 
produce power in a Turboexpander (PRT). In the main fractionator 
(MFC), the reactor eﬄuent is separated into four streams: gas to 
compressor, Cracked Naphtha, Light Cycle Oil (LCO) and Decanted 
Oil (ODEC). At the top of the main fractionator, gas is partially con- 
densed. Part of it is compressed in a Wet Gas Compressor (WGC) 
and sent to the Gas Recovery Section where, through distillation 
and absorption processes, Fuel Gas, C3s, LPG and Cracked Naphtha 
(DC2, ABS, C3S and DC4) are produced. Cold cracked naphtha and 
LCO are used as absorber liquid. There is an integration between 
RFCC and Crude units by means of the hot stream from the main 
fractionator, used to heat the crude oil on PHT1, PHT2, and PHT3. 
The bottom stream of MFC is the ODEC. 
The RTO model shown in Fig. 3 was developed using the RTO 
features in Aspen Plus RTO V8.8, was commissioned over 8 years 
ago and runs about every 2 h. The equation-oriented NLP consists 
of more than 20 
0,0 
0 
0 variables and constraints and comprises up 
to 30 degrees of freedom and 75 chemical species (both pure and 
pseudocomponents). Most of the units are from the Aspen-EO li- 
brary. On the other hand, the EO-based FCC model, called SIMCRAQ 
( 
Marlin and Hrymak, 1997 
), was developed with the Aspen Custom 
Modeler at Petrobras. It should be noted that all of these EO mod- 
els have exact derivatives, which allow fast Newton-type conver- 
gence. Both data reconciliation and optimization steps are solved 
with a large-scale SQP optimization code. Together, these problems 
require about 25 CPU min. to solve with about 50 SQP iterations. 
Our case study problem, shown in Fig. 4 
, has 23 degrees of 
freedom (optimization variables) and 198,834 variables for Aspen 
EO library models. The more detailed SIMCRAQ model consists of 
2724 variables and is more diﬃcult to incorporate and solve. Nev- 
Fig. 4. Schematic diagram of RFCC model (a. riser (tubular reactor); b. reactor ves- 
sel; c. reactor bed; d. regenerator ﬁrst stage dense phase; e. regenerator second 
stage dense phase; f. general dilute phase; g. regenerated catalyst plug valve; h. 
spent catalyst plug valve; i. reactor cyclones; j. second stage dilute phase; k. ﬁrst 
stage dilute phase; l. regenerator; m. stripper; n. regenerator cyclones). 
ertheless, as noted in Ansari et al. (2020) 
, Darby et al. (2011) 
, while 
simpler EO models can be used for separation, heat transfer and 
momentum transfer units, reactor models, such as the FCC unit, 
require additional complexity to provide accurate RTO results. 
The RFCC is divided into two EO models, which are described 
in Matias (2018) 
, Niederberger et al. (20 
0 
0) 
. In Fig. 4 
, the SIMCRAQ 
block refers to the ﬂuidized bed reactor and the catalyst regen- 
3 
X. Chen, K. Wu, A. Bai et al. 
Computers and Chemical Engineering 153 (2021) 107455 
erator, and MFC block refers to the fractionator. SIMCRAQ is the 
main block that we focus on for the reﬁnery optimization. The pre- 
processed feed stream enters with dispersion steam and regener- 
ated catalyst from the bottom of the plug-ﬂow reactor (a). The re- 
actions occur as this mixture advances in the riser. When the inlet 
ﬂow moves into the reactor vessel (b), most of the products are 
removed and sent to the main fractionator. The remainder consists 
primarily of used catalyst with coke, which is sent to the regener- 
ator. After two dilution steps (d, e) in the regenerator, the coke is 
burned to ﬂue gas (n) and the regenerated catalyst is then recycled 
(f, g) to the riser. 
Because the RTO cycle is updated every two hours, it is impor- 
tant for the optimizer to solve the optimization problem within 
the limited time. The complexity of the rigorous model is high 
and the computational cost to solve it is expensive. To address 
this problem, reduced-order models (ROM) can be used to re- 
place the detailed models. In this study, we built a reduced-order 
model to substitute RFCC model (SIMCRAQ), speciﬁcally the re- 
action part, in order to reduce the complexity of the RTO, and 
save computational time and model maintenance costs. For this 
study, a simple, EO-based surrogate (reduced) model replaces SIM- 
CRAQ in the RTO model and SQP solution. On the other hand, 
this model must be updated during runtime by the SIMCRAQ 
model to ensure the accuracy of the RTO solution. To do this, we 
adopt concepts from trust region optimization ( 
Conn et al., 2009 
; 
Nocedal and Wright, 1999 
; Wild, 2009 
) and apply the trust region 
ﬁlter (TRF) strategy ( 
Eason and Biegler, 2016 
, 2018 
; Fletcher et al., 
2002 
; Fletcher and Leyffer, 2002 
). The TRF strategy is an eﬃcient 
approach to include reduced models, but does not compromise the 
optimum of the truth models. Instead, TRF has strong convergence 
properties to the optimum of the truth model, for a general class of 
reduced models ( 
Conn et al., 2009 
; Eason and Biegler, 2016 
, 2018 
). 
The next section describes the TRF approach and Section 3 de- 
scribes a simple PFR model that is used as a surrogate for SIM- 
CRAQ. Section 4 then describes the implementation of this strat- 
egy with Aspen EO ( 
Aspen Custom Modeler User’s Guide 2002 
). 
Section 5 describes the case study and presents the RTO results 
while Section 6 summarizes the paper and provides directions for 
future work. 
2. Surrogate-based TRF optimization strategy 
We consider the RTO problem with both the EO library and de- 
tailed (truth) models as the mixed EO and black box optimization 
problem of the following form: 
min 
z,w 
f 
( 
z, 
w, 
t 
( 
w 
) 
) 
s.t. 
h 
( 
z, 
w, 
t 
( 
w 
) 
) = 0 
g 
( 
z, 
w, 
t 
( 
w 
) 
) ≤0 
(1) 
where the minimization is over z ∈ R 
n 
z and w ∈ R 
n 
w 
; w repre- 
sents the inputs to the detailed truth model, represented as a black 
box function, and z represents the remaining variables in the rest 
of the ﬂowsheet. Here f( 
z, 
w, 
t(w 
) 
) is the economic or model ﬁt- 
ting objective function and h 
( 
z, 
w, 
t(w 
) 
) = 0 represents the EO- 
based plant model. Also, t(w 
) 
: R 
n 
w → R 
n 
t 
, represents the out- 
put of the black box model with respect to w 
. The inequalities 
g( 
z, 
w, 
t(w 
) 
) ≤0 include step bounds for the RTO step, which are 
typically written as upper and lower variable bounds on the de- 
grees of freedom in RTO. Step bounds are imposed based on limi- 
tations of model extrapolation, as well as limitations of the control 
systems in meeting the new setpoints. 
We assume that all the functions f, h, g, t(w) are twice contin- 
uously differentiable for this case; here Aspen Sensitivity Analysis 
provides the derivatives, dt/dw 
. Because of the inherent diﬃculty 
of black box optimization problems, we assume that the black box 
model only accounts for a small part of the whole system. In this 
case, the number of inputs w and the outputs t(w) of the black 
box is small compared with the EO model variables. In our prob- 
lem, the black box input w has dimension 117 and the output t(w) 
has dimension 222. If we separate the black box and EO models, 
we can formulate the optimization problem as follows: 
min 
z,w 
f 
( 
z, 
w, 
y 
) 
s.t. 
h 
( 
z, 
w, 
y 
) = 0 
y 
−t 
( 
w 
) = 0 
g 
( 
z, 
w, 
y 
) ≤0 
(2) 
Problem (2) can be solved using a trust region method 
( 
Nocedal and Wright, 1999 
). At each iteration k 
, a reduced model 
r 
k 
(w 
) is built to locally approximate t(w) within the trust region 
radius ¯
k 
. We then restrict the optimization to stay within the 
trust region where the reduced model is considered suﬃciently ac- 
curate. 
The introduction of the reduced model and trust region con- 
straint leads to the following trust region subproblem (TRSP0) 
. 
min 
x 
f 
( 
x 
) 
s.t. h 
( 
x 
) = 0 
y 
−r 
k 
( 
w 
) = 0 
g 
( 
x 
) ≤0 
∥ 
x 
−x 
k 
∥ ≤¯
k 
(TRSP0) 
where x 
T = [ 
z 
T 
, w 
T 
, y 
T 
]. This problem is formulated about a base 
point x 
k and solved repeatedly at each iteration k 
. The trust re- 
gion constraint ∥ 
x 
−x 
k 
∥ ≤¯
 bounds the movement (extrapolation) 
of x by the trust region size ¯
k 
, which is adjusted based on the 
progress of the iterates x 
k toward the optimal solution of (2). In 
order to guarantee convergence of solutions of (TRSP0) to the so- 
lution of (2), the following accuracy condition is deﬁned for each 
reduced model r 
k 
(w 
) 
. 
Deﬁnition 1 ( 
Conn et al., 2009 
) 
. A model r 
k 
(w 
) is κ-fully linear on 
࢞k if for all { 
w 
: ∥ 
x 
−x 
k 
∥ ≤k 
} 
∥∇ 
r 
k 
( 
w 
) 
−∇t 
( 
w 
) 
∥ ≤κg ¯
k and ∥ 
r 
k 
( 
w 
) 
−t 
( 
w 
) 
∥ ≤κ f ¯
2 
k 
(3) 
for some ﬁnite κg > 0 and κ f > 0 independent of k. 
At each iteration k 
, r 
k 
(w 
) must satisfy the κ-fully linear prop- 
erty within the trust region ¯
k and it may need to be updated 
with information from the truth model. 
Under mild assumptions on sample set geometry, both poly- 
nomial interpolation and Kriging interpolation are examples of κ- 
fully linear reduced models. These models are especially useful 
when the truth model gradients ( 
∇t(w 
) 
) are not available. When 
truth model gradients ∇t(w) are not available, these popular sur- 
rogates (polynomials, kriging interpolation) will still satisfy the κ- 
fully linear property, as long as the criticality phase is used in TRF; 
this phase requires ¯
k → 0 as k → ∞ 
. 
On the other hand, if ∇t(w) is available, then the TRF algorithm 
simpliﬁes ( 
Alexandrov et al., 1998 
; Agarwal and Biegler, 2013 
), be- 
cause surrogate models can be generated using Eq. (4) 
r 
k 
( 
w 
) = ˜ 
r 
( 
w 
) 
+ ( 
t( 
w 
k 
) 
−˜ 
r 
( 
w 
k 
) 
) 
+ 
(∇ 
t 
( 
w 
k 
) 
−∇ 
˜ 
r 
( 
w 
k 
) 
) 
T 
( 
w 
−w 
k 
) 
(4) 
Here ( 
t( 
w 
k 
) 
−˜ 
r 
( 
w 
k 
)) is deﬁned as the Zeroth-order Correc- 
tion (ZOC) and the First Order Correction (FOC) is given by 
(∇t( 
w 
k 
) 
−∇ 
˜ 
r 
( 
w 
k 
)) 
T 
( 
w 
−w 
k 
) 
. where one can see that t( 
w 
k 
) = 
r 
k 
( 
w 
k 
) and ∇t( 
w 
k 
) = ∇ 
r 
k 
( 
w 
k 
) hold at each TRF iteration, no mat- 
ter what function ˜ 
r 
(w 
) is chosen (even ˜ 
r 
(w 
) = 0 
) ( 
Agarwal and 
Biegler, 2013 
). On the other hand, choosing a tailored model for 
˜ 
r 
(w 
) that closely approximates t(w) allows us to improve the per- 
formance of the TRF method. In Section 3 we describe how ˜ 
r 
(w 
) 
was constructed for the FCC truth model. 
4 
X. Chen, K. Wu, A. Bai et al. 
Computers and Chemical Engineering 153 (2021) 107455 
Moreover, the fact that t( 
w 
k 
) = r 
k 
( 
w 
k 
) and ∇t( 
w 
k 
) = ∇ 
r 
k 
( 
w 
k 
) 
for all TRF iterations, also leads directly to satisfaction of the κ- 
fully linear property, for any ˜ 
r 
(w 
) 
. This can be seen as follows. 
From Taylor’s theorem applied to elements j of vector functions t 
and r 
k in (4), we have: 
r 
( 
j 
) 
k ( 
w 
k + 
s 
k 
) = r 
( 
j 
) 
k ( 
w 
k 
) 
+ ∇ 
r 
( 
j 
) 
k ( 
w 
k 
) T 
s 
k + 1 
2 
s 
k 
∇ 
2 
r 
( 
j 
) 
k ∇ 
r 
( 
j 
) 
k ( 
ξk 
) s 
k 
t 
( 
j 
) 
k ( 
w 
k + 
s 
k 
) = t 
( 
j 
) 
k ( 
w 
k 
) 
+ ∇ 
t 
( 
j 
) 
k ( 
w 
k 
) T 
s 
k + 1 
2 
s 
T 
k 
∇ 
2 
t 
( 
j 
) 
k 
ξ ′ 
k 

s 
k 
for some ξ k ξ’ 
k in the segment [w 
k 
,w 
k + 
s 
k 
] 
. Subtracting the sec- 
ond equation from the ﬁrst, and noting the function and gradient 
equalities at w 
k 
, as well as ∥ 
s 
k 
∥ 
2 ≤¯
2 
k 
, leads to: 
| 
r 
( 
j 
) 
k ( 
w 
k + 
s 
k 
) 
−t 
( 
j 
) 
k ( 
w 
k + 
s 
k 
) 
| 
= 1 
2 
| 
s 
T 
k 
∇ 
2 
r 
( 
j 
) 
k ∇r 
( 
j 
) 
k ( 
ξk 
) s 
k −s 
T 
k 
∇ 
2 
t 
( 
j 
) 
k 
ξ ’ 
k 

s 
k 
| 
≤| 
s 
T 
k 
∇ 
2 
r 
( 
j 
) 
k ( 
ξ k 
) 
s 
k 
| 
+ 
| 
s 
T 
k 
∇ 
2 
t 
( 
j 
) 
k 
ξ ’ 
k 

s 
k 
| ≤1 
/ 
2 
( 
κr + 
κt 
) ∥ 
s 
2 
k 
∥ ≤κ f 

2 
k 
where κ f = 1 
/ 
2( 
κr + 
κt 
) 
. Applying Taylor’s theorem again we also 
have: 
∇r 
( 
j 
) 
k ( 
w 
k + 
s 
k 
) = ∇ 
r 
( 
j 
) 
k ( 
w 
k 
) 
+ ∇ 
2 
r 
( 
j 
) 
k ( 
ξk 
) s 
k 
, ∇ 
t 
( 
j 
) 
k ( 
w 
k + 
s 
k 
) 
= ∇ 
t 
( 
j 
) 
k ( 
w 
k 
) 
+ ∇ 
2 
t 
( 
j 
) 
k 
ξ ′ 
k 

s 
k 
Subtracting the second equation from the ﬁrst, and noting the 
gradient equalities at w 
k 
, as well as ∥ 
s 
k 
∥ ≤¯
k 
, leads to: 
|∇ 
r 
( 
j 
) 
k ( 
w 
k + 
s 
k 
) 
−∇ 
t 
( 
j 
) 
k ( 
w 
k + 
s 
k 
) 
| 
= |∇ 
2 
r 
( 
j 
) 
k ∇r 
( 
j 
) 
k ( 
ξk 
) s 
k −∇ 
2 
t 
( 
j 
) 
k 
ξ ’ 
k 

s 
k 
| 
≤|∇ 
2 
r 
( 
j 
) 
k ( 
ξk 
) s 
k 
| 
+ | 
s 
T 
k 
∇ 
2 
t 
( 
j 
) 
k 
ξ ’ 
k 

s 
k 
| 
≤( 
κr + κt 
) ∥ 
s 
k 
∥ ≤κg 
k 
which leads to the κ-fully linear property (3). 
In Eason and Biegler (2016 
, 2018 
), the TRF approach was de- 
veloped for the case where ∇t(w 
) was not available. Their algo- 
rithms are based on a synthesis of concepts from the ﬁlter method, 
a compatibility phase and a criticality phase. Since the Aspen EO 
platform provides ∇t(w 
) to construct r 
k 
(w 
) 
, at virtually no cost 
from the EO sensitivity analysis, our modiﬁed TRF algorithm re- 
moves the criticality phase and k → 0 is not required for conver- 
gence ( 
Fletcher et al., 2002 
; Yoshio and Biegler, 2020 
). 
2.1. Simpliﬁcation of the trust region 
The TRF algorithm in Eason and Biegler (2016 
, 2018 
) requires a 
separate optimization problem (compatibility step) to ensure that 
a feasible point exists for (TRSP0) for any ¯
 > 0 
. To simplify the 
TRF algorithm and remove this step, we apply the approach in 
Yoshio and Biegler (2020 
), partition x 
T = [ 
v 
T 
, 
u 
T 
] and deﬁne a sub- 
set of variables that serve as the degrees of freedom, u ∈ 
U ⊆R 
n 
u . 
Once u is ﬁxed, we assume the remaining variables v can al- 
ways be determined from the equality constraints in (TRSP0). Since 
the degrees of freedom can be drawn from the ﬂowsheet model 
or inputs to the black box model, we further partition the vari- 
ables as u 
T = [ 
z 
T 
u 
, w 
T 
u 
] ⊆R 
n 
u 
, v 
T = [ 
z 
T 
v 
, w 
T 
v 
, 
y 
T 
] 
, z 
T = [ 
z 
T 
v 
, z 
T 
u 
] and 
w 
T = [ 
w 
T 
v 
, w 
T 
u 
] 
. 
To avoid satisfying ∥ 
x 
−x 
k 
∥ ≤¯
 directly, we consider a trust re- 
gion for the degrees of freedom alone ( 
Yoshio and Biegler, 2020 
), 
i.e., ∥ 
u 
−u 
k 
∥ ≤k 
, since the equations can be solved for v for 
any u in the trust region. In order to show the equivalence of 
∥ 
u 
−u 
k 
∥ ≤k to ∥ 
x 
−x 
k 
∥ ≤¯
k 
, we formulate the parametric NLP 
(with u ﬁxed): 
min 
v 
f 
( 
v 
; u 
) 
s.t. h 
( 
v 
; u 
) = 0 
y 
−t 
( 
w 
v 
, 
w 
u 
) = 0 
g 
( 
v 
; u 
) ≤0 
, 
( 
NLP ( 
u 
) 
) 
and assume that its solution, denoted as v 
( 
u 
), satisﬁes the Man- 
gasarian Fromovitz Constraint Qualiﬁcation (MFCQ) and the Second 
Order Suﬃcient Condition (SOSC) for all u ∈ U 
. This property can 
be expected to hold for well-tested models that can be found in 
RTO. (Moreover, if the inequality constraints are not always fea- 
sible under these conditions ( 
Yoshio and Biegler, 2020 
) the TRSP 
problem also can be extended with the addition of penalty vari- 
ables.) With this assumption we can apply the following Lipschitz 
condition: 
∥ 
v 
( 
u 
) 
−v 
( 
u 
k 
) 
∥ ≤L 
∥ 
u 
−u 
k 
∥ 
, ∀ 
u, u 
k ∈ U 
where L > 0 is a Lipschitz constant. Choosing k = 
¯
k 
( 
L 
2 
+1 
) 
1 
/ 
2 we 
can write: 

L 
2 + 
1 
∥ 
u 
−u 
k 
∥ 
2 ≥∥ 
v 
( 
u 
) 
−v 
( 
u 
k 
) 
∥ 
2 + ∥ 
u 
−u 
k 
∥ 
2 = ∥ 
x 
−x 
k 
∥ 
2 
(5) 
Thus, imposing k ≥∥ 
u 
−u 
k 
∥ ⇒ ¯
 k ≥∥ 
x 
−x 
k 
∥ 
. 
Consequently, we replace the trust region constraint in (3) and 
write the following trust region subproblem: 
min 
x 
f 
( 
x 
) 
s.t. h 
( 
x 
) = 0 
y = r 
k 
( 
w 
v 
, 
w 
u 
) 
g 
( 
x 
) ≤0 
∥ 
u 
−u 
k 
∥ ≤k 
( 
TRSP 
) 
We assume the MFCQ always holds for (TRSP) at all iterations 
k. By solving (TRSP) we now generate a sequence of points x 
k 
that converge to the solution of (2). With the Trust Region Filter 
method, this sequence maintains feasibility for the EO model con- 
straints, while simultaneously converging towards optimality of (2) 
and feasibility of the black box constraints y = t(w). 
2.2. Description of the trust region ﬁlter method 
We deﬁne the step determined by the solution of the trust re- 
gion problem as: 
s 
k := x 
∗
s,k −x 
k 
where x 
∗
s,k is the minimizer of (TRSP). If s 
k signiﬁcantly improves 
the objective function or makes suﬃcient progress to feasibility, we 
say this step is successful and assign x 
k 
+1 = x 
k + s 
k 
. Otherwise we 
have to restore the previous iteration, shrink the trust region, and 
solve it again with x 
k 
+1 = x 
k 
. 
Based on the framework in Fletcher et al. (2002 
), Eason and 
Biegler (2016 
, 2018 
) provide a detailed convergence analysis of the 
TRF method. In the TRF algorithm suﬃcient progress to optimality 
is assessed by constructing a ﬁlter of the iterates. As an alterna- 
tive to a merit function, the ﬁlter borrows concepts from multi- 
objective optimization to balance the trade-off between feasibility 
and the objective function ( 
Eason and Biegler, 2018 
). We deﬁne the 
infeasibility of NLP (2) as: 
θk = ∥ 
y 
k −t( 
w 
k 
) 
∥ 
(6) 
and employ a subset of iterations Ƶ ⊂N for which we store ( 
θ, f) 
pairs. 
F 
k = ( 
θ j 
, f 
j 
) : j < k, j ∈ Z 

The ﬁlter points can be interpreted as building a Pareto front 
for the minimization of θ and f. The acceptance of a point for the 
ﬁlter ( 
( 
θ j 
, f 
j 
) ∈ F 
k 
∪ 
( 
θk 
, f 
k 
)) is given by the following conditions: 
θ( 
x 
k + 
s 
k 
) ≤( 
1 
−γθ ) 
θ j or f 
( 
x 
k + 
s 
k 
) ≤f 
j −γ f 
θ j 
(7) 
Moreover, for small θ( 
x 
k 
) 
, we require stronger reductions in 
f( 
x 
k + 
s 
k 
) 
. To enforce this behavior, we evaluate the following 
switching condition: 
f 
( 
x 
k 
) 
−f 
( 
x 
k + 
s 
k 
) ≥κθθ( 
x 
k 
) 
γs 
(8) 
5 
X. Chen, K. Wu, A. Bai et al. 
Computers and Chemical Engineering 153 (2021) 107455 
where γθ , γ f 
, κθ , γs are tuning parameters. 
If (8) holds, we label the step s 
k as an f 
-type step. For the f 
- 
type step, ( 
θk 
, f 
k 
) will not be used to update the ﬁlter, and we 
also increase the trust region. Otherwise, we label s 
k as a θ-type 
step, and add the corresponding pair ( 
θk 
, f 
k 
) to the ﬁlter. For f 
-type 
steps, the improvement of the objective function dominates in (8), 
and this generally occurs when θk is small. For θ- 
type steps, the 
constraint violation tends to dominate the reductions in (7) and 
the ( 
θk 
, f 
k 
) pair should be added to the ﬁlter for the next iteration. 
Moreover, updating the trust region radius k is also inﬂuenced 
by the ﬁlter. When s 
k is unacceptable to the ﬁlter, we return to the 
previous (TRSP) and apply a smaller trust region to achieve a more 
accurate reduced-order model r 
k 
(w 
) 
. Else, if s 
k is an f 
-type step, 
the infeasibility is within a bearable range and it is safe to increase 
the trust region radius. Finally, with a θ-type step, the convergence 
theory in Fletcher et al. (2002 
) allows for very ﬂexible trust region 
updates as long as ࢞k remains bounded ( 
Eason and Biegler, 2016 
). 
For acceptable θ-type steps we apply the following update rule ac- 
cording to a ratio test. 
ρk = θ( 
x 
k 
) 
−θ( 
x 
k + 
s 
k 
) 
∥ 
y 
k −r 
k 
( 
w 
k 
) 
∥ 
= θ( 
x 
k 
) 
−θ( 
x 
k + 
s 
k 
) 
θ r 
( 
x 
k 
) 
−θ r 
( 
x 
k + 
s 
k 
) 
(9) 
where θ r 
( 
x 
k 
) = ∥ 
y 
−r 
k 
(w 
) 
∥ 
. Since the minimizer of TRSP is always 
feasible, we have θ r 
( 
x 
k + 
s 
k 
) = 
0, θ r 
( 
x 
k 
) = θ( 
x 
k 
) 
, and Eq. (9) can be 
simpliﬁed to: 
ρk = 1 
−θ( 
x 
k + 
s 
k 
) 
θ( 
x 
k 
) 
(10) 
Using parameters 0 < η1 < η2 < 1 and 0 < γc < 1 ≤γe 
, the trust 
region is then updated as follows, 
k 
+1 = 
 
γc 
k 
i f ρk < η1 
k 
i f η1 < ρk < η2 
γe 
k 
i f ρk > η2 
(11) 
Moreover, the trust region in (TRSP) is deﬁned by using the RTO 
step bounds, u 
U 
, u 
L : 
u 
k −k 
( 
u 
U −u 
L 
) ≤u ≤u 
k + k 
( 
u 
U −u 
L 
) 
(12) 
To keep the trust region bounds within u 
L ≤u 
k ≤u 
U 
, we write: 
max 
( 
u 
k −k 
( 
u 
U −u 
L 
) 
, u 
L 
) ≤u ≤min 
( 
u 
k + k 
( 
u 
U −u 
L 
) 
, u 
U 
) 
(13) 
and we apply a scaled trust region k which takes a value be- 
tween 0 and 1. Note that for k = 0, we have u = u 
k and for k = 
1 
, we have the original bounds, u 
L ≤u 
k ≤u 
U 
, which corresponds 
to the largest possible trust region for TRSP. 
Finally, if the initial value of u is chosen infeasible, i.e., u 
0 < u 
L 
or u 
0 > u 
U 
, Eq. (13) may generate a lower bound which is higher 
than the upper bound. In this case, we redeﬁne the bounds as: 
u 
trus 
tL = u 
0 if u 
0 < u 
L 
u 
trus 
tU = u 
0 if u 
0 > u 
U 
and use u 
t 
rust 
L and u 
t 
rust 
U to reset the trust region bounds in (12). 
2.3. Statement of the TRF algorithm 
The trust region ﬁlter (TRF) algorithm can be stated as follows. 
1. Initialization: Choose tuning parameters for the algorithm. 
In this work, the parameters are set as: γc = 0 
. 
5 
, γe = 2 
, γ f = 
0 
. 
01 
, γθ = 0 
. 
01 
, κθ = 0 
. 
01 
, γs = 0 
. 
9 
, η1 = 0 
. 
4 
, η2 = 0 
. 
8 
; 
Set the initial trust region 0 to 1.0; 
Initialize the ﬁlter F 
0 = ∅ 
. 
Set the initial point x 
0 
. Record θ0 and f 
0 
. Set k = 0. 
2. Construct the reduced model r 
k 
(w 
) from (4) by evaluating the 
truth model t( 
w 
k 
) 
, surrogate model ˜ 
r 
( 
w 
k 
) 
, ZOC and FOC. 
3. If the infeasibility θk < 10 
−4 
, the optimum is obtained and the 
algorithm terminates. 
4. Solve the trust region subproblem (TRSP) and compute s 
k 
. 
5. Check if x 
+ 
s 
k is acceptable. If (7) holds, then the step is ac- 
ceptable to the ﬁlter, go to step 6. Else, set x 
k 
+1 = x 
k 
, θk 
+1 = θk 
and k 
+1 = γc 
k 
. Set k: 
= k + 1 and go to step 2. 
6. Check the switching condition. If Eq. (8) holds, go to step 7. Else 
go step 8. 
7. This is an f 
-type step. Set x 
k 
+1 = x 
k + 
s 
k 
, k 
+1 = γe 
k 
, f 
k 
+1 = 
f( 
x 
k + 
s 
k 
) 
, θk 
+1 = θ( 
x 
k + 
s 
k 
) 
. Set k: 
= k + 1 and go to step 2. 
8. This 
is 
a θ- 
type 
step. 
Set 
x 
k 
+1 = x 
k + 
s 
k 
, 
f 
k 
+1 = 
f( 
x 
k + 
s 
k 
) 
, θk 
+1 = θ( 
x 
k + 
s 
k 
) 
, and add the pair ( 
θk 
, f 
k 
) to 
the ﬁlter. Check the ratio test (10) and generate the new trust 
region radius accordingly. Set k: 
= k + 1 and go to step 2. 
3. Building the surrogate PFR model for the FCC 
To build the reduced model (4), we develop a surrogate model, 
˜ 
r 
(w 
) to be used to form the reduced model r 
k 
(w 
) in (4) as an 
approximation to t(w 
) 
. As shown in the previous section, conver- 
gence of the TRF method can be shown for any surrogate model, 
but performance improves with accuracy of the surragate. Conse- 
quently, we can choose ˜ 
r 
(w 
) that provides good predictions for 
t(w), and is inexpensive to calculate. Here the reduced model is a 
plug ﬂow reactor (PFR) model with simpliﬁed kinetics and an ana- 
lytic solution. In particular, our PFR surrogate model satisﬁes mass 
balances and can be extrapolated from its ﬁtting region, since it is 
based on ﬁrst principles. These are desirable properties for RTO. 
The truth model, SIMCRAQ in Aspen Plus, models the FCC unit, 
which receives pretreated crude oil feed and converts the inlet 
stream to the petroleum products, including gasoline, LPG, fuel gas, 
coke and acid gas (pure hydrogen sulﬁde, H 
2 
S). The only input 
variable is the feed ﬂowrate of the crude oil (all products and oil 
are represented as pseudo-components in Aspen). We assume the 
component fractions of the crude oil are ﬁxed, although the crude 
component of the gas oil may change with the supply. To provide 
the data for the surrogate model, we change the inlet ﬂowrate and 
solve the simulation problem repeatedly and ﬁt the output data for 
the products for the surrogate model. 
The simpliﬁed PFR model includes a set of differential equa- 
tions and ignores the reactions between the products; the reaction 
model is simpliﬁed as shown in Fig. 5 
. 
Fig. 5. Simpliﬁed reaction network. 
6 
X. Chen, K. Wu, A. Bai et al. 
Computers and Chemical Engineering 153 (2021) 107455 
Fig. 6. Pre-calculated \ 
vartheta 
A and \ 
vartheta 
l under 534 °C. 
Table 1 
Fitted Values of w 
2 
, b 
2 
, w 
3 
, b 
3 (rounded to 6 signiﬁcant digits). 
w 
2 
b 
2 
w 
3 
b 
3 
Gas oil (A) 
−2.36873e-06 
1.14159e-03 
0.00103829 
−0.478247 
Gasoline (B) 
−5.81273e-06 
2.75146e-03 
0.00261768 
−1.18496 
LPG (C) 
−4.51777e-06 
2.15443e-03 
0.00198186 
−0.904264 
Coke (E) 
3.50031e-08 
−5.65547e-05 
−2.06155e-05 
2.15233e-02 
Acid gas (F) 
−3.26698e-04 
8.84972e-07 
7.55408e-07 
−3.26698e-04 
7 
X. Chen, K. Wu, A. Bai et al. 
Computers and Chemical Engineering 153 (2021) 107455 
Table 2 
Fitted Values of w 
2 
, b 
2 
, w 
3 
, b 
3 (rounded to 6 signiﬁcant digits). 
w 
4 
b 
4 
w 
5 
b 
5 
w 
6 
b 
6 
Fuel Gas (D) 
1.904e-07 
−9.967e-05 
−1.0839e-04 
0.0568064 
0.0176478 
−9.20909 
The ﬁrst-order ODE system can be expressed as: 
d 
F 
j 
dV = M 
· F 
(14) 
where: 
ϑ 
XY Ratio of molecular weight of component Y to component X 
F 
i Molar ﬂow rate of component i (kmol/hr) 
k 
i Reaction rate constant for the formation of component i 
r 
i Rate of formation component i 
V Reactor volume ( 
m 
3 
) 
ν Volumetric ﬂow rate ( 
m 
3 
/ 
day 
). 
A Gas oil 
B Gasoline 
C LPG 
D Fuel Gas 
E Coke 
F Acid gas 
In this case, the reaction rates of component lumps in a PFR are 
given by: 
dF 
A 
dV = − k 
1 
+ 
k 
2 
+ 
k 
3 
+ 
k 
4 
+ 
k 
8 
v 

F 
A 
dF 
B 
dV = − ϑ 
BA 
k 
1 
v 

F 
A 
dF 
C 
dV = − ϑ 
CA 
k 
2 
v 

F 
A 
dF 
D 
dV = − ϑ 
DA 
k 
3 
v 

F 
A 
dF 
E 
dV = − ϑ 
EA 
k 
4 
v 

F 
A 
dF 
F 
dV = − ϑ 
FA 
k 
8 
v 

F 
A 
(15) 
From Eq. (15.1) we have: 
F 
A = e 
( 
−ϑ 
A 
V 
) 
F 
A 
0 
(16) 
where: 
ϑ 
A = −

k 
1 + 
k 
2 + 
k 
3 + 
k 
4 + 
k 
8 
v 

(17) 
Then we have: 
F 
j = F 
in 
j + ϑ 
j 
V 
	 
0 
F 
A 
dV 
(18) 
where: 
ϑ 
j = ϑ 
XY 
k 
j 
v , j ∈ { 
B, 
C, 
D, 
E, 
F 
} 
(19) 
Solving for the integral in (18) leads to two equations: 
ln 

F 
out 
A 
F 
in 
A 

= −ϑ 
A 
V 
(19) 
F 
j = 
 ϑ 
j 
ϑ 
A 

F 
in 
A −F 
out 
A 

(20) 
Fig. 6 shows a pre-calculated result, where ϑ 
A and ϑ 
l have 
linear or quadratic (for l = D 
) shapes. The ﬁtting method is the 
curve_ﬁt function from scipy.optimize in the Scipy library, which 
uses nonlinear least squares to ﬁt a regression function. 
We start with a linear equation for F 
in 
A , where θl = w 
1 
F 
in 
A + 
b 
1 . 
Then, both w 
1 and b 
1 are linearly ﬁtted to varying reaction temper- 
atures, w 
1 = w 
2 
T + 
b 
2 and b 
1 = w 
3 
T + 
b 
3 
. As a result, ϑ 
l will have 
the form of: 
ϑ 
l = ( 
w 
2 
T + 
b 
2 
) 
F 
in 
A + 
w 
3 
T + 
b 
3 
(21) 
Table 3 
R 
2 for linear ﬁtting (rounded to 4 signiﬁcant digits). 
ϑ 
A 
ϑ 
B 
ϑ 
C 
ϑ 
E 
ϑ 
F 
R 
2 
0.9938 
0.9951 
0.9948 
0.9972 
0.9929 
Table 4 
Root mean squares of residuals for θD at 3 temperatures. 
533K 
534K 
538K 
Residual Sum 
1.879e-04 
2.111e-04 
3.4504e-05 
with coeﬃcients given in Table 1 
. 
For the fuel gas D, the correlation with respect to F 
A is de- 
termined from a quadratic model similar to the linear ﬁt, starting 
with the general form: 
ϑ 
D = a 

F 
in 
A 
2 + 
b 

F 
in 
A 

+ 
c 
and leading to: 
ϑ 
D = ( 
w 
4 
T + 
b 
4 
) 

F 
in 
A 
2 + ( 
w 
5 
T + 
b 
5 
) 

F 
in 
A 

+ ( 
w 
6 
T + 
b 
6 
) 
with coeﬃcients in Table 2 
. For the given temperature (534 K), 
the following correlation coeﬃcients ( 
R 
2 
) are calculated and pre- 
sented in Table 3 
. 
For the quadratic ﬁtting for ϑ 
D 
, almost every predicted point 
has around 6 × 10 
−4 error with the observed points, which rep- 
resents 0.8% error rate (0.0 
0 
06 / 0.0750). The square roots of the 
residual sum of squares are calculated to present the errors from 
quadratic ﬁtting ( 
Table 4 
). 
While these quadratic ﬁtting errors may make the reduced 
model slightly inaccurate, applying the Zeroth-Order Correction 
(ZOC) and First-Order Correction (FOC) in (14), compensate for 
these errors and provide an accurate value of r 
k at w 
k 
. In this way 
the TRF algorithm determines r 
k 
+ 
1 at the next iteration with func- 
tions and gradients equal to the rigorous model solution t(w 
k 
+ 
1 
). 
The details of the correction terms will be introduced in the next 
section. 
4. Implementation of TRF strategy 
Aspen Custom Modeler V8.8 is employed to add the Zeroth- 
Order correction (ZOC) and First-Order correction (FOC) to 
the ˜ 
r 
(w 
) 
. The TRF block in SIMCRAQ unit adds ZOC and FOC to the 
output of the PFR surrogate model. 
In addition, the components crude oil, gasoline, LPG, fuel gas 
and coke are expressed as a set of pseudo components in As- 
pen Plus because they are not pure chemical components. In ad- 
dition to the pure component and physical property information, 
each stream has EO variables that represent molar or mass ﬂow 
for many lumped or pseudo-components, designated by the preﬁx 
CRQ. Thus, when processing the streams, Aspen will ﬁrst assem- 
ble these CRQ variables into the target products, then calculate the 
chemical reaction results based on chemical components and con- 
vert these to the CRQ results. 
Fig. 7 shows the relationship of reactor models in the SIM- 
CRAQ unit. The CMU_ACMMODEL is the surrogate PFR model and 
SIMCRAQ_ACMMODEL is the original model, which are both pro- 
grammed within Aspen Custom Modeler. CMUSCQEF/SCQEFFL and 
CMUCRQAB/SCQCRQAB are the blocks that receive the real compo- 
nent results and calculate the corresponding CRQ results. We refer 
8 
X. Chen, K. Wu, A. Bai et al. 
Computers and Chemical Engineering 153 (2021) 107455 
Fig. 7. Surrogate models and TRF system. 
to CMU_ACMMODEL, CMUSCQEF and CMUCRQAB together as the 
reduced model (RM) system. Similarly, SIMCRAQ_ACMMODEL, SC- 
QEFFL and SCQCRQAB together form the original cracking model. 
CMUFEEDX is the stream which contains the output information 
( 
˜ 
r 
(w 
) 
) from the RM system. The TRF block will add ZOC and FOC 
to CMUFFEEDX and send the output ( 
r 
k 
(w 
) 
) to the TRFFEEDX. In 
addition, MFFEEDX receives the output from the original crack- 
ing model. When running the simulation or optimization, all three 
streams will be calculated at each iteration but we decide which 
output will go to the next unit by building the EO connections. In 
other words, based on the step of the TRF algorithm, only one of 
three streams will be fed into MFC (main fractionator) unit, and 
the remaining two streams will not affect the results of simulation 
or optimization. In the TRF algorithm, the selection of the 3 out- 
put streams in Fig. 7 enables the calculation of the infeasibility q 
or the correction terms ZOC and FOC. 
Fig. 8 shows the algorithmic steps for the TRF method as well 
as the links between Aspen Plus and Python. The Python code is 
mainly responsible for checking optimality and ﬁlter acceptance as 
well as generating new correction terms. Currently our manual im- 
plementation takes considerable time to collect these data. For ex- 
ample, the number of all EO variables (including all the speciﬁca- 
tions in Aspen) are about 20 
0,0 
0 
0. When inquiring for the ‘Opti- 
mized Variables’ the current manual implementation is too slow 
for on-line use, as the graphical interface in Aspen Plus takes con- 
siderable time to respond to an inquiry. On the other hand, the 
same query in Customize Aspen Plus, responds much faster. Thus, 
we plan to design an automated strategy, which can directly ac- 
cess the Aspen Engine and implement all of the manual steps. In 
future, Customize Aspen Plus will be employed for the optimization, 
as it has a simpler GUI than Aspen Plus and can do the same work. 
The automation of this procedure will be considered as part of fu- 
ture work. 
5. Results and discussion 
5.1. Convergence results 
Three datasets that mirror operation of the RECAP reﬁnery are 
employed to test the trust region method; these are called Day 
14, Day 15 and Day 16. For each dataset, the bounds for degrees 
Fig. 8. Flow chart of the TRF step structure between Aspen Plus and Python. 
of freedom are different according to the step bounds and initial 
value. The large-scale NLP models for SIMCRAQ-RTO and TRF-RTO 
are solved by the DMO (SQP) solver in Aspen Plus V8.8. Note that 
the reﬁnery ﬂowsheet in Fig. 3 is solved by both models, with 
the SIMCRAQ model replaced by its PFR surrogate in the TRF-RTO 
Model. 
The convergence proﬁles of the datasets are shown in Fig. 9 
. For 
each RTO dataset, we also optimized over both RTO models and 
compared the solutions. Each graph in Fig. 9 has 3 curves plotted 
over iterations k 
, k represents the trust region size, θk represents 
the infeasibility and f is the objective function value (proﬁt, which 
is maximized). The convergence tolerance for the TRF optimization 
9 
X. Chen, K. Wu, A. Bai et al. 
Computers and Chemical Engineering 153 (2021) 107455 
Fig. 9. Convergence Proﬁles TRF-RTO Runs for Days 14–16. 
10 
X. Chen, K. Wu, A. Bai et al. 
Computers and Chemical Engineering 153 (2021) 107455 
Table 5 
The step type in three cases. 
iter1 
iter2 
iter3 
iter4 
iter5 
Day 14 
f-step 
θ-step 
f-step 
θ-step 
Day 15 
f-step 
f-step 
θ-step 
f-step 
θ-step 
Day 16 
f-step 
f-step 
θ-step 
f-step 
θ-step 
Table 6 
Average CPU times and (SQP Iterations). 
SIMCRAQ-RTO 
TRF-RTO 
Data Set 
CPU(s) 
Iterations 
CPU(s) 
Iterations 
Day 14 
434.1 
13 
353.4 
10/10/9/5 
Day 15 
422.3 
10 
316.6 
10/10/5/5 
Day 16 
511.7 
10 
406.52 
10/10/10/9/ 
5 
is θk ≤10 
−4 
. For all three datasets, we record the value of the ob- 
jective function, infeasibility θk and the trust region k 
. All three 
optimization cases have similar trends. We set the initial trust re- 
gion as 0 = 
1, which is the maximum value (see (13)). In all cases, 
the objective function improves quickly in the ﬁrst three iterations. 
In the later iterations, the objective function plateaus or even de- 
creases while the infeasibility θk decreases to its tolerance. Inter- 
estingly, the trust region k does not change for all three cases. 
According to (8) and Step 7 in the TRF algorithm, once the in- 
feasibility is small, every increase in objective function will trig- 
ger an f 
-type step. Since we increase the trust region according to 
the TRF algorithm, our initial trust region is at its maximum and 
it remains there during the optimization. Table 5 records the step 
types for the three cases. When the objective function decreases, 
we also have signiﬁcant reduction in the infeasibility, which means 
we maintain a large trust region based on the ratio tests (10) and 
(11). 
Table 6 compares the average CPU times and SQP iterations 
for both RTO models using the DMO optimizer. For TRF-RTO we 
show the SQP iterations for each TRSP problem. In the later TRF 
iterations, we observe that signiﬁcantly fewer SQP iterations are 
needed for the TRSP problems. On the other hand, because the en- 
tire reﬁnery is contained in both RTO models, the CPU times are of 
similar orders of magnitude. Moreover, since both Aspen Plus and 
ACM are closed systems, our current implementation could not be 
structured to optimize the CPU time for the TRF algorithm. Nev- 
Table 8 
Comparison of objective function values. 
Day 14 
Day 15 
Day 16 
Start k$/Day 
56.4 
206.7 
152.7 
SIMCRAQ-RTO k$/Day 
69.3 
218.1 
169.6 
TRF-RTO End k$/Day 
70.4 
220.2 
175.6 
Relative Difference% 
1.59 
0.96 
3.54 
ertheless, the TRF-RTO requires about 20–25% less time than with 
SIMCRAQ-RTO. Also, the EO sensitivity analysis time for dt(w 
) and 
d 
˜ 
r 
(w 
) is about 30 CPU seconds. The overall timing results for the 
three cases show a limited reduction in the average CPU time for 
the TRF-RTO model, because SIMCRAQ accounts for only a small 
part of the reﬁnery. In future work, we plan to replace additional 
reactor and separation blocks by surrogate models in the TRF al- 
gorithm. Because the detailed truth models can be run in paral- 
lel with TRF-RTO, signiﬁcant further time savings can be achieved 
through parallelization, which would fully scale with larger process 
models. 
5.2. Starting and end points 
Starting points (base cases) are obtained by the reconciliation 
in Aspen Plus in the EO parameter estimation mode. The starting 
points initialized from SIMCRAQ or surrogate models are about the 
same, as we chose starting points initialized from SIMCRAQ. Also, 
the initial reconciliation has a simple ZOC, obtained by running the 
reconciliation in another .bkp ﬁle; this just subtracts the solutions 
from MFFEEDX and CMUFEEDX. 
The results are collected in Tables 7 
–9 
. In order to ﬁnd out 
which decision variables show signiﬁcant changes, the relative dif- 
ference between their starting and end values are listed in Table 7 
. 
From this table, we observe that the biggest changes are in the 
Wet Gas Compressor, in the ABS Primary absorber tower liquid 
ﬂow, Turboexpander bypass and MFC Main fractionator tower in- 
termediate reﬂux ﬂow. All of them are related to a better fraction- 
ation and gas recovery, and thus, to an increase on light compo- 
nents production (LPG or Propylene, for instance), the most valu- 
able products at the time, leading to an improvement in the proﬁt. 
The improvement of the objective function values from the start- 
ing points are compared in Table 8 
. We also compare the opti- 
mal points from the TRF-RTO optimization and SIMCRAQ-RTO in 
Table 9 
. 
Table 7 
Relative change in decision variables between starting and end points. 
Decision Variables 
Day 14 (%) 
Day 15 (%) 
Day 16 (%) 
ABS Primary absorber tower liquid ﬂow 
−10.48 
−9.97 
−9.98 
ATM Atmospheric tower side draw stream 
0.07 
3.89 
2.72 
ATM Distillation tower intermediate reﬂux ﬂow 
0.75 
−0.74 
0.015 
ATM Distillation tower diesel withdraw 
1.15 
−1.61 
−1.65 
ATM Distillation tower steam ﬂow in the bottom 
0.00 
6.57 
0.00 
Naphtha stream ﬂow from Distillation to RFCC 
0.00 
0.00 
0.00 
ATM Distillation tower top temperature 
0.10 
−0.99 
−0.04 
ATM Distillation tower feed temperature 
−0.27 
0.27 
0.016 
ATM Deethanizer tower bottom temperature 
−0.22 
0.11 
0.53 
DC4 Debutanizer tower reﬂux ﬂow 
−2.53 
−3.38 
−0.86 
DC4 Debutanizer tower bottom temperature 
−1.45 
−1.44 
−1.45 
MFC Main fractionator tower intermediate reﬂux ﬂow 
−5.93 
7.18 
22.85 
MFC Main fractionator tower LCO stream 
1.29 
0.05 
0.026 
MFC Main fractionator tower top pump around reﬂux stream 
0.00 
0.00 
0.00 
MFC Main fractionator tower top temperature 
−0.10 
0.81 
0.84 
Crude ﬂow 
0.00 
0.00 
0.00 
PRF Pre-ﬂash tower top temperature 
0.99 
−0.58 
−2.49 
Turboexpander bypass 
−11.58 
−8.35 
−5.40 
Atmospheric residue ﬂow 
0.03 
0.00 
0.01 
RFCC reaction temperature 
−0.23 
−0.03 
−0.01 
WGC Wet gas compressor surge recycle 
0.00 
2.60 
7.86 
WGC Valve parameter 
102.95 
102.23 
105.48 
11 
X. Chen, K. Wu, A. Bai et al. 
Computers and Chemical Engineering 153 (2021) 107455 
Table 9 
Comparison of optimization for two RTO models. 
Decision Variables 
Day 14 (%) 
Day 15 (%) 
Day 16 (%) 
ABS Primary absorber tower liquid ﬂow 
0.00 
−0.02 
2.99 
ATM Atmospheric tower side draw stream 
0.00 
−0.02 
2.66 
ATM Distillation tower intermediate reﬂux ﬂow 
0.00 
0.18 
0.02 
ATM Distillation tower diesel withdraw 
0.33 
0.00 
0.00 
ATM Distillation tower steam ﬂow in the bottom 
0.00 
−6.57 
0.00 
Naphtha stream ﬂow from Distillation to RFCC 
−32.03 
0.00 
0.00 
ATM Distillation tower top temperature 
0.00 
0.00 
−0.86 
ATM Distillation tower feed temperature 
0.00 
0.00 
0.13 
ATM Deethanizer tower bottom temperature 
−0.03 
−0.10 
0.20 
DC4 Debutanizer tower reﬂux ﬂow 
0.00 
0.71 
3.68 
DC4 Debutanizer tower bottom temperature 
0.00 
0.00 
0.00 
MFC Main fractionator tower intermediate reﬂux ﬂow 
0.00 
0.67 
0.00 
MFC Main fractionator tower LCO stream 
−2.13 
0.00 
−0.56 
MFC Main fractionator tower top pump around reﬂux stream 
0.00 
0.00 
0.00 
MFC Main fractionator tower top temperature 
0.00 
−0.75 
−0.84 
Crude ﬂow 
0.00 
0.00 
0.00 
PRF Pre-ﬂash tower top temperature 
−0.23 
0.00 
0.51 
Turboexpander bypass 
0.00 
0.57 
1.13 
Atmospheric residue ﬂow 
0.14 
0.00 
0.00 
RFCC reaction temperature 
−0.18 
0.08 
−0.04 
WGC Wet gas compressor surge recycle 
0.00 
0.00 
0.00 
WGC Valve parameter 
−26.17 
16.02 
−14.82 
Fig. 10. Comparison of economic terms for TRF-RTO and SIMCRAQ-RTO. 
12 
X. Chen, K. Wu, A. Bai et al. 
Computers and Chemical Engineering 153 (2021) 107455 
The two RTO models converge to the same convergence toler- 
ance (1.e-4), using the same optimality conditions (and exact gra- 
dients) from the SIMCRAQ-RTO (truth) model. Also, these models 
converge to similar but not identical points, and the optimal proﬁt 
value from TRF-RTO is up to 3.5% higher. We conjecture that this 
slight improvement may be due to allowances of the convergence 
tolerance, as well as an alternate convergence path for the TRF- 
RTO model. Moreover, this result demonstrates that the TRF surro- 
gates and corrections successfully replace the SIMCRAQ model in 
the RTO, and converge to an optimal solution of the SIMCRAQ-RTO 
model. Table 9 indicates the difference in the results of decision 
variables, although this difference has little impact on the product 
values and proﬁt objective function. 
5.3. Economic results 
The proﬁt objective function is deﬁned as: 
PROF 
IT = PRODS −FEEDS −UTILS 
where PRODS represents the value of products; FEEDS represents 
the value of raw materials and UTILS represents the cost for util- 
ities. Fig. 10 compares the difference of economic terms after the 
two RTO runs. By adjusting the operating conditions, the plant is 
able to produce more products and the improvement in product 
value largely determines the improvement in the proﬁt. Otherwise, 
the changes in feed and utility costs are much less signiﬁcant. 
6. Conclusions and future work 
In this study we develop and apply a convergent surrogate- 
based optimization strategy, based on a trust region ﬁlter (TRF) al- 
gorithm, for reﬁnery real-time optimization. To demonstrate this 
approach, we consider a real-world RTO model from Petrobras’ 
reﬁnery process, Reﬁnaria de Capuava (Recap), located in Mauá, 
Brazil. We develop a reduced-order model for the residue FCC 
unit; this is employed in the trust region ﬁlter algorithm to re- 
duce the computational complexity during the optimization. Since 
we assume all functions in the model are twice continuously dif- 
ferentiable and the Mangasarian Fromovitz Constraint Qualiﬁcation 
(MFCQ) holds for all iterations of the Trust Region Sub-Problem 
(TRSP), a simpliﬁed TRF method is applied and the trust region 
constraints are enforced only on the degrees of freedom. 
The TRF strategy, along with the reduced PFR model with 
Zeroth-Order (ZOC) and First Order Corrections (FOC), converges to 
an optimal solution of SIMCRAQ-RTO. To implement the trust re- 
gion algorithm steps in Aspen Plus, Aspen Custom Modeler V8.8 
is used to construct the custom block (TRF) to add ZOC and FOC 
to the RM output. The Trust Region Filter strategy is programmed 
in Python, which can indicate the performance of each TRSP. Also, 
the Python code was also used to generate the updated ZOC, FOC 
and trust region bounds for each TRSP. Three reﬁnery datasets 
are tested for the trust region method and all of them satisfy 
the convergence criteria of the original problem. Thus, the results 
show that the reduced models with the TRF algorithm can replace 
the SIMCRAQ model and converge to the optimal solution. While 
the timing data demonstrate computational improvement, the TRF 
strategy has a strong potential to further reduce the computational 
time with additional reduced models in the process. 
Future work will be devoted to automate the solution strat- 
egy and allow the trust region algorithm to coordinate and solve 
for several reduced models in the TRSPs. Additional reduced-order 
models will be built in the process to replace several additional 
complex black-box (truth) models beyond the RFCC model. Finally, 
the RTO was performed as an oﬄine system and has not been 
tested yet in real-time. This will also be considered for future 
work. 
Author contributions 
All authors contributed signiﬁcantly to this paper. 
Declaration of Competing Interest 
The authors have no conﬂicts of interest. 
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13

Energy Policy 142 (2020) 111491
Available online 24 April 2020
0301-4215/© 2020 Elsevier Ltd. All rights reserved.
An analysis of the efficiency of the oil refining industry in the 
OECD countries 
Chansu Lim a,b, Jongsu Lee a,* 
a Technology Management, Economics and Policy Program, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea 
b Energy Affairs Team, GS Caltex Corporation, 508, Nonhyeon-ro, Gangnam-gu, Seoul, 06141, South Korea   
A R T I C L E  I N F O   
Keywords: 
Production efficiency 
Oil refining industry 
Markowitz portfolio optimization theory 
Efficient frontier 
Panel data analysis 
Dynamic panel model 
A B S T R A C T   
This study investigated the efficiency of the oil refining industry using the two-stage method of Markowitz 
portfolio optimization theory and panel data analysis of about 30 OECD countries from 2005 to 2016, which is a 
new methodology for measuring the efficiency of the oil industry. The oil refining industry’s efficiency is derived 
from the prices of petroleum products (Naphtha, Gasoline, Kerosene, Diesel, and Fuel Oil) using the portfolio 
theory. The panel data was constructed using the following dependent variables, the crude oil production effi­
ciency, energy consumption, renewable energy consumption, and R&D investment. Using the panel data anal­
ysis, empirical analyzes are conducted on how the efficiency of the oil refining industry is affected by explanatory 
variables. The results show that crude oil production and energy use in OECD countries have a negative effect on 
the efficiency of the oil refining industry, and consumption of renewable energy and R&D investment have a 
positive effect. Contrary to conventional perception, the petroleum industry can coexist with the renewable 
energy industry for sustainable development.   
1. Introduction 
The petroleum industry can be decomposed into the upstream sector, 
that is, the exploration and production industry and the downstream 
sector, that is, the oil refinery industry that produces and sells petroleum 
products using crude oil as raw material. Oil refinery produces petro­
leum products such as gasoline, naphtha, kerosene, and light oil through 
refining facilities such as distillation, heavy oil cracking, and desulfur­
ization. Petroleum products are transported through pipelines, vehicles, 
railways, and ships, and are supplied to end consumers through storage 
facilities and, finally, gas stations. In this way, the oil refining industry is 
a capital-intensive business that requires massive investment, such as 
the establishment of infrastructure from the introduction of crude oil to 
refining and sales. 
Since petroleum products are co-products, refineries cannot produce 
only specific petroleum products in the short term, and can only produce 
gasoline, naphtha, and kerosene in specific ratios. In other words, in the 
unit refining industry, certain petroleum products cannot be produced 
immediately according to market demand, but only according to the 
refinery facilities’ constraints. 
The oil industry is one of the most important industry sectors, which 
is hiring hundreds of thousands of workers worldwide and is creating 
hundreds of billions of dollars’ added value globally each year. In re­
gions that house the major NOCs, these oil and gas companies are so 
vital they often contribute a significant amount towards national GDP. 
In case of United States, at the national level, each direct job in the oil 
and gas industry supported an additional 2.7 jobs elsewhere in the US 
economy in 2015, the study indicates. Counting direct, indirect, and 
induced impacts, the industry’s total impact on labor income, including 
proprietors’ income, was $714 billion or 6.7% of national labor income 
in 2015. (API, 2017) Oil refinery transforms crude oil into useful 
products such as liquefied petroleum gas (LPG), gasoline or petrol, 
kerosene, jet fuel, diesel oil and fuel oils. In terms of petrochemical in­
dustry, oil refinery produces olefins and aromatics by fluid catalytic 
cracking of petroleum fractions. Then petrochemical plants produce 
olefins by steam cracking of natural gas liquids like ethane and propane. 
Aromatics are produced by catalytic reforming of naphtha. Olefins and 
aromatics are the building-blocks for a wide range of materials such as 
solvents, detergents, and adhesives. Olefins are the basis for polymers 
and oligomers used in plastics, resins, fibers, elastomers, lubricants, and 
gels. (Meyers, 2004; James, 2006). 
However, the oil industry is facing the crucial environmental issues 
* Corresponding author. 
E-mail addresses: chancelim@gmail.com (C. Lim), jongsu.lee@snu.ac.kr (J. Lee).  
Contents lists available at ScienceDirect 
Energy Policy 
journal homepage: http://www.elsevier.com/locate/enpol 
https://doi.org/10.1016/j.enpol.2020.111491 
Received 4 March 2019; Received in revised form 1 April 2020; Accepted 5 April 2020   
Energy Policy 142 (2020) 111491
2
that are exacerbating business instability, such as climate changes and 
air-pollution emission regulations. Recently Heede (2014, 2019) re­
ported that the 20 fossil fuel companies whose relentless exploitation of 
the world’s oil, gas and coal reserves have contributed to 35% of all 
energy-related carbon dioxide and methane worldwide, totaling 480 
billion tons of carbon dioxide equivalent since 1965. In addition, shale 
gas supply booming suggested that various kinds of raw materials, like 
ethane and propane, could be supplied in place of existing petroleum 
product, like naphtha. (Siirola, 2014) This is an oversupply of the pe­
troleum product sector, resulting in a sharp change in the price of 
naphtha, and its equivalent fraction like gasoline (Kilian, 2016). That is, 
the uncertainty in the oil sector is increased. 
In particular, as mentioned above, the oil refining industry needs 
huge investment in establishing a certain scale of facilities and it shows 
co product characteristics because of the restriction of refining facilities. 
As a result, changes in crude oil prices and the prices of petroleum 
products affect economic efficiency directly. 
Using crude oil and petroleum product price information, the Mar­
kowitz portfolio optimization theory (Markowitz, 1952), which is used 
to analyze financial markets, can be used to determine the rate of pro­
duction that minimizes variance. Until now, the Markowitz portfolio 
optimization theory has been limited to analyzing the risk and efficiency 
of investments in financial markets such as stocks and bonds. However, 
in this study, the theory is extended to analyze the oil refining industry. 
That is, the efficiency index of the oil refining industry is newly proposed 
using the efficient frontier of the petroleum product portfolio, and the 
productivity of the refinery industry at the national level is calculated. 
Therefore, this study focused on the following implications. First, the 
oil refining industry was analyzed by expanding the portfolio optimi­
zation theory that was limited to the financial industry. Second, the 
efficiency index of the oil refining industry was presented through the 
efficient frontier of the petroleum product portfolio. Third, Panel data 
were constructed with the obtained oil refinery efficiency and were used 
to investigate which economic, industrial, and energy factors were 
effective in OECD countries. 
2. Literature review 
2.1. Conventional productivity analysis 
The efficiency and productivity of an industry are typically analyzed 
using DEA. F€
are et al. (1997) estimated the Malmquist productivity 
change index for 17 OECD countries over 1979–1988. Lee et al. (1998) 
and Hsiao and Park (2002) also studied the Malmquist productivity 
change index using manufacturing data from Taiwan and Korea, 
respectively. In addition, Kang and Duk, 2016 used the DEA method to 
derive industry-specific Malmquist energy and environment efficiency 
for the Korean manufacturing industry to examine its productivity 
effect. 
Erbetta and Rappuoli (2003) analyzed the efficiency of technology 
using the DEA method to determine the optimum operating scale of 
Italian gas distribution companies. The analysis was conducted on 46 
gas distribution companies in Italy, using input and output data from 
1994 to 1999. Since there was no structural change during the period, 
the data were pooled to constitute 276 production units to measure ef­
ficiency. Comparing the DEA index with the general model and 
customer density, the average net technical efficiency of city gas com­
panies was measured as 66% in the model considering customer density 
and net technical efficiency was about 3% higher than that in the pre­
vious year. 
2.2. Productivity analysis in the oil industry 
Studies of productivity have mainly applied DEA or case studies to 
qualitatively address the strategy or direction of the development in the 
oil industry. Park and Park (2014) analyzed the productivity of the oil 
industries in Korea, Japan, and the United States from 1991 to 2003 
using the Malmquist productivity index and firm-level information (e.g., 
sales, variable/fixed costs) from oil companies. They found the pro­
ductivity of the US oil industry to be high, suggesting that US oil com­
panies actively promote business diversification and vertical 
integration. In addition, Kwaku et al. (2017) analyzed the efficiency of 
national oil companies using the DEA method, which argues that the 
higher the number of international business activities of such com­
panies, the more efficient they are. 
Jung and Do (2016) suggested that the domestic oil refining industry 
should be expanded to develop resources, upgrade refineries, and the 
petrochemical industry to reduce the influence of OPEC as well as 
expand the supply of and demand for non-traditional oil. Kim and Jung 
(2017) argued for the importance of establishing close cooperation be­
tween the government and private refineries using case studies of oil 
industry policies and the business strategies of major oil companies 
globally. Kim and Kim (2019) stressed the need to revitalize private 
investment and formulate government policy in upstream industries 
such as resource development in the petroleum industry. 
Traditionally, the efficiency of the refining industry has addressed 
energy efficiency rather than production efficiency. A private company, 
Solomon Associates LLC, investigated and analyzed energy use at re­
fineries and derived the Solomon Energy Intensity Index (EII). The oil 
refining plant submits data on energy utilization, throughput, and yield 
to determine the EII of the refinery. The EII is an energy efficiency index 
created to compare actual energy consumption in oil refineries with 
“standard” energy consumption in oil refineries of a similar size and 
composition. Solomon Associates analyzed the production capacity of 
oil refining plants globally to derive the standard energy consumption of 
oil refining plants. The EII is calculated by multiplying the actual energy 
ratio of the oil refinery by the standard energy consumption by 100 
(Worrell et al., 2000). 
2.3. Applying portfolio theory to the financial market 
Markowitz’s portfolio optimization theory has been mainly used to 
analyze the risk and efficiency of investment in financial markets, such 
as stocks and bonds. Karavas (2000) tested the effect of diversified in­
vestments when adding alternative investment assets to traditional in­
vestment portfolios. From 1990 to 1998, four alternative investment 
assets (NAREIT, GSCI, EACM100, and Traditional Alternative) were 
added to the traditional portfolio of investment assets consisting of the 
US S&P 500 and the Salomon Brothers Government and Corporate Bond 
Index, respectively. 
Ennis and Sebastian (2005) examined the effect of the diversification 
of private equity funds on returns, risks, and correlations for US stocks, 
foreign stocks, real estate, private funds, and bonds from 1978 to 2002. 
In the case of including private equity funds, the diversified investment 
effect was based on the Sharp index. In particular, if the portfolio was 
composed of US and foreign stocks rather than a composite asset port­
folio composed of correlations, real estate, and bonds, the diversified 
investment was relatively effective. 
Amin and Kat (2003) investigated the effect of hedge funds’ diver­
sified investment on hedge funds (S&P 500) and bonds (Salomon 
Brothers Treasury Bond Index) and 20 hedge funds from 1994 to 2001. 
C. Lim and J. Lee                                                                                                                                                                                                                               
Energy Policy 142 (2020) 111491
3
They also analyzed the effect of diversification on fund portfolios. The 
results of the analysis showed that the incorporation of hedge funds into 
equity and bond investment assets improved the average diversification 
characteristic. 
Narayan and Narayan (2010) proposed a method of constructing a 
portfolio by analyzing the effect of oil price volatility on the Vietnamese 
stock market. Salem et al. (2011) proposed a method of constructing a 
portfolio based on the stock indices of seven countries including the S&P 
500 and Nikkei 225 index. Bekhet and Matar (2012) proposed a port­
folio composition method based on changes in consumer indices. Milner 
and Vos (2003) used the time-series data of quarterly returns from 1991 
to 2001 to verify the correlation between private equity funds and listed 
stocks as well as the effect of diversification. 
2.4. Applying portfolio theory to the energy industry 
Although Markowitz’s portfolio optimization theory has been used to 
analyze the capital market, research on industrial sectors using portfolio 
theory is limited. Among this strand of research, Awerbuch (2000) and 
Awerbuch and Berger (2003) analyzed the efficiency of the energy in­
dustry using portfolio theory and evaluated the cost of generating gas 
and coal. In particular, Awerbuch (2000) assumed that renewable en­
ergy sources such as wind power and photovoltaic power generation are 
considered to be risk-free. Using price fluctuation data on coal and 
natural gas between 1975 and 1999, he analyzed the correlation be­
tween profit and risk due to the fluctuation in the portfolio between 
coal-fired and gas turbine power generation, assuming that renewable 
energy is mainly risk-free. He found that incorporating renewable en­
ergy into the portfolios of up to 6% of US fossil fuel power plants could 
reduce risk without sacrificing profitability, or increase revenues under 
the same risk. 
Awerbuch and Berger (2003) applied portfolio theory to study the 
EU’s power planning and policy decisions. The EU’s existing power 
generation facilities (e.g., coal, gas, petroleum) as well as all available 
facilities including nuclear power and renewable energy were reviewed. 
In this study, he tried to suggest the directions and problems of the EU’s 
power structure plan in 2010 based on its power structure in 2000. 
Renewable energy, which was largely ignored by the power structure 
review, is an alternative source as part of an efficient portfolio. Compared 
with the fossil fuel generation method, renewable energy incurs almost 
no cost after construction and can be regarded as a risk-free financial 
investment. In addition, considering the profit and risk aspects of the 
correlation of power generation composition, it could be a sufficient 
alternative to ensure a stable profit while reducing the risk. 
Therefore, Markowitz’s portfolio optimization theory has not been 
applied to understand the productivity and efficiency of the oil industry 
to the best of my knowledge. This study has derived the efficiency index 
of the oil refining industry by country through the Markowitz portfolio 
optimization theory, usually used for the construction of optimum stock 
portfolios or electric power and energy portfolios. It is also meaningful 
that the efficiency of the oil refining industry was quantitatively 
analyzed using various panel data models in 30 OECD countries. 
3. Methodology 
In the first step, we calculated the efficiency index of the oil refining 
industry using the Markowitz portfolio optimization theory. In the sec­
ond step, the panel data was structured with the efficiency index calcu­
lated as the dependent variable, and the economic, industrial, and energy 
related indices were used as explanatory variables. Finally, the factors 
affecting the efficiency index of the refinery industry were analyzed by 
econometric modeling, such as various panel data models (Fig. 1). 
3.1. Markowitz portfolio optimization theory 
Markowitz (1952) proposed the portfolio theory for allocating in­
vestment assets in financial markets, minimizing the risk, and maxi­
mizing the expected return rate by using the dispersion and correlation 
of the investment assets (portfolio). The expected value and risk of a 
portfolio are represented by the mean and variance of the distribution, 
which is called the mean-variance model. In the portfolio theory, the 
effect of the diversification of investment assets varies with the 
inter-asset correlation coefficient, which moves between 0 and 1. The 
smaller the correlation coefficient between portfolios, the greater the 
risk reduction, and as the correlation coefficient increases, the risk 
reduction decreases. Markowitz (1952) has demonstrated that the pro­
cess of selecting and organizing portfolios is governed by dominance 
principles, which is to choose an efficient portfolio that has the least risk 
and maximum return among all available portfolios. The set of portfolios 
selected by the dominance principles is called the efficiency curve and 
can be derived through the optimization process. 
In this study, a petroleum product mix constitutes a production 
portfolio. The price of individual petroleum products, in particular, the 
price spread, which is the price difference between crude oil and pe­
troleum products, is set as the average rate of return of individual assets 
according to the portfolio theory. Therefore, the petroleum product 
price spread (ui) calculates the expected return (ui,t) and variance of 
petroleum products (σij) over one unit period. Therefore, equation (1) 
shows that the optimal mix of petroleum products (wi) is calculated by 
minimizing the variance of expected return (Z), and the petroleum 
Fig. 1. Overview of two stage analysis.  
C. Lim and J. Lee                                                                                                                                                                                                                               
Energy Policy 142 (2020) 111491
4
product portfolio’s efficient frontier can be derived. 
Minimize Z ¼
X
N
i;j
σijwiwj
Subject ​ to
X
N
i
uiwi  K
X
N
i
wi ¼ 1
8wi  0
σij ¼ Cov
 ui; uj

¼
P
t
ðui;t   uiÞ
 uj;t   uj

nt   1
N : Total ​ number ​ of ​ petroleum ​ products
wi : ​ Ratio ​ of ​ petroleum ​ product ​ ðiÞ
ui : Expected ​ return ​ of ​ petroleum ​ product ​ ðiÞ
ðPrice ​ spread ​ between ​ reference ​ crude ​ oil ​ and ​ petroleumproduct ðiÞÞ
σij : Covariance ​ of ​ expected ​ returns ​ of ​ petroleum ​ products ðiÞ and ðjÞ
ui;t : expected ​ return ​ of ​ t ​ period ​ of ​ petroleum ​ productðiÞ
K : Minimum ​ expected ​ return ​ on ​ petroleum ​ product ​ portfolio
(1)  
3.2. Portfolio efficiency index 
The derived efficient frontier of the petroleum product portfolio can 
be considered the optimal production curve for the petroleum product 
for one unit period (such as one year)). In other words, it can be assumed 
as the optimal production curve for the oil refining industry. At this 
time, if the national petroleum-product production data of one specific 
country is available, the production efficiency index of the oil industry 
can be defined by the Euclidian distance from the derived optimal 
production curve. This is similar to the methodology of calculating the 
efficiency index through the Efficient Frontier in DEA, (Cooper et al., 
2004). Therefore, in this study, the portfolio efficiency index is defined 
by how close the expected profit and the variance (standard deviation) 
are from the optimal production curve (Fig. 2). This means that the 
portfolio efficiency index for the oil refining industry in one specific 
country can be expressed by equation (2), has a value between 0 and 1, 
and the higher the efficiency index, the closer it is to 1. 
PortFolio Efficiency Index ¼
OT
OA
⇒Inverse ​ of ​ the ​ distance ​ from ​ the ​ efficient ​ frontier
(2) 
This means that the portfolio efficiency index for the oil refining 
industry in a country can be expressed by equation (2), which has a 
value between 0 and 1. The higher the efficiency index, the closer it is to 
1. Efficiency in the portfolio efficient curve can be decomposed into 
volatility (or standard deviation, x-axis) and profitability (or expected 
return, y-axis). In this study, portfolio efficiency is defined by how close 
efficiency is positioned to the portfolio efficiency curve considering both 
volatility and profitability. In other words, the efficiency of the pro­
duction of petroleum products is defined as the proximity to the pro­
duction curve of the petroleum product portfolio, taking both volatility 
and profitability into account at the same time. Hence, the portfolio 
efficiency index is defined as the proximity of the portfolio efficiency 
curve to the origin in the coordinates of volatility (x axis) and profit­
ability (y axis). 
In summary, the annual production data of crude oil and petroleum 
products is used to derive the efficiency production curve for the pro­
duction of petroleum products by year. Next, the efficiency index of the 
country’s oil refining industry is calculated from the OECD’s annual 
petroleum products production rates by country and the derived pro­
duction curves. Then, the panel data is constructed by combining the 
country’s annual oil refining industry’s productivity index and the 
explanatory variables (crude oil production, energy consumption, R&D 
expenditure and renewable energy consumption). 
3.3. Panel data analysis 
The portfolio efficiency index of the oil refining industry by country 
is derived using national petroleum-product production data and the 
optimal production curve (Fig. 2). It is also possible to structure the 
panel data by economic, industrial, and energy related indices of each 
country during the same unit period. 
Therefore, this study applies panel data analysis, which analyzes the 
unobserved heterogeneity, and time effects of individuals. In addition, 
cross-sectional data analysis that is independent of time can analyze the 
dynamic relationship of variables. Therefore, panel data analysis can 
test complex behavioral models that cannot be covered by time series or 
cross sectional data analysis in any way. 
First, a generalized least square (GLS) panel model with hetero­
scedasticity and autocorrelation of panel data is shown in equation (3). 
yi;t ¼ const: þ βxi;t þ εi;tði ¼ 1; 2; ⋯; N; t ¼ 1; 2; ⋯; TÞ
εi;t ¼ ρiεi;t 1 þ νi;t
(3) 
In this case, xi,t and yi,t are explanatory variables and dependent 
variables corresponding to the t period of i individual respectively. If the 
error terms εi, t show a first-order autocorrelation, ρi is a first-order 
autocorrelation coefficient. Furthermore, the estimated β represents 
the influence magnitude of each explanatory variable. 
Second, fixed effect (FE) and random effect (RE) models, which 
reflect the heterogeneity of each individual i, are shown in Equation (4). 
yi;t ¼ const: þ βxi;t þ μi þ εi;tði ¼ 1; 2; ⋯; N; t ¼ 1; 2; ⋯; TÞ
(4)  
where μi is the error term indicating the characteristics of each i object 
that does not change with time considering the fixed and random effect. 
Third, the least square dummy variables (LSDV) model can be 
applied to identify individual characteristics of each entity. If there are N 
entities, N-1 dummy variables are added, as shown in equation (5). In 
addition, the estimated γj represents the influence magnitude of each j 
entity’s characteristic. 
Fig. 2. Portfolio efficient frontier and portfolio efficiency index.  
C. Lim and J. Lee                                                                                                                                                                                                                               
Energy Policy 142 (2020) 111491
5
yi;t ¼ const: þ βxi;t þ
X
N 1
j
γjDj;t þ εi;tði ¼ 1; 2; ⋯; N;
t ¼ 1; 2; ⋯; TÞ
 Dj;t ¼ 1 ; for i ¼ j; Dj;t ¼ 0 ; otherwise i 6¼ j

(5) 
Fourth, the dynamic panel (DP) model using a time-lagged depen­
dent variable as an explanatory variable was applied as equation (6). In 
the DP model, a first-order differential model is used to eliminate the 
error term μi, and the instrumental variable was applied to solve the 
endogenous problem of the explanatory variable. In this study, the 
estimated β vector (β1 β2) was estimated using the difference generalized 
method of moment estimator proposed by Arellano and Bond (1991). 
yi;t ¼ const: þ β1yi;t 1 þ β2xi;t þ μi þ εi;tði ¼ 1; 2; ⋯; N; t ¼ 1; 2; ⋯; TÞ
(6)  
4. Research data 
We use Dubai Crude prices for crude oil and naphtha, gasoline, 
kerosene, diesel and fuel oil for petroleum products in order to represent 
the global oil market. The weekly prices of Dubai Crude and petroleum 
products (naphtha, gasoline, kerosene, diesel and fuel oil) for the period 
from 2005 to 2016 were collected through Petronet (www.petronet.co. 
kr) provided by Korea National Oil Corporation. Fig. 3 shows the price of 
Dubai Crude and Fig. 4 shows the price difference between crude oil and 
petroleum products during the same period. At this time, the prices were 
adjusted using the World Bank’s inflation index for consumer price 
(https://data.worldbank.org). 
The price spread between Dubai Crude and petroleum product was 
used to calculate the expected return and variance (or standard devia­
tion) of oil production for the period from 2005 to 2016. Using Mar­
kowitz portfolio optimization theory, we derive the petroleum product 
portfolio’s efficient frontier, which is the optimal production curve for 
the oil refining industry, shown in Fig. 5. It is worth noting that the 
fluctuation of the optimal production curve for the oil refining industry 
was the greatest at the time of 2008 financial crisis. 
The portfolio efficiency index of the oil refining industry for OECD 
countries was derived using the petroleum product portfolio’s efficient 
frontier and domestic production data of petroleum products by OECD 
countries. Excluding four countries (Estonia, Iceland, Luxembourg, and 
Slovenia) that did not produce crude oil and did not refine petroleum 
products, 30 OECD countries’ domestic production data of petroleum 
products were collected from the International Energy Agency (IEA) Oil 
Information from 2005 to 2016. Appendix A shows the ratio of domestic 
production of petroleum products produced by the 30 OECD countries 
from 2005 to 2016. Using the ratio of domestic production of petroleum 
product, expected return by petroleum product in the year and covari­
ance between petroleum products, the expected return (profitability) 
and volatility of petroleum product profit by country can be calculated. 
In this case, the total profit from the production of petroleum products 
by country is calculated by multiplying the ratio of production by the 
expected return. Next, the volatility of the profit is calculated from the 
product of the weight of production and covariance. This corresponds to 
point A in Fig. 2. After finding point A, we can find point T that meets 
point A and the petroleum product optimal production curve in the di­
rection of the origin. When points A and T are obtained in this way, the 
distances from the origin are calculated by OA and OT, respectively. 
Then, the efficiency index OT/OA is calculated as defined in equation 
(2). Table 1 shows the portfolio efficiency index of the refining industry 
from 2005 to 2016 derived by equation (2). 
In order to explain the efficiency of the oil refining industry, the 
explanatory variable candidates that can affect the oil industry and even 
the energy industry were investigated as shown in Tables 2 and 3. 
If there are many explanatory variables as above, the correlation 
Fig. 3. Price of dubai crude (2005–2016, $/bbl).  
Fig. 4. Price spread between dubai crude and petroleum products (2005–2016, $/bbl).  
C. Lim and J. Lee                                                                                                                                                                                                                               
Energy Policy 142 (2020) 111491
6
between the variables is multicollinearity, which results in inaccurate 
regression. To avoid the multicollinearity between these variables, 
variables with a VIF (Variance Inflation Factors) index of 10 or more 
were excluded from explanatory variables. At the same time, covariance 
analysis of 13 variables excludes highly correlated variables as explan­
atory variables. The variables of GDP and fuel import were excluded for 
the correlation with energy use. The variable of fuel export also 
excluded for the correlation with R&D expenditures. Table 4 and Table 5 
show the VIF test results and covariance matrices for the variables, 
respectively. Through the multicollinearity and covariance analysis, 
crude oil production (crude_prod), energy use (energy_use), renewable 
energy consumption (renew_cons) and R&D expenditure (rnd_exp) were 
selected as explanatory variables. 
The efficiency index of the oil refining industry is a dependent 
variable, and the explanatory variables are crude oil production, energy 
use, renewable energy consumption, and R&D expenditure. Crude oil 
production can explain industrial efficiency in terms of the “curse of 
resources” (Hausmann and Rigobon, 2003). The representative case of 
resource curse is “Dutch Disease”, which is related to the impact of the 
resource wealth on other alternative trade goods (Corden, 1984). Dutch 
disease means that the Netherlands experienced an economic downturn 
in the 1970s, despite a massive natural gas discovery in 1959 that 
resulted in huge wealth. And the economic impact on the resource curse 
is also explained by the fact that resource price volatility leads to 
instability and inefficiency of fiscal revenue and to financial market 
imperfections (Sachs and Warner, 2001). Energy use and renewable 
energy consumption (Adnan and Karık, 2017) (Chang and Hu, 2018) 
affect the efficiency of the energy industry. R&D expenditure is an 
Fig. 5. Portfolio efficiency frontier of petroleum products (2005–2016, yearly, [$/bbl]).  
C. Lim and J. Lee                                                                                                                                                                                                                               
Energy Policy 142 (2020) 111491
7
essential element in corporate growth and innovation (Bilbao-Osorio 
and Rodríguez-Pose, 2004). 
The crude oil production data refer to the IEA (2018) Oil Information 
from 2005 to 2016. Energy use, renewable energy consumption, and 
R&D expenditure data were collected from World Bank Open Data 
(https://data.worldbank.org). 
5. Panel data analysis 
First, the log-Panel GLS model expressed by equation (7) was applied 
to the Portfolio efficiency index of oil refining industry as a dependent 
variable and crude oil production (crude_prod), energy use (energy_use), 
renewable energy consumption (renew_cons) and R&D expenditure 
(rnd_exp) as explanatory variables. 
ln refinery effi;t ¼ const: þ β1 ln crude prodi;t þ β2 ln energy usei;t
þβ3 ln renew consi;t þ β4 ln rnd expi;t þ εi;t
εi;t ¼ ρiεi;t 1 þ νi;t
ð i ¼ 1; 2; ⋯; N; t ¼ 1; 2; ⋯; T Þ
(7) 
The heteroscedasticity of the panel data was tested by White test 
(Table 6). The test statistic, a Lagrange multiplier measure, is distributed 
Chi-squared under the null hypothesis of homoskedasticity. The null 
hypothesis that the panel data is homoskedasticity is rejected as a result 
of white test. Therefore, there is a heteroscedasticity in panel data of 30 
OCED countries’ portfolio efficiency index. 
The Wooldridge test (Wooldridge, 2010) was performed to test the 
first order autocorrelation of the panel data (Table 7). The null hy­
pothesis that there was no first order autocorrelation could not be 
rejected. 
Table 8 shows the estimation results of applying the panel GLS model 
that takes into account the heteroscedasticity and no-autocorrelation of 
the panel data of the 30 OECD countries’ portfolio efficiency index. 
Second, in order to analyze the heterogeneity of each individual 
OECD country i, the between effect, fixed effect, and random effect 
models were applied.   
The BE model estimates the coefficients using the mean of the time 
series observations of the variables. Furthermore, the FE model esti­
mates the coefficient by considering the error term μi as a parameter to 
be estimated. Conversely, the RE model estimates the coefficients by 
assuming the error term μi as a random variable. μi in the FE and RE 
Table 1 
OECD 30 countries’ portfolio efficiency index of oil refining industry.  
Id 
Country 
Year 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 
2016 
1 
Australia 
0.5793 
0.2483 
0.3649 
0.3292 
0.5416 
0.8334 
0.8452 
0.6555 
0.4042 
0.6572 
0.3965 
0.4017 
2 
Austria 
0.8079 
0.3745 
0.7071 
0.3202 
0.7391 
0.5272 
0.5479 
0.9492 
0.8624 
0.5415 
0.6470 
0.7553 
3 
Belgium 
0.7842 
0.3647 
0.6901 
0.5312 
0.7890 
0.9318 
0.6668 
0.7911 
0.5967 
0.7278 
0.5574 
0.5768 
4 
Canada 
0.6164 
0.2707 
0.3950 
0.3797 
0.5645 
0.8619 
0.8513 
0.6716 
0.4103 
0.6258 
0.3969 
0.4043 
5 
Czech Republic 
0.6586 
0.2723 
0.5254 
0.3714 
0.5788 
0.8595 
0.7923 
0.6901 
0.4626 
0.6645 
0.4315 
0.4473 
6 
Chile 
0.7308 
0.3534 
0.5743 
0.4878 
0.7112 
0.9540 
0.7790 
0.6897 
0.4290 
0.6308 
0.4080 
0.4187 
7 
Denmark 
0.6839 
0.2821 
0.5126 
0.3178 
0.7093 
0.7901 
0.9321 
0.8062 
0.6383 
0.7839 
0.5121 
0.5741 
8 
Finland 
0.6858 
0.3031 
0.5571 
0.3719 
0.7347 
0.8743 
0.8688 
0.7957 
0.5967 
0.7346 
0.5277 
0.5620 
9 
France 
0.6955 
0.3133 
0.5585 
0.4570 
0.6650 
0.9465 
0.7387 
0.6890 
0.4293 
0.6420 
0.4330 
0.4313 
10 
Germany 
0.7316 
0.3287 
0.6551 
0.4129 
0.7592 
0.9212 
0.6222 
0.7998 
0.6669 
0.7244 
0.5172 
0.5924 
11 
Greece 
0.6305 
0.2685 
0.4624 
0.3570 
0.6262 
0.8856 
0.8276 
0.7020 
0.4589 
0.6456 
0.4340 
0.4468 
12 
Hungary 
0.7201 
0.3236 
0.6001 
0.3955 
0.7728 
0.9250 
0.7947 
0.7859 
0.6151 
0.7265 
0.5452 
0.5806 
13 
Ireland 
0.6824 
0.3170 
0.5073 
0.4323 
0.6972 
0.9311 
0.8581 
0.7180 
0.4503 
0.6799 
0.4670 
0.4581 
14 
Israel 
0.8031 
0.4049 
0.6318 
0.7020 
0.8282 
0.9741 
0.7184 
0.7469 
0.5232 
0.6912 
0.5423 
0.5338 
15 
Italy 
0.6945 
0.2988 
0.5937 
0.3015 
0.7423 
0.7237 
0.9060 
0.8405 
0.7528 
0.8619 
0.5907 
0.6719 
16 
Japan 
0.7699 
0.3965 
0.7237 
0.8044 
0.8071 
0.9927 
0.5710 
0.7023 
0.4581 
0.6472 
0.4997 
0.4790 
17 
Korea 
0.8726 
0.4643 
0.6427 
0.4596 
0.7918 
0.7364 
0.8016 
0.9441 
0.8395 
0.4929 
0.6412 
0.7393 
18 
Mexico 
0.7194 
0.3257 
0.5991 
0.4221 
0.7226 
0.9230 
0.8067 
0.7446 
0.5286 
0.6970 
0.4815 
0.5054 
19 
Netherlands 
0.7352 
0.3559 
0.5490 
0.4466 
0.8586 
0.9413 
0.9303 
0.7740 
0.5413 
0.7005 
0.5371 
0.5398 
20 
New Zealand 
0.8617 
0.5366 
0.7656 
0.5145 
0.9549 
0.9004 
0.8897 
0.9118 
0.7992 
0.8415 
0.7978 
0.7993 
21 
Norway 
0.7320 
0.3365 
0.4470 
0.6565 
0.7293 
0.9041 
0.7781 
0.6807 
0.4307 
0.6655 
0.4707 
0.4498 
22 
Poland 
0.7580 
0.3981 
0.5495 
0.4410 
0.9450 
0.9353 
0.8259 
0.8699 
0.7651 
0.7680 
0.7032 
0.7351 
23 
Portugal 
0.6826 
0.3042 
0.5394 
0.4346 
0.8138 
0.9182 
0.8955 
0.7915 
0.5692 
0.7225 
0.5250 
0.5462 
24 
Slovak Republic 
0.7406 
0.3144 
0.5705 
0.3489 
0.7820 
0.4812 
0.5937 
0.9718 
0.7221 
0.5728 
0.7263 
0.7236 
25 
Spain 
0.6928 
0.3204 
0.6087 
0.3835 
0.7332 
0.9147 
0.6427 
0.7935 
0.5067 
0.7215 
0.5504 
0.5292 
26 
Sweden 
0.7681 
0.3641 
0.5996 
0.5501 
0.8858 
0.9651 
0.7553 
0.8020 
0.5912 
0.7265 
0.5624 
0.5772 
27 
Switzerland 
0.6596 
0.2985 
0.5046 
0.3487 
0.7031 
0.8910 
0.8354 
0.7534 
0.5563 
0.7098 
0.4731 
0.5149 
28 
Turkey 
0.7600 
0.3546 
0.5969 
0.5532 
0.7128 
0.9763 
0.6466 
0.6570 
0.4438 
0.6369 
0.4222 
0.4327 
29 
United Kingdom 
0.8186 
0.4316 
0.6438 
0.5981 
0.7307 
0.9762 
0.7882 
0.7707 
0.5431 
0.6626 
0.5143 
0.5285 
30 
United States 
0.5568 
0.2417 
0.3206 
0.3512 
0.4593 
0.7745 
0.7177 
0.6007 
0.3427 
0.5676 
0.3468 
0.3448  
BE Model
Meanðln refinery effiÞ ¼ const: þ β1Mean
 ln crude prodi

þ β2Meanðln energy useiÞ
þβ3Meanðln renew consiÞ þ β4Mean
 ln rnd expi

þ εi;t
FE Model& RE Model
ln refinery effi;t ¼ const: þ β1 ln crude prodi;t þ β2 ln energy usei;t
þβ3 ln renew consi;t þ β4 ln rnd expi;t þ μi þ εi;t
ði ¼ 1; 2; ⋯; N;
t ¼ 1; 2; ⋯; TÞ
(8)   
C. Lim and J. Lee                                                                                                                                                                                                                               
Energy Policy 142 (2020) 111491
8
models represents the individual OECD country’s characteristics that do 
not change over time. 
For the BE model, if OECD country A has a 1% increase in energy use 
compared with country B, all things being equal, the efficiency of the oil 
refining industry will decrease by 0.0854% (Table 9). In the case of the 
FE model, the energy use of OECD country A was observed from 2005 to 
2016. If energy use is increased by 1%, all things being equal, the effi­
ciency of the oil refining industry is reduced by 1.3336%. 
By contrast, in the RE model, all things being equal, a 1% increase in 
energy use reduces the efficiency of the oil refining industry by 
0.2157%. In this model, energy consumption as an explanatory variable, 
has the same effect within and between panel entities. Therefore, if 
OECD country A increases energy use by 1% over country B, country A’s 
oil refining industry efficiency declines by 0.2157% over country B, and 
if energy use increases by 1% within country A, the refining industry 
efficiency declines by 0.2157% within country A. 
Next, the Hausman test was performed to evaluate whether the FE 
and RE models are appropriate for the panel data (Table 10). The 
Hausman test is a method for confirming the existence of a systematic 
difference between the two models’ estimated coefficients (Hausman, 
1978). The Hausman test statistic is distributed Chi-squared under the 
null hypothesis that the difference in FE and RE Model’s coefficients is 
not systematic. The Hausman test rejects the null hypothesis that the 
estimated coefficients of the two models are not systematic, with a sig­
nificance level of less than 1%. In other words, the analysis of the oil 
refining industry efficiency in 30 OECD countries showed that the FE 
model is more appropriate. 
Third, the LSDV model was applied to the oil refining industry effi­
ciency to identify the individual characteristics of each country. The 
result of Hausman test above suggests that the FE model is more 
appropriate. Therefore, by applying the LSDV model as an extension of 
the FE model, the impact on the oil refining industry’s efficiency of 
Table 2 
Explanatory variables candidates of analysis.  
Variables 
Definitions 
References 
Dependent 
Variables 
refinery_eff 
Refinery efficiency 
OECD Countries’ Domestic Petroleum Products  
Production & Markowitz Portfolio and Efficient  
Frontiers (2005–2016) 
Explanatory 
Variables 
crude_prod 
Crude oil production (ton) 
Oil Information (IEA) 
(2005–2016) 
oil_cons_transformation 
Oil consumption in transformation (ton) 
oil_cons_industry 
Oil consumption in industry (ton) 
oil_cons_transport 
Oil consumption in transport (ton) 
fuel_export 
Fuel exports (USD) 
World Bank (2005–2016) 
fuel_import 
Fuel imports (USD) 
energy_use 
Energy use (kg of oil equivalent per capita) 
renew_cons 
Renewable energy consumption (kg of oil  
equivalent per capita) 
elec_cons 
Electric power consumption (kWh) 
carbon_emi 
CO2 emissions (ton) 
hightech_export 
High-technology exports (USD) 
rnd_exp 
Research and development expenditure (USD) 
gdp 
GDP per capita (USD)  
Table 3 
Descriptive statistics of explanatory variable candidates data (2005–2016).  
Variables 
Unit 
Mean 
Standard Deviation 
Maximum 
Minimum 
refinery_eff 
– 
0.6284 
0.1814 
0.9924 
0.2417 
crude_prod 
Metric ton 
25,723 
65,800 
465,163 
0 
oil_cons_transformation 
Metric ton 
4224 
9483 
120,056 
0 
oil_cons_industry 
Metric ton 
13,747 
27,061 
179,125 
628 
oil_cons_transport 
Metric ton 
40,479 
104,814 
613,183 
1688 
fuel_export 
Million USD 
25,744 
33,915 
180,793 
23 
fuel_import 
Million USD 
25,954 
38,515 
228,233 
0 
energy_use 
kg of oil equivalent 
3900 
1624 
8404 
1240 
renew_ener_cons 
kg of oil equivalent 
695 
810 
3950 
38 
elec_pow_cons 
kWh 
8015 
4643 
25,083 
1943 
carbon_emi 
ton 
422,097 
988,118 
5,789,727 
30,678 
hightech_export 
Million USD 
37,426 
48,641 
220,884 
394 
rnd_exp 
Million USD 
35,135 
79,314 
503,917 
310  
Table 4 
VIF (Variance Inflation Factors) index for Explanatory Variable Candidates 
(2005–2016).  
Explanatory Variables 
VIF 
1/VIF 
Explanatory 
Variables 
VIF 
1/VIF 
ln_carbon_emi 
49.82 
0.0430 
ln_fuel_import 
5.57 
0.2053 
ln_oil_cons_transport 
32.07 
0.0422 
ln_fuel_export 
3.77 
0.2900 
ln_elec_cons 
22.30 
0.0737 
ln_energy_use 
2.97 
0.3814 
ln_hightech_export 
19.73 
0.0634 
ln_renew_cons 
2.87 
0.3594 
ln_oil_cons_transformation 
17.04 
0.0723 
ln_crude_prod 
2.30 
0.4393 
ln_oil_cons_industry 
13.63 
0.1086 
ln_rnd_exp 
1.49 
0.7066 
Ln_gdp 
8.97 
0.1350    
Mean VIF 
14.12  
C. Lim and J. Lee                                                                                                                                                                                                                               
Energy Policy 142 (2020) 111491
9
individual countries was estimated. 
ln refinery effi;t ¼ const: þ β1 ln crude prodi;t þ β2 ln energy usei;t
þβ3 ln renew consi;t þ β4 ln rnd expi;t
þ
X
N 1
j
γjDj;t þ εi;t
 Dj;t ¼ 1 ; for i ¼ j; Dj;t ¼ 0 ; otherwise i 6¼ j

ði ¼ 1; 2; ⋯; N;
t ¼ 1; 2; ⋯; TÞ
(10) 
Table 11 shows the results of the LSDV model. The characteristics of 
each country have an effect on the oil refining industry efficiency in case 
of Germany, Spain, France, the United Kingdom, Italy, Japan, Mexico, 
and Turkey. Furthermore, negative effects were estimated for all eight 
countries based on Australia (Const.)’s within statistical significance at 
the 10% level. The relative size of the effect on the oil refining industry’s 
efficiency was in the order of Turkey, Mexico, Japan, Italy, Spain, Ger­
many, the United Kingdom, and France. Furthermore, in Germany, 
Spain, France, the United Kingdom, Italy and Japan (which are major 
OECD countries), the efficiency decreased by the insufficient invest­
ment. UFIP (2016) and Cambridge Econometrics (2016) showed that 
European oil refineries are intensifying competition because of the 
overall decline in demand and new refinery facilities built in Asia and 
the Middle East. Europe’s refinery facilities are more expensive than 
upgrading facilities in competitive areas because of their age and their 
competitiveness is relatively low owing to the EU’s environmental reg­
ulations introduced to reduce greenhouse gas emissions. From 2010 to 
2016, the capacity of refinery facilities in OECD countries declined by 
approximately 11%, and 4 refineries were closed in 2016. The European 
refining industry is expected to close 25–30% of its refineries by 2040 
due to major restructuring. In the case of Turkey, the efficiency 
decreased by the increase of energy use from 2005 to 2016, and in the 
case of Mexico, the efficiency declined because of the large amount of 
crude oil production. 
Fourth, the DP model, using the time-lagged dependent variable as 
an explanatory variable, was applied to the efficiency of the oil refining 
industry’s panel data as in equation (10). The panel data on the effi­
ciency of the oil refining industry established in this study is an eco­
nomic variable that shows a change pattern at various times from 2005 
to 2016. Thus, it is very clear that the efficiency of the previous step will 
affect the next. In addition, since the change in efficiency of the oil 
refining industry cannot occur in a short period, the autocorrelation may 
occur due to the persistency effect (Marques and Fuinhas, 2011). This is 
consistent with the result of autocorrelation in the panel data of the 
previous Wooldridge test. Therefore, in this study, it is appropriate to 
analyze the oil refining industry productivity through the dynamic panel 
model.  
Table 5 
Covariance matrix of explanatory variable candidates.   
ln_carbon_emi ln_crude_prod ln_elec_cons ln_energy_use ln_fuel_export ln_fuel_import ln_hightech_export ln_oil_cons_transformation ln_oil_cons_transport ln_oil_cons_industry ln_renew_cons ln_rnd_exp ln_gdp 
ln_carbon_emi 
1             
ln_crude_prod 
0.5484 
1            
ln_elec_cons 
0.0087 
 0.0907 
1           
ln_energy_use 
0.1718 
 0.0055 
0.9262 
1          
ln_fuel_export 
0.5968 
0.4162 
0.3427 
0.4552 
1         
ln_fuel_import 
 0.1711 
0.0318 
0.0919 
0.6354 
 0.1029 
1        
ln_hightech_export 
0.5875 
0.0975 
0.1905 
0.3505 
0.5313 
 0.2010 
1       
ln_oil_cons_transformation 0.6743 
0.2322 
 0.1755 
 0.0680 
0.3935 
 0.0359 
0.4838 
1      
ln_oil_cons_transport 
0.9636 
0.5018 
0.0747 
0.2035 
0.6192 
 0.2204 
0.6141 
0.6028 
1     
ln_oil_cons_industry 
0.9330 
0.4009 
0.1256 
0.2652 
0.6341 
 0.0910 
0.6251 
0.7124 
0.9076 
1    
ln_renew_cons 
 0.3511 
 0.1030 
0.6321 
0.4657 
0.0625 
0.0153 
 0.2864 
 0.4275 
 0.2358 
 0.2998 
1   
ln_rnd_exp 
0.7583 
0.2408 
0.4238 
0.4754 
0.6149 
 0.1698 
0.7725 
0.4589 
0.8244 
0.7678 
 0.0331 
1  
ln_gdp 
 0.0411 
 0.1355 
0.7858 
0.7019 
0.3521 
0.0328 
0.3508 
 0.1857 
0.0996 
0.0319 
0.4696 
0.5324 
1  
Table 6 
White test for heteroscedasticity.  
Null Hypothesis: homoscedasticity in Panel Data 
Chi2 Distribution (14) ¼ 34.51 
Prob > chi2 ¼ 0.0021*** 
***, **, and * refer to the significance at the 1%, 5%, and 10% levels, 
respectively. 
Table 7 
Wooldridge test for autocorrelation in panel data.  
Null Hypothesis (H0): no first-order autocorrelation. 
F (1, 29) ¼ 0.071 
Prob > F ¼ 0.8036  
C. Lim and J. Lee                                                                                                                                                                                                                               
Energy Policy 142 (2020) 111491
10
As a result, all the explanatory variable coefficients were estimated 
with a significance level of less than 5% (Table 12). In the case of using 
the difference generalized method of moment proposed by Arellano and 
Bond (1991), over identification can be a problem because it uses a large 
number of instrumental variables. Therefore Sargan (1958) and Hansen, 
1982 tests were applied to solve the over identification problem. In the 
Arellano and Bond (1991) estimation method, the Sargan test is not 
reliable when there is heterogeneity in the error term (Roodman, 2009). 
In this study, because of the heterogeneity of the Arellano and Bond 
(1991) estimation method, the Hansen test, not the Sargan test, was used 
to test for over identification. The statistics of Hansen test (Table 12) 
show that the DP model using the time-lagged dependent variable as an 
explanatory variable is appropriate. 
DP model analysis shows that the efficiency of the oil refining in­
dustry at period t-1 has a statistically negative effect on the efficiency at 
period t. Therefore, when the efficiency of the refining industry in­
creases by 1% at period t-1 the efficiency decreases by 0.3442% at 
period t. The efficiency in the 30 OECD countries is worsening. 
Crude oil production has a negative effect on the efficiency of the 
refining industry. If crude oil production increases by 1%, the efficiency 
of the refining industry will decrease by 0.1551%. This can be under­
stood as the “Resource Curse” mentioned above. In the 30 OECD 
countries analyzed, the efficiency of the oil refining industry declined as 
crude oil production increased. Therefore the more crude oil producing 
country is, the worse the efficiency of oil refining industry is. In other 
words, the phenomenon of resource curse appears at the national level 
of oil refining industry in 30 OECD countries. Countries producing re­
sources are seeing accelerated exports of resources and increasing im­
ports of industrial goods instead (Corden, 1982; Corden and Neary, 
1982). Likewise, as crude oil production increases, exports of crude oil 
increase, while imports of petroleum products increase, which in turn 
can have a negative effect on the growth and efficiency of the oil refining 
industry. 
The more energy use, the lower the efficiency of the oil refining in­
dustry. If energy use is increased by 1%, the efficiency of the industry is 
reduced by 1.8697%. The more energy used, the greater the tendency to 
use power generation/heating in the form of heavy oil products rather 
than converting or cracking heavy oil products to light oil products. The 
more energy use, the more urgent it is to meet the demand of petroleum 
products itself and then there is no way to improve the efficiency of the 
oil refining industry. 
The more renewable energy consumed, the higher the efficiency of 
oil refining industry. If the consumption of renewable energy is 
increased by 1%, the efficiency of the refining industry will increase by 
0.7767%. In a previous study (Blazejczak et al., 2014), it is estimated 
that in Germany, a 1% expansion of renewable energy by 2030 will 
increase the GDP by 3.1% and productivity will increase by 3.1%. 
Therefore, the improvement in economy/productivity of the industry as 
a result of the spread of renewable energy has also influenced the effi­
ciency improvement of oil refining industry. This result is consistent 
with previous studies. Chien and Hu (2007) also showed that through 
the DEA method, the effects of renewable energy on technical efficiency 
of 45 countries were analyzed during 2001–2002. The analysis shows 
that the use of renewable energy improves the technological efficiency 
ln refinery effi;t ¼ const: þ β1 ln refinery effi;t 1 þ β2 ln crude prodi;t þ β3 ln energy usei;t
þβ4 ln renew consi;t þ β5 ln rnd expi;t þ μi þ εi;t
ð i ¼ 1; 2; ⋯; N; t ¼ 1; 2; ⋯; T Þ
(11)   
Table 8 
Test results for panel GLS(Generalized least square) model.  
Assumption 
Homoscedasticity & no-autocorrelation 
Dependent 
Variables 
refinery_eff 
Coef. 
Std. Err. 
t 
P>|t| 
95% Conf. Interval 
Explanatory 
Variables 
ln_crude_prod 
 0.0071** 
0.0048 
 2.12 
0.0358 
 0.0141 
 0.0019 
ln_energy_use 
 0.2149*** 
0.0621 
 3.53 
0.0009 
 0.3345 
 0.0966 
ln_renew_cons 
0.0332 
0.0245 
1.38 
0.1666 
 0.0141 
0.0800 
ln_rnd_exp 
0.0298 
0.0157 
1.85 
0.0648 
 0.0020 
0.0593 
Constant 
0.5054 
0.3888 
1.30 
0.1938 
 0.2563 
1.2642 
Observations/Groups/Periods 
330/30/11 
Wald Chi2 (4) 
16.07*** (Prob. > Wald Chi2 (4) ¼ 0.0029) 
***, **, and * refer to the significance at the 1%, 5%, and 10% levels, respectively. 
Table 9 
Test results for BE model, FE model and RE model.  
Dependent 
Variables 
ln_refinery_eff 
BE Model 
FE Model 
RE Model 
Explanatory 
Variables 
ln_crude_prod 
 0.0064** 
 0.0270 
 0.0071** 
ln_energy_use 
 0.0854* 
 1.3336*** 
 0.2157*** 
ln_renew_cons 
 0.0150 
0.0928 
0.0326 
ln_rnd_exp 
0.0028 
0.2510** 
0.0295* 
Constant 
0.3171 
4.4234 
0.5063 
***, **, and * refer to the significance at the 1%, 5%, and 10% levels, 
respectively. 
Table 10 
Hausman test.  
- 
FE Model 
Coefficients 
(b) 
RE Model 
Coefficeint 
(B) 
Difference 
(b-B) 
Standard Error 
[diag (Vb-VB)] 
0.5 
ln_crude_prod 
 0.0270 
 0.0071 
0.0200 
0.0664 
ln_energy_use 
 1.3336 
 0.2157 
1.1178 
0.3205 
ln_renew_cons 
0.0928 
0.0326 
0.0603 
0.1216 
ln_rnd_exp 
0.2510 
0.0295 
0.2214 
0.1000 
Null Hypothesis (H0): difference in coefficients not systematic 
chi2 (2) ¼ (b-B)’ [(Vb-VB)-1](b-B) ¼ 36.54*** 
Prob.>chi2 ¼ 0.0000 
***, **, and * refer to the significance at the 1%, 5%, and 10% levels, 
respectively. 
C. Lim and J. Lee                                                                                                                                                                                                                               
Energy Policy 142 (2020) 111491
11
of the whole industry. In addition, Chein and Hu (2008) studied that 
through the structural equation model (SEM), the effects of renewable 
energy on gross domestic product (GDP) were analyzed in 116 countries 
of year 2003. They showed that the use of renewable energy increases 
the overall efficiency of the macroeconomy. 
The more research and development (R&D) expenditure is executed, 
the higher the efficiency of the industry. If R&D expenditure increases by 
1%, the efficiency of the oil refining industry increases by 0.4305%. In 
general, R&D investment increases the productivity of enterprises. Gri­
liches (1981) conducted a study of 133 companies in the United States 
on R&D investment and firm productivity from 1966 to 1977, showing a 
positive relationship. Therefore, R&D expenditure can lead to an in­
crease in productivity of the whole industry and thus improve the effi­
ciency of the oil refining industry. 
6. Conclusion and policy implications 
The international oil market is a free market for the production and 
sales of petroleum products. As stocks and bonds, the oil market is free to 
trade and market participants can freely enter. Thus, participants in the 
petroleum product market can also forecast market dynamics and pre­
pare for trading using portfolio theory to analyze financial markets. If 
portfolio theory could derive the efficient frontier of petroleum products 
that minimizes volatility and maximizes profits, oil refineries could 
change their operating conditions or select the crude oil to achieve the 
optimal petroleum product production mix. In addition, using the effi­
cient frontier of petroleum products based on portfolio theory could help 
them make decisions on investment in facilities for the production of 
petroleum products in the long term. At the national energy industry 
level, it can be used as a tool for formulating energy policy (e.g., 
stockpiling petroleum products in an emergency) by deriving the opti­
mum mix of petroleum product production. 
Table 11 
Test results for least square dummy variables model.  
- 
Coefficient. 
Standard Error 
T 
P>|t| 
95% Conf. Interval 
ln_crude_prod 
 0.0270 
0.0630 
 0.43 
0.6663 
 0.1510 
0.0967 
ln_energy_use 
 1.3336*** 
0.3251 
 4.10 
0.0001 
 1.9720 
 0.6932 
ln_renew_cons 
0.0928 
0.1216 
0.75 
0.4512 
 0.1471 
0.3304 
ln_rnd_exp 
0.2510** 
0.1006 
2.49 
0.0130 
0.0524 
0.4480 
Austria 
 0.2976 
0.2818 
 1.06 
0.2922 
 0.8517 
0.2568 
Belgium 
 0.0388 
1.0685 
 0.04 
0.9710 
 2.1412 
2.0638 
Canada 
0.4091 
0.2984 
1.37 
0.1711 
 0.1781 
0.9964 
Switzerland 
 0.9959 
1.0541 
 0.95 
0.3451 
 3.0702 
1.0781 
Chile 
 0.4721 
0.5375 
 0.88 
0.3802 
 1.5299 
0.5857 
Czech Republic 
0.0913 
0.3763 
0.24 
0.8080 
 0.6485 
0.8313 
Germany 
 0.7219*** 
0.2721 
 2.66 
0.0082 
 1.2570 
 0.1871 
Denmark 
 0.3459 
0.2395 
 1.45 
0.1493 
 0.8175 
0.1253 
Spain 
 0.7346** 
0.3950 
 1.86 
0.0421 
 1.5118 
0.0425 
Finland 
 0.0582 
1.0801 
 0.05 
0.9571 
 2.1835 
2.0675 
France 
 0.5773** 
0.2822 
 2.05 
0.0413 
 1.1325 
 0.0223 
United Kingdom 
 0.6127* 
0.3662 
 1.67 
0.0953 
 1.3332 
0.1078 
Greece 
 0.2642 
0.4896 
 0.54 
0.5893 
 1.2275 
0.6989 
Hungary 
 0.3249 
0.4265 
 0.76 
0.4472 
 1.1639 
0.5142 
Ireland 
 0.4091 
1.0867 
 0.38 
0.7071 
 2.5474 
1.7292 
Israel 
 0.5351 
0.5348 
 1.00 
0.3181 
 1.5875 
0.5174 
Italy 
 0.7452*** 
0.2874 
 2.59 
0.0100 
 1.3111 
 0.1799 
Japan 
 0.8674* 
0.4672 
 1.86 
0.0641 
 1.7867 
0.0521 
Korea, Rep. 
 0.0196 
0.5252 
 0.04 
0.9702 
 1.0527 
1.0137 
Mexico 
 0.9593* 
0.5177 
 1.85 
0.0651 
 1.9783 
0.0596 
Netherlands 
0.0950 
0.2536 
0.37 
0.7092 
 0.4036 
0.5933 
Norway 
0.4380 
0.3962 
1.11 
0.2703 
 0.3415 
1.2177 
New Zealand 
0.2281 
0.3833 
0.59 
0.5530 
 0.5259 
0.9820 
Poland 
 0.3973 
0.3902 
 1.02 
0.3092 
 1.1650 
0.3704 
Portugal 
 0.9184 
1.0788 
 0.85 
0.3950 
 3.0408 
1.2042 
Slovak Republic 
0.1759 
0.5858 
0.30 
0.7641 
 0.9768 
1.3287 
Sweden 
 0.3363 
1.0604 
 0.32 
0.7510 
 2.4231 
1.7505 
Turkey 
 1.1614** 
0.5081 
 2.29 
0.0232 
 2.1607 
 0.1621 
United States 
 0.3543 
0.3421 
 1.04 
0.3011 
 1.0271 
0.3184 
Australia (Const.) 
4.7790 
3.5583 
1.34 
0.1802 
 2.2232 
11.7814 
***, **, and * refer to the significance at the 1%, 5%, and 10% levels, respectively. 
Table 12 
Test results for dynamic panel model.  
Dependent 
Variables 
ln_refinery_eff 
Coef. 
Std. Err. 
t 
P>|t| 
95% Conf. Interval 
Explanatory 
Variables 
ln_ refinery_eff (t-1) 
 0.3442*** 
0.0319 
 10.86 
0.0005 
 0.4049 
 0.2818 
ln_crude_prod 
 0.1551** 
0.0783 
 2.03 
0.0427 
 0.3073 
 0.0041 
ln_energy_use 
 1.8697** 
0.7556 
 2.48 
0.0130 
 3.3479 
 0.3892 
ln_renew_cons 
0.7767*** 
0.2519 
3.10 
0.0028 
0.2863 
1.2678 
ln_rnd_exp 
0.4305*** 
0.1498 
2.89 
0.0057 
0.1400 
0.7198 
Sargan test of over id. restrictions: chi2(45) ¼ 185.64 Prob > chi2 ¼ 0.000. 
(Not robust, but not weakened by many instruments.). 
Hansen test of over id. restrictions: chi2(45) ¼ 29.75 Prob > chi2 ¼ 0.931. 
(Robust, but weakened by many instruments.). 
***, **, and * refer to the significance at the 1%, 5%, and 10% levels, respectively. 
C. Lim and J. Lee                                                                                                                                                                                                                               
Energy Policy 142 (2020) 111491
12
This study investigated the efficiency of the oil refining industry 
using a two-stage method of Markowitz portfolio optimization and panel 
data analysis from about 30 OECD countries from 2005 to 2016. The 
efficiency of the oil refining industry was derived by applying portfolio 
theory based on the price of petroleum products. And the explanatory 
variables describing the efficiency of the oil refining industry (crude oil 
production, energy use, renewable energy consumption, R&D expendi­
ture, etc.) were selected to form panel data for OECD countries from 
2005 to 2016. These panel data generally have heterogeneity and 
endogenous problems of explanatory variables. For this, various panel 
data models were used. Then, the Hausman test showed that FE model 
was an appropriate analytical model for this study, and expanded it to 
analyze the efficiency of the oil refining industry in individual OECD 
countries through LSDV model. Next, DP model was applied to solve the 
endogenous problem of the following explanatory variables and 
analyzed how each variable affects the efficiency of the oil refining in­
dustry such as crude oil production, energy use, renewable energy 
consumption, and R&D expenditure. The results show that crude oil 
production and energy use in OECD countries have a negative effect on 
the efficiency of the oil refining industry, and consumption of new and 
renewable energy and R&D investment have a positive effect. 
If the resource curse in this study is limited to the efficiency of the 
refinery industry rather than the national economy, in terms of the ef­
ficiency of the oil refining industry defined by portfolio theory, the 
productivity of the refinery industry decreases as the production of 
crude oil may increase. In other words, there is an example of the phe­
nomenon of resource curse in 2005–2016. 
As energy use increases, there is no room for investment in individual 
companies or industries to improve energy efficiency, so the efficiency 
of oil refining industry reduces. This means that it is difficult to increase 
the efficiency voluntarily at the industry level. The government should 
encourage investment in the improvement of the efficiency of the oil 
refining industry in order to achieve sustainable development of the 
industry. For example, in Japan, in June 2014, a policy plan to 
strengthen the competitiveness of the oil refining industry was 
announced. It proposed resolving excess refining capacity, optimizing 
equipment through integration, and improving energy efficiency. It also 
recommended upgrading ratios from 45% to 50% and activating the 
M&A (mergers and acquisitions) market of oil refining companies 
(Yoshio, 2014). As of 1983, 49 refineries were restructured to 29 in 
2014, and Idemistu Kosan acquired Showa Shell in 2014. In Europe, 
efforts have been made to increase the efficiency of oil refining industry 
(Reuters, 2013), with the shutdown of refineries processing 1898 
million barrels/day from 2008 to 2013. 
By convention, renewable energy has been opposed to oil, repre­
sented by fossil fuels. Lee et al. (1998) insisted that the strategy for a 
sustainable development is the replacement of fossil fuels by various 
sources of renewable energy. Weight (2009) proposed replacement of 
fossil power plant introducing wind energy in German. And the 
renewable energy technologies provide an excellent opportunity for 
mitigation of greenhouse gas emission and reducing global warming 
through substituting conventional energy sources like oil and coal. (Park 
and Park, 2014) However, in this study, the expansion of renewable 
energy will increase the diversity of energy use and promote the growth 
and efficiency of the related materials and components industries, which 
will also increase the efficiency of the oil refining industry. The eco­
nomic growth led by renewable energy will inevitably lead to growth 
and efficiency of the entire industry, which will positively affect the 
growth and efficiency of the oil industry. 
Contrary to the concern that the demand for petroleum products will 
decrease because of the expansion of the renewable energy supply, it 
will increase the diversity of energy use. In other words, by replacing the 
demand for heavy oil products used for heat source and power genera­
tion with renewable energy sources, the conversion of heavy oil prod­
ucts to light oil products can be accelerated and the efficiency of the 
entire oil refining industry can be increased. Although biofuels in 
renewable energy sources may directly reduce the demand for petro­
leum products, their substitution for gasoline or diesel will ultimately 
lead to a shift to higher-value-added petrochemical products. Therefore, 
the biofuel industry has a positive effect on the efficiency of the oil 
refining industry. Therefore, the government will continue to expand 
renewable energy, which will help the overall growth and efficiency of 
the industry and particularly improve the efficiency of oil refining 
industry. 
R&D investment is positive for improving the efficiency of industry 
overall. In the oil refining industry, however, R&D investment accounts 
for a small portion (1–3%) of total sales. However, it is necessary to 
increase investment in R&D to improve the efficiency of oil refining 
industry. The process automation and logistics optimization, which are 
the oil industry’s conventional R&D activities, should be continued, and 
the more substantial investment should be paid to the cutting-edge R&D 
such as big data analysis, machine learning, and deep learning (neural 
network) technology development, which have recently attracted 
attention in all industries. 
Finally, the oil refining industry in the major OECD developed 
countries (Germany, Spain, France, the United Kingdom, Italy, Japan) 
are showing a structural decline, which is because of the decline and 
stagnation of demand for petroleum products in the market. In partic­
ular, countries in OECD Europe are experiencing a recession in the oil 
refining industry due to reduced demand for petroleum products and 
new refining facilities in Asia and the Middle East. In addition, the 
countries that experienced a sharp increase in energy use among OECD 
countries (Turkey) or those with a large export of crude oil (Mexico) 
were negatively affected by the efficiency of the refinery industry. 
So, in the case of OECD developed countries (Germany, Spain, 
France, the United Kingdom, Italy, Japan), where investment in the oil 
refining industry has been declining, there is a need for institutional 
support for vertical or horizontal integration of the oil refining industry. 
Such policy support enables active mergers and acquisitions between 
refineries within and between these countries, leading to the increased 
efficiency through economies of scale. Next, in the case of Turkey, where 
oil refining industry’s efficiency is low due to high energy use, it is 
necessary to directly increase energy efficiency facilities. In addition, 
investments in the expansion of refining facilities should be pre­
determined in such cases as Mexico, where oil production is high and oil 
refining industry’s efficiency is low. 
If the demand for petroleum products is sharply reduced, especially 
in Korea, the efficiency of the oil refining industry will deteriorate as 
sharply as in the major OECD developed countries. Therefore, it is 
necessary to make an investment in heavy oil upgrading facilities, ver­
tical integration into the petrochemical industry, and restructuring 
through M&A between refineries. 
In contrast to this study, petroleum companies diversify their busi­
ness portfolio by entering upstream business, resource development, 
and petrochemical business, which is a downstream business, and offset 
the risk factors of the entire business. Therefore, the actual petroleum 
industry may differ from the theoretical petroleum industry, and some 
limitations may be considered in the study. 
Another limitation is that in the financial industry, arbitrary changes 
to assets are possible; on the contrary, in the energy industry, it is 
impossible to change certain energy sources drastically. Increasing or 
decreasing energy sources in the short term to build an efficient portfolio 
is difficult. Thus, the change to an optimal oil product production mix 
may in fact be challenging to realize because of facility constraints at oil 
refineries, stockpiling obligations, and limits on the distribution infra­
structure of petroleum products. 
In addition, this study has a limitation that the efficiency of the oil 
refining industry has not been studied in the countries that have recently 
expanded their influence in the refining industry. China, Russia, and 
Saudi Arabia, which are excluded from this study, are very important in 
the global oil industry as oil producing countries. Furthermore, South­
east Asian countries such as Vietnam, Thailand, and Indonesia are 
C. Lim and J. Lee                                                                                                                                                                                                                               
Energy Policy 142 (2020) 111491
13
emerging oil consumption markets. In particular, National Oil Com­
panies (NOCs) in the Middle East are aggressively investing not only in 
producing and selling crude oil, but also in expanding refineries to sell 
petroleum products. The efficiency of the oil refining industry in these 
countries must be included in order to analyze the efficiency at the 
global level more closely. The economies of these countries are growing 
in recent years, and demand for petroleum products is increasing. As a 
result, oil refining facilities have been expanded, which is an important 
factor in changing the efficiency of the global oil refining industry. 
Therefore, further research will need to be done through oil, energy, and 
economic data on oil-producing countries and emerging oil-consuming 
countries. 
CRediT authorship contribution statement 
Chansu Lim: Conceptualization, Methodology, Software, Data 
curation, Writing - original draft, Visualization, Investigation. Jongsu 
Lee: Supervision, Writing - review & editing. 
Appendix A. Supplementary data 
Supplementary data to this article can be found online at https://doi. 
org/10.1016/j.enpol.2020.111491. 
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C. Lim and J. Lee

A novel Metric of Sustainability for petroleum reﬁnery projects
Hamidreza Hasheminasab a, *, Yaghob Gholipour b, Mohammadreza Kharrazi c,
Dalia Streimikiene d
a School of Civil Engineering, College of Engineering, University of Tehran, 16th Azar St., Enghelab Sq., Tehran, Iran
b Engineering Optimization Research Gr., College of Engineering, University of Tehran, 16th Azar St., Enghelab Sq., Tehran, Iran
c Ofﬁce of Sustainable Development, Amir Kabir University of Technology, 424 Hafez Ave, Tehran, Iran
d Lithuanian Energy Institute, Breslaujos 3, Kaunas, LT-44403, Lithuania
a r t i c l e i n f o
Article history:
Received 14 April 2017
Received in revised form
24 July 2017
Accepted 24 September 2017
Available online 9 October 2017
Handling Editor: Cecilia Maria Villas B^
oas de
Keywords:
Sustainable development
Petroleum reﬁnery
Indicator frameworks
Metric of Sustainability
Energy infrastructure projects
a b s t r a c t
Infrastructure projects will continue to be developed in the coming years, particularly in developing
countries such as Iran. These projects can have major impacts on all three pillars of sustainability, i.e.
social, economic, and environmental. Among these projects, energy as well as oil & gas projects can have
an even bigger impact on all three pillars, in terms of magnitude and severity of the consequences. As
such, energy and oil & gas projects are not only important from political and strategic point of view, but
they are also of major signiﬁcance when it comes to principles of sustainability. Therefore, it is important
to introduce methods and solutions to improve the level of sustainability at which such facilities are
designed, constructed, and operated. Although existing studies have suggested various methods to
implement sustainable development principles in infrastructure projects, effective assessment of sus-
tainability and proper deﬁnition of its indicators are scarce in this industry in general. Among oil & gas
projects, Petroleum Reﬁnery Industry (PRI) projects lack a reliable sustainability assessment system,
although in practical terms, many elements of sustainability are incorporated in reﬁneries nowadays.
This research aims to develop a sustainability assessment framework including proper indicators for
such an assessment. This framework can serve as a reference for future research in PRI projects. Sus-
tainability indicators in all three pillars of sustainability are consolidated and structured under the
proposed framework, which is designed to deal with the complicated makeup and operation of oil re-
ﬁnery projects. This article presents a novel Metric of Sustainability (MOS), which can help the decision
makers, most notably strategic managers, to properly set up the process for design, construction, and
operation of new reﬁnery projects or audit and appraise the operating ones.
Based on the proposed framework and the resulting MOS, a real reﬁnery project is assessed according
to the available data and required accuracy. The outputs are then presented in Fig. 3 for discussion. The
results of this case study show that the environmental concerns are the most important issue in
distillation units. Also, GHG emissions in the operating phase have the most adverse effect on the sus-
tainability proﬁle of the case reﬁnery.
© 2017 Elsevier Ltd. All rights reserved.
1. Introduction
The concept of sustainable development is inherently compli-
cated since it has different meanings for different people. Thus,
different interpretations (in particular in multidisciplinary projects
like PRI) lead to diverse types of research (Hall, 2006). As Norgard
puts it:
“[e]nvironmentalists want environmental systems sustained.
Consumers want consumption sustained. Workers want jobs sus-
tained.” (Norgaard, 1988).
Obviously, no single discipline's perspective can ensure the
overall realization of sustainability and sustainable development
adequately.
Oil and gas projects and related industries are crucial when it
comes to enhancing world's economy and increasing national
GDPs. Besides, due to its big economic impact, Oil and Gas Industry
(OGI) is, quite frequently, a key player in politics and strategic
* Corresponding author.
E-mail addresses: hasheminasab@ut.ac.ir (H. Hasheminasab), dalia@mail.lei.lt
(D. Streimikiene).
Contents lists available at ScienceDirect
Journal of Cleaner Production
journal homepage: www.elsevier.com/locate/jclepro
https://doi.org/10.1016/j.jclepro.2017.09.223
0959-6526/© 2017 Elsevier Ltd. All rights reserved.
Journal of Cleaner Production 171 (2018) 1215e1224
decision making in governments. Despite this undeniable inﬂuence
on economic development, OGI is also notorious for its environ-
mental and social devastation, destroying habitats and adversely
affecting the livelihood of communities living near operating plants
or construction sites. The simultaneity of great economic beneﬁt (in
particular in the short-term), tremendous environmental damage
and serious social upheaval (especially in the long-run), and
complicated long-term ﬁscal matters, warrants more research on
how to address sustainability concerns in OGI projects.
The contribution of this paper is to develop a comprehensive,
integrated, and facilitated sustainability indicator framework for
PRI and a novel sustainability metric that helps managers make
better futuristic decisions. Also, this framework and MOS will be
the foundation for future studies in this regard. This study ad-
dresses the following questions, (1) What would be the sustain-
ability indicators for a PRI with regard to three pillars of sustainable
development, (2) What are the factors that could be used to
represent the degree to which various indicators have been
reached, (3) Considering the complicated and multi-disciplinary
nature of PRI projects, how could we make sure that the set of in-
dicators used for sustainability assessment are comprehensive and
valid for PRI projects, (4) How can we measure the degree of sus-
tainability for a given petroleum reﬁnery, and (5) How could all of
these steps be followed for a real reﬁnery project.
2. Literature review
In the context of sustainability in OGI projects, a recent study
was performed in order to incorporate sustainability elements and
investigate opportunities and threats in an oil and gas company's
settings (George, et al., 2016). Other studies have noted rising
expenditure on activities related to sustainability matters such as
environmental remediation and industrial energy management
among petroleum ﬁrms located in the US (Verdantix, 2014). How-
ever, such increases in spending do not reﬂect a concerted effort to
embrace sustainability, especially as new cost-effective methods of
extracting unconventional reserves adversely impact the environ-
ment and have made renewable energy less competitive (Lozanova,
2014).
In recent years, OGI companies have been accused of ‘green-
washing’ in their marketing campaigns and corporate reports
(Pulver, 2007). OGI companies generally perceive that incorpora-
tion of sustainability and long-term thinking into OGI operations,
would result in reducing their proﬁtability. Thus, giant OGI com-
panies prefer to spend their budgets on competitive investment
opportunities. Consequently, they mainly concentrate on devel-
oping other cleaner ways of using fossil fuels, such as carbon cap-
ture and sequestration (CCS) technology rather than renewable
energies such as wind, solar and hydropower which are not eco-
nomic (Webb, 2009). In view of human dependence on non-
renewable energy, which leads to the oil industry's continued ex-
istence, any effort to reduce the negative impact of such a
destructive industry, however minimal, should not be undermined
(George et al., 2016).
Among various OGI sectors, PRI is a key sector, as it provides the
vital commodities for transportation, buildings, and other in-
dustries. In terms of employment, in the European Union alone,
140,000 people are directly employed on PRI facilities, and another
600,000 are involved with the distribution and sale of products;
also many more have jobs that are indirectly related to PRI
(Brussels, 2015). Besides, PRIs are important contributors to GDP,
high-skill jobs, technical know-how, etc. which are valuable, inte-
grated parts of the industrial supply chain. Across the EU, the sector
generates 23 billion Euros a year of added value and 270 billion
Euros in revenue from fuel taxes (Brussels, 2015). Beyond transport
fuels, PRI products are used as feedstock for further downstream
processes that contribute to a wide range of products that we use in
our everyday lives. For instance, PRI products are used as plastics in
packaging, as synthetic ﬁbers in clothing, as detergents in domestic
cleaning, and as agricultural fertilizers. Also, the close integration of
PRI with sectors such as petrochemicals strengthens those valuable
industries. Additionally, it is very hard to replace such products and
their feedstock with valuable and sustainable alternatives. Thus,
although this research is focused on PRI projects, it is related in
many ways to OGI projects in general.
Due to negative sustainability effects, in particular, environ-
mental and human lifestyle impacts in the vicinity of the con-
struction sites, as well as long-term economic problems due to
safety and maintenance costs, OGI is intrinsically an unsustainable
industry. Therefore, in this research, when we talk about cradle-to-
grave and life-cycle sustainability assessment, the goal is to mini-
mize the negative repercussions an oil reﬁnery project could have
on the environment and a country's social and economic sustain-
able development. With that in mind, strategic decisions of OGI
development projects can be made by considering all aspects of
life-cycle sustainability.
2.1. An overview of PRI sustainability
This review will constitute general review of sustainability as-
pects of PRI addressed by various researchers, as well as compila-
tion and screening of sustainability indicators introduced and/or
used in previous research, covering all three pillars of sustainability
individually and as a whole. The latter part, i.e. the compilation of
indicators, will serve as a prerequisite to establishing the sustain-
ability indicator framework that will be pursued in section 3, and
development of the Metric of Sustainability that will be presented
in section 4.
According to our literature review, a variety of studies have been
carried out regarding sustainability in energy and electricity pro-
duction. For instance, various electricity production scenarios were
investigated and compared in terms of their whole life cycle in
order to achieve the target 80% GHG reduction until 2070 in the UK
(Stamford and Azapagic, 2014). Felix and Gheewala (2012) evalu-
ated the environmental impact of electricity production in Tanzania
with a life-cycle approach, and predicted milestones in subsequent
years where signiﬁcant increases would occur in such environ-
mental impacts. Besides, energy sustainability was monitored with
an indicator-based methodology in Greece (Angelis-Dimakis, et, al.
2012). This methodology also was used in order to build a sus-
tainability self-regulated system for urban assessment (Hiremath
et al., 2013). Furthermore, energy sustainability in sewage treat-
ment projects is studied with sensitivity analysis and comparative
studies (Halaby et al., 2017). Besides, as the relationship between
the environment and technology is, however, complex and para-
doxical (Grübler, 1998; Grübler et al., 2002), sustainability of
technology is also assessed with a conceptual framework based on
dynamic system approach (Musango and Brent, 2011). All in all, OGI
projects/plants, although being important parts of energy produc-
tion and electricity supply chain of industries, they have large
negative impacts on sustainable development and its environ-
mental aspects in particular. Since this article is focused on PRI
projects, and due to complex and multidisciplinary nature of such
projects, a comprehensive literature review has been performed, in
order to ensure the proposed list of indicators that will serve as the
basis for the proposed MOS is comprehensive. In addition to in-
dicators, we have also identiﬁed various factors for each indicator,
which can be used as tools to gauge the level of realization of each
indicator.
Energy and environmental issues are always of prominence to
H. Hasheminasab et al. / Journal of Cleaner Production 171 (2018) 1215e1224
1216
both citizens and their governments (Cucchiella and D'Adamo,
2013). The more primitive societies and their technologies were
powered by renewable energies like wind, water, and wood. In
contrast, modern technologies are predominantly powered by
nonrenewable energies like oil, coal, and gas. Moreover, the total
amount of energy in the universe is constant and we have to take
into account that the rapid use of ﬁnite resources and energy
supplies would deﬁnitely be an important concern.
Petroleum and its derivatives such as gasoline, kerosene, diesel,
etc. are among the most prominent and widely used types of fossil
fuel. The realization that fossil fuel resources required for the
generation of energy are becoming scarce and that climate change
is related to carbon emissions to the atmosphere has increased
interest in energy saving and environmental protection (Vine,
2007). On the other hand, petroleum reﬁneries are mega projects
and so complex and costly. Hence, the strategy is to reduce negative
effects (greenhouse gas emissions, environmental contaminants,
safety hazards, etc.) and increase positive ones (Proﬁt, Green
economy,
human
health,
biodiversity,
etc.)
simultaneously
(Mahmoud and Shuhaimi, 2013).
Silvestre studied two important problems in the offshore oil and
gas
industry;
namely
“lack
of
uniformity”
and
“inefﬁcient
enforcement”, and offered some recommendations (Silvestre and
Gimenes, 2017). Clancy proposed a weighing and multi-criteria
decision analysis (MCDA) for assessing and comparing petroleum
vs. wood-based materials (Clancy et al., 2013). Also, Hadidi devel-
oped an optimization model that identiﬁes the best gas emission
mitigation technology with minimum cost for oil reﬁneries (Hadidi
et al., 2016). Some other studies, regard petrochemical projects as
an alternative and by using questionnaire surveys and different
optimization algorithms evaluate the alternatives (Heravi et al.,
2015). However, as it is mentioned before, PRI projects are com-
plex and we would not be able to consider them as a simple
alternative. Thus, it's important to clarify what the intended pur-
pose of an alternative is. This research proposes three levels for data
gathering, i.e. Level 1 which is at the level of utility and process,
Level 2 which is at the level of reﬁnery units, and Level 3 which is at
the level of reﬁnery components. These levels will be based on the
current phase of the project which managers want to make de-
cisions, the level of data which is available and measured, and the
level of authority that auditors have in the project. These three
levels of data gathering make three different frameworks with
different level of accuracies. Finally, combining measurement tool
with the level of data availability will result in the sustainability
framework.
2.2. PRI sustainability indicators
The majority of previous research on sustainable development is
carried out based on three pillars of sustainability (Environmental,
Social, Economic), which dates back to UN Conference in Rio de
Janerioin 1992. Sustainable development of energy and water
projects has been perceived as strategic and important and previ-
ous research in this ﬁeld can be categorized into four main groups
(Dui
c et, al. 2015):
- Energy issues, which are considered in energy systems and
power generation particularly in buildings
- Water issues, which are mainly water scarcity, relations be-
tween energy and water, and wastewater treatment
- Environmental management
- Sustainability methods and schemes
Indicator-based sustainability assessment is an appropriate
methodology in order to ensure the comprehensiveness of the
assessment. As an example, an indicator-based framework was
proposed for sustainability evaluation of geothermal energy pro-
jects (Shortall et al., 2015). Also, indicator-based systems as a self-
regulated system that integrates development and environment
protection is widely used for sustainability assessment of cities
(Hiremath et al., 2013).
For sustainability appraisal in complex systems like the ones
commonly seen in OGI, Indicator-based approaches would deﬁ-
nitely be useful. A major reference in this regard, is a technical
report by United Nations Statistical Commission (UNSC) on the
process for development of an indicator framework for the goals
and targets of the post-2015 development agenda (UN, 2016).1
Sustainability factors derived from this reference, have been
inserted with their corresponding code numbers in the following
tables for easier reference. Moreover, emission estimation protocol
for
petroleum
reﬁneries
which
is
developed
by
the
US
Environmental Protection Agency (EPA) was a primary tool in the
environmental evaluation (EPA, 2015). Based on the cited infor-
mation and using expert judgment via meetings with professionals
in this ﬁeld, the indicators and the related factors are developed in
line
with
the
requirements
of
petroleum
reﬁnery
projects.
Although there is sometimes a considerable overlap between in-
dicators and their corresponding factors, in this study, for the sake
of simplicity in the complex environment of petroleum projects, in
such cases the item has been allocated to the closer side, either as
an indicator or as a factor.
2.2.1. Environmental indicators
Perhaps the most important indicator category in sustainability
studies, which has been regarded as a core in previous studies is the
environmental indicator category. As an example, Jovanovic used
US's EPA TANKS software in order to develop a comprehensive
model for tank farm emissions. In particular, volatile organic
compounds (VOCs) were investigated among other environmental
concerns (Jovanovic et al., 2010). Another piece of work was carried
out to investigate possibilities to reduce CO2 emissions within the
Swedish petroleum reﬁning sector and to estimate the related costs
(Holmgren and Sternhufvud, 2008).
Gas emissions from PRI facilities often contain groups of highly
reactive gasses as deﬁned by the National Ambient Air Quality
Standard (NAAQS) (US EPA, 2015; Hadidi et al., 2016). PRI facilities
are major contributors to Green House Gas (GHG) emissions, toxic
chemicals, ambient particulate matter, Noise effects, industrial
sewerage,
land
and
natural
resource
degradation,
bio-
contamination, and many other negative effects. In this research,
environmental indicators are divided into four major categories
which are as follows:
 Atmosphere
 Water (Fresh Water, Ocean, Sea, Coast)
 Land & Soil pollution
 Natural Resource
 Biodiversity
Upon listing of indicators, corresponding factors were deter-
mined based on a comprehensive literature review. These factors
are presented in Table 1.
2.2.2. Social indicators
Sustainable development is deﬁned as “The possibility that
1 https://sustainabledevelopment.un.org/index.php?
page¼view&type¼111&nr¼6754&menu¼35 (Accessed on 12/18/2016).
H. Hasheminasab et al. / Journal of Cleaner Production 171 (2018) 1215e1224
1217
human and other forms of life will ﬂourish on the Earth forever”
(Ehrenfeld, 2004). Hence, every development needs to satisfy hu-
man needs and sustain social development in the ﬁrst place. From
this perspective, every construction projects, and OGI and PRI
projects in particular, are developed to satisfy different human
need. Besides, it has been found that with growth of sustainability
scope and framework, the social dimension of this framework lacks
increasingly the scientiﬁc basis as well as operational consider-
ations; and thus needs further development (Missimer et al., 2016a,
2016b). What is certain is that, development of these projects must
not result in human difﬁculties and unsustainability in social
development.
Herein, factors of social sustainability are ﬁrst deﬁned using
United Nation's 2015 indicators (UN, 2016), and then interpreted
according to PRI projects needs and requirements. For instance,
“Frequency rates of fatal and non-fatal occupational injuries and
time lost due to occupational injuries by gender” with the code
number of 8.8.2 in UN's indicators published in 2015, was
customized to “Frequency rates of fatal and non-fatal occupational
injuries”. Furthermore, other studies in this regard are investigated
to ensure the comprehensiveness of the social factors deﬁned for
every indicator.
This research aims to categorize social sustainability into ﬁve
indicators which are as follows:
 Poverty & Equality
 Health
 Safety & Security
 Education
 Welfare
Furthermore, a variety of factors by which above-said indicators
can be assessed is deﬁned in Table 2.
2.2.3. Economic indicators
Petrochemical industries have a tendency toward production
cost reduction and increase in or consistency of the quality of the
products simultaneously. Also, an important economic concern in
today's’ market with the high variability of energy prices, is the lack
of reliability in earning estimations (Neelis, 2008) which will
complicate the economic situation in PRI projects. Furthermore, the
economy depends heavily on the international political strategies
and consequently, an unstable economy with many ﬂuctuations,
deeply affects availability of raw materials. On the other side, eco-
nomic performance directly affects the availability of the petroleum
products which will add to its complexity.
OGI projects are economically fascinating projects and govern-
ments are always looking forward to ﬁnding an opportunity to
develop a new project if applicable. In this regard, US petrochemical
industry,
for
instance,
employs
nearly
160,000
people
and
Table 1
Environmental sustainability factors.
Indicator
Source
Code
Factor
Atmosphere
SDG-UN-2015
2.4.1
Emissions of greenhouse gases per ton of reﬁnery product (CO2, CH4, N2O, HFCs, CCl4, CH3CCl3,
CCl3F, CCl2F2, C2Cl3F3)
SDG-UN-2015
7.a.1
Net carbon intensity per kilowatt electricity production
SDG-UN-2015
11.6.2
Level of ambient particulate matter per ton of reﬁnery product
SDG-UN-2015
12.4.1
Level of hazardous chemicals(HAPs) and wastes per ton of reﬁnery production
SDG-UN-2015
12.4.2
Levels of selected contaminants in air caused by to reﬁnery operation: Acid Gas (SOX, NOX),
VOCs, CO, Ozone, Hydrocarbons, H2S, NH3
Shen, 2010
e
Level of noise pollution due to project/plant construction/Operation
Water (Fresh Water, Ocean, Sea, Coast)
SDG-UN-2015
6.3.1
Percentage of wastewater safely treated to standard levels: Cooling Water, Process Water,
Sanitary Sewage water, Storm Water
SDG-UN-2015
6.4.1
the proportion of wastewater contamination in the reﬁnery output to absorbable pollution by
water resource (sea, river, etc.)
SDG-UN-2015
6.4.2
The reﬁnery efﬁciency in water resource usage
SDG-UN-2015
6.5.1
Level of integrity in water resource management in different process units
SDG-UN-2015
14.3.1
Average acidity (PH) of the water resource and water stress value
SDG-UN-2015
12.4.2
Average levels of contaminants in water per ton of reﬁnery production (BOD)
Hall-UNCSD
e
Number of local households and businesses affected by water contamination
Hall-UNCSD
e
Annual withdrawal of ground and surface water as a percentage of reﬁnery demanded water
separately
Land & Soil Pollution
SDG-UN-2015
11.6.1
The amount of solid waste produced by reﬁnery per ton of production
SDG-UN-2015
12.5.1
Per capita waste generation (in kg) for reﬁnery employees
SDG-UN-2015
12.5.2
recycling rate and percentage of industrial waste reuse
SDG-UN-2015
15.3.1
Level of soil degradation due to construction/operation of the project/plant
SDG-UN-2015
12.4.2
Level of contaminants in soil per ton of reﬁnery production
Hall-UNCSD
e
Residence status of the project area (the number of households within a radius of 50 km)
Shen, 2010
e
Level of Protection to landscape and historical sites
Natural Resource
SDG-UN-2015
7.1.2
The extent to which the reﬁnery can satisfy the demand of “non-solid fuel”
SDG-UN-2015
7.2.1
Renewable energy share in the total energy of reﬁnery ﬁnal energy consumption
SDG-UN-2015
7.2.2
The extent of documentation issued in the reﬁnery project/plant on the utilization of renewable
energy
SDG-UN-2015
8.4.1
Indicator of crude oil efﬁciency (ratio of crude oil to product)
SDG-UN-2015
12.2.1
percentage of electricity demand of the reﬁnery which is accommodated by the crude oil in the
reﬁnery processes
SDG-UN-2015
12.2.2
natural resource utilization per reﬁnery worker, as well as per unit production in the reﬁnery
SDG-UN-2015
12.7.2
Percentage of required reﬁnery procurement with approved LCA sustainability assessment
documentations (Equipment, material, etc.)
Hall-UNCSD
e
Certiﬁed wood used in construction/operation of reﬁnery project/plant
Biodiversity
SDG-UN-2015
14.3.2
Level of damage to the ﬂora by reﬁnery construction and operations
SDG-UN-2015
14.4.1
Level of damage to fauna by reﬁnery construction and operation
SDG-UN-2015
15.1.2
Percentage of vegetation coverage in the reﬁnery site and local region
H. Hasheminasab et al. / Journal of Cleaner Production 171 (2018) 1215e1224
1218
generates product shipments and value added of $83.2 billion and
$88.5 billion respectively (Neelis, 2008). Thus, every owner and
operator in this industry seeks to reduce costs, boost proﬁt margins,
and enhance product quality.
In this research, ﬁve indicators are introduced for economic
development as follows:
 Energy consumption
 Financial
 Economic Performance
 Occupation
 Earning
Due to vast economic effects, its signiﬁcance, and complexity, a
variety of quantitative factors is allocated to every introduced in-
dicator (Table 3).
2.3. Level of accuracy
The petroleum derived from crude oil ﬁelds is always trans-
ported to crude oil storage tanks or directly to reﬁneries by pipeline
or shipping. Next, the crude oil transported from the oilﬁelds and
storage tanks to the reﬁneries is converted to reﬁned products such
as gasoline or petrol, kerosene, jet fuel, diesel oil, heating oil, fuel
oil, lubricants, waxes, asphalt, natural gas, and LPG. Subsequently,
the reﬁned products are transported to destinations via different
modes of transportation.
PRI is at the core of the above-said process. Sustainability
assessment, optimization of Process, and choosing the most envi-
ronmentally and socially friendly options with greener economic
attributes will be ensured by quantitative evaluation of indicators.
The main question in this regard, is how a complex alternative for a
PRI project can be thoroughly illustrated and quantitatively
assessed in the framework of indicators while the proposed
framework needs a large amount of information and that may not
be prepared or gathered shortly. This will depend on the amount of
information and/or data which is available in the current phase of
the project. The more information is provided as input, the more
accuracy and detail will be achieved in the outcome of the
assessment. For instance, seeking information in the ﬁrst phases of
a project, will not result in accurate and detailed information.
Hence, the assessment has to be used with a lower level of accuracy.
Or if the necessary information in a reﬁnery were not available and
needs to be collects later, the assessment accuracy level should be
lowered as well. To facilitate the assessment process, this study
proposes three levels for PRI projects (in order to measure in-
dicators and related factors for them). This levels are:
Level 1- Utility & Process: All in all, petroleum reﬁneries are
made up of different parts which are either producer or consumer
of energy and utility. The utility is considered to represent the
portions that produce energy, while the process is considered to
represent the areas that consume energy. Therefore, all of the
sustainability factors are deﬁned as consumers and producers of a
petroleum reﬁnery as a whole.
Level 2- Reﬁnery units: A petroleum reﬁnery is a collection of
units which, from an operating standpoint, has the biggest impact
on the sustainability outcome of the reﬁnery. Thus, if we are in the
operating phase, this level is recommended. These units are deﬁned
below:
 Desalter: this is a processing unit in an oil reﬁnery that removes
salt from the crude oil, (Depending on the quality of the crude
oil, this could be optional in certain circumstances)
 Distillation: atmospheric and vacuum distillation units are
processing units in which desalted crude oil is separated to
different derivatives by a process of heating and cooling
Table 2
Social sustainability factors.
Indicator
Source
Code
Factor
Poverty &
Equality
SDG-UN-2015
1.2.2
Proportion of project human resource living below national poverty line
SDG-UN-2015
3.8.1
Fraction of project human resource protected against impoverishment by out-of-pocket health expenditures
Shell, 2015
e
Staff forums and grievance procedures forming in the project and operation stage
Shell, 2015
e
Gender diversity in all different levels of leadership, management, and expertise
SDG-UN-2015
5.b.2
Proportion of working women with computational skills to men with similar skills
Health
SDG-UN-2015
2.1.2
Annual number of project/plant manpower getting sick because of poisoned and unhealthy food and water
SDG-UN-2015
3.4.1
Annual number of project/plant manpower death because of cardiovascular disease, cancer, or chronic respiratory
disease as a result of exposure to toxic gasses and other substances in the reﬁnery
Shell, 2015
e
Total recordable occupational illness frequency (TROIF)
SDG-UN-2015
3.4.2
Tobacco consumption per capita among project manpower
SDG-UN-2015
3.9.1
Population in local areas in the vicinity of the project/plant exposed to outdoor air, water, and soil pollution caused by
the reﬁnery
SDG-UN-2015
6.2.1
Percentage of project/plant manpower and local people using safely managed sanitation services
SDG-UN-2015
8.3.2
Percentage of project/plant manpower and local people having health insurance coverage
Safety & Security SDG-UN-2015
1.5.1
Number of people having accident in the project/plant per annum
SDG-UN-2015
1.5.2
Level of health and educational facilities in order to prevent accidents' occurrence
Shen, 2010
e
The extent of damage to local cultural heritage as a result of petroleum reﬁnery project/plant construction/operation
Shell, 2015
e
Lost days due to accidents leading to injury or death
SDG-UN-2015
8.8.2
Frequency rates of fatal and non-fatal occupational injuries
Education&
Training
SDG-UN-2015
4.3.1
Number of people who are trained and develop practical skills in the project
SDG-UN-2015
4.4.1
Participation rate of owner/operator in formal and non-formal education and training of staff
SDG-UN-2015
4.4.2
Percentage of staff who are computer and information literate in the project
SDG-UN-2015
4.6.2
literacy rate among project/plant staff
SDG-UN-2015
4.a.1
Number of schools and training centers established in project/plant accommodation and local city
Welfare
SDG-UN-2015
5.4.2
Amount of increase in water resource infrastructure access, which has been made by the project/plant
SDG-UN-2015
6.1.1
Percentage of project/plant staff using safely managed drinking water services
SDG-UN-2015
7.1.1
Amount of increase in electricity infrastructure access, which has been made by the project/plant
SDG-UN-2015
11.1.1
Percentage of project/plant staff living in unsuitable accommodation and informal settlements
SDG-UN-2015
17.19.2
The amount of happiness and national pride among staff due to their occupation
Shen, 2010
e
Scale of serviceability of reﬁnery and its production for local community
H. Hasheminasab et al. / Journal of Cleaner Production 171 (2018) 1215e1224
1219
 Uniﬁers: Different type of uniﬁers may be utilized in a petro-
leum reﬁnery by which the distilled oil will enhance its quality
by lowering its detrimental environmental and health impacts
but with an expensive initial cost.
 Buildings: industrial and non-industrial buildings are of sec-
ondary importance among other units. Because they consume
and produce energy, utility and emissions far less than other
units. So, this research considers the sustainability of reﬁneries
regardless of buildings.
 Storage tanks: Tanks play an important part in a petroleum re-
ﬁnery plant. However, they are like buildings in sustainability
studies. Thus, they are eliminated from the study as well.
Level 3- Reﬁnery Components: A petroleum reﬁnery plant is a
collection of thousands of components which altogether make up
the whole reﬁnery. These components are divided into the
following categories:
- Structural components (ST): backﬁll Material, formwork, con-
crete, rebar, anchor bolts, steel structure, etc.
- Mechanical components (MD): Drum, tower, exchanger, tube,
heater, tank, air cooler, ﬁlter, etc.
- Rotary Equipment's components (RE): Pump, Compressor,
Dryer, Power generator, HVAC equipment, etc.
- Piping Components (PI): Cap, collar, reducer, pipe, elbow, ﬂange,
gasket, spacer, valve, tee, etc.
- Electrical components (EL): Continuous loads, Standby loads,
and intermittent loads (these components are mostly utilities)
- Instrument components (IN): control valve, hand switch, high
alarm, positioner, level gauge, level indicator, etc.
There are some other components, e.g. ﬁre stationary and mo-
bile equipment (HSE components) which are not included in this
study. The whole process of level 2 and 3 are depicted in Fig. 1.
The more detailed information is available; the higher accuracy
level shall be chosen. As we go from level one to three, the amount
of information is deepened. Thus, with regard to the level of
available information, level of accuracy shall be chosen.
Table 3
Economic sustainability factors.
Indicator
Source
Code
Factor
Energy consumption
SDG-UN-2015
7.3.1
achievement rate of the designed energy usage in the operation phase
SDG-UN-2015
7.3.2
The efﬁciency of electricity production in the reﬁnery (output to input)
SDG-UN-2015
7.b.1
The energy economic efﬁciency in the reﬁnery (the economic return per unit of energy consumed)
SDG-UN-2015
9.4.2
Required energy intensity per unit of value added
Financial
Shen, 2010
e
the amount of initial investment or forecasted budget of the project
Shen, 2010
e
payback period (investment & interest) based on analysis of market supply and demand
Shen, 2010
e
Net present value of reﬁnery life cycle costs (including operating costs, maintenance, overhaul, etc.)
Shen, 2010
e
Net present value of reﬁnery life cycle proﬁts (including product sales, etc.)
Shen, 2010
e
Reﬁnery internal return ratio (IRR)
SDG-UN-2015
7.a.2
The foreign direct investment (ﬁnancing) or mobilizing domestic ﬁnancial resources in order to achieve more
advanced technologies and cleaner fuel production
SDG-UN-2015
7.b.2
the level of international cooperation in the reﬁnery project in order to facilitate access to clean and
sustainable energy that is consumed in the project
SDG-UN-2015
9.5.1
number of man-hours in the research and development in order to promote technologies in clean energy
utilization in the reﬁnery project/plant construction/operation
SDG-UN-2015
9.a.1
Investment program (Annual credit ﬂow) in the reﬁnery project
SDG-UN-2015
9.a.2
percentage of ﬁnanciers on the short list who are interested in participation and are willing to pay for the
reﬁnery project
Shen, 2010
e
Level of investment risks in the reﬁnery project
SDG-UN-2015
12.a.1
Amount of spending on R&D by owner to achieve sustainable production and consumption in the Reﬁnery
Project
SDG-UN-2015
12.a.2
The amount of reward which is intended in the contract to cover costs of patent in sustainable production and
consumption of reﬁnery project
Economic Performance
SDG-UN-2015
8.1.1
Level of reﬁnery participation in increasing GDP
SDG-UN-2015
8.1.2
Level of reﬁnery participation in inclusive wealth
SDG-UN-2015
8.2.1
Growth rate of GDP per capita per employed person in the reﬁnery
SDG-UN-2015
8.2.2
Export diversiﬁcation of the reﬁnery products in terms of products and markets
SDG-UN-2015
13.1.2
Amount of ﬁnancial losses due to climate change and natural disasters risks in the reﬁnery project/plant
SDG-UN-2015
9.2.1
Percentage of growth in GDP per capita by the reﬁnery among other available alternative projects
SDG-UN-2015
9.2.2
Ratio of created jobs in the reﬁnery project/plant to job opportunities in other available alternative projects
SDG-UN-2015
9.4.1
Intensity of raw material use per unit of value-added
SDG-UN-2015
9.5.2
Percentage of decrease in value added in exchange for technology upgrades and stability enhancement in the
reﬁnery project/plant
SDG-UN-2015
12.c.1
Amount of reﬁnery products' subsidies per total reﬁnery expenditure
SDG-UN-2015
17.19.1
Average index of Economic Welfare of project manpower and local people
SDG-UN-2015
8.8.2
The ﬁnancial and time losses resulting from accidents in the reﬁnery project/plant
Hall-UNCSD
e
The amount of each reﬁnery product and its means of transportation (kg.km)
Occupation
SDG-UN-2015
8.3.1
Number of job opportunities created by the reﬁnery and local economic boom within different phases of the
project
SDG-UN-2015
8.3.2
Number of small and medium ﬁrms that have developed during the construction and operation of the reﬁnery
project
SDG-UN-2015
8.5.1
Employment to working-age population (15 years and above) ratio by gender and age group
SDG-UN-2015
8.5.2
Unemployment rate in different phases of the project due to different human resource demand (hiring and
ﬁring)
SDG-UN-2015
8.6.1
Number of illiterate and unskilled workforce who acquire skills in the reﬁnery project/plant
Earning
SDG-UN-2015
10.1.2
Reﬁnery project/plant participation in the promotion of regional labor income
SDG-UN-2015
10.2.2
Proportion of labors living below 50% of median income
SDG-UN-2015
11.1.2
Proportion of labors that spends more than 30% of its income on accommodation
SDG-UN-2015
1.3.2
Average health insurance expenditure to income ratio
H. Hasheminasab et al. / Journal of Cleaner Production 171 (2018) 1215e1224
1220
3. The framework of indicators
Rapport et al. believed that a proper economic analysis would
need to consider the monetary value of environmental beneﬁts
(Rapport et al., 2008). As a consequence, a petroleum reﬁnery
evaluation particularly in the ﬁrst phases of a project, would not
only have to assess the ﬁnancial results of the investment in its
traditional sense, and the efﬁciency of the production process, but
the monetary value of environmental and social beneﬁts has to be
factored in as well. The Huge cost of treatment for employees and
locals especially in the operating phase due to illnesses caused by
the reﬁnery, as well as the increase in the cost of environmental
decontamination from toxic air pollutants emitted by the reﬁnery,
are some examples of such environmental and social costs which
have to be considered in a reﬁnery project assessment.
Sustainability indicators under three pillars of sustainable
development are deﬁned and quantiﬁed with a variety of factors as
shown in Tables 1e3. Consequently, based on indicators, factors,
and the available/required level of accuracy, a speciﬁc framework
for every PRI project will be organized in order to evaluate its
sustainability. The general preference in makeup of this framework
is to use a life cycle approach to sustainability evaluation, most
preferably a cradle-to-grave approach; however, due to certain
complexities inherent in this approach, we will sometimes resort to
more simpliﬁed approaches such as gate-to-gate approach. For
example, energy consumption rate is mostly relevant in operation
and construction phases, or air pollution has the biggest impact in
operating condition.
By the proposed framework, there could be a variety of meth-
odologies that can assess the sustainability of PRI projects. Any
methodology will put together a set of indicators that could be used
by managers to reach proper decisions. In this research, a novel
methodology has been used which is illustrated in the following
section.
4. Metric of Sustainability (MOS)
The main question in sustainability assessment of PRI projects is
how the sustainability framework which is comprised of different
factors in diverse ﬁelds, and different units and measurement tools,
could serve the purpose in an integrated way. We borrow from the
approach that was introduced by Chong, in which he proposed a
sustainability measurement tool for life-cycle assessment of waste-
to-energy systems to ensure the integrity of the assessment (Chong
et al., 2016). Accordingly, this study proposes a novel Metric of
Sustainability (MOS) in order to seamlessly integrate inputs and to
achieve reliable outputs, so that, it could be the basis for decision
making in this industry.
For each sustainability factor, we can identify two limits. One is
the maximum value, which is when the factors reaches the limit set
by the relevant standard, which is called here Xmax, and the second
is the minimum value, which occurs when the factor reaches the
ideal condition, deﬁned by the industry's best practices. For
example, based on general and professional rules and regulations in
PRIs, reﬁneries have to keep their efﬂuents and wastes below
speciﬁc standard levels. Thus, keeping below the standard level,
would be a prerequisite for each speciﬁc factor; this would be
regarded as the maximum value for the factor in the proposed MOS.
Also, the best scenario for every factor is identiﬁed based on the
national and local ideal condition. This level is marked as the
minimum amount for the proposed factor. As a result, factors can be
normalized for every indicator, factor, and phase in the following
way:
Xij
p ðTÞ ¼
F 

XmaxþXmin
2


XmaxXmin
2

For P ¼ 2:3
Xij
p ðTÞ ¼ 1 
0
@F 

XmaxþXmin
2


XmaxXmin
2

1
A
For P ¼ 1
where:
F: value for the factor in the current case;
Xmax: maximum value for the factor based on relevant standard;
Xmin: minimum value for the factor based on ideal condition;
i: ith indicator, i ¼ {1, 2, …I};
j: jth factor, j ¼ {1, 2, …J};
p: pth Pillar of sustainability, p ¼ {1 for environment, 2 for
economic, 3 for social};
T: time and phase of the measurement or valuation, T ¼ {1 for
pre-construction, 2 for construction, 3 for post-construction, 4
for product lifecycle};
X: Normalized factor;
Factors could be deterministic or probabilistic and as a result,
the measurement tool has to be deterministic or probabilistic. The
following formulas will sum up the values of factors for various
indicators and phases for any of the three pillars of sustainability:
Fig. 1. Reﬁnery processes.
Sp ¼
8
>
>
>
>
>
>
<
>
>
>
>
>
>
:
X
I
i¼1
X
4
T¼1
X
J
j¼1
Xij
p ðTÞ
; For Discrete values
ZI
1
Z4
1
ZJ
1
Xij
p ðTÞ dJ$dT$dI
; For continuous values
; cp2
8
>
>
>
>
>
>
<
>
>
>
>
>
>
:
1: 2: 3
9
>
>
>
>
>
>
=
>
>
>
>
>
>
;
H. Hasheminasab et al. / Journal of Cleaner Production 171 (2018) 1215e1224
1221
Sustainability pillar weights depend on many items such as
political, market, technical, and technological matters. Thus, for any
speciﬁc project, based on its speciﬁc properties, the weights of
pillars have to be determined by a panel of experts. Based on expert
opinion, the MOS will be calculated after incorporating the relative
weight for each pillar of sustainability, using the following
equation:
MOS ¼
X
3
p¼1
Wp  Sp
where:
Wp: Weight for pth pillar of sustainability;
Other critical factors, which can be helpful for decision makers
are as follows:
Xi
p ¼
8
<
:xi
p ¼
X
T
X
j
Xij
p ðTÞ






ci2f1: 2:…: Ig
9
=
;
cp2f1: 2: 3g
Xij
p
¼
(
xj
p
¼
X
T
Xij
p ðTÞ




cj2f1: 2:…: Jg

)
cp2f1: 2: 3g; ci2f1; …; Ig
XT
p ¼
8
<
:xT
p ¼
X
i
X
j
Xij
p ðTÞ






cT2f1: 2: 3: 4g
9
=
;
cp2f1: 2: 3g
Analytical outputs :
8
>
>
>
>
<
>
>
>
>
:
Cri
p ¼ max
i
n
Xi
p
o
Crj
p ¼ max
j
n
Xij
p
o
CrT
p ¼ max
T
n
XT
p
o
where:
Cri
p: Critical indicator in pth sustainability pillar;
Crj
p: Critical factor in pth sustainability pillar;
CrT
p: Critical phase in pth sustainability pillar;
Also, interlinking statuses could be deﬁned. For instance, what
the important indicator in the construction phase is, or what the
most inﬂuential factor in post construction phase is. Besides,
sensitivity analysis for every factor, indicator, and phase could be
done by the proposed approach as well.
5. Case study
The case study is a real heavy crude oil reﬁnery project which is
in the design phase. In this study, we cannot reveal conﬁdential
information, so we solely discuss the results and measurements.
Since this project is in its initial stages, and detailed information
about the exact components to be used is not available, the second
level of accuracy which is reﬁnery units is chosen to be followed in
this case. Moreover, in this small case, no manufacturer, vendor, and
contractor has got involved yet; therefore, Cradle-to-Gate studies
are not applicable. Besides, the exact target product market and its
destination have not been clariﬁed yet. As such, Gate-to-Grave
studies
will
have
little
accuracy and
availability.
Thus,
the
following reﬁnery units are investigated for Gate-to-Gate assess-
ment according to the proposed framework and MOS for the design
phase alone, in order to reach meaningful results.
- Desulfurization
- Desalter
- Atmospheric distillation
- Vacuum distillation
The Mentioned limitation and assumptions could compromise
the comprehensiveness of the sustainability assessment tool, and
the work needs to be improved as complementary data is gathered.
However, this study was targeting the proper set up of the
assessment methodology, and a complete and comprehensive
assessment will be the subject of upcoming studies.
Gate-to-Gate assessment has its own association in decision
making and different sensitivity analysis in order to achieve the
best and sustain choices in design phase. Moreover, this assessment
can assist designers in having a sustainability-based design.
5.1. Environmental speciﬁcations
There are many air pollutants during construction, including
due to fuel consumption in construction equipment engines and
excavation-backﬁll process, which creates signiﬁcant amounts of
airborne particles. During operation, air pollution is mainly due to
furnace exhaust system and ﬂare's stack. There are many airborne
contaminants including, but not limited to, NOx, SOx, CO2, CO, TSP,
etc. Noise creation is also a major problem, both during construc-
tion and during operation. Major sources of water contamination
are leaking from equipment, sewage, as well as industrial and non-
industrial wastewater. Furthermore, reﬁnery products themselves
tend to undermine sustainability. Thus, the level of damage to the
environment should be considered in the life cycle of the products
as well. In summary, reﬁnery construction, operation, and products
have a great environmental footprint.
In this study, all utilities (electricity, water, fuel …) required for
each unit are taken into account. Also, the total efﬂuent due to
utility services and/or the operation of that unit is considered as its
footprint. Besides, every unit has its share in the ﬂare unit efﬂuents
as well. Therefore, in this case “Atmosphere” and “Water (Fresh
Water, Ocean, Sea, and Coast)” are the prime indicators to monitor
for
environmental
sustainability
assessment.
All
in
all,
the
Fig. 2. Selected factors for implementing the case study measurements.
H. Hasheminasab et al. / Journal of Cleaner Production 171 (2018) 1215e1224
1222
summary output for this pillar is presented in Fig. 3.
For instance, for the atmospheric distillation unit, according to
Iran's Environmental Protection Organization (IEPO), sulfur dioxide
limit is 7.2 PPM, which represents the upper limit (Xmax). However,
project speciﬁcations don't specify a best practice limit for that;
therefore, lower limit (Xmin) is set to be zero. Besides, as stated in
the process design outputs, sour water from atmospheric column
reﬂux drum (as one of the contributors to atmospheric column
efﬂuents) which at a normal continuous ﬂow of 6:3

m3

h

with
density of 974:1

kg
m3

contains 350 PPM of H2S. Sour water will
be routed to wastewater treating plant which is designed to thor-
oughly eliminate H2S. Thus, at the end of the process, atmospheric
column's H2S output will be zero. Therefore:
X15
1
ð2Þ ¼ 1 
 
0  7:2þ0
2
	
7:20
2
	
!
¼ 0
5.2. Social speciﬁcations
The local community in project area had a population of about
29,000, according to 2012 census. Also, Literate population in the
project vicinity is 86% and only 36% are economically active among
which 31 percent have been working in industrial projects and the
rest of the working population have worked in agriculture and
service sectors. Estimated average human resource demand in
construction and operation phases for this project are 4000 and
300 respectively. Hence, the project will have a great inﬂuence on
local community's employment rate.
In this study, as the project is in its ﬁrst stages, some of the
factors which are related to the construction and operation phases,
like “Number of people having an accident in the project/plant” are
not included. Two pivotal indicators in this pillar of sustainability,
which are important at this stage, are “Education” (because of
technical knowledge and know-how which could be learned or
practiced during the project) and “Safety & Security” (because of
design for safety based on existing procedures, standards, rules,
and regulations). The results are summarized in Fig. 3.
For instance, based on previous experience, for the atmospheric
column, 4000 man-hours is needed in the design phase. Thus, 4000
man-hours of engineers gain valuable experience and develop their
knowledge. Besides, according to the EPA (Enterprise Process As-
sets), with the same productivity and quality, 5000engineering
man-hours could be allocated to the design of the atmospheric
column at max. Also, best practices indicate that the minimum
man-hour required for the design of the unit is about 3000, which
will represent the lower limit. Thus, the corresponding factor is
calculated as follows2:
X41
2
ð2Þ ¼ 4000  5000þ3000
2
	
50003000
2
	
¼ 0
5.3. Economic speciﬁcation
Forecasted investment in this project is more than $ 130 million
with an internal rate of return of 33% and a payback period of 3
years. The construction phase will be ﬁnished in 3 years and the
estimated operation phase is about 15 years with the capacity of
35,000 barrels per day of production. Obviously, the project is one
of the contributory factors in the economic development of the
project region and the country.
In this study, there are no international ﬁnances. Also, occupa-
tion and energy consumption status are indistinct and based on the
estimations in preliminary studies and conceptual design phases.
Thus, “Energy Consumption”, “Occupation”, and “Economic Per-
formance” play the important roles in sustainable economic pillar
in this project and during this phase. The results of the economic
pillar are represented collectively in Fig. 2.
For instance, for the atmospheric column, a number of national
and international vendors/manufacturers documents which should
be issued is about 500 drawings or documents. If every document
takes an average 50 man-hours to prepare and revise based on
engineering comments, it will take 25,000 man-hours and about 30
vendors/manufacturers involved in the project in the design phase.
Besides, according to the enterprise process asset, it could be at
least 15,000 and a maximum of 30,000 (number of revisions to
achieve agreement) with the same quality. Hence, the factor will
measure as follows:
X41
3
ð2Þ ¼ 25000  30000þ15000
2
	
3000015000
2
	
¼ 0:33
6. Conclusion
The case study result shows that in the abovementioned scope,
assumptions, and restrictions as a whole, the reﬁnery project is
socially sustainable and environmentally unsustainable. It would
be an important information which can help the managers with
regard to other limitations and policies to make the best decisions.
The atmospheric and vacuum distillation units have the most
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
Desulfuriza on
Desalter
Atmospheric dis
a on
Vacuum dis la on
Economic
Environmental
Social
Fig. 3. Results of the case study and measures.
2 Increasing the man-hour of the project will adversely affect sustainable eco-
nomic development which would be measured in the economic pillar separately.
H. Hasheminasab et al. / Journal of Cleaner Production 171 (2018) 1215e1224
1223
unsustainable environmental impacts among other units in the
reﬁnery project and have the most sustainable economic effects
simultaneously. Hence, the country/region priorities will justify the
tradeoff between economic and environmental concerns. Atmo-
spheric and vacuum distillations make the core in the reﬁnery
process by producing the main products of the reﬁnery unit while
desalter and desulphurization units are preliminary and somewhat
expensive units. Also in this regard, in some other cases uniﬁers
have not economic justiﬁcation and postpone in the future devel-
opment phases. Thus, these two distillation units have the sus-
tainable economy and positive value in the assessment.
Moreover, reﬁnery plant has a positive social sustainability
impacts in different units due to education and training the new
staffs, create a vast variety of job opportunities, etc. However,
desulphurization unit is technology-intensive and new packages
have to be check and train by designers, as well as operators. Hence,
the indicator value in this unit is the highest value in the evaluation
process. Besides, since desulfurization unit is one of the prominent
contributors in enhancing the quality of the product and increase
market share, if the product were included in the assessment
process, desulphurization deﬁnitely would have a greater envi-
ronmental sustainability impact.
The Proposed MOS is completely ﬂexible to satisfy every stan-
dard and speciﬁcation based on the governing policies and prior-
ities,
as
well
as
contractual
and
other
requirements.
Also,
categorizing a PRI project attributes into different levels and
assigning every relevant feature to various categories, helps the
researcher do a sensitivity analysis on one hand, and process design
and optimization using sustainability-based methodologies on the
other hand.
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1224

Energy Conversion and Management 253 (2022) 115149
Available online 31 December 2021
0196-8904/© 2021 Elsevier Ltd. All rights reserved.
Crude oil hierarchical catalytic cracking for maximizing chemicals 
production: Pilot-scale test, process optimization strategy, 
techno-economic-society-environment assessment 
Xin Zhou, Shangfeng Li, Yuan Wang, Jiewenjing Zhang, Zhibo Zhang, Changgui Wu, 
Xiaobo Chen *, Xiang Feng, Yibin Liu, Hui Zhao, Hao Yan *,1, Chaohe Yang 
State Key Laboratory of Heavy Oil Processing, China University of Petroleum, Qingdao, Shandong 266580, People’s Republic of China   
A R T I C L E  I N F O   
Keywords: 
Hierarchical catalytic cracking 
Reaction activation energy 
Optimization method 
Techno-economic-society-environment 
assessment 
A B S T R A C T   
Crude oil direct catalytic cracking can effectively promote the production of chemicals. However, the hetero­
geneity of cracking depth of various distillates seriously restricts its industrialization. This study proposed a novel 
crude oil hierarchical catalytic cracking process for controlling the catalytic cracking depth. The key operating 
parameters were investigated and optimized using a multi-objective optimization strategy. A quantitative 
assessment for the life cycle techno-economic-society-environment of the novel processes was then conducted 
and compared with the conventional process. Results show that the optimized first and second flash unit tem­
peratures are 187 ◦C and 251 ◦C. The optimized first and second riser outlet temperatures are 644 ◦C and 682 ◦C, 
respectively. The conversion rate and olefin yields of the novel process are increased by 1.47% and 1.46%. The 
hydrogen and carbon atoms efficiency in the novel process is 63.17% and 76.21%, which could raise 0.97% and 
1.62% compared with the conventional process. Moreover, the novel process could increase 14.3% and 1.61%% 
in the net present value and internal rate of return. Meanwhile, it decreased by 2.1%, 8.2%, and 2.2% in 
greenhouse gas emissions, wastewater generation, and non-renewable energy consumption, compared with 
conventional crude oil-to-chemicals processes. These findings in this work could promote engineering applica­
tion, process intensification, and key operating parameters optimization of crude oil direct catalytic cracking.   
1. Introduction 
With electric vehicle-owned expected to surge [1] and the growth 
rate of gasoline consumption expected to wane [2], the transition of 
refineries from “fuel types” to “chemical types” has become the 
consensus of the petrochemical industry [3]. It is also the inevitable 
avenue for China to strive to achieve “carbon neutral” before the 2060s 
[4]. In response to this trend, oil companies and research institutions, 
such as Saudi Aramco [5], Shell [6], ExxonMobil [7], Sinopec [8], China 
University of Petroleum (East China) [9], King Abdullah University of 
Science and Technology [10] have flocked to study the technology to 
maximize chemicals from crude oil. 
Currently, the technologies of maximizing chemicals mainly focus on 
two core routes: heavy oil hydrocracking and catalytic cracking to 
achieve the goal of producing more low-carbon olefins and aromatics, as 
depicted in Fig. 1(a)-Fig. 1(b), respectively [11]. The new integrated 
refineries in China mostly adopt the first route, i.e., hydrocracking to 
maximize naphtha production as the core, steam cracking to produce 
ethylene and propylene, and continuous reforming to produce BTX. 
Other refineries employ the last one, i.e., heavy oil catalytic cracking to 
produce propylene and naphtha with high aromatic contents, which 
could also increase BTX by coupled with hydrotreating and aromatics 
extraction units [12]. However, no matter which route is chosen, the 
crude oil processing pathways will be extended, and the investment and 
operation costs will significantly increase. The crude-oil-to-chemical 
(COTC) technology could bypass the traditional atmospheric and vac­
uum units, build a new short processing pathway with low energy 
consumption, and develop new refining chemical integration technol­
ogy of crude oil direct conversion to chemicals, shown in Fig. 1(c)-Fig. 1 
(d) [13]. The COTC technology is the essential process of efficient uti­
lization of petroleum resources and an academic frontier in petroleum 
refining. Tullo believes that the crude oil to chemicals (COTC) tech­
nology could dominate the petrochemical industry in 2020–2030 [14]. 
* Corresponding authors. 
E-mail addresses: 1chenxiaobo@upc.edu.cn (X. Chen), haoyan@upc.edu.cn (H. Yan).   
1 The two authors have the same contribution to this study. 
Contents lists available at ScienceDirect 
Energy Conversion and Management 
journal homepage: www.elsevier.com/locate/enconman 
https://doi.org/10.1016/j.enconman.2021.115149 
Received 3 September 2021; Received in revised form 13 December 2021; Accepted 14 December 2021   
Energy Conversion and Management 253 (2022) 115149
2
The relationship of feedstocks and products among carbon number, 
boiling point, and H/C ratio is shown in Fig. 2 [15]. By comprehensive 
analysis of the H/C ratio of the feedstocks (green dotted line shown at 
the right side in Fig. 2) and products (the left side in Fig. 2), it could be 
found intuitively that the H/C ratio of gasoline is 2.2–2.4, and the H/C 
ratio of diesel is 1.7–1.9. While the H/C ratio of olefins is 2, and the H/C 
ratio of aromatics is lower, ranging from 1.1 to 1.2. When the goal of 
crude oil resources utilization changes from “fuels (gasoline, diesel, 
etc.)” to “chemicals (ethylene, propylene, benzene, etc.),” it is necessary 
to realize the directional conversion of crude oil into chemicals from the 
perspectives of catalyst design, process integration, and intensification. 
Based on the balance of hydrogen and carbon atoms between feedstocks 
and products, the COTC technology meets the hydrogen carbon atom 
balance. Therefore, the COTC processing route is entirely feasible from 
hydrogen and carbon balance. 
From the perspective of technology status, ExxonMobil has created a 
precedent for the commercial application of the crude oil steam cracking 
(COSC) process, as depicted in Fig. 1(d) [16]. It aims to preheat crude oil 
directly, flash, and separate it into two fractions. The light fraction is fed 
to the steam cracking unit to produce olefins, while the heavy fractions 
are exported to refineries. In 2014, ExxonMobil built an industrial 
demonstration plant with an annual output of 1 million tons of ethylene 
in Singapore, as shown in Fig. 1(e) [14]. Although the COSC technology 
prefers light crude oil as feedstocks and still employs the conventional 
high-temperature steam cracking process, its economic benefits are 
significant due to the significantly shortened processing process. The 
industrial operation results show that the COSC process can make a net 
profit of $100–200 per ton of ethylene compared with the conventional 
naphtha cracking process [16]. 
Another alternative process is the crude oil catalytic cracking 
(COCC) technology, as illustrated in Fig. 1(c) [17]. Through deep cata­
lytic cracking, crude oil could directly generate dry gas rich in ethylene, 
liquefied gas containing C3 and C4 olefins, and naphtha rich in light 
aromatics. After separation, low-carbon olefins and light aromatics 
could be obtained and realized crude oil direct catalytic cracking to 
produce petrochemicals. This route can considerably shorten the crude 
oil processing process, reduce the unit investment, and maximize the 
production of chemicals such as low-carbon olefins and light aromatics 
to realize the direct leap from “fuels-type refinery” to “petrochemical- 
type refinery.” 
Compared with the COSC process, the COCC process exhibits 
incomparable advantages as shown below [18]: (1) The first is the 
feedstock adaptability. The COCC process can use heavier paraffin-based 
crude oil as raw material, which adapts to becoming heavier and infe­
rior. (2) The second is the product distribution and the flexibility of 
market regulation. Unlike the COSC process, the COCC process can 
adjust ethylene and propylene ratio widely by adjusting catalyst prop­
erties and process conditions. (3) The third is the economic benefit. 
Previous research [17] indicates that the net present value of the COCC 
process can be increased by 65% compared with the COSC process. A. 
Corma and Michael T. Klein also believe that the COCC process is a 
significant way to produce chemical raw materials at a low cost, which 
has better flexibility and development prospects than the COSC process 
[19,20]. To date, some works focusing on maximizing chemicals from 
crude oil have directly been investigated. The overview of crude oil to 
chemicals is shown in Table 1. Different kinds of catalysts (i.e., USY, FCC 
equilibrium catalysts, and ZSM-5 zeolites) are investigated to convert 
various crude oil (i.e., Daqing crude oil, Arab light oil, and Arab extra 
Nomenclature 
ACU 
aromatic complex unit 
ADU 
atmospheric and vacuum distillation unit 
AEU 
aromatic extraction unit 
CCRU 
catalytic reforming unit 
DCC 
deep catalytic cracking 
DHCRU diesel hydrocracking unit 
DHDT 
deep hydrotreating unit 
DHDTU diesel hydrotreating unit 
FFUT 
first flash unit temperature 
FROT 
first riser outlet temperature 
GHDTU gasoline hydrotreating unit 
GHG 
greenhouse gas 
HCO 
heavy cycle oil 
HFCCU 
heavy oil fluid catalytic cracking unit 
IRR 
internal rate of return 
KDTU 
kerosene hydrogenation unit 
LCO 
light cycle oil 
LHRU 
Light naphtha hydrocracking unit 
NCF 
net cash flow 
NHDTU naphtha hydrotreating unit 
NREC 
non-renewable energy consumption 
NPV 
net present value 
OMUDOV one-million-USD-dollar output value 
OV 
output value 
SCU 
steam cracking unit 
SHDT 
selective hydrotreating unit 
SFUT 
second flash unit temperature 
SROT 
second riser outlet temperature 
TAC 
total annual costs 
TFC 
total fixed costs 
TPR 
total project revenue 
TVC 
total variable costs 
VHCRU 
vacuum residue hydrocracking unit 
WWG 
wastewater generation 
Subscript 
γn
pdc 
the n-th product yields 
φn 
the nonlinear functional relationship among FFUT, SFUT, 
FROT, 
SROT 
and γn
pdc 
η 
mass material subscript entering the crude oil catalytic 
cracking process 
μ 
mass material subscript leaving the crude oil catalytic 
cracking process 
θ 
energy material subscript entering the crude oil catalytic 
cracking process 
τ 
energy material subscript leaving the crude oil catalytic 
cracking process 
RMsum 
raw material mass flows 
RMin
η 
raw material entering the crude oil catalytic cracking 
process 
PFout
μ 
product flow leaving the crude oil catalytic cracking 
process 
PPn
pdc 
product prices (including tax) 
Ein
θ 
energy material entering the crude oil catalytic cracking 
process 
Eout
τ 
energy material leaving the crude oil catalytic cracking 
process 
I 
utility types 
M 
total process unit numbers 
N 
total product numbers 
y 
year  
X. Zhou et al.                                                                                                                                                                                                                                    
Energy Conversion and Management 253 (2022) 115149
3
light oil, etc.) to chemicals at relatively harsh reaction conditions (i.e., 
temperatures: 600–700 ◦C). 
Under the leadership of Prof. Yang and Prof. Shan, the two-stage riser 
catalytic cracking (TSRCC) has been successfully developed and popu­
larized in the industry [30]. In the TSRCC technology, heavy crude oil is 
sent into the first riser, and cycling oil from the fraction system is fed to 
the second riser. Heavy and light feedstocks are in contact with highly 
active regenerated catalysts and react under the most favorable condi­
tions. Moreover, the length of two riser reactors is shorter than the 
traditional FCC (fluid catalytic cracking) riser. More than ten commer­
cial units have confirmed that the TSRCC technology can increase 
chemical yields [31]. The detailed and in-depth research approaches, i. 
e., density function theory calculation [32,33], catalyst engineering and 
design [34,35], process simulation and optimization [36], computa­
tional fluid dynamics design [37], are integrated. An overview of TSRCC 
technology for maximizing chemicals production is described intuitively 
in Table 2. 
As shown in Table 1 and Table 2, the laboratory research and in­
dustrial pilot test show that direct crude oil catalytic cracking effectively 
integrates and utilizes crude oil resources and maximizes petrochemi­
cals efficiently. Meanwhile, it should also be firmly believed that in the 
future, the refinery would be developed directly around the crude oil to 
chemicals process as shown in Fig. 1 (c) and Fig. 1 (d), instead of the 
current state of the art, which is unified or even extensive as depicted in 
Fig. 1. The flow diagrams of the crude oil to chemical processes: (a) the heavy oil hydrocracking processing route; (b) the heavy oil catalytic cracking processing 
route; (c) the crude oil catalytic cracking processing route; (d) crude oil steam cracking processing route; (e) the crude oil steam cracking developed in Singapore by 
Exxon-Mobile in 2014. 
X. Zhou et al.                                                                                                                                                                                                                                    
Energy Conversion and Management 253 (2022) 115149
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Fig. 1 (a) and Fig. 1 (b): crude oil is first fed to the atmospheric distil­
lation unit and vacuum distillation unit, and then separated different 
distillates to produce various products. It should be noted that in this 
study, the basic crude oil-to-chemicals process is shown in Fig. 1(c). The 
overall innovation tendency of COCC technology is to build a new 
process with a short process, low energy consumption, and high yield, 
and ultimately upgrade the traditional refining process. In the future, 
four significant technical difficulties still need to be solved: (1) The first 
is to evaluate the composition of crude oil accurately. (2) The second is 
to accurately control the chemical reaction conversion rate and improve 
product selectivity. (3) The third is to promote the hierarchical con­
version of diesel, heavy oil, and naphtha hydrocarbons and strengthen 
the catalytic cracking reaction of naphtha light hydrocarbon molecules. 
(4) The fourth is to design pretreatment units to achieve process inten­
sification and avoid downstream equipment pollution or even shut down 
due to coking. 
This study is committed to providing feasible strategies to solve those 
above third and fourth technology issues. A novel process model of 
crude oil hierarchical catalytic cracking (CHCC) process was proposed 
based on the TSRCC technology. The molecular-level process model was 
constructed for the first time for direct fractionation of crude oil. 
Coupled with the crude oil preheating flash system model, making full 
use of the technical advantage that the two-stage riser reactor can carry 
out zoning reaction on raw materials, strengthening the role of different 
reaction areas in coordinating the directional conversion of naphtha, 
diesel, and heavy oil into chemicals, this work developed the process 
model of CHCC. The optimal process parameters in the CHCC process are 
Fig. 2. The relationship of feedstocks (at the right side of green dotted line) and 
products (at the left side of green dotted line) among carbon number, boiling 
point, and H/C ratio. 
Table 1 
The state-of-the-art technology of the COTC process.  
Feedstocks 
Process, reactors, and catalysts 
Conditions 
Yields of chemicals 
CH4/C2=
Classification 
Ref. 
AL; AXL; ASLa 
fixed-bed micro-activity test (MAT); 
Equilibrium FCC catalysts/ZSM-5 
T: 600–650 ◦C 
C2-C4 olefins yields: 39.1–42.9 wt% 
Naphtha yields: 23.0–31.7 wt% 
0.26–0.64 
COCC 
technology; 
Chemicals and 
fuels 
[21] 
ASL 
riser simulator; USY and MFI zeolite 
T: 500–575 ◦C 
C2-C4 olefins yields: 10.0–26.8 wt% 
Naphtha yields: 37.3–51.2 wt% 
0.14–0.43 
COCC 
technology; 
Chemicals and 
fuels 
[22] 
ASL 
fixed-bed microactivity test; Equilibrium 
FCC catalysts/MFI 
T: 550–650 ◦C 
C2-C4 olefins yields: 11.6–34.9 wt% 
Naphtha yields: 35.4–53.3 wt% 
0.11–0.63 
COCC 
technology; 
Chemicals and 
fuels 
[20] 
Light crude oil 
Equilibrium FCC catalysts 
T: 560–640 ◦C 
C2-C4 olefins yields: 2.5–21.6 wt% 
Naphtha yields: 40.2–44.8 wt% 
0.50–0.75 
COCC 
technology; 
Chemicals and 
fuels 
[19] 
AL; AXL; ASL 
Microactivity test unit; Equilibrium FCC 
catalysts/MFI 
T: 550 ◦C 
C2-C4 olefins yields: 9.8–21.3 wt% 
Naphtha yields: 29.8–59.9 wt% 
1.53–2.64 
COCC 
technology; 
Chemicals and 
fuels 
[23,24] 
AXL 
fixed-bed micro-activity test 
Equilibrium FCC catalysts/MFI 
T: 550–650 ◦C 
C2-C4 olefins yields: 22.8–32.7 wt% 
Naphtha yields: 24.4–48.3 wt% 
0.38–0.69 
COCC 
technology; 
Chemicals and 
fuels 
[25] 
Crude oila 
Preheat + flash + catalytic cracking 
T: 600–650 ◦C; 
catalyst/oil: 20 
~ 31 
C2-C4 olefins yields: 23.0–31.3 wt% 
Naphtha yields: 47.2–48.9 wt% 
0.60 ~ 
0.63 
COCC 
technology; 
Chemicals and 
fuels 
[26] 
Hydrotreated 
AL 
Flash + conventional hydrotreating +
slurry hydrtreating + steam cracking 
T: 400–900 ◦C; 
Steam/oil: 0.3 
~ 2 
Ethylene: 23.2 wt%; Propylene: 13.3 wt% 
Butadiene: 4.9 wt%; Butene: 4.2 wt% 
0.46 ~ 
0.47 
COSC 
technology; 
Chemicals 
[27] 
Alaskan crude 
oil 
Thermal cracking tube 
T: 829 ~ 843 ◦C; 
Ethylene: 19.3 ~ 20.4 wt%; Propylene: 12.1 
~ 12.2 wt%; Butadiene: 4.7 wt%; 
0.46 ~ 
0.47 
COSC 
technology; 
Chemicals 
[28] 
AL 
Thermal cracking tube 
Steam/oil: 1.2 
Ethylene: 18 wt%; Propylene: 13.8 wt%; 
Butadiene: 2.7 wt%; Butene: 2.9 wt% 
0.56 
COSC 
technology; 
Chemicals 
[29] 
Agbami crude 
oil 
Thermal cracking tube 
Steam/oil: 1.0 
Ethylene:21.5 wt%; Propylene: 12.9 wt%; 
Butadiene: 4.0 wt%; Butene: 4.4 wt% 
0.39 
COSC 
technology; 
Chemicals 
[29] 
a: AL = Arab light; AXL = Arab extra light; ASL = Arab super light. 
X. Zhou et al.                                                                                                                                                                                                                                    
Energy Conversion and Management 253 (2022) 115149
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realized quantitatively. The directional conversion of naphtha to low- 
carbon olefins is strengthened to solve the key scientific problems 
encountered in developing the new technology for producing chemicals 
using crude oil directly and realize a one-step leap forward in the crude- 
to-chemical process. 
2. Methods 
The life cycle techno-economic-society-environment method is car­
ried out to achieve process parameters optimization and evaluate the 
techno-economic and society-environment performance of the proposed 
CHCC process quantitatively. The basic process schematic flowsheet of 
crude oil-to-chemicals is demonstrated in Fig. 1(c). 
2.1. Global framework 
The global framework employed in this study consists of four levels, 
as illustrated in Fig. 3. The first level, conceptual design, mainly focuses 
on developing the conceptual process flowsheets, performing massive 
literature research, and considering the feasibility investigation of the 
proposed process technology. The second level, the pilot-test experi­
ment, mainly includes performing crude oil hierarchical catalytic 
cracking experiments under different process conditions to provide data 
support for constructing the process simulation model. The third level, 
simulation, and optimization, mainly includes developing the proposed 
simulation flowsheet, verifying the accuracy of process simulation, and 
applying the process optimization algorithm to optimize process pa­
rameters. The fourth level, the life cycle society-environment assessment 
(LCSEA) model, mainly includes using the optimal process model to 
quantitatively evaluate the performance from the techno-economic, 
social, and environmental perspectives. 
2.2. Feedstock properties, catalysts, and experimental equipment 
The most critical factors affecting ethylene and propylene mass 
yields are crude oil properties. Moreover, it could also directly affect the 
techno-economic performance. However, the unified and transparent 
standards for crude oil properties are quite limited. Based on the report 
of IHS Markit [16], the APIo of crude oil adopted in the steam cracking 
process developed by ExxonMobil and Saudi Aramco is 43. Combined 
with pilot-test results, it is considered that paraffin-based crude oil 
should be selected as the raw materials, which should have higher 
hydrogen content, lower metal content, sulfur content, and low-carbon 
alkane content. More specifically, the hydrogen content should be 
higher than 12 wt% to ensure the yield of light olefins. The total content 
of nickel and vanadium should be <5 ppmwt% because the reaction rate 
of dehydrogenation and aromatics condensation could be inhibited. To 
avoid affecting product properties, the sulfur content should be lower 
Table 2 
A detailed review of two-stage riser catalytic cracking technology for maximizing chemicals production.  
Feedstocks 
Process, reactors, and catalysts 
Conditions 
Yields of chemicals 
CH4/C2=
Classification 
Ref. 
C4 
pilot-scale riser FCC unit; HZSM-5 + USY 
zeolite catalysts 
T: 510 ◦C; catalyst/oil: 8 
Ethylene: 7.35 wt%; Propylene: 
28.7 wt% 
Butene: 22.9 wt%; Gasoline: 
14.8 wt% 
1.14 
Two-stage FCC 
technology 
[31] 
Naphtha 
pilot-scale riser FCC unit; Y 
zeolite equilibrium FCC catalysts 
T: 480 ◦C 
C2-C4 olefins yields: 
11.88–17.22 wt% 
Naphtha yields: 73.41–82.46 
wt% 
N/A 
Two-stage FCC 
technology 
[38] 
Diesel 
pilot-scale riser FCC unit; HZSM-5 + USY 
zeolite catalysts 
T: 510–560 ◦C; catalyst/oil: 
5 
C2-C4 olefins yields: 
2.64–19.30 wt% 
Gasoline: 12.35–56.1 wt% 
N/A 
Two-stage FCC 
technology 
[34,35] 
Vacuum gas oil 
pilot-scale riser FCC unit; HZSM-5 + USY 
zeolite catalysts 
T: 500 ~ 550 ◦C; catalyst/ 
oil: 7 ~ 18 
C2-C4 olefins yields: 
35.35–27.06 wt% 
Naphtha yields: 22.11–43.29 
wt% 
1.54–2.01 
Two-stage FCC 
technology 
[39,40] 
Daqing residue 
pilot-scale riser FCC unit; HZSM-5 + USY 
zeolite catalysts 
T: 510 ~ 580 ◦C; catalyst/ 
oil: 7 ~ 18 
C2-C4 olefins yields: 
35.35–37.80 wt% 
Naphtha yields: 25.04–26.86 
wt% 
0.40–0.41 
Two-stage FCC 
technology 
[31] 
Sudan crude oil 
pilot-scale riser FCC unit; Y 
zeolite regenated FCC catalysts 
T: 460 ◦C; catalyst/oil: 5 
Naphtha: 39.48 wt%; Diesel: 
29.88 wt% 
LPG: 15.32 wt% 
N/A 
Two-stage FCC 
technology 
[41] 
Daqing crude 
oila 
pilot-scale riser FCC unit; ZSM-5 
zeolite 
T: 530–590 ◦C; catalyst/ 
oil:10 
C2-C4 olefins yields: 
31.37–33.48 wt%  
Naphtha yields: 16.50–23.32 
wt% 
0.51–0.59 
COCC technology; 
Chemicals and fuels 
UWs 
Daqing crude 
oila 
pilot-scale riser FCC unit; ZSM-5 
zeolite 
T: 600–630 ◦C; catalyst/ 
oil:12 
C2-C4 olefins yields: 
32.94–34.54 wt% 
Naphtha yields: 14.50–17.29 
wt% 
0.48–0.53 
COCC technology; 
Chemicals and fuels 
UWs 
Daqing crude 
oila 
pilot-scale riser FCC unit; ZSM-5 
zeolite 
T: 650–690 ◦C; catal/ 
oil:15–20 
C2-C4 olefins yields: 
36.13–40.46 wt% 
Naphtha yields: 10.50–12.14 
wt% 
0.36–0.40 
COCC technology; 
Chemicals and fuels 
UWs 
Zhongyuan 
crudea 
pilot-scale riser FCC unit; ZSM-5 
zeolite 
T: 650–690 ◦C; catalyst/ 
oil:15–20 
C2-C4 olefins yields: 
33.28–37.12 wt% 
Naphtha yields: 10.43–12.51 
wt% 
0.41–0.46 
COCC technology; 
Chemicals and fuels 
UWs 
Changqing 
crudea 
pilot-scale riser FCC unit; ZSM-5 
zeolite 
T: 650–690 ◦C; catalyst/oil: 
15–20 
C2-C4 olefins yields: 
32.34–37.69 wt% 
Naphtha yields: 10.90–13.87 
wt% 
0.41–0.46 
COCC technology; 
Chemicals and fuels 
UWs 
a: The pilot-test results of COCC technology using Daqing and Changqing crude oil are unpublished works (UWs). 
X. Zhou et al.                                                                                                                                                                                                                                    
Energy Conversion and Management 253 (2022) 115149
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than 0.3 wt%. The above indexes limit most crude oil as direct cracking 
feedstocks. Facing the possible uncertainties in the future, it is better to 
use domestic paraffin-based crude oil as cracking feedstocks to ensure 
quality and supply. Fig. 4 demonstrates the major onshore paraffin- 
based crude oil production areas and various fractions in China. 
Table 3 shows more detailed key property indicators for the six kinds 
of paraffin-based crude oil. After a comprehensive comparison, it could 
be observed that the six types of crude oil all meet the primary feedstock 
standards of crude oil direct catalytic cracking. However, considering 
the future production and supply capacity, the paraffin-based Daqing 
crude oil with an annual output of nearly 40 million tons is selected as 
the raw material for the direct cracking of crude oil in the pilot-test and 
process modeling. In this study, the processing capacity of the CHCC and 
COCC processes is 3.0 Mt/year. 
In this study, ZSM-5 zeolites are employed in the CHCC process, and 
catalyst properties are shown in the reference [34]. The CHCC process 
Fig. 3. The global methodology for developing and evaluating the novel CHCC process using the LCSEA model.  
Fig. 4. (a) Major onshore paraffin-based crude oil production areas; (b) detailed fractions of various crude oil.  
X. Zhou et al.                                                                                                                                                                                                                                    
Energy Conversion and Management 253 (2022) 115149
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using Daqing crude oil as feedstocks is executed in the pilot-scale TSRCC 
unit, as shown in Fig. 5. Continuous catalytic cracking reactions and 
catalyst regeneration operations, similar to the commercial catalytic 
cracking unit, could be carried out. The computer controls the display 
and adjustment of main operating parameters. 
2.3. Description, simulation, and verification 
The description, simulation, and model verification of the COCC and 
CHCC processes are illustrated in this section. 
2.3.1. The crude oil catalytic cracking processes 
In the previous work, after being pretreated (i.e., dehydration and 
desalination), Daqing crude is fed to the preheated system and heated to 
210 ◦C [17]. The mixed liquid and gas are entered into the flash unit to 
realize the flash separation. Gas products (fractions <210 ◦C involving 
alkanes, naphtha, and partial kerosene) are sent to the bottom of the 
second riser reactor. Liquid products (fractions＞210 ◦C including 
kerosene, diesel, and heavy oil) entered the first riser reactor. The 
effluent from the riser reactor is then sent into the main fractionator. 
Therefore, a variety of gases and fractions can be separated, including 
dry gas (involving target product ethylene), liquefied petroleum gas 
(involving target product propylene and butene), gasoline (involving 
target product BTX), and diesel, as shown in Fig. 6 (a). 
2.3.2. The highlights of the novel process 
Fig. 6 (a) depicts the conventional crude oil catalytic cracking pro­
cess, while Fig. 6 (b) shows the novel CHCC technology integrating the 
hierarchical gasification process. The purpose of the CHCC process is to 
preheat further and flash separation of raw materials, conduct partition 
reaction of small molecular alkanes, naphtha, kerosene, and diesel, and 
maximize the sufficient cracking reaction of reactants with different 
reactants activation energies to produce more petrochemicals. Daqing 
crude is fed to the preheated system and heated to about 170 ~ 210 ◦C. 
And then, the liquid and gas are entered into the first-stage flash unit to 
realize the flash separation. Gas products are sent into the second riser 
reactor of the two-stage riser reactor unit. The liquid products are sent 
into the second-stage preheated system and heated to 210 ~ 290 ◦C. 
After being flashed by the second-stage flash tank to realize the sepa­
ration of gas and liquid, the gas products (including kerosene and partial 
diesel) are sent into the second riser reactor of the two-stage riser 
reactor. The liquid products (including diesel and heavy oil) enter the 
Table 3 
Detailed key property indicators for the six kinds of paraffin-based crude oil.  
Items 
Hydrogen contents 
Ni + V contents 
Sulfur contents 
Daqing 
12.38 wt% 
2.64 ppmwt% 
0.11 wt% 
Changqing 
12.15 wt% 
1.69 ppmwt% 
0.06 wt% 
Zhongyuan 
12.44 wt% 
4.24 ppmwt% 
0.31 wt% 
Qinghai 
11.98 wt% 
8.42 ppmwt% 
0.02 wt% 
Yumen 
11.95 wt% 
4.88 ppmwt% 
0.13 wt% 
Tuha 
12.61 wt% 
0.67 ppmwt% 
0.10 wt%  
Fig. 5. Diagram for the pilot-scale TSRCC unit.  
X. Zhou et al.                                                                                                                                                                                                                                    
Energy Conversion and Management 253 (2022) 115149
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first riser reactor. Similarly, as the product separation of the COCC 
process, the pyrolysis liquid products of the proposed CHCC process 
from the two-stage riser reactor are sent to the fraction system. It should 
be noted that the optimal flash temperature will be shown in the 
following section. 
Based on the previous research, literature, and industrial unit data, 
the results can intuitively indicate that the high-boiling-point fractions 
in crude oil, including vacuum gas oil, atmospheric residue, and even 
vacuum residue, could increase the production of low-carbon olefins and 
light aromatics through deep catalytic cracking [13]. However, the low- 
boiling-point fractions in crude oil, i.e., straight-run naphtha and diesel, 
are rich in small molecular alkanes. The reaction activation energy is 
about 10 times higher than heavy metals oil [19]. They can not be 
effectively activated in the conventional catalytic cracking process and 
can not be fully converted into low-carbon olefins or light aromatics. 
Hence, effectively converting small molecular alkanes into low-carbon 
olefins through catalytic cracking has become the main bottleneck 
restricting the successful development of new crude-to-chemical tech­
nology. In the CHCC process, the core technology is to add a preheating 
system and a flash system to increase the preheating temperature and 
increase the yield of the target product. Moreover, combined with the 
structural characteristics of the two-stage riser reactor, naphtha and 
diesel catalytic cracking in different regions are realized in other reac­
tion temperature regions [see Fig. 6(b)]. 
2.3.3. Model accuracy verification 
The Aspen HYSYS platform is employed in this study to develop the 
CHCC process. The catalytic cracking unit adopts the twenty-one lum­
ped kinetic model [42]. Fig. 7 shows the Aspen HYSYS simulation 
flowsheet of the proposed CHCC process. Please refer to the previous 
work [17] for more detailed modeling work. The detailed simulation 
process data and process description are shown in the Supporting 
Materials. 
Based on the above scenario, the CHCC process verification is per­
formed. Fig. 8 demonstrates the error graph between the process 
simulation results and pilot-test results. The farther the data points 
shown in Fig. 8 are from the diagonal line, the greater the relative error 
between the simulated and the experimental values and the inaccurate 
model. On the contrary, the relative errors are minor, and the estab­
lished model is accurate. Through comprehensive analysis of Fig. 8, it 
can be intuitively indicated that the relative errors between the exper­
imental and simulated values are pretty minor. These actual results 
could prove the accuracy of the established CHCC process model. Hence, 
the above results and analysis can verify the overall model accuracy of 
the proposed CHCC process model. 
2.4. Techno-economic analysis 
The annual total cost (TAC), net present value (NPV), and internal 
rate of return (IRR) of COCC and CHCC processes are calculated in this 
Fig. 6. Process diagram of the COCC and CHCC processes: (a) the COCC process and (b) the proposed CHCC process.  
X. Zhou et al.                                                                                                                                                                                                                                    
Energy Conversion and Management 253 (2022) 115149
9
work. TAC includes two parts: total annual capital and operating costs. 
In addition, total capital costs consist of total direct costs and indirect 
costs [43]. The total direct cost [44,45] is the total project investment, 
including construction costs, bank loans, etc. The scale index method is 
adopted in the study [46]. Indirect costs mainly consist of landing, 
litigation, management, etc. Operating expenses include raw materials, 
utilities, employee welfare, and maintenance management costs. Utility 
costs mainly consist of power, steam, recycling water, refrigerant, etc. 
These are estimated according to the energy balance in Aspen HYSYS. 
This study considers two financial evaluation indexes: NPV and IRR. 
NPV and IRR are defined as Eqs. (1–2) and Eq. (3), respectively [47,48]. 
NCFy = TPRy −FCy −VCy
(1)  
NPV =
∑
WPL
y = 0
NCFy
(1 + dr)y
(2)  
0 =
∑
WPL
y = 0
NCFy
(1 + IRR)y
(3)  
where net cash flow is the full name of NCF in the year y; TPRy is the 
total project revenue; TFCy and TVCy represent the total fixed and var­
iable costs; the discount rate is the full name of dr 10%; WPL means the 
whole plant life, 20 years. 
2.5. Life cycle society-environment assessment 
The three most crucial evaluation indexes: greenhouse gas emissions 
(GHG), wastewater generation (WWG), and non-renewable energy 
consumption (NREC), are executed and calculated as Eqs. (4–6) in this 
LCSEA model [49]. 
GHGtotal = 298 × N2O + 25 × CH4 + CO2
(4)  
WWGtotal = WWGsulfur−containing + WWGoily−containing
(5)  
NRECtotal = NRECcrudeoil + NRECcrudecoal + NRECnaturalgas
(6)  
where WWGsulfur-containing and WWGoily-containing are sulfur-containing 
and oily-containing wastewater, respectively. NRECcrudeoil, NRECcrude­
coal, and NRECnaturalgas represent non-renewable energy consumption 
types. To assess the social contribution of the two processes, the total 
output value (product revenue and taxes created by the tax policies) is 
taken as the vital comparison index. Based on the previous study [17], 
the functional unit in the proposed LCSEA model should be one-million- 
USD-dollar output value, termed OMUDOV. Hence, available units of 
the three indexes, i.e., GHG, WWG, and NREC, are t CO2 equivalent/ 
OMUDOV, t wastewater/OMUDOV, and MJ/OMUDOV, respectively. Its 
detailed meaning is based on the quantitative GHG (tCO2 equivalent), 
WWG (t wastewater), and NREC (MJ) created by OMUDOV. 
3. Results and discussions 
The results and discussions illustrate the optimization strategy, 
detailed optimized results, and life cycle society-environment 
assessment. 
3.1. The optimization strategy of parameters in the novel process 
A detailed optimization strategy is carried out to solve the optimi­
zation problem of CHCC process parameters in this study. The critical 
process parameters in the CHCC process are first identified and then 
optimized by MatLab software. The method is based on the simu­
lation–optimization technique and facilitated by the Aspen HYSYS- 
MatLab interface [50]. Objective function and constraints are devel­
oped, and the branch and bound (BAB) method [51], which is one of the 
most commonly used algorithms to address the mixed-integer nonlinear 
Fig. 7. Aspen HYSYS simulation flowsheet of the proposed CHCC process.  
X. Zhou et al.                                                                                                                                                                                                                                    
Energy Conversion and Management 253 (2022) 115149
10
programming issue [52], is used to manage the optimization calculation 
model. The characteristic of the BAB method is to expand the feasible 
solution of the problem as a tree branch. This method can solve not only 
pure integer programming but also mixed-integer programming. The 
BAB algorithm’s advantages are obtaining the optimal solution and fast 
average speed [53]. It must be noted that the key to improving the BAB 
algorithm’s efficiency is to determine the boundary values. If the 
boundary is not adequately estimated, the computational efficiency will 
be affected. Hence, in this study, a lot of simulation analysis work was 
performed to determine the range of the decision variables and the 
critical process parameters that affect the decision variables. This 
fundamental work could be given full play to the high efficiency of the 
BAB algorithm by integrating with the above boundary value analysis. 
3.1.1. Analysis of critical operating parameters 
The parameter configuration in the CHCC process is different from a 
conventional single-stage riser. The reaction temperature in the two 
risers is quite different. The first riser outlet temperature is about 
600–650 ◦C to prevent excessive cracking and coking of heavy oil 
molecules. The second riser outlet temperature is about 650-700 ◦C, 
higher than the first riser outlet temperature, to enhance the catalytic 
cracking of naphtha and diesel. Moreover, the hierarchical flash tem­
perature in the Flash 1# and Flash 2#, as shown in Fig. 6(b), are also 
significant parameters. Hence, for the above CHCC process model, four 
vital operating parameters, i.e., first riser outlet temperature (FROT), 
first Flash 1# unit temperature (FFUT), second riser outlet temperature 
(SROT), and second Flash 2# unit temperature (SFUT) should be opti­
mized in this section. 
3.1.1.1. Flash unit temperature. Fig. 9 indicates the product distribution 
results in the CHCC process via changing FFUT and SFUT when FROT 
and SROT are 630 ◦C and 680 ◦C, respectively. As illustrated in Fig. 9(a)- 
Fig. 9(c), the key desired product yields of ethylene, propylene, and C4 
olefins, show a negative correlation trend with FFUT and SFUT. More­
over, reducing the preheat flash temperature is conducive to improving 
coke yields [see Fig. 9(g)]. However, the gasoline, diesel, and slurry 
yields positively correlate with the preheating flash temperature, as 
shown in Fig. 9(d)-Fig. 9(f). This study also defined critical parameters, 
i.e., the mass yield ratio of methane and ethylene (MER), which needs to 
be controlled in a low range to ensure the selectivity of olefins produced 
by the CHCC process. As shown in Fig. 9(h), increasing FFUT and SFUT is 
conducive to reducing MER. However, it contradicts that FFUT and 
SFUT should be decreased to maximize ethylene and propylene. 
Therefore, through the comprehensive analysis of Fig. 9, the suitable 
variation range of FFUT and SFUT [see the green area in Fig. 9(h)] 
should be 180–190 ◦C and 245–255 ◦C, respectively. 
Fig. 8. Model validation for the CHCC process: (a) overall product mass yields; (b) mass yield ratio of methane/ethylene; (c) olefin contents in C4; (d) pyrolysis 
gasoline compositions; (d) diesel compositions; (e) slurry compositions. 
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3.1.1.2. Reaction outlet temperature. Fig. 10 shows the calculation re­
sults of product distribution in the CHCC process via changing FROT and 
SROT when FFUT and SFUT are 190 ◦C and 250 ◦C, respectively. As 
illustrated in Fig. 10(a)-Fig. 10(c), the key desired product yields of 
ethylene, propylene, and C4 olefins, show a positive correlation trend 
with FROT and SROT. Moreover, raising the catalytic cracking tem­
perature is also conducive to improving coke yields [see Fig. 10(g)]. 
However, gasoline, diesel, and slurry yields negatively correlate with the 
FROT and SROT, as shown in Fig. 10(d)-Fig. 10(f). It is worth noting that 
through the comprehensive analysis of Fig. 10(a)-Fig. 10(c), an exciting 
conclusion was found, i.e., when FROT is relatively low (600-620 ◦C), 
the improvement of olefin yields by increasing SROT is very limited and 
not intuitive. However, when FROT is relatively high (＞630 ◦C), the 
effect of growing SROT on increasing olefins is very significant. This also 
shows that to improve the yield of ethylene and propylene, the FROT can 
not be at a low level. Otherwise, if the first riser reactor is at a low re­
action temperature and the SROT is forcibly increased to strengthen the 
cracking of naphtha and diesel, it will only play the opposite role for 
maximizing olefins production in the proposed CHCC process. As for 
MER, similarly, as the changing trend of FFUT and SFUT, the MER needs 
to be controlled in a low range to ensure the selectivity of olefins pro­
duced by the CHCC process. As illustrated in Fig. 10(h), increasing FROT 
and SROT is conducive to reducing MER. However, it contradicts that 
FROT and SROT should be adequately raised to maximize ethylene and 
propylene. Hence, by comprehensive analysis of Fig. 10, the acceptable 
variation range of FROT and SROT are 630-650 ◦C and 670-690 ◦C, 
Fig. 9. The 3D dynamic calculation results of product distribution in the CHCC process via changing FFUT and SFUT (FROT = 630 ◦C; SROT = 680 ◦C): (a) ethylene 
yields; (b) propylene yields; (c) C4 yields; (d) gasoline yields; (e) diesel yields; (f) slurry yields; (g) coke yields; (h) MER. 
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respectively. 
3.1.2. Optimization problem statement 
As defined in Eqs. (7–10), the optimization problem in the CHCC 
process is how to maximize techno-economic performance while 
ensuring the least GHG, the least WWG, and the least NREC 
simultaneously? 
Multi - objective functions : max 
OVtotal =
∑
N
n=1
RMsum⋅γn
pdc⋅PPn
pdc, ∀n ∈N
(7)  
minGHGtotal =
∑
M
m=1
GHGm,tCO2eq, ∀m ∈M
(8)  
minWWGtotal =
∑
M
m=1
WWGm,tH2O, ∀m ∈M
(9)  
minNRECtotal = NRECRM
crudeoil +
∑
M
m=1
∑
I
i=1
NRECm,i∀m ∈M, i ∈I
(10)  
where OVtotal represents the total output value, $/year; RMsum means 
raw material mass flows, ton/year; γn
pdc means product yields, wt%; PPn
pdc 
is the price of products listed in the Supporting Materials, $/ton; The 
Fig. 10. The 3D dynamic calculation results of product distribution in the CHCC process via changing FROT and SROT (FFUT = 190 ◦C; SFUT = 250 ◦C): (a) ethylene 
yields; (b) propylene yields; (c) C4 yields; (d) gasoline yields; (e) diesel yields; (f) slurry yields; (g) coke yields; (h) MER. 
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units of NRECtotal, GHGtotal and WWGtotal are MJ/year, t CO2 eq/year, 
and t H2O/year, respectively; N, M, and I are the total product numbers, 
total process unit numbers, and total utility type numbers, respectively. 
It is challenging to optimize multiple extreme values for specific 
constraints, i.e., maximizing techno-economic performance, minimum 
NREC, minimum GHG, and minimum GHG simultaneously. Hence, it is 
crucial to find the correlation between these extreme values in the 
opposite direction. As mentioned before, the functional units of 
GHGtotal, WWGtotal, and NRECtotal, are tCO2 eq/OMUDOV, t H2O/ 
OMUDOV, and MJ/OMUDOV, respectively. To obtain the minimum 
values of the three objective functions, it is significant first to address the 
optimal operating parameters which create maximum TOV of the CHCC 
process. Moreover, suppose the maximum TOV created by the CHCC 
process is solved. In that case, the GHGtotal, WWGtotal, and NRECtotal of 
CHCC process under constraints must be the minimum for the NREC, 
GHG, and WWG generated by the specific one million total output value. 
3.1.3. The constraint conditions and objective functions 
The proposed optimization problem is herein simplified based on the 
above analysis. The complex formula of constraint conditions and 
objective functions is given and defined as Eqs. (11–18). The constraints 
conditions, Eq. (12) and Eq. (13), guarantee the material and energy 
balance in the CHCC process. Moreover, the constraint conditions, Eqs. 
(14–18), are to limit the changing ranges of decision variables to achieve 
the optimal solution efficiently. It is worth noticing that the four critical 
process operating parameters, i.e., FFUT, FROT, SFUT, and SROT, show 
a nonlinear functional relationship on decision variables γn
pdc , as 
demonstrated in Section 3.1. Hence, the nonlinear functional relation­
ship between process operating parameters and decision variables could 
be developed. 
Objective function :
min - OVtotal = -
∑
N
n=1
RMsum⋅γn
pdc⋅PPn
pdc
(11)  
Constraints :
∑
η
η=1
RMin
η
=
∑
μ
μ=1
PFout
μ
(12)  
∑
θ
θ=1
Ein
θ
=
∑
τ
τ=1
Eout
τ
(13)  
γn
pdc = φn(FFUT, SFUT, FROT, SROT), ∀n ∈N
(14)  
180◦C⩽FFUT⩽190◦C
(15) 
Fig. 11. The flowsheet of model optimization calculation using the 
BAB method. 
Fig. 12. The optimal operating parameters and calculated OVtotal result.  
Table 4 
The material balance and comprehensive analysis.  
Items 
COCC 
CHCC 
Raw materials (wt%) 
Daqing oil 
100 
100 
Total 
100 
100 
Products (wt%) 
CH4 
8.75 
8.29 
ethane 
3.21 
3.60 
ethylene 
18.13 
19.22 
propane 
6.12 
6.23 
propylene 
14.90 
15.27 
C4 
21.72 
23.24 
gasoline 
9.16 
7.13 
diesel 
8.05 
6.75 
slurry 
2.51 
2.74 
coke 
7.45 
7.53 
Total 
100 
100 
Comprehensive analysis 
convention rate (%) 
89.44 
90.91 
methane/ethylene 
0.48 
0.43 
ethylene + propylene (wt%) 
33.03 
34.49  
Table 5 
The COCC and CHCC processes’ energy consumption.  
Items 
COCC 
CHCC 
coke combustion (MJ/t Daqing crude)  
3118.57  
3319.49 
freshwater (MJ/t Daqing crude)  
75.67  
86.38 
cooling water (MJ/t Daqing crude)  
286.95  
374.25 
low-pressure steam (MJ/t Daqing crude)  
−57.95  
−142.64 
middle-pressure steam (MJ/t Daqing crude)  
137.75  
142.76 
high-pressure steam (MJ/t Daqing crude)  
−89.46  
−93.68 
electricity (MJ/t Daqing crude)  
2168.54  
2257.68 
fuel (MJ/t Daqing crude)  
178.79  
197.54 
Total energy consumption (MJ/t Daqing crude)  
5818.86  
6141.78  
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235◦C⩽SFUT⩽245◦C
(16)  
630◦C⩽FROT⩽650◦C
(17)  
670◦C⩽SROT⩽690◦C
(18)  
where RMin
η and PFout
μ 
mean raw material mass flows and product mass 
flows; Ein
θ and Eout
τ represent energy flows in and out the CHCC process; η 
, μ , θ , and τ are the mass material subscripts entering and leaving the 
CHCC process, energy material subscripts entering and leaving the 
CHCC process; φn is the nonlinear functional relationship among FFUT, 
SFUT, FROT, SROT, and γn
pdc . 
3.1.4. Optimal operating parameter solving strategy 
The integrated optimization method is developed via calling the 
Actxserver interface function. The connection method and code are 
shown in the Supporting Materials. Fig. 11 illustrates the flowsheet of 
model calculation using the BAB method. Remarkably, there are two 
judgment processes to control the rapid convergence of the algorithm. 
The first is to judge whether the iteration i > 100 and the residual r <
0.0001. If they are neither satisfied, it means that the program algorithm 
has not converged, nor has it reached the maximum number of itera­
tions. The program will update the calculated process parameters and 
continue to use Aspen HYSYS software to solve. If at least one is met, it is 
necessary to check the algorithm further. The optimal operating pa­
rameters can be obtained if it has converged (r < 0.0001). 
Fig. 12 illustrates the optimal operating parameters and calculated 
OVtotal results. In Fig. 12, the optimized operating parameters are FFUT 
= 187 ◦C, SFUT = 251 ◦C, FROT = 644 ◦C, SROT = 682 ◦C. The 
calculated OVtotal, 2.77 × 109 USD, is obtained by solving the CHCC 
process’s optimization model. Suppose the optimal operating parame­
ters calculated by the optimization model proposed in this study are 
used in real industrial production. In that case, the results show that the 
optimal social and economic benefits could be obtained. It is worth 
noticing that the optimal operating variables are also used in the 
following technical and financial analysis and life cycle assessment. In 
addition, using the above optimal operational parameters, the 
Fig. 13. The hydrogen and carbon atoms utilization and sulfur balance: (a) the hydrogen and carbon utilization of COCC process; (b) the hydrogen and carbon 
utilization of CHCC process; (c) the sulfur balance in the COCC process; (d) the sulfur balance in the CHCC process. 
Table 6 
Techno-economic analysis results.  
items 
unit 
COCC 
CHCC 
Total capital costs    
Total direct cost 
USD/year 
5.124E + 07 
5.510E + 07 
Total indirect cost 
USD/year 
2.716E + 07 
2.920E + 07 
Total operating costs    
Total feedstock cost 
USD/year 
1.939E + 09 
1.939E + 09 
Total utility cost 
USD/year 
5.326E + 07 
6.254E + 07 
TAC 
USD/year 
2.071E + 09 
2.086E + 09  
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optimization of technical and economic performance, life cycle NREC, 
GHG, and WWG could be realized simultaneously. In the results and 
discussions section, the life cycle inventory analysis results indicate the 
minimum life cycle GHG, NREC, and WWG based on the proposed 
functional unit OMUDOV. 
3.2. Optimized results of mass balance and energy balance 
The optimized results of mass balance are listed in Table 4. 
Comprehensive analysis results indicate that the CHCC process’s con­
version rate is 90.91%, and the COCC process is 89.44%. The ethylene +
propylene yields are 34.49 wt% in the CHCC process, 1.04 times greater 
than the COCC process. 
Table 5 shows the energy consumptions of the CHCC and COCC 
processes. The COCC and CHCC processes’ energy consumptions are 
5818.89 MJ/t Daqing crude and 6141.79 MJ/t Daqing crude. In the 
Daqing crude oil catalytic cracking processing stage, the regeneration 
unit’s coke burning is the main link of energy consumption and occupies 
about 53.6%-54.0%. Hence, the regeneration unit is responsible for GHG 
emissions. The above results are discussed in social and environmental 
analyses as shown below. 
3.3. The atoms utilization and balance 
The two processes’ carbon and hydrogen atoms utilization is defined 
as Eq. (19) and Eq. (20). 
UH =
∑I
i=1PH
i × Yi
RMH
× 100%, ∀i ∈I
(19)  
UC =
∑I
i=1PC
i × Yi
RMC
× 100%, ∀i ∈I
(20)  
where UH and UC mean the hydrogen and carbon utilization; PH
i and PC
i 
are the hydrogen and carbon mass content in desired products, (wt%); 
RMH and RMC are the hydrogen and carbon mass content in raw ma­
terials, (wt%); Yi means the expected product mass yields (wt%); i in­
dicates the desired product numbers. 
To further demonstrate the hydrogen and carbon atoms utilization in 
the COCC and CHCC processes, the hydrogen and carbon atoms utili­
zation is shown in Fig. 13. As depicted in Fig. 13 (a), the UH atom uti­
lization in the COCC process is 63.17%. As for the carbon atom 
utilization, UC is 74.59%. The catalytic coke burning process is mainly 
undesirable utilization and hydrogen and carbon atoms loss. In this 
process, the loss of hydrogen and carbon atoms is mostly coke com­
bustion, methane formation, and slurry. In Fig. 13 (b), the hydrogen and 
carbon atoms utilization in the optimized CHCC process is 64.14% and 
76.21%, respectively. Based on these results, the hydrogen and carbon 
atoms utilization in the COCC process is relatively low. Through inte­
grating the hierarchical catalytic cracking technology and optimal 
strategy, the hydrogen and carbon atoms utilization in the optimized 
CHCC process has been improved intuitively. The elemental balance of 
sulfur in the COCC and CHCC processes is depicted in Fig. 13 (c)-Fig. 13 
(d). Moreover, the primary process of sulfur leaving the whole process 
system is concentrated in the crude oil catalytic cracking process (about 
0.05–0.06 wt%), followed by quench and compression process 
(0.03–0.04 wt%). Since most of the sulfur is deposited in oil products 
and coke, the sulfur content in the olefin separation process is relatively 
low, 0.01 wt%. 
3.4. Techno-economic analysis 
A detailed technical and financial analysis is carried out in this sec­
tion. Table 6 lists the results from the techno-economic perspective of 
the COCC and CHCC processes. 
The decomposition distribution of TAC in the COCC and CHCC 
processes is shown in Fig. 14. Data indicates total feedstock costs, 
1.939E + 09 USD/year, have the most significant contribution for the 
TAC, 92.97%-93.65%. This powerfully shows that the feedstock costs 
are the essential indicator. Furthermore, the next two contributing fac­
tors are total indirect costs and utility costs, occupying the proportion of 
2.47%-2.64% and 2.57%-2.99% of the TAC. Total indirect costs are the 
least contributing factor, only occupying 1.31%-1.40% of the TAC. 
Table 7 shows the results of NPV and IRR, which are calculated to 
estimate the anti-risk ability and economic benefit in the CHCC and 
COCC processes. The techno-economic analysis is based on the Brent 
Crude Oil 80$/bbl. The NPV in the CHCC process, 2.23E + 08 USD, is 
1.14 times higher than the COCC process. Moreover, the IRR in the 
CHCC process, 18.74%, is 1.09 times higher than the COCC process. The 
anti-risk ability and economy of the optimized CHCC process are supe­
rior to the COCC process through comprehensive analysis. 
3.5. Life cycle society-environmental assessment 
The life cycle inventory data, including Daqing crude oil feedstocks, 
Fig. 14. The decomposition distribution of TAC: (a) COCC and (b) CHCC processes.  
Table 7 
Results of NPV and IRR.  
Items 
COCC 
CHCC 
NPV, USD 
1.95E + 08 
2.23E + 08 
IRR, % 
17.13 
18.74  
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Energy Conversion and Management 253 (2022) 115149
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cracking products, utility consumptions, and GHG by generating the 
OMUDOV via COCC and CHCC processes, are illustrated in Fig. 15. 
The process inventory data in wastewater generation in the two 
processes is demonstrated in Fig. 16 based on OMUDOV. It should be 
pointed out that sulfur-containing, as well as oil-containing wastewater, 
are both calculated as wastewater in the study. Fig. 16 shows the life 
cycle inventory data of water resources consumption of utilities, i.e., 
desalted water, recycling water, etc. 
The detailed explanation and analysis of the LCSEA model are 
demonstrated in Fig. 17 based on the process inventory data. GHG, 
WWG, and NREC performance are shown in Fig. 17(a)-Fig. 17(c). The 
GHG, WWG, and NREC performance in the CHCC process offer signifi­
cant advantages compared with the COCC process. For example, the CO2 
equivalent emissions in the COCC process is 315.26 tCO2 
eq⋅OMUDOV−1. It is 1.02 times higher than the CHCC process, 308.71 
tCO2 eq⋅OMUDOV−1. The WWG and NREC in the COCC process are 
321.56 tH2O⋅OMUDOV−1 and 80177 MJ⋅OMUDOV−1, 1.09 and 1.02 
times greater than the CHCC process. Compared with the COCC process, 
the proposed CHCC process shows more excellent life cycle social & 
environmental advantages. 
The breakdown results for the LCSEA model are shown in Fig. 17(d)- 
Fig. 17(f) to further recognize the bottlenecks in the two processes. The 
Daqing crude oil catalytic cracking unit exhibits the most significant 
contribution to the GHG and NREC, which occupies 63.6%-64.1% and 
62.6%-62.8% in the total GHG and total NREC, respectively. Through 
comprehensive analysis of Fig. 17(d) and Fig. 17(f), it could be identified 
that the catalytic cracking units are the most significant “bottleneck” for 
GHG and NREC. The quench and compression unit are the most critical 
contributor to the total WWG, accounting for 55.8% and 56.5% in COCC 
and CHCC processes. Moreover, the Daqing crude oil catalytic cracking 
unit occupies another 45.2% and 44.5% in the COCC and CHCC process, 
respectively. Based on the LCSEA results, it is intuitively indicated that 
the Daqing crude oil catalytic cracking unit is the enormous bottleneck 
process. Hence, in future work, optimizing process variables (critical 
parameters in the fraction system) and achieving process strengthening 
(integrating CO2 capture and storage unit) is imminent. 
3.6. Sensitivity analysis 
Performing the sensitivity analysis aims to quantitatively analyze the 
influence of various indexes on economic performance, which seeks to 
identify the anti-risk ability of the project further. Based on Brent 80 
USD/bbl, the sensitivity analysis about Daqing price, ethylene price, 
propylene price, C4 price, gasoline price, and capital investment with 
contingency is executed. Each factor’s influence is depicted in Fig. 18. 
The Daqing price’s fluctuation shows the most significant effect on the 
projected revenue. Specifically, when Daqing price’s fluctuation range 
is ± 20%, the NPV in the COCC and CHCC processes change from −1.96 
× 108 USD to 3.44 × 108 USD and −2.26 × 108 USD to 3.78 × 108 USD, 
respectively. The second factor is the fluctuation of ethylene prices; the 
Fig. 15. Inventory data for NREC and GHG is based on OMUDOV through (a) COCC and (b) CHCC processes.  
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Energy Conversion and Management 253 (2022) 115149
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third is the propylene price’s change, and the next is the gasoline prices. 
The changes of capital investment with contingency exhibit the slightest 
fluctuation. Similarly, in the CHCC process, the greatest and slightest 
impact factors are the Daqing price and capital investment with con­
tingency. It is worth noticing that the CHCC process’s fluctuation range 
is more violent than the COCC process, especially for the tremendous 
change, Daqing crude oil price. 
4. Conclusion 
A novel two-stage riser crude oil hierarchical catalytic cracking 
technology, termed the CHCC process, is proposed to efficiently utilize 
the Daqing crude oil and maximize petrochemicals production. The 
conventional COCC process is also developed and simulated. Further­
more, operating parameters optimization, hydrogen, and carbon atoms 
utilization, life cycle techno-economic-society-environment assessment 
is further identified and carried out. The main conclusions can be 
summarized as follows: 
(a): The optimized operating parameters, first and second flash unit 
temperature, are 187 ◦C and 251 ◦C. The optimized first and second riser 
outlet temperature is 644 ◦C and 682 ◦C, respectively. The calculated 
total output value, 2.77 × 109 US dollar, is obtained by solving the 
optimization model. 
(b): As for the technical performance, the CHCC process’s conversion 
rate is 90.91%, and the COCC process is 89.44%. The total yields of 
ethylene and propylene in the COCC are 33.03 wt%, while the CHCC 
process is 34.49 wt%. Moreover, the hydrogen and carbon atoms effi­
ciency in the CHCC process is 63.17% and 76.21%, which could raise 
0.97% and 1.62% compared with the COCC process. In terms of eco­
nomic, the TAC for the CHCC process is 2.086 × 108 USD/y, 1.01 times 
higher than the COCC process. The NPV and IRR in the CHCC process are 
2.23 × 108 USD and 18.74%, which is 1.14 times and 1.09 times higher 
than the COCC process. 
(c): In terms of social and environmental performance, the proposed 
CHCC process exhibits superior GHG, WWG, and NREC performance 
based on OMUDOV. The GHG, WWG, and NREC of the CHCC process are 
308.71 tCO2 eq⋅OMUDOV−1, 295.15 tH2O⋅OMUDOV−1, and 7.84 × 104 
MJ⋅OMUDOV−1, which could reduce by 2.1%, 8.2%, and 2.2%, 
respectively, compared with the COCC process. 
Thus, the CHCC process using two-stage riser crude oil hierarchical 
catalytic cracking technology shows the best all-around performance 
compared to the conventional COCC process. The optimal social and 
economic benefits could be obtained if the optimization model proposed 
in this study are employed in industrial application. 
Declaration of Competing Interest 
The authors declare that they have no known competing financial 
interests or personal relationships that could have appeared to influence 
the work reported in this paper. 
Fig. 16. The inventory data for WWG is based on OMUDOV through (a) COCC and (b) CHCC processes.  
X. Zhou et al.                                                                                                                                                                                                                                    
Energy Conversion and Management 253 (2022) 115149
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Acknowledgments 
This work is funded by the National Natural Science Foundation of 
China (22108305, 22078364, and 22108307), China Postdoctoral Sci­
ence Foundation funded project (2021M703584), the Fundamental 
Research Funds for the Central Universities (20CX06095A and 
20CX06096A), and the Qingdao Postdoctoral Applied Research Project 
(qdyy20200069 and qydd20200073). 
Appendix A. Supplementary data 
Supplementary data to this article can be found online at https://doi. 
org/10.1016/j.enconman.2021.115149. 
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X. Zhou et al.

Energy efﬁciency improvement in oil reﬁneries through ﬂare gas
recovery technique to meet the emission trading targets
Gabriele Comodi a, *, Massimiliano Renzi b, Mos
e Rossi b
a Dipartimento di Ingegneria Industriale e Scienze Matematiche, Universit
a Politecnica delle Marche, Ancona, Italy
b Libera Universit
a di Bolzano, Facolt
a di Scienze e Tecnologie, Bolzano, Italy
a r t i c l e i n f o
Article history:
Received 21 January 2016
Received in revised form
13 April 2016
Accepted 17 April 2016
Available online 12 May 2016
Keywords:
Reﬁnery
Flare gas recovery system
Liquid ring compressor
Emission trading system
a b s t r a c t
Flare gas recovery is one of the most attractive methods to improve energy efﬁciency in oil reﬁneries to
decrease greenhouse gas emissions. The recovered gas is used to feed reﬁnery processes, granting ad-
vantages in terms of fuel economy and ﬂare stress. This paper presents the results obtained by a feasi-
bility study of a ﬂare gas recovery system in a real reﬁnery; the work focused on: i) the choice and the
design of the ﬂare gas recovery system; ii) the gas treatment and reuse; iii) the economic feasibility, and
the payback period. An experimental campaign has been performed to evaluate both the composition
and the ﬂow rate of the ﬂare gas. Results showed that the ﬂare gas had a strongly variable ﬂow rate and
composition due to the different gas species processed in reﬁnery. A methodology for the system se-
lection is presented: a 400 kg/h liquid ring compression device is chosen; its basic design is described as
well as the chemical treatments of inert gases and hydrogen sulphide (H2S). The yearly energy recovery
was estimated to be 2900 TOE, corresponding to 6600 tons of CDE (Carbon Dioxide Equivalent). Finally,
an economic evaluation was carried out, showing a payback period of about 2.5 years.
© 2016 Elsevier Ltd. All rights reserved.
1. Introduction
The global warming issue is mainly driven by a strong increase
in the carbon dioxide and other greenhouse gases released in the
atmosphere by the human activities.
Oil reﬁneries are energy intensive plants that are responsible of
a signiﬁcant amount of greenhouse gas emissions. In this context,
Gielen et al. [1] analysed different types of approaches to reduce the
emissions of Japanese petrochemical industries. Due to their
contribution to the emission of greenhouse gases, oil reﬁneries are
among the energy intensive sectors included in the EU Emission
Trading System [2,3]. The production process of reﬁneries has a
strong environmental impact due to the signiﬁcant amount of co-
produced gas that is ﬂared as a by-product and large supplies of
gas have emerged. Moreover, ﬂare gas has high concentration of
H2S and other harmful by-products species are the most important
elements to process. A suggested method to control the environ-
mental impact of oil reﬁneries is the prevention of gas ﬂaring. Gas
ﬂaring is a safety practice that consists on burning off the disposed
gases lost through the safety valves of the plant and on the gas
discharged in the blowdown system during unsteady running
conditions, like the shutdowns of the reﬁnery plants. Given the CO2
reduction targets suggested by the Kyoto Protocol, gas ﬂaring in
reﬁneries will be probably forbidden or strongly limited. Therefore,
this operation will be analysed and improved in the future oil re-
ﬁneries processes by studying different methods, including the GTL
(Gas-to-Liquid) production that is deeply investigated by Bjornda-
len et al. [4] and it is considered a suitable alternative to conven-
tional gas ﬂaring. Mourad et al. [5] investigated on burned gas
recovery in order to run the petrochemical industry or, otherwise,
to maintain the rate of oil production. Anomohanran [6] showed
the results of the greenhouse gases emissions produced by gas
ﬂaring activities in Nigeria with the purpose to warn the govern-
ment in order to carry on several activities for their reduction. Xu
et al. [7] investigated on the minimization of ﬂare stack to allow
chemical plant start-up operations in order to decrease the energy
loss caused by the amounts of VOCs (volatile organic compounds)
and highly reactive VOCs released by the ﬂare. Zadakbar et al. [8]
showed a method which consisted in compressing ﬂare gas and
sending it to the fuel gas header for immediate use as fuel gas.
Several authors discussed the advantages of ﬂare gas recovery and
* Corresponding author. Dipartimento di Ingegneria Industriale e Scienze Mate-
matiche, Universit
a Politecnica delle Marche, via brecce bianche 1, Ancona, Italy.
Tel.: þ39 071 220 4761; fax: þ39 071 960 5109.
E-mail address: g.comodi@univpm.it (G. Comodi).
Contents lists available at ScienceDirect
Energy
journal homepage: www.elsevier.com/locate/energy
http://dx.doi.org/10.1016/j.energy.2016.04.080
0360-5442/© 2016 Elsevier Ltd. All rights reserved.
Energy 109 (2016) 1e12
the design of the system in order to decrease the amount of CO2
released in the atmosphere: Abdulrahman et al. [9] analysed the
importance of the ﬂare gas recovery through the CDM (Clean
Development Mechanism) recognised by KP (Kyoto Protocol). Sterk
et al. [10] discussed the subsidising methods granted by the single
countries to encourage the local industries to improve their energy
efﬁciency. Saidi et al. [11], after a resume of the ﬂare gas recovery
advantages, highlighted three different technologies to recover the
ﬂare gas: GTL (gas-to-liquid) technology [12]; gas turbines in order
to produce electrical energy [13]; and ﬁnally the compression
method [14], which consists in compressing the recovered ﬂare gas
and injecting it into the fuel gas header. As aforementioned, the
ﬂare gas is obtained by mixing all the gases released in the oil
reﬁnement processes, so its molecular weight is variable and it
depends on the different running plants situations. After an eval-
uation of the pros and cons of the possible ﬂare gas recovery
technologies, Sonawat et al. [15] explained how to recover the ﬂare
gas using the ejector; in their paper, they gave detailed information
about the design of the recovery system. As regards the liquid ring
compressor, Banwarth [16] supplied a detailed description of the
design steps required for one-stage and two-stage compressor
design. A detailed explanation of the main design points and uti-
lization of the ﬂare gas recovery system and the choice of tech-
nology is reported by Zadakbar et al. [17] for a reﬁnery located in
Iran, and by Fischer et al. [18], for a reﬁnery located in Arkansas
(U.S.A.). In this latter case, liquid ring compressors were used to
recover the gas because this type of machine results to be the best-
suited solution considering the process requirements, efﬁciency,
maintenance and, ﬁnally, the possibility to remove the H2S con-
tained in the ﬂare gas.
In this paper, technical and economic feasibility of a ﬂare gas
recovery system are analysed considering an Italian reﬁnery as a
case study. In particular, the ﬂare gas composition and its ﬂow rate
will be reported over a wide period of reﬁnery plants operations. In
addition, two different technologies for the ﬂare gas recovery are
analysed: the ﬁrst one is the ejector and the second one is the liquid
ring compressor. Using the liquid ring compressor technology, it is
possible to reduce the percentage of H2S inside the ﬂare gas and
thus to operate a ﬁrst treatment of the gas itself; downstream the
compressor a second process, that consists in an amine washing
column, completely removes the H2S. The cleaned ﬂare gas can be
sent to the fuel gas header of the reﬁnery in order to be reused for
feeding the reﬁnery processes. The choice of the recovery tech-
nology and the energy consumption of the liquid ring compressor
are discussed; the work also discusses energetic, environmental
and economic considerations of the introduction of a ﬂare gas
recovery system; ﬁnally, an economic evaluation of the ﬂare gas
recovery technology is presented.
The paper is organized as follows: Section 1 describes the two
commercial technologies to recover ﬂare gas; Section 2 describes
the ﬂare gas system addressed in terms of lay-out, gas analyses
(ﬂow rate, chemical composition, LHV (Low Heating Value)) and
design of the liquid ring compressor; Section 3 reports the results of
the study in terms of energy and economic analyses and emission
reduction; ﬁnally, conclusions are reported.
2. Technologies for the ﬂare gas recovery
One of the most important topic in the design of a ﬂare gas
recovery system is the choice of the most viable technology that
can be applied to the reﬁnery plant.
The two most used technologies in petrochemical industries are
ejector and liquid ring compressor. Goodyear et al. [19] explained in
detail how the ejector works. The ejector is deﬁned as ‘jet pump’ or
‘educator’; for its work, it exploits the Bernoulli's principle: an in-
crease in velocity (kinetic energy) corresponds to a decrease in
pressure and vice versa. In this way, the ﬂare gas is catch and, af-
terwards, compressed at an intermediate pressure between LP
(Low process Pressure) and HP (High process Pressure) with the
help of HP driving ﬂuid, which can be liquid or gas that supplies the
energy required for the ﬁnal compression. The ejector is very cost
effective and it is used in many reﬁnery processes. A single stage
ejector consists of driving nozzle, suction nozzle, mixing chamber,
throat and diffuser. The driving ﬂuid (HP ﬂuid) goes through the
nozzle where its kinetic energy increases in spite of a decrease of
pressure energy: this effect carries on a signiﬁcant pressure drop of
the ﬂuid and the generation of a low-pressure region downstream
the nozzle that allows the LP ﬂuid to be recalled into the ejector.
The driving and the suction ﬂuid are mixed in the mixing
chamber. The driving ﬂuid decelerates while the suction ﬂuid ac-
celerates; indeed, in the throat, the pressure of the mixed gas in-
creases. The mixture goes through the diffuser where its kinetic
energy decreases due to a further rise in pressure; thus the pressure
of the suction ﬂuid increases to the exit pressure, which reaches a
pressure level between the HP and LP, as, reported in Abdelli [20].
Sarshar et al. [21] explained that, if the exit pressure of the mixed
gas is lower than that required for the following reﬁnery processes,
a higher number of ejectors must be installed in series. The feasi-
bility of this solution requires the characteristics of the elaborated
ﬂuid to be almost the same.
In the same way, Banwarth [16] described the second technol-
ogy, the liquid ring compressor, which is commonly used in
Nomenclature
LHV
Low Heating Value [kJ/kg]
b
Compression ratio
c
Speciﬁc heat [kJ/(molK)]
T
Temperature [K]
_
m
Flare gas mass ﬂow rate [kg/h]
_
V
Flare gas volumetric ﬂow rate [m3/h]
r
Flare gas density [kg/m3]
p
Pressure [bar]
Wsp
Speciﬁc work [kJ/kg]
h
Efﬁciency
P
Power [kW]
Subscripts
Stage
Stage
Ovrll
Overall
Inlet
Inlet port
Outlet
Outlet port
Pol
Polytropic transformation
Is
Isentropic transformation
Real
Real transformation
Isobaric Isobaric transformation
Vol
Volumetric
Tot
Total
G. Comodi et al. / Energy 109 (2016) 1e12
2
reﬁneries; it is a rotary volumetric machine that uses a secondary
ﬂuid (water or amine) to compress the ﬂare gas. Its working prin-
ciple resamples the operation of a vane compressor. One of the
most important advantages of using this device with respect to a
simple ejector is the chance of removing dangerous species like H2S
that are present in reﬁneries ﬂare gas.
It consists on an axial impeller enclosed in an external case: on
the impeller, there is a determined number of blades that convey
the gas from the inlet port to the discharge port; gas is enclosed, on
one side, by the blades of the impeller and, on the other side, by the
liquid ﬂuid. Liquid ring compressors can reach high compression
ratios (generally up to 1:31 between the suction and the pressure
sides). The circular motion of the impeller, which is offset with
respect to the housing, compresses the gas.
Fig. 1 reports a section of a liquid ring compressor while Fig. 2
describes the working principle of the liquid ring compressor.
An impeller is installed in the centre of the circular casing,
which is partly ﬁlled with liquid (Fig. 2a). The choice of the typology
of liquid is very important because, in a reﬁnery plant, it can also
accomplish a gas cleaning process; in particular, it allows the gas to
reduce its amount of H2S and simplify the following cleaning
process.
The blades of the impeller can be radially or forward facing; in
this last case, the liquid ring speed and the compression are higher.
The rotation of the impeller pushes the liquid to the external part of
the cylindrical case due to centrifugal forces (Fig. 2b); if the impeller
is eccentrically installed, a rotating liquid ring takes the shape of the
casing meanwhile the impeller rotates. Therefore, a crescent-shaped
space develops between the impeller hub and the liquid ring during
the rotation of the impeller (Fig. 2c). The blades of the impeller
divide this space in several sub-volumes: one of them is facing in the
inlet port and recalls new gas in the compressor. During the rotation,
the gas is compressed due to the reduction of the available volume
until it reaches the discharge port. The secondary liquid compresses
the gas and exchanges heat during its compression.
Table 1 lists the main advantages and disadvantages of the two
abovementioned technologies:
3. Description of the ﬂare gas recovery system
This section shows the methodology used for the choice and the
design of the ﬂare gas recovery system. The methodology has a
general validity but, in this work, it has been speciﬁcally applied to
a test case of an Italian oil reﬁnery having the following running
characteristics:
➢Site surface: about 700,000 m2;
➢Crude oil processing capacity: 3,900,000 tons per year (about
85,000 barrels per day);
➢Storage: over 1,500,000 m3;
➢Crude oil incoming: 100% via sea;
➢Main reﬁnery processes for the production of propane, gasoline,
kerosene and diesel are present in the reﬁnery of this case
study;
➢In the context of Emission Trading System, the Italian govern-
ment awarded the reﬁnery plant an allowance of 495,000 tons
of Carbon Dioxide Equivalent (CDE) for 2015. In order to meet
national targets for emission reduction, the amount of allow-
ances will progressively reduce in the next years down to
446,000 tons of CDE in 2020 [22].
In order to better understand the ﬂare gas recovery proce-
dure, a layout of the overall ﬂare gas recovery system is shown in
Fig. 3.
The ﬂare stack is located at a certain distance from the reﬁnery
and gases are burnt downstream the three-phase separator and the
hydraulic seal; these three devices are arranged in series. They are
linked in the following way: the three-phase separator, the hy-
draulic seal and, ﬁnally, the ﬂare stack and all together constitute
the blowdown system. The ﬂare stack has two main aims: the ﬁrst
one is a routine safety procedure and it is referred to the discharge
of the gas to the blowdown system and consequently to the ﬂare
stack while the reﬁnery is running in unsteady state; this gas can
also come from losses through the machines that link the reﬁnery
plants to the blowdown system. The second one is a non-routine
safety procedure that activates in case of increase of pressure
level inside the reﬁnery plants and it takes place during the shut-
downs, maintenance and the pipeline purge; Fahim et al. [23]
investigated in detail the fundamental approaches of safety in pe-
troleum reﬁneries.
The technology used to recover the ﬂare gas is the liquid ring
compressor: this choice is due to the possibility of the ﬂare gas
treatment, decreasing the amount of H2S, before sending it to the
amine-washing column in order to eliminate it.
With regard to the liquid ring compressor, the design ﬂow
rate takes into account the average mass ﬂow rate of the ﬂare
gas that ﬂows into the blowdown pipe, its pressure levels at the
suction and discharge ports, and the inlet and outlet tempera-
tures of the ﬂare gas. In this paper, the compression process
was modelled as a nearly isothermal compression: this trans-
formation is obtained due to the presence of the liquid ring that
cools the gas during the compression phase and allows the gas to
get a discharge temperature which is only slightly higher than
the inlet one. The nearly isothermal compressions are modelled
with a polytropic transformation with a characteristic index close
to one.
Downstream the liquid ring compressor, ﬂare gas needs to be
puriﬁed and treated before being used as fuel gas. In order to have
this chemical treatment, the ﬂare gas has two washing stages and a
dilution stage: the ﬁrst stage occurs inside the compressor only if
the liquid ring is the amine (MDEA, Methyl diethanolamine),
whereas the second stage occurs inside a drum in which the gas
ﬂows in counter current with a washing ﬂow of amine. Between
these two stages, the ﬂare gas is diluted with other reﬁnery gases to
reduce the molar percentage of the inert gases. The inert gases,
contained inside the ﬂare gas, do not directly participate to the
combustion reaction, but they reduce the low heating value of the
recovered gas; therefore, it is important to limit their share in the
ﬂare gas. In order to control the amount of inert gases contained in
the gas, a speciﬁc component, the KO (Knock Out) drum, is intro-
duced before the washing column. The KO drum is a vessel used to
slow down gases and allows liquids to be separated from the gas
stream. KO drum can be conﬁgured in either horizontal or vertical
arrangement; when horizontal, it is designed with one gas stream
Fig. 1. Section of the liquid ring compressor [16].
G. Comodi et al. / Energy 109 (2016) 1e12
3
inlet, and two outlets, which can then be joined with a manifold.
Another conﬁguration that can be used is with one inlet and a much
larger outlet. In a vertical arrangement, the KO drum can have a side
inlet with a larger exit, which will slow down the gases; the vertical
arrangement can be adopted to obtain a tangential gas inlet. With a
tangential KO drum, the gases enter and spin around the wall of the
vessel; while spinning, the ﬂuid friction of the gas along the wall
allows the entrained liquid to slow down. Bafﬂes are also used in a
vertical drum to disrupt and slow down the gas to exit. Generally,
the KO drum is realized by Carbon Steel or, for corrosive services,
stainless steel. In this case study, the KO drum is located between
the three-phase separator of the recovery system and the amine-
washing column (Fig. 3). The ﬂare gas was mixed in the KO drum
with other gases coming from other reﬁnery processes in order to
achieve the optimal molar percentage of the inert gases contained
in the mixed gas. Then the mixed gas is sent to the amine washing
column before going to the fuel gas header: in this column the
hydrogen sulphide is caught by the MDEA using a counter current
washing procedure. The property of the MDEA to catch the H2S is
important for almost two reasons: i) less emissions of SO2 in the
atmosphere and, ii) recovery of the sulphur as a marketable by-
product.
Finally, the ﬂare gas is ready to go to the fuel gas header. Fig. 4
shows the PFD (Process Flow Diagram) of the ﬂare gas recovery
system that can be applied in this reﬁnery case study.
A detailed description of the ﬂare gas ﬂow rate and composition
will be reported in the following sections.
3.1. Gas ﬂow rate analysis
In order to correctly size the ﬂare gas recovery system, it is
useful to investigate the gas ﬂow rate and its composition. These
data were obtained during a test campaign in the reﬁnery for a
period of 13 months. Table 2 reports the main characteristics of the
ﬂow meter used for the measurement of the ﬂare gas mass ﬂow
rate [24].
Fig. 5 shows the trend of the daily average mass ﬂow rate during
the period “August 2013eAugust 2014”. This was calculated from
the acquired instantaneous mass ﬂow rate that usually is almost
constant throughout the day except for some very high peaks.
These outliers referred to plant unsteady running, start up and
shutdown that caused pressure peaks inside the plants. These
values of gas ﬂow rates, corresponding to instantaneous values
higher than 6000 kg/h, were not considered to size the ﬂare gas
recovery technology, as they did not refer to typical running con-
ditions and because they are over the operating range of the
measurement instrument. During these unsteady running condi-
tions, the ﬂare gas was by-passed directly to the ﬂare for safety
reasons. Given these assumptions, an average value of 443 kg/h was
obtained during the mass ﬂow rate sampling campaign.
In order to better assess the ﬂare gas variability over a year of
operation, the trend of the percentages of gas ﬂow occurrences is
reported in Fig. 6: the gas ﬂow rate value which had the highest
percentages of occurrences was 400 kg/h; this value, with daily
average mass ﬂow rate, will be taken into account to size the liquid
ring compressor.
Fig. 2. Working principle of the liquid ring compressor [16].
Table 1
Pros and cons of ejector and liquid ring compressor technologies.
Ejector:
Liquid ring compressor:
Advantages:
➢Zero emissions to the environment;
➢No maintenance;
➢No rotating parts;
➢Easy to install and to control;
➢Low noise level.
Advantages:
➢The liquid ring compresses the gas and adsorbs heat from it, so the ﬁnal temperature of the gas will not be so high;
➢The suction ﬂuid must not have a determined level of pressure but it can be arbitrary;
➢Efﬁcient operation at partial loads.
Disadvantages:
➢No gas washing treatment;
➢The
secondary
ﬂuid
must
have
a
determined pressure level.
Disadvantages:
➢If the liquid ring is the amine, it is necessary to provide an interconnecting system to bring the amine to an amine washing
column and then to bring back the clean amine to the liquid ring compressor;
➢Foundation where there are installed the liquid ring compressor and the electric engine;
➢Rotating parts;
➢Noise.
G. Comodi et al. / Energy 109 (2016) 1e12
4
The discharge pressure of the liquid ring compressor should be
at least 7 bar abs, which is the level required to overcome the
pressure drops in the pipeline and into the devices used to treat the
gas before entering into the fuel gas header. Due to the high
compression ratio and to the rigid geometric shape arrangement of
the suction and pressure ports, Banwarth [16] suggested to use a
two-stage liquid ring compressor to prevent high temperature at
the discharge port, which can decrease the overall efﬁciency.
Fig. 3. Layout of the ﬂare gas recovery system.
G. Comodi et al. / Energy 109 (2016) 1e12
5
3.2. Flare gas chemical analysis
Besides the evaluation of the ﬂare gas ﬂow rate, also a sampling
campaign of the chemical elements contained in the gas was per-
formed. In particular, the presence of both inert gases (N2 and CO2)
and hydrogen sulphide (H2S) were monitored. Indeed, the former
do not take part in the combustion reduction thus lowering the
LHV, while the latter is a dangerous component for the reliability of
the plant, mostly for machineries and pipeline; for this reason, it is
necessary to eliminate or to reduce its quantity to a few molar
percentages.
The measurements of these chemicals components were carried
out by extracting a test sample on the blowdown system, precisely
between the three-phase separator and the hydraulic seal. Finally,
the chemical compositions were obtained by the laboratory anal-
ysis
performed
inside
the
reﬁnery.
Table
3
lists
the
main
characteristics of the gas chromatograph, which is a Reﬁnery gas
analyser manufactured by PAC with a maximum sample pressure
rating of 25.8 bar for gases, used for the evaluation of the ﬂare gas
mass ﬂow rate [25].
Ten samples were analysed with the aim of evaluating the
content of the single species. The chemical composition of the ﬂare
gas varied strongly depending on the plant running conditions. For
this reason, several measurements were carried out during the
measurement campaign and an average composition was taken
into account for the design of the liquid ring compressor and the
other devices in the plant section. Fig. 7 reports the trend of the
chemical elements contained in the ﬂare gas measured by the gas
chromatograph during the sample campaign and Table 4 shows the
average values of these elements and their physical and chemical
proprieties [26].
Fig. 4. PFD (Process Flow Diagram) of the ﬂare gas recovery system.
Table 2
Main operation and performance data of the measurement system.
Measured quantity
Measurement equipment
Range
Accuracy
Flare gas mass ﬂow rate
GE DigitalFlow™GF 868
0.03e100 m/s
Molecular weight accuracy: 2e120 g/gmol
Mass ﬂow accuracy: 2.4e7%
G. Comodi et al. / Energy 109 (2016) 1e12
6
Thanks to this analysis, it was possible to determine the value of
the speciﬁc heat at constant pressure, cp, and at constant volume,
cv, as well as the value of k (characteristic index of an adiabatic
transformation), that was equal to 1.13.
3.3. Flare gas LHV analysis
The ﬂare gas recovery allows the reﬁnery to achieve economic
saving in the plant's operations. In order to quantify the saving, the
analysis on the average LHV of the ﬂare gas was carried on. During
the chemical analysis, asides from inert gases and hydrogen sul-
phide, also the value of the LHV ﬂare gas was evaluated. The ﬁnal
result of the average ﬂare gas LHV was equal to 39,951 kJ/kg Fig. 8
shows the variations of LHV values in relation to the ﬂare gas mass
ﬂow rate measured during the sampling campaign.
Fig. 5. Daily average mass ﬂow rate in the period “August 2013eAugust 2014”.
Fig. 6. Percentages of occurrences in the period “August 2013eAugust 2014”.
Table 3
Main characteristics of the gas chromatograph.
Species
Sample range
Linear dynamic range
C1eC6 Hydrocarbons
Atmospheric overhead
Hydrocarbons:
<0.01e100%mol
C6þ
Ethylene
Inert Gases:
<0.02e100%mol
He
FCC overhead
H2S:
<0.1e100%mol
H2
Fuel Gas
N2
Recycle Gas
O2
Desulfurizer Gas
H2S
LPG
CO
Propane
CO2
Butane
Benzene
Butadiene
Toluene
Propylene
G. Comodi et al. / Energy 109 (2016) 1e12
7
Fig. 7. Variations of the inert gases (a) and the hydrogen sulphide (b) expressed in molar percentages.
G. Comodi et al. / Energy 109 (2016) 1e12
8
3.4. Design of the liquid ring compressor
The experimental data, reported in the previous sections, were
used for the concept design of the liquid ring compressor, which
recovers the ﬂare gas. The reference operating conditions used for
the sizing are reported in Table 5.
The ﬂare gas in the liquid ring compressor presented traces of
dragging water and its inlet temperature was very variable because
it depends on the temperature of the various gases that ﬂows into
Table 4
Main composition of ﬂare gas.
Chemical element
Molar fraction
cv [J/molK]
cp [J/molK]
N of moles per 1 kg of ﬂare gas
Butane
0.28
20.79
29.14
0.06
Carbon dioxide
1.68
28.81
37.75
0.35
Carbon monoxide
0.30
20.42
28.73
0.06
Ethane
5.78
44.77
53.35
1.23
Ethylene
0.31
34.56
42.89
0.07
Hydrogen
33.94
20.53
28.85
7.33
Hydrogen sulphide
5.48
75.37
83.68
1.17
Iso Butane
1.24
88.00
96.54
0.26
Iso Butylene
0.33
71.84
80.15
0.07
Iso Pentane
2.28
104.03
112.34
0.48
Methane
24.14
27.47
35.84
5.14
Nitrogen
10.87
20.79
29.14
2.31
Normal Butane
3.01
72.25
85.56
0.64
Normal Pentane
1.67
104.03
112.34
0.36
Propane
5.52
64.81
74.01
1.18
Flare gas
96.84a
63.72
72.11
20.61
a The molar faction of Flare Gas is not equal to 100% because the elements that have a lower molar fraction inside the gas are not reported for simplicity. The cv and cp values
are normalized considering a 100% molar fraction of ﬂare gas.
Fig. 8. Variations LHV values.
G. Comodi et al. / Energy 109 (2016) 1e12
9
the blowdown system. In order to keep the temperature of the ﬂare
gas in the suction port of the liquid ring compressor stable, a
controlled heat exchanger using the makeup water pipeline can be
used. The makeup pipeline is a hydraulic circuit that feeds, with
demineralized water, all the reﬁnery processes that require water
for their operations in order to reintegrate the amount that is lost
by evaporation or leakage. In this case study, the temperature of
water in the makeup circuit is equal to 288 K, which is enough to
cool down the ﬂare gas to a temperature of 298 K, before entering
the liquid ring compressor. The ﬂare gas compression is made using
liquid water that operated as a liquid ring in the compressor. As
anticipated, the compression phase was modelled using a poly-
tropic compression with a characteristic index of the polytropic
transformation n close to 1; thus the ﬂare gas compression was
approximated by a nearly-isothermal transformation because the
heat produced by the gas compression was absorbed by the liquid.
As suggested by Banwarth [16], a ﬁnal temperature of 10 K higher
than the initial one was considered. The liquid water is discharged
with the ﬂare gas in the discharge port and it is then separated from
the gas; water is then cooled down and recirculated in the inlet port
of the compressor. Knowing the value of k of the ﬂare gas from its
chemical analysis as well as its pressure and temperature at the
suction and discharge ports, a polytropic index n of 1.04 can be
calculated. Given this value of n, the main thermodynamic and
physical characteristics of the ﬂare gas at the inlet and at the outlet
of the two compression stages can be assessed, as reported in
Table 6.
The following step of the compressor design was the evaluation
of the compression work. In order to obtain the ﬁnal desired
pressure, it was necessary to split the compression in two stages.
The ﬁrst compression in the vanes is described by a polytropic
transformation, which takes place in the ﬁrst stage raising the
pressure of the gas from 1.03 bar to 2.73 bar. A second compression
effect is determined by the volume reduction that occurs between
the ﬁrst and the second stage thanks to an intercooling of the ﬂare
gas by means of the reﬁnery make-up water; owing to the very low
obtained volume difference, this compression work is negligible.
The third and the last compression work is the polytropic
compression in the second stage that raises gas pressure from
2.73 bar to 7 bar. In Table 7 the polytropic, isentropic and volu-
metric efﬁciency are reported, as well as the power required to feed
the liquid ring compressor.
After the evaluation of the speciﬁc work, typical values of
volumetric efﬁciency and organic efﬁciency where used as sug-
gested by Banwarth [16]. Finally, these data allowed to calculate the
mechanical power consumption of the liquid ring compressor
which was evaluated in about 82 kW.
4. Results
4.1. Energy analysis
As the average mass ﬂow rate of ﬂare gas (400 kg/h), its average
LHV value (39,951 kJ/kg) and the running time of the reﬁnery plant
(8000 h/year) are known, it was possible to evaluate the energy
obtained by the combustion of the recovered ﬂare gas. The yearly
primary energy saving obtained by the recovery of the ﬂare gas
corresponds to about 127.8 TJ corresponding to 2550 tons of natural
gas, which can also be expressed as 2900 TOE, or 6600 tons of CDE
(Carbon Dioxide Equivalent).
In addition, the emission trading approach is treated in this
paper; at present, a maximum of 495,000 tons of CDE are awarded
to the reﬁnery. In 2020, the legislator will reduce the allowance to
446,000 tons of CDE, therefore initiatives to improve the reﬁnery's
energetic efﬁciency are required. In this regard, the ﬂare gas re-
covery system covers 13.5% of the 2020 emission reduction target
imposed by ETS (Emission Trading Systems).
In order to assess a correct energetic and economic analysis, also
the electricity required to feed the liquid ring compressor was
calculated: knowing this value, it can be evaluated the recovery
system running cost. The compressor can be couplet with an
electric motor having a pick-up power of 110 kW; assuming a yearly
operation of 8000 h and an average electric motor running power
of 90 kW, the ﬁnal electrical energy consumed by the liquid ring
compressor is equal to 720 MWh.
4.2. Economic analysis
In this section, the main capital costs of the installation of the
recovery system are reported and a feasibility study is assessed. The
main considered costs are: the civil works required for the instal-
lation of the compressor and the interconnecting pipes; the phys-
ical
and
chemical
characteristics
of
the
ﬂare
gas
for
the
measurement systems; the engineering works for the system
design. These costs were provided directly from the purchasing
department of the reﬁnery taking into account the internal costs
applied by the afﬁliated companies that work in situ. The main
additional components required in the system are shown in
Table 5
Speciﬁc references of the running condition to design the
liquid ring compressor.
Design parameter
Value
_
m [kg/h]
400
pinlet [bar]
1.03
poutlet [bar]
7
Tinlet [K]
298
Toutlet [K]
308
bstage
2.65
bovrll
7
Table 6
Physical characteristics of the ﬂare gas (inlet and outlet) of the two compression
stages.
1st compression stage
2nd compression stage
Tinlet [K]
298.00
Tinlet [K]
298.00
Toutlet [K]
308.00
Toutlet [K]
308.00
_
m [kg/h]
400.00
_
m [kg/h]
400.00
_
V inlet [m3/h]
452.44
_
V inlet [m3/h]
176.11
_
V outlet [m3/h]
176.74
_
V outlet [m3/h]
70.91
rinlet [kg/m3]
0.88
rinlet [kg/m3]
2.27
routlet [kg/m3]
2.26
routlet [kg/m3]
5.64
pinlet [bar]
1.03
pinlet [bar]
2.73
poutlet [bar]
2.73
poutlet [bar]
7
Table 7
Running data of the liquid ring compressor.
Running data
Value
Wsp_pol [kJ/kg]
57.72
Wsp_is [kJ/kg]
62.21
Wsp_real in each stage [kJ/kg]
104.72
Wsp isobaric [kJ/kg]
0.05
Wsp_tot [kJ/kg]
412.69
hvol
0.8 [32]
horg
0.6
hpol
0.5512
his
0.5941
_
m [kg/h]
400.00
Povrll [kW]
81.63
G. Comodi et al. / Energy 109 (2016) 1e12
10
Table 8: the higher cost is due to the installation of the recovery
system equipment.
In order to evaluate the feasibility of the investment, Table 9
reports the running costs of the ﬂare gas recovery system.
Considering an Italian electricity cost for large industrial consumers
equal to 0.11 V/kWh [27], the electricity cost to run the liquid ring
compressor is 79,200 V/year. Table 9 also reports the other running
costs of the system.
The positive cash ﬂow derives from: i) the amount of primary
energy saved; ii) the amount of CDE allowances sold (or not
bought). As regards the energy savings, the combustion of a total
amount of 3,566,948.5 Nm3 of methane equivalent are avoided;
considering an average price of natural gas of 0.35 V/Nm3 in Italy
[27], the economic savings correspond to 1,248,432 V/year. For
what concerns the CDE allowances, an average fee of 5.90 V per ton
of carbon dioxide in the period 2012e2014 [28] was taken into
account; the additional revenue is 38,940 V/year. Therefore, a
yearly total cash ﬂow of 1,287,372 V is obtained.
Using the presented assumptions, the cash ﬂow of the ﬂaring
recovery system investment and its PBP (Payback Period), taking
into account different discount rates (10%e15% e 20%), are shown
in Fig. 9. The PBP (Payback period) ranges between 2 years and 1
month and 2 years and 5 months.
5. Conclusions
In this work, the main advantages of the ﬂare gas recovery
system in an oil reﬁnery are presented. The technology is then
applied to a test case of an Italian reﬁnery subject to European ETS
(Emission Trading Systems).
The two most important technologies for the ﬂare gas recovery
are reported: the ejector and the liquid ring compressor. Liquid ring
compressor has the peculiar advantage of running a ﬁrst chemical
cleaning treatment of the gas if the liquid ring is the amine; for this
reason, this technology was selected for the evaluations of this
paper.
The methodology for the design of the system was presented. An
experimental campaign in the reﬁnery deﬁned the average mass
ﬂow rate of the ﬂare gas produced in the reﬁnery; also a chemical
analysis was carried out to deﬁne the species contained in the ﬂare
gas in order to evaluate its LHV (Low Heating Value) potential. The
Table 8
Equipment costs.
Description
Capital cost [kV]
Flare gas recovery system
1000
Piping
350
Interconnections
50
Civil engineering
50
Electric and instrumental adjustments
150
Lock Valves
60
Installation
100
Ruck adjustments
50
N2 analyser installed on the output of the amine washing column
45
New instrument to measure the mass ﬂow rate in the fuel gas
25
Engineering works
90
Total (without Contingency)
1970
Contingency
200
Total (with Contingency)
2170
Table 9
Running costs.
Description
Cost [kV]
Cost of electricity per year
79.2
Utilities
15
Maintenance
5
Total
99.2
Fig. 9. Trend of the sum of cash ﬂow of the overall ﬂaring recovery system and its PBP taking into account different discount rates.
G. Comodi et al. / Energy 109 (2016) 1e12
11
main element analysed were the inert gases (N2 and CO2) and the
hydrogen sulphide (H2S).
According to the experimental measurements and the running
conditions of a real running Italian reﬁnery, the concept design and
the performance of a liquid ring compressor to treat the ﬂare gas
are reported. The average ﬂow rate of the recovered ﬂare gas is
about 400 kg/h with a discharge pressure of 7 bar. A two-stage 82-
kW liquid ring compressor is adopted to the purpose: its main
characteristics and energy consumption are evaluated. The ﬂare
gas, thus, is diluted with other reﬁnery gases to reduce the molar
percentage of the inert gases. In order to reduce the H2S, that is
dangerous for the pipeline corrosion, a two-stage puriﬁcation so-
lution is applied. Downstream the second puriﬁcation stage, the
ﬂare gas is ready to go to the fuel gas header of the reﬁnery. The
amount of ﬂare gas that can be recovered yearly corresponds to
2900 TOE, or 6600 tons of CDE, equal to 127,8 TJ which represent
the 13.5% of the 2020 emission reduction target imposed by ETS.
Also an economic analysis is reported, taking into account the
capital and running cost of the system, which allows the reﬁnery to
evaluate the PBP period of the system: with an interest rate of 15%
the PBP is 2 years and 3 months and the ﬁnal cash ﬂow is 3.8 MV.
Therefore, by applying the presented ﬂare gas recovery solution,
both environmental, economic and energetic advantages could be
achieved.
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G. Comodi et al. / Energy 109 (2016) 1e12
12

Green chemistry contribution towards more equitable global
sustainability and greater circular economy: A systematic literature
review
Cecilia Silvestri a, *, Luca Silvestri b, Antonio Forcina c, Gianpaolo Di Bona d,
Domenico Falcone d
a University of “Tuscia” of Viterbo, Department of Economics and Management (DEIM), Via del Paradiso, 47, 01100 Viterbo, Italy
b University of Rome “Niccol
o Cusano”, Department of Engineering, Via Don Carlo Gnocchi, 3, Rome, 00166, Italy
c University of Naples “Parthenope”, Department of Engineering, Isola C4, Centro Direzionale Napoli, Napoli, Italy
d University of Cassino and Southern Lazio, Department of Civil and Mechanical Engineering, Via G. Di Biasio 43, Cassino, 03043, Italy
a r t i c l e
i n f o
Article history:
Received 30 May 2020
Received in revised form
19 January 2021
Accepted 26 January 2021
Available online 6 February 2021
Handling Editor: Cecilia Maria Villas B^
oas de
Almeida
a b s t r a c t
Green chemistry has been a major driver of sustainable development and had an important diffusion in
recent years. In order to investigate the state of the art in this ﬁeld, a systematic literature review has
been performed, also identifying possible developments for future research. In particular, the aim of this
research is to investigate how Green Chemistry (GC), Sustainability and Circular Economy (CE) concepts
are related to each other and how researchers are addressing and analyzing this relation.
Since the nature of chemistry is to produce intermediate goods that are generally used by other in-
dustries, the focus has been mainly placed on industrial sector. In other words, chemistry involves most
of production systems.
According to systematic literature review methodology, different research questions were formulated,
in order to schematize and to get a comprehensive view about the evolution of green chemistry research.
The selected articles were analyzed through different criteria, including the Triple Bottom Line (TBL)
framework, and were divided into different clusters, according to purposes, impacts and scope of each
research.
The analysis of papers shows that chemical industry is able to contribute to a fair transition towards a
greater economic, environmental and social sustainability. Even if the main focus of GC is the environ-
ment, GC is getting closer to TBL pillars, representing the main tool for chemical industry to implement
Sustainable Chemistry (SC) system and to realize the transition towards sustainability and CE. Finally,
main results were summarized in a framework that shows the connections among systems and tools,
highlighting main synergies.
Results highlight how GC is the tool through which it is possible realize the SC system. In particular, the
SC, in a CE system, can be involved in processes of production and recycling, ensuring more sustainable
environmental, economic and social systems. Furthermore, results show how GC and CE are getting
closer to each other highlighting the ongoing alignment of purposes among different tools and adopted
approaches in a holistic vision.
© 2021 Elsevier Ltd. All rights reserved.
1. Introduction
During last decades, the rapid global population growth and the
increased standard of living contributed in drawing attention to
hazardous materials released into the environment and degrada-
tion of natural resources.
The chemical sector is facing the grand challenge to reach
greener manufacturing processes, through an efﬁcient consump-
tion of raw materials and eliminating or reducing waste. Research
on Green Chemistry (GC) is continuously evolving, especially dur-
ing last years, when several authors proposed GC metrics (Bours
et al., 2017; McElroy et al., 2015) or new business models (Lozano
* Corresponding author.
E-mail address: c.silvestri@unitus.it (C. Silvestri).
Contents lists available at ScienceDirect
Journal of Cleaner Production
journal homepage: www.elsevier.com/locate/jclepro
https://doi.org/10.1016/j.jclepro.2021.126137
0959-6526/© 2021 Elsevier Ltd. All rights reserved.
Journal of Cleaner Production 294 (2021) 126137
et al., 2014a) for the implementation of GC principles (Anastas and
Warner, 1998). According to Manley et al. (2008), GC “challenges
innovators to design and utilize matter and energy in a way that
increases performance and value while protecting human health
and the environment” (p. 473). In fact, the ﬁnal purpose of the GC is
to reduce negative effects on human health and environment
(Schwager et al., 2016), as well as to establish a strong connection
between GC itself and sustainability. According to Manley et al.
(2008), GC is an innovative and non-regulatory approach, driven
by sustainability and then oriented to a global sustainability scope.
To reach sustainability, a complex balance among resources
utilization,
economic
growth
and
environmental
impact
is
demanded. In fact, as stated during the World Summit on Social
Development (2005), sustainability development goals have to be
characterized by three main dimensions, that are: (1) environ-
mental; (2) economic; and (3) social. These three aspects have to be
considered as totally interdependent and they can reinforce each
other (Morelli, 2011). The concept of sustainability development is
also at the basis of the Triple Bottom Line (TBL) framework
(Elkington,1994), that takes into account social, environmental and
economic performance, in order to achieve a sustainable develop-
ment and a new way of thinking for ﬁrm’s business strategies.
GC does not involve the three TBL dimensions a priori. However,
according to Manley et al. (2008), it represents a fundamental
aspect for the sustainability.
Certainly, the three TBL dimensions are related to the concept of
“Sustainable Chemistry” (SC). The German Environment Agency
states that “it is the objective of this concept [SC] to combine, on the
basis of recent scientiﬁc knowledge, the preventive protection of
environment and health with an innovative economic strategy. This
strategy has been designed to create high added-value jobs” (p. 6).
During last decades, GC and SC have been often used inappro-
priately as synonyms (Tundo et al., 2000). In 2017, German Envi-
ronment Agency afﬁrmed that International Sustainable Chemistry
Collaborative Centre should have increased efforts to remove any
ambiguity for GC and SC concepts, because of GC offers useful rules
for the “chemical synthesis”, while SC integrates GC principles by
default, as well as it considers GC principles for the assessing of
products, processes and applications in a holistic perspective. In
fact, GC, deriving from a perspective of design and production,
mainly focuses on technical aspects and engineering issues (i.e.
synthesis processes, atomic economics and utilization of solvent).
On the other hand, SC includes all life cycle stages and surrounding
areas, as well as the direct and indirect relations with environment,
economic and social perspectives (Blum et al., 2017).
In a such context, GC can be considered as a prerequisite and a
fundamental part of SC (Blum et al., 2017), while SC as a system for
chemical development, where chemistry is applied in a safer and
eco-friendly way, being able to face issues related to economic and
social impacts for all life cycle stages (Chen et al., 2020; Marion
et al., 2017).
According to Nosonovsky and Bhushan (2012), GC and SC con-
cepts,
together
with
“Green
Engineering”
(GE)
concept
(Nosonovsky and Bhushan, 2012, p.3), deﬁne the so-called “green
tribology” concept that is based on twelve principles.
The US Environmental Protection Agency (EPA) deﬁnes GE as
‘‘the design, commercialization and use of processes and products
that are technically and economically feasible while minimizing (1)
generation of pollution at the source (2) risk to human health and
the environment’’ (Slater et al., 2005).
Brennecke (2004), analyzing GE and GC deﬁnitions, states that
GE deals with design, marketing and the use of all the types pro-
cesses and products, while GC includes only a subset of these as-
pects, that are: process designs and chemical products. The author
declared: “So Green Chemistry is a subset of Green Engineering!”
(Brennecke, 2004, p.362).
Although these concepts can be used in an interchangeable way,
Marteel-Parrish and Abraham (2014) considers “each of these
concepts embodies slightly different ideas and encompasses
different scopes of activity” (p.22). However, the interdisciplinarity
is undeniable. Both GC and GE “help to balance the need to improve
quality of life while maintaining the health of humans and the
environment” (Matus et al., 2012, p. 193). In this regard, sustain-
ability goes beyond GE e GC concepts (Marteel-Parrish and
Abraham, 2014).
Sustainability and sustainable development are ethical theories
that deﬁne desirable results in a context that takes into account
environmental, economic and social aspects (Voigt et al., 2013).
1.1. Green chemistry and circular economy
According to Voigt et al. (2013), GC can be considered as the
chemical philosophy that encourages “the design of products and
processes that reduce or eliminate the use and generation of haz-
ardous substances” (p.150).
The limitation of hazardous chemical substances in the cycle of
materials is not only a way for protecting the human health and the
environment, but also an opportunity for the future reuse of ma-
terials and, then, for the enhancement of the circular economy (CE)
(European Environmental Bureau, 2017).
CE is deﬁned as “restorative and regenerative by design, and
aims to keep products, components and materials at their highest
utility and value at all times” (Stahel, 2016, p. 436). The European
Environment Agency noted that a “particular concern in the
context of a circular economy is our increasing reliance on chem-
icals. When closing material loops, accumulation of hazardous
substances should, in principle, be prevented. A key challenge in
this respect is striking the right balance between the quantities of
materials to be recycled and their (nontoxic) quality” (European
Environmental Bureau, 2017, p.6). The management of hazardous
chemical substances is considered a priority for ensuring a high
level of the health and environment protection, promoting a cir-
cular approach to the economy (EUROPEAN, 2017). CE aims for
ambitious objectives and one of the main issues is exactly repre-
sented by the presence of hazardous chemical substances that can
enter, or re-enter, in the environment and technosphere. Such
substances can reappear in end products that have been made from
waste, entailing risks for humans and environment (Bodar et al.,
2018). This aspect represents the main motivation for their exclu-
sion from material ﬂows and a key element for obtaining a CE that
operates
to
its
full
potential
(Swedish
Society
for
Nature
Conservation, 2018).
Therefore, it seems evident how CE is strongly related to the
concept of GC. According to Smieja and Babcock (2017), GC is an
integral part of each strategy based on circular business models and
both concepts share several environmental purposes.
Chen et al. (2020) highlights the necessity to deﬁne new stra-
tegies for the GC implementation following a CE perspective. Ac-
cording to Clark et al. (2016), a full synergy between purposes of
chemical industry and CE will be possible when chemistry,
considering both academic and industrial contexts, would have
changed their aspirations to meet CE purposes.
Keijer et al. (2019) also discussed the possibility to reach an ideal
circularity, by reusing chemical products countless times and
considering energy as the only input of the process. In particular,
the authors developed twelve principles for a “circular chemistry”
(p.190). Such approach aims to enlarge the concept of sustainability
from the optimization of processes to the entire lifecycle of
chemical products. The authors analyzed the importance to in-
crease the efﬁciency of resources through the value chain of
C. Silvestri, L. Silvestri, A. Forcina et al.
Journal of Cleaner Production 294 (2021) 126137
2
product, as well as they highlighted the necessity to develop new
chemical reactions for reusing and recycling of chemical sub-
stances. The ﬁnal purpose is to develop a perfect circular system
without any waste generation. Therefore, CE redeﬁnes the concept
of chemical processes, considering them as “sustainable” when
people, resources and proﬁt are taken into account, and then
reproducing the TBL perspective (Elkington, 2018; Keijer et al.,
2019). In fact, several authors demonstrated such relation be-
tween CE and sustainability (Korhonen et al., 2018a; 2018b;
Kristensen and Mosgaard, 2020; Rossi et al., 2020).
The CE concept is also related to other terms, such as “Green
Economy” and “Bioeconomy”. In particular, the latter has a com-
plementary role together with the CE (European Commission,
2017) and it can be deﬁned as “an economy based on the sustain-
able production and conversion of renewable biomass into a range
of bio-based products, chemicals and energy” (De Besi and
McCormick, 2015; N€
ayh€
a, 2019, p.1296). Synergies between these
two concepts are particularly relevant (Kardung et al., 2020). Ac-
cording
to
important
industrial
associations,
such
as
CEPI
(Confederation of European Paper Industries) and EuropaBio (The
European Association for Bioindustries), it is necessary a greater
integration among both concepts, which should be developed
along with other concepts rather than in a parallel manner. In fact,
these associations use and support the “circular bioeconomy”
concept (CEPI, 2017; EuropaBio, 2017).
Also in this concept, the GC has a crucial role. GC innovations
have the prospective to make the use of the biomass more efﬁcient
in terms of costs if compared to fossil raw materials (Kardung et al.,
2020). The constant research of alternatives to the petroleum-
based raw materials by scientists, industrialists and politics, high-
lights the role of the GC in reaching the primary goal of a sustain-
able chemistry in the long term horizon (Ciriminna et al., 2020). If
the twentieth century saw the rise of industries that used the pe-
troleum as the main source for energy and chemistry, the so-called
“the petroleum industries”, the twenty-ﬁrst century is character-
ized by the gradual transition towards “biomass industries” (Clark,
2007, p. 605), where the biological resources are going to be the
main source for both energy and chemistry (Bozell, 2008). Until
today, these biological resources have been used and disposed as
waste or, alternatively, sold for future uses, such as feed for live-
stock (Ciriminna et al., 2020). According to Clark (2007), following
this approach, it is possible to develop a new sustainable society
that is based on renewable resources, where the conversion process
will be performed by the so-called “bioreﬁnery”. In this context, the
implementation of technologies and GC principles directly within
the bioreﬁnery will be essential for minimizing the environmental
impact (Azapagic et al., 2004; Clark, 2007).
The CE implementation and everything that follows require a
legislation oriented to eco-innovations (Stankevi
cien_
e et al., 2020)
and able to promote enterprises in developing innovative solutions
for reducing production waste, as well as for obtaining a “green”
production and consumption (Kalmykova et al., 2016). In other
words, CE is able to generate its beneﬁcial effect if it is implemented
in a “Green Economy” context (Stankevi
cien_
e et al., 2020). The
“Green Economy” is often considered as a concept that includes
elements of CE and bioeconomy (D’Amato et al., 2017), and able to
“result in improved human well-being and social equity, while
signiﬁcantly reducing environmental risks and ecological scarcities.
In its simplest expression, a green economy can be thought of as
one which is low carbon, resource efﬁcient and socially inclusive”
(UNEP, 2011, p. 1).
Such background highlights how the GC focus is in line with
broader concepts of sustainability and CE. In particular, GC gives
theoretical and technological fundamentals for implementing
strategies oriented to transmaterialization and dematerialization
within chemical companies and beyond. These are the conditio sine
qua non for using, in an efﬁcient way, resources that are limited in
availability. Indeed, through the implementation of these two
processes, it is possible a transition towards a sustainable society,
that is characterized by signiﬁcant changes in using energy and
resources (Mulvihill et al., 2011). The GC allows to identify oppor-
tunities for innovating sustainability (Matus et al., 2012). However,
to achieve the goal and fully understand the GC potential, it is
necessary a transformation that involves social, political, economic
and technological factors (Mulvihill et al., 2011).
The increasing number of research studies related to the GC
suggested, to the authors of this study, to investigate literature to
ﬁgure out how and if research efforts, focused on GC, are embracing
the sustainability concept in its broader deﬁnition. In other words,
how GC is approaching main sustainability aspects, including ele-
ments of the TBL and the CE approach.
Basing on this consideration, the aim of this study is to under-
stand and analyze how much such “chemical philosophy” has been
accepted and shared by scientiﬁc communities, as well as industrial
sectors. GC promotes the interdisciplinary design and the devel-
opment of new technologies that represent the principles of sus-
tainability (Mulvihill et al.,
2011).
Since GC aims to reach
sustainability and CE, it is important to understand the state-of-
the-art in this area and how much such concepts are getting
close to each other.
The focus consists in investigating the role of GC in business, in a
perspective of sustainability and CE. In this context, GE was not
taken into account because, in this phase of study, the authors have
maintained the focus on the chemical area.
For this purpose, a systematic literature review (SLR) method-
ology has been used. A SLR is considered an important tool to un-
derstand the existing state of knowledge on given questions.
Compared to traditional review, SLR differs by using replicable,
scientiﬁc and transparent processes (Tranﬁeld et al., 2003a,b),
reducing the risk of bias introduction or lack of critical evaluation
(Briner and Denyer, 2012; Kitchenham, 2004; Tranﬁeld et al.,
2003a,b). Furthermore, the methodological design allows to eval-
uate what is known for the identiﬁed review questions, but also
what is not known, ﬁnding limitations of existing studies and
prospects for further research (Briner et al., 2009).
The article is organized as follow: in Section 2 the aim of the
review is presented, including SLR questions and contributions to
GC that are present in literature; Section 3 deﬁnes the research
methodology; Section 4 includes the material collection phase; in
Section 5 the analysis of articles is carried out. In particular, it is
divided in “Index calculation and database analysis” and “Temporal
distribution and evolution of publications, sources and authors”;
Section 6 and 7 illustrate how structural dimensions and analytical
categories have been identiﬁed; Section 8 contains discussions,
Section 9 “Limitations and future perspective” and Conclusions are
presented in ﬁnal Section 10.
Finally, because of the strong interdisciplinarity among all these
concepts, Table S1, that summarizes concepts, focuses and differ-
ences, has been provided as supplementary ﬁle.
2. Research questions and purpose of the paper e (step 1)
The relation between GC and sustainability is a research topic
that has been widely debated in literature and, today (April 15,
2020), it is possible to ﬁnd seven literature reviews that investi-
gated such relation.
First review was published by García-Serna et al., in 2007 and it
focuses on the concept of “Green Engineering” and how this
approach can promote sustainability.
In 2011, Rodrigues and Joekes discussed some aspects of cement
C. Silvestri, L. Silvestri, A. Forcina et al.
Journal of Cleaner Production 294 (2021) 126137
3
industry in relation to environmental sciences. In particular, au-
thors paid attention on the role of cement chemistry in terms of
sustainability.
Coish et al. (2016) proposed a more technical review focused on
the current state of the “sustainable innovation” (p.1) and on future
challenges for the environmental risk reduction.
Schwager et al. (2016) provided an overview of GC and SC in the
context of “Agenda 2030” for the sustainable development, ﬁnding
that, to explore and to promote the GC and SC potential, innovative
models for business and CE are needed.
In their review, Chaturvedi et al. (2017) discussed how phar-
maceutical companies are becoming interested to sustainability,
considering the relation among environmental aspects, economic
growth and social welfare as the main critical factor.
The topic of CE is also analyzed by Loste et al. (2020), with the
aim to evaluate the GC contributes to drive the transition to CE.
Finally, Nambela et al. (2020) developed a review on the relation
between GC and sustainability. In particular, when such concepts
are applied to the industrial sector of textile colorants.
Table 2 shows main features of seven reviews presented in this
paragraph, focused on relation between GC, sustainability and CE
during last years. In particular, Table 1 gives a clearer and more
concise summarization of some aspects, differentiating existing
literature reviews on GC and sustainability.
In these previous reviews, none of the authors studied how GC is
related to both TBL and CE simultaneously or considered the whole
chemical industry chain. Finally, the SLR approach has never used
before.
The aim of this literature review is to provide a comprehensive
overview of how the research on GC has evolved to address chal-
lenges raised by sustainability, that is characterized by the three
TBL main dimensions (environmental, economic, social), and CE, as
well as identifying potential directions for future research on the
subject.
Such overview can represent a powerful tool to understand and
predict how ﬁrms, operating in the chemical sector, as well as ac-
ademic studies, are dealing the key issues of the sustainability. To
the authors’ best knowledge, a similar study has not been reported
in literature to date.
To achieve these aims, we tried to answer the following research
questions:
RQ1 e How are ﬁrms dealing with green chemistry issue?
RQ2 e What are the obstacles and the possible solutions
encountered by the industrial system?
RQ3 e How are governments and academic research addressing
the policymaking in GC?
RQ4
e
What
are
the
recommended
practices
for
the
implementation of GC by academics and practitioners?
RQ5 e Which are the comprehensive studies that show exper-
imental applications of GC to industrial processes?
RQ6 e What is the relation between GC and the TBL framework?
This SLR aims to answer to these questions and analyze a sig-
niﬁcant part of literature concerning CG, sustainability and CE.
Main innovations were introduced to better understand the topic at
hand. As ﬁrst, the relation among GC, sustainability and CE were
investigated through a systematic approach to obtain a compre-
hensive analysis and a reproducible methodology, including
rigorous research criteria. Moreover, such analysis involves several
industrial sectors to explain how they are approaching CG, sus-
tainability and CE issues. In fact, GC has a wide application area,
being “the study of matter and all of its transformations” (Manley
et al., 2008, p. 744).
3. Methodology
To provide a general framework about GC evolution, a SLR was
carried out. The SLR methodology was ﬁrst adopted in medicine
(Saade et al., 2020) and successively in other ﬁelds, such as social
sciences, engineering (Bastas and Liyanage, 2018; Sassanelli et al.,
2019; Stuck et al., 1999), business and economics (Aquilani et al.,
2017; Colicchia and Strozzi, 2012; Merli et al., 2018). The aim of
SLR is “to identify, evaluate and interpret research relevant to a
determined topic area, research question or phenomenon of in-
terest” (Kitchenham, Charters, 2007; Muller et al., 2019) (p. 398). In
particular, according to Muller et al. (2019), the SLR allows to (1)
summarize the existing evidence in a topic, (2) identify gaps in the
state-of-art, proposing areas for further investigation, and (3)
provide a framework as support for further research activities.
In methodological terms, literature review allows to investigate
a given topic through both qualitative and quantitative content
analysis (Merli et al., 2018; Seuring and Muller, 2008). Several au-
thors proposed speciﬁc frameworks to deﬁne SLR phases. Accord-
ing to Tranﬁeld et al. (2003a,b), the SLR process should follow three
main steps, that are: planning, execution, and reporting. According
to the approach suggested by Briner and Denyer (2012), to conduct
a SLR, the following steps are needed: 1) formulation of research
questions; 2) exploration and analysis of literature, through ad hoc
chosen keywords; 3) inclusion of only papers that meet research
criteria and goals (ﬁt-for-purpose method); 4) construction of a
database, where articles and ﬁndings are evaluated and sorted; and
5) synthesis phase, in which results are extracted from database
and discussed.
Following a transparent protocol, SLRs use a series of selection
criteria that, explicitly, evaluate how much the selected paper is
Table 1
Literature review on green chemistry and sustainability.
Authors
Database Source
Aim
Focus
TBL pillars
García-Serna et al.
(2007)
WoS
Chemical Engineering Journal
Green engineering and chemical
engineering vs sustainability
Process general industry
Environmental
Rodrigues and
Joekes (2011)
Wos
Environmental Chemistry Letters
Cement chemistry and Sustainability
environmental
Process cement industry
Environmental
Coish et al. (2016)
WoS
ACS Sustainable Chemistry &
Engineering
Molecular design for reduced hazard
Molecular design process in
industrial chemicals
Environmental
Schwager et al.
(2016)
WoS
Current Opinion in Green and
Sustainable Chemistry
Green Chemistry and Sustainable
Chemistry
Business model: Chemical
Leasing
Environmental,
Economic, Social
Chaturvedi et al.
(2017)
Scopus
Journal of Cleaner Production
Green chemistry and Sustainable practices Process pharmaceutical industry
Environmental,
Economic, Social
Loste et al. (2020)
WoS
Environmental Science and
Pollution Research
Green chemistry and Circular Economy
Business model
Environmental,
Economic, Social
Nambela et al.
(2020)
WoS
Journal of Cleaner Production
Green synthesis and Sustainability
Process textile colorants
Environmental,
Economic
Source: authors’ elaboration
C. Silvestri, L. Silvestri, A. Forcina et al.
Journal of Cleaner Production 294 (2021) 126137
4
relevant in responding to the research question (Xavier et al., 2017).
For
example,
the
C-I-M-O
(context-intervention-mechanism-
outcome) framework (Briner and Denyer, 2012; Denyer and
Tranﬁeld, 2009) is a common approach used for the selection of
research keywords (Abdul et al., 2014; Caiado et al., 2017; Garza-
Reyes, 2015; Xavier et al., 2017).
Mayring (2004) proposed a model based in four steps: 1) ma-
terial collection; 2) descriptive analysis; 3) category selection; and
4) material evaluation. In particular, such model allows to evaluate
the collected papers through speciﬁc criteria based on topics and
analytical categories (Merli et al., 2018).
In this study, the C-I-M-O framework was adopted to select both
research keywords and articles, while the Mayring (2004) approach
was chosen for the evaluation of papers (Fig. 1).
In particular, through the research keywords analysis, the C-I-
M-O framework allowed the authors to deﬁne the speciﬁc research
focus, excluding articles that do not match with the scope of the
review. The Mayring (2004) model is commonly applied in SLR
concerning sustainability (Klewitz and Hansen, 2014; Merli et al.,
2018; Seuring and Muller, 2008; Shukla and Jharkharia, 2013) and
it also allows to classify a large number of documents, highlighting
main trends.
4. Material collection e (step 2)
This
research
investigated
on
main
electronic
databases,
including Scopus (scopus.com) and Web of Science (WoS) data-
bases, that today are considered as the most complete scientiﬁc
databases by scientiﬁc community (Chadegani et al., 2017; Guz and
Rushchitsky, 2009). Only peer-reviewed journals were considered,
and then excluding reports or conference papers. In fact, the journal
establishment and its availability for the readers are considered as
key aspects to ensure a high quality of the literature review (Guz
and Rushchitsky, 2009; Chadegani et al., 2017; Merli et al., 2018).
The English language is generally considered as the international
academic language (Genç, B., Bada, 2010; Merli et al., 2018), for this
motivation we chose to focus only on English language journals.
The search strings used in both databases are: “green chemistry”
AND “sustainability”; “green chemistry” AND “circular economy”;
“green chemicals” AND “sustainability”; “green chemicals” AND
“circular economy”. The present study considers all articles pub-
lished before April 15, 2020.
In WoS database, the research was conducted by “Topic”, that
includes “Title, Author Keywords, Abstract, Keywords Plus”, while
in Scopus the search ﬁeld type was “Article title, Abstract,
Keywords”.
The C-I-M-O framework consists in the following inclusion/
Table 2
e List of selected journals.
Journal
n
%
Green Chemistry
19
20.0%
Journal of Cleaner Production
10
10.5%
ACS Sustainable Chemistry & Engineering
9
9.5%
Resources Conservation and Recycling
6
6.3%
Chemsuschem
5
5.3%
Current Opinion In Green And Sustainable Chemistry
3
3.2%
Green Chemistry Letters And Reviews
3
3.2%
Chemical Engineering Journal
2
2.1%
Clean Technologies and Environmental Policy
2
2.1%
Green Processing and Synthesis
2
2.1%
Industrial and Engineering Chemistry Research
2
2.1%
Journal of Applied Biomaterials and Functional Materials
2
2.1%
Resources-Basel
2
2.1%
Aci Materials Journal
1
1.1%
Benchmarking
1
1.1%
Business Strategy And The Environment
1
1.1%
Corporate Environmental Strategy
1
1.1%
Energy Science And Engineering
1
1.1%
Environment, Development And Sustainability
1
1.1%
Environmental and Socio-Economic Studies
1
1.1%
Environmental Chemistry Letters
1
1.1%
Environmental Engineering Science
1
1.1%
Environmental Progress
1
1.1%
Environmental Science & Technology
1
1.1%
European Journal of Operational Research
1
1.1%
Environmental Science And Pollution Research
1
1.1%
Foods
1
1.1%
Food Engineering Reviews
1
1.1%
Guocheng Gongcheng Xuebao/The Chinese Journal of Process Engineering
1
1.1%
International Journal of Life Cycle Assessment
1
1.1%
IPPTA: Quarterly Journal of Indian Pulp and Paper Technical Association
1
1.1%
Journal of ASTM International
1
1.1%
Journal of Industrial Ecology
1
1.1%
Journal of Polymers And The Environment
1
1.1%
Journal of The Royal Society Interface
1
1.1%
Physical Sciences Reviews
1
1.1%
Scientiﬁc Reports
1
1.1%
Science of The Total Environment
1
1.1%
Sn Applied Sciences
1
1.1%
Sustainability
1
1.1%
World Transactions on Engineering and Technology Education
1
1.1%
Total
95
100%
Source: Authors’ elaborations
C. Silvestri, L. Silvestri, A. Forcina et al.
Journal of Cleaner Production 294 (2021) 126137
5
exclusion criteria:
 Inclusion criteria: all the papers that analyze GC, sustain-
ability and CE in the industry ﬁeld and that are focused on
chemical sector;
 Exclusion criteria: all the papers that do not focus on
chemical sector, although they analyze GC, sustainability and
CE;
Furthermore, a second ﬁlter was applied to focus on the
following subject areas:
I) Scopus: Social Sciences; Business, Management and Ac-
counting; Economics, Econometrics and Finance; Decision
Sciences; Engineering;
II) WoS: Green Sustainable Science Technology; Environmental
Studies; Engineering Environmental; Management; Food
Science Technology; Multidisciplinary Sciences; Operations
Research Management Science; Social Sciences Interdisci-
plinary or Business.
The inclusion of “Engineering” as subject area is mainly due to
three reasons: (1) the general aim of process engineering is to plan,
implement and execute programs able to guarantee the best op-
erations for companies (Mendoza-chac
on et al., 2016; Sengupta
et al., 2015); (2) the concept of process engineering is followed by
the concept of chemical engineering, which can be applied to all
processes involved in industries (Dal Pont, 2013); (3) the principles
of GC are in accordance with the more recent “Principles of Green
Engineering” (Anastas and Zimmerman, 2003; De Mello et al.,
2017).
According to the aim of this study, all papers that include key-
words
“ﬁrm”,
“organization”,
“company”,
“corporation”,
“enterprise”, “industry”, and “industrial” were included. Fig. 2
shows the selection and evaluation processes.
The initial amount of collected papers from Scopus and WoS was
733 and 583, respectively. Through the application of ﬁlters and
eliminating duplicated papers, the remaining papers were 28 from
Scopus and 53 from WoS, respectively. Additional 14 papers were
included in both databases, and then the ﬁnal number of papers is
95.
All articles were collected in a database by year, title, source title,
authors, number of authors, and nation of the authors. Further-
more, it was taken into account the evidence type (empirical/con-
ceptual
or
literature
review),
research
design
(qualitative/
quantitative), research method (i.e. case study, experiment, inter-
view, questionnaire), and the Triple Bottom Line dimensions
(Environmental, Economic, Social).
5. Descriptive analysis of the papers e (step 3)
5.1. Index calculation and database analysis
Until 2004, Thomson Reuters’ WoS database was the only
database that allowed the possibility to develop bibliometric ana-
lyses, such as literature reviews.
In November 2004, Scopus database was presented by Elsevier
Science and soon became the WoS’ main competitor for the inter-
national market of scientiﬁc databases (S
anchez et al., 2017).
In this context, several indexes and methods were used to
analyze the degree of overlap or similarities among databases. For
example, Meyer’s Index (Meyer et al., 1983) is an index that allows
to compare different databases, including the possibility to evaluate
the capability of a database to cover the same topic or and potential
Fig. 1. Revision process. Authors’ elaboration.
C. Silvestri, L. Silvestri, A. Forcina et al.
Journal of Cleaner Production 294 (2021) 126137
6
traditional and relative overlaps (S
anchez et al., 2017).
In this study, Meyer’s Index was used to understand how much
the database covers the considered topic (Dur
an-S
anchez et al.,
2018). The index assigns the value “1” to primary sources con-
tained in the selected database, such value will be progressively
reduced every time that the same paper is also present in other
databases. For duplicated papers the value is reduced to “0.5”,
triplicated papers to “0.3”, and so on (Dur
an-S
anchez et al., 2018;
Merli et al., 2018; S
anchez et al., 2017). Only two databases were
used in this analysis and then secondary sources, that are contained
in both databases, have a weighting factor of 0.5. Greater Meyer’s
index is and more unique it will be the considered database
(Fabregat-Aibar et al., 2019).
Meyer’s Index ¼ S Articles *weight
Total Articles
(1)
The following equations (2) and (3) show results for Scopus and
WoS databases:
Scopus ¼ 30 þ ð12*0:5Þ
95
¼ 0:378
(2)
WoS ¼ 53 þ ð14*0:5Þ
95
¼ 0:621
(3)
As it is possible to see, WoS database has a more singularity
(62.1%) than Scopus (37.8%).
The Traditional Overlap (TO) index, deﬁned by Gluck (1990),
allows to measure the overlap percentage between two databases,
in this case they are Scopus and WoS.
% Traditional overlap ¼ 100*
jScopus∩WoSj
jScopus∪WoSj

(4)
% Traditional overlap ¼ 100 *
12
95

¼ 12:63%
(5)
At greater value of TO, databases are considered more similar to
each other. In this case, equation (5) shows a similarity of 12.63% or,
in opposite viewpoint, a difference of 87.37%.
5.2. Temporal distribution and evolution of publications, sources
and authors
Articles and ﬁndings were evaluated and sorted in a database.
The analysis of the database shows an increasing trend of number
of papers from 2002 to April 15, 2020, especially from 2014 to 2019,
with the only exception of 2015 (Fig. 3). During this period, more
than half of the total number of papers (63%) was realized. The total
of collected papers is 95.
Fig. 2. Identiﬁcation of relevant articles process. Authors’ elaboration.
Fig. 3. - Temporal distribution of selected articles. Source: Authors’ elaboration.
C. Silvestri, L. Silvestri, A. Forcina et al.
Journal of Cleaner Production 294 (2021) 126137
7
Most of the considered journals have GC and environmental
issues as main topic. The “Green Chemistry” is the journal with the
largest number of papers (20.0%), followed by “Journal of Cleaner
Production” (10.5%) and “ACS Sustainable Chemistry & Engineer-
ing” (9.5%) and the 5 most proliﬁc journals account for 51.6% of
records (Table 2).
Fig. 4 shows the temporal distribution of articles published in
the ﬁrst 5 journals. In 2014, there was the greatest number of
published articles (7) and the simultaneous presence of all the ﬁrst
ﬁve journals. 2016 corresponds to the second year for number of
publications but they were published by only two journals (“Jour-
nal of Cleaner Production” and “ACS Sustainable Chemistry & En-
gineering”). Fig. 4 also shows that the distribution of paper was not
constant over the years. In fact, in some years, any of the ﬁve
journals published articles that match with the scope of this review.
Finally, Table 3 shows the number of authors per article. A total of
34.7% of papers have a number of authors that is greater than or
equal to 5 and 18.9% of papers have between 1 or 3 authors. Papers
with 4 authors represent the 10.5% of the total, that is the lowest
percentage.
6. Category selection and material evaluation e (step 4)
The purpose of this research phase is to analyze main aspects
emerged from papers and categorize them accordingly. For such
purpose, as mentioned in Section 3, the Mayring (2004) model was
adopted. Fig. S1, included in the supplementary ﬁle, shows the
phases followed for the selection process and for the material
evaluation.
In order to determine main aspects that emerged from papers
and deﬁne provisional categories, a criterion, based on theoretical
background and research questions, was developed. Through a
feedback loop, categories were checked and, eventually, remodeled
and veriﬁed (Kohlbacher, 2006).
Generally, methods used for the qualitative analysis are two,
that are the inductive and deductive methods. Each analysis needs
three process phases, that are: 1) preparation, 2) organization, and
3) communication of results. Such two methods differ for the “or-
ganization” phase (Elo et al., 2014). More generally, the inductive
approach moves from the speciﬁc to general, while the deductive
approach, basing on a speciﬁc theory or an existing model, moves
from general to speciﬁc (Polit and Beck, 2004).
Furthermore, the inductive approach deﬁnes categories created
from raw data and without the use of any categorization matrix
based on theory, as it is the case of the deductive approach (Elo and
Kyng€
as, 2008).
For the analysis of papers, six structural dimensions were
selected and, for each of them, analytical categories were deﬁned.
For the deﬁnition of some categories, a deductive approach was
used. The categorization matrix was deﬁned through a preliminary
analysis of articles (Sustainability/TBL, Research methodology,
Geographical focus, Industries). Through the inductive analysis, and
then basing on collected material, two categories were identiﬁed
(Research cluster, Keywords). Table 5 resumes the structural
dimension and relative analytical categories used for material
evaluation. In particular, Table 4 shows, for each category and
structural dimension, the considered approach, that can be induc-
tive or deductive.
7. Material evaluation e (step 5)
In this section, main results obtained from Structural di-
mensions and Analytical categories are described.
7.1. Cluster of research
All the articles collected can be presented in three clusters
within the following items:
(1) “Strategy”: studies concerning how and at which level, GC
principles have been implemented in industrial sectors or in
different Countries of the world. In particular, they highlight
barriers and future perspectives, as well as propose new
business models
for supporting the “strategic” imple-
mentation of GC principles;
(2) “Assessment and Practice”: studies proposing suggestions
and tools for the implementation of CG principles and
methods for assessing environmental performance of their
application. They generally show case studies where GC
principles were successful implemented;
(3) “Technical”: papers that include experimental applications of
GC principles in industrial processes.
Articles in the ﬁrst cluster can be both conceptual and empirical.
Conceptual studies propose models, frameworks or reviews; the
empirical ones can present case studies or in-depth interviews with
academic researchers or environmental managers working in
multinational companies about green chemistry implementation.
Articles in the second cluster offer original suggestions on how
Fig. 4. e Temporal distribution of the ﬁrst ﬁve journals. Source: Authors’ elaborations.
Table 3
e Number of authors per articles.
Authors
n
%
1
18
18.9%
2
16
16.8%
3
18
18.9%
4
10
10.5%
5
33
34.7%
Tot
95
100.0%
Source: Authors’ elaborations
Table 4
e Approach, structural dimensions and analytical categories.
Approach
Structural dimensions
Analytical categories
Inductive
Cluster of research
Strategy
Assessment and Practice
Technical
Inductive
Keywords
Keywords families
Deductive
Sustainability
Economic
Environmental
Social
Deductive
Research methodology
Conceptual
Empirical
Deductive
Industries
Industrial sector
Deductive
Geographical focus
Geographical area analysis
Source: Authors’ elaborations
C. Silvestri, L. Silvestri, A. Forcina et al.
Journal of Cleaner Production 294 (2021) 126137
8
to improve CG principles and how to apply them. Generally, they
propose new indexes or models to assess the sustainability of
chemicals processes or to ﬁgure out the implementation level of GC
principles. They can also report case studies were GC principles
have been successful applied.
The third cluster includes articles that present empirical results
obtained from experiments or in industrial environments.
Fig. 5 shows the temporal distribution of papers for each cluster.
As it is possible to see, the largest cluster is the “Assessment and
Practice” cluster, conﬁrming that, before managing a new approach,
it is needed to know how to measure and assess it. Only in this way,
it is possible to evaluate if the chosen path for achieving sustain-
ability is the right one.
Beside general indicators, several articles present case studies
where GC principles have been implemented in an excellent way.
Such cases result useful to understand which are most effective
tools for the GC implementation.
7.1.1. “Strategy”, “Assessment and Practice” and “technical” clusters
The ﬁrst cluster contains 23 articles published from 2005 to
April 15, 2020, concerning how GC principles have been imple-
mented in industrial sectors or in different Countries of the world.
Within the ﬁrst cluster, articles that propose new methodolo-
gies generally contain in-depth interviews and questionnaires.
Articles that belong to the ﬁrst cluster provide an analysis of the
level of the implementation of GC principles, in several areas of
chemical industry. They focus on both obstacles and future per-
spectives, proposing new business models to support the strategic
application of GC.
According to Ananda et al. (2009), the “green” or “sustainable”
chemistry is considered as the new paradigm to which industry can
refer to meet the corporate environmental responsibility, especially
the chemical sector. GC represents an integral part of the global
competitiveness of the chemical industry, in particular for Austra-
lian industries (Ananda et al., 2009). They developed a “roadmap”
(p.1051) to support the process of “greening of the Australian
chemical industry over the next two decades” (p.1051) and for
achieving competitive advantages. In 2008, Iles (2008) already
argued that the chemical industry would have faced several chal-
lenges towards sustainability, thanks to “the transformation of
chemical technologies, products and infrastructure to use less en-
ergy and matter, be less toxic and draw on renewable energy and
feedstocks” (p.532). However, the author referred not only to
technical aspects, but also to business management for a satisfying
implementation of GC. Indeed, the author stated that the greatest
obstacle is represented by the lack of business strategies able to
make GC actions commercially valuable. Furthermore, the author
focused on chemical industrial efforts in United States, highlighting
the weakness of the market signals and discussing potential
methods to improve the GC market and related obstacles. Finally,
the author pointed out the importance to create an efﬁcient in-
formation ﬂow about GC within the ﬁrm and to establish new re-
lations with supply chain partners, sharing with them information
useful to achieve environmental and productivity goals.
The analysis of the barriers for GC implementation was also
faced by Matus et al. (2012). In particular, they focused on Chinese
chemical industry. Their results indicate that, despite the growing
application of GC principles in China, the greatest barrier to their
Table 5
Articles related to research questions RQ1 RQ2 RQ3.
RQ1 e How are ﬁrms dealing with green chemistry issue?
RQ2 e What are the main obstacles that the industrial system encounters and what are possible solutions?
RQ3 e What is the role of governments and academic research in green chemistry policymaking?
Authors
Methodology
approach
Interdisciplinary approach
Focus
Kirchhoff (2005)
Framework
Eco innovation
Collaboration between industry, academia, and
government
H€
ofer and Bigorra
(2007)
Model
Eco innovation
Companies
Iles (2008).
Model
Sustainability marketing approaches
Companies
Manley et al. (2008)
Qualitative
GE - Eco Innovation
Companies
Ananda et al. (2009)
Model
Business strategies
Companies
Matus et al. (2012)
Qualitative
GE - Eco Innovation
Companies
Watson (2012)
Quantitative
Corporate policy - Eco innovation
Companies
Federsel (2013a,b)
Framework
Business strategies
Companies
Scruggs (2013)
Qualitative
Chemicals Management Strategies
Consumer products
Giraud et al. (2014)
Quantitative
Eco innovation
Collaboration between Industries
Tarasova et al. (2014)
Quantitative
Sociological studies
Companies
Green (2016)
Model
Science and Engineering areas
Policy
Smieja and Babcock
(2017)
Model
Product circularity
Supply chain
Freire (2018)
Framework
GE - Eco Innovation - Analysis of companies’ behavioral intentionality
(willingness)
Companies
Koenig et al. (2019)
Qualitative
GE
Supply chain
Chen et al. (2020)
Qualitative
Product circularity
Chemical management system
Ratti (2020)
Qualitative
Eco innovation
Companies
Source: Authors’ elaborations
Fig. 5. e Temporal distribution of clusters. Source: Authors’ elaboration.
C. Silvestri, L. Silvestri, A. Forcina et al.
Journal of Cleaner Production 294 (2021) 126137
9
implementation is represented by the difﬁculty in reaching a
proper balance between the generation of proﬁt and the environ-
mental protection, as well as the lack of expert human capital. The
same authors also identiﬁed policies as one of the main drivers to
achieve GC implementation. The importance of policies was also
analyzed by Green (2016). The author stated that the reason for the
slow application of GC paradigms is mainly due, in addition to a low
level of knowledge, to the passivity of the whole industrial system
and the lack of political and ﬁnancial incentives. The author argued
that plans for enhancing the sustainability of chemical processes,
for the whole supply chain, can be promoted only by public policies.
One important example is the emerging concept of the “chemical
leasing”, promoted by the United Nations Industrial Development
Organization (UNIDO, 2011), with the aim to integrate policy ini-
tiatives on several settings, including scientiﬁc and technological
ﬁelds, as well as to promote CG and the “sustainable chemistry”
(Lozano et al., 2014a).
The “chemical leasing” concept was also investigated by Lozano
et al. (2014) and Schwager et al. (2016). Lozano et al. (2014a) pro-
posed a critical reﬂection on this concept, stating that, although
both suppliers and users can get economic and environmental
advantages, the “chemical leasing” is restricted only to some
chemicals, that consist in solvents and catalysts. Therefore, Lozano
et al. (2014a) argued a more clear and precise deﬁnition of the
“chemical leasing”, considering it as part of a holistic approach that
takes into account economic, environmental, social and temporal
aspects. Schwager et al. (2016) investigated the potential of
“chemical leasing” to integrate green and sustainable chemistry at
global scale, identifying the need of innovative business models to
facilitate the involvement of different elements, including the in-
dustrial sector.
Freire (2018) came to the same conclusion, although the author
focused his study on the behavioral analysis of ﬁrms rather than the
“chemical leasing”. In particular, studying behavioral and innova-
tive theories, the author developed a general conceptual frame-
work to understand the threshold beyond which ﬁrms are willing
to engage in environmental innovation. Results highlighted the
importance of some stakeholders or strategic partnerships to
develop conditions for technical and non-technical changes within
ﬁrms.
According to Tarasova et al. (2014), although Russia was inter-
ested to join the World Trade Organization in 1993, the lack of
strategical plans for adapting industry to GC paradigms led to
negative results. In other words, Russian companies needed syn-
ergic business models for a proper implementation of GC.
Giraud et al. (2014) considered the collaboration among com-
panies as the key factor to ensure the sustainability of chemical
ﬁrms. The authors argued that such collaboration can help com-
panies to identify more efﬁcient processes, to develop guidelines
for each sector and to deﬁne strategies to turn innovative ideas in
feasible and economic technologies. The same authors underlined
the need to establish a “Roundtable” (p. 2241) among ﬁrms and
various organizations to achieve a mutually and advantageous ex-
change of knowledge and ideas for the application of GC principles.
An example of roundtable is given by the Green Chemistry Institute
Pharmaceutical Roundtable (GCIPR) of the American Chemical So-
ciety (ACS). It was established in 2005 to assist the progress of GC
and pharmaceutical industry at global scale (Koenig et al., 2019).
The authors pointed out the beneﬁts from the partnership among
pharmaceutical ﬁrms and their supply chain partners, especially in
terms of costs saving and logistic optimization. More in details, the
Green Chemistry Institute Pharmaceutical Roundtable has offered
an overview of how ﬁrms, from different sector, faced challenges
and reached cost targets, improving sustainability performance.
Other studies (Chaturvedi et al., 2017; Federsel, 2013a,b; Watson,
2012) conﬁrmed that GC has a central role on pharmaceutical
sector. Watson (2012) tried to determine how many ﬁrms are
actually applying GC based processes and, more in general, sus-
tainability principles. The author analyzes 11 ﬁrms classiﬁed as “big
pharma” (p.251) and afﬁrms that, although satisfactory results have
been already obtained in the implementation of green technolo-
gies, the general framework remains unclear and varies widely.
Federsel (2013a,b) attempted to answer to the question: “What
is the relation between the Green Chemistry initiative and the
pharmaceutical industry?” (p. 3105). The author investigated stages
of GC implementation for the pharmaceutical industry, showing
how, from a difﬁcult beginning, it caught up rapidly and success-
fully. To this end, the author also proposed some case studies of
excellence.
Veleva and Cue (2017) also explored the adoption of GC in “big
pharma” sector, as well as potential drivers, barriers and future
opportunities. In particular, the authors noted that main barriers
are represented by the lack of attention from government in placing
appropriate laws.
Chaturvedi et al. (2017) focused their attention to Indian phar-
maceutical production, stating that the sustainability can be
reached through a holistic approach involving the whole products’
life cycle. They assigned to Indian law the deﬁnitive role of pro-
moter of a successful application of GC, indicating a series of
mandatory guidelines to achieve the sustainability for all functional
elements involved in the supply chain. Furthermore, they high-
lighted the importance to establish a partnership between industry
and academic institutions to create a transparent data and infor-
mation exchange. According to Kirchhoff (2005) and García-Serna
et al. (2007), efforts for such type of collaboration are not only
strategic but also necessary for obtaining a deﬁnitive adoption of
GC supported by a task force of scientists and engineers.
H€
ofer and Bigorra (2008) considered the adoption of sustainable
strategies as the driving force for ﬁrms to pursue a state of health,
safety and wellness for consumer. However, they noted that laws
concerning chemicals have always been weak, although people and
environment are generally exposed to the risk of chemical
pollutants.
Scruggs (2013) showed that, despite the deﬁcit of proper laws,
several companies in the consumer goods industry sector take
autonomous actions to minimize the use of hazardous chemicals.
Furthermore, a proper balance between the increasing of produc-
tion and a safer work environment seems to be one of the most
important objectives for the sustainability.
Manley et al. (2008) considered GC as the innovative approach
able to address the sustainability at “molecular level”, encouraging
scientists to design innovative processes to increase performance
and the value of products, protecting health and environment.
The second cluster contains 43 articles, concerning the identi-
ﬁcation of tools for assessing environmental performance of ﬁrms
and practices for supporting the implementation of GC principles.
The rapid development of process industries produced high pol-
lutants and environmental damages. In this context, GC principles
appear as a powerful tool to address these issues. However, it is also
important to introduce criteria and methods to assess the envi-
ronmental performance, as well as to deﬁne principles for a proper
ecological design (Zhang et al., 2005). Calvo-Flores (2009) intro-
duced some mathematical parameters to describe the sustainabil-
ity of chemical reactions and their environmental impact.
According to Lankey and Anastas (2002), to reach sustainability
goals and allow a more ecological industry, GC and engineering
should work together, designing all life cycle stages of chemicals by
a green design. For example, Zhang et al. (2005) proposed a model
in which engineering methods, for the design of process systems,
are applied for GC implementation.
C. Silvestri, L. Silvestri, A. Forcina et al.
Journal of Cleaner Production 294 (2021) 126137
10
According to Calvo-Flores (2009), the eco-design can also be
applied to industrial synthesis manufacturing processes. However,
although a green technology can appear as eco-friendly, this does
not mean that all processes and chemical reactions involved are
also a priori sustainable, as well as an all-inclusive method to assess
them does not exist.
According to Gonzalez and Smith (2003), to evaluate the sus-
tainability of a chemical process, a set of indicators or metrics for
the sustainability are needed. The GREENSCOPE (Gauging Reaction
Effectiveness for the ENvironmental Sustainability of Chemistries
with a multi-Objective Process Evaluator) is an indicator model,
designed by US Environmental Protection Agency (USEPA), to
evaluate the sustainability of a chemical reaction or process
involving the areas of environment, energy, efﬁciency and economy
(Gonzalez and Smith, 2003). In order to obtain sustainable chemical
processes, Ruiz-Mercado et al. (2014) introduced an innovative
methodological approach based on the simultaneous application of
the GREENSCOPE and the Life Cycle Assessment (LCA). Further-
more, Fadel and Tarabieh (2019) developed a “holistic industrial
environmental index (IEI)” (p.1) to evaluate the sustainability of
industrial chemical processes. His aim was to support industry for
applying innovative and effective indexes that are also valid in
other industrial sectors. Most authors considered the analysis of the
whole life cycle of chemical products and processes (i.e. LCA) as the
authentic key factor to guarantee the sustainability, improving
environmental impacts (Lankey and Anastas, 2002). In this context,
Subramaniam et al. (2016) illustrated, through the use of some
examples, the importance to integrate LCA with the techno-
economic analysis, especially during ﬁrst phases of design. Re-
sults showed a more rationale development of sustainable chemi-
cal processes. Jim
enez-Gonz
alez and Overcash (2014) proposed an
overview of the potentiality of the LCA applied to pharmaceutical
and chemical industry. Sheldon (2007) focused on the evolution of
the so called “E-factor” that is the ﬁrst general metric for green
chemistry, and it is deﬁned as the ratio of the mass of waste and the
mass of product. In particular, the author highlighted the effect of
the E-factor during last ﬁfteen years for the development of phar-
maceutical and chemical sectors and in a context of GC and
sustainability.
Even in this cluster, the pharmaceutical sector is analyzed by
several authors. Curzons et al. (2007), Rosenthal and Lütz (2018)
and Andraos (2019) explored chemical processes that are gener-
ally involved in pharmaceutical industry, assessing their environ-
mental impacts. Curzons et al. (2007) highlighted the necessity of
an easy methodology to provide indicators or metrics to assess the
“greenness” or the sustainability of processes related to Active
Pharmaceutical Ingredient (API). To this end, the authors based
their research on the use of the FLASC™(Fast Life cycle Assessment
of Synthetic Chemistry), that is a tool web-based that meets these
requirements. In the pharmaceutical sector, the importance to
promote a continuous improvement on chemical processes, to
make them more green, is also highlighted by Van Der Vorst et al.
(2009). The authors showed that some methods are particularly
suited to evaluate the greenness, the ecoefﬁciency and sustain-
ability, giving a comprehensive example. The use of a proper
methodology for the assessment is essential because, as stated by
Van der Vorst et al. (2013) “you can only manage what you can
measure” (p.744). Rosenthal and Lütz (2018) analyzed the synthesis
processes for the APIs and stated that, although enzymatic pro-
cesses can contribute to a higher sustainability, there is the ne-
cessity to have reliable tools to measure their environmental
impact.
Andraos (2019) developed a standardized format to produce
accurate reports for the preparation of the green synthesis process,
this format can be also applied to chemical syntheses of APIs.
Another subject of analysis is the unconventional gas industry,
that has begun to use the hydraulic fracturing chemicals to reduce
risks associated with possible “chemical containment failure”. In
this context, Hurley et al. (2016) developed a comparative analysis
of methods for assessment based on the computation of the
chemical risk index, as well as the formulation of a “novel hydraulic
fracturing ﬂuid greenness assessment system” (p. 647). Results
showed relevant beneﬁts associated to such index, in particular in
promoting environmental-best practices for the unconventional
gas industry.
Bours et al. (2017) developed a methodology that integrates LCA
environmental impacts with other hazardous indexes originated
from additive manufacturing activities. Their results originated
from
a
study
in
which
they
explored
several
existing
methodologies.
García-Bustamante et al. (2018) proposed an index to assess the
sustainability of the chemical processes involved in the food sector,
where GC is highly demanded.
A further area investigated by several authors concerns the
application of GC in the cement production, that involves different
materials and processes, but also high volumes. These motivations
led Rodrigues and Joekes (2011) to discuss environmental aspects
related to the cement industry. They considered the improvement
of chemical processes as the key role for achieving sustainability.
Coppola et al. (2018) proposed different solutions to make the
cement industry more sustainable, reducing greenhouse gases
emissions and using renewable resources. In particular, they
focused on “greener” binding agents as an alternative to Portland
cement.
Kadzi
nski et al. (2018) used the Multiple Criteria Decision Aiding
(MCDA) to assess GC implementation for nanoparticles synthesis.
The authors proposed a single framework to identify advantageous
related to common criteria used for sustainability assessment.
Already in 2012, Tian et al. (2012) used the MCDA to classify the
sustainability of three alternatives to glyphosate. Cespi et al. (2016)
used the MCDA to study 1,3-Butadiene production. The authors
highlighted the necessity for researchers to have a more “scientiﬁc”
and quantitative tool to evaluate the sustainability of processes
when they have been designed according to GC principles.
The metrics to assess the “greenness” of a chemical process
attracted the attention of several authors. For example, McElroy
et al. (2015) developed the “metrics toolkit” (p. 3111) to evaluate
the sustainability of reactions in a both quantitative and qualitative
way. It includes a wide range of holistic criteria to establish their
greenness, considering both upstream and downstream phases of
the reaction. The authors also proposed three innovative metrics,
that are: the optimum efﬁciency (OE), the renewable percentage
(RP) and the waste percentage (WP). Sheldon (2017) considered the
possibility to apply the traditional metrics, based on the “E-factor”
and on “atom economy”, to pharmaceutical and others chemical
products. The author concluded that such mass-based metrics have
several limitations in assessing the sustainability for the considered
sector and then he explored other metrics to meet “green” re-
quirements. According to H€
ofer and Bigorra (2008), biomass and
renewable raw materials are the authentic drivers to let industry
and sustainability meet together.
Hjeresen et al. (2002) afﬁrmed that several innovative green
technologies have been developed during last years. Such tech-
nologies are able to provide both economic and environmental
beneﬁts. The authors presented examples of some successful
implementations. Lepech et al. (2009) argued that the connection
among materials development, structural design and sustainability
is one of the main factors that helps the successful application of GC
principles. For this reason, they proposed a framework to design
green materials, showing a comprehensive case study. In particular,
C. Silvestri, L. Silvestri, A. Forcina et al.
Journal of Cleaner Production 294 (2021) 126137
11
they focused their attention on tools that is possible to use during
the products’ development and related beneﬁts.
Hunt et al. (2014) focused on phytoextraction, showing how this
process can be considered as an example of the application of GC.
Sheldon (2016) stressed the fact that is possible to design a more
sustainable world basing on catalysis process. According to Lange
(2009), sustainability can be reached reducing the consumption
of natural resources, waste, hazardous waste and costs, indicating
such four dimensions as “sustainability stresses” (p.587). The
author showed how Shell Company reached, in just a few decades,
the implementation of GC principles, reducing “sustainability
stresses”.
Another important aspect concerning environmental impacts is
represented by toxicity of chemicals. In this context, Coish et al.
(2016) deﬁned the state-of-the-art of the molecular design and
focused on potential toxicity of different phases involved. The aim
was to suggest to chemistry scientists to use innovations on their
design protocols.
Lambin and Corpart (2018) presented the case study of French la
Roquette, a family-owned group in the sector of Food, Nutrition and
Health. The company is noted for its efforts made for introducing
sustainable measures in all its activities. The authors showed how
the company applied GC principles and targets, in accordance with
deﬁnitions made by United Nations.
Aydin and Badurdeen (2019) proposed a design approach for
sustainable products based on “multiple life cycle” and considering
end-of-life treatments. The validation of methodology is performed
through a case study.
Shahid and Mohammad (2013) studied the role that different
biopolymers
have
on
antimicrobial
development,
analyzing
different technologies of green pretreatments and describing main
advantages.
Basing on a partnership developed among the Rowan University
and some pharmaceutical companies, Slater and Savelski (2011)
developed projects to promote the implementation of GC princi-
ples, through examples of successful application. In pharmaceutical
sector, Ott et al. (2014) argued that APIs need a redesign or, more in
general, a rethinking for processes involved. To this end, the au-
thors presented a holistic optimization for processes based on
product’s life cycle, demonstrating that the adopted methodology is
able to produce high environmental beneﬁts. De Soete et al. (2013)
also analyzed APIs’ processes, but they focused on other aspects of
life cycle, i.e. packaging and distribution of products. In particular,
they discussed, through the analysis of a case study, how changing
the production way of batches can bring beneﬁts to the environ-
ment, as reduction of waste and resource consumptions. Their
focus mainly was on making packaging for medical products more
sustainable through the application of GC principles. Packaging is a
particularly important topic, especially since waste, derived from
packaging, have started to represent a signiﬁcant part of urban solid
waste and the cause of several environmental concerns.
Generally, materials for packaging are not biodegradable and
also difﬁcult to recycle or reuse (Davis and Song, 2006). In this
context, Narayan and Balakrishnan (2006) discussed environ-
mental beneﬁts to use biodegradable packaging in terms of landﬁll,
incineration, composting, recycling and possible reuses. In partic-
ular, the authors highlighted how sustainability, industrial ecology,
ecoefﬁciency and GC represent the new paradigms to guide the
development for the next generation of products and processes,
especially for plastics.
Arancon et al. (2013) showed a recent development in strategies
for the valorization of waste from chemicals, including its reuse as
fuels or in other applications. The authors also presented guidelines
for future GC implementation, basing on case studies where stra-
tegies for the valorization of waste have been successful applied.
De Mello et al. (2017) developed a comparative analysis of the
environmental impacts for the production of biodiesel from soy oil,
evaluating the GC implementation and using different metrics to
assess sustainability. Results showed the advantages derived from
both approaches and they also developed a model to design
guidelines for future applications. Finally, Limleamthong et al.
(2016) proposed a new way to identify more eco-friendly chemi-
cal products.
Also in this cluster, a close relation between green chemistry
and circular economy is highlighted. According to Linder (2017), GC
should be considered within the circular economy model, arguing
that chemists have a key role in reaching a more sustainable future.
The author also gave some examples of excellence.
In this context, Nistic (2017) afﬁrmed that circular economy and
GC principles are fully in accordance, sharing same environmental
purposes, i.e. waste reduction. The author showed, through a case
study, how organic waste can be reintroduced as raw material for
the production of chemicals or in value-added materials.
The third cluster contains 29 papers related to the imple-
mentation of GC principles on chemical processes, discussing
technical aspects. Articles focus on chemical processes employed in
industry or that are subject of study for scientiﬁc research.
The chemical synthesis represents one of the aspects most
studied by the scientiﬁc community with the aim to realize more
clean or “green” synthesis (Georgi
ades et al., 2015; Khoo et al.,
2015; Middelkoop et al., 2019; Pati, 2014; Simonetti et al., 2019;
Theingi et al., 2019). Another subject of study is the catalysis
(Adebajo, 2007; Walther, 2014), especially relative to polymers
widely used in industry. For this topic, Mohanty et al. (2002),
Tabone et al. (2010), Hong et al. (2017) and Capezza et al. (2019)
investigated
the
possibility
to
obtain
recyclable
bio-based
polymers.
GC principles can be also applied to solvents involved in
chemical processes. In this context, some authors (Hiltunen et al.,
2016; Lowe and Milbradt, 2011) proposed more sustainable mix-
tures for solvents.
With regards to materials, precious metals are the most widely
studied. In particular, studies focus on the possibility to recover
precious metals through green chemical processes and succes-
sively, in a circular economy perspective (Halli et al., 2018; Yang
et al., 2018), to reintroduce them for the manufacturing of new
products (Izatt et al., 2015; Cui et al., 2020).
As in the second cluster, the third cluster collects several studies
concerning the application of GC on the cement production. In
particular, some authors studied innovative and sustainable mix-
tures derived from industrial or agricultural waste (Debbarma et al.,
2020), the creation of eco-sustainable cements (Coppola et al.,
2018b) and an innovative cement realized with eco-friendly ma-
terials or through a more effective energetic use (Font et al., 2018).
In this context, Muwashee (2020) studied how it is possible to
obtain building materials, starting from cement-based materials.
For the food sector, Barba et al. (2015) focused on technologies
that are in line with GC principles and Romani et al. (2016) studied
on the implementation of GC to olive oil production at industrial
scale. Furthermore, Pathan and Ahmad (2016) demonstrated,
through an empirical study, that is possible to obtain high-
performance vegetable oil basing on organic-inorganic bio hybrid
materials, meeting the 12 GC principles.
For the pharmaceutical sector, Van Der Vorst et al. (2009) and
Wehrs et al. (2019) performed an important analysis to demon-
strate the beneﬁc effect of GC application to achieve a more sus-
tainable production, as well as Lae et al. (2019) stated in their recent
work. Cespi et al. (2016), Mamat et al. (2014) and Molino et al.
(2019) studied the reduction of waste from pharmaceutical in-
dustry, using green processes.
C. Silvestri, L. Silvestri, A. Forcina et al.
Journal of Cleaner Production 294 (2021) 126137
12
7.2. Identiﬁcation of most used keywords in selected papers
In this section, the most used keywords in selected papers have
been identiﬁed. More in detail, for each article, keywords used by
authors were identiﬁed and collected, through a criterion based on
a conceptual similarity (Merli et al., 2018). Furthermore, keywords
were grouped in categories. Among most used keywords, ﬁrst ﬁve
are “Chemical components” (64) followed by Sustainability (46),
Industry (46), Green chemistry (39) e Environmental (29). The
“Sustainability” category includes keywords strictly related to it, as
“sustainability development”, “sustainable infrastructure” or “sus-
tainable bio-composites”, etc. On the other hand, “Green chemis-
try” contains keywords as “green chemicals” and “principles of
green chemistry”. The CE category ranked tenth, with a frequency
equal to 12. Such result shows how CE topic has become relevant in
academic research only in recent years, especially if it is discussed
in relation to GC (Table S2 in the supplementary ﬁle). The most
common family of keywords is “Chemical components” (64), fol-
lowed by “Sustainability” and “Industry” with 46 occurrences.
“Green Chemistry” is the fourth with 39 occurrences, followed by
“Environmental” (29). Finally, “Culture” e “Country” is the less
recurrent.
On the basis of results collected, a Pareto analysis were per-
formed to understand most important categories developed in
relation to GC, sustainability and CE.
Pareto analysis is a widely applied quality tool (Karanikas, 2016),
and it represents a simple and an effective statistical method for
ranking and classifying data in a decreasing order from the highest
frequency of occurrences to the lowest ones (Bajaj et al., 2016). The
total frequency is summed up to 100. More in detail, the Pareto
analysis is based on the 80/20 rule principle. The classiﬁcation of
data is divided into two categories: the “vital few” items that
occupy a substantial amount (80 per cent) of cumulative percent-
age of occurrences, and the “useful many” items that occupy the
remaining
20
per
cent
of
occurrences
(Karuppusami
and
Gandhinathan, 2006). The Pareto analysis has been developed as
a Quality Control tool for processes, although several authors used
such tool also in systematic literature reviews (Aquilani et al., 2017;
Bajaj et al., 2016; Karuppusami and Gandhinathan, 2006).
The purpose of the analysis is to ﬁgure out the most important
keyword families within this research context. For this reason,
keyword families that correspond to “research keywords” used in
this study (Section 4), were not considered. In fact, keyword fam-
ilies “Sustainability”, “Green chemistry” and “Industry” have been
the most common. For the sake of consistency, keyword families of
“Circular Economy” were removed a priori. Fig. 6 shows the results
of the Pareto analysis.
The graph in Fig. 6 represents keyword families in a descending
order. As it is possible to see, there is a clear pointer that overlays
the line graph at 80%. Such pointer separates 80% cumulative
percent, identifying the remaining 20% which represents less
signiﬁcative keywords.
“Chemical components” represents the most important cate-
gory. In fact, “Chemical components” determine the sustainability
of a product and the possibility to reuse it in a circularity approach.
As expected, most of articles focused their attention to such
keyword. “Environmental” is the second keyword family in the
rank. Such result shows the strict relation between chemistry and
environment, and how, for several years, the sustainability concept
has been studied only in an environmental perspective by chem-
istry. This fact is also demonstrated by the third category “LCA”. In
fact, such approach is oriented to assess the environmental impact
of processes of products in a life cycle perspective. Then, LCA has a
key role for the rationale development of sustainable chemical
processes (i.e. Subramaniam et al., 2016). On the other hand, the
presence of the keyword “Hazard” demonstrates how chemistry is
approaching to social dimension of the sustainability concept,
becoming less dangerous for human health. Attention should be
also given to the keyword “Waste”, that is strongly related to CE
concept. Efforts in such context aim in designing chemical products
generated from organic waste (Campos et al., 2020) and in making
chemical products able to have a second life through different ap-
plications (Debbarma et al., 2020). Finally, the presence of the
keyword “Catalysis” in most important keyword families can be
explained considering the aim of this study. In fact the catalysis is
the branch of chemical industry and, as stated by Farrauto (2007),
“most products produced in the chemical and petroleum industries
utilize catalysts to enhance the rate of reaction and selectivity to
desired products” (pag. 271).
7.3. Research methodology used in selected studies
Articles based on a methodological research have been classiﬁed
distinguishing them in conceptual and empirical papers. Further-
more, an additional analysis was performed to distinguish articles
that belongs to the same macro group (Flick, 2014; Jaakkola, 2020;
MacInnis, 2011; Muijs, 2010). In particular, according to MacInnis
(2011), it is possible to deﬁne as conceptual articles all the aca-
demic articles that are based only on ideas and then without any
data. For the same author, conceptual papers also include analytical
articles based on mathematics, when ideas are expressed through
mathematical expression instead of words. Papers that are included
in this category are “conceptual framework”, “integrative model”
and “state-of the art reviews” (MacInnis, 2011, pag. 141). Studies
based on experiments or observations are considered as “Empir-
ical” papers. Empirical papers, in turn, are divided in those that
involve a “qualitative” (based on non-numerical data) and “quan-
titative” approach (based on numerical data analyzed statistically)
(Flick, 2014; Muijs, 2010). Fig. S2, in the supplementary ﬁle, sum-
marizes the model used for the purposes of this study.
Among the 95 articles collected, 70 are based on empirical ev-
idences and 25 are conceptual. In each year, conceptual studies are
always less numerous than empirical ones (Fig. 7). Most of the
empirical papers are qualitative (38 out of 70) and only 32 are
quantitative. The conceptual papers propose models (14 out of 25)
and reviews (7 out of 25). Fig. 8 shows the general classiﬁcation of
articles.
Cases studies and experimental papers are the most common.
Several case studies were presented in articles included in
“Assessment and Practice” cluster. In fact, in this category, authors
propose suggestions and tools for the implementation of CG
Fig. 6. Pareto Analysis. Source: Authors’ elaboration.
C. Silvestri, L. Silvestri, A. Forcina et al.
Journal of Cleaner Production 294 (2021) 126137
13
principles and methods to assess environmental performance of
their application, focusing on excellence cases. For example,
Lambin and Corpart (2018) presented the case of Roquette, that is
considered as the global leader in implementing activities based on
sustainable development. Several case studies were presented for
different industrial sectors, especially for the pharmaceutical sector
(Andraos and Andraos, 2018; Koenig et al., 2019; Rosenthal and
Lütz, 2018), followed by cement (Rodrigues and Joekes, 2011),
textile industry (Shahid and Mohammad, 2013) and paper industry
(Manley et al., 2008). Moreover, case studies were also discussed in
a CE context (Hong and Chen, 2017; Linder, 2017) and for high-
lighting the importance of the integration of LCA with both GC and
engineering principles in a perspective of sustainability and circu-
larity
of
production
phases
(Lankey
and
Anastas,
2002;
Subramaniam et al., 2016).
Experiments are instead the main tool used in articles that have
been included in “Technical” cluster. In fact, such studies involve
the GC application to realize sustainable chemical products or to
use organic waste to make chemicals with a sustainable perspective
(Campos et al., 2020; Hong and Chen, 2017; Pathan and Ahmad,
2016; Romani et al., 2016).
In
this
context,
“Models”
represent
the
most
common
intellectual tool. The “Strategy” cluster includes 14 articles on a
total of 24. Such studies propose new business models to incentive
GC principles implementation, in respect of the environmental
dimension, sustainability, and, above all, the economic dimension.
The most representative example is represented by the leasing
model discussed by Lozano et al. (2018) and Schwager et al. (2016).
The 10 remaining papers have been included in the “Assessment
and Practice” cluster, and they propose models able to evaluate
environmental performance of chemical processes (Calvo-ﬂores,
2009; Sheldon, 2017) deﬁning new criteria and methodology
(Tian et al., 2012; Zhang et al., 2005).
7.4. Identiﬁcation of main industrial sectors
The value of GC is not always perceived because, generally, its
products do not have a direct use. In fact, such products are mainly
intermediate goods, that are used by other industries to produce
end
products.
However,
chemistry
interests all
life
aspects,
including nutrition, health, hygiene and mobility. Many evidences
demonstrate that chemical products are used in all economic ac-
tivities, such as agriculture (5,2%), services (12,7%) and family
consumptions (14,6%), with the largest proportion in industry
(67,5%) (Federchimica, 2019). Industrial sectors have been catego-
rized basing on the Report published by Federchimica (2019), that
identify nine industrial sectors mainly involving the chemistry.
Fig. 9 shows how pharmaceutical is the most studied sector (22),
in terms of GC applied to sustainability and CE. In fact, innovation is
a key element for the pharmaceutical sector, which aims to improve
health and quality of life of patients, as well as to pursue sustain-
ability. In 2017, Europe invested in this sector an estimation of V
35,200 million in R&D (EFPIA, 2018). Other sectors are Food (6) and
Buildings (6). These results conﬁrm how chemistry is investing in
innovations oriented to sustainability, in particular when the same
chemistry involves industrial sectors like Food and Buildings
(Federchimica, 2019).
Fig. 7. Temporal distribution of the papers: Conceptual vs Empirical. Source: Authors’
elaboration.
Fig. 8. Classiﬁcation of the papers. Source: Authors’ elaboration.
C. Silvestri, L. Silvestri, A. Forcina et al.
Journal of Cleaner Production 294 (2021) 126137
14
7.5. Geographical focus
The geographical origin of articles was obtained considering the
country of the ﬁrst author (Fig. 10). The analysis shows that USA is
the country with the largest number of papers (24 papers, that
correspond to 26.7% of the total), followed by Italy and the UK (8
papers per country). However, if the analysis focuses on the au-
thors’ continent of origin, the scenario changes. Europe is the ﬁrst
continent with 43 articles (45.3% of the total), followed by North
America with 26 papers (27.4%) and Asia with 16 papers (16.8%)
(Fig. 11).
Fig. 12 shows the temporal distribution of articles in relation to
the country of the ﬁrst author. As it is possible to see, since 2002
Europe and USA are the countries that ﬁrst investigated such topics.
In particular, during last years, when sustainability and CE began to
be relevant for the world of business. Asia started its research ac-
tivity with a slight delay, with only one article published in 2005,
however, since 2012, the number of publications starts to increase.
7.6. Sustainability
One of the aims of this study was to understand how CG is
approaching the global sustainability. For this reason, articles were
analyzed taking into consideration the TBL context. The structural
dimension and the sustainability category were identiﬁed basing
on the concept of Corporate Social Responsibility (i.e. Jamali et al.,
2006; Knoepfel, 2001) and on SLR developed within the sustain-
ability context (Franciosi et al., 2020; Sassanelli et al., 2019; Ülgen
et al., 2019). Fig. 13 shows how the environmental dimension is
the most studied, with 44 articles in a total of 95. Such result shows
how the GC concept is mainly related to the environmental aspect
of sustainability, although sustainability takes into consideration
also social and economic dimensions.
Instead, articles that analyze simultaneously environmental and
economic dimensions are 24 in a total of 95. This is a particularly
relevant result, because it demonstrates how much the sustainable
development is strongly related to the economic dimension and,
therefore it should not be neglected or considered in conﬂict with
the environmental and social dimensions. In fact, the economic
development is essential for creating employment and having
economic resources that is possible to invest in environmental
protection.
A total of 20 articles out of 95 address sustainability considering
all the three dimensions, demonstrating how, along with business
models based on CE, also GC starts to involve the global sustain-
ability concept. Finally, 7 articles discuss economic and social as-
pects. GC and chemical companies begin to focus attention on
prevention and human health, not only for consumers and citizens,
but also for their employers.
Fig. 14 shows the temporal weight of TBL. As it is possible to see,
the environmental dimension has the greater importance among
collected articles. Fig.16 also highlights two aspects: (1) the domain
of the environmental dimension, and (2) the larger growth of
environmental dimension in respect to other two dimensions. This
result can be explained through the strong attention that has been
paid from society to environmental topics, as well as the increasing
number of discussions on climate change and corporate scandals
Fig. 9. Industrial sectors. Source: Authors’ elaboration.
Fig. 10. Number of papers per country of the ﬁrst author. Source: Authors’ elaboration.
Fig. 11. Continents of the ﬁrst author. Source: Authors’ elaboration.
Fig. 12. Temporal distribution of papers considering different continents. Source:
Authors’ elaboration.
Fig. 13. The TBL dimensions. Notes: Env (environmental), Env Ec (Environmental and
economic), Env Ec Soc (Environmental, economic and social), Env Soc (Environmental
and social). Source: Authors’ elaboration.
C. Silvestri, L. Silvestri, A. Forcina et al.
Journal of Cleaner Production 294 (2021) 126137
15
happening in the environmental area (Ülgen et al., 2019).
8. Discussion
The analysis that was carried out and the identiﬁcation of
clusters allowed to appropriately answer the Research Questions in
a timely manner. The analysis of the ﬁrst cluster allows us to answer
to the ﬁrst three questions:
RQ1 e How are ﬁrms dealing with green chemistry issue?
RQ2 e What are the obstacles and the possible solutions
encountered by the industrial system?
RQ3 e How are governments and academic research addressing
the policymaking in GC?
The chemical sector demonstrated a positive attitude towards
achieving sustainability and the implementation of GC principles.
Furthermore, although the environmental issue is particularly
discussed by the chemical sector at global scale, several barriers still
slow down the new sustainable business model adoption. First of
all, the lack of politic programs aimed to support ﬁrms for the
implementation of green technologies. Governments could exert
social pressure on chemical ﬁrms and develop mandatory guide-
lines to promote green thinking within companies, through a
proper legislation.
According to Green (2016), governments could also provide
ﬁnancial incentives to companies for the human capital formation.
Hence the importance to establish a partnership with academic
institutions, which would have the hard task of training engineers,
scientists and future leaders with experience in sustainability and
the ability for successfully implementing GC principles. The
“chemical leasing” is today a good way to establish a productive
collaboration, but more should be done. The cooperation among
governments, industry and academic institutions appears to be the
key aspect for addressing the issue of the creation of a sustainable
society, where innovative technology and the green thinking can
face such huge global challenge (Kirchhoff, 2005).
According to Matus et al. (2012), the GC gives innovative an-
swers for understanding how to implement the scientiﬁc knowl-
edge in complex scientiﬁc problems. In other words, GC, together
with GE, allows “to use scientiﬁc knowledge to reconcile a very real
need for chemical production and use with the desire to reduce the
hazards e global, physical, and toxicological e associated with these
activities” (Matus et al., 2012, p.193). For this reason, GC is bound to
establish a close collaboration with governments.
The realization of sustainable chemical substances demands
several investments in terms of human and economic resources.
For such reason, it is necessary a synergy among academic in-
stitutions (science), industry (realization of products/services) and
governments. The latter should promote politics oriented (1) to
economically support the most virtuous companies or those that
are concretely oriented to approaches for sustainability and CE; (2)
to sensitize citizens to these topics, and (3) to design training ac-
tivities related to GC. In particular, citizens, through their choices in
terms of consumption and lifestyle, are able to carry out a social
pressure for both politics and companies.
The synergy between science and enterprises is fundamental to
help the transition from an innovative solution, that has been
developed in laboratories, to the actual implementation in chemi-
cal sector.
If GC is implemented in an efﬁcient way, it allows to overcome
conﬂicts between the economic growth and environmental bene-
ﬁts (Matus et al., 2012). Governments and enterprises should
establish this awareness through a complex process of cultural
exchange, that is, at the same time, stimulating and necessary.
According to Albayati and Arrak (2019), “sustainability is very
important in this world at this time” (p. 2727).
As stated by Giraud et al. (2014), “Together, we can accomplish
what no single company can do alone” (p.2241).
Finally, Table 5 summarizes methodologies and interdisciplinary
approaches observed in papers that address research questions
RQ1, RQ2 and RQ3. In particular, Table 7 highlights how GC topic
can be referred to different contexts and analyzed through multiple
approaches.
8.1. RQ4 e what are the recommended practices for the
implementation of GC by academics and practitioners?
As might be expected, tools and practices play a fundamental
role for enabling a proper GC implementation. As stated by the
economist Drucker (2005), to manage and improve a system, it
should be possible to measure it. Such concept can be also applied
to chemistry.
The “green metrics” is a widely debated topic, as proven by the
number of articles presented in this section. The topic is continu-
ously subject to changes and improvements (Calvo-Flores, 2009),
however there is a common opinion that an all-inclusive method
for assessing the real sustainability of a chemical reaction or pro-
cess does not still exist (Gonzalez, and Smith, 2003).
On the other hand, several authors agreed with the idea that GC
principles have to be combined with GE and products that have
been studied from the point of view of the whole life cycle (Lankey
and Anastas, 2002). LCA appears as one of the most analyzed
methodology, for both theoretical and empirical studies. Indeed,
according to Lankey and Anastas (2002), LCA helps to clearly deﬁne
advantages related to environment, as well as the economic ben-
eﬁts derived from the implementation of technologies related to GC
and CE. Measurement indices also appear relevant. In this context,
in 2014, Ruiz-Mercado et al. (2014) introduces a new methodo-
logical approach, proposing a simultaneous implementation of two
methodologies: (1) GREENSCOPE and (2) LCA. This approach allows
the design sustainable chemical processes with an overall positive
impact. Furthermore, the authors highlighted how for the devel-
opment of systems that are both sustainable and CE-oriented, a
greater integration of tools is demanded. This integration can lead
to a change for the industrial perspective toward a systemic
approach.
Through this SLR, it was possible to ﬁnd that several authors are
in agreement in combining the use of LCA with other tools, such as
metrics.
Practical studies showed several case studies where GC princi-
ples have been implemented in a successful manner.
One of the main purposes of articles was to promote GC para-
digms, showing the beneﬁc effects for the environment and pre-
senting efﬁcacy of methodologies and guidelines.
Finally, also in this cluster, the close relation between GC and CE
Fig. 14. Temporal
distribution
of
papers
that
involve (TBL).
Source:
Authors’
elaboration.
C. Silvestri, L. Silvestri, A. Forcina et al.
Journal of Cleaner Production 294 (2021) 126137
16
emerged. Chemistry is generally applied to each industrial process
and then its application has to be rethought in a way that both
sustainability and a CE perspective are taken into account.
Furthermore, GC and CE share same environmental purposes,
including the beneﬁts for society and human health.
The great challenge of the 21st century for the chemical industry
is the transition towards green and sustainable manufacturing
processes, through a more efﬁcient use of raw materials, reducing
waste and avoiding the use of hazardous and toxic substances
(Sheldon, 2016). The union of paradigms allows to achieve the
common purpose.
Finally, Table 6 summarizes methodologies and interdisciplinary
approaches observed in papers that address research questions
RQ4.
8.2. RQ5 e which are the comprehensive studies that show
experimental applications of GC to industrial processes?
In the third cluster, collected articles are focused on technical
aspects related to chemical processes that have been designed for
industrial purposes. Such experimental applications have been
designed for pharmaceutical, food and cement sector. The analysis
of the database also showed that the three clusters are focused on
the same industrial sectors.
What emerges from studies is that there is a growing interest to
sustainability and GC is widely considered as the main tool to
achieve it. The analysis of papers shows how the use of waste
represents a relevant topic for GC implementation. This demon-
strates how GC is getting closer to CE, which aims to be a closed
system and then without waste generation. Table 7 lists main ar-
ticles that address the research question RQ5 and, in particular,
articles where authors applied GC focusing on waste management.
8.3. RQ6 e what is the relation between GC and the TBL
framework?
The analysis of papers shows that chemical industry is able to
contribute to a fair transition towards a greater economic, envi-
ronmental and social sustainability. The main focus of GC is the
environment, since it is part of GC purposes for preventing waste
generation and assessing environmental impacts of products and
processes (Lozano et al., 2014a).
According to Manley et al. (2008), GC is “an innovative, non-
regulatory, economically driven approach towards sustainability”
(p. 743) and deﬁned as “the design, development, and imple-
mentation of chemical products and processes to reduce or elimi-
nate the use and generation of substances hazardous to human
health and the environment” (p.743). Furthermore, Loste et al.
(2019) stated that such approach focuses on reduction of risks
associated to products directly at their source. Therefore, it is
possible to deduce that the relationship between GC and sustain-
ability is particularly strong and many factors should be involved to
address the challenge of a more sustainable development on a
global scale. Kirchhoff (2005) asserted that “green chemistry is an
important tool in achieving sustainability” (p.237).
This SLR also shows that, among TBL pillars (environment, social
and economic), GC is strictly connected to the environmental
dimension. However, although GC is born with environmental
purposes, industrial chemistry faces several global challenges,
including environmental and economic issues (job creation and
business survival), and social (safety and health protection of citi-
zens, workers and consumers). In other words, chemical industry
Table 6
Articles related to RQ4.
RQ4 - What are the main tools or practices that academics and practitioners recommend for the implementation of green chemistry?
Authors
Methodology Approach
Interdisciplinary Approach
Applications
Lankey and Anastas
(2002)
LCA
GC And GE Technologies
Process Catalysis
Gonzalez et al. (2003)
GREENSCOPE
LCA and GE Principles
Process Synthesis
Zhang et al. (2005)
GC Principles
Process System Engineering
Process Synthesis And Design
Curzons et al. (2007)
Fast Life Cycle Assessment
(FLCA)
Engineering
Process Synthesis
Van Der Vorst et al. (2009) LCA
Exergetic Life Cycle Assessment (Elca)
Preparative Chromatographic
Separation Processes
Tabone et al. (2010)
LCA
GC and GE Technologies
Green Design
Tian et al. (2012)
Multi-Criteria Decision
Analysis Method
EG
Process Synthesis
De Soete et al. (2013)
LCA
Exergy Analysis (Ea) And Exergetic Life Cycle Analysis (Elca)
Active Pharmaceutical Ingredient
Hunt et al. (2014)
LCA
Engineering
Phytoextraction
Jim
enez-gonz
alez and
Overcash (2014)
LCA
Life Cycle Inventory, Life Cycle Impact Assessment
Active Pharmaceutical Ingredient
(Api)
Ott et al. (2014)
LCA
GC And GE Technologies
Process Catalysis
Ruiz-Mercado et al.
(2014)
LCA
Greenscope
Synthesis And Design Of Chemical
Processes
Cespi et al. (2016a)
LCA
GC and GE Principles
Production From Biomass
Coish et al. (2016)
LCA
Interdisciplinary Approach (Systems Thinking): Synthetic Chemistry,
Toxicology, Biology, Pharmacology, And Ecology.
Synthetic Chemicals And
Molecular Design
Limleamthong et al.
(2016)
LCA
Data Envelopment Analysis (Dea)
Screening Of Solvents For Co2
Capture
Subramaniam et al.
(2016)
LCA
GC and GE Technologies
Process Catalysis
García-Bustamante et al.
(2018)
Analytical Hierarchy Process (AHP).
Chemical Processes
Sheldon (2018)
E Factors And Atom
Economy
CG and EG
Process Catalysis
Rosenthal and Lütz (2018) LCA
Environmental Factor (E Factor)
Active Pharmaceutical Ingredient
Fadel, C., Tarabieh (2019) LCA
Industrial Environmental Index
Solvent-Based Processes
Source: Authors’ elaborations
C. Silvestri, L. Silvestri, A. Forcina et al.
Journal of Cleaner Production 294 (2021) 126137
17
should redesign their business models, policies, processes and
products, following an approach oriented to sustainability, CE and
all the three TBL pillars. SC represents the proper system able to
apply such approach, containing in its interior the application of GC
principles. In other words, SC ensures a safer use of chemistry for
citizens, consumers and workers, as well as environmental-friendly
solutions. Referring to the concept of the three TBL perspectives, GC
is able to generate beneﬁts “to human health (cleaner air, water,
safer food, and goods), the environment (less consumption of
feedstocks and energy, reduction of pollution or of ecotoxicity), and
business
(better
performance,
reduction
of
raw
materials,
improving competitiveness)” (Loste et al., 2019 p.785).
All the 95 papers were analyzed and classiﬁed to understand
how GC is approaching Sustainability and CE in a TBL perspective.
In particular, Fig. 15 shows the number of papers that involve the
three TBL pillars for each of the three clusters, allowing to better
understand the GC evolution towards global sustainability.
As shown in Fig.15, the environmental topic is the most debated
and each of the study considers and analyzes environmental
aspects.
As mentioned before, the relationship between GC and sus-
tainability is relevant. The reason can be found in the beneﬁcial
effects derived from the implementation of GC principles, including
the reduction or the elimination of waste, hazardous materials and
the use of renewable materials (Hjeresen et al., 2002). What
emerges from the analysis of articles is that the concept of sus-
tainability is implicit on GC.
The economic aspects were widely debated in 40 articles. This
result demonstrates how ﬁrms are abandoning the so-called
“enduring myth” (Hjeresen et al., 2002), according to which it is
not possible to reach environmental beneﬁts without a reduction in
proﬁt. Indeed, companies that implemented GC principles gener-
ally increased their proﬁt, through the reduction of waste and the
recovery of resources (Hjeresen et al., 2002; Loste et al., 2019).
Furthermore, technological innovations can support companies to
reach both economic and environmental goals (Freire, 2018;
Hjeresen et al., 2002).
The social is the less studied dimension, with only 27 articles.
This fact is also in accordance with some results found in literature.
In fact, some authors (Agthe et al., 1978; B€
oschen et al., 2003);
Table 7
Articles that address the research question RQ5.
RQ5 e Are there any comprehensive studies that show experimental applications of GC to industrial processes?
Authors
Methodology approach
Industrial sector of applications
Waste used
Mohanty et al. (2002)
Experiment
Rubber-Plastic
Agricultural and biomass feedstock
Lowe and Milbradt (2011)
Experiment
Crop Protection Industry
Organic waste
Barba et al. (2015)
Experiment
Food
Microalgae
Mamat et al. (2014)
Experiment
Industrial sector in general
Agricultural
Walther (2014)
Experiment
Chemical industry
Renewables
Georgi
ades et al. (2015)
Experiment
Chemical industry
No, but the aim is to generate minimal waste.
Izatt et al. (2015)
Experiment
Metallurgy
No, but the aim is to generate minimal waste.
Cespi et al. (2016b)
Experiment
Industrial sector in general
Epichlorohydrin industry
Pathan and Ahmad (2016)
Experiment
Industrial sector in general
Bio-based hybrid nanocomposite coatings
Romani et al. (2016)
Experiment
Food
Agricultural
Hong and Chen (2017)
Experiment
Chemical industry
Polymers
Coppola et al. (2018a)
Experiment
Buildings
Mortar and concrete
Font et al. (2018)
Experiment
Buildings
Agricultural
Halli et al. (2018)
Experiment
Metallurgy
Metal
Yang et al. (2018)
Experiment
Metallurgy
Metal
Capezza et al. (2019)
Experiment
Pharmaceutics
Agricultural
Molino et al. (2019)
Experiment
Food
Agricultural
Campos et al. (2020)
Experiment
Food
Agricultural
Cui et al. (2020)
Experiment
Chemical industry
Liquid crystal displays
Debbarma et al. (2020)
Experiment
Buildings
Industrial and agricultural
Source: Authors’ elaborations
Fig. 15. TBL pillars in the selected clusters. Source: Authors’ elaboration.
C. Silvestri, L. Silvestri, A. Forcina et al.
Journal of Cleaner Production 294 (2021) 126137
18
highlighted that GC studies concerning social aspects are not yet
adequately debated, being GC mainly focused on environmental
protection (Lozano, 2012; Lozano et al., 2014b).
The cluster in which social aspects are most debated is the
“Strategy” cluster, with 15 articles on a total of 23 papers. This result
shows that also GC is directing its attention towards social aspects.
A more in-depth analysis shows that the three TBL dimensions
are mainly studied by “conceptual” articles. They generally propose
models and frameworks, analyzing the sustainability concept in its
completeness.
Green (2016) highlighted that GC can have beneﬁcial effects not
only for environment, but also for pursuing business objectives and
realize useful products. The author asserted that the model can help
research and chemical production to preserve the environment and
human health. However, according to Green (2016) and Schwager
et al. (2016), it is necessary to improve the knowledge about GC.
Furthermore, in order to explore the potential of GC, they devel-
oped innovative business models able to involve all the actors,
including industry.
Matus et al. (2012) afﬁrmed that GC principles allow to improve
the life quality, protecting the environment and the human health.
The possibility to achieve
a global sustainability is also
addressed by articles containing empirical studies. For example,
Lange (2009) stated that the possibility to reach a more sustainable
development is gaining even more importance, involving envi-
ronmental, economic and social aspects. The author demonstrated,
through a case study, how the application of GC principles allows to
reach the three TBL dimensions.
The analysis of articles also highlighted the close relationship
between sustainability and the circular economy concept. Accord-
ing to Linder (2017) chemistry and, in particular, GC can represent
the main driver for the circular economy. In fact, the authors stated
that chemistry has the key role to lead society towards a sustain-
able future, embracing all the three TBL dimensions.
Sheldon (2017) showed that both sustainable production and
circular economic perspective can be reached through a chemical
production based on biological raw materials, as well as reducing
waste and through a more effective energetic use. The close rela-
tionship between GC and circular economy is very signiﬁcant,
because it shows how an approach based on the three TBL di-
mensions can lead to an easier achievement of objectives. In fact,
sustainability and circular economy concepts generally share some
concerns, such as the attention for the current consumptions that
can affect the wellbeing of future generations (Geissdoerfer et al.,
2017), as well as the necessity to have greener technologies. Ac-
cording to Kirchhoff (2005), GC offers tools to ﬁnd solutions to
these global challenges.
Moreover, the chemical sector supports most of the industries.
For this reason, it should respond to relevant challenges related to
the topic of the sustainability and CE. According to Clark (2007),
before addressing key issues such as the transition towards
renewable resources, the avoiding of hazardous and polluting
processes and the design of eco-compatible and safety products, it
should develop “chemical sustainable products” and “green” sup-
ply chains. The use of renewable biological resources, as source of
precious chemical substances, represents a fundamental starting
point, as well as a common practice in many countries of the world
(Ciriminna et al., 2020). A bioeconomy can contribute to a sus-
tainable and favorable future. For achieving the well-deﬁned bio-
economy purposes, several obstacles should be overcame and the
same GC, through its principles, is trying to do it (Fitzgerald, 2017).
On the base of previous considerations, it is possible to consider
CE as a macrosystem able to generate economic, social and envi-
ronmental values (TBL pillars) and to reuse materials without any
waste generation. In such macrosystem, the sub-system SC, thanks
to GC principles, takes part in production phases and in reuse of
materials. In fact, GC is the main tool of the sustainable chemistry,
and the increasing orientation towards TBL pillars is facilitating its
integration with the CE macrosystem (Fig. 16).
Then, this study suggests that GC, embracing the TBL perspec-
tive, facilitates the transition towards sustainability and CE. How-
ever, the conditio sine qua non corresponds in a signiﬁcant change
in consideration of environmental, economic and social issues, that
should be perceived as codiﬁed in a regulatory framework and
managed by private companies.
According to Matus et al. (2012), the GC application allows to
simultaneously answer to several challenges related to sustain-
ability and CE.
However, in order to make this possible, it is necessary a politic
able to support the integration among tools and subsystems in a
holistic way.
The framework in Fig. 16 aims to understand such systemic
vision. Governments, research, enterprises, citizens and consumers
are the key players of this framework. Each of them, in different
ways, could contribute to reach the idea of sustainability and CE.
The conditio sine qua non is the achievement of a radical cultural
change for addressing global challenges and aimed to a unique
objective, that is the social well-being of all individuals.
9. Limitations and future research
The main aim of this work was to investigate how much the
“chemical philosophy” (Voigt et al., 2013, p. 149) has been under-
stood and shared by both science and industry. The focus was on
the GC implementation in industry in a perspective of sustainability
and CE, giving an overview on how the existing literature addressed
the relations among GC, sustainability and CE. Results highlighted
how GC represents the main tool for realizing principles and con-
cepts of SC and, more in general, of Sustainability and CE. It was
possible to highlight the growing involvement of GC for the
Fig. 16. Framework. Source: Authors’ elaboration. Notes: CE can be considerate as a
macrosystem able to generate economic, social and environmental value (TBL pillars)
and to reuse materials without any waste generation. In such macrosystem, the sub-
system SC, thanks to GC principles, takes part in production phases and reuse of
materials. In fact, GC is the main tool of the sustainable chemistry, and the increasing
orientation towards TBL pillars is facilitating its integration with CE macrosystem.
C. Silvestri, L. Silvestri, A. Forcina et al.
Journal of Cleaner Production 294 (2021) 126137
19
application of the three TBL pillars. In fact, GC is no longer oriented
only to reach environmental sustainability, but also to social and
economic dimensions, and then following a systemic approach.
However, although the scientiﬁc approach that was used for
reaching the purposes of this research, the article presents some
limitations. As ﬁrst, although a transparent approach acknowl-
edged from literature, the article can be limited by some subjec-
tivity in the categorization of information. To limit this aspect, the
deﬁnition of structural dimensions and analytic categories was
structured as suggested by existing literature. In particular, studies
related
to
sustainability
and
CE
topics
were
taken
into
consideration.
Another limitation is represented by the typology of documents
included in the database that consists exclusively in scientiﬁc pa-
pers, without considering the so-called “grey literature” or non-
academic literature (Geissdoerfer et al., 2017; Merli et al., 2018).
Indeed, it appears clear how non-academic literature has a funda-
mental role in giving GC implementation practices, especially in
industrial sector. Royal Society of Chemistry (https://www.rsc.org/)
and ACS Chemistry for Life (https://www.acs.org/content/acs/en.
html) represent the main communities that contribute to scienti-
ﬁc publications on GC issues, but they deal only in making reports
the synthetize strategies, activities and relative impacts. More in
detail, their purpose is twofold: (1) to connect people with chem-
ical science through knowledge, skills and community concepts,
and (2) to make chemical enterprise to grow toward a more sus-
tainable approach, improving the life of people.
The speciﬁc research criteria used in this work represent a
further limitation. Indeed, the study focus on GC topic, neglecting
the relevant topic of the GE, that is described in the Introduction
section. Indeed, in this ﬁrst phase of the study, the attention of
authors was focused on chemistry. However, both concepts are
closed to each other, and this may results in loss of information, in
particular in terms of industrial applications.
These limitations can help to improve and encourage the future
research on this topic. As ﬁrst, it is possible to extent the research to
the “grey literature”, as well as to include GE within research
keywords. This analysis could also allow to better investigate the
relations among concepts described here and the analysis of case
studies, as well as to study relations with CE.
Finally, the review showed that the social impact of such topics
has gained a growing interest within the scientiﬁc community
during last years. It should be interesting to investigate how GC, SC,
Sustainability and CE affect the social well-being.
10. Conclusions
Chemistry, considered in terms of science and industry, has a
crucial role in ﬁnding technological solutions to global changes,
such as the climate change and the degradation of natural re-
sources. In particular, chemistry continuously investigates new
roads to realize the largest number of products in an ever more
efﬁcient way, reducing waste and protecting human health and
environment. In fact, GC, being getting closer to TBL pillars, repre-
sents the main tool for the chemical industry in implementing the
SC system and then realizing the transition towards sustainability
and CE.
In the present study, a systematic literature review of the state-
of-the-art of research concerning green chemistry, circular econ-
omy and global sustainability was conducted. 95 papers were
examined, published between 2002 and April 15, 2020.
The analysis made it possible to answer the six proposed
research questions, concerning the role of companies and in-
stitutions in the development of green chemistry, as well as ﬁnd
main limitations, application tools and impacts on society.
In this regard, it is possible to afﬁrm that companies have a good
aptitude in developing green chemical applications, although the
main limitation is represented by the lack of politic programs
aimed at supporting ﬁrms for the implementation of green tech-
nologies. Finally, the results of the analysis showed that almost all
researches are focused on environmental impacts deriving from
green chemistry applications, while 44% are focused on economic
impacts and only 25% on social impacts.
This study, from a theoretical point of view, aims to identify a
comprehensive framework to explain how GC, sustainability and CE
are interconnected, and how GC is assuming a central role to reach
sustainability during last years. In fact, GC is no longer oriented only
to reach environmental sustainability, but also to social and eco-
nomic dimensions, and then following a systemic approach. In view
of this consideration, it is important to invest more in research to
encourage the development of new processes or chemical syn-
thesis, in addition to existing ones. Research should be conducted in
an academic context, facilitating the subsequent use in industrial
applications (Manley et al., 2008).
The relation between academic research and chemical industry
is strong, being highly interested in transformations and properties
of substances. On the other hand, chemical research, that it
developed within an industrial context, transforms academic
research results in new technology and products, improving the
well-being and quality of life (Federchimica, 2019).
Following this approach, this study also helps enterprise man-
agement to reach a cultural change towards sustainability and CE.
Furthermore, it demonstrates how GC implementation allows to
reach both economic and environmental sustainability, that are
fundamental aspects for the growth of ﬁrms. Results let the authors
to encourage ﬁrms in considering the possibility to generate both
proﬁts and environment protection.
Being the chemical industry upstream in supply chains, it has
more opportunities than other industrial sectors to take part in a
wide range of industrial processes, thanks also to its technological
advances.
A stricter interaction among ﬁrms should be realized, promoting
business models such as the chemical leasing, that allows to use
chemical products in a more efﬁcient way, as well as the waste
reduction. In fact, ﬁrms that work together can realize more than
what they can do individually. Furthermore, a stricter interaction
between ﬁrms and academic research is demanded. In fact, sus-
tainability is a topic that concerns everyone, because several ac-
tions are needed to reach the sustainability. Today, GC frontiers
represent a global challenge for management and research, as well
as an incentive towards more sustainable processes and products.
CRediT authorship contribution statement
Cecilia Silvestri: Conceived and designed the analysis, Collected
the data, Contributed data or analysis tools, Performed the analysis,
Wrote the paper. Luca Silvestri: Conceived and designed the
analysis, Collected the data, Contributed data or analysis tools,
Performed the analysis. Wrote the paper. Antonio Forcina:
Conceived and designed the analysis, Collected the data, Contrib-
uted data or analysis tools, Performed the analysis. Gianpaolo Di
Bona: Conceived and designed the analysis, Collected the data,
Contributed data or analysis tools, Performed the analysis. Dome-
nico Falcone: Conceived and designed the analysis, Collected the
data, Contributed data or analysis tools, Performed the analysis.
Declaration of competing interest
The authors declare that they have no known competing
ﬁnancial interests or personal relationships that could have
C. Silvestri, L. Silvestri, A. Forcina et al.
Journal of Cleaner Production 294 (2021) 126137
20
appeared to inﬂuence the work reported in this paper.
Appendix A. Supplementary data
Supplementary data to this article can be found online at
https://doi.org/10.1016/j.jclepro.2021.126137.
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24

Journal of Cleaner Production 10 (2002) 85–92
www.cleanerproduction.net
Environmental efﬁciency analysis for ENI oil reﬁneries
Maurizio Bevilacqua a,*, Marcello Braglia b
a Dipartimento di Ingegneria Industriale, Universita
` degli Studi di Parma, Viale delle Scienze, 43100 Parma, Italy
b Dipartimento di Ingegneria Meccanica, Nucleare e della Produzione, Universita
` degli Studi di Pisa, Via Bonanno Pisano 25/B, 56126 Pisa,
Italy
Received 26 November 2000; accepted 23 April 2001
Abstract
This paper describes a new model for evaluating the (relative) environmental efﬁciency of the seven AgipPetroli oil reﬁneries
set up in Italy during the 4-year period (1993–96). In particular, the environmental impact is considered in terms of air emissions.
The Data Enveloped Analysis is proposed as an objective benchmark. This multi-criteria technique makes it possible to evaluate
the relative environmental efﬁciency considering six different types of emissions (CO, CO2, SO2, etc.) and the annual quantity of
oil processed. 2001 Elsevier Science Ltd. All rights reserved.
Keywords: Air pollution; Oil reﬁnery; Data envelopment analysis; Environmental performance indicator
1. Introduction
The petrochemical products market has experienced a
great transformation during the last few years, with a
change in products in keeping with market demands. Oil
reﬁneries of the European Community have invested a
great deal of money and resources to update their pro-
duction plants in order to conform with the changing
situation and to keep their offer in line with the mar-
ket demand.
The promotion of new standards to monitor the con-
trol and reduction of pollutant emissions, as for example
those adopted in Italy [1,2], has urged oil companies to
place a greater emphasis on improving their anti-pol-
lutant technologies.
Here, the introduction of the ISO 14031 standard
makes it possible to deﬁne the environmental perform-
ance audit as a recursive task within the ﬁrm, providing
at the same time the tools to be used to deﬁne the per-
formance indicators [3,4]. Such performance indicators
make it possible to perform a benchmarking analysis
between ﬁrms operating in the same industrial sector and
have proved to be effective communication tools for the
* Corresponding author. Tel.: +39-0521-905860/+39-071-2204874;
fax: +39-0521-905705/+39-071-2804239.
E-mail address: maurizio.bevilacqua@unipr.it (M. Bevilacqua).
0959-6526/01/$ - see front matter 2001 Elsevier Science Ltd. All rights reserved.
PII: S0959-6526(01)00022-1
drafting of environmental reports. The environmental
performance indicators may be used both at a macro
level e.g. by governmental institutions for a control pur-
pose, or at a micro level e.g. within the ﬁrm to assess
the actual performance [5].
Reﬁnery energy consumption remained relatively con-
stant during the period 1980–1990 as investment in con-
servation measures compensated for increases due to
additional processing for product quality and demand
changes. This situation will not be mirrored in the period
1990–2010. Reﬁnery speciﬁc energy consumption is
forecasted to grow by 50%–75% by the year 2010 (from
5.8% to between 8.7% and 10.2% of reﬁnery intake) in
order to meet future product quality and demand changes
with limited economic opportunities for further energy
conservation [6].
This analysis is based on the work of A.D. Little [7],
that describes two different scenarios for the possible
developments of the oil market for OECD Europe: Fuel
Oil Decline (FD) in which the percentage of fuel oil is
forecasted to decrease by one third, and Sustained
Growth (SG) in which the percentage of fuel oil is con-
sidered constant (see Table 1).
In the FD scenario the speciﬁc energy consumption is
forecasted to increase by some 75% from 1990 to 2010.
Over 50% of the additional energy is due to the increase
in
required conversion
capacity,
(that reﬂects
the
reduction in demand for the heavy end of the barrel due
86
M. Bevilacqua, M. Braglia / Journal of Cleaner Production 10 (2002) 85–92
Table 1
Speciﬁc energy consumption increase for European reﬁneries
Year–Scenario
1990
2010 FD
2010 SG
Oil processed
613
605
453
[Mt/yr]
speciﬁc energy
consumption
2380
4218
3588
[MJ/t]
Energy penalty (referred to 1990)
1838
1208
[MJ/t]
Gasoil quality (cetane)
607
0
Gasoil quality (% S)
152
168
Fuel oil quality
89
449
Motor gasoline quality (lead)
272
240
Motor gasoline quality (benzene)
379
334
Increase in conversion capacity
339
17
to inter-fuel competition), and to the correction of the
consequential deterioration in distillate cetane quality. A
quote of about 35% of the increase is due to improve-
ments in gasoline quality, in particular for the reduction
of benzene and aromatics levels.
In the situation depicted by the SG scenario, no further
conversion capacity will be needed. The forecasted
growth of 50% in the speciﬁc energy consumption is
40% due to energy consumption in new residue desul-
phurisation capacity with the gasoline quality changes
responsible for a further 50% of the increase.
As a consequence, in the two scenarios evaluated, by
the year 2010 absolute CO2 emissions, which are highly
dependent on the product demand projections and there-
fore on the reﬁnery intake, are forecasted to range from
98 Mt/y to 156 Mt/y, an increase of between 40% and
65% on 1990 levels.
Similar considerations can be made for sulphur diox-
ide emissions. In a survey covering the year 1992, 84
European reﬁneries, processing about 80% of Western
European crude oil throughput, provided comprehensive
data on sulphur in crude oil, petroleum products and sul-
phur emissions in air. This has been compared with pre-
vious surveys covering the years 1979, 1982, 1985 and
1989. The data for Western Europe as a whole illustrate
the following trends [8]:
1. Total crude runs since 1979, having fallen back shar-
ply, have been slowly recovering since 1985.
2. The percentage of sulphur in crude oil has declined
from about 1.4% to fractionally more than 1% — the
lowest point was reached in 1985.
3. Sulphur in oil products for combustion has decreased
by some 60% since 1979. Over the period 1979–1992,
sulphur recovery in the reﬁneries has been improved
from just over 1 Mt/y (10% of the sulphur input) to
nearly 1.9 Mt/y (more than 30% of input), with a
crude oil throughput passing from 680 Mt during
1979 (1.45 wt% sulphur in crude) to 582 Mt in 1992
(1.05 wt% sulphur in crude).
4. Reﬁnery SO2 emissions have followed the crude runs
pattern; however, there has been a steady improve-
ment in relative terms.
Another report examines the sulphur balances of Eur-
opean oil reﬁneries and the sulphur content of oil pro-
ducts for the year 1995 [9]. Comparisons are made with
data from the 1992 CONCAWE survey [8]. Data are
included on the distribution of sulphur levels in the
major product groups to allow assessment of the impact
of regulatory measures. Compared with 1992, there was
a signiﬁcant decrease in sulphur in combustion products
with a corresponding increase in sulphur recovered by
reﬁneries. To provide further continuity with the 1992
report, some data are broken down to show the situation
in four different European regions.
In the US, the EPA and Amoco (Yorktown, VA) [10]
carried out a comprehensive study of emission from the
Amoco oil reﬁnery in Yorktown, Virginia. The object of
the study was to explore pollution prevention options
from petroleum reﬁnery releases to all environmental
media (air, water and land) and to investigate the impact
that such options could have on human and environmen-
tal exposure to airborne emissions. This paper focused
on air pollution from an oil reﬁnery using a technique
based on Data Envelopment Analysis (DEA) to evaluate
the environmental performance of the plants.
2. Agippetroli oil reﬁneries
AgipPetroli is an Eni Group company and performs
reﬁning activities in Italy and Germany. In Italy, Eni
wholly owns and operates six reﬁneries and holds a 50%
interest in the Milazzo reﬁnery in Sicily. Eni owns a
reﬁning capacity (balanced with conversion capacity) of
859,000
barrels/day,
with
approximately
787,000
barrels/day capacity in Italy, accounting for approxi-
mately 39% of Italy’s domestic reﬁning capacity, as
shown in Table 2.
Each AgipPetroli Italian reﬁnery is specialised on the
basis of its logistical conﬁguration, geographic location
and integration with other Eni operations.
The Sannazzaro, Venice–Porto Marghera and Taranto
reﬁneries supply gasoline and other light products to
markets in their respective geographic regions (i.e.,
northwest, northeast and southeast Italy, respectively).
The Sannazzaro and Venice–Porto Marghera reﬁneries
also supply the Swiss, Austrian and Bavarian markets.
The Livorno reﬁnery is the primary Eni production
facility for lubricants. The Gela and Milazzo reﬁneries
87
M. Bevilacqua, M. Braglia / Journal of Cleaner Production 10 (2002) 85–92
Table 2
AgipPetroli reﬁning system in Italy
Primary
balanced
Ownership
Conversion
Oil reﬁnery
reﬁning
share (%)
equivalent (%)
capacity
(bbls/d)
Priolo
100.0
25.4
170,000
Sannazzaro
100.0
38.4
160,000
Gela
100.0
129.4
100,000
Taranto
100.0
69.8
84,000
Livorno
100.0
11.0
80,000
Porto Marghera
100.0
20.4
70,000
Milazzo
50.0
57.7
80,000
Total
—
47.6
744,000
specialise in the processing of extra-heavy crudes
obtained from nearby Eni ﬁelds offshore of Sicily. The
Priolo reﬁnery specialises in the processing of high-par-
afﬁn content crude oil from the Eni Bu-Attifel ﬁeld in
Libya, which is used primarily to produce feedstocks for
the integrated production of oleﬁns in the Priolo petro-
chemical plant. The Gela reﬁnery, with the highest con-
version capacity in the reﬁnery sector, specialises in the
reﬁning of heavy crude oil coming from Eni wells in the
area and the production of lead-free gasoline (with a low
benzene and aromatic content). The Livorno reﬁnery has
recently started the operation of a modiﬁed bitumen pro-
duction plant to enlarge the reﬁnery products offer and
the adjustment of the adopted environmental manage-
ment system to meet the ISO 14001 standard.
The Sannazzaro reﬁnery occupies an area of about 200
ha. It is situated in the southwestern part of the Padania
plain, near to the most highly industrialised area of Italy.
Today the reﬁnery is equipped with three conversion
units and all the plants needed to produce high-octane
components for gasoline. It produces nearly all the elec-
tricity it needs for its functioning, while maintaining a
constant and necessary connection with the national grid.
The Sannazzaro reﬁnery has recently concluded an
exacting and important 5-year plant restructuring pro-
gramme, which has enabled it to meet the continual
changes in market demand in an adequate and ﬂexible
manner.
The Venice reﬁnery situated on the shore of the
lagoon, is today able to reﬁne crude coming mainly com-
ing from the Middle East and to deal with the market’s
increasingly rigid qualitative and quantitative requests.
The production activity has recently undergone profound
and radical transformations in line with the changes that
have characterised the petroleum industry in recent
years.
The Gela reﬁnery, located on the south coast of Sicily,
has a complex production cycle comprising distillation,
thermal conversion and a catalytic plant, and is strictly
linked to the Enichem petrochemical plant. The reﬁnery,
therefore, forms part of an important integrated cycle.
The investments made over the years have made it poss-
ible to continually develop the reﬁning structures and
place the Gela Reﬁnery among the most complex and
advanced reﬁneries in Europe, bringing the reﬁnery
capacity up to 5 million tons a year.
The Livorno reﬁnery, which occupies an area of 150
ha, is unique in the AgipPetroli reﬁning circuit, as it is
able to produce base lubricants and parafﬁns. Some pro-
cessing units have recently been updated and an
advanced co-generation power station has been built to
produce electrical and thermal energy for both the
national grid and the production units. The power plant
is made up of two turbo-gas units fuelled by gas pro-
duced by the reﬁnery and by methane.
The Priolo reﬁnery occupies an area of 400 ha in the
Bay of Augusta on the Ionian sea. The reﬁnery itself
forms part of the Priolo petrochemical complex. The pet-
roleum processing cycle installed in the reﬁnery rep-
resents one of the largest structures for the reﬁning of
crude oil in Italy and has an authorised processing
capacity of 17,600 kt/y. From crudes and straight runs,
it produces virgin naphtha, gas oil and LPG for petro-
chemical units, fuel oil for thermal electrical power sta-
tions, gasolines and kerosene, gas oils and other fuel oils
for the national and international markets. Thanks to its
having a processing cycle integrated into that of petro-
chemical production, the reﬁnery can ensure the pro-
duction of high quality gasoline in terms of low benzene
and aromatic content.
The Taranto reﬁnery, which started its operation in
1967, occupies an area of about 200 ha. The reﬁnery
plants have been gradually modernised, in accordance
with the change in demand. During 1994 a Residue Hyd-
roconversion Unit (RHU) was set up to upgrade the
reﬁnery production cycle. As a consequence of the RHU
plant installation, the environmental impact of the Tar-
anto
reﬁnery
decreased.
The
level
of
technology
employed in this plant is such as to place it at the fore-
front of reﬁning technology. In its present conﬁguration,
its units form three integrated complexes: the original
hydro-skimming reﬁnery, the thermal conversion of resi-
dues and the residual hydration plant. The technological
innovation of the reﬁnery also provides for major meas-
ures to improve instrumentation and plant control.
The Milazzo oil reﬁnery is located on the north coast
of Sicily and covers an area of about 212 ha. The reﬁnery
can handle a wide range of crudes and can produce a
wide variety of petroleum products depending on market
demand. At the beginning of the ’90s an updating pro-
gram was started to ensure a reduction of fuel oil pro-
duction in favour of distillates and an improvement in
distillates quality. The aim was to produce unleaded
gasoline and gas oil with low sulphur content.
The AgipPetroli industrial structure in Italy is based
88
M. Bevilacqua, M. Braglia / Journal of Cleaner Production 10 (2002) 85–92
on the concepts of integration and optimisation of pro-
cessing in the various reﬁneries of the circuit. This has
positive effects on production processes, intermediate
goods, on the quality of products and the economic
results of integrated supply.
Its high conversion capacity is accompanied by exten-
sive diversiﬁcation that allows production of a complete
range of fuels, combustibles and lubricants, high quality
components for ecological products, bases and additives
for lubricants, parafﬁn, bitumen, special products and
feedstock for the petrochemical sector.
3. Data envelopment technique
DEA is a mathematical programming method for
assessing the comparative efﬁciencies of Decision Mak-
ing Units (DMUs, e.g., banks, robots, schools, hospitals,
plants, etc.) where the presence of multiple inputs and
outputs makes comparison difﬁcult, see Dyson et al.
[11,12]. DEA is a non-parametric method that allows
efﬁciency to be measured without having to specify
either the form of the production function or the weights
for the different inputs and outputs chosen. This method-
ology deﬁnes a non-parametric best practice frontier that
can be used as a reference for efﬁciency measures.
The efﬁciency of a many input–many output DMU (in
this case an oil reﬁnery) may be deﬁned as a weighted
sum of its m outputs divided by a weighted sum of its
n inputs:
Efficiency of DMU jEjweighted sum of outputs
weighted sum of inputs
a1y1j+…+amymj
b1x1j+…+bnxnj
where ak is the weight of output k, bk is the weight of
input k, yki is the amount of output k from DMU j, xki
is the amount of input k in DMU j, and where Ej is
conventionally constrained by DEA to be deﬁned in the
range (0–1) [13].
Once the attributes have been deﬁned, DEA makes it
possible to obtain a ranking of all DMUs based on the
concept of their efﬁciency. With respect to s competing
DMUs, the relative efﬁciency, RPEj, for each generic
DMU j (j=1,…,s) can be evaluated by using the model:
maxRPEj
subject to

m
k1
akykj

n
i1
bixij
1 for each DMU j=1,…,s (i)
ake
k=1,…,m
(ii)
bie
i=1,…,n
(iii)
(1)
Restrictions are placed on these assessments to reﬂect
a priori judgements on inputs and/or outputs. The
weights of inputs and outputs (variables of the model)
are bounded (constraints (ii) and (iii)) to be greater than
or equal to some small positive arbitrary quantity e in
order to avoid a performance criterion being totally
ignored (e is set at zero in most DEA applications). The
relative efﬁciency of each DMU is bounded (constraint
(i)) to be lower than or equal to 1 (maximum value).
Then model (1) indicates the combination of weights
most favourable to DMU j, as compared to the other
DMUs. RPEJ represents the maximum relative efﬁciency
for the target DMU. The performances of all DMUs will
emerge when each DMU is considered in turn. As the
cost function varies from problem to problem, weights
may differ accordingly.
Model (1) is fractional linear programming, but it can
be easily transformed into the following linear program
(LP) [13]:
maxHj=
m
k1
akykj
subject to

n
i1
bixij=1
(i)

m
k1
akykj−
n
i1
bixij0 for each DMU j=1,…,s (ii)
ake
k=1,…,m
(iii)
bie
i=1,…,n
(iv)
(2)
4. The DEA air pollution model
The 1996 Agip environmental report is very thorough.
For the ﬁrst time it includes data concerning oil reﬁning
as well as lubricating oil reﬁning, solvent and additive
production, lubricating oil preparation, transport and
storage of petroleum products [14]. This is important
because in oil reﬁneries logistic activities (i.e., stocking,
transport, etc.) can also generate polluting emissions if
the plant structures or the operating procedures are not
adequate.
The relevant data are shown in an aggregate form for
the whole processing operation of the oil reﬁneries and
represent the annual amount, expressed in tons, of the
different pollutant emissions. The pollutants taken into
consideration are peculiar to the reﬁning and distribution
operations of oil products and are those assessed by the
main environmental protection regulations in force in
Italy and in the European Community.
89
M. Bevilacqua, M. Braglia / Journal of Cleaner Production 10 (2002) 85–92
The diffused emissions, not valuable through a direct
measurement, have been estimated using the method-
ologies suggested by the American Petroleum Institute
(API) and CONCAWE, the organisation of the European
oil companies for safety and environmental problems.
The pollution due to reﬁnery product distribution activity
through motor vehicles (i.e., CO emissions) is estimated
on the basis of the kilometric distance covered using the
CORINAIR emission factors.
5. Experimental results
By using the data shown in Table 3, the linear pro-
grams of model (2) are solved using LINDO software.
Table 3 headings use the acronyms TSP to identify Total
Suspended Particles and VOC for Volatile Organic
Compounds. It is worth noting the values of oil
reﬁneries’ CO2 emissions in Table 4. The values have
been evaluated and compared with those reported in
[14]. In particular, for the Gela oil reﬁnery during 1993
we have an energy consumption equal to 1,000,000 TOE
(Tons Oil Equivalent) corresponding to 109 kg of fuel
oil (FO). Considering a mean percentage in weight of
carbon in fuel oil of 85% (data from Perry’s Chemical
Table 3
Oil reﬁneries’ production and emissions
Reﬁnery–year
Oil processed
SO2
NOX
TSP
CO
CO2
VOC
[t/y]
[t/y]
[t/y]
[t/y]
[t/y]
[kt/y]
[t/y]
Gela 93
5,243,000
90,000
9200
690
1000
3300
2650
Gela 94
5,517,000
83,000
9500
710
1050
3300
2700
Gela 95
5,021,000
72,000
8800
620
840
2800
2450
Gela 96
5,320,000
68,000
7200
610
850
2600
2050
Livorno 93
3,900,000
13,000
2200
130
165
1000
1650
Livorno 94
3,900,000
1500
1850
132
165
1250
1520
Livorno 95
3,700,000
11,800
1900
132
130
1250
1550
Livorno 96
4,500,000
13,000
2000
155
152
1400
170
Milazzo 93
4,324,000
5200
1400
340
275
720
2000
Milazzo 94
5,438,000
6400
2100
420
465
1150
2500
Milazzo 95
5,448,000
6000
2400
420
495
1300
2620
Milazzo 96
4,700,000
8100
1900
380
470
1250
2550
Priolo 93
8,300,000
32,000
6100
2200
400
2300
2620
Priolo 94
8,900,000
27,000
6200
1950
405
2350
2600
Priolo 95
8,575,000
17,000
6300
1350
395
2410
2430
Priolo 96
8,350,000
17,500
6400
480
380
2390
2390
Sannazzano 93
7,897,000
6000
5200
450
340
1495
2380
Sannazzano 94
7,565,000
4950
5000
415
475
1400
2250
Sannazzano 95
7,611,000
4750
4500
430
450
1495
2200
Sannazzano 96
8,180,000
4850
5200
440
430
1550
2200
Taranto 93
4,180,000
5300
2000
330
240
805
1200
Taranto 94
4,371,000
8200
2700
520
300
1200
1150
Taranto 95
3,992,000
8100
2370
480
315
1600
1150
Taranto 96
3,970,000
8000
2250
440
305
1800
1000
Venezia 93
3,600,000
4400
1650
240
135
620
1600
Venezia 94
3,300,000
3900
1530
205
128
570
1000
Venezia 95
3,310,000
4300
1700
230
140
630
1000
Venezia 96
3,180,000
3800
1700
260
130
620
900
Engineer Handbook) we can calculate the production of
CO2 and that during the combustion process, for every
kg of carbon 3.67 kg of CO2 are formed; the CO2 pro-
duction can be evaluated as follows:
CO2 production: 109∗0.85∗3.67=3,119,500,000 CO2
kg, corresponding to 3119.5 kt of CO2. The calculated
value agrees well with the one drawn from the AgipPe-
troli environmental report (1996), and reported in the
paper (3300 kt of CO2). Another relevant question is
relative to the amount of crude oil that is converted into
CO2 by the reﬁnery. Using the AgipPetroli environmen-
tal report (1996) data we can see that for the Gela oil
reﬁnery during 1993 there is a CO2 production equal to
3,300,000 t/y, corresponding to about 900,000 t of car-
bon as CO2. With a mean percentage of carbon in crude
oil equal to 85%, the CO2 production corresponds to
about 1,000,000 t/y of crude oil that is converted into
CO2 by the reﬁnery. This value is about 20% of the oil
reﬁnery incoming crude, and does not agree with the
indication of [15] where the typical crude loss to energy
is about 6%. Such a difference can be explained by con-
sidering that the CO2 production for all ENI oil reﬁneries
is due to the sum of the contributions of both the electric
generation power plants and of the oil reﬁning plants.
The ENI oil reﬁneries’ CO2 emissions, divided in CO2
90
M. Bevilacqua, M. Braglia / Journal of Cleaner Production 10 (2002) 85–92
Table 4
Oil reﬁneries’ CO2 emissions
Percentage of
Power plant
Reﬁning plant’s
CO2 evaluated
CO2 evaluated
kton of carbon
kton of carbon
oil converted
Oil processed
fuel
fuel
CO2 evaluated
Oil reﬁnery
CO2 [kt/y]
[kt/y] due to
[kt/y] due to oil (as CO2) due to (as CO2) due to into CO2 by the
[t/y]
consumption
consumption
[kt/y]
power plant
reﬁning
power plant
oil reﬁning
reﬁning
[TOE]
[TOE]
operations
Gela 93
5,243,000
700,000
300,000
3300
3120
2184
936
685
293
5.6%
Gela 94
5,517,000
650,000
310,000
3300
2995
2028
967
636
303
5.5%
Gela 95
5,021,000
600,000
250,000
2800
2652
1872
780
587
244
4.9%
Gela 96
5,320,000
620,000
230,000
2600
2652
1934
717
606
225
4.2%
Livorno 93
3,900,000
225,000
150,000
1000
1170
702
468
220
147
3.8%
Livorno 94
3,900,000
355,000
155,000
1250
1591
1107
484
347
152
3.9%
Livorno 95
3,700,000
365,000
150,000
1250
1607
1139
468
357
147
4.0%
Livorno 96
4,500,000
385,000
175,000
1400
1747
1201
546
376
171
3.8%
Milazzo 93
4,324,000
100,000
150,000
720
780
312
468
98
147
3.4%
Milazzo 94
5,438,000
120,000
300,000
1150
1310
374
936
117
293
5.4%
Milazzo 95
5,448,000
130,000
330,000
1300
1435
406
1029
127
323
5.9%
Milazzo 96
4,700,000
125,000
230,000
1250
1107
390
717
122
225
4.8%
Priolo 93
8,300,000
575,000
160,000
2300
2293
1794
499
562
156
1.9%
Priolo 94
8,900,000
550,000
200,000
2350
2340
1716
624
538
196
2.2%
Priolo 95
8,575,000
605,000
190,000
2410
2480
1887
593
592
186
2.2%
Priolo 96
8,350,000
600,000
210,000
2390
2527
1872
655
587
205
2.5%
Sannazzano 93
7,897,000
195,000
400,000
1495
1856
608
1248
191
391
5.0%
Sannazzano 94
7,565,000
160,000
380,000
1400
1685
499
1185
156
372
4.9%
Sannazzano 95
7,611,000
175,000
410,000
1495
1825
546
1279
171
401
5.3%
Sannazzano 96
8,180,000
180,000
415,000
1550
1856
562
1295
176
406
5.0%
Taranto 93
4,180,000
110,000
200,000
805
967
343
624
108
196
4.7%
Taranto 94
4,371,000
170,000
215,000
1200
1201
530
671
166
210
4.8%
Taranto 95
3,992,000
150,000
265,000
1600
1295
468
827
147
259
6.5%
Taranto 96
3,970,000
140,000
270,000
1800
1279
437
842
137
264
6.7%
Venezia 93
3,600,000
85,000
155,000
620
749
265
484
83
152
4.2%
Venezia 94
3,300,000
87,000
130,000
570
677
271
406
85
127
3.9%
Venezia 95
3,310,000
95,000
140,000
630
733
296
437
93
137
4.1%
Venezia 96
3,180,000
100,000
125,000
620
702
312
390
98
122
3.8%
91
M. Bevilacqua, M. Braglia / Journal of Cleaner Production 10 (2002) 85–92
production due to oil reﬁning and to electricity gener-
ation in power plants, are shown in Table 4. Analysis of
the fuel consumption shows that the percentage of oil
converted into CO2 due to oil reﬁning is in the range
1.9%–6.7%, values comparable with those shown in
[15], where the typical crude loss to energy is shown to
be about 6%.
The simple efﬁciency scores for the 28 oil reﬁneries
are shown in Table 5.
The worst situation was found in the Gela oil reﬁnery.
In that case the environmental performance was always
lower than 0.50 during the analysed operating period.
Such a situation can be explained by considering the fact
that the Gela reﬁnery exploits high sulphur content oils
of local origin. Some important updating work is in pro-
gress on the Gela reﬁnery which, once completed, will
lead to major reductions in the sulphur oxide and nitro-
gen oxide emissions produced by the thermo-electric
power stations of the reﬁnery which are ﬁred by pet-
roleum coke. The high sulphur coke produced by the
processing of national crudes extracted in the area makes
it possible to exploit this important national raw material
found in the area of Gela. After a long process of analy-
sis of all the various abatement technologies available,
the most advanced and best able to meet integrated pol-
lution control criteria was chosen. The SNOX tech-
Table 5
DEA scores for the oil reﬁneries
Reﬁnery–year
DEA score
Gela 93
0.3666506
Gela 94
0.3827551
Gela 95
0.3986396
Gela 96
0.4741302
Livorno 93
1.000000
Livorno 94
1.000000
Livorno 95
0.9607843
Livorno 96
1.000000
Milazzo 93
1.000000
Milazzo 94
0.9368784
Milazzo 95
0.8705569
Milazzo 96
0.8613687
Priolo 93
0.7568378
Priolo 94
0.7948128
Priolo 95
0.7869140
Priolo 96
0.8221532
Sannazzano 93
1.000000
Sannazzano 94
1.000000
Sannazzano 95
1.000000
Sannazzano 96
1.000000
Taranto 93
0.9723304
Taranto 94
0.8022289
Taranto 95
0.7640963
Taranto 96
0.8084119
Venezia 93
1.000000
Venezia 94
1.000000
Venezia 95
0.9416342
Venezia 96
1.000000
nology guarantees such important performances as
maximum efﬁciency in ﬂue cleansing, the extraction of
sulphur oxides in the form of sulphuric acid, the total
absence of liquid efﬂuents and solid waste, minimum
requirements
of
additives,
and
maximum
energy
efﬁciency thanks to the use of ﬂue-gas heat to pre-heat
the combustion air in the boilers. The overall cost of the
investment will come to about 380 billion lire. The pro-
cess involves the oxygenation of sulphur oxides in SO3
and its subsequent conversion into sulphuric acid. The
section
for
the
elimination
of
nitrogen
oxides
(denitriﬁcation) produced by the plant involves the cata-
lytic reduction of nitrogen oxides to elementary nitrogen
and water. Up-line of the SNOX plant there will be an
electrostatic ﬁlter to guarantee an elevated efﬁciency in
the abatement of the dust found in the ﬂue case emitted
by the boilers.
The Livorno reﬁnery was characterised by a high
environmental efﬁciency throughout the observed per-
iod. This fact is also due to some major overhaul work
that the reﬁnery has undertaken in recent years. Some
processing units have been modernised and an advanced
co-generation power station has been built to produce
electrical and thermal energy for both the national grid
and the production units.
A similar situation can be observed for the Venice
reﬁnery. Today the establishment, from a strictly indus-
trial point of view, is situated in a strategic context on
account of its geographical position and its stock of
infrastructure and network of connections. The reﬁnery
ensures the supply of energy products needed for civil
and industrial uses in a vast and economically important
area. Its operational hinterland extends throughout the
entire northwest of the country. The utilities available
for the running of processing plants provide steam, elec-
tricity, compressed air and sea water. After bringing into
service the new thermo-electric power station in 1993,
the Venice reﬁnery generates more energy than that
required by its processing plant.
The analysis of Sannazzaro operating conditions
shows that such a plant is always characterised by the
greatest environmental efﬁciency values. The Sannaz-
zaro reﬁnery has recently concluded an exacting and
important 5-year plant restructuring programme, which
has enabled it to meet the continual changes in market
demand in a suitable and ﬂexible manner. It has become
what in the trade is known as a “white reﬁnery”, i.e.
one producing comparatively small quantities of heavy
products such as fuel oils, bitumens and processing resi-
dues, while reaching a very high level of complexity —
in fact so high as to have it rank among the most modern
and efﬁcient reﬁneries in Europe. The technologies
introduced with the restructuring programme are among
the most advanced in the ﬁeld of reﬁning.
The Milazzo oil reﬁnery environmental performance
was much affected by the fact that in 1996 the pollutant
92
M. Bevilacqua, M. Braglia / Journal of Cleaner Production 10 (2002) 85–92
emissions were inﬂuenced by the reﬁnery turnaround.
The lower availability of gas and low sulphur content
oil therefore led to an increase in SO2 emissions. At the
same time, a decrease in other pollutant emissions was
recorded.
The Priolo reﬁnery shows an increase in environmen-
tal performance for the period observed. This fact can
be interpreted as the consequence of dust ﬁltering plants
that have allowed a signiﬁcant reduction of the emissions
due to this pollutant.
A decrease in the percentage of pollutant emissions
can be noted for the Taranto reﬁnery. An improvement
of the adopted combustion technologies and the avail-
ability of better quality fuels are the main causes behind
the pollutant emissions trend. The reduction of environ-
mental efﬁciency can, on the other hand, be explained
as the consequence of a higher inlet sulphur content of
the fuels used. Owing to this fact the absolute SO2 emis-
sion levels increased during the period observed.
6. Conclusions
The adoption of an environmental management sys-
tem (EMS) can guarantee several beneﬁts, such as
improved environmental performance, reduced liability,
better compliance, improved public image, reduction of
costs and better access to capital, therefore helping the
ﬁrm to be more effective in achieving environmental
goals. One important task for the environmental manage-
ment system to be really effective is the adoption of a
systematic approach to control, measure and improve the
environmental effort of the ﬁrm. An EMS should be built
on the “Plan Do Check Act” base proposed by Shewart
and Deming, following the concept of “continuous
improvement”. The assessment of the environmental
performance of the ﬁrm is a key element of the “Check”
phase and so attention must be paid to the analysis of
the environmental indicators. The environmental analy-
ses may be seen as a multicriteria problem which
includes several quantitative (and also qualitative) fac-
tors. Then, opportune techniques are needed to allow a
thorough analysis of the environmental impact. This
paper reinforces the use of DEA as a good tool for
environmental analyses.
Acknowledgements
This paper has been supported by MURST funding
schemes. The authors wish to thank the referres for their
constructive comments that enabled improvements in the
quality of this paper.
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Review of application of artiﬁcial intelligence techniques in petroleum
operations
Saeed Bahaloo a, Masoud Mehrizadeh b, Adel Najaﬁ-Marghmaleki c, *
a Department of Petroleum Engineering, Amirkabir University of Technology, Tehran, Iran
b Department of Petroleum Engineering, School of Science and Engineering, Khazar University, Baku, Azerbaijan
c Department of Petroleum Engineering, Ahwaz Faculty of Petroleum, Petroleum University of Technology, Ahwaz, Iran
a r t i c l e
i n f o
Article history:
Received 22 July 2021
Received in revised form
7 July 2022
Accepted 8 July 2022
Available online 16 July 2022
Keywords:
Artiﬁcial intelligence
Machine learning
Upstream operation
Oil and gas industry
Petroleum systems
Decision-making
a b s t r a c t
In the last few years, the use of artiﬁcial intelligence (AI) and machine learning (ML) techniques have
received considerable notice as trending technologies in the petroleum industry. The utilization of new
tools and modern technologies creates huge volumes of structured and un-structured data. Organizing
and processing of these information at faster pace for the performance assessment and forecasting for
ﬁeld development and management is continuously growing as an important ﬁeld of investigation.
Various difﬁculties which were faced in predicting the operative features by utilizing the conventional
methods have directed the academia and industry toward investigations focusing on the applications of
ML and data driven approaches in exploration and production operations to achieve more accurate
predictions which improves decision-making processes. This research provides a review to examine the
use cases and application of AI and ML techniques in petroleum industry for optimization of the up-
stream processes such as reservoir studies, drilling and production engineering. The challenges related to
routine approaches for prognosis of operative parameters have been evaluated and the use cases of
performance optimizations through employing data-driven approaches resulted in enhancement of
decision-making workﬂows have been presented. Moreover, possible scenarios of the way that artiﬁcial
intelligence will develop and inﬂuence the oil and gas industry and how it may change it in the future
was discussed.
© 2022 The Authors. Publishing services provided by Elsevier B.V. on behalf of KeAi Communication Co.
Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/
by-nc-nd/4.0/).
1. Introduction
Newer reservoirs for exploration of hydrocarbons are encoun-
tered in deeper depths and in farthest locations. As discussed by
Lantham (2019), the need for petroleum related materials is
increasing daily. Hence, the companies require to take actions for
production optimization, cost reduction, and environmental im-
pacts of hydrocarbon production. It is not possible to achieve these
objectives through using conventional approaches for exploration,
production and management of hydrocarbon resources. However,
with new emerging strategies and modeling methods, companies
can gain more proﬁt by utilizing data-driven technologies. Balaji
et al. (2018) sated that in classical approaches of hydrocarbon
production, the implemented methods fall into three categories
which are mathematic-based, physic-based, and empirical ones.
While the mathematical methods depend on mass, momentum
and energy conservation equations, the empirical approaches
depend on past observations and experiments. Due to drawbacks of
the mathematical methods at different operational situations and
inaccurate results of empirical methods, the use of these ap-
proaches needs several simplifying assumptions, which results in
their improper performance in handling complicated relationships,
noises, and incomplete data. In exploration and production oper-
ations, huge volumes of data are created in various daily processes.
These databases can be used in data driven methodologies and big
data interpretations, to achieve useful decision-making strategies.
The proﬁt of using these models is enhancement and optimization
of hydrocarbon production (Hamzeh, 2016).
The use of soft computing methodologies has demonstrated to
be hopeful in handling various difﬁcult problems for many oilﬁeld
related processes. Various studies have proved that the AI and ML
* Corresponding author.
E-mail addresses: saeed.bahaloo@aut.ac.ir (S. Bahaloo), Mmehrizadeh@khazar.
org (M. Mehrizadeh), adelnajaﬁput90@yahoo.com (A. Najaﬁ-Marghmaleki).
Contents lists available at ScienceDirect
Petroleum Research
journal homepage: http://www.keaipublishing.com/en/journals/
petroleum-research/
https://doi.org/10.1016/j.ptlrs.2022.07.002
2096-2495/© 2022 The Authors. Publishing services provided by Elsevier B.V. on behalf of KeAi Communication Co. Ltd. This is an open access article under the CC BY-NC-ND
license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Petroleum Research 8 (2023) 167e182
methods grasp useful answers for operation related difﬁculties
currently being faced in the industrial operations and can effec-
tively assist in addressing the problems associated with processing
and interpretation of huge databases (Mohaghegh, 2016; Amini and
Mohaghegh, 2019).
Mohammadpoor and Torabi (2018) conducted an investigation
and recognized that more than 80% of the value chain employment
of big data is mainly in petroleum operations. Saputelli et al. (2003)
and (Saputelli et al., 2003) pointed out that big data is growingly
becoming a necessary tool for oil and gas industry. He reviewed the
use cases of AI and ML methods in operations resulted in better
decision makings for petroleum related operations. In the present
work, various ML approaches of relevance to petroleum industry
will be reviewed. Then, the use cases of these algorithms in
different sections of petroleum industry and the ways they can be
utilized to extract useful decision-making information from avail-
able databases will be discussed. We will review the application of
these methods within prediction of petrophysical features and
reservoir studies, drilling and production optimization.
2. Artiﬁcial intelligence methods
2.1. Artiﬁcial neural network (ANNs)
ANNs have been developed to reproduce the operation of the
processing neurons in the brain of humans. Their core characteristic
is that they are able to recognize and handle extremely complicated
nonlinear relations among various parameters (Ali, 1994). In addi-
tion, they are able to gain knowledge and extend it in situations
that they exhibit acceptable outputs in the unseen testing data. In
addition, they are no limitations on ANNs in terms of hypothesizes
of the original method. Overall, they exhibit robust performance in
addressing various forms of tasks and problems, such as approxi-
mation of non-linear functions, recognition and classiﬁcation of
existing patterns, optimizing the problem solution and automatic
control. The main component of these networks is the units called
neurons (Hush and Horne, 1993). The neurons reproduce the
operation of the neurons within the brain. The schematic repre-
sentation of a neuron is displayed in Fig. 1. There are various neu-
rons in the skeleton of ANN associated together by inﬂuent
directions (lines) (Hush and Horne,1993). Their structure should be
trained in order to learn to execute different tasks by tuning
inﬂuent directions between the neurons. Multilayer perceptron
(MLP) networks are the widely utilized sub-branch of ANNs. A MLP
have a structure composed of parallel layers that each layer holds
several neurons, and individual layers are totally linked to subse-
quent layers. Depending on the type of connection among layers,
MLP networks are categorized into feed-forward and feedback
types. Feed-forward networks consists of layers which pass data in
a one-sided path from input to output layer while feedback types
allow interconnection of data in both internal and external di-
rections, which is possible through establishing speciﬁc loops
within the structure of the model. In addition, in another classiﬁ-
cation, MLP networks fall within supervised and unsupervised
frameworks according to the type of their training algorithm (Hush
and Horne, 1993). Overall, there are three major layers in MLP
structure named input, hidden, and output ones. The input layer
feeds the required data into the network. The task of hidden layer
that is the most crucial layer, is to map the received data from input
layer to output layer. The process terminates in the output layer
that determines the network's output. The core stage in the
execution of ANNs is the training phase, where the variables of the
network (weight terms and biases) are adjusted within an iterative
procedure of adaption with the domain that the model is applied.
The training phase lasts until a minimum error is attained. A single
full appearance of all the training datasets to the model within the
training phase is named an iteration or epoch (Haykin and
Lippmann, 1994). The main aim of the training process of this
network is the optimization of the cost function. In another word,
the cost function should be minimized during the training process.
The schematic representation of the framework of MLP is depicted
in Fig. 2. Various training algorithms are proposed including
LevenbergeMarquardt, back-propagation (BP), resilient propaga-
tion, etc. (Haykin and Lippmann, 1994), to facilitate the training
phase of these networks. BP is the most favorite one for using MLP
networks. It beﬁts from the chain rule to characterize the impact of
individual weight terms within the structure of the network toward
to the cost function's derivative. Its execution process initiates
through calculating the cost function derivatives in the most pre-
ceding network layer and next it propagates the computed de-
rivatives in reverse direction toward the input layer (Haykin and
Lippmann, 1994). However, this method exhibits various disad-
vantages along with itself, including the slow convergence, stuck on
local minima and the choice of proper training speed. A solution
suggested to alleviate the training rate issue during this algorithm
is to use a parameter called momentum to update the weight terms.
Hence, despite changing the weight terms during the training
process by using the derivative of cost function, the previous
weight terms also contribute to determine the values of new
weights as training process proceeds. The main drawback of this
Fig. 1. Representation of single neuron operation process (Rahmanifard and Plaksina,
2019).
Fig. 2. Schematic representation of ANN structure.
S. Bahaloo, M. Mehrizadeh and A. Najaﬁ-Marghmaleki
Petroleum Research 8 (2023) 167e182
168
process is that it needs to optimize two variables that are the rate of
training and the momentum term. In conventional adaptive-based
training techniques, the training rate and derivates impact on the
magnitude of the weight. Although the adaptation process of the
training rate is done precisely, it is considerably inﬂuenced with the
unpredictable performance of the derivative itself. Riedmiller and
Braun (1993) proposed a new technique for training process of
these networks. The method directly adjusts the weight step ac-
cording to locally calculated gradients, where the optimization
procedure is carried out according to the order of dimensional
derivative signs in weight domain. This technique differs from
other techniques in that the weight adjustment is independent to
the gradient size. This approach obtains the optimum weight
through utilizing a separate adjustment parameter Dij, which
speciﬁcally evaluates the size of the updated weight. The Lev-
enberge Marquardt (LM) algorithm was originated based on the
Newton's optimization approach (Riedmiller and Braun, 1993;
Mehrizadeh et al., 2020). This technique differs from BP method in
the way that it uses the derivatives to re-calculate weight terms.
The main idea of Newton's approach is:
Newton's approach uses an expansion of the second-order
Taylor series. Ak denotes the second derivative matrix of the cost
function. Newton's approach usually provides faster convergence
compared to the steepest descent approach. However, calculation
of the derivative matrix for neural networks is challenging and
computation demanding. The LM approach was proposed to alle-
viate this problem by by-passing the need for determining the
derivate matrix for Newton approach while maintaining the speed
of training as before (Hagen et al., 1996). The major drawback of the
LM approach is the requirement for keeping large matrixes of
adjustable weights and parameters during its calculations and
convergence process. However, a method was proposed which al-
lows LM not to compute and store the complete matrix to alleviate
the storage problem. The databases for implementation of ANNs
are usually categorized into train, validation, and test sets. The aim
of train portion of data is training the model and optimize the
structure of the model. The portion of data used for validation aims
to control the model performance within the training phase. These
data are not considered during the optimization of the structure of
the network. The portion of data used for test set aims to check the
performance of fully developed network in prediction of the un-
seen data. These data are utilized as an index to evaluate the
generalization capacity of the completed model when it is facing
new input data. They are generally utilized to check how well the
network can reproduce the overall trend of data and avoids
memorization of data points and overﬁtting. Two methods can be
utilized during the training process of these models which are
batch and stochastic (sequential) approaches. During stochastic
method, the optimization of network parameters is done when a
portion of training data is introduced to the network. In batch
method, the optimization of weight terms occurs when the total
training set is introduced to the network in individual training it-
erations. The sequential method offers faster performance because
it needs less available space for storing the weight terms. However,
the batch method offers better preservation of convergence criteria
during optimization process. Moreover, various training techniques
such as use the batch method during their computation process.
2.2. Support vector machines (SVMs)
SVM is a branch of the ML methods and is a helpful technique
implemented in data regression and classiﬁcation by mapping
them to disjointed future-spaces in both the existing input domain
in linear problems and multi-dimensional feature domains for
nonlinear regression and classiﬁcation problems. In recent years,
SVM has been implemented in various areas of research and sci-
ence. Consider the problem is associated with a dataset of input and
output parameters. In SVM, the hyperplanes that split different
classes are calculated in a way to maximize the distance of the
hyperplane from the closest points of different classes. When the
splitting hyperplane is non-linear in the initial input domain, it is
possible
to
generate
linearized
hyperplanes
in
the
multi-
dimensional feature domain by applying speciﬁc functions known
as kernels to input domain. The conceptual visualization of SVM is
represented in Fig. 3. In order to conduct regression analysis by
SVM, the following equation is constructed by the model (Cortes
and Vapnik, 1995):
y ¼ wT4ðxÞ þ b
(1)
In which w and b denote the weight terms and bias values. In
order to obtain w values, the following function should be mini-
mized subject to inequality constraints:
min
(
1
2w2 þ C
X
N
i¼1
xi þ x*
i

)
(2)
yi 
n
wT4ðxiÞ þ b
o
 j þ xi
i ¼ 1; 2; :::; N
xi; x*
i  0
i ¼ 1; 2; :::; N
(3)
In which j is the function approximation term, xi, x*
i are slack
variables and C is an adjustable parameter. Finally, the SVM solves
the regression equation by introducing Lagrange multipliers (di d*
i )
as follows:
yðxÞ ¼
X
N
i¼1
di  d*
i
kðx; xiÞ þ b
(4)
In which k (x, xi) is called a kernel type function.
Least square SVM (LSSVM) is another type of SVM which utilizes
square form of errors in the formulation of cost function and con-
straints with equality type rather than positive error terms and
inequality
type
of
constraints.
Hence,
LSSVM
regression
is
Fig. 3. Visual representation of SVM operation (Choubey and Karmakar 2020).
S. Bahaloo, M. Mehrizadeh and A. Najaﬁ-Marghmaleki
Petroleum Research 8 (2023) 167e182
169
conducted through linearizing the problem instead of using
quadratic programming (Kumar et al., 2018). Recently, the LSSVM
has gained popularity and successfully being utilized in various
applications (Jiang et al., 2018; Kumar et al., 2018; Li et al., 2020;
Song et al., 2020).
Considering the training data as (xi, yi) | xi 2 Rd, yi 2 R, in which
i ¼ 1, 2, …, N, N denotes the number of data points and d is the
number of input parameters, xi and yi denote the input and target
arrays. Utilizing a nonlinear function 4(x), a higher dimension
domain will be considered to project the inputs onto it and perform
the regression computations. The mathematical formulation of this
regression analysis is expressed as follows:
y ¼ wT4ðxÞ þ b
(5)
In which w and b denote the weight terms and bias values, being
predicted within the algorithm computations in Eq. (6).
C ¼ 1
2wTw þ 1
2 g
X
N
i¼1
e2
i
(6)
yi ¼ wT4ðxiÞ þ b þ ei
In which g > 0 is a term called penalty multiplier and ei repre-
sents the error values.
To
make
the
computation process
more
straightforward,
Lagrange formulation is developed as below:
Lðw; b; e; aÞ ¼ 1
2kwk2 þ 1
2 g
X
N
i¼1
e2
i 
X
N
i¼1
ai

wT4ðxiÞ þ ei þ b  yi

(7)
Where ai terms denote the Lagrange factors. By using the partial
derivatives of u, e, b and a, and elimination of u and e terms, the
computation outcomes are as below:
yðxÞ ¼ wT4ðxÞ þ b ¼
X
N
i¼1
aikðx; xiÞ þ b
(8)
In which k (x, xi) is called a kernel type function with positive
values.
It has been shown that the prediction and globalization capa-
bility of LSSVM greatly is inﬂuenced by the type of kernels and their
associated adjustable variables (Sun et al., 2020). Various kernel
types such as RBF, Poly and linear are available to be used in
modeling works and are mathematically represented as follows:
kRBF
xi; xj
 ¼ exp



xi  xj

2 .
2s2
(9)
kPoly
xi; xj
 ¼ xi:xj þ 1d
(10)
kline
xi; xj
 ¼ xi:xj
(11)
The radial basis function (RBF) is the most used kernel that can
perform locally approximations with excellent performance, while
polynomial type kernels (Poly) have good overall approximation
capacity, which offers acceptable capability in handling nonlinear
inputs. Some investigations combine different types of kernel
functions (RBF, Poly and linear) by using weight terms to beneﬁt
from strengths of all functions and to increase the estimation
capability of the LSSSVM model. The formulation of this type of
kernel function can be represented as below:
kxi; xj
 ¼ m1kRBF þ m2kPoly þ m3kline
(12)
In which mi is an adjustable factor associated with RBF, Poly and
linear kernels which are between 0 and 1. By adjusting mi values,
this type of combined kernel function provides optimized features
for various input parameters. The use of this type of integrated
kernel functions the model will be able to better handle the
nonlinear elements and obtains the solution of linearized equations
more efﬁciently.
2.3. Fuzzy logic (FL)
Fuzzy logic is identical to daily experiences because it deals with
decisions made by human. The concept of fuzzy inference system
(FIS) is quite useful in balancing and controlling the incomplete
observations (Bello et al., 2016). The advantages and drawbacks of
FL are as follows:
(a) Advantages: The mathematical tools of FL are helpful in
translation and regulating the human decision-making into
appropriate expressions which could effectively be applied
utilizing computer; FL networks are able to process arbitrary
non-linear
and
complex
problems;
FL
techniques
are
important in solving complicated sequential problems that
are difﬁcult or impossible to be addressed by even advanced
mathematical tools; FL techniques are effective and gain
popularity in incomplete knowledge situations; FL methods
are proved to be effective in addressing reasoning problems.
(b) Disadvantages: FL has several drawbacks including the spe-
cialists in this ﬁeld who contribute in handling problems by
FL, face difﬁculties to organize their judgements as the
problems are associated with various uncertainty and biases;
another issue with FL is that increasing the fuzzy subsets for
individual input parameters results in considerable increase
of the required rules. This can cause increase in uncertainty
of network. Despite the existing disadvantages, FL method
has been efﬁciently utilized for solving complex problems in
various research ﬁelds (Park et al., 2010).
The drawbacks of FL and other AI methods resulted in more
attempts in ﬁnding ways to decrease the inﬂuence of these draw-
backs. One important achievement was development of hybridized
methods. Hybridized approach beneﬁts from integration of several
soft computing techniques. These methods attempt to compensate
the drawbacks of one method with the robustness of other ones.
Recently, the hybridized methods gained considerable attention
and has been effectively applied to various non-linear and complex
problems (Acampora and Loia, 2005). FL has been increasingly
considered in hybridized models. Hybridized approaches using
combinations of FL with AI approaches including neural nets, GA
and SVM have been considerably utilized in academic and indus-
trial applications. Popular methods are neuro fuzzy methods, SVM-
fuzzy techniques, and GAeFL hybridized approach. The integration
of FL and neural nets is named neuro-fuzzy and beneﬁts from the
merits of both methods. Neuro-fuzzy approach integrates the self-
learning capabilities of ANNs and the unique knowledge demon-
stration and inference abilities of FL. This technique offers various
features such as pattern recognition, classiﬁcation, regression and
scientiﬁc modeling. It has been used in various petroleum related
applications. The tendency for gaining more accurate predictions of
actual data is the main concern of hybridized techniques. However,
the more crucial point is not to use the integrated and hybridized
techniques only for improving the performance of the network for a
speciﬁc dataset but instead to use the power of integration and
S. Bahaloo, M. Mehrizadeh and A. Najaﬁ-Marghmaleki
Petroleum Research 8 (2023) 167e182
170
hybridization of models to help the technologies and contribute to
provide universally accurate models for real world problems.
Fuzzy c-means (FCM) is a clustering method that categorizes
individual data points into different clusters based on a speciﬁed
degree which is determined through membership functions (MFs).
Bezdek (2013, 2013) proposed the method for the ﬁrst time as an
enhancement to previous clustering algorithms. The algorithm
demonstrates the process of grouping data in a way to separate the
multidimensional domain to certain clustering sets. FCM is a pop-
ular fuzzy-based clustering method that extends the conventional
c-means technique that is applicable in situations with ambiguity
in determining the exact number of required clusters for catego-
rizing the data (Chiu,1994). FCM is an important method having the
ability of recognizing patterns in huge historical data from petro-
leum related processes. Identifying and clustering these patterns
will be useful to obtain better understandings of the past, present
and future performance of oil and gas ﬁelds that will result in
effective and cost saving ﬁeld development and production
planning.
2.4. Linear regression
This is a statistics-based modeling approach which builds a
linear relationship between a parameter and one or several other
independent parameters within a set of data (xi, yi), in which i ¼ l. 2,
3, …, n. A regression that predicts a parameter (y) based on an in-
dependent parameter (x) is usually known as Linear Regression
(LR). In situations in which the parameter (y) is predicted based on
several independent parameters (x1, x2, x3, …, xn), the regression is
known as Multiple Linear Regression (MLR). The mathematical
formulations for these terms can be represented as below:
Simple LR : y ¼ b0 þ b1x þ ε
(13)
Multiple LR : y ¼ b0 þ b1x1 þ b2x2 þ ::: þ bnxn þ ε
(14)
In which b0, b1, b2, …, bn denote the variables, and ε represents
the regression error which needs to be determined by utilizing the
data points. Maucec and Garni (2019) integrated the LR with the
concept of Analysis of Variance (ANOVA) to describe a new
modeling approach named generalized linear modelling. Fig. 4
shows a basic representation for LR in which the points are
regressed by AB line to predict the Y parameter based independent
parameter X (the black points represent pairs of (xi, yi) data for i ¼ l,
2, 3, .., n).
2.5. Decision tree (DT)
A DT can be used of to address problems related to regression
and classiﬁcation. This method applies Boolean approach to
represent a single or multiple attributes. There is a speciﬁc node
called the root or parental node in the structure of the method. This
node is connected to several other nodes which are usually known
as child nodes and operational nodes. This approach is usually
represented in a graphical tree structure to better visualize and
identify the outcomes of the decisions. The algorithm selects the
parent node from the best attributes and then starts the operation
process. Then, the remained attributes will be selected and the
algorithm will continue the operation until there is no attribute, or
when the operation converges to optimum state. The criterion for
selecting the ﬁttest attribute is based on an index. This index can be
any of the three parameters namely Gini index, entropy or classi-
ﬁcation error which are formulated as follows:
GINIðtÞ ¼ 1 
X
½pðijtÞ2
(15)
EntropyðtÞ ¼ 
X
pðijtÞlog 2 pðijtÞ
(16)
ErrorðtÞ ¼ 1  maxpðijtÞ
(17)
In which, t denotes a speciﬁc node and p(i|t) represents the class
probability. Fig. 5 shows a representation of a yes or no based DT to
select one of A and B terms (to decide to select the A or B). The main
purpose of DT is to continuously divide the data to assign them
within their suitable class (as is displayed in Fig. 5) or until there is
no improvement in the tree structure and accuracy compared to
previous ones. This technique is ﬂexible to alter the training data
during operation, however the ﬁnal output is sensitive to the
amount of data in each class. This problem can be resolved by
utilizing a well-known approach named Random Forest (RF), which
enables the method to analyze multiple trees in parallel by using
the same dataset. RFs are effective in adjusting the model, con-
trolling the noise, and handling large databanks with various at-
tributes. This is because in this approach each tree does not pass its
error to other trees. The output of RF can be determined by
comparing the error of various trees. The ﬁttest tree is considered
as the model output.
2.6. Bayesian belief networks (BBN)
The concept of BBN method originates from Bayes' theorem
beginning from the query that in what way the likelihood of inci-
dent A is interconnected with an existing incident B (Glickman and
Van Dyk, 2007). The mathematical representation of this concept
can be expressed by the P(A|B) symbol, which can be re-calculated
through new instances of B. The needed variables are the past
likelihood in happening of incident A (P(A)), incident B (P(B)) and
incident B with the condition-dependent likelihood of A (P(B|A) or
the probability of occurrence under the tested hypothesis (Ji and
Marefat, 1995; Kruschke, 2015). Hence, the mathematical repre-
sentation of the Bayes’ theorem is as below:
PðAjBÞ ¼ PðBjAÞ:PðAÞ
PðBÞ
(18)
Fig. 4. Representation of a simple LR (Choubey and Karmakar, 2020).
S. Bahaloo, M. Mehrizadeh and A. Najaﬁ-Marghmaleki
Petroleum Research 8 (2023) 167e182
171
The incident B is named a parental node or a factorization of
incident A, while incident A is named a child node of incident B
(Kragt, 2009). The parental nodes in a BBN require to deliver prior
likelihoods, a single or more child nodes and a table for conditioned
likelihood showing the linkage between parent nodes and their
children. By implementing the chain rule, a central table of
conditioned likelihood that consists of n parameters, can be ob-
tained. In another word, the probability is calculated by taking the
values of P(node | parent(node)) and calculating their products using
the individual network nodes (X1, …, Xn) as is formulated in Eq. (19)
(Choubey and Karmakar, 2020):
PðX1; :::; XnÞ ¼
Y
n
i¼1
PðXijX1; :::Xi1Þ ¼
Y
n
i¼1
PðXijParentðXiÞÞ
(19)
In which, (Xi) represent parental nodes of Xi parameters. For
instance, by n parameters, there should be 2n likelihoods. For more
complicated systems, the required likelihoods will drastically rise
to considerably huge numbers, mainly due to occurrence of mul-
tiple states for different parameters (Doolittle et al., 2006). This
disadvantage can be alleviated through extracting the feasible re-
lations between parameters by quantitatively measuring control-
ling alternative node states. However, conversion of qualitative and
expert knowledge-based parameters to probability terms is a
difﬁcult and challenging task (Jakoby et al., 2014). By visualizing the
probabilities in reason-consequence graphical representations
subject to uncertain states, a BBN holding nodes and linkages is able
to utilize expert knowledge to transform different relations into
probabilistic sets. The model permits to arrange existing concepts,
to evaluate feasible possibilities during initialization, execution and
re-calculation stages. The linkages denote the relevant paths and
interrelations of nodes. There is no incoming connection to the
parental nodes and the last child have no output connection
(Sample et al., 2016a). In another word, these techniques owe two
branchesd(i) an acyclic graph with is directed (consists of nodes
and linkages) where the nodes denote the parameters and linkages
denote the tree-based connections between the parameters and,
(ii) Restricted probability distribution lists for evaluation of the
probability-related association between parameters. These pa-
rameters can be non-continuous or continuous. In order to name
this kind of structure as BBN, one need to initialize it by assigning
the prior probabilities to parental nodes and table of conditional
probabilities to child ones. These tables are the core processing
units of BBN for determining the ﬁnal output values of posterior
likelihoods based on existing parent-child relations through
applying the Bayesian concept (Guo and Hsu, 2002).
Netica2 is a tool distributed for generation of Bayesian-based
structures on operating systems. This tool is free for performing
calculations up to 15 parameters. This tool is able to assign
continuous parameters to relevant ranges with designed frame-
works, rather than using non-continuous forms for the input pa-
rameters. The software uses three kinds of nodes named nature,
decision and utility-based nodes. The software generates the tables
of conditional probability during detection of the structure of the
BBN. BBN, uses non-continuous iterations, to determine adequate
number of ﬁttest likelihoods for unseen parameters using available
data (Sample et al., 2016b). First, a comparison between absent
values and logarithm of probabilities will be carried out, next the
performance of the model will be maximized by training the initial
model. This process will be repeated until the convergence of all
likelihoods and achieving stable conditions for logarithm of prob-
abilities. The visualization of this model is depicted in Fig. 6, by
utilizing a basic BBN framework.
2.7. Principal component analysis (PCA)
PCA is an approach for handling and reducing the number of
inputs for problems with large number of input data, to facilitate
their use and interpretation by other modeling algorithms. The
techniques for reducing the dimension of a problem select the
suitable features or remove un-suitable ones by linearly combining
the primary features. PCA linearly transforms the data through
rotation of feature domain, where alignment of data along the di-
rection of maximum variance is more than other directions.
Kormaksson et al. (2015) provided a review of PCA and applied the
method for identifying sweet spots by utilizing well logging infor-
mation. This algorithm transforms an n-dimensional matrix into a
k-dimensional one where k is lower than n. The transformation is
carried out by building a matrix from the foremost k Eigen vectors
based on the covariance Cx values of the original matrix as is
formulated in Eq. (20). Next, the covariance Cy values of converted
matrix is determined by Eq. (21) (Kormaksson et al., 2015; Choubey
and Karmakar, 2020):
Cx ¼ ðX  XÞðX  XÞT
n  1
(20)
Cy ¼ P:Cx:PT
(21)
Fig. 5. A simple DT workﬂow (Choubey and Karmakar, 2020).
Fig. 6. Illustration of the BBN method (Choubey and Karmakar, 2020).
S. Bahaloo, M. Mehrizadeh and A. Najaﬁ-Marghmaleki
Petroleum Research 8 (2023) 167e182
172
2.8. Gradient boosted machine (GBM)
This method is identical to random forest (RF) in that both of
them try to enhance the performance of DTs. However, the
evolvement of trees in RF approach is done in parallel, whereas in
GBM the growth of trees is carried out through received signals of
predecessors, and every tree lies within the updated type of the
initial data. In order to recompense the shortages induced through
the undesirable gradients, the tree's cost function is controlled in
subsequent sequences. Despite slower learning process for boosted
trees, they are less likely to suffer from overﬁtting problems thanks
to their reduced size in comparison with RF (Maucec and Garni,
2019; Choubey and Karmakar, 2020).
2.9. Big data methods and tools (BDMT)
Mohammadpoor and Torabi (2018) as well as Choubey and
Karmakar (2020), provided a detailed review of well-known
methods and main tools for handling of big data which will be
brieﬂy presented here:
2.9.1. Hadoop
Hadoop is a freely available software which integrates the net-
works of a large number of processors. It splits large ﬁles into sub-
sections and maintains them in disseminated storage. This provides
the option of processing the high-dimensional information in
parallel to perform tasks such as interpretation of logging infor-
mation and detection of anomalies in tools functioning using sen-
sors in the petroleum industry.
2.9.2. Mongo DB
This is a NoSQL and document-orientated database tool. Its main
usage is in cases needing active queries and also in cases in which
deﬁning indexes are preferred. Its main usage is for storage of dy-
namic data such as drilling rig information during exploration or
production in onshore or offshore conditions.
2.9.3. Cassandra
Similar to Mongo DB, this is also a distributed and NoSQL
database tool. It is mainly utilized in situations where large data-
bases need to be processed and stored on servers. When gathering
information from huge arrays of sensors, it is required to store the
information in several servers. This tool facilitates the process of
categorizing several databases and simultaneously storing them
from different sensors in various servers.
2.9.4. R programming
R is a new functional-based language for programmers that
makes it easy to perform complicated statistics-based computa-
tional processes as well as generating good-quality visualizing
outputs. It is mainly utilized for statistically analyzing the data and
software development for data driven interpretation.
2.9.5. Datameer
It is a spreadsheet-based programming technology, which offers
inherent tools for data interpretation, manipulation, and modeling
facilities. It has the ability to combine, interpret, and display data
irrespective of source, conﬁguration, and size.
2.9.6. BigSheets
This is a spreadsheet free web app which is identical to Data-
meer in functionality. The IBM developed this tool which enables
users to split large data into useable commercial outputs. It is
frequently applied in cleaning, interpreting, and displaying large
databases. Data like sales by year by non-technical users in the oil
and gas industry.
3. Application of AI in different ﬁelds
3.1. Reservoir studies
The process of reservoir characterization involves the quanti-
tative allocation of corresponding parameters including porosity,
permeability, ﬂuid property and other characteristics to the reser-
voir to gain better understanding about it (Mohaghegh et al., 1994;
Larki et al., 2018). This process also includes identifying geological
structures and uncertainties as well as spatial changes in geological
features. Appropriate execution of characterization process is
important to optimize the production of hydrocarbons. There are
different approaches in characterization of reservoirs in the pe-
troleum industry, but the widely used and best practice approach is
to take cores from reservoir sections. The data obtained from
analyzing the core data are absolute and relative permeability,
porosity, ﬂuid saturations, composition of rocks, size of particles
and geo-mechanical information. While most of the features help
to examine the reservoir potential for storing and producing ﬂuids,
some of them including the composition of rock and geo-
mechanical information aim to conduct the drilling and produc-
tion processes properly. Hu et al. (2017) categorized petroleum
related minerals into minerals susceptible to water, minerals sus-
ceptible to salinity, minerals susceptible to acid, minerals suscep-
tible to alkaline and minerals susceptible to velocity of ﬂowing
ﬂuid. For example, understanding the composition of mineral,
mainly the type of clay and distribution of the desired zone will
help to formulate safer ﬂuids for drilling operations as well as aim
to design practical treatments for stimulation or fracturing opera-
tions. In addition, knowledge of geo-mechanical features of rocks
will help in designing the true density values of ﬂuids in drilling,
better control of stability conditions for wellbore and better control
of sand production. Hence, extracted knowledge from interpreta-
tion of core data will help to better characterize a reservoir and will
be useful for optimization of drilling and production processes.
Currently, it is not possible and practical to conduct coring surveys
in all drilled wells because taking core from reservoirs is time
demanding and costly. This induces uncertainties for development
of a reliable three-dimensional static model of the reservoir as the
available core data are considerably fewer than the existing wells.
Characterization of a reservoir is a difﬁcult and complex task
because of the existing non-linearity and heterogeneity in various
parameters. These challenges can effectively be handled efﬁciently
and precisely through utilizing the soft computing methods such as
ANNs, FL, SVM, GA, etc. Over the past few years, these techniques
were successfully implemented within the petroleum industry and
it was shown that they are able to provide accurate predictions of
uncertain parameters including permeability and facies in wells
with log data and without core data (Cuddy 2000; Hambalek and
Gonzalez, 2003; Khoshmardan et al., 2021).
However, soft computing methods do not need any simpliﬁca-
tions and has shown to perform well to identify various rock types.
The offered regression techniques by soft computing and AI ap-
proaches have been shown to be better than the classical regression
operations mainly in situations where highly uncertain data are
dealt with. Chen et al. (1995) used a regression approach using FL to
evaluate the variables of the Archie equation in a situation where
the observed and modeled data differences were attributed to
various sources including errors in experimentations and ambigu-
ities of parameters in the network (Chen et al.,1995). Fang and Chen
(1997) used FL to estimate porosity and permeability of sandstone
rocks by using 5 composition and texture related input. They
clustered the experimental data into various categories by FCM. The
S. Bahaloo, M. Mehrizadeh and A. Najaﬁ-Marghmaleki
Petroleum Research 8 (2023) 167e182
173
sandstone features of individual clusters were utilized to extend
the fuzzy rules. There were a single if-then rule and ﬁve input pa-
rameters associated with individual clusters. The rules were
applied to conduct estimations in a linguistic manner through
integrating the output of individual rules. Another ﬁeld of reservoir
characterization through which the AI approaches have been uti-
lized is the classiﬁcation of reservoir anisotropic features. Lian et al.
(1998) integrated FL and geo-statistical techniques and obtained
the existing spatial relation between reservoir parameters. They
utilized the outcomes to estimate the distribution of permeability
within the reservoir. Nikravesh et al. (2001) utilized k-means
clustering, neural nets and FCM approaches for ﬁeld characteriza-
tion. They utilized the approaches to cluster three dimensional
seismic features and ﬁnd reasonable relationships among various
clusters and the production log. They succeeded to ﬁnd the best
positions for planned wells according to the useful information
extracted from individual clusters. Nikravesh and Aminzadeh
(2001a) as well as Nikravesh and Aminzadeh (2001a), utilized
neuro-fuzzy approach to optimally propose a number of rules for
nonlinear correlation between permeability and several other pa-
rameters. They extracted the rules by utilizing training data and
implemented the model to estimate permeability values by using
new databases. They obtained accurate predictions by the proposed
model. The work also revealed that the model detected existing
relations between input data. Cuddy (2000) focused on the appli-
cation of FL in extracting lithofacies information and estimation of
permeabilities in different wells without core data by utilizing a
network trained on wells with core data. They compared the results
of FL and ANN and observed better performance for the FL model.
Saggaf et al. (2003) predicted the distribution of porosity from
seismic features in a ﬁeld by utilizing single hidden layer ANN with
regularized BP training algorithm. This technique could overcome
the overﬁtting problem that is usually encountered in conventional
BP algorithms. The approach provided accurate prediction and
fewer adjustment of network variables at the expense of some
computational difﬁculty. Saggaf and Nebrija (2003) summarized
the merits of FL in identifying different rock types by using well log
data. Their analysis revealed that FL can estimate ﬂow units and
rock types in wells with no core data with high precision in com-
parison with classical approaches. This approach can greatly save
coring expenses and time for the industrial operations. Ilkhchi et al.
(2006) utilized data from real offshore gas wells to build FL
frameworks for the studied ﬁeld. They used FCM for rock typing
analysis by utilizing core porosity and permeability information.
They used TakagieSugeno-Kang type fuzzy inference system (FIS)
to estimate the permeability data. Their outcomes revealed that FL
method can be successful applied for estimation of core derived
permeability data. Al-Bulushi et al. (2009) utilized ANN framework
to predict water saturation and distribution of ﬂuids in a reservoir.
The input data for development of the model were a set of well log
data. It was observed that the highest inﬂuential parameter in
prediction of water saturation was the resistivity log. The highest
inﬂuential parameter in prediction of ﬂuid distributions was the
distribution of pressure. The distribution of pressure was obtained
from core information. They achieved a R2 and RMSE value of 0.91
and 0.025 for prediction of water saturation, and for distribution of
ﬂuids, they obtained RMSE value of 0.05 for well log data and 0.046
for core data, respectively. Hamidi et al. (2010) suggested that the
available approaches for classiﬁcation of rocks exhibit sudden
transitions that sometimes do not exist in many situations.
Moghadam et al. (2011) predicted the porosity and permeability
in a reservoir by utilizing ANN and candidate well log data. Six log
parameters were used for their work according to feature extrac-
tion methods, regression analysis, and analysis of correlating pa-
rameters. ANN outcomes based on the well log information was put
into comparison with other classical approaches namely expo-
nential and multilinear regression approaches. The exponential
approach gave a considerably small R2 value of 0.43 for estimation
of core permeability data which indicates that the method is not
applicable for the heterogenous reservoirs. Multilinear regression
approach gave a more accurate performance compared to the
exponential approach with R2 value of 0.89 for porosity and 0.53 for
permeability. However, the method overpredicts high data and
underpredicts the small data. Owing to the nonlinearity and het-
erogeneity of data points the conventional methods were not
effective in prediction of actual core data. Finally, the modeling task
was effectively handled by utilizing ANN with R2 value of 0.99 for
both porosity and permeability data. The merit of soft computing
methods over conventional approaches in characterization of res-
ervoirs is that they are able to handle limited and incomplete da-
tabases that is an important difﬁculty during the primary phases of
reservoir exploration. Soft computing methods do not need any pre
supposition on the complexities of the reservoir to develop an
effective model from measured data (Shokir and Engineering,
2006). AI and computer-based models are important tools that
offer the potential of integrating a huge amount of input data and
being utilized in reservoirs having many drilled wells in consider-
ably fast computation process with nearly automatic execution
process.
Reservoir
characterization
is
an
important
way
of
describing hydrocarbon reservoirs in order to obtain the highest
proﬁt within their production lifetime. Obtaining the highest proﬁt
requires to take into consideration the intrinsic error related to all
measurement, recognitions of uncertainty coming from the heter-
ogenous reservoirs as well as the capability to handle gaps and
deﬁcits in available information. AI-based techniques have per-
formed efﬁciently robust regarding these necessities. However,
future works need to be directed toward the application of com-
bination of AI methods to compensate the shortcomings of in-
dividuals techniques with the robustness of other AI techniques
including ANN, FL, SVM, GA, etc. Despite implementation of several
hybrid models, their capabilities in identifying helpful relations
among different types of reservoir parameters and conducting ac-
curate extensions far from drilled wells have been rarely studied.
Considering the huge volume of associated databases in petroleum
industry
which
has
been
gathered
during
many
years,
in-
vestigations on in what way to consider this information in current
and future applications by utilizing hybridized AI techniques that
beneﬁts from various soft computing methods will reduce the in-
vestment risks and will offer clearer insights for enhanced char-
acterization of reservoirs for in various ﬁelds.
Soumi et al. (2018) and Chaki et al. (2018) utilized various pre/
post processing signals for the estimation of reservoir lithological
characteristics. They applied three types of AI methods including
ANN, ANFIS, and SVR to model the properties by integration of
seismic and well log data. Table 1 provides a more detailed review
of application of various AI and ML techniques in reservoir studies.
3.2. Production
Hydrocarbon production prediction is becoming very crucial
with respect to planning and development of projects involving
environment and ﬁnancial regulations, as well as commissioning
and de-commissioning of facilities. The precise prediction of the
performance of producing wells can deliver roadmaps for produc-
tion optimization, artiﬁcial lifts usages, detecting the requirement
for work-over processes, stimulation of wells, design of facilities,
and
planning
the
enhanced
oil
recovery
processes
(Najaﬁ-
Marghmaleki et al., 2018). Oilﬁeld production optimization is gov-
erned through the nodal analysis process to evaluate the optimum
operational conditions. Large volumes of data are produced during
S. Bahaloo, M. Mehrizadeh and A. Najaﬁ-Marghmaleki
Petroleum Research 8 (2023) 167e182
174
Table 1
Summary of AI and ML applications in reservoir studies.
ML Algorithm
Reference
Size of Data
Predicted Parameter
Result
ANFIS coupled
with GA
Huang et al.
(2001)
563 data points from 4 wells
Permeability
The GA helped to improve the accuracy of ANFIS with R2
value of 0.71
ANN
Huang et al.
(2003)
Not Given
CO2/oil MMP
The model provided accurate outcomes with R2 value of 0.99
for training and 0.936 for testing
ANFIS
Lim and Kim
(2004)
47 data points of an offshore well
Porosity and Permeability
The model predicts the core data better than conventional
approaches with overall R2 value of 0.9993 and 0.9998 for
porosity and permeability data, respectively.
ANFIS
Shokir (2006) 504 data points from two wells
Permeability
The model performed well with R2 value of 0.985
ANN, SVM
El-Sebakhy
(2009)
782 data points
PVT properties (bubble point
pressure and oil formation
volume factor)
The SVM provided better prediction results compared to ANN
and empirical correlations
ANN
Al-Bulushi
et al. (2009)
13 data points for water saturation and
saturation height functions of 53 core
plugs for ﬂuid distribution
Water saturation,
Fluid distribution
The model successfully predicted data with R2 and RMSE of
0.91, 0.025 for water saturations and RMSE of 0.046 for ﬂuid
distribution
GA
Chen et al.
(2010)
Simulation model of a reservoir
Parameters of WAG process
By using the newly developed model, oil recovery can be
increased by 11.4%
ANFIS
Yetilmezsoy
et al. (2011)
258 data points
Water in oil emulsion
formation
The model provided predictions with RMSE of 2.0907 and R2
of 0.967
ANN
Moghadam
et al. (2011)
54 data points from two wells
Porosity and Permeability
Predicted the porosity data with overall R2 value of 0.993 and
the permeability data with overall R2 value of 0.991
Hybrid of ANN, FL
and SVM
Anifowose
and
Abdulraheem
(2011)
723 porosity data points and 1219
permeability data points from three
wells
Porosity and Permeability
The combination of ANN, FL and SVM provided the most
accurate predictions with maximum R2 value of 0.96 for
porosity data and 0.95 for permeability data
ANN, MNN
(Tahmasebi
and
Hezarkhani,
2012)
34 core permeability data points
Permeability
The MNN model performed better than the ANN model. The
R2 value of ANN and MNN models were 0.94 and 0.99,
respectively
ANN, FL, ANFIS
Kenari and
Mashohor
(2013)
Not Given
Water saturation
ANFIS predictions are better than other models
ANN-Fuzzy
Wang et al.
(2013)
231 data point from three wells
Porosity
The model predicted core data with overall R of 0.9504
ANN, LSSVM
coupled with
CSA
Chamkalani
et al. (2013)
4756 data points
Gas compressibility factor
The LSSVM model presented better outcomes with MSE and
R2 values of 0.000014 and 0.999, respectively
ANN coupled with
PSO
Zendehboudi
et al. (2014)
169 data points
Recovery factor and cumulative
steam to oil ratio in SAGD
process
The model showed average error less than 7% and better
performance compared to conventional ANN
ANFIS coupled
with GA
Afshar et al.
(2014)
153 data points
Oil bubble point pressure
The GA helped to improve the accuracy of ANFIS model and
provided an overall R2 value of 0.9909
ANN-Fuzzy
Zerrouki et al.
(2014)
420 data points of a single well
Natural fracture porosity
The model exhibited a R2 value of 0.965. Applying the model
for prediction of data in another well resulted in R2 value of
0.878
ANN coupled with
GA
Xue et al.
(2014)
61 data points
Fracture in core samples
The model provided well-stablished results for prediction of
fractures in core samples
ANFIS
Aïfa et al.
(2014)
690 data points
Porosity and permeability
The model exhibited accurate results with R2 values of 0.99
and 0.97 for porosity and permeability, respectively
ANN coupled with
ICA
Amiri et al.
(2015)
2200 data points from 12 wells
Water saturation
Using ICA improved the performance of ANN model and
increase R2 value from 0.92 to 0.96
ANN
Singh et al.
(2016)
Not Given (data from Kansan gas ﬁeld) Porosity
Acceptable prediction of porosity data with overall R2 value of
0.9714.
ANN
Zhang et al.
(2016)
64 data points
Wettability
The model exhibited accurate predictions with average error
less than 5%
ANN
Gholanlo et al.
(2016)
564 data points from an oilﬁeld
Water saturation
ANN predictions are better than the empirical approaches
ANN, ANFIS
Khan et al.
(2018)
150 data points
Water saturation
ANFIS predictions with testing R2 of 0.956 are better than
ANN
SVM coupled with
PSO
Zhong and
Carr (2019)
94 data points of six wells
Porosity
The model presented robust performance with R2 value of
0.914
ANN
Bruyelle and
Gu
erillot
(2019)
Simulation model of Brugge ﬁeld
Dynamic reservoir parameters
such as pressure, oil and water
rates
The ANN showed much predictive capability than other
methods
ANN
Amini and
Mohaghegh
(2019)
CO2 sequestration simulation model
Dynamic reservoir parameters
including pressure, saturation
and CO2 mole fraction
The proposed method can produce target data with less
computational cost and time compared to the numerical
simulator
ANN, LSSVM
integrated with
CSA
Miah et al.
(2020)
182 data points
Water saturation
The ANN trained by LM method and the LSSVM coupled with
CSA and RBD kernel provided the most accurate predictions
ANN, SVM and
their
integration
with GA and
PSO
Zhang et al.
(2021)
253 core permeability data points
Porosity, FZI, FZI*
The hybrid PSO-SVM method provided the most accurate
predictions compared to other methods
(continued on next page)
S. Bahaloo, M. Mehrizadeh and A. Najaﬁ-Marghmaleki
Petroleum Research 8 (2023) 167e182
175
monitoring the conditions of producing wells in real time mode.
These data include real-time response of pressure and ﬂowing rate
data which are utilized for graphical and analysis purposes for
improved decision-makings. Generally, regression and simulation
approaches are utilized to estimate future production of wells ac-
cording to their pre-existing observed production rates, which is a
time consuming and computational demanding task. On the his-
torical production data. To implement this process in more effort-
less and cost saving ways, AI approaches can be used. This section
focuses on the application of AI approaches in production optimi-
zation processes within petroleum industry. Alimonti and Falcone
(2004) used FL for monitoring the production rate of ﬂuids and
composition of steam during multiphase ﬂow process. In the work
done by Li et al. (2013), the DT approach was utilized to estimate the
ﬂuid production rates through taking into account interrelations
between the input parameters. They compared the performance of
DT and methods and observed that both methods were reliable and
accurate.
However,
further
analysis
of
statistical
parameters
revealed the better classiﬁcation performance of the DT approach.
Chakra et al. (2013) used a branch of ANN to estimate cumulative
ﬂuid production of an oilﬁeld with few numbers of train data. The
results showed that the implemented method is reliable for pre-
diction of production data for short and long run plans. The out-
comes
of
method
according
to
the
performance
measure
parameters were in consistency with the results achieved from the
simulation works. However, the approach needs further improve-
ment to enhance the precision and robustness by involving the
characteristic variables of the reservoir. Yu et al. (2013) proposed
SVM approach for detection of failures in submersible pump
operation by using performance-based variables. The developed
method performed well through reducing the rate of misjudgment
and excellent pattern recognition capability. Kamari et al. (2014)
utilized LSSVM method coupled with CSA to estimate unloading
gradient pressures in wells under gas lift operation to evaluate the
optimum rates for production and injection of ﬂuids. Khamehchi
et al. (2014), utilized ANN coupled with PSO to estimate the sand
production onset. Their results showed that coupling the ANN with
PSO increases it performance accuracy for prediction of actual data
compared to back propagation algorithm. Li et al. (2015) utilized a
soft computing approach for fault detection in wells under pro-
duction by sucker rod pumps to choose clustering index and the
highest appropriate scale variable. Choubineh et al. (2017) showed
that hybridized ANN approach with six input variables of speciﬁc
gravity of oil and gas, gas to oil ratio, size of choke, pressure and
temperature at wellhead estimated the choke critical ﬂow rates of
liquid at installed at wellhead with acceptable correctness as
inferred from statistical performance measure parameters. Okwu
and Adetunji (2018) performed an investigation to optimize the
distribution cost of several products to different locations by uti-
lizing a hybrid method called adaptive neuro-fuzzy inference sys-
tem (ANFIS). Liu et al. (2018) implemented an ANFIS framework
coupled with the PSO algorithm to estimate the weight percentage
of unstable asphaltene. The developed method was trained and
tested utilizing the data points gathered from literature available
works and the accuracy was monitored by statistical parameters.
Bhattacharya et al. (2019) utilized AI approaches coupled with
gathered data from temperature and pressure measurements,
completions, and production logs for prediction of gas production
in a horizontal well after stimulation operations. They utilized ANN,
SVM and RF methods and found that the RF was the ﬁttest algo-
rithm with faster computation time and convergence. Wang and
Chen (2019) investigated the production conditions of wells being
hydraulically fractured by using soft computing methods. They
utilized various AI techniques to predict ﬁrst year production data
of wells. They concluded that among various methods, the RF
approach is more accurate thanks to offering more suitable statis-
tical performance measure parameters. Mahmoud et al. (2019)
provided an ANN technique for optimization of the number of
separation steps, pressure and temperature in multiphase sepa-
rator equipment according to the composition of ﬂowing ﬂuid. The
results revealed that the developed method can be utilized to
predict the operational conditions of separators to enhance the
resulted ﬂuid quality achieved from surface separators. The work
done by Liu et al. (2020) highlighted application of ANN and SVM
methods for prediction of oil production. Their results showed that
the models were able to reproduce the target data with acceptable
accuracy. Crnogorac et al. (2020) utilized a FL optimization tech-
nique to identify the best artiﬁcial lift scenario for several wells. The
appropriate artiﬁcial lift scenarios were identiﬁed and classiﬁed for
selected wells by conducting sensitivity analysis study. Table 2
provides a more detailed review of application of various AI and
ML techniques in production optimizations.
3.3. Drilling operations
The process of well drilling is a difﬁcult job as it includes few
previous knowledge of the characteristics of the sub e surface
settings and the challenge boosts with progress of drilling to higher
depths or deviation of the well path from vertical trajectory. In
addition, presence of drilling events including differential pipe
sticking, lost circulation,
dogleg severity, etc., increases the
complexity and challenges of the process. Recently, AI techniques
were gained popularity for addressing these problems.
Wu et al. (1997) conducted a comparison between FL and sta-
tistical techniques to predict the recovery during the initial stages
of production, primary water injection and during drilling of inﬁll
wells. They observed that FL recovery prediction methods con-
structed by utilizing extracted parameters from statistical method
gives the most accurate outcomes. Garrouch and Labbabidi (2003)
utilized FL in selection of underbalanced drilling (UBD) candidate
wells. They achieved reliable outcomes by studying two real cases,
i.e., the ﬁrst one was a reservoir under extreme depletion and
damage and the second one was a fractures reservoir with low
pressure. Malallah and Nashawi (2005) developed an ANN method
for prediction of fracture gradient of formations during drilling
operations. They utilized data from 16 wells located in various
geological settings. They concluded that the ANN model performs
better than other literature models in prediction of the fracture
gradient. Garrouch and Lababidi (2005) used FL in formations
producing heavy oil from multilaterally completed wells. They
implemented FL to design geometry of the wellbore and comple-
tion methods of the wells. They concluded that in cases with
Table 1 (continued )
ML Algorithm
Reference
Size of Data
Predicted Parameter
Result
ANN, MLR,
XGBoost, GA,
PSO
Chai et al.
(2021)
A full physics reservoir model under
waterﬂooding
History matching and
optimization of ﬁeld
development
The applied methods provided improved history matching
and ﬁeld development workﬂows
S. Bahaloo, M. Mehrizadeh and A. Najaﬁ-Marghmaleki
Petroleum Research 8 (2023) 167e182
176
constraints on operational processes, the fuzzy FL could assist
drillers to properly decide on the most suitable drilling choices to
select. Zarei et al. (2008) used the neuro-fuzzy technique in opti-
mizing the drilling locations of wells. The work revealed that the
use of neuro-fuzzy method effectively saves the interpretation time
which is needed for implementation of the technique.
Drillers usually face several drilling difﬁculties including dif-
ferential pipe sticking. Prevention of these issues could reduce the
cost of drilling and optimize the process of drilling. Murillo et al.
(2009) integrated ANN and FL to examine the risks associated
with pipe stuck within the well planning phase and the real drilling
process. Alireza et al. (2011) applied ANN for prediction of lost
circulation during drilling operations. They used 18 input variables
and real data from 32 wells to build and train the network. They
obtained satisfactory results with correlation coefﬁcient of 0.95,
0.76 and 0.82 for training, validation and testing data sets.
Shadizadeh et al. (2010) developed an ANN method for forecasting
the pipe sticking in drilling wells based on input data for static and
dynamic conditions. They observed an accuracy more than 90% for
the implemented method. Jahanbakhshi et al. (2012) also devel-
oped ANN and SVM frameworks for prediction of pipe sticking in
drilling operations by using 12 input variables and 214 data points.
They obtained better predictive performance for the SVM model in
comparison with the ANN approach.
In incomplete depleted ﬁelds, AI techniques has been used in
operations related to the drilling of inﬁll wells. Drilling inﬁll wells is
useful to help in producing the remaining hydrocarbons and can be
subject to optimization methodologies when the natural reservoir
uncertainty in reservoir is involved using AI methods. Popa (2013)
examined a scenario in which the FL method was utilized to
identify horizontal wells through utilizing a complicated three-
dimensional
fully
constructed
ﬁeld
model
constructed
with
available data from more than 21,000 wells. He concluded that the
use of FL for identifying the horizontal wells increased the ﬁeld
production potential. This increase was explained through the
capability of FL to concentrate on locations with high production
capacity. Yin et al. (2014) developed an ANN model for early
warning of kick by using various input parameters. They trained
their model based on available data of a speciﬁc portion of a single
well and then applied their model to predict the kick occurrence in
other portions of other wells. They obtained acceptable and reliable
results with learning rates of 91.4% and 86.7%. Toreiﬁand Rostami
(2014) developed an ANN model to estimate the ﬁltrate loss dur-
ing drilling operations. The developed model utilized different
drilling and mud information (e.g., location of well, depth, pene-
tration rate, lithology of formation, rate of pump, pressure of mud,
and rheology of mud) as input variables. The model was unfortu-
nately just applicable for certain reservoirs because of its de-
pendency on the location of well and type of formation. Fang et al.
(2017) developed an ANN model for estimation of fracture density
by using several well logs and obtained accurate predictions. They
also utilized their network to predict the fracture density in three-
dimensional model by ﬁrst propagating the input parameters of the
network within the model and then applying the network for
prediction of fracture density. Roy et al. (2018) implemented
various algorithms such as ANN, FL, ANFIS and multiple linear
regression for prediction of fracture gradient using limited data
points. They observed that the ANFIS model provide more accurate
result compared to other methods. Liang et al. (2019) employed
conventional and GA trained ANNs for kick detection based on
available information from drilled wells. They observed better
performance for GA trained network compared to the conventional
network. Anemangely et al. (2018) conducted a comparison
investigation
between
ANN
trained
by
PSO
and
Cuckoo
Table 2
Summary of AI and ML applications in production optimization.
ML Algorithm
Reference
Size of Data
Predicted Parameter
Result
BBN
Ghoraishy et al.
(2008)
Data from 59 wells
Gel treatment
performance
The developed method was able to predict the target data with accuracy over 75%
ANN, DT
Li et al. (2013)
320 oil production
data
Oil production
classiﬁcation
The models are able to acceptable classify the production data with high
classiﬁcation rate
ANN
Chakra et al.
(2013)
Historical production
data
Oil production
The model has high capability of predicting the cumulative oil production even
with limited data
SVM
Yu et al. (2013)
Data from a pumping
unit
ESP failure
The model provides accurate performance recognition capability
LSSVM coupled
with CSA
Kamari et al.
(2014)
87 data points
unloading gradient
pressure
The model average error in predictions was 1.084% with R2 value of 0.9994
ANN coupled
with PSO
Khamehchi et al.
(2014)
20 data points
onset of sand production Coupling the ANN with PSO increased its performance in prediction of target data
SVM
Guo et al. (2015)
Data from 100 wells
ESP failure
The model exhibits acceptable modeling capability and efﬁciently monitor and
predict the working conditions of wells under ESP production
ANN
Jia and Zhang
(2016)
Data from a
production well
Production decline
analysis
The model gives accurate predictions with overall R2 value of 0.9987
SVM coupled
with PSO
Ebrahimi and
Khamehchi (2016)
Data from a reservoir
simulation model
Gas lift optimization
The SVM model performed effectively in comparison with a commercial
simulator
LSSVM coupled
with PSO
Qiao et al. (2017)
Historical production
data of an oilﬁeld
Production rate
Results indicated that the model has acceptable convergence, robust estimation
capability and fast computation process
ANN
Choubineh et al.
(2017)
113 data points
Choke critical frow rate
The model presented predictions with R2 value of 0.981 and average errors less
than 2.09%
LSSVM coupled
with PSO, ANN
Panja et al. (2018) Simulation data of a
share reservoir
Oil and gas recovery,
Producing gas-oil ratio
The developed models have very precise recovery prediction capability at various
operational conditions
ANFIS coupled
with PSO
Liu et al. (2018)
428 data points
Asphaltene weight
percentage
The model represented acceptable results with RMSE of 0.49 and R2 value of 0.96
ANN, SVM, RF
Bhattacharya et al.
(2019)
Production data from
a horizontal well
Daily gas production rate The RF presented most accurate predictions accuracy of 96%. The ANN model was
also better than SVM
ANN
Mahmoud et al.
(2019)
Data from multistage
separator
multistage separator
parameters
The model can be effectively utilized for prediction of separator operating
conditions
FL
Crnogorac et al.
(2020)
Data from different
oilﬁelds
Artiﬁcial lift selection
The model presented acceptable performance with acceptable matches with
previous results of the same oilﬁeld
S. Bahaloo, M. Mehrizadeh and A. Najaﬁ-Marghmaleki
Petroleum Research 8 (2023) 167e182
177
Optimization Algorithm (COA) optimization algorithms with a
conventional ANN framework for prediction of the rate of pene-
tration (ROP) and concluded that training the ANN with COA pro-
vide more efﬁcient solutions to obtain the most precise ROP model.
Abbas et al. (2019) utilized operational and geological input pa-
rameters to develop ANN and SVM models for prediction of lost
circulation using 1120 experimental data. They observed that the
SVM model performs slightly better than the ANN model. In
addition, they also concluded that the accuracy of developed
models greatly depends on the range of the data which was
considered during the learning phase of the models. Sabah et al.
(2019a) investigated the performance of three soft computing
models named ANN, ANFIS and decision tree (DT) for prediction of
lost circulation during drilling operations by using 1900 real ﬁeld
data from 61 wells. Their results showed that by using fewer input
variables, ANN and ANFIS provided better performance compared
to DT, however, by increasing the number of input variables, the
performance of DT improved considerably and performed better
predictions than ANN and ANFIS methods. Sabah et al. (2019b)
compared ANN trained by PSO with simple ANN, SVM, DT and
random forest (RF) in prediction of ROP parameter. The outcomes
showed that the ANN trained by PSO provides the best results.
Table 3
Summary of AI and ML applications in drilling operations.
ML Algorithm
Reference
Size of Data
Predicted
Parameter
Result
ANN
Yilmaz et al.
(2002)
55 data points
Bit type
The model exhibited satisfactory results in both training and testing
scenarios
ANN
Malallah and
Nashawi (2005)
21513 data points of 16 wells Fracture gradient
coefﬁcient
The predictions of the ANN model were better than other correlations
ANN
Shadizadeh et al.
(2010)
195 data for dynamic and
231 data for static conditions
Pipe sticking
The model provided acceptable predictions with average errors less than
5%
ANN
Mohammadpoor
et al. (2010)
167 data points
Bottom hole
pressure
The model can predict data with average error of 2.06% and better
performance compared to existing correlations
ANN coupled with GA and
ACO
Ashena and
Moghadasi
(2011)
160 data points
Bottom hole
pressure
It was observed that both GA and ACO are able to improve the
performance of ANN
ANN
Alireza et al.
(2011)
Not Given
Lost circulation
The model provided precise outcomes with train, test and validation R2
values of 0.95, 0.76, 0.82, respectively
ANN, SVM
Jahanbakhshi
et al. (2012)
214 data points
Pipe sticking
The predictions of the SVM model were better than the ANN
ANN
Toreiﬁand
Rostami (2014)
1630 data points from 38
wells
Lost circulation
The model provided acceptable predictions with R2 value of 0.94
PSO
Humphries et al.
(2014)
A reservoir simulation model Well placement
The model presented acceptable performance in addressing the well
placement optimization problem.
ANN
(Chao et al., 2015) Not Given
Fracture pressure
The model showed predictions with average errors less than 10%
ANN
Vega et al. (2016) 5000 data points
Equivalent
circulation density
The proposed method offered a better and faster control and monitoring
of equivalent circulation density compared to classical methods
GA
Farshi (2008)
Not Given
Well placement
The GA method was modiﬁed to better detect the optimum well
placement locations
ANN
Fang et al. (2017) Not Given
Fracture density
The model performed well in identifying the existing fractures
ANN
Hosseini (2017)
Not Given
Lost circulation
The model provides predictions with R2 value of 0.65
ANN coupled with GA
Xie et al. (2018)
1440 data points
Kick formation
The GA improved the prediction performance and learning time of the
ANN model
ANN, FL, ANFIS
Roy et al. (2018)
46 data points
Fracture toughness The ANFIS model provided the most accurate predictions
ANFIS
Agin et al. (2018) 2400 data points from 61
wells
Lost circulation
The model provided reliable predictions with train, test, and validation
RMSE of 0.08, 0.09 and 0.15, respectively
ANN
Gowida et al.
(2019)
515 data points
Rheological
properties of
drilling ﬂuid
The model predicted target data with R2 value of 0.97 and errors less
than 6.1%
ANN coupled with GA
Liang et al.
(2019)
120 data points
Drilling overﬂow
The GA helped to improve the prediction performance of ANN
DT, ANN, ANFIS
Sabah et al.
(2019a)
1900 data points of 61 wells Lost Circulation
Although all models exhibited good performance, the DT model
provided better predictions.
ANN, SVM
Abbas et al.
(2019)
1120 data points
Lost Circulation
The performance of ANN was better than the SVM approach
ANN
Ahmed et al.
(2019a)
245 data points
Formation pressure
during drilling
The model performed well in prediction of pore pressure data
ANN, SVM, FL
Ahmed et al.
(2019b)
245 data points
Formation pressure
during drilling
All developed methods exhibited R2 values greater than 0.99 and
average errors less than 0.4%
ANN, ANFIS
Abdelgawad et al.
(2019)
2376 data points
Equivalent
circulation density
The models were able to predict the data with R2 value of 0.99 and
average errors less than 0.22%
ANN
Alkinani et al.
(2020)
100000 data points from
more than 2000 wells
Equivalent
circulation density
The model showed acceptable accuracy in prediction of output
parameter
ANN coupled with PSO,
GA, ICA
Ashraﬁet al.
(2019a)
1000 data 500 m of drilling
depth
Rate of penetration Prediction of the ANN coupled by PSO with RMSE of 1.12 were more
accurate than other methods
ANN
Agwu et al.
(2019)
676 data points
Drill cutting settling
velocity
The ANN provided accurate predictions with train and testing RMSE
values of 0.03 and 0.09, respectively
ANN, SVM, RF
Soares and Gray
(2019)
7415 data points
Rate of penetration The developed models where better than the existing literature models
and the most accurate model was RF
LSSVM, SVM, ANN
coupled with GA, PSO
and COA
Mehrad et al.
(2020)
2096 data points from two
wells
Rate of penetration While all developed models performed well, the LSSVM coupled with
COA was superior to all of them
ANN
Hou et al. (2020) data points from 50 wells
Lost circulation
The model provided accurate predictions with accuracy over 90%
S. Bahaloo, M. Mehrizadeh and A. Najaﬁ-Marghmaleki
Petroleum Research 8 (2023) 167e182
178
Ashraﬁet al. (2019b) have focused on the applicability of MLP and
radial basis function (RBF) neural networks in ROP prediction while
training them by various optimization algorithms including ICA,
PSO, GA, ICA, and BBO. The outcomes showed that the MLP model
trained by PSO provides the most reliable predictions. Elkatatny
(2019) tuned the structure of simple ANN by integrating it with
differential evolution algorithm. Then, proposed a novel ROP model
that exhibited more accurate outcomes than existing literature
models. Table 3 provides a more detailed review of application of
various AI and ML techniques in drilling operations.
3.4. Future of AI in petroleum industry
Novel pattern identiﬁcation methods built on the basis of deep
learning have gained popularity in seismic operations, speeding up
the interpretation process with a factor of 10e1000 (Cunha et al.,
2020). It is less likely that the AI methods can hanlde the physical
part (i.e., quantity, fee, and installation location of sensors) of the
seismic process. However, they are beneﬁcial for optimization of
the secondary surveys. The mathematical processing systems and
prediction abilities of ML methods will provide better suggestions
on having cheaper secondary surveys with the lowest damage to
the value of gathered data. In this regard, AI-driven approaches are
an efﬁcient method to speed up and, more importantly, to elimi-
nate the subjective nature of the interpretation procedure (Portugal
et al., 2018). In addition, apart from future application of ML in
seismic processes, a notable section in the well logging data could
be efﬁciently generated with ML. In future, this would allow the
usage of ML in well logging operations. Systems enabling the oil
corporates allocate less resources to the operational section of well
logging. Similarly, core analysis operations can also be accelerated
with a similar approach. There are three main areas for using AI in
reservoir engineering. First one is associated with calculations
performed with conventional reservoir simulators. The simulators
conduct numerical computations of partial differential equations
related to the physics of ﬂow within reservoirs. The calculations are
done on the three-dimensional grid comprising of from 1 million to
several
billions
of
cells.
The
calculations
are
usually
time
consuming, despite using updated workstations and servers, which
restricts the possibility of performing many runs. Hence, this re-
duces the optimization capability for suitable planning for devel-
opment of the ﬁeld. One of the most important roles of AI
techniques is speeding up the reservoir modeling computations.
New surrogate models for various reservoirs with modern calcu-
lations engine according to deep neural networks reduces the
dimension of the mathematical problem and predicts the time
dependent variables in an order 100 to 1000 times faster than the
common simulators. Hence, AI methods are capable of speeding up
the calculations while maintaining the same functionality. The next
area is using AI in upscaling (i.e., converting the data obtained from
different scales of geophysical analysis to a single model for
geological and reservoir studies). The process of upscaling needs a
signiﬁcant level of creativity to be done. There is no unique scien-
tiﬁc procedure to conduct upscaling, and usually reservoir engi-
neers consider several tricks to complete it in such that it sounds
right to them. It brings a signiﬁcant bias into the simulation model.
Since there is no unique and correct framework for this process, a
reservoir engineer may think of enhancing objectiveness by relying
on experience and beneﬁting from a smart system. This can be
carried out correctly with a deep learning algorithm which trained
based on several manual upscaling cases. This brings the advan-
tages of enhanced objectiveness as well as speeding up and
reducing the time of upscaling process.
Reservoirs which are under production and green ﬁelds are also
appealing for AI-driven applications. There are reported ML
applications for different pumps to develop predictive maintenance
tools and decide on the optimum operational regions to minimize
the costs of operation versus production. Most of the pumps have
sensors for recording various parameters such as temperature,
pressure, ﬂow rates, vibrations, etc. There are various cases of using
data-aided models to determine the optimum operation regimes,
failure prediction and prevention, and saving the time of mainte-
nance. In addition, the cost of investment and associated risks can
be signiﬁcantly reduced by gathering the data from the wells which
are under production by various treatment jobs. There are various
reported researches on estimation of the hydraulic fracturing efﬁ-
ciency and evaluation of injectivity problems by using ML tools
(Orlov and Koroteev, 2019). We think that future development of
methods according to mathematical and programming optimiza-
tion will bring the potential for describing the entire job operation
in a smart way. This smart system will enable to choose a speciﬁc
design for treatment of a well.
4. Conclusions
The AI and ML methods are increasingly gaining application in
various sections of petroleum industry as effective approaches
substituting the conventional methods. Different works have
proven that these methods exhibit predictive performance over
90% according to statistical quality assessing evaluations. These
methods have been capable of predicting lithological and strati-
graphic features, lithofacies, and shale sweet spots as well as
detection of seismic horizons. The drilling variables such as ROP,
velocity of settled cuttings and differential sticking are efﬁciently
predicted and optimized and drilling challenges including lost cir-
culation volume estimates or identiﬁcation of drilling hazardous
events were minimized. The use of ML and AI methods to improve
different sections of reservoir studies by estimating reservoir
characteristic variables including porosity, permeability, water
saturation, bubble point pressure, and recovery factor have been
evaluated. In addition, log generation, history matching and ﬂow
rate estimations, have also been addressed in the present work. The
use cases of AI and ML methods in optimization of ﬂuid production
by estimation of oil and gas production rates, well treatment and
artiﬁcial lift planning which improves the production related de-
cision makings have also been discussed. In addition, possible
criteria of the way in which artiﬁcial intelligence can develop and
spread within the oil and gas industry in the future was evaluated.
The AI and ML approaches have considerably saved operation and
computation
time
and
reduced
the
associated
expenses
by
providing efﬁcient predictions and supplying effective solutions for
various operations in the petroleum industry.
Declaration of competing interest
The authors declare that they have no known competing
ﬁnancial interests or personal relationships that could have
appeared to inﬂuence the work reported in this paper.
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S. Bahaloo, M. Mehrizadeh and A. Najaﬁ-Marghmaleki
Petroleum Research 8 (2023) 167e182
182

Treatment of petroleum reﬁnery wastewater by electrochemical methods
Yusuf Yavuz, A. Savaş Koparal ⁎, Ülker Bakır Öğütveren
Anadolu Üniversitesi, Çevre Sor.Uyg. ve Araş. Merkezi, Eskişehir, Turkey
a b s t r a c t
a r t i c l e
i n f o
Article history:
Received 18 November 2009
Received in revised form 3 March 2010
Accepted 5 March 2010
Available online 10 April 2010
Keywords:
Petroleum reﬁnery wastewater
Phenol
Electrochemical oxidation
Electrofenton
Electrocoagulation
Direct and indirect electrochemical oxidation by using boron doped diamond anode (BDD), direct
electrochemical oxidation by using ruthenium mixed metal oxide (Ru-MMO) electrode, and electrofenton
and electrocoagulation by using iron electrode were investigated for the treatment of petroleum reﬁnery
wastewater (PRW). The results have been given at the best operational conditions which were obtained for
each electrochemical method. The results obtained from electrochemical methods were compared to each
other. Complete phenol and COD removal can be achieved in almost all electrochemical methods, except
electrocoagulation, provided that electrolysis time is prolonged. The most efﬁcient method was the
electrofenton process followed by the electrochemical oxidation using BDD anode. Phenol removal of 98.74%
was achieved in 6 min of electrolysis and COD removal of 75.71% was reached after 9 min of electrolysis in
electrofenton. Additionally, 99.53% phenol and 96.04% COD removal were obtained in direct electrochemical
oxidation at the current density of 5 mA/cm2. Initial phenol concentration was reduced to ﬁnal phenol
concentration of 0.91 mg/L after 40 min of electrolysis, and initial COD decreased to 36.7 mg/L and 23.3 mg/L
after 60 min and 75 min of electrolysis, respectively. Electrocoagulation was found to be ineffective for the
treatment of PRW.
© 2010 Elsevier B.V. All rights reserved.
1. Introduction
The petroleum reﬁnery industry converts crude oil into more than
2500 reﬁned products, including liqueﬁed petroleum gas, gasoline,
kerosene, aviation fuel, diesel fuel, fuel oils, and lubricating oils [1].
Large volume of water is used in reﬁnery processes, especially for
distillation, hydro-treating, desalting and cooling systems [1]. The
quantity and characteristics of wastewater generated depend on the
process conﬁguration. Reﬁneries generate polluted wastewater,
containing COD levels of approximately 300–600 mg/L; phenol levels
of 20–200 mg/L; benzene levels of 1–100 mg/L; levels of heavy metals
of 0.1–100 mg/L for chrome and 0.2–10 mg/L for lead; and other
pollutants [2].
Phenol and phenolic compounds, which are constituents of PRW, are
of widespread use in many industries, such as dyes, plastics, polymeric
resin production, pharmaceutical, and oil reﬁnery [3,4]. Therefore, these
compounds are commonly encountered in industrial efﬂuents and
surface water. As a result of the formation potential of carcinogenic
chlorophenols during wastewater treatment, phenol containing efﬂu-
ents have to be treated before disposal [5,6].
The traditional treatment of the wastewater originated from the
reﬁneries is based on the mechanical and physicochemical methods
such as oil–water separation and coagulation followed by biological
treatment. The wastewater arriving at the treatment plant has been
contaminated with a light fraction of aliphatic and aromatic
petroleum hydrocarbons and chlorinated organic substances origi-
nating from the cooling liquids used in the industrial processes [7].
Treatment technologies studied by the researchers for the treatment
of PRW include enhanced photo-degradation [7], fenton and photo-
fenton processes [8], biodegradation [9], membrane bioreactor [10],
ﬂocculation and ceramic membrane ﬁltration [11], and electrochemical
methods [12,13].
Electrochemical treatment methods for water/wastewater con-
taining phenol and derivatives have been attracting great attention for
the last decade [3–5,14–20], and offer many distinctive advantages
such as environmental compatibility, versatility, energy efﬁciency,
safety, selectivity, amenability to automation, and cost effectiveness
[21]. Nevertheless most of these studies have been conducted by
using model wastewaters.
Electrode material which can signiﬁcantly inﬂuence the mecha-
nism and consequently the products of the anodic reaction is one of
the most important parameters in the electrochemical studies and
applications. The results from the studies in which BDD, Ru-MMO, and
iron electrodes had been used were given in this report.
Ruthenium oxide (RuO2) is an important catalyst for the industry
because it is widely used for the Cl2 and O2 production [22,23]. RuO2
has good conductivity, good barrier properties against oxygen
diffusion and high thermal stability at the temperatures as high as
800 °C. Ruthenium and ruthenium oxide have also excellent chemical
stability [24].
Desalination 258 (2010) 201–205
⁎ Corresponding author. Tel.:+90 222 3213550; fax: +90 222 323 95 01.
E-mail address: askopara@anadolu.edu.tr (A.S. Koparal).
0011-9164/$ – see front matter © 2010 Elsevier B.V. All rights reserved.
doi:10.1016/j.desal.2010.03.013
Contents lists available at ScienceDirect
Desalination
journal homepage: www.elsevier.com/locate/desal
When oxide anodes (MOx) such as Ru-MMO are used, it is possible
to produce hydroxyl radicals (OH•) unselective strong oxidizing
agents. The mechanisms of the oxidation of organic matter and the
formation of hydroxyl radicals on an oxide anode are represented
below (Eq. (1)) [25];
MOx + H2O→
kOH
MOx OH·


+ Hþ + e−
ð1Þ
The hydroxyl radicals are adsorbed at the electrode surface and
degradation of organic matter occurs according to Eq. (2);
R + MOx OH·


z→
kc
z= 2⋅O2 + zHþ + ze−+ MOx
ð2Þ
BDD, as a novel catalyst, has a great potential for electrochemical
applications, especially in the treatment of wastewater and drinking
water due to the extraordinary chemical inertness and the opportu-
nity of using such electrodes (anodes as well as cathodes) in very
aggressive media. The electrochemical properties of diamond provide
a wide variety of applications due to the extreme electrochemical
window (N3 V) before the formations of hydrogen at the cathode and
oxygen at the anode [26–28].
Once BDD anodes are used, they allow direct production of the
hydroxyl radicals (OH•) from the electrolysis of water with very high
current efﬁciencies [29] according to Eq. (3).
H2O→OH· + e−+ Hþ
ð3Þ
Electrocoagulation is based on in situ formation of the coagulant as the
sacriﬁcial anode corrodes due to an applied current. This technique
combines three main interdependent processes operating synergistically
to remove pollutants: electrochemistry, coagulation and hydrodynamics
[30]. The mechanism of EC is highly dependent on the chemistry of the
aqueous medium, especiallyon conductivity. Other characteristicssuch as
pH, particle size, and chemical constituent concentrations will also
inﬂuence the EC process. According to Mollah et al. [31], oxidation of iron
in an electrolytic system produces iron hydroxide, Fe (OH)n, where n=2
or 3. Two mechanisms have been proposed for the production of Fe
(OH)n; one of them has been given below in reactions between (4) and
(7) and more details have been given elsewhere by Mollah et al. [31]:
Anode:
4Fe→4Fe2+ + 8e−
ð4Þ
4Fe2+ + 10H2O + O2→4Fe OH
ð
Þ3 + 8Hþ
ð5Þ
Cathode:
8Hþ + 8e−→4H2
ð6Þ
Overall:
4Fe + 10H2O + O2→4Fe OH
ð
Þ3 + 4H2
ð7Þ
In EF process, Fe2+ and H2O2 can be generated on-site electrochem-
ically, either separately or concurrently. H2O2 can be electrogenerated by
the reduction of dissolved oxygen (Eq. (8)), and Fe2+ by the reduction of
Fe3+ (Eq. (9)) or the oxidation of a sacriﬁcial iron anode (Eq. (4)) [32,33]:
O2 + 2Hþ + 2e−→H2O2
ð8Þ
Fe3+ + e−→Fe2+
ð9Þ
The reaction between H2O2 and Fe2+ produces also hydroxyl
radical according to the reaction (10).
H2O2 + Fe2+ →Fe3+ + OH· + OH−
ð10Þ
Efﬁciency of EF process depends heavily on factors such as the
quantities of H2O2 and Fe2+ cation, pH of the solution, current density
and supporting electrolyte concentration [33,34].
In this study, feasibility of the PRW treatment by using different
electrochemical methods has been studied using an actual wastewater.
Performances of these electrochemical methods were also evaluated
and compared with each other.
2. Experimental methods
PRW, provided from a national oil reﬁnery plant located in Kocaeli
province of Turkey, was treated by different electrochemical methods.
All experimental studies were accomplished at ambient temperature
(20–25 °C) with a wastewater volume of 300 mL. PRW used had a
phenol concentration of 192.9 mg/L and a COD of 590 mg/L. Electrical
conductivity of PRW was 15.63 mS/cm. Wastewater preserved with
concentrated H2SO4 was stored in refrigerator for the use in the
experiments.
Electrochemical oxidation of PRW by using Ru-MMO electrode
was carried out in a parallel plate reactor consisted of mainly 4 com-
partments of total volume of 270 mL. 4 anodes and 4 cathodes made
of Ru-MMO in the dimensions of 3×4 cm which were supplied from
Magneto special anodes B.V. (Schiedam, The Netherlands) were con-
tained in each compartment. Studies were carried out in the batch
mode, and wastewater was recirculated by means of a peristaltic pump.
This experimental setup can be found elsewhere in more detail [3].
Direct and indirect electrochemical oxidation studies by using BDD
anode, and electrocoagulation and electrofenton studies by using iron
electrodes were accomplished in a bipolar trickle tower (BTT) reactor.
Both diamond and iron anodes were shaped as Raschig ring. The
thermo-jacketed BTT reactor of 125 mL was 2.5 cm in an inner diameter.
The feeder electrodes were 22.5 cm apart from each other. There were
26 layers in the reactor, and four electrodes in each layer. All electrode
layers were isolated from each other by using a dielectric material. The
detailed explanation including experimental setup can be found else-
where [4]. BDD anodes on niobium substrate were purchased from
Metakem GmbH (Usingen, Germany), and iron electrodes were pro-
vided from local suppliers.
During the experiments, samples were withdrawn from the
reactor at predetermined time intervals.
2.1. Analysis
NaCl (MERCK) was used in indirect electrochemical oxidation
studies, and H2O2 (MERCK) was used in electrofenton studies. Phenol
was determined according to the standard methods (direct photo-
metric method; ASTM 5530 D) [35], and COD was measured using a
COD reactor (HACH) and a spectrophotometer. All COD and phenol
analysis were accomplished in duplicate and average values have
been reported. All chemicals used were in analytical grade.
Statron 3234.4 and 3234.9 model power supplies, OGSM 3900 digital
multimeter, Multiﬁx MC 1000 PEC model peristaltic pump, Orion 420 A
model pH meter, Shimadzu UV-1700 model spectrophotometer were
used as auxiliary equipments in the experimental studies.
3. Results and discussion
3.1. Electrochemical oxidation by using Ru-MMO electrode
Ru-MMO electrode was employed in a parallel plate reactor for the
electrochemical oxidation of PRW. Best experimental conditions were
202
Y. Yavuz et al. / Desalination 258 (2010) 201–205
reached at the current density of 20 mA/cm2, and the ﬂow rate of
24.83 mL/min. This study was carried out without any need of sup-
porting electrolyte.
After 210 min of electrolysis, 94.5% phenol removal was reached.
However, COD removal is more difﬁcult since the wastewater gener-
ally has a variety of organic and inorganic contaminants and longer
electrolysis time is necessary for COD removal compared to phenol
removal [3]. COD removal of nearly 70% was obtained with Ru-MMO
anode after 300 min of electrolysis as shown in Fig. 1a. Since efﬂuent
complied with the discharge standards after 300 min of electrolysis,
complete removal of COD was not aimed.
3.2. Electrocoagulation and electrofenton by using iron electrode
Electrocoagulation and electrofenton studies were carried out
using a BTT reactor. Raschig ring shaped iron electrodes were used in
both studies. In electrocoagulation, without addition of any support-
ing electrolyte, 6.27% phenol and 2.26% COD removal values could be
obtained at a current density of 1 mA/cm2 after 120 min of elec-
trolysis. Variation of remaining phenol concentration and COD with
respect to time can be seen in Fig. 1b. Phenol removal of 98.74%
was achieved in 6 min of electrolysis and COD removal of 75.71% was
reached in 9 min of electrolysis provided that 1000 mg/L of H2O2 was
added in electrofenton studies carried out at the same experimental
conditions as electrocoagulation (Fig. 1c).
It is clear from these results that just electrocoagulation is
inefﬁcient for the treatment of PRW.
High removal efﬁciency was attained in a short time period by
applying electrofenton method. However, removal rate decreased
when H2O2 in media consumed after a given time period. If the
discharge limits are not met in this situation, continuous feeding of
H2O2 to the electrochemical reactor can be suggested to solve this
problem according to Yavuz [33]. Yavuz [33] has demonstrated that
feeding type of H2O2 has an important factor affecting the perfor-
mance of an electrochemical system.
3.3. Direct and indirect electrochemical oxidation by using BDD electrode
Direct and indirect electrochemical oxidation studies were also
performed in BTT reactor utilizing Raschig ring shaped BDD electrodes
in these studies.
Typical results obtained from direct electrochemical oxidation by
using BDD electrode have been given in Table 1. Phenol removal of
99.53% and COD removal of 96.04% were achieved in direct
electrochemical oxidation at the current density of 5 mA/cm2. Initial
phenol concentration of 192.9 mg/L was reduced to ﬁnal phenol
concentration of 0.91 mg/L at the end of 40 min of electrolysis. On the
Fig. 1. Variation of phenol and COD with time in (a) electrochemical oxidation by using Ru-MMO electrode (I=7200 mA, i=20 mA/cm2, Q=24.83×10−3 L/min, t=20 °C),
(b) electrocoagulation (I=176 mA, i=1 mA/cm2, Q=36.3×10−3 L/min, t=20 °C), (c) electrofenton (1000 mg/L H2O2, I=176 mA, i=1 mA/cm2, Q=36.3×10−3 L/min,
t=20 °C), (d) direct electrochemical oxidation by using BDD electrode (I=882 mA, i=5 mA/cm2, Q=36.3×10−3 L/min, t=20 °C), (e) indirect electrochemical oxidation by using
BDD electrode (0.05 M NaCl, I=530 mA, i=3 mA/cm2, Q=24.83×10−3 L/min, t=20 °C).
203
Y. Yavuz et al. / Desalination 258 (2010) 201–205
other hand, initial COD of 590 mg/L decreased to ﬁnal COD values of
36.7 mg/L and 23.3 mg/L after 60 min and 75 min of electrolysis,
respectively.
In indirect electrolysis, in the presence of 0.05 M NaCl, 98.9%
phenol removal at 60th min, and 95.48% COD removal at 90th min
were reached at the current density of 3 mA/cm2.
Time coarse variations of phenol and COD in direct and indirect
electrochemical oxidation by using BDD electrode have shown in
Fig. 1d and e. The graphics of these two studies have similar trends.
However, higher current density was utilized to obtain same
efﬁciency in direct electrochemical oxidation than that in indirect
electrochemical oxidation although the results were similar. There-
fore, it can be concluded that lower current density is required to
reach same removal efﬁciency in the presence of NaCl. Additionally,
there is no signiﬁcant difference in total energy consumption values
between these two methods.
3.4. Comparison of electrochemical methods
Prevailing mechanism in direct and indirect electrooxidation, and
electrofenton studies is degradation by OH• radicals generated
electrochemically, except electrocoagulation. While degradation also
takes place at the electrode surfaces in Ru-MMO and BDD electrodes,
coagulation occurs in electrofenton additionally. In electrocoagula-
tion, after coagulant is generated electrochemically, complexation or
electrostatic attraction followed by coagulation will occur. Perfor-
mance of the electrocoagulation was considered to be limited due to
the high amount of soluble organic pollutants and low amount of
suspended solids contained in PRW. Thus, OH• radical formation
potential becomes more important for the successful treatment of
PRW. OH• radical formation potential of Ru-MMO and BDD electrodes
and electrofenton mechanism is well known today and was given in
the Introduction section. In this situation, results obtained from the
studies are compatible with that in the literature.
Time coarse variations of remaining phenol concentration, COD,
and energy consumption in the electrochemical methods studied
have been summarized in Figs. 2–4, respectively.
According to the results given in Fig. 2, electrochemical methods can
be put in order for phenol removal from best to worst as follows:
electrofenton, direct and indirect electrochemical oxidation by using
BDD (almost same), direct electrochemical oxidation by using Ru-MMO,
and electrocoagulation. Phenol concentration in wastewater decreased
gradually with time in all methods studied. However, phenol degrada-
tion reaction occurred very fast in electrofenton compared to the other
methods with a ﬁrst-order rate constant of 0.7825 1/min. Initial phenol
concentration of 192.9 mg/L was decreased to 2.43 mg/L in a very short
time period (only 6 min) by using electrofenton method (Fe2+−H2O2).
On the other hand, time necessary to reach phenol removal of over 95%
varied 40–60 min for direct and indirect oxidation at BDD anode. It
should be noted that the extension of electrolysis time leads to increase
in energy consumption. Thus, the removal of pollutants in a very short
time period makes the electrochemical method favorable for the
industrial wastewater treatment applications. Therefore electrofenton
can be suggested and utilized easily as a process step in a real waste-
water treatment plant. In addition, it took 210 min to achieve nearly 95%
phenol removal by using Ru-MMO anode. According to the results,
longer time is necessary for Ru-MMO electrode to reach an acceptable
removal value for pollutants compared to the other methods. All these
performance differences between the electrode materials used can be
attributed their speciﬁc OH• radical formation potential. Furthermore,
additional reactions occurred at the electrode and/or in bulk solution
also contribute the treatment efﬁciency.
Variation of COD with time depending on the electrode materials
has been plotted in Fig. 3. As it can also be seen from Fig. 3 that
variation of COD had a similar trend with the variation of remaining
phenol concentration (Fig. 2). These two ﬁgures reveal that the
electrochemical methods studied have exhibited similar performance
for both phenol and COD removal. In a matter of fact, phenol
concentration is included in COD value. Therefore it can be concluded
that if an electrochemical method is successful for phenol removal,
will also be successful for COD removal, and vice versa. It should be
noted that COD removal is more difﬁcult than phenol removal because
of complex nature of COD. Results obtained from Figs. 2 and 3 have
proved this situation. As seen from these ﬁgures the removal
efﬁciency of COD is lower than the removal efﬁciency of phenol for
a given time period. For instance, in direct electrochemical oxidation
studies by using BDD anode removal efﬁciencies of phenol and COD
after 10 min of electrolysis were 79.87% and 54.80%, respectively.
Same situation was also valid for other electrochemical methods
studied. It can be said that longer electrolysis time is required for COD
removal than that of phenol removal for all electrochemical method.
One of the most important parameters in the electrochemical
studies as well as in other wastewater treatment methods is energy
Table 1
Typical results obtained in direct electrochemical oxidation studiesby using BDD electrode
(I=882 mA, i=5 mA/cm2, Q=36.3×10−3 L/min, t=20 °C).
Time
(min)
Applied potential
(V)
Cout
(mg/L)
COD
(mg/L)
Removal
efﬁciency
(%)
Energy consumption
(kWh/g)a
Phenol
COD
0
285
192.90
590
–
–
–
5
285
70.10
296
63.67
49.72
0.94
10
284
38.80
266
79.87
54.80
1.68
15
283
11.50
176
94.02
70.06
2.32
20
280
4.55
126
97.64
78.53
2.92
30
270
2.73
103
98.58
82.48
3.49
40
265
0.91
60
99.53
89.83
4.05
50
260
–
50
–
91.52
–
60
250
–
36.7
–
93.78
–
75
247
–
23.3
–
96.04
–
a Energy consumption values were calculated in the base of phenol removal.
Fig. 2. Time coarse variation of phenol concentration with electrode materials employed.
Fig. 3. Variation of COD values with time depending on the electrode materials.
204
Y. Yavuz et al. / Desalination 258 (2010) 201–205
consumption. Besides high removal efﬁciency, low energy require-
ment is also a necessity for a treatment system. In electrochemical
studies, energy consumption is directly proportional with current
density applied and inversely proportional with electrical conductiv-
ity of the solution.
Cumulative energy consumption values have shown in Fig. 4. Aver-
age energy consumption values were 0.143 kWh/g in electrofenton
method and 4.05 kWh/g in direct electrochemical oxidation by using
BDD electrode. However, it was determined as high as 31.949 kWh/g for
electrocoagulation. This is approximately ten times higher than the
energy consumption values obtained in other methods. According to the
results, electrocoagulation was not a suitable method from the energy
consumption angle.
In the processes studied, COD or phenol removal rate is proportional
to the concentration of the organic pollutant and phenol concentration,
respectively. Therefore, the kinetics for COD or phenol removal can be
written as:
−d
dt C
½  = k C
½ 
ð11Þ
Rearranging and integrating Eq. (11) gives
ln Ct
½

Co
½
 = −k⋅t
ð12Þ
where, Co is the initial COD or phenol concentration of the wastewater
in mg/L, and Ct is the COD or phenol concentration in mg/L at time t.
Plotting Ct/Co on the y axis versus t on the x axis on semilog scale will
result in straight line with the slope of k. Removal efﬁciency values,
ﬁrst-order rate constants, and correlation coefﬁcients for phenol and
COD for electrochemical methods studied were listed in Table 2. The
lowest rate constant was obtained in electrocoagulation with a
numerical value of 0.0007 1/min, and the highest rate constant value
of 0.7825 1/min was achieved in electrofenton for phenol removal.
There is ∼1100 fold difference between these two values. Further-
more, rate constants were 0.0002 1/min in electrocoagulation and
0.1806 1/min in electrofenton for COD removal, respectively. Accord-
ing to Table 2, the fastest reaction took place in electrofenton among
the electrochemical methods studied.
4. Conclusions
Treatment of PRW by several electrochemical methods was
investigated in this study. For this aim, direct and indirect electrochem-
ical oxidation by using boron doped diamond (BDD) and ruthenium
mixed metal oxide (Ru-MMO) electrodes, and electrofenton and
electrocoagulation by using iron electrodes were employed.
Optimum experimental conditions were obtained for all electro-
chemical methods employed, and the results were evaluated for these
conditions. Then, performances of ﬁve electrochemical systems were
determined and compared each other for the treatment of PRW.
In general, all electrochemical methods studied except electro-
coagulation were found to be successful for the treatment of PRW
according to the results obtained. The most efﬁcient method was the
electrofenton process followed by either direct or indirect electro-
chemical oxidation using BDD anode. Electrocoagulation was found to
be ineffective for the treatment of PRW.
Acknowledgements
This study was supported by Anadolu University Research Fund
with Grant no: 01.02.52.
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[32] Z. Qiang, J. Chang, C. Huang, Water Res. 37 (2003) 1308.
[33] Y. Yavuz, Sep. Purif. Technol. 53 (2007) 135.
[34] L. Szpyrkowicz, C. Juzzolino, S.N. Kaul, Water Res. 35 (2001) 2129.
[35] APHA, Standard Methods for the Examination of Water and Wastewater20th ed,
APHA, AWWA, WEF, 1998.
Fig. 4. Variation of energy consumption values with electrode materials in the experimental
studies.
Table 2
First-order rate constants for electrochemical methods studied.
Method
Electrode
material
Removal
efﬁciency, %
First-order
rate constant,
k, 1/min
R2
Phenol COD
Phenol COD
Phenol COD
Electrooxidation
Ruthenium
MMO
94.49
70.06
0.0162 0.0041
0.7921 0.9415
Electrocoagulation
Iron (Fe)
8.23
6.27
0.0007 0.0002
0.5868 0.7320
Electrofenton
Fe2++H2O2
98.74
75.71
0.7825 0.1806
0.9485 0.8645
Electrooxidation
BDD
99.53
96.04
0.1478 0.0492
0.9353 0.8685
Electrooxidation
BDD+NaCl
98.90
95.48
0.0876 0.0393
0.7974 0.6347
205
Y. Yavuz et al. / Desalination 258 (2010) 201–205

Journal
 of
 Process
 Control
 31
 (2015)
 30–44
Contents
 lists
 available
 at
 ScienceDirect
Journal
 of
 Process
 Control
j
 our
 na
 l
 ho
 me  pa
 g
 e:
 www.elsevier.com/locate/jprocont
Dynamic
 modeling
 and
 optimization
 of
 an
 industrial
 ﬂuid
 catalytic
cracker
Hasan
 Sildir a,  Yaman
 Arkun a,∗,  Ummuhan
 Canan b,  Serdar
 Celebi b,  Utku
 Karani c,  Ilay
 Er c
a Department
 of
 Chemical
 and
 Biological
 Engineering,
 Koc
 University,
 Rumeli
 Feneri
 Yolu,
 Sariyer,
 Istanbul
 34450,
 Turkey
b TUPRAS
 R&D
 Department,
 Kocaeli,
 Turkey
c TUPRAS
 Izmir
 Reﬁnery,
 Izmir,
 Turkey
a
 r  t
 i c  l e  
i  n  f  o
Article
 history:
Received
 24
 October
 2014
Received
 in
 revised
 form
 26
 March
 2015
Accepted
 7
 April
 2015
Available
 online
 4
 May
 2015
Keywords:
Fluid
 catalytic
 cracking
Discrete
 lumping
Dynamic
 reactor
 modeling
Parameter
 estimation
Optimization
a
 b
 s
 t  r  a
 c  t
Fluid  Catalytic
 Cracking
 (FCC)  is  an  important
 process
 which  is  used  to  convert
 heavy
 petroleum
 fractions
into
 more
 valuable  lighter
 products.
 In  this  work,  the  FCC  process  consists
 of  the
 reactor,  the  regenerator
and
 the fractionation
 units.
 Modeling
 is challenging
 due  to  the  complex
 reaction
 chemistry
 and  the
interactions
 among
 the  different
 process
 units.  The  reaction
 medium
 is  modeled
 by  the  method
 of  discrete
lumping
 that uses  narrow  fractions.
 As  a result,
 the  number
 of  discrete
 lumps
 (or  pseudo-components)
 to
model
 the  process
 increases
 and  this
 enables
 better  prediction
 of fractionation
 products.
 For the  reactor,
we present
 a new  kinetic
 model
 that  includes
 a yield
 function
 for  the  cracking
 products.
 Kinetic
 constants
and
 heat of cracking
 are  correlated
 with  the  average
 boiling
 point  of the  pseudo-components.
 These
correlations
 are  next  used  in
 the  development
 of  ﬁrst-principles
 models  for the  riser  and  the  regenerator
units.
 In  addition,  an  empirical
 model  is  constructed
 for  the  purpose
 of  predicting
 the  individual
 amounts
of
 the  fractionation
 products
 from  the  reactor’s
 efﬂuent.
 Using  parameter
 estimation,
 model  parameters
are
 estimated
 from
 actual  industrial
 data.  Model
 predictions
 match  the  plant  measurements
 closely.
Simulation
 and
 optimization
 results
 show  that  the  developed
 model  offers  signiﬁcant
 potential
 for  use
in
 real-time
 optimization
 and  control.
©  2015  Elsevier
 Ltd.
 All  rights
 reserved.
1.
 Introduction
Fluid
 catalytic
 cracking
 (FCC)
 is
 one
 of
 the
 most
 important
reﬁnery
 processes.
 It
 is
 used
 for
 cracking
 high
 molecular
 weight
hydrocarbon
 feed-stocks
 to
 smaller
 molecules
 which
 boil
 at
 rela-
tively
 lower
 temperatures.
 In
 the
 reﬁnery
 under
 study
 here,
 heavy
vacuum
 gas
 oil
 (HVGO)
 feed
 is
 converted
 to
 off-gas,
 liqueﬁed
petroleum
 gas
 (LPG),
 whole
 crack
 naphtha
 (WCN),
 light
 cycle
 oil
(LCO)
 and
 clariﬁed
 oil
 (CLO).
 LPG
 and
 WCN
 are
 usually
 the
 primary
products.
 Approximately,
 45%
 of
 naphtha
 in
 the
 world
 is
 produced
by
 FCC
 [1].
The
 existing
 FCC
 plant
 in
 the
 reﬁnery
 consists
 of
 a
 reaction
 unit
which
 is
 followed
 by
 the
 fractionation
 unit
 that
 separates
 the
 reac-
tor
 efﬂuent
 into
 the
 ﬁnal
 products.
 A
 simpliﬁed
 process
 ﬂow
 sheet
is
 shown
 in
 Fig.
 1.
 Pressure
 and
 temperature
 transmitters
 are
 rep-
resented
 by
 PT
 and
 TT,
 respectively.
 The
 block
 that
 is
 marked
 with
“GC”
 represents
 the
 gas
 analyzer.
 The
 reaction
 unit
 is
 composed
of
 the
 riser
 and
 the
 regenerator.
 HVGO
 is
 fed
 to
 the
 bottom
 of
 the
∗Corresponding
 author.
 Tel.:
 +90
 212
 338
 1313;
 fax:
 +90
 212
 229
 6674.
E-mail
 address:
 yarkun@ku.edu.tr
 (Y.
 Arkun).
riser
 after
 it
 is
 dispersed
 through
 a
 nozzle
 system.
 After
 disper-
sion,
 the
 feed
 vaporizes
 upon
 contact
 with
 the
 hot
 catalyst
 coming
from
 the
 regenerator.
 Dispersion
 of
 the
 feed
 provides
 more
 heat
transfer
 which
 in
 turn
 increases
 the
 efﬁciency
 of
 feed
 vaporiza-
tion.
 Some
 amount
 of
 lift
 steam
 is
 also
 added
 to
 provide
 drag
 force
to
 catalyst
 particles.
 Steam
 and
 the
 vaporized
 feed
 lift
 the
 catalyst
particles
 upward
 through
 the
 riser.
 In
 the
 riser,
 vaporized
 hydrocar-
bons
 crack
 to
 smaller
 molecules
 on
 the
 catalyst
 surface.
 In
 addition
to
 the
 cracking
 reactions,
 some
 amount
 of
 coke
 is
 deposited
 on
 the
catalyst
 surface
 which
 reduces
 the
 catalyst’s
 activity.
 At
 the
 riser
exit,
 deactivated
 catalyst
 particles
 are
 separated
 and
 transferred
back
 to
 the
 regenerator
 whereas
 the
 vapor
 hydrocarbons
 are
 sent
 to
the
 fractionation
 unit
 where
 they
 are
 separated
 into
 the
 end
 prod-
ucts.
 In
 the
 regenerator,
 the
 coke
 on
 the
 catalyst
 in
 burned
 with
air
 and
 fresh
 hot
 catalyst
 is
 transferred
 back
 to
 the
 riser
 inlet.
 The
gaseous
 products
 of
 combustion
 reactions
 are
 further
 processed
in
 the
 CO
 burner.
 Operating
 conditions
 and
 design
 variables
 for
 a
particular
 day
 are
 summarized
 in
 Table
 1.
In
 the
 riser,
 hydrocarbon
 compounds
 are
 converted
 to
 smaller
molecules
 which
 boil
 at
 lower
 temperatures.
 There
 is
 a
 high
 num-
ber
 of
 chemical
 species
 in
 the
 reaction
 medium.
 In
 practice,
 each
species
 contributes
 to
 the
 cracking
 tendency
 of
 a
 hydrocarbon.
 In
http://dx.doi.org/10.1016/j.jprocont.2015.04.002
0959-1524/©
 2015
 Elsevier
 Ltd.
 All
 rights
 reserved.
H.
 Sildir
 et
 al.
 /
 Journal
 of
 Process
 Control
 31
 (2015)
 30–44
 
31
Nomenclature
˛
 
tuning
 parameter
 the
 deactivation
 function
ˇ
 
tuning
 parameter
 for
 preexponential
 factor
ˇc
tuning
 parameter
 for
 combustion
 reactions
kc0
tuning
 parameter
 for
 combustion
 reactions
K3c0
tuning
 parameter
 for
 combustion
 reactions
K3h0
tuning
 parameter
 for
 combustion
 reactions
Eˇ
tuning
 parameter
 for
 combustion
 reactions
Ec0
tuning
 parameter
 for
 combustion
 reactions
E3c
tuning
 parameter
 for
 combustion
 reactions
E3h
tuning
 parameter
 for
 combustion
 reactions
ε
 
the
 void
 fraction
p
tuning
 parameter
 for
 the
 yield
 function

 
tuning
 parameter
 for
 preexponential
 factor
p
tuning
 parameter
 for
 the
 yield
 function
cat
the
 bulk
 catalyst
 density
 (kg/m3)
g
the
 gas
 density
 (kg/m3)

 
The
 parameter
 set

 
The
 temperature
 cut
 point
 (K)
˚
 
the
 catalyst
 activity
 coefﬁcient
ϕ
 
the
 coking
 tendency
Ai
the
 pre-exponential
 factor
 of
 ith
 pseudo
 component
(m2/kg
 cat
 h)
Ariser
the
 crossectional
 area
 of
 the
 riser
 (m2)
Aregen
the
 crossectional
 area
 of
 the
 regenerator(m2)
BPi
the
 boiling
 point
 of
 ith
 pseudo
 component
 (K)
˙
C
the
 coke
 mass
 ﬂow
 rate
 (kg/h)
˙
C0
the
 coke
 ﬂow
 rate
 at
 the
 riser
 inlet
 (kg/h)
CD
the
 drag
 coefﬁcient
EA,i
the
 activation
 energy
 of
 ith
 pseudo-component
(kJ/mol)
E1
tuning
 parameter
 for
 the
 activation
 energy
E2
tuning
 parameter
 for
 the
 activation
 energy
Hc,coke
the
 heat
 of
 combustion
 of
 coke
 (kJ/kg)
Hc,i
the
 heat
 of
 combustion
 of
 ith
 pseudo
 component
(kJ/kg)
	Hi
the
 heat
 of
 cracking
 of
 ith
 pseudo-component
(kJ/kg)
	HC,CO
the
 heat
 of
 combustion
 in
 reaction
 I
 (kJ/mol)
	HC,CO2
the
 heat
 of
 combustion
 in
 reaction
 II
 (kJ/mol)
	HCO,CO2
the
 heat
 of
 combustion
 in
 reaction
 III
 and
 IV
(kJ/mol)
	HH2,H2O
the
 heat
 of
 combustion
 in
 reaction
 V
 (kJ/mol)
	Hvap,i
the
 heat
 of
 vaporization
 of
 ith
 pseudo—component
(kJ/kg)
˙
MCO
the
 molar
 ﬂow
 rate
 of
 CO
 (mol/h)
˙
MCO2
the
 molar
 ﬂow
 rate
 of
 CO2 (mol/h)
˙
MO2
the
 molar
 ﬂow
 rate
 of
 O2 (mol/h)
˙
MN2
the
 molar
 ﬂow
 rate
 of
 N2 (mol/h)
˙
MHVGO
the
 mass
 ﬂow
 rate
 of
 the
 feed
 (mol/h)
˙
MH2O
the
 molar
 ﬂow
 rate
 of
 H2O
 (mol/h)
˙
MCO,DenseToDilute
the
 molar
 ﬂow
 rate
 of
 CO
 that
 leaves
 the
dense
 bed
 (mol/h)
˙
MCO2,DenseToDilute
the
 molar
 ﬂow
 rate
 of
 CO2 that
 leaves
 the
dense
 bed
 (mol/h)
˙
MN2,AirToDense
the
 molar
 ﬂow
 rate
 of
 N2 from
 the
 air
 to
 the
dense
 bed
 (mol/h)
˙
MH2O,
 Dense
 To
 Dilute
the
 molar
 ﬂow
 rate
 of
 H2O
 that
 leaves
the
 dense
 bed
 (mol/h)
˙
MO2,DenseToDilute
the
 molar
 ﬂow
 rate
 of
 O2 that
 leaves
 the
dense
 bed
 (mol/h)
˙
MO2,in
the
 molar
 ﬂow
 rate
 of
 O2 from
 the
 air
 to
 the
 dense
bed
 (mol/h)
Mcat
total
 catalyst
 holdup
 in
 the
 dense
 bed
 (kg)
˙
Mi
the
 mass
 ﬂow
 rate
 of
 ith
 pseudo—component
 (kg/h)
˙
Mi
the
 mass
 ﬂow
 rate
 of
 pseudo—components
 (kg/h)
˙
Mcat
the
 mass
 ﬂow
 rate
 of
 catalyst
 (kg/h)
˙
Msteam
the
 mass
 ﬂow
 rate
 of
 steam
 (kg/h)
˙
Mcat
The
 catalyst
 circulation
 rate
 (kg/h)
MW
 
the
 average
 molecular
 weight
 (kg/mol)
MWi
the
 molecular
 weight
 of
 ith
 pseudo—component
(kg/mol)
MWcoke
the
 molecular
 weight
 of
 coke
 (kg/mol)
MWH2
the
 molecular
 weight
 of
 hydrogen
 (kg/mol)
N
 
the
 number
 of
 pseudo—components
NBP
 
the
 normal
 boiling
 point
 (K)
P
 
Pressure
 (bar)
PCO
the
 partial
 pressure
 of
 CO
 (bar)
PO2
the
 partial
 pressure
 of
 O2 (bar)
QR,DenseBed
the
 heat
 released
 per
 volume
 due
 to
 combustion
reactions
 in
 the
 dense
 bed
 (kJ/m3)
QR,DiluteBed
the
 heat
 released
 per
 volume
 due
 to
 combustion
reactions
 in
 the
 dilute
 phase
 (kJ/m3)
R
Ideal
 gas
 constant
TB,i
the
 
boiling
 
point
 
temperature
 
of
 
ith
pseudo—component
 (K)
T
 
the
 temperature
 (K)
TDilute
the
 temperature
 of
 dilute
 phase
 exit
 (K)
TMixZone
the
 mixing
 zone
 temperature
 (K)
THVGO
the
 liquid
 feed
 temperature
 (K)
TRegen
The
 dense
 phase
 regenerator
 temperature
 (K)
TSteam
The
 steam
 temperature
 (K)
VDenseBed
the
 volume
 of
 the
 dense
 bed
 (m3)
YRiser
coke
the
 coke
 weight
 fraction
 on
 catalyst
 which
 travels
from
 the
 riser
 to
 regenerator
YRegen
coke
the
 coke
 weight
 fraction
 on
 catalyst
 which
 travels
from
 the
 regenerator
 to
 riser
Ycoke
the
 coke
 weight
 fraction
 on
 catalyst
Ycoke
H2
the
 H2 weight
 fraction
 in
 coke
acoke
the
 tuning
 parameter
 for
 heat
 of
 combustion
 of
 coke
bcoke
the
 tuning
 parameter
 for
 heat
 of
 combustion
 of
 coke
ac
the
 tuning
 parameter
 for
 heat
 of
 combustion
bc
the
 tuning
 parameter
 for
 heat
 of
 combustion
cp,O2
the
 heat
 capacity
 of
 oxygen
 (kJ/mol
 K)
cp,N2
the
 heat
 capacity
 of
 nitrogen
 (kJ/mol
 K)
cp,CO
the
 heat
 capacity
 of
 CO
 (kJ/mol
 K)
cp,CO2
the
 heat
 capacity
 of
 CO2 (kJ/mol
 K)
cp,H2O
the
 heat
 capacity
 of
 H2O
 (kJ/mol
 K)
cp,cat
the
 heat
 capacity
 of
 catalyst
 (kJ/kg
 K)
cp,avg
the
 average
 heat
 capacity
 (kJ/mol
 K)
cp,liquid,i
the
 heat
 capacity
 of
 ith
 pseudo—component
 in
 liq-
uid
 phase
 (kJ/kg
 K)
cp,gas,i
the
 heat
 capacity
 of
 ith
 pseudo—component
 in
 gas
phase
 (kJ/kg
 K)
cp,steam
the
 steam
 heat
 capacity
 (kJ/kg
 K)
dcat
the
 catalyst
 diameter
 (m)
g
the
 gravitational
 acceleration
 (m/s2)
ki
the
 cracking
 rate
 constant
 of
 ith
 pseudo-component
(mol/m3 h)
kC,CO
the
 reaction
 rate
 constants
 in
 reactions
 I
 (1/bar
 h)
kC,CO2
the
 reaction
 rate
 constants
 in
 reactions
 II
 (1/bar
 h)
kCO,CO2,c
the
 
reaction
 
rate
 
constants
 
in
 
reactions
 
III
(mol/kgcat
 h
 bar2)
kCO,CO2,h
the
 
reaction
 
rate
 
constants
 
in
 
reactions
 
IV
(mol/h
 bar2)
kH2,H2O
the
 reaction
 rate
 constants
 in
 reactions
 V
p
the
 yield
 function
rC,CO
the
 rate
 of
 reaction
 I
 (mol/m3 h)
32
 
H.
 Sildir
 et
 al.
 /
 Journal
 of
 Process
 Control
 31
 (2015)
 30–44
rC,CO2
the
 rate
 of
 reaction
 II
 (mol/m3 h)
rCO,CO2,c
the
 rate
 of
 reaction
 III
 (mol/m3 h)
rCO,CO2,h
the
 rate
 of
 reaction
 IV
 (mol/m3 h)
vcat
the
 velocity
 of
 the
 catalyst
 (m/h)
vg
the
 gas
 velocity
 (m/h)
wi
the
 weight
 fraction
 of
 the
 ith
 pseudo—component
z
 
the
 axial
 position
 (m)
order
 to
 model
 such
 complex
 mixtures,
 Quann
 and
 Jaffe
 [2]
 pre-
sented
 a
 structure
 oriented
 lumping
 (SOL)
 approach
 which
 takes
the
 molecular
 structure
 into
 account.
 However,
 industrial
 applica-
tion
 of
 SOL
 is
 difﬁcult
 since
 the
 analysis
 of
 petroleum
 fractions
 at
the
 molecular
 level
 is
 still
 limited.
 Complexity
 of
 detailed
 models
has
 motivated
 the
 development
 of
 simpler
 lumped
 models.
 In
 the
discrete
 lumping
 approach
 [3],
 the
 mixture
 is
 assumed
 to
 be
 com-
posed
 of
 pure
 pseudo-components
 (PCs)
 that
 are
 characterized
 by
an
 intrinsic
 property
 such
 as
 average
 normal
 boiling
 point
 (NBP).
 In
the
 discrete
 lumping
 literature,
 most
 studies
 prefer
 to
 use
 a
 small
number
 of
 PCs
 to
 facilitate
 modeling
 and
 to
 reduce
 the
 number
of
 unknown
 kinetic
 constants.
 In
 early
 studies
 [4–6]
 the
 reaction
medium
 is
 represented
 by
 3
 lumps
 (feed,
 gasoline
 and
 light
 gases,
coke).
 Limited
 ﬂexibility
 of
 these
 models
 has
 motivated
 the
 intro-
duction
 of
 additional
 lumps.
 In
 some
 studies,
 coke
 and
 light
 gases
are
 considered
 as
 separate
 lumps
 [7–9].
 Later
 studies
 also
 include
diesel
 as
 another
 lump
 [10–12].
 Vargas
 et
 al.
 used
 a
 6-lump
 model
and
 estimated
 parameters
 from
 reﬁnery
 data
 [13].
 In
 some
 detailed
studies
 10-lump
 models
 are
 used
 [14–16].
 Gupta
 et
 al.
 [3]
 devel-
oped
 a
 new
 kinetic
 model
 considering
 a
 large
 number
 of
 lumps.
 It
is
 assumed
 that
 each
 pseudo-component
 gives
 two
 other
 pseudo-
components
 in
 one
 single
 cracking
 reaction
 step.
 They
 estimate
the
 kinetic
 constants
 using
 a
 probability
 based
 empirical
 approach
that
 considers
 all
 feasible
 reactions
 (e.g.
 in
 the
 order
 of
 10,000
for
 50
 PCs).
 The
 novelty
 of
 the
 kinetic
 model
 developed
 for
 the
riser
 in
 this
 work
 is
 due
 to
 its
 yield
 function
 which
 eliminates
 the
search
 for
 kinetic
 constants
 over
 a
 large
 number
 of
 reactions
 as
done
 in
 [3].
 In
 our
 model
 when
 the
 jth
 PC
 cracks,
 it
 is
 assumed
that,
 it
 can
 form
 all
 the
 lighter
 PCs
 with
 different
 yields.
 Speciﬁ-
cally,
 we
 construct
 a
 yield
 function
 p(i,j)
 that
 speciﬁes
 the
 amount
of
 the
 ith
 PC
 formed
 from
 cracking
 of
 the
 jth
 PC.
 The
 yield
 function
Fig.
 1.
 Simpliﬁed
 process
 ﬂow
 diagram
 of
 FCC
 plant
 in
 the
 reﬁnery.
parameters
 are
 updated
 from
 plant
 data
 and
 thus
 it
 is
 assumed
 that
the
 resulting
 yield
 function
 is
 a
 good
 approximation
 of
 the
 true
distribution.
Typical
 FCC
 models
 in
 the
 literature
 include
 the
 riser
 and
 the
regenerator
 without
 considering
 the
 fractionation
 unit.
 A
 detailed
modeling
 of
 fractionation
 is
 out
 of
 the
 scope
 of
 this
 study
 as
 well.
However,
 here,
 we
 are
 interested
 in
 predicting
 the
 amounts
 of
 the
ﬁnal
 products
 WCN,
 LCO,
 CLO,
 LPG
 and
 off-gas.
 For
 this
 purpose,
an
 empirical
 model
 is
 developed
 by
 making
 use
 of
 temperature
 cut
points
 (TCPs).
 This
 eliminates
 the
 need
 for
 any
 rigorous
 fraction-
ation
 unit
 modeling
 to
 calculate
 the
 product
 distribution.
 In
 this
fashion
 we
 can
 predict
 how
 the
 riser
 and
 the
 regenerator
 operating
conditions
 affect
 the
 individual
 product
 amounts.
Our
 modeling
 objective
 is
 to
 derive
 a
 practical
 nonlinear
 model
which
 captures
 the
 dominant
 steady-state
 and
 dynamic
 features
of
 the
 plant.
 To
 this
 end,
 the
 riser
 and
 the
 regenerator
 units
 are
modeled
 using
 ﬁrst
 principles.
 Details
 of
 these
 models
 are
 pre-
sented
 next.
2.
 Modeling
 of
 the
 riser
The
 riser
 includes
 the
 mixing
 and
 the
 reaction
 zones
 which
 are
modeled
 next.
2.1.
 The
 mixing
 zone
The
 volume
 in
 which
 the
 liquid
 feed
 is
 combined
 with
 the
 hot
catalyst
 at
 the
 riser
 inlet
 is
 called
 the
 mixing
 zone.
 It
 is
 widely
accepted
 in
 the
 literature
 that
 vaporization
 occurs
 in
 a
 small
 frac-
tion
 of
 the
 riser
 [17,18].
 Therefore,
 the
 mixing
 zone
 is
 taken
 as
a
 small
 volume
 and
 modeled
 separately
 from
 the
 reaction
 zone.
Table
 1
Typical
 design
 variables
 and
 operating
 conditions.
FEED
Flowrate
 (kg/h)
 
63,403.67
SG
 
0.93
Dist.
 Vac.
 (C)
 
10%
 
440.00
50%
 
496.00
90%
 
552.00
EP
 
590.00
Products
Off
 gas
 (kg/h)
 
4340.66
LPG
 (kg/h)
 
11,553.88
WCN
 (kg/h)
 
27,810.51
LCO
 (kg/h)
 
4531.58
CLO
 (kg/h)
 
9739.21
Operating
 conditions
Air
 rate
 (N
 m3/h)
 
41,561.68
Catalyst
 circulation
 (kg/min)
 
10,470.52
Feed
 temperature
 (C)
 
202.74
Riser
 outlet
 temperature
 (C)
 
522.50
Riser
 pressure
 (Psig)
 
28.96
Dense
 phase
 temperature
 (C)
 
673.23
Dilute
 phase
 temperature
 (C)
 
693.65
Regenerator
 pressure
 (Psig)
 
34.19
Catalyst
Reactor&Stripper
 (kg)
 
19,125.00
Regenerator
 (kg)
 
76,950.00
Average
 Partical
 Size
 (m)
 
91.00
Type
 
Conﬁdential
Dimensions
Riser
 diameter
 (m)
 
0.88
Riser
 height
 (m)
 
20.00
Regenerator
 diameter
 (m)
 
5.44
Regenerator
 height
 (m)
 
16.45
H.
 Sildir
 et
 al.
 /
 Journal
 of
 Process
 Control
 31
 (2015)
 30–44
 
33
Fig.
 2.
 The
 mixing
 zone.
Crucial
 inputs
 to
 the
 mixing
 zone
 are
 shown
 in
 Fig.
 2.
 The
 amount
of
 lift
 steam
 is
 negligible
 compared
 to
 the
 mass
 of
 catalyst
 and
 feed
[8].
The
 catalyst
 to
 oil
 mass
 ratio
 is
 usually
 high
 and
 the
 catalyst
temperature
 is
 much
 greater
 than
 the
 feed
 vaporization
 tempera-
ture.
 In
 addition,
 the
 feed
 is
 dispersed
 in
 the
 mixing
 zone
 to
 provide
turbulence
 for
 efﬁcient
 vaporization.
 Due
 to
 high
 heat
 transfer
 rate
between
 the
 catalyst
 and
 the
 hydrocarbons,
 the
 catalyst
 and
 the
vaporized
 hydrocarbons
 can
 be
 assumed
 to
 have
 the
 same
 temper-
ature
 when
 they
 leave
 the
 mixing
 zone.
 There
 is
 no
 reaction
 in
 the
mixing
 zone
 since
 catalyst
 is
 active
 only
 when
 the
 hydrocarbons
are
 in
 the
 gaseous
 phase
 [19].
Under
 these
 conditions,
 a
 steady-state
 energy
 balance
 for
 the
mixing
 zone
 results
 in:
TMixZone

TRegen
˙
Mcat ×
 cp,catdT
 =
N

i=1
TB,i

THVGO
˙
Mi ×
 cp,liquid,idT
 +
N

i=1
	Hvap,i
+
N

i=1
TMixZone

TB,i
˙
Mi ×
 cp,gas,idT
 +
TMixZone

Tsteam
˙
Msteam ×
 cp,steamdT
 
(1)
In
 Eq.
 (1),
 the
 left
 hand
 side
 is
 the
 heat
 released
 by
 catalyst
 parti-
cles.
 The
 ﬁrst
 term
 in
 the
 right
 hand
 side
 is
 the
 energy
 that
 is
 needed
to
 increase
 the
 temperature
 of
 the
 liquid
 hydrocarbons
 to
 their
 boil-
ing
 points.
 The
 second
 term
 is
 vaporization
 of
 hydrocarbons,
 and
the
 third
 term
 is
 superheating
 of
 those
 hydrocarbons
 to
 the
 mix-
ing
 zone
 temperature.
 The
 last
 term
 is
 the
 energy
 added
 to
 the
 lift
steam.
 An
 expression
 similar
 to
 Eq.
 (1)
 was
 already
 used
 in
 the
literature
 [20,21].
 Heat
 capacities
 for
 the
 pseudo-components
 are
estimated
 from
 [22].
 Heats
 of
 vaporization
 are
 obtained
 from
 HYSYS
and
 there
 are
 some
 correlations
 in
 the
 literature
 which
 provide
similar
 predictions
 as
 well
 [23,24].
After
 TMixZone is
 determined
 from
 Eq.
 (1),
 the
 velocity
 of
 the
 gas
(vg)
 leaving
 the
 mixing
 zone
 can
 be
 calculated
 from
 the
 ideal
 gas
law
 as
 a
 result
 of
 high
 temperature
 and
 low
 pressure
 operating
conditions
 at
 the
 reaction
 zone
 inlet:
vg =
˙
MHVGOR
 ×
 TMixZone
MW
 ×
 P
 ×
 Ariser
(2)
2.2.
 Reaction
 zone
The
 reaction
 zone
 starts
 right
 after
 the
 mixing
 zone.
 Vaporized
hydrocarbons
 and
 catalyst
 particles
 travel
 along
 the
 reaction
 zone
where
 the
 cracking
 reactions
 occur.
 At
 the
 riser
 exit,
 deactivated
catalyst
 particles
 are
 sent
 to
 the
 regenerator
 and
 vapor
 hydrocar-
bons
 are
 sent
 to
 the
 fractionation
 unit
 where
 they
 are
 separated.
The
 riser
 is
 a
 two-phase
 (vapor
 and
 catalyst)
 moving
 bed
 reactor.
We
 assume
 that
 the
 system
 is
 adiabatic
 and
 there
 are
 no
 heat
 and
mass
 transfer
 resistances
 between
 the
 vapor
 and
 catalyst
 phases
and
 both
 phases
 are
 assumed
 to
 be
 in
 plug
 ﬂow
 [3,12,18].
 Hydro-
dynamics
 of
 the
 riser
 is
 not
 well
 understood
 [25]
 and
 including
0 
5 
10 
15 
20
0 
10 
20 
z [m]
 velocity [m/s]
gas velocity
catalyst velocity
Fig.
 3.
 The
 catalyst
 and
 the
 gas
 velocity.
the
 radial
 direction
 would
 increase
 the
 modeling
 uncertainty.
 In
addition,
 radial
 distribution
 has
 not
 been
 observed
 to
 be
 signiﬁcant
[13].
 Under
 these
 simpliﬁcatons,
 although
 the
 riser
 is
 a
 two-phase
reactor,
 it
 is
 modeled
 as
 a
 single
 phase,
 one-dimensional,
 plug-ﬂow
model.
The
 cracking
 reactions
 are
 assumed
 to
 be
 ﬁrst
 order
 and
irreversible.
 Riser
 dynamics
 is
 very
 fast
 relative
 to
 the
 much
 slower
regenerator
 which
 dominates
 the
 overall
 dynamic
 behavior
 [8,15].
Therefore,
 the
 riser
 can
 be
 assumed
 to
 be
 at
 pseudo-steady
 state
and
 its
 modeling
 equations
 are
 derived
 only
 for
 the
 steady-state
conditions.
2.2.1.
 Hydrodynamics
The
 pressure
 drop
 is
 mainly
 governed
 by
 gravitational
 forces
and
 acceleration
 of
 particles
 and
 is
 calculated
 using
 [27]:
∂P
∂z
=
 −
(1
 −
 ε)cat +
 εg

g
 −∂
∂z

(1
 −
 ε)catv2
cat +
 εgv2
g

(3)
where
 the
 void
 fraction
 ε
 is
 given
 by
(1
 −
 ε)
 =
˙
Mcat
vcatcatAriser
(4)
The
 catalyst
 velocity
 is
 calculated
 from
 [26,27]:
∂cat
∂z
=
 CD
3
4
g × 
vg −
 vcat
2
dcatcatcat
+

g −
 cat

g
catcat
(5)
CD is
 the
 drag
 coefﬁcient;
 dcat is
 the
 average
 diameter
 of
 the
catalyst
 particles
 (m);
 cat and
 g are
 catalyst
 and
 gas
 phase
 den-
sities,
 respectively.
 In
 our
 case,
 the
 catalyst
 particles
 are
 small
 and
the
 catalyst
 velocity
 is
 close
 to
 the
 gas
 velocity
 as
 shown
 in
 Fig.
 3.
Assuming
 the
 same
 velocity
 for
 gas
 and
 catalyst
 reduces
 the
 com-
putational
 load
 and
 does
 not
 result
 in
 signiﬁcant
 changes
 in
 our
results.
2.2.2.
 The
 kinetic
 model
The
 cracking
 rate
 constant
 depends
 on
 many
 parameters
 includ-
ing
 feed
 and
 catalyst
 properties.
 Bollas
 et
 al.
 [28]
 deﬁned
 a
 feed
index
 to
 relate
 parafﬁnic,
 oleﬁnic,
 and
 aromatic
 content
 of
 the
 feed
to
 rate
 constants
 based
 on
 empirical
 correlations.
 Ginzel
 [29]
 inves-
tigated
 the
 inﬂuence
 of
 feed
 quality
 on
 cracking
 performance.
 In
summary,
 limited
 knowledge
 of
 chemical
 composition
 for
 complex
feeds
 provides
 only
 some
 qualitative
 understanding
 of
 kinetics.
 In
our
 case,
 we
 have
 deﬁned
 the
 pre-exponential
 factor
 Ai and
 the
activation
 energy
 Ei as
 a
 function
 of
 boiling
 point
 only.
 In
 this
 fash-
ion
 unnecessary
 details
 and
 feed
 analysis
 are
 avoided
 while
 using
readily
 available
 boiling
 point
 data
 which
 is
 most
 representative
 of
oil
 fractions
 [30].
 Therefore,
 the
 pre-exponential
 factor
 and
 activa-
tion
 energy
 of
 the
 ith
 pseudo
 component
 are
 parameterized
 by
 its
boiling
 point,
 NBPi:
Ai =
 ˇ
 ×
 NBP
i
(6)
EA,i =
 E1 −
 E2 ×
 NBPi
(7)
34
 
H.
 Sildir
 et
 al.
 /
 Journal
 of
 Process
 Control
 31
 (2015)
 30–44
-20
 0 
0
20
 
0
40
 
0
60
 
0
0.00
  
0.02
  
0.04
  
0.06 
0.08
  
NBP [oC]
p(i,j)
45 oC
345 oC
633 oC
-20
 0 0
20
 
0
40
 
0
60
 
0
0.00
  
0.01
  
0.02
  
0.03
  
0.04
  
0.05
  
NBP [oC]
p(i,j)
λp = 35
λp = 1.7
λp = 0.44
-20
 0 
0
20
 
0
40
 
0
60
 
0
0.00
  
0.01
  
0.02
  
0.03 
0.04
  
NBP [oC]
p(i,j)
λp = 0.38
λp = 0.76
λp = 1.5
)
c
(
)
b
(
)
a
(
Fig.
 4.
 Product
 distributions
 of
 speciﬁc
 pseudo-components.
The
 rate
 constant
 for
 each
 PC
 is
 calculated
 from
ki =
 Aie−EA,i/RT
(8)
Parameters
 E1,
 E2,
 ˇ
 and
 
 are
 estimated
 from
 plant
 data.
 Eq.
(6)
 is
 in
 the
 form
 of
 a
 power
 law
 [31]
 since
 higher
 boiling
 materials
have
 higher
 cracking
 rate
 constants
 [32].
 Eq.
 (7)
 is
 proposed
 based
on
 experimental
 evidence
 that
 activation
 energy
 is
 higher
 for
 low
boiling
 materials
 in
 general
 [33–35].
 The
 nature
 of
 the
 activation
energy
 also
 explains
 why
 the
 light
 materials
 react
 more
 slowly
 and
the
 average
 activation
 energy
 increases
 when
 lighter
 molecules
 are
formed
 in
 the
 cracking
 reactions
 [34].
In
 the
 kinetic
 model
 proposed
 by
 Gupta
 [3]
 each
 pseudo-
component
 gives
 two
 other
 pseudo-components
 in
 one
 single
cracking
 reaction
 step.
 In
 our
 model
 when
 the
 jth
 PC
 cracks,
 it
 can
form
 all
 the
 lighter
 PCs
 with
 different
 yields.
 The
 yield
 function
 p(i,j)
determines
 the
 amount
 of
 the
 ith
 PC
 formed
 from
 cracking
 of
 the
jth
 PC.
 Construction
 of
 the
 yield
 function
 is
 challenging
 since
 the
reactions
 are
 highly
 dependent
 on
 catalyst
 properties,
 feed
 con-
tent,
 operating
 conditions
 and
 many
 other
 unknown
 factors
 [36].
In
 addition,
 available
 reﬁnery
 data
 is
 limited
 since
 measurements
can
 be
 taken
 at
 the
 riser
 exit
 only,
 which
 limits
 the
 analysis
 of
 the
intermediate
 products.
 Hernandez
 et
 al.
 used
 a
 beta
 distribution
function
 [37]
 for
 similar
 purposes.
 In
 our
 case,
 we
 make
 use
 of
 lit-
erature
 results
 on
 the
 riser
 behavior
 to
 construct
 the
 yield
 function.
For
 example,
 Gilbert
 et
 al.
 [38]
 observed
 that
 the
 amount
 of
 light
materials
 is
 positively
 correlated
 with
 the
 contact
 time.
 In
 addition,
the
 catalyst
 type
 inﬂuences
 the
 results
 signiﬁcantly,
 which
 means
that
 the
 yield
 function
 might
 be
 catalyst
 dependent.
 The
 results
 in
[27,37,39,40]
 demonstrate
 that
 there
 is
 a
 temperature
 and
 compo-
sition
 proﬁle
 along
 the
 riser.
 Thus,
 starting
 from
 the
 riser
 inlet,
 the
yields
 of
 cracking
 reactions
 to
 intermediate
 products
 should
 be
 in
signiﬁcant
 amounts,
 and
 those
 intermediate
 products
 should
 next
be
 consumed
 by
 secondary
 reactions.
 In
 order
 to
 determine
 the
distribution
 of
 primary
 products,
 we
 focused
 on
 the
 experiments
that
 provide
 minimum
 contact
 time
 and
 conversion.
 The
 product
distributions
 of
 parafﬁnic
 and
 oleﬁnic
 petroleum
 feed-stocks
 are
presented
 in
 [32]
 with
 a
 low
 contact
 time.
 The
 product
 distribution
of
 a
 speciﬁc
 petroleum
 cut
 is
 also
 presented
 in
 [41].
 Based
 on
 those
studies,
 the
 following
 yield
 function
 is
 constructed
 to
 calculate
 the
products
 distribution
 of
 a
 speciﬁc
 PC:
p (i,
 j) = 1
PT
j

p ×
 NBPj
2
 ×
 NBP3
i
e
−p×NBPj
2(p×NBPj)
2NBPi
(NBPi−p×NBPj)
2
(9)
where
 p and
 p are
 adjustable
 parameters.
 PT
j is
 the
 normalization
factor
 so
 that
j

i=1
p(i,
 j)
 =
 1:
PT
j =
j−1

i=1

p ×
 NBPj
2
 ×
 NBP3
i
e
−p×NBPj
2(p×NBPj)
2NBPi
(NBPi−p×NBPj)
2
(10)
p(i,j)
 for
 three
 PCs
 that
 have
 different
 NBPs
 are
 presented
 in
Fig.
 4a.
Fig.
 4a.
 shows
 that
 higher
 boiling
 PCs
 have
 wider
 product
 distri-
butions
 whereas
 light
 products
 can
 produce
 fewer
 types
 of
 species.
The
 yield
 function
 expressed
 by
 Eq.
 (9)
 is
 ﬂexible
 as
 it
 generates
 a
large
 class
 of
 different
 product
 distributions
 by
 tuning
 its
 param-
eters.
 For
 example,
 different
 possible
 product
 distributions
 of
 the
heaviest
 PC
 are
 presented
 in
 Fig.
 4b
 and
 c
 when
 those
 parameters
are
 changed.
 The
 yield
 function
 parameters
 are
 updated
 from
 plant
data
 and
 thus
 it
 is
 assumed
 that
 the
 resulting
 yield
 function
 will
 be
a
 good
 approximation
 of
 the
 true
 distribution.
In
 the
 riser,
 along
 with
 cracking
 reactions,
 some
 portion
 of
hydrocarbons
 is
 deposited
 on
 the
 solid
 catalyst
 due
 to
 coking.
 Coke
deposition
 causes
 signiﬁcant
 deactivation
 of
 catalyst
 even
 though
the
 mass
 of
 the
 coke
 is
 signiﬁcantly
 less
 than
 that
 of
 the
 catalyst
 and
feed.
 The
 amount
 of
 coke
 should
 be
 predicted
 accurately
 to
 calcu-
late
 deactivation
 and
 the
 reaction
 rates
 in
 the
 regenerator.
 When
few
 lumps
 are
 used
 to
 characterize
 the
 reaction
 medium,
 usually
coke
 is
 considered
 a
 separate
 lump
 [7–9].
 The
 coking
 tendency
 is
100
200
300
400
500
600
700
800
900
0.0 
0.5 
1.0 
NBP [K]
Cumulative
Mass Fraction
Riser Effluent
CLO
LCO
WCN
LPG
off-gas
HVG
 
O
Fig.
 5.
 Boiling
 point
 curves
 of
 petroleum
 fractions.
 Vertical
 lines
 represent
 the
 NBP
 of
 the
 corresponding
 PC.
H.
 Sildir
 et
 al.
 /
 Journal
 of
 Process
 Control
 31
 (2015)
 30–44
 
35
affected
 by
 feedstock,
 operating
 conditions,
 catalyst
 type,
 and
 reac-
tor
 design
 [42].
 It
 is
 known
 that
 most
 of
 the
 coke
 formation
 occurs
early
 in
 the
 riser
 [43,44].
 It
 is
 found
 in
 [44]
 that
 catalyst
 to
 oil
 ratio
is
 positively
 correlated
 with
 coke
 formation.
 In
 addition,
 heavy
 and
aromatic
 hydrocarbons
 increase
 coking
 [28,45].
 In
 [28],
 the
 Conrad-
son
 carbon
 of
 the
 feed
 is
 found
 to
 be
 an
 indicator
 of
 coking
 tendency.
Heavy
 hydrocarbons
 form
 most
 of
 the
 coke
 since
 they
 are
 richer
 in
carbon
 [8].
 It
 is
 clear
 that
 coke
 is
 a
 product
 of
 cracking
 reactions
and
 it
 should
 be
 included
 in
 the
 kinetic
 model.
 We  have
 introduced
a
 coking
 tendency
 parameter
 (ϕ)
 in
 our
 model.
 For
 each
 cracking
reaction,
 ϕ
 fraction
 of
 the
 reacting
 material
 is
 converted
 to
 coke.
2.2.3.
 Mass
 balance
It
 is
 customary
 to
 characterize
 the
 composition
 of
 petroleum
fractions
 by
 a
 boiling
 point
 curve.
 Such
 curves
 are
 obtained
 from
 an
industry-standard
 ASTM
 laboratory
 test
 in
 which
 distilled
 volume
fractions
 of
 the
 sample
 are
 recorded
 as
 a
 function
 of
 temperature.
Next
 mass
 fractions
 can
 be
 computed
 (e.g.
 using
 the
 oil
 manager
of
 HYSYS)
 and
 plotted
 versus
 temperature.
 In
 our
 case,
 the
 boil-
ing
 point
 curves
 of
 HVGO,
 WCN,
 LCO
 and
 CLO
 are
 measured
 in
 the
reﬁnery.
 LPG
 and
 off-gas
 composition
 is
 analyzed
 using
 gas
 chro-
matography
 and
 this
 data
 can
 be
 used
 to
 obtain
 the
 boiling
 point
curve
 of
 these
 products
 as
 well.
 The
 boiling
 point
 curves
 of
 5
 indi-
vidual
 products
 and
 the
 feed
 HVGO
 are
 shown
 in
 Fig.
 5.
 It
 is
 not
possible
 to
 sample
 and
 perform
 a
 similar
 distillation
 assay
 on
 the
riser
 output
 before
 fractionation
 due
 to
 safety
 and
 other
 physical
restrictions.
 Because
 of
 lack
 of
 this
 data,
 we
 blend
 the
 individual
boiling
 point
 curves
 of
 the
 5
 products
 in
 HYSYS
 to
 obtain
 the
 boil-
ing
 point
 curve
 of
 the
 riser
 efﬂuent.
 The
 riser
 efﬂuent
 boiling
 point
curve
 reconstructed
 in
 this
 fashion
 is
 shown
 in
 Fig.
 5
 as
 well.
The
 normal
 boiling
 range
 (NBP)
 in
 Fig.
 5
 is
 quite
 large;
 there-
fore,
 few
 PCs
 fail
 to
 characterize
 such
 mixtures.
 In
 order
 to
 model
the
 narrow
 fractions,
 the
 reactor
 efﬂuent
 is
 divided
 into
 pseudo-
components
 which
 have
 a
 boiling
 range
 of
 10 ◦C.
 Since
 there
 is
 very
little
 material
 in
 very
 light
 products,
 the
 boiling
 point
 range
 for
these
 pseudo-components
 is
 selected
 wider
 in
 order
 to
 reduce
 the
total
 number
 of
 PCs.  The
 vertical
 lines
 in
 Fig.
 5
 represent
 the
 NBP
of
 the
 corresponding
 pseudo
 component.
 The
 average
 cumulative
amount
 of
 the
 material
 which
 boils
 at
 a
 speciﬁc
 temperature
 is
 on
the
 y
 axis.
The
 following
 steady-state
 mass
 balance
 holds
 for
 the
 ith
pseudo-component
 (PC)
 at
 any
 axial
 position,
 z:
∂ ˙
Mi

∂z
=
 −ki ×
˙
Mi
vg
× (1
 −
 ε) ×
 cat ×
 ˚
 +
N

n>i
p(i,
 n)
 × (1
 −
 ϕ)
×
 kn ×
˙
Mn
vg
× (1
 −
 ε) ×
 cat ×
 ˚
 
(11)
where
˙
Mi is
 the
 mass
 ﬂow
 rate
 of
 ith
 PC;  ki is
 the
 cracking
 rate
constant
 (m3/kg
 cat
 h)
 of
 ith
 PC,
 and
 ˚
 is
 the
 catalyst
 activity
 coef-
ﬁcient.
 The
 ﬁrst
 term
 in
 the
 right
 hand
 side
 denotes
 cracking
 of
 ith
PC
 to
 smaller
 molecules;
 the
 second
 term
 is
 the
 formation
 of
 that
component
 from
 cracking
 of
 larger
 molecules.
 The
 yield
 function
p(i,n)
 determines
 the
 amount
 of
 the
 ith
 PC
 formed
 from
 cracking
 of
the
 nth
 PC.
 For
 each
 reaction,
 ϕ
 fraction
 of
 the
 reacting
 material
 is
converted
 to
 coke.
 The
 value
 of
 ϕ
 is
 estimated
 from
 plant
 data.
 The
catalyst
 activity
 coefﬁcient
 ˚
 is
 calculated
 as
 explained
 below.
The
 overall
 mass
 balance
 between
 the
 inlet
 and
 outlet
 of
 the
riser
 gives:
N

i=1
˙
Mi + ˙
C = ˙
MHVGO + ˙
C0
(12)
where ˙
C0 is
 the
 coke
 ﬂow
 rate
 (kg/h)
 on
 the
 catalyst
 entering
 the
riser.
 The
 coke
 mass
 ﬂow
 rate ˙
C
 leaving
 the
 riser
 is
 computed
 from
Eq.
 (13):
˙
C
 = ˙
MHVGO −
N

i=1
˙
Mi + ˙
C0
(13)
Catalyst
 activity
 depends
 on
 the
 coke
 fraction
 on
 the
 catalyst
since
 coke
 is
 the
 physical
 reason
 for
 deactivation.
 An
 exponential
type
 deactivation
 is
 used
 [27]. There
 are
 other
 types
 of
 expressions
in
 the
 literature
 as
 well
 [25].
 Catalyst
 activity
 coefﬁcient
 is
 then
deﬁned
 by:
˚
 =
 e−˛
˙
C/ ˙
Mcat

(14)
where ˙
C/ ˙
Mcat is
 the
 coke
 fraction
 on
 the
 catalyst
 and
 ˛
 is
 a
 tuning
parameter.
2.2.4.
 Energy
 balance
The
 following
 steady-state
 energy
 balance
 holds
 at
 any
 axial
position,
 z:
∂ ˙
M
 ×
 cp,avg ×
 T
 + ˙
Mcat ×
 cp,cat ×
 T
∂z
=
N

i=1
	Hi ×
 ki ×
˙
Mi
vg
× (1
 −
 ε) ×
 cat ×
 ˚
 
(15)
where
 cp,avg is
 the
 average
 heat
 capacity
 and
 	Hi is
 the
 heat
 of
cracking
 of
 ith
 PC.
The
 heat
 of
 cracking
 of
 the
 ith
 pseudo
 component
 is
 calculated
from
 [3]:
	Hi =
 Hc,coke ×
 ϕ
 +
i

j=1
p(j,
 i)
 ×
 (1
 −
 ϕ)
 ×
 Hc,j −
 Hc,i
(16)
where
 Hc,coke is
 the
 heat
 of
 combustion
 of
 coke;
 Hc,i is
 the
 heat
 of
combustion
 of
 the
 ith
 pseudo
 component.
Since
 heat
 of
 combustion
 is
 sensitive
 to
 feed
 content
 which
 can-
not
 be
 directly
 measured
 in
 the
 reﬁnery,
 we
 use
 a
 power-law
 type
equation
 to
 relate
 heat
 of
 combustion
 to
 NBP:
Hc,i =
 acNBPbc
i
(17)
where
 ac and
 bc are
 adjustable
 parameters.
Hc,coke is
 considered
 to
 be
 a
 function
 of
 API
 gravity
 of
 the
 feed:
Hc,coke =
 acokeAPIHVGO +
 bcoke
(18)
2.2.5.
 Prediction
 of
 ﬁnal
 product
 distribution
When
 the
 temperature
 cut-points
 [46]
 (TCPs)
 used
 in
 the
 frac-
tionation
 unit
 are
 known,
 the
 amounts
 of
 the
 ﬁnal
 products
 (off-gas,
LPG,
 WCN,
 LCO
 and
 CLO)
 can
 be
 predicted
 from
 the
 distribution
of
 PCs  in
 the
 riser
 efﬂuent.
 This
 is
 shown
 in
 Fig.
 6
 for
 a
 particu-
lar
 day
 of
 plant
 operation.
 The
 solid
 curve
 represents
 the
 boiling
point
 curve
 of
 the
 riser
 efﬂuent
 predicted
 by
 the
 model
 and
 it
 is
obtained
 through
 Eq.
 (11).
 Since
 the
 actual
 riser
 efﬂuent
 boiling
point
 curve
 cannot
 be
 measured
 in
 the
 plant,
 we
 blend
 the
 avail-
able
 ﬁnal
 products’
 boiling
 point
 curves
 to
 construct
 the
 actual
 riser
efﬂuent
 boiling
 point
 curve.
 This
 is
 shown
 by
 the
 dashed
 curve
 in
Fig.
 6.
Each
 vertical
 dashed
 line
 in
 Fig.
 6
 represents
 a
 TCP,
 whose
numerical
 value
 is
 also
 shown.
 The
 yield
 of
 each
 cut
 (off-gas,
 LPG,
WCN,
 LCO
 and
 CLO)
 is
 computed
 from
 the
 intersection
 of
 TCPs
 with
the
 boiling
 point
 curve.
 We  compute
 the
 TCPs
 from
 plant
 data
 as
follows.
Let
 P(NBP)
 deﬁne
 a
 polynomial
 that
 relates
 the
 NBP
 to
 the
 corre-
sponding
 cumulative
 mass
 of
 plant
 riser
 efﬂuent.
 The
 parameters
 of
36
 
H.
 Sildir
 et
 al.
 /
 Journal
 of
 Process
 Control
 31
 (2015)
 30–44
100
200
300
400
500
600
700
800
900
254 
14684 
29115 
43546 
57977 
NBP [K]
Cumulative mass
[kg/h]
Off-
 gas 
LPG 
WCN 
LCO 
CLO
T: 185.9
T: 279
T: 484.4
T: 546.2
Model Riser Effluent
Plant Riser Effluent
Fig.
 6.
 Riser
 efﬂuent
 curves
 and
 TCPs.
P(NBP)
 are
 calculated
 using
 least
 squares
 to
 match
 the
 plant
 boiling
point
 curve
 and
 the
 mass
 balance
 constraints:
P(NBPh)
 = ˙
M
(19)
P(NBPl)
 =
 0
 
(20)
where ˙
M is
 the
 total
 mass
 ﬂow
 rate
 at
 the
 riser
 exit;
 NBPh and
 NBPl
are
 the
 highest
 and
 the
 lowest
 boiling
 point
 temperatures
 in
 the
medium,
 respectively.
There
 are
 4
 temperature
 cut
 points
 for
 5
 products
 as
 shown
 in
Fig.
 6.
 The
 cut
 points
 are
 calculated
 from:
Min

4

i=1
⎛
⎝P 
i

−
i

j=1
Mi,p
⎞
⎠
2
(21)
where
 

 
is
 
the
 
vector
 
of
 
4
 
cut
 
points:
 

 =

offgas−LPG LPG−WCN WCN−LCO LCO−CLO

.
 P(i)
 is
 the
 total
 mass
which
 boils
 up
 to
 the
 temperature
 i;
 Mi,p is
 the
 amount
 ith
 product
from
 the
 plant.
For
 instance,
 offgas−LPG is
 the
 cut
 point
 temperature
 between
off-gas
 and
 LPG.
 Thus
 offgas−LPG should
 satisfy
 the
 following:
P 
offgas−LPG

=
 Moffgas
(22)
where
 Moffgas is
 the
 mass
 ﬂow
 rate
 of
 off-gas.  Similarly
 for
 LPG−WCN:
P 
LPG−WCN

=
 Moffgas +
 MLPG
(23)
Once
 the
 cut
 points
 are
 determined
 from
 the
 plant
 data,
 they
can
 be
 used
 to
 calculate
 the
 amounts
 of
 the
 ﬁnal
 products
 from
the
 model
 riser
 efﬂuent
 curve.
 Note
 that
 if
 the
 model
 and
 the
 plant
riser
 efﬂuent
 curve
 overlap
 perfectly,
 the
 ﬁnal
 products
 amounts
are
 predicted
 exactly
 by
 the
 model.
3.
 Regenerator
In
 the
 regenerator
 coke
 is
 burnt
 to
 increase
 the
 catalyst
 activity.
The
 compressed
 air
 is
 fed
 to
 the
 bottom
 of
 the
 regenerator
 after
passing
 through
 the
 distributors.
 Oxygen
 in
 the
 air
 reacts
 with
 the
coke
 and
 gaseous
 combustion
 products
 are
 further
 processed
 in
the
 CO
 boiler.
 Since
 combustion
 reactions
 are
 highly
 exothermic,
regenerated
 catalyst
 is
 transferred
 back
 to
 the
 riser
 mixing
 zone
 at
a
 high
 temperature
 and
 provides
 energy
 for
 the
 endothermic
 reac-
tions
 occurring
 in
 the
 riser.
 It
 is
 assumed
 that
 the
 regenerator
 pres-
sure
 is
 kept
 constant
 by
 manipulating
 the
 ﬂue
 gas
 ﬂow
 rate
 [15].
We  assume
 that
 the
 regenerator
 has
 two
 physical
 regimes
described
 by
 the
 dense
 bed
 and
 the
 dilute
 phase
 (see
 Fig.
 1)
 which
are
 modeled
 next.
3.1.
 Modeling
 of
 the
 dense
 bed
The
 dense
 bed
 is
 the
 lower
 part
 of
 the
 regenerator
 where
 there
 is
a
 high
 concentration
 of
 catalyst
 particles.
 As
 air
 travels
 through
 the
dense
 bed,
 oxygen
 reacts
 with
 coke.
 The
 air
 ﬂow
 rate
 is
 not
 so
 high
 to
carry
 the
 catalyst
 particles
 away
 but
 it
 is
 enough
 to
 generate
 mixing
in
 the
 dense
 bed.
 In
 practice,
 there
 might
 be
 catalyst
 concentration
and
 temperature
 gradients
 in
 the
 dense
 bed
 despite
 the
 mixing.
 This
may
 be
 important
 in
 some
 cases
 [47,48]
 in
 which
 case
 the
 dense
 bed
is
 further
 divided
 into
 two
 phases:
 emulsion
 and
 bubble
 phases.
 In
our
 case,
 we
 do
 not
 have
 measurement
 points
 in
 the
 regenerator
to
 know
 whether
 this
 is
 signiﬁcant
 or
 not.
 Therefore,
 in
 order
 to
keep
 it
 simple,
 we  have
 modeled
 the
 dense
 bed
 as
 a
 well-mixed
CSTR.
 Similar
 assumptions
 have
 found
 applications
 in
 many
 other
modeling
 studies
 [10,15].
 Primary
 reactions,
 rate
 expressions
 and
heat
 of
 reactions
 are
 presented
 in
 Table
 2.
Mcat (kg)
 is
 the
 total
 catalyst
 mass
 holdup
 in
 the
 dense
 bed.
 We
assume
 that
 the
 catalyst
 holdup
 is
 kept
 constant
 by
 manipulating
the
 slide
 valves
 on
 the
 spent
 and
 regenerated
 catalyst
 circulation
lines
 [15].
 VDenseBed (m3)
 is
 the
 volume
 of
 the
 dense
 bed;
 YRegen
coke
is
the
 coke
 mass
 fraction
 of
 the
 catalyst
 in
 the
 dense
 phase;
 MWcoke
is
 the
 molecular
 weight
 of
 coke.
 kC,CO,
 kC,CO2,
 kCO,CO2,c,
 kCO,CO2,h and
kH2,H2O are
 the
 reaction
 rate
 constants
 in
 reactions
 I,
 II,
 III,
 IV
 and
V,
 respectively.
 Combustion
 rate
 expressions
 (rC,CO,
 rC,CO2, rCO,CO2,c
and
 rCO,CO2,h)
 are
 also
 shown
 in
 Table
 2. PCO (bar)
 and
 PO2 (bar)
are
 the
 partial
 pressures
 of
 CO
 and
 O2.
 CO
 combustion
 can
 occur
through
 both
 catalytic
 and
 homogenous
 paths
 (reactions
 III–IV).
Arthur
 [49]
 studied
 the
 combustion
 of
 carbon
 without
 catalyst
 and
determined
 the
 ratio
 of
 kC,CO and
 kC,CO2 at
 various
 temperatures.
Arbel
 et
 al.
 [15]
 used
 this
 to
 infer
 combustion
 rate
 constants
 of
reactions
 I
 and
 II.
 Even
 though
 the
 reaction
 medium
 is
 composed
of
 several
 types
 of
 elements,
 the
 reaction
 mechanism
 is
 not
 fully
deﬁned
 due
 to
 catalyst
 inﬂuence
 and
 hydrodynamic
 issues.
 In
 the
literature
 different
 reaction
 mechanisms
 and
 numerical
 values
 for
the
 reaction
 orders
 have
 been
 proposed
 [15,47,50].
 We  have
 consid-
ered
 the
 orders
 of
 reactions
 as
 unknown
 parameters [a1,
 a2.
 .
 .a8]
which
 are
 estimated
 from
 plant
 data.
 Unlike
 C
 and
 CO
 combustion
reactions,
 burning
 of
 H2 is
 so
 fast
 that
 it
 is
 assumed
 to
 occur
 instan-
taneously
 [51].
 Even
 though
 H2 does
 not
 inﬂuence
 the
 kinetics
 of
Table
 2
Reactions
 and
 rate
 expressions
 in
 the
 regenerator.
Reaction
 number
Reaction
Reaction
 rate
 expression
 
Heat
 of
 reaction
(I)
 
C
 +
 1/2O2
kC,CO
→CO
 
rC,CO =
 kC,CO ×

Mcat
VDenseBed ×
YRegen
coke
MWcoke
a1
×
PO2
a2
	HC,CO
(II)
 
C
 +
 O2
kC,CO2
→
 CO2
rC,CO2 =
 kC,CO2 ×

Mcat
VDenseBed ×
YRegen
coke
MWcoke
a3
×
PO2
a4
	HC,CO2
(III)
 
CO
 +
 1/2O2
kCO,CO2,c
→
 
CO2
rCO,CO2,c =
 kCO,CO2,c ×
Mcat
VDenseBed × (PCO)a5 ×
PO2
a6
	HCO,CO2
(IV)
 
CO
 +
 1/2O2
kCO,CO2,h
→
 
CO2
rCO,CO2,h =
 kCO,CO2,h × (PCO)a7 ×
PO2
a8
	HCO,CO2
(V)
 
H2 +
 1/2O2
kH2,H2O
→
 
H2O
 
Instantaneous
 
	HH2,H2O
H.
 Sildir
 et
 al.
 /
 Journal
 of
 Process
 Control
 31
 (2015)
 30–44
 
37
the
 regenerator
 by
 its
 concentration,
 the
 thermal
 effects
 are
 signif-
icant
 because
 of
 its
 high
 combustion
 energy
 [52].
 Expressions
 for
the
 rate
 constants
 are
 given
 in
 Eqs.
 (24)–(27)
 [15].
kC,CO = ˇce−Eˇ/RTkc0e−Ec0/RT
ˇce−Eˇ/RT +
 1
(24)
kC,CO2 =
ˇce−Eˇ/RT
ˇce−Eˇ/RT +
 1
(25)
kCO,CO2,c =
 k3c0e−E3c/RT
(26)
kCO,CO2,h =
 k3h0e−E3h/RT
(27)
where
 ˇc,
 kc0,
 K3c0,
 K3h0,
 Eˇ,
 Ec0,
 E3c and
 E3h are
 adjustable
 param-
eters
 for
 which
 approximate
 values
 are
 available
 [15].
Unlike
 the
 riser,
 the
 dynamics
 of
 the
 dense
 bed
 are
 signiﬁcant
due
 to
 its
 large
 catalyst
 holdup.
 The
 approximate
 residence
 time
 of
the
 catalyst
 in
 the
 dense
 bed
 is
 about
 5–7
 min.
 The
 mass
 balance
for
 the
 coke
 is
 given
 by
Mcat
MWcoke
d
dt

YRegen
coke

= ˙
Mcat
YRiser
coke
MWcoke
−˙
Mcat
YRegen
coke
MWcoke
−
rC,CO +
 rC,CO2

VDenseBed
(28)
where
 (rC,CO +
 rC,CO2)
 is
 the
 combustion
 rate
 of
 coke.
 Note
 that
 the
time
 constant
 of
 the
 plant
 is
 primarily
 affected
 by
 Mcat,
 which
 is
kept
 constant
 by
 controlling
 the
 catalyst
 level
 in
 the
 regenerator.
The
 gaseous
 species
 have
 a
 residence
 time
 of
 3–4
 s
 in
 the
 dense
bed.
 Thus,
 we
 have
 assumed
 pseudo
 steady-state
 for
 the
 gaseous
phase.
 The
 mass
 balance
 for
 CO
 in
 the
 dense
 phase
 is
 as
 follows:
−˙
MCO,DenseToDilute +
 rCOVDenseBed =
 0
 
(29)
˙
MCO,DenseToDilute is
 the
 molar
 ﬂow
 rate
 of
 CO
 that
 leaves
 the
 dense
phase.
 rCO is
 calculated
 from:
rCO =
 kC,CO

Mcat
VDenseBed
×
YRegen
coke
MWcoke
a1
× 
PO2
a2 −
 kCO,CO2,c
×
Mcat
VDenseBed
× (PCO)a5 × 
PO2
a6 −
 kCO,CO2,h × (PCO)a7 × 
PO2
a8
(30)
Similarly,
 the
 material
 balance
 for
 CO2 gives:
−˙
MCO2,DenseToDilute +
 rCO2VDenseBed =
 0
 
(31)
and
rCO2 =
 kC,CO2

Mcat
VDenseBed
×
YRegen
coke
MWcoke
a3
× 
PO2
a4
+
 kCO,CO2,c
Mcat
VDenseBed
× (PCO)a5 × 
PO2
a6 +
 kCO,CO2,h(PCO)a7
×
PO2
a8
(32)
H2 is
 converted
 to
 water
 instantly,
 and
 water
 ﬂow
 rate
 that
leaves
 the
 dense
 bed
 is
 calculated
 from:
˙
MH2O,DenseToDilute = ˙
McatYRiser
coke
Ycoke
H2
MWH2
(33)
where
 Ycoke
H2
is
 the
 H2 weight
 fraction
 in
 coke.
 Material
 balance
 for
O2 is
 given
 by
˙
MO2,in −˙
MO2,DenseToDilute −˙
McatYRiser
coke
Ycoke
H2
MWH2
1
2 +
 rO2VDenseBed =
 0
(34)
where
 MO2,in is
 the
 molar
 ﬂow
 rate
 of
 O2 into
 the
 dense
 bed
 from
the
 supply
 air;
˙
MO2,DenseToDilute is
 the
 molar
 ﬂow
 rate
 of
 O2 that
leaves
 the
 dense
 bed.
rO2 is
 calculated
 from:
rO2 =
 −kC,CO ×

Mcat
VDenseBed
×
YRegen
coke
MWcoke
a1
× 
PO2
a2 1
2 −
 kC,CO2 ×

Mcat
VDenseBed
×
YRegen
coke
MWcoke
a3
× 
PO2
a4
−kCO,CO2,c ×
Mcat
VDenseBed
× (PCO)a5 × 
PO2
a6 × 1
2 −
 kCO,CO2,h × (PCO)a7 × 
PO2
a8 × 1
2
(35)
The
 energy
 balance
 for
 the
 dense
 bed
 is
 as
 follows:
d
dt

Mcat +
 MO2 +
 MN2 +
 MCO +
 MCO2 +
 MH2O

cp,avgTRegen

=
 ˙
MO2,in ×
 cp,O2 ×
 Tair + ˙
MN2,in ×
 cp,N2 ×
 Tair + ˙
Mcat ×
 cp,cat ×
 TRiser

−
⎛
⎝
˙
MO2,DenseToDilute ×
 ×cp,O2 ×
 TRegen + ˙
MN2,DenseToDilute ×
 cp,N2 ×
 TRegen + ˙
Mcat ×
 cp,cat ×
 TRegen+
˙
MCO,DenseToDilute ×
 cp,CO ×
 TRegen + ˙
MCO2,DenseToDilute ×
 cp,CO2 ×
 TRegen + ˙
MH2O,DenseToDilute ×
 cp,H2O ×
 TRegen
⎞
⎠
+QR,DenseBedVDenseBed + ˙
McatYRiser
coke
Ycoke
H2
MWH2
	HH2,H2O
(36)
where
 TRegen is
 the
 dense
 phase
 temperature
 (K);
 Tair is
 the
 air
 tem-
perature
 (K),
 and
 QR,DenseBed is
 the
 heat
 released
 per
 volume
 due
 to
combustion
 reactions:
QR,DenseBed =
 kC,CO

Mcat
VDenseBed
YRegen
coke
MWcoke
a1
PO2
a2	HC,CO +
 kC,CO2

Mcat
VDenseBed
YRegen
coke
MWcoke
a3
PO2
a4	HC,CO2
+kCO,CO2,c ×
Mcat
VDenseBed
× (PCO)a5 × 
PO2
a6	HCO,CO2 +
 kCO,CO2,h × (PCO)a7 × 
PO2
a8	HCO,CO2
(37)
38
 
H.
 Sildir
 et
 al.
 /
 Journal
 of
 Process
 Control
 31
 (2015)
 30–44
3.2.
 Modeling
 of
 the
 dilute
 phase
In
 contrast
 to
 the
 dense
 bed,
 there
 is
 negligible
 amount
 of
 cata-
lyst
 particles
 in
 the
 dilute
 phase.
 Thus,
 solid
 coke
 does
 not
 exist
in
 the
 dilute
 phase.
 The
 dilute
 phase
 can
 be
 approximated
 by
pseudo-steady-state
 operation
 due
 to
 high
 superﬁcial
 velocity
 of
the
 gaseous
 phase.
 The
 dilute
 phase
 is
 modeled
 as
 an
 adiabatic
 plug
ﬂow
 reactor
 in
 which
 CO
 is
 burnt
 homogenously
 only.
The
 material
 balance
 of
 CO
 at
 an
 axial
 position,
 z,
 is
 given
 by
d
dz
 ˙
MCO

=
 −kCO,CO2,h × (PCO)a7 × 
PO2
a8Aregen
(38)
The
 balance
 for
 CO2:
d
dz
 ˙
MCO2

=
 kCO,CO2,h × (PCO)a7 × 
PO2
a8Aregen
(39)
The
 balance
 for
 O2:
d
dz
 ˙
MO2

=
 −kCO,CO2,h × (PCO)a7 × 
PO2
a8Aregen × 1
2
(40)
The
 differential
 energy
 balance
 is
 given
 by:
−d
dz
 ˙
MO2 ×
 cp,O2 ×
 T
 + ˙
MN2 ×
 cp,N2 ×
 T
 + ˙
MCO ×
 cp,CO ×
 T
 + ˙
MCO2 ×
 cp,CO2 ×
 T
 + ˙
MH2O ×
 cp,H2O ×
 T
+kCO,CO2,h × (PCO)a7 × 
PO2
a8	HCO,CO2Aregen =
 0
(41)
4.
 Parameter
 estimation
Parameter
 estimation
 is
 necessary
 to
 determine
 reasonable
 val-
ues
 for
 the
 model
 parameters.
 The
 data
 used
 in
 the
 parameter
estimation
 include
 a
 wide
 range
 of
 operating
 conditions
 and
 dif-
ferent
 types
 of
 feed
 stocks.
A
 nonlinear
 weighted
 steady-state
 parameter
 estimation
 prob-
lem
 is
 deﬁned
 by:
min


Xp −
 Xm ()TW 
Xp −
 Xm ()
(42)
where
 Xp is
 the
 vector
 of
 plant
 measurements;
 Xm()
 is
 the
 vector
of
 the
 model
 predictions
 obtained
 with
 parameter
 set
 ,
 and
 W
 is
the
 weighting
 matrix.
 For
 the
 riser,
 plant
 measurements
 include
the
 product
 amounts
 and
 the
 exit
 temperature.
 For
 the
 regenera-
tor,
 plant
 measurements
 are
 the
 exit
 gas
 composition
 and
 dense
phase
 temperature,
 dilute
 phase
 temperature
 and
 coke
 fraction
 of
the
 catalyst.
The
 following
 parameters
 are
 estimated
 for
 the
 riser:
riser = 
ˇ
 

E1
E2
p
p
˛
 
ac
bc
acoke
bcoke
 
(43)
The
 following
 parameters
 are
 estimated
 for
 the
 regenerator:
regen = 
ˇc
kc0
K3c0
K3h0
Eˇ
Ec0
E3c
E3h
a1
a2
a3
a4
a5
a6
a7
a8

(44)
For
 complex
 models
 of
 this
 type,
 some
 of
 the
 parameters
 are
usually
 redundant
 or
 correlated.
 This
 lack
 of
 identiﬁability
 often
occurs
 due
 to
 insufﬁcient
 data
 or
 the
 presence
 of
 a
 large
 number
of
 parameters
 [53,54].
 Among
 the
 parameter
 set
 ,
 a
 subset
 of
 the
most
 sensitive,
 identiﬁable
 parameters
 have
 to
 be
 chosen
 to
 deter-
mine
 reliable
 estimates.
 For
 this
 purpose
 the
 Fisher
 information
matrix
 was
 used
 [55].
 The
 covariance
 of
 the
 inverse
 of
 the
 Fisher
information
 matrix
 is
 a
 lower
 bound
 for
 the
 covariance
 matrix
 of
the
 parameter
 errors:
Cov( ˆ

 −
 ∗)
 ≥
 I−1
F (∗)
 
(45)
where
 IF =
1
2 FFT is
 the
 Fisher
 information
 matrix,
 and
 F
 is
 the
sensitivity
 matrix
 of
 model
 outputs
 to
 the
 parameters.
 2 is
 the
variance
 of
 the
 measurement
 noise
 or
 output
 error;
 ( ˆ
 −
 ∗)
 is
 the
error
 in
 the
 parameters
 with
 * and ˆ

 being
 the
 true
 and
 estimated
Table
 3
Estimated
 parameters.
Parameter
 
Nominal
value
95%
 conﬁdence
interval
riser
p
0.76
 
0.04
p
1.64
 
0.86

 
0.069
 
0.00001
ˇ
 
0.024
 
0.001

 
3.32
 
0.06
bc
−0.054
 
0.00009
regen
a2
0.65
0.004
a6
0.90
 
0.006
a8
0.95
 
0.044
parameter
 values,
 respectively.
 In
 inequality
 (45)
 small
 eigenval-
ues
 of
 IF will
 give
 large
 lower
 bounds
 and
 thus
 large
 covariances
for
 the
 parameter
 errors.
 This
 means
 that
 certain
 parameters
 have
poor
 (large)
 conﬁdence
 intervals
 and
 cannot
 be
 identiﬁed.
 Follow-
ing
 the
 algorithm
 proposed
 in
 [54],
 the
 number
 of
 parameters
 was
successively
 reduced
 until
 the
 minimum
 eigenvalue
 of
 the
 Fisher
information
 matrix
 is
 above
 a
 threshold
 value
 speciﬁed
 by
 the
 user.
The
 eliminated
 parameters
 were
 treated
 as
 constant
 and
 not
 used
for
 further
 tuning.
 Although
 different
 methods
 exist
 to
 recover
 from
identiﬁability
 problems
 [56],
 this
 approach
 was
 found
 to
 be
 easy
to
 use
 and
 gave
 good
 results
 [57].
 We  have
 used
 the
 algorithm
 in
[54]
 to
 reduce
 the
 number
 of
 parameters
 until
 the
 minimum
 eigen-
value
 of
 FIM
 is
 above
 a
 threshold
 value
 and
 conﬁdence
 regions
 are
acceptable.
 The
 resulting
 identiﬁable
 reduced
 set
 of
 parameters
 for
the
 riser
 and
 the
 regenerator
 are
 given
 in
 Table
 3
 with
 their
 nominal
values
 and
 95%
 conﬁdence
 intervals.
5.
 Steady-state
 results
The
 main
 outputs
 of
 the
 riser
 are
 the
 exit
 temperature
 (TRiser)
and
 the
 product
 distribution.
 Note
 that
 the
 model
 uses
 the
 same
TCPs
 as
 the
 plant
 and
 this
 makes
 the
 predicted
 ﬁnal
 products
 com-
parable
 to
 the
 actual
 data.
 The
 available
 data
 under
 consideration
include
 10
 sets
 of
 measurements
 with
 different
 feeds
 and
 steady-
state
 operating
 conditions.
 Four
 days
 were
 used
 for
 training
 (	)
 and
the
 remaining
 was
 used
 for
 prediction
 (•)
 as
 shown
 in
 Fig.
 7.
 The
model
 predicts
 the
 overall
 steady-state
 performance
 of
 the
 riser
quite
 well,
 considering
 that
 45
 degree
 line
 corresponds
 to
 perfect
estimation.
In
 Table
 4
 the
 errors
 in
 Fig.
 7
 are
 quantiﬁed
 by
 using
 the
 absolute
deviation
 (AD)
 and
 root
 mean
 square
 error
 (RMSE).
 Note
 that
 the
average
 absolute
 deviations
 for
 product
 amounts
 are
 less
 than
 10%
of
 their
 nominal
 values.
 Average
 temperature
 prediction
 error
 in
the
 riser
 is
 around
 2
 K.
A
 similar
 comparison
 is
 made
 for
 the
 regenerator
 as
 shown
 in
Fig.
 8.
 Note
 that
 the
 model
 captures
 the
 overall
 plant
 behavior
 of
the
 regenerator
 as
 well.
 In
 parallel
 with
 ﬁndings
 in
 [15],
 prediction
of
 after
 burn
 gas
 phase
 compositions
 is
 more
 challenging
 both
 as
a
 result
 of
 hydrodynamic
 effects
 and
 difﬁculty
 in
 measuring
 the
average
 gas
 phase
 composition
 accurately.
H.
 Sildir
 et
 al.
 /
 Journal
 of
 Process
 Control
 31
 (2015)
 30–44
 
39
787  
792 
797
 
 
787 
792 
797 
Model
Plant
TRiser [K]
3224 
4798
 
 
6372
 
 
3224 
4798 
6372 
Model
Plant
off-ga
 s [kg
 /h]
11350 
15313
 
 
1927
 
5
11350 
15313 
19275 
Model
Plant
LPG [kg/h]
22574 
30730
 
 
38886
 
 
22574 
30730 
38886 
Model
Plant
WCN [kg
 /h]
4532 
9633
 
 
14734
 
 
4532 
9633 
14734 
Model
Plant
LCO [kg
 /h]
4134 
13213
 
 
2229
 
1
4134 
13213 
22291 
Model
Plant
CLO [kg/h]
Fig.
 7.
 Comparison
 of
 model
 and
 plant
 for
 the
 crucial
 riser
 variables.
Table
 4
AD
 and
 RMSEs
 for
 the
 riser
 variables.
TRiser [K]
Off-gas
 [kg/h]
 
LPG
 [kg/h]
 
WCN
 [kg/h]
LCO [kg/h]
 
CLO
 [kg/h]
Training
ADAVERAGE
1.8
 
785
 
849
 
2923
 
973
 
1674
ADMAX
3.9
 
1993
 
1425
 
4039
 
1583
 
1995
ADMIN
0.2
 
187
 
409
 
2127
 
160
 
993
RMSE
 
2.3
 
1074
 
948
 
3034
 
1107
 
1722
Validation
ADAVERAGE
1.8
 
286
 
1001
 
1225
 
1156
 
3497
ADMAX
4.9
 
1993
 
2201
 
4039
 
1778
 
5605
ADMIN
0.1
 
12
 
12
 
292
 
160
 
242
RMSE
 
2.4
 
1293
 
1275
 
1396
 
1293
 
3874
4.9  
8.5 
12
 
.1 
4.9 
8.5 
12.1 
Model
Plant
CO [%]
7.0  
9.5 
11
 
.9 
7.0 
9.5 
11.9 
Model
Plant
CO2 [%]
0.0  
1.1  
2.2 
0.0 
1.1 
2.2 
Model
Plant
O2 [%]
0.1  
0.3  
0.5 
0.1 
0.3 
0.5 
Model
Plant
Ycoke
Regen [%]
959.5 
971
 
.1 
982
 
.7 
959.5 
971.1 
982.7 
Model
Plant
TDil
 ute [K]
944.1 
949
 
.6 
955
 
.2 
944.1 
949.6 
955.2 
Model
Plant
TRegen [K]
Fig.
 8.
 Comparison
 of
 regenerator
 model
 and
 plant
 measurements.
40
 
H.
 Sildir
 et
 al.
 /
 Journal
 of
 Process
 Control
 31
 (2015)
 30–44
Table
 5
AD
 and
 RMSEs
 for
 the
 regenerator
 variables.
CO
 [%]
 
CO2 [%]
 
O2 [%]
 
N2 [%]
 
Y Regen
coke
[%]
 
TDilute [K]
 
TRegen [K]
Training
ADAVERAGE
1.3
 
0.5
 
0.3
 
0.4
 
0.03
 
1.2
 
1.6
ADMAX
2.7
 
1.0
 
0.6
 
1.0
 
0.06
 
2.1
 
2.5
ADMIN
0.3
 
0.2
 
0.0
 
0.0
 
0.01
 
0.4
 
0.1
RMSE
 
1.35
 
1.03
 
1.12
 
0.49
 
0.05
 
2.53
 
3.5
Validation
ADAVERAGE
1.1
 
0.9
 
0.3
 
0.4
 
0.02
 
3.2
 
2.0
ADMAX
2.0
 
1.7
 
1.3
 
0.7
 
0.05
 
6.3
 
4.6
ADMIN
0.4
0.1
0.0
0.1
0.00
 
1.2
 
0.4
RMSE
 
1.55
 
1.45
 
0.77
 
0.53
 
0.025
 
5.1
 
4.2
In
 Table
 5,
 the
 absolute
 deviation
 (AD)
 and
 root
 mean
 square
errors
 (RMSE)
 are
 given.
 Average
 temperature
 prediction
 error
 in
the
 regenerator
 is
 around
 2
 K
 and
 prediction
 error
 in
 the
 gaseous
products
 amounts
 is
 less
 than
 10%.
6.
 Potential
 application
 areas
 of
 the
 model
The
 developed
 model
 can
 be
 used
 for
 various
 purposes
 including
operator
 training,
 monitoring,
 optimization
 and
 control.
 Detailed
treatment
 of
 all
 these
 applications
 is
 beyond
 the
 scope
 of
 this
 paper.
We
 present
 below
 the
 important
 features
 of
 the
 model
 that
 are
relevant
 to
 optimization
 and
 control
 studies.
6.1.
 Plant
 operating
 window
 and
 economic
 optimization
The
 complex
 interactions
 of
 the
 riser
 and
 the
 regenerator
 must
be
 well
 understood
 in
 order
 to
 realize
 the
 full
 economic
 potential
of
 the
 FCC
 plant.
 Using
 the
 model,
 we
 have
 evaluated
 the
 steady-
state
 solutions
 corresponding
 to
 feasible
 values
 of
 manipulated
variables.
 The
 resulting
 operating
 window
 is
 shown
 in
 Fig.
 9
 for
a
 particular
 feed.
 In
 this
 operating
 window,
 contours
 of
 important
process
 variables
 and
 the
 plant’s
 proﬁt
 are
 plotted
 as
 a
 function
of
 catalyst
 circulation
 rate
 ( ˙
Mcat)
 and
 air
 ﬂow
 rate
 ( ˙
Mair).
 Once
the
 degrees
 of
 freedom
 are
 ﬁxed
 (i.e.
˙
Mcat
and
˙
Mair values
 are
assigned),
 the
 steady-state
 solution
 and
 the
 proﬁt
 can
 be
 read
 from
the
 displayed
 contours.
For
 example
 the
 point
 labeled
 by
 (·
 ·
 ·) corresponds
 to
 the
 nom-
inal
 steady-state
 operation
 in
 the
 plant.
 In
 Fig.
 9,
 in
 addition
 to
 the
riser
 and
 regenerator
 temperature
 contours,
 we
 have
 also
 shown
the
 economic
 proﬁt
 which
 is
 given
 by:
J
 =
5

i=1
Pi × ˙
Mp,i −
 PHVGO × ˙
MHVGO −
 U
 
(46)
where
 Pi is
 the
 price
 of
 the
 ith
 product; ˙
Mp,i is
 the
 mass
 ﬂow
 rate
of
 the
 ith
 product;
 PHVGO is
 the
 price
 of
 the
 feed; ˙
MHVGO is
 the
 mass
ﬂow
 rate
 of
 the
 feed;
 J
 is
 the
 net
 proﬁt;
 U
 is
 the
 utility
 cost.
 In
 our
case,
 the
 utility
 cost
 is
 negligible
 compared
 to
 the
 economic
 value
 of
the
 product
 and
 the
 feed.
 The
 prices
 of
 products
 are
 determined
 by
interactions
 among
 different
 plants
 in
 the
 reﬁnery
 and
 the
 market
demand
 for
 different
 products.
 In
 order
 to
 compute
 these
 prices,
the
 planning
 department
 uses
 a
 reﬁnery-wide
 linear
 programming
algorithm.
 Once
 the
 prices
 are
 set,
 the
 optimum
 steady-state
 oper-
ating
 point
 can
 be
 calculated
 from:
Max
˙
Mcat, ˙
Mair
5

i=1
Pi × ˙
Mp,i −
 PHVGO ×HVGO −U
s.t.
˙
Mair,min ≤˙
Mair ≤˙
Mair,max
˙
Mcat,min ≤˙
Mcat ≤˙
Mcat,max
Tregen ≤
 Tregen,max
Triser ≤
 Triser,max
(47)
925
930
930
935
935
935
940
940
940
940
945
945
945
945
950
950
950
950
955
955
955
955
960
960
960
965
965
970
970
975
catalyst c
 irculation
air flow rate
785
785
785
790
790
790
795
795
795
800
800
800
805
805
805
810
1650
1700
1700
1700
1750
1750
1750
1800
1800
1800
1850
1850
1850
1900
1900
1900
1950
1950
1950
2000
2000
2000
2050
2050
2050
6.3
6.
 
4
6.
 
5 
6.6
6.
 
7
6.
 
8 
6.9
x 10
5
3.95
4
4.05
4.1
4.15
4.2
4.25
4.3
4.35
x 10
4
Trege
 
n
Triser
Profit
Fig.
 9.
 Riser,
 regenerator
 temperatures
 and
 plant
 proﬁt
 in
 the
 operating
 window.
H.
 Sildir
 et
 al.
 /
 Journal
 of
 Process
 Control
 31
 (2015)
 30–44
 
41
0 
0.2438
 
0.4875
 
0.7313
0.975
947.0 
951.7 
956.4 
time [h]
[K]
Regenerator Dense Bed Temperature
0
0.2438
0.4875
0.7313
0.975
969.9262
974.4919
979.0576
time [h]
[K]
Regenerator Dilute Phase Temperature
0
0.2438
0.4875
0.7313
0.975
0.13 
0.15 
0.16 
time [h]
[%]
Coke Content on Fresh Cat
0
0.2438
0.4875
0.7313
0.975
793.0 
796.3 
799.6 
time [h]
[K]
Riser Temperature
0
0.2438
0.4875
0.7313
0.975
147.2 
149.4 
151.6 
time [h]
[kgmol/h]
CO2 flow rate
0
0.2438
0.4875
0.7313
0.975
0.2 
0.4 
0.6 
time [h]
[kgmol/h]
O2 flow rate
0 
0.2438
 
0.4875
 
0.7313
0.975
7563.6 
8124.3 
8684.9 
time [h]
[kg/h]
CLO
0
0.2438
0.4875
0.7313
0.975
5338.3 
5416.8 
5495.4 
time [h]
[kg/h]
LCO
0 
0.2438
 
0.4875
 
0.7313
0.975
27662.1 
27975.1 
28288.1 
time [h]
[kg/h]
WCN
0
0.2438
0.4875
0.7313
0.975
10361.3 
10543.1 
10724.9 
time [h]
[kg/h]
LPG
Fig.
 10.
 Dynamic
 response
 of
 the
 plant
 to
 a
 step
 increase
 in
 the
 air
 ﬂow
 rate
 by
 1000
 m3/h.
The
 constraints
 on
 the
 catalyst
 circulation
 rate
 and
 the
 air
 ﬂow
rate
 are
 capacity
 constraints,
 and
 temperatures
 are
 constrained
due
 to
 safety.
 In
 the
 absence
 of
 any
 temperature
 constraints,
the
 optimum
 steady-state
 corresponds
 to
 maximum
 values
 of
˙
Mcat
and
˙
Mair which
 is
 the
 upper
 right
 corner
 in
 Fig.
 9.
 When
the
 temperatures
 are
 constrained,
 the
 optimum
 shifts
 to
 differ-
ent
 operating
 points
 where
 different
 constraints
 become
 binding.
For
 example,
 when
 TRegen,max =
 955
 K,
 the
 optimum
 operating
 point
shifts
 to
 the
 rectangle
 (·
 ·
 ··  ·
 ·)  where
 catalyst
 circulation
 and
 regen-
erator
 temperature
 (instead
 of
 air
 ﬂow
 rate)
 are
 at
 their
 maximum
limits.
 If ˙
Mcat,max is
 658,353
 kg/h
 and
 TRegen,max =
 955
 K,
 the
 opti-
mum operating
 regime
 further
 shifts
 to
 the
 ellipse
 (·
 ·
 ·)  where
both
 regenerator
 temperature
 and
 catalyst
 circulation
 constraints
become
 active.
In
 addition
 to
 computation
 of
 the
 optimum
 steady-state,
 Fig.
 9
provides
 signiﬁcant
 insight
 into
 understanding
 of
 the
 inﬂuence
 of
manipulated
 variables
 on
 the
 plant
 proﬁtability.
 As
 seen
 from
 the
contours,
 the
 proﬁt
 is
 more
 sensitive
 to
 the
 air
 ﬂow
 rate
 compared
to
 the
 catalyst
 circulation.
 Physically,
 the
 air
 ﬂow
 increases
 the
extent
 of
 the
 reaction
 in
 the
 regenerator
 and
 a
 hotter
 temperature
catalyst
 with
 less
 coke
 content
 is
 transferred
 to
 the
 riser.
 This
 favors
cracking
 at
 constant
 catalyst
 circulation.
 On
 the
 other
 hand,
 increas-
ing
 the
 catalyst
 ﬂow
 rate
 at
 constant
 air
 ﬂow
 rate
 also
 increases
 the
cracking
 reactions
 as
 a
 result
 of
 increased
 catalyst
 concentration
 in
the
 riser;
 but
 the
 extent
 is
 not
 so
 signiﬁcant
 because
 of
 decrease
 in
the
 temperature.
6.2.
 Model
 dynamics
Dynamic
 analysis
 of
 the
 model
 helps
 to
 better
 understand
 the
interactions
 between
 the
 operation
 of
 the
 riser
 and
 the
 regenera-
tor.
 Fig.
 10
 shows
 the
 model’s
 dynamic
 response
 to
 an
 increase
 in
the
 air
 ﬂow
 rate
 by
 1000
 m3/h.
 The
 amount
 of
 air
 supply
 deter-
mines
 the
 extent
 of
 combustion
 reactions.
 When
 the
 air
 input
increases,
 more
 combustion
 reactions
 occur,
 the
 coke
 content
 of
the
 catalyst
 decreases
 and
 the
 combustion
 product
 CO2 increases
while
 O2 decreases.
 At
 the
 same
 time,
 more
 heat
 is
 released
 due
to
 increased
 combustion.
 Consequently,
 the
 temperature
 in
 the
dense
 and
 dilute
 phases
 increases.
 A
 relatively
 hotter
 catalyst
 is
transferred
 to
 the
 riser
 and
 the
 riser
 temperature
 increases
 as
 well.
This
 high
 temperature
 operation
 results
 in
 more
 cracking
 as
 seen
in
 product
 ﬂow
 rates.
 The
 amount
 of
 heavy
 products
 (CLO
 and
 LCO)
decreases
 whereas
 the
 amount
 of
 the
 light
 products
 (WCN,
 LPG)
increases.
Next
 we
 study
 the
 response
 to
 catalyst
 circulation
 change.
 Fig.
 11
shows
 the
 model’s
 dynamic
 response
 to
 a
 5%
 increase
 in
 the
 catalyst
circulation
 rate,
 keeping
 the
 air
 ﬂow
 constant.
 When
 the
 catalyst
circulation
 rate
 increases
 suddenly,
 more
 energy
 is
 transferred
 from
the
 regenerator
 to
 the
 riser
 and
 the
 riser
 temperature
 increases
 sud-
denly
 due
 to
 its
 small
 time
 constant.
 The
 sudden
 initial
 increase
 in
the
 riser
 temperature
 favors
 more
 cracking,
 initially.
 As
 a
 result
of
 temperature
 drop
 in
 the
 regenerator,
 the
 combustion
 reactions
slow
 down
 and
 the
 coke
 fraction
 on
 the
 catalyst
 increases.
 Due
 to
both
 decrease
 in
 the
 regenerator
 temperature
 and
 increase
 in
 coke
amount
 on
 the
 regenerated
 catalyst,
 the
 riser
 temperature
 even-
tually
 drops.
 Note
 that
 at
 steady-state
 the
 amounts
 of
 the
 light
products
 slightly
 increase
 as
 a
 result
 of
 increased
 catalyst
 concen-
tration
 in
 the
 riser.
Next
 we  study
 the
 effect
 of
 feed
 composition
 by
 processing
 a
heavier
 feed
 with
 pseudo-components
 boiling
 at
 higher
 tempera-
tures
 as
 shown
 in
 Fig.
 12.
Since
 the
 cracking
 reactions
 in
 the
 riser
 are
 endothermic,
more
 energy
 is
 consumed
 when
 a
 heavier
 feedstock
 is
 processed,
resulting
 in
 the
 riser
 temperature
 drop
 as
 shown
 in
 Fig.
 13.
 This
is
 followed
 by
 the
 decline
 in
 the
 regenerator
 temperature
 and
increased
 coke
 content
 on
 the
 catalyst.
 Since
 the
 feed
 is
 heavier,
conversion
 to
 heavy
 products
 (CLO
 and
 LCO)
 increases
 whereas
 the
amount
 of
 the
 light
 products
 (WCN,
 LPG)
 decreases.
Finally,
 the
 feed
 ﬂow
 rate
 is
 increased
 by
 10%.
 The
 results
 are
shown
 in
 Fig.
 14.
 Increased
 space
 velocity
 results
 in
 less
 contact
time
 with
 the
 catalyst
 and
 decreases
 both
 the
 riser
 and
 regenerator
temperatures.
 Less
 cracking
 occurs
 and
 heavier
 products
 (CLO
 and
LCO)
 are
 favored
 and
 the
 amount
 of
 the
 light
 products
 (WCN,
 LPG)
decreases.
42
 
H.
 Sildir
 et
 al.
 /
 Journal
 of
 Process
 Control
 31
 (2015)
 30–44
0 
0.2438 
0.4875 
0.7313 
0.975
939.0 
943.0 
947.0 
time [h]
[K]
Regenerator Dense Bed Temperature
0
0.2438
0.4875
0.7313
0.975
960.2 
964.5 
968.8 
time [h]
[K]
Regenerator Dilute Phase Temperature
0
0.2438
0.4875
0.7313
0.975
0.16 
0.17 
0.19 
time [h]
[%]
Coke Content on Fresh Cat
0
0.2438
0.4875
0.7313
0.975
792.0 
794.8 
797.7 
time [h]
[K]
Riser Temperature
0 
0.2438 
0.4875 
0.7313 
0.975
7364.2 
8024.5 
8684.8 
time [h]
[kg/h]
CLO
0
0.2438
0.4875
0.7313
0.975
5311.6 
5403.5 
5495.3 
time [h]
[kg/h]
LCO
0 
0.2438 
0.4875 
0.7313 
0.975
27662.2 
28030.7 
28399.3 
time [h]
[kg/h]
WCN
0
0.2438
0.4875
0.7313
0.975
10361.3 
10575.3 
10789.3 
time [h]
[kg/h]
LPG
Fig.
 11.
 Dynamic
 response
 of
 the
 plant
 to
 a
 5%
 increase
 in
 the
 catalyst
 circulation
 rate.
600
65
 
0 
700
75
 
0 
800
85
 
0 
900
0
1
2
3
4
5
6
7 x 104
NBP [K]
Cumulative Mass [kg/h]
Nominal Fee
 d
Hea
 vier Feed
Fig.
 12.
 Heavier
 feed
 as
 characterized
 by
 its
 boiling
 point
 curve.
0 
0.2438 
0.4875 
0.7313 
0.975
922.0 
934.5 
947.0 
time [h]
[K]
Regenerator Dense Bed Temperature
0
0.2438
0.4875
0.7313
0.975
940.0 
954.4 
968.8 
time [h]
[K]
Regenerator Dilute Phase Temperature
0 
0.2438 
0.4875 
0.7313 
0.975
0.16 
0.18 
0.21 
time [h]
[%]
Coke Content on Fresh Cat
0
0.2438
0.4875
0.7313
0.975
764.9 
775.1 
785.2 
time [h]
[K]
Riser Temperature
0 
0.2438 
0.4875 
0.7313 
0.975
9879.1  
11259.2 
12639.3 
time [h]
[kg/h]
CLO
0
0.2438
0.4875
0.7313
0.975
5645.2 
5735.0 
5824.8 
time [h]
[kg/h]
LCO
0
0.2438
0.4875
0.7313
0.975
25285.0 
26039.6 
26794.2 
time [h]
[kg/h]
WCN
0
0.2438
0.4875
0.7313
0.975
9170.7 
9555.2 
9939.7 
time [h]
[kg/h]
LPG
Fig.
 13.
 Dynamic
 response
 of
 the
 plant
 to
 a
 heavier
 feed.
H.
 Sildir
 et
 al.
 /
 Journal
 of
 Process
 Control
 31
 (2015)
 30–44
 
43
0
0.2438
0.4875
0.7313
0.975
924.6 
935.8 
947.0 
time [h]
[K]
Regenerator Dense Bed Temperature
0
0.2438
0.4875
0.7313
0.975
943.6 
956.2 
968.8 
time [h]
[K]
Regenerator Dilute Phase Temperature
0
0.2438
0.4875
0.7313
0.975
0.16 
0.18 
0.20 
time [h]
[%]
Coke Content on Fresh Cat
0
0.2438
0.4875
0.7313
0.975
768.2 
776.2 
784.3 
time [h]
[K]
Riser Temperature
0
0.2438
0.4875
0.7313
0.975
12736.5 
14060.4 
15384.3 
time [h]
[kg/h]
CLO
0
0.2438
0.4875
0.7313
0.975
6309.8 
6354.5 
6399.3 
time [h]
[kg/h]
LCO
0 
0.2438 
0.4875 
0.7313 
0.975
27163.4 
27904.9 
28646.4 
time [h]
[kg/h]
WCN
0 
0.2438 
0.4875 
0.7313 
0.975
9764.4  
10116.7 
10469.0 
time [h]
[kg/h]
LPG
Fig.
 14.
 Dynamic
 response
 of
 the
 plant
 to
 a
 10%
 step
 increase
 in
 the
 feed
 ﬂow
 rate.
All
 the
 simulated
 dynamic
 trends
 are
 compatible
 with
 what
 is
observed
 in
 the
 plant.
 The
 magnitudes
 of
 the
 steady-state
 gains
and
 time
 constants
 obtained
 from
 model
 responses
 are
 also
 con-
sistent
 with
 plant
 operators’
 experience.
 A
 thorough
 validation
 of
the
 dynamic
 model
 by
 performing
 step
 responses
 is
 planned
 as
 part
of
 a
 future
 model
 based
 control
 implementation
 in
 the
 plant.
7.
 Conclusions
Using
 the
 method
 of
 discrete
 lumping,
 we
 have
 developed
 a
model
 for
 an
 industrial
 ﬂuid
 catalytic
 cracker.
 The
 model
 predicts
the
 riser
 and
 regenerator
 temperatures
 and
 product
 yield
 under
dynamic
 and
 steady-state
 conditions.
 It
 uses
 sufﬁcient
 number
 of
pseudo-components
 to
 characterize
 narrow
 boiling
 fractions
 to
improve
 the
 predictions.
 Empirical
 correlations
 that
 describe
 the
reaction
 mechanism
 with
 few
 parameters
 are
 constructed
 from
literature
 data.
 In
 particular,
 a
 new
 yield
 function
 for
 the
 kinetic
model
 of
 the
 riser
 is
 developed.
 Also,
 an
 empirical
 approach
 to
estimate
 the
 fractionation
 products
 is
 presented.
 This
 allows
 the
prediction
 the
 ﬁnal
 product
 amounts
 from
 the
 boiling
 point
 curve
of
 the
 riser
 efﬂuent.
Model
 parameters
 are
 determined
 from
 plant
 data
 using
 param-
eter
 estimation.
 Steady-state
 model
 predictions
 predict
 the
 plant
data
 closely.
 Simulations
 show
 that
 the
 model
 also
 explains
 the
dynamic
 interactions
 between
 the
 riser
 and
 regenerator.
 Among
many
 potential
 applications,
 the
 model
 is
 especially
 suited
 for
 real-
time
 optimization
 and
 control.
 In
 this
 paper
 we  have
 shown
 how
the
 feasible
 operating
 window
 and
 optimum
 steady-state
 operat-
ing
 conditions
 can
 be
 ascertained
 from
 the
 model.
Acknowledgements
The
 authors
 gratefully
 acknowledge
 the
 ﬁnancial
 support
 of
TUPRAS
 Reﬁneries.
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Energy efﬁciency optimization in scheduling crude oil operations of
reﬁnery based on linear programming
NaiQi Wu a, ZhiWu Li a, *, Ting Qu b
a Institute of Systems Engineering, Macau University of Science and Technology, Taipa, Macau
b School of Electrical and Information Engineering, Jinan University (Zhuhai Campus), Zhuhai 519070, China
a r t i c l e i n f o
Article history:
Received 13 September 2016
Received in revised form
10 March 2017
Accepted 29 July 2017
Available online 31 July 2017
Keywords:
Oil reﬁnery
Crude oil operations
Scheduling
Energy efﬁciency
a b s t r a c t
For sustainable development, a reﬁnery is required to save energy as much as possible so as to reduce the
emission of greenhouse gas. In crude oil operations, oil transportation from storage tanks to charging
tanks via a pipeline consumes a large portion of energy. It is vitally important to minimize energy
consumption for this process. Since the oil ﬂow resistance is proportional to the square of oil ﬂow rate,
the relation between energy efﬁciency and ﬂow rate is nonlinear, which makes the problem complicated.
This work addresses this important issue by formulating a linear programming model for the considered
problem such that it can be efﬁciently solved. A real-world industrial case study is used to demonstrate
the applications and signiﬁcance of the proposed method.
© 2017 Elsevier Ltd. All rights reserved.
1. Introduction
Facing with global and increasingly intensive market competi-
tion, great attention is paid to the scheduling operations and con-
trol of discrete manufacturing systems (Bai et al., 2016; Chen et al.,
2017a; 2017b; Qiao et al., 2015; and Wu and Zhou, 2012a, 2012b,
and Wu et al., 2013) to name a few of studies made in recent years.
Also, a plant in the process industry has to be well operated such
that it is competitive. It is known that, with advanced information
technology applied to modify the operations, a process plant can be
made more proﬁtable (Moro, 2003). During the last two decades,
extensive attention from both academia and industry community
has been paid to the optimization of the operations in reﬁneries, a
type of most important process industries. A reﬁnery can be
operated in a hierarchical way with three layers: production
planning, short-term scheduling, and unit control at the upper,
middle, and lower layers, respectively. With linear programming-
based commercial software tools, such as PIMS (Process Industry
Modeling System) (Aspen Technology Inc, 1999) and RPMS (Re-
ﬁnery and Petrochemical Modeling System) (Bonner and Moore,
1979), an optimal plan can be efﬁciently found at the upper layer.
Meanwhile, at the lower layer, advanced process control techniques
are widely realized for unit control in reﬁneries, resulting in sig-
niﬁcant productivity improvement. However, thanks to the lack of
efﬁcient techniques and software tools for short-term scheduling at
the middle layer, the three layers cannot be integrated such that a
global optimum cannot be achieved and a short-term schedule has
to be obtained manually by a planner. Hence, extensive efforts have
been made for developing effective and efﬁcient techniques for
scheduling of reﬁneries.
Since the operations in a reﬁnery contains a discrete-event
process, its short-term scheduling problem is essentially combi-
natorial and NP-hard (Floudas and Lin, 2004). Moreover, to obtain
such a schedule, one needs to deﬁne activities to be performed and
sequence them simultaneously, leading to the fact that the widely
used heuristics and meta-heuristics cannot be applied since these
methods require that the jobs to be scheduled should be known in
advance. Hence, mathematical programming models are adopted
to formulate the scheduling problem of a reﬁnery with two cate-
gories, namely discrete and continuous-time representations.
By discrete-time representation, the scheduling horizon is dis-
cretized into a number of uniform slots such that the scheduling
problem can be formulated as a mixed integer linear programming
(MILP) model (Shah, 1996; Lee et al., 1996; Pinto et al., 2000;
Glismann and Gruhn, 2001; Jia et al., 2003; Rejowski and Pinto,
2003; Saharidisa et al., 2009; Mendez et al., 2006; and Yuzgee
et al., 2010). To make a schedule obtained by such a model
* Corresponding author.
E-mail addresses: nqwu@must.edu.mo (N. Wu), zhwli@xidian.edu.cn (Z. Li),
quting@jnu.edu.cn (T. Qu).
Contents lists available at ScienceDirect
Journal of Cleaner Production
journal homepage: www.elsevier.com/locate/jclepro
http://dx.doi.org/10.1016/j.jclepro.2017.07.222
0959-6526/© 2017 Elsevier Ltd. All rights reserved.
Journal of Cleaner Production 166 (2017) 49e57
practically applicable, the time duration for the time slots should be
short enough, leading to a huge number of discrete variables for a
practical application problem. Hence, it is almost impossible to be
solved by the existing software tools such as the famous CPLEX and
LINGO (Floudas and Lin, 2004; and Wu et al., 2011).
To make the problem computationally tractable and solvable,
models with continuous time-representation are developed such
that the time points for the start and end of an event can be exactly
found (Jia et al., 2003; Jia and Ierapetritou, 2004; Karuppiah et al.,
2008; Li et al., 2002, 2012; Mouret et al., 2009, 2011; and Shah
et al., 2009). By such a model, it is indeed that the number of
discrete variables can be signiﬁcantly reduced. Especially, by the
priority-slot-based modeling method presented by Mouret et al.
(2009, 2011), the problem size can be signiﬁcant reduced. Never-
theless, it pays an expense of introducing non-linear constraints,
resulting in a mixed integer non-linear programming (MINLP)
model. It is well-known that an MINLP problem is very difﬁcult to
solve. Furthermore, in order to build such a model, one needs to
decide the number of operations to be performed in advance,
which is unrealistic for practical applications in general (Floudas
and Lin, 2004).
In addition to different modeling methods, efforts have been
made to improve computational efﬁciency by developing solution
methods. For example, event-based methods (Yuzgee et al., 2010;
and Furman et al., 2007), an outer-approximation algorithm (Jia
et al., 2003), and a decomposition algorithm (Shah et al., 2009) to
improve the efﬁciency of solving such a problem. Indeed, the
aforementioned solution methods can reduce the computation
requirements to some extent. However, all these methods belong to
enumeration in nature. With the NP-hard nature of the problem,
they cannot resolve the computational complexity problem for
real-life applications. To avoid solving an MINLP problem, based on
the model obtained by the priority-slot-based modeling method, a
two-stage algorithm is proposed to solve the MINLP problem
(Mouret et al., 2009, 2011). At Stage 1, an MILP model is formed by
removing all the non-linear constraints from the MINLP model. This
MILP problem is then solved to obtain a solution for the integer
variables. Generally the solution obtained from solving the MILP is
not feasible for the original MINLP, since the non-linear constraints
are removed. Then, at Stage 2, the solution for the integer variables
obtained at Stage 1 is substituted into the original MINLP to obtain a
non-linear programming (NLP) model without an integer variable.
Then, the NLP is solved to obtain a schedule. However, a solution
may not be found for the NLP problem even if a feasible schedule
exists for the original MINLP problem.
Thus, to make the problem formulated by mathematical pro-
gramming models solvable, in modeling, some constraints are
ignored, which, unfortunately, results in an inefﬁcient or unrealistic
schedule for real-life scenarios (Mendez et al., 2006). This means
that there is a gap between academic research and applications. As
the best knowledge of the authors, up to now, there is no software
tool of short-term scheduling of oil reﬁneries for practical use.
To bridge such a gap, the key is to develop a computationally
efﬁcient approach for a good solution other than an exactly optimal
solution. Notice that, with a large number of discrete variables, the
solution space is very large. On the other hand, with a large number
of constraints for the problem, the feasible solution space must be
small. Thus, it is extremely difﬁcult to solve such a problem by any
enumeration method. With this observation, a Petri net-based
control-theoretic approach for crude oil operations is proposed
(Wu et al., 2008a, 2009, 2015). By this approach, the dynamic
behavior of the process is modeled with a hybrid Petri net model by
treating the plant as a hybrid system with the interaction of
discrete-event and continuous processes (Wu et al., 2007; 2008b).
From the viewpoint of control theory, schedulability is analyzed
to establish schedulability conditions for different scenarios (Wu
et al., 2011, 2008a, 2009, 2010a, 2010b, 2016a; and Zhang et al.,
2017). These conditions determine the feasible space, i.e., the
feasibility conditions, such that the problem can be decomposed
into two sub-problems: reﬁning scheduling and detailed sched-
uling. With the schedulability conditions as constraints, the
reﬁning scheduling problem can be solved to obtain a realizable
and optimal reﬁning schedule by using linear programming-based
methods (Wu et al., 2012; 2016b). Then, given a realizable and
optimal reﬁning schedule, a detailed schedule to realize it can be
found in a recursive way (Wu et al., 2011, 2008a, 2009, 2010a,
2010b, 2016a). Furthermore, with the schedulability conditions,
genetic algorithm can be used to ﬁnd an optimal detailed schedule
(Hou et al., 2017). Accordingly, a short-term schedule for crude oil
operations can be efﬁciently found although it may not be globally
optimal.
It should be pointed out that all the aforementioned studies
mainly focus on searching for techniques to cope with computa-
tional complexity while some objectives are optimized without
taking sustainability into account. With continuous climate change
resulting from anthropogenic greenhouse gas emissions, there is a
great concern on energy saving for sustainable development. It is
well-known that process industries are characterized by high en-
ergy consumption. It is vitally important to minimize energy con-
sumption in operating a reﬁnery such that greenhouse gas
emissions can be reduced as much as possible. A reﬁnery process is
very complex and energy-intensive, and signiﬁcant energy can be
saved if it is well-operated in the sense of energy efﬁciency. Thus, it
is very important to reduce energy consumption in the sense of cost
reduction and sustainable development. This work focuses on the
energy saving issue in scheduling the process of crude oil
operations.
In the existing studies on scheduling crude oil operations, a
variety of objectives are optimized. They include: 1) minimizing
cost resulting from crude oil inventory, oil tanker waiting, and oil
unloading (Lee et al., 1996; Jia et al., 2003; and Jia and Ierapetritou,
2004); 2) maximizing productivity (Pinto et al., 2000; and Wu et al.,
2012, 2016b) and minimizing the number of tanks used (Pinto et al.,
2000; Zhang et al., 2017); 3) minimizing the number of oil type
switches in oil transportation via a pipeline (Lee et al., 1996; Hou
et al., 2017); 4) minimizing the remaining oil in a tank when it is
unloaded (Shah, 1996); and 5) maximizing the processing effec-
tiveness of different oil types processed by different distillers (Wu
et al., 2012; 2016b). However, as far as the authors know, there is
no research report on how to save energy in scheduling crude oil
operations, which is crucial to our society. This motivates us to
conduct this study.
As pointed out in (Wu et al., 2005), in crude oil operations, crude
oil transportation from storage tanks to charging tanks via a
pipeline consumes a large portion of energy. In order to save energy
in the process of crude oil operations, it is signiﬁcant to minimize
the energy consumption for oil transportation. The aim of this work
is two folds: 1) to develop a method to minimize the energy con-
sumption in oil transportation from storage tanks to charging
tanks, and 2) the proposed method should be computationally
efﬁcient such that it is practically applicable.
Generally, in a reﬁnery, crude oil is transported from storage
tanks to charging tanks via a pipeline. When delivering liquid
material through a pipeline, the ﬂow resistance in a pipeline is
proportional to the square of ﬂuid velocity, i.e., is highly non-linear
as pointed out by Cafaro et al. (2015). Hence, the transportation rate
of the pipeline is not proportional to the power applied, in other
words, the energy consumption is non-linear with respect to the oil
ﬂow rate. This makes the problem of optimizing the energy efﬁ-
ciency
in
oil
transportation
very
complicated.
Thus,
a
N. Wu et al. / Journal of Cleaner Production 166 (2017) 49e57
50
computationally efﬁcient method for optimizing energy con-
sumption in oil transportation should avoid non-linearity and at
the same time obtain satisfactory performance.
For a long-distance pipeline, at each pumping station, there are
a number of sets of machines for pumping the oil. In practice, the oil
ﬂow rate is regulated by changing the number of sets of machines
that are in operation. Based on such an operation mode, this work
tackles the energy efﬁciency issue and a linear programming-based
approach is proposed to minimize the energy consumption in
crude oil operations. It is done as follows: a short-term schedule is
found with the maximal ﬂow rate of the pipeline to maximize the
productivity without taking energy efﬁciency into account. This
problem can be efﬁciently solved by the Petri net-based theoretic-
control approach proposed in (Wu et al., 2008a, 2009, 2012, 2016b).
Based on the obtained schedule, this work minimizes the energy
consumption
for oil
transportation via
a pipeline.
A linear
programming-based technique is developed to solve it. By doing so,
non-linearity is avoided and it is computationally very efﬁcient.
The remainder of this work is organized as follows. Next section
brieﬂy introduces the process of crude oil operations and its short-
term scheduling problem. Section 3 states the energy optimization
problem in crude oil operations and presents the linear program-
ming formulation for it. A real-world industrial case study is given
to demonstrate the application and signiﬁcance of the proposed
method in Section 4. Section 5 concludes the work.
2. The process and its short-term schedule
Before presenting the problem discussed in this work and the
method for it, we brieﬂy introduce the processes of a reﬁnery. An
illustrative view of general/typical oil reﬁnery processes can be
depicted in Fig. 1. A reﬁnery contains a number of storage tanks
located at a port near the plant, a pipeline, a number of charging
tanks in the plant, a number of distillers for oil distillation, and a
variety of other production units. In the viewpoint of operations,
there are two phases: 1) crude oil operations and 2) production.
Crude oil arrives at the port through oil tankers by sea. It is then
unloaded into storage tanks. From the storage tanks, oil is delivered
to charging tanks through the pipeline. Charging tanks are used to
feed oil to the distillers for distillation. This process forms the ﬁrst
phase, called crude oil operations. The products obtained from
distillation are further processed by other production units to
obtain various components. These components are blended to form
the ﬁnal products, which is the production phase. A short-term
scheduling problem for the process of crude oil operations is well
recognized as being the most difﬁcult one in a reﬁnery. That an-
swers why we ﬁnd no implementable theory and software tools to
enable industrial-size applications.
To meet the market demands, a reﬁnery should process a
number of crude oil types with different components. Each distiller
can process some crude oil types, but not all, which in turn requires
a tank (storage or charging tank) to hold one oil type at any time.
Before oil can be processed by a distiller, brine must be separated
from oil. To do so, it requires that, after ﬁlling a storage or charging
tank, crude oil must stay in it for some time before it can be dis-
charged. This is called an oil residency time constraint. Besides, any
tank cannot be charged and discharged simultaneously. There is
another requirement that a distiller must work continuously and
cannot be stopped unless there is a planned maintenance. The
above requirements pose a large number of constraints on the
process of crude oil operations.
To schedule the process of crude oil operations is to decide the
tasks to be performed and sequence them. A task is a discrete event
for the process. In the execution of a task, oil is delivered in a
continuous way, resulting in a hybrid system with both discrete-
event and continuous processes. When a task is executed, the
system is transformed from a state to another such that a task can
be seen as a control command. Thus, the scheduling problem of
crude oil operations is to determine the commands (tasks) and can
be studied from a perspective of hybrid system control as done by
Wu et al. (2011, 2008a, 2009, 2010a, 2010b, 2016a, 2012, and 2016b;
and Zhang et al., (2017). By the control-theoretic-based approach, a
task is deﬁned as follows.
Deﬁnition 2.1.
A task (TS) is deﬁned as TS ¼ {OT, SP, DP, V, a, b},
where OT denotes an oil type; SP the source from which the oil comes,
DP the device to which the oil is delivered; V the amount of oil to be
processed; and a and b the start and end time points for a task.
For easy implementation and simplicity for ﬁnding a schedule,
the oil ﬂow rate in a task is set to be a constant, i.e., f ¼ V/(b -a). In
crude oil operations, there are three types of TSs: UTSs for oil
unloading from a tanker to storage tanks, DTSs for oil delivering
from storage tanks to charging tanks, and FTSs for oil feeding from
charging tanks to distillers. With the deﬁnition of TSs, a short-term
schedule SCHD for crude oil operations can be described as
SCHD ¼ {UTS1, UTS2, …, UTSw, DTS1, DTS2, …, DTSx, FTS1, FTS2, …,
FTSk}
(2.1)
Thus, the scheduling problem of crude oil operations is to ﬁnd
an SCHD such that all the aforementioned requirements and con-
straints are met, while some objectives are optimized. With the
maximal oil ﬂow rate of a pipeline, such a schedule can be efﬁ-
ciently found by the control-theoretic-based approach to optimize
productivity and oil type processing effectiveness (Wu et al., 2008a,
2009, 2012, and 2016b). A schedule for a scenario from a reﬁnery
Tanker
Storage
tanks
Pipeline
Charging
tanks
Distillers
Other
production
units
Blend
header
Finished
products
Crude oil operations
Production
Fig. 1. The illustrative view of a general oil reﬁnery.
N. Wu et al. / Journal of Cleaner Production 166 (2017) 49e57
51
obtained by using this approach is shown in Figs. 2 and 3, where DSi
represents Distiller i. In Fig. 2, the distiller feeding schedule is given
by presenting the case when a charging tank is used to feed a type
of oil into a distiller. For example, during time interval (0, T1),
Charging Tank #1 is used to feed Oil Type #1 into Distiller 1. While,
in Fig. 3, an oil transportation schedule from storage tanks to
charging tanks is given by presenting the case when a type of oil is
charged into a charging tank. For instance, during time (0, A1), Oil
Type #1 is charged into Charging Tank #2.
To make a schedule feasible, a charging tank can feed a distiller
only when the oil in the charging tank is ready for feeding. For
example, the charging of Oil Type #1 into TK #3 ends at A2 as shown
in Fig. 3 such that it can be ready at T2 when TK #3 starts to feed DS1
as shown in Fig. 2. Note that, for the schedule shown in Figs. 2 and 3,
TK #1 is used to feed Oil Type #1 into DS1 at time zero, which
means that Oil Type #1 should be charged into TK #1 before time
zero, i.e., it is done during the last scheduling horizon.
By observing the schedule shown in Fig. 3, during time (A2, A3),
the pipeline is stopped and no oil is transported. Note that, in
transporting oil via the pipeline, the oil ﬂow resistance is propor-
tional to the square of the oil velocity and is highly non-linear to the
velocity (Cafaro et al., 2015). This implies that the higher the oil
ﬂow rate in the pipeline is, the more energy is consumed. Thus,
when there is idle time for the pipeline, one may reduce the oil ﬂow
rate to save energy if the obtained schedule is still feasible. Also, the
transportation of some oil parcels can be delayed by reducing the
oil ﬂow rate without affecting the distiller feeding schedule. In this
way, energy consumption can be further reduced. This problem is
coped with in the next section.
3. Problem formulation and solution method
As aforementioned, to ﬁnd a schedule for crude oil operations is
to decide a series of TSs and, by the control-theoretic-based
approach, the oil delivering rate for each TS is set to be a con-
stant. Since the scheduling problem of crude oil operations is
extremely complicated, it is difﬁcult to efﬁciently ﬁnd such a
schedule by optimizing productivity and energy consumption
simultaneously. However, with the maximal oil transportation rate
via a pipeline, a schedule to maximize the productivity can be
efﬁciently found. Based on such a schedule, this section discusses
how to optimize energy consumption by regulating the oil trans-
portation rate in the obtained DTSs.
3.1. Problem statement
Given a schedule with the maximal oil transportation rate for
DTSs, to minimize energy consumption, we examine whether some
parcels of oil in the DTSs can be delayed without impact on the
feasibility of the schedule. If so, we can delay the transportation of
some parcels by reducing the transportation rate.
A pipeline system in a reﬁnery used to transport crude oil from
storage tanks to charging tanks can be illustrated by Fig. 4. It is
composed of a pipeline and a number of pumping stations that
provide the power for the oil transportation. There are a number of
sets of machines at each pumping station. The power provided by
the system is dependent on the number of sets of machines that are
in operation at each station. In such a system, given the number of
sets of machines in operation at each station, the oil transportation
rate can be regulated in a range. Since the relation between the
pumping power and oil rate is highly non-linear (Cafaro et al., 2015)
and the range that can be regulated is generally small. In practice,
when the oil transportation rate needs to be increased, one puts
more sets of machines in operation at each pumping station. In fact,
the oil transportation rate is not proportional to the number of sets
of working machines either and is highly non-linear instead. Take a
reﬁnery as an example. When one set of machines is used at each
pumping station, the transportation rate of the pipeline is 20,000
tons per day. When two sets of machines are used, the rate is
30,000 tons per day. If three sets are used, it is 33,000 tons per day
only.
Note that the scheduling of crude oil operations is a routine job.
Fig. 2. The Gantt chart for distiller feeding schedule.
#2
TK #2
#7
TK #9
#5
TK #8
#7
TK #5
#1
TK 
#3
Time 
(H)
0
A1
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
TK #2
#1
TK #1
#4
170
180
TK 
#6
#4
A2
A3
A4
A5
A6
A7
A8
10
Fig. 3. The Gantt chart for oil transportation schedule via a pipeline.
N. Wu et al. / Journal of Cleaner Production 166 (2017) 49e57
52
To make a method for the routine scheduling problem applicable to
complex and practical application problems, it must be simple and
computationally efﬁcient. With high non-linearity, it is extremely
difﬁcult to optimize the energy consumption if the domain of oil
transportation rate is in a real number space. Thus, to search for an
efﬁcient method for the scheduling problem of crude oil opera-
tions, one must avoid the non-linearity. To do so, it is reasonable to
adopt the operational mode used in the practice, which regulates
the oil transportation rate by changing the number of sets of ma-
chines at each station. Also, given the number of sets of machines to
be operated at each station, there is a most energy-efﬁcient oil
transportation rate. Thus, it is justiﬁed that, given the number of
sets of machines to be operated, the most energy-efﬁcient oil
transportation rate is applied. Based on this analysis, we present a
simple and computationally efﬁcient method to minimize the en-
ergy consumption in scheduling crude oil operations as follows.
Based on the above analysis, to minimize energy consumption is
to decide the oil transportation rate for each DTS. Since the number
of sets of machines at each station is limited and known, there are
only a limited number of selections on oil transportation rate for
the amount oil for each DTS. To do so, we assume that there are n
selections. Then, given a DTS ¼ {OT, SP, DP, V, a, b}, we divide V into n
parcels V1, V2, …, and Vn such that each parcel is delivered with
different rate as shown in Fig. 5. Nevertheless, the feasibility needs
to be guaranteed, i.e., when a charging tank is scheduled to be
charged, it must be emptied, and when the oil in a charging tank is
scheduled to be fed into a distiller, the oil residency time constraint
must be satisﬁed. Speciﬁcally, for the schedule given in Figs. 2 and
3, by regulating the oil transportation rate of DTSs, Charging Tank
#5 can be charged only after time point T4 and its charging should
be ended U time units earlier than time point T8 when Tank #5 that
holds Oil Type #7 is used to feed DS3, where U is the oil residency
time.
In summary, to minimize energy consumption for oil trans-
portation via a pipeline, for each DTS ¼ {OT, SP, DP, V, a, b}, one
needs to optimally divide V into n parcels V1, V2, …, and Vn such that
they are transported with ﬂow rate levels 1, 2, …, and n, respec-
tively. A linear programming-based method can be developed to
achieve this purpose.
3.2. A linear programming-based method
Given a short-term schedule for crude oil operations obtained
by the control-theoretic-based approach, assume that there are k
DTSs, each of which is performed to charge a charging tank. With a
single pipeline, these DTSs are sequenced such that DTS iþ1 should
be performed just after DTS i. Notice that if DTS iþ1 is scheduled to
be performed immediately after DTS i is performed, then when DTS i
is delayed, DTS iþ1 must be delayed too. However, if there is an
interruption for the pipeline between performing DTSs i and iþ1, a
delay of performing DTS i does not necessarily result in a delay of
performing DTS iþ1. Thus, to present the method, the DTSs are
grouped.
The DTSs obtained by the control-theoretic-based approach are
divided into d groups such that, in group Gi, there are ki DTSs with
k1 þ k2 þ … þ kd ¼ k. We use DTSij to denote the j-th DTS in group
Gi, and Aij and Bij to denote the time points when DTSij starts to
charge a charging tank and ends the charging, respectively. Note
that Aij and Bij are given by the schedule obtained by the control-
theoretic-based approach, i.e., they are known. Then, with this
grouping, we have Bij ¼ Ai(jþ1), i.e., in the same group, the DTSs are
performed one after another without interruption. However,
Bi(ki) < A(iþ1)1 must hold. In other words, between groups Gi and
G(iþ1), the pipeline is schedule to be idle for some time. Since the
schedule obtained by the control-theoretic-based approach is
known, given i, whether Bi(ki) < A(iþ1)1 holds or not is known too.
This implies that the number of groups is known, i.e., d is a decided
parameter. Based on this grouping of DTSs, we present the
following notations to formulate the considered problem.
Parameters and sets.
n: the number of sets of machines usable at each pumping
station;
S ¼ {1, 2, …, n}: the set of the number of sets of machines;
d: the number of groups of DTSs;
G ¼ {1, 2, …, d};
Nki ¼ {1, 2, …, ki};
DTSij: the j-th DTS in Group i2G and j2Nki that is decided by a
given schedule;
Gi ¼ {DTSi1, DTSi2, …, DTSi(ki)};
Vij: the amount of oil to be transported by performing DTSij;
TKij: the charging tank to be charged by performing DTSij that is
decided in the given schedule;
Aij: the time point when TKij starts to be charged by performing
DTSij as given by the schedule;
Bij: the time point when charging TKij ends as given by the
schedule;
Tij: the time point when TKij charged by performing DTSij begins
to feed a distiller as given by the schedule;
U: the oil residency time;
fi: the most energy-effective oil transportation ﬂow rate when i
sets of machines are used at each pumping station;
Ci: the cost coefﬁcient when i sets of machines are used at each
pumping station;
Storage tanks
Pumping 
station 2
Pumping 
station 3
Charging tanks
Pipeline
Pumping 
station 1
Fig. 4. Illustration of a pipeline system.
DTS 1
DTS 1
Flow rate
level 1
Flow rate
level 1
Flow rate
level 2
Flow rate
level 2
Flow rate
level n
Flow rate
level n
DTS 2
DTS 2
DTS k
DTS k
C11
C1k
C12
Cnk
Cn2
C22
C2k
C2k
C21
C21
Cn1
Cn1
Fig. 5. Optimization of energy consumption by regulating the ﬂow rate of DTSs.
N. Wu et al. / Journal of Cleaner Production 166 (2017) 49e57
53
Decision variables.
xijh: the amount of oil in DTSij to be transported by using the
most energy-efﬁcient ﬂow rate with h sets of machines being
used at each pumping station, i2G, j2 Nki, and h2S;
ti1: the time point when TKi1 starts to be charged by performing
DTSi1 after oil transportation rate is regulated.
Given a schedule obtained by the control-theoretic-based
approach, the above listed sets and parameters are known except
Ci. To formulate the addressed problem, one needs to determine Ci.
With high non-linearity, the higher the oil transportation rate is,
the more power is consumed by transporting one unit of crude oil,
i.e., Ci > Ci-1 holds. For the linear programming problem presented
below, if we set Cd > Ch with d s h, by solving the following linear
programming problem, higher priority must be given to the oper-
ation mode that uses h sets of machines than the one that uses
d sets of machines. Thus, to optimize the problem addressed in this
paper, we need only set Ci > Ci-1 regardless of the actual values of
Ci's. Let 4 denote the nominal power in KWs when one set of ma-
chines is in operation at each station. Then, with i sets of machines
are in operation, the power is i4 in KWs, and the energy consumed
for delivering one ton of oil is i4/fi in KWhours. However, in
solving an optimization problem, we can obtain the same result no
matter we set Ci to be i4/fi or i/fi. Thus, it is reasonable to let Ci ¼ i/fi
such that we have Ci > Ci-1. Then, we formulate the problem as
follows.
Minimize J ¼
X
i2G
X
j2Nki
X
h2S
Chxijh
(3.1)
Subject to
t11  A11
(3.2)
X
h2S
x1jh ¼ V1j; j2Nk1
(3.3)
t11 þ
Xj
g¼1
X
h2S
x1gh
.
fh þ U  T1j; j2Nk1
(3.4)
ti1  Ai1, i2G\{1}
(3.5)
ti1  tði1Þ1 þ
X
j2Nkði1Þ
X
h2S
xði1Þjh
.
fh; i2G\f1g
(3.6)
X
h2S
xi jh ¼ Vij; i2G\f1g; j2Nki;
(3.7)
ti1 þ
Xj
g¼1
X
h2S
xigh
.
fh þ U  Tij; j2Nki; i2G\f1g
(3.8)
xijh  0 and ti1  0
(3.9)
Since Ch represents the energy consumed for transporting one
unit of crude oil, by Objective (3.1), the total energy consumption is
minimized by regulating oil transportation rate. Constraint (3.2)
guarantees that oil transportation can be done when a charging
tank is available as speciﬁed by the given schedule. Constraint (3.3)
states the conservativeness property of crude oil in a DTS.
Constraint (3.4) guarantees that the time when the oil in the
charged charging tank is ready is earlier than Tij when the charging
tank is used to feed a distiller, i.e., it guarantees that the time delay
caused by regulating the oil transportation is in a permissive range
such that the oil residency time constraint is satisﬁed. Constraint
(3.9) presents the non-negative requirement.
As above discussed, between two Groups G(i-1) and Gi, there is an
idle time, or we have Ai1 > B(i-1)(k(i-1)). However, after delaying the
transportation of oil of DTSs in G(i-1), this may no longer hold. Since
a pipeline cannot be used to perform two DTSs simultaneously, a
DTS in Gi can be performed only after all the DTSs in G(i-1) have been
executed. Constraints (3.5) and (3.6) state that when a DTS in Gi is
performed, the pipeline is available, and at the same time, charging
tank TKi1 that is necessary for performing DTSi1 is released. Con-
straints (3.7) and (3.8) have the same meaning as that of (3.3) and
(3.4).
Notice that the domain of xijh's and ti1's is real number, and the
objective and constraints are linear. Hence, this is a linear pro-
gramming formulation and can be efﬁciently solved by commercial
software tools. Notice that a frequent switch from transporting one
type of oil to another via a pipeline is very costly and the number of
such switches should be minimized in scheduling crude oil oper-
ations. The number of DTSs for an obtained schedule is not large in
general. Consequently, the proposed linear programming formu-
lation cannot be large. Thus, the proposed method is simple and
computationally efﬁcient. In this way, the proposed method mini-
mize energy consumption and at the same time it is practically
applicable due to its computational efﬁciency.
4. Industrial case study
This section uses a real-life scenario from a reﬁnery in southern
China to show the application of the proposed method. The reﬁnery
is located at the southern China and is one of the largest reﬁneries
in China. It has three distillers and a long-distance pipeline for
delivering oil from storage tanks to charging tanks. These distillers
are designed for processing different types of oil such that multiple
types of oil should be processed by the reﬁnery. The distance from
the storage tanks to charging tanks is more than 20 km, so is the
pipeline. The maximal oil processing capacity of the three distillers
is 375 tons, 230 tons, and 500 tons per hour, respectively. For the
pipeline, there are three sets of machines at each pumping station.
If one set, two sets, and three sets of machines are put into oper-
ation, the corresponding most energy-efﬁcient oil transportation
rate via the pipeline is 20,000 tons, 30,000 tons, and 33,000 tons
per day (or 833.333 tons, 1250 tons, and 1375 tons per hour),
respectively.
As a routine, the reﬁnery needs to present a short-term schedule
every 10 days. The case presented here is one of the scenarios and a
schedule is found by the control-theoretic-based method (Wu et al.,
2008a, 2009, and 2012). For this case problem, since the total oil
processing capacity is 375 þ 230 þ 500 ¼ 1105 tons per hour that is
less than 1250 tons per hour by using two sets of machines at each
pumping station, we can treat 1250 tons per hour as the maximal
oil transportation rate via the pipeline for scheduling the process. In
other words, there are two oil transportation modes: 1) one set of
machines at each station is in operation and 2) two sets of machines
at each station are in operation with f1 ¼ 20,000/24 ¼ 833.333 tons
per hour and f2 ¼ 30,000/24 ¼ 1250 tons per hour, respectively. In
this way, with the initial state of the charging tanks shown in
Table 4.1, the obtained schedule is shown in Figs. 6 and 7.
For the obtained schedule by the control-theoretic-based
method, there are nine DTSs and they form two groups with
G1 ¼ {DTS11, DTS12, DTS13, DTS14} and G2 ¼ {DTS21, DTS22, DTS23, DTS24,
DTS25}. For this schedule, Charging Tanks #129 with Oil #3, #128
with Oil #2, and #116 with Oil #4 have been already charged during
the last scheduling horizon such that they can be used to feed DS1,
DS2, and DS3 at time zero, respectively. For example, 34000 tons of
N. Wu et al. / Journal of Cleaner Production 166 (2017) 49e57
54
oil #2 in tank #128 are fed into Distiller 2 ﬁrst. At the same time,
40,000 tons of oil #2 are transported into tanks #180 and #181.
Then, 21,200 tons of oil #2 are fed into Distiller 2 and 18,800 tons of
oil #2 in tank #181 are left for the use of the next scheduling ho-
rizon. It should also be noticed that, among the DTSs, the oil
transported to Charging Tanks #128 and #127 by performing DTS24,
DTS25 is not processed during the current scheduling horizon but
for the next horizon and the time when it is processed is unknown.
Hence, we do not need to consider these two DTSs for energy
reduction.
From the given schedule, by using ST to stand for storage tanks,
we have DTS11 ¼ {#2, ST, #180, 20000, 0, 16}, DTS12 ¼ {#2, ST, #181,
20000, 16, 32}, DTS13 ¼ {#1, ST, #127, 34000, 32, 59.2}, DTS14 ¼ {#1,
ST, #182, 20000, 59.2, 75.2}, DTS21 ¼ {#6, ST, #116, 34000, 92.8, 120},
DTS22 ¼ {#6, ST, #117, 34000, 120, 147.2}, DTS23 ¼ {#1, ST, #129, 9000,
147.2,154.4}, T11 ¼130.4 h, T12 ¼ 217.4, T13 ¼ 72, T14 ¼162.7, T21 ¼164,
T22 ¼ 232, and T23 ¼ 216.03. Also by deﬁnition, we have C1 ¼ 1/
f1 ¼ 0.0012 and C2 ¼ 2/f2 ¼ 0.0016. For this case problem, we have
U ¼ 6 h. From Fig. 7, we can observe that only B14 < A21 holds, which
implies that there are two groups of DTSs, i.e., d ¼ 2. Then, we can
formulate the linear programming model for the problem as
follows.
Minimize J ¼ C1  (x111 þ x121 þ x131 þ x141 þ x211 þ x221 þ x231)
þ C2  (x112 þ x122 þ x132 þ x142 þ x212 þ x222 þ x232)
Subject to
t11  0
x111 þ x112 ¼ 20000
t11 þ x111/833.333 þ x112/1250 þ 6  130.4
x121 þ x122 ¼ 20000
t11 þ x111/833.333 þ x112/1250 þ x121/833.333 þ x122/
1250 þ 6  217.4
x131 þ x132 ¼ 34000
t11 þ x111/833.333 þ x112/1250 þ x121/833.333 þ x122/1250 þ x131/
833.333 þ x132/1250 þ 6  72
x141 þ x142 ¼ 20000
t11 þ x111/833.333 þ x112/1250 þ x121/833.333 þ x122/1250 þ x131/
833.333 þ x132/1250 þ x141/833.333 þ x142/1250 þ 6  162.7
t21  t11 þ x111/833.333 þ x112/1250 þ x121/833.333 þ x122/
1250 þ x131/833.333 þ x132/1250 þ x141/833.333 þ x142/1250
Table 4.1
The initial state of the charging tanks.
Tank
Capacity (Ton)
Type of oil ﬁlled
Volume (Ton)
Distiller feeding
Tank #129
34,000
Crude oil #3
27,000
Distiller 1
Tank #128
34,000
Crude oil #2
30,000
Distiller 2
Tank #116
34,000
Crude oil #4
27,000
Distiller 3
Tank #117
34,000
Crude oil #5
30,000
Tank #115
34,000
Crude oil #5
25,000
Tank #127
34,000
Tank #182
20,000
Tank #180
20,000
Tank #181
20,000
Fig. 6. The distiller feeding schedule for the case problem.
#180
#181
0
20
40
60
80
100
120
140
160
180
200
Time (H)
#2
#1
#129
#117
#127
#2
#127
#182
#1
#116
#6
#1
#3
#6
#2
#128
A11
A12
A13
A14
B14
A21
A22
A23
A24
A25
Fig. 7. The oil transportation schedule for the case problem.
N. Wu et al. / Journal of Cleaner Production 166 (2017) 49e57
55
t21  92.8
x211 þ x212 ¼ 34000
t21 þ x211/833.333 þ x212/1250 þ 6  164
x221 þ x222 ¼ 34000
t21 þ x211/833.333 þ x212/1250 þ x221/833.333 þ x222/1250 þ 6  232
x231 þ x232 ¼ 9000
t21 þ x211/833.333 þ x212/1250 þ x221/833.333 þ x222/1250 þ x231/
833.333 þ x232/1250 þ 6  216.03
xijh  0 and ti1  0
As pointed out above, for the obtained formulation, there are
only 16 variables and 17 constraints, which is small and easy to
solve.
It
is
solved
by
using
CPLEX
with
x111 ¼ x121 ¼ x141 ¼ x212 ¼ x222 ¼ x232 ¼ 0, x112 ¼ 20000, x122 ¼ 20000,
x131 ¼17000, x132 ¼ 17000, x142 ¼ 20000, x211 ¼ 34000, x221 ¼ 34000,
and x231 ¼ 9000. The obtained schedule is illustrated by the Gantt
chart in Fig. 8, where an orange bar presents that the oil is trans-
ported by using two sets of machines, while a green bar represents
that one set of machine is used. By this schedule, Aij's and Bij's are
modiﬁed as A12 ¼ 16, A13 ¼ 32, A14 ¼ 66, B14 ¼ 90, A21 ¼ 92.8,
A22 ¼ 133.6, A23 ¼ 174.4, and A24 ¼ 185.2 such that it is feasible.
Notice that A14 þ 6 ¼ 72 ¼ T13 to guarantee that the Oil Type #1
charged to Charging Tank #117 is usable at time T13. To do so, Oil
Type #2 charged into Charging Tanks #180 and #181, and part of Oil
Type #1 charged to #117 should be transported by using two sets of
machines, otherwise an obtained schedule is infeasible.
By observing Fig. 8, it can be seen that higher priority is given to
the use of one set of machines since this mode is more energy-
efﬁcient.
By
the
schedule
obtained
by
using
the
proposed
method, we have J ¼ 228. However, by the schedule given in Fig. 7,
we have J ¼ 273.6. This implies that the objective is reduced by
(273.6e228)/273.6 z 16.7%, i.e., signiﬁcant energy is saved.
5. Conclusions
It is commonly recognized that, to be competitive in a global
market, an oil reﬁnery should be well operated. Since the sched-
uling problem of a reﬁnery is extremely complicated and chal-
lenging, much attention has been paid to this issue. In this research
ﬁeld, the main focus is on ﬁnding an efﬁcient approach such that
the scheduling problem is computationally solvable and some ob-
jectives are optimized. In the existing methods, the objectives
include maximizing productivity, minimizing oil inventory, mini-
mizing changeover, and so on. However, no much work is found to
take energy efﬁciency as an objective in scheduling an oil reﬁnery.
Due to the great effect of greenhouse on the global climate, an
enterprise is required to be sustainable, i.e., energy efﬁciency is
vitally important. This work addresses this critical issue in sched-
uling a reﬁnery.
In our previous work, we present a control-theoretic-based
approach to the scheduling problem of crude oil operations, by
which a schedule can be efﬁciently found. Based on the approach,
this work studies the energy efﬁciency problem in crude oil oper-
ations. Since oil transportation from storage tanks to charging tanks
consumes a large part of energy in crude oil operations, the
objective of this work is to reduce the energy consumption in
transporting oil via a pipeline. With the relation between oil
transportation rate and energy consumption being highly non-
linear, to obtain a practically applicable method, it is crucial to
avoid non-linearity. By considering the fact that, in practice, the oil
transportation rate is regulated by changing the number of sets of
machines at each pumping station and, for a given number of sets
of machines, there is a most energy-efﬁcient oil transportation rate,
only a limited number of rates to be selected. Based on such an
operation mode, a method is proposed to formulate the energy
efﬁciency problem of scheduling crude oil operations as a linear
programming problem. By this formulation, energy saving is real-
ized by delaying the transportation of some oil parcels if possible
with lower oil ﬂow rate. In this way, we avoid non-linearity and
integer variables in the model. A real-life case study is presented to
show the application of the proposed method. It is found that, for
such a case problem, there are less than 20 constraints and it is easy
to solve. Also, signiﬁcant energy can be saved. Thus, the proposed
method not only optimizes the energy consumption, but also is
applicable to real-life problems due to its computational simplicity.
Note that the linear programming formulation is an approxi-
mate model for the non-linear process. It is useful to do comparison
study with a non-linear model. Also, there are more issues for en-
ergy saving in scheduling crude oil operations. For example, there
are high fusion oil types whose fusion point is higher than 30 C.
Hence, when such oil types are transported from one place to
another via a pipeline, they need to be heated. Then, they are stored
in tanks and cool down. When they are to be processed, they need
to be heated again. Also, when the middle products come just from
a device, they are very hot. Then, they are stored in tanks and cool
down. However, when they go to the next processing step, they
need to heat up. In this way, large amount of energy is consumed,
which can be greatly saved if the operations are properly sched-
uled. This is the issues for our future research work.
Acknowledgments
This work was supported in part by Science and Technology
Development Fund (FDCT) of Macau under Grants 106/2016/A3 and
078/2015/A3, and National Natural Foundation of China under
Grants 61273036 and 61603100.
References
Aspen Technology Inc, 1999. PIMS System Reference. Cambridge, MA, USA, version
11.0. .
Bai, L.P., Wu, N.Q., Li, Z.W., Zhou, M.C., Oct. 2016. Optimal one-wafer cyclic
#180
#181
0
20
40
60
80
100
120
140
160
180
200
Time (H)
#2
#1
#129
#117
#127
#2
#127
#182
#1
#116
#6
#1
#3
#6
#2
#128
A11
A12
A13
A14
B14 A21
A22
A23
A24
A25
220
240
Fig. 8. Gantt chart for the optimized oil transportation schedule.
N. Wu et al. / Journal of Cleaner Production 166 (2017) 49e57
56
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N. Wu et al. / Journal of Cleaner Production 166 (2017) 49e57
57

Chemical
 Engineering
 Research
 and
 Design
 1
 5
 2
 (
 2
 0
 1
 9
 )  85–94
Contents
 lists
 available
 at
 ScienceDirect
Chemical
 Engineering
 Research
 and
 Design
j
 ourna
 l
 h
 omepage:
 www.elsevier.com/locate/cherd
Increasing
 operational
 efﬁciency
 through  the
integration
 of  an oil  reﬁnery
 and  an  ethylene
production
 plant
Elham
 Ketabchi ∗,
 Evgenia
 Mechleri,
 Harvey
 Arellano-Garcia
Department
 of
 Chemical
 and
 Process
 Engineering,
 University
 of
 Surrey,
 Guildford,
 GU27XH,
 United
 Kingdom
a
 r  t  i
 c
 l  e  
i  n
 f
 o
Article
 history:
Received
 11
 February
 2019
Received
 in
 revised
 form
 31
 July
 2019
Accepted
 23
 September
 2019
Available
 online
 1
 October
 2019
Keywords:
Integration
Optimisation
Modelling
Oil
 reﬁnery
Petrochemical
 plant
Ethylene
 production
 plant
a
 b  s
 t  r  a  c  t
In
 this
 work,
 the
 optimal
 integration
 between
 an
 oil
 reﬁnery
 and
 an
 ethylene
 production
plant
 has
 been
 investigated.
 Both
 plants
 are
 connected
 using
 intermediate
 materials
 aim-
ing
 to
 remove,
 at
 least
 partially,
 the
 reliance
 on
 external
 sourcing.
 This
 integration
 has
 been
proven
 to
 be
 beneﬁcial
 in
 terms
 of
 quality
 and
 proﬁt
 increase
 for
 both
 production
 systems.
Thus,
 three
 mathematical
 models
 have
 been
 formulated
 and
 implemented
 for
 each
 plant
individually
 as
 well
 as
 for
 the
 integrated
 system
 as
 MINLP
 models
 aiming
 to
 optimise
 all
three
 systems.
 Moreover,
 a
 case
 study
 using
 practical
 data
 is
 presented
 to
 verify
 the
 fea-
sibility
 of
 the
 integration
 within
 an
 industrial
 environment.
 Promising
 results
 have
 been
obtained
 demonstrating
 signiﬁcant
 proﬁt
 increase
 and
 enhanced
 operability
 in
 both
 plants.
©
 2019
 Institution
 of
 Chemical
 Engineers.
 Published
 by
 Elsevier
 B.V.
 All
 rights
 reserved.
1.
 
Introduction
The
 importance
 of
 petroleum
 reﬁning
 and
 reﬁneries
 are
 shown
 in
 our
everyday
 lives
 in
 various
 forms
 such
 as
 transportation,
 usage
 in
 house-
hold
 and
 commercial
 products
 and
 even
 in
 pharmaceuticals.
 These
industries
 are
 mainly
 dependant
 on
 crude
 oil
 as
 it
 is
 their
 main
 feed,
therefore,
 any
 change
 in
 crude
 oil
 price
 will
 have
 a
 direct
 impact
 on
these
 industries.
 An
 increase
 in
 feed
 price
 encourages
 the
 industry
to
 develop
 new
 ways
 to
 increase
 the
 proﬁt
 margin.
 Following
 this,
 all
resources
 are
 required
 to
 be
 used
 in
 a
 highly
 efﬁcient
 way
 which
 can
be
 achievable
 through
 adequate
 integration.
 Other
 than
 the
 impact
 of
crude
 oil
 price,
 other
 considerations
 that
 have
 substantial
 effects
 on
the
 plants
 are
 the
 strict
 environmental
 regulations
 which
 have
 raised
the
 cost
 of
 producing
 clean
 fuels
 (Fahim
 et
 al.,
 2010).
Most
 of
 the
 technological
 change
 in
 reﬁneries
 over
 the
 last
 20
years
 has
 been
 because
 of
 environmental
 concerns
 that
 lead
 to
 the
change
 and
 alteration
 of
 the
 existing
 processes.
 This
 “cause
 and
 effect”
response
 to
 environmental
 mandates
 have
 had
 a
 large
 impact
 on
the
 reﬁneries’
 economic
 performance,
 causing
 increased
 costs
 (Lefﬂer,
2008).
 Nevertheless,
 these
 mandates
 have
 opened
 avenues
 to
 seek
alternative
 pathways
 to
 step
 towards
 a
 more
 environmentally
 friendly
∗Corresponding
 author.
E-mail
 address:
 e.ketabchi@surrey.ac.uk
 (E.
 Ketabchi).
industry
 while
 also
 investigating
 solutions
 to
 maintain
 the
 industry’s
operation.
Moreover,
 ﬂuctuations
 in
 oil
 reﬁnery
 and
 petrochemical
 plants’
proﬁtability
 has
 led
 these
 industries
 to
 look
 for
 new
 ways
 to
 maintain
their
 proﬁt
 while
 staying
 competitive.
Taking
 all
 of
 this
 into
 account,
 to
 maintain
 the
 operability
 of
 reﬁner-
ies,
 improve
 proﬁt
 margins
 and
 product
 quality
 while
 also
 considering
environmental
 regulations,
 one
 valid
 approach
 represents
 the
 integra-
tion
 with
 compatible
 systems,
 which
 may
 result
 in
 a
 beneﬁcial
 outcome
for
 the
 considered
 production
 systems.
 So
 for
 instance,
 Shell
 Global
(2017)
 recently
 proposed
 a
 hydrocracker-ethylene
 cracker
 integration
 to
reduce
 ethylene
 cracker
 feedstocks
 while
 using
 the
 hydrogen
 produced
from
 the
 ethylene
 plant
 in
 the
 hydrocracking
 unit
 in
 the
 reﬁnery.
 This
was
 carried
 out
 at
 industrial
 scale
 in
 Singapore,
 Germany,
 Netherlands,
and
 USA.
The
 study
 of
 modelling
 oil
 reﬁneries
 is
 well
 established.
 However,
they
 are
 intrinsically
 non-linear
 and
 complex
 to
 simulate,
 therefore,
most
 of
 the
 proposed
 models
 aim
 at
 reducing
 this
 challenging
 problem.
In
 an
 oil
 reﬁnery
 the
 conventional
 production
 planning
 is
 usually
conducted
 focusing
 on
 the
 optimisation
 of
 individual
 or
 group
 of
 units
within
 the
 production
 but
 not
 considering
 the
 utility
 system
 (Burkhard
et
 al.,
 2017).
 For
 example,
 a
 detailed
 formulation
 of
 a
 reﬁnery
 model
 was
carried
 out
 by
 Kancijan
 et
 al.
 (2015)
 in
 which
 the
 modelling
 of
 the
 main
units
 was
 focused
 on
 while
 also
 modelling
 6
 stages
 of
 the
 distillation
unit
 and
 merging
 them
 into
 the
 general
 pattern
 of
 the
 reﬁnery.
 This
https://doi.org/10.1016/j.cherd.2019.09.028
0263-8762/©
 2019
 Institution
 of
 Chemical
 Engineers.
 Published
 by
 Elsevier
 B.V.
 All
 rights
 reserved.
86
 
Chemical
 Engineering
 Research
 and
 Design
 1
 5
 2
 (
 2
 0
 1
 9
 )
 85–94
Nomenclature
Sets
 and
 Subsets
C
 
Set
 of
 commodities
u
 
Units
m
 
Operation
 mode
f(u)
 
Cracking
 furnaces-subset
 of
 u
RR(c)
 
Subset
 of
 C
 of
 raw
 materials
 of
 the
 oil
 reﬁnery
RE(c)
 
Subset
 of
 C-
 raw
 material
 of
 the
 ethylene
 plant
CF(c)
 
Subset
 of
 C
 of
 fuel
 oil
 or
 fuel
 gas
CE(c)
 
Subset
 of
 C
 of
 ethylene
 plant
 product
CU(c)
 
Subset
 of
 C
 of
 intermediates
CFGE(c)
 
Subset
 of
 C
 of
 fuel
 gas
 ﬂowing
 out
 of
 the
 ethy-
lene
 plant
CP(c)
 
Subset
 of
 C
 of
 production
 product
 of
 the
 oil
reﬁnery
FGHER(c)
 Subset
 of
 C
 of
 fuel
 oil
 and
 gasoline
 and
 hydro-
gen
 ﬂowing
 out
 of
 the
 ethylene
 plant
FGRE(c)
 
Subset
 of
 C
 of
 fuel
 gas
 ﬂowing
 out
 of
 the
 reﬁnery
FORE(c)
 
Subset
 of
 C
 of
 fuel
 oil
 ﬂowing
 out
 of
 the
 reﬁnery
CHRE(c)
 Subset
 of
 C
 of
 chemical
 products
 ﬂowing
 into
the
 ethylene
 plant
CRE(c)
 
Subset
 of
 C
 of
 raw
 materials
 ﬂowing
 into
 the
ethylene
 plant
IPR(c)
 
Subset
 of
 C
 of
 inventorial
 commodities
 (produc-
tion
 product)
 in
 the
 reﬁnery
IER(c)
 
Subset
 of
 C
 of
 intermediate
 materials
 ﬂowing
into
 the
 reﬁnery
IRE(c)
 
Subset
 of
 C
 of
 intermediate
 materials
 ﬂowing
out
 of
 the
 reﬁnery
HER(c)
 
Subset
 of
 C
 of
 hydrogen
 ﬂowing
 into
 the
 reﬁn-
ery
OMU(m)
 Subset
 of
 operation
 mode
 on
 unit
BL(u)
 
Subset
 of
 U
 of
 blending
 headers
UP(u)
 
Subset
 of
 U
 of
 processing
 units
FB(u)
 
Set
 of
 cracking
 furnaces
 and
 boilers
r(c)
 
Raw
 material-subset
 of
 c
 in
 cracking
 furnace
SO(c)
 
Set
 of
 output
 products
 c
 of
 separation
 unit
 u
US(u)
 
Set
 of
 separation
 units
 in
 ethylene
 plant
P
 
Property
t
 
Time
 horizon
SI(c)
 
Set
 of
 input
 material
 c
 of
 separation
 unit
 u
CI(c)
 
Set
 of
 feed
 material
 of
 operation
 m
 on
 unit
 u
CO(c)
 
Set
 of
 products
 of
 operation
 m
 on
 unit
 u
FI(c)
 
Set
 of
 input
 material
 c
 of
 furnace
 f
FO(c)
 
Set
 of
 output
 products
 c
 of
 furnace
 f
Parameters
CDF
 
Pre-exponential
 factor
 regarding
 impact
 of
 cok-
ing
 deposition
 on
 product
 yields
LCF
 
Linearized
 coke
 factor
FCCF
 
Pre-linear
 
coefﬁcient
 
fuel
 
consumption
 
of
cracking
 furnace
CRF
 
Pre-linear
 factor
 for
 coking
 reaction
FOTF
 
Pre-linearized
 factor
 regarding
 impact
 of
 outlet
temperature
 of
 furnace
 on
 product
 yields
FCCFT
 
Pre-linear
 coefﬁcient
 regarding
 fuel
 consump-
tion
 of
 cracking
 furnace
 f
 related
 to
 coil
 outlet
temperature
Cfd
 
Pre-linear
 coefﬁcient
 regarding
 fuel
 consump-
tion
 of
 cracking
 furnace
 f
 related
 to
 dilution
steam
Emc
 
Fixed
 maintenance
 cost
 regarding
 either
 pro-
duction
 unit
 or
 utility
 equipment
 u
 when
 the
operation
 is
 on
Ec
 
Activation
 energy
 of
 coking
 reaction
Mpr
 
Molar
 concentration
 of
 propylene
SEC
 
Material
 switching
 cost
 coefﬁcient
 regarding
cracking
 furnace
 f
FS
 
Separation
 factor

 
Coke
 density

 
Ideal
 gas
 constant
DP(c,t)
 
Market
 demand
 for
 ﬁnal
 product
 c
pri(c)
 
Price
 of
 material
 c
SM(c,t)
 
Supply
 of
 material
 c
Dfc(c)
 
Constant
 value
 regarding
 aggregated
 model
 of
fuel
 consumption
 of
 cracking
 furnace
 f
 when
cracking
 material
(u,c)
 
Pre-linear
 coefﬁcient
 of
 unit
 load
 on
 product
yield
Cfp(u,c)
 Pre-linear
 coefﬁcient
 of
 separation
 unit
 top
pressure
 on
 product
 yield
pi(u,t)
 
Operation
 cost
 of
 the
 production
 unit
(u,m,c)
 Yield
 ratio
 of
 the
 material
 c
 of
 unit
 u
 in
 opera-
tion
 mode
 m
p(u,c)
 
Fixed
 yield
 of
 the
 product
 c
 of
 cracking
 furnace
f
SIL(c,t)
 
Safety
 inventory
 level
 of
 commodity
 c
IC(c,t)
 
Inventory
 cost
 of
 commodity
 c
INC(c,t)
 
Inventory
 capacity
 of
 commodities
 c
PU(c,p)
 
Property
 of
 intermediates-upper
 limit
PL(c,p)
 
Property
 of
 intermediates-lower
 limit
PROI(c,
 p)
 Property
 p
 of
 intermediate
 product
 c
Variables
Ct
 (u,
 t)
 
Coke
 thickness
 of
 furnace
FOT
 (u,
 c,
 t)
 Outlet
 temperature
 of
 furnace
 in
 period
 t
FB
 (u,
 c,
 t)
 Fuel
 c
 consumed
 by
 furnace
 or
 boiler
 of
 period
t
FC
 (u,
 c,
 t)
 Amount
 of
 commodities
 c
 consumed
 in
period
 t
 in
 unit
 u
FC1
 (u,
 m,
 c,
 t)
 Amount
 of
 commodities
 c
 consumed
 in
period
 t
 in
 unit
 u
 on
 operation
 m
FF
 (u,
 m,
 t)
 Flow
 rate
 of
 unit
 u
 of
 period
 t
 with
 operation
mode
 m
FP
 (u,
 m,
 c,
 t)
 Amount
 of
 commodities
 c
 produced
 in
period
 t
 of
 unit
 u
 on
 operation
 m
 in
 the
 reﬁnery
FP1
 (u,
 c,
 t)
 Amount
 of
 commodities
 c
 produced
 in
 period
t
 of
 unit
 u
 in
 the
 ethylene
 plant
FPP(c,
 t)
 Amount
 of
 commodities
 c
 produced
 of
 period
 t
FU
 (u,
 t)
 Flow
 rate
 of
 unit
 u
 of
 period
 t
MC(c,
 t) Raw
 material
 consumed
 in
 period
 t
MER(c,
 t)
 Amount
 of
 material
 c
 from
 the
 ethylene
 plant
to
 the
 reﬁnery
IN(c,
 t)
 
Material
 inventory
 of
 c
 of
 period
 t
MRE(c,
 t)
 Amount
 of
 material
 c
 from
 the
 reﬁnery
 to
 the
ethylene
 plant
PC(c,
 t)
 
Amount
 of
 commodity
 c
 purchased
 of
 period
 t
PEN(c,
 t)
 Penalty
 difference
 between
 real
 inventory
 and
expected
 inventory
 of
 commodity
 c
Prof
 
Overall
 proﬁt
Rc
 (c,
 u,
 t)
 Coking
 reaction
 rate
 regarding
 furnace
 f
 pro-
cessing
 material
 in
 period
 t
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87
RD
 (u,
 c,
 t)
 Dilution
 steam
 consumption
 of
 furnace
 f
when
 consuming
 material
SC(c,t)
 
Amount
 of
 commodity
 c
 sold
 or
 delivered
 in
period
 t
B
 (u,
 c,
 t)
 Binary
 variable
 denotes
 whether
 the
 unit
 u
operates
 with
 consuming
 material
r
 
in
 period
 t
Bm
 (u,
 m,
 t)
 Binary
 variable
 that
 denotes
 whether
 pro-
cessing
 unit
 u
 is
 on
 with
 operation
 m
 of
 t
Z
 (u,
 t)
 
Binary
 variable
 denoting
 whether
 the
 material
consumed
 of
 cracking
 furnace
 is
 changed
 in
period
 t

 (u,c,t)
 
Yield
 ratio
 of
 the
 product
 c
 of
 cracking
 furnace
f
 consuming
 raw
 material
Tp
 (u,
 t)
 Top
 pressure
 of
 separation
 unit
EDF(c,
 t)
 Fuel
 gas
 consumed
 in
 utility
 generation
 equip-
ment
was
 then
 simulated
 using
 nonlinear
 constraints.
 Based
 on
 simulation
studies,
 Kancijan
 et
 al.’s
 objective
 was
 to
 maximise
 the
 proﬁt
 while
 tak-
ing
 the
 market
 requirements
 into
 account.
 Another
 example
 regarding
studies
 on
 nonlinear
 modelling
 of
 the
 oil
 reﬁnery
 is
 Guerra
 et
 al.’s
 work
which
 consists
 of
 two
 papers,
 one
 concerning
 the
 formulation
 of
 the
process
 models
 and
 the
 other
 presenting
 case
 studies.
 In
 Guerra
 et
 al.’s
formulation
 section,
 the
 crude
 distillation
 unit
 (CDU)
 and
 the
 ﬂuid
 cat-
alytic
 cracking
 unit
 (FCC)
 in
 the
 oil
 reﬁnery
 were
 modelled
 empirically
and
 nonlinearly
 with
 the
 success
 of
 being
 validated
 through
 rigorous
process
 simulators
 (Guerra
 and
 Le
 Roux,
 2011a,
 b).
 Further
 develop-
ments
 of
 the
 CDU
 model
 were
 achieved
 by
 Alattas
 et
 al.
 aiming
 to
reﬂect
 the
 true
 nature
 of
 the
 model
 through
 adding
 nonlinearity
 to
existing
 linear
 reﬁnery
 planning
 models.
 This
 has
 resulted
 in
 higher
proﬁt
 margins
 when
 compared
 to
 common
 models,
 such
 as
 swing-cut
models,
 but
 with
 an
 increase
 in
 order
 of
 magnitude
 (Alattas
 et
 al.,
 2011).
Menezes
 et
 al.
 have
 also
 tackled
 this
 issue
 by
 improving
 the
 swing-cut
model
 for
 improved
 accuracy
 of
 the
 model.
 They
 have
 achieved
 this
 via
taking
 into
 consideration
 that
 the
 model
 requires
 corresponding
 light
and
 heavy
 swing-cuts
 avoiding
 overestimation
 of
 the
 proﬁt
 (Menezes
et
 al.,
 2013).
Guerra
 and
 Le
 Roux
 however,
 were
 able
 to
 formulate
 a
 nonlinear
reﬁnery
 planning
 with
 empirical
 models
 for
 CDU
 and
 FCC
 by
 repre-
senting
 accurate
 models
 to
 overcome
 limitations
 of
 both
 linear
 and
nonlinear
 empirical
 models
 for
 CDUs
 and
 FCC
 units
 (Guerra
 and
 Le
Roux,
 2011b).
Despite
 favoring
 accurate
 models
 for
 improved
 results,
 their
 com-
plexities
 must
 be
 considered
 in
 which
 various
 studies
 have
 aimed
 to
tackle
 this
 such
 as
 Shah’s
 research.
 They
 have
 proposed
 approaches
 for
linearization
 and
 model
 decomposition
 due
 to
 the
 high
 computational
expense
 to
 reach
 an
 optimal
 solution
 for
 real-life
 reﬁnery
 applications
when
 they
 developed
 a
 short-term
 scheduling
 model
 for
 large-scale
reﬁneries
 (Shah,
 2015).
 This
 leads
 to
 the
 fact
 that
 although
 an
 accurate
model
 is
 required
 for
 the
 representation
 of
 an
 oil
 reﬁnery,
 the
 balance
between
 accuracy
 and
 non-complexity
 is
 an
 important,
 yet
 challeng-
ing
 aspect
 of
 this
 area
 of
 research
 that
 is
 also
 highly
 dependent
 on
 the
application
 and
 end
 goal
 of
 the
 work.
Petrochemical
 plants
 have
 also
 been
 studied
 in
 terms
 of
 produc-
tion
 planning
 and
 optimisation.
 One
 seminal
 example
 is
 the
 work
 of
Diaz
 and
 Bandoni
 in
 which
 they
 have
 presented
 the
 operational
 opti-
mization
 of
 a
 real
 ethylene
 production
 plant
 in
 operation
 using
 the
outer-approximation
 method
 for
 a
 mixed
 integer
 nonlinear
 program-
ming
 (MINLP)
 problem.
 They
 were
 able
 to
 reach
 convergence
 in
 few
iterations
 while
 also
 opening
 avenues
 to
 introduce
 this
 method
 to
 be
considered
 as
 a
 standard
 option
 in
 large
 scale
 conditions
 (Díaz
 and
Bandoni,
 1996).
Moreover,
 different
 approaches
 have
 been
 implemented
 regard-
ing
 the
 ethylene
 production
 plant
 via
 MINLP
 modelling,
 including
feedstock
 management
 in
 terms
 of
 vessel
 arrivals
 and
 storage
 tanks
alongside
 the
 consideration
 of
 operation
 conditions
 for
 the
 plant
 side
scheduling
 (Tjoa
 et
 al.,
 1997).
 Other
 relevant
 modelling
 aspects
 and
details
 of
 an
 ethylene
 plant
 have
 been
 studied
 such
 as
 the
 optimal
reaction
 conditions
 for
 the
 steam
 cracking
 of
 ethane
 to
 obtain
 a
 higher
ethylene
 yield
 (van
 Goethem
 et
 al.,
 2013).
 Improved
 operating
 condi-
tions
 were
 also
 investigated
 in
 a
 non-linear
 real-world
 and
 validated
operational
 planning
 model
 aiming
 to
 maximise
 product
 revenues
while
 considering
 utility
 and
 feedstock
 cost
 (Gubitoso
 and
 Pinto,
 2007).
Zhao
 et
 al.
 (2016)
 incorporated
 a
 short-term
 multi-period
 MINLP
planning
 model
 for
 the
 ethylene
 production
 that
 incorporated
 the
 use
of
 energy
 in
 both
 the
 thermal
 cracking
 and
 the
 downstream
 process
 to
explore
 the
 potential
 for
 an
 increase
 in
 the
 production
 margin
 and
 the
reduction
 of
 energy
 losses.
Considering
 that
 a
 fair
 amount
 of
 research
 has
 been
 carried
 out
on
 the
 individual
 plants
 in
 terms
 of
 modelling,
 it
 is
 of
 importance
 to
investigate
 work
 carried
 out
 on
 the
 combination
 of
 these
 plants
 to
 also
identify
 the
 existing
 gap
 in
 this
 area.
Al-Qahtani
 and
 Elkamel
 have
 published
 a
 paper
 regarding
 the
integration
 of
 the
 aforementioned
 plants
 with
 the
 consideration
 of
 a
relevant
 aspect
 of
 reﬁnery
 planning
 using
 different
 crude
 combination
alternatives.
 They
 have
 also
 considered
 production
 capacity
 expansion
while
 focussing
 on
 the
 simultaneous
 analysis
 of
 process
 network
 inte-
gration
 alternatives
 in
 a
 multisite
 reﬁning
 system
 and
 a
 petrochemical
system.
 Through
 a
 mixed-integer
 linear
 program
 (MILP),
 their
 overall
objective
 was
 minimizing
 total
 annualized
 cost.
 In
 their
 formulation
they
 have
 considered
 individual
 component
 ﬂows
 rather
 than
 bilinear
mixing
 equations
 in
 order
 to
 avoid
 complexities.
 However,
 the
 ﬁner
details
 of
 the
 petrochemical
 plant
 is
 not
 presented
 in
 the
 work
 and
 the
main
 focus
 is
 on
 the
 oil
 reﬁnery
 (Al-Qahtani
 and
 Elkamel,
 2008).
In
 addition,
 Zhao
 et
 al.
 developed
 an
 integrated
 optimisation
approach
 connecting
 an
 oil
 reﬁnery
 with
 an
 ethylene
 plant
 while
 con-
sidering
 the
 potential
 of
 increasing
 the
 overall
 proﬁt,
 formulating
 a
MINLP
 model
 to
 optimise
 the
 production
 planning
 of
 process
 units
 in
the
 oil
 reﬁnery
 and
 the
 ethylene
 plant
 concurrently.
 They
 proposed
though
 to
 simplify
 the
 complex
 model
 using
 the
 Lagrangian
 algorithm
in
 order
 to
 obtain
 a
 MILP
 problem
 (Zhao
 et
 al.,
 2017).
This
 work
 aims
 at
 implementing
 the
 proposed
 integration
 by
 using
relevant
 recent
 data
 as
 well
 as
 conducting
 model-based
 studies
 and
optimization
 analyses
 in
 order
 to
 obtain
 an
 improved
 understanding
of
 those
 parameters
 that
 have
 a
 bigger
 impact
 on
 the
 integration
 of
a
 conventional
 oil
 reﬁnery
 with
 a
 petrochemical
 plant,
 in
 this
 case,
an
 ethylene
 plant.
 This
 integration
 requires
 upgrading
 the
 reﬁnery’s
by-products
 and
 main
 products
 by
 reducing
 operating
 costs
 while
upgrading
 the
 feed
 availability
 and
 quality
 of
 petrochemical
 plants.
This
 combination
 is
 beneﬁcial
 for
 both
 plants
 in
 terms
 of
 lowering
 costs
and
 improving
 the
 plant’s
 efﬁciency
 as
 well
 as
 supplying
 feed
 for
 the
petrochemical
 plant.
 However,
 based
 on
 various
 research
 presented
and
 available,
 even
 though
 the
 accuracy
 of
 the
 models
 is
 of
 impor-
tance,
 their
 complexities
 could
 cause
 problems
 that
 would
 hinder
 the
progression
 of
 implementing
 this
 idea.
 Therefore,
 our
 aim
 is
 to
 prove
this
 concept
 while
 demonstrating
 a
 model
 that
 not
 only
 has
 the
 key
requirements
 of
 representing
 the
 plants
 to
 a
 good
 level
 of
 accuracy
 but
would
 also
 ensure
 the
 success
 of
 the
 model
 through
 a
 simpler
 model
compared
 to
 previous
 work.
 It
 is
 of
 importance
 that
 through
 this
 con-
nection,
 intermediate
 material
 of
 both
 plants
 is
 utilised
 for
 improving
and
 producing
 products
 as
 well
 as
 feeding
 the
 utility
 system
 demand.
This
 should
 be
 done
 in
 a
 way
 so
 that
 the
 suitable
 interaction
 between
the
 plants
 followed
 by
 efﬁcient
 material
 utilization
 are
 applied.
 More-
over,
 since
 both
 plants
 are
 prone
 to
 market
 volatility,
 yield
 maximising
and
 proﬁt
 increase
 alongside
 plant
 optimisation
 are
 key
 differentiators
for
 the
 competitiveness
 of
 reﬁneries.
2.
 
Problem
 statement
The
 key
 contribution
 in
 this
 work
 is
 the
 synergy
 between
complex
 plants
 in
 a
 way
 to
 achieve
 a
 beneﬁcial
 outcome,
 in
this
 case,
 an
 oil
 reﬁnery
 and
 an
 ethylene
 production
 plant.
Through
 the
 proposed
 integration,
 economic
 beneﬁt
 would
 be
88
 
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Fig.
 1
 –
 Schematic
 representation
 of
 the
 integration
 between
 an
 oil
 reﬁnery
 and
 an
 ethylene
 production
 plant
 (Ketabchi
et
 al.,
 2018).
Table
 1
 –
 List
 of
 abbreviations
 used
 in
 Fig.
 1
 with
 their
corresponding
 description.
Abbreviation
 used
 in
 Figure
 1
 
Description
DM
 
Demethaniser
DE
 
Deethaniser
E-FR
 
Ethylene
 Fractionator
DPRO
 
Depropaniser
PRO-FR
 
Propylene
 Fractionator
DB
 
Debutaniser
DPEN
 
Depentaniser
achieved
 for
 both
 plants
 in
 terms
 of
 providing
 an
 alternate
source
 of
 feedstock
 as
 well
 as
 intermediate
 material
 required
for
 both
 involved
 production
 plants.
 For
 this
 purpose,
 each
plant
 is
 to
 be
 modelled
 and
 optimised
 individually
 as
 well
as
 the
 ﬁnal
 integrated
 plant.
 Thus,
 three
 mathematical
 mod-
els
 have
 been
 derived
 and
 implemented
 considering
 their
integration
 method
 as
 a
 MINLP
 problem.
 A
 schematic
 of
 the
integration
 can
 be
 seen
 in
 Fig.
 1.
 A
 Table
 is
 also
 provided
 for
the
 abbreviations
 used
 in
 the
 aforementioned
 ﬁgure
 (Table
 1).
The
 pathway
 of
 integrating
 the
 two
 plants
 will
 be
 explored
while
 deﬁning
 the
 units
 used
 in
 each
 plant
 as
 well
 as
 the
 cor-
responding
 streams.
 Firstly,
 the
 oil
 reﬁnery
 process
 and
 its
units
 will
 be
 discussed.
 This
 plant
 consists
 of
 the
 main
 feed
of
 crude
 oil
 being
 converted
 into
 the
 ﬁnal
 products.
 These
units
 are
 as
 follows:
 CDU,
 FCC,
 Delayed
 Coking
 Unit
 (DCU),
Catalytic
 Reforming
 Unit
 (CRU),
 Hydro-Treating
 unit
 (HT),
 Gas
Desulfurization
 unit
 (DS)
 and
 blenders.
The
 primary
 process
 being
 the
 distillation
 process
 occurs
 in
the
 CDU,
 in
 which
 the
 products
 are
 lightest
 to
 heaviest:
 Light
Straight-Run
 Naphtha
 (LSRN),
 Heavy
 Straight-Run
 Naphtha
(HSRN),
 Raw
 Kerosene
 (RKERO),
 Light
 Gas
 Oil
 (LGO),
 Atmo-
spheric
 Gas
 Oil
 (AGO),
 Vacuum
 Gas
 Oil
 (VGO)
 and
 the
 heaviest
product,
 residue
 (RESID).
 The
 produced
 RESID
 then
 enters
 the
DCU
 that
 produces
 additional
 VGO.
 RKERO
 is
 then
 consumed
in
 the
 HDS
 that
 produces
 kerosene
 (KERO)
 as
 a
 ﬁnal
 product
that
 is
 then
 stored
 in
 a
 product
 tank.
Furthermore,
 the
 produced
 VGO
 alongside
 AGO
 then
 enters
the
 FCC
 which
 generates
 the
 main
 products
 of
 Cracked
 Gas
 Oil
(CGO),
 Cracked
 Gasoline
 (CG)
 and
 Fuel
 Oil
 (FO).
 Parallel
 to
 these
products,
 by-products
 of
 propylene,
 Fuel
 Gas
 (FG)
 and
 ethane
are
 also
 produced
 from
 the
 FCC
 in
 which
 the
 ethane
 ﬁnds
 use
in
 the
 cracking
 furnaces
 of
 the
 ethylene
 production
 plant.
 On
the
 other
 hand,
 the
 propylene
 produced
 will
 be
 stored
 in
 a
 tank
as
 a
 ﬁnal
 product
 in
 the
 aforementioned
 plant
 For
 example,
 FO
and
 FG
 are
 used
 in
 the
 oil
 reﬁnery
 to
 fuel
 the
 utility
 system
though
 their
 initially
 high
 sulphur
 content,
 which
 requires
these
 to
 be
 desulphurised
 in
 the
 DS
 prior
 to
 use.
Furthermore,
 the
 CGO
 from
 the
 FCC
 enters
 the
 HT
 produc-
ing
 diesel
 and
 FO
 as
 well
 as
 FG
 which
 is
 a
 by-product
 of
 this
process.
 The
 diesel
 from
 the
 HT,
 gathered
 with
 portions
 of
 LGO
and
 AGO
 are
 all
 blended
 in
 the
 diesel
 blender
 (DB).
 The
 DB
 then
produces
 two
 types
 of
 diesel.
 The
 former
 blend
 produces
 −10#
diesel,
 while
 when
 blending
 diesel
 with
 portions
 of
 CGO
 will
result
 in
 0#
 diesel
 product.
Another
 unit
 previously
 mentioned,
 the
 CRU,
 produces
 FG,
Naphtha
 and
 Reformer
 Gasoline
 (RG).
 This
 RG
 is
 then
 blended
with
 Methyl
 tert-butyl
 ether
 (MTBE)
 as
 well
 as
 LSRN
 in
 the
gasoline
 blender
 (GB)
 that
 gives
 the
 output
 of
 90#
 gasoline.
Concurrently,
 CG,
 HSRN
 and
 MTBE
 are
 blended
 in
 the
 GB
producing
 93#
 gasoline.
 The
 gasoline
 and
 diesel
 blending
 pro-
cesses
 have
 speciﬁcations
 in
 terms
 of
 octane
 number
 and
 pour
point.
 For
 example,
 the
 octane
 number
 of
 gasoline
 90#
 and
 93#
should
 be
 higher
 than
 90
 and
 93,
 respectively.
 While
 diesel
−10#
 and
 0#
 should
 have
 a
 pour
 point
 (the
 temperature
 below
which
 the
 ﬂow
 characteristics
 of
 the
 substance
 are
 lost)
 lower
than
 −10 ◦C
 and
 0 ◦C,
 respectively.
 The
 most
 desired
 products
of
 the
 reﬁnery
 consist
 of
 two
 kinds
 of
 gasoline
 (90#
 gasoline
and
 93#
 gasoline),
 two
 kinds
 of
 DIE
 (−10#
 diesel
 and
 0#
 diesel),
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 Engineering
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89
KERO
 and
 FO.
 In
 more
 detail,
 −10#
 diesel
 means
 that
 −10 ◦C
is
 the
 lowest
 temperature
 that
 diesel
 remains
 pourable
 and
 in
this
 process
 the
 requirement
 is
 to
 have
 a
 pour
 point
 less
 than
−10 ◦C
 for
 −10#
 diesel.
Streams
 produced
 in
 the
 oil
 reﬁnery
 can
 either
 be
 sold
 as
individual
 products
 or
 used
 beneﬁcially
 in
 the
 ethylene
 plant.
One
 of
 the
 key
 objectives
 of
 this
 integration
 is
 to
 maintain
 a
balance
 of
 material
 usage
 in
 both
 plants.
 This
 is
 especially
 the
case
 with
 ethylene
 and
 ethane
 that
 are
 wasted
 through
 heat-
ing
 and
 boiling
 within
 the
 reﬁnery
 that
 could
 otherwise
 be
used
 in
 the
 ethylene
 production
 plant.
 Additionally,
 the
 propy-
lene
 produced
 by
 the
 FCC
 unit
 can
 be
 deployed
 in
 the
 ethylene
plant
 to
 produce
 a
 wide
 range
 of
 chemicals.
The
 considered
 ethylene
 production
 plant
 is
 presented
 as
follows:
 a
 series
 of
 parallel
 cracking
 furnaces
 having
 the
 feed
of
 Naphtha,
 Ethane,
 AGO,
 and
 Hydrocracking
 Vent
 Gas
 Oil
(HVGO),
 which
 are
 used
 directly
 from
 the
 oil
 reﬁnery.
 Follow-
ing
 this
 unit
 in
 which
 cracking
 takes
 place,
 quenching
 and
compressing
 occurs
 followed
 by
 separation
 units.
 These
 units
are
 namely,
 a
 demethaniser,
 deethaniser,
 depropaniser,
 debu-
tanizer,
 and
 depentaniser
 alongside
 a
 propylene
 and
 ethylene
fractionator
 to
 further
 separate
 ethane
 and
 propane
 from
ethylene
 and
 propylene.
 Other
 products
 from
 these
 units
 are
hydrogen,
 butadiene,
 benzene,
 C4 and
 C5, which
 have
 also
partly
 use
 in
 the
 oil
 reﬁnery.
 The
 produced
 hydrogen
 is
 more
valuable
 to
 the
 reﬁnery
 plant
 than
 to
 the
 ethylene
 plant.
Moreover,
 a
 portion
 of
 hydrogen
 can
 be
 transferred
 to
 the
 oil
reﬁnery,
 as
 well
 as
 to
 FO
 and
 CG
 from
 the
 furnace
 output.
The
 proposed
 integration
 is
 beneﬁcial
 in
 many
 ways,
 imple-
menting
 changes
 in
 requirements
 for
 each
 plant
 that
 should
lead
 to
 mutual
 improvements
 while
 also
 increasing
 produc-
tion
 levels
 using
 available
 process
 technologies.
 Each
 plant
model
 is
 formulated
 separately
 including
 their
 integration
 to
minimise
 externally
 purchased
 feed
 material,
 with
 the
 overall
model
 based
 on
 the
 previous
 work
 of
 Zhao
 et
 al.
 (2017).
 Here,
the
 time
 dependency
 of
 some
 variables
 and
 parameters
 were
removed
 in
 addition
 to
 assigning
 values
 to
 some
 parameters,
instead
 of
 a
 range,
 to
 streamline
 the
 problem
 such
 as
 the
 sup-
ply
 demand
 relationship.
 Regarding
 time
 dependency,
 we
 have
chosen
 the
 price
 of
 commodities
 sold
 and
 purchased
 not
 to
 be
time
 dependent
 in
 our
 study.
 This
 is
 beneﬁcial
 in
 a
 way
 that
would
 lead
 to
 less
 complications
 when
 simulating
 the
 model,
less
 infeasibilities
 and
 compilation
 time.
2.1.
 
Oil
 reﬁnery
 model
A
 model
 is
 proposed
 to
 represent
 the
 process
 of
 reﬁning
 crude
oil
 to
 the
 manufacturing
 of
 the
 products
 in
 accordance
 with
the
 process
 description.
 Eq.
 (1)
 deﬁnes
 the
 relation
 between
commodities
 sold
 and
 the
 market
 demand
 and
 have
 been
chosen
 to
 be
 equal
 in
 this
 case.
 Material
 inventory
 balance
for
 the
 ﬁnal
 products
 is
 presented
 in
 Eq.
 (2),
 being
 equal
 to
the
 sum
 of
 commodities
 produced
 in
 the
 processing
 units
 of
the
 reﬁnery
 considering
 the
 commodities
 consumed.
 Eq.
 (3)
shows
 the
 inventory
 balance
 for
 ﬁnal
 products
 in
 the
 blender,
which
 is
 the
 sum
 of
 the
 amount
 of
 commodities
 produced
 in
the
 blending
 headers
 subtracting
 the
 number
 of
 production
materials
 sold.
 Eq.
 (4)
 evidences
 the
 commodities
 consumed
for
 blending
 is
 equal
 to
 commodities
 produced
 in
 the
 blending
headers,
 while
 Eq.s
 (5)
 and
 (6)
 represent
 inequality
 constraints
for
 blending
 processes.
 Eq.
 (7)
 denotes
 that
 the
 ﬂowrate
 of
 unit,
u
 on
 operation
 mode,
 m
 is
 equal
 to
 the
 unit
 capacity
 (ﬂow
 rate
in
 unit
 u-processing
 units
 in
 the
 oil
 reﬁnery)
 with
 Bm
 being
the
 binary
 variable
 indicating
 whether
 the
 unit
 is
 active
 with
operation
 mode
 m.
 Eq.
 (8)
 stipulates
 that
 only
 one
 operation
of
 unit,
 u
 (processing
 units
 in
 the
 reﬁnery)
 is
 allowed
 in
 each
time
 period,
 t.
 Another
 equation
 that
 is
 critical
 in
 this
 model
 is
the
 sum
 of
 the
 ﬂow
 rate
 under
 all
 operation
 modes
 equals
 the
ﬂow
 rate
 of
 each
 processing
 unit,
 as
 shown
 in
 Eq.
 (9).
 Addition-
ally,
 the
 ﬂow
 rate
 of
 the
 processing
 unit
 in
 the
 reﬁnery
 is
 equal
to
 the
 sum
 of
 commodities
 consumed,
 represented
 in
 Eq.
 (10)
and
 the
 fraction
 of
 ﬂow
 rate
 is
 also
 equal
 to
 the
 commodities
produced
 in
 the
 processing
 units
 of
 the
 reﬁnery,
 Eq.
 (11).
DP (c,
 t) =
 SC (c,
 t) ∀c
 ∈
 CP,
 t
 
(1)
IN (c,
 t) =
 IN (c,
 t
 −
 1) +

u
 ∈
 UP

m
 ∈
 OMU(m)
FP (u,
 m,  c,
 t)
−

u
 ∈
 UP

m
 ∈
 OMU(m)
FC1 (u,
 m,
 c,
 t) ∀c
 ∈
 IPR(C),
 t
 
(2)
IN (c,
 t) =
 IN (c,
 t
 −
 1) +

u
 ∈
 BL(U)

m
 ∈
 OMU(m)
FP (u,
 m,
 c,
 t)
−SC(c,
 t)∀c
 ∈
 CP,
 t
 
(3)

c
 ∈
 CI(c)
FC1 (u,
 m,
 c,
 t)
=

c
 ∈
 CO(c)
FP (u,
 m,
 c,
 t) ∀u
 ∈
 UBL,
 m
 ∈
 OMU(m),
 t
 
(4)

c
 ∈
 CI(c)
PROI (c,  p) *FC1(u,
 m,
 c,
 t)
 ≥

c
 ∈
 CO(c)
FP (u,
 m,
 c,
 t) *PL (c,
 p)
∀u
 ∈
 BL
 (U),
 m
 ∈
 OMU(m)
 ,
 p
 ∈
 P,
 t
 
(5)

c
 ∈
 CI(c)
PROI (c,  p) *FC1(u,
 m,
 c,
 t)
 ≤

c
 ∈
 CO(c)
FP (u,
 m,
 c,
 t) *PU (c,
 p)
∀u
 ∈
 BL (U) ,
 m
 ∈
 OMU (m) ,
 p
 ∈
 P,
 t
 
(6)
Bm ( u,
 m,
 t) *FU (u,
 t) =
 FF(u,
 m,
 t)∀u
 ∈
 UP,
 m
 ∈
 OMU(m),
 t
 
(7)

m
 ∈ OMU(m)
Bm (u,
 m,
 t) ≤
 1∀u
 ∈
 UP,
 t
 
(8)

m
 ∈ OMU(m)
FF (u,
 m,
 t) =
 FU (u,
 t) ∀u
 ∈
 UP,
 t
 
(9)
FF (u,
 m,
 t) =

c
 ∈
 CI(c)
FC1 (u,
 m,
 c,
 t) ∀u
 ∈
 UP,
 m
 ∈
 OMU
 (M),
 t
(10)
FP (u,
 m,
 c,
 t) =
 ˛ (u,
 m,
 c) *FF (u,
 m,
 t) ∀u
 ∈
 UP,
 m
 ∈
OMU  (M),
 c
 ∈
 CO(c),
 t
 
(11)
2.2.
 
Ethylene
 production
 plant
 model
The
 ethylene
 plant
 has
 been
 considered
 to
 consist
 of
 crack-
ing
 processes
 taking
 place
 in
 parallel
 furnaces
 and
 multiple
separation
 units
 to
 obtain
 the
 desired
 ﬁnal
 products.
 Here,
Eq.
 (12)
 represents
 the
 inventory
 balance
 for
 raw
 material
 in
the
 ethylene
 plant
 speciﬁcally
 that
 it
 is
 equal
 to
 the
 sum
 of
90
 
Chemical
 Engineering
 Research
 and
 Design
 1
 5
 2
 (
 2
 0
 1
 9
 )
 85–94
commodities
 produced
 in
 the
 plant
 considering
 the
 recycle
ﬂow
 rate
 of
 ethane
 produced
 in
 the
 separation
 train,
 plus
the
 supply
 of
 raw
 material
 while
 subtracting
 the
 commodi-
ties
 consumed
 in
 the
 furnace.
 Inventory
 balance
 and
 demand
constraints
 for
 the
 ﬁnal
 products
 are
 shown
 in
 Eq.
 (13).
 The
inventory
 is
 equal
 to
 the
 sum
 of
 the
 amount
 of
 commodi-
ties
 produced
 in
 the
 ethylene
 plant
 (recycle
 ﬂowrate
 of
 ethane
produced
 in
 the
 separation
 train)
 minus
 the
 amount
 of
 sold
commodities
 and
 minus
 the
 fuel
 consumption
 of
 the
 boiler.
Moreover,
 the
 economic
 penalty,
 (PEN(c,
 t)),
 is
 introduced
 in
Eqs.
 (14)
 and
 (15)
 as
 a
 necessary
 factor
 to
 be
 considered
 in
 the
plant
 inventory,
 when
 the
 inventory
 of
 the
 material
 or
 prod-
uct
 exceeds
 the
 security
 level,
 MIS.
 Exceeding
 the
 security
 level
will
 lead
 to
 a
 loss
 in
 the
 overall
 proﬁt
 as
 the
 economic
 penalty
is
 included
 in
 the
 objective
 function.
 It
 should
 also
 be
 noted
that
 the
 inventory
 capacity
 must
 exceed
 the
 material
 inven-
tory
 as
 shown
 in
 Eq.
 (16).
 Another
 aspect
 that
 needs
 to
 be
considered
 is
 the
 material
 balance.
 Material
 balance
 for
 Fuel
gas/fuel
 oil
 is
 presented
 in
 Eq.
 (17),
 indicating
 the
 fact
 that
 the
sum
 of
 Fuel
 gas/fuel
 oil
 purchased
 and
 produced
 is
 greater
 or
equal
 to
 the
 sum
 of
 Fuel
 gas/fuel
 oil
 consumed
 by
 the
 cracking
furnaces
 and
 boiler.
 Furthermore,
 the
 fuel
 oil
 produced
 in
 the
furnace
 is
 assumed
 to
 be
 equal
 to
 the
 amount
 of
 methane
and
 propane
 produced,
 which
 is
 demonstrated
 in
 Eq.
 (18).
During
 the
 cracking
 process
 in
 the
 furnace,
 coke
 is
 formed.
The
 consideration
 of
 this
 phenomenon
 is
 included
 in
 this
model
 in
 Eq.
 (19).
 This
 equation
 calculates
 the
 reaction
 rate
of
 coke
 formation
 through
 the
 use
 of
 Arrhenius’
 Law
 as
 well
as
 coke
 thickness
 demonstrated
 in
 Es.
 (19)
 and
 (20)
 developed
by
 Berreni
 and
 Wang
 (2011).
 Eq.
 (21)
 shows
 that
 the
 product
yield
 model
 is
 equal
 to
 the
 sum
 of
 ﬁxed
 yield,
 fractions
 of
 fur-
nace
 outlet
 temperature
 and
 coke
 thickness.
 Another
 material
balance
 is
 presented
 in
 Eqs.
 (22)
 and
 (23)
 concerning
 the
 rela-
tionship
 between
 the
 commodities
 consumed/produced
 in
the
 furnace
 and
 the
 ﬂow
 rate
 in
 the
 furnace.
 In
 more
 detail,
they
 portrait
 the
 commodities
 produced
 in
 the
 ethylene
 plant,
which
 is
 equal
 to
 the
 commodities
 consumed
 multiplied
 by
their
 ﬁxed
 yield
 in
 Eq.
 (22)
 while
 Eq.
 (23)
 depicts
 that
 the
 sum
of
 commodities
 consumed
 (raw
 material
 in
 this
 case)
 should
be
 equal
 to
 the
 ﬂow
 rate
 of
 the
 corresponding
 unit.
 Eq.
 (24)
regards
 a
 constraint
 for
 yield
 fractions
 that
 should
 have
 a
 sum
of
 less
 than
 one,
 while
 Eq.
 (25)
 shows
 that
 the
 commodities
consumed
 speciﬁcally
 in
 the
 cracking
 furnace
 should
 be
 equal
to
 the
 ﬂowrate.
 Eq.
 (26)
 deﬁnes
 that
 for
 each
 furnace,
 only
 one
material
 can
 process
 in
 any
 given
 period
 with
 B
 (u,
 c,
 t)
 being
the
 binary
 variable
 that
 denotes
 whether
 the
 unit
 u
 operates
with
 consuming
 material
 r
 in
 period
 t
 for
 the
 ethylene
 produc-
tion
 plant.
 Eq.
 (27)
 details
 the
 changeover
 condition
 variable,
 z
(u,
 t),
 for
 cracking
 furnaces
 and
 is
 introduced
 in
 the
 objective
function
 as
 a
 penalty
 in
 case
 of
 unstable
 operation.
 Eq.
 (28)
shows
 the
 production
 of
 each
 product,
 FP1
 (u,c,t),
 from
 the
corresponding
 separation
 unit
 as
 a
 linear
 function
 of
 ﬂowrate.
Operating
 condition,
 Tp
 (u,t),
 corresponds
 to
 the
 top
 pressure
of
 the
 separation
 column
 and
 unit
 separation
 factor,
 Fs.
 Lastly,
Eq.
 (29)
 shows
 that
 fuel
 consumed
 by
 furnace
 or
 boiler
 is
 equal
to
 the
 sum
 of
 the
 linear
 function
 of
 ﬂowrate,
 outlet
 tempera-
ture
 of
 furnace
 and
 dilution
 steam.
IN (c,
 t) =
 IN (c,
 t
 −
 1) +

f
 (u)
 ∈
 FI(u,c)
FC (f
 (u),
 c,
 t) +
 SM (c,
 t)
−

f  (u)
 ∈ FI(u,c)
FC (f
 (u),
 c,
 t) ∀c
 ∈
 RE(C),
 f
 (u),
 t
 
(12)
IN (c,
 t) =
 IN (c,
 t
 −
 1) +

u
 ∈
 SO(u,c)
FP1 (u,
 c,
 t) −
 SC(c,
 t)
−

u
 ∈
 FB(U)
FB (u,
 c,
 t) ∀c
 ∈
 CE,
 t
 
(13)
PEN (c,
 t) ≥
 IN (c,
 t) −
 SIL(c,
 t)∀c,
 t
(14)
−PEN (c,
 t) ≤
 IN (c,
 t) −
 SIL(c,
 t)∀c,
 t
 
(15)
IN (c,
 t) ≤
 INC(c,
 t)∀c,
 t
 
(16)
PC (c,
 t) +
 FPP (c,
 t) ≥

u
 ∈
 FB(u)
FB (u,
 c,
 t) ∀c
 ∈
 CF,
 t
(17)
FPP (c,
 t) =

u
FP1(u,
 methane,
 t)
+

u
FP1(u,
 propane,
 t)∀c
 ∈
 CF,
 u
 ∈
 US,
 t
 
(18)
Rc (r (c) ,
 f (u) , t) =
 CRF
 ∗
 *FOT (f (u) ,
 r,
 t) *Ec*Mpr∀f
 (u),
r(c),
 t
 
(19)
Ct(f (u) ,
 t)
 =
t

1

r
LCF*Rc (r(c),
 f (u) ,
 t) **B(u,
 r(c),
 t)
∀f
 (u),
 r(c),
 t
 
(20)
˛ (f
 (u),
 c,
 t) =
 ˛p (f
 (u),
 c) +
 CDF (r(c),
 f
 (u),
 c) *Ct (f (u) ,
 t)
+FOTF*FOT(f
 (u),
 r(c),
 t)∀f
 (u),
 r(c),
 c
 ∈
 FO(c)
 
(21)
FP1(f
 (u),
 c,
 t)
 =
 ˛p (r(c),
 f (u) ,
 c) *FC(f
 (u),
 r(c),
 t)∀f
 (u),
 r(c),
c
 ∈
 FO(c),
 t
 
(22)

r
FC (f
 (u),
 r(c),
 t) =
 FU(f  (u),
 t)∀f
 (u),
 r(c)
 ∈
 FI(c),
 t
 
(23)

c
˛p (r,
 f
 (u),
 c) ≤
 1∀f
 (u),
 r(c),
 c
 ∈
 FO(c)
 
(24)
B (u,
 r(c),
 t) *FU (u,
 r(c)) =
 FC(u,
 r(c),
 t)∀u
 ∈
 f
 (u),
 r(c),
 t
 
(25)

r(c)
B (f,  r(c),
 t) ≤
 1∀f
 (u),
 t
 
(26)
Z (u,
 t
 +
 1) ≥
 B (u,
 r(c),
 t
 +
 1) −
 B (u,
 r(c),
 t) ∀u
 ∈
 f
 (u),
 r(c),
 t
 
(27)
FP1 (u,
 c,
 t) =
  (u,
 c) *FU (u,
 t) +
 Cfp (u,
 c) *Tp2 (u,
 t)
+Fs
 ∀u
 ∈
 US,
 c
 ∈
 SO
 (u,
 t),
 t
 
(28)
FB (f,
 c,
 t) =
 FCCF*FC (f (u) ,
 r (c) ,
 t) +
 FCCFT*FOT (f (u) ,
 r (c) ,
 t)
+Cfd*RD (f
 (u),
 r(c),
 t) +
 Dfc(c)∀
 f
 (u),
 c
 ∈
 CF,
 t
 
(29)
Chemical
 Engineering
 Research
 and
 Design
 1
 5
 2
 (
 2
 0
 1
 9
 )
 85–94
 
91
2.3.
 
Interconnection
 section
 model
 —
 inventory
balance
 for
 ethylene
 production
 plant
This
 section
 covers
 the
 material
 balance
 of
 transferred
 mate-
rial
 from
 the
 reﬁnery
 to
 the
 ethylene
 plant
 and
 vice
 versa.
Eq.
 (30)
 shows
 the
 inventory
 balance
 of
 raw
 material
 to
the
 ethylene
 plant
 from
 the
 reﬁnery,
 while
 Eq.
 (31)
 demon-
strates
 the
 inventory
 balance
 of
 chemicals
 ﬂowing
 into
 the
ethylene
 plant
 from
 the
 oil
 reﬁnery
 as
 depicted
 in
 Fig.
 1.
 The
supply
 of
 fuel
 gas
 is
 presented
 in
 Eq.
 (32).
 This
 supply
 consists
of
 the
 amount
 purchased
 from
 the
 market,
 transported
 from
the
 reﬁnery
 and
 produced
 by
 the
 ethylene
 complex.
 Eq.
 (33)
denotes
 the
 relationship
 of
 ﬂow
 streams
 for
 fuel
 oil,
 cracked
gasoline
 and
 hydrogen.
IN (c,
 t) =
 IN (c,
 t
 −
 1) +
 MRE (c, t) +

u
 ∈
 SO(c)
FP1 (u,
 c,
 t) +
 SM (c,
 t)
−

f
 ∈ FI(c)
FC (f,
 c,
 t) ∀c
 ∈
 RE,
 f,
 t
 
(30)
IN (c,
 t) =
 IN (c,
 t
 −
 1) +
 MRE (c, t) +

u
 ∈
 SO(c)
FP1 (u,
 c,
 t)
−SC (c,
 t) ∀c
 ∈
 CHRE,
 t
 
(31)
PC (c,
 t) +
 MRE (c,
 t) +
 FPP(c,
 t)
 ≥

u
 ∈
 FB(u)
FB(u,
 c,
 t)∀c
 ∈
 CFGE,
 t
(32)
MER (c,
 t) =

u
 ∈
 SO(c)
FP1(u,
 c,
 t)
 ∀c
 ∈
 FGHER(C),
 t
 
(33)
2.4.
 
Interconnection
 section
 model
 —
 inventory
balance
 for
 oil
 reﬁnery
 plant
This
 section
 covers
 the
 material
 balance
 of
 transferred
 mate-
rial
 from
 the
 reﬁnery
 to
 the
 ethylene
 plant
 and
 vice
 versa.
The
 ﬁrst
 equation
 in
 this
 section
 represents
 the
 inven-
tory
 balance
 of
 products,
 including
 the
 portion
 that
 will
 be
transferred
 to
 the
 ethylene
 production
 plant.
 Eq.
 (35)
 is
 the
inventory
 balance
 of
 cracked
 gasoline
 ﬂowing
 back
 to
 the
 reﬁn-
ery
 as
 intermediate
 products
 from
 the
 ethylene
 plant
 whereas
Eq.
 (36)
 shows
 the
 inventory
 balance
 of
 fuel
 oil.
 The
 amount
 of
fuel
 oil
 in
 the
 inventory
 balance
 is
 comprised
 of
 the
 amount
produced
 in
 the
 reﬁnery
 plus
 the
 amount
 produced
 in
 the
ethylene
 plant,
 subtracting
 the
 amount
 sold.
 Similar
 to
 Eq.
 (36),
Eq.
 (37)
 represents
 the
 inventory
 balance
 for
 fuel
 gas.
 Eq.
 (38)
is
 the
 inventory
 balance
 for
 hydrogen
 that
 is
 mostly
 produced
in
 the
 ethylene
 production
 plant
 and
 utilised
 in
 the
 Hydro-
Treating
 Units
 of
 the
 reﬁnery.
 Eqs.
 (39–41)
 represent
 the
 mixing
relationship
 for
 ﬂow
 streams.
IN (c,
 t) =
 IN (c,
 t
 −
 1) +

u
 ∈
 UP

m
 ∈
 OMU(m)
FP (u,
 m,
 c,
 t)
−

u
 ∈
 UP

m
 ∈
 MU
FC1 (u,  m,
 c,
 t) −
 MER (c,
 t) ∀c
 ∈
 IRE(C),
 t
 
(34)
IN (c,
 t) =
 IN (c,
 t
 −
 1) +

u
 ∈
 UP

m
 ∈
 OMU(m)
FP (u,
 m,
 c,
 t)
−

u ∈
 UP

m
 ∈
 MU
FC1 (u,  m,
 c,
 t) +
 MER(c,
 t)∀c
 ∈
 IER(C),
 t
 
(35)
IN (c,
 t) =
 IN (c,
 t
 −
 1) +

u
 ∈
 UP

m
 ∈
 OMU(m)
FP (u,
 m,  c,
 t)
+MER (c,
 t) −
 SC(c,
 t)∀c
 ∈
 FORE,
 t
 
(36)
IN (c,
 t) =
 IN (c,
 t
 −
 1) +

u
 ∈
 BL(U)

m
 ∈
 OMU(m)
FP (u,
 m,
 c,
 t)
−MRE (c,
 t) −
 EDF (c,
 t) ∀c
 ∈
 FGRE,
 t
 
(37)
IN (c,
 t) =
 IN (c,
 t
 −
 1) +
 MER (c,  t)
−

u
 ∈
 UP

m
 ∈
 OMU(m)
FC1 (u,
 m,
 c,
 t) +
 PC (c,
 t) ∀c
 ∈
 HER(C),
 t
 (38)
MRE (lsrn,
 t) =
 MRE (naphtha,
 t) ∀t
 
(39)

u
 ∈
 UP

m
 ∈
 OMU(m)
FP (u,
 m,
 ethane,
 t) =
 MRE (ethane,
 t) ∀t
 
(40)

u
 ∈
 UP

m
 ∈
 OMU(m)
FP (u,
 m,
 propylene,
 t) =
 MRE (propylene,
 t) ∀t
 
(41)
2.5.
 
Objective
 function
 deﬁnition
The
 integrated
 model’s
 objective
 function
 aims
 to
 maximize
the
 proﬁt.
This
 function
 consists
 of
 the
 revenue
 of
 reﬁnery
 and
ethylene
 plant
 sales,
 the
 raw
 material
 cost
 for
 both
 plants,
inventory
 costs,
 equipment
 and
 maintenance
 costs.
 This
objective
 function
 is
 detailed
 in
 Eq.
 (42)
 where
 pri
 (c)
 repre-
sents
 the
 price
 of
 material
 and
 SC
 (c,
 t)
 the
 amount
 of
 material
sold.
 The
 second
 and
 third
 terms
 are
 the
 revenue
 of
 the
 ethy-
lene
 plant
 products
 and
 intermediates,
 respectively.
 The
 next
three
 terms
 correspond
 to
 the
 costs
 for
 raw
 material,
 inventory
and
 processing
 into
 account
 with
 PC
 (c,
 t)
 being
 the
 amount
of
 material
 purchased,
 IN
 (c,
 t)
 the
 material
 inventory,
 INC
 (c,
t)
 inventory
 cost,
 FR
 (u,
 t)
 the
 ﬂow
 rate
 and
 OPC
 (u)
 the
 price
of
 unit
 operation.
 The
 remaining
 terms
 show
 the
 cost
 for
 the
supply
 for
 the
 ethylene
 plant,
 penalty
 and
 material
 switching
costs
 as
 well
 as
 the
 cost
 of
 operating
 the
 separation
 columns.
Prof
 =

t

c
 ∈
 CP(C)
pri (c) *SC (c,
 t) +

t

c
 ∈ CE(C)
pri (c) *SC (c,
 t) +

t

c
 ∈
 CU(C)
pri (c) *SC (c,
 t) −

c
 ∈
 RR(C)

t
pri (c) *PC (c,
 t) −

t

c
 ∈
 CU(C)
IN (c,
 t) *IC (c) −

t

u
 ∈
 UP(U)
FU (u,
 t) *pi (u,
 t) −

c
 ∈
 RE(C)

t
pri (c) *SM (c,
 t) −

t

c
 ∈
 RE(C)
PEN (c) *IC (c) −

t

u ∈ US(U)
FU (u,
 t) *pi (u,
 t) −

t

f
Z (u,
 t) *SEC(f
 (u))
 
(42)
92
 
Chemical
 Engineering
 Research
 and
 Design
 1
 5
 2
 (
 2
 0
 1
 9
 )
 85–94
2.6.
 
Model
 implementation
This
 model
 has
 been
 implemented
 in
 GAMS
 version
 24.8.5
 for
optimisation.
 Each
 model
 has
 been
 implemented
 separately
for
 comparison
 and
 to
 also
 optimise
 the
 plants
 individually.
 All
three
 models
 were
 mathematically
 formulated
 while
 the
 inte-
grated
 version
 was
 formulated
 as
 an
 MINLP
 problem,
 solved
with
 the
 BARON
 solver
 (Tawarmalani
 and
 Sahinidis,
 2004)
 with
a
 compilation
 time
 of
 33.12
 s
 for
 the
 oil
 reﬁnery
 model,
 3.4
 min
for
 the
 ethylene
 plant
 and
 6.7
 min
 for
 the
 integration.
The
 compilation
 time
 is
 signiﬁcantly
 lower
 when
 compared
to
 other
 studies,
 mainly
 due
 to
 the
 simpliﬁcation
 of
 the
 model
leading
 to
 the
 attainment
 of
 results
 in
 short
 order.
 Below
 is
presented
 a
 graph
 demonstrating
 various
 papers
 and
 their
compilation
 time
 while
 modelling
 an
 oil
 reﬁnery
 and
 an
 ethy-
lene
 production
 plant.
As
 can
 be
 seen
 in
 Fig.
 2
 a
 series
 of
 papers
 have
 been
compared
 in
 terms
 of
 compilation
 times
 which
 includes
 both
pre-processing
 and
 optimization
 time
 for
 the
 modelling
 and
optimization
 of
 an
 oil
 reﬁnery
 and
 an
 ethylene
 production
plant
 separately,
 while
 the
 time
 for
 the
 model
 in
 this
 study
 has
also
 been
 marked
 amongst
 the
 previous
 studies.
 It
 is
 evident
that
 the
 compilation
 time
 for
 this
 paper
 is
 considered
 to
 be
 in
a
 good
 range,
 being
 among
 the
 studies
 that
 have
 been
 able
 to
conduct
 the
 simulation
 at
 impressively
 low
 times.
 However,
 it
must
 be
 noted
 that
 due
 to
 the
 complexities
 of
 each
 model
 the
time
 can
 differ
 which
 is
 not
 necessarily
 due
 to
 any
 shortcom-
ings
 of
 the
 prior
 research.
 Nonetheless,
 it
 is
 an
 advantage
 of
this
 research
 to
 be
 able
 to
 conduct
 the
 simulation
 in
 a
 rela-
tively
 short
 time
 when
 compared
 to
 other
 studies
 making
 this
simulation
 easier
 to
 conduct.
3.
 
Case
 study
The
 proposed
 model
 has
 been
 constructed
 in
 a
 way
 to
 cover
the
 full
 period
 of
 both
 plants
 however,
 in
 this
 case
 study,
 a
 spe-
cial
 case
 of
 integration
 has
 been
 considered
 while
 regarding
the
 model
 as
 a
 multi-period
 network.
This
 study
 will
 explore
 the
 integration
 of
 a
 conventional
UK
 oil
 reﬁnery
 and
 an
 ethylene
 production
 plant
 on
 a
 single
period
 aiming
 to
 maximise
 proﬁts
 of
 both
 plants
 as
 well
 as
the
 integration.
 The
 proposed
 mathematical
 model
 requires
 a
number
 of
 parameters
 such
 as
 price,
 capacity,
 properties,
 sup-
ply,
 demand,
 product
 speciﬁcations
 and
 process
 yield,
 which
are
 not
 calculated
 but
 deﬁned
 using
 various
 databases
 for
 UK
reﬁneries
 and
 petrochemical
 plants
 (BOC,
 2017;
 Deloitte,
 2012;
Oil
 and
 Gas
 Journal,
 2014).
 This
 model
 contains
 a
 total
 of
 42
equations
 with
 23
 variables
 while
 considering
 the
 demand
 of
material
 to
 be
 equal
 to
 the
 amount
 of
 material
 sold
 as
 well
as
 using
 only
 one
 type
 of
 crude
 oil
 for
 the
 oil
 reﬁnery
 sec-
tion,
 assuming
 the
 maximum
 capacity
 of
 200,000
 bbl/day
 in
the
 CDU
 with
 the
 decision
 variables
 of
 FP1
 (u,
 c,
 t),
 FPP
 (c,
 t)
 and
FU
 (u,
 t)
 being
 the
 products
 obtained
 from
 the
 Ethylene
 plant,
oil
 reﬁnery
 and
 the
 capacity
 of
 the
 units
 involved
 in
 this
 opti-
mization
 problem,
 respectively.
 Furthermore,
 the
 physical
 and
chemical
 properties
 of
 the
 materials
 and
 their
 intermediates
from
 the
 blending
 processes
 were
 considered.
The
 main
 units
 implemented
 in
 GAMS
 for
 the
 oil
 reﬁnery
were
 the
 CDU,
 FCC,
 CRU,
 DS
 and
 blenders
 having
 the
 prod-
ucts
 and
 intermediates
 of
 gasoline,
 diesel,
 kerosene,
 fuel
 oil,
fuel
 gas,
 ethane
 and
 propylene.
 Meanwhile,
 the
 main
 units
modelled
 for
 the
 ethylene
 production
 plant
 are
 the
 series
 of
furnaces
 and
 separation
 units.
 These
 units
 produce
 ethylene,
Table
 2
 –
 Amount
 of
 material
 produced
 in
 the
 oil
 reﬁnery
sent
 to
 the
 ethylene
 production
 plant-
 comparison
before
 and
 after
 integration.
Material
 
Amount-before
integration
 (t)
Amount-after
integration
 (t)
Ethane
 
15
 
10
Naphtha
 
20
 
30
AGO
 
345
 
345
HVGO
 
385
 
380
Propane
 
5
 
2
FG
 
20
 
29
Table
 3
 –
 The
 capacity
 of
 units
 chosen
 by
 the
 solver
 to
optimise
 the
 plant
 before
 and
 after
 integration.
Unit
 
Capacity-before
integration
 (t)
Capacity-after
integration
 (t)
CDU
 
200000
 
200000
FCC
 
80000
 
85,824
HDS
 
70000
 
85,824
CRU
 
82000
 
82,460
GB
 
200000
 
237,551
DB
 
200000
 
237,551
propylene,
 butadiene,
 benzene,
 C4 and
 C5.
 It
 must
 be
 noted
that
 due
 to
 one
 of
 the
 aims
 being
 the
 avoidance
 of
 model
 com-
plexity
 while
 also
 maintaining
 accuracy
 as
 much
 as
 possible,
there
 were
 various
 assumptions
 considered
 in
 this
 work.
 Apart
from
 the
 few
 mentioned
 in
 the
 previous
 paragraph,
 no
 utility
system
 such
 as
 cooling
 unit
 models,
 energy
 consumption
 and
generation
 of
 furnaces
 and
 compressors
 or
 boiler
 and
 turbine
models
 were
 considered.
 Various
 parameters
 were
 given
 ﬁxed
values
 or
 were
 calculated
 to
 produce
 a
 ﬁxed
 number
 rather
than
 having
 a
 range
 and
 lastly,
 only
 mass
 integration
 is
 being
investigated
 in
 this
 research.
To
 describe
 the
 decision
 variables,
 the
 feed
 required
 for
 the
ethylene
 plant
 is
 sourced
 from
 the
 reﬁnery
 products
 having
the
 values
 obtained
 from
 the
 optimisation
 of
 the
 oil
 reﬁnery
presented
 below
 while
 comparing
 their
 values
 prior
 and
 after
the
 integration.
 The
 products
 obtained
 in
 the
 ethylene
 produc-
tion
 plant
 are
 also
 demonstrated
 through
 comparison
 before
and
 after
 the
 integration.
 To
 investigate
 the
 impact
 of
 integra-
tion
 on
 the
 oil
 reﬁnery,
 the
 changes
 in
 the
 unit
 capacity
 of
 the
modelled
 oil
 reﬁnery
 units
 are
 also
 presented
 in
 Table
 3.
As
 seen
 in
 Table
 2,
 there
 is
 a
 slight
 increase
 in
 the
 pro-
duction
 of
 Naphtha
 and
 FG
 after
 the
 integration
 taking
 place.
This
 is
 due
 to
 the
 introduction
 of
 streams
 from
 the
 ethylene
production
 plant
 to
 the
 oil
 reﬁnery.
 For
 instance,
 introduc-
ing
 Hydrogen
 from
 the
 ethylene
 production
 plant
 entering
 the
CRU
 would
 result
 in
 the
 increase
 of
 Naphtha
 and
 FG
 produc-
tion
 that
 can
 be
 seen
 in
 the
 results
 of
 this
 integration.
 This
increase
 is
 beneﬁcial
 for
 the
 system
 as
 it
 can
 be
 used
 in
 the
cracking
 furnaces
 producing
 a
 cycle
 that
 produces
 more
 inter-
mediates
 for
 the
 oil
 reﬁnery
 while
 providing
 more
 feed
 for
 the
ethylene
 plant
 all
 over
 again.
 However,
 some
 of
 the
 materials
produced
 in
 the
 oil
 reﬁnery
 have
 decreased
 such
 as
 Ethane,
HVGO
 and
 propane.
 They
 are
 used
 in
 the
 ethylene
 production
plant,
 as
 feed
 for
 the
 cracking
 furnaces
 and
 product
 tanks
 in
the
 aforementioned
 plant.
 Due
 to
 the
 change
 in
 stream
 levels
after
 the
 integration,
 the
 capacity
 and
 ﬂows
 in
 the
 oil
 reﬁnery
units,
 which
 have
 been
 modelled,
 have
 also
 changed
 respec-
tively
 as
 noted
 in
 Table
 3.
The
 results
 demonstrated
 in
 Table
 4
 are
 indicative
 of
 the
changes
 occurring
 after
 integration
 regarding
 the
 ethylene
Chemical
 Engineering
 Research
 and
 Design
 1
 5
 2
 (
 2
 0
 1
 9
 )
 85–94
 
93
Fig.
 2
 –
 Comparison
 of
 compilation
 time
 regarding
 the
 modelling
 of
 an
 oil
 reﬁnery
 and
 an
 ethylene
 production
 plant
 with
previous
 studies
 (Shah
 et
 al.,
 2015)
 (Guyonnet
 et
 al.,
 2009)
 (Zhao
 et
 al.,
 2014)
 (Menezes
 et
 al.,
 2013)
 (Alattas
 et
 al.,
 2011)
 (Zhao
et
 al.,
 2016)
 (Díaz
 and
 Bandoni,
 1996)
 (Gubitoso
 and
 Pinto,
 2007)
 (Zhao
 et
 al.,
 2011)
 (Tjoa
 et
 al.,
 1997).
Table
 4
 –
 Amount
 of
 intermediate
 material
 produced
 in
the
 ethylene
 production
 plant-
 comparison
 before
 and
after
 integration.
Material
 
Amount-before
integration
 (t)
Amount-after
integration
 (t)
CG
 
300
 
370
FO
 
200
 
150
Hydrogen
 
50
 
50
Table
 5
 –
 Proﬁt
 of
 the
 oil
 reﬁnery,
 ethylene
 production
plant,
 and
 the
 proposed
 integration.
Plant
 
Proﬁt
 (million
 UK
 pounds)
Oil
 reﬁnery
 
1.28
Ethylene
 production
 
0.475
Integrated
 oil
 reﬁnery
and
 ethylene
production
 plant
13.46
production
 plant
 intermediates.
 There
 is
 an
 increase
 in
 the
production
 of
 CG,
 which
 is
 due
 to
 the
 increase
 of
 FG,
 Naph-
tha,
 AGO,
 HVGO
 and
 Ethane
 feed
 to
 the
 cracking
 furnaces.
This
 leads
 to
 the
 beneﬁcial
 use
 of
 CG
 in
 the
 GB
 units
 in
 the
oil
 reﬁnery.
 The
 levels
 of
 FO
 produced
 from
 this
 plant
 have
decreased,
 which
 can
 be
 explained
 through
 the
 fact
 that
 it
enters
 the
 oil
 reﬁnery
 and
 is
 blended
 in
 the
 product
 tank
that
 could
 be
 utilised
 for
 further
 processes.
 This
 highlights
the
 close
 and
 successful
 connection
 between
 the
 two
 plants
that
 has
 proven
 that
 the
 proposed
 model
 has
 in
 fact
 comple-
mented
 both
 plants
 as
 explained
 regarding
 Tables
 2–4.
 The
maximization
 of
 the
 proﬁt
 of
 each
 plant
 including
 the
 inte-
gration,
 the
 outcome
 is
 presented
 in
 Table
 5.
 As
 seen,
 there
is
 a
 signiﬁcant
 increase
 in
 the
 proﬁt
 after
 integration,
 which
demonstrates
 the
 relevance
 of
 the
 proposed
 plant
 integration.
This
 high
 increase
 is
 due
 to
 the
 interaction
 in
 both
 plants
 and
the
 use
 of
 intermediate
 streams
 for
 the
 beneﬁt
 of
 each
 plant,
and
 thus,
 aiming
 to
 replace
 the
 externally
 purchased
 feed.
Nevertheless,
 it
 must
 be
 noted
 that
 not
 taking
 utility
 costs
 into
consideration
 would
 be
 part
 of
 the
 substantial
 proﬁt
 increase.
A
 breakdown
 of
 the
 process
 costs
 for
 each
 plant,
 material
inventory
 cost,
 inventory
 capacity
 and
 material
 price
 utilised
in
 the
 models
 are
 presented
 in
 Tables
 6–8.
Table
 6
 –
 Operating
 costs
 used
 in
 the
 model
 for
 units
 in
the
 ethylene
 plant,
 oil
 reﬁnery
 and
 their
 integration.
Process
 unit
Operating
 cost
 ($/bbl
 feed)
CDU
 
0.15
CRU
0.6
FCC
 
0.65
HDS
 
0.65
GB
 
0.2
DB
 
0.2
Separation
 unit
 
0.3
Boiler
 
0.1
Table
 7
 –
 Inventory
 costs
 used
 in
 model
 for
 ethylene
plant,
 oil
 reﬁnery
 and
 their
 integration.
Material
 
Inventory
 cost
 ($/day)
Gasoline
 
1427.868
Diesel
 
255.95
FO
 
869.0475
Kerosene
 
91.66
Propylene
 
260.035
Crude
 oil
 
714257.8
FG
 
869.0475
Ethylene
 
190.255
C4
238.095
Butadiene
 
119.0475
C5
5714.28
Benzene
 
238.095
Hydrogen
62500
4.
 
Conclusion
In
 this
 work,
 a
 mixed-integer
 nonlinear
 programming
 model
has
 been
 proposed
 for
 the
 integration
 of
 an
 oil
 reﬁnery
 and
an
 ethylene
 production
 plant.
 Using
 data
 from
 a
 typical
 oil
reﬁnery
 and
 ethylene
 plant
 in
 the
 UK,
 the
 models
 were
 imple-
mented
 in
 GAMS
 and
 using
 different
 relaxation
 techniques
 to
tackle
 complexities
 and
 errors.
 Through
 our
 proposed
 model,
we
 were
 able
 to
 achieve
 less
 complexity
 while
 considering
accuracy
 to
 have
 the
 possibility
 of
 achieving
 the
 intended
outcome
 of
 higher
 proﬁt,
 operational
 efﬁciency
 and
 less
dependency
 on
 fossil
 fuel
 which
 is
 maintained
 through
 the
introduced
 concept
 of
 industrial
 symbiosis
 where
 both
 plants
beneﬁt
 through
 the
 connection
 having
 a
 reduced
 need
 to
 pur-
chase
 feed
 for
 one
 of
 the
 plants.
 This
 simpliﬁed
 approach
enables
 a
 smoother
 pathway
 for
 application,
 removing
 many
94
 
Chemical
 Engineering
 Research
 and
 Design
 1
 5
 2
 (
 2
 0
 1
 9
 )
 85–94
Table
 8
 –
 Material
 costs
 used
 in
 the
 model
 for
 the
ethylene
 plant,
 oil
 reﬁnery
 and
 their
 integration.
Material
 
Price
 ($/bbl)
Gasoline
 
55.18
Diesel
 
51.85
FO
 
12.88
Kerosene
 
83.33
Propylene
 
5602.9
Ethylene
 
150.8
C4
22.2
Butadiene
 
244.4
C5
12333.3
Benzene
 
99.28
Ethane
 
0.465
Naphtha
 
4058.8
AGO
10
Hydrogen
 
37.037
Crude
 oil
 
47
FG
 
13
obstacles
 that
 were
 present
 in
 previous
 works
 ensuring
 oper-
ational
 efﬁciency
 and
 a
 step
 towards
 an
 environmentally
friendly
 industry.
Each
 plant
 was
 optimised
 individually
 followed
 by
 their
integration
 which
 has
 presented
 valuable
 results
 in
 terms
 of
producing
 higher
 value
 products
 while
 maximising
 the
 proﬁt
for
 the
 plants
 individually.
 Moreover,
 after
 the
 integration
 of
the
 plants,
 there
 is
 a
 signiﬁcant
 proﬁt
 increase
 showing
 the
integration
 to
 be
 of
 considerable
 advantage
 while
 beneﬁting
from
 product
 availability,
 synergy
 from
 joint
 infrastructure
while
 dampening
 the
 inﬂuence
 of
 ﬂuctuations
 of
 feed
 and
product
 costs.
 The
 high
 increase
 in
 proﬁt
 is
 mainly
 due
 to
the
 use
 of
 intermediates
 from
 the
 oil
 reﬁnery
 as
 opposed
 to
purchasing
 them
 from
 an
 outside
 source.
 Not
 only
 does
 the
plant
 beneﬁt
 from
 reducing
 the
 external
 feed
 purchases,
 it
 also
beneﬁts
 from
 upgrading
 the
 quality
 of
 the
 ﬁnal
 products.
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 M.F.,
 Alsahhaf,
 T.A.,
 Elkilan,
 A.E.,
 2010.
 Fundamentals
 of
Petroleum
 Reﬁning,
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 ed.
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Lefﬂer,
 W.L.,
 2008.
 Petroleum
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 Global,
 2017.
 Hydrocracker-Ethylene
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 .
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 2017)
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 oil
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 Improvements
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 petroleum
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 process
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 petroleum
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 2011.
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 crude
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 in
 reﬁnery
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 optimization.
 Ind.
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 B.C.,
 Kelly,
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 Grossmann,
 I.E.,
 2013.
 Improved
swing-cut
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 planning
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 scheduling
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oil-reﬁnery
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18324–18333,
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Shah,
 N.K.,
 2015.
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 M.S.,
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 1996.
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Journal of Cleaner Production 413 (2023) 137447
Available online 16 May 2023
0959-6526/© 2023 Elsevier Ltd. All rights reserved.
Thermodynamic analysis and techno-economic assessment of fluid catalytic 
cracking unit in the oil refining process 
Masoud Nahvi a, Ahmad Dadvand Koohi a,*, Mehdi Sedighi b 
a Chemical Engineering Department, Engineering Faculty, University of Guilan, Rasht, Iran 
b Department of Chemical Engineering, University of Qom, Qom, Iran   
A R T I C L E  I N F O   
Handling Editor: Mingzhou Jin  
Keywords: 
Energy and exergy analysis 
Exergoeconomic analysis 
FCC 
Light olefins 
Oil refining 
A B S T R A C T   
Fluid catalytic cracking is an important, expensive, and energy-consuming unit that produces light olefins, gases, 
and naphtha from heavy residues. This study aims to increase light products and reduce heavy products at the 
FCC unit, simulated using Aspen HYSYS software. Exergoeconomic analysis using the Sankey diagram was 
performed for the first time for this unit, which provides the possibility of determining the important quantities 
in the process from a thermodynamic and thermo-economic perspective. Based on the results, the atmospheric 
tower consumes the most energy (about 64%) and is the most destructive unit which is responsible for 61% of 
exergy destruction. In terms of energy consumption, the FCC unit is ranked second with 16%. However, the FCC 
unit with 11% share is the third destructive unit, but an exergy efficiency of 98% was observed in this unit. A 
total of 267 MW of the tower’s exergy was destroyed, making it the second most destructive unit in the plant. 
Compared to other process units, the FCC unit accounts for 9% of the total cost, while the fractionator tower is 
the most expensive unit in the process at 48%. The Sankey chart shows crude oil has the highest cost stream at 
$744,145 per hour. The FCC unit has the highest exergoeocomic factor, while the cooler has the lowest exer­
goeconomic factor, with 2.4 M$/h cost of exergy destruction, as well as the lowest exergy efficiency.   
1. Introduction 
The growing demand for chemical products has increased interest in 
employing units that uniquely convert fuels (Altawell, 2020; David and 
Jones, 2006). Despite the concern about olefins in gasoline, light olefins 
are valuable products in the chemical and petrochemical industries 
(David and Jones, 2006; Ghannadzadeh and Sadeqzadeh, 2016). Heavy 
materials such as gas oil are converted in FCC units into lighter and more 
valuable products such as liquefied petroleum gas and gasoline (Ahmed 
and Ateya, 2016; Gary et al., 2007; Haydary and Pavlík, 2009; 
Sadeghbeigi, 2020; Sotelo et al., 2019). Many studies and research have 
been conducted to determine which parameters affect process efficiency 
and product growth, to present a feasible method that will reduce the 
energy cost for FCC, and to examine the impact of parameters on the 
process (Morar and Agachi, 2009; Nuhu et al., 2012; Rajeev et al., 2015). 
According to Yakubu et al. (John et al., 2017), gasoline, coke, and gas 
are produced more in the presence of a Cat/Oil ratio of 2–6.5 as the riser 
diameter increases from 1 m to 1.35 m and 1.6 m. According to Rajeev 
et al. (2015) second cracking occurs when the riser height exceeds 70 m, 
resulting in a reduction in naphtha production. Industrial plants such as 
refineries can contribute to long-term energy sustainability (Gao et al., 
2012; Hashemi et al., 2019; Leal-Navarro et al., 2019). Overall exergy 
efficiency, irreversibility, utility exergy, and stage-by-stage efficiency 
were calculated (Mestre-Escudero et al., 2020), mapping mercaptan 
oxidation units to determine the maximum exergy loss in the device. As 
a ratio of energy quantities, efficiency is often used to assess and 
compare various systems (Dincer and Rosen, 2021). To improve sys­
tems, it is reasonable to identify the aspects that can be improved. 
Exergy analysis can therefore be applied to assess the process efficiency 
and operating conditions (Gollangi and NagamalleswaraRao, 2022; 
Leal-Navarro et al., 2019). Exergy analysis, in particular, provides effi­
ciencies, which define how closely actual performance approaches the 
ideal and more clearly identifies thermodynamic losses than energy 
analysis. Exergy loss reduces the useful effect of the process or increases 
the consumption of its driving system (Kaushik and Singh, 2014; Szar­
gut, 2005). As such, exergy methods are often used by researchers for 
analyzing, assessing, designing, improving, and optimizing processes 
and systems (Dincer and Rosen, 2021; Yang et al., 2020). Hybrid systems 
using biomass as a fuel for both electricity and heat production have 
been comprehensively evaluated through multiple approaches such as 
* Corresponding author. Tel.: +98 1333690274; fax: +98 1333690271. 
E-mail address: dadvand@guilan.ac.ir (A. Dadvand Koohi).  
Contents lists available at ScienceDirect 
Journal of Cleaner Production 
journal homepage: www.elsevier.com/locate/jclepro 
https://doi.org/10.1016/j.jclepro.2023.137447 
Received 29 January 2023; Received in revised form 4 April 2023; Accepted 8 May 2023   
Journal of Cleaner Production 413 (2023) 137447
2
energy, exergy, exergoeconomics, and environmental analysis, with the 
gasifier being the most destructive unit (Wu et al., 2020). In a 
thermo-economic analysis of LPG units, Saadi et al. (2019) map the 
exergy destruction in column sections, identifying the most exergy 
destructive sections as the reboiler, condenser heat loss, and cooling 
section. An advanced exergoeconomic analysis was applied to a com­
bined energy system using the cryogenic energy of LNG using the 
Modified Production Structure Analysis (MOPSA) method, and both 
conventional and advanced exergy analyses yielded parabolic solar 
collectors were found to have the highest exergy destruction and cost 
rates (¨
Ozen and Koçak, 2022). For the synthesis of monochloroethane 
(MCM) from hydrochlorination of methanol, Gollani and Naga­
malleswaraRao (Gollangi and NagamalleswaraRao, 2022) proposed an 
integrated exergetic evaluation. (Chang et al., 2021; Martínez Gonz´
alez 
et al., 2019; ¨
OZDEM˙
IR and GENÇ, 2022; Sajedi et al., 2015). To intro­
duce the optimal energy supply design, the performance of the CCHP 
system is compared under two different operating scenarios. Finally, a 
parametric study is used to optimize the performance of the selected 
CCHP system using the optimal energy supply design (Norani and 
Deymi-Dashtebayaz, 2022). The impact of key operating parameters on 
exergy efficiency was investigated to determine the optimal operating 
conditions to achieve maximum exergy efficiency of integrated meth­
anol synthesis and methanol-olefin systems where the plant has the rate 
of highest exergy destruction and total cost rate (Detchusananard et al., 
2022). Pandey et al. used the 4 E (energy, exergy, exergoeconomics, and 
enviroeconomic) analytical approach to investigate major innovations, 
practical implications, and proper requirements of solar water heating 
systems with and without phase change materials (Pandey et al., 2021). 
Okereke et al. (2020) proposed a method to identify hidden eco­
nomic losses in mixer systems, suggesting changes in system operation 
and design as a means to reduce costs and increase profitability. As 
energy loss and destruction have a significant economic impact on 
processes, it is important to use a method that combines economics with 
other disciplines such as energy and exergy to determine the cost impact 
on equipment. This method is called exergoeconomics (Julio et al., 
2021; Mehrpooya et al., 2016; Nami et al., 2019; Zahedi et al., 2021). 
The effective removal of heavy hydrocarbons before liquefaction pro­
cesses introduced by a modified version of Galileo self-refrigerated to 
study the economical removal of C5
+ components has attracted the in­
terest of researchers (Yousefikhanghah et al., 2021). To determine each 
piece of equipment’s cost, an economic assessment is required before 
performing an exergoeconomic analysis (He et al., 2020). Hashemi et al. 
(2019) conducted an energy assessment using exergoeconomics and 
sensitivity analysis to increase the efficiency of the sulfur recovery unit, 
but the reaction furnace was the most destructive component exergy, 
separators and mixers had the least exergy destruction. 
Exergy loss per total capital investment was calculated for each type 
of industrial heat pump using exergoeconomic criteria (Wang et al., 
2020). The economic feasibility and exergoeconomic concept of alter­
native hydrogen production were examined by tracing the flow of 
exergy through the system and pricing both waste and products 
(AlZahrani and Dincer, 2021). Noorpoor and Mazare (2018) evaluated 
the thermodynamics and thermoeconomics of installing a combustion 
air preheater on a fired heater. Various CO2 capture and utilization 
mechanisms are investigated to determine which components have 
higher exergy destruction and investment costs to offer potential stra­
tegies for enhancing the processes, and to enhance an understanding of 
the cost formation of the inventive system based on technical and eco­
nomic feasibility (He et al., 2020). Energy analysis reveals destructive 
sections and energy losses throughout the process (John et al., 2019; 
Mehrpooya and Ansarinasab, 2015). By improving energy, efficiency, 
and exergy, other parameters such as exergoeconomics and cost balance 
were also improved. 
The paper presents an energy analysis of a conceptually designed 
FCC unit that is simulated using Aspen HYSYS (V11). To study the effects 
of exergy destruction on plant exergy efficiency, an exergy analysis was 
conducted. The chemical exergy of the FCC unit was also calculated. The 
formation of exergy and cost of the process is shown for the first time 
through Sankey diagrams to evaluate the process from an efficiency and 
economic standpoint. Atmospheric gas oil (AGO) and naphtha are also 
added to the feed of FCC units to change the molecular weight of the 
feed. As a cracking reaction occurred in the FCC unit, chemical exergy 
was calculated and incorporated. In addition, various factors have been 
studied regarding energy loss and exergy destruction of equipment. 
Moreover, light hydrocarbons used for the production of light olefins are 
produced in the extraction zone of oil refineries. The fractionator col­
umn was also simulated separately to split different types of products. 
For the exergetic analysis of the FCC unit, the compression, cracking, 
separation, and refrigeration sections of a refining plant were studied. A 
thermodynamic and economic analysis is performed to identify poten­
tial improvements for energy-intensive systems based on insight into the 
overall plant’s performance. Lastly, this work represents the most 
comprehensive energy and exergy-based analysis of the FCC unit in oil 
refining. 
2. Process description 
2.1. Process simulation 
A conceptual integrated FCC plant, shown in Fig. 1, was designed 
and simulated using Aspen HYSYS software. To obtain the final product, 
extraction, and fractionation are used. Enthalpy, mass exergy, temper­
ature, pressure, entropy, and mass flow of the designed FCC process are 
shown in Table 1. The following assumptions and simplifications were 
used for the plant simulation and analysis:  
1. Plant operates at steady-state,  
2. Throughout the process, heat loss and pressure drop are neglected.  
3. Potential and kinetic energies are not taken into account. 
2.1.1. Zone 1 – extraction phase 
Feeder provides Iranian heavy oil crude as petroleum feed for the 
process. Laboratory and pilot plant data are compiled in crude oil assays 
to determine the properties of the crude oil (David and Jones, 2006). In 
the feeder box (feeder), the inlet feed temperature is increased to 315◦C. 
Vapor Flash (S2 in the vapor phase) is an intermediate product that is 
sent to the upper section of the atmospheric column after entering a flash 
drum at 200 kPa absolute pressure and 315◦C temperature (T-101). 
Along with the high-pressure steam (atmospheric steam) injected into 
the bottom trays of the column (T-101), liquid (liquid phase) is fed to the 
bottom trays of the column (T-101). Water and naphtha gas are 
extracted from the top section of the column, which operates at 148 kPa. 
After mixing with other components, the temperature of the output 
naphtha is reduced to around 43.7 ◦C in the following stages of the 
process. Strippers are also injected with atmospheric gas oil steam (S5) 
and diesel steam (S4). Kerosene side streams are boiled up in (Reb) for 
further purification and use. Products that did not separate in the col­
umn, such as diesel, kerosene, and AGO, are separated in the stripper 
section. Three pumps around (PAs) were added to restore mass balance 
in the entire column, where the side draw is then recycled back into the 
main tower. A portion of the circulating quench draw is cooled in the 
PA-2 cooler, while the remainder is cooled in series with the PA-3 cooler 
(PA-1 and PA-2). Because of its prominent position and to ensure the 
maximum quantity of valuable product, the Naphtha1 (S8) stream (S20 
and S21, respectively) is separated by TEE-101. 
2.1.2. Zone 2 – extraction phase and cracking zone 
The residual liquid of the atmospheric column (S12) is transmitted to 
the vacuum tower after passing through TEE-100. In addition, a vacuum 
steam (S15) system has been added to the tower to improve trans­
forming and transferring through trays. The vacuum tower has 16 trays 
M. Nahvi et al.                                                                                                                                                                                                                                  
Journal of Cleaner Production 413 (2023) 137447
3
and a pump at the top. A hydrocarbon mixture (heavy vacuum gas oil 
(HVGO), vacuum residue (VR), AGO, Naphtha) exits the mixer (S22), 
enters the cooler, and exits the cooler as feed for the FCC section. The 
output stream (S22) from the mixer (MIX-100) is cooled to 65 ◦C after 
passing through a cooler (E-100). In the following section, cooled heavy 
hydrocarbons are thermally cracked and converted in stages. 
A riser reactor with a diameter of 1 m and a height of 70 m maximizes 
the thermal cracking process (Rajeev et al., 2015). There is just as much 
importance to the specification design of this unit as it is to the main 
fractionator tower. Zeolites for FCC applications are Type X, Type Y, and 
ZSM-5. The pore size of ZSM-5 is smaller than that of Y zeolite, and the 
pore arrangement of ZSM-5 is different from Y zeolite. Furthermore, the 
shape selectivity of ZSM-5 allows it to preferentially crack long-chain 
low-octane normal paraffins and some olefins in gasoline fractions 
(Sadeghbeigi, 2020). Therefore, ZSM-5 was used as a catalyst due to its 
suitable conversion properties. ZSM-5 is a versatile zeolite that enhances 
olefin yield and octane rating. 
2.1.3. Zone 3 – extraction phase 
Along with fractionator steam, cracked hydrocarbon from the FCC 
unit is fed into the bottom section of the fractionator column (T-103). In 
addition, rich oil and steam are injected into the column. Cracked gases 
and other overhead vapors are partially condensed in the condenser and 
sent to the light olefins splitter (X-100) via a mixer. The top section of 
the column cools and recycles naphtha and light cycle oil (LCO) draws. 
Gases from the upper part of the fractionator column are recirculated to 
the condenser at 105 ◦C. Rich oil was imported to the tower to increase 
the amount of LCO and heavy cycle oil (HCO). After being sent to a 
splitter, the mixture of naphtha and gases (S34) increases the number of 
valuable products for facilitating and other uses. 
3. Process analysis 
3.1. Energy analysis 
An energy interaction for a system may be divided into two parts: 
heat (Q) and work (W). The energy balance for a control mass can be 
written as follows using the first law of thermodynamics (FLT) (Dincer 
and Rosen, 2021): 
δQ = δE + δW
(1) 
Eq. (1) can be integrated from an initial state (in) into a final state 
(out) to yield: 
Qin−out = Eout −Ein + Win−Out
(2) 
Energy efficiency (ηE) can be calculated by applying the energy 
balance equation to each piece of equipment and calculating the total 
energy consumed (Dincer and Rosen, 2021): 
ηE =
∑Eout,i
∑Ein,i
× 100
(3)  
3.2. Exergy analysis 
Exergy analysis necessitates a thorough understanding of the envi­
ronment and dead state used in these calculations. The dead state con­
dition is typically determined by temperature, pressure, and chemical 
composition (Zhu et al., 2018). The reference temperature (T0) and 
reference pressure (P0) values are 298 K/25 ◦C & 101.325 kPa/1 bar 
(Gollangi and NagamalleswaraRao, 2022). Heat flow, energy, exergy 
efficiency, and exergy destruction (due to irreversible entropy genera­
tion within the system) can be predicted using mass, energy, and exergy 
balance equations. As a result, the exergy balance equation is as follows 
(Dinçer and Zamfirescu, 2016): 
Exin −Exout = Exloss
(4)  
∑
Exin = Exsys +
∑
Exout + Id
(5) 
Exin represents the inlet exergy, Exout is the outlet exergy and Id is the 
amount of exergy destruction. Exergy can also be divided into several 
components, such as potential, kinetic, physical, and chemical exergy. 
Many industrial processes in practice may overlook kinetic and potential 
exergy. As a result, a matter stream’s total molar exergy can be 
expressed as follows: 
Ex = Exphy. + Exche. + ExPotential + Exkinetic
(6)  
Ex = Exphy. + Exche.
(7) 
The molar physical exergy (Exphy.) of the stream represents the 
maximum amount of work that can be obtained by reversibly trans­
ferring 1 mol of the stream from its operating state (T and P) to the 
determined temperature (T0) and pressure (P0) state, which is known as 
the environment reference conditions presented below. 
Exphy. = m × exphy.
(8) 
exphy. is specific physical exergy of material streams, which can be 
Fig. 1. PFD of conceptually simulated FCC unit and fractionator column in the oil refining process.  
M. Nahvi et al.                                                                                                                                                                                                                                  
Journal of Cleaner Production 413 (2023) 137447
4
obtained as follows: 
exphy. = (h −h0) −T0(s −s0)
(9) 
The molar chemical exergy of a fluid (Exche.) is the amount of work 
that can be generated by 1 mol of fluid under reference conditions 
(Abdollahi-Demneh et al., 2011). Chemical exergy is thus the result of 
unrestricted (thermomechanical and chemical) equilibrium with the 
environment. Szargut proposed a mathematical model for chemical 
exergy in a material stream (Szargut, 2005): 
Exche. = ΔG0
f +
∑
niexch,i
(10) 
Exche. is the total chemical exergy of a compound, ΔG0
f is the standard 
Gibbs energy for the formation of the substance and can be calculated as 
follows: 
ΔG0
f = ΔGR −ΔGP
(11) 
ΔGR and ΔGP are the standard Gibbs energy of the reactant and 
product respectively. (ni), is the number of atoms in element (i), and 
(exch,i) is the standard chemical exergy of each elemental compound 
which can be obtained as below (Dincer and Rosen, 2021): 
niexch,i =
(
ni,Rexch,i,R −ni,Pexch,i P
)
(12) 
ni,Rexch,i,R and ni,Pexch,i P is the standard chemical exergy of reactant 
and product elements. The chemical exergy of all substances and ma­
terial streams involved in this process is summarized in Table 1s. Heat 
loss is the sole cause of exergy loss in a component. When Ti is the 
component’s temperature and T0 is the reference temperature, the 
exergy loss is the sum of the heat loss and the component’s exergy, 
resulting in destruction. As a result, the associated exergy of heat and 
exergy rate balance for component k in the system can be expressed as 
Eqs. (13) and (14). 
Exheat =
(
1 −T0
Ti
)
Q
(13)  
Exin −Exout + Exheat = Exloss
(14) 
When chemical and physical exergy are added to total exergy, the 
total exergy efficiency is expressed as follows (Gollangi and Naga­
malleswaraRao, 2022): 
ψEx =
∑Exout,i
∑Exin,i
× 100
(15)  
3.3. Economic analysis 
Thermodynamics and economic concepts are combined in thermo- 
economics to evaluate and optimize energy systems (Okereke et al., 
2020). The cost index, for example, is used to determine the cost of 
equipment so that it can be converted into the required daily costs. The 
Chemical Engineering Plant Cost Index (CEPCI), used in this study and 
also mentioned by Hashemi et al. (2019) is defined as follows. 
PECreference = PECorigin.
(CEPCIreference
CEPCIorigin
)
(16) 
The PEC is the cost of purchasing equipment. The constant-escalation 
levelized factor (CELF) and capital recovery factor are two economic 
analysis parameters: 
CRF = i × (1 + i)n
(1 + i)n −1
(17)  
CELF = k(1 + kn)CRF
(1 −K)
(18)  
Table 1 
Thermodynamic properties of the process stream.  
Stream 
Fluid 
T 
(◦C) 
P 
(KPa) 
Mass flow 
(Kg/h) 
Specific 
enthalpy (KJ/ 
Kg) 
Specific 
entropy (KJ/ 
Kg-◦C) 
Mass exergy 
(KJ/Kg) 
Stream 
Fluid 
T 
(◦C) 
P 
(KPa) 
Mass flow 
(Kg/h) 
Specific 
enthalpy (KJ/ 
Kg) 
Specific 
entropy (KJ/ 
Kg-◦C) 
Mass exergy 
(KJ/Kg) 
S1 
Crude feed 
315 
200 
904,443 
−1380 
3.63 
250.2 
S19 
Vacuum 
Residue 
280 
7.56 
4,058,120 
−1571 
3.34 
163.2 
S2 
Vapor Flash 
315 
200 
366,965 
−1246 
3.77 
301.7 
S20 
N 1 
43.7 
148 
179,005 
−2188. 
1.46 
1.23 
S3 
Liquid Flash 
315 
200 
537,478 
−1471 
3.53 
210.3 
S21 
N 2 
43.7 
148 
26,344 
−2188 
1.46 
1.23 
S4 
Diesel Steam 
380 
1034 
3887 
−12,702 
10.07 
1026 
S22 
To Cooling 
178 
7.24 
634,149 
−1750 
3 
67.7 
S5 
AGO Steam 
321 
200 
2300 
−12,808 
10.07 
747 
S23 
FCC Feed 
65 
7 
634,149 
−2020 
2.29 
3.19 
S6 
Atmospheric 
Stream 
380 
1034 
29,939 
−12,702 
10.07 
1026 
S24 
Fractionator 
Feed 
540 
340 
614,494 
−842.4 
4.87 
656 
S7 
Water 
43.7 
148 
205,350 
−15,842 
3 
2.50 
S25 
Rich Oil 
250 
500 
51,712 
−1628 
3.23 
139 
S8 
Naphtha 1 
43.7 
148 
205,350 
−2188 
1.46 
1.23 
S26 
LCO Steam 
240 
1300 
900 
−13,010 
9.50 
912 
S9 
Kerosene 
203 
174 
13,778 
−1757 
2.70 
90.01 
S27 
Fractionator 
Steam 
240 
1200 
1531 
−13,010 
9.50 
903 
S10 
Diesel 
209 
178 
122,592 
−1745 
3 
93.71 
S28 
Gases 
105 
255 
346,912 
−2199 
3.76 
83.7 
S11 
AGO 
250 
181 
49,160 
−1635 
3.24 
134.8 
S29 
Naphtha 2 
105 
255 
103,493 
−1993 
1.41 
19 
S12 
Atmospheric 
Residue 
302 
186 
513,819 
−1512 
3.47 
191.6 
S30 
LCO 
265 
276 
44,000 
−1655 
2.56 
143 
S13 
Residue 1 
302 
186 
293,787 
−1512 
3.47 
191.6 
S31 
HCO 
284 
276 
158233 
−1589 
2.74 
167 
S14 
Residue 2 
302 
186 
220,032 
−1512 
3.47 
191.6 
S32 
Heavy 
Naphtha 
447 
288 
10000 
−1294 
2.34 
361 
S15 
Vacuum Steam 
232 
1103 
5000 
−13010 
4.28 
886.5 
S33 
Slurry Oil 
493 
301 
6000 
−1189 
2.50 
431 
S16 
Over flash 
283 
6.61 
112,755 
−1842 
4.28 
261 
S34 
To Separating 
135 
255 
450,406 
−2152 
3.22 
70.4 
S17 
LVGO 
289 
7 
79 
−1548 
3.40 
173.4 
S35 
Light Olefins 
142 
6.61 
57,170 
651.3 
4.20 
−123.1 
S18 
HVGO 
288 
7 
164.5 
−1546 
3.40 
174.2 
S36 
Bottoms 
142 
6.61 
393,237 
−2391 
4.04 
−39.41  
M. Nahvi et al.                                                                                                                                                                                                                                  
Journal of Cleaner Production 413 (2023) 137447
5
K = 1 + rn
1 + ieff
(19) 
ieff is the annual effective interest rate, (n) is the plant’s economic 
lifetime and (rn) is the nominal escalation rate. The following equations 
can be used to calculate levelized annual carrying charges, which are the 
costs of holding a security or physical commodity for a period of time 
and include insurance, storage, interest, and other incidental costs, as 
well as levelized operating and maintenance costs for the unit’s lifetime 
(He et al., 2020; Wang et al., 2014): 
CCl = IC × CRF
(20)  
OMCl = φ ×
∑
ICk × CELF
(21) 
IC is the installation cost, φ is the maintenance factor. Aspen Eco­
nomic Analyzer (APEA) computes component IC and PEC and displays 
them in Table 2S. The feature includes design procedures as well as cost 
data for various materials. The cost balance equation states the 
following for all (kth) components (Hashemi et al., 2019): 
∑
p(out)
˙
Cp(out),k +
˙
Cw,k = ˙
Cq,k +
∑
F(in)
˙
˙
CF(in),k + ˙
Zk
(22)  
˙
CF(in) = ˙
cF(in) ˙
ExF(in) = ˙
cF(in)
(
˙
mF(in)exF(in)
)
(23)  
˙
Cp(out) = ˙
cp(out) ˙
Exp(out) = ˙
cp(out)
(
˙
mp(out)exp(out)
)
(24)  
˙
Cw = ˙
cw ˙
Exw = ˙
cwW
(25)  
˙
Cq = ˙
cq ˙
Exq
(26) 
˙
cF(in), ˙
cp(out), ˙
cw, ˙
cq denote average costs per unit of exergy, ˙
Zk is the 
cost rate of capital investment, operating, and maintenance. Cost terms 
can also be defined as follows (Hamedi et al., 2022; Hashemi et al., 2019; 
Nami et al., 2019): 
Zk = ZCI
k + ZOM
k
(29)  
ZCI
k =
CCl
3600 × N ×
PEC
∑PEC
(30)  
ZOM
k
=
OMCl
3600 × N ×
PEC
∑PEC
(31) 
N is the annual operating hours, ZCI
k is capital investment, ZOM
k 
is 
maintenance and operating cost, respectively. In addition to investi­
gating exergoeconomic performance, two indexes for innovative sys­
tems are introduced: exergoeconomic factor (fk) and relative cost 
difference (rk). The following is how the exergy destruction cost rate and 
exergy economic factor are calculated: 
fk =
˙
Zk
˙
Zk +
˙
CD,k
(32)  
rk = cp,k −cF,
cF,k
(33)  
˙
CD,k = ˙
cF,k ˙
ExD,k
(34) 
The costs of energy destruction are not directly accounted for in the 
equipment cost balance. The exergy and exergoeconomic balances of 
each piece of equipment are added together to calculate this parameter 
(Hashemi et al., 2019). The main parameters and assumptions used to 
calculate the economic indicators are shown in Table 2. 
4. Results and discussion 
4.1. Energy and exergy evaluation 
Table 3 displays the chemical and physical exergies calculated by 
adding the ones from each compound involved in each stream, as well as 
the system’s total exergy for each current. According to the study, the 
first extraction section accounted for the majority of irreversibilities in 
the plant, accounting for approximately 61% of the total exergy 
destruction of 812 MW. In terms of exergy, the third extraction zone 
(with a share of 23%) and the cracking section (with a share of 15%) are 
the two most destructive sections of the plant. The two most important 
phenomena that destroy exergy are chemical reactions and heat trans­
fer. At higher temperatures, heat transfer destroys more exergy. Ac­
cording to Hamedi et al. (2022), in chemical plants, thermodynamic 
irreversibility accounts for 70–80% of exergy destruction and separation 
processes account for 5–15%. 
As a result, this statement is supported in that the sections devoted to 
separation processes, such as the flash drum, mixers (MIX-100 & 101), 
and tees (TEE-100 & 101), as well as the cooling and splitting system 
associated with them, contribute only 2–4% of the plant’s overall exergy 
destruction. The most energy is lost as a result of various chemical re­
actions in the extraction and cracking section. According to Leites et al. 
(2003), the priority for reducing exergy destruction should be to reduce 
the thermodynamic irreversibility of chemical reactions. Fig. 2 depicts 
the exergy destruction and component efficiency in various plant zones. 
The atmospheric column (T-101) is responsible for the greatest loss of 
exergy in the first extraction zone. 
The flash drum (V-100), which destroys 700 kW of exergy, is the next 
component in this zone. The exergy efficiency of the atmospheric col­
umn is 45.5%, as shown in Fig. 2, with the inefficiency due to chemical 
and heat losses. Furthermore, the low exergy destruction of V-100 
resulted in a 99% efficiency. It has been demonstrated that, while some 
Table 2 
Parameters for investment analysis.  
Parameters 
Unit 
Value 
CRF 
% 
0.0936 
Maintenance Factor 
– 
6 
The annual rate of changes 
– 
0.9722 
Oil price 
$ 
115 
CEPCI 
% 
1.6575 
Interest rate 
% 
8 
Hours of operation per year 
Hour 
6000 
Project lifespan (Economic lifetime) 
Years 
25 
Nominal escalation rate 
% 
5  
Table 3 
Exergy of main streams.  
Stream 
Chemical 
Exergy (kW) 
Physical 
Exergy 
(kW) 
Stream 
Chemical 
Exergy (kW) 
Physical 
Exergy 
(kW) 
S1 
– 
62,862 
S19 
– 
18404 
S2 
– 
30,755 
S20 
– 
61.3 
S3 
– 
31,408 
S21 
– 
9.03 
S4 
– 
1108 
S22 
– 
11931 
S5 
– 
477 
S23 
7,848,879 
563 
S6 
– 
8539 
S24 
7,781,717 
111,968 
S7 
– 
143 
S25 
– 
1998 
S8 
– 
70.3 
S26 
– 
228 
S9 
– 
344 
S27 
– 
384 
S10 
– 
3191 
S28 
– 
8070 
S11 
– 
1841 
S29 
– 
543 
S12 
– 
27,348 
S30 
– 
1748 
S13 
– 
15,637 
S31 
– 
7353 
S14 
– 
11,711 
S32 
– 
1003 
S15 
– 
1231 
S33 
– 
719 
S16 
– 
8176 
S34 
– 
8812 
S17 
– 
3.80 
S35 
– 
−1956 
S18 
– 
7.96 
S36 
– 
−4305  
M. Nahvi et al.                                                                                                                                                                                                                                  
Journal of Cleaner Production 413 (2023) 137447
6
physical exergy is lost between the inlet and outlet streams of V-100, the 
chemical exergy between the inlet and outlet streams of the drums 
compensates for the loss of physical exergy due to separation phenom­
ena (Hamedi et al., 2022). A breakdown of exergy destruction within the 
extraction zone revealed that the T-101 column accounted for 98% of 
the first extraction zone’s exergy destruction. Fig. 3 depicts the input 
and output enthalpies as well as the energy consumption of the process 
equipment (Gollangi and NagamalleswaraRao, 2022). This unit’s 
refrigeration and condensing system uses energy to separate valuable 
products from upper trays. 
When compared to other components, the T-101 column emits more 
energy into the surrounding environment (Fig. 3). The separator (V-100) 
contributes approximately 16% input energy and 10% output to this 
zone, resulting in lower energy consumption than other components due 
to the high heating value of the input stream in the separator as a result 
of the feed streams. It is also comparable to the results of Gollani and 
NagamalleswaraRao in terms of exergy balance (Gollangi and Naga­
malleswaraRao, 2022). The amount of exergy involved with each sub­
unit of oil refining production zones is depicted in Fig. 4. The additional 
energy consumed in the extraction zone to improve separation is largely 
responsible for the T-101 exergy destruction. The majority of the exergy 
in the T-101 subunit is destroyed in the condenser, where endothermic 
reactions destroy 42% of the total exergy. 
A higher reaction temperature (145–300 ◦C (Dincer and Rosen, 
2021)) and more feedstocks involved make the heat required in this 
process more significant than in other zones. Because of the energy 
needed for condensing, this unit is the second most energy-intensive in 
the process. Therefore, its condenser is responsible for 69% of the exergy 
destruction in the atmospheric column. However, more heat input leads 
to more irreversible loss, as the condenser temperature varies greatly 
(about 102 ◦C) during condensing. Exergy loss in the main tower is 
significant as a result of mole separation in trays, which can be attrib­
uted to chemical losses, and heat waste during separation is another 
source of exergy destruction. Furthermore, there is a significant amount 
of net liquid and vapor flow in tower trays, resulting in physical and 
chemical exergy losses. 
Due to differences in the flow, molecular weight, and heat capacity of 
the inlet streams from TEE-100 and T-101, as well as the pressure drop of 
the product streams from T-102, vacuum towers, T-102, and MIX-100 
are jointly responsible for exergy destruction in zone 2. T-102 has 
high exergy efficiencies and low total exergy destructions due to the 
same amount of chemical exergy flowing in and out of this column. The 
Fig. 2. Exergy efficiency and exergy destruction of components in the process.  
Fig. 3. Variation of enthaplies and energy consumption of components in the process.  
M. Nahvi et al.                                                                                                                                                                                                                                  
Journal of Cleaner Production 413 (2023) 137447
7
T-102 tower is responsible for 86.3% of the exergy destruction in the 
second zone. Exergy destructions in T-102 are caused by increasing the 
temperature and pressure of the high-pressure and temperature steam 
streams (HP, respectively) to better separate, convert, and condense 
different streams at different phases and temperatures. T-102 and TEE- 
101, however, have exergy efficiencies of 93% and 99%, respectively. 
The mixing of two streams with different temperatures and chemical 
compositions destroys a significant amount of exergy in the MIX-100 
(FCC feed dilution mixer). MIX-100 has the lowest exergy efficiency 
value (58.7%) in zone 2. Fig. 3 illustrate that the vacuum tower (T-102), 
is an efficient component compared to other towers because it does not 
have a condenser and reboiler attached to it. This unit consumes about 
912 kW of energy, which is not significantly higher than other units 
(Morar and Agachi, 2009; Nuhu et al., 2012). 
The feed pressure is low as a result of passing through the vacuum 
section and the difference in input and output pressures of the FCC unit, 
resulting in negative exergy. There is also a temperature and molecular 
weight difference between the input and output flow to the unit, 
resulting in a significant difference in input and output exergy. The input 
exergy is low due to the low pressure and temperature of the unit feed 
flow. Because of its high molecular weight, the feed is also heavy. The 
feed becomes lighter and its molecular weight decreases as the tem­
perature and pressure of heavy materials in the FCC unit rise. Because 
the output exergy exceeds the input exergy, the difference between the 
two is negative. 
To avoid undesired reactions that reduce the yield of gases and light 
products, the reactor plenum temperature (riser plug reactor) must be 
controlled to reduce the temperature of the cracked hydrocarbon from 
entry to exit. As a result, the FCC unit is responsible for 71% of the 
exergy destruction in the cracking section. The thermal component of 
physical exergy is reduced by lowering the temperature difference be­
tween the outlet stream and the reference environment. A negative 
physical exergy exists due to the lower outlet pressure than the reference 
environment pressure. The feed pressure falls below the ambient pres­
sure as it passes through the cooler and vacuum tower, which is 
approximately 7 kPa. Furthermore, differences in outlet and inlet tem­
peratures, as well as molecular weight streams, contribute to the unit’s 
negative physical exergy. As previously stated, this negative exergy is 
irrelevant because the reference and operating environments are not 
identical (Rosen, 2009). The overall exergy of the unit in Fig. 2 remains 
positive because the thermal component of the physical exergy is greater 
than the fictitious negative pressure component. This unit destroyed 
67.2 MW of exergy due to chemical exergy, as shown in Fig. 2. 
Furthermore, the pressure inside the reactor should be low since higher 
pressures promote undesired reactions. 
A cooler, E-100, was provided to the hydrocarbon feed stream to 
preserve the partial pressure of hydrocarbons low while also maintain­
ing feed temperature. Excess feed temperature increases feed conversion 
but also results in increased exergy destruction of the cracking section 
(Nuhu et al., 2012), Due to heat and physical losses, E-100 contributes 
59 MW to total exergy destruction. The FCC unit efficiency was 98%, 
which is favorable for this unit, but the cooler with 5% efficiency has the 
lowest efficiency in the plant because both units operate across T0 
(Dincer and Rosen, 2021). The values of exergy efficiency are almost 
identical to those reported by Nuhu et al. and Hamedi et al. (Hamedi 
et al., 2022; Nuhu et al., 2012). Exothermic combustion reactions within 
the reactor in the riser characterize the cracking section of the FCC unit. 
Inside the riser, the exergy is destroyed for a variety of reasons during a 
cracking section. Finally, in most energy-intensive processes, such as 
refining, the cracking reactions release energy that can be used, as 
shown in Fig. 3, which consumes 16% of the energy. E-100 consumes 4% 
of total energy due to the heat capacity of the flow mixture and heat loss 
during the refrigeration process. According to a variety of studies about 
combustion and cracking, heat transfer, mass transfer, and chemical 
reaction are responsible for most of the exergy losses (Gollangi and 
NagamalleswaraRao, 2022; Hamedi et al., 2022). 
The fractionator column causes approximately 267 MW of exergy 
loss in the third extraction zone, making it the most destructive 
component in this zone. T-103 destroys exergy due to nonuniform 
driving forces of heat and mass transfer due to the mixing of streams 
with different pressures, temperatures, and chemical compositions, as 
well as direct heat exchange between them in the column. By increasing 
the number or volume of packings in extraction columns, equilibrium 
can be approached at some points, thereby reducing exergy destruction 
during the absorption process. Furthermore, the X-100 and MIX-101 
destroy about 11% of the total exergy of the fuel in the zone. Accord­
ing to Fig. 2, X-100 has a high exergy efficiency of 71%, while T-103 has 
a low exergy efficiency of 16%. According to Table 3, the physical 
exergy of cracked gas, which is used to define the fractionator column’s 
product, is significantly greater than the physical exergy of the inlet 
stream. As shown in Fig. 3, this column accounts for 14% of total energy 
consumption. This was due to the high energy demand for condensing 
the light products, as well as significant energy loss from fuel separation 
in the tower. The heat capacity of the inlet stream from the fractionator 
column results in 12% energy input and 8% output in the splitter (X- 
100), for a total energy consumption of 2%. 
According to Nuhu et al. (2012) fractionator columns are responsible 
for major exergy loss in this unit. The main tower accounted for 12% of 
the exergy destruction in T-103. According to Fig. 4, the total amount of 
exergy destroyed at the plant in the fractionator column was 32 MW. 
Furthermore, because of the high flow rate, overcoming the transport 
phenomenon has resulted in a significant loss of exergy, resulting in the 
destruction of 20% of the exergy in the fractionator column by sepa­
rating and condensing a large amount of light key products. The oper­
ating condition of the condenser has a significant impact on heat loss 
and chemical exergy. As Dincer and Rosen also noted, a reduction of 
Fig. 4. Exergy destruction of subunits in the oil refining process.  
M. Nahvi et al.                                                                                                                                                                                                                                  
Journal of Cleaner Production 413 (2023) 137447
8
86 ◦C in tower upper product temperature combined with a 1400 ton/h 
condenser inlet flow rate results in significant destruction of this 
component (Dincer and Rosen, 2015). 
A higher crude feed mass flow rate will result in a lighter product at 
working conditions. Since the plant’s throughput has increased from 
90,000 bbl/day to 205,000 bbl/day, the cracking zone’s exergy 
destruction has been calculated to be 239.7 MW, which represents a 
slightly higher share of 37% in total exergy destruction. The study of the 
oil refining process, as a complex chemical processing plant, revealed 
that many components of the process were destroyed by considerable 
amounts of exergy. Excess exergy destruction is caused by non- 
uniformity of driving forces and molar variation in materials from the 
reference condition, which is referred to as chemical exergy losses in 
different parts of the components. A small driving force necessitates 
unnecessary capital investments, whereas a large driving force necessi­
tates excessive exergy consumption (Leites et al., 2003). 
4.2. Exergoeconomic assessment 
Fig. 5 shows a Sankey diagram of the oil refining plant cost flow. The 
cost flow diagram helps to understand how the cost is formed in the 
system. When evaluating the process from an exergoeconomic stand­
point, the cost of exergy destruction, the exergoeconomic factor, and the 
relative cost difference were all taken into account. The exergoeconomic 
evaluation parameters are shown in Table 4. The price of crude oil was 
estimated to be around 115 $/h (prices of other products are listed in 
Table 3S). In Fig. 6, the exergoeconomic factors (fk) and total cost rates 
( ˙
Zk + CD,k) of the plant components are signified separately for each 
zone, in descending order of the total cost rates. 
As shown in Fig. 5, crude feed, stream 1 (S1), received from the 
plant’s Feeder (FEEDER) at a cost rate of 744,145 $/h (3.28 $/MJ), 
enters the first extraction zone’s flash drum (V-100), where it is sepa­
rated and distributed between various sections of the extraction zone 
based on the flow rate. The flash drum installation cost is 406,000 $, 
according to the Aspen HYSYS economic analyzer. T-101 accounts for 
9% of total installation costs, with the installation of the condenser ac­
counting for 14% and the main tower of the T-101 column accounting 
for 80%, as shown in Fig. 7. According to Fig. 6, the total cost rate in 
zone 1 is highest in the atmospheric column (T-101). V-100 has an 
exergoeconomic factor of about 0.05%, indicating that it has a nearly 
equal share of thermodynamic inefficiencies and investment cost in its 
high total cost rate. 
T-101 is the zone’s next component with a high priority for 
improvement. Except for V-100 (flash drum), the exergoeconomic fac­
tors of this component are less than 1%, indicating that they must be 
increased to improve their thermodynamic performance (Fig. 7). 
Because exergy-related costs in the atmospheric column are high, 
increasing investment costs are not only a constraint but also a necessary 
solution to improve and optimize T-101 column performance. Because 
the atmospheric column’s reboiler (T-101) operates at temperatures 
above ambient, exergy is transferred from the reboiler’s hot stream to 
the column. To produce dilution steam for the separation process, MP 
steam, at a cost rate of 59 $/h, and two HP streams, at a cost rate of 1690 
$/h, enter the T-101 column. Its condensate leaves the atmospheric 
column condenser at 57 $/h as water. Zone 2 has the highest cost rate for 
the vacuum tower, as shown in Fig. 6. (T-102). 
The atmospheric residue exits the column at 332,335 $/h, splitting 
into two streams, S13 at 190,088 $/h and S14 at 142,246 $/h, both of 
which enter the vacuum tower. Because of the high product streams, the 
cost of exergy destruction is low in this tower, as shown in Fig. 6. T-102 
thus has an exergoeconomic factor of 0.1%. T-102 has a total cost rate of 
38,000 $/h and a total exergy destruction rate of 26,000 $/h. The high 
total cost is due to nearly high investment costs as well as thermody­
namic inefficiencies. The exergoeconomic factors of V-100 and T-102 
(crude feed separators and extraction towers) are extremely high. T-102 
accounts for 4% of total installation costs and 2% of total purchasing 
costs, as illustrated in Fig. 7. 
These components’ investment costs can thus be reduced primarily 
to reduce their total cost rates. The thermodynamic performance of the 
X-100 and the other two TEEs should be improved, but their cost rates 
cannot be considered because there is insufficient data for design and 
their investment cost is less than that of the main components. The MIX- 
100 received the naphtha (N1) stream (S20) with a cost rate of 219,552 
$/h, the HVGO (S18) and VR (S19) with a cost rate of 219,069 $/h, and 
the AGO with a cost rate of 39,924 $/h. Exergy destruction is typically a 
hidden cost that can only be discovered through exergoeconomic anal­
ysis. Fig. 7 shows that the E-100 component accounts for 1% of the total 
installation cost, while heat and physical losses contribute to the lowest 
exergy efficiency. 
An increase in exergoeconomic factor (fk) indicates a more signifi­
cant impact of investment rate on cost, while a decrease in fk indicates 
increased exergy destruction is beneficial to better exergoeconomic 
performance. This component has the lowest exergoeconomic factor due 
to the amount of exergy lost in the cooler, so investment cost must be 
Fig. 5. Sankey diagram of cost flow in the studied oil refining process.  
M. Nahvi et al.                                                                                                                                                                                                                                  
Journal of Cleaner Production 413 (2023) 137447
9
Table 4 
Comparative results of the FCC unit and column section.  
Component 
cf,k ($ /MJ)
cp,k ($ /MJ)
CD,k ($ /h)
Zk ($ /h)
fk (%)
rk(%)
V-100 
3.2882 
6.645 
8268 
4.7 
0.052 
102 
T-101 
0.0374 
0.0113 
1,812,523 
99.18 
0.001 
69.6 
T-102 
0.0268 
0.0315 
26,554 
36.44 
0.107 
88.24 
E-100 
11.14 
23.60 
2,367,624 
5.39 
0.0002 
111.3 
FCCU 
0.0169 
0.0168 
22,752 
116.6 
0.51 
0.53 
T-103 
2.1 
0.112 
882,851 
561.7 
0.046 
94.6 
Total 
17.5 
30.5 
5,120,564 
824 
0.128 
77.8  
Fig. 6. Total cost rates, cost of exergy destruction, and exergoeconomic factors of the components in the oil refining process.  
Fig. 7. Total installation cost and purchasing equipment cost of oil refining process.  
M. Nahvi et al.                                                                                                                                                                                                                                  
Journal of Cleaner Production 413 (2023) 137447
10
considered in addition to exergy-related costs. Furthermore, when the 
expenses of installation and operation cost (with a share of 128,600 $, 
which is the lowest installation cost rate) of E-100 with an exergy 
destruction rate are compared, it is clear that the amount of destruction 
is greater than the total cost of the process, which is another reason for 
the recession in the exergoeconomic factor. Cooled mixtures of heavy 
hydrocarbon from the E-100 at a cost rate of 478,550 $/h are entered 
into the FCC unit expanded through the riser reactor by cracking with 
catalyst, producing 637 kW at 49.4 $/h, which is the unit’s cost, as 
shown in Fig. 5. Due to the high cost of the catalyst, riser, and cyclone 
technology, the FCC unit is the most expensive component when 
compared to the other components shown in Fig. 7. 
The fluid catalytic cracking reactor with zeolite catalysts has the 
highest exergoeconomic factor of 0.51%, as shown in Fig. 6. FCC unit’s 
exergoeconomic factor is high due to differences between energy 
destruction cost rate and total cost. Furthermore, the FCC unit in this 
zone, which differs significantly from the other components of the 
process, has the greatest diversity of total cost rate from destruction cost 
rate. As a result, before improving total costs, their thermodynamic ef­
ficiency should be improved to reduce total cost rates while increasing 
investment costs. The T-103 column has the highest total cost rate in 
zone 3, as shown in Fig. 6. The column was filled with rich oil (S25) at a 
cost of 39,255 $/hr. Because the T-103 column’s condenser condenses 
the refrigerant stream at a cost of 523,555 $/h, its exergy dissipation is 
proportional to the exergy of the gas stream. 
T-103 represents 48% of total installation costs and 17% of total 
purchasing costs. Furthermore, the main tower installation accounts for 
94% of the total cost, with the condenser accounting for 5%. T-103 has 
an exergoeconomic factor of 0.063% based on the difference between 
chemical exergy loss and heat loss during the separation process. The 
high total cost rate indicates nearly equal shares of thermodynamic in­
efficiencies and investment costs. T-103’s exergoeconomic factors 
necessitate a dramatic increase in the investment cost, thereby 
decreasing their total costs, by using much better-designed equipment 
and increasing their thermodynamic efficiency. 
The total cost rate of the entire plant ( ˙
Zk + CD,k) is approximately 
5,121,388 $/h, of which 824 $/h is the levelized investment and 
maintenance cost (Ztot) (Table 4). The investment cost contributes a 
greater share of the 78% increase in exergy unit cost (30.5 $/MJ) than 
the exergy unit cost of the fuel (17.5 $/MJ) according to the exer­
goeconomic factor for the entire plant (50.61%). Exergy loss from the 
plant due to heat release into the atmosphere costs 81 dollars per 
kilowatt-hour. A relative cost difference indicates an increase in the cost 
of the stream as a result of passing through equipment and designates 
the ability of equipment to lower the cost per unit of exergy per product. 
The potential for optimization is greater, and optimization on equip­
ment with a greater relative cost difference is easier. Equipment with a 
greater relative cost difference should be given special consideration, 
especially if the operating and maintenance costs, as well as the costs of 
exergy destruction, are high. 
It is common for chemical processing plants or utility systems that 
produce steam and electricity to base their monetary costs on the market 
price of the energy and/or material resources, as well as the profit 
intended by their producers. This leads to significant errors because the 
product’s price should reflect the true value and utility of the resources 
(Dincer and Rosen, 2021). Because exergy is the only property of any 
energy or mass stream that reflects its true thermodynamic value, it 
would be logical to value various products of a chemical processing 
plant or utility system using exergoeconomic analysis, because it is the 
only property that reflects the product’s true thermodynamic value 
(Dincer and Rosen, 2021; Hamedi et al., 2022). 
5. Conclusion 
In this study, an FCC unit with a fractionator column was simulated 
using Aspen HYSYS to investigate the production of light olefins from 
heavy residuals. The results of the energy and exergy analysis revealed 
that 61% (8% from separating, 42% from condensing) of exergy was 
destroyed in the atmospheric column, and with a consumption rate of 
64%, the atmospheric tower (T-101) consumed the most energy. The 
vacuum tower has the lowest exergy destruction of any refining plant, 
resulting in the highest exergy efficiency of 93%. The FCC unit destroyed 
11% of the exergy in the process, but consumed about 16% of the en­
ergy, making it the second most energy-consuming unit in the process. 
Additionally, 20% of the exergy is lost in fractionator columns due to 
separating (about 4%) and condensing (about 16%). A fractionator 
column (T-103) is responsible for 14% of the total energy used. There­
fore, cooler (E-100) and atmospheric column (T-101) yielded the lowest 
rates of exergy efficiency. For the first time, integrated exergy and 
exergoeconomic analysis are presented in this work, which uses a San­
key diagram to examine how the costs of steam, oil feedstock, interest 
rates, and maintenance affect exergoeconomic indicators. The crude 
feed has the highest cost flow rate, with a share of 744,145 $/h, ac­
cording to the Sankey diagram. The findings show that the fractionator 
column was the most expensive component, accounting for 40% of the 
overall installation costs for this column. The fractionator column’s 
exergoeconomic factor was 0.063%. The FCC unit had the second 
highest cost out of all units in the plant, comprising 9% of the total 
installation cost. It also had the highest economic exergy factor at 0.5%, 
indicating that it is both expensive and has high total and maintenance 
costs in comparison to the cost of exergy destruction. Furthermore, the 
cooler had the least economic exergy factor in the entire plant, indi­
cating that 1% of the total installation cost is due to the cooler compo­
nent. The cooler was found to have the lowest exergy efficiency as well 
as the lowest exergy economic factor in terms of economic analysis. 
Credit author statement 
Masoud Nahvi: Investigation, Formal analysis, Simulation and 
Analysis, Data curation, Writing – original draft. Ahmad Dadvand 
Koohi: Supervision, Conceptualization, Methodology, Resources, 
Writing – review & editing. Mehdi Sedighi: Supervision, Conceptuali­
zation, Methodology, Resources, Writing – review & editing. 
Declaration of competing interest 
The authors declare that they have no known competing financial 
interests or personal relationships that could have appeared to influence 
the work reported in this paper. 
Data availability 
Data will be made available on request. 
Appendix A. Supplementary data 
Supplementary data to this article can be found online at https://doi. 
org/10.1016/j.jclepro.2023.137447. 
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M. Nahvi et al.

Fuel 288 (2021) 119678
Available online 9 December 2020
0016-2361/© 2020 Elsevier Ltd. All rights reserved.
Review article 
Multiobjective optimization and analysis of petroleum refinery catalytic 
processes: A review 
Hamdi A. Al-Jamimi a,b, Galal M. BinMakhashen a,b, Kalyanmoy Deb c, Tawfik A. Saleh d,* 
a Information and Computer Science Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia 
b Research Institute, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia 
c Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA 
d Chemistry Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia   
A R T I C L E  I N F O   
Keywords: 
Multiobjective optimization 
Petroleum refinery 
Pareto-optimal solution 
Evolutionary computing 
Environmentally friendly approach 
A B S T R A C T   
Multiobjective optimization (MOO) techniques are of much interest with their applications to petroleum refinery 
catalytic processes for finding optimal solutions in the midst of conflicting objectives. The rationale behind using 
MOO is that if objectives are in conflict, a set of trade-off optimal modeling solutions must be obtained to help 
management select the most-preferred operational solution for a refinery process. Using MOO does not involve 
hyperparameters thereby reducing the expensive parameter tuning tasks. A true MOO method allows numerous 
Pareto-based optimal solutions to be identified so that management and decision-makers’ preference information 
can be used to finally select a single preferred solution. This review discusses MOO algorithms and their ap­
plications in petroleum and refinery processes. The survey provides insights into the fundamentals, metrics, and 
relevant algorithms conceived for MOO in petroleum and refinery fields. Also, it provides a deeper discussion of 
state-of-the-art research conducted to optimize conflicting objectives simultaneously for three main refinery 
processes, namely hydrotreating, desulfurization, and cracking. Finally, several research and application di­
rections specific to refinery processes are discussed.   
1. Introduction 
The petroleum refinery industry is a substantial contributor to in­
ternational economics. It produces various important chemicals as well 
as many types of fuels that constitute a considerable part of the global 
market [1,2]. Different types of processes are involved in petroleum 
refining. Examples of these include i) treating processes, such as 
hydrotreating, hydrodesulfurization, and chemical sweetening, ii) con­
version processes like alkylation, polymerization, and cracking, and iii) 
feedstock and product handling [3]. The petroleum industry and 
refining technologies are moving fast toward a new scientific generation 
and vision. Due to the large capacity of refining units and the 
improvement in its chemical production process, it may lead to signifi­
cant economic profits to the industry. The refiners aim at maximizing 
one primary product while minimizing other undesired components 
simultaneously. However, such objectives are mostly conflicting with 
each other due to the complexity of the refinery processes. Hence, 
optimizing a refinery process must determine compromise solutions to 
reveal trade-offs among conflicting objectives [4]. By considering, a 
single objective and optimizing for its associated solution, with a 
disregard of other important objectives, may lose its usefulness and 
significance in the whole solution process. Consequently, no individual 
solution satisfies the optimality of all-important objectives. The neces­
sity to consider more than one objective when optimizing the refinery 
processes imposed new challenges to the field of multiobjective opti­
mization (MOO). MOO allows processing all desired objectives directly 
and simultaneously, and finds a set of trade-off (compromised) solutions 
that are optimal. Usually, such optimization (either with or without 
constraints) yields a group of trade-offs ideal solutions, popularly called 
“Pareto-optimal solutions” [5]. 
Many studies modeled and optimized certain petroleum refinery 
processes. Most of the conducted research studies were based on a 
formulation of a single objective function. The practical solution to such 
an optimization problem endues a solo optimal solution. Although some 
existing problems can be tackled as a single optimization, it is compli­
cated to find a particular solution that covers all aspects of optimality 
[6,7]. MOO algorithms were the subject of active research for opti­
mizing refinery processes by simultaneously considering several objec­
tives concerning a set of constraints. In petroleum refining, there are 
* Corresponding author. 
E-mail address: tawfik@kfupm.edu.sa (T.A. Saleh).  
Contents lists available at ScienceDirect 
Fuel 
journal homepage: www.elsevier.com/locate/fuel 
https://doi.org/10.1016/j.fuel.2020.119678 
Received 8 August 2020; Received in revised form 11 October 2020; Accepted 31 October 2020   
Fuel 288 (2021) 119678
2
many types of processes including crude desalting, crude pre-heat train, 
alkylation, steam reforming for hydrogen production, etc. 
This study aims to provide a broad view and analysis of MOO algo­
rithms in the optimization of petroleum refinery processes and to 
emphasize the crucial role of MOO in that domain. This would 
encourage researchers in petroleum and petrochemical industries to 
benefit from it. In this study, we provide a comprehensive review started 
by providing an in-depth discussion on MOO fundamentals, metrics, and 
relevant algorithms designed for oil refinery and petrochemical pro­
cesses. This study reviews the up-to-date reports conducted to optimize 
simultaneously three main objectives of refinery processes, namely, 
hydrotreating, desulfurization, and cracking. Also, the study gives a 
summary of the role of applying MOO algorithms in refinery processes 
optimization. Finally, it highlights various open research challenges and 
identifies possible future trends for MOO in the oil refinery. 
2. Multiobjective optimization 
This section introduces some fundamental discussions to make the 
current work comprehensive and self-contained. It also briefly presents 
the different types of MOO algorithms and examples of applying MOO in 
different disciplines. 
2.1. Formulation and background concepts 
The MOO optimization framework encompasses three phases: 
problem formulation, optimization, and decision-making. The formu­
lation of a constrained MOO problem focuses on three aspects: i) the 
objective functions to be simultaneously minimized or maximized, ii) 
the decision variables used as parameters of the stated function, and iii) 
the constraints of objective functions (Fig. 1(a)). Optimization, in gen­
eral, includes either minimization or maximization problems. We 
consider a minimization problem for the sake of simplicity. Eq. (1) 
shows a minimization problem, however, all presented approaches and 
ideas apply to a maximization problem, as well. 
Minimize[f1(x), f2(x), ..., fn(x) ],
(1) 
subject to m as: 
gi(x) ≤0, i = 1, 2, ..., m,
(2) 
and to p as: 
hj(x) = 0, j = 1, 2, ..., p,
(3) 
The number of objective functions is denoted by n, x =
[x1, x2, ⋯., xd]T is a vector of decision variables, and m, p is the number of 
inequality/equality constraints. The aim is to identify some sets among 
all x = (x*
1, x*
2, ⋯., x*
n) vectors that satisfy Eq. (2) and Eq. (3), and yield 
the optimal results by considering the predefined objectives [8]. 
Fig. 1(b) shows an optimization that indicates the selection and 
application of the appropriate MOO algorithm. Usually, it is impractical 
to have an optimum solution for all stated objectives due to the possible 
conflicts. Thus it is expected to have a group of solutions that are optimal 
and have equal quality [9]. As shown in Fig. 1(c), such Pareto-optimal 
front is the main goal in MOO where the domain experts then can 
judge and select the most applicable solution to the current case. A 
process engineer can establish and understand tradeoffs and process 
performance using the MOO results. Hence, the decision-maker, based 
on his/her expertise and intuition, needs to choose the most appropriate 
solution for implementation or particular regions of the tradeoff surface 
for further exploration. In addition, “innovization” (innovation-through- 
optimization) can be used to explore the Pareto front intelligently and 
deduce common patterns from the obtained Pareto-optimal solutions. 
For example, Equations (4) and (5) show two different desired ob­
jectives; the minimization of sulfur concentration and aromatic content 
in the product: 
Minf(TR, PR, ⋯) = outlet concentration of sulfur
(4) 
Nomenclature 
AMOPSO Adaptive multiobjective particle swarm optimization 
HDA 
Hydro-dearomatization 
HDN 
Hydro-denitrogenation 
HDS 
Hydro-desulfurization 
HDT 
Hydrotreating 
LHSV 
Liquid Hourly Space Velocity 
MODE-TL Differential evolution with taboo list 
MOO 
Multiobjective Optimization 
MOP 
Multiobjective Problem 
NSGA 
Non-Dominated Sorting Genetic Algorithm 
NSGA II Non-Dominated Sorting Genetic Algorithm II 
SA 
Simulated annealing 
SAMTLO Self-adaptive multiobjective teaching–learning based 
optimization 
SOP 
Single Objective Problem 
TBR 
Trickle-Bed Reactor 
AMOPSO Adaptive multiobjective particle swarm optimization 
MOPDE-CES Multiobjective parallel differential evolution with a 
competitive evolution strategy 
SA-MTLBO Self-adaptive multiobjective teaching–learning- 
based optimization 
FCCU 
Fluidized-bed catalytic cracking unit  
Fig. 1. Multiobjective optimization framework.  
H.A. Al-Jamimi et al.                                                                                                                                                                                                                          
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Minf(TR, PR, ⋯) = total aromatics content
(5) 
The two-objective problem is sketched in a graphical form in Fig. 2. 
To optimize the objectives, different experiments are conducted under 
different conditions, called decision variables. These variables include, 
but are not limited to, temperature, pressure, etc. The Pareto-optimal set 
is represented by realistic non-dominated solutions resulting from 
optimization. Point W and surrounding points are not on the Pareto- 
optimal front as they are dominated by both points X and Y. We 
notice improvement of objective-1 by moving from point X to Y, while 
objective-2 worsened (increases), and vice versa. Therefore, the points X 
and Y are not strictly dominated by any point, thus they are equally good 
as non-dominated points. The imaginary point Z has improved values for 
both objectives. Usually, such a point is unrealizable due to the objec­
tives’ contradictory. In the following, the objectives optimized in each 
study are presented in addition to other related important aspects. 
2.2. MOO algorithms 
Various algorithms have been designed to address multiobjective 
problems in the literature. Such algorithms are designed to deal with 
simultaneously multiple objectives while identifying optimum configu­
rations or solutions. Thereinafter, we briefly describe some common 
MOO techniques based on reviewed papers. In summary, the methods 
are grouped into five categories; genetic algorithms, particle swarm 
optimization, differential evolution, simulated annealing, and desir­
ability function methods. 
2.2.1. Genetic algorithms 
A genetic algorithm (GA) was created as a search algorithm in the 
Sixties and Seventies by John Holland [10]. The basic aim of genetic 
algorithms was to mimic natural selection and to find better solutions 
from the search space iteratively using a population of solutions. Gold­
berg and his other Holland’s students showed that GAs can discover 
“optimal or near-optimal solutions” for optimization problems [11]. 
These algorithms draw their inspiration from various hypotheses of 
biological evolution where cascading improvements can be passed from 
ancestors to successors through inheritance. In genetic algorithms, in­
formation is recombined at each generation to produce new offspring. 
Therefore, the fittest offspring is then chosen to inherit its strongest 
features to their successors (or new solutions). 
GAs have received tremendous attention recently, particularly in 
MOO. Such efforts introduce many variations of GAs where each algo­
rithm offers different features to solve optimization problems.  
• Vector Evaluated Genetic Algorithm (VEGA)- This algorithm evaluates 
every non-overlapping part of the population using a different 
objective function but allowed recombination operators to occur on 
the entire population [12]. VEGA is a general form of a standard 
single-objective GA, where it returns a vector of solutions instead of a 
single solution. Despite two specific modifications, VEGA could not 
maintain various Pareto-optimal solutions due to the lack of an 
efficient diversity-preserving operator [11].  
• Nondominated Sorting GA (NSGA)- It was introduced as Evolutionary 
Algorithms (EAs) for MOO [13], followed by Goldberg’s suggestion 
of the use of a diversity-preserving operator. Although NSGA was 
shown to find and maintain multiobjective Pareto-optimal front so­
lutions. It requires a tunable sharing parameter [14].  
• NSGA-II - It is an upgrade over the original NSGA that eliminates the 
need to share parameters and has modified two main phases: 1) 
fitness assignment and 2) fitness sharing. The fitness assignment is 
introduced to reduce time to reach convergence, while the fitness 
sharing phase is mainly introduced to expand the solution diversity 
[14]. In NSGA-II, the convergence time is reduced to a one-level of 
magnitude better than NSGA. Moreover, NSGA-II uses a crowding 
distance concept to estimate a solution density/sharing and to avoid 
a tunable sharing parameter. NSGA-II has also been extended for 
handling more than three objectives by NSGA-III [7].  
• Niched-Pareto GA (NPGA) – It uses a tournament selection which 
assumes a single solution to be found for a given problem [15]. NPGA 
has modified the convergence process to maintain multiple Pareto- 
optimal generations by adding domination tournaments and deter­
mining the winner by using the sharing of non-dominant 
tournaments.  
• Strength Pareto EA (SPEA) - The algorithm employs an external group 
of solutions (i.e. archive) besides maintaining a regular population. It 
starts with an initial population and an empty limited archive. Then, 
it iteratively performs three steps. The first step is to keep non­
dominated solutions in the created archive. Then, in the second step, 
it updates the archive in each iteration to remove dominated or 
duplicate solutions. The size of the archive was pre-defined manu­
ally. Therefore, SPEA performs clustering on archiving solutions to 
preserve the characteristics of the non-dominated front and avoided 
exceeding that pre-defined threshold. Finally, fitness values are 
calculated for both archived and population members [16]. The 
SPEA is further improved in several studies such as [17,18].  
• Noninferior Surface Tracing EA (NSTEA)- This algorithm performs 
seeding of the initial population besides employing Pareto optimality 
[19]. It was evaluated and compared against popular MOEA algo­
rithms and has shown promising performance. Besides its simple 
concepts that have permitted it to be used for single-objective opti­
mization, it reduces computational requirements for the iterative 
sorting and comparisons required when determining Pareto 
optimality.  
• MOEA based on Decomposition (MOEA/D) – It provides another 
methodology to solve multiobjective problems. It approaches solu­
tions by decomposing the problem into single objective subproblems. 
Then, it optimizes these sub-problems using an evolutionary 
approach concurrently and collaboratively. Such problem-specific 
knowledge in MOEAs is found to be effective in problem search 
spaces [20]. 
2.2.2. MO particle swarm optimization (MOPSO) 
PSO is a continuous search technique which mimicks the coordinated 
movement of natural objects (birds, fishes, etc.). Three aspects of their 
movement is idealized in terms of updating a population into a new one. 
Fig. 
2. A 
hypothetical 
two-objective 
optimization 
problem: 
Pareto 
optimal Concept. 
H.A. Al-Jamimi et al.                                                                                                                                                                                                                          
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However, PSO is similar to the GAs where both start with a set of random 
populations, then using some fitness functions to evaluate the current 
population. Both use randomness to find optimality. PSO is also 
employed in solving multiobjective problems as well. Similar to the 
SPEA algorithm, it uses non-dominated solutions for selecting its global 
best and keeps two archives to save the global best and the local best 
individuals [21]. 
2.2.3. Differential evolution (DE) 
This algorithm maintains a set of candidate solutions and performs 
three processes: recombination, evaluation, and selection. The recom­
bination involves creating new candidate solutions that are weighted by 
a difference between members of two arbitrarily picked populations and 
added to a third population. The evaluation handles a perturbation ef­
fect on population members to the broader population spread relatively. 
Then, in the selection process, this perturbation resorts into a self- 
organized problem space that bounds to known areas of interest [22].  
• Multiobjective Differential Evolution (MODE) - It inherits all features of 
the basic DE algorithm and modifies the way a selection process was 
performed. It implements NSGA’s non-dominated sorting/ranking 
for its selection process [23]. First, it combines the newly generated 
offspring and parents. Then, it performs the “non-dominated sorting” 
on the merged population. Then, the choice relies on the crowding 
distance and nondominated rank [24].  
• MODE with Taboo List (MODE-TL)- It redefines the classical DE steps 
with a taboo list. MODE-TL creates a taboo list, performs a taboo 
check, and the algorithm convergence is determined by the number 
of generations. First, constrained by decision-variables bounds, a 
population of non-dominated individuals in d-dimensional space is 
initialized randomly. Then, it computes a scaled difference of three 
individuals to build a mutant vector. All these individuals are 
selected randomly. Finally, MODE-TL uses a Taboo search to avoid 
revisiting searched spaces by keeping a record in a Taboo list [25].  
• Generalized DE (GDE3)- It mimics the NSGA-II framework, but the 
creation of offspring solutions is made using the DE operators [26]. 
2.2.4. Simulated annealing (SA) 
This method mimics the natural annealing process of solids to opti­
mize trade-off solutions [27,28]. The annealing process has two phases; 
1) it increases the temperature of the heating bath to a maximum that 
equals to melt solid, 2) it decreases the temperature of the heating bath, 
carefully, until particles rearrange in a state of the solid. Therefore, it 
ensures the atoms possessing a minimum energy state. In general, it 
starts with an initial design (i.e., population). Then, new designs are 
emerging randomly in the neighborhood of the current design to identify 
optimal solutions.  
• Multiobjective Simulated Annealing (MOSA)- It adopts a dominance- 
based energy function [29]. The energy is calculated for a partic­
ular solution based on the entire set of dominated solutions. If the 
true Pareto front does not exist, MOSA estimates a Pareto-optimal 
front using a group of mutually nondominated solutions detected 
so far in the space of solutions. MOSA uses a unity-based energy 
function to avoid fluctuations in the set of possible solutions. 
• Archived MOSA- It introduces the sorting of the nondominated so­
lutions to control the size of the crowd concept and without loss of 
diversity [28]. To maintain these concepts Archived MOSA in­
corporates soft-limit (SL) and hard-limit (HL). The SL allows non­
dominated solutions to be generated and added to the sorted archive. 
Whereas, the hard-limit is maintained by applying clustering on the 
sorted nondominated solutions to reduce the size from SL to HL. 
2.2.5. Desirability function approach (DF) 
This approach uses a geometric average as a desirability function to 
help in ranking solutions for better decision making (DM) process. It 
transforms all estimated solutions into a normalized space. Then, it 
computes the desirability of all solutions using the utility function. 
Usually, the desirability is described using ranks where the higher the 
rank the better the desirable solution. Thus, the ideal response is ranked 
with a value of ρ. A threshold can be set to filter responses outside the 
acceptable range that have lower ranks [30]. 
2.2.6. Teaching-learning-based optimization (TLBO) 
TLBO is a nature-inspired algorithm to simulate the influence of 
teachers on learners [31]. However, this method is independent of any 
algorithmic parameters and uses common controlling thresholds. TLBO 
divides the optimization process into two-phases: teaching and learning. 
The difference between the two-phases is the process style. In the first 
phase, a supervised learning process is taking place where a teacher is 
leading the learning process, while in the second phase; the learning 
process is continued by encouraging the interactions among learners 
themselves. 
2.2.7. Methods with a priori articulation of preferences 
• ε-Constraint- is a method designed to discover Pareto optimal solu­
tions based on the optimization of one objective while treating the 
other objectives as constraints bound by some allowable range εi. 
The problem is repeatedly solved for different values of εito generate 
the entire Pareto set. It is also known as trade-off method, which 
means that the decision-maker specifies a trade-off among the mul­
tiple objectives. In the ε-constrained method, one of the objectives is 
optimized while the others are treated as constraints. This idea is 
express in Eqs. (6) and (7). Although £-Constraint method is quite 
simple technique, it is computationally intensive. Furthermore, the 
solutions found are not necessarily globally non-dominated [32]. 
minfr(x)
(6)  
s.t.fi(x) ≤εi, i = 1, ⋯., N; i ∕
= r
(7)  
where εi is the limiting value of fi desired by the decision-maker. 
• Weighted Sum – this method combines all the multiobjective func­
tions into one scalar, composite objective function using the 
weighted sum as shown in Eq. (8) 
F(x) = w1f1(x) + w2f2(x) + ⋯+ wnfn(x)
(8)  
where the solution strongly depends on the chosen weighting co­
efficients w = (w1, w2, ⋯.wn). Weight of an objective is chosen in pro­
portion to the relative importance of the objective. Although this 
method is simple, it is not trivial to set the weight vectors to obtain a 
Pareto-optimal solution in the desired region in the objective space. In 
addition, weighted sum method cannot find certain Pareto-optimal so­
lutions in the case of a nonconvex objective space[33]. 
2.3. General view on the MOO applications 
There are countless studies where MOO has been applied as a 
promising field of optimization. The literature reveals that tens of 
research studies employ MOO in different research fields. Due to the 
enormous number of studies that used MOO algorithms; it is laborious to 
produce a detailed review of them. Thus, this section gives a glance at 
the most popular applications. 
MOO has been employed to optimize numerous industrial applica­
tions including data mining [34], transportation [35], scheduling [36], 
manufacturing [37]. In addition, a variety of scientific applications has 
used different MOO algorithms. This includes, but not limited to, 
chemistry [38], physics [39], medicine [40]. Noticeably, MOO algo­
rithms are applied tremendously in engineering disciplines as the related 
problems have clear mathematical models that facilitate MOO. Among 
H.A. Al-Jamimi et al.                                                                                                                                                                                                                          
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Table 1 
Existing studies relating to the application of MOO in the three refinery processes.  
Refinery Process 
Study 
Optimization Algorithm 
Process 
Objectives 
Hydrotreating 
Bayat et al., 2014 [48] 
Genetic algorithms 
Catalytic dehydrogenation of 
paraffin 
(1)Max(olefinproductionrate)(2)Max(selectivity)
Weifeng et al., 2007 [49] 
Genetic algorithms 
An industrial naphtha continuous 
catalytic reforming process 
(1)Max(yieldofthearomatics)(2)Min(eld of heavy aromatics)
Bakhshi Ani et al., 2015 [50] 
Genetic algorithms 
Hydrotreating process 
(1)Min(sulfurcontent)(2)Min(aromaticsamount)
Wu et al., 2018 [41] 
ε-constrains method 
Hydrogenation reaction kinetics 
(1)Min(operatingcost)(2)Min(environmentalimpactsofanHDTprocess)
Desulfurization 
Srinivas et al., 2013 [51] 
Genetic algorithms 
HDS and HDA of diesel 
(1)Min(sulfurcontent)(2)Max(aromaticsconversion)
Miranda-Galindo et al., 2014 [24] 
Differential evolution 
HDS of diesel 
(1)Min(sulfurcompounds)(2)Min(emissionsofCO2)(3)Min(annualcost)
Behin and Farhadian, 2017 [52] 
Desirability function approach 
Oxidative desulfurization 
(1)Min(sulfurcontent)(2)Min(aromaticsconcentration)
Cracking 
Punase et al., 2019 [53] 
Genetic algorithms 
Ethanol steam reforming 
(1)Max(hydrogenmolefraction)(2)Min(molefractionsofCO + CO2)
Bhutani et al., 2006 [54] 
Genetic algorithms 
Hydrocracking 
Case:1(1)Max(keroseneproduction)(2)Min(makeupH2flowrate)
Case:2(1)Max(heavydiesel)(2)Min(makeupH2flowrate)
Case:3(1)Max(heavy −endproducts)(2)Min(productionoflight −ends)
Jiang and Du, 2017 [55] 
Genetic algorithms 
Ethylene cracking furnace 
(1)Max(averagebenefits)(2)Min(averagecokingamount)
Wang and Tang, 2013 [56] 
Differential evolution 
Naphtha pyrolysis process 
(1)Max(yieldofethylene)(2)Max(yieldofpropylene)
Yu et al., 2015 [57] 
Teaching learning-based optimization 
Naphtha pyrolysis process 
(1)Max(ethyleneproduction)(2)Max(propyleneproduction)(3)Max(yieldofbutadiene)
Yu et al., 2018 [58] 
Teaching learning-based optimization and 
differential evolution 
Ethylene cracking furnace 
(1)Max(productyield)(2)Min(fuelconsumption)
Geng et al., 2016 [59] 
Particle swarm optimization 
Ethylene cracking furnace 
(1)Max(ethyleneproduction)(2)Max(propyleneproduction)
Li et al., 2007 [21] 
Particle swarm optimization 
Ethylene cracking furnace 
(1)Max(ethyleneproduction)(2)Max(propyleneproduction)
Sankararao and Gupta, 2007 [60] 
Genetic algorithms and simulated 
annealing 
Fluidized-bed catalytic cracking 
(1)Max(yieldofgasoline)(2)Min(COinthefluegas)(3)Min(airflowrate)
H.A. Al-Jamimi et al.                                                                                                                                                                                                                          
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those: electrical engineering [41]; aerospace engineering [42], civil 
engineering [43]; robotics [44]; software engineering [45]; energy [46], 
and petroleum engineering [47]. 
The above surge of MOO application is not surprising, because most 
practical problems in industries and societies involve more than one goal 
to ascertain the practicality and applicability of the final solution. It is 
also not surprising that MOO algorithms are applied to refinery 
processes. 
3. Application of MOO in refinery processes 
In this work, among several refinery processes to obtain clean and 
environment-friendly fuels, we reviewed the application of MOO to 
optimize the three main refinery processes namely: hydrotreating 
(HDT), desulfurization (DS), and cracking (CR). Table 1 lists the studies 
on the application of MOO in such refinery processes along with the 
associated algorithms, and objectives to be optimized. In each study, 
different objectives are optimized. It should be mentioned that the 
ethylene cracking furnace is the main unit to produce raw materials such 
as ethylene and propylene in the petrochemical industry. The plant with 
the facilities for cracking naphtha at a high temperature, >1000 K to 
produce petrochemical feedstocks such as ethylene, propylene, mixed- 
C4, and pyrolysis gasoline, is called the naphtha cracking plant. 
3.1. Hydrotreating process 
The Hydrotreatment process plays a significant role in clean oil 
production. It assists in removing sulfur and other heteroatoms from 
petroleum products. Bakhshi et al. [50] studied the reactions of three 
processes hydrodesulfurization (HDS), hydrodearomatization (HDA), 
and hydrodenitrogenation (HDN). They assessed the effects of different 
operational parameters on many hydrotreating reactions. The parame­
ters used include pressure, temperature, H2/oil ratio, and liquid hourly 
space velocity (LHSV). NSGA-II has been employed to find the optimal 
configuration for the investigated reactor. The performance of the model 
has been evaluated using real experimental pilot-plant data [61]. 
Bayat et al. [48] studied comprehensively the optimization of the 
heavy paraffin-dehydrogenation reactor. The main production included 
hydrogen and olefin. They employed a dynamic MOO for industrial 
paraffin-dehydrogenation radial-flow fixed-bed reactor (RF-FBR). The 
olefin production rate and selectivity were stated as the objectives to be 
optimized, while the inlet temperature of the gas, total molar flow rate, 
and pressure was considered as the decision variables. NSGA-II tech­
nique was employed to optimize the intended LAB plant. The simulation 
results exposed that the catalyst deactivation can assist in reducing the 
olefin production rate over time. 
Although the reduction of sulfur content in the final product is 
desired, it has cost in terms of consuming utilities and affecting the 
environment. The tradeoff between these conflicting objectives moti­
vated researchers to employ MOO to find a set of Pareto optimal fronts 
with good convergence. For instance, Wu et al. [41] investigated the 
reduction of environmental impacts of an HDT process and minimizing 
operational cost simultaneously. They used hydrogenation reaction ki­
netics for the optimization of the operating conditions. Note that in real 
plants the reactor could be vertical rather horizontal. There is a reaction 
zone provided by the HDT reactor for hydrogen and feed oil under two 
operating conditions, i.e., high pressure and temperature. The steam, 
electricity, and fuel gas were used to increase these two operators. 
Simultaneously, to increase product quality hydrogen was used to 
eradicate unwanted components, such as sulfur, aromatics, and nitro­
gen. The experiments revealed that the reduction of hydrogen in the 
process would decrease extremely the economic and environmental 
effects. 
Another application of MOO algorithms was to optimize an indus­
trial naphtha continuous-catalytic-reforming process. To optimize the 
aromatic production, Weifeng et al. [49] stated two objectives to be 
optimized simultaneously maximizing aromatics yield and minimizing 
heavy aromatics. Five decision variables were considered to be inves­
tigated: inlet temperature, LHSV, latent aromatic content of naphtha 
charge, reaction pressure, and the ratio of mole flow of the hydrogen to 
mole flow of naphtha charge. In summary, MOO was actively adopted in 
hydrotreating problems, highlighting either possible reactor configura­
tion or/and correct input quantities of all factors for the production of 
the best possible outcome(s). 
3.2. Desulfurization process 
The desulfurization process (DS) is considered as one of the crucial 
catalytic units in which the sulfur should be eliminated. The optimiza­
tion of DS is viewed as a nonlinear and multivariable problem owing to 
the process’s nature and its design variables. Hence, the optimal design 
of DS involves selecting the suitable operational conditions and 
configuration to optimize objectives simultaneously. For instance, the 
reduction of sulfur content may result in undesirable environmental and 
economic side effects. Thus, while the process minimizes sulfur content 
in the final product, CO2 emissions increase as well as the cost of the 
process. To optimize these three objectives simultaneously, Miranda- 
Galindo et al. [24] reported a MOO application of HDS of diesel fuel 
using distillation with side reactor. They identified the needed operating 
conditions by optimizing the three contradicting objectives. The results 
showed that the separation schemes of distillation with side reactors 
may offer better performance for HDS process in comparison to reactive 
distillation. They used the MODE-TL algorithm that avoids revisiting 
solutions by developing a Taboo list concept [25]. The Taboo list uses 
the original population to be arbitrarily initialized, while its update 
comes from the newly generated trails resulting in new nondominated 
sorting and ranking. 
One of the fractions that lower the fuel is the high aromatic con­
centration in fuel products. Thus, in addition to HDS, clean fuels 
research including HDA as a key process to improve fuel quality. How­
ever, there will be a conflict when trying to minimize both HDS and HAD 
at the same time. For this purpose, Srinivas et al., [51] investigated the 
optimal conditions that can minimize sulfur content and maximize total 
aromatics conversion at the exit of the trickle-bed reactor (TBR). First, 
they formulated two independent optimization problems by considering 
one of the objectives at a time and solved them simple GA. While one 
problem was to minimize the outlet concentration of sulfur, another one 
was to maximize the total conversion of aromatics. The two objective 
functions were used simultaneously to formulate a MOO problem that 
was later resolved using NSGA-II method. 
In addition, Behin and Farhadian [52] studied the undesirable 
impact of the oxidative desulfurization (ODS) process on the diminution 
of the aromatic. In the experiments, they considered four decision var­
iables: time, superficial gas velocity, ultraviolet irradiation, and ultra­
sound irradiation. They stated two objectives, that is, desulfurization 
maximization and de-aromatization minimization, and used MCDM 
approach to evaluate them. Also, they employed graphical multi- 
response optimization techniques to optimize the desulfurization oper­
ating variables. Similar to hydrotreating, MOO is shown to optimize up 
to three competing objectives to identify the best operating parameters 
and possible values for a list of high-quality solutions. Such solutions 
require experts to select suitable for their particular case-optimization 
problem. 
3.3. Cracking process 
Cracking is one of the key refinery processes in which large hydro­
carbon molecules are broken down into smaller and more useful ones. A 
cracking process that occurs with hydrogenation is called hydrocracking 
(HDC). It is used to transform heavy-petroleum-feedstock into lighter 
products by eradicating sulfur and aromatics concertation. Different 
needs to be considered to improve the HDC process. These include 
H.A. Al-Jamimi et al.                                                                                                                                                                                                                          
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optimizing several correlated parameters with their interference such as 
catalyst design, catalysts, and temperature, pressure, minimizing utility 
consumption, and utilizing the best resources. Due to the effectiveness of 
MOO algorithms in such cases, Bhutani et al. [54] employed MOO to 
optimize the industrial important objectives and operational constraints 
of an industrial HDC unit. To optimize simultaneous objectives, they 
investigated three different cases: (i) maximizing kerosene production 
and minimizing makeup H2 flow rate, (ii) maximizing heavy diesel and 
minimizing makeup H2 flow rate, and (iii) maximizing heavy-end 
products and minimizing the production of light-ends. 
Another model is reported by Punase et al. [53] to optimize a fixed- 
bed-catalytic reactor. They targeted improving two objectives that are 
maximization of hydrogen mole fraction in the product gas and mini­
mization of the mole fractions of the greenhouse gases. For this purpose, 
they employed NSGA-II to conduct MOO of an isothermal fixed-bed ESR 
reactor. Also, Jiang and Du [55] investigated the optimization of 
average benefits and the average coking amount in the case of the fixed- 
feed-rate of the ethylene-cracking-furnace. They considered a multi­
objective scheduling optimization problem that deals with the sched­
uling of multiple feeds on parallel units. They used an improved hybrid 
encoding NSGA-II with mixed discrete variables (MDNSGA-II) to attain 
the derived optimization. 
The steam-cracking furnace in petrochemical plants is another 
crucial device used in the pyrolysis process. It uses naphtha, ethane, 
propane, butane, gas oil, etc. For example naphtha-based steam cracking 
processes to produce ethylene and propylene. To identify the optimal 
operation configuration that may lead to an increase in the production of 
ethylene and propylene, Wang et al. [56] built a hybrid prediction 
model that integrates the least-squares support machines with PSO. The 
prediction model helped in estimating the ethylene and propylene yield 
based on finding an optimized operation configuration using MOPDE- 
CES. The findings exposed that the increase in ethylene and propylene 
yields can significantly improve the operation of an ethylene plant. 
Fig. 3 illustrates a framework that can be used for optimizing the 
stated objectives simultaneously. Yu et al. [57] employed a new SA- 
MTLBO algorithm to maximize the yields of ethylene, propylene, and 
butadiene. The used approach proved its ability to find good spread 
nondominated solutions and afford additional choices for decision- 
makers for improving the cracking furnaces utilization. The promising 
results encouraged the authors to employ SA-MTLBO to develop the 
MOO model in another industrial cracking-furnace-system [58]. Two 
objectives were stated to simultaneously increase the key products and 
reduce the fuel consumed per unit. They reported skillful guidelines to 
design a MOO system that can be adapted to optimize other industrial 
production processes. 
Another example is to optimize the ethylene-cracking-furnace that 
was reported by Geng et al. [59]. They considered two different cases of 
studies with different objectives and different cracking cycle types. Final 
optimal solutions are determined using a fuzzy consistent matrix, where 
the decision-makers can pick the suitable operation optimization con­
ditions for a set of solutions. 
MOO algorithms can optimize the multiobjective of naphtha 
cracking. Li et al. [21] employed MOPSO in a naphtha industrial 
cracking furnace to optimize the yield rates of ethylene and propylene 
simultaneously. Also, a hybrid model using MOPSO and an artificial 
neural network was applied in the optimization operation. 
Fluidized catalytic cracker is a pillar in petroleum refining. For 
optimizing fluidized-bed catalytic cracking unit (FCCU), Sankararao and 
Gupta [60] used MOSA algorithm to solve different industrial FCCU 
optimization problems. One of the optimization problems has two ob­
jectives that focusing on maximizing gasoline yield and minimizing CO 
in the flue gas, while another optimization problem is considered a third 
objective (i.e. minimizing the CO in the flow-air) in addition to the 
above-mentioned objectives. Also, Palit [62] reviewed the challenges in 
the application of MOEA in the design of FCCU and other chemical 
engineering systems. He aimed to enlighten the importance of MOEA 
and its immense application in designing chemical engineering systems 
and petroleum-refining units. In summary, MOO methods can help re­
searchers to find the best levels of correlated parameters where the 
decision space might be ambiguous. 
4. Optimization criteria and merits 
This section categorized the comparison criteria into two parts. The 
first category discusses the criteria related to the MOO algorithms, while 
the second one focuses on the parameters and conditions associated with 
the refinery process. 
4.1. Criteria of multiobjective optimization 
To build the right model, it is required to formulate the problem by 
identifying the objectives to be optimized, their constraints, and the 
used decision variables. Moreover, the appropriate selection of the MOO 
algorithm is an important and vital part of achieving satisfactory results. 
4.1.1. Multiobjective algorithm 
Several MOO algorithms, such as GAs, PSO, SA, decomposition, 
differential equations, have been employed to discover sets of “Pareto- 
Fig. 3. The framework of multiobjective operational optimization [58].  
H.A. Al-Jamimi et al.                                                                                                                                                                                                                          
Fuel 288 (2021) 119678
8
optimal solutions” for solving problems in the petroleum industry. 
NSGA-II has been applied in several studies and it showed a prom­
ising performance. For instance, it was used to attain optimal conditions 
that help maximize sulfur reduction and aromatic conversion at the exit 
of the trickle-bed reactor [51]. Furthermore, NSGA-II was employed to 
optimize the production of hydrogen and olefin yields using the heavy 
paraffin dehydrogenation reactor [48], as well as to optimize the yields 
of HDC unit [54]. 
Owing to the complexity of the refinery processes and the nature of 
the decision variables, various improved variations of the NSGA-II al­
gorithm were employed to obtain better results. For instance, an 
improved hybrid encoding NSGA-II with mixed discrete variables 
(MDNSGA-II) was employed for ethylene cracking furnace [55], and an 
adapted version of NSGA-II was used for a diesel fuel hydrotreating 
reactor [50]. Recently, the jumping-gene-adaptation of NSGA-II has 
been used to optimize a fixed bed catalytic reactor [53]. 
However, some studies used different MOO algorithms to find the 
optimal configuration that may lead to optimizing more than one 
objective in the refinery processes. For example, the MOO method based 
on MODE-TL has been used for the optimization of HDS of diesel fuel 
using distillation with side reactions. Also, other optimization methods, 
including simulated annealing and particle swarm were used to optimize 
the cracking process [21,59,60]. The optimization of the cracking pro­
cess was reported using another method, namely SAMTLO, in two 
different studies [57,58]. 
4.1.2. Optimized objectives 
Most of the real problems in science and engineering have simulta­
neous objectives to be optimized. Engineers and practitioners intend to 
optimize and obtain “Pareto-optimal solutions”. That is, the final 
solution must define the best trade-off between competing objectives. 
Some studies have investigated different scenarios for the sake of opti­
mizing various contradictory objectives in each scenario based on in­
dustrial practice [54]. Table 1 demonstrates the refinery processes and 
considered objectives optimized in the surveyed studies. 
4.1.3. Decision variables 
It is essential to understand the trends of decision variables 
commonly used in various problems as they are at the core of any 
optimization process. The optimization of refinery processes has been 
conducted by considering several objectives under different experi­
mental variables. Table 2 lists various decision variables used along with 
the corresponding studies. It is noticeable that the essential conditions 
required in the refinery processes, such as temperature, pressure, liquid 
space velocity, etc., appeared at different stages. 
4.1.4. Constraints 
In practice, real-world problems involve several conflicting objec­
tives along with constraints on what combinations of those objectives 
are attainable. For example, when minimizing the sulfur content in the 
final product, subject to the cost as well as the emission level re­
quirements. The number of constraints varies among studies according 
to different factors. Table 3 demonstrates examples of constraints stated 
in different studies. 
Moreover, the practitioners identified the lower/upper bounds of the 
operating conditions’ values. Although there are common decision 
variables used in different studies, their lower and upper bounds vary 
from one operational unit to another in line with the tackled problem. 
Table 4 shows examples of decision variables along with the lower and 
upper limits for them. 
Table 2 
The decision variables used in the compared studies.   
TR 
DS 
CR 
Decision Variables 
[41] 
[50] 
[48] 
[49] 
[52] 
[51] 
[53] 
[58] 
[55] 
[59] 
[57] 
[21] 
[60] 
[54] 
Inlet temperature   
✓ 
✓  
✓         
LHSV 
✓ 
✓  
✓  
✓         
Latent aromatics content of naphtha charge    
✓           
Reaction pressure (Pr)   
✓ 
✓  
✓         
H2/Oil ratio  
✓  
✓           
Total molar flow rate (Ft)   
✓            
Pressure 
✓      
✓        
Time  
✓   
✓          
Superficial gas velocity     
✓          
UV irradiation     
✓          
US irradiation     
✓          
Feed Flow rate (FHVGO)        
✓      
✓ 
Recycle gas mass flow rate (MRG)              
✓ 
Temperature (TRG) 
✓             
✓ 
Recycle oil temperature (TRO)              
✓ 
Recycle oil mass fraction (FRO)              
✓ 
Quench flow rates to the catalyst beds (Q1, Q2, and Q3)              
✓ 
Ratio of team-to-naphtha            
✓   
Outlet temperature            
✓   
Outlet pressure            
✓   
Gasoline yield               
Percent CO in the flue gas             
✓  
Air flow rate (kg/s)             
✓  
Air preheat temperature (K)             
✓  
Feed preheat temperature (K)             
✓  
Catalyst flow rate (kg/s)             
✓  
Feedstock flow          
✓ 
✓    
Dilution steam ratio          
✓     
Coil inlet temp          
✓     
Coil outlet temperature (COT)        
✓   
✓    
Steam hydrocarbon ratio (SHR)        
✓   
✓    
Feed temperature  
✓             
Initial partial pressure of H2S  
✓             
Coil output pressure           
✓    
Isothermal temperature       
✓        
water to ethanol (mole fraction) ratio       
✓         
H.A. Al-Jamimi et al.                                                                                                                                                                                                                          
Fuel 288 (2021) 119678
9
4.2. Refinery process criteria 
4.2.1. Refinery process 
MOO has been used to optimize different refining processes at 
different levels, Table 2. The refinery process generally includes oper­
ation where several parameters and operational conditions are opti­
mized for better yield of the targeted products. Conditions and 
parameters include pressure, temperature, type of gas flow, catalyst 
amount, type, reactor size and type, feed type and composition, cracking 
furnace type and its parameters, etc. Thus, there are several parameters 
to be optimized. This depends on the operational unit and its specifi­
cations, aim, and attained yield. Based on that the parameters to be 
optimized are based on the unit hence type of optimization varies from 
refinery unit to another refinery unit. Thus, the parameters to be opti­
mized are to be determined by the operational department and decision- 
makers. In the technology of the oil refinery such as catalytic reforming, 
catalytic cracking, hydrocracking, simulation in a single application on 
some devices have achieved some success. The classic planning models 
of refinery production employ input–output relationships without 
employing decision-making variables associated with the unit’s opera­
tion (type-of-feed, pressure, temperature, catalyst amount, type, etc.) for 
different refinery units. However, optimizing a single device has its re­
strictions. For example, the optimal solution is not possible in the 
multiplant optimal solutions since a single device optimization requires 
proper intermediate information for complete optimization. To sum up, 
it is imperative to further improve the refining process simulation and 
optimization technology. 
Table 3 
Examples of constraints presented in the literature.  
Study 
No. of 
Constraints 
Used Constraints 
Description 
[41] 
3 
Sprod ≤SU
ProdNprod ≤NU
ProdAprod ≤AU
Prod  
Environmental 
standards govern 
the optimal 
impurity 
contents of 
sulfur, nitrogen, 
and aromatics. 
[55] 
5 
Mass balance: 
FiTcycle = ∑
jDi.jti.j∀i  
Equality 
between the feed 
processed by 
cracking furnace 
system and that 
arrived into the 
plant. 
Integer constraint: 
0 ≤ni,j ≤N∀i,j  
The sub-cycle 
number of 
processing feed 
should be an 
integer value 
with a certain 
limit. 
Timing constraints: 
(a) Δti,j = ni,jti,j +ti,j∀i, j 
(b) ∑
iΔti,j ≤Tcycle∀i,j 
(c)Δti,j ≥0,ti,j ≥0,Tcycle > 0∀i,j  
(a) Feed time 
should equal to 
the time of 
processing and 
decoking. 
(b) Processing 
and decoking 
time allocated 
should be less 
than or equal to 
the cycle time 
horizon. 
(c) Time-of-feed 
should equal to 
zero when the 
corresponding 
sub-cycle 
number is zero. 
[59] 
8 
(a) Yiethy ≥Lethy 
(b) Yiprop ≥Lprop 
(c) Tw ≤Tu 
(d) Ql ≤QNaphtha ≤Qu 
(e) DSRl ≤DSRi ≤DSRu  
(f) COTl ≤COTi ≤COTu 
(g) NPFM 
(h)Il ≤i ≤Iu  
(a) The yield of 
ethylene of any 
day should be 
greater than the 
lower bound of 
ethylene. 
(b) The yield of 
propylene of any 
day should be 
greater than the 
lower bound of 
propylene. 
(c) The tube wall 
temperature 
should be less 
than the upper 
limit 
(d) The feedstock 
flow governs by 
the lower and 
upper limits 
(e) Dilution 
steam ratio 
governs by the 
lower and upper 
limits 
(f) Coil outlet 
temperature 
governs by the 
lower and upper 
limits 
(g) NPFM is the 
mechanistic 
model of full-  
Table 3 (continued) 
Study 
No. of 
Constraints 
Used Constraints 
Description 
cycle ethylene 
cracking furnace 
process. 
(h) The cracking 
cycle is restricted 
by its lower and 
upper limits.  
Table 4 
Constraints of decision variables presented in some studies.  
Study 
Decision Variable 
Lower limit 
Upper limit 
[53] 
Temperature (K) 
823 
923 
Pressure (kPa) 
101.325 
151.988 
H2O/E 
3.5 
10 
[41]. 
Temperature (◦C) 
280 
340 
Pressure (MPa) 
6 
8 
[52] 
Time (min) 
15 
45 
Ug (cm/s) 
0.05 
0.15 
Desulfurization yield (%) 
50 
98 
De-aromatization (%) 
35.3 
94.6 
[48] 
Temperature (K) 
723 
773 
Pressure (kPa) 
200 
300 
[60] 
Trgn (K) 
700 
950 
Tfeed (K) 
575 
670 
Tair (K) 
450 
525 
Fcat (kg/s) 
115 
290 
Fair (kg/s) 
11 
46 
[54] 
MRG (kg/h) 
7000 
9000 
FHVGO (kL/h) 
95 
115 
TRG (K) 
630 
680 
TRO (K) 
610 
655 
FRO (-) 
0.70 
0.92 
Q1 (kg/h) 
1400 
2200 
Q2 (kg/h) 
1400 
2200 
Q3 (kg/h) 
1200 
1900  
H.A. Al-Jamimi et al.                                                                                                                                                                                                                          
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4.2.2. Catalyst and reactor 
Catalysts are used, in some units of the refinery in several processes 
as cracking, conversion, etc. Catalysts including its type, amounts, 
density, porosity, etc., and the type of reactor including its dimensions, 
size, length of the catalyst bed, etc. are some of the important parame­
ters to be optimized for optimum yield. For example, different types of 
reactors have been optimized in HDS by considering different parame­
ters such as the catalyst type reactors. 
Behin and Farhadian [52] conducted several experiments in a semi- 
batch mode reactor. The reactor was a draft tube airlift of 45 cm height 
and 10 cm inside diameter. In each experiment, 3.5 L of nonhydro­
treated kerosene as feedstock with a total sulfur content of 1,553 ppm 
was introduced into a reactor. Another example is the catalyst consisting 
of Nickel-based with Ni (II)-Al (III) with lamellar double hydroxide 
(LDH) as catalyst precursor was tested for ethanol steam reforming in 
sorption-enhanced steam reforming (SESR). The parameters of the 
reactor are the diameter of the reactor, length of the catalyst bed, 
porosity of the bed, fractional catalysts volume in the reactor, and the 
density of the catalyst. In this work, an isothermal ethanol steam- 
reforming reactor was reported. The findings exposed that the used 
kinetic model is effective and reliable for a fixed-bed reactor to do ESR 
over Ni-based catalysts as the model predictions are close to the real data 
[53]. Moreover, such reactions could also be studied using different 
types of reactors such as fluidized bed, membrane, fixed bed, and wall- 
coated microchannel. 
Miranda-Galindo et al. [24] used MOO to optimize five configura­
tions for distillation in a side reactor for HDS including an alternative 
using reactive distillation, shown in Fig. 4. They used the kinetic ex­
pressions of sulfur compounds to carry out the intended simulation of 
the distillation process with side reactors in the HDS. The used compo­
nents include Benzothiophene, Thiophene, Dibenzothiophene, and 4,6- 
Dimethyldibenzothiophene. The scheme DRI offers the best trade-offs 
for total-annual cost, sulfur flow rate, and CO2 emissions for the HDS 
process. 
Bhutani et al. [54] reported on hydroprocessing that was conducted 
to convert heavy oils to middle distillates in the hydrogen-rich atmo­
sphere. They elevated both temperature and pressure, then they con­
ducted a process in packed bed catalytic reactors. An ideal catalyst 
consisting of silica-alumina (or low- or high-zeolite SiO2-Al2O3) was 
used to promote cracking activity, in addition to the base metal 
Fig. 4. Schemes of distillation for HDS process: “(a) a recirculation (DRSI), (b) two side streams (DRSII), (c) distillation-reaction (DRI), (d) sidestream (DRSL), (e) 
three side reactor and sidestream (DR3R), and (e) reactive distillation (RD)” [24]. 
H.A. Al-Jamimi et al.                                                                                                                                                                                                                          
Fuel 288 (2021) 119678
11
components (Pd, Pt, Ni, and Mo) to encourage hydrogenation. The 
conversion of n-paraffins and naphthenes in phtha yields aromatics and 
isoparaffins over bifunctional catalysts such as Pt-SdA1203 in four re­
actors [49]. Dynamic MOO of heavy paraffin dehydrogenation was 
carried out over Pt–Sn–Al2O3 catalyst for the production of olefin in an 
industrial RF-FBR [48]. 
4.2.3. Yield type 
According to the process to be optimized, the type of outputs from 
that process can be determined. The outputs from refinery processes 
have the same nature in terms of being oil derivatives. Table 5 demon­
strates the yield of each study. The refinery process is not limited to a 
particular type of fuel. For example, different processes such as desul­
furization [24], HDC [54], and hydrotreating [50], were applied on 
diesel fuel. Another example of applying desulfurization on kerosene is 
presented in [52]. 
5. Multi-criterion decision-making in energy 
Pareto front optimized solutions require some expertise to decide on 
selecting the best possible solution among them that satisfies their 
needs. Selecting such a Pareto front solution involves a set of tradeoffs 
among other solutions acquired from a MOO exercise. A decision- 
making process is a non-trivial task for an operator, which may 
require some guidelines. Multi-criteria decision-making (MCDM) is a 
technique of evaluating multiple conflicting criteria (i.e. trade-off so­
lutions) for the sake of selecting the best solution. 
MCDM techniques become popular in various applications including 
energy decision-making processes [63-67]. The main concern in these 
studies is sustainability in process design for energy planning. Dinh et al. 
[66] used the analytic hierarchy process (AHP) method to analyze a 
decision-making process choose among three alternatives of biodiesel 
feedstocks. They considered economic, safety, social issues, and envi­
ronmental impacts while developing the selection process (i.e., take a 
decision). Moreover, Othman et al. [65] proposed a systematic method 
for sustainability assessment and design selection in chemicals and in­
dustries using AHP. Their approach is considered hard and soft in­
dicators for sustainable design. They illustrated their method using 
biodiesel process design. MOGA [68], which is a framework of genetic 
algorithms proposed to search for Pareto optimal solutions of MOO 
problems, has been utilized for a decision-making process design [69]. 
MOGA was employed to identify an optimal low-carbon design alter­
native that satisfies the carbon footprint constraint and reduces the cost 
in terms of time and effort [69]. 
6. Discussions 
Many MOO algorithms have been employed to find solutions for 
various refinery processes with a different number of parameters and 
conflicting objectives. The focus of the different studies was to achieve a 
Pareto optimal front, where a diverse range of solutions exists. The 
analysis of the Pareto front solutions can provide useful and deeper 
knowledge of the intrinsic properties of the optimization problem. To 
explore the Pareto front intelligently “innovization” can be used to learn 
and deduce common patterns from the obtained Pareto-optimal solu­
tions [70,71]. The common patterns stay as rules that can be utilized by 
future applications. Exploring the application of MOO and “innoviza­
tion” on refinery processes would result in a better global Pareto front 
approximation with the possibility of highlighting many particular re­
gions of interest. The innovization can decipher new and innovative 
design principles by finding commonality principles among multiple 
solutions. 
The optimization in the oil and refinery field can be conveniently 
formulated as MOO problems that comprise a relatively large number of 
objectives; however, the surveyed studies concentrated on optimizing 
two or three objectives. However, Section 4.1.2 showed that many re­
finery solutions were optimized with a maximum of three objectives, but 
rarely exceeded that. The optimization problems that have more than 
three objectives are often called many-objective problems. 
The optimization of an industrial HDC unit was reported to produce 
valuable products via processing the heavy distillates [54]. A hydro­
treater (HT) reactor packed with two beds, followed by a hydrocracker 
(HC) reactor packed with 4 beds. A model for HT and HC was used and 
the reaction products were taken into eight components (e.g. light 
naphtha, liquefied petroleum gas, heavy naphtha, kerosene mass flow, 
light diesel mass flow rate, heavy diesel mass, a flow rate of unconverted 
recycle oil, and flow rate of unconverted oil product. The HC model was 
developed based on assumptions, including a plug-flow reactor without 
axial diffusion, adiabatic, and steady-state operation. The quantities of 
H2, H2S, and NH3 are known from the stoichiometry of reactions for the 
sulfur and nitrogen compounds, i.e. benzothiophene and quinolone. The 
quantity of other light components is assumed to be constant throughout 
the HT process. Objectives were selected depending on industrial pri­
orities. Moreover, they were applied for maximizing diesel, kerosene 
and naphtha production while minimizing off-gases, LPG production, 
and hydrogen consumption. Because of several objectives, the MOO 
problem has been divided into three cases:  
• Max (kerosene production) and Min (makeup H_2 flow rate)  
• Max (heavy diesel) and Min (makeup H_2 flow rate)  
• Max (heavy-end products) and Min (production of light-ends) 
An extended version of NSGA-II, named NSGA-III [72], was recently 
proposed to handle many-objective optimization problems. Despite 
NSGA-III works well with problems that have multiple objectives, it has 
not yet been utilized in the refinery field. 
In practice, optimization problems change with time; thus they need 
to be treated as a dynamic optimization problem [73]. The changes in oil 
and refinery optimization problems can be either in the objective 
functions, constraints, decision variables, or any combination of them. 
Nevertheless, there is a lack of studies in addressing dynamic MOO 
problems. Referring to the studies investigated in this review, there were 
Table 5 
List of refinery processes and objectives optimized in the surveyed studies.   
[48] 
[49] 
[50] 
[41] 
[51] 
[24] 
[52] 
[53] 
[54] 
[55] 
[56] 
[57] 
[58] 
[59] 
[21] 
[60] 
olefin 
✓                
aromatic  
✓ 
✓  
✓  
✓          
sulfur   
✓ 
✓  
✓ 
✓          
nitrogen   
✓              
hydrogen mole fraction        
✓         
Kerosene         
✓        
heavy diesel         
✓        
ethylene          
✓ 
✓ 
✓ 
✓ 
✓ 
✓  
propylene           
✓ 
✓ 
✓ 
✓ 
✓  
butadiene            
✓     
Gasoline                
✓ 
CO                
✓  
H.A. Al-Jamimi et al.                                                                                                                                                                                                                          
Fuel 288 (2021) 119678
12
no adequate dynamic MOO procedures used to track Pareto-optimal 
frontiers. 
The decision-making task is essential to take full advantage of the 
application of MOO algorithms. Finding the best solution includes two 
tasks is an optimization that discovers optimal solutions trading-off 
between conflicting objectives. Then, a decision-making task is to 
select a distinct solution from them [74]. Nevertheless, we found that 
the concern when solving MOO problem was limited to find a complete 
set of Pareto-optimal solutions. The refinery optimization studies mostly 
focused on the optimization aspects to find a set of Pareto-optimal so­
lutions in a problem. Thus, it would be advantageous to employ an 
interactive process to carry out integral MOO and decision-making. The 
process starts as solving a real MOO problem, then the decision-maker 
would assist in choosing a single preferred solution. 
The optimization of the refinery processes parameters under uncer­
tainty, such as decision variables or objective/constraint functions, has 
not been addressed. However, existing studies are designed to deal with 
optimization under uncertainty, such as the refinery planning [75] and 
integrated oil supply chain [76,77]. There are other real-world appli­
cations using MOP in Chemical Engineering [78]: modeling of chemical 
processes, polymer extrusion and other applications [79–84]. Moreover, 
it is believed that many-objectives optimization problems with their 
applications in petroleum and refinery processes are still of great interest 
from the academic and industrial point-of-views. The application of 
MOO contributes to the technical breakthrough in solving the resulting 
oil refinery problems. However, there are still several areas that pose 
various challenges. Fig. 5 demonstrates that optimization applications 
can be employed in various refinery-related problems. In summary, 
previous studies lack a discussion of an end-to-end methodology. Such a 
method should help in optimizing competing objectives, listing Pareto 
optimal fronts, and highlights the best decision. 
7. Conclusions and future directions 
This review has revealed a range of optimization methods used in oil 
refineries to optimize simultaneous objectives in different processes. The 
optimization and decision-making are the two milestones of any MOO 
application. Usually, decision-makers need definitive solutions based on 
their preferences, which they deem appropriate in practice. MOO gives 
the practitioners more conscious and better choices through a deep 
understanding of the problem and the existing alternatives. The review 
has presented state-of-the-art research conducted to optimize simulta­
neously of refinery processes, namely, hydrotreating, desulfurization, 
and cracking. Finally, it has highlighted a variety of open research 
challenges and identified possible future trends for MOO in the oil re­
finery. In addition, it has been argued that ‘Innovization’ concept can be 
employed to decipher innovative design principles by finding com­
monality principles among multiple trade-off solutions. This would 
assist practitioners, not only to find a feasible solution, but also key 
knowledge for future applications, which would be extremely useful for 
practical problems. 
Declaration of Competing Interest 
The authors declare that they have no known competing financial 
interests or personal relationships that could have appeared to influence 
the work reported in this paper. 
Acknowledgment 
The authors would like to acknowledge the help and support pro­
vided by King Fahd University of Petroleum and Minerals (KFUPM) 
through funding the project number DF181023. 
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H.A. Al-Jamimi et al.

An initiative towards sustainability in the petroleum industry: A review
S. Bathrinath ⇑, N. Abuthakir, K. Koppiahraj, S. Saravanasankar, T. Rajpradeesh, R. Manikandan
Department of Mechanical Engineering, Kalasalingam Academy of Research and Education, Krishnankovil 626126, Tamil Nadu, India
a r t i c l e
i n f o
Article history:
Received 15 December 2020
Received in revised form 2 January 2021
Accepted 10 February 2021
Available online 5 March 2021
Keywords:
Sustainability
Petroleum industry
Social sustainability
Environmental sustainability
a b s t r a c t
Rapid industrialization has affected the environment to a large extent. Degradation of the environment
has altered the biodiversity’s nature, resulting in loss of ecosystem and raising the temperature.
Because of the adverse environmental impact, the industrial community faces a wide level of criticism
from society. Pressure from society forces the industrial community to adopt sustainable manufacturing
practices. Although sustainable manufacturing practice offers a wide range of beneﬁts, there are several
difﬁculties faced by the industrial community in adopting sustainability. One of the industrial sector fac-
ing societal pressure is the petroleum industry. The petroleum industry is facing challenges in all three
spheres of sustainability, i.e., social, economic, and environmental. This study provides an overview of
the challenges related to the petroleum industry. The outcome of the study will beneﬁt the research scho-
lar and policy makers involved in sustainability assessment.
 2021 Elsevier Ltd. All rights reserved.
Selection and peer-review under responsibility of the scientiﬁc committee of the 3rd International Con-
ference on Materials, Manufacturing and Modelling.
1. Introduction
Because of rapid industrialization, the environment has been
degraded at an exponential range. The industrial community’s
hunger for energy, raw material, and water has led to unchecked
exploitation of the available environmental resources. Most of
the industries are not concerned with the adverse impact of their
industrial activity. After the Brundtland report (1987), the indus-
trial community started emphasizing the industries’ environmen-
tal performance [1]. As a result, the concept of integrating
sustainability in industrial activity has been the prime focus among
the industrial community for the past few decades [2]. Though the
industrial community has expressed their interest in sustainability,
the journey is full of obstacles. The concept of sustainability covers
three broad aspects, namely environmental, economic and social
aspects. Hence, an integrated, holistic approach is required for
the industrial community in adopting sustainable manufacturing
practices. The concept of sustainability is often mistaken as the
concept of the triple bottom line (TBL). However, the sustainability
concept goes beyond TBL [3]. A sustainable manufacturing practice
emphasizes the products and processes that are economically
viable, have a minimal adverse environmental impact, conserve
natural resources and safeguard the employees and the environ-
ment. Adoption of sustainable manufacturing practice will give a
competitive edge to the industrial community. To ripe the beneﬁt
of sustainable manufacturing, the industrial community need to
adopt sustainable manufacturing practice as a long term strategy.
The possibility of moving rapidly towards sustainable consump-
tion and production is not a cake walk for any industry. Such tran-
sition needs severe disruptions in terms of individual, region and
organization [4]. Such transition involves fundamental socio-
economical and organizational reconﬁguration. In general, an orga-
nization involves three subsystem namely production, distribution
and consumption. Such transition refers to a fundamental shift of a
sectoral regime, in terms of the interaction between three analyt-
ical levels: niches, the regime, and the landscape [5]. Four types
of transition pathways have been suggested for sustainability tran-
sition:
transformation,
technological
substitution,
re-
conﬁguration, de-alignment and re-alignment.
Nowadays, the industries show more interest in sustainable
manufacturing practices due to pressure from the customers, gov-
ernment and environmental regulators. The transition towards sus-
tainable manufacturing needs a large amount of capital investment,
technological support and organizational change. In this sense, the
transition towards sustainable manufacturing is easy for large-
scale industries compared to small scale industries [6]. However,
in many developing countries, the number of small scale industries
outnumbers large scale industries. The small scale industries are
very instrumental in the economic growth of developing countries.
India, a developing country’s economy, is largely dependent on the
small scale industry’s economic activities. Industries like the petro-
https://doi.org/10.1016/j.matpr.2021.02.330
2214-7853/ 2021 Elsevier Ltd. All rights reserved.
Selection and peer-review under responsibility of the scientiﬁc committee of the 3rd International Conference on Materials, Manufacturing and Modelling.
⇑Corresponding author.
E-mail address: bathri@gmail.com (S. Bathrinath).
Materials Today: Proceedings 46 (2021) 7798–7802
Contents lists available at ScienceDirect
Materials Today: Proceedings
journal homepage: www.elsevier.com/locate/matpr
leum industry, leather industry and chemical industry largely affect
the environment [7]. The industrial progress towards sustainable
manufacturing in developing is still at a nascent stage compared
to developed countries. For measuring the performance of the
industries towards sustainability, various sustainability assessment
methodologies like strategic environmental assessment (SEA),
social life cycle assessment (SLCA), eco-efﬁciency (EE) analysis
and health impact assessment (HIA) have been used.
In September 2015, the United Nations adopted 17 Sustainable
Development Goals, covering the global challenges facing human-
ity, including poverty, inequality, health, education, energy, cli-
mate, the environment, and prosperity [8]. The organization’s
2030 Agenda for Sustainable Development is a continuation of its
Eight Millennium Development Goals, with, among notable addi-
tions, the inclusion of energy access as a separate goal. Energy is
central to many of these goals through its linkage to the economy,
education, health, the environment, and water. In 2019, nearly a
billion people (more than 13% of the world population) do not have
access to electricity, and 3 billion people lack access to clean cook-
ing solutions, even within oil and gas producing/exporting coun-
tries [9]. Petroleum reserves in a country presents its policy
makers with a challenging and complex task. The challenge
includes formulating and agreeing on policies that will shape the
country’s petroleum sector and guide the translation of the newly
discovered resources into equitable and sustainable economic and
social growth for the nation over the long term [10].
This study focuses on the adverse environmental impact cre-
ated by the petroleum industry as it affects the environment, local
community and the workers to a large extent. The major issues
faced by the petroleum industries are the disposal of oil residues
generated from storage, processing, and transportation facilities
[11]. The petroleum industry is one of the core industries in India.
The petroleum sector which involves reﬁning, transportation, and
marketing, contributes about 15% to India’s GDP [12]. The petro-
leum industry is highly known for its capability in earning foreign
currency, which accounts for 17% of the total exports.
For this study, extent literature survey was carried out. Litera-
ture reviewed for the study were referred from leading science
database like Science Direct, Web of Science, Google Scholar, Wiley
and Taylor and Francis. Keywords and Boolean operators like issues
OR challenges in sustainability, Problem AND barriers faced by pet-
roleum industries, challenges faced by developing AND developed
countries in sustainability were used.
2. Environmental sustainability
The petroleum industry, which involves exploration of the earth
crust, converts the raw material into petrol. During the conversion
process, different products like gasoline, lubricating oil, and waxes
are produced [13]. Environmental impacts of the petroleum indus-
try may inﬂuence species, populations, assemblages, or ecosystems
by modifying various ecological parameters (e.g., variety, biomass,
productivity, etc.). At the project level, potential impacts unit of
measurement
is
sometimes
assessed
through
some
formal
methodology, termed associate environmental impact assessment
(EIA) [14]. Environmental management ways in which, consider
those to avoid and minimize the environmental impacts of comes,
unit of measurement set throughout the EIA methodology and can
become conditions of operation. As a result, this part of the EIA
methodology is especially necessary for preemptively avoiding
serious impacts to the marine setting [9]. Routine oil and gas activ-
ities can have reconsidered environmental effects throughout each
of the foremost phases of exploration, production, and decommis-
sioning [15]. During the exploration section, impacts could result
from backhanded (sound and trafﬁc) and direct physical (anchor
chains, drill cuttings, and boring Fluids) unsettling inﬂuence. More
direct physical impacts occur at intervals in the assembly section
as pipelines area unit organized and boot the number of discharged
created water will increase. Placement of infrastructure on the sea
bottom, like anchors and pipelines, will directly disturb the bottom
and cause a transient increase in native alleviation [16]. Coastal
and marine biological resources are highly affected by the spills
of the petroleum industry during transportation.
Although Iran is the ﬁfth largest producer of oil in the world,
Iran is unable to meet the local demands in term of transportation
and gasoline needs [17]. This is because of low efﬁciency of Iranian
reﬁneries and negative impact of the reﬁneries on the environment
and the society. Another study in Ghana context reveals that coun-
tries are facing many difﬁculties in moving towards sustainability.
The ﬁsherfolk in the Western Region of Ghana are under high
socioeconomic vulnerability because of decreased ﬁsh catch and
declining coastal livelihoods [18]. This is the result of adverse envi-
ronmental impact. In particular, the developing countries are fac-
ing more challenges in comparison with developed countries.
Being devoid of technological assistance and ﬁnancial assistance,
the developing countries are struggling in moving towards sustain-
ability. Despite sufﬁcient funds from the government and the
implementation of a diversiﬁcation strategy, it has been observed,
Ghana is characterized by high budget deﬁcits and borrowing
levels [19]. This indicate numerous technological developments
based on R&D activities are required in the transition towards sus-
tainability. For this, the developing countries need immense sup-
port from the developed countries in term of technology and
capital. Supply chain players in emissions-intensive industries
are not likely to be motivated to make appropriate green improve-
ments because signiﬁcant investment is required for innovation
and process improvement. Properly planned government interven-
tion can increase the supply chain performance and may assist in
achieving sustainable goals [20]. The Nigerian oil industry is
divided into three sectors: the upstream petroleum sector which
comprises of exploration, production and the downstream which
deals with reﬁning of crude oil for domestic consumption, market-
ing, transportation and the midstream which deals with the natu-
ral gas. Lack of working reﬁneries and heavy pressure on
infrastructure from resultant importation has been a key cause of
supply shortages. In this regards, the implication of lean six sigma
has been suggested as a viable option in mitigating the environ-
mental impact [21]. The lean concept has been used to improve
operational and technical aspects, contractor/supplier relation-
ships, team organization and project management practice in the
petroleum industry. For this four elements are critical, namely
leadership and commitment from management, employee involve-
ment, cooperation and trust with contractors/suppliers and lean
project management. These elements are the pillars that are
founded on lean philosophy and principles to support technical/-
operational improvement in the organization [22]. Petroleum poli-
cies tend to vary signiﬁcantly from country to country. As a result,
there is no one-size-ﬁts-all policy and there are no clear-cut
answers to the many potential policy dilemmas associated with
the discovery of petroleum resources.
3. Social sustainability
Besides having several environmental drawbacks, the Petroleum
industries are also criticized for their adverse impact on society
[23]. The social dimension of sustainability concerns the impacts
an organization has on the social systems such as labour practices,
human rights and relationship with communities within which it
operates. The indicators surround around labour practices and
decent work, human rights, society and product responsibility
S. Bathrinath, N. Abuthakir, K. Koppiahraj et al.
Materials Today: Proceedings 46 (2021) 7798–7802
7799
[24]. Responsibility towards social justice issues is the ability of a
ﬁrm to take actions and be accountable for its social and environ-
mental impacts on the society. The work identifying with organiza-
tion
of
these
Rules
covers
endorsement
of
Reﬁneries,
Petrochemicals/Oil and Gas Processing Plants, transport of oil via
land and pipeline, Flameproof other appropriately ensured electri-
cal contraption and other well-being hardware for use in zones
loaded down with combustible gases and vapor’s, authorizing of
Petroleum apportioning/administration station, Petroleum stock-
piling establishments, Tank trucks for transportation by street, air-
plane 7 refuelled and issuance of Certiﬁcate of Gas Free in regard of
Vessels/Ships conveying oil for moor passage, man section or hot
work, up to most recent 10 years (2010–2019) [25-28]. The cus-
tomer, communities and the business impact were identiﬁed and
managed by the way called social sustainability. The company pro-
vides more importance in social sustainability to maintain good
relationships with people and society. The safety of the workers in
each location will be considered by social sustainability and it won’t
allow any compromise on the safety of the workers [29]. The work-
ers will not allow to a dangerous working environment without any
safety measures. The quality in the brand and the product is due to
the poor social sustainability, and it makes risk and improper safety
practices. Attainable social sustainability makes the company more
transparent and increases ethics in every operation. Social sustain-
ability enhances safe practices in the company [14].
According to a report by the World Health Organization (WHO),
one of the causes of cancerous diseases for men and women, which
have increased in recent years in Iraq, are the petroleum industrial
risks. In addition, according to the annual statistical collection
issued by the Central Statistical Organization in Iraq, the number
of cancer disease cases for 2012, 2013amounted to 21101, 23308,
respectively, and the number of deaths due to the same disease
for the same years 10,278 and 8341, respectively [30-32]. One of
the greatest challenges arising from the activities of the interna-
tional oil companies (IOCs) in Nigerian petroleum industry is the
devastation caused on the environment. The effects of this devas-
tation, most often than not, have grave consequences on the enjoy-
ment of human rights by the local population. One of the methods
of ensuring good corporate human rights practices by the IOCs has
been through corporate social responsibility (CSR) strategies. Lack
of marketization, excess oil reﬁning capacity, high external depen-
dency, environment pollution and unstable international trading
relationship are identiﬁed as the challenges in the transition
towards sustainability [33-35]. Government should call for the
merger of small and medium-sized reﬁneries and the construction
of integrated reﬁning and chemical enterprises, thereby to acceler-
ate the phase-out of obsolete reﬁning capacity. Next, diversiﬁca-
tion of the oil supply sources can be contributed by increasing
domestic production, enriching the source of oil imports, enhanc-
ing overseas investment and establishing strategic petroleum
reserves. Finally, domestic petroleum enterprises’ anti-risk capa-
bility should be improved in response to volatile oil prices brought
by international trade disputes. Domestic oil reﬁning capacity has
been increasing because the local governments blindly support
local reﬁning and chemical enterprises [36,37]. China imposed
strict controls on oil imports to help maintain order in the domes-
tic market. China’s petroleum market was characterized by low
efﬁciency, leading to a decline in oil production and heavy losses
suffered by petroleum enterprises.
4. Economic sustainability
The resource conservation that deﬁnes the value of the resource
today and the future is called an economy. It will help to indicate
the assets, savings and debts and also patents [38,39]. The long
term cost of the resource is considered economic sustainability in
the form of human and material. Economic sustainability helps
develop the business and reduce the loss in savings and other
resources. It will act as the baseline of emerging business activities
and prove safe business practice. In moving towards economic sus-
tainability, the petroleum industry needs to ensure process safety.
Any fault in the execution of the process results in complete dam-
age to the plant [40]. Further, during petroleum transportation,
caution must be exercised as the petroleum products are highly
inﬂammable. The friction of the transporting vehicle may also
result in accidents [41-44]. The oil industry plays a central role
in the modern economy and society [45]. However, intensive and
extensive exploitation of oil sources often leads to environmental
degradation, thus raising sustainability concerns. Despite the
importance of the oil industry, the literature largely neglects the
sustainability related challenges in the management of supply
chains. Supplier Selection strategy impacts upon Execution func-
tion in terms of contract and quality standards compliance. As a
consequence, oil and gas distribution companies have to continu-
ously monitor and assess their supplier commitment to contract
requirements and its compliance with quality of service/product
standards and regulations. Another area of interest is suppliers’
sustainable oriented technical capability—its capability to pos-
sesses equipment and technologies [46-48]. Development brings
about political, social, economic, and environmental changes to
enhance the overall quality of life sustainably. It should be the
responsibility of medium- and large-scale mining companies to
incorporate CSR to encourage the sustainable development of local
communities in nearby mining operations.
Supplier Selection strategy impacts upon Execution function in
terms of contract and quality standards compliance. As a conse-
quence, oil and gas distribution companies have to continuously
monitor and assess their supplier commitment to contract require-
ments and its compliance with quality of service/product stan-
dards and regulations [49]. Another area of interest is suppliers’
sustainable oriented technical capability—its capability to pos-
sesses equipment and technologies. In terms of Execution function,
use of fuel efﬁcient vehicles, to some extent even hybrid or electric
vehicles may become part of their business model. As a conse-
quence, preference for environmental-friendly transport modes
has to be considered by oil and gas distribution companies, making
them prone to use more fuel efﬁcient suppliers both form corpo-
rate social responsibility reasons and to cut costs [50]. All this
may determine a need for transport safety training focus, adequate
consideration of safety and health risks of transport mode and
responsible inspection and maintenance of vehicles.
5. Conclusion
The study aims to provide an overview of the petroleum indus-
try’s challenges, particularly for the petroleum industry located in
India, a developing country. In this study, all three major domains
of sustainability are discussed in detail. The outcome of the study
will provide a comprehensive summary of the sustainability issues
in the petroleum industry. The reason for selecting the petroleum
industry is that it leaves an adverse environmental impact. Hence,
it becomes essential to analyze the challenges faced by the petro-
leum industry.
 From the above information, it is very clear that minimal impor-
tance has been paid by the petroleum industries in moving
towards sustainability. In term of social sustainability, it is very
essential for
industries to adopt sustainability. Switching
towards sustainability will bring in a change about the view
of the industry.
S. Bathrinath, N. Abuthakir, K. Koppiahraj et al.
Materials Today: Proceedings 46 (2021) 7798–7802
7800
 In term of economic sustainability, the petroleum industry have
to adopt lean six sigma strategy. Minimization of materials and
activities may bring down the cost incurred in these activities.
Further, it will bring a change in the ﬁnancial activities of the
industrial community.
 Also, the petroleum may consider the adverse environmental
impact created by the industry during processing stage. Opting
for green manufacturing and sustainable manufacturing may
lower the consumption of non-renewable resources.
CRediT authorship contribution statement
S. Bathrinath: Conceptualization. N. Abuthakir: Writing - orig-
inal draft. K. Koppiahraj: Validation. S. Saravanasankar: Supervi-
sion. T. Rajpradeesh: Methodology. R. Manikandan: Resources.
Declaration of Competing Interest
The authors declare that they have no known competing ﬁnan-
cial interests or personal relationships that could have appeared
to inﬂuence the work reported in this paper.
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7802

Resources Policy 65 (2020) 101543
Available online 25 November 2019
0301-4207/© 2019 Elsevier Ltd. All rights reserved.
Assessment of oil refinery performance: Application of data envelopment 
analysis-discriminant analysis 
Amani Mohammed Atris 
School of Environment and Society, Tokyo Institute of Technology, 3-3-6, Shibaura, Minato-ku, Tokyo, 108-0023, Japan   
A R T I C L E  I N F O   
Keywords: 
Refinery 
Efficiency 
Data envelopment analysis 
Discriminant analysis 
Operational efficiency 
A B S T R A C T   
Refineries are vital not only in the oil and gas industry but also in many other industries. Refinery products are 
essential for many industrial sectors and end-users. However, the market for refinery products has witnessed a 
significant transformation due to changes made in products to meet market demand and environmental regu­
lations. This study examines refineries’ operational efficiency and conducts an efficiency-based rank assessment 
using an unbalanced panel dataset comprised of oil and gas refineries in four global regions (U.S. and Canada; 
Europe; Asia-Pacific; Africa and the Middle East) covering 2008 to 2017. This study applies a combination of data 
envelopment analysis and data envelopment analysis–discriminant analysis to examine the efficiency-based rank 
for oil and gas refineries. A Kruskal–Wallis rank sum test is conducted to examine whether the average efficiency- 
based ranks measures change over time and whether they differ among the four regions. Moreover, a Wilcoxon 
rank sum test is utilized to investigate whether the adjusted efficiency averages differ between any two of the 
regions under study over the 10 years between 2008 and 2017. The results indicate that the U.S. and Canada 
display superior performance among the four regions, likely because that region contains major companies and 
complex refineries that use highly advanced technology.   
1. Introduction 
The oil and gas (O&G) industry’s supply chain consists of an upper 
sector (exploration and development) and a lower sector (refinery and 
sales). Refineries are vital in the O&G industry. Their products, such as 
petrochemicals, gasoline, and diesel, are crucial products for many in­
dustrial sectors and end-users. Oil refinery products fuel most of the 
world’s transportation modes. Furthermore, industrial and commercial 
activities and electricity generation use products refined from crude oil. 
The market for refinery products has witnessed a significant trans­
formation over the last few decades due to changes in products made to 
meet market demand and environmental regulations. Although the 
refining market is mature and globally widespread, the profit margins of 
refineries in developed countries have dropped significantly in the past 
few years due to the high prices of refined oil products. The decline in 
refineries’ profit margins has been driven mainly by the increase in the 
prices of raw materials (e.g., crude oil prices and proposed local gov­
ernment taxes), which has raised the total costs of final refined oil 
products. Accordingly, the corporate leaders of refineries are facing 
pressures that could lead to future divestitures of their business. On the 
other hand, developing countries have witnessed increases in oil 
consumption. As a result, refineries in Asia, the Middle East, and Eastern 
Europe have significantly upgraded their facilities and expanded their 
production scale over the past decade. 
Refineries must consider their configuration processes in order to 
adapt to global market changes. Refinery configuration is the combi­
nation of refinery process units used to make refined products. 
A great number of process units increases refinery complexity, and 
refinery type defines a refinery’s complexity level. There are four types 
of refinery: topping, hydro skimming, conversion or cracking, and deep 
conversion or coking refineries. Topping refineries are the simplest type; 
they work only on crude distillation and basic operation process. Hydro- 
skimming refineries include crude distillation process units and more 
advanced units, such as those for catalytic reforming, hydro treating, 
and product blending, which upgrade naphtha to gasoline and control 
the sulfur amount. Cracking refineries include not only hydro-skimming 
characteristics but also catalytic cracking and hydrocracking processes, 
so that they can convert the heavy crude oil fraction to lighter products 
(e.g., gasoline, petrochemical, jet and diesel fuel). Coking refineries are 
the most complex type. They can convert the heaviest oil residuals to 
lighter streams and valuable products. Cracking and coking refineries 
are required to meet the market demand for qualified light products. 
E-mail address: mohammed.a.aa@m.titech.ac.jp.  
Contents lists available at ScienceDirect 
Resources Policy 
journal homepage: http://www.elsevier.com/locate/resourpol 
https://doi.org/10.1016/j.resourpol.2019.101543 
Received 29 August 2019; Received in revised form 7 November 2019; Accepted 13 November 2019   
Resources Policy 65 (2020) 101543
2
Refinery complexity depends on several factors, including product de­
mand, local economics and regulations, and crude oil composition (e.g., 
heavy/light, sour/sweet). All of these factors must be considered in 
order to enhance efficiency and meet rapidly changing regional and 
global market trends. The key goal is to gain higher market share and 
achieve a higher level of profitability in order to maintain competi­
tiveness in the market. 
The profitability of refineries depends on many factors (see Ban­
dyopadhyay et al. (2019); Cerd
a et al. (2018); Zhao et al. (2017); Kor­
otin et al. (2017); Zhang et al. (2001)) such as the prices of crude oil and 
other raw material inputs, the characteristics of the regional markets in 
which the refinery operates and sells its products, refinery operations, 
process capacity, process complexity, the current structure of the re­
finery industry, and the operational efficiency of the refinery company.1 
Global market regulations and standards, which require certain 
environmental performance levels for refinery operations and major 
refined products, affect all refineries. Thus, refineries must take a global 
perspective when facing global market challenges. 
One global environmental protection factor is that the International 
Maritime Organization (IMO), launched in October 2016, will become 
effective in January 2020. This initiative aims to reduce the amount of 
sulfur emissions in bunker fuel from its current 3.5% to a maximum of 
0.5%.2 Hence, refineries have to produce a low-sulfur product in order to 
meet the environmental regulation. The downstream oil market has to 
adapt to this regulation, and also become more flexible in the face of 
global challenges by enhancing refineries’ ability to meet the global 
market requirements imposed by falling oil prices (an uncontrollable 
factor), new regulations on sulfur in marine bunker fuel, and other 
factors such as regional expansion (e.g., the Asia-Pacific region and the 
Middle East). 
Refinery market capacity varies across regions. Fig. 1 shows the ca­
pacity trends in the four regions examined in this study,3 showing that 
the Asia-Pacific region has the highest capacity, followed by the U.S. and 
Canada, Europe, and the Middle East and Africa. Asia Pacific and the 
Middle East and Africa have seen increasing trends over the last 10 
years. On the other hand, the capacity of refineries in Europe has 
decreased since 2012, while capacity in the U.S. and Canada region is 
relatively stable, with only a slight increase since 2015. 
These regional capacity trends are consistent with future market 
expectations according to which Asia is expected to lead, with a capital 
expenditure (capex) of US$254 billion allocated for announced and 
planned projects up to 2022; this is followed by Africa and the Middle 
East, with US$117 billion and US$88 billion, respectively.4 On the other 
hand, Western Europe has seen many refinery shutdowns, and a 
maximum of 800,000 barrel/day is at risk of shutdown because of sig­
nificant challenges caused by changes in crude oil prices, decreasing 
demand for refineries’ products, and environmental regulations.5 These 
regional factors are affecting refinery capacity. 
The purpose of this study is to investigate O&G refineries’ opera­
tional efficiency in four global regions (U.S. and Canada, Europe, the 
Asia-Pacific, and Africa and the Middle East) using an unbalanced panel 
dataset comprised of 696 global O&G refineries between 2008 and 2017, 
and discuss policy implications for the refineries industry operations in 
every region by obtaining empirically efficiency based rank measures. 
This study uses a combination of data envelopment analysis (DEA) 
and DEA-discriminant analysis (DEA-DA). DEA classifies refineries into 
two categories (efficient and inefficient) based on their efficiency scores. 
Then, DEA-DA is utilized to evaluate all refineries’ operational effi­
ciency scores and ranks to get an adjusted efficiency score for each re­
finery. DEA-DA reduces the number of efficient refineries and generates 
a single efficient decision-making unit (DMU) to present a wild industry 
assessment based upon the efficiencies ranks. Further, due to a lack of 
statistical inference methods used to complement DEA and DEA-DA 
computations, a Kruskal–Wallis rank-sum test is conducted to examine 
whether the average adjusted efficiency-based ranks measures change 
over time and whether they differ among the four regions. Moreover, a 
Wilcoxon rank-sum test is utilized to investigate whether the adjusted 
efficiency averages differ between any two of the regions under study 
over the ten years between 2008 and 2017. Combining these methods 
aids the decision making of corporate leaders and policymakers because 
it allows them to compare operational performance among regional 
refineries in terms of their efficiency level and of their overall ranking. 
Corporate leaders tend to pay more attention to their firms’ ranking in 
their industry sector than to their comparative level of efficiency. Effi­
ciency measures and rankings reveal the position of a refinery within the 
industry and indicate which refineries have room for performance 
improvement. As mentioned, our performance assessment examines 
differences across four regions: the U.S. and Canada, Europe, the Asia- 
Pacific, and Africa and the Middle East. 
The remainder of this paper is organized as follows. Section 2 re­
views the literature on DEA studies of O&G refineries. Section 3 dis­
cusses the study’s dataset, DEA and DEA-DA formulations, and rank sum 
tests (Kruskal–Wallis and Wilcoxon). Section 4 discusses the study’s 
empirical results on the refineries’ efficiency and rankings based on our 
regional classification. Section 5 presents the study’s conclusion and 
outlines potential future research possibilities. 
2. Literature review 
2.1. DEA studies applied to O&G refineries 
The DEA method is widely used in many studies as a holistic 
approach to evaluating the relative performance of a group of peer units, 
called DMUs. Glover and Sueyoshi (2009) reviewed prior studies and 
documented the contribution of Professor William W. Cooper in terms of 
the historical development of the DEA. Sueyoshi et al. (2017) conducted 
a survey of 693 studies that applied the DEA to energy and the envi­
ronment, classifying them into groups according to topic. Mardani et al. 
(2018) summarized 145 DEA studies on energy and environmental is­
sues. They provided an overview of studies using different DEA appli­
cation types along with an explanation of their inputs and outputs. 
Furthermore, many studies have applied the DEA in combination 
with a productivity index. For example, Chan Oh and Hildreth (2014) 
measured technical improvements of energy efficiency in the automo­
tive industry. Yang and Li (2017) conducted efficiency evaluations and 
policy analysis of industrial wastewater control in China. Hsu (2013) 
performed an international comparison of the efficiency of government 
health expenditures. F€
are et al. (2004) evaluated environmental per­
formance using a formal index number (a kind of environmental pro­
ductivity index), and Chowdhury et al. (2014) investigated productivity, 
efficiency, and technological changes with and without case-mix used as 
output categories in Ontario hospitals between 2002 and 2006. Feng 
et al. (2018) analyzed the sources of green total-factor productivity 
(GTFP) changes and its inefficiency of China’s metal industry (MI) from 
2000 to 2015, from regional and provincial view. Song et al. (2018) 
proposed a comprehensive decomposition framework that integrated 
production-theoretical decomposition analysis with index decomposi­
tion analysis to distinguish the driving factors of CO2 emissions from 
1 See: THAILAND INDUSTRY OUTLOOK 2018–20, Refinery Industry. 
(https://www.krungsri.com/bank/getmedia/ab98f86c-6415-4f61-97c7-121 
26ba58c65/IO_Refinery_2018_EN.aspx).  
2 See (http://www.seatrade-maritime.com/images/PDFs/SOMWME-whitep 
aper_Sulphur-p2.pdf).  
3 Data source: BP Statistical Review of World Energy (2018)(https://www. 
bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-eco 
nomics/statistical-review/bp-stats-review-2018-full-report.pdf).  
4 See 
(https://www.hydrocarbons-technology.com/comment/china-nigeri 
a-drive-global-refinery-capacity-additions-capex/).  
5 See 
(https://www.spglobal.com/platts/en/market-insights/latest-news/ 
oil/091418-more-refineries-at-risk-of-closure-in-europe-new-capacities-else 
where-platts-summit). 
A. Mohammed Atris                                                                                                                                                                                                                            
Resources Policy 65 (2020) 101543
3
China’s iron and steel industry between 2000 and 2014. Further, they 
analyzed the different characteristics and drivers of CO2 emissions at the 
national, regional and provincial levels. Feng et al. (2019) examined the 
sustainability of China’s MI since the 21st century through an overview 
and an analysis of GTFP to elucidate the current situation of the energy 
and environment in MI Sector from 2000 to 2015. Ma et al. (2019) 
examined the impact of government regulation on energy and CO2 
emissions performance in China’s mining industry. 
Our review finds that DEA studies on O&G companies, particularly 
studies on O&G refineries, are limited compared with DEA studies for 
other applications, despite the importance of refineries’ products and 
their massive economic impact in the petroleum and petrochemical in­
dustry. Table 1 summarizes DEA studies on O&G refineries, including 
the authors’ names; the studies’ methodology, inputs, and outputs used 
for efficiency assessment; and a brief description of each study. 
Azadeh et al. (2017) evaluated the reciprocal impacts of managerial 
and organizational factors and RE for 41 gas refineries in Iran using DEA 
and statistical methods. The DEA results indicated that two RE factors 
(learning and flexibility) had the most influence on the managerial and 
organizational factors and that the managerial factors had a weaker 
influence on RE than the organizational factors had. The results of the 
statistical methods showed that the data were reliable and revealed a 
strong direct relationship between the two main factors (organizational 
and managerial). Azadeh et al. (2015) applied a combined ANN and 
multivariate approach to evaluate the performance of five gas refineries 
in Iran from 2005 to 2009 by considering two cases (financial indicators 
and financial/non-financial indicators). The refineries were ranked 
using DEA, PCA, ANN, and NTX. A sensitivity analysis found that the 
DEA was the most noise-resistant of the methods and that the results for 
the financial indicators and those for the combined operational in­
dicators differed slightly. 
Bevilacqua and Braglia (2002) evaluated the environmental effi­
ciency of seven AgipPetroli oil refineries owned by Italy’s Eni Group 
Company. In particular, they examined the environmental impact of 
their air emissions from 1993 to 1996. They confirmed that an appli­
cation of an environmental management system (EMS) could generate 
various benefits, such as reduced liability, improved public image, better 
compliance, reduced costs, and better access to capital. Francisco et al. 
(2012) used DEA models to evaluate environmental efficiency on the 
basis of an undesirable output associated with production processes. 
They found that results obtained without using undesirable output as a 
basis could be misleading and that environmental regulations seemed to 
be less effective for efficient refineries. The study also showed the re­
finery age, as an uncontrollable variable, had no significant effect on the 
environmental efficiency of the refinery. 
Li et al. (2017) established a sustainability evaluation index system 
for refinery firms comprising 17 representative indexes for refineries’ 
sustainability development. They applied a DEA-based model to a sus­
tainability assessment of 15 refineries in China. They provided recom­
mendations for enhancing the refineries’ sustainability based on the 
results, such as reducing the total cost per unit, enhancing investments 
in science and technology to maintain continuing innovation, increasing 
the social contribution rate and employee benefits, and considering 
energy-conservation and emissions-reduction policies in order to ach­
ieve clean production. Song and Zhang (2009) presented a novel method 
of evaluating and predicting oil refineries’ performance based on DEA 
and SVM models. They found that SVM could be used to predict whether 
an oil refining enterprise was DEA-efficient because the SVM’s predic­
tion result was identical to that of the DEA model. Al-Najjar and 
Al-Jaybajy (2012) utilized DEA to examine the relative efficiency of a 
sample of oil refineries in Iraq from 2009 to 2010. They proposed using 
their research on oil refineries in Iraq in order to determine how best to 
apply DEA to measure efficiency and overcome efficiency problems. 
Mekaroonreung and Johnson (2010) measured the technical efficiency 
of 113 U.S. oil refineries in 2006 and 2007 by comparing multiple DEA 
methods. They considered undesirable output in the production process 
and found that domestic refineries could improve efficiencies regardless 
of the DEA assumptions used and that environmental regulations 
reduced the amount of potentially desirable outputs produced by some 
facilities. Managi et al. (2004) examined the total factor productivity of 
offshore O&G production in the Gulf of Mexico from 1947 to 1998. The 
study utilized DEA to measure productivity and decomposed it into 
technological change, efficiency change, and scale change. They applied 
DEA to the dataset. The results indicated that productivity declined for 
the first 30 years of the study period but that technological change 
outpaced depletion, causing productivity to increase rapidly, particu­
larly in the last five years of the study period. Sueyoshi (2000) proposed 
a stochastic DEA model that included future information, called a “DEA 
future analysis.” The suggested approach was used to plan the restruc­
turing strategy of a Japanese petroleum company. The results indicated 
Fig. 1. Refining capacity of the four regions.  
A. Mohammed Atris                                                                                                                                                                                                                            
Resources Policy 65 (2020) 101543
4
Table 1 
DEA studies applied to oil and gas refineries.  
Authors 
Methodology 
Description 
Inputs 
Outputs 
Azadeh et al. (2017)  
- DEA  
- Statistical methods 
(Spearman’s test & 
alpha test) 
This study measured the mutual impact of 
Resilience Engineering (RE) and managerial and 
organizational factors for 41-gas refineries in 
Iran.  
- Commitment management  
- Learning  
- Awareness  
- Flexibility  
- Self-organization  
- Redundancy  
- Managerial factors  
- Organizational factors 
Azadeh et al. (2015)  
- DEA  
- Principal 
Component 
Analysis (PCA)  
- Numerical 
Taxonomy (NTX)  
- Artificial Neural 
Network (ANN)  
- Statistical methods 
(T-test) 
This study presented a combined approach for 
evaluating the performance of 5 gas refineries in 
Iran over the period 2005–2009.  
- Number of personnel  
- Total costs (except the cost 
of goods sold (COGS))  
- Personnel education cost  
- R&D cost  
- Fixed non-current assets  
- Stock turnover  
- Asset turnover ratio  
- Current assets turnover 
ratio  
- Amount of refinery’ s fuel 
consumption/amount of 
received sour or sweet gas  
- Return on sales  
- Operating earnings  
- Net income  
- Return on assets  
- Capital return  
- The amount of gas sent to the torch/the 
amount of received sour or sweet gas  
- Operating capacity divided by nominal 
capacity  
- Operating capacity of each LPG 
production unit divided by design 
capacity of each LPG production  
- Operating capacity of each refrigeration 
and dew point control unit divided by 
design capacity of each refrigeration and 
dew point control unit  
- Operating capacity of each sulfur 
production unit divided by design 
capacity of each sulfur production unit  
- Operating capacity of each liquids 
stabilization unit divided by design 
capacity of each liquids stabilization unit  
- Operating capacity of each dehydration 
unit divided by design capacity of each 
dehydration unit. 
Bevilacqua and 
Braglia (2002) 
DEA 
This study examined the environmental 
performance of the seven AgipPetroli oil 
refineries set up in Italy from 1993 to 1996  
- Power planet fuel 
consumption  
- Oil processed  
- Refining planet fuel 
consumption  
- SO2  
- NOx  
- Total Suspended Particles (TSP)  
- Volatile Organic Compounds (VOC)  
- CO  
- CO2 
Francisco et al. 
(2012) 
DEA 
This study assessed the environmental efficiency 
for 10 oil refineries in the public sector in Brazil 
in 2004  
- Idleness percentage of the 
operating plant  
- The amount of water 
consumed  
- Desirable: (refinery production volume)  
- Undesirable: (generated effluents) 
Li et al. (2017) 
DEA 
This study examined the sustainable performance 
for 15 refineries in China  
- Asset-liability ratio  
- Comprehensive energy 
consumption per unit of 
output  
- Entire cost per unit  
- Solid waste emissions per 
unit of output  
- Wastewater emissions per 
unit of output  
- Waste gas emissions per 
unit of output  
- Employee turnover rate  
- Return on assets  
- Asset turnover  
- Investment intensity in science and 
technology  
- R&A personnel  
- Comprehensive commodity rate  
- Environmental protection cost per 10 
thousand Yuan of output 
“three wastes”  
1 disposal rates  
2 social contribution rate  
3 income per capital. 
SONG and ZHANG. 
(2009) 
DEA 
Support Vector 
Machine (SVM) 
This study presented a method for evaluating and 
predicting oil refineries performance, using two 
deferent techniques.  
- Manpower cost index  
- Operation cost index  
- Return on average capital employed  
- Operation cost added value index  
- Manpower cost added value index  
- Assets added value index 
Al-Najjar and 
Al-Jaybajy (2012) 
DEA 
This study measured the relative efficiency of 12 
oil refineries in Iraq over the period 2009–2010. 
The study revealed that there is a waste or 
underutilization of resources at the inefficient 
refineries.  
- Crude oil  
- Workforce  
- Electricity  
- Land  
- Naphtha  
- Gasoline  
- Kerosene  
- Fuel oil 
Mekaroonreung and 
Johnson (2010) 
DEA- variable returns 
to scale (VRS) 
This study Investigated the technical efficiency of 
113 US oil refineries in operation over two years, 
2006–2007. They found that domestic refineries 
could improve efficiencies regardless of the 
different DEA assumptions. Further, 
environmental regulations reduced the amount of 
potentially desirable outputs produced by some 
facilities.  
- Equivalent distillation as a 
proxy of capital  
- Energy  
- Crude oil  
- Gasoline  
- Distillate  
- Toxic release 
Managi et al. (2004) 
DEA 
This study examined the hypothesis that 
technological change has offset depletion for 
offshore oil and gas production in the Gulf of 
Mexico using a unique micro-level data set over  
- Number of platforms  
- Ave. platform size 
Number of exploration wells  
- Oil production  
- Gas production 
(continued on next page) 
A. Mohammed Atris                                                                                                                                                                                                                            
Resources Policy 65 (2020) 101543
5
that large gas stations worked more efficiently than small ones. 
Moreover, the O&G industry is a part of the mining, quarrying and 
O&G extraction sector, where crude oil extraction is considered as liquid 
minerals. Therefore, it is worthy of mentioning the other applications of 
DEA on mining and quarrying industries. For example, Zhu et al. (2018) 
utilized slacks-based global DEA to analyze the GTFP of mining and 
quarrying industry and its five sub-industries in China from 1991 to 
2014 concerning to technology, scale, and management. Further, they 
employed a Malmquist index to distinguish the key factors that account 
for the changes of GTFP. The results indicated that the GTFP of the 
mining and quarrying industry improved during the period of study due 
to technological progress, whereas declining in scale efficiency and 
management efficiency curb the growth of GTFP. Li et al. (2019) used a 
modified dynamic DEA SBM model to estimate the coal production ef­
ficiencies and land damage in 24 Chinese provinces from 2011 to 2016. 
They found that seven provinces were fully efficient in all the study’s 
years, efficiencies of coal industry labor force, fixed assets, and coal 
production dropped significantly. However, the land damage was un­
clear, and Beijing and Guangxi had the least mining quantities, but the 
greatest need for land damage improvements. Roman et al. (2017) 
applied two-step DEA to evaluate the efficiency of the mining and 
quarrying industry of Visegrad 4 countries between 2011 and 2015. The 
results revealed that the Slovak Republic is the most inefficient country. 
In addition, they employed the double bootstrapped efficiencies that 
reduce lacks of DEA, and then truncated regression. The results of the 
proposed model show that Gross investments in machinery, equipment, 
construction and alteration of buildings and Human Development Index 
(HDI) have a positive impact on efficiency. Wang and Zhang (2018) 
investigated four key conceptual dimensions of innovation for relative 
technological innovation of China’s coal mine and developed groups of 
indicators to assess the performance of coal mine’s technological inno­
vation activities. Then, they developed an index system based on DEA to 
audit the progress of innovation and development. They found that the 
proposed index system and model could effectively assess coal mine’s 
relative technological innovation capability and get the input gap to­
wards efficiency, safety, and sustainability. Hosseinzadeh et al. (2016) 
applied bootstrap DEA to distinguish the balance of efficiency gains and 
losses for 33 Australian mining firms from 2008 to 2014. The results 
indicated that mining companies involved in exploration and extraction 
activities have been less efficient than those involved in metal process­
ing or mining services. 
2.2. Contribution of this study 
A careful review of DEA studies on O&G refineries indicates that, to 
the best of our knowledge, no previous study has applied a combination 
of DEA and DEA-DA to O&G refineries. Furthermore, this study 
examines more firms than have been examined in previous studies; the 
larger dataset allows a broader analysis. To address the shortcomings of 
previous O&G studies, this study uses a unique unbalanced panel dataset 
for O&G refineries in the four regions listed above covering the 10 years 
from 2008 to 2017. Further, this study reflects the impact of global 
market challenges and the requirement on refinery efficiency. Then, this 
study addressed some practical suggestions to improve the overall re­
finery operational efficiency, which have gained importance in modern 
society as one of the primary energy efficiency issues. 
Moreover, this study investigates differences in the adjusted 
efficiency-based ranks among four regions by the Kruskal-Wallis rank 
sum test, in which the null hypothesis is that there are no differences in 
the average adjusted efficiency levels among the four regions or between 
different periods. As the mathematical structure of DEA applications 
lack statistical inferences, the Kruskal–Wallis rank sum test is required to 
investigate the null hypothesis where different groups or periods have 
the same distribution for average adjusted efficiency levels.6 This study 
also utilizes a Wilcoxon rank sum test to check for differences in average 
adjusted efficiency levels between region pairs, where the null hypoth­
esis is that there is no apparent difference in the average adjusted effi­
ciency levels between any two regions. 
2.3. Hypotheses 
This study proposes three null hypotheses based on the results of the 
previous studies on O&G refineries discussed above. 
The operational efficiency of refineries over the world is determined 
by various vital factors, such as proximity to oil sources, the intensity of 
oil product consumption, oil transportation routes, and refinery con­
struction. Furthermore, refineries vary by type and oil-refining tech­
nology, which condition the complexity level of a refinery. 
A refinery’s complexity is reflected in its oil-refining capacity utili­
zation and the technology used by the refinery to obtain highly purified 
oil products. Differences in complexity levels are driven mainly by the 
regional and locational characteristics of the refineries. Therefore, based 
on these refinery characteristics, we propose that there are significant 
differences in average adjusted efficiency levels among the four regions 
and between any two regions. Our first hypothesis is thus as follows: 
H0.
The average adjusted efficiency measures are invariantly distrib­
uted between the four regions. There is no difference between the av­
erages of their adjusted efficiency levels. 
Table 1 (continued) 
Authors 
Methodology 
Description 
Inputs 
Outputs 
the period 1947–1998. The results indicated that 
the technological change has outpaced depletion 
and productivity has increased rapidly, 
particularly in the most recent five years of the 
study period.  
- Number of development 
wells  
- Average drilling distance for 
exploratory wells  
- Average drilling distance for 
development wells  
- Produced water  
- Weighted innovation index  
- Horizontal & directional 
drilling (exploratory)   
- Horizontal & directional 
drilling (development) 
Sueyoshi (2000) 
A stochastic DEA 
This study suggested the DEA approach for 
planning the restructuring strategy of a Japanese 
petroleum company in 1998–1999. The results 
showed that large gas stations operated more 
efficiently than small ones.  
- Number of employees  
- Size of gas station  
- Operation cost  
- Gasoline  
- Petrol  
6 Bootstrap DEA is proposed by Simar and Wilson (1998, 2000) as a way to 
equip DEA with statistical inference using a sample technique. This study does 
not use such an application. 
A. Mohammed Atris                                                                                                                                                                                                                            
Resources Policy 65 (2020) 101543
6
The second hypothesis is as follows: 
H0.
The average adjusted efficiency levels in any two regions are 
equal. No statistically significant differences are observed between any 
two regions. 
Over the last few decades, international oil refinery regulations have 
been strengthened in order to reduce pollution. Environmental regula­
tion by the IMO is one of the most significant challenges facing refineries 
all over the world. The IMO is pressuring refineries to produce low- 
sulfur products. For instance, refineries in North America and Europe 
have to reduce their sulfur oxide content to levels under 0.1%. While 
Asia and the Middle East are witnessing the construction of many 
complex refineries and anticipating a projected capacity increase of 7 
million additional barrels per day by 2023, the IMO is radically changing 
refineries’ processes (complexity) in order to reduce the sulfur content 
of fuel oil used in ships from 3.5% to a maximum of 0.5%. The IMO’s 
regulations will come into effect in January 2020, which is outside our 
study period. Therefore, the third null hypothesis is as follows: 
H0.
The average adjusted efficiency measures are invariantly distrib­
uted over the 10 years of the study period. No statistically significant 
shifts in average adjusted efficiency levels occur over time. 
3. Methodology 
3.1. Data 
This study uses an unbalanced panel dataset comprising O&G re­
fineries located in the four regions listed above. The dataset was 
collected using S&P CAPITAL IQ PLATFORM7 covering 2008 to 2017. 
This study uses two DEA models: the first is the DEA radial model (input 
oriented), which is used for efficiency assessment; the second is the DEA- 
DA model, which is used for group classification and ranking via a 
financial dataset that contains four inputs and three outputs. These are 
summarized below. 
[Four Inputs].  
 Number of employees: This variable reflects one of the main input 
factors used in the production process, along with capital and ma­
terials. This variable is often used as an index for company size and 
scale. It is the main factor considered in M&A (mergers and acqui­
sitions) decisions because their main target is often to reduce the 
number of employees and labor costs.  
 Total assets: This variable reflects the total amount of investment, 
cash, equipment, receivables, and all other assets as reported in the 
balance sheet. Moreover, total assets reflects the borrower’s fiscal 
strength. This input consists of two kinds of assets: current and fixed. 
More total assets produce more revenue and income. This variable 
can also be used as a firm size index.  
 Total cash and short-term investments: This variable reflects a kind 
of current assets that are highly liquid; these can be used quickly if 
the company needs quick money or cash. Higher cash and short-term 
investments allow firms to hedge against unexpected future fluctu­
ations in profits and to enhance their liquidity ratios.  
 Total debt: This variable reflects the volume of loans and liabilities 
that the company uses to finance its investments, including long- and 
short-term liabilities. Higher total debt to total assets increases the 
degree of leverage and financial risk. 
[Three outputs].  
 Total revenue: This reflects the total amount of money that the 
company receives from the sales of products and services in a specific 
period. Higher revenue indicates that the company is successful in its 
business sector.  
 Net income: This variable indicates the net amount of money (profit) 
that the company earned in a specific period. This variable is 
calculated by subtracting all the business expenses from the amount 
of total revenue. Increased net income indicates a net increase in 
shareholders’ equity, which will be distributed as dividends among 
the shareholders.  
 Total enterprise value (TEV): This determines the overall economic 
value of the company. It is calculated as the market price of a stock 
multiplied by the total number of shares outstanding. This variable is 
an important measure for analyzing potential takeover targets. 
Table 2 presents descriptive statistics for the data on refining com­
panies operating in the U.S. and Canada, Europe, the Asia-Pacific, and 
Africa and the Middle East. The average, standard deviation, minimum, 
and maximum are denoted by “Avg.,” “S.D.,” “Min.,” and “Max.,” 
respectively. In 2008, the Asia-Pacific region exceeds the other three 
regions in all average production factors. This is due to the increased 
demand for refinery products in their markets, particularly China and 
India. Large-scale upgrading is ongoing as a response to this increase in 
market demand. From 2014 to 2017, U.S. and Canada region shows the 
highest values for all production factors except total debt and net income 
in 2014 and 2016. Since 2014. U.S. shale oil has created a boom in the 
crude oil industry in this region and led the US to become a crude oil 
exporter in 2017 for the first time. Shale oil made up more than 1/3 of 
the onshore production of crude oil in the U.S. 
The Asia-Pacific region has the highest total debt among all regions 
in 2008, and from 2011 to 2014, the values were $2421.4, $2492.8, 
$2788.6, $2320.8, and $2277.5 million US, respectively. The U.S. and 
Canada region shows the highest total revenue from 2010 to 2017, at 
$11611.1, $16002.6, $24897.1, $28504.8, $20873.1, $19835.8, 
$17426.9, and $22248.6 million US$, respectively. The input and 
output datasets show the highest average levels for all regions in 2017, 
except for total revenue in 2013, which displays the highest value 
($16254.1 million US). 
3.2. Models 
3.2.1. DEA 
The DEA method is a holistic approach to evaluating firms’ efficiency 
levels which DMUs use multiple inputs and outputs to produce goods/ 
services. The DEA is unique in that it compares the performance of each 
DMU to that of all other DMUs. This study applies a radial input-oriented 
DEA model under the assumption of VRS technology. This model re­
duces inputs to produce a certain amount of outputs and removes the 
scale economy or diseconomy of a DMU from the efficiency score. This 
model is often used in empirical studies of DEA. 
The mathematical symbols used to illustrate production factors are 
as follows:  
(a) Xj ¼ ðx1j; x2j; …; xmjÞT > 0: a column vector of m inputs of the j-th 
DMU (j ¼ 1;…;n), and  
(b) Yj ¼ ðy1j; y2j; …; ysjÞT > 0: a column vector of s outputs of the j-th 
DMU (j ¼ 1; …; n), where superscript “T” indicates a vector 
transpose. The inequality (>) implies that the relationship is 
applied to all components of the two column vectors. 
In addition to the above production factors, which are given as an 
observed dataset, this study uses the following symbols (unknown), 
which are measured by applying the DEA:  
(c) dx
i  0: unknown slack variable of the i-th input (i ¼ 1;…;m),  
(d) dy
r  0: unknown slack variable of the r-th output (r ¼ 1;…;s), 
7 See S&P CAPITAL IQ PLATFORM: https://www.spglobal.com/marketinte 
lligence/en/index. 
A. Mohammed Atris                                                                                                                                                                                                                            
Resources Policy 65 (2020) 101543
7
Table 2 
Production Factors for the refineries’ Companies among four regions on Average.  
Variable 
Outputs 
Inputs 
Net Income 
Total 
Revenue 
Total Enterprise 
Value 
Employees 
Total 
Assets 
Total Cash & ST 
Investment 
Total Debt 
Unit 
Millions$ 
Millions$ 
Millions$ 
Employee 
Millions$ 
Millions$ 
Millions$ 
2008 
Avg. 
U.S. and Canada 
151.3 
10711.2 
1625.0 
2760.7 
2795.4 
109.7 
747.2 
Europe 
59.0 
5674.2 
1711.0 
1472.2 
2004.0 
123.7 
542.2 
Asia- Pacific 
418.3 
14331.7 
5691.9 
5417.4 
7922.0 
603.7 
2421.4 
Africa and Middle 
East 
63.5 
4705.3 
948.1 
937.9 
1636.5 
312.9 
466.1 
Total 
Avg.  
267.2 
11171.1 
3629.9 
3728.5 
5193.3 
399.4 
1550.7 
2009 
Avg. 
U.S. and Canada 
38.2 
2547.7 
790.7 
879.0 
1039.9 
59.3 
289.6 
Europe 
225.5 
9074.4 
4586.6 
6061.3 
6417.5 
262.7 
1959.7 
Asia-Pacific 
67.6 
7789.0 
2391.0 
3532.9 
3438.1 
471.8 
1254.8 
Africa and Middle 
East 
102.3 
3168.8 
990.3 
922.5 
1549.5 
303.6 
521.2 
Total 
Avg.  
85.6 
5813.5 
1983.2 
2675.7 
2838.7 
324.9 
966.3 
2010 
Avg. 
U.S. and Canada 
76.1 
11611.1 
2704.9 
2796.0 
4675.9 
469.6 
1022.6 
Europe 
230.0 
10147.0 
3789.2 
4978.8 
5943.8 
311.6 
1542.8 
Asia-Pacific 
161.1 
8453.5 
3033.1 
3332.6 
4565.6 
438.0 
1435.4 
Africa and Middle 
East 
94.9 
5218.5 
3637.4 
1505.3 
4068.3 
544.7 
1673.8 
Total 
Avg.  
141.4 
8815.1 
3167.3 
3138.7 
4694.1 
445.2 
1402.2 
2011 
Avg. 
U.S. and Canada 
355.5 
16002.6 
2553.0 
3551.2 
5360.8 
416.9 
964.8 
Europe 
153.3 
10258.3 
2418.7 
4132.8 
4772.9 
265.3 
1306.2 
Asia-Pacific 
300.2 
13587.5 
3773.6 
3699.6 
7453.6 
434.8 
2492.8 
Africa and Middle 
East 
83.9 
4857.8 
2151.6 
1244.5 
2840.5 
170.0 
1102.4 
Total 
Avg.  
265.6 
12551.7 
3047.6 
3351.9 
5897.3 
370.9 
1739.0 
2012 
Avg. 
U.S. and Canada 
638.9 
24897.1 
5532.8 
4240.1 
7712.6 
791.8 
1237.1 
Europe 
154.8 
13141.4 
3114.4 
4350.0 
5547.0 
299.4 
1346.9 
Asia-Pacific 
166.6 
15580.4 
4970.4 
3884.5 
8137.7 
438.2 
2788.6 
Africa and Middle 
East 
79.9 
4841.8 
1900.0 
980.6 
2581.1 
223.7 
1134.9 
Total 
Avg.  
276.4 
15718.6 
4310.4 
3459.2 
6636.8 
476.5 
1880.3 
2013 
Avg. 
U.S. and Canada 
596.5 
28504.8 
7770.0 
4489.2 
9004.8 
903.9 
1314.4 
Europe 
152.0 
12704.4 
3521.2 
5865.2 
6249.7 
327.2 
1227.8 
Asia-Pacific 
165.5 
14239.7 
3725.8 
3725.8 
7027.6 
366.5 
2320.8 
Africa and Middle 
East 
63.0 
3486.2 
1234.6 
729.4 
1720.5 
197.6 
592.4 
Total 
Avg.  
265.6 
16254.1 
4407.5 
3618.2 
6633.0 
482.7 
1671.1 
2014 
Avg. 
U.S. and Canada 
564.1 
20873.1 
6388.7 
4094.1 
7427.7 
625.8 
1488.6 
Europe 
30.9 
5967.3 
2979.6 
1879.7 
3065.0 
117.4 
848.2 
Asia-Pacific 
108.9 
11925.9 
3171.5 
2931.8 
6296.1 
495.9 
2277.5 
Africa and Middle 
East 
75.6 
2720.6 
1239.3 
1041.8 
1361.5 
190.9 
448.2 
Total 
Avg.  
271.7 
13570.7 
4067.4 
3022.7 
5795.3 
479.8 
1626.5 
2015 
Avg. 
U.S. and Canada 
838.1 
19835.8 
9706.8 
6012.2 
10721.8 
770.2 
2307.8 
Europe 
252.3 
8602.6 
3386.8 
4412.9 
4707.9 
592.3 
1245.2 
Asia-Pacific 
49.1 
3452.7 
1468.3 
1235.5 
1620.4 
146.5 
493.3 
Africa and Middle 
East 
97.5 
1784.2 
1239.4 
885.9 
1125.6 
126.8 
392.5 
Total 
Avg.  
280.5 
7891.0 
3726.4 
2763.7 
4192.4 
354.6 
1022.7 
2016 
Avg. 
U.S. and Canada 
379.9 
17426.9 
10694.0 
5916.4 
11748.1 
900.1 
2817.8 
Europe 
392.4 
7132.1 
3433.9 
4291.3 
4655.8 
502.3 
1002.7 
Asia-Pacific 
71.0 
2877.4 
1262.9 
1262.5 
1606.1 
161.4 
424.3 
Africa and Middle 
East 
78.6 
1779.9 
1191.1 
972.8 
1572.0 
198.6 
553.4 
Total 
Avg.  
182.3 
6442.6 
3614.0 
2630.6 
4235.1 
375.5 
1051.3 
2017 
Avg. 
U.S. and Canada 
1026.1 
22248.6 
14951.9 
6611.3 
14869.3 
997.7 
3627.1 
Europe 
467.7 
9330.4 
4646.2 
4126.4 
5316.7 
603.9 
911.6 
Asia-Pacific 
311.5 
8054.9 
5993.2 
6069.6 
7436.7 
629.5 
2232.6 
(continued on next page) 
A. Mohammed Atris                                                                                                                                                                                                                            
Resources Policy 65 (2020) 101543
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(e) λ ¼ ðλ1; …; λnÞT: unknown column vector of “intensity” or 
“structural” variables,  
(f) ε: a small number to be prescribed by the DEA researcher, in this 
study ε ¼ 0:0001. 
The input-oriented radial DEA model used in this study is as follows: 
Minimizeθ þ ε
h X
m
i¼1
dx
i þ
X
s
r¼1
dy
r
i
s:t:
X
n
j¼1
xijλj þ dx
i ¼ θxij
ði ¼ 1; …; mÞ;
X
n
j¼1
yijλj   dy
r ¼ yrj
ðr ¼ 1; …; sÞ;
X
n
j¼1
λj ¼ 1;
λj  0ðj ¼ 1; …; nÞ; θ : URS; dx
i  0ði ¼ 1; …; mÞ; dy
r  0ðr ¼ 1; …; sÞ;
(1)  
where URS is unrestricted. 
3.2.2. DEA-DA 
The DEA method is a management science technique, and DA is a 
statistical methodology. The DEA-DA is a research tool that combines 
managerial and statistical approaches to predict group membership in 
sampled data. This study applies a DEA-DA model and classifies all 
DMUs into efficient (E) and inefficient (IE) groups as formulated in 
Model (2): 
Min:
M
X
j2E
Zj þ
X
j2IE
Zj
st:
 X
m
i¼1
vixij þ
X
s
r¼1
wryrj þ σ þ Mzj  0; j 2 E;
 X
m
i¼1
vixij þ
X
s
r¼1
wryrj þ σ   Mzj   ε; j 2 IE;
X
m
i¼1
vi þ
X
s
r¼1
wr ¼ 1;
vi  εζi; i ¼ 1; :::; m; wr  εζr; r ¼ 1; :::; s;
X
m
i¼1
ζi ¼ m;
X
s
r¼1
ζr ¼ s; σ : URS; vi  0 for all i; wr  0 for all r;
Zj : binary for all j; ζi : binary for all i; and ζr : binary for all r:
(2)  
here, M is a prescribed large number, and ε is a prescribed small number. 
It is necessary to specify the two numbers before solving Model (2). The 
objective function minimizes the total number of incorrectly classified 
DMUs by counting a binary variable (Zj). In this classification, the 
inefficient group (IE) has less priority than the efficient group (E). 
Therefore, we add M to the efficient group in the objective of Model (2). 
The discriminant score is expressed by  σ ðj 2 EÞ and   σ   ε ðj 2 IEÞ, 
respectively. The small number (ε) is incorporated into Model (2) in 
order to avoid a case where an observation exists on an estimated 
discriminant function. 
All the DMUs are classified by a discriminant function ð   P
m
i¼1
vixij þ
P
s
r¼1
wryrj þ σÞ. Unknown weights (vi for i ¼ 1;:::; m;wr
for r ¼ 1;:::;s) 
indicate the slope of the discriminant function. (Note that vi and wr are 
dual variables (multipliers) in the DEA but are weights in the DEA–DA.) 
The constraints (P
m
i¼1
vi þ P
s
r¼1
wr ¼ 1; vi  εζi; i ¼ 1;:::;m; wr  εζr; r ¼
1; :::; sÞ) indicate that all the weights are positive, so that the discrimi­
nant function is a full model. The sum of the unknown weights is unity. 
This restriction is often referred to as “normalization.” Model (2) in­
corporates binary variables (ζi: binary for all i, and ζr: binary for all r) to 
count the number of positive weight estimates. It is possible to change 
the number of binary variables to set the weights at a number lower than 
the actual number (i.e., it is possible to reduce the number of binary 
variables according to the importance of each factor and depending on 
whether you need to deal with zero and/or negative data). Such a 
change depends upon the degree of freedom between the number of 
observations (DMUs) and the number of weights. After applying Model 
(2) to the dataset, we obtain an optimal solution and compute the 
following score for the j-th DMU: 
ρj ¼  X
m
i¼1
v
i xij þ
X
s
r¼1
w
ryrj þ σ
for all j ¼ 1; :::; n:
(3) 
Using the ρj score, the adjusted efficiency score is computed for the 
refineries through the following procedure:  
(a) Find the maximum and minimum values of ρ by maxj ρj and 
minj ρj.  
(b) Find the range between them by 
(b-1) range (A) ¼ maxj ρj   minj ρj if minj is non-negative and 
(b-2) range (B) ¼ maxj ρj þ

minj

 if minj ρj is negative.  
(c) The adjusted efficiency score for the j-th DMU is measured by 
(c-1) Efficiency ¼ ½ρj  minj= ½range ðAÞ if minj ρj is non- 
negative and 
(c-2) Efficiency ¼ ½ρj þminj=½range ðBÞ if minj ρj is negative. 
3.2.3. Rank sum tests 
This study utilizes the Kruskal–Wallis rank sum test (H) to examine 
the null hypotheses regarding whether the different groups and periods 
have the same distribution. This study reorders all DMUs in ascending 
order based on their average adjusted efficiency scores to compute the 
test (Sueyoshi and Goto, 2012). Let Rjt denote the rank of the j-th DMU in 
the t-th group (or period). The rank sum of all DMUs in the t-th group (or 
period) is Rt ¼ P
n
j¼1
Rjt where n stands for the number of companies at the 
t-th group (or period). The KW test is mathematically expressed as 
follows: 
Table 2 (continued) 
Variable 
Outputs 
Inputs 
Net Income 
Total 
Revenue 
Total Enterprise 
Value 
Employees 
Total 
Assets 
Total Cash & ST 
Investment 
Total Debt 
Unit 
Millions$ 
Millions$ 
Millions$ 
Employee 
Millions$ 
Millions$ 
Millions$ 
Africa and Middle 
East 
116.2 
2215.3 
1358.8 
940.3 
1649.4 
241.8 
587.4 
Total 
Avg.  
435.8 
9934.7 
6738.3 
4986.8 
7571.4 
627.3 
2040.3 
Total 
Avg. 
All 
3364.81 
439.03 
1516.01 
5474.22 
253.64 
10962.30 
3960.15 
Min 
1.00 
0.04 
0.00 
2.08 
0.12 
0.38 
1.61 
Max 
140483 
10474 
30342 
109937 
5106 
165960 
100833 
S.D. 
8244.02 
917.41 
3416.99 
11648.72 
659.89 
22079.99 
8916.45  
A. Mohammed Atris                                                                                                                                                                                                                            
Resources Policy 65 (2020) 101543
9
H ¼
12
NðN þ 1Þ
X
T
t¼1
R2
t
n   3ðN þ 1Þ:
(4)  
here, the number (N) refers to the total number of DMUs in all groups (or 
periods). The statistic (H) follows the x2 distribution with a degree of 
freedom (df ¼ T-1). Hollander and Wolfe (1999) have described the 
Kruskal–Wallis rank sum test in detail. 
When multiple DMUs have the same rank, the H statistic needs to be 
adjusted as follows: 
Hc ¼

12
NðN þ 1Þ
X
T
t¼1
R2
t
n   3ðN þ 1Þ
  
1  P q
N3   N

:
(5)  
here, q ¼ z3   z where z denotes the number of observations on the same 
rank. 
3.2.4. Wilcoxon rank sum test 
This study applies a Wilcoxon rank sum test (also known as the 
“Mann–Whitney test”) to examine the null hypotheses regarding 
whether there is no significant statistical difference between any two 
regions (i.e., whether the medians of the two regions are equal; Mann 
and Whitney, 1947). The rankings of the DMUs are calculated according 
to their average adjusted efficiency scores. As both samples are larger 
than 10, the Wilcoxon rank sum test is mathematically treated as 
follows: 
μA ¼ nAðnA þ nB þ 1Þ
2
(6)  
σA ¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ffi
nAnBðnA þ nB þ 1Þ
12
r
(7)  
z ¼ RA   μA
σA
(8)  
here, μA is the mean for group A (i.e., the expected value for group A), σA 
is the variance of group A, RA is the sum of the rankings of group A, and Z 
is the critical value. 
4. Empirical results 
The number of companies and their efficiency results obtained from 
model 1 are summarized in Table 3. The total number of refineries in 
each region is 178 in the U.S. and Canada, 66 in Europe, 328 in the Asia- 
Pacific region, and 124 in Africa and the Middle East. These results show 
that the highest efficiency score is for the U.S. and Canada, where 46 
firms achieved an efficiency score of 1, and 61 refineries received scores 
of less than 0.5; these represent 26% and 34%, respectively, of the U.S. 
and Canada sample. Europe has the lowest number of firms because of 
the wave of refinery shutdowns over the last decade Four refineries in 
Europe are fully efficient, with an efficiency score of 1, and 42 com­
panies have efficiency scores less than 0.5; these firms represent 6% and 
64% of the European sample, respectively. The Asia-Pacific region has 
the highest number of refineries in the sample. Only 40 of the region’s 
refineries are efficient, and 208 obtained efficiency scores less than 0.5; 
these refineries represent 12% and 63% of the Asia-Pacific sample, 
respectively. Africa and the Middle East region has 11 efficient refineries 
and 56 firms with scores less than 0.5; these represent 9% and 45% of 
the sample, respectively. 
Table 4 shows a comparison of average efficiency scores using the 
conventional DEA and average adjusted efficiency scores using DEA-DA. 
To clarify the difference, for 2014, the average efficiency scores and 
average adjusted efficiency scores are 0.3200 and 0.5270 for Europe 
0.7103 and 0.6543 for the U.S. and Canada, respectively. The DEA-DA 
results show that applying this discriminant analysis to an industry- 
wide evaluation is important for determining the real efficiency per­
formance in each region. 
Fig. 2 depicts the average adjusted efficiency scores of the four re­
gions from 2008 to 2017. U.S. and Canada region shows the best per­
formance, followed by the Asia-Pacific, Africa and the Middle East, and 
Table 3 
Summarized efficiency results of model 1.  
Efficiency score 
1 
0.9 
0 .8 
0 .7 
0 .6 
0 .5 
less than 0.5 
Number of companies 
U.S. and Canda 
46 (26%) 
8 (4%) 
14 (8%) 
17 (10%) 
16 (9%) 
16 (9%) 
61 (34%) 
178 
Europe 
4 (6%) 
1 (2%) 
1 (2%) 
2 (3%) 
7 (11%) 
9 (14%) 
42 (64%) 
66 
Asia-Pacific 
40 (12%) 
11 (3%) 
10 (3%) 
15 (5%) 
26 (8%) 
18 (5%) 
208 (63%) 
328 
Africa and Middle East 
11 (9%) 
4 (3%) 
11 (9%) 
12 (10%) 
13 (10%) 
17 (14%) 
56 (45%) 
124  
Table 4 
Average of efficiency and adjusted efficiency scores.  
Region 
Year 
Average of 
efficiency score 
Average of 
adjusted 
efficiency 
Rank 
U.S. and 
Canada 
Avg. 
2008 
0.6761 
0.5589 
21 
2009 
0.7069 
0.6589 
39 
2010 
0.5805 
0.5825 
31 
2011 
0.6281 
0.6184 
35 
2012 
0.7087 
0.6578 
38 
2013 
0.7497 
0.6661 
40 
2014 
0.7103 
0.6543 
37 
2015 
0.6694 
0.6416 
36 
2016 
0.5654 
0.5310 
13 
2017 
0.6599 
0.6040 
34 
Europe 
Avg. 
2008 
0.4243 
0.6015 
33 
2009 
0.4650 
0.5205 
8 
2010 
0.4753 
0.5612 
24 
2011 
0.4301 
0.5508 
17 
2012 
0.4447 
0.5096 
3 
2013 
0.4315 
0.5100 
4 
2014 
0.3200 
0.5270 
10 
2015 
0.4376 
0.5305 
11 
2016 
0.4899 
0.5413 
14 
2017 
0.5848 
0.5771 
28 
Asia-Pacific 
Avg. 
2008 
0.5454 
0.5583 
20 
2009 
0.4584 
0.5112 
5 
2010 
0.4815 
0.5647 
25 
2011 
0.4942 
0.5600 
22 
2012 
0.4978 
0.5076 
2 
2013 
0.5123 
0.5308 
12 
2014 
0.3876 
0.4874 
1 
2015 
0.4949 
0.5807 
29 
2016 
0.4581 
0.5456 
15 
2017 
0.5145 
0.5689 
26 
Africa and 
Middle East 
Avg. 
2008 
0.6263 
0.5559 
19 
2009 
0.6694 
0.5820 
30 
2010 
0.6358 
0.5712 
27 
2011 
0.5393 
0.5529 
18 
2012 
0.6202 
0.5859 
32 
2013 
0.5527 
0.5177 
6 
2014 
0.5823 
0.5244 
9 
2015 
0.5633 
0.5610 
23 
2016 
0.5145 
0.5202 
7 
2017 
0.4870 
0.5486 
16 
Note: the DEA-DA Model obtains the adjusted efficiency score. This study pro­
posed M ¼ 10000 and ε ¼ 0.0001 for the computation of the DEA-DA model. The 
optimal solution of DEA-DA is v1 ¼ 0.0001, v2 ¼ 0.1307, v3 ¼ 0.1535, 
v4 ¼ 0.2287, w1 ¼ 0.1727, w2 ¼ 0.0174, w3 ¼ 0.2968 and σ ¼ 0.008. 
A. Mohammed Atris                                                                                                                                                                                                                            
Resources Policy 65 (2020) 101543
10
Europe. The high performance in North America is due to the shale 
boom, which changed the position of the U.S. in the global oil market, 
particularly in the petrochemicals industry. Petrochemical companies 
and gas investors in the U.S. and Canada have invested greatly in re­
fineries and natural gas plants, as well as the extraction and processing 
of natural gas liquids (e.g., ethane, pentane, propane, butane). More­
over, the low cost of ethane in the U.S. enhances the efficiency of U.S. 
refineries and gives U.S. producers a competitive advantage. 
Further, U.S. refineries exceeded 17 md/d in 2017, with a 92% ca­
pacity utilization rate. Most of this capacity comes from the Gulf Coast 
region, and it is expected to promote U.S. capacity over the next few 
years. The increase in U.S. crude oil output, along with the inflows of 
discounted Canadian crude oil, are the main drivers of the change in the 
U.S. position in the global oil market. 
The Asia-Pacific region is endeavoring to boost the productivity and 
efficiency of its O&G refineries and reserves to meet increasing energy 
demand. China, India, and some parts of Thailand, Indonesia, and 
Malaysia have mature O&G reserves. Emerging markets such as the 
Philippines and Myanmar have begun developing new oil fields. The 
main O&G industry players in the Asia-Pacific region are divided into 
national oil companies (NOCs) and international oil companies (IOCs). 
NOCs include the China National Offshore Oil Corporation (CNOOC), 
India’s Oil and Natural Gas Corporation (ONGC), Malaysia’s Petronas, 
Thailand’s PTT, and Vietnam’s PetroVietnam. NOCs are leading the 
investment and development of the regional O&G industry. Meanwhile, 
IOCs such as Exxon Mobil, BP, Chevron, Shell, and Murphy are helping 
to expand the Asia-Pacific O&G market through many joint ventures. 
For instance, Papua New Guinea’s InterOil was acquired by Exxon 
Mobil, BP plans to expand its Tangguh LNG project in Indonesia, and 
Exxon Mobil and Chevron are investing in oil ventures in Kazakhstan to 
transport crude oil to China. Other IOCs and independents are also 
participating in O&G market activities, including Reliance (one of In­
dia’s largest private sector companies), Murphy, and Shell. 
Africa and the Middle East region (particularly the Middle East) is 
increasing its refinery capacity and investing more in technology to 
integrate its upstream and downstream sectors.8 Saudi Arabia is a sig­
nificant oil-producing nation not only in the region but also in the world. 
It is working on maintaining its capacity and enhancing its refineries’ 
throughput via joint ventures. For instance, Saudi Aramco closed the 
Jeddah refinery in 2017 to enhance its domestic business. The company 
is working to expand its downstream business overseas through joint 
ventures in emerging markets in Asia (e.g., China, Malaysia, and 
Indonesia); it is also expanding in the U.S. by consolidating operations at 
the Motiva plant (600 kb/d) in a joint venture with Shell. Saudi Aramco 
currently refines only 30% of its crude oil production; it aims to double 
its refining capacity and increase its crude productivity. To this end, 
Saudi Aramco and SABIC petrochemical company signed an agreement 
to construct a 400 kb/d crude-producing chemical complex by 2025. 
Other Middle Eastern countries, such as Bahrain, Kuwait, and Oman, are 
also pursuing overseas expansion. The UEA has been less active in 
expanding its capacity. However, ADNOC is starting main upgrades to 
refine sour crude through its Ruwais refinery. 
The Middle East is expected to attain the world’s highest crude 
productivity by 2023. Africa has a low average utilization rate, of under 
70%. However, it is working to increase its refinery capacity. Nigeria 
will drive capacity growth in Africa and the Middle East by adding 
1.933 md/d by 2022.9 The worst performance is seen in Europe due to 
the changes in their market patterns caused by the switchover to electric 
vehicles and buses and because Europe is moving toward a low-carbon 
economy and the use of fuel-mix energy. 
Refineries’ margins and fuel oil production are susceptible to oil 
price fluctuations, as shown in Fig. 2. Average adjusted efficiency in all 
regions increased in 2015 due to declining oil prices in 2014, which 
affected all refineries’ margins and profits due to the increased demand 
for their products. However, in 2016, all regions recorded a drastic 
decrease in average adjusted efficiency due to OPEC’s decision to cut 
their oil production to increase oil prices. 
Table 5 presents the Kruskal–Wallis rank sum test results for the 
average adjusted efficiency scores. Test 1 investigates the null hypoth­
esis (H0) that the average adjusted efficiency measures are uniformly 
distributed among the four regions, while test 2 examines the null hy­
pothesis (H0) that the average adjusted efficiency scores are uniformly 
distributed over the 10 years analyzed in this study. The test statistics (H 
statistics) are 14.847 for test 1 and 3.902 for test 2, with 3 and 9 degrees 
of freedom (df) respectively. The critical values for the statistics are 
Fig. 2. Average adjusted efficiency scores of the four regions.  
8 Sueyoshi and Wang (2014), and Atris and Goto (2019) discussed the role of 
supply chain and vertical structure in enhancing the overall efficiency in the 
O&G industry. 
9 See https://www.hydrocarbonengineering.com/refining/03092018/nigeri 
a-will-propel-refining-capacity-growth-in-the-middle-east-and-africa/. 
A. Mohammed Atris                                                                                                                                                                                                                            
Resources Policy 65 (2020) 101543
11
7.815 and 16.919, respectively, at the 5% significance level. The null 
hypotheses are thus rejected for test 1 but not for test 2. In other words, 
the average adjusted efficiency did not uniformly distribute among the 
four regions (the adjusted efficiencies were, on average, different among 
the four regions). Contrariwise, the average adjusted efficiency was 
uniformly distributed over the 10 years from 2008 to 2017 (there was no 
difference across the 10 years). 
Table 6 presents the Wilcoxon rank sum test results, separately, be­
tween the U.S. and Canada and the other three regions. The test in­
vestigates the null hypothesis (H0) that the mean of the average adjusted 
efficiency level between the U.S. and Canada and the other regions is 
equal. The results indicate that the Z statistics between the U.S. and 
Canada on one hand and the Asia-Pacific region, Europe, and Africa and 
the Middle East on the other are  3.0240,  2.8730, and  3.0990, 
respectively, with p-values of 0.0025, 0.0041, and 0.0019, respectively, 
at the 5% significance level. These results reject the null hypothesis (H0) 
that there is no statistically significant difference in the average adjusted 
efficiency mean between the U.S. and Canada and the other three 
regions. 
Table 7 shows the Wilcoxon rank sum test statistics between Europe 
and the other three regions. The test investigates the null hypothesis 
(H0) that there is no difference in means average adjusted efficiency 
levels between any two regions. The critical values (Z) between Europe 
and the Asia-Pacific region, Africa and the Middle East, and the U.S. and 
Canada are 0.1510, 0.7560, and  3.0990, respectively, and their p- 
values are 0.8798, 0.4497, and 0.0019, respectively, at the 5% signifi­
cance level. Consequently, we do not reject the null hypothesis (H0) that 
there is no statistically significant difference in average adjusted effi­
ciency mean between Europe on one hand and the Asia-Pacific region 
and Africa and the Middle East on the other. Therefore, we reject the null 
hypothesis regarding the EU region and U.S. and Canada, as shown in 
Table 6. 
Table 8 shows the Wilcoxon rank sum results of the test between 
Africa and the Middle East and the other three regions. The test in­
vestigates the null hypothesis (H0) that there is no difference in mean 
average adjusted efficiency levels between any two regions. The critical 
values (Z) between Africa and the Middle on one hand and the Asia- 
Pacific region, Europe, and the U.S. and Canada on the other are 
0.7560, 0.7560, and  2.8730, respectively, and their p-values are 
0.4497, 0.4497, and 0.0041, respectively, at the 5% level of significance. 
As a result, we do not reject the null hypothesis (H0) between Africa and 
the Middle East and the Asia-Pacific region and Europe. This indicates 
that there is no statistically significant difference in average adjusted 
efficiency mean between Africa and the Middle East and the Asia–Pacific 
region and Europe. Therefore, we reject (H0) between Africa and the 
Middle East and the U.S. and Canada (see Table 6). 
Table 9 shows the Wilcoxon rank sum results for the test between the 
Asia-Pacific region and the other three regions. The test investigates the 
null hypothesis (H0) that there is no difference in mean adjusted effi­
ciency level between any two regions. The critical values (Z) between 
the Asia-Pacific region on one hand and Africa and the Middle East, the 
EU, and the U.S. and Canada on the other are 0.7560, 0.1510, and 
 3.0240, respectively, and their p-values are 0.4497, 0.8798, and 
0.0025, respectively, at a 5% significance level. Thus, we do not reject 
the null hypothesis (H0) between the Asia-Pacific region and Africa and 
the Middle East and Europe. This indicates that there is no statistically 
significant difference in average adjusted efficiency mean between the 
Asia-Pacific region and Africa and the Middle East and Europe. There­
fore, we reject (H0) between the Asia-Pacific region and the U.S. and 
Canada (see Table 6). 
5. Conclusion 
This study examined the efficiency of O&G refineries in four global 
regions—the U.S. and Canada, the Asia-Pacific region, Africa and the 
Middle East, and Europe—from 2008 to 2017 using a unique unbalanced 
dataset comprised of 696 refineries operating in the four regions. The 
study utilized the averages of the refineries’ adjusted efficiencies to 
simplify the discussion because the dataset was unbalanced and the 
sample was large. The study applied a combination of DEA and DEA-DA 
to provide an efficiency-based ranking of the O&G refineries. 
A Kruskal–Wallis rank sum test was used to test whether the average 
adjusted efficiencies measures differed among the four regions and if 
they changed over time. Furthermore, a Wilcoxon rank sum test was 
utilized to investigate whether the average adjusted efficiency levels 
differed between any of the two regions over the study’s 10-year sample 
period. 
The results indicate that the U.S. and Canada outperform the other 
three regions; this is followed by the Asia-Pacific region, Africa and the 
Middle East, and Europe, in that order. The study also found a statisti­
cally significant difference among the four regions. Moreover, the 
average adjusted efficiency ranks were invariant from 2008 to 2017. 
This result might be due to the differences between the refineries’ types, 
technological complexity levels, and capacities: The U.S. has many 
advanced technologies available with which to extract light tight oil 
(LTO) as well as restrictive environmental regulations, which give U.S. 
refineries a competitive advantage in adapting to global market changes 
and requirements. 
Furthermore, the operations of integrated U.S. oil companies (such 
as Majors) boost the region’s global position through the many joint 
Table 5 
Kruskal-wallis rank sum test.   
df 
H statistics 
Critical value 
P value 
Test 1 
3 
14.340 
7.815 
0.002 
Test 2 
9 
6.918 
16.919 
0.646  
Table 6 
Wilcoxon rank sum test of U.S. and Canada region.  
Region 
W-Test 
The Asia- 
Pacific 
Africa and the Middle 
East 
Europe 
U.S. and 
Canada 
Z 
 3.0240 
 2.8730 
 3.0990 
P. 
Value 
0.0025 
0.0041 
0.0019  
Table 7 
Wilcoxon rank sum test of Europe.  
Region 
W-Test 
The Asia- 
Pacific 
Africa and the Middle 
East 
U.S. and 
Canada 
Europe 
Z 
0.1510 
0.7560 
 3.0990 
P. 
Value 
0.8798 
0.4497 
0.0019  
Table 8 
Wilcoxon rank sum test of Africa & the middle east.  
Region 
W-Test 
The Asia- 
Pacific 
Europe 
U.S. and 
Canada 
Africa and the Middle 
East 
Z 
0.7560 
0.7560 
 2.8730 
P. 
value 
0.4497 
0.4497 
0.0041  
Table 9 
Wilcoxon rank sum test of the Asia-Pacific.  
Region 
W-Test 
Africa and the Middle 
East 
Europe 
U.S. and 
Canada 
The Asia- 
Pacific 
Z 
0.7560 
0.1510 
 3.0240 
P. 
Value 
0.4497 
0.8798 
0.0025  
A. Mohammed Atris                                                                                                                                                                                                                            
Resources Policy 65 (2020) 101543
12
ventures being conducted with other countries. Vertical integration is a 
crucial factor in enhancing refinery efficiency because it reduces risks 
and increases profitability at every stage, from the wellhead to the 
gasoline station. Vertical integration and joint ventures also help re­
fineries achieve a balance between conducting operations and protect­
ing themselves against market instability. 
A 2018 oil market report10 stated that the U.S. was expected to be a 
significant player in massive integrated petrochemical projects for pro­
ducing ethylene. This will help the U.S. to not only cover the growth in 
its domestic market but also export to other markets. Further, the U.S. 
and Canada will become the leading suppliers of crude oil over the next 
five years. The U.S. will export LTO to Europe to address its deficit of 
African light oil and also to the Asia-Pacific region to satisfy the market 
demand for petrochemical raw material. This illustrates the vital role of 
vertical integration and resource proximity in refinery efficiency. 
The Asia-Pacific region shows the highest O&G capacity and activity 
levels, as well as increasing adjusted efficiency trends. As a result, the 
Asia-Pacific region has become an attractive market for major oil com­
panies, which are increasing their investments by engaging in many 
joint ventures, particularly in petrochemical plants. 
Africa and the Middle East have also started to integrate their re­
fineries through many joint ventures in order to generate more complex 
refining and petrochemical products domestically. That implies the 
importance of petrochemical and complex refining products, which are 
considered key to gaining more global market share, thereby enhancing 
refining efficiency. 
Despite the expected refinery shutdown waves in Europe due to the 
IMO regulation in 2020, the demand for refinery products slightly 
increased in 2017 because of the global trend toward high-consuming 
vehicles (SUVs). This increase suggests that changes in demand pat­
terns affect refinery products and that Europe should invest in tech­
nology in order to overcome their resource limitations. 
Based upon the results, various management/policy recommenda­
tions were proposed to enhance the refineries’ operational efficiency in 
each region. These include (1) Processing of incremental amounts of 
domestic LTO by U.S. refiners is likely to have a positive impact on re­
finery efficiency. However, the U.S. needs to import different qualities of 
crude oil to maximize its throughput, considering its mix of refining 
capacity. Hence, it would be uneconomic to run refineries only with 
light crude oil; (2) Adjustment of refinery structure and control the scale 
of industry is required to improve the scale efficiency of refined and 
petrochemical products, for instance, the Asia-Pacific region has to 
adjust their refineries capacity to adapt rising in petrochemical demand 
leads by China, India and Southeast Asia, particularly, in chemical 
products; (3) The future for oil producers lies in developing a value- 
added sector of refining and petrochemicals. Therefore, the Middle 
East region has to engage in many joint ventures through intensive 
capital investment to expand into the downstream sector and to refine its 
crude oil domestically, which creates more value from their crude oil 
throughput and diversify the region’s economies; (4) The Asia-Pacific 
has to accelerate the application of new technologies to the refineries. 
Whereas, turning heavy oil into high-quality products also requires more 
advanced molecular processing than is possible with simple refining or 
distillation; (5) European refineries have to launch joint ventures or 
cooperate with electric vehicle (EV) producers to offset their losses 
incurred when converting to other energy resources; (6) Europe region 
should not add more costs on the refining sector and stimulates R&D to 
unlock innovation to develop the low-carbon technologies for refineries 
and their products. 
One limitation of this study is that it does not examine the effects of 
M&A policies on the global refining market. This issue will be discussed 
in future research as an extension of this study. 
Acknowledgment 
The author gratefully thanks prof. Mika Goto for her support. 
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A. Mohammed Atris

Chemical Engineering Science 282 (2023) 119331
Available online 29 September 2023
0009-2509/© 2023 Elsevier Ltd. All rights reserved.
Multi-objective optimization strategy for industrial catalytic cracking units: 
Kinetic model and enhanced SPEA-2 algorithm with economic, CO2, and 
SO2 emission considerations 
Lei Wan a, Kai Deng a, Xiangyang Li b, Liang Zhao a, Jian Long a,* 
a Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China 
b Sinopec Jinan Refining and Chemical Company, Jinan, Shandong 250101, China   
A R T I C L E  I N F O   
Keywords: 
Catalytic cracking 
Reaction-regeneration model 
Path evolution operator 
Improved SPEA-2 
A B S T R A C T   
The multi-objective optimization of the economics and environmental protection of the fluidized catalytic 
cracking reaction-regeneration (FCC-RR) process can achieve a balanced production of light olefins, and other 
chemicals while mitigating pollutant gas emissions. This paper proposes a novel multi-objective optimization 
framework that integrates the FCC-RR lumped kinetic model and SPEA-2 algorithm based on the path evolution 
reproduction operator. The proposed FCC-RR model not only provides insights into the influence of various 
reaction variables on product yields from a mechanistic perspective but also accurately simulates the distribution 
of products during the reaction-regeneration process. Additionally, we present an improved SPEA-2 algorithm 
that incorporates the path evolution operator and applies it to a real FCC-RR device to speed up the optimization 
process in terms of solution speed and quality. The results of the case study demonstrate that the proposed 
optimization framework has significant advantages in solving multi-objective optimization in the catalytic 
cracking process.   
1. Introduction 
1.1. Background 
In the past two decades, economic and population growth has fueled 
the growth of oil demand (Alabdullah et al., 2020). With the advance­
ment of technology and upgrading of installations, refineries are 
showing rapid growth in refining capacity and processing volume (Sha 
et al., 2022). Simultaneously, the advancements in efficiency, the tran­
sition towards light olefins energy systems (Song et al., 2022), and the 
significant rise in electric vehicle adoption have prompted numerous 
nations to contemplate strategies for phasing out gasoline and diesel 
fuels. Consequently, this shift has directly resulted in a decrease in the 
demand for gasoline and diesel while generating a surge in the demand 
for light olefins and other chemical compounds, the petrochemical in­
dustry has the responsibility to meet the global market demand by 
expanding the production capacity of chemicals (Tanimu et al., 2022). 
With the trend of decreasing gasoline and diesel production and 
increasing chemicals such as light olefins, optimizing production pro­
cesses to improve economic benefits is critical for refineries to achieve 
sustained commercial success in a competitive market environment. 
The petroleum refining sector, one of the crucial promoters of socio- 
economic development, is also a major source of air emissions and other 
environmental emissions (Sun et al., 2019). Carbon dioxide and sulfur 
dioxide are among the primary pollutant gas emitted by refineries. The 
emission of greenhouse gases is the primary contributor to global 
warming and climate change, and carbon dioxide is of serious consid­
eration for being the most vital component of greenhouse gases (Obobisa 
et al., 2022). On the other hand, SO2 is a harmful gas that has a 
straightforward bearing on air quality and human health (Chu et al., 
2018). Sulfur dioxide and nitrogen oxides emission engender a series of 
profound environmental challenges, including acid rain and severe air 
pollution. The production of sulfur dioxide and nitrogen oxide com­
pounds exposes the refining industry to significant operational and 
environmental problems (Wang et al., 2020). The enforcement of 
rigorous emission standards (Long et al., 2021) mandates the inclusion 
of multi-objective optimization, encompassing the pursuit of economic 
benefits and environmental protection, as an indispensable fundamental 
element of modern industrial production. This strategic approach es­
tablishes the underpinning for future sustainable development. 
* Corresponding author. 
E-mail address: longjian@ecust.edu.cn (J. Long).  
Contents lists available at ScienceDirect 
Chemical Engineering Science 
journal homepage: www.elsevier.com/locate/ces 
https://doi.org/10.1016/j.ces.2023.119331 
Received 31 July 2023; Received in revised form 10 September 2023; Accepted 25 September 2023   
Chemical Engineering Science 282 (2023) 119331
2
At the heart of current refining technology, catalytic cracking is now 
capable of transforming raw oil into high value-added oil and chemical 
feedstocks in an efficient, green, and economical way. The FCC process 
is capable of producing fuel oils such as gasoline and diesel, offering 
high economic benefits. Specifically, gasoline and diesel produced from 
catalytic cracking units account for approximately 70 % and 30 % of the 
total gasoline and diesel production, respectively (Yang et al., 2023). 
Additionally, the process can also produce light olefins and other 
chemicals that are in demand in the market (Bai et al., 2019). In the 
process of FCC, the nature of the catalyst will have an effect on the 
product, so some scholars have carried out the optimization of the cat­
alytic cracking reaction through the study of the catalyst. Zhang Quan 
et al. (Quan et al., 2023) investigated the effects of acidity, textural 
properties and metal species loading of ZSM-5 zeolite on the cracking 
performance of EDC. I. Istadi et al. (2022) evaluated the reactivation and 
modification of fluid catalytic cracking (RFCC) catalysts using sulfuric 
and citric acids as acid treatment rejects by varying the concentration of 
the acid solution. On the other hand the distribution of FCC products can 
be improved by optimizing the FCC process. Zhou Xing et al. (Xin et al., 
2021) proposed a new direct catalytic cracking process for crude oil, the 
COCC process, and the new COCC process shows great potential in the 
production of ethylene and propylene from crude oil. Zhou Xing et al. 
(Xin et al., 2022) proposed a novel layered catalytic cracking process for 
crude oil to control the depth of catalytic cracking and to promote the 
engineering application, process enhancement and optimization of key 
operating parameters for direct catalytic combustion of crude oil. 
Furthermore, the catalytic cracking process is responsible for a sig­
nificant portion of the overall CO2 and SO2 emissions in refineries. Given 
the complexity and high coupling of the catalytic cracking process, it 
becomes essential to prioritize its optimization within the refinery op­
erations. The reaction-regeneration system serves as the central unit in 
an FCC unit, making it crucial to model and optimize this system 
accordingly (Xu and Cui, 2018). 
1.2. Previous research 
In the context of multi-objective optimization for the FCC-RR system, 
the accuracy of process models and the effectiveness of optimization 
algorithms are two vital factors that affect the optimization results. The 
establishment of a precise process model holds paramount importance 
as it offers a comprehensive comprehension and depiction of the cata­
lytic cracking process, facilitating the provision of precise inputs for 
optimization algorithms. Meanwhile, efficient and reliable optimization 
algorithms play a crucial role in striking a balance among multiple 
optimization objectives and determining the set of solutions that 
perform optimally in all aspects (Tian et al., 2022). In turn, this facili­
tates feasible and high-performance optimization strategies for catalytic 
cracking processes in refineries. 
Currently, the modeling methods for the catalytic cracking process 
are mainly composed of three types: lumped kinetic model (Xiang et al., 
2021), molecular-based kinetic model (Ali et al., 2022), and data-driven 
model (Zhang et al., 2019). Lumped kinetic models could reflect the real 
mechanism of the catalytic cracking reaction, and at the same time, 
these models are fast and accurate, which are useful for modeling and 
optimization of the catalytic cracking process. The lumped kinetic model 
is to divide the hydrocarbon mixture into multiple lumps based on the 
principle of kinetic similarity without using the molecular structure of 
petroleum hydrocarbons, and numerous achievements have been made 
in the previous research on the lumping method. In recent years, Yakubu 
M. John et al. (John et al., 2019)have proposed a six-lump model to 
simulate the detailed steady-state model of an industrial fluid catalytic 
cracking (FCC) unit, A. Golrokh Sani et al.(Sani et al., 2018) proposed an 
8-lump model for further analysis of the distribution of olefins in the 
reaction, A nine-lump model to simulate VGO catalytic cracking was 
proposed by Ali Afshar Ebrahimi et al. (Afshar Ebrahimi et al., 2018), 
Yupeng Du et al. (Du et al., 2019) proposed a 10-lump model to simulate 
the catalytic cracking process, Tianyue Li, Jian Long, et al. (Li et al., 
2022) proposed an 11-lump model to simulate a fluid catalytic cracking 
unit, and Yang Chen et al. (Chen et al., 2020) proposed a 12-lump model 
for the simulation of fluid catalytic cracking of heavy oil for purifying 
gasoline and improving the light olefins production. With respect to the 
investigation of the regeneration process of catalytic cracking, Aysar T. 
Jarullah et al.(Jarullah et al., 2017) proposed an industrial process 
catalytic cracking simulator and Binay Singh et al.(Singh et al., 2017) 
proposed a seventeen-lump model, which is designed to simulate in­
dustrial FCC unit. Yet, the work of this unit was limited to the reactions 
of carbon elements and hydrogen elements on the catalyst and failed to 
consider adequately the reactions of other elements, especially those of 
sulfur elements that are associated with the production of the polluting 
gas SO2. 
Although previously proposed models have provided accurate por­
trayals of petroleum products such as gasoline and diesel, the descrip­
tion of chemical product distribution is still inadequate. Nevertheless, as 
the trend toward less gasoline and diesel use and more chemical use 
takes hold, the ability to accurately describe the distribution of chem­
icals in the catalytic cracking process is critical to optimize it. Therefore, 
it has become particularly important to develop an integrated model 
capable of calculating the distribution of chemicals such as light olefins. 
In the past, mainly four categories of multi-objective algorithms have 
been utilized to tackle multi-objective optimization problems in re­
fineries (Trivedi et al., 2017). The first type is the Pareto-based multi- 
objective evolutionary algorithm (MOEA), which includes algorithms 
such as NSGA-II (Ward and Pini, 2022), SPEA-2 (Naghshbandy and 
Faraji, 2019), PESA-II (Haber et al., 2022), and SPEA-2 + SDE (Li et al., 
2014). The second type is decomposition-based MOEA, which includes 
algorithms like ENS-MOEA (Xie et al., 2022), and NSGA-III (Farmahini 
et al., 2018). The third type is indicator-based MOEA, which includes 
algorithms such as IBEA (Li et al., 2019), and MOEA/IGD-NS (Taher­
nezhad-Javazm et al., 2022). The fourth type is model-based MOEA. 
Notably, when existing MOEAs are used to resolve practical problems, 
they face some difficulties such as low search efficiency, poor quality, 
and instability of the optimization results due to the complexity of the 
practical problems, which have not been effectively solved by existing 
studies. In recent years, significant advancements have been made in 
enhancing environmental selection and mating selection within many 
multi-objective algorithms. However, the research on designing repro­
duction operators remains relatively limited (Song et al., 2020). While 
efficient environmental selection operations can allow the fittest in­
dividuals to evolve to the next generation, it does not inherently produce 
better individuals. Producing good individuals and selecting the best 
individuals are equally important, and one cannot be without the other. 
If the algorithm does not produce good individuals, then the best envi­
ronmental selection operation will not allow the population to evolve. 
Given that the environment selection has been well studied, this paper is 
therefore improved based on the reproduction algorithm. 
The reproduction operators commonly employed in multi-objective 
algorithms, such as Simulation of binary crossover operators (SBX 
(Deb and Agrawal, 1995)), Differential Evolution operators (DE (Das 
and Suganthan, 2011)), and their variations, have a drawback in 
effectively capturing the valuable information generated throughout the 
evolutionary process. As a result, these operators may not fully exploit 
the potential of the evolving population in generating high-quality so­
lutions. These operators fail to consider important factors like the di­
rection and distribution of individual movements in the decision space. 
To address this limitation, the MO-CMA-ES (Igel et al., 2007) algorithm 
was developed. However, it should be noted that this algorithm lacks 
information exchange between different individuals. 
1.3. Motivation and main contributions 
Accurately modeling the FCC-RR system and using a multi-objective 
algorithm based on a path-based reproduction operator for the process 
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Chemical Engineering Science 282 (2023) 119331
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can accurately calculate the yield of each product and the emission of 
pollutants, and can quickly achieve multi-objective optimization of 
economic benefits, CO2 emissions, and SO2 emissions. Accordingly, a 
balance between economic benefits and environmental protection can 
be achieved. 
In this study, we focus on the FCC-RR system within a real-world 
refinery and aim to construct an accurate mechanism model and sub­
sequent optimization. To address the need for a comprehensive model 
that can calculate multiple chemicals and pollutant gases, we propose a 
novel FCC-RR kinetic model. Besides, considering the challenges of slow 
convergence and unstable solution quality faced by conventional multi- 
objective algorithms, we introduce a path-based reproduction operator 
into the well-known multi-objective algorithm SPEA-2, aiming to 
leverage path-based information for improved performance. The pro­
posed model and optimization approach is validated using industrial 
data, demonstrating their superiority in terms of achieved results. 
The main contribution of this paper is as follows:  
(1) We have proposed a mechanistic-based FCC-RR kinetic model 
that can simultaneously calculate multiple chemicals and assess 
pollutant gases.  
(2) An improved SPEA-2 algorithm (SPEA2-M) incorporating a path 
evolution reproduction operator is proposed.  
(3) The improved algorithm was applied to an actual FCC-RR unit to 
optimize it with significant improvements in economic benefits, 
CO2 emissions, and SO2 emissions. 
2. Problem statement 
The actual catalytic cracking process is rather complex and highly 
coupled. It can convert heavy petroleum fractions into high value-added 
light oil and chemicals. The FCC-RR system is the core system of cata­
lytic cracking, which provides the required high-temperature catalytic 
environment for the cracking of raw materials. As shown in Fig. 1, We 
have developed an FCC-RR system model consisting of 18 lumps riser 
reactor model and 13 lumps regenerator model. The whole FCC-RR 
system reaction flow is: the raw oil enters the reactor from the bottom 
of the riser, immediately vaporizes after contact with the high- 
temperature regenerated catalyst, and accelerates up with the gas 
before the lift. Under the influence of high temperatures and catalysts, 
crude oil is subjected to a series of intricate chemical reactions during 
the rising process, including cracking, isomerization, and hydrogen 
transfer. The generated oil and gas then pass through fractional distil­
lation and reabsorption systems before passing through the separation 
system to produce gasoline, diesel, and other oil products as well as 
propylene, ethylene, LPG, and other chemicals. As the catalyst un­
dergoes deactivation due to carbon accumulation, the spent catalyst is 
separated from the oil and gas using a separator located at the top of the 
reactor. It is then transferred to the regenerator, where the carbon is 
removed through air combustion. This process restores the catalyst’s 
activity, transforming it into a regenerated catalyst that re-enters the 
bottom of the lift tube to participate in the reaction, utilizing the high 
temperature generated by combustion. During the regenerator reaction, 
flue gas is produced, which contains harmful gases such as sulfur dioxide 
(SO2) and greenhouse gases (CO2). In the FCC-RR process, altering some 
of the operating conditions such as reactor pressure, temperature, etc. 
can change the product distribution and pollutant emissions, so it is 
imperative to select the appropriate optimization variables to optimize 
them. However, the current lumped modeling approach predominantly 
focuses on the investigation of products such as gasoline and diesel, 
while there is limited research on the distribution of light olefins such as 
propylene and ethylene. For this reason, in response to the current trend 
of reducing gasoline and diesel production, our proposed FCC-RR system 
model has the capability to simulate the production of multiple chem­
icals while maintaining computational advantages. This facilitates 
timely optimization during the FCC-RR process (Chachuat et al., 2009). 
Therefore, it is necessary to establish a multi-objective optimization 
framework based on the proposed FCC-RR model to integrate economic 
benefits, CO2 emissions, and SO2 emissions. 
Meanwhile, for the optimization process, the previous optimization 
algorithm does not consider enough information at the time of path 
generation, which leads to a slow solution, a reproduction operator 
based on path regeneration is introduced and combined with the multi- 
objective algorithm SPEA-2. The sophisticated algorithm SPEA2-M is 
applied to the multi-objective optimization in the FCC-RR system. 
3. Modeling of FCC-RR 
3.1. Reaction mechanism of the catalytic cracking reaction 
Catalytic cracking is the most core heavy oil lightening process in 
modern refineries, converting heavy oil fractions into light hydrocar­
bons. Hydrocarbon molecules undergo a complex parallel-sequential 
catalytic cracking reaction on the cracking catalyst (Zhang et al., 
Fig. 1. Diagram of FCC-RR model.  
L. Wan et al.                                                                                                                                                                                                                                     
Chemical Engineering Science 282 (2023) 119331
4
2014). In the catalytic cracking reaction system, there are mainly pri­
mary and secondary reactions. Fig. 2 shows the parallel-sequential chain 
of the catalytic cracking reaction of heavy oil fractions. 
The primary reactions in catalytic cracking involve the breaking of 
bonds in alkane, cycloalkane, and aromatic compounds present in the 
feedstock. These reactions are followed by various secondary reactions. 
At the molecular level, catalytic cracking reactions involve five main 
types of reactions, as indicated in Table 1, along with their respective 
reaction equations. Table 1 provides a clear and concise representation 
of the reactions between different hydrocarbons and cracking catalysts. 
From a macroscopic perspective, the catalytic cracking reactions of 
various hydrocarbons follow the parallel order reaction kinetics. Table 2 
shows a brief description of the two types of reactions and reaction 
equations on the solid and gas phases of the regenerator. 
3.2. FCC-RR lift tube reactor model 
3.2.1. Eighteen lumps division 
In this work, the design of the lumped model is carried out to divide 
lumps based on the mechanism of parallel-sequential reactions in cata­
lytic cracking. During the analysis of the feedstock composition, four 
lumps are examined. However, considering that resin and asphaltene 
primarily participate in the coking reaction and account for a relatively 
small proportion of the FCC process, these two components, namely 
asphaltene and resin, are grouped together as a single lump. Due to the 
poor nature of diesel fuel obtained by catalytic cracking, the analysis of 
the composition of catalytic diesel fuel (monocyclic aromatics and 
polycyclic aromatic hydrocarbons content) can help to achieve high- 
value utilization of poor-quality catalytic crack diesel fuel and is an 
important way to assist in enhancing the efficiency of catalytic plants. 
Gasoline is the main product of a catalytic cracking unit and is designed 
according to its gasoline hydrocarbon family composition. Butene, 
propylene, and ethylene are important chemicals and are classified as 
Fig. 2. Heavy oil catalytic cracking parallel-sequential reaction chain.  
Table 1 
The main types of reactor and chemical equations in the reactor (Pashikanti and 
Liu, 2011).  
Reaction Class 
Chemical reaction equation 
Cracking 
Cm+nH2[(m+n)+2]→CmH2m+2 + CnH2n+2 
C(m+n)H2(m+n)→CmH2m + CnH2n 
Ar −C(m+n)H2(m+n)+1→Ar −CmH2m−1 + CnH2n+2 
C(m+n)H2(m+n)(naphthene)→CmH2m−1(naphthene)
Isomerization 
x −CnH2n→y −CnH2n 
n −CnH2n→i −CnH2n 
n −CnH2n+2→i −CnHn+2 
C6H12(naphthene)→C5H9 −CH3(naphthene)
Hydrogen Transfer 
CnH2n(naphthene) + CmHm(olefin)→Ar −CxH2x+1(aromatic)
+CpH2p+2(paraffin)wherex = m + n −6 −p 
Dehydrogenation and 
Dealkylation 
i −CnH2n−1 + CmHm−1→Ar + C(m+n−6)H2(m+n−6)
n −C2H2n+2→CnH2n + H2 
Aromatic Ring 
Condensation 
Ar −CHCH2 + R1CH −CHR2→Ar −Ar + H2  
Table 2 
The main types of the regenerator and chemical equations in the regenerator.  
Reaction Class 
Chemical reaction equation 
Reaction on the solid phase 
Ccat + 0.5 + σ
1 + σ O2→
σ
1 + σ CO2 +
1
1 + σ CO 
Hcat + 0.25O2→0.5H2O 
Scat + O2→SO2 
Ccat + NO→CO + + 0.5N2 
Hcat + Ccat + Ncat→HCN 
Reaction on the gas phase 
CO + 0.5O2→CO2 
NO + 0.5O2→NO2 
HCN + 1.75O2→0.5H2O + NO + CO2 
CO + NO→0.5N2 + CO2  
Table 3 
The lump division of each lump of the reactor model.  
Lump 
Fraction 
Chemical composition 
Symbol 
Lump 
Fraction 
Chemical composition 
Symbol 
1 
Heavy oil 
Saturates 
SS 
10 
Gasoline 
Olefins 
Go 
2 
Aromatics 
SA 
11 
Naphthenes 
Gn 
3 
Resins + Asphaltenes 
SR 
12 
Aromatics 
Ga 
4 
Diesel 
Paraffins 
Dp 
13 
Gas 
Butene 
C4=
5 
Olefins 
Do 
14 
propylene 
C3=
6 
Naphthene 
Dn 
15 
Liquefied gas 
LPG 
7 
Monocyclic aromatics 
Das 
16 
Ethylene 
C2=
8 
Polycyclic aromatics 
Dam 
17 
Dry gas 
DR 
9 
Gasoline 
Paraffins 
Gp 
18 
Coke  
C  
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Chemical Engineering Science 282 (2023) 119331
5
separate lumps. The remaining products are classified as liquefied gas 
(C3 = -C4 = ) and dry gas (C1 = –C2 = ) according to the carbon content. 
Table 3 shows the distribution of each of the 18 lumps. 
Following the principle of kinetic similarity, the lump reaction 
network of the reactor model is constructed, as depicted in Fig. 3. The 
network consists of a total of 120 reactions distributed among the 18 
lumps that have been divided. 
3.2.2. Eighteen-lump kinetic model of riser reactor 
The reactions in a catalytic cracking industrial lift tube reactor are so 
complex that it is impractical to consider all factors in the process of 
building a model of an industrial lift tube reactor. Hence a reasonable 
simplification is needed before building the model, starting with the 
following assumptions: (1) All reactions are primarily irreversible. (2) 
The reaction process is considered from a macroscopic point of view and 
all reactions involved are considered as phase reactions. (3) The gas flow 
state in the riser reactor involved in the reaction is assumed to be 
isothermal, gaseous, and ideal piston flow. Internal mass diffusion 
within the material is neglected. (4) The catalyst deactivation involved 
in the reactions is characterized by catalyst time-varying deactivation 
and it is assumed that all reactions proceed on the same acidic active 
center (i.e., catalyst time-varying deactivation affects all reactions to the 
same extent), is not selective and is only related to catalyst residence 
time. (5) The reaction rate was corrected for the severe effect of heavy 
aromatic and alkaline nitride adsorption in the feed involved in the re­
action on the reaction results using empirical equations for heavy aro­
matic adsorption deactivation and alkaline nitride adsorption 
deactivation. (6) It is assumed that none of the gases produce coke. The 
lumped reaction rate equations are as follows: 
da
dt = −K⋅a⋅ρ
SWH
A
(1) 
Here a is the mass percentage concentration vector of the reaction 
lump and a is an 18 lumps column vector. A is the catalyst deactivation 
factor which contains the influence of nitrogen, heavy aromatics, and 
coke deposition. Whereby A is obtained from the expression: 
A = φ(Cc)f(A)f(N)
(2) 
φ(Cc)
is the catalyst coking deactivation equation, φ(CC) =
(1 + β⋅CC)−M. Where β is the catalyst coking deactivation factor, Cc 
representing coke on the regenerated catalyst (wt%). f(A) is the heavy 
aromatic adsorption deactivation equation, and f(A) = (1 + kACA)−1. 
Where kA is a heavy aromatic adsorption deactivator, CA is the residual 
carbon content of the raw oil, m%.f(N) is the alkali nitrogen adsorption 
deactivation equation, f(N) = (1 + kNCNtCϕ−1
C/O)
−1. Where CN is the al­
kali nitrogen content of the raw oil(wt%), tC is the residence time of the 
catalyst, and ϕC/O is catalyst-to-oil ratio (CTO), kg/kg. 
In addition, it should be added that K is the reaction rate constant 
matrix and ρ is the density of the gas. An example is given here for 
saturated hydrocarbons, where 15 reactions can be carried out in the 
reaction network. Then its reaction rate equation can be described as: 
daSS
dt = −ρ
SWH
⋅A ×
( ∑
15
i=1
ki
)
× aSS
(3)  
where ki is denoted as the rate constant of reaction i, in accordance with 
the Arrhenius equation (i.e., k =
k0⋅e−Ea
RT). k0 and Ea are the 
Fig. 3. The eighteen lumps reaction network of reactor.  
Table 4 
The lump division of each lump of the regenerator model.  
Lump 
Item 
Description 
Symbol 
Lump 
Item 
Description 
Symbol 
1 
Catalyst 
Carbon on catalyst 
Ccat 
8 
Gas phase 
Water molecules 
H2O 
2 
Hydrogen on the catalyst 
Hcat 
9 
Sulfur Dioxide 
SO2 
3 
Sulfur on the catalyst 
Scat 
10 
Hydrocyanic acid 
HCN 
4 
Nitrogen on the catalyst 
Ncat 
11 
Nitrogen monoxide 
NO 
5 
Gas phase 
Oxygen 
O2 
12 
Nitrogen Dioxide 
NO2 
6 
Carbon Dioxide 
CO2 
13 
Nitrogen 
N2 
7 
Carbon monoxide 
CO 
14 
—— 
—— 
——  
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Chemical Engineering Science 282 (2023) 119331
6
corresponding pre-exponential factor and activation energy of the con­
version reaction process, respectively. T is the reaction temperature and 
R is the molar gas constant, taken as 8.3145 under the SI system. The rest 
of the reaction rate equations are given in the Appendix. 
3.3. FCC-RR system regenerator model 
3.3.1. Thirteen lumps division 
A complete FCC-RR system model is composed of a riser reactor 
model and a regenerator model. In the FCC process, coking-prone 
components such as asphaltenes and resins form ordered or disordered 
high carbon-to-hydrogen ratio structures called coke after dehydroge­
nation and are deposited on the catalyst surface and in the pore chan­
nels, resulting in catalyst deactivation (Tang et al., 2017). In this 
scenario, a regenerator model is constructed to utilize air to scorch off 
the coke covering the catalyst surface, thereby restoring the catalyst to 
activity. 
In the construction of the regenerator model, as in the case of the 
reactor model, the coking kinetics was studied using a coking kinetic 
approach to the coking molecule CxHyNaSb on the catalyst surface, 
which was treated as four components, plus the other reactions in the 
coking reaction system, and ultimately the whole reaction system con­
sisted of thirteen components, each of which is shown in Table 4 below. 
In the catalyst regeneration process, two reactions occur, one on the 
catalyst and the other on the gas phase. The overall reaction is illustrated 
in Fig. 4. 
3.3.2. Thirteen-lump reaction kinetic model of regenerator 
To streamline the model and facilitate calculations, certain as­
sumptions are made regarding the regenerator reaction. These as­
sumptions include: (1): The dense phase is divided into the bubble phase 
(flat push flow) and the emulsion phase (fully mixed flow). (2): There is 
no reaction in the bubble phase, only mass transfer, heat transfer and gas 
phase reaction in the emulsion phase, and gas–solid phase reaction on 
the catalyst surface. (3): In the bubble phase, the bubble volume during 
the bubble ascent will increase with its height and the change of bubble 
phase change is instantaneous. The time lag of the bubble phase is not 
considered. 
The formula for the parameter σ needed in the mathematical model is 
first given in Equation (4), where σ signifies the molar ratio between CO2 
and CO generated throughout the combustion process and R denotes the 
molar gas constant valued at 8.314 under the SI system. 
σ = CO2
CO
⎧
⎪
⎨
⎪
⎩
9.55 × 10−4 × e
5585
RT (T < 803)
1
1 + (T −803) × 6.1 × 10−3 (T⩾803)
(4)  
where T is the reaction temperature, K. Taking N2 on the gas phase as an 
example, the reaction equation in the regenerator is shown below: 
dbN2
dt = 0.5×KCO NO ×bCO ×bNO
ub
+0.5×(1−εb)×(1−εe)×KC ×bC ×bNO
us
(5)  
where KC = −KC0e−
EC0
RT , KC0 represents the pre-exponential factor in the 
reaction and EC0 represents the activation energy. us,ub represent solid- 
phase velocity and gas-phase velocity respectively. εb , εe represent 
emulsion phase porosity, and bubble phase fraction, respectively. bN2 
represents the molar concentration of N2 in the reaction. The same 
represents the CO and NO molar concentration in the reaction of the 
regenerator, respectively. The complete formula for each component is 
shown in the Appendix. 
3.4. Reactor and regenerator coupling process 
The catalysts involved in the cracking reaction gradually experience 
surface area coking and a decrease in activity within the riser reactor, 
known as spent catalysts. These spent catalysts are then introduced into 
the regenerator, where they undergo a coke combustion process to 
remove the coke deposition on the catalyst surface and restore their 
activity to eventually form a regenerated catalyst. The regenerated 
catalysts are subsequently transported back to the reactor through 
catalyst transfer pipelines to participate in new cracking reactions. The 
following steps are implemented by coupling the calculation of the 
catalyst cycle volume and the catalyst coke content: First, initialization 
is performed in the reaction and regeneration modules, assuming zero 
carbon fixation of the regenerated catalyst. Next, the information about 
the catalyst to be generated and the regenerated catalyst is initialized. 
Then, an iterative calculation process is performed by cycling through 
the reaction module and regeneration module. In this process, 
Fig. 4. Regeneration reaction process.  
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Chemical Engineering Science 282 (2023) 119331
7
calculations are iteratively performed until the regenerated catalyst and 
the spent catalyst reach a state where no further changes occur in their 
information. At this point, the current results are used as the input for 
subsequent iterations. The coupling flow of the FCC-RR system is shown 
in Fig. 5. 
During the reactor model calculation, there are 120 reaction paths (i. 
e.,120 reaction rate constants involved in the reaction system), corre­
sponding to 120 conversion reaction pre-exponential factor k0 and re­
action activation energy Ea. Together with the catalyst deactivation 
factor, alkali nitrogen, and heavy aromatic adsorption factor involved in 
the reaction rate equation, there are 244 parameters in total. During the 
calculation of the regenerator model, a total of nine sets of reactions 
were included, involving a total of 18 reactions pre-exponential factor 
kC0 and reaction activation energy EC0. The results of the system of 
differential equations are calculated by the fourth-order Runge-Kutta 
method. The optimal combination of kinetic parameters is solved by a 
heuristic optimization algorithm (genetic algorithm) with the objective 
of minimizing the sum of squares due to error (SSE). In other words, by 
setting a reasonable range of kinetic parameters and iterative calcula­
tion conditions of the optimization algorithm, the combination of kinetic 
parameters that can meet the requirements of model accuracy is 
obtained. 
4. Optimization methods 
4.1. Mathematical basic 
In this section, a reproduction operator that can efficiently use the 
information in the path evolution (PE) process is introduced. To begin 
with, define some variables that need to be explained. xi denotes any one 
of the populations. Centerg represents the mean point of all individuals in 
Fig. 5. FCC-RR model calculation flow chart.  
Fig. 6. Schematic diagram of generation of offspring individuals based on the 
evolution path. 
Table 5 
PE algorithm framework.  
Algorithm 1: PE algorithm framework 
Input: Decision variables: Xg and Xg-1, Virtual Pareto front layers: VFNg, 
Evolutionary path steps: α, Minimum normalized evolutionary path length in nep: 
minC, Upper limit of decision variables: UB, Lower limit of decision variables: LB, 
A number of offspring individuals: N0. 
Output: Offspring individual decision variables: Xnext 
I : ep = CalculateEvolutionPath
(
Xg - 1, Xg, minC, UB, LB
)
1:for i = 1 : D 
2: nep(i) = (Centerg(i) −Centerg - 1(i))/(UB(i) −LB(i)) nep: normalized evolutionary 
path 
3:end for 
4:if max(nep) < minC 
5:index = randi(D,1)
6:Centerg(index) = LB(index) + rand(0, 1) × (UB(index) −LB(index))
7:return to line 2 
8:end if 
9:Computationale evolutionary path: ep = Centerg −Centerg−1 
10:return ep 
II:Xtemp1 = GeneratePotentialSolution(Xg, ep, α, No)
1:for i = 1 : No 
2:for j = 1 : No 
3:Xtemp1(i,j) = α*ep(j)*rand(0, 1) + Xg(i,j)
4:end for 
5:end for 
6:return Xtemp1 
III:Xtemp2 = PolynomialMutation(Xtemp1)
IV:Xnext = GeneSharing(Xg, Xtemp2, VFNg)
1:F1 = find(VFNg = 1)
2:for i = 1 : length(Xtemp2)
3:jrand = randi(D,1)
4: for j = 1 : D 
5:F1rand = randi(length(F1),1)
6: if rand(0, 1) > GSRandjrand ∕
= j GSR: Gene sharing rate 
7:Xnext(i,j) = Xg(F1(F1rand),j)
8: else 
9:Xnext(i,j) = Xtemp2(i,j)
10: else if 
11: else for 
12: else for 
13: return Xnext  
L. Wan et al.                                                                                                                                                                                                                                     
Chemical Engineering Science 282 (2023) 119331
8
the gth generation population, where Centerg(j) = ∑N
i = 1xi
j/N, N rep­
resents the size of the population, and D is the number of the decision 
variable. The equations to be utilized for the designed reproduction 
operator are shown in Equation 6. 
Centerg = mean
(
x1, x2, x3, ..., xN)
where xi =
(
xi
1, xi
2, xi
3, ..., xi
D
)
ep = Centerg −Centerg−1
(6) 
Here ep = Centerg-Centerg-1 is used to generate offspring individuals, 
which are simply followed in real-time. For a simplified illustration of 
the offspring generation process, Fig. 6 demonstrates that we initially 
assume a population size of N = 5 and a decision variable of D = 2. The 
first step involves calculating the path ep and then determining the ep 
direction, indicating that increasing the values of both x1 and x2 im­
proves the quality of the offspring individuals. Next, the calculated ep is 
multiplied by a forward progress length α to obtain the maximum for­
ward distance of the offspring individuals (represented by dashed ar­
rows). Using each parent as the starting point, the children are generated 
within the rectangle pointed by α ∗ep. In this case, the five offspring 
individuals are located within the region of the rectangle formed by five 
points in the figure below. 
For speeding up the convergence and facilitating the dissemination 
of the most favorable genes in the population, gene-sharing operations 
are incorporated into the algorithm. 
4.2. PE design process 
The algorithm 1 in Table 5 presents the overall framework of the PE, 
which first calculates the evolutionary path ep, then calculates the 
offspring individuals Xtemp1, enhances the genetic diversity in the pop­
ulation by employing polynomial variation in this step and finally shares 
the good genes in the parent in the offspring band by gene sharing. 
In the algorithm, VFNg is the number of virtual Pareto front layers of 
individuals in the gth generation. 
In the proposed algorithm 1, considering that all individuals need to 
be engaged in the evolution of the population, it can be achieved by 
calculating the population of centroids. With lines 2–9 in algorithm 1 in 
Table 5, we can ensure that Centerg and Centerg-1 are significantly 
different to ensure that the population will not be trapped in a local 
optimum. The final calculation of the evolutionary path ep =
Centerg −Centerg−1. Individuals in the offspring are calculated by algo­
rithm formula II, then genetic diversity in the population is increased by 
algorithm formula III polynomial mutation, and finally, genes that are 
superior in the parent are passed on to be shared in the offspring by the 
gene-sharing operation. 
4.3. Parameter adaptive process design 
The overall architecture of the reproduction operator (PE) has been 
described above, and there is an acritical parameter in the reproduction 
operator used: α. The magnitude of the value can determine how much 
can be gone along the evolutionary path at most. In our experiments, we 
use an adaptive way to adjust the parameters α. Equation 7 gives the 
process of how α is adaptively taken. 
α =
{
min(α*ω, αub), ifpsucc > ptarget
succ
max(α/ω, αlb), otherwise
(7)  
where ptarget
succ represents the survival rate of the offspring and is an arti­
ficially set parameter with a size between 0 and 1 (i.e., the percentage of 
offspring individuals evolving to the next generation). Psucc denotes the 
actual offspring individual survival rate, whose value is equal to the 
individuals in the next generation of offspring divided by the population 
size N. ω ∈(1, +∞) determines the growth rate of α. 
In the early stages of evolution: psucc > ptarget
succ .α will increase at the 
rate of ω. During this period, most individuals in the decision space are 
well away from the Pareto optimal solution, it is simple to generate non- 
dominated individuals and shift towards the Pareto optimal solution set 
at a very high speed. Set a maximum αub to avoid the generation of 
erroneous offspring by moving too far in a certain direction during its 
follow-up. 
As the population continues to evolve, when the population has 
evolved to lie near the Pareto optimal solution at this point, 
psucc < ptarget
succ . α decreases in this period at a rate of ω. At this stage, the 
algorithm has basically achieved convergence and continues to search 
locally in the Pareto solution set. This enriches the diversity of pop­
ulations. To ensure that each gene is slightly perturbed, a lower limit αlb 
is set. 
4.4. Improved SPEA-2 algorithm for integrated PE 
In this section, we discuss how to integrate the proposed path-based 
reproduction operator into the SPEA-2 algorithm and designate the 
improved algorithm as SPEA2-M. The specific steps are shown in Al­
gorithm 2 in Table 6. 
In this algorithmic framework, “…” and “……” represent the other 
parameters and operations of the original algorithm, respectively. Two 
populations are randomly generated in lines 1 and 2 of the algorithm 
framework: P-1 and P0. Line 3 uses the environmental selection in the 
original algorithm to select N newborn individuals to update P0. The 
purpose of this operation is to ensure that the overall quality of P0 is 
better than that of P-1, which eventually results in a more efficient initial 
evolutionary path. In lines 4 to 6, the value of the VFNg element cor­
responding to the adaptively optimal half of the individuals is set to 1, 
and the values of the other elements of VFNg are set to 2. In the algo­
rithm’s main loop logic, the novel reproduction operator is applied in 
line 10 to generate offspring. Line 11 calculates the objective function 
values for the individuals, and line 12 updates all individuals in the 
parent population. Once the environment selection calculation is 
completed in line 14, the parameters VFNg and α in the novel repro­
duction operator are updated in lines 15 to 18. Subsequently, the path- 
based reproduction operator was incorporated into the SPEA-2 algo­
rithm as a crucial component in the evolution of the algorithm’s 
Table 6 
Combining reproduction operators with SPEA-2.  
Algorithm 2: SPEA2-M (Combining reproduction operators with SPEA-2) 
Input: α0αInitialvalue, αubTheupperlimitofα,αlbLowerlimitofα 
minC (Minimum normalized evolutionary path length in nep),Ptarget
succ 
Output: Last generation populations: P 
1:P−1 = RandomInitialize(N)
2:P0 = RandomInitialize(N)
3:
[
P0, FitnessValue, psucc,...
]
= EnvironmentalSelection(P−1,P0,...)
4:VFN0 = 2*ones(1,N)
5:index = FitnessValueOptimal half of the index 
6:VFN0(1,index) = 1 
7:......
8:While termination criterion not fulfilled do 
9:......
10: Xnext = PE(Xg−1, Xg, VFNg, α, minC, UB, LB, N) PE calculation is shown in Algorithm 
1 
11:Og = FunctionEvalution(Xnext)
12:Pg−1 = Pg 
13:......
14:[Pg,FitnessValue,Psucc,...] = EnvironmentalSelection(Og,Pg,...)
15:VFNg = 2 × ones(1,N)
16:index = FitnessValueOptimalhalfofthe index 
17:VFNg(1,index) = 1 
18:Adjustα by equation 7 
19:......
20:end while 
21:return P  
L. Wan et al.                                                                                                                                                                                                                                     
Chemical Engineering Science 282 (2023) 119331
9
offspring. This integration aims to expedite the convergence of the al­
gorithm, reduce solution time, and simultaneously enhance the solution 
quality. The logic block diagram illustrating this integration is presented 
in Fig. 7. 
The fundamental procedure of the SPEA2-M algorithm is as follows: 
N: Population size, N: reserve set size, t (current number of 
iterations), 
T (upper limit of preset number of iterations). 
Step 1: Initializing the population (process decision variable data 
set) P0 and the empty reserve set P0, when the number of iterations t = 0. 
Step 2: Calculating the fitness of individuals F(i) in the initial 
population. 
Step 3: Environment Selection: Copy all the non-dominated solution 
sets in Pt and Pt to Pt+1, and if the number of Pt+1 is greater than N, 
perform the pruning operation as described in the environment selection 
below; otherwise, select the dominated solution sets in Pt and Pt to add 
to Pt+1. 
Step 4: When t > T or other termination conditions are satisfied, the 
custom output variable A is output as the set of decision variables rep­
resented by the non-dominated solution of Pt+1. 
Step 5: Selection operation, tournament selection for Pt+1 into the 
mating pool. 
Step 6(Integrated arithmetic): Feed the data set in the mating pool 
as input to the new reproduction operator program for computation, and 
store the result in the set Pt+1, with the number of iterations returning to 
Step2. 
Next, the calculation of the fitness function and the environment 
selection are described separately. The formula for the degree of adap­
tation is shown below: 
S(i) = {j|j ∈Pt + Pt ∩j ≺i}
(8)  
R(i) =
∑
j∈Pt+Pt,i<j
S(j)
(9) 
Where S(i) denotes the number of individuals dominated by indi­
vidual i. R(i) denotes the original adaptation value, which consists of the 
sum of the total number of all individuals dominated by each individual j 
that dominates i. The k-neighborhood was chosen to estimate the den­
sity. The distance of everyone to the reserve set and all individuals in the 
population is calculated, and the kth individual is selected in increasing 
order as σk
i . Where K =
̅̅̅̅̅̅̅̅̅̅̅̅̅
N + N
√
, 2 is added to ensure that the value is 
within the interval (0,1). 
D(i) =
1
σk
i + 2
(10)  
F(i) = R(i) + D(i)
(11) 
In terms of environment selection:  
(1) When |pt+1|〈|N|, all individuals in Pt +Pt select the top N −|pt+1|
according to the fitness ranking. 
Fig. 7. SPEA2-M algorithm flowchart.  
L. Wan et al.                                                                                                                                                                                                                                     
Chemical Engineering Science 282 (2023) 119331
10
(2) When |pt+1|〉|N|, j ∈Pt+1。Eliminate individual i in each iteration, 
where i satisfies 
Di⩽Dj ⇔∀0 < k < |Pt+1| : σk
i = σk
j ∪
∃0 < k <
⃒
⃒
⃒Pt+1
⃒
⃒
⃒:
[(
∀0 < l < k : σl
i = σl
j
)
∩σk
i < σk
j
]
(12) 
Where σk
i represents the distance between individual i and the kth 
neighbor. The above formula indicates that all individuals are sorted 
based on their distances to their neighbors. The neighbor individual with 
the minimum distance is selected for removal (k and i may not neces­
sarily have the smallest distance to each other). If there are multiple 
individuals with the same minimum distance, the second smallest dis­
tance is considered, while keeping the minimum distance. This process 
continues iteratively, reducing population crowding and ensuring pop­
ulation diversity. 
Compared to other operators, the path-based reproduction operator 
in this method ensures that all individuals are aligned in the same 
evolutionary direction. This improves the convergence speed of the 
operator while preserving population diversity. All individuals partici­
pate in the calculation of evolutionary paths, resulting in a more robust 
evolutionary direction. Additionally, the operator incorporates gene 
sharing operations, which involve cross operations targeting offspring 
individuals and superior parent individuals. This further enhances the 
convergence of the algorithm. The convergence effect is depicted in 
Fig. 8. 
The SPEA-2 algorithm incorporates the path-based reproduction 
operator as a crucial link in the growth of the algorithm’s offspring in an 
effort to hasten convergence, shorten solution time, and enhance solu­
tion quality. Although the population initialization in the initial algo­
rithm simply generates a random set of initial populations in a range of 
values, which is universal but does not work well for decision variables 
with complex conditions, the algorithm is further improved here for the 
characteristics of the refinery process due to the complexity of the re­
finery process. For instance, the three following choice variables: the 
mass flow rates of the hydrogenated wax oil feed, the tank wax oil feed, 
and the tank residual oil feed. There are two techniques for these three 
choice variables’ input, and the associated pseudo-codes are displayed 
in Table 7 and Table 8, respectively. Strategy 1: The three decision 
variables’ total feed remains constant, but there are upper and lower 
bound constraints on each. Strategy 2: The three decision variables are 
input in a certain proportion. 
Like the above decision variables with specific requirements, the 
corresponding initialization algorithm is written and integrated into 
SPEA-2, and then the corresponding switching mode is set for use. 
Compared with the initialization process of the original SPEA-2 algo­
rithm, the optimized SPEA-2 algorithm in this chapter is more applicable 
to most of the complex input conditions of the refinery process, more 
functional, and simpler to optimize the operation. At the same time, for 
some characteristics of the mechanism model, this chapter adds some 
optimization mechanisms to the algorithm, such as the objective mini­
mization mechanism and the mechanism model convergence judgment 
processing mechanism, which solves a series of problems that may occur 
in the optimization process. 
4.5. Evaluation metrics for algorithms 
In this study, the performance of the proposed path-based repro­
duction operator was evaluated using several metrics, including Inver­
ted Generational Distance (IGD)(Ozcelikkan et al., 2022), Generational 
Distance (GD)(Ozcelikkan et al., 2022), Hyper Volume(HV) (Ozcelikkan 
et al., 2022), and Diversity Metric(DM) (Petchrompo et al., 2022). A 
smaller GD value indicates better convergence of the algorithm, while a 
smaller IGD value indicates better overall performance. A larger HV 
value signifies better overall performance and a larger DM value in­
dicates a wider range of solutions. To validate the effectiveness of the 
proposed path-based reproduction operator, we conducted tests on 
mathematical problems such as ZDT and WFG, comparing the results 
with three other multi-objective optimization algorithms. 
The performance of the proposed path-based optimization algorithm 
on the ZDT problem is depicted in Fig. 9. In the bi-objective problem, the 
Fig. 8. Illustration of Convergence Effects Pre and Post Evolution (Left: early stage of evolution; Right: late Evolution).  
Table 7 
Three Decision Variables Initialize Population Strategy 1 pseudocode.  
STRATEGY 1: 
Input: All parameter values: Parameter, Population size: N, Lower bound for each 
decision: variable: 
DecDown, Upper limit of each decision variable: DecUp. 
Output: Primary Populations: Dec. 
1:ReadParameter 
2:for i = 1 : N 
3:Dec(i,1) = rand(DecDown1,DecUp1)
4:Dec(i,2) = rand(DecDown2,DecUp2)
5:Dec(i,3) = Total −Dec(i,1) −Dec(i,2)
6:end for 
7:return Dec  
Table 8 
Three Decision Variables Initialize Population Strategy 2 pseudocode.  
STRATEGY 2: 
Input: All parameter values:Parameter, Population size: N, Lower bound for each 
decision variable:DecDown, Upper limit of each decision variable:DecUp. 
Output: Primary Populations: Dec. 
1:ReadParameter 
2:for i = 1 : N 
3:Dec(i,1) = rand(DecDown1,DecUp1)
4:Total = Dec(i,1)/k1 
5:Dec(i,2) = Total*k2 
6:Dec(i,3) = Total*k3 
7:end for 
8:return Dec  
L. Wan et al.                                                                                                                                                                                                                                     
Chemical Engineering Science 282 (2023) 119331
11
path-based optimization algorithm demonstrates advantages across all 
evaluation metrics. 
The performance of different algorithms on the tri-objective math­
ematical problem is shown in Fig. 10. In the tri-objective optimization 
problem, the modified SPEA-2 algorithm (SPEA2-M) also demonstrates 
remarkable performance across all evaluation metrics. 
5. Case study 
5.1. Industrial fluid catalytic cracking plants 
Industrial catalytic cracking processes are characterized by being 
multi-modal, multi-scale, long time-delayed, highly nonlinear, strongly 
Fig. 9. Performance of different algorithms in the ZDT problem of bi-objective optimization.  
Fig. 10. Performance of different algorithms in the WFG problem of tri-objective optimization.  
L. Wan et al.                                                                                                                                                                                                                                     
Chemical Engineering Science 282 (2023) 119331
12
coupled, and continuously dynamic complex systems. This significantly 
increases the difficulty of modeling industrial catalytic cracking pro­
cesses. Additionally, uncertainties related to feedstock, catalyst, and 
equipment performance, which are commonly encountered in petro­
leum refining and chemical production, further amplify the demand for 
operational optimization. 
This paper is based on the modeling and optimization research 
conducted on a catalytic cracking unit with an annual processing ca­
pacity of 3.5 million tons in the East China region. The industrial cata­
lytic cracking unit consists of three main parts: the reaction-regeneration 
system, fractionation system, and absorption stabilization system. It 
includes equipment such as the riser reactor, regenerator, main frac­
tionation tower, absorption tower, reabsorption tower, stabilization 
tower, main fan, and gas compressor. The riser reactor employs the MIP 
process. The unit is equipped with a real-time database containing over 
3,000 measurement points and more than 500 quality analysis tags. 
In order to ensure the accuracy of the modeling, an initial selection of 
73 feature variables, deemed influential based on expert knowledge, was 
Fig. 11. Illustration of Simulation Modeling and Optimization Techniques for FCC-RR unit.  
Table 9 
Upper and Lower Bounds on Decision Variable Constraints.  
Names of Decision Variables 
unit 
Lower 
Bound 
Upper 
Bound 
Feed temperature (Tfeed) 
◦C 
210 
220 
First reaction zone outlet temperature (Tfr) 
◦C 
515 
530 
Feed Pressure (Pfeed) 
KPa 
850 
900 
Reactor Pressure (Preaction) 
KPa 
210 
230 
Hydrogenated wax oil feed mass flow rate (oil 
1) 
t/h 
225 
240 
Wax oil feed mass flow rate in tank area (oil 2) 
t/h 
105 
120 
Residual oil feed mass flow rate in the tank 
area (oil 3) 
t/h 
10 
45 
Regeneration catalyst activity (CA) 
% 
55 
61 
Oxygen content in flue gas (OC) 
v% 
0 
6  
L. Wan et al.                                                                                                                                                                                                                                     
Chemical Engineering Science 282 (2023) 119331
13
made. Hourly averaged data spanning one year of continuous produc­
tion from a 3.5 million-ton catalytic cracking unit at an Eastern China 
refinery was collected. Due to partial data unavailability on-site, a total 
of 309 valid data points covering multiple production cycles were 
gathered throughout the year. Following data preprocessing, which 
involved the removal of outliers, duplicates, missing values, and treat­
ment of fluctuating data, a dataset comprising 200 samples and 56 
features was retained, thereby further enhancing the quality of the 
modeling dataset. The actual refinery data was utilized to model and 
optimize the design of the FCC-RR system. The specific workflow for the 
modeling and optimization process is illustrated in Fig. 11. 
5.2. Optimization problem 
5.2.1. Variable selection and constraints 
Before optimization, the core operating conditions that will largely 
influence the catalytic cracking reaction-regeneration system are taken 
out for optimization. Selected core operating conditions are shown in 
Table 9, which contains feed temperature (Tfeed), reactor pressure 
(Pfeed), first reaction zone outlet temperature (Tfr), reactor pressure 
(Preaction), hydrogenated wax oil feed mass flow rate, wax oil feed mass 
flow rate in tank area, residual oil feed mass flow rate in the tank area, 
regeneration catalyst activity (CA) and Oxygen content in flue gas (OC). 
Considerable effort has been dedicated to ensuring that the upper and 
lower limits we have determined are not only reasonable but also 
practical. We engaged FCC experts and operators to provide specific 
values, meticulously analyzed these values, and communicated them 
comprehensively to ascertain their feasibility in industrial practice. 
The formula for the decision variables affecting the product distri­
bution is given below. 
Tfeed
min < Tfeed < Tfeed
max
(13)  
Pfeed
min < Pfeed < Pfeed
max
(14)  
Tfr
min < Tfr < Tfr
max
(15)  
Flowoil
min < Flowoil < Flowoil
max(oil ∈{1, 2, 3})
(16)  
Preaction
min
< Preaction < Preaction
max
(17)  
CAmin < CA < CAmax
(18)  
OCmin < OC < OCmax
(19)  
5.2.2. Optimized targets for emission reduction and revenue enhancement  
(1) Economic benefits 
In this section the design of the optimization objective is carried out 
based on the above bringing in the decision variables and then finding 
each product, firstly the formula for the economic benefits of the whole 
process is given in Equation 20–24. The products considered in this 
study that can generate economic benefits include gasoline, diesel, 
propylene, ethylene, butene, liquefied gas, dry gas, and coke. Where y1, 
y2, and y3 represent the quality of tank wax oil, tank residue oil, and 
refined wax oil respectively, and Vall represents the volume of the set as a 
whole. F represents process compliance and f catalyst represents catalyst 
replenishment. 
Earningstotal = EarningsGet −CostRaw −CostLabor −CostCatalyst
(20)  
EarningsGet =
∑
j∈products
PricejajVall
(21)  
CostRaw =
∑
k∈oil{y1,y2,y3}
Pricekyk
(22)  
CostLabor = PriceLabor × F
(23)  
CostCatalyst = PriceCatalyst × fCatalyst
(24)    
(2) CO2 Emission 
The second optimization target is the greenhouse gas CO2 produced 
in the production, and the CO2 emissions produced in the catalytic 
cracking process are noted as EmissionsCO2. The carbon dioxide emissions 
generated throughout the entire process are calculated using Equation 
25, where Vreg represents the volume of flue gas in the regenerator and 
44 represents the molecular weight of carbon dioxide. 
EmissionsCO2 = bCO2Vreg
22.4
× 44
where bCO2 comes from the equation 5 and Appendix
(25)    
(3) SO2 Emission 
Similarly, the definition of sulfur dioxide emissions is provided by 
the calculation formula shown in Equation 26, where 64 represents the 
molecular weight of sulfur dioxide. 
EmissionsSO2 = bSO2Vreg
22.4
× 64
where bSO2 comes from the equation 5 and Appendix
(26)  
5.2.3. Multi-objective problem design 
In this section, the design of the entire multi-objective optimization 
process is focused on prioritizing the economic benefits of the catalytic 
cracking process. Therefore, the primary objective is to maximize the 
overall economic performance while minimizing carbon dioxide and 
sulfur dioxide emissions. The complete multi-objective optimization 
formulation is presented in Equation 27, where X represents the afore­
mentioned decision variables. 
Table 10 
Accuracy Results for Eighteen-Lump Kinetic Modeling for Each Individual Lump.  
Lump 
Actual mean 
value (%) 
Model Mean 
(%) 
Absolute error 
(%) 
Relative Error 
(%) 
SS 
3.089  
3.069  
0.019 
0.64 
SA 
1.874  
1.920  
0.046 
2.45 
SR 
0.887  
0.711  
0.176 
19.9 
Dp 
2.057  
1.854  
0.203 
9.86 
Do 
0  
0.143  
0.143 
– 
Dn 
1.277  
1.311  
0.034 
2.65 
Das 
5.083  
5.135  
0.052 
1.03 
Dam 
11.429  
11.306  
0.123 
1.08 
Gp 
14.766  
14.750  
0.015 
0.10 
Go 
9.934  
9.988  
0.054 
0.54 
Gn 
4.594  
4.696  
0.103 
2.23 
Ga 
16.561  
16.426  
0.134 
0.81 
C4=
3.894  
3.874  
0.019 
0.50 
C3=
5.554  
5.693  
0.139 
2.50 
LPG 
7.725  
7.663  
0.061 
0.79 
C2=
0.760  
1.014  
0.254 
33.4 
DR 
3.744  
3.846  
0.102 
2.72 
C 
6.406  
6.478  
0.072 
1.12  
L. Wan et al.                                                                                                                                                                                                                                     
Chemical Engineering Science 282 (2023) 119331
14
⎧
⎪
⎪
⎨
⎪
⎪
⎩
maximize refinery economic benefits by eq 20 - 24
minimize CO2 emissions by eq 25
minimize SO2 emissions by eq 26
s.t. Temperature constraints from eq(13, 15)
Pressure constraints from eq(14, 17)
Feed flow rate of each raw oil constraints from eq(16)
Regeneration catalyst activity constraints from eq(18)
Oxygen content in flue gas constraints from eq(19)
(27)  
5.3. Results and discussion of modeling and optimization for industrial 
installations 
5.3.1. Results of 18-lump reactor model based on industrial installations 
By utilizing the data collected from the FCC-RR unit, the kinetic 
model presented in Chapter 3 can be integrated with a genetic algorithm 
to determine the 18-lump reaction kinetic model. In this process, 100 
sets of parameter estimates are selected for the model, while another 50 
different sets are reserved for validating the effectiveness of the model. 
The test results, displayed in Table 10, demonstrate the performance of 
the model using the 50 testing datasets. The experimental findings 
substantiate the efficacy of the proposed 18-lump modeling approach for 
most products. For a visual representation of the experimental results, 
refer to Fig. 12, which illustrates the absolute errors associated with the 
18-lump model for the validation datasets. Notably, the product calcu­
lations remain well within an acceptable range. 
Fig. 12. 18-Lump riser reactor model absolute error.  
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Chemical Engineering Science 282 (2023) 119331
15
Subsequently, we conducted further sensitivity analysis on the 
temperature of the first reaction zone (FRZ). We examined the impact of 
temperature variation in the FRZ on the various lumps, as depicted in 
Fig. 13. With increasing temperature, there was an upward trend in the 
aromatics content of gasoline, while the yield of gasoline olefins 
decreased. The yields of diesel, liquefied gas, and propylene exhibited a 
decreasing trend, which is consistent with the expected behavior. As the 
cracking depth increased, larger molecules were further converted into 
smaller ones. Additionally, the intensified process of hydrocarbon 
molecule dehydrogenation at higher temperatures resulted in an 
increased coke yield. 
5.3.2. Results of 13-lump regenerator model based on industrial 
installations 
The results of the regenerator fit are shown in Table 11. For the target 
objects to be optimized, greenhouse gas CO2 and harmful gas SO2, the 
predicted values of the model are within an acceptable range of error 
from the real values, so the model can be used for simulation and 
optimization. 
5.3.3. Optimization based on the improved SPEA2-M algorithm 
In this study, the operational data is used to generate decision vari­
ables, which are then input into the MATLAB-based catalytic cracking 
mechanism model. The values of objective function values are auto­
matically derived from the model. Subsequently, the PlatEMO algorithm 
platform is employed to perform multiple calculations and optimize the 
decision variable values, aiming to achieve optimal objective function 
values. We chose all four algorithms for our calculations, setting their 
maximum number of iterations to 10,000, the number of populations to 
100, and the decision variables to 9 and setting upper and lower bounds 
for each decision variable, all other parameters use the default param­
eters in the algorithm in the PlatEMO algorithm. 
To evaluate the convergence and adaptability of the proposed path- 
based reproduction operator combined with the SPEA-2 algorithm in the 
complex catalytic cracking process, the average IGD variation is 
analyzed using four different optimization algorithms across various 
objective function values. Fig. 14 presents the performance comparison 
of the ARMOEA, NSGA-II, SEPA-2, and SPEA2-M algorithms in solving 
different multi-objective problems. In practical optimization problems, 
obtaining the true Pareto frontier is often challenging. In this study, 
multiple calculations are performed using different input conditions to 
approximate the Pareto frontier. The performance of the algorithms is 
evaluated by comparing the normalized average objective function 
value and the average solution time. The Inverted Generation Distance 
(IGD) formula is used to quantify the quality of the obtained solutions. 
The IGD formula is given below: 
IGD(Q, P) =
∑
v∈Pd(Q, v)
|P|
(28)  
where P is the set of points uniformly distributed on the real pareto 
surface, |P| is the number of individuals of points uniformly distributed 
on the real pareto surface, Q is the set of pareto optimal solutions found 
by the algorithm, and d(Q, v) is the minimum Euclidean distance be­
tween individuals v in the set of P points and the population Q. 
Fig. 13. Results of the yield of each lump with the temperature change of the FRZ.  
Table 11 
Regenerator lumps accuracy results.  
Product 
MAV 
MMV 
AE 
RE (%) 
CO2 (v %) 
16.63 
16.3 
0.33 
1.98 
SO2 (mg/m3) 
150 
147 
3 
2 
N2 (v %) 
73.17 
73.98 
0.81 
1.11 
H2O (v %) 
7.8 
7.68 
0.13 
1.67 
NO (v %) 
0 
0 
0 
0 
HCN (v %) 
0 
0 
0 
0 
CO (v %) 
0 
0 
0 
0 
O2 (v %) 
2.13 
2.14 
0.014 
0.65 
NO2 (mg/m3) 
457.5 
450.73 
6.77 
1.48 
*Mean Actual Value (v %): AMV, Mean Model Value (v %): MMV, Absolute Error 
(v %): AE, Mean Relative Error (%): RE. 
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Chemical Engineering Science 282 (2023) 119331
16
The population centroid evolutionary trajectory is shown in Fig. 15. 
The provided figure depicts the evolutionary trajectories of population 
centroids in the decision space for different algorithms on the problems 
of economic benefits, CO2 emission, and SO2 emission. The algorithms 
presented in the figure, from left to right and top to bottom, are 
ARMOEA, NSGA-II, SPEA-2, and SPEA2-M. It can be observed that the 
SPEA2-M algorithm exhibits the fastest convergence speed among the 
compared algorithms during the evolutionary process. This advantage 
allows the SPEA2-M algorithm to significantly reduce the computational 
resources required for evolution. 
Fig. 14. IGD of different algorithms for each product problem in catalytic cracking. *(a): Liquefied gas, Diesel production, and CO2 emissions. (b): Diesel production, 
CO2 emissions, and SO2 emissions. (c): Gasoline production, energy consumption, and SO2 emissions. (d): Gasoline production, CO2 emissions, and SO2 emissions. 
(e): Economic benefits, energy consumption, and CO2 emissions. (f): Liquefied gas production, CO2 emissions, and SO2 emissions. 
L. Wan et al.                                                                                                                                                                                                                                     
Chemical Engineering Science 282 (2023) 119331
17
Based on the analysis of Fig. 16, it can be observed that the ARMOEA 
algorithm, when compared with the other three algorithms in the 
context of this optimization model, does not produce a uniformly 
distributed optimal Pareto solution set. This indicates that the ARMOEA 
algorithm may not be well-adapted to this particular optimization 
model. On the other hand, the other three algorithms demonstrate better 
results, particularly the SPEA2-M algorithm, which exhibits the most 
favorable optimization outcomes. Based on the experimental compari­
son, it can be concluded that the SPEA-2 algorithm is more adaptable to 
the optimization model and achieves faster convergence to lower IGD 
values. Therefore, the SPEA-2 algorithm serves as the framework for 
further improvement. 
As depicted in Fig. 17, the final optimization problem shows that the 
enhanced SPEA-2 algorithm (SPEA2-M) outperforms others in terms of 
average IGD. The results of the verification demonstrate that the elite 
strategy and shared parameters incorporated into the new path-based 
reproduction operator in this paper are well-aligned and effective. 
Furthermore, when compared with other algorithms, the improved 
Fig. 15. Evolutionary trajectories of population centers obtained by different algorithms on economic benefits, CO2 emissions, and SO2 emissions.  
Fig. 16. Use four algorithms to solve the Pareto optimal solution set generated 
by minimizing CO2 emissions, minimizing SO2 emissions, and maximizing 
economic benefits. 
Fig. 17. Four algorithms for solving the problems of minimizing CO2 emissions, 
minimizing SO2 emissions, and maximizing economic benefits generate Mean 
IGD diagrams. 
Table 12 
Comparison table of optimization conditions before and after optimization.  
Decision variable name 
Before 
optimization 
After 
optimization 
Feed temperature (◦C)  
216.8  
210.0 
Feed pressure (kPa)  
880.3  
850.0 
First reaction zone outlet temperature (◦C)  
527.8  
515.0 
Reactor pressure (kPa)  
222.9  
210.0 
Hydrogenated wax oil feed mass flow rate 
(t/h)  
230.0  
225.0 
Wax oil feed mass flow rate in the tank area 
(t/h)  
110.0  
119.1 
Residual oil feed mass flow rate in the tank 
area (t/h)  
40.0  
41.2 
Regeneration catalyst activity (%)  
58.0  
57.5 
Oxygen content in flue gas (v%)  
2.4  
2.0  
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Chemical Engineering Science 282 (2023) 119331
18
algorithm exhibits faster convergence and superior computational 
resource efficiency in obtaining Pareto solution sets with closely 
matched values. This showcases the algorithm’s ability to achieve fast 
and accurate optimization results, providing a significant advantage 
over alternative approaches. The final optimal set of decision variables is 
obtained as shown in Table 12. 
The optimized Pareto optimal solution set partial objective function 
values are shown in Table 13. 
The initial values before optimization are as follows: economic 
benefits of 215102.8 Yuan/h, CO2 emission of 95223.1 kg/h, and SO2 
emission of 125.6 kg/h. After optimization, focusing on economic ben­
efits, the values are improved to 226753.6 Yuan/h for economic bene­
fits, 71798.2 kg/h for CO2 emission, and 81.8 kg/h for SO2 emission. The 
significant improvement in these values demonstrates the effectiveness 
of the optimization process. The optimized operation data can serve as 
valuable guidance for refining operations, enabling simultaneous im­
provements in economic benefits and reductions in CO2 and SO2 
emissions. In practical production, refineries bear a significant re­
sponsibility for achieving substantial economic benefits. Therefore, 
when selecting from the Pareto solution set, which includes solutions 
where all three optimization objectives have seen improvements, the 
solution that yields the highest economic benefit is chosen as the opti­
mization outcome. The optimization results are visually presented in 
Fig. 18. 
6. Conclusion 
This study presents a novel lumped mechanism modeling approach 
for catalytic cracking, addressing the limitations of previous methods by 
incorporating consideration for specific chemical products. The pro­
posed approach is well-aligned with current objectives of reducing oil 
consumption and increasing chemical production. Our findings high­
light the reliance on empirical knowledge rather than scientific foun­
dations when determining decision parameters in the production 
process. To tackle this challenge and mitigate the significant CO2 and 
SO2 emissions associated with catalytic cracking while increasing eco­
nomic benefits, an intelligent optimization algorithm is introduced. 
By integrating a path-based reproduction operator with the SPEA-2 
algorithm, we successfully optimize the proposed model to maximize 
the economic benefits of the catalytic cracking process while simulta­
neously reducing SO2 and CO2 emissions. Promising pilot results indi­
cate that the enhanced optimization algorithm, SPEA2-M, exhibits faster 
convergence and yields superior Pareto optimal solutions within the 
lumped model. Its applicability and adaptability to the optimization 
needs in the refinery industry are noteworthy. 
This study makes significant contributions to the field of catalytic 
cracking, offering an improved and comprehensive approach for 
addressing oil reduction, enhancing chemical production, and promot­
ing environmental sustainability. The proposed lump mechanism 
modeling approach, along with the intelligent optimization algorithm, 
paves the way for more efficient and environmentally friendly opera­
tions in the refinery sector. 
Table 13 
Pareto optimal solution partial objective function value.  
CO2 
emissions 
(kg/h) 
SO2 
emissions 
(kg/h) 
Economic 
benefits 
(CNY/h) 
CO2 
emissions 
(kg/h) 
SO2 
emissions 
(kg/h) 
Economic 
benefits 
(CNY/h)  
50259.7  
56.9  
182596.7  
52022.9  
58.4  
191016.9  
49340.2  
55.8  
180175.7  
52540.9  
59.2  
192526.3  
56989.3  
64.4  
202068.1  
49446.8  
55.9  
180572.1  
52440.3  
58.9  
192245.9  
53242.1  
59.9  
194139.9  
51810.9  
58.1  
190423.9  
48956.3  
55.5  
178493.1  
50376.3  
57.1  
183166.5  
51982.9  
58.4  
190973.9  
53170.7  
60.0  
193849.9  
52735.8  
59.2  
192889.3  
49416.4  
55.9  
180472.2  
53112.0  
59.6  
193780.3  
49388.2  
55.9  
180276.9  
49712.7  
56.3  
181241.9  
57071.0  
64.6  
202331.4  
50116.6  
56.8  
182631.8  
50673.1  
57.8  
183033.9  
53839.1  
60.6  
195505.3  
53146.37  
59.7  
193836.5  
53114.2  
59.7  
193702.0  
53199.6  
59.9  
194042.2  
63121.2  
79.6  
213829.2  
50005.3  
56.7  
181931.8  
50311.9  
57.2  
182752.2  
53092.0  
59.6  
193802.4  
71798.2  
81.8  
226753.6  
Fig. 18. Comparison of target values before and after optimization.  
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Chemical Engineering Science 282 (2023) 119331
19
CRediT authorship contribution statement 
Lei Wan: Methodology, Software, Writing – original draft, Visuali­
zation. Kai Deng: Software, Visualization. Xiangyang Li: Resources, 
Conceptualization. Liang Zhao: Conceptualization, Supervision, Inves­
tigation, Visualization. Jian Long: Methodology, Software, Investiga­
tion, Visualization. 
Declaration of Competing Interest 
The authors declare that they have no known competing financial 
interests or personal relationships that could have appeared to influence 
the work reported in this paper. 
Data availability 
The authors do not have permission to share data. 
Acknowledgments 
This work was supported by National Natural Science Fund for 
Distinguished Young Scholars (61925305), National Natural Science 
Foundation of China (62373155, 62373154,61973124), and Major 
Program of Qingyuan Innovation Laboratory (Grant No. 00122002).  
Appendix 
Modeling part parameters  
ρ 
Gas mixture density 
A 
Catalyst deactivation factor 
φ(Cc)
Catalyst coking deactivation 
f(A)
Heavy aromatic adsorption deactivation 
f(N)
Alkali nitrogen adsorption deactivation 
β 
Catalyst coking deactivation factor 
Cc 
Coke on the regenerated catalyst 
kA 
Heavy aromatic adsorption deactivator 
CA 
Residual carbon content of the raw oil 
kN 
Basic nitrogen adsorption coefficient 
CN 
Alkali nitrogen content of the raw oil 
tC 
Residence time of the catalyst 
ϕC/O 
Catalyst-to-oil ratio 
k0 
Corresponding pre-exponential factor 
Ea 
Activation energy 
R 
Molar gas constant 
T 
Reaction temperature 
EC0 
Activation energy 
ub 
Gas-phase velocity 
us 
Solid-phase velocity 
εb 
Emulsion phase porosity 
εe 
Bubble phase fraction 
bN2 
Molar concentration of N2 in the reaction 
SWH 
Real weight space velocity  
Formulas 
Part A: Reactor modeling equations 
The reaction rate equation for component SS: 
daSS
dt = −ρ
SWH
⋅A × (k1 + k2 + k3 + k4 + k5 + k6 + k7
+k8 + k9 + k10 + k11 + k12 + k13 + k14 + k15)×aSS
(1) 
The reaction rate equation for component SA: 
daSA
dt
= −ρ
SWH
⋅A × (k16 + k17 + k18 + k19 + k20 + k21 + k22
+k23 + k24 + k25 + k26 + k27 + k28 + k29 + k30)×aSA
(2) 
The reaction rate equation for component SR: 
daSR
dt
= −ρ
SWH
⋅A × (k31 + k32 + k33 + k34 + k35 + k36 + k37
+k38 + k39 + k40 + k41 + k42 + k43 + k44 + k45)×aSR
(3) 
The reaction rate equation for component Dp: 
daDp
dt
= −ρ
SWH
⋅A × ((k46 + k47 + k48 + k49 + k50 + k51 + k52 + k53) × aDp
−k1aSS −k16aSA −k31aSR)
(4) 
L. Wan et al.                                                                                                                                                                                                                                     
Chemical Engineering Science 282 (2023) 119331
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The reaction rate equation for component Do: 
daDo
dt
= −ρ
SWH
⋅A × ((k54 + k55 + k56 + k57 + k58 + k59 + k60 + k61) × aDo
−k2aSS −k17aSA −k32aSR)
(5) 
The reaction rate equation for component Dn: 
daDn
dt
= −ρ
SWH
⋅A × ((k62 + k63 + k64 + k65 + k66 + k67 + k68 + k69 + k70 + k71) × aDn
−k3aSS −k18aSA −k33aSR −k54aDo)
(6) 
The reaction rate equation for component Das: 
daDas
dt
= −ρ
SWH
⋅A × ((k72 + k73 + k74 + k75 + k76 + k77 + k78 + k79 + k80 + k81) × aDas
−k4aSS −k19aSA −k34aSR −k55aDo −k62aDn)
(7) 
The reaction rate equation for component Dam: 
daDam
dt
= −ρ
SWH
⋅A × ((k82 + k83 + k84 + k85 + k86 + k87 + k88 + k89 + k90) × aDam
−k5aSS −k20aSA −k35aSR −k56aDo −k63aDn −k72aDas)
(8) 
The reaction rate equation for component Gp: 
daGp
dt
= −ρ
SWH
⋅A × ((k91 + k92 + k93 + k94 + k95 + k96) × aGp −k6aSS
−k21aSA −k36aSR −k46aDp −k73aDas −k82aDam)
(9) 
The reaction rate equation for component Go: 
daGo
dt
= −ρ
SWH
⋅A × ((k97 + k98 + k99 + k100 + k101 + k102) × aGo −k7aSS −k22aSA
−k37aSR −k47aDp −k57aDo −k64aDn −k74aDas −k83aam)
(10) 
The reaction rate equation for component Gn: 
daGn
dt
= −ρ
SWH
⋅A × ((k103 + k104 + k105 + k106 + k107) × aGn −k8aSS
−k23aSA −k38aSR −k65aDn −k97aGo)
(11) 
The reaction rate equation for component Ga: 
daGa
dt
= −ρ
SWH
⋅A × ((k108 + k109 + k110 + k111 + k112 + k113) × aGa
−k9aSS −k24aSA −k39aSR −k75aDas −k84aDam −k98aGo −k103aGn)
(12) 
The reaction rate equation for component C4=: 
daC4=
dt
= −ρ
SWH
⋅A × ((k114 + k115 + k116) × aC4= −k10aSS −k25aSA −k40aSR −k48aDp
−k58aDo −k66aDn −k76aDas −k85aDam −k91aGp −k99aGo −k104aGn −k108aGa)
(13) 
The reaction rate equation for component C3=: 
daC3=
dt
= −ρ
SWH
⋅A × ((k117 + k118) × aC3= −k11aSS −k26aSA −k41aSR −k49aDp −k59aDo
−k67aDn −k77aDas −k86aDam −k92aGp −k100aGo −k105aGn −k109aGa −k114aC4=)
(14) 
The reaction rate equation for component LPG: 
daLPG
dt
= −ρ
SWH
⋅A × ((k119 + k120) × aLPG −k12aSS −k27aSA −k42aSR
−k50aDp −k68aDn −k78aDas −k87aDam −k93aGp −k110aGa)
(15) 
The reaction rate equation for component C2=: 
daC2=
dt
= −ρ
SWH
⋅A × (−k13aSS −k28aSA −k43aSR −k51aDp −k60aDo
−k69aDn −k79aDas −k88aDam −k94aGp −k101aGo
−k106aGn −k111aGa −k115aC4= −k117aC3= −k118aLPG)
(16) 
The reaction rate equation for component DR: 
L. Wan et al.                                                                                                                                                                                                                                     
Chemical Engineering Science 282 (2023) 119331
21
daDR
dt = −cs × (−k14aSS −k29aSA −k44aSR −k52aDp −k70aDn −k80aDas
−k89aDam −k95aGp −k112aGa −k116aC4= −k118aC3= −k120aLPG)
(17) 
The reaction rate equation for component C in reactor: 
dac
dt = −ρ
SWH
⋅A × (−k15aSS −k30aSA −k45aSR −k53aDp −k61aDo −k71aDn
−k81aDas −k90aDam −k96aGp −k102aGo −k107aGn −k113aGa)
(18)  
Part B: Regenerator modeling equations 
The reaction rate equation for component C in regenerator: 
dbC
dt = −1
us
(
KCbO2bC + KHCNb1.75
O2 bHCN + KC NObNOb0.5
O2
)
(19) 
The reaction rate equation for component H: 
dbH
dt = −1
us
KHbO2bH
(20) 
The reaction rate equation for component S: 
dbS
dt = −1
us
KS × bO2 × bS
(21) 
The reaction rate equation for component N: 
dbN
dt = −1
us
KHCNbHbCbN
(22) 
The reaction rate equation for component NO2: 
dbNO2
dt
= 1
ub
KNO O2bNOb0.5
O2
(23) 
The reaction rate equation for component NO: 
dbNO
dt
= (1 −εb)(1 −εe)
us
KC NObCObNO
+ 1
ub
(
KHCN O2b1.75
O2 bHCN −KNO O2bNOb0.5
O2 −KCO NObCObNO
)
(24) 
The reaction rate equation for component H2O: 
dbH2O
dt
= (1 −εb)(1 −εe) × 0.5
us
× KHbO2bH + 0.5
ub
KHCN O2bO2bHCN
(25) 
The reaction rate equation for component O2: 
dbO2
dt
= −(1 −εb)(1 −εe)
us
×
(0.5 + σ
1 + σ × KCbO2bC + 0.25 × KHbO2bH + KSbO2bS
)
+ 1
ub
( −0.5×KCO O2b0.5
O2 bCOb0.5
H2O
+1.75 × KHCN O2b1.75
O2 bHCN + 0.5 × KNO O2bNOb0.5
O2 )
(26) 
The reaction rate equation for component N2: 
dbN2
dt
= 0.5 × KCO NObCObNO
ub
+ 0.5 × (1 −εb)(1 −εe) × KCbCbNO
us
(27) 
The reaction rate equation for component HCN: 
dbHCN
dt
= (1 −εb)(1 −εe) × KHCNbHbCbN
us
−KHCN O2b1.75
O2 bHCN
ub
(28) 
The reaction rate equation for component SO2: 
dbSO2
dt
= (1 −εb)(1 −εe)
us
KSbO2bS
(29) 
The reaction rate equation for component CO2: 
L. Wan et al.                                                                                                                                                                                                                                     
Chemical Engineering Science 282 (2023) 119331
22
dbCO2
dt
=
(1 −εb)(1 −εe) ×
σ
1 + σ
us
× KCbO2bC
+ 1
ub
×
(
KCO O2b0.5
O2 bCOb0.5
H2O + KCO NObCObNO + KHCN O2b1.75
O2 bHCN
)
(30) 
The reaction rate equation for component CO: 
drCO
dt
=
(1 −εb)(1 −εe) ×
1
1 + σ
us
(KCbO2bC + KC NObCbNO)
−1
ub
(
KCO NObCObNO + KCO O2b0.5
O2 bCOb0.5
H2O
)
(31)  
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L. Wan et al.

Industrial
 Crops
 and
 Products
 77
 (2015)
 516–526
Contents
 lists
 available
 at
 ScienceDirect
Industrial
 Crops
 and
 Products
jo
 u
 r
 n
 al
 homep
 age:
 www.elsevier.com/locate/indcrop
Optimization
 of
 catalytic
 cracking
 process
 for
 upgrading
 camelina
 oil
to
 hydrocarbon
 biofuel
Xianhui
 Zhao,
 Lin
 Wei ∗,  Shouyun
 Cheng,
 James
 Julson
Department
 of
 Agricultural
 and
 Biosystems
 Engineering,
 South
 Dakota
 State
 University,
 Brookings,
 SD
 57007,
 USA
a
 r  t
 i c  l e  
i  n  f  o
Article
 history:
Received
 16
 March
 2015
Received
 in
 revised
 form
 1
 September
 2015
Accepted
 11
 September
 2015
Available
 online
 30
 September
 2015
Keywords:
Camelina
 oil
Hydrocarbon
 biofuel
Orthogonal
 experiment
Catalytic
 cracking
Zn/ZSM-5
a
 b
 s
 t  r  a
 c  t
Catalytic
 cracking
 of
 camelina
 oil  over  Zn/ZSM-5
 catalyst
 in  a ﬁxed-bed
 tubular
 reactor  was investigated.
An
 optimization
 study
 on  the  catalytic
 cracking
 conditions
 based  on  nine  well-planned
 orthogonal
 exper-
iments
 was  carried  out.  Three  main
 operation
 conditions
 including
 reaction
 temperature,
 liquid hourly
space
 velocity
 and  oil  extraction
 press
 frequency
 were
 studied  to  examine
 their  effects
 on the  yield
and
 quality
 of hydrocarbon
 biofuel
 produced.
 Characterization
 of the  catalyst,
 hydrocarbon
 biofuel
 and
non-condensable
 gas  was  conducted.
 There  was no
 signiﬁcant
 difference
 between
 the  bulk  structures
of
 fresh  Zn/ZSM-5
 and  used  Zn/ZSM-5.
 Small
 ZnO  particles
 dispersed
 well  on  the
 parent  ZSM-5.
 Hydro-
carbon
 biofuel
 contained
 65.18%
 hydrocarbons
 and  its properties
 including
 dynamic
 viscosity,
 density
and
 higher
 heating
 value
 were  improved
 after upgrading,
 compared
 to  camelina
 oil.  It
 was  found
 that
the
 oil  extraction
 press
 frequency
 was  the  most  important
 factor  and  liquid  hourly
 space  velocity  was
the
 least  important
 factor  for  the  hydrocarbon
 biofuel
 production.
 In addition,
 the  optimum
 conditions
for
 camelina
 oil  upgrading
 were a
 combination
 of  reaction
 temperature
 of  550 ◦C,
 a  liquid
 hourly  space
velocity
 of 1.0 h−1 and  an  oil  extraction
 press  frequency
 of 15  Hz.
©  2015  Elsevier
 B.V.  All  rights
 reserved.
1.
 Introduction
Conventional
 transportation
 fuel
 produced
 from
 petroleum
 has
caused
 greenhouse
 gas
 emissions.
 Renewable
 fuel
 such
 as
 bio-jet
fuel,
 biodiesel
 and
 ethanol
 is
 an
 effective
 alternative
 energy
 source.
Exploring
 alternative
 energy
 sources,
 especially
 with
 the
 growing
importance
 of
 bio-jet
 fuel
 in
 the
 aviation
 industries,
 has
 been
 paid
attention
 to
 by
 researchers.
 It
 has
 been
 proved
 that
 renewable
 fuel
can
 signiﬁcantly
 reduce
 greenhouse
 gas
 emissions
 compared
 with
conventional
 petroleum
 fuel.
 Under
 this
 situation,
 new
 crops
 such
as
 camelina
 and
 jatropha
 have
 been
 studied
 as
 new
 feedstocks
for
 renewable
 fuel
 production.
 In
 addition,
 camelina
 and
 jatropha
are
 inedible
 plants,
 which
 do
 not
 compete
 with
 the
 food
 industry.
Camelina
 is
 a
 low
 input
 oilseed
 crop
 that
 can
 provide
 sustainable
biofuel.
 It
 has
 a
 high
 oil
 content,
 high
 tolerance
 to
 cold
 weather
and
 drought
 conditions,
 short
 growth
 cycle,
 and
 resistance
 to
 com-
mon  pathogens
 (Li
 and
 Mupondwa,
 2014).
 Camelina
 is
 considered
as
 an
 important
 renewable
 energy
 source
 due
 to
 its
 widespread
production
 and
 high
 yield.
Well-known
 methods
 for
 biofuel
 production
 from
 vegetable
oil
 mainly
 include
 thermal
 cracking,
 transesteriﬁcation,
 hydropro-
cessing
 and
 catalytic
 cracking.
 Thermal
 cracking
 is
 a
 technique
 to
∗Corresponding
 author.
 Fax:
 +1
 6056886764.
E-mail
 address:
 lin.wei@sdstate.edu
 (L.
 Wei).
convert
 vegetable
 oil
 to
 biofuel
 at
 a
 high
 temperature
 and
 high
 pres-
sure
 without
 catalyst.
 The
 thermal
 cracking
 process
 involves
 either
a
 free
 radical
 mechanism
 or
 a
 carbonium
 ion
 mechanism.
 How-
ever,
 the
 cost
 is
 relatively
 higher
 and
 the
 gasoline
 yield
 is
 relatively
lower
 compared
 with
 catalytic
 cracking
 (Li
 et
 al.,
 2009b).
 Transes-
teriﬁcation
 of
 vegetable
 oil
 is
 a
 process
 in
 which
 solvents
 such
 as
methanol
 and
 ethanol
 in
 the
 presence
 of
 catalyst
 are
 used
 to
 chem-
ically
 break
 the
 molecules
 of
 vegetable
 oil
 into
 glycerol
 and
 esters
of
 the
 renewable
 fuel.
 The
 product
 of
 this
 process
 is
 considered
 as
biodiesel,
 but
 biodiesel
 is
 not
 suitable
 for
 future
 bio-jet
 fuel
 produc-
tion
 because
 it
 exhibits
 some
 drawbacks
 such
 as
 low
 oxidative
 and
storage
 stability,
 high
 density
 and
 viscosity,
 and
 water
 absorbency
(Ribeiro
 et
 al.,
 2014;
 Zhao
 et
 al.,
 2015a,c;
 Habibullah
 et
 al.,
 2015).
Hydroprocessing
 of
 vegetable
 oil
 requires
 severe
 conditions
 such
 as
high
 catalyst
 activity
 and
 high
 hydrogen
 pressure.
 Although
 some
noble
 metal
 catalysts
 possess
 a
 high
 activity,
 their
 high
 price
 and
limitation
 of
 availability
 become
 an
 issue.
 During
 hydroprocessing,
oxygen
 atoms
 are
 removed
 in
 the
 form
 of
 CO,
 CO2 and
 H2O
 with
 an
introduction
 of
 hydrogen
 (Zhao
 et
 al.,
 2015c).
Catalytic
 cracking
 of
 vegetable
 oil
 has
 the
 potential
 to
 produce
biofuel
 similar
 to
 petroleum
 based
 fuel.
 Triglycerides
 in
 vegetable
oil
 are
 cracked
 into
 fatty
 acids
 through
 a
 decomposition.
 Then,
 fatty
acids
 are
 converted
 into
 products
 such
 as
 gasoline,
 heavy
 hydrocar-
bons
 and
 light
 gaseous
 hydrocarbons
 (Rao
 et
 al.,
 2010).
 Catalysts
with
 long
 lifetime
 and
 excellent
 selectivity
 can
 reduce
 the
 process-
ing
 cost
 and
 improve
 the
 product
 yield.
 Distillation
 technology
 can
http://dx.doi.org/10.1016/j.indcrop.2015.09.019
0926-6690/©
 2015
 Elsevier
 B.V.
 All
 rights
 reserved.
X.
 Zhao
 et
 al.
 /
 Industrial
 Crops
 and
 Products
 77
 (2015)
 516–526
 
517
be
 used
 to
 separate
 an
 oil
 mixture
 with
 wide
 boiling
 ranges
 into
products
 with
 narrower
 boiling
 ranges.
 The
 oil
 mixture
 is
 heated
to
 vaporize
 the
 lighter
 components
 (Zhao
 et
 al.,
 2015c;
 Greg
 et
 al.,
2006).
 After
 vegetable
 oil
 is
 upgraded
 into
 liquid
 hydrocarbons,
 the
liquid
 hydrocarbons
 will
 be
 delivered
 to
 a
 petroleum
 reﬁnery
 to
produce
 bio-jet
 fuel.
Li
 et
 al.
 (2009a)
 used
 cottonseed
 oil
 to
 produce
 biofuel
 using
 a
catalytic
 cracking
 method
 in
 a
 ﬁxed-ﬂuidized
 bed.
 The
 effect
 of
 res-
idence
 time,
 catalyst-to-oil
 ratio
 and
 reaction
 temperature
 on
 the
yield
 of
 bio-gasoline
 and
 light
 fuel
 oil
 was
 studied.
 It
 was
 found
that
 catalyst-to-oil
 ratio
 and
 reaction
 temperature
 were
 important
variables.
 In
 addition,
 the
 catalytic
 cracking
 method
 could
 be
 used
to
 convert
 cottonseed
 oil
 to
 value
 added
 gasoline.
 Several
 stud-
ies
 in
 the
 literature
 have
 shown
 that
 the
 composition
 of
 vegetable
oil
 had
 an
 inﬂuence
 on
 the
 yield
 and
 distribution
 of
 biofuel.
 For
example,
 Ooi
 et
 al.
 (2004)
 found
 that
 the
 composition
 of
 feedstock
including
 used
 palm
 oil
 and
 palm
 oil
 fatty
 acids
 mixture
 affected
 the
conversion
 and
 liquid
 fuel
 distribution
 during
 the
 catalytic
 crack-
ing
 process.
 Al-Sabawi
 et
 al.
 (2012)
 compared
 various
 vegetable
oils
 for
 the
 biofuel
 production
 and
 found
 that
 the
 palm
 oil
 cracking
produced
 more
 liqueﬁed
 petroleum
 gas
 and
 light
 C2-C4 oleﬁns
 than
soybean
 oil
 and
 canola
 oil.
 Yang
 et
 al.
 (2013)
 studied
 that
 the
 con-
centration
 of
 the
 C18 fatty
 acid
 mixture
 (including
 C18:0,
 C18:1 and
C18:2)
 had
 a
 signiﬁcant
 effect
 on
 the
 biofuel
 distribution.
 Morgan
et
 al.
 (2012)
 studied
 the
 deoxygenation
 of
 soybean
 oil
 under
 nitro-
gen
 atmosphere
 over
 supported
 nickel
 catalysts.
 Oxygen
 atoms
 in
the
 carboxyl
 group
 of
 the
 fatty
 acid
 structure
 were
 removed
 in
 the
form
 of
 CO
 and
 CO2.
 The
 catalytic
 cracking
 of
 the
 fatty
 acid
 chains
produced
 liquid
 hydrocarbons
 (C5-C17).
 Unsaturated
 triglycerides
as
 a
 feed
 led
 to
 the
 formation
 of
 lighter
 hydrocarbons
 and
 higher
amounts
 of
 coke
 than
 using
 saturated
 triglycerides
 as
 a
 feed.
 How-
ever,
 only
 a
 few
 researchers
 have
 studied
 the
 catalytic
 cracking
of
 inedible
 vegetable
 oil
 for
 biofuel
 production.
 In
 addition,
 it
 is
important
 to
 optimize
 the
 catalytic
 cracking
 process
 conditions
 to
produce
 biofuel
 with
 a
 high
 quality.
Zeolite
 catalyst
 includes
 ZSM-5,
 Beta,
 SBA-15,
 mordenite,
 Mor,
FCC,
 etc.
 It
 was
 reported
 to
 be
 promising
 to
 convert
 biomass
 derived
feedstocks
 into
 chemicals
 and
 biofuels.
 Among
 the
 zeolite
 catalysts,
ZSM-5
 has
 a
 high
 acidity
 and
 is
 effective
 to
 convert
 triglycerides
 into
liquid
 fuel.
 ZSM-5
 can
 increase
 the
 formation
 of
 aromatics
 and
 is
widely
 used
 in
 the
 petroleum
 reﬁnery
 process
 (Zhao
 et
 al.,
 2015c;
Zhang
 et
 al.,
 2011).
 The
 loading
 of
 metals
 including
 Al,
 Ca,
 Cr,
 Cu,
 Li,
Mg
 and
 Ni
 into
 ZSM-5
 showed
 that
 they
 could
 enhance
 the
 active
acid
 sites
 on
 the
 surface
 of
 ZSM-5
 catalyst
 (Zakaria
 et
 al.,
 2012).
However,
 the
 research
 on
 biofuel
 production
 using
 inedible
 veg-
etable
 oil
 over
 ZSM-5
 catalyst
 doped
 with
 Zn
 is
 lacking.
 Actually,
ZnCl2 has
 a
 high
 activity
 and
 stereoselectivity
 and
 is
 easily
 available
as
 well
 (Demirbas,
 2003).
The
 objective
 of
 this
 work
 is
 to
 explore
 optimum
 operation
 con-
ditions
 in
 which
 camelina
 oil
 underwent
 catalytic
 cracking
 over
 the
Zn/ZSM-5
 catalyst
 in
 a
 ﬁxed-bed
 reactor.
 The
 effect
 of
 the
 operation
conditions
 on
 the
 hydrocarbon
 biofuel
 yield
 and
 properties
 such
 as
moisture
 content,
 density,
 dynamic
 viscosity
 and
 higher
 heating
value
 (HHV)
 was
 discussed.
 The
 main
 compounds
 of
 hydrocarbon
biofuel
 and
 non-condensable
 gas
 were
 determined.
2.
 Materials
 and
 methods
2.1.
 Feedstock
 and
 device
 preparation
Camelina
 (sativa)
 seeds
 were
 purchased
 from
 Hancock
 Seed
Company,
 Dade,
 Florida,
 USA.
 The
 moisture
 content
 and
 bulk
 den-
sity
 of
 camelina
 seeds
 was
 4.92%
 and
 0.76
 g/mL,
 respectively.
 The
camelina
 seeds
 were
 used
 for
 oil
 extraction
 at
 the
 Advanced
 Bio-
fuel
 Laboratory
 in
 the
 Department
 of
 Agricultural
 and
 Biosystems
Table
 1
Main
 physicochemical
 properties
 of
 camelina
 oil.
Physicochemical
 properties
 
Camelina
 oil
Main
 constituents
 (area%)
 
Linolenic
 acid
 (C18H30O2):
 78.67–84.47
Density
 (g/mL)
0.88–0.89
Moisture
 content
 (wt.%)
 
0.06–0.08
Dynamic
 viscosity
 (20 ◦C,
 cP)
 
58.85–59.65
HHV
 (MJ/kg)
 
39.41–39.65
Engineering,
 South
 Dakota
 State
 University
 (SDSU)
 using
 a
 cold
press
 method.
 A
 M70
 Oil
 Press,
 purchased
 from
 Oil
 Press
 Company
(Eau
 Claire,
 Wisconsin,
 USA),
 was
 used
 for
 the
 oil
 extraction
 from
camelina
 seeds
 and
 the
 nozzle
 size
 (inside
 diameter)
 of
 the
 Oil
 Press
was
 5.59
 mm.
 The
 camelina
 seeds
 were
 pressed
 at
 three
 different
frequencies
 (15
 Hz,
 20
 Hz
 and
 25
 Hz),
 which
 were
 related
 to
 the
screw
 rotation
 speed
 of
 Oil
 Press.
 The
 yield
 of
 camelina
 oil
 obtained
from
 the
 cold
 press
 of
 camelina
 seeds
 at
 15
 Hz,
 20
 Hz
 and
 25
 Hz
 was
36.7%,
 36.6%
 and
 36.1%,
 respectively.
 The
 camelina
 oil
 produced
at
 these
 three
 different
 frequencies
 was
 used
 for
 upgrading
 tri-
als.
 Some
 physicochemical
 properties
 of
 the
 cold-pressed
 camelina
oil
 are
 shown
 in
 Table
 1
 (Zhao
 et
 al.,
 2014b;
 Zhao
 et
 al.,
 2015b).
The
 linolenic
 acid
 content
 of
 the
 cold-pressed
 camelina
 oil
 was
higher
 than
 that
 of
 the
 camelina
 sativa
 oil
 (27.1–39.7%)
 reported
by
 Abramovic
 and
 Abram
 (2005).
 Crowley
 and
 Frohlich
 (1998)
 have
studied
 that
 the
 fatty
 acid
 composition
 of
 camelina
 oil
 was
 inﬂu-
enced
 by
 both
 variety
 and
 environment.
 For
 example,
 the
 linolenic
acid
 content
 (percentage
 of
 total
 fatty
 acids)
 of
 a
 camelina
 sativa
seed
 was
 between
 12.3%
 and
 32.0%.
 However,
 the
 linolenic
 acid
content
 of
 a
 new
 transformed
 docosahexaenoic
 acid
 (DHA)
 produc-
ing
 camelina
 sativa
 seed
 was
 38.6–54.1%
 due
 to
 the
 incorporation
of
 microalgal
 genes
 (Mansour
 et
 al.,
 2014).
2.2.
 Catalyst
 preparation
The
 zeolite
 powders
 were
 purchased
 from
 Zeolyst
 International,
Kansas
 City,
 Kansas,
 USA.
 The
 atomic
 ratio
 of
 silica-to-alumina
 of
the
 zeolite
 was
 15.
 The
 catalyst
 of
 ZSM-5
 doped
 with
 20
 wt.%
 Zn
(Zn/ZSM-5)
 was
 prepared
 using
 a
 wet
 impregnation
 method.
 Zinc
chloride
 purchased
 from
 Fisher
 Scientiﬁc
 was
 used
 to
 provide
 Zn.
The
 catalyst
 blended
 with
 boehmite
 and
 guar
 gum
 was
 pressed
 in
 a
strip
 shape
 at
 about
 6.9
 ×
 104 Pa
 through
 a
 sieve
 in
 a
 hydraulic
 press
(Owatonna
 Tool
 Company,
 Minnesota).
 The
 catalyst
 was
 sealed
 and
stored
 at
 room
 temperature
 for
 12
 h
 and
 then
 calcined
 in
 the
 air
 at
550 ◦C
 for
 4
 h.
 The
 catalyst
 was
 stored
 in
 a
 sealed
 vial
 in
 a
 des-
iccator
 for
 upgrading
 use
 (Zhao
 et
 al.,
 2015b,c).
 In
 this
 study,
 the
fresh
 Zn/ZSM-5
 catalyst
 was
 used
 as
 a
 control
 compared
 to
 the
 used
Zn/ZSM-5
 catalyst
 (Zhao
 et
 al.,
 2015a).
 The
 used
 Zn/ZSM-5
 catalyst
was
 obtained
 after
 being
 used
 for
 a
 typical
 catalytic
 cracking
 of
camelina
 oil.
2.3.
 Catalyst
 characterization
The
 bulk
 structure
 of
 fresh
 Zn/ZSM-5
 and
 used
 Zn/ZSM-5
 was
examined
 using
 an
 automated
 multipurpose
 X-ray
 Diffractome-
ter
 (XRD,
 SmartLab,
 Rigaku
 Corporation).
 The
 scan
 range
 was
 5–50
degrees
 with
 a
 scan
 speed
 of
 2◦/min.
 The
 tube
 voltage
 and
 tube
current
 were
 40
 kV
 and
 44
 mA,
 respectively.
 A
 Fourier
 transform
infrared
 spectroscopy
 (FT-IR)
 analysis
 of
 the
 catalyst
 samples
 was
performed
 on
 a
 Nicolet
 6700
 FT-IR
 spectrometer
 using
 KBr
 pel-
lets.
 The
 surface
 area,
 pore
 volume
 and
 pore
 size
 of
 the
 catalysts
were
 measured
 using
 a
 Micromeritics
 TriStar
 3000
 automated
 gas
adsorption
 analyzer.
 The
 physisorption
 analysis
 with
 liquid
 nitro-
gen
 was
 carried
 out
 using
 an
 ASAP
 2010
 Micropore
 Analyzer
 at
 77
 K.
The
 used
 Zn/ZSM-5
 was
 pretreated
 in
 the
 air
 at
 600 ◦C
 to
 remove
the
 coke
 which
 could
 pollute
 the
 apparatus.
 The
 surface
 area
 was
518
 
X.
 Zhao
 et
 al.
 /
 Industrial
 Crops
 and
 Products
 77
 (2015)
 516–526
calculated
 using
 a
 Brunauer-Emmett-Teller
 (BET)
 model
 for
 the
nitrogen
 adsorption
 isotherm.
 The
 pore
 size
 was
 calculated
 using
4
 VT/SBET (VT refers
 to
 the
 adsorption
 total
 pore
 volume
 and
 SBET
refers
 to
 the
 BET
 surface
 area).
 The
 pore
 size
 distribution
 was
 deter-
mined
 using
 a
 Barrett–Joyner–Halenda
 (BJH)
 adsorption
 model.
 The
micropore
 volume
 and
 mesopore
 volume
 were
 analyzed
 through
a
 density
 functional
 theory
 (DFT).
 The
 carbon
 (C)
 content
 of
 used
Zn/ZSM-5
 was
 determined
 using
 a
 CE-440
 Elemental
 Analyzer
(Exeter
 Analytical,
 Inc.)
 based
 on
 ASTM
 D4057.
 The
 surface
 mor-
phology
 of
 the
 catalysts
 was
 observed
 using
 a
 scanning
 electron
microscope
 (SEM,
 Hitachi
 S-3400N).
 An
 FEI
 Talos
 F200X
 transmis-
sion
 electron
 microscope
 (TEM)
 equipped
 with
 FEI’s
 ChemiSTEM
technology
 was
 used
 to
 observe
 the
 morphology
 and
 the
 elemental
distribution
 of
 the
 catalyst
 samples.
 Scanning
 transmission
 electron
microscope
 (STEM)
 with
 a
 high-angle
 annular
 dark
 ﬁeld
 (HAADF)
detector
 and
 a
 super-X
 energy-dispersive
 spectrometer
 (EDS)
 was
equipped.
 The
 accelerating
 voltage
 used
 was
 200
 kV.
 Each
 powder
sample
 was
 suspended
 in
 ethanol
 and
 sonicated
 for
 30
 min.
 The
suspension
 was
 allowed
 to
 settle
 for
 5
 min.
 After
 that,
 the
 sam-
ple
 was
 dispersed
 on
 a
 300
 mesh
 copper
 grid
 coated
 with
 a
 holey
carbon
 ﬁlm
 (Zhao
 et
 al.,
 2015b,c;
 Zhao
 et
 al.,
 2013;
 Liu
 et
 al.,
 2013).
2.4.
 Oil
 upgrading
The
 upgrading
 of
 camelina
 oil
 was
 run
 in
 a
 ﬁxed-bed
 reactor
with
 about
 25
 g
 catalyst
 and
 the
 test
 runs
 were
 duplicated
 with
 the
same
 catalyst.
 Fig.
 1
 shows
 a
 process
 ﬂow
 diagram
 of
 the
 camelina
oil
 upgrading
 system
 mainly
 consisting
 of
 oil
 injection,
 cracking
reaction,
 condensing,
 distillation
 and
 product
 collection.
 The
 sys-
tem
 was
 purged
 with
 nitrogen
 at
 a
 pressure
 of
 1.38
 ×
 105 Pa
 (20
 psi).
Camelina
 oil
 was
 pumped
 until
 N2 had
 been
 blown
 for
 15
 min
 to
remove
 the
 air
 throughout
 the
 upgrading
 system.
 Before
 camelina
oil
 went
 through
 a
 reactor,
 it
 was
 evaporated
 to
 oil
 vapor
 in
 a
 pre-
heater,
 which
 aimed
 to
 let
 it
 have
 a
 better
 contact
 with
 the
 catalyst
in
 the
 reactor.
 The
 preheater
 and
 reactor
 both
 contain
 a
 stainless
steel
 tube
 ﬁxed
 coaxially
 in
 a
 furnace.
 The
 tube
 in
 the
 reactor
 is
508
 mm
 long
 with
 a
 25.4
 mm
 internal
 diameter.
 When
 the
 oil
 vapor
got
 in
 contact
 with
 the
 catalyst,
 catalytic
 cracking
 reactions
 took
place.
 The
 produced
 oil
 gas
 was
 cooled
 down
 through
 a
 condenser
system
 at
 −10.0 ◦C
 and
 collected
 in
 the
 form
 of
 liquid.
 Some
 of
 the
oil
 gas
 could
 not
 be
 cooled
 down
 using
 this
 condenser
 system
 and
it
 was
 considered
 as
 non-condensable
 gas
 in
 this
 study.
 The
 non-
condensable
 gas
 went
 through
 a
 ﬁlter
 in
 order
 not
 to
 pollute
 the
environment
 and
 it
 would
 be
 collected
 through
 a
 gas
 sampling
 bag
for
 its
 composition
 analysis
 (Zhao
 et
 al.,
 2015b,c).
 The
 collected
 liq-
uid
 was
 then
 distilled
 using
 a
 vapor
 and
 a
 condenser.
 The
 vapor
 is
 a
standard
 heating
 and
 controls
 system
 of
 TM
 series
 (Glas-Col,
 Terre
Haute,
 Indiana,
 USA).
 The
 vapor’s
 temperature
 was
 set
 as
 230 ◦C
 and
the
 top
 half
 of
 its
 ﬂask
 was
 covered
 with
 a
 heat
 preservation
 cotton.
The
 condenser
 was
 set
 at
 −5.0 ◦C
 and
 the
 condensing
 product
 was
considered
 as
 hydrocarbon
 biofuel.
2.5.
 Orthogonal
 array
 experimental
 design
 and
 statistical
 analysis
In
 this
 study,
 the
 experiments
 were
 based
 on
 an
 orthogonal
array
 experimental
 design
 (OA9 matrix)
 (Wu and
 Leung,
 2011).
The
 following
 three
 variables
 were
 analyzed:
 reaction
 tempera-
ture
 (factor
 A),
 liquid
 hourly
 space
 velocity
 (LHSV,
 factor
 B)
 and
oil
 extraction
 press
 frequency
 (factor
 C).
 Three
 typical
 reaction
temperatures,
 450 ◦C,
 500 ◦C
 and
 550 ◦C,
 were
 assessed
 according
to
 the
 cracking
 activity
 of
 the
 catalyst,
 the
 liquid
 product
 yield
and
 quality.
 Three
 LHSVs
 including
 0.2
 h−1,
 0.6
 h−1 and
 1.0
 h−1
were
 tried.
 It
 was
 found
 from
 our
 previous
 research
 that
 the
 fatty
acid
 composition
 of
 camelina
 oil
 produced
 at
 different
 frequen-
cies
 had
 a
 minor
 difference,
 which
 might
 affect
 the
 upgrading
 of
camelina
 oil
 (Zhao
 et
 al.,
 2014b).
 Therefore,
 the
 oil
 extraction
 press
Table
 2
Trials
 in
 OA9 matrix.
Trial
 No.
 
Factors
A
 (◦C)
 
B
 (h−1)
 
C
 (Hz)
1
 
450
 
0.2
 
15
2
 
450
 
0.6
 
20
3
 
450
 
1.0
 
25
4
 
500
 
0.2
25
5
 
500
 
0.6
 
15
6
 
500
 
1.0
 
20
7
 
550
 
0.2
 
20
8
 
550
 
0.6
 
25
9
 
550
 
1.0
 
15
frequencies
 including
 15
 Hz,
 20
 Hz
 and
 25
 Hz
 were
 considered
 as
a
 variable
 in
 the
 present
 study.
 Nine
 trials,
 shown
 in
 Table
 2,
 are
carried
 out
 to
 complete
 the
 optimization
 process.
 The
 run
 order
of
 these
 trials
 was
 randomized
 without
 any
 subjective
 or
 personal
bias
 and
 each
 trial
 was
 repeated.
 The
 data
 was
 analyzed
 through
 a
range
 analysis
 to
 reﬂect
 the
 optimal
 operation
 conditions.
 To
 inves-
tigate
 whether
 the
 factors’
 effect
 on
 the
 hydrocarbon
 biofuel
 yield
and
 quality
 was
 signiﬁcant
 or
 not,
 an
 analysis
 of
 variance
 (ANOVA)
was
 used,
 followed
 by
 a
 test
 of
 a
 multiple
 comparison:
 least
 sig-
niﬁcant
 difference
 (LSD).
 The
 ANOVA
 and
 LSD
 test
 were
 performed
using
 a
 statistical
 software
 SPSS
 (version
 19,
 IBM
 Company),
 and
differences
 were
 considered
 statistically
 signiﬁcant
 at
 p
 <
 0.05
 (Ye
et
 al.,
 2013).
In
 a
 range
 analysis,
 Kji,
 K′ji,
 Rj are
 three
 important
 parameters
(i
 =
 1,
 2,
 3).
 Kji is
 deﬁned
 as
 the
 sum
 of
 all
 levels’
 evaluation
 indexes
in
 each
 factor
 (j
 =
 A,
 B,
 C).
 K′ji,
 to
 determine
 the
 optimal
 level
 for
each
 factor,
 is
 deﬁned
 as
 the
 mean
 value
 of
 Kji.
 Rj,
 to
 evaluate
 the
importance
 of
 these
 factors,
 is
 deﬁned
 as
 the
 range
 value
 between
the
 maximum
 and
 minimum
 value
 of
 K′ji.
 A
 higher
 Rj means
 a
greater
 importance
 of
 the
 factor
 and
 the
 optimal
 level
 for
 each
 fac-
tor
 is
 obtained
 when
 K′ji is
 the
 highest
 or
 lowest
 depending
 on
 the
characteristic
 (Wu  and
 Leung,
 2011).
2.6.
 Physicochemical
 characterization
The
 obtained
 hydrocarbon
 biofuel
 was
 characterized
 based
 on
its
 density,
 dynamic
 viscosity,
 moisture
 content,
 HHV,
 composi-
tion
 and
 yield.
 The
 hydrocarbon
 biofuel
 sample
 was
 tested
 for
three
 times
 by
 the
 ratio
 of
 its
 mass
 to
 volume
 at
 room
 temper-
ature
 to
 obtain
 the
 density.
 Dynamic
 viscosity
 was
 determined
using
 a
 Viscoanalyzer
 (REOLOGICA
 Instruments
 AB
 Company)
 at
20 ◦C.
 Moisture
 content
 was
 tested
 using
 a
 Karl
 Fischer
 Titrator
V20
 (Mettler
 Toledo
 Company),
 based
 on
 ASTM
 E1064.
 The
 HHV
was
 determined
 using
 a
 Calorimeter
 System
 (C
 2000,
 IKA-Works,
Inc.),
 which
 is
 within
 ASTM
 D4809
 (Zhao
 et
 al.,
 2015b,c).
 The
 yield
of
 hydrocarbon
 biofuel
 was
 deﬁned
 by
 the
 following
 equation:
Yield
 of
 hydrocarbon
 biofuel
 = mass
 of
 hydrocarbon
 biofuel
mass
 of
 camelina
 oil
 feed
×
 100
(1)
where,
 the
 mass
 of
 camelina
 oil
 feed
 was
 deﬁned
 as
 the
 mass
 of
camelina
 oil
 that
 was
 pumped
 into
 the
 ﬁxed-bed
 reactor.
The
 major
 compounds
 of
 hydrocarbon
 biofuel
 were
 ana-
lyzed
 using
 a
 gas
 chromatography–mass
 spectrometry
 (GC–MS)
(Agilent
 GC-7890A
 and
 MSD-5975C).
 The
 capillary
 column
 was
30
 m
 ×
 0.25
 mm
 ×
 0.25
 m
 DB-5MS.
 Hydrogen
 was
 used
 as
 a
 car-
rier
 gas
 with
 a
 ﬂow
 rate
 of
 1.0
 mL/min.
 One
 microliter
 of
 the
 sample
was
 introduced
 through
 an
 injection
 port
 operated
 in
 a
 splitless
mode
 at
 285 ◦C.
 The
 splitless
 time
 was
 30
 s.
 The
 original
 column
temperature
 was
 175 ◦C
 with
 a
 holding
 time
 of
 6.00
 min.
 Then,
 the
temperature
 reached
 to
 260 ◦C
 after
 5.67
 min
 at
 a
 rate
 of
 15 ◦C/min.
The
 holding
 time
 at
 this
 temperature
 was
 15.68
 min.
 The
 major
X.
 Zhao
 et
 al.
 /
 Industrial
 Crops
 and
 Products
 77
 (2015)
 516–526
 
519
Fig.
 1.
 Process
 ﬂow
 diagram
 of
 camelina
 oil
 upgrading
 to
 hydrocarbon
 biofuel.
compounds
 were
 identiﬁed
 through
 the
 National
 Institute
 of
 Stan-
dards
 and
 Technology
 (NIST)
 mass
 spectral
 library
 and
 literature
(Cheng
 et
 al.,
 2015a,b;
 Zhao
 et
 al.,
 2015b,c;
 Zhao
 et
 al.,
 2014a,b).
 The
main
 constituents
 of
 non-condensable
 gas
 were
 determined
 using
 a
GC
 system.
 An
 Agilent
 GC
 (7890A)
 with
 a
 50
 m
 ×
 0.53
 mm
 ×
 15
 m
19095P-S25
 column
 was
 used.
 A
 standard
 gas
 mixture
 was
 used
for
 the
 calibration
 and
 a
 ﬂame
 ionization
 detector
 (FID)
 was
 used
for
 the
 analysis
 of
 C1-C5 hydrocarbons.
 For
 FID,
 its
 heater
 temper-
ature,
 H2 ﬂow
 rate
 and
 air
 ﬂow
 rate
 were
 300 ◦C,
 30
 mL/min
 and
400
 mL/min,
 respectively.
 A
 thermal
 conductivity
 detector
 (TCD)
was
 used
 for
 the
 analysis
 of
 H2,
 CO
 and
 CO2.
 For
 TCD,
 its
 heater
temperature
 and
 reference
 ﬂow
 rate
 were
 200 ◦C
 and
 45
 mL/min,
respectively
 (Bezergianni
 et
 al.,
 2009;
 Zhao
 et
 al.,
 2015b,c).
3.
 Results
 and
 discussion
3.1.
 Catalyst
 characterization
3.1.1.
 XRD
 analysis
 of
 catalyst
The
 XRD
 analysis
 gives
 information
 about
 the
 bulk
 structure
 of
the
 catalyst.
 Fig.
 2(1)
 shows
 the
 XRD
 spectra
 of
 fresh
 Zn/ZSM-5
 and
used
 Zn/ZSM-5
 catalyst.
 The
 main
 peaks
 of
 fresh
 Zn/ZSM-5
 were
observed
 at
 2
 =
 7.90◦,
 2
 =
 8.80◦and
 2
 =
 23.00◦.
 The
 peak
 posi-
tion
 and
 intensity
 of
 fresh
 Zn/ZSM-5
 are
 in
 good
 agreement
 with
the
 previously
 reported
 spectrum
 of
 zeolite
 (Sharma
 et
 al.,
 2010;
Zakaria
 et
 al.,
 2012;
 Zhao
 et
 al.,
 2015b).
 There
 was
 no
 big
 difference
between
 the
 bulk
 structures
 of
 fresh
 Zn/ZSM-5
 and
 used
 Zn/ZSM-
5.
 The
 used
 Zn/ZSM-5
 retained
 major
 structural
 elements
 of
 zeolite
after
 being
 used
 for
 the
 upgrading
 of
 camelina
 oil.
 However,
 the
intensities
 of
 fresh
 Zn/ZSM-5
 and
 used
 Zn/ZSM-5
 were
 slightly
 dif-
ferent,
 which
 might
 result
 from
 a
 certain
 amount
 of
 coke
 deposited
on
 the
 used
 Zn/ZSM-5
 catalyst.
3.1.2.
 FT-IR
 spectrum
 of
 catalyst
Fig.
 2(2)
 displays
 the
 FT-IR
 spectra
 of
 fresh
 Zn/ZSM-5
 and
 used
Zn/ZSM-5
 based
 on
 the
 framework
 vibrations,
 ranging
 between
4000
 cm−1 and
 400
 cm−1.
 There
 was
 no
 large
 band
 position
 shifts
for
 fresh
 Zn/ZSM-5
 and
 used
 Zn/ZSM-5,
 which
 supports
 the
 prior
discussion
 on
 XRD
 analysis
 of
 fresh
 Zn/ZSM-5
 and
 used
 Zn/ZSM-
5
 that
 their
 bulk
 structures
 were
 similar.
 Both
 fresh
 Zn/ZSM-5
 and
Table
 3
The
 N2 adsorption–desorption
 analysis
 of
 fresh
 Zn/ZSM-5
 and
 used
 Zn/ZSM-5.
Fresh
 Zn/ZSM-5
 
Used
 Zn/ZSM-5
BET
 surface
 area
 (m2/g)
171.96
288.67
Mesopore
 surface
 area
 (m2/g)
 
47.95
 
87.94
Total
 pore
 volume
 (cm3/g)
 
0.25
 
0.39
Micropore
 pore
 volume
 (cm3/g)
 
0.01
 
0.02
Mesopore
 pore
 volume
 (cm3/g)
 
0.11
 
0.18
Pore
 size
 (Å)
 
57.01
 
54.27
used
 Zn/ZSM-5
 exhibited
 O-H
 groups
 in
 the
 3608–3630
 cm−1 band.
The
 O H
 groups
 might
 originate
 from
 the
 O
 H
 groups
 because
of
 the
 framework
 Al
 or
 from
 the
 O
 H
 groups
 of
 adsorbed
 water.
The
 water-bending
 vibration
 in
 the
 bond
 at
 1580–1620
 cm−1 was
observed
 for
 both
 fresh
 Zn/ZSM-5
 and
 used
 Zn/ZSM-5.
 It
 indicates
that
 there
 was
 adsorbed
 water
 presented
 in
 the
 catalyst,
 which
might
 occur
 during
 the
 incipient
 wet
 impregnation
 process
 (Zhao
et
 al.,
 2015b,c).
 The
 Si
 O
 Si
 symmetric
 stretching
 vibration
 at
around
 796
 cm−1 and
 the
 double
 ring
 vibration
 band
 near
 545
 cm−1
were
 observed
 for
 both
 fresh
 Zn/ZSM-5
 and
 used
 Zn/ZSM-5.
 Sim-
ilar
 observations
 were
 found
 in
 the
 study
 of
 Zakaria
 et
 al.
 (2012).
These
 ﬁndings
 suggest
 the
 formation
 of
 only
 ZSM-5
 phase
 in
 the
fresh
 Zn/ZSM-5
 and
 used
 Zn/ZSM-5.
3.1.3.
 N2 adsorption–desorption
 analysis
 of
 catalyst
Table
 3
 lists
 the
 surface
 area,
 pore
 size
 and
 pore
 volume
 of
 fresh
Zn/ZSM-5
 and
 used
 Zn/ZSM-5.
 The
 BET
 surface
 area,
 mesopore
 sur-
face
 area
 and
 pore
 volume
 of
 used
 Zn/ZSM-5
 were
 higher
 than
 those
of
 fresh
 Zn/ZSM-5.
 The
 pore
 size
 of
 used
 Zn/ZSM-5
 was
 slightly
lower
 than
 that
 of
 fresh
 Zn/ZSM-5.
 The
 used
 Zn/ZSM-5
 was
 already
calcined
 before
 the
 N2 adsorption–desorption
 analysis,
 suggest-
ing
 that
 the
 used
 Zn/ZSM-5
 catalyst
 might
 be
 recycled
 for
 further
cracking
 of
 camelina
 oil
 after
 the
 calcination
 treatment.
Fig.
 2(3)
 shows
 the
 N2 adsorption–desorption
 isothermal
 curves
of
 fresh
 Zn/ZSM-5
 and
 used
 Zn/ZSM-5.
 Both
 the
 fresh
 Zn/ZSM-
5
 and
 used
 Zn/ZSM-5
 exhibited
 a
 combination
 of
 type
 I
 and
type
 IV
 isotherm
 with
 enhanced
 uptake
 of
 nitrogen
 at
 a
 higher
relative
 pressure
 (p/p0),
 according
 to
 the
 IUPAC
 classiﬁcation
 of
physisorption
 isotherms
 (Perry
 and
 Green,
 1997).
 In
 addition,
the
 fresh
 Zn/ZSM-5
 and
 used
 Zn/ZSM-5
 both
 displayed
 a
 typical
520
 
X.
 Zhao
 et
 al.
 /
 Industrial
 Crops
 and
 Products
 77
 (2015)
 516–526
Fig.
 2.
 Characterization
 of
 (a)
 fresh
 Zn/ZSM-5
 and
 (b)
 used
 Zn/ZSM-5:
 (1)
 XRD
 spectra;
 (2)
 FT-IR
 spectra;
 (3)
 N2 adsorption–desorption
 isotherm
 curves;
 and
 (4)
 pore
 size
distribution.
hysteresis
 loop
 between
 the
 relative
 pressures
 of
 0.4–1.0,
 which
might
 be
 attributed
 to
 the
 crystalline
 agglomerates
 that
 caused
the
 mesoporous
 structure.
 Furthermore,
 the
 adsorption
 quantity
 at
low
 relative
 pressures
 for
 both
 fresh
 Zn/ZSM-5
 and
 used
 Zn/ZSM-5
was
 high,
 conﬁrming
 the
 presence
 of
 microporous
 adsorption
 (Zhao
et
 al.,
 2015b,c).
 These
 ﬁndings
 are
 in
 accord
 with
 the
 study
 of
 Jae
et
 al.
 (2011).
 The
 calculated
 micropore
 and
 mesopore
 volumes
 are
shown
 in
 Table
 3.
 Used
 Zn/ZSM-5
 presented
 a
 higher
 adsorption
quantity
 than
 fresh
 Zn/ZSM-5;
 in
 other
 words,
 used
 Zn/ZSM-5
 had
a
 higher
 pore
 volume
 than
 that
 of
 fresh
 Zn/ZSM-5.
 Fig.
 2(4)
 shows
the
 BJH
 pore
 size
 distribution
 of
 fresh
 Zn/ZSM-5
 and
 used
 Zn/ZSM-5.
The
 BJH
 pore
 size
 model
 conﬁrmed
 the
 presence
 of
 mesoporosity
 in
both
 fresh
 Zn/ZSM-5
 and
 used
 Zn/ZSM-5.
 In
 addition,
 used
 Zn/ZSM-
5
 presented
 a
 higher
 pore
 volume
 than
 that
 of
 fresh
 Zn/ZSM-5
 when
the
 pore
 width
 was
 lower
 than
 30
 nm.
3.1.4.
 SEM,
 STEM
 and
 EDS
 of
 catalyst
Fig.
 3(1)
 shows
 the
 surface
 morphologies
 of
 fresh
 Zn/ZSM-5
 and
used
 Zn/ZSM-5.
 There
 was
 no
 big
 difference
 between
 the
 surface
morphologies
 of
 the
 fresh
 Zn/ZSM-5
 and
 used
 Zn/ZSM-5.
 The
 fresh
Zn/ZSM-5
 and
 used
 Zn/ZSM-5
 both
 had
 a
 3-dimensional
 pore
 sys-
tem
 and
 contained
 small
 pores.
 There
 were
 some
 small
 particles
dispersing
 on
 the
 surface
 of
 fresh
 Zn/ZSM-5
 and
 used
 Zn/ZSM-
5.
 This
 is
 supported
 by
 the
 STEM
 images
 of
 fresh
 Zn/ZSM-5
 and
used
 Zn/ZSM-5,
 shown
 in
 Fig.
 3(2),
 where
 small
 white
 shades
 dis-
persed
 on
 the
 fresh
 Zn/ZSM-5
 and
 used
 Zn/ZSM-5.
 These
 shades
might
 be
 attributed
 to
 the
 small
 ZnO
 particles
 dispersing
 on
 the
parent
 zeolite.
 Some
 small
 dark
 particles
 dispersed
 on
 the
 used
Zn/ZSM-5,
 suggesting
 that
 the
 carbonaceous
 substances
 generated
after
 the
 upgrading
 of
 camelina
 oil
 deposited
 on
 the
 used
 Zn/ZSM-
5.
 The
 carbon
 content
 of
 used
 Zn/ZSM-5
 was
 17.49%,
 conﬁrming
 the
existence
 of
 carbonaceous
 substances.
 These
 ﬁndings
 are
 corrobo-
rated
 by
 the
 suggestions
 made
 by
 Zhang
 et
 al.
 (2012).
 The
 TEM/EDS
X.
 Zhao
 et
 al.
 /
 Industrial
 Crops
 and
 Products
 77
 (2015)
 516–526
 
521
Fig.
 3.
 Characterization
 of
 (a)
 fresh
 Zn/ZSM-5
 and
 (b)
 used
 Zn/ZSM-5:
 (1)
 SEM
 images;
 and
 (2)
 STEM
 images.
elemental
 maps
 are
 shown
 in
 Fig.
 4.
 Uniform
 distribution
 of
 Zn
 was
observed
 on
 both
 fresh
 Zn/ZSM-5
 and
 used
 Zn/ZSM-5
 except
 some
Zn
 aggregates.
 The
 distribution
 of
 C
 on
 used
 Zn/ZSM-5
 conﬁrms
 that
carbonaceous
 substances
 were
 generated
 after
 the
 fresh
 Zn/ZSM-5
was
 used
 for
 the
 upgrading
 of
 camelina
 oil.
3.2.
 Physical
 properties
 of
 product
 and
 statistical
 analysis
The
 effect
 of
 various
 parameters
 on
 the
 physical
 properties
 of
hydrocarbon
 biofuel
 is
 listed
 in
 Table
 4.
 Some
 of
 the
 camelina
 oil’s
undesirable
 properties
 such
 as
 dynamic
 viscosity,
 density
 and
 HHV
were
 improved
 after
 the
 upgrading.
 With
 the
 range
 analysis
 result
in
 Table
 5,
 the
 physical
 properties
 of
 hydrocarbon
 biofuel
 produced
under
 the
 three
 effect
 factors
 were
 compared.
 The
 large
 molecules
in
 camelina
 oil
 were
 cracked
 into
 smaller
 ones
 over
 Zn/ZSM-5
so
 that
 the
 dynamic
 viscosity
 of
 hydrocarbon
 biofuel
 decreased
largely.
 The
 dynamic
 viscosity
 of
 hydrocarbon
 biofuel
 produced
 in
this
 study
 varied
 from
 1.93
 cP
 to
 3.65
 cP,
 which
 was
 close
 to
 the
dynamic
 viscosity
 (3.41–3.46
 cP)
 of
 hydrocarbon
 fuel
 reported
 by
Xu
 et
 al.
 (2010).
 Furthermore,
 the
 dynamic
 viscosity
 of
 hydrocar-
bon
 biofuel
 produced
 under
 the
 eighth
 and
 ninth
 trial
 conditions
was
 lower
 than
 the
 maximum
 dynamic
 viscosity
 of
 jet
 fuel
 mea-
sured
 at
 25 ◦C
 (2.00
 cP)
 (Zhao
 et
 al.,
 2015c).
 Based
 on
 the
 Rj value,
the
 three
 factors’
 level
 of
 signiﬁcance
 was
 oil
 extraction
 press
 fre-
quency
 (0.87)
 >
 reaction
 temperature
 (0.71)
 >
 liquid
 hourly
 space
velocity
 (0.26),
 suggesting
 that
 a
 small
 change
 in
 oil
 extraction
press
 frequency
 could
 produce
 a
 signiﬁcant
 change
 in
 the
 dynamic
viscosity
 of
 hydrocarbon
 biofuel.
 The
 lowest
 dynamic
 viscosity
 of
hydrocarbon
 biofuel
 for
 each
 level
 was
 distinguished
 as
 the
 reaction
temperature
 of
 550 ◦C
 (2.04
 cP),
 the
 liquid
 hourly
 space
 velocity
 of
0.2
 h−1 (2.41
 cP)
 and
 the
 oil
 extraction
 press
 frequency
 of
 25
 Hz
(2.17
 cP).
 The
 reaction
 temperature
 and
 oil
 extraction
 press
 fre-
quency
 produced
 a
 signiﬁcant
 effect
 on
 the
 dynamic
 viscosity
 of
hydrocarbon
 biofuel
 according
 to
 the
 ANOVA
 results.
 A
 following
multiple
 comparison
 analysis
 for
 the
 hydrocarbon
 biofuel
 property
is
 shown
 in
 Table
 6.
It
 appears
 dynamic
 viscosity
 of
 hydrocarbon
 biofuel
 obtained
 at
550 ◦C
 was
 signiﬁcantly
 different
 from
 dynamic
 viscosity
 obtained
at
 450 ◦C
 and
 500 ◦C.
 The
 comparison
 between
 the
 dynamic
 viscos-
ity
 obtained
 at
 450 ◦C
 and
 500 ◦C
 did
 not
 appear
 to
 be
 statistically
different.
 Also,
 the
 dynamic
 viscosity
 of
 hydrocarbon
 biofuel
obtained
 at
 20
 Hz
 was
 signiﬁcantly
 different
 from
 dynamic
 viscosity
obtained
 at
 15
 Hz
 and
 25
 Hz.
Water
 is
 a
 byproduct
 produced
 during
 the
 catalytic
 cracking
of
 camelina
 oil,
 which
 might
 lead
 to
 a
 higher
 water
 content
 in
hydrocarbon
 biofuel
 than
 that
 of
 camelina
 oil.
 The
 water
 content
of
 hydrocarbon
 biofuel
 was
 between
 0.09%
 and
 0.24%,
 which
 was
lower
 than
 the
 water
 content
 of
 light
 oil
 produced
 after
 the
 upgrad-
ing
 of
 bio-oil
 studied
 by
 Xu
 et
 al.
 (2008).
 In
 addition,
 the
 water
content
 of
 hydrocarbon
 biofuel
 produced
 under
 the
 second
 and
ninth
 trial
 conditions
 was
 in
 the
 water
 content
 range
 of
 jet
 fuel
(Zhao
 et
 al.,
 2015c).
 The
 three
 factors’
 level
 of
 signiﬁcance
 was
 reac-
tion
 temperature
 (0.06)
 =
 liquid
 hourly
 space
 velocity
 (0.06)
 >
 oil
extraction
 press
 frequency
 (0.04).
 The
 lowest
 moisture
 content
 of
hydrocarbon
 biofuel
 for
 each
 level
 was
 distinguished
 as
 the
 reac-
tion
 temperature
 of
 450 ◦C
 (0.12%),
 the
 liquid
 hourly
 space
 velocity
of
 0.6
 h−1 or
 1.0
 h−1 (0.13%)
 and
 the
 oil
 extraction
 press
 frequency
of
 15
 Hz
 (0.13%).
 The
 reaction
 temperature,
 liquid
 hourly
 space
velocity
 and
 oil
 extraction
 press
 frequency
 all
 contributed
 as
 a
 sig-
niﬁcant
 factor
 for
 the
 water
 content
 of
 hydrocarbon
 biofuel.
 There
522
 
X.
 Zhao
 et
 al.
 /
 Industrial
 Crops
 and
 Products
 77
 (2015)
 516–526
Fig.
 4.
 TEM/EDS
 elemental
 maps
 of
 (a)
 fresh
 Zn/ZSM-5
 and
 (b)
 used
 Zn/ZSM-5.
was
 no
 signiﬁcant
 difference
 between
 the
 water
 content
 obtained
at
 450 ◦C
 and
 550 ◦C,
 between
 the
 water
 content
 obtained
 at
 0.6
 h−1
and
 1.0
 h−1,
 and
 between
 the
 water
 content
 obtained
 at
 15
 Hz
 and
20
 Hz.
 Other
 comparisons
 between
 pairs
 of
 means
 appear
 to
 be
statistically
 different.
X.
 Zhao
 et
 al.
 /
 Industrial
 Crops
 and
 Products
 77
 (2015)
 516–526
 
523
Table
 4
Physical
 properties
 of
 hydrocarbon
 biofuel
 obtained
 from
 camelina
 oil.
Trial
 No.
 
Product
 
Dynamic
 viscosity
 (20 ◦C,
 cP)
 
Moisture
 content
 (wt.%)
 
Density
 (g/mL)
 
HHV
 (MJ/kg)
Camelina
 oil
 
58.85–59.65
 
0.06–0.08
 
0.88–0.89
 
39.41–39.65
1
 
Hydrocarbon
 biofuel
2.49
 ±
 0.16
0.13
 ±
 0
 
0.82
 ±
 0
 
43.74
 ±
 0.06
2
 
Hydrocarbon
 biofuel
 
3.65
 ±
 0.02
 
0.09
 ±
 0
 
0.82
 ±
 0
 
44.77
 ±
 0.07
3
 
Hydrocarbon
 biofuel
 
2.11
 ±
 0.06
 
0.14
 ±
 0
 
0.81
 ±
 0
 
43.79
 ±
 0.07
4
 
Hydrocarbon
 biofuel
 
2.47
 ±
 0.06
 
0.24
 ±
 0.01
 
0.82
 ±
 0
 
43.42
 ±
 0.06
5
 
Hydrocarbon
 biofuel
 
2.42
 ±
 0.06
 
0.16
 ±
 0
 
0.81
 ±
 0
 
43.58
 ±
 0
6
 
Hydrocarbon
 biofuel
 
3.21
 ±
 0.08
 
0.15
 ±
 0
 
0.83
 ±
 0
 
43.43
 ±
 0.01
7
 
Hydrocarbon
 biofuel
 
2.26
 ±
 0.06
 
0.19
 ±
 0
 
0.82
 ±
 0
 
43.15
 ±
 0.01
8
 
Hydrocarbon
 biofuel
1.93
 ±
 0.05
0.14
 ±
 0.01
0.81
 ±
 0
43.19
 ±
 0.14
9
 
Hydrocarbon
 biofuel
 
1.93
 ±
 0.06
 
0.09
 ±
 0.01
 
0.82
 ±
 0
 
43.68
 ±
 0.29
Hydrocarbon
 biofuel
 
3.41–3.46
 Xu
 et
 al.
 (2010)
 
0.52
 Xu
 et
 al.
 (2008)
 
0.88
 Chen
 et
 al.
 (2010)
 
40.82
 Chen
 et
 al.
 (2010)
Jet
 fuel
 (ASTM
 standard)
 Zhao
 et
 al.
 (2015c)
 
≤2.00
 (25 ◦C)
 
<0.10
 
0.78–0.84
 
≥42.80
Table
 5
Range
 analysis
 and
 ANOVA
 results
 for
 the
 properties
 of
 hydrocarbon
 biofuel.
Value
 name
 
Reaction
 temperature
 (factor
 A)
 
LHSV
 (factor
 B)
 
Oil
 extraction
 press
 frequency
 (factor
 C)
1a
2b
3c
4d
1
 
2
 
3
 
4
 
1
 
2
 
3
 
4
K1
8.25
 
0.36
 
2.45
 
132.30
 
7.22
 
0.56
 
2.46
 
130.31
 
6.84
 
0.38
 
2.45
 
131.00
K2
8.10
 
0.55
 
2.46
 
130.43
 
8.00
 
0.39
 
2.44
 
131.54
 
9.12
 
0.43
 
2.47
 
131.35
K3
6.12
 
0.42
 
2.45
 
130.02
 
7.25
 
0.38
 
2.46
 
130.90
 
6.51
 
0.52
 
2.44
 
130.40
K′1
2.75
 
0.12
 
0.82
 
44.10
 
2.41
 
0.19
 
0.82
 
43.44
 
2.28
 
0.13
 
0.82
 
43.67
K′2
2.70
 
0.18
 
0.82
 
43.48
 
2.67
 
0.13
 
0.81
 
43.85
 
3.04
 
0.14
 
0.82
 
43.78
K′3
2.04
 
0.14
 
0.82
 
43.34
 
2.42
 
0.13
 
0.82
 
43.63
 
2.17
 
0.17
 
0.81
 
43.47
R
 
0.71
0.06
0.00
 
0.76
 
0.26
 
0.06
 
0.01
 
0.41
 
0.87
 
0.04
 
0.01
 
0.31
Sum
 of
 squares
 
1.89
 
0.01
 
0.00
 
1.97
 
0.26
 
0.01
 
0.00
 
0.51
 
2.70
 
0.01
 
0.00
 
0.31
Degree
 of
 freedom
 
2
 
2
 
2
 
2
 
2
 
2
 
2
 
2
 
2
 
2
 
2
 
2
Mean
 square
 
0.95
 
0.01
 
0.00
 
0.99
 
0.13
 
0.01
 
0.00
 
0.25
 
1.35
 
0.00
 
0.00
 
0.16
F
 
17.32
 
23.75
 
2.59
 
10.40
 
2.40
 
26.54
 
13.70
 
2.69
 
24.71
 
11.95
 
4.82
 
1.66
Signiﬁcant
 difference
Yes
 
Yes
 
No
 
Yes
 
No
 
Yes
 
Yes
 
No
 
Yes
 
Yes
 
Yes
 
No
a Dynamic
 viscosity.
b Moisture
 content.
c Density.
d HHV.
Table
 6
Multiple
 comparison
 analysis
 for
 the
 properties
 of
 hydrocarbon
 biofuel.
Value
 namea
K′i
K′i –
 2.04
 
K′i –
 2.70
 
Value
 namea
K′i
K′i –
 2.17
 
K′i –
 2.28
A1 (450 ◦C)
2.75
 
0.71e
0.05
 
C2 (20
 Hz)
 
3.04
 
0.87e
0.76e
A2 (500 ◦C)
 
2.70
 
0.66e
C1 (15
 Hz)
 
2.28
 
0.11
A3 (550 ◦C)
 
2.04
 
C3 (25
 Hz)
 
2.17
Value
 nameb
K′i
K′i –
 0.12
 
K′i –
 0.14
 
Value
 nameb
K′i
K′i –
 0.13
 
K′i –
 0.13
A2 (500 ◦C)
 
0.18
 
0.06e
0.04e
B1 (0.2
 h−1)
 
0.19
 
0.06e
0.06e
A3 (550 ◦C)
 
0.14
 
0.02
 
B2 (0.6
 h−1)
 
0.13
 
0
A1 (450 ◦C)
 
0.12
 
B3 (1.0
 h−1)
 
0.13
Value
 nameb
K′i
K′i –
 0.13
 
K′i –
 0.14
 
Value
 namec
K′i
K′i –
 0.81
 
K′i –
 0.82
C3 (25
 Hz)
 
0.17
 
0.04e
0.03e
B1 (0.2
 h−1)
 
0.82
 
0.01e
0
C2 (20
 Hz)
 
0.14
 
0.01
 
B3 (1.0
 h−1)
 
0.82
 
0.01e
C1 (15
 Hz)
 
0.13
 
B2 (0.6
 h−1)
 
0.81
Value
 namec
K′i
K′i –
 0.81
 
K′i –
 0.82
 
Value
 named
K′i
K′i –
 43.34
 
K′i –
 43.48
C1 (15
 Hz)
 
0.82
 
0.01
 
0e
A1 (450 ◦C)
 
44.10
 
0.76e
0.62e
C2 (20
 Hz)
 
0.82
 
0.01e
A2 (500 ◦C)
 
43.48
 
0.14
C3 (25
 Hz)
 
0.81
 
A3 (550 ◦C)
 
43.34
a Dynamic
 viscosity.
b Moisture
 content.
c Density.
d HHV.
e Signiﬁcant
 difference.
Density
 of
 oil
 is
 important
 in
 the
 airless
 combustion
 systems
and
 the
 densities
 of
 all
 hydrocarbon
 biofuels
 decreased
 after
the
 camelina
 oil
 upgrading.
 The
 density
 of
 hydrocarbon
 biofuel
produced
 in
 this
 study
 was
 lower
 than
 that
 of
 hydrocarbon
 oil
(0.88
 g/mL)
 via
 the
 deoxy-liquefaction
 of
 sunﬂower
 oil
 studied
 by
Chen
 et
 al.
 (2010). In
 addition,
 the
 density
 of
 hydrocarbon
 biofuel
was
 in
 the
 range
 of
 0.81–0.83
 g/mL,
 which
 was
 in
 the
 density
 range
of
 jet
 fuel
 (0.78–0.84
 g/mL)
 (Zhao
 et
 al.,
 2015c).
 Furthermore,
 the
Rj value
 for
 the
 density
 under
 the
 reaction
 temperature
 factor
 was
zero,
 suggesting
 that
 the
 reaction
 temperature
 had
 no
 signiﬁcant
524
 
X.
 Zhao
 et
 al.
 /
 Industrial
 Crops
 and
 Products
 77
 (2015)
 516–526
Fig.
 5.
 The
 yield
 of
 hydrocarbon
 biofuel
 produced
 under
 different
 conditions.
effect
 on
 the
 density
 of
 hydrocarbon
 biofuel.
 This
 was
 conﬁrmed
by
 the
 analysis
 of
 variance
 that
 the
 reaction
 temperature
 did
 not
produce
 a
 signiﬁcant
 effect
 on
 the
 density
 of
 hydrocarbon
 biofuel.
The
 liquid
 hourly
 space
 velocity
 and
 oil
 extraction
 press
 frequency
had
 a
 signiﬁcant
 inﬂuence
 on
 the
 density
 of
 hydrocarbon
 biofuel.
The
 density
 of
 hydrocarbon
 biofuel
 obtained
 at
 0.6
 h−1 was
 sig-
niﬁcantly
 different
 from
 density
 obtained
 at
 0.2
 h−1 and
 1.0
 h−1,
and
 the
 density
 obtained
 at
 20
 Hz
 was
 signiﬁcantly
 different
 from
density
 obtained
 at
 15
 Hz
 and
 25
 Hz.
The
 HHVs
 of
 all
 hydrocarbon
 biofuels
 increased
 after
 the
catalytic
 cracking
 of
 camelina
 oil,
 which
 might
 be
 due
 to
 the
 con-
version
 of
 fatty
 acids
 to
 hydrocarbons
 during
 the
 cracking
 process.
The
 HHV
 of
 hydrocarbon
 biofuel
 was
 between
 43.15
 MJ/kg
 and
44.77
 MJ/kg,
 which
 was
 higher
 than
 both
 the
 HHV
 of
 hydrocar-
bon
 oil
 through
 the
 deoxy-liquefaction
 of
 sunﬂower
 oil
 studied
by
 Chen
 et
 al.
 (2010)
 and
 the
 minimum
 HHV
 of
 jet
 fuel
 (Zhao
et
 al.,
 2015c).
 The
 three
 factors’
 level
 of
 signiﬁcance
 was
 reac-
tion
 temperature
 (0.76)
 >
 liquid
 hourly
 space
 velocity
 (0.41)
 >
 oil
extraction
 press
 frequency
 (0.31).
 The
 highest
 HHV
 of
 hydrocarbon
biofuel
 for
 each
 level
 was
 distinguished
 as
 the
 reaction
 temper-
ature
 of
 450 ◦C
 (44.10
 MJ/kg),
 the
 liquid
 hourly
 space
 velocity
 of
0.6
 h−1 (43.85
 MJ/kg)
 and
 the
 oil
 extraction
 press
 frequency
 of
 20
 Hz
(43.78
 MJ/kg).
 Only
 the
 reaction
 temperature
 produced
 a
 signiﬁcant
effect
 on
 the
 HHV
 of
 hydrocarbon
 biofuel.
 The
 HHV
 of
 hydrocar-
bon
 biofuel
 obtained
 at
 450 ◦C
 was
 signiﬁcantly
 different
 from
 HHV
obtained
 at
 500 ◦C
 and
 550 ◦C.
Based
 on
 the
 above
 analysis
 of
 the
 hydrocarbon
 biofuel
 qualities,
the
 reaction
 temperature
 factor
 was
 more
 important
 than
 the
 liq-
uid
 hourly
 space
 velocity
 factor.
 The
 oil
 extraction
 press
 frequency
factor
 had
 a
 bigger
 impact
 on
 the
 dynamic
 viscosity
 of
 hydrocarbon
biofuel
 than
 the
 reaction
 temperature
 factor,
 but
 the
 oil
 extraction
press
 frequency
 factor
 had
 a
 smaller
 impact
 on
 the
 moisture
 con-
tent
 of
 hydrocarbon
 biofuel
 than
 the
 liquid
 hourly
 space
 velocity
factor.
 According
 to
 the
 quality
 analysis
 of
 hydrocarbon
 biofuel
 for
each
 level
 and
 the
 property
 range
 of
 jet
 fuel,
 the
 optimal
 quality
 of
hydrocarbon
 biofuel
 was
 found
 to
 occur
 at
 a
 reaction
 temperature
of
 550 ◦C,
 liquid
 hourly
 space
 velocity
 of
 1.0
 h−1 and
 oil
 extraction
press
 frequency
 of
 15
 Hz.
3.3.
 The
 yield
 of
 product
 and
 statistical
 analysis
After
 the
 camelina
 oil
 upgrading,
 the
 produced
 hydrocarbon
biofuel
 yield
 is
 shown
 in
 Fig.
 5.
 The
 range
 of
 the
 hydrocarbon
 bio-
fuel
 yield
 varied
 from
 45.74%
 to
 59.81%,
 which
 was
 lower
 than
 the
Fig.
 6.
 The
 relative
 content
 of
 components
 in
 a
 non-condensable
 gas
 sample.
yield
 of
 hydrocarbon
 oil
 (74.2%)
 obtained
 by
 deoxy-liquefaction
of
 sunﬂower
 oil
 studied
 by
 Chen
 et
 al.
 (2010). It
 indicates
 that
other
 operation
 conditions
 such
 as
 catalyst
 species,
 catalyst-to-oil
ratio
 and
 condensing
 temperature
 of
 the
 ﬁxed-bed
 reactor
 could
be
 considered
 in
 the
 future
 to
 improve
 the
 hydrocarbon
 biofuel
yield.
 With
 the
 range
 analysis
 result
 in
 Table
 7,
 the
 yield
 of
 hydro-
carbon
 biofuel
 obtained
 under
 three
 effect
 factors
 was
 compared.
For
 the
 yield
 of
 hydrocarbon
 biofuel,
 the
 three
 factors’
 level
 of
importance
 was
 oil
 extraction
 press
 frequency
 (5.31)
 >
 reaction
temperature
 (5.11)
 >
 liquid
 hourly
 space
 velocity
 (3.92).
 The
 highest
hydrocarbon
 biofuel
 yield
 for
 each
 level
 was
 distinguished
 as
 the
reaction
 temperature
 of
 450 ◦C
 (55.75%),
 liquid
 hourly
 space
 veloc-
ity
 of
 0.2
 h−1 (55.80%)
 and
 oil
 extraction
 press
 frequency
 of
 20
 Hz
(57.18%).
 Based
 on
 the
 analysis
 of
 variance,
 each
 of
 these
 three
 fac-
tors
 did
 not
 produce
 a
 signiﬁcant
 effect
 on
 the
 hydrocarbon
 biofuel
yield.
 Combining
 the
 above
 hydrocarbon
 biofuel
 quality
 analysis,
the
 optimal
 conditions
 for
 hydrocarbon
 biofuel
 production
 could
 be
expressed
 as:
 reaction
 temperature
 of
 550 ◦C,
 liquid
 hourly
 space
velocity
 of
 1.0
 h−1 and
 oil
 extraction
 press
 frequency
 of
 15
 Hz.
3.4.
 Constituents
 of
 hydrocarbon
 biofuel
 and
 non-condensable
gas
Table
 8
 shows
 the
 major
 compounds
 of
 a
 hydrocarbon
 biofuel
sample.
 After
 the
 upgrading
 of
 camelina
 oil,
 the
 produced
 hydrocar-
bon
 biofuel
 mainly
 contained
 hydrocarbons
 and
 oxygenates
 such
 as
acid,
 alcohol,
 ester
 and
 ketone.
 The
 hydrocarbons
 ranged
 from
 C15
to
 C28 and
 their
 total
 content
 was
 65.18%,
 indicating
 that
 fatty
 acids
in
 camelina
 oil
 were
 mostly
 converted
 to
 hydrocarbons
 through
cracking
 over
 the
 Zn/ZSM-5
 catalyst.
 In
 addition,
 more
 than
 20%
alcohols
 and
 ketone
 were
 produced.
 The
 production
 of
 hydrocar-
bon,
 alcohol
 and
 ketone
 is
 a
 good
 sign
 since
 it
 means
 some
 of
 the
oxygen
 atoms
 in
 camelina
 oil
 had
 been
 removed
 through
 a
 series
 of
reactions
 such
 as
 deoxygenation.
 However,
 there
 were
 still
 12.63%
acid
 and
 esters
 containing
 in
 hydrocarbon
 biofuel,
 suggesting
 that
further
 reﬁning
 is
 necessary
 in
 the
 future
 to
 convert
 more
 acid/ester
to
 hydrocarbon.
A
 typical
 GC
 analysis
 of
 the
 non-condensable
 gas
 produced
 from
catalytic
 cracking
 of
 camelina
 oil
 is
 shown
 in
 Fig.
 6.
 During
 the
catalytic
 cracking
 of
 camelina
 oil,
 the
 generated
 non-condensable
gas
 mainly
 contained
 H2,
 CO2,
 CO,
 and
 C1-C5 light
 hydrocarbons.
Hydrogen
 was
 generated
 from
 the
 dehydrogenation
 reactions
 and
CO
 was
 produced
 from
 the
 decarbonylation
 reactions
 taking
 place
during
 the
 catalytic
 cracking
 of
 camelina
 oil
 (Zhao
 et
 al.,
 2015c).
The
 content
 sum
 of
 H2 and
 CO
 was
 low
 (0.67%).
 CO2 was
 pro-
duced
 from
 the
 decarboxylation
 and
 the
 content
 of
 CO2 was
 the
highest
 among
 the
 components
 of
 non-condensable
 gas,
 which
 is
 a
X.
 Zhao
 et
 al.
 /
 Industrial
 Crops
 and
 Products
 77
 (2015)
 516–526
 
525
Table
 7
Range
 analysis
 and
 ANOVA
 results
 for
 the
 yield
 of
 hydrocarbon
 biofuel.
Value
 name
 
Reaction
 temperature
 (factor
 A)
 
LHSV
 (factor
 B)
 
Oil
 extraction
 press
 frequency
 (factor
 C)
K1
167.26
 
167.40
 
155.60
K2
151.91
155.65
171.53
K3
166.44
 
162.56
 
158.48
K′1
55.75
 
55.80
 
51.87
K′2
50.64
 
51.88
 
57.18
K′3
55.48
 
54.19
 
52.83
R
 
5.11
 
3.92
 
5.31
Sum
 of
 squares
 
99.52
 
46.50
 
95.91
Degree
 of
 freedom
2
 
2
 
2
Mean
 square
 
49.76
 
23.25
 
47.95
F
 
2.51
 
1.17
 
2.42
Signiﬁcant
 difference
 
No
 
No
 
No
Table
 8
The
 main
 constituents
 of
 a
 hydrocarbon
 biofuel
 sample.
Compound
 
Molecular
 formula
 
Relative
 content
 (%)
Acid
 
1.89
Hexadecanoic
 acid,
 2-methyl-
 
C17H34O2 (C17:0)
 
1.89
Alcohol
 
20.52
Z-2-tridecen-1-ol
 
C13H26O
 (C13:1)
 
0.09
Z,E-3,13-Octadecadien-1-ol
 
C18H34O
 (C18:2)
 
6.86
n-Tetracosanol-1
 
C24H50O
 (C24:0)
12.63
1-Hexacosanol
 
C26H54O
 (C26:0)
 
0.94
Ester
 
10.74
Heptadecanoic
 acid,
 14-methyl-,
 methyl
 ester
 
C19H38O2 (C19:0)
 
0.76
Hexadecanoic
 acid,
 (3-bromoprop-2-ynyl)
 ester
 
C19H33BrO2 (C19:2)
 
0.96
E,Z-2,15-octadecadien-1-ol
 acetate
C20H36O2 (C20:2)
3.09
Di-n-octyl
 phthalate
 
C24H38O4 (C24:3)
 
5.93
Hydrocarbon
 
65.18
Cyclopentadecane
 
C15H30 (C15:0)
 
28.99
Hexadecane
 
C16H34 (C16:0)
 
16.40
1-Nonadecene
 
C19H38 (C19:1)
 
0.16
Cyclohexane,
 1-(1,5-dimethylhexyl)-4-(4-methylpentyl)-
 
C20H40 (C20:0)
 
6.58
Eicosane
 
C20H42 (C20:0)
 
6.55
Heneicosane
 
C21H44 (C21:0)
 
1.23
Docosane
 
C22H46 (C22:0)
 
3.04
Cyclotetracosane
 
C24H48 (C24:0)
 
1.42
Tetracosane
 
C24H50 (C24:0)
 
0.24
Pentacosane
 
C25H52 (C25:0)
 
0.05
Octacosane
 
C28H58 (C28:0)
 
0.52
Ketone
 
1.38
2-Nonadecanone
 
C19H38O
 (C19:0)
 
1.38
Other
 
0.29
good
 sign.
 Based
 on
 Huang’s
 study
 report
 (Huang
 et
 al.,
 2015),
 the
favorite
 pathway
 to
 remove
 oxygen
 atom
 is
 to
 form
 CO2 during
 the
upgrading
 process.
The
 total
 content
 of
 C1-C5 light
 hydrocarbons
 was
 1.10%.
 The
production
 of
 CH4 indicates
 that
 the
 Zn/ZSM-5
 catalyst
 was
 capable
in
 the
 cracking
 and
 converting
 of
 fatty
 acids
 into
 the
 smallest
 frac-
tion
 of
 hydrocarbon.
 Other
 complicated
 chemical
 reactions
 such
as
 dehydration,
 dehydrogenation
 and
 deoxidation
 might
 produce
C2-C5 hydrocarbons.
 The
 dehydrogenation,
 dehydration,
 decar-
bonylation
 and
 decarboxylation
 taking
 place
 during
 the
 upgrading
of
 camelina
 oil
 could
 be
 illustrated
 in
 Eq.
 (2)
 (Zhang
 et
 al.,
 2011;
Zhao
 et
 al.,
 2015b;
 Zakaria
 et
 al.,
 2012;
 Zhang
 et
 al.,
 2007).
Camelinaoil(CxHyOz)
 →
 CxHy +
 H2 +
 H2O
 +
 CO
 +
 CO2
(2)
Some
 released
 hydrogen
 might
 react
 with
 some
 unstable
 light
hydrocarbons
 such
 as
 C2H4 and
 C3H6 to
 produce
 more
 stable
 hydro-
carbons
 (Zhao
 et
 al.,
 2015b;
 Zhang
 et
 al.,
 2009).
 Meanwhile,
 some
short-chain
 hydrocarbons
 such
 as
 C3H8 might
 decompose
 to
 C2H4
because
 of
 the
 C
 C
 bond
 cleavage
 (Li
 et
 al.,
 2009b).
 The
 content
 of
H2,
 CO
 and
 light
 hydrocarbons
 was
 low,
 but
 they
 have
 the
 potential
to
 be
 recycled
 for
 the
 production
 of
 energy
 and
 liquid
 fuel
 in
 the
future
 (Zhang
 et
 al.,
 2015).
4.
 Conclusions
The
 peak
 position
 and
 intensity
 of
 fresh
 Zn/ZSM-5
 catalyst
 show
the
 spectrum
 of
 zeolite.
 There
 is
 no
 big
 difference
 between
 the
bulk
 structures/surface
 morphologies
 of
 fresh
 Zn/ZSM-5
 and
 used
Zn/ZSM-5.
 The
 carbon
 content
 of
 used
 Zn/ZSM-5
 is
 17.49%,
 but
 used
Zn/ZSM-5
 might
 be
 regenerated
 for
 further
 cracking
 of
 camelina
 oil
after
 a
 calcination
 treatment.
 Three
 experimental
 conditions
 affect
the
 hydrocarbon
 biofuel
 yield
 and
 qualities
 differently.
 Based
 on
 the
yield
 and
 qualities
 of
 hydrocarbon
 biofuel,
 the
 oil
 extraction
 press
frequency
 is
 the
 most
 important
 factor
 and
 liquid
 hourly
 space
velocity
 is
 the
 least
 important
 factor.
 The
 optimum
 conditions
 for
hydrocarbon
 biofuel
 production
 could
 be
 expressed
 as:
 reaction
temperature
 of
 550 ◦C,
 liquid
 hourly
 space
 velocity
 of
 1.0
 h−1 and
oil
 extraction
 press
 frequency
 of
 15
 Hz.
Fatty
 acids
 in
 camelina
 oil
 are
 mostly
 converted
 to
 hydrocarbons
through
 the
 catalytic
 cracking
 over
 Zn/ZSM-5.
 Some
 undesirable
properties
 of
 camelina
 oil
 including
 dynamic
 viscosity,
 density
 and
HHV
 are
 improved
 after
 upgrading.
 Non-condensable
 gas
 mainly
containes
 H2,
 CO2,
 CO
 and
 C1-C5 light
 hydrocarbons,
 which
 might
be
 produced
 from
 multiple
 reactions
 such
 as
 dehydrogenation,
decarbonylation,
 decarboxylation
 and
 deoxidation.
 In
 the
 future,
other
 operation
 conditions
 such
 as
 catalyst-to-oil
 ratio,
 catalyst
species
 and
 condensing
 temperature
 of
 the
 ﬁxed-bed
 reactor
 could
526
 
X.
 Zhao
 et
 al.
 /
 Industrial
 Crops
 and
 Products
 77
 (2015)
 516–526
be
 considered
 to
 further
 improve
 the
 hydrocarbon
 biofuel
 yield
 and
quality.
Acknowledgements
This
 study
 was
 funded
 by
 the
 U.S.
 Department
 of
 Transporta-
tion
 through
 NC
 Sun
 Grant
 Initiative
 under
 Grant
 No.
 SA0700149.
The
 authors
 would
 like
 to
 thank
 Dr.
 Douglas
 Raynie,
 Ms.
 Changling
Qiu
 and
 Ms.
 Shanmugapriya
 Dharmarajan
 for
 helping
 in
 the
 GC/MS
measurement,
 Dr.
 Qiquan
 Qiao
 and
 Mr.
 Ashish
 Dubey
 for
 helping
in
 the
 XRD,
 FT-IR
 and
 SEM
 analysis,
 Dr.
 Lee
 Pullan
 for
 helping
 in
the
 TEM
 analysis,
 and
 Dr.
 Zhengrong
 Gu
 and
 Ms.
 Xiaomin
 Wang
for
 helping
 in
 the
 BET
 measurement.
 The
 XRD
 equipment
 is
 sup-
ported
 by
 the
 NSF
 MRI
 grant
 (Award
 No.
 1229577).
 All
 the
 support
in
 experiment
 from
 Mr.
 Yang
 Gao,
 Yinbin
 Huang,
 Zhongwei
 Liu
 and
Yong
 Yu
 is
 gratefully
 acknowledged.
 However,
 only
 the
 authors
 are
responsible
 for
 the
 opinions
 expressed
 in
 this
 paper
 and
 for
 any
possible
 error.
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Review
Simulation and optimization technologies for petroleum waste management
and remediation process control
X.S. Qin a,b, G.H. Huang a,b,*, L. He a
a Faculty of Engineering, University of Regina, Regina, Saskatchewan S4S 0A2, Canada
b Chinese Research Academy of Environmental Science, North China Electric Power University, Beijing 100012-102206, China
a r t i c l e
i n f o
Article history:
Received 10 October 2007
Received in revised form 18 May 2008
Accepted 2 July 2008
Available online 10 August 2008
Keywords:
Review
Petroleum waste management
Multiphase simulation
Optimization
a b s t r a c t
Leakage and spill of petroleum hydrocarbons from underground storage tanks and pipelines have posed
signiﬁcant threats to groundwater resources across many petroleum-contaminated sites. Remediation of
these sites is essential for protecting the soil and groundwater resources and reducing risks to local
communities. Although many efforts have been made, effective design and management of various
remediation systems are still challenging to practitioners. In recent years, the subsurface simulation
model has been combined with techniques of optimization to address important problems of contam-
inated site management. The combined simulation-optimization system accounts for the complex
behavior of the subsurface system and identiﬁes the best management strategy under consideration of
the management objectives and constraints. During the past decades, a large number of studies were
conducted to simulate contaminant ﬂow and transport in the subsurface and seek cost-effective reme-
diation designs. This paper gives a comprehensive review on recent developments, advancements,
challenges, and barriers associated with simulation and optimization techniques in supporting process
control of petroleum waste management and site remediation. A number of related methodologies and
applications were examined. Perspectives of effective site management were investigated, demon-
strating many demanding areas for enhanced research efforts, which include issues of data availability
and reliability, concerns in uncertainty, necessity of post-modeling analysis, and usefulness of devel-
opment of process control techniques.
 2008 Elsevier Ltd. All rights reserved.
1. Introduction
Flow
and
transport
of
petroleum-derived
contaminants,
commonly referred to as non-aqueous phase liquids (NAPLs) in the
subsurface is of enormous importance to our society. Problems
with these contaminations arise from disposal dumps, leaking
storage tanks and accidental spills of hydrocarbons used in petro-
leum industry (Essaid et al., 1993; Kobus, 1996; Garg and Rixey,
1999; Tam and Byer, 2002). There are thousands of sites that have
been seriously contaminated by petroleum products in Canada,
posing signiﬁcant threats to human health and natural environ-
ment, and jeopardizing future opportunities for economic devel-
opment (CCME, 1996; NRTEE, 1997).
Although chemical properties and site conditions vary from site
to site, the basic principles governing the fate and transport of
NAPLs are the same. These principles may be used to understand
the contamination problem and to evaluate remediation practices
(Mercer and Cohen, 1990). Non-aqueous phase liquid (NAPL)
migration in the subsurface is affected by many factors such as the
volume of release and the properties of NAPL and soil media. When
introduced into the subsurface, gravity causes the NAPL to migrate
downward through the vadose zone as a distinct liquid. This
vertical migration is also accompanied to some extent by lateral
spreading due to the effect of capillary forces and medium spatial
variability (Schwille, 1988). As the NAPL progresses downward
through the vadose zone, it leaves residual liquid trapped in the
pore spaces due to surface tension effects. In addition to migration
of NAPL, some of the immiscible ﬂuid may volatilized and form
a vapor extending beyond the NAPL (Abriola, 1989; Mercer and
Cohen, 1990). When the NAPL reaches the groundwater table, the
light non-aqueous phase liquid (LNAPL) will spread laterally along
the capillary fringe while the dense non-aqueous phase liquid
(DNAPL) will displace water and continue its migration under
pressure and gravity forces. In heterogeneous media, the NAPL
distribution will be highly complicated (Pinder and Abriola, 1986).
As the NAPL encounters ﬂowing water, soluble components may
dissolve to form a solute plume that can migrate due to hydraulic
gradients (Mackay et al., 1985). The dissolved phase is of signiﬁcant
* Corresponding
author.
Center
for
Studies
in
Energy
and
Environment,
University of Regina, Regina, Saskatchewan, Canada S4S 0A2, Canada. Tel.: þ1 306
585 4095; fax: þ1 306 585 4855.
E-mail address: huang@iseis.org (G.H. Huang).
Contents lists available at ScienceDirect
Journal of Environmental Management
journal homepage: www.elsevier.com/locate/jenvman
0301-4797/$ – see front matter  2008 Elsevier Ltd. All rights reserved.
doi:10.1016/j.jenvman.2008.07.002
Journal of Environmental Management 90 (2009) 54–76
concern to site managers since these toxic substances may migrate
through subsurface environment for a considerable distance, and
readily contaminate the drinking water sources. Generally, the
transport of dissolved contaminants in groundwater is controlled
by three main processes including advection, hydrodynamic
dispersion (mechanical dispersion and molecular diffusion), and
geochemical processes (Corapcioglu and Baehr, 1987; Panday et al.,
1995). The three processes operate simultaneously. Their individual
impact on the overall contaminant transport may vary with the
type of solute, solid matrix and other geochemical conditions (Bear,
1979; Helmig, 1997). This combination of physical mechanisms (i.e.
groundwater
ﬂow,
contaminant
advection,
and
dispersion)
produces conditions under which contaminants placed on or in the
ground can percolate through the unsaturated zone to saturated
groundwater
zone
where
they are
transported,
often
great
distances, by the movement of the groundwater. By the mecha-
nisms of groundwater transport described above it is clear that
groundwater contamination is a particularly difﬁcult problem
because of the ability of groundwater to travel large distances,
spreading the contaminant form a relatively small source to a large
area (Panday et al., 1997).
Once contaminants are trapped in subsurface and/or mixed
with groundwater, remedial actions should be undertaken. The
goal of remediation is to remove or contain contaminants at the
contaminated site. The high cost of soil and groundwater cleaning
up and the urgency of the problems motivated the identiﬁcation of
efﬁcient ways in designing site speciﬁc techniques for imple-
menting remediation actions. Studies for modeling site remedia-
tion processes were also reported. They were useful for dynamic
evaluation/prediction and/or real-time control of the remediation
systems. For example, Campagnolo and Akgerman (1995) proposed
a model for simulating soil vapor extraction systems that was used
for biodegradation of petroleum-contaminated soils. Christodou-
latos and Mohiuddin (1996) suggested generalized models for
prediction of pentachlorophenol adsorption by natural soils.
Generally,
the
above
models
were
mainly
for
remediation
processes. Their outputs would be useful for further examination of
environmental and economic implications, as well as risks to the
surrounding communities (Theelen, 1997; Wang et al., 1998;
Swartjes, 1999). Moreover, the management of polluted sites
requires a clear understanding of the type(s) of pollutants that are
present, their concentration and spatial/temporal distribution,
their movement in environment to potential receptor points, and
their effects on human health (Paustenbach, 1989; Phillips and
Chapple, 1995; Rangan et al., 1997; Tam and Byer, 2002).
Therefore, in order to design cost-effective remediation strate-
gies, it is necessary to fully understand the governing physical,
chemical and biological processes of ﬂow and transport of
contaminants in porous media, and analyze tradeoffs between
system cost and process efﬁciency of various site remediation
alternatives. Mathematical models are recognized as effective tools
that could help achieve these goals (Korsakova, 1996; Mason and
Kueper, 1996; Helmig, 1997). During the past decades, a large
number of studies were conducted to simulate contaminant ﬂow
and transport in the subsurface (Gierke et al., 1990; Forsyth and
Shao, 1991; Falta, 1998; Maqsood et al., 2003), and seek cost-
effective remediation designs (Gorelick et al., 1984; Ahlfeld, 1990;
Dougherty and Marryott, 1991; Chang et al., 1992; Culver and
Shoemaker, 1992; McKinney and Lin, 1995, 1996).
This paper will review the previous research on process simu-
lation and optimization for remediation processes where critical
issues that deserve further exploration will be identiﬁed. As a vast
number of literatures in such a ﬁeld exist, it is not our intention to
provide a complete discussion of all these studies. Instead, we will
focus on those most representative works in each of the ﬁeld, and
discuss signiﬁcant challenges. The paper will be structured as
follows: (1) technology advancement pathway for process simula-
tion; (2) technology advancement pathway for process optimiza-
tion; (3) complexities of model applications; and (4) future
perspectives.
2. Technology advancement pathway for process simulation
The migration of NAPLs in the underground environment is
controlled
by
numerous
physical,
chemical,
and
biological
processes (Mackay et al., 1985). The issues related to process
modeling of NAPL remediation not only include mathematical
formulation which is used to describe the nature of multiphase
ﬂow and transport in subsurface, but also contain information
about the sources of pollutants, the spatial information of subsur-
face, and the properties of pollutants, soils and groundwater, as
well as the remediation processes (Abriola, 1989; Borden and Kao,
1992; Andric
ˇevic
´ et al., 1994; Andric
ˇevic
´ and Cvetkovic, 1996;
Ghomshei and Meech, 2000). Multiphase ﬂow and transport model
is one of the major components of process models for describing
the physics of multiphase ﬂow in porous media during remedia-
tion. Since 1960s, a signiﬁcant number of numerical simulators
were developed for modeling NAPL ﬂow and transport in multi-
phase subsurface systems (Helmig, 1997; Huber and Helmig, 1999;
Zheng and Bennett, 2002). Excellent reviews of these simulators
were given by Abriola (1989), Mercer and Cohen (1990), Russell
(1995), and Miller et al. (1998).
Although multiphase ﬂow and transport modeling has become
a well-known practice in the petroleum engineering for character-
ing the contaminant plume, the different motivation of the petro-
leum and environmental remediation engineering promoted the
development of models that are generally aimed at simulation and
design of speciﬁc remediation systems. As hydrocarbon contami-
nants in subsurface involve different phases, remediation of NAPL
contamination often requires a combination of various techniques.
For example, free-phase NAPL should be recovered by oil extraction;
then the dissolved phase can be removed by pump-and-treat
techniques; the residual saturation left behind might be cleaned up
by soil vapor extraction or bioventing techniques. Since pump-and-
treat technology has been well developed for many years, we will
focus our review on a number of emerging technologies.
2.1. Surfactant-enhanced aquifer remediation (SEAR) models
Surfactants have been studied and evaluated for many years in
the petroleum industry for enhanced oil recovery (EOR) (Nelson
and Pope, 1978; Baran et al., 1998). Enhanced solubility and mobi-
lization are the two main mechanisms for the recovery of entrap-
ped organic residuals in surfactant-enhanced aquifer remediation
(Brown et al., 1994; Pennell et al., 1994; Delshad et al., 1996). In
general, any numerical model that simulates phase migration
subject to mass transfer between phases can be used to simulate
surfactant/cosolvent ﬂushing of soil and groundwater provided
that
appropriate
constitutive
relationships
are
incorporated.
Although many multiphase multicomponent models appear in the
published literatures, only some of these have been used to actually
simulate surfactant/cosolvent systems. Very few models are avail-
able for describing the main phenomena associated with surfac-
tant-enhanced remediation (Fortin et al., 1997). Wilson (1989) ﬁrst
developed a mathematical model for simulating the in situ
surfactant ﬂushing of hydrophobic organic compounds from soil
and aquifers. Wilson and Clarke (1991) considered adsorption of
the surfactant and solubilization of the contaminant in examining
surfactant remediation at the ﬁeld and laboratory-scale when
developing a two-dimensional area, single-phase ﬂow model.
Abriola et al. (1993) developed a one-dimensional numerical model
to simulate surfactant-enhanced solubilization of NAPL in porous
X.S. Qin et al. / Journal of Environmental Management 90 (2009) 54–76
55
media subject to non-equilibrium mass transfer between NAPL and
an aqueous surfactant solution. The calibrated model simulations
exhibited good agreement with measured efﬂuent concentrations,
supporting the utility of the conceptual modeling approach. Brown
et al. (1994) presented a three-dimensional ﬁnite difference model
capable of simulating the solute transport in a four-phase system
including a surfactant-induced microemulsion phase. The model
could incorporate a variety of equilibrium phase behavior rela-
tionships, allowing it to simulate alcohol ﬂooding, surfactant
ﬂooding, and combined surfactant/cosolvent ﬂooding applications.
Mason and Kueper (1996) presented a one-dimensional model
where the non-equilibrium mass transfer term accounted for high
non-wetting phase saturations found in NAPL pools. The developed
model was compared to laboratory column experiments involving
the solubilization of pooled PCE (perchloroethylene or tetra-
chloroethylene). Delshad et al. (1996) advanced a three-dimen-
sional multicomponent multiphase compositional ﬁnite difference
simulator for analyzing surfactant-enhanced remediation of aqui-
fers contaminated by NAPLs. A phase behavior model was devel-
oped for addressing a full range of the commonly observed micellar
and microemulsion behavior pertinent to SEAR. The other surfac-
tant related properties such as adsorption, interfacial tension,
capillary pressure, capillary number and microemulsion viscosity
were modeled based on the phase behavior model. Reitsma and
Kueper (1996) proposed a one-dimensional model to simulate the
use of alcohol ﬂooding to remove DNAPL from below the water
table. The results of this study demonstrated that slug deterioration
due to hydrodynamic dispersion could dramatically inﬂuence the
degree of mass removal achieved in an alcohol ﬂood. White and
Oostrom (1998) developed a numerical model to investigate the
main processes associated with surfactant-enhanced NAPL reme-
diation in porous media. The model coupled four non-linear mass
balance conservation equations that incorporated aqueous- and
NAPL-phase migration and transport of aqueous phase dissolved
surfactant and organics. The model was used to simulate experi-
ments described by Pennell et al. (1996) in which the NAPL
perchloroethylene was ﬂushed from sand columns using different
surfactant solutions.
More recently, Rathfelder et al. (2001) presented a numerical
model of surfactant-enhanced solubilization and applied it to the
simulation of NAPL recovery in two-dimensional heterogeneous
laboratory
sand
tank
systems.
Model
parameters
(including
viscosity, density,
solubilization
capacity,
surfactant sorption,
interfacial tension, permeability, capillary retention functions, and
interphase mass transfer correlations) were derived from inde-
pendent, small-scale, batch and column experiments. Model
predictive capability was assessed for the evaluation of the micellar
solubilization of tetrachloroethylene (PCE) in the two-dimensional
systems. Later, Rathfelder et al. (2003) investigated the migration
behavior of chlorinated solvents (i.e. PCE) under conditions of
surfactant-facilitated interfacial tension (IFT) reduction through
comparison
of
model
predictions
with
observations
from
controlled laboratory experiments. The PCE inﬁltration experi-
ments were modeled with the immiscible ﬂow simulator M-
VALOR, which was a two-dimensional three-phase ﬂow model that
employed an iterative implicit pressure, explicit saturation (IMPES)
block-centered ﬁnite difference algorithm (Abriola et al., 1992).
Other related studies can be found in Conrad et al. (2002) and Liu
(2005). Notable SEAR models published to date are summarized in
Table 1, where the evolutionary pathway and structural changes in
modeling technology are listed.
2.2. Modeling of enhanced biodegradation
In situ bioremediation has been one of the most promising
techniques for the remediation of petroleum-contaminated sites
(Waddill and Widdowson, 1998). However, design of in situ
bioremediation under speciﬁc on-site conditions may remain to be
a challenging issue, mainly due to difﬁculties in gaining insight into
the complex source and medium conditions in subsurface systems.
Modeling of in situ non-aqueous phase liquid (NAPL) biodegrada-
tion is potentially useful in assessing the transport and fate of
contaminants, designing the cleanup operations, and estimating
the durations of such restoration operations (Chen et al., 1992).
Over the past years, numerous biodegradation models were
proposed (Bedient and Rifai, 1992; de Blanc, 1998), with a majority
of efforts focused on mechanisms and kinetics in biodegradation of
petroleum-contaminants.
Molz et al. (1986) developed a one-dimensional model for
aerobic biodegradation of organic contaminants in groundwater
coupled with advective and dispersive transport. A microcolony
approach was used in the modeling effort, where the microcolonies
of bacteria were represented as disks of uniform radium and
thickness attached to aquifer sediments. Widdowson et al. (1988)
developed a model to simulate the biodegradation of generic
organic carbon in laboratory columns. This was one of the ﬁrst
models to incorporate multiple electron acceptors. The concen-
tration changes of two electron acceptors and one additional
nutrient were simulated. Srinivasan and Mercer (1988) advanced
a one-dimensional, ﬁnite difference model for simulating biodeg-
radation and sorption processes in saturated porous media. The
model formulation allowed for accommodating a variety of
boundary conditions and process theories. Aerobic biodegradation
was
modeled
using
a
modiﬁed
Monod
function;
anaerobic
biodegradation was modeled using Michaelis–Menten kinetics.
Celia et al. (1989) presented a numerical biodegradation model to
simulate cometabolism, aerobic and anaerobic metabolism with
multiple substrates. Semprini and McCarty (1991) developed a one-
dimensional and non-steady-state model with features similar to
those in Molz et al. (1986) and Borden and Bedient (1986). The
model considered processes of advection, dispersion, and sorption,
as well as Monod kinetics for electron donor and electron acceptor.
The model was veriﬁed with ﬁeld bioremediation results for
Table 1
Process simulation models for SEAR
Reference
MTa
Dimb
PHc
Compd
VTe
IMTf
Hg
ATh
MCi
Wilson, 1989; Wilson
and Clarke, 1991
S
2
a
N-a, S-a
F
L
HO
O-LM,
O-FM
ES
Abriola et al., 1993
S
1
a
N-a, S-a
L
R
HO
O-LM,
S-LM
ES
Brown et al., 1994
M
3
a, n,
m, s
N-m,
N-a
N/A
R
HG
O-LI,
S-LM
ES,
EM
Mason and Kueper,
1996
M
1
a, n, s
N-a
L
R
HO
N/A
ES
Delshad et al., 1996
M
3
a, n,
m, s
N-m,
N-a
N/A
R
HG
O-LI,
S-LM
ES,
EM
White and Oostrom,
1998
M
3
a, n,
g, s
N-a, S-a
L
R
HO
O-LI,
S-LM
ES,
EM
Rathfelder et al., 2001
M
2
a, n, s
N-a, S-a
L
R
HG
O-LI,
S-LM
ES
Rathfelder et al., 2003
M
2
a, n
N/A
L
N/A
HO
N/A
EM
a MT (model type): M ¼ multiphase numerical model, S ¼ single-phase numerical
model.
b Dim (dimensionality).
c PH (considered phases): a ¼ aqueous, n ¼ NAPL, m ¼ microemulsion, s ¼ solid,
g ¼ gas.
d Comp. (composition): N-l ¼ NAPL in phase l (l ¼ a, n, m, and s), S-l ¼ surfactant
in phase l.
e VT (veriﬁcation type): F ¼ ﬁeld-scale test, L ¼ laboratory column test.
f IMT (interface mass transfer): L ¼ local equilibrium model, F ¼ ﬁrst-order
kinetic model, R ¼ rate-limited exchange model.
g H (heterogeneity): HO ¼ homogenous, HG ¼ heterogeneous.
h AT (adsorption): O- ¼ organic sorption, S- ¼ surfactant sorption, LM ¼ Lang-
muir, FD ¼ Freundlich, LI ¼ Linear.
i MC (mechanisms): ES ¼ enhanced solubilization, EM ¼ enhanced mobilization.
X.S. Qin et al. / Journal of Environmental Management 90 (2009) 54–76
56
chlorinated aliphatic compound. Semprini and McCarty (1992)
developed a one-dimensional model to simulate the biodegrada-
tion
process
of
chlorinated
hydrocarbons
through
injecting
methane and oxygen into an aquifer at the Moffet Field Naval Air
Station, USA. The study results illustrated that inhibition kinetics
were necessary to be considered to accurately represent changes of
methane and contaminant concentrations.
Chang and Alvarez-Cohen (1995) developed a mechanistic
model to describe the kinetics of the cometabolic degradation of
chlorinated organics by oxygenase-expressing cultures over a range
of substrate conditions. Modiﬁcations of both Michaelis–Menten
and Monod kinetics were proposed to incorporate the effects of
product toxicity, reducing energy limitation, and competitive
inhibition together with cell growth and decay. Huesemann (1995)
presented a predictive model for estimating the extent of petro-
leum hydrocarbon biodegradation in contaminated soils through
a series of solid- and slurry-phase soil bioremediation experiments.
The predictive algorithm was based on the observed outputs of
average reduction of 86 individual hydrocarbon classes and their
respective initial concentrations. Norris et al. (1999) presented
a mathematic model for simulating bioremediation process with
oxygen-releasing solids; the model could generate the maximum
well spacing in an array of wells, which would help result in
effective remediation within a speciﬁed distance of a plume of
contaminated groundwater passing through the array. More
recently, Schoefs et al. (2004) developed a reduced-order model of
biodegradation in unsaturated soils that allowed the estimation of
contaminant depletion through using available on-line measure-
ments and decoupling the intrinsic biodegradation kinetics from
limiting factors imposed by ﬁeld conditions such as oxygen transfer
and contaminant bioavailability. The developed model could be
used to facilitate real-time monitoring of contaminant biodegra-
dation, using online carbon dioxide measurement.
The above mentioned studies devoted to enhancing our
understanding on the fundamental theory of microbial kinetics in
modeling biodegradation of petroleum-contaminants. The studied
problems were normally restricted to either completely-mixed or
one-dimensional experimental tests, with ﬂow and transport
processes being omitted. Over the past years, a number of studies
were undertaken to address in situ bioremediation processes
coupled with contaminant transport in two-dimensional or three-
dimensional domains.
Borden and Bedient (1986) developed the ﬁrst version of the
BIOPLUME model which could be used to simulate the simulta-
neous growth, decay, and transport of microorganisms combined
with the transport and removal of hydrocarbons and oxygen. Fol-
lowed by this study, Borden et al. (1986) used the model to simulate
biodegradation at the Conroe superfund site in Texas. Oxygen
exchange with the unsaturated zone was simulated as ﬁrst-order
decay in hydrocarbon concentration. The loss of hydrocarbon due
to horizontal mixing with oxygenated ground water and resulting
biodegradation was simulated by generating oxygen and hydro-
carbon distributions independently and then combining by super-
position.
Simulated
oxygen
and
hydrocarbon
concentrations
closely matched the observed values. Rifai et al. (1987) proposed
the second version of the program (BIOPLUME II) which allowed for
prediction of naturally occurring biodegradation as well as in situ
bioremediation
through
incorporating
instantaneous
reaction
model for the aerobic and ﬁrst-order kinetics for the anaerobic
biodegradation. MacQuarrie et al. (1990) developed a similar
approach to Borden et al. (1986) and Rifai et al. (1987) to develop
a biodegradation model. The advection-dispersion equation was
coupled with a dual-Monod relationship. The authors concluded
that in a two-dimensional shallow aquifer setting, an organic
plume experiences mass loss, spreading controlled by the avail-
ability of dissolved oxygen, and skewing in the direction of ground
water ﬂow. Later on, MacQuarrie and Sudicky (1990) used the
model developed by MacQuarrie et al. (1990) to examine plume
behavior in uniform and random ﬂow ﬁelds. The study results
demonstrated that, in uniform ground water ﬂow, a plume origi-
nating from a high-concentration source would experience more
spreading and slower normalized mass loss than a plume from
a lower initial concentration source as the dissolved oxygen was
more quickly depleted. Chen et al. (1992) presented a multiphase
model that accounted for four-phases: solid, water, NAPL and air. It
could
reﬂect
mass
exchanges
among
the
four-phases,
two
substrates, two electron acceptors, one additional limiting nutrient,
and two microbial populations. The model illustrated how multiple
electron acceptors, nutrients, and microbial populations could be
simulated. Although the model was not applied to a real case, it
could be modiﬁed to account for multiphase or three-dimensional
domain. This study formed a basis for many follow-up modeling
efforts. The model of Sarkar et al. (1994) was based on the
sophisticated platform of the multi-component, multiphase, three-
dimensional model UTCHEM. The model was used to simulate
bacteria and substrate efﬂuent concentrations, as well as the
changes in the permeability of the porous media in laboratory
columns. Similar to that of Chen et al. (1992), this model could serve
as a platform for incorporating other biodegradation processes.
Fabritz (1995) presented a numerical model capable of simulating
two-dimensional saturated steady-state ﬂow aquifers with advec-
tion and dispersion of multiple reactive solutes. It was a general-
purpose model that provided ﬂexibility in input parameters and
conﬁgurations. However, the model was limited to two-dimen-
sional simulation and needed to consider transient ﬂow and
multiple ﬂows to simulate the in situ biodegradation processes. The
BIOMOC presented by Essaid and Bekins (1997) was developed
through modiﬁcations of an existing transport model (MOC) which
was developed originally by Konikow and Bredehoeft (1978). BIO-
MOC was a two-dimensional, multi-species reactive solute trans-
port model with sequential aerobic and anaerobic degradation
processes. A number of mathematical expressions for biological
transformation rates from the literature were included as options in
the code. These included single, multiple and minimum Monod
kinetics, competitive, non-competitive, and substrate inhibition.
More recently, de Blanc (1998) added biodegradation capabil-
ities to a three-dimensional, multiphase, multi-component porous
media ﬂow model (UTCHEM). The biodegradation model described
biological transformations of organic contaminants originating
from NAPL sources, and could accommodate multiple substrates,
electron acceptors, and biological species. External mass transfer
resistances to biodegradation, inhibition, nutrient limitations,
enzyme competition, porosity reduction (caused by biological
growth), and permeability reduction (caused by biological growth)
could also be simulated. Ahn et al. (1999) performed experiments
in soil-columns to test the ability of a mathematical model which
was developed for naphthalene transport and biodegradation.
Model prediction for transport and degradation were based on
predetermined parameters that described naphthalene desorption
kinetics and the utilization of naphthalene by the test bacterium.
Gallo and Manzini (2001) developed a fully coupled numerical
model for two-phase ﬂow with NAPL transport and biodegradation
kinetics in porous media. The governing equations were grouped
into two subsystems, with one being used to describing phase
pressure and saturations, and the other one to reﬂecting contami-
nant concentration and bacterial population distribution. A set of
numerical experiments were used to demonstrate the effectiveness
of the method. Huang et al. (2006a) developed a modeling system
through integrating an enhanced in situ biodegradation (EISB)
process model with a three-dimensional multiphase multicompo-
nent (3DMM) ﬂow and transport model into a general framework.
A multi-dimensional pilot-scale physical model was used to verify
X.S. Qin et al. / Journal of Environmental Management 90 (2009) 54–76
57
the developed modeling system. Based on multiplicative Monod
kinetics, the biodegradation model was capable of simulating
substrate competition, nutrient limitation, product toxic inhibition,
and aerobic cometabolism.
Notable bioremediation models published to date are summa-
rized in Table 2, where the evolutionary pathway and structural
changes in modeling technology are listed. Generally, the main
approaches used for modeling biodegradation kinetics could be
grouped into: (1) ﬁrst-order decay model (Borden et al., 1986;
Semprini and McCarty, 1991); (2) Monod kinetic model (Srinivasan
and Mercer, 1988; Celia et al., 1989; Semprini and McCarty, 1991;
Essaid and Bekins,1997; Norris et al.,1999; Huang et al., 2006a); (3)
bioﬁlm model (Molz et al., 1986; Widdowson et al., 1988; Chen
et al., 1992; Sarkar et al, 1994; de Blanc, 1998); and (4) instanta-
neous reaction model (Rifai et al., 1987). Many of these biodegra-
dation
models
combined
physical,
chemical
and
biological
processes into a general framework, and could supply useful
information for the management of in situ bioremediation.
2.3. Modeling of free-product recovery
Processes of site remediation include free-product recovery,
cleanup of residual phase adsorbed on soil particles, and removal of
miscible/dissolved contaminants in groundwater. In the past years,
free-product
recovery
attracted
more
attentions
due
to
its
economic and temporal efﬁciencies (Campagnolo and Akgerman,
1995; Kirshner et al., 1996; Gerhard et al., 2001; Jennings and Patil,
2002; Mihopoulos et al., 2002). Much attention was paid to the
development and implementation of remediation technologies for
free-product cleanup (Nadim et al., 2000; USEPA, 2001). However,
few studies were reported in modeling the processes of free-
product recovery (Campagnolo and Akgerman, 1995; Mihopoulos
et al., 2000, 2001, 2002; Jennings and Patil, 2002).
Parker et al. (1994) developed a numerical model, named
ARMOS, to model free-product migration and recovery in an
unconﬁned aquifer. Based on the assumption of local vertical
equilibrium, the area ﬂow equations in ARMOS for water and
hydrocarbon were derived with reduced dimensionality and non-
linearity. The proposed model was capable of simulating free-phase
hydrocarbon under natural gradients as well as under conditions
involving hydrocarbon skimming with or without water pumping,
and could be used to evaluate environmental impacts of hydro-
carbon releases and to compare alternative remediation measures.
Waddill and Parker (1997a) presented an enhanced semi-analytical
algorithm for calculating recovery and trapping of free-phase oil.
The semi-analytical model used an analytical solution for steady-
state drawdown in an unconﬁned aquifer due to water pumping.
The inﬂuence of hysteresis on the oil–water capillary fringe was
incorporated into the calculation of oil trapping below a rising oil–
water interface. Field data from a pipeline leak were evaluated by
the semi-analytical model for hypothetical scenarios involving oil
recovery from three wells and a falling regional water table. Results
demonstrated that the proposed model captured many of the
trends of transient oil recovery. Later on, Waddill and Parker
(1997b) used ARMOS coupled with stochastic analysis to evaluate
the effects of subsurface heterogeneities on the recovery of LNAPLs.
Oil recovery in three heterogeneous cases was compared to an
‘‘equivalent’’
homogeneous
soil
with
effective
parameters
computed as the geometric means of the stochastic parameters.
The study results demonstrated that soil heterogeneities did not
greatly affect oil recovery or trapping in subsurface, and the
geometric mean soil properties provided a useful estimation of the
potential for oil recovery from oil spills. Charbeneau et al. (2000)
proposed two simple models for predicting LNAPL recovery rates
using wells and vacuum enhanced systems. The models incorpo-
rate vertical variations in LNAPL saturation and relative perme-
ability through use of effective LNAPL-layer values. Compared with
ARMOS, the presented model was simple to use and could
contribute to a preliminary design of effective free-product
recovery systems. However, the applicability of the model was
restricted by the assumption of vertical equilibrium and the inca-
pability in addressing multiple well interactions.
Among the existing remediation measures, coupled removal of
free-phase petroleum products with hydrocarbon vapor and/or
contaminated groundwater has aroused much interest. Dual-phase
(or two-phase) vacuum extraction (DPVE), similar to bioslurping,
was a cost-effective emerging technology to enhance remediation
efﬁciency by simultaneously recovering petroleum hydrocarbons in
multiple phases under enhanced-vacuum conditions (O’Melia and
Parson, 1996). The previous studies related to this remediation
technology
focused
on
its mechanism, implementation,
and
process control (Lamarre et al., 1997; Bailey and Schneider, 1998;
Zahiraleslamzadeh et al., 1999; Roth et al., 1999; Vaughn and
Turner, 2001). For example, USEPA (1997) reported the character-
istics, designs, performances, and costs of typical dual-phase
extraction systems, and provided a list of vendors which had
designed and installed full-scale systems. The related modeling
studies were very limited (Li et al., 2003a; Yen et al., 2003; Yen and
Chang, 2003). Li et al. (2003a) proposed a ﬁnite element multiphase
ﬂow model to simulate the dual-phase vacuum extraction reme-
diation process. This model was computationally efﬁcient based on
vertical integration of governing equations for water, oil, and gas
ﬂow. Yen et al. (2003) proposed a numerical ﬁnite element bio-
slurping simulation model to assess LNAPL recovery efﬁciency in
Table 2
Process simulation models for bioremediation
Reference
Dima VTb
Typec
Subd EAe AV-DISf Co-Ihg CAh
MKi
Borden et al., 1986;
Borden and
Bedient, 1986
2
F
AE
S
S
Yes
No
D
IR
Molz et al., 1986
1
N/A AE
S
S
Yes
No
D
BF
Rifai et al., 1987
2
F
AE, AN S
S
Yes
No
D
IR, FD
Srinivasan and
Mercer, 1988
1
L
AE, AN S
S
Yes
No
D
MO, FD
Widdowson et al.,
1988
2
L
AE
S
M
Yes
No
D
BF
Celia et al., 1989
1
L
AE, AN M
S
Yes
Yes
D
MO
MacQuarrie et al.,
1990
2
L
AE
S
S
Yes
No
D
MO
Semprini and
McCarty, 1992
1
F
AE
M
S
Yes
Yes
D, A FD
Chen et al., 1992
1
L
AE
M
M
Yes
Yes
D
BF
Sarkar et al., 1994
3
L
AE
M
S
Yes
Yes
D, A MO
Fabritz, 1995
2
N/A AE
M
S
Yes
Yes
D, A MO
Chang and Alvarez-
Cohen, 1995
0
L
AE
M
M
No
Yes
D
MO
Essaid and Bekins,
1997
2
F
AE, AN M
S
Yes
Yes
D
MO, FD
de Blanc, 1998
3
L
AE
M
M
Yes
Yes
D, A MO,
FD, IR
Norris et al., 1999
2
L
AE
M
S
Yes
No
D
MO
Ahn et al., 1999
1
L
AE
S
S
Yes
No
D, A MO
Gallo and Manzini,
2001
1
N/A AE
S
S
Yes
No
D
MO
Schoefs et al., 2004
0
L
AE
S
S
No
No
D, A MO
Huang et al., 2006a
3
L
AE
M
S
Yes
Yes
D, A MO
a Dim ¼ dimension.
b VT (veriﬁcation type): F ¼ ﬁeld-scale test, L ¼ laboratory column test.
c Type: AE ¼ aerobic biodegradation, AN ¼ anaerobic biodegradation.
d Sub (No. of substrates): M ¼ multiple, S ¼ single.
e EA (No. of electron acceptors): M ¼ multiple, S ¼ single.
f AV-DIS ¼ consideration of advection-dispersion process.
g Co-Ih ¼ effects of co-metabolism or substrate inhibition.
h CA (contaminant availability): D ¼ dissolved phase, S ¼ adsorbed phase.
i MK (microbial kinetics): BF ¼ bioﬁlm model, MO ¼ Monod kinetics, FD ¼ ﬁrst-
order decay model, IR ¼ instantaneous reaction model.
X.S. Qin et al. / Journal of Environmental Management 90 (2009) 54–76
58
a heterogeneous, anisotropic unconﬁned aquifer. The model could
simulate three-phase (water, oil, and gas) ﬂow and transport in
groundwater and gas phase ﬂow in the unsaturated zone. It
modeled the recovery and migration of LNAPLs with vacuum
enhanced recovery and multispecies dissolved (groundwater) and
gas phases (unsaturated zone) transport in heterogeneous, aniso-
tropic porous media. The bioslurping model implementation was
assessed using a case study from Southern Taiwan.
The above mentioned approaches for free-product recovery
were presented as either analytical equations or two-dimensional
numerical models. Selection of appropriate models depends on the
complexities of the site conditions or the requirement of under-
standing for system design. However, most of these modeling
approaches were based on the assumption of vertical equilibrium
which may not be applicable in a highly heterogeneous site
conditions (Charbeneau et al., 2000).
2.4. Modeling of soil vapor extraction and bioventing
Soil vapor extraction (SVE) and bioventing (BV) are air-induced
remediation technologies focus on cleaning up the volatile organic
contaminants from the subsurface using air injection and/or
vacuum extraction. SVE and BV are similar, in that they both
employ vadose zone wells and pumps to generate gas ﬂow through
the unsaturated zone, but differ fundamentally in the mechanism
of contaminant removal (Rathfelder et al., 2000). In SVE, air is
introduced into the vadose zone to increase the volatilization of
contaminant and a vacuum is created by extracting the air and
contaminant vapor using extraction wells (Regalbuto et al., 1988;
Johnson et al., 1994; Sawyer and Kamakoti, 1998). In BV, the activity
of the indigenous bacteria is enhanced by inducing air (or oxygen)
ﬂow into the unsaturated zone (using extraction or injection wells)
and, if necessary, by adding nutrients (Conner, 1988; Dupont, 1993;
Hinchee, 1994).
SVE and BV systems are becoming common practices for cleaning
up contaminated sites in recent years, due to their proven cost-
effectiveness and ﬂexible design/operation requirement (Pedersen
and Curtis, 1991; Rathfelder et al., 1995). However, the intrinsic
complexities associated with multiphase-ﬂow patterns, interphase
mass transfer and media conditions lead to difﬁculties in system
design,
operation
and
assessment.
Mathematical
models
are
important tools to help improve understanding of these processes,
and have been extensively investigated for many years (Baehr et al.,
1989; Sepehr and Samani, 1993; Lundegard and Andersen, 1996;
Jennings and Patil, 2002; Rahbeh and Mohtar, 2007).
A large number of physical and system variables will impact the
design, the operation, and the efﬁciency of SVE and SV systems
(Rathfelder et al., 1991). Generally, mechanisms affecting the fate of
volatile organic chemicals (VOCs) in soils include advection,
dispersion, mass transfer, sorption, and biodegradation. Chemical
parameters, such as vapor pressure, Henry’s Law constant, and
sorption afﬁnity govern the contaminants potential to partition into
the gas and solid phases. Field conditions such as soil type,
heterogeneity, porosity and moisture distribution impact the gas
ﬂow capacities. Over the past years, many numerical models were
developed to investigate the vapor ﬂow and contaminant transport
in porous media, with partial or complete considerations to the
above mentioned effects. Most of these modeling studies can be
roughly divided into three groups of studies, focusing on: (1) gas
ﬂow
behavior;
(2)
transport
model
with
local
equilibrium
assumptions in interphase mass transfer; and (3) non-equilibrium
mass transfer.
2.4.1. Models focusing on gas ﬂow behavior
Many earlier studies were conducted to develop methods that
may be used to help design gas extraction and gas control systems
for SVE and BV systems (Massmann, 1989; McWhorter, 1990; Baehr
and Hult, 1991; Mohr and Merz, 1995). Models that could predict
the distribution of subsurface gas pressures and velocities were
developed to help select design variables such as well spacing, well
conﬁguration, and blower or pump speciﬁcations.
Krishnayya et al. (1988) presented a two-dimensional ﬁnite
element model for predictions of ﬂow and pressure ﬁelds resulting
from soil vapor extraction operations. Predicted pressure ﬁelds
were compared against laboratory measurements showing close
agreement. Massmann (1989) developed an expression for vapor
ﬂux that was similar in a form to the transient groundwater ﬂow
equation. The method was applied to a ﬁeld extraction test in which
gas pressures in an observation probe were measured as a function
of time. McWhorter (1990) developed an analytical model that
included the non-linear effects of compressible ﬂow, assuming
a transient ﬂow in a one-dimensional, radially symmetric ﬂow ﬁeld
in the vicinity of a vertical well. The method was used to estimate
air conductivity for a set of data from a hypothetical extraction test.
Baehr and Hult (1991) developed a two-dimensional, steady-state
analytical model for predicting the distribution of air pressure in
the unsaturated zone resulting from a pneumatic test. Welty et al.
(1991) proposed three-dimensional air ﬂow model, later referred to
as AIR3D, adapted from the USGS ground water ﬂow code MOD-
FLOW. The model was capable of simulating three-dimensional air
ﬂows through a heterogeneous, anisotropic unsaturated zone
where air ﬂow was induced through dry wells or trenches, as in
vapor extraction remediation. Shan et al. (1992) advanced an
analytical method for estimating air conductivity through homo-
geneous and anisotropic media. The top boundary of the ﬂow ﬁeld
was assumed to be a constant pressure boundary which may not be
applicable for some ﬁeld tests. Horizontal and vertical permeabil-
ities were estimated by using type curves for typical well conﬁg-
urations. Sepehr and Samani (1993) developed a three-dimensional
gas ﬂow model named GAS3D for simulating the dynamics of gas
ﬂow in soil venting systems. The model considered the effects of
partial penetration and partial screening of vapor extraction wells
as well as the heterogeneity and anisotropy of the soil. The simu-
lated pressure around a vapor extraction well was veriﬁed and
validated by comparing the results of the ﬁnite difference solution
to the actual ﬁeld measurements and the results of an analytical
solution under homogeneous and isotropic conditions. Massmann
and Madden (1994) proposed two analytical procedures (i.e.
a modiﬁed Theis analysis and a modiﬁed Hantush analysis) to
estimate air conductivity and porosity based on ﬁeld tests using air
extraction and injection in both horizontal and vertical wells. Mohr
and Merz (1995) proposed a two-dimensional air ﬂow model to
evaluate pilot test data and estimate remediation rates for SVE
systems. The model predictions of soil vacuum versus distance
were statistically compared to pilot test data for 65 SVE wells at 44
sites. The radius of inﬂuence and in situ bioremediation rates could
be estimated using the proposed air ﬂow model. Sawyer and
Kamakoti (1998) coupled an air ﬂow simulation model (AIR3D) to
a mixed-integer programming model to determine the optimum
number of wells, their locations and pumping rates for soil vapor
extraction. The proposed SVE management model could be useful
in the design process in cases of short remediation times when the
installation costs of wells could be signiﬁcant.
2.4.2. Models with local equilibrium mass transfer
More sophisticated models coupling contaminant transport
models in multiple phases have been developed assuming local
equilibrium between liquid and vapor phases. Baehr and Cor-
apcioglu (1987) used a one-dimensional multiphase composi-
tional model to evaluate, hypothetically, the transport of organic
contaminant from the unsaturated zone into ground water. Wil-
son et al. (1987) developed a two-dimensional model tracking
X.S. Qin et al. / Journal of Environmental Management 90 (2009) 54–76
59
a two-component vapor phase, where the source of the contam-
inant vapors was a ﬂoating product pool. Predictions compared
favorably with experimental results. A major conclusion was vapor
extraction processes should be modeled as a multi-component
mixture for both vapor and liquid phases. Stephanatos (1988)
developed a two-dimensional ﬁnite difference model for transport
of a single volatile species in a two-phase air–water system. The
model was applied to a ﬁeld contamination site. Independent
predictions of venting removal rates for toluene and xylene show
generally good agreement with measured rates, although tailing
effects were not captured. As an extension to the work of Baehr
and Corapcioglu (1987), Baehr et al. (1989) developed a one-
dimensional multicomponent compositional transport model to
predict radially symmetric air ﬂow induced by venting from
a single well. It was the ﬁrst attempt to conduct a formal analysis
of the transport process involved with BV (or in situ air stripping).
Johnson et al. (1990) developed a simple predictive ‘‘screening
model’’ based on local chemical equilibriums for estimating the
performance of a BV system. Analytical expressions were provided
for describing various behaviors of a venting operation, such as
time required for steady-state vapor ﬂow, height of water table
upwelling, temperature effects on vapor ﬂow, venting-induced
vapor and liquid compositional changes, and venting rates in
heterogeneous soils with free-product boundaries.
2.4.3. Models with non-equilibrium mass transfer
Later on, many researchers have indicated that the assumption
of local equilibrium may not adequately describe contaminant
movement under conditions found in vapor extraction applications.
As a result, non-equilibrium interphase mass transfer was proposed
and widely used in many SVE/BV modeling studies. Sleep and Sykes
(1989) developed a numerical model simulating non-equilibrium
volatilization and dissolution from an immobile NAPL. The model
includes three-phases: water, air, and NAPL, with only gas phase
being mobile. Both saturated and unsaturated conditions can be
simulated. Gierke et al. (1990) developed a numerical model for
accounting mechanisms of affecting the fate of non-degradable
VOCs in soils in laboratory columns of unsaturated soil. Non-equi-
librium associated with air–water mass transfer was reﬂected
through adjusting Henry’s constant. Later, Gierke et al. (1992)
derived a mathematical model for examining the impact of gas
advection, gas diffusion, gas–water mass transfer, gas–water par-
titioning, sorption, and intra-aggregate diffusion on subsurface
movement of organic vapors. The model was used to simulate
experimental data, focusing on non-equilibrium gas transfer
between the intra-aggregate and the soil air phases. The study
demonstrated that vapor extraction performance in moist, aggre-
gated soils would be affected by non-equilibrium transport.
Brusseau (1991) developed a one-dimensional model with
enhanced capabilities in addressing effects of soil heterogeneity
and rate-limited sorption. This model considered an immobile
water phase, a mobile air phase and a solid phase, and was able to
simulate both equilibrium-based and rate-limited sorption. The
performance of the model was evaluated by comparing predicted
simulations to data obtained from column tests by Brown and
Rolston (1980) and Gierke et al. (1992). Later, Brusseau et al. (1992)
developed a numerical advective-dispersive solute transport model
that explicitly accounted for multiple sources of non-equilibrium
and transformation reactions during steady-state ﬂow in porous
media.
A
multiprocess
non-equilibrium
with
transformation
(MPNET) model was formulated for cases where non-equilibrium
was described as ﬁrst-order processes. Rathfelder et al. (1991)
developed a two-dimensional numerical ﬂow and transport model
for investigating ﬁeld-scale SVE operations. Mass storage in four-
phases (i.e., water, solid, NAPL and vapor) was accounted for.
Illustrative examples demonstrated the inﬂuence of chemical, ﬁeld,
and system parameters in vapor extraction operations. However,
the study only assumed the gas phase as mobile, which restricted
the model’s applicability to unsaturated zone conditions with low
moisture contents. Armstrong et al. (1994) developed a numerical
model for simulating the
rate-limited
extraction
of
volatile
compounds
governed
by
ﬁrst-order
kinetic
mass
transfer
processes. The study showed that for a given condition, increasing
the ﬂow rate has little effect beyond a certain point. It was also
shown that pulsed pumping was generally less efﬁcient than
continuous pumping at a low rate.
The above mentioned transport models were based on the
assumption of single-phase gas ﬂow, where the biodegradation
effects were mostly neglected. This limited their applicability to
ﬁeld problems. A number of improvements were made by
a number of researchers. For example, McClure and Sleep (1996)
developed a three-dimensional ﬁnite difference model for simu-
lation of bioventing. The model incorporated three-phase (i.e. air,
water, and organic) ﬂow with equilibrium interphase mass transfer
and dispersive transport of organic compounds, oxygen, and carbon
dioxide. Biodegradation limitations due to both substrate and
oxygen availability were modeled using the dual Monod formula-
tion. Poulsen et al. (1996) developed a two-dimensional numerical
model for simulating migration of a volatile contaminant portioned
between NAPL, air, water and solid phases in unsaturated zone.
Non-equilibrium mass-transfer equations were used for NAPL-air
and mobile-immobile water phases. Results demonstrated that
rate-limited mass transfer only altered the cleaning up rate, but did
not change the cumulative amounts of contaminant removal. The
study also indicated that heterogeneity in soil air conductivity
would have a signiﬁcant inﬂuence on both the time required for
cleanup and on the migration pattern of the contaminants. Rath-
felder et al. (2000) advanced a comprehensive two-dimensional
model, named Michigan soil vapor extraction remediation (MISER)
model, to investigate the performance of ﬁeld-scale SVE and BV
systems through integrating processes of multiphase ﬂow, multi-
component compositional transport with non-equilibrium inter-
phase mass transfer, and aerobic biodegradation.
Recent works have been devoted to the reﬁnement and vali-
dation of predictive interphase mass transfer models applicable
under a variety of conditions (Hoeg et al., 2004), the evaluation of
the feasibility of applying passive SVE modes (Jennings and Patil,
2002), the consideration of soil fracturing in low permeability soil
conditions and NAPL spreading under non-equilibrium conditions
for SVE (Schulenberg and Reeves, 2002; Kneafsey and Hunt, 2004),
and the continued quantiﬁcation of microbial processes under
unsaturated ﬁeld conditions.
2.5. Modeling of air sparging
Air sparging (AS) is the process of injecting air directly into
groundwater to promote volatilization of both NAPL and dissolved
volatile
organic
contaminants,
and
enhance
biodegradation
(Downey and Elliott, 1990; Ahlfeld et al., 1994; Benner et al., 2002).
In many cases, AS can be combined with SVE to extract the
contaminated vapors from the unsaturated zone (Marley et al.,
1992; Johnson et al., 1993). The earliest AS applications appeared in
European countries in mid-1980s, where removal of chlorinated
solvents was enhanced by injecting air into the saturated zone
(Bohler et al., 1990). More recently, AS has been used in North
America for the cleanup of saturated zones contaminated with
NAPL contaminants (Ahlfeld et al., 1994). Johnson et al. (1993)
provided an important review on a number of issues related to
technical details, basic physical and biological process, design
criteria, and monitoring requirement of AS systems. More recently,
efforts have been made to develop effective mathematical tools for
gaining better understandings of AS processes in order to facilitate
X.S. Qin et al. / Journal of Environmental Management 90 (2009) 54–76
60
system design and operation. Since early 1990s, a number of
modeling studies were published in this area, which formed
fundamental theoretical frameworks for AS systems (Lundegard
and Andersen, 1996; McCray, 2000). The developed AS models
could mainly be categorized into two groups: (1) conceptual
reactor models; and (2) multiphase ﬂow and transport models
(Rabideau and Blayden, 1998; McCray, 2000).
2.5.1. Conceptual reactor models
Reactor models (or compartment models) were based on the
assumption that the aqueous, air or other phases stay in individual
compartment, and mass transfer (either in equilibrium or non-
equilibrium) occurred between two different compartments. Each
compartment was considered as a completely-mixed (or non-
dimensional) reactor, with lumped-parameters being used for
modeling (Wilson,1992). Sellers and Schreiber (1992) developed an
analytical reactor model for simulating air ﬂow patterns generated
during AS processes. It was assumed that the air traveled in discrete
bubbles, and mass transfer between water and air phases was rate-
limited. The model output was an exponential expression of
contaminant aqueous concentration versus time. However, this
bubble-ﬂow model was only suitable for large-pore media as
mentioned by Hein et al. (1997). Wilson (1992) and Wilson et al.
(1992) presented a multi-compartment model for simulating the
removal of dissolved VOCs from aquifers by AS techniques. The
water phase was divided into two well mixed compartments (i.e.
sparged-water and stagnant compartments) with mass exchanges
by advection. The effects of air ﬂow rate, groundwater ﬂow rate,
aquifer thickness, number of theoretical transfer units (related to
axial dispersion), Henry’s constant and initial VOC concentration
were investigated. A local equilibrium mass transfer equation
described by Henry’s Law was used in the study. A number of
follow-up works for enhancing the multi-compartment model was
presented in the next few years. For example, Roberts and Wilson
(1993) used the compartment model to describe the removal of
dispersed DNAPL dropets from contaminated aquifers by AS; Wil-
son et al. (1994b) incorporated the kinetics of NAPL solution and
contaminant diffusion from low-permeability porous layers to their
previous models; Go
´mez-Lahoz et al. (1994) investigated the
diffusion-induced concentration rebound after shutdown of an AS
system; Wilson et al. (1994a) considered effects of air channeling to
their models based on the assumption that the sparging air moved
through persistent channels in the aquifer and VOC transport to the
sparging air was caused by diffusion/dispersion; more recent
enhancement to Wilson’s models was achieved by considering the
biodegradation effects (Wilson and Norris, 1997) and random
features of air channels (Wilson et al., 1998).
Chao et al. (1998) conducted a series of air sparging column
experiments to investigate the non-equilibrium air–water mass
transfer of VOCs in the saturated porous media. A lumped param-
eter model based on a ‘‘two-zone’’ concept was developed to esti-
mate the air–water mass transfer coefﬁcients. The obtained results
were suitable for mass transfer in the immediate vicinity of
a sparging well and might not be applicable in ﬁeld-scale AS
systems. Rabideau and Blayden (1998) developed an analytical
reactor model to predict the removal of VOCs from ground water by
AS. Removal of dissolved contaminants was realized through
volatilization, advection, and ﬁrst-order decay. The air sparging
zone was considered as a reactor consisting of two completely-
mixed compartments that exchange mass with each other. Only
one of the two compartments exchanged mass with the vapor
phase introduced by sparging. Non-equilibrium mass transfer
between aqueous and vapor phases was calculated based on the
equation
proposed
by
Gvirtzman
and
Gorelick
(1992).
The
proposed model was appealing in its simplicity in predicting
tailing/rebound effects, but weak in describing detailed multiphase
behaviors involved with AS processes. Later, Rabideau et al. (1999)
provided a two-compartment air-channel model to analyze the
performance of an AS system designed for source zone remediation
at a case study site. The study was restricted to simple contami-
nation scenario where free NAPL was not present and TCE was not
expected to undergo biotransformation. Similar to the model
proposed by Chao et al. (1998), Braida and Ong (2000) developed
a one-dimensional radial diffusion model with a ﬁrst-order air–
water mass transfer boundary condition to predict VOC volatiliza-
tion from air sparging of contaminated soil columns. The air
channels in the soil column was represented by a composite of
evenly spaced cylindrical air channels surrounded with non-
advective water saturated region. The model predicted fairly well
the change in the VOC concentrations in the exhaust air, the ﬁnal
average aqueous VOC concentration, and the total mass removed.
2.5.2. Multiphase ﬂow models
The reactor models treated the remediation zone as various
mixed compartment. This assumption may not be appropriate in
real-world aquifer systems, where complex soil heterogeneity,
anisotropy and capillary effects exist. Multiphase models were
proved to be valuable alternatives in analyzing the physiochemical
processes associated with AS, and have aroused much attention.
Ahlfeld et al. (1994) reviewed the basic physics of air ﬂow through
saturated porous media for AS systems, and discussed a number of
implications for the practical application of AS. A conceptual model
of the detailed behavior of an air sparging system was constructed
using elements of multiphase ﬂow theory and the results of recent
experimental work. He concluded that the previous understanding
of a radius of inﬂuence about a sparge point was ambiguous and
must be carefully applied. Unger et al. (1995) used a numerical
model (CompFlow) to study the mechanisms controlling vacuum
extraction, coupled with air sparging, as a means for remediation of
heterogeneous formations contaminated with DNAPLs. They found
that the effects on sparging performance caused by heterogeneity
were small in comparison to the effects of changing design
conﬁgurations and operating conditions. van Dijke and van der Zee
(1995) presented a two-phase numerical ﬂow model to simulate AS
processes in a homogeneous axially symmetric porous media. The
model was based on the mixed form of the Richards equation for
both the air and water phases. The dependence of capillary pres-
sure and relative permeability on saturations was modeled based
on the equations proposed by Parker et al. (1987). Lundegard and
Anderson (1996) used a multiphase, multicomponent ﬁnite differ-
ence simulator known as TETRAD to simulate AS systems. Detailed
description of TETRAD was given by Vinsome and Shook (1993).
The impact of geological and engineering parameters on the ﬂow of
air injected below the water table in an unconﬁned sandy aquifer
was investigated. They concluded that the multiphase simulation
was a powerful means of investigating the sensitivity of AS systems
to many geological and engineering factors such as injection pres-
sure, rate, and depth, as well as soil heterogeneity and anisotropy.
However, their simulation only addressed the general response of
simple aquifer and well conﬁguration (a single air injection well).
They also focused their study on air ﬂow and did not consider issues
of VOC removal.
McCray and Falta (1997) used a ﬁnite difference, multiphase-
ﬂow, contaminant transport code (named T2VOC) to simulate
removal of NAPLs from subsurface by AS. The T2VOC, developed by
Falta et al. (1995), assumed that the multiphase system consisted of
three mass components (i.e. water, air and organic chemical), and
three mobile phases (i.e. gas, aqueous and NAPL). The code well
modeled the two-dimensional air sparging experiments from Ji
et al. (1993) which included both homogeneous and heterogeneous
permeability distributions. Field-scale simulations of air sparging
for hypothetical DNAPL and LNAPL spills in various hydrogeologic
X.S. Qin et al. / Journal of Environmental Management 90 (2009) 54–76
61
settings were also performed. The results indicated that multiphase
numerical models were helpful in the analysis of AS systems. Hein
et al. (1997) conducted both laboratory tests and model simulations
to gain knowledge of the air ﬂow and water behavior around air
injection wells in a cylindrical reactor. The simulations were per-
formed by using T2VOC for an isothermal, axisymmetric, homo-
geneous, isotropic system under two-phase (air and water) ﬂow
conditions. The modeling results of air ﬂuxes at various distances
from the sparge well compared satisfactorily with experimental
measurement. It was also pointed out that the practical use of
a multiphase ﬂow model would require collection of input data
with a considerable amount of effort and cost.
More recently, Benner et al. (2000) used a commercial simulator,
BIOVENTING, to investigate the effects of volatilization and
microbial degradation on total product hydrocarbon (TPH) removal
in a drum storage site using in situ AS operations. The BIOVENTING
was a multiphase, multicomponent numerical simulator that was
capable of modeling in situ air injection or extraction system
designs for the removal of organic contaminants from soil and
groundwater with considerations of chemical partitioning and
biodegradation, as well as soil heterogeneity and anisotropy. The
study results indicated that the microbial degradation was the most
signiﬁcant contributor to the removal of contaminant mass. As
a follow-up study, Benner et al. (2002) performed numerical
simulations using BIOVENTING based on the ﬁeld data from ﬁve AS
sites to analyze effects of a number of soil, contaminant, and AS
system design factors on contaminant removal time and overall
remediation cost. Mei et al. (2002) advanced a numerical model for
simulating the axisymmetric ﬂow in steady air venting/sparging
processes, assuming that the subsurface ﬂow was a two-phase
problem, where only air was moving and water remained
stationary. The study investigated the effect of air compressibility
on modeling outputs which was normally ignored by the previous
studies, and revealed that the presence of air channels and bubbles
may strongly affect the transport of chemicals. Rahbeh and Mohtar
(2007) developed a numerical model for the design and operation
of AS and SVE systems under heterogeneous soil conditions. The
model consisted of a steady-state air ﬂow module (to determine the
capillary pressure head distribution), and a multiphase (including
aqueous,
gaseous,
and
solid
phases)
contaminant
transport
module. Results of two case studies showed that biodegradation
played a major role in the remediation of the contaminated sites.
As implied by many previous studies, AS is intrinsically
a multiphase ﬂow problem, involving the simultaneous movement
of air, water, and possibly liquid hydrocarbons (Lundegard and
Andersen, 1996). Thus, the multiphase ﬂow and transport models
for AS process simulation were more sophisticated than the
conceptual reactor models in describing system dynamics of
multiphase ﬂow, contaminant removal, soil heterogeneity, and
microbial degradation of organic contaminants. However, applica-
bility of these models was limited by the vast data requirement in
ﬁeld-scale applications (Rabideau and Blayden, 1998).
Generally, many process simulation models have been devel-
oped and used to help examine impacts of operating variables on
system performances. The construction of a model that is able to
simulate the contaminant-transport and remediation processes
involves different tasks from a variety of disciplines. These tasks
normally include the derivation of a conceptual framework,
development of a mathematical model, adoption of a proper solu-
tion scheme, and comparison with experimental measurements.
Over the past decades, the range of these models has increased in
variety, complexity, and sophistication. From the large number of
physical processes mentioned, it is evident that the mathematical
modeling of remediation processes provides quantitative descrip-
tions of contaminant behaviors under various operating conditions,
which are normally impossible to be completely tested through
experiments due to restrictions of labor and materials. The simu-
lation model for a speciﬁc remediation process, with properly
calibrated parameters, can help predict system responses under
any given operating conditions. This makes it possible in identi-
fying cost-effective remediation designs.
As with the process simulation models, parameter estimation is
of great importance to the model validations. Although a large
number of models have been presented in literature, very few of
them have been convincingly validated with sufﬁcient laboratory
data. In addition, site remediation efforts are normally complicated
with multiple factors such as free-product recovery, vapor phase
extraction, residual-phase removal, and groundwater-pollution
control. Even though there were a number of efforts on modeling of
individual system components, these studies could not effectively
reﬂect interactions among various subsurface processes. Thus,
integrated studies are desired for supporting more effective deci-
sions of petroleum waste management.
3. Technology advancement pathway for process
optimization
Efﬁcient design of aquifer remediation projects has been a chal-
lenge to practitioners ever since the beginning of the environmental
remediation industry. The clean-up of impacted sites is normally
costly and time-consuming (Huang et al., 2006b). It is thus impor-
tant to develop tools that can help identify the most cost-efﬁcient
way of implementing remediation plans. Simulation models, when
used alone, cannot guarantee that the design strategy is optimal.
They can handle ‘‘what if’’ questions, but are weak in seeking
answers to ‘‘which one is the best’’ (Huang, 1998; Huang and Xia,
2001).
Simulation-optimization
methods
have
proved
to
be
powerful tools to reduce remediation costs and facilitate effective
design/operation for remediation purposes, and have become an
area of extensive investigations over the past decades (Johnson
and Rogers, 1995; McKinney and Lin, 1994, 1995, 1996; Mansﬁeld
et al., 1998; Minsker and Shoemaker, 1998a; Smalley et al., 2000;
Zheng and Wang, 2002; Huang et al., 2006a). The developed
optimization methods can generally be categorized into ﬁve groups:
(1) classical linear/non-linear programming method; (2) mixed-
integer programming method; (3) multi-objective programming;
(4) dynamic programming method; (5) inexact programming; and
(6) artiﬁcial intelligence (AI) based programming. The detailed
review on each of these groups is provided as follows.
3.1. Classical linear/non-linear programming
Since
mid-1970s,
linear
programming
has
been
used
in
a number of groundwater quantity/quality management based on
simpliﬁed transport models or management options (Molz and
Bell, 1977; Willis, 1979; Gorelick and Remson, 1982; Colurallo et al.,
1984; Atwood and Gorelick, 1985; Ahlfeld and Heidari, 1994). Many
of these studies focused on hydraulic head gradient control for
groundwater reclamation through application of a series of
extraction/injection wells to create a hydraulic head ﬁeld that
controlled plume migration. The corresponding management
model aimed to minimize the cost of pumping while maintaining
inwardly directed hydraulic head gradients along the contaminant
plume boundary. Gorelick (1983) provided an extensive literature
review on early studies of this topic. A relatively recent study was
conducted by Ahlfeld and Heidari (1994), where a linear optimi-
zation model coupled with simulation was developed to investigate
the optimal hydraulic control problem for groundwater systems.
The proposed model used a simpliﬁed way of capturing the key
elements of the problem and could provide guidance and insight to
site managers. However, since simpliﬁcations were required to
X.S. Qin et al. / Journal of Environmental Management 90 (2009) 54–76
62
remove system non-linearities, the general applicability of linear
programming techniques is limited (Culver and Shoemaker, 1992).
Non-linear programming models were proposed by a number of
researchers, where constraints of contaminant concentrations were
considered. Gorelick et al. (1984) were the ﬁrst to couple ground-
water simulation methodology with non-linear optimization.
Representation of the full advection-dispersion equation in an
optimization formulation was proposed for a groundwater reme-
diation problem. The cost of pumping was minimized while
requiring that concentrations, as modeled using two-dimensional,
ﬁnite element, transient, advection-dispersion transport code
SUTRA (Voss, 1984), be held below speciﬁed values at particular
points. The non-linear programming code MINOS (Saunders, 1977)
employed SUTRA as a subroutine for function and derivative eval-
uations.
This
combined
non-linear
simulation-optimization
method was demonstrated on both steady-state and transient
management problems at down-gradient nodes while minimizing
total pumping. Wagner and Gorelick (1987) and Ahlfeld et al. (1988)
used non-linear programming to minimize the pumping rates in
a remediation problem (either pumping for contaminant removal
or recharge for in-ground dilution). This objective was assumed to
represent the minimization of the total operating costs, as well as
the implicit minimization of the capacity of the treatment plant.
Ahlfeld (1990) used a non-linear model to include the transport
equation as a constraint (concentration is constrained to certain
values at certain future points in time).
A number of efforts were made to include capital costs of well
installation in steady-state remediation non-linear models. This
entailed representing the discontinuous ﬁxed-cost function by
a continuous function containing the ﬁxed cost multiplied by
a penalty. The penalty coefﬁcient was either zero or one, depending
on whether the well was to be built or not. For examples, McKinney
and Lin (1992) used a polynomial penalty coefﬁcient; Karatzas and
Pinder (1996) used an outer approximation method to solve
a concave non-linear remediation model where they considered
a continuous cost function with an exponential penalty coefﬁcient;
McKinney and Lin (1995) compared the polynomial penalty coef-
ﬁcient method and the exponential penalty coefﬁcient method.
Later on, McKinney and Lin (1996) proposed a penalty coefﬁcient
method as a means of incorporating the capital costs into a non-
linear model, under homogeneous and heterogeneous soil condi-
tions. The consideration of heterogeneity produced different well
locations and pumping rates. Even if these methods could accom-
modate capital costs, their use to deﬁne time-varying policies
would require signiﬁcant extra computational effort. Karatzas and
Pinder (1993) presented an outer approximation (OA) method for
reaching global minimization of concave objective functions over
a closed convex set of constraints in groundwater management
problems. The ﬁxed charges were incorporated into the objective
function in an exponential form and the problem was solved as
a concave minimization problem. To demonstrate the potential of
OA, two applications of groundwater management problems were
presented, where the results were compared with an existing
solution obtained using a different optimization approach. As an
extension to their previous work, Karatzas et al. (1996) extended
the work of Karatzas and Pinder (1996) to a multi-period approach
for the design of groundwater remediation systems using the OA
method. Application of this extended OA to example problems
showed the cost-effectiveness of a multi-period design compared
to a single period design, but it also revealed an associated increase
in computational effort for a multi-period design due to the
increase in problem dimensionality.
The above-mentioned studies used classical linear/non-linear
algorithms
for
dealing
with
groundwater
quantity/quality
management problems. Recently, many studies have been con-
ducted to explore enhanced or new non-linear algorithms. For
example, Papadopoulou et al. (2003) and Papadopoulou et al.
(2007) extended the work of Karatzas et al. (1996) through devel-
opment of a more sophisticated and computationally efﬁcient OA
method for handling the case of non-convex constraints of
groundwater quality management models. More related studies
can be found in Wang and Ahlfeld (1994), Bear and Sun (1998),
Spiliotopoulos et al. (2004), Matott et al. (2006), Finsterle (2006).
3.2. Mixed-integer and multi-objective programming
A number of studies formulated the aquifer management
models as mixed-integer programs involving both continuous and
integer variables.
Willis
(1976) used
a mixed-integer linear
programming model (MILP) to determine the optimal treatment
processes associated with an injection well ﬁeld for the treatment of
municipal wastewater and the disposal of the efﬂuent by under-
ground injection. Sawyer and Ahlfeld (1992) developed a mixed-
integer programming model that considered a simpliﬁed version of
the cost function of extraction and/or injection strategy, including
the linear costs of well installation and operation in a plume
containment system. Morgan et al. (1993) developed a mixed-
integer-chance-constrained programming (MICCP) method to ﬁnd
the globally optimal trade-off curve for maximum reliability versus
a minimum pumping objective in a pump-and-treat scheme. The
uncertainty of the physical aspects (hydraulic conductivity) was
incorporated through the coefﬁcients of
the constraints for
a steady-state case. Sawyer et al. (1995) developed a mixed-integer
optimization model that included operating and installation costs
to design and operate a set of extraction and injection wells. A
sensitivity analysis indicated that the ratio of two cost components
had an important impact on the numerical solution. Misirli and
Yazicigil (1997) compared the performance of quadratic program-
ming,
linear
programming
and
mixed-integer
programming
deterministic models built to identify the best management alter-
natives for the containment of a groundwater pollution plume.
Results indicated that considerable savings could be accomplished
through mixed-integer programming models including all types of
costs.
Ritzel and Eheart, 1994 used genetic algorithm to solve a multi-
objective
groundwater
pollution
containment
problem.
Two
variations of a multiple objective GA were formulated: a vector-
evaluated GA (VEGA) and a Pareto GA. For the zero-ﬁxed cost
situation, the Pareto GA was shown to be superior to the VEGA and
was shown to produce a trade-off curve similar to that obtained via
another
optimization
technique,
mixed-integer
chance-con-
strained programming (MICCP). Meyer et al. (1994) described
a multi-objective model, taking uncertainty into account, to design
a monitoring network aiming to minimize the number of moni-
toring wells, to maximize the probability of detecting a contami-
nating leak and to minimize the expected area of contamination
once detected. The model was solved by a SA algorithm that per-
formed well for the large number of realizations considered in the
Monte Carlo simulation of groundwater ﬂow and contaminant
transport. Erickson et al. (2002) used a niched Pareto genetic
algorithm (NPGA) for optimizing a pump-and-treat system. In the
case study, the problem is to simultaneously minimize the remedial
design cost and contaminant mass remaining at the end of the
remediation horizon. The results showed that NPGA was effective
in dealing with this class of groundwater optimization problems.
McPhee and Yeh (2006) developed an experimental-design-based
methodology for groundwater management. In their method, the
multiobjective programming problem for parameter estimation
was formulated and solved using a combination of genetic algo-
rithm
and
gradient-based
optimization
techniques.
Gaussian
quadrature and Bayesian decision theory were also combined for
selecting the best design under parameter uncertainty. Results
X.S. Qin et al. / Journal of Environmental Management 90 (2009) 54–76
63
showed that the uncertainty analysis was able to identify complex
interactions among the model parameters that may affect the
performance of the experimental designs as well as the attain-
ability of management objectives.
3.3. Dynamic programming
To describe the sequential time nature of environmental prob-
lems, dynamic programming (DP) models were proposed. A DP
model deﬁnes optimal policies over a set of management periods,
in consideration of operating costs of pumping and treatment. It is
effective for dealing with problems with sequential or multi-stage
decision makings. The overall optimization problems are normally
divided into many smaller optimization problems, leading to
reduced computational cost (Andric
ˇevic
´ and Kitanidis, 1990). The
DP method allows the decision variables such as pumping rates at
a certain stage be identiﬁed by the state of the system at that time
horizon rather than be determined at the initial stage. Such
a philosophy is ideally suited for help design feedback controls of
site remediation.
A number of researchers have addressed systems with linear
dynamics. Murray and Yakowitz (1979) presented a constrained
differential dynamic programming (DDP) algorithm and applied it
to the management of a multi-reservoir system. Makinde-Odusola
and Marino (1989) used a feedback method of dynamic program-
ming to determine optimal hydraulic management policies for
a conﬁned aquifer. Andric
ˇevic
´ (1990) applied DDP, combined with
an extended Kalman ﬁlter, to the hydraulic management of
a conﬁned aquifer with consideration of parameter uncertainties.
Lee and Kitanidis (1991) advanced an adaptive method (dual-
control method with small perturbation approximation) based on
DDP algorithm for seeking optimal pump-and-treat strategies,
when aquifer information was limited. The effectiveness of the
method was demonstrated using Monte Carlo simulations for
a two-dimensional conﬁned aquifer with multiple wells along with
constraints on state and decision variables.
A number of studies solved a non-linear groundwater optimal
control problem through a two-stage iterative algorithm, referred
to as successive approximation linear quadratic regulator (SALQR)
(Culver and Shoemaker,1992). The simulation model ran in the ﬁrst
stage to obtain the aquifer response (described as the system state)
from a speciﬁc pumping strategy (described as the control vector).
In the second stage, determine the derivatives of the objective
function and transition equations based on linearized simulation
dynamics, and obtain an improved pumping strategy. Repeat these
two stages until the pumping strategy converge into an optimal
value. Jones et al. (1987) was the ﬁrst to use SALQR algorithm for
computing optimal pumping policies for managing water supply
from an unconﬁned aquifer. The study focused on hydraulic
management, without consideration of groundwater-quality issues
and system uncertainty.
Andric
ˇevic
´ and Kitanidis (1990) presented an approximate dual-
control method with SALQR algorithm for solving groundwater
remediation problems. The method used a stochastic method to
account for and reduce parameter uncertainties derived from
spatial variability of hydrogeological parameters (e.g. transmissivty,
dispersivity and storativity). The methodology was applied to
a hypothetical one-dimensional bounded aquifer system with
a single extraction well. Chang et al. (1992) formally discussed the
SALQR algorithm, and illustrated its capacity of solving ground-
water quality control problems with time-varying pumping policy
and water quality constraints. The study used a hyperbolic penalty
function method for addressing a large number of constraints
associated with the optimal control problems. The study concluded
that time-varying policies were more cost-effective than time-
invariant policies. Culver and Shoemaker (1992) developed a ﬁnite
element groundwater ﬂow and transport model with SALQR algo-
rithm to determine optimal time-varying groundwater pump-and-
treat reclamation policies. Management periods were groups of
simulation time steps during which the pumping policy remains
constant. The study results indicated that the SALQR algorithm with
management periods could signiﬁcantly reduce the computational
requirements for non-steady optimization of groundwater recla-
mation and other management applications. Culver and Shoemaker
(1997) described a model for determining dynamic policies using
a control theory algorithm, quasi-Newton differential dynamic
programming (QNDDP), in conjunction with a ﬁnite element
groundwater simulation model. The study results indicated that for
short time-varying policies (about 6 months or less), the consid-
eration of treatment plant capital costs has a tremendous impact on
the optimal policies selected; for large management periods the
consideration of such costs only had a slight effect. Huang and
Mayer (1997) provided a dynamic formulation of the optimization
management model where pumping rates and well locations are
simultaneously treated as decision variables. The results revealed
that dynamic pumping rates and well locations can produce a more
cost-effective groundwater remediation policy than the previous
static ones. Mansﬁeld et al. (1998) proposed a method for
exploiting the sparsity of a ﬁnite element model within an opti-
mization model using the SALQR approach, in an attempt to reduce
the
computational
effort.
Mansﬁeld
and
Shoemaker
(1999)
extended their earlier work on derivative-based optimization for
cost-effective remediation to unconﬁned aquifers, which had more
complex, non-linear ﬂow dynamics than conﬁned aquifers. Exact
derivative equations were presented, and two computationally
efﬁcient approximations, the quasi-conﬁned (QC) and head inde-
pendent from previous (HIP) unconﬁned-aquifer ﬁnite element
equation derivative approximations, were presented and demon-
strated to be highly accurate.
However, these models only examine remediation systems with
pump-and-treatment
technology.
A
number
of
optimization
studies were also devoted to improving designs of in situ biore-
mediation. Minsker and Shoemaker (1996) made the ﬁrst attempt
in this area. In their study, a SALQR-based optimization model to
bioremediation was developed, where highly non-linear equations
related to biomass growth was incorporated. As a follow-up study,
Minsker and Shoemaker (1998a) discussed computational issues
that encountered in their previous studies, and proposed a number
of strategies for improving performance of the model. The
addressed issues included increased computational complexity,
difﬁculties in convergence of the model, and convergence to local
minima. The strategies developed were useful for any application of
optimization
techniques
to
non-linear
processes
in
water
resources. Minsker and Shoemaker (1998b) presented a coupled
optimal control and simulation model to select injection and
extraction well sites and pumping rates for cost-effective in situ
bioremediation design. It was found that time-varying pumping
strategies for in situ bioremediation were signiﬁcantly less expen-
sive than time-invariant pumping strategies, despite the typically
shorter duration of in situ bioremediation. Yoon and Shoemaker
(1999) compared three major classes of algorithms (evolutionary
algorithms, direct search methods and derivative-based optimiza-
tion methods) that could be used to identify the most cost-effective
policy for bioremediation problems, and indicated that the SALQR
was the fastest algorithm. Liu and Minsker (2002) presented
a multiscale derivative method for solving a set of successive
approximation linear quadratic regulator model. The method is
applied to in situ bioremediation design, in terms of which
a substantial reduction in computing time compared was revealed.
To solve the dynamic optimal groundwater management problems,
Hsiao and Chang (2002) proposed a procedure that integrates
a
genetic
algorithm
with
constrained
differential
dynamic
X.S. Qin et al. / Journal of Environmental Management 90 (2009) 54–76
64
programming (CDDP) methods. The case study showed that the
CDPP model would affect the ﬁxed costs associated with installing
wells. Liu and Minsker (2004) developed a full multiscale approach
to partial differential equation (PDE) constrained optimization, and
used it to solve a SALQR model for optimal control of in situ
bioremediation. Application of the method to a bioremediation
case study with about 6500 state variables converges in about
8.8 days, compared to nearly 1 year using the previous model.
3.4. Inexact programming
Since many operating parameters are associated with uncer-
tainties, a number of inexact optimization techniques were also
developed for addressing such complexities (Wagner and Gorelick,
1987; Andric
ˇevic
´ and Kitanidis, 1990; Morgan et al., 1993; Tiede-
man and Gorelick, 1993; Chan, 1994; Gorelick, 1997; Guan and Aral,
1999; Chan Hilton and Culver, 2005; Yan and Minsker, 2006).
Wagner
and
Gorelick
(1987)
investigated
the
groundwater
management issues when the modeling parameters were not
adequately known. In their study, parameters were estimated by
using a non-linear multiple regression and the risk-qualiﬁed solu-
tions were gained through the chance-constrained optimization
technique. Wagner and Gorelick (1989) developed a non-linear
optimization model for minimizing pumping while meeting water
quality standards. The stochastic inverse problem approach was
adopted to quantify and characterize the statistical uncertainty
associated with parameters of heterogeneous systems. The log-
hydraulic conductivity was modeled as a realization of random ﬁeld
with exponential covariance function. These studies did not
consider the ﬁxed capital costs associated with well installation. In
fact, the drilling costs could be much more important than costs
related to pump size (Cunha, 2002).
Andric
ˇevic
´ and Kitanidis, 1990 presented a stochastic optimi-
zation method to account for and reduce the parameter uncertainty
in aquifer remediation systems. The method can elucidate the dual-
control aspect of the stochastic control and optimize the objective
function by improving the accuracy of estimation of system
parameters (e.g., dispersivity, transmissivity). Wagner et al. (1992)
developed a non-linear model including uncertainty in hydraulic
conductivity and in the objective function (they minimize the
expected total costs of operating wells), to contain a plume of
contamination; this was solved using stochastic programming with
recourse. This method involved a two (or more) stage decision
process. The problem unfolded after the stochastic elements of
decision were realized. Tiedeman and Gorelick (1993) formulated
a problem using stochastic non-linear programming approach for
design of a groundwater remediation system in an aquifer located
in southwest Michigan. It is found that the ability of probabilistic
constraints to accommodate local variations in model prediction
uncertainty in highly important. The post-optimization results also
showed that an increased reliability level for hydraulic containment
would not signiﬁcantly reduce the plume cleanup time. Provencher
and Burt (1994) presented two methods for approximating the
optimal groundwater pumping schemes for several interrelated
aquifers in a stochastic setting. The ﬁrst method used a dynamic
programming (DP) algorithm where values of the function were
estimated by Monte Carlo simulation combined with a curve-ﬁtting
technique. The second used a Taylor series approximation to the
functional equation of the DP. A simple groundwater management
problem was applied for illustrating the methods, and the results
indicated that the two methods yielded nearly identical estimates
of the optimal pumping schemes. Sawyer and Lin (1998) extended
the model proposed by Sawyer and Ahlfeld (1992) to incorporate
uncertainty both in groundwater simulation models and in
the cost coefﬁcients used in the objective function. Chance-
constrained programming was applied, and the model thus became
a non-linear mixed-integer model. Aly and Peralta (1999b) pre-
sented a methodology to address the stochastic nature of hydraulic
conductivity in the design of
pump-and-treat systems. The
methodology incorporated neural-network-based surrogates to
model the response surface instead of the stochastic simulator, and
then used a genetic algorithm to ﬁnd the global optimal solutions of
the non-linear formulation. The method was applied to a real
example for illustration, with the results showing that a trade-off
between reliability and treatment facility size could be produced.
More recently, Thurston and Srinivasan (2003) presented
a chanced optimization framework for green engineering decision
making issues; it could deal with the unavoidable tradeoffs that
arise from all the ‘‘pollution prevention pays’’ opportunities. Guan
and Aral (2004) proposed a fuzzy mathematical programming
approach to solve groundwater remediation management prob-
lems. Based on the parameter uncertainty, two optimization
models were formulated, where the pumping rates were repre-
sented as either deterministic or fuzzy decision variables. The two
models were then converted to computationally efﬁcient algo-
rithms through the application of fuzzy set theory. To illustrate the
performances of the two models, several numerical examples were
provided; the optimal solutions acquired from the proposed
method showed improvements in the effectiveness of the reme-
diation system, and could support identiﬁcation of desired pump-
ing rates. Moreover, the results were compared with those obtained
from probabilistic analysis to demonstrate the ﬂexibility and reli-
ability of the identiﬁed remediation strategies through the fuzzy
method. Chan Hilton and Culver (2005) used a robust genetic
algorithm approach for help determine the best remediation
design, with uncertainties associated with hydraulic conductivity
being considered. The approach was applied to two pump-and-
treat remediation cases for contaminated aquifers of varying
heterogeneity.
3.5. AI-based programming
Recently, there has been a growing interest in using artiﬁcial
intelligence to solve groundwater remediation problems. This is
mainly because of the following three reasons. The ﬁrst is the
challenge existing in the solution process that usually requires
incorporating
complex
simulation
models
into
optimization
frameworks. This leads to the difﬁculty in seeking the globally
optimal
solutions
with
conventional
non-linear
optimization
algorithms. In response to this concern, a set of AI-based heuristic
approaches have been more and more used for solving complex,
discontinuous groundwater remediation problems. The second is
the difﬁculty in obtaining the derivatives with respect to decision
variables. These AI-based heuristic programs normally begins with
only an approximate method of solving a problem within the
context of some goal, and then uses feedback from the effects of the
solution to improve its own performance (Reeves, 1990). In solving
optimization problems, heuristic methods do not require deriva-
tives as are needed in non-linear programming. Thus, over the past
years, a large number of applications of heuristic approaches in
groundwater engineering were reported (Cunha, 2002). The last is
the requirement for alleviating the computation burden resulting
from the repeatedly calling the simulation models. To address this
problem, some researchers proposed to create approximation
surrogates to replace the initial simulation models. Currently,
artiﬁcial neural network (ANN) and regression analysis methods
have been proved to be promising in approximating the complex
simulation models.
3.5.1. Genetic algorithm
Genetic algorithm (GA) is a stochastic searching method that
mimics natural evolution and is based on the concept of ‘‘survival of
X.S. Qin et al. / Journal of Environmental Management 90 (2009) 54–76
65
the ﬁttest’’ (Goldberg, 1989; Muhlenbein,1997; Reeves,1990). GA is
implemented as a computer simulation in which a population of
abstract representations (called chromosomes) of candidate solu-
tions (called individuals, creatures, or phenotypes) to an optimi-
zation problem evolves toward better solutions (Goldberg, 1989).
The evolution usually starts from a population of randomly
generated individuals and happens in generations. In each gener-
ation, the ﬁtness of every individual in the population is evaluated,
multiple individuals are stochastically selected from the current
population (based on their ﬁtness), and modiﬁed (recombined and
possibly randomly mutated) to form a new population. The new
population is then used in the next iteration of the algorithm.
Commonly, the algorithm terminates when either a maximum
number of generations has been produced, or a satisfactory ﬁtness
level has been reached for the population. If the algorithm has
terminated due to a maximum number of generations, a satisfac-
tory solution may or may not have been reached.
McKinney and Lin (1994) incorporated GA to simulation models
to solve groundwater management problems. The results indicated
that GA could effectively help obtain globally (or, at least near
globally) optimal solutions. The formulation of a GA-based opti-
mization model was found straightforward and could provide
solutions which were as good as or better than those obtained by
linear or non-linear programming methods. Huang and Mayer
(1997) proposed a new optimization formulation for dynamic
groundwater remediation management, with well locations and
pumping rates being used as decision variables, and applied it
a hypothetical, three-dimensional, contaminated aquifer systems.
The results indicated that the optimal well locations and pumping
rates obtained with the moving-well model were less expensive
than solutions obtained with a comparable ﬁxed-well model. Wang
and Zheng (1997) developed a simulation-optimization model for
the design of pump-and-treat remediation systems under a variety
of ﬁeld conditions. The model integrated GA with MODFLOW and
MT3D in to a general framework. The superiority of the proposed
method was demonstrated through comparison with the trial-and-
error method. Later, Wang and Zheng (1998) coupled GA and SA
with
MODFLOW
for optimal
management of
ground water
resources under general conditions. The strengths and limitations
of the GA and SA based models are evaluated by comparing the
results with those obtained using linear programming, non-linear
programming,
and
differential
dynamic
programming.
Three
example problems were examined in the study. The GA and SA
based models yielded nearly identical or better solutions than the
various programming methods. SA tended to outperform GA in
terms of the number of forward simulations needed, but it used
more
empirical
control
parameters
which
were
difﬁcult
to
determine.
More recently, Guan and Aral (1999) proposed a new compu-
tational procedure, referred to as progressive genetic algorithm
(PGA), to solve the optimal design problem of groundwater
management. PGA is a sub-domain method, which combines
standard genetic algorithm with ground water simulation models
in an iterative solution process and provides a powerful tool for the
solution of highly non-linear optimization problems. Yoon and
Shoemaker (1999) compared computational performance of eight
optimization algorithms used to identify the most cost-effective
policy for in situ bioremediation of contaminated ground water.
Three major classes of algorithms were considered in the compar-
ison: evolutionary algorithms, direct search methods, and deriva-
tive-based optimization methods. Based on the three problems
considered, the SALQR was found to be the fastest algorithm. Chan
Hilton and Culver (2000) compared two methods (i.e. the additive
penalty and the multiplicative penalty methods) for constraint
handling within the genetic algorithm framework for optimal
groundwater remediation design. These results demonstrated that
the multiplicative penalty method was a robust method, capable of
ﬁnding feasible and optimal or near-optimal solutions while using
a range of weights. Smalley et al. (2000) presented a groundwater
management model that could simultaneously predict risk and
propose cost-effective options for reducing risks to acceptable
levels under uncertainty. The model combined a noisy GA with
a numerical fate and transport model and an exposure and risk
assessment model. Results from an application to a site from the
literature showed that the noisy GA was capable of identifying
highly reliable designs from a small number of samples, showing
a signiﬁcant advantage for computationally intensive groundwater
management models.
More recently, Zheng and Wang (2002) conducted an optimi-
zation demonstration project of pump-and-treat system for the
containment and cleanup of a large trichloroethylene (TCE) plume
through simulation-optimization (S/O) approach. The optimization
techniques used in the study were based on evolutionary algo-
rithms coupled with a response function approach for achieving
greater computational efﬁciency. Bayer and Finkel (2004) used
simple genetic algorithms (SGAs) with binary encodings and
derandomized
evolution
strategies
(DESs)
with
real-valued
encodings to adapt well capture zones for the hydraulic optimiza-
tion of pump-and-treat systems. The results indicated that the DES
was a more robust optimization method for the selected advective
control problem than the SGA one. Yan and Minsker (2006)
advanced an adaptive neural network genetic algorithm for the
design of a groundwater remediation system with an improved
computational efﬁciency; the results showed that the proposed
method was effective in reducing computational efforts in opti-
mizing large-scale groundwater remediation systems.
3.5.2. Simulated annealing
Simulated annealing (SA) is inspired in the physical annealing
process. The randomized nature of the procedure permits asymp-
totic convergence to optimal solutions under mild conditions
(Reeves, 1990). The improvement of the current solution is ach-
ieved by generating small displacements in an iterative fashion. The
value of the objective function for each solution is calculated and
then evaluated. If the displacements implied an improvement, the
solution is automatically accepted. Otherwise it is accepted
according to a given probability (Metropolis criteria). Dougherty
and Marryott (1991) used SA to ﬁnd an optimal remediation design,
considering constant capital costs for well installation and linear
costs for the operation. The approach was illustrated by example
applications to idealized problems of groundwater ﬂow and
selection of remediation strategy, including optimization with
multiple groundwater control technologies. The results demon-
strated the ﬂexibility of the method and its potential for solving
groundwater management problems. Later, Marryott et al. (1993)
applied the SA method to analyze alternate design strategies for
groundwater remediation at a contaminated ﬁeld site. A number of
applications indicated that the computational requirement of
simulated annealing was comparable to other non-linear optimi-
zation techniques. Kuo et al. (1992) proposed three non-linear
optimization formulations to address problems of pump placement
and pumping-rate selection in designing contaminated ground-
water remediation systems using SA algorithms. It was found that
SA algorithm required less computation times than conventional
non-linear optimization techniques. Rizzo and Dougherty (1996)
developed and applied a multi-period SA approach to a large-scale
groundwater contamination management problem. Incorporating
real costs and penalized constraints that were imposed at an
arbitrary set of times and spatial areas into the objective function,
they compared the multi-period and single period solutions, giving
special attention to the tradeoffs between the competing goals of
minimizing both costs and cleanup violations.
X.S. Qin et al. / Journal of Environmental Management 90 (2009) 54–76
66
The above studies have demonstrated that the GA and SA are
advantageous over conventional optimization techniques in their
easiness and ﬂexibility in solving complex and discontinuous
optimization problems. Many other related studies can also be
found in Erickson et al. (2002), Chan Hilton and Culver (2001), and
Babbar and Minsker (2006). In recent years, development of
efﬁcient searching algorithms is still an area of intensive investi-
gations. For example, tabu search, inspired in the human memory
process, was used by Zheng and Wang (1999) to solve ground-
water
remediation
design
problems.
Lee
and
Ellis
(1996)
compared eight heuristic algorithms used to deﬁne the optimal
design of a groundwater monitoring network. Although, heuristic
approaches are highly ﬂexible and straightforward for handling
groundwater quality
management
problems, they are often
computationally
intensive and
require hundreds, sometimes
thousands, of evaluations of the objective function to reach
termination criteria (Johnson and Rogers, 2000). This computa-
tional hurdle is primarily derived from the time-consuming ﬂow
and transport modeling process, which needs to be run many
times in heuristic search.
3.5.3. Artiﬁcial neural networks
Rogers and Dowla (1994) proposed an ANN-based groundwater
management model for optimizing aquifer remediation. The ﬂow
and transport model generated a set of sample data upon which the
network could be trained. The study results indicated that the ANN-
based management solutions were consistent with those resulting
from
a
more
conventional
optimization
technique,
which
combined solute transport modeling and non-linear programming
with a quasi-Newton search. ANN is a mathematical tool (or
computational tool) for modeling complex non-linear relationships
(Peterson and Soderberg, 1997). It consists of an interconnected
group of artiﬁcial neurons and processes information using a con-
nectionist approach to computation. In most cases, it is an adaptive
system that changes its structure based on external or internal
information that ﬂows through the network during the learning
phase (Peterson and Soderberg, 1997). Artiﬁcial neural networks
(ANNs) impose fewer constraints on the functional form of the
relationships between input and output variables, making them
a viable choice for application when the complexity of the system is
difﬁcult to anticipate (Johnson and Rogers, 2000). Johnson and
Rogers (2000) examined the impact of using ANN and linear
approximators (LA) on the quality and quantity of solutions
obtained
from
simulated-annealing-driven
searches
on
two
different groundwater remediation problems. The study demon-
strated that the ANN approximations matched the quality of the
solutions provided by a full numerical ﬂow and transport model.
Yan and Minsker (2006) demonstrated that the conventional S/R/O
methods
could
lead
to
suboptimal
solutions,
and proposed
a dynamic modeling approach, named adaptive neural network
genetic algorithm (ANGA), for improvement. Application of a ﬁeld-
scale case demonstrated the advantages of ANGA in reducing the
computational efforts in large-scale water resources management
problems.
3.5.4. Regression-approach-based methods
The effectiveness of neural network models for facilitating
simulation-optimization studies has been demonstrated by many
studies. A potential problem associated with ANN is that the
training process is normally time-consuming, especially when the
number of training data is large. Development of alternative
techniques with better efﬁciencies is a potential area that
deserves further investigations (Qin et al., 2007a, 2008a). There-
fore, regression approaches have been widely used for improving
computational efﬁciency of the optimization problems. Alley
(1986) adopted various regression equations, based on 20 runs of
a two-dimensional contaminant transport model to describe
relationships between pumping and recharge rates of decision
wells to the concentration of contaminants at control locations.
Lefkoff and Gorelick (1990) used multi-linear regression method
to predict changes in groundwater salinity as a function of
hydrologic conditions and water use decisions. The regression
model was built based on 600 runs of a transport model. Ejaz and
Peralta (1995) used regression equations based on 729 runs of an
advective-dispersive model to predict downstream concentra-
tions of several constituents from the upstream ﬂow rate and
constituent
concentration.
Cooper
et
al.
(1998)
developed
a simulation/regression/optimization (S/R/O) model to predict,
analyze and optimize the LNAPL recovery process. A number of
power-form non-linear regression equations were provided to
describe relationships between system responses and time-
varying water-pumping rates. A limitation of the study was the
negligence of system cost in the optimization model. Aly and
Peralta (1999a) proposed a S/R/O approach for dealing with
single- and multiple-planning period problems in ground water
remediation. The response-surface method was used to represent
relationships between system responses (i.e. response variable,
CMAX) and pump-and-treat schemes. The results demonstrated
that
the
response
functions
could
dramatically
reduce
the
computational efforts compared to all embedded approaches.
Zheng and Wang (2002) used the response function approach,
embedded within a simulation-optimization framework, to mimic
the simulation model in an approximate manner. They indicated
that the regression method required a minimal amount of
computational time. Huang et al. (2003) proposed an integrated
simulation-optimization
approach
for
supporting
real-time
dynamic modeling and process control of surfactant-enhanced
remediation
at
petroleum-contaminated
sites.
Subsurface
modeling was combined with a dual-response surface method to
develop a system for generating optimum operation conditions
under various site conditions, through the support of a non-linear
optimization model. Yen and Chang, 2003(a,b) proposed a S/R/O
method for providing cost-effective solutions for bioslurping
operations. However, the study only used a linear regression
method which was weak in approximating highly non-linear
systems. Huang et al., 2006b proposed a statistical forecasting
system for supporting remediation design and process control
based on techniques of NAPL-biodegradation simulation and
stepwise-cluster analysis (SCA). A unique contribution of this
research was the development of a multivariate inference system
associated with simulation and optimization efforts for tackling the
complexities in situ bioremediation practices. Qin et al. (2007b)
presented a simulation-based process optimization system through
integrating a multidimensional simulator, a multivariate statistical
tool and an optimization model within a general framework for
supporting decisions of surfactant-enhanced aquifer remediation
(SEAR). Application of the proposed method to a hypothetical PCE
spill-and-remediation case demonstrated the effectiveness of the
proposed method. He et al. (2008a) presented an integrated
simulation-optimization method for the design of a pump-and-
treat system. To speed up the optimization process, stepwise
quadratic surface analysis was used to reproduce the relationships
between pumping rates and contaminant concentrations. A total of
32 proxy models reﬂecting such relationships were then incorpo-
rated into the optimization problem, which could be solved with
improved computational efﬁciency. Qin et al. (2008b) proposed an
integrated simulation-optimization system for supporting deci-
sions of the dual-phase vacuum extraction (DPVE) processes. The
system coupled a DPVE process simulator, a multivariate regression
tool and a non-linear optimization model into a general framework.
The forecasting system was then embedded into a multiobjective
optimization framework, where the objectives were to minimize
X.S. Qin et al. / Journal of Environmental Management 90 (2009) 54–76
67
the operation cost and maximize the remediation efﬁciency. He
et
al.
(2008b)
developed
a
non-linear
chance-constrained
programming (NCCP) model for optimizing SEAR processes, where
the SCA was used to create a set of proxy simulators for quantifying
the relationships between operating conditions (i.e., pumping rate)
and probabilities of benzene levels in violation of standard.
Typical optimization models published to date are summarized
in Table 3, where the type of remediation, system objective, deci-
sion variables, time variability, and main optimization algorithms
are listed. From literature review, it is found that implementing
a remediation program is usually costly. Thus it is important to
develop techniques that can help identify the most cost-effective
way of implementation. Undoubtedly, process optimization models
have proved to be powerful and useful methods to solve design and
operation problems for remediation processes. Simulation models,
when used alone, can hardly ensure that the design strategy
obtained is optimal. Development of simulation-based process
optimization models allow us seek answers to ‘‘what if’’ questions,
and thus facilitate the site managers in ﬁnding least-cost strategies
for remediation (Cunha, 2002).
Table 3
Process optimization models
Optimization method
Reference
RAa
OFb
DVc
Cased
TMe
MAf
Classic linear or non-linear programming
Lefkoff and Gorelick, 1986
P&T
OP
PR
H
TV
LP
Ahlfeld and Heidari, 1994
P&T
OP
PR
F
TV
LP
Gorelick et al., 1984
P&T
OP
PR
F
TI
GRG
Ahlfeld et al., 1988
P&T
OP
PR
F
TI
GRG
Karatzas and Pinder, 1993
P&T
CA, OP
PR
H
TI
OA
Karatzas and Pinder, 1996
P&T
CA, OP
PR
H
TI
OA
Karatzas et al., 1996
P&T
CA, OP
PR
H
TV
OA
McKinney and Lin, 1996
P&T
CA, OP
PR
H
TI
GRG
Papadopoulou et al., 2003
P&T
CA, OP
PR
H, F
TI
OA
Papadopoulou et al., 2007
P&T
CA, OP
PR
H, F
TV
OA
Mixed-integer programming
Sawyer and Ahlfeld, 1992
P&T
CA, OP
PR, WN
H
TI
MIP
McKinney and Lin, 1995
P&T
CA, OP
PR, WN
H
TI
MIP
Sawyer et al., 1995
P&T
CA, OP
PR, WN
H
TI
MIP
Misirli and Yazicigil, 1997
P&T
CA, OP
PR, WN
H
TV
MIP
Dynamic programming
Andric
ˇevic
´ and Kitanidis, 1990
P&T
OP
PR
H
TV
SALQR
Lee and Kitanidis, 1991
P&T
CA, OP
PR
H
TV
DDP
Culver and Shoemaker, 1992
P&T
OP
PR, WL, WN
H
TV
SALQR
Chang et al., 1992
P&T
OP
PR
H
TV
DDP
Culver and Shoemaker, 1997
P&T
CA, OP
PR
H
TV
DDP
Minsker and Shoemaker, 1996
BR
OP
PR
H
TV
SALQR
Mansﬁeld et al., 1998
P&T
OP
PR
H
TV
SALQR
Minsker and Shoemaker, 1998a,b
BR
OP
PR
H
TV
SALQR
Mansﬁeld and Shoemaker, 1999
P&T
OP
PR
H
TV
SALQR
Liu and Minsker, 2002
BR
OP
PR
F
TV
SALQR
Hsiao and Chang, 2002
P&T
CA, OP
PR, WL, WN
H
TV
DDP
Liu and Minsker, 2004
BR
OP
PR
F
TV
SALQR
Inexact programming
Wagner and Gorelick, 1987
P&T
OP
PR
H
TI
CCP
Morgan et al., 1993
P&T
CA, OP
PR
H
TI
MICCP
Meyer et al., 1994
MND
CA, EN
WL, WN
F
TV
MCS
Sawyer and Lin, 1998
P&T
CA, OP
PR, WN
H
TI
MICCP
Guan and Aral, 2004
P&T
OP
PR
H
TI
FP
Chan Hilton and Culver, 2005
P&T
CA, OP
PR, WL
H
TI
NGA
AI-based programming
McKinney and Lin, 1994
P&T
CA, OP
PR
H
TI
GA
Ritzel and Eheart, 1994
P&T
CA, OP
PR, WL, WN
H
TI
GA
Rogers and Dowla, 1994
P&T
OP
PR
H
TI
ANN
Rizzo and Dougherty, 1996
P&T
CA, OP
WL
L
TV
SA
Huang and Mayer, 1997
P&T
CA, OP
PR, WL
H
TV
GA
Johnson and Rogers, 2000
P&T
CA, OP
PR, WN
H
TI
ANN
Erickson et al., 2002
P&T
CA, OP, EN
PR
H
TI
GA
Cooper et al., 1998
FPR
EN
PR
H
TV
PNR
Zheng and Wang, 2002
P&T
EN
PR
F
TV
RSR
Huang et al., 2003
SEAR
OP, EN
PR
F
TI
RSR
Yen and Chang, 2003a,b
BS
OP
PR
F
TI
LR
Yan and Minsker, 2006
P&T
CA, OP
PR, WL
H, F
TV
ANGA
Huang et al., 2006b
BR
OP, EN
PR
L
TI
SCA
He et al., 2008a
P&T
EN
PR
F
TI
RSR
He et al., 2008b
SEAR
EN
PR
L
TI
SCA
Qin et al., 2008b
DPVE
OP, EN
PR
F
TI
SCA
a RA (remediation actions): P&T ¼ pump-and-treat, MND ¼ monitoring network design, BR ¼ bioremediation, SEAR ¼ surfactant-enhanced aquifer remediation, FPR ¼ free-
product recovery, BS ¼ bioslurping, DPVE ¼ dual-phase vacuum extraction.
b OF (objective functions): OP ¼ operating cost, CA ¼ capital cost, EN ¼ environmental performance.
c DV (decision variables): PR ¼ pumping (or injection) rates, WL ¼ well locations, WN ¼ well numbers.
d Case: F ¼ ﬁeld-scale problem, L ¼ laboratory-scale problem, H ¼ hypothetical case.
e TM (time mode): TV ¼ time-varying strategy, TI ¼ Time-invariant strategy.
f MA (main algorithm): LP ¼ linear programming, GRG ¼ generalized reduced gradient algorithm, OA ¼ outer approximation, SALQR ¼ successive approximation linear
quadratic regulator, DDP ¼ differential dynamic programming, MIP ¼ mixed-integer programming, CCP ¼ chance-constraint programming, MICCP ¼ mixed-integer chance-
constraint programming, NGA ¼ noisy genetic algorithm, FP ¼ fuzzy programming, MCS ¼ Monte Carlo simulation, FP ¼ fuzzy programming, ANN ¼ artiﬁcial neural network,
GA ¼ genetic algorithm, SA ¼ simulated annealing, ANGA ¼ adaptive neural network genetic algorithm, PNR ¼ power-form non-linear regression, RSR ¼ response-surface
regression, LR ¼ linear regression, SCA ¼ stepwise cluster analysis.
X.S. Qin et al. / Journal of Environmental Management 90 (2009) 54–76
68
Generally, seeking optimal solutions to groundwater remedia-
tion problems can be challenging since the process often requires
coupling an optimization algorithm with complex simulation
models to evaluate potential solutions (Yan and Minsker, 2006).
While a variety of optimization methods have been developed for
supporting
decisions
of
soil
and
groundwater
remediation
management, application of these optimization techniques to real-
world systems is still a difﬁcult task (Culver and Shoemaker, 1992).
As site remediation efforts are complicated with multiple factors,
purposes, and criterion, there is a lack of efﬁcient numerical
methods
for
optimization
of
groundwater/soil
remediation
systems. Successful application of these methods can thus be
extended for supporting more effective decisions of petroleum
waste management.
4. Complexities of model applications
The basic theories and mathematical formulations for process
simulation and optimization of remediation practices have been
investigated for several decades, and are becoming increasingly
sophisticated. Theoretically, it is possible to resolve remediation
problems considering all the features that characterize real-world
sized situations and producing robust solutions. However, simula-
tion of NAPL transport in subsurface and its remediation process
requires a signiﬁcant amount of hydrogeological, contaminant,
and/or biological data. Enormous data is also required when per-
forming uncertainty analysis, risk assessment, optimization, and
decision making. It is very likely that the simulation results or
optimization solutions will be as good as the data and assumptions
used to perform the analysis.
For simulation models, limited ﬁeld-scale observations exist for
many multiphase systems of concern. Many standard physical,
chemical or biological equations (e.g. constitutive relations, mass
transfer, and sorption, decay) used for modeling the multiphase
ﬂow and transport phenomena were mainly based on column
experiments, and may need modiﬁcations when applied in large-
scale applications. For management models, the lack of information
related to cultural, social, economic and political factors often
hinders the development of effective management strategies.
Therefore, researchers have to work within limited scopes where
the required data are available, making it meaningless for the
wordings of comprehensive analysis, global optimization, and
systematic consideration. It is suggested that the completion of
modeling or design based on the available data means halfway only,
and the remaining half is to examine how information that are
unavailable but may present as implicit knowledge of decision
makers or stakeholders could be incorporated within the research
framework.
The acquisition, validation and processing of all data needed for
a simulation and optimization study require the dedication of large
amounts of project resources. These resources include not only the
data acquisition costs, but also engineering time, computing costs,
and investments in software, development, support, and mainte-
nance. It is thus necessary to develop efﬁcient data-analysis tools in
order to help site managers spend reasonable amount of efforts in
site
characterization,
with
data
reliability
and
resources
consumption being properly balanced.
Regarding
optimization
models,
the
linear
programming
methods are often over simpliﬁed, and unrealistic for many real-
world
applications.
The
conventional
non-linear
methods
(including classical non-linear, dynamic, mixed-integer non-linear
methods) are strong in strictness of their mathematical represen-
tation and reasoning, but less attractive in practical applications
due to a requirement of complex non-linear manipulations (i.e.
calculation of gradients or derivatives) and excessive computa-
tional efforts. Furthermore, the functions of typical groundwater
system components (e.g. system cost) may be either discontinuous
(e.g. well ﬁeld capital costs) or highly complicated (e.g. treatment
costs), making it difﬁcult to calculate or estimate the related
derivatives of these functions with respect to the decision variables
(McKinney and Lin, 1994).
Recent developments in the ﬁeld of operations research,
together with hardware and software performance improvements,
are making it possible to reduce the difﬁculty of coupling simula-
tion and optimization techniques into a general framework through
either highly-efﬁcient searching algorithms or surrogating models.
More importantly, these techniques require the simulation models
to run in a separate mode; this brings signiﬁcant ﬂexibility in
transferring the optimization framework to other types of reme-
diation systems such as surfactant ﬂushing, soil vapor extraction,
and free-product recovery. However, the heuristic approaches rely
heavily on computational capabilities of computers and will be
extremely time-consuming in solving highly complicated real-
world problems. More efﬁcient algorithms of searching are desir-
able, to be developed in future studies. In addition, when using the
sampling data for deriving the surrogates, no methods can be
available to help evaluate the suitability or quality of the data used.
Such a topic deserves further investigations.
5. Perspectives
Remediation of petroleum-contaminated soil and groundwater
systems has become a major element of environmental programs.
However, effective contaminated site management is a complicated
task, involving a variety of factors with multi-component, multi-
period, and multi-objective features. Many traditional remediation
programs were based on empirical equations or personal experi-
ences, which may not represent optimal clean-up strategies at
a contaminated site. In addition, interrelated environmental,
institutional, engineering, and economic conditions at a site may
further complicate decisions of site management. Simulation and
optimization models based on the principles of subsurface engi-
neering and operations research have been developed and used to
evaluate
groundwater
hydraulic
control
methods,
examine
contaminant fate and transport mechanisms, and determine
optimal management strategies for remediation programs. Our
ultimate goal is to obtain desired levels of environmental protec-
tion, while the resource requirement would be as lower as possible.
However, the possibility for accomplishing this goal could be
affected by many constraints, limitations and complexities that
exist in the contaminated site management systems. Facing these
challenges, it is essential to gain insight into these barriers in order
to identify effective approaches for overcoming or mitigating them.
In the following sections, a number of perspectives will be
discussed.
5.1. Modeling under uncertainty
Numerical modeling was often based on a number of assump-
tions to represent a physical system by a mathematical formulation.
The modeling parameters were usually speciﬁed deterministically.
However, in natural porous media systems, many parameters may
show uncertain natures. The uncertainties can be related to aquifer
heterogeneity, as well as physical, chemical and biological proper-
ties of the NAPLs being released and transported. Such property
parameters could vary largely from one site to another and also
exhibit great spatial variability even within the same site. There-
fore, mathematical modeling of subsurface systems is largely
complicated by the presence of uncertainties, and it should
accommodate the uncertainties in the simulation processes. It is
recognized that success of site risk assessment and remediation
system design depends signiﬁcantly on whether the numerical
X.S. Qin et al. / Journal of Environmental Management 90 (2009) 54–76
69
models have appropriately quantiﬁed and incorporated these
uncertainties into simulation processes. A model used for risk
assessment and remediation design that underestimates the
pollution problems might cause severe environmental and health
consequences. On the other hand, a remediation system design
based on a model that overestimate the problem might lead to
waste of resources. Therefore, it is a challenging problem facing
subsurface researchers to accurately evaluate the uncertainties, so
as to provide reasonable information of contaminant transport and
fate for further decision making.
The increasing awareness of the uncertain nature of porous
media has led to signiﬁcant research efforts towards a better
understanding of ﬂow and transport processes in subsurface. The
main approaches include stochastic and fuzzy-set-based methods
(Gelhar, 1993; Hoffman et al., 1999; Hu and Huang, 2002; Hu et al.,
2002; Tam and Byer, 2002; Zheng and Bennett, 2002). However,
most of the previous studies relied on individual fuzzy or stochastic
methods to deal with the uncertainties (Li et al., 2003b). Such
treatment can hardly characterize the system complexities where
different levels of information quality exist (e.g., intervals, proba-
bilistic
distributions,
and
fuzzy
membership
functions).
For
example, when sufﬁcient information is available to represent the
uncertainties as probabilistic distributions while an interval model
is used, then probabilistic information will have to be simpliﬁed
into intervals, leading to waste of the valuable information. On the
other hand, when only limited or imprecise information is available
while a stochastic model is used, then detailed probabilistic
distributions will need to be generated based on unrealistic
assumptions, resulting potential errors with the modeling inputs.
In general, such manipulations in the uncertain modeling inputs
would result in considerable under- or over-estimation in the
simulation and risk assessment results.
The underestimated
predictions may introduce risk to human health, while the over-
estimated ones may lead to economic loss due to over-conservative
remediation designs. Therefore, in order to effectively model the
complex physical, chemical and biological processes associated
with the contaminant transport and remediation processes, accu-
rate representation and quantiﬁcation of the uncertain, dynamic
and interactive features of real-world problems must be obtained
(Unger et al., 1995).
Petroleum
waste
management
systems
have
interactive,
dynamic and uncertain features. Uncertainties may be associated
with determination of system parameters, reﬂection of interactive
relationships, formulation of modeling approaches, interpretation
of research outputs, and implementation of recommended policies.
Often, to quantify such systems, a number of simpliﬁcations were
made, such as linear, continuous, static, single-objective, or deter-
ministic assumptions. These simpliﬁcations, however, would be
responsible to risks of system errors and failures. How to effectively
reﬂect these complexities without taking these risks has been
a challenging question facing environmental researchers. Although
a few mixed-integer, dynamic, and multi-objective programming
methods have been proposed to mitigate these concerns, there is
a lack of advancing more efﬁcient techniques for solving large-scale
problems. This will be an important subject of future studies.
Effective consideration of system uncertainties is also a major
concern when developing programming models for supporting
contaminated site management. It is always challenging for site
managers or environmental regulatory agencies when decisions
have to be made in ambivalent circumstances, when the many goals
are likely to be at variance and data may be unavailable or inaccu-
rate, since there will be repercussions on a major economic activity.
Over the past years, such a complexity was tackled by a number of
researchers such as Ahlfeld et al. (1988), Morgan et al. (1993),
Ranjithan et al. (1993), and Bear and Sun (1998) based on stochastic
approaches (e.g. stochastic programming or chance-constrained
programming). However, due to system non-linearities, the related
studies are still limited. A vast number of advanced optimization
approaches based on integrated fuzzy, stochastic and/or interval
programming used in other environmental ﬁelds may be helpful
(Huang, et al., 1994; Yeomans et al., 2003; Liu et al., 2003; Nie, et al.,
2007). As a result,
the
inherent
complexities
in subsurface
contamination problems provide an adequate reason for a focused
effort to more in-depth and effective simulation and optimization
technologies with consideration of system uncertainties. This effort
will lead to more accurate predictions on temporal and spatial
variations of contaminant behaviors in subsurface and more robust
decision supports for site managers, and bring enormous environ-
mental and economic beneﬁts.
5.2. Extending optimization theory to a broader scope
As shown in the review, a magnitude of studies has focused on
removal of dissolved phase hydrocarbons through either pump-
and-treat or bioremediation technologies. Investigation of cost-
effective management strategies for other techniques such as AR,
SVE, BV, and SEAR was relatively limited. Applying the existing
simulation-optimization philosophy to these systems seems an
attractive choice. However, the speciﬁc technical details associated
with various technologies may lead to signiﬁcant differences in
deﬁning the decision variables and cost functions, as well as envi-
ronmental and engineering constraints. Recent explorations on
SEAR, DPVE, and Bioslurping processes (He et al., 2008b; Qin et al.,
2007b, 2008b) have shown that extending optimization theory to
a broader scope of site remediation technologies will become an
area of active research.
5.3. Developing process control techniques
Process control was deﬁned as the process of planning and
regulating, with the objective of performing the process in an
effective and efﬁcient way. In petroleum waste management and
site remediation studies, process control is based on an integrated
simulation and optimization approach as well as other assistant
techniques. Over the past decades, many research works have been
conducted to determine dynamic process control policies for
petroleum waste management through integrating simulation and
optimization techniques (Johnson and Rogers, 1995; McKinney and
Lin, 1994, 1995, 1996; Mansﬁeld et al., 1998; Minsker and Shoe-
maker, 1998a,b; Smalley et al., 2000; Zheng and Wang, 2002).
However, these methodologies mainly focused on the optimization
part of the controlling system, and paid less attention to the
improvement of system efﬁciencies. In fact, practical site manage-
ment requires effective designs of process control systems in order
to identify signiﬁcant variations in operational performance,
determine root causes, make corrective actions and verify results.
Although the related studies fall within the scope of control engi-
neering, it is still imperative to develop or customize advanced
process control techniques to improve efﬁciencies of site manage-
ment. However, due to complexities of various remediation
systems, direct studies in developing process control techniques in
such a ﬁeld were very limited.
It was identiﬁed from vast literatures that approaches of model
predictive control and artiﬁcial intelligent (AI) process control were
effective tools in improving product quality and efﬁciency and
reducing costs of industrial production and pollution control (Rao
and Rawlings, 2000; Cannas et al., 2001; Guh, 2003). They are
potential control methods that can be used for subsurface reme-
diation studies. A number of attempts have been made in more
recent years. Hu et al. (2003) developed an on-line fuzzy process
controller for supporting in situ bioremediation systems, where
pumping rates could be adjusted real-time based on the measured
X.S. Qin et al. / Journal of Environmental Management 90 (2009) 54–76
70
pollution levels. Later, Hu et al. (2006) developed a dynamic model
predictive control system for in situ bioremediation. The control
system included an optimization tool that consisted of a simulation
model and an optimization function. Huang et al. (2008) proposed
a framework of an integrated process control system for improving
remediation efﬁciencies and reducing operating costs based on
physical and numerical models, stepwise cluster analysis, non-
linear optimization and artiﬁcial neural network. In brief, a great
number of process control studies exist but very few of them
addressed soil and groundwater remediation processes. It is an area
deserving extensive investigations.
5.4. Applying post-modeling analysis
Systems simulation and optimization models for site remedia-
tion are primarily developed for reﬂecting the impacts of human
activities, exploring interactions among various system compo-
nents, making tradeoffs among different objectives, and thus sup-
porting decisions that lead to maximized environmental and
economic efﬁciencies (Thomas et al., 1990). However, due to the
complexity of the study system, the uncertain inputs/outputs, the
dynamic and interactive system characteristics and their variations
under the changing social, economic, legislational, and environ-
mental conditions may affect practical applicability of many model
solutions.
Post-modeling analysis (PMA) is desirable, to be carried out to
deal with such problems. PMA is a methodology that uniﬁes an
optimization model with a rule-based system and enables the
multi-objective decision modeling that considers both numeric and
symbolic objectives and decision variables (Lee and Song, 1996).
The solutions obtained through optimization models only answer
the question ‘what to do’ in a decision process. Then in PMA, these
obtained solutions will be analyzed and interpreted for helping
decision makers to identify desired tradeoffs between economic
development and environmental protection. Further clariﬁcation
and interpretation could then be carried out for comprehensively
evaluating potential groundwater quality management scenarios
and for generating decision alternatives. Therefore, PMA is useful
for accommodating changing goals or to experiment with ‘‘what
if’’-type scenarios and to see how possible changes will affect the
strategic management plan. It would provide a bridge between the
systems simulation and optimization and the practical application.
However, the related studies in the ﬁeld of petroleum waste
management and site remediation is very limited.
6. Concluding remarks
Leakage and spill of petroleum hydrocarbons from underground
storage tanks and pipelines have posed signiﬁcant threats to
groundwater
resources
across
many
petroleum-related
sites.
Remediation of these contaminated sites is essential for protecting
the soil and groundwater resources and reducing risks to local
communities. Although many efforts have been made, effective
design and management of various remediation systems are still
challenging to practitioners. In recently years, the subsurface
simulation model has been combined with techniques of optimi-
zation to address important problems of contaminated site
management.
The
combined
simulation-optimization
system
accounts for the complex behavior of the subsurface system and
identiﬁes the best management strategy under consideration of the
management objectives and constraints. To reach the objective of
cost-effective site management, identiﬁcation of barriers in site
characterization, uncertainty treatment, post-modeling analysis,
and decision supports would be essential before any short or long-
term plans can be made.
In this paper, recent developments, advancements, challenges,
and barriers associated with simulation and optimization tech-
niques in supporting process control of petroleum waste manage-
ment and site remediation were analyzed. A number of related
methodologies and applications were examined. Perspectives of
effective site management were investigated, demonstrating many
demanding areas for enhanced research efforts, including issues of
data availability and reliability, concerns in uncertainty, necessity of
post-modeling analysis, and usefulness of decision supports.
Acknowledgements
This
research
was
supported
by
the
Major
State
Basic
Research Development Program of MOST (2005CB724200 and
2006CB403307), and the Natural Science and Engineering Research
Council of Canada. The authors deeply appreciate the editors and
the anonymous reviewers for their insightful comments and
suggestions.
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76

Procedia Chemistry  
10 ( 
2014 
)  
7 – 14 
1876-6196 Crown Copyright © 2014 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license 
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
Peer-review under responsibility of Tomsk Polytechnic University
doi: 
10.1016/j.proche.2014.10.003 
Available online at www.sciencedirect.com
ScienceDirect
XV International Scientific Conference “Chemistry and Chemical Engineering in XXI century” 
dedicated to Professor L.P. Kulyov 
Multiobjective optimization of industrial petroleum processing units 
using Genetic algorithms 
Stanislav Y. Ivanov* and Ajay K.Ray 
University of Western Ontario, Department of Chemical and Biochemical Engineering, London, ON, N6A 3K7, Canada 
Abstract 
For many years most of refining processes were optimized using single objective approach, but practically such complex 
processes must be optimized with several objectives. Multiobjective optimization allows taking all of desired objectives directly 
and provide search of optimal solution with respect to all of them. Genetic algorithms proved themselves as a powerful and 
robust tool for multi-objective optimization. In this article, the review for a last decade of multi-objective optimization cases is 
provided. Most popular genetic algorithms and techniques are mentioned. From a practical point it is shown which objectives are 
usually chosen for optimization, what constraint and limitations might impose multi-objective optimization problem formulation. 
Different types of petroleum refining processes are considered such as catalytic and thermal. 
 
© 2014 The Authors. Published by Elsevier B.V. 
Peer-review under responsibility of Tomsk Polytechnic University. 
Keywords: multiobjective optimization; petroleum refining, genetic algorithm 
1. Introduction 
It is hardly possible to diminish importance of the key roles of petroleum refining in modern chemical industry. It 
produces different types of fuels (e.g. gasoline, diesel, furnace fuel, etc.) or wide variety of valuable chemicals 
which constitutes significant part of global market. Due to its importance, optimization of refining processes is 
essential. Capacities of modern units are high, and hence, even small performance improvements might lead to 
significant economical profits. 
 
 
* Corresponding author. Tel.: +1-519-697-9429. 
E-mail address: sivanov@uwo.ca 
Crown Copyright © 2014 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license 
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
Peer-review under responsibility of Tomsk Polytechnic University
8  
 
Stanislav Y. Ivanov and Ajay K. Ray /  
Procedia Chemistry  
10 ( 
2014 
)  
7 – 14 
Conventional methods of optimization for many years had been based on formulation of single objective function 
and search of its minimum (or maximum). In case of complex industrial processes either most important objective 
was chosen or single objective function in some way related to economic effect (e.g., profit maximization or cost 
minimization). Practically solution of such optimization problem yields single-point solution. This approach has 
some obvious disadvantages. Optimization of only one objective while disregarding the others might lose some 
practically meaningful solution and at times solution may be practically irrelevant. Nevertheless, sometimes relation 
between real objectives and their economic effect is not clear which makes difficult formulation of single objective 
function. Moreover, cost or profit functions are site-specific and time-specific and solution may not be useful. 
Multi-objective optimization with its concepts and methods allow overcoming issues mentioned above. Applying 
multi-objective approach for solving real-life optimization problems, it becomes possible to take into account all of 
desired objective functions and treat them directly regardless of any explicit relation to economic efficiency. It is 
especially important for petroleum refining processes due to its complexity, i.e. variety of components in feedstock 
and products, diversity of chemical reactions, number of units included into processing scheme. Such nature of oil 
refining processes makes multi-objective optimization “a more advanced” tool in a search of optimal solution(s). 
2. Solution of multiobjective optimization problem 
Any multi-objective optimization (minimization) problem (MOO), regardless of area of application, is 
formulated as following: 
 
  
 
1
2
Minimize I(x) = [I (x), I (x)...I (x)]
n
  
(1) 
Subject to: 
x
S

 
g (x) 
 0, i = 1, 2...K
k
d
 
h (x) = 0, j = 1, 2...J
j
 
where n is a number of objectives, gk and hj are inequality and equality constraints quantities of K and J 
respectively, x - set of decision variables, S - decision domain for x. However, optimization might include both 
minimization and maximization of objectives, for the sake of simplicity we will consider minimization problem. All 
the ideas and approaches can be easily extended for maximization problems. 
 
Fig. 1 Pareto front for two-objective optimization problem in objective domain 
Unlike in case of single objective optimization, the non-trivial solution for problem (1) is not a single point, but a 
number of points called Pareto-optimal solutions. 
 
Stanislav Y. Ivanov and Ajay K. Ray /  
Procedia Chemistry  
10 ( 
2014 
)  
7 – 14 
9
Pareto-optimal point: a point x
S

 is called Pareto optimal point if and only if there is no other point x'
S

 
such that 
(x)
I (x') < I
i
i
  for all objectives simultaneously. 
Set of Pareto-optimal points for problem (1) is called Pareto front and represents a solution of multi-objective 
optimization problem. Let’s consider simple two-objective problem (Fig. 1). Here solid line is a Pareto front. Points 
1 and 2 are two representative points belonging to Pareto front. If we move from point 1 to 2, objective 2 is 
improving (decreasing) while objective 1 is worsening (increasing). Hence, point 1 and 2 are equally-good non 
dominated points. Point 3 is not Pareto optimal point since it has both objective values “worse” than any of points 
from Pareto front. Point 4 is imaginary point where both objectives 1 and 2 have minimum values. This point is 
unreachable due to conflictive nature of objectives and equality and inequality constraints from (1) which might 
introduce limitation to search space. 
3. Use of Genetic algorithms for multiobjective optimization 
There’s a number of different method for solving MOO, i.e. search of Pareto-optimal solutions1,2. In last several 
years, numerous of modifications on methods based on genetic algorithms (GAs) experienced a significant growth 
in popularity for solving MOO problems. Original concept of genetic algorithm was established by Holland3 and 
evolved further by Goldberg4. Nowadays many of different adaptations of GAs exist but all of them have the same 
basic principles, which are: 
x 
GA works with number of decision points (called chromosomes) instead of single one 
x 
Doesn’t use derivatives of objective functions in MO search 
x 
GA operators are probabilistic in nature 
GAs have proved themself as a very robust optimum search methods. First of all, it is a global optimum search 
procedure, which overcome the drawbacks of majority of derivative-based methods. GAs can treat continuous or 
discrete functions (or decision variables); they can find optimum for multi-modal functions or converge to non-
convex Pareto front5. 
To understand GA’s main principles, one should consider Simple Genetic Algorithm (SGA). It is necessary to 
notice that SGA deals with coded variables. Any discrete or continuous variable is represented in a form of binary 
string (i.e. sequence of 0’s and 1’s). More detailed description about mapping of real variable into binary can be 
found elsewhere4. SGA first initializes a random set of N solutions (called “population”).  
Each member of the set (called “individual”) represents a single set of decision variable with corresponding value 
of objective function(s). After population is created, SGA form a “mating pool”– an intermediate population, 
members of which are copied from original population - through reproduction. The chance of an individual to be 
chosen for mating is proportional to its objective function value; the lower the value, the higher the probability. In 
other words, the mating pool will be inhabited more with individuals who have lower objective values. 
When mating pool is formed, SGA performs genetic operation over its individuals. The nature of these operators 
is not similar to any mathematical operators. Like the reproduction, they mimic the behavior of real genetic 
operators in nature. That’s what makes GA an outstanding search procedure from any other mathematical 
techniques. Firstly, SGA choses two strings to carry out a crossover operator with probability pcrossover (~0.5-0.7). 
Single-point crossover operator choses a random position at binary sequence and swap subsequences of two 
individuals (Figure 2). Another following conventional GA operator is mutation. It simply alters single bit (from 1 
to 0 or vice versa) in binary sequence with probability pmutation (~0.01-0.1). 
 
10  
 
Stanislav Y. Ivanov and Ajay K. Ray /  
Procedia Chemistry  
10 ( 
2014 
)  
7 – 14 
Fig.2 Single-point crossover operation 
After these operations are done, one has a new generation of individuals. The reproduction, crossover and 
mutation are carried out once again. This drives the optimization search to an optimum until some termination 
criteria is satisfied. Usually it is a number of generations (practically around ~250-1000). 
The following procedure describes the basics of GA. Multi-objective genetic algorithms utilize the same 
principles combined with MOO concepts of Pareto optimality. Modern GAs are more complex than SGA. They 
utilize modified genetic operator (e.g. two-point crossover), might treat real variables directly or implement different 
selection algorithms, but still principles remain the same. Here is the list of some of the modifications and 
adaptations: Multi-objective GA (MOGA)6, Vector Evaluated Genetic Algorithm (VEGA)7, NSGA-II8, Differential 
Evolution9 and many others. 
Based on literature review over the last decade authors would like to emphasise that practically many of chemical 
engineering optimization problems were solved using NSGA-II. This algorithm has proved itself as very powerful 
tool for MOO. Detailed description of NSGA-II can be found in original source8. 
The main advantages of NSGA-II among other multi-objective GAs are notable and they are: 
x 
relative simplicity of algorithm 
x 
provides better convergence to a Pareto front 
x 
provides wider distribution of solutions along Pareto front 
x 
uses concept of elitism, which allow to carry individuals with better objective values through 
generations 
x 
relatively low computational cost due to Non-domination Sorting Approach 
x 
with Constrained tournament method it is possible to treat constraints directly without increasing 
computational time. 
4. Multiobjective optimization of petroleum refining processes 
Petroleum refining processes might be considered on different levels. Usually feedstock for refining unit includes 
variety of components which yields to multi-product outflow containing desired and undesired components. 
Commonly units operate under high temperature and pressure, which significantly contributes to cost of final 
product and impose restrictions to process operation parameters. Majority of processes utilize heterogeneous 
catalysts, which are also sensitive to process conditions. Nevertheless, typical refining process consists of many 
auxiliary units, the performances of which can significantly affect optimal conditions. All of these increases 
complexity of refining process. 
In such scenario, application of multi-objective optimization becomes essential for improvement of unit 
performances. To carry out MOO of refining process it is vital to formulate real-life objectives in addition to 
implementation of relevant constraints. 
Critical literature review showed a growing interest for use of genetic algorithms in multi-objective optimization 
of petroleum refining processes in the last several years. 
In Bhutani et al.10 authors performed a multi-objective optimization of an industrial hydrocracking unit, which is 
used to process heavy distillates to valuable products in presence of hydrogen. The unit considered mainly consisted 
 
Stanislav Y. Ivanov and Ajay K. Ray /  
Procedia Chemistry  
10 ( 
2014 
)  
7 – 14 
11
of two reactors in series – a hydrotreater (HT) and a hydrocracker (HC). Both are packed bed reactors with 2 and 4 
beds respectively. 
Authors utilized a simplified model for HT and first-principle model for HC. Reaction products were lumped into 
8 components (e.g. liquefied petroleum gas (LPG), light naphtha, heavy naphtha, etc.). Kinetic scheme were 
developed for these pseudo components. The HC modeling was based on the following assumptions: plug-flow 
reactor without axial diffusion, adiabatic, steady state operation. 
Objectives were chosen based on industrial priorities and they were applied to maximize diesel, kerosene and 
naphtha production and to minimize off-gases, LPG production and hydrogen consumption. Due to many important 
objectives MOO problem was divided into 3 two-objective cases for simplicity: 
x 
Maximization of kerosene flow rate and minimization of hydrogen consumption 
x 
Maximization of diesel flow rate and minimization of hydrogen consumption 
x 
Maximization of high-value end products (“HE” which is sum of all products except LPG) and 
minimization of low-value end products (“LE” which is sum of light components) 
Additional constraints were introduced into MOO problem formulation. HC bed’s inlet and outlet temperatures 
were limited with upper value to ensure stable HC operation. Liquid hourly space velocity in the reactor was 
bounded to guarantee proper hydrodynamic regime in the reactor. Also, conversions of feed per one pass and overall 
throughput of the reactor was imposed to lie within certain range to maintain unit operation on reasonable level. 
MOO was carried out using real-coded NSGA-II with 50 individuals (chromosomes) and for 200 generations. 
Each optimization case yielded in Pareto optimal front. Solutions were represented in two-dimensional objective 
domain with corresponding values for (optimum) decision variables. 
Figure 3 illustrates Pareto front for maximization of HE and minimization of LE case. One can see from the plot, 
objectives are competing (conflicting). It is impossible to satisfy both objectives simultaneously. The resulted Pareto 
from is given to a decision maker (a unit manager, engineer, researcher), who can pick a certain desired point to 
operate industrial HC. There’s no better points beside provided in the Pareto solution. 
It is be noted that real industrial operating point (represented by a solid square) are far from all optimum solutions 
(hollow squares). The results clearly show that some improvements are possible to make just by changing process 
operating condition to improve its performance. 
In the work of Kasat et al.11, MOO of industrial Fluidized Catalytic Cracking (FCC) unit was performed. Authors 
utilized lumped kinetic scheme with empirical reactor model to simulate FCC unit behaviour. They formulated three 
multi-objective cases to solve: a) maximization of gasoline yield and minimization of CO in a flue gas with 
constraints limiting amount of coke on catalyst, b) maximization of gasoline yield and minimization of air feed rate 
in regenerator with constraints limiting amount of CO in flue gas and c) maximization of gasoline yield, 
minimization of air feed rate in regenerator and minimization of CO amount in a flue gas with constraints like in 
previous two cases.  
NSGA-II was used as a multi-objective optimization method. Authors obtained two and three dimensional Pareto 
fronts. They reported that varying constraints and choosing different objective functions it is possible to adapt 
proposed approach to any existing FCC unit to improve its performance. Later Kasat and Gupta12 carried out the 
same MOO case as in Kasat et al.11, but they introduced an improved genetic operator called jumping gene (JG) (for 
a description of JG principles one can refer to original source). Authors reported a better distribution of individuals 
along the Pareto front as well as faster convergence of modified algorithm. Faster convergence might be beneficial 
for MOO, since majority of industrial units’ simulations has high computational cost. 
12  
 
Stanislav Y. Ivanov and Ajay K. Ray /  
Procedia Chemistry  
10 ( 
2014 
)  
7 – 14 
 
Fig.3 Pareto optimal front for two-objective optimization problem for industrial hydrocracking unit: 
Maximization of high-value end (HE) products and minimization of low-value end (LE) 
Note: values are given in dimensionless form due to proprietary reasons. (Adapted with permission from Ind. Eng. Chem. Res. 45(4) N. Bhutani, 
A. K. Ray and G. P. Rangaiah, "Modeling, Simulation and Multi-objective Optimization of an Industrial Hydrocracking Unit", p1354-1372. 
Copyright 2006 American Chemical Society) 
 
Weifeng et al.13 proposed a multi-objective optimization of naphtha catalytic reformer. The fixed bed unit with 4 
radial-flow reactors was studied in the proposed research. Unit was mainly operated for production of aromatics. 
The radial flow model combined with lumped kinetics was utilized assuming no axial or radial dispersion effects in 
the reactor. In addition to reactor, models of auxiliary units were also included (e.g. separator, heat-exchangers). 
Formulation MOO problem authors chose to a) maximize aromatics yield and b) minimize yield of heavy aromatics. 
Main process variables affecting these objective values were taken as each reactor inlet temperatures, process 
pressure, and hydrogen/hydrocarbon molar ratio. Objective values and decision variables were bounded based on 
industrial practice. 
Neighbourhood and archived genetic algorithm (NAGA) was used to solve the problem; Pareto-optimal set of 
solutions was obtained. Authors noted that among all decision has conflicting effect for chosen objectives, but inlet 
temperature in fourth reactor which allows increasing aromatics yield while decreasing heavy aromatics. 
Rahimpour et al.14,15 carried out similar multi-objective cases for non-conventional type naphtha catalytic 
reformer with 3 reactors in series, where each reactor in coupled with heat-exchanger, thereby excluding the 
necessity of inter-stage heating. They maximized production of aromatics, hydrogen and aniline. Notable point is 
that they included a catalyst distribution between reactors as a decision variables, unlike to Weifeng et al.13. 
MOO problems were solved using objectives sum method with differential evolution. Authors came up with 
single-point optimal solution and optimal profiles for operating conditions along reactors length. It was reported 
significant increase in objective values for all chosen objectives. Also, results were compared with performance of 
conventional naphtha reformer; advantages of a new reactor were shown. 
Iranshahi et al.16 performed design and operation stage MOO of combined tubular membrane (refereed as “M”) 
and radial-flow spherical (referred as “S”) for two different unit arrangement in series (called as SMS and SMM). 
Authors chose hydrogen and aromatic production as objective function. Problem was constrained by hydrogen-to-
hydrocarbons ratio. 
Like in two previous works, differential evolution genetic algorithm was used to maximize sum of objectives. 
Constraints were handled in a form of penalty function. Single optimal solution for each of proposed reactor 
arrangements and results were compared. Show that optimization allowed improving performance of both reactor 
arrangements, however drew up a conclusion that SMS arrangement performs better comparing to SMM. 
 
Stanislav Y. Ivanov and Ajay K. Ray /  
Procedia Chemistry  
10 ( 
2014 
)  
7 – 14 
13
Work of Wang and Tang 17 utilizes data-based model to simulate and optimize performance of industrial naphtha 
pyrolysis unit. Authors used multi-objective genetic algorithm based on differential evolution approach called 
MOPDE-CES. Pyrolysis mainly is used for decomposition of higher hydrocarbons into light products, especially 
gases. The objectives for MOO were chosen as: maximize a) ethylene and b) propylene yield and no constraints 
were introduced. The number of Pareto-optimal solutions was obtained. Additionally, performance of MOPDE-CES 
and NSGA-II for solving the same MOO case was compared. Authors reported good convergence of MOPDE-CES 
to Pareto front comparing to NSGA-II with less computational cost. 
Bayat et al.18 performed MOO of paraffin dehydrogenation unit (PacolTM process), which is a part of linear 
alkylbenzenes (LAB) plant. The process is aimed for conversion of C10-C14 paraffins into corresponding olefins for 
further benzene alkylation. The dehydrogenation reactor is a fixed-bed radial flow reactor. To simulate its behaviour 
authors developed non-steady-state (to take into account catalyst deactivation) one-dimensional homogeneous 
model including mass and heat balance over the reactor. Objectives chosen for MOO were: maximize a) production 
and b) selectivity of olefins. 
MOO was carried out using NSGA-II. Solution of optimization problem yielded into several Pareto fronts with 
respect to time. It was shown how do Pareto-optimal solution are shifting due to catalyst deactivation. Also one 
solution of entire Pareto front was proposed as “the most acceptable” to operate the industrial unit. 
5. Conclusions 
Conventional refinery consists of processes of different types – heterogeneous and homogeneous catalytic, 
thermal, physical (such as distillation or mixing), and genetic algorithms for MOO might be applied to any type 
them. They differ in the nature of reactions, products, unit arrangements or technologies applied. 
In this light, to account all of these, the importance and significance of MOO for petroleum refining processes is 
evident. Multi-objective genetic algorithms are very robust technique to carry out MOO of industrial processes. 
They allow taking into account all complexity of considered problem and finding optimal solution(s). Genetic 
algorithms can easily treat several objectives and constraints simultaneously. This is often vital for industrial cases 
since solution provided by single-objective optimization methods might be irrelevant in real life. 
However, use of genetic algorithms in optimization of industrial oil refining processes is relatively new field of 
research comparing to other fields in chemical engineering. It makes it an attractive field for further investigations. 
References 
1. Branke J, Deb K, Miettinen K, Slowinski R. Multiobjective Optimization. Interactive and Evolutionary Approaches. : Springer; 2008. 
2.  Miettinen K. Nonlinear Multiobjective Optimization. : Kluwer Academic Publishing; 1999. 
3. Holland JH. Adaptation in natural and artificial systems :an introductory analysis with applications to biology, control, and artificial 
intelligence. 1 MIT Press ed. Cambridge, Mass.: MIT Press; 1992. 
4. Goldberg DE. Genetic algorithms in search, optimization, and machine learning. Reading, Mass.; Don Mills, Ont.: Addison-Wesley Pub. Co; 
1989. 
5. Deb K. Multi-objective optimization using evolutionary algorithms. 1st ed. Chichester, England; New York: John Wiley & Sons; 2001. 
6. Fonseca CM, Fleming PJ. Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization. 1993. 
7. James David Schaffer. Some Experiments in Machine Learning Using Vector Evaluated Genetic Algorithms (Artificial Intelligence, 
Optimization, Adaptation, Pattern Recognition)Vanderbilt University; 1984. 
8. Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary 
Computation. 2002;6(2):182-197. 
9. Storn R, Price K. Differential evolution - A simple and efficient heuristic for global optimization over continuous spaces. J Global Optimiz. 
1997; 11(4):341-359. 
10. Bhutani N, Ray AK, Rangaiah GP. Modeling, simulation, and multi-objective optimization of an industrial hydrocracking unit. Ind Eng Chem 
Res. 2006; 45(4):1354-1372. 
11. Kasat RB, Kunzru D, Saraf DN, Gupta SK. Multiobjective optimization of industrial FCC units using elitist nondominated sorting genetic 
algorithm. Ind Eng Chem Res. 2002; 41(19):4765-4776. 
12. Kasat RB, Gupta SK. Multi-objective optimization of an industrial fluidized-bed catalytic cracking unit (FCCU) using genetic algorithm (GA) 
with the jumping genes operator. Comput Chem Eng. 2003; 27(12):1785-1800. 
13. Hou Weifeng, Su Hongye, Mu Shengjing, Chu Jian. Multiobjective optimization of the industrial naphtha catalytic reforming process. Chin J 
Chem Eng. 2007;15(1):75-80. 
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14. Rahimpour MR, Iranshahi D, Pourazadi E, Bahmanpour AM. Boosting the gasoline octane number in thermally coupled naphtha reforming 
heat exchanger reactor using de optimization technique. Fuel. 2012; 97:109-118. 
15. Pourazadi E, Vakili R, Iranshahi D, Jahanmiri A, Rahimpour MR. Optimal design of a thermally coupled fluidised bed heat exchanger reactor 
for hydrogen production and octane improvement in the catalytic naphtha reformers. Can J Chem Eng. 2013; 91(1):54-65. 
16. Iranshahi D, Rahimpour MR, Paymooni K, Pourazadi E. Utilizing DE optimization approach to boost hydrogen and octane number, through a 
combination of radial-flow spherical and tubular membrane reactors in catalytic naphtha reformers. Fuel .2013; 111:1-11. 
17. Wang X, Tang L. Multiobjective Operation Optimization of Naphtha Pyrolysis Process Using Parallel Differential Evolution. Ind Eng Chem 
Res. 2013;52(40):14415-14428. 
18. Bayat M, Dehghani Z, Rahimpour MR. Dynamic multi-objective optimization of industrial radial-flow fixed-bed reactor of heavy paraffin 
dehydrogenation in LAB plant using NSGA-II method. Journal of the Taiwan Institute of Chemical Engineers 2013(In press).

Process Safety and Environmental Protection 188 (2024) 64–72
Available online 11 May 2024
0957-5820/© 2024 Institution of Chemical Engineers. Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and
similar technologies.
Plant wide modelling and thermodynamic optimization of a petroleum 
refinery for improvement potentials 
Adil Sana a, Iftikhar Ahmad a,*, Husnain Saghir a, Manabu Kano b, Hakan Caliskan c,*, 
Hiki Hong d,* 
a School of Chemical and Materials Engineering (SCME), National University of Sciences & Technology (NUST), Sector H-12, Islamabad 44000, Pakistan 
b Department of Systems Science, Kyoto University, Kyoto 606-8501, Japan 
c Department of Mechanical Engineering, Faculty of Engineering and Natural Sciences, Usak University, Usak 64200, Türkiye 
d Department of Mechanical Engineering, Kyung Hee University, Yongin 17104, Republic of Korea   
A R T I C L E  I N F O   
Keywords: 
Plant wide modelling 
Chemical process 
Thermodynamics 
Exergy 
Petroleum refinery 
A B S T R A C T   
In this work, plant wide modelling of a petroleum refinery was conducted for the optimization. An Aspen 
HYSYS® model of a petroleum refinery was used for collection of the information to perform the thermodynamic 
(exergy) analysis. The plant wide model consisted of alkylation unit, reformer, naphtha hydrotreater, catalytic 
cracker, diesel hydrotreater, isomerization unit, hydrocracker, and kerosene hydrotreater. Seventeen (17) in­
dependent input streams and twenty-eight (28) output streams of the plant were considered for the analysis. The 
physical and chemical exergies of the streams were summed for assessing the overall exergy efficiency, exergy 
destruction, and improvement potentials of the plant. Exergy efficiency of the plant was 91.38 % with exergy 
destruction of 704054.64 kW and exergetic improvement potential of 60707.50 kW. The current study gives an 
insight into the plant-wide analysis ranging from physical exergies of the process streams and utilities to the 
chemical exergies of the fuels (product) produced in the refineries.   
1. Introduction 
Depletion of energy resources have driven the search for sustainable 
operation of industries. Petroleum refinery being an energy intensive 
industry is also the focus of research. According to a report of US 
Department of Energy, Energy of 794 TBTU/year (26 %) could be 
conserved in petroleum refinery by adopting efficient operation and 
technological upgradation. Exergy analysis based on first and second 
law of thermodynamics has been supportive in the quest for realizing 
energy efficient design and operation of process plants (Hinderink et al., 
1996a). In the exergy analysis, the quality of the exergy and its degra­
dation in processes is accounted (Dogbe et al., 2018). In 1950 s, the 
concept of exergy in process industry was introduced (Moran and 
Sciubba, 1994). The energy crises of the early 1970 s further pushed 
researcher toward means for realizing energy efficient processes that 
triggered the use of exergy analysis (Faruque Hasan et al., 2007). 
Several studies have been reported in literature regarding the exergy 
analysis of the petroleum refinery. Researchers have focused on indi­
vidual sections as well as equipment to calculate the exergy losses and to 
identify the sources of exergy losses in petroleum refining industry. 
Various research works on distillation columns have been reported such 
as exergoeconomic analysis (Rivero et al., 1999, 2004), irreversibility 
rate and exergy efficiency (Anozie et al., 2009), (Darabi et al., 2015). 
Similarly, Mestre-Escudero et al. performed exergy analysis of oxidation 
unit of a refinery to find a tradeoff between product quality and exergy 
efficiency (Mestre-Escudero et al., 2020). Hu et al. performed exergy 
analysis on natural gas liquid recovery processes, according to which air 
cooler and column contributed more to the total exergy destruction (Hu 
et al., 2018). Rivero et al. performed exergy analysis of several equi­
pment/units such as adiabatic distillation, absorption heat pumps, 
coking–gasification-combined cycle co-and tri-generation, and fuel cells 
(Rivero, 2002). Junior et al. applied exergy analysis on offshore plat­
forms of separation processes of petroleum refinery and found that 
major exergy consumers were compressions and heating operations in 
the plant (De Oliveira and Van Hombeeck, 1997). Exergy analysis of 
delayed coker revealed exergy efficiency of 81.58 % (Ibrahim et al., 
2021). In another study on delayed coker, the effect of preheating of feed 
through a Heat Exchanger Network on overall exergy efficiency of the 
unit was investigated (Lei et al., 2012). Lei et al. applied exergy analysis 
on integrated heat exchange and fractionating processes of delayed 
coking units, according to which it had 97.3 % exergy efficiency and 
* Corresponding authors. 
E-mail addresses: iftikhar.salarzai@scme.nust.edu.pk (I. Ahmad), hakan.caliskan@usak.edu.tr (H. Caliskan), hhong@khu.ac.kr (H. Hong).  
Contents lists available at ScienceDirect 
Process Safety and Environmental Protection 
journal homepage: www.journals.elsevier.com/process-safety-and-environmental-protection 
https://doi.org/10.1016/j.psep.2024.05.006 
Received 13 February 2024; Received in revised form 24 April 2024; Accepted 2 May 2024   
Process Safety and Environmental Protection 188 (2024) 64–72
65
38.1 % improvement potential (Lei et al., 2016). Chen et al. proposed a 
flowsheet of delayed coking units and did exergy analysis, according to 
which 37.2 % of energy consumption can be decreased. The relation of 
optimal stages with exergy efficiency of distillation columns was also 
studied (Chen, 2004). Akram et al. analyzed the effect of process un­
certainty on exergy efficiency of naphtha reforming process (Akram 
et al., 2018). Agbo et al. quantified equipment-wise exergy destruction 
and exergy efficiency of a naphtha hydrotreating unit (Agbo et al., 
2019). Bandyopadhyay et al. performed exergy and pinch analysis on 
diesel hydrotreating unit and found that valves, air coolers and fired 
heaters had the highest exergy improvement potentials (Bandyo­
padhyay et al., 2019). Chegini et al. conducted a study on hydrocracking 
process and found that 5.96 MW exergy could be recovered from hot 
stock flue gasses (Goodarzvand-Chegini and GhasemiKafrudi, 2017). 
Similarly, exergy analysis of fluid catalytic cracking (FCC) unit was 
performed. The identified improvement potentials were worth 
3.83 MW, exergy efficiency was 61.2 %, and total exergy losses were 
more than 660 MW (Goodarzvand-Chegini and GhasemiKafrudi, 2017; 
Al-Mutairi, 2014; Nuhu et al., 2012). Ibrahim et al. applied exergy 
analysis on amine regeneration unit and concluded that regenerators 
had 80 % of overall exergy destruction (Ibrahim et al., 2022). Caballero 
et al. performed exergy analysis on FCC plant separation processes and 
concluded that fractionator column had maximum energy improvement 
potential (Nuhu et al., 2012). Samad et al. performed exergy analysis of 
reactive sections in petroleum refinery to identify the improvement 
potentials. ANN based surrogate models were utilized in genetic algo­
rithm based optimization framework (Samad et al., 2023a). 
The exergy analysis has been performed in various simulators like 
Aspen HYSYS, Aspen Plus, and other software like MATLAB, MS Excel, 
and Fortran (Li and Lin, 2016). For instance, Li and Lin developed Aspen 
Plus model of liquid from coal (LFC) and then performed its exergy 
analysis. Aspen HYSYS based exergy analysis of various industrial pro­
cesses has been performed such as CO2 removal from syngas and CO2 
capture from offshore primary petroleum processing plant (Feyzi et al., 
2017; Carranza S´
anchez and de Oliveira, 2015). Aspen HYSYS has been 
used for exergy analysis of enzymatic hydrolysis reactors and cumene 
production plant (Ojeda and Kafarov, 2009; Samad et al., 2023b). 
Kochunni et al. used Aspen HYSYS for comparison of reverse LNG 
boil-off gas reliquefaction system in terms of exergy efficiency 
(Kochunni and Chowdhury, 2017). Ojeda et al. used computer based 
tools for exergy analysis, and LCA, using Aspen-HYSYS, Aspen HX-NET, 
and SimaPro life cycle assessment software for the identification of 
environmental impacts as well as exergy losses in the biofuels produc­
tion process from lignocellulosic biomass (Ojeda et al., 2010). Gour­
melon et al. used ProSimPlus for the purpose of process simulation and 
exergy analysis (Gourmelon et al., 2015). Ghannadzadeh et al. discussed 
the general methods for exergy balance in ProSimPlus process simulator 
(Ghannadzadeh et al., 2012). 
Exergy analysis on individual sections of the petroleum refinery has 
been reported in literature. However, a holistic perspective regarding 
the exergy losses and improvement potentials a plant wide exergy 
analysis is not yet investigated. This study aims to fill this gap by per­
forming the plant wide exergy analysis of petroleum refining industry 
including all the major units. Contributions of this work are listed below:  
• Physical exergy of all input and output streams of the petroleum 
refinery plant are calculated in Aspen HYSYS.  
• Chemical exergy of all input and output streams of plant wide model 
of refinery are calculated. 
• Exergy destruction, exergetic improvement potential and exergy ef­
ficiency of the plant are calculated based on physical and chemical 
exergy of all input and output streams. 
Rest of paper is ordered as follows. Section 2 describe fundamentals 
of exergy analysis. Section 3 describes the process flow diagram of pe­
troleum refinery in Section 3.1 and in Section 3.2, the brief introduction 
of input and output streams followed by methodology in Section 4. The 
calculated results and the improvement potentials have been discussed 
and the streams of petroleum refinery have been ranked in Section 5. 
Section 6 concludes the work. 
2. Fundamentals of exergy analysis 
Exergy analysis integrates both first and second laws of thermody­
namics and allows the process engineers to design the process more 
energy efficient by recognizing, computing, and minimizing the process 
irreversibility. Exergy is the maximum possible work that can be ob­
tained through reversible process by bringing a matter from its initial 
state to a state of chemical and thermodynamic equilibrium with the 
environment as shown in Fig. 1. A schematic representation of terms 
associated with exergy are summarized in Fig. 1. 
2.1. Physical exergy 
Physical exergy is maximum work that can be attained through 
physical processes by taking a stream from an initial state to a thermo- 
mechanical equilibrium state with the environment. On molar basis, 
physical exergy is defined as 
Exphy = Δactual→0
[
L
(∑
n
i=1
xiHl
i −T0
∑
n
i=1
xiSl
i
)
+V
(∑
n
i=1
yiHv
i −T0
∑
n
i=1
yiSv
i
)]
(1)  
where Si and Hi denote enthalpy and entropy respectively at the 
Nomenclatures 
Ex 
Molar exergy ( kJ
mol)
H 
Molar enthalpy ( kJ
mol)
T 
Temperature (K) 
˙
m 
Molar flowrate (mol
s )
Exphy 
Physical exergy ( kJ
mol)
S 
Entropy 
(
kJ
K mol
)
L 
Liquid fraction 
V 
Vapor fraction 
x 
Molar fraction 
Exo
chem,REF−i
Chemical exergy 
R 
Ideal gas constant 
P 
Pressure (kPa) 
G 
Gibbs energy (kJ) 
ΔfGi
0 
Gibbs energy of formation at reference temperature(To) 
and pressure (Po)(kJ) 
Exchem 
Chemical molar exergy 
Ex 
Total exergy 
Exds 
Exergy destruction 
Exin 
Exergy of input streams 
Exout 
Exergy of output streams 
η 
Exergy efficiency 
IP 
Exergetic improvement potential 
HHV 
Higher heating value 
(
kJ
mol
)
RON 
Research octane number  
A. Sana et al.                                                                                                                                                                                                                                    
Process Safety and Environmental Protection 188 (2024) 64–72
66
reference pressure and temperature of pure chemical components of 
material stream. L, V and xi are liquid fraction of the stream, vapor 
fraction of the stream, and the molar fraction of component i, respec­
tively (Hinderink et al., 1996b). 
2.2. Chemical exergy 
Chemical exergy is the maximum possible work accomplished by 
taking a material stream from a state of thermo-mechanical equilibrium 
to a state of chemical and thermo-mechanical equilibrium with the 
environment. The mathematical form for calculation of chemical exergy 
of components which do not exist in reference environment is given 
below: 
Ex0
chem,i=Δf Gi
0−∑
j
vjEx0
chem,j
(2)  
where Ex0
chem,iis standard chemical exergy of any species i, Ex0
chem,jis stan­
dard chemical exergy of the element j in species i and ΔfGi
0 is Gibbs 
energy of formation at the reference temperature and pressure i.e To and 
Po, respectively, the higher heating value “HHV” is equal to specific 
chemical exergy (Dincer and Rosen, 2021). The chemical exergy of 
multicomponent material stream is determined by; 
E
xchem=L0 ∑
n
i=1
x0,iEx0l
chem,i
+V0 ∑
n
i=1
y0,iEx0v
chem,i
(3)  
where Exchemis chemical molar exergy (kJ/mol), L is liquid fraction and V 
is vapor fraction. 
The sum of physical exergy and chemical exergy is called total 
exergy. 
Ex = Exphy + Exchem
(4)  
2.3. Process irreversibility or exergy destruction 
Process irreversibility indicates that the total amount of exergy 
destroyed through each process unit. It is the difference in exergy of 
input and output streams (Dogbe et al., 2018). 
Exds = Exin −Exout
(5)  
2.4. Exergy Efficiency 
It is a benchmark that depicts the nearness of a system to the ideal. It 
delivers a more evocative assessment of the performance of a process 
than orthodox energy efficiency. Among numerous exergy efficiency 
expressions anticipated in the literature, the simplest and most 
commonly used is fraction of the output exergy to input exergy of a 
process. 
η = Exout
Exin
(6)  
2.5. Exergetic improvement potential 
Improving exergy efficiency and diminishing irreversibility are 
restricted by scientific and monetary limitations. Therefore, the exer­
getic improvement potential is assessed to depict the magnitude and 
compare conceivable improvement potentials of processes. Exergetic 
improvement potential is a resultant of exergy efficiency and irrevers­
ibility. 
IP = (1 −η)Exds
(7)  
3. Process description 
In this section, process flow diagram in Aspen HYSYS environment of 
the petroleum refinery is described followed by details of input and 
output streams considered in this study. 
3.1. Process flow diagram (PFD) of petroleum refinery 
The Process Flow Diagram (PFD) of the petroleum refinery is shown 
in Fig. 2. The main processes in the PFD units are delayed coker, cata­
lytic cracking unit, hydrotreaters, reformer, hydrocracker, saturated gas 
plant, unsaturated gas plant, alkylation unit, amination unit, hydrogen 
plant, and isomerization unit. The delayed coker (DLC coker) is one of 
the types of coker which consists of furnace of multiple passes to heat the 
residual oil feed to its thermal cracking temperature. This produces 
petroleum coke and coker gas oil by conversion of long chain and heavy 
hydrocarbon molecules of the residual oil (Gary et al., 2007). Catalytic 
cracking (CCU cat cracker) produces gasoline, olefinic gases, and other 
petroleum products by converting the hydrocarbon fractions, high mo­
lecular weight, and high boiling point of crude oils. Originally the pro­
cess of thermal cracking was used for cracking of petroleum 
hydrocarbons but now almost it has been totally replaced by catalytic 
cracking, as it yields greater volumes of high-octane rating gasoline and 
produces by-product gases, with more carbon-carbon double bonds (i.e. 
olefins), which are of greater financial values than the gases produced by 
thermal cracking (Gary et al., 2007). The Diesel hydrotreating (DHT Dist 
Hydrotreater) also called catalytic hydrogen treating is used to reduce 
unwanted components from straight-run diesel fraction at moderate 
pressures and higher temperatures in a reactor by selectively reacting 
these components with hydrogen (Hoehn et al., 2017). To produce jet 
fuel and kerosene, the kerosene hydrotreating (KHT Kero Hydrotreater) 
is used for upgradation of raw kerosene distillate. The problem of 
corrosion in fuel handling, aircraft engines and storage facilities can 
occur due to sulfur and mercaptans present in the raw kerosene cuts 
which come from the crude distillation unit while the problem of color 
stability in product is caused by the presence of nitrogen in the raw 
kerosene feed from some crude oils (Parkash, 2003). The NSP Naphtha 
splitter is a splitting unit which splits hydrotreated naphtha into heavy 
naphtha and light naphtha. The feed of the reformer is prepared by the 
removal of nitrogen and sulfur from the heavy naphtha streams which is 
treated by the help of naphtha hydrotreater (NHT Naphtha Hydro­
treater) (Aitani, 2004). Catalytic Reformer (LPR Reformer) produces 
major blending product for gasoline by converting low octane straight 
run naphtha fractions into low sulfur and high-octane reformate. The 
byproduct of catalytic reformer is hydrogen which is consumed in pro­
cesses of hydrocracking and hydrotreating (Alfke et al., 2000). The 
process of hydrocracking takes place in the HCD hydrocracker which 
produce the high value transportation products and petrochemical 
feedstock by conversion of low value petroleum feed-stocks (Ward, 
1993). SGP Sat Gas Plant separates the gas liquids from the refinery gas 
coming from the distillation units and other process units. The wet gas 
Fig. 1. Concept of Exergy. where P,T denotes pressure and temperature 
respectively while molar flowrate is denoted by ˙
m (Akram et al., 2018). 
A. Sana et al.                                                                                                                                                                                                                                    
Process Safety and Environmental Protection 188 (2024) 64–72
67
streams from the fraction receivers, TCC, overhead accumulators of 
delayed coker and FCC is used in UGP Unsat Gas Plant for recovery of 
light hydrocarbons (C3 and C4 olefins). In the alkylation unit (alky), high 
octane gasoline, alkylate, is produced by the conversion of isobutane 
and low-molecular-weight alkenes (mainly a blend of propene and 
butene) in the presence of acid catalyst such as sulfuric acid (H2SO4). 
The amine acid gas removal (AMN Amine) is used for the removal of 
carbon dioxide (CO2) and hydrogen sulfide (H2S) from gases by utilizing 
the aqueous solutions of numerous alkylamines (amines) (Towler and, 
1997, 1998). To meet the demand of Hydrogen, hydrogen plant (HYD 
Hydrogen Plant) is required in petroleum refinery. To prepare feed for 
alkylation unit, isobutane is produced by isomerization process (IS4 C4 
Isom). Plant Fuel System (PFS) is used in petroleum refinery for the 
purpose of collection, preparation and distribution of gas fuel and liquid 
fuel (Bahadori, 2016). SRU Sulfur Recovery denotes unit that recovers 
elemental sulfur by conversion from hydrogen sulfide (H2S). For this 
purpose, Claus process is used (Jafarinejad, 2016). Gasoline, Distillate 
and Fuel Oil Blending processes are the final step in petroleum refinery 
process in which finished product is obtained by the mixing of optimum 
blend of components among various petroleum streams. 
3.2. Streams of petroleum refinery 
Input and output streams considered in this plant are discussed in 
Sections 3.2.1 and 3.2.2 respectively. 
3.2.1. Input streams 
The input streams considered for this analysis are provided in  
Table 1. The “CD1 Feed” and “CD2 Feed” streams are of sweet and sour 
crudes respectively. “Purchased C4M” is the stream which consist of i- 
Butane, 1-Butene and n-Butane and it is input of the Unsaturated Gas 
Plant which is denoted by “UGP Unsat Gas Plant” in the process flow 
diagram. “iC4” consist of i-butane and input of “SFA Sulf Acid Alkyl­
ation”. “H2S adjusted to SAMN” consist of H2S and input of AMN Amine. 
“Natural Gas” consist of Methane and input of HYD Hydrogen Plant. “hyl 
to DHT” consist of hydrogen and input of DHT Dist Hydrotreater. “h2 to 
KHT” consist of hydrogen and input of KHT Kero Hydrotreater. “hyl to 
NHT” consist of hydrogen and input of NHT Naphtha Hydrotreater. “hyh 
to HCD” consist of hydrogen and input of HCD hydrocracker. “h2 to 
Isom” consist of hydrogen and input of IS4 C4 Isom. “hyl to PFS” consist 
of hydrogen and input of PFS Plant Fuel System. “Purchased nC4” 
consist of n-Butane and input of IS4 C4 Isom. “H2S adjusted to SRU” and 
“Total Sul” both streams consist of H2S and they are inputs of SRU Sulfur 
recovery. 
3.2.2. Output streams 
The output streams considered for this analysis are listed in Table 2. 
“VR2 to Fuel Oil” consist of pseudo-components and output of Crude 
Units. “DLC Coke” consist of pseudo-components and output of DLC 
Fig. 2. Process Flow diagram of Petroleum Refinery.  
Table 1 
Input streams data  
Input 
Flowrate kg/ 
h 
Input 
Flowrate kg/ 
h 
CD2 Feed  
350200.00 
hyl to NHT  
102.02 
CD1 Feed  
233300.00 
hyh to HCD  
2010.61 
Purchased C4M  
7277.29 
h2 to Isom  
9.40 
iC4  
17843.82 
hyl to PFS  
2025.30 
H2S adjusted to SAMN  
1637.03 
Purchased nC4  
12840.00 
Natural Gas  
339.61 
H2S adjusted to SRU  
1799.93 
hyl to DHT  
34.15 
Total Sul  
589.96 
h2 to KHT  
29.74 
hyl to TGT  
0.02  
A. Sana et al.                                                                                                                                                                                                                                    
Process Safety and Environmental Protection 188 (2024) 64–72
68
Coker. “SGP c5+ and others” consist of Hydrogen, Ethylene, H2S, Pro­
pene, 1-Butene and pseudo-components and output of X-100. “iC4 to 
SFA” consist of i-Butane and output of SGP Sat Gas Plant. “SFA nc3” 
consist of Propane and output of SFA Sulf Acid Alkylation. “Alk. loss” 
consist of pseudo-components and output of TEE-100. “IS4 to SFA” 
consist of i-Butane and stream in SFA Sulf Acid Alkylation. “H2S total” 
consist of H2S and output of MIX-100. “h2 to HCD” and hyl from SLPR” 
consist of Hydrogen and output of HYD Hydrogen Plant. “PFS C1 to 
HYD” consist of Methane and output of PFS Plant Fuel System. “C2+ to 
Fuel” consist of Hydrogen, Ethylene, Ethane, Propane and pseudo- 
components and output of PFS Plant Fuel System. “H2S to SRU” 
consist of H2S and output of SRU Sulfur Recovery. Sulfur” consist of 
pseudo-components and output of SRU Sulfur Recovery. “Total Sul” 
consist of H2S and output of SRU Sulfur Recovery. “URG Regular” and 
“UPR Premium” consist of n-Butane and pseudo-components and output 
of Gasoline Blending. “LRG Leaded Reg” consist of n-Butane and pseudo- 
components and output of Gasoline Blending. “JET Kero/Jet” consist of 
pseudo-components and output of Distillate Blending. “HSF Hi Sulfur 
Fuel Oil, LSF Low Sulfur Fuel Oil and FO loss” consist of pseudo- 
components and outputs of Fuel Oil Blending. “Diesel and Export 
Diesel” consist of pseudo-components and outputs of TEE-100. “LPG” 
consist of Propane and traces of i-Butane and output of MIX-100. 
4. Methodology 
The methodology implemented in this work, shown in Fig. 3, was 
comprised of the following phases:  
I. Extraction of Process Information from Aspen HYSYS Model: 
Process information is extracted from the Aspen HYSYS model, 
which serves as the basis for subsequent calculations. This step 
involves accessing the Aspen HYSYS model through spreadsheet 
to gather data on input and output streams, operating conditions, 
and unit operations within the process. 
II. Importing Data into Microsoft Excel: After extracting the pro­
cess information from the Aspen HYSYS model, the data is im­
ported into Microsoft Excel for further analysis.  
III. Identification of Input and Output Streams: In this stage, the 
process model’s input and output streams from the simulated 
model were established. The material flow into and out of various 
units of the operation is represented by these streams. Every 
stream’s key information regarding its composition, flow rate, 
temperature, pressure, etc. was extracted into the Microsoft 
Excel.  
IV. Chemical and Physical Exergies Calculation: Following the 
identification of the input and output streams, each stream’s 
physical exergies are determined. Eq. (1) based on variables such 
as: temperature, pressure, and specific enthalpy is used in this 
computation. Each input and output stream’s chemical exergies 
are computed subsequent to the physical exergies. Chemical 
exergy-specific Eqs. (2,3) were used, involving the stream’s 
chemical composition and reference state characteristics.  
V. Calculation of Total Exergy: The total exergy of the input and 
output streams is determined by computing the physical and 
chemical exergies of each stream and added them together using 
Eq. (4). A comprehensive evaluation of the stream’s potential 
work content is given by the total exergy. 
VI. Based on Eqs. (5,6 and 7), exergy destruction or process irre­
versibility, exergetic improvement potential and exergy effi­
ciency were calculated respectively. 
5. Result and discussions 
In Section 5.1, calculations of plant wide exergy destruction or 
process irreversibility, exergy efficiency and energetic improvement 
potential have been discussed. 
5.1. Plant wide exergy analysis 
Plant wide exergy analysis performed in this study include exergy 
destruction, exergy efficiency and exergetic improvement potential. 
Physical exergy of input and output streams are discussed in Section 
5.1.1. Section 5.1.2 describe chemical exergy analysis of input and 
output streams and are ranked from largest to smallest value. Section 
5.1.3 describes the overall exergy analysis of petroleum refinery and the 
recommendations by which exergy efficiency can be enhanced while 
exergy destruction can be reduced. 
5.1.1. Physical exergy 
By the help of the extracted data, physical exergies of stream were 
calculated in Microsoft excel. The physical exergies of input and output 
Table 2 
Output streams data.  
Output 
Flowrate kg/ 
h 
Output 
Flowrate kg/h 
VR2 to Fuel Oil  
9.62 
H2S to SRU  
5485.00 
DLC Coke  
5407.00 
Sulfur  
589.96 
NSP water  
0.01 
Total Sul  
589.96 
SGP c5+ and 
others  
9.38 
URG Regular  
222600.00 
iC4 to SFA  
2094.00 
UPR Premium  
5004.00 
SFA nc3  
4283.98 
LRG Leaded Reg  
26090.00 
Alk. Loss  
509.29 
JET Kero/Jet  
68110.00 
IS4 to SFA  
17880.00 
Dist loss  
0.00 
H2S total  
4988.80 
HSF Hi Sulfur Fuel Oil  
20190.00 
h2 to HCD  
2010.61 
LSF Low Sulfur 
FuelOil  
72131.85936 
Hyl  
0.00 
FO loss  
1.539 
hyl from SLPR  
2200.63 
Diesel  
106701.3845 
PFS C1 to HYD  
4749.92 
Export Diesel  
573.7667075 
C2+ to Fuel  
21839.21 
LPG  
32890  
Fig. 3. Methodology employed for Exergy Analysis.  
A. Sana et al.                                                                                                                                                                                                                                    
Process Safety and Environmental Protection 188 (2024) 64–72
69
streams are ranked from highest to lowest values in Table 3 and Table 4 
respectively. Top five input streams with respect to values of physical 
exergies are “CD2 Feed”, “CD1 Feed”, “Purchased nC4”, “Purchased 
C4M” and “iC4” with the values of 204.19 kW, 132.72 kW, 129.83 kW, 
94.42 kW and 76.86 kW respectively. The lowest input stream with the 
respect to values of physical exergy is “hyl to TGT” and “Total Sul” with 
the value of approximately equal to 0 kW and 0.03 kW respectively. Top 
five output streams with respect to values of physical exergies are “LSF 
Low Sulfur FuelOil”, “Diesel”, “JET Kero/Jet”, “URG Regular” and “IS4 
to SFA” with the values of 8367.51 kW, 2602.79 kW, 1691.97 kW, 
1366.52 kW and 475.86 kW respectively. The reason for higher physical 
exergy value is that these streams have higher flowrates, temperature, 
and pressure. The lowest output stream with the respect to values of 
physical exergy is "C2+ to Fuel" with the value of −12.78 kW, the reason 
for being lowest is that it has low flowrate, temperature, and pressure. 
For side-by-side comparison, highest five input and output streams with 
respect to physical exergies are graphically represented in Fig. 4 and  
Fig. 5 respectively. 
5.1.2. Chemical exergy 
For the calculation of chemical exergy of streams, the standard 
chemical exergy of components of streams are used. Chemical exergies 
of input and output streams has been calculated, shown from highest to 
lowest value in Table 5 and Table 6. Top five input streams with respect 
to values of chemical exergies are “CD2 Feed”, “CD1 Feed”, “iC4”, 
“Purchased nC4” and “Purchased C4M” with the values of 
4244715.83 kW, 2822217.14 kW, 243712.03 kW, 175783.7 kW and 
98496.17 kW respectively. The lowest input stream with the respect to 
values of chemical exergy is “hyl to TGT” with the value of 0.65 kW. Top 
five output streams with respect to values of chemical exergies are “URG 
Regular”, “Diesel”, “LSF Low Sulfur Fuel Oil”, “JET Kero/Jet” and “LPG” 
with the values of 2424179.18 kW, 1310055.89 kW, 881611.61 kW, 
868345.74 kW and 457662.05 kW respectively, the higher values are 
due to higher flowrates, temperature and pressure of streams. The 
lowest output stream with the respect to values of chemical exergy is 
“hyl” with the value of approximately equal to 0 kW because it has low 
flowrate almost equal to zero. For side-by-side comparison, highest five 
input and output streams with respect to chemical exergies are graphi­
cally represented in Fig. 6 and Fig. 7 respectively. 
5.1.3. Overall exergy analysis 
Total exergy is sum of the physical and chemical exergy of each 
stream. By summing the values of both chemical and physical exergy, 
total exergy of input and output streams have been calculated which are 
shown from highest and lowest in Table 7 and Table 8 respectively. Top 
five input streams with respect to values of total physical exergy are 
“CD2 Feed”, “CD1 Feed”, “Utilities”, “iC4” and “Purchased nC4” with 
the values of 4244920.02 kW, 2822349.86 kW, 391852.93 kW, 
243788.89 kW and 175913.52 kW respectively. The lowest input stream 
with the respect to value of total exergy is “hyl to TGT” with the value of 
0.65 kW. Top five output streams with respect to values of total exergy 
are “URG Regular”, “Diesel”, “LSF Low Sulfur Fuel Oil”, “JET Kero/Jet” 
and “LPG” with the values of 2425545.70 kW, 1312658.68 kW, 
889979.13 kW, 870037.71 kW and 457669.01 kW respectively, the 
higher values are due to higher flowrates, temperature, and pressure of 
streams. The lowest output stream with the respect to values of total 
exergy is “hyl” with the value of approximately equal to 0 kW because it 
has low flowrate almost equal to zero. For side-by-side comparison, 
highest five input and output streams with respect to total exergies are 
graphically represented in Fig. 8 and Fig. 9 respectively. 
By comparison between exergy of top five input streams, it can be 
Table 3 
Physical exergy of input streams.  
Input 
Exergy (kW) 
Input 
Exergy (kW) 
CD2 Feed  
204.19 
H2S adjusted to SRU  
4.17 
CD1 Feed  
132.72 
H2S adjusted to SAMN  
3.54 
Purchased nC4  
129.83 
hyl to NHT  
3.25 
Purchased C4M  
94.42 
hyl to PFS  
1.29 
iC4  
76.86 
hyl to DHT  
1.09 
hyh to HCD  
64.05 
h2 to KHT  
0.95 
Natural Gas  
29.74 
Total Sul  
0.03 
h2 to Isom  
6.90 
hyl to TGT  
0.00  
Table 4 
Physical exergy of output streams.  
Output 
Exergy (kW) 
Output 
Exergy (kW) 
LSF Low Sulfur FuelOil  
8367.51 
H2S to SRU  
12.69 
Diesel  
2602.79 
H2S total  
10.80 
JET Kero/Jet  
1691.97 
Sulfur  
7.89 
URG Regular  
1366.52 
LPG  
6.97 
IS4 to SFA  
475.86 
Alk. loss  
5.20 
HSF Hi Sulfur Fuel Oil  
214.01 
VR2 to Fuel Oil  
2.18 
LRG Leaded Reg  
91.46 
Total Sul  
0.03 
hyl from SLPR  
70.10 
SGP c5+ and others  
0.01 
h2 to HCD  
55.00 
FO loss  
0.01 
UPR Premium  
36.20 
NSP water  
0.00 
iC4 to SFA  
29.47 
Dist loss  
0.00 
DLC Coke  
26.52 
Hyl  
0.00 
SFA nc3  
18.47 
PFS C1 to HYD  
-2.61 
Export Diesel  
14.00 
C2+ to Fuel  
-12.78  
Fig. 4. Physical exergy of input streams.  
Fig. 5. Physical exergy of output streams.  
Table 5 
Chemical exergy of input streams.  
Input 
Exergy (KW) 
Input 
Exergy (KW) 
CD2 Feed  
4244715.83 
H2S adjusted to SAMN  
7459.78 
CD1 Feed  
2822217.14 
Natural Gas  
5202.38 
iC4  
243712.03 
hyl to NHT  
3977.59 
Purchased nC4  
175783.70 
Total Sul  
2688.40 
Purchased C4M  
98496.17 
hyl to DHT  
1331.23 
hyl to PFS  
78960.48 
h2 to KHT  
1159.35 
hyh to HCD  
78387.73 
h2 to Isom  
366.49 
H2S adjusted to SRU  
8202.08 
hyl to TGT  
0.65  
A. Sana et al.                                                                                                                                                                                                                                    
Process Safety and Environmental Protection 188 (2024) 64–72
70
inferred that the input streams have very nominal values with respect to 
the value of utilities section. As there are ample reaction zones and 
energy losses in the petroleum refinery which causes the lowering of 
temperature and pressure due to which the utilities energy input is used. 
The input streams contain low quality and raw form of fuels which have 
low octane number. The purpose of petroleum refinery is to convert 
these raw crudes into high quality fuels having high octane numbers by 
passing through different separation and reactive zones and absorbing 
exergy by the help of utilities. In the plant-wide operations, the drop in 
physical exergy is transformed in the form of chemical exergy. 
As Plant wide total input exergy of all input streams is 
Table 6 
Chemical exergy of output streams.  
Output 
Exergy (kW) 
Output 
Exergy (kW) 
URG Regular  
2424179.18 
DLC Coke  
39050.54 
Diesel  
1310055.89 
iC4 to SFA  
28599.98 
LSF Low Sulfur FuelOil  
881611.61 
H2S to SRU  
24994.54 
JET Kero/Jet  
868345.74 
H2S total  
22733.43 
LPG  
457662.05 
Export Diesel  
7044.58 
LRG Leaded Reg  
283488.33 
Alk. loss  
5548.82 
C2+ to Fuel  
249330.96 
Total Sul  
2688.40 
IS4 to SFA  
244206.12 
Sulfur  
1507.69 
HSF Hi Sulfur Fuel Oil  
234428.33 
VR2 to Fuel Oil  
109.79 
hyl from SLPR  
85795.80 
SGP c5+ and others  
100.04 
h2 to HCD  
78387.77 
FO loss  
15.64 
PFS C1 to HYD  
72762.62 
Dist loss  
0.01 
SFA nc3  
59611.94 
NSP water  
0.00 
UPR Premium  
54498.52 
hyl  
0.00  
Fig. 6. Chemical exergy of input streams.  
Fig. 7. Chemical exergy of output streams.  
Table 7 
Total Exergy of Input Streams.  
Input 
Total Exergy 
(kW) 
Input 
Total Exergy 
(kW) 
CD2 Feed  
4244920.02 
H2S adjusted to SAMN  
7463.33 
CD1 Feed  
2822349.86 
Natural Gas  
5232.11 
Utilities  
391852.93 
hyl to NHT  
3980.84 
iC4  
243788.89 
Total Sul  
2688.43 
Purchased nC4  
175913.52 
hyl to DHT  
1332.32 
Purchased C4M  
98590.58 
h2 to KHT  
1160.30 
hyl to PFS  
78961.77 
h2 to Isom  
373.40 
hyh to HCD  
78451.78 
hyl to TGT  
0.65 
H2S adjusted to SRU  
8206.24 
H2S adjusted to SAMN  
7463.33  
Table 8 
Total Exergy of Output Streams.  
Output 
Total Exergy 
(kW) 
Output 
Total Exergy 
(kW) 
URG Regular  
2425545.70 
DLC Coke  
39077.06 
Diesel  
1312658.68 
iC4 to SFA  
28629.45 
LSF Low Sulfur 
FuelOil  
889979.13 
H2S to SRU  
25007.23 
JET Kero/Jet  
870037.71 
H2S total  
22744.23 
LPG  
457669.01 
Export Diesel  
7058.58 
LRG Leaded Reg  
283579.79 
Alk. loss  
5554.02 
C2+ to Fuel  
249318.18 
Total Sul  
2688.43 
IS4 to SFA  
244681.98 
Sulfur  
1515.58 
HSF Hi Sulfur Fuel 
Oil  
234642.35 
VR2 to Fuel Oil  
111.98 
hyl from SLPR  
85865.90 
SGP c5+ and 
others  
100.05 
h2 to HCD  
78442.78 
FO loss  
15.64 
PFS C1 to HYD  
72760.01 
Dist loss  
0.01 
SFA nc3  
59630.40 
NSP water  
0.00 
UPR Premium  
54534.72 
hyl  
0.00  
Fig. 8. Total exergy of input streams.  
Fig. 9. Total exergy of output streams.  
Table 9 
Exergy Analysis.  
Total Exergy (kW) 
Total Input  
8165266.98 
Total Output  
7461212.34 
Exergy Destruction  
704054.64 
Exergy Efficiency  
0.91 
Exergetic Improvement Potential  
60707.50  
A. Sana et al.                                                                                                                                                                                                                                    
Process Safety and Environmental Protection 188 (2024) 64–72
71
8165266.98 kW and total output exergy is 7461212.34 kW as shown in  
Table 9. Based on these calculations, the exergy destruction and exer­
getic improvement potential value calculated by Eq. (5) and Eq. (7) is 
704054.64 kW and 60707.50 kW respectively. As the exergy destruction 
or process irreversibility is a measure of the quantity of exergy destroyed 
in a process. Exergetic improvement potential is estimated to specify the 
magnitude and compare possible improvement potentials of processes. 
Value of exergy efficiency calculated by Eq. (6) is 91.38 %. Irrevers­
ibility phenomena is caused by:  
• Non-homogeneities produced from mixing two or more components 
with diverse temperatures, pressures, and concentrations.  
• The effect of dissipation is caused by electric resistance, friction, 
inelasticity, pressure drop or viscosity.  
• Chemical reactions in which produced entropy is proportional to 
extent of reaction (Ghannadzadeh, 2013; Le Goff, 1979) 
An integrated strategy focusing on multiple units and processes is 
needed to increase the effectiveness of physical and chemical energy 
utilization in the petroleum refining sector. In order to reduce energy 
losses and improve efficiency in real-time, advanced process control 
systems should be utilized. Dynamically controlling process parameters 
can be used to reduce energy consumption and maintain product quality 
standards in cracking units by optimising feedstock ratios (de de de 
Oliveira Junior and de Oliveira, 2013). Surplus heat from catalytic 
reforming processes to preheat feed streams, can reduce energy con­
sumption and minimize the exergy losses linked to heat transfer by 
implementing pinch analysis. An effective way to address thermal in­
efficiencies and minimize exergy losses in vital parts like piping, vessels, 
and equipment is to upgrade insulation materials and methods in high 
temperature environments. There is potential for significant energy 
savings when antiquated compressors, turbines, and pumps are swapped 
out for more effective models (Mehdizadeh-Fard et al., 2018). One more 
efficient way to improve separation efficiency and reduce energy con­
sumption is to optimise distillation columns using cutting-edge control 
strategies and intensive tray/column designs. Optimising tray designs in 
crude distillation units can enhance vapor-liquid contact and improve 
separation efficiency, thereby mitigating the exergy losses that are 
linked to separation processes (Sajedi et al., 2015). Energy consumption 
and process efficiency can be increased by employing process intensi­
fication techniques like membrane separation, reactive distillation, and 
microreactor systems. Furthermore, the optimisation of chemical pro­
cesses is essential for reducing energy-intensive procedures and the 
corresponding energy losses. For example, reducing energy re­
quirements and minimising exergy losses can be achieved by optimising 
catalyst regeneration processes in hydrotreating units, which will ulti­
mately lead to increased overall efficiency (Bandyopadhyay et al., 
2019). The exergy efficiency can be improved and exergy destruction or 
process irreversibility can be decreased by the implementations of en­
gineering solutions, which will make the process more energy efficient, 
feasible and economical. Improvement in exergy efficiency and mini­
mizing the exergy destruction are restricted by economic and techno­
logical constraints. However, it is very important to evaluate the 
suggested engineering solutions before their implications on the basis of 
economic evaluation through exergoeconomics studies. 
6. Conclusions 
In plant-wide model, exergy of 391852.93 kW of utility was 
consumed. The reason behind this consumption is the fact that the pe­
troleum refinery is overall comprised of endothermic processes which 
comsume the exergy. Total exergy of all input streams was 
8165266.98 kW and total output exergy was 7461212.34 kW. The 
exergy requirements for the conversion of low quality, low RON of raw 
fuels into high quality (high RON) fuels are met up with the help of 
utilities streams. The drop in physical exergy is also transformed in the 
form of chemical exergy. The process model was found 91.38 % exergy 
efficient with exergy destruction of 704054.64 kW and exergetic 
improvement potential of 60707.50 kW. The current study is solely 
performed on the input and output streams of plant wide petroleum 
refinery but in the future studies, exergy analysis can be executed on 
equipment level for investigating the equipment level improvement 
potentials. 
CRediT authorship contribution statement 
Adil Sana: Conceptualization, Data curation, Formal analysis, 
Funding acquisition, Investigation, Methodology, Project administra­
tion, Resources, Software, Validation, Visualization, Writing – original 
draft, Writing – review & editing. Iftikhar Ahmad: Conceptualization, 
Data curation, Formal analysis, Funding acquisition, Investigation, 
Methodology, Project administration, Resources, Software, Supervision, 
Validation, Visualization, Writing – original draft, Writing – review & 
editing. Husnain Saghir: Conceptualization, Data curation, Formal 
analysis, Funding acquisition, Investigation, Methodology, Project 
administration, Resources, Software, Visualization, Writing – original 
draft, Writing – review & editing. Manabu Kano: Conceptualization, 
Data curation, Formal analysis, Funding acquisition, Investigation, 
Methodology, Project administration, Resources, Software, Validation, 
Visualization, Writing – original draft, Writing – review & editing. 
Hakan Caliskan: Conceptualization, Data curation, Formal analysis, 
Funding acquisition, Investigation, Methodology, Project administra­
tion, Resources, Software, Supervision, Validation, Visualization, 
Writing – original draft, Writing – review & editing. Hiki Hong: 
Conceptualization, Data curation, Formal analysis, Funding acquisition, 
Investigation, Methodology, Project administration, Resources, Soft­
ware, Supervision, Validation, Visualization, Writing – original draft, 
Writing – review & editing. 
Declaration of Competing Interest 
The authors declare that they have no known competing financial 
interests or personal relationships that could have appeared to influence 
the work reported in this paper. 
Acknowledgment 
This study is supported by the Higher Education Commission (HEC), 
Pakistan, under the National Research Program for Universities (NRPU) 
Project No. 10215/ Federal. 
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A. Sana et al.

Fuel 328 (2022) 125292
Available online 29 July 2022
0016-2361/© 2022 Elsevier Ltd. All rights reserved.
Full Length Article 
Optimized catalytic pyrolysis of refinery waste sludge to yield clean high 
quality oil products 
Ali Kamali a, Setareh Heidari b, Abooali Golzary c, Omid Tavakoli d,*, David A. Wood e 
a Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD 20742, USA 
b Department of Chemical Engineering, Auburn University, USA 
c School of Environment, College of Engineering, University of Tehran, Tehran, Iran 
d School of Chemical Engineering, College of Engineering, University of Tehran, Tehran, Iran 
e DW Energy Limited, Lincoln, United Kingdom   
A R T I C L E  I N F O   
Keywords: 
Refinery oil sludge (ROS) 
Upgraded oil product 
Pyrolysis 
Response surface methodology 
Nickel-zeolite catalysts (Ni/HZSM-5) 
A B S T R A C T   
The production of clean oil products from oily waste can help the oil industry to become more efficient and 
sustainable. A novel technique is developed that takes waste refinery oily sludge (ROS) as feedstock to produce a 
clean high quality oil product. The cost of the feedstock supply is very low as ROS is a waste product that is costly 
to dispose of with potential adverse consequences for the ecosystem if improperly discarded. The novel process 
involves a two-stage tubular fixed-bed reactor capable of converting the ROS into a clean oil product and 
simultaneously upgrade its quality. To systematically optimize the process variables, the response surface 
methodology (RSM) is applied. A meticulous mathematical model with an error of less than 5% estimates the 
optimum point for achieving the maximum product yield. The upgrading process involves a specifically syn­
thesized, highly selective metal (Ni/Co/Mo) loaded zeolite catalysts. The synthesized catalysts are carefully 
characterized using XRD, FTIR, ICP, and SEM analysis. The detailed compositions of the upgraded products are 
also characterized using GC–MS CHNS/O elemental, and GC techniques. Among the synthesized catalysts, the 3 
wt% Ni/HZSM-5 catalyst exhibited superior performance in reducing unfavorable oxygen, sulfur, and nitrogen 
contents in the oil products. Notably, the higher heating value of the upgraded product generated with nickel- 
zeolite catalysts (Ni/HZSM-5) is 44.24 MJ/kg which is in the range of naturally occurring crude oils (typically 
42 to 47 MJ/kg). This feature highlights the quality and value of the final product.   
1. Introduction 
Today, the sustainability of energy resources has become a major 
concern worldwide due to the continued rise in energy demand and 
energy insecurity with pressing concerns associated with the need to 
reduce greenhouse gas emissions from fossil fuels [1,2]. Despite the 
noticeable attempts made to transform renewable energies to electricity 
and transportation biofuels in order to deal with the universal energy 
shortage and environmental crisis, the contribution of renewable energy 
supply worldwide remains less than 20%. This is much less than supply 
from crude-oil refined products. Indeed renewable energy sources face 
fundamental infrastructure barriers inhibiting their rapid and large- 
scale development [3]. 
The global shortages of energy supply coupled with volatility in oil 
prices and emissions concerns have led many communities to seek 
alternative and reliable energy sources. In this regard, renewable, sus­
tainable bio-based chemicals and biofuels can offer promising advan­
tages over conventional fossil fuels by decreasing the greenhouse gas 
footprint of global transportation and reducing reliance on fossil fuel- 
derived energy and chemicals. So far, first-generation bio-fuels (i.e. bio- 
ethanol and biodiesel), principally derived from vegetable oil and food- 
crops, are extensively commercialized in some regions [3,4]. These 
conventionally produced biofuels offer huge benefits in terms of pro­
ducing invaluable fuel supply, with little toxicity, less green-house gas 
emissions, and the potential for long-term sustainability in compar­
ison with non-renewable fossil fuels. Yet, when it comes to the cost of 
the raw materials plus operations and supply prices, it is difficult to 
justify the diversion of valuable and essential food-crops into biofuel 
feedstocks. The mass production of direct food-crop-based biofuels 
undermines food production, and tends to inflate the prices of certain 
* Corresponding author. 
E-mail address: otavakoli@ut.ac.ir (O. Tavakoli).  
Contents lists available at ScienceDirect 
Fuel 
journal homepage: www.elsevier.com/locate/fuel 
https://doi.org/10.1016/j.fuel.2022.125292 
Received 3 May 2022; Received in revised form 24 June 2022; Accepted 11 July 2022   
Fuel 328 (2022) 125292
2
crops, thereby exacerbating food availability and costs [5]. In this re­
gard, new areas of research have arisen regarding the development of 
non-food-crop-based biofuels, also known as advanced-generation bio­
fuels, which can be mainly derived from either non-edible vegetable oils 
or waste biomass or, in some cases waste cooking or engine oils. The 
primary advantages of the aforementioned classes of biofuels are asso­
ciated with their environmental benefits together with economic prof­
itability. Raw materials used in producing advanced-generation biofuels 
(and alternative fuels when referring to products generated from waste 
Fig. 1. Schematic diagram and photograph of the experimental setup for converting ROS to high quality fuel products (see also the graphical abstract).  
Fig. 2. The refinery oily sludge, as feedstock (left), and the oil product (right).  
Table 1 
Design of experiments of the non-catalytic pyrolysis of refinery oily sludge and 
the impacts of pyrolysis operating conditions on the efficiency of the generated 
product.  
Standard 
order 
Sequence of 
experiments 
T 
(◦C) 
Nitrogen 
flow rate 
(ml/min) 
Thermal 
heating rate 
(◦C/min) 
Product 
yield (wt. 
%) 
9 
1 
400  
0.2 
15  
17.16 
5 
2 
400  
0.6 
15  
15.84 
13 
3 
600  
0.2 
15  
19.81 
1 
4 
600  
0.6 
15  
17.07 
6 
5 
500  
0.2 
10  
18.48 
10 
6 
500  
0.6 
10  
17.12 
8 
7 
500  
0.2 
20  
19.75 
7 
8 
500  
0.6 
20  
17.82 
3 
9 
400  
0.4 
10  
18.43 
4 
10 
600  
0.4 
10  
19.87 
12 
11 
400  
0.4 
20  
19.66 
2 
12 
600  
0.4 
20  
21.12 
11 
13 
500  
0.4 
15  
18.45  
Table 2 
Summary of the results of regression analysis and p values for different equation 
models.  
Model 
p-Value 
Linear regression model 
Quadratic regression model 
Simple  
0.0544  
0.0083 
Square root  
0.0546  
0.0104 
Natural logarithm  
0.0549  
0.0131 
Decadic logarithm  
0.0549  
0.0131 
Inverse square root  
0.0553  
0.0164 
Inverse function  
0.0559  
0.0204 
Quadratic  
0.0545  
0.0055 
Cubic  
0.0552  
0.0042  
Table 3 
P-values and the statistical significance of components of the quadratic regres­
sion model to estimate the pyrolysis efficiency of refinery oily sludge.  
Expression 
P-value 
Is this parameter statistically significant? 
model  
0.0052 
Yes 
Q  
0.0026 
Yes 
T  
0.0029 
Yes 
H  
0.0066 
Yes 
Q × T  
0.0568 
Yes 
H × Q  
0.2154 
No 
H × T  
0.7271 
No 
(Q)2  
0.0062 
Yes 
(T)2  
0.1209 
No 
(H)2  
0.0082 
Yes  
A. Kamali et al.                                                                                                                                                                                                                                 
Fuel 328 (2022) 125292
3
or spent fossil fuel products) seem to be more economical than direct ­
food-crop-based biofuels. More importantly, the application of waste 
biomass and/or waste oils, as feedstocks, in biofuel production can not 
only significantly reduce the cost related to waste management, trans­
portation, and treatment processes but also mitigate the detrimental 
impacts of waste disposal on our environment [4]. 
One of the major types of thermochemical conversion processes 
capable of producing biofuel directly from biomass feedstock is fast 
pyrolysis, involving a rapid, high thermal decomposition of biomass 
feedstock in the absence of oxygen [5,7]. Pyrolysis can produce a liquid 
product that can be easily stored and transported. The output is com­
parable to refinery low grade oil and can be used directly. Pyrolysis of 
petroleum sludge emits less NOX and SOX pollutants and also concen­
trates heavy metals in petroleum sludge in the final solid product. The 
solid product (char) typically makes up 30 to 50% by weight of the main 
petroleum sludge and can be used as an adsorbent to remove various 
contaminants such as H2S and NOX in gas streams. The advantages of 
pyrolysis-derived biofuels and alternative fuels are however outstripped 
by their high oxygen content, demanding multiple upgrading strategies 
for compatibility with most conventional grades of fuel. In turn, the 
existence of high oxygen concentrations in pyrolysis products does result 
in a decrease in their energy densities along with an increase in their 
acidity and instability. This reduces their commercial feasibility for 
conversion to fuels for use in the majority of internal combustion en­
gines. However, integrating pyrolysis technology with catalytic 
upgrading processes, not only can significantly improve the product 
quality by decreasing its oxygen content, but also shorten reaction time 
and lower reaction temperature. 
Among various catalysts tested for the pyrolysis process of biomass/ 
waste feedstocks to biofuel, the Zeolite Socony Mobil–5 (ZSM-5; with 
generic chemical formula NanAlnSi96–nO192⋅16H2O) catalysts have 
demonstrated promising results. They can produce deoxygenated bio­
fuel and waste-to-fuel products, while concurrently enhancing their 
enrichment in C8 −C17 aromatic hydrocarbons. In this regard, Hernando 
Fig. 3. The oil product yield versus pyrolysis temperature and nitrogen gas flow rate at different thermal heating rates: a) 10 ◦C/min, b) 15 ◦C/min, and c) 20 
◦C/min. 
Table 4 
Optimum conditions for oil production from ROS through the non-catalytic 
pyrolysis process.   
Temperature 
(◦C) 
Nitrogen 
gas flow 
rate (ml/ 
min) 
Thermal 
heating 
rate (◦C/ 
min) 
Oil 
production 
yield (wt.%) 
Optimum point 
achieved by 
the proposed 
model (Eq. (3)) 
500  
0.327 
20  
20.19 
Optimum point 
achieved by 
the 
experiments 
500  
0.3 
20  
19.35  
A. Kamali et al.                                                                                                                                                                                                                                 
Fuel 328 (2022) 125292
4
et al. [6] reported about 50 wt% decrease in biofuel oxygen concen­
tration when using metal oxide ZSM-5 catalysts in a two-step (thermal/ 
catalytic) conversion of lignocellulose feedstock to biofuel. Similarly, 
Ma et al. [7] evaluated the effects of ZSM-5 based catalysts on the 
characteristics of oil products applied to the hydrothermal liquefaction 
of macroalgae. They reported a considerable improvement in produc­
tion yields and energy densities, by up to approximately 80% and 70%, 
respectively. 
A wide range of feedstock is being studied worldwide for the gen­
eration of cleaner alternative fuels to those produced directly by the 
refining of crude oil. Waste cooking oil, industrial fats, and animal fats 
have been extensively used as feedstock to produce eco-friendly biofuel 
[3,4]. Yet, their supply is typically limited in terms of quantities avail­
able in specific locations and is generally insufficient for large-scale 
mass production. Most recently, Jafari et al. [8] used a novel, in situ 
catalytic pyrolysis system for the production of biodiesel from wet 
microalgae using supercritical carbon dioxide, and reported results 
of approximately 25% biodiesel production yields. Nevertheless, bio- 
fuel production from microalgal species faces technological and eco­
nomic scale-up challenges. The process challenges include algae culti­
vation, harvesting, flocculation, centrifugation and lipid extraction are 
typically involved in algal feedstock preparation [8–10]. These con­
straints have accelerated the search for alternative sources of feedstock 
available in commercial quantities at low cost, such as refinery oily 
sludge. 
1.1. Refinery oily sludge 
For many years, oily sludge produced in petroleum refineries has 
been considered a hazardous waste, demanding appropriate disposal. 
The treatment and disposal of that waste requires a high level of pro­
tection isolating it from the environment and human contact, resulting 
in costly handling [11]. Refinery oily sludge (ROS) is primarily 
composed of a complex mixture of petroleum hydrocarbons (e.g. 
aliphatic and aromatic compounds), plus toxic metallic chemical ele­
ments (e.g. lead, arsenic, mercury and cadmium), sediment (mineral 
and rock grains) and water accumulated as part of crude oil exploitation 
and refining operations [12,13]. A current increment in the crude oil 
exploitation accompanied by the severe ecological risk, imposed by 
improper waste disposal, has raised the global awareness of the need to 
develop a more sustainable and efficacious waste management strate­
gies for ROS. Despite the emerging technologies for the treatment of 
industrial wastes including extraction with tunable solvents [14–17], 
filtration [18–26], and biodegradation [27,28], most of these technol­
ogies are either in their infancy or uneconomical when applied at the 
industrial scale. 
During the past few years, the concept of ’waste-to-wealth’, 
addressing the utilization of waste materials to create value-added 
products such as fuels, chemicals, and fertilizers, has received much 
attention [29–32]. This approach has stimulated this work, leading to 
the development of a two-stage, catalytic pyrolysis system to transform 
toxic ROS, as feedstock, into a high-value, cleaner upgraded oil product 
with potential to supplement fuel supply. 
The impacts of determining key pyrolysis parameters, including 
temperature, nitrogen gas flow rate, and thermal heating rate on the 
pyrolysis efficiency, have been systematically examined with Design 
Expert-11 software. This has made it possible to define a precise math­
ematical model to estimate the optimum point for achieving the highest 
product yield. The emphasis is to synthesize robust, highly selective ­
metal loaded HZSM-5 zeolite catalysts to upgrade the quality of the oil 
product to meet standards and specifications. Previous studies have 
mainly focused on improving product yields through catalytic/non- 
catalytic pyrolysis processes. Consequently, far less attention has been 
previously paid to waste-to-oil product quality management, there­
by limiting or frustrating the commercial utilization of the fuel products 
as compared to conventional refinery-produced fuels. 
1.2. Novel aspects of this study 
ROS has not previously been examined for the production of 
marketable upgraded oil products using a direct catalytic pyrolysis 
process in the presence of Ni/Co/Mo loaded HZSM-5 zeolite catalysts. 
The impacts of governing parameters on the product yield from the 
pyrolysis of ROS has been evaluated via Design Expert-11 simulation 
software. This has, for the first time, made it possible to formulate an 
accurate quadratic model describing this system. Its application de­
termines the optimal condition for the non-catalytic/catalytic conver­
sion of ROS to upgraded market-quality oil products. The effects of 
catalyst type using pure HZSM-5 and metal/HZSM-5 zeolites on product 
yield and quality in terms of compositions, higher heating value (HHV), 
Fig. 4. The interplays between temperature and nitrogen flow rate on oil product yield from non-catalytic pyrolysis of refinery oily sludge under the constant thermal 
heating rates of a) 20 ◦C/min and b) 10 ◦C/min. 
A. Kamali et al.                                                                                                                                                                                                                                 
Fuel 328 (2022) 125292
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and emission characteristics have been comprehensively studied. To do 
so, a state-of-the-art, two-stage tubular fixed-bed reactor capable of 
sequentially converting ROS into the pyrolysis oil and upgrading the 
product quality has been designed, constructed, and implemented. 
The laboratory-scale apparatus involves pyrolysis and reformer vessels, 
heating systems, temperature controllers, pipes, regulators, and related 
accessories, specifically customized to meet the requirements for the 
catalytic pyrolysis process of ROS conversion into useable fuel products. 
2. Materials and methods 
2.1. Materials 
Feed (ROS) was supplied from an Iranian oil refinery and imple­
mented as the feedstock sample. The catalyst preparation followed the 
procedure of Gopakumar et al. [33]. The ZSM-5 catalysts with silica to 
alumina ratio of 20 to 1 were purchased from Merck. The HZSM-5 was 
then successfully synthesized through the calcination of ZSM-5 at 550 ◦C 
for 3 h. Metal loaded catalysts were synthesized via the wet impregna­
tion method using HZSM-5 and a set of metal species (cobalt (Co), nickel 
(Ni), and molybdenum (Mo)). The impregnation of the parent catalyst 
(HZSM-5) was conducted in 20 ml of an aqueous solution containing 3 
wt% 
of 
metal 
salts, 
i.e., 
Co(NO3)2⋅6H2, 
Ni(NO3)2⋅6H2 
and 
(NH4)6Mo7O24⋅4H2O for 30 min. The metal salt composition of 3 wt% 
was selected based on previous studies [34,35]. Subsequently, the so­
lutions were dried at 120 ◦C, then samples were calcined in air at 500 ◦C 
for 3 h, and reduced in H2/N2 (%5 H2) for 2 h at 500 ◦C. Finally, the 
metal loaded catalysts, denoted as Co/HZSM-5, Ni/HZSM-5, and Mo/ 
HZSM-5 were obtained. 
2.2. Experimental process setup 
The designed and manufactured combination of instruments to carry 
out the conversion of ROS into a high-quality oil product consists of: (I) 
pyrolysis vessel: a cylinder with an inner diameter of 83 mm and a 
height of 230 mm; (II) reformer vessel: a cylinder with an inner diameter 
of 55 mm, but similar in height to the pyrolysis vessel; (III) heating 
system: the heating of the pyrolysis and reformer vessels is conducted 
using electric elements (IV) temperature controllers: the temperature of 
the vessels are monitored with two PID temperature gauges in 
conjunction with two type-K thermocouples; (V) Nitrogen cylinder: ni­
trogen gas is used as gas flow, supplied from the cylinder with a purity of 
99.999% (VI) pipes, regulator and related accessories: all equipment is 
made of stainless steel-316 at the nominal diameter of 1/4 in., capable of 
Fig. 5. The recorded effects of a) temperature, b) nitrogen flow rate and c) thermal heating rate on oil product yield during the non-catalytic pyrolysis process of 
refinery oily sludge. 
A. Kamali et al.                                                                                                                                                                                                                                 
Fuel 328 (2022) 125292
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withstanding pressures up to 250 bar. Fig. 1 illustrates the novel and 
specifically customized experimental setup implemented for the waste- 
to-oil conversion process of ROS. 
2.3. Methodology 
The pyrolysis vessel is fed with 30 g of the feedstock. Prior to initi­
ating any experiments, a continuous flow of nitrogen gas, as an inert 
gas, is passed through the pyrolysis chamber to remove the oxygen and 
to sweep the exhausted gases during the pyrolysis reaction. After about 
2 hours, the pyrolysis reactions are conducted at a temperature of 500 ◦C 
for half an hour. The released pyrolysis gases are rapidly condensed into 
a collector bottle filled with isopropyl alcohol (IPA, obtained from 
Merck) which is placed into an ice-water bath. The uncondensed gases 
were swept into a gasbag for GC analysis. The IPA solvent is then 
evaporated under vacuum at 45 ◦C for 50 min, and the remaining 
mixture is weighed to compute the raw oil product yield by applying Eq. 
(1). 
Oil Product Yield(%) = Mass of produced oil(g)
Mass of feed(g)
× 100%
(1) 
The catalytic pyrolysis process is then conducted under the opti­
mized conditions using a fixed feed to catalyst ratio of 100:5 (wt./wt.), 
and the effects of catalyst type on the oil product yield along with its 
quality are comprehensively evaluated. Fig. 2 shows images of the ROS 
feed used in this study versus the upgraded oil product. 
2.4. Design of experiments 
To achieve the optimal conditions for running a process, there are 
Fig. 6. X-ray diffraction patterns of the HZSM-5 and metal (Ni/Co/Mo) loaded 
HZSM-5 catalysts. 
Fig. 7. SEM images of the synthesized catalysts: a) HZSM-5, b) 3 wt% Ni/HZSM-5 c) 3 wt% Co/HZSM-5 d) 3 wt% Mo/HZSM-5.  
A. Kamali et al.                                                                                                                                                                                                                                 
Fuel 328 (2022) 125292
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two approaches: One is to apply a trial-and-error strategy and the other 
approach is developing a design of experiment (DOE) to establish a 
quantitative relationship between the main parameters of the process 
and the process outputs, and consequently obtain the best experimental 
design [8]. DOE strategy is typically used to reduce the number of 
experimental runs. The trial-and-error method, however, may waste a 
large quantity of experiment materials especially in the prolonged tests. 
Besides, in some circumstances, it may not be possible to recover the 
reactants and/or catalysts; hence, these materials must be disposed of 
after each experiment/trial until achieving the desired results. Thus, in 
the present research, the justified, robust, and comprehensive DOE 
strategy was used to investigate the impact of the key operating vari­
ables (temperature, nitrogen flow rate, and thermal heating rate) 
together with their interplays on oil product yield. 
According to previous work, the detrimental parameters affecting 
the pyrolysis yield are temperature, pressure, thermal heating rate, ni­
trogen gas flow rate, catalyst type, and conversion time duration [36]. 
Design Expert 11 software was used to obtain the experimental design. 
The Response Surface Method (RSM) using the Box-Behnken design 
technique [37] was employed to optimize temperature, thermal heating 
rate along with the nitrogen gas flow rate. The ranges of temperature 
(400–600 ◦C), thermal heating rate (10–20 ◦C/min) and nitrogen gas 
flow rate (0.2–0.6 ml/min) were selected based on exploratory 
experiments. 
Three influential parameters each at three levels – temperature (400, 
500, and 600 ◦C), nitrogen gas flow rate (0.2, 0.4, and 0.6 ml/min), and 
thermal heating rate (10, 15, and 20 ◦C/min) were considered in the test 
design. Subsequently, a set of experimental tests, based on the design of 
experiments, were carried out and pyrolysis yields were calculated for 
each run. A statistical analysis of pyrolysis yield data was then con­
ducted to systematically evaluate how the degree of design parameters 
influenced oil product yield. In this regard, regression analysis was used 
to identify the correlation between the dependent variable (pyrolysis 
yield) and three independent variables i.e., temperature, thermal heat­
ing rate and nitrogen gas flow rate. The level of significance of compo­
nents for regression models were determined based on p-values. 
Generally, in null hypothesis significance testing, a p-value less than 0.1 
(more precisely less than 0.05) is considered statistically significant. 
Once optimal conditions for the non-catalytic conversion of the re­
finery oily sludge to oil product were established, further conversion 
processes were conducted using HZSM-5, 3 wt% Ni/HZSM-5, 3 wt% Co/ 
HZSM-5 and 3 wt% Mo/HZSM-5 catalytic systems. 
2.5. Feed, catalyst and product characterizations 
2.5.1. Feed characterizations 
2.5.1.1. Elemental composition and higher heating value analysis. The 
elemental composition of the refinery oily sludge including carbon, 
hydrogen, nitrogen, and sulfur was analyzed using Vario ELIII 
500
1000
1500
2000
2500
3000
3500
4000
Intensity (a.u.)
Wavenumber (cm-1)
 3%Co/HZSM-5
 3%Mo/HZSM-5
 3%Ni/HZSM-5
 HZSM-5
Fig. 8. Fourier transform infrared spectroscopy spectra for the prepared cata­
lyst samples. 
Table 5 
Structural properties of the synthesized catalysts in terms of specific surface area 
(SBET (m2/g)), total pore volume (VP (cm3/g)), average pore diameter (nm) and 
metallic element contents (ICP (wt.%)).  
Catalyst samples 
SBET (m2/ 
g) 
VP (cm3/ 
g) 
Average pore diameter 
(nm) 
ICP (wt. 
%) 
HZSM-5  
327.47  
0.16  
1.88  
– 
3 wt% Ni/ 
HZSM-5  
276.59  
0.14  
2.08  
2.97 
3 wt% Co/ 
HZSM-5  
296.39  
0.15  
1.96  
2.94 
3 wt% Mo/ 
HZSM-5  
327.02  
0.15  
1.97  
2.92  
Table 6 
The weight percentage of the oil product, solid and gases during non-catalytic 
and catalytic pyrolysis of ROS at T = 500 ◦C, nitrogen flow rate = 0.3 (ml/ 
min) and thermal heating rate = 20 ◦C/min.  
Type of process 
Catalyst type 
Oil product 
(wt.%) 
Solid 
(wt.%) 
Gas (wt. 
%) 
Non-catalytic 
pyrolysis process 
–  
19.35  
15.72  
64.93  
Catalytic pyrolysis 
process 
HZSM-5  
18.63  
15.82  
65.55 
3 wt% Ni/ 
HZSM-5  
15.77  
15.58  
68.66 
3 wt% Co/ 
HZSM-5  
17.2  
15.54  
67.26 
3 wt% Mo/ 
HZSM-5  
17.92  
15.66  
66.42  
No Catalyst
HZSM-5
3%Ni/
HZSM-5
3%Mo/
HZSM-5
3%Co/
HZSM-5
0
20
40
60
80
100
Selectivity (%)
 CO2
 CH4
 H2
Fig. 9. The compositions of gas products measured by GC analysis.  
A. Kamali et al.                                                                                                                                                                                                                                 
Fuel 328 (2022) 125292
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equipment with an SS column 6 by 5 mm by 2 m – HayeSep Q. The 
oxygen content in the feedstock was also computed using the difference 
method. The results of the elemental analysis were then used to calculate 
the HHV using the Dulong Equation (Eq. (2)) [38]. 
HHV
(MJ
Kg
)
= 0.338C + 1.428
(
H −O
8
)
+ 0.095S
(2)  
2.5.2. Catalysts’ characterization 
2.5.2.1. Scanning electron microscopy. Scanning electron microscopy 
(SEM) was conducted using an AIS2100-Seron Technology model to 
analyze the morphology of the synthesized catalysts. 
2.5.2.2. Inductively coupled plasma. In order to meticulously analyze the 
composition of the synthesized catalysts, inductively coupled plasma 
(ICP) – optical emission spectrometry (OES) was carried out by using an 
Agilent-7900 ICP device. 
2.5.2.3. X-ray 
diffraction. The 
crystallographic 
structure 
and 
morphology of the prepared catalysts were examined using an X-ray 
diffraction (XRD) instrument (PW1730, Netherlands). 
2.5.2.4. Brunauer–Emmett–Teller. The porosity and the specific surface 
area of the synthesized catalysts were precisely characterized using 
the Brunauer–Emmett–Teller (BET) method [39] using a physical gas 
adsorption instrument (BELSORP MINI II). 
2.5.2.5. Fourier transform infrared. In order to detect the chemical 
structure and the changes in the functional groups after the catalytic 
modification process, Fourier Transform Infrared Spectroscopy (FTIR) 
analysis was performed by an infrared spectroscopy instrument (Avatar- 
Thermo). 
2.5.3. Product characterizations 
2.5.3.1. Gas chromatography-mass spectrometry. A gas chromatography- 
mass spectrometry (GC–MS) analyzer (Agilent GC 6890 N, MS 5973 N 
with an HP-5 column (30 m × 0.25 mm × 0.25 µm)) was used to identify 
the chemical compounds in the oil product. For this purpose, Helium gas 
(purity of 99.999%) was utilized as a carrier gas. The temperature 
program, starting from 40 ◦C for 5 min, was incremented in 10 ◦C steps 
to 225 ◦C, and then kept constant at 225 ◦C for 30 min. 
2.5.3.2. Gas chromatography. For the quantitative analysis of the 
gaseous pyrolysis products, gas chromatography (GC) was carried out 
using a Young Lin Acme 6100 GC device, equipped with an HID detector 
with two filled columns (Agilent 19,095Q-P04 (Q)) and a molecular 
sieve column. 
2.5.3.3. Elemental composition and higher heating value analysis. The 
elemental composition and higher heating value of the oil product were 
analyzed through the same procedure described in section 2.5.1.1, and 
the results were compared to those of the feedstock. 
3. Results and discussion 
3.1. Design of experiments (DOE) 
3.1.1. Suitable model for estimating the yield of oil product from refinery 
oily sludge 
Table 1 summarizes the effects of pyrolysis temperature, nitrogen gas 
flow rate, and thermal heating rate on the oil product yield during the 
pyrolysis of ROS. 
The DOE data (Table 1) was evaluated numerically to determine the 
best fitting model to calculate oil product yield as a function of tem­
perature (T), nitrogen flow rate (Q), and thermal heating rate (H). The 
best predictive model was selected based on the minimum p-value (min 
P) approach and/or maximum regression coefficient. Table 2 lists the 
results of regression analysis and p-values for various equation models 
evaluated for the non-catalytic pyrolysis process applied to ROS. 
The p-values (Table 2) indicate that the cubic equation model best 
describes the efficiency of oil product yield from ROS. From these re­
sults, it was also concluded that fitting the data using the quadratic 
regression achieved more significant relationships than those obtain 
through linear regression. 
Table 3 provides the p-values and level of significance of components 
for second-order regression models to estimate the pyrolysis efficiency 
of the ROS conversion process. Variables with statistically insignificant 
parameter estimates (i.e., P > 0.1) were eliminated from the model, 
thereby leaving a model based on the remaining variables with the high 
regression coefficient of 0.985 (Eq. (3)). This high regression coefficient 
demonstrates the potential usability of the model to successfully predict 
the yield of oil product from ROS under the different experimental 
conditions. 
(Oil Product Yield %)3 = 12503.38072 + 30405.39728 × Q −21.59454 × T
−1214.03092 × H −20.38347 × Q × T
−30852.81293 × Q2 + 0.038370 × T2
+ 44.77319 × H2
(3) 
Using the proposed equation (Eq. (3)), 3-dimensional graphs were 
constructed showing oil product yield as a function of the nitrogen gas 
flow rate and the pyrolysis temperature under various thermal heating 
rates (Fig. 3). According to Fig. 3, medium temperature, medium N2 flow 
rate, and high thermal heating rate were recognized as favorable con­
ditions for oil product generation through the pyrolysis process. The 
impacts of the aforementioned operating parameters on oil product 
yield are discussed in section 3.1.2. 
A numerical method was then applied using the Design Expert-11 
software to determine the optimal operating conditions of pyrolysis of 
ROS. The results of the optimization attempts are provided in Table 4. 
While taking into consideration the results provided in Table 4, it can 
be reasonably concluded that the obtained theoretical optimum values 
are located close to the experimentally derived values (errors <5%). 
Table 7 
Results of N, S, H, O elemental analysis and higher heating value measurements for the feedstock and the liquid phase of the pyrolysis product.   
C (wt.%) 
H (wt.%) 
N (wt.%) 
S (wt.%) 
O (wt.%) 
H/C 
N/C 
S/C 
O/C 
HHV(MJ/Kg)1 
Feed (moisture: 27.6 wt%; ash content: 14.7 wt%) 
61.73  
9.42  
4.07  
2.35  
22.43  
1.83  
0.06  
0.014  
0.27  
30.54 
Non-catalytic pyrolysis 
72.22  
9.95  
2.13  
1.93  
13.77  
1.65  
0.03  
0.01  
0.14  
36.34  
Catalytic pyrolysis 
HZSM-5  
75.37  
10.14  
2.02  
1.66  
10.81  
1.61  
0.02  
0.008  
0.11  
38.18 
3 wt% Ni/HZSM-5  
84.59  
11.07  
1.75  
1.23  
1.47  
1.57  
0.02  
0.005  
0.01  
44.24 
3 wt% Co/HZSM-5  
80.69  
10.92  
1.97  
1.54  
4.88  
1.62  
0.02  
0.007  
0.05  
42.14 
3 wt% Mo/HZSM-5  
77.47  
10.38  
1.91  
1.26  
8.98  
1.61  
0.02  
0.006  
0.09  
39.52  
1 HHV Calculated with Eq. (2). 
A. Kamali et al.                                                                                                                                                                                                                                 
Fuel 328 (2022) 125292
9
This result verifies the accuracy of the developed quadratic model to 
estimate the optimum temperature, nitrogen flow rate, and thermal 
heating rate for the pyrolysis processes applied to ROS. 
3.1.2. Effects of different parameters on oil product yield obtained by 
pyrolysis of refinery oily sludge 
Fig. 4 demonstrates the interactions between temperature and ni­
trogen flow rate on oil product yield at the constant thermal heating 
rates of 10 ◦C/min and 20 ◦C/min. According to Fig. 4, at a constant 
nitrogen flow rate, oil product yield increased with the increase of 
temperature. The liquid yields at the low nitrogen flow rate are more 
substantial than those achieved at the high nitrogen flow rate. 
Fig. 5 illustrates the individual effects of each parameter on the oil 
production efficiency. According to Fig. 5(a), at lower temperatures (T 
<450 ◦C) the oil product yield remained almost the same at close to 
20%; by a further increment in the temperature, oil product yield 
increased very gradually to reach approximately 21% at 600 ◦C. 
Increasing the temperature above 500 ◦C to 600 ◦C therefore had a 
negligible impact on oil product yield (less than 1%). Given that 
outcome, and to prevent any unnecessary further energy consumption, a 
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
1.56
1.58
1.60
1.62
1.64
1.66
 No Catalyst
 HZSM-5
 3%Ni/ HZSM-5
 3%Mo/ HZSM-5
 3%Co/ HZSM-5
H/C
O/C
(a) 
(b) 
Fig. 10. Van Krevelen diagrams of: (a) the oil products generated from ROS pyrolysis; (b) a range of organic materials, fuels and pyrolysis products (modified after 
Wood, 2022 [56]). 
A. Kamali et al.                                                                                                                                                                                                                                 
Fuel 328 (2022) 125292
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temperature of 500 ◦C was chosen for further process system 
optimization. 
Fig. 4(b) and Fig. 5(b) show that for a constant temperature, oil 
product yield increased with an increase in nitrogen flow rate to reach 
its peak, followed by a downward trend in oil production with a further 
increase in nitrogen flow rate. This reverse trend in the oil product yield 
may be associated with the interplay between two parallel reaction 
pathways in the pyrolysis process, i.e., hydrolysis and repolymerization. 
It may be explained in terms of the higher the nitrogen flow rate, the 
lower gas the residence time. That lower residence time probably lessens 
the opportunity for the produced gases to take part in aromatic ring 
formation, and consequently decreases the oil product yield. On the 
other hand, at a low nitrogen flow rate, the repolymerization and/or 
hydrocracking of the intermediate components may be conducted, 
generating either heavy aromatics and polyaromatic coke or gaseous 
and volatile compounds, thereby also reducing oil product yield [35]. 
Thus, it is important to maintain optimal nitrogen flow rate during the 
pyrolysis of ROS. 
Fig. 5(c) shows the recorded effects of the thermal heating rate on oil 
product yield during pyrolysis conversion of ROS at constant tempera­
ture and nitrogen flow rate of 500 ◦C and 0.327 ml/min, respectively. 
Oil product yield that started at just below 19% at the thermal heating 
rate of 10 ◦C/min, fell slightly to just over 18% for the thermal heating 
rate of 15 ◦C/min. Oil product yield then experienced a moderate rise 
(roughly 10%) by increasing the thermal heating rate from 15 ◦C/min to 
20 ◦C/min. 
Fig. 11. GC–MS analysis of the oil products from: a) non-catalytic pyrolysis b) catalytic pyrolysis by HZSM-5c) catalytic pyrolysis by 3 wt% Ni/HZSM-5 d) catalytic 
pyrolysis by 3 wt% Co/HZSM-5 e) catalytic pyrolysis by 3 wt% Mo/HZSM-5. 
No Catalyst
HZSM-5
3%Ni/
HZSM-5
3%Mo/
HZSM-5
3%Co/
HZSM-5
16
20
24
28
32
Aromatic Selectivity (%)
Fig. 12. The selectivity of aromatic compounds produced throughout the 
catalytic/non-catalytic pyrolysis of refinery oily sludge. 
Fig. 13. The SEM images of the remaining solid phase (char) after the pyrolysis 
process of refinery oily sludge. 
A. Kamali et al.                                                                                                                                                                                                                                 
Fuel 328 (2022) 125292
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3.2. Characterization of the synthesized catalysts 
3.2.1. X-ray diffraction analysis 
Phase analysis of the X-ray diffraction (XRD) patterns of the HZSM-5 
and metal/HZSM-5 catalysts are illustrated in Fig. 6. 
No extra diffraction peaks are detected in the XRD patterns of the 
synthesized impregnated catalysts compared to that of the original 
HZSM-5 (Fig. 6). These similar reflection patterns suggest that no sig­
nificant loss of zeolite crystallinity occurred after modification [40]. The 
intensified peak located at 23–25◦can be attributed to the strong crys­
tallinity of the HZSM-5. However, a slight decrease in the intensities of 
these peaks after metal impregnation may be interpreted as a lattice 
distortion of the zeolite that occurs after metal incorporation. 
The diffractograms of the metal loaded HZSM-5 catalyst samples do 
not detect intensified peaks corresponding to the metal oxides. This 
observation, however, does not, in itself, disprove the existence of metal 
oxides in the surface of the HZSM-5 catalyst, as these substances may 
still be present either as amorphous phases and/or well dispersed fine 
particles in the external surface of the zeolite [41]. 
3.2.2. Scanning electron microscopy analysis 
The morphologies of the synthesized HZSM-5 and metal/HZSM-5 
catalysts were characterized by scanning electron microscopy (SEM) 
analysis as shown in Fig. 7. 
HZSM-5 (Fig. 7(a)) has a common hexagonal framework and sheet- 
like structure. The HZSM-5 samples have an agglomerated appearance 
(Fig. 7a) which may be attributed to the interconnection of small par­
ticles, the reason why they tend to grow into cubic shapes [42]. After 
impregnation by Ni and Co and Mo, the morphology has not undergone 
obvious changes (compare Fig. 7(a) with those in Fig. 7(b–d)). Mean­
while, the metal particles are not evident in the SEM images since they 
are only presented in trace amounts and probably exist as nano-scale 
particles [43]. 
3.2.3. Fourier-transform infrared spectroscopy 
Infrared spectroscopy (FT-IR) in the region of approximately 
400–4000 cm−1 was used to analyze the framework vibrations of the 
catalysts, with the results shown in Fig. 8. No obvious band position 
shifts after metal modification can be identified (Fig. 8). Thus, it can be 
concluded that no isomorphous replacement of the HZSM-5 architecture 
has occurred. This supports the results of the XRD analysis reported in 
section 3.2.1. 
The H-O-H bending vibration at 1620–1640 cm−1 is identifiable in 
the HZSM-5 and the metal/HZSM-5 catalysts (Fig. 8). This indicates the 
presence of adsorbed water molecules in the samples, occurring through 
the incipient wet impregnation step. Furthermore, the bands at around 
3700 cm−1 and 3600 cm−1 can be attributed to the Al-OH groups and 
the bridged O–H groups, respectively [44,45]. 
3.2.4. Surface area, pore density, mean pore size and metallic content 
The structural characteristics of the synthesized catalysts in terms of 
specific surface area (SBET (m2/g)), total pore volume (VP (cm3/g)), and 
average pore diameter (nm) were determined using the Brunauer- 
Emmett-Teller method [46] and the results are provided in Table 5. 
The introduction of Ni, Co and Mo into the HZSM-5 framework 
corresponds to a reduction in the specific surface area and pore volume 
of the catalysts (Table 5). This may be due to either partial or complete 
blockage of some of the pores of the catalysts by agglomerations of 
small, probably nano-scale, metal particles. Furthermore, the actual 
metallic contents of the metal/HZSM-5 catalysts, achieved by using 
inductively coupled plasma testing, proved the successful incorporation 
of the metal elements into the zeolite catalysts; within errors of less than 
± 1.5%, which are considered to be negligible. 
3.3. Production yield 
The weight percentage yields of the products including oils, solids 
and gases obtained through non-catalytic and catalytic pyrolysis of ROS 
under optimum operating conditions (T = 500 ◦C, nitrogen flow rate =
0.3 (ml/min) and thermal heating rate = 20 ◦C/min) are summarized in 
Table 6. Accordingly, the incorporation of the catalysts into the pyrolysis 
process led to a very small decrease in the oil product yield (up to 2%), 
while offering a moderate increase in the gas production (up to 4%). The 
former may be due to the very high activity of the metal/HZSM-5 cat­
alysts, most notably associated with the 3 wt% Ni/HZSM-5 catalyst. The 
latter seems to be so, given the fact that saturated hydrocarbon mole­
cules can undergo a cracking process on solid acid catalysts, increasing 
the gas production [47,48]. The constant production of char (roughly 
16%) was achieved from both catalytic and non-catalytic pyrolysis of 
ROS. 
3.3.1. Gas-phase result 
The compositions (from GC analysis) of the gaseous products are 
illustrated in Fig. 9. The produced gases are composed of hydrogen, 
methane, and carbon dioxide. Overall, all catalytic pyrolysis processes 
displayed a noticeable increase in terms of their gas production. In 
addition, a significant difference could be observed in the percentages of 
carbon dioxide, methane and hydrogen generated. The hydrogen to 
carbon dioxide ratio is about 10 to 1 for all metal catalysts, with much 
less carbon dioxide produced than methane. The water-gas shift reaction 
(i.e., CO + H2O ↔ H2 + CO2) seems to be responsible for both hydrogen 
and carbon dioxide increments [49,50]. These gases are also generated 
through dehydrogenation and deoxygenation reactions. Similar results 
have been reported for the pyrolysis of oil in the presence of metal- 
loaded HZSM-5 catalysts [51,52]. 
The maximum selectivity of H2 and CO2 were reached with the 3 wt 
% Ni-loaded HZSM-5 catalyst, which could be due to the important role 
of Ni in boosting dehydrogenation and deoxygenation/decarboxylation 
reactions, respectively. 
3.3.2. Liquid phase analysis 
The simultaneous CHNS analysis was performed to quantify the 
concentrations of carbon, hydrogen, nitrogen, and sulfur in the ROS 
(feedstock) and the oil product; the results of combustion CHNS ana­
lysis are shown in Table 7. 
Generally, the lower the H:C ratio, the better the quality of generated 
aromatic compounds and consequently the better the quality of pro­
duced oil [53]. The feedstock (ROS) is composed of 22.43 wt% oxygen 
(Table 7). The excessive presence of oxygen in the feed is the main 
reason for its undesirable properties such as low energy density [54]. 
The ratio of H:C decreased from 1.65 for the non-catalytic pyrolysis 
process to 1.57 for the catalytic pyrolysis process facilitated by the 3 wt 
% Ni/HZSM-5 catalyst (Table 7). These H:C values are noticeably lower 
than those of the ROS feed (1.85). A similar trend is observed with 
respect to the S:C ratios. The N:C ratios in the oil products formed by the 
catalytic pyrolysis process are also two-third of those produced by the 
non-catalytic process. 
Typically, biofuels and waste-to-oil products exhibit high oxygen 
contents resulting in low stability over time and a low heating value. 
Improvement is therefore necessary to eliminate the oxygen to make its 
properties resemble crude oil refinery fuel products. The 3 wt% Ni/ 
HZSM-5 catalyst exhibited the best catalytic performance, because it 
reduced the oxygen content of the oil products by approximately 65% 
(Table 7). 
For the 3 wt% Ni/HZSM-5 catalytic pyrolysis of ROS, the higher 
heating value of the oil product reached 44.24 MJ/Kg, compared with 
30.54 MJ/Kg for ROS feed and 36.34 MJ/Kg for oil product from the 
non-catalytic pyrolysis process, respectively. Notably, the magnitude of 
the HHV obtained by using the 3 wt% Ni/HZSM-5 catalyst is close to that 
of crude oil, clearly indicating the significance of the present research. 
A. Kamali et al.                                                                                                                                                                                                                                 
Fuel 328 (2022) 125292
12
This result suggests that 3 wt% Ni/HZSM-5 can be applied as a prom­
ising candidate for catalytic upgrading of ROS and other waste-to-oil 
processes. 
3.4. Van Krevelen diagram 
A beneficial comparison with common crude oils and their de­
rivatives is the typical Van Krevelen diagram, plotting the H:C atomic 
ratio versus the O:C atomic ratio (Fig. 10). High-quality waste-to-oil 
products are expected to meet the H:C and O:C atomic ratios of 1.5 to 2 
and 0, respectively [55]. 
Thus, when combining the results of Fig. 10(a) and Table 7, it can be 
concluded that the oil product generated over 3 wt% Ni/HZSM-5 cata­
lyst has the best quality, in terms of O:C ratio, compared to the oil 
products generated by the alternative conditions tested. Fig. 10(b) 
provides a reference point for the pyrolysis product compositions of this 
study, placing them in the context of other organic materials, fossil fuels 
and pyrolysis products. 
3.5. GC–MS analysis of the oil products 
The changes in the chemical composition of oil products, generated 
by catalytic and non-catalytic pyrolysis conversion of ROS under opti­
mized operating conditions, were analyzed using GC–MS (Fig. 11). 
The oil products are composed of a broad range of chemicals with 
distinct molecular sizes and structures including aliphatic, aromatics 
(monoaromatics and polyaromatics) and saturated/unsaturated fatty 
acids (Fig. 11). Table A in the Supporting Information section compares, 
in detail, the components in the oil products detected by gas 
chromatography-mass spectrometry (GC–MS) analyzer for each process. 
Fig. 12 compares the percentage of overall aromatic components 
obtained throughout the non-catalytic pyrolysis of ROS with the pyrol­
ysis conversion of ROS by either original HZSM-5 or metal loaded 
HZSM-5. It is apparent that the incorporation of the synthesized cata­
lysts into the combined two-stage pyrolysis-catalysis process caused 
significant changes in the compositional characteristics of the oil prod­
ucts. Among the synthesized catalysts, the 3 wt% Ni/HZSM-5 catalyst 
achieved the greatest catalytic activity, i.e., 32.51% ROS conversion to 
aromatics-enriched oil product which was around 22% higher than that 
of the unmodified HZSM-5 catalyst. This finding may be correlated to 
the high density of the accessible acid sites in the Ni/HZSM-5, fostering 
dehydrogenation and deoxygenation reactions which are promoted by 
nickel-metal supported on zeolite [57]. This is simultaneously associ­
ated with the catalytic carboxylation and dehydrogenation of gaseous 
compounds, increasing the production of H2 and CO2 gases in the output 
product. 
3.6. Solid products of pyrolysis of ROS 
The solid phase product (~16 wt%, Table 6) is essentially char. 
Fig. 13 shows an SEM micrograph of that solid phase. 
The surface of the char, the solid residue of the 500 ◦C pyrolysis 
process (Fig. 13) displays a porous and sponge-like texture. The presence 
of hydrocarbon in the generated char along with its unique structural 
features make them suitable for use in several applications such as 
adsorbent for metal removal from aqueous solutions [58–60]. Hence, 
the solid products also have potential as a marketable product. 
4. Conclusion 
Three metal/HZSM-5 catalysts (Ni/HZSM-5, Co/HZSM-5 and Mo/ 
HZSM-5) containing 3 wt% metal loading were successfully prepared 
by the wet impregnation method and examined in a two-stage pyrolysis 
refinery oily sludge (ROS) to oil upgrading system. The morphology and 
structure of the synthesized catalysts were characterized using XRD, 
FTIR, and SEM analysis. The loading of Ni, Co and Mo did not alter the 
zeolite crystalline structure; However, BET surface area as well as total 
pore volume of metal/HZSM-5 catalysts became reduced compared to 
those found in the original HZSM-5.The impacts of influential parame­
ters on the oil product yield in the pyrolysis of ROS was evaluated using 
Design Expert-11 software, and a precise quadratic model was estab­
lished for this system. This model facilitated the establishment of 
optimal conditions for the non-catalytic/catalytic conversion of ROS to 
an upgraded oil product. The catalytic activity of the synthesized cata­
lysts was then systematically studied for a two-step (thermal/catalytic) 
conversion of ROS to oil product under the optimized operating condi­
tions (i.e., T = 500 ◦C, nitrogen flow rate = 0.3 ml/min and thermal 
heating rate = 20 ◦C/min). The following conclusions are drawn:  
• The implementation of metal-enhanced, zeolite-based catalysts in 
the catalytic pyrolysis process of ROS led to a slight decrease in oil 
product yield (up to 2%), coupled with an increase in the gas pro­
duction yield (up to 4%). 
• Among the synthesized catalysts, the 3 wt% Ni/HZSM-5 demon­
strated superior performance in lowering the oxygen, sulfur, and 
nitrogen contents of the organic phase of the oil product from the 
pyrolysis process.  
• Based on the Van Krevelen diagram, the oil product generated over 3 
wt% Ni/HZSM-5 catalyst achieved the best quality, in terms of fuel 
specification, among the oil products generated by the other cata­
lysts evaluated.  
• The energy density of the oil product significantly improved from 
30.54 MJ/Kg (non-catalytic pyrolysis process) to 44.24 MJ/Kg (3 wt 
% Ni/HZSM-5 catalyst). 
• The higher heating value of the oil product obtained from the py­
rolysis conversion of ROS catalyzed by 3 wt% Ni/HZSM-5 is com­
parable to that of crude oil, suggesting: I) ROS can be applied as a 
potential waste-to-oil feedstock for viable upgraded oil production; 
and II) 3 wt% Ni/HZSM-5 can be applied as a promising catalyst for 
the catalytic upgrading of the oil product. 
The processes described are significant for waste-to-fuel applications 
because they potentially make use of a toxic waste material (ROS) 
produced in substantial volumes by crude-oil refineries. ROS is costly to 
treat and dispose of, consequently, its effective conversion avoids po­
tential environmental damage, and generates a valuable high quality 
liquid fuel products from that waste. 
Credit authership contribution statement 
Ali Kamali: Performing design of experiments analysis, Acquisition 
of data, analysis and interpretation of data, Writing – review & editing. 
Setareh Heidari: Writing – review & editing. Abooali Golzary: 
Conceptualization, Methodology. Omid Tavakoli: Conceptualization. 
David A. Wood: Conceptualization, Methodology, Writing – review & 
editing. 
Declaration of Competing Interest 
The authors declare that they have no known competing financial 
interests or personal relationships that could have appeared to influence 
the work reported in this paper. 
Data availability 
Data will be made available upon request. 
Appendix A. Supplementary data 
Supplementary data to this article can be found online at https://doi. 
org/10.1016/j.fuel.2022.125292. 
A. Kamali et al.                                                                                                                                                                                                                                 
Fuel 328 (2022) 125292
13
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A. Kamali et al.

Life cycle assessment of petroleum reﬁning process: A case study in
China
Yeye Liu a, Sheng Lu a, Xuejun Yan b, Sulian Gao b, Xiaowei Cui c, Zhaojie Cui a, *
a School of Environmental Science and Engineering, Shandong University, Qingdao, China
b Shandong Provincial Jinan Eco-environment Monitoring Centre, Jinan, China
c School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
a r t i c l e i n f o
Article history:
Received 30 October 2019
Received in revised form
2 February 2020
Accepted 3 February 2020
Available online 6 February 2020
Handling Editor. Cecilia Maria Villas B^
oas de
Almeida
Keywords:
Petroleum reﬁning process
Life cycle assessment
VOCs emission characteristic
Climate change
Government management
a b s t r a c t
Climate change mainly caused by transportation fuel consumption has attracted global concern. In life
cycle environmental burdens generated from transportation fuel production, petroleum reﬁning stage is
the hotspot. However, the in-depth environmental analysis of petroleum reﬁning is very limited. China is
the second largest petroleum reﬁning country, so it is very essential to break petroleum reﬁning stage
into speciﬁc unit to analyze how Chinese reﬁnery can be improved in environment performance. To
achieve this goal, a systematic life cycle analysis of the environmental burden generated from petroleum
reﬁning process was conducted to identify the control emphasis and seek potential improvement
measures. Sensitive analysis and volatile organic compounds (VOCs) emission characteristics were
additional discussed to improve the accuracy of results. The signiﬁcant burdens generated from petro-
leum reﬁning process were freshwater ecotoxicity and climate change. Crude oil extraction and transport
dominated most environmental categories, which indicated that the environmental problem exist in
upstream supply chain. Catalytic cracking, feedstock and product handling, catalytic reforming, crude oil
distillation, cooling water system and diesel hydrotreating were the major control units due to their
direct emissions and electricity consumption. VOCs (e.g., acrolein and chloroﬂuorocarbons) produced
from reﬁnery fugitive emissions were the main substances for reﬁnery to reduce human toxicity, ozone
depletion and photochemical oxidant formation inﬂuences. 14% of Climate change were derived from
organic chemicals emission in this study, which suggested that VOCs-related carbon emission should be
involved in current carbon accounting work or greenhouse gases (GHGs) studies on the petroleum
reﬁning industry. The identiﬁed control emphasis included equipment leaks from core reﬁning units,
storage tank emissions control, energy structure optimization and catalysts consumption intensity
reduction. Some feasible and useful reduction measures targeted the control emphasis were proposed for
policy makers and reﬁnery managers to formulate reduction strategies and improve the sustainability of
the petrochemical industry.
© 2020 Published by Elsevier Ltd.
1. Introduction
Climate change and energy security are the common global
problems. Oil is the largest source of global energy consumption
(~31.8% of total world energy consumption)(BP, 2019b). Oil prod-
ucts are widely used as fuel and raw materials for petrochemical
industry. In BP’s prediction, oil will continue to take an import part
in the global energy system for decades to come (BP, 2019a). Many
reﬁning processes are needed to convert primary oil that cannot be
directly used into petroleum products more suitable for con-
sumption. Petroleum reﬁning process is related not only energy
security but also environmental burdens. In life-cycle petroleum
product production and usage (including crude oil extraction,
transportation, reﬁning and fuel combustion), more than 40% of
environmental impacts (e.g., climate change, ozone depletion, hu-
man toxicity, photochemical oxidant formation, terrestrial ecotox-
icity) are attributable to the petroleum reﬁning stage (Morales
et al., 2015). Those environmental damages would subsequently
inﬂuence the global environment through the extensive applica-
tion of petroleum products. China is the second largest oil reﬁning
country, accounting for 15.5% of world total crude distillation
* Corresponding author.
E-mail address: cuizj@sdu.edu.cn (Z. Cui).
Contents lists available at ScienceDirect
Journal of Cleaner Production
journal homepage: www.elsevier.com/locate/jclepro
https://doi.org/10.1016/j.jclepro.2020.120422
0959-6526/© 2020 Published by Elsevier Ltd.
Journal of Cleaner Production 256 (2020) 120422
capacity in 2017. To deal with environment pollution problems of
petroleum reﬁning industry, Chinese government has published
increasingly strict stand (MEEC, 2015b) to promote reﬁnery’s
technological progress and ﬁne management. Especially, with the
serious situation of air pollution characterized by high concentra-
tion of ground-level ozone and ﬁne particulate matter, the petro-
leum reﬁning industry has become a focus of Chinese government
because of VOC emissions which are key precursors for the for-
mation of ozone and PM (Yuan et al., 2013). A systematic analysis of
environmental burden generated from petroleum reﬁning process
is highly necessary to understand where are the most crucial
emphasis
and
how
to
improve
reﬁnery’s
environmental
performance.
Life cycle assessment (LCA) is an efﬁcient method to quantify the
environmental impacts from all stages of certain products, pro-
cesses or activities, and has been extensively used in various areas.
With regard to the petroleum industry, numbers of Life cycle en-
ergy and GHG emissions analysises for transportation fuels (e.g.,
gasoline, diesel, jet fuel, natural gas) production have been con-
ducted in Europe (Furuholt, 1995), USA (Yang et al., 2016), Korea
(Jang and Song, 2015) and China (Shen et al., 2006). They analyzed
environmental impacts from different perspectives. For example,
Khan (2018) performed a comparative Well-to-Tank (WTT) energy
consumption and GHG emissions analysis of 10 petroleum and
natural gas fuels (i.e., gasoline, diesel and CNG) pathways (e.g., local
crude, imported crude, imported). Rahman et al. (2015) quantiﬁed
well-to-wheel (WTW) GHG emissions for fuels derived from ﬁve
North American conventional crudes. Masnadi et al. (2018) model
and analyze upstream life cycle greenhouse intensities and energy
use of crude oil extraction and transport for some of the major
global oilﬁelds that supply the Chinese oil market. Except energy
consumption and GHG emissions, more comprehensive environ-
mental categories were considered in studies of (Restianti and
Gheewala, 2012) which added acidiﬁcation and human toxicity
impacts when evaluated environmental impact of gasoline pro-
duction and use in Chile. Their results indicated that petroleum
reﬁning activities were the hotspots as well as the tailpipe emis-
sions from car use. In order to reduce GHG emissions and depen-
dence on fossil fuels, life cycle energy and GHG emissions were
additional assessed for oil sands-derived transportation fuels
(Nimana et al., 2015), biofuels (Wang and Cheng, 2018) and alter-
native production ways of liquid fuels (Zhou et al., 2017) and oleﬁns
(Keller et al., 2019).
However, the above studies were all conducted for speciﬁc pe-
troleum product, rather than the petroleum reﬁning process. Pe-
troleum reﬁning is generally treated as an integrated stage and
cannot speciﬁc in sub reﬁning unit in previous LCA studies. Reﬁning
unit is the basic component of all reﬁneries and each unit has its
own emission characteristic. Analyzing the environmental impacts
from unit level is very necessary to understand where burdens
originated from so as to obtain more targeted control measures to
improve the environmental performance of the petroleum reﬁning
industry. The achieved information can shed light on the best
combination of various reﬁnery process units to reduce environ-
mental pressures. Most LCA studies for petroleum fuels focused on
energy use and GHG emissions lacking a holistic environment
impacts analysis. In this regard, this study aims to (1) quantify the
life cycle environmental impacts of various petroleum reﬁning
process from sub-reﬁnery angle to holistically understand what
impacts they produce, (2) identify the key factors, units and sub-
stances inﬂuencing the environmental burdens to seek the future
control emphasis, and (3) propose feasible reduction measures and
control emphasis for government and reﬁnery to promote envi-
ronment ﬁne management and sustainability for the petrochemical
industry.
2. Materials and methods
2.1. Scope deﬁnition
Function unit work as a quantiﬁed reference for related inputs
and outputs of an investigated system (ISO14040, 2006). In this
study, reﬁning 10000 tons of crude oil is used as the function unit.
Fig. 1 illustrated the system boundary through gate-to-gate
approach. The system boundary starts with receipt of crude oil
for storage at the reﬁnery, include general petroleum handling and
reﬁning operations and terminates with shipping the reﬁned
products from the reﬁnery. The core reﬁning operations involve
atmospheric and vacuum distillation unit, catalytic reforming unit,
catalytic cracking unit, delayed coking unit, diesel hydrotreating
unit, gasoline adsorptive desulfurization (s-zorb), liquid gas sepa-
ration (LPG), methyl tert-butyl ether (MTBE) production, acid gas
removal, feedstock and product handling stage (FPHS) (i.e., storage,
blending, loading and unloading operations). Public system is
considered in this study, including power station, freshwater
treatment, desalting station, sour water stripping, wastewater
treatment plant, hydrogen production, sulfur recovery and cooling
water system. Each process involves raw materials and auxiliary
chemicals production, energy and water production and con-
sumption, direct air and wastewater emissions, and solid waste
disposal. In reﬁnery, the environmental burdens generated from
upstream crude oil extraction and transport are shared by all
reﬁning processes through oil material ﬂow. In other words, the
burden of crude oil extraction and transport should be allocated to
the whole reﬁnery, rather than one speciﬁc process. In this regard,
the system boundary is divided into reﬁnery level and speciﬁc unit
level. At reﬁnery level, the whole reﬁnery is regard as an integrated
unit, while speciﬁc unit is considered as basic unit at speciﬁc unit
level where crude oil extraction and transport is eliminated.
2.2. Life cycle inventory
Table 1 lists the LCI result of petroleum reﬁning process. All
materials, energy and water consumption, direct emissions, and
waste disposal are presented based on function unit. In this study,
reﬁnery fuel gas and steam and a small percentage of electricity
were produced from reﬁnery itself. The amount of steam and
electricity presented in inventory are net consumption values. The
surplus steam is offered to surrounding community and the value
of this part is set as negative.
2.3. Assessment method and data collection
The life cycle impact assessment was conducted by the ReCiPe
method (Goedkoop et al., 2009). Based on the input-output mate-
rials and emissions, 15 midpoint categories, including climate
change, ozone depletion, particulate matter formation, human
toxicity, ionizing radiation, photochemical oxidant formation,
terrestrial acidiﬁcation, terrestrial ecotoxicity, freshwater eutro-
phication, freshwater ecotoxicity, marine eutrophication, marine
ecotoxicity and fossil depletion, were selected to assess the envi-
ronmental burdens generated from petroleum reﬁning process. The
endpoint categories were not considered in this study due to the
relatively high uncertainty. In order to understand the signiﬁcant
environmental categories produced from petroleum reﬁning pro-
cess, normalization was conducted to make the categories dimen-
sionless so that the contribution of each impact categories can be
analyzed.
The LCI data listed in Table 1 and Fig. 1 were collected from a
medium-scale reﬁnery located in Shandong Province of China. The
materials and energy consumption data were derived from
Y. Liu et al. / Journal of Cleaner Production 256 (2020) 120422
2
measuring instrumentation inserted in pipelines. The pollutants
data (i.e., emissions to air, emission to water, solid wastes) were
derived from reﬁnery routine monitoring reports which conducted
once a week, except VOCs which were estimated by a compre-
hensive method combined material balance and speciﬁc models, as
shown in Table 2. The background data of material production (e.g.,
crude oil, electricity, steam, chemicals) were obtained from the
Chinese LCI database. Whereas, the background data of most
auxiliary materials (e.g., corrosion inhibitor, antichlor, desulfurizer,
catalysts) haven’t found in Chinese or Europe database. Thus, these
materials are replaced by their raw materials which are used to
synthesize auxiliary materials in this study. The background data of
solid waste disposal were collected from Europe database due to
the lack of related Chinese data.
With regard to VOCs emissions from reﬁning units, Checking
guideline for VOCs pollution sources in the petrochemical industry
(checking guideline) (MEEC, 2015a) estimated VOCs from equipment
leaks by combing correlation equations method that relate mass
emission rate (unit: kg/h) to detected net screening value (unit:
ppm) and average EF method for millions of inaccessible seals.
Fig. 1. System boundary of petroleum products production.
Table 1
Life cycle inventory of the whole reﬁnery (values were presented per function unit).
Category
Substance
Unit
Amount
Material
Crude oil
t
10000
Methyl alcohol
t
44.41
Natural gas
t
1.86
Water
t
7427.28
NaOH
t
6.43
HCl
t
5.92
Catalyst
t
5.32
Sodium hypochlorite
t
1.23
Polyacrylamide
t
0.74
Liquid ammonia
t
0.46
Corrosion inhibitor
t
0.22
Others
t
2.20
Energy
Electricity
kwh
565007.51
Steam
t
154.17
Emissions to air
PM2.5
t
0.16
PM10
t
0.17
SO2
t
0.71
NOX
t
1.54
VOCs
t
3.21
CO
t
7.22
CO2
t
453.21
NH3
kg
4.32
nickel
kg
31.18
Emissions to water
Wastewater
t
2961.00
Oils
kg
1.63
COD
kg
118
Total phosphorus
kg
9.34
Total nitrogen
kg
20.91
sulﬁde
kg
0.08
Cyanide
kg
0.06
Nickel
kg
0.30
Arsenic
kg
0.01
Methylbenzene
kg
0.17
Ethylbenzene
kg
0.08
Xylene
kg
0.54
Benzene
kg
0.18
Solid waste
Dead catalysts
t
5.94
Reﬁnery sludge
t
2.56
Table 2
Estimation methods for VOCs emissions from reﬁnery.
VOCs emission source
Estimation method
Reﬁning unit
Oil mass balance
Storage tank
Theoretical model a
Product loading
Measurement
Stationary combustion
Carbon balance
Process vent
Measurement
Waste water treatment
Measurement
Flare system
Measurement
Cooling tower
Emission factor b
a Tank-speciﬁc model presented in Checking guideline; b: VOCs emission
factor of cooling tower is 7.19E-07 t-VOCs/m3-circulating water which is
presented in checking guideline.
Y. Liu et al. / Journal of Cleaner Production 256 (2020) 120422
3
However, the detected net screening values were instantaneous
and only represented emission level in the monitoring period
rather than the average emission level for a period. The value
changed largely with the operation condition and general main-
tenance practices. The equation parameters were from American
reﬁneries. The gap between American and China may lead to re-
sults’ uncertainty. To ensure the accuracy and localization, the oil
mass balance was selected to estimate VOCs emissions from
reﬁning units which can eliminated the aforementioned inﬂuence.
For stationary combustion sources, carbon balance was adopted to
calculate VOCs emission amount, rather than direct measurement.
Measurement is an effective method to estimate speciﬁc pollutants,
such as CO2, SO2, PM. However, VOCs are a family of Chemical or-
ganics. The results cannot ensure all VOC compositions were
measured. For example, oxygenated VOCs were commonly ignored
in current VOCs species proﬁle (Wei et al., 2016) resulting the
underestimated VOCs emission concentration. Carbon balance
method estimated emissions from origin fuel and can cover all
possible emission ways. This method was also used by (Zhang et al.,
2000) to calculate emissions from household stoves. The detailed
estimation procedures of each source were presented in supply
material S1eS3.
2.4. Case introduction
A typical state-owned petroleum reﬁnery with a reﬁning ca-
pacity of 7.5 million tons crude oil per year, located in northern
China, was selected as the case. The reﬁnery mainly produced
gasoline, diesel, liqueﬁed gas, petroleum coke, propylene and
paving asphalt. Catalytic cracking unit installed desulfurization and
dust removal facility. Wastewater treatment system were all
covered and collected waste gas were vented to catalytic combus-
tion device. VOCs emissions from loading operations were disposed
by catalytic incinerators after recovery. Storage tanks were divided
into three basic types: vertical ﬁxed roof, external ﬂoating roof,
internal ﬂoating roof. All of them had no control devices. The oil
scheme of this reﬁnery is shown in Fig. 1.
3. Results
3.1. Normalization result
Table 3 lists the LCIA midpoint result of reﬁning 10000 tons
crude oil. Normalization is performed to identify the key environ-
mental impact categories generated from crude oil reﬁning based
on world’s normalization factors (Sleeswijk et al., 2008). The
detailed normalization method is shown in supporting material S4.
Fig. 2 presents the normalized results of the midpoint categories for
the whole reﬁnery (scope 1). Result shows that the most signiﬁcant
environment burdens generated from petroleum reﬁning were
freshwater eutrophication (FET) and climate change (CC). The im-
pacts on particulate matter formation (PMF), ozone depletion (OD),
human toxicity (HT), photochemical oxidant formation (POF),
terrestrial acidiﬁcation (TA) and freshwater ecotoxicity (FE) were
moderate. Meanwhile, petroleum reﬁning processes had slight ef-
fects on ionizing radiation (IR), terrestrial ecotoxicity (TET) and
fossil depletion (FD). The rest environmental categories were
marginal and can be negligible (i.e., ME, MET). Among these cate-
gories, the environmental impacts on PMF, CC, OD, HT and POF are
major associated to human health, and FET, TA are related to
ecosystem quality. The results implied that petroleum reﬁning
process has more noticeable damage on human health and
ecosystem quality compared with resource scarcity.
3.2. Main contributors and substances
Identiﬁcation of main contributors are vital to targeted where
are the pivotal in the life cycle environmental impacts. In this part,
only the identiﬁed eight categories (i.e., PMF, CC, OD, HT, POF, TA,
FET, FE) were involved. Fig. 3 presents the main contributors to the
identiﬁed categories at the entire reﬁnery level (scope 1). It was
clearly that crude oil dominated most categories, except OD which
was mainly derived from reﬁnery on-site emissions. On-site
emissions additionally had signiﬁcant impact on HT. The result
indicated that it is upstream crude oil supply chain rather than
reﬁnery
itself
that
produce
environmental
burdens
during
Table 3
LCIA results of reﬁnery (values were presented per function unit).
Impact category
Abbreviation
Unit
Amount
Climate change
CC
kg CO2-Eq
7206225.85
Ozone depletion
OD
kg CFC-11-Eq
18.53
Particulate matter formation
PMF
kg PM10-Eq
8883.80
Human toxicity
HT
kg 1,4-DCB-Eq
657858.55
Ionizing radiation
IR
kg U235-Eq
224174.06
Photochemical oxidant formation
POF
kg NMVOC
29083.80
Terrestrial acidiﬁcation
TA
kg SO2-Eq
27521.62
Terrestrial ecotoxicity
TET
kg 1,4-DCB-Eq
1444.55
Freshwater eutrophication
FE
kg P-Eq
295.95
Freshwater ecotoxicity
FET
kg 1,4-DCB-Eq
14359.03
Marine eutrophication
ME
kg N-Eq
26.81
Marine ecotoxicity
MET
kg 1,4-DCB-Eq
11432.11
Fossil depletion
FD
kg oil-Eq
10438738.24
Fig. 2. Normalization result of petrolem reﬁning process at the reﬁenry-level.
Y. Liu et al. / Journal of Cleaner Production 256 (2020) 120422
4
petroleum reﬁning. Except crude oil production, the major con-
tributors to PMF, CC, POF, TA and FE categories were on-site
pollutant emissions and electricity consumption.
Fig. 4, Fig. 5 and Fig. 6 further identiﬁed the key contributions at
the speciﬁc unit level where crude oil factor was not considered,
including the main contributions of crucial units to the identiﬁed
categories, the contributions of key factors to crucial units and the
contributions of public units to the identiﬁed categories. For POF,
HT and OD categories, on-site emissions from catalytic cracking,
FPHS, cooling water system, catalytic reforming and diesel hydro-
treating were the major contributor. These three categories are
mainly related to VOCs. The VOCs species distinction of each unit
accounted for their contribution differences to the three categories.
With regard to PMF, CC and TA, steam, electricity and on-site
emissions in catalytic reforming, crude oil distillation and sour
water stripping were the major contributors. Catalytic cracking unit
played an effective role in PMF, CC and TA due to the output steam
generated from catalytic cracking ﬂue gas energy recovery satisﬁed
approximately 70% of reﬁnery’s steam demand. That signiﬁcantly
reduced the whole reﬁnery’s environmental pressure on the
aforementioned
categories.
Though
steam
is
an
important
contributor for individual units (see Fig. 5), it is not the main
contributor for the entire reﬁnery (see Fig. 3). Acid gas removal unit
had additional impact on PMF due to its steam usage. The impacts
on FET and FE were primarily attributable to catalytic cracking,
catalytic reforming and wastewater treatment process. Catalysts
generation and usage were the reason for the larger contribution of
catalytic cracking and reforming unit. The contribution of waste-
water treatment to FET was derived from catalytic cracking nickel
emissions to water. The impact of wastewater treatment on FE was
originated from phosphate contained in each unit discharged
wastewater.
Based on the above analysis, the environmental reduction per-
formance of petroleum reﬁning industry depended largely on up-
stream crude oil extraction and transport processe from the angle
of whole life cycle. In terms of reﬁnery itself, catalytic cracking,
FPHS, catalytic reforming, crude oil distillation, cooling water sys-
tem and diesel hydrotreating should be responsible for the most of
environmental burdens generated from petroleum reﬁning. Direct
emissions from these units accounted for more than 50% of the total
unit-level environmental impacts, except PMF, CC and TA cate-
gories where electricity consumption contributed the most. The
smaller contribution of on-site emission to PMF, CC and TA likely
because the main energy fuel in reﬁnery are reﬁnery fuel gas and
natural gas which produced less pollutants compared with heavy
oil or coal after combustion. The other reason may beneﬁt by the
steam that generated from reﬁnery itself. Though ﬁne particular
matter pollution is serious in China, it was not the control emphasis
for the petroleum reﬁning industry.
Key substances at unit-level scope are presented in Fig. 7. VOCs
generated from reﬁnery on-site fugitive emission (e.g., equipment
leakage, storage and blending, product loading, stationary com-
bustion) were the key substances for the crucial impact categories.
For example, more than 79% of HT was attributable to acrolein and
nearly 100% of OD was put down to tetrachloromethane and CFCs.
VOCs were responsible for 47% of POF category, the remaining of
Fig. 3. Contributions of main processes at the entire reﬁnery level.
Fig. 4. Contributions of crucial units to the identiﬁed categories.
Y. Liu et al. / Journal of Cleaner Production 256 (2020) 120422
5
which were mainly caused by NOx from on-site emissions. For CC
category, the major contributor was CO2, of which 57% were from
electricity production and 42% were from on-site emissions. It’s
worth noting that approximately 14% of CC impacts were from
methane and fugitive non-methane hydrocarbons (e.g., CFCs, hal-
ocarbons). The results indicated that most current GHGs-related
studies or carbon accounting work may underestimated the value
of carbon emissions due to the ignoring of organic chemicals. The
impact on PMF were mainly caused by SO2, NOx, PM, while SO2 and
NOx were also the main substances for TA impact. These substances
were primarily derived from on-site emissions and electricity
production. Nickel generated from catalytic cracking unit and cat-
alysts production contributed signiﬁcantly to FET. Bromine gener-
ated from various catalysts production to water also played
effective role in FET. Phosphate to water was the leading substance
for FE impact. On the whole, acrolein, CFCs, tetrachloromethane,
CO2, NOx, SO2 and nickel were the priority control substances. With
the worsening environmental pollution caused by ozone and PM,
the control of VOCs is more and more urgent for the petroleum
reﬁning industry.
Fig. 5. Contributions of key factors to the crucial units: (a) crude distillation, (b) catalytic reforming, (c) catalytic cracking, (d) diesel hydrotreating, (e) FPHS.
Fig. 6. Contribution of public units to the auxiliary system.
Y. Liu et al. / Journal of Cleaner Production 256 (2020) 120422
6
3.3. Sensitivity analysis
The sensitivity analysis of main contributors was conducted by
evaluating the inﬂuence of a 5% reduction of individual contribu-
tors to the identiﬁed key categories (see Fig. 8). Results exhibited
that the decrease of crude oil consumption could achieve the
maximal environment performance due to its great variation to the
overall environmental impacts. However, reﬁneries have tried their
best to control crude oil loss. The potential of improving crude oil
utilization level to reduce environmental burdens is not big. A
decrease in direct VOCs emissions could arouse signiﬁcant ﬂuctu-
ation in OD, HT and POF impacts. Though obvious reduction of PM,
CC and TA burdens were observed due to the direct emissions
change, electricity consumption was the factor that result in the
highest variation in the aforementioned three categories. Control-
ling VOCs emissions, improving the utilization efﬁciency of elec-
tricity were the key points to alleviating the overall environmental
pressures for the petroleum reﬁning industry.
Fig. 7. Main substances to the environmental categories at unit-level.
Fig. 8. Sensitive analysis of main contributors.
Y. Liu et al. / Journal of Cleaner Production 256 (2020) 120422
7
4. Discussion
The results of main substance and sensitive analysis indicated
that VOCs generated from reﬁnery on-site operation are the main
substances resulting OD, HT and POF burdens in life cycle reﬁning
process. It is important to analyze the accuracy of VOCs emission
amount. Table 4 summarizes the integrated VOCs emissions factor
(EF) reported by current studies. The result of EEA represents Eu-
ropean average industrial emission level using the same method
presented in checking guideline. China has not got industrial VOCs
EF so far due to the VOCs control work just being started. This study
obtained 0.32 kg/t (the detailed information was provided in sup-
plementary materials S5) which was in the same order with that of
checking guideline (0.25), EEA (0.07-0.61) and lower than (Wei et al.,
2016) (1.44). The difference between this study and (Wei et al.,
2016) is derived from the different estimation method and re-
ﬁnery control level. This study calculated VOCs emissions from
reﬁning units by oil mass balance method (see Fig. S1) based on
annual input-output data, while other emission sources were
estimated by methods presented in Checking guideline. Wei et al.
(2016) achieved the EF for a petroleum reﬁnery in Beijing using
ISC3 model based on measured ambit VOCs concentrations. How-
ever, ISC3 model is difﬁcult to branch out into speciﬁc unit. The
different processing ﬂow scheme, production technology, and
pollutant control level also result the discrepancy. In terms of VOCs
species, 107 compositions including alkanes, alkenes, alkynes, ar-
omatics, halocarbons and oxygenated VOCs (OVOCs) were detected
for reﬁnery units, storage tank, stationary combustion sources and
wastewater treatment plant in this study. The chemical group
patterns of VOCs emission from core reﬁning units were illustrated
in Fig. 9. The result shows that alkanes are the major VOCs in
reﬁning units with a range of 48%e71% when included OVOCs.
However, OVOCs are usually ignored in current species studies (Wu
and Xie, 2017) or health risk assessment (Zhang et al., 2018). If
OVOCs are exclude, the proportion of alkanes in core reﬁning units
is 64%e80% which is similar to previous study (63.8%e85.9%) in
Yangtze River Delta (Mo et al., 2015) and (62.4%e75.4%) in Beijing
(Wei et al., 2014).
How to reduce VOCs emission is a hot topic in China. Reﬁning
units and storage tanks, accounting for 83% of total VOCs emissions
together in this study, were the key points for VOCs reduction. With
regard to equipment leaks in reﬁning units, enhancing the Leak
Detection and Repair (LDAR) frequency of catalytic cracking and
catalytic reforming unit are important. Replacing old connection
equipment is additional crucial to prevent VOCs emission from the
source. For government, it is an effective measure to install
continuous emission monitoring system (CEMS) at reﬁnery’s up-
wind and downwind site to understand VOCs emission level and
analyze reﬁnery’ s reduction performance. CEMS data of upwind
and downwind site also beneﬁt to precisely locate VOCs emission
source and can be provided as evidence of enforcing the law. If
possible, CEMS could also be equipped in core reﬁning units and
should be connected to government monitoring center so as to
obtain the VOCs emission situation timely, targeted and reﬁned. It’s
helpful for reﬁnery to conduct the LDAR work and reduce labor
cost. In terms of storage tank emissions, diesel tanks are the control
emphasis contributing 59% to the total tank emissions. The pro-
portion of diesel stored by ﬁxed-roof tanks, inner and external
ﬂoating-roof tanks are 92%, 2% and 6% in this study. Their corre-
sponding VOCs EF calculated by this study are 0.261, 0.073 and
0.036 kg/t-turnover. The EF of ﬁxed-roof tank is nearly 3 and 7
times as high as that of inner and external ﬂoating-roof tanks. If
ﬁxed-roof tanks are all replaced by ﬂoating-roof tanks, approxi-
mate 43%e52% of tank emissions can be reduced. Subsequently,
OD, HT and POF will decrease by 28%, 35% and 17%. Though diesel is
generally stored by ﬁxed-roof tanks in China, adopting ﬂoating-roof
tanks probably be an important measure to reduce VOCs emissions
with the increasing strict VOCs control stand and outstanding
ozone problem. Using oil products on-line blending technology
where oil products are blended in pipeline, if possible, is beneﬁcial
to lessen oils volatilization compared with tank blending technol-
ogy. That is also recommended in Comprehensive treatment scheme
for VOCs in key industries (MEEC, 2019)published by the Ministry of
Ecology and Environment of China in 2019. In heavy pollution
weather, reﬁnery can stop the operations of oil blending and
loading and reduce reﬁning amount to alleviate environmental
burdens.
Enormous energy is required in reﬁnery to raise the tempera-
ture of feedstocks and process streams. Energy consumption
structures adopted by reﬁneries are various. Some reﬁneries use
reﬁnery fuel gas as the primary fuel for nearly all petroleum re-
ﬁnery process heaters and boilers to preheat feed materials. Energy
contained in catalytic cracking ﬂue gas is also collected to generate
electricity and steam. The shortage in energy will be provided by
outsourcing electricity, just as this study adopted. Some reﬁneries
use outsourcing electricity as the primary fuel. Catalytic cracking
Table 4
Comparison of the integrated VOCs emission factor among different methods.
Emission source
Emissions factor (kg-VOCs/t-crude oil)
This study
Checking guideline
EEAa
Wei et al. (2016)
Reﬁnery
0.32
0.25
0.07e0.61
1.44
a EMEP/EEA air pollutant emission inventory guidebook 2016.
Fig. 9. VOCs species pattern of core reﬁning units.
Y. Liu et al. / Journal of Cleaner Production 256 (2020) 120422
8
ﬂue gas is used to generate stream only, and reﬁnery fuel gas are all
sold. The outsourcing electricity can further be divided into coal-
based electricity and hydropower. In this study, the outsourcing
electricity were coal-based electricity because the investigated case
relied on coal-based electricity. As aforementioned, direct emis-
sions from electricity consumption and fuel combustion produced
obvious inﬂuence on PM, CC and TA impacts. Analyzing the impacts
of different energy consumption type are vital to provide valuable
information on how to choose the best way to reduce the three
environmental burdens. Fig. 10 presents the environmental bur-
dens generated by different energy type when the same amount of
energy is provided (reﬁnery ﬂue gas 1ton, electricity 3652.4 kwh,
3.5 MPa steam 10.8 tons). Results showed that the impacts gener-
ated from hydropower consumption is the minimum, followed by
reﬁnery fuel gas, steam and coal-based electricity. That means
approximate 9%, 12% and 7% of CC, PM and TA burdens can be
reduced if only hydropower is involved. Nevertheless, hydropower
is not abundant in China, only accounting for 18% of national power
grid in 2017 (NBSC, 2018), and is restricted by region. Compared
with coal-based electricity, reﬁnery fuel gas can decrease more
than 80% of burdens when provide equivalent energy. In conclu-
sion, for most reﬁneries, using reﬁnery fuel gas as much as possible
is an effective way for the petroleum reﬁning industry to reduce
environmental burdens. Using the surplus energy contained in tail
gas (e.g., catalytic cracking unit, sulfur recovery unit, delay coking
unit) to generate electricity is also workable to decrease the
outsourcing coal-based electricity consumption. For the region that
rely on coal-based electricity, promoting cleaner energy introduc-
tion to adjust local energy structure is also crucial to alleviate the
environmental pressure of petroleum reﬁning processes.
Catalyst generation and consumption is another point that
might need attention for reﬁnery. At scope 2 level, on-site nickel-
based catalysts usage contributed 62% to FET, while upstream cat-
alysts generation contributed 47% to FE and 28% to FET (see Figs. 3
and 4). The catalysts consumption intensity of catalytic cracking
unit in this study is 1.0 kg/t-feed which is much higher than the
industrial average level of 0.6 kg/t-feed (MEPC, 2003). The higher
intensity of catalysts not only add the burdens of upstream catalysts
production and subsequent hazardous waste disposal but also
increase the potential of reﬁnery on-site nickel loss. For instance,
nickel emitted to air and water are all derived from catalytic
cracking unit in this study. Many factors inﬂuence the catalyst
consumption intensity, such as catalyst type, catalyst reaction ef-
ﬁciency, catalyst regeneration efﬁciency, catalyst regeneration ﬂue
gas disposal efﬁciency and so on. Establishing ﬁne catalyst material
balance for catalytic cracking and catalytic reforming unit to seek
the detail loss points and reasons, taking targeted measures from
raw materials replacement, reaction control to end fuel gas treat-
ment are necessary and effective to reduce the environmental
impacts on FET and FE.
5. Conclusions
This study conducted a systematic life cycle environmental
burden assessment for petroleum reﬁning process from reﬁnery-
level and speciﬁc unit level. VOCs emission characteristic were
discussed and some feasible reduction suggestions were proposed.
The reﬁnery-level results showed that the signiﬁcant environ-
mental burdens were freshwater ecotoxicity and climate change.
Crude oil extraction and transport (contributing 55%e98% to each
category) was the dominated factor for most categories. The
reﬁning unit-level results revealed that catalytic cracking, FPHS,
catalytic reforming, crude oil distillation, cooling water system and
diesel hydrotreating should be responsible for the most of envi-
ronmental burdens due to their direct emissions and electricity
consumption. VOCs control were the priority for environment
burdens reduction. The VOCs emission factor is 0.32 kg-VOCs/t-
crude oil for a medium-scaled reﬁnery based on oil mass balance.
Controlling VOCs emission (e.g., acrolein, CFCs) from reﬁning units
(e.g., catalytic cracking, catalytic reforming, hydrogen production)
and storage tanks, optimizing energy structure, reducing catalysts
consumption intensity were necessary for the petroleum reﬁning
industry.
This study provided valuable future control directions for gov-
ernment and enterprise to make more targeted policies on petro-
leum reﬁning industry toward sustainable development. The sub
reﬁnery information is useful for the improvement of Chinese local
LCA database of the petroleum reﬁning industry and shed light on
Fig. 10. Environmental burdens of different energy consumption (reﬁnery fuel gas 1 ton, electricity and hydropower 3652 kwh, steam 10.8 tons).
Y. Liu et al. / Journal of Cleaner Production 256 (2020) 120422
9
the possible combinations of various reﬁning units to reduce
environmental burden. VOCs emission information can serve as
reference for the industrial VOCs emission factor development.
However, many limitations still remain in this study. All the ﬁnd-
ings are obtained through a case study, and thus cannot represent
industrial situation well. The ability of crude oil reﬁning, the quality
of the crude oil feedstock and the processing ﬂow scheme are all
likely to inﬂuence the results. More different cases are necessary to
investigate further. The fundamental studies about VOCs estima-
tion and monitoring and speciﬁc local standard should be con-
ducted to improving the VOCs reduction performance.
Declaration of competing interest
The authors declare that they have no known competing
ﬁnancial interests or personal relationships that could have
appeared to inﬂuence the work reported in this paper.
CRediT authorship contribution statement
Yeye Liu: Conceptualization, Investigation, Writing - original
draft. Sheng Lu: Formal analysis, Writing - review & editing. Xue-
jun Yan: Resources, Funding acquisition. Sulian Gao: Resources,
Funding acquisition. Xiaowei Cui: Writing - review & editing.
Zhaojie Cui: Supervision, Writing - review & editing, Software.
Acknowledge
This study was supported by the project of Heavy Air Pollution
Reason and Control Research (No. DQGG0209) and the Major
Project of Social and Livelihood (201509001-2).
Appendix A. Supplementary data
Supplementary data to this article can be found online at
https://doi.org/10.1016/j.jclepro.2020.120422.
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10

Toward petroleum-free with plant-based chemistry
Farid Chemat, Maryline Abert Vian and
Harish Karthikeyan Ravi
Depletion of fossil resources, global warming, and increasing
world's population represent major Damocles' sword for hu-
manity to ensure its future against famine, climate change, and
the end of the petroleum era. Solutions will come from pro-
duction of plant-based chemicals, which is not new, nor a
historical artifact. On earth, 99% of the biomass alive is
composed of plants and microorganisms. Because of this
biodiversity, it could be sufficient as a comprehensive sus-
tainable resource of reagents for industry. Against this back-
ground, what will be needed for the industry worldwide to solve
the long-standing unresolved problem of how to convert plant
biomass into reagents, ingredients, and products with accept-
able societal, environmental, and cost levels? There is an
urgent need for conceptual leap in fundamental and applied
research by original ‘multi-scales understanding.' Mitigating
climate change fueled by anthropogenic activities with plant-
based green chemistry to establish a circular bio-based
economy while adhering to the Sustainable Development
Goals is the scope of this perspective review.
Addresses
Avignon University, INRAE, UMR 408, GREEN Extraction Team,
Avignon, F-84000, France
Corresponding author: Chemat, Farid (Farid.Chemat@univ-avignon.fr)
Current Opinion in Green and Sustainable Chemistry 2021,
28:100450
This review comes from a themed issue on Sustainable Chemistry
and the UN SDGs (2021)
Edited by Zhimin Liu and Klaus Kümmerer
Available online 19 January 2021
For complete overview of the section, please refer the article
collection - Sustainable Chemistry and the UN SDGs (2021)
https://doi.org/10.1016/j.cogsc.2021.100450
2452-2236/© 2021 Elsevier B.V. All rights reserved.
Introduction
Plant-based chemistry has been used probably since the
discovery of ﬁre. Egyptians and Phoenicians, Jews and
Arabs, Indians and Chinese, Greeks and Romans, and
even Mayas and Aztecs all possessed a culture of using
plants as a source of reagents for cosmetic, perfumery,
medicine, food ingredients and products, colors and
dyes, and building materials. Until the start of the pe-
troleum era, plant-derived biomass, because of plant
biodiversity,
was
the
primary
source
of
reagents,
ingredients, and products for food and nonfood appli-
cations. The spectacular growth of petroleum-based
processes led to a withdrawal from those based on
plant biomass. Almost all major economies in the
developed and developing world have mature reﬁneries,
which could transform petrol, a complex liquid mixture
to a variety of reagents and products essential and vital
for our life and modernity.
Depletion of fossil resources, global warming, and
increasing world’s population represent major Damo-
cles’ sword(s) for humanity to ensure its future against
famine, climate change, and the end of petroleum era,
which are interconnected. If we consider that these
fossil resources have been formed from a large number of
plants, algae, and zooplankton, and also the point that on
earth, 99% of the biomass alive is composed of plants
and microorganisms, and it is evident that they could be
sufﬁcient as comprehensive sustainable resources for
reagents in chemical and food industries for millions of
years. The future of humanity could be secured by
establishing a sustainable and circular economy that
relies on the biodiversity with not only plants as green
solution, but also macro and microalgae as blue solution,
and microorganisms as white solution, and insects as
brown solution.
Nowadays, based on research and innovations in the
20th century, we know that in a technical point of view,
almost all petroleum based-chemicals and materials
could be substituted by their plant-based counterparts.
However, the cost of bio-based production in many cases
exceeds the cost of petrochemical production. With a
petroleum reﬁnery, we can separate and transform petrol
to huge number of alkanes and aromatic compounds as
building blocks; by contrast; agri-food industries largely
adhere to the ‘one cultivation - one product paradigm.’
The problem is ‘the missing link’: how to convert plant
biomass into reagents, ingredients, and products for in-
dustries worldwide with acceptable societal, environ-
mental, and cost levels. Solutions require innovations
that break away from the past rather than simple con-
tinuity with ‘Plant-Based’ green chemistry.
Green chemistry is based on principles [1] that could
reduce the environmental impact of nonrenewable re-
sources (petrol, gas, and coal) on chemical and food
industries: perfumery, pharma, food, ﬁne chemicals,
pesticides. Many industries and academia adhere to
these principles and change the face of chemistry. The
Available online at www.sciencedirect.com
ScienceDirect
Current Opinion in
Green and Sustainable Chemistry
www.sciencedirect.com
Current Opinion in Green and Sustainable Chemistry 2021, 28:100450
next challenge will be the use of starting chemical ma-
terials from renewable resources such as plants and
microorganisms, but also to obtain these synthons and
ingredients by sustainable, green extraction and sepa-
ration technologies (Figure 1).
Extraction: unlocking the gateway to plant-
based green chemistry
The primary raw material for the obtention of petro-
chemical feedstocks is crude petroleum oil, which is in
turn reﬁned and fractionated as per requirements. The
key distinction between the reﬁning of petroleum crude
oil and bioreﬁning of plant biomasses is in the apparent
state of the initial material. In the case of crude oil
reﬁning, the starting material (i.e. crude oil) is in a
liquid or slurry state with impurities. Whereas, in the
case of plant matrices, the initial biomass predominantly
exists in solid state. Benign extraction techniques are
the ﬁrst and foremost step employed to retrieve the
natural plant chemicals such as the primary and sec-
ondary metabolites. Implementation of a nondestruc-
tive extraction technique as the primary step in a
bioreﬁnery or cascading use scheme would enable
further improvement in biomass valorization, resulting
in the obtention of several chemical constituents
(Figure 2), including essential oils, waxes, sterols, tri-
glycerides, phospholipids, fatty acids, polyphenols, pig-
ments, proteins, and carbohydrates [2,3]. Extraction
process comprises several unit operations namely pre-
treatment of plant material (thermal or nonthermal
drying, size reduction, etc.) and post-treatment of liquid
extract, which includes separation, concentration, puri-
ﬁcation, etc. The principal unit operation in this dy-
namic is the solideliquid extraction, which is the most
common in plant material-based extraction systems.
Optimization of the extraction parameters is crucial, and
failure to do so often might result in time-consuming
and
energy-intensive
processes
[4].
The
growing
importance of green extraction and its application for
plant-based biomass reﬁnery has been the subject of
numerous research and review articles [5e8]. The uti-
lization of bio-based synthons and ingredients undeni-
ably offers various advantages, but the transformation
has to be feasible and cannot come at any cost [9]. The
viability of embracing bio-based products has to be
reasonable in terms of economy and ecology; that way,
the sustainability aspect of the transformation process is
preserved. The ideal scenario is not only to have bio-
based ingredients but also to have bio-based in-
gredients extracted with green technologies.
Future challenges: green intensification
techniques and alternative solvents
The major impediments in the transformation of
biomass for the provision of chemical compounds and
bioproducts are the inherent complexity and variation in
their physical and chemical compositions. This neces-
sitates the adoption of highly complex and tedious
processing conditions, which drives up the operational
costs and the low conversion efﬁciency of biomass to
products could jeopardize the economic viability of the
operation [2,10]. The application of innovative extrac-
tion technologies and intensiﬁcation techniques such as
ultrasound, microwave, instant controlled pressure drop,
sub- or supercritical ﬂuid, pulsed electric ﬁelds, extru-
sion, ohmic, ultraviolet (UV), infrared (IR), and solar-
assisted as a standalone process or in synergy can be
used for exhaustive recovery of bioactive compounds
Figure 1
Classes of natural plant chemicals.
2
Sustainable Chemistry and the UN SDGs (2021)
Current Opinion in Green and Sustainable Chemistry 2021, 28:100450
www.sciencedirect.com
from plant matrices (Figure 3). Incorporation of these
green extraction and intensiﬁcation tools considerably
enhances the efﬁciency, drastically reduces the time,
energy, and volume of the solvent when compared to the
conventional setup [11].
Finding a suitable alternative to replace petroleum-
based solvent for the green extraction of natural prod-
ucts is an intricate task. The ideal alternative solvent
should possess the following traits: high solvency; high
ﬂash points; low toxicity; lower environmental impact;
easily biodegradable; origin from renewable resources,
reasonable priced, and easily recyclable without any
deleterious effect to the environment. Several studies
focusing on alternative solvents (Figure 4) for the green
extraction of natural compounds have been communi-
cated and are summarized in Ref. [12].
‘One cultivation – multi-product’: a
biorefinery paradigm
Alerts over the last few decades ﬁrmly pushed two
critical aspects of plant biomass from agriculture and
food industries largely adhering to the one cultivation
e one product paradigm. The diminished use of the
byproducts because they are dilute resources, and the
high cost of recovery associated with it are the current
obstacles in biomass valorization. The ﬁnal objective is
not to provide solutions for a type of biomass, but
rather general models applicable to every kind of po-
tential biomass substrates. To facilitate the transition of
fossil centric global economy to a sustainability-based
bioeconomy, a forward-looking approach with a step-
wise adaptation of global reﬁning schemes for the
production of bioenergy, biofuels, and bio-based prod-
ucts should be considered. Authors Champagne and
Matharu consolidated the processes involved in the
reﬁnement of biomass to biofuels and bioproducts
along the entire supply chain into pretreatment, frac-
tionation, modiﬁcation, and conversion routes [13].
Pretreatment processes have ubiquitous applications in
the conversion of lignocellulosic biomass [14] and can
be employed optionally for other plant-based biomasses
as well. Within this framework, the fractionation pro-
cess for biomass valorization can be categorized into dry
and wet processing modes. Fractionation of biomass can
be accomplished with different strategies: a) bio-
reﬁning: the ideal goal of any bioreﬁning scheme is to
completely valorize the biomass into a spectrum of bio-
based products (food, feed, and platform chemicals) of
economic value and exhaust the potential functional-
ities offered by the matrix. This is usually achieved in a
single step or multiple steps by employing various unit
operations sequentially. b) Cascading use: systematic
effort to exploit the biomass for higher added-value
products before utilizing it as a source for energy [15].
The deconstruction and cracking approach are other
plausible alternative strategies for biomass valorization
[9].
Biorefinery(ies) as success stories for
plant-based chemistry
The objective of bioreﬁning is to valorize all the plant’s
components into products of economic signiﬁcance
[16]. At present, there are several types of bioreﬁnery
schemes that can be employed depending on the
different inputs and outputs emerging from the trans-
formation process of plant biomass. Almost 80% of the
grapes harvested (77.8 million tons in 2018) is used for
viticulture. The complete valorization of the sheer
amount of by-products emerging from winemaking by
Figure 2
Extraction: a key component in plant-based green chemistry.
Plant-based green chemistry 2.0 Chemat et al.
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Current Opinion in Green and Sustainable Chemistry 2021, 28:100450
means of an integrated bioreﬁnery approach is an
excellent example (Figure 5).
An innovative approach, which seems surprising, is the
utilization of insect larvae for the bioconversion of
different biomasses. Insect-based products for food and
feed applications has already garnered signiﬁcant atten-
tion [17]. They are considered as an alternative to con-
ventional protein sources like ﬁshmeal and soybean meal.
The black soldier ﬂy (BSF) in particular can be reared on
a diverse range of substrates, including agricultural
wastes
and
food
industry
by-products.
Insect
Figure 3
Overview of green extraction and intensification techniques.
Figure 4
Alternative solvents for green extraction.
4
Sustainable Chemistry and the UN SDGs (2021)
Current Opinion in Green and Sustainable Chemistry 2021, 28:100450
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bioconversion is exceptionally advantageous in terms of
overcoming the physical and chemical variation chal-
lenges in the biomass. Their ability to thrive in a wide
array of substrate with varying nutritional composition
can be exploited and used to fabricate a structured
bioconversion system, which ensures thorough utilization
of the biomass and obtain a standardized nutritional
composition in the larvae. For instance, 1,000 kg of fruit
and vegetable wastes through larvae mediated biocon-
version yields 125 kg of fresh larvae and almost 250 kg of
frass [18]. Though the proximate composition of larvae
may vary, the bioconversion ensures assimilation of high-
value components (protein, lipids, minerals, and other
nutrients) in the larval biomass, which can be retrieved
by traditional bioreﬁning or fractionation techniques.
The lipid fraction of the insect ﬁnds applications in feed,
food, biodiesel, and cosmetic formulations. The protein
meal is widely considered as replacements for conven-
tional protein sources. Chitin has multifarious applica-
tions and frass from insects can be used as fertilizers and
soil amendments. Even the conversion of complex
lignocellulosic biomass (corn stover, rice straw) can be
effectuated with insects along with co-conversion agents
(microbes, enzymes) [19e21]. The conversion efﬁciency
of lignocellulosic biomass to platform chemical or inter-
mediary products along with techno-economic analysis
and life cycle assessment of the process could be
compared with insect bioconversion for similar bio-
masses. Such comparisons could shed light on the eco-
nomic
aspects,
environmental
impact,
technical
feasibility, and overall implications of the individual sys-
tems and in turn aid in the industrial-scale adaptation of
the process for guaranteed proﬁtability and sustainability.
Plant-based green chemistry: Sustainable
Development Goals perspective
The Sustainable Development Goals (SDGs) put forth
by the United Nations (UN) is a culmination of criteria
and strategies that serve as the blueprint to achieve a
better and more sustainable future for all. The 17 goals
are all interconnected, and achieving even one of this
ambitious goal by the year 2030 will have a positive
ripple effect on the rest. Adapting green extraction and
reﬁning processes for the production of platform
chemicals, energy, biofuels, active pharmaceutical in-
gredients,
and
other
chemical
intermediates
will
signiﬁcantly accelerate the progress toward a sustainable
Figure 5
Scheme of a wine biorefinery.
Plant-based green chemistry 2.0 Chemat et al.
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Current Opinion in Green and Sustainable Chemistry 2021, 28:100450
bio-economy.
Authors
Anastas
and
Zimmerman
addressed the challenges of sustainable chemistry rele-
vant to SDGs [22]. We postulate that the plant-based
green chemistry approach can have an overall impact
on the 17 SDGs, and it certainly inﬂuences nine of them
directly, namely (Figure 6), (i) good health and well-
being: replacing petroleum solvents with alternative
bio-based solvents for example, n-hexane with 2-meth-
yloxolane can change the way edible oil reﬁning func-
tions; (ii) clean water and sanitation: sourcing of
biomolecules like chitin and chitosan, which acts as a
ﬂocculant in water treatments, larvae-mediated waste
management in animal agriculture could drastically
reduce the industrial run-offs in animal agriculture; (iii)
affordable and clean energy: plant lipids for biodiesel
production, biogas generation from plant biomass as a
last resort in the bioreﬁning scheme or cascading use;
(iv) industry, innovation, and infrastructure: plant-based
green chemistry has already paved the way for the
implementation of several innovative extraction and
processing techniques in industrial scale. Further push
by
incentivizing
the
companies
that
gravitate
toward green processes will spur more innovation for
clean label products; (v) sustainable cities and com-
munities: self-sufﬁcient communities and smart cities
with urban vertical farms is a possibility in the near
future; (vi) responsible consumption and production:
biodegradable plastics from plant-based constituents
like starch and biopolymers along with regulatory push
can boost responsible consumption; (vii) climate action;
(viii) life below water; and (ix) life on land: lower
greenhouse emissions as a result of transformation to
plant-based biomass, reduced carbon footprint, and
sustainable plant and animal agriculture practices are
few of the aspects that can contribute to the achieve-
ment of the goals outlaid.
Conclusions and future prospects
Plant-based green chemistry could be one of the solu-
tions from the past to the future of humanity, focusing
on ecologic and economic chemistry, and as a success
story of the evolution of green chemistry in the 21st
century toward a petroleum-free world. The most crit-
ical aspect is not only to have 100% bio-based product
but to obtain 100% ‘sustainable’ bio-based products,
with net positive carbon impacts and limiting the use of
petroleum solvents and nonrenewable energy. There are
many key challenges and barriers at different levels from
microscale
(petroleum-free),
mesoscale
(detextura-
tion), to macroscale (bioreﬁnery) to achieve a concep-
tual leap in fundamental and even in applied research
for conversion of plant biomass to reagents and in-
gredients with societal, environmental, and cost impacts
to levels acceptable to secure humanity against the
inevitable end of the petroleum era.
Declaration of competing interest
The authors declare that they have no known competing
ﬁnancial interests or personal relationships that could
have appeared to inﬂuence the work reported in this
paper.
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Current Opinion in Green and Sustainable Chemistry 2021, 28:100450

224 (2023) 211579
Available online 23 February 2023
2949-8910/© 2023 Elsevier B.V. All rights reserved.
Multi-objective optimization of petroleum engineering problems using a 
hybrid workflow: Combination of particle swarm optimization, fuzzy logic, 
imperialist competitive algorithm and response surface methodology 
Mohammad Sadegh Karambeigi a, Atefeh Hasan-Zadeh b, Mohammad Saber Karambeigi c,d, 
Seyyed Ali Faal Rastegar b, Masoud Nasiri e, Yousef Kazemzadeh f,g,* 
a Ministry of Education, Hamedan Department of Education, Farhang St., Hamedan, Iran 
b Fouman Faculty of Engineering, College of Engineering, University of Tehran, 43581-39115, Iran 
c Petroleum Industry Innotech Park, , Tehran 1933713173, Iran 
d The Graduate School of Management and Economics, Sharif University of Technology, Tehran, 863911155, Iran 
e Faculty of Chemical, Gas and Petroleum Engineering, Semnan University, Semnan, 35195-363, Iran 
f Enhanced Oil Recovery Research Center, School of Chemical and Petroleum Engineering, Shiraz University, Shiraz, Iran 
g Department of Petroleum Engineering, Faculty of Petroleum, Gas, and Petrochemical Engineering, Persian Gulf University, Bushehr, Iran   
A R T I C L E  I N F O   
Keywords: 
CEOR 
Multi-objective optimization 
PSO 
Fuzzy logic 
Neuro-simulation 
Design of experiments 
A B S T R A C T   
Optimization techniques are used to find the strategies for chemically enhanced oil recovery in a sandstone 
reservoir. This research develops a multi-objective optimization methodology by combining experimental design 
methods and artificial intelligence techniques. The capability of this hybrid artificial intelligence methodology is 
evaluated in the optimal design of control variables to achieve the highest performance of a surfactant/polymer 
injection project into a sandstone reservoir. In the first step, a two-level full factorial design is used to screen 
initial control variables. Thereafter a response surface methodology (RSM) is employed to optimize the RF and 
NPV of a CEOR application. The neuro-simulation technique provides the required outputs for screening and 
RSM designs. The performance of network is improved using the imperialist competitive algorithm (ICA). Having 
precise fitness functions, multi-attribute optimization was performed using particle swarm optimization (PSO) 
and fuzzy logic (FL). This paper discusses the advantages of different perspectives over single-objective ap­
proaches. Using the RF-objective PSO algorithm, RF exceeded 64% of original oil in place (OOIP), while the 
profit of project slumped to $5.90 MM. On the other hand, NPV-attribute PSO increased NPV to $8.48 MM. 
Meanwhile, RF, as the technical success of the project, plunged to less than 53% OOIP. However, the proposed 
multi-objective algorithm increased RF to 57% OOIP with NPV of $8.11 MM, solving the trade-off between 
technical and economic terms. The results of this study indicate the efficacy of proposed hybrid workflow for 
multi-attribute decision-making of CEOR field implementation.   
1. Introduction 
The oil industry is a major player in world economics. Supply chain 
management is one of the upstream industry’s strategic focuses, and oil 
production plays a key role in this area since it helps smooth out market 
swings. Although coronavirus pandemic has reduced economic activity 
and oil price is highly volatile, Energy Information Administration (EIA) 
reported that daily production is still above 90 million barrels in 2021, 
which shows that this fossil fuel is consistently the key player in the 
world energy map. On the demand side, EIA announced that oil 
consumption was increased by more than 5% over the last year ((Energy 
Information Administration, 2021)). 
In general, the efficiency of primary and secondary production sce­
narios is less than 50% in almost reservoirs, and an immense amount of 
oil remains intact that is the main target of tertiary (EOR) recovery 
methods ((Ahmadi and Shadizadeh, 2012; Santanna et al., 2009; 
Thomas, 2007)). 
A wide range of EOR techniques can be classified into three main 
categories: thermal, gas, and chemical methods ((Gharibshahi et al., 
2015)). Chemical-enhanced oil recovery (CEOR) is defined as the 
* Corresponding author. 
E-mail address: yusefkazemzade@aut.ac.ir (Y. Kazemzadeh).  
Contents lists available at ScienceDirect 
Geoenergy Science and Engineering 
journal homepage: www.sciencedirect.com/journal/geoenergy-science-and-engineering 
https://doi.org/10.1016/j.geoen.2023.211579 
Received 7 June 2022; Received in revised form 8 December 2022; Accepted 12 February 2023   
Geoenergy Science and Engineering 224 (2023) 211579
2
process in which a type or collection of chemicals (e.g., alkaline, sur­
factant and polymer) are used to improve interfacial and/or rheological 
properties of displacing fluids ((Sheng, 2010a; Thomas and Farouq Ali, 
2001)). There are great deals of operational (field) experiences, such as 
Daqing, Shengli, Huabei, and Xingjiang fields in China and Yates field in 
West Texas ((Sheng, 2010b)), proving its outstanding performance to 
recover residual oil trapped in the reservoir ((Iglauer et al., 2010; Liu 
et al., 2015; Zhang et al., 2010)). 
However, implementing CEOR is a difficult process since several 
technical, operational, economic, and most importantly environmental 
factors must be taken into account at the same time. This is because each 
oilfield is unique and has unique features. The high cost of chemicals, 
the difficulty of the operation itself, the severe reservoir conditions, and 
potential environmental hazards ((Muggeridge et al., 2014; Stoll et al., 
2011)). To alleviate some negative effects of such challenges, optimi­
zation of the process due to the effective parameters can pave the way 
for the successful application of this method in pilot and field scales 
((Carrero et al., 2007; Fathi and Ramirez, 1984; Zerpa et al., 2005)). In 
such circumstances, optimal decisions need to be taken in the presence 
of trade-offs between two or more conflicting objectives ((Isebor and 
Durlofsky, 2014; Yasari et al., 2013)). It is defined as multi-criteria de­
cision making. Water flooding optimization in petroleum reservoirs 
((Hourfar et al., 2019)), different methodologies were proposed in the 
literature for the optimization of CEOR approaches; such as 
simulation-based analysis ((AlSofi and Blunt, 2014; Wu et al., 1996)), 
surrogate-based optimization ((Carrero et al., 2007), (Zerpa et al., 
2005)) sensitivity analysis of key parameters ((Anderson et al., 2006)), 
and experimental design ((Prasanphanich et al., 2012; Douarche et al., 
2014)). Despite their efficiency, they have efficiently solved the problem 
as single-objective optimization of either net present value (NPV) or 
recovery factor (RF). Conventional single-objective optimization ap­
proaches may ignore the trade-off viewpoint; therefore, multi-objective 
optimization approaches should be utilized. 
To do that, the current paper proposes a new workflow in which 
design of experiments (DOE) and artificial intelligence techniques are 
coupled to develop a hybrid algorithm for multi-objective optimization 
of surfactant/polymer (SP) flooding in a sandstone reservoir in terms of 
simultaneous maximization of RF and NPV. The other methods as a 
trade-off solution in managing conflicting objectives can be found in 
various fields, even outside of petroleum engineering ((Tai Chui and 
Lytras, 2019; Gustafsson et al., 2019; Chamseddine et al., 2020)). 
The general overview of workflow can be described in Fig. 1. The 
paper discusses the workflow, as well as the priority of multiple per­
spectives over single-objective insight in its different stages. 
2. Background 
2.1. Design of experiments (DOE) 
DOE is defined as a systematic rigorous approach to planning reli­
able, reproducible and accurate professional research. When managing a 
process, it is required to understand the meaningful connections among 
the factors affecting the process and their corresponding response(s) to 
that process. DOE methodologies facilitate the systematic determination 
of such cause-and-effect relationships. 
There are different areas in which DOE can be applied to generate a 
collection of valid information regarding the process ((Montgomery, 
2012)). It was used for screening of significant factors and statistical 
modeling of the process. The former was done by two-level design, and 
the latter was carried out using the response surface methodology. 
2.1.1. Two-level factorial design 
Ranking the importance of factors is the preliminary step of experi­
mentation whereby a large number of factors that might be important 
are analyzed, and insignificant factors are determined and ignored from 
the major DOE plan, saving time and budget ((Sellstr¨
om et al., 1992)). 
Factorial two-level design One of the best methods for concurrently 
screening a large number of parameters to determine whether key ones 
warrant further inquiry is to create first-order models. 
2.1.2. Response surface methodology (RSM) 
RSM is comprised of a group of statistical and mathematical tech­
niques. When a response or a set of responses are affected by indepen­
dent control variables, RSM can be applied to find a functional 
relationship among them ((Khuri and Mukhopadhyay, 2010)). RSM 
models the process via the fitting of the polynomial equation(s) to the 
provided data ((Ba and Boyaci, 2007)). 
Compared to one-factor-at-a-time approach of experimental design 
in which only one factor is changed while others are kept at a constant 
level, RSM can vary the levels of factors simultaneously while it keeps 
the number of required runs minimum. Other aspects of RSM applica­
tions are process optimization and representing the interaction of factors 
((Bezerra et al., 2008; Ghaedi et al., 2015)). Therefore, it is known as an 
interesting multivariable statistical protocol for the design of experi­
ments ((Asfaram et al., 2016; Jeirani et al., 2013)). 
2.2. Artificial neural networks (ANN) 
ANN is a nonlinear processing paradigm that mimics the operational 
Fig. 1. General overview of the workflow.  
M.S. Karambeigi et al.                                                                                                                                                                                                                         
Geoenergy Science and Engineering 224 (2023) 211579
3
principles of the human neural network ((Shabanzadeh et al., 2015)). 
Neuro computing was born in 1943 when McCulloch and Pitts published 
an article on how neurons might work ((McCulloch and Pitts, 1943)), 
and from then on it was successfully used to solve a broad spectrum of 
complex problems in many areas of industry e.g. quality control 
((Anderson and Whitcomb, 2004)), science e.g. pattern recognition 
((Widrow et al., 1994)), finance e.g. stock market prediction ((Bah­
rammirzaee, 2010; Wong and Selvi, 1998)), medicine e.g. medical 
diagnosis ((Baxt, 1995; Patel and Goyal, 2007)), data-mining e.g. 
knowledge discovery ((Lu et al., 1996)), and energy e.g. forecast of 
crude oil production ((Kalogirou, 2000)). ANN is a parallel structure 
made up of layers of closely interconnected processors called neurons, 
which perform comparable tasks to the axons of biological neural net­
works. When example data are supplied, it may learn the empirical 
relationship between an input and an output of a process. ANN is of great 
interest in petroleum engineering studies ((Al-Bulushi et al., 2012; 
Mohaghegh, 2000; Talaat et al., 2018)) because of its unique features: It 
is a model-free function without the requirement of any knowledge 
regarding the process. It has a high tolerance to noisy data, the ability to 
learn rather than find a solution or mathematical modeling, and finally 
the ability to generalize ((Ahmadloo et al., 2010; Al-Dousari and Gar­
rouch, 2013; Salahshoor et al., 2013)). 
According to Kolmogorov’s theorem (from 1957), a multi-layer 
perceptron (MLP) network can act as a universal predictor ((Nezama­
badi-Pour, 2015)) MLP algorithm is recognized as one of the most 
popular paradigms for the construction of ANN ((Silva et al., 2007)). The 
architecture of an MLP comprises the input, one or more hidden and 
output layers. There are different numbers of neurons in each layer. 
Direct links with their weights connect the neurons of the current layer 
and those on the proceeding layer. 
The received signals from previous layers are collected by the output 
layer, creating the response of the network. The most effective training 
technique is backpropagation, which introduces a large amount of 
available data to the network and adjusts the weights to achieve a 
training objective that is typically the smallest difference between the 
output(s) of introduced (real) data and the MLP response. Eventually, 
MLP learns the behavior of presented data which can be used for future 
predictions. 
2.3. Particle swarm optimization (PSO) 
PSO is a population-based stochastic search method that was pro­
posed by Kennedy and Eberhart (1995)). Their idea was inspired by 
social behaviors of animals, such as bird flocking, fish schooling and 
bees swarming ((Khulal et al., 2016; Senthamaraikkannan et al., 2016)). 
PSO belongs to the family of swarm intelligence approaches in which 
collective behaviors of living creatures are studied to develop algorithms 
to solve scientific and engineering problems ((Reddy and Kumar, 
2007)). 
PSO algorithm starts with the random generation of a population of 
possible solutions referred to as a swarm of particles, each of which is 
characterized with a position (x) and a velocity (v) ((Li et al., 2015)). The 
position of each individual (particle) denotes its current location in an 
N-dimensional search space. At each iteration, the fitness of particles is 
evaluated using fitness function(s) and compared to the best individual 
fitness achieved so far by the particle. Moreover, the best experiences of 
all particles in a swarm are compared to select the best global solution. 
Thereafter, all particles move to new positions in the search space in the 
next iteration as Eq. (1): 
xi(t + 1) = xi(t) + vi (t + 1)
(1) 
The velocity vector of each individual is updated accordingly: 
vi(t + 1) = ωvi(t) + C1φ1(pbesti −xi(t)) + C2φ2(gbest −xi(t))
(2)  
Where ω is inertia weight factor, C1 and C2 are acceleration coefficients, 
φ1 and φ2 are random weights taken from numbers uniformly distrib­
uted in the interval (0,1), pbesti is personal best of ith iteration and 
defined as the best location found so far by a particle, and gbest is global 
best and denotes the best global solution among all of the pbesti achieved 
so far ((de Pina et al., 2011; Zendehboudi et al., 2014; Zhu et al., 2011)). 
There are three components (terms) in the velocity update equation 
(Eq. (2)). The first term is the inertia component in which the previous 
flight trajectory is considered for the movement of the particle. The 
cognitive element, which serves as the particle’s internal memory, is the 
next concept. It makes an effort to relocate the particle to areas where it 
has shown a high level of individual fitness. The last word is the social 
component, which directs the particle toward the best results of recent 
nearby particles ((Gustafsson et al., 2019), (Ciaurri et al., 2011)). The 
stochastic effect of cognitive and social components is provided by 
random weights (φ1 and φ2). 
The exploring of particles in the search space proceeds until stopping 
criterion is satisfied which can be defined as the maximum number of 
iterations. Eventually, the last gbest is the optimization solution. 
2.4. Fuzzy logic (FL) 
FL is based on fuzzy sets, which were formalized by Zadeh (1965)). It 
was inspired by the process of human thinking and cognition. The notion 
of graded membership is the basis of FL. It mathematically models 
approximate reasoning and is the most suitable approach to deal with 
information that is uncertain, imprecise, and vague with no sharp 
boundaries, such as computational perception ((Lababidi et al., 2004; 
Sedighi et al., 2014; Solo and Gupta, 2007)). 
Traditional (Boolean) logic is built on classical sets in which the 
membership of an element is a bivalent condition (0 or 1): the element 
either belongs to the set (1) or does not (0). By contrast, the elements of 
fuzzy sets have a degree of membership. Fuzzy sets permit a gradual 
transition from membership to non-membership ((Mohaghegh, 2000)). 
It provides via the definition of membership function as Eq. (3) which 
represents the concept of partial truth: 
A = {(x, μA(x))|x ∈X}
(3)  
Where A is fuzzy set, x is an element of X as a domain of points, and μA(x)
is the membership function which quantifies the belonging degree of x 
to the fuzzy set ((Khatami et al., 2008; Nashawi and Malallah, 2009; 
Nowroozi et al., 2009)). 
2.5. Imperialist competitive algorithm (ICA) 
ICA is a new optimization strategy based on human social-political 
evolution ((Atashpaz-Gargari and Lucas, 2007)). It was introduced in 
2007, and from then on it was used to solve many engineering problems 
((Gerist and Maheri, 2019; Hosseini and Al Khaled, 2014)). The results 
of its different applications indicate the success of the proposed algo­
rithm, especially in petroleum engineering ((Ahmadi and Chen, 2019; 
Ameli and Mohammadi, 2018)). 
In fact, imperialism means expanding a country’s domain of power 
and sovereignty beyond its borders. One country may control other 
countries either using direct sovereignty or by more covert methods, 
such as control of markets, commodities, and raw materials, the latter 
being new colonialism. In this algorithm, the politics of assimilation and 
colonial competition form the core. 
To start the algorithm, an array of optimization variables (Eq. (4)) is 
formed which is known as “country": 
Country =
[
p1, p2, p3, …, pNvar
]
(4)  
Where, for example, p1 is language, p2 is economic policy, p3 is religion, 
and so on. We are looking for the best country to solve the optimization 
problem, i.e., the best set of problem parameters, such as Eq. (5): 
M.S. Karambeigi et al.                                                                                                                                                                                                                         
Geoenergy Science and Engineering 224 (2023) 211579
4
cost = f(country) = f
(
p1, p2, p3, …, pNvar
)
(5) 
As a consequence of applicable decisions, the successful imple­
mentation of CEOR approaches in the most efficient and economical way 
depends on process optimization using methodologies supporting mul­
tiple perspectives. For this purpose, a new hybrid workflow was intro­
duced which combined the abilities of experimental design approaches 
as well as artificial intelligence techniques to model and optimized the 
process in the presence of multiple criteria. 
3. Problem statement and methodology 
3.1. Case study 
Benoist sandstone reservoir was used. It is located in Marion County, 
Illinois, USA. The reservoir is sandstone with 50 ft thickness of pay zone. 
The injection pattern of the reservoir is an inverted five-spot. Table 1 
summarizes the reservoir data ((Prasanphanich, 2009)). 
The residual oil trapped in the reservoir was tertiarily recovered 
using CEOR flooding. Water flooding and chemical flooding were 
simulated using UTCHEM simulator ((Delshad et al., 1996; Pope and 
Nelson, 1978)), and recovery factor was calculated. Economic evalua­
tions were performed at 50 USD/bbl of oil price. Other variables to 
calculate NPV are in Table 2. The detailed procedures for reservoir 
simulation and economic analysis were thoroughly discussed elsewhere 
((Prasanphanich, 2009)). 
The process of surfactant/polymer flooding was modeled due to RF 
and NPV as functions of eight control variables which were as follows: 
surfactant slug, surfactant concentration in surfactant slug, polymer 
concentration in surfactant slug, polymer drive, and polymer concen­
tration in polymer drive, the ratio of vertical to horizontal permeability, 
the salinity of polymer drive, and salinity of post-flush water injection. 
The sampling domain of these variables in search space is presented in 
Table 3. 
The analysis of variance (which is mentioned in the following), and 
the mathematical equation obtained from the modeling (Eqs. (13) and 
(14) in the sequel) show the significant effect of all of the parameters A 
to F to determine the outputs. This, along with other technical reasons 
related to the effectiveness of all considered parameters in recovery 
factors and the profit of the CEOR, will justify the selection of the pa­
rameters A to F. 
3.2. Workflow 
Fig. 2 shows the algorithm of the workflow in which different tools 
were utilized. 
In brief, after screening the most significant variables (first stage), 
the algorithm began modeling surfactant/polymer injection using RSM 
and neuro-simulation, providing objective functions. Thereafter, a 
multi-purpose approach optimized the process via coupling of PSO/FL 
or ICA/FL. The final step was decision-making based on the results of 
previous outputs. The outcome of algorithm is the optimum value of 
effective parameters while a trade-off between technical and economic 
objectives is considered. The detailed description of each stage is as 
follows. 
3.2.1. Screening of influential factors 
The surfactant/polymer flooding had eight initial parameters. They 
were ranked by two-level full factorial design. Limiting levels for each 
factor were extracted from high and low values of each variable in the 
sample domain (Table 3). The design required 2k runs that k is the 
number of initial factors ((Ferreira et al., 2007; Valle et al., 2009; 
Ghobadi Nejad et al., 2019)). The response of each run was provided via 
the neuro-simulation approach. Thereafter, responses were fed into 
software (Design Expert 7.0.0) to analyze using a two-level full factorial 
design. 
3.2.2. Development of objective functions 
Precise objective functions are the essential requirement of present 
optimization algorithm. Mathematical relationships between indepen­
dent variables and outputs of the process were established using statis­
tical modeling. For this purpose, RSM was used through a series of n 
experiments was designed. In the next step, the corresponding responses 
(outputs) of each run be determined. Thereafter, quadratic equations 
were fitted to the data. The validation of equations fitted to the data was 
examined via the analysis of variance (ANOVA). 
Among different methodologies of RSM, the central composite 
design (CCD) was selected as one of the most popular RSM designs. In 
Table 1 
The properties of the simulation model ((Prasanphanich, 2009)).  
Parameter 
Value 
Reservoir size (ft) 
1169(i) × 1168(j) × 53(k) 
Number of grid blocks 
41(i) × 41(j) × 6(k) 
Reservoir pore volume (MM bbl) 
2.082 
Reservoir pressure (psi) 
300 
Reservoir temperature (◦F) 
82 ((Ware, 1983)) 
Average porosity (%) 
15.3 
Permeability range (md) 
84–414 
Initial water saturation (%) 
30 
Water viscosity (cp) 
0.89 
Oil viscosity (cp) 
6.8 
Injection wells 
4 
Production wells 
9 
Distance between injection and production wells (ft) 
330  
Table 2 
Input variable of economic analysis ((Prasanphanich, 2009)).  
Category 
Parameters 
Unit 
Value 
INITIAL CAPITAL 
COSTS 
Facilities and equipment 
$ 
500 
Workover, drilling, and leasehold 
costs 
$ 
0 
OPERATING COSTS 
Water flood operating cost 
$/month 
10 
Chemical injection cost 
$/bbl 
0.1 
Produced water cost 
$/bbl 
0.1 
Oil treatment cost 
$/bbl 
0.1 
overhead cost 
% 
10 
COMMODITY 
PRICES 
Oil price 
$/bbl 
50 
Surfactant price 
$/lb 
2 
Polymer price 
$/lb 
1 
TAXATION 
Royalty 
% 
12.5 
Severance & Ad valorem tax rate 
% 
0.046 
Effective income tax rate 
% 
38.25 
EOR tax credit rate 
% 
0 
GENERAL 
Inflation rate 
% 
3 
Escalation (oil and chemical prices 
and operating cost) 
% 
3 
Real discount rate 
% 
10 
Real reinvestment rate 
% 
10  
Table 3 
Range of independent variables for screening design of the process ((Prasan­
phanich, 2009)).  
Factor 
Unit 
Symbol 
Minimum 
Maximum 
Surfactant slug size 
resePV 
A 
0.097 
0.259 
Surfactant concentration 
Vol. 
fraction 
B 
0.005 
0.03 
Polymer concentration in 
surfactant slug 
wt% 
C 
0.1 
0.25 
Polymer drive size 
PV 
D 
0.324 
0.648 
Polymer concentration in 
polymer drive 
wt% 
E 
0.1 
0.2 
kv/kh ratio 
– 
F 
0.01 
0.25 
Salinity of polymer drive 
meq/ml 
G 
0.3 
0.4 
Salinity of water post-flush 
meq/ml 
H 
0.3 
1.03  
M.S. Karambeigi et al.                                                                                                                                                                                                                         
Geoenergy Science and Engineering 224 (2023) 211579
5
this method, three types of experiments are designed: factorial design 
(2k), axial design (2 × k) and several center points by which computing 
error and reproducibility of data are evaluated. The functional rela­
tionship is a polynomial function consisting of linear and quadratic 
terms as Eq. (6): 
Y = α0 +
∑
k
i=1
αiXi +
∑
k
i=1
∑
k
j=1
αijXiXj
(6)  
Where Y is the output, k is the number of screened variables, α0 is the 
constant value, αi is the linear coefficient, αij is the quadratic coefficient, 
Xi is the single factor, and XiXj is the interaction factor. 
Similar to previous stage, neuro-simulation approach was used to 
generate the required responses of each run. 
3.2.3. Neuro-simulation approach 
The routine approach to provide the responses (here RF and NPV) of 
CEOR processes is the application of simulation methodologies (e.g., 
UTCHEM simulator). However, they need reliable static (geological) and 
history-matched dynamic models ((Cavalcante et al., 2019)) of the 
reservoir, which are often not made available to the general public. 
Furthermore, many input data are necessary for a reliable simulation 
model. Instead, an alternative solution is gathering the data of simula­
tion outputs from the open literature to develop a surrogate model by 
which required responses are generated. Hence, data-dependent para­
digms act very well among which the neuro-simulation approach is the 
best where by the proxy model is generated using artificial neural net­
works (ANN). 
The data were collected ((Prasanphanich, 2009)), and the noisy part 
was removed. Then, they were randomly divided into two separate 
datasets: training (80% of the data) and evaluation (remaining 20%) 
Fig. 2. The workflow of multi-objective optimization.  
M.S. Karambeigi et al.                                                                                                                                                                                                                         
Geoenergy Science and Engineering 224 (2023) 211579
6
datasets. Inputs and outputs of each individual data in different datasets 
were normalized using the mean and standard deviation of variables. 
Three steps were included in ANN modeling: training, generalization, 
and operation. 
Between inputs and outputs was in training phase in which the 
network learned. A multilayer backpropagation network composed of 
input, one hidden, and output layers were selected. The default number 
of neurons in the hidden layer was equal to the number of inputs 
(influential factors). Transfer functions of hidden and output layers were 
tangent sigmoid and purelin types, respectively. Levenberg-Marquardt 
was considered the default training algorithm. The stopping criterion 
of the training phase was the mean square error of 1 × 10−5. 
In generalization phase, ANN performance was improved using the 
evaluation dataset. To this end, the number of neurons in the hidden 
layer was changed from 1 to 15. Consequently, 10 training algorithms 
were assessed: Batch training with weight and bias learning rules 
(trainb), BFGS quasi-Newton (trainbfg), Bayesian regularization 
(trainbr), Fletcher-Powell conjugate gradient (traincgf), Gradient 
descent with adaptive learning rate (traingda), Gradient descent with 
momentum (traingdm), Gradient descent with momentum and adaptive 
learning rate (traingdx), Levenberg-Marquardt (trainnlm), Powell-Beale 
conjugate gradient (traincgb), and Polak-Ribiere conjugate gradient 
(traincgp). 
To compare ANN efficiency in different network structures, three 
statistical parameters were used to quantify the computing error. They 
were MAPE (mean absolute percentage error), SMAPE (symmetric 
MAPE) and MSE (mean square error) as Eqs. (7)–(9): 
MAPE = 100
n
∑⃒
⃒
⃒
⃒
yp −yr
yr
⃒
⃒
⃒
⃒
(7)  
SMAPE = 100
n
∑⃒
⃒yp −yr
⃒
⃒
(|yp|+|yr|
2
)
(8)  
MSE = 1
n
∑(
yp −yr
)2
(9)  
Where n is the number of data in the dataset, yp is predicted data by ANN 
and yr is real data. Error analysis based on just one parameter (either 
MAPE or MSE) may be misleading. Therefore, it should be performed by 
a combination of diverse parameters ((Chai and Draxler, 2014; Mathews 
and Diamantopoulos, 1994; Shcherbakov et al., 2013)). 
The essential answers for full factorial and CCD designs were then 
retrieved in the operation phase as the last stage of ANN modeling after 
having an optimal efficient ANN. 
3.2.4. Improvement of ANN using ICA 
The performance of neuro-simulation was improved using ICA. 
Checking out the various situations, algorithm parameters are set as 
follows: number of initial countries = 100, number of initial imperialists 
= 50, number of decades = 50, revolution rate = 0.3, assimilation co­
efficient = 2, assimilation angle coefficient = 0.5, gamma = 0.02 (which 
total cost of empire = cost of imperialist + (zeta × zeta × mean) (cost of 
all colonies)), damp ratio = 0.99, uniting threshold = 0.02. Apparently 
the method is still somehow a black box to the authors. 
3.2.5. Multi-criteria optimization methodology 
Surfactant/polymer flooding was optimized using multi-purpose 
PSO-FL methodology. In the first iteration, a swarm of 25 particles 
was randomly positioned in the search domain. Then, RF and NPV of 
each individual particle were calculated using fitness (objective) 
functions. 
In each iteration, the values of (pbest)i and gbest were determined. In 
so far as the simultaneous presence of two responses (RF and NPV), the 
optimization was a multi-attribute problem. One efficient solution to 
such problems is to combine multiple objectives into a unique function. 
For this purpose, PSO was coupled with fuzzy logic. Domain trans­
formation was done via the fuzzification of mathematical objective 
functions to generate fuzzy membership functions as Eq. (10): 
μf (Fk) =
⎧
⎪
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎪
⎩
0
Fk < Fmin
k
Fk −Fmin
k
Fmax
k
−Fmin
k
Fmin
k
Fmin
k
< Fk < Fmax
k
1
Fk > Fmax
k
(10)  
Where μf(Fk) is fuzzy membership function of k th objective function 
(k = 1 for RF and k = 2 for NPV), Fk is the k th response, Fmax
k 
and Fmin
k 
are maximum and minimum values of each objective function, respec­
tively. Each fuzzy membership function represented a fuzzy region of 
acceptability. Thereafter, a new function called zeta function was 
defined as: 
ζi =
{μf (F1), μf (F2)
}
i
(11)  
Where ζi is satisfaction factor, μf(F1) is RF fuzzy membership function 
and μf(F2) is NPV fuzzy membership function, respectively. The sub­
scribe i denotes the particle number (from 1 to 25). The algorithm 
evolved towards the maximum values of ζi (as close to unity as possible). 
The fitness of particles was evaluated based on ζi. Then, pbesti values 
were determined so pbesti of each particle has experienced its maximum 
fitness (ζi) so far. Thereafter, the multi-criteria solution was selected as 
follows: 
gbest = Max {pbesti}
(12) 
In the next iterations, the positions of particles were updated ac­
cording to Eqs. (11) and (12), and a new swarm was generated. The 
constant inertia (ω) of 0.7298 and acceleration coefficients (C1 and C2) 
of 1.49618 were assigned ((Cai et al., 2019; Van Den Bergh and Engel­
brecht, 2006)) by which the velocity factor components were calculated 
and new placement of particles was identified. The above steps were 
repeated to update values of pbesti and gbest until the algorithm reached 
the stopping criterion. The last gbest is the solution of the problem. 
The coupling of PSO with FL was found to be a powerful multi- 
attribute optimization method whereby the scenario of surfactant/ 
polymer injection into the reservoir was simultaneously optimized in 
terms of the highest oil recovery factor and maximum profit of the 
project. 
4. Results and discussion 
A workflow for multi-objective optimization of petroleum industry 
problems was developed. The performance of the workflow was exam­
ined in a case study of a complex tertiary oil recovery method. The re­
sults are presented and discussed as follows. 
4.1. Screening of the most effective factors 
Among available EOR techniques, CEOR is known as a complicated 
process. On the one hand, its complexity and uncertainty necessitate 
considering parameters when precise modeling or optimization is 
intended. On the other hand, time and budget limitations increase the 
application cost of complex models. A standard strategy is performing a 
screening study to determine the possible influential factors. Factorial 
designs are common for determining the linear influence of initial fac­
tors ((Lundstedt et al., 1998)). To this end, two-level full factorial design 
was used. Having eight initial factors (Table 3), a total of 256 sets of runs 
were planned. The corresponding outputs were provided by 
neuro-simulation methodology. We discuss the validation of this proxy 
model in the next section. 
The full factorial design was then analyzed using ANOVA at the 5% 
significance level (p-value<0.05). Factors A (surfactant slug size), B 
M.S. Karambeigi et al.                                                                                                                                                                                                                         
Geoenergy Science and Engineering 224 (2023) 211579
7
(surfactant concentration), C (polymer concentration in surfactant slug), 
D (polymer drive size), E (polymer concentration in polymer drive), and 
F (kv/kh ratio) were recognized as important factors for both RF and 
NPV. Factor H (salinity of water post-flush) had the influence on neither 
RF nor NPV and factor G (salinity of polymer drive) was an influential 
parameter just for RF. 
The most significant factors affecting NTG and RF are not the same 
(Fig. 3):; factor A (surfactant slug volume) for RF and factor B (surfactant 
concentration) for NPV, respectively. Previous investigations showed 
that surfactant slug size is one of the most important factors affecting oil 
recovery ((Lorenz, 1989)). Moreover, the surfactant cost includes the 
major portion of the chemical cost for any CEOR scenario ((Meyers, 
1981)), and that is why NPV has been substantially influenced by sur­
factant concentration (see Fig. 4). 
This finding highlights the significance of multi-attribute approaches 
compared to single-objective techniques for the optimization of complex 
problems in petroleum engineering where factors affect responses 
differently. More specifically, it is clear that the ordering of effective 
factors for RF and NPV were different since RF evaluates technical 
perspectives while NPV includes economic circumstances. It demon­
strates the superiority of algorithms over single-objective techniques 
when trade-off effects of goals are taken into account. A union of 
important factors regarding each response was taken as multi-attribute 
screened factors. Therefore, factors A to G were selected as the final 
influential parameters on which principal experimental design (CCD 
plan) was focused. 
4.2. Validation of neuro-simulation approach 
The outputs of the full factorial and CCD designs in each run were 
provided using the neuro-simulation technique. ANN was trained and 
validated based on the observations of input-output behaviors of 
available data gathered from the results of previous simulation studies in 
the literature. ANN was first trained with a multi-layer backpropagation 
network by introducing the training dataset. MAPE and SMAPE of RF 
response in the training phase were 18.85% and 16.64%, respectively. 
They were calculated as 10.03% and 9.52% for NPV, respectively. 
The results of training phase were not acceptable, and therefore the 
efficiency of the network should be increased in the generalization phase 
whereby the performance of trained network to estimate newly- 
presented unseen data was examined over the evaluation dataset. For 
this purpose, further attempts were made to select the best training al­
gorithm and the number of neurons in the hidden layer. 
Bets training algorithm was Bayesian regularization back­
propagation by which overfitting is prevented and outstanding gener­
alization performance is provided for regression problems ((Karambeigi 
et al., 2011)). It shows MAPE values of RF and NPV predictions in 
generalization phase were substantially reduced from 51.24% to 
14.95%–6.13% and 3.28% when default training algorithm (Lev­
enberg-Marquardt) was replaced with Bayesian regularization algo­
rithm as the most efficient training algorithm. 
The optimum number of neurons was then selected when it was 
varied from 1 to 15 and the trends of MAPE were followed. Fig. 5 shows 
the best prediction efficiency was achieved when the number of neurons 
was nine. Hence, MAPE decreased further to 3.53% and 1.95%, 
respectively. The prediction MSE of two responses was calculated as 
1.09% original oil in place (OOIP) and 0.04$ MM, respectively. The 
cross-plots of ANN estimation and actual data in training and general­
ization phases are shown in Fig. 6. 
These values indicate that the MPL structure containing three input, 
one hidden and output layers trained with Bayesian regulation back­
propagation algorithm and having nine neurons in the hidden layer was 
the optimal ANN network to be successfully applied for the accurate 
prediction of unknown data in operation (third) stage in which required 
outputs of screening (full factorial) and principal (CCD) designs were 
provided. 
4.3. ICA for ANN improvement 
By default, ICA is an optimization algorithm. In this paper, however, 
it was u to improve the performance of the ANN. Hence, the inputs and 
two outputs are given to the optimal network obtained in subsection 5.2 
and it reaches with ICA to the optimum level of training and testing of 
both outputs simultaneously. The obtained network has the necessary 
performance for multi-objective optimization techniques used in the 
sequel. 
These are the main parameters of the algorithm that we will not 
elaborate on since the full description can be found in the references 
((Xing and Gao, 2014)). Finally, total performance (for both outputs 
simultaneously) by choosing parameters introduced in section 4.2.4., 
was reported to be 96%, which also confirms the low MSE obtained for 
RF and NPV separately. 
Using imperialist competitive algorithm to improve the performance 
of the neural network, it is no longer necessary to manually adjust the 
network and apply the various methods as shown in Fig. 3. 
4.4. Generation of objective functions using RSM 
After initial variables were ranked, the principal experimental plan 
was designed using CCD to find objective functions. Based on seven 
significant factors that remained from the results of the screening stage, 
and according to Eq. (6), CCD proposed a set of 152 runs. Thereafter, the 
corresponding output for each run was generated using the neuro- 
simulation approach. Following this, the obtained outputs were fed 
into software to fit second-order models using RSM. 
Table 4 shows the results of ANOVA composing of a collection of 
various statistical tests to evaluate the quality of fitted models. It shows 
the fitted quadratic functions on the data were statistically significant in 
Fig. 3. Pareto charts of the main effect of initial factors (yellow line is t-Value 
limit = 1.97). 
M.S. Karambeigi et al.                                                                                                                                                                                                                         
Geoenergy Science and Engineering 224 (2023) 211579
8
99.9% confidence level as the F Values were calculated as 171 for RF 
model and 163 for NPV model. Furthermore, the values of Prob > F were 
less than 0.0001 for both models. 
The coefficient of determination (R2) quantifies the proportion of 
variance explained by predictors of the proposed model. R2 values of 
second-order models were 0.981 and 0.980 for RF and NPV responses, 
respectively. Although R2 is a conventional indicator, it alone cannot 
guarantee the goodness-of-fit because it continuously increases as long 
as any predictor is added to the equation. Therefore, artificial 
improvement of model may occur. To compensate for this effect, 
adjusted R-squared is considered as a modified form of R2 by which the 
degree of relationship is evaluated better in the sampling domain. R2
adj 
increases when useful predictors are added to the model and will be 
reduced if unnecessary terms are included in the equation. Hence, a 
more precise estimation of how the model generalizes can be provided. 
R2
adj of quadratic models for two responses were determined as 0.975 and 
0.975 which show high explanatory power of the fitted models. More­
over, the predictive ability of the regression model for future estimations 
is examined when predicted R-squared is considered. R2
pred of proposed 
models for two outputs were calculated as 0.940 and 0.935 which had a 
reasonable agreement with R2
adj. The favorable values of these statistical 
parameters proved the acceptable fitness of second-order equations. 
There are two other statistical parameters in the ANOVA table. The 
reproducibility of model is measured using the coefficient of variance 
(CV) which is defined as the ratio of the standard deviation to the mean. 
It is a useful parameter for the evaluation of model reliability. The values 
of less than 10% are suggestive of a favorable predictor model. The 
proposed models were reproducible as the CV of their second-order 
equations for RF, and NPV outputs were calculated as 1.99% and 
4.10%, respectively. Furthermore, the contrast in predicted response 
relative to its associated error is measured using an adequate precision 
parameter. The adequate precision of regression models can be ensured 
with a signal to noise ratio greater than 4. They were calculated as 
63.159 and 55.197 by which the successful application of fitted models 
to navigate the search space is indicated. 
Different statistical parameters in ANOVA table confirmed RF and 
NPV were efficiently modeled in terms of seven screened factors. 
Eventually, the developed second-order Eqs. (13) and (14) were 
extracted as objective functions for PSO-FL optimization. 
RF = + 28.73447 + 166.55027A + 3127.84554B + 131.82572C
−47.28657D −52.68134E −26.96072F −252.22730G −1539.24953AB
−17.56655AC + 15.34398AD −71.13774AE −14.44152AF
−159.15490AG −2026.35227BC + 526.65048BD −475.31914BE
−160.10240BF −1335.44628BG + 30.26061CD −122.86153CE
+ 1.44758CF −206.94019CG + 39.70949DE + 8.53134DF
+ 35.65359DG + 26.19122EF + 68.10743EG −2.04736FG
−29.94598A2−51966.24377B2+74.23545C2+28.68177D2
+ 366.60976E2+61.01211F2 + 462.02976G2
(13)  
NPV = −5.06951 + 50.50774A + 561.65623B + 34.61332C
−4.88624D6.43122E −2.89839F −13.74147G −1571.95191AB
−7.54189AC + 1.82158AD −18.83260AE −1.81436AF −29.95762AG
−421.44173BC + 102.08395BD −153.49988BE −38.26693BF
−229.78350BG + 6.38777CD −24.47320CE −0.025723CF
−39.92990CG + 7.19773DE + 1.49304DF + 6.70727DG + 4.42405EF
+ 11.68099EG −0.54155FG
−37.41904A2−7557.48052B2−9.31668C2+0.94855D2
+ 21.37747E2+3.35373F2 + 39.05747G2
(14)  
Where A to F are representative of seven control variables (Table 3). RF 
Fig. 4. Performance of different training algorithms for the training of MLP network.  
Fig. 5. Influence of the number of neurons in hidden layer on the prediction 
efficiency of MLP structure. 
M.S. Karambeigi et al.                                                                                                                                                                                                                         
Geoenergy Science and Engineering 224 (2023) 211579
9
as well as NPV are the responses. 
The accuracy of the obtained equations in predicting the output 
values with respect to the given inputs can be seen in Fig. 7. 
One of the most important applications of RSM rather than its ability 
to establish objective functions is to extract the interaction of factors. 
The analysis of interaction between different factors revealed that there 
was an interaction between surfactant slug size (A) and surfactant 
concentration (B) as shown in Fig. 8. It shows their interactions were 
meaningful as the opposite trends of variations for RF and NPV re­
sponses as functions of factor A were observed when factor B was 
changed from its low level (0.01 vol frac.) to the high level (0.025 vol 
frac.). 
In other words, RF and NPV were conflicting objectives, and multi- 
objective optimization should be performed to find the optimal design 
of control variables for the successful application of surfactant/polymer 
flooding. 
4.5. Multi-objective optimization 
Having validated objective functions, multi-purpose optimization 
could be consequently performed. There was a swarm of 25 particles in 
each PSO iteration, and the optimization algorithm was continued to 60 
iterations. Two extremes of each control variable were low and high 
levels of screened factors (Table 3). The optimization goal was set for the 
simultaneous maximization of RF and NPV. 
In each iteration, the placement and velocity of every particle in the 
search domain were randomly determined. Following this, two re­
sponses were calculated using objective functions obtained in Eqs. (13) 
and (14). Thereafter, zeta function (Eq. (11)) was employed to find the 
unique satisfaction index of each particle. 
Furthermore, the values of pbesti and gbest were determined in each 
iteration. Proceeding with PSO-FL optimization methodology, ζ 
increased as demonstrated in Fig. 9 and two responses (outputs) pro­
gressed through their maximum values as plotted in Fig. 10 in which RF 
was replaced with residual oil saturation (ROS) using Eq. (15) for better 
visualization of the variations. The less ROS, the more RF. 
RSO = 100 −RF
(15)  
4.6. Decision making 
Table 5 presents the optimal arrangement of influential variables 
whereby gbest was met as the goal of the optimization problem. 
The PSO-FL algorithm proposed the optimum values of effective 
factors as follows: surfactant slug size of 0.259 PV containing surfactant 
concentration of 0.0088 vol fraction and polymer concentration of 0.25 
wt%, polymer drive size of 0.648 PV composed of 0.2 wt% polymer and 
Fig. 6. The cross-plots of ANN prediction versus actual data: (a) training phase of RF, (b) training phase of NPV, (c) generalization phase of RF, and (d) generalization 
phase of NPV. 
Table 4 
The results of ANOVA for the response surface model.  
Statistical results 
RF response 
NPV response 
Model F value 
171.26 
162.51 
Model prob > F 
<0.0001 
<0.0001 
R-squared 
0.9810 
0.9800 
Adjusted R-squared 
0.975 
0.974 
Predicted R-squared 
0.9401 
0.935 
CV% 
1.99 
4.10 
Adequate precision 
63.159 
55.197  
M.S. Karambeigi et al.                                                                                                                                                                                                                         
Geoenergy Science and Engineering 224 (2023) 211579
10
0.3009 meq/ml salt, and kv/kh ratio of 0.0176. Implementing the CEOR 
scenario by this procedure, the algorithm was estimated RF of 57.21 % 
OOIP and NPV of $8.11 MM. 
For the approval of this decision, the optimal scenario was compared 
with the RSM method which is a conventional approach to be utilized in 
the process optimization ((Rastegar et al., 2016)). Table 5 (column 2) 
shows the optimal scenario proposed by RSM. It predicted the recovery 
factor as 56.99%OOIP and estimated the net present value as $7.94 MM. 
Comparing two approaches, one found that the PSO-FL methodology 
could search more efficiently the sampling domain and has presented 
better results. 
Multi-objective optimization of surfactant-polymer flooding consid­
ered both technical and economic conditions simultaneously which are 
essential for decision-making for the field operations. To highlight the 
priority of this viewpoint, the current workflow was then compared with 
the PSO single-objective algorithm. Table 5 (column 3) demonstrates the 
results of RF-objective PSO algorithms regardless of economic issues and 
Table 5 (column 4) shows the optimum factors to achieve the highest 
NPV regardless of technical conditions. Although the recovery factor 
increased from 57.21% OOIP (PSO-FL methodology) to 64.04% OOIP 
(RF-objective PSO algorithm), NPV was drastically reduced from $8.11 
MM to $5.90 MM. In other words, the recovery factor was improved 
11.94% OOIP when just a technical goal has been considered but the 
profit of CEOR approach has decreased 27.26%. Hence, decisions based 
on just technical affairs regardless of economic conditions may threaten 
the successful implementation of field-scale CEOR projects. 
On the other hand, NPV-objective of PSO algorithm resulted in the 
improvement of project profit as NPV increased from $8.11 MM (PSO-FL 
approach) to $8.48 MM (single objective of NPV) while the oil pro­
duction of the field decreased substantially as recovery factor was 
reduced from 57.21% OOIP to 52.53% OOIP. Comparing the optimum 
scenarios presented by multi-objective and single-objective algorithms 
revealed the outstanding performance and priority of such methodolo­
gies for multi-purpose decision-making in the management of field 
development plans. 
5. Conclusion 
Based on the results of this study, the following conclusions can be 
drawn.  
1. Algorithms which can consider the presence of trade-offs between 
two or more competing technical and economic objectives have 
Fig. 7. Comparison between predicted values and actual values of RF and NPV.  
Fig. 8. The interactive effects of surfactant slug size and surfactant concen­
tration on (a) RF (b) NPV. 
M.S. Karambeigi et al.                                                                                                                                                                                                                         
Geoenergy Science and Engineering 224 (2023) 211579
11
priority over single-objective approaches that may ignore the trade- 
offs so that their solutions can satisfy just one objective while they 
may be unacceptable with respect to the other objective(s).  
2. Four main stages were included in the structure of the workflow: 
screening, modeling, optimization, and decision-making. The case 
study to assess the effectiveness of proposed workflow was a sur­
factant/polymer flooding project in a sandstone reservoir. The ob­
jectives of the problem were RF as the technical aspect and NPV as 
the economic index.  
3. A Two-level full factorial design was used to screen eight initial 
variables. For RF, they were descendingly ranked as surfactant slug 
(A), polymer concentration in surfactant slug (C), polymer concen­
tration in polymer drive (E), polymer drive size (D), surfactant 
concentration (B), kv/kh ratio (F), the salinity of polymer drive (G), 
and the salinity of water post-flush (H). Thus, the descending order of 
their effect on NPV was as follows: B, C, E, D, A, F, G, H. Based on the 
ANOVA, factors A to G as the union of the most significant factors for 
both responses were determined as influential factors. 
4. Neuro-simulation technique was efficient to generate required out­
puts of experimental designs in both the screening and modeling 
stages. In this regard, multilayer perceptron structure with Bayesian 
regularization backpropagation training algorithm and the optimal 
number of neurons in hidden layer showed the best performance of 
artificial neural network.  
5. ICA was to optimize ANN structure automatically compared to 
manual change of training algorithm and the number of hidden layer 
neurons. It improved the performance of training and evaluation 
phases because manual approach was time consuming.  
6. In the modeling stage, CCD as the best design of RSM was used to fit 
second-order equations to the generated data. The evaluation of 
goodness-of-fit for quadratic models using different parameters of 
ANOVA confirmed that the CCD approach could develop highly ac­
curate objective functions. 
7. Comparing PSO-FL methodology with another multi-objective opti­
mization developed by CCD approach as well as single-objective (RF- 
objective or NPV-objective) PSO algorithm indicated the marked 
preference of PSO-FL technique by which the recovery factor 
increased to 57.21% OOIP with NPV of $8.11 MM as the maximum 
profit of the project. 
8. Although this workflow had promising results, the inherent limita­
tion of such hybrid artificial intelligence algorithms is their de­
pendency to the data. Hence, this workflow can be applied to solve 
other petroleum industry problems in which different objectives are 
conflicting. 
Author contribution statements 
Mohammad Sadegh Karambeigi, Atefeh Hasan-Zadeh, Mohammad 
Saber Karambeigi, Seyyed Ali Faal Rastegar, Masoud Nasiri and Yousef 
Kazemzadeh contributed to the design and implementation of the 
research, to the analysis of the results and to the writing of the 
manuscript. 
Declaration of competing interest 
The authors declare that they have no known competing financial 
interests or personal relationships that could have appeared to influence 
the work reported in this paper. 
Data availability 
No data was used for the research described in the article. 
Acknowledgement 
The authors are thankful to Stat-Ease, Minneapolis for the provision 
of the Design Expert package. 
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Contents lists available at ScienceDirect
Journal of Environmental Chemical Engineering
journal homepage: www.elsevier.com/locate/jece
Current technologies and future directions for treating petroleum reﬁneries
and petrochemical plants (PRPP) wastewaters
Shahryar Jafarinejada,b,⁎, Sunny C. Jianga
a Civil and Environmental Engineering, University of California, Irvine, CA, USA
b Department of Chemical Engineering, College of Engineering, Tuskegee University, Tuskegee, AL, USA
A R T I C L E I N F O
Keywords:
Petroleum
Wastewater treatment plant
Reﬁnery
Petrochemical
A B S T R A C T
Water scarcity and wastewater management pose signiﬁcant challenges to petroleum reﬁneries and petro-
chemicals plants (PRPP). The escalating demand of petroleum products in pace with the world population
growth and technological development requires new water management strategies that encourage reducing
water consumption, reusing treated wastewater and remediating environmental impacts (R3). New innovations
and design of PRPP wastewater treatment plant (WWTP) are necessary to incorporate the principal of R3 to meet
both the industrial water needs and the environmental regulatory requirements. The goal of this review is to
summarize the state of technologies for wastewater treatment in the PRPP in order to identify areas of future
improvements. We begin with a systematic survey of water quality characteristics of PRPP discharge and the
regulatory requirements for eﬄuent water. We then examine the current WWTP technologies, conﬁguration and
operation in managing PRPP wastewaters, followed by discussion on PRPP WWTPs in Iran. Lastly, based on the
gaps identiﬁed in the system analysis, we share our vision for future opportunities in improving the design and
operation of PRPP WWTPs. Speciﬁcally, we recommend new hybrid technologies to increase treatment capacity,
improve eﬄuent water quality, and manage shock loadings of toxic materials and organics.
1. Introduction
The need for petroleum and other energy resources are growing
with the population growth and worldwide economic development. The
global energy demands are expected to increase 37% from the current
level by 2035 [1,2] with a large portion of the energy relying on pet-
roleum products. However, scarcity of water resources and concerns of
environmental pollution [3–5] present great challenges to petroleum
industry. The nexus of water and petroleum energy production is now
well recognized. New water management and wastewater treatment
strategies are critically needed to help the petroleum industry and to
protect the environment.
The petroleum industry can roughly be divided into four sectors: 1)
exploration, development and production; 2) hydrocarbon processing
(reﬁneries and petrochemicals plants); 3) storage, transportation, and
distribution; and 4) retail or marketing [6–8]. These four sectors are
also known as upstream, midstream and downstream processes [9,10].
Although environmental impacts are associated with every sector of the
industry, hydrocarbon processing by petroleum reﬁneries and petro-
chemicals plants (PRPPs) uses the largest amount of water and also
generates large amounts of wastewater [8]. Discharging of these ef-
ﬂuents without eﬀective treatment technologies to separate water and
oil not only pollutes the environment but also reduces the product re-
covery and misses the opportunity for recycle the water for reuse in
other PRPP processes [8,11,12].
Worldwide, PRPP produces more than 2500 diﬀerent types of va-
luable products from crude oil [13]. In 2015, approximately 95 million
barrel per day (mbpd) of oil was consumed globally, up from 84.7 mbpd
in 2005 [14]. The amount of water required and wastewater produced
depends on the size of the plant, type of crude oil used, products gen-
erated and the complexity of operation [4,15]. It is estimated that
80–90% of the water supplied to PRPP comes out as wastewater since
water does not enter into the ﬁnal product [8,16]. According to Coelho
et al. [17], petroleum reﬁnery processes generate around 0.4–1.6 times
the volume of wastewater per volume of crude oil processed [4,17].
Thus, based on 95 mbpd of consumed oil in 2015, PRPP would generate
between 38 and 152 mbpd of wastewater globally. The PRPP process
conﬁguration can aﬀect the quantity and quality of the wastewaters
generated greatly. For example, only 3.5-5 m3 of wastewater is gener-
ated per ton of crude oil processed when cooling water is recycled
https://doi.org/10.1016/j.jece.2019.103326
Received 10 March 2019; Received in revised form 13 July 2019; Accepted 27 July 2019
⁎ Corresponding author at: Civil and Environmental Engineering, University of California, Irvine, CA, USA; Department of Chemical Engineering, College of
Engineering, Tuskegee University, Tuskegee, AL, USA.
E-mail address: sjafarinejad@tuskegee.edu (S. Jafarinejad).
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[8,18,19]. However, petrochemical plants generate 131,400 m3 waste-
water per 500,000 metric tons ethylene production annually [8,20].
Wastewaters are produced by diﬀerent processes and operations in
PRPP. Some wastewaters are generated directly from production pro-
cesses such as vapor condensation, process water and spent caustic in
crackers, and aromatic plants. Others are from cooling tower blow
down, pump and compressor cooling. Surface runoﬀfrom paved utility
area drains collecting stormwater contaminated by crude oil, chemical
solvents, and spilled petroleum products is another signiﬁcant source of
environmental pollutants from PRPP [8,20–23]. Large PRPPs also
generate considerable quantity of sanitary wastewater [22,21–23].
These diverse types of wastewater may be combined in some cases but
can be also segregated during treatment processes.
It is clear that eﬀective wastewater treatment and reuse technolo-
gies are crucial to manage the wastewater from the PRPP [24]. Such
technologies are beneﬁcial to petroleum industries by: 1) providing
additional source of water through water recycling, 2) improving the
crude oil recovery, and 3) reducing and remediating the impact to the
environment [4]. The strategy of recycle, reuse and reduce (R3) should
be incorporated as a fundamental principal for the design and operation
of future PRPP wastewater treatment plants (WWTPs). In this study, we
carried out a review and analysis on quantity and quality of wastewater
eﬄuent from PRPP. We conducted literature research to compare the
eﬃciency and unit process design of current wastewater treatment
technologies for pollutant removal from PRPP wastewater. PRPP
WWTP in Iran was investigated and ﬁnally, future perspectives were
discussed.
2. PRPP wastewaters quality and discharge guidelines
There is a long list of organic and inorganic pollutants associated
with PRPP wastewater. Water quality parameters such as oil and grease
(O&G), total hydrocarbon content (THC), total petroleum hydrocarbon
index (TPH-index), biochemical oxygen demand (BOD), soluble BOD
(SBOD), chemical oxygen demand (COD), soluble COD (SCOD), total
organic carbon (TOC), ammoniacal nitrogen, total nitrogen, total sus-
pended solids (TSS), total dissolved solids (TDS), and total metals are
often used as bulk measurements to characterize the water quality.
Special metals such as Cd, Ni, Hg, Pb, and vanadium, speciﬁc organics
such as cyanides, phenols, benzene, toluene, ethylbenzene, and xylene
(BTEX), and inorganics such as ﬂuorides, phosphates, sulﬁdes, chlor-
ides, and other micropollutants are also measured in some studies.
General physical parameters include pH (acids, alkalis), hardness, tur-
bidity, heat, taste and odor producers are used in some cases as water
quality indicators [8,25–29]. The literature review and monitoring data
retrieved from PRPP indicate that the quality of the PRPP wastewaters
vary signiﬁcantly from plant to plant depending on the characteristics
of the crude oil and the process used in the PRPP [30]. Table 1 sum-
marizes the typical range of PRPP wastewater quality parameters [8]
and the eﬄuent discharge guidelines provided by the World Bank
Group (WBG) [8,31].
In addition to the discharging concentration requirements, WBG
guidelines also require that the eﬄuent should not result in a greater
than 3 °C temperature increase at the edge of the mixing zone or 100 m
from the point of discharge for direct dumping to surface waters. These
guideline values are assumed to be achievable under usual PPRP op-
erating conditions using appropriately designed and operated WWTP
facilities through the application of pollution prevention and control
techniques [31,32]. However, practical experience reveals that it has
been increasingly challenging for PRPPs to meet such discharging
guidelines due to the complexity of the PRPP wastewaters.
3. Current PRPP WWTP technologies
Current PRPP WWTP shares many similarities with municipal
WWTP
design,
including
following
unit
treatment
processes:
pretreatment; primary treatment; secondary treatment; and tertiary
treatment or polishing [22,23,26,33,34]. Unique to PRPP WWTP, pri-
mary wastewater treatment include two stages: namely primary oil and
water separation followed by secondary oil and water separation.
American petroleum institute (API) separator, corrugated plate inter-
ceptor (CPI) separator, parallel plate interceptor (PPI) separator, tilted
plate interceptor (TPI) separator, hydrocyclone separators, and buﬀer
and/or equalization tanks are the most common technologies applied in
the primary oil and water separation. Suites of technologies that share
the similar principal are used in the secondary separation. They include
dissolved air ﬂotation (DAF), dissolved gas ﬂotation (DGF), induced air
ﬂotation (IAF), induced gas ﬂotation (IGF)) [22,23,26,30,33,34]. First
stage oil and water separation is usually applied when oil concentra-
tions in the wastewater exceed ˜500 mg/L. Second stage of primary
treatment is employed to remove small oil droplets and suspended so-
lids, oil emulsions and oil wetted solids that have not been separated in
the ﬁrst stage of primary treatment [8,30]. A review of technologies
currently used in the treating PRPP wastewater around the world is
presented in Table 2.
In secondary treatment, similar to the principal of municipal WWTP
biological reactor, PRPP WWTP uses microbial activities to consume/
degrade remaining dissolved oil and other organic pollutants [8,12,22].
Microorganisms in the secondary reactor can be either enriched from
naturally occurring microbial communities by the individual treatment
plant or be purchased from commercial vendors to seed in the reactor.
The commercial vendors also provide speciﬁc groups of microbial
consortia that have acclimatized to speciﬁc organics that are toxic or
recalcitrant to microbial degradation [29]. Over 200 species of bacteria,
yeasts, and fungi have been reported to degrade hydrocarbons ac-
cording to Zobell [62] and Zhu et al. [63]. Hassanshahian and Cappello
[64] reported that 79 bacterial genera, including 9 cyanobacterial
genera, 103 fungal genera, and 14 algal genera can degrade or convert
hydrocarbons.
In secondary bioreactor, dissolved oil and recalcitrant organics are
oxidized into simple end products such as CO2, H2O, and CH4 under
aerobic, anaerobic or semi aerobic conditions. A C:N:P ratio of 100:5:1
has been shown to be optimal for microorganisms growth in bioreactor
Table 1
List of commonly measured water quality parameters in PRPP wastewater and
World Bank Group (WBG) eﬄuent discharging guidelines.
Parameters
Typical PRPP Wastewater
WBG Guidelines [31]
mg/L
mg/L
O&G
12.5 – 20223
10
BOD
90 – 685
30
COD
300 – 600
125
TSS
28.9 – 950
30
Phenol
0.2 – 200
0.2
pH
6.7 - 10.8
6 – 9
Turbidity
10.5 - 159.4 (NTU)
BTEX
1 -100
Benzene
0.05
Benzo(a)pyrne
0.05
Total cyanide
1
Free cyanide
0.1
Heavy metals
0.01 – 100
Total Chromium
0.5
Hexavalent Chromium
0.05
Copper
0.5
Iron
3
Lead
0.1
Nickel
0.5
Mercury
0.03
Arsenic
0.1
Vanadium
1
Total N
10
Total P
2
Sulﬁde
0.2
Temperature
< 3 at edge of mixing
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Table 2
Technologies and conﬁgurations applied in diﬀerent PRPP WWTPs around the world.
Company
/Location
Wastewater treatment
Sludge treatment
Eﬄuent purpose
Reference
Reﬁnery of Barrancabermeja-Colombia
Oily wastes collection system, API, equalization basin,
neutralization pond, coagulation/ﬂocculation, ﬂotation pond,
biological phenol removal pond, oxidation and sedimentation
pond
Gravitational and thermal dehydration using vapor injection,
injection of a polymer followed by centrifugation, biodegradation,
and ﬁnal disposal
Surface discharge or recycling
[35]
PEMEX reﬁnery, Mexico
API, DAF, biological process (activated sludge), immersed
reinforced hollow-ﬁber UF (ZeeWeed® UF system), and RO
membrane
RO permeate to reﬁnery for reuse and RO
reject to discharge
[36,37]
A Mexican reﬁnery
Sumps, equalization, screens, equalization, API, equalization, CPI,
equalization, DAF, oxidation lagoon, stabilization lagoon,
activated sludge, sedimentation, chlorination
Discharge to surface water body, irrigation,
and ﬁreﬁghting
[38]
Chevron reﬁnery, Canada
Sour water stripping, API, DAF, aeration with equalization, Deep
Shaft Technology Inc. (DSTI™) activated sludge bioreactor,
dissolved air ﬂotation, clariﬁer and ﬁnal eﬄuent polishing by
bioﬁlters
Route to the Greater Vancouver Regional
District sewer system, where it receives further
treatment prior to being discharged
[39]
Marathon petroleum oil reﬁnery, USA
Screening, equalization, DAF, ZeeWeed MBR system
Sludge is thickened in a ﬁlter press and then hauled oﬀsite
Discharge into the City of Ashland’s
wastewater treatment system
[40]
The Frontier oil reﬁnery in Cheyenne,
WY, USA
API, equalization tank, ﬂash tank, ﬂoc tank, bio tank or APS,
clariﬁer (In this WWTP, the IFAS process was applied to upgrade
the existing WWTP for better nitriﬁcation)
Sludge from API to sludge thickening tank and sludge from clariﬁer
to aerobic digester tank
Discharge to surface water
[41,42]
Amazonas oil reﬁnery, Equador
CPI, DAF, activated sludge
[43]
Lindsey reﬁnery, UK
API, chemical ﬂocculation or DAF, equalization tank, aerated
lagoon, trickling ﬁlter, clariﬁer
Discharge to surface water
[44]
Humber reﬁnery, UK
Equalization tank, activated sludge unit, water cascade, clariﬁer,
holding pond and air ﬂotation unit for ﬁnal polishing
Sludge from clariﬁer is processed for use as an agricultural
fertilizer
Discharge to surface water
[44]
Porto reﬁnery, Portugal
API, PPI, neutralization, coagulation, ﬂotation (DAF),
conventional activated sludge, clariﬁcation, chlorination,
mechanical aeration, and ﬁltration
Thickening (gravity thickener), clariﬁcation, and ﬁnal disposal
Discharge into the sea or water reuse in the
reﬁnery
[45]
Kırıkkale TUPRAS reﬁnery, Turkey
Equalization, Oil separator, ﬂocculation, DAF, activated sludge
process, Sedimentation
Thickening and dewatering
Discharger into the Kızılırmak river, one of the
most important rivers of Black Sea Basin
[46]
A reﬁnery in the western region of
India
Sumps, equalization, TPI, ﬂash mixer, coagulation/ﬂocculation,
DAF or tilted plate ﬂotation tank (TPF), neutralization tank, bio-
tower, intermediate clariﬁer, activated sludge system or aeration
tank, ﬁnal clariﬁer, pressure sand ﬁlters and granular activated
carbon (GAC) ﬁlters
Sludge thickener, dewatering centrifuge, lagoons, and disposal
Discharge
[47]
Haldia reﬁnery, India
API, DAF, bio-tower, ﬁltration, high rate contact type clariﬁer,
and RO plant
[48]
Panipat reﬁnery, India
TPI, DAF, bio-tower, aeration, clariﬁer, ﬁltration, pressure sand
ﬁlter, and dual media ﬁlter,
Gravity thickener and centrifuge
[48]
Bhatinda reﬁnery, India
Raw wastewater treatment plant, SBR, and MBR
Sludge bioremediation
[48]
Gujarat reﬁnery, India
TPI/CPI, DAF, biological treatment by plastic media bio-tower
trickling ﬁlter and activated sludge process, ﬁltration by PSF and
ACF
Dewatering of sludge
Reuse as makeup to cooling tower
[49]
Mangalore reﬁneries & petrochemicals
limited (MRPL), India
API, TPI/CPI, DAF, ﬁltration by DMF and ACF, biological
treatment by SBR and MBR. Treated water reuse/recycle systems
consisting of UF and RO membrane units followed by degasser.
Bioremediation of oily sludge and dewatering. The bioremediation
plant consists of treating thickened oily sludge in a conﬁned batch
bioreactor with indigenously prepared microbes for conversion as
bio-sludge that can be transferred to non hazardous land ﬁll sites.
Reuse/recycle
[49]
Pertamina reﬁnery unit IV, Cilacap,
Indonesia
API, CPI, equalization tank, DAF, aeration tank, sedimentation
tank, and clean water tank
Sludge from aeration tank to belt ﬁlter press
Discharge
[2]
A reﬁnery in the Philippines
Pre-separator, DAF, aeration pond, clariﬁer, impounding basin
Discharge to Manila Bay
[50]
Tehran reﬁnery, Iran
API, evaporation basin, chemical addition, ﬂoatation unit,
biological treatment unit, clariﬁer, chlorination, deep bed
ﬁltration
(Sludge from ﬂotation unit to sludge basin)/(sludge from biological
treatment unit to sludge treatment, evaporation and/or landﬁll)
[51]
Reﬁnery in southwestern of Iran
Oil/water separator, equalization, DAF, secondary treatment
Sludge thickening
Discharge
[52]
(continued on next page)
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Table 2 (continued)
Company
/Location
Wastewater treatment
Sludge treatment
Eﬄuent purpose
Reference
QP Reﬁnery Mesaieed, Qatar
Equalization tank, ﬂow splitter, CPI, equalization, neutralization,
aeration basin, clariﬁer, pressure sand ﬁlter, activated carbon
ﬁlter, and EDR plant
Treated water from EDR is reused to reﬁnery
for cooling tower and rejected water from EDR
is discharged to sea
[53]
Dora reﬁnery, Baghdad, Iraq
API separators, chemical tank, DAF, aeration tank, secondary
clariﬁer, and chlorination
Sludge thickener, sludge treatment by CaO, O2, and steam
Discharge
[54]
Kuwait National Petroleum Company
(KNPC)
TPI/CPI, DAF, biological treatment by carrousel type activated
sludge process, ﬁltration by continuous ﬂow sand ﬁlters
Dewatering of sludge
[49]
The reﬁneries in some Middle East
countries
Diversion pit, API separators, equalization tank, DAF, aeration
basins, clariﬁcation, sand ﬁlters
sludge receiving pond, sludge centrifuge, sludge hopper,
evaporation pond
Discharge to river/sea
[55]
Suez oil processing company (SOPC),
oil reﬁnery in Egypt
API, screw type pump, ﬂash mixing equipment, coagulation/
ﬂocculation, chemical preparation and feeding system, DAF, and
air saturation system
Sludge dewatering system
Discharge to sea
[56]
SAMIR Reﬁning company
Predecanteur, separator, packaging station, aeroﬂotation,
aeration, clariﬁer
Sludge thickener and sludge treatment
Discharge to the sea
[57]
Petroleum reﬁnery at the San Francisco
Bay area
(Sour water to stripping, chemical ﬂocculation, activated sludge
oxidation)/(Desalter brine slop, etc. to primary separations, API,
air ﬂotation unit), aeration, holding pond, submerged diﬀuser
Discharge
[58]
Reﬁnery in the Chicago area
API, equalization tank, sedimentation tank, activated sludge
oxidation, clariﬁer, chlorination, treated water holding basin, and
multi-cone aerators
Centrifuge and landﬁll
Discharge to canal
[58]
Petroleum reﬁnery located on Taiwan's
southwest Coast
CPI, Neutralization tank, DAF, deep activated sludge aeration
tanks A/B/C/D, MBR, and eﬄuent pit
[59]
Yokogawa Corporation of America
API, DAF, activated sludge treatment/nitriﬁcation basin,
chlorination, and sand ﬁlter
Sludge thickening, sludge digestion, sludge dewatering, and
disposal
Discharge
[60]
MARINER plus s.r.o. (Flottweg,
Solenis)
Acid/lye, API, ﬂotation, aeration tank, and clariﬁer
(Sludge from API to pretreatment and Flottweg Tricanter)/(sludge
from clariﬁer to Flottweg Decanter)
Discharge
[61]
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[8,12,29]. Amendments are often added to the bioreactor to reach the
optimal C:N:P ratio in order to achieve the best biodegradation activ-
ities. Again similar to municipal WWTP, PRPP WWTP biological
treatment processes can employ diverse technologies including sus-
pended growth processes such as activated sludge process (ASP),
powdered activated carbon treatment (PACT) process, sequencing
batch reactors (SBRs), continuous stirred tank bioreactor (CSTB), and
membrane bioreactors (MBRs). For small scale treatments, attached
growth processes such as trickling ﬁlters (TFs), ﬂuidized bed bioreactor
(FBB) and rotating biological contactor (RBC) are also commonly found
in treatment applications [8,12,23,29,65,66]. Hybrid systems that
combine the suspended and attached growth processes are adapted
more recently in order to enhance oil removal eﬃciencies and improve
eﬄuent quality [29]. A nitriﬁcation or a combined nitriﬁcation/deni-
triﬁcation system can also be adapted to removal high concentration of
ammonia or nitrogen that may be present in some of the PRPP waste-
water [23,26]. Natural treatment through aerated lagoons can also ef-
fectively promote oil degradation [67].
The performance of diﬀerent biological systems around the world
for treating PRPP wastewater eﬄuent at industrial scale is compared in
Table 3. In general, BOD removal eﬃciencies can be in the range of
88–97% for activated sludge, > 95.07% for PACT process, > 97% for
MBR, 50–95% for aerated lagoon, 95% for DSTI™activated sludge
bioreactor followed by a DAF clariﬁer and eﬄuent polishing bioﬁlters,
60–85% for trickling ﬁlter (Table 3).
Among diverse secondary treatment technologies, activated sludge
was the most used option around the world (Table 2). Improved re-
movals of BOD, COD and refractory organics; improved stability to
shock loads and toxic upsets; less tendency to foam in aerator [66,75];
the operational ﬂexibility enhancement of the WWTP [68]; lower ef-
ﬂuent toxicity [69,75]; metals, volatile organic compound (VOC)/odor,
and color control and minimization [68,69]; improved sludge settling
[75] or simpliﬁed solids management [69] have generally been listed as
advantages of PACT process over conventional activated sludge (CAS).
SBR technology may promote the mineralization of the wastewater
containing toxic compounds [76]; but it has limited application in the
PRPP WWTP [8,12,23]. MBR technology can successfully be used to
treat PRPP wastewaters, but its application is not common and facing
some challenges such as higher cost-eﬀectiveness. In the petroleum
industry, where further tertiary treatment such as reverse osmosis (RO)
is required, MBR can be preferred to the other option of using media
ﬁltration and microﬁltration after biological treatment [8,23]. It is
anticipated that the application of MBR technology in the PRPP WWTPs
will be increased with decreasing the membrane cost [8,77]. DSTI™
activated sludge bioreactor has had limited application in PRPP WWTP
[39]. Because of the current stringent eﬄuent standards for PRPP,
aerated lagoons are used less frequently for wastewater treatment
[8,23]. When a high-quality discharge is not required, TF may be used
in reﬁneries and/or it may also be applied upstream of an ASP to treat
wastewater [8,49,72].
Regardless of the technology employed in the secondary treatment,
the performance of secondary treatment can be aﬀected by diﬀerent
factors, including wastewater ﬂow and quality, aeration rate, food/
microorganism (F/M) ratio, sludge loading, sludge volume index (SVI),
sludge age or sludge retention time (SRT), mixed liquor suspended
solids (MLSS), and wastewater temperature [29]. In addition, SBR
performance may be aﬀected by organic loading rate, hydraulic re-
tention time (HRT), SRT, DO, and inﬂuent characteristics such as COD,
solids content, C/N ratio [78,79].
Orbecido et al. [80] assessed and contrasted aerobic biological
treatment methods including CAS, SBR, IFAS, and MBR for a petroleum
reﬁnery WWTP with respect to three criteria: i) economic including the
capital and operating cost; ii) environmental including the treated ef-
ﬂuent quality, ability to adjust to hydraulic and contaminant loading,
ability to answer oil entry, and land footprint; iii) technical including
the need of pretreatment and secondary clariﬁer, reliability and validity
of systems, and complexity to perform and control. Their results de-
monstrated that SBR was the most precedent option followed by CAS.
The sensitivity analysis indicated that the ranking of the alternative
treatment technology weighed heavily by the economic and environ-
mental aspects of the technology [80]. Jafarinejad [81] studied the
performance and economics of CAS process replacing by SBR tech-
nology in a full-scale two train petroleum reﬁnery WWTP using com-
puter simulation. He reported that under the design criteria and op-
erational conditions used in the study, the treated eﬄuent investigated
parameters from both petroleum reﬁnery WWTPs containing CAS +
CAS and SBR + CAS processes complied with the regulated treated
eﬄuent standards and the energy and amortization costs for both plants
Table 3
Performance of the diﬀerent biological systems for the treating wastewater eﬄuents from PRPP at industrial scale.
Process
Performance
References
PACT process
COD, BOD, and oil and grease removals for a reﬁnery wastewater were 79.05%, > 95.07%, and 95.27%,
respectively. Eﬄuent soluble BOD, soluble COD, and oil and grease for a petrochemical wastewater were < 30,
135, and 5 mg/L, respectively.
[8,68,69]
SBR
BP reﬁnery Ltd utilized SBR technology for upgrading of a lagoon system applied for secondary treatment of
petroleum reﬁnery wastewater during a major expansion of an existing reﬁnery; using a HRT of 36 h and SRT of
40 days the total COD in the petroleum reﬁnery wastewater was decreased to 50-150 mg/L.
[12,70,71]
MBR
WWTP in Marathon petroleum oil reﬁnery, which consists of screening, equalization, DAF, ZeeWeed MBR
system, is able to remove more than 97% of the BOD, 70% of the COD, 90% of the oil and grease, 92% of the
BTEX, and 92% of the TSS present in the wastewater.
[40]
IFAS
IFAS technology has provided consistently superior treatment eﬃciency than a conventional activated sludge
process. The ammonia removal data has reported a 73% improvement in the IFAS bioreactor with only a small
ﬁll fraction of media.
[41,42]
Aerated lagoon
According to Pombo et al. [67], removal eﬃciencies can be between 80% and 90% for TSS, 65% and 80% for
COD, and 50% and 95% for BOD, depending on the type of system. In addition, according to Bush [72], removal
eﬃciencies can be 75-95% for BOD, 60-85% for COD, 40-65% for suspended solids, 70-90% for oil, 90-99% for
phenol, and 95-100% for sulﬁdes.
[67,72]
Activated sludge
88-95% BOD reduction and 98-99% phenol removal for reﬁnery wastewater; 95-97% BOD removal for
petrochemical wastewater
[73]
Deep Shaft Technology Inc. (DSTI™) activated
sludge bioreactor
The Chevron reﬁnery WWTP in Burnaby, BC, which consists of a deep shaft bioreactor followed by a DAF
clariﬁer and eﬄuent polishing bioﬁlters, is able to degrade approximately 75% of the COD and 95% of the BOD
present in the wastewater.
[74]
Trickling ﬁlter
The trickling ﬁlters from the Lindsey reﬁnery have shown good BOD removal eﬃciencies (75-100%) but the
poorest eﬃciency of COD removal (28-54%). According to Bush [72], removal eﬃciencies can be in the range
of 60-85% for BOD, 30-70% for COD, 60-85% for suspended solids, and 50-80% for oil, depending on the ﬁlter
type, its loadings, medium type, etc.
[44,72]
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were higher in comparison with the operation, maintenance, material,
and chemical costs [81].
Tertiary treatment or polishing step, which takes place downstream
of the secondary treatment is traditionally achieved through sand ﬁl-
tration, activated carbon ﬁltration [22,23,33,34,72], or chemical oxi-
dation [23,34,72]. More recently membrane separation technologies
including ultraﬁltration (UF) [8,26,33] and RO [26] have been applied
to PRPP wastewater treatment to improve the eﬄuent quality for dis-
charge or reuse [34]. Other advanced treatment technologies including
ion exchange, electrodialysis (ED) and electrodialysis reversal (EDR)
are proposed and applied in small scale applications. Advanced oxida-
tion processes (AOPs) using hydrogen peroxide/ultraviolet (H2O2/UV),
ozonation process, Fenton and photo-Fenton, heterogeneous photo-
catalysis, electrochemical oxidation, wet air oxidation (WAO) and su-
percritical water oxidation (SCWO) have also been proposed or tested
in laboratory to remove recalcitrant chemical pollutants to discharge or
reuse requirement. However, most of the advanced processes have not
been employed in the industrial scale PRPP WWTP [8,23].
3.1. PRPP WWTP design and conﬁgurations
Fig. 1 shows a typical PRPP wastewater treatment system that in-
cludes equalization, primary and secondary oil/water separation, bio-
logical treatment, biological or secondary clariﬁcation, tertiary treat-
ment and solids handling [8,82,83]. Equalization system is applied to
smooth out ﬂuctuations or variations in ﬂow and composition of the
wastewater inﬂuent [23,72,82] in order to minimize potential spikes in
loads to the downstream processes. Flow equalization reduces the size
of the downstream units and the cost of the overall WWTPs. Con-
centration equalization minimizes contaminant shock loading to the
bioreactor [8,23]. Studies show equalization signiﬁcantly improves the
eﬀectiveness of biologically-based system [8,72]. Equalization system
may be located upstream of the primary oil/water separation (API se-
parator) [23], upstream of the secondary oil/water separation (the
DAF/IAF) [23,72,82], or downstream of the secondary oil/water se-
paration (the DAF/IAF) [23,82] as illustrated in Fig. 1. If upstream of
the API separator is selected for the location of equalization system, due
to the separation tendency of oil and solids contained in the oily was-
tewater in this tank, hardware (piping/pumps and controls) should be
supplied for the elimination of free oil and solids from the tank to
prevent accumulation of these materials. Equalization system must be
cleaned once or twice a year depending on the solids and oil content of
the oily wastewater [8,23].
Wastewater segregation and/or segregated wastewater treatment in
the petroleum reﬁneries is beneﬁcial for improving treatment eﬃciency
and reuse of treated water. Such practice is especially important in
water-scarce regions, yet it is not commonly practiced. Segregation may
be based on the total dissolved solids (TDS) content of the wastewater
to separate into: 1) Low TDS water including stripped sour water,
stormwater, and miscellaneous wastewater; and 2) High TDS water
including desalter eﬄuent, tank bottom sediment and water (BS&W),
and spent caustic. Fig. 2 illustrates a segregated wastewater treatment
system that includes two parallel trains with the identical unit opera-
tions except the step of primary oil/water separation. An API separator
is not needed for the low TDS train due to the low suspended solids
content of the wastewater [8,23].
As already shown in Table 2, PRPP WWTPs’ technologies and con-
ﬁgurations can vary from plant to plant. Three simpliﬁed process ﬂow
diagrams (PFDs) of TUPRAS petroleum reﬁnery WWTP (Fig. 3), PEMEX
reﬁnery WWTP (Fig. 4), and Barrancabermeja-Colombia reﬁnery
WWTP (Fig. 5 (a) for wastewater treatment section and Fig. 5 (b) for
oily sludge treatment section) are presented as examples of diverse
design. There have not been direct comparisons of the treatment eﬃ-
ciencies and eﬄuent water quality for diverse technologies and design
conﬁgurations amongst diﬀerent PRPPs around the world.
3.2. PRPP WWTP in Iran
Iran is one of the largest producers and exporters of petroleum
(crude oil and natural gas) in the world and the National Iranian Oil
Company (NIOC) is one of the world’s largest oil and gas companies by
proven reserves [8]. There are many PRPPs in Iran which large quan-
tities of wastewaters are generated from their activities and processes
that must be treated [84].
The simpliﬁed PFD of a reﬁnery WWTP for the oily wastewater in
Iran is shown in Fig. 6. The purpose of this reﬁnery WWTP is to process
the reﬁnery wastewater to meet the quality for surface discharge to
evaporation pond and to recycle the treated eﬄuent for the use as
cooling towers make-up water [81]. The plant has been designed to
process and/or treat waste streams of desalter oily water, oily water
sewer, oily water from sour water stripper, non-oily water sewer and
sanitary sewer. The wastewater inﬂuent is ﬁrst separated by two API
separators into two equalization basins. The waste stream ﬂowing into
two DAF units is treated by H2SO4 or NaOH for pH control, followed by
Fig. 1. A typical PRPP wastewater treatment system (modiﬁed from [8,82,83]).
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Fig. 2. Segregated wastewater treatment system (modiﬁed from [8,23]).
Fig. 3. The simpliﬁed process ﬂow diagram of TUPRAS petroleum reﬁnery WWTP in Turkey (modiﬁed from [46]).
Fig. 4. The simpliﬁed process ﬂow diagram of PEMEX reﬁnery WWTP in Mexico (modiﬁed from [36]).
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coagulant dosing with polyelectrolyte and mixing through a static
mixer. A secondary coagulant aid such as ferric chloride is then added
in the DAF and oily scum is scraped oﬀfrom the surface to return to oily
sludge thickener. Nutrients including P and Urea are amended in the
waste stream entering the two aeration basins, each equipped with four
mechanical aerators for biological treatment. The mixed liquor is then
ﬂowed into two clariﬁers, which then send sludge to an aerobic digester
and supernatant to ﬁlter units. Disinfection of the secondary eﬄuent is
achieved through injection of chlorine from chlorination units. Total
organic carbon (TOC) is monitored continuously in-stream of the ﬁnal
eﬄuent to provide feed back as if the water quality is suﬃcient for
reuse in cooling tower or to divert the eﬄuent to the evaporation pond
if quality does not meet cooling tower makeup speciﬁcations. The ﬁl-
tration may also be reprocessed through DAF to improve water quality
for cooling tower reuse.
The sanitary sewage (not shown in Fig. 6) consists of two lines, each
one composed by a comminutor, an equalization basin, an aeration
basin, a clariﬁer basin, a chlorine contact chamber. The tertiary treat-
ment unit consists of three solid accumulation chambers, three dual
ﬁlter basins, a clear water storage chamber and two eﬄuent chambers.
The storm-water is recovered in a basin, skimmed to remove ﬂoatable
oil, and sent to the main treatment downstream API separators. The
desalter oily water eﬄuent is routed through two oil/water separators
before it joins the oily process water upstream of the equalization ba-
sins.
In the most PRPP WWTPs in Iran, the treatment purpose is the
compliance with the regulations set by regulatory agencies. Due to the
water scarcity in Iran, water reuse issue must be the driving force for
the PRPPs to treat wastewaters with suitable treatment technologies.
On the other hand, among diverse secondary treatment technologies,
activated sludge is the most used option in PRPP WWTPs in Iran. Thus,
advanced treatment technologies should be implemented to make
Fig. 5. The simpliﬁed process ﬂow diagram of wastewater treatment section (a) and oily sludge treatment section (b) of Barrancabermeja-Colombia reﬁnery WWTP
(modiﬁed from [35]).
Fig. 6. The simpliﬁed process ﬂow diagram of a reﬁner WWTP in Iran for the oily wastewater.
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wastewaters suitable for reuse. In addition, Iranian petroleum industry
and researchers should pay more attentions to the commercialization of
innovative processes and designs, computer modeling and simulation of
PRPP WWTPs behavior for process optimization, modernization and
modiﬁcation to plant conﬁguration, energy saving, and implementation
of instrumentation, control and automation methods.
4. Advancements in PRPP WWTPs and future trends
Continues development of technology in PRPP WWTP is necessary
to keep pace with the increase in demand of petroleum products, ex-
haustion of high quality oil reserve, heightened environmental aware-
ness among human society and stricter discharge regulations focusing
on environmental protection. Such new technology developments
should base on the past PRPP WWTP design and operation experience
and the speciﬁc local conditions. The new technologies should con-
tribute to fundamental shift of mentality from wastewater discharge to
recycle and reuse within the industry. The new technologies should also
include on-line monitoring and smart feedback loop to ensure eﬄuent
water quality for environmental protection and decisions for retreat or
reuse.
PACT system designed by Siemens Water Technologies [85] is an
example of new technology for PRPP wastewater treatment. This
system, recently adapted by China Petroleum & Chemical Corporation
(Sinopec Corp.) at Anqing reﬁnery in Anhui Province, China, combines
biological treatment and carbon adsorption into a single, synergistic
treatment step to remove organics. The post treatments include a
Zimpro wet air regeneration (WAR) hydrothermal unit and a hydro-
clear sand ﬁltration system to increase the ﬁnishing water quality. A
ﬁrst membrane based PRPP wastewater treatment plant was designed,
constructed and operated by General Electric (GE) in 2012. The ad-
vanced GE ZeeWeed UF and Mobile RO are used for a petroleum
company of Trinidad & Tobago Ltd (PETROTRIN) in the West Indies
[86]. In 2014, a Russian oil company Bashneft took an additional step
to incorporate GE's ZeeWeed MBR, EDR and RO technologies for the
treatment of reﬁnery wastewater from the Bashneft-Ufaneftekhim oil-
processing complex and other enterprises of the Northern Industrial
Block of Ufa [87]. The membrane-based technologies are likely to be-
come more common in the future years because of their dramatic im-
provements in eﬄuent water quality to meet the requirement for reuse
and environmental protection [88]. The recent advancements in
membrane permeability and antifouling property make them attractive
options in upgrading the PRPP WWTP. However, wastewater treat-
ments are cost centers in reﬁneries - they aren't proﬁt centers. As such,
PRPP WWTPs frequently don't have suﬃcient resources to implement
technology improvement with signiﬁcant impact on the ﬁnancial
bottom line. These departments often struggle trying to get ﬁnancial,
manpower, and interdepartmental support from the ﬁnished product
departments.
To balance the cost of technology improvement in WWTP and ﬁn-
ishing water quality requirement for reuse, future trends in designing
and constructing of WWTPs for the PRPP should focus on hybrid
technologies to improve the treatment capacity and pollutant removal
upon existing technology without the signiﬁcant increase in design and
operational cost. There is a great potential for PRPP wastewater treat-
ment to incorporate membrane separation technologies such as MBR as
additional treatment trains due the compact size of membrane systems
in comparison with bioreactors [89]. Furthermore, forward osmosis
(FO) that separates water from salt and organics with minimal energy
requirement and low membrane fouling propensity may become an
energy eﬃcient option for oily wastewater treatment with reduce op-
erational cost [90,91]. Advanced oxidation processes (AOPs) have also
been proposed for removal of toxicity and recalcitrant organics in the
eﬄuent of treated PRPP wastewater. The ﬁnal selection of treatment
technologies should consider the balancing of economic, environmental
and technical suitability and regulatory constraints.
5. Conclusions
Oil companies are among largest companies in the world. They are
also the largest water consumers of the world. Extreme pressures exist
to improve upon environmental considerations and to reduce or opti-
mize operating costs. Technology enhancement in PRPP wastewater
treatment is the best approach to achieve both goals through reduction
of environmental impact and reuse the water for product generation.
The existing PRPP WWTP around the world treats oily inﬂuent through
primary treatment, secondary treatment, and tertiary treatment or
polishing. However, the design and conﬁguration of the plants are
highly diverse and regional speciﬁc. With the increase of petroleum
products, water scarcity and stricter environmental regulations, new
technologies to improve the PRPP wastewater treatment eﬃciency and
eﬄuent water quality that ﬁt for reuse are needed. Amongst diverse
technologies that are being explored, membrane separation shows po-
tential own to its relatively low cost and signiﬁcant improvement in
eﬄuent water quality. Finally the selection of PRPP wastewater treat-
ment technology should balance the economic, environmental and
technical suitability and regulatory constraints.
Declaration of Competing Interest
There are no conﬂicts to declare.
Acknowledgments
Financial support from DOE U.S.- China CERC-WET is acknowl-
edged.
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1

Research Paper
Numerical simulation and optimization of an industrial ﬂuid catalytic
cracking regenerator
Guangwu Tang a, Armin K. Silaen a, Bin Wu a, Dong Fu a, Dwight Agnello-Dean b, Joseph Wilson c,
Qingjun Meng c, Samir Khanna c, Chenn Q. Zhou a,⇑
a Center for Innovation through Visualization and Simulation, Purdue University Northwest, 2200 169th Street, Hammond, IN 46323, United States
b BP Company North America, East Chicago, IN 46323, United States
c BP Reﬁning and Logistics Technology, Naperville, IL 60563, United States
h i g h l i g h t s
 A CFD model has been developed for a commercial FCC regenerator.
 Fluidized bed with combustion has been simulated.
 The model has been validated with plant data.
 5% oxygen enrichment for operation has been proposed.
a r t i c l e
i n f o
Article history:
Received 13 January 2016
Revised 17 September 2016
Accepted 10 October 2016
Available online 11 October 2016
Keywords:
Regenerator
CFD
Eulerian
Kinetic theory
Drag model
Hydrodynamics
Combustion
a b s t r a c t
Fluid catalytic cracking (FCC) is one of the most important conversion processes in petroleum reﬁneries.
FCC regenerator is a key part of an FCC unit to recover the solid catalyst reactivity by burning off the
deposited coke on the catalyst. In this paper, a three-dimensional multi-phase, multi-species reacting
ﬂow computational ﬂuid dynamics (CFD) model was established to simulate the ﬂow and the reactions
inside an FCC regenerator. The Euler-Euler approach, where the two phases (gas and solid) are considered
to be continuous and fully inter-penetrating, is employed. A modiﬁed drag force model was applied on
the CFD model with appropriate inﬂuential cluster diameters of particle grouping phenomenon. The
developed CFD model was validated by using plant data. With the validated CFD model, the ﬂow charac-
teristics and the inner-phase and inter-phase reactions were studied. The results showed that the effect
of oxygen enrichment on the catalysts recovery efﬁciency was limited when the oxygen enrichment
exceeded 5%.
 2016 Elsevier Ltd. All rights reserved.
1. Introduction
Fluid Catalytic Cracking (FCC) process is one of the most impor-
tant processes in petroleum reﬁnery, which was paid considerable
attention over the past 70 years [1]. FCC units play a pivotal role in
petroleum reﬁning industry that mainly converts low-value, high-
boiling feedstock like gas oil into valuable products such as gaso-
line and middle distillates by using cracking catalyst [2,3]. The
FCC zeolite catalyst particles belong to Group A particles which
have mean size and density ranges of d < 500 lm, 1.4 g/cm3 <
q < 4 g/cm3, respectively [4]. During the FCC process, the FCC
catalyst loses its reactivity due to the deposition of coke which is
generated during the complicated chemical reactions. Since there
are 40–50 tons of solid catalyst circulated in one FCC unit every
minute [5], recycling the deactivated catalyst is critical for the
entire process to reduce costs. Recovery of the catalyst reactivity
by burning off the coke on the catalyst in the FCC regenerator is
also important to maintain its cracking efﬁciency. Therefore, it is
of great importance to understand the ﬂuidized-bed ﬂow proper-
ties and reactions inside the FCC regenerator to achieve high
efﬁciency and low cost.
Fluidized bed technique is widely used in industrial processes,
which has been comprehensively studied and optimized using
numerical modeling. Both Lagrangian and Eulerian algorithms are
used to simulate the gas-solid two phase ﬂow, which consider
the gas phase as a continuum [6–8]. However, Lagrangian algo-
http://dx.doi.org/10.1016/j.applthermaleng.2016.10.060
1359-4311/ 2016 Elsevier Ltd. All rights reserved.
⇑Corresponding author.
E-mail addresses: tangg@pnw.edu (G. Tang), asilaen@pnw.edu (A.K. Silaen),
wu7@pnw.edu (B. Wu), fudong1985@gmail.com (D. Fu), Dwight.Agnello-Dean@bp.
com (D. Agnello-Dean), Joseph.Wilson2@bp.com (J. Wilson), qingjun.meng@bp.com
(Q. Meng), samir.khanna@bp.com (S. Khanna), czhou@pnw.edu (C.Q. Zhou).
Applied Thermal Engineering 112 (2017) 750–760
Contents lists available at ScienceDirect
Applied Thermal Engineering
journal homepage: www.elsevier.com/locate/apthermeng
rithm solves the Newtonian equations of motion for each particle
and takes into consideration of particle collisions effects. Particle
collision laws are applied for the solid-solid collision based on
the energy dissipation by means of restitution coefﬁcient and fric-
tion [9,10]. Fluidization phenomena simulation by using Lagran-
gian algorithm has been reported in lab scale [11]. It is difﬁcult
to use Discrete Phase Model (DPM) to model industrial powder
process [12]. Lagrangian model also requires more computational
resources compared to an Eulerian model and most of the applica-
tion are limited to big particles (d > 500 lm) [9,10,13]. Gas and
particles are all considered as continuums and fully interpenetrat-
ing in Eulerian algorithm. The interactions between two continu-
ous phases are described by generalized Navier-Stokes equations
with additional closure laws to describe particle-particle and
particle-wall interactions. Kinetic theory of granular ﬂow provides
explicit closures for Geldart A particles, such as FCC catalyst, with
both efﬁciency and accuracy [14–19].
The main task for FCC regenerator is to consume the coke
(including carbon and hydrogen) on the catalyst surface through
combustion and also to elevate the catalyst bulk temperature to
ensure optimal conditions for cracking reactions. However, major-
ity of the CFD studies of ﬂuidized bed devices available in the liter-
atures
focused
on
isothermal
process
[20].
Due
to
the
computational cost, the application of the fundamentals-oriented
models, such as the Eulerian-Eulerian multiphase models applying
the kinetic theory of granular ﬂow, for simulation of industrial-
scale circulating ﬂuidized bed furnaces has been small [21].
Improvements in numerical techniques and computing power
have provided opportunities to advance CFD application on simu-
lating the reactions in a ﬂuidized bed. Lu et al. [22–24] studied
the heat transfer and hydrodynamics in a ﬂuidized bed using
kinetic theory of granular ﬂow without considering reactions.
Due to the complexity of multiphase combustion process in ﬂu-
idized bed which involves homogeneous and heterogeneous reac-
tion process, only few literatures [25–27] considered CFD for
combustion in ﬂuidized bed while overlooking three dimensional
effects. Adamczyk et al. [28–29] applied a dense discrete phase
model to simulation circulating ﬂuidized bed using ANSYS Fluent.
For FCC process, only experiments, empirical and semi-empirical
1D and 2D models are reported in studying the reactions in FCC
regenerator [30–34]. Therefore, applying the advanced CFD tech-
nique on the FCC catalyst regeneration process to optimize the
design and operation is of great signiﬁcance. And the CFD method
is expected to substitute empirical models in future [20].
This paper employs Computational Fluid Dynamics (CFD) to
simulate a commercial regenerator. Based on the multiphase Eule-
rian model with kinetic theory of granular ﬂow, the air-catalyst
two phase multi-species reacting ﬂow inside the regenerator was
modeled. The ﬂow hydrodynamics was also obtained by using a
modiﬁed drag model to calculate the gas-solid interaction drag
force. Restitution coefﬁcient 0.9 was applied to deﬁne the solid-
solid collision forces [35]. A reaction model which includes
homogeneous and heterogeneous combustion was developed to
simulate the multiphase reactions, temperature and species distri-
butions in the FCC regenerator. The simulation results are com-
pared with the plant data. The effect of the important parameter,
Nomenclature
a
absorption coefﬁcient
C
linear-anisotropic phase function coefﬁcient
CD
drag coefﬁcient
dp
diameter of particle, m
d
p
particle effective mean diameter in dense bed, 300 lm
d0
p
particle
effective
mean
diameter
in
dilute
phase,
150 lm
T
temperature, K
Tip
phase temperature
g
gravity acceleration, m/s2
Yi
species i mass fraction
g0
radial distribution function
G
incident radiation
h
speciﬁc enthalpy
P
pressure, Pa
Re
Reynolds number
Nu
Nusselt number
Pr
Prandtl number
Re
p
cluster Reynolds number
t
time, s
u
velocity, m/s
qip
bulk density of phase i
VOFip
volumetric fraction of phase ip
MWi
molecular weight of species i
st
stoichiometric coefﬁcient of reactants
Tref
reference temperature (298 K)
qg
!
the heat ﬂux
Sg
source term that includes sources of enthalpy
Q qg
the intensity of heat exchange between gas and solid or
liquid phase
hqg
the interphase enthalpy
Kg
the thermal conductivity of gas phase
v
!
g
the velocity of phase g
v
!
p
the velocity of phase p
_
mpg
the mass transfer from p phase to g phase
Sq
the source term
u
!
g
superﬁcial gas velocity, m/s
u
!
p
superﬁcial particle velocity, m/s
I
unit stress tensor
Greek letters
a
volume fraction
b
inter-phase momentum exchange coefﬁcient, kg/m3 s
c
collisional dissipation of energy ﬂuctuation, kg s3/m
Cdil;H
dilute diffusion coefﬁcient for energy ﬂuctuation, kg s/m
CH
diffusion coefﬁcient for the energy ﬂuctuation, kg s/m
H
granular temperature, m2/s2
lp
particle phase shear viscosity, Pa s
lg
gas phase shear viscosity, Pa s
kp
solid bulk viscosity, Pa s
kg
gas bulk viscosity, Pa s
q
density, kg/m3
s
stress tensor, Pa
e
the coefﬁcient of restitution, 0.9
rs
scattering coefﬁcient
k
reaction rate constant
Superscripts and subscripts
i; j; k
direction coordinate
g
gas phase
max
maximum
p
particle phase
NR
total number of reactants
G. Tang et al. / Applied Thermal Engineering 112 (2017) 750–760
751
such as oxygen enrichment, on the ﬂow and reactions has been
investigated.
2. CFD model and methodology
The Euler-Euler approach, in which the two phases (gas and
solid)
are
considered
to
be
continuous
and
fully
inter-
penetrating, is employed. The kinetic theory of granular ﬂow was
applied in this simulation, with a Reynolds number of approxi-
mately 4  105. Gao et al. [18,19] reported that the laminar viscous
model was able to accurately predict ﬂows in the same range of
Reynolds number. Therefore, the laminar viscous model was
applied in this study. Almuttahar et al. [36] also reported that even
in the case of high density circulating ﬂuidized bed, laminar model
can give better prediction to the hydrodynamics compared to tur-
bulence models. Chang, et al. [37] applied laminar model to calcu-
late viscous force on an industrial turbulent ﬂuidized bed reactor
and validated the axial bed density proﬁle with industrial data.
Reaction and heat transfer models are needed in order to model
the main function of FCC regenerator to recover catalyst cracking
reactivity by burning off the coke on the catalysts. Finite-rate
model was used for simulating the combustion. Heterogeneous
reaction model with Gunn model [38] for interphase heat transfer
were incorporated into the model. Finite-rate combustion model
was used for simulating chemical reactions. Both gas phase reac-
tion and gas-solid interface reactions were deﬁned. P1 radiation
model was used for modeling the radiation heat transfer. The main
reactions considered in this model were carbon oxidization, hydro-
gen combustion and CO combustion. The reaction rates were
included into the model through User Deﬁned Function (UDF)
scripts based on the kinetics referred from literatures.
2.1. Governing equations
2.1.1. Conservation of mass
Gas-phase continuity equation
@ðagqgÞ
@t
þ r  ðagqgv
!
gÞ ¼ ð _
mpg  _
mgpÞ þ Sg
ð1Þ
Solid-phase continuity equation
@ðapqpÞ
@t
þ r  ðapqpv
!
pÞ ¼ ð _
mgp  _
mpgÞ þ Sp
ð2Þ
where the ﬁrst term is the time rate of change of gas phase or solid
phase, the second term is the advection of the gas phase or solid
phase. Each computational cell is shared by gas and solid phase
with the total of solid and gas volume fraction is 1.
ap þ ag ¼ 1
ð3Þ
2.1.2. Conservation of momentum
Gas-phase momentum equation
@
@t
ðagqgv
!
gÞ þ @
@xj
ðagqgv
!
gv
!
gÞ ¼ agr  p þ r  

sg þ bðu
!
g  u
!
pÞ
þ qgag g
!
ð4Þ
Solid-phase momentum equation
@
@t
ðapqpv
!
pÞ þ @
@xj
ðapqpv
!
pv
!
pÞ ¼ apr  p þ r  

sp  bðu
!
g  u
!
pÞ
þ qpap g
!
ð5Þ
where the terms on the left hand of the equation are the time rate of
change of gas or solid phase and advection term. On the right hand
of the equation, body force (gravity, pressure), shear force term and
phase interaction terms are considered. b is the gas-solid interphase
drag coefﬁcient.
The ﬂuctuations that occur in the solid phase are modeled from
the kinetic theory of gases modiﬁed to account for inelastic colli-
sions between particles.
Solid-phase turbulent ﬂuctuating energy equation
3
2
@apqpH
@t
þ rðapqpv
!
pHÞ


¼ ðppI þ 

spÞ
: rv
!
p þ r  ðkHrHÞ  cH þ £gp
ð6Þ
where ðppI þ 

sÞ : rv
!
p is the generation of energy by the solid
stress tensor, kHrH is the diffusion energy, cH the collisional dissi-
pation of energy, £gp the energy exchange between the gas phase
and the particle phase.
In order to close the governing relations, constitutive Eqs. (7)–
(17) are used in this model.
Gas-phase stress tensor


sg ¼ aglgðru
!
g þ ru
!T
gÞ þ ag
kg  2
3lg


ru
!
gI
ð7Þ
Solid-phase stress tensor


sp ¼ aplpðru
!
p þ ru
!T
pÞ þ ap kp  2
3lp


ru
!
pI
ð8Þ
Solid-phase pressure
pp ¼ apqp½1 þ 2ð1 þ eÞapg0H
ð9Þ
Radial distribution function
g0 ¼
1 
ap
ap;max

1
3
"
#1
ð10Þ
Granular temperature
H ¼ 1
3 hu0
pu0
pi
ð11Þ
Solids phase shear viscosity (Gidaspow kinetic theory)
lp ¼ 10qpdp
ﬃﬃﬃﬃﬃﬃﬃﬃ
pH
p
96ð1 þ eÞg0
1 þ 4
5 ð1 þ eÞg0ap

2
þ 4
5a2
pqpdpg0ð1 þ eÞ
ﬃﬃﬃﬃ
ﬃ
H
p
r
ð12Þ
Solids phase shear viscosity (Syamlal-O’Brien kinetic theory)
lp ¼ apqpdp
ﬃﬃﬃﬃﬃﬃﬃﬃ
pH
p
6ð3  eÞ
1 þ 2
5 ð1 þ eÞð3e  1Þg0ap

2
þ 4
5a2
pqpdpg0ð1 þ eÞ
ﬃﬃﬃﬃ
ﬃ
H
p
r
ð13Þ
Solids bulk viscosity
kp ¼ 4
3apqpdpg0ð1 þ eÞ
ﬃﬃﬃﬃ
ﬃ
H
p
r
ð14Þ
Diffusion coefﬁcient (Gidaspow kinetic theory)
CH ¼ 150qpdp
ﬃﬃﬃﬃﬃﬃﬃﬃ
pH
p
384ð1 þ eÞg0
1 þ 6
5 ð1 þ eÞg0ap

2
þ 2a2
pqpdpg0ð1 þ eÞ
ﬃﬃﬃﬃ
ﬃ
H
p
r
ð15Þ
Diffusion coefﬁcient (Syamlal-O’Brien kinetic theory)
CH ¼
15apqpdp
ﬃﬃﬃﬃﬃﬃﬃﬃ
pH
p
4ð41  33
2 ð1 þ eÞÞ 1 þ 3
5 ð1 þ eÞ2ð2e  1Þg0ap

þ
8
15p
41  33
2 ð1 þ eÞ


ð1 þ eÞapg0

ð16Þ
752
G. Tang et al. / Applied Thermal Engineering 112 (2017) 750–760
Collisional dissipation of energy ﬂuctuation
cH ¼ 12ð1  e2Þg0
dp
ﬃﬃﬃﬃ
p
p
qpa2
pH3=2
ð17Þ
The transfer of the kinetic energy of random ﬂuctuations in the
particle velocity from the solid phase to the gas phase is:
£gp ¼ 3KpgH
ð18Þ
A modiﬁed drag model was used with considering the cluster-
ing phenomenon in both dense dilute phase and dilute phase.
The inﬂuential cluster diameters in the dense phase and the dilute
phase were determined in previous research [39]. The modiﬁed
drag model is listed in Table 1.
2.1.3. Conservation of energy
Gas-phase energy equation
@
@t
ðagqghgÞ þ r  ðagqghgu
!
gÞ ¼ ag
@Pg
@t
þ 

sg : r ug
! r qg
! þSg
þ
X
2
q¼1
ðQ qg þ _
mqghqg  _
mgqhgqÞ
ð19Þ
Solid-phase energy equation
@
@t
ðapqphpÞ þ r  ðapqphpu
!
pÞ ¼ ap
@Pp
@t
þ 

sp : r up
! r qp
! þSp
þ
X
2
q¼1
ðQgp þ _
mgphgp  _
mpghpgÞ
ð20Þ
where g represents gas phase, p represents solid phase, q represents
any other two phases except the calculated phase in each equation.
hg is the speciﬁc enthalpy of the gas phase, qg
! is the heat ﬂux, Sg is a
source term that includes sources of enthalpy (such as radiation),
Q qg is the intensity of heat exchange between gas and solid or liquid
phase. hqg is the interphase enthalpy.
Q qg ¼ CqgðTq  TgÞ
ð21Þ
Cqg is the volumetric heat transfer coefﬁcient between solid or
liquid phase and gas phase, which related to the phase q Nusselt
number, Nuq.
Cqg ¼ 6KgaqagNuq
d
2
q
ð22Þ
Kg is the thermal conductivity of gas phase.
For Gunn heat transfer model [38], the
Nup ¼ ð7  10ag þ 5a2
gÞð1 þ 0:7Re0:2
p Pr
1
3Þ
þ ð1:33  2:4ag þ 1:2a2
gÞRe0:7
p Pr
1
3
ð23Þ
Pr ¼
cpglg
Kg
ð24Þ
P1 Radiation Model
In FCC regenerator, another heat transfer form is radiation due
to the combustion occurs inside with high temperature around
1000 K. Therefore, thermal radiation should be considered. The
radiation heat ﬂux as function of incident radiation intensity in
P1 model is:
qr ¼ 
1
3ða þ rsÞ  Crs rG
ð25Þ
where a is the absorption coefﬁcient, rs is the scattering coefﬁcient,
G is the incident radiation, C is the linear-anisotropic phase function
coefﬁcient.
rqr ¼ aG  4an2rT4
ð26Þ
The expression for rqr can be directly substitute into the
energy equation to account for the heat sources due to the
radiation.
2.1.4. Conservation of species
Species transport equations are used to model the species
which are involved in the chemical reactions. For each phase k,
predict the local mass fraction of each species, Yk
i , through the solu-
tion of a convection-diffusion equation for i species, when applied
to multiphase mixture can be expressed in the following form:
@
@t
ðq ga gY g
i Þ þ r  ðq ga gY g
i v
! gÞ ¼ r  a gJi
! g þ a gS g
i þ a gR g
i
þ
X
n
p¼1
ð _
mpigi  _
mgipiÞ þ Rate
ð27Þ
where R g
i is the net rate of production of homogeneous species i by
chemical reaction for gas phase. _
mpigi is the mass transfer source
between species i and j from solid phase to gas phase. Rate is the
heterogeneous reaction rate. a g is the volume fraction for gas phase.
Table 1
Modiﬁed drag coefﬁcient with zone classiﬁcation.
Void fraction
Drag force model
Drag force coefﬁcient
60.80
Ergun [40]
b1 ¼ 150 apð1agÞlg
agðd
pÞ2
þ 1:75 qgapju
*
pu
*
gj
d
p
0.8–0.933
ZP
b2 ¼ 5
72 CD
apagqgju
*
pu
*
gj
d
pð1agÞ0:293
CD ¼
24
agRe
p ð1 þ 0:15ðagRe
pÞ0:687Þ
ðRe
p 6 1000Þ
0:44
ðRe
p > 1000Þ
(
Re
p ¼ qgd
pju
*
gu
*
pj
lg
0.933–0.990
Wen and Yu [41]
b3 ¼ 3
4 CD
apagqgju
*
pu
*
gj
d0
p
a2:65
g
CD ¼
24
agRe0
p ð1 þ 0:15ðagRe0
pÞ
0:687Þ
Re0
p ¼ qgd0
pju
*
gu
*
pj
lg
0.990–1.00
Schiller and Naumann [42]
b4 ¼ 3
4 CD
agapqgju
*
pu
*
gj
d0
p
CD ¼
24
Re0
p ð1 þ 0:15Re00:687
p
Þ
ðRe0
p 6 1000Þ
0:44
ðRe0
p > 1000Þ
(
Re0
p ¼ qgd0
pju
*
gu
*
pj
lg
G. Tang et al. / Applied Thermal Engineering 112 (2017) 750–760
753
S g
i is the rate of creation by addition from the dispersed phase plus
any user-deﬁned sources.
In FCC regenerator, deposited coke on the solid catalyst surface
reacts with gas phase species O2 and CO2. Such interphase chemi-
cal reactions are also known as heterogeneous reactions. The
heterogeneous phase interaction reaction rates used for the reac-
tions [43] are:
Rate ¼ k
Y
NR
i¼1
YiqipVOFip
MWi
 1000

st
 103 kmol
m3 s
ð28Þ
k ¼ A
Tip
Tref

b
exp
E
R Tip


ð29Þ
where Yi is ith species mass fraction. NR is the total number of reac-
tants in a given inter-phase reaction. qip is the bulk density of phase
ip. VOFip is the volume fraction of phase ip. MWi is the molecular
weight of the reactant species. st is the stoichiometric coefﬁcient
of reactant in the given reaction. k is the rate constant. Tip is phase
temperature. Tref is reference temperature. A is pre-exponential fac-
tor, and E is activation energy.
2.2. Simulation code and solution method
This simulation was using the commercial software package
ANSYS Fluent. The Finite Volume Method (FVM) [44] was used
to solve the partial differential equations by discretized the equa-
tions using an upwind differencing scheme. The Phase Coupled
SIMPLE algorithm was applied to adjust the pressure and velocities
after each iteration. The transient approach was used to solve this
problem due to the real ﬂow characteristics. UDF scripts were cou-
pled into the main solver to calculate the ﬂow and reactions. The
drag coefﬁcient between the gas phase and solid phase based on
previous developed modiﬁed drag model was applied by using
UDF scripts. The reaction rates for four reactions are based on
the heterogeneous reaction rate, which were also written as UDF.
Each simulation was performed over 1200 s until the reaction
reached a steady state. The calculations were using High Perfor-
mance Computer (HPC) cluster at Purdue University Northwest.
Detailed simulation parameters are listed in Table 2.
3. Boundary and initial conditions
3.1. Computational domain
The computational geometry of the regenerator being studied is
shown in Fig. 1(a). The regenerator cylindrical vessel includes nine
two-stage cyclones, two levels of air rings, two spent catalyst
inlets, and two regenerated catalyst outlets. The regenerator
start-up operation normal starts with charging a certain amount
of clean catalyst at the bottom. Fluidization air is then injected into
the catalyst bed to form a ﬂuidized bed. The continuous operation
process then starts to recycle the spent catalyst from the riser reac-
tor. The spent catalyst with a certain amount of transport air is fed
into the regenerator through the spent catalyst inlets located at the
bottom of the geometry. Air is continuously injected into the
regenerator through the upper and lower air ring distributors
located in the bottom to ﬂuidize the catalyst bed and combust
the coke on the catalyst surface. The catalysts carried by the gas
to the top are separated from the gas in the two-stage cyclones
and recycled back into the catalyst bed. This two stage group
cyclones are generally equipped in FCC regenerators with the out-
let duct of ﬁrst stage cyclone directly coupled to the inlet duct of
the secondary stage cyclone as shown in Fig. 2. This group cyclones
has advantages of high and ﬂexible handling capacity, high efﬁ-
ciency, high separation precision and relatively low cost [45,46].
The regenerated catalysts are removed from the regenerator
through the regenerated catalyst outlets with high temperature
and recovered reactivity. A total number of 2 million cells was used
for this simulation. The details of the mesh is shown in Fig. 1(b).
Mesh independent study can be found in previous work [39].
3.2. Operating conditions
Typical operational boundary conditions for this particular FCC
regenerator was applied to the developed CFD model as shown in
Table 3. The amount of regenerated catalyst extracted from the
regenerator is equal to the amount of spent catalyst input from
the spent catalyst inlet. The walls were set as no-slip wall bound-
ary conditions for both solid phase and gas phase. Each phase con-
tains multiple species. According to the plant measurements, the
catalyst used for this simulation contains 1 percent coke, with car-
bon count for 92% and the remaining 8% is hydrogen. The gas phase
contains species O2, N2, CO, CO2, H2O. In addition, the catalyst goes
through the cyclone was deﬁned 100% recycled back to the regen-
erators with all the species concentrations unchanged as shown in
Fig. 2, which was expressed by UDF. The simulation was conducted
based on the initial startup condition.
3.3. Reaction mechanisms
The carbon and hydrogen combustion reactions were taken into
consideration in this CFD model. The global reaction mechanisms,
kinetics and heat generation are listed in Table 4. [47–49] The
kinetic parameters for reaction 1 (R1) and reaction 2 (R2) are
obtained from the experimental work done by Kanervo et al.
[47], in which reaction rates of combustion of coke on FCC solid
catalyst particles to produce CO and CO2 in a reactor were mea-
sured. The kinetic parameters from the experimental work of
hydrogen combustion in carbonaceous deposits on zeolite-type
cracking catalysts reported by Wang et al. [48] was used for the
Table 2
Simulation parameters.
Parameters
Value and methods
Gas-solid two phase
ﬂow model
Eulerian-Eulerian model with kinetic theory
(granular)
Solver
Pressure based and transient
Flow type
Laminar
Wall boundary
condition
No slip
Time step used
0.01 s
Pressure velocity
coupling scheme
Phase Coupled SIMPLE
Under-relaxation factors
Pressure 0.3, momentum 0.7, volume fraction 0.5,
granular temperature 0.2. Species 0.6
Maximum solid packing
volume fraction
0.6
Spatial discretizations
Gradient: green-gauss cell based momentum,
energy, species: second order upwind, volume
fraction: ﬁrst order upwind
Gas mixture density
Incompressible ideal gas law
Superﬁcial velocity
0.6–0.7 m/s
Absorption coefﬁcient
Gray WSGG with domain based mean beam
length
Scattering coefﬁcient
0
Solid mixture density
Volume weighted mixing law
Catalyst particle density
1440 kg/m3
Catalyst particle mean
diameter
75 lm
Solid particle absorption
efﬁciency
0.9
Solid particle scattering
efﬁciency
0.1
754
G. Tang et al. / Applied Thermal Engineering 112 (2017) 750–760
hydrogen combustion. The kinetics for the CO combustion is
adopted from the work by Dryer and Glassman [49]. This kinetics
does not include the effects CO combustion promoter which is
used in the actual regenerator operations.
4. Results and discussions
4.1. Simulation results
The model has been validated by plant data under typical oper-
ation condition as shown in Table 5. The catalyst circulation ﬂow
rate through cyclones were predicted by the CFD model, which
agrees with the real measurement data monitored in plant. Pres-
sure drop between two locations at different heights (one in the
dense zone and the other in the dilute zone) inside the regenerator
is measured during the regenerator operation. The error of the
pressure drop calculated by the model relative to the average of
the pressure drop range of the actual regenerator is 4%. Tempera-
ture ﬁeld and the major species volume fractions has also been
compared with plant data. The relative errors between the temper-
ature predicted by the model and measurements in both dense
zone and dilute zone are within 1%. The relative error of CO is high.
However, it should be noted that the CO concentration at the
cyclone inlets is extremely low. This high relative error of CO con-
centration is due to the fact that, the kinetics for the CO combus-
(a) 
Center cross-section
Upper air ring 
Lower air ring 
(b) 
Fig. 1. (a) Computational domain with boundaries, (b) mesh.
Fig. 2. Cyclone boundary conditions.
G. Tang et al. / Applied Thermal Engineering 112 (2017) 750–760
755
tion used in the model does not take into account any CO combus-
tion promoters. In the real operations, CO combustion promoters
are added into the catalyst bed to promote CO combustion in the
dense bed. It is desired to have as much CO as possible burned
inside the dense bed thus the amount of CO that goes CO combus-
tion in the dilute zone can be minimized. More detailed validation
process can be found in previous work [39].
In this simulation, due to the transient ﬂow properties, a tran-
sient simulation processes was applied to simulate ﬂuid ﬂow and
combustion reactions which was started from initial startup condi-
tion to a quasi-steady state. The ﬂuidization process of the solid
FCC catalyst in the regenerator is shown in Fig. 3.
Fig. 3 shows that at the startup conditions, where the catalyst
bed is resting at the bottom of the FCC regenerator. With the input
of air from the air rings, the ﬂuidization phenomenon took place.
The catalysts were ﬁrstly ﬂuidized by the air with a high ﬂuidized
bed due to the initial high accumulated momentum. After the ﬂow
reached a quasi-steady state after 100 s, it shows similar ﬂow pat-
terns but different ﬂow property values at speciﬁc locations over
time. The catalyst solid volume fraction contour shows that there
are four zones along the regenerator height: dense zone, sub-
dense zone, dilute zone and sub-dilute zone. The sub-dense zone
is also called the ﬂuidized bed; while in the dense zone, the solid
catalyst is difﬁcult to be ﬂuidized due to the geometry effect. Dilute
zone has low catalyst concentration which is between the ﬂuidized
bed and cyclones. The region above the cyclones almost has no cat-
alyst concentration which called sub-dilute zone as shown in Fig. 3.
The ﬂuidized bed combustion process also occurs with the
development of ﬂuid ﬂow. The transient process of temperature
evolution at ﬁve different normalized regenerator heights were
plotted in Fig. 4. The temperature value is the mass weighted aver-
age temperature on the cross sectional area.
Fig. 4 shows the transient temperature evolution process inside
a FCC regenerator. It is shown that the combustion occurs immedi-
ately after the spent catalysts are transported into the regenerator.
The gas temperature evolution inside the FCC reheating furnace
can be divided into three stages. The gas temperature increases
linearly at the ﬁrst 200 s. Then, the rate of temperature evolution
increases dramatically from 300 s to 600 s. After 600 s, the temper-
ature increase rate reduces and reaches a steady state after 1000 s.
In addition, among the ﬁve different levels, the level h/H = 0.15 and
h/H = 0.24 are in the ﬂuidized bed. The temperature evolution pro-
ﬁles at different heights indicate that the temperature in the ﬂu-
idized bed is lower than temperature in the dilute phase and the
Table 3
Boundary conditions.
Spent catalyst inlet
Upper air ring
Lower air ring
Regenerated catalyst outlet
Velocity (m/s)
5.6
12.9
15.7
1.1
Gas volume fraction
0.05
1
1
0
Solid volume fraction
0.95
0
0
1
Table 4
Reaction mechanisms and kinetics.
Reactions
Heat of reaction (kJ/mol)
Pre-exponential factor
Activation energy (J/mol)
R1
C + ½ O2 ? CO
118
0.155  108 s1atm1
159,000
R2
C + O2 ? CO2
403
0.376  108 s1atm1
110,000
R3
CO + ½ O2 ? CO2
283
0.149  1012 kg1m3atm2
212,000
R4
H2 + ½ O2 ? H2O
600
3.3  107 kg1s1atm1
157,700
Table 5
Relative errors of the simulation results when compared with
plant data.
Parameters
Relative error
Catalyst circulation
2.74%
DP (in H2O)
4.0%
Dense zone temperature
0.50%
Dilute zone temperature
0.29%
C reduction rate (%)
1.7%
Volume fraction at outlets:
CO (ppm)
8881.8%
CO2
1.2%
O2 (vol%)
14.9%
t=0 s
t=10 s
t=100 s
t=300 s
t=500 s
t=800 s
Fig. 3. Solid volume fraction proﬁles changing with time.
756
G. Tang et al. / Applied Thermal Engineering 112 (2017) 750–760
maximum difference is approximately 20 K. This temperature dif-
ference indicates a CO combustion occurred above the ﬂuidized
bed. At 1260 s, after the ﬂow and reaction reached quasi-steady
state, the mass weighted average catalyst volume fraction and
the temperature proﬁles along the FCC regenerator height were
plotted in Fig. 5.
Fig. 5 shows that the average temperature in the ﬂuidized bed is
lower than that in the dilute zone. The transition region between
the ﬂuidized bed and the dilute zone raises the gas temperature
due to the CO combustion. The spikes at the top region (h/
H = 0.89) on both the averaged solid volume fraction and temper-
ature proﬁles are due to the effect of cyclone geometry. The cata-
lyst accumulates on the cyclone top surface while the high
temperature ﬂue gas emits from the cyclones.
Fig. 6 shows the mass weighted average mass fractions proﬁles
of species C<s> and H2 in the solid catalyst along the regenerator
height after the ﬂow reaching the quasi-steady state. It indicates
that the mass fraction of C<s> and H2 in the catalyst at the regen-
erated catalyst outlet are 0.09% and 0.0015%, respectively. By com-
Fig. 4. Gas temperature evolution at different levels along the regenerator height.
Fig. 5. Mass weighted average solid volume fraction and gas temperature along regenerator height.
Fig. 6. Mass weighted average species mass fraction in solid phase along regen-
erator height.
G. Tang et al. / Applied Thermal Engineering 112 (2017) 750–760
757
paring to the original content 0.92% and 0.08%, the reduction rate
for C<s> and H2 after combustion are 90% and 98%, respectively.
The C<s> and H2 on the catalysts are further reduced in the dilute
zone which increases the gas temperature at the dilute zone. The
mass weighted average species concentrations in the gas phase
along the regenerator height after reaction reached steady state
are shown in Fig. 7.
According to Fig. 7, CO2, H2O and O2 are the major species, while
the CO concentration is approximately zero. Along the regenerator
height, the CO2 mass fraction increases above the ﬂuidized bed. O2
mass fraction decreases greatly above the lower air ring, and fur-
ther deceases to a small percentage above the ﬂuidized bed. Due
to the fast hydrogen combustion, the H2O mass fraction increases
above the ﬂuidized bed and remain stable below the cyclone.
4.2. Effect of oxygen enrichment
The ultimate goal for optimizing the existing FCC regenerator is
to improve the FCC catalyst reactivity. Increasing the coke reduc-
tion without damaging the cyclone structure and catalyst itself
by high temperature are the basic criteria for optimization. The
optimal catalyst temperature in the dense is approximately
1000 K. The simulation on FCC regenerator under the typical oper-
ating condition indicates that the coke reduction efﬁciency can be
further improved. There are several parameters can be adjusted to
improve the coke reduction: air ﬂow rate, oxygen enrichment,
geometry change. Oxygen enrichment is a technique that widely
applied in the industrial furnace to improve combustion efﬁciency.
Oxygen enrichment technique has been applied on current FCC
regenerator operation process to improve the recovery of catalyst
reactivity. Therefore, the effect of oxygen enrichment on the cata-
lyst reactivity recovery process needs to be investigated based on
the validated CFD model. The effect of 5% and 10% oxygen enrich-
ment on the reaction inside the FCC regenerator has been studied.
The effect of oxygen enrichment on the mass weighted average
temperature ﬁeld inside the FCC regenerator is shown in Fig. 8.
According to Fig. 8 and 5% oxygen enrichment would increase
approximately 6 K in both the ﬂuidized bed and the dilute zone
along the FCC regenerator height. When further increase the oxy-
gen enrichments to 10%, the average temperature increased
approximately 2 K in the dilute zone, which indicates that the
combustion of the carbon and hydrogen in the solid phase reached
a limit as shown in Fig. 9.
According to Fig. 9, with the increasing of oxygen enrichment,
the mass weighted average carbon and hydrogen mass fractions
Fig. 7. Mass weighted average species mass fractions in gas phase along regener-
ator height.
Fig. 8. Effect of oxygen enrichments on average gas temperatures along regenerator
height.
Fig. 9. Effect of oxygen enrichments on species C<s> and H2 in the solid phase along the regenerator height.
758
G. Tang et al. / Applied Thermal Engineering 112 (2017) 750–760
in the ﬂuidized bed decrease, which indicates an improvement in
the reduction rates. The reduction rate of C<s> increases from
90% to 94.5% and the H2 reduction rate increases from 98% to
99% when the oxygen enrichment is 5%. However, after oxygen
enrichment exceeds 5%, the effect of oxygen enrichment on
improving carbon and hydrogen reactions is limited. The limitation
is also indicated by the mass weighted average gas species plots in
Fig. 10.
According to Fig. 10, with the increasing of oxygen enrichment,
the CO2 mass fraction increased in both ﬂuidized bed and the
dilute zone. The oxygen penetrates into in the bottom packed
bed and increases the CO2 mass fraction signiﬁcantly. However,
the CO2 mass fraction stabilizes in the dilute phase when the oxy-
gen enrichment exceeds 5%. Due to the fast reaction rate of hydro-
gen combustion, the oxygen enrichment has limited effect on the
species H2O distribution. It can be seen that with the increasing
of oxygen enrichment, the mass fraction of H2O increases at the
normalized height around 0.3 where the transition between ﬂu-
idized bed and dilute phase occurs. However, the H2O mass frac-
tion in the dilute zone keeps constant, which indicates that the
hydrogen combustion occurred fast and oxygen enrichment effect
is limited.
5. Conclusions
A three-dimensional multi-phase, multi-species reacting ﬂow
computational ﬂuid dynamics (CFD) model has been established
to simulate the ﬂow characteristics and chemical reactions inside
an FCC regenerator. The multiphase Eulerian model with kinetic
theory of granular ﬂow is employed. A modiﬁed drag model is used
to calculate the drag force during solid catalyst ﬂuidization pro-
cess. Heterogeneous reaction model was employed where inter-
phase reactions are considered. The ﬁnite-rate combustion model
was used which use Arrhenius expressions. The developed CFD
model was validated by using the plant data. This CFD model is
able to predict the ﬂow ﬁeld, temperature distribution and species
concentrations. The transient ﬂow characteristics and the inter-
phase reactions were studied. The effect of oxygen enrichment
on the catalyst reactivity recovery was investigated, and the results
show that coke reduction rate can be improved from 90% to 94.5%
with 5% oxygen enrichment. The effect of oxygen enrichment is
limited when it exceeds 5%. This simulation did not consider the
CO combustion promoters, which could introduce errors on the
CO concentration.
Acknowledgements
The authors would like to thank BP Reﬁning and Logistics Tech-
nology for providing funding for this project and Center for Innova-
tion through Visualization and Simulation for providing all the
resources required for this work.
This research was partially supported by U.S. Department of
Energy Grant DE-NA000741 under the administration of the
National Nuclear Security Administration.
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 
Abstract—Petroleum refining is a chemical process in which the 
raw material (crude oil) is converted to finished commercial products 
for end users. The fluid catalytic cracking (FCC) unit is a key asset in 
refineries, requiring optimised processes in the context of engineering 
design. Following the first stage of separation of crude oil in a 
distillation tower, an additional 40 per cent quantity is attainable in 
the gasoline pool with further conversion of the downgraded product 
of crude oil (residue from the distillation tower) using a catalyst in 
the FCC process. Effective removal of sulphur oxides, nitrogen 
oxides, carbon and heavy metals from FCC gasoline requires greater 
separation efficiency and involves an enormous environmental 
significance. The FCC unit is primarily a reactor and regeneration 
system which employs cyclone systems for separation.  Catalyst 
losses in FCC cyclones lead to high particulate matter emission on 
the regenerator side and fines carryover into the product on the 
reactor side. This paper aims at demonstrating the importance of FCC 
unit design criteria in terms of technical performance and compliance 
with environmental legislation. A systematic review of state-of-the-
art FCC technology was carried out, identifying its key technical 
challenges and sources of emissions.  Case studies of petroleum 
refineries in Nigeria were assessed against selected global case 
studies. The review highlights the need for further modelling 
investigations to help improve FCC design to more effectively meet 
product specification requirements while complying with stricter 
environmental legislation. 
 
Keywords—Design, 
emissions, 
fluid 
catalytic 
cracking, 
petroleum refineries.  
I. INTRODUCTION 
HE main processes in FCC designs are reaction, 
separation and regeneration. Operating conditions of FCC 
cyclones involve varying temperature and pressure of gas flow 
(reactor 525 oC, regenerator 700 oC) and the loading of the 
catalyst with highly abrasive properties. Failure mechanisms 
arising from the FCC process include coke depositions on 
reactor gas outlets, plugging of cyclone dipleg, catalyst 
carryovers in the cyclones, erosion or attritions in the cyclones 
due to higher throughputs, and reduced cyclone efficiency 
subsequent to an increased capacity [1]. Refiners highly seek 
FCC capacity increase for the better economic performance of 
the operation. However, this often introduces a loophole for 
significant catalyst losses by either the mechanisms of poor 
catalyst flowability or dipleg plugging. The cyclone dipleg is 
 
C. R. Nnabalu is with the School of Engineering, Systems Power and 
Energy Division, University of Glasgow, Glasgow, G12 8QQ UK (phone: 
447724921334; e-mail: r.nnabalu.1@ research.gla.ac.uk).  
G. Falcone is with the School of Engineering, Systems Power and Energy 
Division, University of Glasgow, Glasgow, G12 8QQ UK (e-mail: 
gioia.falcone@glasgow.ac.uk). 
I. Bortone is with the School of Water, Energy and Environment, Cranfield 
University, Bedford, MK43 0AL UK (e-mail: imma.bortone@cranfield.ac.uk) 
relative to its backup height for sustaining catalyst loads. 
Increase in capacity and, subsequently, an increase in pressure 
drop leads to dipleg plugging with catalysts, and on reaching 
the cyclone’s bottom, catalysts losses may occur due to it 
being carried over. The inadequate performance of the unit 
would lead to significant catalyst losses, operation at reduced 
capacity intake, and reduced process performance in achieving 
specified product yields and emission limits. FCC separation 
systems are installed in a variety of ways such as single, two-
stage, third-stage separation (TSS) and fourth-stage separation 
(FSS) [2]-[4]. According to the Climate and Clean Air 
Coalition (CCAC) [5], a majority of countries in 2020 would 
adopt the use of low sulphur diesel fuels with less than 50 
parts per million (ppm) sulphur content by 2020, and ultra-low 
sulphur fuels with less than 10 ppm sulphur content by 2030. 
A 90% reduction in black carbon and atmospheric particulate 
matter emissions from vehicles is also expected by 2030. 
II. 
FCC UNIT EVOLUTION 
The Houdry process developed the first cracking reactions 
which occurred on a fixed bed up until the fluidisation regime 
[6]. Refiners now prefer the fluidised bed processes which 
have advanced in technology as detailed in the various FCC 
types discussed below.  
A. Upflow Unit 
The Upflow unit also known as Model I (1942) was the first 
commercial FCC unit in the fluidisation regime developed by 
Standard Oil Development Co. (SOD) with cyclones located 
externally. The regenerator and reactor system circulates the 
catalyst using an up-flow configuration in multiple vessels as 
shown in Fig. 1. The non-heat balanced process employed 
preheating, catalyst cooling and a low pressure operated 
regenerator. Although the up-flow reactor pipe featured a 
section with a wider diameter, it allowed an insufficient 
contact time and a less dense bed required for the natural clay 
catalyst used; this led to catalyst losses until the invention of 
zeolite containing catalysts which require less contact time. 
The catalyst losses experienced with the up-flow unit led to 
the development of a modified unit known as Model II, with 
catalyst down-flow configuration and elongation of the section 
of the reactor with a wider diameter thus, allowing a dense bed 
and sufficient contact time [7]. 
B. Stacked Unit 
A stacked unit features a regenerator below the reactor 
vessel as shown in Fig. 2; the UOP stacked design (1947) was 
the first of its kind developed with the feature of spent catalyst 
stripping. The spent catalyst flows to the regenerator section 
The Role of Fluid Catalytic Cracking in Process 
Optimisation for Petroleum Refineries  
Chinwendu R. Nnabalu, Gioia Falcone, Imma Bortone 
T
World Academy of Science, Engineering and Technology
International Journal of Chemical and Molecular Engineering
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by gravity while the regenerated catalyst is carried on by feed 
vapour to the cracking reactor bed [8].  
 
 
Fig. 1 Upflow Unit -Model I FCC unit by SOD  [7] 
 
 
Fig. 2 Stacked unit by UOP [8] 
C. 
Orthoflow Unit 
M.W. Kellogg Cooperation (now Kellogg Brown and Root 
Inc.) developed the Orthflow unit (1951) [8] with a riser 
reactor fed to two-stage reactor cyclones overhead in the 
internals of regenerator vessel as shown in Fig. 3. A light 
crude processing refinery in Warri, Nigeria, owned by 
Nigerian National Petroleum Cooperation (NNPC) which uses 
the M.W. Kellogg unit is in use with a carbon monoxide (CO) 
boiler for complete combustion of CO to carbon dioxide 
(CO2). There are fewer chances of coke deposition given that 
coking is more significant with heavier feedstocks [9]. 
Advanced models of the Orthoflow design developed by 
Kellogg Brown and Root Inc. (KBR) features four regenerator 
cyclones in two stages are shown in Fig. 4. 
 
  
Fig. 3 M.W. Kellog Orthoflow Unit [10] 
D. 
 Side by Side – Two Vessel Unit 
SOD (now ExxonMobil) developed a side by side unit in 
1952 known as Model IV with a U bend feature as shown in 
Fig. 5. Standard two vessel units are widely employed, and the 
most common are the Exxon Flexicracking unit (see Table I) 
[11], the Shaw and Axen design, and the UOP model shown in 
Fig. 6 [12]. An example is the UOP FCC unit in Port Harcourt 
refinery where the reactor is an all riser cracking system. The 
regenerator system is designed for complete CO combustion 
and feeds hot flue gas to a flue gas cooler for heat recovery via 
steam generation. There are other FCC models with a side by 
side configuration designed for two stage regeneration. The 
UOP design performance has improved with piped spent 
catalyst distributor [13]. 
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Fig. 4 KBR orthoflow Unit [12] 
E. Standard Two Vessel Unit with TSS and FSS 
Shell first proposed riser cracking unit in 1957, a feature 
maintained by new FCC designs following the innovation of 
zeolite catalysts [8]. According to Shell Global Solutions, 
Shell FCC cyclones has attained greater separation efficiency 
(99.9%) by improvement of cyclone geometry, mechanical 
design and construction materials; this is now a customised 
Shell FCC design, unlike standard unit designs equipped with 
separation systems made by cyclone manufacturers. The 
solutions feature the application of a set of axial cyclones in 
parallel in a TSS at the regenerator for meeting particulate 
emission legislation (50 milligrams of dry NOx per 
normalised meter cube of exhaust [mg/Nm3]), other than 
power recovery expander (rotor blade) protection. A FSS with 
a single cyclone processes the TTS under-flow gas. 
Furthermore, the afterburn conditions that lead to catalyst 
losses in the regenerator are reduced by an improved 
construction of the cyclone crossover and plenums (single 
point anchoring, shot-creting the lining method) and a new 
type of refractory material. The design improvements include 
cyclone geometry modifications to reduce wall thickness 
erosion 
and 
coke 
depositions, 
cyclone 
compartment 
redefinition as per catalyst flow analysis from its interior into 
the dipleg, adjustments to the tolerance of cyclone suspensions 
and plenums to bear after-burn conditions with low risks of 
crack formation, allowing quick repair to parts other than the 
cyclone. Shells standard two vessel design features close-
coupled reactor cyclones and direct-coupled regenerator 
cyclones. Notably, other features that make up Shell’s 
innovations reported having no significant erosion effects are 
the direct coupling of rough-cut steam-stripping reactor 
cyclone to 2nd stage cyclones, the inclusion of vortex stabiliser 
device at the 2nd stage reactor cyclones for improving the 
pressure balance in the cyclone [14]. 
 
 
Fig. 5 Model IV SOD (1952) [7] 
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Fig. 6 UOP High-Efficiency Regenerator [12] 
 
 
Fig. 7  Shell external reactor design [14] 
F. Side by Side – External Reactor with TSS and FSS 
Shell’s heavy feedstock processing unit in Stanlow refinery, 
UK is an external reactor design. Fig. 7 shows shell external 
reactor design with a rough-cut cyclone for pre-stripping 
directly coupled to 2nd stage cyclones. Its main feature for 
technology improvement is an external reactor riser with the 
advantage of less stagnant areas hence, reduced chances of 
coke deposition and accessibility for blasting of reactor dipleg 
to re-establish flow in the case of blockages. Gas carry-under 
due to dipleg catalyst overloading is suppressible by adequate 
sealing of primary and riser end cyclones by lowering its 
bottom end in the stripper bed; as applied in a third-party Shell 
unit in the USA (a one vessel internal reactor and regenerator 
system). As reported, TSS and FSS have been applied to the 
unit to meet particulate legislation, with the added advantage 
of the recovered catalyst (typically about 200mg/Nm3) reused 
in the reactor and regenerator system. Other safeguards against 
catalyst losses are temperature indicators, DP measurement, 
gate valves and fluidisation gas injection provisions at the 
dipleg externals of the 2nd stage reactor cyclones [14]. 
Summarily, Table I highlights the features of various FCC unit 
types. 
 
TABLE I 
VARIOUS FCC UNIT DESIGN FEATURES 
FCC Unit Designs 
Features 
Upflow unit - Model I 
 External multi-cyclone systems. 
 Smaller reactor diameter and less contact time, 
less dense bed 
 Catalyst up-flow configuration. 
 No centrifugal fans, low-pressure regenerator, 
non-pressure balanced. 
 Quick separation. 
 Catalyst coolers, feed preheat as non-heat 
balanced
Downflow unit 
 Internal multi cyclone system. 
 Larger reactor for increased catalyst circulation 
rate.
Stacked unit - UOP 
 Spent catalyst stripping. 
 Catalyst up-flow to the reactor by feed vapours 
due to the configuration. 
M. W. Kellogg 
Orthoflow 
 Riser reactor, reactor cyclones stacked above 
regenerator, 
 Stripper below reactor cyclones.
KBR Orthoflow 
 Closed reactor cyclone system 
 Multistage stripper. 
 Two-stage regeneration hence coke reduction in 
regenerated catalyst 
Side by side -Model IV 
by SOD 
 Pressure balanced. 
 Catalyst flow rate not adjustable over a wide 
range as controlled by changes in differential 
pressure between reactor and regenerator 
 Catalyst downflow configuration.
Side by side Exxon 
Flexicracking 
 Transfer line reactor with steam stripper elevated 
above regenerator level. 
 Maintained at heat balance, nearly adiabatic. 
Side by side - UOP 
high Efficiency 
 All riser cracking. 
 Complete combustion of CO to CO2. 
Side by side - Shaw 
and Axen 
 Two-stage regeneration for reducing the rate of 
catalyst deactivation and improving regeneration. 
Side by side - Shell 
two vessel unit 
 TTS and FFS included. 
Side by side - Shell 
External Reactor 
Design 
 TTS and FFS included. 
 External reactor, hence less stagnant area. 
 Used mainly for processing heavier feedstock. 
III. FCC SEPARATION SYSTEMS 
Cyclones are gas-solid separators used in two main points 
in the FCC process; separation of the product gases from the 
used catalyst (in the reactor) and separation of regenerated 
catalyst from flue gas (in the regenerator). On the reaction of a 
catalyst with the long chain hydrocarbon (feedstock) in the 
reactor, the molecules are broken down into olefins, separated 
from the catalyst and fed to a fractionator. The catalysts used 
(spent-catalyst) becomes inactive due to coke deposition on 
the surfaces, hence are fed to the regenerator for reactivation. 
Recycling of the spent-catalyst occurs in the regenerator 
where coke is removed by combustion to reactivate the acid 
sites on the catalyst for reuse in the reactor. A cyclone in the 
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regenerator separates the reactivated catalyst (regenerated 
catalyst) from flue gases which are products of the combustion 
reaction. The heat produced from combustion vaporises the 
regenerator flue gases while the regenerated catalyst is fed to 
meet a new stream of feedstock in the reactor repeating the 
reaction, 
separation 
and 
regeneration 
processes. 
The 
regenerated catalyst is high in metal concentrations and 
becomes less active while recirculated continuously. Hence 
fresh catalyst additions are required. Hydro cyclones are often 
used downstream of the reactor to meet the product quality 
requirements.  The fluid medium in the hydro cyclone is liquid 
whereas cyclones separate particles in a gas or liquid stream 
using centrifugal force[15]. 
Given the flow field pattern within a cyclone system 
cyclone, the various types are standard reverse flow and 
uniflow. Table II gives a summary of their differences. 
 
TABLE II 
DIFFERENCES BETWEEN CYCLONE TYPES 
Cyclone Types 
Features 
Uniflow or axial 
 No conical section for particle collection hence compact 
construction easily integrated into pipes 
 particle leaves the cyclone body with a small portion of 
the inlet gas stream 
 Lower energy consumption due to lower pressure loss 
 Clean gas leaves the cyclone body in the same inlet 
direction. 
 Commonly installed in series 
Reverse flow 
 The conical section included for collecting particles, 
often bulky when designed for processing high gas flow 
rates. 
 Particles hit the sides and slide down to the conical 
section. 
 Prone to higher corrosion, an effect of higher tangential 
velocity due to flow field pattern. 
 Vortex reversal; gas stream enters the cyclone, the clean 
gas flow reverses and leaves at the top 
 Usually installed standalone. 
 
Uniflow cyclones are built with no conical section and are 
commonly installed in series. The inlet and outlet are in the 
same direction such that the clean gas product flows out of the 
cyclone body from the same inlet direction. Uniflow cyclones 
are otherwise known as axial or swirl tube. Unlike uniflow 
cyclones flow, in reverse flow cyclones, the inlet is at a 
tangent to the top of the cyclone. The dust particles collide 
with the side and fall to the bottom of a conical section 
included in the design. The vortex reversal within the cyclone 
system causes the clean gas product to flow to the centre and 
leave the cyclone body from the top[15], [16]. 
A. Close-Coupled and Direct-Coupled Cyclone Systems 
Close coupled cyclone systems are installed with the 
primary cyclone in an orientation such that separation of 
catalyst from products occurs immediately after the cracking 
reaction; hence the product gas feeds are prevented from 
further cracking at the fractionator[14]. Avidan et al. [17] 
discussed the use of closed cyclone systems as a riser 
termination device; separation of the catalyst from the product 
occurs in a short contact time to discriminate non-selective 
post-riser cracking hence, obtaining the desired product yields. 
However, a direct-coupled cyclone (DCC) system gives a 
minimum vapour residence time between the riser exit and 
reactor outlet compared to rough-cut and close-coupled 
cyclones. A DCC systems with negative pressure (operation at 
reduced pressure relative to the dilute phase of the reactor 
vessel) have minimal hydrocarbon blowdown at the primary 
reactor cyclone dipleg in comparison to close coupled or 
rough cut cyclones with positive pressure (irrespective of if 
the dipleg of the positive pressure device is submerged in a 
stripper bed or otherwise)[18]. 
IV. ADVANCES IN FCC CYCLONE OPTIMISATION 
The FCC process optimisation is in the context of 
mechanical design and model-based control [8]. Additives are 
often introduced in the processes to promote combustion, 
modify the product yields and, for removal of SOx, NOx, and 
metals. A good choice of composite materials can be used to 
achieve enhanced structural performance. The choice of 
catalyst is also an essential factor to consider as a safeguard 
against attrition [14]. Ultimately, an improved cyclone 
separation system is essential for maximising output, yielding 
specified product quality and meeting emission requirements. 
By design performance, there are standard and high-efficiency 
cyclone systems.  
A Shell third-party unit in the USA adopts primary reactor 
cyclones close coupled (to avoid post-riser coke formations), 
further improved to perform a pre-stripping function as well as 
separation by the exclusion of the cyclone dipleg area and 
injection of steam for catalyst stripping. A vortex stabiliser 
added in the cyclone bottom improves pressure balance and 
dampens the effect of high particle velocities [14]. 
Computational fluid dynamics (CFD) modelling has played 
a vital role in FCC cyclone optimisation. Other than 
improvements in construction materials and methods, under a 
given pressure loss, the separation efficiency of the cyclone 
may be optimised. Lower pressure drops in the process 
correspond to a reduced energy cost. The separation (or 
collection) efficiency is an evaluation of the performance of 
the process in collecting the particles. Pressure drops in 
cyclones increase with increasing gas flows. In making a 
dimensional analysis, pressure drop, and the separation 
efficiency are two critical parameters for optimisation of the 
FCC separation process [19]. 
Various CFD investigations have analysed the geometric 
parameter and shape adjustments as well as the inclusion of 
additional structure to the cyclone body. Direct coupling of 
cyclones in two stages has often improved their overall 
performance.  
A CFD study on reverse flow cyclones highlights a method 
of achieving the optimum values of a cyclone system’s 
dimensions by supplying permissible values of pressure drop, 
inlet gas velocity, height and cyclone diameter. Some 
geometric parameters have an impact on efficiency, but no 
effect on the systems pressure drop. These parameters include 
the dust discharge pot diameter, the ratio of the height of the 
separation space to the diameter of the cyclone, the outlet tube 
insert depth, the width to height ratio of the inlet cross-section 
and the inlet configuration. The dust hopper dimension may 
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benefit from operation margin considerations but is known not 
to affect efficiency and the systems pressure loss.  On the 
other hand, cyclone geometric parameters with a significant 
effect on efficiency and pressure drop include the cyclone 
diameter, the ratio of the inlet cross-section to the cyclone 
cross-section (at a given inlet velocity) and the ratio of the exit 
tube entrance diameter to the cyclone diameter [20]. 
According to Gimbun et al. [21], higher collection 
efficiency is achieved with a smaller cone tip diameter but 
leads to higher pressure drops.  
The dipleg length has been found to impact significantly on 
collection efficiency [22]. 
A cyclone with a variable cross-sectional area and its vortex 
length stretched produced a secondary swirling flow which is 
favourable for the particle collection efficiency with a particle 
diameter less than 5 µm [23]. 
Elsayed [24] reported an optimised vortex finder shape by 
the adjoint method for improved performance. Given studies 
comparing various cyclone designs, the vortex finder shape 
affects the collection efficiency and pressure drop. [25] Also, 
the eccentricity of the vortex finder is detrimental to the 
collection efficiency and pressure drop. [26] 
Huang et al. [27] investigated the impact of adding a 
laminarizer with 15 tubes at the entrance of the cyclone and 
found it to improve the collection efficiency but leading to 
higher pressure drops. 
Considering that fine particle may be trapped by tangential 
velocity on the cyclone sidewalls, Zhang et al. [28] 
investigated two new cyclone designs with the redistribution 
of several collection areas (dustbins) at the sides of the 
cyclone body in addition to the collection at the bottom. The 
first case employs a cross-sectional separation area of six 
variable diameters connected in series while the second case 
replicates the interior of the Stairmand high-efficiency cyclone 
[29] with an outer shell-like dustbin included. The two novel 
designs were found to be more efficient than the Stairmand 
high-efficiency cyclone by 3% and 33.9% respectively. As 
reported, the collection efficiency of the first case was 
improved 6% with a particle diameter of less than 1µm. On 
the other hand, the second case showed lower collection 
efficiency due to reduced tangential velocity in the interior 
with the range of 1.6µ – 3.1 µm particles diameters. 
V. 
CONCLUSION 
The FCC unit processes the heavier portion of the oil barrel; 
hence this shows the critical role it plays in the era of stricter 
environmental legislation and cyclone separators are essential 
for improving the overall FCC processes. In optimising FCC 
cyclones, it is vital to have a precise understanding of the flow 
regimes in the process. The centrifugal forces within the 
cyclone displace the particles contained in the catalyst and 
product mixture. In most cases, the products from primary 
cyclone flow through the vortex finder to secondary cyclones 
connected in series to provide greater collection efficiency 
before feeding it to the fractionation section. Various studies 
show improvement of collection efficiency with an increase in 
flow rate, but also the impact on the pressure within the 
system. Vortex stabilisers can be included at the bottom of the 
cyclone to improve the pressure balance; however, this does 
not result in overall system performance, due to a reduction in 
separation efficiency. The shell custom FCC unit discussed 
highlights 
two 
features 
to 
take 
into 
consideration: 
submergence in a stream stripper bed and blasting of the 
externals of the reactor and regenerator system to re-establish 
flow. An erosion modelling of the separation system needs to 
take account of the loading conditions considering the thermal 
stresses and strains absorbed. Lessons learned from other 
refiners are often valuable in improving existing equipment; 
however, it is essential to assess the undesirable impact of 
alternative solutions in plant operations. In order to meet 
specified product quality and stringent legislation on 
particulate emission, FCC revamping requires optimised 
separation systems and their virtual testing to eliminate 
unexpected afterburn conditions. Research efforts to date have 
yielded the optimisation of FCC cyclones, usually with 
improvement in a given parameter, but shortfalls in another. 
The stricter emission and fuel quality regulations imposed by 
CACC call for a collaborative effort within the petroleum 
industry. There is a need for further engineering design and 
CFD modelling of FCC separation systems towards improved 
overall system performance. 
ACKNOWLEDGEMENT 
C. R. Nnabalu thanks Petroleum Technology Development 
Fund (PTDF) for sponsoring this research. 
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Acknowledgment II
12 years ago I started working with Rasool Khosravanian when he worked on his PhD
project on multivariate analysis of casing setting depth in Iran. We have since cooperated
over the years, and 2 years ago, he moved to Norway as an invited researcher sponsored
by Equinor. We decided then that we should write a book on optimization. Actually,
many models exist, but they needed to be put in the right context for optimization
analysis.
Rasool has been very dedicated to the book project and has put in a considerable
effort. Whenever I visited him, he was working on the book. Luckily we got Joanne
Stone, a language professional, into the project and she has improved both the language
and the structure considerably.
The oil industry is now working towards digitalization at all levels. We hope that our
book will contribute to this development with the ultimate goal of increased efﬁciency at
a lower cost. Since the way the petroleum industry is working is changing, we also plan to
implement courses at the University of Stavanger introducing multivariate computerized
solutions in well engineering to prepare the students for the digitalized industry.
We are highly appreciative of the positive support we have received from the
University of Stavanger’s Department of Energy and Petroleum, from Equinor and Aker
BP, and from the many individuals with whom we have discussed our work.
Stavanger, July 2021
Bernt S. Aadnøy
xiiij

Research
Unconventional and Intelligent Oil & Gas Engineering—Perspective
Intelligent Petroleum Engineering
Mohammad Ali Mirza, Mahtab Ghoroori ⇑, Zhangxin Chen ⇑
Department of Chemical and Petroleum Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
a r t i c l e
i n f o
Article history:
Received 9 December 2021
Revised 1 May 2022
Accepted 17 June 2022
Available online 19 July 2022
Keywords:
Artiﬁcial intelligence
Machine learning
Intelligent reservoir engineering
Text mining
Intelligent geoscience
Intelligent drilling engineering
a b s t r a c t
Data-driven approaches and artiﬁcial intelligence (AI) algorithms are promising enough to be relied on
even more than physics-based methods; their main feed is data which is the fundamental element of
each phenomenon. These algorithms learn from data and unveil unseen patterns out of it. The petroleum
industry as a realm where huge volumes of data are generated every second is of great interest to this
new technology. As the oil and gas industry is in the transition phase to oilﬁeld digitization, there has
been an increased drive to integrate data-driven modeling and machine learning (ML) algorithms in dif-
ferent petroleum engineering challenges. ML has been widely used in different areas of the industry.
Many extensive studies have been devoted to exploring AI applicability in various disciplines of this
industry; however, lack of two main features is noticeable. Most of the research is either not practical
enough to be applicable in real-ﬁeld challenges or limited to a speciﬁc problem and not generalizable.
Attention must be given to data itself and the way it is classiﬁed and stored. Although there are sheer
volumes of data coming from different disciplines, they reside in departmental silos and are not accessi-
ble by consumers. In order to derive as much insight as possible out of data, the data needs to be stored in
a centralized repository from where the data can be readily consumed by different applications.
 2022 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and
Higher Education Press Limited Company. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/).
1. Introduction
Artiﬁcial intelligence (AI) seeks to mimic human decision mak-
ing. The subset of AI techniques known as machine learning (ML)
enables computers to learn how to act outside the conﬁnes of their
programmed behavior, through the use of external data. ML has
revolutionized myriad industries and ﬁelds of study, with incredi-
bly wide-ranging applications from stock market analysis to self-
driving cars. With the advent of Internet of Things (IoT) devices
and big data (where a high volume of data is generated at high
velocity and with many different varieties), ML is one of the most
important technologies to ensure that actionable insights can be
gleaned from big data.
In the oil and gas industry, model types are divided into three
main categories: physical, mathematical, and empirical models
[1]. A physical model is a scaled-down or scaled-up version of an
object that is developed to simplify the understanding of how a
physical object or scenario looks or operates. These models have
the disadvantages of being costly and time-consuming to develop,
and may not be sufﬁciently accurate in some cases. Empirical mod-
els are established based on experiments; they are subject to a
variety of errors, such as human and measurement errors, and
are not generalizable. Mathematical models encode physical laws
to simulate the underlying physics; however, they require many
assumptions and simpliﬁcations [1]. To deal with the challenges
in these three model types, derive insights, and make intelligent
decisions in a timely manner, a more promising technique is
required. This is where ML can be applied, due to its ability to cap-
ture and act upon insights from vast datasets that could never be
handled through purely programmatic rules, due to the complexity
of the relationships between data and the insights gleaned from
the data.
The oil and gas industry is rapidly transitioning to oil-ﬁeld
digitization, and there has been an increased drive to apply data-
driven modeling and ML algorithms to various petroleum engi-
neering challenges. Data-driven modeling uses mathematical
equations derived from data analysis, as opposed to knowledge-
driven modeling, in which logic is the main tool to represent a the-
ory [2,3]. While there may be data-driven algorithms that do not
learn from data (and thus cannot be called ML), ML is a subset of
data-driven approaches that demonstrate a form of AI. Fig. 1 sum-
marizes different types of ML algorithms.
https://doi.org/10.1016/j.eng.2022.06.009
2095-8099/ 2022 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company.
This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
⇑Corresponding authors.
E-mail addresses: mahtab.ghoroori@ucalgary.ca (M. Ghoroori),
zhachen@ucalgary.ca (Z. Chen).
Engineering 18 (2022) 27–32
Contents lists available at ScienceDirect
Engineering
journal homepage: www.elsevier.com/locate/eng
ML has been widely used in different areas of the petroleum
industry, including geoscience, reservoir engineering, production
engineering, and drilling engineering. The next four sections pre-
sent a critical review and perspective of the application of ML in
each of these areas.
1.1. Intelligent geoscience
Geoscience has utilized algorithms such as decision trees,
Markov chains, and K-means clustering as early as the 1960s.
Markov chains have been utilized in sedimentology [4], hydrology
[5], and well-log analysis [6]. Preston and Henderson [7] used
K-means clustering to interpret the periodicity of sediment depos-
its. Early application of decision trees can be found in economic
geology and perspective mapping [8,9]. Due to a variety of factors,
including a lack of computational power and immaturity of the
ﬁeld, ML adoption did not perfectly satisfy initial expectations;
hence, little development of AI occurred in the 1970s. Zhao and
Mendel [10] employed recurrent neural networks (NNs) to perform
seismic deconvolution in the 1980s, which can be considered a
resurgence of interest in AI. A shift from knowledge-driven to
data-driven ML occurred in the 1990s, when the ﬁrst review
of
NNs
in
geophysics
was
published
by
McCormack
[11].
McCormack’s review explored pattern recognition and presented
a summary of NN applications over the previous 30 years, along
with applied samples of seismic trace editing and automated
well-log analysis. Deep learning (DL) and, more speciﬁcally, convo-
lutional neural networks (CNNs) were revitalized in the 2010s,
when Waldeland and Solberg [12] applied a small CNN to seismic
data for salt recognition. Fault interpretation [13–15], horizon
Fig. 1. Different types of ML algorithms. DBSCAN: density-based spatial clustering of applications with noise; HDBSCAN: hierarchical density-based spatial clustering of
applications with noise.
M.A. Mirza, M. Ghoroori and Z. Chen
Engineering 18 (2022) 27–32
28
picking [16], and facies classiﬁcation [17,18] are other applications
of CNNs in geoscience. Mosser et al. [19] applied a generative
adversarial network (GAN)—that is, an ML model in which two
NNs work together competitively to make more accurate predic-
tions—early on in geoscience to perform pore scale modeling of
three-dimensional (3D) porous media. GANs have also been uti-
lized in seismogram generation and geostatistical inversion [20].
Seismicity is another important ﬁeld in geoscience in which ML
has become widely used. Mousavi et al. [21] used ML algorithms to
discriminate deep micro-seismic events from shallow ones based
on features of waveforms recorded on surface receivers. He et al.
[22] used an ML algorithm to improve the risk management of
induced seismic events. The proposed model was a set of simple
closed-form expressions, with the advantages of high transparency
and fast execution speed, providing the operator with the greatest
chance of success. Industrial activities such as mining, oil- and gas-
ﬁeld depletion, wastewater injection, and geothermal operations
can induce seismicity [23,24]. In western Canada, the seismicity
induced from hydraulic fracturing (HF) has galvanized public and
academic attention [25]. Investigating the correlations between
induced seismicity and HF has been exceedingly challenging for a
long time, due to the complexity introduced by strongly coupled
geomechanical, geophysical, and geological behaviors. Thus, there
is plenty of room for exploring ML applications in seismicity.
1.2. Intelligent reservoir engineering
ML algorithms have become popular in various areas of reser-
voir engineering, particularly in reservoir characterization and in
pressure, volume, and temperature (PVT) computations. A two-
layer NN was developed by Gharbi and Elsharkawy [26] to esti-
mate bubble point pressure and a formation volume factor for oil
reservoirs. In another study, a radial basis function and multi-
layer perceptron NN were employed to estimate a formation vol-
ume factor, isothermal compressibility, and brine salinity [27].
Wang et al. [28] used artiﬁcial neural networks (ANNs) in a compo-
sitional reservoir simulation for phase equilibrium calculations,
including phase stability tests and phase splitting calculations. A
combination
of
two
approaches—namely,
a
support
vector
machine (SVM) and fuzzy logic—was utilized to predict permeabi-
lity and porosity using real-life well logs as an input feed [29]. Patel
and Chatterjee [30] utilized classiﬁcation algorithms to carry out
quick and accurate rock typing (i.e., classifying reservoir rock into
different categories based on similarities). In the presence of ran-
dom noise, the performance of an ANN with a single hidden layer
was explored by An [31] to establish a model to predict the thick-
ness of a low-velocity layer. The proposed approach was also
applied on an oil ﬁeld in northern Alberta, Canada, to construct a
distribution map of porosity-net pay thickness, based on which
four wells were drilled and the ﬁeld production increased by
almost 20% [32]. Jamialahmadi and Javadpour [33] utilized a radial
basis function NN using depth measurements and the porosity of
core data as inputs to estimate the permeability of an entire oil
ﬁeld in southern Iraq. An ensemble ML model (i.e., a random forest
algorithm) was developed by Wang et al. [34] to predict time-lapse
saturation proﬁles at well locations using actual production and
injection data from a structurally complicated and highly faulted
offshore oil ﬁeld as the major inputs. A new framework for the pre-
diction of multiple reservoir parameters (i.e., porosity, saturation,
lithofacies, and shale content) was developed by introducing an
extreme learning machine (ELM), which is one of the most
advanced ML techniques [35]. In comparison to the classic
single-layer feed-forward NN approach, the proposed method
requires fewer computing resources and less training time without
sacriﬁcing accuracy.
1.3. Intelligent production engineering
Production prediction/optimization and HF are two other ﬁelds
in the energy industry in which ML has grown popular. Many
parameters must be taken into account for production prediction
and optimization, including the recovery process, proppant type,
well spacing, treatment rate, and number of fracturing stages.
Although the optimization of operational parameters can save mil-
lions of dollars and signiﬁcantly enhance unconventional reservoir
production, traditional reservoir simulations are computationally
expensive, which adds up when taking different variations of reser-
voir characteristics into account [36,37]. Hence, production predic-
tion and optimization are good candidates for AI applications, as
shown by the recent development and analyses of ML algorithms
for various recovery processes, such as water and chemical ﬂoods
and steam injection [38–40]. Dang et al. [41] utilized an NN for
the multidimensional interpolation of relative permeability to
overcome the impacts of different parameters (i.e., the polymer,
surfactant, and salinity) during hybrid recovery processes. Produc-
tion forecasting for wells in different reservoirs using geological,
core, and log data is a widely used ML application in this domain
[42,43]. Tadjer et al. [44] utilized DeepAR and Prophet (a time
series ML algorithm) as alternatives to decline curve analysis for
short-term oil and gas well forecasting. Using an NN to predict
bottom hole pressure in vertical wells, which is a crucial parameter
in the design of production facilities, is another application of ML
in this area [45]. A long short-term memory (LSTM) model along
with a feature-selection method was applied to predict the daily
production rates of shale gas wells in the Duvernay Formation in
Canada [46]. Horizontal well placement optimization was investi-
gated by Popa and Connel [47] via stratigraphic performance
estimation using a combination of fuzzy logic and NN.
In the last two decades, the growing number of HF jobs has
resulted in a substantial amount of measured data that can be used
to construct ML prediction models. A study was conducted by
Mohaghegh [48] to map a natural fracture network in Utica shale
using fuzzy logic cluster analysis. He et al. [49] developed a model
to optimize HF design in shale gas reservoirs using AI and fuzzy
logic analysis. A novel model was developed using an SVM to
determine the hydraulic apertures of rough rocks [50]. Yang et al.
[51] established a data analytics approach combining design
parameters
derived
from
acoustic
wireline
logs
and
post-
fracturing analysis to optimize fracturing treatment design. The
obtained fracturing optimization algorithm was validated using
production logging tool data and deep shear-wave imaging along
horizontal wells in the Marcellus shale reservoirs. An integrated
approach combining ML, reservoir simulation, and HF was pre-
sented by Wang and Sun [52] to optimize well spacing in Permian
shales, considering a typical well for each representative region in
this large area. A reinforcement learning algorithm was applied by
Bangi and Kwon [53] to achieve a uniform proppant concentration
along fractures in order to improve HF productivity; the research-
ers coupled dimensionality reduction with transfer learning to
speed up the learning process. Duplyakov et al. [54] presented a
model based on a combination of boosting algorithms and ridge
regression to predict the cumulative oil production of a well com-
pleted with multistage fractures. A case study was performed on
74 hydraulically fractured wells in the Montney Formation in
Alberta, Canada, to predict cumulative production proﬁles over a
ﬁve-year period using well spacing, rock mechanical properties,
and completion parameters as input features [55]. A proxy model
was developed to predict cumulative gas production for shale
reservoirs using a deep NN based on production, completion, and
HF data as input features; this model was validated using ﬁeld data
for 1239 horizontal wells in the Montney Formation [56].
M.A. Mirza, M. Ghoroori and Z. Chen
Engineering 18 (2022) 27–32
29
1.4. Intelligent drilling engineering
Considering that huge volumes of real-time data are being pro-
duced daily during drilling operations, drilling engineering has also
beneﬁted greatly from the application of ML. Due to the volatility
of oil prices in recent years, methods to ensure good economics
in a variety of price scenarios have been sought. In order to achieve
this, ML has become increasingly common to alleviate drilling
challenges in real time. Drilling operation optimization and stuck
pipe prediction are two of the most critical areas in drilling engi-
neering and have been frequently investigated using AI applica-
tions.
Mohaghegh
[57]
utilized
an
ANN
for
the
real-time
identiﬁcation of drilling anomalies and their related nonproductive
time (NPT). An ML model was developed by Unrau et al. [58] to
determine a real-time alarm threshold in order to detect anomalies
in ﬂow rates and mud volume data during drilling operations. This
model assists in the early detection of lost circulation and mini-
mizes false alarm creation. Reinforcement learning algorithms
were applied by Pollock et al. [59] to reﬁne a pretrained NN based
on 14 horizontal wells in the Permian and Appalachia basins. The
reﬁned model managed to minimize tortuosity and deviations
from planned trajectories with a less than 3% error. Zhao et al.
[60] applied ML algorithms to derive a trend of different drilling
parameters in order to identify anomalous incidents and propose
remedial actions accordingly. An attempt was made to apply ML
algorithms to the optimization of a rate of penetration (ROP) using
drilling features such as weights on bit, ﬂow rate, and rotations per
minute [61]. Goebel et al. [62] developed an ML model to predict
future stuck pipes based on the monitoring and investigation of
various parameters including ROP, pipe rotation, inclination angle,
and ﬂow rate. A year later, real-time risk prediction during drilling
was presented by Dursun et al. [63]. ML algorithms were coupled
with data mining and natural language processing (NLP) tech-
niques to investigate daily drilling reports (DDRs) for two onshore
ﬁelds in the Middle East in an exceptionally short time, in order to
categorize productive and NPT and discover critical contributing
factors of NPT [64].
2. Challenges and opportunities
ML algorithms can be very effectively applied to address three
main types of problems: building surrogate models for understood
problems to reduce computational costs; building ML models for
problems that require human intervention and knowledge for
analysis; and building ML models for complex problems that were
previously impractical to be addressed. ML yields the fastest suc-
cess in realms in which the environment is straightforward, data
is easily available, and decisions are not expensive. Although most
cases of ML use in the petroleum industry do not meet any of these
criteria, as the environments are usually heterogeneous, decisions
are expensive (e.g., drilling a well), and data is sporadic, the invest-
ment in longer term gains through the effective application of ML
can provide a great deal of value, although thoughtful design and a
high degree of collaboration with domain experts is required [65].
Applying ML algorithms to petroleum engineering problems
requires a variety of challenges to be overcome. One challenge is
that the data often comes in a high volume (i.e., large amounts of
data), with a wide range of variety (i.e., many different data for-
mats) and veracity (i.e., data inconsistency and inaccuracy), and
at a high velocity (i.e., a high rate of data inﬂux). Massive amounts
and varieties of data are being produced daily from downhole and
surface sensors installed on operational equipment in the petro-
leum industry. The industry utilizes structured and unstructured
data to keep track of production, safety, and maintenance. Acquir-
ing accurate data in the petroleum industry is usually difﬁcult or
impossible, and can be expensive. As a result, obtaining sufﬁcient
quantities of high-quality data for training and veriﬁcation is a
prevalent challenge in the petroleum industry, which causes
uncertainties and noise in training data. In turn, such issues com-
promise the generalizability and accuracy of ML models. In addi-
tion, raw data is often not ready for ML algorithms and needs to
be preprocessed and cleaned. Subsurface uncertainties and data-
processing time delays are also important considerations. More-
over, such data usually resides in departmental silos, and the cor-
responding models are either unavailable or not open to others due
to conﬁdentiality concerns and competitive edges—a problem that
is particularly prevalent in academic environments [65]. Further-
more, model explainability is important for geoscience, since it
can be just as important to know the reason for a result as it is
to know the result itself.
Perhaps due to the challenges mentioned above, ML adoption in
geoscience is not moving as quickly as in many other ﬁelds.
Although ML is a promising technique for using big data to dis-
cover input–output relationships and derive insights, ML perfor-
mance can be affected by the high dimensionality of the data.
This may lead to misleading correlations and impractical and unre-
liable clustering. It is noteworthy that data is usually ambiguous in
its initial state; thus, different preprocessing techniques are
required to identify salient features and make the ML model
capable of learning a system’s behavior. There is a risk that missing
data and a lack of system stability will introduce biases into ML
models, making it problematic to extract beneﬁcial knowledge
from data [66]. Moreover, the considerations and challenges of uti-
lizing data from diverse sources should be taken into account. Pri-
vacy, security, and ethics related to data are also very important
aspects to consider. Hybrid modeling, which integrates ML algo-
rithms with physics-based methods, can be considered as a way
to mitigate the abovementioned problems. Furthermore, transfer
learning—in which a pretrained model is used as a starting point,
and then a model is trained on top of it by considering one’s own
training data—is a relatively recent ML technique that can poten-
tially be beneﬁcial in geoscience contexts.
3. Perspectives
The potential of ML has not been fully used in two areas of the
petroleum industry—namely, reservoir simulation and text mining.
Reservoir simulation involves differential equations (DEs) that ade-
quately illustrate physical property changes over time and space
and are thus useful for describing physical phenomena in nature.
There are many problems in science and engineering that require
solving complicated DEs. However, DEs are remarkably difﬁcult
to solve, and their associated simulations are extremely complex
and computationally intensive. This level of complexity requires
the use of giant computers to perform simulations and justiﬁes
the interest in AI among researchers in this area. Utilizing DL,
which involves NNs with more than one hidden layer, is a promis-
ing technique that will speed up solving DEs and save scientists
and engineers a great deal of time and effort. Caltech researchers
have introduced a new DL technique for solving DEs that is more
accurate, generalizable, and 1000 times faster than traditional DL
algorithms [67]. This new adaptation is based on deﬁning the input
and output in a Fourier space, as opposed to a Euclidean space in
traditional DL. This development will not only lessen the depen-
dency on supercomputers but also raise the computational capac-
ity to efﬁciently model more intricate problems.
The petroleum industry is just beginning to harness the power
of ML for smart reporting and extracting information from text
documents. Daily drilling and completion reports are two of the
main text-based documents in the industry that contain important
M.A. Mirza, M. Ghoroori and Z. Chen
Engineering 18 (2022) 27–32
30
text, as well as a variety of other types of data such as depths, cas-
ing sizes, hole sizes, and perforation depths. NLP and DL algorithms
can be used to develop models for automated quality control of
operations and performance improvement, providing approaches
that are far more efﬁcient than the traditional approach of relying
on the knowledge of subject matter experts [68]. Several studies
have investigated text processing in the petroleum industry, with
a focus on topics including the text mining of operational data
for risk management and issue prediction [69], producing metrics
and pattern recognition based on contextual analysis of reports
[70,71], and reports classiﬁcation [71]. Although the literature con-
tains such studies on using text mining techniques to mitigate
text-based challenges in the industry, there is still great potential
for ML in this area, and it must be further explored.
4. Concluding remarks
Data-driven approaches and AI algorithms hold enough promise
that they may someday be relied upon even more than physics-
based methods. Their main feed is data, which is the fundamental
element of each scenario. These algorithms learn from data and
reveal unseen patterns. Within the petroleum industry, there is
great interest in using this technology to gain insight from the
huge volumes of data that are generated every second. Many stud-
ies explore AI applicability in various subdisciplines of this indus-
try; however, there is a noticeable lack of two main features; that
is, most of the research on this topic is either not practical enough
to be applicable in real-ﬁeld challenges or limited to a speciﬁc
problem and not generalizable. Attention must be given to data
itself and to how it is classiﬁed and stored. Although tremendous
volumes of data are produced in different disciplines, such data
remains within departmental silos and is not accessible by others.
To derive as much insight as possible from data, the data must be
stored in a centralized repository from which it can be readily con-
sumed for different applications. Between data acquisition and the
application of AI and ML techniques, data must be processed in
order to effectively extract features and ensure that the data can
effectively support the algorithms. Although AI and ML techniques
are increasingly important within the petroleum and reservoir
engineering domain, they are only part of a holistic system. In
order to ensure that this system can deliver value, careful consid-
eration is required to apply algorithms to this challenging domain,
and the right type, quality and volume of data must be available
and effectively processed to achieve the desired outcomes. Thus,
although AI is a critical tool to efﬁciently manage the world’s
underground resources, data is the key to fully exploit the
possibilities.
Compliance with ethics guidelines
Mohammad Ali Mirza, Mahtab Ghoroori, and Zhangxin Chen
declare that they have no conﬂict of interest or ﬁnancial conﬂicts
to disclose.
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M.A. Mirza, M. Ghoroori and Z. Chen
Engineering 18 (2022) 27–32
32

Anish Thomas and M.V. Pavan Kumar*
Multi-objective optimization of a ﬂuid
catalytic cracking unit using response
surface methodology
https://doi.org/10.1515/cppm-2022-0018
Received April 5, 2022; accepted October 12, 2022; published online November 21, 2022
Abstract: In oil refineries, fluid catalytic cracking (FCC) is a major unit consisting of several process variables
and multiple products. Since FCC units are given prime importance as they are contributing a large share in
profits, the optimal operation of FCC is always desirable while considering the changing economic scenarios
with respect to the products. However, optimization of FCC is quite challenging due to the complex nature of
the process. In this work, using Aspen HYSYS V9® catcracker module, process data of FCC was obtained using
central composite design (CCD). Second order regression equations for the selected responses were obtained
using Analysis of variance (ANOVA) approach. The interaction effects of feed ﬂow, feed temperature, feed
pressure, air blower discharge temperature and catalyst circulation rate on responses (yield of products) were
presented. Further, the optimization was performed based on a multi-response optimization technique in the
Design expert software and the optimal values of the input variables were obtained for the chosen objectives
(representing various operation scenarios). The optimal operation scenarios that were obtained for the ob-
jectives were validated successfully. This work highlights the use of statistics based soft computing techniques
for the optimization of complex chemical engineering operations such as FCC.
Keywords: 21-lump kinetic model; Aspen HYSYS; design expert; fluid catalytic cracking unit; response surface
methodology.
1 Introduction
The need for residual oil upgradation paved the way to many rapid developments in the area of the catalytic
cracking of crude petroleum in the early 20th century, most importantly, a major invention known as Houdry’s
fixed bed catalytic cracking process and its commercialization [1]. The Houdry’s ﬁxed bed reactor technology
was later upgraded to ﬂuid catalytic cracking (FCC) process, and FCC is an important operation in every
reﬁnery worldwide now and FCC units chieﬂy convert heavier feed stocks into valuable lighter products such
as gasoline, LPG, propylene, naphtha, diesel etc. FCC units are the largest producer of high octane gasoline and
light end products in reﬁneries [2]. Since FCC units contribute to major proﬁts for oil reﬁneries and operate at
their maximum capacity, the optimal operation of FCC units in the light of ever changing economic targets and
environmental constraints has become quite essential over the last four decades. Since FCC unit operation is
highly complex in nature involving many process variables, a great deal of research has been focussed on the
development of reliable yet simple modelling and simulation tools for the optimization and control.
Ali et al. developed a dynamic model of a UOP stacked FCC unit involving coupled ordinary differential
equations and performed sensitivity studies to determine the control loop interactions [3]. This model was
modiﬁed by Malay et al. for control and optimization studies [4]. Han and Chung developed a model of side-by-
side type FCC unit and studied control and optimization of the process [5]. Secchi et al. developed a dynamic
*Corresponding author: M.V. Pavan Kumar, Department of Chemical Engineering, National Institute of Technology Calicut, Kerala,
India, PIN-673601, E-mail: malladi@nitc.ac.in
Anish Thomas, Department of Chemical Engineering, National Institute of Technology Calicut, Kerala, India, PIN-673601,
E-mail: anish_p140084ch@nitc.ac.in
Chem. Prod. Process Model. 2023; 18(3): 469–485
model of a UOP stacked FCC unit (designed to operate in partial combustion mode) and validated the results
using industrial data [6]. Almeida and Secchi performed dynamic real time optimization (DRTO) using the
developed dynamic model and presented a short comparison with real time optimization (RTO) results, in
which DRTO showcased better beneﬁts over RTO [7]. John et al. developed a gPROMS based optimization model
for the maximization of gasoline and a 4.5% increase in gasoline yield was reported [8]. Kasat and Gupta
carried out multi-objective optimization of a ﬂuid catalytic cracking unit using non-dominated sorting genetic
algorithm with jumping genes (NSGA-II-JG) [9]. The computational time required for optimization was
signiﬁcantly reduced when compared to NSGA-II. Later, Sankararao and Guptha carried out multiobjective
optimization using jumping gene adaptations of simulated annealing - MOSA-JG and MOSA-aJG, for an FCC
unit [10]. The results were compared with the jumping gene adaptations of NSGA developed by Kasat and
Gupta and the superiority of MOSA-aJG was shown. Bohorquez et al. used surrogate models for the multi-
objective optimization of an FCC unit [11]. For GLN (Naphtha) values, the PSO algorithm exhibited better
performance than GA and SQP-GA. Overall, for an FCC unit, a simple model can reduce the computational time
for optimization. However, the model should be reliable and applicable for a wide range of operation sce-
narios. Although a single objective function (for maximizing the proﬁt) can be used in the optimization, multi-
objective optimization is more appropriate as several constraints cannot be accommodated meaningfully in a
single objective. Hence, multi-objective optimization is more desirable in the case of FCC units due to the
greater number of variables. In recent years, multi-objective optimization using statistical methods has gained
more attention in several disciplines. Due to its simplicity, elegance, ease of understanding and reliability,
optimization using response surface methodology is more popular now. The optimization of FCC units using
statistical methods was also reported in the literature. Cuadros et al. combined factorial design technique with
genetic algorithm to optimize the conversion of an FCC unit [12]. Two variables, namely regenerated catalyst
slide valve opening and feed ﬂow rate, were considered for the optimization and a conversion increase of 8.71%
was reported. Ridzuan et al. employed central composite design technique to develop a statistical model for
wax deposition in Malaysian crude oil [13]. The wax deposition was minimized more than hundred fold by
using the multi-response optimization technique in design expert software. For the crude oil hydrotreating
process, Bhran et al. employed response surface methodology for the optimization of the process to achieve
maximum conversion of the contaminants [14]. The applicability of the statistical tools for the successful
optimization of many industrial processes were also reported in the literature. Das and Mishra used response
surface methodology coupled with box-behnken design to optimize the conditions for the preparation of
activated carbon from biomass [15]. Optimal operating conditions were obtained using multi-response opti-
mization technique based on desirability approach. Wang et al. used central composite design to optimize
biodiesel production from trap grease [16]. Trap grease with high free fatty acid content was converted into
biodiesel (89.33% ester content) by keeping the three factors (molar ratio of methanol to trap grease, sulfuric
acid concentration and reaction time) at their optimum values. Bas and Boyaci did a critical analysis on the
application of response surface methodology on its usability and limitations [17].
The optimization levels of FCC is classified into four distinct stages: a) design stage, b) optimal tuning of
operating conditions, c) optimal set points and d) optimal control [12]. In the optimization levels given above,
the second one i.e., optimal tuning of operating conditions must be given prime importance as it pertains to the
daily operation of the unit, which forms a basis for the selection of optimal set points in the attempts to control
the process. Hence, in this study, central composite design (CCD) is employed for optimizing products’ yield
and conversion of a FCC unit. A 21-Lump kinetic model of a FCC unit (simulated in Aspen HYSYS V9®) is used to
obtain the reduced statistical model. The regression equations are obtained using response surface method-
ology. Multi-objective optimization is performed in Design Expert software to optimize the conversion,
products’ yield and reactor outlet temperature. The optimization study using RSM inherently includes the
interaction effects among all salient parameters of the unit [17]. This work focuses mainly on the optimization
of the yield of a FCC which is operating at different operational regimes. The study involves more factors and
responses, and production oriented objectives. In Table 1, the different optimization strategies used for the FCC
are tabulated for an easy comparison of the tools and objectives of the previous studies with the current work.
470
A. Thomas and M.V. Pavan Kumar: Multi-objective optimization of a FCCU using RSM
2 Process description
The schematic diagram of a FCC Unit is shown in Figure 1. and the operation of the unit is described here
brieﬂy. The preheated feed oil enters the riser through the feed nozzle, where the feed is atomized using
dispersion steam. Endothermic cracking reactions are triggered as soon as the regenerated catalyst comes in
contact with the atomized feed in the riser. Entrained hydrocarbons from the catalyst are removed in
the subsequent steam stripping section of the reactor. Coke is formed as a side product during cracking. The
entire coke formed is deposited on the catalyst surface, blocking the catalyst pores, and thus reducing the
catalyst activity. The used catalyst is transferred into the regenerator section, where the coke formed during
the reaction stage is burnt off using air supply from the regenerator air blower. The exothermic heat of the
combustion reactions is transferred to the riser section via circulation of the catalyst to support endothermic
cracking reactions in the riser. The vapour phase products of the riser-reactor are sent to the fractionator for
further processing. Cyclones are provided in both reactor and regenerator sections to prevent the escape of
solid catalyst particles via hydrocarbon vapours (products) and ﬂue gases respectively.
3 Materials and methods
3.1 Aspen HYSYS FCC unit model
The steady state simulation of an FCC unit is performed in Aspen HYSYS V9® CatCracker module. A rigorous 21-Lump kinetic model
for the cracking reactions is used in the module. Table 2 shows the dimensions of the hardware and the base case operating
conditions for a FCCU unit operating in partial combustion mode. The feedstock and catalyst used are Vacuum Gas Oil (VGO) and
Vision 59 respectively. The steady state data for the FCC unit simulation in the current work is adapted from Han and Chung [18].
Table : Comparison of proposed optimization strategy with other strategies discussed in the literature.
Reference
Factors Response Objectives Optimization
Description
John et al.
()


Gproms based
optimization
Developed gPROMS based optimization model for the
maximization of gasoline and a .% increase in gasoline
yield was reported
Kasat and
Guptha
()


NSGA-II,
NSGA-II-JG
Compared NSGA-II-JG with NSGA-II where the computational
time required for optimization was signiﬁcantly reduced for
the former
Sankararao
and Guptha
()


&Simulated
Annealing
Jumping gene adaptations of simulated annealing - MOSA-JG
and MOSA-aJG was used and the results were compared with
the jumping gene adaptations of NSGA developed by Kasat
and Gupta and the superiority of MOSA-aJG was shown
Bohorquez
et al. ()


PSO,SQP, GA,
GA-SQP
Used surrogate models for optimization and for GLN
(Naphtha) values, PSO algorithm exhibited better perfor-
mance than GA and SQP-GA
Cuadros
et al. ()


GA
Two variables, namely regenerated catalyst slide valve
opening and feed ﬂow rate, were considered for the optimi-
zation and a conversion increase of .% was reported
Current work


Desirability
based
approach
For the chosen objectives, the optimal values of the inputs
were obtained. The results of the optimization were validated
using Aspen HYSYS catcracker module.
A. Thomas and M.V. Pavan Kumar: Multi-objective optimization of a FCCU using RSM
471
3.2 Experimental design and statistical analysis
Response Surface methodology is a collection of mathematical and statistical techniques, which can be used to find out the
correlation between the factors and responses. RSM offers plethora of information from a limited number of experiments. Central
composite design (CCD) is the most frequently used and flexible for fitting second order models [19]. CCD is a full factorial design
with a central point and additional axial points. Since more samples are required for CCD, the curvature of the design space can be
investigated [20]. On the other hand, Box-behnken design (BBD) does not contain an embedded factorial design [21, 22].
Assuming that all variables are measurable, the response surface can be expressed as:
y = f(x1, x2, x3, … … … … .xk)
(1)
Where y is the response and x1,x2,x3,………….xk are the factors.
Experimental data from the model is fitted to a second order polynomial equation, represented as
y = β0 + ∑
k
i=1
βixi + ∑
k
i=1
βiix2
i + ∑
k−1
i=1
∑
k
j=2
βijxixj + ε
(2)
where β0, βi, βii, βij are the coefﬁcients (obtained by least square method) xi and xj are the input factors, y is the response and ε is the
approximation error [23].
In the current work, CCD is used to develop RSM model for the salient response variables of FCC unit using Design Expert v11®
(Stat-Ease, Inc. USA.2017). Table 3 depicts the FCC process variables with their levels used in this study. For the ﬁve factors, seven
responses were chosen namely 1. light end products’ yield, 2. naphtha yield, 3. LCO yield, 4. bottoms yield, 5. coke yield,
6. conversion and 7. reactor outlet temperature. The experimental range is chosen within ±10% of each factor’s design value.
Figure 1: A schematic of side-by-side type FCCU.
472
A. Thomas and M.V. Pavan Kumar: Multi-objective optimization of a FCCU using RSM
3.3 Optimization
The numerical optimisation technique used in the Design Expert is a simultaneous optimization technique devised by Derringer
and Suich (1980) [24]. Design points as well as a set of random points are checked to ﬁnd out the most desirable solution.
Constraints are set in the form of design goals which are applicable to both factors and responses. Design goals can be prioritized
based upon the importance of a goal. A carefully chosen combination of these goals constitutes the overall desirability function,
D. The search begins at random points and proceeds up to the steepest slope to ﬁnd the local maximum/minimum. 100 random
starting points are used in this optimization, which increases the probability of ﬁnding the global maximum. The goal of this
optimization is to maximise the desirability objective function represented as:
D = (d1(y1).d2(y2) … …dk(yk))
1/k = ( ∏
k
i=1
di)
1/k
(3)
where k is the number of responses and di(yi) denotes the individual desirability. Let y be the response, T be the target, U and L be the
upper and lower limits respectively, then for the response maximisation, the desirability can be represented as:
d =
⎧
⎪
⎪
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎪
⎪
⎩
0 
y < L
(y −L
T −L)
r
L ≤y ≤T
1
y > T
(4)
For response minimisation, desirability can be represented as:
Table : Dimensions and base case conditions of FCCU.
Variable
Description
Base case values
FF
Feed volume ﬂow (tonne/h)
.
TF
Feed temperature (K)

PF
Feed pressure (kPa)

Sg
Speciﬁc gravity of feed
.
FD
Dispersion steam mass ﬂow (tonne/h)
.
TD
Dispersion steam temperature (K)

FS
Stripping steam mass ﬂow (tonne/h)
.
TS
Stripping steam temperature (K)

PS
Stripping steam pressure (kPa)

TR
Reactor plenum temperature (K)
.
PR
Reactor pressure (kPa)

PRg-R
Regenerator-reactor pressure difference (kPa)

xoxygen
Oxygen mole % in ﬂue gas (%)

xCO
Carbon monoxide mole % in ﬂue gas (%)
.
xCO
Carbon dioxide mole % in ﬂue gas (%)
.
Tamb
Ambient air temperature (K)

FC
Catalyst circulation rate (tonne/h)

TRG
Regenerator dense bed temperature (K)

ΔTRG
Regenerator ﬂue gas-dense bed temperature difference (K)
−.
TCY
Regenerator cyclone temperature (K)

FA
Regenerator inlet air ﬂow rate (tonne/h)
.
TA
Regenerator air blower discharge temperature (K)
.
LR
Riser length (m)

DR
Riser diameter (m)
.
LS
Stripping section length (m)
.
DS
Stripping section diameter (m)
.
LRG
Regenerator length (m)

DRG
Regenerator diameter (m)
.
A. Thomas and M.V. Pavan Kumar: Multi-objective optimization of a FCCU using RSM
473
d =
⎧
⎪
⎪
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎪
⎪
⎩
1 
y < T
(U −y
U −T)
r
T ≤y ≤U
0
y > U
(5)
If a particular target value needs to be attained, desirability can be represented as:
d =
⎧
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎩
0 
y < L
(y −L
T −L)
r1
L ≤y ≤T
(U −y
U −T)
r2
T ≤y ≤U
0
y > U
(6)
where r,r1 and r2 are the weights.
The desirability objective function for FCC optimization is represented as:
D = (d1(YLE).d2(YN).d3(YLCO).d4(YB).d5(YC).d6(Conv).d7(TR))1/7 = ( ∏
7
i=1
di)
1/7
(7)
Optimization results are also validated using Aspen HYSYS® catcraker model values. The objectives, based on the desired
operation scenario, deﬁned for the FCC, are given below. Note that each objective depicts a multi-objective optimization problem:
–
Objective 1: Maximize the conversion while minimizing LCO, bottoms and coke yield
–
Objective 2: Maximize light end products’ yield while minimizing LCO, Bottoms and coke yield
–
Objective 3: Maximize conversion, light end products’ yield, and reactor outlet temperature while minimizing LCO, Bottoms
and coke yield
–
Objective 4: Maximize naphtha yield while minimizing LCO, bottoms and coke yield
–
Objective 5: Maximize light end products’ yield, naphtha yield and conversion while minimizing LCO, Bottoms and coke yield
–
Objective 6: Maximize LCO yield while minimizing bottoms and coke yield
–
Objective 7: Maximize LCO yield and Naphtha yield
–
Objective 8: maximize light end products’ yield, naphtha yield and LCO yield while minimizing bottoms and coke yield
–
Objective 9: Maximize naphtha yield and LCO yield, when light end products yield is 18.49% (−2% of the base case value)
–
Objective 10: Maximize naphtha yield and light end products’ yield, when LCO yield is 17.18% (−2% of the base case value)
–
Objective 11: Maximize light end products’ yield and LCO yield, when naphtha yield is 38.3% (−2% of the base case value)
4 Results and discussion
4.1 Analysis of variance (ANOVA) and regression model representation
A five-factor seven-level central composite design was used for CCD to understand the influence of process
variables such as feed flow rate, feed temperature, feed pressure, air blower discharge temperature and
catalyst circulation rate on product yields, conversion and reactor outlet temperature. The data obtained using
Table : Levels of parameters used in the experimental design.
Factor
Name
Coded variable level
Low
Center
High
−1
0
+1
A
Feed ﬂow rate FF, (tonne/h)

.

B
Feed temperature TF, (K)
.

.
C
Feed pressure PF, (KPa)



D
Regenerator air blower discharge temperature TA, (K)

.

E
Catalyst circulation rate FC, (tonne/h)



474
A. Thomas and M.V. Pavan Kumar: Multi-objective optimization of a FCCU using RSM
CCD (50 experiments) is shown in Table 4. A conﬁdence interval of 95% is taken in all cases, which implies
that those factors whose p-value is greater than 0.05 have no signiﬁcant effect on response. The results of
ANOVA and ﬁt statistics are shown in Table 5. The statistically signiﬁcant model terms considered for the
regression equation are shown in the table. Since the F value for all the models are high, the models are
considered signiﬁcant. The R2 value for all the models is close to unity. Also, the predicted R2 values are in a
reasonable agreement with the adjusted R2 values, since the difference between them is less than 0.2.
Adequate precision, which is the signal to noise ratio, is greater than 4 (desirable value) in all cases
indicating the adequacy of the signal. The predicted versus actual plots for all responses are shown in
Figure 2a–g. The data points are well distributed near the central line in all the cases suggesting a very good
ﬁt between actual and predicted values. The predicted versus actual plot in the case of Naphtha in Figure 2b
are comparatively not near to the 45° line but acceptable. The quadratic models thus obtained with the
application of multiple regression analysis on the data and the ﬁnal equations are shown below.
YLE = 20.60 −5.83A + 4.85B + 0.9425D + 4.48E −2.32AB −0.4610AD −2.23AE + 1.7BE + 1.93A2 + 1.39B2
+ 0.6963E2
(8)
YN = 40.14 + 1.88A −2.59B −0.5427D −1.75E + 3.04AB + 0.558AD + 2.99AE −2.23BE −2.32A2 −1.54B2
−1.19E2
(9)
YLCO = 19.13 + 1.48A −0.6872B −0.1228D −1.06E
(10)
YB = 13.66 + 2.77A −1.28B −0.2239D −1.86E −0.7256AB −0.1086AD −0.7386AE + 0.5099BE
+ 0.0881DE + 0.4714A2 + 0.2531B2 + 0.4658E2
(11)
YC = 5.62 −0.256A −0.3229B −0.0589D + 0.1668E + 0.0702AB + 0.0161AD + 0.0513AE −0.0517BE
−0.0122DE −0.0227A2 −0.04B2 −0.0307E2
(12)
Table : The actual design of experiments and responses using CCD.
Run
Factors
Response
FF
TF
PF
TA
FC
YLE
YN
YLCO
YB
YC
Conv
TR
(t/h)
(K)
(KPa)
(K)
(t/h)
(%)
(%)
(%)
(%)
(%)
(%)
(K)


.



.
.
.
.
.
.
.


.



.
.
.
.
.
.
.

.


.

.
.
.
.
.
.
.

.


.

.
.
.
.
.
.
.


.



.
.
.
.
.
.
.


.



.
.
.
.
.
.
.


.



.
.
.
.
.
.
.


.



.
.
.
.
.
.
.


.



.
.
.
.
.
.
.

.


.

.
.
.
.
.
.
.


.



.
.
.
.
.
.
.


.



.
.
.
.
.
.
.


.



.
.
.
.
.
.
.

.


.

.
.
.
.
.
.
.

.

.
.

.
.
.
.
.
.
.
A. Thomas and M.V. Pavan Kumar: Multi-objective optimization of a FCCU using RSM
475
Table : ANOVA and ﬁt statistics for CCD based model.
Variables Significant terms
Model
F-value
Lack of fit
Standard
deviation
R2
Adjusted
R2
Predicted
R2
Adequate
precision
YLE
A, B, D, E, AB, AD, AE, BE,
A, B, E
.
Not
signiﬁcant
.
.
.
.
.
YN
A, B, D, E, AB, AD, AE, BE,
A,B, E
.
Not
signiﬁcant
..
.
.
.
YLCO
A,B,D,E
.
Not
signiﬁcant
..
.
.
.
YB
A, B, D, E, AB, AD, AE, BE,
DE, A, B, E
.
Not
signiﬁcant
..
.
.
.
YC
A, B, D, E, AB, AD, AE, BE,
DE, A, B, E
.
Not
signiﬁcant
..
.
.
.
Conv
A, B, D, E, AB, AE, BE, A,
E
.
Not
signiﬁcant
..
.
.
.
TR
A, B, D, E, AB, AD, AE, BD,
BE, DE, A, E
.Not
signiﬁcant
..
.
.
.
Figure 2: Plot of actual response versus predicted response for (a) light end products yield, (b) naphtha yield, (c) LCO yield,
(d) bottoms yield, (e) coke yield, (f) conversion, and (g) reactor outlet temperature using CCD.
476
A. Thomas and M.V. Pavan Kumar: Multi-objective optimization of a FCCU using RSM
Conv = 67.17 −4.25A + 1.97B + 0.3467D + 2.92E + 0.7814AB + 0.8009AE −0.5805BE −0.4067A2
−0.5226E2
(13)
TR = 813.04 −14.73A + 14.1B + 2.63D + 12.61E + 1.87AB + 0.1894AD + 0.7375AE −0.3319BD −1.7BE
−0.2044DE + 0.5136A2 −0.8853E2
(14)
4.2 Interaction of process variables on response for CCD
The interaction effect of some key process variables on the light end products’ yield (YLE) is shown in Figure 3a–d.
For a ﬁxed TF, TA and FC values, YLE decreases with the increase of FF. For a ﬁxed FC, an increase of TF
increases YLE. As shown in Figure 3b, for ﬁxed FF, the increase of TA has a minor negative effect on YLE. However,
the interaction between TF and FC is a positive effect on YLE. For increased catalyst circulation rate and/or feed
temperature, the increased cracking is the reason for the increase in the yield of light end products.
The variation of Naphtha yield with respect to these key process variables is shown in Figure 4a–d. The
non-linear nature of the interaction of FF with TF and FC is evident in the tested operation window as shown in
all cases. The naphtha yield (YN) decreases with the increase of TF. Multiple steady state behaviour for the
Naphtha yield in the reported operation range is quite evident from the plots. For a ﬁxed value of TF, TA or FC, an
increase of FF initially increases YN and then decreases after attaining a maximum value of YN. Due to the
multiplicity nature of YN, deviations are observed in the actual versus predicted in Figure 2b.
The effect of the variation in the process variables is not much pronounced in the case of LCO yield (data
not shown). The effect of interaction of process variables on bottoms yield (YB) is shown in Figure 5a–e. The
bottom yield decreases for an increase in TF. For a ﬁxed FC value, an increase of TA does not change the bottoms
yield. Overall, an increase of catalyst circulation rate (FC) and feed inlet temperature (TF) leads to more
Figure 3: Combined effect of (a) feed ﬂow rate and feed temperature, (b) feed ﬂow rate and regenerator air blower discharge
temperature, (c) feed ﬂow rate and catalyst circulation rate and (d) feed temperature and catalyst circulation rate on light end
products yield using CCD.
A. Thomas and M.V. Pavan Kumar: Multi-objective optimization of a FCCU using RSM
477
cracking, thus increase of the percentage of light end products and decrease of yield of bottoms, and vice versa.
The value of YC increases with the decrease of FF for ﬁxed TF, TA and FC values as shown in Figure 6a–c,
respectively. For ﬁxed FC, the effect of the change of TF is much pronounced than that of TA for the YC. From
these ﬁndings, it can be seen that the changes in FC, TF and FF have a greater inﬂuence on the yields of
products. To understand this aspect further, the conversion (Conv) of the unit is plotted in Figure 7a–c.
Increase of FF leads to decrease in the conversion for ﬁxed catalyst circulation rate and/or feed temperature. It
is obvious because FF and FC must be balanced in order to maintain the conversion at its desired value. Any
imbalance thus changes the yields of products. For ﬁxed FF, increase of FC and TF increases the conversion and
increase of the yield of light end products. The variation of reactor outlet temperature (TR) with respect to the
variation of chosen process variables is depicted in Figure 8a–f. The increase of FF for a ﬁxed value of TF, TA or
FC decreases the TR. It is obvious that the increased ﬂow rate necessitates additional energy for the endothermic
cracking reactions. With no such additional supply with a ﬁxed catalyst circulation rate, the temperature at the
reactor exit and conversion decreases (refer Figure 7a, c).
In this study, a set of 50 experimental runs are required for the CCD and it involves axial (star) points and
corner points, which can depict the FCC process in a satisfying manner. In the actual process, operating
conditions may drift to the points outside the designed operation regime. Hence, the inclusion of axial
points, which are outside the design space, can lead to a model representing these unexpected scenarios in
an efficient manner and improves overall accuracy of the predictions. In order to test the performance of the
statistical models outside of the design space, a set of eight experimental runs are performed for ±12% of base
case design value as illustrated in Figure 9. The design values are also validated against the Aspen HYSYS
model values. CCD and Aspen HYSYS model values for ±12% of base case design are shown in Table S23
(Supplementary material). The values predicted by the statistical model developed in the study is closely
matching the results obtained from Aspen HYSYS. It highlights that central composite design approach is
well suitable to model FCC units. In a previous study by Rakic et al. four experimental designs were
compared for the suitability for the optimization and advocated the efﬁcacy of CCD for the optimization
using the grid point search methodology [25]. It is also one reason for the selection of CCD for the
Figure 4: Combined effect of (a) feed ﬂow rate and feed temperature, (b) feed ﬂow rate and regenerator air blower discharge
temperature, (c) feed ﬂow rate and catalyst circulation rate and (d) feed temperature and catalyst circulation rate on naphtha yield
using CCD.
478
A. Thomas and M.V. Pavan Kumar: Multi-objective optimization of a FCCU using RSM
optimization of FCC unit in this study. However, response surface methodology is used for the optimization
in the current work.
4.3 Optimization
Response surface methodology is an excellent tool for finding optimal process conditions. The best possible
solutions are found by numerical optimization using a desirability-based approach. The objectives defined for
the FCC unit and the predicted optimal values for CCD are shown in Table 6. The percentage deviation of model
values from the base case value is also given in Table 6. Objectives 1–3 mainly deal with the maximization of
conversion and light end products’ yield while minimizing LCO, bottom and coke yields. Objectives 4 & 5 deal
mainly with naphtha yield maximization while minimizing LCO, bottom and coke yields. Objectives 6–8 deal
with maximising LCO yield while minimizing bottom and coke yields. Objectives 9–11 ﬁx a target value (−2% of
base case value) for one of these three (light end products yield, naphtha yield and LCO yield) while maximizing
the other two.
Conversion and light end products’ yield are maximized by increasing the catalyst circulation rate and/or
decreasing the feed flow rate, which in turn increases the catalyst to oil ratio. When catalyst circulation rate
Figure 5: Combined effect of (a) feed ﬂow rate and feed temperature, (b) feed ﬂow rate and regenerator air blower discharge
temperature, (c) feed ﬂow rate and catalyst circulation rate, (d) feed temperature and catalyst circulation rate and (e) regenerator
air blower discharge temperature and catalyst circulation rate on bottoms yield using CCD.
A. Thomas and M.V. Pavan Kumar: Multi-objective optimization of a FCCU using RSM
479
increases, the reactor temperature also increases. The optimization results for the objectives are explained
here. For objective 1, an increase in conversion of 5.58% (from the base case value) is obtained. For objective 2,
the light end products’ yield increased by 24.61%. In the case of objective 3, the deviation of light end products’
yield, conversion and reactor outlet temperature from the base case values are 27.45, 5.99 and 4.74%
respectively. Naphtha yield increases initially with catalyst to oil ratio till it reaches the over-cracking point,
after which the yield reduces substantially [26, 27]. For objective 4, Naphtha yield reduces by 5.94%. However,
for objective 5, when both light end products’ yield and naphtha yield are maximized, the naphtha yield falls to
11.82%. LCO yield increases initially with conversion till it reaches the start of overcracking, after which the
LCO yield reduces with conversion. To increase the LCO yield, the conversion should be lower, which also
Figure 7: Combined effect of (a) feed ﬂow rate and feed temperature, (b) feed ﬂow rate and catalyst circulation rate and (c) feed
temperature and catalyst circulation rate on conversion using CCD.
Figure 6: Combined effect of (a) feed ﬂow rate and feed temperature, (b) feed ﬂow rate and regenerator air blower discharge
temperature, (c) feed ﬂow rate and catalyst circulation rate, (d) feed temperature and catalyst circulation rate and (e) regenerator
air blower discharge temperature and catalyst circulation rate on coke yield using CCD.
480
A. Thomas and M.V. Pavan Kumar: Multi-objective optimization of a FCCU using RSM
results in an increase in bottoms yield [28]. It is observed from the tables that for objective 6, the LCO yield
increases by 1.17%. For objective 7, the LCO yield increases by 2.45% and Naphtha yield reduces to 2.53% of the
base case value. For the simultaneous maximization of LCO, naphtha and light end products’ yield in objective
8, LCO yield goes below its base case value. The desirability plot and optimization ramps for each objective is
illustrated in S1-S22 (Supplementary material).
The predicted results are validated using catcracker of Aspen HYSYS. It is observed that the predicted
optimal values are in close agreement with the values obtained in the module and the errors are tabulated in
Table 7. Mean absolute error (MAE), mean square error (MSE) and root mean square error (RMSE) were
calculated. It is observed that these error values in all cases are well below the acceptable limit.
2
4
6
8
0
20
40
60
YLE (%)
2
4
6
8
0
20
40
60
YN (%)
2
4
6
8
15
20
25
YLCO (%)
2
4
6
8
10
15
20
25
30
YB (%)
2
4
6
8
4
5
6
7
Experimental runs
YC (%)
2
4
6
8
50
60
70
80
Experimental runs
Conversion (%)
2
4
6
8
750
800
850
900
Experimental runs
TR (K)
CCD Model
Aspen HYSYS Model
Figure 9: Validation of CCD model with Aspen HYSYS model values for ±12% of base case design value.
Figure 8: Combined effect of (a) feed ﬂow rate and feed temperature, (b) feed ﬂow rate and regenerator air blower discharge
temperature, (c) feed ﬂow rate and catalyst circulation rate, (d) feed temperature and regenerator air blower discharge
temperature, (e) feed temperature and catalyst circulation rate and (f) regenerator air blower discharge temperature and catalyst
circulation rate on reactor outlet temperature using CCD.
A. Thomas and M.V. Pavan Kumar: Multi-objective optimization of a FCCU using RSM
481
Table : Multiobjective optimization using CCD and validation of the optimized values using Aspen HYSYS.
Obj S/
w
FF
TF
PF
TA
FC
YLE
YN
YLCO
YB
YC
Conv
TR
D
(tonne/h)
(K)
(KPa)
(K)
(tonne/h)
(%)
(%)
(%)
(%)
(%)
(%)
(K)

DX
.
(−.%)
.(%)

(−.%)

(.%)
.
(.%)
.
(.%)
.
(−.%)
.
(−.%)
.
(−.%)
.
(−.%)
.
(.%)
(.%) .
HY
.
.


.
.
.
.
.
.
.
.

DX
.
(−.%)
.(%)
.
(.%)

(.%)
.
(.%)
.
(.%)
.
(−.%)
.
(−.%)
.
(−.%)
.
(−.%)
.
(.%)
(.%) .
HY
.
.
.

.
.
.
.
.
.
.
.

DX

(−.%)
.(%)
(%)

(.%)

(.%)
.
(.%)
.
(−.%)
.
(−.%)
.
(−.%)
.
(−.%)
.
(.%)
.
(.%)
.
HY

.



.
.
.
.
.
.
.

DX
.
(−.%)
.(%)
(−%)

(.%)
.
(−.%)
.
(.%)
.
(−.%)
.
(−.%)
.
(−.%)
.
(−.%)
.
(.%)
.
(.%)
.
HY
.
.


.
.
.
.
.
.
.
.

DX
.
(−.%)
.(%)
(−%)

(.%)
.
(−.%)
.
(.%)
.
(−.%)
.
(−.%)
.
(−.%)
.
(−.%)
.
(.%)
.
(.%)
.
HY
.
.


.
.
.
.
.
.
.
.

DX
.
(.%)
.(%)
(−%)

(.%)
.
(−.%)
.
(−.%)
.(−.%)
.
(.%)
.
(.%)
.
(−.%)
.
(−.%)
.
(−.%)
.
HY
.
.


.
.
.
.
.
.
.
.

DX
.
(.%)
.
(.%)
(−%)

(−.%)
.
(−.%)
.
(−.%)
.
(−.%)
.
(.%)
.
(.%)
.
(−.%)
.
(−.%)
.
(−.%)
.
HY
.
.


.
.
.
.
.
.
.
.

DX
.
(.%)
.(%)

(−.%)

(.%)
.
(.%)
.
(.%)
.
(−.%)
.
(−.%)
.
(−.%)
.
(−.%)
.
(.%)
.
(.%)
.
HY
.
.


.
.
.
.
.
.
.
.

DX
(.%)
.
(.%)
.
(−.%)

(.%)
.
(−.%)
.
(−%)
.
(−.%)
.
(.%)
.
(.%)
.
(−.%)
.
(−.%)
.
(−.%)
.
HY

.
.

.
.
.
.
.
.
.
.

DX
.
(−.%)
.
(−.%)
(%)

(.%)
.
(.%)
.
(.%)
.
(−.%)
.
(−%)
.
(−.%)
.
(.%)
.
(.%)
.
(.%)
.
HY
.
.


.
.
.
.
.
.
.
.

DX
.
(.%)
.(%)
.
(−.%)

(.%)
.
(.%)
.
(.%)
.(−.%)
.(%)
.
(.%)
.
(−.%)
.
(−.%)
.
(.%)
.
HY
.
.
.

.
.
.
.
.
.
.
.
Obj, objective; S/w, software used; DX, design expert; HY, Aspen HYSYS; D, desirability.
482
A. Thomas and M.V. Pavan Kumar: Multi-objective optimization of a FCCU using RSM
5 Conclusions
An extensive study of the interaction effects of feed flow rate, feed temperature, feed pressure, air blower
discharge temperature and catalyst circulation rate was performed using Design expert software. It is
observed that feed pressure and air blower discharge temperature has less effect on conversion and prod-
ucts’ yield in an FCC unit whereas the interactions between FF and TF, FF and FC along with TF and FC have
considerable effect on FCC conversion and products’ yield. The steady state behaviour of the FCC unit was
well represented by the chosen statistical models. Furthermore, multi-objective numerical optimization was
done using Design Expert software for the statistical model developed. For the chosen 11 objectives, the
optimal values of the inputs were obtained. The results of the optimization were validated using Aspen
HYSYS catcracker module. The study shows that statistical tools can offer great advantage for the optimi-
zation of FCC units.
Nomenclature
ANOVA
analysis of variance
BBD
box-behnken design
CCD
central composite design
Conv
conversion
DRG
regenerator diameter
DS
stripping section diameter
DR
riser diameter
FCC
ﬂuid catalytic cracking
FF
feed volume ﬂow
FC
catalyst circulation rate
FA
regenerator inlet air ﬂow rate
FD
dispersion steam mass ﬂow
FS
stripping steam mass ﬂow
LR
riser length
LS
stripping section length
LRG
regenerator length
MAE
mean average error
MSE
mean square error
RMSE
root mean square error
Table : Validation error between HYSYS and CCD model values.
Obj
EYLE
EYN
EYLCO
EYC
EConv
ETR
MAE
MSE
RMSE
EYB

.
−.
.
.
−.
−.
−.
.
.
.

.
−.
.
.
−.
−.
−.
.
.
.

.
−.
.
.
−.
−.
−.
.
.
.

−.
.
−.
.
.
.
.
.
.
.

.
−.
−.
.
−.
−.
.
.
.
.

.
−.
−.
−.
−.
.
−.
.
.
.

.
−.
.
.
−.
−.
.
.
.
.

−.
.
−.
−.
.
.
.
.
.
.

.
−.
.
−.
−.
−.
−.
.
.
.

−.
.
−.
.
.
.
.
.
.
.

−.
.
−.
−.
.
.
.
.
.
.
Obj, objective; MAE, mean absolute error; MSE, mean square error; RMSE, root mean square error.
A. Thomas and M.V. Pavan Kumar: Multi-objective optimization of a FCCU using RSM
483
PF
feed pressure
PS
stripping steam pressure
PR
reactor pressure
PRg-R
regenerator-reactor pressure difference
RSM
response surface methodology
Sg
speciﬁc gravity of feed
TR
reactor plenum temperature
TS
stripping steam temperature
TCY
regenerator cyclone temperature
Tamb
ambient air temperature
TRG
regenerator dense bed temperature
TD
dispersion steam temperature
TF
feed temperature
TA
regenerator air blower discharge temperature
ΔTR
regenerator ﬂue gas-dense bed temperature difference
xoxygen
oxygen mole % in ﬂue gas
xCO
carbon monoxide mole % in ﬂue gas
xCO2
carbon dioxide mole % in ﬂue gas
YLE
light end products’ yield
YN
naphtha yield
YLCO
light cycle oil yield
YB
bottoms yield
YC
coke yield
Author contributions: All the authors have accepted responsibility for the entire content of this submitted
manuscript and approved submission.
Research funding: None declared.
Conﬂict of interest statement: The authors declare no conﬂicts of interest regarding this article.
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Hoboken: John Wiley & Sons; 1995.
25. Rakic T, Kasagic-Vujanovic I, Jovanovic M, Jancic-Stojanovic B, Ivanovic D. Comparison of full factorial design, central
composite design and box–behnken design in chromatographic method development for the determination of ﬂuconazole and
its impurities. Anal Lett 2014;47:1334–47.
26. Biswas J, Maxwell IE. Octane enhancement in ﬂuid catalytic cracking II. Operation in the overcracking regime. Appl Catal, A
1990;58:19–27.
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cracking unit. Arabian J Sci Eng 2019;44:5475–87.
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Supplementary Material: The online version of this article offers supplementary material (https://doi.org/10.1515/cppm-2022-
0018).
A. Thomas and M.V. Pavan Kumar: Multi-objective optimization of a FCCU using RSM
485

A. Maadhah, Y. Fujiyama, H. Redhwi,  M. Abul-Hamayel, A. Aitani, M. Saeed, and C. Dean 
 
April 2008 
                The Arabian Journal for Science and Engineering, Volume 33, Number  1B 
17 
 
 
A NEW CATALYTIC CRACKING PROCESS TO MAXIMIZE 
REFINERY PROPYLENE 
Ali G. Maadhah    
 Chemical Engineering Department 
King Fahd University of Petroleum & Minerals 
Dhahran, Saudi Arabia 
Yuuichirou Fujiyama   
Refinery Integration R&D Group  
Fuel Research Laboratory, Nippon Oil Corporation 
8, Chidoricho, Naka-ku, Yokohama, 231-0815, Japan 
Halim Redhwi and Mohammed Abul-Hamayel  
Chemical Engineering Department  
King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia 
Abdullah Aitani* and Mian Saeed  
Center for Refining and Petrochemicals, Research Institute 
King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia 
and Christopher Dean 
Research & Development Center 
 Saudi Aramco, Dhahran, 31311, Saudi Arabia 
 
  
اﻟﺨـﻼﺻ
   : ـﺔ ـ
ﺗﺘﻨﺎو
ل هﺬﻩ اﻟﻮرﻗﺔ ﻣﻮﺿﻮع ﺗﻄﻮﻳﺮ ﻋﻤﻠﻴﺔ ﺟﺪﻳﺪة ﻟﻠﺘﻜﺴﻴﺮ اﻟﺤﻔﺰي اﻟﻤﻤﻴﻊ 
)HS-FCC
 (
 ﻋﻨﺪ ﻋﻮاﻣﻞ ﺗѧﺸﻐﻴﻞ ﻣﺮﺗﻔﻌѧﺔ ﺑﻬѧﺪف زﻳѧﺎدة إﻧﺘﺎﺟﻴѧﺔ
 
اﻟﺒﺮوﺑﻴﻠﻴﻦ ﺑﻮﺳ
ﺎ
 
ﻃﺔ ﻓﺮﻳﻘﻲ ﺑﺤﺚ ﻣﻦ اﻟﻤﻤﻠﻜﺔ اﻟﻌﺮﺑﻴﺔ اﻟﺴﻌﻮدﻳﺔ واﻟﻴﺎﺑﺎن
. 
وﻟﻘﺪ ﺗﻢ ﺗﺸﻐﻴﻞ اﻟﻌﻤﻠﻴﺔ ﻓѧﻲ ﻣѧﺼﻨﻊ ﺗﺠﺮﻳﺒѧﻲ ﺷѧﺒﻪ ﺻѧﻨﺎﻋﻲ ﺑﻄﺎﻗѧﺔ 
)30
 (
ًﺑﺮﻣﻴﻼ
 ﺑﺎﻟﻴﻮم ﺑﺎﻟﻘﺮب 
 
ﻣﻦ ﻣﺼﻔﺎة أراﻣﻜﻮ اﻟﺴﻌﻮدﻳﺔ
،
 إﺿﺎﻓﺔ إﻟﻰ وﺣﺪة دﻓﻘﻴﺔ ﺑѧﺎردة 
)
 
ﺑѧﺪون ﻟﻘѧﻴﻢ
( 
ﺑﻄﺎﻗѧﺔ 
)500
 (
 ﺑﺮﻣﻴѧﻞ ﺑѧﺎﻟﻴﻮم ﻓѧﻲ ﺷѧﺮآﺔ ﻧﻴﺒѧﻮن
 
ﻟﻠﺒﺘﺮول ﻓﻲ اﻟﻴﺎﺑѧﺎن
. 
وﻣѧﻦ ﻣﻼﻣѧﺢ اﻟﻌﻤﻠﻴѧﺔ اﻟﺠﺪﻳѧﺪة إﺿѧﺎﻓﺔ ﺗﻌѧﺪﻳﻼت ﻣﻴﻜﺎﻧﻴﻜﻴѧﺔ 
إ
ﻟѧﻰ ﻋﻤﻠﻴѧﺔ اﻟﺘﻜѧﺴﻴﺮ اﻟﺘﻘﻠﻴﺪﻳѧﺔ 
)FCC
 (
 ﻣѧﻊ ﺗﻐﻴѧﺮات ﻓѧﻲ ﻇѧﺮوف
 
اﻟﺘﺸﻐﻴﻞ وﺗﺮآﻴﺒﺎت اﻟﺤﻔѧﺎز
. 
وﺗﺘﻤﺜѧ 
 
ﻞ ﻣﻼﻣѧﺢ اﻟﺘѧﺸﻐﻴﻞ اﻟﺮﺋﻴѧﺴ
ﻴ
ﺔ ﻟﻠﻌﻤﻠﻴѧﺔ ﺑﻨﻈѧﺎم ﺗﻔѧﺎﻋﻠﻲ ذات دﻓѧﻖ ﻣѧﻦ أﻋﻠѧﻰ إﻟѧﻰ أﺳѧﻔﻞ ، وإرﺗﻔѧﺎع درﺟѧﺔ ﺣѧﺮارة 
 
اﻟﺘﻔﺎﻋﻞ وﻗﺼﺮ زﻣﻦ ﺗﻼﻣﺲ اﻟﻠﻘﻴﻢ ﻣﻊ اﻟﺤﻔﺎز
،
 إﺿﺎﻓﺔ إﻟﻰ 
ارﺗﻔﺎع
 ﻣﻌﺪل اﻟﺤﻔﺎز ﺑﺎﻟﻨﺴﺒﺔ ﻟﻠﺰﻳﺖ
. 
وﺗﻢ إﺟﺮاء اﻟﺘﺠѧﺎرب اﻟﻤﻌﻤﻠﻴѧﺔ ﻓѧﻲ وﺣѧﺪﺗﻴﻦ ذات 
 
دﻓﻖ ﻣﺨﺘﻠﻒ
،
 إﺿﺎﻓﺔ إﻟﻰ ﻣﺼﻨﻊ ﺗﺠﺮﻳﺒѧـﻲ ﺷѧﺒﻪ 
 
ﺻѧﻨﺎﻋﻲ ﺑﺎﺳѧﺘﺨﺪام ﺣﻔѧﺎزات
،
 
 وإﺿѧﺎﻓﺎت وﻟﻘѧﺎﺋﻢ زﻳѧﻮت ﻣﺨﺘﻠﻔѧﺔ
. 
ً 
وأﻇﻬѧﺮت ﻧﺘѧﺎﺋﺞ اﻟﻔﺤѧﺺ ﺗﻔﻮﻗѧﺎ
ﻷداء اﻟﻤﻔﺎﻋﻞ ذات اﻟﺪﻓﻖ ﻣﻦ أﻋﻠﻰ إﻟﻰ أﺳﻔﻞ وذﻟﻚ ﻟﻤﻨﻌﻪ اﻟﺨﻠﻂ اﻟﻌﻜﺴﻲ ﻣﻤﺎ أدى إﻟﻰ زﻳﺎدة إﻧﺘﺎﺟﻴﺔ اﻷﻟﻴﻔﻴﻨﺎت اﻟﺨﻔﻴﻔѧﺔ 
) 
اﻹﻳﺜﻴﻠѧﻴﻦ واﻟﺒѧﺮوﺑﻴﻠﻴﻦ 
واﻟﺒﻴﻮﺗﻴﻠﻴﻦ 
( 
 
وﺗﻘﻠﻴﻞ إﻧﺘﺎج اﻟﻐﺎز اﻟﺨﻔﻴﻒ
. 
وﺑﺎﺳ
ﺘﺨﺪام زﻳﺖ اﻟﻐﺎز اﻟﻔﺮاﻏѧﻲ اﻟﻤѧﺸﺘﻖ ﻣѧﻦ ﺑﺘѧﺮول ﺧѧﺎم ﺑﺮاﻓﻴﻨѧﻲ وﺣﻔѧﺎز ﺧѧﺎص ﻧѧﻮع 
)USY
 (
 ذات
ﺛﺒﺎت ﻋﺎل وﺻﻠﺖ 
إﻧﺘﺎﺟﻴﺔ
 اﻟﺒﺮوﺑﻴﻠﻴﻦ إﻟﻰ 
)25
 (%
 وزن ، وإﻧﺘﺎﺟﻴﺔ اﻟﺠﺎزوﻟﻴﻦ إﻟﻰ
)30
 (%
 وزن ﻋﻨﺪ ﻇﺮوف ﺗﺸﻐﻴﻞ اﻟﻌﻤﻠﻴﺔ
   .
  
  
 
 *To whom correspondence should be addressed 
E-mail:  maitani@kfupm.edu.sa  
Phone 03 860-3007; Fax 03 860-4509 
Paper Received 9 May 2006; Revised 11 December 2006; Accepted 20 December 2006 
A. Maadhah, Y. Fujiyama, H. Redhwi,  M. Abul-Hamayel, A. Aitani, M. Saeed, and C. Dean 
 
The Arabian Journal for Science and Engineering, Volume 33, Number  1B 
 
         April 2008 
18 
ABSTRACT 
A novel high-severity FCC process that maximizes propylene production is under 
development by research teams in Saudi Arabia and Japan. The process has been 
proven in a 30-BPD demonstration plant at Saudi Aramco’s refinery, Saudi Arabia, 
and at a 500-BPD cold flow model at Nippon Oil Corporation, Japan. The HS–FCC 
process combines mechanical modifications to conventional FCC with changes in 
process variables and catalyst formulations. The process main operating features are 
a down-flow reactor system, high reaction temperature, short contact time, and high 
catalyst to oil ratio. Experimental runs were conducted in a downer- and riser-type 
pilot plants and a demonstration plant using various catalysts, additives and feed oils. 
The experimental results demonstrated the advantage of downer in suppressing back-
mixing, thus increasing the yield of light olefins (ethylene, propylene, and butylenes) 
and reducing dry gas. Using paraffinic crude base VGO feed and proprietary USY 
FCC catalyst, propylene yield of 25 wt% (feed basis) and gasoline yield of 30 wt% 
were obtained under HS-FCC reaction conditions. 
Key words: aromatics, cracking, downer, FCC catalysts, gasoline, octane, olefins, 
propylene, riser. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
A. Maadhah, Y. Fujiyama, H. Redhwi,  M. Abul-Hamayel, A. Aitani, M. Saeed, and C. Dean 
 
April 2008 
                The Arabian Journal for Science and Engineering, Volume 33, Number  1B 
19 
A NEW CATALYTIC CRACKING PROCESS TO MAXIMIZE REFINERY PROPYLENE 
 
1. INTRODUCTION 
Increasing the yield of the valuable light olefins, especially propylene and butylenes, remains a major challenge for 
many integrated refineries. As global petrochemical demand for propylene continues to grow, opportunities for 
improved production routes will emerge. Propylene is used as a feedstock for a wide range of polymers, product 
intermediates, and chemicals. Major propylene derivatives include polypropylene, acrylonitrile, propylene oxide, oxo-
alcohols, and cumene. These derivatives account for almost 90% of global propylene demand. In 2005, global demand 
for propylene reached about 67 million tons and its annual demand is expected to increase by 4.8% during 2005–2010 
[1,2]. The processing of propylene to polypropylene improves refinery margins and increases revenues because of the 
high value of polypropylene.  
Propylene has been considered a by-product of ethylene production via steam cracking of naphtha or other 
feedstocks. However, this route has not been able to keep up with propylene demand. To make up this shortfall, 
refineries capture propylene from fluid catalytic cracking (FCC) units and purify it to either chemical grade or polymer 
grade propylene. Currently, FCC supplies 30 wt% of the world’s propylene and the remainder is co-produced from 
steam cracking.  
FCC continues to be the dominant conversion process for gasoline and light olefins production with a global capacity 
of 14.2 million BPD. While refiners are under pressure to process heavier crudes, the FCC product slate is increasingly 
shifting towards light olefins production (mainly propylene). More stringent specifications for gasoline are needed in the 
future, for which the current FCC product slate is not optimal, because of high aromatics and olefins content.  
2. FCC OPTIONS TO MAXIMIZE REFINERY PROPYLENE 
The conventional FCC unit is typically operated at low to moderate severity with flexibility to swing between 
maximum distillate and maximum gasoline mode [2,3]. This unit yields 3–4 wt% propylene. Improvements in FCC 
catalysts, process design, hardware, and operation severity can boost propylene yield up to 25 wt% or higher. In FCC 
practice, there are several options to increase the selectivity to light olefins [2]. The major options are as follows: 
• 
Dedicated FCC catalysts 
• 
ZSM-5 additives 
• 
Higher severity operation (higher cracking temperature) 
• 
Naphtha recycle and  
• 
Dedicated new petrochemical FCC processes 
Conventional FCC catalyst compositions contain a catalytic cracking component and amorphous alumina which is 
necessary to provide the bottoms conversion. Catalytic cracking components are crystalline compounds such as 
faujasite-type Y zeolite. Amorphous alumina may also be used as a binder to provide a matrix with enough binding 
function to properly bind the crystalline cracking component when present. ZSM-5 based additive containing a small 
pore zeolite (5.5 to 7.5 Å) is commonly added to the cracking catalyst in FCC to enhance gasoline octane and LPG 
olefins production, especially propylene.  
One cost effective way to increase the propylene yield from the FCC unit is the use of specialized catalysts that 
contain ZSM-5 zeolite. An increasing number of refiners use as much as 10 wt% of ZSM-5 additives to obtain more than 
9 wt% propylene. Because of its unique pore structure, ZSM-5 limits access to only linear or slightly branched 
hydrocarbon molecules within the gasoline boiling point range. ZSM-5 based additive acts mainly by cracking C6+ 
gasoline olefins to smaller olefins such as propylene and butylenes [4]. These catalysts and additives increase the yield 
of propylene and other light olefins at the expense of gasoline and distillate products.  
The cracking of light hydrocarbons is another excellent option for an FCC-based refiner to produce and recover 
propylene. Naphtha is the most common feedstock used in FCC cracking units for the incremental production of 
propylene. Various process schemes for naphtha cracking in the FCC unit have been suggested and the simplest option 
consists of feeding and cracking naphtha together with gas oil feed. Naphtha may also be injected at the bottom of the 
fluidized riser reactor, before regenerated catalyst contacts gas oil feed, where it may be cracked at higher temperature 
and catalyst/oil (C/O) ratio [5]. 
A. Maadhah, Y. Fujiyama, H. Redhwi,  M. Abul-Hamayel, A. Aitani, M. Saeed, and C. Dean 
 
The Arabian Journal for Science and Engineering, Volume 33, Number  1B 
 
         April 2008 
20 
The need for a higher cracking temperature, a shorter contact time, and a higher C/O ratio lead to the conclusion that 
the mechanical restrictions of existing FCC units prevent the optimization of the conventional process for maximum 
olefin production [6]. Despite the various FCC technologies available to increase propylene yield, intense research 
activity in this field is still being conducted. Table 1 presents a summary of emerging FCC processes for enhancing 
refinery propylene [2].  
 
Table 1.   Emerging FCC-Based Propylene Technologies [2] 
Process Name 
Developer/Licensor 
Propylene 
Yield, wt% 
Remarks 
Deep Catalytic 
Cracking (DCC-I and 
DCC-II) 
RIPP-Sinopec/Stone Webster 
14.6-28.8 
Commercialized, several 
plants in and outside 
China. 
Catalytic Pyrolysis 
Process (CPP) 
RIPP-Sinopec/Stone Webster 
24.6 
VGO and heavy feeds, 
commercial trials in China. 
High-Severity FCC 
(HS–FCC) 
Nippon/KFUPM/JCCP/Saudi 
Aramco 
17-25 
Downer, high severity 
(temperature, C/O), 500 
BPD cold flow model. 
Indmax 
Indian Oil Co./ABB Lummus 
17-25 
Upgrades heavy cuts at 
high C/O 15-25. 
Maxofin 
ExxonMobil and KBR 
18 
Variations with Superflex 
to increase propylene 
NEXCC 
Fortum 
16.1 
High C/O, short contact 
time, multi-port cyclones. 
PetroFCC 
UOP 
22 
Additional reaction 
severity along with RxCat 
design. 
Selective Component 
Cracking (SCC) 
ABB Lummus  
24 
High Severity operation 
(temperature, C/O) 
High-Olefins FCC 
Petrobras 
20-25 
Downer, high temperature, 
C/O. 
Ultra Selective 
Cracking 
IFP/Stone Webster/Total 
NA 
200 BPD downer Demo 
unit  
3. FEATURES OF HS–FCC 
The main objective of the high-severity FCC process is to produce significantly more propylene and high octane 
number gasoline.  The conceptual process and preliminary feasibility study of the HS–FCC process started in the mid 
1990s [7]. For a 36 000 BPD conceptual unit and depending on the operating mode (gasoline mode operates at 550 ºC), 
the HS–FCC doubles the amount of light olefins. At high-olefins mode (600 ºC), the unit provides three times more light 
olefins accompanied with a minimum loss in gasoline as shown in Figure 1. The production of propylene is 2 to 4 times 
higher than in the conventional FCC process. The study showed the high feasibility of HS–FCC even though the olefin 
yield was not so high during the forepart of the development. 
The special features of this new process (Table 2) include rapid feed vaporization, down-flow reactor, high severity, 
short contact time, and high C/O ratio. Since the FCC process involves successive reactions, the desired products such as 
olefins and gasoline are considered intermediate products. A suppression of back-mixing by using the downer reactor is 
the key to achieving maximum yield of these intermediates. Compared to conventional FCC processes, the HS–FCC has 
modifications in the reactor/regenerator and stripper sections [8–12]. An efficient product separator suppresses side 
reactions (oligomerization and hydrogenation of light olefins) and coke formation accelerated by condensation. 
A. Maadhah, Y. Fujiyama, H. Redhwi,  M. Abul-Hamayel, A. Aitani, M. Saeed, and C. Dean 
 
April 2008 
                The Arabian Journal for Science and Engineering, Volume 33, Number  1B 
21 
Table 2. Main Features of HS–FCC Process 
Main Feature 
Remarks 
Down-flow Reactor 
• Minimizes back-mixing 
• Reduces undesirable by-products 
High Reaction Temperature 
• Operates at high temperatures (550 to 650°C) 
• Enhances thermal and catalytic cracking 
High C/O Ratio 
• Compensates reduced conversion  
• Enhances catalytic cracking 
Short Contact Time 
• Reduces thermal cracking 
• Reduces undesirable successive reactions 
0
20
40
60
80
100
Conventional
FCC
HSFCC Gasoline
Mode
HSFCC Light
Olefin Mode
Product  yields, (wt%)
Ethylene
Propylene
Butylenes
Gasoline
LCO+HCO
Others
 
Figure 1.  Comparison of typical product yields of conventional and HS–FCC processes 
 
3.1. Down-Flow Reactor  
A down-flow reactor system has been adopted for the HS–FCC process. The downer permits higher C/O ratios 
because the lifting of catalyst by vaporized feed is not required. As with most reactor designs involving competing 
reactions and secondary product degradation, there is a concern over catalyst-feed contacting, back-mixing, and control 
of the reaction time and temperature. The down-flow reactor would ensure plug flow without back-mixing.  
3.2. High-Reaction Temperature  
The HS–FCC unit is operated under considerably higher reaction temperatures (550 to 650°C) than conventional 
FCC units. Operating conventional FCC units at these temperatures degrades products, reduces yields, and makes excess 
light gases which often limit the production capability of a FCC unit. Under these reaction temperatures, two competing 
cracking reactions, thermal cracking and catalytic cracking, take place. Thermal cracking contributes to the formation of 
lighter products, mainly dry gas and coke, while catalytic cracking increases propylene yield. HS–FCC yields of 
undesirable dry gas, coke, and 1,3-butadiene are much lower confirming reduced thermal cracking. 
A. Maadhah, Y. Fujiyama, H. Redhwi,  M. Abul-Hamayel, A. Aitani, M. Saeed, and C. Dean 
 
The Arabian Journal for Science and Engineering, Volume 33, Number  1B 
 
         April 2008 
22 
3.3. Short Residence Time 
The short residence time (less than 0.5 sec) of feed and product hydrocarbons in the downer should be favorable for 
minimizing thermal cracking. Undesirable secondary reactions such as polymerization reactions and hydrogen-transfer 
reactions, which consume olefins, are suppressed. In order to attain the short residence time, the catalyst and the 
products have to be mixed and dispersed at the reactor inlet and separated immediately at the reactor outlet.  For this 
purpose, a high efficiency product separator has been developed capable of suppressing side reactions (oligomerization 
and hydrogenation of light olefins) and coke formation accelerated by condensation [11]. 
3.4. High C/O Ratio 
 In order to compensate for the decrease in conversion due to the short contact time, the HS–FCC process can be 
operated at high C/O ratios, in the range of 15 to 40.  As mentioned earlier, the other advantage of operation at high C/O 
is the enhanced contribution of catalytic cracking over thermal cracking. By increasing the C/O ratio, the effects of 
operating at high reaction temperature (thermal cracking) are minimized. High C/O maintains heat balance thereby 
achieving high reaction temperature. It also increases conversion and maximizes light olefins production. 
4. EXPERIMENTAL 
The HS–FCC process has successfully passed several phases of testing at pilot plant level (0.1 BPD), demonstration 
plant level (30 BPD), and cold flow models (30 and 500 BPD). The chronology of various testing stages and units used 
are presented in Table 3.  
 
Table 3. Stages of HS–FCC Process Development 
Parameters 
Riser Pilot 
Plant 
Downer Pilot 
Plant 
Demonstration 
Plant 
Cold Flow Model 
Capacity, BPD 
0.1 
0.1 
30 
30 
500 
Location 
Yokohama 
Dhahran 
Ras Tanura 
Yokohama 
Yokohama 
Engineering 
Contractor 
Xytel 
Xytel 
Chiyoda 
Chiyoda 
Chiyoda 
Completion Date 
1999 
2001 
2005 
2000 
2007 
Remarks 
Davison’s 
Circulating 
Riser (DCR) 
Modified 
DCR-catalyst 
with hopper 
Various feed oils, 
catalysts, and 
additives 
fluidization, feed injection 
and catalyst-product 
separation 
 
Table 4. Operating Conditions of Downer and Riser Pilot Plants 
Type of Reactor 
Parameter 
Downer 
Riser 
Reactor Outlet Temperature  
°C 
600 
600 
Pressure (Stripper Top) 
kPa 
98 
98 
Feed Rate 
kg/h 
0.4-1.2 
0.7-1.0 
Feed Preheat 
°C 
280 
280 
Catalyst Inventory 
L 
8 
2 
Steam Pretreatment for 6h 
°C 
810 
810 
Circulation rate 
kg/h 
13-40 
13-18 
Cat/Oil ratio 
kg/kg 
13-40 
13-30 
A. Maadhah, Y. Fujiyama, H. Redhwi,  M. Abul-Hamayel, A. Aitani, M. Saeed, and C. Dean 
 
April 2008 
                The Arabian Journal for Science and Engineering, Volume 33, Number  1B 
23 
4.1. Pilot Plant 
Experiments were conducted in a 0.1 BPD downer pilot plant and a similar conventional riser type pilot plant. Both 
pilot plants were operated at similar conditions, catalyst (low-activity USY) and hydrotreated VGO. Typical operating 
conditions of the two pilot plants are presented in Table 4. The base catalyst was a low cracking activity catalyst 
containing H-USY type zeolite with low acid site density. In some experiments, the catalyst was blended with 10 wt% 
commercial ZSM-5 additive (supplied by Grace, USA). The base catalyst and additive were deactivated with 100% 
steam at 810°C for 6h before evaluation in a fluid-bed steamer. Typical properties of catalyst and additive are presented 
in an earlier publication [13].  
The 0.1 BPD pilot plant consists of a downer reactor, stripper, regenerator, and catalyst hopper. Feed oil is charged 
into the upper part of the downer reactor together with dispersion steam. Regenerated catalyst is also charged to the top 
of the reactor from the catalyst hopper. At the outlet of the downer, product hydrocarbons are separated immediately 
from catalyst in the stripper, where heavy oil adsorbed on the spent catalyst is stripped by steam, and then spent catalyst 
is sent to the regenerator. Catalyst circulation rate was calculated from the delta coke and coke yield.  
4.2. Demonstration Plant 
The 30 BPD demonstration plant was constructed by Chiyoda at a site near Saudi Aramco’s refinery in Ras Tanura 
(Figure 2). The plant was operated for a period of 18 months including several planned shutdowns for inspection and 
modification. The main purpose of operation was to confirm the operability of feed nozzle and injection systems.  
Stripper
Combustion air
as lift medium
Feed
Product
Hopper
Steam
Regenerator
Lift Line
Lift Line
Downer
Air
Flue Gas
  
Figure 2. Schematic diagram of 30 BPD HS-FCC demonstration plant 
The first stage product-catalyst separator of the Demo plant was also a new design different from conventional 
cyclone separator. The demonstration plant consists of the following sections:  
• Feed oil and catalyst mixer 
• Reaction section (downer reactor) 
• Product and catalyst separator 
• Stripper 
• Regenerator with a riser-type lift line, and  
• Catalyst hopper. 
A. Maadhah, Y. Fujiyama, H. Redhwi,  M. Abul-Hamayel, A. Aitani, M. Saeed, and C. Dean 
 
The Arabian Journal for Science and Engineering, Volume 33, Number  1B 
 
         April 2008 
24 
4.3. Feedstock Oil and Product Analysis 
Vacuum gas oil and atmospheric residue, both virgin and hydrotreated, were evaluated. Table 5 presents typical 
properties of the various feed oils. The gaseous product containing C1 to C4, and some of the C5 hydrocarbons, hydrogen 
and nitrogen was analyzed by TCD and FID gas chromatography. The liquid product was analyzed by simulated 
distillation chromatography to determine the percentage of gasoline, LCO and HCO fractions. Coke deposited on spent 
catalysts was determined by burning the coke in the presence of oxygen and analyzing the combustion gas by infrared 
analyzer. 
Table 5. Typical Properties of Various Feed Oils 
Arabian Light Crude* 
Low-Sulfur Crude East Asia 
Property 
VGO* 
AR  
VGO 
AR 
Density @15°C, g/cc 
0.897 
0.914 
0.862 
0.877 
Viscosity @100°C, mm²/sec 
8.31 
15.91 
5.22 
9.56 
Conradson Carbon, wt% 
0.15 
2.74 
0.18 
4.73 
C wt% 
85.9 
86.5 
85.9 
86.2 
H wt% 
14.0 
13.4 
14.0 
13.6 
Composition  
H/C ratio 
1.94 
1.85 
1.94 
1.88 
Basic Nitrogen, ppm 
18 
160 
170 
400 
                    * Hydrotreated vacuum gas oil (VGO) and atmospheric residue (AR) 
5. RESULTS 
5.1. Pilot Plant Results  
The results of downer pilot and riser pilot plant are compared in Figure 3. Dry gas formation was suppressed due to 
the elimination of back-mixing in the downer and the total yield of useful products is higher at all conversion levels as 
compared to riser.  
 
3
5
7
9
70
75
80
85
90
Conversion
Dry Gas,  (wt%)
Downer
Riser
60
65
70
75
70
75
80
85
90
Conversion (wt%)
Light Olefins+Gasoline,  (wt%)
Riser
Downer
Figure 3.  Comparison of product yields from riser and downer pilot plants 
The advantage of downer operation over riser is clearly shown in Figure 4. The downer, operated at HS–FCC 
conditions, offers more gasoline at the same yield of light olefins compared to the riser. At the same gasoline yield, the 
downer offers higher light olefins yield. Several studies were conducted with different feed oils, catalysts, and olefin 
boosting additive combinations.   
A. Maadhah, Y. Fujiyama, H. Redhwi,  M. Abul-Hamayel, A. Aitani, M. Saeed, and C. Dean 
 
April 2008 
                The Arabian Journal for Science and Engineering, Volume 33, Number  1B 
25 
0
10
20
30
40
50
60
0
10
20
30
40
Light Olefins (wt%)
Gasoline  (wt%)
Downer
Riser
More light olefins at 
same gasoline
More gasoline at 
same light olefins
 
Figure 4.  Advantage of downer versus riser for light olefins 
The quality of feed also has an impact on the cracking activity and yield structure. Aromatic base feeds are difficult 
to crack and produce more coke, whereas paraffinic base feeds are easily cracked and can produce more light olefins. A 
set of experiments were conducted using different feed oils of various origins. VGO and AR obtained from paraffinic 
low-sulfur crude of East Asia showed high reactivity. The results are presented in Table 6 [14].  
Table 6. Effect of Feed Oil Type on Product Yields 
Arabian Light Crude 
Low Sulfur Crude East 
Asia 
 
Yields 
VGO 
AR 
VGO 
AR 
C2 
4.3 
3.8 
5.4 
5.1 
C3 
20.7 
19.2 
25.0 
23.8 
C4 
17.4 
16.6 
19.5 
18.5 
Light olefins 
(wt% feed basis) 
Total  
42.4 
39.5 
49.9 
47.4 
Dry gas 
6.5 
6.2 
7.8 
7.9 
LPG 
42.4 
39.7 
49.8 
46.7 
Gasoline 
33.6 
35.1 
29.0 
29.2 
LCO+ 
14.5 
14.6 
11.0 
11.9 
Others  
(wt% feed basis) 
Coke 
2.9 
4.5 
2.3 
4.3 
Hydrotreated feed oils gave high propylene and gasoline yield even when hydrotreated AR was used. At low C/O 
ratios, the performance of Arabian Light VGO was better, whereas at higher C/O ratios, the conversion of paraffinic 
VGO was higher. In terms of light olefins yield, paraffinic VGO gave the best performance. At a C/O ratio of 40, 
paraffinic VGO yielded 50 wt% light olefins (25 wt% propylene). The gasoline yield was found higher for hydrotreated 
VGO compared to paraffinic VGO. A similar trend was also observed for the two atmospheric residue feed oils. 
Figure 5 shows the features of the developed proprietary catalyst for HS–FCC. The proprietary catalyst with an 
additive gave higher propylene yield compared to a conventional catalyst mixed with 10% of the same additive. Table 7 
compares the yields of light olefins, LPG, gasoline, LCO, HCO, and coke make for a base catalyst and with and without 
10 wt% ZSM-5 additive. The base catalyst yielded about 29 wt% olefins and 45 wt% gasoline compared to 39 wt% light 
olefins and 34 wt% gasoline in catalyst blended with additive.  
A. Maadhah, Y. Fujiyama, H. Redhwi,  M. Abul-Hamayel, A. Aitani, M. Saeed, and C. Dean 
 
The Arabian Journal for Science and Engineering, Volume 33, Number  1B 
 
         April 2008 
26 
 
 
 
 
 
 
 
 
 
 
 
Figure 5.  Advantage of proprietary catalyst 
 
Table 7. Comparison of ZSM-5 addition in a 0.1 BPD Downer Pilot Plant 
Product Yields wt% 
Base Catalyst 
Base +10wt% ZSM-5 
Dry Gas 
4.6 
5.5 
Propylene 
10.7 
18.4 
Butylenes 
16.1 
17.8 
Total C3-C4 Olefins 
28.7 
39.3 
LPG 
30.9 
40.5 
Gasoline 
45.4 
34.0 
Light Cycle Oil (LCO) 
9.4 
9.3 
Heavy Cycle Oil (HCO) 
6.6 
7.1 
Coke 
3.1 
3.5 
The rise in propylene was accompanied with a drop in gasoline yield since the additive is effective in catalytic 
systems where the catalyst have low-hydrogen transfer activity. While fresh ZSM-5 is sufficiently active to crack 
gasoline paraffins, ZSM-5 in the FCC, which has been subjected to severe hydrothermal conditions, is not active enough 
to crack gasoline paraffins [13, 15,16]. 
5.2. Demonstration Plant versus Pilot Plant  
The operation of the demonstration plant was very successful and the obtained results were very close to those of the 
0.1 BPD pilot plant. The two plants were operated using conventional FCC catalyst and untreated VGO to compare the 
difference of the scale. The yields of light olefins, gasoline, LCO, HCO, and coke in the pilot and demonstration plants 
are compared in Table 8. At a C/O ratio of 30, the conversion in both plants was high about 80 wt %. Gasoline yields 
were similar in both plants; and a small decrease in the yield of light olefins occurred in the demonstration plant. Coke 
make was also higher in the demonstration plant. The results in Table 8 confirm that the pilot plant and demonstration 
plant performed similarly. It also confirmed that scaling up the process was successful. 
The experimental results of demo plant operation using a catalyst (a blend of conventional catalyst, developed 
proprietary catalyst and ZSM-5 based additive) and feed oil (obtained from the bottom of the first stage of hydrocracker) 
are shown in Table 9. The propylene yield was 20 wt% accompanied with a drop in gasoline yield. This drop was likely 
due to ZSM-5 accelerating the catalytic cracking of the gasoline lighter components to produce low molecular weight 
olefins.  
0
5
10
15
20
25
75
80
85
90
C onver
si
on (
221℃)
,
 
 
m ass%
Pr
opyl
ene 
yi
el
d, m ass%
C onvent
i
onal
C at
al
yst
Pr
opr
i
et
ar
y
C at
al
yst
Additive
10%
0
5
10
15
20
25
75
80
85
90
C onver
si
on (
221℃)
,
 
 
m ass%
Pr
opyl
ene 
yi
el
d, m ass%
C onvent
i
onal
C at
al
yst
Pr
opr
i
et
ar
y
C at
al
yst
Additive
10%
A. Maadhah, Y. Fujiyama, H. Redhwi,  M. Abul-Hamayel, A. Aitani, M. Saeed, and C. Dean 
 
April 2008 
                The Arabian Journal for Science and Engineering, Volume 33, Number  1B 
27 
Table 8. Effect of ZSM-5 Addition on HS–FCC Product Yields at 600°C Using Untreated VGO 
Conventional FCC 
HS–FCC Pilot Plant 
HS–FCC Demo Plant 
Product 
Yields wt% 
Base 
Catalyst 
Base 
+10wt% 
ZSM-5 
Base   
Catalyst 
Base 
+10wt% 
ZSM-5t 
Base 
Catalyst 
Base + 
8wt% ZSM-
5* 
Dry Gas 
5.3 
5.8 
4.6 
5.5 
5.4 
10.4 
Propylene 
7.5 
13.0 
10.7 
18.4 
10.6 
20.4 
Butylenes 
8.8 
13.6 
16.1 
17.8 
13.4 
19.0 
Total C3-C4 
Olefins 
16.3 
26.6 
28.7 
39.3 
25.7 
43.9 
Gasoline 
43.4 
34.1 
45.4 
34.0 
36.0 
35.7 
Light Cycle 
Oil 
15.0 
15.4 
9.4 
9.3 
10.5 
1.1 
Heavy Cycle 
Oil 
14.3 
13.4 
6.6 
7.1 
7.7 
4.4 
Coke 
2.3 
2.1 
3.1 
3.5 
9.1 
2.3 
      *Feed oil used was a hydrotreated VGO 
The analysis of gasoline showed an octane number of 99 RON and 71% aromatics, 14% olefins, 5% n-paraffins, and 
4% naphthenes. The gain of gasoline octane results from the combined effects of reducing low octane compounds 
together with the relative increase in concentration of desirable high-octane hydrocarbons (i.e., aromatics and 
naphthenes). Isomerization of light C5 – C6 linear olefins is also claimed to play a role in overall octane enhancement.  
Table 9. Performance of HS–FCC with Hydrotreated Feed in Demo Plant 
Feed Oil 
1st Stage Hydrocracker Bottom 
Catalyst System 
Proprietary 
Conventional 
Additive 
32% 
60% 
8% 
H2-C2 
10.4 
LPG 
46.0 
(Propylene) 
(20.4) 
Gasoline 
35.7 
LCO + 
5.5 
Product Yields, wt% 
Coke 
2.3 
5.3. Cold Flow Model 
A 500 BPD cold-flow circulating fluidized-bed apparatus made out of plexiglass is currently being operated to study 
the hydrodynamics of catalyst dispersion, product separation and catalyst pattern in downer reactor. A relatively new 
electrical capacitance tomography (ECT) technology is used to study the gas-solid mixing patterns in the feed injector 
and reactor [17]. The parts were designed using fluid dynamics simulation models. The results of the cold-flow model 
will be used to confirm the accuracy of the simulation model and in scale–up to commercial capacity. The total catalyst 
inventory of the model is about 21 tons at a catalyst circulation rate of 1.2 tons/min.  Refinery equilibrium FCC catalyst 
and air are used to simulate the vaporized feed and cracked oil. The total height of the model reaches 35 meters 
comprising catalyst feed hopper, collection hopper, and cyclones. 
A. Maadhah, Y. Fujiyama, H. Redhwi,  M. Abul-Hamayel, A. Aitani, M. Saeed, and C. Dean 
 
The Arabian Journal for Science and Engineering, Volume 33, Number  1B 
 
         April 2008 
28 
6. CONCLUDING REMARKS 
Stand-alone fuel refinery is not any more profitable and FCC is no more a fuel producer only. With proper design 
and operation, the HS–FCC process is in best position to produce light olefins for petrochemicals usage along with fuel 
products. Catalytic cracking under high severity in a downer-type reactor boosts overall conversion and enhances the 
production of gasoline and light olefins. Based on the intrinsic features of HS–FCC, maximum propylene yield and 
aromatics can be obtained by the combination of an optimized catalyst system and operating conditions. The recovery of 
propylene and paraxylene enhances the economics of an upgraded refinery with HS–FCC option. 
ACKNOWLEDGMENTS 
The authors acknowledge the support of NOC, Saudi Aramco, KFUPM, and Japan Cooperation Center, Petroleum 
(JCCP) in publishing this paper. 
REFERENCES 
[1]  
M. Walther, “Refinery Sources Will Fill the Future Propylene Gap”, Oil & Gas Journal, January 27, 2003, pp. 52–54. 
[2]  
A.  Aitani,  “Propylene Production”,  in :Encyclopedia of Chemical Processing. New York : Taylor & Francis, 2006, pp. 
2461–2466. 
[3] 
T. Chan and K. Sundaram, “SCC: Advanced FCCU Technology for Maximum Propylene Production”, Presented at the 
AIChE 1999 Spring Meeting 2nd International Conference in Refinery Processing, Houston, March 14-18, 1999. 
[4] 
J. Fu and M. Xu, “Using ZSM-5 Additive with DMS Based FCC Catalyst for Increased Propylene Production”, 
Presented at the 7th International Symposium on Advances in FCC, ACS 232nd Meeting, San Francisco, September 
2006.  
[5] 
A. Corma, F. Melo, L. Sauvanaud, and F. Ortega, “Different Process Schemes for Converting Light Straight Run and 
FCC Naphthas in a FCC Unit for Maximum Propylene Production”, Applied Catalysis A: General, 265 (2004), pp.195–
206. 
[6] 
J. Jakkula, “A Novel Catalytic Cracking Technology for Olefins Production”, Presented at the European Refining 
Technology Conference, London, November 1997.  
[7]  
Y. Fujiyama, H. Redhwi, A. Aitani, R. Saeed, and C. Dean, “Demonstration Plant for New FCC Technology Yields 
Increased Propylene”, Oil & Gas Journal, September 26, 2005, pp. 62–67. 
[8]  
Y. Fujiyama, “Process for Fluid Catalytic Cracking of Oils”, US Patent 5,904,837, May 18, 1999. 
[9]  
T. Ino and S. Ikeda, “Process for Fluid Catalytic Cracking of Heavy Fraction Oil”, US Patent 5,951,850, September 14, 
1999. 
[10]  Y. Fujiyama, et al., “Process for Fluid Catalytic Cracking of Heavy Fraction Oils”, US Patent 6,045,690, April 4, 2000. 
[11]  S. Nishida and Y. Fujiyama, “Separation Device”, US Patent 6,146,597, November 14, 2000. 
[12]  T. Ino, T. Okuhahra, M. Abul-Hamayel, A. Aitani, and A. Maghrabi, “Fluid Catalytic Cracking Process for Heavy Oil”, 
US Patent No. 6,656,346, December 2, 2003. 
[13] 
A. Aitani, T. Yoshikawa, and T. Ino, “Maximization of FCC Light Olefins by High Severity Operation and ZSM-5 
Addition”, Catalysis Today, 60 (2000), pp. 111–117. 
[14] 
M. Saeed, M. Siddiqui, A. Aitani, M. Abul-Hamayel, T. Okuhara, and T. Ino, “Impact of Downer Reactor and High 
Severity Conditions on the Product Slate of FCC Process”, Proceedings of the Middle East Petrotech 2003, Manama, 
Bahrain, September 29-October 01, 2002. 
[15] 
J. Buchanan, “Gasoline Selective ZSM-5 FCC Additives”, Applied Catalysis A: General 171 (1998), pp. 57–64. 
[16]  T. Okuhara, T. Ino, M. Abul-Hamayel, A. Maghrabi, and A. Aitani, “Effect of ZSM-5 Addition on Product Distribution 
in a High Severity FCC Mode”, Petroleum Science and Technology, 19 (2001), pp. 685–695. 
[17] 
F. Kuhn, J. Schouten, R. Mudde, C. van den Bleek, and B. Scarlett, “Analysis of Chaos in Fluidization Using Electrical 
Capacitance Tomograpgy”, Measurement Science and Technology, 7 (1996), pp. 361–368.

Journal of Advanced Research in Fluid Mechanics and Thermal Sciences 73, Issue 1 (2020) 109-130 
109 
 
 
Journal of Advanced Research in Fluid 
Mechanics and Thermal Sciences 
 
Journal homepage: www.akademiabaru.com/arfmts.html 
ISSN: 2289-7879 
 
Simulation of Liquefied Petroleum Gas Recovery from Off 
Gases in a Fuel Oil Refinery 
 
 
Rizwan Ahmed Qamar1, Asim Mushtaq2,*, Ahmed Ullah1, Zaeem Uddin Ali1 
 
1 
Chemical Engineering Department, NED University of Engineering & Technology, Karachi, Sindh, Pakistan 
2 
Polymer and Petrochemical Engineering Department, NED University of Engineering & Technology, Karachi, Sindh, Pakistan 
 
 
ARTICLE INFO 
ABSTRACT 
Article history: 
Received 9 April 2020 
Received in revised form 30 April 2020 
Accepted 1 May 2020 
Available online 5 July 2020 
With the growing need and demand for petroleum fuels in the industrial, automotive 
and domestic sectors, it has become the need of time to address this problem. The 
important question is, what engineers can do in this regard. There is a simple solution 
to cater this problem, the production trends worldwide are increasing every year, but 
still, the demand keeps increasing exponentially. One way is to stress emphasis on 
reducing the wastage of these precious fuels and, at the same time, implement 
methodologies and techniques to recover these. The research caters to cope up with 
the shortage of LPG within Pakistan. Since LPG is a fuel that is being used in the 
industrial, automotive as well as domestic sector, adequate responsive measures 
should be taken to meet the market requirements. This research focuses on the 
technique to recover LPG from refinery off-gases. Since off-gases usually are flared of 
by most of the refineries leading to potential loss of lighter end fuels like LPG. The 
article encloses a detailed proposal of a recovery plant that makes use of simple 
techniques and efficient process accompanied by economical and feasible solutions.  
Keywords: 
 
Liquefied Petroleum Gas; refinery; 
recovery; Aspen Hysys; cryogenic; 
absorption; flare  
Copyright © 2020 PENERBIT AKADEMIA BARU - All rights reserved 
 
1. Introduction 
 
The liquefied petroleum gas abbreviated as LPG is an efficient nontoxic, odorless, environment-
friendly hydrocarbon fuel which comprises propane and butane primarily with some fractions of 
ethane and pentane [1,2]. As the fuel represents the family of lighter hydrocarbons from C1-C5, it is 
also termed as gas liquids. The concentration of propane and butane in LPG varies by the season. 
That is, the fuel sold includes more propane in winter while butane is a prime component in summer. 
Powerful odorant ethanol is added in an odorless fuel for leakage detection. The international 
standard of LPG is EN 589. The LPG was developed at the beginning of the 20th century, which is 
indeed very late in the oil and gas business [1,2]. 
                                                          
 
* Corresponding author. 
E-mail address: engrasimmushtaq@yahoo.com (Asim Mushtaq) 
 
https://doi.org/10.37934/arfmts.73.1.109130 
Open 
Access 
Journal of Advanced Research in Fluid Mechanics and Thermal Sciences 
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110 
 
It was developed when the scientist was working on the problem of quick gasoline evaporation. 
In 1911, an American scientist found that there are two gases propane and butane in gasoline, which 
causes its quick evaporation so they should be removed. He found methods to remove these gases 
from gasoline and these removed gases are LPG. LPG was initially presented in Pakistan in 1966. At 
first, the Pakistan LPG market was a completely directed one, where supply was confined to just 
indigenously delivered LPG and where the Government of Pakistan controlled evaluating. A change 
happened in 1994-1995 when imports were permitted, prompting total industry deregulation by 
April 2001. Even though LPG's offer in the nation's energy prerequisites has been nominal to date, 
over the previous decade, development by volume has been at a pace of around 14% per annum. 
This is required to keep developing fundamentally with expanding LPG supplies accessible from both 
imported and local sources [3,4].  
LPG’s boiling point varies as it depends upon its mixture composition amount of propane and 
butane. It ranges from -42 °C to 0 °C. LPG is fuel and like normal fuel, it produces carbon dioxide and 
water when it is combusted with a sufficient amount of air. Less amount of air than required results 
in incomplete combustion which gives toxic gas that is carbon mono oxide. LPG is kept as a fluid under 
pressure. It is colorless and its weight is around a half portion of that of an equivalent volume of 
water. The pressure of LPG inside a closed container is equivalent to the vapor pressure of the fluid 
and gaseous LPG and relates to its temperature. Butane is twofold heavy as air and propane are one 
and half times heavy as air. Subsequently, the vapor may stream along the ground and into channels, 
reducing to the least degree of the environment and be exploded at a considerable distance from the 
source of spillage. The temperature required to ignite LPG in the air will be around 500°C. The calorific 
estimation of LPG is about 2.5 times higher than that of the primary gas, so more heat is delivered 
from a similar volume of gas. LPG is an odorless, colorless and non-toxic gas. It is provided industrially 
with an additional odorant to help location by smell LPG is a magnificent dissolvable of oil and elastic 
item and is for the most part non-acidic to steel and copper composites. The flashpoint of LPG is - 
104°C. LPG is propane (C3H8), butane (C4H10), or a blend of propane/butane [5]. 
Cooking is the most universal energy-consuming activities. LPG is a versatile fuel and provides 
many benefits associated with cooking. This fuel provides an easily controllable flame, which is blue 
so that the right level of flame can be maintained. As this is the cleaner fuel and doesn’t produce soot 
or smell, which increases cooker or burner life plus doesn’t damage or burn cooking appliances. It 
provides moist heat, which helps cook more delicious food. LPG is considered to be a blessing in rural 
areas as before LPG people have to go and collect wood and charcoal and when it burns, it emits 
hazardous gases which not only discomfort women doing the cooking but also disturb the people 
around it. LPG is available in cylinders and has not only made cooking easy and enjoyable but also 
made the life of people comfortable and healthy [5,6]. As one of the cleanest conventional fuels 
accessible, LPG supplements sustainable power sources and advances which rely upon certain 
climate conditions or light. LPG additionally empowers exceptionally proficient decentralized 
generation through little self-containing generators and micro-combined power and heat. For these 
sorts of confined force generation, LPG's carbon impression is under that of diesel and essentially 
lower than gasoline. LPG can be utilized in numerous applications in the modern part, to be specific 
in space and procedure heating, controlling mechanical stoves, creation of nourishment, ovens, 
heaters, manufacturing of packing material just as in fueling forklift trucks in distribution centers 
[5,6]. 
The benefits of LPG are clean-burning, no sediment, burners have a more extended life, so 
maintenance is low, no spillage as it disintegrates at air temperature and pressure. In a flash, 
controllable fire temperature abstains from scaling and decarburizing of parts, naturally benevolent 
fuel, with negligible sulfur substance and sulfur-free emissions. Exceptionally high proficiency with 
Journal of Advanced Research in Fluid Mechanics and Thermal Sciences 
Volume 73, Issue 1 (2020) 109-130 
111 
 
direct firing framework Instant heat for quicker warm-up and chill off can be utilized for an 
assortment of utilizations. Force, payload, acceleration and voyage speed are similar to those of a 
proportionate vehicle fueled on gas. Propane has a high octane rating of 104, in the middle of 
Compressed Natural Gas (CNG) (130) and ordinary unleaded fuel (87). The high octane rating 
empowers the propane to blend better with air and to consume more completely than gasoline, 
producing less carbon. With less carbon development, flash fittings regularly last more and oil 
changes are required less frequently. Since it consumes in the motor in the gaseous stage, propane 
brings about less corrosion and motor wear than does gasoline [7].  
LPG has two causes: 60% is recuperated during the extraction of flammable gas and oil from the 
earth, and the staying 40% is delivered during the refining of unrefined petroleum. Unrefined 
petroleum is a liquid found usually in rock, comprising mostly complex hydrocarbons, with some 
extra natural material. It is the major fuel utilized on the earth and is utilized in the creation of 
numerous manufactured materials like plastics. It is profoundly flammable dark, the thick liquid that 
comprises of hydrocarbon molecules. It has been found in normal underground reservoirs. It 
produces oil-based commodities and records for about 40% of the world's essential vitality 
utilization. At the point when handled, it yields gaseous petrol and chemical residue, which are 
additionally energized. It is classified on basis of viscosity and the degree of polluting influences (like 
sulfur) present in it. The basic arrangement wt.% ranges for practically all unrefined oils carbon 83.9-
86.8%, hydrogen 11.0-14.0%, sulfur 0.06-8.00%, oxygen 0.08-1.82%, nitrogen 0.02-1.70% and metals 
0.00-0.14% [7,8].  
The process of refining oil includes numerous stages. LPG is created from oil at a few of these 
stages, including atmospheric refining, cracking, reforming and others. It is created because the gases 
of which it is created (butane and propane) are caught inside the unrefined petroleum. To balance 
out the raw petroleum before pipeline or tanker circulation, these 'related' or characteristic gases 
are additionally handled into LPG. In unrefined petroleum refining, the gases that makeup LPG are 
the main items delivered while in transit to making heavier fuels, for example, diesel, stream fuel, 
fuel oil and gas. Generally, three percent of a typical barrel of unrefined petroleum is refined into 
LPG, while forty percent of a barrel could be changed into LPG.  
At the point when gas is drawn from the earth, it is a blend of a few gases and fluids. Methane is 
sold by gas utilities like petroleum gas, establishes around 90 % of this blend. Of the staying 10 % is 
propane and 5 % is different gases, for example, ethane and butane. Before natural gas can be 
distributed or utilized, the gases (which are marginally heavier than methane, the significant 
constituent of natural gas) are separated. Contingent upon the wetness of a creating gas field, gas 
fluids usually contain 1% to 3% of the unprocessed gas stream [9]. 
 
1.1 Various Processes for Production of LPG 
 
In a common vapor separation framework, the feed gas move in the membrane and the LPG 
segments especially permeate, making an LPG advanced permeate stream. The permeate is 
compressed and LPG is recuperated as a fluid in the condenser. The buildup stream from the 
membrane exhausted in LPG and enhanced in hydrogen and lighter hydrocarbon gases (ethane, 
methane), remains at pressure and can be sent straightforwardly to the fuel headers or for additional 
hydrogen purification. Now and again, the hydrogen purity is sufficiently high to permit direct recycle 
to other refinery methods is appeared in Figure 1 [10]. Vapour separation technology includes gas 
reception, gas sweetening, drying and filtration, followed by LPG recovery using a combination of 
turbo-expander technology and hydrocarbon washing to achieve high recovery of LPG. The plant also 
includes fractionation of LPG into its pure constituents, propane and butane. The distillation process 
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is driven by a hot oil system and air cooling. The sales gas is then compressed in gas turbine driven 
centrifugal compressors. 
 
 
Fig. 1. Vapour separation technology 
 
Figure 2 illustrates PRO-MAX technology. The rich low-pressure gas is chilled and partially 
condensed by cross heat exchange along with the refrigerant. The remaining vapor then enters an 
absorber. The re-circulating lean oil employed is a stream with high ethane content and low propane 
content. This is the ideal reflux composition to achieve high overall propane recovery. The de-
ethanizer is a conventional distillation system employing propane refrigerant for the overhead 
condenser and some type of heat input (typically steam or hot oil) for tower re-boil. A slipstream of 
the tower reflux stream is withdrawn, subcooled by cross-exchange and recycled to the absorber. 
The cross-exchange warms the cold overhead vapors from the absorber and de-ethanizer. This 
combined residue gas stream (comprising the ethane and lighter components) can then be routed to 
the plant fuel system. Removal of the LPG components from the plant fuel gas system may also 
improve the operation of fired equipment throughout the facility [3,10]. 
 
 
Fig. 2. Pro-Max LPG technology 
 
Depending on the feed pressure, some limited amount of feed compression may still be required 
with PRO-MAX. Depending on the required residue gas delivery pressure, some limited amount of 
residue compression may also be required. It is most often advantageous to employ a turbo-
expander to help balance overall plant chilling requirements. Propane recovery levels approaching 
100% are typical with PRO-MAX. The only practical limitation of the technology is the composition of 
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the de-ethanizer overhead being constrained by the use of propane (or similar) refrigerant. For leaner 
gas and with increasing feed pressures, the relative amounts of light components (specifical 
methane) that are condensed and fed to the de-ethanizer also increase. To maintain a low propane 
level in the de-ethanizer overhead then requires lower and lower deethanizer condenser 
temperatures. For propane refrigeration, -40 deg F represents the lowest practical limit to avoid 
vacuum operation. The deethanizer operating pressure can be raised to help mitigate this overhead 
temperature, but a practical pressure limit is about 500 psi due to critical pressure considerations 
[10,11]. 
One way to recover LPG from off-gases is to use the process of oil absorption since LPG is readily 
absorbed in kerosene under low temperature conditions. The off-gases from the platformer unit of 
a refinery are mainly composed of LPG and lighters, which account for C1-C5 components of the 
crude. Out of the off-gases, LPG absorbs in kerosene undercooled and moderate pressure conditions, 
whereas the lights that are composed of C1 and C2 of the crude components remain unabsorbed. 
The off-gases from the platformer unit of the refinery are introduced into the gas absorption 
column, the liquid and gas flow in a counter-current direction to each other. Lean kerosene, which is 
cooled to around 40°C, is introduced from the top of the absorption column while off-gases from the 
base of the column. The mass transfer takes place and the heavier components of the off-gases that 
are LPG are absorbed in lean kerosene, whereas the lighters leave the column from its top. Kerosene, 
now rich with LPG, is taken out from the bottom and introduced into a stripper where LPG and 
kerosene are separated. LPG is taken from the top of the stripper and kerosene from its bottom 
[12,13]. 
LPG is a cleaner fuel and is playing its role in the development of the agriculture sector all over 
the world. It is widely in use for many agricultural processes like thermal desiccation, fueling of farm 
vehicles, crop-drying, and insect repellent. LPG is the fuel that is extensively used by the automotive 
sector. On comparison of LPG with other fossil fuels, it has been found out that it has lesser toxic gas 
emissions, which result in a cleaner environment and protection of human health. Moreover, it 
mitigates the threat of climate change, which can cause a severe problem in the future. The other 
positive point of this fuel is its high octane number, which protects the vehicle and increases its 
performance. The most feasible recovery process is through absorption in kerosene due to the 
following advantages it has over other techniques. This process utilizes all the available raw materials 
within a refinery that are kerosene obtained from the crude oil is used for absorption purposes, so 
the cost for purchasing oil for absorption is cut down. The process is relatively simple as it involves 
the addition of only an absorption column and a stripper for the recovery process to the current 
refinery unit. The process is easily understandable and simple in operation as compared to other 
techniques that may involve designing of turboexpanders, catalytic reactors or de-methanizer, de-
propanizer, debutanizer. The raw material kerosene can be recovered back by distillation 90% of the 
LPG can be recovered from off-gases through this process [14,15]. 
The objective of this research includes the simulation that can be used as a tool to acquire 
comprehensive information required for the design of a real plant, for experiment, control and 
optimization purposes. The benefits of the simulation include precise design information to ensure 
the process feasibility along with the process flow diagrams and multiple design cases or options that 
save valuable cost. The best retrofit option for the chemical plant, optimization to get the process 
highest performance point and sensitivity analysis, evaluating the process key control variables and 
degree of operating constancy. The simulation of the recovery of LPG recovery from off-gases by 
using cryogenic and absorption techniques. 
 
 
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2. Methodology  
2.1 Simulation Approach 
 
In this research, simulation of the LPG recovery system is carried out using Aspen Hysys version 
8.8. The major components in a refinery are typically hydrocarbons for which the appropriate fluid 
package is Peng-Robinson. The main aspect of process simulation is the choice of an appropriate 
physical property method that will exactly analyze different physical properties. The main purpose of 
this simulation was to recover the maximum amount of LPG from the off-gases coming out from 
different units of the refinery is shown in Figure 3. Recovery of LPG has a trade-off with the purity of 
LPG, so extracting an optimum figure among the two parameters is important to make the system 
best efficient and economical. All the parameters of off-gases, solvent, and different units are based 
on real data, which was taken from the refinery [13,16]. 
 
 
Fig. 3. Off gases data 
 
The refinery off-gases come from different units, including alkylator, plate former, distillation 
column and hydrocracker. These off-gases are mostly at high temperatures, so their sensible heat is 
removed and then they combine into a header. The off-gases composition mentioned in Figure 4 is 
the composition of the gases present in this header. 
 
 
Fig. 4. Off gases composition 
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2.2 Process Flow Diagram of Cryogenic Technique 
 
Different equipment like LNG heat exchanger, separator, distillation column, compressors and 
expander are used in this technique. Figure 5 shows the recovery of LPG through the cryogenic 
technique. 
 
 
Fig. 5. Recovery of LPG through Cryogenic technique 
 
Figure 6 shows the LNG heat exchanger datasheet. 
 
 
Fig. 6. LNG heat exchanger 
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Figure 7 shows the expander compressor K-100 datasheet. 
 
 
Fig. 7. Expander K-100 
 
Figure 8 and Figure 9 show the expanders of compressor K-101 and K-102 datasheets. 
 
 
Fig. 8. Compressor K-101 
 
 
Fig. 9. Compressor K-102 
 
Figure 10 shows the distillation column parameter. 
 
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Fig. 10. Distillation column parameter 
 
2.3 Process Flow Diagram of Absorption Technique 
 
Figure 11 shows the recovery of LPG through the absorption technique. 
 
 
Fig. 11. Process flow diagram for the absorption of 
LPG in oil 
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Figure 12 shows the conditions of the kerosene (solvent), which is entered at the top of the 
absorption column. The major factor is the optimum flow rate of the solvent, which can recover most 
of the LPG from the off-gases stream [16,17]. 
 
 
Fig. 12. Lean oil (solvent) parameters 
 
To find out the optimum flow rate of the solvent, the case study is important. As it is clear from 
Figure 13 that almost 95% of LPG is recovered while having a flow rate of approximately 10 MMSCFD. 
 
 
Fig. 13. A case study for solvent optimum flow rate 
 
Figure 14 shows the connections of lean oil absorber. 
 
 
Fig. 14. Connections of lean oil absorber 
 
Figure 15 shows the conditions and parameters of the lean oil absorber. 
 
 
Fig. 15. Conditions and parameters of lean oil absorber 
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Figure 16 shows the optimum number of plates for the absorber column. If place 10 plates in the 
absorber column can get negligible LPG losses, which will certainly improve the percent recovery 
[18,19]. 
 
 
Fig. 16. Composition versus tray position from the top 
 
Figure 17 case study concludes that if no of the trays in the absorber unit is lea than 10 plates, 
then adequate losses of LPG will occur in the column. To save LPG from losses and to erect an 
absorption column with minimum cost, 10 plates are optimum for this column. 
 
 
Fig. 17. A case study for optimization of LPG loss 
 
2.4 De-Ethanizer Column 
 
Figure 18 shows the condition and properties of the de-ethanizer column and Figure 19 shows 
the specifications and parameters of the de-ethanizer column. 
 
 
Fig. 18. Condition and properties of the de-ethanizer column 
 
Number of stages 
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Fig. 19. Specifications and Parameters of the de-ethanizer column 
 
Figure 20 case study provides us information about the maximum phase separation spot in the 
stripper column. At 10th plate can have optimum results, which will help us in generating healthy 
recovery of LPG [20]. 
 
 
Fig. 20. A case study of flow versus tray position from the top of the de-
ethanizer column 
 
Vent rate is an important specification on the de-ethanizer column, as it can be a cause for LPG 
loss along with ethane at the top of the column. Figure 21 observed the case study. The optimum 
vent rate is 0.53 MMSCFD. Almost 98% of ethane is recovered at this vent rate, which means only 2 
% of LPG losses occur, which is a negligible loss [11,21]. 
 
 
Fig. 21. A case study for maximum ethane recovery from de-
ethanizer 
 
 
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2.5 De-Propanizer Column 
 
Figure 22 and Figure 23 show the conditions and design parameters of the de-propanizer column. 
 
 
Fig. 22. Conditions of de-propanizer column 
 
 
Fig. 23. Design parameters of the de-propanizer column 
 
The bottom product of the de-propanizer solvent is to be recycled, so it mustn't contain light 
fractions in it. Figure 24 shows an optimum column height by calculating the best no of plates, which 
give maximum phase separation. The recycled solvent carries kerosene along with C5 and C6 
components. 
 
 
Fig. 24. A case study of flow versus tray position from the top of de-
propanizer 
 
 
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3. Results  
 
This LPG is a valuable fuel, is gaining a key reputation in the global market due to its widespread 
use. The crisis can be addressed to a certain extent by making purposeful use of fuel resources 
available currently, which includes solar energy, ethanol and biogas diesel. LPG is being recovered by 
different processes like cryogenic process, double-wall column process, membrane process, and 
absorption process through different solvents. This research aims to do a comparative analysis 
between two techniques, cryogenic technique and absorption technique using kerosene as a solvent. 
This study recovered more than 90% LPG by both techniques and with a reasonable purity level. 
Absorption technology and cryogenic technique are simulated using ASPEN HYSYS V8.8 and 
calculations are further rechecked linking values from ASPEN HYSYS to Microsoft EXCEL and by 
applying different formulae. The operating cost found for the cryogenic technique is higher than the 
absorption process. The results showed that the absorption technology is better than cryogenic 
technique while issues like compressors compressibility factor, high cost and life-cycle of equipment 
would be resolved. 
 
3.1 Comparative Analysis of Cryogenic and Absorption Techniques for the Recovery of LPG from Off 
Gases 
 
Liquefied petroleum gas abbreviated as LPG is an efficient nontoxic, odorless, environment-
friendly hydrocarbon fuel which comprises propane and butane primarily with some fractions of 
ethane and pentane. As the fuel represents the family of lighter hydrocarbons from C1-C5, it is also 
termed as gas liquids. The concentration of propane and butane in LPG varies by the season. That is, 
the fuel sold includes more propane in winter while butane is a prime component in summer. A 
powerful odorant ethanethiol is added in an odorless fuel for leakage detection. The international 
standard of LPG is EN 589. The LPG was developed at the beginning of the 20th century, which is 
indeed very late in the oil and gas business. It was developed when the scientist was working on the 
problem of quick gasoline evaporation and in 1911, an American scientist found that there are two 
gases propane and butane in gasoline, which causes its quick evaporation so they should be removed. 
He found methods to remove these gases from gasoline and these removed gases are LPG. LPG is 
fuel and like normal fuel, it produces carbon dioxide and water when it is combusted with a sufficient 
amount of air. Less amount of air than required results in incomplete combustion, which gives toxic 
gas that is carbon mono oxide [11,14]. 
 
3.2 Economic Evaluation 
 
The economic investigation of the cryogenic and absorption process is carried out utilizing ASPEN 
Economic Analyzer V8.8. It is a cost assessing programming that gives CAPEX evaluations and OPEX 
estimates for relating and screening various procedure plans. It depends on model-based estimation 
to produce capital cost and operating cost estimates. Key highlights incorporate interactive 
equipment to decide working expenses and venture investment analysis and programmed 
generation of block and procedure stream diagrams. It is coordinated with process simulators 
systems Aspen HYSYS and Aspen Plus, sparing time and eliminating mistakes brought about by 
physically moving information between process structure and estimation departments. With 
Activated Economics, process simulation clients can create CAPEX and OPEX assessments utilizing a 
similar expense evaluating programming as estimators [22]. 
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The simulated cryogenic and absorption methods are analyzed through the ASPEN Economic 
analyzer. The equipment used in the absorption process includes pumps, reboiler absorbers and 
absorption column. And the equipment utilized in cryogenic is Heat Exchanger, coolers, expanders, 
separators, compressors, mixer and distillation column. The economic analysis for the equipment 
capital cost is carried for an off-gases flow rate of 10 MMSCFD. For the off-gases stream of 10 
MMSCFD, the equipment cost of the Expander procedure is incredibly higher than that of the 
absorption procedure. For the expander procedure, the equipment cost is 10.07 million USD and for 
the equipment procedure, it is 7.8 million USD, as appeared in Figure 25 [3,5]. 
 
 
Fig. 25. Cryogenic versus Absorption equipment’s 
 
Operating cost is looked at dependent on utility utilization by each procedure. The significant 
utility in the absorption procedure is steam that is us in reboiler for the recovery of the lean oil 
solvent. Different utilities are cooling water and power. In the expander procedure, the significant 
utility utilization is power and cooling water is additionally utilized. Operating examination is made 
for flue gas stream rate running somewhere in the range of 1 and 200 MMSCFD. The working expense 
is higher for the expander procedure when contrasted with the assimilation procedure. For 10 
MMSCFD stream of off-gases, the working or utility expense of the cryogenic procedure is 356.77 
USD/hr and for the retention procedure, it is 239.07 USD/hr. This gap in working cost increments as 
the flow rate of off-gases increments. Figure 26 shows the comparison. 
 
 
Fig. 26. Cryogenic versus Absorption utility cost 
 
As discussed above that the major utility for the absorption process is steam, whereas, for the 
expander process, the major utility is electricity. In the expander process, 91% of the total operating 
cost came from electricity consumption and 9% from cooling water consumption. And for the 
absorption process, steam constitutes 59% of total operating cost, cooling water 26% and electricity 
15%. The pie charts are shown in Figure 27. 
 
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(a) 
 
(b) 
Fig. 27. Utility distribution cost (a) Cryogenic (b) Absorption 
 
Figure 28 shows the absorption and cryogenic product purity. The limitation is the maximum 
allowable heavier hydrocarbons (C5+) in LPG is 7%. 
 
 
Fig. 28. Absorption and cryogenic products purity 
 
Figure 29 shows the utility cost of absorption and cryogenic techniques. 
 
 
Fig. 29. Absorption versus Cryogenic utilities 
 
The most feasible recovery process is through absorption in kerosene due to the advantages it 
has over other techniques. This process utilizes all the available raw materials within a refinery that 
are kerosene obtained from the crude oil is used for absorption purposes, so the cost for purchasing 
oil for absorption is cut down. According to this research work, the expander partition has a higher 
working expense than the absorption strategy, which is financially advantageous. Another factor of 
decision is utilities accessible since membrane separation, for the most part, required electricity and 
little amount of water. Thusly membrane separation is an earlier choice in the remote zones where 
utilities like cooling water and steam are not effectively accessible. Convincingly, expander separation 
is efficient for little to the medium progression of off-gases and higher stream rates; absorption is 
the favored procedure [21].  
 
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3.3 Equipment Designing of Absorption Column 
 
Absorption is utilized to separate gas mixtures; expel contaminations, contaminants, toxins, or 
catalyst harms from a gas; and recoup significant chemicals. Figure 30 shows the lean oil absorber 
structure parameters. In the absorber segment, plate sections can deal with a wider range of liquid 
and gas stream proportions than packed columns. Packed columns are not reasonable for extremely 
low fluid rates. The productivity of a plate can be projected with more conviction than the equal term 
for packing (HTU or HETP). Plate columns can be planned with more assertion than packed columns. 
There is some uncertainty in every case that great fluid distribution can be retained up all through a 
packed column under every single working condition, particularly in large segments. It is simpler to 
make cooling in a plate segment; coils can be presented on the plates. It is simpler to have a 
withdrawal of side-streams from plate segments [4]. 
 
 
Fig. 30. Lean oil absorber design parameters 
 
3.4 Equipment Designing of Distillation Column 
 
Distillation is a procedure that isolates at least two segments into an overhead distillate and 
bottoms. The bottoms item is only fluid, whereas the distillate might be fluid or fume or both. The 
separation method involves three things. Initial, a subsequent stage must be shaped so both fluid 
and fume stages are available and can reach each other on each phase inside a separation column. 
Besides, the segments have various volatilities with the goal that they will separate among the two 
stages to an alternate degree. In conclusion, the two stages can be separated by gravity and 
mechanical methods. Distillation contrasts from absorption and stripping in that the subsequent 
stage is made by thermal methods. Figure 31 shows the de-ethanizer segment plan parameters. Table 
1 shows the ideal temperature distinction. 
 
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Fig. 31. De-ethanizer column design parameters 
 
Distillation is the most affordable method for separating mixtures of fluids. If comparative 
volatilities of segments, distillation turns out to be extravagant (Douglas, 1988) and extraction or 
receptive refining ought to be thought of. Sieve trays are metal plates with openings in them. Vapour 
goes upward through the fluid on the plate. The direction of fume and fluid stream through a plate 
and a column. Each plate has two conductors on both sides, called down comers. Fluid falls through 
the down comers by gravity from one plate to another below it [4,12]. 
 
Table 1 
Optimum temperature difference 
Parameters 
Temperature ‘K’ 
Condenser 
Refrigeration 
3-10 
Cooling water 
6-20 
Pressurized fluid 
10-20 
Boiling water 
20-40 
Air 
20-50 
Reboiler 
Process fluid 
10-20 
Steam 
10-60 
Hot oil 
20-60 
 
A weir on the plate assurances that there is some fluid (holdup) on the plate and is structured 
such that the holdup is at a suitable height, to such an extent that the bubble caps are protected by 
fluid. Being lighter, fume streams up the section and is forced to go through the fluid, by the openings 
on each plate. The section of fumes on every plate is termed as the active tray zone. 
Figure 32 shows the de-ethanizer section plan parameters of dynamic vessel particulars. Steam 
boiler is an equipment used to change over the fluid into high-pressure fume. As a rule, the fluid is 
bubbled in a shell with the assistance of hot channels (tubes). On the outside surface of the cylinders, 
fluid changes its stage by watching heat (dormant warmth + reasonable warmth). Thusly, the 
necessary high temperature of the hot cylinders is kept up by circling low weight or high-pressure 
steam inside the cylinders. Because of the temperature affectability of the material and pace of fume 
development, bubbling is done inside or outside of the cylinders [18]. 
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Fig. 32. De-ethanizer column design parameters of dynamic vessel specification 
 
The tube bundle is inundated in a pool of fluid at the base of the column in a larger than usual 
shell. Kettle reboiler is additionally called a "submerged bundle reboiler." The height of the tube 
bundle is generally 40 to 60% of the shell ID. The submergence of the tube bundle is guaranteed by 
a flood weir at the height of regularly 5-15 cm from the upper surface of topmost cylinders—the 
favorable position reasonable for vacuum activity and high vaporization rate up to about 80% of the 
feed. Low heat transfer rate than different kinds as there is no fluid course (low speed); not proper 
for fouling liquids; kettle reboiler isn't appropriate for heat-delicate materials as it has higher 
habitation time. 
The change from the fluid stage to the fume stage is known as vaporization and the opposite 
stage move is condensation. The change from a fluid to fume or fume to fluid happens at one 
temperature (named as saturation or equilibrium temperature) for the pure liquid compound at a 
given pressure. The mechanical act of vaporization and condensation happens at practically constant 
pressure; hence, the stage change happens isothermally. Condensation happens by two diverse 
physical components that are film condensation and drop-wise condensation [23]. The idea of the 
condensation relies on whether the condensate (fluid shaped from fume) wets or doesn't wet the 
strong surface. If the condensate wets the surface and streams on a superficial level as a film, it is 
called film condensation. At the point when the condensate doesn't wet the strong surface and the 
condensate is collected as droplets, it is drop-wise buildup. The heat transfer coefficient is around 4 
to multiple times higher for dropwise condensation. The condensate frames a fluid film on the 
uncovered surface if there should be an occurrence of film condensation. The heat transfer 
coefficient is lower for film condensation because of the obstruction of this fluid film. Dropwise 
condensation generally happens on new, spotless and cleaned surfaces. The heat exchanger utilized 
for condensation is known as a condenser. In modern condensers, film condensation ordinarily 
happens [5,18].  
 
3.5 Equipment Designing of Centrifugal Pump 
 
These are a sub-class of dynamic axisymmetric work-engrossing turbomachinery, utilized to move 
liquids by the change of rotational active vitality to the hydrodynamic vitality of the liquid stream. 
Figure 33 shows the centrifugal siphon P-100 structuring. 
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Fig. 33. Centrifugal pump P-100 Design 
 
The accessible Net Positive Suction Head (NPSH or NPSHA) a proportion of how close the liquid 
at a given point is too blazing thus to cavitation. The required NPSH (NPSHR) the head an incentive 
at a particular point (the inlet of a siphon) required to shield the liquid from enrapturing the siphon 
thus. Bubbling starts when the pressure in the fluid is diminished to the fume pressure of the liquid 
at the actual temperature. To portray the potential for bubbling and cavitation, the distinction 
between the all-out head on the suction side of the siphon - near the impeller, and the fluid fume 
pressure at the actual temperature can be utilized [23]. The fluids fume head at the real temperature 
can be stated as; 
 
hv = pv / γvapor  
 
 
 
 
 
 
 
 
 
 
  (1) 
 
where; hv = vapor head in m or inch, pv = vapor pressure in m or inch, γvapor = specific weight of the 
vapor in N/m3 or lb/ft3. 
 
The vapor pressure in a liquid relies upon the temperature. Water, the most normal liquid, begins 
bubbling at 20 oC if the absolute pressure is 2.3 kN/m2. For a 47.5 kN/m2 absolute pressure of, the 
water begins bubbling at 80 oC and 101.3 kN/m2 (typical climate), the bubbling beginnings at 100 oC 
[3,6,7,19]. 
 
4. Conclusions 
 
This research focuses on bridging up with the uprising demand of LPG in the country since it aims 
at the recovery of LPG from waste gases in a fuel oil refinery. The research has been proved to be 
beneficial for the industry as it generates profit out of waste gases as well as for the market since it 
would cater to the shortage of LPG in the country. The economic analysis and cost estimation have 
yielded the project feasible as well as highly profitable. This research encloses the detailed 
description of the process, designing of equipment followed by basic knowledge regarding LPG, its 
use, applications, storage considerations and environmental aspects. The scope of this study can be 
elaborated and it can be applied to the recovery of LPG from refinery off-gases. The waste gases 
currently being flared at these plant sites can be used to recover LPG from them and cope up with 
the shortage in the country. 
 
 
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Acknowledgement 
The authors would like to acknowledge the Department of Chemical Engineering and Department of 
Polymer and Petrochemical Engineering, NED University of Engineering & Technology, Karachi, 
Pakistan for supporting in this research work. 
 
References  
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Safari, Ayoub, and Masoud Vesali-Naseh. "Design and optimization of hydrodesulfurization process for liquefied 
petroleum gases." Journal of Cleaner Production 220 (2019): 1255-1264. 
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About the author
Abdolhossein Hemmati Sarapardeh is currently an Assistant Professor
at the Department of Petroleum Engineering, Shahid Bahonar University
of Kerman, Iran. He is also an adjunct professor at College of
Construction Engineering, Jilin University, Changchun, China; and visit-
ing scholar at Institute of Research and Development, Duy Tan
University,
Da
Nang,
Vietnam
and
Faculty
of
Environment
and
Chemical Engineering, Duy Tan University, Da Nang, Vietnam. He was
previously a visiting scholar at the University of Calgary, Canada. He
earned a PhD in petroleum engineering from the Amirkabir University of
Technology, an MSc in hydrocarbon reservoir engineering from the
Sharif University of Technology, and a BSc in petroleum engineering
from the Amirkabir University of Technology. His research interests
include enhanced oil recovery processes, heavy oil systems, nanotechnol-
ogy, and applications of intelligent models in the petroleum industry. He
has been awarded as a distinguished graduate MSc student, was an honor
PhD student, and a recipient of the National Elites
Foundation
Scholarship. He was named Outstanding Reviewer in five prestigious
journals including Journal of Fuel and Journal of Petroleum Science and
Engineering, published by Elsevier. He has published over 90 journal arti-
cles, several conference proceedings, and earned one patent.
Sassan Hajirezaie is currently a PhD candidate at Princeton University
studying civil and environmental engineering. He earned a Master of
Science in petroleum engineering from the University of Oklahoma, and
a Bachelor of Science in petroleum engineering from Sharif University of
Technology. His research focuses on carbon capture and storage (CCS),
application of machine learning models in unconventional oil and gas pro-
duction, and renewable energy sources. He has published many journal
articles, peer reviewed at several journals, and is a member of the Society
of Petroleum Engineers and the American Geophysical Union.
Menad Nait Amar received the BSc degree, the MSc degree, and the
PhD degree in Petroleum/reservoir Engineering at University M’hamed
Bougara of Boumerdes, Algeria in 2013, 2015, and 2018, respectively. His
research interests include machine learning, optimization and data mining,
ix
and their applications in the oil industry. He is currently an Engineer
Researcher at Sonatrach and an Assistant Professor within the Faculty of
Hydrocarbons and Chemistry at the University M’hamed Bougara of
Boumerdes in Algeria.
Aydin Larestani is a Research Assistant in Department of Petroleum
Engineering, Shahid Bahonar University of Kerman, Iran. He is currently
an MSc student at the University of Kerman and a member of the Iranian
Oil Industry Youth Committee in the World Petroleum Council. He is
the first ranked student in MSc in hydrocarbon reservoir engineering at
the Shahid Bahonar University of Kerman and first ranked graduate in
Bachelor of Science in drilling engineering. He was the secretary of petro-
leum engineering scientific association from 2015 to 2018. His research
interests include applications of intelligent models in the petroleum indus-
try, chemical enhanced oil recovery, thermal EOR, interfacial tension,
and heavy oil.
x
About the author

1                                                                            NIGERIAN JOURNAL OF TECHNOLOGICAL DEVELOPMENT, VOL. 19, NO.1, MARCH 2022 
*Corresponding author: isalaudeen@atbu.edu.ng                                                                                                     doi: http://dx.doi.org/10.4314/njtd.v19i1.1 
Re 
Pr 
Pwf 
Pwh 
Psep 
Reservoir 
Separator 
Flow line 
Wellhead 
Figure 1: Schematic of production system. 
 
ABSTRACT: This research attempted to optimize petroleum production system of well X in Field Y in Niger Delta 
region of Nigeria located in Gulf of Guinea by adopting Nodal analysis technique. A non-commercial software known 
as Nodal Analysis Program   was used for the analysis. The dataset available from offset well were used as the input 
parameters to the software for the selection of the most economical production string for the new well. The production 
system has two adjustable components: vertical tubing and nearly horizontal flowline. The flowline inclination is -3.0 
degree to the horizontal. The productivity index of the well was obtained in order to know the deliverability of the 
well. Several combinations of the tubing and flowline have been used in the analysis of the production system. The 
optimal configuration of the production system components is selected by the maximum operating flow rate of 1118 
stb/day. The stable operational region is determined with the assumption that the system will be stable above the flow 
rate corresponding to the minimum on the outflow performance relation (OPR) curve. The introduction of the gas lift 
into the optimal system configuration increased the operating oil rate from 1118 stb/day to production rate of 
approximately 1287 stb/day, but the operating oil rate decreased with higher gas injection rate to 1115 stb/day. The 
optimal gas injection rate is selected by highest operating oil rate. The fluctuations in the oil price did not change the 
selection of the optimal configuration and gas injection rate. The investigation of the flow regime in the system before 
and after gas lift has revealed that the effect of gas injection on the flow regime is minor, probably due to low injection 
rate. Disperse flow was the flow regimes investigated and established for vertical flow (tubing) before and after gas 
injection. While on the other hand, elongated bubble was established to be the flow regime in flowline before gas 
injection and slug flow after gas injection in the flowline. 
 
KEYWORDS: Nodal Analysis Program, IPR-OPR Curve, Optimization, Operating points, Flow regimes  
[Received Apr. 6, 2021; Revised Oct. 27, 2021; Accepted Dec. 12, 2021]                             Print ISSN: 0189-9546 | Online ISSN: 2437-2110
 
I. 
INTRODUCTION 
Proper design and selection of production string to give 
optimal operational conditions is a must before embarking on 
any oil gas production operation.  Nodal analysis entails a 
process, which uses nodes to converge the oil and gas 
production rate and optimize the total production system (Guo, 
et al, 2007). Figure 1 shows a schematic diagram of a simple 
production system. Hand calculations, though, tedious and 
cumbersome can be performed to compute the flow rates and 
pressures at the different nodes. However, with advances in 
technology, several computer programs such as PIPE SIM, 
PROSPER, can be applied to simplify and accelerate the 
process. In this research, production system of well X in field 
Y in Niger Delta region of Nigeria is optimized using the 
NODAL ANALYSIS PROGRAM. The nodal analysis 
software, developed at the University of Tulsa, served as an 
alternative package to commercial software. The tool computes 
the necessary data needed to plot the inflow performance 
relation curve (IPR) and outflow performance relation curve 
(OPR) for critical analysis. In an attempt to carry out the 
optimization plan, optimal flow was determined and  
 
production component such as tubing and flowlines was 
selected in the most economically feasible manner. A gas lift 
injection was modeled at different injection rates, flow regimes 
both in the tubings as well as the flowlines were determined, 
and finally stable and unstable regions were identified. 
 
 
 
 
 
 
 
 
 
 
 
 
Optimization of Petroleum Production System using 
Nodal Analysis Program  
Ibraheem Salaudeen1*, Daniyar Bopbekov2, Abdulsalam Abdulkarim3  
1Department of Petroleum Engineering, Abubakar Tafawa Balewa University Bauchi, Nigeria. 
2Department of Petroleum Engineering, Nazarbayev University, Nur-Sultan, Kazakhstan. 
3 Department of Petroleum and Gas Processing Engineering Technology, Bonny Island, Nigeria. 
2                                                                            NIGERIAN JOURNAL OF TECHNOLOGICAL DEVELOPMENT, VOL. 19, NO.1, MARCH 2022 
 
  Figure  2: Illustration of Nodal Analysis graph(Lea and Rowlan, 
2019b). 
Lea and Rowlan (2019) explained the application of 
nodal analysis for gas or oil well modelling which simulates 
and model  well’s performance  through flow components that 
ensure flow assurance such as  chokes, tubings, flow lines etc., 
completion effects such as perforations and well deliverability 
in terms of  inflow performance. Many researchers have 
applied nodal analysis to diverse problems in oil and gas 
industry (Odjugo et al., 2020, Al-Qasim et al. 2019, Fan and 
Sarica 2019, Wilson 2015, Lea 1988, Dala et al. 2015, James 
and Rowlan 2019a, Abdullahi et al., 2019, Noor et al., 2017, 
Duncan et al., 2015, Soomro et al., 2015, Widyasari et al., 
2019, Al-Ruheili et al., 2012) 
The determination of well deliverability entails 
combination of inflow performance that describes reservoir 
deliverability and wellbore outflow performance that defines 
the production string resistance to flow.  Oil and gas properties 
are strong function of pressure and temperature which vary 
with location within the oil and gas production system. To 
model and simulate the fluid flow in the production system, the 
system is usually divided into discrete nodes that separate 
system into different components to easily evaluate the fluid 
properties at each element. The system analysis for 
determination of fluid production rate and pressure at a 
specified node is called Nodal analysis in petroleum 
engineering (Guo, et al, 2007, Dala et al. 2015, Jansen, 2017). 
Guo  et al (2007) explained that pressure continuity is the 
basis for performing Nodal analysis where a unique pressure 
value exist at a given node regardless of whether the pressure 
is evaluated from the performance of upstream equipment or 
downstream equipment. The performance curve (pressure–rate 
relation) of upstream equipment is called “inflow performance 
curve”; the performance curve of downstream equipment is 
known as outflow performance curve. The operating points are 
selected based on the intersection of the IPR and OPR curve 
(Figure 2).  For convenience, nodal analysis is usually 
conducted using the bottom-hole or wellhead as the solution 
node, as the pressure is normally measured at either the 
bottom-hole or the wellhead.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
        Al-Anazi et al., 2017 adopted nodal analysis to optimize 
smart 
wells, 
improved 
recovery, 
reduced 
Operating 
Expenditure (OPEX) and they concluded that nodal analysis is 
a powerful tool capable of simulating downhole conditions for 
inflow-outflow performance optimization of fluid from the 
reservoir. 
In this paper, nodal analysis program is applied to 
optimize the production system of well X in filed Y in Niger 
Delta region of Nigeria by selecting the optimum tubing and 
flowline sizes among different options available - 2, 3, and 3.5 
inches considering the cost of steel as well as the average crude 
oil price as at March 2021 with possible fluctuations of plus or 
minus $20 based on predictions made by the research team. 
Stable regions and flow regimes were identified. The gas lift 
injection was also modelled to determine the best injection rate 
based on the optimized production string-tubing and flowlines. 
The software is a non-commercial software and has not been 
used for nodal analysis for the new well considered in our 
study.    
 
II. THEORY AND MATHEMATICAL BACKGROUND 
A)  Single phase productivity index 
The IPR curve is linear for the values of the flowing 
bottomhole pressure above bubble point, because the flow is 
single phase. The maximum single-phase flow rate is 
calculated by:  
           𝑞𝑏= 𝐽(𝑃
̅ −𝑃
𝑏)                                                                  (1) 
where qb is the oil flowrate (stb/d) at bubble point pressure, J 
is the productivity index (stb/d/psi) and 𝑃
̅, 𝑃
𝑏 (Psia) are average 
reservoir and bubble point pressure respectively (Ahmed and 
Mckinney, 2005, Guo et al., 2007). 
When the pressure in the bottomhole drops below bubble 
point pressure, the IPR is not linear because the flow is 
multiphase as gas starts to come out of the solution. At that 
point, the bottom hole pressure is described to be below 
saturation or bubble point pressure. The flow rate is related to 
the pressure difference by: 
𝑞𝑜= 𝑞𝑏+
𝑃
𝑏𝐽𝑃𝑏
1 + 𝑉[1 −(1 −𝑉) (𝑃
𝑤𝑓
𝑃
𝑏
) −𝑉(𝑃
𝑤𝑓
𝑃
𝑏
)
2
]      ( 2) 
where qo is oil flowrate (stb/d) at any pressure below bubble 
point pressure,  𝐽𝑃𝑏  is Productivity index (stb/d/psi) at bubble 
point pressure, V is Vogel coefficient with a constant value of 
0.8, 𝑃
𝑤𝑓 is bottomhole flowing pressure (Psia). 
Combining Eqs. (1) and (2) gives Eq. (3).  
𝑞0 = 𝐽(𝑃−𝑃
𝑏) + 
𝑃𝑏𝐽𝑝𝑏
1+𝑉[1 −(1 −𝑉) (
𝑃𝑤𝑓
𝑃𝑏) −𝑉(
𝑃𝑤𝑓
𝑃𝑏)
2
] (3)
 
By letting 𝐽= 𝐽𝑝𝑏, the oil flow rate becomes: 
𝑞0 = 𝐽𝑝𝑏(𝑃−𝑃
𝑏+ 
𝑃𝑏
1+𝑉[1 −(1 −𝑉) (
𝑃𝑤𝑓
𝑃𝑏) −𝑉(
𝑃𝑤𝑓
𝑃𝑏)
2
])(4) 
  Applying Vogel coefficient into the equation and rearranging 
Eqn. (4), we have: 
𝐽𝑝𝑏=
𝑞𝑜
𝑃−𝑃𝑏+ 
𝑃𝑏
1+0.8[1−(1−0.8)(
𝑃𝑤𝑓
𝑃𝑏)−0.8(
𝑃𝑤𝑓
𝑃𝑏)
2
]
                   (5)      
 
Eq. (5) is used to calculate single phase productivity 
index of the well (Guo et al., 2007). In order to investigate the 
flow regimes in the tubing as well as the flowline, superficial 
velocity of the phases flow is required. Superficial phase 
SALAUDEEN et al:  OPTIMIZATION OF PETROLEUM PRODUCTION SYSTEM USING NODAL ANALYSIS                                                                          3 
Table 1: Input parameters required by the software. 
Fluid Specific Gravities 
 
Flowrate Data 
Oil Specific Gravity 
45 (API)  
 
Test Oil Flowrate 
1,500 (STB/day)  
Water Specific Gravity 
1.01 
 
Formation Gas-Oil Ratio 
400 (SCF/STB)  
Gas Specific Gravity 
0.65 
 
Water Cut 
0 (%)  
Reservoir Data 
 
Gas Lift Data 
Reservoir Pressure 
2,250 (psig)  
 
Gas Lift Included 
NO 
Bubble Point Pressure 
1,250 (psig)  
 
Gas Injection Depth 
4,500 (ft) 
Test Bottom Hole Flowing Pressure 
853 (psig)  
 
Injection Gas Specific Gravity 
0.68 
Reservoir Pressure at Test Conditions 
2,250 (psig)  
 
Injection Gas-Oil Ratio 
285 (SCF/STB)  
Operating Conditions 
 
Correlations to Use 
Separator Pressure 
450 (psig)  
 
Solution Gas & Bubble Point Correlation 
Kartoatmodjo  
Separator Temperature 
80 (deg F)  
 
Oil Formation Volume Factor Correlation 
Kartoatmodjo  
Well Head Pressure 
0 (psig)  
 
Oil Viscosity Correlation 
Kartoatmodjo  
Well Head Temperature 
160 (deg F)  
 
Z Factor Correlation 
Standing 
Bottom hole Temperature 
220 (deg F)  
 
Tubing Pressure Drop Correlation 
Beggs - Brill Model  
Choke Included 
YES  
 
Flowline Pressure Drop Correlation 
Beggs - Brill Model  
Bean Choke Size 
64 (1/64 in)  
 
 
 
Tubing Data 
 
Flowline Data 
Tubing Depth 
4,500 (ft)  
 
Flowline Length 
15,000 (ft)  
Tubing Inner Diameter 
3 (in)  
 
Flowline Angle 
--3(deg) 
 
 
 
Flowline Inner Diameter 
3.5 (in)  
Economics Data 
 
 
Price of steel* 
$6per ft per in. 
 
 
 
Price of oil (exclude gas production) 
$70 per STB 
 
 
 
*Example: 20-foot section of 3-inch diameter pipe = | 20 ft | | $5/ ft-in | | 3 in | = $ 300 
 
velocities can be calculated knowing the flow rate and conduit 
diameter as given by Eqs. (6) and (7). 
𝑣𝑠𝑂=
𝑞𝑂
𝐴𝑝                                                                              (6)  
 𝑣𝑠𝐺=
𝑞𝐺
𝐴𝑝                                                                             (7) 
where, 𝐴𝑝 𝑖𝑠 area to flow, 𝑞𝑂 𝑖𝑠 oil rate (bbl/day), 𝑞𝑔 𝑖𝑠 gas rate 
(cuft/day). 
The gas rate at the surface before the gas lift is determined 
as the amount of gas that left the solution at a particular 
pressure and temperate, which can be obtained using: 
      𝑄𝑔= 𝑄𝑜(𝑅𝑠𝑖−𝑅𝑠(𝑝, 𝑇))                                                  (8) 
After the gas lift, Eqn. (9) can be applied for analysis. 
      𝑄𝑔= 𝑄𝑜(𝐺𝑖𝑛𝑗+ 𝑅𝑠𝑖−𝑅𝑠(𝑝, 𝑇))                                   (9) 
where, 𝑅𝑠(𝑝, 𝑇) 𝑖𝑠 solution gas-oil ratio at specific 
pressure and temperature (SCF/STB), 𝐺𝑖𝑛𝑗 𝑖𝑠 injected gas-oil 
ratio (SCF/STB). 
Since the phase volumes change with pressure and 
temperature, it is necessary to calculate the flow rate at the 
point of interest adopting Eqns. (9) and (10). 
      𝑞𝑔= 𝑄𝑔𝐵
𝑔(𝑝, 𝑇)                                                              (10)   
      𝑞𝑜= 𝑄𝑜𝐵𝑜(𝑝, 𝑇)                                                               (11) 
where, 𝑞𝑔− gas flow rate in (cuft/day), 𝐵
𝑔− gas formation 
volume factor (cuft/SCF), 𝑞𝑜− oil flow rate in (bbl/day), 𝐵𝑜−
 oil formation volume factor (bbl/STB). Details of Eqs. (6) 
through (11) can be found in (Brill and Beggs, 1991). 
 
III.    METHODS 
 Nodal analysis program software which is an excel based 
program that constructs IPR and OPR curves for the node at 
the bottom hole and determines the operating pressure(s) and  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
flow rate(s) for the production system was used. The software 
requires  production system parameters and selection of an 
appropriate correlations to successfully run. Parameters of the 
production system required for the software to successfully run 
range from fluid properties to production string geometries. 
Correlations used in the software include Kartoatmodjo’s 
correlation for the oil properties, Standing’s for the z factor and 
Beggs and Brill model for the pressure drop along the tubing 
and flowline.   
Once appropriate input parameters are keyed in or 
imported into the software, the calculation of the OPR and IPR 
curves can be initiated and comparisons can be made between 
OPR and different IPRs. The program will output the IPR, 
OPR and operating conditions on the separate sheet. Error 
message will pop up if the system does not have any operating 
point. 
Field Y is located in Niger Delta Nigeria-West Africa. 
The full reservoir description can be found in our previous 
paper (Abdullahi et al. 2019) and some research papers 
published by other investigators such as (Chukwu, 1991, 
Burke, 2000). 
  
IV. RESULTS AND DISCUSSION 
From the available data gathered, the single-phase 
productivity of the well was determined to be 1.12 stb/day/psi 
which shows the potential of the well in terms of the volume 
of oil that can be delivered economically to the surface. In 
order to appropriately optimize the production string, the 
flowline was firstly optimized using the available dimensions 
to construct IPR – OPR to obtain the operating points such as 
flow rates, bottom hole pressure, well head pressure and 
separator pressure. The general input parameters are shown in 
Table 1. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4                                                                            NIGERIAN JOURNAL OF TECHNOLOGICAL DEVELOPMENT, VOL. 19, NO.1, MARCH 2022 
 Table 4 Operating points. 
 
Flowlines 
ID 
Qo  
(STB/day) 
Pwf  
(psia) 
Pwh  
(psia) 
Psep  
(psia) 
2.5 
973.37 
1461.66 
460.69 
464.70 
3.0 
1069.92 
1367.77 
407.99 
464.70 
3.5 
1118.23 
1321.4 
382.07 
464.70 
 
     Table 2: Oil revenue and pipe cost for different flowline diameters. 
 
Flowline 
ID 
(inches) 
Tubing 
ID 
Steel Price 
($/ft-in) 
Pipe cost 
($) 
Qo 
(stb/d) 
Oil Price 
($/stb) 
Revenue ($) 
7 days 
365 days 
2.5 
3.0 
6 
306,000 
973 
70 
476770 
24860150 
3.0 
3.0 
6 
351,000 
1069.92 
70 
524260.8 
27336456 
3.5 
3.0 
6 
396,000 
1118.23 
70 
547932.7 
28570777 
 
Table 3: Operating points for different tubing internal diameters. 
 
Tubing ID 
Qo  
(STB/day) 
Pwf  (psia) 
Pwh  (psia) 
Psep  (psia) 
2.0 
1052.68 
1384 
379.21 
464.70 
3.0 
1118.23 
1321.4 
382.07 
464.7 
3.5 
1106.91 
1332.29 
381.56 
464.7 
 
Figure 3: IPR- OPR curve for 2.5’’, 3.0’’ and 3.5’’ flowline internal diameter. 
0
500
1000
1500
2000
2500
0
500
1000
1500
2000
Pwf  [psia]
Qo  [BPD]
2.5 in Flowline  IPR
Curve
2.5 in Flowline OPR
Curve
3 in Flowline  IPR Curve
3 in Flowline OPR Curve
3.5 in Flowline IPR Curve
3.5 in Flowline  OPR
Curve
Appropriate values of flowlines diameters and other 
necessary data were imported into the software, and the 
software was run. The operating points displayed and output 
from the software are shown in Table 2. 
Figure 3 shows the optimized production rate for each 
flowline internal diameter (ID) considered. The IPR of each 
flow line were overlain in the plot while the OPR was clearly 
and separately plotted. It can be observed that flow line with 
3.5 inches internal diameter had the highest production rate of 
1118 stb/day and flow line with 2.5 inches internal ID has the 
least flow rate per day. The operating points of each flowline 
are displayed in Table 2. 
Table 3 shows oil revenue and pipe cost for different 
flowline diameters considered. From the table, the flowline 
with 3.5 inches has the highest production rate and as such it 
has the highest revenue of $28, 570,770. Therefore, internal 
diameter of 3 inches and 3.5 inches are selected as the 
optimized tubing and flowline of the production string 
respectively. Furthermore, the optimized value of the internal 
diameter of 3.5 inches for flowline is used to optimize for 
tubing diameter.  Figure 4 shows the IPR-OPR curve for each 
scenario and the operating points are summarized in Table 4. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
From Figure 4, the OPR curves for tubing with 3.0 and 
3.5 inches’ seem to overlap but, tubing with 3.0 inches internal 
diameter has slightly higher production rate (1118.23 stb/day) 
than the tubing with 3.5 inches (1106.91). The economic 
analysis was carried out to finally select the best tubing, 
considering cost and oil price (Table 5). 
From the table, the tubing with 3 inches internal diameter has 
the highest revenues per annum as well as the highest net 
income compared with 2-inches and 3.5-inches tubing. 
Therefore, the recommended production string for well X 
should consist of 3 inches and 3.5 inches internal diameter for 
tubing and flowline respectively. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
SALAUDEEN et al:  OPTIMIZATION OF PETROLEUM PRODUCTION SYSTEM USING NODAL ANALYSIS                                                                          5 
Table 5: Oil revenue and pipe cost for different tubing diameters. 
Tubing ID 
(inches) 
Flowline 
ID (inches) 
Steel Price 
($/ft-in) 
Pipe cost 
($) 
Qo (stb/d) 
Oil 
Price 
($/stb) 
Revenue ($) 
7 days 
365 days 
2.0 
3.5 
6 
369,000 
1052.68 
70 
515813.2 
26895974 
3.0 
3.5 
6 
396,000 
1118.23 
70 
547932.7 
28570777 
3.5 
3.5 
6 
409,500 
1106.91 
70 
542385.9 
28281551 
 
Figure 4: IPR-OPR curve for tubing geometry. 
0
500
1000
1500
2000
2500
0
500
1000
1500
2000
Pwf  [psia]
Qo  [BPD]
2 in Tubing  IPR Curve
2 in Tubing OPR Curve
3 in Tubing  IPR Curve
3 in Tubing OPR Curve
3.5 in Tubing IPR Curve
3.5 in Tubing  OPR Curve
Figure 5: IPR-OPR curve with varying gas -oil injection ratio. 
0
500
1000
1500
2000
2500
0
500
1000
1500
2000
Pwf  [psia]
Qo  [BPD]
0 scf/stb  IPR Curve
0 scf/stb  OPR Curve
1000 scf/stb IPR Curve
1000 scf/stb OPR Curve
2000 scf/stb IPR Curve
2000 scf/stb  OPR Curve
3000 scf/stb  IPR Curve
3000 scf/stb  OPR Curve
4000 scf/stb  IPR Curve
4000 scf/stb  OPR Curve
5000 scf/stb  IPR Curve
5000 scf/stb  OPR Curve
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
A)  Modeling of Gas Injection 
 
The optimum flowline and tubing diameters selected from 
the previous section were used to model gas injection into the 
tubing at a depth of 4,500 ft, with injection volume factors of 
1,000, 2,000, 3,000, 4,000, and 5,000 SCF/STB respectively. 
The injected gas gravity is 0.68. The IPR and OPR curves with 
the varying gas injection rates are presented in Figure 5.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
The optimum production rate for each injection gas-oil ratio in 
scf/stb obtained from Figure 5 are used to generate the oil 
revenue possible per annum as displayed in Table 6. From the 
table, it is observed that 1000 scf/stb injection rate has the 
highest production rate, highest oil revenue and highest net 
income. As the gas-oil injection ratio increases the flow rate 
decreases. It can also be observed from Figure 6 that, the 
optimum gas injection rate increased oil production as  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6                                                                            NIGERIAN JOURNAL OF TECHNOLOGICAL DEVELOPMENT, VOL. 19, NO.1, MARCH 2022 
Figure 6: IPR-OPR for gas injection and no gas injection scenario. 
0
500
1000
1500
2000
2500
0
500
1000
1500
2000
Pwf  [psia]
Qo  [BPD]
0 scf/stb IPR Curve
0 scf/stb OPR Curve
1000 scf/stb IPR Curve
1000 scf/stb OPR Curve
Table 6: Oil revenue, net income and pipe cost for different gas injection rates. 
Injection Gas-
Oil Ratio 
(SCF/STB) 
Tubing ID 
(in) 
Flowline 
ID (in) 
Pipe cost 
($) 
Qo 
(STB/day) 
Oil price 
$/STB 
Revenue ($) 
Net Income ($) 
7 days 
365 days 
7 days 
365 days 
0 
3.0 
3.5 
396,000 
1118.23 
70 
547,933 
28,570,777 
151,933 
19,599,365 
1000 
3.0 
3.5 
396,000 
1286.53 
70 
630,400 
32,870,842 
234,400 
22,989,303 
2000 
3.0 
3.5 
396,000 
1264.71 
70 
619,708 
32,313,341 
223,708 
22,647,115 
3000 
3.0 
3.5 
396,000 
1219.43 
70 
597,521 
31,156,437 
201,521 
21,845,758 
4000 
3.0 
3.5 
396,000 
1169.61 
70 
573,109 
29,883,536 
177,109 
20,951,690 
5000 
3.0 
3.5 
396,000 
1114.61 
70 
546,159 
28,478,286 
150,159 
19,949,583 
 
Table 6: Net income for different tubing diameters at 
different oil prices. 
Flowline ID 
Net income for 1 year ($) 
50 $/STBs 
70 $/STBs 
90 $/STBs 
2.5 inch 
18,842,410 
26,526,974 
34,211,538 
3.0 inch 
20,011,698 
28,174,777 
36,337,856 
3.5 inch 
19,791,608 
27,827,051 
35,952.494 
 
 
 
 
Table 7: Net income for different flowline diameters at 
different oil prices. 
Flowline 
ID 
Net income for 1 year ($) 
50 $/STBs 
70 $/STBs 
70 $/STBs 
2.5 inch 
17,451,250 
24,554,150 
31,657,050 
3.0 inch 
19,175,040 
26,985,456 
34,795,872 
3.5 inch 
20,011,698 
28,174,777 
36,337,856 
 
Table 9: Net income for different gas injection rates at different oil prices. 
Injection Gas-Oil 
Ratio 
(SCF/STB) 
             Net income for 1 year ($) 
50$/STBs 
70$/STBs 
90 $/STBs 
0 
20,011,698 
28,174,777 
36,337,856 
1000 
23,083,173 
32,474,842 
41,866,511 
2000 
22,684,958 
31,917,341 
41,149,724 
3000 
21,858,598 
30,760,437 
39,622,276 
4000 
20,949,383 
29,487,536 
38,025,689 
5000 
19,945,633 
28,082,286 
36,218,939 
 
Table 8: Operating points before and after gas lift. 
Operating points 
Before gas Lift 
After Gas Lift 
Qo  (STB/day) 
1118.23 
1286.53 
Pwf  (psia) 
1321.4 
1126.15 
Pwh  (psia) 
382.07 
506.51 
Psep  (psia) 
464.70 
464.70 
 
compared to a scenario when there was no gas injection. 
Sensitivity analysis was also run to checkmate the optimized 
system. This was done by factoring fluctuation of plus or minus 
$20 into the selection plan. Tables 7, 8 and 9 give the details 
of the oil revenue as well as the net income for tubing and 
flowline at different oil prices as well as at different gas 
injection scenario. From Tables 7, 8 and 9, it can be concluded 
that 3.0 inches internal diameter tubing, 3.5 inches internal 
diameter flowlines and gas injection rate of 1000 scf/stb can be 
selected for the optimal running of the well. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
B) Flow Regime of the Optimized Production String before 
and after Gas Injection 
The flow in the production system is mostly multiphase- two-
phase, because the bottom hole pressure is below the saturation 
pressure which implies that gas comes out of the solution as 
the pressure decreases. When the gas lift is applied, additional 
amount of free gas is introduced into the system, which affects 
the flow pattern. The flow regime in the system is not uniform 
and changes with superficial velocities of the phases and the 
orientation of the conduit (vertical for tubing and nearly 
horizontal for flowline). There 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
SALAUDEEN et al:  OPTIMIZATION OF PETROLEUM PRODUCTION SYSTEM USING NODAL ANALYSIS                                                                          7 
Figure  7: Flow regime maps for vertical pipes James F. Lea and 
Rowlan 2019a. 
Figure 8: Flow regime map for horizontal pipes (Mandhane et al., 1974). 
Table 10: Operating Points before and after gas lift. 
Operating points 
Before gas Lift 
After Gas Lift 
Qo  (STB/day) 
1118.23 
1286.53 
Pwf  (psia) 
1321.4 
1126.15 
Pwh  (psia) 
382.07 
506.51 
Psep  (psia) 
464.70 
464.70 
 
 
are various flow regime maps for the two-phase flow in the 
literature (Brill and Beggs, 1991, Lea and Rowlan 2019a). 
Most of them attempted to map flow regimes on the plot of 
superficial liquid velocity versus superficial gas velocity. Two 
widely used maps have been selected to evaluate the flow 
regimes in this paper: one for vertical pipe proposed by Lea 
and Rowlan 2019a (Figure 7) and the second one for horizontal 
pipe proposed by Mundhane et al., 1974 (Figure 8). 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
The operating points before and after gas injection with the 
optimized system is presented in Table 10. Since the diameters 
of the flowline and the tubing in the bottomhole are known, 
only flow rates are to be determined. The oil rate is determined 
by the software, as well as other operating parameters like 
pressure throughout the system, which are shown in Table 10. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  Investigation of flow regimes was carried out with 
available data using Eqs. (7 - 10). In addition, Al-Marhoun’s 
correlations (Al-marhoun, et al, 2015) are applied for the oil 
formation volume factor and solution gas-oil ratio, while gas 
formation volume factor is calculated for the 1:1 mixture of 
methane and ethane, using z-factors graph. The points of 
interest are the bottom of the tubing and the flowline. Thus, 
fluid properties are determined for the pressure and 
temperature in the bottomhole and the flowline. The flowline 
pressure and temperature are assumed to be an average 
between the wellhead and the separator. 
 
V. CONCLUSION 
The Nodal Analysis Program software has been 
successfully applied to the production system and an optimum 
configuration is selected based on the economic feasibility and 
other relevant factors. Introduction of the gas lift and its effect 
on the system performance has been evaluated as well. Finally, 
the flow regime nature in the two sections of the system were 
investigated before and after the gas lift application. The 
selection of the piping diameter is justified by the oil revenue 
for the operating period and the cost associated with the piping 
installation. The pipe cost depends on the size of the pipe. The 
optimized geometry for the production string where best 
operating conditions of pressure and flow are achieved consist 
of 3.5-inch flowline and 3.0-inch tubing.  
The system has different operating point with changing 
flowline ID. The decrease in the flowline ID hinders the flow, 
as the frictional pressure drop becomes more dominant at the 
higher oil rates that results in the intersection of the OPR and 
IPR curves at the lower oil rates. The gas-oil ratio injection rate 
of 1000 scf/stb was optimized for the gas lift operation because 
higher injection rate was found to be associated with 
decreasing oil rate and increasing frictional pressure drop. A 
stable flow is observed based on the flow regime identified 
before and after gas injection. Hence, non-commercial 
software is proved to be effective in optimizing production 
system and solving some other production related problems. 
 
ACKNOWLEDGEMENT 
The authors would like to thank the software expert that 
released the key to the software used for the study. 
 
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Abstract – Increasing gasoline and propylene production 
from Fluid Catalytic Cracking Units (FCCUs) has been 
widely discussed over the past few years. Today, one of the 
main industrial challenges is how to balance the design and 
operation of FCCU between maximum gasoline and 
propylene production.  With market demands and prices of 
both products fluctuating, it is desirable to operate the unit 
at optimal operating settings to achieve optimum yields. This 
paper proposes a new Mixed Integer Nonlinear programing 
problem (MINLP) formulation that aims to minimize the 
total cost of gasoline and propylene production by obtaining 
optimal operating settings that generate both products 
optimal yields as per their respective economic value. A case 
study is developed to verify the formulation effectiveness. 
The problem is solved using Matlab genetic algorithm 
toolbox. The results reveal that the model reacts correctly to 
prices and demands market fluctuations under various 
situations.  
 
 
I.  INTRODUCTION 
 
 
Fluidized Catalytic Cracking (FCC) is a process by 
which crude oil and high molecular weight oils are 
converted to more premium grades of lighter-weight 
hydrocarbons in various petroleum refineries [1]. The 
process involves the separation of octane products in the 
presence of a catalyst to produce valuable hydrocarbons, 
gaseous products and coke (deposited on the catalyst). 
Every FCCU consists of two main components: a reactor 
where the heavy feed is cracked into its constituents and a 
regenerator where the coke is burned in the presence of 
air to regenerate the catalyst to its original state. The 
competitive nature of the petrochemical industry is the 
impetus behind the constant technological and economic 
improvement of all FCCU processes. The goal is often to 
maximize product quality and quantity while maintaining 
an economic edge and adhering to environmental and 
health regulations. Due to its vital role in oil refineries, 
FCCUs have long served as the benchmarking tool used 
to evaluate their performance [2]. 
 
FCCU is a multivariable, non-linear process with 
several operational constants that limit production rates 
making it suited for optimization. Studies by a number of 
scientists in the field of academia and research were 
carried out [3-6], each adding to the complexity of the 
study and incorporating factors that affected the overall 
performance of the FCCU. Of all those studies, [7] 
provides a dynamic FCCU model with constraints that 
were considered as challenges in the chemical process 
control community. Moreover, the model proposed by [7] 
uses a Model IV type reactor, which to date, remains as 
the most commonly used FCCU reactor. Operational 
details and technical information on the Model IV type 
reactor is discussed in detail in [8]. 
FCCU operation and products yields are strongly 
influenced by the unit’s operating settings. Moreover, 
external factors such as an addition of ZSM-5 additive 
will affect the products yields significantly. Utilization of 
ZSM-5 enhances cracking of low octane linear 
hydrocarbons from gasoline into light olefins. Thus, light 
olefins production increase whereas gasoline yield 
generally drops [9].  
In order to optimize FCCU, yield model is required. 
This model captures the major effects and interactions 
taking place in the unit.  In this study, a six-lump yield 
model is utilized which will be discussed in the section II.  
Optimal yield of desired products can be achieved by 
providing the FCCU controller with optimal settings.  
Optimal yield of each product depends on economic 
factors such as the product’s demands and selling prices, 
feedstock purchase price, and ZSM-5 additive price. 
Hence, optimization of the unit operation can result in 
substantial economic benefits due to the fact that this 
process involves large throughput. Increase of profits 
ranging from 3-5% is predicted if an online optimization 
routine was applied to the FCC unit process [10]. 
Therefore, this paper proposes a mixed integer non-linear 
programming (MINLP) problem formulation for model 
IV FCCU. Such formulation is suggested due to the 
nature of the problem and that MINLP formulations were 
previously developed in literature for oil industry 
applications and proved their effectiveness [11–13]. 
 
II. YIELD MODEL 
 
 
A kinetic yield model for the cracking of vacuum gas 
oil (VGO) is necessary to estimate the yield of different 
product resulting from the VGO cracking. The yield 
model is based on the lumping technique by grouping 
chemical species with similar behavior into small number 
of lumps [14]. 
Different lump models have been proposed in the 
literature [15–20]. However, for reasons of data 
availability the study is based on a six lump model with 
twelve kinetic constants and catalyst deactivation as 
demonstrated by [16]. The six lumps are: gasoline (C3 -
493 K), C3’s (propane and propylene), C4’s (butane, i-
butane and butenes), dry gas (H2, C1 – C2), coke and 
unconverted VGO (Fig. 1). 
Kinetic expressions developed by [16] are functions 
of product yields, coke deactivation and kinetic constants. 
For the period of concern, it is assumed that there will be 
no catalyst decay. The estimation for the kinetic 
parameters was done for a typical VGO with API gravity 
A Novel Optimization Formulation of Fluid Catalytic Cracking Unit 
(Presented at the 5th IESM Conference, October 2013, Rabat, Morocco) © I4e2 2013 
Khaled Saleh, Hebatallah Ibrahim, Majd Jayyousi, Ali Diabat 
Masdar Institute of Science and Technology, Abu Dhabi, United Arab Emirates 
 (ksaleh@masdar.ac.ae, hibrahim@masdar.ac.ae, mjayyousi@masdar.ac.ae, adiabat@masdar.ac.ae)  
 
 
 
 
 
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of 24 and a commercial equilibrium catalyst decoked at 
853 K during 3 h [16]. Moreover, [16] obtained the 
kinetic parameters for the product yields at 773 K, where 
average deviation was less than 5%. 
 
Fig. 1. Six-lump kinetic yield model [16] 
 
The resultant gasoline and C3 yields with respect to 
VGO conversion rate are illustrated in Fig. 2. Since the 
optimization model  presented later needs a relationship 
between the product yields and the VGO conversion rate; 
using  data obtained from the yield model, regression 
techniques were deployed to develop the expressions (1) 
and (2) that represent the weight fraction of the gasoline 
and the C3 products (as these two lumps are the main 
focus of this study) as a function of the VGO conversion 
rate through the cracking process, and the change caused 
to the products yields in the case of using a ZSM-5 
additive.  
 
       
g
Add
gasoline
Shift
X
conv
conv
conv
conv
Wt






5326
.
1
2757
.
4
855
.
7
7591
.
4
2
3
4
(1) 
        
3
2
3
4
3
0714
.
0
6589
.
0
2289
.
1
7696
.
0
C
Add
C
Shift
X
conv
conv
conv
conv
Wt





 (2) 
 
where conv is the weight fraction conversion of gas oil 
feed. WtGasoline and WtC3 are the weight fraction of the 
VGO feedstock converted into gasoline and C3 
respectively. XAdd is a binary variable that takes the value 
of 1 in case there is a need to use ZSM-5 additive. Finally, 
Shiftg and ShiftC3 represent the percentage change that 
affects gasoline and C3 products when 10% of ZSM-5 
additive is used.  
The shift in yield due to the addition of ZSM-5 was 
added to the equation, to allow for the increase of C3 
products on the account of gasoline in case of ZSM-5 
addition. According to [9], the addition of 10% ZSM-5 
with base catalyst in a conventional reactor will increase 
the yield of propylene by 5.5% (because propylene is 
grouped with propane in the C3 lump, it is assumed that 
C3 products yield increase by 5.5%), and decrease the 
yield of gasoline by 9.3% (Fig. 2).  
 
Fig. 2. Gasoline and C3 product yields with respect to VGO conversion 
rate and effect of 10% of ZSM-5 additive. 
 
The main aim is to link the FCC unit operating 
conditions with products yields to find the operating set 
points with varying conditions. Thus, VGO conversion 
rate is estimated using the following expression 
developed by [17]: 
      
avg
f
r
T
E
k
r
r
r
e
e
COR
k
k
k
Conv






)
1
)(
(
(
1
2
1
2
2




  
(3) 
 
where k1 and k2 are the kinetic frequency factors taking 
values of (6.4456 lb oil s/lb catalyst) and (1.7229e6 lb oil 
s/lb catalyst) respectively. Ef is the ratio of activation 
energy of cracking reaction and gas constant (13723.96oF) 
[17, 18]. COR is the Catalyst to Oil ratio can be found 
from expression (4) [18, 19]: 
 
                                    
VGO
RGC
F
F
COR 
                           (4) 
 
where FRGC (lb/s) is the flow rate of the catalyst into the 
riser while FVGO (lb/s) is the flow rate of VGO into the 
riser. Moreover, r (s) is the residence time of the catalyst 
in riser, and it is obtained from the material balance for 
the oil balance based on the work of [20]: 
 
                                
VGO
v
ris
ris
r
F
A
h


                        (5) 
 
where hris is the height of the reactor riser (60 ft). Aris is 
the cross-sectional area of reactor riser (9.16 ft2) vis the 
vapor density at reactor riser conditions (0.57 lb/ft3). ɛ is 
the void fraction in riser which is not occupied by the 
catalyst computed by the following equation [18, 19]: 
  
        
t
t
sg
sp
sg
t
sp
sg
t
U
U
U
U
U
U
U
U
U
2
4
)
(
)
(
2 







     (6) 
 
0.4
0.5
0.6
0.7
0.8
0.9
1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
VGO convertion (wt fraction) 
Products yeild (wt fraction)
product yields with respect to converted VGO
 
 
Gasoline Base Case
Gasoline with 10% ZSM-5
C3 Base Case
3 with 10% ZSM-5
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where Usg (ft/s) is the superficial velocity of vapor in 
riser, Usp (ft/s) is the superficial velocity of catalyst in 
riser and Ut   (ft/s) is the terminal Velocity of catalyst in 
riser in which can be computed as follows: 
 
                           
ris
v
VGO
sg
A
F
U

3048
.
0

                        (7) 
 
                          
ris
c
RGC
sp
A
F
U

3048
.
0

                            (8) 
 
                          
D
v
v
c
p
t
C
g
d
U



3
)
(
4


                        (9) 
 
where CD is the terminal velocity drag coefficient (0.43 if 
Reynolds number > 103 ). c is the density of catalyst in 
the riser (45 lb/ft3). g is the gravitational acceleration 
(32.174 ft/s2). dp is the catalyst diameter (2.4606 * 10-5 ft) 
[18]. Moreover, Tavg (oF) (in equation (3)) is the average 
temperature in riser used in yield optimization model and 
can be calculated as follows: 
 
                      
ris
avg
T
T
T
7
.
0
31
.
0
0 

                 (10) 
where  
            
pVGO
VGO
pc
RGC
pVGO
VGO
reg
pc
RGC
C
F
C
F
T
C
F
T
C
F
T



2
0
             (11) 
             
94
.
460
)
1
(
3
)
25
(
)
25
(
2




T
e
e
T
VGO
VGO
F
F
     
(12) 
 
where T2 (oF)  is the temperature of fresh feed entering 
reactor riser. Tris (oF) is the temperature of reactor riser. 
Treg (oF) is the temperature of regenerator bed. CpVGO is the 
heat capacity of oil feed (1 BTU/lb oF). Cpc is the heat 
capacity of catalyst (0.31BTU/lboF). T3 (oF) is the furnace 
firebox temperature [18, 19]. 
The preheat furnace is used to heat the gas oil feed 
prior to entering the reactor riser section. From the steady 
state energy balance of the furnace, the firebox flow rate 
F5 (scf/s) can be calculated using equation (13) [18, 19]: 
 
                        
3
2
1
5
T
a
T
a
F
LM 

                         (13) 
 
where TLM (oF) is the furnace log mean temperature 
difference, and can be obtained by [18,19]: 
 
               
)
(
)
(
ln
)
(
)
(
2
3
1
3
2
3
1
3
T
T
T
T
T
T
T
T
TLM






                      (14)  
 
where T1 is the temperature of fresh feed entering furnace 
(460.94 oF). a1, and a2 are furnace heat loss parameters 
with values (0.02462 ft3/s oF) and (1.536 * 10-4 ft3/s oF) 
receptivity.  Both parameters were calculated by [17] 
using steady-state data from the FCC process simulator.  
 
III. PROBLEM FORMULATION 
 
The key assumptions in this study are that the main 
products of economic value are gasoline and C3s (propane 
and propylene) and that there is a penalty cost imposed 
whenever the production of a product is less than its 
demand. The main objective of the optimization model is 
to minimize the total cost (C) subject to equipment, 
process, and demand constraints. Furthermore, this 
problem is formulated as MINLP problem. Therefore, the 
cost minimization objective function can be represented 
as follows: 
 
Minimize C = 
)
(
)
(
)
(
)
(
3
)
(
3
)
(
3
)
(
)
(
)
(
)
(
)
(
)
(
3
)
(
3
)
(
)
(
)
(
*
)))
*
(
(
,
0
max(
*
)))
*
(
(
,
0
max(
*
*
)
*
*
(
i
Add
i
Add
i
VGO
i
C
i
C
i
C
i
VGO
i
Gasoline
i
Gasoline
i
Gasoline
i
VGO
i
C
i
C
i
Gasoline
i
Gasoline
N
i
i
VGO
X
C
F
Wt
D
P
F
Wt
D
P
F
S
Wt
S
Wt
C








(15)
            
 
where i is identifier which indicates that the price/demand 
change within period N. CVGO is the cost of VGO ($/lb) 
which differs at each i. DGasoline (lb/s), DC3 (lb/s), SGasoline 
($/lb),  and SC3 ($/lb)  are the gasoline/C3 demands and 
selling prices respectively. PGasoline ($/lb) and PC3 ($/lb) are 
the penalties imposed when gasoline or C3 production 
does not meet demand respectively. Finally, CAdd ($) is 
the cost of ZSM-5 additive.  
 
The main optimization decision variables are the flow 
rate of VGO into the riser FVGO (i) (lb/s), flow rate of the 
catalyst into the riser FRGC (i) (lb/s), temperature of reactor 
riser Tris (i) (0F), temperature of regenerator bed Treg (i) (0F), 
furnace firebox temperature T3 (i) (0F), and XAdd (i) for N 
period. Thus, the total number of variables is 6*N. In 
addition to these variables, the fuel flow rate to preheat 
furnace F5 (i) (lb/s) is considered as an operating setting 
which is determined using equation (13). 
 
The model should satisfy FCC model IV equipment 
constraints as it represents physical limitations which 
affect the operation of the unit. According to [18, 19], 
these constraints can be expressed as follows: 
 
                       
132
75
)
(


i
VGO
F
i
   
                  
(16) 
                               
0
)
(

i
RGC
F
i
   
                         
(17) 
                        
5
.
39
0
)
(
5


i
F
i
   
                      
(18) 
                     
994
980
)
(


i
ris
T
i
   
                   
(19) 
                  
1400
1265
)
(


i
reg
T
i
   
                
(20) 
                    
1700
650
)
(
3


i
T
i
   
                   
(21) 
 
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In order to ensure realistic operation of FCCU, 
process constraints on VGO conversion and residence 
time of catalyst inside riser are imposed represented by 
equations (22) and (23) respectively [16, 21]. 
 
                           
95
.
0
)
(

i
conv
i
   
                    
(22) 
                            
10
2
)
(


i
r

i
   
                       
(23) 
 
 
In this way, the problem becomes one where the main 
six operating settings must be found for each case i that 
would minimize the total production cost satisfying all the 
constraints. 
 
IV. CASE STUDY AND RESULTS 
 
 
This section describes the case study developed in 
order to test and verify the effectiveness of the proposed 
problem formulation. Table 1 shows the parameters 
provided to the model and the resultant optimal settings. 
In this study, the period of consideration is nine days (i.e. 
N=9). It is assumed that at the beginning of each day (i) a 
new set of parameters are received. These parameters 
include gasoline and C3 selling prices and demands, VGO 
purchase cost, gasoline and C3 penalty cost which is 
presumed to be 30% of their respective selling prices, and 
ZSM-5 additive cost. Thus, for each day the model 
utilizes the parameters and accordingly provides seven 
optimal settings to FCCU controller which will enable the 
unit to produce optimal product yields that minimizes the 
cost and honors the equipment and process limitations. 
The unit will operate at these settings for the whole day 
until the next day when change in parameters occur. The 
parameters values are chosen in this study in a way to 
develop 9 different cases in order to test the model 
response against different price and demand fluctuation. 
 
The proposed model formulation is highly nonlinear 
and of a high dimensional order. Thus, heuristic 
techniques such as the genetic algorithm (GA) are often 
chosen in such a task due to their ease of implementation. 
Since developing the algorithm is out of scope of this 
study, GA Matlab Toolbox is used to solve this case study 
[22]. GA solves optimization problems by mimicking the 
principles of biological evolution, repeatedly modifying a 
population of individual points using rules modeled on 
gene combinations in biological reproduction. 
 
The results obtained in Table 1 indicated correct 
response of the model formulation in various situations. 
For instance, gasoline price is much higher than C3 price 
on day 1 and vice-versa on day 2 while maintaining all 
TABLE I  
CASE STUDY PARAMETERS AND OPTIMAL OPERATION SETTINGS 
Time interval i (day) 
1 
2 
3 
4 
5 
6 
7 
8 
9 
Gasoline Sell ($/lb) 
0.2 
0.055 
0.07 
0.07 
0.07 
0.07 
0.05 
0.05 
0.05 
C3 Sell ($/lb) 
0.055 
0.2 
0.07 
0.07 
0.07 
0.07 
0.13 
0.13 
0.13 
VGO Purchase ($/lb) 
0.0399 
0.0399 
0.0399 
0.0399 
0.0399 
0.0399 
0.0399 
0.0399 
0.0199 
Gasoline Penalty($/lb) 
0.06 
0.0165 
0.021 
0.021 
0.021 
0.021 
0.015 
0.015 
0.015 
C3 Penalty($/lb) 
0.0165 
0.06 
0.021 
0.021 
0.021 
0.021 
0.039 
0.039 
0.039 
ZSM-5 Purchase ($) 
0.23 
0.23 
0.23 
0.23 
0.23 
0.23 
0.1 
0.8 
0.23 
Gasoline Demand (lb/s) 
30 
30 
30 
30 
75 
75 
30 
30 
30 
C3 Demand (lb/s) 
5 
5 
10 
20 
5 
13 
15 
15 
15 
Gasoline Yield (lb/s) 
74.16893 
48.5645 
73.2705 
73.29815 
73.92657 
73.71267 
45.06358 
37.88924 
56.07596 
C3 Yield (lb/s) 
6.986473 
20.24582 
8.037523 
8.035476 
7.437874 
7.678455 
15.00036 
8.319504 
17.72918 
Fvgo (lb/s) 
131.9999 
131.9808 
131.9587 
131.998 
131.9984 
131.9998 
108.651 
83.1706 
132 
Frgc (lb/s) 
1192.186 
2418.872 
1184.162 
1291.532 
1379.94 
1225.894 
1625.25 
1856.06 
1982.318 
F5 (SCF/s) 
22.42498 
28.04926 
15.25512 
11.9982 
12.01814 
24.84942 
13.96984 
15.13487 
14.00013 
COR 
9.031718 
18.32746 
8.973732 
9.784483 
10.45421 
9.287092 
14.95844 
22.3163 
15.01757 
Tr  (0F) 
990.6989 
993.9565 
990.6165 
983.9281 
983.9972 
985.2968 
992.0443 
989.7904 
984.9326 
T3 (0F) 
1450.824 
1699.893 
1133.348 
989.1074 
989.9903 
1558.182 
1088.537 
1164.282 
1077.756 
Treg (0F) 
1279.599 
1399.972 
1384.827 
1365.138 
1286.182 
1328.72 
1335.386 
1354.667 
1321.031 
r 
2.219164 
2.000007 
2.221382 
2.199562 
2.182391 
2.212464 
2.514796 
3.025568 
2.072476 
ZSM-5 Additive (XAdd) 
0 
1 
0 
0 
0 
0 
1 
0 
1 
VGO conv (wt) 
0.811205 
0.946634 
0.844486 
0.84436 
0.826349 
0.833848 
0.911788 
0.949972 
0.902125 
Cost ($) 
-9.95124 
-1.22418 
-0.3852 
-0.17538 
-0.40623 
-0.2918 
0.23195 
0.603049 
-2.25179 
Objective C 
-13.8508 
 
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other parameters equal. The resultant gasoline and C3 
yields were proportional to the price change. On day 1 
both demands were satisfied with maximum gasoline 
produced while on day 2 ZSM-5 additive is used to 
maximize C3 yield in response to its high price. 
Moreover, the price of ZSM-5 additive on day 6 is much 
lower than on day 7 whereas the price of C3 is higher than 
gasoline and all other parameters are kept equal on both 
days. Thus, on day 6 the additive was used to meet C3 
demand however, on day 7 a penalty on C3 was paid as its 
demand was not met and the additive was not utilized. For 
both days FVGO value did not hit upper bound. On the 
other hand, on day 9 the additive price returned to have 
moderate price whereas the VGO purchase price reduced 
to half of its original price. This resulted in maximization 
of C3 yield with aid of ZSM-5 additive as response to its 
respective high selling price. Unlike the case on day 6 and 
7, on day 9 the products yields were limited by FVGO 
hitting upper bound.  
 
 
V.  CONCLUSION 
 
 
This paper proposed MINLP formulation for FCCU 
in order to obtain optimal operating settings that minimize 
the cost in a market where prices and demands of oil 
products fluctuate on a daily basis. The formulation was 
based on a six lump yield model that predicts the 
conversion of VGO to valuable products. A case study 
was developed in order to test the model formulation. The 
results indicated that the model responded correctly to 
change in prices and demands. Optimal operating settings 
are provided to FCCU controller allowing the unit to 
generate optimal yields based on the current prices and 
demands of the respective products. 
 
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bed reactors." AIChE Journal 16, no. 3 (1970): 397-404. 
[17] Ancheyta, Jorge, and Rogelio Sotelo. "Kinetic modeling of 
vacuum gas oil catalytic cracking." Revista de la Sociedad 
Química de México 46, no. 1 (2002): 38-42. 
[18] Khandalekar, Prasad D. "Control and optimization of 
fluidized catalytic cracking process." PhD diss., Texas Tech 
University, 1993. 
[19] Ellis R.C," Supervisory optimization of a fluidized catalytic 
cracking unit," Diss, Texas Tech University,1996. 
[21] Sundaralingam R, "Optimization of a model IV fluidized 
catalytic cracking unit," Diss, University of Toronto, 2001. 
[21] Jacob, Solomon M., Benjamin Gross, Sterling E. Voltz, and 
Vern W. Weekman. "A lumping and reaction scheme for 
catalytic cracking." AIChE Journal 22, no. 4 (1976): 701-
713. 
[22] Muldowney, Gregory P. "FCC process with upflow and 
downflow reactor." U.S. Patent 5,468,369, issued 
November 21, 1995. 
[22] Toolbox, MATLAB Optimization. "The MathWorks Inc." 
Natick, MA (2002).  
Authorized licensed use limited to: ULAKBIM UASL - Bilkent University. Downloaded on August 19,2024 at 08:04:52 UTC from IEEE Xplore.  Restrictions apply.

Acknowledgment I
I would like to express my very great appreciation to Professor Bernt Sigve Aadnøy for
his support, guidance, and collaboration during the preparing of this book. Without his
experience, knowledge, and support, this book would not exist.
I would like to acknowledge my gratitude to Equinor (Academia Program) and Aker
BP which sponsored my postdoctoral position and provided me with the opportunity to
further my research into oil well optimization.
I would also like to thank Øystein Arild, Head of the Department of Energy and
Petroleum Engineering, for his support during my postdoctoral studies at the University
of Stavanger.
My grateful thanks are also extended to Joanne Stone who is a professional native
English language editor for her help in editing this book and for her valuable time and
suggestions during the preparation of this book. The assistance provided by Elsevier in
producing this book was greatly appreciated. Thanks to everyone on the publishing
team.
Not least, I want to thank my wife and daughter, Doctor Maryam and Sana, thank
you!
Stavanger, July 2021
Rasool Khosravanian
xij

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Application of Data Envelopment Analysis to Measure the Technical 
Efficiency of Oil Refineries: A Case Study 
Dr. Sabah M. Al-Najjar (Corresponding author) 
Department of Business Management, College of Administration and Economics 
University of Baghdad, Baghdad, Iraq 
Tel: 964-7709-726-485   E-mail: sabanajar@yahoo.com 
 
Mustafa A. Al-Jaybajy 
Technical College of Management, Foundation of Technical Institutes, Baghdad, Iraq 
Tel: 964-7711-281-288   E-mail: mustafajabajy@yahoo.com 
 
Received: June 25, 2012        Accepted: July 13, 2012         Online Published: September 12, 2012 
doi:10.5430/ijba.v3n5p64          URL: http://dx.doi.org/10.5430/ijba.v3n5p64 
 
Abstract 
This paper is an attempt to implement the Data Envelopment Analysis (DEA) approach to measure the relative 
efficiency of a sample of oil refineries in Iraq over a period of two years, 2009-2010. We demonstrate that DEA is an 
effective tool for the Ministry of Petroleum (MOP) for monitoring and controlling the performance of oil refineries, 
which are growing as an important sector in Iraq. The authors followed a case study methodology where data about 
the inputs and outputs of refineries are gathered and analyzed to compute the relative efficiency of the refineries. 
Based on the results obtained, 50% of the refineries were efficient in 2009, while 58% of them were efficient in 2010, 
and the overall efficiency of the refineries studied was about 82% and 87% respectively. Later, inefficient refineries 
were investigated closely to identify the areas in which the use of resources manifest decreasing returns to scale. We 
concluded the paper with some recommendations on the applicability of the DEA for oil refinery efficiency 
evaluation. Due to the absence of research work, in this discipline, in the oil sector in Iraq, this study shall augment 
our knowledge on how oil refineries in Iraq may apply DEA to measure their efficiency, and how they might use the 
results to overcome efficiency problems. Although the results of the present paper are limited to the oil refineries 
studied; the DEA approach could trigger the attention of policy makers in the MOP to apply DEA to improve the 
efficiency of other DMUs. In addition, other manufacturing and service sectors in Iraq could, also, benefit from this 
approach.  
Keywords: Data envelopment analysis, Technical efficiency, Oil refineries, Oil refinery performance, Iraq 
1. Introduction 
In 2009, Iraq was the world's 12th oil producing country, and the fourth largest proven oil reserves after Saudi Arabia, 
Canada, and Iran. Only a small portion of Iraq's known fields are in the development process. The country may be 
one of the few places in the world where great reserves (proven and unknown) have slightly been exploited. The 
energy sector in Iraq is heavily dependent upon oil. Revenues from crude oil accounted for over 2/3 of the GDP in 
2009. Iraqi refineries are somewhat eroded infrastructure, and run at utilization rates of 50% or more. Regardless of 
several attempts to improve the refineries in recent years, the sector has not been able to meet domestic demand of 
about 600 000 bbl/d. Iraq reportedly has nearly 600 000 b/d of refining capacity at several facilities. But because of 
looting, sabotage, deferred maintenance, and unreliable electric power supplies, refinery operations are insufficient 
for domestic needs (Kumins, 2005). The refineries produce, mainly, heavy fuel oil and some other needed refined 
products. Therefore, Iraq relies on imports for about 30% of its gasoline and 17% of its LPG (MOP, 2009). To 
alleviate this problem, Iraq adopted a strategic plan for 2008-2017 to increase the refining capacity to 1.5 million bpd 
(US Commercial Services, 2012). Therefore, at this time, analyzing the performance of national oil refineries is 
important for many reasons. Firstly, oil refineries are national and dominate the proven oil reserves. Secondly, oil 
refineries are expected to supply, at least, the domestic needs for different fuel types. Thirdly, the oil sector 
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dominates the economy and is considered the major source of economic development and GDP especially in the 
developing countries such as Iraq (Stevens, 2008) (Hartley & Medlock, 2007). 
In fact oil refineries seek to create value by virtue of their national mission, and the shareholder is the government 
which tries to maximize the social welfare. Oil refineries can create value by various links in the oil industry value 
chain. This chain starts from the oil fields and moves through: production, processing, transportation, and market. 
The oil fields are the gift of nature; however, the production stage is the important link that is related to field 
recovery factors and production costs. The production link of the refinery is a function of its technical efficiencies. 
So far, the authors are not aware of any previous research on measuring the efficiency and productivity of oil 
refineries in Iraq via the DEA approach. Therefore, much work is needed to measure the relative efficiency of oil 
refineries to identify areas of inefficiencies. This shall help in improving the use oil refinery resources, and reduce 
the dependence on fuel types imported from abroad to satisfy domestic needs. The present study is important for 
three reasons: it increases our knowledge and understanding about measuring the technical efficiency of oil refineries 
in Iraq, it coincides with and supports the MOP's efforts to improve the performance of oil refineries, and finally, it is 
the first study of this type in this domain. The results of this study are expected to provide policy makers at the MOP 
with some helpful insights in developing national strategies directed towards improving the efficiency of national oil 
refineries in Iraq.  
2. Oil Industry in Iraq: A Concise Background  
Established in 1964, the Iraq National Oil Company (INOC) was intended to develop the concession areas taken over 
from international oil companies that had previously controlled Iraq’s oil sector. The company was granted the 
exclusive rights by law to develop Iraq’s oil reserves and granting new concessions to foreign oil companies was 
rendered illegal. Iraq realized that it needed to enhance the technical capabilities of INOC and sought assistance from 
countries that were not involved in the country’s colonial history and the consortium Iraq Petroleum Company (IPC), 
which included the precursor companies to British Petroleum, Royal Dutch Shell, Exxon and Mobil Corporations 
and CFP of France, and had run Iraq’s industry since the British colonial mandate in the 1920s. Iraq concluded a 
services contract with Entreprise Des Recherches et des Activites Petrolieres (ERAP) of France for technical 
assistance in Southern Iraq and the offshore. The agreement did not grant any concessionary rights to the French firm. 
In 1976 Iraq established a new Ministry of Petroleum, the Ministry was commissioned to perform functions of 
planning and direct construction of petroleum sector infrastructure. In 1987, a major reorganization of the oil sector 
took place and INOC became part of the Ministry of Petroleum itself. 
In addition to being hurt severely during the Iraq-Iran war, and during the period of sanctions which followed the 
invasion of Kuwait by Iraq, the oil industry faced different problems immediately after 2003. According to Jaffe 
(2006) many oil reconstruction projects fell victim to insurgency attacks, such as a major damage has been sustained 
to the Baiji gas oil separation plant which halted the processing of 300 000 b/d via Turkey. Water injection activities 
at the Rumaila fields experienced prolonged delays. Lack of technical training and experience hindered the optimum 
implementation of water injection activities to boost production potential at the field. The lack of adequate security 
poses a major challenge for the government in the oil sector. Sectarian and regional strife undermines the ability to 
operate facilities or the sector as a whole either efficiently or effectively. Intimidation of key experts, either those 
trained abroad or those holding critical positions, has become a serious problem and hundreds of oil industry leaders 
have been killed or purged from the sector (Jaffe, 2006). Combining these factors with a looming gap in technical 
and managerial expertise due to Iraq’s relative isolation over the past 25 years as the energy industry rapidly evolved, 
has seriously eroded the capacity of the Iraqi government to manage the oil sector. 
In the present time, the Iraqi oil industry remains structured around both regional lines and functional duties based on 
the 1987 organizational plan. Generally speaking The Minister of Oil is the functional head of the industry, with 
several undersecretaries reporting directly to him. Below this hierarchy are state-run companies functionally defined, 
each led by a Director General and other senior staff (Jaffe, 2006). 
With the exclusion of the Kurdistan Region, there are three national oil companies in Iraq: 
 
The North Refineries Company (NRC) runs six refineries: Salladin, Kirkuk, Baiji, Haditha, Kasak, and 
Qayarah. 
 
The Midland Refineries Company (MRC) manages four refineries: Dura, Najaf, Samawa, and Karbala. 
 
The South Refineries Company (SRC) directs three refineries: Basrah, Nassiriay, and Ammara. 
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The country's refining capacity is estimated at 600 000 b/d. Two major refineries are located at Baiji. Large oil 
refineries are also located at Dura. Iraqi oil refineries were seriously damaged during the years of war and the sector 
remains dilapidated and in need of massive repair (Jaffe, 2007).  
3. DEA: A Theoretical Background 
Data Envelopment Analysis (DEA) is an approach to measure the relative efficiency of Decision Making Units 
(DMU’s) (Taylor, 2001). DMU’s could be organizations, divisions, or units that use similar inputs and produce 
similar outputs. DEA is defined as a linear programming technique which identifies the best practice among a sample 
of units, and measures efficiency based on the difference between best practice and the observed units (SCRC, 1997). 
Best practice could be identified at the organizational, national, and international levels. In essence, DEA attempts to 
measure the technical efficiency (TE). The later is expressed as the potential to increase quantities of outputs from a 
given quantities of inputs. 
This approach was first proposed by Charnes et. al. (1978), and later extended by Banker, Charnes, and Cooper 
(1984). The work of Charnes et. al. is actually based on Farrell’s input and output method to measure efficiency. 
Farrell’s work entitled “The Measurement of Productive Efficiency”, was introduced in 1957 in the Journal of Royal 
Statistical Society (Tone et. al. 2000). Farrell’s TE considers multiple inputs and outputs simultaneously to measure 
the efficiency of organizations using one input to produce one output, or uses one input to produce two outputs, or 
uses two inputs to produce one output. Farell’s technique plots an efficiency frontier or a group of best performers. 
The efficiency frontier is the curve plotting the minimum amount of an input (or combination of inputs) required to 
produce a given quantity of output (or combination of outputs). The best performers are plotted on the efficient 
frontier to indicate that they use their resources more efficiently, than others, to create outputs.  
To explain some of the concepts brought by Farrell, we consider Table 1 which represents the sales (output) of eight 
stores generated by workers or salespersons (input). The last row of Table 1 is the ratio of sales/workers which is 
referred to as efficiency and is computed by the following equation: 
Efficiency = output(s)/input(s)                              (1) 
It appears that store B, a DMU, is the most efficient one, while store F is the least efficient DMU. By plotting the 
data provided by Table 1, we obtain Figure 1. From this figure, the line OO’ which passes through B represents the 
efficiency frontier. All the points below OO’ are said to be inefficient. Hence, OO’ contains or “envelopes” the rest 
of the points on Figure 1. Using the least squares method (Clark, 1978), it is possible to derive the regression line for 
the data presented by Table 1: 
y= 0.67x                                      (2) 
Where y is sales, and x is the number of workers. By plotting this line on Figure 1, we obtain Figure 2. From the last 
figure we notice that the regression line passes in the middle of the data. The points below the regression line refer to 
inferior performance, while those above the regression line are considered to have excellent performance. It is 
evident from Figure 2 that the regression analysis does not identify the best practice or the benchmark for 
performance. This explains why organizations prefer DEA over regression analysis in measuring performance 
(Ghosh, 2008).  
Farrell, also, proposed the Input-Oriented Measure of TE manifested in Figure 3. Here, a company uses two inputs 
X1, X2 to produce one output Q. If the company produces along QQ’, then it is technically efficient. However, if the 
company uses a level of input that corresponds to D to produce one unit of Q, then the company is said to be 
inefficient. The level of inefficiency is measured by the distance CD. This distance represents the amount by which 
the inputs must be reduced to achieve technical efficiency without reducing inputs. Meanwhile, CD/OD represents 
the ratio by which the inputs must be reduced to reach technical efficiency. In other words TE=1-CD/OD, thus the 
TE is somewhere between 0-1. Assuming the X1,X2 prices are fixed, then the distributed efficiency is represented by 
the ratio of OB/OC, and the distance BC is the amount by which the costs of inputs must be reduced to produce at p’. 
Furthermore, Figure 4 provides a schematic representation of Farrell’s Output-Oriented Measure of Technical 
Efficiency where a company uses one input X1 to produce two outputs Q1, Q2. In this figure, pp’ represents the 
production frontiers. All the points that lie on pp’ (such as B) are technically efficient, while all the points that fall 
below pp’ are technically inefficient, such as A. The distance AB is the measure of technical inefficiency, or the 
amount by which outputs may be increased without increasing inputs. The ratio OB/OC is the measure of distributed 
efficiency, or the ratio by which returns may be increased without affecting the inputs. From the above discussion it 
is evident that the Farrell’s method is limited by the number of inputs/outputs. 
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To overcome the limitation of the Farrell’s work, Charnes, Cooper, and Rhode (Charnes et. al., 1978) introduced 
their CCR DEA model that can handle multiple inputs and multiple outputs to measure TE. In the presence of 
multiple input and output factors, technical efficiency is defined as follows: 
Technical Efficiency = weighted sum of outputs                      (3) 
weighted sum of inputs 
Assuming that there are n DMUs, each one has m inputs and s outputs, then technical efficiency of the p’s DMU is 
given by the following model proposed by Charnes et. al. (1978): 
 




m
j
ji
x
j
u
s
k
kp
y
k
v
1
1
max
 
i
m
j
ji
y
j
u
s
k
kp
y
k
v
t
s






1
1
1
.
                                         (4) 
                                   
j
k
j
u
k
v
,
0
,


 
Where, k=1 to s, j=1 to m, i=1 to n, yki = amount of output k produced by DMU i, xji= amount of input j used by 
DMU i, vk= weight assigned to output k, uj= weight assigned to input j.  
Because of the difficulty of solving fractional linear programs, Charnes et. al. converted the above model into a more 
simplified model which is expressed below (Talluri, 2000). 
                                     


s
k
kp
y
k
v
1
max
 
                                    



m
i
j
jp
x
j
u
t
s
1
.
.
 
                         







s
k
m
j
i
ji
x
j
u
ki
y
k
v
1
1
0
                                      (5) 
                                   
j
k
j
u
k
v
,
0
,


 
The previous model is executed n times to identify the relative efficiency scores of all DMUs involved in the 
evaluation. Inputs and outputs that maximize the efficiency of each DMU are selected for each DMU. The DMU is 
considered efficient if it obtains a score of 1, otherwise the DMU is inefficient (Cooper et. al., 2006).  
In order to identify benchmarks for the inefficient DMUs, DEA provides a set corresponding efficient units that may 
be used as benchmarks to improve the inefficient DMUs. The solution of the following dual form of the above linear 
model provides the possible benchmarks for the inefficient units. 
                                 

min
 
                          





n
i
j
jp
x
ij
x
i
t
s
1
0
.
.


 
                             





n
i
k
kp
y
ki
y
i
1
0

 
                                            
i
i

0

 
Where 
     
iables
dual
s
score
efficiency
var




  
 (6) 
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Model (4) and its dual form are known to be DEA models with constant returns to scale (CSR). CSR indicates that 
doubling the inputs of a DMU will result in doubling the outputs, too (SCRC,1997). In other words there are no 
economies or diseconomies of scale, and that the size of the organization is not considered appropriate for measuring 
efficiency. To overcome this limitation of the DEA CCR model, Banker, Charnes, and Cooper extended the CCR 
model to handle problems with variable returns to scale (VRS). The new model, BCC, referred to by the initials of 
the authors, is capable of dealing with problems that exhibit decreasing, constant, and increasing returns to scale 
(Banker et. al.,1984). 
According to Ghosh (2008), DEA has the following characteristics: 
 
It is a nonparametric optimization method that determines production frontiers. 
 
It is a linear programming method that constructs frontiers to calculate efficiency relative to peers, and then 
decides which peer can be set as benchmark for other DMUs. 
 
It is a generalization of the Farrell’s single-input single-output technical efficiency to multiple-input 
multiple-output through constructing a virtual single output to virtual single input ratio. 
 
DEA considers multiple factors and does not necessitates parametric assumptions of traditional multivariate 
methods. 
 
Inputs and outputs may assume different units. 
Furthermore, the following are some limitations of the DEA (SCRC, 1997) (Kuosmanen et. al., 2007): 
 
Since DEA is a deterministic model (and descriptive in nature) it therefore provides results that are sensitive 
to input measurements errors.  
 
DEA attempts to measure the efficiency of a particular sample relative to best practice. Hence, it is not 
useful to compare the scores between two different studies. 
 
DEA results are sensitive to output and input specification, and the size of the sample. Large sample size 
tend to produce lower average efficiency scores. While including few DMUs relative to the number of 
inputs and outputs will tend to inflate the efficiency scores. 
 
Since DEA is a non-parametric approach, therefore statistical tests are not applicable. 
Despite these limitations, DEA has received an increasing importance during the last two decades, and it has been 
used as a tool for evaluating and improving the performance of different organizations (manufacturing and service). 
According to Charnes et. al. (1994), DEA is extensively applied in performance evaluation and benchmarking in 
hospitals, bank branches, libraries, production plants, etc. In addition, Tavaresx (2002) developed a DEA database 
which included 3,203 references, 2,152 authors and 1,242 keywords. The references are distributed over seven 
publication types as shown in Figure (5).  
4. Literature Review 
In a world of global competition, success is dependent on the proper use of inputs to generate outputs. Financial and 
operational problems could result from failure to optimize the efficient use of resources. Hence, researchers exerted 
great efforts to develop approaches that help businesses to improve the use of resources. The DEA was one of the 
most popular approaches proposed to improve the efficient use of resources. Since its introduction by Charnes, 
Cooper, and Rhode, the DEA approach has attracted the attention of academicians and practitioners all over the 
world. It has also seen a wide variety of applications to evaluate the performance of various types of DMUs engaged 
in different activities in different environmental contexts, and in different countries (Cooper et. al., 2004). The DEA 
applications were evident in service and manufacturing sectors.  
Odeck & Alkadi (2001) attempted to evaluate the performance of Norwegian bus companies subsidized by the 
government. The authors used the DEA approach to measure efficiency in this sector. Several issues were addressed 
in this context, such as: efficiency rankings, distribution and scale properties in the bus industry, potentials for 
efficiency improvements in the sector, the impact of ownership, etc. The findings of this study show that the average 
bus company exhibits increasing return to scale in production of its services. The implications of DEA results are 
discussed and concluding remarks offered. Banker et. al. (2002) attempted to measure the productivity of Acer to 
determine whether the introduction of information technology at the firm in 1998 had some impact on the company's 
performance. Based on different efficiency ratings, the authors concluded that the introduction of information 
technology resulted in productivity increases in 1997-1999. The findings of the study assisted the applicability of the 
DEA approach to measure the productivity of the firm at different points in time. Mahadevan (2002) sought to 
explain the productivity growth performance in the manufacturing sector in Malaysia using a panel of data of twenty 
eight industries from 1981-1996. The author applied the DEA approach to compute and to decompose the Malmquist 
index of total factor productivity (TFP) growth into technical change, change in technical efficiency and change in 
scale efficiency. The rationale behind this decomposition was to identify the sources that were crucial for policy 
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formulation. The study revealed that the annual TFP growth of the Malaysian manufacturing sector was low at 0.8% 
and this was due to small gains in both technical change and technical efficiency, with industries operating close to 
optimum scale. Wang (2006) believes that no one performance measurement tool can provide a composite picture 
about the performance of a firm, therefore he proposed the use of DEA and the Balanced Scorecard (BSC) 
approaches to determine whether these two approaches are appropriate to Acer firm with information about the firm's 
performance between 2001-2003. The author reports that the two approaches provided illuminating information 
about Acer's performance, and that other firms could benefit from both approaches. Oliveira et. al. (2007) mention 
that the last two decades were characterized by high oil prices, thus many countries in the world were vulnerable to 
this phenomenon. On the other side, the production of oil and gas sponsored the industrialization of many countries 
worldwide including South America countries. The authors analyzed the performance of some South America 
countries using the DEA to measure the efficiency regarding the usage and dependency on production, consumption, 
and proved resources of oil and gas. The authors claim that their study could be extended to evaluate other countries 
around the world. Zhou et. al. (2008) argue that DEA has gained an immense popularity in the energy and 
environmental sectors in recent years. Thus, the authors presented a literature survey on the application of DEA to 
the energy and environmental studies. The most popular DEA techniques were introduced first, and then followed by 
a classification of 100 publications in this field. The authors concluded that DEA is gaining more popularity in the 
energy and environmental studies, and that there is a lack in literature review in this field. They also believe that the 
classification of DEA studies reached in their study is useful to researchers entering this exciting field. Motivated by 
the rise of energy prices in the transportation sector, Malhotra et. al. (2008) applied the DEA approach to analyze the 
performance of seven North American Class I freight railroads. The authors analyzed the financial ratios of a firm as 
opposed to its peers. The DEA brought out the firms that were operating more efficiently compared to other firms in 
the industry. The study pointed out the areas where poor performing firms need to improve. Mekaroonreung & 
Johnson (2009) used DEA as a method for evaluating the technical efficiency of 113 U.S. oil refineries in 2006 and 
2007. The authors implemented several measures based on the DEA approach; these measures were compared to 
study the impact of disposability assumptions. The authors demonstrated that oil companies can improve efficiencies 
regardless of the assumption of disposability of bad outputs. Sepehrdoust (2011) applied the DEA to evaluate the 
housing industry performance in many states, in Iran, based on the data collected from the Statistical Center of Iran 
from 2006-2009. The author reported that only 37% of the states studied operated efficiently and the average 
efficiency score obtained by all states was around 94%. The author proposed some measures that could be applied by 
the government to stimulate the efficiency of the housing sector in Iran. Ines and Martinez (2011) used the DEA to 
measure energy efficiency development in the non-energy-intensive sectors (NEISs) in Germany and Colombia 
through a production-bases theoretical framework using data from 1998-2005. The authors compared energy 
efficiency performances at two levels of aggregation and then applied different alternative models. The results 
indicated considerable variations in energy efficiency performance in the NEISs of the two countries studied. Ajalli 
et. al. (2011) investigated the problem of separability in DEA where the number of DMUs is lower compared to the 
number of input and output. The authors evaluated 23 provincial gas companies considering the higher output rates 
of each provincial gas company. To achieve the objectives of the study, an integrative model was developed using 
the Anderson-Peterson Method along with DEA. The results contributed to the increased power of evaluation, 
separability, and adequate ranking of the companies studied. 
The above review is no way exhaustive about the widespread use of DEA, however, it demonstrates the applicability 
of this approach to a multiplicity of sectors. The benefits obtained from this approach shall continue to trigger 
interests among researcher to pursue more developments and applications of the DEA. 
5. Research Problem and Objectives 
The literature review provides DEA applications in different business sectors and in different countries. However, 
the authors did not encounter any study that measures and documents the performance of oil refineries in Iraq. 
Currently, the Office of the General Inspector (OGI) evaluates the refinery company performance based on the 
performance of the following units within each company: Legal, Managerial, Contractual, Auditing, and Financial. 
The evaluation is done using a form that contains several questions which are supposed to be answered by the 
functional directors at the end of the year. By reviewing the annual evaluation reports of the refineries, the authors 
observed that neither the criteria nor the weights used to measure the refinery's performance are uniform. Hence, it is 
not possible know precisely which refineries are using their resources more efficiently than the others, nor does the 
present method assists the MOP to analyze the inefficiency problems within each refinery. The research problem lies 
in the absence of a formal approach to measure the technical efficiency of oil refineries at the MOP. The authors 
believe that this work is worthwhile, and shall shed the light on this area. The findings of this paper provide a clear 
indication of the refineries which are using their resources efficiently. This information can be applied by the MOP 
to augment decision making with information regarding best practices for the oil refineries. The present study is 
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significant at this time because it coincides with the reconstruction efforts of the MOP to enhance the oil industry in 
Iraq. 
The present research attempts to achieve the following objectives: 
 
Developing and applying a DEA model to measure the TE of a sample of oil refineries (DMUs) in Iraq. 
 
Comparing the TE of the studied refineries to identify the refinery(ies) that could be used as a benchmark. 
 
Identifying and explaining the reasons that impede the refineries from reaching efficiency frontiers. 
 
Computing the quantities by which inputs should be reduced so that inefficient refineries can attain the 
efficient production frontiers of the oil industry in Iraq. 
6. Research Methodology 
In this work a case study approach was followed to compute and analyze the technical efficiency of the refineries 
studied. The case study approach was also used by Oliveira et. al. (2007), Ajalli et. al. (2011), Mekaroonreung & 
Johnson (2009) and Ines and Martinez (2011) to measure the TE in the energy industry. The sample of the refineries 
studied consists of 12 refineries under the direction of NRC, MRC and SRC. For confidentiality purposes, the names 
of the refineries shall be referred to by DMU1, DMU2, …..DMU12. To measure the TE of the sample studied, the 
authors followed the following steps: 
 
Sample Selection: twelve out of thirteen refineries were selected for this study. One refinery was 
excluded from the study due to difficulties in data collection. 
 
Data Collection: for the purposes of this study, four inputs {crude oil (m3), workforce (workers), 
electricity (Kw/h) and land (hectares) }, and four outputs {naphtha (m3) , gasoline (m3), kerosene (m3) 
and fuel oil (m3)} were identified and fed into the DEA model. Tables (2) and (3) present the 
input/output data for all the refineries involved in this study during 2009, 2010 respectively. 
 
Model Selection: the DEA CCR with constant returns to scale model developed by Charnes & 
Cooper (1978) and presented by (4) is used to measure the TE of the refineries. 
 
Model Development: twelve models/year (one for each refinery) are developed to evaluate the 
relative efficiency scores of each DMU involved in the study during 2009 and 2010.  
Several software packages are available to solve the DEA model such as DEA windows, Frontier Analyst, 
DEAFrontier, etc. (Lin et. al., 2009). In this study, we preferred to use more generic software to perform the 
calculation, therefore we selected the Excel 2003 Solver for this purpose.  
7. DEA Application 
Using the DEA CCR model with constant returns to scale and the input- output data presented in Tables (2) and (3), 
a DEA model was developed to calculate the TE for each refinery during 2009 and 2010. For instance, the DEA 
model developed to compute the TE for DMU2 in 2009 is presented below: 
Max Z= 111188x1+101833x2+64845x3+320098x4 
S.T 
617740y1+270y2+6048y3+1110y4=1 
854368X1+452196X2+218503X3+1921998X4 ≤ 3733935y1+4249y2+77900000y3+855y4 
111188x1+101833x2+64845x3+320098 ≤ 617740y1+270y2+6048y3+1110y4 
81893x1+84833x2+48741x3+240454x4 ≤ 482216y1+549y2+5135y3+323y4 
40100x1+38674x2+20764x3+119262x4 ≤ 225340y1+182y2+2300y3+600y4 
1195126x1+1433560x2+823529x3+2646866x4 ≤ 40359283y1+2980y2+12042000y3+2000y4  
761253x1+828516x2+569705x3+1898627 ≤ 27231760y1+2220y2+57864000y3+1350y4                   (7) 
4663x1+9268x2+3330x3+22573x4 ≤ 347393y1+204y2+225000y3+80y4 
0x1+201068x2+211824x3+859847x4 ≤ 10112105y1+247y2+360000y3+423y4 
2167x1+37063x2+0x3+89404x4 ≤ 1127080y1+160y2+221000y3+128y4 
642100x1+1446603x2+591398x3+3492130x4 ≤ 8019877y1+4510y2+100174y3+8000y4 
50849x1+168141x2+86832x3+673677x4 ≤ 7136817y1+845y2+4712y3+4000y4 
648x1+180x2+107x3+197x4 ≤ 4000y1+375y2+2700y3+600y4 
x1, x2, x3, x4  ≥ 0 
y1, y2, y3, y4  ≥ 0 
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The computer output for this model is presented in Table 4. From this table it appears that the TE of this refinery is 1, 
which indicates that this refinery is using its resources efficiently and it could be used as a benchmark for other 
inefficient refineries. Thus, a total of 24 runs were conducted for all the refineries. 
8. Results  
Table (5) lists the refineries according to their TE calculated by the DEA models. It appears that 6 out of 12 (50%) 
refineries attained TE in 2009, while seven refineries (58%) were technically efficient in 2010. Some DMUs 
(refineries) were technically efficient in both years such as DMU1,2,3,5,6. DMU4,8,11,12 improved their efficiency in 
2010 compared to 2009. In addition, DMU7,9,10 experienced a decline in TE in 2010. The average TE of all the 
refineries in 2009 was 82%, while in 2010 the average TE was 87%, these results coincide with the estimates 
reported by Jaffe (2007). The annual average improvement achieved in 2010 was about 6%. The least TE in 2009 
and 2010 was achieved by DMU7. The inefficient refineries in 2010 (7,8,9,10 and 11) should reduce the use of their 
resources by 71%, 13%, 34%, 2%, and 40% respectively to reach the efficient production frontiers. The amounts by 
which resources should be reduced by the inefficient refineries are provided in Table (6). From this table, the total 
annual underutilization at the inefficient refineries from crude oil, workers, electricity and land is 6 637 308 m3, 
1530 workers, 144 459 Kw/h and 3355 hectares respectively. To discriminate the efficient refineries, Hingsworth 
and Parkin (1995) suggest that it is worth identifying the number of times that an efficient DMU acts as a peer. In our 
case the peer refineries are those that scored efficient in both years (DMU1,2,3,5,6), these DMUs can be considered as 
better performing units due to their outstanding operations. 
To identify the causes of inefficient operations, the authors conducted several interviews with directors at the MOP. 
The following are the most frequent causes that were delineated: 
 
Frequent electric power interruptions. 
 
Insurgency attacks, sabotages and looting of crude oil.  
 
Suboptimal utilization of the land available for refineries. 
 
Excessive workforce due to the return of fired employees before 2003. 
 
Hydrogen and propane shortages. 
 
Shortages of fuel required to operate the refineries. 
 
Maintenance activities are not performed as planned. 
 
Shortage of trained personnel. 
 
Shortage of capacity to store finished products. 
 
Underutilization of workforce. 
 
Use of inadequate spare parts. 
 
Shortage of heavy equipments (bulldozers, cranes, etc) to facilitate the refinery's operations. 
Unless these problems are resolved and resources are restructured, the inefficient refineries shall remain inefficient in 
the future. 
9. Conclusions 
Performance measurement tools can help organizations to evaluate the allocations of their resources in order to 
determine the way those resources may be managed and allocated to value-adding activities. Hence, DEA can also 
assist in identifying areas where resources are misallocated. In this study we demonstrated that the DEA is a 
powerful non-parametric approach for measuring the TE of the refineries studied, and it can provide a summary 
measure of the relative performance of each refinery. It is clear that the DEA approach offers illuminating 
information to the MOP which can benefit from such information regarding decision making for the oil refineries. 
Based on the results obtained, 50% of the refineries were efficient in 2009, while 58% of them were efficient in 2010. 
This may be due to the improvements in the security conditions realized in 2010. The overall efficiency of the 
refineries studied was about 82% and 87% in 2009 and 2010 respectively. It is interesting to note that the oil industry 
in Iraq is not effectively under the pressures (at least now) of environmental regulations. The present study revealed 
that there is a waste or underutilization of resources at the inefficient refineries. Those inefficient refineries manifest 
decreasing returns to scale and need to reorganize their structure of inputs in order to reach efficiency production 
frontiers. Although this study is not a large scale, it provides policy makers at the MOP with an insight about the 
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relative performance of the oil refineries, and in deriving strategies to reconstruct their inputs to eliminate waste and 
optimize outputs. 
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Table 1. Data for single input-output 
H 
G 
F 
E 
D 
C 
B
A 
Stores
 
8
  
6
  
5
  
5
  
4
  
3
  
3
 
2
 
Workers
 
5
  
3
  
2
  
4
  
3
  
2
  
3
 
1
  
Sales
 
0.625
 
0.5
 
0.4
  
0.8
  
0.75
 
0.67
 
1
 
0.5
  
Sales/Workers
 
Source: Ghosh, 2008. 
 
Table 2. Inputs and outputs for 2009 
Inputs**
  
Outputs*
  
  
4
  
3
  
2
  
1
  
4
  
3
  
2
  
1
  
DMUs
 
855
  
77900000
 
4249
  
3733935
  
1921998
 
218503
 
452196
  
854
 
368
  
DMU1
 
1110
  
6048
  
270
  
617740
  
320098
  
64845
  
101833
  
111188
  
DMU2
  
323
  
5135
  
549
  
482216
  
240454
  
48741
  
84833
  
81893
  
DMU3
  
600
  
2300
  
182
  
225340
  
119262
  
20764
  
38674
  
40100
  
DMU4
  
2000
  
12042000
 
2980
  
40359283
 
2646866
 
823529
 
1433560
 
1195126
 
DMU5
  
1350
  
57864000
 
2220
  
27231760
 
1898627
 
569705
 
828516
  
761253
  
DMU6
  
80
  
225000
  
204
  
347393
  
22573
  
3330
  
9268
  
4663
  
DMU7
  
423
  
360000
  
247
  
10112105
 
859847
  
211824
 
201068
  
0
  
DMU8
  
128
  
221000
  
160
  
1127080
  
89404
  
0
  
37063
  
2167
  
DMU9
  
8000
  
100174
  
4510
  
8019877
  
3492130
 
591398
 
1446603
 
642100
  
DMU10
  
4000
  
4712
  
845
  
7136817
  
673677
  
86832
  
168141
  
50849
  
DMU11
  
600
  
2700
  
375
  
4000
  
197
  
107
  
180
  
648
  
DMU12
 
Source: MOP. 
*Outputs: 1=naphtha, 2=gasoline, 3=kerosene, 4=fuel oil 
**Inputs: 1=crude oil, 2=workers, 3=electricity (Kw/h), 4=land 
 
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Published by Sciedu Press                        74                           ISSN 1923-4007  E-ISSN 1923-4015 
Table 3. Inputs and outputs for 2010 
Inputs**
  
   Outputs*
  
4
 
3
  
2
 
1
 
4
 
3
  
2
  
1
  
DMUs
 
855
 
77911296
  
4321
 
4687599
 
2396900
 
2728920
 
652019
 
967667
  
DMU1
1110
 
7086
  
304
 
1123375
 
605782
 
109763
 
187724
 
196213
  
DMU2
 
323
 
5451
  
575
 
861415
 
439386
 
85401
  
152452
 
158328
  
DMU3
 
600
 
2300
  
256
 
316388
 
170265
 
32220
  
60350
  
48098
  
DMU4
 
2000
 
105346000
 
2899
 
49422658
 
3165386
 
959075
 
1847619
 
1290367
 
DMU5
 
1350
 
69830000
  
2995
 
31383883
 
2117908
 
898346
 
900685
 
1050931
 
DMU6
 
80
 
2250
  
241
 
9790
 
2030
 
129
  
665
  
0
  
DMU7
 
423
 
360000
  
290
 
9827616
 
811115
 
211754
 
234040
 
0
  
DMU8
 
128
 
221952
  
142
 
1808842
 
13926
 
0
  
64301
  
0
  
DMU9
 
8000
 
4590
  
102213
 
8271227
 
3633443
 
468384
 
1545962
 
348263
  
DMU10
 
4000
 
888
  
4638
 
7708797
 
309512
 
9123
  
194931
 
181033
  
DMU11
 
600
 
400
  
4000
 
12000
 
258465
 
357
  
60644
  
49500
  
DMU12
Source: MOP. 
*Outputs: 1=naphtha, 2=gasoline, 3=kerosene, 4=fuel oil 
**Inputs: 1=crude oil, 2=workers, 3=electricity (Kw/h), 4=land 
Table 4. Computer solution for DMU2-2009 
Report Created: 7/13/2011 2:57:42 PM 
Microsoft Excel 12.0 Sensitivity Report 
Adjustable Cells 
 
 
  
  
Final 
Reduced
Objective 
Allowable 
Allowable 
 
Cell 
Name 
Value 
Cost 
Coefficient 
Increase 
Decrease 
 
$C$20 
x1= x1 
1.30681E-06
0
111188
0 
0
 
$C$21 
x2= x1 
1.93636E-06
0
101833
0 
0
 
$C$22 
x3= x1 
5.68058E-06
0
64845
0 
0
 
$C$23 
x4= x1 
9.03335E-07
0
320098
0 
0
 
$E$20 
y1= x3 
1.88363E-07
0
0
0 
0
 
$E$21 
y2= x3 
0.000887291
0
0
0 
0
 
$E$22 
y3= x3 
0
0
0
0 
1E+30
 
$E$23 
y4= x3 
0.000580245
0
0
0 
0
Constraints 
 
 
  
  
Final 
Shadow 
Constraint 
Allowable 
Allowable 
 
Cell 
Name 
Value 
Price 
R.H. Side 
Increase 
Decrease 
 
$L$5 
DMU1 
4.969543981
0
0
1.35850828 
1.185877638
 
$L$6 
DMU2 
1
1
0
0.31869405 
0.219069229
 
$L$7 
DMU3 
0.765373537
0
0
0.132593301 
0.113350852
 
$L$8 
DMU4 
0.352974994
0
0
1E+30 
0.19910472
 
$L$9 
DMU5 
11.40682054
0
0
0.613091918 
3.314325252
 
$L$10 
DMU6 
7.550470225
0
0
1E+30 
0.332107724
 
$L$11 
DMU7 
0.063347137
0
0
1E+30 
0.229515844
 
$L$12 
DMU8 
2.369352993
0
0
0.628619437 
0.967747257
 
$L$13 
DMU9 
0.155360764
0
0
1E+30 
0.273177528
 
$L$14 
DMU10 
10.15429152
0
0
2.29354365 
1.417922429
 
$L$15 
DMU11 
1.493843312
0
0
1E+30 
2.921211317
 
$L$16 
DMU12 
0.001981138
0
0
1E+30 
0.679653384
 
$M$6 
DMU2 
1
1
1
1E+30 
1
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Table 5. Listing of refineries according to TE achieved in 2009 & 2010 
DMUs 
2009 
2010 
1 
1 
1 
2 
1 
1 
3 
1 
1 
4 
0.68 
1 
5 
1 
1 
6 
1 
1 
7 
0.34 
0.29 
8 
0.57 
0.87 
9 
0.79 
0.66 
10 
1 
0.98 
11 
0.55 
0.60 
12 
0.89 
1 
 
Table 6. Computation of wasted and target resources at the inefficient DMUs in 2010 
Actual 
Resources 
Target 
Resources
Amount
Wasted 
% of Wasted
 Resources
Inputs 
DMU 
9790 
2839 
6951 
0.71 
Crude oil m3 
DMU 7 
241 
70 
171 
0.71 
Workers 
2250 
653 
1597 
0.71 
Electricity (Kw/h)
80 
23 
57 
0.71 
Land (Hectares)
9 827 616 
8 550 026
1 277 590
0.13 
Crude oil m3 
DMU 8 
290 
252 
38 
0.13 
Workers 
360 000 
313 200 
46 800 
0.13 
Electricity (Kw/h)
423 
368 
55 
0.13 
Land (Hectares)
 1 808 842 
1 193 836
615 006
0.34 
Crude oil m3 
DMU 9 
142 
94 
48 
0.34 
Workers 
221 952 
146 488 
75 464 
0.34 
Electricity (Kw/h)
128 
85 
43 
0.34 
Land (Hectares)
7 708 797 
4 625 278
3 083 519
0.40 
Crude oil m3 
DMU 10 
888 
533 
355 
0.40 
Workers 
4638 
2783 
1855 
0.40 
Electricity (Kw/h)
4000 
2400 
1600 
0.40 
Land (Hectares)
8 271 227 
6 616 981
1 654 245
0.02 
Crude oil m3 
DMU 11 
4590 
3672 
918 
0.02 
Workers 
102 213 
81 770 
20 443 
0.02 
Electricity (Kw/h)
8000 
6400 
1600 
0.02 
Land (Hectares)
 
 
www.sciedu.ca/ijba 
International Journal of Business Administration 
Vol. 3, No. 5; 2012 
Published by Sciedu Press                        76                           ISSN 1923-4007  E-ISSN 1923-4015 
 
Figure 1. Efficiency frontier and feasible production set 
 
Figure 2. Regression line and efficiency frontier 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3. Input-oriented technical efficiency 
Source: Ghosh, 2008,p. 51. 
 
www.sciedu.ca/ijba 
International Journal of Business Administration 
Vol. 3, No. 5; 2012 
Published by Sciedu Press                        77                           ISSN 1923-4007  E-ISSN 1923-4015 
 
 
 
 
 
 
 
 
 
 
 
Figure 4. Output-oriented technical efficiency 
Source: Ghosh, 2008, p.52. 
 
1469
1259
171
127
115
50
12
0
200
400
600
800
1000
1200
1400
1600
event paper
journal paper
dissertation
book chapter
research paper
book
special journal
Publication Type
Number
  
Figure 5. DEA publication number by type 
Source: Tavares, 2002, p.4.

Numerical optimization of self-compacting mortar mixture containing
spent equilibrium catalyst from oil reﬁnery
Sandra Nunes a, *, Carla Costa b
a CONSTRUCT-LABEST, Department of Civil Engineering, Faculty of Engineering (FEUP), University of Porto, Portugal
b Department of Civil Engineering, High Institute of Engineering of Lisbon (ISEL), Lisbon, Portugal
a r t i c l e i n f o
Article history:
Received 15 September 2016
Received in revised form
27 April 2017
Accepted 27 April 2017
Available online 28 April 2017
Keywords:
Spent equilibrium catalyst (ECat)
Pozzolanic addition
Industrial by-product
Statistical factorial design
Self-compacting concrete (SCC)
a b s t r a c t
As the oil reﬁning industries continue to grow, the production of waste catalysts generated in that
process is expected to also increase. It would be of great value both economically and ecologically if these
wastes could be reused as an addition in self-compacting concrete (SCC). This paper uses statistical
factorial design approach, namely a central composite design, to conduct a proper experimental plan to
design SCC mortar mixtures incorporating spent equilibrium catalyst (ECat), a waste generated by the oil-
reﬁnery industry. The mathematical empirical models derived (which were also experimentally vali-
dated) revealed the inﬂuence of mixture design parameters, and their coupled effects, on the mortars’
properties namely, deformability, viscosity, compressive strength, resistivity and ultrasonic pulse ve-
locity. A numerical optimization technique was applied to the derived models to select the best mixture,
which maximizes simultaneously durability and eco-efﬁciency and minimize cost, while maintaining
self-compactability. The current study revealed that ECat can be successfully applied in SCC mortars, as a
high volume cement replacement material (up to VEcat=Vp ¼ 19.7%) due to its high pozzolanic activity.
Nevertheless, for powder-type SCCs, cement/ECat blends must be combined with other ﬁner additions to
complete the powders distribution curve increasing the viscosity and stability of paste phase, in the fresh
state.
© 2017 Elsevier Ltd. All rights reserved.
1. Introduction
Civil engineering plays a key role in developing speciﬁc actions
towards a smarter, sustainable and inclusive society, important
pillars of the EU Strategy 2020, while contributing to job creation
and economy growth. As such, new developments in civil engi-
neering are needed to promote the mitigation of human impact on
the environment, the improvement of life quality and the efﬁcient
use of resources (Torgal and Jalali, 2011). Concrete industry, in
particular, can make a major step towards a more sustainable so-
ciety since it allows incorporation of recycled waste materials from
other industries either as partial surrogates of the aggregates (Dash
et al., 2016) or of cement. These supplementary cementitious ma-
terials, with pozzolanic activity, comprise, among others, agricul-
tural wastes (Aprianti et al., 2015), waste foundry sand, coal bottom
ash, cement kiln dust and wood ash (Siddique, 2014). More and
more, it is a common practice to incorporate these materials into
hydraulic binders as a solution to their ﬁnal conﬁnement. The
incorporation of these industrial by-products in concrete produc-
tion offers many advantages such as: (a) environmental, mitigating
solid waste disposal of these materials in landﬁlls as well as
reducing consumption of natural resources and CO2 emissions in
cement plants; (b) economic, decreasing the demands for cement
thus reducing the cost and consumption of energy as well as
turning wastes into products with added-value; and (c) techno-
logical, giving rise to novel construction materials with improved
engineering properties both in the fresh and hardened states.
Within this scope, this paper addresses the reuse of the spent
equilibrium catalyst (ECat), generated in the ﬂuid cracking catalytic
(FCC) units present in most of the oil reﬁneries, incorporated in self-
compacting concrete (SCC). FCC units perform one of the most
signiﬁcant processes in the oil industry by converting crude oil into
more valuable oil products such as high octane gasoline. This pro-
cess requires the presence of a catalyst that after some time of use
loses the required catalytic activity and is withdrawn from the
process, becoming a waste, ECat, which currently is mainly disposal
of in landﬁlls. The quantity of total ECat generated is signiﬁcant. Oil-
reﬁneries worldwide withdraw approximately 840 metric kt/y of
* Corresponding author. Rua Dr. Roberto Frias, s/n, 4200-465, Porto, Portugal.
E-mail addresses: snunes@fe.up.pt (S. Nunes), carlacosta@dec.isel.pt (C. Costa).
Contents lists available at ScienceDirect
Journal of Cleaner Production
journal homepage: www.elsevier.com/locate/jclepro
http://dx.doi.org/10.1016/j.jclepro.2017.04.161
0959-6526/© 2017 Elsevier Ltd. All rights reserved.
Journal of Cleaner Production 158 (2017) 109e121
ECat (Ferella et al., 2016), of which 20% by European reﬁneries
(ECCPA, 2006). Portugal generates up to 780 metric t/y of ECat
(Sines Reﬁnery, 2015).
The ECat is an aluminosilicate constituted by a zeolite with
faujasite-type structure incorporated in a matrix essentially of
alumina and other amorphous aluminosilicates. The zeolite phase
is a crystalline structure consisting of interconnected tunnels and
cages that confers high speciﬁc surface area (>100 m2/g) and,
consequently, provides high reactivity. The exact composition of
the ECat used in the different reﬁneries depends on the manufac-
turer and on the process that is going to be used. However, the
physical and chemico-mineralogical properties of ECat provided by
the same reﬁnery remain stable among different samples over long
periods of time.
When incorporated in cement-based materials, the ECat-
including the generated in Portugal (Costa and Marques, 2012) -
exhibits strong pozzolanic activity observed by means of both
thermogravimetric methods (Paya et al., 2003) and mechanical
strength tests (Pay
a et al., 2013). ECat pozzolanic high-reactivity is
similar to that of metakaolin (Allahverdi et al., 2011). The assess-
ment of the leaching of heavy metals from ECat indicated that they
are below than the regulatory limits speciﬁed both by the Envi-
ronmental Protection Agency of Taiwan (Su et al., 2000) and in the
European Directive (Antiohos et al., 2006). Therefore, reusing ECat
into hydraulic binders will not result in further pollution.
Previous research studies also showed that when the spent
catalyst is used as a partial cement surrogate the spread ﬂow value
of fresh mortars decreases with increasing its content (Allahverdi
et al., 2011), the bleeding is reduced and the setting time be-
comes shorter (Su et al., 2000). Due to the additional strength-
provider products, that arise from the pozzolanic reaction of ECat,
cement replacement with ECat up to 15e20 wt% leads to strength
enhancement (Costa and Marques, 2012) regardless the type of
cement (Zornoza et al., 2007). In addition, the pozzolanic products
also promote the reﬁnement of capillary porosity of the cement
matrix as well as the decrease in their interconnectivity improving
the resistance against water absorption and chloride ion diffusion
(Castellanos et al., 2014). It was also observed a reduction on
alkaliesilica reaction occurring susceptibility (Costa et al., 2014)
because zeolite-based materials are good adsorbents for trapping
alkalis reducing their availability to participate in the reaction with
the aggregates. Similarly to the results found with other pozzolan
blended cement-based materials, the ECat incorporation leads to a
decrease in the resistance to the CO2 inward diffusion i.e, in the
carbonation resistance (Castellanos et al., 2014). This result is
attributed to the consumption of Ca(OH)2 in the pozzolanic reac-
tion. However, it was observed that this spent catalyst does not
worsen the corrosion level of steel reinforcement under carbon-
ation attack for low w/b ratio (G
omez, 2007) or under chloride
ingress attack (Zornoza et al., 2008). Replacement of Portland
cement by spent catalyst might result in a reduction or an increase
of 90-day shrinkage of hardened cement paste, depending on
replacement level (Allahverdi et al., 2011). From a technological
perspective, previous studies revealed that spent ECat may be a
constituent of mortars and concretes for different applications such
as, concrete repair mortars (Costa et al., 2013), traditional concrete
(Pacewska et al., 2002), high strength concrete and self-compacting
concrete (SCC) (Martínez, 2007) as well as ultra-high strength ﬁbre
reinforced concrete (Torregrosa, 2013) since they give rise to con-
struction materials that meet the requirements lay down out in the
corresponding standards.
Among the aforementioned ECat potential applications, its
reuse in SCC is a very interesting since it is able to address long term
performance, cost-effectiveness, and environmental issues in an
integrated way. In fact, the SCC ability to ﬂow under its own weight
tends to produce a more dense and uniform material with long-
term improved performance (Okamura et al., 2000). In particular,
the powder-type SCC production requires a signiﬁcant amount of
powder materials in order to ensure ﬁlling ability, passing ability
and segregation resistance. These powder materials may comprise
industrial wastes such as agro-industrial waste (Nagaratnam et al.,
2016), recycled ground glass powder (Nunes et al., 2013) and coal-
and biomass-ﬁred ash (Sua-iam and Makul, 2015). SCC technology
will, along with the mentioned advantages, lead to increased pro-
ductivity and reduction of construction period due to the ease of
placing SCC in heavily reinforced areas difﬁcult to access and to the
reduced effort in accomplishing the casting tasks, in general.
Furthermore, it leads to considerable reduction of the acoustic
noise levels in the construction site. However, the research studies
about the use of spent catalysts from oil reﬁnery in SCC are rare, and
will be further exploited in the current work.
Concrete mix-design is becoming a very complex problem since
concrete has to incorporate an increasing number of constituent
materials (cement, several additions, one or more type of admix-
tures, recycled and/or natural aggregates) and it has to meet several
requirements related to safety, serviceability, durability, cost and
environmental impacts. It became very difﬁcult for designers to
foresee all the consequences of changing certain mixture parame-
ters in such a multidisciplinary problem due to the complex
interactive relationship among the design variables. Another chal-
lenge is how to obtain the necessary information about all the
constituent materials and their interactions to guide manufacturers
toward the optimal mixture composition within a reasonable time
and targeting different engineering properties. To achieve these
aims the design of experiments (DOE) is a systematic statistical
approach that allows the exploration of the relationships between
design variables (mixture parameters) and responses (e.g. fresh and
hardened concrete properties) giving a better overall system un-
derstanding, while minimizing the number of experiment runs. The
experimental plan is designed to allow estimating main effects,
interaction and quadratic effects of the design variables, and
therefore the response surfaces of interest can be obtained. In
addition, it offers valid basis for developing empirical models that
allow determining optimal settings of the design variables for the
optimization of the ﬁnal product properties. Several research works
on SCC can now be found in literature employing the DOE approach
(Shi et al., 2015) to help understand the effect of mixture parame-
ters on key fresh and hardened SCC properties and facilitate the test
protocol required to optimize SCC (Khayat et al., 2000), to quantify
SCC mixtures robustness (Nunes et al., 2009) and to develop me-
dium strength SCC (Sonebi, 2004).
From the foregoing literature survey, it was not found
research on the optimal design of SCC mixtures incorporating
ECat with a systematically statistical technique. Thus, the effect
of ECat incorporation on the fresh and hardened properties of
SCC needs to be carefully and individually evaluated. This
research requires an adequate experimental design to be chosen
so that appropriate experimental data can be collected and
analyzed by statistical methods, resulting in sound conclusions.
For this purpose, in a ﬁrst stage, paste composition can be
designed independently to rest of the mixture, by performing
tests at the mortar level (Nunes et al., 2009). The objectives of the
present study include: (1) to conduct a proper experimental plan
based on a DOE technique to explore the relationships between
the responses (mortars fresh and hardened properties) and the
design variables (paste mixture parameters), (2) to establish
explicit relationships between the response variables and design
variables, (3) to determine the optimal settings of the design
S. Nunes, C. Costa / Journal of Cleaner Production 158 (2017) 109e121
110
variables to satisfy the performance requirements using numer-
ical optimization techniques.
2. Experimental program
2.1. Materials characterization
The powder materials used to prepare the SCC mortars inves-
tigated in the present study were ordinary Portland cement (CEM I
42.5R), limestone ﬁller and spent equilibrium catalyst (ECat)
generated by Portuguese reﬁnery company PETROGAL S.A. at Sines.
Table 1 presents relevant chemical and physical properties of
the powder materials as well as the pozzolanic reactivity of the
ECat. The bulk chemical compositions were obtained by X-ray
ﬂuorescence spectrometry (XRF) using a PANalytical model Axios
equipment and by loss on ignition (LOI) evaluated following
European standard EN 196-2. The densities were determined by
helium pycnometry using an AccuPyc 1330 instrument from
Micromeritics. Particle size distributions (PSD) were obtained by
laser diffraction using a CILAS PSA 1064 equipment. Fig. 1 pre-
sents the cumulative PSD of the powder materials. These results
show that ECat particles are coarser than the cement and ﬁller
particles although possessing a narrower particle size range.
Since 93.72% of ECat particles have a size smaller than 0.125 mm,
ECat was included in the powders content of paste and was also
taken into account in calculating the volumetric water/powder
ratio, in accordance to EFNARC (EFNARC, 2005). The speciﬁc
surface
area
(SSA)
of
ECat
was
determined
by
nitrogen
adsorption-desorption isotherm at 77 K using a Micromeritics
ASAP 2000 apparatus.
The pozzolanic reactivity of the ECat was assessed both directly
by means of the modiﬁed Chapelle test method (following the
procedure described in French standard NF P 18e513) and
indirectly evaluating the Strength Activity Index (SAI) deﬁned by
the American standard ASTM C311 as the ratio (in %) between the
compressive strength of a 20% (w/w) ECat/cement blended mortar
and the compressive strength of a plain cement mortar, at 28 curing
days (Costa and Marques, 2012). Both these results suggest that the
ECat presents a high pozzolanic activity. In fact, the Chapelle test
result of 1540 mg of Ca(OH)2 consumed during the pozzolanic re-
action per g of ECat is higher than those reported in literature which
are, typically, lower than 1000 mg of lime consumption per g of the
pozzolanic
material
under
evaluation.
Namely,
metakaolins
consume between 560 and 1140 mg of Ca(OH)2/g, ﬁne sepiolite
consumes 90 mg of Ca(OH)2/g (Andrejkovi
cov
a et al., 2011) and
different types of ﬂy ashes consume between 540 e 725 mg of
Ca(OH)2/g (Antiohos et al., 2007). Also the SAI value of 95% is
signiﬁcantly higher than the expected value of 80% if only the
dilution effect is considered (Costa and Marques, 2012).
Fig. 2 shows the shape and a detail of the surface texture of ECat
particles observed by scanning electron microscopy (SEM) using
secondary electron (SE) mode. The image reveals that ECat consist
mainly of almost spherical shape particles with a highly rough
porous textured surface.
The aggregate used to prepare the SCC mortars was natural
siliceous sand conforming to the standard EN 196-1 (round-shaped
particles having a diameter in the range of 0.08e2 mm, density of
2630 kg/m3, water absorption (Asd) 0.3%, by mass). The admixture
used was a polycarboxylate ether-based with a high-range water
reducing superplasticizer (liquid, brown, density 1050 kg/m3, and
26% of solid content, gSp).
2.2. Mixtures formulation
SCC proportions can be established based on the following
mixture variables: water to powder volume ratio ðVw=VpÞ; water to
cement weight ratio (w=c); ﬁller to cement weight ratio (f =c);
superplasticizer to powder weight ratio (Sp=p) and sand to mortar
volume (Vs=Vm), as suggested by Okamura et al. (2000). The volu-
metric composition of the mortar mixture is considered ﬁrst with
subsequent conversion to proportions by weight. Mixture pro-
portions of mortar prepared in this study, per cubic meter, were
obtained using the following formulation.
For a volume of mortar Vm ¼ 1 m3 and a given value of ðVs=VmÞ
the sand volume ðVsÞ can be obtained from
Table 1
Chemical and physical properties of the powder materials (cement, ﬁller and ECat)
and pozzolanic activity of ECat.
CEM I 42.5R
Limestone ﬁller
ECat
Bulk chemical composition (%, by mass)
SiO2
19.41
0.97
40.30
Al2O3
5.45
0.29
54.45
Fe2O3
3.23
0.14
0.45
CaO
62.57
55.31
0.06
MgO
1.91
0.20
0.15
SO3
2.89
0.05
0.00
K2O
1.10
0.02
0.02
Na2O
0.00
0.00
0.43
TiO2
0.27
0.02
0.72
P2O5
0.10
0.00
0.50
MnO
0.05
0.01
0.00
SrO
0.07
0.01
0.00
V2O5
0.00
0.00
0.33
NiO
0.00
0.00
0.42
La2O3
0.00
0.00
0.87
LOI
2.70
43.42
1.05
Physical properties
Density (kg/m3)
2980
2680
2690
Mean particle size (mm)
16.67
25.69
91.65
d50 (mm)*
12.11
13.13
87.29
d90 (mm)*
37.93
63.92
138.11
Speciﬁc Surface Area (m2/kg)
1052
1000
150,070
Absorption coefﬁcient,
AEcat (%, by mass)
e
e
29.7
Pozzolanic Activity
SAI (%)
e
e
95
Chapelle pozzolanicity
(mg Ca(OH)2/g ECat
e
e
1540
* Stoppage to scrape material adhering to the mixing bowl; mixing at low speed
(140 ± 5 rotations$min1).
0
10
20
30
40
50
60
70
80
90
100
1
10
100
Cumulative volume percentage passing (%)
Particle diameter (μm)
CEM I 42.5R
Limestone filler
ECat
Fig. 1. Cumulative particle size distributions of CEM I 42.5R, limestone ﬁller and ECat.
S. Nunes, C. Costa / Journal of Cleaner Production 158 (2017) 109e121
111
Vs ¼ ðVs=VmÞ  Vm
(1)
and for a given value of ðVw=VpÞ, the powder volume ðVpÞ and the
water volume (Vw) can be deﬁned as
Vp ¼
1  Vs
1 þ Vw
Vp

(2)
Vw ¼ Vw
Vp
  Vp
(3)
From the Vp and Vw values the weight values of water (ww),
cement (wc), limestone ﬁller (wf ) and ECat (wEcat) can be deter-
mined as follows
ww ¼ Vw  rw
(4)
wc ¼
ww
w=c
(5)
wf ¼ ðf =cÞ  wc
(6)
wEcat ¼
 
Vp  wc
rc
 wf
rf
!
 rEcat
(7)
where rw, rc, rf and rEcat represent the density of water, cement,
limestone ﬁller and ECat, respectively. From the superplasticizer
dosage ðSp=pÞ and the weight values of cement, ECat and limestone
ﬁller the liquid weight of superplasticizer (wSp) is given by
wSp ¼ ðSp=pÞ 

wc þ wf þ wEcat

(8)
Dry aggregate content (wSd) can be obtained as follows
wSd ¼ Vs  rsd
(9)
where rsd represent the density of reference sand. The water added
to the mixture has to be corrected (wwc) by subtracting the water
content of the superplasticizer and adding the water needed for
saturating the aggregate and ECat, from a dry state, as follows
wwc ¼ ww  wSp 

1  gSp

þ wSd  Asd þ wEcat  AEcat
(10)
where gSp represent the solid content of superplasticizer, and Asd
and AEcat represent the absorption coefﬁcients of the reference sand
and ECat, respectively.
2.3. Experimental design
Among the many DOE techniques available, a central composite
design (CCD) was adopted to investigate the effects of three key
mixture parameters - (Vw=Vp); (w=c) and (Sp=p) - on different
mortar properties, namely, the slump ﬂow diameter, V-funnel ﬂow
time, compressive strength, electric resistivity and ultrasonic pulse
velocity.
A CCD consists of a 2k (or 2kp fractional) factorial design where
k is the number of design variables (p is the size of the fraction of
the full factorial used), augmented by additional axial points and
central points (Montgomery, 2013). When, as in the case of the
present study, k equals to 3 and a complete factorial design is
chosen p equals to zero, the experimental plan of the CCD can be
schematically represented as it is shown in Fig. 3. This design
provides ﬁve levels for each design variable (-a, 1, 0, þ1, þa), and
the total number of experiment runs N is given by
N ¼ 2k þ 2k þ nc
(11)
where 2k, 2k and nc represent, respectively, the number of factorial
points, axial points, and central points. Considering the number of
replicated central runs, nc ; equals to 6 a total of 20 runs were
performed. In order to make the design rotatable the a value, in
coded units, equals to (2k)1/4 (Montgomery, 2013). In this case
a ¼ ð23Þ1/4 ¼ 1.682. Table 2 lists the coded and the actual values of
the ﬁve levels for each design variable adopted in this CCD plan. The
range of the design variables was established based on a set of
preliminary tests that conﬁrmed the self-compactability of the
mortars with the most extreme compositions namely, the least and
the most ﬂuid mixtures.
Besides the design variables considered in the CCD, the
following mixture parameters were kept constant in the current
study: (i) (f =c) equal to 0.20 and (ii) Vs=Vm ratio equal to 0.475. Note
that a cement CEM I combined with limestone ﬁller such that the
percentage of f =c ¼ 20% is equivalent to a cement CEM II/A-L,
Fig. 2. SEM (SE mode) imaging of ECat particles: (a) particles shape; (b) detail of
particle surface texture.
Fig. 3. Schematic representation of a rotatable CCD for three design variables.
S. Nunes, C. Costa / Journal of Cleaner Production 158 (2017) 109e121
112
according to standard NP EN 197-1, which is currently the most
used cement in Portugal for structural concrete preparation. The
chosen Vs=Vm value is higher than the value recommended by
Okamura et al. (0.40) but is a more typical value of European SCC
mixtures (Domone, 2006). Higher Vs=Vm ratio value allows to
reduce the paste content in SCC and, therefore, to obtain more cost-
efﬁcient mixtures.
2.4. Specimen’s preparation and test methods
The mixtures were prepared in batches of 1.6 L using a two-
speed mixer in accordance with the standard EN 196-1. Table 3
presents the mixing procedure followed for mortars preparation.
Since the high speciﬁc surface of the ECat with water afﬁnity leads
to a signiﬁcant water absorption, AECat (Table 1) the mixing time at
the beginning of the mixing procedure was extended in relation to
what is typical to enable the ECat saturation.
The mortars fresh state properties were characterized using the
mini-slump ﬂow and the mini-V-funnel tests performed as rec-
ommended by Okamura et al. (2000). After the fresh state mortar
tests, prismatic specimens (4  4  16 cm3) were prepared
following the procedure prescribed by the standard EN 196-1
except the compaction step. These specimens were kept under
water in a chamber under controlled environmental conditions
(Temp. ¼ 20 C) until they reach the testing ages for assessment of
the hardened mortars’ properties.
Both electrical resistivity and ultrasonic pulse velocity tests
were performed at 3, 7, 14, 21, 28, 56 and 90 days.
The electrical resistivity was assessed using the two electrodes
technique made of stainless steel (4  4 cm2) which were placed on
the farthest opposite ends of the mortar prisms, in saturated con-
ditions, and applying a 30 V voltage to monitor the current in-
tensity (Fig. 4). The electrical resistivity was then computed
applying Ohm’s Law as follows:
Resist ¼ V  A=ðL  IÞ
(12)
where Resist is electric resistivity (U.m); V is voltage (Volts); A
and L, are, respectively, the cross area (m2) and length (m) of the
test specimen through which current passes and; I is current in-
tensity (Amp.).
The ultrasonic pulse velocity (UPV) was measured by direct
transmission method (in accordance with the standard EN 12504-
4:2007) using a PROCEQ Tico test equipment with two 54 kHz
transducers (transmitter and receiver) ﬁrmly coupled to the
farthest opposite ends of the mortar prims using petroleum jelly as
the couplant between the transducers and the specimens (Fig. 5).
Since the UPV test equipment provides the time of the ultrasonic
pulse propagation through the mortars between the two trans-
ducers, UPV was computed by dividing the propagated path length
to the pulse transit time.
The compressive strength test was performed at 28 and 90 days
following the procedure described in the standard EN 196-1.
3. Test results and discussion
Fig. 6 presents the evolution of the electrical resistivity,
ultrasonic pulse velocity and compressive strength results over
time. These results show that mortars exhibit a signiﬁcant increase
of both electrical resistivity and compressive strength from 28 to 90
days, of 77% and 19% on average, respectively, which can be
attributed to the high pozzolanic activity of ECat (Table 1). The
development of compressive strength of ECat/cement blended
mortars until 90 days had already been reported elsewhere (Pay
a
et al., 2013). Following up the trend in the other properties, the
UPV also increases with the curing age (Martínez-Molina et al.,
2014). However, its increment between 28 days and 90 days is
negligible, of 3% on average. Since typically the compressive
strength-UPV relationship is exponential (Demirboga et al., 2004)
is expected that beyond a given curing age the UPV values become
almost asymptotic and, thus, their increments are signiﬁcantly
lower than those in strength.
In view of developing numerical optimization tools i.e, predic-
tion empirical models able to maximize improved performance of
SCC mortars incorporating ECat one decided to model the hardened
mortar properties at the age of 90 days. However, since in the case
of electrical resistivity a statistical analysis of the experimental
results obtained at the age of 90 days revealed the existence of an
outlier for the factorial point F2 in CCD plan marked with an arrow
in Fig. 6 (a), the modelling of this property was established by
ﬁtting the results obtained at the age 56 days instead of those for 90
days.
DOE approach was also used to optimize SCC mortars incorpo-
rating other additions namely limestone, metakaolin (Figueiras
et al., 2011) and glass powder (Nunes et al., 2013), having the
same volume of standard sand. Table 4 provides a summary of the
CCD plan and materials used in each case as well as the range of
resistivity results obtained at 28 days, including the case of the
current study. Comparing the results obtained in the current study
with the results obtained with a binary mixture of cement and
limestone ﬁller one can easily conclude that the incorporation of
ECat improves the resistivity of mortar. Nevertheless, at the age of
28 days the beneﬁcial effect of ECat is not so strong as compared to
very reactive materials like Metakaolin 1 shown in Table 4
(Figueiras et al., 2011) and glass powder (Nunes et al., 2013).
Table 5 shows test results used in the modelling, namely, the
spread ﬂow diameter (Dﬂow), V-Funnel ﬂow time (Tfunnel), elec-
trical resistivity at 56 days (Resistiv, 56d), ultrasonic pulse velocity
at 90 days (UPV, 90d) and compressive strength at 90 days (fcm,
90d) obtained for each mortar mixture considered in the CCD plan.
In the case of the run number 3 (corresponding to the factorial
point F3 in CCD plan, Fig. 3) it was not possible to measure the ﬂow
time due to occurrence of blocking near the exit section of the V-
funnel, thus a very high value of 100 was adopted as the Tfunnel
result for the modelling purposes.
Table 6 presents the descriptive statistical parameters of both all
the experimental results used in the modelling (section 4) as well
as only for the central points. From these data it may be observed
that with the experimental plan implemented a wide range of
mortar properties was covered with Dﬂow ranging from 121 to
315 mm, Tfunnel ranging from 3.6 to 20.2 s, Resist, 56d ranging
from 62 to 207 Ohm.m, UPV, 90d ranging from 3880 to 4752 m/s
and fcm, 90d ranging from 36 to 84 MPa. In all cases, the coefﬁcient
of variation found for results of total points is signiﬁcantly higher
Table 2
Experimental region of the design variables of the CCD plan (and corresponding coded values).
Design variables
Low axial (1.682)
Low factorial (1.00)
Central (0)
High factorial (þ1.00)
High axial (þ1.682)
Vw=Vp
0.866
0.900
0.950
1.000
1.034
w=c
0.406
0.44
0.490
0.54
0.574
Sp=p
1.78%
1.85%
1.95%
2.05%
2.12%
S. Nunes, C. Costa / Journal of Cleaner Production 158 (2017) 109e121
113
than the coefﬁcient of variation found for results of only the central
points (experimental error), which is a necessary condition for
ﬁnding a good ﬁtting model. Moreover, the range of Dﬂow and
Tfunnel results obtained seems to be adequate, since it includes
both target values for Dﬂow and Tfunnel of 250 mm and 10s,
respectively, according to Okamura et al. (2000).
4. Response models
4.1. Models identiﬁcation
A CCD allows the identiﬁcation of empirical models by ﬁtting
second-order polynomial equations to the experimental data for
each response (mortars properties). The general form of the
second-order models is expressed as follows:
y ¼ b0 þ
X
i
bixi þ
X
i
biix2
i þ
X
i < j
bijxixj þ ε
(13)
where y represents the model response variables that in the pre-
sent study are the mortar properties Dﬂow, Tfunnel, Resist,56d,
UPV, 90d and fcm,90d; xi correspond to the design variables
considered i.e, (Vw=Vp); (w=c) and (Sp=p); b are the tuning co-
efﬁcients (in which b0 is the independent term, bi represents the
linear effect of xi , bii represents the quadratic effect of xi and bij
represents the linear-by-linear interaction between xi and xj); and ε
is the ﬁtting error.
The commercial software Design-Expert was used to analyse the
results for each response variable e Dﬂow; Tfunnel; Resist,56d;
UPV, 90d and fcm, 90d e and to ﬁt the empirical models using
regression analysis as well as to obtain the analysis of variance
(ANOVA). A detailed description of all steps involved in models
identiﬁcation is provided in (Nunes et al., 2013). Table 7 presents
the estimated model coefﬁcients, in terms of actual values of design
variables, including the residual error term (ε), along with the
correlation coefﬁcients.
ANOVA
showed
that
these
models
are
signiﬁcant
when
describing the effect of Vw=Vp, w=c and Sp=p on the modelled re-
sponses. Since R2 and R2adj values are signiﬁcantly high, >0.90, the
large proportion of the variability of response variables is explained
by the obtained regression models. Residual analysis did not reveal
any obvious model inadequacies or indicate serious violations of
the normality assumptions, except in the case of Tfunnel. This
problem was overcome after a variable transformation of the type
(1/y).
Replicate runs of central points were spread out in time to get a
rough check on the stability of the process during the experimental
programme. The accuracy of the derived models can be assessed by
comparing the residual standard deviation (see Table 7) and the
standard deviation calculated from the central points (see Table 6).
A good ﬁtting can be expected when residual standard deviation
does not exceed the experimental error by far. In this study, the
standard deviation measured on the central points was always
close to the residual standard deviation, except in the case of
Tfunnel. This is due to the very low standard deviation observed in
central point results, which is typical of high ﬂuidity mixtures.
4.2. Models validation
Four additional mixtures not used to derive the models (shown
in Table 8) were prepared to validate the predicting capability of the
proposed models. These four mixtures were used along with the six
central point mixtures of the CCD plan (run numbers 15e20 in
Table 5) to compare the measured and predicted values of each
response variable. The ratio between predicted-to-measured values
for Dﬂow; Tfunnel; Resist,56d; fcm, 90d and UPV, 90d ranged be-
tween 0.88 and 1.18; 0.81 and 1.21; 0.95 and 1.15; 0.94 and 1.30;
0.99 and 1.07, respectively. These ratios indicate good accuracy for
the proposed models. Furthermore, the experimental results of the
four mixtures in Table 8 fall within or very close to the limits of the
Table 3
Mortars mixing procedure.
Task
Duration
mixture ECat with 70% of water
1.0 min
resting
4.0 min
add cement þ limestone ﬁller þ ﬁne
aggregate and mixture
1.0minþaþ1.0min
add 30% water þ superplasticizer and mix
1.0minþaþ1.0min
resting
2.0 min
mixture
1.0min
a Stoppage to scrape material adhering to the mixing bowl; mixing at low speed
(140 ± 5 rotations$min1).
Fig. 4. Experimental setup for electrical resistivity testing.
Fig. 5. eExperimental setup for UPV testing.
S. Nunes, C. Costa / Journal of Cleaner Production 158 (2017) 109e121
114
respective prediction intervals. Thus, one can expect the estab-
lished models to be sufﬁciently accurate to predict the analyzed
fresh and hardened properties.
4.3. Signiﬁcant effects on the response variables
The estimated models expressed in terms of coded values
permit to compute the relative inﬂuence of linear, interaction, and
quadratic effects of the design variables on the response variables
represented in Fig. 7. In this ﬁgure, higher values indicate higher
inﬂuence of the design variable in the response. Moreover, a posi-
tive value reﬂects that to an increase in the design variable leads to
an increase the response variable and vice-versa. The results pre-
sented in Fig. 7 clearly show that w=c is the variable with higher
effect on almost all the response variables being only exceeded by
the effect of Vw=Vp on Tfunnel response. These results also show
that within the range of Sp=p dosages selected for this study, this
variable only has a signiﬁcant effect on UPV, 90d and fcm, 90d re-
sponses and yet its effect is relatively small. For this reason, the
variable Sp/p will be kept constant with the value of 1.95% which
corresponds to the middle of experimental range adopted for this
variable in the current study (Table 2).
An interaction effect between Vw=Vp and w=c was found to be
signiﬁcant for both fresh state properties. This interaction effect
represents the inﬂuence of ECat. From the formulation presented in
section 2.2 it is possible to compute the ECat volume fraction in
powders materials as follows:
VEcat
Vp
¼ 1  Vw
Vp
w=c

 
1
rc
þ f =c
rf
!
 rw
(14)
Since f =c was kept constant, Equation (14) reveals that the ECat
volume fraction in powders mixture depends only on the Vw=Vp
and w=c. This relation is also represented graphically in Fig. 8 to
help the visualization of the effect of these two design variables on
ECat volume fraction in powders mixture.
5. Mixtures optimization
5.1. Exploitation of statistical models using response surface
methods
From the numerical ﬁtted models response surface graphs or
contour plots can be generated offering an opportunity to visually
(a)
(b)
(c)
Fig. 6. Evolution of resistivity (a), ultrasound pulse velocity (b) and compressive strength (c) results over time, for all mixtures in the experimental plan.
S. Nunes, C. Costa / Journal of Cleaner Production 158 (2017) 109e121
115
Table 4
Summary of materials, CCD plan parameters and range of resistivity results (at 28 days) of different experimental plans.
Reference
Materials
CCD plan
Design variables
Experimental region of design variables [-a,þa]
Resist, 28d (Ohm.m)
(Figueiras et al., 2011)
CEM I 42.5 R
23
Vw/Vp
[0.650; 0.900]
[39.1; 67.2]
Limestone ﬁller
a ¼ 1.682
w/c
[0.320; 0.510]
Standard sand
nc ¼ 4
Sp/p
[0.550%; 0.650%]
nf ¼ 8
na ¼ 6
CEM I 42.5 R
24
Vw/Vp
[0.700; 1.000]
[87.4; 437.3]
Limestone ﬁller
a ¼ 2.000
w/c
[0.370; 0.490]
Metakaolin 1
nc ¼ 6
Sp/p
[0.985%; 1.030%]
Standard sand
nf ¼ 16
mtk/c
[0.05; 0.20]
na ¼ 8
CEM I 42.5 R
24
Vw/Vp
[0.650; 0.950]
[55.7; 200.7]
Limestone ﬁller
a ¼ 2.000
w/c
[0.360; 0.480]
Metakaolin 2
nc ¼ 6
Sp/p
[0.978%; 1.023%]
Standard sand
nf ¼ 16
mtk/c
[0.05; 0.20]
na ¼ 8
(Nunes et al., 2013)
CEM I 42.5 R
23
Vw/Vp
[0.732; 1.068]
[88.3; 504.4]
Glass powder
a ¼ 1.682
w/c
[0.40; 0.60]
Standard sand
nc ¼ 4
Sp/p
[1.17%; 1.33%]
nf ¼ 8
na ¼ 6
Current study
CEM I 42.5 R
23
Vw/Vp
[0.866; 1.043]
[55.4; 132.2]
Limestone ﬁllera
a ¼ 1.682
w/c
[0.406; 0.574]
ECat
nc ¼ 4
Sp/p
[1.78%; 2.12%]
Standard sand
nf ¼ 8
na ¼ 6
Notes: Vs/Vm ¼ 0.475 in all experimental plans; nc: no of central points; nf: no of factorial points; na: number of axial points; different superplasticizer type was used in each
experimental plan.
a (f/c) ﬁxed at 0.20.
Table 5
Mixtures proportions identiﬁed by coded values of the design variables and experimental test results used in the modelling.
Run number
Point type
Coded values
Dﬂow (mm)
Tfunnel (s)
Resist, 56d (Ohm.m)
UPV, 90d (m/s)
fcm, 90d (MPa)
Vw/Vp
w/c
Sp/p
1
F1
1
1
1
253.8
6.22
108.6
4477.6
70.8
2
F2
1
1
1
294.0
4.21
71.4
4619.8
73.3
3
F3
1
1
1
121.2
100a
199.6
4060.9
40.8
4
F4
1
1
1
212.3
3.75
143.3
3976.8
38.4
5
F5
1
1
1
260.2
5.94
108.8
4602.1
77.2
6
F6
1
1
1
276.5
4.60
62.4
4678.4
69.0
7
F7
1
1
1
131.0
20.22
206.9
3986.7
41.2
8
F8
1
1
1
236.2
3.68
158.4
4166.7
45.6
9
CC1
1.682
0
0
179.2
12.47
193.6
4177.6
50.4
10
CC2
1.682
0
0
256.7
3.56
87.0
4461.0
62.3
11
CC3
0
1.682
0
314.6
5.72
70.3
4752.5
83.8
12
CC4
0
1.682
0
125.8
13.29
186.0
3880.4
35.8
13
CC5
0
0
1.682
219.7
5.56
121.8
4270.5
53.8
14
CC6
0
0
1.682
246.5
5.42
135.8
4424.0
64.2
15
C1
0
0
0
229.0
5.24
124.1
4232.8
56.2
16
C2
0
0
0
238.3
5.29
138.7
4297.2
55.7
17
C3
0
0
0
234.3
6.23
138.9
4304.9
61.2
18
C4
0
0
0
216.5
6.10
136.2
4336.0
56.8
19
C5
0
0
0
241.5
4.72
135.8
4297.2
57.6
20
C6
0
0
0
254.5
4.53
124.8
4320.4
55.4
a Adopted value for modelling purposes due to the occurrence of blocking near the exit section of the V-funnel.
Table 6
Descriptive statistics of the results for the total points used in the modelling and for central points.
Dﬂow (mm)
Tfunnel (s)
Resist, 56d (Ohm.m)
UPV, 90d (m/s)
fcm,90d
(MPa)
N¼20 total points
minimum
121.3
3.56
62.4
3880.4
35.8
maximum
314.6
20.22a
206.9
4752.5
83.8
mean
227.1
6.67
132.6
4316.2
57.5
standard deviation
52.4
4.19
42.0
238.9
13.2
coefﬁcient of variation (%)
23.1%
62.8%
31.6%
5.5%
23.0%
nc¼6 central points
minimum
216.5
4.53
124.1
4232.8
55.4
maximum
254.5
6.23
138.9
4336.0
61.1
mean
235.7
5.35
133.1
4298.1
57.2
standard deviation
12.7
0.69
6.8
35.4
2.1
coefﬁcient of variation (%)
5.4%
13.0%
5.1%
0.8%
3.7%
a Excluding the value attributed for run number 3.
S. Nunes, C. Costa / Journal of Cleaner Production 158 (2017) 109e121
116
analyse how certain mixture parameters inﬂuence the studied SCC
properties and serving as a basis for ﬁnding the optimal solution.
Fig. 9 (a) and (b) show, respectively, the contour plots for the
Dﬂow and Tfunnel, respectively, as a function of Vw=Vp and w=c.
These contour plots were built from the models presented in
Table 7, keeping the dosage of superplasticizer with ﬁxed value
equal to 1.95%. The simultaneous analyses of these two ﬁgures
reveal that it is not possible to obtain an adequate ﬂow diameter of
Dﬂow~250 mm along with a relatively high ﬂow time of Tfun-
nel~10s, as recommended by Okamura et al. (2000) for powder-
type SCCs (EFNARC, 2006) since these two target values of the
mortars fresh properties do not lie in the same experimental vari-
ables region. This might be explained by the very low range particle
size distribution of the ECat that contributes to lack of particles in
the range 20e60 mm in the powder materials (see Fig. 1) which
might be responsible for a decrease in packing density. This is
consistent with a decrease of the plastic viscosity of mixtures and,
consequently, with a reduction in the ﬂow time. Nevertheless,
these mortar properties are admissible for combination- or vis-
cosity type SCCs, which are characterized by lower powder content
(EFNARC, 2006).
Fig. 10 (a) and (b) show, respectively, the electrical resistivity at
56 days and compressive strength at 90 days as a function of Vw=Vp
and w=c. It is interesting to observe that the maximums of these
two response variables (electrical resistivity and compressive
strength) do not coexist in the same experimental variables region.
Such as it was previously referred, the values presented in Fig. 7
reveal that while the compressive strength is mainly determined
by the w/c ratio, the resistivity is affected by both design variables
Vw=Vp and w=c, which are the same design variables that also
determine the content of ECat volume fraction in the powder ma-
terials (Equation (14)). The simultaneous analysis of Figs. 8 and 10
(a) clearly reveals that resistivity increases with an increase of
ECat volume fraction in paste. This empirical relationship can be
attributed to the additional C-A/S-H products formed during the
pozzolanic reaction of the ECat that might promotes a continuous
reduction of the pore connectivity of the mortars matrices.
5.2. Mixture optimization targeting different engineering
requirements
Based on the regression models obtained (Table 7), the numer-
ical optimization technique can be used to determine the optimal
values of design variables to be used in the mortars preparation
Table 7
Reduced quadratic models (actual values of design variables), error term and correlation coefﬁcients.
Fitted models
ε, std. dev.a
R2/R2adj
Dﬂow ¼ 3450.085e2862.854  Vw/Vp-7665.238  w/cþ6987.500  Vw/Vp  w/c
12.653
0.942/0.931
1/(Tfunnel) ¼ 5.413e7.274  Vw/Vp-9.381  w/cþ17.664  Vw/Vp  w/c-8.080  w/c2
0.017b
0.946/0.932
Resist, 56d ¼ 247.984e537.992  Vw/Vpþ807.597  w/c
8.340
0.961/0.956
UPV, 90d ¼ 5042.381 þ 1158.345  Vw/Vp-5350.488  w/cþ407.745  Sp/p
45.250
0.964/0.957
fcm, 90d ¼ 165.654e300.327  w/cþ19.993  Sp/p
3.113
0.945/0.938
a Error term is a random and normally distributed variable with mean zero.
b Corresponding value for (Tfunnel) is 3.3 s.
Table 8
Experimental and predicted results of conﬁrmation mixtures (not used to derive the numerical models).
Vw/Vp
w/c
Sp/p
Dﬂow (mm)
Tfunnel (s)
Resist, 56d (Ohm.m)
UPV, 90d (m/s)
fcm, 90d (MPa)
0.900
0.48
1.60%
Experimental result
183.3
8.64
131.20
4229.1
53.9
Predicted value
212.8
7.53
151.4
4169.0
53.5
95% PI lowa
181.7
5.67
131.7
4020.4
43.8
95% PI higha
243.9
11.23
171.2
4317.7
63.2
0.900
0.48
1.60%
Experimental result
180.5
9.33
146.34
4177.5
52.7
Predicted value
212.8
7.53
151.4
4169.0
53.5
95% PI lowa
181.7
5.67
131.7
4020.4
43.8
95% PI higha
243.9
11.23
171.2
4317.7
63.2
1.00
0.54
2.05%
Experimental result
241.8
4.17
145.16
4177.5
45.9
Predicted value
221.3
3.91
146.1
4147.3
44.5
95% PI lowa
187.7
3.31
125.7
4029.6
36.9
95% PI higha
254.9
4.79
166.4
4265.1
52.1
0.900
0.48
2.36%
Experimental result
242.8
6.25
131.39
4304.9
57.6
Predicted value
212.8
7.53
151.4
4477.6
68.6
95% PI lowa
181.7
5.67
131.7
4317.8
58.2
95% PI higha
243.9
11.23
171.2
4637.5
79.1
a Lower and higher limits of prediction interval with 95% conﬁdence level.
Fig. 7. Relative signiﬁcance of design variables on each response variable.
S. Nunes, C. Costa / Journal of Cleaner Production 158 (2017) 109e121
117
while imposing practical constraints such as self-compactability, a
target strength value, maximum ECat incorporation, minimum cost
and/or others.
First of all, for performing the optimal design in terms of self-
compactability the optimization problem was deﬁned as indi-
cated in the column A of Table 9. The corresponding optimal values
of design variables are represented by the grey region in Fig. 11,
independent of
superplasticizer dosage. The relatively small
optimal region obtained indicates that, with the assumptions
adopted in this study, the workability requirements limit the
number of possible optimal solutions.
Thereafter, different optimizations can be performed in order to
ﬁnd the mixture proportions that leads to the best overall perfor-
mance targeting eco-efﬁciency, economy or different engineering
properties. For this purpose, the mixture requirements have ﬁrst to
be speciﬁed. Typically, hardened state behaviour in terms of
strength and durability requirements are dependent on the appli-
cation type and on the exposure conditions.
By way of example, the common speciﬁcations of maximize
durability, minimize cost and maximize eco-efﬁciency were the
goals assumed in the current work to perform a mixture optimi-
zation considering also the hardened state properties. In this case,
compressive strength maximization was not considered due to the
high values of strength reached at 90 days. To achieve these goals
the optimization constraints indicated in column B of Table 9 were
used in the optimization process.
In fact, electrical resistivity maximization improves the concrete
resistance to penetration of liquid and gas substances (Andrade and
D’Andrea, 2010) and, therefore, its durability. Since ECat is a
polluting waste material, higher content of ECat in the paste con-
tributes both to reduce the cost and to increase the eco-efﬁciency of
the mixture. For this reason it was included the maximization of
VEcat=Vp as a goal. The resulting optimal mixture design variables
and predicted response values of the overall optimal solution are
presented in Table 10. In this mixture VEcat=Vp amounts to 19.7%.
This value corresponds to a high volume cement of replacement.
In order to assess the validity of the developed regression
models, a mortar using the optimized design variables was pre-
pared and tested. The experimental results obtained are also
included in Table 10. The comparison between the predicted values
and the experimental results obtained (all lying in the 95% conﬁ-
dence interval) again conﬁrm the accuracy of the obtained models.
The ﬁnal spread area of the optimized mixture after ﬂow test is
presented in Fig. 12. For similar results in the fresh state (see
Table 5), the optimized mixture compared to mixtures F1 and F5,
0.90
0.92
0.95
0.97
1.00
0.44
0.46
0.49
0.52
0.54
Vw/Vp
w/c
10
15
20
25
30
Fig. 8. Contour plot of ECat volume fraction (%) in powders mixture.
0.90
0.92
0.95
0.97
1.00
0.44
0.46
0.49
0.52
0.54
Vw/Vp
w/c
160
180
200
220
240
260
280
0.90
0.92
0.95
0.97
1.00
0.44
0.46
0.49
0.52
0.54
Vw/Vp
w/c
4
6
8
10
12
14
16
18
20
(a)
(b)
Fig. 9. Contour plots of Dﬂow (a) and Tfunnel (b) while maintaining.Sp=p ¼ 1:95%
S. Nunes, C. Costa / Journal of Cleaner Production 158 (2017) 109e121
118
exhibits improved resistivity (from 109 to 139 Ohm.m) and
increased VEcat=Vp (from 16% to 20%). This shows the numerical
optimization allowed to further improve the mortar properties in
terms of durability, cost and eco-efﬁciency, while maintaining the
properties in the fresh state.
The next stage of this study will be carried out on the concrete
level. SCC concrete mixtures composition will be obtained by
considering the paste mixture proportions deﬁned in Table 10 and
replacing reference sand by real aggregates (ﬁne and coarse ag-
gregates). Tests on concrete are then necessary to optimize the
aggregate skeleton and paste content. The experimental design
technique can also be applied at this stage to optimize SCC mixtures
considering as independent variables, those variables related to the
aggregates.
6. Conclusions
The current study revealed that spent equilibrium catalyst from
oil reﬁnery can be successfully applied in SCC mortars, as a high
volume cement replacement material up to 23%. The use of this
spent material widens the types of additions available for SCC
saving landﬁll usage, reducing CO2 emissions since decreases
cement content and turning an otherwise polluting waste into a
value-added by-product. In opposition to other additions, ECat is
very well consistent in terms of particle size distribution and
composition, thus not requiring increased monitoring of deliveries.
0.90
0.92
0.95
0.97
1.00
0.44
0.46
0.49
0.52
0.54
Vw/Vp
w/c
70
90
110
130
150
170
190
0.90
0.92
0.95
0.97
1.00
0.44
0.46
0.49
0.52
0.54
Vw/Vp
w/c
45
50
55
60
65
70
(a)
(b)
Fig. 10. Contour plots of Resist, 56d (a) and fcm, 90d (b) while maintaining.Sp=p ¼ 1:95%
0.90
0.92
0.95
0.97
1.00
0.44
0.46
0.49
0.52
0.54
A: Vw/Vp
B: w/c
Df low: 250
Df low: 270
Tf unnel: 6
Tfunnel: 8
Fig. 11. Optimum region for SCC (optimization constraints A).
Table 9
Optimization constraints.
A
B
Design Variables:
Vw=Vp
In range
In range
w=c
In range
In range
Sp=p
In range
In range
Response Variables:
Dﬂow (mm)
In range [250, 260]
In range [250, 260]
Tfunnel (s)
In range [6, 8]
Maximize
Resist,56d (Ohm.m)
None
Maximize
fcm, 90d (MPa)
None
None
UPV, 90d (m/s)
None
None
VEcat=Vpaste
None
Maximize
Notes: Range of design variables (coded values) was set at [2.0; 2.0], respectively;
lower and higher weight values of constraints were both set equal to 1; importance
was set equal for all constraints.
S. Nunes, C. Costa / Journal of Cleaner Production 158 (2017) 109e121
119
Moreover, based on presented experimental results and on the
mathematical empirical model derived, the following conclusions
can be drawn:
 The mortars’ compressive strength and UPV are mainly deter-
mined by the w/c ratio.
 The resistivity is affected by both design variables Vw=Vp and
w=c, which are the same design variables that also determine
the content of ECat volume fraction in the powder materials.
 An increase of ECat volume content in paste was found to
strongly inﬂuence workability and resistivity of mortar. It de-
creases mortar ﬂow diameter and increases both ﬂow time and
resistivity of mortar.
 Since ECat is a relatively coarse material as compared to the
most commonly used additions in SCC, the optimization tech-
nique applied to the models lead to a ﬁnal optimized self-
compacting mortar mixture exhibiting a relatively low V-fun-
nel ﬂow time. At the concrete level this can lead to low stability
mixtures, which can be mitigated by a wise choice of aggregates
or by the use of a viscosity agent.
 A signiﬁcant increase of compressive strength and resistivity
was observed from 28 to 90 days, which can be attributed to the
pozzolanic activity of ECat.
 To further increase the number of possible SCC mortar solutions
incorporating ECat, this spent material should be investigated in
ternary or even quaternary mixtures of binders, combining
cement and ECat with ﬁner and less reactive additions, such as
limestone ﬁller, ﬂy ash or slag.
Acknowledgements
This work was ﬁnancially supported by: Project POCI-01-0145-
FEDER-007457 - CONSTRUCT - Institute of R&D In Structures and
Construction funded by FEDER funds through COMPETE2020 -
Programa
Operacional
Competitividade
e
Internacionalizaç~
ao
(POCI) e as well as by Project Reference PTDC/ECM/113115/2009
funded by national funds through FCT, Fundaç~
ao para a Ci^
encia e
a Tecnologia and by PETROGAL S.A. company. Acknowledgements
are also due to Sines Reﬁnery, CIMPOR, - Cimentos de Portugal,
SGPS and Sika Portugal SA for supplying, respectively, ECat,
cement and admixtures. The assistance of In^
es Laginha and
Marco Freitas Prata in conducting the mortars preparation and
testing is gratefully acknowledged.
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Vw/Vp
w/c
Sp/p
Dﬂow
(mm)
Tfunnel
(s)
Resist,56d
(Ohm.m)
UPV, 90d (m/s)
fcm,90d
(MPa)
0.88
0.45
1.95%
Predicted value
250
6.75
139.1
4431.0
69.0
95% PI lowa
214.6
5.07
118.2
4312.4
61.6
95% PI higha
285.4
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160.1
4549.7
76.3
Experimental result
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4411.8
67.6
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S. Nunes, C. Costa / Journal of Cleaner Production 158 (2017) 109e121
121

CHAPTER 1
Introduction
Contents
1.1 Overview
1
1.2 Preprocessing of data
2
1.2.1 Data cleaning
2
1.2.2 Data integration
2
1.2.3 Data transformation
3
1.2.4 Data reduction
3
1.2.5 Data discretization
4
1.2.6 Data statistics
4
1.3 Processing of data
5
1.3.1 Data training
5
1.3.2 Data validation and testing
5
1.4 Postprocessing of data
6
1.4.1 Statistical analyses for models’ evaluation
6
1.4.2 Graphical error analysis for models’ evaluation
9
1.5 Applicability domain of a model
19
1.5.1 Identification of experimental data outliers
19
1.6 Sensitivity analysis on models’ inputs
21
1.6.1 Relevancy factor analysis
21
1.7 The areas of intelligent models applications in the petroleum industry
21
References
22
1.1 Overview
In this chapter, the main statistical and graphical approaches used to
analyze the performance of artificial predictive models in the oil and gas
industry are described. In addition, data preprocessing steps, as necessary
steps to refine a data bank before performing statistical and graphical error
analyses, are presented. These processes eliminate unreliable data points to
ensure the development of more accurate predictive models. These unre-
liable data points could be false data or outliers that should be removed
from a dataset. Error analysis techniques are used to evaluate the perfor-
mance and accuracy of predictive models. The basis of these techniques is
measuring the deviation of predictions from the measured data points
1
Applications of Artificial Intelligence Techniques in the Petroleum Industry
DOI: https://doi.org/10.1016/B978-0-12-818680-0.00001-1
© 2020 Elsevier Inc.
All rights reserved.
using different mathematical formulations. Graphical error analyses, on the
other hand, are used to enable an easier way to compare the performance
of multiple predictive models and select the most accurate one. These
techniques are a representation of the outcome from statistical techniques
and are used to facilitate the process of model selection. In this chapter,
first, a brief description of different procedures for data analysis is pro-
vided, and then some examples of these procedures are given. The defini-
tions and formulations of statistical error analyses are presented along with
specific examples using data from real oil and gas operations. In addition,
different graphical error analyses with graphical examples are presented.
Dew point pressure was chosen as a candidate parameter to describe these
statistical and graphical methods better.
1.2 Preprocessing of data
Preprocessing of data means the steps that are necessary to take before
using the data for processing purposes. These steps are described next.
1.2.1 Data cleaning
Data cleaning consists of eliminating the inconsistencies in the data,
smoothing the noisy data, and compensating the missing data points. The
data that need to be cleaned include the data that were added to the data
bank by mistake or data outliers. For example, when the data points rep-
resent minimum miscibility pressure of gasoil systems, negative values
would not be a reasonable representative of pressure values since pressure
cannot be a negative value. Therefore, these data points should be
removed. On the other hand, in order to use a dataset for model develop-
ment, every data point in the set should have all the inputs that the model
uses for predictions. If a data point does not have even one of the inputs,
it should be removed during the data cleaning stage. Outliers are another
group that should be eliminated from a dataset. For example, if the target
output of the model is reservoir temperature and one of the data points is
273 K, it should be removed from the data as reservoir temperature can-
not be 0°C.
1.2.2 Data integration
Data can be represented differently. During this step, all the different
representations are combined, and a unique and consistent representation
2
Applications of Artificial Intelligence Techniques in the Petroleum Industry
is made. For example, when the data represent the components of
injected gas, the percentages can be either reported individually or as
ratios. Another example is the different ways to present critical tempera-
ture and pressure values when predicting the viscosity of a gas. These crit-
ical values could be either reported individually or as pseudocritical values
at certain temperature and pressure values. The units of data points also
need to be consistent. For example, if the predictive model uses tempera-
ture in Kelvin, all the temperature values need to be converted to Kelvin
during the data integration stage.
1.2.3 Data transformation
Normalization, generalization, and aggregation of data are called data
transformation. For example, data points are usually normalized between
21 and 1 or 0 and 1. There are different formulations to do that. One of
the common approaches is dividing all the data points by the maximum
data point. This method would normalize all the values between a small
value and one. Another common formula is xn 5 ðx 2 xminÞ= xmax 2 xmin
ð
Þ,
which locates all the data points between 0 and 1. If the data need to be
normalized between 21 and 1, the following formula can be used:
xn 5 2 3

ðx 2 xminÞ= xmax 2 xmin
ð
Þ

2 1. Another statistical approach for
data normalization is achieved by xn 5 x 2 xavg


=σ where xavg represents
the mean and σ denotes the standard deviation (SD). When the data
points are large numbers in the orders of 106 and above, the log of data
points can be used for normalization.
1.2.4 Data reduction
Data reduction consists of a reduction in the representation of data in
a data bank. In some instances, there are a greater number of inputs
than necessary, and they can be reduced. Dimensional analysis is usu-
ally helpful in reducing the number of input parameters. Also, some-
times, there are too many data points in the dataset, and a percentage
of the data points can be discarded without hurting the model devel-
opment process. In some cases, for example, when the heavy compo-
nents of oil need to be considered for model development, an
average value over the input parameters (i.e., average critical temper-
ature) can be considered as one input and that decreases the needed
computations.
3
Introduction
1.2.5 Data discretization
Several data points can be eliminated by dividing continuous attributes by
the range of attributed intervals during the data discretization process.
Generally, averaging the data points that are all located within the same
interval is an effective approach to perform this. For example, when there
are many data points in the set and within certain ranges and one data
point would be a sufficient representative of each range, averaging over
the interval is used to reduce the computations.
1.2.6 Data statistics
1.2.6.1 Skewness
In addition to minimum, maximum, average, and standard deviation of a
data set, sometimes other statistical parameters such as skewness and kur-
tosis are used to better describe the distribution of data points in a data
set. The asymmetry of the probability distribution of a random variable
about its mean is measured by skewness. Skewness can be positive, nega-
tive, or undefined. In unimodal distributions, positive skewness occurs
when the tail of the distribution is toward the right side, and the mass of
the distribution is concentrated on the left side. Negative skewness occurs
when the tail of the distribution is toward the left side, and the mass of
the distribution is concentrated on the right side. Undefined skewness
occurs in symmetric distributions when one tail is fat, and one tail is
long. In asymmetric distributions, one tail is long and thin, and one tail is
fat and short.
The formula and a schematic figure for negative and positive skewness
are shown in the following equation and Fig. 1.1:
b1 5 m3
s3 5
1=n P
n
i51
xi2x
ð
Þ3
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
1= n21
ð
Þ P
n
i51
xi2x
ð
Þ2
r
3 5
1=n P
n
i51
xi2x
ð
Þ3
1= n21
ð
Þ P
n
i51
xi2x
ð
Þ2

3=2 ; (1.1)
1.2.6.2 Kurtosis
The tailedness of a probability distribution of a random variable is mea-
sured by kurtosis. Similar to skewness, kurtosis is a measure of the
shape of a probability distribution. A positive kurtosis means that many
data points are located in the tails of the distribution, while a negative
4
Applications of Artificial Intelligence Techniques in the Petroleum Industry
distribution entails few data points in the tails. For a dataset with n
values, kurtosis is calculated as follows:
g2 5 m4
m2
2
5
P
n
i51
xi2x
ð
Þ4
1=n P
n
i51
xi2x
ð
Þ2

2
(1.2)
A normal distribution has a kurtosis of 3. Some authors prefer to use
excess kurtosis which is kurtosis minus 3.
1.3 Processing of data
1.3.1 Data training
Data training is performed to construct a predictive model. Usually, 70%
80% of the data points are used to train the model, and the rest is used for
validation and testing. The dataset needs to go through the preprocessing
steps before being used during the training stage, as it was explained before.
1.3.2 Data validation and testing
In order to evaluate the ability of a model in predicting new data points,
data validation and testing are performed to investigate how accurately
the current data points can be predicted, and therefore new predictions
can be made reliably. This step is performed to evaluate the effectiveness
of the training stage. Usually, 15% of the data points are assigned to data
validation, and 15% are used for data testing. In some cases the validation
Figure 1.1 Illustration of data distribution with respect to the skew values.
5
Introduction
stage is skipped, and all the data points, excluding the training set, are
used for data testing.
1.4 Postprocessing of data
1.4.1 Statistical analyses for models’ evaluation
Statistical analysis methods are used to evaluate the performance of a
model. This is generally done by comparing the model predictions with
the
experimental
values
by
introducing
various
error
calculation
approaches. Here, some of the main statistical techniques are presented.
1.4.1.1 Average percent relative error (APRE)
In this technique, the relative deviation of predicted data points by a
model from the corresponding experimental data is calculated. This
parameter is sometimes called the average relative deviation. This parame-
ter is defined as follows:
Er 5 1
n
X
n
i51
Ei
(1.3)
where Ei is the relative deviation of a represented/predicted (denoted by
rep./pred) value from an experimental (denoted by exp) value based on
the following formula:
Ei 5
μiexp 2 μirep:=pred
μiexp
"
#
3 100.i 5 1; 2; 3; . . .; n
(1.4)
1.4.1.2 Average absolute percent relative error (AAPRE)
Sometimes called average absolute percent relative deviation, this tech-
nique is the same as average percent relative error except the absolute
values of errors are considered in the calculation of the final error value,
as shown in the following equation:
Ea 5 1
n
X
n
i51
jEij
(1.5)
Care should be taken when using APRE and AAPRE results. While
APRE is a measure of relative error, AAPRE indicates the absolute error
value, and the smaller it is for a model, the more accurate the model is.
To clarify this issue, five model performances are reported in Table 1.1.
6
Applications of Artificial Intelligence Techniques in the Petroleum Industry
While all models have the same AAPRE values, they have very different
APRE results. Models A and B indicate a uniform distribution of outputs
because their APRE values are relatively small. However, model C under-
estimates the measured values because, based on Eq. 1.4, it generally
predicts smaller values than the experimental values, which causes an
underestimation of the results. Similarly, model D overestimates the results
as its APRE is a negative value. The APRE reported for model E is unre-
alistic because the average of absolute error values calculated by AAPRE
cannot be smaller than APRE.
Table 1.2 shows an example of how to calculate AAPRE from absolute
percent relative error. By taking an average of the absolute percent relative
error values, AAPRE can be obtained as reported in the same table.
1.4.1.3 Root mean square error (RMSE)
The data dispersion around zero is calculated using this technique.
Generally, the smaller this value generated by a model, the more accurate
Table 1.1 Comparison between average percent relative error (APRE) and average
absolute percent relative error (AAPRE).
Models
A (%)
B (%)
C (%)
D (%)
E (%)
AAPRE
20
20
20
20
20
APRE
2
22
15
215
221
Result
Uniform
distribution
Uniform
distribution
Underestimate
Overestimate
Impossible
Table 1.2 Calculation of average absolute percent relative error from absolute
percent relative error values.
Experimental
Calculated
Absolute percent relative error (%)
1
1
1.2
20
2
10
12
20
3
100
130
30
4
10
7
30
5
50
42
16
6
37
49
32
7
40
28
30
8
50
60
20
9
10,000
12,000
20
Average absolute percent relative error
24.22
7
Introduction
that model is in predicting the measured values. Root mean square error
is calculated as follows:
RMSE 5
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
1
n
X
n
i51
Oiexp2Oirep:=pred

2
s
(1.6)
where Oiexp and Oirep represent the experimental and predicted out-
puts, respectively.
In the following equation the root mean square error (RMSE) calcula-
tion for the sample data points in Table 1.2 is presented:
RMSE5
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
1
9
121:2
ð
Þ2 1 10212
ð
Þ2 1 1002130
ð
Þ2 1 1027
ð
Þ2 1 50242
ð
Þ2 1 37249
ð
Þ2 1 40228
ð
Þ2 1 50260
ð
Þ2 1 10000212000
ð
Þ2
	

v
u
u
t
RMSE5
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
1
9 0:0414190019164114411441100143106
	

v
u
u
t
RMSE5
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
ﬃ
1
9 1:43103 143106
	

v
u
u
t
RMSE5 666:78
1.4.1.4 Standard deviation (SD)
The degree of data scattering is quantified using this technique, and similar
to RMSE, a smaller SD value generated by a model means a more accu-
rate predictive model.
SD is calculated as follows:
SD 5
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
1
n 2 1
X
n
i51
Oiexp2Oirep:=pred
Oiexp

2
s
(1.7)
1.4.1.5 Coefficient of determination (R2)
This parameter is used to quantify how close the prediction values are
to the experimental values. This is done by comparing the R2 values,
and a closer value of R2 to unity is equivalent to a more accurate
model.
8
Applications of Artificial Intelligence Techniques in the Petroleum Industry
Coefficient of determination is calculated as follows:
R2 5 1 2
P
n
i51
Oiexp2Oirep:=pred

2
P
n
i51
Oirep:=pred2O

2
(1.8)
1.4.2 Graphical error analysis for models’ evaluation
Graphical approaches are used to visualize the performance of a model
based on the error between the model’s predictions and experimental
data. Here, some of the main graphical approaches are presented.
Fig. 1.2 is an example of using graphical AAPRE analysis to compare
the performance of the models in Table 1.2. As the figure shows, while
model 5 is the most accurate one, model 6 can make predictions with the
least accuracy. This graphical technique significantly helps when there are
numerous models, and their performances are to be evaluated.
1.4.2.1 Error distribution curve
In this technique, the data points are plotted around the zero-error line to
investigate whether or not the predictive model has an error trend as the
values of predictions (outputs) increase.
Fig. 1.3 is an example of using graphical error distribution analysis to
evaluate the error trend of a model in predicting dew point pressure
values. This figure represents a model that uniformly predicts the
Figure 1.2 Graphical comparison of AAPRE values generated by different models.
AAPRE, Average absolute percent relative error.
9
Introduction
experimental values with high accuracy over a wide range of data points.
As can be seen, the data points lie close to the zero-error line, and regard-
less of the increase or decrease in the values, they follow the same trend.
There are not many scattered or outlier data points, and there is no sys-
tematic or random separation of the data points from the zero-error line.
This figure indicates that there is no error trend in the model performance
as the pressure values increase, which means this model is a good candi-
date to be used for any range of data points within the allowed domain of
the model. This model has been developed based on enough number of
input data points during the training stage. A final remark about this
Figure 1.3 Error distribution of a model that uniformly makes predictions.
Figure 1.4 Error distribution of a model that fails in making accurate predictions.
10
Applications of Artificial Intelligence Techniques in the Petroleum Industry
model is that even small deviations from the zero-error line predicted by
this model are balanced, which means this model does not suffer from
even a small systematic error.
Fig. 1.4 shows a model that suffers from a random error in predicting
experimental values. This model is the least accurate model with the larg-
est AAPRE value among all the example models presented in this chapter.
The large AAPRE is equivalent to large deviations from the zero-error
line, as can be seen in this figure. This model is not appropriate to be
used for any range of data points, and all the data points randomly deviate
from the zero-error line. This poor performance can be caused due to
using not appropriate model structure during the model development or
not having enough data points during the model training stage. Using a
better structure or better training algorithm for the model helps improve
the performance of such models.
Fig. 1.5 shows an example of a model that underestimates the mea-
sured data points. Under these conditions, the relative error is a positive
number, which can be observed in this figure, and predictions deviate
from the correct values. However, Fig. 1.5 indicates a model that under-
estimates only a portion of the data points (larger values) and does a fair
job in uniformly predicting the other data points (smaller and medium-
range numbers). This model would be a good candidate to use when
only small- and medium-range values are to be predicted. Nevertheless,
Figure 1.5 Error distribution of a model that underestimates predictions.
11
Introduction
this model indicates an error trend and should not be considered a reliable
model.
Fig. 1.6 shows a model that consistently underestimates the values and
is not appropriate to use for any range of data points. In this case a system-
atic error is observed in predicting the data points, and almost all the
experimental points are predicted to be smaller than their true values. The
data points significantly deviate from the zero-error line, and a significant
systematic error in predictions is evident. This error can be due to the
lack of data points or wrong model structure during the model develop-
ment stage.
Fig. 1.7 represents a model that overestimates the experimental data
points. Overestimation occurs when the predicted values tend to be larger
than the experimental values, as described in Eq. (1.4). Under these condi-
tions the relative error is a negative number, as can be seen in this figure,
and predictions deviate from the correct values. However, Fig. 1.7 indicates
a model that overestimates only a portion of the data points (larger values)
and does a relatively fair job in uniformly predicting the other data points
(smaller and medium-range numbers). Even though this model would be a
good candidate to use when only small- and medium-range values are to
be predicted, it still shows an error trend and should be used with caution.
Fig. 1.8 shows a model that consistently overestimates the values and is
not appropriate to use for any range of data points. In this case, a system-
atic error is observed in predicting the data points, and almost all the
Figure 1.6 Error distribution of a model that underestimates predictions.
12
Applications of Artificial Intelligence Techniques in the Petroleum Industry
experimental points are predicted to be larger than their true values. The
data points significantly deviate from the zero-error line, and this does not
improve by decreasing or increasing the values. This error can be due to
the lack of data points or wrong weights and biases during the model
development stage.
Figure 1.7 Error distribution of a model that overestimates predictions.
Figure 1.8 Error distribution of a model that overestimates predictions.
13
Introduction
1.4.2.2 Crossplots
In this approach, the predicted data points are plotted against the
measured data points along with a unit slope line. The closer the data
points to the 45-degree line, the more accurate the predictive model is.
Here, some examples of crossplots for different models are presented.
Fig. 1.9 is an example of using graphical crossplot analysis to evaluate the
error trend of a model in predicting dew point pressure values when the
pressure increases. As can be seen, the data points lie close to the unit-
slope line, and regardless of the increase or decrease in the values, they
follow the same trend. There are not many scattered or outlier data points
(in this case none), and there is no systematic or random separation of the
data points from the unit-slope line.
Fig. 1.10 shows a model that suffers from a random error in predicting
experimental values. This model is the least accurate model with the larg-
est AAPRE value among all the example models presented in this chapter.
This model is not appropriate to be used for any range of data points, and
all the data points randomly deviate from the unit-slope line. This poor
performance can be caused due to using inappropriate structure or training
algorithm during the model development or not having enough data
Figure 1.9 Crossplot of a model that uniformly makes predictions.
14
Applications of Artificial Intelligence Techniques in the Petroleum Industry
Figure 1.10 Crossplot of a model that fails in making accurate predictions.
Figure 1.11 Crossplot of a model that underestimates predictions.
15
Introduction
points during the model training phase. Using a better structure or
training algorithm helps improve the performance of such models.
Fig. 1.11 shows an example of a model that underestimates the measured
data points. As was discussed previously, underestimation occurs when the
predicted values tend to be smaller than the experimental values, as described
in Eq. (1.4). Under these conditions the relative error is a positive number,
and predictions deviate from the correct values. However, Fig. 1.11 indicates
a model that underestimates only a portion of the data points (larger values)
and does a fair job in uniformly predicting the other data points (smaller and
medium-range numbers). This model would be a good candidate to use
when only small- and medium-range values are to be predicted.
Fig. 1.12 shows a model that consistently underestimates the values and
is not appropriate to use for any range of data points. In this case, a system-
atic error is observed in predicting the data points, and almost all the
experimental points are predicted to be smaller than their true values. The
data points significantly deviate from the unit-slope line, and this deviation
is pronounced at larger values. This error can be due to the lack of data
points or wrong model structure during the model development stage.
Fig. 1.13 represents a model that overestimates the experimental data
points. Overestimation occurs when the predicted values tend to be larger
than the experimental values, as described in Eq. (1.4). Under these
Figure 1.12 Crossplot of a model that underestimates predictions.
16
Applications of Artificial Intelligence Techniques in the Petroleum Industry
Figure 1.13 Crossplot of a model that overestimates predictions.
Figure 1.14 Crossplot of a model that overestimates predictions.
17
Introduction
conditions, the relative error is a negative number, and predictions deviate
from the correct values. However, Fig. 1.13 indicates a model that overes-
timates only a portion of the data points (larger values) and does a fair job
in uniformly predicting the other data points (small- and medium-range
numbers). This model would be a good candidate to use when only
small- and medium-range values are to be predicted.
Fig. 1.14 shows a model that consistently overestimates the values and is
not appropriate to use for any range of data points. In this case a systematic,
error is observed in predicting the data points, and almost all the experi-
mental points are predicted to be larger than their true values. The data
points significantly deviate from the unit-slope line, and this deviation is
evident at larger values. This error can be due to the lack of data points or
wrong weights and biases during the model development stage.
1.4.2.3 Cumulative frequency plots versus absolute percent relative
error
In this technique, the data points with absolute percent relative errors
smaller than certain increasing values are cumulatively plotted against the
absolute percent relative error. Fig. 1.15 is an example of using graphical
cumulative frequency analysis to evaluate the performance of different
models in predicting oil viscosity. An example conclusion from this
Figure 1.15 Cumulative frequency plot of different models for the prediction of
undersaturated crude oil viscosity [1].
18
Applications of Artificial Intelligence Techniques in the Petroleum Industry
figure would be that the suggested model shows superiority to all other
models by predicting 80% of the data points with an absolute relative
error of less than 10%.
1.4.2.4 Group error
In this approach, the error values associated with the data in different
ranges are calculated and plotted. The data are divided into different
ranges, and their error within each range is calculated and plotted.
1.4.2.5 3-D plots
If a model’s prediction errors are to be plotted as a function of two inde-
pendent variables, a 3-D plot is an effective graphical technique to use.
1.5 Applicability domain of a model
Applicability domain of a model is the domain in which the training set
of the model has been developed, and it is applicable to make predictions
based on this domain.
1.5.1 Identification of experimental data outliers
Outlier detection is a technique to identify a group of data points that are
different from other data points within a dataset [2]. Outlier detection
techniques can be done numerically or graphically, depending on the
application [26]. The Leverage approach is a well-known technique for
outlier detections, which works based on the data residuals (the deviation
of a model’s predictions from experimental values) [2,3,5,6]. In leverage
approach, a hat matrix (H) is defined to determine the hat indices or
leverage of data points as follows:
H 5 XðXTXÞ21XT
(1.9)
where X represents a two-dimensional matrix with N rows (data points)
and k columns (model parameters). Also, T is for transpose multiplier.
The diagonal components of H represent the hat values of data. The
obtained H values from Eq. (1.11) are used in Williams plot to identify
the outlier and suspected data points graphically. This plot is also used to
obtain the correlation between the H indices and cross-validation resi-
duals. In fact, a Williams plot is a plot in which standardized residuals are
plotted versus hat values and different areas of valid data, suspected data,
and out of leverage data are determined.
19
Introduction
The standardized residuals (SR or cross-validation residuals) for each
data point are defined as follows:
SRi 5
ei
RMSE
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
1 2 Hii
ð
Þ
p
(1.10)
where ei is the deviation of the predicted data point from its experi-
mental value (predicted output-experimental data), RMSE is the root
mean square error of the model, and Hii is the hat index of the ith data
point.
In leverage approach, warning leverage (H) is defined to accept or
reject the model outputs and measurements. This parameter is defined as
H 5 3(k 1 1)/N, and usually, a value of 3 with an SD of 6 3 from the
mean is chosen to cover 99% of the scattered data. Under the conditions
where the majority of data points end up within the intervals of
0 # Hii # H and 23 # SRi # 3, one can conclude that the developed
model and its predictions are valid and reliable and also the experimental
data used for model development are reliable and valid.
The data points within the ranges of 23 # SR # 3 and H # H are
called good high leverage points and are outside the applicability domain
of the proposed model. The data points that are located in the ranges
of SR , 2 3 or 3 , SR (Regardless of their H value) are called bad
high leverage points and are known as experimentally suspected data
Figure 1.16 The Williams plot of a model for predicting solubility of CO2 in
brine [14].
20
Applications of Artificial Intelligence Techniques in the Petroleum Industry
points, which could be generated by an error during the experimental
measurements. An example of a Williams plot is depicted in Fig. 1.16.
1.6 Sensitivity analysis on models’ inputs
Sensitivity analysis entails how the uncertainty in a model’s inputs affects
the uncertainty of the model’s output. Sensitivity analysis can be con-
ducted for a variety of reasons, including evaluating the extent of correla-
tion between a model’s outputs and its inputs, searching for errors in the
model’s structure, and simplifying a model by eliminating its inputs that
do not affect its output.
1.6.1 Relevancy factor analysis
Relevancy factor analysis is a reliable technique to evaluate the impact of a
model’s inputs on its output. The relevancy factor measures how much effect
each input parameter has on the output, and a higher r value measured for
an input in this technique indicates a larger impact by that input on the out-
put. Here, the directional relevancy factor is introduced that can also identify
whether an input has a direct relationship with the output or a reverse one.
The following formula measures the directional relevancy factor [79]:
r Inpk; μ
ð
Þ 5
P
n
i51
Inpk;i 2 Inpave;k


μi 2 μave


ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
P
n
i51
Inpk;i2Inpave;k

2P
n
i51
μi2μave

2
r
(1.11)
where lnpk,i and lnpkavg denote the ith value and the average value of the
kth input, respectively (k represents the model inputs). μi stands for the
ith value of the predicted output, and μavg is the average value of the pre-
dicted output values.
1.7 The areas of intelligent models applications in the
petroleum industry
Intelligent models have been developed and used in various applications
in the oil and gas industry. These include the application of intelligent
models in reservoir and production engineering, drilling engineering, and
exploration engineering. Some specific examples are oil and gas viscosity
prediction [1,10,11], minimum miscibility pressure and interfacial tension
[12], and solution gas ratio [13].
21
Introduction
References
[1] S. Hajirezaie, et al., Development of a robust model for prediction of under-
saturated reservoir oil viscosity, J. Mol. Liq. 229 (2017) 8997.
[2] A. Leroy, P. Rousseeuw, Robust Regression and Outlier Detection Wiley Series in
Probability and Mathematical Statistics, Wiley, New York, 1987.
[3] C.R. Goodall, 13 Computation using the QR decomposition, Handb. Stat. 9 (1993)
467508.
[4] F. Gharagheizi, et al., Evaluation of thermal conductivity of gases at atmospheric
pressure through a corresponding states method, Ind. Eng. Chem. Res. 51 (9) (2012)
38443849.
[5] P. Gramatica, Principles of QSAR models validation: internal and external, QSAR
Comb. Sci. 26 (5) (2007) 694.
[6] A.H. Mohammadi, et al., A novel method for evaluation of asphaltene precipitation
titration data, Chem. Eng. Sci. 78 (2012) 181185.
[7] G. Chen, et al., The genetic algorithm based back propagation neural network for
MMP prediction in CO2-EOR process, Fuel 126 (2014) 202212.
[8] S. Esfahani, S. Baselizadeh, A. Hemmati-Sarapardeh, On determination of natural gas
density: least square support vector machine modeling approach, J. Nat. Gas Sci.
Eng. 22 (2015) 348358.
[9] M. Fathinasab, S. Ayatollahi, A. Hemmati-Sarapardeh, A rigorous approach to pre-
dict nitrogen-crude oil minimum miscibility pressure of pure and nitrogen mixtures,
Fluid Phase Equilib. 399 (2015) 3039.
[10] S. Hajirezaie, et al., A smooth model for the estimation of gas/vapor viscosity of
hydrocarbon fluids, J. Nat. Gas Sci. Eng. 26 (2015) 14521459.
[11] S. Hajirezaie, S. Ayatollahi, Soft Computing Model for Prediction of Sour and
Natural Gas Viscosity. The 8th International Chemical Engineering Congress &
Exhibition (IChEC 2014) Kish, Iran, 24-27 February, 2014. Available at: https://
www.sid.ir/FileServer/SE/219e201408743.pdf
[12] S. Ayatollahi, et al., A rigorous approach for determining interfacial tension and min-
imum miscibility pressure in paraffin-CO2 systems: application to gas injection pro-
cesses, J. Taiwan Inst. Chem. Eng. 63 (2016) 107115.
[13] S.-M. Tohidi-Hosseini, et al., Toward prediction of petroleum reservoir fluids prop-
erties: a rigorous model for estimation of solution gas-oil ratio, J. Nat. Gas Sci. Eng.
29 (2016) 506516.
[14] N.-A. Menad, A. Hemmati-Sarapardeh, A. Varamesh, S. Shamshirband, Predicting
solubility of CO2 in brine by advanced machine learning systems: Application to
carbon capture and sequestration, Journal of CO2 Utilization 33 (2019) 8395.
22
Applications of Artificial Intelligence Techniques in the Petroleum Industry

Modeling and Optimization of a Model 
Catalytic Cracking Unit 
IV Fluidized 
Robert C. Ellis, Xuan Li, and James B. Riggs 
Dept. of Chemical Engineering, Texas Tech University, Lubbock, TX 79409 
The problem of on-line optimization of a model IVjluidized catalytic cracking (FCC) 
unit is analyzed. The “process”was modeled by combining a model IV FCC unit dy- 
namic simulator (McFarlane et al., 1993; Khandalekar, 1993) with a ten-lump yield 
model (Jacob et al., 1976; Arbel et al., 1995) for the reactor section. The steady-state 
optimization model consisted of macroscopic steady-state models of the regenerator and 
reactor, a simpl$ed reactor yield model, and models of the uarious process constraints. 
The steady-state optimization problem was solved using successiue quadratic program- 
ming sofnvare (NPSOL, Gill et al., 1986) and the optimum process set points were 
implemented on the process simulator using a nonlinear constraint controller 
(Kandalekar and Rigs, 1995). The relatiue per$ormance of constraint control, off-line 
optimization, and on-line optimization is compared for different feed characteristics and 
product pricing structures. 
Introduction 
The FCC unit is a refinery process that converts high- 
molecular-weight gas oils into more valuable, lighter hydro- 
carbon products. Since the FCC unit is capable of converting 
large quantities of heavy feed into more valuable gasoline in 
a matter of seconds, efficient operation of the FCC unit is 
essential to the economic health of a refinery. The FCC unit 
is a multivariable nonlinear process which has a number of 
operational constraints that limit unit production rates. Since 
FCC units are economically important and involve complex 
interactions between product yields and production rates, 
they are an ideal candidate for optimization. 
A number of articles have been written about modeling of 
an FCC unit. Ford et al. (1976) developed a distributed pa- 
rameter model of the regenerator in an FCC unit using a 
detailed kinetic combustion model. Lee and Grove (1985) 
presented a model of an FCC unit based on macroscopic 
models of the reactor and regenerator. Monge and Georgakis 
(1987) developed a dynamic model of an FCC unit and used 
it to examine the dynamic behavior of the process. McFar- 
lane et al. (1993) developed a dynamic FCC unit model with 
constraints that was posed as a challenge problem for the 
chemical process control community. They used a distributed 
Correspondence concerning this article should be addressed to J. B. Riggs. 
Current address of R. C. Ellis: Aspen Technology Inc., 9896 Bissonnet, Houston, 
TX 97042. 
2068 
September 1998 
parameter model of the regenerator but used only a continu- 
ous stirred-tank reactor (CSTR) for the reactor section, which 
did not include a yield model. The FCC simulator used in 
this work is based on a modified version of the McFarlane 
FCC model. Theologos et al. (1997) used a 3-D fluid flow and 
reaction yield model of the FCC reactor to investigate the 
effects of injection geometry on the yield of the desired prod- 
ucts. 
FCC optimization requires a detailed yield model capable 
of predicting the yields of valuable products and gasoline oc- 
tane value. Early FCC yield models predicted feed conver- 
sion based on process operating conditions and cracking data 
for different gas oil feeds (Blanding, 1953). Weekman and 
Nace (1970) proposed a kinetic-based FCC reaction network 
consisting of three kinetic lumps, which was later expanded 
to ten lumps by Jacobs et al. (1976). An updated version of 
the ten-lump model was proposed by Arbel et al. (1995) that 
considered modern FCC units and allowed for catalyst char- 
acterization based on experimental data. The development of 
more rigorous, molecular-based, FCC yield models is an area 
of active research (Liguras and Allen, 1989; Liguras et al., 
1992). 
The light gases produced by the FCC unit process include 
hydrocarbons with molecular weights of up to C,. Light gas 
correlations based on industrial yield data have been pro- 
posed to provide a general description of light gas yields as a 
Vol. 44, No. 9 
AIChE Journal 
function of feed conversion and feed gravity (Gary and 
Handwerk, 1983; Maples, 1993). Detailed reaction schemes 
for the production of light gases have been proposed by John 
and Wojciechowski (1975) and Corma et al. (1984). 
Models used to predict the octane number of gasoline pro- 
duced in an FCC unit vary in degrees of sophistication. Rig- 
orous molecular-based models have been proposed (Liguras 
and Allen, 19901, along with correlations developed from in- 
dustrial data (Maples, 1993). Other sources of octane infor- 
mation from an industrial viewpoint are prevalent throughout 
the literature (Desai and Haseltine, 1989; Pierce and Log- 
winuk, 1985; Leuenberger, 1988). 
The primary impetus for supervisory optimization of an 
FCC unit is economic. Successful implementations of unit 
optimization have been published (Van Wijk and Pope, 1993; 
Lauks et al., 1993). Dynamic Matrix Control Corporation 
(DMC) reported that the implementation of an on-line opti- 
mization routine to a process will increase unit profitability 
by 3-5% (DMC, 1990). Other benefits of an on-line opti- 
mization routine include smoother constraint handling and 
off-line "what if' studies. 
Process Overview 
The FCC process (Figure 1) consists of three subsections: 
reactor, regenerator, and main fractionator. The reactor of 
an FCC unit consists of a feed riser line and a catalyst disen- 
gaging zone. The riser section is a long vertical pipe which is 
partially contained in the reactor vessel. After being heated 
to a temperature of 600-800°F (316-427"C), the gas oil feed 
is injected into the riser where it is mixed with hot catalyst 
(1,200- 1,400"F, 649-760°C) from the regenerator. The cata- 
lyst supplies the reaction sites and thermal energy required 
to carry out the endothermic catalytic cracking reactions. The 
temperature of the product and catalyst as it leaves the riser 
is approximately 900-1,000"F (482-538°C). The residence 
time for the catalyst in the riser is on the order of 2-10 s. 
The short residence time minimizes gasoline cracking and 
catalyst deactivation due to coking, resulting in better yields 
of the more valuable products. After the catalyst and hydro- 
carbons exit the riser section the catalyst is removed from the 
hydrocarbon stream in the disengaging zone of the reactor 
vessel. The hydrocarbon gas stream is sent to the main frac- 
tionator for separation while the spent catalyst returns to the 
regenerator where a portion of the coke formed during the 
cracking reactions is removed from the catalyst by combus- 
tion with air. 
In the regenerator, heated combustion air is mixed with 
the spent catalyst to bum off the coke produced during the 
cracking process. Carbon dioxide, carbon monoxide, water, 
and excess air are released from the regenerator as flue gas. 
Since the combustion of coke from the catalyst is an exother- 
mic reaction, the regenerated catalyst has increased thermal 
energy that is required for the cracking reactions in the riser 
section. The temperatures of the reactor and regenerator will 
remain stable if the thermal energy balance between the two 
vessels is satisfied. Energy balance closure will be realized 
when the thermal energy required to vaporize and crack the 
gas oil feed is equal to the thermal energy released from the 
combustion of coke from the catalyst. The excess oxygen in 
the flue gas is controlled to attain efficient combustion in the 
regenerator. 
The hydrocarbon products from the reactor are separated 
into various components in the main fractionator. The prod- 
uct streams from the main fractionator include light gases 
(C, and lighter), gasoline, heavy fuel oil (HFO), light fuel oil 
Downstroam 
Sqmratorr 
Gar 
oil 
Figure 1. Model IV fluid catalytic cracking unit. 
AIChE Journal 
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(LFO), cycle oil, and fractionator bottoms. In the current 
study, HFO and cycle oils are lumped together as a single 
product. 
Overall Approach 
The simulation study of on-line optimization of an FCC 
unit involves a number of different models, both steady-state 
and dynamic. The “process” is represented by a dynamic 
model of a model IV FCC unit (McFarlene et al., 1993; 
Khandalekar, 1993) which is combined with a steady-state 
yield model for the FCC reactor. The dynamic simulator cal- 
culates the time-varying states of the FCC unit at any point 
in time while the yield model uses the reactor conditions to 
calculate the conversion and the distribution of products. 
The “process”(dynamic simulator and detailed yield model) 
is controlled by a nonlinear constraint controller. The nonlin- 
ear constraint controller uses a steady-state energy balance 
on the reactor, dynamic energy, and oxygen balances for the 
regenerator, and models of the various constraints in order to 
maintain operation at the specified reactor temperature, re- 
generator temperature, and 0, level in the flue gas from the 
regenerator while maximizing the feed rate to the FCC unit. 
Finally, the supervisory optimization algorithm (SOA) 
chooses the set points for the reactor temperature, regenera- 
tor temperature, 0, level in the flue gas from the regenera- 
tor, and the process feed rate. The models used in the SOA 
consist of steady-state FCC unit models, steady-state models 
of the constraints and an approximate yield model. A succes- 
sive quadratic programming (SQP) optimization engine uses 
the SOA models to identify the economic-based optimum op- 
erating point. 
It should be pointed out that the models used both by the 
constraint controller and by the SOA are parameterized on- 
line using “process” data in order to keep the models in 
agreement with the process when significant process model 
mismatch occurs. 
In summary, the SOA identifies the set points for the con- 
straint controller. Then the constraint controller adjusts the 
manipulated variables in the “process” in order to maintain 
operation at the desired operating point. The hierarchy of 
optimization/constraint control/process model is shown in 
Figure 2. 
Process Model 
The process model consists of the dynamic FCC unit model 
combined with the detailed yield model for the reactor. 
FCC unit dynamic model 
The model IV FCC unit dynamic simulator used in this 
study was originally developed by McFarlane et al. (1993) us- 
ing ACSL (advanced continuous simulation language). Khan- 
dalekar (1993) converted the ACSL code to FORTRAN and 
benchmarked the simulator against open-loop responses pro- 
vided in the Amoco/Lehigh University Model IV FCC indus- 
trial challenge problem statement (McFarlane et al., 1993). 
The mathematical models that make up the FCC unit sim- 
ulator consist of a set of time-dependent differential equa- 
tions that were integrated using a fourth-order Runge-Kutta 
method with a step size of 0.25 s. The regenerator was de- 
Economic Information From 
Management 
Feed Quality 
Supervisory Optimization 
Algorithm 
FCCU Dynamic 
Simulation 
Figure 2. Supervisory optimization 
flow diagram. 
scribed using a set of spatial differential equations that were 
evaluated using an explicit Euler method with a step size of 
0.15 ft (0.05 m). To reduce computation time, the differential 
equations for the regenerator were integrated after every 
three steps in the time domain without a significant loss of 
accuracy. 
Reactor model 
The model by McFarlane et al. (1993) used a continuous 
stirred-tank reactor (CSTR) model that used empirical pa- 
rameters to predict conversion, reactor temperature, and coke 
content of the spent catalyst. In order to study FCC opti- 
mization, the CSTR model was replaced by a plug-flow reac- 
tor (PFR) model that predicts reactor temperature, the prod- 
uct distribution based on a tcn-lump model, the product dis- 
tribution of the light gases, the coke content on the catalyst, 
the octane of the gasoline produced assuming pseudo- 
steady-state operation, and perfect gas oil/catalyst contact- 
ing. 
The differential material balance equations for the compo- 
nent lumps, the differential energy balance, and the differen- 
tial material balance for the deposition of coke on the cata- 
lyst were integrated along the height of the riser tube using 
LSODE (Hindmarsh, 1980). LSODE was required to inte- 
grate the reactor riser equations due to the stiffness caused 
by the coke deposition kinetic equations. The temperature of 
the reaction mixture and the product distribution predicted 
at the outlet of the riser were used in empirical algebraic 
equations to calculate the distribution of light gas products 
and the octane of the gasoline produced. 
September 1998 Vol. 44, No. 9 
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- PI 
Ph 
Ash 
b Arl - 
As1 
u 
Figure 3. Ten-lump FCC reaction 
network. 
Ten-Lump Model. A ten-lump FCC unit reactor-riser 
model (Jacob et al., 1976; Arbel et al., 1995) was incorpo- 
rated into the dynamic simulator to provide a more complete 
description of the gas oil cracking kinetics. This model char- 
acterizes the gas oil feed on the basis of molecular structure 
and molecular weight. The ten-lump model is shown in Fig- 
ure 3, and the boiling ranges of the individual lumps are 
shown in Table 1. The light gas oil (LGO) components with 
boiling points (1) greater than 648°F (342°C) and (2) between 
432°F and 648°F (222°C and 342°C) are each grouped in four 
lumps: paraffins, naphthenics, aromatics, and aromatics with 
substituent branches. The heavy gas oil (HGO) has the same 
four lumps but each lump has a boiling point greater than 
648°F (342°C). Gasoline contains C,s up to a boiling point of 
432°F (222°C). The "C" lump contains coke and light gases 
(C, to C4). The kinetic models used for this reaction net- 
work, which were taken from Jacob et al. (1976) and Arbel et 
al. (19951, assumed first-order reactions with Arrhenius rate 
Table 1. Boiling Range of Lumps in Ten-Lump FCC 
Reaction Network 
Boiling 
Lump 
Description 
Range 
'h 
wt. % paraffinic molecules 
T > 648oF* 
Nh 
wt. % naphthenic molecules 
T > 
648"F* 
As, 
wt. % aromatic substituent 
T > 
648"F* 
Ar, 
wt. % carbon atoms among 
T > 
648"F* 
molecules 
aromatic rings 
p/ 
wt. '
%
 paraffinic molecules 
432 < 
T < 
648oF** 
4 
wt. 5
%
 naphthenic molecules 
432 < 
T < 
648"F** 
As, 
wt. % aromatic substituent 
432 < 
T < 
6480F** 
Ar/ 
432 < T < 
648OF** 
molecules 
aromatic rings 
wt. 5% coke and . 
. 
. 
wt. % carbon atoms among 
G 
wt. % gasoline 
(C5s-432"F) 
C 
(Cl-C4, and coke) 
I. Butene 
4. Propene 
3. Isobutane 
5. Propane 
3. Butane 
6. Gases < 
C2 
*342"c 
**222-342"C 
AIChE Journal 
September 1998 
constants for each of the 20 reactions shown in Figure 3. The 
differential material balance for coke deposition on the cata- 
lyst in the riser was based on previous work by Voorhies 
(1945), Gross et al. (1974), and Krambeck (1991). This model 
used an Arrhenius rate constant and includes a coking ten- 
dency factor that was expressed as an empirical function of 
the local product distribution (Gross et al., 1974). 
The light gas model predicts the yields 
of light gases based on the outlet conditions from the riser 
and the inlet feed quality while honoring the framework set 
by the ten-lump model. In order to predict the yields of n- 
butane, butylenes, i-butane, propane, propylene, and gases 
lighter than propane, expressions based on literature data 
(Gary and Handwerk, 1983) were developed. The feed grav- 
ity (MI) 
was estimated based on the composition of the feed, 
molecular weight information, and carbon-to-hydrogen ratio 
using standard correlations (Winn, 1957). These gas yields 
were scaled for consistency with the ten-lump yield model by 
selecting the value of K. The expressions for the light gases 
were estimated using the following equations: 
for butane 
Light Gas Model. 
Wt.n-butane 
(0.037424X" - 
0.06856 ApI+0.909615), 
(1) 
Pn - 
butane 
=K- 
Pfeed 
for butylenes 
(0.1476X" - 
0.06067 API 
- 
1.69867), 
Pbutylene 
Wt.butylene = 
~ Pfeed 
(2) 
for i-butane 
(O.lOlX, - 
0.098667 API - 
4.20733), 
P
z
 - 
butane 
Wt.,-butane 
= 
K- Pfeed 
(3) 
for propylene 
[(1.366 EXP(0.01956XJ +0.26125 API 
Ppropylene 
=K- 
Pfeed 
-6.00875)], 
(4) 
for propane 
Ppropane t0.36089 EXP (0.02655XJ1, 
(5) 
Wt.propane = 
K ~ Pfeed 
for C
,
 gases and lighter gases 
4, 
Wt.C2S 
= K - 
[0.366EXP(0.03322XU) 
-0.310 API+7.13]. 
Pfeed 
(6) 
Vol. 4
4
,
 
No. 9 
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Gasoline Octane Model. The motor octane number (MON) 
was estimated using information from the literature (Leuen- 
berger, 1988; Desai and Haseltine, 1989) and the expression 
is shown below. 
MON = 72.5 
+O.05(Tr - 
900°F) +0.17(Xw,.- 
0.55) (7) 
The research octane number (RON) was determined using 
a correlation developed from literature data (Gary and 
Handwerk, 1983). 
RON = 1.2931 
MON + 
12.06897 
(8) 
Modeling benchmarking 
The process model was quantitatively benchmarked 
(Kandalekar and Riggs, 1995) against the results presented 
by McFarlane et al. (1993). In addition, the yield model and 
octane model were qualitatively benchmarked against pub- 
lished data. A comparison between the predications from the 
yield model developed in this work and published results ver- 
sus conversion were made for C, yields (Nelson, 1973; 
Voorhies et al., 1964; Wachtel et al., 1972; Maples, 19931, C, 
yields (Maples, 19931, gasoline yield (Khandalekar, 19931, 
coke yields (Maples, 1993), and motor octane number (Desai 
and Haseltine, 1989). These results show a good representa- 
tion of the available data by the yield and octane models. For 
the specific comparisons between the model and the pub- 
lished data, see Ellis (1996). 
Nonlinear Constraint Controller 
The nonlinear multivariable constraint controller used in 
this study is based on using generic model control (Lee and 
Sullivan, 1988). This constraint controller is designed to max- 
imize unit feed rate for a particular operating point by main- 
taining operation against the operative constraints while 
maintaining the reactor temperature, regenerator tempera- 
ture, and the oxygen constant for the flue gas at their respec- 
tive set points. For a detailed description of the constraint 
controller, the reader is referred to Khandalekar and Riggs 
(1995). 
Process models 
The constraint controller uses a macroscopic steady-state 
energy balance for the reactor, and macroscopic dynamic en- 
ergy and oxygen balances on the regenerator. The adjustable 
model parameters (enthalpy of gas oil cracking, A Hcrack, 
and 
the frequency factor for coke combustion) were updated with 
on-line data using the nonlinear model parameterization ap- 
proach used by Rhinehart and Riggs (1991). 
Constraint models 
The constraint controller considers the following con- 
straints which were set by McFarlane et al. (1993): 
(1) Maximum tube temperature in the firebox. 
(2) Maximum fuel flow rate to the firebox. 
(3) Maximum catalyst circulation rate (AP). 
(4) Maximum regenerator air flow rate. 
(5) Maximum flow rate through the wet gas compressor. 
Nonlinear models were used to model each of these con- 
straints. The operative constraint was used to set the gas oil 
feed rate to the process. It should be pointed out that the 
maximum flow rate through the wet gas compressor used in 
the study was 10% lower than the value specified by McFar- 
lane et al. (1993). This was done in order that the wet gas 
compressor would act as an operative constraint. 
Supervisory Optimization Algorithm 
The supervisory optimization algorithm (SOA) is the high- 
est level in the process/controller/optimization hierarchy and 
determines the economic optimum operating point for the 
FCC unit. The SQP algorithm, used to solve the optimization 
problem, consists of the economic objective function, the col- 
lection of process constraints, and the optimization engine 
uses four decision variables. 
The regenerator temperature, riser temperature, gas oil 
feed flow rate, and stack gas oxygen concentration were cho- 
sen as decision variables for optimization. The four equations 
used to describe the FCC unit were solved simultaneously for 
four process variables using Newton’s method (Riggs, 1994). 
The remaining process variables were determined using ex- 
plicit expressions and then the objective function value was 
evaluated. The economic objective function used in this study 
is shown below 
(10) 
The values for p, are based on the economic climate and are 
tabulated in Table 2 for the various cases evaluated in this 
study. The economic optimization problem was also subject 
to the process constraints. In addition to the constraints pre- 
viously listed, there were upper and lower limits placed on 
both independent and dependent variables in the FCC unit 
process. Table 3 lists the upper and lower limits for the con- 
strained variables considered here. 
1 
F = [ c 
PlWt., + ( P ,  + 
Poctfoct)Wt., - 
Pfeed Fo,, (9) 
ia 
I = 1 , r  # 9 -  12 
MON + 
RON 
fa 
= 
2 
- 
85.69 
Optimization model for the FCC unit 
The FCC unit model used for process optimization was de- 
Table 2. Product Prices for Three Modes of Operation Studied* 
Mode 
PHFO 
PLFO 
P ~ ~ s o I ~ ~ c  
Pnc, 
P=c, 
Pic, 
P=c, 
P”C 
, 
P 5 
c, 
Poctane 
Gasoline 
0.02773 
0.07203 
0.09716 
0.05695 
0.08496 
0.07903 
0.09547 
0.05579 
0.0600 
0.00348 
LFO 
0.02828 
0.10084 
0.09230 
0.05695 
0.08496 
0.07903 
0.09547 
0.05579 
0.0600 
0.00116 
Lt. Gas 
0.02773 
0.07203 
0.08097 
0.07909 
0.09346 
0.08448 
0.09984 
0.05582 
0.0660 
0.00116 
*Note: Values are expressed as $/lb except for poctdnr. 
w h ~ c h  
is $/octane. 
2072 
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Table 3. Process Constraint Used in Optimization 
Number 
Bound 
Variable 
Bound 
Constraint 
Lower 
Process 
Upper 
1 
1,265"F 
T,e g 
1,400"F 
2 
980°F 
T. 
994°F 
3 
4 
5 
6 
7 
8 
9 
10 
11 
12 
13 
14 
75 Ib/s 
0.500% 
650°F 
0 
14.7 psig 
14.7 
psig 
5,100 RPM 
0 
-5 psia 
0 
45,000 SCFM 
0 
F",, 
132 Ib/s 
T3 
1,700"F 
F
5
 
39.5 SCM 
p 4  
54 psig 
'6 
54 psig 
sa 
6,100 RPM 
OZ sg 
2% 
Fsucn,lift - 
Fsurge,lrft 
None 
P
h
 - 
P
A
 
2 psia 
"
.
 
20 ft 
55,000 SCFM 
Fw, 
0.90 Ib mol/s 
LV 
Fsucn,cornb 
SI conversion: "C = PF - 
32)/1.8; kg 
= Ib = 0.454; kPa = psi x 6.89; L/s = 
CFM x 0472. 
veloped using macroscopic steady-state phenomenological 
models and empirical expressions. The models and expres- 
sions that comprise the optimization model are presented in 
the following order: 
(1) Reactor-riser 
(2) Reactor yield model 
(3) Regenerator 
(4) Firebox and preheat furnace 
(5) 
Pressure balances 
Reactor-Riser. A reactor-riser macroscopic, steady-state 
energy balance was used to develop an approximate model 
for the reactor-riser. The reactor model assumes that the heat 
lost by the regenerated catalyst and the feed is equal to the 
heat consumed by the cracking reaction occurring in the riser 
(11) 
The gas oil conversion Xwt, 
is estimated using the follow- 
ing expression developed by Khandalekar and Riggs (1995) 
Tr,avg 
= 
0.3T0 
+0.7Tr 
(14) 
Reactor-Yield 
Model. A simplified version of the ten-lump 
yield model, which was used for the "process," was used by 
the SOA. The simplified model used two assumptions which 
were designed to make the simplified yield model more 
tractable. 
(1) The average riser temperature Tr,avg 
was used to evalu- 
ate rate constants. 
(2) The coke on the catalyst in the reactor was assumed to 
remain constant. A weighted average was used between the 
top and bottom of the riser. 
These assumptions allowed for the direct calculation of the 
reactor yield from the operating conditions in the reactor-riser 
and provided for some structured mismatch between the de- 
tailed yield model and the simplified yield model which was 
used by the SOA. 
Steady-state macroscopic energy and oxygen 
balances were used to develop expressions to determine the 
regenerator temperature and excess oxygen in the flue gas. 
Several assumptions were made in the development of the 
regenerator model. 
(1) The combustion of coke was modeled using the follow- 
ing reaction: 
Regenerator. 
c+02 
+co2. 
(2) The depletion of oxygen across the regenerator bed was 
modeled as a plug-flow reactor. (Note that the constraint 
controller modeled the regenerator as a CSTR.) 
(3) With regard to coke content on the catalyst, the cata- 
lyst bed was assumed to be a well-mixed CSTR; therefore, 
the concentration of carbon on the catalyst was assumed con- 
stant throughout the catalyst bed. 
(4) No reaction occurs in the dilute phase of the regenera- 
tor, above the catalyst bed. 
These assumptions made it possible to obtain a tractable 
regenerator model at the expense of process/model mis- 
match. 
The steady-state energy balance for the regenerator is 
shown below 
+[I-exp j ~ l o o k p B c ~ g c z b e d ) ] ~ ~ c o k e  
= 0.0 (15) 
"
s
 
An empirical expression for the concentration of carbon on 
regenerated catalyst as a function of flue gas oxygen concen- 
tration and regenerator temperature was developed by Cut- 
ler (1990) 
where 
Steady-state reference parameters were determined using 
state simulation data and nonlinear regression. The approxi- 
mate model for estimation of the concentration of carbon on 
spent catalyst was offered by Lee et al. (1985). 
(17) 
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The steady-state oxygen balance for the regenerator is deter- 
mined by equating the reduction in the moles of oxygen in 
the air fed to the regenerator with the number of moles of 
oxygen consumed by the coke combustion reaction 
where C, is 2.78 based on assuming the complete combustion 
of coke and the coke composition as CH. 
The preheat furnace is used to heat the 
gas oil feed prior to entering the reactor-riser reaction. The 
macroscopic energy balance around the preheat furnace is 
given by 
Preheat Furnace. 
Qtotal= 
Qtrans + 
Q ~ o s s  
(19) 
where Qtotal 
is determined based on assuming complete com- 
bustion of the fuel; Qtrans 
is calculated using a log mean tem- 
perature difference; and Qloss 
is modeled as directly propor- 
tional to the heat load in the firebox. Therefore, this equa- 
tion becomes 
F, AH, = 
UAfLM(T3, 
T I )  
+ aF,T, 
(20) 
In addition, the heat absorbed by the feed, QtranS, 
is also equal 
to the sensible heat change of the feed. 
Equation 21 is used to calculate the firebox temperature, T3. 
Then Eq. 20 is used to calculate the fuel flow rate to the 
firebox. If the upper limit on T3 is exceeded, T3 is set to its 
upper limit, If F, is in excess of its upper limit, F, is set at its 
upper limit and Foil 
is calculated from the simultaneous solu- 
tion of Eqs. 20 and 21. 
The air injection 
to the regenerator and the stack gas valve maintain the pres- 
sure in the regenerator. The pressure difference between the 
reactor and the regenerator and the height of catalyst in the 
stand pipe are used to transport catalyst to the reactor. The 
gaseous products are produced in the reactor flow to the main 
fractionator driven by pressure difference. The LFO and HFO 
are separated in the main fractionator. Gasoline and light 
gases are separated in the main fractionator and leave from 
the overhead of the main fractionator to the suction side of 
the wet gas compressor. The pressure distribution through an 
FCC unit is maintained by the valves on the regenerator air 
blowers, regenerator stack gas valve, wet gas compressor suc- 
tion valve, and bypass valve. In the simulator, the reactor 
pressure was maintained constant by adjusting the wet gas 
compressor suction valve. A constant pressure drop between 
the main fractionator and the reactor is assumed. The regen- 
erator pressure and wet gas compressor suction pressure are 
updated based on assumed fluid dynamics. 
The total air-flow rate to the regenerator Fa,, is specified 
as a degree of freedom by the constraint controller. Since the 
sum of the lift air F,, and combustion air F,, must equal the 
total air-flow rate, F,, and F,, are set as follows (McFarlane 
et al., 1993) 
Pressure Distribution/Flow Calculations. 
for Fair 
< 
58.94 Ib/s (17.96 kg/s) 
F,, = 12.41 Ib/s (3.78 kg/s) 
F,, = 46.53 lb/s (14.18 kg/s) 
for 58.94 Ib/s (17.96 kg/s) < Fair 
< 66.07 Ib/s (20.14 kg/s) 
F,, = 46.53 Ib/s (14.18 kg/s) 
F,, = Fa,, - 
F
c
a
 
for Fa,, 
2 
66.07 Ib/s (20.14 kg/s) 
F,, = 
19.54 lb/s (5.96 kg/s) 
Fca = 
Fair - 
FIa 
(22) 
Since the regenerator pressure remains relatively constant 
during normal operation, its measured value was assumed 
constant for optimization calculations. The pressure 
drop/flow equations for the air blowers were used along with 
the effect of catalyst weight (McFarlane et al., 1993) to calcu- 
late the pressure at the bottom of the catalyst bed. 
For the wet gas compressor, the ratio of the wet gas flow 
rate to the reactor feed rate is empirically modeled as a func- 
tion of the reactor temperature 
The flow/pressure drop equations for the wet gas compressor 
presented by McFarlane et al. (1993) are then used to cal- 
culate the inlet pressure to the wet gas compressor. The 
pressure drop between the wet gas compressor and the main 
fractionator was calculated using the flow equation given by 
McFarlane et al. (1993). Finally, the reactor pressure was cal- 
culated by assuming a constant pressure drop between the 
reactor and the main fractionator (McFarlane et al., 1993). 
The optimiza- 
tion engine selects the four degrees of freedom (Treg, 
Tr, F,,,, 
and 02.sg) 
and with these values the model equations are 
solved simultaneously for all the needed steady-state operat- 
ing conditions for the FCC unit. The reactor conditions are 
then used to calculate the distribution of products produced 
by the reactor using the approximate yield model. Finally, the 
product prices and octane value are combined with the prod- 
uct flow rates to calculate the economic objective function 
values. Also, the process operating conditions are used to cal- 
culate the values of the process constraints. 
In order to solve for the steady-state operating conditions, 
Newton’s method (Riggs, 1994) is used to solve a black-box 
function of four unknowns: T2, 
T3, Frgc, 
Fa,,. 
This black-box function is implemented in the following se- 
quence: 
(1) Determine combustion and lift air flow rates (Eq. 22). 
(2) Calculate the pressure distribution through the FCC 
(3) Using Eqs. 12, 13, and 14, calculate the gas oil conver- 
(4) Calculate the amount of coke on the regenerated cata- 
(5) Calculate the amount of coke on the spent catalyst us- 
(6) Determine the error in the furnace heat balance equa- 
Solution Procedure for Optimization Model. 
unit. 
sion, XWt 
lyst, Crgc, 
using Eq. 16. 
ing Eq. 17. 
tion (Eq. 21). 
2074 
September 1998 Vol. 44, NO. 
9 
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(7) Determine the error in the steady-state energy balance 
for the regenerator Eq. 15. 
(8) Determine the error in the steady-state oxygen balance 
on the regenerator Eq. 18. 
(9) Determine the error in the steady-state energy balance 
on the reactor Eq. 11. 
Summarizing, Newton's method selects values of T,, T,, 
Frgc, 
and Fa,, 
until Eqs. 21, 15, 18, and 11 are simultaneously 
satisfied. 
After the four-dimensional black-box function is solved, the 
approximate reactor yield model is evaluated to calculate the 
distribution of products. The economic objective function can 
then be calculated directly. Also, with the process conditions 
calculated from the solution of the black-box function, each 
of the process constraints can be evaluated. 
Model parameterization 
Benefits of FCC unit optimization can only be realized if 
the FCC unit optimization model accurately describes the 
FCC process. To reduce process-model mismatch, adjust- 
ment parameters, which were common to the process and 
optimization model and whose exact values were not pre- 
cisely known, were identified and updated. 
Certain parameters were set based on on-line updating and 
certain parameters were evaluated off-line. The parameters 
ko, [preexponential for coking kinetics (Eq. 1
.
5
1
1
 and k, [rate 
constant for the three-lump model (Eq. 12)] were evaluated 
off-line. These parameters were estimated using steady-state 
data from the process simulator using a least squares analy- 
sis. 
The following parameters were updated on-line: 
k,-frequency 
factor for coke formation (Eq. 17). The 
value of k, is calculated each time the constraint controller is 
called (T = 0.5 minutes). The value of k, is filtered using a 
first-order exponential filter with filter value of 0.05. 
AH,,,,, -heat 
of cracking (Eq. 11). The value of AHcrack 
is calculated using the reactor energy balance (Eq. 1) each 
time the constraint controller is called (T = 0.5 minutes). The 
value of AHcrdck 
is filtered using a first-order exponential fil- 
ter with a filter value of 0.05. 
k,-frequency 
factor for gasoline formation (Eq. 12). 
The conversion is estimated on-line by using the product flow 
rates from the main fractionator (Xwt 
= 
Fgasohne/Fproduct). 
Eq. 
12 can then be used to calculate k,. This value of k, is calcu- 
lated once every 10 minutes and filtered with a first-order 
exponential filter with a filter factor of 0.01. 
It should be pointed out that both AHcrack 
and k, are be- 
ing adjusted using the reactor energy balance. Since AHcrack 
is being updated on a much higher frequency than k,, AH,,,,, 
is really responsible for removing mismatch for the reactor 
energy balance, while k, is slowing tracking conversion 
changes. 
Process-model mismatch between the simulation product 
yields and the optimization yield model was minimized by 
adjusting the frequency factors in the optimization yield 
model. In order to reduce the dimensionality of the parame- 
terization problem, the FCC unit products were grouped as 
HFO, LFO gasoline, and light gases. Six parameters were 
used to adjust the frequency factors such that the yields pre- 
dicted by the optimization yield model were consistent with 
the process data. The frequency factors were multiplied by 
the adjustable parameters. Filtered process yield data were 
used as parameterization targets. The SQP optimization al- 
gorithm NPSOL (Gill et al., 1986) was used to determine the 
adjustable frequency factors based on minimizing the sum of 
the squares of the errors between the predicated product 
yields and the measured product flows from the main frac- 
tionator. 
The product of the yield model frequency factors and the 
six parameters was used to adjust the ten-lump optimization 
yield model frequency factors. This parameterization was im- 
plemented prior to each optimization cycle. 
Results 
Table 4 lists a comparison between the gains obtained from 
the process simulator and the optimization model at the base 
case conditions for feed API equal to 27. Each of the gains 
for the three outputs (reactor temperature, regenerator tem- 
perature, and oxygen in the flue gas) and the three inputs 
(fuel flow rate to feed preheater, flow rate of regenerated 
catalyst, and air flow to the regenerator) show reasonable 
agreement between the simulator and the optimization model. 
The diagonal gains (i.e., A
T
J
A
 F,, ATreg/A 
FrgF, 
and 
A02,sg/A Fair) 
have better agreement than the off-diagonal 
gains. These results indicate that the optimization model is 
reasonable, but there is significant difference between the 
process simulator and the approximation model that is used 
to optimize the process simulator. 
Table 5 shows the effect of parameter variation on the 
change in the optimal objective function. The results were 
obtained by optimizing the process simulator using the opti- 
mization model with different optimization model parame- 
ters. Note that the optimization objective function is most 
sensitive to changes in k02 and reasonable variations in other 
model parameters have a relatively small effect on the opti- 
mum objective function value. 
Table 4. Comparison Between the Gains Obtained from the Simulator 
and Optimization 
Model at Base Case 
Conditions, 
API = 
27 
A PV/A F, 
A PV/A Frgc 
A PV/AFa,, 
- 
0.552"FXft s 
O.O5TF/Ilb/s) 
O.O7o"F/Ilb/s) 
AT,, model 
- 
0.334"FAft 2) 
/s) 
O.04loF/(lb/s) 
0.449"FXlb/s) 
AT,, simulator 
AT,,,, simulator 
1.460°F/iftp) 
- 
0.122"F/ilb/~) 
- 
1.838"F/(lb/~) 
ATreg, 
model 
0.599"F/(ft /s) 
- 
0.173"F/ilb/~) 
- 
1.598"F/(lb/~) 
- 
0.096 rnol %Aft s 
0.013 mol %/(lb/s) 
0.089 rnol %/(lb/s) 
AO,,,,, model 
- 
0.236 rnol %Aft ?) 
p) 
0.036 mol %/(lb/s) 
0.101 rnol %/(lb/s) 
AO,,,,, simulator 
SI conversion: "C = ("F - 
32)/1.8; L/s = ftys x 28.3. 
AIChE Journal 
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1998 Vol. 44, No. 9 
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Table 5. Summary of Results from Sensitivity Analysis 
Study: The Effect of Parameter Uncertainty on 
Optimal Objective Value 
Change in 
Treg. 
Tr, 
F,,,l, 
AObj. F, 
Parameter 
“F 
“F 
Ib/s 
%O,,, 
% 
k ,  
+7.5% 
k ,  +5.0% 
A Hcrack 
+ 
2.5% 
ko, +5.0% 
k ,  - 
7.5% 
k, - 
5.0% 
A Hcrack 
- 
2.5% 
ko, -5.0% 
1,265 
1,265 
1,265 
1,265 
1,265 
1,265 
1,265 
1,265 
994.00 
993.70 
991.63 
994.00 
994.00 
994.00 
991.60 
994.00 
104.12 
104.27 
104.18 
104.12 
104.12 
104.12 
105.32 
104.12 
0.5 
0.067 
0.5 
-0.111 
0.5 
-0.111 
0.5 
- 
0.223 
0.5 
0.089 
0.5 
- 
0.089 
0.5 
0.200 
0.5 
- 
0.601 
SI conversion: “C = (“F - 
32)/1.8; kg = Ib x 0.454 
Table 6 shows the sensitivity of the economic objective 
function to specified changes in the reactor temperature, re- 
generator temperature, oxygen in the flue gas, and feed flow 
rate to the FCC unit. Note that the change in the oxygen 
level in the flue gas has the least effect on the unit profitabil- 
ity while the riser temperature has the largest effect on the 
profitability. 
Table 6. FCC Unit Objective Function Sensitivity Gain 
Analysis, Feed API = 
27, Gasoline Mode 
T, 
7 
Trrp 
Fo,l, 
AObj.F, 
Run 
“F 
“F 
%O,,,, 
Ib/s 
% 
Base 
992 
1,270 
0.750 
i02 
AT, 
994 
1,270 
0.750 
102 
1.105 
AT,,, 
992 
1,268 
0.750 
102 
0.276 
AO?, 
S
,
 
992 
1,270 
0.850 
102 
0.069 
U”,, 
992 
1,270 
0.750 
104 
0.437 
SI conversion: “C = (“F 
- 
32)/1.8; kg = Ib x 0.454. 
Table 7 compares the optimization performance for base 
case operation, constraint control, off-line optimization, and 
the “true” 
optimum. The base case operation assumed opera- 
tion at fixed levels in the decision variables (T,,, = 1,270”F, 
T, = 
994”F, 
%02,,, 
= 0.75) and a fixed feed rate (for API = 27, 
Foil 
= 102 Ib/s; for API = 23, F,,, = 
105 Ib/s). Constraint con- 
trol maintains the same values for the decision variable at the 
base case but maximizes F,, while observing all constraints. 
Off-line optimization results were obtained by using the con- 
straint controller to maximize the oil feed rate while using 
the optimization model to determine the optimum values for 
the decision variables. The off-line optimization results were 
Table 7. Comparison of Base Case, Constraint Control, Off-line Optimization, and True Optimum 
Case 
Treg 
T, 
F0,I 
Active 
Number 
API 
Mode 
Approach 
(“F) 
PF) 
(Ib/S) 
%O,,,, 
AObj. F 
Constraints 
1 
27 
Gasoline 
Base 
1,270 
994 
102 
0.75 
Constraint control 
1,270 
994 
104.12 
0.75 
1.13% 
5, 14 
Off-line optimization 
1,265 
994 
104.12 
0.5 
1.82% 
5, 14 
Opt. using simulator 
1,265 
994 
104.12 
0.5 
1.82% 
5, 14 
Constraint control 
1,270 
990 
109.4 
0.75 
0.77% 
5, 14 
Off-line optimization 
1,265 
994 
107.36 
0.786 
5.62% 
5, 14 
Opt. using simulator 
1,265 
994 
107.36 
0.786 
5.62% 
5, 14 
Constraint control 
1,270 
990 
109.38 
0.75 
0.77% 
5. 14 
Off-line optimization 
1,267.12 
994 
107.36 
0.5 
4.95% 
5. 13, 14 
Opt. using simulator 
1,267 
994 
107.37 
0.5 
4.97% 
5, 13, 14 
Constraint control 
1,270 
994 
104.12 
0.75 
1.46% 
5, 14 
Off-line optimization 
1,265 
991.9 
105.11 
0.745 
2.04% 
5, 14 
Opt. using simulator 
1,265 
992 
105.09 
0.75 
2.01% 
5, 14 
Constraint control 
1,270 
990 
109.4 
0.75 
1.98% 
5, 14 
Off-line optimization 
1,265 
992.8 
107.7 
0.885 
3.75% 
5, 14 
Opt. using simulator 
1,265 
992.6 
107.9 
0.874 
3.68% 
5, 14 
Constraint control 
1,270 
990 
109.38 
0.75 
1.22% 
5, 14 
Off-line optimization 
1,267.12 
994 
107.36 
0.5 
4.38% 
5, 13, 14 
Opt. using simulator 
1,267 
994 
107.37 
0.5 
4.35% 
5, 13, 14 
Constraint control 
1,270 
994 
104.12 
0.75 
1.39% 
5, 14 
Off-line optimization 
1,265 
994 
104.12 
0.5 
1.94% 
5, 14 
Opt. using simulator 
1,265 
994 
104.12 
0.5 
1.94% 
5, 14 
Constraint control 
1,270 
990 
109.4 
0.75 
1.57% 
5, 14 
Off-line optimization 
1,265 
994 
107.36 
0.786 
4.09% 
5, 14 
Opt. using simulator 
1,265 
994 
107.36 
0.5 
4.09% 
5, 14 
2 
23 
Gasoline 
Base 
1,270 
990 
105 
0.75 
3 
23C 
Gasoline 
Base 
1,270 
990 
105 
0.75 
4 
27 
LFO 
Base 
1,270 
994 
102 
0.75 
5 
23 
LFO 
Base 
1,270 
990 
105 
0.75 
6 
23C 
LFO 
Base 
1,270 
990 
105 
0.75 
7 
27 
Lt. Gas 
Base 
1,270 
994 
102 
0.75 
8 
23 
Lt. Gas 
Base 
1,270 
990 
105 
0.75 
9 
23C 
Lt. Gas 
Base 
1,270 
990 
105 
0.75 
Constraint control 
1,270 
990 
109.38 
0.75 
1.57% 
5, 14 
Off-line optimization 
1,267.12 
994 
107.36 
0.5 
4.09% 
5, 13, 14 
Opt. using simulator 
1,267 
994 
107.37 
0.5 
4.23% 
5, 13, 14 
2076 
September 1998 Vol. 44, No. 9 
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 15475905, 1998, 9, Downloaded from https://aiche.onlinelibrary.wiley.com/doi/10.1002/aic.690440914 by Bilkent University, Wiley Online Library on [19/08/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
obtained by parameterizing the optimization models for each 
case study and using the resulting models to determine the 
optimum operating conditions. The true optimization results 
were obtained by allowing the SQP optimizer to select the 
degrees of freedom and then applying these degrees to the 
process simulator until the simulation lined out at steady- 
state. The steady-state results were used to calculate the value 
of the economic objective function and the constraint values. 
The SQP optimizer used the objective function value and 
constraint values to select new values for the degrees of free- 
dom until an optimum set of operating conditions was deter- 
mined. 
Nine separate cases are considered. Three different sets of 
economic parameters are considered (gasoline, LFO, and 
light gas modes, see Table 2). For each mode, three different 
feed types were considered: API 
= 
27; API 
= 
23; and API = 
23 with coking tendency increased by 5%. 
The changes in objective function value (A Obj. F )  from 
base case conditions were based on results obtained from the 
process simulator. Note that in all cases, the results for off-line 
optimization were essentially equivalent to the true optimum 
results. For the gasoline mode, the results for constraint con- 
trol showed an average increase in profitability over base case 
operation of 1%, while for the LFO and light gas modes, the 
increase in profitability was about 1.5%. The average incre- 
mental benefit for off-line optimization over constraint con- 
trol for feed with API = 27 was 0.6%. The average incremen- 
tal benefit for off-line optimization over constraint control 
for both cases with API = 
23 was over 3%. 
In all cases, the wet gas compressor constraint and the pre- 
heater tube temperature constraint were operative. For the 
three cases in which the coking factor was increased, the air 
blower constraint was also encountered. When the wet gas 
compressor and the air blower constraints are both operative, 
the optimizer will drive the process to a system pressure such 
that there is a proper compromise between these constraints. 
As system pressure is increased, the wet gas compressor ca- 
pacity will increase but the combustion air blower capacity 
will decrease. 
All cases except cases 4 and 5 resulted in a reactor temper- 
ature at the maximum level (994"F, [532"C]). Since the wet 
gas compressor constraint is operative at the maximum riser 
temperature, decreasing the riser temperature results in an 
increase of the FCC unit feed rates. In the LGO mode (i.e., 
winter pricing structure), lower riser temperatures result in 
large feed rate increases and more profitable operation. Some 
variation in the optimum 0, in the flue gas was observed 
although this decision variable does not have a major effect 
on process profitability. In addition, small changes in the op- 
timum TIeg were observed for the case in which the coking 
tendency of the feed increased. 
Figure 4 shows a comparison between off-line and on-line 
optimization. The off-line results were based on applying 
constraint control with the optimization decision variables set 
at optimum levels (see Table 7) for LGO mode for an API of 
the feed equal to 23. On-line optimization uses model param- 
eterization in order to respond to changes in the process. Ini- 
tially, both cases are operated at base case conditions. At 
time equal to 267 minutes, both on-line and off-line opti- 
mization are applied with constraint control. After some 
startup variations, both optimizers line out with a 3.3% im- 
AIChE Journal 
September 1998 
8 ,  
I 
L
I
 
I 
rcl 
0 
$
4
 
4 
c 
E
2
 
8 
a 
0 
I
I
 
c. t. resumed 
I 
8 
0 
0 
0 
tn 
W 
0 
0 
0 
0 
0 
r 4  
m 
d 
Time(minutes) 
Figure 4. Comparison of on-line optimization with 
off-line optimization. 
b.c.-base 
case; c.t.-coking 
tendency; 0.0.-optimum 
operation. 
provement over base case operation. At time equal to 420 
minutes, the coking tendency of the feed is increased by 3%. 
Note that after 50 minutes the on-line approach results in 
about 0.8% more profitable operation than the off-line ap- 
proach. At time equal to 545 minutes, the coking tendency is 
returned to its original value and the off-line and on-line re- 
sults become equal again. 
Additional Discussion on FCC Optimization 
The model IV FCC unit that was considered in this paper 
has a number of very specific characteristics, especially with 
regard to the process constraints. Each FCC unit has its own 
particular set of constraints. Since the optimum of an FCC 
unit involves operation on the constraints, the specific combi- 
nation of constraints will determine which constraints are op- 
erative during optimal operation. For example, the maximum 
firing rate for the feed furnace can severely restrict the FCC 
processing rate when the wet gas compressor is overdesigned 
or can be a nonfactor when the wet gas compressor is under- 
designed. 
A model IV FCC unit is an older design that does not have 
a slide valve for controlling the catalyst circulation rate and is 
not constructed of materials that allow higher riser tempera- 
tures. The slide valve provides an extra degree of freedom. In 
effect, the slide valve allows operation against the air blower 
constraint more frequently than for a Model IV FCC unit. In 
addition, the slide valve protects against flow reversal in the 
transfer lines and thus allows for operation closer to the flow 
reversal limit. The higher riser temperatures provide higher 
reactor conversion. In certain cases, the maximum reactor 
temperature may be such that over-cracking of the gasoline 
may result, significantly reducing gasoline yield. In such cases, 
it is usually more profitable to operate at reactor tempera- 
tures that are less than the maximum even for the gasoline 
mode. In such cases, the incremental benefit of on-line opti- 
mization would be expected to be larger than observed in this 
study based on the results that were developed for the winter 
mode for this study. 
In this work, several constraints that are commonly en- 
countered in industrial FCC operation were not considered. 
For example, this work did not model the main fractionator. 
Vol. 44, No. 9 
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Main fractionator flooding can be an operative constraint for 
an FCC optimization. Also, constraints of FCC operation can 
result due to constraints in the gas recovery unit, that is, the 
series of columns responsible for separating the light gas pro- 
duced by the FCC unit. In addition, the effect of ambient 
temperature changes on the capacity of the wet gas compres- 
sor was not modeled, but can be important for industrial FCC 
units. 
Conclusion 
This study has examined a wide range of operation of a 
model IV FCC unit. It was shown that constraint control and 
off-line optimization each provide significant incremental 
economic benefit. On-line optimization is shown to provide 
an additional improvement when unmeasured changes in the 
coking tendency of the feed occur. 
Notation 
a =furnace heat loss parameter, 1.535 
x 
CF,,ir 
=heat capacity of air, 0.244 Btu/lb/”F 
Cp,oil 
=heat capacity of gas oil feed, 0.74 Btu/lb catalyst/”F 
Btu/SCF/”F 
CF,, 
=heat capacity of catalyst, 0.31 Btu/lb catalyst/”F 
Crgc 
=weight fraction of coke on regenerated catalyst, lb coke/lb 
Crgc, 
ref =weight fraction of coke on regenerated catalyst reference, 
C, =weight fraction of coke on spent catalyst, lb coke/lb cata- 
C, =wet gas production parameter, 0.0088438 Ib mol/lb feed 
C, =wet gas production parameter, 0.00004 lb mol/lb feed/”F 
C, =constant in the oxygen balance expression, 2.78 
E =activation energy of coke combustion reaction, Btu/lb mol 
E,, =activation energy of coke formation reaction, Btu/lb mol 
EF 
=activation energy for conversion of feed in three-lump 
f , ,  
=octane differential used in objective function evaluation, 
catalyst 
lb coke/lb catalyst 
lyst 
model, Btu/lb mol 
octane number 
F =economic objective function for optimization, $/s 
Fair 
=air flow rate into regenerator, Ib/s 
F,,, =gas oil feed flow rate, Ib/s 
Frgc 
=flow rate of regenerated catalyst, lb/s 
FgaSOllne 
=gasoline flow rate from the main fractionator, lb/s 
Fproduct 
=total product flow rate from the main fractionator, Ib/s 
F,,,,, comb =combustion air blower inlet suction flow, ICFM 
FSucn, 
lift = 
lift-air blower inlet suction flow ICFM 
Fsurge, = 
lift-air blower surge flow, ICFM 
F 
=flow rate of wet gas to wet gas compressor, lb/s 
=flow of fuel to furnace, SCF/~ 
i =1-10, lumps in the ten-lump model: 1-P,,, 2-N,, 3-C,,, 
4-A,, 5-P,, 6-N,, 7-C,,, 
&A,, 9-G lump, 10-C lump; 
13-18, species in the light gas: 13-butane, 14-butene, 15- 
isobutane, 16-propylene, 17-N-propane, %Gases s C,. 
k =reaction rate of depletion of oxygen, Ib oil (s)/lb catalyst 
k ,  =frequency factor for the formation of coke, lb coke/lb 
catalyst 
2.4 
ko2 
=frequency factor for the depletion of oxygen in the regen- 
erator, lb coke (s)/ft3 
k ,  
=kinetic frequency factor for the formation of gasoline, Ib 
oil (s)/lb catalyst 
k ,  =frequency factor for conversion of feed in three-lump 
model, lb oil(s)/lb catalyst 
K =light gas scaling constant, 0.01 
L, =level of catalyst in standpipe, ft 
d 
= 
molecular weight (lb/lb mol) 
02, 
a,r =mole fraction of 0, in air, 0.21 
02, 
ref =mole percent oxygen reference, 0.00771 
1 for API = 
27; 
O,, 5g =concentration of oxygen in regenerator stack gas, mol% 
pfeed 
=cost of gas oil feed, $/lb 
0.01489 for API 
= 
23 
2078 
September 1998 
p, 
=product value used in the objective function, $/ib 
p,,ct 
=differential gasoline octane value used in objective func- 
tion, $/lb/octane number 
P4 =reactor pressure, psia 
P6 =regenerator pressure, psia 
QI,,, =heat loss from furnace, Btu/s 
Q,ot,l 
=total heat generated in furnace, Btu/s 
Qtranc 
=heat transferred to feed oil, Btu/s 
R =universal gas constant (10.73 ft3 psia/lb mol/”R) 
sa =actual speed of lift air blower, RPM 
t, =catalyst residence time in riser, s 
To =temperature of catalyst and feed entering riser, “F 
T, =temperature at the outlet of the riser, “F 
T’,, =temperature of air entering regenerator, 270°F 
T,, 
=average temperature in riser used in yield optimization 
Tr,ref 
=base temperature for reactor riser energy balance, 999°F 
model, “F 
Treg 
=temperature of regenerator bed, “F 
Treg, 
ref =regenerator reference temperature, 944°F 
Ti 
=temperature of fresh feed entering furnace, “F 
T2 
=temperature of fresh feed entering reactor riser, “F 
T3 
=furnace firebox temperature, “F 
UAf 
=furnace overall heat-transfer coefficient, 25 Btu/s/”F 
us =superficial velocity in regenerator, ft/s 
wtt, =weight fraction of ten lump species, lb/lb feed 
Xw, 
= 
weight fraction conversion 
X,, =volume fraction conversion 
zbed 
=height of catalyst bed in regenerator, ft 
AH, =heat of combustion of furnace fuel 1,000 Btu/SCF 
AHcoke 
=heat of combustion of coke, Btu/lb coke 
h 
P =pressure difference between regenerator and reactor, psig 
pB =volume fraction of catalyst in the re enerator 
pL 
=density of light gas component i, lb/ft3 
pfeed 
=density of gas oil feed to riser (Ib/ft $ 
) 
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120
Chem. Educ. Res. Pract., 2012, 13, 120–127
This journal is c The Royal Society of Chemistry 2012
Cite this: Chem. Educ. Res. Pract., 2012, 13, 120–127
Ensuring sustainability of tomorrow through green chemistry integrated
with sustainable development concepts (SDCs)w
Mageswary Karpudewan,* Zurida Ismail and Wolﬀ-Michael Roth
Received 15th October 2011, Accepted 31st December 2011
DOI: 10.1039/c1rp90066h
The purpose of this article is to describe a best practice: an approach to teaching chemistry that
our quantitative research has shown to produce large diﬀerences between experimental and
control groups in terms of achievement, pro-environmental attitudes, values, and motivation. Our
interest in teaching chemistry by focusing on sustainable development arises from the
environmental concerns that as the country of this study, Malaysia is facing in many diﬀerent
areas—including rampant logging and pollution. As educators, we are interested in educating
future generations so that they can cope with the environmental challenges that not only this
nation but also the world as a whole is increasingly facing. The ‘‘green chemistry’’ approach we
describe here may be just the answer that other developing nations and emergent economies in
closing the gap with industrialized nations. We provide a detailed description of how green
chemistry has been implemented in a curriculum for pre-service science teachers and in the
curriculum of secondary school chemistry.
Introduction
The purpose of this paper is to describe a green chemistry
approach—which we have used in the secondary school chemistry
curriculum and in chemistry teaching methods courses—as an
example of a ‘‘good/eﬀective’’ practice, evidence for which our
experimental work has provided (e.g., Karpudewan et al.,
2009, 2011; Karpudewan et al., 2011, in press-a, in press-b).
The unique feature of this paper is its concern for the nature of
the practice rather than for the evaluation (which we provide
elsewhere) in the hope that science educators generally but
those working in countries with emergent economies especially
are inspired to use the approach to prepare their nations for
coping with pollution and environmental exploitation. We
begin by outlining the societal and political background to
our development of a green chemistry curriculum. We move to
describing the fundamental ideas underlying the education for
sustainable development and green chemistry. In the context
of this paper, we provide and discuss some of the outcomes of
the experimental work that we have obtained from studies on
the integration of green chemistry as laboratory-based peda-
gogy in chemistry teaching methods course. We conclude this
introduction with a sketch of the adaptations that we made to
the regular chemistry curriculum to produce a green chemistry
curriculum.
Societal and political background
Malaysia is a developing nation with an emergent economy.
The country is striving to achieve the status of a fully developed
country by the year 2020, a goal explicitly stated in the Sixth
Malaysian Plan (Economic Planning Unit, 1991). To achieve this
status, the nation requires an annual growth of 7% (in real terms)
over
the
30-year
period
since
the
plan
was
articulated
(1990–2020), leading to an eightfold increase of its 1990 GDP
of RM115 billion. To achieve such an ambitious goal for
economic growth, Malaysia draws on its natural resources,
new constructions, the opening of new industries, and the
development of power plants to support these industries. How-
ever, all of these activities potentially have tremendous impact on
the environment. It is therefore of profound importance to
manage resources and construction so that the development is
sustainable over a time span that exceeds the 30-year plan.
In addition to the environmental challenges from sanctioned
developments, there are challenges that come from other
activities, including the illegal logging of prime forests. To allow
Malaysia—or all those developing nations and emergent economies
in a similar situation—to set in place a sustainable economy also
requires a population suﬃciently informed about how to deal with
the challenges to environment and environmental health.
Educating citizens by introducing programs that target
secondary school students and today’s pre-service teachers
who will teach future generations of students on the issue of
sustainability appears to be a be viable approach to developing
a pro-environmental orientation in a nation as a whole. We
anticipate this based on observations in Switzerland, where the
(pro-, anti-) environment-related discourses of 15–16-year old
Universiti Sains Malaysia and Griﬃth University, Queensland,
Australia. E-mail: kmageswary@usm.my,
mageswary_karpudewan@yahoo.com
w This article is part of a themed issue on sustainable development and
green chemistry in chemistry education.
Chemistry Education
Research and Practice
Dynamic Article Links
www.rsc.org/cerp
PAPER
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121
students reﬂect those of Swiss society as a whole, which means
that there is a reproduction of adult discourses among the
young who then have children themselves reﬂecting the
discourses of their parents (Zeyer and Roth, 2009, 2011).
Changing orientations toward the environment, therefore, requires
targeting future generations (of parents) and their teachers,
because these would then mediate the environmental and
sustainability discourses of subsequent generations. With this
goal in mind, we conducted empirical studies to test whether
secondary school students and pre-service teachers would
change toward more positive levels of their understanding,
attitudes, values, and motivations toward environmental
issues. Our eﬀort of integrating green chemistry in teacher
education curriculum is consistent with the calls for higher
education to make sustainability education a requirement for
all undergraduates; and adopting green chemistry into sec-
ondary school curriculum is consistent with the understanding
that students have to have opportunities to be educated about
sustainability from secondary school levels on (Rowe, 2007).
In addition to the other eﬀects we intend to bring about, the
implementation of green chemistry also contributes to the
improvement of science education (Alberts, 2005; Kennedy,
2007), to its goal of contributing to the building and sustaining
of lively scientiﬁc communities that are able to address global
problems, and to the maintenance of high levels of scientiﬁc
literacy among the general public (van Eijck and Roth, 2007).
Green chemistry also has the potential to contribute to the
education of scientists who actively work in their community
for bringing about sustainable practices related to the environ-
ment and environmental health (Roth, 2009). Green chemistry
allows students to participate in decision making over real issues in
their everyday worlds and, therefore, allows them to contribute to
global environmental problems by acting appropriately on
local matters.
Education for sustainable development
Various deﬁnitions were given for the term sustainable develop-
ment. Basically, sustainable development is deﬁned in a widely
quoted statement: ‘‘the development that meets the needs of the
present without comprising the abilities of future generations to
meet their own needs’’ (World Commission on Environment
and Development, 1987). Education is central for sustainability,
because it eﬀects the implementation of sustainable development
plans, impacts the decision-making capacities of the society, and
determines the quality of life. Education that is intended to support
sustainable development is known as education for sustainable
development. Education for sustainable development is a value-
laden education, which centers on human beings and asks human-
kind to change its behaviors. The ultimate focus of education for
sustainable development is to create a sense of responsibility that
takes into account the social, economic, and environmental eﬀects
on human life forms (Burmeister et al., 2012). Education for
sustainable development thereby becomes a guiding principle
for classroom learning, life beyond the classroom, as well as for
completing education. Therefore, education for sustainable develop-
ment emphasizes active student participation as an important aspect
that fosters a sense of responsibility to actively contribute to the
development of a sustainable way of living.
In an education for sustainable development, the content of
the curriculum and pedagogy employed to deliver the subject
matter are central to the particular values, worldviews, and
attitudes that are fostered (Fien, 2000). The author recom-
mends the curriculum to be holistic and pedagogy to be
student centered. Both goals can be achieved when everyday
life examples and knowledge are welcomed as the grounding
for students’ subsequent learning (Anderson, 2007). Integra-
tion of everyday life examples and knowledge leads to an
interdisciplinary perspective on subject matter (Roth, 2003).
This is so because sustainable knowledge systems bridge the
gap between the knowledge and application (David et al.,
2003). Similarly, sustainable development concepts also allow
teaching and learning to integrate diﬀerent curriculum subjects
and, therefore, support students to participate in interdisci-
plinary problem statements and solution ﬁnding (Yencken
et al., 2000). Suitable sustainable development concepts
inclusive of concepts such as carrying capacity, steady-state
economy, ecospace, ecological footprint, natural resource
accounting,
eco-eﬃciency,
life-cycle
analysis,
sustainable
consumption, local-global link, interdependence, intergener-
ational equity, intra-generational equity, interspecies equity,
and basic human needs (Yencken et al., 2000).
Green chemistry
‘‘Green chemistry’’ refers to the use of chemistry that prevents
pollution. To prevent pollution, green chemistry employs
materials, processes, or practices that reduce or eliminate the
creation of pollutants or wastes. The term also refers to
practices that reduce the use of hazardous and nonhazardous
materials, energy, water, or other resources as well as protect
natural resources through eﬃcient use (EPA, 1996). The
implementation of the green chemistry into practice is guided
by 12 principles that underlie a green approach to chemistry
(Anastas and Warner, 1998). Through application and exten-
sion of these 12 principles, green chemistry can contribute to
sustainable development (Wardencki et al., 2005). The princi-
ples of green chemistry comprise the following: it is better to
prevent waste than treating it; atom economy; using less
hazardous material synthesis method; designing safer chemicals;
design for energy eﬃciency; use of renewable feedstock; reduce
derivatives; catalysis; design for degradation; real-time analysis
for population prevention; and inherently safer chemistry for
accident prevention (Anastas and Warner, 1998). In this, green
chemistry is not a new branch of science. Rather, it is a new way
of thinking about science in a responsible manner so that the lives
of future generations are not compromised by today’s actions.
Implementing green chemistry into curriculum comes with the
hope of building a foundation that leads to a sustainable chemical
industry in support of a sustainable society (Braun et al., 2006).
In schools, green chemistry allows students to make
connections between the discipline of chemistry, other disciplinary
subject matters, and aspects of their lives. For example, green
chemistry has the potential to overcome a major barrier of current
environmental education (Lianne, 2005), which separates pristine
environments (nature) and the home, where the practices occur
that impact the nature. Green chemistry provides students with
the opportunity to act in an environmentally appropriate manner,
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122
Chem. Educ. Res. Pract., 2012, 13, 120–127
This journal is c The Royal Society of Chemistry 2012
because students understand how their chemistry directly aﬀects
the settings in which chemicals are used and where pollutants and
wastes are dispersed. It has been shown that students who engage
in environmental action tend to develop deep understandings of
the sciences that they directly apply as part of their actions (Roth
and Barton, 2004). Studies have suggested that implementation of
green chemistry education can overcome the limitations of the
current environmental education because it enhances critical
thinking and problem solving skills as well as encourages students
to look into sustainable development both locally and globally
(Parrish, 2007). This study also lights that green chemistry
enhances the communicative skills of the students. Students learn
to address the environmental problems, as they felt empowered
towards solving the problems (Haack et al., 2005).
Green chemistry laboratory manual
In the course of implementing green chemistry experiments
into pre-service teachers’ curriculum and secondary school
chemistry curriculum, a laboratory manual was produced
containing 27 activities (Karpudewan et al., 2011). These
experiments/activities were obtained from various sources
(e.g., Cann and Connelly, 2000; Warren, 2001; Kirchhoﬀ
and Ryan, 2002) and tailored to suit our Malaysian context.
We also developed several new activities. Prior to the develop-
ment of the manual, a series of workshops involving in-service
teachers (Fig. 1), pre-service teachers and secondary school
students (Fig. 2) were conducted to identify the feasibility of
integrating
these
activities
into
the
oﬃcial
curriculum.
Through these workshops feedback was obtained from
teachers and students, including their views in relation to the
activities. The feedback was used to further improve the
activities before the ﬁnal version was completed.
The activities in the manual cover various topics that are an
integral part of the secondary school chemistry curriculum. In
the laboratory manual, the existing experiments in the chemistry
curriculum were presented in such a way that environmental
concerns were highlighted. To extend the learning beyond the
walls of the classroom, and therefore to make the learning of
chemistry relevant with the everyday life of the students, appro-
priate sustainable development concepts were also integrated
into the curriculum. All the activities begin with pre-lab ques-
tions, which are followed by pre-lab discussions and a study of
the procedures required for conducting the chemistry experi-
ments at the heart of the activities. For answering the pre-lab
questions, students have to review the relevant literature and
discuss their answers within a peer group. The pre-lab questions
thereby engage the student in reﬂection that set the stage for
understanding the laboratory work in the applied context of a
real-world problem. The pre-lab questions also introduce the
materials and processes used in the experiment. The questions
also require students to investigate the historical and current,
real-world aspects of the experiment. The safety precautions and
concerns as well hazardousness of the chemical and correct
means of disposing the chemicals are discussed thoroughly in
this section. The conversion of traditional experiments into green
chemistry experiments also involved adding a new dimension to
the students’ tasks: they are asked to analyze and explain how
their actions in relation to materials used in the experiment can
and will contribute to sustainable development and the environ-
mental health of the nation as experienced by future generations.
During the post-lab discussions students not only discuss the
observation and ﬁndings of the experiments as they traditionally
do but also in view of with the impacts of the pertinent chemistry
on economy, environment, and society on Malaysia as well as on
the world more generally. For this purpose the students have to
draw on the sustainable development concepts, which therefore
become the discursive resources they learn to deploy for the
purpose of making arguments for a greener approach to the use
Fig. 1
In service teachers working with the experiments.
Fig. 2
Secondary school students involved in the experiment.
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Chem. Educ. Res. Pract., 2012, 13, 120–127
123
of chemical processes. For example, the production of biodiesel
from palm oil, an important resource produced in Malaysia, is
one of the experiments included in the manual (see below). (It is
evident that in other developing nations and emerging econo-
mies, curriculum developers will choose relevant local and
national resources for contextualizing their green chemistry
eﬀorts.) Two sustainable development concepts are addressed:
natural resource accounting and life cycle analysis (Yencken et al.,
2000). In relating the experiment to life cycle analysis, students
ﬁnd all aspects included that are inherent in the process of
producing biodiesel from the growing of palms to the use of
biodiesel (including waste generation and management). This
involves, for example, studying the starting materials (renewable
or non-renewable sources used in the industrial process), energy
and chemicals used to develop the biodiesel, or the wastes
deriving from the production and consumption of biodiesel.
Students evaluate the relevance of these factors to the local and
national economy, environment, and the society as a whole.
In this way, green chemistry makes way for student-centered
learning. In other words, studying the chemistry involved in
the local context and with local relevance gives rise to deep
learning, a process where learners do not rote memorize and
regurgitate facts taught by teachers but engage with their
hearts and minds (i.e., cognitive and aﬀective domain) to
enhance their understanding of issues that directly aﬀect their
everyday lives (Greeno, 1998). In the appendix, we exemplify
green chemistry by means of a detailed description of one
activity with local application: the production of biodiesel.
Toward a greener chemistry: the production of
biodiesel
The production of biodiesel described in the appendix is one of the
experiments that students conduct as part of the green chemistry
curriculum. This description exempliﬁes our overall eﬀort, which
has led to substantial changes in students’ knowledge, pro-environ-
mental attitudes, motivations to act pro-environmentally, and the
values students adopt. That is, there is experimental evidence for
the quality of this revised chemistry teaching practice. Thus, for
example, after going through a course in green chemistry
pre-service teachers’ acquisition of environmental concepts (Tradi-
tional Environmental Concepts [TECs] and Sustainable Develop-
ment Concepts [SDCs] improved and it appeared that the pre-
service teachers developed deeper understandings of SDCs than
TECs (Karpudewan et al., 2009). Understanding of SDCs is
imperative, as it overcomes the limitation of TECs. For instance
TECs basically imparts the knowledge about the environment and
actions necessary to overcome pollution whereby in this scenario
the environment is perceived diﬀerently than the human; however,
SDCs describe the whole ecosystem as integral part of everyday
living and the learners begin to relate the consequences of their
actions on the whole ecosystem.
In terms of environmental values, introduction of green
chemistry
experiments
as
a
laboratory-based
pedagogy
changes pre-service teachers’ environmental value from initially
being egocentric towards being more homocentric and ecocentric
(Karpudewan et al., in press-a). The results of the study indicate
that after going through series of green chemistry experiments
pre-service teachers’ egocentric value orientation decreased
signiﬁcantly, homocentric orientation increased, however, the
increase is not signiﬁcant. The ecocentric orientation value
orientation of the pre-service teachers’ improved signiﬁcantly.
Individual with egocentric value will engage in the activities that
beneﬁt themselves without considering whether or not the activities
beneﬁt the environment. Homocentric individual will justify their
actions from the perspective of whether the actions beneﬁt the
humanity and ecocentric individual attempt to protect the environ-
ment for its intrinsic worth. Results of another study, involving
diﬀerent cohort of pre-service teachers indicates that pro-environ-
mental attitude and self-reported behavior measured with New
Ecological Paradigm scale and self-reported behavior survey
changed substantially (Karpudewan et al., in press-b). An increase
in the total pro-NEP stance measured in the percentage was
obtained for the entire 15 items in the NEP scale among the pre-
service teachers experienced green chemistry. Additionally, for the
self-reported behavior statistically reliable diﬀerences were obtained
between the pre-test and post-test means on every one of the eight
pro-environmental items included in the survey.
A survey was conducted to identify the students’ view/
perceptions on the implementation of green chemistry as a
laboratory-based pedagogy (Karpudewan et al., 2011). A
majority of them agreed that the green chemistry experiments
are accordance with syllabus requirement; it is easier to learn
chemistry concepts with green chemistry experiments; the
equipments and materials are readily available; suﬃcient time
was allocated to conduct the experiments; and the experiments
are safe to be conducted in schools. However, a majority also
indicated that they are not sure whether they have better
understanding of SDCs and how to apply in their everyday
life. Additionally, these pre-service teachers are not sure
whether the procedures to conduct the experiments are simple
and easy to be followed.
The ﬁndings obtained from our eﬀort in integrating green
chemistry experiments into pre-service teachers’ curriculum, as
reported in various previously published work, explicitly
indicates that there are substantial in evidence in green chemistry
promoting pro-environmental attitudes, environmental values
and knowledge (Karpudewan et al., in press-a, in press-b;
Karpudewan et al., 2009).
Teaching methods that go with green chemistry
Green chemistry goes beyond simply doing the experiment and
linking it to concepts. Throughout the unit, students are
supported in making linkages to other aspects of their life.
For example, to bring in the real-world situation, the experiment
on biodiesel was further extended with activities comparing heat
of combustion of petroleum diesel and biodiesel and comparison
of energy required to travel for 100 km and the cost comparison
between biodiesel and petroleum diesel. The integration of
experiment on production of biodiesel as one of the green
chemistry experiments is consistent with the claim that this will
foster controversial discussions in the classroom and one possible
way to improve students’ motivation and attitude towards
chemistry and its importance to society (Eilks, 2002). Fig. 3
shows another extension that allows students to understand that
there is much more soot produced in the burning of petroleum
diesel than in the burning of biodiesel. Through this hands-on
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This journal is c The Royal Society of Chemistry 2012
activity students observe the diﬀerences in the burning of the
two fuels.
There are further extensions that involve students taking a
particular side in a mock debate. The mock debate in our
context is slightly diﬀerent from previously reported studies on
teaching of biodiesel (e.g. Eilks, 2002) and bioethanol usage
(e.g. Feierabend and Eilks, 2011). The students were requested
to provide opinion for the question of ecological evaluation
based on a public debate concerning biodiesel that appeared in
the newspaper (Eilks, 2002). For this debate, students review
further relevant literature on the diﬀerences between these two
fuels. They conduct a life-cycle analysis (similar to cradle-
to-grave analysis used in Eilks, 2002) of both fuels, and based
on that then they debate whether opting to use biodiesel is
really green. For the purpose of debate, two groups are
appointed. One group supports and argues that using biodiesel
is green. The other group is tasked with arguing against the
view. While debating, the students are intrinsically involved in
understanding the arguments of the side they have to defend to
convince the other side. In their argument the students draw on
real-world examples, for example, on the fact that petroleum
diesel adds to the air pollution due to forest burning in Sumatera
and Indonesia, and which raises the air pollution index in
Malaysia. The other group raises the question why the Indonesian
(in Sumatera) burns the forest in the ﬁrst place, which, as they
ﬁnd out, they do to plant palm trees for the purpose of
biodiesel. That is, students learn that there are costs as well
as beneﬁts with the production of biodiesel from palm oil.
Yet another extension that allows students to understand
chemistry concepts that have real-world pertinence: they
determine, for example, the heat of combustion for biodiesel
and petroleum diesel using the apparatus shown in Fig. 4.
After they have calculated the combustion heat, energy level
diagrams are drawn for both the fuels. They calculate bond
energy: DH = bond broken  bond formed as well as the
energy required to travel 100 km using either fuel. The
diﬀerences in the energy are converted to number of moles
and volume of biodiesel consumed.
Finally, we end the discussion on biodiesel with a role-play. The
implementation of role-play is consisted with notion that this form
of expression raises learners’ awareness of the societal dimension
of the issues being discussed; and it highlights the dialogues taking
place and various options available for consideration and diﬀerent
special interest groups which take part in the process (Feierabend
and Eilks, 2010). It is proposed that the government is planning to
impose the use of biodiesel in the near future. Students are asked
to respond to this based on the role they are assigned: a palm tree
plantation worker, an active member of Greenpeace (a NGO of
environmentalist), a CEO of a company that provides goods and
transportation service in Malaysia, and as a member of the general
public. As palm tree plantation worker, students provide their
views to the governmental plan talking about, for example, why
biodiesel is being produced commercially in Malaysia as well the
future of biodiesel. As an active Greenpeace member, students
imagine having just returned from the Bali Climate Talks and that
they are getting worry about the condition of mother earth. As
CEO of a transportation company, which consumes a large
amount of diesel every year and expects that more fuel will be
used due to the increase of customers in the coming year, they
provide arguments about why or why not diesel-powered engine
should be converted to biodiesel engines. Finally as a member of
general public, the students comment on the government’s plan
and speculate about how the prices of other goods will be aﬀected
by the plan.
Implementation and evaluation of green chemistry
In this section, we describe where and how we implemented
the green chemistry curriculum and evaluated its impact.
Green chemistry in the teacher education curriculum
The chemistry teaching methods course is oﬀered in the School
of Educational Studies at Universiti Sains Malaysia. It is a
compulsory course taken by the third-year students enrolled in
the science education degree program. Upon completing this
program, the pre-service teachers may be appointed as teachers,
Fig. 3
The soot developed from burning of biodiesel (left) and
petroleum diesel (right).
Fig. 4
Apparatus used to determine heat of combustion.
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Chem. Educ. Res. Pract., 2012, 13, 120–127
125
teaching chemistry to Form 4 and Form 5 (secondary school)
students. Currently, the course content covers instructional
theory, pedagogical content knowledge, and various instruc-
tional strategies used to teach chemistry. This includes
constructivism,
inquiry
methods,
project-based
learning,
active learning, and traditional laboratory-based pedagogy.
The pre-service teachers are exposed to various instructional aids
that may be used to teach chemistry eﬀectively. Fundamentally,
the course lays a foundation of pedagogical and pedagogical
content knowledge as well as relevant educational theories.
We began the implementation of green chemistry during the
2006/2007 and 2007/2008 semesters as a laboratory-based
pedagogy. The ﬁrst and second cohorts of the implementation
consisted of 110 and 263 pre-service teachers, respectively.
This eﬀort constitutes a concrete implementation of the
university’s sustainability endeavors. During the semester,
the pre-service teachers worked on ten experiments. They also
simulated teaching a lesson in green chemistry, where students
act in the role of the teacher. For this purpose they were
required to prepare a lesson on the activity assigned to them.
Upon completing the lesson, the students were required to
submit a completed lab report. The students were continu-
ously assessed on lesson plans, lab reports and simulated
teaching. Two quizzes were administered, during the middle
of semester (after completing the ﬁrst ﬁve experiments) and
towards the end of the semester (after completing all the 10
experiments). In their ﬁnal written examinations, students
were asked to justify the introduction of green chemistry in
secondary school curriculum. Our empirical work shows large
eﬀects on the environmental values (Karpudewan et al., in
press-a) and acquisition of sustainable development concepts
(Karpudewan et al., 2009). Throughout the semester the
students were exposed to the green chemistry experiments
and eﬀect of green chemistry experiments on environmental
value change and acquisition of environmental concepts was
measured for three times using repeated measure design. In all
but one instance, statistically reliable eﬀects were found
(Table 1). Green chemistry also shows large eﬀects on improving
the environmental attitudes (Karpudewan et al., in press-b) when
compared to students who did the traditional experiments on the
same chemistry topic (Table 2).
Green chemistry in secondary school chemistry curriculum
The integration the sustainable development concepts into the
green chemistry experiments was extended to the secondary
school level, where they were introduced as part of the students’
practical work. In Malaysia, practical work is compulsory and
constitutes an essential component of teaching and learning
chemistry. For the purpose of this research the experiments in
relation to topic of rate of reaction and chemistry of carbon
compound were introduced to the students. Table 3 indicates the
list of experiments conducted by the high school students. The
eﬀect of the green chemistry on student motivation towards
learning chemistry were measured using a qualitative approach
and the eﬀect on students understanding of chemistry concepts
was measured using a previously validated chemistry achieve-
ment test (CAT). Again, there were large eﬀect sizes (d = 3.27)
when compared to the control group (t(65) = 14.89, p o
0.0001),
where
students
did
the
equivalent
chemistry
experiments.
Conclusion
During the recent climate change meeting in Copenhagen
(December 2009), Malaysia agreed to reduce carbon emissions
from 187 million tones in 2005 to 74.8 million tones in 2020, a
reduction to 40% of the original levels. To achieve such an
ambitious target, the nation as a whole has to contribute both
on personal and collective levels. The current secondary school
students, who will be joining the workforce in the near future,
require some fundamental understanding of sustainable develop-
ment and what this implies for the individual and collective
practices. Our work shows that green chemistry has a tremendous
potential in changing relevant chemistry concepts, attitudes,
motivations for acting pro-environmentally, and pro-environmental
values. We fundamentally hope—though this remains to be
studied—that our own endeavor of teaching green chemistry
leads to sustainable change and development of the Malaysian
people. We designed green chemistry in the hope that it
will assist our people to actively contribute to sustainable
growth and a planet that is made more livable because
of how each of us behaves individually and contribute to
collective decision-making.
Appendix
Pre-lab questions
1. What is the characteristic of cooking oil that allows it to be
used as starting material of biodiesel?
2. Use structural features of cooking oil and methanol to
describe how biodiesel is formed.
3. What are the diﬀerences between petroleum diesel and
biodiesel in terms of their structures?
4. Describe what you know about the starting materials
used for biodiesel production and to what extend it has been
implemented in Malaysia.
5. What are the contributions of present generation for the
future generations with the change from petroleum diesel to
biodiesel?
Pre-lab discussion
In 1895 Rudolf Diesel designed an engine that run on vegetable
oil. However, the high viscosity of vegetable oil and the lower
production costs of petroleum diesel resulted in Diesel’s modiﬁ-
cation of the engines to run on petroleum diesel. At the time,
awareness about the pollution created by petroleum diesel did not
exist. Today, however, it is well understood that the use of
petroleum diesel contributes to air pollution; air pollution, in
turn, impacts the climate both locally and globally as well as
having immediate eﬀects on human health (e.g., under smog
conditions). Petroleum diesel forms particulates and carcinogenic
compounds. It contributes to a net increase in greenhouse gases
such as carbon dioxide, sulfur dioxide, and nitrous oxide.
In addition, petroleum diesel is a product obtained by fractional
distillation of petroleum, a non-renewable (fossil-fuel) resource.
Furthermore, petroleum diesel is much more diﬃcult to degrade
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than biodiesel is. A viable substitute for the demand for diesel fuel
is biodiesel, a methyl ester of fatty acids found in vegetable oils.
Biodiesel is produced through transesteriﬁcation reaction (Fig. 5).
During this reaction the bonding between glycerol and the fatty
acids are cleaved. A methyl group is added to the end of the fatty
acid, which is called biodiesel, and the other products are glycerol
and the sodium hydroxide catalyst. Discarded cooking oil or palm
oil is suitable starting materials for the production of biodiesel.
Purpose
1. To produce biodiesel from cooking oil.
2. To identify the properties of biodiesel.
3. To compare and diﬀerentiate the properties of biodiesel
and petroleum diesel.
Equipment and chemicals
100 mL Erlenmeyer ﬂask, pestle and mortar, magnetic stir
plate, hot plate, 20 mL graduated cylinder, 100 mL graduated
cylinder, thermometer, stop watch, 125 mL separation funnel,
retort stand, methanol, sodium hydroxides pellets, cooking oil.
Procedure
1. Measure 14 mL of methanol into 100 mL Erlenmeyer ﬂask.
2. Weigh 0.5 g of sodium hydroxide. Crush the NaOH
pellets into a powder using a mortar and pestle and transfer
this powder into the Erlenmeyer ﬂask containing methanol.
3. The NaOH can be dissolved with continuous stirring on
magnetic stir plate for about 5 to 10 min.
4. Use a graduated cylinder to measure 60 mL of cooking oil
and add this to the methanol solution in the Erlenmeyer ﬂask.
5. Using a hot plate, gently heat the solution to a tempera-
ture between 35 1C to 50 1C for 20 min with continuous
stirring so that the mixture does not separate into two layers.
6. Pour the warm reaction mixture into 125 mL separation
funnel and allow the solution to cool and partition into two
product layers.
7. Draw oﬀthe bottom layer, which contains glycerol,
residual methanol; trace water and salts into a small weighed
(tared) beaker.
8. The top layer in the separation funnel is the biodiesel.
Gravity ﬁltrations can be used to ﬁlter the biodiesel (Fig. 6).
9. Measure the volume of biodiesel collected and calculate
the percentage of biodiesel conversion based on the starting
volume of oil and volume of biodiesel produced.
10. Compare the viscosity of vegetable oil and biodiesel.
Post-lab questions
1. Describe how the biodiesel production can be used to imple-
ment the concept of natural resource accounting: a strategy that
helps household or government calculate its real wealth, i.e., the
volume of total economic production minus the value of the
natural and social capital consumed to achieve it.
2. Life cycle analysis is a management tool for identifying
the net ﬂows of resource and energy used in the production,
consumption and disposal of product or service in order to
leverage eco-eﬃciency gains. Describe how this concept can be
taught while teaching biodiesel production experiment.
3. What changes did you see between the characteristic of
the starting materials and ﬁnal oil?
Table 1
The outcome of changes in environmental values as reported in Karpudewan et al., (in press-a) and knowledge Karpudewan et al., (2009)
Measure
Categories
Statistic
Findings
Environmental Values
Overall values (combination of all the three
categories of values)
Repeated measure one-way
ANOVA
F(2,218) = 180.40, p o 0.0001
Egocentric
F(2,218) = 12.53, p o 0.00333
Homocentric
F(2,218) = 0.003, p 4 0.0167
Ecocentric
F(2,218) = 9.43, p o 0.00333
Knowledge
Traditional Environmental Concepts
Repeated measure one-way
ANOVA
F(2,208) = 3.784, p o 0.05
Sustainable Development Concepts
F(2,208) = 59.56, p o 0.05.
Table 2
The outcome of comparison of environmental attitudes between experimental group treated with green chemistry and control group with
traditional lesson as reported in Karpudewan et al., (in press-b)
Measure
Mean (M) and Standard Deviation (SD)
t-test results
Control group
Experimental group
Environmental Attitudes
M = 2.68, SD = 0.42
M = 3.13, SD = 0.29
t(261) = 11.02, p = 0.004
Table 3
List of experiments conducted each week throughout the
entire study
Week
Experiment
1
Eﬀect of temperature on the rate of reaction
2
Eﬀect of concentration on the rate of reaction
3
Eﬀect of concentration on the rate of reaction
4
Biosynthesis of ethanol
5
Bromination of an alkene
6
Preparation and distillation of cyclohexene
Fig. 5
A transesteriﬁcation reaction produces biodiesel (and glycerine)
from the reagents vegetable oil and methanol in the presence of the
sodium hydroxide catalyst.
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Chem. Educ. Res. Pract., 2012, 13, 120–127
127
4. Based on the answer for question three, explain why
biodiesel is more easily degradable than petroleum diesel.
5. Is biodiesel really green? Explain at least one argument in
support of the idea that biodiesel is a greener fuel. Also present
one argument that biodiesel is not a greener fuel.
6. In the commercial production of biodiesel, 1200 kg of
vegetable oil produces 1100 kg crude biodiesel. How does your
yield compare to this?
7. Describe the green chemistry principle that could be
incorporated into this experiment.
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(left) Biodiesel being separated from the byproducts in a separation funnel. (right) Biodiesel developed after removal of glycerol and
methanol (bottom layer).
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CHAPTER 6
Application of intelligent models
in exploration engineering
Contents
6.1 Overview
279
6.2 Geochemistry
279
6.3 Geophysics
282
6.4 Petro-physics
283
6.5 Geo-mechanical characterization of organic-rich shales
284
6.6 Brittleness index in shale gas and tight oils
285
6.7 Total organic carbon determination
286
6.8 Shear wave velocity
288
6.9 Flow units
289
6.10 Facies identification from well log
290
References
292
6.1 Overview
Over the last few years, there has been a dramatic increase in application
of intelligent models in exploration engineering. Some of the entries in
this field contain specific knowledges which have been utilized for further
investigations and understanding purposes in other domains. The vast
majority of intelligent models’ applications in exploration engineering
include optimization, modeling, and classification. A number of subdo-
mains, namely, geochemistry, geophysics, petro-physics, and others, have
been identified common, attractive in this context.
This chapter draws upon illustrative insights into how intelligent mod-
els are introduced in exploration engineering and brought reliability to
this field.
6.2 Geochemistry
Geochemistry is one of the most important branches in exploration engi-
neering. It aims at explaining the mechanisms behind key geological
279
Applications of Artificial Intelligence Techniques in the Petroleum Industry
DOI: https://doi.org/10.1016/B978-0-12-818680-0.00006-0
© 2020 Elsevier Inc.
All rights reserved.
systems by applying the principles and tools of chemistry. The gained
results from this science provide an invaluable guidance for multidisciplin-
ary exploration tasks. However, due to the expensive cost of the analyses
needed for generating geochemical maps and data to employ during
the exploration tasks, researchers have shown an increased interest in
the application of intelligent techniques to build accurate predictive/
classification paradigms based on gathered information from prior geo-
chemical investigations.
Ziaii et al. [1] applied neuro-fuzzy technique for identifying anomalies
in mining geochemistry. The proposed workflow in their study was
applied to various real-world geochemical projects. Their outcomes
highlighted noticeable improvements, comparing the results obtained
from applying multivariate statistical analysis.
Sun et al. [2] applied Kohonen neural network (KNN) and the factor
analysis to geochemical data pattern recognition for a PbZnMoAg
mining area around Sheduolong in Qinghai Province, China. Their results
showed that KNN effectively classifies the samples, eliminates many sam-
ples with background values.
Twarakavi et al. [3] applied support-vector machines (SVM) and
robust least-squares SVM to predict arsenic concentrations in the sedi-
ments of Circle City, Alaska. Their models are based on gold concentra-
tion distribution present within the sediments. The results of their study
showed improvements in the reliability of the predictions compared to
artificial neural network (ANN).
Beucher et al. [4] implemented ANN for soil mapping in the
Sirppujoki river catchment area, southwestern Finland. According to their
results, the proposed paradigm in their study showed consistent perfor-
mance and allowed the creation of valid distribution maps.
Gonbadi et al. [5] applied various intelligent techniques, including
AdaBoost, SVM, and random forest (RF), to distinct porphyry Cu-related
geochemical anomalies in Kerman Province of Iran. The developed mod-
els in their investigation were trained with borehole and surface rock sam-
ples from drilled zones. Then, these paradigms were utilized to generate a
classified map illustrating anomalous areas in the undrilled parts of the dis-
trict. The outcomes of the study showed satisfactory prediction capabilities
for the applied techniques.
O’Brien et al. [6] proposed a classification model using RFs to catego-
rize mineral chemistry using 533 of gahnite compositions (i.e., Mg, Al, V,
Cr, Mn, Fe, Co, Ni, Zn, Ga, and Cd) from the Broken Hill deposit and
280
Applications of Artificial Intelligence Techniques in the Petroleum Industry
11 minor Broken Hilltype deposits in the Broken Hill domain. Their
results demonstrated the high classification ability of RF in the considered
geochemistry project.
Kirkwood et al. [7] applied quantile regression forests (based on RF)
to examine the support of high-resolution auxiliary information to create
accurate geochemical maps. Their research work detailed the performance
analysis of proposed intelligent model to predict element concentrations,
loss on ignition, and pH in the soils of South West England. Their model
was able to predict the quadratic relationship of their data points with
high accuracy as can be seen in Fig. 6.1.
Zaremotlagh and Hezarkhani [8] introduced two paradigms using deci-
sion tree and ANN to identify the geochemical distribution patterns of light
rare earth elements in the Choghart deposit, Central Iran. Their findings
revealed the efficiency of the implemented intelligent techniques, and the
possibility of generating distribution patterns as geochemical maps.
Hajirezaie et al. extended their work on using predictive methods to
estimate reservoir properties [911] to geochemical investigation of a for-
mation under exploration in the Gulf of Mexico [12,13]. They developed
Figure 6.1 A regression coefficient of 0.98 was obtained when predicting the data
points in the work done by Kirkwood et al. [7].
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Application of intelligent models in exploration engineering
two models to estimate mineral precipitation based on the mixing of sea
water with the formation water in Gulf of Mexico and quantified the
impact of various geochemical parameters on the amount to mineral pre-
cipitation in this formation.
6.3 Geophysics
Geophysics through its various topics plays a crucial role in many explora-
tion tasks, mainly those related to oil industry. It helps to get an overview
about deep soils. Intelligent models such as ANN, SVM, and evolutionary
algorithms for data classification and learning are increasingly powerful
tools in many geophysics’ tasks.
Essenreiter et al. [14] proposed multilayer perceptron (MLP) for iden-
tifying and eliminating multiple reflections in seismic data. Their results
showed the high ability of MLP in the considered geophysical problem.
He and Zhou [15] proposed a three-layer feed-forward neural net-
work (FNN) to geophysical parameter inversion. The developed paradigm
in their work was trained using 5 and 10 numerically modeled records of
the vertical component, then, a three-layer transverse isotropic model was
created randomly to test the network. The learning phase of their model
was done by means of genetic algorithm (GA). The achieved result
revealed a high degree of reliability for the proposed model in dealing
with the geophysical parameter inversion task.
Kaftan et al. [16] established two paradigms using MLP neural net-
works and radial basis function (RBF) neural networks (RBFNN) to
model synthetic gravity data and Seferihisar gravity data. The modeled
parameters are related to the structure, namely, the depths, the density
contrasts, and the locations of the structures. Their results showed that
both implemented paradigms provide consistent results. In addition, it was
concluded that generally RBFNN increased the training speed and exhib-
ited better performance.
Mojeddifar et al. [17] performed a comparative investigation between
three types of adaptive neuro-fuzzy inference system (ANFIS) algorithms
and a pseudo-forward equation to characterize the North Sea Reservoir
(F3 block) based on seismic data. The ANFIS models were established
using different clustering schemes, namely, grid partitioning, subtractive
clustering method (SCM), and fuzzy c-means clustering. The obtained
results showed acceptable performance for the applied methods in
identifying the investigated geophysical parameters.
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
Karimpouli and Fattahi [18] implemented adaptive network-based
fuzzy inference system (ANFIS) to establish a predictive paradigm for P-
and S-wave impedances that are vital for different tasks such as reservoir
characterization, fluid detection, and rock physical modeling. The pro-
posed schemes were applied on real-field data from a carbonate reservoir
in Iran. The achieved results revealed that the ANFIS model based on
SCM exhibits the best predictions of P- and S-wave impedances with
high degree of accuracy and robustness.
Moghtased-Azar and Zaletnyik [19] conducted a comparison study
between various polynomial schemes and ANN for modeling the hori-
zontal deformation field of the Cascadia subduction zone, as determined
from Global Positioning System analyses of the Pacific Northwest
Geodetic Array. Their results showed that ANN model, including seven
neurons in its hidden layer, with radial basis transfer function outper-
formed the other proposed paradigms.
Eshaghzadeh and Hajian [20] introduced the so-called modular feed-
forward neural network that consists of three similar one-layer FNNs to
predict the shape factor, depth, and amplitude coefficient parameters
related to simple geometric-shaped models. The implemented technique
was applied to real gravity field anomalies measured over an iron deposit
in Kerman province, Iran, and the results were very consistent.
Al-Garni [21] applied ANN to interpret spontaneous potential (self-
potential) anomalies related to simple geometric-shaped models for esti-
mating the parameters of different simple geometrical bodies. The results
showed acceptable agreement between the outcomes derived by the neu-
ral network and those from the real-field data.
6.4 Petro-physics
Petro-physics is the study of rock properties by focusing on their pore size
information and how hydrocarbon fluids fill these pores. Several para-
meters have been studied using petro-physics such as porosity, permeabil-
ity,
water
saturation,
and
lithology.
Well
logs
are
the
primary
petrophysical tools used for studying reservoir parameters. Well logs
include information about reservoir formation, which is obtained using
the inserted measurement tools in a borehole using strings and then petro-
physical samples are taken for further analysis. Combination of petrophysi-
cal, geological, and geophysical results generates a full picture of the target
reservoir to be used in reservoir engineering. Generally, petrophysical
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Application of intelligent models in exploration engineering
measurements are time taking and expensive. Application of artificial
intelligence (AI) has made it possible to generate the needed petrophysical
information at a much faster pace and lower cost. Here, some case studies
are presented that have information about the usage of AI for studying
petrophysical data.
Nyein et al. [22] employed ANN to predict water saturation and
porosity, and to identify lithology of a sand reservoir. They used density
and resistivity logging data as well as gamma ray data to develop two
intelligent models for prediction of water saturation and porosity. Their
models were in good agreement with the results from conventional tech-
niques using the well logging data. They were also able to successfully
define the pay zones for new wells in the studied shaly sand reservoir.
Kohli and Arora [23] used neural network with a LevenbergMarquardt
learning algorithm to predict well log response. They used the well log
data from different offset wells to predict permeability. By focusing on the
optimization of the training stage of the model development, they showed
that their model is time- and cost-effective and avoids the need for the
complex analysis of cores.
Malvi´
c [24] used neural networks to study the clastic facies prediction in
Lower Pannonian sediments of the Sava depression in Croatia. They pre-
dicted positions of a formation’s facies based on the positions of the reser-
voir sandstone and lithology. Even though their results did not predict the
position of marlstone positions accurately, their developed neural network
model showed about 90% accuracy in predicting the sandstone lithology.
Eskandari et al. [25] employed neural networks to predict shear wave
velocity from the well-log data. They used various numerical techniques
to optimize their model parameters using advanced numerical techniques
such as evolutionary computing and fast training. This helped their net-
work rapidly converge to accurate results while avoiding local minima.
Their model was able to predict the shear wave velocity with a regression
coefficient of 0.96 for the generalization stage of neural network and 0.94
for the multiple regression. They proposed that their model did not have
the need for setting a sensitive parameter such as learning rate.
6.5 Geo-mechanical characterization of organic-rich shales
Accurate investigation of geo-mechanical properties plays a crucial role in
proper characterization of unconventional reservoirs, namely, organic-rich
shales. This subdomain of exploration engineering is increasingly focusing
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
on the application of intelligent models in prediction, classification, and
optimization purposes.
Determination of lithofacies is among the main challenges in studying
shale reservoirs. The gathered results from this information are primordial
for hydraulic fracture stimulation and give an insight about organic-matter
and gas concentration. Wang and Carr [26] utilized conventional logs
across the Appalachian basin and applied ANN models to predict shale
lithofacies. Their investigation was formulated as a multiclass classification
problem and they employed decomposition technology of one-versus-
the-rest in a single ANN and pairwise comparison method in a modular
approach. The predicted shale lithofacies allowed the construction of a
three-dimensional shale lithofacies model.
Wang et al. [27] applied SVM to recognize shale lithofacies from well
conventional logs, which is known as a very complex and nonlinear prob-
lem. Their proposed paradigm SVM classifier was coupled with optimiza-
tion techniques, including grid searching, GA, and particle swarm
optimization (PSO). In the same context, the authors tried various kernel
functions during the training phase. The obtained results showed that
SVM classifiers outperformed ANN classifiers from different points of
view, such as stability and the computational time cost. In addition, it was
found that the hybridization SVM-PSO with radial basis function as ker-
nel is the best-established model. The lithofacies predicted using the SVM
classifier was used to build a 3D Marcellus Shale lithofacies model.
6.6 Brittleness index in shale gas and tight oils
Brittleness is another vital information for proper screening of hydraulic
fracturing candidates in unconventional reservoirs such as tight oils and
shale gas. This parameter can be modeled based on mechanical parameters
and mineralogical data. Shi et al. [28] carried out a modeling investigation
to relate the well log data and core mineralogy-based brittleness. The
authors applied two data-driven techniques, namely, back propagation
ANN (BPANN) and least-squares support-vector regression (LS-SVR).
The employed data points were from one well in Jiaoshiba shale gas field,
China. Their results revealed that LS-SVR approach is more accurate
than the BPANN method at same conditions. In addition, Shi et al. [29]
implemented two types of ANN, namely, MLP and RBFNN, for pre-
dicting rock brittleness by exploiting conventional well logs as inputs.
The proposed techniques were applied on a single well in the Santanghu
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Application of intelligent models in exploration engineering
tight oil formation in the Xinjiang basin, China. Their results highlighted
the ability of the two ANN models in predicting rock brittleness. In addi-
tion, it was concluded that RBFNN is better than MLP from the predic-
tion performance perspective. According to the performance indicators,
the predictive performance of the RBF model was found to be better
than that of the multilayer perception model.
Luo et al. [30] chose approximately 2,000 horizontal wells targeting
Mid Bakken Formation with detailed completion and production infor-
mation, and after performing stratigraphic and petrophysical analyses, they
collected the regional variation in depth, thickness, porosity, and water
saturation of the Bakken Shale Petroleum System. This latter was gathered
as a database that was applied for building a neural network model to
identify the relationship between the first-year oil production with the
reservoir properties. Their results showed that the developed paradigm
provided a reliable performance as it identified the best well location,
understood the effectiveness of the completion strategy, and predicted the
well production.
6.7 Total organic carbon determination
Total organic carbon (TOC) is an important parameter in reservoir eval-
uation and exploration, and source rock characterization. An increase in
TOC indicates the presence and extension of a source rock, while a
reduction in TOC would be interpreted as a lack of hydrocarbon
source. TOC also determines the geophysical characterization of shale
gas resources, which impacts the response form organic-based rocks.
Rock evaluation pyrolysis is a traditional approach for identification of
the hydrocarbon potential of reservoirs. Conventional interpretation of
gamma ray, acoustic, and resistivity logs has been used in the past to
determine the level of TOC in reservoirs. These techniques can estimate
an acceptable level of a reservoir’s TOC, but they usually tend to be
time-consuming and expensive to perform. Therefore, many engineers
have attempted to use AI approaches to predict TOC from well log
data.
For instance, Tan et al. [31] developed an RBF model to predict the
TOC of a shale gas reservoir in China. They used lab-measured TOC
results to construct their model. They used neutron log, density log,
acoustic log, deep induction log, and gamma ray during their model-
development stage. Their TOC predictions had mean square error (MSE)
286
Applications of Artificial Intelligence Techniques in the Petroleum Industry
values of as low as 0.3 and regression coefficients of as high as 0.86. They
suggested that their RBF model can predict TOC more accurately than
traditional ANN methods and added that the primary advantage of RBF
is that it has only one hidden layer and does not need to define the num-
ber of hidden layers in advance.
In a 2012 study, Sfidari et al. [32] developed a two-step technique to
predict TOC from well-log data. In their first approach, they categorized
their well log data into different electrofacies (EF). In order to identify the
EF, they compared the intelligent data clustering methods (self-organizing
maps) against statistical methods, including K-means clustering and hierar-
chical cluster analysis. In addition, they performed cluster validity tests to
choose the best approach to categorize the data into a certain number of
EF, and for each of the EF, they developed a different ANN to predict
the TOC values form well-log data. In their second approach, they used a
similar ANN model for the entire interval to predict TOC values. They
compared their models to each other and to a conventional Δ Log R
technique and suggested that clustering the data into multiple EF using
the self-organizing maps would result in more accurate predictions com-
pared to constructing one model for the entire interval as a single cluster.
They also suggested that in general, intelligent models are more accurate
than the conventional approaches. They were able to achieve MSE values
as low as 0.0073.
In a 2016 study, Shi et al. [33] used an extreme machine learning
(EML) method to estimate TOC values from well-log data. They com-
pared their single-layer feed-forward model to the conventional multi-
layer LevenbergMarquardt method and suggested that the EML model
is not only more accurate than the conventional ANN model, but it can
also perform at a faster pace and a more efficient computational cost.
They were able to achieve a root mean square error value of 0.30 and a
regression coefficient of 0.93.
In 2017, Mahmoud et al. [34] used 442 datapoints from Barnett shale
and developed an ANN model to estimate TOC using well-log data. The
input parameters for their model included gamma ray, bulk density, resis-
tivity, and sonic transit time. They used the data from Barnett shale to
train and test their model and then employed their model to estimate the
TOC values of Devonian shale. Their model was able to predict the
Barnett TOC values with a regression coefficient of 0.93 and an average
absolute deviation percentage of 0.91 for Barnett shale. They suggested
that their model outperforms the current models developed for Devonian
287
Application of intelligent models in exploration engineering
shale as well, with a regression coefficient of 0.89 and an average absolute
deviation percentage of 0.99.
6.8 Shear wave velocity
Mechanical properties of rocks have a great influence on development
and production of hydrocarbons. Wave velocity is an important parameter
that can give an accurate description of rocks’ mechanical properties. This
parameter is rarely recorded during drilling processes and is usually mea-
sured from cores in the lab. However, core sampling from lengthy reser-
voir layers is cumbersome and expensive. That is the primary reason that
artificial methods have received much attention in the field of wave
velocity prediction.
In 2007, Rezaee et al. [35] employed ANNs, fuzzy logic, and neuro-
fuzzy logic to predict shear wave velocity from well log data. They used
the data of two sandstone reservoirs in Australia for training their models,
and used the data from a third well in the same reservoir for model testing
and validation. They were able to obtain a low MSE of 0.05 for their pre-
dictions, and suggested that their developed models are successful candi-
dates for performing intelligent reservoir characterization at a high pace
and accuracy.
In 2012, Güllü [36] developed ANN and gene expression programing
models to predict shear wave velocity of an earthquake-recording site in
California. In their models, they used site-to-source distance, spectral
accelerations, peak ground acceleration, and the magnitude of the record-
ings. They were able to achieve a regression coefficient of 0.70 and a
mean absolute error value of less than 58. Even though their model was
able to predict shear wave velocity within an acceptable error range, they
suggested that using the gene expression programing approach without
engineering interpretations of shear wave velocity is not sufficient to char-
acterize an earthquake recording site.
In a 2014 study, Maleki et al. [37] employed empirical correlations
and artificial intelligent techniques to estimate shear wave velocity from
the well log data of a formation in an oilfield in the south of Iran. The
regression models that they used for estimation of shear wave velocity
included Brocher, Castagna, and Carroll correlations and the AI techni-
ques included back propagation neural network and SVR. They con-
cluded that Castagna correlation and SVR are the most accurate
regression and intelligent models, respectively. Even though they were
288
Applications of Artificial Intelligence Techniques in the Petroleum Industry
able to achieve a regression coefficient of 0.97 and rapid prediction of
shear wave velocity using the SVR model They suggested that in order to
have a reliable estimation of shear wave velocity, it is crucial to run shear
wave velocity during wire line logging operations and that merely relying
on artificial models is not recommended.
In 2019, Wang and Peng [38] combined extreme learning machine
(ELM) with technique of mean impact value (MIV) to estimate the
shear wave velocity values of a shale gas reservoir in Ordos Basin in
China from well log data. In order to construct their model, they used
3201 data points and compared the performance of their model with
LevenbergMarquardt ANN and suggested that their ELM model out-
performs the former model at a lower computational cost. In addition,
they used the same number of data points from another well to com-
pare their model with convolutional neural network, SVR, and four
well-known correlations to predict shear wave velocity and concluded
that their ELM model coupled with the MIV analysis is more accurate
and robust in estimating shear wave velocity values of the gas shale res-
ervoir. They were able to obtain a high regression coefficient of 0.97
and a low root mean square error value of 0.08 by using their ELM-
MIV model.
6.9 Flow units
A flow unit is a certain layer of reservoir in which the petrophysical and
geological properties do not significantly change, and fluid flow can be
defined uniformly. Fluid flow properties within hydraulic flow units can
be used to obtain a better characterization of a reservoir. Permeability data
is the most important source of flow unit definition. Permeability data can
be obtained by taking cores and well logs of the reservoir. However, these
tasks are generally time-consuming and expensive. Therefore, flow unit
prediction is another area where application of AI has become popular.
In 2003, Aminian et al. [39] employed statistical techniques to identify
flow units within a reservoir in West Virginia using core analysis data.
They developed an innovative approach to test and train neural networks
to predict permeability values form well log data. They used the reser-
voir’s production history and combined neural network and statistical
methods to characterize flow units and estimate permeability values. They
were able to achieve a regression coefficient of 0.77 in their permeability
predictions. They proposed that combining neural networks with the plot
289
Application of intelligent models in exploration engineering
of reservoir cumulative storage capacity versus cumulative flow capacity
can successfully characterize flow units within a reservoir.
In a 2014 study, Hatampour and Ghiasi-Freez [40] used GA to predict
the flow units of an Iranian reservoir. The developed GA-based formula
was able to predict flow zone index (FZI) with a regression coefficient of
0.85. Their formula was able to accurately characterize 68 out of 81 m of
the reservoir with respect to its actual flow units. They attributed the
errors of the formula to the heterogeneities in the fluid flow and mineral-
ogical properties.
In a similar study, Hatampour et al. [41] developed ANNs and linear
regression to develop a relationship between seismic data and FZI. In
addition to a probabilistic neural network (PNN), they developed an
RBF model as well as an MLP model. They concluded that PNN can
predict FZI more accurately than RBFN, MLFN, and regression techni-
ques. In their study, they obtained a regression coefficient of 0.79 and an
estimation error of 4.46% using PNN. They utilized this model to
develop a three-dimensional hydraulic flow unit model of a carbonate gas
field in the Persian Gulf.
6.10 Facies identification from well log
Identification of formation facies from well logs is an important task in
subsurface geological exploration and modeling. Determining different
lithofacies to characterize a reservoir is traditionally performed using core
samples taken from underground formations. However, there are techni-
cal and financial barriers to the number of cores and the depth interval
from which the cores can be taken. Well logs are good alternative candi-
dates to perform reservoir characterization, but manually reading and
interpreting well logs can be a cumbersome task. Accordingly, using AI to
interpret well logs has recently received much attention to determine
lithological facies.
In a 2002 study, Bhatt and Helle [42] employed modern neural net-
works to identify lithological facies from well logs. In their work, they
introduced recurrent back-propagating neural networks modified from
time-series forecasting. Suggesting that recurrent ANNs enhance facies
classification by removing vague identifications, their results indicated a
90% accuracy in classifying facies of the Ness formation in the North Sea.
In a similar study, Kapur et al. [43] used back propagationbased neu-
ral networks to predict facies from well logs, including gamma ray,
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
resistivity, neutron, and density logs. Their results showed an accuracy of
75%93% in recognizing the debris flow, shoreface, lower shoreface, and
turbidites facies. They argued that their work can be used as a reference
for future data-collection operations where it is critical to know which
logs are more important to take, and also for fields where quantitative rec-
ognition of numerous logs can be a cumbersome task.
In 2009, Tang et al. [44] developed a workflow for preprocessing and
cleanup of core data and facies prediction using PNN. They digitized the
core description and reduced the number of facies to decrease the compu-
tational cost needed for their predictions. Their facies recognition results
showed a better performance compared to multivariate statistical algo-
rithms with an accuracy above 70%. In addition, they used overall
Table 6.1 Summary of a number of intelligent models developed for reservoir
exploration and evaluation.
Author(s)
Model
Type of study
Error
Tan et al. [31]
RBF
TOC
MSE 5 0.3
R2 5 0.86
Sfidari et al. [32]
ANN
TOC
MSE 5 0.007
Shi et al. [33]
EML
TOC
RMSE 5 0.3
Mahmoud et al.
[34]
ANN
TOC
R2 5 0.93
Maleki et al. [37]
BPNN and SVR
Shear wave
velocity
R2 5 0.97
Güllü [36]
ANN and GEP
Shear wave
velocity
R2 5 0.70
Rezaee et al. [35]
ANN, FL, NFL
Shear wave
velocity
MSE 5 0.05
Wang and Peng
[38]
EML-MIV
Shear wave
velocity
R2 5 0.97
RMSE 5 0.08
Hatampour et al.
[41]
GA
Flow units
R2 5 0.85
Aminian et al. [39]
ANN
Flow units
R2 5 0.77
Bhatt and Helle
[42]
ANN
Facies
identification
R2 5 0.90
Tang et al. [44]
PNN
Facies
identification
R2 5 0.70
ANN, Artificial neural network; BPNN, back propagation neural network; EML, extreme machine
leaning; FL, fuzzy logic; GA, genetic algorithm; GEP, gene expression programing; MSE, mean
square error; MIV, mean impact value; NFL, neuro-fuzzy logic; PNN, probabilistic neural network;
RBF, radial basis function; RMSE, root mean square error; SVR, support-vector regression; TOC,
Total organic carbon.
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Application of intelligent models in exploration engineering
confidence and discriminant ability to quantify the uncertainties in their
facies predictions. They applied their model to 15 wells is Wafra
Maastrichtian reservoir in Kuwait and suggested improved understanding
of the depositional setting and facies distribution of the reservoir.
In a different study, Saggaf and Nebrija [45] developed a fuzzy logic
inference method to predict lithological facies from well log data. They
suggested that their model is superior to alternative methods for predicting
facies such as neural and statistical methods. Their model showed a good
agreement with the results taken from a cored well in a carbonate forma-
tion. They suggested that their model is easier to reproduce, noniterative,
and computationally more efficient compared to the existing techniques
in predicting lithological and depositional facies. Table 6.1 summarizes the
information from some of the recent works on reservoir exploration.
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294
Applications of Artificial Intelligence Techniques in the Petroleum Industry

CHAPTER 2
Intelligent models
Contents
2.1 Artificial neural networks
23
2.1.1 Multilayer perceptron neural network
24
2.1.2 Radial basis function neural network
26
2.2 Fuzzy logic systems
28
2.3 Adaptive neuro-fuzzy inference system
31
2.4 Support vector machine
33
2.4.1 Ordinary support vector machine
33
2.4.2 Least-square support vector machine
35
2.5 Decision tree
37
2.5.1 Random forest
38
2.5.2 Extra trees
40
2.6 Group method of data handling
40
2.6.1 Hybrid group method of data handling
40
2.7 Genetic programming
42
2.7.1 Multigene genetic programming
43
2.8 Gene expression programming
44
2.9 Case-based reasoning
46
2.10 Committee machine intelligent system
47
References
48
In this chapter, various intelligent models such as artificial neural networks
(ANNs), fuzzy logic (FL) systems (FLSs), and adaptive neuro-fuzzy infer-
ence system (ANFIS) are discussed in detail.
2.1 Artificial neural networks
The architectures of ANNs are inspired by biological neurons and the
human brain system, which are able to solve complex problems by relat-
ing the inputs and outputs. This capability has made the ANNs applicable
in many fields, including electronics, aerospace, medical, and oil and gas
industry. These nonlinear learning mathematical models can be used in
23
Applications of Artificial Intelligence Techniques in the Petroleum Industry
DOI: https://doi.org/10.1016/B978-0-12-818680-0.00002-3
© 2020 Elsevier Inc.
All rights reserved.
different manners, such as pattern identification, function approximation,
optimization, and data processing [1,2].
ANNs consist of two basic elements: processing elements and inter-
connections or links. Processing elements, which are called nodes or neu-
rons, are responsible for processing information, while links establish
connections between neurons. Radial basis function (RBF) and multilayer
perceptron (MLP) are the most popular neural networks. Between the
aforementioned networks, the most fundamental difference is that the
neurons use various methods to process the information. Fig. 2.1 repre-
sents a flowchart of an ANN model development process.
2.1.1 Multilayer perceptron neural network
An MLP network consists of several layers. The first and the last layers are
the input and output layers that refer to input data and output of the
model, respectively. Hidden layers are the intermediate layers between
the input and output layers [3]. In general, the role of hidden layers is to
make a relationship between the inputs and the desired output. Usually,
the desired property or parameter is the output of the model, and the
quantity of neurons in the input layer is equivalent to the input variables.
Empirical procedures are needed to define the number of hidden layers
and neurons in each hidden layer.
In most cases, it is appropriate to consider an MLP with a single hid-
den layer [4]. However, for more complex systems, two hidden layers can
be used. Each neuron in the hidden layer is linked into all of the existing
elements in the prior and the subsequent layer. The quantity of every sin-
gle neuron in the output layer and a hidden layer is defined as the sum-
mation of the amount of each neuron in the previous layer multiplied by
a specific weight for that particular neuron, and a bias term is eventually
added to the summation. Afterward, an activation function is employed
to pass the resultant value. Some of the usable activation functions are
listed next.
Sinusid:f x
ð Þ 5 sinðxÞ
(2.1)
Linear 5 Purelin:f x
ð Þ 5 x
(2.2)
Tansig 5 Tanh:f x
ð Þ 5 ex 2 e2x
ex 1 e2x 5
2
1 2 e22x 2 1
(2.3)
24
Applications of Artificial Intelligence Techniques in the Petroleum Industry
Binary step:f x
ð Þ 5
x
for
x , 0
2 x
for
x $ 0

(2.4)
Logsig 5 Sigmoid:f x
ð Þ 5
1
1 1 e2x
(2.5)
Figure 2.1 Flowchart of an ANN model development process. ANN, Artificial neural
network.
25
Intelligent models
Arctan:f x
ð Þ 5 tan21ðxÞ
(2.6)
Consider a two-hidden-layer MLP network and arctan, sinusid, and
purelin as the activation functions for the two hidden layers and the out-
put layer, respectively. Then, the following equation represents the calcu-
lation of the output:
Output 5 Purelin

w3 3 sinusid w2 3

arctan w1 3 x
ð
Þ 1 b1


1 b2


1 b3

(2.7)
where b1, b2, and b3 are the bias terms for each layer, and w1, w2, and w3
are the weight matrixes for the first, second hidden layers, and the output
layer, respectively. Generally, in the case of using two hidden layers, the
activation functions used for the first and second hidden layers are tansig
and logsig, respectively.
2.1.2 Radial basis function neural network
Another well-known neural network is the RBF network that is applica-
ble in both regression and classification. The RBF is based on the theory
of function approximation. In 1988 Broomhead and Lowe introduced the
RBF neural network [5]. Neural networks have two main types: feedfor-
ward and backward; and RBF as well as MLP is a feedforward network.
Fig. 2.2 illustrates a schematic of a typical RBF ANN. These networks are
capable of treating randomly distributed data, determining accurate results,
and generalizing to a high dimension space easily [5]. RBF networks are
Figure 2.2 Schematic illustration of a typical RBF. RBF, Radial basis function.
26
Applications of Artificial Intelligence Techniques in the Petroleum Industry
extensively used in several mathematical research and physical properties
approximations [69].
Generally speaking, an RBF neural network can be considered as a
feedforward three-layer network that consists of an input layer and an
output layer, connected through a hidden layer [10]. The input layer pro-
vides the feed of the hidden layer. The input layer comprises n input neu-
rons, in which n is equal to the input variables. The main element of an
RBF network is the hidden layer that is responsible for transmitting the
data from input space to a higher dimensionality hidden space [11]. In the
hidden layer, each point is located at the center of a specific space with a
certain radius, and in each neuron the distance between the input vector
and its center is determined. The center vector consists of cluster centers
that are reported by Oij, where j is representing the number of center
vectors (j 5 1,. . .,N). Consider that N is always lower than or equal to the
number of input data points used to train the model [10]. For instance,
consider a network that has 10 input variables and 400 datasets for train-
ing, then, i ranges from 1 to 10, and N should be lower than or equal to
400. The Euclidean distance is employed to measure the distance between
the inputs and centers:
rj 5
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
X
n
i51
xi2Oij

2
s
(2.8)
In this equation, n is equal to 10 for a model with 10 input variables.
In order to transfer the Euclidean distance from each neuron in the
hidden layer to the output, radial basis transfer functions are employed.
Some of these functions are represented next:
1. Linear function
g r
ð Þ 5 r
(2.9)
2. Gaussian function
g r
ð Þ 5 exp
r2
2σ2

	
with spread coefficient σ . 0
(2.10)
3. Multiquadratic function
g r
ð Þ 5 r21σ2

1=2 with spread coefficient σ . 0
(2.11)
27
Intelligent models
4. Cubic function
g r
ð Þ 5 r3
(2.12)
5. Generalized multiquadratic function
g r
ð Þ 5 r21σ2

β with spread coefficient σ . 0; 0 , β , 1
(2.13)
6. Thin plate spline function
g r
ð Þ 5 r2lnðrÞ
(2.14)
7. Inverse multiquadratic function
g r
ð Þ 5 r21σ2

21=2 with spread coefficient σ . 0
(2.15)
The spread coefficient (σÞ, which must be specified empirically, repre-
sents the width or radius of the bell shape. The output of the model is
calculated as:
yk 5
X
N
j51
wjgkj :Xk 2 Oj:


k 5 1; . . .; M
and
j 5 1; . . .; N
(2.16)
where N represents the number of nodes in the hidden layer, w is con-
nection weight, O expresses the center, and
:X 2 O:


denotes the
Euclidean distance that is calculated using Eq. (2.8).
There are two primary parameters in the Gaussian RFB: the spread
coefficient of Gaussian function (σ) and the number of neurons in the
hidden layer, which can be lower than or equal to the number of input
datasets, as already stated. However, as the number of neurons increases,
the network becomes more complex, but the error will be lower. In addi-
tion, as the spread coefficient becomes larger, the network will be smaller
that may lead to faster execution but higher error. Considering the fact
that numerous neurons and small values of spread coefficient might fail to
predict the testing data accurately (although this condition may result in
good accuracy for training data), these two variables must be optimized.
Trial and error method is the conventional method to optimize them,
while metaheuristic algorithm can optimize them more accurately.
2.2 Fuzzy logic systems
According to researchers, in the case of real and complex processes, mea-
surements
and
process
modeling
will
never
be
exact,
and
some
28
Applications of Artificial Intelligence Techniques in the Petroleum Industry
uncertainties such as randomness, incompleteness, and ignorance of data
exist in the process model. FL was first introduced by Zadeh [12] to
model human reasoning from imprecise and incomplete information
through defining vague terms and permitting the establishment of a rule
base. FL is capable of combining human experiential knowledge and han-
dling such complex systems from an engineering point of view. FLSs usu-
ally correspond to reasoning and inference on a higher level.
Consider a total space (X) and one of its members (x). The following
expression represents the set (α), which is defined by (Xα), on the total
space (X) through mapping the total space to the set {0,1}.
Xα:X- 0; 1
f
g
x-Xα x
ð Þ 5
0
x=
2α
1
xAα

(2.17)
So, the function Xα takes the value of 1 if x belongs to α, and if x is
not a part of α, the function becomes equal to 0. The following equation
is equivalent to Eq. (2.17) in the FLS:
mα:X- 0; 1
f
g
x-mα x
ð Þ
(2.18)
where mα is the membership function (MF) [13]. The mapping of space
X in the crisp logic is a Boolean set (“yes” and “no,” or “1” and “0”),
while it is a domain (0 # mα # 1) in the FL. In the literature, several MFs
have been introduced, which are summarized in Table 2.1.
Fig. 2.3 illustrates the general algorithm for the FLS. Through the
fuzzy
MFs
and
linguistic
variables,
the
input
data
is
fuzzified.
Afterward, the ifthen rules, FL operators, and fuzzy rules are applied
in the fuzzy inference process, where the fuzzy inputs are mapped to
fuzzy outputs. The fuzzy inference has various names such as FL con-
troller, fuzzy model, fuzzy (expert) system, fuzzy rulebased system,
and fuzzy associative memory. The system will not be capable of
decision-making unless the fuzzy output be defuzzified. Maximum
defuzzifier, center average, and center of gravity are the most conven-
tional algorithms used in defuzzification stage. FL has been widely used
in science, engineering, and business for various purposes such as func-
tion approximation, analysis of complex problems, decision-making,
risk assessment, classification, and process control.
29
Intelligent models
Table 2.1 The most frequent membership functions in fuzzy logic.
Function
Membership function,
mα(x)
Comments
Gaussian
e2 x2c
ð
Þ2=2α2
c: locates peak
α2: variance, α . 0
Generalized
Gaussian
e2 x2c
ð
Þβ=α
c: locates center of the
curve
β: controls width of the
curve tip
α: standard deviation
α . 0 and β . 0
Sigmoid
1
1 1 e2αðx2cÞ
c: crossover point
α: slope at x 5 c
S-function
0
x , α
2
 x2α
β2α
	2
α # x , β
1 2 2
 x2c
c2α
	2
β # x # c
1
x . c
8
>
>
>
>
>
>
>
<
>
>
>
>
>
>
>
:
α , β , c
b: crossover 5 (α 1 c)/2
mα(b) 5 0.5
Trapezoidal
0
x , α
x 2 α
β 2 α α # x , c
1
β # x , c
d 2 x
d 2 c c # x , d
0
x $ d
8
>
>
>
>
>
>
>
>
>
>
<
>
>
>
>
>
>
>
>
>
>
:
α , β , c , d
β and c: locate peak
Triangular
0
x , α
x 2 α
β 2 α α # x , β
c 2 x
c 2 b β # x # c
0
x . c
8
>
>
>
>
>
>
>
<
>
>
>
>
>
>
>
:
α , β , c
β: locates peak
Generalized
bell shape
1
11 x2c
j
j
ð
Þ2β
α . 0 and β . 0
c: center of the curve
Figure 2.3 The general algorithm for a fuzzy logic system.
2.3 Adaptive neuro-fuzzy inference system
ANFIS, a combination of ANN and FLS, is a hybrid neuro-fuzzy technique
that is proposed to benefit from the advantages of both neural networks and
FLSs and to overcome the deficiencies of ANN and FLS [14]. ANFIS is a
kind of rule-based adaptive models, in which rules are developed during the
training phase of the network [15]. In order to establish a FIS, MFs, which
are tuned by ANNs, and fuzzy ifthen rules are utilized by this network.
One of the main FLSs is TakagiSugenoKang that is capable of creating
ifthen rules using the pattern of input and output [6,1618].
Fig. 2.4 illustrates a typical ANFIS network with m inputs and four
inference rules. Ii stands for the inputs, and the output of the model is
reported by Y. In the following, an example of ifthen rules for creating
a fuzzy model is given:
Rule
1:
IF
I1 5 A1,
I2 5 B1,
I3 5 C1,
and
I4 5 D1,
THEN
y1 5 a1I1 1 b1I2 1 c1I3 1 d1I4 1 f1
Rule
2:
IF
I1 5 A2,
I2 5 B2,
I3 5 C2,
and
I4 5 D2,
THEN
y2 5 a2I1 1 b2I2 1 c2I3 1 d2I4 1 f2
Figure 2.4 Schematic illustration of a typical ANFIS network. ANFIS, Adaptive
neuro-fuzzy inference system.
31
Intelligent models
Rule
3:
IF
I1 5 A3,
I2 5 B3,
I3 5 C3,
and
I4 5 D3,
THEN
y3 5 a3I1 1 b3I2 1 c3I3 1 d3I4 1 f3
Rule
4:
IF
I1 5 A4,
I2 5 B4,
I3 5 C4,
and
I4 5 D4,
THEN
y4 5 a4I1 1 b4I2 1 c4I3 1 d4I4 1 f4
In the aforementioned rules, A, B, C, and D are the fuzzy sets of input
parameters. The statements after the IF conditions and THEN conditions
are called antecedents and consequences, respectively. ANFIS network
comprises five layers, and the function of each layer is summarized next:
Layer 1: input nodes
In this layer the input data is transformed into linguistic terms. Nodes
are predefined linguistic terms, which are created based on the selected
MFs in the previous step, and each input parameter is linked to its nodes.
Among different MFs, the two-parameter symmetric Gaussian MF is usu-
ally used as the input MF, which is represented next [15,1922]:
O1;i 5 p I
ð Þ 5 exp 2 I2Z
ð
Þ2
2σ2

	
(2.19)
In this equation, the output of the layer is reported by O, Z stands for
the Gaussian MF center, and σ shows the variance term. These parameters
should be optimized during the training process by the ANFIS model.
Layer 2: rule nodes
In this layer, which is also called the firing strength layer, the accuracy
and efficiency of conditions are evaluated. Each node is labeled as Π that
multiplies all of the inputs and provides the output of the second layer
using Eq. (2.20).
O2;i 5 Wi 5 PAiðIÞ 3 PBiðIÞ
(2.20)
Layer 3: normalization nodes
In this layer, the calculated values of firing strengths are normalized by
Eq. (2.21) to differentiate the firing strength of each rule from the total
firing strength of whole rules.
O3;i 5 W i 5 Wi
Wt
5
Wi
P Wi
(2.21)
Layer 4: consequent nodes
In the fourth layer, the linguistic terms of the output of the model are
defined. The effect of each role on the output of the model is measured
by Eq. (2.22).
O4;i 5 W iyi 5 W i aiI1 1 biI2 1 . . . 1 fi
ð
Þ
(2.22)
32
Applications of Artificial Intelligence Techniques in the Petroleum Industry
where ai, bi, and fi are linear variables and should be optimized and
adjusted by ANFIS model based on the minimizing the deviation
between the predicted value and the target value.
Layer 5: output nodes
The existing node in the fifth layer utilizes the weighted average
summation approach to calculate the overall output.
O5;i 5 Y 5
P Wiyi
P Wi
5
X
W iyi 5 final output
(2.23)
2.4 Support vector machine
2.4.1 Ordinary support vector machine
Support vector machine (SVM), which is a learning machine method
based on statistical learning theory and the concept of structural risk mini-
mization, was first introduced in 1960s by Vapnik and coworkers [23,24].
Since SVM-based models are capable of modeling the nonlinear problems
perfectly with fewer adjustable optimization parameters compared to
ANN-based frameworks, they can be named the best alternative to ANN
systems. Generally, SVM is used for regression and classification problems
[25]. The ordinary SVM utilizes kernel function (an adaptive margin-
based loss function) to map the sample linear or nonlinear data to a very
high-dimensional space (hypersurfaces). The dimension of this space is
remarkably higher than the original space. By introducing N as the num-
ber of data points, and n as the number of input variables, the dimension
of the input x is N 3 n. As mentioned previously, for a given dataset com-
prising the input matrix X 5 (x1, x2,. . ., xN) and an output vector y 5 (y1,
y2,. . ., yN), SVM is capable of constructing an optimized linear regression
by a nonlinear mapping function Φx, as shown in Eq. (2.24).
q x
ð Þ 5 wTΦx 1 b
(2.24)
In this equation, q(x) is the regression function of SVM, w is the
weight vector, Φx represents the kernel function, and b is a bias term.
Utilizing the Lagrange multipliers and optimality constraints, the q(x) can
be reformulated as:
q x
ð Þ 5
X
n
i51
βi 2 β
i


K x; xi
ð
Þ 1 b
(2.25)
33
Intelligent models
where βi and β
i are the Lagrange multipliers and kernel function is
reported by K x; xi
ð
Þ. A kernel function can be any function that satisfies
Mercer’s condition. In fact, a kernel function is the inner product of two
vectors x and xi in the feature space Φx and Φxi. Utilizing kernel function
may enable us to deal with feature spaces of any arbitrary dimension with-
out calculating the mapped values directly. The parameters of the kernel
function should be optimized by the user. Gaussian RBF is usually
employed as the kernel function, due to great performance in capturing
the nonlinear relationship and fewer parameters to be set.
K xi; xj


5 exp
2:xi2xj:
2
σ2
 
!
(2.26)
In the abovementioned formula, σ represents the width parameter of
RBF kernel function. Fig. 2.5 illustrates the structure of an SVM method.
Ordinary SVM method is suffering from several limitations, among
which the necessity to solve a large-scale quadratic programming problem
is more severe than others. A modified SVM named least-squares SVM
(LSSVM), which is able to solve linear programming instead of quadratic
programing problems, was introduced by Suykens and Vandewalle [26] to
overcome this constraint by reducing the complexity of the optimization
process.
Figure 2.5 The structure of an SVM method. SVM, Support vector machine.
34
Applications of Artificial Intelligence Techniques in the Petroleum Industry
2.4.2 Least-square support vector machine
As mentioned previously, SVM models correspond to quadratic program-
ming and nonlinear equations, which might make them computationally
unaffordable. Suykens and Vandewalle [26] introduced LSSVM with the
aim of simplifying and ameliorating the original scheme. LSSVM has the
advantage of solving a set of linear equations, while it keeps the outstanding
performance and qualities of the antecedent model. The capability of con-
verting the inequality limitations into equality constraints and utilizing the
cost function (CF) with a sum of square errors will enable the LSSVM
method to achieve linearization. In essence, the regression error should be
adjusted during the calculations of an SVM model, while it is mathemati-
cally characterized and reported in an LSSVM model [26,27].
LSSVM has received increasing attention in the last few years, since it
is an improved, widely accepted version of SVM with higher convergence
rate and lower complexity due to its linearization of the equation sets.
This method is aimed to detect a quick solution with respect to converg-
ing to a global optimum. In order to determine the w and b in Eq. (2.24),
Vapnik suggested the minimization of the following CF [28]:
CF 5 wT
2 1 c
X
n
j51
ξj 2 ξ
j


(2.27)
which is subjected to the below constraints:
ξj; ξ
j $ 0
j 5 1; 2; . . .; N
wTUΦ xj
 
1 b 2 yj # ε 1 ξ
j
j 5 1; 2; . . .; N
yj 2 wTUΦ xj
 
2 b # ε 1 ξj
j 5 1; 2; . . .; N
8
<
:
(2.28)
where xj and yj stand for the input and output of the jth data point,
respectively. The slack variables are shown by ξj and ξ
j , and ε represents
the fixed precision of the function approximation. The coefficient c is the
SVM tuning parameter that defines the amount of deviation from a pre-
defined precision ε. Choosing a very small ε to develop a high-accuracy
model can lead to locating some data points outside of the ε precision,
and consequently, the solution may be infeasible. Therefore, the safe error
margin is determined by the slack variables.
Utilizing the Lagrangian method, the CF and its constraints can be
minimized as follows [27]:
35
Intelligent models
L β; β
ð
Þ 5 2 1
2
X
N
j;i51
βj 2 β
j


βi 2 β
i


K xj; xi


2 ε
X
N
j51
βj 2 β
j


1
X
N
j51
yj βj 2 β
j


(2.29)
X
N
j51
βj 2 β
j


5 0; βj; β
j A 0; c
½

(2.29a)
K xi; xj


5 Φ xj
 TΦ xi
ð Þ
j 5 1; 2; . . .; N
(2.29b)
As mentioned previously, βi and β
i are Lagrangian multipliers. The
final form of SVM is as proposed in Eq. (2.25). To avoid a quadratic pro-
gramming, which needs to determine the unknown parameters, βi; β
i ,
and b, in Eq. (2.25), LSSVM has emerged. Eq. (2.30) is illustrating the
reformulated SVM [27].
CF 5 wTw
2
1 γ
2
X
N
j51
e2
j
(2.30)
which is subjected to the following constraint:
yk 5 wTΦ xj
 
1 b 1 ej
(2.31)
In Eq. (2.28), γ $ 0 is the tuning parameter of LSSVM, and ej is the
regression error term. In fact, the first term in this equation is a typical L2
norm on the regression weights, and the second term corresponds to
regression error. γ defines the relative weight of the second term com-
pared to the first term of the CF, which should be optimized by the user.
The Lagrange function for this problem is represented in Eq. (2.32). The
regression error is defined by the constraint as the deviation between the
estimated and experimental value. The ingenuity of the LSSVM scheme is
in the definition of error. It is noteworthy to mention that in the ordinary
SVM, all the existing errors smaller than the predefined precision (ε) are
neglected.
L w; b; e; β
ð
Þ 5 wTw
2
1 γ
2
X
N
j51
e2
k 2
X
N
j51
βj wTΦ xj
 
1 b 1 ej 2 yj


(2.32)
36
Applications of Artificial Intelligence Techniques in the Petroleum Industry
The method of Lagrangian multipliers insinuates that the derivatives of
Eq. (2.32) should be equated to zero to optimize the CF.
@L
@b 5 0.
X
N
j51
βj 5 0
@L
@w 5 0.w 5
X
N
j51
βjΦ xj
 
@L
@ej
5 0.βj 5 γej
j 5 1; 2; . . .; N
@L
@aj
5 0.wTΦ xj
 
1 b 1 ej 2 yj 5 0
j 5 1; 2; . . .; N
8
>
>
>
>
>
>
>
>
>
>
>
>
>
>
<
>
>
>
>
>
>
>
>
>
>
>
>
>
>
:
(2.33)
Obviously, there are 2N 1 2 unknowns as well as 2N 1 2 equations.
So, solving the system of equations leads to defining the parameters of
LSSVM. Moreover, all SVM-based models involve a kernel function,
since they are organized to be usable in both linear and nonlinear
regression conditions. There are two tuning parameters (γ; σ2Þ if RBF
is employed as the kernel function. These tuning parameters can be
defined during the learning process by the minimization of the devia-
tion between the LSSVM model and the experimental data. The mean
square error (MSE) of the outputs obtained from the LSSVM model is
calculated as:
MSE 5 1
n
X
n
i51
Orep:=predi2Oexpi

2
(2.34)
where n represents the number of samples from initial population, O is
the output, rep./pred and exp stand for represented/predicted and experi-
mental values, respectively.
2.5 Decision tree
Decision trees (DTs) are one of the most applicable machine learning
algorithms. Simplicity and interpretability, capability of being graphically
represented, and low computational cost of DTs are the features responsi-
ble for their increasing use. A DT consists of a hierarchically organized set
of conditions or restrictions that are applied from a root to a leaf of the
tree (terminal node) [29,30]. The interpretation of a hierarchical tree
37
Intelligent models
structure is easier compared to an ANN due to their transparency. There
are two distinguishable methodologies within DTs: regression trees and
classification trees. In order to induce the DT from a dataset, an evalua-
tion measure of each of the evidential features is used to maximize the
internode heterogeneity.
According to literature, the first step to induce the DT is selecting the
optimal splitting measurement vectors. The process begins by splitting
the parent node (root or dependent variable) into binary pieces, where
the leaves (child nodes) are purer than the root. In order to maximize the
purity of the resulting tree, which means the highest reduction in the
impurity, all candidate splits are investigated by the DT and the optimal
split is selected [30]. This splitting procedure is as follows:
Δi s; t
ð
Þ 5 i t
ð Þ 2 pLi tL
ð Þ 2 pRi tR
ð Þ
(2.35)
where s stands for candidate split at node t, Δi s; t
ð
Þ is a measure of impu-
rity reduction from split s, i(t) represents the impurity before splitting, and
i(tL) and i(tR) show the impurity after splitting. The node t is divided by s
into the right child node tR and left child node tL with a proportion of pR
and pL, respectively.
Many approximations can be used to measure the impurity. Gini
index [30], gain-ratio [29], and chi-square [31] are the most frequent
ones.
2.5.1 Random forest
Random forest (RF) is a combination of numerous DT algorithms to pre-
dict or classify the value of a variable [3234]. Ensemble learning algo-
rithms are more robust and accurate than single classifiers [35,36]. In
2001, Breiman introduced RFs as a promising and novel classifier. RFs
are capable of handling thousands of input variables and estimating the
importance
of
each
variable
in
the
classification
without
variable
elimination.
In an RF, each tree helps to assign the most frequent class to the input
data with a single vote [3234]. Fig. 2.6 illustrates the common structure
of an RF.
In an RF algorithm, the generalization error is reduced due to utilizing
a random subset of predictive variables or input features in the division of
each node, instead of using the best variables. In order to make the trees
38
Applications of Artificial Intelligence Techniques in the Petroleum Industry
grow from various training datasets with the aim of increasing the diver-
sity of the trees, bagging or bootstrap aggregation is used by the RF algo-
rithm. Bagging is a method to create training data through randomly
resampling the original dataset. Each training subset consists of a specified
proportion of the training dataset. “Out-of-bag” or “OOB” refers to
another subset that comprises samples that are absent in the training sub-
set. It should be noticed that the nonselected elements form a different
OOB subset for each tree through the bootstrapping process. These latter
OOB subsets can be subsequently applied to evaluate the performance. In
this way, an unbiased estimation of the generalization error is computed
by the RF [32]. Gini index is employed by the RF classifier as a measure
for selecting the best split.
To generate a prediction model through an RF classifier, only two
parameters are needed to be defined: the number of predictive variables
(n) and the number of classification trees (m). In another word, each of
the elements of the dataset is classified by a number of trees (m) utilizing a
predefined number of random predictive variables (n) in order to classify a
new dataset.
According to Breiman [35], as the number of trees increases, the
generalization error converges and overfitting is not a problem. On the
Figure 2.6 Structure of a random forest.
39
Intelligent models
contrary, reducing the number of predictive variables results in weakening
of each individual tree of the model, which leads to reduce the correlation
between the trees and improve the accuracy of the model. Therefore, it is
necessary to choose a large number of trees and to minimize the generali-
zation error by optimizing the number of predictive variables.
The relative importance of each variable can be assessed by the RF
classifiers. In order to evaluate the importance of each variable, the RF
changes one of the input variables and keeps the rest of the variables con-
stant. Then, the reduction in accuracy of the model is measured utilizing
the OOB error estimation [32].
2.5.2 Extra trees
Extra trees (ETs) are also known as extremely randomized trees and are
first proposed by Geurts et al. [37]. ET is introduced as an extension of
RF algorithm that is less probable to overfit a dataset [37]. In this method
as well as RF, a random subset of features is utilized to train each base
estimator. The aim of using the ET algorithm is to diminish the variance
of the prediction model through employing stronger randomization tech-
niques. There are two main differences between ET and RF:
•
ET splits the nodes by randomly choosing the best feature along with
the corresponding value.
•
ET utilizes the whole input training set to grow each tree instead of
applying a bagging procedure.
2.6 Group method of data handling
One of the most promising families of ANNs is named group method of
data handling (GMDH) that is also known as polynomial neural network
[38]. GMDH is capable of supplying a user-friendly polynomial formula
while it models complex systems. The subtlety of the GMDH lies in
employing multiple nodes in the intermediate layers. In the conventional
GMDH method, which was introduced by Ivakhnenko [39], each
GMDH node calculation is based on a quadratic polynomial model com-
prising of the previous neuron.
2.6.1 Hybrid group method of data handling
An extensive version of GMDH, namely, hybrid GMDH, is proposed to
overcome some generalization lacks of conventional GMDH that consists
40
Applications of Artificial Intelligence Techniques in the Petroleum Industry
of more interactions between the variables and nodes. Therefore, the
hybrid version represents more flexibility to model more complex systems
[40]. Hybrid GMDH corresponds to the following rule:
yi 5 a 1
X
m
i51
X
m
j51
?
X
m
k51
cij?kxn
i xn
j ?xn
kn 5 1; 2; . . . ; 2d
(2.36)
where the inputs and output of the model are represented by xij?k and
yi, respectively. cij?k; m; and d, respectively, stand for polynomial coeffi-
cients, the input parameters numbers, and the size of layers.
The full-form mathematical formulation may be derived utilizing the
partial polynomials with predefined orders, which leads to create new
nodal variables (z1; z2; . . .). Considering a system consisting of two neu-
rons coupled with a quadratic polynomial expression, the following equa-
tion can be obtained:
zGMDH
i
5 α0 1 α1xi 1 α2xj 1 α3xixj 1 α4x2
i 1 α5x2
j
(2.37)
The least-square method (LSM) may be employed to adjust the coeffi-
cients of Eq. (2.37) as:
δ2
j 5
X
Nt
i51
yi2zGMDH
i

2j 5 1; 2; . . . ;
m
2

	
(2.38)
In the above-shown equation, Nt is the size of training set, and m is
the number of variables. In order to deal with this problem, a general
matrix may be defined as [38,41]:
y 5 ATX
(2.39)
Utilizing the LSM yields:
AT 5 yXT XXT

-1
(2.40)
where A is the (α0; α1; α2; α3; α4; α5), y is the (y1; y2; . . . ; ym), and T
represents the transpose of the matrix. The observed data should be
divided into training and testing subsets. The coefficients of the model
may be obtained using the training subset, and the testing subset is
employed to find the best combination of two variables based on the
following condition:
41
Intelligent models
δ2
j 5
X
N
i5Nt11
yi2zGMDH
i

2 , εj 5 1; 2; . . . ;
m
2

	
(2.41)
A new variable will be stored only if this condition is satisfied, other-
wise it will be eliminated by the algorithm. In each iteration, the magni-
tude of the deviation between the estimated data and observed ones is
calculated, and once the minimum value is reached, the algorithm will be
terminated.
2.7 Genetic programming
Evolutionary algorithms (EAs) are stochastic search methods inspired by
the Darwin theory of evolution of species [42]. EAs evolve a population
(a set of candidate solutions). Evolutionary operators are applied to find
better solutions in the search process. Genetic programming (GP) was first
introduced by Koza [43] and is defined as an EA that explores a program
space, while genetic algorithms explore a solution space. Therefore, each
individual of the population is a tree-like structure program. These pro-
grams can be mathematical or logical expressions.
Initially, a population composed of individuals is generated randomly.
Then, from a terminal set and a function set, the individual of a tree is
randomly generated. The function set represents logical and arithmetic
operators (1, 2 , 3 , 4, sin, cos, etc.), and the terminal set shows vari-
ables or constants. The population is evaluated by a fitness function that
measures the error between the real output from the data and output pro-
duced by a solution. Root-mean-square error (RMSE) and MSE are con-
ventional fitness functions. GP usually employs tournament selection to
select individuals from the population with respect to fitness values [43].
Afterward, the following genetic operators are applied to produce the
individuals of the next generation:
Mutation: This operator selects a solution and mutates it randomly to
produce a new individual. This operation can be a node mutation or a
subtree mutation. In the case of subtree mutation, as shown in Fig. 2.7,
the subtree of a randomly selected node of the parents is replaced by a
newly generated one. In a node mutation, only the selected node is
substituted by a function or terminal.
Crossover: This operator selects to individuals to produce a new solu-
tion. In each parent, two nodes are randomly selected, and the subtree of
42
Applications of Artificial Intelligence Techniques in the Petroleum Industry
the first parent is replaced by the subtree of the second parent, as shown
in Fig. 2.8.
Reproduction: This operator copies the individual and transfers it to
the next generation without any modification.
Finally, the best individual in the population is represented as the
output.
2.7.1 Multigene genetic programming
Multigene GP (MGGP) or multibranches GP was introduced in 1998
[44] as an advancement of GP. In order to improve the fitness of solutions
generated by GP, MGGP linearly combines low depth GP blocks.
MGGP provides simpler models than those of standard GP due to the use
of smaller trees [45]. In this algorithm, each individual consists of one or
more genes (trees) and the output of the model is the summation of
weighted outputs of two or more trees in a multigene program plus a bias
term. Fig. 2.9 represents an illustration of a pseudo-linear MGGP model
and its corresponding mathematical expression, where b0 is bias term, w1
and w2 are the gene weights, and x1 and x2 are input variables. However,
the symbolic regression algorithm is employed to determine the weights
in the original version of MGGP, the resulting pseudo-linear model can
show completely nonlinear behavior. Nonlinear regression techniques
Figure 2.7 Subtree mutation operation example.
43
Intelligent models
have been also applied to optimize the weights of the genes. A schematic
flowchart of MGGP is represented in Fig. 2.10.
2.8 Gene expression programming
Gene expression programming (GEP) was first introduced in 2001 [46]
and is classified as an advanced soft computing technique. This method is
Figure 2.8 An example of a crossover operation.
Figure 2.9 A pseudo-linear MGGP model and its mathematical expression. MGGP,
Multigene genetic programming.
44
Applications of Artificial Intelligence Techniques in the Petroleum Industry
considered as a part of the group of EAs and can be applied to evolution-
ary principles. GEP is capable of generating an explicit mathematical
expression for the case being studied. GEP is presented as an improved
version of GP that was introduced by Koza [47], which is able to over-
come the shortcomings of GP.
GEP process aims to find the best expression model (as the other EAs)
by utilizing the chromosomes that are capable of codifying and reporting
the possible solutions. Expression tree is another key feature of GEP that
is obtained by transforming the employed chromosomes into real candi-
dates. In addition, genes are employed, which consist of terminals and a
head containing functions. Each gene comprises a terminal set (i.e., x, y,
z) and a fixed-length list of symbols that denotes kinds of operators (i.e.,
3 , 4, 1 , 2 , O, log) [48]. Fig. 2.11 illustrates a chromosome with three
genes and corresponding mathematical relation.
The following steps illustrate how a GEP works:
Figure
2.10 A
schematic
flowchart
of
MGGP.
MGGP,
Multigene
genetic
programming.
45
Intelligent models
1. GEP setting parameters: The aim of this step is to define the key para-
meters of the model the length of genes, the stopping criteria, and the
size of the population.
2. Population initialization: generate initial chromosomes randomly (vari-
ous probable mathematical expression).
3. Employing a fitness function to evaluate the chromosomes.
4. Choosing the fittest characteristics and use them for the subsequent
generation.
5. Tournament selection is applied in order to select the characteristics
and a new offspring will be generated through the recombination of
these individuals. There are two kinds of recombination in GEP: one
point and two points.
6. Mutation operator: It mutates the genomes by adjusting an element by
another, and it has a principle role in GEP.
7. The sequences are transposed and inserted somewhere in a chromo-
some. This step aims to activate and mutate parts of the genome in the
chromosome.
The steps from 3 to 7 will be reiterated until the stopping criteria are
satisfied.
2.9 Case-based reasoning
Case-based reasoning (CBR) is capable of solving problems through
remembering past and similar cases [49]. In a CBR, it is considered that
similar problems have similar solutions, which is analogous to the human
problem-solving behavior. A CBR is based on a set of activities such as
case representation, indexing, case storage, and a CBR cycle, as shown in
Figure
2.11 A
schematic
of
three-gene
chromosome
and
its
mathematical
expression.
46
Applications of Artificial Intelligence Techniques in the Petroleum Industry
Fig. 2.12. Case representation applies to the knowledge to be included
about cases and recognizing a proper structure to describe cases. To facili-
tate case retrieval, indexing specifies indices to cases. Case storage relates
to arranging an appropriate case-base structure for the collected cases to
enable their effective retrieval. The CBR cycle consists of four phases as
follows [50]:
1. Retrieve: In the case base, most similar cases to a given target case are
searched. Various similarity functions such as Euclidean and Manhattan
distance are employed to rank and sort the obtained results. In order
to better preserve transitivity, reflexivity, and symmetry, an inverse
exponential function of the similarity measure can be employed.
2. Reuse: In the case of sufficient similarity between the retrieved cases
and the target case, the solutions of the retrieved cases need no modifi-
cation and can be reused directly.
3. Revise: If the target case is not sufficiently similar to the retrieved
cases, the solutions to retrieved cases should be revised through consid-
ering the differences between the given target case and retrieved cases.
4. Retain: In this phase, the target (new) case and its solutions are
retained in the case base for future reuse.
Therefore, CBR is considered as a self-learning system.
2.10 Committee machine intelligent system
Practically, various intelligent models are introduced, among which the
best model is selected and the others are discarded. Therefore, it will be a
waste attempt to try to train a discarded model. To overcome this
Figure 2.12 A schematic of a CBR process. CBR, Case-based reasoning.
47
Intelligent models
deficiency, it is needed to generate a committee machine (committee of
machines) through combining the intelligent models. Committee machine
intelligent system (CMIS) was first introduced by Nilsson in 1965 [51]
and is defined as an ANN that is capable of dividing and overcoming
in order to solve a problem. Committee machines are divided into the
following classifications:
1. static structure and
2. dynamic structure
In fact, the solution of various models is merged in order to find a bet-
ter solution and introduce an accurate solution. A common approach to
combine these different solutions is to employ a simple averaging or
weighted averaging to linearly combine the solutions [52,53]. In the case
of using simple averaging, all solutions equally contribute to the final solu-
tion, which is the basic deficiency of this averaging, since the less accurate
solutions should contribute less to the final solution. However, in the case
of utilizing weighted averaging, the accuracy of each model determines its
contribution and a coefficient for that solution in the linear combination.
The sum of the coefficients in the linear combination is equal to unity. In
another word, the absolute coefficient of each model in the linear equa-
tion reflects the contribution of the model to the final solution of CMIS
model.
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[41] A. Hemmati-Sarapardeh, E. Mohagheghian, Modeling interfacial tension and mini-
mum miscibility pressure in paraffin-nitrogen systems: application to gas injection
processes, Fuel 205 (2017) 8089.
[42] Goldberg, D.E., Genetic Algorithms in Search, Optimization, and Machine
Learning., Addison-Wesley Longman Publishing Co., Inc. 1989.
[43] J.R. Koza, J.R. Koza, Genetic Programming: On the Programming of Computers
by Means of Natural Selection, vol. 1, MIT Press, 1992.
[44] M. Hinchliffe, M. Willis, M. Tham, Chemical process systems modelling using mul-
tiobjective genetic programming, Genet. Program. (1998) 134139.
[45] D.P. Searson, GPTIPS 2: An Open-Source Software Platform for Symbolic Data Mining,
in Handbook of Genetic Programming Applications, Springer, 2015, pp. 551573.
[46] C. Ferreira, Gene expression programming: a new adaptive algorithm for solving
problems, Complex Syst. 13(2) (2001) 87129.
[47] Koza,
J.R.,
Genetic
Programming
II:
Automatic
Discovery
of
Reusable
Subprograms. Cambridge, MA The MIT Press, vol. 13(8), 1994, p. 32.
[48] L. Teodorescu, D. Sherwood, High energy physics event selection with gene expres-
sion programming, Comput. Phys. Commun. 178 (6) (2008) 409419.
[49] J.L. Kolodneer, Improving human decision making through case-based decision aid-
ing. AI Mag. 12 (1991) 5252.
[50] A. Aamodt, E. Plaza, 1994. Case-based reasoning: Foundational issues, methodologi-
cal variations, and system approaches. Ai Commun. 7, 3959.
[51] N.J. Nilsson, Learning Machines, 1965.
[52] M.P. Perrone, L.N. Cooper, When Networks Disagree: Ensemble Methods for
Hybrid Neural Networks, Institute for Brain and Neural Systems, Brown University,
Providence, RI, 1992.
[53] S. Hashem, B. Schmeiser, Approximating a Function and Its Derivatives Using
MSE-Optimal Linear Combinations of Trained Feedforward Neural Networks,
Purdue University, Department of Statistics, 1993.
50
Applications of Artificial Intelligence Techniques in the Petroleum Industry

CHAPTER 3
Training and optimization
algorithms
Contents
3.1 Overview
51
3.2 Genetic algorithm
52
3.3 Differential evolution
55
3.4 Particle swarm optimization
56
3.5 Ant colony optimization
59
3.6 Artificial bee colony
61
3.7 Firefly algorithm
62
3.8 Imperialist competitive algorithm
63
3.9 Simulated annealing
65
3.10 Coupled simulated annealing
66
3.11 Gravitational search algorithm
67
3.12 Cuckoo optimization algorithm
68
3.13 Gray wolf optimization
70
3.14 Whale optimization algorithm
71
3.15 LevenbergMarquardt algorithm
73
3.16 Bayesian regularization algorithm
75
3.17 Scaled conjugate gradient algorithm
75
3.18 Resilient backpropagation algorithm
76
References
76
3.1 Overview
The resolution of optimization problems encountered in the oil industry
has become a central subject as these kinds of problems are generally
known to be time-consuming and requires intensive calculation means.
Given the efficiency, robustness, and the low run-time of machine learn-
ing (ML) methods, hybridization of these substitution paradigms with rig-
orous optimization algorithms ensures twofold benefits: the first consists of
improving the learning process of ML methods when these latter are cou-
pled with the optimization techniques for searching their proper control
parameters and the second is the significant reduction of the run-time
51
Applications of Artificial Intelligence Techniques in the Petroleum Industry
DOI: https://doi.org/10.1016/B978-0-12-818680-0.00003-5
© 2020 Elsevier Inc.
All rights reserved.
when ML methods are employed as proxy coupled with optimization
techniques for systems optimization tasks. In this chapter, the first purpose
of the hybrid ML-optimization techniques is investigated by highlighting
the different families of optimization algorithms. Before proceeding to
these details, Fig. 3.1 summarizes the main workflow of the hybridization
process between ML methods and the optimization techniques.
3.2 Genetic algorithm
Genetic algorithm (GA) is an evolutionary algorithm that is known to be
very efficient in resolving a wide variety of optimization problems with
different degrees of complexity. Indeed, the first version of GA that was
introduced by Holland [1] and extended later by Goldberg and Holland
[2] is considered the famous and first nature-inspired algorithm. GA is classi-
fied beneath population-based optimization techniques, and this means that
the optimization process is done using a set of individuals that represent pos-
sible solutions of the problem. GA applies the so-called genetic operators as a
sort of guide toward better and unvisited regions in the search realm.
Selection, elitism, crossover, and mutation are the fundamental genetic
operators. Before proceeding to the search process using these operators, an
initial population of possible solutions is generated randomly and codified in
a form of chromosomes. The codification differs from a problem to another
according to its nature. Among the well-known forms, we find binary
encoding, which is preferred for continuous problems and permutation
encoding, to be proper for combinatorial problems. Fig. 3.2A and B shows
examples of binary and permutation encodings.
During the optimization process, a fitness function is considered as a
mean to evaluate the quality of the individuals in the population and dif-
ferentiate between them. When GA is applied for the learning phase of
ML methods, mean square error is the frequently applied fitness function.
The iterative process of the genetic operators is based on a replacement
approach that is done by substituting inappropriate elements with new ones
with respect to their fitness values. From the chronological standpoint, elit-
ism is generally the first operator that is applied after the evaluation. This
operator means the survival of a specified number of fittest individuals and
their direct transition to the new generation. The elitism operator is followed
by the selection operator that allows the consideration of a set of individuals
in the population on the basis of their performance to give birth to new
52
Applications of Artificial Intelligence Techniques in the Petroleum Industry
Start 
Random splittingof data 
Training data
Testing data
Initialization of the control parameters
Optimization algorithms
ML methods
Train ML
Calculate ﬁtness function
Meet stopping
criteria ? 
Parameters optimization
No
Yes
Final values of parameters
Testing ML
Meet stopping
criteria ? 
No
Yes
Obtain the properparameters 
End
Figure 3.1 Flowchart of the hybridization procedure: ML-optimization algorithms.
ML, Machine learning.
(A)
1
0
1
0
0
0
1
2
4
6
7
0
8
1
(B)
Figure 3.2 Examples of (A) binary and (B) permutation encodings.
53
Training and optimization algorithms
offspring by the crossover operator. The selection of these individuals is
carried out using one of the following methods:
•
Tournament selection: two (or more) individuals are randomly selected,
their fitness functions are compared, and the best fit is selected.
•
Roulette wheel selection: this method is inspired by the lottery wheel on
which individuals are represented by sectors that are proportional to their
fitness. The selection is made in a proportion to the fitness of the indivi-
duals. Thus for a particular individual i with a fitness value fi, the proba-
bility (Pi) of its selection from the population of a size n is given by:
Pi 5
fi
P
n
i51
fi
(3.1)
•
Ranking selection: it classifies the population according to the fitness
function, then, each individual is given a rank. The better the individ-
ual, the higher is the rank. Afterward, the principle of selection by
roulette is applied on the rows of the individuals and not on the value
of the fitness function.
Crossover operator is then applied on the selected individuals. This
operator is considered the principal operator for GA as it allows it to
explore the research space by diversifying the population. This operator
usually manipulates the chromosomes of two parents to generate two off-
spring with a given probability Pco. There are different types of crossing,
such as the crossing at one point (single point) and two points as illustrated
in Fig. 3.3.
Figure 3.3 Illustration of one point and two points crossover.
54
Applications of Artificial Intelligence Techniques in the Petroleum Industry
The last genetic operator is mutation. This latter reflects the exploitative
character of GA. It corresponds to a minor change in the genetic code
applied to an individual in order to introduce diversity and thus avoid falling
into local optimums. This operator is applied with a probability Pm. Single
point and multiple points are the well-known mutation types. Fig. 3.4 shows
how the mutation (single point) is done on a given chromosome.
This process of operators is repeated until a termination condition is
met. This latter is generally specified as the maximum number of iteration
or a satisfactory cutoff accuracy. The pseudocode of GA is given next.
Algorithm I. Genetic algorithm
1. Generate an initial population
2. Initialize the probabilities Pco and Pm
3. Repartition of the numbers of individuals to be generated using the
different operators
4. Choose the mutation type
5. Repeat
6. Evaluation of the individuals based on the fitness function
7. Elitism
8. Selection
9. Crossover
10. Mutation
11. Stopping criteria
12. Return the best found solution
3.3 Differential evolution
Differential evolution (DE) is another population-based and evolutionary-
inspired algorithm. It was introduced by Storn and Price [3,4]. DE is derived
from GA as it applies approximately the same operators namely, mutation,
crossover, and selection when exploring and exploiting the search space. In
contrast to the case of GA, these operators are formulated and applied in
another way in DE's iterative process. Furthermore, mutation operator is the
Figure 3.4 Mutation operator.
55
Training and optimization algorithms
principal operator in DE, where it leads optimization process, while selection
operator offers guidance toward the prospective regions. The following steps
summarize the procedure of optimization in DE for a minimization problem
formulated with an objective function f ðÞ and d variables:
•
Initialization: an initial population of n possible solutions is generated
randomly and encoded in form of vectors x according to the decision
variables of the problem.
•
Mutation: for an iteration ðt 1 1Þ, and for each vector xit from the pre-
ceding iteration, the following equation is applied to deliver the
mutant vector:
ut11
i;j
5 xt
r1;j 1 F 3 xt
r2;j 2 xt
r3;j


(3.2)
where j 5 1; 2; . . . ; d and r1; r2; r3Af1; 2; . . . ; ng are the different random
indexes. F is the mutation factor that is chosen randomly from [0,2],
and uit11 is called donor vector.
•
Crossover: by applying this operator a trial vector tut11
i
is created using
the donor vector with respect to a predefined crossover probability
(Pco), this process is done as follows:
tut11
i;j
5
ut11
i;j ; if randi;j # Pco


or j 5 Irand
xt
i;j; if ðrandij . PcoÞ and j 6¼ Irand

(3.3)
where j 5 1; 2; . . . ; d; randijA½0 1; IrandAð1; 2; . . . ; dÞ is a randomly
selected index; and Pco is the crossover probability A [0 1].
•
Selection: the resulted vector from mutation and crossover is assessed
and its quality is compared to that of the original vector, the fittest one
is chosen to survive. The selection operator of DE is done as follows:
xt11
i
5
tut11
i
; if f

tut11
i

# f xt
i
 
xt
i; otherwise
(
(3.4)
where f is the fitness function.
These steps are reiterated until a termination condition is satisfied.
3.4 Particle swarm optimization
Particle swarm optimization (PSO) is a metaheuristic optimization algo-
rithm, originating from the work of Kennedy and Eberhart in 1995 [5],
and improved later by Clerc [6]. PSO is considered the well-known smart
56
Applications of Artificial Intelligence Techniques in the Petroleum Industry
swarmbased algorithms [7]. This method of optimization is inspired
from the concept of self-organization and collaboration between group—
living animals such as birds and fish during their movements. Three prin-
ciples are repeated during these movements for each individual:
•
Repeat the previous movement.
•
Redirect to its best past position.
•
Move to the best (past) position of its group of informants.
An analogy of the individuals and the aforementioned principles are
adapted in the algorithm of PSO as follows: a swarm of particles, which
are potential solutions to the optimization problem, are moving around
the research space to find the global optimum. The displacement of a par-
ticle is influenced by the following three components:
•
A physical component: the particle tends to follow its current direction of
movement.
•
A cognitive component: the particle tends to go to the best site that has
been visited.
•
A social component: the particle tends to rely on the experience of its
congeners and thus moves toward the best site already reached by its
neighbors.
In the case of an optimization problem, the quality of a position is deter-
mined by the value of the objective function at that point. Fig. 3.5 illustrates
an example of the direction of flight of a particle in a research space.
In a search space of dimension d, each particle i of the swarm at a
given iteration t is characterized by its position xi;t and speed vi;t. Each
particle keeps in mind the best position by which it has already passed.
Figure 3.5 Scheme of particle displacement principle.
57
Training and optimization algorithms
This position is noted by pbestt. The best position reached by all the
swarm particles is indicated by gbestt. Thus the motion equations of a par-
ticle at a new iteration t 1 1 are given by [6]:
vi;t11 5 χ 3

vi;t 1 c1r1 pbesti;t 2 xi;t


1 c2r2 gbestt 2 xi;t




(3.5)
and
xi;t11 5 xi;t 1 vi;t11
(3.6)
where χ 5 2= 2 2 ϕ 2
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
ﬃ
ϕ2 2 4ϕ
p



 is the convergence factor; ϕ 5 c1 1 c2,
c1 indicates the cognitive component factor, c2 is the social component
factor; and r1 and r2 are random variables between 0 and 1. Generally,
ϕ 5 4:1 (where c1 5 c2 5 2:05), and thus χ 5 0:729 gives the best perfor-
mances [6].
PSO starts with a random initialization of the swarm in the search
space, and at each iteration of the algorithm, each particle is moved
according to the equations described earlier. Once the displacement of the
particles is done, the new positions are evaluated. The pbestt11 and gbestt11
are then updated as follows:
pbesti;t11 5
pbesti;t; iff

pbesti;tÞ # f xi;t11


xi;t11; otherwise
(
(3.7)
gbestt11 5 min f pbesti;t11


	

(3.8)
where f is the objective function to minimize. The procedure of optimi-
zation using PSO is summarized as follows:
Algorithm II. Particle swarm optimization
1. Initialize randomly n particles: positions and vitesses
2. Evaluate the positions of particles and distinguish pbesti;0 et gbest0
3. Repeat
4. Moving particles according to Eqs. (3.5) and (3.6)
5. Evaluate the new positions of the particles based on the objective
function.
6. Update pbesti;t et gbestt
7. Stopping criteria
8. Return the best found solution
These steps are repeated until a specified condition is verified.
58
Applications of Artificial Intelligence Techniques in the Petroleum Industry
3.5 Ant colony optimization
Ant colony optimization (ACO) is a metaheuristic optimization technique
based on the behavior of ants. It was developed in the early of 1990s by
Dorigo [8]. The original idea comes from the observation of the exploita-
tion of food resources by ants. Although ants have limited cognitive abili-
ties individually, they are able to find the shortest path between a food
source and their nest collectively.
When looking for food, ants initially explore the area surrounding
their nest in a random way. If an ant finds a food source, it evaluates it
and returns some food to the nest. During the return journey, the ant
deposits a pheromone trail on the path followed. The deposited phero-
mone depends on the quantity and quality of the food, and this guides
other ants to this food source. Pheromone trails represent indirect com-
munication (this communication system is known as stigmergy) among
ants, allowing them to find the shortest paths between their nest and food
sources. Fig. 3.6 illustrates this process.
This real ant colony capability is adopted in the artificial ACO to find
approximate solutions to optimization problems. However, as the first
version of ACO works for discrete domains only, the application of
Figure 3.6 Selection of the shortest path by an ant colony. An ant finds a food
source (F) then returns to the nest (N) by spreading the pheromone along its path.
59
Training and optimization algorithms
pheromone strategy was generalized for continuous domains [9]. This is
done by making the so-called solution archive that includes a list of candi-
date solutions leading to a Gaussian mixture probabilistic model. The
ACO in continuous domains is known as Estimation of Distribution algo-
rithm because of the evolutionary interaction between the probabilistic
model and solution archive [10]. The main steps of this algorithm are
summarized as follows:
•
Initialization: an initial population of n ants is generated randomly in a
search domain, and their fitness functions are evaluated.
•
Generation of the solution archive: the initial solutions are sorted according
to their fitness. The best and the worst solutions are represented by x1
and xn, respectively.
•
Weight attribution: a weight is attributed to solutions regrouped in the
archive by applying the following equation:
wi ~
1
ﬃﬃﬃﬃﬃ
ﬃ
2π
p
αn exp 21
2
i21
αn

2
"
#
(3.9)
and
X
n
i51
wi 5 1
(3.10)
•
Generation of the probabilistic model: the following equation is applied to
the probabilistic model consisting of the Gaussian mixture:
Gk x k
½ 
ð
Þ 5
X
n
i51
wiN x k
½ ;μi k
½ ; σi k
½ 


(3.11)
N x;μ; σ
ð
Þ 5
1
ﬃﬃﬃﬃﬃ
ﬃ
2π
p
σ exp 21
2
x2μ
σ

2


(3.12)
where k is the decision variable and x[k] means the kth element in x.
The probabilistic paradigm is accomplished by calculating the mean and stan-
darddeviationoftheGaussianmixtureasshowninthefollowingexpressions:
μi k
½  5 xi½k
(3.13)
σi k
½  5
η
n 2 1
X
n
i051
xi k
½  2 xi0 k
½ 


(3.14)
where η . 0 is a balance factor between the exploration and exploitation.
60
Applications of Artificial Intelligence Techniques in the Petroleum Industry
•
Sampling: m new samples are generated as the offsprings of the previous
archive by using g 5 G1; G2; . . . ; Gnx


. An evaluation of the offspring
is done based on the fitness function.
•
Selection: in this step, a new solution archive is built by the offspring
and l found best solutions. The fittest solution in the archive is there-
fore the best solution of the optimization process.
This process is repeated until a stopping condition is satisfied.
3.6 Artificial bee colony
Artificial bee colony (ABC) is an intelligent swarm optimization technique
that was introduced by Karaboga [11]. The mathematical formulation of
ABC algorithm is inspired from the autoorganization and the foraging
behavior of the honeybees. As the other evolutionary algorithms, ABC
proceeds initially by creating an initial population of bees, whose positions
imitate possible solutions for the problem. The population called also col-
ony is split into three groups of bees swapping information between them
to improve the quality of solutions. These groups are employed,
onlooker, and scout bees. The role of employed bees consists of exploring
and searching the food sources, while the onlooker bees select the proper
sources of foods based on the dance shared by the employers. Each
employed bee becomes a scout bee if it fails improving the quality of its
food after a while. The following steps describe the main adapted
sequences of ABC algorithm:
•
Initialization: an initial colony is created and reported randomly in the
search space by attributing position vectors xi for each i employed bee.
The number of employed bees is the same as the number of onlooker
bees, which is also the same as that of the solutions [12]. The positions
of bees are equivalent to the sources of food, and hence, their quality
is evaluated based on the fitness function.
•
Employed bees: each bee i updates its position by exploiting the gath-
ered information from the preceding iteration ðtÞ as follows:
xi t 1 1
ð
Þ 5 xi t
ð Þ 1 λi xi t
ð Þ 2 xα t
ð Þ
ð
Þ
(3.15)
where α is a random index from f1; 2; . . . ; colony sizegα6¼i and λi is a
random number from [0,1].
The new position is evaluated using the fitness function, and if it
outperforms the old one, this new position is accepted as the actual
source of food; otherwise, it is discarded.
61
Training and optimization algorithms
•
Onlooker bees: after completing the exploration phase by the employers,
they expose their fitness values to the onlooker bees. Accordingly, the
onlookers will select proper sources with respect to a probability
P calculated as follows:
Pi 5
fiti
P
e
i51
fiti
(3.16)
where fiti is the fitness value for the ith bee and e is the number of
employed bees. As in the case of the employer bees, each onlooker
bee will update its position if its fitness value outperforms the previous
value [12].
•
Scout bees: each employed bee becomes a scout bee if no improvement
is reported in its fitness value after a specified number of iterations. To
do so, its position is substituted randomly from the search space.
The fittest position in the colony is that providing the highest quality
of food, and hence, this position is the most qualified to be the optimum
solution.
The aforementioned steps are repeated until a termination criterion is
fulfilled.
3.7 Firefly algorithm
Firefly algorithm (FFA) is another smart swarm inspired algorithm that
was introduced by Yang [13]. As indicated by its appellation, the founda-
tion of FFA is imitated from the real behavior of fireflies that shed light
[13]. This engendered light is considered a communication mean between
fireflies, and it is also used to attract preys. Accordingly, the mathematical
formulation of FFA, reflecting the movements of fireflies is based on the
generated lights and its intensity. In this context, lighter firefly is attracted
toward randomly traveling brighter fireflies. Hereafter, the less bright will
displace the brighter one. The attractiveness and the intensity decrease
with distance. Firefly will move randomly if there is no available brighter
firefly [13]. The brightness of every firefly represents the quality of the
solutions. The following equation summarizes the movement of a firefly i
toward a brighter firefly j for a given iteration ðt 1 1Þ:
xi
t11 5 xi
t 1 β0e2γri;j2 xi
t 2 xj
t


1 α rand 2 1
2


(3.17)
62
Applications of Artificial Intelligence Techniques in the Petroleum Industry
where
the
attraction
effect
is
shown
by
β0e2γri;j2 xit 2 xjt


;
and
α rand 2 ð1=2Þ


represents the randomization term, where α corresponds
to the randomization coefficient; β0 is the intensity of light at distance
r 5 0, and for most cases, its value is equal to 1; γ is called absorption
coefficient and its value is distributed over [0,N]. The distance between
any two fireflies i and j located at xi and xj, respectively, is the Cartesian
distance, that is, ri;j 5
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
P
d
k51
xi;k 2 xj;k


s
.
After making all the movements by the fireflies, the fittest one is con-
served. The traveling process is iterated until a termination condition is
satisfied.
3.8 Imperialist competitive algorithm
Imperialist competitive algorithm (ICA) is a new population-based algo-
rithm that mimics the human being’s sociopolitical evolution. ICA was
introduced by Atashpaz-Gargari and Lucas [14]. As the other population-
based algorithms, ICA begins with initial populations that are called coun-
tries. These latter are split into two categories; colony and imperialist (in
optimization terminology, countries with the least cost), which together
form empires. Imperialist competitive is another terminology that is asso-
ciated with ICA, and it consists of attempting to gain more colonies. Due
to competition process, the powerful imperialists will be intensified in the
power. In addition, an empire is distorted, once it misses all of its colonies.
In the same context, the most influential imperialist remains in the world,
and all the countries become its colonies at the end of algorithm. The
main steps of ICA are summarized as follows:
•
Generating initial empire: an initial population of possible solutions is
created randomly and codified in form of countries according to the
dimensionality d of the problem. The following array is defined for
each country:
country 5 P1; P2; . . . ; Pd
½

(3.18)
Afterward, the countries are evaluated based on the cost function
(fitness function):
cost 5 f country
ð
Þ 5 f
P1; P2; . . . ; Pd
½

ð
Þ
(3.19)
63
Training and optimization algorithms
ICA includes different control parameters, namely, the number of
initial countries (Ncountry), number of imperialist (Nimp), and the
remaining Ncol of the population that will be the colonies each of
which belongs to an empire [14]. After the initialization of the afore-
mentioned parameters, normalized cost of an imperialist is applied to
split equivalently the colonies among imperialists as follows:
Ck 5 ck 2 maxi Ci
f
g
(3.20)
where ck is the cost of kth imperialist and Ck is its normalized cost.
Therefore, the power of each imperialist is gained as follows [14]:
Pk 5
Ck
P
Nimp
i51
Ci


















(3.21)
The normalized power of an imperialist depends on its colonies.
Accordingly, the initial number of an imperialist will be [14]:
N  Ck 5 round Pk  Ncol
f
g
(3.22)
where N  Ck means the initial number of colonies of kth empire and
Ncol is the number of all colonies. In order to divide the colonies
among imperialists, N  Ck of the colonies is randomly chosen and
allocated to each imperialist. The colonies combined with the imperi-
alist form the kth empire.
•
Moving the colonies of an empire toward the imperialist: the imperialist
countries attempt to improve their colonies and include them as a part
of their imperialists. This process is implemented by the movement
the colonies toward their suitable imperialist. This movement process
is illustrated in Fig. 3.7. According to this figure, the colony travels
toward the imperialist by x units. x corresponds to a random variable
with uniform distribution [14]:
xBU 0; θ 3 L
ð
Þ
(3.23)
where L and θ are distance between an imperialist and colony and a num-
ber greater than 1, respectively. If, after this movement, one of the colonies
has more power than its relevant imperialist, they will switch positions.
•
The total power of an empire: total cost of an empire is resulted by the
following equation:
64
Applications of Artificial Intelligence Techniques in the Petroleum Industry
T  Ck 5 costimperialistk 1 ω 3 mean cost colonies of empirek


	

(3.24)
where T  Ck is the total cost of the kth empire, and ω , 1 is a positive
number. A high value of ω means that the total power is highly related to
the colonies, whereas low value indicates the dominance of the imperialist
compared to the colonies. Generally, a value of 0.1 is suitable for most
problems [14].
3.9 Simulated annealing
Simulated annealing (SA) was introduced by Kirkpatrick et al. [15] and
ˇ
Cerný [16] based on the work done by Metropolis et al. [17], which was
able to describe the evolution of a thermodynamic system based on the
Boltzmann distribution. The inspiration of SA comes from metallurgy,
where, to reach the low-energy states of a solid, it is heated to very high
temperatures, after it is allowed to cool down slowly so that the atoms
have time to order regularly (this process is called annealing). The projec-
tion of this metallurgical process into optimization gave a simple and effi-
cient method (SA) based on the following analogies: the objective
function to optimize is assimilated to the energy of the material, and the
temperature is represented by a control parameter defining the cooling
scheme, thus obtaining a minimum energy solid is equivalent to the
search for the overall optimum of the objective function.
The principle of SA is to iteratively browse the solution space as fol-
lows: at the beginning of the search, a solution denoted by x0 is initially
generated randomly. This latter has an initial energy E0 and an initial tem-
perature T0 that is generally high. Over the course of the iterations,
Figure 3.7 Movement of colonies toward their related imperialist.
65
Training and optimization algorithms
the temperature decreases and an elementary change is made on the solu-
tion. This modification changes the energy of the system ΔE. If this varia-
tion is negative, the new solution improves the objective function and
reduces the energy of the system, and, therefore, it is accepted. If the found
solution is less good than the previous one, then it will be accepted with a
probability P calculated according to the following Boltzmann distribution:
P E; T
ð
Þ 5 exp 2ΔE
T


(3.25)
Depending on the Metropolis criterion, a number EA 0; 1
½
 is compared
to the probability P(E,T). If P E; T
ð
Þ # E, the new solution is accepted. The
functioning of the Metropolis criterion is interpreted as follows:
•
If ΔE 5 f x
0
 
2 f x
ð Þ , 0; exp 2ΔE=T


. 1, and thus E is always
lower than this value, and we accept the solution xt.
•
If ΔE . 0
•
and T is high, then exp 2ΔE=T


D1, any neighbor is accepted.
•
and T low, then exp 2ΔE=T


D0, unlikely to be accepted.
The pseudocode of SA is represented in Algorithm III.
Algorithm III. Simulated annealing
1. Initialize temperature: T’T0ðmaxÞ
2. Generate a random solution x0, and x’x0
3. Calculate f ðx0Þ associated with the initial solution x0
4. Repeat
5. Generate a random neighbor x
0 to x
6. Calculate ΔE 5 f x
0
 
2 f x
ð Þ
7. If ΔE , 0 then x’x
0
8. Otherwise accept x
0 as a new solution with respect to probability
PðE; TÞ
9. End if
10. Update T based on the cooling pattern: T’αT with αA 0; 1
½

11. Stopping criteria
12. Return the optimal solution xopt
3.10 Coupled simulated annealing
Due to the lower quality of some solutions resulted by SA, a modified
version of this latter consisting of the coupled simulated annealing (CSA)
was introduced [18]. This version aims to get better accuracy without
slowing down the convergence speed, mainly by escaping more easily
66
Applications of Artificial Intelligence Techniques in the Petroleum Industry
from local optima. Suykens and Vandewalle demonstrated the effective-
ness of the coupled principle among local optimization processes in non-
convex problems [19]. This leads to the improvement of the optimization
results. It is worth noting that the key difference between SA and CSA is
the acceptance probability [18].
3.11 Gravitational search algorithm
Gravitational search algorithm (GSA) is a physic inspired algorithm that
imitates the natural interaction forces between masses. GSA was devel-
oped by Rashedi et al. [20]. Newton’s law of gravity stipulates that the
gravitational force F, between two particles in the universe can be
expressed as follows:
F 5 G M1M2
R2
(3.26)
where M1 and M2 are the masses of two particles distant from each other
with a distance R, and G is the gravitational constant. On the other hand,
Newton’s second law that relates force with mass M and acceleration a is
defined as follow:
F 5 Ma
(3.27)
GSA starts the optimization process by creating a population of parti-
cles X, of size l, presenting possible solutions of the studied problem.
During the optimization process, these particles are changing their posi-
tions denoted by a vector x in a d-dimensional space, with vector x repre-
senting the particle position. For a given iteration t the force between
two particles i and j in the search space is expressed as follow [20]:
Fd
ij t
ð Þ 5 G t
ð Þ Mi t
ð ÞMj t
ð Þ
Rij t
ð Þ 1 ε
xd
j t
ð Þ 2 xd
i t
ð Þ


(3.28)
In the aforementioned equation, ε is a small constant, and G is the
gravitational constant that is specified iteratively using the following
equation:
G t
ð Þ 5 G t0
ð Þ tβ
0
t β , 1
(3.29)
where G t0
ð Þ denotes the initial gravitational constant, and R is the
Euclidian distance between the two particles. Rashedi et al. [20] proposed
67
Training and optimization algorithms
the use of R instead of R2 based on empirical indication during computa-
tional experiments. The resulted overall force that influences each particle
i is obtained as follows:
Fd
i t
ð Þ 5
X
l
jAKbest;j6¼i
ϕ1jFij t
ð Þ
(3.30)
where ϕ1j is a randomly generated number from the interval [0,1], and
Kbest is the set of best particles, with size set to l0 (in this case l0 5 l) at the
beginning of the optimization and decreased linearly over iterations. The
acceleration of mass called as law of motion in GSA terminology is calcu-
lated as shown in the following equation:
ai 5 Fd
i t
ð Þ
Mi t
ð Þ
(3.31)
with Mi points out the inertia mass for particle i, assessed with
Mi t
ð Þ 5
mi t
ð Þ
P
l
j51
mj t
ð Þ
(3.32)
and
mi t
ð Þ 5 fiti t
ð Þ 2 worst t
ð Þ
best t
ð Þ 2 worst t
ð Þ
(3.33)
where fit points out the actual fitness value for a particle i at iteration t,
worst and best mean the worst and best fitness values in the population at
iteration t, correspondingly. The update in the velocity and position of
each particle is done as shown in the following equations:
vd
i t 1 1
ð
Þ 5 ϕ2ivd
i t
ð Þ 1 ad
i t
ð Þ
(3.34)
xd
i t 1 1
ð
Þ 5 vd
i t 1 1
ð
Þ 1 xd
i t
ð Þ
(3.35)
where ϕ2i is a random generated uniformly from the interval [0,1], x and
v point out the position and velocity of particles, respectively.
3.12 Cuckoo optimization algorithm
Cuckoo optimization algorithm (COA) is a population-based metaheuris-
tic algorithm developed first by Yang and Deb [21] and extended later by
68
Applications of Artificial Intelligence Techniques in the Petroleum Industry
Rajabioun [22]. This algorithm mimics the known egg-laying behavior
observed in some cuckoo species, consisting of laying their eggs in some
nest of other birds to avoid the responsibility of raising offspring. Cuckoos
investigate the most appropriate zones to lay eggs in order to maximize
their eggs survival rate [22]. To this end, the deposited eggs with high
degree of similarity to eggs of the hosted bird have more chance to be
conserved by this latter. The adaptation of the real behavior of cuckoos to
the COA is described as follows.
An initial population of n nests called also “habitats” is created and dis-
tributed randomly in the search space. In the same context, a random
number of eggs are attributed to each cuckoo inside their egg-laying
domain. This latter, that is, egg-laying domain, is described by a radius
(R) that is defined as follows:
Ri 5 α 3 NEi
N
3 ub 2 lb
ð
Þ
(3.36)
where NEi and N represent the number of devoted eggs to ith cuckoo
and total number of eggs, respectively; α is an integer; ub and lb are
the
minimum
and
maximum
bounds
of
the
decision
variables,
correspondingly.
The laid of the eggs in the nests of the host birds with respect to the
radius R is followed by the evaluation of the quality of these eggs, and
only the fittest ones are kept. The cuckoo offspring live until their
mature, and then they travel toward the better profit habitats. During
this traveling process, the cuckoos may accomplish just a percentage of
the trail and can also be distracted. The generation of new habitat by
this migration process for a given cuckoo ith is expressed mathemati-
cally as:
xi;j t 1 1
ð
Þ 5 xi;j t
ð Þ 1 ϕ 3 xbest
j
2 xi;j t
ð Þ


(3.37)
where ðt 1 1Þ and t point out the new and the previous iteration, respec-
tively, and best represents the best habitat in each iteration, and ϕ is a
control parameter that is determined as follows:
ϕ 5 MC 3 rand
(3.38)
where the motion coefficient MC is a variable to manage the distraction
from original way and rand is a random number generated uniformly in
the range 0 and 1. To conserve the fittest cuckoos a maximum number of
cuckoos with higher profit will be remained, and the rest is discarded.
69
Training and optimization algorithms
The above-described steps are reiterated until a stopping condition is
satisfied.
3.13 Gray wolf optimization
Gray wofl optimization (GWO) is a recent group—living metaheuristic
algorithm [23]. It was introduced by Mirjalili et al. [23]. The mathematical
formulation of GWO describing the main steps to follow during the
searching process is inspired from the hunting tactic of gray wolves and
their leadership skills. At the beginning of the search, an initial population
of wolves reflecting possible solutions for the problem is created ran-
domly. This population includes four groups of wolves according to their
guiding and leading importance when performing a hunt. These groups
are alpha ðαÞ, beta ðβÞ, delta ðδÞ, and omega ðωÞ. The first three groups,
that are, α; β, and δ wolves represent the three best wolves in the popula-
tion, respectively. These groups lead the hunting process and guide the
group of wolves ðωÞ, which include the remaining wolves, toward better
solutions.
The initialization step is followed by the so-called circling process
around the prey. This task is done based on the following equation:
X t 1 1
ð
Þ 5 Xp t
ð Þ 2 A  D
(3.39)
where XpðtÞ represents the prey vector that is assigned to the best wolf,
that is, α, t points out the iteration, and D is evaluated as indicated
below:
D 5 C  Xp t
ð Þ 2 X t
ð Þ




(3.40)
The included parameters in the above-formulated equations, namely,
A and C are expressed as follows:
A 5 2a  r1 2 a
(3.41)
C 5 2r2
(3.42)
where XðtÞ represents the position of the gray wolf; a is generally
decreased linearly from 2 to 0; r1 and r2 are random from [0,1]. It is worth
mentioning that the parameters A and C gain the gray wolves random
relocations nearby the prey.
70
Applications of Artificial Intelligence Techniques in the Petroleum Industry
Afterward, a repositioning of positions of the wolves ðωÞ is done
according to the positions of groups α; β, and δ. This update in the posi-
tions is expressed by the following equations [23]:
X t 1 1
ð
Þ 5 X1 1 X2 1 X3
3
(3.43)
where X1; X2; X3 are defined as:
X1 5 Xα t
ð Þ 2 A1  Dα
(3.44)
X2 5 Xβ t
ð Þ 2 A2  Dβ
(3.45)
X3 5 Xδ t
ð Þ 2 A3  Dδ
(3.46)
where the positions of α, β, and δ are indicated by Xα, Xβ, and Xδ,
respectively; Dα, Dβ, and Dδ are expressed as follows:
Dα 5 C1  Xα t
ð Þ 2 X t
ð Þ




(3.47)
Dβ 5 C2  Xβ t
ð Þ 2 X t
ð Þ




(3.48)
Dδ 5 C3  Xδ t
ð Þ 2 X t
ð Þ




(3.49)
where C1, C2, and C3 are random vectors, and XðtÞ points out the cur-
rent solution.
The mechanism of updating omega ðωÞ positions is summarized in
Fig. 3.8.
By performing all the abovementioned steps by each wolf, the gained
positions are evaluated based on the fitness function, and the fittest found
value is assigned as the new position of the gray wolf α if it performs the
previous one. GWO is ended upon satisfying a stopping condition.
3.14 Whale optimization algorithm
Whale optimization algorithm (WOA) is a nature-inspired search
algorithm, recently introduced by Mirjalili and Lewis [24]. WOA is an
imitation of the back whales hunting mechanism that passes through
encircling prey, spiral bubble-net feeding maneuver and the look for
prey [24].
71
Training and optimization algorithms
As a population-based algorithm, WOA proceeds initially by generat-
ing a random population of possible solutions. Each element in WOA can
be applied as a search agent. These latter are evaluated basing on the fit-
ness function, and accordingly, their positions are updated progressively
using the gathered information in each iteration. The updating mecha-
nism is performed according to the equations that is shown next for a
given iteration t 1 1
ð
Þ:
X t 1 1
ð
Þ 5
X t
ð Þ 2 AD if p , 0:5
D
0eblcos 2πt
ð
Þ 1 X t
ð Þ if p $ 0:5

(3.50)
In the abovementioned equation, X is the best found solution (the
prey), p represents a random number in [0,1] which points out the proba-
bility of updating the shape of the position as spiral or circular, with a
50% chance for each of the shapes. D
0 corresponds to the distance
between the whale i and the prey, that is, D
0 5 X k
ð Þ 2 X k
ð Þ



. b is a con-
stant for defining the spiral shape, and l is a random number in [ 2 1,1].
The rest of the appearing terms in the above mentioned equation are
defined as follows:
Figure 3.8 Omega ðωÞ gray wolf relocation according to positions of α; β, and δ
wolves.
72
Applications of Artificial Intelligence Techniques in the Petroleum Industry
D 5 CX k
ð Þ 2 X k
ð Þ




(3.51)
A 5 2ar 2 a
(3.52)
C 5 2r
(3.53)
where a is a number decreasing linearly from 2 to 0 over the distance
course and r is a random from [0,1]. A must be between 21 and 1, oth-
erwise, the update process of the position will be conducted circularly
with respect to a randomly selected searching agent Xrand t
ð Þ as exhibited
by the following equation:
X t 1 1
ð
Þ 5 Xrand t
ð Þ 2 AD
(3.54)
From the real adaptation perspective, the first part of Eq. (3.50)
demonstrates the encircling process of WOA, while the second part
shows the bubble-net technique. As exploitation and exploration are
two fundamental processes of any population-based algorithm, these latter
are guaranteed in WOA through the tuning of the control parameters a
and c.
The update process is followed by the evaluation of the new gained
positions. Accordingly, if the fittest found searching agent outperforms
the previously existing one, its position will be changed to this newly
obtained position, otherwise, the best position will remain the same.
Finally, this process is reiterated until a specified termination criterion is
fulfilled.
3.15 LevenbergMarquardt algorithm
LevenbergMarquardt algorithm (LMA) is a least squarebased local
minimization algorithm. LMA is the frequently employed algorithm for
optimizing the weights and bias of multilayer perceptron (MLP) para-
digms as it has a high ability to reach their proper values regardless how
the starting point is remote [25,26]. LMA is developed based on the work
of Levenberg [27] and Marquardt [28]. This method is very close to
Newton’s method, and the only difference lies in the introduction of a
parameter ς, called the regularization parameter or LevenbergMarquardt
parameter, to stabilize Newton’s method. This parameter is updated
automatically according to the convergence of each iteration.
73
Training and optimization algorithms
Consider the case of approximating a function f or identify its rela-
tionship with its inputs using MLP and given a set of observations
y 5 fyð1Þ; yð2Þ; . . . ; yðnÞgT (a single output) with their corresponding master
points X 5 fxð1Þ; xð2Þ; . . . ; xðnÞgT. The resulted approximation by MLP is
denoted F x; w
ð
Þ, where w is the vector of weights arranged in a certain
order between the layers. According to LMA, the optimum adjustment in
the weights of the MLP is given by:
Δw 5 H1ςI
½
21g
(3.55)
where I is the identical matrix which has the same size as the Hessian
matrix H; ς is a regularization parameter that forces the sum of the matri-
ces ðH 1 ςIÞ to be vertible.
The learning of the MLP is carried out by minimizing the objective func-
tion (error function) En (w) averaged on the master points used as follows:
En w
ð Þ 5 1
2n
X
n
i51
yðiÞ2F xðiÞ;w



2
(3.56)
The gradient and the Hessian of the objective function are respectively
defined by:
g w
ð Þ 5 @En w
ð Þ
@w
5 2 1
n
X
n
i51
yðiÞ 2 F xðiÞ;w



 @F xðiÞ;w


@w
(3.57)
and
H w
ð Þ 5 @2En w
ð Þ
@w2
5 1
n
X
n
i51
@F x i
ð Þ;w


@w

 @F x i
ð Þ;w


@w

T
2 1
n
X
n
i51
yðiÞ 2 F xðiÞ;w



 @2F xðiÞ;w


@w2
(3.58)
By substituting these two equations in that of Δw, the weight adjust-
ment is calculated in each iteration using LMA. However, the complexity
of calculating the expression of Δw is sometimes significant, especially
when the size of the weight vector w is large (due to the Hessian calcula-
tion). To reduce this problem, the second term of the Hessian expression
is eliminated, and the approximation of Hessian is given as follows:
H w
ð Þ  1
n
X
n
i51
@F x i
ð Þ;w


@w

 @F x i
ð Þ;w


@w

T
(3.59)
74
Applications of Artificial Intelligence Techniques in the Petroleum Industry
The regulation parameter ς plays an important role in the efficiency of
LMA. If ς 5 0, the algorithm is reduced to Newton method. If ς is very
large, the matrix ςI will dominate the Hessian matrix H and the algo-
rithm will function as the gradient descent algorithm. Therefore, in each
iteration, the value of ς will be selected large enough to ensure that
ðH 1 ςIÞ is vertible. The recommended steps for choosing ς are summa-
rized as follows [29]:
•
Calculate En w
ð Þ for an iteration ðt 2 1Þ.
•
Select a modest value for ς.
•
Calculate Δw (using its expression) for the iteration t and evaluate
En w 1 Δw
ð
Þ.
•
If En w 1 Δw
ð
Þ . En w
ð Þ, increase the value of ς with a chosen factor
(e.g., 10) and repeat the previous steps.
•
If En w 1 Δw
ð
Þ , En w
ð Þ, reduce the value of ς with a chosen factor
(e.g., 10) and update the weights as w-w 1 Δw.
3.16 Bayesian regularization algorithm
Bayesian regularization (BR) algorithm is another frequently applied algo-
rithm for training MLP. BR can be considered as a delivery from LMA as
it uses its concept when updating the weights and biases of MLP [30,31].
To do so, an objective function consisting of a combination of squared
errors and squared network weights is minimized [32]. Therefore, the fol-
lowing cost function is formulated:
F ω
ð Þ 5 αEw 1 βEn
(3.60)
where En and Ew are the sums of network errors and squared network
weights, respectively, and α and β present the parameters of the objective
function F ω
ð Þ. These latter are obtained from Bayes’ theorem.
Gaussian distribution is performed in BR algorithm to select the train-
ing set and setup the weight vector. This step is followed by the selection
of suitable values for α and β that can be done by conducting some alge-
braic operations. Then, the objective function F ω
ð Þ is minimized using
LMA, and accordingly, the weights are updated. This calculation process
is reiterated until the satisfaction of a convergence condition [32].
3.17 Scaled conjugate gradient algorithm
The traditional backpropagation algorithm based on the negative descent
direction strategy presents a lack of convergence speed when updating the
75
Training and optimization algorithms
MLP weights [32]. To overcome this matter, the conjugate gradient is
applied and the beforehand obtained error minimization is conserved, as
stated in the following equation:
P0 5 2 g0
(3.61)
where 2g0 and P0 present the directions of search and conjugate (or the
steepest descent), respectively.
Then, the optimum distance for the search direction is determined by
applying a line search, as shown in the following [33]:
xt11 5 xt 1 αtgt
(3.62)
The search direction is resulted as follows [33]:
Pt 5 2 gt 1 βtPt21
(3.63)
According to the approach applied to calculate β, many versions of
the conjugate algorithm can be distinguished [33]. Instead of the line
search technique, computationally cheaper methods such as scaled conju-
gate gradient (SCG) can be applied. SCG uses CG algorithm combined
with the trust region technique [34].
3.18 Resilient backpropagation algorithm
The frequently applied steepest descent method to train MLP may be dis-
turbed due to the nearly zero slope resulted for large inputs mainly when
sigmoid and tansig are the considered transfer functions (as both of them
map the input from an infinite range into a finite output) [25].
Consequently, the resilient backpropagation (RB) algorithm was devel-
oped for this derivative issue. In RB algorithm, only the derivative's sign
is considered when the direction of weights is updated [35].
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78
Applications of Artificial Intelligence Techniques in the Petroleum Industry

RESEARCH ARTICLE
Open Access
Evaluation of oil removal efficiency and
enzymatic activity in some fungal strains for
bioremediation of petroleum-polluted soils
Fariba Mohsenzadeh1*, Abdolkarim Chehregani Rad2 and Mehrangiz Akbari3
Abstract
Background: Petroleum pollution is a global disaster and there are several soil cleaning methods including
bioremediation.
Methods: In a field study, fugal strains were isolated from oil-contaminated sites of Arak refinery (Iran) and their
growth ability was checked in potato dextrose agar (PDA) media containing 0-10% v/v crude oil, the activity of
three enzymes (Catalase, Peroxidase and Phenol Oxidase) was evaluated in the fungal colonies and bioremediation
ability of the fungi was checked in the experimental pots containing 3 kg sterilized soil and different concentrations
of petroleum (0-10% w/w).
Results: Four fungal strains, Acromonium sp., Alternaria sp., Aspergillus terreus and Penicillium sp., were selected as
the most resistant ones. They were able to growth in the subjected concentrations and Alternaria sp. showed the
highest growth ability in the petroleum containing media. The enzyme assay showed that the enzymatic activity
was increased in the oil-contaminated media. Bioremediation results showed that the studied fungi were able to
decrease petroleum pollution. The highest petroleum removing efficiency of Aspergillus terreus, Penicillium sp.,
Alternaria sp. and Acromonium sp. was evaluated in the 10%, 8%, 8% and 2% petroleum pollution respectively.
Conclusions: Fungi are important microorganisms in decreasing of petroleum pollution. They have bioremediation
potency that is related to their enzymatic activities.
Keywords: Enzymatic activity, Fungi, Petroleum removing, Soil pollution
Background
Petroleum pollution is a global disaster that is a com-
mon phenomenon in the oil-bearing and industrial
regions [1]. Petroleum pollution of environments is dan-
gerous for plants, animals and people [2,3]. Iran, as an
oiled country, contained a lot of petroleum polluted-
environments and the pollutions are increasing in recent
years [4].
There are several soil cleaning methods including
burning, washing, chemical applying and bioremediation
[5]. Bioremediation is using of plants and microorgan-
isms to remove or detoxify environmental contaminants.
Bioremediation has been intensively studied over the
past two decades, driven by the need for a low-cost, sus-
tainable with natural environment, and in-situ alterna-
tive to more expensive engineering-based remediation
technologies [1,6,7]. Bioremediation has been applied to
remove crude oil [8-11], motor oil [12], and diesel fuel
[13] from soil but the removal efficiency is highly vari-
able [14].
Bioremediation
of
petroleum-polluted
media
were
done
using
plants
or
plant-associated
micro
flora
[15,16]. There are different economically and environ-
mentally important uses for microorganisms, such as re-
mediation and rehabilitation of petroleum contaminated
soils [11,17-22].
Bioremediation of petroleum-contaminated soils is
mainly based on biodegradation by the fungal strains
that are present in the associated with plants or in the
soils of petroleum polluted sites [23]. Some prior
* Correspondence: fmohsenzade@gmail.com
1Laboratory of Microbiology, Department of Biology, Bu-Ali Sina University,
Hamedan, Iran
Full list of author information is available at the end of the article
IRANIAN JOURNAL OF 
ENVIRONMENTAL HEALTH
SCIENCE & ENGINEERING
© 2012 Mohsenzadeh et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the
Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work is properly cited.
Mohsenzadeh et al. Iranian Journal of Environmental Health Sciences & Engineering 2012, 9:26
http://www.ijehse.com/content/9/1/26
researchers reported that some fungal species are resist-
ant to petroleum-pollution and they are capable to re-
move soil pollution. The results of Ulfig et al. [24]
indicated that keratinolytic fungi, especially Trichophy-
ton ajelloi, is a potential tool for assessment of soil pet-
roleum
hydrocarbon
contamination
and
associated
bioremediation progress. Fungal strains namely Alter-
naria alternate, Aspergillus flavus, Curvularia lunata,
Fusarium solani, Mucor racemosum, Penicillium nota-
tum and Ulocladium atrum were isolated from the soils
in the petroleum polluted areas in Saudi Arabi [25].
Eggen and Majcherczykb [17] showed that white rot fun-
gus, Pleurotus ostreatus, was able to remove polycyclic
aromatic hydrocarbons (PAH) from contaminated soil.
Little attention has been paid to the role of fungal spe-
cies in the environmental biotechnology and bioremedi-
ation of petroleum pollution, specially in Middle Eastern
region [18,25]. Some fungal strains including Alternaria
alternate, Aspergillus flavus, Curvularia lunata, Fusar-
ium solani, Mucor racemosum, Penicillium notatum and
Ulocladium atrum were isolated from the soils in the
petroleum-polluted areas in Iran [11]. The aim of this
research was to collect fungal strains from petroleum-
polluted soils of Arak refinery, evaluation of their ability
in removing of petroleum pollution in experimental con-
ditions and determination of their enzymatic activity
during petroleum removing.
Methods
The studied area
The Arak oil refinery, located near the Arak city in the
center of Iran was selected in this study. The city is
located in the central part of Iran (34° 5' 8" North, 49°
41' 2" East) with elevation average about 1723 meters
above sea level. The population of the city is 503673.
Arak is the capital city of Markazi province and is
mostly arid or semiarid, subtropical along Caspian coast.
It rains most in winter and is moderately warm in sum-
mer. Its annual precipitation is 317.7 mm, mean annual
temperature is 11.8°C and 46% humidity.
Arak oil refinery is located at 25 km far away from
Arak city. Arak refinery is a relative new refinery with
the production capacity of 22434 barrel in day that
funded in 1992. Soil characters of the area was evalu-
ated as sandy loam containing 80% sand, 12% loam, 6%
sludge and 2% organic material with pH 6.8. Chemical
composition of the used crude oil in the refinery is
13.4% saturated hydrocarbons, 40% aromatic hydrocar-
bons, 46.6% polar compounds (Refinery office data).
Due the oil refining activities in this region, a high
degree of petroleum pollution (5-10%) was reported in
the refinery areas [16]. The identification of soil con-
tamination was also possible based on a visual examin-
ation of the soil.
Selection of fungal strains
Since the amounts of microorganisms in the around of
plant roots are up to 200 times more than soil [13], root
samples were harvested from the plants growing in the
polluted area of Arak refinery, and sliced into segments
with 1 cm length, washed and then dried. The samples
were kept in Sodium hypo chloride 1% (30 sec) and then
ethanol 70% (30 sec), for removing the peripherally
attached microorganisms, and dried after washing with
distilled water [13]. The samples were kept in potato
dextrose agar (PDA) media containing lactic acid. The
Petri dishes were incubated in 25 ± 2°C for 4 days. Then,
different fungal colony were isolated and cultured separ-
ately in PDA [16]. Fungal specimens were examined
under light microscope after preparations and identified
using morphological characters and taxonomical keys
provided in the mycological keys [26-28]. The specimens
were also sent to the department of mycology in our
university for confirmation of their scientific names.
Determination of the fungal growth ability under
petroleum pollution
The growth assay was used to find the resistant fungal
species to petroleum contamination of the soil. The
assays were conducted by comparing the growth rates of
fungal strains, as colony diameter, on the oil contami-
nated and control Petri dishes. Test dishes were pre-
pared by adding crude oil to warm PDA solution. In
order to have a uniform concentration of oil in all plates,
the solution was thoroughly mixed with a magnetic stir-
rer, right before it was added to the plates. Different con-
centrations of oil/PDA mixture (2, 4, 6, 8 and 10% v/v)
were prepared. Pure PDA was used in control plates. All
dishes were incubated with 2 mm plugs of fungal myce-
lia taken from agar inoculums plate. The dishes were
incubated at 25±2°C in an incubator. Fungal mycelia ex-
tension on the plates (colony diameter) was measured
using with measuring tape after 7 days and compared
with the control plates.
Evaluation of petroleum removing
The four fungal strains that showed the highest resistant
and growth ability in the prior stage, were chosen for
this study. They are common and native fungi that iso-
lated from the studied petroleum polluted area. Ninety-
six pots were selected for this study and divided in to
four groups; each group containing 24 pots and used for
each fungal strain. Each pot was filled with 3kg of sterile
agricultural soil and mixed with 3g of the studied fungi.
The experimental groups were as groups A, B, C, and D.
Each group including one of the above-mentioned fungal
strains and sub-groups are growing in the pots added
different concentrations (0, 2, 4, 6, 8 and 10% w/w) of
crude oil.
Mohsenzadeh et al. Iranian Journal of Environmental Health Sciences & Engineering 2012, 9:26
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The pots were incubated in a greenhouse in the
temperature of 25±2°C for three months. The soil of ex-
perimental and control pots were homogenized separ-
ately and were kept in 4°C at refrigerator until future
study. Concentrations of crude oil (TOG%) were deter-
mined and compared in the soil of experimental and
control pots.
Determination of Total Oil and Grease (TOG)
The soil samples from experimental and control pots
were collected separately. Each sample, without fungal
segments, was homogenized and stored at 4°C until fur-
ther processing. TOG was analyzed according to the
EPA method 9071 A and EPA Method 3540 B [29]. Five
gram of the soils in two replicates were acidified with
hydrochloric acid to pH=2 and dehydrated with magne-
sium sulphate monohydrate. After 15 min, samples were
transferred into paper extraction thimbles and placed
into a Soxhlet type apparatus. TOG was extracted with
dichloromethane for 8 h. The extract was filtered
through filter paper (Whatman No. 4) with 1g sodium
sulphate. The solvent was evaporated with a rotary evap-
orator and the weight of dry extract was determined.
Percentage of TOG decreasing was calculated based on
soil weight and compared in the experimental and con-
trol pots.
Determination of enzymatic activity
For the assays of Catalase (CAT) and Peroxidase (POX)
enzymes, mycelia (200 mg) was homogenized in an ice-
cooled mortar, grounded in 1 ml of 100 mM potassium
phosphate buffer (pH 7.4) and centrifuged at 10,000 rpm
for 10 min under cooling and the supernatant was used
for enzyme assay. The activities of CAT and POX were
determined according to Aebi [30]. CAT activity was
determined by measuring the decomposition of H2O2
and the decline in absorbance at 240 nm was followed
for 3 min. The reaction mixture contained 50 mM phos-
phate buffer (pH 7.0), 15 mM H2O2, and 0.1 ml of
enzyme extract, was used which started the reaction in
3 ml. The activity of POX evaluated by measuring the
oxidation of guaiacol and the increase in absorbance at
470 nm was recorded for 3 min. The reaction mixture
contained 50 μl of 20 mM guaiacol, 2.8 ml of 10 mM
phosphate buffer (pH 7.0), and 0.1 ml enzyme extract.
The reaction was started with 20 μl of 40 mM H2O2.
The activity was defined as differences of optical den-
sity per min, for each mg of fresh weight of samples
(Δ OD/ min/ mg FW).
The Phenol Oxidase (POD) activity was studied in the
extracts of fungi growing in PDA media with different
concentrations. The procedure adopted by Tate [31] and
Theorell [32] was followed. Pure standard horseradish
POD (SIGMA, USA) of RZ value 3.04 was used as
standard. The POD activity was measured using guaiacol
(1.11 mg/ml density) as chromogenic on spectropho-
tometer. The extract free of all cellular components was
heated at 65°C for three minutes in a water bath and
then cooled promptly by placing in ice bucket for inacti-
vation of catalase activity.
Statistical analysis
In order to detect a significant difference between the
experimental groups and control ones analysis of vari-
ance (ANOVA) followed by the least significant differ-
ence test (LSD) that was performed between studied
groups [33]. Each data was represented as the means ±
SD of 5 samples for experimental groups and also 5 for
control.
Results
Isolated fungi
The fungi growing in the petroleum-polluted areas of
Arak refinery were isolated and their growth ability
was checked under petroleum pollution. Four fungal
strains that showed the highest abundance in the pol-
luted area and also the highest growth ability were
chosen and identified by morphological characters and
taxonomical keys. The results of the taxonomic deter-
mination for the fungi showed that the selected fungal
species that present in the petroleum polluted soils
are: Acromonium sp., Alternaria sp., Aspergillus terreus
and Penicillium sp.
Fungal growth ability under petroleum pollution
The growth ability of the isolated fungal strains was
carried out under different concentrations of crude oil
and was expressed as diameter of the colony (Figure 1).
The results showed that the all above-mentioned fungi
are resistant to petroleum pollution and they made a
sufficient colony in 2% crude oil concentration; mean-
while, only some of them are resistant to higher pollu-
tion. Among the studied fungi, Alternaria sp. showed
the highest resistance to 10% petroleum pollution (with
47.50 mm diameter of colony after 7 days growth),
and three fungal strains including Aspergilus terreus
(31.25 mm), Penicilium sp. (23 mm), and Acromonium
sp. (20.50 mm) were also relatively resistant ones. The
colony diameters were determined after 7 days growth
in the different concentrations of petroleum polluted
PDA media (Figure 1).
Bioremediation
Three
months
after
growing
of
fungal
strains
in
petroleum-contained soils, concentration of petroleum
was determined in the experimental pots and compared
with the beginning of experiment. The obtained data
showed
that
the
concentration
of
petroleum
was
Mohsenzadeh et al. Iranian Journal of Environmental Health Sciences & Engineering 2012, 9:26
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decreased considerably in the all experimental pots
(Figure 2). The data showed that the all studied fungal
strains were able to decrease petroleum pollution. For
Aspergillus terreus, the most decreasing of petroleum
was evaluated in the pots containing 10% petroleum
(44% decreasing) and the lowest decrease was in the
pots
containing
2%
petroleum
(20%)
(Figure
2).
Acromonium sp. was also cause to decrease amount of
crude oil in the growing pots. The highest removal
ability was in the pots containing 2% petroleum (50%
decreasing) and the lowest one was in the pots with
10% pollution (28%) (Figure 2).
For Alternaria sp. petroleum removing in the pots
with 8% crude oil is the highest (55% decreasing) and
Figure 1 The growth ability of the isolated fungal strains under different percentage of petroleum pollution. Results showed that
Alternaria sp. has the highest and Acromonium sp. the lowest growth ability. Each data represented the mean±SE of five samples.
Figure 2 Petroleum removing (%) by the studied fungal strains. Results showed that Penicillium sp. and Alternaria sp. are more effective
fungai in high petroleum-polluted media. Each data represented the mean±SE of five samples.
Mohsenzadeh et al. Iranian Journal of Environmental Health Sciences & Engineering 2012, 9:26
Page 4 of 8
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the lowest decrease (50%) was in the pots with 2%. Fi-
nally, for Penicillium sp., the highest decreasing of pet-
roleum
was
evaluated
in
the
pots
containing
8%
petroleum (54% decreasing) and the lowest decrease was
in the pots containing 2% petroleum (25%) (Figure 2).
Based on the results, the all fungi are effective in petrol-
eum removing from soil of the pots.
Enzymatic assay
The activity of three enzymes, catalase (CAT), peroxid-
ase (POX) and polyphenol oxidase (POD), were deter-
mined in the above-mentioned fungi during the growth
in the media with different concentrations of petroleum
pollution. After two weeks growing in the media con-
taining crude oil, enzymatic activity were determined in
the colonies of experimental Petri dishes and compared
with control ones. Results showed that the activity of
the enzymes was increased considerably in the all-
experimental Petri dishes (Table 1). For Acromonium sp.,
the highest activity of catalase (CAT) was in the group
growing in 2% petroleum pollution and it was decreased
in the other experimental groups armed with increasing
of crude oil concentrations. The lowest activity was
observed in the control group. In Alternaria sp. activity
of CAT was increased with increasing of petroleum pol-
lution. The highest activity was determined in the col-
onies growing in the media containing 8% crude oil, and
in the all-experimental groups were more than control
ones (Table 1). Similar pattern was also observed for
Penicillium sp. and its highest CAT activity was in the
group treated with 8% crude oil. In Aspergillus terreus,
straight relationship was observed between the CAT
activity and crude oil concentrations (Table 1). The
lowest activity was observed in the control group and
the highest was in the group treated with 10% petrol-
eum pollution.
Results showed that the activity of peroxidase (POX) in
the fungal strains growing in the petroleum-polluted
media, was different with control ones. In Acromonium
sp. (Table 1), POX activity was decreased with increasing
of petroleum pollution and the highest activity was in
non-polluted media. In Alternaria sp., POX activity was
increased with increasing of petroleum pollution. The
highest activity was observed in the group with 8% pollu-
tion but then decreased in the group containing 10% pol-
lution. The lowest activity was evaluated in control group.
In Aspergillus terreus, POX activity was increased with
petroleum pollution straightly (Table 1). So, the highest
activity was in 10% pollution and the lowest one was in
non-polluted group. In Penicillium sp. the highest activity
was in the group containing 8% petroleum pollution and
the activity of POX in the groups with 6 and 10% was also
higher than non-polluted group but in the groups with 2
and 4% pollution are near to control ones (Table 1).
Phenol oxidase (POD) activity was compared in the
fungi growing in petroleum-polluted and control media.
Results showed that in Acromonium sp. the highest ac-
tivity was in the groups treated whit 2 and 4% petrol-
eum. In other experimental groups its activity was
similar with control ones (Table 1). In Alternaria sp.,
POD activity was increased with increasing the petrol-
eum pollution until 8%, but it was decreased slightly in
the group growing in media with 10% petroleum pollu-
tion (Table 1). In Aspergilus terreus, POD activity was
increased with increasing of petroleum pollution and the
highest activity was determined in the group growing in
media with 10% petroleum pollution. Finally, for Penicil-
lium sp., the highest POD activity was evaluated in the
group growing in the media containing 8% petroleum
pollution and the lowest activity was in the group grow-
ing in the non-polluted media (Table 1).
Discussion
Study on the fungal species showed that Acromonium
sp., Alternaria sp., Aspergillus terreus and Penicillium
sp. were the common fungi, with high frequency in the
petroleum polluted areas. It seems that petroleum pollu-
tion could not inhibit the growth and variation of fungal
strains in petroleum polluted areas. It seems that the
fungal species used oil compounds as nutrients and pet-
roleum pollution cause to increase fungal growth. The
similar
results
were
reported
by
some
researchers
[11,16-21]. Penicillium oxalicum was also isolated from
petroleum-polluted soils and reported as degradability
potential microorganism for bioremediation of crude
oil [34].
The In vitro growth test of the isolated fungi showed a
species-specific response. All of the studied fungal
strains were able to growth in 2% v/v oil pollution and
therefore could be useful for the remediation of light soil
pollution. Although the growth of fungal species were
reduced by increasing oil concentrations (more than 4%
v/v), but all of them were still able to growth in the high
concentrations of petroleum. They were produced suffi-
cient colonies in the high-polluted media but with a lag-
ging time. It seems that they could be used also for oil
degradation in the soils with high pollution effectively.
Our results are accordance with the some finding of
other researchers about other different fungal species
[11,16-21].
Results of this research showed that the amounts of
petroleum pollution were decreased in the presence of
the studied fungal strains considerably. It means that
the fungal strains were able to degrade crude oil and
consumption of its components. Although there are
several reports about the fungal ability in removing of
petroleum and its derivers from the polluted soils
[11,16,18,20,21], but this is the first report about the
Mohsenzadeh et al. Iranian Journal of Environmental Health Sciences & Engineering 2012, 9:26
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Table 1 Different Enzyme activity (Unit/mg) in fungal strains under different petroleum concentrations
0%
2%
4%
6%
8%
10%
A
B
C
A
B
C
A
B
C
A
B
C
A
B
C
A
B
C
Acromonium sp.
2±0.1
13±2
24±3
7.5±0.3
28±2
13±3
6±1.5
26±3
12±2
5±0.3
13±2
7±1
3.2±0.2
12±1
6±1
3±o.2
11±2
5±1
Alternaria sp.
1.3±0.2
11±2
3±0.2
2.4±0.4
22±3
8±1.4
2.5±0.4
24±3
9±1
4±0.5
28±2
11±0.8
6.2±0.2
40±2
15±2
5±0.2
32±3
12±2
Aspergillus terreus
1.5±2
25±0.3
6±1
30±2
33±3
11±2
3.8±0.3
35±2
13±3
4±0.3
36±4
16±2
4.4±0.2
36±4
18±2
6.5±0.2
40±3
24±5
Penicillium sp.
3±0.1
32±1
12.5±2
3.2±0.2
51±5
14±2
4±0.2
68±4
16±1
4.1±0.2
70±3
24±2
8.5±0.1
88±5
35±4
4.5±0.2
58±4
24±3
A, Catalase; B, Phenol Oxidase; C, Peroxidase.
Mohsenzadeh et al. Iranian Journal of Environmental Health Sciences & Engineering 2012, 9:26
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petroleum removing ability of the studied fungal strains.
The results of our study proposed the above-mentioned
fungi for using in remediation of petroleum-polluted
environments in a field study. It means that the data
of this study indicated that isolated fungi Acromonium
sp., Alternaria sp., Aspergillus terreus and Penicillium
sp. may have the potential for bioremediation of soil
in highly polluted conditions especially in semi-dry
regions.
Enzymatic assay indicated that activities of the all
studied enzymes were increased with the increasing of
petroleum pollution. In Acromonium sp., only the activ-
ity of catalase was decreased with the increasing of pet-
roleum pollution. In other fungal strains, there is a sharp
rise of enzymatic activity in the petroleum-polluted
media. Penicillium sp. showed the highest catalase activ-
ity and the lowest one is observed in Aspergillus sp. For
peroxidase and phenol oxidase, the highest activity was
evaluated in Penicillium sp. and the lowest one was in
Acromonium sp. Kotik et al. [35] reported that enzymatic
activity of microorganisms were increased in the petrol-
eum polluted soils and they were able to find hydrolase
epoxide as a new enzyme that has major role in the
degradation of crude oil. High activity of catalase and
peroxidase was also reported in the soil microorgan-
isms in petroleum-polluted soils [36,37] that is accord-
ance with our results. Tang et al. [38] applied ryegrass
and effective microorganisms for bioremediation of
petroleum polluted soils and increasing of enzymatic
activity was observed in the soil microorganisms includ-
ing Edwardsiella tarda, Bacterium aliphaticum, Bacillus
megaterium, Bacillus cereus, Pseudomonas maltiphilia,
Fusarium
vertiaculloide,
Botryodiphodia
thiobroma,
Fusiarum oxysporum, Cryptococcus neofomas, Aspergil-
lus niger and Candida tropicalis. Ugochukwu et al. [36]
reported that biochemical analysis revealed that except
B. aliphaticum, which had high lipase activity, fungal
isolates generally recorded higher lipase activities than
bacterial isolates.
Based on the results of this study, it seems that fungal
strains are the most effective organisms that are abun-
dant in petroleum polluted soils and they were able to
digest and remove petroleum compounds enzymatically.
Based on our results they were able to grow in
petroleum-polluted media effectively and Alternaria
sp. is the most resistant fungal strain to high degree of
petroleum pollution than others. Bioremediation tests
with the fungal strains showed that they are effective
in decreasing of petroleum pollution from environment
and based on the results Alternaria sp. and Penicillium
sp. were the most effective ones. This means that
the fungal strains had bioremediation potency for
petroleum-polluted media and their enzymatic activity
have a major role in degradation of petroleum.
Conclusion
Our results showed that the studied fungi were able to
growth in the subjected petroleum concentrations and
Alternaria sp. showed the highest growth ability in the
petroleum containing media. Results of bioremediation
tests showed that the studied fungi were able to decrease
petroleum pollution. The highest petroleum removing
efficiency of Aspergillus terreus, Penicillium sp., Alter-
naria sp. and Acromonium sp. was evaluated in the 10%,
8%, 8% and 2% petroleum pollution respectively. Enzym-
atic activities were increased in the fungal colonies grow-
ing in the oil-polluted media; this means that the fungal
enzymes have a critical role for petroleum degradation.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
MA, is a MSc student and this manuscript was wrote based on her thesis
results. She analyzed the Samples. ACR, is the supervisor of the thesis and
supervised the methods and the project. He wrote the original research plan
of the project and also edited the manuscript. FM, is co-supervisor of the
thesis. She wrote the original manuscript and participated in planning of
sample analysis. All authors read and approved the final manuscript.
Acknowledgements
This research was done using a financial support provided by Islamic Azad
University (Broujerd Branch) and also Bu-Ali Sina University partly.
Author details
1Laboratory of Microbiology, Department of Biology, Bu-Ali Sina University,
Hamedan, Iran. 2Laboratory of Plant Cell Biology, Department of Biology,
Bu-Ali Sina University, Hamedan, Iran. 3Department of Biology, Islamic Azad
University, Broujerd Branch, Broujerd, Iran.
Received: 24 July 2012 Accepted: 8 December 2012
Published: 15 December 2012
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doi:10.1186/1735-2746-9-26
Cite this article as: Mohsenzadeh et al.: Evaluation of oil removal
efficiency and enzymatic activity in some fungal strains for
bioremediation of petroleum-polluted soils. Iranian Journal of
Environmental Health Sciences & Engineering 2012 9:26.
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Mohsenzadeh et al. Iranian Journal of Environmental Health Sciences & Engineering 2012, 9:26
Page 8 of 8
http://www.ijehse.com/content/9/1/26

CHAPTER EIGHT
Advanced approaches and technology
for casing setting depth optimization
Key concepts
1. Casing setting depth involves all rig processes and directly affects the well con-
struction cost. However, it depends on many factors and elements. We have designed
a multivariate approach that includes nearly all equipment costs, rig costs, and so on.
We also evaluated geological uncertainty. Based on real data from the Iranian Field, one
ﬁeld example shows that well costs can be reduced by between 2.4% and 15% by
using the multivariate approach.
2. We present a new integrated method for selection of casing seat locations that includes
six criteria.
3. Remote real-time pore pressure monitoring with a predrill pore pressure model
reduces risk and cost by providing signiﬁcant insight into wellbore stability and
allowing for casing seat optimization.
4. Managed pressure drilling (MPD) reduces the number of casings required. It may also
be used actively to make drilling a section faster and more efﬁcient, and to allow for
longer sections to be drilled.
8.1 Introduction
The process of designing a petroleum well involves many activities and technical
areas. These cover various engineering problems, which can be solved with contribu-
tions from applied mathematics. With the increasing emphasis on optimal three-
dimensional wells, effective mathematical tools for analyzing and understanding the
relevant issues in drilling engineering are now attracting much attention.
Cost efﬁciency is a strong driver in the petroleum industry. For this reason, drilling
optimization is of interest. The placement of the casing points can have a signiﬁcant
economic impact. Determining the optimal locations of the casing points for wells in oil
and gas reservoirs can provide major cost savings. Finding these optima depends on a
complex combination of geological, petrophysical, ﬂow regime, and economic
parameters. However, decisions about the development plan are made in the presence of
uncertainty at every step of the modeling, starting with the measurement and processing
of raw data (seismic data, well logs, geology, etc.). Geological uncertainty about the
reservoir geometry and petrophysical properties is one of the uncertainties that could
Methods for Petroleum Well Optimization
 2022 Elsevier Inc.
ISBN 978-0-323-90231-1, https://doi.org/10.1016/B978-0-323-90231-1.00010-8
All rights reserved.
303j
inﬂuence casing point section (CPS) problem decisions signiﬁcantly. This chapter in-
troduces a “Full” approach to incorporate the geological uncertainty in the selection of
the best casing point for a set of predeﬁned scenarios.
8.2 Problem statement
To reach the reservoir or the target, a number of casing strings are usually required.
The purpose of each string is to seal off the formations above to allow the next hole
section to be drilled. After the casing is installed, it is cemented to provide pressure
integrity. We give a short description of each casing type in the following (Fig. 8.1):
•
Conductor pipe: This is the ﬁrst casing string to be run and consequently has the
largest diameter. It is generally set approximately 50e100 m below ground level or
the seabed. Its function is to seal off unconsolidated formations at shallow depth.
•
Surface casing: The surface casing is run after the conductor and is generally set
200e800 m below ground level or the seabed. The main functions of the surface
casing are to seal off any freshwater sands and to support the wellhead and blowout
preventer (BOP) equipment.
•
Intermediate casing: The intermediate casing is set to seal off or protect any problem
area and to provide safety for further drilling.
•
Production casing: This serves to isolate the hydrocarbons during production. It is the
protective housing for the pumps and other production equipment.
•
Liner string: A liner is a casing string that does not extend to the surface, being hung
instead from a liner hanger set inside the previous casing string but usually within
about 300 ft (91 m) of its bottom. The liner is not tied back to the wellhead.
•
Production tubing: This is the transport conduit for the hydrocarbons from the
reservoir.
The size and setting depth of these casing strings depends almost entirely on the
geological and pore pressure conditions in the location in which the well is being drilled.
Some typical casing string conﬁgurations used throughout the world are shown in
Fig. 8.2.
Other factors that can impact shoe depths are as follows:
1. Operating and regulatory requirements. These need to be fully understood prior to
commencing design.
2. Pressure management and wellbore stability concerns. Wellbore stability can be a
function of mud weight, deviation, or stress uncertainties within speciﬁc formation
interval sections.
3. Shallow geohazards.
4. Deeper geological, reservoir, well conditions, or operationally related hazards.
304
Methods for Petroleum Well Optimization
8.2.1 Casing and bit selection
The chart in Fig. 8.3 can be used to select the casing bit sizes required to fulﬁll many
drilling programs. To use the chart, start by determining the casing or liner size of the
previous size pipe to be run and follow the chart from that point. The ﬂow of the chart
Figure 8.1 A casing points proﬁle.
Figure 8.2 Typical casing string conﬁgurations. Modiﬁed from Khosravanian, R., Aadnøy, B.S., 2016.
Optimization of casing string placement in the presence of geological uncertainty in oil wells: offshore
oilﬁeld case studies. J. Petrol. Sci. Eng. 142.
Advanced approaches and technology for casing setting depth optimization
305
then indicates the hole sizes that may be required to set that size pipe. Blue lines indicate
commonly used bits for that pipe size, and these can be considered to have adequate
clearance to run and cement the casing or liner (i.e., 4½00 casing inside 61/800 hole). The
red lines indicate the less common hole sizes used (i.e., 500 inside a 61/800 hole). The
selection of one of these red paths requires special attention to the drilling phase of the
well. This selection process is repeated until the anticipated number of casing sizes has
been reached.
8.2.2 Well cost estimation
The starting point of a new well project is always to deﬁne the well. The purpose of the
well, whether it is an exploration well, a prospect appraisal well, or a ﬁeld development
well, deﬁnes the depth, production capacity, and the application of the well. Then
the well design must be deﬁned starting with the wellhead system and the sizes of the
Figure 8.3 Casing and bit size selection chart. Modiﬁed from Aird, P., 2019. Deepwater Drilling Well
Planning, Design, Engineering, Operations, and Technology Application Book.
306
Methods for Petroleum Well Optimization
casing and production tubulars. Afterward, the designer looks for a suitable drilling unit.
Deep wells often require large hoisting capacity, whereas shallower but long horizontal
wells often require high torque capacity due to high wellbore friction.
For well optimization, all costs are affected by the depth of the casing strings, so this is
what we want to investigate here. As the number of casings increases, the trip time,
installation time, cement required, and cementing time will also increase. The ability to
handle larger casings requires more expensive rigs, tools, pumps, compressors, and
wellhead control equipment. Increasing the number of casing strings from three to four
may result in an approximately 10%e20% increase in well cost. Increasing the number of
strings from four to ﬁve may increase the cost by approximately 20%e30%.
Of all the petroleum exploration activities, drilling oil wells could be considered to be
one of the riskiest and most expensive ventures. Record-high oil prices and a shortage of
supply of drilling rigs, especially deepwater drilling rigs, have boosted the rig daily rental
rate dramatically over the past decade. For that reason, one of the objectives in drilling a
hydrocarbon well is to make the hole in the ground as quickly as possible. However,
there are three basic considerations for successful drilling operations. First of all, the well
needs to be drilled in a safe manner. Health, safety, and the environment (HSE) are
always the top priorities, although this may result in delays to operations or extra costs.
Second, the well must fulﬁll the requirements for its purpose whether that is an
exploration well, a prospect appraisal well, or a ﬁeld development well. However,
regardless of the well type, there are minimum demands for all wells. They should be
drilled without damaging the borehole and the potential formations. They should also
allow for formation testing, data gathering, hydrocarbon production, or other postdrill
activity. The third basic consideration is that the overall well cost should be minimized.
This topic has been the point of interest for the industry for a long time. Several oil
companies have put a great deal of effort into improving drilling efﬁciency and reducing
drilling time to lessen the overall well cost.
A few decades ago, there was not a strong link made between the various activities
such as drilling, completion, and production in the planning stages. Drilling design was
largely based on drilling performance and not so much on the later application of the
well. Today, a well project is designed based with much greater consideration of the
connection between the various activities. As an example, the reservoir is now drilled
with ﬂuids that minimize formation damage to improve later production. Kerzner
(2001) identiﬁes the following key issues for a drilling project.
A well should be completed:
1. within the allocated time period,
2. within the budgeted cost,
3. at the proper performance or speciﬁcation level,
4. to the customer’s satisfaction, and
5. without disturbing the main workﬂow of the organization.
Advanced approaches and technology for casing setting depth optimization
307
HSE regulations are a key concern, guiding and controlling all activities within a well
project. Time and cost are also key issues for drilling, but they cannot override the other
constraints mentioned earlier. With these concerns in mind for the design of a well
project, the objective is to provide good forecasts for the well to enable planning.
8.2.3 Challenges
From a management perspective, the aim is to complete well construction while keeping
time and cost to the minimum. However, achieving a correct estimate of well
construction cost and duration is not straightforward. There are major challenges related
to well cost estimation, some of which are described in the following (Kullawan, 2011):
1. One main source of information for the model inputs is historical data from other
wells. However, there can be shortcomings in the data acquired, or the collected data
may not have a sufﬁcient level of detail. Furthermore, the available data may not be
relevant to the wells for which estimates are being made.
2. Well construction processes are associated with risks of undesirable events. These
events, such as waiting on weather (WOW) and a kick event, can cause delays to well
operations. The total operation time is the summation of the trouble-free time (TFT)
and the nonproductive time (NPT). We may deﬁne TFT as the time required for
planned operations, and NPT as additional time needed to carry out any unplanned
operations. The challenge in preparing estimates for the authority for expenditures
(AFEs) is that an increase in the duration of the project caused by NPT creates the risk
of exceeding the planned budget. Thus, accurate forecasting of well cost with an
appropriate allowance being made for NPT is needed to allocate an adequate budget
for drilling the well; there should be neither insufﬁcient funding nor funds left un-
spent at the end of the project.
3. As well as unwanted events, geological and technical factors add many uncertainties
to the planned operations. The drilling processes may take longer to complete than
expected. This is where the probabilistic approach plays a signiﬁcant role.
8.3 Mathematical approach: casing string placement
optimization under uncertainty
From the well trajectory shown in Fig. 8.4, a general equation to calculate the well
path is developed for a horizontal well. This equation is composed of seven segments: a
kick-off segment, three segments for build and drop, two hold segments, and ﬁnally a
lateral section in the target layer HD.
308
Methods for Petroleum Well Optimization
8.3.1 Sources of uncertainty
Statistics show that there are uncertainties in well construction projects. It is possible for
10%e20% of well activity time to be spent dealing with unplanned events such as cir-
culation losses and stuck pipe. These can be caused by many different factors. Of course,
good planning tries to include a forecast for the downtime resulting from dealing with
problems. However, such forecasts are uncertain because of the following:
Figure 8.4 Vertical plan of a general 2D horizontal well trajectory. Modiﬁed from Khosravanian, R.,
Aadnøy, B.S., 2016. Optimization of casing string placement in the presence of geological uncertainty in oil
wells: offshore oilﬁeld case studies. J. Petrol. Sci. Eng. 142.
Advanced approaches and technology for casing setting depth optimization
309
•
The geological uncertainty related to the reservoir geometry and the distribution of
petrochemical properties. This has the most direct effect on the different forecasts that
form the basis for CPS problem decision-making.
•
Uncertainty about the pore and fracture pressure, which may lead to kicks, circu-
lation losses, and stuck pipe.
•
Measurement errors.
•
Uncertainty about the actual behavior of the rock and ﬂuid when subjected to
external stimuli.
•
Modeling limitations.
8.3.2 Utility function
The impact of decisions on the success or failure of a project can be signiﬁcant. Decisions
should be based on the most relevant and accurate planning tools available. The deﬁ-
nition of the problem as the maximization of expected utility rather than the monetary
value, as well as the whole process of decision tree construction, transforms the problem
to the decision-maker’s attitude toward risk. Utility or preference theory explains how
this transformation is possible. The utility function is the tool to quantify the decision-
maker’s risk attitude. The shape of the utility function determines whether the decision-
maker is risk-neutral, risk-averse, or risk-prone. A risk-neutral decision-maker has a
linear utility function, which is equivalent to basing decisions purely on monetary value.
A risk-averse decision-maker has a concave utility function, which corresponds to the
avoidance of uncertain areas of the search space even if they might have the possibility of
greater ﬁnancial gain. The risk-prone decision-maker has a convex utility function,
which represents the willingness to take some risk for the chance of greater ﬁnancial
gain. A simple analytical utility function has the exponential form (Guyaguler and
Horne, 2001):
UðxÞ ¼ a þ berx
(8.1)
A normalized version of Eq. (8.1) is calculated with a ¼ 1 and b ¼ 1. The
curvature of the utility function determines the risk attitude of the decision-maker. The
magnitude of risk aversion of a given utility function, U, is given by:
RðxÞ ¼  U00ðxÞ
U0ðxÞ
(8.2)
The problem of casing string placement can be studied within the decision analysis
framework since the problem consists of the decision on the well path suitable for well
completion and the probable events thereafter.
310
Methods for Petroleum Well Optimization
The term R(x) in Eq. (8.2) is also referred to as the Arrow-Pratt measure of absolute
risk aversion or the risk aversion coefﬁcient. The risk aversion coefﬁcient is a constant for
the exponential utility function and is equal to the exponent r in Eq. (8.2).
8.3.3 The casing point selection problem decision tree
We illustrate the decision tree for the CPS problem in Fig. 8.5. Decisions are made at the
decision nodes (square nodes). The decision in this example corresponded to ﬁnding the
well trajectory that would be suitable for well completions. Due to imperfect infor-
mation about conditions, each decision leads to an event i (geological scenario) with
different probabilities of occurrence Pi. The outcome of an event is the best objective
function pi. Each event is also assigned a utility value Ui, which is a measure of the
satisfaction of the decision-maker for the possible range of outcomes. Satisfaction for any
given outcome depends on the risk attitude of the decision-maker. The risk attitude is
quantiﬁed in the form of a mathematical function called the utility function, which is
used to translate an outcome pi into utility. The utility function simply returns a utility
value given the pi.
The event nodes are collapsed into a single expected utility value. The decision with
the maximum utility is then chosen. Thus, optimization by evaluating a subset of the
decisions can be carried out to determine the decision that leads to maximum expected
utility (Fig. 8.6).
E fU ðTrajectory iÞg ¼
X
NGeological Scenarios
i ¼ 1
UiPi
(8.3)
Figure 8.5 The CPS problem decision tree for three trajectories and three geological scenarios. CPS,
casing point selection. Modiﬁed from Khosravanian, R., Aadnøy, B.S., 2016. Optimization of casing string
placement in the presence of geological uncertainty in oil wells: offshore oilﬁeld case studies. J. Petrol. Sci.
Eng. 142.
Advanced approaches and technology for casing setting depth optimization
311
8.3.4 Full approach methodology
In this section, we describe a general methodology that can be applied to any type of
CPS problem, with a case study to demonstrate the application of the methodology. The
best scenario deﬁned by the Full approach takes into account the uncertainty in the
geological model by using multiple models. The steps of the proposed Full approach are
as follows:
1. Propose n scenario of the well path is suitable for well completion.
2. Propose L scenario of the geological model l ¼ 1 . L. The notation for the
geological model L was intentionally simple, but actually L is a spatially distributed
vector of numerical models representing top structure, lithology, and thickness.
3. Deﬁne and solve the mathematical model under proposed geological scenarios:
s ¼ 1 . S separately. Under each scenario, a best solution is determined by the
model, or in other words, we should ﬁnd the optimum of pi for each well trajectory.
After that, we can transform optimum pi to the utility value (Fig. 8.7).
4. The decision with the maximum expected utility is chosen (Fig. 8.8).
To introduce ﬁnancial considerations into the optimization process, the costs can be
prescribed as follows:
TCi ¼ drilling cost þ casing cost
¼
( X
P
i ¼ 1
X
T
j ¼ 1
X
R
k ¼ 1
X
W
l ¼ 1
BijklXijklLDrilling
i
þ
X
S
i ¼ 1
X
T
j ¼ 1
X
W
k ¼ 1
CijkYijkLcasing
i
)
(8.4)
pi is calculated as the objective function under different geological scenarios.
Objective Function ¼ Maximize pi ¼ Maximize
(
NPV ¼
X
N
t ¼ 1
CFt
ð1 þ iÞt
)
(8.5)
Figure 8.6 The casing placement decision tree with event nodes collapsed into expected utility.
Modiﬁed from Khosravanian, R., Aadnøy, B.S., 2016. Optimization of casing string placement in the
presence of geological uncertainty in oil wells: offshore oilﬁeld case studies. J. Petrol. Sci. Eng. 142.
312
Methods for Petroleum Well Optimization
Depending on the choice of risk-aversion factor, pi is transformed into utility (Eq.
8.7). If a risk-neutral decision is used, a straight-line utility curve, which is indifferent to
pi, is used. Otherwise, an exponential utility curve is used (Eq. 8.7) (Fig. 8.7).
pscaled
i
¼
pi
constant
(8.6)
Ui ¼
8
>
>
<
>
>
:
pscaled
i
r ¼ 0
1  erp Scaled
i
1  er
rs0
(8.7)
After calculating the utility value of each objective function under different scenarios
including A, B and C, the expected utility is calculated by multiplying each utility value
with the probability of occurrence of that utility. It is clear at this stage that we should
select decisions that have the maximum amount of expected utility (Ozdogan, 2004).
Figure 8.7 Utility curves for different risk-aversion coefﬁcients. Modiﬁed from Hammond, J.S., 1967.
Better decisions with preference theory. Harv. Bus. Rev., 123e141 (NovembereDecember Edition);
Guyaguler, B., Horne, R.N., 2004. Uncertainty Assessment of Well Placement Optimization. Society of Pe-
troleum Engineers Reservoir Evaluation & Engineering, February, 24e32.
Advanced approaches and technology for casing setting depth optimization
313
8.3.5 Field cases
8.3.5.1 RSH oilﬁeld, Iran
In this illustration, the operator plans to have wells drilled in the oilﬁeld by a drilling
contractor employing two jack-up drilling rigs. The satellite platform (W0) houses only
one rig at a time, but the main (W4) platform can accommodate two rigs at opposite
sides of the platform if required. The proposed number of wells to be drilled is shown in
Table 8.1.
Fig. 8.9 shows the plan view of RSH oilﬁeld.
Figure 8.8 Maximum expected utility in full approach methodology. Modiﬁed from Khosravanian, R.,
Aadnøy, B.S., 2016. Optimization of casing string placement in the presence of geological uncertainty in oil
wells: offshore oilﬁeld case studies. J. Petrol. Sci. Eng. 142.
314
Methods for Petroleum Well Optimization
Table 8.1 The proposed number of the drilling wells.
Drilling platform
W0 platform
W4 platform
Total wells
Producing wells
6
11
17
Water injectors
4
6
10
Gas injector
1
1
1
Water disposal
0
1
1
Total wells
11
19
30
Figure 8.9 Plan view of the wells in the RSH oilﬁeld. Modiﬁed from Khosravanian, R., Aadnøy, B.S., 2016.
Optimization of casing string placement in the presence of geological uncertainty in oil wells: offshore
oilﬁeld case studies. J. Petrol. Sci. Eng. 142.
Advanced approaches and technology for casing setting depth optimization
315
After the geologists and reservoir engineers have identiﬁed drilling targets, it is then
the drilling engineer’s job to design the wellbore trajectory for these targets. In the case of
the drilling wells in the RSH oilﬁeld, many factors were considered by the engineers
such as collision risk, trajectory geometric shape, true vertical depth and horizontal
departure, inclination and azimuth, build-up rate, tortuosity, and torque and drag. All
these factors should be evaluated together during the trajectory design. Three optimized
well trajectory proﬁles are proposed for the CPS problem, and casing points should be
optimized for these. Fig. 8.10 shows W2, W7, and W19, with trajectory proﬁles I, II,
and III, respectively, for optimizing casing points.
8.3.5.1.1 Drilling plan in RSH oilﬁeld
8.3.5.1.1.1 First hold section: drilling 17.500 hole/133/800 casing This hole will be drilled
to  900e1000 m to provide support to the wellhead and casing and to allow for
the installation of the ﬁrst BOP stack (or diverter) to ensure the safe drilling of the next
hole section. A 133/800 casing string will be run to the surface, and a single-stage cement
job will be performed.
Figure 8.10 Well paths suitable for well completions optimized for the CPS problem. CPS, casing point
selection. Modiﬁed from Khosravanian, R., Aadnøy, B.S., 2016. Optimization of casing string placement in
the presence of geological uncertainty in oil wells: offshore oilﬁeld case studies. J. Petrol. Sci. Eng. 142.
316
Methods for Petroleum Well Optimization
8.3.5.1.1.2 Second hold section: drilling 12¼00 hole/95/800 casing This section will be drilled
from a 133/800 casing shoe to set a 95/800 casing at the selected reservoir with the casing
shoe 5 m inside the formation. This casing will allow the installation of the 5000 psi
BOP stack to ensure the safe drilling of the next hole section.
8.3.5.1.1.3 Third hold section: drilling 8½00 hole/500 slotted liner The 8½00 hole section will
be drilled horizontally from the 95/800 casing shoe to the planned section with total
depth (TD) at  4200 m within the selected formation. The setting depth may vary
depending on reservoir target, actual conditions, and completion. This horizontal
section of the hole will be cased with a 500 slotted liner in the hydrocarbon-bearing
zone and will not be cemented. The 500 slotted liner will be run with the liner
hanger set at an approximate 100 m inside the 95/800 casing.
To show the optimization of other wells, another two wells from the case study are
selected by using Lingo 8. W7 and W19 are optimized using the same procedure as for
the W2 well, and the results obtained from solving the models are discussed in the
following. W7 is a well with three casing points in 133/800, 95/800, and 500. The results are
shown in Figs. 8.11 and 8.12. The next step is to run the model using data gathered from
RSH oilﬁeld for the other wells. The optimization plan for different casing points is
summarized in Table 8.2.
Figure 8.11 Optimization plan for W2 well. Modiﬁed from Khosravanian, R., Aadnøy, B.S., 2016. Opti-
mization of casing string placement in the presence of geological uncertainty in oil wells: offshore oilﬁeld
case studies. J. Petrol. Sci. Eng. 142.
Advanced approaches and technology for casing setting depth optimization
317
Fig. 8.13 shows the optimum intervals for casing points in each section; the selection
of these intervals depends on the decision-maker. We take the average of the upper and
lower points of each interval as the ﬁnal optimum points.
Figure 8.12 Optimization plan for W19 and W7 wells. Modiﬁed from Khosravanian, R., Aadnøy, B.S.,
2016. Optimization of casing string placement in the presence of geological uncertainty in oil wells:
offshore oilﬁeld case studies. J. Petrol. Sci. Eng. 142.
Table 8.2 Optimization plan of casing points for RSH oilﬁelds.
Drilling hole
Slotted liner
Well name
133/800
95/800
700
500
TMD
TVD
TMD
TVD
TMD
TVD
TMD
TVD
W2
1185
975
1919
1470
4270
1533
W7
1235
1208
2272
2011
2756
2136
4238
2149
W19
1358
1200
3000
2020
5018
2066
From Khosravanian, R., Aadnøy, B.S., 2016. Optimization of casing string placement in the presence of geological
uncertainty in oil wells: offshore oilﬁeld case studies. J. Petrol. Sci. Eng. 142.
318
Methods for Petroleum Well Optimization
8.4 Multiple criteria approach: casing seat selection method
Well integrity has become an important subject in recent years. It implies that one
or two well barriers must be in place at all times in a well. The casing is an essential part of
any barrier. A number of elements are involved in the selection of the depth of a casing.
These relate to pore pressure, geomechanics, and well control. However, these elements
are evaluated separately in the well design process.
This part of our study presents a new integrated method for selection of casing seat
locations that has six criteria:
1. the fundamental gas-ﬁlled casing;
2. the minimum mud weight to drill next section;
3. the kick margin;
4. the riser margin;
5. the weak point in the well (this compares casing shoe strength with burst strength
below the wellhead; the objective is to avoid failures below the wellhead, and to
ensure that the casing shoe represents the weak point in the well); and
6. the tubing leak for the production casing.
All these criteria are deﬁned for the project and integrated into a generalized casing
depth model. Here the casing depth is chosen by deciding on acceptable kick margins
and casing qualities. The model is ideal for sensitivity and uncertainty analysis as all six
Figure 8.13 Casing setting depth intervals (meters) for W19 well. Modiﬁed from Khosravanian, R.,
Aadnøy, B.S., 2016. Optimization of casing string placement in the presence of geological uncertainty in oil
wells: offshore oilﬁeld case studies. J. Petrol. Sci. Eng. 142.
Advanced approaches and technology for casing setting depth optimization
319
criteria are satisﬁed for the solutions chosen, and it is valid both for vertical wells and
deviated wells. We give examples of these.
This multiple criteria casing seat selection method is considered as a step toward a
more systematic approach to ensuring well integrity, and also as a tool for optimizing
casing type-casing depth. Casing seat depths are usually selected based on pore pressure,
fracture pressure constraints, and operational and wellbore stability constraints.
In this chapter, we will deﬁne the design constraints for the six criteria, and we will
derive a common model which encompasses all these criteria. The result is a model
which shows the consequences of the casing seat being shifted, and it also deﬁnes the
minimum strength casing to be used. For this example, we deﬁne the six criteria as
follows:
•
The gas-ﬁlled casing criterion (this assumes that the well is ﬁlled with formation gas
and shut in): the top of the casing must be sufﬁciently strong to handle this.
•
Minimum mud weight required to drill the next section: in this case, it is from the
casing shoe to the depth of next openhole section.
•
The kick margin: if the casing shoe does not provide full well integrity.
•
The riser margin: if the well is drilled from a ﬂoating rig.
•
The weak point in the well: this must be below the shoe and not below the wellhead.
•
Leaking tubing criterion: the production casing must be sufﬁciently strong to handle
a leak in the production tubing during testing or production.
Because all pressures involved are hydrostatic, the solution depends on the true vertical
depth. The solution is therefore valid for all wellbore inclinations, provided the true
vertical depth is used. Note that for highly deviated wells, a low hydrostatic pressure may
become very high as the kick ﬂuid is circulated up the well. The relationship between the
projected height of the kick and the actual kick volume should therefore always be
evaluated (Aadnøy et al., 2012).
8.4.1 Casing seat selection criteria
8.4.1.1 Criterion 1: gas-ﬁlled casing
This is the most fundamental criterion. The casing should be sufﬁciently strong to
withstand the pressures during shut-in if the well control fails and the entire well be-
comes ﬁlled with reservoir ﬂuid. This is visualized in Fig. 8.14. Assume the casing is
entirely ﬁlled with reservoir ﬂuid as during a well control situation. Then the casing
should be sufﬁciently strong to withstand the pressures. The wellhead pressure is equal to
the reservoir pressure minus the hydrostatic weight of the reservoir ﬂuid. The lower the
reservoir ﬂuid density, the higher the wellhead pressure. Low-density gas therefore results
in a high wellhead pressure. This is visualized in Fig. 8.14.
The internal load pressure on the top of the casing is:
Pint
wh ¼ Po  0:098 dresðD  DwhÞ
(8.8)
320
Methods for Petroleum Well Optimization
For a subsea wellhead, the external pressure depends on whether the casing is
installed through the riser or not. When installing the casing without a riser, the external
pressure is equal to the seawater pressure on the wellhead. If the casing is installed in mud
through the riser, the external pressure can be assumed to be equal to the mud pressure at
the wellhead depth. However, after installation, the external pressure is not measured and
cannot be veriﬁed. Because designing a drilling plan using seawater pressure provides a
more conservative design, it is often applied for all casing strings. The external load
pressure is then given by the equation:
Pext
wh ¼ 0:098 dwðDwh  haÞ
(8.9)
The burst loading on the casing is the difference between the inside and outside
pressure. Including the safety factor, this becomes:
Pburst  SF ½Po  0:098 fdresðD  DwhÞ þ dwðDwh  haÞg
(8.10)
Or expressing the pore pressure as a gradient:
Pbrust
0:098 SF  ðdo  dresÞD  ðdw  dresÞDwh þ dwha
(8.11)
Figure 8.14 Deﬁnitions of gas-ﬁlled casing. Modiﬁed from Aadnoy, B.S., 2010. Modern Well Design,
second ed. CRC Press/Balkema.
Advanced approaches and technology for casing setting depth optimization
321
Rearranging equation Eq. (8.11) then yields the maximum permissible pore pressure
gradient for the next openhole section if the casing strength is given:
d0  1
D
 PBurst
0:098SF þ ðD  DwhÞdres þ ðDwh  haÞdw

(8.12)
This criterion only ensures that the casing is sufﬁciently strong at the top. The next
criterion considers the openhole below the casing.
8.4.1.2 Criterion 2: mud weight to drill next openhole section
This is the classic casing seat criterion, often called the down-up principle. For a given
mud weight that exceeds the pore pressure at the bottom, the formation at the shoe must
not fracture. This is shown in Fig. 8.15 which shows the 133/800 casing shoe. This is
expressed as the equation:
Pwf  P0  0:098dmudðD  DcÞ
(8.13)
Or expressed as gradient when assuming dmin
mud ¼ d0:
dwf  do
(8.14)
Figure 8.15 Mud weight constraints. Modiﬁed from Aadnoy, B.S., 2010. Modern Well Design, second ed.
CRC Press/Balkema.
322
Methods for Petroleum Well Optimization
For the shallower casings, the principle is no longer applicable as here pore pressure is
normal. Other criteria, such as wellbore stability issues, cost optimization issues, or
operational issues, should be used here.
8.4.1.3 Criterion 3: the kick margin
This criterion deﬁnes the condition where the openhole below the wellhead is sufﬁ-
ciently strong for the well to be drilled. This is also shown in Fig. 8.14. If gas enters the
well from the reservoir, the load below the casing shoe will be larger than when mud ﬁlls
the entire well. The reason is that the weight of the gas is less than the weight of the mud,
thereby communicating a higher pressure upwards.
The scenario shown in Fig. 8.15 has zero kick margin as the mud weight gradient
equals the pore pressure gradient at the bottom and the fracture gradient below the shoe.
In practice, a margin is used for the fracture pressure. The fracture margin is a factor of
safety which is directly related to the kick margin; a high fracture margin gives a high
kick margin.
The load below the casing shoe is simply the reservoir pressure minus the hydrostatic
pressure caused by the gas and mud in the interval between the reservoir and the casing
shoe. Equilibrium is reached when the load below the casing shoe equals the fracture
pressure of the rock.
Pwf  Po  0:098½dreshres þ dmudðD  Dc  hresÞ
(8.15)
Or as a gradient:
dwf Dc  doD þ ðdmud  dresÞhres  dmudðD  DcÞ
(8.16)
Because the casing depth is an unknown, the fracture pressure is not a constant but a
function of depth.
dwf ¼ a þ bDc
(8.17)
For a given setting depth, the maximum permissible kick volume, represented by hres,
can be calculated.
hres 

dwf  dmud

Dc þ ðdmud  d0ÞD
dmud  dres
(8.18)
If we assume that we drill the well as deep as can without expecting a kick, we can
assume that the mud weight is equal to the maximum pore pressure gradient in the
openhole section. Reducing Eq. (8.18) yields:
hres  dwf  dmud
dmud  dres
Dc ¼ dwf  dmax
0
dmax
0
 dres
Dc
(8.19)
Advanced approaches and technology for casing setting depth optimization
323
As pointed out by Rabia (1987) and Santos et al. (2011), the effect of compressibility,
thermal effects, and annular capacity will affect the calculated kick margin volume.
Santos et al. (2011) shows that the annular capacity generally is more important for short
openhole sections and that the effect of compressibility can become signiﬁcant for long
openhole sections. Neglecting the temperature effect generally gives a more conservative
solution. For marginal design factors, these effects should be evaluated.
Santos et al. (2011) also shows that other effects can signiﬁcantly affect the kick
tolerance calculations. Choke operator error, annular and choke-line friction, kick after
ﬂow, and inﬂux density correction are all factors that inﬂuence the calculated results.
Simple calculations may result in an overly conservative design, and it is important to
consider all possible effects if a more aggressive approach is desired.
Note also that different rules may apply for exploration wells and development wells
when considering what pressure should be used to represent the kick pressure. The kick
pressure can be accounted for by introducing a pseudo pore pressure, and this is
explained in the next section.
8.4.1.4 Criterion 4: the riser margin on ﬂoating rigs and other pore pressure
replacements
The theory behind the riser margin is that if the riser breaks suddenly, the hydrostatic
head of the mud in the riser is replaced by seawater, leading to lower bottomhole
pressure. The right-hand side of Fig. 8.16 must balance the pore pressure at the bottom
of the well. Equilibrium demands that:
Po ¼ 0:098½dmudðD  DwhÞ þ dwðDwh  haÞ
(8.20)
Or, expressed as a gradient:
do ¼ dmud

1  Dwh
D

þ dw
ðDwh  haÞ
D
(8.21)
Fig. 8.17 shows the result of the riser margin. If we drill with a heavier mud, pore
pressure will be balanced if the riser breaks off.
From Fig. 8.17, we now see that the casing setting depth is restricted by the minimum
mud weight rather than by the pore pressure itself. However, even if a riser margin is
applied, a kick still has to be accounted for, and it is assumed that the minimum mud
weight represents the potential kick pressure. Hence, the pore pressure in setting depth
calculations is replaced with what we can call a pseudo pore pressure. The pseudo pore
pressure for the riser margin is obtained by rearranging Eq. (8.21) as Eq. (8.22):
d0
0 ¼ dmin
mud ¼ d0D  dwðDwh  haÞ
D  Dwh
(8.22)
324
Methods for Petroleum Well Optimization
Figure 8.17 Effects of riser margin. Modiﬁed from Aadnoy, B.S., 2010. Modern Well Design, second ed.
CRC Press/Balkema.
Figure 8.16 Illustration of deﬁnition of riser margin. Modiﬁed from Aadnoy, B.S., 2010. Modern Well
Design, second ed. CRC Press/Balkema.
Advanced approaches and technology for casing setting depth optimization
325
Note that such a pseudo pore pressure is also applied in kick tolerance calculations.
Typically, a different kick pressure is assumed for exploration wells than for development
wells. The kick pressure used for calculations may be a correction of the pore pressure
itself, or a correction of the applied mud weight. Thus, in pressure load and setting depth
calculations, the real pore pressure is replaced with a pseudo pore pressure that represents
the possible kick pressure.
The riser margin is applicable for ﬂoating drilling rigs operating in moderate water
depths. For deepwater drilling, the riser margin often results in too high mud weights,
beyond the fracture strength of the well. For deepwater applications, the riser margin is
therefore usually neglected.
8.4.1.5 Criterion 5: the weak point in the well
Typically, surface and intermediate casing strings have reduced integrity. They are not
designed to take the full production load of a well. The kick margin is a way to ensure
integrity. If the reservoir ﬂuid volume entering the wellbore is less than the kick margin,
there is full integrity. Conversely a larger inﬂux volume leads to loss of integrity as the
casing shoe may fracture when circulating out a kick.
In this section, we will compare the casing burst strength at the wellhead to the
formation strength below the casing. The objective is to ensure that the weak point is the
casing shoe. A failed casing at the wellhead may be catastrophic, whereas circulation loss
below the casing can be handled operationally.
A “worst-worst” scenario is that the pore pressure is higher than predicted, resulting
in a gas-ﬁlled well that is loaded toward failure. If the casing below the wellhead is loaded
toward burst, the corresponding pressure below the casing shoe is deﬁned by the
wellhead pressure plus the hydrostatic weight of the gas in the casing.
Pburst
SF þ 0:098 dresðDc  DwhÞ ¼ 0:098 dshoe Dc
(8.23)
The pressure gradient at the shoe becomes:
dshoe ¼
Pburst
0:098 DcSF þ dres
Dc  Dwh
Dc
(8.24)
The condition for the weak point to be below the shoe is that it fails below the casing
that is before the casing burst at the wellhead, or:
dshoe  SF$dwf
(8.25)
dcritical
shoe
¼ SF$dwf
(8.26)
8.4.1.6 Criterion 6: leaking tubing
The aforementioned analysis relates to the shallower casing strings, such as the surface
and the intermediate casing strings. The objective is to determine casing depths. The
326
Methods for Petroleum Well Optimization
depth of the production casing is often selected from reservoir criteria, for example, by
setting the production casing into the caprock above the reservoir. The production
casing will always have full integrity. We have, however, included the production casing
in this analysis because during production the casing may fail at the level of the pro-
duction packer. This will be addressed in the following.
During well testing or production, a leak may occur at the top of the production
tubing just below the wellhead. The production tubing is usually installed on a packer at
the bottom of the well. If a leak occurs, high pressure is transmitted to the inside of the
production casing and the production casing is subjected to a burst load.
Because the leaking tubing criterion is only relevant for the production casing, full
well integrity may be assumed. Below the wellhead, the inside pressure of the tubing is
equal to the inside pressure during gas-ﬁlled casing. Therefore, the burst load below the
wellhead is also equal to the burst load during gas-ﬁlled casing. The internal and external
pressures of the casing can now be expressed as follows:
Pext ¼ 0:098dwðDwh  haÞ þ 0:098doutside fluidðz  DwhÞ
(8.27)
Pint ¼ PGas filled casing þ 0:098dinside fluidðz  DwhÞ
(8.28)
The position of maximum load will now depend on the density of the ﬂuids inside
and outside the casing string, but typically the top of the packer becomes the critical
position. In the following, we will assume that the packer depth is the critical factor and
that as a worst-case scenario the packer is located at the casing seat depth. This can, for
example, be a likely scenario for the completion of a horizontal section. The maximum
load can be expressed as follows:
PBurst  SF

PGas filled casing þ 0:098

dinside fluid  doutside fluid

 ðDc  DwhÞ  0:098dwðDwh  haÞ

(8.29)
Inserting the gas-ﬁlled casing pressure and expressing as a gradient yields:
PBurst
0:098SF  d0D  dresðD  DwhÞþ

dinside fluid  doutside fluid

ðDc  DwhÞ dwðDwh  haÞ
(8.30)
This can now be solved for the casing setting depth:
Dc  Dwh þ
PBurst
0:098SF þ dresðD  DwhÞ þ dwðDwh  haÞ  d0D
dinside fluid  doutside fluid
(8.31)
Here, we observe that if dinside fluid  doutside fluid, the leaking tubing criterion becomes
equal to the gas-ﬁlled casing criterion. If dinside fluid > doutside fluid, the leaking tubing
criterion will reduce the permissible setting depth of the casing string compared with the
gas-ﬁlled casing criterion.
Advanced approaches and technology for casing setting depth optimization
327
We summarize the conditions considered in Table 8.3.
To show some practical applications, we created a series of scenarios in which we
apply each of the six criteria.
8.4.2 Scenarios
8.4.2.1 Scenario 1: gas-ﬁlled casing while drilling next openhole section
A gas-ﬁlled casing and the ability to drill the next openhole section are absolute re-
quirements. If we combine Eqs. (8.12) and (8.14), we end up with the casing setting
depth:
dwf  dmax
0
¼ 1
D

PBurst
0:098$SF þ ðD  DwhÞdres þ ðDwh  haÞdw

(8.32)
where the fracture pressure gradient is a function of depth, dwf ¼ a þ bDc. Inserting this
into Eq. (8.32) yields:
Dc  dmax
0
 a
b
¼
PBurst
0:098$SF þ ðD  DwhÞdres þ ðDwh  haÞdw  aD
bD
(8.33)
8.4.2.2 Scenario 2: riser margin
If we add the riser margin to the result of scenario 1, the pore pressure representing the
kick pressure is replaced by the pseudo pore pressure in Eq. (8.33). Thus, the minimum
fracture gradient is now restricted by the pseudo pore pressure representing the mini-
mum allowable mud weight in the well. However, the maximum wellhead pressure is still
Table 8.3 Conditions considered in the study.
Criterion
Fluid in
annulus
Condition
Requirement
Failure position
1. Gas-ﬁlled
casing
Gas
PBurst 
SF$Pmax
load ðDwhÞ
Absolute
Casing: below
wellhead
2. Mud weight
Mud
dwf ðDcÞ  d0ðDÞ
Absolute
Formation: casing shoe
3. Kick margin
Mud and gas
dwf ðDcÞ  d0ðDÞ
Optional
Formation: casing shoe
4. Riser margin
Mud plus
seawater
dwf ðDcÞ  d0
0ðDÞ
Optional
Formation: casing shoe
5. Weak point
Gas
PBurst 
SF$Pmax
wf ðDcÞ
Optional
Formation: casing shoe
6. Leaking
tubing
Gas
PBurst 
SF$Pmax
load ðDcÞ
Absolute
(prod
casing)
Casing: packer/casing
shoe
From (Aadnøy et al., 2012).
328
Methods for Petroleum Well Optimization
determined by the gas-ﬁlled casing as described in Eq. (8.10). The casing setting depth
can now be determined by combining Eqs. (8.10), (8.12), and (8.22).
Dc ¼ d0
0  a
b
¼
PBurst
0:098bðD  DwhÞSF  a  dres
b
(8.34)
8.4.2.3 Scenario 3: kick margin
Here, we will evaluate the maximum kick margin for scenario 1. That is, how much gas
can enter the well without fracturing the formation below the casing shoe. If the casing is
set at a depth such that there is no margin between the fracture gradient at the casing shoe
and the maximum pore pressure gradient, by default there is no kick margin at all.
Therefore, the casing is set deeper than the minimum depth to create a safety margin.
This safety margin is deﬁned by the kick margin.
If the section is being drilled with the minimum possible mud weight, dmud ¼ dmax
0
,
we observe that the maximum kick margin is equal to:
hres ¼ dwf  dmax
0
dmax
0
 dres
Dactual
c
(8.35)
where Dactual
c
> Dminimum
c
. If the calculated kick margin is larger than the volume of the
openhole section, then we have full well integrity.
hres > D  Dc0Full well integrity
hres < D  Dc0Reduced well integrity
(8.36)
8.4.2.4 Scenario 4: kick margin and riser margin
If we now apply a riser margin in addition to the kick margin, we observe that the pore
pressure gradient is replaced with a pseudo pore pressure gradient (dmax
0
is replaced by d0
0).
In riser margin calculations, or other kick tolerance calculations, the applied pseudo pore
pressure gradient is always larger than the initial pore pressure gradient. Replacing the
maximum pore pressure gradient in Eq. (8.35) with a higher value results in a reduced
kick margin.
8.4.2.5 Scenario 5: weak point
Here, we compute the constraints for the weak point. Using Eqs. (8.24e8.26) and the
fracture gradient equation (Eq. 8.17), one can solve the casing constraint as:
Dc <  0:5
SF$a  dres
SF$b

þ
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
ﬃ
1
4
SF$a  dres
SF$b
2
þ
C
SF$b
s
(8.37)
Advanced approaches and technology for casing setting depth optimization
329
where:
C ¼
Pburst
0:098SF  dresDwh
(8.38)
8.4.3 Numerical example
We now present a numerical example of casing seat selection analysis. The analysis was
performed expressing the fracture gradient curve as a single linear curve. A more
complex fracture gradient curve does not change the procedure of analysis but does
require more comprehensive calculations.
The casing seat that will be studied is the 133/800 casing as shown in Figs. 8.17 and
8.19. We assume that the next openhole section is being drilled to 2400 mTVD. Two loss
zones are shown at about 1100 and 1600 mTVD depth. The examples assume a safety
factor against burst of SF ¼ 1.3 and a reservoir ﬂuid density of 0.20 s.g.
Converting the fracture gradient into a curve on the form dwf ¼ a þ bDc yields a
fracture curve as shown in Fig. 8.18.
In the following, we summarize other well data (Table 8.4) and present the casing
burst pressures for selected casings (Table 8.5).
Fig. 8.19 shows the results of the setting depth calculations from scenarios 1 and 2.
Assuming that the casing strength is equal to the pressure load resulting from the
openhole section, a minimum casing shoe depth of 1300 mTVD is determined. If a riser
margin has to be accounted for, the calculated setting depth increases to approximately
1530 mTVD.
These results are in good agreement with what we could expect by inspecting
Fig. 8.17.
Applying Eq. (8.10) to compare the casing strength with the pressure loads shows that
the required casing strength is equal to 314 bar when including a safety factor of 1.2.
Looking at Table 8.5, we ﬁnd that the best 133/800 casing will be casing no. 4.
0
400
800
1200
1600
2000
2400
2800
1
1.2
1.4
1.6
1.8
2
2.2
Depth (m)
Fracture gradient (s.g.)
Figure 8.18 Single fracture gradient curve.
330
Methods for Petroleum Well Optimization
In the example, we have decided to place the casing seat at 1670 mTVD to seal off the
lost circulation zone. The next step is to determine the resulting kick margins based on
the setting depth. If we can ignore the riser margin, Eq. (8.35) gives us a kick margin of
110 m (hres), while if the kick margin is included, a riser margin of 31 m (hres) is found.
As expected, we get less kick margin when the riser margin has to be accounted for.
This example shows the coupling between the margin design criteria and the casing
setting depth, if a shallow casing setting depth is chosen, the risk associated with criteria
that are violated must be addressed.
8.5 Real-time approach: casing seat optimization using remote
real-time well monitoring
Remote real-time pore pressure monitoring using logging-while-drilling (LWD)
services coupled with a predrill pore pressure model reduces risk and cost by providing
signiﬁcant insight into wellbore stability and allowing for casing seat optimization.
Table 8.4 Well data.
Density
Depths
Mud
Reservoir
Seawater
Dwh (m)
hf
D
1.6
0.20
1.03
225
25
2400.00
0
400
800
1200
1600
2000
2400
2800
1
1.2
1.4
1.6
1.8
2
2.2
Depth (m)
Fracture gradient (s.g.)
Fracture gradient
Gas-filled casing and next section
Including riser margin
Selected casing depth
Figure 8.19 Casing setting depth calculations.
Table 8.5 Burst pressure for six different casing strings.
Pburst (bar)
Casing 1
Casing 2
Casing 3
Casing 4
Casing 5
Casing 6
SF ¼ 1.2
166.7
220.8
275
329.2
383.3
437.5
Advanced approaches and technology for casing setting depth optimization
331
Incorrect determination of the formation pressure while drilling can lead to the
formation being fractured if the mud weight is too high (principally in the orientation of
maximum horizontal stress), resulting in losses to the fractures and any permeable zones
governed by the quality of the mud cake. Excessive overbalance also raises the risk of
differential sticking of the BHA during stationary periods. Conversely, if the mud weight
is too low, wellbore breakouts can occur (in the orientation of the minimal horizontal
stress), leading to both wellbore instability and increased risk of kicks or the swabbing of
formation ﬂuid into the wellbore from permeable zones.
If the safe mud weight window, indicated by the green arrow in Fig. 8.20, is not
maintained by sustaining optimal Mw, this can result in breakouts or fractures to either
side. The challenge is limited not just to the unknown magnitude of the pore pressure
but also to onset depths due to uncertainty in the velocity model.
For example, when drilling in remote areas, in the absence of offset data, pore
pressure is determined as a function of seismic rock velocity. The velocity model and
corresponding pore pressure model form the basis for exploration well planning. These
models directly impact the casing design, number of sections, and mud weight plan. For
frontier exploration drilling, the relationship is not well established; this reliance is high
risk, and a high-technology approach can reduce this risk.
In addition, formation-pressure-while-drilling (FPWD) technology can be added to
the seismic and sonic technologies to calibrate the predrill pore pressure model. The
acquired formation pressures, coupled with the while-drilling petrophysical data, allow
for the velocity-to-pore-pressure transform and normal compaction trend lines to be
calibrated and reduce the uncertainty of the pore pressure model ahead of the bit. The
calibrated models permit the operator to make decisions conﬁdently and continue
drilling in a single section to intercept key targets, potentially eliminating casing strings,
with the assurance that the kick tolerance and safe overbalance are maintained at an
optimal level to reduce the risk of mud losses or kicks.
Figure 8.20 Safe mud weight window (in green) with breakouts or fractures to either side if the
optimal Mw is not sustained.
332
Methods for Petroleum Well Optimization
8.5.1 Predrill pressure prediction
Before drilling, simulator software is used to model the pressure in the pressure cell
(bordered by geological faults) where the well is located. The model is run several
hundred times, each time varying slightly the parameters that inﬂuence the overpressure,
for which certain measurements are not available. Each simulation results in a pressure
prognosis for the well. Pore pressures prediction methods are presented in Fig. 8.21.
The pore pressure model is used to determine the number of casing sections required,
where the casing shoes should be placed, and the limits for the mud weight window. The
model has the potential for substantial uncertainty due to the inherent uncertainty,
vertically and laterally, in the surface and subsurface data, not to mention the resolution.
The predrill pore pressure model (see Fig. 8.22) shows an example with four different
pore pressure proﬁles, with a disparity of up to 1.8 ppg. The model, which is inﬂuenced
by offset formation pressure data, shows a potential abnormal pressure ramp starting from
1500 m BML from 8.6 to 10.7 ppg.
8.5.2 Pressure prediction while drilling
When drilling starts, the driller provides the owner of the project with real-time re-
sistivity and sonic data via the e-Drilling system (a system for real-time drilling simu-
lation). Using these measurements, the pressure is calculated based on Eaton’s resistivity
and sonic log equations (Eaton 1972 and 1975, respectively):
Figure 8.21 Pore pressure prediction method.
Advanced approaches and technology for casing setting depth optimization
333
Resistivity
Sonic log
pobs ¼ s  ðs pnÞ

Rsh;obs
Rsh;n

nr
(8.39)
With nr ¼ 1.2
pobs ¼ s  ðs pnÞ

DTsh;n
DTsh;obs

ns
(8.40)
With ns ¼ 3.0
where s ¼ overburden stress (Pa) and is given as a function of the average formation bulk
density; pn ¼ normal pore pressure (Pa) and is a function of the average formation ﬂuid density;
and Rsh ¼ shale resistivity (Um) and DTsh ¼ shale travel time (s/m).
Note that: “obs” refers to the observed (actual pressured) condition; “n” refers to the hydrostatic
(normally pressured) condition.
The corrected drilling exponent for a normally compacted/pressured formation can be
inferred from offset wells at the same depth, or by extrapolation of trend data in the
normally compacted/pressured formation. From the Monte-Carlo runs and the
observed pressure calculated from the resistivity, sonic, and drilling exponent, one can
calculate weights (wi) given to each simulation run (i), based on the log data information.
In basin modeling, these weights are calculated based on observed pressures (Pobs) from a
calibration measurement as follows.
wi ¼
N
P
N
i ¼ 1
an

pmodðiÞ
n
 pobs
n
2
(8.41)
Figure 8.22 Predrill pore pressure model. Modiﬁed from Kemper, J., Richards, M.L., Taylor, M., Kelsall,
N.R., Turner, M., Puech, J.C., 2013. Real time velocity and pore pressure model calibration in exploration
drilling. In: Offshore Europe Oil and Gas Conference and Exhibition Held in Aberdeen, UK, 3e6 September.
334
Methods for Petroleum Well Optimization
p ¼
P
I
i ¼ 1
pmod i
ð Þ  wi
P
I
i ¼ 1
wi
(8.42)
where: N is the number of calibration depths; “n” refers to the well depth; an is a weight
of importance applied to each calibration depth; Pn
mod(i) is the modeled overpressure for
depth “n” in run “i”; and Pn
obs is the observed (measured) overpressure for the calibration
depth.
The aforementioned equations are rewritten based on equivalent expressions in Sylta
and Krogstad (2003) where the stochastic variable is oil or gas column height H, rather
than overpressure p. In this implementation, we calculate Pn
obs as the arithmetic average of
the pore pressure calculated according to resistivity, sonic, and drilling data. If data for
one of these variables is missing, the variable is ignored. The owner of the project then
updates the calculated pressure as often as required and updates the weight and hence
predicts the most probable pressure at greater depths (Luthje et al., 2009). As more and
more information is gathered, a better prediction is found for the pressure at greater
depths (Fig. 8.23).
8.5.3 Gulf of Mexico well case study
We now present the results of a shelf job in the Gulf of Mexico (GoM) that allowed an
operator to drill successfully in a very tight hydraulic envelope and even eliminate a string
Figure 8.23 Pressure modeling while drilling. Modiﬁed from Hantschel, T., Hidalgo, J.C., MacGregor, A.,
2015. Geological Pore Pressure Prediction: An Application of Petroleum System Modeling Technology.
Upstream Technology Leadership Webinar Series, Schlumberger Technology.
Advanced approaches and technology for casing setting depth optimization
335
of casing (Goobie et al., 2008). The uncertainty in the pore pressure prediction ahead of
the bit can be signiﬁcantly reduced by model updating. The LWD measurements allow
the predrill velocity-to-pore pressure transforms to be updated while drilling, using the
velocities from the sonic tool and pressures from the LWD formation pressure tool. This
calibrated transform is then applied to revise the predrill pore pressure model while
drilling and thus provide an estimate ahead of the bit.
The techniques described here using real-time measurements allowed the operator to
extend both the 95/800 intermediate casing and 700 liner to total depth. As a result, a critical
casing string was pushed 1287 ft deeper than planned, and the need for a preplanned 500
liner was eliminated. This saved casing expense, as well as slim-hole drilling and
completion costs.
The operator approached a turnkey company to drill a prospect in Vermillion Block
338. After studying the potential risks, the turnkey company insisted that one of the
conditions of acceptance was that a 75/800 drilling liner would be used. An extra string of
500 casing would then be required at TD. This would have mitigated some of the drilling
risk and allowed the turnkey company to successfully penetrate the reservoir; however, it
substantially increased the overall AFE of the project. Using real-time LWD measure-
ments to optimize drilling, the operator thought a string of casing could be eliminated by
extending the depth of the 95/800 intermediate casing string and the 700 replacement liner
to total depth. If successful, this would eliminate the proposed 75/800 liner. Because there
was no longer a need for a pass-through diameter to accommodate a 500 liner, a less
expensive 700 liner was used. This saved not only casing cost, but also avoided difﬁculties
associated with slim-hole drilling and completion.
The predrill pore pressure model was available both on the rig and at the onshore
operation support center (OSC). A Drill MAP was also created detailing the expected
hazards and risk mitigation process for the well. The downhole measurements from the
suite of LWD tools provided annular pressure, sonic, formation pressures, gamma ray,
and resistivity, together with observations from the rig surface data. These were used to
update and reﬁne the earth model while drilling and to reduce uncertainty ahead of the
bit (Fig. 8.24). Combining the predrill model with the LWD data allowed the drilling
team to better understand the well hydrodynamics. The LWD annular pressure tool
provided the swab and surge pressures from the equivalent static density minimum
(ESDmin) and equivalent static density maximum (ESDmax) real-time data points, and
information on the annular hole-loading. The well-speciﬁc velocity information from
the LWD sonic was used to further deﬁne the geopressure predictions in shales and
update the predrill model. The LWD formation pressure tool gave formation pressure
measurements for the permeable formations, which were used for model calibration and
updating. It was assumed that the shale and sand pore pressures were in equilibrium. The
gamma ray tool assisted with lithology discrimination, while the resistivity data were
used for an independent pore pressure evaluation.
336
Methods for Petroleum Well Optimization
The predrill pore pressure model aims at incorporating all the new information
acquired while drilling to reduce the uncertainty, not only at the current depth but also
ahead of the bit. To reduce uncertainty, drilling operations closely monitor the well
conditions and update the predrill model with measurements from the LWD tools.
Fig. 8.25 shows the ﬁrst update of the model. Pressure measurements while drilling and
real-time sonic data (blue curve) are used to derive a locally calibrated velocity-to-pore
pressure transform. This is applied to checkshot velocities (red curve) extracted from the
GoM cube, thus by updating the model ahead of the bit, uncertainty is reduced with
every update (see also Fig. 8.24). As a quality control, mud weights from an offset well are
also displayed, together with the formation integrity test (FIT) values for this well.
Fig. 8.26 shows a further update of the predrill model using real-time sonic and for-
mation pressure information. To constrain the pore pressure in the deeper sections, the
updated transform derived from the incoming real-time sonic data and calibrated using
the pressure measurements was also applied to the sonic log of an offset well (dark brown
line in Fig. 8.26). The resulting pore pressure prediction in these deeper sections allows
for conﬁdence in the derived transform, as shown by the good agreement between
pressure prediction and mud weights at this offset well. Real-time data were continually
monitored, allowing daily updates of the velocity-to-pore-pressure transform (Fig. 8.27).
Figure 8.24 Real-time results are compared with model and updated. Modiﬁed from Tollefsen, E.,
Goobie, R.B., Noeth, S., Sayers, C., den Boer, L., Hooyman, P., Akinniranye, G., Cooke, J., Thomas R., Carter,
E., 2006. PPI technology services, optimize drilling and reduce casing strings using remote real-time well
hydraulic monitoring. In: International Oil Conference and Exhibition in Mexico, 31 August-2 September,
Cancun, Mexico.
Advanced approaches and technology for casing setting depth optimization
337
The pore pressure based on the real-time sonic data was in very good agreement with the
prediction based on the extracted checkshot velocities, especially for depths below
9000 ft TVDSS.
Fig. 8.25 shows the ﬁrst update of the model using pressure measurements and real-
time sonic (blue curve) to derive a locally calibrated velocity-to-pore-pressure transform.
Figure 8.26 Second pore pressure update.
Figure 8.25 First update of model.
338
Methods for Petroleum Well Optimization
This is applied to checkshot velocities (red curve) extracted from the GoM cube, thus
updating the model ahead of the bit. Offset well mud weights are plotted for comparison.
Fig. 8.26 shows the second pore pressure update at the drilling location comparing
offset well data with real-time data. To constrain the pore pressure in deeper sections, the
derived transform was also applied to the sonic log of an offset well (the dark-brown line
in Fig. 8.26).
Fig. 8.27 shows a composite wellbore hydrodynamics proﬁle, in which the following
areas of data are highlighted:
(A) The top of the pore pressure ramp as conﬁrmed by both the sonic, resistivity, and
formation pressure-while-drilling measurements.
(B) The discrepancy between 7000 and 8000 ft with the predrill model (represented by
thedashedpurpleline)andthemeasuredformationpressuresisanexcellentexampleof
Figure 8.27 Composite wellbore hydrodynamics proﬁle displaying all the data. Modiﬁed from Goobie,
R.B., Tollefsen, E., Noeth, S., Sayers, C. M., den Boer, L., Hooyman, P., Akinniranye, G., Cooke, J., Thomas, R.,
Carter, E., 2008. Remote Real-Time Well Monitoring and Model Updating Help Optimize Drilling Perfor-
mance and Reduce Casing Strings. September SPE Drilling & Completion.
Advanced approaches and technology for casing setting depth optimization
339
the importance of incorporating measurements in real time to update the predrill
model to reduce the pore pressure uncertainty. In this case, the predrill model is based
on an extraction from a 3D cube covering the northern GoM. The 3D cube is con-
structed using velocity data from offset wells. For this method, the proximity of offset
wells to the drilling location also contributes to the initial uncertainty in the predrill
model. T
o further reduce the pore pressure uncertainty, surface seismic data speciﬁc to
the area (GoM Block) can be incorporated into the predrill modeling process.
(C) The annular pressure tool showed the equivalent circulating density (ECD) rapidly
approaching the fracture gradient. Initially the drilling operator felt that this must be
due to a mechanical malfunction of the annular pressure tool strain gauge. The
drilling team noted that if the formation pressure tool were deployed, it could be
used to resolve the discrepancy. Unfortunately, before testing, the formation failed.
(D) Both the sonic and formation pressure tools were removed from the borehole at
11,400 ft. This decision was made, because the zones of greatest concern had been
reached at 11,000 ft. Pore pressure had closely tracked the pore pressure model for
2500 ft, and the predrill analysis of other offset wells showed that every other well
was completed with no further increases in mud weight.
(E) The maximum ECD was limited to 17.5 lb/galUS as indicated by the green dashed
line. This was set and monitored from the point where lost circulation occurred
until the casing point to avoid losses at the point of fracture.
8.5.3.1 Casing seats scenarios
The well proﬁle on the left in Fig. 8.28 shows the casing program as planned by the
turnkey company. Note that the 95/800 casing, shown to 7000 ft, was to constrain a mud
weight of up to 13 lb/galUS. In fact, it was necessary to raise the mud weight to 13 lb/
galUS at 6800 ft; therefore, the casing would have been placed 200 ft shallower than
planned. The casing program in the middle was established by the operator as the
best-case scenario and required real-time veriﬁcation of the wellbore hydrodynamics.
The best-case scenario shows the 95/800 casing pushed to 8500 ft. Pushing to this depth
with 13 lb/galUS mud turned out to be extremely optimistic as determined by the real-
time pore pressure prediction and formation pressure measurements. The casing pro-
gram on the right shows the ﬁnal design. Real-time hydrodynamics allowed the operator
to conﬁdently raise the mud weight to 15.2 lb/galUS before placing the 95/800 casing in
the borehole at 8187 ft.
The operator achieved the goal of eliminating the preplanned 500 casing and avoiding
a slim-hole completion. LWD technologies and OSC services addressed drilling-related
issues and pore pressure model updating in real time. This technique has the following
effects:
•
It reduces time lost due to kicks and losses.
•
It reduces lost-in-hole risk.
•
It pushes casing seats as deep as possible.
•
It maintains borehole hydraulics.
340
Methods for Petroleum Well Optimization
•
It eliminates casing.
•
It increases ROP by drilling closer to balance and avoiding unnecessary ﬂow and
bottom-up checks.
•
It results in safer drilling practices.
8.6 Technological approach: reduced number of casings using
unconventional drilling methods
The last decade has seen the advent of new unconventional drilling methods, such
as managed pressure drilling and casing/liner drilling. Numerous ﬁeld applications have
proved that both of these technologies bring value to drilling operations when used in
appropriate situations. It is thought that a combination of the two might yield additional
beneﬁts. Examples of candidates thought to beneﬁt from using such a combination
include the following:
•
highly depleted reservoirs,
•
formations with very narrow mud windows, and
•
consecutive layers with different pressure regimes.
When drilling wells through complex formations in overbalance, the tapering
(or telescope) effect is encountered. Conventionally, the diameter of the wellbore is
reduced in each section after a new casing string is set, as the bit has to ﬁt through the
casing to drill ahead. Each successive string thus reduces the diameter of the hole, which
may eventually prevent the running of a production liner, if the wellbore diameter is too
narrow. Additionally, it is often necessary to set additional casing strings, called
Figure 8.28 Casing seat optimization using remote real-time well monitoring.
Advanced approaches and technology for casing setting depth optimization
341
contingency casings if wellbore instability problems are encountered. MPD provides
increased control over the bottomhole pressure (BHP). This enables operators to drill
with smaller margins and maintain BHP between pore pressure and fracture gradient
without setting the casing prematurely, enabling casing points to be set deeper. The same
property enables the operator to drill through narrow mud windows, which are, for
example, prevalent in depleted reservoirs and high-pressure high-temperature (HPHT)
reservoirs. This has the potential to eliminate contingency casing strings and certain
casing strings, allowing the operator to drill wells that would be undrillable using
conventional techniques. This is illustrated in Fig. 8.29; it can be observed that the well
drilled using MPD (right) has smaller casing dimensions than the one drilled using
overbalanced drilling (left) right up until the high-pressure reservoir is penetrated.
8.6.1 Riserless drilling
Declining hydrocarbon reserves and increasing demand are pushing oil and gas com-
panies to explore areas that present high economical risk and technical problems. This
includes deepwater exploration, which looks quite promising but at same time presents
new challenges. Riserless drilling is a new innovative solution for this, which consists of a
mud circulation system without a riser. Subsea mud pumps are used to pump drilling
mud from the seabed up to the rig ﬂoor. In this section, we will look closely at riserless
drilling and the technology available to accomplish this. Moreover, we will also look at
the main disadvantages of having a riser installed in deep water drilling that have spurred
engineers to look toward new innovative technology and at the advantages of riserless
drilling. Riserless drilling is an MPD method without the use of a marine riser. It
simultaneously addresses two major challenges in deep and ultradeep operations:
Figure 8.29 Overbalanced drilling casing program (left) versus managed pressure drilling casing
program (right). Modiﬁed from Montilva, J., Mota, J. and Billa, R., April 24, 2012. Onshore MPD System
Enables Lower Mud Weights for Challenging Wells. Drilling Contractor. www.drillingcontractor.org/
onshore-mpd-system-enables-lower-mud-weights-for-challenging-wells-15662.
342
Methods for Petroleum Well Optimization
•
It resolves the issues of a narrow window between the formation pressure and the
pore pressure by isolating the system from the environment using the MPD method.
•
It removes the limitations resulting from the use of the marine riser.
A riserless mud recovery (RMR) system enables reuse of the drilling mud. Fig. 8.30
shows an RMR system in which drilling mud is pumped through the drill pipe, comes
out through the drill bit, is returned to the seabed around the drill pipe, and is then
captured and pumped to the surface. The pumping process is carried out by the subsea
pump, located as shown in Fig. 8.30. Although riserless drilling has large potential
beneﬁts for deepwater applications, because of the cost of installing and removing the
riser, the technology has not been used in practice for complete drilling in deepwater. It
is envisaged that this will change in the near future.
The pressure margin between pore and fracture pressure is very narrow under
deepwater conditions, which is illustrated in Fig. 8.31. This calls for the installation of
several casing and liner strings, which results in the reduction of the borehole size to the
point where it may no longer be ﬁt for production before it has reached the reservoir
depth. This is a signiﬁcant limitation on the depths that can be reached, hindering the
attainment of signiﬁcant hydrocarbon reserves in deepwater (Fig. 8.32).
Figure 8.30 Deepwater RMR. RMR, riserless mud recovery. Modiﬁed from AGR Drilling, 2010. Power-
Point Presentation of AGR Subsea Inc. On RMR vs. Conventional Drilling. September 9e11, https://www.
iodp.org/241-12-what-is-riserless-mud-recovery-cohen/ﬁle.
Advanced approaches and technology for casing setting depth optimization
343
Figure 8.31 Conventional drilling provides a narrow drilling window in offshore ﬁelds because of the
extra hydrostatic pressure of mud in the rise.
Figure 8.32 Casings when using conventional drilling (left) versus dual gradient drilling (right). Riserless
dual gradient drilling (DGD) using a specialized subsea pump placed on the seaﬂoor during top hole
drilling has been widely used on offshore subsea wells prior to installing the blowout preventer (BOP).
Riserless DGD systems, known as riserless mud recovery (RMR), have been developed to allow riserless
sections to be drilled with weighted mud while taking returns back to surface. This allows the operator to
set surface casing strings deeper, thereby reducing the total number of liners/casing strings in the well.
344
Methods for Petroleum Well Optimization
8.6.2 Managed pressure drilling technology
MPD is a relatively recent technology. The main principle of MPD is to manipulate the
annular pressure proﬁle throughout the wellbore. This is controlled through the hy-
drostatic ﬂuid column in addition to the application of a surface pressure known as
backpressure. The backpressure is normally done by a choke which can vary from
manual to semi or automatic, thus maintaining the desired pressure proﬁle during the
operation. MPD focuses not simply on the bottomhole pressure but also on the entire
pressure proﬁle (Sobreiro de Oliveira, 2018).
The International Association of Drilling Contractors (IADC) deﬁnes MPD as an
adaptive drilling process used to precisely control the annular pressure proﬁle throughout
the wellbore. While conventional drilling uses the hydrostatic pressure of the drilling mud
to manage pressure in the well, MPD uses a combination of surface pressure, hydrostatic
pressure of the mud, and annular friction to balance the exposed formation pressure.
Other goals of MPD, which can be considered as important drivers for the use of this
technique, are the elimination of one or more casing strings (Figs. 8.33e8.35), the ability
to drill longer extended reach drilling wells (ERD) with constant BHP
, to control
shallow gas and water ﬂows (deepwater) and also to provide a safer drilling environment.
To accomplish MPD, the application of a combination of the following techniques is
necessary:
•
backpressure,
•
a variable ﬂuid density,
•
the ﬂuid(s) rheology,
•
circulation friction factor, and
•
hole geometry.
Figure 8.33 Single gradient drilling ﬂuid creates high formation stress at the mudline and results in
ﬁve casing points being needed to stay within the drilling margin. Modiﬁed from Offshore Magazine,
2012. Dual-gradient Technology Expands Deepwater Drilling Opportunities. https://www.offshore-mag.
com/drilling-completion/article/16760118/dualgradient-technology-expands-deepwater-drilling-
opportunities.
Advanced approaches and technology for casing setting depth optimization
345
8.6.2.1 Principles of managed pressure drilling
The MPD system consists of surface and subsurface tools. The MPD process controls the
annular pressure proﬁle safely. As mentioned earlier, the main target is to avoid any NPT
incident caused by a narrow pressure proﬁle. The MPD is a closed and pressurized
circulating ﬂuid system. Using the appropriate tools while drilling, the well pressure is
controlled by dynamic pressure, static pressure, and backpressure. The equivalent weight
of the mud in the hole at the time is thus determined in the following ways.
8.6.2.1.1 Circulating (dynamic)
Conventional drilling during circulation:
ECD ¼ MWHP þ AFP
(8.43)
Figure 8.35 Optimal dual-gradient system minimizes mudline stress and results in the fewest casing
points. Modiﬁed from Offshore Magazine, 2012. Dual-gradient Technology Expands Deepwater Drilling
Opportunities.
https://www.offshore-mag.com/drilling-completion/article/16760118/dualgradient-
technology-expands-deepwater-drilling-opportunities.
Figure 8.34 The dual-gradient technique lowers stress at the mudline while maintaining the same
pressure at total depth and eliminating casing points. Modiﬁed from Offshore Magazine, 2012. Dual-
gradient
Technology
Expands
Deepwater
Drilling
Opportunities.
https://www.offshore-mag.com/
drilling-completion/article/16760118/dualgradient-technology-expands-deepwater-drilling-opportunities.
346
Methods for Petroleum Well Optimization
MPD during circulation:
ECD ¼ MWHP þ BP þ AFP
(8.44)
where: MWHP is the mud weight hydrostatic pressure; AFP is the annular friction
pressure; and BP is the surface backpressure.
8.6.2.1.2 Not circulating (static)
During connection, under no-circulation conditions, the annular friction part will
disappear, and the well pressure will be due to static mud weight, which is the case for
conventional drilling. In a narrow window, the well pressure could be lower than the
formation and collapse pressure and hence cause problems such as well collapse and kick.
However, to solve this problem, the MPD system maintains the well pressure within the
narrow window by applying backpressure.
8.6.2.2 Conventional drilling during static
ESD ¼ MWHP
(8.45)
MPD during static:
ESD ¼ MWHP þ BP
(8.46)
Comparing Eqs. (8.45) and (8.46), the amounts of surface backpressure during static
conditions will be roughly equal to the circulating annular friction pressure (AFP) when
the last stand was drilled in. There are several conﬁgurations which are available for MPD
equipment. They vary in accordance with the objective of the work and the reservoir
characteristics. To make an accurate choice of the equipment necessary for MPD op-
erations, there is a series of relevant inputs and considerations to take into account for
each case. Fig. 8.36 shows the surface and subsurface equipment as listed in the
following:
•
rotating control devices,
•
drilling chokes,
•
choke manifold,
•
ﬂowmeter,
•
oil/gas separators, and
•
nonreturn valves, downhole isolation valves, downhole measurement.
8.6.2.3 Advantages
As already mentioned, MPD allows the drilling of narrow operational windows between
pore pressure (PP) and fracture gradient (FG). The advantages and disadvantages of MPD
include those shown in Table 8.6.
Advanced approaches and technology for casing setting depth optimization
347
Figure 8.36 MPD system arrangements. MPD, managed pressure drilling. Modiﬁed from Nas, S., 2011.
Kick detection and well control in a closed wellbore IADC/SPE 143099, weatherford solution. In: IADC/SPE
Managed Pressure Drilling and Underbalanced Operations Conference and Exhibition. Denver, Colorado,
USA.
Table 8.6 The advantages and disadvantages of MPD.
Advantages
Disadvantages
•
Reduces the number of casings
•
Reduces the number of trips and cost
for cementing operation
•
Reduces nonproductive time
•
Reduces the overall drilling cost
•
Drills undrillable formations, which is
challenging for conventional methods
•
Allows drilling of a highly fractured
formation
•
Controls annular pressure precisely during
drilling and connection
•
Enhances early kick loss detection
•
High-pressure high-temperature application
•
It is an application-
speciﬁc method
•
Equipment footprint is typically not
as extensive
•
Extensive training of personnel is
required
•
Complex to operate
•
High capex (high initial cost of
project)
348
Methods for Petroleum Well Optimization
8.7 Summary
The conclusions in this chapter are drawn using different approaches:
8.7.1 Mathematical approach
Using cost of equipment, services, and consumables in a well, a multivariate cost model
was constructed. Using this casing setting, depth optimization was performed.
•
The multivariate method was furthermore expanded, now including geological
uncertainty using a utility framework.
•
On average over the RSH oilﬁeld, the consideration of geological uncertainty,
through multiple scenarios using the Full approach, provides better decisions
regarding the best points of wells, as savings at least of 2.4%e15.2% are calculated.
•
The extension of the casing point planning (CPS problem) to the uncertainty
environment is a good tool with which to determine casing setting depths of wells.
•
The more data available, the smaller the uncertainty and the better the decisions.
8.7.2 Multiple criteria approach
This presents a new integrated method for selection of casing seat locations that includes:
•
the fundamental gas-ﬁlled casing criterion,
•
the minimum mud weight to drill the next section,
•
the kick margin,
•
the riser margin,
•
assessment of the weak point in the well, and
•
the tubing leak criterion for the production casing.
The weak point criterion compares casing shoe strength with burst strength below the
wellhead. The objective is to avoid failures below the wellhead and to ensure that the
casing shoe represents the weak point in the well. All these criteria are deﬁned and
integrated into a generalized casing depth model. Here the casing depth is chosen by
deciding on acceptable kick margins and casing qualities. The model is ideal for sensi-
tivity and uncertainty analysis as all ﬁve criteria are satisﬁed for the solutions chosen, and
it is valid both for vertical wells and deviated wells.
8.7.3 Real-time approach
Real-time hydrodynamic monitoring succeeded in identifying fracture points, allowed
drilling to proceed in the constraints of a very tight mud envelope, and pushed a 95/800
casing depth 1287 ft deeper than planned. This eliminated an entire casing section and
saved several rig days.
Advanced approaches and technology for casing setting depth optimization
349
8.7.4 Technological approach
•
MPD increases the operating ability where a narrow operational window exists be-
tween pore pressure and fracture pressure as expected.
•
MPD reduces the number of casing strings needed to reach planned TVD.
8.8 Problems
Problem 1: Casing base design
We will design an intermediate casing. The design parameters are as follows:
•
Depth of casing: 1820 m
•
Depth to seabed: 225 m
•
Depth to sea level: 25 m
•
Depth to top of cement: 1685 m
•
Depth next openhole section: 2365 m
•
Pore pressure gradient, next section: 1.55 s.g.
•
Fracture design gradient at shoe: 1.77 s.g.
•
Formation ﬂuid density: 0.76 s.g.
•
Mud density: 1.50 s.g.
•
Cement density: 1.45 s.g.
The casing data are:
•
133/800 grade X-70, 72 lbs/ft. casing
•
Weight: 107.1 kg/m
•
Cross-sectional inner area: 772 cm2
•
Burst strength: 510 bar
•
Collapse resistance: 199 bar
•
Pipe body yield strength: 1016  103 daN
Compute the design factors for casing burst, collapse and tension. Assuming that
minimum safety factor for burst, collapse and tension is 1.10.
Problem 2: Casing setting depth optimization
We want to investigate if the casing is set at an optimal setting depth and if it should be set
shallower. We assume that the difference between a 17½00 hole and a 12¼00 hole is 2 m/
h, covering drilling, installing, and cementing the casing.
Assuming that minimum safety factor for burst, collapse, and tension is 1.10:
1. How shallow can the casing be set?
2. How much time is saved?
3. Which parameter is critical; burst, collapse or tension?
4. Identify other issues caused by setting the intermediate casing shallower.
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Methods for Petroleum Well Optimization
Problem 3: Investigation of setting depth criteria
The two previous problems are using criteria 1 and 2. We want to investigate the effect
on setting depths from the other criteria.
1. Assuming an annular capacity of 0.02 m3/m and a kick margin of 8 m3, what is the
shallowest depth at which the casing can be set?
2. If we are drilling in 300 m of water, will the riser margin limit the setting depth of
the casing?
3. Is the weak point in the well at the wellhead or below the casing shoe?
Problem 4: Inﬂuence of casing shoe depth on sustained casing
pressure
In this problem, four generic cases with unfavorable intermediate casing shoe setting
depth causing sustained casing pressure (SCP) will be presented (Inger, 2012). Find a
solution to how SCP may be avoided. Rough casing costs estimations related to the
different solutions are to be evaluated when deciding which option to choose
(Figs. 8.37e8.40).
Figure 8.37 Case 1: leak below production packer. Cement outside the 95/800 casing is set below the
production packer. A leak below the production packer may therefore lead to ﬂuid ﬂowing into
the formation or SCP in annulus “b” and/or annulus “c”. The primary barrier is shown in blue and the
secondary barrier in red shading (Inger, 2012).
Advanced approaches and technology for casing setting depth optimization
351
Figure 8.38 Case 2: casing shoe above unsealed high-pressure formation. Fluid from the high-
pressure zone enters the well causing pressure build-up in annulus “b.” The primary barrier is
shown in blue and the secondary barrier in red shading (Inger, 2012).
Figure 8.39 Case 3: casing shoe set in weak formation. 133/800 casing shoe is set in weak formation. The
casing shoe and formation cannot handle the pressure of the leaked ﬂuid. The shoe and surrounding
formation cracks and ﬂuid is allowed to enter the formation and/or migrate along the 133/800 casing into
Annulus “c.” The primary barrier is shown in blue and the secondary barrier in red shading (Inger, 2012).
352
Methods for Petroleum Well Optimization
Problem 5: Limitations of riserless drilling
•
Explain the disadvantages and limitations of riserless drilling.
•
Do you have any ideas for how to solve seal problem for riserless drilling as shown in
Fig. 8.41?
Figure 8.40 Case 4: leak below production casing shoe. Leak below liner hanger packer migrating
into annulus “a”, “b,” and surrounding formation. The primary barrier is shown in blue and the sec-
ondary barrier in red shading (Inger, 2012).
Figure 8.41 Sketch of a seal problem for riserless drilling
Advanced approaches and technology for casing setting depth optimization
353
Nomenclature
Bijkl drilling cost per foot for bit type j in rotary speed k and bit weight l in section i of well
CFt cash ﬂow in period t
Cijk casing cost per foot for casing grade j with weight coupling k in section i of well
D total well depth from drill ﬂoor
Dcas depth to casing shoe
dfrac fracture gradient below shoe
dmud relative mud density
DO reservoir pressure gradient
dres relative density of reservoir ﬂuid
dshoe pressure gradient at shoe when casing top is loaded to burst
dw relative seawater density ¼ 1.03 s.g.
Dwh depth to wellhead at seabed from drill ﬂoor
ha air gap drill ﬂoor sea level
hres height of reservoir gas entering wellbore
i interest rate
Lcasing length of casing in section i
Pburst the burst strength of the casing
PO pore pressure at the bottom of next openhole section; considered constant
Pwf fracture pressure below casing shoe; is considered variable as the casing depth is not ﬁxed
Pwh burst load below wellhead
SF safety factor for burst; deﬁned as burst strength/burst load
TCi total cost of drilling and casing and etc in period i
Xijkl 1 if we select rotary speed in k state and bit weight in l state for bit type j in section i of well, otherwise is 0
Yijk 1 if we select casing type j in section i with coupling type k, otherwise is 0
References
Aadnoy, B.S., 2010. Modern Well Design, second ed. CRC Press/Balkema.
Aadnøy, B.S., Ka
˚rstad, E., Belayneh, M.A., 2012. Casing Depth Selection Using Multiple Criteria. Paper
SPE 150931 Presented at the Drilling Conference and Exhibition 2012 IADC/SPE. Society of Pe-
troleum Engineers.
AGR Drilling, 2010. PowerPoint Presentation of AGR Subsea Inc. On RMR vs. Conventional Drilling.
September 9e11. https://www.iodp.org/241-12-what-is-riserless-mud-recovery-cohen/ﬁle.
Aird, P
., 2019. Deepwater Drilling Well Planning, Design, Engineering, Operations, and Technology
Application Book.
Eaton, B.A., 1972. Graphical method predicts geopressures worldwide. World Oil 182 (6), 51e56.
Eaton, B.A., 1975. The equation for geopressure prediction from well logs. In: Paper SPE 5544 of the 50th
Annual Fall Meeting of the SPE-AIME, Dallas, USA.
Goobie, R.B., Tollefsen, E., Noeth, S., Sayers, C.M., den Boer, L., Hooyman, P
., Akinniranye, G.,
Cooke, J., Thomas, R., Carter, E., 2008. Remote Real-Time Well Monitoring and Model Updating
Help Optimize Drilling Performance and Reduce Casing Strings. September SPE Drilling &
Completion.
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Guyaguler, B., Horne, R., 2001. Uncertainty assessment of well placement optimization. In: Paper SPE
71625 Presented at the 2001 SPE Annual Technical Conference and Exhibition. New Orleans,
Louisiana, 30 Septembere3 October.
Guyaguler, B., Horne, R.N., February 2004. Uncertainty Assessment of Well Placement Optimization.
Society of Petroleum Engineers Reservoir Evaluation & Engineering, pp. 24e32.
Hammond, J.S., 1967. Better decisions with preference theory. Harv. Bus. Rev. 123e141 (November-
December Edition).
Hantschel, T., Hidalgo, J.C., MacGregor, A., 2015. Geological Pore Pressure Prediction: An Application of
Petroleum System Modeling Technology. Upstream Technology Leadership Webinar Series,
Schlumberger Technology.
Inger, K.E., 2012. Inﬂuence of Casing shoe Depth on Sustained Casing Pressure. Master’s Thesis. Nor-
wegian University of Science and Technology, Trondheim.
Kemper, J., Richards, M.L., Taylor, M., Kelsall, N.R., Turner, M., Puech, J.C., 2013. Real time velocity
and pore pressure model calibration in exploration drilling. In: Offshore Europe Oil and Gas Con-
ference and Exhibition Held in Aberdeen, UK, 3e6 September.
Kerzner, H., 2001. Project Management: A Systems Approach to Planning, Scheduling and Controlling,
seventh ed. John Wiley & Sons, New Y
ork.
Khosravanian, R., Aadnøy, B.S., 2016. Optimization of casing string placement in the presence of
geological uncertainty in oil wells: offshore oilﬁeld case studies. J. Petrol. Sci. Eng. 142.
Kullawan, K., 2011. Risk Based Cost and Duration Estimation of Well Operations. Master’s Thesis. Sta-
vanger University, Stavanger.
Luthje, M., Helset, H.M., Hovland, S., 2009. New integrated approach for updating pore-pressure pre-
dictions during drilling. In: Paper Presented at the SPE Annual Technical Conference and Exhibition.
New Orleans, Louisiana, USA, 4e7 October. SPE-124295-MS.
Montilva, J., Mota, J., Billa, R., April 24, 2012. Onshore MPD System Enables Lower Mud Weights for
Challenging Wells. Drilling Contractor. www.drillingcontractor.org/onshore-mpd-system-enables-
lower-mud-weights-for-challenging-wells-15662.
Nas, S., 2011. Kick detection and well control in a closed wellbore IADC/SPE 143099, weatherford
solution. In: IADC/SPE Managed Pressure Drilling and Underbalanced Operations Conference and
Exhibition. Denver, Colorado, USA.
Offshore Magazine, 2012. Dual-gradient Technology Expands Deepwater Drilling Opportunities. https://
www.offshore-mag.com/drilling-completion/article/16760118/dualgradient-technology-expands-
deepwater-drilling-opportunities.
Ozdogan, U., 2004. Optimization of Well Placement under Time-dependent Uncertainty. M.Sc. Thesis.
Department of Petroleum Engineering, Stanford University, p. 88.
Rabia, H., 1987. Fundamental Casing Design. Petroleum Engineering and Development Studies, vol. 1.
Graham and Trotman.
Santos, H.M., Catak, E., Valluri, S., 2011. Kick tolerance misconceptions and consequences to well design.
In: Paper Presented at the 2011 SPE/IADC Drilling Conference and Exhibition Held in Amsterdam,
The Netherlands, 1e3 March.
Sobreiro de Oliveira, L., 2018. MPD-ﬁeld Case Studies, Modelling and Simulation Studies. M.Sc. Thesis.
University of Stavanger, Stavanger.
Sylta, Ø., Krogstad, W
., 2003. Estimation of oil and gas column heights in prospects using probabilistic
basin modelling methods. Petrol. Geosci. 9, 243e254. https://doi.org/10.1144/1354-079302-563.
Tollefsen, E., Goobie, R.B., Noeth, S., Sayers, C., den Boer, L., Hooyman, P
., Akinniranye, G., Cooke, J.,
Thomas, R., Carter, E., 2006. PPI technology services, optimize drilling and reduce casing strings
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Mexico, 31 August-2 September, Cancun, Mexico.
Advanced approaches and technology for casing setting depth optimization
355

CHAPTER ONE
Introduction to digital twin, automation
and real-time centers
Key concepts
1. A ﬁve-dimension model provides reference guidance for understanding and
implementing digital twin. We look at the frequently used technologies and tools
for digital twin to provide a guide to how digital twin models could be employed in
the future.
2. Oil and gas operators and service providers are now undergoing digital transformation
to enable them to thrive in a digital environment and to gain a competitive
advantage. Drilling generates large volumes of data from many sources, which leads the
industry into the world of big data. As technology is advancing rapidly, the industry
has started to talk about drilling automation, machine learning, artiﬁcial intelligence,
and big data analytics. T
o be able to use these, drillers need employees with the
appropriate technical expertise and data that is provided in the correct digital format.
1.1 Digital twin technology
1.1.1 Digital twins
A digital twin is a virtual and simulated model or a true replica of a physical asset. It is a
computerized companion of the physical asset and can be used for various purposes as
depicted in Fig. 1.1 below.
The model in Fig. 1.1 speciﬁcally ﬁnds expression through ﬁve enabling compo-
nents: sensors and actuators from the physical world, integration, data, analytics, and the
continuously updated digital twin application. These constituent elements are explained
at a high level below (Parrott and Warshaw, 2017):
•
Sensors: Sensors distributed throughout the manufacturing process create signals
that enable the twin to capture operational and environmental data pertaining to the
physical process in the real world.
•
Data: Real-world operational and environmental data from the sensors are aggre-
gated and combined with data from the enterprise.
•
Integration: Sensors communicate the data to the digital world through integration
technology (which includes edge, communication interfaces, and security) creating a
two-way link between the physical world and the digital world.
Methods for Petroleum Well Optimization
 2022 Elsevier Inc.
ISBN 978-0-323-90231-1, https://doi.org/10.1016/B978-0-323-90231-1.00006-6
All rights reserved.
1j
•
Analytics: Analytics techniques are used to analyze the data through algorithmic
simulations and visualization routines that are used by the digital twin to produce
insights.
•
Digital twin: The “digital” side of Fig. 1.1 is the digital twin itself, an application
that combines the components above into a near real-time digital model of the
physical world and processes. The objective of a digital twin is to identify intolerable
deviations from optimal conditions along any of the various dimensions. Such a
deviation is a case for business optimization; either the twin has an error in the logic
(hopefully not), or it has identiﬁed an opportunity for saving costs, improving quality,
or achieving greater efﬁciencies. The identiﬁcation of an opportunity may result in
an action in the physical world.
•
Actuators: Should an action be warranted in the real world, the digital twin
produces the action by way of actuators, subject to human intervention, which
triggers the physical process.
Below we describe the key technologies for digital twin from three perspectives: data-
related technologies, high-ﬁdelity modeling technologies, and model-based simulation
technologies. Fig. 1.2 presents the technology architecture for digital twin.
Figure 1.1 Visualization of digital twin creation. Modiﬁed from Offshore Engineer, 2019. Visualization of
Digital Twin Creation. https://www.oedigital.com/news/470548-digital-twin-taking-shape-of-the-offshore-
ecosystem.
2
Methods for Petroleum Well Optimization
1.1.1.1 Data-related technologies
Data are the basis of digital twin. Sensors, gauges, RFID tags and readers, cameras,
scanners, and other equipment should be chosen and integrated to collect total-element
data for digital twin. Data then should be transmitted in a real-time or near real-time
manner. However, the data required for digital twin are usually of big volume, high
velocity, and great variety, and such data are difﬁcult and costly to transmit to the digital
twin in the cloud server. Thus, edge computing is an ideal method for pre-processing the
collected data to reduce the network burden and eliminate the chances of data leakage,
and real-time data transmission is made possible by 5G technology. Data mapping and
data fusion are also needed to understand the collected data.
1.1.1.2 High-ﬁdelity modeling technologies
The model is the core of digital twin. Models of digital twin comprise semantic data
models and physical models. Semantic data models are trained by known inputs and
outputs, using artiﬁcial intelligence methods. Physical models require comprehensive
understanding of the physical properties and their mutual interaction. Thus, multi-
physics modeling is essential for high-ﬁdelity modeling of digital twin.
Figure 1.2 Technology architecture for digital twin. Modiﬁed from Liu, M., Fang, S., Dong, H., Xu, C.,
2021. Enabling technologies and tools for digital twin. J. Manuf. Syst. 58 (Part B), 3e21. https://doi.org/10.
1016/j.jmsy.2019.10.001.
Introduction to digital twin, automation and real-time centers
3
1.1.1.3 Model-based simulation technologies
Simulation is an important aspect of digital twin. Digital twin simulation enables the
virtual model to interact with the physical entity bi-directionally in real time.
Kritzinger et al. (2018) classiﬁed three uses of the term “digital twin”, based on the
level of data integration between the physical asset and digital representation in the
described digital twin: digital model (DM), digital shadow (DS), and digital twin (DT).
When there is no automatic real-time data communication between the physical asset
and the digital representation, as in Fig. 1.3, then the described digital twin is classiﬁed as
a “digital model”. When there is automatic real-time communication from the physical
representation to the digital twin but not from the digital representation to the physical
asset, as in Fig. 1.4, then the described digital twin is classiﬁed as a “digital shadow”.
Only when there is automatic real-time communication both from the physical asset to
the digital representation and from the digital representation to the physical asset, as in
Fig. 1.5, is the described digital twin classiﬁed as a proper digital twin.
1.1.2 Five-dimension digital twin model
The ﬁve-dimension digital twin model can be formulated as Eq. (1.1) (Tao et al., 2018a).
MDT ¼ ðPE; VM; Ss; DD; CNÞ
(1.1)
Figure 1.3 Data ﬂow in a digital model. Modiﬁed from Kritzinger, W., Karner, M., Traar, G., Henjes, J.,
Sihn, W., 2018. Digital twin in manufacturing: a categorical literature review and classiﬁcation. IFAC-Pap.
Online 2018 51 (11), 1016e1022.
Figure 1.4 Data ﬂow in a digital shadow. Modiﬁed from Kritzinger, W., Karner, M., Traar, G., Henjes, J.,
Sihn, W., 2018. Digital twin in manufacturing: a categorical literature review and classiﬁcation. IFAC-Pap.
Online 2018 51 (11), 1016e1022.
4
Methods for Petroleum Well Optimization
where: PE are physical entities; VM are virtual models; Ss are services; DD is digital twin
data; and CN are connections. The ﬁve-dimension DT model expressed in this formula
is illustrated in Fig. 1.6.
1.1.2.1 Physical entities in digital twin
DT is used to create virtual (digital) models of physical entities to simulate their behaviors
digitally (Tao et al., 2018b). The physical world is the foundation of DT. For the
purposes of DT, the physical entity may be a device or product, physical system, activities
process, or even an organization. These entities act according to physical laws and are
subject to uncertainty in their environments. Physical entities can be divided into three
levels, according to their function and structure: unit level, system level, and system of
system (SoS) level (Tao et al., 2019).
1.1.2.2 Virtual models in digital twin
Virtual models should be faithful replicas of physical entities that reproduce the geom-
etries, properties, behaviors, and rules of the original. The three-dimension geometric
Figure 1.5 Data ﬂow in a digital twin. Modiﬁed from Kritzinger, W., Karner, M., Traar, G., Henjes, J., Sihn,
W., 2018. Digital twin in manufacturing: a categorical literature review and classiﬁcation. IFAC-Pap. Online
2018 51 (11), 1016e1022.
Figure 1.6 Five-dimension digital twin model.
Introduction to digital twin, automation and real-time centers
5
model describes a physical entity in terms of its shape, size, tolerance, and structural
relationships. Based on physical properties (e.g., speed, wear, and force), the physical
model reﬂects the physical phenomena of the entity, such as deformation, delamination,
fracture, and corrosion. The behavior model describes the behaviors (e.g., state
transition, performance degradation, and coordination) and response mechanisms of the
entity to changes in the external environment. The rule model endows DTwith logical
abilities such as reasoning, judgment, evaluation, and autonomous decision-making, by
following the rules extracted from historical data for the physical entity or supplied by
domain experts.
1.1.2.3 Digital twin data
Twin data is a key driver of digital twin. DT deals with multi-temporal scale, multi-
dimension, multi-source, and heterogeneous data. Data can be acquired in the
following ways: obtained from physical entities, including static attribute data and
dynamic condition data; generated by virtual models, which reﬂect the simulation result;
obtained from services, and describe the service invocation and execution; and in the
form of knowledge provided by domain experts or extracted from existing data.
1.1.2.4 Services in digital twin
With product-service integration now taking place in all aspects of modern society, more
and more enterprises have begun to realize the importance of service. Service is an
essential component of DT in light of the paradigm of Everything-as-a-Service (XaaS).
First, DT provides users with application services related to simulation, veriﬁcation,
monitoring, optimization, diagnosis and prognosis, prognostic and health management
(PHM), etc. Secondly, a number of third-party services are needed to build a functioning
DT, such as data services, knowledge services, algorithms services, etc. Lastly, the
operation of DTrequires the continuous support of various platform services, which can
accommodate customized software development, model building, and service delivery.
1.1.2.5 Connections in digital twin
Digital representations are connected dynamically with their real counterpart to enable
advanced simulation, operation, and analysis. DT has six pairs of connections between
physical entities, virtual models, services, and data: physical entities and virtual models
(CN_PV); physical entities and data (CN_PD); physical entities and services (CN_PS);
virtual models and data (CN_VD); virtual models and services (CN_VS); and services
and data (CN_SD) (Tao et al., 2018a). These connections enable information and data
exchange between the four parts.
Through its integration with mobile internet, cloud computing, big data analytics
and other technologies, DT is potentially applicable to many ﬁelds which require the
mapping, fusion, and co-evolution of the physical and virtual spaces. The applications of
DT are illustrated in Fig. 1.7.
6
Methods for Petroleum Well Optimization
1.1.3 Value of digital twin
Building on a report from Oracle, eight value additions of digital twin have been identiﬁed
(Rasheed et al., 2020):
•
Real-time remote monitoring and control: Generally, it is almost impossible to gain
an in-depth view of a very large system physically in real time. A digital twin can be
accessed anywhere. Not only can the performance of the system be monitored but it
can also be controlled remotely using feedback mechanisms.
•
Greater efﬁciency and safety: It is envisioned that digital twinning will enable greater
automation, with humans interfering as and when required. Thiswill allow dangerous,
dull and dirty jobs to be allocated to robots, with humans controlling them remotely.
In this way humans will be able to focus on more creative and innovative jobs.
•
Predictive maintenance and scheduling: Comprehensive digital twinning will ensure
that multiple sensors monitoring the physical assets will generate big data in real time.
Smart analysis of that data will allow faults in the system to be detected early or to be
anticipated before they occur. This will enable better scheduling of maintenance.
•
Scenario and risk assessment: A digital twin, or to be more precise a digital sibling of the
system, will enable what-if analyses, resulting in better risk assessment. It will be possible
to perturb the system to synthesize unexpected scenarios and study the response of the
system aswellastheresultofthecorrespondingmitigationstrategies.Usingadigitaltwin
is the only way to perform this kind of analysis without jeopardizing the real asset.
•
Better intra-team and inter-team synergy and collaborations: With greater autonomy
and all the information at their ﬁngertips, teams can better utilize their time in
improving synergies and collaborations, leading to greater productivity.
Figure 1.7 Different application ﬁelds of digital twin. Modiﬁed from Qi et al. (2021).
Introduction to digital twin, automation and real-time centers
7
•
More efﬁcient and informed decision-making: The availability of quantitative data and
advanced analytics in real time will enable more informed and faster decision-making.
•
Personalization of products and services: With detailed knowledge of historical re-
quirements and the preferences of the various stakeholders, as well as evolving market
trends and competitive environments, demand for customized products and services
is bound to increase. For the factories of the future, a digital twin will enable faster
and smoother gear shifts to address changing needs.
•
Better documentation and communication: Readily available information in real
time combined with automated reporting will help keep stakeholders well informed
about the drilling operations, improving transparency.
1.1.4 Modeling basis used in digital twin development
The basis of the digital twin system is established from the ﬁrst principles of physics.
The digital twin uses all available real-time drilling data, both surface and downhole.
These are linked to and combined with real-time modeling to supervise and optimize
the drilling process. The real-time data, well conﬁguration data, and other relevant data
are used to visualize the wellbore and the drilling process status in 3D in real time. The
digital twin is composed of the following (Mayani et al., 2020):
•
An advanced and fast integrated drilling simulator, integrating transient hydraulic,
thermal, and mechanical models: The integrated drilling simulator models the different
drilling sub-processes dynamically. The interactions between the sub-processes are also
modeled in real time. This simulator is used to perform forward-looking simulations
automatically and can be used for planning revisions on the ﬂy (what-if) as well.
•
Automatic correction and quality checking of the drilling data: This ensures the data
are suitable for processing by computer models.
•
Algorithms that monitor the drilling process in real time by means of a combination
of time-based drilling data and real-time modeling of data results.
•
Algorithms for diagnosis of the drilling state.
•
A 3D visualization (Virtual Wellbore), with dynamic visualization of the downhole
process.
•
Data ﬂow and computer infrastructure.
1.1.5 Monitoring of the drilling wells using digital twin
Digital twin was ﬁrst used in drilling monitoring with the introduction of advanced
monitoring, which is the most recent evolution in drilling monitoring. Automated
monitoring offers real-time simulations, comparing simulation results with measure-
ments in real time, detecting diagnostics automatically as well as manually, and self-
detection of problems. In the next stage of this evolution drilling operations will
move toward real-time optimization. It will be possible to perform forecasting simula-
tions, “forward-looking” simulations, “what-if” simulations and predictive analysis.
8
Methods for Petroleum Well Optimization
The digital twin virtual well consists of an advanced mathematical model which
includes a complex mechanical model as well as a hydraulic model. Examples of the
parameters that these models can calculate include:
•
pressure
•
SPP (standpipe pressure)
•
ECD (equivalent circulating density)
•
temperature
•
choke pressure
•
pore and fracture pressure
•
pit gain
•
cuttings concentration
•
rate of penetration
•
wellbore stability
•
torque and drag
•
torque on the bit
•
torque at the surface
•
hook load
•
static, axial, and rotational friction
•
string elongation
•
block speed
Real-time mathematical models utilize real-time drilling data sent from the rig. The
models compare real-time downhole measurements with the modeled parameters to
monitor downhole conditions during drilling and casing operations. This allows the early
detection of symptoms of downhole deterioration, which are shown in the automatic
diagnostic messages provided by the model. Thus, the use of digital twin helps to improve
drilling and casing performance based on downhole conditions (Figs. 1.8 and 1.9).
The digital twin of the well is visualized in both the 2D and 3D real-time views during
drilling. The 2D view uses all available real-time drilling data including surface and downhole
data in combination with advanced monitoring models to monitor and provide advisory for
more optimal drilling. The various drilling models interact, and the measured values and the
calculated results are visualized in real time in a graphical user interface. The 2D virtual well
contains the wellbore geometry, tubular properties, drill string, temperature proﬁle, pressure
proﬁle including pore and fracture proﬁle, and risk messages icons. ECD variations and the
comparison with pore and fracture pressure are also visible in the 2D illustration.
The diagnostic technology is combined with the 3D visualization to create a “virtual
wellbore”. The 3D visualization together with diagnostic updates and virtual gauges can
provide a better understanding of well conditions throughout the drilling operation. Bit
depth, ECD values and all other information can be monitored using sensors. This data
can be included and updated in the visualization.
Fig. 1.10 shows a typical 2D illustration, and Fig. 1.11 shows a typical 3D visual-
ization. The 2D and 3D views of the digital twin can be used during the whole
life cycle of the drilling operation as well as during post analysis, training, and
Introduction to digital twin, automation and real-time centers
9
Figure 1.8 Sketch of a well with a digital twin illustrating how estimated pressure values can be
extracted everywhere along the ﬂow path. A similar extraction can be done for all modeled variables
(such as ﬂow rates, densities, and ECD). Modiﬁed from Gjerstad, K., Bergerud, R., Thorsen, S.T., 2020.
Exploiting the full potential in automated drilling control by increased data exchange and multi-
disciplinary collaboration, SPE Annual Technical Conference and Exhibition, Virtual Event Held 26e29
October 2020.
Figure 1.9 Real-time mathematical models utilize real-time drilling data. Modiﬁed from Bergerud, R.,
August 2016. Powerpoint Presentation: Drilltronics Drilling Process Automation e Statfjord C, Ronny
Bergerud Operation Manager Drilltronics. https://docplayer.net/191389103-Drilltronics-drilling-process-
automation-statfjord-c.html.
10
Methods for Petroleum Well Optimization
Figure 1.10 2D visualization of digital twin during drilling in auditorium. (Aker BP’s ﬂexible & multi-
purpose Onshore Collaboration Center, Photographer: Bjørn O. Bådsvik).
Figure 1.11 Real-time 3D visualization on video wall in rig-room. (Aker BP’s ﬂexible & multipurpose
Onshore Collaboration Center).
Introduction to digital twin, automation and real-time centers
11
experience transfer. For experience transfer and learning the whole operation can be
replayed and displayed.
The properties of the wellbore, drill string, and reservoir are set using ﬁeld devel-
opment planning and well construction software that create the basis for the intended
design and modeled physical constraints. A high-level view of this complete system is
shown in Fig. 1.12. By integrating all the modeling parts, available data from past wells
and live access to real-time data, it is possible to create the richest uniﬁed representation
of well construction preparation or execution. The uniﬁed set of models are a
combination of plans and actuals, including their respective interpretation and execution
uncertaintyethus helping to achieve the highest possible level of ﬁdelity based on the
latest information. Models are continually updated, and plans are kept “live”; consistency
is achieved due to their uniﬁed representation (see Fig. 1.12).
Digital twins provide us with the ability to investigate the future by combining
historical data, real-time data, and physics-based models. There are many applications in
the oil and gas domain that apply the data to physics-based models to make predictions
about the various drilling processes, systems, and associated equipment.
1.1.6 The concept of digital twinning for well construction
Well construction involves a multitude of physical processes, measurements, control
applications, analyses, and decision loops, spanning a range of temporal resolutions and
response times or delays. For simplicity, our discussion divides resolution and delay into four
groups (see Fig. 1.13): sub-second, sub-minute, intermediate (minutes), and long (hours).
This range of requirements for data resolution and delay in system response indicates
that it is likely that closed-loop, sub-second response needs will be met by a locally
controlled rig-speciﬁc system. Sub-minute, intermediate, and long responses will cover
the broader process, in which drilling data are applied automatically or manually to
inﬂuence or make decisions about the ongoing drilling process, as is believed to be the
norm today. It is suggested that a new term, control-time, be introduced for this.
Figure 1.12 Digital twinning for well construction. Modiﬁed from Germain, O., McMullin, D., Tirado, G.,
2018. Using an E&P digital twin in well construction. In: Embracing the Digital Twin for E&P an iEnergy
eBook. Halliburton Landmark, pp. 27e35.
12
Methods for Petroleum Well Optimization
Control-time describes the resolution required of the system surface data and control
system algorithms for the control of drilling-machinery parameters such as pump rate,
hook load, pipe rotation, pipe velocity, and rate of penetration (ROP). The resolution of
the downhole data available to the control system during normal operations is lower,
currently constrained by the bandwidth of the mud-pulse telemetry. However, the
introduction of wired-pipe technology will make these measurements also available to
the control system with sub-second resolution. There is also a question of the time it
takes to perform a measurement. Some measurements are instantaneous, such as motor
torque or standpipe pressure, while others take time and may involve applications of
models, such as derived fracture pressure from a leak-off test or wellbore friction from a
friction test. However, measurements with a long control time are generally not applied
as feedback variables in closed-loop control algorithms, although they could trigger
action. For example, as a result of taking a leak-off test, the fracture pressure constraint
may be updated in the automated system. In another case, friction analysis may be
applied to detect potential hole-cleaning issues, with possible triggering of mediating
action (Thorogood et al., 2010).
1.2 Drilling automation
Automation is broadly deﬁned as a technology dealing with the application of
mechatronics and computers in the production of goods (manufacturing automation)
and services (service automation). Automation can also be deﬁned as the replacement of
human labor by electronic or/and mechanical devices. This deﬁnition has broadened
over time. First, it covers many processes: in the case of drilling, for example, not solely
the operation of the drill. Second, the human labor that it replaces can be both physical
and mental (Iversen et al., 2013).
Figure 1.13 Typical timescales in drilling-process management. Modiﬁed from Thorogood, J., Aldred, W.
D., Florence, F., Iversen, F., 2010. Drilling automation: technologies, terminology, and parallels with other
industries. SPE Drill. Complet. 25 (4), 419e425. https://doi.org/10.2118/119884-PA, SPE/IADC Drilling
Conference and Exhibition, Amsterdam, The Netherlands, 17e19 March 2009. https://doi.org/10.2118/
119884-PA.
Introduction to digital twin, automation and real-time centers
13
Reasons for implementing automation are:
1. a shortage of labor,
2. a high cost of labor,
3. to increase productivity,
4. to reduce costs, and
5. to reduce process lead time.
The evolution of automation can be divided into three eras: mechanization, semi-
automation, and local automation. Mechanization means replacing human labor by
mechanical power which provides more torque and force. Semi-automation means some
of the mechanical operations are automated but skilled human operators are needed to
feed the automated machines with the required data. Local automation removes the need
for a human interface from the semi-automated operation.
There are three basic categories of automation: ﬁxed automation, programmable
automation, and ﬂexible automation (Fig. 1.14).
Sheridan (2002) refers to the human-automation system which he divides into two
categories, mechanization, and computerization. Here mechanization means replacing
human labor by machines that are physically controlled by a human. Computerization
means that the process is operated or controlled by a computer, which is itself controlled
by a human, thus providing an interface between human and machine.
Sheridan (2002) categorized automation into four types:
1. mechanization and sensing integration;
2. data processing and decision-making;
3. mechanical and information action; and
4. open-loop operation on closed control.
The precise deﬁnition of automation varies according to the industry and technology to
which it is applied.
Drilling automation is used by drilling engineers and is an example of Sheridan’s
human-automation system. Computers are used to control and manage the parameters
affecting the drilling operation such as ﬂow rate, downhole pressure (DHP), mud weight
(MW), pore pressure (PP), fracture pressure (FP), and so on.
Within drilling, the application of automation is expanding to include drilling
machinery, sensor technology, control systems, and computer and communications
technology. This explosion of technology is leading a change in drilling automation from
the machine level to fully integrated operations (Iversen et al., 2013).
Figure 1.14 The three basic categories of automation.
14
Methods for Petroleum Well Optimization
Deﬁning and recognizing automation as a term or/and as a process level is important
to identifying how it can be applied to the operations of the different segments of the oil
and gas industry, such as contractors, services, and operating companies.
1.2.1 Automation levels
Automation levels range from a fully manual system (meaning no automation) to a fully
automated system, with the semi-automated levels in between having varying degrees of
manual and automated operations. A semi-automated system contains decision and
action options, which are either assigned to the operator or the computer. If the
computer is assigned fewer decision and action options than the operator, then the level
is closer to the fully manual level; if the operator is assigned fewer decision and action
options than the computer, the level is closer to the fully automated level.
1.2.2 Modeling
Making a model is a process of using pre-existing (historical) data and real-time data. Thus,
modeling uses the work done and the optimization processes. There are some parameters
that affect real-time data and thus affect modeling (Thorogood et al., 2010), such as:
•
functionality type
•
frequency
•
set point
•
reaction time
The functionality of drilling operations could be classiﬁed as an open-loop system. The
exception is if it is imitated by many real-time issues requiring closed loop. The affecting
parameters are:
•
a ﬂexible and scalable model accepting additional functionality;
•
missing data;
•
limited data transmission bandwidth;
•
diagnostic algorithm effect on bandwidth;
•
modal accuracy estimating under abnormal situations such as missing data;
•
fast set-point change under sudden parameters change; and
•
physical machine response.
There are many drilling models today, including the earth seismic model, the drilling
optimization model, and the ﬂuid model, that control drilling operations such as ROP
,
cement circulation, tripping, wellbore pressure, and drill string vibration. These models
currently work independently, but through automation it may be possible to integrate
them into a general drilling automation system based on safety, performance, and
economics.
Well construction depends on the analysis of formation behavior based on infor-
mation taken from previous drilling operations or study reports. This information is used
to build up the automation models, and the information can be updated manually,
although an electronic source is recommended to ensure high-quality automation.
Introduction to digital twin, automation and real-time centers
15
Remote support and decision-making have both a direct and indirect relationship
with the drilling procedures and data resolution that are used for estimations to help in
decision-making. This requires the parameters to be updated and then fed back into the
models for automated optimization. Time-scale analysis is a central item in the updating
of parameters that helps in decision-making on how to manage and update the whole
automation system.
Data resolution and response time are important factors for the well instruction
processes. Resolution and delay are divided into four groups: sub-second, sub-minute,
intermediate (minutes), and long (hours) (Fig. 1.13).
The sub-second response works in a speciﬁc system while the other responses work
in wider operations.
Resolution and delay are also called control-time, which deals with resolution and
control algorithms to control the drilling operation’s various parameters. Control-time is
divided into instantaneous and long time, where long time cannot be used as a feedback
in the control algorithm.
1.2.3 Data communication
In the past, the data used for monitoring was adjusted by the operators, using normal-
ization/rows, log transformations, and various ﬁlters. With the coming of mechanical
drilling, some companies started to use data to plan a drilling program as well as for
monitoring. Data recovery started with the advent of electronic communication which
made it possible to access data via a network connection to be used widely as a planning
tool. For an automated system, the data used depend on some conditions such as
availability, completeness, and accuracy.
The short-trend operations accepted some incorrect data, but long-trend operations
did not because it affected the operations’ performance.
Unconditional data exchange creates problems in the system, so it is important to
choose the right data exchange by following a standard communication protocol. In
general, the protocols and protocol responses should follow the system’s requirements
and data requirements where some data depict slow changes and other data depict a
quickly changing situation.
System integration is one of the complexities facing automation drilling because of
many factors such as:
•
poor quality of the information about the system available to the operator;
•
the need to avoid information overload between the system and the operator,
especially when connecting multiple services;
•
the need to initiate standard change-management techniques which have an effect on
the magnitude of process changes; and
•
the system security, which is a challenge for the industry because of the potential for
miscommunication between the different parties (operator, contractor, and third
parties).
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Methods for Petroleum Well Optimization
The machine and model interface is an important issue in automation drilling where
machines emulate human actions to execute a process, with the help of real-time data
from the models’ sensors to update the data for the machine action. This type of
continuous communication in the system improves the drilling operation and provides
standards of automation that keep up with technological advances (Thorogood et al.,
2010).
1.2.4 Modes of automation
Automation modes are classiﬁed according to the feature level of the operator and the
automation system in that mode. Broadly, there are three automation modes, fully
manual, semi-automated, and fully automated. Within the semi-automated mode there
are ﬁve modes, which are differentiated from each other by the responsibility/feature
level of the operator and the automation system. There are, therefore, seven modes in
total, brieﬂy described below (see Thorogood et al., 2010; and Ornaes, 2010). Some of
the terms used need additional explanation, such as envelope protection, closing the
loop, multilevel control structures, feedback control, supervisory control, optimization,
and autonomy. We will provide this in the following sections.
Mode 0: “Direct manual control” mode. In this mode, the driller will receive no
support at all from the automation system. The driller is presented with raw signals and
simple alarms associated with top-side hardware.
Mode 1: “Assisted manual control” mode. The signiﬁcant contribution of the
automation system in this mode is the introduction of software that analyzes the current
situation of the well and presents the information to the driller. This will improve the
quality of the decision-making of the driller.
Mode 2: “Shared control” mode. This is the ﬁrst mode at which the automation
system will start to directly interfere with the operation of equipment. The main feature
of this mode will be envelope protection.
Mode 3: Management by delegation. Some of the operator’s tasks are delegated to
the automation system and are fully automated by a closed-loop controller.
Mode 4: Management by consent. The automation system provides regulated
multiple control loops, where models describing the well reach the right control loop.
The operator feeds the automation system with the operation to be performed, the
operation goals, the chosen variables, and their desired values.
Mode 5: Management by exception. The automation system decides on the next
operational mode by additional logic, and the operator’s role is to monitor and interfere if
the system does not behave as expected.
Mode 6: Autonomous operation. A fully automated system, and the role of the
operator is just to monitor the system.
In all seven modes, the operator retains authority over the operation and remains the
main decision-maker for the whole operation to address any risks that may arise.
Introduction to digital twin, automation and real-time centers
17
1.2.4.1 Envelope protection automation
An envelope protection system takes the well conditions into consideration when calcu-
lating the boundaries to be implemented at an offshore installation (Iversen et al., 2009).
Therefore, the envelope protection may take the following issues into consideration
(see Fig. 1.15):
•
Envelope protection sets boundaries and limitations depending on the well condi-
tions and information available.
•
The protection system will only interfere when the driller/operator exceeds these
boundaries.
•
Envelope boundaries must be calculated dynamically and updated according to the
new well conditions.
•
The dynamic calculations require a computational model that is highly expensive.
•
Envelope protection reduces the frequency of critical situations arising but does not
eliminate them.
1.2.4.2 Closed-loop automation
Closed-loop control is a higher level of automation than the envelope protection system.
In this control system, the set point/control value is deﬁned and set by the operator either
manually or automatically. If automatically, the operator uses an automated system to ﬁnd
and update the set point. The closed-loop control system uses an algorithm to calculate
the deviation in the real-time measurement from the set point and then activates an order
or process to return the operation to the set point. This type of control system requires a
large amount of data for the multilevel control structure and decision-making for the
whole operation, which can be supplied by a data acquisition system. Figs. 1.16 and 1.17
show a closed-loop system.
1.2.4.3 Multilevel control structure
The timescale is the key element in the structure of a multilevel control system. The
timescale ranges from zero for the upper level to inﬁnity for the lowest level because of
Figure 1.15 Envelope protection automation. Modiﬁed from Breyholtz, Ø., Nikolaou, M., 2012. Pre-
senting a Framework for Automated Operations. Society of Petroleum Engineers, pp. 118e126. https://doi.
org/10.2118/158109-PA.
18
Methods for Petroleum Well Optimization
the difference in the timescale between levels. The higher-level co-ordinates with the
lower level to reach the goal of the control system. Optimization and decision-making
also depend on the timescale/time length, so deﬁning the control level type (higher or
lower) is necessary for deciding on the appropriate systematic control hierarchy for an
operation such as drilling, for which multilevel control systems are not currently being
used. There are three proposed levels for a drilling automation system, which are:
•
feedback control level
•
supervisory control level
•
optimization level
Figure 1.16 Closed-loop controller. Modiﬁed from Breyholtz, Ø., Nikolaou, M., 2012. Presenting a
Framework for Automated Operations. Society of Petroleum Engineers, pp. 118e126. https://doi.org/10.
2118/158109-PA.
Figure 1.17 Multilevel structure. Modiﬁed from Breyholtz, Ø., Nikolaou, M., 2012. Presenting a Frame-
work for Automated Operations. Society of Petroleum Engineers, pp. 118e126. https://doi.org/10.2118/
158109-PA.
Introduction to digital twin, automation and real-time centers
19
Control levels in the oil industry
Feedback control
Feedback control is used to keep the controlled values equal to the set point. There are
many controllers used as feedback controllers, but not all of them are appropriate to the
oil industry. The best-known controller in the oil industry is the proportional integral
derivative (PID) controller. This is also referred to as the signal-input/signal-output
controller, because it uses one input to control one output. PID requires high tuning
quality to ensure the system performance because poor tuning will mean controlled
variables cannot be kept at set-point values. The use of another controller is recom-
mended if the drilling dynamics are nonlinear, which increases the complexity of the
system, and if the use of PID controllers is uneconomic because of the high cost of the
development, maintenance, and tuning of the PID system.
Supervisory control
This level of control can regulate all the low-level feedback controllers by determining the
set point of the controlled values. Supervisory control can repair the failed controllers
provided that this does not result in unbudgeted costs for improvement, maintenance, and
tuning of the controllers. There are many strategies employed in this type of control, but the
most useful one is model predictive control (MPC) because of its ability to handle many
variable control issues such as actuator boundaries and operator restrictions. MPC using a
ﬁnite-horizon open loop to solve control problems depends on the instance state of the
well/operation. MPC models can be obtained mathematically or experimentally and can be
linear or nonlinear, but the nonlinear model is more complicated and complex
Optimization
This level of automation is used to improve the whole drilling operation’s performance by
ﬁnding the effective operational condition of the well. It is not essential in highly
automated modes, where it is only used to optimize the control system or mode. There is
a direct relationship between this level of automation and the supervisory control level.
The optimization output is used as an input for the supervisory level, where it includes
the optimal values that need to be deﬁned and calculated related to economic and
operational conditions. Some of these variables are constant, such as time varying
disturbances, and others are variable such as degree of freedom of optimization. To get the
optimum optimization model/level, the relevant variables have to be deﬁned and solved
and the solving result has to be perfect. This will lead to parameters being maintained at
the set points to achieve an almost optimal operation and reduce losses compared to the
designed range. When the operation is kept within the designed range/window, the cost
will be reduced, because fewer models are needed to optimize the operation, and it relies
on a steady-state model that depends upon the low-level feedback controllers and their
ability to measure and regulate all the operation disturbances.
Autonomy
This level is the highest level of automation, where the operator’s role is to decide on the
level of automation to be used for the drilling operation, but not to interfere in the
operation itself. The automation system will be able to make appropriate decisions itself
and to change the conditions according to the current well status.
20
Methods for Petroleum Well Optimization
1.3 Real-time centers
Over the years, the role of the real-time operation center or onshore collaboration
center has evolved beyond just facilitating fast decision-making and cost saving. As
drilling engineering becomes more and more complicated, the real-time center needs to
be able to cater for the whole drilling cycle. To deliver a complete real-time commu-
nication solution, integrated real-time operation center services were created with the
intention of providing end-to-end solutions to the oil and gas operators, starting with the
well planning phase, covering the well execution, and extending all the way to post-job
analysis. The diagram below shows the integrated real-time operation services deployed
in an operator’s ofﬁce, and the illustrations show the layout of the center in the ofﬁce
space. This solution covers collaborative well planning, well engineering and planning,
real-time data aggregation and visualization, real-time monitoring and interventions,
predictive modeling, drilling optimization, training and mentoring, and ﬁnally data
management and archiving (Iskandar et al., 2018) (Figs. 1.18e1.20).
In the section below we explain in more detail the function of each of the services
and how each one contributes to the success of an integrated real-time operation.
1.3.1 Collaborative well planning
A complete real-time operation cycle starts with a collaborative well planning discussion,
especially for challenging environment well cases. This discussion session involves all the
stakeholders in the center from different backgrounds. During the planning stage three-
dimensional (3D) integrated platform technology is used. This seamlessly integrates both
geoscience and engineering data for detailed analysis.
Typically, drilling a well involves two ﬁelds of expertise: geoscience and drilling
engineering. Historically, communication between the two has always been an issue
because, despite having a common goal, the two ﬁelds have different primary interests.
Figure 1.18 Integrated real-time operation center services.
Introduction to digital twin, automation and real-time centers
21
The main concern for Geology & Geophysics (G&G) is to reach the depth of interest,
but that is sometimes challenging to achieve according to the engineering principles.
The drilling engineer’s primary concern is to drill quickly and safely, which sometimes
requires the G&G team to adjust its geological target.
Communication between different departments was usually done via emails or phone
calls, and it could take several months to agree on a ﬁnalized well plan. The generally
imperfect communication between geologists, geophysicists, reservoir engineers, and
drilling engineers involved in well planning exacerbates this uncertainty. The end goal of
Figure 1.19 Aker BP’s onshore collaboration center, Stavanger. (Photographer: Arne Bru Haug, Bjørn
O. Bådsvik).
Figure 1.20 Real-time operation network architecture. Modiﬁed from Iskandar, F.F., A iddin, M.S.Z.,
Nazzeri, N., Aziz, A.A., Atemin, A., 2018. Integrated real-time operation centre: a complete solution towards
effective and efﬁcient drilling operation. Paper Presented at: Offshore Technology Conference Asia Held in
Kuala Lumpur, Malaysia, 20e23 March 2018. https://doi.org/10.4043/28598-MS.
22
Methods for Petroleum Well Optimization
a collaborative well planning session is to have a formal binding agreement between all
parties, ratiﬁed by all stakeholders prior to the start of drilling. An effective collaborative
well planning session is likely to result in the smooth running of the drilling process.
1.3.2 Well engineering and planning
Once the reservoir and drilling target has been ﬁnalized, the Drilling Application
Engineer (DAE) will perform a study and conduct a consultation on the trajectory
design, BHA design, torque and drag analysis, casing stress check, etc. At this stage, all the
technical aspects of well planning are veriﬁed via simulation. The DAE will also provide a
predictive torque and drag roadmap for various drilling parameters based on speciﬁed
friction factors. Most importantly, the DAE is responsible for highlighting any risks
associated with the well together with providing potential solutions.
1.3.3 Real-time data aggregation and visualization
Real-time data aggregation and visualization depends on the operator, and the network
architecture usually utilizes the operator’s existing infrastructure to preserve data
conﬁdentiality. A data aggregation server, commonly referred to as a rig server, is
deployed on the rig site at the beginning of each job. The server is connected to the data
acquisition systems of all the service companies such as mud loggers, MWD
(measurement-while-drilling) providers, cementing operators, and well-test consultants,
to centralize all drilling data. The data is stored locally on the rig before being transmitted
to the town via an internet network.
End users, such as the drilling engineer and G&G team, are then able to visualize the
data through a web-based application on a desktop or mobile device. The real-time
operation (RTO) engineer’s job is to monitor data streaming from the rig site through
the network and to prevent data loss by acting immediately if any problems arise. At the
same time, they are responsible for solving any problems the end user has with the data
visualization.
1.3.4 Real-time monitoring and interventions
A monitoring specialist is what makes the operation center a real-time center. Real-time
monitoring (RTM) is a process through which operational personnel can review,
evaluate, and adjust data on a database or a system (such as offshore drilling, well
completions, or production). RTM allows operational personnel to review the overall
processes and functions performed on the data in real time. Typically, RTM software or
an RTM system provides visual insights into the data, which can be collected from
multiple or various sources on the MODU (mobile offshore drilling unit). RTM can also
provide instant notiﬁcations or alerts concerning speciﬁc data-driven or administrator-
speciﬁed events, such as when a data value goes out of a speciﬁed range.
Introduction to digital twin, automation and real-time centers
23
A team of monitoring specialists look at the data logs 24/7 to ensure smooth drilling
operations. The people who staff a real-time operation center or real-time operation
center are typically highly experienced, with a minimum of 10 years’ experience of
working on a rig site. Based on their experience, monitoring specialists are expected to
identify any potential hazards while drilling and to alert the people on the rig site and in
the town using a trafﬁc light system to highlight the incident severity. In many situations,
the proactive measures taken by the oil workers in town have helped to reduce if not
prevent any negative impact from an incident on the rig.
1.3.5 Predictive modeling
One of the highlights of a real-time operation center is that it is designed to allow
communication between multiple software systems, especially those that support
Wellsite Information Transfer Standard Markup Language (WITSML, the petroleum
industry standard for sharing data). Typically, in an integrated real-time operation center,
there are several software systems running simultaneously to enhance the monitoring.
The most recent technology to enhance monitoring is predictive modeling software
which is able to predict the hole conditions in real time as well as in advance using the
applied drilling parameters.
The RTO engineers are responsible for ensuring that the data from the rig site is fed
into the predictive modeling application in real time. A dedicated calculation server for
predictive modeling is installed to catch the real-time data from a WITSML store. The
software calibrates the simulated model by applying the actual parameters used while
drilling. By having the combination of hydraulic, thermodynamic, and mechanical in-
puts, the monitoring engineer is then able to predict the behavior of cuttings bed height
development according to the parameter being used. Inefﬁcient drilling parameters
applied while drilling, such as slow revolutions per minute (RPM) and ﬂow rate, will
result in additional trips such as wiper trips and backreaming activities to clean the hole.
The development of the cutting proportion and cutting bed height generated in pre-
dictive modeling is based on the number of solids generated calculated on the ROP
, mud
carrying capacity, trajectory, ﬂow rate, and RPM. These help to identify in which section
of a well the cuttings are being accumulated at the low side, even with pumping or
without pumping, if the applied drilling parameters (RPM and ﬂow rate) are insufﬁcient.
1.3.6 Drilling optimization and detailed technical analysis
A dedicated drilling optimization specialist (DOS) is responsible for ﬁnding the best
possible solution to any technical problem for which there are usually several possible
contributing factors. The DOS is responsible for interpreting data from various
downhole and surface sensors and identifying the best possible solution for optimizing
the drilling operation. On top of that, DOS is responsible for performing post-run
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Methods for Petroleum Well Optimization
analysis, event reports (for example stuck pipe), identifying what was learned from the
incident, and setting out the best practices that would prevent such issues from
happening again.
1.3.7 Training and mentoring
A highly sophisticated real-time center does not necessarily translate into a collaborative
working environment. Although the security level to get into this room is high, there is
usually a wide range of authorized personnel, especially from among those involved in
the drilling operation. This includes the G&G team, drilling engineers, and the subject
matter experts to encourage knowledge transfer from the experts to the junior engineers.
The DAE and the monitoring specialist are responsible for managing the knowledge
ﬂow obtained throughout the drilling event and to document all lessons learnt and best
practices for future reference. Typically, during the drilling operation, the monitoring
specialist will facilitate the discussion among the junior engineers. This is to bridge the
knowledge gap and speed up the development of the junior engineers.
1.3.8 Data management and archiving
After the entire process from pre-planning to post analysis has been completed, the RTO
Engineer is responsible for ensuring that all the data are being kept in a proper manner
for future reference. This includes raw data from the rig site, logs, interventions, daily
reports, failure reports, best practices, and lessons learnt. The data are kept in a standard
folder structure and all the ﬁles are named following a standard format. This is important
to keep the data clean.
1.4 Summary
1. Digital twin represents an advancement in digitalization. It is increasingly being
applied and in an expanding range of areas, such as smart manufacturing, building
management, smart city, healthcare, the oil and gas industry, and many more.
2. The integrated real-time operation center has proved to be a success and has made
drilling operations run more smoothly. Integrated real-time operations do not just
save money. They help to increase drilling workﬂow efﬁciency, identify the root
cause of a failure event utilizing historical data, and increase participation and
collaboration between multiple disciplines. They also centralize data and information
from various high-tech software. In the next decade the industry must focus on ways
to use all the data that have been generated, especially from the integrated real-time
operation centers, to automate simple decisions and help guide the operator through
more complex decision-making with the use of artiﬁcial intelligence and big data
analytics.
Introduction to digital twin, automation and real-time centers
25
1.5 Problems
Problem 1: Drilling systems automation
The Drilling Systems Automation (DSA) Roadmap Report is a ﬁrst of its kind in the oil
and gas drilling industry. The purpose of that report is to describe a vision for DSA and
the steps that may be taken to move the industry forward and to affordably achieve this
vision. The latest version e V 19 05 31 is available at https://dsaroadmap.org/drilling-
systems-automation. Using the report:
•
Explain the DSA decision- and control-making framework in the control systems
section of the report.
•
Explain the key challenges of drilling systems automation.
Problem 2: Real-time centers (drilling operation centers or onshore
collaboration centers)
T
wo phases in the evolution of drilling operations centers are identiﬁed based upon pub-
lished information, as shown in Fig. 1.21. The ﬁrst generation of centers was short-lived,
failing to survive the slump in oil price and drilling activities during the late 1980s (for an
examination of this see Booth, 2011). The second generation had the beneﬁt of the sig-
niﬁcant evolution of information technology in the intervening years and is part of a broader
trend toward integrated operations and collaborative work processes. The centers shown in
Fig. 1.21 are identiﬁed from a review of several operators’ strategies and operations.
1. Which companies use digital twin in drilling or offer uniquely powerful software
systems for control and monitoring of the real-time dynamic and integrated drilling
process automation in the oil and gas industry?
2. Update to the present for all companies that have drilling operation centers or
onshore collaboration centers.
Figure 1.21 Drilling operations centers e timeline of signiﬁcant initiatives. Modiﬁed from Booth, J.,
June 2011. Real-time drilling operations centers: a history of functionality and organizational purpose e
the second generation. SPE Drill. Complet. 26 (02). https://doi.org/10.2118/126017-PA.
26
Methods for Petroleum Well Optimization
Problem 3: Digital drilling ecosystem
This problem looks at the approach taken by one of the oil and gas operators in Europe
toward an open, standardized, and structured digital drilling ecosystem. This system
enables the exchange of plans between systems while imposing standardized plan
structures for the well construction, time estimation, and time planners on the rig action
plan, and ensuring their connection to a rig control system (Fig. 1.22).
1. How is a smart hub used for uniting drilling data?
2. Show how the smart hub connects to other systems in a high-level sketch.
3. Is this approach able to reduce drilling time by connecting autonomous drilling with
a highly efﬁcient digital planning and execution process?
Problem 4: Drillbotics competition
The Society of Petroleum Engineers’ Drillbotics competition challenges student teams
from around the world to design, build, and operate a small rig that can autonomously
drill a vertical wellbore through a rock sample. Teams are judged not only on how
quickly the rig drills the sample and the quality of the wellbore, but also on how much
they learn in designing and building the rig (Fig. 1.23).
Figure 1.22 A conceptual overview of the smart hub. Modiﬁed from Caso, C., Bennett, P., Isaachsen, J.,
Ask, K.K., Stray, T., Andresen, P.A., Straumsheim, C.F., 2020. Toward the redeﬁnition of drilling plan and
execution via a digital drilling ecosystem. Paper Presented at: IADC/SPE International Drilling Conference
and Exhibition Held in Galveston, Texas, 3e5 March 2020. https://doi.org/10.2118/199600-MS.
Introduction to digital twin, automation and real-time centers
27
1. What are the Drillbotics competition guidelines regarding the criteria below?
•
Phase I: simulation/model/algorithm.
•
Phase II: construction cost limitation, performance, quality of the wellbore.
2. Describe the Drillbotics rock sample provided for the competition for evaluating the
performance and controller system of the rig.
Problem 5: Microservices
The objective of the well construction optimization platform is to improve visibility on
drilling operations in real time and to allow the driller to use this and data from other
sources to optimize performance. To do so, the platform goes beyond simply transferring
information between the rig and ofﬁce. It also integrates Microservices e data from
different vendors, various disciplines, and other wells e into a collaborative, multidis-
ciplinary solution (see Fig. 1.24).
1. Name the Microservices provided on this platform and discuss their respective roles.
2. Consider how many Microservices need to be integrated on this platform to support
a safe drilling operation.
3. Discuss the dataﬂow connections between the different Microservices that are needed
on a professional platform.
Figure 1.23 A schematic of a nano rig. Modiﬁed from https://drillbotics.com/.
28
Methods for Petroleum Well Optimization
References
Bergerud, R., August 2016. Powerpoint Presentation: Drilltronics Drilling Process Automation e Statfjord
C,
Ronny
Bergerud
Operation
Manager
Drilltronics.
In:
https://docplayer.net/191389103-
Drilltronics-drilling-process-automation-statfjord-c.html.
Booth,J.,June2011. Real-timedrillingoperationscenters:ahistoryoffunctionalityandorganizationalpurposee
the second generation. SPE Drill. Complet. 26 (Issue 02). https://doi.org/10.2118/126017-PA.
Breyholtz, Ø., Nikolaou, M., 2012. Presenting a Framework for Automated Operations. Society of
Petroleum Engineers, pp. 118e126. https://doi.org/10.2118/158109-PA.
Caso, C., Bennett, P
., Isaachsen, J., Ask, K.K., Stray, T., Andresen, P
.A., Straumsheim, C.F
., 2020. Toward
the redeﬁnition of drilling plan and execution via a digital drilling ecosystem. In: Paper Presented at:
IADC/SPE International Drilling Conference and Exhibition Held in Galveston, Texas, 3e5 March
2020. https://doi.org/10.2118/199600-MS.
Germain, O., McMullin, D., Tirado, G., 2018. Using an E&P digital twin in well construction. In:
Embracing the Digital Twin for E&P an iEnergy eBook. Halliburton Landmark, pp. 27e35.
Herbert, M., James, R., Aurlien, J., 2008. ConocoPhillips onshore drilling centre in Norway, a virtual tour
of the centre including a link up with offshore. In: Paper Presented at: SPE Intelligent Energy
Conference and Exhibition Held in Amsterdam, The Netherlands, 25e27 February 2008. https://
doi.org/10.2118/111372-MS.
Iskandar, F
.F
., Abiddin, M.S.Z., Nazzeri, N., Aziz, A.A., Atemin, A., 2018. Integrated real-time operation
centre: a complete solution towards effective and efﬁcient drilling operation. In: Paper Presented at:
Offshore Technology Conference Asia Held in Kuala Lumpur, Malaysia, 20e23 March 2018. https://
doi.org/10.4043/28598-MS.
Iversen, F
., Gressga
˚rd, L.J., Thorogood, J., Balov, M.K., Hepso, V
., March 2013. Drilling automation:
potential for human error. SPE Drill. Complet. 28 (Issue 01).
Iversen, F
.P
., Cayeux, E., Dvergsnes, E.W
., Ervik, R., Welmer, M., Balov, M.K., 2009. Offshore ﬁeld test
of a new system for model integrated closed-loop drilling control. In: Paper (SPE 112744) Was
Accepted for Presentation at the IADC/SPE Drilling Conference, Orlando, Florida, 4e6 March 2008,
and Revised for Publication. Original Manuscript Received for Review 17 December 2007. Revised
Paper Received for Review 28 November 2008. Paper Peer Approved 26 May 2009. Paper Presented
at: SPE/IADC Drilling Conference and Exhibition, Amsterdam, The Netherlands, 17e19 March
2009. https://doi.org/10.2118/112744-PA.
Kritzinger, W
., Karner, M., Traar, G., Henjes, J., Sihn, W
., 2018. Digital twin in manufacturing: a cate-
gorical literature review and classiﬁcation. IFAC-Pap. Online 2018 51 (11), 1016e1022.
Figure 1.24 Overview of well construction optimization platform. Modiﬁed from https://www.weather
ford.com/en/products-and-services/drilling/drilling-services/well-construction-optimization-platform/.
Introduction to digital twin, automation and real-time centers
29
Mayani, M.G., Baybolov, T., Rommetveit, R., Ødegaard, S.I., Koryabkin, V
., Lakhtionov, S., 2020.
Optimizing drilling wells and increasing the operation efﬁciency using digital twin technology. In:
Paper Presented at: IADC/SPE International Drilling Conference and Exhibition Held in Galveston.
Texas, 3e5 March 2020. https://doi.org/10.2118/199566-MS.
Mayani, M., Rommetveit, R., Oedegaard, S.I., Svendsen, M., 2018. eDrilling, drilling automated realtime
monitoring using digital twin. In: Abu Dhabi International Petroleum Exhibition and Conference
Held in Abu Dhabi, UAE, 12e15 November 2018.
Offshore Engineer, 2019. Visualisation of Digital Twin Creation. https://www.oedigital.com/news/
470548-digital-twin-taking-shape-of-the-offshore-ecosystem.
Ornæs, J.I., 2010. Closed-loop control for decision-making applications in remote operations. In: Paper
Presented at: The IADC/SPE Drilling Conference and Exhibition, New Orleans, 2e4 February 2010.
https://doi.org/10.2118/126907-MS.
Parrott, A., Warshaw, L., 2017. Industry 4.0 and the Digital Twin e Manufacturing Meets its Match.
Deloitte
Consulting
LLP
.
https://www2.deloitte.com/us/en/insights/focus/industry-4-0/digital-
twin-technology-smart-factory.html.
Qi, Q., Tao, F
., Hu, T., Anwer, N., Liu, A., Wei, Y
., Wang, L., Nee, A.Y
.C., 2021. Enabling technologies and
tools for digital twin. J. Manuf. Syst. 58 (Part B), 3e21. https://doi.org/10.1016/j.jmsy.2019.10.001.
Qi, Q., Tao, F
., Hu, T., Anwer, N., Liu, A., Wei, Y
., Wang, L., Nee, A.Y
.C., October 2019. Enabling
technologies and tools for digital twin. J. Manuf. Syst. 29 https://doi.org/10.1016/j.jmsy.2019.10.001.
Rasheed, A., Omer San, O., Trond Kvamsdal, T., 2020. Digital Twin: Values, Challenges and Enablers
from a Modeling Perspective, vol. 8. Institute of Electrical and Electronics Engineers (IEEE),
pp. 21980e22012. https://doi.org/10.1109/ACCESS.2020.2970143.
Sheridan, T.B., 2002. Humans and Automation: Systems Design and Research Issues. Human Factors and
Ergonomics Society/Wiley, Santa Monica/New Y
ork.
Tao, F
., Zhang, M., Liu, Y
., Nee, A.Y
.C., 2018a. Digital twin driven prognostics and health management
for complex equipment. CIRP Ann. 2018 Manuf Technol. 67 (1), 169e172. https://doi.org/10.1016/
j.cirp.2018.04.055.
Tao, F
., Cheng, J., Qi, Q., Zhang, M., Zhang, H., Sui, F
., 2018b. Digital twin-driven product design,
manufacturing and service with big data. Int. J. Adv. Manuf. Technol. 94 (9e12), 3563e3576.
Tao, F
., Qi, Q., Wang, L., Nee, A., 2019. Digital twins and cyberephysical systems towards smart
manufacturing and industry 4.0: correlation and comparison. Engineering 5, 653e661.
Thorogood, J., Aldred, W
.D., Florence, F
., Iversen, F
., 2010. Drilling automation: technologies, termi-
nology, and parallels with other industries. SPE Drill. Complet. 25 (4), 419e425. https://doi.org/
10.2118/119884-PA. SPE/IADC Drilling Conference and Exhibition, Amsterdam, The Netherlands,
17e19 March 2009.
Further reading
Cayeux, E., Mihai, R., Carlsen, L., Stokka, S., 2020. An approach to autonomous drilling. In: Paper
Presented at: IADC/SPE International Drilling Conference and Exhibition Held in Galveston, Texas,
3e5 March 2020. https://doi.org/10.2118/199637-MS.
Open-source code
Y
ou can develop your models using open-source code for different tools available
on https://github.com/.
Examples include:
Drillbotics
Open smart hub
30
Methods for Petroleum Well Optimization

CHAPTER SIX
Mechanical speciﬁc energy and drilling
efﬁciency
Key concepts
1. The strength of the mechanical speciﬁc energy (MSE) plot is that it more accurately
illustrates the actual efﬁciency of the drilling process. This information is crucial in
cost saving and cost justiﬁcation and, if utilized correctly, could in turn save time as
well as costs.
2. The applications of digital drilling optimization using speciﬁc energy are presented
including real-time identiﬁcation of rate of penetration (ROP), subsurface lithology,
and pore pressure prediction.
3. A new multidimensional space model for formation drillability prediction is presented.
4. A method is presented to calculate the drilling energy ﬂow in the drill string and to
enable better drilling energy management by maximizing useful energy consumption
and reducing energy waste. This method provides a new way to understand and improve
drilling efﬁciency.
6.1 Introduction to mechanical speciﬁc energy
6.1.1 Drilling efﬁciency
Drilling efﬁciency, where the optimal (high) penetration rate is achieved, is an important
cost-saving measure. Drilling for petroleum requires a complex system, which relies on
many different factors such as bit size, bit efﬁciency, torque, weight on bit (WOB),
revolutions per minute (RPM), ﬂow rate, mud rheology, and formation hardness. This
makes achieving and maintaining a high ROP a challenging task, requiring more than
just supplying the drill string with sufﬁcient power.
6.1.2 Causes of inefﬁciency
In the study of drilling efﬁciency, more than 40 different categories of penetration
limiters have been identiﬁed (Dupriest, 2006). Of these 40 categories, only four are
directly related to the bit. The categories can be divided into two groups.
The ﬁrst group includes factors that limit input energy. Such problems are usually
caused by equipment insufﬁciencies and are often too expensive to rectify. These
Methods for Petroleum Well Optimization
 2022 Elsevier Inc.
ISBN 978-0-323-90231-1, https://doi.org/10.1016/B978-0-323-90231-1.00008-X
All rights reserved.
193j
problems could be rig limits such as insufﬁcient rig top drive or rotary torque. Problems
could also be caused by other limits, such as insufﬁcient drill string makeup torque,
weight of the drill collars, bit durability, hole cleaning, or directional targeting control.
The second group includes factors that create inefﬁciency or founder. These factors
prevent the energy from being properly transferred to the formation, and so only a
portion of the supplied energy is used for efﬁcient drilling. The most common problems
of this kind are bit balling, bottomhole balling, and vibrations.
6.1.2.1 Bit balling
Bit balling is a common issue when drilling in claystone and shale formations, and it
reduces the efﬁciency of the drilling progress. Bit balling, as shown in Fig. 6.1, comes
from the accumulation of materials within the cutting structure of the bit and can happen
at any time during drilling.
Bit balling is commonly identiﬁed when reduced ROP
, reduced torque, and
increased standpipe pressure (SPP) are observed, as the accumulation of materials hinders
the ﬂow of ﬂuid through the passage between the wellbore wall and the balled bit. The
chances of bit balling can be limited by avoiding too high WOB and too high hydrostatic
pressure in the wellbore. If bit balling is expected, and a polycrystalline diamond compact
(PDC) bit is used, this needs a large junk slot area. If a rock bit is used, steel tooth bits are
preferred. It is also important that the bit nozzles are not extended and that the center jet
is not blocked. The center jet is important for efﬁciently ﬂushing out the accumulated
materials (Drilling formulas, 2014).
If bit balling does occur, the operation should be stopped until the issue is resolved.
Bit balling is most commonly solved by increasing RPM and mud ﬂowrate, while
Figure 6.1 A PDC bit balled during shale drilling.
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Methods for Petroleum Well Optimization
simultaneously lowering the WOB. In some cases, the pumping of a high viscosity pill
might be necessary. A reduction in SPP indicates that the removal of debris has created a
clear path.
6.1.2.2 Bottomhole balling
Another common problem is bottomhole balling. This problem is mostly experienced
when hard formation bits grind, creating ﬁner particles, which in turn clog the hole.
This is commonly associated with the “chip hold-down effect”, where the particles
broken loose from the formation are held in place by differential pressure, making them
difﬁcult to move. Fig. 6.2 shows a chip that is held in place by the pressure exerted by
the mud.
Bottomhole balling is usually identiﬁed by the observation of reduced ROP and
reduced torque, differing from bit balling by there being no change in standpipe pressure.
Bottomhole balling could be avoided by increasing the hydraulic horsepower and not
using an insert bit.
6.1.2.3 Vibrations
Vibrations in the drill string are a common cause of drilling inefﬁciency. Vibrations
usually occur when one or more of the following factors are present, usually in com-
bination with high WOB and relatively high RPM: lithological transitions, use of
underreamer, poor bottomhole assembly (BHA) design, and/or poor parameter man-
agement (Abbott, 2014). The most common problems associated with vibrations are
complications in drilling that cause additional stress to both the wellbore and the drill
string (Ahmadi and Altintas, 2013). This type of stress could cause severe fatigue and
damage to the drill string over time, resulting in tool failure and an additional trip. This is
costly for the operation in terms of both time and money. Continuous stress to the
wellbore often results in reduced wellbore surface quality, creating additional obstacles
during tripping and circulation. A reduced ROP could indicate vibrations, but vibrations
Figure 6.2 The chip hold-down effect. The chip is kept in place by the differential pressure between
bottomhole pressure (Pbh) and pore pressure (Pp).
Mechanical speciﬁc energy and drilling efﬁciency
195
are also sometimes measured by sensors tracking in real time. There are three categories
of vibrations, lateral, torsional, and axial, as illustrated in Fig. 6.3.
Axial vibrations, also known as bit bounce, are vibrations along the trajectory of the
wellbore. This type of vibration mostly affects the bit cutters and the bearings but also
prevents energy from being efﬁciently transferred to the formation.
Torsional, or stick-slip, vibrations happen when a part of the drill string intermit-
tently gets stuck at high frequency, while the part of the drill string above the stuck
section keeps rotating. The drill string then gathers potential energy as the string itself
gets twirled. At one point, the torque becomes too high for the wellbore to hold, and the
formation lets go of the drill string. The drill string will then rotate rapidly as the
torsional energy is released. If the problem is not solved, the drill string will repeatedly
get stuck until enough energy is worked up for it to be released. This type of vibration
causes fatigue to drill collar connections and may also damage the bit.
The most harmful type of vibrations are the lateral vibrations. Here the drill string
moves in a circular motion around the larger diameter of the wellbore. This type of
behavior damages the surface of the wellbore and can also cause severe fatigue to the drill
string components. Lateral vibrations may come as backward whirl and forward syn-
chronous whirl, differentiated by the direction in which the rotary motion against the
wellbore occurs. This is illustrated in Fig. 6.4.
When vibrations have been identiﬁed as the problem, the driller should reduce WOB
and stay below the critical RPM. If this does not help, the string design should be re-
evaluated.
A summary of the main features of the different vibration modes is given in Table 6.1.
Figure 6.3 The three types of vibrations acting on the drill string.
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Methods for Petroleum Well Optimization
6.1.3 Regions of drilling efﬁciency
The traditional “drill-off” curve, depicted in Fig. 6.5, neatly represents three regions of
drilling efﬁciency, which are uniquely tied to bit dysfunction and characteristic MSE
responses (Dupriest and Koederitz, 2005). Region 1 is dominated by inefﬁcient drilling
from an inadequate depth of cut (DOC). An inadequate DOC causes low bit
Figure 6.4 Lateral vibrations.
Table 6.1 The main features of the different vibration modes.
Axial
Torsional
Lateral
Mode of
vibration
Bit bounce
Stick-slip
Whirl
Motion
Up/down movement
along drill string axis
Twisting about the
drill string axis
Bending or whirl,
transverse to the drill
string axis
Main cause
Hard formation
Vertical hole
Roller Cone bits
Aggressive PDC bits
Friction between
wellbore and BHA
High-angle wells
Aggressive side-cutting bits
Friction
Washed-out hole
Unstable BHA/unstabilized
drill string
Frequency
(Aadnøy
et al., 2009)
1e10 Hz
<1 Hz
Bit whirl: 5e100 Hz
BHA whirl: 5e20 Hz
Symptoms
seen
at surface
Large WOB
ﬂuctuations
Rig/top-drive shaking
Reduced ROP
Top drive stalling,
torque and RPM
ﬂuctuations and
Reduced ROP
Increased surface torque
Reduced ROP
Postdrilling
evidence
Early bearing failure
Broken cutters
BHA failure
Damaged cutters
Over torqued
connections
Twist-offs
BHA failure
Damaged cutters and/or
stabilizers
Overgauge holes, BHA
failure washouts
One-sided wear on BHA
components
Real-time
mitigation
actions
Increase WOB and
decrease RPM
Decrease WOB and
increase RPM
Increase WOB and
decrease RPM
BHA, bottomhole assembly; ROP
, rate of penetration; RPM, rotations per minute; WOB, weight on bit.
Based on Larsen, L.K., 2014. T
ools and Techniques to Minimize Shock and Vibration to the Bottomhole Assembly Master
Thesis University of Stavanger.
Mechanical speciﬁc energy and drilling efﬁciency
197
aggressiveness from “chamfer” drilling. Bit whirl is likely to dominate this region due to
the bit cutters not being fully buried in the formation. MSE in this region is very high
and erratic, indicating that large amounts of energy put into the system are being
dissipated outside the system in the form of excessive vibrations. Any additional energy
input into the system will disproportionately increase the output of the system (ROP).
Region 2 represents efﬁcient drilling. Drilling efﬁciency improves dramatically with
sufﬁcient WOB to bury the cutters, adequately constraining lateral bit movement and
preventing bit whirl.
6.1.4 Analyzing trends in mechanical speciﬁc energy
In earlier years the drillability was deﬁned as the d-exponent (Jordan and Shirley, 1966).
Teale in 1965 (Teale, 1965) proposed MSE as the energy needed by the drill bit to
efﬁciently destroy a volume of rock as the drilling operation proceeds. This expresses the
relationship between input energy and penetration rate. Analyzing and baselining trends
in MSE are a convenient way to identify in which region of the drill-off curve the bit is
being operated. Real-time MSE surveillance facilitates a continuous detection of
changes in drilling efﬁciency, which allows the optimum selection of drilling parameters
by sufﬁcient parameter exploration or so-called “step-tests”. In other words, MSE will
indicate if a change in a drilling parameter is moving you closer to, or further away, from
the maximum expected performance. Postdrill MSE analysis may provide quantitative
data to identify drilling inefﬁciencies and bit dysfunction in historical reference wells,
providing a cost justiﬁcation for proposing changes in the current system to extend the
founder point of the next well (Dupriest and Koederitz, 2005). Extending the founder
point, which is the onset of bit dysfunction in any well, may improve drilling perfor-
mance, and BHA tool and bit longevity considerably.
Operational considerations and the ability to diagnose several bit dysfunctions from
characteristic MSE responses and analysis are presented in detail by Berge-Skillingstad
Figure 6.5 Characteristic WOB versus ROP drill-off curve. ROP, rate of penetration; WOB, weight on bit.
Modiﬁed from Berge-Skillingstad, M., Anderssen, V.H., 2018. MSE: Next Generation Digital Drilling Opti-
mization. Department of Geoscience and Petroleum. NTNU. Trondheim.
198
Methods for Petroleum Well Optimization
and Anderssen (2018). MSE is expected to change adaptively as formation lithology and
compressive rock strength changes. However, the incremental changes in MSE from
different compressive rock strengths pale in comparison to the large (and often erratic)
ﬂuctuations in MSE from the transition from efﬁcient to inefﬁcient drilling (Dupriest
and Koederitz, 2005). Baselining trends in MSE, combined with pattern recognition and
analysis to determine drilling efﬁciency, onset, and type of bit dysfunction, have proved
to be a strong operational diagnostic tool in ﬁeld applications.
Fig. 6.6 shows an MSE curve from a well in which bit balling is occurring. The
footage where the MSE is high indicates that there is dysfunction (in this case, bit
balling). When the bit drilled from a shale back into a sand, the MSE fell, indicating the
bit’s cutting structure had cleaned up and was then operating efﬁciently. Changes in rock
hardness also affect the energy required, but this effect is minor when compared with the
energy increase when bit dysfunction occurs. Therefore, these large changes in MSE are
very useful in showing dysfunction. When combined with other information, changes in
MSE can also be used to determine the cause of the problem.
Changes in MSE can be related to effects of dysfunction shown in Fig. 6.7. If the
MSE increases when a change is made, the performance moves further way from drilling
efﬁciency, which is indicated by the dashed line. If the MSE decreases, the performance
moves closer to the dashed line. For example, the curve for whirl shows that if WOB is
Figure 6.6 Example mechanical speciﬁc energy plot showing severe bit dysfunction in shales due to
bit balling and efﬁcient drilling in sands. Nozzles were changed during a trip to increase bit cleaning,
and the MSE curve now shows both shales and sands being drilled efﬁciently. MSE, mechanical
speciﬁc energy; ROP, rate of penetration; WOB, weight on bit.
Mechanical speciﬁc energy and drilling efﬁciency
199
increased, the ROP performance moves closer to the predicted line, which means that
inefﬁciency due to whirl is decreasing, and we would expect the MSE to decrease. This is
used as a diagnostic. If the WOB is increased, and the MSE declines, we know that whirl
was the initial cause of dysfunction. As shown in Fig. 6.7, there is no other dysfunction
that improves as WOB is increased (i.e., moves closer to the dashed line). To identify
some of the other forms of founder, it is necessary to observe additional data or to have
more information about the drilling conditions. This is discussed in the sections below.
Regardless of the cause of dysfunction, the manner in which the driller uses the MSE
to maximize real-time performance is the same. To maximize performance, the driller
must conduct step tests by changing one parameter at a time (WOB, RPM, or GPM).
•
If the MSE declines, the dysfunction is getting better and performance is improving,
then the driller should continue with more of the same change (that is, increase
WOB).
•
If the MSE increases, the dysfunction is becoming worse, and performance is
declining, then the driller should change the parameter in the other direction (that is,
reduce the WOB).
•
If the MSE stays the same, performance is on the straight-line portion of the drill-
off curve in Fig. 6.8A, and then the driller should continue increasing WOB to
founder.
It should be emphasized that the driller cannot simply observe the MSE curve to di-
agnose most root causes or to determine the next action. Step tests must be conducted,
and the MSE response to the change observed. It is the response that is diagnostic.
Figure 6.7 Founder, or rock-cutting dysfunction, causes the depth of cut and ROP to be less than they
should be for a given WOB, reducing drilling efﬁciency. The order in which the various dysfunctions
are seen as WOB is increased will vary and must be determined by the driller in an organized step test.
ROP, rate of penetration; WOB, weight on bit; MSE, mechanical speciﬁc energy.
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Methods for Petroleum Well Optimization
6.2 Mechanical speciﬁc energy: next-generation digital drilling
optimization
The MSE surveillance process allows the driller to detect changes in the efﬁciency
of the drilling system more or less continuously. This improves performance by
(1) allowing the optimum operating parameters to be identiﬁed easily, and (2) providing
the quantitative data needed to justify the costs of design changes to extend the current
limits of the system. MSE analysis has resulted in redesign in areas as diverse as well
control practices, bit selection, BHA design, makeup torque, directional target sizing,
and motor differential ratings. The use of MSE surveillance is a key feature in a family of
well planning and operational practices.
6.2.1 Maximizing drill rates with real-time surveillance of mechanical
speciﬁc energy
Real-time MSE surveillance is used to ﬁnd the founder point for the current system, and
in some cases the cause of the founder. MSE is a ratio. It quantiﬁes the relationship
between input energy and ROP
. This ratio should be constant for a given rock, which is
to say that a given volume of one type of rock requires a given amount of energy to
destroy. The relationship between energy and ROP derived by Teale (1965) is:
MSEzðApproximatelyÞ Input Energy
Output ROP
(6.1)
MSE ¼ 480  Tor  RPM
Dia2  ROP
þ 4  WOB
Dia2  p
(6.2)
Figure 6.8 (A) The graphs show a straight-line response of ROP to WOB, indicating an efﬁcient bit up
to the founder point. The driller must keep WOB at or below the founder point. (B) The result of drilling
process changes that elevate the founder point to a higher WOB. The WOB that the driller can now
apply without foundering is increased, as is the achievable ROP. ROP, rate of penetration; WOB, weight
on bit.
Mechanical speciﬁc energy and drilling efﬁciency
201
To improve the usefulness of MSE surveillance in ﬁeld operations, the speciﬁc energy
equation originally derived by Teale has been adjusted to include a mechanical efﬁciency
factor (EFFM).
MSEadj ¼ MSE  EFFM
(6.3)
At perfect efﬁciency, the MSE would equal the rock compressive strength. However,
as shown in Fig. 6.9, bits are typically only 30%e40% efﬁcient at peak performance.
Consequently, even when the bit is operating at peak efﬁciency in the linear portion of
the drill-off curve, Teale’s MSE value would be around three times the rock compressive
strength. Multiplying the equation by an assumed mechanical bit efﬁciency reduces the
value to one that is closer to the compressive strength (MSEadj).
In ﬁeld operations, the operator always sets the EFFM uniformly to 0.35, regardless
of bit type or WOB. Although this value is known from lab data to commonly vary from
0.30 to 0.40, the error introduced was in the past considered to be acceptable. Calcu-
lations of MSE from surface measurements contain even larger sources of error, and the
ﬁeld plots are only used qualitatively as a trending tool. Any error in EFFM will tend to
shift the curve uniformly. Although the value may be incorrect, the uniformity of the
shift allows the curve still to be used effectively as a visual trending tool.
Fig. 6.10 shows an example of an MSEadj display that is similar to what is viewed by
rig site personnel. The inputs to the equation and a few other key data are plotted in the
Figure 6.9 Notional depiction of bit mechanical efﬁciency. In region I of the classic drill-off curve, the
WOB is inadequate to achieve the minimum threshold in depth of cut (DOC) required for efﬁcient
drilling. Above this threshold, bit mechanical efﬁciency is typically 30%e40%. WOB, weight on bit.
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Methods for Petroleum Well Optimization
three tracks on the left, and the MSEadj is shown in the track on the far right. The data are
collected from surface sensors and routed to the data contractor’s computer. As drilling
progresses, the calculated MSEadj is displayed alongside other mechanical drilling curves.
Plots can be generated on time-based or footage-based scales and displayed on any of the
rig-site monitors. Although the intent is for MSEadj analysis to be conducted in real time
by rig site personnel, the information is also transmitted offsite to the drilling engineer,
typically in 15-second updates. Each user has the ﬂexibility to change the scaling on each
track and the length of interval displayed. Fig. 6.10 shows a plot from a section of hole
that was drilled in the same manner as the offsets in this 30-year-old ﬁeld. The interval
was drilled with an IADC 1-1-7-tooth bit, 20 k lb WOB, and water-based mud.
The formations were soft, with rock strengths in both the sands and shales of less than
2 ksi. If the bit were efﬁcient, the MSEadj curve would have been a straight line with a
value of around 2 ksi. Instead, the MSEadj rises to values exceeding 25 ksi in the shales
and drops back to 2 ksi in the sands. The system requires as much energy to drill the
shales as if the rock compressive strength were 25 ksi. The founder that is occurring in
the shales is assumed to be due to bit balling because the other two causes of founder
(bottomhole balling and vibrations) are unlikely in this situation. When the bit enters
sand and the buildup of shale cuttings on its surface is cleared, the cutting structure
Figure 6.10 MSEadj rises well above the baseline trend when the system is operating beyond its
founder point. MSE, mechanical speciﬁc energy.
Mechanical speciﬁc energy and drilling efﬁciency
203
becomes efﬁcient again, and the ROP climbs back to around 300 fph while the MSEadj
drops back close to rock strength. The bit was drilling in a mature area at an average ROP
similar to that of the offset record well.
In this case study, the crew knew that the bit slowed in shales and sped up in sands, but
before analyzing the MSE data they had viewed this as a result of the shale simply being
“slower drilling”. The example therefore illustrates the usefulness of a tool that provides
an objective assessment of performance. The MSEadj plot makes it clear that this bit and
the record offset well were both inefﬁcient and that it should be possible to drill the
entire interval at 300e400 fph if the founder problem in the shales were addressed. This
was done on subsequent wells with a PDC bit and enhanced hydraulics.
In the aforementioned example, the MSE plot is being used in real time, but only as a
passive learning tool. Fig. 6.11 shows the preferred approach to using the MSEadj plot,
which is to conduct frequent methodical tests to identify system limits. In this case study,
after drilling out of the surface casing with an 8½00 bit in water-based mud, an “MSE
Weight-Test” was conducted during which the WOB was raised from 5 to 11 k lb in 2 k
lb increments. Each time the weight was raised, the MSEadj was observed to see if it had
increased; an increase in MSEadj would indicate that the system was foundering. In this
case, it was essentially unchanged, and the bit was operating just as efﬁciently at 200 fph as
it was at 100 fph. An “MSE RPM-Test” was then conducted at 2130 ft by raising the
rotary speed from 60 to 120 rpm, and the MSEadj again was unchanged. The bit
continued to be efﬁcient at close to 400 fph.
Figure 6.11 WOB and RPM tests are conducted by observing the MSE while increasing parameters. If
the MSE remains close to the baseline value while raising WOB, the bit is as efﬁcient at the high load as
before. ROP will continue to increase linearly with WOB. RPM tests are conducted in a similar manner.
Spurious spikes are disregarded unless they persist for several feet. They occur at connections and are
associated with light WOB while reestablishing the bottomhole pattern. MSE, mechanical speciﬁc
energy; ROP, rate of penetration; RPM, rotations per minute; WOB, weight on bit.
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Methods for Petroleum Well Optimization
As shown conceptually to the right in Fig. 6.11, the bit is operating in the linear
portion of the drill-off curve throughout both tests. In contrast, the high MSE in the
surface hole shows that the tooth bit used in this interval was balling in shales. Continued
testing and modiﬁcations to the hydraulics in subsequent wells eventually resulted in
continuous drill rates of over 500 fph throughout the production hole. Even at these
elevated ROPs, the bit design and high hydraulics are now adequate to prevent founder.
6.2.1.1 Friction losses in the drill string
The drilling team or drilling engineer should always be aware that the MSEadj may
contain inaccuracies and should only be used as a trending tool. The most signiﬁcant
source of error is drill string friction. The MSEadj calculated from surface data contains
torque that was created by friction between the pipe and borehole. This torque distorts
the curve so that the bit appears to be consuming much more energy than is actually the
case. Where high frictional losses exist, the MSEadj values may exceed rock strength by
several hundred thousand psi and yet the bit is operating efﬁciently, and the high values
are due entirely to drill string friction.
This issue should be resolved to some degree when software has been developed to
utilize downhole data. However, even with downhole data, it is likely that the MSE
curve will continue to be used primarily as a trending tool. The drill team views the
overall shape of the curve to develop a sense of what the baseline MSEadj might be and
then makes judgments based on the change in trend as various parameters are tested.
A spreadsheet has been developed to aid the user in determining what portion of the
MSEadj might be due to an assumed amount of string friction, but this adjustment is not
routinely made in real time. Instead, rig-site personnel depend heavily on pattern
recognition and trend analysis.
6.2.1.2 Bit balling
Fig. 6.12 shows another example of the MSEadj response during bit balling. The bit had
an initial HSI of 5.2 hp/in2, and it drilled at the previous record well pace, with an
average ROP of around instantaneous 150 fph. However, because rig site personnel saw
that the energy consumption was high, with an MSEadj in excess of 30 ksi in the shales,
they concluded that the bit was balling and pulled it. Despite the record pace, this crew
had a low tolerance for inefﬁciency due to their prior experience with MSEadj testing in
other ﬁelds. The replacement bit was almost identical in design but was nozzled for an
HSI of 11.5 hp/in2. The increase in hydraulics enabled the cutting structure to remain
clean at much higher ROP
. After the change in hydraulics was made, the MSEadj was
observed to approximately equal rock compressive strength, indicating that the cutting
structure remained clean, and both sands and shales were drilled uniformly at more than
350 fph for the next 3000 ft.
Mechanical speciﬁc energy and drilling efﬁciency
205
Fig. 6.13 demonstrates the manner in which hydraulics affect founder and ROP
. The
level of HSI required for a given bit and formation depends on the desired ROP
. There is
no single threshold of hydraulics at which balling ceases to occur. Hydraulics do not
eliminate balling; they only extend the founder point so that balling occurs at a higher
WOB and ROP
. Real-time surveillance of MSE weight tests has enabled the relationship
between hydraulics and ROP to be quantiﬁed, with potential implications in equipment
contracting. The ability to quantify the founder point has also begun to make it possible
to distinguish between bits that were previously thought to have similar performance.
6.2.1.3 Bottomhole balling
Bottomhole balling becomes more likely when insert bits are used in hard formations
because of the crushing action of insert bits, though the phenomenon may appear to
some degree any time that high WOB is utilized in hard rock. The MSEadj curve shown
on a time scale in Fig. 6.14 is considered to be characteristic of bottomhole balling.
Figure 6.12 Founder point was extended in water-based mud to beyond 350 fph by reducing nozzle
size to increase HSI.
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Methods for Petroleum Well Optimization
Figure 6.13 Desired hydraulics in water-based mud depend on the desired ROP. ROP, rate of
penetration.
Figure 6.14 Probable bottomhole balling with insert bit. High MSEadj value and lack of variation
(þ5%) suggests that the bit is rotating on bottomhole cake with little interaction with the rock.
Mechanical speciﬁc energy and drilling efﬁciency
207
A 77/800 insert bit is drilling 25-ksi rock in water-based mud. The MSEadj is elevated to a
very high level of 800 ksi, which is to say that the system is using the same amount of
energy as if we were drilling a rock of higher strength. The conclusion that this is due to
bottomhole balling comes primarily from elimination of the alternatives. Bit balling does
not occur in very hard rock. Although vibrations are very common, and they can create
MSEadj values of this magnitude, the smooth curve lacks the character expected with
vibrations. The variation in MSEadj is less than 5%. The lack of variation is interpreted as
the behavior of a bit rotating on powder with little direct contact with resistant rock.
Vibrations tend to generate wide variations in torque. In Fig. 6.14, this bit does not
appear to be reacting with the bottom of the hole to generate torque.
6.2.1.3.1 Vibrations
Fig. 6.15 shows a series of MSE Weight and RPM tests run in 5e10 ksi rock. This
example demonstrates some commonly observed vibrational behaviors. The MSEadj was
initially 30e40 ksi. When the WOB was decreased at 8270 ft, the MSEadj dropped
dramatically and ROP increased. Because bit balling and bottomhole balling appeared
unlikely in the given situation, the drop in MSE was assumed to have occurred because
the reduction in WOB moderated vibrations. When the weight was raised back to its
original value at 8500 ft, the MSE also climbed and the ROP fell, indicating the return of
vibrations. At 8580 ft, the WOB was reduced to very low levels and the MSE climbed
much higher, possibly due to inadequate depth of cut (DOC) or severe whirl.
Altogether, the testing shows that the highest ROP is achieved at around 12e15 k
WOB. However, this could be determined by running drill rate tests without the beneﬁt
of the MSEadj curve. The value of the MSEadj curve is that it shows the drill team that the
ROP is being limited by vibrations and not by changes in rock strength. The changes
observed in MSE far exceeded any reasonable change that might have occurred in
conﬁned compressive strength. To drill faster, a design change is needed to eliminate or
constrain vibrations at a WOB higher than 15 k lb.
Fig. 6.16 shows a second example of vibrational founder in an 8½00 hole in 5-ksi
compressive strength rock. At the beginning of this interval, the MSE baseline is
around 250 ksi with frequent spikes of up to 500 ksi. The baseline is elevated because of
drill string friction losses, and the spikes in this example are believed to be due to the
tendency for this particular bit to develop a whirling pattern and vibrate when traversing
the highly laminated stratigraphy. In an attempt to reduce vibrations, the crew increased
WOB and decreased the rotary speed at 10,200 ft. This is a common operational
mitigation for whirl. The MSEadj declined, and ROP increased. The ROP response
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Methods for Petroleum Well Optimization
could have been explained without the MSEadj curve because ROP normally increases
with increased WOB. However, the ROP response should have only been proportionate
to the WOB increase, but in this case, it exceeded the increase. The fact that the response
was disproportionately high resulted in the decline in MSEadj. The bit did not simply
drill faster due to increased WOB; it became more efﬁcient with the WOB with which it
was provided. MSEadj testing can be used in this manner to adjust parameters to mitigate
vibrations, but only within a certain range. If the vibrational tendency is too great, the
drilling team must modify the bit or system to achieve a signiﬁcant improvement.
Figure 6.15 Initial MSEadj suggests mild vibrational founder. However, the system became more
efﬁcient at reduced WOB. Energy loss returned when the weight was raised back. A ﬁnal test at very
low weight showed even greater inefﬁciency, possibly due to increased whirl or low depth of cut.
WOB, weight on bit.
Mechanical speciﬁc energy and drilling efﬁciency
209
Fig. 6.17 shows the onset of severe whirl when an aggressive PDC encountered a
short interval in which the rock strength increased from around 3 to 8 ksi. The MSE
increased by over 50 ksi, indicating the onset of vibrational founder. The crew raised the
WOB to try to maintain ROP and severely damaged the bit within 100 ft of drilling.
Caliper logs also showed a signiﬁcantly oversized hole drilled by a whirling bit. When
another stringer with the same MSE pattern was encountered 500 ft deeper, the WOB
and RPM were reduced to protect the bit. After the MSE showed that the stringer had
been fully penetrated, drilling parameters were returned to normal, and the bit was
eventually pulled at target depth with no indication of damage.
Fig. 6.18 illustrates an important additional aspect of vibrational founder. The
amplitude of the vibrations that may reduce ROP can be quite small. The left-hand track
shows a plot of MSEadj. At some depths, the value exceeds 1000 ksi. The corresponding
vibrations are shown on the right-hand axis. There is a clear correlation between the
two, but the RMS vibrational levels causing this extreme inefﬁciency are generally less
Figure 6.16 High MSEadj with PDC in 5-ksi compressive strength rock assumed to be due to whirling
vibrations. MSE declined when WOB was raised and RPM reduced. MSE, mechanical speciﬁc energy;
RPM, rotations per minute; WOB, weight on bit.
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Methods for Petroleum Well Optimization
than 3 Gs. Because the industry has been primarily concerned with tool damage,
vibration-monitoring subs do not typically transmit a vibration warning until acceler-
ations of 25e50 Gs are observed. Consequently, operators are not aware that these issues
exist or that there may be a very large opportunity to improve ROP
.
Fig. 6.18 also shows that all forms of vibration do not affect ROP equally. The highest
G forces are observed between 8350 and 8400 ft, and yet the MSEadj is relatively low.
The accelerations on this axis are interpreted as stick-slip, and the MSEadj behavior is
consistent with lab reports showing stick-slip to have only a moderate effect on ROP
.
ROP effects cannot be predicted from vibrational data alone, nor can the nature of the
vibrations be understood from MSEadj surveillance alone. However, the combination
may provide both the technical understanding of the changes that need to be made
and form the cost justiﬁcation required to make them, both for operators and for the
designers of bits.
Figure 6.17 ROP dropped from 90 to 20 fph in hard stringer. However, the increase in MSEadj to 70 ksi
greatly exceeds possible change in rock strength, indicating that most of the ROP loss was due to
vibrations. ROP, rate of penetration.
Mechanical speciﬁc energy and drilling efﬁciency
211
6.2.1.4 Bit dulling
Fig. 6.19 shows an example of a dulling trend with an 8½00 insert bit in 20-ksi rock.
Dulling trends may be very distinct. In this particular case, the early trend was masked by
high drill string torque in the directional hole and vibrations. When the insert bit dulls,
energy consumption tends to increase steadily over the last 50e100 ft. The trend suggests
that the bit stays relatively efﬁcient throughout most of its life, but once the dulling
begins, the tooth proﬁle ﬂattens fairly rapidly. PDC bits have been seen to become
inefﬁcient within a shorter interval. The operator’s knowledge of the expected bit life
and offset performance is a key factor in deciding to pull the bit based on an observed
MSE trend.
6.2.2 Hydromechanical speciﬁc energy
Teale (1965) deﬁned MSE as the amount of energy required to remove a unit volume of
rock. It amounts to the combination of energies due to axial and torsional loads (Eq. 6.4).
However, the MSE does not necessarily represent the total energy consumed in breaking
Figure 6.18 Correlation can be seen between MSE and downhole vibrational accelerations. Vibrations
of less than 3 Gs are affecting efﬁciency severely. From 8350 to 8400 ft, the amplitude of the vibrations
in one axis is higher, but this speciﬁc type of vibration does not affect drilling efﬁciency as greatly. MSE,
mechanical speciﬁc energy.
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Methods for Petroleum Well Optimization
and removing the rock fragments beneath the bit because the hydraulic energy term is
omitted in the model.
MSE ¼ WOB
Ab
þ 120p  N  T
Ab  ROP
(6.4)
Hydromechanical speciﬁc energy (HMSE) is the total energy consumed during the
drilling of a well (Mohan et al., 2015; Chen et al., 2016; Wei et al., 2016; Oloruntobia
and Butt, 2019). HMSE combines the axial, rotary, and hydraulic energies:
HMSE ¼
Axial Energy
Rock Volume Drilled þ
Torsional Energy
Rock Volume Drilled þ
Hydraulic Energy
Rock Volume Drilled
(6.5)
Figure 6.19 Dulling trend with insert bit. Replaced with PDC bit and motor.
Mechanical speciﬁc energy and drilling efﬁciency
213
HMSE ¼ WOB
Ab
þ 120p  N  T
Ab  ROP
þ 1154  DPb  Q
Ab  ROP
(6.6)
Pessier and Fear (1992) expressed the downhole torque (T) as a function of WOB,
bit-speciﬁc coefﬁcient of sliding friction (m), and bit diameter (Eq. 6.7).
T ¼ m  DbWOB
36
(6.7)
Eq. (6.8) is obtained by substituting Eq. (6.7) into Eq. (6.6).
HMSE ¼ WOB
Ab
þ 13:33m  N  WOB
AbROP
þ 1154  DPbQ
AbROP
(6.8)
In the expanded form, the HMSE is given by:
HMSE ¼ WOB
Ab
þ 120p  N  T
AbROP
þ 1154  h  DPb  Q
AbROP
(6.9)
where: WOB is the downhole weight on bit (lbs); Ab is the bit area (in2); N is the rotary
speed (rpm); T is the torque on bit (lb-ft); ROP is the rate of penetration (ft/h); DPb is
the bit pressure drop (psi); Q is the ﬂow rate (gpm); and h is the hydraulic energy
reduction factor.
Due to accelerated ﬂuid entrainment immediately below the jet nozzles during
drilling, only a portion (25%e40%) of the available bit hydraulic energy actually reaches
the bottom of the hole (Warren, 1987). The hydraulic energy reduction factor converts
the jet hydraulic energy into the bottomhole hydraulic energy. Therefore, a hydraulic
energy reduction factor (h) is introduced to convert the bit hydraulic energy into the
bottomhole hydraulic energy (Eq. 6.10).
HMSE ¼
WOB
Ab
þ 13:33m  N  WOB
Ab  ROP
þ 1154  h  DPb  Q
Ab  ROP


NPP
ECD

(6.10)
6.2.3 Hydromechanical speciﬁc energy for lithology prediction
For polycrystalline diamond compact (PDC) bits, the hydraulic energy reduction factor
(hPDC Bit) is expressed as a function of junk slot area and total ﬂow area (Oloruntobi
et al., 2018), and it is given by:
hPDC Bit ¼ 1 
 JSA
TFA
0:122
(6.11)
where JSA is the junk slot area (in2) and TFA is the total ﬂow area (in2).
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Methods for Petroleum Well Optimization
For roller-cone bits, the hydraulic energy reduction factor is expressed as a function
of bit area and total ﬂow area (Warren, 1987):
hRoller cone bit ¼ 1 
0:15 Bit area
TFA
0:122
(6.12)
The pressure drop at the bit nozzle is expressed as a function of circulating ﬂuid
density, volumetric ﬂow rate, and nozzle total ﬂow area:
DPb ¼
MW Q2
10858 TFA2
(6.13)
where: DPb is the bit pressure drop (psi); MW is the mud weight (ppg); Q is the ﬂow rate
(gpm); TFA is the total ﬂow area (in2).
The HMSE consumed while drilling with PDC bits can be obtained by combining
Eqs. (6.10), (6.11) and (6.13):
HMSEPDC ¼ WOB
Ab
þ 120pNT
AbROP þ
1154 MW Q3

1 
 JSA
TFA
0:122
10858 AbROP TFA2
(6.14)
The HMSE consumed while drilling with roller-cone bits can be obtained by
combining Eqs. (6.10), (6.12), and (6.13):
HMSERoller cone bit ¼ WOB
Ab
þ 120pNT
AbROP þ
1154 MW Q3

1 
0:15 Bit area
TFA
0:122
10858 AbROP TFA2
(6.15)
When plotted against depth on semilog, the HMSE computed using Eqs. (6.14) or
(6.15) should be able to clearly identify the various stratigraphic units being penetrated.
If available, downhole measurements of torque and WOB from the measurement while
drilling (MWD) tools should be used to estimate the HMSE. Using the drilling
parameters obtained from surface measurements to estimate the HMSE can introduce
signiﬁcant errors, especially in moderately to highly deviated (>20 degrees inclination)
wells due to the presence of friction between the drill string and the borehole walls. The
application of drilling data obtained from surface measurements to compute the HMSE
is possible in vertical wells since the friction between the drill string and the walls of the
borehole is usually negligible.
It is acknowledged that the HMSE may be affected by several factors other than
lithology. These factors include bit wear, bit type, rock compaction, and differential
pressure between the bottomhole pressure (dictated by equivalent circulating density:
ECD) and the formation pore pressure. Bit wear will cause a reduction in the rate of
penetration and thus an increase in the HMSE.
Mechanical speciﬁc energy and drilling efﬁciency
215
In a normally compacted series, rock compaction typically increases with depth.
Hence, the energy (HMSE) required to break and remove a unit volume of rock will also
increase with depth. Although lithology is the major factor controlling HMSE changes,
if the effects of other factors on the HMSE can be minimized, HMSE changes can
mainly be attributed to lithological variations. The objective here is not to eliminate but
to minimize the effects of other parameters on the HMSE to the extent that the lithology
effect dominates the drilling process. For practical purposes, the effects of bit wear, bit
type, and rock compaction on the HMSE can be minimized by analyzing the HMSE
over short intervals (  2000 ft) drilled with a single bit. The short intervals will ensure
that the bit dulling and rock compaction are within the tolerable range, and the single bit
will ensure the effect of bit type is eliminated. Therefore, over short intervals, any de-
viation in the HMSE trend will indicate either a lithological change or a change in
differential pressure.
The changes caused by differential pressure are more gradual: a gradual decrease in
the HMSE may indicate drilling through the pressure transition zones as pore pressure
increases, while a gradual increase in the HMSE may indicate that the amount of
overbalance is becoming excessive. The effect of differential pressure on the speciﬁc
energy will be more pronounced at low values of overbalance than at high values of
overbalance (Vidrine and Benit, 1968; Black et al., 1985; Bourgoyne et al., 1986).
However, the changes caused by lithology are typically abrupt and easily identiﬁed. Since
lithology identiﬁcation is the objective, any sudden changes in the HMSE trend when
plotted against depth will indicate the lithological boundary. Although short intervals of
analysis are recommended, this does not mean the entire hole section cannot be
analyzed. All that is required is to divide the hole section into several intervals and then
analyze individual intervals. The chosen intervals of analysis do not necessarily have to be
uniform.
6.2.3.1 Field example
To demonstrate the usefulness of the proposed methodology and its applications at the
wellsite, we consider the case study of an exploratory gas well (Well A) located
approximately 83 km northwest of Port Harcourt in the central swamp region of the
Niger Delta basin. Well A is slightly deviated with maximum inclination of 14.6 degrees.
Figs. 6.20 and 6.21 display the recorded drilling parameters and wellbore pressures for
two separate intervals in Well A. The recorded data include torque, rotary speed, ﬂow
rate, ROP
, WOB, equivalent circulating density, mud weight, and pore pressure. The
bottomhole pressure is estimated from the ECD. The recorded drilling parameters were
obtained from surface measurements. The errors associated with using the drilling data
obtained from surface measurements to compute the HMSE in this well are negligible
because the well maximum inclination is low (<15 degrees), the intervals under
consideration are short (  2000 ft), the kick-off point is deep (7878 ft), and the dogleg
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Methods for Petroleum Well Optimization
Figure 6.20 The plots of drilling parameters and wellbore pressures versus depth for Well A in in-
terval 1. Modiﬁed from Oloruntobia, O., Butt, S., January 2020. Application of speciﬁc energy for lithology
prediction. J. Petrol. Sci. Eng. 184 106402.
Figure 6.21 The plots of drilling parameters and wellbore pressures versus depth for Well A in
interval 2. Modiﬁed from Oloruntobia, O., Butt, S., January 2020. Application of speciﬁc energy for
lithology prediction. J. Petrol. Sci. Eng. 184 106402.
Mechanical speciﬁc energy and drilling efﬁciency
217
severities (DLS) do not exceed 1.5 degrees/100 ft anywhere across the intervals. Over
short intervals in low inclination wells at low DLS, changes in friction forces between the
drill string and the borehole walls can be negligible. Since lithology identiﬁcation using
the HMSE concept is based on observing trend changes, any changes in HMSE trends
over short intervals at low DLS in low inclination wells will most likely be due to other
factors (such as lithology) rather than the friction forces between the drill string and the
borehole walls.
In interval 1 (Fig. 6.20), the recorded drilling parameters were acquired in the 1600-
hole section drilled with a single roller-cone (milled tooth) bit from 8695 to 9420 ft. The
interval was drilled with water-based mud, and the total ﬂow area (TFA) of the roller-
cone bit was 1.1689 in2. The formation pore pressure was normal across all the pene-
trated rocks. In interval 2 (Fig. 6.21), the recorded drilling parameters were acquired in
the 12¼00-hole section drilled with a single PDC bit from 9690 to 11,690 ft. The TFA of
the PDC bit was 1.2003 in2, and its junk slot area (JSA) was 21.28 in2. The interval was
drilled with oil-based mud, and the formation pore pressures vary across the penetrated
rocks. The interval consisted of both normally pressured zones and two depleted sands.
Fig. 6.22A shows the GR-depth and HMSE-depth plots for interval 1. The HMSE is
computed using Eq. (6.10) because the interval was drilled with a roller-cone bit. An
excellent agreement in trend is observed between the gamma ray (GR) and the HMSE.
This clearly demonstrates the applicability of the HMSE to lithology identiﬁcation.
Abrupt changes in the HMSE trend indicate lithological changes. In shale formations as
indicated by high GR, higher energy is consumed in breaking the rocks. However, in
sand formations as indicated by low GR, lower energy is consumed in breaking the
rocks. A shale baseline drawn through the interval indicates that the shale formation
between 8695 and 8826 ft required lower energy to drill than the remaining deeper shale
formations. This is probably due to bit dulling and rock compaction effects on the
HMSE.
Fig. 6.22B shows the GR-depth, VR-depth, and HMSE-depth plots for interval 2.
The velocity ratio (VR) is derived from the ratio of compressional to shear-wave
velocities.
Note that the display unit of VR is in 1/100 s for ease of interpretation. The HMSE is
computed using Eq. (6.9) because the interval was drilled with a PDC bit. A good
agreement exists between the conventional lithology identiﬁers and the HMSE. In shale
formations, as indicated by high GR and high VR, lower energy is consumed in
breaking the rocks. In sand formations, as indicated by low GR and low VR, higher
energy is consumed in breaking the rocks. The formation tops are clearly visible with
abrupt changes in the HMSE. Remarkably, the HMSE is able to identify the very tiny
sands (minor reservoirs), conﬁrming the accuracy of the proposed methodology. Across
the interval, the HMSE shale baseline is fairly constant between 9690 and 11,252 ft.
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Methods for Petroleum Well Optimization
Deeper than 11,252 ft, the shale intervals begin to shift from the shale baseline, possibly
due to bit dulling and/or rock compaction effects. If a longer interval of analysis is
considered, the effects of bit dulling and rock compaction on the HMSE may be highly
pronounced, making evaluations more complex and difﬁcult. This is the main reason
why short intervals of analysis are recommended. The different responses of roller-cone
and PDC bits in the same lithology are mainly due to their cutting actions. Each bit type
drills the hole in a different manner; the roller-cone bit crushes the formations, while the
PDC bit shears the formations. The abrupt changes in the HMSE at the top of the
formation indicate that the effect of lithology on the HMSE dominates the drilling
process.
6.2.4 Hydromechanical speciﬁc energy for pore pressure prediction
In this part, a new pore pressure prediction technique based on the concept of HMSE is
being proposed. The new technique can provide a reliable means of estimating the
Figure 6.22 (A) The GR-depth and HMSE-depth plots for Well A in interval 1. (B) The GR-depth, VR-
depth, and HMSE-depth plots for Well A in interval 2. Modiﬁed from Oloruntobia, O., Butt, S.,
January 2020. Application of speciﬁc energy for lithology prediction. J. Petrol. Sci. Eng. 184 106402.
Mechanical speciﬁc energy and drilling efﬁciency
219
formation pore pressure from the drilling parameters in the absence of reliable downhole
measurements at relatively low cost.
Due to the jet impact of the drilling ﬂuid on the formation, an equal and opposite
(pump-off) force is exerted on the bit. The pump-off/jet-impact force will reduce the
WOB (Eq. 6.16).
HMSE ¼
WOBe
Ab
þ 13:33m  N  WOBe
AbROP
þ 1154  h  DPb  Q
Ab  ROP


NPP
ECD

(6.16)
For ﬁxed cutter bits, the value of the bit-speciﬁc coefﬁcient of sliding friction (m) will
depend on lithology, rock strength, mud weight, blade count, bit wear, and cutter sizes
(Caicedo et al., 2005; Guerrero and Kull, 2007). However, for ﬁeld applications, the
value of m often stays within a narrow range: 0.18e0.24 for roller-cone bits and 0.5e0.8
for PDC bits under different operating conditions (Wei et al., 2016). To minimize the
errors in the computation of HMSE, it is reasonable to assume average values of 0.21 for
the roller-cone bit and 0.65 for the PDC bit. For PDC bits, the hydraulic energy
reduction factor (h) is expressed as a function of the ratio between junk slot area (JSA)
and jet total ﬂow area (TFA) as shown in Eq. (6.11) (Oloruntobi et al., 2018). For roller-
cone bits, the model proposed by Warren (1987) provides good estimates (Eq. 6.12). The
hydraulic energy reduction factor model proposed by Rabia (1989) is more complex and
may not be suitable for applications where there are variations in nozzle sizes within the
same bit.
WOBe ¼ WOB  h  Fj
(6.17)
Fj ¼ 0:000516  MW  Q  Vn
(6.18)
Vn ¼ 0:32  Q
TFA
(6.19)
The formulas for the pressure drop at the bit, effective weight on bit, jet impact force,
and jet velocity are presented in Eqs. 6.13, 6.17, 6.18 and 6.19, respectively.
As the depth of burial increases in a normally compacted series, the energy (HMSE)
required to break and remove a unit volume of rock will also increase. However, sub-
surface overpressure conditions with lower effective stress will require less energy to drill
than the normally compacted series at the same depth, leading to the reversal of the
HMSE trend.
220
Methods for Petroleum Well Optimization
6.2.4.1 Methodology of pore pressure prediction
1. Compute the HMSE at the depth of interest using Eqs. (6.11), (6.12), and Eqs.
(6.16)e(6.19). If there are wide variations/ﬂuctuations in HMSE values due to
different lithologies being penetrated, the HMSE should be estimated over clean
shale intervals only to remove any lithological effects on HMSE.
2. Display the plot of HMSE against depth on a semilog.
3. Establish the normal compaction trend (NCT) over the entire interval.
4. Estimate the pore pressure gradient at any given depth using an energy-based Eaton’s
model (Eq. 6.19). The value of the speciﬁc energy ratio exponent (m) will vary from
one region to another. It can be obtained by calibrating Eq. (6.19) to any known
overpressure intervals in the offset or current wells. If the current well being drilled is
used as the calibration well, Eq. (6.19) should be preferably calibrated to the pressure
transition zones where kick intensity is reduced.
Gpp ¼ Gob 

Gob  Gnp


HMSEo
HMSEn
m
Figure 6.23 The plots of drilling parameters against depth for Well B. Modiﬁed from Oloruntobia, O.,
Butt, S., February 2019. Energy-based formation pressure prediction. J. Petrol. Sci. Eng. 173, 955e964.
Mechanical speciﬁc energy and drilling efﬁciency
221
6.2.4.2 Field example of pore pressure prediction
To demonstrate the applicability of the new pore pressure prediction technique, we
consider the case study of a recently drilled high-pressure high-temperature exploratory
well (Well B) in the tertiary deltaic system of the Niger Delta. Well B is located
approximately 80 km northwest of Port Harcourt in the central region of the basin. The
well is a near vertical sidetrack well drilled to a total depth of 17,265 ft with a maximum
inclination of 6.8 degrees.
In this example, all depths are referenced to true vertical depth (TVD) below the
rotary table (RT).
Table 6.2 provides information about the type of bit and BHA used to drill the hole
sections of interest. The dull grade for the bit used to drill the 55/800 hole was not available
because the bit was lost in the hole due to a stuck pipe incident following a well-killing
operation. Only the 12¼00, 8½00, and 55/800 hole sections are under consideration here.
These intervals contain the normally compacted series, pressure transition zones, and
overpressure formations. The top/big hole sections have been excluded from the analysis
because of limited data acquisitions, and the sections contain loose continental sands with
no overpressure or hydrocarbon bearing intervals. Fig. 6.23 displays the plots of the
Figure 6.24 The formation bulk density and overburden pressure/gradient proﬁles for Well B.
Modiﬁed from Oloruntobia, O., Butt, S., February 2019. Energy-based formation pressure prediction. J.
Petrol. Sci. Eng. 173, 955e964.
222
Methods for Petroleum Well Optimization
recorded drilling parameters from surface measurements while drilling the well. Where
the BHA contains mud motor, total rotary speed is obtained using Eq. (6.20).
Total rotary speed ¼ Surface string rotation þ ½Q  Motor STFR
(6.20)
Table 6.2 Well B: well and bit data summary.
Hole
size
Bit data
BHA type
Intervals (ft)
TFA
(in2)
JSA
(in2)
Bit dull grade
12¼00
PDC bit
(HCC, Q 506 F)
RSS
10,099e15,080 1.2824 31.48
2-5-WT-G-X-I-
CT-BHA
12¼00
PDC bit
(HCC, QD
507 FHX)
RSS
15,080e15,193 1.2962 21.28
1-2-CT-S-X-I-
NO-TD
8½00
PDC bit
(HCC, DP 506 F)
RSS
15,193e15,601 0.8399 15.55
1-1-WT-S-X-I-
NO-DTF
8½00
PDC bit
(HCC, DP 506 F)
RSS
15,601e16,556 1.0301 15.55
2-2-BU-A-X-I-
PN-TD
55/800
PDC bit
(HCC, QD 406 FHX)
Mud
motor
16,556e17,265 0.8437 4.295
N/A
BHA, bottomhole assembly; JSA, junk slot area; TFA, total ﬂow area.
Figure 6.25 The HMSE and pore pressure proﬁle for Well B. HMSE, hydromechanical speciﬁc energy.
Modiﬁed from Oloruntobia, O., Butt, S., February 2019. Energy-based formation pressure prediction. J.
Petrol. Sci. Eng. 173, 955e964.
Mechanical speciﬁc energy and drilling efﬁciency
223
To determine the overburden pressure (Eq. 6.21), the formation bulk density logs
from the offset wells were integrated with the formation bulk density logs from the well
(Well B) to produce the equation of best ﬁt (Eq. 6.22). The equation of best ﬁt was
further constrained by the leak-off test (LOT) data in the ﬁeld since the Niger Delta basin
operates under a normal faulting regime where Sv > sH > sh. The equation of best ﬁt
was used to estimate the formation bulk density values in intervals where formation bulk
density logs were not acquired. The overburden gradient (Gob) was obtained by dividing
the overburden pressure at the depth of interest by the true vertical depth. The plots of
formation bulk density, overburden pressure/gradient, and equation of best ﬁt are dis-
played in Fig. 6.24. Eq. (6.21) is an improvement to the formation bulk density pre-
diction model presented by Oloruntobi et al. (2018) for the central region of the Niger
Delta based on a new set of offset-well data.
Sv ¼ 0:433
Z
z
0
rbdz
(6.21)
rb ¼ 1:136 Z0:0833
(6.22)
Fig. 6.25A shows the plot of HMSE versus depth for Well B. Since the lithological
effect on the HMSE is minimal in this well, the HMSE values are estimated across the
various stratigraphic units from 10,997 to 17,265 ft. From the plot, the normal
compaction trend (NCT) can be visibly identiﬁed from 10,997 to 15,060 ft. In these
intervals, the total energy required to break and remove a unit volume of rock beneath
the bit (HMSE) increases with depth due to a decrease in rock porosity and an increase in
effective stress. Depth intervals that lie on the NCT correspond to the normally com-
pacted series in the ﬁeld. Based on the salinity of the formation waters in the region, the
average normal pore pressure in the intervals that lie on the NCT is 8.66 ppg (0.45 psi/
ft). In the intervals just below 15,060 ft (top of pressure transition zones), subsurface
overpressure conditions cause the HMSE to depart from the NCT to lower values. The
overpressure intervals with lower effective stress needed less energy to drill than the
normally compacted series at the same depth. The magnitude of overpressure is directly
correlated to the amount of deviation from the NCT.
Fig. 6.25B shows the comparison between pore pressure estimates derived from the
HMSE concept (Eq. 6.19) and actual pore pressure measurements. An excellent
agreement is observed between the predicted and measured formation pore pressure.
The actual pore pressure measurements were obtained from the wireline pressure sam-
pling tool and drilling kick data at the formations/depths of interest. Since the actual
formation pore pressure in the ﬁeld is known up to 16,567 ft (from offset wells) prior to
drilling Well A, Eq. (6.19) is calibrated to these intervals to determine the value of the
speciﬁc energy ratio exponent (m). The value of the speciﬁc energy ratio exponent (m) is
224
Methods for Petroleum Well Optimization
0.28. The predicted formation pore pressure is normal from 10,997 to 15,060 ft with an
average value of 0.45 psi/ft. At the depth just below 15,060 ft (onset of overpressure), the
formation pore pressure increases from 0.45 to 0.72 psi/ft at 15,630 ft. The formation
pore pressure then increases further from 0.72 psi/ft at 15,630 ft to 0.9 psi/ft at the
bottom of the well. The actual formation pore pressure at the bottom of the well was
obtained from gas kick data. While drilling with a mud weight (MW) of 0.87 psi/ft at the
bottom of the well (17,265 ft), a gas kick was taken with stabilized shut-in drill pipe
pressure (SIDPP) of 530 psi. This results in formation pore pressure of 0.9 psi/ft.
6.3 Rock drillability assessments
6.3.1 Drillability d-exponent
The drillability d-exponent normalizes the ROP by removing the effects of external
drilling parameters such as pressure and rock strength. This exponent increases with
depth in normally pressured formations, proportionally to the rock strength. When
drilling into abnormally pressured shale, however, the exponent will decrease with
depth. Here the drilling bit encounters an undercompacted section, where the decreased
density and increased porosity result in a more drillable formation. If all other drilling
parameters are unchanged, the rate of penetration will increase in this section. ROP also
increases by having less pressure differential between drilling ﬂuid and pore pressure.
These abnormal pressure zones are detected far earlier by a bit with no wear, than by a
worn-down bit. A dull bit may be far into the abnormally pressured zone before the
transition is detected. A projected plot of the d-exponent is shown in Fig. 6.26 (Ablard et
al., 2012).
Figure 6.26 Example of d-exponent plot. Modiﬁed from Ablard, P., Bell, C., Cook, D., Fornasier, I., Poyet,
J-P., Sharma, S., Fielding, K., Lawton, L., Haines, G., Herkommer, M.A., McCarthy, K., Radakovic, M., Umar,
L., 2012. The expanding role of mud logging. Oilﬁeld Rev. Spring 2012 24 (1), 31e32.
Mechanical speciﬁc energy and drilling efﬁciency
225
Using changes of ROP values by themselves as indicators of abnormal pressure is not
ideal. Therefore, the drillability exponent is used to normalize or correct the drilling rate.
This gives a more effective indicator of pore pressure and abnormally pressured zones.
The basic drillability exponent (d) originates from work by Bingham (1965), and the
mathematical formulation of the d-exponent is given as Eq. (6.23).
d ¼
log
 ROP
60 RPM

log
12 WOB
106dB

(6.23)
This equation tries to correct the ROP for changes in WOB, RPM, and hole size. In
1971, Rehm and McClendon produced a corrected d-exponent for changes in mud
weight (Rehm and McClendon, 1971). The corrected d-exponent (dc) is given by Eq.
(6.24).
dc ¼ d
NPP
ECD

(6.24)
Here, NPP is the normal pore pressure gradient, and ECD is equivalent circulating
density. This correction is universally used as it makes the exponent more sensitive to
mud weight changes and to increasing pore pressure, yet it is without a thorough
theoretical basis (Rabia, 2002).
Three limitations of the drillability exponent have been expressed (Rabia, 2002).
•
The drillability exponent requires clean shale or clean argillaceous limestone.
•
A large increase in mud weight results in lower values of the corrected drillability
exponent (dc).
•
The corrected drillability exponent (dc) is affected by lithology, type of bit, bit wear,
poor hydraulics, unconformities, and motor or turbine runs.
6.3.2 Formation drillability prediction
Formation drillability is an indicator which describes the difﬁculty degree of drilling into
the rock under certain conditions. The main purpose of a drillability study is to classify
rock to choose the right type of drill bit to enhance the drilling speed and lower the
drilling cost. In such a study, kernel principal component analysis (KPCA) is used to
extract the feature of the parameters, and then quantum particle swarm optimizatione
support vector machine (QPSO-SVM) is utilized as the fusion algorithm. The com-
parison of the results of this prediction function with the results of a backpropagation
neural network (BP-NN) indicates that this method is better than a BP-NN in a variety
of performance conditions and has the advantages of higher accuracy and better
generalization ability.
226
Methods for Petroleum Well Optimization
The concept expression of the model is sketched out in Fig. 6.27, and the main
characteristics are as follows:
1. The proposed heterogeneous space is a continuous multidimensional space, and each
point in the space represents an information source of some kind.
2. The basic data space can be divided into several clusters, corresponding to several
information source categories. Each cluster has a “center”; in addition, each corre-
sponding basic data space, such as seismic and well logging data, can also be char-
acterized with a number of dimensional vectors.
3. The clusters are not isolated, and they have a strong correlation with each other.
Based on the multidimensional heterogeneous space conceptual model, feature-level
information fusion is adopted as the fusion framework, as shown in Fig. 6.28.
Feature-level fusion is an intermediate-level fusion process. First, each sensor extracts its
own feature vector, and then the fusion center completes the fusion process of the feature
Figure 6.27 Sketch map of a multidimensional heterogeneous model. Modiﬁed from Ma, H., August
2011. Formation drillability prediction based on multi-source information fusion. J. Petrol. Sci. Eng. 78 (2),
438e446.
Figure 6.28 Map of a multidimensional heterogeneous model with fusion framework. Modiﬁed from
Ma, H., August 2011. Formation drillability prediction based on multi-source information fusion. J. Petrol.
Sci. Eng. 78 (2), 438e446.
Mechanical speciﬁc energy and drilling efﬁciency
227
vector. Generally speaking, the extracted feature information should be a sufﬁcient
representation of the information or sufﬁcient statistics from the information. The
advantage of the feature-level information fusion is that it can achieve considerable
compression of information, which is conducive to real-time information processing.
Also, the fusion result can give the maximum feature information required by the
decision-making analysis because the extracted features are directly related to the
decision-making analysis. The most widely used feature extraction methods include,
among others, PCA, KPCA, ICA, and kernel independent component analysis (KICA).
In addition, rough sets, random sets, evidence theory, Bayesian network, fuzzy set, gray
correlation, and SVM are among the commonly utilized multisource information fusion
methods.
The formation drillability prediction model is established by means of feature
extraction, data association, and multisource information fusion. In the model, the
closely related seismic data (seismic layer velocity), well-logging data (acoustic velocity,
formation density, shale content), mud logging data (drilling pressure, rotary speed,
hydraulic horsepower, bottomhole differential pressure, rate of penetration), and for-
mation depth are taken as inputs, and the formation drillability is taken as the output.
The structural diagram of the formation drillability prediction model is shown in
Fig. 6.29.
SVMs, derived from Vapnik’s statistical learning theory (Vapnik, 1995, 1998), have
become a popular technique among machine learning models due to their ability to
approximate complex nonlinear mapping directly from the inputeoutput data with a
Figure 6.29 Structural diagram of the formation drillability prediction model.
228
Methods for Petroleum Well Optimization
simple topological structure (Fig. 6.30). Through the use of a kernel, an input space of
data can be transformed into nonlinear and high-dimensional space. The algorithm
eventually generates a sparse prediction function by choosing only a selected number of
training points, known as support vectors. SVM implements an approximation of the
structural risk minimization principle through a balanced trade-off between empirical
error and model complexity. Thereby, SVM achieves global optimization.
Here, we use the Lagrange dual theory with the introduction of kernel function K(xi,
xj) ¼ 4(xi)$4(xj) to address the quadratic programming problem with the nonlinear
inequality constraints. Hence, we get the dual optimal problem:
max
a;a’
(
LD ¼ 1
2
X
k
i ¼ 1
X
k
j ¼ 1

ai  a
i
	
aj  a
j

K

xi; xj

þ
X
k
i ¼ 1
yi

ai  a
i
	
 ε
X
k
i ¼ 1

ai
þ a
i
	
)
(6.25)
subject to:
X
ai  a
i
	
¼ 0; ci: 0  ai  C
(6.26)
Figure 6.30 The sketch map of SVM. SVM, support vector machine. Modiﬁed from Ma, H., August 2011.
Formation drillability prediction based on multi-source information fusion. J. Petrol. Sci. Eng. 78 (2),
438e446.
Mechanical speciﬁc energy and drilling efﬁciency
229
where ai is a Lagrange multiplier.
Solving the dual problem, we get the solution:
fðxÞ ¼ u$fðxÞ þ b ¼
X
k
i ¼ 1

ai  a
i
	
Kðx; xiÞ þ b:
(6.27)
The kernel function has many forms. In this chapter, we select the radial basis
function (RBF) kernel, which is known to handle complex nonlinear problems well:
KðXi; XÞ ¼ exp

 kX  Xik2
2g2

:
where g is the Gaussian width. Two major RBF parameters applied in SVM, C and g,
must be set appropriately. The complexity and generalization ability of the SVM model
are determined by the two parameters and especially by the relation between them.
Among them, parameter C represents the cost of the penalty.
In QPSO, each particle converges to its own random point Pi ¼ (Pi1, Pi2, ., Pid).
The updates of the particles are accomplished according to the following iterative
equations:
mBest ¼ 1
M
X
M
i ¼ 1
Pi
(6.28)
P ¼ a1$Ppj þ a2$Pgj
a1 þ a2
(6.29)
xðt þ 1Þ ¼ P  b$jmBest  xðtÞj$lnð1=mÞ
(6.30)
The execution of QPSO-SVM is as follows:
Step 1: Take penalty coefﬁcient C and the width coefﬁcient of RBF g as the position
vector of the particle. Initialize the position vector of all particles in the population
randomly.
Step 2: Evaluate the ﬁtness function for each particle.
Step 3: Compare the particle’s ﬁtness evaluation with the particle’s best solution. If
the current value is better, then update the best value and position of the particle.
Step 4: Compare the ﬁtness with the population’s overall previous best. If the current
value is better, then update the value and position of the global best particle.
Step 5: Calculate mBest using Eq. (6.28).
Step 6: Calculate random point P of all particles using Eq. (6.29).
Step 7: Update the position of all particles using Eq. (6.30).
Step 8: Repeat Step 2 to Step 7 until a stop criterion is satisﬁed.
230
Methods for Petroleum Well Optimization
Two wells with similar geological characteristics (Well d1 and Well zh1) in a block of
the Xinjiang oilﬁeld are selected as the research objects in our example.
The parameters in the QPSO-SVM method are set as follows: the number of
particles is 40, the maximum number of iterations is 100, and the contraction expansion
coefﬁcient decreases from 0.9 to 0.4 linearly with the increase of the number of
iterations. The formation drillability prediction results of the two methods for Well d1
are shown in Fig. 6.31. The correlation between predicted formation drillability
with the QPSO-SVM method and actual formation drillability of Well d1 is shown in
Fig. 6.32.
To verify the feasibility and universality of the proposed method, the data of Well
d1 are selected as the training sample, then the formation drillability model is estab-
lished with the QPSO-SVM method and a BP-NN, and after that the formation
drillability of Well zh1 is predicted. Prediction results of the two methods are shown
in Fig. 6.33, and the correlation between predicted formation drillability with the
QPSO-SVM method and actual formation drillability of Well zh1 is shown in
Fig. 6.34.
Figure 6.31 Formation drillability prediction result of Well d1.
Mechanical speciﬁc energy and drilling efﬁciency
231
Figure 6.33 Formation drillability prediction of Well zh1.
Figure 6.34 The correlation between predicted and actual formation drillability of Well zh1.
Figure 6.32 The correlation between predicted and actual formation drillability of Well d1.
232
Methods for Petroleum Well Optimization
6.4 Drilling system energy beyond mechanical speciﬁc energy
6.4.1 Assessing the energy loss
Signiﬁcantly, more energy is needed at the bit to drill a certain volume of rock inefﬁ-
ciently. Therefore, the measured MSE for such a system may be split up (Fig. 6.35) into
the expected energy needed, equal to the unconﬁned compressive strength (UCS), and
the extra energy needed to account for dissipated energy.
The cutting force required at the bit is related to the UCS in an ideal state. The other
main terms inﬂuencing energy consumption at the bit make up the dissipated energy.
Therefore, at the outset, it is assumed that the extra energy is expended on torque, drag,
hydraulics, and vibrations (Fig. 6.36).
MSE seems to be a reasonable attempt to assess the energy loss at the bit; unfortu-
nately, there are no mathematical terms yet included in the general form of the MSE
equation (Eq. 6.9) that assign energy losses to the different processes responsible for
wasted energy if drilling inefﬁciently.
Translating the idea behind the MSE model of the bit to the total system could be the
ﬁrst approach to introducing an energy balance throughout the total system. In this case,
MSE would be a single mathematical term of an energy balance, as would be the other
energy-consuming superior groups like T&D, hydraulics, and drilling dynamics. A
visualization of the idea behind an energy balance for an ideal working system is pre-
sented in Fig. 6.37.
Every single term has its foundation in the basic differential equations used to
describe that term. A reference value for the energy input may be modeled closer to
reality with advanced simulation than is done in this section. Provided that the well is
drilled without any complications, the total energy consumed by each term in this
different superior group will be equal to the predicted energy input. If the energy used is
higher than the expected energy input, this is an indication of inefﬁciencies or drilling
problems. These drilling problems contribute to every single term introduced in
Fig. 6.37, as is shown in Fig. 6.38.
The splitting of the drilling problems within the energy balance could be extended to
a maximum, resulting in a mind map that becomes more and more complex. We do not
discuss this process further here, but we have introduced it to give a better understanding
Figure 6.35 Splitting up MSE. MSE, mechanical speciﬁc energy. Modiﬁed from Lackner, D., 2015.
Concept and Framework to Asses [sic] the Energy Losses along the Drillstring. Master Thesis. Mon-
tanuniversität Leoben.
Mechanical speciﬁc energy and drilling efﬁciency
233
of what these drilling problems might be. An energy balance used as a control framework
of continuous real-time measurements could, therefore, help to indicate if the system is
operating efﬁciently or if it is the reason that an excessive amount of energy is wasted. An
energy balance for controlling and monitoring could help to increase the efﬁciency of
the drilling process itself and simultaneously provide an additional safety tool by iden-
tifying drilling problems at an early stage.
6.4.2 Energy ﬂow in the drill string
As ﬁxed-blade drag bits with PDC replace roller-cone bits as the major cutting structure
in the oil and gas industry, more energy is required to fracture the rock at a faster ROP
.
Reaching the desired well target depth (TD) at high efﬁciency becomes more
Figure 6.36 MSE and the missing terms. MSE, mechanical speciﬁc energy. Modiﬁed from Lackner, D.,
2015. Concept and Framework to Asses [sic] the Energy Losses along the Drillstring. Master Thesis.
Montanuniversität Leoben.
Figure 6.37 Proposed energy balance for an ideal working system. Modiﬁed from Lackner, D., 2015.
Concept and Framework to Asses [sic] the Energy Losses along the Drillstring. Master Thesis. Mon-
tanuniversität Leoben.
Figure 6.38 Breakdown of the drilling problems within the energy balance. Modiﬁed from Lackner, D.,
2015. Concept and Framework to Asses [sic] the Energy Losses along the Drillstring. Master Thesis.
Montanuniversität Leoben.
234
Methods for Petroleum Well Optimization
challenging when complex well trajectory designs are adopted in the extended-reach
drilling applications. A comprehensive analysis of energy in drilling systems is the key
to drilling energy management and performance improvement. MSE, the mechanical
work done to remove a unit volume of rock, has been widely adopted to measure drilling
efﬁciency. Two work components (“thrust” and “rotary”) are considered in the MSE
formula as follows:
MSE ¼ SWOB
Abit
þ 120$p$RPM$STOR
Abit$ROP
(6.31)
Here, Abit is the bit area, and SWOB, RPM, STOR, and ROP are, respectively,
weight-on-bit, rotary speed, torque, and penetration rate, all measured at the surface.
Nowadays, the majority of BHA include a positive displacement motor (PDM) to
provide additional power to the bit. The mechanical power output at motor can be
included in the MSE formula.
MSE ¼ SWOB
Abit
þ 120$p$RPM$STOR þ Pdiff $Q
Abit$ROP
(6.32)
Here, Pdiff and Q are the motor differential pressure and input ﬂow rate. To further
correlate MSE with the actual rock strength and improve the usefulness of MSE sur-
veillance, Dupriest and Koederitz (2005) introduced a drilling efﬁciency factor (EFFM)
to the MSE calculation.
MSEadj ¼ MSE$EFFM
(6.33)
In the ﬁeld execution phase, the operator always sets EFFM uniformly to 0.35,
regardless of bit type or drilling parameters. Teale’s original MSE equation has been
modiﬁed (Armenta, 2008; Mohan et al., 2009) to include the bit’s hydraulic effect on
MSE correlation. Pessier et al. (2012) used the MSE and ROP cross-plot to investigate
overall drilling performance and system capability. MSE is an indicator that measures the
overall efﬁciency, and the trend change in MSE can be qualitatively used to identify
formation transition or drilling dysfunction. However, showing the detailed energy ﬂow
in the drilling system is not enough to make it possible to identify the root causes of
drilling inefﬁciency.
Drilling is a process in which energy is supplied from surface devices, gravity acting
on the drill string, and the mud motor placed downhole. The energy is transferred
through the drill string and is used to cut the formations at the bottom of the wellbore to
extend its length. Part of the energy input may parasitize on the drill string in the forms
of elastic strain and kinetic energy; other portions of the input energy may be dissipated
due to shock and vibration (S & V), and interactions of tubulars with environments.
From an energy point of view, drilling optimization aims to minimize the energy loss and
to make as full use as is practical of the energy input to the bit to drill the formations.
Mechanical speciﬁc energy and drilling efﬁciency
235
Fig. 6.39 explains the energy inputs and outputs, residual energy components in the drill
string, and detailed energy ﬂow in the drilling system.
In this section, we aim to develop a systematic procedure for calculating the various
forms of energy in a drilling system and evaluating the detailed tubular energy ﬂow.
A transient drilling dynamics model acts as the backbone for this procedure to simulate
the time history of dynamic responses of the entire drilling system. Tracing the history of
energy inputs, energy consumption for rock removal, parasitizing energy in tubulars, and
energy loss provides a new perspective on drilling efﬁciency and drill string integrity.
This method enables a holistic drilling energy management workﬂow, which may be
used to facilitate the drilling job planning, execution, and postwell evaluation.
6.4.3 Theory of drilling energy
The calculation formulas for drilling energy variables are discussed in this section. First,
as a dynamic energy system, the drill string takes energy inputs from surface devices (top
drive and draw works), gravity potential energy, and a mud motor deployed downhole.
Figure 6.39 Energy ﬂow in the drill string. Modiﬁed from Chen, W., Shen, Y., Zhang, Z., Bogath, C.,
Harmer, R., 2019. Understand drilling system energy beyond MSE. In: SPE Annual Technical Conference and
Exhibition, 30 September - 2 October, Calgary, Alberta, Canada. Society of Petroleum Engineers. https://
doi.org/10.2118/196050-MS.
236
Methods for Petroleum Well Optimization
The work (energy input) done by the surface torque applied by the top drive (STOR)
may be deﬁned as:
WSTOR ¼
Z
STOR$dðREVtableÞ
(6.34)
where REVtable represents the surface rotation revolution angle at the drill string top.
The work done by the axial forces may be expressed as:
WAXIAL ¼
Z  Z
WTDSðxÞ $ cosðIncðxÞÞdx

$dðPDÞ 
Z
HKL$dðPDÞ
(6.35)
Here, the ﬁrst term is the work done by gravity force, and the second term is the
work done by the hook load (HKL) applied by draw works. WTDS and Inc(x) is the drill
string linear weight and the wellbore inclination angle at the location of x. PD is the drill
string axial penetration distance or drilling distance. The negative sign ahead of the
second term indicates that the direction of pipe penetration is opposite to the hook load
direction during normal drilling operation. Because the surface weight on bit (SWOB) is
deﬁned as the drill string weight minus the hook load, Eq. (6.35) can be simpliﬁed as:
WAXIAL ¼
Z
SWOB$dðPDÞ:
(6.36)
When a mud motor is used, the motor hydraulic energy input may be calculated by
the expression:
WPDMHydro ¼
Z
Pdiff $dV
(6.37)
where Pdiff and V are the pressure drop across the motor and ﬂow volume passing the
motor. The total energy applied to the drill string may be expressed as:
Winput ¼ WSTOR þ WAXIAL þ WPDMHydro
(6.38)
Once the total energy applied to the drill string is calculated, various energy con-
sumption terms may be calculated to determine how the input energy is distributed.
Effective use of the input energy is to drill and remove the formation (that is, lengthen
the wellbore). Reaction axial force (DWOB) and torque (DTOB) at the drill bit are
generated as the bit cuts the rock. Energy used by drilling formations is equal to the work
done by the DWOB and DTOB.
WRockCut ¼
Z
DWOB$dðUBit AxialÞ þ
Z
DTOB$dðREVbitÞ:
(6.39)
Here, UBit Axial and REVbit are the bit axial displacement and rotation revolution
angle.
Mechanical speciﬁc energy and drilling efﬁciency
237
There are two types of mechanical energy parasitizing on the drill string: strain and
kinetic energy. The strain energy is mechanical energy stored in an elastic material upon
deformation caused by mechanical loading. For a drill string, the strain energy can be
decomposed into three components: (1) tensile strain energy caused by axial force;
(2) bending strain energy caused by bending moment; and (3) torsional strain energy
resulting from torque. The drill string can be decomposed into a series of short beams,
each of which has a uniform cross section. It can be assumed that piecewise there is
constant force distribution in one beam. The strain energy may be calculated by the
expression:
USE ¼ p2L
2AE þ M2L
2EIbend
þ
T2L
2GIpolar
;
(6.40)
where P
, M, and Tare the internal axial force, bending moment, and torque in one beam,
respectively. L is the length of one beam segment. A, Ibend ¼ p(OD4  ID4)/64, and
Ipolar ¼ p(OD4  ID4)/32 are the cross-sectional area, bending moment of inertia, and
polar moment of inertia, respectively. E and G are the Y
oung’s modulus and shear
modulus of the material.
Here, we use the ﬁnite element method (FEM) to simulate the internal mechanical
forces of the drill string. In FEM, the drill string is meshed using 3D (three-dimensional)
beam elements. For each beam element, the foregoing strain energy is calculated
using Eq. (6.40). The total strain energy is the sum of the strain energy of each beam
element.
USE;total ¼
X
i ¼ all element

USE;i
	
(6.41)
Kinetic energy is the energy that an object possesses due to its motion. The kinetic
energy may be decomposed into a translation component and a rotary component. In
FEM, the translational and rotational velocities are calculated at the two end nodes of
each beam element (see Fig. 6.40). The beam mass is considered to be lumped,
concentrated at the center of the element.
The kinetic energy due to translational motion may be calculated by the expression:
UKTran ¼ 1
2 m

 Vc
!
:
(6.42)
Here, m is the mass of the beam, and vc
! is the average translational velocities at the
beam element center, which can be calculated based on the node velocities:
vc
! ¼ ðv1
! þ v2
!Þ=2:
(6.43)
238
Methods for Petroleum Well Optimization
Axial rotational kinetic energy may be calculated by the expression:
UKRot ¼ 1
2Jxu2
xc
(6.44)
Here, Jx ¼ m

OD2þID2
8

is the polar mass moment of inertia of the beam element,
and uxc ¼ ðux1þux2Þ
2
is the average axial rotation speed at the beam center. There is one
minor rotational kinetic energy term caused by the tilting motion of the beam with
respect to the axis perpendicular to the beam axis. If you consider the beam element is
relatively short in FEM, the tilting rotational kinetic energy can be negligible. The total
kinetic energy of the drill string is the sum of kinetic energy of each FEM beam element.
UKE;total ¼
X
i ¼ all element

UKTran;i þ UK Rot;i

(6.45)
Some of the input energy may be dissipated due to shock, vibration, and frictional
contact between the drill string and the wall of the wellbore, and ﬂuid and material
damping due to the drill string motion. The energy loss can be considered as the negative
work done by the frictional force and damping forces to the drilling system. Based on the
law of energy conservation, the energy loss can be expressed as:
Eloss ¼ Winput  WRockCut  USE;total  UKE;total
(6.46)
The calculated energy loss provides insights not only into the overall percentage of
wasted energy but also into the cause of energy loss. Minimizing the energy loss could
serve as an objective for drilling optimization and drilling efﬁciency improvement.
Figure 6.40 (A) Translational velocities of the beam element, and (B) rotation speed of the beam.
Modiﬁed from Chen, W., Shen, Y., Zhang, Z., Bogath, C., Harmer, R., 2019. Understand drilling system
energy beyond MSE. In: SPE Annual Technical Conference and Exhibition, 30 September - 2 October,
Calgary, Alberta, Canada. Society of Petroleum Engineers. https://doi.org/10.2118/196050-MS.
Mechanical speciﬁc energy and drilling efﬁciency
239
6.5 Summary
This chapter deals with the efﬁciency of drilling processes and how to accurately
determine and monitor this efﬁciency. This is done by evaluating the input energy versus
the output drilling progress. It gives a presentation of the current methods used for
efﬁciency determination, and the factors that typically inﬂuence drilling efﬁciency.
Among these is MSE, which is a good measure of the efﬁciency of a drilling process.
Real-time monitoring of MSE, when applied, could increase drilling efﬁciency as it will
give the operator the opportunity to make the adjustments required to maintain an
optimal penetration rate.
The quantum particle swarm optimization method is combined with the SVM
method to form a new information fusion method, which is applied to the formation
drillability prediction. Compared with the traditional BP-NN algorithm, this algorithm
has the advantage of higher accuracy and better generation ability. Separately, these
methods have been used to build the formation drillability proﬁle in a certain region
quickly, providing the basis for drilling bit selection and rate of penetration prediction,
for example.
The approach calculates the drilling energy in detail and complements the MSE
method. The calculated drilling energy provides a more holistic viewpoint for energy
ﬂow, BHA dynamics, and drilling efﬁciency, which is expected to aid real-time inter-
pretation of MSE. The method can be implemented in the simulation workﬂow during
the planning phase to facilitate bit selection, BHA design, and drilling parameter opti-
mization to achieve better energy efﬁciency.
6.6 Problems
Problem 1: MSE simulation and drilling efﬁciency
In this problem, we classiﬁed the data into four quartiles (Q1 to Q4) based on the ROP
and MSE values. It should be noted that this classiﬁcation was made based on com-
munications with experts working in drilling operations. In this graph Fig. 6.41, any
ROP above 50 ft/h indicates an excellent drilling performance. The impact of MSE on
the drilling performance was then incorporated, assuming that the lower MSE indicates
better drilling efﬁciency.
The ﬁrst quartile (Q1) is deﬁned with the highest rate of penetration and lower MSE;
this quartile would present the best drilling conditions, meaning that less energy is
required to drill a signiﬁcant rock section. The worst drilling conditions can be deﬁned
as the fourth quartile which has the lowest ROP and the highest drilling energy. Q2 and
Q3 can be considered to be moderate drilling conditions with acceptable drilling efﬁ-
ciency. It is important to note that this classiﬁcation can be changed according to the
240
Methods for Petroleum Well Optimization
situation. For example, the worst drilling conditions in this example could just represent
drilling through a stronger formation or with a more worn-out bit; hence, preliminary
information about the formation is highly important. Table 6.3 gives the parameters for
generating 5000 stochastic data for this problem for a speciﬁc formation (Hassan et al.,
2020).
1. Show and analyze a plot and clustering for the speciﬁc energy (MSE) against the
drilling ROP for these 5000 data based on Fig. 6.41.
2. Is the ROP/MSE ratio capable of assessing the drilling operations in real time? Can the
proﬁle of the ROP/MSE ratio be displayed along with the drilling parameter to provide
a quickand more reliable evaluationof the ongoing drilling operation?Y
oucan consider
the ROP/MSE ratio for depths between 0 and 10,000 ft for the seven zones.
Problem 2: MSE optimization and drilling efﬁciency
The most signiﬁcant relationships for calculating MSE are presented in Table 6.4.
Ranges of variations of these parameters for two wells are presented in Table 6.5 for
generating 5000 stochastic data for this problem in a speciﬁc formation.
Table 6.3 Statistical information for data generation for problem 1.
Parameter
RPM (rpm)
TRQ (kft.lbf)
WOB (klbf)
Q (gal/min)
ROP (ft/h)
Minimum
15.9
35.0
0.0
243.3
74.8
Maximum
99.8
154.4
25.9
917.9
191.5
Mean
49.2
112.7
8.8
648.6
136.4
Range
83.9
119.4
25.9
674.6
116.6
Variation
386
1390
33
24,576
393
Standard deviation
19.6
37.3
5.6
156.8
19.8
Skewness
0.5
1.0
0.2
0.3
0.1
ROP
, rate of penetration; RPM, rotations per minute; WOB, weight on bit.
Figure 6.41 Cross-plot for the mechanical speciﬁc energy (MSE) against the rate of penetration (ROP).
Mechanical speciﬁc energy and drilling efﬁciency
241
1. Calculate the value of MSE using the relationships mentioned in Table 6.4.
2. Design, model, and optimize using the cuckoo-search optimization algorithm for
achieving the maximum ROP and minimum MSE.
3. Refer to the data in Table 6.5. Perform a Monte Carlo simulation based on this
information to analyze the MSE for at least two models in Table 6.4, their standard
deviation, and minimum and maximum values.
Table 6.4 MSE model.
1, Teale (1965)
MSE ¼ WOB
Ab þ 120p$RPM$Tor
Ab$ROP
2, Pessier and Fear
(1992)
MSE ¼ WOB$

1
Ab þ13:33$mb$RPM
Db$ROP

& mb ¼ 36
Tor
Db$WOB
3, Dupriest and
Koederitz
(2005)
MSE ¼ 0:35

WOB
Ab þ120p$RPM$Tor
Ab$ROP

4, Cherif (2012)
MSE ¼ Em

4WOB
pD2
b þ480 RPM$Tor
pD2
b$ROP

5, Chen et al. (2016) MSE ¼ Em$WOBb$

1
Ab þ13:33$mb$RPM
Db$ROP

&WOBb ¼ WOB$emSgbmb
6, Al-Sudani (2017)
MSE ¼
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
ﬃ
WOB$ð120p$RPMÞ2$Tor
gc$

Db
12
2
v
u
u
u
t
ROP
MSE, mechanical speciﬁc energy.
Table 6.5 Statistical information for data generation.
Parameters
Well A
Well B
Minimum
value
Mean
value
Maximum
value
Minimum
value
Mean
value
Maximum
value
Depth (m)
2873
3132
3391
2404
2716
3028
Bit size (in)
6.125
6.125
6.125
8.5
8.5
8.5
Rotation speed
(rpm)
47.715
236.195
3725.300
45.62
135.10
187.80
Weight on bit (lbf) 0
7478.337 32,780.120
1367.30
6926.03
15,424.38
Torque (daN$m)
0
113.539
222.789
56.74
90.62
108.78
Penetration rate
(ft/h)
0.582
9.611
151.319
2.20
12.02
19.17
Flow rate (gal/
min)
68.507
291.723
20,687.334
232.93
332.54
412.35
Tooth wear (%)
0
1.370
3.008
0
1.21
3.55
242
Methods for Petroleum Well Optimization
Figure 6.42 The distribution comparison of ROP and MSE for test intervals. MSE, mechanical speciﬁc
energy; ROP, rate of penetration.
Figure 6.43 The structured method for drilling performance optimization strategy in interval 2.
Problem 3: Operating parameters optimization
We provide the joint MSE-ROP values distribution for two intervals. Fig. 6.42 shows
the performance when the driller used two different methods to set the parameter values
for drilling two test interval sections. The formation in interval 2 is deep with higher
rock strength than interval 1.
1. Interpret the two intervals in Fig. 6.42 considering dampened vibration and stick-
slip.
2. Update the structured method in Fig. 6.43 considering new techniques and
technology.
Nomenclature
BHA bottomhole assembly
CCS conﬁned compressive strength (MPa)
dc e exponent corrected d-exponent
dcn dc e exponent from the normal compaction trend at a given depth
dco computed dc e exponent from the measured data at a given depth
Dia bit diameter (inches)
ECD equivalent circulating density (ppg or psi/ft)
EFFM mechanical efﬁciency, ratio
ft feet
HMSE hydromechanical speciﬁc energy (psi)
HRSE hydrorotary speciﬁc energy (psi)
JSA junk slot area (in2)
m HMSE exponent
MSE mechanical speciﬁc energy (psi)
MW mud weight (ppg)
N rotary speed (rpm)
NCT normal compaction trend
NPP normal pore pressure (ppg or psi/ft)
PDC polycrystalline diamond compact
Q ﬂow rate (gpm)
ROP rate of penetration, ft/h
RPM bit rotating speed, revolutions per minute
STFR speed to ﬂow ratio
T torque on bit (lb-ft)
TFA total ﬂow area (in2)
Tor torque (ft-lbs)
TVD true vertical depth
VR velocity ratio
WOB weight on bit (lbs force)
B formation porosity (fraction)
DPb bit pressure drop (psi)
Δtn normal compaction shale travel time at a given depth (microsecond/ft)
244
Methods for Petroleum Well Optimization
ΔtO observed shale travel time at a given depth (microsecond/ft)
m bit coefﬁcient of sliding friction
rb formation bulk density (g/cm3)
se vertical effective stress (psi)
sH maximum horizontal stress (psi)
sh minimum horizontal stress (psi)
smax vertical effective stress at the onset of unloading (psi)
h hydraulic energy reduction factor
Ab bit area (in2)
Cb bulk compressibility (psi)
Cp pore compressibility (psi)
Db bit diameter (in)
Fj jet impact force (lbs)
Gnp normal pore pressure gradient at a given depth (psi/ft)
Gob overburden pressure gradient at a given depth (psi/ft)
Gpp pore pressure gradient at a given depth (psi/ft)
HMSEn HMSE from the normal compaction trend at a given depth (psi)
HMSEo computed HMSE from the measured data at a given depth (psi)
M bit coefﬁcient of sliding friction
MSEadj adjusted mechanical speciﬁc energy (psi)
Rn normal compaction trend shale resistivity at a given depth (ohm-m)
Ro observed shale resistivity at a given depth (ohm-m)
Sv overburden pressure (psi)
Vmax compressional velocity at the onset of unloading (m/s)
Vn nozzle/jet velocity (ft/s)
Vp compressional velocity (m/s)
WOBe effective weight on bit (lbs)
Z depth (ft-TVD)
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Rehm, McClendon, R., 1971. Paper presented at the Fall Meeting of the Society of Petroleum Engineers
of AIME. New Orleans, Louisiana, October 1971. Paper Number: SPE-3601-MS. https://doi.org/10.
2118/3601-MS.
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Teale, R., 1965. The concept of speciﬁc energy in rock drilling. Int. J. Rock Mech. Min. Sci. Geomech.
Abstr. 2 (1), 57e73. https://doi.org/10.1016/0148-9062(65)90022-7.
Vapnik, V
.N., 1995. The Nature of Statistical Learning Theory. Springer, New Y
ork.
Vapnik, V
.N., 1998. Statistical Learning Theory. Wiley, New Y
ork.
Vidrine, D.J., Benit, E.J., 1968. Field veriﬁcation of the effect of differential pressure on drilling rate.
J. Petrol. Technol. 20, 676e682. https://doi.org/10.2118/1859-PA (SPE 1859).
Warren, T.M., 1987. Penetration rate performance of roller cone bits. SPE Drill. Eng. 2, 9e18.
Wei, M., Li, G., Shi, H., Shi, S., Li, Z., Zhang, Y
., 2016. Theories and applications of pulsed-jet drilling
with mechanical speciﬁc energy. SPE J. 21, 303e310.
Open-source code
Y
ou can develop your models using open-source code for different tools available
on https://github.com/
Examples include:
Pore pressure prediction using seismic velocity and well log data
Using well logs to predict lithology
Mechanical speciﬁc energy and drilling efﬁciency
247

CHAPTER NINE
Data mining in digital well planning and
well construction
Key concepts
1. Data mining is the emerging science and industry of applying modern statistical and
computational technologies to the problem of ﬁnding useful patterns hidden within
large databases. This chapter presents a study on clustering and classifying methods.
2. Large-scale collection and interpretation of ﬁeld data, what is known as “data
mining,” can be considered to be an important tool for understanding the impact of
different parameters on the rate of penetration (ROP) to estimate the recommended
range of rheological properties, which will result in improving the ROP
.
3. Detecting a kick in its early stages gives the crew more time to control it, resulting in
safer and more efﬁcient drilling operations. Five models have been developed and
evaluated for optimizing kick detection. They are decision tree, k-nearest neighbor
(KNN), sequential minimal optimization (SMO) algorithm, artiﬁcial neural
network (ANN), and Bayesian network. The models were trained to detect kicks
based on actual kick cases.
9.1 Data mining techniques
9.1.1 Introduction to data mining
With the enormous amount of data stored in ﬁles, databases, and other repositories, it is
increasingly important, if not essential, to develop powerful means for the analysis and
interpretation of data and for the extraction of knowledge that could help in decision-
making. Data mining, also popularly known as knowledge discovery in databases (KDD),
has been described as “the nontrivial process of identifying valid, novel, potentially
useful, and ultimately understandable patterns in data” (Fayyad et al., 1996). Fig. 9.1
shows data mining as a step in an iterative knowledge discovery process.
The iterative process (Pujari, 2001) consists of the following steps:
1. Data selection: At this step, the data relevant to the analysis are decided on and
retrieved from the data collection.
2. Data integration: At this stage, multiple data sources, often heterogeneous, may be
combined in a common source.
3. Data cleaning (preprocessing): Also known as data cleansing, this is a phase in which
noisy data and irrelevant data are removed from the collection of selected data.
Methods for Petroleum Well Optimization
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ISBN 978-0-323-90231-1, https://doi.org/10.1016/B978-0-323-90231-1.00005-4
All rights reserved.
357j
4. Data transformation in data mining is done to combine unstructured data with
structured data to analyze it later. It is also important when the data are transferred to
a new cloud data warehouse. When the data are homogeneous and well-structured, it
is easier to analyze and look for patterns. Data transformation in data mining involves:
data smoothing; data aggregation; discretization; generalization; attribute construc-
tion; and normalization.
5. Data mining: This is the crucial step in which clever techniques are applied to extract
data patterns that are potentially useful.
6. Pattern evaluation: In this step, strictly interesting patterns representing knowledge
are identiﬁed based on given measures.
7. Knowledge representation: In this ﬁnal phase, the discovered knowledge is visually
presented to the user. This essential step uses visualization techniques to help users
understand and interpret the data mining results.
KDD is an iterative process. Once the discovered knowledge is presented to the user, the
evaluation measures can be enhanced, the mining can be further reﬁned, new target data
can be integrated, and the data transformed again, to get different, more appropriate
results.
9.1.2 Data mining techniques
Researchers have identiﬁed the two fundamental goals of data mining as prediction and
description. Prediction makes use of existing variables in the database to predict future
values of interest. Description focuses on ﬁnding patterns that describe the data and
presenting them for user interpretation. The relative emphasis of prediction and
description differ with respect to the underlying application and the technique. There
are several data mining categories that fulﬁll these objectives: association rule mining,
clustering, and classiﬁcation mining using the techniques such as decision tree, genetic
algorithms, machine learning, and neural networks. There are seven data mining cate-
gories considered in this chapter.
Figure 9.1 An overview of the steps comprising the KDD process. KDD, knowledge discovery in
databases. Modiﬁed from Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., 1996. From data mining to
knowledge discovery in databases. Al Mag. 17 (3), 37e54.
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Methods for Petroleum Well Optimization
1. Tracking patterns. One of the most basic techniques in data mining is learning to
recognize patterns in the data sets. This is usually the recognition of some aberration
in the data that happens at regular intervals, or an ebb and ﬂow of a certain variable
over time. For example, lost circulation may be seen increasing at speciﬁc depth or
abrasive wear in bits occurs when faced with a speciﬁc formation.
2. Classiﬁcation. Classiﬁcation is a more complex data mining technique that forces
researchers to collect various attributes together into discernible categories, which they
can then use to draw further conclusions, or which serve some function. For example, if
a researcher is evaluating data on pore pressure, mud density parameters, and drilling
histories, they might be able to classify wells as “low,” “medium,” or “high” blowout
risks. They could then use these classiﬁcations to learn even more about those wells.
3. Association. Association is related to tracking patterns but is more speciﬁc to depen-
dently linked variables. In this case, the researcher will look for speciﬁc events or at-
tributes that are highly correlated with another event or attribute. For example, bit
balling islikely tohappenwhendrilling into clay formation, decreasingthe ROPaswell.
4. Outlier detection. In many cases, simply recognizing the overarching pattern
cannot give a clear understanding of the data set. It is also necessary to identify
anomalies or outliers in the data.
5. Clustering. Clustering is very similar to classiﬁcation but involves grouping chunks
of data together based on their similarities. For example, well log cluster analysis is an
innovative approach that provides the explorationist with an efﬁcient tool for
analyzing, screening, and ﬁltering a large volume of well log data, to identify and map
potential hydrocarbon accumulations. The method involves seeking high-density
areas (clusters) in the multivariate space of well log data, to deﬁne classes of similar
log responses.
6. Regression. Regression, used primarily as a form of planning and modeling, is used
to identify the likelihood of a certain variable, given the presence of other variables.
For example, through a multiple regression analysis of detailed drilling data taken
over short intervals, the best mathematical model can be chosen for optimal drilling
and abnormal pressure detection by the d-exponent method.
7. Prediction. Prediction is one of the most valuable data mining techniques, since it is
used to project the types of data that the researcher will see in the future. In many cases,
just recognizing and understanding historical trends is enough to chart a fairly accurate
prediction of what will happen in the future, for example, prediction of drilling
trajectory in directional wells via a new rock-bit interaction model, prediction of
failures while drilling, prediction of drilling ﬂuid loss, and drilling stuck pipe prediction.
9.1.3 Cluster analysis
Clustering is deﬁned as unsupervised learning where the objects are grouped on the basis
of some similarity inherent among them. There are different methods for clustering the
objects, such as hierarchical, partitional, grid, density-based, and model-based clustering.
Fig. 9.2 shows the taxonomy of clustering approaches (Fraley and Raftery, 1998).
Data mining in digital well planning and well construction
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9.1.3.1 Hierarchical clustering methods
In hierarchical clustering (HC) methods, clusters are formed by iteratively dividing the
patterns using a top-down or bottom-up approach. There are two hierarchical methods:
agglomerative and divisive HC (Murtagh, 1984). The agglomerative method follows the
bottom-up approach, which builds up clusters starting with a single object and then
merging these atomic clusters into larger and larger clusters, until all of the objects are
ﬁnally lying in a single cluster or until certain termination conditions are satisﬁed. The
steps of agglomerative clustering are as follows:
1. Make each point a separate cluster.
2. Form clusters until the clustering is satisfactory.
3. Merge the two clusters with the smallest intercluster distance.
4. End.
The divisive HC method follows a top-down approach. This subdivides the cluster into
smaller and smaller pieces until each object is a cluster on its own or until the process
satisﬁes certain termination conditions such as a desired number of clusters are obtained
or the distance between the two closest clusters is above a certain threshold distance. The
steps of divisive clustering are as follows:
1. Construct a single cluster containing all points.
2. Break down this cluster and subsequent clusters until the clustering is satisfactory.
3. Split the cluster that yields the two components with the largest intercluster distance.
4. End.
Figure 9.2 Taxonomy of clustering approaches. Modiﬁed from Fraley, C., Raftery, A.E., 1998. How many
clusters? Which clustering method? Answers via model-based cluster Analysis. Comput. J. 41 (8), 578e588.
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The hierarchical methods usually lead to the formation of dendrograms, as shown in
Fig. 9.3.
The HC methods could be further grouped into three categories based on similarity
measures or linkages (Jain et al., 1999): single-linkage clusters, complete-linkage clusters,
and average linkage clusters. We also describe enhanced HC, which addresses some of
the criticisms of HC.
9.1.3.1.1 Single-linkage clustering
Single-linkage clustering is often called the connectedness method, the minimum
method, or the nearest-neighbor method. In single-linkage clustering, the link between
two clusters is made by a single-element pair, namely the two elements (one in each
cluster) that are closest to each other. In this clustering, the distance between the two
clusters is determined by the nearest distance from any member of one cluster to any
member of the other cluster, which also deﬁnes similarity. If the data are equipped with
similarities, the similarity between a pair of clusters is considered to be equal to the
greatest similarity of any member of one cluster to any member of the other cluster.
Fig. 9.4 shows the mapping of single-linkage clustering. The criteria between two sets of
clusters A and B are as follows:
minfdða; bÞ: a ˛ A; b ˛ Bg
(9.1)
Figure 9.3 Hierarchical clustering dendrogram (agglomerative).
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361
9.1.3.1.2 Complete-linkage clustering
In complete-linkage clustering, also called the diameter method, the maximum method,
or the furthest-neighbor method, the distance between two clusters is determined by the
longest distance from any member of one cluster to any member of the other cluster.
Fig. 9.5 shows the mapping of complete-linkage clustering. The criteria between two
sets of clusters A and B are as follows:
maxfdða; bÞ: a ˛ A; b ˛ Bg
(9.2)
9.1.3.1.3 Average-linkage clustering
In average-linkage clustering, also known as the minimum variance method, the distance
between two clusters is determined by the average distance from any member of one
cluster to any member of the other cluster. Fig. 9.6 shows the mapping of average-
linkage clustering. The criteria between two sets of clusters A and B are as follows:
1
jAjjBj
X
a˛A
X
b˛B
dða; bÞ
(9.3)
9.1.3.1.4 Enhanced hierarchical clustering
The common criticism of classical HC algorithms is that they lack robustness and are,
therefore, sensitive to noise and outliers. The main deﬁciency of HC (Nagpal et al.,
2013) is that after the two points of the clusters are linked to each other, they cannot
Figure 9.4 Mapping of single-linkage clustering.
Figure 9.5 Mapping of complete-linkage clustering.
Figure 9.6 Mapping of average-linkage clustering.
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Methods for Petroleum Well Optimization
move into other clusters in a hierarchy. This means that HC algorithms are not capable of
correcting possible misclassiﬁcations. The computational complexity of most of HC
algorithms is at least O(N ^
2), and this high cost limits their application in large-scale data
sets. Other disadvantages of HC include the tendency to form spherical shapes and
reversal phenomenon in which the normal hierarchical structure is distorted (Xu and
Wunsch, 2005). With the requirement of large-scale data sets in recent years, the HC
algorithms have been enriched with some new techniques as modiﬁcations to the
classical HC methods.
We describe a selection of algorithms that use HC with some enhancements:
•
Balanced iterative reducing and clustering using hierarchies (BIRCH) (Zhang et al.,
1996) contains the idea of cluster features (CFs). CF is the triplet (n, LS, SS) where n
is the number of data objects in the cluster, LS is the linear sum of the attribute values
of the objects in the cluster, and SS is the sum of the squares of the attribute values of
the objects in the cluster. These are stored in a CF-tree form, so there is no need to
keep all tuples or all clusters in main memory, but only the CF tuples (Periklis, 2002).
The main advantages of BIRCH lie in two characteristics; the ability to deal with
large data sets and the robustness to outliers (Zhang et al., 1996). Also, BIRCH can
achieve a computational complexity of O(N).
•
Clustering using representatives (CURE) (Guha et al., 1998) is a clustering technique
for dealing with large-scale databases, which is robust toward outliers and accepts
clusters of various shapes and sizes. Its performance is good with 2-D data sets.
BIRCH and CURE both handle outliers well, but CURE’s clustering quality is
better than that of BIRCH (Guha et al., 1998). Conversely, in terms of time
complexity, BIRCH is better than CURE as it attains computational complexity of
O(N) compared to CURE with O(N^
2 log N).
•
ROCK (Guha et al., 1999) is applied to categorical data sets that follow the
agglomerative HC algorithm. It is based on the number of links between two records;
links capture the number of other records, which are very similar to each other. This
algorithm does not use any distance function.
•
CHAMELEON (George et al., 1999) is a HC algorithm, where clusters are merged
only if the interconnectivity and closeness (proximity) between two clusters are high
relative to the internal interconnectivity of the clusters and closeness of items within
the clusters. One limitation of CHAMELEON is that it is known for low-
dimensional spaces and it is not applied to high dimensions.
9.1.3.2 Partition clustering methods
Partitional clustering is the opposite of HC; here, data are assigned into k-clusters
without any hierarchical structure by optimizing a criterion function (Lam and
Wunsch, 2014). The most commonly used criterion is the Euclidean distance, which
ﬁnds the minimum distance between points with each of the available clusters and assigns
the point to the cluster. The algorithms (Nagpal et al., 2013) studied in this category
Data mining in digital well planning and well construction
363
include, among others: k-means, PAM (Kaufman and Rousseeuw, 1990), CLARA
(Kaufman and Rousseeuw, 1990), CLARANS (Ngand and Han, 2002), fuzzy c-means,
and DBSCAN. Fig. 9.7 shows the partitional clustering approach.
9.1.3.2.1 K-means clustering
The k-means algorithm is one of the best-known, benchmarked, and simplest clustering
algorithms (Lam and Wunsch, 2014). It is mostly applied to solve clustering problems. In
this procedure, the given data set is classiﬁed through a user-deﬁned number of clusters,
k. The main idea is to deﬁne k centroids, one for each cluster. The objective function J is
given as follows wherejj*jj
^2 is a chosen distance measure between a data point xi( j) and
the cluster center Cj. Fig. 9.8 shows the ﬂow diagram of the k-means algorithm.
Minimize J ¼
X
k
j ¼ 1
X
n
i ¼ 1


xðjÞ
i
 cj



2
(9.4)
The procedure of the k-means algorithm is composed of the following steps (Saxena
et al., 2017):
1. Initialization: Suppose we decide to form k-clusters of the given data set. Now take k
distinct points (patterns) randomly. These points represent the initial group centroids.
Figure 9.7 Partitional clustering approach.
Figure 9.8 Flow diagram of k-means algorithm.
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Methods for Petroleum Well Optimization
As these centroids will change after each iteration before clusters are ﬁxed, there is no
need to spend time choosing the centroids.
2. Assign each object to the group that has the closest centroid.
3. When all objects have been assigned, recalculate the positions of the k centroids.
4. Repeat steps 2 and 3 until the centroids no longer move. This produces a separation
of the objects into groups from which the metric to be minimized can be calculated.
9.1.3.2.2 Fuzzy c-means clustering
Fuzzy c-means (FCM) is a clustering method which allows one point to belong to two or
more clusters unlike k-means where each point is assigned to only one cluster. This
method was developed by Dunn (1973) and improved by Bezdek (1981). The procedure
of fuzzy c-means (Xu and Wunsch, 2005) is similar to that of k-means. It is based on
minimization of the following objective function, where m is the fuzzy partition matrix
exponent for controlling the degree of fuzzy overlap, with m > 1.
Jm ¼
X
N
i ¼ 1
X
c
j ¼ 1
um
ij

xi  vj

2; 1 < m < N
(9.5)
Fuzzy overlap refers to how fuzzy the boundaries between clusters are; that is the
number of data points that have signiﬁcant membership in more than one cluster. uij is
the degree of membership of xi in the cluster j, xi is the i-th pattern of D-dimension data,
vj is the j-th cluster center of the D-dimension, and jj*jj is any norm expressing the
similarity between any measured data and the center (Saxena et al., 2017).
The procedure for FCM is as follows:
1. Set up a value of c (number of clusters).
2. Select initial cluster prototype V1, V2, ., Vc from Xi, i ¼ 1, 2, ., N.
3. Compute the distance jjXi  Vjjj between objects and prototypes.
4. Compute the elements of the fuzzy partition matrix (i ¼ 1, 2, ., N; j ¼ 1, 2,
., c):
uij ¼
"Xc
l¼1

xi  vj


kxi  vlk
#1
5. Compute the cluster prototypes ( j ¼ 1, 2, ., c):
Vj ¼
PN
i¼1u2
ijxi
PN
i¼1u2
ij
6. Stop if the convergence is attained or the number of iterations exceeds a given limit.
Otherwise, go back to step 3.
Data mining in digital well planning and well construction
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9.1.4 Classiﬁcation
Classiﬁcation is a form of data analysis that extracts models describing important data
classes. Such models, called classiﬁers, predict categorical (discrete, unordered) class la-
bels. For example, we can build a classiﬁcation model to categorize bits selection on
geomechanical factors in different formations. Such analysis can help provide us with a
better understanding of the data at large. Many classiﬁcation methods have been pro-
posed by researchers in machine learning, pattern recognition, and statistics. Most al-
gorithms are memory resident, typically assuming a small data size. Recent data mining
research has built on such work, developing scalable classiﬁcation and prediction tech-
niques capable of handling large amounts of disk-resident data. Classiﬁcation has
numerous applications including exploration, production, and drilling optimization. In
Chapter 7, we described neural networks, decision tree, and SVM methods.
Classiﬁcation is a learning function that maps (classiﬁes) a data item into one of several
predeﬁned classes (see Fig. 9.9).
9.2 Data mining application in digital drilling engineering
9.2.1 Data mining applied to real-time drilling
Data mining applied to real-time drilling data repositories draws upon the analysis of
nontrivial real-time drilling parameter values and leverages historical data from previous
wells and structured technical knowledge of drilling specialists. By doing so, it reduces
the decision-making time during crucial drilling operations. A knowledge management
strategy for drilling operations can therefore be established based on knowledge re-
positories and inference routines methods, enabling the generation of a preemptive data
model for each operative scenario during drilling operations.
Using real-time monitoring and data mining services, oil and gas operators can
intervene to prevent problematic drilling events from occurring. By consistently pre-
dicting adverse events, drilling projects can be safely completed ahead of time and under
budget, with optimum efﬁciency (Table 9.1).
9.2.1.1 Infrastructure of data streaming
Oil and gas companies are increasingly using data to improve drilling practices. Rigs
provide massive amounts of data to help engineers optimize well performance to reduce
Figure 9.9 Main classiﬁcation classes.
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Methods for Petroleum Well Optimization
downtime and associated costs. Executives are monitoring the evolution of big data
technology, especially analytics and machine-learning capabilities. The resulting design
of efﬁcient preventative-maintenance schedules can reduce operational problems.
Drilling operations around the world are facing challenges related to geological
complexity, lack of specialists, and increasing data volumes:
1. Drilling costs have dramatically increased, particularly in deep waters and in highly
heterogeneous formations.
2. The complexity and volume of data generated at the rig site has signiﬁcantly
increased.
3. There are not enough specialists available to expediently monitor, assess, and analyze
the data at the rig site.
4. Bringing the data to the specialists is less expensive than sending specialists to every
rig.
Drilling data are collected, aggregated, transmitted, visualized, and exploited in real time
by applying the appropriate technology and procedures (Fig. 9.10). As a result, drilling
staff located at the rig site beneﬁt from preventive alerts and technical recommendations
that allow optimization of the drilling process. Real-time data are standardized to ensure
interoperability, that is, to permit integration of data stores with engineers’ software in
real time, according to the oil industry standard for drilling data, wellsite information
transfer standard markup language (WITSML). WITSML is an open standard, available
for all operators and contractors, and has been implemented worldwide.
Real-time data are stored in a WITSML store. Data can then be retrieved for
visualization, monitoring, and assessment purposes. Data can also be logically structured
as a data warehouse for data mining purposes, yet this particular use is less frequent in the
industry. Nowadays, real-time operation centers (RTOCs) are commonly based on
Table 9.1 Data aggregation and real-time drilling operations management.
Improved operational
efﬁciency
Reduces invisible lost time
and nonproductive time.
True reﬂection of well
conditions with real-time
models.
Reporting and analytics
Daily well reports, analytics, and
comparison of wells or assets.
Real-time analytics
Real-time visualization and
analytics for ROP
, drag and
torque, etc.
Reduced operational
challenges
Solves hole circulation, kick
detection, tripping issues,
etc.
Learnings from the past
Incorporates past learnings to
improve efﬁciency and to
optimize well trajectory.
Management of complex
operations
Supports operations like
HPHT, managed pressure,
deep water, etc.
Data mining in digital well planning and well construction
367
WITSML for monitoring and reporting a set of variables to help drilling engineers to
improve ﬁeld operations performance and to avoid undesirable incidents occurring
during daily activities. Real-time services integrate drilling data and display it at an
RTOC. The RTOC’s specialized engineering team use the data to monitor the ﬁeld and
to watch for any operative events that, according to their knowledge, could cause or are
causing an undesirable incident in the ﬁeld. Nevertheless, this applied knowledge is not
recorded, so it is not available for future decision-making.
9.2.1.2 Improving decision support systems with data mining techniques
The challenge to implementing knowledge management in the drilling process is
deﬁning the workﬂow to allow the connection of technical data with the expertise of the
specialists. Given the nature of decision-making during drilling operations, an interactive
knowledge management workﬂow is thus required, involving explicit knowledge
analysis, implicit knowledge use, and data mining to extract useful knowledge from the
available data and information (Fig. 9.11).
The main components of the workﬂow are: the data warehouse; a real-time algo-
rithm; the data-mining process; the decision-making process; actions taken; and
documentation of the new drilling scenario and the solution (Jaime et al., 2012). We
describe these components in the text box.
Figure 9.10 Infrastructure of data streaming in real-time drilling operations (Operator IT Environ-
ment). Modiﬁed from Dismas Bismo Tjitrosoemarto, 2017. Achieving well construction efﬁciency through
an independent real-time data platform to enable ‘Monitor-By-Recommendation’ of critical operations.
Technical-Sales Asia Paciﬁc e Oil & Gas, Kongsberg Digital.
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Methods for Petroleum Well Optimization
Figure 9.11 System architecture of drilling engineering data warehouse.
Data warehouse
The data warehouse is fed by the following:
•
Real-time data collected at the rig site. Data collected are classiﬁed as follows:
1. Surface parameters, such as torque, hook load, bit depth, RPM, and ROP.
2. Downhole tool (LWD/MWD/PWD) parameters, such as gamma ray, resistivity, inclina-
tion, azimuth, and bottom pressure.
3. Mudlogging data, such as lithology, chromatography, and descriptions.
•
Correlation and historical data from previous wells. This includes wireline logging data.
•
Documented previous drilling scenarios and applied solutions.
The data and information that compose the data warehouse are structured by
variables. The main variables are as follows:
•
Well name, location, ﬁeld, formation target, year of drilling.
•
Operative conditions: rig name and capacities, crew, mud type.
•
Real-time data, from all data families, such as surface parameters (mud ﬂow rate, torque,
block position, hook load, standpipe pressure, rotary, etc.), downhole parameters (re-
sistivity, trajectory, etc.), and mudlogging data (lithology).
•
Correlation well name, location, ﬁeld, formation target, year of drilling.
•
Drilling scenarios or alert conditions, such as losses, gains, and drag, as well as operative
ranges deﬁned for normal operations, and also solutions implemented for each scenario.
(Continued)
Data mining in digital well planning and well construction
369
Data warehouse (cont'd)
Algorithm
It is necessary to deﬁne a predictive model for the identiﬁcation of undesirable alert conditions,
such as partial losses, drags and gains. The model is deﬁned as a set of operative conditions,
parameters, and value ranges that lead to an undesirable operative scenario. For example, if
the value of the standpipe pressure and output ﬂow parameters diminishes, and the ﬂow
rate varies with time, a partial loss of drill ﬂuid occurs at the rig site. A full set of alert conditions
are created, each deﬁned by the relevant behavior of drilling parameters. This set of alert con-
ditions constitutes the predictive model. Once the predictive model is deﬁned, it is represented
as a real-time algorithm that identiﬁes the existence of any predeﬁned alert condition scenario
by interacting with real-time data ﬂowing from the data warehouse. Input of the real-time al-
gorithm is real-time data plus the data corresponding to the last 8 h. This allows the algorithm
not only to identify variations in values with regard to a value range but also to determine pat-
terns indexed in time in each of the involved parameters. If an alert condition is identiﬁed by
applying the algorithm to the real-time data ﬂow, an operative alert is generated. This alert trig-
gers a decision-making process for whether or not to take an action, anddif neededdwhat
action to take.
Data mining process
Data mining has been applied to oil and gas data warehouses to elucidate patterns and non-
explicit knowledge that is embedded in explicit data. The output information of the described
real-time algorithmdan alert conditiondalso triggers a simple query for the data warehouse,
based upon the variables listed before. The query pulls data from the data warehouse corre-
sponding to scenarios similar to the one that triggered the alert condition. This discriminated
set of data feeds a data mining process, which analyzes all variables, ranges, and drilling sce-
narios and selects the conditions that have a higher statistical weighting on a particular drilling
scenario, given the current operative conditions. Conditions are then presented to a drilling
specialist at the RTOC, along with the alert condition and the query results, providing the
specialist with more data from which to make a more accurate and faster decision.
Decision-making process
As mentioned above, as well as the implicit knowledge of the RTOC staff and the preemptive
alert generated from the real-time algorithm, the decision-making process involves the useful
knowledge generated from the data-mining process, thus leveraging and shaping the aim of
the entire process.
Actions
The decision made by the RTOC staff is communicated directly to ﬁeld staff at the rig site, to
diminish the time elapsed between the event that triggered the alert and the solution of
that event. Actions taken by the ﬁeld personnel are correspondingly registered back at the
data warehouse, to keep track of the complete cycle and store, not only drilling scenarios,
but factual and feasible solutions as well. Actions are integrated into the new, documented dril-
ling scenario with its correspondent solution and are stored in the data warehouse as a docu-
mented drilling scenario.
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Methods for Petroleum Well Optimization
9.2.2 Impact of rheological properties on rate of penetration
This section looks at a study using data collected from mud logging data, daily drilling
reports (DDRs), and geological information. Statistical and sensitivity analyses were
performed to identify the relationship between ROP and drilling ﬂuid rheological
properties. The correlation coefﬁcient (CC), which is also called the Pearson
productemoment correlation, was utilized to understand the effect of solid content
(SC), yield point (Yp), and plastic viscosity (PV) on ROP
. The results showed that SC is
the most inﬂuential rheological property on ROP
, then PV and ﬁnally Yp. In addition,
this work demonstrates how bit hydraulics can be improved by means of modifying the
rheological properties rather than adjusting the ﬂow rate or nozzle size. Large-scale
collection and interpretation of ﬁeld data, in other words “data mining,” can be
considered to be an important tool in understanding the impact of different parameters
on the ROP to estimate the recommended range of rheological properties, which will
result in improving the ROP (see Al-Hameedi et al., 2019).
9.2.2.1 Methodology
Real-ﬁeld data from more than 1000 wells were collected from 10 oilﬁelds in Iraq, using
DDRs, daily mud reports, daily mud logging reports, and end of well reports. The data
were combined in one set, and data preprocessing was performed. Table 9.2 shows the
properties of the data utilized.
Data warehouse (cont'd)
Documentation of the new drilling scenario and solution
Registration of results is the key for the model to be useful in real-time operations. It is clear that,
when ﬁrst implemented, the time elapsed between an undesirable event and the solving action
may be similar to the time that would elapse if a conventional, empirical model were imple-
mented. Nevertheless, as the model is used and the data warehouse is fed with more scenarios,
decisions, and actionsdthat is, as explicit knowledge is stored at the warehousedthe output
will be more precise and will provide the required knowledge and information to make a faster
decision.
Table 9.2 Properties of the data used in the study.
Parameter
Minimum
Maximum
Standard deviation
PV (cp)
6
29
3.41
Yp (lb/100 ft2)
11
30
4.45
SC (%)
2
10
1.86
ROP (m/hr)
2
13
2.49
From Al-Hameedi, A.T.T., Alkinani, H.H., Dunn-Norman, S., Flori, R.E., Alsaba, M.T., Amer, A.S., Al-Bazzaz, W
.H.,
March 2019. An assessment of the impact of rheological properties on rate of penetration using data mining techniques. In:
International Petroleum T
echnology Conference Held in Beijing, China, 26e28.
Data mining in digital well planning and well construction
371
Descriptive data analytics were conducted to better comprehend the effect of SC, PV
,
and Yp on the ROP
. The raw data were ﬁrst inspected for outliers. Boxplots were used to
eliminate the outliers, such that any data point falling outside the minimum or the
maximum limit of the whiskers was eliminated. Then, the data were tested for normality
to evaluate whether they were normally distributed or not. When the data are not
normally distributed, the ﬁrst action should be to try to convert the data to normally
distributed data. Depending on the skewness of the data, there are many approaches that
can be used to convert the data to a normal distribution (Alkinani et al., 2018). If that
does not work, the last option should be using some of the analysis methods that are used
for nonnormally distributed data such as Kendall, Spearman, or Hoeffding correlations.
However, there are many limitations to these methods, including, among others, using
the rank of the data, not the data point itself.
One of the most common methods used to evaluate the linear relationship between
two normally distributed variables is the correlation coefﬁcient. The CC was used to
assess the relationship between ROP and SC, Yp, and PV to optimize the drilling
operation. The CC ranges from 1 to þ1. A CC of þ1 indicates the strongest positive
relationship between two variables. In other words, if one variable increases, the other
will increase as well. Conversely, a CC of 1 indicates the highest negative relationship
between two variables. In other words, if one variable increases, the other will
dramatically decrease. If the CC is zero, this indicates that there is no relationship
between the two variables. The correlation coefﬁcient can be calculated using Eq. (9.6).
CC ¼
Pn
i¼1ðxi  xÞðyi  yÞ
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
ﬃ
Pn
i¼1ðxi  xÞ2Pn
i¼1ðyi  yÞ2
q
:
(9.6)
where X
¯ and Y
¯ are the mean of the x and y variables, n is the number of data points, xi
and yi are the values of x and y at the i-th individual. Moreover, in this study, a sensitivity
analysis tornado chart was performed to better understand the effect of SC, Yp, and PV
on ROP
. Fig. 9.12AeC shows plots of ROP against SC, PV
, and Yp, respectively. The
CCs were: SC 0.72; PV 0.68; and Yp 0.28. Since all CCs were found to be
negative, this implies that the ROP has an inverse relationship with SC, PV
, and Yp; as
SC, PV
, and Yp increase, ROP will be decreased.
SC has the highest negative CC, which makes it the most inﬂuential drilling ﬂuid
rheological property on ROP
. Fig. 9.13 shows a sensitivity analysis tornado chart for the
drilling ﬂuid rheological properties on ROP
. This plot will help to understand how much
each drilling ﬂuid rheological parameter impacts ROP
. Using Fig. 9.13, ROP can be
optimized in such a way that if high ROP is desired, the ﬁrst thing that should be checked
is the amount of SC in the drilling mud, since it is the most inﬂuential parameter.
9.2.3 Kick detection using data mining
The amount of real-time data generated and collected during a drilling operation is
enormous. These data points are categorized as surface and subsurface parameters based
372
Methods for Petroleum Well Optimization
Figure 9.12A ROP versus SC. ROP, rate of penetration; SC, solid content. Modiﬁed from Al-Hameedi,
A.T.T., Alkinani, H.H., Dunn-Norman, S., Flori, R.E., Alsaba, M.T., Amer, A.S., Al-Bazzaz, W.H., March 2019. An
assessment of the impact of rheological properties on rate of penetration using data mining techniques.
In: International Petroleum Technology Conference Held in Beijing, China, 26e28.
Figure 9.12B ROP versus PV. ROP, rate of penetration; PV, plastic viscosity. Modiﬁed from Al-Hameedi,
A.T.T., Alkinani, H.H., Dunn-Norman, S., Flori, R.E., Alsaba, M.T., Amer, A.S., Al-Bazzaz, W.H., March 2019. An
assessment of the impact of rheological properties on rate of penetration using data mining techniques.
In: International Petroleum Technology Conference Held in Beijing, China, 26e28.
Figure 9.12C ROP versus Yp. ROP, rate of penetration; Yp, yield point. Modiﬁed from Al-Hameedi, A.T.T.,
Alkinani, H.H., Dunn-Norman, S., Flori, R.E., Alsaba, M.T., Amer, A.S., Al-Bazzaz, W.H., March 2019. An
assessment of the impact of rheological properties on rate of penetration using data mining techniques.
In: International Petroleum Technology Conference Held in Beijing, China, 26e28.
Data mining in digital well planning and well construction
373
on the location of measurement. Surface parameters are measured on the surface.
Therefore, they are not limited by downhole transmission restriction and can have high
data collection frequency. Subsurface parameters are usually associated with logging
while drilling (LWD) and measurement while drilling (MWD). They are transmitted to
the surface in real time using mud pulse, with at best a rate of a few data points each
minute. During all phases of the drilling operation, surface parameters are continuously
collected, whereas subsurface parameters require a continuous column of mud to transfer
data to the surface in real time. With surface parameters, data can be obtained before,
during, and after all events that take place during a drilling operation. The goal here is to
use the huge amount of data available and continuously generated to predict and mitigate
unpleasant events before they take place (see Alouhali and Aljubran, 2018).
In data mining, instance refers to a case or occurrence of anything, and it consists of
class and attributes. In the study described here, each data instance represents a single
time step. Attributes are the surface parameters, and the class parameter is either kick or
no kick. Due to the high frequency of data collection (one instance every 5 s), a large
amount of data was generated. Drilling data were collected from wells that had a well
control incident (a kick) during the drilling operation. This was then reviewed, cleaned,
and labeled (kick, no kick) to build a training data set. The study started with over 1
million instances of real-time drilling operations containing mostly no kick data and few
kick data. These were used to build a training data set; then, after reﬁnement, this
number was reduced to just above 122,000 instances. Instance reduction (sometimes
referred to as data set condensation) is an important step in the data preprocessing stage
that can be applied in many data mining analyses before starting with the model
development. This step ensures that the data used in the analysis is relevant, and it also
reduces the noise and outliers for better performance. Even though most data analysts
Figure 9.13 Tornado chart sensitivity analysis. Adapted from Al-Hameedi, A.T.T., Alkinani, H.H., Dunn-
Norman, S., Flori, R.E., Alsaba, M.T., Amer, A.S., Al-Bazzaz, W.H., March 2019. An assessment of the
impact of rheological properties on rate of penetration using data mining techniques. In: International
Petroleum Technology Conference Held in Beijing, China, 26e28.
374
Methods for Petroleum Well Optimization
would say more data are always better, with the reduced data set, the prediction models
were seen to improve, and processing time was much shorter. Each instance has 14
attributes reﬂecting relevant surface drilling parameters. Tenfold cross-validation was
used to tune the parameters and select features to optimize each model. Overall, ﬁve
classiﬁers were evaluated: decision tree, KNN, sequential minimal optimization (SMO)
algorithm, artiﬁcial neural network (ANN), and Bayesian network. The performance of
these models was calculated and compared using metrics such as precision, recall,
m-measure, MCC, ROC Area, PRC Area, mean absolute error, root mean squared
error, relative absolute error, root relative squared error, and kappa statistic.
9.2.3.1 System architecture
Fig. 9.14 illustrates the overall architecture of the system. It consists of various agents that,
along with the data from surface sensors, perform sensor calibration and event detection.
The models are predicting kicks using only surface parameters such as pressure gauges,
ﬂow meters, hook load, ROP
, torque, pump rate, and weight on bit. The system shown
in Fig. 9.14 has three main functions:
1. real-time data gathering from the mud circulation sensors,
2. real-time data gathering from the hoisting and rotary sensors, and
3. application of the data mining algorithm, such as the ANN in Fig. 9.15.
9.2.3.2 Data mining evaluation criteria
The performance of each model was compared using common data analytic metrics and
coefﬁcients. Some of these metrics measure the accuracy of the model such as precision,
recall, m-measure, MCC, ROC Area, and PRC Area. The others measure and quantify
the mistakes or error of each model such as mean absolute error, root mean squared error,
Figure 9.14 System architecture for input data.
Data mining in digital well planning and well construction
375
relative absolute error, and root relative squared error. The confusion matrix is the ﬁrst
step in evaluating the model’s performance where it displays four important parameters:
true positive, false positive, true negative, and false negative.
Relying on one type of measurement to evaluate the performance of a model often
gives a misleading result, and it is always better to use more than one to get a holistic view
of how accurate the model is by trying to maximize the accuracy parameters and
minimizing the errors. A brief description of each of the metrics is outlined in Fig. 9.16.
True Positive (TP): Number of instances correctly classiﬁed as a kick.
False Positive (FP): Number of instances incorrectly classiﬁed as a kick.
True Negative (TN): Number of instances correctly classiﬁed as not a kick.
False Negative (FN): Number of instances incorrectly classiﬁed as not a kick.
Precision = TP/(TP+FP)
Recall = TP/(TP+FN)
F1 Score = 2(Recall  Precision)/(Recall + Precision)
MCC = ((TPTN)e (FPFN))/[(TP +FP)(TP + FN)(TN + FP)(TN + FN))]1/2
Figure 9.15 Artiﬁcial neural network algorithm for kick detection.
Figure 9.16 Metrics measure the accuracy.
376
Methods for Petroleum Well Optimization
9.2.3.2.1 ROC area
Receiver operator characteristic (ROC) curves show how the number of correctly
classiﬁed positive instances varies with the number of incorrectly classiﬁed negative
instances. The area under the ROC curve (AUC) is a measure of how well a parameter
can distinguish between two diagnostic groups (diseased/normal). The closer the ROC
area value to 1, the more accurate is the model. ROC curves have a disadvantage if the
classes are unbalanced.
9.2.3.2.2 Precisionerecall curve area
The precisionerecall curve (PRC) area plot precision versus recall then calculates the
area under the curve, similar to the ROC area; the closer the value to 1, the more ac-
curate is the model. PRCs have an advantage over ROC curves when the data are
skewed in favor of one class over the other, which is the case in this study.
9.2.3.2.3 Kappa statistic
The kappa statistic is a measurement comparing the observed or actual accuracy of the
classiﬁer or algorithm with expected accuracy (random chance). The kappa statistic was
originally developed to measure the agreement between two human raters, which then
was modiﬁed and adopted by the machine learning community to evaluate the
performance of the classiﬁer. Values for the kappa statistic range from 0 to 1 where
0 means no match and 1 means a perfect match.
Errors were also calculated to provide additional metrics for evaluating the perfor-
mance of the models. They are best used to measure the performance of regression
models. The errors listed in Table 9.3 are also a good metric for measuring the
performance of models with binary attributes, such as in this case study. The difference
between the mean absolute error (MAE) and root mean squared error (RMSE) is that
MAE is linear, giving all individual difference the same weight, whereas RMSE squares
the errors before they are averaged, which gives a higher penalty for a large error.
9.2.3.3 Artiﬁcial neural network
A backpropagation ANN, namely, multilayer perceptron (MLP) was used. An ANN
usually consists of three types of nodes: input nodes, hidden nodes, and output nodes.
The input nodes provide information from the outside world to the network and are
collectively referred to as the input layer. No computation is performed in any of the
input nodes; they receive the information from the user or outside world and then pass
them to the hidden nodes. The hidden nodes have no direct connection with the outside
world. They only deal with the input nodes and output nodes. They perform
computations on the data they received from the input nodes and then transfer the
information to the output nodes. All the hidden nodes together form the hidden layer. A
deep ANN is a normal ANN with several hidden layers. The output nodes are together
referred to as the output layer. They are responsible for transferring the information from
Data mining in digital well planning and well construction
377
the network to the outside world. Fig. 9.15 shows an overview of the ANN model used
for kick detection. The results of the model are listed in Table 9.4.
9.2.3.4 Comparison models evaluation
Table 9.5 shows the results of each classiﬁer evaluated in the study: decision tree, KNN,
sequential minimal optimization (SMO) algorithm, ANN, and Bayesian network.
A decision tree model is recommended to detect the kick because it both has high
accuracy and requires low computational power, allowing it to be implemented as edge
processing if required. KNN has slightly higher accuracy, but with a trade-off in terms
of the computational power required as KNN takes much longer to evaluate new
inputs than all the other models. An ANN and a Bayesian network are also good
candidates for this application, but with slightly less accuracy than the decision tree
and KNN models. Lastly, SMO’s subpar performance is mainly due to the type of
data used in this case study. Although SMO can be used for classiﬁcation problems, it is
more suitable for regression analysis and has better performance with a different set
of data.
Table 9.3 Error calculation formulae.
MAE ¼ 1
N
X
N
i ¼ 1


b
qi  qi



Mean absolute error
RMSE ¼
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
ﬃ
1
N
X
N
i ¼ 1
	
b
qi  qi

2
v
u
u
t
Root mean squared error
RAE ¼
XN
i¼1


^
qi  qi



XN
i¼1


q  qi



Relative absolute error
RRSE ¼
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
ﬃ
XN
i¼1
	
^
qi  qi

2
XN
i¼1
	
q  qi

2
v
u
u
u
u
t
Root relative squared error
Table 9.4 Artiﬁcial neural network performance.
Metric
Value
Precision
0.988
Recall
0.852
F-measure
0.915
MCC
0.917
ROC area
0.928
PRC area
0.878
Kappa statistic
0.9143
From Alouhali, R., Aljubran, M., December 2018. Drilling through data: automated kick detection using data mining. In:
SPE International Heavy Oil Conference and Exhibition Held in Kuwait City, Kuwait, 10e12.
378
Methods for Petroleum Well Optimization
9.3 Summary
1. This chapter presents a new approach that uses data mining tools to achieve a higher
level of automation and optimization.
2. It discusses the development of a new model and procedure to detect kicks in real
time while drilling with high accuracy. With this model, the detection rate and speed
are high, giving the drilling crew enough time to react to the kick and maintain the
wellbore stability and mud rheology.
3. Analysis shows a clear relationship between the rheological properties and ROP
,
where higher ROP can be achieved at lower values of plastic viscosity and yield
points. In addition, it was clear that solid content has the highest impact on the ROP
.
9.4 Problems
Problem 1: Data mining
Consider the following data points:
A1¼ (2,10), A2¼ (2,5), A3¼(8,4), A4¼(5,8), A5¼(7,5), A6¼(6,4), A7¼(1,2),
A8¼(4,9).
1. Using the min approach, max approach, and centroid approach, and the resulting
dendrogram, create the necessary proximity matrices using Euclidean distance.
Problem 2: K-means clustering algorithm
Consider the ﬁve data points shown in the following:
P1:(1,2,3), P2:(0,1,2), P3:(3,0,5), P4:(4,1,3), P5:(5,0,1).
1. Apply the k-means clustering algorithm to group those data points into two
clusters using the L1 distance measure. Suppose that the initial centroids are C1:(1,0,0)
and C 2:(0,1,1). Use the following table as a template to illustrate each of the k-means
iterations (Table 9.6).
Table 9.5 Performance of the ﬁve models.
Metric
Decision tree
KNN
Bayesian network
SMO
ANN
Precision
0.989
0.992
0.767
0.754
0.988
Recall
0.972
0.987
0.919
0.51
0.852
F-measure
0.98
0.99
0.836
0.609
0.915
MCC
0.98
0.989
0.838
0.617
0.917
ROC area
0.993
0.999
0.998
0.754
0.928
PRC area
0.971
0.988
0.919
0.39
0.878
Kappa statistic
0.98
0.989
0.8342
0.6057
0.9143
From Alouhali, R., Aljubran, M., December 2018. Drilling through data: automated kick detection using data mining. In:
SPE International Heavy Oil Conference and Exhibition Held in Kuwait City, Kuwait, 10e12.
Data mining in digital well planning and well construction
379
2. Explain how the k-means termination condition has been reached.
3. For your ﬁnal clustering, calculate the corresponding SSE (sum of squared error)
value.
SSE ¼
X
K
i ¼ 1
X
x˛Ci
dist2ðmi; xÞ
x is a data point in cluster Ci and mi is the representative point for cluster C.
Problem 3: Decision tree to predict wellbore stability
Consider the training data set shown in the table. The ID3 algorithm can be performed
to derive a decision tree to predict whether a depth is likely to be stable for drilling
(Table 9.7).
1. Calculate the following information entropy: H(wellbore stability jdepth<¼3000)¼
2. Assume that result to select “depth” as the ﬁrst testing attribute at the top level of the
decision tree. Calculate H(wellbore stability jWell Kick) in the subtable of
“depth<¼3000”: H(wellbore stability jWell Kick)¼
Table 9.6 A template to illustrate each of the k-means iterations.
Cluster
Old centroids
Cluster elements
New centroids
1
2
Table 9.7 Training data set for wellbore stability.
Depth (m)
Inclination
Well Kick
Mud weight
Wellbore stability
3000
High
No
Low
No
3000
High
No
Medium
No
3001 to 4000
High
No
Medium
No
>4000
Medium
No
High
Y
es
>4000
Low
Y
es
High
Y
es
>4000
High
Y
es
Medium
No
3001 to 4000
Low
Y
es
High
Y
es
3000
Medium
No
Low
No
3000
Low
Y
es
High
Y
es
>4000
Medium
Y
es
High
Y
es
3000
Medium
Y
es
High
Y
es
3001 to 4000
Medium
No
High
Y
es
3001 to 4000
High
Y
es
High
Y
es
>4000
Medium
Y
es
Low
No
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Methods for Petroleum Well Optimization
Problem 4: Hierarchical clustering tree
The proximity matrix (i.e., distance matrix) for ﬁve data objects (p1 ., p5) is shown in
Table 9.8. Apply agglomerative HC to build the HC tree of the data objects. Merge the
clusters by using max distance and update the proximity matrix correspondingly. Make
sure you show each step of clustering clearly.
Problem 5: Data mining for rate of penetration prediction
Consider the information and data set for the ROP model shown in Problem 9 in
Chapter 2. The RapidMiner software can be run to derive different techniques to
predict ROP (RapidMiner software Manual: https://docs.rapidminer.com/downloads/
RapidMiner-v6-user-manual.pdf).
1. Present at least three methods of data mining for ROP prediction.
Problem 6: Data mining techniques for optimum bit selection
Consider the depth and IADC code for the seven wells shown in the table. Use at least
three data mining techniques for presenting a model prediction for determining
optimum bit selection and rank your model: (Table 9.9)
•
At ﬁrst, consider drilling intervals of 50 m. For example, you can consider a
1500 m total depth of 30 intervals.
Table 9.8 Distance matrix.
p1
p2
p3
p4
p5
p1
0
1
5
9
10
p2
1
0
3.5
8
7
p3
5
3.5
0
3
4
p4
9
8
3
0
0.5
p5
10
7
4
0.5
0
Table 9.9 Depth and IADC code for seven wells.
X1
X2
X3
X4
X5
X6
X7
Depth
(m)
IADC
code
Depth
(m)
IADC
code
Depth
(m)
IADC
code
Depth
(m)
IADC
code
Depth
(m)
IADC
code
Depth
(m)
IADC
code
Depth
(m)
IADC
code
150
d
150
d
150
d
150
d
150
d
150
d
150
d
403
111
302
131
680
537
480
131
479
131
235
131
401
537
544
131
681
537
910
111
695
537
650
131
347
537
918
131
715
527
874
131
1295
111
718
537
757
131
620
131
920
131
1072
111
1443
111
1457
131
1102
131
1359
131
1358
537
1156
131
1412
131
1263
111
1410
131
1400
131
1395
131
1408
131
Data mining in digital well planning and well construction
381
•
Rank your model using different software such as MATLAB, Weka, and RapidMiner
software.
•
Performance measurements for the ranking include Precision, Recall, MCC, ROC
Area, and PRC Area.
Problem 7: Standard data platform
A standardized independent self-managed real-time data management infrastructure to
facilitate seamless ﬂow of real-time technical data transparently throughout the organi-
zation showed in Fig. 9.17.
1. Explain how data mining techniques help to achieving well construction efﬁciency
through a below independent real-time data platform of critical operations.
2. Which early drilling problems could be predicted using the data mining model?
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382
Methods for Petroleum Well Optimization
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Guha, S., Rastogi, R., Shim, K., 1999. ROCK: a robust clustering algorithm for categorical attributes. In:
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., 1990. Finding Groups in Data: An Introduction to Cluster Analysis. Wiley.
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Murtagh, F
., 1984. A survey of recent advances in hierarchical clustering algorithms which use cluster
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Periklis, A., 2002. Data Clustering Techniques. University of Toronto.
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Open-source code
Y
ou can develop your models using open-source code for different tools available
on https://github.com/
Examples include:
•
Source Code for Machine Learning in the Oil and Gas Industry.
•
Data Mining about petroleum engineering.
•
Automated well log correlation using Python.
•
A tool to track drilling data in 3D using downhole measurements.
•
Data mining code including decision tree, k-means and etc.
•
Data mining applied to oil well using k-means and DBSCAN.
•
Well trajectory planning.
Data mining in digital well planning and well construction
383

CHAPTER 7
Weaknesses and strengths of
intelligent models in petroleum
industry
Contents
7.1 Overview
295
7.2 Intelligent models versus theoretical models
295
7.3 Intelligent models versus empirical correlations
297
7.4 Effect of the number of actual data
298
7.5 Validation of the developed models
299
References
300
7.1 Overview
The previous chapters shed light on how intelligent models bring improve-
ments in solving many problems related to petroleum industry from various
standing points of view, including accuracy, robustness, and generalization.
Fig. 7.1 summarizes the main noted enhancements ensured by these models
when they have been applied in petroleum industry. However, in this con-
text, it is necessary to underline the weaknesses and strengths of these intel-
ligent models. To this end, this chapter aims at highlighting these main
points by giving some deep comparisons with frequently applied counter-
part paradigms, namely, the theoretical- and empirical-based approaches.
7.2 Intelligent models versus theoretical models
It is well known that many parameters encountered in the petroleum
industry can be delivered from some advanced theoretical approaches
when they are included in multitasking-based computational frameworks
[1]. Flow models and thermodynamic equations such as equations of state
are among the frequently included theoretical approaches in many petro-
leum industry tasks [2]. The theoretical approaches are based on prior
295
Applications of Artificial Intelligence Techniques in the Petroleum Industry
DOI: https://doi.org/10.1016/B978-0-12-818680-0.00007-2
© 2020 Elsevier Inc.
All rights reserved.
knowledge which are developed to explain a phenomenon and further
investigate its behavior under different circumstances. A deep mathemati-
cal formulation is generally needed for achieving more representative the-
oretical paradigms. The latter are known, generally, to be simple-to-use
techniques. However, due to the diversity of information and data related
to given parameters aimed to be modeled, the inflexibility of theoretical
procedures employed for this purpose, the considered suppositions, and
sometimes ignorance of influential parts in the modeling, many theoretical
models fail in getting satisfactory results when modeling several kinds of
parameters [35]. Besides the accuracy issue, some theoretical models are
frequently related to extensive calculus tasks, which results in the applica-
tion of some theoretical and numerical strategies for resolving the formu-
lated problems to get better estimations. Therefore, this kind of
demarches can lead to computational efforts, which reduces the simplicity
of theoretical techniques.
On the other hand, intelligent models are resulted from pattern recog-
nition, systems identification, and cognitive processes, which are based on
Figure 7.1 A summary of intelligent models' impact.
296
Applications of Artificial Intelligence Techniques in the Petroleum Industry
advanced learning tools that employ data, regardless of its variety, to track
and recognize relationships between parameters. The intelligent models
can be gained in a form of explicit expression, such as the models that use
genetic programing, gene expression programing, group method of data
handling, multilayer perceptron (MLP), and radial basis function, or in a
form of computer-aided model when applying other machine-learning
techniques. There are many learning rules that are integrated in machine
learning methods which allow achieving better estimations. It is worth
mentioning that most of these learning schemes are known to be light
mathematical approaches that do not need advanced resolving techniques.
In addition, many well-known mathematical calculation issues that may
be encountered in the learning phase of intelligent models such as matrix
inversion, quadratic programming problems, and local minima cases are
overcome by several tricks, for example, adding the regularization param-
eter in LevenbergMarquardt algorithm for MLP training, applying
sequential minimal optimization for support vector regression learning
phase and optimizing the control parameters of the other intelligent mod-
els using metaheuristic algorithms [69]. Another very important point
that gives the advantage of additional accuracy for the models resulted
from machine learning methods is the inclusion of the so-called cross-vali-
dation step during the learning phase of the intelligent models. This step
helps with adapting the proper control parameters of the applied method
by estimating the out-of-sample error, and hence the most representative
model will be identified and generated [10]. Moreover, this step helps in
overcoming ambiguities in the data utilized in the training phase of the
intelligent model.
7.3 Intelligent models versus empirical correlations
Several parameters that are involved in many petroleum industry tasks are
determined by means of experimental approaches. These techniques allow
a real-time monitoring of the phenomenon and provide physical under-
standings of existing processes between the different parameters. As the
experimental approaches are expensive and time-consuming, and as their
gained results are delivered in discrete manner with respect to the experi-
mental conditions that do not allow their direct utilization for continues
simulation tasks, representative models should be created accordingly. For
this purpose, the gained results from the performed laboratory measure-
ments are frequently utilized for establishing empirical correlations for the
297
Weaknesses and strengths of intelligent models in petroleum industry
studied parameters. The empirical procedure consists of using empirical
evidence to gain a certain degree of knowledge about the phenomenon.
Most of the available empirical correlations in the petroleum industry are
known to be derived from experimental studies for specific cases and
under predefined conditions. Pressure volume temperature and produc-
tion parameters are among the well-known examples that are described
by empirical correlations [11]. The latter are known to be simple-to-use
methods for predicting the considered parameters. However, most of the
available empirical correlations in petroleum industry are established for
specific cases with limited ranges of experimental conditions [12,13]. In
addition, most of the correlations exhibit low degrees of accuracy due to
the strategies employed for elaborating the expressions. These issues lead
to the restriction of the applicability realm of the empirical correlations.
Besides, several prior empirical correlations fail to capture the expected
trends existing between the desired output and the independent variables,
mainly for conditions outside the establishment ones.
In intelligent models, the gathered experimental data are also a basic
element for building the paradigms. These latter can be computer-aided
models or explicit models, according to the utilized intelligent technique.
Generally, the applicability of the intelligent models is recommended to
the intervals that have been used for training these models. However, in
contrast to the empirical techniques, intelligent models learn the utilized
data and identify the patterns between the input parameters and the out-
put. The available learning rules and hybridizations with optimization
algorithms allow the improvement of the provided performances by the
intelligent models. Moreover, machine-learning techniques deal easily
with large data and can recognize patterns and specific trends that would
not be identified by the empirical correlations. Machine learning methods
show very satisfactory performances at handling data that are multidimen-
sional and multivariety, and they can do this in real time and in the pres-
ence of uncertainties.
7.4 Effect of the number of actual data
The reliability of the intelligent models is related to the quality and
amount of experimental data used in their development. It is clear that
using a dataset with extensive range of experimental data points will result
in a better predictive model. Besides, the number of actual data has an
important effect on the accuracy and the recognition of the existing
298
Applications of Artificial Intelligence Techniques in the Petroleum Industry
relationship between the input parameters and the output of the studied
system. As the goal of modeling using intelligent models is to get para-
digms with a high ability of generalization, it is obvious that utilizing a
database with a large size and wide range will allow a good understanding
and gathering of a wide variety of information about the system, and
hence, the identification will be made in an effective manner. As illus-
trated in the previous chapters, most of the established intelligent para-
digms in petroleum industry exhibited very satisfactory performances by
dealing with databases of different sizes. Generally, utilizing more than
100 experimental points for building the intelligent models leads to reli-
able results. However, some published works demonstrated the high abil-
ity of the intelligent models when modeling systems with databases
include a moderate number of experimental points [1317]. This can be
explained by the learning procedure and wide range of the inputs applied
for the training of the intelligent models which consists of dividing data to
training, validation, and test sets. In this context, the training part is
employed for building of the models, while the validation and test parts
are applied as adaptive check points for adjusting the models and as blind
data for testifying their robustness, respectively. However, it is necessary
to note that for some complex systems, it is not practical to use a small
database mainly when time is a paramount parameter in the system.
Reservoir simulation tasks are among the examples where large database is
mandatory for getting reliable results when using intelligent models
[9,1822].
7.5 Validation of the developed models
To ensure a high generalization of the intelligent models and avoid the
overfitting issue during the establishment of these models, a good choice
of their control parameters, such as weights and biases for MLP and the
hyperparameters for SVR, should be performed properly. A technique
called “cross validation,” is usually considered for this choice during the
learning stage of the intelligent models. This technique involves using
two independent sets of data to train the model: one for pure learning
(adjusting the control parameters) and the other for validation (validating
its generalization on data not included during learning). The termination
criterion is then to stop the learning step when the training set has lower
error and the validation set achieves its minimum value of error. This
reflects the difference between the actual data and the predictions of the
299
Weaknesses and strengths of intelligent models in petroleum industry
model. It is worth mentioning that a third subset of data is generally
considered to test the generalization of the intelligent models after learn-
ing and validation. This additional subset is known to be blind data that
has not been involved during the establishment of the model.
References
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SPE. Vol. 20, 2020.
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Energy Rev. 81 (2018) 313329.
[4] A.
Hemmati-Sarapardeh,
A.
Shokrollahi,
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Mohammadi, A. Naseri, Reservoir oil viscosity determination using a rigorous
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bitumen mixture viscosity in solvent-assisted oil recovery process using GEP and
GMDH modeling approaches, Pet. Sci. Technol. 37 (2019) 16401647.
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M.M. Husein, B. Dabir, Modeling minimum miscibility pressure during pure and
impure CO2 flooding using hybrid of radial basis function neural network and evo-
lutionary techniques, Fuel 220 (2018) 270282.
[7] Y. Kaneda, H. Mineno, Sliding window-based support vector regression for predict-
ing micrometeorological data, Expert Syst. Appl. 59 (2016) 217225.
[8] J.C. Platt, Advances in kernel methods. Chapter: Fast Training of Support Vector
Machines Using Sequential Minimal Optimization, MIT Press, Cambridge, MA, USA, 3,
1999, 185208.
[9] N.A. Menad, Z. Noureddine, An efficient methodology for multi-objective optimi-
zation of water alternating CO2 EOR process, J. Taiwan Inst. Chem. Eng. 99 (2019)
154165.
[10] Y.S. Abu-Mostafa, M. Magdon-Ismail, H.-T. Lin, Learning From Data, vol. 4,
AMLBook, New York, 2012.
[11] M. Sanni, Petroleum Engineering: Principles, Calculations, and Workflows, vol. 237,
John Wiley & Sons, 2018.
[12] G. Chen, K. Fu, Z. Liang, T. Sema, C. Li, P. Tontiwachwuthikul, et al., The
genetic algorithm based back propagation neural network for MMP prediction in
CO2-EOR process, Fuel 126 (2014) 202212.
[13] H. Sayyad, A.K. Manshad, H. Rostami, Application of hybrid neural particle swarm
optimization algorithm for prediction of MMP, Fuel 116 (2014) 625633. Available
from: https://doi.org/10.1016/j.fuel.2013.08.076.
[14] E.M.E.-M. Shokir, CO2oil minimum miscibility pressure model for impure and
pure CO2 streams, J. Pet. Sci. Eng. 58 (2007) 173185. Available from: https://doi.
org/10.1016/J.PETROL.2006.12.001.
[15] H. Kaydani, A. Mohebbi, A. Hajizadeh, J. Dakhelpour, Developing a formula based
on a hybrid neural genetic algorithm for the prediction of minimum miscibility
pressure, Energy Sources, A: Recover. Util. Environ. Eff. 36 (2014) 679688.
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[16] M.-A. Ahmadi, M. Ebadi, Fuzzy modeling and experimental investigation of mini-
mum miscible pressure in gas injection process, Fluid Phase Equilib. 378 (2014)
112. Available from: https://doi.org/10.1016/J.FLUID.2014.06.022.
[17] M.K. Emera, H.K. Sarma, Use of genetic algorithm to estimate CO2oil minimum
miscibility pressure—a key parameter in design of CO2 miscible flood, J. Pet. Sci. Eng.
46 (2005) 3752. Available from: https://doi.org/10.1016/J.PETROL.2004.10.001.
[18] N.A. Menad, N. Zeraibi, K. Redouane, Optimization of WAG process using
dynamic proxy, genetic algorithm and ant colony optimization, Arabian J. Sci. Eng.
43 (2018) 63996412.
[19] S.D. Mohaghegh, C.A. Modavi, H.H. Hafez, M. Haajizadeh, M.M. Kenawy, S.
Guruswamy, Development of surrogate reservoir models (SRM) for fast track analysis
of complex reservoirs, in: Intelligent Energy Conference and Exhibition, 2006,
pp. 150. doi:10.2118/99667-MS.
[20] S.D. Mohaghegh, Reservoir simulation and modeling based on artificial intelligence
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nition, in: SPE Digital Energy Conference and Exhibition, 2011.
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advanced data-driven analytics, Geosci. Front. 7 (2016) 1120.
301
Weaknesses and strengths of intelligent models in petroleum industry

CHAPTER ELEVEN
Monte Carlo simulation in wellbore
stability optimization
Key concepts
1. We establish and apply a full approach methodology for wellbore stability optimi-
zation under uncertainty. It involves six major steps: Monte Carlo simulation (MCS),
failure criterion, data screening, probability box (P-box) and Bhattacharyya coefﬁ-
cient, safe mud weight window (SMWW) stochastic sensitivity analysis, and ﬁnally
updating the model.
2. The minimizing of geomechanical wellbore integrity problems by analyzing data
through robust plotting and advanced statistical methods is explained.
3. A clear logic is provided for SMWW and minimizing SMWW uncertainties
by creating dynamic simulations and by altering parameters with statistical
uncertainty.
4. Monte Carlo simulation, or probability simulation, is a technique used to understand
the impact of risk and uncertainty in different models. The application of MCS in
wellbore stability using summary statistics is proposed.
5. Pressure overlapping zones are proposed as a novel uncertainty quantiﬁcation (UQ)
metric by creating a Monte Carlo simulation to model a complex wellbore stability
system.
11.1 Basic multivariate statistics
The expected value of a random variable gives a crude measure of the “center of
location” of the distribution of that random variable. For instance, if the distribution is
symmetric about a value m, then the expected value equals m. To reﬁne the picture of a
distribution around the value’s “center of location,” we need some measure of spread (or
concentration) around that value. The simplest measure to calculate for many distri-
butions is the variance. There is an enormous body of probability literature that deals
with approximations to distributions, and bounds for probabilities and expectations,
expressible in terms of expected values and variances.
Methods for Petroleum Well Optimization
 2022 Elsevier Inc.
ISBN 978-0-323-90231-1, https://doi.org/10.1016/B978-0-323-90231-1.00002-9
All rights reserved.
439j
11.1.1 Mean
Let X1; X2; :::; Xn be n observations of a random variable X. We wish to measure the
average of X1; X2; :::; Xn in some sense. One of the most commonly used statistics is the
mean (Fig. 11.1), mX, deﬁned by the formula:
mX ¼ X ¼ 1
n
X
n
i ¼ 1
Xi
(11.1)
Let X be the continuous random variable taking values in [a,b] and f ðxÞ the
probability density function (PDF). Then, the expected value of the continuous random
variable X is:
EðXÞ ¼ m ¼
Zb
a
xf ðxÞdx
(11.2)
11.1.2 Variance
Next, we wish to obtain some measure of the variability of the data. The statistics most
often used are the variance and the standard deviation sX ¼
ﬃﬃﬃﬃﬃﬃ
s2
X
p
. We have:
sX ¼
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
1
n
( X
n
i ¼ 1
X2
i  1
n
 X
n
i ¼ 1
Xi
!2)
v
u
u
t
(11.3)
It is easy to show that the variance is simply the mean squared deviation from the
mean.
Let the continuous random variable be X taking values in [a,b] and f ðxÞ the
probability distribution. Let m ¼ EðXÞ be the expected value of X. Then, the variance
of the continuous random variable X is (Fig. 11.1):
Var X
ð Þ ¼ s2 ¼ E½X  E X
ð Þ2 ¼
Zb
a
ðx  mÞ2f x
ð Þdx
(11.4)
Figure 11.1 Large and small variance of probability distribution.
440
Methods for Petroleum Well Optimization
11.1.3 Covariance
Next, let (X1, Y1), (X2, Y2), ., (Xn, Yn) be n pairs of values of two random variables X
and Y
. We wish to measure the degree to which X and Y vary together, as opposed to
being independent. The ﬁrst statistic we will calculate is the covariance sXY given by:
sXY ¼ 1
n
( X
n
i ¼ 1
XiYi  1
n
 X
n
i ¼ 1
Xi
!  X
n
i ¼ 1
Yi
!)
(11.5)
It is a quantitative measure of the extent to which the deviation of one variable from
its mean matches the deviation of the other variable from its mean. It is a mathematical
relationship that is deﬁned as:
CovðX; YÞ ¼ Ef½X  EðXÞ½Y  EðYÞg is the covariance of X and Y:
(11.6)
CovðX; YÞ ¼ EðXYÞ  EðXÞEðYÞ
VarðX þ YÞ ¼ VarðXÞ þ VarðYÞ þ 2CovðX; YÞ
11.1.4 Correlation coefﬁcient
Actually, a much better measure of correlation can be obtained from the formula:
rXY ¼
P
n
i ¼ 1
XiYi  1
n
 P
n
i ¼ 1
Xi
  P
n
i ¼ 1
Yi

ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
ﬃ
(
P
n
i ¼ 1
X2
i  1
n
 P
n
i ¼ 1
Xi
2)(
P
n
i ¼ 1
Y2
i  1
n
 P
n
i ¼ 1
Yi
2)
v
u
u
t
(11.7)
The quantity rXY is known as the coefﬁcient of correlation of X and Y
.
•
Correlation measures the strength of the linear association between two quantitative
variables.
•
Y
ou get the correlation coefﬁcient from your calculator or computer.
•
The correlation coefﬁcient has a value between 1 and þ1.
•
The correlation coefﬁcient has no units (Fig. 11.2).
Figure 11.2 Correlation coefﬁcient between 1 and þ1.
Monte Carlo simulation in wellbore stability optimization
441
11.1.5 Skewness
Skewness is a measure of the degree to which the sample population deviates from
symmetry with the mean at the center (Fig. 11.3).
Skewness ¼ EððX  mÞ3Þ=s3;
(11.8)
Skewness ¼ S(xi  x)3/[(n  1)S3], n is at least 2.
•
If S < 0, the distribution is negatively skewed (skewed to the left).
•
If S ¼ 0, the distribution is symmetric (not skewed).
•
If S > 0, the distribution is positively skewed (skewed to the right).
11.1.6 Kurtosis
Kurtosis is a statistical measure that deﬁnes how heavily the tails of a distribution differ
from the tails of a normal distribution. In other words, kurtosis identiﬁes whether the
tails of a given distribution contain extreme values.
Kurtosis ¼ E

ðX  mÞ4
=s4;
(11.9)
Kurtosis ¼ S(xi  x)4/[(n  1)S4], n is at least 2.
The types of kurtosis are determined by the excess kurtosis of a particular
distribution. The excess kurtosis can take positive or negative values, as well as values
close to zero.
11.1.6.1 Mesokurtic
Data that follow a mesokurtic distribution show an excess kurtosis of zero or close to
zero. It means that if the data follow a normal distribution, they follow a mesokurtic
distribution.
11.1.6.2 Leptokurtic
Leptokurtic indicates a positive excess kurtosis. The leptokurtic distribution shows heavy
tails on either side, indicating the large outliers.
Figure 11.3 Skewness coefﬁcient.
442
Methods for Petroleum Well Optimization
11.1.6.3 Platykurtic
A platykurtic distribution shows a negative excess kurtosis. The kurtosis reveals a
distribution with ﬂat tails. The ﬂat tails indicate the small outliers in a distribution.
11.1.7 Quartiles
Quartiles are measures of central tendency that divide a group of data into four
subgroups:
•
Q1: 25% of the data set is below the ﬁrst quartile.
•
Q2: 50% of the data set is below the second quartile.
•
Q3: 75% of the data set is below the third quartile.
11.1.8 Probability density function
X is a continuous random variable if its range space Rx is an interval or a collection of
intervals (Fig. 11.4).
F(x), the probability density function (PDF) of X, satisﬁes:
Pða  X  bÞ
Zb
a
f ðxÞdx
(11.10)
1. f ðxÞ  0, for all x in RX
2.
R
RX
f ðxÞdx ¼ 1
3. f ðxÞ ¼ 0; if x is not in RX
Properties:
1. PðX ¼ x0Þ ¼ 0; because
R x0
x0 f ðxÞdx ¼ 0
2. Pða  X  bÞ ¼ Pða < X  bÞ ¼ Pða  X < bÞ ¼ Pða < X < bÞ
Figure 11.4 Probability density function (PDF).
Monte Carlo simulation in wellbore stability optimization
443
11.1.9 Cumulative distribution function
The cumulative distribution function (CDF) is denoted by F(x). It measures the prob-
ability that the random variable X is x; that is, F(x) ¼ P(X  x) (Fig. 11.5).
If X is discrete, then:
FðxÞ ¼
X
all
xix
pðxiÞ
(11.11)
If X is continuous, then:
FðxÞ ¼
Z x
N
f ðtÞdt
(11.12)
Properties:
F is a nondecreasing function: If a < b; then FðaÞ  FðbÞ
2: limx/NFðxÞ ¼ 1
3: limx/NFðxÞ ¼ 0
All probability questions about X can be answered in terms of the CDF
, for example:
Pða < X  bÞ ¼ FðbÞ  FðaÞ; for all a < b
(11.13)
Figure 11.5 Probability density function (left) and cumulative distribution function (right) of a normal
distribution.
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Methods for Petroleum Well Optimization
11.1.10 Percent-point function
The percent-point function (PPF) is the inverse of the CDF
. For this reason, the percent-
point function is also commonly referred to as the inverse distribution function. That is,
for a distribution function, we calculate the probability that the variable is less than or
equal to x for a given x. For the percent-point function, we start with the probability and
compute the corresponding x for the cumulative distribution. Mathematically, this can
be expressed as:
Pr½X  GðaÞ ¼ a or alternatively x ¼ GðaÞ ¼ GðFðxÞÞ
(11.14)
11.1.11 Distribution
11.1.11.1 Uniform distribution
The uniform distribution is a continuous probability distribution. By integration, we
obtain the probability function (pf ):
f ðxÞ ¼
8
>
<
>
:
1
b  a
a  x  b
0
elsewhere
(11.15)
FðxÞ ¼
8
>
>
>
>
<
>
>
>
>
:
0
x  a
x  a
b  a
a  x  b
1
b  x
(11.16)
The
expected
value
and
variance
of
the
uniform
random
variable
is
mX ¼ E½X ¼ aþb
2 ; Var½X ¼ s2
X ¼ ðbaÞ2
12 .
11.1.11.2 The normal distribution
The best-known distribution is the normal distribution, also known as the Gaussian dis-
tribution.
The
normal
probability
law
with
parameters
m
and
s
is
fXðxÞ ¼
1
s ﬃﬃﬃﬃ
2p
p
eðxmÞ2=2s2, for N < x < N, s > 0. The expected value and variance
of the normal random variable is mX ¼ E½X ¼ m; Var½X ¼ s2
X ¼ s2. The standard
normal random variable (the unit normal) is fZðzÞ ¼
1
ﬃﬃﬃﬃ
2p
p
ez2=2, m ¼ 0, s ¼ 1.
1. The new random variable: If X is normal with mean m and variance s2, then: Xþ b
is normal with mean m þ b and variance s2; aX is normal with mean am and variance
a2s2; aX þ b is normal with mean am þ b and variance a2s2.
Monte Carlo simulation in wellbore stability optimization
445
2. Switching from a nonunit normal to the unit normal: Z ¼ Xm
s , mZ ¼ 0,
sZ ¼ 1.
For a normal curve, approximately 68.4% of the observations fall within 1 standard
deviation of the mean, 95.4% within 2 standards deviation of the mean, and 99.7%
within 3 standard deviations (Fig. 11.6).
By standardizing a normal distribution, we eliminate the need to consider mx and sx;
that is, we have a standard frame of reference. The distributions you will encounter most
frequently are shown in Table 11.1.
•
Lognormal distribution: a normal distribution, plotted on an exponential scale.
Often used to convert a strongly skewed distribution into a normal one.
•
Weibull distribution: mainly used for reliability or survival data.
•
Exponential distribution: exponential curves.
•
Uniform distribution: when everything is equally likely.
11.1.12 Conﬁdence coefﬁcient and conﬁdence level
The level of conﬁdence, c, is the probability that the interval estimate contains the
population parameter (Fig. 11.7).
The difference between the point estimate and the actual population parameter
value is called the sampling error. When m is estimated, the sampling error is the
difference m e x. Since m is usually unknown, the maximum value for the error can be
Figure 11.6 Relationship between standard deviation and normal curve.
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Methods for Petroleum Well Optimization
Table 11.1 Distributions.
Notation
FXðxÞ
fXðxÞ
E½X
V½X
MX s
ð Þ ¼ EðesXÞb
Uniform
Unif ða; bÞ
8
>
>
<
>
>
:
0
x < a
x  a
b  a
a < x < b
1
x > b
f x
ð Þ ¼
8
<
:
1
b  a a  x  b
0
elsewhere
aþb
2
ðbaÞ2
12
esbesa
sðbaÞ
Normal
N ðm; s2Þ
FðxÞ ¼
R x
N 4ðtÞdt
4ðxÞ ¼
1
s ﬃﬃﬃﬃ
2p
p
exp

 ðxmÞ2
2s2

m
s2
exp

ms þ s2s2
2

Lognormal
lnN ðm; s2Þ
1
2 þ 1
2 erf
	
ln xm
ﬃﬃﬃﬃﬃ
2s2
p

1
x ﬃﬃﬃﬃﬃﬃﬃ
2ps2
p
exp

 ðln xmÞ2
2s2

emþs2=2

es2  1

e2mþs2
Exponentiala
ExpðbÞ
1  ex=b
1
bex=b
b
b2
1
1 s
b
ðs < bÞ
Gammaa
Gammaða; bÞ
gða;bxÞ
GðaÞ
ba
GðaÞxa1ebx
a
b
a
b2
0
B
@
1
1 s
b
1
C
A
a
ðs < bÞ
Beta
Betaða; bÞ
Ixða; bÞ
GðaþbÞ
GðaÞGðbÞxa1ð1  xÞb1
a
aþb
ab
ðaþbÞ2ðaþbþ1Þ
1 þ P
N
k ¼ 1
 Y
k1
r ¼ 0
a þ r
a þ b þ r
!
sk
k!
Weibull
Weibullðl; kÞ
1  eðx=lÞk
k
l

x
l
k1
eðx=lÞk
lG

1 þ1
k

l2G

1 þ2
k

 m2
P
N
n ¼ 0
snln
n! G

1 þn
k

aWe use the rate parameterization where b ¼ 1
l. Some textbooks use b as scale parameter instead.
bThe ﬁrst moment is E(X), The second moment is E(X
2), The third moment is E(X
3),. The n-th moment is E(Xn).
calculated using the level of conﬁdence. Given a level of conﬁdence, the margin of error
(sometimes called the maximum error of estimate or error tolerance), E, is the greatest
possible distance between the point estimate and the value of the parameter that it is
estimating.
E ¼ zcsx ¼ zc
sﬃﬃ
ﬃ
n
p
(11.17)
When n  30, the sample standard deviation, s, can be used for s.
A c-conﬁdence interval for the population mean m is:
x  E < m < x þ E
(11.18)
If the level of conﬁdence is 90%, this means that we are 90% conﬁdent that the
interval contains the population mean, m. (Figure 11.7A).
The corresponding z-scores are  1.645.
If the level of conﬁdence is 99%, this means that we are 99% conﬁdent that the
interval contains the population mean, m. (Figure 11.7B).
The corresponding z-scores are  2.575.
The probability that the conﬁdence interval contains m is c (Fig. 11.8).
Figure 11.7 Level of conﬁdence.
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Methods for Petroleum Well Optimization
11.2 Uncertainty assessment of wellbore stability
There is a digital twin change taking place in smart well construction today. More
and precise data will become generated in real-time drilling for a vast number of analyses.
Wellbore stability optimization in the digital well construction process is a complicated
matter and includes several key parameters. Today, there are many in-house drilling
analytics tools used by service and consulting companies. There are six steps to wellbore
stability optimization as shown in Fig. 11.9.
Figure 11.8 Normal or t-distribution.
Figure 11.9 Wellbore stability optimization steps.
Monte Carlo simulation in wellbore stability optimization
449
11.2.1 Uncertainty propagation
The Monte Carlo method is generally used to perform an uncertainty study. This
technique provides an efﬁcient and straightforward way to propagate uncertainties and
has become the industry standard for this purpose.
Uncertainty propagation shows how the uncertainty of input parameters spreads into
the output of the model at hand. Fig. 11.10 shows the steps.
1. Construct a probability density function (PDF) for each input parameter (PDF
reﬂects the state of knowledge about the value of the parameter).
Generate one set of input parameters by using random numbers according to PDFs
assigned to those parameters.
2. Quantify the output function using the above set of random values. The obtained
value is a realization of a random variable (X).
3. Repeat steps 1 to 2 n times (until a sufﬁcient number of samples is obtained; e.g.,
1000) producing n independent output values. These n output values represent a
random sample from the probability distribution of the output function.
4. Generate statistics from the obtained sample for the output result: mean, standard
deviation, s, conﬁdence interval, etc.
11.2.2 Safe mud weight window for wellbore stability
As an uncertainty solution in model simulation, veriﬁcation and validation has received
increasing attention in the drilling industry. Geomechanical modeling consists of
computing the stresses around the wellbore and comparing them to a failure model. In
this part, two different failure criteria are used and evaluated under uncertainty. Wellbore
stability depends on many factors such as geology, wellbore path, petrophysics, and the
operational execution. One of the main results is the assessment of the allowable mud
weight window, the SMWW
. At the end, we must also decide if a pessimistic or
Figure 11.10 The general procedures of a Monte Carlo simulation. Modiﬁed from Bratvold, R.B., Begg,
S.H., 2010. Making Good Decisions. Richardson, Texas, USA: Society of Petroleum Engineers.
450
Methods for Petroleum Well Optimization
optimistic decision is made, based on uncertainty considerations. Fig. 11.11 illustrates the
relationship of mud weight, wellbore stability, and the in situ stress magnitudes of various
types of rock failure models. A common interpretation of Fig. 11.11 follows. When the
mud pressure is less than the pore pressure, the wellbore may have splintering failure or
spalling. When the mud pressure is less than the shear failure gradient, the wellbore has
shear failure or breakout. If the mud pressure is too high, drilling-induced hydraulic
fractures are generated, which may cause losses in mud drilling. To maintain wellbore
Figure 11.11 SMWW for wellbore stability steps. SMWW, safe mud weight window.
Monte Carlo simulation in wellbore stability optimization
451
stability, the mud weight should therefore be within an appropriate range when a hole is
drilled in the ground; the borehole wall stress and the mud pressure will balance the
stresses and pressures that exist inside the formation.
At high wellbore pressure, a tensile failure occurs, whereas at low wellbore pressure, a
shear failure occurs leading to a collapsed wellbore. Often the wellbore then becomes
oval or elliptic; this is called wellbore breakout. The basic stress model used is what is
referred to as the Kirsch equation, which assumes a pressure step at the wellbore wall
caused by an impermeable mud cake. For example, during stimulation operations with
water, there is no mud cake and different equations apply. In general, the SMWW is
deﬁned with the fracture limit as the upper limit, and the collapse pressure or the pore
pressure as the lower limit. Many models exist, from the simple linear elastic model to
more complex nonlinear models. However, there is usually not sufﬁcient data to justify
models that are complex. This lack of data also makes MCS in wellbore stability, an
excellent candidate for uncertainty analysis. It is important to understand that the in situ
stress state may be the most uncertain parameter. The consequences of wellbore
instability problems are circulation losses caused by too high mud weight, or by poor
hole cleaning which is often caused by wellbore collapse. At low pressures, we may see
wellbore collapse causing stuck pipe, landing problems of casing strings, and poor cement
jobs. The two major unplanned events that can occur during drilling are stuck pipe and
circulation losses, both closely related to wellbore stability.
Random input variables can be modeled with different types of probability distributions
such as normal, uniform, triangular, lognormal, and quadratic distributions. A real
distribution can beestablishedifthere are sufﬁcient geomechanics data. Ifnot, a distribution
that best explains the data set can be assumed. Fig. 11.12 shows the 11 examples of input
probability distributions.
Figure 11.12 Different types of input probability distributions.
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Methods for Petroleum Well Optimization
The selection of a distribution graph may differ, depending on data availability.
Drilling often uses the normal distribution. For data sets with evidence of mode or most
likely value, it is recommended that triangular and uniform distributions be considered.
For small samples from which unrepresentative data points have been removed through
rigorous analyses, uniform distributions are the preferred choice. If distribution
parameters are known, then the distribution is deﬁned. For example, the normal
distribution is deﬁned by its mean and standard deviation. The uniform distribution is
deﬁned by its minimum and maximum values, while the triangular distribution is
deﬁned by its minimum, most likely, and maximum values. Measures of dispersion,
variance, standard deviation, and P10 to P90 show the extent to which a given data set is
spread around the mean (or P50, for a symmetric distribution). The MCSs used here are
based on the procedure deﬁned by Williamson et al. (2006). This has four steps:
1. Select a failure criteria model:
•
nonpenetrating Kirsch solution for wellbore fracturing;
•
MohreCoulomb for collapse (see section 11.3.1); or
•
modiﬁed Lade criterion, also for wellbore collapse (used in problem 1).
2. Perform data gathering and determine the lower and upper limits for input variables:
The input parameters (now random variables) with assumed uncertainties are shown
in Table 11.2.
3. Select distribution for input variables: All the inputs are assigned a stochastic
distribution.
4. Perform output generation and interpretation of the results.
By using a range of possible values, instead of a single guess, a realistic span can be
created. When a model is based on ranges of estimates, the output of the model will also
be in the range of estimates. The steps of the MCS and the SMWW workﬂow for
wellbore stability using MohreCoulomb criteria are illustrated in Fig. 11.13.
Table 11.2 Uncertainty estimation for ﬁrst run of fracture and collapse pressure.
Input parameter
Most likely value
Uncertainty
estimation (%)
Range of magnitude
sH
1.8 s.g.
10
1.62e1.98 s.g.
sh
1.5 s.g.
5
1.43e1.58 s.g.
Po
1.05 s.g.
30
0.74e1.37 s.g.
a
30 degrees
20
24e36 degrees
so
0.5 s.g.
50
0.25e0.75 s.g.
From Udegbunam, J.E., Aadnøy, B.S., Fjelde, K.K., 2014. Uncertainty evaluation of wellbore stability model predictions.
J. Petrol. Sci. Eng. 124 (2014) 254e263. https://doi.org/10.1016/j.petrol.2014.09.033.
Monte Carlo simulation in wellbore stability optimization
453
11.2.3 Well geomechanical model design
A geomechanical model reveals the mechanical behavior of rock and wellbore and is
used to better manage the drilling programs as shown in Fig. 11.13. According to
geomechanics, well drilling generates signiﬁcant changes in the local stress ﬁeld of the
formation due to losses of supporting material. The drilling operation induces radial and
tangential stresses on the wellbore wall that result in shear stresses. At certain positions,
the induced stress may be higher than the rock strength and the rock will fail, causing
borehole collapse. This geomechanical behavior can be addressed by knowing the
features of the rock so that it is possible to prevent or to minimize instability problems.
Mechanical earth models (MEMs) are presented for wells. Models describing the rock’s
elastic and strength properties, in situ stresses, and pore pressure as a function of depth
are established. MEMs consist of continuous proﬁles of the following rock mechanical
data and parameters along the well trajectories in different formations:
1. comprehensive information about the mechanical stratigraphy;
2. formation elastic properties, including dynamic and static Y
oung’s modulus and
Poisson’s ratio;
3. rock strength parameters, including UCS, friction angle, and tensile strength;
Figure 11.13 Phases of a typical Monte Carlo simulation.
454
Methods for Petroleum Well Optimization
4. pore pressures and a leak-off test (LOT); and
5. the in situ stress state, including minimum and maximum horizontal stresses, the
azimuth of the minimum horizontal stress, and magnitude of vertical stress.
11.2.4 Stress transformation and equations
To get the stresses at the wall of the borehole, Aadnøy and Looyeh (2011) itemized the
four steps shown in Fig. 11.14, to be taken in the order presented.
The principal in situ stresses in the rock formation need to be transformed to a
different Cartesian coordinate system to align with the orientation of the drilled hole.
The stress and direction of the drilled wellbore is deﬁned by its inclination (g), which is
the angle with respect to the vertical, the azimuth (4) and the position of the wellbore
with reference to the x-axis, q (Aadnøy and Looyeh, 2011). The transformation of the
stress components yields the subsequent equations:
sx ¼

sH cos2 4 þ sh sin2 4

cos 2 g þ svsin 2 g
sy ¼ sHsin 2 4 þ shcos 2 4
szz ¼

sH cos2 4 þ sh sin2 4

sin 2 g þ svcos 2 g
sxy ¼ 1
2 ðsh  sHÞsin 2 4 cos g
sxz ¼ 1
2

sH cos2 4 þ sh sin2 4  sv

sin 2 g
syz ¼ 1
2 ðsh  sHÞsin 2 4 sin g
(11.19)
Figure 11.14 Stress transformation.
Monte Carlo simulation in wellbore stability optimization
455
Kirsch Equations
sr ¼ Pw
sq ¼ sx þ sy  pw  2

sx  sy

cos 2 q  4sxy sin 2 q
sz ¼ szz  2v

sx  sy

cos 2 q  4vsxy sin 2 q/planestrain
sz ¼ szz/planestrain
srz ¼ 0
sqz ¼ 2

 sxy sin q þ syz cos q

(11.20)
After the successful transformation of the stress equations as given by Eq. (11.19),
steps 1 and 2 are completed. To achieve steps 3 and 4, governing equations were
developed, some logical assumptions made, and boundary conditions applied; the
resultant Kirsch equations are deﬁned as follows.
Considering an isotropic solution, and taking r ¼ a, Eq. (11.19) becomes
Eq. (11.20) as shown and illustrated in Fig. 11.15.
The radial stress is highly controllable by the driller (for example, pressure of drilling
mud), whereas the two remaining stresses are less inﬂuential as they are controlled by the
far-ﬁeld stresses.
In this chapter, we use two models to calculate collapse pressure to prevent rock
failure. They are MohreCoulomb and modiﬁed Lade. Based on the Kirsch equation
with the assumption q ¼ 90, it means that there is no breakout in the borehole wall
because breakout is avoided during drilling or it can be minimized to achieve wellbore
stability. So, the stress around the borehole can be calculated as shown in Eq. (11.21).
Figure 11.15 Stresses on a borehole wall. Modiﬁed from Aadnøy, B.S., Bell J.S., 1998. Classiﬁcation of
drilling induced fractures and their relationship to in-situ stress direction, publisher: society of petro-
physicists and well-log analysts, petrophysics. SPWLA J. Form. Eval. Reserv. Descr. 39 (06). Paper Number:
SPWLA-1998-v39n6a2. Published: 01 November 1998.
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Methods for Petroleum Well Optimization
As seen from Kirsch’s equation, fracture occurs when the minimum in situ stress is
exceeded. In drilling operations, these equations use a nonpenetrating boundary con-
dition and become Eq. (11.21) (Aadnøy et al., 2007). We assume here that sH ¼ sh.
pwf ¼ 2sh  Pp
(11.21)
A nonpenetrating boundary condition is when ﬂuids build up a ﬁlter cake barrier
during the drilling operation, assuming a perfect mud cake so there will be no ﬁltration
loss. A penetrating boundary condition is when ﬂuid is pumped into the formation
(Aadnøy et al., 2008).
11.2.5 Borehole failure
Borehole failure depends on many interrelated factors such as orientation, formation
pore pressure, rock compressive strength, wellbore azimuth, and the in situ stress
magnitude. Three orthogonal stresses, axial stress, tangential stress, and wellbore pressure
sq ¼ 3sx  sy  Pwc
sa ¼ szz þ 2y

sx  sy

sr ¼ Pwc
q ¼ 90
Rock failure is governed
by principal
stresses, and the
solution for principal
stress is below
(Aadnøy, 1988):
si ¼ Pwc
sj ¼ 1
2 ðsq þ saÞ
þ 1
2
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
ðsq  saÞ2 þ 4s2
qz
q
sk ¼ 1
2 ðsq þ saÞ
 1
2
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
ðsq  saÞ2 þ 4s2
qz
q
Vertical well
sqZ ¼ 0
s1 ¼ sq ¼ 3sx  sz  Pwc
s3 ¼ Pwc
s’
1 ¼ 3sx  sy  Pwc  P0
s’
3 ¼ Pwc  P0
(11.21)
Monte Carlo simulation in wellbore stability optimization
457
cause shear failure, and a single tensile stress causes tensile failure (Aadnøy et al., 2009).
There are, in all, nine possible modes of failure: six modes of shear failure and three
modes of tensile failure. Shear, or compression failure, is when the pressure inside the
borehole is lower than the pore pressure (underbalanced drilling condition) and may
eventually cause collapse or breakouts of parts of the borehole wall. Tensile failure is
when the wellbore pressure exceeds the formation’s fracture pressure (overbalanced
drilling condition), and it may lead to fracturing of the borehole wall (Aadnøy and
Looyeh, 2011). The different failure modes may happen independently, sequentially, or
simultaneously. The geometry of borehole shear failure can be categorized into six
modes based on the magnitude of axial stress (sa), radial stress (sr), and tangential stress
(sq). These are presented in Fig. 11.16.
11.2.6 Probability distribution
Stochastic simulation guarantees that, if the probabilistic distribution of oil well design
parameters or related parameters are precisely deﬁned, the probabilistic distribution of
output variables (for example, collapse and fracture pressure) can be illustrated in a
manner that more properly deﬁnes the response of the drilling operation. A compre-
hensive approach is presented here leading to independent deterministic analyses, fol-
lowed by stochastic analyses, which are concluded via the best comparison between a
deterministic numerical model and a probabilistic model to present the best solutions.
Fig. 11.17 shows how this approach may lead to better decision-making in drilling
operations.
Figure 11.16 Modes of shear failure and tensile failure for vertical wellbores. Adapted from Rezmer-
Cooper, I., Bratton, T., Krabbe, H., 2000. The use of resistivity-at-the-bit images and annular pressure
while drilling in preventing drilling problems. SPE Drill. Complet. 16 (01), 35e42. Paper Number: SPE-
70130-PA. https://doi.org/10.2118/70130-PA.
458
Methods for Petroleum Well Optimization
Fig. 11.17 shows the interference of the PDF of Qk (loads are the collapse pressure
with X1 ~ N [m1, s1 square]) and Rk (the resistance R is the wellbore fracture pressure
with X2 ~ N [m2, s2 square]). The overlapping area denotes the probability of failure.
The smaller the overlapping zone, the more reliable the wellbore will be, meaning the
risk of wellbore collapse will be lower. Conversely a big overlap zone indicates a higher
risk of wellbore collapse.
We have two normal distributions deﬁned by their averages and standard deviations,
where m1 < m2. In Fig. 11.17, the green variable corresponds to X1. Let c denote the
point of intersection where the PDFs meet in the overlapping zone of plot, then the area
of our intersection zone is:
PðX1DcÞ þ PðX2CcÞ ¼ 1  F1 c
ð Þ þ F2 c
ð Þ ¼ 1  1
2 erf
c  m1
ﬃﬃﬃ
2
p
s1

þ 1
2 erf
c  m2
ﬃﬃﬃ
2
p
s2

(11.22)
where erf(.) is the error function and point c is the solution to f1(x) ¼ f2(x) within the
overlapping zone, which is calculated in Eq. (11.23):
c ¼
m2s2
1  s2

m1s2 þ s1
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
ﬃ
ðm1  m2Þ2 þ 2

s2
1  s2
2

log
s1
s1

s

s2
1  s2
2
(11.23)
Figure 11.17 Probability densities for the resistances and loads of wellbore collapse used in this
chapter.
Monte Carlo simulation in wellbore stability optimization
459
11.3 Numerical examples
The mud pressure is the key factor to be determined and calculated due to its main
role in well stability. The determination of SMWW provides a safe range of mud
pressures that can be applied with low risk. To demonstrate the power of the MCS
procedure, we provide an example taken from North Sea ﬁeld data. The simulated real
data in the columns belong to North Sea ﬁelds, which represent the different input
quantities, linked row by row according to the functional relationship for fracture and
collapse pressure. The outputs and histogram from this set of calculations now show the
many possible amounts, which can be referred to as the measured, the mean of which
provides an estimate of the measured and the distribution of values. The data in the
output column can now be further evaluated. Examples of output analyses include the
following:
•
Plotting a frequency histogram for input data using Excel chart functions such as sH,
sh, Po, a, and so.
•
Analyzing the shape of the distribution based on visual control of the frequency
graph.
•
Presenting useful statistics such as mean, mode, median, and standard deviation using
standard Excel functions.
•
Moving or copying the output into another column and sorting from smallest to
largest, then excluding the lowest 2.5% and highest 2.5% of values to give a 95%
coverage interval of pressure. The Excel PERCENTILE function can be used to
determine the required coverage interval boundaries as shown in Fig. 11.18.
Figure 11.18 Calculation of the coverage interval for collapse and fracture pressure.
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Methods for Petroleum Well Optimization
•
Calculating skewness and kurtosis. These statistics could provide additional support
when considering the shape of the output, assessing its closeness to normality or
when determining the coverage interval. This is fulﬁlled in two parts: deterministic
and stochastic analyses.
11.3.1 MohreCoulomb shear failure criterion for safe mud weight
window
The MohreCoulomb failure model only uses maximum and minimum principal stress.
The failure model can be described by Eq. (11.24) (Aadnøy and Hansen, 2005).
s ¼ s0 þ s0 tana
Where:
s ¼ 1
2

s0
1  s0
3

cosa
s’ ¼ 1
2

s0
1 þ s0
3

 1
2

s0
1  s0
3

sina
(11.24)
Combining these equations arrives at:

s0
1  s0
3



s0
1 þ s0
3

sin a ¼ 2s0 cos a
(11.25)
11.3.1.1 Deterministic prediction
For a deterministic analysis, the well stability analysis includes fracture and collapse
pressure as shown in Fig. 11.19. In this calculation, the single-point estimates of the
geopressures will give an optimistic SMWW
. This may lead to drilling problems.
However, it is impossible to analyze the associated risk and uncertainty based on these
ﬁxed input data.
Let
sh ¼ 1:5 sg sH ¼ 1:8 sg; P0 ¼ 1:05 sg; a ¼ 30+; s0 ¼ 0:5 sg.
Pwf ¼ 3sh  sH  p0 ¼ 3  1:5  1:4  1:05 ¼ 1:65 sg ¼ 13:91ppg
Pwc ¼ 1
2 ð3sH  shÞð1  sin aÞ þ P0 sin a  s0 cos a ¼
1=2  ð3  1:8  1:5Þ  ð1  sin 30Þ þ 1:05  sin 30  0:5  cos30 ¼ 1:076sg ¼ 9ppg
Monte Carlo simulation in wellbore stability optimization
461
11.3.1.2 Stochastic prediction
Making estimates about SMWW in drilling projects is something every drilling manager
does regularly. Sometimes, based on our experience and knowledge of operations,
making those estimates is easy. However, at other times, proposing and implementing an
estimate is complicated. Even after we make a good mud weight estimate using
comparative rock strength criteria, we may not feel conﬁdent about that estimate. Here is
an MCS estimation technique to use when we are faced with a deterministic estimation
of mud weight.
Here, we are using the statistical solution method, based on the MohreCoulomb
failure criterion; we can deﬁne the required strength properties. We must also deﬁne a
friction angle range and pore pressure. First, we will run our simulation with 5000 data
generation, with the uncertainty for each parameter taken from Table 11.2. These data
are from a ﬁeld in the North Sea in 1700 m depth.
The results of running the Excel program are shown in Fig. 11.20. These are not
realistic results, and we could not use them for practical purposes in the ﬁeld. In the
overlap area, there was collapse and fracture in the well at the same time, and this is
impossible. We need to provide the most reliable set of results for use as inputs for the
analysis methods currently employed in drilling engineering. There is a signiﬁcant
overlap between the collapse and fracture pressure that appears in the simulation. The
overlap between the two statistical samples under the normal distribution is related to the
Bhattacharyya coefﬁcient in Eq. (11.26). The Bhattacharyya coefﬁcient can be used to
ﬁnd the relative closeness of the two normal histograms being presented.
DBhattacharyya ¼ 1
4 In
1
4
s2
1
s2
2
þ s2
2
s2
1
þ 2

þ 1
4
ðm1  m2Þ2
s2
2 þ s2
2

(11.26)
Figure 11.19 Range of values for safe mud weight management of the well derived from the deter-
ministic calculation.
462
Methods for Petroleum Well Optimization
We are looking for the intersection between the green graph and the blue graph.
That intersection is larger when the two distributions are similar, and Fig. 11.21 shows
our simulation is weak. It is therefore necessary for us to ﬁnd a decision bound, criterion,
etc., for our data generation to achieve a better simulation of collapse and fracture
Figure 11.20 Unrealistic results base N¼5000 trials with uncertainty from Table 11.3.
Figure 11.21 Finding a decision bound, criterion, etc. for better simulation.
Monte Carlo simulation in wellbore stability optimization
463
pressure in our case study. Realistic results for SMWW can be predicted by applying
Fig. 11.23 the SMWW workﬂow for wellbore stability analysis using the Mohre
Coulomb criteria. We now calculate the histogram plot of collapse and fracture pressure
for 20,000 Monte Carlo trials generated by Excel with uncertainty in estimation (%5)
for all input data as in Table 11.2. The distribution for the entire input variable is normal.
Maximum and minimum collapse and fracture pressure for 20,000 input data are
determined in Fig. 11.24. Looking at the new histogram makes it easy to determine that
we have overlap, but it is a very small range compared with the previous overlap interval.
One of the likely disadvantages of an MCS is that a ﬁrst run of trials does not by itself
show the reliability of the results. However, the greater the number of MCS trials, the
more “stable” will be the output standard deviation (that is, the standard uncertainty of
the measured). This feature of MCS can thus be used as a direct method for determining
the number of trials for a given case study. Fig. 11.22 shows a typical plan for ﬁnding the
number of trials required in strong MCSs.
We focus on the procedure presented in Fig. 11.23 for a screening of data for the
input parameters; data screening should be carried out prior to our Monte Carlo pro-
cedure. Often, data screening procedures are so tedious that they are ignored. Then, after
an analysis produces unexpected results, the data are investigated closely and carefully.
This program needs to be applied to the whole data screening process. By seeing the
overlap zone of the histograms, we will be able to verify most of our data assumptions
before beginning the actual analysis.
As explained in Fig. 11.23, there were several data recorded in the Excel ﬁle that are
not covered by the equation in Table 11.3, and we focused on working on these three
Perform 20 Mont Carlo simulation with 1000 trials each and calculate the standard 
deviation of the output. Record the standard deviation for each of the 20 simulations. 
Repeat the 20 simulations, but now with 10,000 trials each. Again, calculate the 
standard deviation of the measured for each of the 20 simulations.
Again, repeat with 20 simulations for 100,000 trials each. The variation of the 
standard deviation will decrease as the number of trials increases.
Figure 11.22 Typical plan for ﬁnding preferred number of trials.
464
Methods for Petroleum Well Optimization
bounds for screening data in our Excel ﬁle. This stress state is assumed because a large
extent of the North Sea area has a normal fault stress regime. Based on this screening, we
run an example so that we describe it in more detail.
Let: sv ¼ 2.0 s.g., sH ¼ 1.93 s.g., sh ¼ 1.75 s.g., Po ¼ 1.45 s.g., a ¼ 20 degrees,
so ¼ 0.25 s.g.;
A ¼ 7  sin 20 degrees; B ¼ 5  3sin 20 degrees; C ¼ 1.45(1 þ sin 20 degrees) þ
2(0.0634 e 0.25cos 20 degrees);
A ¼ 6.658; B ¼ 3.974; C ¼ 1.603.
Let X1 ¼ shA ¼ 11.65 s.g.; X2 ¼ sHB ¼ 7.67 s.g.; X3 ¼ X2 þ C ¼ 9.273 s.g.:
For bound 1:
shA  sHB þ C
11.65 > 9.27
For bound 2:
sHA  svB þ C
12.85 > 9.55
For bound 3:
shA  svB þ C
11.65 > 9.55
We will run the procedure again for two cases. We are going to execute a proposed
procedure using Excel programming based on statistical methods for the cases shown in
Fig. 11.25 (case 1:sH ¼ 1:2sh) and Fig. 11.28 (case 2:sH ¼ sh).
Data generaon
Rock strength criterion selecon
1. Histogram plot of fracture and collapse pressure 
2. Summary stascs Q5, Q25, Q50, Q75 and Q95 
No
SMWW
Start
Calculate stresses around a well
Simulaon is good?
Yes
Screening data
Determine the bounds base on stress state
Figure 11.23 SMWW workﬂow for wellbore stability.
Monte Carlo simulation in wellbore stability optimization
465
For case 1, the lowest 2.5% and highest 2.5% of values (based on row number) to give
a 95% coverage interval of pressure are between 1.19 and 1.37 s.g., as shown in
Fig. 11.26.
Figure 11.24 Unrealistic results base N¼20,000 trials and uncertainty in Table 11.2.
Table 11.3 Bounds for in situ stresses for boreholes in the three principal stress directions.
Stress state
Bound 1
Bound 2
Bound 3
Normal fault
shAsHB+C
sHAsvB+C
shAsvB+C
Strike-slip fault
shAsHB+C
svAsHB+C
shAsvB+C
Reverse fault
shAsHB+C
svAsHB+C
svAshB+C
A¼7sin a, B¼53sin a, C¼Po (1+sin a)+2(dsocos a).
466
Methods for Petroleum Well Optimization
This part of the procedure is focused on calculating an initial interval of SMWW in
case 2. The safe mud pressure window is constrained to the speciﬁc casing setting depth
being designed, which implies that a repeated calculation should be executed for each hole
section. In addition, as previously speciﬁed, the deterministic approach relies strongly
upon proper measurements of the geomechanical properties of the near-wellbore con-
ditions and cannot take into account uncertainties under this setting (Fig. 11.27).
Let
sh ¼ 1:4 sg sH ¼ 1:4 sg; P0 ¼ 1:05 sg; a ¼ 30+; s0 ¼ 0 sg.
Pwf ¼ 3sh  sH  p0 ¼ 32  1:54  1:05 ¼ 1:75 sg
Pwc ¼ 1
2 ð3sH  shÞð1  sin aÞ þ P0 sin a  s0 cos a ¼
1=2  ð3  1:4  1:4Þ  ð1  sin 30Þ þ 1:05  sin 30  0  cos 30 ¼ 1:225sg
Figure 11.25 Histogram of collapse and fracture for case 1.
Figure 11.26 Range of values for safe mud weight management derived from a stochastic calculation
for Case 1.
Monte Carlo simulation in wellbore stability optimization
467
Based on Table 11.3, this calculation also matches the MCSs (see Fig. 11.28).
For case 2, the lowest 2.5% and highest 2.5% of values (based on row number) to give
a 95% coverage interval of pressure are between 1.32 and 1.44 s.g., as shown in
Fig. 11.29.
The numbers in Table 11.4 provide a quick summary of the data and are particularly
useful for comparing fracture and collapse pressure statistics. There are two main types of
summary statistics present in this evaluation including Q25 and Q75. Measures of central
tendency provide different versions of the average, including the mean, the median, and
the mode. Measures of dispersion provide information about how much variation there
is in the data, including the range and the standard deviation.
Figure 11.27 Range of values for safe mud weight management in case 2 derived from a deter-
ministic calculation.
Figure 11.28 Histogram of collapse and fracture for case 2.
468
Methods for Petroleum Well Optimization
According to Fig. 11.25, for case 1, the SMWW range, where fracture or collapse
cannot happen, with 95% conﬁdence, is between 1.19 and 1.37 s.g.
According to Fig. 11.28, for case 2, the SMWW range, with 95% conﬁdence, is
between 1.32 and 1.44 s.g.
11.4 Summary
Selecting the appropriate SMWW is critical in drilling offshore and onshore
operations, to ensure the safe and economic delivery of a high-quality wellbore. Making
selections from alternative well designs can lead to enhanced well stability, lower capital
costs, and a reduction in drilling time. However, typically, there is a trade-off for these
beneﬁts, with certain designs leading to lower costs and others leading to higher
productivity or higher risk during drilling operations in the long term.
Figure 11.29 Range of values for safe mud weight management in case 2 derived from a stochastic
calculation.
Table 11.4 Summary statistics for fracture and collapse pressure using screening data under case 1
and case 2.
Monte Carlo simulation in wellbore stability optimization
469
•
Borehole stability research requires a large amount of ﬁeld data, which are not always
available, especially in exploratory drilling. The Monte Carlo method is widely used
in engineering for sensitivity analysis, and it contributes to quantitative risk analysis
when applied to analyze the uncertainty of the wellbore stability.
•
The results show that an SMWW can be calculated between the implemented
calculation (deterministic) and the Monte Carlo method. In this way, the proposed
method could satisfy the needs of the drilling engineering application.
•
One of the aspects of the Bhattacharyya coefﬁcient as an uncertainty quantiﬁcation
(UQ) metric is to runthe stochastic sensitivityanalysis, by quantifying the importance of
input parameters, according to the uncertainty interval of the SMWWas output. This
issue needs an accurate UQ metric to show how much the uncertainty interval of the
SMWW can be adjusted, when the epistemic uncertainty space of the input parameters
is reduced. The graphs of the probability distribution of collapse pressure and fracture
pressure are shown in the P-box format. The red circle part of the graph indicates the
Bhattacharyya coefﬁcient, which can assess the wellbore stability uncertainty for
different PDFs of collapse pressure and fracture pressure as shown in Fig. 11.30.
Figure 11.30 Relationship between collapse and fracture pressure by the P-Box and Bhattacharyya
distance.
470
Methods for Petroleum Well Optimization
11.5 Problems
Problem 1: Determining mud weight by using modiﬁed Lade
criterion
The Lade criterion for failure of frictional materials is given by Eq. (11.27) (Ewy, 1999):

I00
1
3=I00
3 ¼ 27 þ h Where:
I00
1 ¼ ðs1 þ S1  P0Þ þ ðs2 þ S1  P0Þ þ ðs3 þ S1  P0Þ
I00
3 ¼ ðs1 þ S1  P0Þðs2 þ S1  P0Þðs3 þ S1  P0Þ
(11.27)
The input parameters for a vertical well with assumed uncertainties are shown in
Table 11.5 in a 1700 m depth.
Present the following graphs and calculations:
1. Histogram of collapse in modiﬁed Lade.
2. SMWW in modiﬁed Lade without overlap.
3. Summary statistics for collapse and fracture pressure.
Problem 2: Probability distribution ﬁtting
The calculation model for collapse and fracture pressure is assumed as follows (Guan and
Sheng, 2017):
rc ¼ hð3sH  shÞ
ðK2 þ hÞ
þ esc  rp

K2  1

ðK2 þ hÞ

2CK
0:00981  HðK2 þ hÞ
(11.28)
K ¼ cot

45
	  a
2

(11.29)
rf ¼ 3sh  sH  esc  rp þ St=0:00981  H
(11.30)
Table 11.5 Lower and upper limits for input variables.
Input parameter
Most likely value
Uncertainty in estimation (%)
sH
1.7 s.g.
10
sh
1.4 s.g.
5
Po
1.01 s.g.
20
a
45 degrees
25
so
0.5 s.g.
30
Monte Carlo simulation in wellbore stability optimization
471
According to the probability distribution of parameters in Table 11.6, 4000 random
values are generated based on MCS. Put the random values into the collapse and fracture
pressure calculation models.
1. Determine the probability distribution ﬁtting of formation collapse pressure and
fracture pressure before and after screening or adjustment.
2. Determine the safe drilling ﬂuid density window with a reliability of 50%.
3. Determine the absolute safety zone.
Problem 3: SMWW in a deviated well
Use the data for problem 1 to determine the SMWW in a deviated well. Y
ou can assume
information about inclination and azimuth.
Problem 4: Overlapping area of two lognormal distributions
Deﬁne X1wLognormal(ln(m1),s2) and X2wLognormal(ln(m2),s2), where m2>m1>0
and that there is a deﬁnite proportion, h˛(0,1), between X1 and X2 such that:
8
>
<
>
:
f1ðxÞ ¼
h
xs
ﬃﬃﬃﬃﬃﬃ
2p
p
eðInðxÞInðm1ÞÞ2
2s2
(11.31)
8
>
<
>
:
f2ðxÞ ¼ 1  h
xs
ﬃﬃﬃﬃﬃﬃ
2p
p
eðInðxÞInðm2ÞÞ2
2s2
(11.32)
Table 11.6 Probability distribution of geomechanical parameters.
Parameters
Distribution form
Characteristic
parameter (1250 m)
Characteristic
parameter (1750 m)
Vertical principal
stress sv (g/cm3)
N (m, s2)
m ¼ 2.133, s ¼ 0.08
m ¼ 2.15, s ¼ 0.02
Maximum horizontal
principal stress sH (g/cm3)
N (m, s2)
m ¼ 1.91, s ¼ 0.06
m ¼ 1.99, s ¼ 0.01
Minimum horizontal
principal stress sh (g/cm3)
N (m, s2)
m ¼ 1.648, s ¼ 0.013
m ¼ 1.817, s ¼ 0.02
Rock tensile
strength St (MPa)
N (m, s2)
m ¼ 2.28, s ¼ 0.156
m ¼ 1.84, s ¼ 0.302
Formation pore
pressure rp (g/cm3)
N (m, s2)
m ¼ 1.08, s ¼ 0.093
m ¼ 1.39, s ¼ 0.099
Internal friction
angle a (degree)
N (m, s2)
m ¼ 32.7, s ¼ 0.02
m ¼ 32.6, s ¼ 0.04
Cohesion C (MPa)
N (m, s2)
m ¼ 4.5, s ¼ 0.03
m ¼ 3.6, s ¼ 0.06
472
Methods for Petroleum Well Optimization
where f1 and f2 represent the h-scaled PDFs of X1 and X2, respectively.
1. Given m1, m2, s, and h, how is the overlapping area of the two probability distri-
bution curves, OVL ¼ f(m1, m2, s, h), formulated?
2. Calculate the overlapping area. See an illustrative plot below, where OVL ¼ f(m1 ¼ 5,
m2 ¼ 10, s ¼ 20%, h ¼ 50%) is shaded in yellow.
Problem 5: Calculation of probability of failure under two normal
distributions and determination of Bhattacharyya coefﬁcient
Given the stress distribution with a mean, mx, of 1500 and standard deviation, sx, of 20, and
given a strength distribution with a mean, my, of 1600 and standard deviation, sy, of 30:
1. Determine the probability of failure.
2. Determine the number of intersection points.
3. Find the overlapping area of the stress and strength normal distributions. Also
determine the Bhattacharyya coefﬁcient.
Nomenclature
C cohesion (MPa)
erf(.) the error function
H depth (m)
LOT leak-off test
MCS Monte Carlo simulation
Monte Carlo simulation in wellbore stability optimization
473
MEM mechanical earth models
Q5, Q10, etc. quantiles
SMWW safe mud weight window
s standard deviations
sH maximum horizontal stress
sh minimum horizontal stress
sv overburden stress
sr radial stress
sq tangential stress
sqq induction tangential stress
szz induction axial stress
srr induction radial stress
s1 maximum principal stress
s2 intermediate stress
s3 minimum principal stress
m averages
h parameter of modiﬁed Lade
r; q; z cylindrical coordinate system
x; y; z Cartesian coordinate system
so cohesive rock strength
a angle of internal rock friction
esc effective stress coefﬁcient, 0.8.
r density
rc
collapse pressure (g/cm3)
rf
formation pore pressure (g/cm3)
ɳ nonlinear correction factor, 0.95
Co uniaxial compressive strength
C cohesive rock strength
L1 parameter of modiﬁed Lade
L3 parameter of modiﬁed Lade
Mw mud weight
N number of trials in a Monte Carlo simulation
pw mud weight
pwf fracture pressure
pwc collapse pressure
p0 formation pore pressure in modiﬁed Lade
p probability
S parameter of modiﬁed Lade
So cohesive rock strength
St rock tensile strength (MPa)
s:g: speciﬁc gravity
T uniaxial tensile strength
UCS uniaxial compressive strength
References
Aadnøy, B.S., 1988. Inversion technique to determine the in-situ stress ﬁeld from fracturing data. In: Paper
SPE 18023, Presented at the 63rd SPE Annual Technical Conference and Exhibition, Houston, Texas.
https://doi.org/10.2118/18023-MS.
474
Methods for Petroleum Well Optimization
Aadnøy, B.S., Belayneh, M., Arriado, M., Flatboe, R., 2008. Design of well barriers to combat circulation
losses. SPE Drill. Complet. 23 (03), 295e300. https://doi.org/10.2118/105449-PA. SPE-105449-PA.
Aadnøy, B.S., Bell, J.S., 1998. Classiﬁcation of drilling induced fractures and their relationship to in-situ
stress direction, publisher: society of petrophysicists and well-log analysts, petrophysics. SPWLA J.
Form. Eval. Reserv. Descr. 39 (06). Paper Number: SPWLA-1998-v39n6a2. Published: 01 November
1998.
Aadnøy, B.S., Hansen, A.K., 2005. Bounds on in-situ stress magnitudes improve wellbore stability analyses.
J. Petrol. Sci. Eng. 115e120. https://doi.org/10.2118/87223-PA.
Aadnøy, B.S., Kaarstad, E., Belayneh, M., 2007. Elastoplastic fracture model improves predictions in
deviated wells. In: Paper Presented at the SPE Annual Technical Conference and Exhibition,
Anaheim, California, USA, November 2007. https://doi.org/10.2118/110355-MS. Paper Number:
SPE-110355-MS.
Aadnøy, B., Looyeh, R., 2011. Petroleum Rock Mechanics: Drilling Operations and Well Design. Gulf
Professional Publishing, Boston.
Aadnøy, B.S., Cooper, I., Miska, S.Z., Mitchel, R.F
., Payne, M.L., 2009. Advanced Drilling and Well
Technology. Society of Petroleum Engineers, Richardson, Texas, USA.
Bratvold, R.B., Begg, S.H., 2010. Making Good Decisions. Society of Petroleum Engineers, Richardson,
Texas, USA.
Ewy, R.T., 1999. Wellbore stability prediction by use of a modiﬁed lade criterion. SPE Drill. Complet. 14,
85e91. https://doi.org/10.2118/56862-PA.
Guan, Z.-C., Sheng, Y
.-N., 2017. Study on evaluation method for wellbore stability based on uncertainty
analysis. J. Appl. Sci. Eng. 20 (4), 453e457. https://doi.org/10.6180/jase.2017.20.4.06.
Rezmer-Cooper, I., Bratton, T., Krabbe, H., 2000. The use of resistivity-at-the-bit images and annular
pressure while drilling in preventing drilling problems. SPE Drill. Complet. 16 (01), 35e42. https://
doi.org/10.2118/70130-PA. Paper Number: SPE-70130-PA.
Udegbunam, J.E., Aadnøy, B.S., Fjelde, K.K., 2014. Uncertainty evaluation of wellbore stability model
predictions. J. Petrol. Sci. Eng. 124 (2014), 254e263. https://doi.org/10.1016/j.petrol.2014.09.033.
Williamson, H.S., Sawaryn, S.J., Morrison, J.W
., 2006. Monte Carlo techniques applied to well forecasting:
some pitfalls. SPE Drill. Complet. 21 (3), 216e227.
Open-source code
Open-source code for different tools is available on https://github.com/ such as:
•
Monte Carlo Simulation with Python
•
Bhattacharyya distance
•
Advanced Statistical Estimation
Open-source code for different tools is also available on https://www.mathworks.com
such as:
•
Monte Carlo Estimation Examples
•
Calculate overlapping area of two CDF’s
•
Find overlapping surface area of two distributions
•
Mean and 3-sgima for Lognormal distributions
•
Advanced Statistical Estimation of Variance and Standard Deviation
•
Bhattacharyya distance between two Gaussian classes
•
Find intersection of 2 normal distribution
Monte Carlo simulation in wellbore stability optimization
475

CHAPTER FIVE
Wellbore hydraulics and hole cleaning:
optimization and digitalization
Key concepts
1. The ﬁrst part of this chapter describes the general aims of optimization and lists its
functions, while introducing the basics of the hydraulic and drilling ﬂuid systems.
Understanding the principles of such a complex subject as optimization is of primary
importance to enhance drilling efﬁciency by well monitoring. These principles have
a major inﬂuence on the elements of the whole drilling process, from the spud of the
well until the end of the completion phase.
2. Piecewise equations for the cuttings bed and the drill pipe are presented to show how
to ﬁnd the optimal height of the cuttings bed.
3. There are several indexes that evaluate hole cleaning efﬁciency while drilling. The
index that is the core of the developed model is the carrying capacity index (CCI),
which is deﬁned as a measure of the ability of a mud system to circulate the cuttings to
the surface. The index is inﬂuenced mainly by the drilling ﬂuid properties and ﬂow
hydraulics, both of which are controllable factors. This allows the rig crew to make
adjustments on location to ensure sufﬁcient cleaning of the cuttings.
4. The Reelwell drilling method (RDM) prevents the accumulation of cuttings in the
well due to its superior hole cleaning capability, even at low ﬂow. By transporting the
cuttings rapidly to the surface, formation evaluation is improved, and contamination
of the mud is avoided, as mixing and grinding of cuttings is prevented.
5. Digitalization can enhance hole-cleaning effectiveness and monitor borehole stability
through accurate measurement and analysis of cuttings recovered from the well.
5.1 Hydraulic optimization
5.1.1 Introduction
Traditional selection of the hydraulic parameters of a drilling system often involves an
optimization procedure. Typically, the ﬂow rate directly beneath the drill bit is selected
for optimization. Common optimization criteria are the maximization of the hydraulic
energy delivered through the bit nozzles and the maximization of the jet impact force.
Although these criteria seem reasonable at a ﬁrst glance, a closer look at the total
hydraulic system reveals that they may have limitations.
Methods for Petroleum Well Optimization
 2022 Elsevier Inc.
ISBN 978-0-323-90231-1, https://doi.org/10.1016/B978-0-323-90231-1.00007-8
All rights reserved.
149j
In this section, we will approach the hydraulic optimization problem in a non-
traditional way. We will use a semiempirical approach for the pressure drop model.
Furthermore, in addition to the classical optimization methods, such as maximum
hydraulic horsepower and jet impact, new criteria are derived, which take the ﬂow rate
and cuttings transport into account. The reason for this is that the classical criteria have
not been adequate for the evolution of deeper and long-reach wells.
5.1.2 The hydraulic system
5.1.2.1 Pressure losses
In this chapter, we will deﬁne some simple equations to perform pressure drop
calculations in the hydraulic system. First, we will investigate some properties of the
various ﬂow regimes. Bourgoyne et al. (1986) give a good overview of the equations
needed to calculate friction losses in tubing and annuli for non-Newtonian ﬂuids.
In general, we deal with two ﬂow regimes. In the laminar ﬂow regime, the ﬂuid
moves along deﬁned paths, and the ﬂow equations are determined analytically. In the
turbulent ﬂow regime, in contrast, ﬂuid moves in a chaotic manner. There are no
analytical models available for this case, and therefore, correlations have to be established
using the friction factor concept. In general, we can say that the following relations exist
between pressure drop and ﬂow rate for Newtonian ﬂuids:
For laminar ﬂow:
Pemq
(5.1)
For turbulent ﬂow:
Perfq2
(5.2)
where P ¼ pressure drop, q ¼ ﬂow rate; m ¼ viscosity, r ¼ ﬂuid density, and f ¼ friction
factor.
Note that the pressure drop for ﬂow in pipes depends on the ﬂow regime; in laminar
ﬂow, the pressure drop is proportional to the viscosity and the ﬂow rate, and in turbulent
ﬂow, the pressure drop is proportional to the density and the ﬂow rate squared. Eqs. (5.1)
and (5.2) are valid for Newtonian ﬂuids. For non-Newtonian ﬂuids, more complex
relations exist, as described by Bourgoyne et al. (1986). However, the trends are similar,
and since we are not going to use these equations in the following analysis, they will not
be addressed further.
Fig. 5.1 illustrates the hydraulic system on a ﬂoating drilling rig. Inside the drill pipe,
the ﬂow velocity is high because of the small cross-sectional area. The velocity increases
signiﬁcantly over the bit nozzles. The inside of the drill string is usually in turbulent ﬂow.
In the annulus, the section along the bottomhole assembly (BHA) may be in turbulent
ﬂow or in laminar ﬂow, but the rest of the annulus, including the riser, is usually in
laminar ﬂow.
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Methods for Petroleum Well Optimization
Seen in the context of Eqs. (5.1) and (5.2), we observe that we have a mixture of ﬂow
regimes. Therefore, the total pressure loss consists of a mixture of those two equations.
From a functionality point of view, the ﬂow across the bit nozzles removes drilled
cuttings away from the drill bit. The ﬂow in the annulus has the function of transporting
these cuttings up the wellbore for disposal on the drilling rig. The pressure drop can be
split into two parts:
1. The pressure drop across the nozzles, which aids the drilling process by cleaning and
providing hydraulic power.
2. The pressure drop in the rest of the system (the system pressure drop). This is also
called the parasitic pressure drop as it does not contribute to the drilling process.
If we consider the hydraulic system of Fig. 5.1 as a whole, we can split the total pressure
drop into a useful and a parasitic group, as follows:
P1 ¼ P2 þ P3
(5.3)
Figure 5.1 The hydraulic system. Aadnoy (2010).
Wellbore hydraulics and hole cleaning: optimization and digitalization
151
where P1 ¼ pump pressure, P2 ¼ pressure drop across bit nozzles, and P3 ¼ parasitic
pressure loss, or system losses.
We will brieﬂy consider the parasitic pressure loss. Instead of modeling each element
of the system using Eqs. (5.1) and (5.2) and Fig. 5.1, and totaling the contributions, we
will use one simple equation that describes the whole system.
P3 ¼ Cqm
(5.4)
where C ¼ proportionality constant and m ¼ ﬂow rate exponent.
Typically, the pressure losses in the annulus or the laminar parts of the system are of
the order 10%e20% of the total pressure drop. The losses inside the drill string dominate
the parasitic pressure loss. Since this usually is turbulent, Eq. (5.2) dominates the process.
Therefore, Eq. (5.4) is dominated by turbulent ﬂow, which results in an exponent of
slightly less than two.
The pressure drop across the drill bit nozzles must also be evaluated. The ﬂow rate
through the nozzles is given by the continuity equation, where v is velocity and A is area:
q ¼ vaAa ¼ vbAb ¼ constant
(5.5)
or : va ¼ q
Aa
;
vb ¼ q
Ab
(5.6)
Using the conservation of energy principle, and assuming an incompressible and
frictionless system, the pressure drop across the bit nozzles is:
v2
a
2 þ Pa
r ¼ v2
b
2 þ Pb
r
(5.7)
P2 ¼ Pa  Pb ¼ r
2

v2
b  v2
a

(5.8)
where subscript a refers to the drill pipe and b to the nozzles, r is ﬂuid density, and g is the
gravitational constant.
Two simpliﬁcations will now be introduced. First, the velocity inside the drill string is
negligible compared with the nozzle velocity. Therefore, the drill string ﬂow velocity is
neglected. Second, experimental measurements have shown that the ﬂow is not ideal
and is somewhat lower than predicted by the aforementioned equation. A discharge
coefﬁcient of 0.95 is often used. Introducing these elements and the continuity relation,
the aforementioned equation can be expressed as:
vo ¼ 0:95
ﬃﬃﬃﬃﬃﬃﬃ
ﬃ
2P2
r
r
or
P2 ¼
rq2
2A20:952
(5.9)
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Methods for Petroleum Well Optimization
We have now deﬁned all the elements required to use the total pressure drop equation
(Eq. 5.3). In the following, we will present an example to demonstrate its application.
From an exploration well, the following system losses or parasitic losses have been
measured at different well depths (Table 5.1).
It is difﬁcult to show the continuous pressure dropeﬂow rate behavior on a plot
because we have only a few discrete measurements. However, taking the logarithm on
both sides of Eq. 5.4, we obtain:
lnP3 ¼ lnC þ mlnq
(5.10)
If the logarithmic relationship given by Eq. 5.4 is correct, the data should plot as
straight lines on a logelog plot, with a slope equal to m. This is performed in Fig. 5.2.
The numerical value of the slope can be obtained by using Eq. (5.10) on two data sets and
subtracting, as shown in the following using the two ﬁrst entries of the data set in
Table 5.1:
m ¼ lnð100 = 173Þ=lnð2228 = 3000Þ ¼ 1:84
Repeating this process for the two other depth intervals yields the same value.
Inserting this value and a data set from Table 5.1 into Eq. (5.4), we obtain an expression
for each of the three depth intervals:
P3 ¼ 6:92  105q1:84
at 1200 m depth
P3 ¼ 8:72  105q1:84
at 2200 m depth
P3 ¼ 10:36  105q1:84
at 3200 m depth
The three pressureeﬂow rate functions are shown in Fig. 5.2. We see clearly the
advantage of using a logelog plot. Having only two measurements, the complete
pressure range can be established.
Table 5.1 Parasitic pressure losses.
Depth (m)
Pressure drop (bar)
Flow rate (L/min)
1200
100
2228
173
3000
2200
103
2000
218
3000
3200
123
2000
259
3000
From Aadnøy, B.S., 2010. Modern Well Design, Second Edition. CRC Press/Balkema.
Wellbore hydraulics and hole cleaning: optimization and digitalization
153
We also observe that it is only the scaling factors in front of each of the three
aforementioned expressions that are different. A plot also showed that this constant is
a linear function with depth. Solved as a depth-dependent function, the three
aforementioned expressions can therefore be presented in a single equation as follows:
P3 ¼ ð4:86 þ 0:00172DÞ105q1:84
(5.11)
A few words about Eq. (5.11) are needed. If the calculation is based on a few
measurements as in the example, care should be taken to avoid extrapolating beyond the
range of the measurements. If measurements are not available, hydraulic modeling can be
used.
Eq. (5.11) is linear with depth. This is intentional, as typically one type of BHA is
used, and drill pipes are only added when deepening the well. The pressure drop increase
from adding drill pipes is a linear function, with a constant ﬂow rate.
Finally, the exponent m contains both a viscosity and a density element as indicated in
Eqs. (5.1) and (5.2). If signiﬁcant changes are introduced in the ﬂuid properties or in the
equipment, this exponent is no longer expected to stay constant.
We will use the aforementioned parasitic pressure loss function but note that the same
ideas can be approached with more complex modeling.
5.1.2.2 Classical optimization criteria
Now we will brieﬂy show how to derive the two classical hydraulic optimization criteria
that have been used for decades: the maximum hydraulic horsepower and the maximum
jet impact.
Figure 5.2 System pressure losses for various depth intervals. Aadnoy (2010).
154
Methods for Petroleum Well Optimization
The hydraulic horsepower across the bit nozzles is given by:
HP ¼ P2q
(5.12)
We can replace the pressure drop across the bit as the difference between the pump
pressure and the parasitic pressure losses, Eqs. (5.3) and Eq. (5.4):
HP ¼ ðP1  CqmÞq
(5.13)
To ﬁnd the maximum hydraulic horsepower across the bit, Eq. (5.13) is differentiated
and set equal to zero:
dðHPÞ
dq
¼ P1  Cðm þ 1Þqm ¼ 0
(5.14)
qm ¼
P1
Cðm þ 1Þ
(5.15)
The fraction of parasitic pressure loss over total pump pressure to obtain maximum
hydraulic horsepower can be expressed as:
P3
P1
¼
1
m þ 1
(5.16)
This process is repeated for the jet impact criterion. Imagine that a jet impinges the
bottom of the hole. If the ﬂuid momentum is destroyed in the impact, then the jet impact
force is given by:
F2 ¼ m v2  v1
t
0F2 ¼ rqv
(5.17)
Inserting Eq. (5.9), the impact force can be expressed in terms of bit pressure loss and
ﬂow rate:
F2 ¼ 1:344
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
ﬃ
rq2P2
p
(5.18)
Again inserting Eqs. (5.3) and (5.4), we obtain:
F2 ¼ 1:344
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
rq2ðP1  CqmÞ
q
(5.19)
Differentiating this equation and setting equal to zero yields:
dF2
dq ¼ 2P1  Cðm þ 2Þqm ¼ 0
qm ¼
2P1
Cðm þ 2Þ
(5.20)
Wellbore hydraulics and hole cleaning: optimization and digitalization
155
and the fraction parasitic pressure loss for the maximum jet impact force is:
P3
P1
¼
2
m þ 2
(5.21)
Eqs. (5.16) and (5.17) deﬁne the fraction for parasitic pressure losses that gives the
maximum hydraulic horsepower and jet impact beneath the drill bit. The following
example demonstrates the application of these concepts.
Example 5.1:
Assume that we consider the hydraulic system used in the previous section. The
exponent is here m ¼ 1.84. The fractions of pressure drops are then given by Eqs. (5.16)
and (5.21):
For maximum hydraulic horsepower:
Fraction parasitic pressure drop: P3
P1
¼
1
1:84 þ 1 ¼ 0:35.
Fraction pressure drop across drill bit: 1e0.35 ¼ 0.65.
For maximum jet impact:
Fraction parasitic pressure drop: P3
P1
¼
2
1:84 þ 2 ¼ 0:52.
Fraction pressure drop across drill bit: 1e0.52 ¼ 0.48.
When using these criteria, the drill bit nozzles have to be selected to satisfy the
fractions deﬁned before.
We observe that there is a considerable spread in the pressure loss fractions depending
on the criterion chosen. A pertinent question to ask is what criterion is best. Is there
physical signiﬁcance in these criteria? In all the analytical and experimental work seen so
far, these two criteria have been used. We will expand our examination with new
criteria, but ﬁrst we will investigate some cases in an attempt to evaluate the short-
comings of the classical approach.
5.1.2.3 Example of shortcomings of the classical approach
The two classical optimization criteria have been used extensively by drilling engineers.
A key assumption is that the drill bit works best under these conditions. We will
investigate this further.
A numerical example is shown in Fig. 5.3. The equation for parasitic pressure loss
from the previous section is plotted. It has also been assumed that the pump is working
under a maximum constant pressure. This is often the case for the deeper sections of the
well.
The total pressure is 300 bar. The difference between the total pump pressure and the
parasitic pressure is equal to the pressure drop across the nozzles. We observe that for low
parasitic pressure loss, the bit loss is high, and vice versa. Fig. 5.3 also indicates the
maxima for the two previously deﬁned optima. If a high bit nozzle pressure loss is
wanted, the ﬂow rate must be low. To summarize, we have so far compared three criteria:
156
Methods for Petroleum Well Optimization
the maximum hydraulic horsepower, the maximum jet impact force, and the maximum
pressure drop. They all result in different ﬂow rates.
Next, let us consider another element, which has not been addressed yet, namely the
carrying capacity of the drilling ﬂuid.
Lermo (1993) investigated the relationships between the hydraulic optimization
criteria and the cuttings transport in the wellbore. Fig. 5.4 shows an example from a
simulation of a deep well. It shows two curves for parasitic pressure losses, one for a
standard rotary assembly and one for an application with a downhole motor. Also shown
are the two pressure levels deﬁning the optimum conditions from the previous
discussion. We observe that the maximum jet impact force has optima at 2520 and
1800 L/min, while the optima are at 2520 and 3190 L/min for maximum hydraulic
power. However, also shown is a line at 2720 L/min, which is the minimum ﬂow rate to
ensure hole cleaning. In this case, the ﬂow rates given by the two classical criteria will be
insufﬁcient to clean the well, except for the case with a rotary assembly and maximum jet
impact criterion.
To summarize this discussion, the following elements may have importance in
hydraulic optimization:
Cleaning or providing impact beneath the drill bit:
•
maximum hydraulic horsepowerdor maximum jet impact forcedor maximum
pressure
Transporting drilled cuttings out of well:
•
minimum ﬂow rate
Figure 5.3 The total, parasitic and bit pressure loss at 1200 m depth. Aadnoy (2010).
Wellbore hydraulics and hole cleaning: optimization and digitalization
157
We will not draw any ﬁrm conclusions regarding the hydraulic process beneath the drill
bit. There is probably some optimum for the interaction between the rock and the
drill bit, but it is not certain which physical mechanism dominates. There are several
functions, such as pure cleaning beneath the bit, or mechanical work on the rock itself.
There may be various optima depending on the rock properties.
Regardless of the criteria chosen for the drill bit, the cuttings must not be allowed to
accumulate in the annulus. Therefore, the minimum transport ﬂow rate must be used,
even if it violates other hydraulic criteria. The shortcomings of the classical optimization
criteria have been recognized for many years. For deep, inclined and long-reach wells,
the ﬂow rates used are often higher than predicted by the classical models. The industry
had, however, lacked a systematic way to handle this issue, and in the following section, a
model to solve the problem will be proposed.
The hole cleaning issue will not be pursued further here, but for more on this subject,
the reader is referred to Zamora and Hanson (1990), Sifferman and Becker (1992), and
Hemphill and Larsen (1993).
5.1.3 Hydraulic optimization
As shown in the previous section, the ﬂow rate might be a limiting factor for cutting
cleaning purposes. Before proceeding, we will investigate the various elements of the
hydraulic system and the effects of the ﬂow rate on each component, with reference to
Fig. 5.1.
A brief discussion of the elements in Table 5.2 would include the aspect of wear. The
majorityof the pressure losses occur before the drill bit. A ﬂow rate that is too high gives rise
to pressures that may promote premature drill string failure, or so-called washout. It should
therefore be mentioned that there could be a penalty for loading the system too much.
Figure 5.4 Example case showing need for carrying capacity. Aadnoy (2010).
158
Methods for Petroleum Well Optimization
The system after the drill bit is mostly limited by the cuttings transport process. This is
a central problem and must be properly handled. Therefore, this element must always be
satisﬁed regardless of other optimization criteria.
The central issue in this section is the new optimization criterion. However, in the
practical application of these criteria, cuttings transport models must be used to deﬁne
the minimum ﬂow rates for a given well. Please refer to Section 5.2 for an assessment of
ﬂow rate.
5.1.3.1 A new method for hydraulic optimization
Traditionally, performance criteria that relate to the physical process are chosen for
hydraulic optimization. Commonly used criteria are the maximum hydraulic horse-
power and the maximum jet impact force, as discussed earlier.
The ﬂow rate is believed to be the critical parameter for improving the transport of
drilled cuttings. Recent trends in offshore drilling are also for higher ﬂow rates than those
recommended by the traditional criteria. However, the ﬂow rate itself is not suited to
being a performance criterion, as it does not take into account other parts of the process.
We need some criteria to select hydraulic parameters in a systematic way in a drilling
operation. In the following, another way to approach the problem will be proposed.
Hydraulic horsepower spent across the bit nozzles is equal to the product qP2, and its
maximum is deﬁned by Eq. (5.16). The jet impact force is obtained by multiplying the
ﬂow rate with
ﬃﬃﬃﬃﬃ
P2
p
instead of P2 as for the hydraulic power, as shown in Eq. (5.17). We
will take advantage of the pattern indicated earlier and deﬁne a nonphysical variable, which
is the product of q n=2
ﬃﬃﬃﬃﬃ
P2
p
, where the parameter n is deﬁned as a performance index.
Differentiating this function and setting equal to zero gives the optima:
d

q n=2
ﬃﬃﬃﬃﬃ
P2
p

dq
¼ 0
(5.22)
Eqs. (5.3) and (5.4) express the nozzle pressure drop as pump pressure and parasitic
pressure:
P2 ¼ ðP1  CqmÞ
(5.23)
Table 5.2 Overview of the hydraulic system.
Position
Flow regime
Limitation
Critical parameter
1. Surface piping
Turbulent
Wear
2. Inside drill string
Turbulent
Wear
3. Inside drill collars
Turbulent
Wear
4. Through nozzles
Turbulent
Wear
5. Outside drill collars
Turbulent/laminar
Washout
Flow rate
6. Outside drill pipe
Laminar
Cuttings transport
Flow rate
7. Inside riser
Laminar
Cuttings transport
Flow rate
From Aadnøy, B.S., 2010. Modern Well Design, Second Edition. CRC Press/Balkema.
Wellbore hydraulics and hole cleaning: optimization and digitalization
159
Combining the aforementioned two equations and solving, we obtain a general
equation for optimization criteria as follows:
qm ¼
nP1
Cðm þ nÞ
P3 ¼
P1n
m þ n
(5.24)
A number of performance criteria can be derived using the performance indices.
Table 5.3 summarizes these.
The following steps deﬁne the application of the hydraulic optimization principles:
•
determine the ﬂow rate q that ensures proper cleaning of the borehole;
•
select the performance index;
•
compute the system loss P3 and bit loss P2; and
•
compute the nozzle area A.
5.1.4 Optimum nozzle and ﬂow rate selection
The well that is studied in the ﬁeld case is vertical and classiﬁed as a wildcat well. The
problem to be addressed is the design of the hydraulics for the 12¼00 section in the
interval 1200e3200 m. A PDC bit was chosen to drill this section. This bit had one
center nozzle and ﬁve nozzles between the blades.
First, a study was conducted to summarize the experiences of earlier bit runs with this
particular drill bit. Table 5.4 gives a summary of some of the parameters. Of particular
interest was the observation that 8 of the 18 drill bits were pulled with plugged center
nozzles. For this reason, the use of a larger center nozzle than the standard 12/3200 was
recommended to improve cleaning beneath the bit. There is no correlation between the
Table 5.3 Overview of optimization criteria.
Performance
index
Equation
Criterion
Fraction
parasitic
pressure loss
Flow rate
1
qP2
Maximum
HP
1
m þ 1
P1
Cðm þ 1Þ
2
q
ﬃﬃﬃﬃﬃ
P2
p
Maximum jet
impact
2
m þ 2
2P1
Cðm þ 2Þ
3
q3=2
ﬃﬃﬃﬃﬃ
P2
p
New A
3
m þ 3
3P1
Cðm þ 3Þ
4
q2
ﬃﬃﬃﬃﬃ
P2
p
New B
4
m þ 4
4P1
Cðm þ 4Þ
5
q5=2
ﬃﬃﬃﬃﬃ
P2
p
New C
5
m þ 5
5P1
Cðm þ 5Þ
From Aadnøy, B.S., 2010. Modern Well Design, Second Edition. CRC Press/Balkema.
160
Methods for Petroleum Well Optimization
drilling rate and the number of plugged center nozzles. The experience is that once the
center nozzle is plugged, the drilling rate decreases signiﬁcantly, resulting in pulling of
the bit. Table 5.4 can be used to argue that the center nozzle is particularly sensitive in
this drill bit design and that this must be addressed further in the hydraulics design.
Fig. 5.5 shows the parasitic pressure losses at 1200 m depth, at 2200 m, and at
3200 m. Also shown is the maximum allowed pump pressure at 290 bars. Three other
criteria are shown, namely the minimum ﬂow rate acceptable for the measurement while
drilling system (MWD), the ﬂow rate at which the ﬂow becomes turbulent around the
BHA, and the minimum ﬂow rate to ensure good hole cleaning. It is the latter factor that
dominates this design, and this is usually the case when designing a well. Therefore, we
will accept turbulent ﬂow around the BHA.
These data are redrawn to a logelog scale in Fig. 5.6. For this well, the slope is
m ¼ 1.84. The ﬁve optimization criteria of Table 5.5 are also shown.
Criterion “New B” was chosen for the operation, mainly because the ﬂow rate was
above the critical ﬂow for cuttings transport, 2520 L/min. From Fig. 5.6, we also see that
the ﬂow rate should ideally vary from 3250 L/min at 1200 m to 2580 L/min at 3200 m,
with about 2880 L/min at 2200 m. The classical criteria recommend a lower ﬂow rate.
The data for “New B” are summarized in Table 5.5.
Table 5.4 Summary of earlier bit runs.
No. of bits
Nozzles
q (L/min)
ROP (m/hr)
Remarks
1
5  16, 1  12
2960
1.5
Plugged center nozzle
2
5  19, 1  12
2660
9.8
3
5  16, 1  12
2600
13.6
4
5  19, 1  12
2300
18.2
5
5  18, 1  12
2400
14.9
Plugged center nozzle
6
6  12
2600
18.3
7
5  14, 1  12
2400
15.4
8
5  15, 1  12
2450
24
9
5  14, 1  12
2400
4.8
10
5  14, 1  12
2350
23.8
11
5  19, 1  12
20
Plugged center nozzle
12
5  19, 1  12
30
13
5  18, 1  12
10
14
5  18, 1  12
22
Plugged center nozzle
15
5  19, 1  12
7
Plugged center nozzle
16
5  18, 1  12
27
Plugged center nozzle
17
5  19, 1  12
16
Plugged center nozzle
18
5  19, 1  12
19
Plugged center nozzle
ROP
, rate of penetration.
From Aadnøy, B.S., 2010. Modern Well Design, Second Edition. CRC Press/Balkema.
Wellbore hydraulics and hole cleaning: optimization and digitalization
161
Figure 5.6 Determination of ﬂow ranges and optimization criteria. Aadnoy (2010).
Figure 5.5 Parasitic pressure losses and ﬂow rate constraints.
Table 5.5 Hydraulic parameters for the ﬁeld case.
Criterion
Percentage parasitic
pressure loss
Flow range (L/min)
Maximum hydraulic power
54
2800e2220
Maximum jet impact
52
2850e2280
New A
63
3070e2450
New B
69
3250e2580
New C
73
3370e2800
From Aadnøy, B.S., 2010. Modern Well Design, Second Edition. CRC Press/Balkema.
162
Methods for Petroleum Well Optimization
The result of the bit run was 1238 m drilled, which was a company record with this
particular bit type. No nozzle plugging was observed. Although this ﬁeld case is not
proof for the new models presented here, it shows that sound preplanning does give
results. In this case, two changes were introduced: a higher ﬂow rate in the whole system
and an increased ﬂow rate in the center nozzle of the bit.
The three lines of Fig. 5.6 deﬁne the depth levels 1200, 2200, and 3200 m. As the
well deepens, the parasitic pressure loss increases due to the added drill pipe. To avoid
exceeding the maximum pump pressure of 290 bar, the ﬂow rate has to be gradually
decreased. From Table 5.5 and the New B criterion, we see that the proposed ﬂow rate is
3250 L/min at 1200 m depth, decreasing to 2580 L/min at 3200 m. During an actual
drilling operation, the ﬂow rate will gradually be reduced by monitoring the pump
pressure.
The actual nozzle selection process will be demonstrated using the following
example:
At 1200 m depth, the ﬂow rate is 3250 L/min, and the fraction parasitic loss is
obtained from Table 5.3:
4
m þ 4 ¼
4
1:84 þ 4 ¼ 0:69 or; 0:69  290 bar ¼ 200 bar
and the pressure loss across the nozzles:
290  200 ¼ 90 bar
The nozzle area required can be calculated with Eq. (5.9):
A ¼ q
ﬃﬃﬃﬃﬃﬃﬃﬃ
r
2P2
r
1
0:95
Using the units of density (kg/L), ﬂow rate (L/min), and pressure (bar), the nozzle
area in in2 can be obtained by dividing the equation above by 376. The result is:
A ¼ 3250
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
1:65
2  90
r
1
0:95  376 ¼ 0:87 in2
Table 5.6 Optimal nozzle selection for New B criterion.
Depth (m)
Nozzles (inches)
1200
Five13/32, one16/32
2200
Five12/32, one16/32
3200
Five11/32, one16/32
From Aadnøy, B.S., 2010. Modern Well Design, Second Edition. CRC Press/Balkema.
Wellbore hydraulics and hole cleaning: optimization and digitalization
163
Using ﬁve 13/3200 nozzles and one 16/3200 nozzle, this area is approximately obtained:
A ¼ 5 p
4
13
32
2
þ p
4
16
32
2
¼ 0:85 in2
Repeating this process at the two other depths, we can deﬁne the nozzle program as
follows, if we assume a drill bit with six nozzles (Table 5.6).
In a practical application, we have to consider the penetration length of each drill bit,
as we can only change nozzles when pulling the drill bit. If we assume one drill bit for
each of the intervals of Table 5.6, ﬁve 13/3200 and one 16/3200nozzles may be used in the
interval 1200e2200 m. However, since we are approaching the critical ﬂow rate for
cuttings transport in the bottom interval, we will recommend using the nozzles designed
for a 3200 m depth.
If more or fewer bit runs are expected, the nozzle selection must be evaluated
accordingly.
5.1.5 Proposed optimization criteria for various well types
Lermo (1993) undertook a larger analysis of the cuttings transport velocity and the
optimization criteria. He evaluated various well types and depths and arrived at the
following tabulated recommendation. For cuttings transport analysis, he used several
commercially available simulators, which were considered to be the state of the art. We
will not address this analysis in detail but show some conclusions of Lermo’s work.
Table 5.7 shows some of the results for the 12¼00 hole section. Although the table
deﬁnes the criteria suitable for drilling the wells, the right-hand column proposes a
stronger criterion for each hole length if hole cleaning is believed to be a problem. If
signiﬁcant hole collapse takes place, for example, a stronger requirement may be applied
to ensure good cuttings transport.
Table 5.7 Proposed optimization criteria for a typical 12¼00 hole.
Hole length
Vertical holes
Deviated wells
drilled with motor
Deviated wells
without motor
Stronger
requirements
Less than 2500 m
Max HP or Max
jet impact
Max HP or Max
jet impact
Max jet impact
New A
2500e4000 m
Max HP or Max
jet impact
Max jet
impact
New A
New B
Deep (5000 m)
Max HP or
jet impact
Maximum jet
impact or New A
New B
New C
From Aadnøy, B.S., 2010. Modern Well Design, Second Edition. CRC Press/Balkema.
164
Methods for Petroleum Well Optimization
Other data for the proposed hole are drill pipe 675 m of 500, rest 65/800; drill
collars 120 m 81/800 outside diameter, 2.8100 ID; Mud density 1.65 s.g.; yield point 32 lbf/
100 sq ft.; and plastic viscosity of 42 cP
.
For the 17½00 sections, the cuttings carrying capacity became more critical. For
vertical wells, the criteria of Table 5.3 ensured adequate cuttings transport. However,
when wells were inclined with deviation exceeding 45 degrees, a signiﬁcant increase in
ﬂow rate was required. Lermo recommended the use of three mud pumps instead of two,
and the optimization procedure also involved pump liner size selection. In general, the
two criteria New B and New C were the result of the optimization process.
The effects of changing drill pipe size were also investigated. By increasing the drill
pipe size, the parasitic pressure losses decrease and the ﬂow velocity in the annulus
increases, both improving the total process. Fig. 5.7 illustrates this in a parasitic pressure
drop/ﬂow rate plot. The three curves represent the system losses for three drill pipe sizes.
The criterion New C is applied in each of the cases, and we observe that a higher ﬂow
rate results with larger drill pipe, mainly because of reduced parasitic pressure losses. The
following ﬂow rates resulted from this analysis (see also Table 5.8):
Figure 5.7 Pressure losses and ﬂow rates with different drill pipe sizes, using the New C criterion.
Aadnoy (2010).
Table 5.8 Minimum ﬂow rate versus drill pipe size.
Drill pipe size (inch)
Minimum ﬂow rate (L/min)
5
3490
5½
3800
65/8
4370
From Aadnøy, B.S., 2010. Modern Well Design, Second Edition. CRC Press/Balkema.
Wellbore hydraulics and hole cleaning: optimization and digitalization
165
By increasing drill pipe size, we also add ﬂexibility to the hydraulic design.
Other data: hole size 17½00; drill pipe length 2323 m; drill collars 177 m 800 outside
diameter (OD), 2.8100 inside diameter (ID); mud density 1.50 s.g.; yield point 28 lbf/100
sq ft.; and plastic viscosity 29 cP
.
In this section, we have shown that the two classical hydraulic optimization criteria
may be inadequate for some wells. Since experience shows that poor hole cleaning often
is associated with borehole problems, new optimization criteria are needed. Therefore,
in this section, new criteria have been derived. These are aimed at providing sufﬁcient
ﬂow rate to clean the wellbore and should provide a methodology for designing the
complete hydraulic system.
Before ending this section, a couple of elements related to the hydraulic system
should be mentioned. An emerging mechanism is the effect of drill string rotation on the
pressure drop. Rotation typically leads to increased pressure loss, and the reader is
referred to publications by Lockett et al. (1993), Marken and Saasen (1992), Oudeman
and Bacarreza (1995), and Cartalos and Dupuis (1993).
Finally, more recently, it has become understood that hole cleaning is an important
factor for drill string torque and drag. For long-reach wells, good hole cleaning must be
obtained to be able to reach the target. Aarrestad and Blikra (1994) and Alfsenen et al.
(1995) present ﬁeld cases demonstrating this.
5.2 Hole cleaning
It is important to bring the drilled cuttings out of the wellbore. If they accumulate,
the drill string may get stuck. Also, excess cuttings in the annulus may lead to an increase
in bottomhole pressure, which again may lead to circulation losses.
When a wellbore increases in size due to wellbore collapse, sometimes larger rock
pieces must be transported out of the wellbore for the same reasons as given for drilled
cuttings.
The ﬂow rate and the drilling rate must stay within certain limits to ensure good hole
cleaning. Usually, hydraulic simulators are used to determine the minimum ﬂow rate. In
this section, we will present a simpler approach using a few charts and equations.
Luo et al. (1992) wrote an excellent paper on hole cleaning, where the authors
presented the physics of hole cleaning and also a practical way to apply the models. They
reﬁned and expanded this work in Luo et al. (1994). This section is based on the
information given in these publications. These papers still capture the state of the art to a
large extent as seen in the API (2006). Luo et al. address hole cleaning only for wells with
an inclination exceeding 30 degrees. For lower angle inclinations, we have extrapolated
the angle factor of Table 5.10 to the vertical.
166
Methods for Petroleum Well Optimization
5.2.1 Effect of parameters on hole cleaning
The aim of hole cleaning and cuttings transportation is to prevent cuttings from settling
and to carry them to the shale shaker. The functions of the mud are to remove and lift
cuttings away from the drill bit face, as well as cooling the bit.
A full understanding of the cutting transportation mechanism has been a critical issue
for decades. Hence, determining precisely the factors affecting it is a challenge. No
universally accepted theory can account for all the observed phenomena. However,
many researchers have concluded that the ability of the mud to carry the rock fragments
is related to mud type, density and rheology, mud ﬂow rate, or annular mud velocity.
Cuttings size and density, hole angle, rotations per minute (RPM), rate of penetration
(ROP), and drill pipe eccentricity have an impact as well.
Table 5.9 displays all parameters impacting on cuttings transport and the hydraulic
module. It shows that drill pipe eccentricity has an indirect effect on both cutting
transport and hydraulics, while hole size, mud properties, cuttings, and the ﬂow rate have
a direct impact on hydraulics calculations and hole cleaning optimization.
For example, rotation can improve hole cleaning even more effectively when
working together with other parameters. This level of enhancement due to pipe rotation
Table 5.9 Parameters and their effect on hydraulics.
Parameter
Hole cleaning
Hydraulics
Hole angle
Signiﬁcant negative effect
Indirect effect
Hole size
Direct effect
Direct effect
Cuttings size
Indirect effect
Direct effect
Cuttings density
Indirect effect
Direct effect
Cuttings shape
Indirect effect
Direct effect
Mud density
Direct effect
Direct effect
Mud rheology
Direct effect
Direct effect
Mud type
Direct effect
Direct effect
Flow rate
Signiﬁcant positive effect
Direct effect
ROP
Indirect effect
Direct effect
RPM
Signiﬁcant positive effect
Indirect effect
Pills
Direct effect
Indirect effect
Drill pipe eccentricity
Indirect effect
Indirect effect
Drill string size
Indirect effect
Direct effect
ROP
, rate of penetration; RPM, rotations per minute.
Table 5.10 Angle factors for deviated holes.
Hole angle (degree) 0
25
30
35
40
45
50
55
60
65
70e80 80e90
Angle factors (AF)
2.03a 1.51 1.39 1.31 1.24 1.18 1.14 1.10 1.07 1.05 1.02
1.0
aExtrapolated value.
From Aadnøy, B.S., 2010. Modern Well Design, Second Edition. CRC Press/Balkema.
Wellbore hydraulics and hole cleaning: optimization and digitalization
167
is a function of the simultaneous combination of mud rheology, cuttings size, and mud
ﬂow rate. Also, it was observed that the dynamic behavior of the drill pipe (steady-state
vibration, unsteady-state vibration, whirling rotation, true axial rotation parallel to hole
axis, etc.) plays a major role in the improvement of hole cleaning. With rotation,
the cuttings resting on the lower side of the hole will stir up into the upper side, where
the ﬂow is effective (Sanchez et al., 1999). Fig. 5.8 shows the effectiveness of the drill
pipe rotation.
5.2.2 Cuttings transport mechanisms
The cuttings will sink due to gravity. The ﬂuid velocity and the viscosity will attempt to
carry them up the well. In addition to these opposite effects on particles, there are
depositional/erosional forces acting on cuttings beds. The complete model is complex
and requires numerical solutions. Luo et al. (1992) used experimental data from ﬂow
loops to determine the parameters of the model. The following controllable variables
were found:
1. mud ﬂow rate
2. ROP
3. mud rheology
4. mud ﬂow regime
5. mud weight
6. hole angle
7. hole size
They also deﬁned some uncontrolled variables such as:
1. drill pipe eccentricity
2. cuttings density
3. cuttings size
These were put into seven dimensionless groups that were ﬁtted to experimental data.
Figure 5.8 Impact of rotation on cuttings bed.
168
Methods for Petroleum Well Optimization
5.2.3 Hole cleaning model
Figs. 5.9e5.11 provide the results of these studies. The procedure for using these ﬁgures
is as follows:
•
Enter the rheology factor chart with the plastic viscosity (PV) and yield point (YP)
values and read off the value of the rheology factor (RF).
•
Get the angle factor (AF) from Table 5.10.
•
Calculate the transport index (TI) based on RF
, AF
, and MW:
TI ¼ RF  AF  MW
(5.25)
Figure 5.9 Rheology and hole cleaning charts for the 17½00 section. Modiﬁed from Luo, Y., Bern, P.A.,
Chambers, B.D., Kellingray, D.S., February 15e18, 1994. Simple Charts to Determine Hole Cleaning Re-
quirements in Deviated Wells. Paper IADC/SPE 27486 Presented at The1994 IADC/SPE Drilling Conference,
Dallas.
Figure 5.10 Rheology and hole cleaning charts for the 12¼00 section. Modiﬁed from Luo, Y., Bern, P.A.,
Chambers, B.D., Kellingray, D.S., February 15e18, 1994. Simple Charts to Determine Hole Cleaning
Requirements in Deviated Wells. Paper IADC/SPE 27486 Presented at The1994 IADC/SPE Drilling Conference,
Dallas.
Wellbore hydraulics and hole cleaning: optimization and digitalization
169
•
Enter the appropriate ROP chart with the TI and the desired (or maximum ﬂow
rate), read off the CFR for hole cleaning (or the maximum safe ROP).
•
If the hole is washed out, ﬁnd the ﬂow rate correction a from Table 5.11. Then use
Eq. (5.26) to calculate the CFR for the washout hole section:
CFRwashout ¼ a  CFRgauge
(5.26)
The following example is also adapted from Luo et al. (1994).
Example 5.2:
A horizontal 8½00 hole section is to be drilled with a 1.45 s.g. mud where PV ¼ 25 cP
and YP ¼ 18 lbf/100 ft2. We want to know:
1. What is the maximum safe ROP if the mud pumps can deliver a maximum 450
GPM?
2. If it is anticipated that we can drill at a ROP of 20 m/h, what ﬂow rate will be
required to clean the well.
3. If we suspect that the hole has been washed out to 1000, what ﬂow rate should we
pump?
Solution:
Maximum safe ROP
. From the rheology factor chart Fig. 5.11, it may be found that
RF ¼ 0.91. From the angle factors in Table 5.10, it is found that AF ¼ 1. The transport
index comes from Eq. (5.25):
TI ¼ 0:91  1:0  1:45 ¼ 1:32
Then from the ROP chart in Fig. 5.11 at a TI of 1.32, it can be found that, if the
maximum achievable ﬂow rate is 450 GPM, the maximum ROP that can be drilled
without causing hole cleaning problems is about 23 m/h.
Flow at 20 m/h. If we anticipate that we can drill at an ROP of 20 m/h, then the ﬂow
required to clean the hole is 440 GPM.
Table 5.11 Flow rate correction factors for washout holes.
8½00 hole
12¼00 hole
17½00 hole
Washout size (00)
a
Washout size (00)
a
Washout size (00)
a
9
1.12
13
1.10
18
1.03
10
1.38
14
1.24
19
1.09
11
1.65
15
1.39
20
1.16
12
1.94
16
1.53
21
1.22
13
2.24
17
1.68
22
1.28
14
2.55
18
1.82
23
1.34
From Aadnøy, B.S., 2010. Modern Well Design, Second Edition. CRC Press/Balkema.
170
Methods for Petroleum Well Optimization
Washed-out hole. However, if the hole is suspected being washed out to 1000 and we
still plan to drill at a rate of 20 m/h, it may be found from Table 5.11 that the ﬂow rate
should be corrected by a factor of a ¼ 1.38, that is:
CFRwashout ¼ 1:38  440 ¼ 607:2 GPM
Under these circumstances, measures must be taken either to increase the maximum
achievable ﬂow rate (e.g., by using larger drill pipes) or to adjust drilling parameters
(e.g., mud YP).
5.2.4 Cuttings transport and settling
A cuttings particle in suspension within a moving or dynamic drilling ﬂuid is subject to
several forces. The ﬁrst set of forces are the static ones: the gravity force (Fg) and the
buoyancy force (Fb). In addition, there is a dynamic force: the frictional force (Clark and
Bickham, 1994). The frictional force is decomposed into a drag force (FD) following the
ﬂow direction and a lift force (FL) perpendicular to the ﬂow movement (see Fig. 5.12).
The drag and lift forces depend on the local velocity of the ﬂuid around the cutting
particle. If we consider only a bulk ﬂuid velocity (averaged over the whole cross section),
then it is possible to calculate a terminal velocity, compared with the ﬂuid, for a cuttings
particle. If the component of the terminal velocity in the direction of the wellbore axis is
positive, then the cuttings particle is transported.
To calculate the size of the cuttings bed in the annulus at a given depth, we need to
account for the actual packing efﬁciency of particles in the bed (that is the ratio of the
Figure 5.11 Rheology and hole cleaning charts for the 8½00 section. Modiﬁed from Luo, Y., Bern, P.A.,
Chambers, B.D., Kellingray, D.S., February 15e18, 1994. Simple Charts to Determine Hole Cleaning
Requirements in Deviated Wells. Paper IADC/SPE 27486 Presented at The1994 IADC/SPE Drilling Conference,
Dallas.
Wellbore hydraulics and hole cleaning: optimization and digitalization
171
actual particle volume to the occupied volume). For the monodispersed packing
problem (a single size), the maximum packing efﬁciency is
p
ﬃﬃﬃﬃﬃ
18
p
¼ 0:74048 , which is
the highest possible density among all possible lattice packing, as demonstrated by Gauss
in 1831. In practice, when the spheres are added randomly, the packing is irregular, and
the maximum achievable density is lower than the best lattice packing. It has been
demonstrated that with jammed packing, the packing efﬁciency cannot exceed the limit
of 63.4% in the most compact way or 55% for loose packing (Song et al., 2008). The
polydispersed (n-components mixture) packing problem is extremely complex. In that
case, we consider that the different particle sizes are stacked on top of each other with the
largest particle at the bottom and the smallest at the top. However, for the binary hard
sphere packing problem (two sizes) with coarse and ﬁne particles, there exist solutions for
calculating the packing efﬁciency of the mixture of particles. Zheng et al. (1995) have
proposed:
PEmix ¼ PEc þ ð1  PEcÞPEf

eXf ln

Xf
 5
4PEcexp

 4
r

(5.27)
where PEmix is the packing efﬁciency of the mix of coarse and ﬁne particles; PEc is the
packing efﬁciency of the coarse particles; PEf is the packing efﬁciency of the ﬁne
particles; Xf is the volume fraction of ﬁne particles; r is the size ratio of coarse particles to
ﬁne particles; and e is Euler’s number.
Figure 5.12 Force acting on a cuttings particle in suspension. Modiﬁed from Cayeux, E., Mesagan, T.,
Tanripada, S., Zidan, M., Fjelde, K.K., 2013. Real-Time Evaluation of Hole Cleaning Conditions Using a
Transient Cuttings Transport Model. SPE-163492-MS, SPE/IADC Drilling Conference, 5-7 March 2013,
Amsterdam, The Netherlands.
172
Methods for Petroleum Well Optimization
All these packing efﬁciencies are for a very large region so that the effect of
boundaries is insigniﬁcant. This hypothesis is true as long as the cuttings size is small
compared with the radius of the borehole. Thereafter, it is possible to determine the area
occupied by cuttings Ac at a given depth by accounting for the packing efﬁciency. For a
monodispersed system or a binary dispersion with coarse and ﬁne particle:
Ac ¼ npd3
6LPE
(5.28)
where: n is the number of particles in the control volume; d is the diameter of the
particles; PE is the packing efﬁciency; and L is the length of the controlled volume.
For the case of a multidispersed cuttings bed, the area occupied by the cuttings is:
Ac ¼
Pk
i¼1
npd3
i
6PEi
L
(5.29)
where: k is the number of different particle sizes; ni is the number of particles of the i-size
in the controlled volume; di is the diameter of the particles of the i-size; and PEi is the
packing efﬁciency for the particle of the i-size.
Then, it is possible to ﬁnd the free area in the annulus:
Af ¼ pr2
w  Ac  pr2
p
(5.30)
where: Af is the free area in the annulus cross section; rw is the radius of the wellbore;
and rp is the radius of the drill pipe.
Finally, to ﬁnd the height of the cuttings bed (see Fig. 5.13), one needs to solve the
following piecewise equation:
if hc  rw  e  rp
Ac ¼ acos

rwhc
rw

r2
w  ðrw hcÞ
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
r2
w  ðrw  hcÞ2
q
(5.31)
if hc > rw  e  rp and hc
 rw  e þ rp
Ac ¼ acos
rw  hc
rw

r2
w  ðrw  hcÞ
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
r2
w  ðrw  hcÞ2
q


acos
rw  hc  e
rp

r2
p  ðrw  hc  eÞ
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
r2
p þ ðrw  hc  eÞ2
q

if hc > rw  e þ rp
Ac ¼ acos

rwhc
rw

r2
w  ðrw hcÞ
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
r2
w  ðrw  hcÞ2
q
 pr2
p
Wellbore hydraulics and hole cleaning: optimization and digitalization
173
5.3 Real-time assessment of the hole cleaning efﬁciency
5.3.1 Hole cleaning strategy
Hole cleaning can be divided into three different sections based on wellbore deviation.
Different inclinations have different cuttings transportation phenomena and, as a result,
have different hole cleaning strategies. As illustrated in Figs. 5.14 and 5.15, inclinations
between 35 and 60 degrees are more difﬁcult to clean, while the near-vertical sections
are the easiest.
5.3.1.1 Angles of 0e35 degrees
Drilling nearly vertical wells is considered to be the least challenging hole-cleaning
regime since cuttings usually have a long distance to settle. In addition, the drill pipe
Figure 5.13 The different conﬁgurations for the cuttings bed and the drill pipe. Modiﬁed from Cayeux,
E., Mesagan, T., Tanripada, S., Zidan, M., Fjelde, K.K., 2013. Real-Time Evaluation of Hole Cleaning
Conditions Using a Transient Cuttings Transport Model. SPE-163492-MS, SPE/IADC Drilling Conference, 5-7
March 2013, Amsterdam, The Netherlands.
Figure 5.14 Schematic of hole cleaning problem in deviated well.
174
Methods for Petroleum Well Optimization
is usually concentric in vertical wells, which results in a uniform axial velocity in the
annulus cross section. The main objective in vertical wells is to combat and exceed
cuttings slip velocity by controlling the mud ﬂow rate and viscosity. Cuttings slip velocity
can be calculated using API Recommended Practice 13D (2006).
5.3.1.2 Angles of 35e60 degrees
Intermediate angles have the highest cuttings concentration due to hole geometry
(Mohammadsalehi and Malekzadeh, 2011). This section is considered the most
challenging to clean for several reasons. First of all, gravity causes the drill pipe to lie on
the low side of the wellbore, resulting in different velocity ﬂow regimes due to pipe
eccentricity. In addition, cuttings only have a few inches to fall to form a bed. Finally, an
avalanche of cuttings usually forms when the mud pumps are shut off, resulting in the
cuttings sliding to the bottom of the well.
5.3.1.3 Angles of 60e90 degrees
Near-horizontal wells are less challenging to clean than the intermediate hole sections
since avalanches are not observed when shutting off the pumps. Pipe rotation aids the
cleaning of horizontal section holes by mechanically agitating cuttings, moving the
cuttings from low to high velocity ﬂow regimes within the annulus.
In holes inclined less than 30 degrees, the cuttings are effectively suspended by the
ﬂuid shear and do not form beds (zones 1 and 3). In such cases, conventional transport
calculations based on vertical slip velocity are applicable. Beyond 30 degrees, the cuttings
form beds on the low side of the hole, which can slide back down the well, causing
annular pack-off. Cuttings that form on the low side of the hole can either move en masse
as a sliding bed (zone 4) or may be transported at the bed/drilling ﬂuid interface as dunes
or nipples (zone 2). The ideal zones for good hole cleaning are zones 1 and 2 (Fig. 5.16).
5.3.2 Real-time modeling
Hole cleaning evaluation tools and methods are not as effective if the results are not
obtained immediately. Hole cleaning issues must be addressed without delay to avoid an
Figure 5.15 Hole cleaning difﬁculty level at different inclinations. Modiﬁed from Al Rubaii, M.M. 2017.
The Impact of Hole Cleaning on Rate of Penetration. MS thesis, King Fahd University of Petroleum and
Minerals, Dhahran, Saudi Arabia.
Wellbore hydraulics and hole cleaning: optimization and digitalization
175
increased risk of cuttings accumulations and possibly stuck pipe incidents. Real-time
models allow the drilling crew to evaluate hole cleaning and promptly take corrective
actions, such as adjustments to the surface parameters, modiﬁcations to drilling ﬂuid
properties, or dedicated circulations to clear the cuttings to the surface. Conventional rig
sensors may not be sufﬁcient to run certain models in real time, and thus, the addition of
advanced sensors may be necessary in these cases. Automating hole-cleaning evaluation
by calculating and displaying the CCI in real time requires mud weight and rheology
sensors that are not typically present in conventional rigs.
Development and testing of the CCI model in real time involves multiple steps that
are executed through the established process:
1. real-time data gathering and processing;
2. real-time calculations;
3. CCI curve generation; and
4. integration of the model.
The ﬁrst step in building the real-time model is data gathering. The developed algorithm
automatically retrieves drilling data from rig sensors as well as other sources, such as
reports that may contain relevant well information. The amount of data generated by the
rig sensors every minute is huge, and these data are impossible for human brains to
capture consistently. Data can generally be transferred from the rig to databases through
various languages and protocols and may require some processing for the data to be
useful. The data necessary to perform the calculations may be dynamic, such as ﬂow
rates, or static, such as hole size and casing size (Alawami et al., 2019).
Figure 5.16 Cutting transport mechanism in vertical and deviated wells. Modiﬁed from API
Recommended Practice 13D (2006).
176
Methods for Petroleum Well Optimization
5.3.3 Carrying capacity index
In simple terms, the CCI can be deﬁned as a measure of the ability of the drilling ﬂuid
system to carry the cuttings all the way to the surface. Good hole cleaning is indicated
when the cuttings have sharp edges (Robinson and Morgan, 2004). Round edges
indicate a tumbling action in the annulus as cuttings are not transported to the surface
quickly. The CCI values are expected to be 1.0 or greater for good hole cleaning
conditions. When the CCI values are 0.5 or less, the cuttings tend to be rounded and
small, due to longer residence time in the annulus. To calculate the CCI index, some
drilling ﬂuid properties, as well as some well details, are needed.
The index utilizes the mud weight, consistency index, and the annular velocity as
inputs. Determining the consistency index requires measurements of the drilling ﬂuid
rheology, speciﬁcally plastic viscosity and yield point. The annular velocity calculations
require the ﬂow rate and the clearance area between the outer diameter of the drill pipe
and the wellbore wall or the inner diameter of the casing string. The CCI index can be
calculated as follows:
CCI ¼ MW  K  Av
400; 000
(5.32)
where: MW ¼ mud weight (lb/gal); K ¼ consistency index (equivalent cp); and
Av ¼ annular velocity of the drilling ﬂuid (ft/min).
The consistency index is calculated through the following equation:
K ¼ 5111nðPV þ YPÞ
(5.33)
where: n ¼ power law index; PV ¼ plastic viscosity (cp) ¼ q600  q300; and YP ¼ yield
point (lb/100 ft2) ¼ 2q300  q600.
The power law index is deﬁned in the following equation:
n ¼ 3:22 log
2PV þ YP
PV þ YP

(5.34)
Annular velocity (ft/min) can be calculated as follows:
Av ¼
GPM
7:481  clearance area
(5.35)
where GPM ¼ drilling ﬂuid ﬂow rate in gal/min and clearance area ¼ the area between
the drill pipe and the wellbore wall or the casing string (ft2).
The clearance area can be calculated as follows:
Clearance area ¼ p
4  ID2Casing  OD2Drill pipe
144
(5.36)
where IDCasing ¼ the inner diameter of the casing string (in inches) and ODDrillpipe ¼ the
outer diameter of the drill pipe (in inches).
Wellbore hydraulics and hole cleaning: optimization and digitalization
177
In general, increasing the consistency index (K) and the annular velocity (Av) leads to
higher CCI values and thus better hole cleaning. It should also be noted that increasing
annular velocity increases annular frictional loss and so increases ECD, which might
induce loss of mud circulation. The CCI equation is applicable in hole sections with
inclinations of less than 25 degrees. Determination of the CCI allows for better and more
accurate hole cleaning modeling, which reduces the risks of NPT (nonproductive time)
incidents and enhances the drilling ROP
.
5.3.3.1 Carrying capacity index curve generation
The developed code utilizes the retrieved data to perform the initial calculations to
determine the needed variables such as annular velocity and the K constant. The CCI
calculations are then performed in real time to allow for instant evaluation of hole
cleaning.
Once the CCI values are calculated, the developed code displays the result as a curve
versus time, as shown in Fig. 5.17. Displaying the results as a curve makes it easier for
drilling crew and engineers to monitor hole cleaning while drilling. It also makes it faster
and easier to remotely detect trend anomalies that may require further investigation
(Alawami et al., 2019).
5.3.3.2 Integration of the model
The ﬁnal stage of the process is as important as any other step. The availability of a hole
cleaning evaluation index, without connection to the ongoing operations or the surface
parameters associated with it, is not useful by itself. Therefore, the integration of the CCI
Figure 5.17 Visualization of the CCI index values plotted against time. CCI, carrying capacity index.
Modiﬁed from Alawami, M., Bassam, M., Gharbi, S., Al Rubaii, M., October 2019. A Real-Time Indicator for
the Evaluation of Hole Cleaning Efﬁciency. SPE/IATMI Asia Paciﬁc Oil & Gas Conference and Exhibition
Held in Bali, Indonesia. pp. 29e31.
178
Methods for Petroleum Well Optimization
model into existing platforms that are used to monitor drilling operations is essential
to have a complete picture of the hole cleaning conditions, the potential causes of
suboptimal efﬁciency, and how to address it properly.
Fig. 5.17 shows a typical behavior of a real-time CCI curve. These values, as discussed
previously, are dependent on various parameters such as mud weight, rheological
properties, ﬂow rate, casing size and hole size. A change in one of these parameters would
cause a shift in the CCI values. The ﬁrst few points in the curve (Fig. 5.17) have CCI
values higher than 2.0, which indicate very adequate hole cleaning during that period.
These results assure the drilling team that no changes are necessary to optimize hole
cleaning while drilling the speciﬁed section with the current parameters and ﬂuid
properties. The CCI values decrease afterward to around 1.50, which is still higher than
the generally acceptable value of 1.0. This decrease in CCI can be attributed to higher
ROP
, which leads to a higher cuttings concentration and thus higher PV
, which decreases
the CCI values. A change in the mud weight or a reduction in ﬂow rate would trigger
similar changes to the CCI curve. The value of 1.50 remains an indication of adequate
hole cleaning with an opportunity to optimize even further. The lowest points in
Fig. 5.17 show CCI values around 0.75, which would alert the drilling team to a
potential hole cleaning problem. This should result in immediate intervention, with an
adjustment to the inﬂuential parameters such as ﬂow rate or dedicated circulation to
clean the hole of excess cuttings. Taking such corrective actions would improve hole
cleaning and ultimately increase the CCI values.
5.4 New methods for drilling hydraulics
5.4.1 Reelwell drilling method
The Reelwell drilling method (RDM) uses a dual drill string (DDS) with a separate
inner pipe leading the return ﬂuid to the topside facilities. The DDS connects to the top
drive with an adapter and can be directly connected to any standard BHA. The return
drill ﬂuid is led through entrance ports and an inner pipe valve directly above the BHA.
The inner pipe valve closes during pipe connections and isolates the return pipe from the
well. Since the inlet to the inner pipe is set above the BHA, the rest of the annulus,
between the DDS and the formation, remains in near static conditions. This has several
positive effects on drilling parameters such as hole cleaning. Figs. 5.18 and 5.19 show the
RDM system and the inner pipe valve (Jonassen, 2017).
5.4.1.1 Principal description of system
The concept of the drilling system is to apply a concentric DDS together with an
integrated return pump. The DDS, shown in Fig. 5.19, has the returning conduit in the
inner pipe. The supply ﬂuid ﬂows through the annulus of the dual drill pipe. The DDS is
handled like a standard drill pipe, and the connections are made by threading the outer
pipe, as with a normal drill pipe.
Wellbore hydraulics and hole cleaning: optimization and digitalization
179
The inner pipe allows the return ﬂuid to be lifted by an integrated pump from the
bottom of the well, to the topside facilities. Without the pump, the mud and cuttings
would ﬂow up the well and onto the sea ﬂoor, as with conventional top-hole drilling. To
obtain a full return system, the mud level in the well needs to be controlled. The pump
type, motor type, and solution for regulating the mud level in the well must be obtained.
The principle layout of the system is illustrated in Fig. 5.20.
5.4.1.2 The single pump system and the multiple pump system
Now that the pump type and the power source for the return pump have been
selected, the solution for controlling the mud level in the well has been established, and
the overall system design has been set. Two systems have been selected for further
evaluation. They are called the single-pump system and the multiple-pump system. The
two systems are described in the following sections. Fig. 5.20 shows the buildup of the
two alternative systems.
The DDS in conjunction with a return pump and return pump motor are the key
components in the two systems. In the multiple-pump system, the single pump and
motor are replaced by a series of pumpemotor sets positioned up the DDS. The goal is
to reduce the required pressure capacity of the pumps. The number of pumpemotor sets
Figure 5.18 Reelwell drilling method. Modiﬁed from Jonassen, I.L.G., 2017. Evaluation of a Top Hole Full
Return Drilling System Applying A Concentric Dual Drill String and an Integrated Pump. Master’s Thesis,
Faculty of science and Technology, Stavanger University.
Figure 5.19 Dual drill string Reelwell. Modiﬁed from Jonassen, I.L.G., 2017. Evaluation of a Top Hole Full
Return Drilling System Applying A Concentric Dual Drill String and an Integrated Pump. Master’s Thesis,
Faculty of science and Technology, Stavanger University.
180
Methods for Petroleum Well Optimization
in the multiple-pump system can be adjusted to the requirements of the top-hole and the
desired pressure capacity of the return pumps. The top-hole level tank (THLT) is also a
key component of the systems, permitting monitoring of the mud level in the well.
Other items of equipment necessary to complete the systems are:
•
top-drive adapter (TDA),
•
drill-string valve (DSV),
•
check valve,
•
ﬂow control unit (FCU), and
•
operation station.
Figure 5.20 Principle illustration of the single- and multiple-pump systems. Modiﬁed from Jonassen,
I.L.G., 2017. Evaluation of a Top Hole Full Return Drilling System Applying A Concentric Dual Drill String
and an Integrated Pump. Master’s Thesis, Faculty of science and Technology, Stavanger University.
Wellbore hydraulics and hole cleaning: optimization and digitalization
181
5.4.1.3 Frictional pressure loss calculation method
Pressure loss in a ﬂowing ﬂuid is caused by friction between the ﬂuid particles, and
between the ﬂuid particles and the adjacent surroundings. Parameters affecting the
pressure loss are density, viscosity, ﬂow rate, ﬂow regime, conduit geometry, and
rheological parameters. The return pump needs to overcome the frictional pressure loss
to obtain ﬂow. The frictional pressure loss also impacts the available pressure loss to the
mud motor and the drill bit, and it should be calculated for the whole system. The
frictional pressure loss calculation is divided into three separate segments:
5.4.1.4 Example pressure distribution single-pump system
An example is presented to illustrate the pressure distribution of the single-pump system
(Fig. 5.21). Table 5.12 shows the parameter values selected for the example and the
assumptions made with regard to the pressure estimation.
The calculations and pressure distribution are shown in Tables 5.13 and 5.14:
5.4.1.5 The multiple pump system pressure distribution
The pressure distribution of the multiple-pump system is built on the same foundations
as the single-pump system. The pressure down the annulus of the DDS will increase with
added hydrostatic pressure and will decrease with pressure loss in surface equipment,
annulus, and mud motors. The pressure drops across the mud motors are functions of the
required pump power. The same formulas have been applied as for the single-pump
system, but they are adjusted for several pumpemotor sets and DDS segments.
In the inner pipe, the discharge pressure in each pump should be sufﬁcient to
pump the ﬂuid and solids to the next pump. This is repeated until the drill ﬂoor is
reached.
There are uncertainties with regard to the imbalance between the pumpemotor sets.
An imbalance between the pumpemotor sets implies a higher pressure increase in one of
Inner pipe frictional
pressure loss
Annulus frictional pressure loss
Surface-connection
pressure loss
DPfIP ¼ LPr0:8
P Q1:8
P m0:2
P
CMND4:8
iIP 100
DPfTJ ¼
LTJ  r0:8
M  Q1:8
A  m0:2
M
CMN  100

DiTJ þ DoIP
1:8
DiTJ  DoIPC
3
DPfPB ¼
LPB  r0:8
M  Q1:8
A  m0:2
M
CMN  100ðDiPB þ DoPBÞ1:8ðDiPB  DoIPÞ3
PSC ¼ CSC  rS 
 Q
100
1:86
100
DPfIP is the frictional
pressure loss in the
inner pipe (bar).
DPfTJ is the frictional pressure loss in the tool
joints (bar).
DPfPB is the frictional pressure loss in the pipe body
part of the annulus (bar).
PSC is the frictional pressure loss
in the surface connections
(psi).
(5.37)
(5.38 and 5.39)
(5.40)
182
Methods for Petroleum Well Optimization
Figure 5.21 Pressure distribution, single-pump system. Modiﬁed from Jonassen, I.L.G., 2017. Evaluation
of a Top Hole Full Return Drilling System Applying A Concentric Dual Drill String and an Integrated Pump.
Master’s Thesis, Faculty of science and Technology, Stavanger University.
Table 5.12 Example pressure distribution, input values.
Parameter
Value
Parameter
Value
Drill bit size
2600
Bypass ﬂow
200 lpm
Water depth
250 m
Density applied mud
1100 kg/m3
Well length
500 m
Density return ﬂuid
1230 kg/m3
Height to drill ﬂoor
40 m
Viscosity supply
18.36 cP
Height BHA
27 m
Viscosity return
20.52 cP
Height motor
18 m
Cutting contents
10%
Height pump
13 m
Motor efﬁciency
0.72
Flow
900 lpm
Pump efﬁciency
0.72
BHA, bottomhole assembly.
From Jonassen, I.L.G., 2017. Evaluation of a Top Hole Full Return Drilling System Applying A Concentric Dual Drill
String and an Integrated Pump. Master’s Thesis, Faculty of science and T
echnology, Stavanger University.
Wellbore hydraulics and hole cleaning: optimization and digitalization
183
the pumps, leading to a lower necessary differential pressure over the next pump. This
would cause an uneven pressure distribution in the annulus conduit as well, with a higher
differential pressure over the motor associated with the higher performing pump. Due to
the motor bypass, the ﬂow through the motors can be differentiated, giving root to
variable RPM between each pumpemotor set.
5.4.2 Hole cleaning and wellbore risk reduction service
An example digitalization, or the application of information technology to drilling
problems, is the CLEAR hole cleaning and wellbore risk reduction service, provided by
Geoservices, a Schlumberger company. The downhole equipment and the associated
software and dashboard display are used to monitor hole cleaning effectiveness and
wellbore stability, providing real-time data to help the drilling team continually improve
drilling performance and reduce NPT throughout the operation. The weight of cuttings
reaching the surface is continuously measured and analyzed as the cuttings come off the
shale shakers. By comparing measured and theoretical volumes, the CLEAR service can
detect inadequate hole cleaning early (Fig. 5.22). The system has two levels as follows:
Level 1: Basic monitoring: cuttings ﬂow meters, software, cuttings ﬂow trend, caving
tracking, and standard services for the mud logging crew.
Level 2: Advanced monitoring: torque & drag monitoring, integration into a hole
condition monitoring package, trending & statistical analysis, and secondary hole
cleaning evaluation.
Figure 5.22 Cutting weight measurement at shaker to detect abnormal cutting return rate. Modiﬁed
from Schlumberger (https://www.slb.com).
184
Methods for Petroleum Well Optimization
Table 5.13 Formula and calculation for single-pump system.
Parameter
Formula
Calculation
Value
DPfIP
¼ LPr0:8
P Q1:48
P
m0:2
P
CMND4:8
iIP
¼ 7271:230:89001:820:520:2
901:632:954:8100
20:1 bar
DPfTJ
¼
LTJr0:8
M Q1:8
M m0:2
M
CMNðDiTJþDoIPÞ
1:8ðDiTJDoIPCÞ
3
¼
331:10:88101:818:360:2
706:96100ð5þ4:291Þ1:8ð54:291Þ3
7:86 bar
DPfPB
¼
LPBr0:8
M Q1:8
M m0:2
M
CMNðDiPBþDoPBÞ1:8ðDiPBDoIPÞ3
¼
7271:10:88101:818:360:20:01
706:91ð5:906þ3:504Þ1:8ð5:9063:504Þ3
4:35 bar
DPfA
¼ DPfTJ þ DPfPB
¼ 7:86 þ 4:35
¼ 12:22 bar
Pp
g½rPðhDF þ hSW þ hW  hBHA  hMÞ
 rSCðhW  hBHA  hMÞ
 rSWhSW þ PfIP þ 0; 5PfSE þ Pmin
¼ 120  9.81  ð1230 ð250 þ 500 þ 40  45 13Þ =100000Þ  20.1  5
¼ 45:65 bar
HPPout
¼ PP  100  QP=44750
¼ 46100900/44750
¼ 92.51 kW
HPPin
¼ HPPout
hP
¼ 92:51
0:72
¼ 128:48 kW
HPMout
¼ HPMin
hM
¼ 128:48
0:72
¼ 178:44 kW
Modiﬁed from Jonassen, I.L.G., 2017. Evaluation of a Top Hole Full Return Drilling System Applying A Concentric Dual Drill String and an Integrated Pump. Master’s Thesis, Faculty of
science and T
echnology, Stavanger University.
Wellbore hydraulics and hole cleaning: optimization and digitalization
185
Table 5.14 Example pressure distribution, single-pump system.
Pressure distribution
P1
The maximum working pressure of the DDS is
345 bar, the input pressure is limited to 320 bar
to avoid over pressurizing the equipment due to
unexpected pressure increases
¼ 320 bar
P2
¼ P1  PfA  0:5  PfSE
þg  rM  ðhDF þ hSW þ hW  hBHA  hMÞ
¼ 320  12:22  5 þ ð9:81 1100 40 þ250 þ500 45Þ=10000 ¼ 346 bar
P3
¼ P2  PM þ g  rM  hM
¼ 346  130:58 þ ð9:81 1100 18 =10000Þ
¼ 234:84 bar
P4
¼ g½rSCðhW hBHA hMÞ
þ rPðhBHA þ hMÞ
þ rSWhSW þ 3
¼
9:81
100000 ½1100 ð500 45Þ þ1230 45 þ1025 250 þ 3
¼ 82:66 bar
P5
¼ g½rSChW þrSWhSW
¼
9:81
100000 ½1100 ð500 45Þ þ1025 250
¼ 74:23 bar
P6
¼ P5 þ PP
¼ 74:23 þ 45:65
¼ 119:88 bar
P7
¼ P6  grPðhSW þhW þhDF
 hBHA  hM  hPÞ  PfIP  PfSE
¼ 119:88  9:81 
1230
100000 ð250 þ 500 þ 40  45  13Þ  20:1  5 ¼ 6:45 bar
PBHA ¼ P3  P4
234.84  82.66
¼ 152:18 bar
Modiﬁed from Jonassen, I.L.G., 2017. Evaluation of a T
op Hole Full Return Drilling System Applying A Concentric Dual Drill String and an Integrated Pump. Master’s Thesis,
Faculty of science and Technology, Stavanger University.
186
Methods for Petroleum Well Optimization
5.5 Summary
The following conclusions are drawn in this chapter using different approaches:
•
Hydraulic optimization criteria for various well types.
•
A transient cuttings-transport model makes it possible to better predict the down-
hole conditions because it is able to represent phenomena that evolve over time, such
as cuttings bed build-up or removal.
The CCI allows immediate intervention in cases where the wellbore is not cleaned
properly and the risks of stuck pipes may be high. To obtain the full beneﬁts of the CCI,
the model must be integrated and displayed alongside other real-time curves to allow for
better analysis of the hole cleaning situation.
Two technologies, RDM and CLEAR for hydraulics and hole-cleaning systems,
were presented in this chapter.
5.6 Problems
Problem 1: Nozzle size design
During the drilling of the 12¼00 section of an exploration well, it was decided to use the
maximum hydraulic horsepower criterion. The data are as follows:
Flow rate exponent: m ¼ 1.67
Mud density: 1.25 kg/L
Mud pump pressure: 300 bar
Flow rate: 2430 L/min
1. Using Table 5.3, determine the parasitic pressure loss.
2. Determine the pressure drop across the bit nozzles.
3. Determine the nozzle sizes, assuming that a three-cone rock bit is used.
During the operation, drilling problems were observed. It was concluded that the hole
cleaning was not adequate and that the ﬂow rate had to be increased to clean the hole.
Based on a hydraulic simulator, it was decided to increase the ﬂowrate to 3400 L/min.
The parasitic pressure loss was now 196 bar.
4. Calculate the percentage parasitic pressure loss. Using Table 5.3, determine which
optimization criterion now best ﬁts the system.
5. Determine the pressure drop across the bit nozzles.
6. Determine the nozzle sizes for the three-cone bit.
7. Compare and discuss the two optimization criteria used.
Problem 2: Pressure distribution in the multiple-pump system
The following illustration shows the multiple-pump system, conﬁgured with four
pumpemotor sets. Table 5.15 shows the parameter values selected for the case discussion
and also the assumptions regarding the pressure estimation.
1. Determine the pressure distribution graph for a multiple-pump system (Fig. 5.23).
Wellbore hydraulics and hole cleaning: optimization and digitalization
187
Figure 5.23 Pressure distribution, multiple-pump system. Modiﬁed from Jonassen, I.L.G., 2017.
Evaluation of a Top Hole Full Return Drilling System Applying A Concentric Dual Drill String and an
Integrated Pump. Master’s Thesis, Faculty of science and Technology, Stavanger University.
Table 5.15 Parameters for multiple-pump system.
Parameter
Value
Parameter
Value
Drill bit size
2600
Flow
900 lpm
Water depth
250 m
Bypass ﬂow
200 lpm
Well length
500 m
Density applied mud
1100 kg/m3
Length between each pump-motor set
191.25 m
Density return ﬂuid
1230 kg/m3
Height to drill ﬂoor
40 m
Viscosity supply
18.36 cP
Height BHA
17 m
Viscosity return
20.52 cP
Height motor
8 m
Cutting contents
10%
Height pump
8 m
Motor efﬁciency
Pump efﬁciency
0.72
BHA, bottomhole assembly.
From Jonassen, I.L.G., 2017. Evaluation of a T
op Hole Full Return Drilling System Applying A Concentric Dual Drill
String and an Integrated Pump. Master’s Thesis, Faculty of science and Technology, Stavanger University.
188
Methods for Petroleum Well Optimization
Problem 3: Hydraulics and hole cleaning digitalization
1. Explain how the CLEAR service (see Section 5.4.2) could help the drilling team
improve drilling performance and reduce NPT throughout the drilling operation.
2. How could we convert from weight to volume for the cuttings removed from the
hole based on mass ratio?
3. What is an accurate formulation and calculation for the theoretical volumes?
4. Present at least four KPIs of the CLEAR service for hole cleaning.
Problem 4: Reelwell drilling method (RDM)
1. Explain the advantages and disadvantages of the RDM method.
Nomenclature
C constant value
L length (m)
CMN constant number
CSC constant value for calculation of pressure loss in surface connections
DiIP inner diameter of inner pipe (m)
DiPB inner diameter of outer pipe (m)
DiTJ inner diameter tool joints (m)
DoIP outer diameter of inner pipe (m)
DoIPC outer diameter of inner pipe connections (m)
DoPB outer diameter of outer pipe (m)
g speciﬁc gravity (m/s2)
DBHADM height of motor and BHA (m)
DDF drill ﬂoor height from sea level (m)
hSW seawater depth (m)
hW TVD well (m)
LP length pipe (m)
LPB length pipe body (m)
LTJ length tool joint (m)
PBHA u=s nozzles pressure loss in BHA upstream nozzles (bar)
PBHA pressure loss in BHA (bar)
PM in
power input to motor (kW)
PM out
power out from motor (kW)
PP in
power input to pump (kW)
PP out power output from pump (kW)
PPout pump outlet pressure (bar)
PfBH pressure due to friction in bottomhole (bar)
PfIP frictional pressure loss in inner pipe (bar)
PhHSI hydraulic power at bit per square diameter (HP/in2)
mM viscosity in annulus (cP)
mP viscosity in inner pipe (cP)
rC average density cuttings (kg/m3)
Wellbore hydraulics and hole cleaning: optimization and digitalization
189
rM average density mud in annulus (kg/m3)
rP average density mud in return pipe (kg/m3)
rSC average density static column (kg/m3)
rSW seawater density (kg/m3)
Examples include:
•
A Hydraulic Program in Excel-Macro.
•
Integrated web application for the OpenLab Drilling Simulator.
•
MPD Hydraulics (https://github.com/APMonitor/drilling/tree/master/mpd_
hydraulics).
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Methods for Petroleum Well Optimization
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Further reading
Alawami, M., Bassam, M., Gharbi, S., Al Rubaii, M., October 2019. A Real-Time Indicator for the
Evaluation of Hole Cleaning Efﬁciency. SPE/IATMI Asia Paciﬁc Oil & Gas Conference and
Exhibition Held in Bali, Indonesia, pp. 29e31.
Open-source code
Y
ou can develop your models using open-source code for different tools available
on https://github.com/
Wellbore hydraulics and hole cleaning: optimization and digitalization
191

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Contents
About the author
ix
1. Introduction
1
1.1
Overview
1
1.2
Preprocessing of data
2
1.2.1
Data cleaning
2
1.2.2
Data integration
2
1.2.3
Data transformation
3
1.2.4
Data reduction
3
1.2.5
Data discretization
4
1.2.6
Data statistics
4
1.3
Processing of data
5
1.3.1
Data training
5
1.3.2
Data validation and testing
5
1.4
Postprocessing of data
6
1.4.1
Statistical analyses for models’ evaluation
6
1.4.2
Graphical error analysis for models’ evaluation
9
1.5
Applicability domain of a model
19
1.5.1
Identification of experimental data outliers
19
1.6
Sensitivity analysis on models’ inputs
21
1.6.1
Relevancy factor analysis
21
1.7
The areas of intelligent models applications in the petroleum industry
21
References
22
2. Intelligent models
23
2.1
Artificial neural networks
23
2.1.1
Multilayer perceptron neural network
24
2.1.2
Radial basis function neural network
26
2.2
Fuzzy logic systems
28
2.3
Adaptive neuro-fuzzy inference system
31
2.4
Support vector machine
33
2.4.1
Ordinary support vector machine
33
2.4.2
Least-square support vector machine
35
2.5
Decision tree
37
2.5.1
Random forest
38
2.5.2
Extra trees
40
v
2.6
Group method of data handling
40
2.6.1
Hybrid group method of data handling
40
2.7
Genetic programming
42
2.7.1
Multigene genetic programming
43
2.8
Gene expression programming
44
2.9
Case-based reasoning
46
2.10
Committee machine intelligent system
47
References
48
3. Training and optimization algorithms
51
3.1
Overview
51
3.2
Genetic algorithm
52
3.3
Differential evolution
55
3.4
Particle swarm optimization
56
3.5
Ant colony optimization
59
3.6
Artificial bee colony
61
3.7
Firefly algorithm
62
3.8
Imperialist competitive algorithm
63
3.9
Simulated annealing
65
3.10
Coupled simulated annealing
66
3.11
Gravitational search algorithm
67
3.12
Cuckoo optimization algorithm
68
3.13
Gray wolf optimization
70
3.14
Whale optimization algorithm
71
3.15
LevenbergMarquardt algorithm
73
3.16
Bayesian regularization algorithm
75
3.17
Scaled conjugate gradient algorithm
75
3.18
Resilient backpropagation algorithm
76
References
76
4. Application of intelligent models in reservoir and production
engineering
79
4.1
Reservoir fluid properties
80
4.1.1
One-phase properties
80
4.1.2
Two-phase properties
115
4.2
Rock properties
140
4.3
Enhanced oil recovery
154
4.3.1
Enhanced oil recovery processes
155
4.3.2
Minimum miscibility pressure
166
4.4
Well test analysis
180
vi
Contents
4.5
Formation damage
183
4.6
Asphaltene
192
4.7
Production pipelines
198
4.8
Wax
200
4.9
Other applications
201
References
203
5. Application of intelligent models in drilling engineering
229
5.1
Drilling fluids
229
5.2
Lost circulation problem
232
5.3
Stuck pipe
238
5.4
Flow patterns and frictional pressure loss of two-phase fluids
251
5.5
Rate of penetration
253
5.6
Other applications
270
References
273
6. Application of intelligent models in exploration engineering
279
6.1
Overview
279
6.2
Geochemistry
279
6.3
Geophysics
282
6.4
Petro-physics
283
6.5
Geo-mechanical characterization of organic-rich shales
284
6.6
Brittleness index in shale gas and tight oils
285
6.7
Total organic carbon determination
286
6.8
Shear wave velocity
288
6.9
Flow units
289
6.10
Facies identification from well log
290
References
292
7. Weaknesses and strengths of intelligent models in petroleum
industry
295
7.1
Overview
295
7.2
Intelligent models versus theoretical models
295
7.3
Intelligent models versus empirical correlations
297
7.4
Effect of the number of actual data
298
7.5
Validation of the developed models
299
References
300
Index
303
vii
Contents

CHAPTER THREE
Wellbore friction optimization
Key concepts
1. Drilling operations are moving into deeper waters, driving towards extended targets,
under extreme high-pressure, high-temperature (HPHT) conditions, with more com-
plex well paths and in ever tougher environments. Recent wells have been drilled to
more than 10 km from the platform, and companies are planning to extend this to
beyond 12 km. Well friction is one of the most important limiting factors in drilling
to such levels; therefore, elementary and advanced models for wellbore friction are
presented in this chapter.
2. Different examples are used to investigate the minimum friction of the well.
3.1 Elementary models for wellbore friction
The greater prevalence of extended-reach directional wells today means that
tubulars are exposed to greater amounts of torque and drag (T&D). If this torque and
drag is not evaluated, this can result in stuck pipe, pipe failures, and costly ﬁshing jobs.
Normally, torque and drag predictions are created on in-house simulators. However,
although they are a good tool for planning, these simulators have limited availability.
To provide more insight into the role of friction, we look below at a study undertaken
by Aadnøy and Andersen (1998). The authors derived explicit analytical equations to
model drill string tension for hoisting or lowering of the drill string. These are developed
for straight sections, build-up sections, drop-off sections, and side bends. From these
equations, the authors derived both constant curvature models and a new modiﬁed
catenary model. Here, we look at Aadnøy and Andersen’s new catenary model for
arbitrary entry and exit inclinations. Equations are provided to allow the calculation of
the total friction in a well from the sum of the contributions from each hole section, and
equations to determine well friction in a three-dimensional well. Expressions for torque
and drag based on the tension equations, and equations for combined motion and
drilling with a motor are also given. Examples are provided in the following to
demonstrate the application for ordinary production wells, catenary wells, long-reach
wells, and horizontal wells. We also look at Aadnøy and Andersen’s optimization
criteria to design the well for minimum friction.
Methods for Petroleum Well Optimization
 2022 Elsevier Inc.
ISBN 978-0-323-90231-1, https://doi.org/10.1016/B978-0-323-90231-1.00009-1
All rights reserved.
75j
Fig. 3.1 shows a number of different well geometries, including the inclinations at the
top and bottom. The equations for the construction of these well geometries are given in
Tables 3.1 and 3.2 shows 3D analytical model equations.
In this section, we will derive the elementary formulae for wellbore friction for
straight pipes, and for curved pipes, and show some applications. In the rest of this
chapter, we will derive more complex models and their applications.
3.1.1 Friction in straight wellbore sections
In a vertical well section, there is theoretically no contact between the wellbore and the
pipe, and therefore theoretically no friction. Of course, there is contact with the drilling
mud, but this viscous drag is considered negligible. In the following, we will derive
equations for friction in deviated straight sections using one parameter, the coefﬁcient of
friction. This includes both mechanical contact and viscous drag.
We look in the following at the forces shown on the force balance for a straight
inclined pipe in Fig. 3.2.
Total pipe weight multiplied by the buoyancy factor (BF) gives the effective weight
when the pipe is submerged in a drilling ﬂuid:
BFwL & BF ¼ b ¼ 1  rmud
rpipe
Here, w is unit weight (lb/ft) and L is the pipe length.
Figure 3.1 Forces and geometries of various curved hole proﬁles. Modiﬁed from Aadnøy, B.S.,
Andersen, K., 2001. Design of oil wells using analytical friction models. J. Petrol. S. Eng. 32 53e71. https://
doi.org/10.1016/S0920-4105(01)00147-4.
76
Methods for Petroleum Well Optimization
Table 3.1 Geometrical projections for various section proﬁles.
Section proﬁle
Section length Ds
Vertical projection Dz
Horizontal projection Dx
Horizontal projection Dy
(a) Straight inclined
Ds
Ds cos a
Ds sin a
(b) Drop-off
R(a2  a1)
R(sin a2  sin a1)
R(cos a2  cos a1)
(c) Build-up
R(a2  a1)
R(sin a2  sin a1)
R(cos a2  cos a1)
(d) Right-side bend
R(42  41)
0
R(cos 42  cos 41)
R(sin 42  sin 41)
(e) Left-side bend
R(42  41)
0
R(cos 42  cos 41)
R(sin 42  sin 41)
(f) Modiﬁed catenary
F1
w ½sin a1 sinhfAg cos a1
F1 sin a1
w
½coshfAg B
Dx
(g) Entrance modiﬁed
catenary
R a
2
R sin a
2
R(1  cos a
2)
where
A ¼

wx
F1 sin a1
þ sinh1ðcot a1Þ

, B ¼ cosh

sinh1ðcot a1Þ

,
R ¼ F0 þ ðwDsÞ2 þ 2wDsF0 cos a1
wF0 sin a1
, and tan a
2 ¼ wDs þ F1 cos a1
F1 sin a1
Subscript 1 is the deepest position, and subscript 2 is the highest position.
Aadnøy, B.S., Andersen, K., 2001. Design of oil wells using analytical friction models. J. Petrol. S. Eng. 32 53e71. https://doi.org/10.1016/S0920-4105(01)00147-4.
Wellbore friction optimization
77
Table 3.2 3D analytical model equations.
Section proﬁle
Torque
Drag
Straight inclined without
pipe rotation
Fig. 3.2
T ¼ mrbwDL sin a
Without axial pipe motion
F2 ¼ F1 þ bDLwðcos a m sin aÞ
For curved wellbore
section without pipe
rotation
T ¼ mrN ¼ mrF1jq2 q1j
Without axial pipe motion
F2 ¼ F1emjq2q1j þ bwDL

sin a2sin a1
a2a1

Combined axial motion
and rotation for straight
pipe section
T ¼ rmbDL sin a cos j
F2 ¼ F1 þ bwDL cos a
 mbwDL sin a sin j
Combined axial motion
and rotation for curved
pipe section
T ¼ mrN ¼ mrF1jq2 q1jcos j
F2 ¼ F1 þ F1

emjq2q1j  1
	
sin jþ
bwDL
sin a2  sin a1
a2  a1

3D wellbore section
without pipe rotation for
BHA
T ¼ mrj  F1ðq2 q1Þ  bwRa sin a1ða2 a2Þ
2bwRaðcos a2  cos a1Þj
Without axial motion
F2 ¼ F1emjq2q1j þ KbwRaðsin a2 sin a1Þ
Combined motion in 3D
bends for BHA
T ¼ mrj  F1ðq2  q1Þ
bwRa sin a1ða2  a2Þ  2bwRaðcos a2  cos a1Þjcos j
F2 ¼ F1 þ bwRaðsin a2 sin a1Þ
þ

F1

emjq2q1j  1
	
þðK  1ÞbwRaðsin a2  sin a1Þ
	
sin j
Weight term:
K ¼ Að1  mÞ2ðsin a2  eABmða2a1Þ sin a1Þ  2Bmðcos a2  eABmða2a1Þ cos a1Þ
ð1 þ m2Þðsin a2  sin a1Þ
BHA, bottomhole assembly.
Aadnøy, B.S., Andersen, K., March 3e6, 1998. Friction Analysis for Long-Reach Wells. IADC/SPE 39391, IADC/SPE Drilling Conference, Dallas, TX; Aadnøy, B.S., Fazaelizadeh, M.,
Hareland, A., October 2010. A 3D analytical model for wellbore friction. J. Can. Petrol. Technol. 49 (10). https://doi.org/10.2118/141515-PA.
This weight is decomposed into two force components: one along the plane and one
normal to the plane:
BFwL cos a
and
BFwL sin a
Obviously, the component along the plane is the force required to pull the pipe in a
frictionless environment. However, we will introduce the Coulomb law of friction,
which states that for a body in motion, the frictional resistance is independent of velocity
and is equal to the normal force multiplied by the coefﬁcient of friction m, or:
 mBFwL sin a
Friction is always opposing the direction of motion; therefore, a minus or a plus sign
must be used.
Again, with reference to Fig. 3.2, the force to pull or lower a pipe along a plane with
inclination a becomes:
F2 ¼ F1 þ BFwLðcos a  m sin aÞ
(3.1)
Here, upward pull is deﬁned as positive, and downward lowering is deﬁned as
negative. The axial friction when pulling or lowering pipes is called drag. F2 is the top
force, and F1 is the force below the pipe.
Example 3.1: Maximum wellbore angle
Assume a straight inclined wellbore, where we want to lower a pipe inside. Weight is
pulling the pipe down, but wellbore friction acts the opposite way and opposes motion.
Assuming a coefﬁcient of friction of m ¼ 0.2, what is the maximum inclination the
wellbore can have before sliding is arrested?
Figure 3.2 Force balance for a straight inclined pipe.
Wellbore friction optimization
79
Solution:
Obviously, we have two opposing forces: weight is pulling down and friction is
restricting motion. The point at which the pipe will stop sliding is when weight equals
friction, or the argument inside the brackets in Eq. (3.1) equals zero:
ðcos a  m sin a ¼ 0Þ
a ¼ tan1

1
m

¼ tan1

 1
0:2

¼ 78:7 degrees
(3.2)
This simple calculation has signiﬁcant applications. In particular, during completion
operations, it is required that weight exceeds friction to get the completion string to the
bottom. In this example, wellbore inclination should be less than 78.7 degrees for a
successful completion operation.
3.1.1.1 Torque
There is also friction when rotating the string. This is called torque. The top drive must
submit enough energy to rotate the string. In fact, the rotaticonal resistance is given by
the same mechanisms as the axial drag. Fig. 3.3 shows that when pulling the pipe, axial
friction is equal to the normal force (weight) multiplied with the coefﬁcient of friction.
When rotating the pipe, the same frictional force results, except that it now acts in a
tangential direction. The resulting torque is the frictional force multiplied by the radius
of rotation, or:
T ¼ rðmBFwL sin aÞ
(3.3)
Figure 3.3 Axial and rotational friction in a tubular.
80
Methods for Petroleum Well Optimization
Example 3.2: Pipe size and torque
We are drilling with a 3.500, 15.5 lbs/ft drill pipe with a diameter of the connection
diameter of 127 mm. We want to replace this pipe with a 500 25.6 lbs/ft pipe with a
connection diameter of 168 mm. How much increase in torque do we expect?
Solution:
When a drill string rotates, we assume that the connections are the contact points
between the pipe and the wellbore. The radius in Eq. (3.3) is therefore the radius of the
connection and not the pipe radius. We insert the information given in Eq. (3.3) and
obtain:
Tð3:500Þ ¼ 127 mm
2
ðmBFð15:5 lbs=ftÞÞL sin aÞ
Tð500Þ ¼ 168 mm
2
ðmBFð25:6 lbs=ftÞL sin aÞ
(3.4)
Combining the two equations, we obtain the following increase in torque:
Tð500Þ
Tð3:500Þ ¼ 168 mm
127 mm
25:6 lbs=ft
15:5 lbs=ft ¼ 1:32  1:65 ¼ 2:18
(3.5)
This example shows that using the larger pipe results in a 32% increase in torque due
to increased connection diameter and in a torque increase of 65% due to an increase in
normal force (weight). Total torque increase is 118%.
3.1.2 Friction in curved wellbore sections
The previous derivation for a straight pipe is simple. The curved pipe is more complex.
We will show in the simplest possible way how these solutions are obtained.
A pipe bend is shown in Fig. 3.4. We observe that the normal force no longer de-
pends solely on pipe weight, but also on pipe tension. High pipe tension leads to high
friction. This effect is called the capstan effect. We will demonstrate the solution by
assuming a weightless pipe.
Performing a force balance in the radial and tangential directions results in:
Tangential direction: dF ¼ mFdq
(3.6)
Radial direction: dN ¼ Fdq
(3.7)
Wellbore friction optimization
81
Combining these equations and integrating over an angle q result in:
F2 ¼ F1emq
(3.8)
where the following sign convention applies:
þ means that the pipe is pulled upward
 means that the pipe is lowered
For a rotating string, the same contact force applies, and only the friction direction is
tangential. The torque for pipe that is just rotated is:
T ¼ mrN ¼ mrF1jqj
(3.9)
Example 3.3: The severity of the capstan effect
We will now compare two cases: a straight pipe and a curved pipe that has an initial
tension in the bottom of the pipe of 10 klbs and a drag of 2.3 klbs. Then bottom tension
is increased to 100 klbs. What is the change in tension for each pipe? Assume that both
pipes are weightless.
Solution:
We assume that the coefﬁcient of friction is 0.2, and the angle of the curved pipe is 60
degrees. For the straight pipe, we use Eq. (3.10).
F2 ¼ F1 þ bwLðcos a  m sin aÞ
(3.10)
We see that added pipe tension has no effect when pulling this pipe. The frictional
drag remains the same regardless of tension, and pulling force is 100 þ 2.3 ¼ 102.3 klbs.
For the curved pipe we will use Eq. (3.11), which is:
F2 ¼ F1emq ¼ F1e0:260 degrees
p
180 degrees ¼ 1:23F1
(3.11)
Figure 3.4 Force balance for a curved pipe section. Modiﬁed from Aadnøy, B.S., Fazaelizadeh, M.,
Hareland, A., October 2010. A 3D analytical model for wellbore friction. J. Can. Petrol. Technol. 49 (10).
https://doi.org/10.2118/141515-PA.
82
Methods for Petroleum Well Optimization
For the two cases, the top pipe tension becomes:
F2 ¼ 1:23 klbs  10 klbs ¼ 12:3 klbs
F2 ¼ 1:23 klbs  100 klbs ¼ 123 klbs
Change in tension becomes 123  12.3 ¼ 110.7 klbs.
To summarize, friction was constant at equal to 2.3 klbs for the straight pipe. For the
curved pipe, friction increased from 2.3 to 10.7 klbs, an increase of 8.4 klbs, which is due
to the capstan effect.
The effect of pipe weight must be addressed for the curved tubular. Initial models
were valid in a vertical plane only due to gravity. However, Aadnøy et al. (2010) argued
that since pipe tension far exceeds the section weight for most of the tubular length, a
simpliﬁed solution can be used. This is:
F2 ¼ F1emjq2q1j þ BFwL
sin a2  sin a1
a2  a1

(3.12)
Here þ is for hoisting and  is for lowering. The parameter q is the dogleg (DL) of
the wellbore (Fig. 3.5).
Figure 3.5 The dogleg severity in three-dimensional space. Modiﬁed from Aadnøy, B.S., Fazaelizadeh,
M., Hareland, A., October 2010. A 3D analytical model for wellbore friction. J. Can. Petrol. Technol. 49 (10).
https://doi.org/10.2118/141515-PA.
Wellbore friction optimization
83
3.1.3 Two-dimensional friction modeling
In the following, we will show how a simple two-dimensional friction analysis is carried
out. Two dimensions mean that the well path is located in a vertical plane with a constant
azimuth. The following rules apply for the analysis:
•
Always start from the bottom of the well. The bottom force of the string is usually:
F ¼ 0 if the drill string hangs off bottom;
F ¼ Fbit if a bit force is applied.
•
Split the well into geometries, which are vertical, drop-off, sail-section, build-up, and
vertical to top.
•
Compute forces, where the top force of one section is the bottom force on the
previous section.
The equations to use are:
For straight sections: F2 ¼ F1 þ BFwLðcos a  m sin aÞ
(3.13)
T ¼ rðmBFwL sin aÞ
(3.14)
Bends in two dimensions: F2 ¼ F1emja2a1j þ BFwL
sin a2  sin a1
a2  a1

(3.15)
T ¼ mrN ¼ mrF1ja2  a1j
(3.16)
Bends in three dimensions: F2 ¼ F1emjq2q1j þ BFwL
sin a2  sin a1
a2  a1

(3.17)
T ¼ mrN ¼ mrF1jq2  q1j
(3.18)
Here þ means hoisting and  means lowering. Index 2 is the top, and index 1 is the
bottom of each section.
The aforementioned rules and the equations will be applied to the following example
wellbore below.
Fig. 3.6 shows an S-shaped well that is drilled in a vertical plane. The total length is
2111 m, and the drill string consists of 161 m of 800  300 drill collars (2.13 kN/m), and
1950 m of 500e19.5 lbs/ft drill pipe (0.285 kN/m). The drill collar radius is 0.1 m, and
the drill string connection radius is 0.09 m. The well is ﬁlled with 1.3 s.g. drilling mud,
and the coefﬁcient of friction is estimated to be 0.2. The bottomhole assembly (BHA)
starts out just below the drop-off section and is vertical. In this case, there is no change in
azimuth, and the DL of Eq. (3.38) becomes equal to the change in inclination.
The buoyancy factor is (pipe density 7.8 s.g.): BF ¼ 1e1.3/7.8 ¼ 0.833.
84
Methods for Petroleum Well Optimization
Assuming that the drill bit is off bottom, we will compute the forces starting from the
bottom of the well. For simplicity, the frictional factors of the bends are:
emq ¼ e
0:2

45 p
180
	
¼ e0:157 ¼
8
<
:
1:17
0:855
Table 3.3 shows the forces in the drillstring under different conditions. The net
weight of the BHA: 0.833  2.13(kN/m)  161 (m) ¼ 286 kN.
The buoyed pipe weight: 0.833  0.285 kN/m ¼ 0.237 kN/m.
Fig. 3.6 shows the geometry of the well.
When calculating torque, we will use two scenarios: (1) with bit off bottom and
(2) with a bit force of 90 kN. The static weight for the last case is simply obtained by
subtracting the bit force throughout the string.
From Fig. 3.7, it is obvious that the build-up and drop-off bends have a dominating
effect on well friction. This is further seen in Fig. 3.8, which shows the torque. When the
bit force is applied, the tension in the string decreases, leading to less string torque.
Fig. 3.8 shows the reduction in string tension that leads to a reduced string torque.
The numerical values are given in Table 3.4. When the bit force is applied, the driller
Figure 3.6 Geometry of S-shaped well. Modiﬁed from Aadnøy, B.S., Fazaelizadeh, M., Hareland, A.,
October 2010. A 3D analytical model for wellbore friction. J. Can. Petrol. Technol. 49 (10). https://doi.org/
10.2118/141515-PA.
Wellbore friction optimization
85
Table 3.3 Forces in the drill string during hoisting and lowering.
Position
Static weight (kN)
Hoisting (kN)
Lowering (kN)
Well bottom
0
0
0
Bottom
drop-off
286
286
286
Bottom sail section
286 þ 0.237 
120 ¼ 286 þ 28.4 ¼ 314.4
286  1.17 þ 28.4 ¼ 363
286 
0.855 þ 28.4 ¼ 272.9
Top sail section
314.4 þ 0.237 
925 ¼ 314.4 þ 219.2 ¼ 533.6
363 þ 0.237  1308 (cos
45 degrees þ 0.20 sin 45
degrees) ¼ 626
272.9 þ 0.237  1308(cos
45 degrees  0.20 sin 45
degrees) ¼ 448.3
Top build-up section
533.6 þ 0.237 
120 ¼ 533.6 þ 28.4 ¼ 562
626  1.17 þ 28.4 ¼ 760.9
448.3 
0.855 þ 28.4 ¼ 411.7
Top well
562 þ 0.237 
335 ¼ 562 þ 79.4 ¼ 641.4
760.9 þ 79.4 ¼ 840.3
411.7 þ 79.4 ¼ 491.1
Aadnøy, B.S., Fazaelizadeh, M., Hareland, A., October 2010. A 3D analytical model for wellbore friction. J. Can. Petrol. Technol. 49 (10). https://doi.org/10.2118/
141515-PA.
86
Methods for Petroleum Well Optimization
observes an increase in torque of 6.46 kNm, to a total value of 22 kNm. However, due to
wellbore friction in the string torque, the bit torque is actually 9 kNm. From this
example, it is obvious that the bit torque increase is always higher than the torque in-
crease at surface for a deviated well when a bit force is applied.
3.1.4 Three-dimensional friction modeling
Fig. 3.9 shows this three-dimensional well. It is complex, as its direction changes in
three-dimensional space. The analysis is similar to the analysis of the two-dimensional
example, except that the bends are not restricted to a vertical plane but are in a three-
dimensional plane. The three-dimensional equation for bends is used, which uses the
dogleg severity (DLS) instead of the wellbore inclination.
The pipe data are the same as for the two-dimensional example. The results of
performing the analysis are shown in Fig. 3.10. Clearly, a different friction picture is seen
in this well, which has three bends. Increasing the total angle change of a well increases
friction signiﬁcantly.
Figure 3.7 Torque and drag for the S-shaped well. Modiﬁed from Aadnøy, B.S., Fazaelizadeh, M.,
Hareland, A., October 2010. A 3D analytical model for wellbore friction. J. Can. Petrol. Technol. 49 (10).
https://doi.org/10.2118/141515-PA.
Wellbore friction optimization
87
3.1.5 Combined axial motion and rotation
There are certain well operations where the drill string is rotated and hoisted at the same
time. Typical operations include the following:
•
Backreaming during tripping. Sometimes the hook load becomes excessive during
tripping. One remedy to reduce the hook load is to ream or rotate the drill string during
tripping.
•
Rotating liner. In highly inclined wellbores, sometimes the driller spins the liner
during installation to get the pipe to bottom.
•
Casing drilling. A novel drilling method is to use the casing as a drill string. The
casing is not retrieved but left in the bottom of the well section.
The aforementioned examples improve well operations by reducing axial friction. In the
following, we will discuss the mechanisms. Fig. 3.3 shows that for a pipe that is pulled or
rotated, the frictional force is the same. Because friction always acts opposite to motion,
it changes from axial to tangential friction on the pipe.
Figure 3.8 Torque during drilling. Modiﬁed from Aadnøy, B.S., Fazaelizadeh, M., Hareland, A., October
2010. A 3D analytical model for wellbore friction. J. Can. Petrol. Technol. 49 (10). https://doi.org/10.2118/
141515-PA.
88
Methods for Petroleum Well Optimization
Table 3.4 Torque in drill string during drilling and with bit off bottom.
Position
Static
weight,
bit off
bottom
(kN)
Torque off bottom (kNm)
Static weight, 90 kN
bit force
Torque in string (kNm)
Torque in well (kNm)
Well bottom
0
0
90
0
22  13.0 ¼ 9
Bottom
drop-off
286
0
286  90 ¼ 196
0
9
Bottom sail
314.4
0.2  0.09  286  p/4 ¼ 4.04
314.4 
90 ¼ 224.4
0.2  0.09  196 
p/4 ¼ 2.77
9 þ 0.2  0.09  196 
p/
4 ¼ 9 þ 2.77 ¼ 11.77
Top sail
533.6
4.04 þ 0.2  0.09  0.237 
1308  sin 45
degrees ¼ 4.04 þ 3.95 ¼ 8.0
533.6 
90 ¼ 443.6
2.77 þ 3.95 ¼ 6.72
11.77 þ 3.95 ¼ 15.72
Top build-up
section
562
8.0 þ 0.2  0.09  533.6  p/4
¼ 8.0 þ 7.54 ¼ 15.54
562  90 ¼ 472
6.72 þ 0.2 
0.09  443.6  p/
4 ¼ 6.72 þ 6.27 ¼ 13.0
22
Top well
641.4
15.54
641.4 
90 ¼ 551.4
13.0
22
Aadnøy, B.S., Fazaelizadeh, M., Hareland, A., October 2010. A 3D analytical model for wellbore friction. J. Can. Petrol. Technol. 49 (10). https://doi.org/10.2118/141515-PA.
Wellbore friction optimization
89
Figure 3.9 Three-dimensional well shape. Modiﬁed from Aadnøy, B.S., Fazaelizadeh, M., Hareland, A.,
October 2010. A 3D analytical model for wellbore friction. J. Can. Petrol. Technol. 49 (10). https://doi.org/10.
2118/141515-PA.
Figure 3.10 Well friction for three-dimensional well. Modiﬁed from Aadnøy, B.S., Fazaelizadeh, M.,
Hareland, A., October 2010. A 3D analytical model for wellbore friction. J. Can. Petrol. Technol. 49 (10).
https://doi.org/10.2118/141515-PA.
90
Methods for Petroleum Well Optimization
For a straight pipe section, the drag and the tangential friction forces are from Eqs.
(3.1) and (3.3):
DF ¼ mBFwL sin a
(3.19)
T
r ¼ mBFwL sin a
(3.20)
The frictional capacity of this pipe is given by the product of the normal force and the
coefﬁcient of friction. Clearly for a combined motion, this limit will not be exceeded.
Fig. 3.11 shows the frictional capacity for any motion. If the speciﬁc friction is known in
one direction, the resulting friction in the other direction can be calculated from:
F2 ¼ F1 þ BFwL cos a  mBFwL sin a sin j
(3.21)
T ¼ rmBFwL sin a cos j
(3.22)
The implications of Fig. 3.11 are that if we spin the pipe fast, most axial drag dis-
appears and torque takes nearly all friction. This is what happens when we rotate in a
liner. The rotation reduces the axial friction.
Example 3.4. Spinning in a liner
A wellbore has an inclination of 80 degrees. The coefﬁcient of friction is 0.3.
(a) See if it is possible to run a liner into the well.
(b) If not, suggest using rotation.
Solution:
(a) From Example 3.1, we can determine the maximal wellbore inclination that will
allow the pipe to slide into the well:
ðcos a  m sin a ¼ 0Þ / a ¼ tan1

1
m

¼ tan1

 1
0:3

¼ 73:3 degrees
F
r
T
x
y
ψ
∆
Figure 3.11 Relations between torque and drag for a straight section. Modiﬁed from Aadnøy, B.S.,
Fazaelizadeh, M., Hareland, A., October 2010. A 3D analytical model for wellbore friction. J. Can. Petrol.
Technol. 49 (10). https://doi.org/10.2118/141515-PA.
Wellbore friction optimization
91
If the well has an inclination of 80 degrees, the liner will not slide into the well. There are
two ways to improve this situation. One is to push the liner in by installing drill collars
higher up in the string to push the liner into place or to reduce axial drag by rotation.
(b) With reference to Fig. 3.11, we assume that rotation is so fast that the x approaches
zero. We assume that the effective axial friction coefﬁcient is 0.05. The maximum
inclination to provide sliding is now:
ðcos a  m sin a ¼ 0Þ / a ¼ tan1

1
m

¼ tan1

 1
0:05

¼ 87:1 degrees
Now sliding can take place up to 87 degrees, and since the wellbore is 80 degrees, the
liner can be installed. This shows that rotation reduces axial drag and allows tubulars to be
installed in highly inclined wellbores.
3.2 Advanced models for wellbore friction
3.2.1 The Johancsik model
One of the earliest signiﬁcant contributions to wellbore friction in deviated wells was by
Johancsik et al. (1984). Johancsik, Friesen, and Dawson presented a model based on force
equilibrium, which is used in most numerical simulators today. This equation calculates
load increments throughout the drill string. It reads:
Fn ¼
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
ðF1Da sin aÞ2 þ ðF1Df þ wBF sin aÞ2
q
(3.23)
Tension increment: DF1 ¼ BFw cos 4  mFn
(3.24)
Torsion increment: DM ¼ mFnr
(3.25)
The pioneering work of Johancsik, Friesen, and Dawson is in line with the current
understanding of well friction analysis. This model will not be pursued further here as it
is mainly applicable for discretized analysis.
3.3 Application of friction models to wells
3.3.1 Radius of curvature well path model
Once the azimuth and inclination at two wellbore positions are known, the displace-
ments in different directions can be deﬁned by assuming a curved shape between the two
positions. Fig. 3.12 shows a vertical projection of the well path.
92
Methods for Petroleum Well Optimization
The relationship between the wellbore, the radius of the circular segment, and the
angle is (inclination angles given in radians):
DL ¼ Raða1  a2Þ
(3.26)
The vertical projected height is:
DV ¼ Rasin a1  Rasin a2 ¼ DLðsin a1  sin a2Þ
a1  a2
(3.27)
The vertical projected height is used to compute the axial pipe weight. To ﬁnd the
changes in the north and east coordinates, the wellbore trajectory is projected onto a
horizontal plane, as shown in Fig. 3.13.
In this projection, the wellbore is assumed to be circular. From the horizontal pro-
jection in Fig. 3.13, the circular segment can be expressed as:
DD ¼ Rfcos a2  Rfcos a1
(3.28)
This is the distance of the circular projection of the wellbore in the horizontal plane
and is connected to the radius Rf and azimuth angles by the relation:
DD ¼ Rfðf1  f2Þ
(3.29)
Figure 3.12 Projection of a wellbore in a vertical plane. Modiﬁed from Aadnøy, B.S., Fazaelizadeh, M.,
Hareland, A., October 2010. A 3D analytical model for wellbore friction. J. Can. Petrol. Technol. 49 (10).
https://doi.org/10.2118/141515-PA.
Wellbore friction optimization
93
The changes DN and DE now follow from Fig. 3.13:
DN ¼ Rfðsin f1  sin f2Þ
(3.30)
DE ¼ Rfðcos f2  cos f1Þ
(3.31)
The complete expressions are obtained on inserting for Rf and DD:
DN ¼ DL ðcosa2  cosa1Þðsinf1  sinf2Þ
ða1  a2Þðf1  f2Þ
(3.32)
DE ¼ DL ðcos a2  cos a1Þðcos f2  cos f1Þ
ða1  a2Þðf1  f2Þ
(3.33)
3.3.1.1 Positions for straight well sections
With reference to Fig. 3.13, we may assume that the x-axis points north and the y-axis
points east. Deﬁning the unit vector to have a length DL, the displacements for a straight
section can be deﬁned as follows:
•
Vertical projection (to compute axial pipe weight):
DV ¼ DLcos a
(3.34)
Figure 3.13 Projection of wellbore in a horizontal plane. Modiﬁed from Aadnøy, B.S., Fazaelizadeh, M.,
Hareland, A., October 2010. A 3D analytical model for wellbore friction. J. Can. Petrol. Technol. 49 (10).
https://doi.org/10.2118/141515-PA.
94
Methods for Petroleum Well Optimization
•
Horizontal projections in the northern and eastern directions:
DN ¼ DLsin a cos f
(3.35)
DE ¼ DLsin a sin f
(3.36)
Again, the horizontal projections are not used in the examples but are presented to deﬁne
the complete set of equations to deﬁne the geographical positions of the borehole.
3.3.2 The dogleg severity
Standard surveying techniques measure wellbore inclination and azimuth. These are used
to determine vertical depth and geographic reach. Furthermore, two slope parameters
called DL and DLS are computed. The DL is the absolute change of direction, and the
DLS is the derivative of the DL. The DL can be determined by the following equations,
where index 1 and index 2 refer to two consecutive survey measurements, or to the start
and end of a longer wellbore section:
cosq ¼ sin a1sin a2cosðf1  f2Þ þ cos a1cos a2
(3.37)
DLðdegreesÞ ¼ 180
p jqðradÞj
(3.38)
Deﬁning the distance between these measurements as DL, the derivative is:
DLS ¼
DL
DLðmÞ ðdegrees = mÞ
(3.39)
It is customary in the oil industry to present the DLS as degrees per 30 m or per
100 ft.
Although the inclination a is measured in a vertical projection and the azimuth 4 in a
horizontal projection, the DL is measured in an arbitrary plane, as shown in Fig. 3.14.
Inspection of this ﬁgure reveals that it depends on both inclination and azimuth. We will
utilize these properties when we present general friction models that are not restricted to
a plane.
3.3.3 The catenary well path
One of the challenges in directional drilling is to design a well path for minimum
friction. In the early phases of long-reach drilling, various well geometries were inves-
tigated from the perspective of minimizing friction. One of the early ideas was published
by McClendon and Anders (1985). They proposed the use of a catenary well proﬁle to
minimize well friction. They described a catenary curve as “the natural curve that a
cable, chain or other line of uniform weight assumes when suspended between two
Wellbore friction optimization
95
points. The similar suspension of a drill string would also form a catenary curve. A
common example is the curve formed by a telephone line hanging between two utility
poles.”
In other words, it is suggested that the well path be constructed so that the drill string
will hang freely with a catenary shape inside the well. Because of low contact between
the wellbore and the drill string, friction will be minimal.
The catenary concept is used to a large degree for marine riser analysis, but it has not
been much used for drilling. The model is complex, and the beneﬁts are not proven. We
will now present a simple approach to the catenary model and investigate its potential for
friction reduction in petroleum wells.
Early models of the catenary solution were simplistic. However, Aadnøy et al. (2006)
presented a complete analysis where the catenary well path could assume any angle at the
bottom. They also included the build-up section from vertical to the start of the catenary.
This is shown in Fig. 3.15.
In this ﬁgure, the bottom can assume any angle a1, whereas the top angle depends on
the force F and the horizontal departure: see Aadnøy et al. (2006) for derivations of the
equations. The geometry of the catenary well path is given by the following implicit
equation:
z ¼ F1 sin a1
BFw

cosh

wx
F1sin a1
þ sinh1ðcot a1Þ

þ cosh ðsinh ðcot a1ÞÞ

( 3.40)
X
Y
Z
2
2
1
1
2
1
R
R
α
α
ϕ
ϕ
θ
Figure 3.14 The dogleg in three-dimensional space. Modiﬁed from Aadnøy, B.S., Fazaelizadeh, M.,
Hareland, A., October 2010. A 3D analytical model for wellbore friction. J. Can. Petrol. Technol. 49 (10).
https://doi.org/10.2118/141515-PA.
96
Methods for Petroleum Well Optimization
And the total length of the string becomes:
s ¼
F1
BFw

sin a1sinh

wx
F1 sin a1
þ sinh1ðcot a1Þ

 cot a1

(3.41)
The entrance from vertical to the start of the catenary is given by:
R ¼ ðwDsÞ2 þ 2wDsF1 cos a1 þ F2
1
wF1 sin a1
(3.42)
Here, we will only present results of the comparison of the catenary well path with a
conventional well path by Aadnøy et al. (2006). However, if readers are particularly
interested, they can construct a complete catenary proﬁle and perform an analysis using
the equations.
Example 3.4: Comparing a conventional well path with a catenary well path
Fig. 3.16 shows the two well paths that we will compare. The data for the wells are
taken from Aadnøy et al. (2006). One difference between the two paths is that the
conventional build starts at a 900 m depth, whereas the catenary proﬁle starts at a 200 m
depth. Both wellbores enter the sail section at the same angle and depth.
The results of the drag analysis are shown in Fig. 3.17 for both cases.
From Fig. 3.17, we observe that there is less friction through the catenary section
than through the conventional well path. However, the catenary picks up more friction
in the entrance section. The resulting hook load at surface becomes the same for the
two cases.
The aforementioned example shows that, although locally the catenary can give
reduced friction, the total friction in the wellbore is the same. The advantage is that a
Figure 3.15 A freely hanging drill string assuming a catenary shape. Modiﬁed from Aadnøy, B.S., Fabiri,
V., Djurhuus, J., February 21e23, 2006. Construction of Ultra-long Wells Using a Catenary Well Proﬁle.
IADC/SPE Drilling Conference in Miami, Florida, U.S.A.
Wellbore friction optimization
97
Figure 3.16 Conventional and catenary well path. Modiﬁed from Aadnøy, B.S., Fabiri, V., Djurhuus, J.,
February 21e23, 2006. Construction of Ultra-long Wells Using a Catenary Well Proﬁle. IADC/SPE Drilling
Conference in Miami, Florida, U.S.A.
Figure 3.17 Drag forces for the two well paths. Modiﬁed from Aadnøy, B.S., Fabiri, V., Djurhuus, J.,
February 21e23, 2006. Construction of Ultra-long Wells Using a Catenary Well Proﬁle. IADC/SPE Drilling
Conference in Miami, Florida, U.S.A.
98
Methods for Petroleum Well Optimization
local friction reduction may have operational advantages. Catenary well proﬁles are
difﬁcult to design because they require precise control over pipe tension and also the
build rate changes continuously.
3.4 Design of oil wells using analytical friction models
Wellbore friction is a critical parameter in deviated wellbores. In the following, we
will present some examples of well designs used in the oil industry, starting with the
simplest well.
3.4.1 Well with build-and-hold proﬁle
The well under consideration in this section is shown in Fig. 3.18. It is vertical to the
kick-off point and built with a constant radius to a sail angle, which is kept constant to
the bottom of the well.
Tables 3.5 and 3.6 list the equations required to model the drag, torque, and the static
weight for this case.
The static weight is just the unit weight multiplied by the projected height, regardless
of the inclination of the well. For the example well, assume a unit weight of the BHA of
3 kN/m (200 m long), drill pipe of 0.3 kN/m (2000 m from BHA to end build section),
a 60 degrees inclination, a build radius of 500 m, a kick-off depth of 1500 m, and a mud
weight of 1.56 s.g., which leads to a buoyancy factor BF ¼ 1 
rmud
rdrill pipe (Eq. 3.2) of 0.8.
Figure 3.18 Build-sail type well. Modiﬁed from Aadnøy, B.S., Andersen, K., 2001. Design of oil wells using
analytical friction models. J. Petrol. S. Eng. 32, 53e71. https://doi.org/10.1016/S0920-4105(01)00147-4.
Wellbore friction optimization
99
Table 3.5 Designing model for drag during pulling and lowering of drill string for an example build-sail type well.
Force
Equationsdpulling string
Equationsdlowering string
Bit force
F1 ¼ 0
F1 ¼ 0
Force top bottomhole
assembly
F2 ¼ F1 þ wBHALBHAðcos a þm sin aÞ
F2 ¼ F1 þ wBHALBHAðcos a m sin aÞ
Force top of sail section
F3 ¼ F2 þ wDPLDPðcos a þm sin aÞ
F3 ¼ F2 þ wDPLDPðcos a m sin aÞ
Force at kick-off position
F4 ¼ ðF3 þwDPR sin aÞema
F4 ¼

F3 þwDPR
1þm2 ½ð1 m2Þsin a 2m cos a

ema þ 2mwDPR
1þm2
Force on top of well
F5 ¼ F4 þ wDPLKOP
F5 ¼ F4 þ wDPLKOP
Aadnøy, B.S., Andersen, K., 2001. Design of oil wells using analytical friction models. J. Petrol. S. Eng. 32, 53e71. https://doi.org/10.1016/S0920-4105(01)00147-4.
100
Methods for Petroleum Well Optimization
The friction coefﬁcient is 0.15. The torque is computed both with the bit off bottom and
with a bit force of 150 kN, resulting in a bit torque of 6 kNm. With these numbers, the
target is located at 3033 m vertical depth, and the horizontal departure is 2155 m.
Inserting these numbers into the appropriate equations in Tables 3.5 and 3.6 results
in:
Drag when pulling
F5 ¼ 0 þ 302 þ 302 þ 224 þ 360 ¼ 1188 kN
Lowering
F5 ¼ 0 þ 178 þ 178 þ 18 þ 360 ¼ 734 kN
Static weight
F5 ¼ 0 þ 240 þ 240 þ 104 þ 360 ¼ 944 kN
Torque, bit off bottom
T5 ¼ 0 þ 6.24 þ 6.24 þ 10.94 þ 0 ¼ 23.45 kNm
Torque, bit force 150 kN
T5 ¼ 6 þ 6.24 þ 6.24 þ 8.87 þ 0 ¼ 27.35 kNm
This well design is illustrated in Fig. 3.19. The static pipe weight curve in Fig. 3.19A has
a constant slope regardless of inclination as discussed before. It is clear that the build-up
bend contributes signiﬁcantly to well friction. As discussed, any time a change in the
direction of the well takes place, the friction becomes a function of the tension in the
string, not only of the weight itself. This results in a multiplicative effect as seen. This
effect is also seen in the torque curve shown in Fig. 3.19B. When the bit is on the
bottom, however, effective string tension is lowered, resulting in less torque through the
bend. From this simple example, one may conclude that to minimize friction, the
number of direction changes in the well path should be kept to a minimum.
3.4.2 Constructing a modiﬁed catenary well proﬁle
A catenary build proﬁle can provide an absolute minimum of well friction if it is properly
designed. The term modiﬁed catenary refers to the model, which does not require a
horizontal direction at the bottom but can be designed with any sail angle.
Table 3.6 Designing model for static weight and torque during drilling for an example build-sail type
well.
Force/torque
Equationsdstatic weight
Equationsdtorque
At bit
F1 ¼ 0
T1 ¼ 0
Top bottomhole assembly
F2 ¼ F1 þ wBHALBHA cos a
T2 ¼ T1 þ mwBHALBHAr sin a
Top of sail section
F3 ¼ F2 þ wDPLDP cos a
T3 ¼ T2 þ mwDPLDPr sin a
At kick-off position
F4 ¼ ðF3 þwDPR sin aÞ
T4 ¼ T3 þ mr½ðF3 þwDPR sin aÞa
þ2wDPRð1  cos aÞ
On top of well
F5 ¼ F4 þ wDPLKOP
T5 ¼ T4
Aadnøy, B.S., Andersen, K., 2001. Design of oil wells using analytical friction models. J. Petrol. S. Eng. 32, 53e
71. https://doi.org/10.1016/S0920-4105(01)00147-4.
Wellbore friction optimization
101
Fig. 3.20 shows a long-reach well. The well has a horizontal reach of 6000 m, and the
maximum sail angle is 80 degrees due to friction. In our example, this well is planned to
be drilled with a downhole motor and no rotation of the drill string. The string must
therefore be able to slide into the borehole. To minimize friction, it is decided to
construct a catenary proﬁle from 2000 to 3000 m vertical depth. During this critical
Figure 3.20 Proﬁle of example catenary well (A and B). Modiﬁed from Aadnøy, B.S., Andersen, K., 2001.
Design of oil wells using analytical friction models. J. Petrol. S. Eng. 32, 53e71. https://doi.org/10.1016/
S0920-4105(01)00147-4.
Figure 3.19 Well friction (A and B). Modiﬁed from Aadnøy, B.S., Andersen, K., 2001. Design of oil wells
using analytical friction models. J. Petrol. S. Eng. 32, 53e71. https://doi.org/10.1016/S0920-4105(01)
00147-4.
102
Methods for Petroleum Well Optimization
phase, tension at the bottom of the catenary is 300 kN, the buoyed pipe weight is
0.25 kN/m, and the coefﬁcient of friction is 0.15. Using Table 3.1, the following
geometrical equations result:
Here we had sinh(A) and cosh(A) in the below equations.
Ds ¼ 1182sinh A
ð Þ  208
(3.43)
Dz ¼ 1182cosh A
ð Þ  1200
(3.44)
Where:
A ¼
n
x
1182 þ 0:1754
o
B ¼ 1:0154
The well path is constructed by computing the vertical height for each value of x.
One starts at the bottom of the catenary section and works upward. The measured along-
hole length Ds is also computed. Fig. 3.20 shows that the catenary ends at 80 degrees but
has a starting inclination of 21.7 degrees. A short build-up section is required to go from
vertical to the starting value of the catenary. Assuming continuity in the angle and the
slope (derivative) at the matching point between the two proﬁles, this would require a
build-up radius of 8665 m. To make this transition shorter, a build-up radius of 1000 m
was chosen instead.
The build rate for the catenary is computed at 1.72 degrees/30 m, and the build rates
for the various sections are shown in Fig. 3.20B.
During drilling, the theoretical friction in the catenary section is zero, and the total
well friction is due to the short build-up section above the catenary and in the sail section
below. This should clearly represent a minimum; however, when pulling the drill string,
more friction develops. There are two obvious drawbacks to using a catenary proﬁle: (1)
the axial load must be controlled accurately to minimize friction, and (2) a constantly
changing build rate is required. Therefore, unless one is prepared for the increased
complexity and follow-up that is required to drill a catenary, it is not worth the effort.
For this reason, catenary proﬁles are not used very much.
3.4.3 Comparing trajectories for a long-reach well
In the following, friction analysis will be conducted on a long-reach well, using the
equations of Tables 3.5 and 3.6. A target is located at a depth of 2950 mTVD (true
vertical depth). The total well depth is 3100 mTVD with a horizontal reach of 7528 m.
The rig has a hoisting capacity of 4454 kN (1,000,000 lbs) and a top-drive torque of 35
kNm (25,800 ft-lbs). Assume that the hoisting capacity is sufﬁcient, but that the top drive
is a limiting factor. This exercise will determine which well proﬁle results in the lowest
friction. A comparison of the well trajectories is shown in Fig. 3.21. All well paths are
designed to build from vertical to a sail angle, which is kept in the reservoir.
Wellbore friction optimization
103
The maximum sail angle is taken from the friction coefﬁcient used in the previous
well, about 0.15 average for open and cased hole. To ensure that the drill string will slide
when orienting, the inclination should not exceed tan1(1/0.15) ¼ 81.47 degrees
(Eq. 3.2). The maximum sail angle, as a function of well friction, is shown in Fig. 3.22.
Figure 3.21 Well trajectories for the example long-reach well. Modiﬁed from Aadnøy, B.S., Andersen, K.,
2001. Design of oil wells using analytical friction models. J. Petrol. S. Eng. 32, 53e71. https://doi.org/10.
1016/S0920-4105(01)00147-4.
Figure 3.22 Critical sail angle versus friction coefﬁcient. Modiﬁed from Aadnøy, B.S., Andersen, K., 2001.
Design of oil wells using analytical friction models. J. Petrol. S. Eng. 32, 53e71. https://doi.org/10.1016/
S0920-4105(01)00147-4.
104
Methods for Petroleum Well Optimization
Table 3.7 shows the friction for the four well proﬁles. The modiﬁed catenary proﬁle
gave the lowest torque. The undersection proﬁle gave a little higher torque but was
better than the standard well proﬁle and the minimum DL proﬁle. Most of the torque is
generated in the long sail section.
The hook loads for the four well proﬁles are also shown in Table 3.7. The hook loads
are similar, but the standard proﬁle gives a higher pickup load than the other proﬁles.
The undersection proﬁle has the lowest torque. The maximum load is still less than half
of the capacity of the drill pipe. Therefore, tension is not the limiting factor. The
example shows that all four well proﬁles developed similar amounts of friction. The
choice of well proﬁle was not signiﬁcant. Of course, this conclusion is only valid for the
case considered. One advantage that the modiﬁed catenary proﬁle has, compared with
the minimum DL proﬁle, is the fact that the build-up friction is generated over a shorter
length (975 vs. 2758 m). Friction reduction subs may therefore be applied over a much
shorter length, resulting in less cost. A signiﬁcant part of the friction of the modiﬁed
catenary is due to the build-up before the catenary starts. If a slant rig can be used, this
friction could be reduced to a minimum.
3.4.4 Ultralong-reach well design
Today’s record long-reach wells have a horizontal reach of more than 10 km. It is fully
feasible, however, to extend this toward or even beyond 12 km by a well-planned design
and operational follow-up. The well proﬁle should be as simple as possible, consisting of
a vertical section, a build section, and a sail angle toward the target. Drop-off into the
reservoir should be avoided, if possible, to minimize friction.
The sail angle should, in general, be as high as possible, to reduce axial tension and
hence friction in the curved hole sections. Usually the reservoir depth is such that the
target is within reach only if a very high sail angle is used. The maximum sail angle is
given by the friction coefﬁcient. Therefore, another requirement is low friction. This
can be obtained by using oil muds or friction reducers.
The wells will often be designed with a constant sail angle into the reservoir. The
transition from the vertical to this sail angle may follow a modiﬁed catenary curve or a
minimum DL proﬁle, as these will provide minimum friction. To limit the load on the
Table 3.7 Drag and torque for the example long-reach well.
Well proﬁle
Pulling force (kN)
Lowering force (kN)
Torque (kNm)
Modiﬁed catenary
1360
593
28.6
Minimum dogleg
1332
609
30.9
Undersection
1321
568
29.5
Standard
1350
543
30.4
Aadnøy, B.S., Andersen, K., 2001. Design of oil wells using analytical friction models. J. Petrol. S. Eng. 32, 53e
71. https://doi.org/10.1016/S0920-4105(01)00147-4.
Wellbore friction optimization
105
drill pipe, a high buoyancy can be beneﬁcial. The disadvantage of a dense ﬂuid is the
compromise between buoyancy and friction. Usually, more particles in the mud will
increase the friction (Aadnøy, 1996).
In long wells, hydraulic friction may limit the ﬂow rate, thus leading to poor hole
cleaning. Increased pipe size will reduce this problem. Since increased pipe size leads to
increased pipe weight, drill pipes of alternative materials may be required. Today, drill
pipes are available in aluminum, titanium, and composites.
In the following, we extend the well in our example from a reach of 7528 m to
12 km. Fig. 3.23 shows the resulting well path. Maintaining the same sail angle, this well
will reach the target at a depth of 3767 mTVD (from RKB). The build-up section
chosen is the minimum DL proﬁle.
An analysis of the hydraulics and hole cleaning problems of such a long well
concluded that the drill string should have an outer diameter of at least 5.500. This analysis
therefore assumes a 65/800 drill pipe in the upper 2755 m and a 5.500 drill pipe down to the
BHA.
Due to the fact that the vertical depth of the well is not deep, the hook load is
reasonably low. Table 3.8 shows the results. Using steel drill pipe, the hook load is about a
third of the hoisting capacity on the rig, and less than the strength of the pipe. The hook
load is therefore not a limiting factor during drilling of the well.
Table 3.8 also shows the torque for the case of using 5.500 drill pipe throughout. The
total predicted torque is 53.9 kNm. Although a few drilling rigs can handle torque of this
magnitude, it is too high for many drilling rigs. Using an all-steel drill string, the well
would probably not reach the target.
Figure 3.23 Extending an example ultralong-reach well to a horizontal reach of 12 km. Modiﬁed from
Aadnøy, B.S., Andersen, K., 2001. Design of oil wells using analytical friction models. J. Petrol. S. Eng. 32,
53e71. https://doi.org/10.1016/S0920-4105(01)00147-4.
106
Methods for Petroleum Well Optimization
In this example, it was decided to try using a light drill pipe. Most of the torque is
generated from tension from the sail section, which is converted into torque in the build
section. The sail section was therefore ﬁrst modeled with a drill pipe made of titanium.
As seen in Table 3.8, the torque drops to 33.1 kNm. Since composite drill pipes (8.72 lb/
ft) now are available (Hareland et al., 1997), the sail section was modeled with this as well.
The cumulative torque was now down to 19.7 kNm. Bit torque must be added to all
these values.
From this study, it is concluded that it is fully possible to drill an extended reach well
to 12 km and even longer. Indeed, by using lighter drill pipes as shown, it should be
possible to drill several kilometers beyond that. The most important limitation is believed
to be the hydraulic system; the driller probably needs a higher-pressure mud pump.
3.4.5 2D well path optimization
Here, the example build sail well from Section 3.4.1 will be used to investigate the
optimum path of the well. Speciﬁcally, the depth to the kick-off point (KOP) that gives
the minimum well friction will be determined. Minimum drag and torque can be found
in Tables 3.5 and 3.6.
Minimum pull force:
dF5
da ¼ 0/tan a ¼
2ðwBHALBHA þ wDPLDPÞ þ wDPR
ð1  m2ÞðwBHALBHA þ wDPLDPÞ  mwDPR
(3.45)
Minimum lowering force:
dF5
da ¼ 0/tan a ¼
2mðwBHALBHA þ wDPLDPÞ  wDPR
mwDPR  ð1  m2ÞðwBHALBHA þ wDPLDPÞ
(3.46)
Minimum torque:
dT
da ¼ 0/tan a ¼ 2ðwBHALBHA þ wDPLDPÞ þ awDPR
aðwBHALBHA þ wDPLDPÞ  3wDPR
(3.47)
Table 3.8 Comparison of torque and drag in 12 km horizontal reach well using 5.500 drill pipes of
various materials in the sail section. Assume 65/800 steel pipe from surface to start of sail section.
Drill pipe
Hook load (kN)
Torque (kNm)
Pulling
Static
Lowering
Build-up
Sail section
Total
Steel
1790
1750
600
10.2
43.7
53.9
Titanium
1330
1300
600
6.2
26.9
33.1
Composite
1020
1010
600
3.7
16.0
19.7
Aadnøy, B.S., Andersen, K., 2001. Design of oil wells using analytical friction models. J. Petrol. S. Eng. 32, 53e
71. https://doi.org/10.1016/S0920-4105(01)00147-4.
Wellbore friction optimization
107
For example, determine optimal kick-off depth and well inclination.
Using Example 3.4.1, and inserting the data for that well into the aforementioned
equations, the following optimal inclinations are obtained:
Pulling (Eq. 3.45)
a ¼ 68 degrees
Lowering (Eq. 3.46)
a ¼ 0 degrees
Torque (Eq. 3.47)
a ¼ 90 degrees
Maximum sail angle amax ¼ tan11
m
a ¼ 81.5 degrees
From torque considerations, and also from pull considerations, a deep KOP is preferred;
however, to minimize friction during lowering, a shallow KOP is the best. This dem-
onstrates that an exact optimum does not exist, but an optimization criterion has to be
selected. The drill string is heavily loaded during rotation and during pulling; therefore,
these are selected to be optimized. Lowering is usually not critical and will be neglected;
however, for long horizontal wells, lowering can be critical. Different optimization
criteria may apply for different well types.
From the simple geometry studied here, the following observations can be drawn.
The build-up section contributes most to well friction, and minimum friction can be
obtained by placing the KOP deep. The assumption is that lowering friction is
considered unimportant. The sail angle should have an inclination between 68 and 81.5
degrees. The kick-off point should be between 1740 and 2173 m.
A kick-off depth of 2100 m, resulting in a sail angle of 76 degrees, as a compromise
between the pull force and minimum torque will be used. If the real coefﬁcient of
friction should increase slightly (for example, due to ﬁll in the hole), the drill string will
still slide.
Equations can be derived for more complex geometries, and a solution is obtained as
shown earlier. For general three-dimensional geometries, Rudolf et al. (1998) and
Suryanarayana and McCann (1998) have derived nonlinear optimization procedures.
3.5 Summary
•
Friction models for a number of different well geometries are presented.
Explicit equations are given to model both the rotary torque and the drag forces asso-
ciated with hoisting or lowering of the drill string. Equations to compute well friction
for a fully three-dimensional well path are also given.
•
3D analytical model for wellbore friction can be used in a real-time software module
to detect the onset of drilling problems.
•
Summary of major equations is given on the next page:
108
Methods for Petroleum Well Optimization
Main equations for wellbore friction optimization.
Drag and torque in straight sections
Drag forces in straight sections
F2 ¼ F1 þ wDs(cosa  msina)
Torque in straight sections
T ¼ mwDsrsina
Drag in a drop-off bend
Pulling
F2 ¼ F1emða2a1Þ þ wR
1þm2
( 
1  m2
ðsin a2  emða2a1Þ sin a1Þ
2mðcos a2  emða2a1Þ cos a1Þ
)
Lowering
F2 ¼ F1emða2a1Þ þ wRfsin a2 emða2a1Þ sin a1g
Drag in a build-up bend
Pulling
F2 ¼ F1emða2a1Þ  wRfsin a2 emða2a1Þ sin a1g
Lowering
F2 ¼ F1emða2a1Þ  wR
1þm2
( 
1  m2
ðsin a2  emða2a1Þ sin a1Þ
2mðcos a2  emða2a1Þ cos a1Þ
)
Weight of pipe
F2 ¼ F1  wrðsin a2 sin a1Þ
Drag in a side bend
F2 ¼ F1emða2a1Þ
Torque in a drop-off bend
T ¼ mrfF1 þwR sin a1gða2 a1Þ  2mrwRðcos a2 cos a1Þ
Torque in a build-up bend
T ¼ mrfðF1 þwR sin a1Þja2 a1jg þ 2mwRrðcos a2 cos a1Þ
Torque in a side bend
T ¼ mr
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
F2
1 þ ðwRÞ2
q
ðf2 f1Þ
The following summarize Djurhuus’s equations (Aadnøy, 2006)
Drag in a bend is given by
F2 ¼ F1emða2a1Þ þ wR
1þm2
( 
1  m2
ðsin a2  emða2a1Þ sin a1Þ
2ðmÞðcos a2  emða2a1Þ cos a1Þ
)
Torque in a bend is given by
T ¼






r,m
2
4  1
mF1ðemða2a1Þ 1Þ þ wR
1þm2
8
<
:
2ðcos a1  cos a2Þ 

1  m2

1
m

ðemða2a1Þ  1Þsin a1
þ2ðmÞðsin a1  sin a2Þ þ 2ðemða2a1Þ  1Þcos a1
9
=
;
3
5






Torque in static string
T ¼ jr $m½F1 $ða2 a1Þ þw $R $½2ðcos a1 cos a2Þ ða2 a1Þsin a1j
Wellbore friction optimization
109
3.6 Problems
Problem 1: Weight in air, weight in suspension, and hook load
calculation
Y
ou are in charge of drilling a 2000 m deep well. The mud density is 1.5 s.g., and the
density of the steel pipes is 7.85 s.g. At this depth, the drill string is composed of:
1800 m 500 drill pipe, 19.5 lbs/ft or 29 kg/m
200 m 800  300 drill collars, 218.8 kg/m
Perform the following (for more information about force equations see Aadnøy et al.,
2006):
(1) Show the weight in air and the weight in suspension on a plot.
(2) Calculate and plot the piston force and the deviatoric force. Convince yourself that
the deviatoric force is equal to the buoyant force using the Archimedes principle.
During a connection operation, the drill string is accidentally dropped into the well. The
drill bit is jammed so hard into the ﬁll at the bottom that the bottom surface is no longer
exposed to the mud pressure. In other words, there is no piston force at the bottom.
(3) What hook load must be applied to lift the string?
(4) To aid in lifting the string you consider applying hydraulic pressure. What would the
effect be on the pressure inside the drill string and/or in the annulus?
Problem 2: Compute the torque and tension along the string
We will investigate the torque and drag friction in a deviated well consisting of a vertical
well, a build-up section, and a constant sail angle into the reservoir. The data are as
follows (for more information about equations force see Aadnøy et al., 2006):
Vertical depth: 2000 m
Horizontal displacement: 2000 m
Sail angle: 60 degrees
Mud density: 1.7 s.g.
BHA: 200 m of 800  300 drill collars, 218.8 kg/m
Drill pipe: 5 00, 29 kg/m
Friction coefﬁcient: 0.2
(1) Assuming a build-up radius of 250 m, calculate the tension along the string.
(2) Calculate the tension if the build-up radius is 500 m.
(3) Compute the hook load during hoisting and lowering for the aforementioned two
cases.
(4) Compute the torque for the aforementioned two cases.
(5) Tabulate and discuss the results from this analysis. Which parameter dominates the
torque and the drag?
110
Methods for Petroleum Well Optimization
Problem 3: Compute the tension, hook load and torque
The well of problem 2 will be modiﬁed to include a drop-off section at the bottom of the
well. All other data are identical. The drop-off is from the 60 degrees sail angle to vertical
at the bottom of the well.
(1) Calculate the tension if the build-up and drop-off radii are 250 m.
(2) Calculate the tension if the build-up and the drop-off radii are 500 m.
(3) Compute the hook load during hoisting and lowering for the aforementioned two
cases.
(4) Compute the torque for the aforementioned two cases.
(5) Tabulate and discuss the results from this analysis. Which parameter dominates the
torque and the drag? Which section is most important, the build-up or the drop-off?
Nomenclature
BF buoyancy factor: 1  rmud
rpipe
BHA bottomhole assembly
D well depth (ft)
DL, q dogleg, change in wellbore direction (degrees)
DLS dogleg severity (degrees/100 ft)
F force along drill string (kN) or (lbs)
F1 force at the bottom of section
F2 force at the top of section
L length of pipe (ft)
N normal force (lbs)
P pressure (psi)
r pipe/connection radius
R radius of build-up or drop-off bend (m)
RKB drill ﬂoor reference
T torque (ft-lbs)
V velocity
w weight (lbs) or unit weight of drill pipe (lb/ft)
WOB weight-on-bit
Nr rotary pipe speed
RB radius of bend in vertical plane
Ra radius of bend in vertical plane
DF additional force applied to catenary
Ds measured length along hole section
Dx; Dy; Dz projected distances
j angle between axial and tangential pipe velocities
x; y coordinates in the horizontal plane
z vertical coordinate
a1 wellbore inclination at the bottom of bend
a2 wellbore inclination at the top of bend
m coefﬁcient of friction
Wellbore friction optimization
111
r mud or ﬂuid density (ppg)
4 azimuth of wellbore (rad)
References
Aadnøy, B.S., 1996. Modern Well Design, ﬁrst ed. Balkema, Rotterdam, Netherlands.
Aadnøy, B.S., Andersen, K., March 3e6, 1998. Friction Analysis for Long-Reach Wells. IADC/SPE
39391, IADC/SPE Drilling Conference, Dallas, TX.
Aadnøy, B.S., Andersen, K., 2001. Design of oil wells using analytical friction models. J. Petrol. S. Eng. 32,
53e71. https://doi.org/10.1016/S0920-4105(01)00147-4.
Aadnøy, B.S., Fabiri, V
., Djurhuus, J., February 21e23, 2006. Construction of Ultra-long Wells Using a
Catenary Well Proﬁle. IADC/SPE Drilling Conference in Miami, Florida, U.S.A.
Aadnøy, B.S., Fazaelizadeh, M., Hareland, A., October 2010. A 3D analytical model for wellbore friction.
J. Can. Petrol. Technol. 49 (10) https://doi.org/10.2118/141515-PA.
Hareland, G., Lyons, W
.C., Baldwin, D.D., Briggs, G., Bratli, R.K., March 4e6, 1997. Extended Reach
Composite
Materials
Drill
Pipe.
SPE/IADC37646,
1997
SPE/IADC
Drilling
Conference,
Amsterdam.
Johancsik, C.A., Friesen, D.B., Dawson, R., 1984. Torque and drag in directional wells e predictions and
measurements. J. Pet. Technol. 36, 987e1992.
McClendon, R.T., Anders, E.O., March 6e8, 1985. Directional Drilling Using the Catenary Method.
Paper SPE/IADC 13478 Presented at the 1985 SPE/IADC Drilling Conference, New Orleans, La.
Rudolf, R.L., Suryanarayana, P
.V
.R., McCann, R., Rupani, R.A., 1998. An algorithm and program to
plan optimal horizontal well paths. ETCE 98-4532, Energy Sources Tech. Conf., ASME.
Suryanarayana, P
.V
.R., McCann, R.C., Rudolf, R.L., Rupani, R.A., 1998. Mathematical technique
improves directional well-path planning. Oil Gas J. 96 (34), 57e63.
Further reading
Aadnøy, B.S., 2006. Mechanics of Drilling. Shaker Verlag GmbH, Germany.
Open-source code
Y
ou can develop your models using open-source code for different tools available
on https://github.com/
Examples include:
Torque and drag calculations (https://github.com/pro-well-plan)
112
Methods for Petroleum Well Optimization

CHAPTER 5
Application of intelligent models
in drilling engineering
Contents
5.1 Drilling fluids
229
5.2 Lost circulation problem
232
5.3 Stuck pipe
238
5.4 Flow patterns and frictional pressure loss of two-phase fluids
251
5.5 Rate of penetration
253
5.6 Other applications
270
References
273
In this chapter, the applications of artificial intelligence techniques in dril-
ling engineering are reviewed. Herein, various aspects of drilling engi-
neering have been considered (Fig. 5.1).
In drilling engineering, intelligent models have been applied to various
purposes, including frictional pressure loss (FPL), mud rheological proper-
ties, filtration loss, rate of penetration (ROP), and mud cake permeability
predictions. Among various intelligent models, artificial neural networks
(ANNs), fuzzy logic systems, genetic algorithms (GAs), support vector
machines (SVMs), particle swarm algorithm, hybrid intelligent systems,
and case-based reasoning have been utilized recently.
5.1 Drilling fluids
Drilling fluid (also known as drilling mud) is one of the most important
parts of a drilling operation. The fulfillment of a drilling operation is
directly proportional to the employed drilling mud. Drilling fluid is an
oil-based or water-based fluid that plays several significant roles such as
cleaning the bottom hole, transporting the cuttings to the surface, cooling
down the bit and drilling pipes, lubricating the well, controlling the for-
mation pressure to prevent from kick and blowout, and stabilizing the
229
Applications of Artificial Intelligence Techniques in the Petroleum Industry
DOI: https://doi.org/10.1016/B978-0-12-818680-0.00005-9
© 2020 Elsevier Inc.
All rights reserved.
well. Hence, knowing the mud properties and its related issues are impor-
tant tasks in order to drill a well successfully.
According to literature, ANNs have been used for the following
purposes:
1. mud density prediction
2. mud rheological properties prediction
3. filtration loss and mud cake permeability prediction
4. lost circulation prediction
5. hole cleaning and cuttings transport efficiency
6. mud flow pattern prediction in the wellbore annulus
7. FPL prediction
8. settling velocity of cuttings in mud prediction
Fuzzy logic systems have been applied to various aspects of drilling
fluid engineering, such as mud density prediction, stuck pipe prediction,
the consequences of kick, and lost circulation. Moreover, hybrid systems
have been utilized to deal with several practical problems, including lost
circulation, kicks, and effects of high-temperature and high-pressure
(HTHP) conditions on mud densities.
GAs were employed to determine the rheological properties and parti-
cle settling velocity in the mud. Also, SVMs were applied to predict the
Figure 5.1 Various aspects of drilling engineering considered in this chapter.
230
Applications of Artificial Intelligence Techniques in the Petroleum Industry
following issues: stuck pipe, viscosity and density, fluid flow pattern, FPL,
mud rheology, and lost circulation. The particle swarm algorithm has
been applied to improve the prediction of the lost circulation problem.
The case-based reasoning is another intelligent model used in mud engi-
neering for different purposes, such as wellbore leakage prediction, hole
cleaning, and lost circulation predictions.
In 2003, Osman and Aggour [1] employed an ANN model to attain
precise predictions of drilling fluid density as a function of pressure, tem-
perature, and mud type (oil-based, or water-based). The generated model
could predict the density with a root mean square error (RMSE) of
0.0056 and an average absolute percent error (AAPE) of 0.367.
In 2012, Rooki et al. [2] and Wang et al. [3] utilized intelligent mod-
els in order to predict the characteristics of drilling fluids. Rooki devel-
oped a different technique through GAs to determine the rheological
parameters for HerschelBulkley drilling fluids, while Wang introduced a
universal SVM approach to predict the density of synthetic, oil-based, and
water-based drilling fluid at HTHP conditions. This SVM model could
provide predictions with an R2 of 0.9994.
A year later, Razi et al. [4] developed a multilayer perceptron (MLP)
neural network model along with the LevenbergMarquart learning algo-
rithm to predict rheological properties of water-based drilling fluids. They
considered concentration, temperature, and shear rate as input variables and
apparent viscosity, yield point (YP), and plastic viscosity (PV) as the outputs
of the network. The developed model had three layers and 3:3:1 structure
and was able to predict YP, PV, and apparent viscosity with an R-square
error (R2) of 0.986, 0.965, and 0.993 for each one, respectively.
Then, the density of environmental-friendly muds canola, jatropha, and
diesel oil-based muds were predicted through a backpropagation neural net-
work model to address the unreliable experimental data and unavailable cor-
relation in 2015 by Fadairo et al. [5]. In this year, Shadravan et al. [6] used
ANNs and Gaussian process regression to design drilling fluids, and Ataallahi
and Shadizadeh [7] modeled blowouts in Iranian drilling operations through
a fuzzy logic approach.
In 2016, Tatar et al. [8] utilized the least-squares SVM (LSSVM) and
MLP neural network models to calculate brine density. They considered
concentration, temperature, and pressure as the input variables. The proposed
models could predict the brine density with an R2 of 1.000000 and 0.999999
for LSSVM and multilayer perceptron neural network (MLPNN) models,
respectively. Also, Elkatatny et al. [9] proposed a mathematical model using
231
Application of intelligent models in drilling engineering
ANNs and transformed the ANNs black box into a white box to acquire an
observable mathematical model. The rheological properties of drilling fluid
were predictable through this white box.
Chhantyal et al. [10] applied ANN and SVM techniques to predict
the viscosity of non-Newtonian fluids and pointed out that the SVM
model (with mean absolute percentage error (MAPE) 5 2.74%) had a bet-
ter performance compared to the ANN model (MAPE 5 23%). In this
year, Zhou et al. [11] developed a novel hybrid model based on the tradi-
tional backpropagation neural network (BPNN) and particle swarm opti-
mization (PSO) method that was capable of considering the influence of
drilling fluid components. In their model, the drilling fluid component
data (oil-phase and water-phase volume fractions) and mud density at
standard conditions (0 MPa, 20°C) were the input parameters.
In 2017, Elkatatny [12] converted the ANN black box model to a
white box one, similar to his previous work, in order to predict the rheo-
logical parameters of water-based drilling fluid with an average absolute
error of less than 6%. Fig. 5.2 shows the efficiency of the proposed model
in predicting the rheological parameters. In this year, Chhantyal et al. [13]
employed a radial basis function (RBF) neural network to predict the
mass flow using the levels from ultrasonic scanning array. In another
work, Chhantyal et al. [14] applied SVM, FL, and ANN algorithms to
estimate the fluid flow rate and considered the time series of the levels
from the array of ultrasonic level sensors as the input of the model. They
selected the feedforward neural network with the Bayesian regularization
learning algorithm as the best model for this prediction.
In 2018, Ahmadi et al. [15] utilized two hybrid models and the fuzzy
inference system (FIS) to predict the mud density at HTHP conditions.
One of the two hybrid models comprises PSO and ANN (called
PSOANN) and the other one was a coupling of GA and FIS (called
GAFIS). They concluded that the PSOANN model provides more
precise predictions and can be applied to predict the mud density at
HTHP conditions. Fig. 5.3 exhibits the accuracy of the predictions of the
PSOANN model. Table 5.1 provides a summary of the applications of
intelligent models in the area of drilling fluids [1620].
5.2 Lost circulation problem
Lost circulation is a major problem encountered in the drilling process
and is defined as the unwanted loss of the whole or a portion of drilling
232
Applications of Artificial Intelligence Techniques in the Petroleum Industry
mud into a formation. This occurrence can lead to some severe problems,
including pipe stuck, formation damage, blowout, and wellbore instabil-
ity, leading to an increase in time and cost of the operation. Therefore, it
is a common practice in the industry to try to have the least amount of
lost circulation. This issue is influenced by various parameters such as the
type and weight of drilling fluid and its rheological properties, pump
pressure, and formation pore pressure.
Figure 5.2 Predicted value versus actual value of rheological parameters. Adapted
from S. Elkatatny, Real-time prediction of rheological parameters of KCL water-based
drilling fluid using artificial neural networks, Arabian J. Sci. Eng. 42 (4) (2017)
16551665.
233
Application of intelligent models in drilling engineering
In 2005, Jeirani and Mohebbi [21] designed an approach based on
ANNs to predict the permeability of filter cake and filtrate volume. They
considered pressure drop, time, and water and NaCl weight percent as
the input variables, and the developed model was able to provide predic-
tions with an R-square error of 0.9815 for filtrate volume and 0.9433 for
cake permeability.
Moazzeni et al. [22] applied a feedforward backpropagation neural
network to predict lost circulation in different areas of Maroun oilfield.
Two years later, they employed ANNs with the aim of estimation of
mud loss severity in various parts of the same oilfield through using the
operational parameters [23]. In 2011, Alireza et al. [24] introduced opera-
tional parameters of the aforementioned oilfield to ANNs in order to esti-
mate lost circulation severity, stuck pipe severity, and stuck pipe position.
The estimations of the developed model exhibited a good match with the
field observations.
In 2013, Deregeh et al. [25] used the adaptive neuro FIS (ANFIS) algo-
rithm for an earlier kick detection through measurable drilling parameters.
They utilized subtractive clustering to generate the initial FIS, and the relative
mean absolute error (RMAE) of the developed model was 15.97%.
Figure 5.3 Crossplot of experimental data versus PSOANN model predictions.
PSOANN, particle swarm optimizationartificial neural network. Adapted from M.A.
Ahmadi, et al., An accurate model to predict drilling fluid density at wellbore conditions,
Egypt. J. Pet. 27 (1) (2018) 110.
234
Applications of Artificial Intelligence Techniques in the Petroleum Industry
Table 5.1 Summary of the applications of intelligent models in the area of drilling
fluids.
References
Intelligent model
(s)
Type of study
conducted
Input parameters
Error (R2)
Osman and
Aggour [1]
ANNBP
Mud density
of WBM
and OBM
Pressure,
temperature,
type of drilling
mud, and
initial mud
density at
surface
conditions
0.9998
Rooki et al.
[2]
GA
Prediction
of the
rheological
parameter
for
Herschel
Bulkley
drilling
fluids
Flow index, fluid
consistency,
and yield stress
0.9972
Wang et al.
[3]
SVMRBF
Mud density
prediction
at HTHP
conditions
Pressure,
temperature,
initial density,
and type of
drilling fluid
0.9994
Razi et al. [4]
ANNMLP
Modeling of
plastic
viscosity,
yield point,
and
apparent
viscosity
Shear rate,
concentration,
and
temperature
0.986
0.965
0.993
Fadairo
et al. [5]
ANNBP
Prediction of
downhole
OBM
density
Temperature
0.99675
(Canola
OBM),
0.99414
(Jatropha
OBM),
0.99852
(Diesel
OBM)
Shadravan
et al. [6]
ANNBP
Mud design
Temperature,
ingredient A
content, fluid
density

(Continued)
235
Application of intelligent models in drilling engineering
Table 5.1 (Continued)
References
Intelligent model
(s)
Type of study
conducted
Input parameters
Error (R2)
Ataallahi
and
Shadizadeh
[7]
Fuzzy
logic
Blowout
Flow rate,
duration,
and impact

Tatar et al. [8]
ANNMLP
Predicting
brine
density
Concentration,
pressure, and
temperature
0.99999
SVM
1.0
Elkatatny
et al. [9]
ANN
Estimating
the drilling
fluid
rheological
properties
Solid percent,
marsh funnel
viscosity, and
mud density
0.9205
0.954
0.917
Ejimofor
[16]
ANN
Predicting
the mass
flow rate of
drilling
fluids
Pressure drop
(PDT12),
density,
upstream level
(LT-15),
middle
level (LT-17),
downstream
level (LT-18)
0.993
Hoang [17]
ANNBP
Prediction
of mud
viscosity
Shear stress,
density
0.98
SVMRBF
2.7%
(MAPE)
Chhantyal
et al. [18]
Static
ANN
Predicting
the mud
flow rate
Ultrasonic level
measurements
(LT-15, LT-17,
and LT-18)
8.5%
(MAPE)
SVM
7.7%
(MAPE)
Dynamic ANN
5.6%
(MAPE)
Zhou et al.
[11]
PSOBPNN
(hybrid)
Prediction of
the density
of WBM
and OBM
at HTHP
Oil volume
fraction, water
volume
fraction,
pressure,
temperature,
and initial mud
density

Ahmadi et al.
[16]
LSSVM
Estimation of
mud
density
Temperature,
pressure, and
initial mud
density
0.9999
(Continued)
236
Applications of Artificial Intelligence Techniques in the Petroleum Industry
Afterward, Toreifi et al. [26] utilized two modular neural networks and
PSO in order to predict the quantity and quality of lost circulation in Maroun
oilfield to minimize the lost circulation. They reported that the accuracy of
prediction models in quality and quantity of the lost circulation was 0.98% and
0.94%, respectively. Jahanbakhshi et al. [27] also developed two models
through ANNs to estimate the lost circulation in naturally fractured reservoirs.
One of the developed models was designed to predict lost circulation through
nongeomechanical variables, while the other one was able to consider both
nongeomechanical and geomechanical variables as input parameters. They
investigated the effect of geomechanical parameters on lost circulation by
Table 5.1 (Continued)
References
Intelligent model
(s)
Type of study
conducted
Input parameters
Error (R2)
Chhantyal
et al. [18]
ANN
Viscosity
prediction
Shear stress and
density
23%
(MAPE)
SVM
2.74%
(MAPE)
da Silva Bispo
et al. [19]
ANNMLP
Predicting the
apparent
viscosity of
WBMs
Temperature,
concentrations
of xanthan
gum,
bentonite, and
barite
0.9486
Elkatatny [12]
ANN
Rheological
properties
of WBM
Solid percent,
marsh funnel
viscosity, and
mud density
.0.90
Chhantyal
et al. [13]
FFANNBR
Mud flow
rate
prediction
Ultrasonic level
measurements
(LT-1, LT-2
and LT-3)
0.94
0.91
FBANNRTRL
0.83
Fuzzy logic
SVMRBF
0.89
Chhantyal
et al. [14]
ANNRBF
Mud flow
rate
prediction
Ultrasonic level
measurements
(LT-1, LT-2
and LT-3)

Ahmadi et al.
[15]
Fuzzy logic
Estimation of
mud
density
Temperature,
pressure, and
initial mud
density
0.7237
PSOANN
(hybrid)
0.9964
0.9397
GAFIS (hybrid)
ANN, Artificial neural network; BP, Backpropagation; BPNN, backpropagation neural network; BR, Bayesian
regularization; FFANN, feed-forward artificial neural network; FIS, fuzzy inference system; GA, genetic algorithm;
HTHP, high-temperature and high-pressure; LSSVM, least square support vector machine; MAPE, mean absolute
percentage error; MLP, multilayer perceptron; OBM, oil-based mud; PSO, particle swarm optimization; RBF, radial
basis function; RTRL, real-time recurrent learning; SVM, support vector machine; WBM, water-based mud.
237
Application of intelligent models in drilling engineering
comparing the two developed ANN models and concluded that the model
with geomechanical parameters has lower error and higher correlation coeffi-
cient. A year later, in 2015, Jahanbakhshi and Keshavarzi [28] applied the SVM
algorithm to investigate the lost circulation quantitatively and qualitatively in
natural and induced fractured rocks. The developed model could provide esti-
mations with an R-square error of 0.9851.
In 2016, Behnoud far and Hosseini [29] aimed to determine the amount
of circulation loss during underbalanced drilling (UBD) of Asmari formation
in an Iranian oilfield. They utilized ANNs and GA to find the relations of
parameters corresponding to lost circulation, and to determine the optimum
flow rate, pump pressure, and mud weight, respectively. Their developed
model was able to make predictions with an R2 of 0.9991.
In 2019, Sabah et al. [30] developed smart models to predict the
amount of lost circulation in Maroun oilfield. The developed models
were based on decision trees (DT), ANFIS, ANNs, and a hybrid ANN,
namely, GAMLP, among which the hybrid model showed the highest
error and the DT model exhibited the best performance with an R2 of
0.9355 and RMSE of 0.091. Fig. 5.4 illustrates the error comparison
between the developed models.
Later in this year, Abbas et al. [31] applied ANNs and SVM algorithms
to generate reliable models in order to predict the lost circulation solution
in the case of both deviated and vertical wells. They used the datasets col-
lected from 385 wells drilled in Southern Iraq and considered 19 input
parameters such as mud weight, weight on bit (WOB), azimuth, loss rate,
circulating pressure, and ROP. The developed model based on SVM
showed better performance compared to another model with R2 values of
0.97 and 0.95 for training and testing datasets, respectively.
Later, Abbas et al. [32] utilized ANNs and SVM algorithms to predict
the occurrence of lost circulation using the datasets used in their previous
work. Again, they concluded that the predictions of the SVM-based
model are more reliable and more precise. Table 5.2 summarizes the
applications of intelligent models in predicting lost circulation problems
[3335].
5.3 Stuck pipe
The stuck pipe has always been a worldwide concern in the drilling
industry since it can cause extra costs and time to the drilling operations.
Stuck pipe refers to a situation in which the movement or/and rotation of
238
Applications of Artificial Intelligence Techniques in the Petroleum Industry
the drill string is suddenly frozen or restricted. The severity of this phe-
nomenon varies from minor inconvenience to major complications. A
severe stuck pipe case leads to the loss of the drill string or complete loss
of the well [36]. This occurrence is influenced by diverse parameters such
as differential pressure, accumulation of cuttings, improper mud design,
inappropriate drilling properties, and poor hole cleaning [37].
Figure 5.4 The (A) RMSE and (B) R2 comparison between the models developed by
Sabah et al. [30] RMSE, root mean square error. Adapted from M. Sabah, et al. [30],
Application of decision tree, artificial neural networks, and adaptive neuro-fuzzy infer-
ence system on predicting lost circulation: a case study from Marun oil field, J Pet. Sci.
Eng. 177 (2019) 236249.
239
Application of intelligent models in drilling engineering
Table 5.2 The application of intelligent models in predicting lost circulation
problems.
References
Intelligent
model(s)
Type of study
conducted
Input parameters
Error (R2)
Jeirani and
Mohebbi
[21]
ANNBP
Filtration
volume and
mud cake
permeability
of WBM
Water and NaCl
weight
percent,
pressure drop,
time
0.9815
(filter
volume)
0.9433
(cake
permeability)
Sheremotov
et al. [35]
Fuzzy
logic
Lost circulation
problem


Ghaffari et al.
[33]
SONFIS
Analysis of
mud loss
patterns
RPM loss (bbl):
the rate of
mud loss and
pump
pressure, yield
point, plastic
viscosity, solid
content,
funnel
viscosity, mud
weight

Moazzeni
et al.
[22,23]
ANNBP
Predicting the
amount of
lost
circulation
Amount of lost
circulation in
2 days before
the considered
day, amount
of lost
circulation in
the day before
the considered
day, drilling
mud fluid loss
gained from
API filter
press device,
θ300 and θ600
achieved from
rotational
viscometer,
solid percent
of drilling
fluid, mud
weight,
average pump
pressure,
0.82
(Continued)
240
Applications of Artificial Intelligence Techniques in the Petroleum Industry
Table 5.2 (Continued)
References
Intelligent
model(s)
Type of study
conducted
Input parameters
Error (R2)
average
output of
pump, bit
size, easting
and northing
of considered
well, Asmari
formation top
from ground
surface, length
of open hole
at the end of
the considered
day, drilling
time in the
considered
day, drilled
depth in the
considered
day, depth of
the well from
sea level and
ground
surface in the
considered
day
Alireza et al.
[24]
ANNBP
Predicting the
amount of
lost
circulation
Amount of lost
circulation in
2 days before
the considered
day, amount
of lost
circulation in
the day before
the considered
day, drilling
mud fluid loss
gained from
API filter
press device,
0.76543
(Continued)
241
Application of intelligent models in drilling engineering
Table 5.2 (Continued)
References
Intelligent
model(s)
Type of study
conducted
Input parameters
Error (R2)
θ300 and θ600
achieved from
rotational
viscometer,
solid percent
of drilling
fluid, mud
weight,
average pump
pressure,
average
output of
pump, bit
size, easting
and northing
of considered
well, Asmari
formation top
from ground
surface, length
of open hole
at the end of
the considered
day, drilling
time in the
considered
day, drilled
depth in the
considered
day, depth of
the well from
sea level and
ground
surface in the
considered
day
Lu et al. [34]
Fuzzy logic
Drilling fluid
leak zone
Mud flow rate,
mud viscosity,
mud density,
well depth,
formation
pressure

(Continued)
242
Applications of Artificial Intelligence Techniques in the Petroleum Industry
Table 5.2 (Continued)
References
Intelligent
model(s)
Type of study
conducted
Input parameters
Error (R2)
Deregeh
et al. [25]
ANFIS
Kick detection
Depth
15.97%
(RMAE)
Toreifi et al.
[26]
Modular
neural
network
Lost circulation
prediction
YP, PV, 10 s/
10 min gel,
solid content,
viscosity, mud
pump
pressure, filter
cake, the
current depth,
depth of
formation tip,
the flow rate
of mud pump,
penetration
rate,
formation
type, annulus
volume, mud
pressure,
geographic
coordinates
(east and
north)
0.94
Jahanbakhshi
et al. [27]
ANNMLP
Lost circulation
prediction
Young modulus,
natural
fracture
orientation,
tensile
strength,
uniaxial
compressive
strength,
minimum
horizontal
stress, API
fluid loss, mud
filtrate
viscosity, solid
percent, 10 s/
10 min gel
strength, PV,
YP, the
0.94
(Continued)
243
Application of intelligent models in drilling engineering
Table 5.2 (Continued)
References
Intelligent
model(s)
Type of study
conducted
Input parameters
Error (R2)
temperature
in the lost
interval,
average pump
pressure,
ROP, ECD,
porosity,
formation
permeability,
differential
pressure, hole
depth
Jahanbakhshi
and
Keshavarzi
[28]
ANNMLP
Lost
circulation
prediction
Young modulus,
natural
fracture
orientation,
tensile
strength,
uniaxial
compressive
strength,
minimum
horizontal
stress, API
fluid loss, mud
filtrate
viscosity,
Solid percent,
10 s/10 min
gel strength,
PV, YP, the
temperature
in the lost
interval,
average pump
pressure,
ROP, ECD,
porosity,
formation
permeability,
differential
pressure, hole
depth
0.8770
0.9851
SVM
(Gaussian
kernel
and
polyno-
mial
kernel
functions)
(Continued)
244
Applications of Artificial Intelligence Techniques in the Petroleum Industry
Table 5.2 (Continued)
References
Intelligent
model(s)
Type of study
conducted
Input parameters
Error (R2)
Behnoud far
and
Hosseini
[29]
ANNBP
Estimation of
lost
circulation
volume
Mud weight,
mud flow
rate, depth,
pump pressure
0.9991
Sabah et al.
[30]
DT
Quantitative
prediction
of lost
circulation
Depth, northing,
easting, hole
size, WOB,
pump rate,
pump
pressure,
viscosity, θ300,
θ600, gel
strength,
drilling time,
drilling
meterage,
solid percent,
bit rotational
speed,
formation
type, pore
pressure, mud
pressure,
fracture
pressure
0.9355
ANNMLP
0.9073
ANFIS
0.8855
ANNRBF
0.8445
GAMLP
0.8267
Abbas et al.
[31]
ANNs
Predicting the
lost
circulation
solution
Losses rate,
lithology,
mud weight,
flow rate,
ROP,
circulating
pressure,
inclination,
solid content,
fluid loss,
RPM, WOB,
YP, PV,
viscosity, gel
10v, gel 100,
azimuth,
measured
depth, hole
size
0.88 (training)
SVM
0.84 (testing)
0.97 (training)
0.95 (testing)
(Continued)
245
Application of intelligent models in drilling engineering
According to literature, differential sticking and mechanical sticking
are two main categories of the stuck pipe problems. Differential pipe
sticking (DPS) problems refer to the stuck pipe problems caused by differ-
ential pressure between the hydrostatic column induced by the drilling
mud and formation pressure. When an overbalanced drilling mud exerts
considerable pressure against a permeable zone (usually a depleted reser-
voir), the drilling filtrate penetrates the formation, and mud cake will be
created between the bottom hole assembly (BHA) and the permeable
zone. The increase in the thickness of the mud cake results in embedding
the drill string in the mud cake that leads to sticking the drill pipe into
the wellbore. Hence, as the pore pressure reduces in mature oilfields, the
risk of differential sticking rises. Nevertheless, the use of low-weight muds
can lead to wellbore instability and other problems such as mechanical
sticking.
Mechanical sticking refers to the other stuck pipe situations that are
caused by any other factors rather than differential pressure. Wellbore
instability and inadequate hole cleaning are the main reasons for
mechanical sticking problems [38]. Wellbore instability is usually caused
Table 5.2 (Continued)
References
Intelligent
model(s)
Type of study
conducted
Input parameters
Error (R2)
Abbas et al.
[32]
ANN
Predicting the
lost
circulation
occurrence
Lithology, mud
weight, flow
rate, ROP,
circulating
pressure,
inclination,
solid content,
fluid loss,
RPM, WOB,
YP, PV,
viscosity, gel
10v, gel 100,
azimuth,
measured
depth, hole
size
0.87 (training)
SVM
0.83 (testing)
0.92 (training)
0.91 (testing)
ANFIS, Adaptive neuro fuzzy inference system; ANN, artificial neural network; API, american petroleum institute;
BP, backpropagation; DT, decision tree; ECD, equivalent circulating density; MLP, multilayer perceptron; PV,
plastic viscosity; RMSE, root mean square error; ROP, rate of penetration; SONFIS, self-organizing neuro-fuzzy
inference system; SVM, support vector machine; WBM, water-based mud; WOB, weight on bit; YP, yield point.
RPM, Revolutions per minute
246
Applications of Artificial Intelligence Techniques in the Petroleum Industry
by shale layers owing to their tendency to swell when a compressive
failure happens due to low wellbore pressure [39]. One of the crucial
parts of a successful drilling operation is a sufficient hole cleaning.
Improper removing of the cuttings causes the cuttings to deposit inside
the wellbore and pipe sticking. Hole cleaning issue becomes a more cru-
cial issue in deviated wells, especially when the inclination angle is
between 30 and 60 degrees [36].
Early identification of a stuck pipe problem and its causing factor is a
crucial task since any improper reaction to the problem could easily make
it worse. In this way, Siruvuri et al. [40], in 2006, employed ANNs to
investigate the causes of differential pipe sticking. They used a three-layer
feedforward network and backpropagation learning algorithm. The pre-
dictions of the proposed model in pipe sticking and also the process and
forces needed to free the pipe were well matched with real incidents from
the Gulf of Mexico. The subsequent attempt to predict differential pipe
sticking was made by Miri et al. [41] in 2007. They also utilized two
three-layer feedforward ANNs, MLP and RBF, with the backpropagation
(BP) training algorithm to predict DPS problems. Nine drilling and mud
characteristics were used as inputs of the network, such as PV, gel
strength, mud viscosity, and differential parameters. They claimed that the
proposed models could estimate the probability of DPS in offshore oil-
fields of Iran.
Two years later, in 2009, Murillo et al. [42] were the first scholars to
use fuzzy logic, in addition to ANNs, in order to determine the risk of
pipe sticking. They considered 185 datasets (58 nonstuck, 68 mechanical
sticking, and 59 differential sticking) with 20 input parameters such as
measured depth, mud weight, YP, torque, and WOB. They declared that
the proposed models could be utilized both during well planning and dur-
ing drilling operations.
The next application of ANNs in stuck pipe prediction was reported
in 2010 by Shadizadeh et al. [38]. They used 275 datasets (160 nonstuck
and 115 stuck cases) collected from an Iranian oilfield and considered
mud properties, hole geometry, BHA size, drill pipe size, formation pres-
sure, depth, hydraulics, inclination angle, and mud loss volume as the
inputs of the network. The authors claimed that the developed model is
able to estimate the risk of either differential or mechanical sticking before
any drilling operation.
After 2 years, the first application of SVMs in predicting stuck
piperelated problems was proposed by Jahanbakhshi et al. [43] and
247
Application of intelligent models in drilling engineering
Albaiyat and Heinze [44]. Jahanbakhshi et al. developed an SVM-based
model to overcome most of the restrictions and shortcomings of ANNs in
predicting DPS problems. The authors stated that the developed SVM
approach was able to predict the occurrence of differential pipe sticking in
horizontal and sidetracked wells in Iranian offshore oilfields more accu-
rately compared to ANNs. Albaiyat and Heinze also implemented ANNs
and SVMs to predict stuck pipe occurrences by utilizing mud properties,
drilling parameters, and directional characteristics. The proposed models
were able to provide predictions with an accuracy of over 83%.
In 2013, Chamkalani et al. [37] generated a robust LSSVM model that
could predict differential and mechanical pipe sticking using drilling para-
meters with superior performance. They considered diverse drilling para-
meters, including PH, YP, PV, mud weight, measured depth, gel strength,
and solid percent from various wells as the input variables. They used coupled
simulated annealing optimization technique to tune the LSSVM parameters.
The developed model was able not only to predict the pipe sticking, but also
to classify the sticking type (differential or mechanical).
Later in this year, Naraghi et al. [45] were the first scholars who
attempted to develop a fuzzy logicbased model, namely, the active
learning method. They considered various parameters such as initial gel
strength, YP, PV, WOB, and RPM as the inputs of the model, while
probability was the model output. They claimed that the proposed model
could make predictions for pipe sticking with 100% accuracy.
In 2014, Rostami and Manshad [46] employed SVM and ANN algo-
rithms and developed two models. They acquired 185 datasets from eight
different wells in Maroun oilfield of Iran and considered minimum WOB,
nozzle size, initial gel strength, salt concentration, and temperature filtrate loss
as input parameters. A comparison between the two proposed models
revealed that both of them were able to predict the stuck pipe accurately.
Two years later, in 2016, Jahanbakhshi and Keshavarzi [47] investi-
gated the application of various ANN methods for predicting the differen-
tial pipe sticking. They proposed an artificial probabilistic neural network
(PNN) and compared this network with conventional ANNs (MLP and
RBF). Twelve input parameters were considered, such as depth, differen-
tial pressure, filtrate viscosity, and hole size. They reported the accuracy %
of the PNN, MLP, and RBF neural networks as 90.63, 87.50, and 78.13,
respectively, and introduced the PNN as the most reliable predictive tool
in DPS prediction. Table 5.3 represents a summary of the applications of
intelligent models in stuck pipe prediction.
248
Applications of Artificial Intelligence Techniques in the Petroleum Industry
Table 5.3 A summary of the applications of intelligent models in stuck pipe
prediction.
References
Intelligent
model(s)
Type of study
conducted
Input parameters
Error (R2)
Siruvuri et al.
[40]
ANN
Predicting the
differential
pipe
sticking
OBM sticking model:
hole depth,
oilwater ratio,
chlorides, lime,
HPHT fluid loss,
emulsion ability,
gel strength, YP,
PV
Stuck:
0.01369
(MSE)
WBM sticking
model: hole depth,
differential
pressure, PH,
inhibitor
concentration, gel
strength, YP, PV,
total hardness,
chlorides, MBT,
and API fluid loss
Nonstuck:
0.01847
(MSE)
Miri et al.
[41]
ANNMLP
Predicting the
differential
pipe
sticking
Hole depth,
differential
pressure, initial and
10 min gel
strength, YP, PV,
mud filtrate
viscosity, solid
percent, and API
fluid loss
0.00547
(MSE)
ANNRBF
0.00419
(MSE)
Murillo et al.
[42]
ANN
Predicting the
mechanism
of pipe
sticking
Gel strength, mud
weight, YP, PV,
chloride filtrate,
torque and drag on
the string, ROP,
drill collar length,
bit size, bit
rotational speed,
WOB, flow rate,
and depth (vertical
and measured)

Fuzzy logic
Shadizadeh
et al. [38]
ANNBP
Stuck pipe
prediction
Gel strength, PV, YP,
PH, geometric
factor, and
differential pressure

(Continued)
249
Application of intelligent models in drilling engineering
Table 5.3 (Continued)
References
Intelligent
model(s)
Type of study
conducted
Input parameters
Error (R2)
Jahanbakhshi
et al. [43]
ANNBP
Stuck pipe
prediction
before its
occurrence
YP, PV, gel strength,
hole size, BHA
length, mud
filtrate viscosity,
solid content, fluid
loss, still-pipe time,
hole depth, and
differential pressure
82.8%
(accuracy)
SVM-
Gaussian
kernel
function
Albaiyat and
Heinze
[44]
ANNBP
Stuck pipe
prediction
before its
occurrence
Drilling parameters,
mud properties,
and well direction
characteristics

SVM-Linear
kernel
and RBF
Chamkalani
et al. [37]
SVMRBF
Stuck pipe
prediction
PH, flow rate, YP,
PV, gel strength,
drill collar length,
drill collar (OD),
formation pressure,
open hole, oil/
water ratio, water/
oil ratio, solid
percent, calcium
concentration,
angle of well,
measured depth,
RPM, ROP, cross
section of annulus,
formation loss,
differential
pressure, fluid loss,
and TVD
0.94
Zhu et al.
[48]
ANNBP
Model of
pipe
sticking
prewarning
Water loss, the
thickness of mud
cake, sand content,
funnel viscosity,
mud density, pump
pressure,
displacement,
revolution, WOB,
and sticking point
depth

Naraghi
et al. [45]
Fuzzy
logic
active
Stuck pipe
prediction
Mud type, lubricant,
solids level, fluid
loss, mud cake
properties,
100%
(accuracy)
(Continued)
250
Applications of Artificial Intelligence Techniques in the Petroleum Industry
5.4 Flow patterns and frictional pressure loss of two-phase
fluids
Pressure loss is an inevitable occurrence in every piping system that hap-
pens due to friction, elevation changes, and turbulence caused by sudden
changes in direction. Pressure loss calculation has always been an impor-
tant practice in well control or drilling operations. In addition, inaccurate
estimation of pressure losses can cause a variety of drilling difficulties,
including inappropriate selection of required power supply and mud
pump system, kicks, lost circulation, and stuck pipe [49].
On the other hand, the pressure loss is dependent on the fluid flow
pattern that is influenced by various elements such as pressure, temper-
ature, flow rate, and fluid rheology. Based on the fact that distinguish-
ing the flow pattern is essential to determine the pressure gradient, an
accurate prediction of the flow pattern reduces costs, and maximizes
efficiencies [50]. An accurate prediction of flow patterns (FP) leads to a
Table 5.3 (Continued)
References
Intelligent
model(s)
Type of study
conducted
Input parameters
Error (R2)
learning
method
measured depth,
YP, PV, gel
strength, WOB,
and RPM
Rostami and
Manshad
[46]
ANNRBF
Prediction of
Stuck Pipe
Nozzle size,
minimum WOB,
temperature filtrate
loss, initial gel
strength, and salt
concentration
0.1139
(RMSE)
SVMRBF
0.0676
(RMSE)
Jahanbakhshi
and
Keshavarzi
[47]
PNN
Prediction
and
sensitivity
analysis of
DPS
Differential pressure,
hole depth, mud
filtrate viscosity,
fluid loss, solid
content, PV, YP,
gel strength, BHA
length, hole size,
and still pipe time
(SPT)
90.63%
(accuracy)
MLP
84.38%
(accuracy)
RBF
78.13%
(accuracy)
ANN, Artificial neural network; API, american petroleum institute; BHA, bottom hole assembly; BP,
backpropagation; DPS, differential sticking problems; HPHT, high pressure high temperature; MLP, multilayer
perceptron; MSE, mechanical specific energy; OBM, oil-based mud; OD, outer diameter; Ph, power of hydrogen;
PNN, probabilistic neural network; PV, plastic viscosity; RBF, radial basis function; RMSE, root mean square error;
ROP, rate of penetration; SVM, support vector machine; TVD, true vertical depth; WOB, weight on bit; YP, yield
point.
251
Application of intelligent models in drilling engineering
reduction in formation damage, an increase in the ROP, and
production improvement [49].
In this way, several attempts have been made using intelligent models
since 2009, when Ozbayoglu and Ozbayoglu [51] investigated the appli-
cation of ANNs in predicting FP and FPLs of two-phase fluids. In their
study, 18 different types of ANN models were considered and compared
with each other. They considered liquid and gas superficial velocity as
input parameters, and two outputs of the models were FPL and type of
flow pattern. The accuracy of the ANNs to predict FPL and type of low
patterns was 6 30% and 6 5%, respectively.
Later in 2011 Oladunni and Trafalis [52] employed ANNs and SVM
algorithms to predict the flow pattern of a single-phase, non-Newtonian
fluid in a pipe/annulus. They utilized a dataset from several fields in
Oklahoma with 92 data points. Flow rate, density, PV, drill collar outer
diameter (OD), mud type, and pipe diameter were considered as input
variables. The authors stated that the ANN and SVM model could make
predictions with an R2 of 0.9028 and 0.9528, respectively.
Three years later, Shahdi and Arabloo [53] developed an LSSVM
model to estimate the FPL in the presence of drilling cuttings. They con-
sidered hole inclination, pipe rotation, ROP, and flow rate of each phase
as input variables. The estimated results showed a very high correlation
coefficient of more than 0.99.
In 2015, Sorgun et al. [54] utilized an SVM model to predict the pressure
loss of Newtonian and non-Newtonian fluids. The proposed model could pro-
vide predictions with an AAPE less than 5.09% and 5.98% for Newtonian and
non-Newtonian fluids, respectively. In this year, Rooki [49] employed an
intelligent model to estimate the pressure loss of HerschelBulkley drilling
mud. They used an MLP neural network with six input parameters and
10 hidden layers. The inputs of the model were liquid flow rate, yield stress,
consistency index, flow behavior index, the eccentricity of the annulus, and
diameter ratio. The proposed neural network could estimate the pressure loss
with an average absolute percent relative error (AAPRE) of 4.32%.
Then, Rooki [55] conducted another study in 2016 toward the appli-
cation of a General Regression Neural Network (GRNN) in estimating
the pressure loss of HerschelBulkley drilling fluids. The considered
inputs were the same as those of their previous work, and the correlation
coefficient of the GRNN model was reported as 0.99. Fig. 5.5 shows the
capability of the proposed GRNN model as a crossplot of experimental
versus predicted data.
252
Applications of Artificial Intelligence Techniques in the Petroleum Industry
Barati-Harooni et al. [56] investigated the capability of RBF neural
networks in estimating the FPL of two-phase drilling fluid in deviated
wells. In their study, drilling cuttings were considered as the third phase.
The authors used four input parameters that were considered as input
variables in the study of Shahdi and Arabloo [53]. The developed model
could estimate the FPL with an RMSE of 0.008783. The accuracy of the
proposed model in comparison to the LSSVM model developed by
Shahdi and Arabloo [53] is shown in Fig. 5.6.
Table 5.4 represents a summary of the applications of intelligent
models in the area of predicting the FP and pressure losses.
5.5 Rate of penetration
Drilling rate or ROP is a measurement of a drilling operation efficiency.
Drilling time and costs are highly affected by ROP. In order to improve
Figure 5.5 Accuracy of GRNN model proposed by Rooki in pressure loss estimation.
GRNN, General Regression Neural Network. Adapted from R. Rooki, Application of gen-
eral regression neural network (GRNN) for indirect measuring pressure loss of
HerschelBulkley drilling fluids in oil drilling, Measurement 85 (2016) 184191.
253
Application of intelligent models in drilling engineering
drilling efficiency, ROP should be optimized. ROP depends on many
factors such as WOB, formation type, bit type, and RPM. In recent dec-
ades, so many attempts have been made to predict and optimize the ROP
using intelligent models. Hence, we focus on journal papers in this sec-
tion; however, Table 5.5 summarizes all studies at the end of this section.
In 2008, Akin and Karpuz [60] employed ANNs to predict several
drilling parameters related to diamond bit drilling, such as ROP, WOB,
and RPM. They used a feedforward four-layer network to estimate the
Figure 5.6 The accuracy of the model proposed by Barati-Harooni et al. [56] in pre-
dicting the FPL. Adapted from A. Barati-Harooni, et al., Prediction of frictional pressure
loss for multiphase flow in inclined annuli during underbalanced drilling operations,
Nat. Gas Ind. B 3 (4) (2016) 275282.
254
Applications of Artificial Intelligence Techniques in the Petroleum Industry
Table 5.4 Summary of the applications of intelligent models in the flow patterns (FP) and pressure losses prediction.
References
Intelligent model(s)
Type of study conducted
Input parameters
Error (R2)
Ozbayoglu
And
Ozbayoglu
[51]
ANNJordan/
Elman
Prediction of FP and FPL of
two-phase flow
Liquid and gas superficial velocity
0.005 (RMSE for
FP)
0.005 (RMSE for
FPL)
Oladunni and
Trafalis
[52]
ANNBP
FP prediction in the wellbore
annulus
Mud type, drill collar OD, pipe diameter, PV, fluid
density, flow rate
0.9028
SVMpolynomial
and Gaussian
RBF
0.9528
Shahdi and
Arabloo
[53]
LSSVMRBF
Estimation of FPL
In situ flow rate, hole inclination, pipe rotation
speed, ROP
.0.99
Sorgun et al.
[54]
SVM
Estimation the pressure loss of
Newtonian and non-
Newtonian fluids
RPM, inner and outer pipe radii, and fluid velocity
0.0011 (RMSE
for Newtonian
fluids)
0.07 (RMSE for
non-
Newtonian
fluids)
Rooki [49]
ANNMLP
Estimating the pressure loss of
HerschelBulkley fluids
Liquid flow rate, yield stress, consistency index, flow
behavior index, the eccentricity of the annulus,
and diameter ratio
0.999
(Continued)
Table 5.4 (Continued)
References
Intelligent model(s)
Type of study conducted
Input parameters
Error (R2)
Rooki [55]
ANNGRNN
Estimating the pressure loss of
HerschelBulkley fluids
Liquid flow rate, yield stress, consistency index, flow
behavior index, the eccentricity of the annulus,
and diameter ratio
0.98
Barati-
Harooni
et al. [56]
ANNRBF
Estimation of FPL
In situ flow rate, hole inclination, pipe rotation
speed, ROP
0.9965
ANN, Artificial neural networks; BP, back propagation; FP, flow patterns; FPL, frictional pressure loss; GRNN, General Regression Neural Network; LSSVM, least square support vector
machine; MLP, multilayer perceptron; OD, outer diameter;PV, plastic viscosity; RBF, radial basis function; RPM, revolutions per minute; RMSE, root mean square error; ROP, rate of
penetration; SVM, support vector machine.
Table 5.5 Summary of the applications of intelligent models in rate of penetration (ROP) prediction.
References
Intelligent
model(s)
Type of study
conducted
Input parameters
Error (R2)
Bilgesu et al. [57]
ANN
ROP prediction
RPM, WOB, footage, gross hours drilled, mud circulation rate,
formation type, and bit size/type
0.980
Fonseca et al.
[58], Mendes
et al. [59]
ANNLM
ROP prediction
TVD, RPM, and WOB
0.8880.988
Akin and Karpuz
[60]
ANNBP
Estimating
drilling
parameters
RQD, DFI, depth, and formation type

Moran et al. [61]
ANNBP
Predicting the
ROP and bit
wear
Rock strength, rock type, abrasion, WOB, RPM, and mud weight

Moradi et al. [62]
Fuzzy logic
(hybrid)
Drilling rate
prediction
Jet impact force, fractional bit tooth wear, equivalent mud density,
pore pressure gradient, rotary speed, bit diameter, WOB, and
TVD
15.68 (MSE)
Rahimzadeh
et al. [63]
ANN
ROP prediction
Hydraulic jet impact force, rock strength, pore pressure, bit tooth
wear, mud weight, RPM, WOB, and TVD
0.825
Bataee and
Mohseni [64]
ANNLM
Predicting the
proper ROP
Mud weight, RPM, WOB, depth, and bit diameter

Arabjamaloei and
Shadizadeh
[65]
ANN
ROP prediction
Depth, WOB/[1000 3 nozzle size (dn)], RPM, (ρ 3 q)/
(350 3 θ 3 dn), TBH/average efficiency of bit, ECD, hydrostatic
head
0.7401
Gidh et al.
[66,67]
ANN
ROP prediction
RPM, WOB, flow rate

(Continued)
Table 5.5 (Continued)
References
Intelligent
model(s)
Type of study
conducted
Input parameters
Error (R2)
Amar and
Ibrahim [68]
ANN ELM
ROP prediction
ECD, pore pressure gradient, Reynolds number function, tooth
wear, rotary speed, bit weight, and depth
17.13%
(APRE)
ANNRBF
9.6%
(APRE)
AlArfaj et al. [69]
ANNELM
ROP prediction
ECD, pore pressure gradient, Reynolds number function, tooth
wear, rotary speed, bit weight, and depth
26.74%
(APRE)
ANNRBF
36.11%
(APRE)
Arabjamaloei and
Dehkordi [70]
ANN (BP)
ROP prediction
Average compressive strength of formation, standpipe pressure, mud
flow rate, RPM, bit type and size
0.8912
ANFIS
0.7978
Monazami et al.
[71]
ANNLM
ROP estimation
Drill collar outside diameter, drill collar length, kickoff point,
azimuth, inclination angle, WOB, flow rate, bit rotation speed,
mud weight, solid percent, PV, YP, and measured depth
0.98913
Jahanbakhshi
et al. [72]
ANNBP
ROP prediction
Bit hydraulic power, bit wear, bit type, mud PH, solid percent, gel
strength, YP, PV, mud type, porosity, permeability, formation
drillability, hole size, ECD, pump pressure, hole depth, the density
of the overlying rock, differential pressure, RPM, and WOB
0.91555
Ning et al. [73]
ANN
ROP prediction
UCS, WOB, RPM, mud density, apparent viscosity, bit type/size,
gross hours drilled, and drillability coefficient
,10%
(ARE)
Bataee et al. [74]
ANNLM
ROP prediction
and
optimization
Mud weight, RPM, WOB, depth, bit diameter
0.857403
Zare and
Shadizadeh
[75]
ANNBP
ROP estimation
Average compressive strength of the formation, ECD, mud flow rate,
RPM, WOB, bit working hours, drilled interval, depth, total flow
rate, bit type, and size of the bit
,20%
(error)
Basarir et al. [76]
ANFIS
ROP prediction
UCS, RQD, bit load, and bit rotation
0.897
Duan et al.
[77,78]
ANNBP
ROP prediction
Delivery capacity, RPM, WOB, mud density, formation drillability,
and pressure difference
,15%
(error)
Garavand and
Esmaeilian
[79]
Committee
machine
Prediction of the
penetration
rate
RPM, depth, WOB, torque, pump pressure, and the amount of
injected mud
88%
(accuracy)
Bodaghi et al.
[80]
ANNBP
ROP estimation
Bit tooth wear, bit size, formation lithology, interval drilled, WOB,
rotary speed, well deviation, pump pressure, pump rate, mud
weight, and mud viscosity
0.9519
HPGSVM
0.9569
GASVM
0.9644
CSSVM
0.9648
Shi et al. [81]
ANNLM
ROP prediction
in offshore
drilling
Mud viscosity, mud type, WOB, RPM, pump pressure, formation
abrasiveness and drillability, UCS, bit type and size
0.90
ANNELM
0.92
USA
0.94
Kahraman [82]
ANNMLP
Estimation of
penetration
rate
Relative abrasiveness, tensile strength, RPM
0.99
UCS, and thrust
Jiang and
Samuel [83]
ANN-
Bayesian
regulariza-
tion
ROP
optimization
Gamma ray, mud flow rate, RPM, WOB, and depth
.0.99
Hegde et al. [84]
Random
forest
ROP prediction
WOB, RPM, UCS, and mud flow rate
0.84
Ensemble
ROP
model
0.72
Hegde and Gray
[85]
Random
forest
ROP prediction
and
optimization
WOB, RPM, UCS, and mud flow rate
0.96
(Continued)
Table 5.5 (Continued)
References
Intelligent
model(s)
Type of study
conducted
Input parameters
Error (R2)
Tewari and
Dwivedi [86]
ANNBP
Predicting the
ROP
Pore gradient, ECD, jet impact, tooth wear, RPM, bit weight, bit
number, and depth
0.86174
Ansari et al. [87]
CSVRICA
ROP prediction
Interval layer, inner row2, 1/mud weight, 1/WOB, pressure2, pump
rate2, RPM2, and formation type2
0.9061
Eskandarian et al.
[88]
RF (4
variables)
ROP estimation
Four variables: WOB, mud weight, incline, azimuth
0.7653
RF (6
variables)
Six variables: WOB, mud weight, incline, azimuth, pump pressure,
and PV
0.751
MONMLP
(4 variables)
0.7618
MONMLP
(6 variables)
0.8009
Diaz et al.
[89,90]
ANN
ROP prediction
Drilled depth, pore pressure gradient, ECD, WOB, bit diameter,
RPM, fractional tooth height worn away, and flow rate

Bezminabadi
et al. [91]
ANN
Predicting the
ROP
Depth, WOB, RPM, flow rate, mud weight, neutron porosity
hydrogen index, differential pressure, resistivity logs, gamma ray
0.87
Amer et al. [92]
ANNBP
ROP prediction
Bit type, bit IADC codes, bit diameter, bit status, measured depth,
TVD, WOB, RPM, torque, pump flow rate, standpipe pressure,
mud weight, and percentage of different minerals in formation
samples
0.780.99
Ayoub et al. [93]
ANFIS
ROP prediction
Depth, bit size, RPM, WOB, and mud weight
0.98
Elkatatny [94]
ANNLM
Estimating the
ROP
WOB, RPM, drilling torque, standpipe pressure, pump rate, mud
density, and PV
0.99
Hegde and Gray
[95]
RF
Optimizing the
ROP, TOB,
and MSE
RPM, WOB, flow rate, and UCS

Diaz et al. [96]
ANNBP
ROP prediction
Depth, pore pressure gradient, ECD, WOB/bit diameter, RPM,
fractional tooth height worn away, and Reynolds number
.0.90
Yavari et al. [97]
ANFIS
ROP prediction
WOB and RPM
.0.8625
Momeni et al.
[98]
ANNBP
ROP prediction
PV, depth, WOB, RPM, bit size, mud weight, flow rate
0.9473
Anemangely
et al. [99]
MLPCOA
ROP prediction
RPM, WOB, flow rate, compressional wave slowness, shear wave
slowness
0.921
MLPPSO
0.769
Ahmed et al.
[100]
ANN
ROP prediction
Depth, WOB, RPM, torque, flow rate, standpipe pressure, mud
weight, and bit size
0.710.85
ELM
0.730.85
SVM
0.810.91
LSSVM
0.840.94
Soares and Gray
[101]
ANN
Predicting the
ROP
Depth, WOB, RPM and flow rate
 15%
(absolute
error)
SVM-
Gaussian
kernel
function
 14%
(absolute
error)
RF
10.29
(absolute
error)
Ashrafi et al.
[102]
ANNMLP
ROP prediction
WOB, RPM, gamma ray, pump pressure, pore pressure, pump flow
rate, density log and shear wave velocity
0.83
MLPPSO
0.933
MLPGA
0.8931
MLPICA
0.85
MLPBBO
0.8563
ANNRBF
0.8107
RBFPSO
0.8775
RBFGA
0.8819
RBFICA
0.8601
RBFBBO
0.8579
(Continued)
Table 5.5 (Continued)
References
Intelligent
model(s)
Type of study
conducted
Input parameters
Error (R2)
Sabah et al. [103]
ANNMLP
ROP prediction
WOB, RPM, gamma ray, pump pressure, pore pressure, pump flow
rate, density log and shear wave velocity
0.9284
ANNRBF
0.8815
SVM
0.9687
MLPPSO
0.941
Decision tree
0.8975
RF
0.9185
ANFIS, Adaptive neuro fuzzy inference system; ANN, artificial neural network; ARE, average relative error; BBO, biogeography-based optimizer; BP, backpropagation; CS, cuckoo search;
CSVR, committee support vector regression; DFI, discontinuity frequency index; ECD, equivalent circulating density; HPG, hybrid of pattern search and grid search; ELM, extreme learning
machine; GA, genetic algorithm; ICA, imperialist competitive algorithm; IADC, international association of drilling contractors; LM, LevenbergMarquardt; LSSVM, least square support
vector machine; MLP, multilayer perceptron; MON, monotone; MSE, mechanical specific energy; PSO, particle swarm optimization; PV, plastic viscosity; Ph, power of hydrogen; RBF,
radial basis function; RPM, revolutions per minute; RF, random forest; RQD, rock quality designation; SVM, support vector machine; TBH, total bit hours; TOB, torque on bit; TVD, true
vertical depth; UCS, uniaxial compressive strength; USA, upper layer solution aware; WOB, weight on bit; YP, yield point.
desired parameters. The authors considered rock type, depth, rock quality
designation, and discontinuity frequency index as input variables. They
stated that the validation error was 10.29% for the final network.
After two years, Moradi et al. [62] proposed a hybrid fuzzy-based
intelligent approach toward ROP prediction. Their datasets were col-
lected from nine wells drilled in the Khangiran gas field of Iran. They
considered jet impact force, fractional bit tooth wear, equivalent mud
density, pore pressure gradient, rotary speed, bit diameter, WOB, and
true vertical depth as the inputs of the model. They claimed that their
approach showed higher accuracy in ROP prediction compared to con-
ventional methods.
In 2011, Arabjamaloei and Shadizadeh [65] developed an ANN-based
model to estimate the ROP. They used field data collected from Ahwaz
oilfield in Iran. Then, they obtained the optimum value of operating
parameters to maximize the ROP using GAs as the optimization algo-
rithm. The authors stated that the proposed model could satisfactorily pre-
dict the ROP in shaly formations of the Middle East, which are similar to
Pabde and Gurpi formations of Iranian oilfields.
A year later, Arabjamaloei and Dehkordi [70] investigated the effi-
ciency of different approaches in the area of ROP prediction. In addition,
they introduced a new model using fuzzy logic and used it in comparison
with other methods. They concluded that in the case of having a large
databank, ANNs are the most accurate predictive models. Another
attempt to predict ROP was made by Monazami et al. [71] in this year.
They developed an ANN-based model using the real data obtained from
a southern Iranian oilfield. They proposed a three-layer feedforward neu-
ral network that could predict the ROP with an mechanical specific
energy (MSE) of 0.0591.
In 2013, Ning et al. [73] constructed an algorithm based on ANNs
considering uniaxial compressive strength (UCS), apparent viscosity, mud
density, RPM, WOB, gross hours drilled, drillability coefficient, and bit
type/size as input variables and ROP as the output of the model. They
utilized the analytic hierarchy process to determine the weight of each
input. The authors claimed that the proposed model was able to predict
the ROP with an average relative error of lower than 10%.
In 2014, Bataee et al. [74] and Basarir et al. [76] were the scholars
who attempted to predict the ROP through ANNs and ANFIS, respec-
tively. Bataee et al. [74] considered depth, bit size, RPM, mud weight,
and WOB as input parameters to optimize the drilling variables and then
263
Application of intelligent models in drilling engineering
estimate the ROP. Basarir et al. [76] investigated the application of
ANFIS in predicting the ROP of diamond bit drilling. In their study,
UCS, rock quality designation, bit load, and bit rotation were considered
as inputs of the model. The datasets were collected from seven wells in
Turkey by the Mineral Research and Exploration Institute of Turkey
(MTA). The authors represented the following figure as the cross-
correlation graph (Fig. 5.7).
Several intelligent approaches were proposed for ROP prediction in
2015, among which Garavand and Esmaeilian [79] and Bodaghi et al. [80]
employed an imperialist competitive algorithm (ICA)-based committee
machine and SVMs to predict the ROP. Garavand and Esmaeilian [79]
initially developed three independent models: fuzzy logic, neuro-fuzzy,
and ANNs. Then, the ICA was employed to integrate the output of each
model. The integrated outputs were considered as the inputs of the com-
mittee machine that could provide prediction with an accuracy of 88%.
Bodaghi et al. [80] employed an SVM model along with a hybrid of
Figure 5.7 Cross-correlation graphs for overall dataset [76]. Adapted from H. Basarir,
L. Tutluoglu, C. Karpuz, Penetration rate prediction for diamond bit drilling by adaptive
neuro-fuzzy inference system and multiple regressions, Eng. Geol. 173 (2014) 19.
264
Applications of Artificial Intelligence Techniques in the Petroleum Industry
pattern search and grid search (hybrid of pattern search and grid search
(HPG)), GA, and cuckoo search (CS) algorithm as optimization algo-
rithms. The authors reported that using CS could enhance the accuracy of
the model more than the GA. The comparison between the developed
SVM-based model and a backpropagation neural network revealed that
the SVM model was the more reliable model.
In the subsequent year, Shi et al. [81] investigated the application of
two new models: extreme learning machine (ELM) and upper layer solu-
tion aware (USA), an efficient learning model, in the field of ROP pre-
diction. They considered mud properties, WOB, rotary speed, bit type,
hydraulics, rock mechanical properties, and formation type as input vari-
ables. The datasets used in their work were obtained from an oil reservoir
at the Bohai Bay, China. They stated that the comparison between the
proposed models and conventional ANN showed the competency of all
models with a better generalization performance and faster learning speed
for ELM and USA. Later in this year, Kahraman [82] assessed the perfor-
mance of the multiple regression method and ANNs in ROP prediction
of diamond drilling considering the relative abrasiveness, the tensile
strength, and the UCS as inputs of the models. He reported that ANNs
could estimate the ROP more reliably.
In 2017, Hegde et al. [84] compared the performance of three tradi-
tional models with three intelligent approaches in ROP prediction. They
considered Hareland model, Motahhari model, and Bingham model as
traditional models as well as random forest, linear regression model, and
an ensemble ROP model as data-driven models. The inputs of the intelli-
gent models were considered as WOB, RPM, unconfined compressive
strength, and flow rate. Fig. 5.8 evaluates the performance of each model
with respect to their normalized error rate.
Afterward, Hegde and Gray [85] researched the applicability of
machine learning in ROP prediction and optimization. The authors uti-
lized random forest as the predictive model and employed a brute force
algorithm to modify the surface measured parameters such as RPM,
WOB, and mud flow rate. They claimed that the proposed method could
effectively optimize surface parameters to gain the maximum ROP.
Then, Ansari et al. [87] proposed a new hybrid approach, namely,
committee support vector regression based on an ICA (CSVRICA) to
predict the ROP. They used the actual data obtained from nineteen
drilled wells. The authors considered many influencing factors such as
mud weight, RPM, WOB, bit size/type, and pump rate as input
265
Application of intelligent models in drilling engineering
variables. The predictions of the CSVRICA model showed a good
correlation with the field data with the regression coefficient of 0.9061.
Another attempt to predict the ROP was made by Eskandarian et al.
[88] in 2017. They investigated the application of random forest and
monotone MLP (MONMLP) in ROP prediction. They used 226 data
points obtained from five wells in an Iranian oilfield. The authors consid-
ered 13 variables and reported the importance of each one in Fig. 5.9,
where WOB and mud weight showed the most impact on the ROP.
The input variables were reduced to 4 and 6 variables through the cubist
model. Eventually, MONMLP with six input parameters was proposed
as the most reliable approach with the R2 of 0.9472 and 0.8009 for train-
ing and testing datasets, respectively.
In another investigation, Ayoub et al. [93] proposed an ANFIS model
to predict the ROP considering WOB, RPM, mud weight, bit size, and
depth as input parameters. They used a dataset consisting of 504 data
points gathered from a Sudanese oilfield. The correlation coefficient of
the ANFIS model was reported as 98%.
Figure 5.8 Evaluation of predictive models used in the study of Hegde et al [84].
Adapted from C. Hegde, et al., Analysis of rate of penetration (ROP) prediction in drilling
using physics-based and data-driven models, J. Petrol. Sci. Eng. 159 (2017) 295306.
266
Applications of Artificial Intelligence Techniques in the Petroleum Industry
Figure 5.9 The importance of each parameter on the ROP in the study of Eskandarian et al [88]. ROP, Rate of penetration. Adapted from S.
Eskandarian, P. Bahrami, P. Kazemi, A comprehensive data mining approach to estimate the rate of penetration: application of neural network,
rule based models and feature ranking, J. Petrol. Sci. Eng. 156 (2017) 605615.
In 2018, Elkatatny [94] employed ANNs to estimate the ROP using
3333 data points collected from an offshore field in the Middle East.
WOB, RPM, drilling torque, standpipe pressure, pump rate, mud density,
and PV were considered as input parameters. He stated that mud density
and PV had the highest impacts on the ROP with a correlation coeffi-
cient of 0.85 and 0.74, respectively. The proposed model had 20 neurons
and could estimate the ROP with an AAPE of 5.6%.
Afterward, Hegde and Gray [95] applied intelligent models to opti-
mize drilling operations. They developed models for ROP, torque on bit
(TOB), and MSE based on random forest algorithm considering UCS,
RPM, flow rate, and WOB as input variables. A metaheuristic optimiza-
tion algorithm was used to optimize these models. The authors claimed
that the optimization of the MSE model could increase the ROP (20%)
and reduce the MSE (15%) and TOB (7%), which results in longer bit
life, while the optimization of ROP model could increase the ROP by
28%; however, it could undesirably increase the MSE and TOB.
Yavari et al. [97] were the subsequent scholars who investigated the appli-
cation of an intelligent model (ANFIS) in predicting the ROP. They assessed
the performance of the analytical models [Bourgoyne and Young (BY)
model and HarelandRampersad model] and the ANFIS model in ROP
prediction. The models were developed using modular test data, sonic log
data, and drilling data obtained from different sections of the South Pars gas
field. The modeling results revealed that the predictions of the ANFIS model
were more reliable when the data points were numerous.
Momeni et al. [98] applied ANNs and GAs to optimize controllable
drilling parameters and maximize the ROP. A visible mathematical
model, obtained through converting the ANN black box to a white box,
was used as the objective function of GAs. Therefore, the optimum value
of drilling variables could be obtained through a combination of ANNs
and GAs. The proposed model could predict the ROP with an MSE of
0.0037.
Anemangely et al. [99] conducted another research to estimate the
ROP through an MLP neural network. They assessed the performance of
the cuckoo optimization algorithm (COA) and PSO algorithm coupled
with the MLP network. Among various influential parameters, five vari-
ables were chosen as input parameters, including RPM, WOB, flow rate,
compressional wave slowness, and shear wave slowness. The authors
introduced the MLPCOA as a highly reliable model since it could
estimate the ROP with an R2 of 0.921.
268
Applications of Artificial Intelligence Techniques in the Petroleum Industry
In 2019, Ahmed et al. [100] evaluated the performance of four
intelligent algorithms in ROP prediction. They employed ANNs, SVM,
LSSVM, and ELM as intelligent models and considered drilling parameters
of two wells (measured depth, bit size, mud weight, flow rate, standpipe
pressure, rotary torque, RPM, and WOB) as input parameters. According
to the reported results, the LSSVM model showed the highest perfor-
mance to predict the ROP with a correlation coefficient of 0.94. The
authors also investigated the impact of including the specific energy con-
cept as another input variable. This assumption could improve the RMSE
of the LSSVM model by 3%9% for each well.
In another study in 2019, Soares and Gray [101] compared the predic-
tive capabilities of four analytical and three intelligent models in ROP
prediction. They considered Bingham, BY, Hareland and Rampersad, and
Motahhari models as analytical predictive models and ANNs, SVM, and
random forest as machine learning algorithms. They reported the random
forest and BY model as the most accurate models among the intelligent
and analytical models, respectively, with the superiority of random forest.
In this year, Ashrafi et al. [102] used petrophysical logs and drilling
data obtained from a well drilled in Maroun oilfield of Iran to construct
intelligent ROP models. They developed eight hybrid ANN models con-
sidering eight variables as the inputs: WOB, RPM, shear wave velocity,
pore pressure, pump pressure, pump flow rate, gamma ray, and density
log. They utilized four evolutionary algorithms, including the ICA,
biogeography-based optimizer, PSO, and GA, to train the ANNs (MLP
and RBF) models. Finally, they compared the performance of the devel-
oped models with two multiple regression models and conventional
ANNs. They reported the PSOMLP and PSORBF models as the
most reliable ROP predictors with RMSE of 1.12 and 1.4, respectively.
The authors also stated that evolutionary algorithms are superior over
backpropagation methods to train ANNs. Fig. 5.10 shows the perfor-
mance of the PSOMLP and PSORBF models.
Sabah et al. [103] conducted another research to compare several
intelligent models in ROP prediction. The authors considered the
same inputs as Ashrafi et al. [102] did. They investigated the perfor-
mance of four predictive models, including MLP neural network, RBF
neural network, SVM, and a hybrid model consisting of MLP and
PSO algorithm. They concluded that the MLPPSO model could
provide the most accurate predictions, while the SVM model was the
second reliable model. As already mentioned, Table 5.5 represents a
269
Application of intelligent models in drilling engineering
summary of the applications of intelligent models in ROP prediction
[5759,61,63,64,6669,72,75,77,78,83,86,8992,96,104].
5.6 Other applications
Apart from the aforementioned applications of intelligent models in dril-
ling engineering, these models have been used with the aim of various
other purposes recently. This section provides a summary of the other
applications of machine learning models in drilling engineering. In 2012,
Rooki et al. [105] applied ANNs to predict the terminal settling velocity,
an important parameter in many industrial applications, of solid spheres
falling through non-Newtonian and Newtonian fluids, which is a
required task in a drilling operation. They considered particle diameter,
acceleration of gravity, fluid consistency index, flow behavior index, and
liquid and solid density as input parameters. The proposed BPNN model
could predict the terminal settling velocity of solid spheres with an R2 of
0.986. Also, Fig. 5.11 shows the capability of the developed model.
In another study, Goldstein and Coco [106] considered submerged
specific gravity of the particle, kinematic viscosity of the fluid, and the
nominal diameter of the settling particle as input variables to construct an
intelligent model based on GAs with the aim of predicting the settling
velocity of noncohesive particles in 2014. The provided predictions of
their model showed regression coefficient (R2) of 0.97.
Figure 5.10 Crossplot for (A) PSOMLP and (B) PSORBF [102]. PSO, Particle swarm
optimization; MLP, multilayer perceptron; RBF, radial basis function. Adapted from S.B.
Ashrafi, et al., Application of hybrid artificial neural networks for predicting rate of pene-
tration (ROP): a case study from Marun oil field, J. Petrol. Sci. Eng. 175 (2019) 604623.
270
Applications of Artificial Intelligence Techniques in the Petroleum Industry
The terminal settling velocity of particles is affected by a variable named
wall factor. Wall factor is introduced to quantify the extra retardation exerted
on falling particles by the walls of a fluid medium [107]. In 2014, Li et al.
[107] were able to predict the wall factor utilizing ANNs and SVM models
with an R2 of 0.973 and 0.9844, respectively. They gathered a dataset with
513 data points and considered particle diameter, fluid density, tube-to-
sphere-diameter ratio, fluid consistency index and behavior index, particle
density, and diameter-to-length ratio as input parameters.
In this year, Rooki et al. [108] developed ANNs to estimate the hole
cleaning efficiency in foam drilling. They considered annulus eccentricity,
the quality and velocity of foam, RPM, temperature, and pressure as the
inputs. The proposed model could provide predictions for the concentra-
tion of the cuttings with an R2 of 0.914. Later, in 2017, Rooki and
Rakhshkhorshid [109] researched the application of RBF neural network
in predicting the hole cleaning efficiency in UBD operation. The devel-
oped model could predict the concentration of drilling cuttings with an
AAPE of 5.7%. Table 5.6 summarizes the other applications of intelligent
models in drilling engineering.
Figure 5.11 Crossplot of predicted versus measured terminal velocities [105].
Adapted from R. Rooki, et al., Prediction of terminal velocity of solid spheres falling
through Newtonian and non-Newtonian pseudoplastic power law fluid using artificial
neural network, Int. J. Miner. Process, 110 (2012) 5361.
271
Application of intelligent models in drilling engineering
Table 5.6 Summary of the other applications of intelligent models in drilling
engineering.
References
Intelligent
model(s)
Type of study
conducted
Input parameters
Error
(R2)
Rooki et al. [2]
ANNBP
Settling velocity
of particles in
Newtonian
and non-
Newtonian
fluids
Acceleration due to
gravity, flow
behavior index,
fluid consistency,
fluid density,
particle diameter,
and particle
density
0.986
Goldstein and
Coco [106]
Genetic
algorithm
Predicting the
particle
settling
velocity
submerged specific
gravity of the
particle, kinematic
viscosity of the
fluid, and nominal
diameter of the
settling particle
0.97
Li et al. [107]
ANNBP
Prediction of
the wall
factor effect
on particle
settling
diameter-to-length
ratio, tube-to-
sphere diameter
ratio, flow
behavior index,
fluid consistency
index, particle
diameter, particle
density, and fluid
density
0.973
Rooki et al. [108]
ANNBP
Hole cleaning
efficiency of
foam fluid
Annulus eccentricity,
foam velocity,
foam quality, pipe
rotation,
temperature, and
pressure
0.914
Kamyab et al. [110]
ANN
Friction factor
of cuttings
slip
Sphericity of the
particle and
logarithm of
Reynolds number

Rooki and
Rakhshkhorshid
[109]
ANNRBF
Cuttings
transport
modeling of
foam fluid
Annulus eccentricity,
foam velocity,
foam quality, pipe
rotation,
temperature, and
pressure
0.922
ANN, Artificial neural network; BP, backpropagation; RBF, radial basis function.
272
Applications of Artificial Intelligence Techniques in the Petroleum Industry
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Applications of Artificial Intelligence Techniques in the Petroleum Industry

CHAPTER SEVEN
Data-driven machine learning solutions
to real-time ROP prediction
Key concepts
1. Machine learning (ML) algorithms are used more and more in the petroleum in-
dustry. ML algorithms have proved efﬁcient in analyzing and identifying patterns in
large amounts of drilling data, commonly referred to as “big data.” Big drilling data
are used to train learning algorithms to increase their performance.
2. These techniques will help drilling operation teams with future planning as they can
learn from data and act in real time, and ultimately, this will improve decision-
making.
3. A novel rate of penetration (ROP) correlation was derived from the artiﬁcial neural
network (ANN) model using the biases and weights of neuron connections among
input, hidden, and output layers.
7.1 Introduction
ML algorithms use computational methods to “learn” information directly from
data without relying on a predetermined equation as a model. The algorithms adaptively
improve their performance as the number of samples available for learning increases.
In recent years, ML algorithms have aided in solving the ROP problem in drilling
engineering. Fig. 7.1 shows that the ﬁeld of ML is a subset of artiﬁcial intelligence (AI)
and deep learning is a subset of ML. The term data science is applied to a ﬁeld using
techniques from AI, ML, deep learning, and computer science.
AI describes machines that can perform tasks resembling those of humans. So, AI
implies machines that artiﬁcially model human intelligence. AI systems help us manage,
model, and analyze complex systems. It is the superset that has ML and deep learning as a
subset.
ML uses algorithms to parse data, learn from that data, and make informed decisions
based on what it has learned.
Deep learning structures algorithms in layers to create an “ANN” that can learn and
make intelligent decisions on its own. Deep learning is a subﬁeld of ML. While both fall
under the broad category of AI, deep learning is what powers the most human-like AI.
Data science is a broad ﬁeld that spans the collection, management, analysis, and
interpretation of large amounts of data with a wide range of applications. It integrates all
Methods for Petroleum Well Optimization
 2022 Elsevier Inc.
ISBN 978-0-323-90231-1, https://doi.org/10.1016/B978-0-323-90231-1.00001-7
All rights reserved.
249j
the terms above and summarizes or extracts insights from data (exploratory data analysis)
and makes predictions from large data sets (predictive analytics). The ﬁeld involves many
different disciplines and tools, including statistical inference, domain knowledge
(expertise), data visualization, experiment design, and communication. Data science
helps answer the question “what if?” and it plays a crucial role in building ML and AI
systems, and the needs of ML and AI help drive the development of data science.
7.1.1 Types of machine learning
Supervised learning: Input data are tagged. Supervised learning establishes a learning
process, compares the predicted results with the actual results of the “training data” (that
is, input data), and continuously adjusts the predictive model, such as for classiﬁcation
and regression problems, until the predicted results of the model reach an expected
accuracy. Common algorithms include decision trees, Bayesian classiﬁcation, least
squares regression, logistic regression, support vector machines (SVMs), neural net-
works, and so on.
Unsupervised learning: The input data have no tags, but algorithms are used to
infer the intrinsic links of the data, such as clustering and association rule learning.
Common algorithms include independent component analysis, k-means, and apriori
algorithms.
Reinforcement learning: Input data are used as feedback to the model, empha-
sizing how to act based on the environment to maximize the expected beneﬁts. The
difference between this and supervised learning is that reinforcement learning does not
require the correct input/output pairs and does not require precise correction of
suboptimal behavior. Reinforcement learning is more focused on online planning and
requires a balance between exploration (in the unknown) and compliance (existing
knowledge) (Fig. 7.2).
Figure 7.1 Venn diagram representing the components of AI. AI, artiﬁcial intelligence. Adapted from
Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y., 2016. Deep Learning. MIT Press, Cambridge.
250
Methods for Petroleum Well Optimization
Figure 7.2 Types of machine learning.
Data-driven machine learning solutions to real-time ROP prediction
251
Deep learning: Deep learning is a subset of machine learning, and is essentially a
neural network with three or more layers. While a neural network with a single layer can
still make approximate predictions, additional hidden layers can help to optimize and
reﬁne for accuracy. These multilayer neural networks attempt to simulate the behaviour
of the human brain allowing the networks to “learn” from large amounts of data.
Although these networks do not match the brain’s ability, in individual tasks they are
achieving unprecedented levels of accuracydto the point where deep learning algorithms
can outperform humans at classifying images and can beat the world’s best GO player.
This chapter shows that ML for ROP prediction could be classiﬁed under ﬁve
methods: ANN, SVM, fuzzy inference system, neurofuzzy, and ensemble, as shown in
Table 7.1.
The previous approaches (traditional models and statistical models) are started by
preselecting a speciﬁc model. In contrast, ML techniques are able to learn complex
patterns during the training (or learning) phase, without having to specify an ROP
model. After the learning phase, the trained model is able to make ROP predictions
given novel inputs.
The future of drilling operations lies in the ever-increasing implementation of data-
driven modeling and its applications in predicting and optimizing highly uncertain
downhole environments. With various wellbore complexities exaggerating the overall
well cost, achieving drilling efﬁciency with the highest possible ROP is now more
imperative than ever.
Table 7.1 Classiﬁcation of ROP prediction models.
ANN
SVM
Fuzzy
inference
system
Neurofuzzy
Ensemble
Multilayer
perceptron
Convolutional
neural network
Recursive neural
network
Recurrent neural
network
Long short-term
memory
Sequence-to-
sequence
models
Shallow neural
networks
Linear support
vector regression
(SVR)
Nonlinear SVR
Mamdani
Sugeno
hybrid
Neurofuzzy refers to
combinations of artiﬁcial
neural networks and fuzzy
logic
ANFIS: Adaptive neurofuzzy
inference system
DENFIS: Dynamic evolving
neural-fuzzy
Stacking
Blending
Bagging
Boosting
ANN, artiﬁcial neural network; SVM, support vector machine.
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Methods for Petroleum Well Optimization
Although there are numerous ML methods and approaches currently in use, each
algorithm has its particular advantages and limitations. Therefore, each data set and
problem should be considered on a case-by-case basis. In essence, there is no universal
objective model for all conditions because the correlations are complicated. Generally,
the typical approaches fail to generate accurate ROP predictions given the added
complications of bottomhole conditions. Conventionally, ROP optimization requires
discovering the sweet spot between weight on bit (WOB) and rotary speed (RPM,
revolutions per minute) for effective drilling. However, ROP is a complex parameter
affected by many other parameters.
7.2 Data piping in real time
Data collection or processing is the transferring of the data to a condition in which
it can be processed; after that, nonlinear (multiple) regression and ML techniques are
used to ﬁnd the coefﬁcient to estimate and matrix weights for the drilling ROP problem.
The drilling ROP is determined with a certain level of accuracy. The optimization
process requires data piping, which is regarded as a critical and very important step. The
data should be piped to the central computer where the optimization takes place. The
sequence for the optimization cycle is as follows:
1. From the rig site, data should be piped in real time to a central computer.
2. Optimized drilling parameters are estimated by the central computer which processes
the data using the information already in its database.
3. For application of the results, the optimized drilling parameters should be piped back
to the drilling rig site.
7.3 Drilling rate of penetration optimization workﬂow
There are a lot of data records generated during well operations, which are
processed by the various specialists involved:
1. drilling parameters (RPM, WOB, etc.);
2. bit size and type (polycrystalline diamond compound e PDC, diamond impregnated,
rollercone diamond hybrid);
3. type of vibration (lateral, torsional, axial, whirl);
4. properties of lithology (rock type, pressure gradient, compressive strength, etc.);
5. bottomhole assembly (positive displacement motor e PDM), rotary steerable systems
(RSS), turbine, and vertical control system);
6. hydraulic efﬁciency; and
7. mechanical efﬁciency.
The real-time data are streamed from the rig. A hydraulics specialist determines the real-
time equivalent circulating density (ECD) from the incoming data and determines if the
mud is in good condition. The specialist then provides the ECD information to the
Data-driven machine learning solutions to real-time ROP prediction
253
geomechanics specialist, who focuses on pore pressure, fracture gradient, and wellbore
stability issues. This helps the geomechanics specialist determine downhole conditions.
A bit specialist uses this information to determine the appropriate bit required.
Choosing the wrong bit can cost an operator an extra trip, with the additional expen-
diture ranging from a few hundred dollars to hundreds of thousands of dollars depending
on the location and well depth. The drilling supervisor is responsible for keeping and
updating the plan. This real-time input is provided by the hydraulics, geomechanics, and
bit specialists. The drilling supervisor compares the original plan from the collaborative
well-planning workﬂow with actual data and updates it with real-time parameters. One
of the main advantages of this ROP drilling workﬂow is that it operates in a proactive
manner and can optimize the wellbore.
Optimizing ROP drilling requires following a workﬂow that delivers the right
combination of drilling data to address the speciﬁc challenges of the well to be drilled.
The drilling ROP optimization workﬂow application monitors drilling parameters and
drilling performance in real time and makes recommendations for WOB surface, RPM,
and hydraulics to maximize instantaneous ROP
, and to improve on-bottom drilling
performance and the time-depth curve. The ROP optimizer system will automatically
detect formation changes and transitions and will adjust drilling parameter recom-
mendations to optimize ROP in the new formation (Fig. 7.3).
Data Collecon  
Combine data from mulple sources 
Clean and prepare data 
Make data easily available for analysis
Exploratory Data Analysis
Beer understand 
relaonships 
Formulate quesons 
Modeling 
Explicitly model relaonships 
Use models to answer the 
quesons 
Visualizaon and Reporng 
Summarize what has been learned 
Transfer informaon to decision makers 
Idenfy new data to collect 
Figure 7.3 ROP workﬂow. ROP, rate of penetration.
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Methods for Petroleum Well Optimization
Most ROP ML challenges are related to handling the big data and ﬁnding the right
model. It takes time to ﬁnd the best ROP model to ﬁt the drilling data. Choosing the
right model is a balancing act. Highly ﬂexible models tend to over ﬁt data by modeling
minor variations that could be noise. On the other hand, simple models may assume too
much. There are always trade-offs between model speed, accuracy, and complexity.
Preprocessing the ROP might require specialized knowledge and tools. For example, to
select features to train an object detection algorithm requires specialized knowledge of
image processing. Different types of drilling data require different approaches to pre-
processing. The components of the architecture and their respective roles are listed here.
7.3.1 Sensor
Oil rigs are becoming huge sources of big data that can help engineers back at the
operation centers achieve greater safety, optimize yields, and reduce rig downtime.
Automatic acquisition of data eliminates human bias and errors, providing managers with
a more accurate understanding of their operations to help facilitate decisions.
For an efﬁcient drilling process, the monitoring, control processes, and drilling
parametersenhancementshouldbeonareal-timebasis,meaningasitoccurs.Thisstartsatthe
well site with the data acquisition of downhole and surface measurements from the sensors.
Mud-logging systems of modern drilling rigs provide numerous sensor data. The sensor
measurements are the indicators used to monitor different states of the drilling process.
Real-time measurements of the following sensor data are usually available as surface
measurements: hook load, block position, ﬂow rates, pump pressure (PP), borehole and bit
depth, RPM, torque, ROP
, and WOB. In this section, we look at how collected sensor
measurements from mud-logging systems are used to detect the state of different drilling
operations. Detailed data analysis shows that the surface sensor measurements can be
considered as one of the main sources of information about a drilling operation. For this
purpose, an ML model is constructed to interpolate sensor data measurements.
7.3.2 Machine learning model
The ML life cycle involves seven major steps, which are given in Fig. 7.4. The drilling
ROP optimizing model has already delivered signiﬁcant improvements using the ML
algorithm based on the real-time surface and downhole measurements and parameters of
the top drive, drill pipe, mud, and bit, which quickly and reliably detect minute changes
in actual downhole bit response. The ML algorithm calculates parameters, such as WOB
and RPM, which delivers optimum ROP
. The ROP optimizer can be designed to
monitor and control drilling parameters in a closed loop continuously throughout the
drilling operation without the need for direct human intervention.
7.3.3 Remote operation center
Drilling rigs are typically located in remote areas cut off from the broadband world. We
need technologies to transport massive terabyte volumes of data in real time. Decisions
Data-driven machine learning solutions to real-time ROP prediction
255
are made together, on demand, and in real time via reliable, secure wellsite-to-center
connections.
7.3.4 Rig control system
This system controls the equipment on the rig. It can be operated from the rig, but an API
(application programming interface) can be implemented to allow the equipment to be
remotely controlled. This interface solution depends on the rig control system provider.
Gathering Data 
This step includes the below tasks: 
1.
Idenfy various data sources 
2.
Collect data 
3.
Integrate the data obtained from diﬀerent 
sources 
Data preparaon 
This step can be further divided into two processes:
1.
Data exploraon: 
It is used to understand the nature of data that 
we have to work with. We need to understand 
the characteriscs, format, and quality of data. 
A beer understanding of data leads to an 
eﬀecve outcome. In this, we ﬁnd correlaons, 
general trends, and outliers. 
2.
Data pre-processing: 
The next step is preprocessing of data for its 
analysis. 
Data Wrangling 
It is not necessary to use all the data that has been 
collected as some of the data may not be useful. In real-
world applicaons, collected data may have various issues, 
including: 
1.
Missing values 
2.
Duplicate data  
3.
Invalid data 
4.
Noise 
So, we use various ﬁltering techniques to clean the data. 
Data Analysis 
The aim of this step is to build a machine learning model 
to analyze the data using various analycal techniques and 
review the outcome. It starts with the determinaon of 
the type of the problems, where we select the machine 
learning techniques, such as classiﬁcaon, regression, 
cluster analysis, associaon, etc., then build the model 
using prepared data, and evaluate the model. 
1.
Selecon of analycal techniques 
2.
Build models 
3.
Review the result 
Train Model 
We train our model to improve its performance for a 
beer outcome. We use data sets to train the model using 
various machine learning algorithms. Training a model is 
required so that it can understand the various paerns, 
rules, and features. 
Test Model 
Once our machine-learning model has been trained on a 
given data set, then we test the model. In this step, we 
check for the accuracy of our model by providing it with a 
test data set. Tesng the model determines the 
percentage accuracy of the model as per the requirement 
of project or problem. 
Deployment 
The last step of the machine learning life cycle is 
deployment, where we deploy the model. 
Figure 7.4 Machine learning life cycle.
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Methods for Petroleum Well Optimization
7.3.5 Automation console
This console allows the driller to interact with the automated processes. The console is
sited next to the driller and allows them to select “advise mode,” in which they can
control the parameters WOB and RPM. It can also perform an emergency disconnect,
stopping all automated activities.
An integrated system for National-Oilwell Varco’s drilling automation system is
shown in Fig. 7.5.
7.4 Statistical and data-driven rate of penetration model
This section looks at the ﬁve ML methods for ROP: ANN, SVM, fuzzy inference
systems, neurofuzzy, and ensemble models. For each method, additional information is
given in Table 7.1 earlier in this chapter.
7.4.1 Multiple-linear regression
ROP is a vital parameter and directly affects drilling time and costs. Several approaches to
predicting the ROP have been proposed, including mathematical models and AI. Pre-
vious research has shown that AI methods, such as neural networks and adaptive neuro-
fuzzy inference systems, are superior to conventional methods in the prediction of the
drilling rate. However, many complex analytical ROP models have also been developed
in recent years that are able to predict the drilling rate with high accuracy. Therefore,
identifying the most accurate models and the conditions in which each works best can be
highly effective in reducing drilling time as well as drilling cost.
To look into this, a study by Yavari et al. (2018) used drilling data for a selection of
geological formations in an existing gas ﬁeld to ﬁnd the best ROP prediction models in
each case. The study selected one phase of the gas ﬁeld where the geological formations
Figure 7.5 Functionalities of National-Oilwell Varco Operating System (NOVOS). Modiﬁed from Etaje,
D.C., 2018. Identifying the Optimum Zone for Reducing Drill String Vibrations (Master’s thesis). University
of Calgary, Calgary, AB. https://doi.org/10.11575/PRISM/32358.
Data-driven machine learning solutions to real-time ROP prediction
257
penetrated by the wells were, from shallowest to deepest, the Asmari, Ilam, Sarvak,
Upper limestone, Dashtak, Surmeh, and Kangan. The authors calculated a drilling rate
for the different formations according to the Bourgoyne and Y
oung (BY) model and an
adaptive-neurofuzzy inference system (ANFIS).
They concluded that ANFIS is more accurate than the Bourgoyne and Y
oung (BY)
model in processing large amounts of data to predict the drilling rate.
The ROP model for each formation was constructed from a database including data
collected from drilling sites in the gas ﬁeld:
•
the daily mud logging reports (DMLR), with drilling data including drilling rate,
WOB, rotational speed (RPM), pump ﬂow rate, mud weight, bit type and bit wear;
•
modular dynamic test (MDT) data, used to estimate the pore pressure; and
•
sonic log data, used to calculate uniaxial compressive strength.
The study’s data set consisted of 721 data records divided randomly into two parts, of
which 70% (504 records) was used to construct (train) the model, and the remaining 30%
(217 data records) was used to test the developed model. The data used in this study are
displayed graphically in Figs. 7.6 and 7.7.
Several models have been suggested to predict the ROP
. The model established by
Bourgoyne and Y
oung (1974) is one of the most complete drilling models, and it is used
for roller cone bits. The BY drilling model is deﬁned by Eq. (7.1). In this model, eight
variables are used to model the inﬂuence of different drilling parameters: f1 is formation
drillabilitydthe effect of formation strength, bit type, mud type, and solid content,
which are not included in the drilling model; f2 is the normal compaction; f3 is the under
compaction; f4 is the effect of overbalance on penetration rate; f5 is WOB; f6 is rotary
speed; f7 is tooth wear; and f8 is the effect of bit hydraulics.
ROP ¼ ðf1Þðf2Þðf3Þðf4Þðf5Þðf6Þðf7Þðf8Þ
(7.1)
The individual equations are shown in Fig. 7.8, where D is the true vertical-well
depth in ft; gp is the pore-pressure gradient in lbm/gal; rc is the equivalent circulating
density in lbm/gal;

WOB
db

t
is the threshold bit weight per inch of bit diameter at which
the bit begins to drill, measured in 1000 lbf/in.; h is the fractional tooth wear; Fj is the
hydraulic impact force beneath the bit in lbf; and a1 through a8 are the constants that
must be chosen on the basis of local drilling conditions.
The multiple regression method is utilized to determine the model constants (a1
through a8 ) for each formation. Initially, the BY model needs to be expressed in a linear
258
Methods for Petroleum Well Optimization
form by taking the natural logarithms of both sides of Eq. (7.1). This is shown in
Eq. (7.2), and Table 7.2 contains the values of X2 to X8 in the linear form of the BY
model.
Y ¼ LnðROPÞ ¼ Ks þ a1X1 þ a2X2 þ a3X3 þ a4X4 þ a5X5 þ a6X6 þ a7X7
(7.2)
The X and Y values (Eq. 7.2) are then determined using the data set. The general
form of multiple-linear regression (MLR) for a problem with k constants is as shown in
Figure 7.6 Drilling data from daily drilling reports of one of the subject wells. Modiﬁed from Yavari, H.,
Khosravanian, R., Sabah, M., Wood, D.A., 2018. Application of adaptive neuro-fuzzy inference system and
mathematical ROP models for prediction of drilling rate. Iranian J. Oil Gas Sci. Technol. 7 (3), 1e13.
Data-driven machine learning solutions to real-time ROP prediction
259
Eq. (7.3), where n is the number of data records involved (Eren, 2010). By solving this
matrix, the constants can be determined:
(7.3)
MLR is an accurate method for determining the constant coefﬁcients of a model.
However, the authors noted that when applied to the BY model (among others), MLR
leads to meaningless constants (e.g., calculating negative ROPs) because of the quality
of the existing data (e.g., anomalous data points in certain data records). In this situ-
ation, the aberrant data points need to be removed from the data set. In addition, there
are some numerical methods for determining the constant coefﬁcients. The authors
therefore used a simulated annealing algorithm (SAA) to determine the BY model
constants.
7.4.1.1 Simulated annealing algorithm
It was noted in the study that simulation of physical annealing had been used successfully
in optimization (Kirkpatrick et al., 1983). The parameters of the SAA include the design
variables (X0), the energy state (E(X0)), which is equivalent to the objective function, the
initial temperature (T0), the freezing temperature (Tf), the length of the Markov chain
(L), and temperature decrement factor (a) (Granville et al., 1994). SAA uses a Metropolis
criterion to escape from the local optimum point and to have a better chance of
obtaining the global optimum point. The acceptance probability of the Metropolis
criterion is as follows (Metropolis et al., 1953):
P ¼ exp

DE
T

(7.4)
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Methods for Petroleum Well Optimization
Figure 7.7 Drilling data from daily drilling reports, pore pressure, and modular dynamic test data.
Modiﬁed from Yavari, H., Khosravanian, R., Sabah, M., Wood, D.A., 2018. Application of adaptive neuro-
fuzzy inference system and mathematical ROP models for prediction of drilling rate. Iranian J. Oil Gas Sci.
Technol. 7 (3), 1e13.
Figure 7.8 Drilling parameters considered to have an effect on the rate of penetration in the BY
model. BY, Bourgoyne and Young.
Data-driven machine learning solutions to real-time ROP prediction
261
There are six steps involved in the SAA approach:
1. The ﬁrst step is to determine the best algorithm parameters; as there is no direct way
to do so, a trial-and-error approach is used.
2. The next step is to generate a random set of constant coefﬁcients.
3. Using these constants, the ROP values are calculated, and the RMSE1 for all the data
set is determined using Eq. (7.5).
4. A neighboring set of constant coefﬁcients is then generated (Table 7.3), and the
RMSE2 is determined for this new solution.
Table 7.3 Recommended bounds for each constant coefﬁcient of the Bourgoyne and Young model.
Coefﬁcients
Lower bound
Upper bound
a1
0.5
1.9
a2
0.000001
0.0005
a3
0.000001
0.0009
a4
0.000001
0.0001
a5
0.5
2
a6
0.4
1
a7
0.3
1.5
a8
0.3
0.6
Bourgoyne, A.T., Millheim, K.K., Chenevert, M.E., Y
oung, F
.S., 1991. Applied Drilling Engineering (vol. 2), Society of
Petroleum Engineers Richardson, TX; Rahimzadeh, H., Mostoﬁ, M., Hashemi, A., 2011. A new method for deter-
mining Bourgoyne and Y
oung penetration rate model constants. Petrol. Sci. T
echnol. 29 (9), 886e897. https://doi.org/
10.1080/10916460903452009.
Table 7.2 Coefﬁcients of the run -1 linear form of the Bourgoyne and Young model.
Characteristic
Variable
Amount
Normal compaction parameter
X1
2:303  ð10000  DÞ
Undercompaction parameter
X2
2:303  D0:69 

gp  9

Pressure differential parameter
X3
2:303  D 

gp  rc

Bit weight parameter
X4
Ln
0
B
@
W
d


W
d

t
4
W
d

t
1
C
A
Rotary speed parameter
X5
Ln

N
60

Tooth wear parameter
X6
h
Hydraulic parameter
X7
Ln

Fj
1000

Yavari, H., Khosravanian, R., Sabah, M., Wood, D.A., 2018. Application of adaptive neuro-fuzzy inference system and
mathematical ROP models for prediction of drilling rate. Iranian J. Oil Gas Sci. T
echnol. 7 (3), 1e13.
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Methods for Petroleum Well Optimization
5. If RMSE2 is lower than RMSE1, the new solution is considered as the best solution,
else if P (Solution) > Random (0e1), then the RMSE2 is chosen as the best solution.
6. The temperature decreases ðTNew ¼ a TCurrentÞ, and the process returns to step 4.
The process is repeated until the freezing temperature is met.
In their study, Yavari et al. considered the objective function to be the root mean square
error (RMSE), calculated using Eq. (7.5).
RMSE ¼
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
1
n
X
n
i ¼ 1

ROPreal  ROPpredicted
2
s
(7.5)
Bourgoyne and Y
oung’s recommended bounds for each of the eight coefﬁcients are
shown in Table 7.3.
Yavari et al. determined the best algorithm parameters for the SAA for the study
using a trial-and-error approach. These are shown in Table 7.4.
To illustrate the performance of the SAA for determining the constant coefﬁcients of
the BY model for a formation, Yavari et al. (2018) used the Surmeh formation (Fig. 7.9).
The ﬁrst diagram shows that the determined constants can predict the ROP with an
RMSE value equal to 2.97318. The second diagram shows the determined constant
coefﬁcients. The third diagram shows that the algorithm stops when the temperature
reaches the freezing temperature.
The calculated constants are summarized in Table 7.5. When SAA is used for
determining the constant coefﬁcients of mathematics models, sometimes a set of
completely different constants are obtained after each run. In this situation, the pa-
rameters of the algorithm must be changed to reach the global optimum.
7.4.2 Adaptive neurofuzzy inference system model
7.4.2.1 Artiﬁcial intelligence models
It has been established that an ANN can predict ROP accurately within the range of
input data when a large data set is available. Table 7.6 contains the drill-off test data of the
Surmeh formation in a 12.2500 hole section which was drilled with a PDC bit. The mud
weight was 9.6 ppg, and the ﬂow rate was 870 gpm.
Table 7.4 The best algorithm parameters.
No
SAA parameters
Value
1
T0
1000
2
a
0.925
3
Length of Markov chain
8
4
Tf
0.0002
SAA, simulated annealing algorithm.
Yavari, H., Khosravanian, R., Sabah, M., Wood, D.A., 2018. Application of adaptive neuro-fuzzy inference system and
mathematical ROP models for prediction of drilling rate. Iranian J. Oil Gas Sci. Technol. 7(3), 1e13.
Data-driven machine learning solutions to real-time ROP prediction
263
Figure 7.9 Performance of simulated annealing algorithm for determining the Bourgoyne and Young
model in Surmeh formation. Yavari, H., Khosravanian, R., Sabah, M., Wood, D.A., 2018. Application of
adaptive neuro-fuzzy inference system and mathematical ROP models for prediction of drilling rate.
Iranian J. Oil Gas Sci. Technol. 7(3), 1e13.
Table 7.5 Bourgoyne and Young model constants for each formation.
Formation
a1
a2
a3
a4
a5
a6
a7
a8
Asmari
1.535
0.000075
0.000002
0.0001
0.595
0.94
0.895
0.49
Ilam
1.125
0.000101
0.000459
0.000002
0.5
0.405
1.4999
0.595
Sarvak
1.289
0.000079
0.000002
0.000002
0.505
0.98
0.3067
0.435
Upper limestone
1.259
0.00001
0.000043
0.000002
0.865
1
0.305
0.595
Dashtak
1.329
0.000044
0.00012
0.000002
0.505
0.95
0.301
0.3046
Surmeh
1.831
0.000021
0.0001
0.000001
0.619
0.516
0.375
0.342
Kangan
1.525
0.000001
0.000001
0.000001
0.79
0.995
0.303
0.410
Yavari, H., Khosravanian, R., Sabah, M., Wood, D.A., 2018. Application of adaptive neuro-fuzzy inference system and
mathematical ROP models for prediction of drilling rate. Iranian J. Oil Gas Sci. T
echnol. 7(3), 1e13.
Table 7.6 Drill-off test data for Surmeh formation.
WOB (klb)
5
8
11
13
14
15
17
14
14
14
14
RPM (rpm)
180
180
180
180
180
180
180
190
200
210
220
ROP (m/h)
5.5
9.12
13.46
14
14.21
13.5
13.09
15.31
16.5
17.35
18
Yavari, H., Khosravanian, R., Sabah, M., Wood, D.A., 2018. Application of adaptive neuro-fuzzy inference system and
mathematical ROP models for prediction of drilling rate. Iranian J. Oil Gas Sci. T
echnol. 7(3), 1e13.
264
Methods for Petroleum Well Optimization
The drill-off test data were used by Yavari et al. to construct an ANN model to
predict ROP with the structure shown in Fig. 7.10. The WOB and RPM are considered
as input variables, and ROP is the output of the model. The available data sets consisted
of 80% for the pure network learning process, 10% for validation, and 10% for testing
purposes. A two-layered feed-forward backpropagation algorithm with 10 neurons in
the hidden layer was used. The LevenbergeMarquardt (LM) algorithm was selected for
training the network.
Fig. 7.11 shows the relationship between WOB, RPM, and ROP using the con-
structed ANN model. As can be seen, the ANN is able to predict the ROP only within
the range of input data. When there is not enough input data with sufﬁcient distribution,
it cannot be used as a reliable model for the prediction of ROP
.
Figure 7.10 Structure of suggested ANN for prediction of ROP. ANN, artiﬁcial neural network; ROP,
rate of penetration.
Figure 7.11 Relationship between WOB, RPM, and ROP using designed ANN model. ANN, artiﬁcial
neural network; ROP, rate of penetration; RPM, revolutions per minute; WOB, weight on bit. Yavari, H.,
Khosravanian, R., Sabah, M., Wood, D.A., 2018. Application of adaptive neuro-fuzzy inference system and
mathematical ROP models for prediction of drilling rate. Iranian J. Oil Gas Sci. Technol. 7(3), 1e13.
Data-driven machine learning solutions to real-time ROP prediction
265
An ANFIS was then used to construct an ROP prediction model with the drill-off
test data, with 80% for training, 10% for testing, and 10% for checking purposes, as
for the ANN. In the model (see Fig. 7.12), WOB and RPM are the input variables, and
ROP is the output of the model. Gaussian membership functions are used to convert
input data to fuzzy input, and three membership functions are chosen for each linguistic
variable.
Fig. 7.13 shows the relationship between WOB, RPM, and ROP using the study’s
ANFIS model design. It is noticeable that ANFIS performs better than ANN in that it is
a reliable model for prediction of ROP when only a few input data records with suf-
ﬁcient distribution are available.
The study concluded that when large data sets are not available, ANFIS can provide
more accurate and reliable results than ANN.
7.4.2.2 Theoretical basis and justiﬁcation for adaptive neurofuzzy inference
system applied to rate of penetration prediction models
Models that are based on fuzzy inference systems use linguistic terms and IF-THEN rules
instead of numerical terms. Linguistic variables have their values expressed as words or
sentences in a natural language describing degrees of membership. A fuzzy set, which
belongs to these linguistic variables, is an extension of a crisp set in which each element
Figure 7.12 Structure of designed ANFIS model for prediction of ROP. ANFIS, adaptive neurofuzzy
inference system; ROP, rate of penetration.
266
Methods for Petroleum Well Optimization
can have binary membership, that is, either full membership or no membership.
However, fuzzy sets allow partial membership, in which an element can partially belong
to one or more than one set (Nedjah et al., 2005). So, in a crisp set, the membership level
of x element in fuzzy set A can be expressed by the membership function mAðxÞ where
this has values between 0 and 1:
mA x
ð Þ ¼
8
<
:
1
if x ˛A
implying full membership
0
if x ;A
implying non-membership
(7.6)
In ANFIS, input data are converted to fuzzy input by membership functions.
Following this, fuzzy inputs are entered into the neural network block. This block is
connected to an inference engine which includes a rule base. A backpropagation al-
gorithm is used to train the inference engine and to determine appropriate rules that can
reproduce meaningful dependent variable values. After training, the rules, which are
generated, are applied to the data set from the neural network to yield optimum output.
The output obtained from the neural network block is then converted into crisp values
by a defuzziﬁcation algorithm (Sugeno et al., 1988). This analytical sequence is shown in
Fig. 7.14.
There are ﬁve layers in the structure of the neurofuzzy system. These layers are
fuzziﬁcation, rules, normalization, defuzziﬁcation, and output as shown in Fig. 7.15.
Each of these layers include nodes, which process the fuzzy inputs. Since the ANFIS only
allows one model output, the outputs of these nodes are combined to yield a single crisp
Figure 7.13 Relationship between WOB, RPM, and ROP using designed ANFIS model. ANFIS, adaptive
neurofuzzy inference system; ROP, revolutions per minute; ROP, rate of penetration; WOB, weight on bit.
Data-driven machine learning solutions to real-time ROP prediction
267
output. Then the derived output is reentered into the model as an input and compared
with the actual set value. If there is any deviation, the error signal is generated and
becomes the input for the next iteration of the ANFIS model. Following a series of
iterations, the results converge to a stable system with minimal errors between predicted
and measured values (Mathur et al., 2016).
The Takagi, Sugeno, and Kang (TSK) fuzzy inference system is used to construct
the ANFIS model, which consists of two rules (Sugeno et al., 1988). The TSK ROP
model involves two inputs:
WOB and RPM, one output ROP , and fuzzy sets
A1; A2; B1 and B2 . A and B are fuzzy sets of variables WOB and RPM; respectively. In
the ANFIS model, the relation between inputs and output is expressed by the following
IF-THEN rules:
Figure 7.14 High-level schematic for the sequence involved in a fuzzy neural network. Modiﬁed from
Mathur, N., Glesk, I., Buis, A., 2016. Comparison of adaptive neuro-fuzzy inference system (ANFIS) and
Gaussian processes for machine learning (GPML) algorithms for the prediction of skin temperature in
lower limb prostheses. Med. Eng. Phys. 38 (10), 1083e1089.
Figure 7.15 ANFIS architecture involving two rules and two inputs. ANFIS, adaptive neurofuzzy
inference system.
268
Methods for Petroleum Well Optimization
Rule 1:
if WOB is A1 and RPM is B1; then ROP1 ¼ p1 WOB þ q1 RPM þ r1
Rule 2:
if WOB is A2 and RPM is B2; then ROP2 ¼ p2 WOB þ q2 RPM þ r2
p1, q1, r1 and p2, q2, r2 are consequent parameters. A1; A2; B1 and B2 are the fuzzy
sets that represent the linguistic labels. Each layer in the ROP ANFIS model consists of
the following node functions.
Layer 1 is the fuzziﬁcation layer. In this layer, the crisp value enters node i which is
converted into a fuzzy value associated with fuzzy set Ai or Bi. Then, the membership
level of this input is determined by a membership function of the respective fuzzy set.
The output of each node is calculated by using the following equations:
O1:i ¼ mAiðWOBÞ
for i ¼ 1; 2
(7.7)
O1:i ¼ mBiðRPMÞ
for i ¼ 1; 2
(7.8)
Layer 2 is the ﬁrst rule layer. The nodes of this layer are ﬁxed, and they multiply the
membership levels of all inputs according to each rule as follows, where O2:i denotes the
output of layer 2, and wi is the ﬁring strength:
O2:i ¼ wi ¼ mAiðWOBÞmBiðRPMÞ
for i ¼ 1; 2
(7.9)
In this layer, each node calculates the ﬁring strength of each rule via multiplication,
and the rule that has the high ﬁring strength matches the input data. The number of
nodes is equal to the number of rules in this layer.
Layer 3 is the second rule layer. In this layer, each node calculates the ratio of the
ﬁring strength of each rule to the sum of all rules. The ﬁring strength is calculated as
follows, where wi represents normalized ﬁring strength:
O3:i ¼ wi ¼
wi
w1 þ w2
for i ¼ 1; 2
(7.10)
Layer 4 is the defuzziﬁcation layer. The node function in this layer is calculated as
follows, where wi is normalized ﬁring strength, which is calculated from layer 3, ROPi
can be a polynomial function or constant number.
	
pi: qi: ri

is a consequent parameter
set for rule i (Jang, 1993):
O4:i ¼ wi  ROPi ¼ wi  ðpi WOB þ qi RPM þ riÞ
for i ¼ 1; 2
(7.11)
Data-driven machine learning solutions to real-time ROP prediction
269
Layer 5 is the output layer. It only has one node, and this node calculates the sum of
output of all nodes from layer 4 to produce the overall ANFIS output as follows:
overall output ¼ O5:i ¼
X
i
wiROPi ¼
P
iwiROPi
P
iwi
for i ¼ 1; 2
(7.12)
This ROP ANFIS model developed using the training data subset is then used to
predict the ROP for each data set record of the test subset to determine its accuracy.
7.4.2.3 Prediction of drilling rate by adaptive neurofuzzy inference system
The ANFIS model was constructed from the same data set as the BY mathematical ROP
model. After classiﬁcation of the data set, the ANFIS model was trained using MATLAB
software. The TSK fuzzy inference system was used to construct the ANFIS model, and a
hybrid rule algorithm was utilized to train the adaptive network. Model inputs included
depth, WOB, rotational speed, ﬂow rate, mud weight, pore pressure, and bit wear. The
only output is ROP
. In the model constructed, three parameters of membership function
are considered for each input data record. The membership function type is trimf, which
consists of three constants. The linguistic expressions for input data, except for bit type,
are low (L), moderate (M), and high (H). These linguistic labels state the relation be-
tween input and output data via fuzzy IF-THEN rules. The linguistic labels and cor-
responding membership functions for the Sarvak formation are summarized in Table 7.7.
In this study, IF-THEN rules are created according to the relationship between input
and output for each record of the training subset of the data set. The created rule base
contains 2187 rules, for example:
Rule 1:
if Depth is L and WOB is H and RPM is H and flow rate is H and mud weight is Land
pore pressure is L and bit wear is L then ROP1 ¼ f ðdepth: WOB.Þ is H
The last step is defuzziﬁcation, and drilling ROP is converted from a fuzzy expression
into a crisp value. A plot of predicted and measured penetration rates together with best
ﬁt line and correlation coefﬁcient

R2
values for the testing data set is given in Fig. 7.16.
7.4.2.4 Model performance analysis
Of the models compared here, ANFIS has the least amount of error compared with the
Bourgoyne and Y
oung model, with its average error less than 10% in all the studied
formations. Therefore, the ANFIS ROP model can be considered to be the most
appropriate tool for predicting the drilling ROP
. Fig. 7.17 shows the measured and
predicted values of ROP using the two different models evaluated. The amount of the
residual error for each of the developed models is shown in Fig. 7.18. Clearly, the residual
270
Methods for Petroleum Well Optimization
errors yielded by the ANFIS ROP model are lower than yielded by widely used ROP
model BY
.
In other words, the deviation of predicted values from measured values is less in
ANFIS. Analytical models consider the effect of a limited number of parameters to
predict the drilling rate, while there is no limitation on the number of input variables in
ANFIS, and the effect of any variable on drilling ROP can be considered and included.
However, mud weight, pore pressure, and formation strength do not need to be used as
input parameters if they are almost constant during drilling a formation. For those
reasons, ANFIS has the best performance in all studied formations. However, it is worth
noting that the AI systems, such as ANFIS, work better than other approaches only when
a large amount of data exists. ANFIS can predict ROP accurately within the range of
input data but is not able to predict the ROP beyond that range. In situations where a
large data set is not available to train the models, conventional mathematical methods are
likely to be superior to inference systems. Mathematical ROP models can predict the
ROP in all ranges, and they need less input data than ANFIS (Table 7.8).
Table 7.7 Linguistic labels and corresponding membership functions for the Sarvak formation.
Parameter
Linguistic term
Parameters of membership function
a
b
c
TVD
Low
827.5
995
1162.5
Moderate
995
1162.5
1330
High
1162.5
1330
1497.5
WOB
Low
2.45
4.6
11.65
Moderate
4.6
11.65
18.7
High
11.65
18.7
25.75
RPM
Low
8.5
85
161.5
Moderate
85
161.5
238
High
161.5
238
314.5
Flow rate
Low
2718.5
3312
3905.5
Moderate
3312
3905.5
4499
High
3905.5
4499
5092.5
Mud weight
Low
9.41
9.43
9.45
Moderate
9.43
9.45
9.47
High
9.45
9.47
9.49
Pore pressure
Low
8.51
8.53
8.55
Moderate
8.53
8.55
8.57
High
8.55
8.57
8.59
Bit wear
Low
0.101
0.0161
0.1332
Moderate
0.0161
0.1332
0.2503
High
0.1332
0.2503
0.3674
RPM, revolutions per minute; TVD, true vertical depth; WOB, weight on bit. There was no signiﬁcant change in mud
weight and pore pressure in the Sarvak formation.
Data-driven machine learning solutions to real-time ROP prediction
271
Figure 7.16 Measured versus predicted drilling rate for each formation applying the ANFIS ROP model. ANFIS, adaptive neurofuzzy inference
system; ROP, rate of penetration. Modiﬁed from Yavari, H., Khosravanian, R., Sabah, M., Wood, D.A., 2018. Application of adaptive neuro-fuzzy
inference system and mathematical ROP models for prediction of drilling rate. Iranian J. Oil Gas Sci. Technol. 7 (3), 1e13.
272
Methods for Petroleum Well Optimization
Another novel correlation was derived from the ANN model using the biases and
weights of neuron connections among input, hidden, and output layers. The new ROP
correlation is shown in Eq. (7.13) (Abdulmalek and Abdulwahab, 2019).
Figure 7.17 Measured and predicted drilling rates by different approaches.
-30
-20
-10
0
10
20
Residual error
ANFIS
Bourgoyne & Young
Figure 7.18 Residual errors of predicted values by ANFIS and Bourgoyne and Young models. ANFIS,
adaptive neurofuzzy inference system.
Table 7.8 Average percentage error for ANFIS and Bourgoyne and Young for each formation.
Formation type
ANFIS model (%)
Bourgoyne and young model (%)
Asmari
3.75
36.91
Ilam
3.01
13.93
Sarvak
10.79
14.88
Upper limestone
0.75
10.18
Dashtak
8.01
9.17
Surmeh
9.64
15.15
Kangan
6.32
14.44
ANFIS, adaptive neurofuzzy inference system.
Data-driven machine learning solutions to real-time ROP prediction
273
ROPn ¼
" X
N
i ¼ 1
w2i

e

w1i;1WOB þ w1i;2SPP þ w1i;3RPM þ w1i;4Flow pump rate
þw1i;6Torque þ w1i;5rmud þ w1i;7Viscosity Funnel þ w1i;8; ViscosityPlastic
þw1i;9 Yield point þ w1i;10solid þ b1i
2#
þ b2
(7.13)
7.4.3 The decision tree model
To discuss the decision tree model, MLP
, RBF
, and SVR, we use the data from a vertical
well shown in Table 7.9. The major ROP prediction parameters can be divided into two
main categories: mud logging data and well logging data. Mud logging by a dynamic
monitoring station encompasses WOB, bit rotational speed, ﬂow rate, PP
, and mud
weight. Well logging data are added from petrophysical logs, including compressional
and shear sonic wave logs, density logs, and gamma ray and neutron porosity logs, to
form a bank of input information. It is notable that in our gathered data set, the bit type
and formation type were constant, so these parameters have not been considered as input
vectors for developing the models.
Because there are different sampling rates for well logging and mud logging data, an
upscaling method was implemented to average the well logs so that the two sets of data
could be integrated into a uniform data set. The parameters, their range, unit, and
average are presented in Table 7.9. The overall data set consists of 1000 records divided
randomly into two parts: 70% (700 data sets) of the overall data was used to construct the
models, and the remaining 30% (300 data sets) was used to test the developed models.
The selected data are normalized to enhance accuracy of the systems; the following
Table 7.9 Statistical information for input and output parameters.
Coded factor
Parameter
Unit
Minimum
Maximum
Average
X1
Neutron porosity
e
0.3
0.43
0.35
X2
Density
gr/cc
2.21
2.42
2.37
X3
Shear wave velocity
us/ft
226.31
319.82
261.28
X4
Compressional wave velocity
us/ft
100.48
121.45
108.3
X5
Gamma ray
GAPI
70
117
102.47
X6
Weight on bit
1000 lb
0.32
22.23
10.75
X7
Bit rotational speed
RPM
117.29
143.63
135.58
X8
Pump pressure
PSI
3418
3830
3681.32
X9
Bit ﬂow rate
GPM
862.94
921.52
906.73
X10
Mud weight
kg/L
1.5
1.8
1.68
X11
Eaton pore pressure
kg/L
1.19
1.59
1.47
X12
Rate of penetration
m/h
2.27
35.12
24.86
274
Methods for Petroleum Well Optimization
relation is used for normalization of the input and output data between 1 and 1
(Deosarkar and Sathe, 2012):
xn
i ¼ 2  xi  xmin
xmax  xmin
 1
(7.14)
In Eq. (7.14), i is the number of parameters, and xmax and xmin represent the
maximum and minimum values of xi, respectively (Fig. 7.19).
7.4.3.1 Noise reduction
Measured data from the real world are always affected by noise. Even in the best
controlled conditions, errors in the measured data are typically around 5% or more (Orr,
1998; Redman, 1998). The statistical and ML literature offer various deﬁnitions for
Perform noise reducon using 
Savitzky–Golay (SG) method 
Data Collecon and Management 
Combine data from mulple sources 
Clean and prepare data 
Make data easily available for analysis 
Up-scaling and merging data set and 
noise reducon 
Feature selecon 
Training the models 
ROP predicon results 
Up scaling
Data set
Feature selecon using GA coupled with MLP 
Stascal models
Arﬁcial neural network 
RF
DT
SVR
MLP
Outputs
RBF 
Mud logging
Well logging 
Figure 7.19 Machine learning workﬂow.
Data-driven machine learning solutions to real-time ROP prediction
275
noise, but the common deﬁnition is that noisy data can inﬂuence the learning process
and increase learning time (Anemangely et al., 2018). Also, the extraction of the rules
from data and generalization of the trained model to the new data are big problems in the
presence of noise within the data (Lorena and de Carvalho, 2004; Garcia et al., 2015).
Noisy seismic data will produce inappropriate results when the low signal-to-noise ratio
(SNR) data are integrated. Detecting the source of the noise is a key step toward noise
reduction. Shift changes of drilling engineers, workovers and drilling tools replacement,
drilling string vibrations, washouts, and alterations in geological layers are possible
sources of errors in the data acquired at well (Anemangely et al., 2018).
We applied the SavitzkyeGolay (SG) smoothing ﬁlter to remove noise from the data.
In this method, the effect of noise from data is reduced using a polynomial function, with
the results of this function replacing initial (acquired) values. On the basis of least squares
error, a polynomial function of order n is ﬁtted to a number of points within an interval.
The order of the polynomial should be odd and greater than the chosen number of the
points within this interval. The data structure will be preserved if the order of
the polynomial is increased or the number of points within the interval is decreased. On
the other hand, a decrease in polynomial order or an increase in the number of points
within the interval eliminates part of the data structure and brings about further
smoothing. Therefore, determination of the optimal polynomial order and number of
points within the interval is of paramount signiﬁcance. To determine the optimal values
of the two parameters, daily reports for the well being studied and the geologic setting of
the ﬁeld were investigated. Based on sensitivity analysis, the optimal order of the
polynomial and number of points within the interval for the mud logging data were
deﬁned as 3 and 13, respectively. Undertaking the same analysis on petrophysical logs,
the optimal order of the polynomial and number of points within the interval were
deﬁned as 5 and 17, respectively.
7.4.3.2 Feature selection
ML is not advised for feature ranking because of the constraints imposed by the data set
and the constraints of the derived models. Feature ranking is a decision-making index
that identiﬁes the importance of features and can be used to conﬁne the dimensions of
the data set. Application of this approach offers the beneﬁts of shortening training time,
clarifying model interpretation, reducing overﬁtting, and lowering costs of data
collection (James et al., 2013). As the results of variable ranking and the estimation of
importance rely on the predictive model picked for the feature ranking approach (Szlek
and Mendyk, 2015), selecting the appropriate model to use remains a challenge.
In this study, we perform feature ranking, using a genetic algorithm along with an
ANN. Similar to the other metaheuristic algorithms, the initial population was
randomly generated in this method. After that, two different crossover and mutation
operations were executed on the population. During the population generation, the
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Methods for Petroleum Well Optimization
ﬁtness of each individual was acquired using the “rank-based” method, which is a
customary way of calculating ﬁtness. Accordingly, the error for each individual was
calculated based on a predeﬁned multilayer perceptron model. At the next stage, the
population was sorted based on the individual’s errors. The selection pressure was
deﬁned and applied to the roulette wheel selection method to recognize the features
with most impact. First, just the feature with the best ﬁtness was selected, and then the
number of features selected was increased to identify the amount of error reduction
when different numbers of features were selected.
The selected commixture of features can be varied in each step of this process. This
procedure makes it possible to assess the trend of reduction in the error rate associated
with increasing the number of features. The structure of the multilayer perceptron model
was composed of two hidden layers, with nine neurons in the ﬁrst layer and four in the
second layer. To remove the possible error from the initial random selection of weights
and biases, the neural network was run 10 times for each step of feature selection and the
average error was considered. The data set was randomly divided into two sets: the
training set using 70% of the data and the testing set using 30%. The total drilling costs for
each selection can be calculated from the associated costs of each set, using Eq. (7.15). To
avoid the model being overtrained, the training and testing error weights (Wtrain and
Wtest) were set to 0.45 and 0.55, respectively.
RMSEmodel ¼ Wtrain  RMSEtrain þ Wtest  RMSEtest
(7.15)
The results of applying feature selection to the desired data are presented in
Table 7.10. Associated error values in that table are plotted in Fig. 7.20. As this ﬁgure
shows, increasing the number of inputs makes the error curve decrease at the lower slope,
so that changes in error would be negligible once the number of the model’s input
parameters exceeds eight.
Table 7.10 Results of feature selection using genetic algorithm.
Number of inputs
Selected inputs
RMSE
1
X8
3.2909
2
X11, X6
2.1849
3
X11, X6, X7
1.602
4
X11, X9, X6, X8
1.3787
5
X11, X9, X8, X6, X7
1.3294
6
X7, X6, X9, X11, X2, X8
1.2397
7
X2, X8, X7, X11, X6, X9, X3
1.1964
8
X6, X5, X9, X11, X8, X3, X2, X7
1.1462
9
X5, X3, X2, X1, X9, X7, X6, X8, X11
1.1345
10
X8, X2, X1, X7, X3, X5, X4, X11, X9, X6
1.1332
11
X1, X5, X7, X2, X6, X11, X4, X3, X9, X10, X8
1.1315
RMSE, root mean square error.
Data-driven machine learning solutions to real-time ROP prediction
277
For this reason, the model with eight input vectors was selected to estimate ROP in
the next stage. The selected variables are mostly consistent with the ﬁndings in the
literature (Bezminabadi et al., 2017; Anemangely et al., 2018).
7.4.3.3 The regression tree
The classiﬁcation and regression tree (CART) technique belongs to a class of
nonparametric supervised learning tools implemented in a wide range of ﬁelds of
occupation and industries. It has proved to be effective in the execution of a cause-and-
effect analysis. Dynamic test (DT) methods in data mining ﬁt into one of two categories:
“the classiﬁcation tree” or “the regression tree.” The type of tree is decided from the
output variable type; if it is naturally categorical, then the classiﬁcation tree is formed; if
it is continuous, then it is sent into the regression tree (Breiman, 2017). In the study in
this section, the output variable is the amount of rate of penetration, which is considered
a continuous variable, so a regression tree analysis is used.
The procedure underlying both the classiﬁcation and regression tree DTs is the same.
DTs come out of separating observations into subgroups by creating splits based on
predictors (Maucec et al., 2015). This process is also known as binary recursive parti-
tioning, following a binary splitting process in which parent nodes are descended into
two child nodes, and subsequently the rows of the tree go down until reaching the
terminal nodes with no further splitting (Singh, 2017). The DT begins with identifying
the splitting criteria on the basis of input variables and minimizing the square error
between the observed and calculated value of the output variable. Then it produces one
root node and two child nodes (Singh, 2015). The same pattern is applied for each child
node repeatedly to produce further splitting. At last, the DT generates a logical sequence
of splitting criteria based on input variables, and a DT diagram shows a scheme of the
procedure.
In this study, a complex regression tree with a ﬁvefold cross-validation is used to ﬁt an
optimized regression tree for the training data set, and to achieve an accurate predictive
0
0.5
1
1.5
2
2.5
3
3.5
0
2
4
6
8
10
12
RMSE
Number of inputs
Figure 7.20 Results of applying genetic algorithm for feature selection method (associated error with
different number of inputs).
278
Methods for Petroleum Well Optimization
model. The optimal regression tree for the drilling rate with statistical information for
each node is shown in Fig. 7.21. The ﬁrst splitting decision is performed based on PP
.
The parent node in this tree shows that there are a total of 700 observations with a mean
value of 24.91 and standard deviation of 4.18.
7.4.4 Multilayer perceptron neural network model
Multilayer perceptron neural network (MLPNN) as one of the most common and
practical types of ANN is a branch of AI. This network can satisfactorily estimate values
for nonlinear correlations. The structure of an MLPNN comprises three main sections:
an input layer, hidden layer(s), and an output layer. A relationship is established between
input and output parameters through hidden layer(s) (Lashkarbolooki et al., 2012). The
output of MLPNN can be explained as in Eq. (7.16):
Yjk ¼ Fk
 X
Nk1
i ¼ 1
WijkYiðk1Þ þ Bik
!
(7.16)
where Yjk and Bik are the neuron j’s output from k’s layer and bias for neuron j in layer k,
respectively. Wijk represents the weight that is chosen on a random basis in the initial
stage of training procedure. Fk is the transfer function that may be considered in many
different forms such as identity function, binary step function, binary sigmoid, bipolar
Figure 7.21 Optimal regression tree with statistical information for each node shown in a text box.
Sabah, M., Talebkeikhah, M., Wood, D.A., Khosravanian, R., Anemangely, M., Younesi, A., 2019. A machine
learning approach to predict drilling rate using petrophysical and mud logging data. Earth Sci.
India 12, 319e339.
Data-driven machine learning solutions to real-time ROP prediction
279
sigmoid, Gaussian, and linear functions. Backpropagation (BP) algorithms are conven-
tionally used for training MLPNNs. Examples of these BP algorithms are scaled con-
jugate gradient (SCG), LM, gradient descent (GD), and resilient backpropagation (RP).
The value of each node in the hidden layers/output layer is calculated by multiplying
the weight of each node in the previous layer by the desired node in hidden layers/output
layer, and these values are summed. A bias value is then added to the obtained results, and
the resulting value is passed to the activation level through a transfer function to produce
the output. There are different activation functions, which can be used in the hidden
layer(s) and the output layer. The most widely used activation functions are Tansig in the
hidden layer(s) and Purelin in the output layer (Yilmaz and Kaynar, 2011). The deﬁnition
of these transfer functions are as follows:
Purelin: f ðxÞ ¼ x
(7.17a)
Tansig: f ðxÞ ¼
2
1 þ expð 2xÞ  1
(7.17b)
The MLP network model is considered to contain one hidden layer assigning Tansig
for the hidden layer and Purelin for the output layer activation function as in Eq. (7.18),
where: b2 and b1 are the bias vectors of the hidden and output layers, respectively; w1 is
the matrix of weight devoted to the hidden layer; and w2 states the same concept for the
output layer:
Output ¼ Purelinðw2  tansigðw1  x þ b1Þ þ b2Þ
(7.18)
The LM algorithm is applied as the training function for the model in this task.
Several hidden layers can be used in these types of neural network approaches,
although the possibility of reduction to a single layer is also approved in most cases.
280
Methods for Petroleum Well Optimization
However, more than one hidden layer can be employed for developing an MLPNN
procedure to predict precisely the required parameters that have high uncertainty. In this
study, two hidden layers in the MLPNN were considered to provide sufﬁcient accuracy,
with the Tansig transfer functions implemented for the hidden layers and Purelin transfer
functions for the output layer. As mentioned, the network’s performance is highly
dependent on the number of neurons in its hidden layer(s). Therefore, as a parallel study,
different numbers of neurons were used in the hidden layers to analyze the efﬁciency of
different networks using the mean square error (MSE) method. The MSE value of
training data versus the number of neurons in the hidden layers is shown in Fig. 7.22.
In this case, the optimum number of neurons was four neurons in the ﬁrst hidden layer
and six neurons in the second hidden layer, since this kind of network yields minimum
MSE error for training the data set. The properties of the developed MLPNN are shown
in Table 7.11.
7.4.5 The radial basis function neural network model
The radial basis function (RBF) neural network (RBFNN) is a neural network proposed
by Moody and Darken in the late 1980s. The feasibility of this type of neural network in
treating arbitrarily scattered data, easily generalizing for several dimensional space, and
providing spectral accuracy has made it a particularly popular alternative for MLP
Figure 7.22 Number of neurons in hidden layers versus MSE of training data set. MSE, mean square error.
Sabah, M., Talebkeikhah, M., Wood, D.A., Khosravanian, R., Anemangely, M., Younesi, A., 2019. A machine
learning approach to predict drilling rate using petrophysical and mud logging data. Earth Sci.
India 12, 319e339.
Data-driven machine learning solutions to real-time ROP prediction
281
(Elsharkawy 1998; Lashkenari et al., 2013). In addition, RBF network models are also
known for their accuracy in modeling nonlinear data and the feasibility of being trained
in a single direct procedure rather than using an iterative solution, as in MLP (Venkatesan
and Anitha, 2006). The structure of RBF is similar to MLP
, but the main difference
between these methods is that RBF has only one hidden layer, which consists of a
number nodes called RBF units. Each RBF network has two important parameters
describing the center location of functions and its deviation.
The hidden unit measures the distance between an input data vector and the center of
its RBF
. The RBF reaches its peak when the distance between its center and the input
data vector is zero, and then declines gradually as this distance goes up. There is only one
single hidden layer in an RBF network, and there are only two sets of weights: one
connecting the hidden layer to the input layer, and the second connecting the hidden
layer to the output layer. Those weights connecting the hidden layer to the input layer
contain the parameters of the basis functions. The weights connecting the hidden layer to
the output layer are used to form linear combinations of the activations of the basis
functions (hidden units) to generate the outputs. Since the hidden units are nonlinear,
the outputs of the hidden layer may be combined linearly so that processing is rapid
(Yilmaz and Kaynar, 2011). The system outputs can be shown in the following form,
where wT is the transposed output of layer vector, and fðxiÞ is the kernel function,
which is typically in Gaussian form:
f ðxiÞ ¼ wTfðxiÞ
(7.19)
Different methods such as random selection of centers, clustering, and density esti-
mation can be used to ﬁnd the centers in RBF networks. In this study, clustering is
selected. Since the k-means algorithm is more accurate for ﬁnding the centers, it is often
used for data clustering. The general form of approximation is displayed in the following
equation, where ci stands for the centers, N refers to the number of clusters used in the
Table 7.11 MLPANN properties.
Network type
Multilayer perceptron
Training function
Trainlm (LevenbergeMarquardt backpropagation)
Number of layers
3
Number of ﬁrst hidden layer neurons
4
Transfer function of ﬁrst hidden layer(s)
TANSIG
Number of second hidden layer neurons
6
Transfer function of second hidden layer
Tansig
Number of output layer neurons
1
Transfer function of output layer
Purelin
Performance function
MSE
Other parameter(s)
Default
MLPANN, multilayer perceptron artiﬁcial neural network; MSE, mean square error.
282
Methods for Petroleum Well Optimization
model, yk displays outputs obtained from the model, and M expresses the number of
inputs and outputs:
yk ¼
XN
i¼1wifkiðkxk  cikÞ
i ¼ 1.:N; k ¼ 1.:M
(7.20)
The maximum number of neurons (MNN) is a substantially determinant tuning
parameter in the description of these network structures. Here a trial-and-error
approach is taken to determine the optimum values of these parameters, although
there are different optimization algorithms that could be applied for this purpose
(Najaﬁ-Marghmaleki et al., 2017). Various RBF types of neural networks have been
developed by changing this parameter, together with performance monitoring based on
the MSE value calculation of the training data set. Fig. 7.23 shows the behavior of the
approach by considering the MSE values from the training data responses to the
maximum number of neurons. This ﬁgure shows the optimum value was observed at 40
for MNN.
7.4.6 Support vector regression model
In this method, the estimation of unknown values is conducted by employing an optimal
linear regression model in a new feature space, which is imaged by extending input data
mapped from the original space into a higher m-dimensional space (Vapnik, 2013). Now,
if the proposed training data in a p-dimensional input vector is assumed, together with a
Figure 7.23 MSE value of RBF-NN versus MNN. MNN, maximum number of neurons; RBFNN, radial basis
function neural network. Sabah, M., Talebkeikhah, M., Wood, D.A., Khosravanian, R., Anemangely, M.,
Younesi, A., 2019. A machine learning approach to predict drilling rate using petrophysical and mud
logging data. Earth Sci. India 12, 319e339.
Data-driven machine learning solutions to real-time ROP prediction
283
one-dimensional target vector, the ﬁnal formulization would be deﬁned as follows,
where fðxÞ is a nonlinear mapping function, and w and b are the weighting vector and
bias term of the regression equation, respectively (Bodaghi et al., 2015):
f ðxÞ ¼ wTfðxÞ þ b
(7.21)
The following risk function should be minimized using slack variables ( di; d
i ) to
determine the optimum values of w and b, where c is a constant parameter indicating the
trade-off between the ﬂatness and estimation error:
R f
ð Þ ¼ 1
2j w
j jj2 þ c
X
1
i ¼ 1

did
i

(7.22)
Subjected to
8
>
>
>
>
<
>
>
>
>
:
di  wTfðxiÞ  b  ε þ di
wTfðxiÞ þ b  di  ε þ d
i
di; d
i  0
Eq. (7.22) is solved based on a dual problem formulation and expressing Lagrange
multipliers, ai; a
i ˛½0; c, from which the following solution is acquired:
f ðxÞ ¼
X
l
i ¼ 1

ai  a
i

k

xi; x
i

þ b
(7.23)
k

xi; x
i

is the called kernel function. In this study, a three-order polynomial function is
used as a kernel function, which yields better results than the linear and Gaussian functions:
k

xi; x
i

¼

1 þ x
i xi
3
(7.24)
The most-used SVR kernel functions are listed in Fig. 7.24.
284
Methods for Petroleum Well Optimization
According to the brief description of the support vector regression (SVR), c and ε are
the two main constructive parameters of the SVR method, which need to be determined
through optimization techniques. Conventionally, a hybrid of grid search and pattern
search is used as an optimization method, as can be seen in several published research
articles concerning SVR application. In this method, optimization begins with a grid
search to arrive at a region close to the global optimum point. Following this, a pattern
search is executed over the constrained search zone surrounding the best point discov-
ered by the grid search. This combination removes the individual imperfections of grid
search and pattern search in obtaining SVR parameters.
In this section, we analyze the predicting performance of the models, using various
graphical and statistical methods to choose the best model. Regression plots of predicted
values versus measured data for the developed models are shown in Fig. 7.25. In this
ﬁgure, the vertical axes are predicted values, and the horizontal axes are measured values.
It is clear that the values of the regression coefﬁcient for most of the developed models
are highly acceptable, indicating that the proposed models are sufﬁciently accurate for the
prediction of drilling rate. However, this ﬁgure reveals that the SVR and MLPNN
models have higher regression coefﬁcients and better prediction performance than the
other developed models. The relative deviations of the developed models are presented
in Fig. 7.26. It is clear that the smallest deviations belong to the SVR and MLP models,
and the highest deviation belongs to the DT model. In other words, the deviation of the
estimated values predicted by SVR and MLP from the actual values is less than for the
other models. Another graphical method that is used to assess the prediction perfor-
mance of these models is shown in Fig. 7.27, where the measured data and predicted
Figure 7.24 Popular SVM kernel functions.
Data-driven machine learning solutions to real-time ROP prediction
285
values are plotted against the sequence of testing data points. Here the MLP model is the
most accurate predictor because predicted values obtained using MLP are in good
agreement with measured data.
There are also various statistical performance indices to assess the performance of the
models. In this study, variance account for (VAF), RMSE, performance index (PI), and
R2 are used to compare the prediction capacity of developed models, as in the following
equations, where z is the model output, y is the actual output, and p is the number of data
records in the data set. The VAF and RMSE approach has been used by Yılmaz and
Yuksek (2008) and Basarir et al. (2014), and the R2 and RMSE approach has been used
by Boyacioglu and Avci (2010).
Figure 7.25 The cross-plot for (A) DT, (B) SVM, (C) MLP, and (D) RBF. DT, dynamic test; MLP, multilayer
perceptron; RBF, radial basis function; SVM, support vector machine. Sabah, M., Talebkeikhah, M.,
Wood, D.A., Khosravanian, R., Anemangely, M., Younesi, A., 2019. A machine learning approach to predict
drilling rate using petrophysical and mud logging data. Earth Sci. India 12, 319e339.
286
Methods for Petroleum Well Optimization
RMSE ¼
 
1
p
X
p
r ¼ 1
ðyr  zrÞ2
!1
2
(7.25)
PI ¼

r þ VAF
100

 RMSE
(7.26)
VAF ¼

1  varðyr  zrÞ
varðyrÞ

 100
(7.27)
Figure 7.26 Relative deviations of developed models: (A) DT, (B) SVM, (C) MLP, and (D) RBF. DT, dynamic
test; MLP, multilayer perceptron; RBF, radial basis function; SVM, support vector machine.
Sabah, M., Talebkeikhah, M., Wood, D.A., Khosravanian, R., Anemangely, M., Younesi, A., 2019. A machine
learning approach to predict drilling rate using petrophysical and mud logging data. Earth Sci.
India 12, 319e339.
Data-driven machine learning solutions to real-time ROP prediction
287
R2 ¼ 1 
Pp
r¼1ðyr  zrÞ2
Pp
r¼1

yr  yr;mean
2
(7.28)
The accuracy of the proposed models is indicated by small values of RMSE and high
values of R2, PI, and VAF
. Table 7.12 indicates the values of RMSE, VAF
, PI, and R2 for
the developed models. MLP produces the highest values of R2 and VAF
, and also the
lowest RMSE, expressing the best performance among the models. The second-most-
accurate model is SVR, which has slightly lower prediction efﬁciency than MLP
. Both
models demonstrate good well drilling prediction performance.
0
5
10
15
20
25
30
35
40
0
20
40
60
80
100
120
140
160
180
200
220
240
260
280
300
Drilling rate
Number of data
Measured
Predicted
Figure 7.27 Measured and predicted drilling rate versus sequence of data points for SVR model as the
best
predictor.
SVR,
support
vector
regression.
Sabah,
M.,
Talebkeikhah,
M.,
Wood,
D.A.,
Khosravanian, R., Anemangely, M., Younesi, A., 2019. A machine learning approach to predict drilling
rate using petrophysical and mud logging data. Earth Sci. India 12, 319e339.
Table 7.12 Performance indices.
Model
Data set
RMSE
R2
VAF
PI
Decision tree
Test
1.8
0.8511
84.75
0.0395
Train
1.15
0.924
92.41
0.73
All
1.38
0.8975
89.76
0.4633
SVR
Test
1.17
0.9163
91.68
0.6973
Train
0.4896
0.9877
98.77
1.49
All
0.7636
0.9687
96.87
1.18
MLP
Test
1.3
0.9172
91.7
0.5753
Train
1.1
0.9365
93.66
0.7978
All
1.15
0.9284
92.85
0.7372
RBF
Test
1.49
0.8996
89.83
0.3521
Train
1.48
0.8723
87.33
0.326
All
1.48
0.8815
88.16
0.3355
MLP
, multilayer perceptron; RBF
, radial basis function; RMSE, root mean square error; SVM, support vector machine.
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Methods for Petroleum Well Optimization
The results show that decision trees do not exhibit the same high reliability and
effectiveness as ANNs in the estimation of the drilling rate. Moreover, the lowest pre-
diction accuracy among the implemented models is seen in DT (Sabah et al., 2019).
7.5 Summary
1. The comparison of the results of using the ANFIS and the BY mathematical ROP
model to predict the ROP in a gas ﬁeld suggests the following conclusions:
•
ANFIS is an accurate tool for the prediction of ROP
, but its performance depends
on various factors such as the number, accuracy, distribution of input data, and
type of membership functions.
•
Trial and error should be used to determine the proper membership functions,
since there is no direct way to do so. However, this method is time-consuming,
especially when the number of input variables increases, and sometimes it is not
possible to construct a reliable model for prediction of ROP using ANFIS.
•
Although mathematical ROP models have less accuracy than ANFIS, they can be
constructed with less data, and they always provide a reasonable estimation of
ROP
. Therefore, it is better to use ANFIS and mathematical ROP models
simultaneously for the prediction of drilling rate in a particular formation.
2. A large data set from drilling reports and petrophysical logs, including 11 variables,
was inspected. The implementation of an SG ﬁlter to reduce the effect of noise on
data not only shortened the training time but also provided the model with signif-
icantly lower error. In the next stage, a genetic algorithm along with an ANN was
applied to denoised data as a feature selection method to select the superior features
and reduce the input vector. Finally, eight variables were selected as the input data for
developing the models. The parameters that were considered as inputs were WOB,
bit rotational speed, pump ﬂow rate, standpipe pressure, pore pressure, gamma ray,
density log, and sonic wave velocity. The overall data set included 1000 data points
divided randomly into two parts; 70% of the overall data was used to construct the
models and the remaining 30% was used to assess the prediction performance of the
developed models. Statistical and graphical analysis showed that the prediction per-
formance of the developed models is sufﬁciently acceptable in the prediction of the
drilling rate.
7.6 Problems
Problem 1: Artiﬁcial neural network model for rate of penetration
The architecture of the backpropagation using an ANN model and a summary of the
statistics are presented in Fig. 7.28. The data ranges presented in the table are within the
Data-driven machine learning solutions to real-time ROP prediction
289
Figure 7.28 ANN model for ROP. ANN, artiﬁcial neural network; ROP, rate of penetration. Modiﬁed from Abdulmalek, A., Abdulwahab, A., 2019. New
artiﬁcial neural networks model for predicting rate of penetration in deep shale formation. Sustainability 11 (22), 6527. https://doi.org/10.3390/
su11226527.
290
Methods for Petroleum Well Optimization
acceptable known ranges for generating at least 50 numbers for each parameter by
stochastic methods. We want to execute several ANN trials to achieve the optimal se-
lection of the layers, a number of neurons and training, transfer, and network functions.
A two-layer ANN model is used for various number of neurons (1e20).
1. Present the results of two-layer trials at a different number of neurons.
2. Present the results of the best neuron number for each training function.
3. Present the results of various transfer functions.
Problem 2: Rate of penetration prediction and optimization
In this example, various drilling parameters were recorded nearly every meter, and
Table 7.13 lists typical values for 14 of these. The recorded drilling data were used to
predict ROP using the multiple regression approach and ANNs.
1. Present the architecture of the ANN employed during training and forecasting.
2. Present multiple regression and ANN ROP predictions for different degrees of
ﬁltering.
3. What is the average percentage error of multiple regression and ANN ROP pre-
dictions plotted against different n values that correspond to different degrees of
smoothness?
4. Estimate the correlation coefﬁcients of multiple regression and ANN ROP pre-
dictions against the degree of smoothness represented by n, as well as the correlation
between measured and smoothed ROP data sets.
Problem 3: Advanced model for rate of penetration prediction and
optimization
The structure of the ROP model can be divided into three stages as shown in Fig. 7.29.
In the ﬁrst stage, an integrated soft sensing method is introduced to establish the for-
mation drillability (FD) fusion submodel (level 1). In the second stage, the inputs are
selected by the mutual information method. In the last stage, the ROP submodel (level 2)
is established by the improved PSO-RBF neural network method, and it can be used for
the optimization and control of the ROP
.
Formation drillability fusion submodel stage
The input parameters have low-value density and data noise, making a single model
unsuitable for describing the FD. Therefore, various extreme learning machine models
are built, and several weak learners are combined into a strong learner (FD) by methods
such as the Nadaboost algorithm.
Data-driven machine learning solutions to real-time ROP prediction
291
Table 7.13 The recorded drilling data were used to predict rate of penetration.
Depth
gp
(lb/
gal)
rc (lb/
gal)
W
(kbls) d(in)
N (rpm) h()
P (lb/
gal)
q (gal/
min)
u (Cp)
dn (in)
W/
d (klb/
in)
h(cP)
Measured
ROP
(m)
(ft)
(m/h) (ft/h)
332.00
1089.24
10
8.87
13.10 12.25
40.80
0.0018
8.75
874.04
5.00
0.574
1.069 115.68
0.78
2.56
333.00
1092.52
10
8.84
14.24 12.25
54.40
0.0027
8.75
577.40
5.00
0.574
1.163 154.94
1.24
4.06
334.00
1095.80
10
8.84
7.63 12.25
57.80
0.0036
8.75
556.21
5.00
0.574
0.623 159.08
1.28
4.21
796.00
2611.55
9
9.04
26.24 12.25
62.70
0.0404
8.91
529.72
10.00
0.574
2.142 217.33
2.70
8.87
797.00
2614.83
9
9.04
25.57 12.25
62.70
0.0412
8.91
529.72
10.00
0.574
2.088 217.33
3.08 10.09
798.00
2618.11
9
9.04
27.56 12.25
61.80
0.0420
8.91
529.72
10.00
0.574
2.250 217.33
2.84
9.33
1266.00
4153.54
11
9.56
31.15 12.25
62.80
0.2675
9.50
524.42
16.00
0.574
2.543 104.89
2.43
7.97
1267.00
4156.82
11
9.56
31.13 12.25
62.80
0.2688
9.50
524.42
16.00
0.574
2.541 104.89
2.30
7.54
1268.00
4160.11
11
9.56
30.91 12.25
62.80
0.2700
9.50
524.42
16.00
0.574
2.523 104.89
2.31
7.57
1736.00
5695.54
11
9.49
31.90 12.25
65.40
0.0490
9.41
524.42
17.00
0.574
2.604 127.78
1.52
5.00
1737.00
5698.82
11
9.49
28.90 12.25
65.40
0.0499
9.41
524.42
17.00
0.574
2.359 127.78
1.33
4.36
1738.00
5702.10
11
9.49
31.70 12.25
64.00
0.0508
9.41
524.42
17.00
0.574
2.588 127.78
1.74
5.72
2206.00
7237.53
10.5
9.65
30.67 12.25
63.60
0.1989
9.58
529.72
20.00
0.750
2.503 115.19
2.70
8.87
2207.01
7240.85
10.5
9.65
30.36 12.25
59.50
0.2001
9.58
513.83
20.00
0.750
2.478 116.57
2.21
7.24
2208.00
7244.09
10.5
9.65
30.23 12.25
64.30
0.2012
9.58
524.42
20.00
0.750
2.467 115.64
3.03
9.94
2685.01
8809.09
14
9.93
28.66 8.50
90.00
0.0360
9.50
423.78
17.00
0.574
3.372
72.36
0.99
3.24
2685.32
8810.11
14
9.93
28.66 8.50
90.00
0.0362
9.50
423.78
17.00
0.574
3.372
72.36
1.24
4.07
2686.00
8812.34
14
9.93
28.66 8.50
90.00
0.0369
9.50
423.78
17.00
0.574
3.372
72.36
2.72
8.92
3167.61 10392.42
14
10.58
28.66 8.50
90.00
0.0385
9.91
476.75
17.00
0.750
3.372 101.11
2.96
9.71
3168.37 10394.92
14
10.58
28.66 8.50
90.00
0.0393
9.91
476.75
17.00
0.750
3.372 101.11
3.04
9.97
3169.63 10399.05
14
10.58
28.66 8.50
90.00
0.0407
9.91
476.75
17.00
0.750
3.372 101.11
5.04 16.54
3634.00 11922.57
14
10.56
27.56 8.50
60.00
0.1893
9.83
423.78
26.00
0.750
3.242 126.17
0.92
3.02
3635.19 11926.48
14
10.56
27.56 8.50
60.00
0.1922
9.83
423.78
26.00
0.750
3.242 126.17
1.19
3.90
3636.25 11929.95
14
10.56
27.56 8.50
60.00
0.1948
9.83
423.78
26.00
0.750
3.242 126.17
1.41
4.64
4104.22 13465.29
14
11.13
31.97 8.50
75.00
0.2655 10.41
423.78
29.00
0.750
3.761 123.99
1.07
3.51
4105.05 13468.01
14
11.13
31.97 8.50
75.00
0.2695 10.41
423.78
29.00
0.750
3.761 123.99
0.83
2.72
Adapted from Diaz, M.B., Kwang, Y.K., Kang, T
.H., Shin, H.-S., March 2018. Drilling data from an enhanced geothermal project and its pre-processing for ROP forecasting
improvement. Geothermics 72, 348e357. https://doi.org/10.1016/j.geothermics.2017.12.007.
292
Methods for Petroleum Well Optimization
Correlation analysis stage
The FD, mud logging parameters, and operational drilling parameters have nonlinear
relationships with each other. For this reason, the mutual information method is
introduced to select the inputs for the ROP submodel.
Rate of penetration submodel stage
Five parameters, namely, FD, depth, SWOB, RPM, and MW
, are selected as the inputs of
the ROP submodel. To establish the ROP submodel, a radial basis function neural
Figure 7.29 Structure of the ROP model. ROP, rate of penetration. Modiﬁed from Gan, C., Cao, W.,
Wu, M., Liu, K.-Z., Chen, X., Hu, Y., Ning, F., July 2019. Two-level intelligent modeling method for the rate of
penetration in complex geological drilling process. Appl. Soft Comput. 80, 592e602.
Data-driven machine learning solutions to real-time ROP prediction
293
network optimized by the improved particle swarm optimization (RBFNN-IPSO)
[algorithm] is proposed (Tables 7.14 and 7.15).
1. Evaluate RMSE results of the test set for the FD modeling methods using RBF
, a
deep learning method, such as stacked denoising autoencoder (SDAE), and SVR.
Table 7.14 Measured seismic wave time and drilling parameters.
Depth
m
T_measured
ms
Depth
m
T_measured
ms
Depth
m
T_measured
ms
462
340
1345
930
2448
1530
579
4430
1518
1030
2653
1630
718
530
1695
1130
2871
1730
864
630
1877
1230
3114
1830
1017
730
2062
1330
3391
1930
1178
810
2252
1460
3671
2030
Table 7.15 ROP, formation characteristic parameters and operational drilling parameters.
Depth
m
FD
SV
m/s
SWOB
KN/cm
RPM
r/min
BSHH
Kw/cm2
MW
g/cm3
ROP
m/h
80
2.46
1385
0.112
96
0.384
1.05
27.9
152
3.33
2463
1.12
96
0.384
1.05
16.9
320
3.38
2520
3.22
96
4.39
1.08
14.1
643
3.50
2642
3.86
96
4.6
1.14
9.2
1092
3.87
3006
3.86
96
3.3
1.16
6.8
1327
4.20
3307
3.86
96
2.54
1.17
5.1
1340
4.31
3400
3.86
56
2.56
1.17
2.2
1751
4.45
3512
7.07
56
2.42
1.14
3.8
2109
4.69
3705
7.07
56
2.63
1.19
2.3
2326
4.85
3833
7.07
56
2.65
1.19
2.1
2401
4.97
3918
7.07
56
2.64
1.21
2.1
2434
5.01
3950
7.07
56
2.64
1.22
1.7
2655
5.12
4029
7.07
56
3.54
1.24
1.5
2756
5.48
4280
7.07
56
3.54
1.25
1.4
2793
5.66
4400
6.43
56
3.54
1.24
0.7
2808
5.93
4574
2.57
56
3.56
1.24
0.7
2819
5.90
4556
3.7
56
7.39
1.21
0.5
3023
6.17
4720
6.94
56
7.71
1.17
2.1
3201
7.24
5314
8.33
56
7.94
1.17
1.7
3342
7.82
5600
8.33
56
7.86
1.17
1.8
3450
7.82
5600
8.33
56
7.04
1.16
1.8
3485
7.82
5600
7.41
60
7.04
1.17
2.2
294
Methods for Petroleum Well Optimization
2. Evaluate RMSE results of the test set for all ROP derived from the following
methods:
•
single-level RBF
•
two-level RBF
•
two-level SDAE
•
two-level SVR
Problem 4: Training and testing error
In this problem, we will consider the effect of training sample size n on a logistic
regression classiﬁer with d features. The classiﬁer is trained by optimizing the conditional
log-likelihood. The optimization procedure stops if the estimated parameters perfectly
classify the training data or they converge. The plot in Fig. 7.30 shows the general trend
for how the training and testing error changes as we increase the sample size n ¼ |S|.
Analyze this plot and identify which curve corresponds to the training and test error.
Speciﬁcally:
1. Which curve represents the training error?
2. In one word, what does the gap between the two curves represent?
Problem 5: Neural network
Given the truth table (Table 7.16), is it possible for a perceptron to learn the required
output? Explain the reasoning behind your decision.
Figure 7.30 Training error.
Data-driven machine learning solutions to real-time ROP prediction
295
Problem 6: Neural network
The truth table of a function is given in Table 7.17. Based on the partial perceptron given
below, complete the weights for X1, X2, and X3 to implement the required function.
The threshold T of the perceptron is 2.
Problem 7: Decision tree model
In an object recognition task, it is known that the objects come from one of two classes,
C1 or C2. Each instance of an object X has four features, X ¼ (x1, x2, x3, x4). An
experiment has collected 14 instances, and their feature values and classiﬁcation are
shown in Table 7.18.
Design a decision tree classiﬁer to class unknown feature vectors X1 ¼ (1, 3, 1, 2),
X2 ¼ (2, 2, 2, 2) and X3 ¼ (3, 1, 1, 1).
Problem 8: Modiﬁed Bourgoyne & Young’s model
One of the major problems in predicting the rate of penetration in horizontal and in-
clined wells is hole cleaning. For this reason, the ROP model that has been applied for
vertical wells often does not transfer directly to either horizontal or inclined wells.
Table 7.16 Truth table.
Input X1
0
0
1
1
Input X2
0
1
0
1
Output Y
0
1
1
0
Author.
Table 7.17 Truth table of a function.
Input X1
0
0
1
1
Input X2
0
1
0
1
Input X3
1
1
1
1
Output Y
0
0
0
1
Author.
296
Methods for Petroleum Well Optimization
To address this problem, a modiﬁed Bourgoyne & Y
oung’s model has been proposed
which incorporates hole cleaning:
ROP ¼ ð f1Þð f2Þð f3Þð f4Þð f5Þð f6Þð f7Þð f8Þð f9Þð f10Þð f11Þ
The effect of Hole Cleaning ( f9),( f10), ( f11) is deﬁned by:
f9 ¼
0
B
@
Abed=Awell
0:2
1
C
A
a9
f10 ¼
Vactual
Vcritical
a10
f11 ¼
 Cc
100
a11
Abed Area of cuttings bed, ft
^
2
CC Cuttings concentration for a stationary bed (by volume), corrected for viscosity
vactual mud velocity in annulus, ft/s
Table 7.18 Feature values and classiﬁcation of an object X.
X¼(x1, x2, x3, x4)
Classiﬁcation
x1
x2
x3
x4
C
1
1
1
1
C1
1
1
1
2
C1
2
1
1
1
C2
3
2
1
1
C2
3
3
2
1
C2
3
3
2
2
C1
2
3
2
2
C2
1
2
1
1
C1
1
3
2
1
C2
3
2
2
1
C2
1
2
2
I
C2
2
2
1
2
C2
2
1
2
1
C2
3
2
1
2
C1
1
3
1
2
¼?
2
2
2
2
¼?
3
1
1
1
¼?
Author.
Data-driven machine learning solutions to real-time ROP prediction
297
vcritical mud critical velocity in annulus, ft/s
Y
ou can ﬁnd more information about this new model presented in the thesis “Rate of
Penetration Estimation Model for Directional and Horizontal Wells”, by Reza Ettehadi
Osgouei: (https://etd.lib.metu.edu.tr/upload/12608737/index.pdf)
1. Test the model’s performance using your ﬁeld data in directional and horizontal wells
and machine learning algorithms.
2. Compare the performance of the modiﬁed model with the result of using Bourgoyne
& Y
oung’s original model.
Nomenclature
a constant coefﬁcients of the Bourgoyne and Y
oung model
ANFIS adaptive neurofuzzy inference system
ANN artiﬁcial neural network
BY Bourgoyne and Y
oung model
d bit diameter
D true vertical depth (ft)
E energy function
ECD equivalent circulation density (ppg)
FD formation drillability
FIS fuzzy inference system
h bit wear
h fractional tooth worn away
N rotary speed
N rotary speed at Bourgoyne and Y
oung model (rpm)
O output of each node in ANFIS
q ﬂow rate
ROP rate of penetration (ft/hr)
RPM rotary speed (rpm)
SAA simulated annealing algorithm
SWOB speciﬁc weight on bit
UCS uniaxial compressive strength of the rock (psi)
W weight on bit
WOB weight on bit (klb)
X,Y variables
db bit diameter (in)
dn bit nozzle diameter
Fj jet impact force (lbf)
gp pore pressure gradient of the formation
T0 initial temperature (C)
Tf freezing temperature (C)
wf wear function
a temperature decrement factor at SAA
298
Methods for Petroleum Well Optimization
h mud viscosity
m membership function
r mud density
rc equivalent circulating mud density at the hole bottom
s uniaxial compressive strength of the rock (psi)
References
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Bourgoyne, A.T., Millheim, K.K., Chenevert, M.E., Y
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., 2013. The Nature of Statistical Learning Theory. Springer Science & Business Media.
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., Anitha, S., 2006. Application of a radial basis function neural network for diagnosis of
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Further reading
Diaz, M.B., Kwang, Y
.K., Kang, T.H., Shin, H.-S., March 2018. Drilling data from an enhanced
geothermal project and its pre-processing for ROP forecasting improvement. Geothermics 72,
348e357. https://doi.org/10.1016/j.geothermics.2017.12.007.
Gan, C., Cao, W
., Wu, M., Liu, K.-Z., Chen, X., Hu, Y
., Ning, F
., July 2019. Two-level intelligent
modeling method for the rate of penetration in complex geological drilling process. Appl. Soft
Comput. 80, 592e602.
300
Methods for Petroleum Well Optimization
Open-source code
Y
ou can develop your models using open-source code for different tools available
on https://github.com/
Examples include:
•
Implementing SVM Kernel Functions in Python
•
ROP_prediction: Data Mining about Petroleum Engineering
•
ROP simulation generated by program - (Drilling Simulator): https://github.com/
Atashnezhad/Petroleum_Engineering
•
Machine Learning algorithms in MATLAB
•
Drilling Rate of Penetration (ROP) prediction using machine learning.
•
ROP prediction using deep neural network
•
Oil-and-Gas-ML-Application-ROP-Prediction
•
MIT Deep Learning
Data-driven machine learning solutions to real-time ROP prediction
301

CHAPTER FOUR
Wellbore trajectory optimization
Key Concepts
•
The determination of the trajectory of a complex wellbore is very challenging due to
the variety of possible well types, as well as the numerous complex drilling variables
and constraints. We present a complete review of constraints potentially affecting the
optimal well trajectory.
•
Three-dimensional well path design by single- and two-objective optimization is
carried out for the optimization of complex wellbore trajectories by applying ﬁve
evolutionary algorithms: genetic algorithm (GA); particle swarm optimization (PSO);
ant colony optimization (ACO); artiﬁcial bee colony (ABC); and harmony search
(HS).
•
A novel algorithm has been presented to ﬁnd the best well trajectory in wellbore
stability analysis.
4.1 Introduction
The drilling industry today relies on multivariate methods for petroleum well
optimization. These are used to analyze performance during all phases of well
construction and operations, including planning, drilling, and completion to ensure their
success. The determination of the trajectory of a complex wellbore is very challenging
due to the variety of possible well types, as well as the numerous complex drilling
variables and constraints. The well types include, among others, directional wells, cluster
wells, horizontal wells, extended reach wells, redrilling wells, and complex structure
wells. The drilling variables and constraints include wellbore length, inclination hold
angles, azimuth angles, dogleg severity (DLS), lateral length, casing setting depths, and
true vertical depth.
This chapter provides a roadmap of the various prediction models available for the
directional driller to use in the ﬁeld in real time. These models allow the driller to
anticipate and mitigate operational problems, and this translates into improved drilling
efﬁciency. In this chapter, we aim to bridge the current knowledge gap and provide a
comprehensive treatise on well trajectory optimization and, more generally, well
trajectory prediction and the underlying model parameters. In the following, we
describe the new methodology of combining the various models available.
Methods for Petroleum Well Optimization
 2022 Elsevier Inc.
ISBN 978-0-323-90231-1, https://doi.org/10.1016/B978-0-323-90231-1.00011-X
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113j
4.2 Constraints potentially affecting the optimal well trajectory
Rock properties, torque and drag, cost, and the relative location of existing wells
affect the optimal well trajectory, and all can potentially cause problems. To enable
engineers to steer the bit into the proﬁtable payzone, we need to design a platform for
both oil companies and directional drilling contractors to use for directional well path
planning, survey data management, and anticollision analysis. The well path design
should be planned in conjunction with wellbore stability evaluation, offset well analysis,
and relief well planning to deliver a drillable trajectory (Fig. 4.1).
4.2.1 Geomechanical constraints
The key point of well trajectory planning is to forecast and minimize all possible risks
associated with geomechanical conditions and technological parameters. An optimal
solution can be obtained with the use of a detailed geomechanical analysis. The
numerical model of the near wellbore zone is more informative than the analytical
solution. The geomechanical tools necessary for optimizing the trajectory are: image
analysis; geomechanical modeling; seismic ﬁeld analysis; near-wellbore numerical
simulation; and full 3D geomechanical modeling. Microimages help to determine the
presence of cavernousness, natural and induced fractures, geological boundaries, and
Figure 4.1 Constraints affecting the optimal well trajectory.
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Methods for Petroleum Well Optimization
bedding planes. Especially useful is a tool for determining the presence of collapse in the
areas of kick-off sidetracks. Geomechanical modeling helps to determine favorable
intervals for shearing and optimal mud density. To assess the risks during the sidetracking
operation, statistical analysis of the actual data needs to be carried out, taking into
account the spatial orientation of the sidetrack and the direction relative to the currently
acting stress state. A 3D geomechanical model of an oilﬁeld or gas ﬁeld makes it possible
to predict elastic properties and the strength of rock and near-wellbore stress state along
the well path. This allows us to predict the zones of possible drilling complications and to
minimize them.
3D geomechanical model building includes the following steps (Ovcharenko et al.,
2016):
1. Data audit: Collection and analysis of available data; selection of offset wells; and
analysis of the data quality for geomechanical modeling.
2. 1D geomechanical model: Build a model for offset wells; deﬁne reservoir conditions
to a ﬁrst approximation; and quality control (QC) of 1D model using wellbore
stability (WBS) analysis.
3. Preparing for 3D modeling: Seismic data cube analysis; geological model analysis; and
tectonic stress analysis.
4. 3D modeling: Computation of elastic and rock strength properties; and stress cube
computation.
5. QC of 3D model: Calibrating 3D MEM with 1D MEM; and calibrating with direct
measurementdmicroseism data and minifrac tests.
An important step in well trajectory optimization is the analysis of the mud weight
window versus wellbore inclination and well azimuth for every well section. Thus, the
optimal trajectory is estimated based on technological limitations: maximum weight on
bit and torque; well length; curvature; mud weight; and wellbore stability. The trajectory
also has to be based on constraints including the geological target, drill bit parameters,
well construction limitations, mud weight, maximum DLS, weight on bit and torque,
etc. As a result of all these constraints, it may not be feasible to drill the optimal well from
the perspective of wellbore stability. Nevertheless, all-inclusive analysis for well
construction provides a way to improve wellbore stability and to lower drilling risks
within the existing limitations. Figs. 4.2 and 4.3 illustrate the improvement in wellbore
stability achieved by optimizing well inclination change zone by zone.
Well trajectory design is used to determine the optimal safe mud weight window. In
addition, solving wellbore instability problems and decreasing the drilling risks are the
signiﬁcant considerations in well trajectory design. Wellbore stability boundaries are
calculated using the earth stresses, pore pressure, the mechanical properties of the rock,
and well orientation at each depth level. Mud weight is also selected to minimize
wellbore stability hazards, avoid wellbore instability, or, if that is unavoidable, select a
more “controllable” wellbore deformation.
Wellbore trajectory optimization
115
Figure 4.2 Wellbore stability improvement using the mud weight window through trajectory
optimization. Modiﬁed from Alchibaev, D.V., Glazyrina, A.Y., Ovcharenko, Y.V., Kalinin, O.Y., Lukin, S.V.,
Martemyanov, A.N., Zhigulskiy, S.V., Chebyshev, I.S., Sidelnik, A.V., Bazyrov, I.S., 2017. Application of 3D and
near-wellbore geomechanical models for well trajectories optimization. SPE Russian Petroleum Technol-
ogy Conference held in Moscow, Russia, 16e18 October.
Figure 4.3 Mud weight window with consideration of horizontal wellbore azimuth. Source: Author.
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Methods for Petroleum Well Optimization
4.2.2 Anticollision constraints
Anticollision (also called collision avoidance) is a measure taken by every drilling
operator to ensure that the wells are drilled in a safe manner. Anticollision monitoring is
a complex and demanding process that in the past was often not given high priority in
drilling programs. However, in the United States, for example, allowable well density
gradually increased from one well per 25 acres (101,171 m2) to one well per 10 acres,
with a proposed increase to one well per 5 acres (20,234 m2) in 2007 (Mahajan, 2018).
With this a worldwide trend, anticollision monitoring, has become a prime objective of
well planning (Poedjono et al., 2007). Generally, this process is carried out by applying a
set of rules called collision avoidance policies. The separation factor (SF) is one of the
leading collision avoidance indicators in the oil industry; the SF is deﬁned in Fig. 4.4.
In the calculation of the SF
, the center-to-center distance is the distance between the
reference and offset wells calculated by the closest 3-D approach method in feet, and
minimum separation is the summation of the radii of the EoU (ellipse of uncertainty) of
both wells in feet. The SF criterion deﬁnes the limit of risk and the actions that should be
carried out to mitigate the risks. However, all the other collision avoidance policies also
require an accurate understanding of the magnitude of uncertainty associated with the
positioning of planned (or reference) wells and offset wells. These uncertainties are
deﬁned by a combination of survey tools that measure inclination, azimuth, and depth of
the wellbore, and an appropriate error model that is applied to the survey tool to quantify
the uncertainties. In terms of visual representation, and from the standpoint of wellbore
placement, the uncertainties are represented by EoU. This ellipse of uncertainty is
generated at every survey station; these are usually a point at every 100 ft of measured
depth. As seen in Fig. 4.5, this ellipse has ﬁve components (Bang et al., 2009). These are
as follows:
1. verticalduncertainty in depth measurement;
2. horizontalduncertainty in determining the size of EoU;
Figure 4.4 Separation factor.
Wellbore trajectory optimization
117
3. along-holeduncertainty in determining the stretch of EoU;
4. high-sideduncertainty in determining the high side of the borehole; and
5. lateralduncertainty in determining the orientation of EoU.
An unintentional collision between two wellbores may have serious economic conse-
quences and also serious consequences in terms of health, safety, and the environment
(HSE). It is therefore important to evaluate the probability of such an event in the well
planning phase and reevaluate this at critical stages during the drilling phase. To increase
oil ﬁeld development to meet demand, in recent years drilling has occurred more
frequently in dense well spacing in developed oil ﬁelds, using inﬁll drilling technology, or
in high-density cluster wells, using cluster well drilling technology. However, the risk of
drilling such wells is increased by the possibility of well-collision accidents, which has
limited the use of these technologies. If we can accurately forecast such incidents and take
active measures to reduce or even eliminate the risk, then these two drilling technologies
could have a broader application.
4.2.3 Offset well constraints
An essential part of well planning is the offset well reviews. Offset performance can be
analyzed, and the optimal drilling parameters identiﬁed for a planned well in a given
formation or section by predicting rates of penetration on the proposed trajectory. Offset
drilling data provide some of the best well histories available. These show casing depth,
bits, mud properties, and geological trouble areas such as lost circulation or overpressured
areas. Offset drilling data help the driller plan the timetable for drilling and prepare an
appropriate authority for expenditure.
4.2.4 Well control constraints
Engineering and operational planning of the relief well should prevent any uncontrolled
hydrocarbon releases occurring during the execution of the drilling project, but, if a
Figure 4.5 Components of ellipse of uncertainty. Modiﬁed from Bang, J., Torkildsen, T., Bruun, B.T.,
Havardstein, S.T., 2009. Targeting challenges in northern areas due to degradation of wellbore positioning
accuracy. Presented at the SPE/IADC Drilling Conference and Exhibition, Amsterdam, Netherlands, 17e19
March. SPE-119661-MS. https://doi.org/10.2118/119661-MS.
118
Methods for Petroleum Well Optimization
blowout were to occur, it ensures that the response to such an unprecedented event
would be sufﬁcient and robust. We need to design the relief-well trajectory, with
logging-while-drilling technology for sensing resistivity vertically below the drill bit. Oil
and gas extraction in harsh and vulnerable environments requires drilling operations to
be safe and efﬁcient. Following the Deepwater Horizon oil spill in the Gulf of Mexico in
2010, industry rules on relief well planning were revised. As of 2012, the Norwegian
Petroleum Safety Authority (PTIL) will not approve any wells designed with the need for
two relief wells if a blowout occurs (Fig. 4.6). Several operating companies have had to
change their original casing design to ﬁt the PTIL’s new requirement. The operating oil
and gas companies have initiated in-house research on dual relief well drilling due to this
new regulation. Some of these companies have separately done research on different dual
relief well methods, but none of this research has been published or shared outside the
company.
The US government now requires operators to submit two complete relief well plans
to obtain a deepwater drilling permit. Trajectory planning software allows the drilling
engineer to validate the relief well plans early in the trajectory design stage. Thus, rapid
evaluation of key uncertainty parameters is possible early in the well planning process as
soon as these parameters are conﬁrmed, or simulations can be quickly updated as new
data are obtained.
The detailed design of the S-curve is usually dependent on the formation behavior
and the limitations of the survey tools. If the formation consists of unconsolidated rocks
or weak shale, drilling at the desired doglegs is a considerable challenge. The maximum
DLS allowable is 4e5 degrees per 30 m of wellbore length, but this gradient is kept as
low as possible to avoid borehole instabilities (Oskarsen et al., 2016). A smaller inter-
ception hole in the blowing well may be a result of weak formation if the required casing
design does not prevent borehole instabilities (Fig. 4.7).
Magnetic survey methods are used to locate the blowing wellbore when drilling a
relief well, as this is the only way proximity range tools are able to detect the blowing well
Figure 4.6 Schematic of a S-curved relief well (left), and plan view of development well and relief-well
trajectories (right). Modiﬁed from Upchurch, E.R., Falkner, S., Nguyen, C., Russell, K., 2017. Blowout pre-
vention and relief-well planning for the wheatstone big-bore gas-well project. SPE Drill. Complet. 32(3).
https://doi.org/10.2118/174890-PA.
Wellbore trajectory optimization
119
precisely. With this method, the interception point is restricted to a maximum of 10 m
below the deepest point of the blowing well where there is sufﬁcient steel or tools to
create a magnetic ﬁeld. If no magnetic material is present in the openhole section of the
blowing well, the last set casing shoe is the deepest possible intersection point; a casing is
often the only steel source able to create a sufﬁcient magnetic ﬁeld for the survey tools to
register. The most frequent point of interception is right below the last set casing shoe.
There are two different interception points that are applied around the last set casing
shoe: one is around 10 m above the last set casing shoe, and the other one is 10e20 m
below (Kallhovd, 2013). A deeper intersection point will increase the hydrostatic head,
increase the frictional pressure drop, and allow a lower-density kill ﬂuid to be used.
Precise determination of the well path is important to facilitate intersection of the well
bore with a relief well (blowout). The three categories of relief well trajectory design
shown in Fig. 4.8 are simple intercept, parallel track, and oriented intercept.
Surface-seismic-while-drilling (SSWD) technology (Fig. 4.9) could make it possible
to intersect the blowing well below the casing shoe. This method is not dependent on
the presence of any casing or steel tubular in the well to identify the relative wellbore
positions. The SSWD method is based on a surface seismic source generator and a
Figure 4.7 (A) Relief well trajectory (S-curve); (B) the relief well is aligned toward the blowing well with
an angle of 3e4 degrees. Modiﬁed from Haugen, I., 2011. Killing a High Rate Blowout Well through a
Relief Well. Master’s thesis, University of Stavanger.
120
Methods for Petroleum Well Optimization
Figure 4.8 Types of relief well directional designs. Modiﬁed from Goobie, R. B., Allen, W. T., Lasley, B. M.,
Corser, K., Perez, J. P., 2015. A guide to relief well trajectory design using multidisciplinary collaborative well
planning technology. Soc. Petrol. Eng. https://doi.org/10.2118/173097-MS.
Figure 4.9 Schematic of simulated relief well by SSWD. SSWD, surface seismic while drilling. Source:
Author.
Wellbore trajectory optimization
121
receiver array located on the seabed. Preliminary simulations indicate that it is possible to
locate the well paths of the two wells based on the seismic data. This method allows
real-time seismic monitoring of the well paths without interfering with the drilling
operation. This can allow for more precise relative wellbore positioning. The seismic
method may also be used in conventional well-killing operations to provide additional
information on the position of the two wellbores and potentially reduce the time needed
to drill the relief well. If an extended openhole section exists below the last set casing
shoe, SSWD may be used to intersect the blowing well at a deeper point. This offers
several advantages to the killing operation. Since SSWD does not need steel to be present
in the blowing wellbore to facilitate the direct intersection of the blowing well and the
relief well, this makes it possible to intersect a blowing well at the bottom of the openhole
section (Evensen et al., 2014).
4.3 Well path optimization
4.3.1 Three-dimensional well path design by single-objective
optimization
The comparative study in this section addresses the optimization of complex wellbore
trajectories by applying ﬁve evolutionary algorithms: GA; PSO; ACO; ABC; and HS.
The optimization of the gas or oil well path design using these algorithms involves
determining the combined wellbore length of a complex well involving multiple straight
and curved sections of various inclinations and orientations. The objective function is to
minimize the combined wellbore length subject to a number of speciﬁed constraints. As
measured depth drilled is typically directly proportional to the drilling cost, it follows
that the shortest overall wellbore design is likely to be the cheapest, although other
factors such as torque and casing design also play important roles in requiring multiple
objectives to be optimized (for example, Mansouri et al., 2015). The particular well path
targets and constraints applied are those used by Shokir et al. (2004), and further utilized
by Atashnezhad et al. (2014) and Khosravanian et al. (2018), to illustrate the performance
of tuned-PSO and other algorithms. They are also used by Mansouri et al. (2015) to
illustrate the multiobjective optimization performance of GA. The lengths of the curved
sections of the wellbore in the example are calculated by the radius-of-curvature method,
based on the curves being achieved at constant rates of curvature (Figs. 4.10 and 4.11).
The curvatures of the curved wellbore sections are achieved using the following
formulae (Shokir et al., 2004). Symbols and abbreviations used in Eqs. (4.1)e(4.4) are
explained in Figs. 4.10 and 4.11, and in the nomenclature section.
a ¼
1
DMD
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
ðq2  q1Þ2 sin2
f2 þ f1
2

þ ðf2  f1Þ2
s
(4.1)
122
Methods for Petroleum Well Optimization
r ¼ 1
a ¼ 180  100
p  T
(4.2)
DMD ¼ r 
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
ﬃ
ðq2  q1Þ2 : sin2
f2 þ f1
2

þ ðf2  f1Þ2
s
(4.3)
The wellbore scenario evaluated in this study consists of seven component sections
constituting the complete well path, as illustrated in Fig. 4.11. The overall wellbore
length is therefore calculated by summing the lengths of the seven component sections
that are calculated separately for each well path design considered.
TMD ¼ DKOP þ D1 þ D2 þ D3 þ D4 þ D5 þ HD
(4.4)
Each of the metaheuristic algorithms mentioned has been tuned for the wellbore path
optimization example (that is as deﬁned in Figs. 4.10 and 4.11 and Table 4.1) and then
evaluated and compared for performance (that is objective function value and compu-
tational time consumed), associated with four different numbers of iterations executed
Figure 4.10 Calculation of the length for a deviated section of the well trajectory describing the terms
used to deﬁne the different angles and components of the wellbore trajectory. MD, measured depth;
TVD, true vertical depth. After Khosravanian, R., Mansouri, V., Wood, D.A., Alipour, M.R., 2018. A
comparative study of several metaheuristic algorithms for optimizing complex 3-D well-path designs. J.
Petrol. Explor. Prod. Technol. 8, 1487e1503. https://doi.org/10.1007/s1320 2-018-0447-2; Atashnezhad, A.,
Wood, D.A., Fereidounpour, A., Khosravanian, R., 2014. Designing and optimizing deviated wellbore
trajectories using novel particle swarm algorithms. J. Nat. Gas Sci. Eng. 21, 1184e1204. https://doi.org/10.
1016/j.jngse.2014.05.029.
Wellbore trajectory optimization
123
Figure 4.11 The vertical plane of a horizontal well with the operational parameters (from Khosra-
vanian et al., 2018) developed from the wellbore scenario studied originally by Atashnezhad et al.
(2014), and Shokir et al. (2004). Khosravanian, R., Mansouri, V., Wood, D.A., Alipour, M.R., 2018. A
comparative study of several metaheuristic algorithms for optimizing complex 3-D well-path designs. J.
Petrol. Explor. Prod. Technol. 8, 1487e1503. https://doi.org/10.1007/s1320 2-018-0447-2; Atashnezhad, A.,
Wood, D.A., Fereidounpour, A., Khosravanian, R., 2014. Designing and optimizing deviated wellbore
trajectories using novel particle swarm algorithms. J. Nat. Gas Sci. Eng. 21, 1184e1204. https://doi.org/10.
1016/j.jngse.2014.05.029; Shokir, E.M., Emera, M.K., Eid, S.M., Wally, A.W., 2004. A new optimization model
for 3-D well design. Emir. J. Eng. 9(1), 67e74.
Table 4.1 Constraints applied to the example well path.
TVD
Minimum ¼ 10,850 ft. Maximum ¼ 10,900 ft.
HD
2500 ft
Dogleg severity
T1, T2,T3,T4,T5  5/100 ft.
Minimum value of inclination angles
41 ¼ 10, 42 ¼ 40, 43 ¼ 90
Maximum value of inclination angles
41 ¼ 20, 42 ¼ 70, 43 ¼ 95
Minimum value of azimuth angles
q1 ¼ 270, q2 ¼ 270, q3 ¼ 270
q4 ¼ 330, q5 ¼ 330, q6 ¼ 355
Maximum value of azimuth angles
q1 ¼ 280, q2 ¼ 280, q3 ¼ 280
q4 ¼ 340, q5 ¼ 340, q6 ¼ 360
Kick-off point depth
Minimum ¼ 600 ft. Maximum ¼ 1000 ft.
Drawdown point depth
Minimum ¼ 6000 ft. Maximum ¼ 7000 ft.
Third build point depth
Minimum ¼ 10,000 ft. Maximum ¼ 10,200 ft.
First casing setting depth
Minimum ¼ 1800 ft. Maximum ¼ 2200 ft.
Second casing setting depth
Minimum ¼ 7200 ft. Maximum ¼ 8700 ft.
Third casing setting depth
Minimum ¼ 10,300 ft. Maximum ¼ 11,000 ft.
HD, horizontal displacement; TVD, true vertical depth.
After Shokir, E.M., Emera, M.K., Eid, S.M., Wally, A.W
., 2004. A new optimization model for 3-D well design. Emir. J.
Eng. 9(1), 67e74.
124
Methods for Petroleum Well Optimization
(which are 200, 2000, 5000, and 10,000 iterations). Table 4.2 shows the results of the
comparison after 200 iterations, with the performance trends for the objective function
illustrated in Fig. 4.12. Note that the results of the different algorithms presented in
Tables 4.2e4.4 are from Khosravanian et al. (2018), and the algorithm codes used in that
Table 4.2 Comparative results of tuned metaheuristic algorithms applied to the wellbore
optimization example, each evaluated with 200 iterations.
GA
ACO
ABC
HSO
PSO
Optimal solution
founddTMD (feet)
15,040
15,284
15,024
15,066
15,023
Computational time
(seconds)
18
357
13
0.62
3.84
ABC, artiﬁcial bee colony; ACO, ant colony optimization; GA, genetic algorithm; HSO, harmony search optimization;
TMD, total measured depth.
From Khosravanian, R., Mansouri, V
., Wood, D.A., Alipour, M.R., 2018. A comparative study of several metaheuristic
algorithms for optimizing complex 3-D well-path designs. J. Petrol. Explor. Prod. Technol. 8, 1487e1503. https://doi.
org/10.1007/s1320 2-018-0447-2.
0
20
40
60
80
100
120
140
160
180
200
1.505
1.51
1.515
1.52
1.525
1.53
x 10
4
Min. Wellbore Length (feet)
Iteration
Figure 4.12 Performance trends for GA, ABC, HSO, PSO, and ACO algorithms applied to the wellbore
optimization example for 200 iterations. The trends show that ACO performs the worst. Based upon
200 iterations, the ABC algorithm shows the best performance, because of its faster convergence
toward an acceptable optimum. ABC, artiﬁcial bee colony; ACO, ant colony optimization; GA, genetic
algorithm; HSO, harmony search optimization; PSO, particle swarm optimization. Modiﬁed from
Khosravanian, R., Mansouri, V., Wood, D.A., Alipour, M.R., 2018. A comparative study of several meta-
heuristic algorithms for optimizing complex 3-D well-path designs. J. Petrol. Explor. Prod. Technol. 8,
1487e1503. https://doi.org/10.1007/s1320 2-018-0447-2.
Wellbore trajectory optimization
125
study have been reexecuted on the same computer (Intel Core i5 2430M 2.4 GHz, 4 GB
DDR3 Memory) used to evaluate the other algorithms assessed in this study. This en-
sures that the computational times reported here for the PSO algorithm are consistent
with those reported for the other algorithms studied. Of course, running these algo-
rithms on different computer systems with different processor speeds is likely to lead to
different results in terms of computation time, although the objective function search
trends and solutions derived would be the same as those presented here.
Table 4.3 Comparative results of tuned metaheuristic algorithms applied to the wellbore
optimization example, each evaluated with 2000 iterations.
GA
HSO
PSO
ACO
ABC
Optimal solution
founddTMD (feet)
15,024
15,024
15,023
15,239
15,023
Computational time
(seconds)
24
2.01
210
1350
2200
ABC, artiﬁcial bee colony; ACO, ant colony optimization; GA, genetic algorithm; HSO, harmony search optimization;
PSO, particle swarm optimization; TMD, total measured depth.
Table 4.4 Comparison of the best results achieved by each tuned metaheuristic algorithms applied to
the wellbore optimization example.
GA
HSO
ABC
ACO
Optimal solution founddTMD (feet)
15,023
15,024
15,023
15,239
Number of iterations needed to be
fully converged
2000
2000
200
2000
Computational time (seconds)
27
2.01
13
1257
KOP (feet)
877
1000
996.338
827
Second BU depth (feet)
7000
7000
6999.9864
7000
Third BU depth (feet)
10,200
10,200
10,200
10,200
First inclination (deg)
10
10
10
13
Second inclination (deg)
40
40
40
40
Third inclination (deg)
90
90
90
92
First azimuth (deg)
270
270
270
276
Second azimuth (deg)
280
280
276
272
Third azimuth (deg)
270
272
273
279
Fourth azimuth (deg)
340
337
338
339
Fifth azimuth (deg)
337
339
340
333
Sixth azimuth (deg)
356
359
357
359
First DLS (degree/100 feet)
0.75
0.82
0.84
0.96
Second DLS (degree/100 feet)
1.71
1.69
1.69
1.75
Third DLS (degree/100 feet)
3.31
3.31
3.27
3.38
ABC, artiﬁcial bee colony; ACO, ant colony optimization; DLS, doglog severity; GA, genetic algorithm; HSO, harmony
search optimization; TMD, total measured depth.
From Khosravanian, R., Mansouri, V
., Wood, D.A., Alipour, M.R., 2018. A comparative study of several metaheuristic
algorithms for optimizing complex 3-D well-path designs. J. Petrol. Explor. Prod. T
echnol. 8, 1487e1503. https://doi.
org/10.1007/s1320 2-018-0447-2.
126
Methods for Petroleum Well Optimization
It is apparent from Table 4.2 and Fig. 4.12 that better convergence toward an
acceptable minimum objective function value is achieved by the ABC algorithm after 200
iterations, taking just 13 s of computational time. HSO completes 200 iterations in only
0.62 s, but it fails to converge to an acceptable minimum value for the objective function.
The ABC algorithm reaches its ﬁnal convergence by 200 iterations and does not need
further iterations to improve upon those results. The GA and HSO algorithms need
further iterations to ﬁnd better minimum solutions for the objective function. On the
other hand, the discrete ACO algorithm performs much worse than the other algorithms
evaluated, failing to even come close to an acceptable minimum value for the objective
function and taking the most computational time. It is concluded that the ACO algorithm
as conﬁgured is not suitable for problems such as the wellbore path optimization example,
with a wide, continuous feasible solution space and multiple local optima. Because the
GA, HSO, and ACO need more iterations to reach their ﬁnal convergence, further
iterations were executed for these three algorithms. Table 4.3 and Fig. 4.13 show the
comparative performance of these algorithms after 2000 iterations are completed.
This comparative study addressing the optimization of complex wellbore trajectories
by applying ﬁve evolutionary algorithms (GA, PSO, ACO, ABC, and HS) leads to the
following conclusions:
1. HS, PSO, and ABC, as conﬁgured for the complex wellbore problem evaluated,
result in very rapid convergence toward the global optimum trajectory total measured
0
200
400
600
800
1000
1200
1400
1600
1800
2000
1.5
1.505
1.51
1.515
1.52
1.525
1.53 x 10
4
Iteration
Min Wellbore Length (feet)
Figure 4.13 Performance trends for GA, PSO, ABC, ACO, and HSO algorithms applied to the wellbore
optimization example for 2000 iterations. The trends show that PSO and ABC do not have any
improvement at iterations above 200, whereas the GA and HSO reach their ﬁnal convergence during
the execution of the 2000 iterations. ABC, artiﬁcial bee colony; ACO, ant colony optimization; GA,
genetic algorithm; HSO, harmony search optimization; PSO, particle swarm optimization.
Wellbore trajectory optimization
127
depth (TMD) of 15,023 feet. This demonstrates their suitability for optimizing
problems with continuous solution spaces involving multiple local optima that
potentially obscure the global optimum.
2. HS, despite its simplicity, performs the best compared with other algorithms
considered in terms of computational speed. Although HS requires a high number of
iterations of the algorithm to achieve convergence to the global optimum, it does so
in the shortest algorithm execution time.
3. GA achieves convergence to the global optimum but takes longer to do so than HS,
PSO, and ABC. On the other hand, ACO fails to converge toward an acceptable
optimum for the wellbore trajectory problem evaluated. It is also shown to be un-
suitable for solving this type of problem without signiﬁcant modiﬁcations or hy-
bridization, which adds excessive computation time to its execution.
4.3.2 Three-dimensional well path design by two-objective
optimization
Optimizing wellbore trajectories to reach an offset subsurface location, involving a
complex combination of vertical, deviated, and horizontal well components, requires
the minimization of both wellbore length and frictional torque on the drill string. This is
particularly relevant to shallow horizontal wells, which are often limited in their extent
by torque. By minimizing both wellbore length and torque, it is likely that a wellbore
designed to reach a speciﬁc target can be drilled more quickly and cheaply than other
potential trajectories. However, these two objectives are often in conﬂict with each other
and are related in a highly nonlinear manner. A multiobjective genetic algorithm
(MOGA) methodology is developed and applied with two objective functions, which
are wellbore length and torque, to develop a set of Pareto optimal solutions that can aid
the selection of less risky/less costly well trajectory designs. The MOGA performance is
compared with single-objective function studies of a speciﬁc wellbore scenario. The
results indicate that the MOGA methodology outperforms single-objective function
approaches, leading to rapid convergence toward a set of Pareto optimal solutions.
Given that the wellbore trajectory in the scenario analyzed consists of seven parts, the
torque must be calculated for each part separately and summed to provide the total
torque. The value of T from the aforementioned relation is the second objective function
expressed by Eq. (4.5):
T ¼ Tvertical þ T1 þ T2 þ T3 þ T4 þ T5 þ Thorizontal
(4.5)
The calculation of the torque commences from the bottom of the drill string (that is
when the drill string is at the wellbore’s total depth, TD, then the T calculation starts at T
horizontal) and continues, stepwise, upward to the well head (meaning the last
component added to the calculation is at Thorizontal). For the wellbore scenario evaluated,
the torque calculation involves the following assumptions:
1. The drill string has no axial movements (just rotation).
2. The drill string has a 0.1 ft. radius and 0.3 kN/ft. weight.
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Methods for Petroleum Well Optimization
3. The friction factor is 0.2, and the buoyancy factor is 0.7.
Fig. 4.14 illustrates the GA evolutionary trends for the two objective functions in four
runs with different GA behavioral-parameter setups. The beneﬁt of using a parameter-
adaption approach in searching the feasible space, and in enhancing the convergence
trend and convergence time, is clear from the trends shown in Fig. 4.14. Table 4.5
Figure 4.14 Objective function trends compared for adaptive GA behavioral parameters versus
constant-value GA behavioral parameters. The four runs illustrated were all conducted using the same
initial population; i.e., they all begin at the same points on the left sides of the two graphs. Run 1
(adaptive parameters) shows the best convergence because of the high mutation probability applied
in the initial iterations and ﬁnds lower value optima for the objective functions because of the high
crossover probability applied in later iterations. Run 4 shows a smoother trend line and worst
convergence toward its optima and ﬁnds the least attractive optima of the four runs, because it
applies the lowest mutation probability (0.2). The torque scale is in units of N.ft  104, and the length
scale is in units of ft  104. From GA, genetic algorithm. Mansouri, V., Khosravanian, R., Wood, D.A.,
Aadnøy B.S., 2015. 3-D well path design using a multi-objective genetic algorithm. J. Nat. Gas Sci. Eng.,
27(Part 1), 219e235. https://doi.org/10.1016/j.jngse.2015.08.051.
Wellbore trajectory optimization
129
speciﬁes the values of PC, PM, and RM for each run. The best performing (lower) line in
Fig. 4.14 represents Run 1 (Table 4.5), the case using adaptive GA behavioral parameters.
The three other lines, Runs 2, 3, and 4, use constant values for the GA behavioral
parameters, across all iterations. More rapid convergence and better results are clearly
achieved using adaptive behavioral parameters.
The complexity of the well path makes it impossible to observe and characterize an
explicit relationship between torque on the drill string and well path parameters.
However, it seems likely that in feasible solutions with deeper kick-off (that is angle
build-up) points, a higher DLS is required to reach the target zone. This causes more
friction torque on the drill string.
The Pareto frontier in typical two-objective optimization problems, such as the one
described here, shows an ascendant trend in the value of one objective function’s value
versus a descendant trend in the value of the other. In the case studied, the Pareto frontier
established does not mean that the lowest torque is along the longest well path or that the
shortest well path results in the highest torque on the drill string. The relationship
between those objective functions is nonlinear, and the GA optimization has identiﬁed
some high-performing samples that deﬁne the Pareto frontier; there are likely to be other
solutions, not found by the algorithm due to the behavioral constraints imposed, that
would extend, or provide ﬁner detail, along that frontier.
The solutions that yield absolute minimum values of each objective function should
be subsets of the real Pareto optimal solution set. As shown in Fig. 4.15, the dots
corresponding to single-objective function GA optimization for the torque and the
wellbore length (triangle shaped) do extend the trace of the sample of Pareto optimal
solutions (spherical shaped) in both directions. This lends weight to the conclusion that
the dots related to single-objective function GA optimization, as shown in Fig. 4.15, do
indeed represent a subset of the real (complete) Pareto optimal solution set.
By deﬁnition, all the solutions along the Pareto frontier have certain optimal char-
acteristics (meaning they are Pareto optimal solutions). Once the Pareto frontier is
deﬁned, the challenge is to identify which one of those Pareto optimal solutions should
Table 4.5 Key GA behavioral parameter values applied to four runs, for which the two objective
function trends are illustrated in Fig. 4.14.
Run 1
Run 2
Run 3
Run 4
PC
Values adapted as
iterations progress
0.2
0.5
0.8
PM
0.8
0.5
0.2
RM
0.5
0.5
0.5
Minimum torque
(N.ft) obtained
11,745
11,784
11,847
11,877
Minimum wellbore
Length (ft) obtained
15,023
15,022
15,035
15,033
GA, genetic algorithm.
From Mansouri, V
., Khosravanian, R., Wood, D.A., Aadnøy B.S., 2015. 3-D well path design using a multi-objective
genetic algorithm. J. Nat. Gas Sci. Eng., 27(Part 1), 219e235. https://doi.org/10.1016/j.jngse.2015.08.051.
130
Methods for Petroleum Well Optimization
be selected for the well design. Analyzing the cost and time to achieve each Pareto
optimum solution is one way to discriminate, but that is beyond the scope of this chapter.
Table 4.6 shows selected Pareto optimal solutions from MOGA, together with the
results of single-objective function GA for the wellbore length and torque functions for
the wellbore scenario studied. Whereas it seems likely that in the optimum solutions
under single-objective function GA, the lowest torque is along the well path that has the
lowest DLSs, and the lowest length of the wellbore has the highest DLSs, it is not so clear
cut for solutions along the Pareto frontier. There may be other geometric factors in
addition to DLS that are inﬂuencing the wellbore length versus torque relationship.
4.4 Well trajectory optimization for preventing wellbore
instability
Typical trajectory-monitoring models only consider the geometrical approach,
without considering the rock mechanics and operational constraints in a single
algorithm. Trajectory corrections could compromise the wellbore stability and drilling
efﬁciency. For this reason, a model is needed to couple the trajectory-control model with
the rock mechanics and mechanical and hydraulic effects in the trajectory correction.
This approach allows the driller to see the “big picture” when reviewing the designed
well path.
Figure 4.15 Pareto frontier obtained for the two objective functions, wellbore length and torque,
during MOGA, together with the results of single-objective function GA for the wellbore length and
torque functions for the wellbore scenario studied. GA, genetic algorithm; MOGA, multiobjective
genetic algorithm. Modiﬁed from Mansouri, V., Khosravanian, R., Wood, D.A., Aadnøy B.S., 2015. 3-D well
path design using a multi-objective genetic algorithm. J. Nat. Gas Sci. Eng., 27(Part 1), 219e235. https://
doi.org/10.1016/j.jngse.2015.08.051.
Wellbore trajectory optimization
131
4.4.1 Constraint range of the inclination and azimuth angle
To identify the effective stresses around the wellbore, and to learn about the potential
failure of the wellbore, modeling the stresses at the borehole is one of the most
commonly used methodologies. Aadnøy and Looyeh (2011) applied analytical methods
to obtain the stress state at the directional wellbores using in situ principal stresses. When
a borehole is drilled to create a space in the rock mass, stress is transmitted to the wellbore
wall, leading to stress concentration. This concentration can be calculated utilizing a
common procedure. In the following, we present the elastic solution procedure. Con-
version of the three coordinate systems is shown in Fig. 4.16.
4.4.1.1 The MohreCoulomb criterion
On the basis of the stress distribution in Fig. 4.16, the collapse pressure model can be
deduced by using the failure criterion. The MohreCoulomb (M-C) criterion is
commonly used in the analysis of wellbore stability and can be expressed as follows:
s ¼ s tan 4 þ c0
(4.6)
Table 4.6 Results of MOGA and results of previous studies on the same wellbore trajectory scenario.
MOGA design (Pareto optimal Solutions)
Single-
objective
GA on
torque
Single-
objective
GA on
length
TMD (ft.)
15,131 15,190 15,117 15,022 15,042 15,021 15,160 15,077 (15,228)
15,019
Torque
(N.ft.)
11,769 11,752 11,772 11,834 11,812 11,860 11,761 11,779 11,738
(12,257)
TVD (ft)
10,853 11,850 10,854 10,850 10,855 10,850 10,850 10,856 10,850
10,850
DKOP (ft)
1000
1000
1000
1000
1000
1000
1000
1000
994
1000
DD (ft)
6998
6998
6998
6998
6998
6998
6998
6998
6968
70,000
DB (ft)
10,166 10,166 10,166 10,197 10,197 10,197 10,197 10,166 10,097
10,200
41 (degree)
10
10
10
10
10
10
10
10
10
10
42 (degree)
40
40
40
40
40
40
40
40
40
40
43 (degree)
92
94
91
90
90
90
90
90
92
90
q 1(degree)
270
270
270
270
270
270
270
270
270
270
q2 (degree)
280
280
280
280
280
280
280
280
280
280
q3 (degree)
280
280
280
280
280
278
278
280
280
276
q4 (degree)
331
331
331
333
331
333
333
331
330
340
q5 (degree)
331
331
331
333
331
333
333
331
330
340
q6 (degree)
357
357
357
357
357
357
357
357
359
356
T1 (/100 ft) 0.83
0.83
0.83
0.83
0.83
0.83
0.83
0.83
0.82
0.83
T3 (/100 ft) 1.65
1.65
1.65
1.66
1.66
1.66
1.66
1.65
1.62
1.68
T5 (/100 ft) 3.23
3.23
3.23
3.38
3.38
3.38
3.38
3.23
3.00
3.31
GA, genetic algorithm; MOGA, multiobjective genetic algorithm; TMD, total measured depth; TVD, true vertical depth.
From Mansouri, V
., Khosravanian, R., Wood, D.A., Aadnøy B.S., 2015. 3-D well path design using a multi-objective
genetic algorithm. J. Nat. Gas Sci. Eng., 27(Part 1), 219e235. https://doi.org/10.1016/j.jngse.2015.08.051
132
Methods for Petroleum Well Optimization
The M-C criterion also can be represented by the maximum and minimum principal
stresses. By applying Eq. (4.6), we can obtain the following, where sn and sn are the
maximum and minimum principal stresses, respectively, given in MPa. c0 and f are the
cohesion and the internal friction angles of the rock, respectively. The MohreCoulomb
criterion uses unconﬁned compressive strength (UCS) and angle of internal friction (f)
to assess the failure, and then it can be expressed in terms of the maximum and minimum
principal stresses, s1 and s3, and we can deﬁne a function, 4.7, to determine the s1:
s1 ¼ sc þ qs3
(4.7)
q ¼ tan 2

45 þ f
2

¼ 1 þ sin f
1  sin f
sc ¼ 2c tan

45 þ f
2

¼ 2c cos f
1  sin f
(4.8)
Therefore, we can deﬁne a function, F, to determine the shear failure:
F ¼ ðsc þ qs3Þ  s1
(4.9)
Once the function F is lower than 0, the shear failure will occur. Thus, we can predict
the safe MW by Eq. (4.9). Based on the aforementioned model, we can obtain the
constraint range of the inclination angle and azimuth angle of the trajectory optimization
model by inversion. Combined with the M-C criterion, the ﬁnal wellbore stability
constraints can be presented as the formula (Xu and Chen, 2018):
8
<
:
alb  a  aub
flb  f  fub
(4.10)
Figure 4.16 Generalized stress transformation system for a deviated well.
Wellbore trajectory optimization
133
4.4.1.2 The MogieCoulomb failure criterion
To overcome the dilemma encountered in the analysis of wellbore stability, various
researchers have proposed a large number of true triaxial failure criteria. Although these
true triaxial failure criteria could predict the inﬂuence of the intermediate stress, s2,
much more accurately, most of these failure criteria required a greater number of special
strength parameters. The MG-C criterion can be expressed as:
sox ¼ a þ bsm;2 & soct ¼ 1
3

ðs1  s2Þ2 þ ðs2  s3Þ2 þ ðs3  s1Þ21
2
sm;2 ¼ 1
2 ðs1 þ s3Þ
(4.11)
Here, a is the intersection of the line on soct-axis, and b is the inclination of the line.
The strength parameters a and b are related to the friction angle and the cohesive strength
of the rock and can be calculated by following equations:
a ¼ 2
ﬃﬃﬃ
2
p
3 c0 cos 4
(4.12)
b ¼ 2
ﬃﬃﬃ
2
p
3
sin 4
(4.13)
This criterion can be expressed as the following mathematical formula:
FMGC ¼ a þ 1
2 ðs1 þ s3Þb  1
3

ðs1  s2Þ2 þ ðs2  s3Þ2 þ ðs3  s1Þ21
2
(4.14)
If the formula is less than 0, the rock shear failure occurs. Based on the aforementioned
model, we can obtain the constraint range of the inclination angle and azimuth angle of
the trajectory optimization model by inversion. Combined with the MogieCoulomb
(MG-C) criterion, the ﬁnal wellbore stability constraints can be presented as a formula:
8
<
:
alb  a  aub
flb  f  fub
(4.15)
4.4.2 Algorithm to achieve the optimum well trajectory
Preventing wellbore instability is one of the main concerns in well trajectory design in
the petroleum ﬁeld. In this section, we look at the study of a novel algorithm that has
been suggested to ﬁnd the best well trajectory in wellbore stability analysis (Kasravi et al.,
2017). FLAC3D software was utilized to carry out the stability analyses. For validation of
the method, an analytical method based on elastic solution was applied to compare the
134
Methods for Petroleum Well Optimization
results. Due to the time-consuming process of optimization, the team tried to develop a
proxy model to describe the behavior of the actual wellbore simulator. If a limited
number of simulations can be performed on the simulator (FLAC3D numerical code)
and provide a high coefﬁcient of determination for the proxy, the simulator can be
replaced by this proxy model. Nonlinear state and complex processes are other problems
in the optimization process. Sometimes, an analytical method cannot ﬁnd the best so-
lution. In such cases, conducting an intelligent method with application of feedback
control may be useful. The proposed algorithm that we look at here covers the problems
mentioned. The ﬂowchart of a GA-proxy feedback control algorithm for the global
minimum mud pressure required (GMMPR) determination is shown in Fig. 4.17.
In Fig. 4.17A, a group of azimuth and inclination angles has been selected, and
minimum mud pressure required (MMPR) for these samples has been calculated using a
feedback control algorithm. To build a proxy model, input variables consisting of azi-
muth, inclination angle, and mud weight, with the normalized yield zone area (NYZA)
as the output variable, have been proposed until the coefﬁcient of determination of
proxy is not satisﬁed. Then a new sample is selected and added to other samples to build a
convenient proxy model. In Fig. 4.17A, GA in association with the feedback control
algorithm has been used to ﬁnd the optimum well trajectory.
As illustrated in Fig. 4.17B, the proxy model was applied to wellbore stability analysis
instead of FLAC3D numerical code. In the following, we explain the major steps of the
proposed methodology.
Failure occurs when F is less than or equal to zero. According to this equation, mud
weight that prevents failure in each mode of failure can be calculated. The calculations of
the induced stresses are conducted using a generated MATLAB ﬁle, and the mud
pressure is estimated in different trajectories to prevent wellbore instability.
In many instances in deep well drilling, rock exhibits large deformations consistent
with a plastic yielding behavior rather than a pure linear elastic behavior. To enable
realistic solutions to be obtained for the wellbore drilling problem, a more complex
elastoplastic model is therefore required (Chen et al., 2012). Elastoplastic calculations
include an incremental stressestrain relation and a yield surface (Han et al., 2005). In this
method, to analyze the wellbore stability risk, a criterion based on the size of the yielded
zone (that is NYZA) was used. Due to the susceptibility of the yielded zone to swelling
during trip and to mechanical erosion by drill string, the size of this zone indicates the
likelihood of an instability problem (Hawkes et al., 2002). Because the NYZA was
considered as the output and the mud pressure as the input of ﬁnite difference code, the
NYZA was adjusted at a constant (NYZA ¼ 1) using the trial-and-error method. The
process based on blind trial-and-error solution is time-consuming, so a proportional
controller is proposed to deal with the problem. It is characterized by intelligent
repeatability and varied attempts that are continued until success. For the purpose of
optimization, GA, proxy models, proportional controller, and ﬁnite difference code
were considered as the four constituents of the algorithm being studied (see Fig. 4.18).
Wellbore trajectory optimization
135
Figure 4.17 Proposed algorithm to achieve the optimum well trajectory: (A) proxy modeling and
(B) optimization algorithm for wellbore stability analysis using the analytical method based on
elastoplastic assumption. Modiﬁed from Kasravi, J., Safarzadeh, M.A., Hashem, A., 2017. A population-
feedback control based algorithm for well trajectory optimization using proxy model. J. Rock Mech.
Geotech. Eng. 9(2), 281e290. https://doi.org/10.1016/j.jrmge.2016.07.010.
136
Methods for Petroleum Well Optimization
The proxy model approximates the existing wellbore model. It should be able to
describe the extremely nonlinear response of the real model, be easy to build, and be
simple in its application (Zubarev, 2009). The purpose of using a metamodel is to acquire
the maximum amount of information on the model in a minimum number of simula-
tions to reduce optimization time. The proper selection of input data can create a high-
ﬁdelity proxy model. As speciﬁed, to attain high-ﬁdelity proxy model, it is essential to
select sampling data in all of the search space with a low interval. Random sampling,
Latin hypercube sampling, and orthogonal sampling are well-known sampling
algorithms in the surrogate modeling ﬁeld. Random sampling has been selected as the
sampling method because it is simple for calculation, and the wellbore stability model is
not very complicated. Surrogate models (proxies) were developed using artiﬁcial neural
network (ANN) to approximate the design space with higher efﬁciency. ANN is widely
used to approximate complex systems that are difﬁcult to model using conventional
modeling techniques such as mathematical modeling. As is known, ANN is a network
with nodes or neurons analogous to the biological neurons. The nodes are inter-
connected to the weighted links and organized in layers. The performance of a neural
network depends mainly on the weights of its connections. If correct weights can be
trained, then an ANN can do an exceptional job (Liu et al., 2009). The neural model
used in the study consisted of an input layer with three nodes (mud weight, azimuth, and
inclination), an output layer with one node (NYZA), and one hidden layer with seven
nodes. A schematic process of the ANN with a hidden layer is shown in Fig. 4.18.
4.4.3 Well trajectory optimization
Estimation of stability around a wellbore during mud circulation was conducted using
ﬁnite difference method. In this context, the dimensions of the generated model for the
Figure 4.18 Structure of ANN and proxy model. ANN, artiﬁcial neural network. Modiﬁed from Kasravi,
J., Safarzadeh, M.A., Hashem, A., 2017. A population-feedback control based algorithm for well trajectory
optimization using proxy model. J. Rock Mech. Geotech. Eng. 9(2), 281e290. https://doi.org/10.1016/j.
jrmge.2016.07.010.
Wellbore trajectory optimization
137
well, using FLAC3D numerical code, were 12 m  12 m  2 m, and the diameter of the
wellbore was considered to be 0.077 m. The ranges of inclination and azimuth angle are
shown in Fig. 4.19. The M-C plasticity model was used for materials that yield when
subjected to shear loading. The M-C model is the most applicable for general
engineering studies. Also, the M-C parameters of cohesion and friction angle are usually
available more often than other properties of geoengineering materials (Itasca, 2012).
To obtain a better description of the stress state around the wellbore, a combination of
an elastoplastic constitutive model and the M-C failure criterion was employed to
analyze the effect of inclination and azimuth, as well as in situ stress ﬁeld, on the stability
of the wellbore. The input data for mechanical stability analysis of the well are shown in
Table 4.7.
These parameters were selected based on a previous study in this ﬁeld. The vertical
stress was determined according to the density log. Horizontal stresses were predicted
and evaluated from the leak-off test and formation microimager (FMI) log. Rock
properties were estimated by shear and compressional waves. Finally, a rock properties
evaluation was performed by laboratory data. The in situ stress regime in this ﬁeld is
reverse fault: SH > Sh > SV.
A radial cylinder mesh was carried out to perform stability analysis: in a radial cyl-
inder model, 12 reference points are used to create a model.
The elastic method was utilized to analyze the mechanical stability of a depleted well
in an oilﬁeld. Fig. 4.20 illustrates the hydrostatic mud pressure required for wellbore
Figure 4.19 Dimensions of wellbore. Modiﬁed from Kasravi, J., Safarzadeh, M.A., Hashem, A., 2017. A
population-feedback control based algorithm for well trajectory optimization using proxy model. J. Rock
Mech. Geotech. Eng. 9(2), 281e290. https://doi.org/10.1016/j.jrmge.2016.07.010.
138
Methods for Petroleum Well Optimization
Table 4.7 Geomechanical parameters for the depleted well.
Tensile
strength,
T (MPa)
Cohesion,
c (MPa)
Friction
angle,
4 ()
Poisson’s
ratio, n
Pore
pressure,
Pp (MPa)
Vertical
stress,
Sv (MPa)
Maximum
horizontal
stress, SH (MPa)
Minimum
horizontal
stress, Sh (MPa)
Safe mud
pressure,
Pw (MPa)
Azimuth,
AZ ()
Inclination,
INC ()
Depth,
H (m)
0
7.6
43
0.29
35.4
79.2
92.3
84.6
40.5
90
30
3399
From Kasravi, J., Safarzadeh, M.A., Hashem, A., 2017. A population-feedback control based algorithm for well trajectory optimization using proxy model. J. Rock Mech.
Geotech. Eng. 9(2), 281e290. https://doi.org/10.1016/j.jrmge.2016.07.010.
Wellbore trajectory optimization
139
stability versus azimuth and inclination angle calculated by using the M-C criterion. It
reveals that the collapse pressure of a vertical borehole (i ¼ 0) is higher than that of the
depleted borehole (except that the azimuth is close to the minimum horizontal direc-
tion), so the depleted boreholes are more stable than the vertical boreholes. It is also
obvious that drilling in the direction of maximum horizontal stress (where a ¼ 0),
regardless of the inclination, can better avoid borehole collapse. So drilling parallel to the
maximum horizontal stress direction is the most stable state in this case. Therefore, a
depleted borehole with an inclination of 51 that is drilled in the maximum horizontal
stress direction is the best one, and corresponding mud pressure in this trajectory is
45.43 MPa. The worst scenario is drilling a horizontal well in the minimum horizontal
stress direction. Fig. 4.20 also shows that the predicted MMPR to prevent instability in
the current trajectory (a ¼ 90 ; i ¼ 30) is 49.3 MPa.
As shown in Fig. 4.21, the optimization code obtains the GMMPR after 100 iter-
ations, which is related to the best well trajectory, equal to 37.75 MPa (about 1.1 MPa
less than the calculated pressure in the current well trajectory), so the best well trajectory
was obtained at the azimuth of 350.4 and the inclination angle of 66.9. The appropriate
performance of the feedback controller in this methodology for optimal trajectory is
shown in Fig. 4.22. As depicted in this ﬁgure, increasing the mud pressure leads to lower
NYZA, and the borehole will be more stable until the NYZA is equal to 1 at Pw ¼
37:75 MPa.
Figure 4.20 Mud pressure versus azimuth and inclination angle. Modiﬁed from Kasravi, J., Safarzadeh,
M.A., Hashem, A., 2017. A population-feedback control based algorithm for well trajectory optimization
using proxy model. J. Rock Mech. Geotech. Eng. 9(2), 281e290. https://doi.org/10.1016/j.jrmge.2016.07.010.
140
Methods for Petroleum Well Optimization
Table 4.8 compares these two methods and reveals that the optimum azimuth differs
slightly. However, the optimum inclination values for the two methods are different. It is
shown that the predicted mud pressure for the elastic method is higher than the elas-
toplastic solution. The main reason is that, in the elastoplastic method, even after the
stress around the borehole exceeds the formation strength, it does not fail completely or
collapse in the borehole wall. It should be noted that the GMMPR of elastoplastic
analysis is close to the practical safe mud pressure.
Figure 4.22 Performance of feedback controller for optimum well trajectory. Modiﬁed from Kasravi, J.,
Safarzadeh, M.A., Hashem, A., 2017. A population-feedback control based algorithm for well trajectory
optimization using proxy model. J. Rock Mech. Geotech. Eng. 9(2), 281e290. https://doi.org/10.1016/j.
jrmge.2016.07.010.
Figure 4.21 MMPR versus iteration. MMPR, minimum mud pressure required. Modiﬁed from Kasravi, J.,
Safarzadeh, M.A., Hashem, A., 2017. A population-feedback control based algorithm for well trajectory
optimization using proxy model. J. Rock Mech. Geotech. Eng. 9(2), 281e290. https://doi.org/10.1016/j.jrmge.
2016.07.010.
Wellbore trajectory optimization
141
4.5 Summary
•
For each evolutionary algorithm conﬁgured, a key set of control parameters exist that
require a degree of tuning for the algorithms to optimize speciﬁc problems efﬁciently
(that is to converge as rapidly as possible to the global optimum, while avoiding being
trapped at local optima). The results of this study with respect to wellbore trajectory
problems indicate that applying constant values to these control parameters typically
does not result in their optimum performance. Rather, the best results were achieved
by applying dynamically varying values to those control parameters; meaning values
that change with each iteration of an algorithm, depending on how the optimization
trend evolves. For instance, if an algorithm becomes trapped for a number of iter-
ations at or in the vicinity of a local optima, a change in control parameter value(s)
can be triggered that forces the algorithm to widen its exploration activities and
minimize its local search around that local optima. This approach is considered to
have potential applications for other complex optimization problems involving
multiple local optima that potentially prevent an algorithm from reaching the global
optimum.
•
This chapter introduced a new algorithm for analyzing wellbore stability using GA-
proxy feedback control, and it could reduce time of optimization process. The high
coefﬁcient of determination of test and train data veriﬁes the ability of ANN to
predict NYZA from the simulation of the wellbore model. Therefore, this network
can be used instead of FLAC3D software during the optimization process.
Furthermore, acquiring data to build a high-ﬁdelity proxy model is automatically
performed by coupling MATLAB and FLAC3D codes. In an elastoplastic solution,
the optimum azimuth of the wellbore is near the maximum horizontal stress direction
(350.4 degrees), and the optimum inclination angle is 66.9 degrees. But in an elastic
solution, at a wellbore inclination of 51 degrees, the MMPR for wellbore stability
was found at the azimuths of 180 degrees and 360 degrees, which represents drilling
Table 4.8 Comparison of two methods.
Method
Optimum
azimuth ()
Optimum
inclination ()
GMMPR
(MPa)
Current
trajectory mud
pressure (MPa)
Current
trajectory
mud pressure
error (%)
Elastic
0
51
45.43
49.3
22
Elastoplastic
350.4
66.9
37.75
38.86
4
GMMPR, global minimum mud pressure required
From Kasravi, J., Safarzadeh, M.A., Hashem, A., 2017. A population-feedback control based algorithm for well trajectory
optimization using proxy model. J. Rock Mech. Geotech. Eng. 9(2), 281e290. https://doi.org/10.1016/j.jrmge.2016.
07.010.
142
Methods for Petroleum Well Optimization
in the maximum horizontal stress direction as the safest drilling direction. Com-
parison of the simulated results with ﬁeld data shows that the GMMPR predicted by
the mentioned algorithm is close to the practical safe mud pressure. In the direction of
the maximum horizontal stress, failure happens due to the combination of shear and
tensile stresses, but in the minimum horizontal stress direction, failure does not occur.
4.6 Problems
Problem 1: New mathematical modeling
Considering the 3D complexity wellbore trajectory with multiple variables and complex
constraint conditions that are presented in Section 4.3.1:
1. Propose and compare how we can develop a new fast algorithm for the problem
discussed in Section 4.3.1 for enhancing real-time processing ability and higher
optimization accuracy and speed.
2. Propose and discuss the new mathematical modeling for optimizing the energy path
to reach the target.
3. Propose and discuss the new mathematical modeling for optimizing the total drilling
cost to reach the target.
4. Propose and discuss the new mathematical modeling for optimizing the SF along a
directional well trajectory to minimize collision risk.
5. Propose and discuss the new ACO algorithm for optimizing the problem in Section
4.3.1 with signiﬁcant modiﬁcations or hybridization.
MATLAB code and more information for this problem is available in Atashnezhad et al.,
2014 in the Open-source code. Y
ou can assume whatever other information you need to
solve it.
Problem 2: Probabilistic constraints
Formulate and solve the well path optimization in Section 4.3.1 in the presence of
probabilistic constraints that are presented in Chapter 2.
Problem 3: Uncertainty anti-collision analysis of cylindrically shaped
wells (volumetric safety factor)
•
Formulate a mathematical equation for the volumetric anti-collision analysis of
cylindrically shaped wells for use in the safety factor calculation in three-dimensional
drilling (see Fig. 4.23).
•
Present an algorithm using line geometry and dual number algebra to explore the
geometry of cylindrical wells anti-collision analysis in three-dimensional drilling by
calculating the distance d and the angle q between two lines (see Fig. 4.24).
Wellbore trajectory optimization
143
For example, line S1 can be deﬁned by points c
!and f
! or point c
! and direction
vector s
! (see Fig. 4.24 and Eqs. 4.15 and 4.16).
S1 ¼
 
f
!  c
!


 f
!  c
!


; c
!
f
!  c
!


 f
!  c
!


!
¼ ð s
!; c
! s
!Þ
(4.15)
S2 ¼
 
g
!  d
!


 g
!  d
!


; d
!
g
!  d
!


 g
!  d
!


!
¼

w
!; d
! w
!	
(4.16)
Fig. 4.24 Different vectors in volumetric anti-collision analysis.
Figure 4.23 Contact point of a cylinder-cylinder collision of two wells.
144
Methods for Petroleum Well Optimization
Nomenclature
DLS dogleg severity (degrees/100 ft)
GMMPR global minimum mud pressure required
MMPR minimum mud pressure required
NYZA normalized yielded zone area
r, q, z borehole cylindrical coordinate system
x, y, z wellbore rectangular coordinate system
q absolute change in direction
ø wellbore azimuth
m coefﬁcient of friction
r density
4 the friction angle of the rock (degrees)
c0 the cohesive strength of the rock (MPa)
s the shear stress on the failure plane (MPa)
De error
sV
, sH, sh in situ stress coordinates
References
Aadnøy, B.S., Looyeh, R., 2011. Petroleum Rock Mechanics: Drilling Operations and Well Design. Gulf
Professional Publishing, Boston.
Alchibaev, D.V
., Glazyrina, A.Y
., Ovcharenko, Y
.V
., Kalinin, O.Y
., Lukin, S.V
., Martemyanov, A.N.,
Zhigulskiy, S.V
., Chebyshev, I.S., Sidelnik, A.V
., Bazyrov, I.S., 2017. Application of 3D and near-
wellbore geomechanical models for well trajectories optimization. In: SPE Russian Petroleum Tech-
nology Conference held in Moscow, Russia, 16e18 October.
Atashnezhad, A., Wood, D.A., Fereidounpour, A., Khosravanian, R., 2014. Designing and optimizing
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(November), 1184e1204. https://doi.org/10.1016/j.jngse.2014.05.029.
Bang, J., Torkildsen, T., Bruun, B.T., Havardstein, S.T., 2009. Targeting challenges in northern areas due to
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119661-MS.
Chen, S.L., Abousleiman, Y
.N., Muraleetharan, K.K., 2012. Closed-form elastoplastic solution for the
wellbore problem in strain hardening/softening rock formations. Int. J. GeoMech. 12 (4), 494e507.
Evensen, K., Sangesland, S., Johansen, S.E., Raknes, E.B., Arntsen, B., 2014. Relief well drilling using
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Texas, USA, 4e6 March.
Goobie, R.B., Allen, W
.T., Lasley, B.M., Corser, K., Perez, J.P
., March 17, 2015. A Guide to Relief Well
Trajectory Design Using Multidisciplinary Collaborative Well Planning Technology. Society of
Petroleum Engineers. https://doi.org/10.2118/173097-MS.
Han, G., Stone, T., Liu, Q., Cook, J., Papanastasiou, P
., 2005. 3D elastoplastic FEM modelling in a
reservoir simulator. In: Proceedings of SPE Reservoir Simulation Symposium. SPE. https://doi.org/
10.2118/91891-MS.
Haugen, I., 2011. Killing a High Rate Blowout Well through a Relief Well. Master’s thesis. University of
Stavanger.
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Hawkes, C.D., Smith, C.P
., McLellan, P
.J., 2002. Coupled modeling of borehole instability and multiphase
ﬂow for underbalanced drilling. In: Proceedings of IADC/SPE Drilling Conference. SPE. https://
doi.org/10.2118/74447-MS.
Itasca Consulting Group, 2012. Inc. FLAC3D Manual. Itasca Consulting Group, Inc., Minneapolis, USA.
Kallhovd, A., 2013. Evaluation of a Dual Relief Well Operation. Master of Science Thesis. Department of
Petroleum Engineering and Applied Geophysics, Norwegian University of Science and Technology.
Kasravi, J., Safarzadeh, M.A., Hashem, A., 2017. A population-feedback control based algorithm for well
trajectory optimization using proxy model. J. Rock Mech. Geotech. Eng. 9 (2), 281e290. https://
doi.org/10.1016/j.jrmge.2016.07.010. April.
Khosravanian, R., Mansouri, V
., Wood, D.A., Alipour, M.R., 2018. A comparative study of several
metaheuristic algorithms for optimizing complex 3-D well-path designs. J. Petrol. Expl. Prod. Technol.
8, 1487e1503. https://doi.org/10.1007/s1320 2-018-0447-2.
Liu, D., Yuan, Y
., Liao, S., 2009. Artiﬁcial neural networks for optimization of gold-bearing slime smelting.
Expert Syst. Appl. 36 (9), 11671e11674. https://doi.org/10.1016/j.eswa.2009.03.016.
Mahajan, N.H., 2018. End-To-End Well Planning Strategies for Alaska North Slope Directional Wells.
Thesis for the Degree of Master of Science in Petroleum Engineering. University of Alaska Fairbanks.
Mansouri, V
., Khosravanian, R., Wood, D.A., Aadnøy, B.S., 2015. 3-D well path design using a multi-
objective genetic algorithm. J. Nat. Gas Sci. Eng. 27 (Part 1), 219e235. https://doi.org/10.1016/
j.jngse.2015.08.051. November.
Oskarsen, R.T., Rygg, O.B., Cargol, M., 2016. Challenging offshore dynamic kill operations made
possible with the relief well injection spool. In: Presented at the SPE Deepwater Drilling and
Completion Conference, Galveston, Texas, 14e15 September. https://doi.org/10.2118/180279-MS.
SPE-180279MS.
Ovcharenko, Y
., Lukin, S., Tatur, O., Kalinin, O., Kolesnikov, D., Esipov, S., Zhukov, V
., Demin, V
.,
Volokitin, Y
., Sednev, A., Podberezny, M., 2016. Experience in 3D geomechanical modeling, based on
one of the West Siberia oilﬁelds. In: Paper Given at the SPE Russian Petroleum Technology Con-
ference and Exhibition Held in Moscow, Russia, 24e26 October.
Poedjono, B., Akinniranye, G., Conran, G., Spidle, K., San Antonio, T., 2007. A comprehensive approach
to well-collision avoidance. In: Presented at the American Association of Drilling Engineers National
Technical Conference, Houston, 10e12 April. AADE-07-NTCE-28.
Shokir, E.M., Emera, M.K., Eid, S.M., Wally, A.W
., 2004. A new optimization model for 3-D well design.
Emir. J. Eng. 9 (1), 67e74.
Upchurch, E.R., Falkner, S., Nguyen, C., Russell, K., 2017. Blowout prevention and relief-well planning
for the wheatstone big-bore gas-well project. SPE Drill. Complet. 32 (3) https://doi.org/10.2118/
174890-PA. September.
Wang, W
., Wen, H., Jiang, P
., Zhang, P
., Zhang, L., Xian, C., Li, J., Zhao, C., Li, Q., Xie, Q., 2019.
Application of anisotropic wellbore stability model and unconventional fracture model for lateral
landing and wellbore trajectory optimization: a case study of shale gas in Jingmen Area, China. In:
International Petroleum Technology Conference Held in Beijing, China, 26e28 March.
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constraints. In: Proceedings of the 37th Chinese Control Conference, July 25e27, Wuhan, China.
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In: SPE annual technical conference and exhibition. SPE. https://doi.org/10.2118/124815-MS.
Open-source code
Y
ou can develop your models using open-source code for different tools available
on https://github.com/.
Examples include:
Trajectory Optimization using Differential Evolution Algorithm in MATLAB
146
Methods for Petroleum Well Optimization
https://github.com/Atashnezhad/Petroleum_Engineering/blob/master/Codes/
Metahurestic%20Algorithms%20and%20Drilling%20Projects/PSO-Algorithm-master/
MATLAB%20code/PSO%20codes.txt.
https://github.com/Atashnezhad/Petroleum_Engineering.
https://github.com/pro-well-plan.
Wellbore trajectory optimization
147

Identifying Applications of Machine Learning and Data
Analytics Based Approaches for Optimization of Upstream
Petroleum Operations
Rakesh Kumar Pandey, Anil Kumar Dahiya,* and Ajay Mandal
1. Introduction
In the mid-19th century, the ﬁrst oil well was drilled, which
marked the beginning of the world oil economy. This era
embarked on the phase of evolution and development of society
in every aspect, with huge dependence on petroleum products,
which subsequently led to tremendous growth in terms of tech-
nological advancements in the exploration and production
activities over time. With the existing petro-
leum ﬁelds approaching toward depletion,
the global need for energy resources is
increasing simultaneously. This continu-
ously rising demand has led us to move
to complex environments for oil and gas
exploration
and
production
activities.
Moreover, the real-time monitoring of
reservoirs and well operations produces
enormous data, which require efﬁcient
processing and analysis tools for decision-
making regarding ﬁeld development and
asset management.[1,2] Subsequently, the
need for optimum facilities design,[3] trans-
portation[4]
and
reﬁning[5]
activities
at
reduced cost has also grown. Several
attempts[6–9] are also being made to develop
unconventional ﬁelds commercially based
on technoeconomic viability.
The implementation of computer-based
techniques has proven to be promising
toward many challenging problems for var-
ious oilﬁeld operations. Several researchers
have shown that the machine learning (ML) and data analytics
(DA) approaches hold promising solutions toward the opera-
tional challenges being encountered at present in the industry
and can efﬁciently help in resolving the issues related to inter-
pretation and analysis of large datasets.[10–15] Nikravesh and
Aminzadeh[16] have shown the use of neural networks (NNs)
and fuzzy logic for mining petroleum data. Li and Li[17] combined
a NN and cluster analysis to put forth a predictive model for
identifying complex lithology. Fath et al.[18] proposed a novel
approach for bubble point pressure prediction using an NN
model with
reservoir temperature, solution
gas oil
ratio
(GOR), oil gravity, and gas speciﬁc gravity as input attributes.
Ahmadi and Bahadori[19] showed the use of a supervised learning
algorithm to determine the well placement and conning occur-
rence in horizontal wells. Maucec et al.[20] performed data min-
ing and ML on well stimulation data for enhancing the prediction
capabilities. Gupta et al.[21] applied DA for safeguarding real-time
electrical submersible pumping operations. Cadei et al.[22] fore-
casted operational upsets using advanced analytics in an
upstream production system. Dongxiao et al.[23] presented in
their research that long short-term memory (LSTM), cascaded
LSTM, and a fully connected NN can be utilized for generating
synthetic well logs by supplementing the missing logging input
data. Ghorbani et al.[24] proposed an artiﬁcial Intelligence (AI)-
based approach to predict the ﬂow rate of oil from an oriﬁce
R. K. Pandey
Department of Petroleum and Energy Studies
DIT University
Dehradun 248009, India
Dr. A. K. Dahiya
Data Science Research Group
School of Computing
DIT University
Dehradun 248009, India
E-mail: anil.kumar1@dituniversity.edu.in
Dr. A. Mandal
Department of Petroleum Engineering
Indian Institute of Technology (IIT–ISM)
Dhanbad 826004, India
The ORCID identiﬁcation number(s) for the author(s) of this article
can be found under https://doi.org/10.1002/ente.202000749.
DOI: 10.1002/ente.202000749
Over the past few years, machine learning and data analytics have gained tre-
mendous attention as emerging trends in the oil and gas industry. The usage of
modern tools and high-end technologies produces a large amount of hetero-
geneous data. The processing and managing of this data at higher speed for
performance analysis and prediction for ﬁeld development and planning has
become a signiﬁcant area of research. Several challenges that are encountered in
forecasting the operational characteristics using the traditional approaches have
led to research based on implementation of machine learning and data analytics
techniques in exploration and production activities to attain higher accuracy,
which allows making informed choices. Herein, a review is presented to evaluate
the applications and scope of machine learning and data analytics in the oil and
gas industry to optimize the upstream operations, including exploration, drilling,
reservoir, and production. The challenges associated with traditional methods for
forecasting the operational parameters are identiﬁed and case studies associated
with performance optimization using predictive models that have aided in
improving the decision-making process are discussed.
REVIEW
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meter and compared the results with other alternatives.
Mohaghegh[25] showed the implementations of an AI- and
ML-based smart proxy model for estimating the oil discharge rate
into the Gulf of Mexico during the aftermath of Deepwater
Horizon. Fatai et al.[26] integrated seismic attributes with wireline
data to estimate the permeability of a carbonate reservoir by six
different ML techniques based on rigorous parametric study.
Wood[27] obtained an effective result for stratigraphy and
lithofacies prediction on a single type of well by combining
the optimized nearest neighbor algorithm with the well-log
representations. Shen et al.[28] established event recognition
for hydraulic fracturing in a real-time environment using deep
learning (DL) techniques. Khalifah et al.[29] used ML techniques
to forecast permeability and perform diagenesis in tight reser-
voirs. Several other researches have been conducted showing
the
applications
of
computer-based
approaches
upstream,
midstream, and downstream, although this work is mainly
concerned with identifying the scope of analytics tools and
ML algorithms in upstream oil and gas operations.
In this article, the recent developments and applications of ML
and DA in the upstream oil and gas industry have been reviewed.
The second part of this study discusses the methodologies, tools,
and performance indicators that are being utilized for data
visualization and prediction purposes. In the third part, the
implementations and applications of predictive models and
data-driven tools in exploration and drilling, reservoir engineer-
ing, and production sectors of the upstream petroleum industry
are covered. The fourth part provides suggestions and discussion
based on research gap analysis and future scope of work.
2. ML and DA Methodologies
In this section, the AI, ML, DL, and DA methods along with
some of the commonly used tools and statistical measures are
introduced and discussed.
2.1. ML and DA Conceptualization
In this era of technological evolution, AI has opened abundant
scope for development in apparently all sectors, including
business, marketing, education, science and engineering, medi-
cine, and law. The need to meet the current challenges that these
industries are facing has drawn the attention of researchers
worldwide. Hopgood[30] deﬁned AI as the science of mimicking
human mental faculties in a computer. Since its inception in
1955, AI has been through several lows and highs, with
numerous disappointments and success stories. AI covers a wide
range of applications based on which it has been classiﬁed from
time to time. Figure 1 shows the relationships among diversiﬁed
areas of AI.
ML involves teaching machines to handle large datasets for
recognizing patterns and extracting relevant information with
enhanced efﬁciency. Computer science and technology focusing
on building machines when combined with statistical tools and
inferences provides a remarkable outcome as ML. As shown in
Figure 2, ML has broadly been classiﬁed into supervised
learning,
unsupervised
learning,
semi-supervised
learning,
and reinforcement learning.
In supervised learning, the model is trained with a labeled
training dataset (xðiÞ
j , yðiÞ
j ), where xj is a feature vector of dimen-
sion i ¼ 1, 2, 3,.…, m. The supervised learning algorithm aims to
produce a model that permits deducing the label for the input
feature vector x as the output information. Unsupervised learn-
ing either changes the input vector x to a meaningful value which
can be utilized to solve practical problems or into another vector
where the input dataset of unlabeled training examples (xðiÞ
j ) with
x as the feature vector is used. The semi-supervised learning
dataset has both labeled and unlabeled examples, although the
unlabeled dataset is usually greater in quantity. It has a similar
goal to that of the supervised learning algorithm but the unla-
beled training examples help in improving the model perfor-
mance. In the reinforcement learning algorithm, the model is
capable of making decisions of the actions for an environment
without prior knowledge of the learner with an agenda of opti-
mization by maximizing the expected average reward.
Figure 3 shows a schematic diagram of an artiﬁcial neural
network (ANN) with three layers (two hidden layers with three
neurons each and one output layer with a single neuron) where
X0 is the bias term, X1 and X2 are the input attributes, y is the
model output, a½i
j denotes the activation output of the jth neuron
Figure 1. Schematic illustration of relationships among diversiﬁed areas of AI. Reproduced with permission.[31] Copyright 2020, ICT Institute.
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in the ith layer with i ¼ 1, 2, 3,……, n and j ¼ 1, 2, 3,……, m, and a½i
0
represents the bias unit for i ¼ 1, 2, 3,……, n.
DL, also referred to as “hierarchical learning” or “deep struc-
tured learning,” is an extension of ML and a multilayered NN
composed of algorithms to permit training of the model by itself
on huge datasets. The architectures of DL, such as the deep
neural network (DNN), convolutional neural network (CNN),
and recurrent neural network (RNN), are based on a set of algo-
rithms attempting learning data representations. DL is particu-
larly useful when dealing with an enormous dataset with a large
number of features for automatically learning data representa-
tions. DL has diverse applications, including image recognition,
natural language processing, automatic speech recognition, rec-
ommendation systems, and bioinformatics.
DA is considered the scientiﬁc approach of transforming data
into insights for making better decisions with the use of data,
statistical analysis, quantitative methods, information technol-
ogy, and mathematical or computer-based models. Based on
the kind of information desired and the phases of the workﬂow,
it can be subdivided into the following. 1) Descriptive analytics
depicts a summarized view of data-based facts and ﬁgures in a
conveniently interpretable form. 2) Diagnostic analytics allows us
to examine the source or reason behind the occurrence of an
event using correlations, data mining, and data discovery.
3) Predictive analytics helps in predicting the probability of an
occurrence based on current events using data mining, regres-
sion, time series analysis, and forecasting. 4) Prescriptive analyt-
ics indicates the best course of action to make decisions for
optimizing the output with the help of decision analyses, simu-
lations, and optimization models.
Figure 2. Representation of ML classiﬁcation.
Figure 3. Schematic representation of a three-layered ANN.
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DA has gradually evolved over time because of higher comput-
ing speeds, storage facilities, and availability of new statistical
tools.
2.2. ML and DA Processing
In this section, some of the common ML and DA processing
application program interfaces (APIs) are discussed.
2.2.1. Python
Python is an interpreted high-level object-oriented programming
language that supports both structured and functional program-
ming methods. It is an easy to learn, write, read, and maintain
open source platform with vast libraries such as NumPy,
Matplotlib and SciPy, and Scikit-Learn that serve as excellent
ML and analytics tools.
2.2.2. R
It is a widely used programming language that features data
modeling and statistical computing. It supports third-party
graphical user interface such as Jupyter and RStudio with the
libraries implementing a wide range of ML features, statistical
and graphical techniques and time series analysis.
2.2.3. Julia
Julia is a recently developed high-level dynamic programming
language well suited for mathematical analysis and scientiﬁc
computing with high performance efﬁciency. The main features
of this language include multiple dispatch, a dynamic type sys-
tem, a built-in package manager, optimally typed, parallel, and
distributed computing, and efﬁcient support for Unicode.
2.3. Performance Indicators
This section discusses the commonly used indicators to evaluate
the performance of ML and DA algorithms.
2.3.1. Confusion Matrix
A confusion matrix is not exactly a performance metric but forms
the basis for other metrics. It provides a tabular representation of
the model predictions versus the actual labels. Accuracy of the
confusion matrix is given as
Accuracy ¼ True positives þ true negatives
Total samples
(1)
2.3.2. Classiﬁcation Accuracy
Classiﬁcation accuracy is a measure of correct predictions. It is
the ratio of the number of correct predictions to the total number
of predictions, expressed in percentage (%).
Classification accuracy ¼ Number of correct predictions
Total number of predictions  100
(2)
2.3.3. F1-Score
It is the harmonic mean between precision and recall, which is
used to measure the test’s accuracy. The F1-score ranges from
[0,1] and it is expressed mathematically as
F1-score ¼ 2  Precision  recall
Precision þ recall
(3)
where precision and recall are class-speciﬁc performance met-
rics. Precision and recall are given as
Precision ¼
True positive
True positive þ false positive
(4)
Recall ¼
True positive
Ture positive þ false negative
(5)
The true class labels are indicated by highest F1-score value.
2.3.4. Receiver Operating Characteristic (ROC) Curve
It is the plot of the true positive rate versus the false positive rate
for various threshold values which shows the performance of a
binary classiﬁer.
2.3.5. Area Under Curve (AUC)
AUC is a measure of performance of a binary classiﬁer on all
possible threshold values. It measures the area under an ROC
curve and varies in the range of [0,1]. A higher value of AUC
suggests better model performance.
2.3.6. Mean Absolute Error (MAE)
MAE is the absolute average of the difference between target val-
ues and predicted values. It is expressed as
MAE ¼ 1
N
X
N
i¼1
jyi  ˆ
yij
(6)
where N is the total number of data points and yi and ˆ
yi are the
target values and predicted values of the model, respectively.
MAE is one of the most commonly used performance indicators
for continuous variables. MAE may range from 0 to ∞and is a
negatively oriented score.
2.3.7. Mean Squared Error (MSE)
MSE is the average of the square difference between target values
and predicted values. It provides an ease in gradient computation
as compared to MAE. The model can focus more on large errors,
as their effect become more pronounced than that of small
errors. Mathematically, it is represented as
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MSE ¼ 1
N
X
N
i¼1
ðyi  ˆ
yiÞ2
(7)
A good model performance is indicated by a small MSE value.
2.3.8. Root Mean Square Error (RMSE)
RMSE is the root of the average of the square difference between
target values and predicted values.
RMSE ¼
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
1
N
X
N
i¼1
ðyi  ˆ
yiÞ2
v
u
u
t
(8)
A lower RMSE value indicates better model performance.
RSME is particularly useful when large errors are undesirable.
2.3.9. Average Relative Error (ARE)
ARE is deﬁned as the average of the ratio of difference between
target
values
and
predicted
values
to
the
target
values.
Mathematically, it is depicted as
ARE ¼ 1
N
X
N
i¼1
yi  ˆ
yi
yi

(9)
It may be expressed in fraction or percentage. Lower values of
ARE denote better model performance.
2.3.10. Average Absolute Relative Error (AARE)
AARE can be mathematically written as
AARE ¼ 1
N
X
N
i¼1




yi  ˆ
yi
yi




(10)
It may be expressed in fraction or percentage. A smaller AARE
is desirable.
2.3.11. Squared Correlation Coefﬁcient (R2)
R2, also called the coefﬁcient of determination, is a statistical
measure that provides a comparison of sum of squares of the
residual errors with the total sum of squares. It is represented
mathematically as
R2 ¼ 1 
PN
i¼1 ðyi  ˆ
yiÞ2
PN
i¼1 ðˆ
yi  ¯
yÞ2
(11)
The best possible value for R2 is 1. The nearer the value of R2 is
to 1, the better is the model ﬁtted.
2.3.12. Variance Accounted for (VAF)
The best performance of a model is indicated by the highest
value of VAF expressed in percentage. It can be mathematically
given as
VAF ¼

1  Varðyi  ˆ
yiÞ
VarðyiÞ

 100
(12)
3. ML and DA Applications in Upstream
Petroleum Industry
This section of the article focuses on the limitations in predicting
the operational characteristics using traditional approaches and
discussing case studies of the research that has been conducted
to implement the ML and DA tools and techniques in various
sectors of the upstream oil and gas industry, which have been
mainly categorized into exploration and drilling operations, res-
ervoir engineering, and petroleum production system.
3.1. ML and DA in Exploration and Drilling Operations
Geological and geophysical surveys are performed to identify
potential subsurface hydrocarbon accumulations. Prospective
areas are considered for drilling wildcat wells to conﬁrm the pres-
ence of fossil fuels. Appraisal wells are drilled to delineate the
reservoir boundaries for estimating the petroleum reserves with
technoeconomic feasibility considerations. Further, development
wells are drilled based on the ﬁeld development plan in the zones
of interest for hydrocarbon recovery. Several ML and DA techni-
ques have been utilized by researchers for improving and opti-
mizing these operations. This section has further been divided
into two subsections: 1) ML and DA in geological and geophysical
exploration activities and 2) ML and DA in drilling operations.
3.1.1. ML and DA in Geological and Geophysical Exploration
Activities
Traditional methods of geological and geophysical modeling
involve generating computerized representations of subsurface
observations obtained from various geological and geophysical
surveys for evaluating the structural and stratigraphic description
along with estimation of reservoir rock and ﬂuid characteristics.
A large amount of heterogeneous data are collected from these
surveys, requiring processing prior to incorporation into models.
Moreover, the visualization and interpretation of subsurface fea-
tures becomes difﬁcult due to the complexity of the acquired
data. Simultaneously, the approaches available to estimate fea-
tures such as sweet spot, shear wave velocity, seismic horizon,
and kerogen characteristics are limited, error-prone as well as
cost- and time-consuming. Alternatively, application of ML
and DA tools can overcome these problems and increase the
exploration success rates by implementing suitable data visuali-
zation and prediction algorithms. Table 1 summarizes case stud-
ies of applications of ML and DA in exploration activities.
Case Studies: ML and DA in Geological and Geophysical
Exploration Activities: Rastegarnia et al.[32] utilized AI systems
to extract electrofacies volumes and the 3D ﬂow zone index
(FZI) from a large volume of 3D seismic data. Multi-resolution
graph based clustering (MRGC) was used to optimize the elec-
trofacies volumes and multiattribute analysis was used to create a
3D FZI model. The electrofacies models are further improved
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using the probabilistic neural network (PNN), and the 3D FZI
model was improved using a radial basis function (RBF) network,
multilayer feed forward network (MLFFN), and PNN. The results
based on the RMSE indicated that PNN-based models could suc-
cessfully be used to estimate the FZI and electrofacies volume.
Qian et al.[33] proposed a support vector machine (SVM)-based
algorithm in combination with multiscale and multiresource
information from geology, seismic, drilling, and logging for
sweet spot prediction and characterization of shale reservoirs
with high R2 accuracy based on pore pressure, total organic car-
bon content, brittleness, porosity, fracture pressure gradient, and
fracture density properties.
Wang and Peng[34] presented an extreme learning machine
(ELM) model based on the mean impact value to estimate the
shear wave velocity (Km s1) from well-log data. The study also
presented a comparison of the ELM model with ANN, SVM,
Table 1. Summary of case studies on applications of ML and DA in geological and geophysical exploration activities.
Ref no.
and [year]
Objective[s]
of the study
Input data source [s] [ML] methodologies
Output [s]
Performance indicators
[32]
2016
3D FZI prediction
Seismic attributes
Multi Attribute
Analysis, PNN,
MLFFN and RBF
3D FZI volumes
RMSE
Method
Training data
Validation data
Multiattribute analysis
2.44
3.8
MLFFN
1.44
6.26
RBF
2.37
3.22
PNN
1.9
2.96
Electrofacies
prediction
NMR logs, seismic
attributes
MRGC and PNN
Electrofacies
volumes
RMSE
Method
Training data
Validation data
PNN
11
24
[33]
2018
Sweet spot
prediction
Geology, seismic,
drilling, and logging
SVM
Sweet spot location
Indicator
Method
R2
SVM
0.96
[34]
2019
Shear wave velocity
estimation and
performance
comparison
Well logs
ELM, ANN, SVM,
CNN, and EFs
Shear wave velocity
Indicator
Method
R
MAE
VAF (%) RMSE Time (s)
ELM
0.9724 0.0004 94.0330 0.0795
0.3127
ANN
0.9634 0.0071 92.8202 0.0913
4.9135
SVM
0.9326 0.0083 86.3742 0.1155
5.2803
CNN
0.9751
0.0003
94.1956 0.0737
473.26
EF
by
Pickett
0.8187 0.0591 49.5297 0.1908
–
Castagna et al.
0.8032 0.0109 49.9192 0.1890
–
Eskandari et al.
0.7801 0.0102 47.2385 0.2010
–
Brocher
0.8026
0.2106
64.2130 0.3068
–
[35]
2019
Seismic horizon
identiﬁcation
Seismic data
CNN
Single and multiple
seismic horizons
Indicator
CNN
R2
RMSE
Single horizon tracking
0.998
1.78
Multiple horizon tracking
0.992
2.68
[36]
2019
Kerogen
characterization
Heater temperature
transient data
Numerical
simulation coupled
with ANN
Kerogen content and
activation energy
Scaled RMSE
Simulation
Best match
Worst match
Type I kerogen
2.3243e-4
3.0465e-1
Type II kerogen
6.5310e-7
9.5122e-2
Type III kerogen
3.8565e-4
9.9701e-2
SVM classiﬁcation
Types of kerogen
Time, t (days)
110
120
130
140
150
Classiﬁcation
performance (%)
81.7
81.7
82.0
82.3
80.7
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CNN, and empirical formulas (EFs). The four statistical perfor-
mance indicators, namely, the RMSE, VAF, MAE, and correla-
tion coefﬁcient (R), suggested that ELM provided improved
performance and higher computational speed. Figure 4 shows
the comparison of models based on the performance measures
R, RMSE, and VAF.
Yang et al.[35] demonstrated that the CNN is an effective
method for tracking the single and multiple seismic horizon from
discontinuous and heterogeneous data with minimized error and
reduced time consumption. The data training of the models for
single and multiple horizon tracking was performed using only
around 1% and 1.5% of the 3D seismic data volume, respectively,
which provided an efﬁcient RMSE (ms) and R2 performance.
Lee[36] developed a numerical simulation model coupled with
an ANN with monitored heater temperatures under constant
heat inﬂux as the input data to determine the kerogen character-
istics, including the activation energy (KJ mole1) and kerogen
content (vol%). Moreover, the SVM classiﬁer was used to classify
the heater temperature transient data into three kerogen types.
3.1.2. ML and DA in Drilling Operations
Drilling a well is a challenging task because it involves no or min-
imal prior information of the subsurface characteristics and the
complexity increases with increasing depth or deviation of the
well from its vertical path. Moreover, occurrence of drilling prob-
lems such as lost circulation, pipe sticking, shale sloughing, and
dogleg severity add up to make the job highly crucial. Several
traditional approaches, such as measurement while drilling
(MWD) and logging while drilling, have evolved over time for
optimizing the drilling operations and simultaneously monitor-
ing a well. However, erroneous predictions in optimizing drilling
operations results in loss of huge investments in terms of money
as well as time and causes drilling site accidents, including ﬁsh-
ing and blowout, which may also lead to abandonment of the
well. Some case studies have been discussed where ML- and
DA-based approaches were adopted for prediction of lost circu-
lation, rate of penetration (ROP), drilled cuttings settling velocity,
and accidental events to optimize the drilling operation perfor-
mance. Table 2 summarizes the case studies of applications of
ML and DA in drilling operations.
Case Studies: ML and DA in Drilling Operations: Agin et al.[37]
conducted research for estimation of lost circulation during
drilling operations with the application of an adaptive neuro-
fuzzy inference system (ANFIS). Class of regression was
performed to obtain a function for data modeling and error
calculations. Subtractive clustering was implemented for train-
ing the fuzzy inference system model. Finally, the ANFIS model
was trained, tested, and checked to predict lost circulation
volume (bbl) and the performance of the model was evaluated
using RMSE.
Ashraﬁet al.[38] presented eight hybrid and two simple ANN
models that were trained by four different algorithms to
predict the ROP (m h1). The performance indicators used in this
study are RMSE, R, VAF (%), and performance index (PI).
Figure 5 shows a comparison of the eight hybrid and two simple
ANN models based on the performance measures R, RMSE, and
VAF. The radial basis function–particle swarm optimization
(RBF–PSO) and multilayer perception–particle swarm optimiza-
tion (MLP–PSO) models estimated the ROP with the highest
accuracy.
Gurina et al.[39] have demonstrated an ML algorithm to detect
accidental events in directional wells by comparing real-time
MWD data with past data. The proposed model performs anom-
aly detection by analyzing the similarity of events using time
series comparison and gradient boosting classiﬁcation. The area
under precision-recall curve (PR-AUC) and area under receiver
operating characteristic curve (ROC–AUC) have been utilized in
this work to assess the model quality.
Optimization of the prespeciﬁed key performance indicators,
including the ROP (ft h1) and mechanical speciﬁc energy (psi)
was conducted in the research[40] using a coupled end-to-end
model for drilling optimization by taking into account all the
parameters of interest, including weight on bit (WOB), rotary
speed, ﬂow rate, and rock strength. The researchers utilized
the random forest (RF) algorithm for developing models for each
individual parameter under consideration, which were coupled
using an ML algorithm.
The drilled cuttings settling velocity (m s1) prediction model
was developed by Agwu et al.[41] using an ANN. The authors used
MSE, sum of square error (SSE), RMSE, and R2 as the perfor-
mance metrics to obtain the accuracy, which was in agreement
with the experimental results. The developed model was also able
Figure 4. Comparison of ELM, ANN, SVM, CNN, and EF based on the
performance measures a) R, b) RMSE, and c) VAF (%).
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to reduce the iterations and minimize errors as compared to the
traditional approaches, and it could overcome the limitations
associated with the ﬂow regime and cuttings shape factor.
3.2. ML and DA in Reservoir Engineering
ML has been widely incorporated in different aspects of reservoir
engineering, including estimation of petrophysical properties,
reservoir simulation, reserve estimation, reservoir performance
prediction, well logging, well testing, and enhanced oil recovery
(EOR). This section has further been divided into four subsec-
tions: 1) ML and DA in reservoir performance optimization,
2) ML and DA in well-logging operations, 3) ML and DA in
well-testing operations, and 4) ML and DA in EOR.
3.2.1. ML and DA in Reservoir Performance Optimization
The typical activity in reservoir performance optimization is to
tune ﬁeld parameters such as solution GOR, ﬂow rates, and
bottomhole pressure to increase the oil recovery factor. ML-
and DA-based case studies have been discussed later. Table 3
summarizes the case studies of applications of ML and DA in
reservoir performance optimization.
Case
Studies:
ML
and
DA
in
Reservoir
Performance
Optimization: Aulia et al.[42] applied the Association Rules
(ARULES) to analyze the results of reservoir simulation, which
revealed the interaction and contribution of parameters in chang-
ing the oil recovery factor. An ANN was used to rank the signiﬁ-
cance of the parameters. Two methods including Haykin’s and
Table 2. Summary of case studies on applications of ML and DA in drilling operations.
Ref no.
and [year]
Objective[s]
of the study
Input data source[s]
[ML] methodologies
Output[s]
Performance indicators
[37]
2018
Lost circulation
prediction
Drilling data
ANFIS
Amount of lost
circulation
RMSE
Optimization
Training
Testing
Checking
Backpropagation
method
0.083733
0.091039
0.15432
Hybrid method
0.087428
0.09404
0.15615
[38]
2018
ROP prediction
Petrophysical logs,
mud logs
MLP–PSO, MLP–GA,
MLP–ICA, MLP–BBO,
MLP, RBF–PSO, RBF–GA,
RBF–ICA, RBF–BBO
and RBF
ROP
Indicator
Model
RMSE
R
VAF (%)
PI
MLP–PSO
1.12
0.933
93.39
0.7752
MLP–GA
1.43
0.8931
89.44
0.4023
MLP–ICA
1.67
0.85
85.19
0.102
MLP–BBO
1.8
0.8563
85.72
0.0209
MLP
1.68
0.83
83.75
0.0632
RBF–PSO
1.4
0.8775
87.86
0.4093
RBF–GA
1.52
0.8819
88.28
0.2946
RBF–ICA
1.5
0.8601
86.81
0.2902
RBF–BBO
1.61
0.8579
85.92
0.17
RBF
1.76
0.8107
81.46
0.0539
[39]
2019
Accidental event
detection in directional
drilling
MWD data, past accident
well and their mud
log data
Gradient boosting
classiﬁcation of DTs
Classiﬁcation and
ranking of
accidents
Thershold, s ¼ 0.7
ROP–AUC
PR–AUC
0.908
0.6086
[40]
2019
Drilling parameter
optimization
WOB, rotary speed,
ﬂow rate, rock strength
RF
ROP, mechanical
speciﬁc energy
Average performance
ROP
Increased by 26 %
Mechanical speciﬁc
energy
Decreased by 49 %
Drilling vibration classiﬁcation
F1-Score
0.94
[41]
2019
Drilled cuttings settling
velocity prediction
Cuttings sphericity,
cuttings density,
Cuttings Diameter,
mud viscosity, and
mud density
ANN
Drilled cuttings
settling velocity
R2
SSE
MSE
RMSE
MAE
MAPE (%)
0.6118
2.74
0.00807
0.0898
0.065
0.675
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Trenn’s for determining hidden neurons was used and data
splits of 66% and 80% were considered.
Gholami et al.[43] used a genetic algorithm (GA) for selecting
the most suitable input logs and performed permeability (mD)
prediction using support vector regression (SVR) and relevance
vector regression (RVR). RVR yielded more accurate results
compared to SVR for the tested wells. Figure 6 shows a compari-
son of the performance of SVR and RVR using the measures
RMSE and R2.
Fath et al.[44] developed an RBF and multilayer perception
(MLP) based prediction model for determining the solution
GOR (scf stb1) using bubble point pressure, oil gravity, gas spe-
ciﬁc gravity, and reservoir temperature as the input variables.
Based on statistical and graphical error analysis, performance
evaluation of these models was conducted. R, standard deviation
(SD), RMSE, average percentage relative error (APRE), average
absolute percentage relative error (AAPRE), and maximum
APRE were used as the performance indicators. The validation
of the models was performed and the results were found to be in
agreement with empirical correlations.
3.2.2. Well-Logging Operations
Lithology and ﬂuid identiﬁcation are the principal tasks of well-
logging operations and are one of the most signiﬁcant require-
ments of the upstream embranchments. Identifying the lithology
using traditional approaches such as core analysis and geophys-
ical modeling incurs high cost and petrophysical logs combined
with the cross-plots lose their efﬁciency on large datasets,
whereas lithology predictions from drilled cuttings are usually
not very adequate. Moreover, complexities and nonlinearity of
the datasets limit their accurate measurements. Using the ML
and DA approaches, the data from one or more sources such
as seismic attributes, core analysis, well test, and logging of
the same lithology can be accumulated to form a cluster based
on which a corresponding predictive model can be built. Further,
the selections of input attributes, efﬁcient data partitioning as
well as sizes of the training and testing data sets for the model
are important factors for reducing the data perplexity and obtain-
ing accuracy in predictions. In the case studies discussed subse-
quently, ML and DA were adopted for lithology and ﬂuid
identiﬁcation,
log
reconstruction,
and
well
correlations.
Table 4 summarizes the case studies of applications of ML
and DA in well-logging operations.
Case Studies: ML and DA in Well-Logging Operations: Li et al.[45]
established a lithology prediction model with the application of
self-organizing feature map (SOM) cluster analysis to data opti-
mized by a K-means algorithm and utilized the decision tree (DT)
and SVM for building a ﬂuid identiﬁcation model. The proposed
model predicted the lithology and ﬂuids with an accuracy of
91.30% and 95.65%. Also, the accuracy rates for different ﬂuids’
identiﬁcation were 94.31% and 86.97% with DT and SVM,
respectively.
Sebtosheikh and Salehi[46] examined the effect of training data
size and kernel function on SVM performance for predicting the
lithology by taking six different training/testing data partitioning
cases. The results as shown in Figure 7 indicate that the polyno-
mial kernel function based SVM model performed well for
smaller training data sets with the least misclassiﬁcation
error rate.
Salehi et al.[47] conducted a study to estimate the nonrecord
logs, including other existing conventional wireline logs, by uti-
lizing an MLP network with an error backpropagation algorithm.
The performance indicators R and MSE were calculated as
shown in Figure 8 and it was observed that the proposed model
could perform predictions with high accuracy.
Brazell et al.[48] presented a pattern matching based well-log
correlation model using a supervised deep CNN for reservoir
characterization, development mapping, and regional explora-
tion purposes with 96.6% accuracy.
3.2.3. ML and DA in Well-Testing Operations
The recorded pressure response in a well against the created ﬂow
disturbances are utilized to evaluate parameters such as
Figure 5. Comparison of eight hybrid and two simple ANN models based
on the performance measures a) R, b) RMSE, and c) VAF (%).
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permeability, formation damage, initial reservoir pressure, well-
bore storage, reservoir heterogeneity, and openhole ﬂow poten-
tial. Data acquisition may be performed either at the surface or
downhole. Before parameter estimation, it is desirable to identify
the reservoir model. Traditional approaches such as type curves
and derivative plots are used as for model identiﬁcation and res-
ervoir characterization by matching the actual pressure response
plots with the mathematical model plots. ML- and DA-based tools
can assist in meeting the well test objectives with technical via-
bility and economic feasibility. Case studies on ML and DA appli-
cations
in
well-testing
operations
have
been
discussed
subsequently and summarized in Table 5.
Case Studies: ML and DA in Well-Testing Operations: Vaferi
et al.[49] developed a two-layered MLP network with one hidden
layer for identifying reservoir models using drawdown well test
data. A 12-neuron network in the hidden layer of the MLP was
obtained as a suitable conﬁguration with MSE and mean relative
error (MRE) analysis of test data and a scaled conjugate gradient
training algorithm. The MSE goal for all networks was consid-
ered equal to 106.
Aulia et al.[50] implemented the RF algorithm to generate a
modiﬁed tornado chart for systematic well test planning based
on Latin hypercube Monte Carlo (LHMC) uncertainty study to
obtain critical subset wells whose performance was a crucial fac-
tor in optimization of an oilﬁeld. The authors presented the effect
of water cut and oil recovery factor response on one-variable-at-
a-time (OVAT) and RF sensitivity analysis. The wells having
maximum impact on the oil recovery factor and water cut were
identiﬁed based on this analysis.
Hasanvand and Berneti[51] demonstrated the application of a
three-layered backpropagation ANN trained by the Levenberg
Marquardt (LM) algorithm to predict the multiphase ﬂow meter
oil ﬂow rate at unmanned platforms with line pressure and tem-
perature recorded using digital sensors as input parameters. The
performance indicators R2 and MSE provided a good accuracy of
the application model.
A novel well test model identiﬁcation approach was presented
with the help of time series subsequence representing a class for
pressure transient tests by Ahmadi et al.[52] The authors used a
fast-shapelet algorithm to form a new feature space using
extracted shapelets and applied different binary classiﬁers, includ-
ing logistic regression (LR), SVM, PNN, and RF, to them. The clas-
siﬁcation performance was improved using an ensemble learner
and the maximum F-score was obtained as shown in Figure 9.
Table 3. Summary of case studies on applications of ML and DA in reservoir performance optimization.
Ref no.
and [year]
Objective[s]
of the study
Input data source[s]
[ML]
methodologies
Output[s]
Performance indicators
[42]
2010
Selection and ranking
of most inﬂuential
parameters
Reservoir simulation
results
ARULES and
ANN
Rank of importance
of parameters
66% data split with early stop
Indicators
H N methods
R
MSE
Haykin
0.730
0.005
Trenn
0.873
0.003
80% data split with early stop
Indicators
H N methods
R
MSE
Haykin
0.842
0.003
Trenn
0.907
0.003
[43]
2014
Permeability prediction
Well logs
RVR and SVR
Permeability
Training/testing data (%): 70/30
Indicator
Method
R2
(train)
R2
(test)
RMSE
(train)
RMSE
(test)
SVR
0.990
0.96
0.55
0.82
RVR
0.995
0.98
0.42
0.49
[44]
2018
Solution GOR
Prediction
PVT data (bubble point
pressure, oil gravity,
gas speciﬁc gravity,
and reservoir temperature)
RBF and MLP
Solution GOR
Method
Indicator
MLP
RBF
APRE (%)
1.5313
0.3603
AAPRE (%)
14.8979
11.9538
Maximum APRE (%)
66.1501
48.0236
RMSE
120.6314
99.8150
SD
0.1917
0.1565
R
0.96953
0.97818
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3.2.4. MA and DA in EOR
The main objective of an EOR project development is to increase
the overall recovery efﬁciency. To achieve that, the selection of an
appropriate EOR process for a candidate well and optimization of
the associated parameters is essential and simultaneously a com-
plex task that involves integrating rock and ﬂuid criteria govern-
ing technoeconomic performance. At present, there are no fully
established traditional methods which can be implemented to
perform this task. ML and DA techniques can increase the suc-
cess rate by avoiding the usually practiced trial-and-error
approaches. The case studies discussed subsequently for imple-
mentations of ML and DA in EOR have been summarized in
Table 6.
Case Studies: ML and DA in EOR: Ramos and Akanji[53]
performed a neuro-fuzzy simulation study combining an NN
and fuzzy logic with integration of box plot data analysis,
which provided a screening technique for selecting the suitable
EOR method for the candidate reservoirs in the Angolan oil
ﬁelds. The performance was evaluated with RMSE and the
nondimensional error index (NDEI). On the basis of the
depth (m) criterion, the RMSE and NDEI have been shown in
Table 6.
In the work done by Belazreg et al.,[54] data mining (DM) and
simulation approaches have been used together to develop a pre-
dictive tool for recovery factor determination in an immiscible
hydrocarbon water alternate gas (WAG) process. First, simula-
tion was performed over the WAG model. The simulated results
were input into regression and group methods of data handling
to create the predictive model for the WAG incremental recovery
factor, which was tested with R and RMSE.
Khojastehmehr et al.[55] proposed a method for selecting a
suitable technique to apply an EOR project in an oilﬁeld world-
wide. The technique for order of preference by similarity to ideal
solution (TOPSIS) method with a multiple criteria decision mak-
ing (MCDM) approach was used to screen numerous ﬁelds
under the EOR techniques based on several rock and ﬂuid prop-
erties and conditions, and an analytic hierarchy process (AHP)
was applied to determine the relative signiﬁcance of the reservoir
parameters. This technique was recommended as much more
prompt and cost effective compared to simulation or pilot
projects.
Soft sensors have been developed and implemented for ﬂow
rate (m3 h1) measurements in steam-assisted gravity drainage
(SAGD) wells using data-driven models based on partial least
squares
regression
or
multivariate
linear
regression
by
Sedghi et al.[56]
3.3. ML and DA in Petroleum Production System
Oil and gas production forecasting becomes highly essential in
terms of project planning and development, including economic
and environmental considerations, as well as facilities commis-
sioning and decommissioning. Accurate estimates of well perfor-
mance can provide guidance for optimizing the production rate,
installation of artiﬁcial lifts, identifying the need for work-over
jobs, well stimulation operations, facilities design, and schedul-
ing the secondary and tertiary recovery methods. This section has
further been divided into three subsections: 1) ML and DA in
petroleum production optimization, 2) ML and DA in artiﬁcial
lift techniques, and 3) ML and DA in well problem identiﬁcation
and solutions.
3.3.1. ML and DA in Petroleum Production Optimization
Production optimization in oil ﬁelds is concerned with the nodal
analysis to determine the most suitable operating conditions.
Real-time monitoring of wells during production operations pro-
duces large datasets, including dynamic pressure response and
ﬂow rate data, which are used for visualization and interpretation
to
improve
the
decision-making
process.
Typically,
time-
consuming curve ﬁtting and simulation methods are used to
predict primary well productions based on historical production
data. To perform this task with ease and cost effectiveness,
ML- and DA-aided techniques can be utilized. Discussed subse-
quently are case studies for application of ML and DA in produc-
tion optimization, which have been summarized in Table 7.
Case Studies: ML and DA in Petroleum Production Optimization:
In the study presented by Li et al.,[57] prediction of oil production
has been performed using a neural decision Tree (NDT) model
by considering interconnectedness among the input variables. A
comparison of C4.5, ANN, and NDT model showcases the per-
formance accuracy of each model. The results based on MAE,
MSE, and classiﬁcation rate showed signiﬁcant classiﬁcation
accuracy with a lesser number of instances using the NDT
model.
Figure 6. Comparison of performance of SVR and RVR for train-test data
ratio (%) of 70:30 using indicators a) RMSE and b) R2.
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Chithra Chakra et al.[58] applied a higher order neural network
(HONN) to predict cumulative oil production (m3) from a con-
ventional oil ﬁeld with limited training data. It was found to be a
satisfactory tool to forecast the cumulative oil production for
short-term as well as long-term planning. The model outputs
based on the performance measures MSE, RMSE and mean
absolute percentage error (MAPE) were in agreement with the
results obtained from the simulation studies. However, the
method seeks for reﬁnement to improve the accuracy and effec-
tiveness by including the static and dynamic reservoir character-
istic parameters.
Choubineh et al.[59] demonstrated that a hybrid ANN-based
model with six input parameters including oil speciﬁc gravity,
gas speciﬁc gravity, gas liquid ratio, choke size, wellhead
pressure and temperature predicted liquid critical ﬂow rates
Table 4. Summary of case studies on applications of ML and DA in well-logging operations.
Ref no.
and [year]
Objective[s]
of the study
Input data source[s]
[ML] methodologies
Output[s]
Performance indicators
[45]
2012
Lithology and
ﬂuid identiﬁcation
Core analysis, well log
data and well test data
K-means, SOM,
DT, and SVM
Lithology and
ﬂuid
Coincidence rates
Lithology identiﬁcation
and core analysis
91.30%
Fluid identiﬁcation
and produced ﬂuid data
95.65%
DT, SVM
Multiphase ﬂuids
Accuracy rates
DT
SVM
94.31%
86.97%
[46]
2015
Lithology prediction
Seismic attribute data
and petrophysical logs
SVM
Lithology
Misclassiﬁcation rate (%)
Kernel
Data size
(train:test)
RBF
Polynomial
Normalized
polynomial
90:10
12.95
12.95
17.49
80:20
15.18
16.30
18.60
60:40
18.41
16.69
21.43
40:60
18.89
17.73
22.72
20:80
18.78
18.20
23.07
10:90
29.05
26.23
24.19
[47]
2016
Nonrecord log
estimation
Flushed zone resistivity,
ﬂushed zone water saturation
and formation water saturation
MLP
True resistivity
Data
Indicator
Training
Validation
Test
Total
MSE
0.0001
0.00015
0.00009
–
R
0.9973
0.9982
0.9953
0.9972
Bulk density, neutron porosity,
and ﬂushed zone resistivity
Sonic log
MSE
0.004
0.0016
0.0035
–
R
0.9695
0.9796
0.9870
0.9735
Deep resistivity and
microspherical focusing log
Shallow resistivity
MSE
0.001
0.0027
0.004
–
R
0.9801
0.9202
0.9554
0.9667
[48]
2019
Well Log Correlation
Wireline Logs
Correlation Data
Deep CNN
Pattern-matched
log correlations
Model accuracy
96.6%
Classiﬁcation AUC (validation dataset)
0.954
Figure 7. Effect of training data size and kernel function on SVM
performance.
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(Stb D1) at the wellhead choke with high accuracy based on R2,
RMSE, ARE, and AARE.
Bhattacharya et al.[60] demonstrated supervised data driven ML
algorithms integrated with data obtained from surface pressure
and temperature measurements, production logs, completions,
and
a
ﬁber-optic
monitoring
system
for
estimating
gas
production of a stimulated horizontal well. Among the tested
algorithms including ANN, RF, and SVM, maximum accuracy
was obtained for the RF at lesser computational time and
reduced cost.
The research conducted by Liu et al.[61] presented ensemble
empirical mode decomposition (EEMD) based ANN, SVM,
and LSTM models. The performance measures as shown in
Figure 10 indicate that EEMD–LSTM was able to perform time
series oil production (ton D1) forecasting with signiﬁcant supe-
riority in terms of the largest determination coefﬁcient and least
number of errors.
3.3.2. ML and DA in Artiﬁcial Lift Techniques
The selection of artiﬁcial lift techniques for a candidate well
is an important aspect for an optimal hydrocarbon recovery.
Further, the undesirable working conditions, such as sand
production, gas obstruction, parafﬁn deposition, and liquid
below pump intake depth, reduce the efﬁciency of artiﬁcial lifts
installed on a well and may also be damaging to the equipment.
To reduce or avoid such occurrences, proper monitoring of
wells and pumps is desired. The downhole working conditions
are commonly analyzed using the dynamometer cards. Some
case studies of pattern recognition, classiﬁcation, and clustering
methods using ML and DA for selection of lifting methods,
identiﬁcation of abnormalities, and optimization of pump
operations have been discussed here and are summarized in
Table 8.
Case Studies: ML and DA in Artiﬁcial Lift Techniques: Yu
et al.[62] presented an SVM-based diagnostic model for identiﬁ-
cation of a submersible pump’s working conditions based on its
characteristic
parameters.
The
proposed
SVM
model
Figure 8. MLP performance on training, validation, and test data using
a) R and b) MSE.
Table 5. Summary of case studies on applications of ML and DA in well-testing operations.
Ref. no.
and [year]
Objective[s]
of the study
Input data source[s]
[ML] methodologies
Output[s]
Performance indicators
[49]
2011
Reservoir model
identiﬁcation
Drawdown test data
MLP network
Type of reservoir
model
Optimal model conﬁguration and performance
Hidden neurons
MRE
MSE
R
12
0.088810
3.11  107
0.9997317
[50]
2014
Critical Subset
Wells Identiﬁcation
Simulated bottomhole
pressure for each well,
ﬁeld oil recovery factor
and water cut
RF and OVAT
sensitivity analysis
Effect of water cut
and oil recovery factor
on OVAT and RF
Wells having maximum impact on oil recovery
factor and water cut were identiﬁed.
[51]
2015
Multiphase ﬂoe
meter oil ﬂow
rate prediction
Line pressure and
temperature
Three-layer
backpropagation
network trained by LM
Oil ﬂow rate
Indicator
Data
R2
MSE
Train
0.98968
6.9  103
Test
0.98741
9.5  103
[52]
2016
Well test model
identiﬁcation
Pressure derivative plots
LR, PNN, SVM,
and RF binary classiﬁers
Reservoir models
F-score (total)
LR
0.90
SVM
0.58
PNN
0.83
RF
0.96
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outperformed the learning vector quantization (LVQ) classiﬁer
by minimizing the misjudgment rate and outstanding pattern
recognition performance.
Kamari et al.[63] implemented a novel least squares SVM pro-
gram optimized using coupled simulated annealing to predict
unloading gradient pressures (psi ft1) in gas lift wells to deter-
mine the optimal production and injection rates with the design
ﬂuid rate and tubing size as the input attributes.
Li et al.[64] demonstrated an unsupervised fast black hole–
spectral clustering (FBH-SC) learning paradigm for fault diag-
nostics in sucker rod pumping wells with the application of
the CritC function to select the clustering number and the most
suitable scale parameter.
Crnogorac et al.[65] presented a fuzzy logic based optimization
model to select the most suitable artiﬁcial lift technique to be
implemented on wells. Sensitivity analysis was performed and
the artiﬁcial lifts were selected and ranked for implementation
on the candidate wells.
3.3.3. Well Problem Identiﬁcation and Solutions
Well problems, including scaling, formation damage, parafﬁn
and wax deposition, sand production, and excessive water and
gas production, tend to decrease the hydrocarbon recovery efﬁ-
ciency. The identiﬁcation of these problems and the remedial
actions necessitate well interventions, which incur high invest-
ments as well as loss of production. Moreover, they also increase
the cost of production and can damage the operational equip-
ment. The case studies discussed subsequently showcase the
implementation of ML and DA techniques to overcome these
challenges and obtain optimal production. Table 9 summarizes
the case studies of applications of ML and DA in well problem
identiﬁcation and solutions.
Case Studies: ML and DA in Well Problem Identiﬁcation and
Solutions: Khamehchi et al.,[66] used a PSO–ANN most to predict
the onset of sand production by estimating the critical total draw-
down (MPa). When compared with the back propagation ANN,
the performance of the PSO–ANN was more effective in forecast-
ing sand production by optimizing weights of the NN and mini-
mizing uncertainties.
Table 6. Summary of case studies on applications of ML and DA in EOR.
Ref no.
and [year]
Objective[s]
of the study
Input data source[s]
[ML] methodologies
Output[s]
Performance indicators
[53]
2017
Selection of Suitable
EOR Process
Laboratory Studies,
Reservoir Simulation,
Successful EOR Projects
Worldwide
Neuro–Fuzzy Simulation
Model integrated with
Box Plot Analysis
Screening and Ranking
of EOR
Best match (train:test ¼ 80:20)
RMSE
40
NDEI
0.04
[54]
2019
WAG incremental
recovery factor
determination
Reservoir simulation
results of reservoir and
ﬂuid properties
Regression and group method
of data handling technique
based predictive model
WAG incremental
recovery factor
Regression
Indicator
Data
MAE
RMSE
R
R2
Training
2.45989 3.57118 0.766466 0.587464
Validation
2.44422 3.56289 0.761228 0.57932
Group method of data handling
Indicator
Data
MAE
RMSE
R
R2
Training
1.86852 2.89307 0.853969 0.729258
Validation
1.86476 1.86476 0.848507 0.719913
[55]
2019
Suitable EOR
method selection
Rock and ﬂuid data
TOPSIS method with AHP
Screening and Ranking
of EOR
EOR Processes Ranked As Per Their
Applicability In Reservoirs
[56]
2020
Flow rate
measurement
Pump Operational
Characteristics, Wellhead
Flow Rate, Emulsion ﬂow rate
and downhole temperature
Soft sensors and data-driven
model
Produced ﬂuid ﬂow
rate
Indicator
Sensor prediction
R
RMSE
Overall
0.86
40.14
Figure 9. Performance of LR, SVM, PNN, and RF classiﬁers using F-score
measure.
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Wang and Chen[67] evaluated the production performance of
hydraulically fractured wells using a DM approach. They esti-
mated the well production (mbo) for the ﬁrst year using an
NN, SVM, adaptive boosting, and RF. The RF model outper-
formed the other supervised learning approaches by providing
greater prediction accuracy with the MSE and R2 performance
measures.
Liu et al.[68] developed a novel ANFIS network with the help of
PSO to forecast the unstable asphaltene weight percentage for
asphaltene-related modeling. The proposed model was trained
and tested using the open literature data and the performance
was evaluated using RMSE and R2.
Mahmoud et al.[69] presented an ANN algorithm to optimize
the number of stage separation, separator pressure (KPa), and sep-
arator temperature (C) based on ﬂuid composition. The perfor-
mance measures AAPE and R2 indicated that the proposed model
can be used to estimate the separator operating conditions for
improving the quality of crudes obtained from surface facilities.
4. Suggestions and Discussion
The review indicates that the ML and DA techniques have been
widely implemented in upstream petroleum operations to
increase
the
prediction
accuracy
of
crucial
parameters.
Most of the studies have predicted only one output parameter
using multiple input attributes. The input data are generally
obtained
from
different
sources
such
as
core
analysis,
seismic, well logs, well tests, production history, and simulation
results, which are utilized to train the model. The performance
indicators have been applied in most of the studies to evaluate
the accuracies of predictive models. Signiﬁcantly high accuracies
of model performance have been reported in forecasting the
parameters, which suggests that the ML and DA techniques
are highly reliable in planning and developing oil and gas
projects.
The analysis and suggestions for future work have been
included in the following subsections: 1) exploration and drilling
Table 7. Summary of case studies on applications of ML and DA in petroleum production optimization.
Ref no.
and [year]
Objective[s]
of the study
Input data source[s]
[ML] methodologies
Output[s]
Performance indicators
[57]
2013
Petroleum production
prediction
Production history data,
core analysis and
pressure data
NDT, C4.5,
and ANN
Oil ﬂow rate
Indicator
Model
MAE
MSE
Classiﬁcation
rate (%)
C4.5
0.3031
6.0156
69.68
ANN
0.1282
0.0652
76.25
NDT
0.2875
5.4375
71.25
[58]
2013
Cumulative oil production
prediction
Oil production ratio data
HONN
Cumulative oil
production
Overall best performance range
MSE
RMSE
MAPE (%)
0.002–0.005
0.039–0.072
4–7.6
Oil, gas, and water
production ratio data
Best model performance
MSE
RMSE
MAPE (%)
0.001
0.036
3.990
[59]
2017
Liquid critical ﬂow
rate prediction
Oil speciﬁc gravity, gas speciﬁc
gravity, gas liquid ratio,
choke size, wellhead pressure
and temperature
Hybrid ANN
Wellhead choke
liquid critical
ﬂow rates
R2
RMSE
ARE (%)
AARE (%)
0.981
714
2.09
6.5
[60]
2019
Gas production
estimation
Surface pressure and
temperature, distributed
acoustic sensing,
distributed
temperature sensing,
petrophysical
logs, geomechanical logs,
and ﬂow scanner
production log
RF, ANN
and SVM
Daily Gas Production
Model
Accuracy (%)
RF
96
ANN
95
SVM
89
[61]
2020
Time series oil production
forecasting
Available oil production
series data
EEMD–LSTM,
EEMD–ANN, and
EEMD–SVM
Time series oil
production
Indicator
Model
RMSE
MAPE
MAE
R2
EEMD–LSTM
0.992
2.731
0.286
0.957
EEMD–ANN
1.375
4.889
0.496
0.901
EEMD–SVM
1.535
9.036
1.239
0.894
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operations, 2) reservoir engineering, 3) petroleum production
system, and 4) overall suggestions.
4.1. Exploration and Drilling Operations
The PNN model has shown higher performance accuracy.
The validation data error for the RBF and MLFFN methods
are high because of the smaller training data size. Training an
ANN requires a large dataset to provide better prediction
accuracy.[32]
The multiresource and multiscale data used in the SVM
algorithm
have
yielded
signiﬁcant
sweet
spot
prediction
accuracy for shale reservoirs. Alternate methods such as ELM
can be used to increase accuracy. More of the performance
measures should be used to validate the model performance.[33]
The study conducted for shear wave velocity estimation is use-
ful, especially for wells where deploying shear wave velocity
equipment is not cost effective. MAE for CNN and ELM is rela-
tively better. The reported MAE values should not be negative as
MAE is absolute in nature. Feature optimization technique
should be used to improve performance.[34]
Feature optimization in a CNN can be performed to investi-
gate the model performance for more accurate prediction in
tracking single and multiple horizons.[35]
A mixed type of kerogen is not considered for classiﬁcation.
Moreover, the classiﬁcation performance of the SVM is relatively
low. Therefore, other methods can be investigated to improve
accuracy.[36]
The input parameter selection, data preparation, and model
optimization have been well demonstrated. More studies can
be performed to reduce the validation error.[37]
The PSO, GA, ICA, and BBO optimization has improved the
ROP prediction performance. The research has been well dem-
onstrated with adequate performance measures. Studies on
investigating the effect of other input parameters can be con-
ducted for better results.[38]
Studies on investigating the possibility of predicting failures
and accidental events can be performed.[39]
Figure 10. Performance of EEMD–SVM, EEMD–ANN, and EEMD–LSTM
using measures a) RMSE, b) MAE, and c) MAPE.
Table 8. Summary of case studies on applications of ML and DA in artiﬁcial lift techniques.
Ref no.
and [year]
Objective[s] of the study
Input data source[s]
[ML] methodologies
Output[s]
Performance indicators
[62]
2013
Identiﬁcation of submersible
pump working conditions
Structural and running
characteristics
SVM and LVQ
Working condition
diagnostics
Model
Average misjudgment rate (%)
SVM
5.7
LVQ
14.9
[63]
2014
Unloading pressure gradient
prediction in gas lifts
Well operational
conditions
Least squares SVM
Unloading pressure
gradient
AAPRE
APRE
RMSE
R2
1.0846581
0.099497
0.003526
0.9994
[64]
2015
Fault diagnostics in sucker
rod pump wells
Dynamometer card data
FBH–SC
Downhole conditions
fault diagnostics
Clustering accuracy range
0.80–0.97
[65]
2020
Artiﬁcial lift selection
Well, reservoir, and ﬂuid
characteristics, well operating
conditions, economic factors
Fuzzy logic
Selection and ranking
of artiﬁcial lift Techniques
Artiﬁcial lifts were ranked as per their
applicability on candidate wells.
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More investigations can be performed to optimize the drilling
parameters using other methods such as random K–nearest
neighbor and comparing the performance.[40]
The model performance has been shown and compared with
other models very well. Studies on feature selection and
optimization can further assist in increasing the prediction
performance.[41]
4.2. Reservoir Engineering
To assess the applicability of the predictive model for complex
reservoirs, the actual ﬁeld data should be used for validation.
A larger dataset can be used to avoid the case of underﬁtting
and obtain more reliable results.[42]
The logs for the complete wellbore interval are usually not
available. Therefore, more studies can be conducted for propos-
ing better methods.[43]
In the absence of experimental approaches, the proposed
models can be utilized. DL concepts can be investigated to gain
higher accuracy.[44]
The accuracy of multiphase ﬂuid identiﬁcation can be
increased using several alternative techniques using the available
hybrid techniques.[45]
Attribute selection and parameter optimization have been per-
formed to obtain better performance and predictions. Latest fea-
ture selection techniques such as ﬁrst ﬂy can be incorporated for
reducing the misclassiﬁcation rate.[46]
The careful feature selection and normalization technique has
yielded high prediction accuracy of the model.[47]
Optimization techniques such as PSO can be used to
increase the model performance. Studies focusing on incorporat-
ing tools to reduce the requirements of user inputs can be
done.[48]
The range of parameters such as skin and also the dataset size
can be increased to improve the prediction accuracy of the MLP
network. Recognition of other oil formation models can also be
investigated using the available ML and DL techniques.[49]
The dataset considered in the study is good. More alternative
hybrid ensemble learner based studies can be conducted and
suitable metrics can be used to validate the performance.[50]
Use of larger training datasets for ANNs increases the perfor-
mance accuracy. More datasets can be considered in future
studies.[51]
Work has been conducted with proper feature selection.
Ensemble learning can further improve the classiﬁcation
performance.[52]
A soft computing approach can be implemented and investi-
gated to get better performance results.[53]
The proposed predictive model has improved the performance
accuracy. Optimized CNN-based studies can be further exam-
ined to obtain higher accuracy.[54]
More of the EOR methods can be included to increase the
applicability of the method.[55]
The performance accuracy and reliability of soft sensor mod-
els in online implementation are well demonstrated but the R
Table 9. Summary of case studies on applications of ML and DA in well problem identiﬁcation and solutions.
Ref no.
and [year]
Objective[s]
of the study
Input data source[s]
[ML] methodologies
Output[s]
Performance indicators
[66]
2014
Sand production
prediction
Total vertical depth,
transit time, formation
cohesive strength
and effective overburden
vertical stress
PSO–ANN Back
Propagation–ANN
Critical Total
Drawdown
Indicator
Model
APRE
AAPE
MSE
R2
Backpropagation–
ANN
6.4
8.9
4.6e-04
0.979
PSO–ANN
3.24
5.48
2.4e-04
0.995
[67]
2018
Well production
performance
prediction
Well locations, true vertical
depth, well lateral length,
wellbore direction, fracture
stages, total volumes
of proppant and
fracturing ﬂuid type
RF, SVM, NN, and
adaptive boosting
First year
well production
Model
Indicator
RF
Adaptive
boosting
SVM
NN
Average MSE
0.3659
0.3851
0.6260
0.4910
Average R2
0.6310
0.6131
0.3736
0.5064
[68]
2018
Unstable asphaltene
weight percentage
forecasting
Temperature, dilution ratio,
precipitant type
and oil originality
ANFIS–PSO
Weight percentage
of unstable asphaltene
in oil
Data
Indicator
Train
Test
Total
RMSE
0.49256
0.43864
0.48777
R2
0.95402
0.96328
0.95864
[69]
2019
Separation parameter
optimization
Fluid composition
ANN
Separator pressure
and temperature
Indicator
Data
AAPE (%)
R2
Optimum separator
pressure
Train
1.6
0.98
Test
1.4
0.99
Optimum separator
temperature
Train
1.1
0.99
Test
0.65
0.995
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value is relatively low. A new methodology should be designed to
increase overall accuracy.[56]
4.3. Petroleum Production System
Classiﬁcation accuracy of NDT and C4.5 is lower as the input
features do not completely cover the problem space. The inter-
dependencies of inputs as well as the effect of other factors such
as rock formation should be examined.[57]
More careful selection of the number of neurons as well as
time lag can be performed for more accurate oil production
forecasts.[58]
Implementing regularization in nonlinear regression can be
done to further examine the performance results for predicting
wellhead choke liquid critical ﬂow rates.[59]
The performance can be improved by adding more input
parameters such as hydraulic fractures and related attributes
to the model.[60]
The uncertainty quantiﬁcation on the predicted results should
be investigated.[61]
The average misjudgment rate obtained from the SVM is
higher as compared to the LQV. A deep CNN with optimization
techniques can be investigated to attain improved accuracy in the
working
condition
diagnostics
for
sucker
rod
pumping
systems.[62]
The optimized least squares SVM provided a trustworthy tech-
nique for prediction of injection and production rates in gas lift
operation.[63]
A new model should to be designed to reduce the operation
time of the algorithm for fault diagnosis of downhole sucker rod
pumps.[64]
The proposed model provided a satisfactory clustering accu-
racy for artiﬁcial lift selection and ranking using suitable input
parameters.[65]
The PSO-optimized ANN has resulted in signiﬁcant predic-
tion accuracy. The ANN trained with a larger dataset can provide
reliable prediction. The training data size of the model can be
increased to predict the sand production.[66]
Data and feature selection has been done adequately. More
techniques such as MLP with optimization can be studied to
increase the model performance.[67]
More optimization techniques such as the fruit ﬂy algorithm
can be used in the study and the model performance can be
assessed using the performance measures.[68]
The proposed ANN model along with optimization techniques
can be investigated to obtain improved performance results.[69]
4.4. Overall Suggestions
The overall suggestions are as follows. 1) Alarge dataset should
be used to overcome the underﬁtting problem. 2) Hybrid models
can be used instead of simple ML techniques. 3) Regularization
should be implemented to reduce the underﬁtting as well as over-
ﬁtting problem. 4) Latest feature selection and reduction techni-
ques should be used for better model performance. 5) Optimized
DL techniques should be used instead of ML and NN.
6) Sophisticated sensors can be used to collect accurate data.
5. Conclusions
The ML and DA techniques are being implemented in all the
areas of the upstream petroleum industry as integrated tools
as well as alternatives to the traditional approaches. Various
researchers have shown that the predictive models provide an
accuracy of more than 90% based on statistical analysis.
The prediction models have been able to forecast lithology, stra-
tigraphy, facies, and sweet spots along with identiﬁcation of seis-
mic horizons, reservoirs, and ﬂuids. The drilling parameters,
including ROP and cuttings settling velocity, are optimized as well
as estimated, along with minimization of drilling problems by lost
circulation volume prediction or detection of accidental events.
The applications of ML and DA tools to enhance various
aspects of reservoir engineering by predicting reservoir perfor-
mance parameters such as solution GOR, bubble point pressure,
permeability, and oil recovery factor have been discussed. In addi-
tion, the well log correlation, log reconstruction, well test model
identiﬁcation and ﬂow rate prediction, and selection of most efﬁ-
cient EOR processes have also been reviewed in this study.
The applications of ML and DA have been discussed for opti-
mizing the petroleum production system by predicting the oil
and gas ﬂow rates, artiﬁcial lift selection, and well problem iden-
tiﬁcation, which helps in improving decision-making.
The ML and DA methods have signiﬁcantly minimized the
time consumption and associated cost by supplementing high
accuracy based predictions and providing intelligent solutions
to oil and gas operations.
Acknowledgements
This work was partially supported by the Oil Industry Development Board,
Ministry of Petroleum and Natural Gas, Government of India (Grant No.:
4/3/2020-OIDB). The authors would like to express their sincere gratitude
to IIT–ISM, Dhanbad, and DIT University, Dehradun, for providing the
adequate research environment and support. The authors also wish to
appreciate the valuable contribution of the researchers whose work has
been referred to for completing this review work. The authors are highly
thankful to the reviewers for their valuable suggestions to improve the
quality of this work.
Conﬂict of Interest
The authors declare no conﬂict of interest.
Keywords
performance
indicators,
predictive
models,
statistical
evaluation,
upstream operation
Received: August 25, 2020
Revised: October 17, 2020
Published online: November 9, 2020
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Rakesh Kumar Pandey is working as an assistant professor at DIT University. He has four years of
working experience in industry and academics. He has worked under projects of downhole well test data
acquisition and interpretation, and slickline operations in the petroleum industry. He pursued his
postgraduate degree in petroleum engineering from the Indian Institute of Technology (Indian School of
Mines), Dhanbad. His research interests include petroleum production optimization, reservoir
performance analysis, oil and gas well testing, enhanced oil recovery, reservoir simulation, data analytics,
and machine learning.
Anil Kumar Dahiya is working as a professor and head of the Data Science Research Group, DIT
University. He has more than 24 years’ teaching and industrial experience. His research interests include
image processing algorithms, cryptography, artiﬁcial intelligence, signal and systems, and neural systems.
He has done various Government of India projects as principal investigator.
Ajay Mandal is working as a professor and the head of the Department of Petroleum Engineering, Indian
Institute of Technology (Indian School of Mines), Dhanbad. He obtained his Ph.D. degree and did
postdoctoral research as DST Young Scientist from IIT-Kharagpur.
www.advancedsciencenews.com
www.entechnol.de
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Optimization of Propylene Production Process from Fluid Catalytic Cracking
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European Journal of Advances in Engineering and Technology, 2016, 3(9): 81-87 
 
Research Article 
ISSN: 2394 - 658X 
 
81 
 
Optimization of Propylene Production Process from Fluid Catalytic 
Cracking Unit 
 
Azubuike LC, Okonkwo E, Egbujuo W and Chike-Onyegbula C 
 
Department of Polymer and Textile Engineering, School of Engineering and Engineering Technology,  
Federal University of Technology Owerri, Nigeria 
izuegbulilian@yahoo.com 
_____________________________________________________________________________________________
 
ABSTRACT  
 
This study explored the flexibility of FCC unit of a typical Refinery, in Optimizing Propylene, a feedstock for the 
petrochemical industry. This was achieved using Aspen Hysys (version 7.1), a chemical process software to 
systematically manipulate the reactor’s temperature, pressure and catalyst activity variables in the FCCU. Result 
from the simulation showed that increase in temperature, pressure and ZSM-5 additive increases the propylene 
product yield and also enhances the MON and RON of gasoline. It is observed also that, there is a drastic rise in 
olefin product yield as the temperature rises and an increase in ZSM-5 catalyst additive. The product yield variation 
at a catalyst additive of 0.196 of a gradual increase in the temperature, it is seen that the reactor would only operate 
at an optimum temperature range of 520 – 540oC  
 
Key words: Propylene, Optimization, FCCU, ZSM-5 zeolite 
_____________________________________________________________________________________ 
 
INTRODUCTION 
 
Refineries also produce petrochemicals in addition to fuels. Petroleum refining processes generate light olefins as 
well as aromatics (especially benzene, toluene, and xylenes). In periods of high gasoline demand, refining 
operations tend to be optimized to maximize production of fuels; chemical co-production mode is not emphasized. 
In the future scenarios, there will be opportunities to shift from fuels emphasis to increased chemical production 
[5]. In addition to recovering chemical products co-produced in the refinery, transitional streams can be channelled 
to chemical production facilities such as steam crackers for ethylene and propylene production, and naphtha 
reformers for aromatics production. Again, disposition of intermediate refinery streams to chemical production 
facilities will become more advantageous as refinery fuel product demand is impacted by the new trends [5]. A 
large proportion of propylene is produced by steam cracking of light naphtha and during the fluid catalytic 
cracking process. Maximization of propylene production has become the focus of most refineries because it is in 
high demand and there is a supply shortage from modern steam crackers, which now produce relatively less 
propylene [1]. The flexibility of the fluid catalytic cracking (FCC) to various reaction conditions make it possible 
as one of the means to close the gap between supply and demand. The appropriate modification of the FCC process 
is accomplished by the synergistic integration of the catalyst, temperature, reaction-residence time, coke make, and 
hydrocarbon partial pressure [1]. 
 
Propylene is second in importance to ethylene as a raw material for petrochemical manufacture. The largest source 
of petrochemical propylene is that produced as the primary byproduct of ethylene manufacture. Ethylene plants 
charging liquid feedstock typically produce about 15 wt% propylene and provide almost 70 percent of the propylene 
consumed by the petrochemical industry [4].  Since Steam crackers produce more ethylene than propylene, and its 
construction is tied to the demand for ethylene, there is need to create more channels for the production of 
propylene. One of such is FCC produced propylene. This will help in meeting with the increase in demand in 
propylene and bridge gap in future. Also, since the construction of FCC units is driven by the demand for gasoline 
instead of propylene, most of the increased propylene supply will have to come from proper utilization of FCC 
flexibility towards increasing propylene. 
 
 
Azubuike et al                                                           Euro. J. Adv. Engg. Tech., 2016, 3(9):81-87      
______________________________________________________________________________ 
82 
LITERATURE REVIEW 
 
Fluid Catalytic Cracking development started in the 1930's. From different findings, under proper conditions, finely 
divided solids could be made to flow like liquids. Such small particles offered advantages in heat transfer and mass 
diffusion over the large catalyst pellets used in other processes. For catalytic cracking, fluid phase seemed to be very 
advantageous also from the point of view of very quick heat transfer because of strong endothermic effect during 
cracking of feed and strong exothermic effect in the coke-burning regeneration [3]. 
 
A major breakthrough in catalyst technology occurred in the mid-1960s with the development of zeolite catalysts. 
These sieve catalysts demonstrated vastly superior activity, gasoline selectivity, and stability characteristics 
compared to the amorphous silica-alumina catalysts then in use. The availability of zeolite catalysts served as the 
basis for most of the process innovations that have been developed in recent years [2]. The continuing development, 
first in catalyst activity and then in process design led to achieving more product within the dilute phase of the riser, 
or riser cracking as it is commonly called. In 1971, UOP commercialized a new design based on this riser cracking 
concept, which was then quickly extended to revamps of many of the existing units. Commercial results confirmed 
the advantages of this system compared to the older designs. Riser cracking provided a higher selectivity to gasoline 
and reduced gas and coke production that indicated a reduction in secondary cracking to undesirable products [2] 
Although the mechanical configuration of individual FCC units may differ, their common objective is to upgrade 
low value feedstock to more valuable products. 
 
Since the start-up of the first commercial FCC unit in 1942, many improvements have been made. These 
improvements have enhanced the unit’s mechanical reliability and its ability to crack heavier, lower value feedstock. 
The FCC has a remarkable history of adapting to continual changes in market demands [6].This trend has continued 
throughout the years as process designs emphasize greater selectivity to desired primary products and a reduction of 
secondary by-products. 
 
Description of FCC Unit of a Typical Refinery 
The purpose of the Fluid Catalytic Cracking (FCC) process of any refinery is to convert feed, heavy oil to lower 
boiling, high value products, primarily C3-C4 LPG, gasoline and light cycle oil. This is achieved using vapor phase 
chemical reactions in the presence of specialized FCC cracking catalyst during which the long molecular chain of 
the feed is cracked into shorter chain molecules. Heat for the cracking process is supplied by the hot regenerated 
catalyst which vaporizes the finely atomized oil feed and sets the stage for the rapid and selective cracking process. 
The vaporization and cracking reactions occur in the reactor riser. As by-product of the reaction, fuel gas, slurry oil, 
and coke are also generated in the reactor riser. The reaction section of this unit particularly includes reactor, 
catalyst regenerator and product separation. 
 
 
Fig. 1 FCC Unit Block Flow Diagram Of Typical Refinery 
 
IMPORTANT DESIGN FEATURES OF FCC UNIT 
Feed Nozzles 
The feed (VGO, HDO and Steam) enters the Riser through the distributor nozzles at the Riser base and meets hot 
regenerated catalyst. 
 
Reactor Riser 
The cracking reactions take place during the residence time in the riser as the reaction mixture (composed of the 
feed and the catalyst) accelerates toward the Riser ‘tee’ Separator System at the top of the riser. Riser design allows 
reaction to take place in selective environment; maximizes catalytic reactions while minimizing thermal reactions. 
 
Riser Terminal 
‘Tee’ shaped outlet replaced the riser cyclones giving a good catalyst oil separation and lower equipment cost. 
Azubuike et al                                                           Euro. J. Adv. Engg. Tech., 2016, 3(9):81-87      
______________________________________________________________________________ 
83 
 
Fig. 2 Riser Reactor 
 
Fig. 3 Cyclone System 
 
Reactor/Regenerator Cyclones 
The vapor/flue gas leaving the Reactor/Regenerator will carry off the smaller catalyst particles with it. The cyclones 
are used to remove most of the catalyst particles from the Reactor/Regenerator vapor/flue gas. The larger particles 
are removed through inertial forces, which tend to keep the particle moving in a straight line to collide with the wall, 
and centrifugal forces, which tend to throw the particle outward to collide with the wall. The collisions slow the 
speed of the particle and it tends to fall into the cyclones dip legs and return to bed. The drag forces of the gas will 
tend to carry the catalyst particles with it, but only the smaller ones are light enough to stay with the gas, because the 
inertial and centrifugal forces acting on them are small. The catalyst carry over must be max. 1000 wt. ppm in MCB. 
 
Azubuike et al                                                           Euro. J. Adv. Engg. Tech., 2016, 3(9):81-87      
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84 
Disengager 
The reactor in this case can be called ‘Disengager’ as very little reaction takes place here.  
 
Slide Valves 
Slide valves are used to control catalyst/flue gas flow. All are gate valves provided with independent hydraulic oil 
system to ensure a reliable and stable operation. There are Regenerated Catalyst S/V and Spent Catalyst S/V. 
 
Reactor Stripper 
The steam to the stripping section is distributed through a number of small holes in the chest. A rate of 1-2 kg /1.000 
kg catalyst is normal. It allows efficient contact between the catalyst and steam to displace the volatile hydrocarbons 
contained on and in the catalyst particles before they enter the regenerator, where coke will be burnt off. The 
displaced hydrocarbon vapors and most of the steam go up. 
 
Plenum Chamber 
The plenum chamber is a dome shape design which receives the flue gas from all the six pairs of regenerator 
cyclones. The Flue Gas leaving the Plenum Chamber enters the Orifice Chamber. 
Regenerator Air grid 
The Air Grid is Dome shape design with about 900 nozzles. The Air Grid distributes the combustion air evenly 
across the bed in the regenerator. A well distributed source of combustion air is essential for good, evenly 
distributed catalyst regeneration without after burn. The Air Grid is designed to operate satisfactorily at the 
minimum turndown design for the unit. The pressure drop across the grid is kept above 0.07 Kg/cm² at turndown to 
maintain adequate distribution and prevent intrusion of catalyst below the Air Grid and avoid associated erosion. 
 
Torch Oil Nozzles 
During the start-up, torch oil is used to heat the catalyst to its operating temperature. The torch oil nozzles provide 
fine spray of heavy oil injected into the dense bed of air preheated catalyst when extra heat is needed. The torch oil 
is used when the temperature in lower Regenerator is above 400⁰C (ideal 415-430⁰C).  
 
Expansion Joints 
Expansion joints are critical equipment in FCC, an expansion joint is used to allow movement of the system as it 
heats up. The expansion joints at the Regenerator/Reactor stand pipes accommodate the relative expansion 
difference between the Regenerator and Reactor  
 
Orifice Chamber 
The orifice chamber is provided to reduce the pressure of the flue gas leaving the regenerator. The orifice chamber 
has six grids with different number of holes. 
 
Flue Gas Cooler 
The purpose of the Flue Gas Section is to recover thermal energy from the flue gas leaving the Regenerator. The 
energy is used to produce 42Kg/cm² superheated steam. Recovering this energy increase the efficiency of the unit.  
 
Stack 
The Stack is provided at the outlet of the Flue Gas Cooler and Incinerator to safely evacuate the flue gas to 
atmosphere. 
 
Fig. 4 FCC Design 
Azubuike et al                                                           Euro. J. Adv. Engg. Tech., 2016, 3(9):81-87      
______________________________________________________________________________ 
85 
 
 
Fig. 6 Product Yields From the FCC 
 
PROCESS SIMULATION PROCEDURE 
Collection of Data 
Operating Data and Process flow Diagram of Fluid Catalytic Cracking Unit (FCC) were collected from a typical 
refinery and a model was developed with the data in a simulator, using Aspen Hysys (version 7.1)   
 
Process Description 
Fig 5 shows the process flow diagram as modeled using HYSYS version 7.1. The Simulations were performed using 
the data collected. The procedures for process simulation mainly involve defining chemical components (crude 
assay), selecting a thermodynamic model, choosing proper operating units and setting up input conditions (flow rate, 
temperature, pressure, catalyst information and other conditions). Data on most components, such as water, 
hydrocarbons, oxygen, CO, CO2, NO2, SO2, is available in the HYSYS component library. To represent the refinery 
process and FCC unit in Aspen HYSYS, a process flow diagram (PFD) was built, In Simulation Basic Manager, a 
fluid package was selected along with the components which are to be in the input stream. In the process, Peng- 
Robinson was selected as the fluid package as it is able to handle hypothetical components (pseudo-components). 
Most of the heat utilities information was assumed in order to develop the model. The main processing units include 
riser reactor, Regenerator, Distillation Column, Vacuum Distillation column, Valves, Cooler and heaters. After the 
input information and operating unit models were set up, the process steady- state simulation was executed by 
Hysys. Mass and energy balances of each unit, as well as operating conditions and model of FCC was obtained. Fig 
5.0 shows the FCC design on the simulator window. 
Fig. 5 Process Flow Diagram as Modelled on HYSYS 
 
Azubuike et al                                                           Euro. J. Adv. Engg. Tech., 2016, 3(9):81-87      
______________________________________________________________________________ 
86 
Table -1 Simulation Result as Analyzed In an Excel Spreadsheet 
 
Input Factors 
Output Parameter 
Reactor/ 
Riser 
Temp(0c) 
Reactor 
Pressure 
(Kpa) 
ZSM-5 
activity 
Propylene    
(%) 
Butenes      
(%) 
Naphta      
(C5-
430F)(%) 
LCO       
(430-650)(%) 
MON          
(C5-265) 
MON            
(265-430F) 
RON           
(C5-265F) 
RON            
(265-430F) 
530 
340 
0 
4.61710732 
6.17494898 
45.9893284 
15.90459288 
94.4339326 
92.6327056 
83.5137382 
80.7633724 
530 
340 
0.02 
5.1170526 
6.58267848 
44.9362021 
15.88636928 
94.8653922 
93.0053071 
83.9309656 
81.1249196 
530 
340 
0.04 
5.61693061 
6.99036744 
43.8825284 
15.8684183 
95.29688 
93.3777985 
84.3481403 
81.4863369 
530 
340 
0.06 
6.11674294 
7.39801019 
42.8282498 
15.85076585 
95.728396 
93.750179 
84.7652552 
81.8476215 
530 
340 
0.08 
6.61649034 
7.80559887 
41.7732808 
15.83345204 
96.1599406 
94.1224452 
85.1823 
82.208767 
530 
340 
0.1 
7.11617225 
8.21312196 
40.7174903 
15.81653991 
96.5915155 
94.4945898 
85.5992594 
82.5697616 
530 
350 
0 
4.65638905 
6.20961771 
45.811058 
15.92865423 
94.4344504 
92.6305939 
83.5120241 
80.7606847 
530 
360 
0 
4.69418269 
6.24218105 
45.619294 
15.95916173 
94.4354605 
92.6264737 
83.5086796 
80.7554409 
530 
370 
0 
4.73042689 
6.27285101 
45.4224988 
15.99126483 
94.4366478 
92.6216311 
83.5047488 
80.7492776 
530 
380 
0 
4.76516359 
6.30181815 
45.224937 
16.02287551 
94.4378706 
92.6166439 
83.5007005 
80.7429302 
530 
390 
0 
4.79847774 
6.32923447 
45.0282475 
16.05350709 
94.4390888 
92.6116752 
83.4966673 
80.7366064 
530 
400 
0 
4.83045672 
6.3552153 
44.8328069 
16.08335588 
94.4403068 
92.6067076 
83.492635 
80.730284 
520 
340 
0 
4.26588037 
5.80297106 
45.7068598 
16.86834591 
93.5933542 
90.9503488 
83.4970105 
79.3749182 
530 
340 
0 
4.61710732 
6.17494898 
45.9893284 
15.90459288 
94.4339326 
92.6327056 
83.5137382 
80.7633724 
540 
340 
0 
5.18107187 
6.71189614 
44.9740829 
14.91453477 
95.28052 
94.2905544 
83.5105721 
82.1206345 
550 
340 
0 
6.1165791 
7.50294108 
41.688036 
13.96876107 
96.1367077 
95.9092469 
83.4756217 
83.4280613 
560 
340 
0 
7.60235802 
8.60187633 
35.0724516 
13.17600639 
97.0040576 
97.4824165 
83.4037185 
84.6775496 
570 
340 
0 
9.73697153 
9.97196762 
24.5421887 
12.59833162 
97.8815365 
99.0142724 
83.2982801 
85.874457 
 
 
Fig. 7 Reactor Temperatures against Product Yield 
 
 
Fig. 8 Reactor Pressure against Product Yield 
 
Azubuike et al                                                           Euro. J. Adv. Engg. Tech., 2016, 3(9):81-87      
______________________________________________________________________________ 
87 
 
Fig. 9 Effect of Zsm-5 Activity on the Product Yield 
 
Table- 2 Optimum Temperature Range for Optimization 
 
Reactor / Riser 
Temp (Oc) 
Reactor Pressure 
(KPa) 
ZSM-5 
activity 
Propylene (%) 
butenes (%) 
Naphta 
(C5-430F)(%) 
LCO 
(430-650)(%) 
520 
340 
0.196 
8.795880038 
9.556082501 
35.99234671 
16.70227108 
530 
340 
0.196 
9.511785182 
10.16218232 
35.55409605 
15.78327463 
540 
340 
0.196 
10.66524033 
11.03901431 
33.40036432 
14.8454727 
 
RESULT AND DISCUSION 
 
The result of the simulation was analyzed in an excel spreadsheet Table- 1, the product yield from the FCC unit is 
shown on fig 6.0. Process variables discussed in this section are, reactor temperature, reactor pressure, and Zsm-5 
activity. From Fig 7, shows that increase in the reactors temperature, results to drastic drop in PMS production after 
530oC, large drop in LCO Production, drastic Increase in Olefins production, Convergence of propylene and 
butylenes production having a lager % increase in butylenes yields. In Fig 8, an increase in the Reactor pressure, 
results in slight drop in PMS production, negligible drop in LCO Production, Slight Increase in Olefins production. 
In Fig 9, an increase in the ZSM-5 catalyst causes the drop in PMS production, slight drop in LCO Production, 
increase in Olefins production, Greater percentage increase in propylene than butylene production, and also it is 
observed that ZSM-5 additive truncates at 0.196% addition in the HYSYS simulation. Table -2 shows that at the 
optimum temperature range of 520 – 540oC and at catalyst activity of 0.196, there is increase in olefin production. 
 
CONCLUSION 
 
Based on this study it can be concluded that increase in riser reactor temperature, increases the yield of propylene in 
an FCC unit. There is sharp decrease in PMS and LCO yield when the temperature is increased. The MON and RON 
octane number are not affected with the increase in temperature. There is no significant effect of change in pressure 
on the product yield. Also increase in ZSM-5 additive results in greater %Increase in propylene production from 
FCC unit. There is a drop in PMS and LCO production with increase in ZSM-5 activity. ZSM-5 additive truncates at 
0.196% addition in the hysys simulation and since there is a drastic rise in the olefin product yield as the temperature 
rises and also an increase in ZSM-5 catalyst additive, the product yield variation at a catalyst additive of 0.196 of a 
gradual increase in the temperature. Shows that the reactor would only operate normal at a temperature range of 
(520 – 540) oC 
REFERENCES 
 
[1] AM Aaron,  Maximizing Propylene Production via FCC Technology, Appl Petrochem Res, Springerlimk 
Publication, USA, 2015. 
[2] CL Helmer and FL Smith, UOP Fluid Catalytic Cracking Process,  Chapter 3.3, UOP LLC Des Plaines, Illinois, 
2004. 
[3] P Hudec, FCC Catalyst- Key Element in Refinery, 45th International Petroleum Conference, Bratislava, Slovak 
Repulic, 2011. 
[4]  NM Philip, Future Refinery- FCC Role in Refinery/Petrochemical Intergration, Kellog, Brown and Root Inc. 
Houston Texas, USA, 2001. 
[5]  P Ruzika, Opportunities for Refinery and Petrochemical Plant Integration, Carmagen Engineering Inc. New 
Jersy, 2014. 
[6] R Sadeghbeigi, Fluid Catalytic Cracking Handbook, Design, Operation and Troubleshooting of FCC 
Facilities,2nd  Edition, Gulf Professional Publishing,  USA,  2000. 
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CHAPTER TWELVE
Case-based reasoning (CBR) in digital
well planning and construction
Key concepts
1. A case-based reasoning (CBR) model comprising general knowledge (well-known
rules and theories) and speciﬁc knowledge (stored in cases) is presented.
2. Well design is key to decreasing the costs and risks involved in well drilling. The
experience acquired by engineers is an important factor in good drilling design.
However, individuals’ knowledge can be lost through changes of personnel, poten-
tially resulting in problems and costs that could have been avoided. This chapter
represents an initiative to model a case-based architecture for petroleum well design.
3. Lost circulation represents a very costly and time-consuming challenge in drilling
through depleted geological zones, and lost circulation has been difﬁcult to accurately
predict and manage. These objectives can be achieved by creating a system using CBR,
which reviews a database of past incidents, determines those most similar in nature, and
provides recommendations based on previous successes and failures.
12.1 Basic concepts
12.1.1 Knowledge-based systems
The concept of knowledge-based systems is derived from the ﬁeld of artiﬁcial intelli-
gence (AI). The ﬁeld of AI is aimed at an understanding of human intelligence to build
computer programs that are capable of simulating one or more intelligent behaviors.
Intelligent behaviors include cognitive skills such as thinking, problem-solving, learning,
understanding, reading emotions, consciousness, intuition, creativity, and language
capacity. These days some intelligent behaviors, such as problem solving, learning, and
understanding, can be handled by computer programs (Eshete, 2009; Champandard,
2008).
Computer programs that try to solve problems in a similar way to a human expert by
using knowledge about the application domain and problem-solving techniques are
known as knowledge-based systems. Human experts apply problem-solving techniques
to use their knowledge of the domain to solve problems. Knowledge-based systems
handle problems in the same way. They represent the knowledge about the application
domain and apply one or more techniques that process that knowledge to solve
Methods for Petroleum Well Optimization
 2022 Elsevier Inc.
ISBN 978-0-323-90231-1, https://doi.org/10.1016/B978-0-323-90231-1.00003-0
All rights reserved.
477j
problems. Every knowledge-based system has two building blocks, which are known as
the knowledge base and the inference engine (Eshete, 2009).
The knowledge base contains all the knowledge about the domain that is required to
handle problems. The knowledge can be acquired from experts, documents, books, and/
or other sources. It is formalized and organized with a technique called knowledge
representation. There are several ways to represent knowledge in the knowledge base,
including cases and rules.
The second component of a knowledge base system is the inference engine. After the
system gets the required knowledge, it needs to be instructed on how to use the
knowledge to solve problems. The inference engine represents the reasoning technique
that manipulates, uses, and controls the knowledge to solve the problems, such as CBR
and rule-based reasoning.
In the following sections, we will brieﬂy discuss two knowledge-based systems: case-
based systems, which use cases for knowledge representation and CBR for reasoning
techniques; and rule-based systems that use rules for knowledge representation and
rule-based reasoning for reasoning techniques.
12.1.2 Case-based systems
Human beings handle situations by recalling their experience of similar situations. If the
situation is novel, people try to handle it by relating it to some aspect of different
experienced situations. People normally learn from their successes and failures how to
handle future similar situations successfully or without repeating mistakes. Remem-
bering and reusing solutions to previous problems, and learning from experiences to
apply in future, is natural and useful (Aamodt and Plaza, 1994; Kolodner, 1993; Leake,
1996). Case-based systems are designed to work in the same way, with the basic idea that
similar problems have similar solutions. Case-based systems are knowledge-based systems
that solve problems by remembering a similar past situation and reusing its solution and/
or the lessons learned from it. Case-based systems combine problem-solving and
learning from new experiences for future use (Aamodt and Plaza, 1994; Kolodner,
1993). The knowledge base of a case-based system represents situations or domain
knowledge in the form of cases, and its inference engine uses a CBR method to solve
new problems or to handle new situations.
The general structure of a CBR system is shown in Fig. 12.1. Here the general
knowledge is a store of many solved cases, which together build a model for a CBR
system. When a new unsolved case is introduced into the model, a new solution will be
retrieved. A CBR system is able to read the unsolved case (as input data), and it retrieves
the best similar solved case (as output data). The solution of this solved case will be
suggested and approved for the unsolved case, and a new solution will be derived.
The new solution can be applied directly to the new problem or modiﬁed according to
the differences between the input and output cases.
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Methods for Petroleum Well Optimization
CBR should not be seen as a database; it is more like a knowledge base. Databases
normally contain digital numbers, symbols, and characters. The data need to be trans-
ferred to pieces of information. The processed or learned information is called
knowledge (Aamodt, 2004). For clariﬁcation, some examples are given in Table 12.1.
In the CBR process, the knowledge is acquired and captured from the cases (speciﬁc
knowledge) and merged with general knowledge. The combined knowledge is retained
and organized in the system’s memory to reuse it for new problems when needed. When
a new case is solved, it is added to the case base. In this way, the body of knowledge
expands itself in a generic framework, and it can be used for different applications where
the domain has uncertainties and incomplete information. The core of a CBR is a
structural model which deals with the problem domain.
12.1.2.1 The case-based reasoning process
CBR can be considered as a machine, which reads a new unsolved case, matches it with
the many solved cases that have been stored in the machine memory, and retrieves the
Figure 12.1 A simple schematic for a case-based reasoning system.
Table 12.1 Typical examples of data transferred to information and information to knowledge.
Data
Information
Knowledge
1.1 s.g.
Low mud density
Low mud density leads to hole collapse
100 bar
Low pore pressure
Low pore pressure causes mud loss
$61
High oil price
High oil price causes more drilling activity
From Abdollahi, J., 2007. Analysing Complex Oil Well Problems through Case-Based Reasoning. Thesis for the degree of
Philosophiae Doctor (PhD), Norwegian University of Science and T
echnology.
Case-based reasoning (CBR) in digital well planning and construction
479
most similar solved case. Therefore, the output of the machine is a proposed solution to
the new unsolved case. A simple schematic for the CBR process is shown in Fig. 12.2.
Cases are used to represent the domain knowledge of a case-based system. A case
refers to speciﬁc experience or to knowledge tied to speciﬁc situation that is worth
remembering for future use. So cases in the knowledge base represent a collection of
speciﬁc experienced, captured, and learned situations for the application domain
(Aamodt and Plaza, 1994; Kolodner, 1993). Each case consists of three main parts (Leake,
1996):
•
Description of the situation/problem: This describes the speciﬁc circumstances,
conditions of a situation, and state of the environment when this particular case is
recorded.
•
Solution: This provides knowledge of how the problem in the description was solved
or treated in a particular instance.
•
Outcome: This describes the ﬁnal result or consequence, and feedback gained from
following the proposed solution.
12.1.2.2 The case-based reasoning cycle
CBR, as its name indicates, uses cases to reason about a given problem. In the problem-
solving process, it reuses similar previous cases to understand the current problem, and to
either suggest a solution based on the successful outcome of the previous case or reject a
solution that resulted in failure in the previous case. A CBR technique follows four
processes to accomplish its reasoning task:
1. Retrieve: retrieving the most similar previously solved cases.
Figure 12.2 The functionality of case-based reasoning.
480
Methods for Petroleum Well Optimization
2. Reuse: reusing the retrieved cases by copying or integrating the proposed solution.
3. Revise: revising or adapting the proposed solution.
4. Retain: retaining the new generated solution for future use.
Fig. 12.3 describes the sequence of those processes (Aamodt and Plaza, 1994; Lopez
et al., 2006; Kolodner, 1993; Leake, 1996).
Revise
In a case-based system, proposing a solution is not the only goal, the system also aims to learn from the consequences of applying the  
simulator, by getting feedback from a human expert for the application domain or by applying the solution in the real world and seeing the  
 result. This process may take hours, days, or months until the result is realized. The system learns from the result, whether it is success or   
failure. If it is failure, the fault in the reasoning needs to be repaired, and an explanation of why the failure occurred should be given to 
  
 prevent such failures with similar problems in the future.
When a new problem occurs, this process tries to identify the descriptive features of the new problem, and it searches previous cases that 
match with the new situation based on the identified features. Identifying descriptive features involves identifying properties that describe the
new problem, leaving out those that do not describe it strongly, and representing the descriptive features in a case format. There are 
algorithms that are capable of  doing this task. Searching similar previous cases is performed by matching the new case with saved old cases 
from the knowledge base. It results in a collection of similar cases. The final step of the retrieval process is to select the best matched case or 
a set of cases from the collection of similar cases. The degree of  similarity is measured by using similarity assessment methods. The quality 
of the retrieval process depends on its descriptive feature identifying algorithm, searching algorithm and similarity assessment method.                  
Reuse
The selected case in the retrieval process can be used : to understand the new situation when it is not clear by itself ; to propose a solution 
based on the solution taken in the selected case ; or to prevent an incorrect solution to the problem based on the failure of the proposed 
solution in the selected case. Proposing a solution can be performed in two ways: reusing the solution as it was or by adapting it. When the 
selected case and the new case do not have significant differences, the solution in the selected case will be proposed for the new problem. 
Whereas, if there is a significant difference between them, the solution in the selected case is adapted, based on the unique features of the
 new case. This process is known as adaptation.                       
Retain
Case-based systems upgrade their domain knowledge by learning from new experiences while problems are being solved. After the proposed 
solution for the given problem is evaluated in the revise process, the retain process identifies new experiences that are useful and worth 
remembering, and decides how to merge these with existing knowledge. This type of learning is known as incremental learning because it always 
adds knowledge that is new and useful in addition to the existing knowledge.    
proposed solution.  This process evaluates how good the proposed solution is for the given problem. The evaluation is performed by using a   
Retrieve
Figure 12.3 Cyclical process of case-based reasoning. Modiﬁed from Smiti, A., Elouedi, Z., 2013.
Modeling competence for case based reasoning systems using clustering. In: Proceedings of the Twenty-
Sixth International Florida Artiﬁcial Intelligence Research Society Conference.
Case-based reasoning (CBR) in digital well planning and construction
481
The ﬁrst step in building a CBR model is the “representation of cases” as well as
knowledge. This means deﬁning and describing the cases in the model to recall and reuse
them for reasoning. The main challenges for case representation are:
1. the searching and matching process for cases;
2. integrating new cases in the existing memory (model);
3. selecting the type of data which should be stored in cases, qualitatively and
quantitatively;
4. organizing and indexing cases for effective retrieval and reuse; and
5. integrating cases with the general domain knowledge.
In the retrieval step (ﬁrst step), the closest similar solved cases are matched and retrieved.
This step has three substeps, executed in this order: (1) identify features; (2) make initial
match; and (3) search and select. Both solved and unsolved cases are deﬁned in the CBR
model as a set of descriptors (features); CBR systems should identify features of cases by
either syntactical similarities (superﬁcial) or semantical similarities. For example, the
CYRUS and ARC systems perform syntactical similarities, while PROTOS and
CREEK perform semantical similarities (Aamodt and Plaza, 1994). In CREEK, the
CBR processes are supported by an explanation engine to explain the reasoning (through
the CBR processes) for the user or to report an internal explanation that the CBR system
may create while reaching the goal of the reasoning tasks. This engine has three subtasks
(Aamodt, 1994a):
1. Activate: making active the related features or concepts of the cases within the
network knowledge structure (ontology).
2. Explain: creating and explaining the derived information within the activated
knowledge (from last step).
3. Focus: focusing and selecting a conclusion which satisﬁes the goal.
These subtasks are illustrated in Fig. 12.4. They have an initial state description (input)
and ﬁnal state description (output).
In the reuse step, the focus is on the retrieved case solution for the new case in terms
of the similarities and differences of the attributes of the two cases (input and output), and
Figure 12.4 The case-based reasoning process and the explanation engine. Modiﬁed from Aamodt, A.,
1994a. Explanation-driven case-based reasoning. In: Wess, S., Althoff K.D., Richter M.M. (Eds.) Topics in
Case-Based Reasoning. EWCBR 1993. Lecture Notes in Computer Science (Lecture Notes in Artiﬁcial In-
telligence), vol. 837. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58330-0_93.
482
Methods for Petroleum Well Optimization
the system tries to select and transfer some part of the retrieved case to the new case. In
this way, the suggested solution will be derived. In the revise step, the suggested solution
will be evaluated and veriﬁed. The outcome of the revise step can be either to use the
retrieved solution directly or to adapt the solution by using the domain knowledge base.
In the retain step, the new cases will be evaluated to store in the existing model.
One of the important output parameters of the CBR process is the similarity match
percentage (fraction of 100) between the input case (new unsolved case) and the
retrieved cases (previous solved cases). The retrieved cases will be ranked based on the
similarity percentage. The total similarity percentage comprises two matches: direct
match (syntactical similarities) and indirect match (semantical similarities). In the direct
match part, the similarly is exact between the input ﬁndings of the unsolved case and the
output ﬁndings of the solved case, which is one-by-one similarity. However, in the
indirect match part, the ﬁndings between the two cases are not necessarily full similarities
as they are in the direct match, but they have different relations between them (for
example, causal relations, associational relations, and structural relations).
12.1.2.3 Similarity assessment
A robust retrieval process requires an effective similarity assessment. Two different
mechanisms are used to compute the values of similarity between a new problem case
and a case in the case base. Linear similarity is used for those features that have numeric
values. Semantic similarity, relying on concepts abstraction, is being used for direct or
indirect matches of symbolic feature values. The indirect match is used when the model-
based module is utilized.
In CREEK, the match similarity is the function of a number of related ﬁndings,
predictive strength (degree of sufﬁciency), and importance (degree of necessity), which is
discussed in Section 12.2.5. The similarity function between input and output cases is
given as (Lippe, 2001):
simðCm; CreÞ ¼
P
n
i ¼ 1
P
m
j ¼ 1
sim

fi; fj

 relevance factorfj
P
m
j ¼ 1
relevance factorefj
(12.1)
The relevance factor is a number that represents the combined predictive strength
and importance of a ﬁnding of a case, and sim( f1, f2) is given by:
For symbolic concepts
For linear concepts
simðf1; f2Þ ¼
(
1
if f1 ¼ f2
0
if f1 s f2
simð f1; f2Þ ¼ 1 




f1f2
MaxMin




(12.2)
(12.3)
Case-based reasoning (CBR) in digital well planning and construction
483
The linear approach explicitly computes the values of similarity according to the min-
imum and maximum values of each concept. The maximum and minimum of each
feature give an interval, and the values of the two cases are compared on this scale, giving
a value of 0 if the difference between the values is the same as the difference between the
minimum and the maximum, and a value of 1 if the values are the same.
12.1.2.4 Cases
In the CBR language, a case is usually denoted by a problem situation or an episode.
Some situations recur with regularity, and the solution of the problem in one instance of
that situation is likely to be applicable in another. Cases are descriptions of situations or
episodes that contain valuable knowledge. A case can be considered as special experience
which is worth keeping in the memory (case base) for applying in the future. In CBR
language, this knowledge is called “speciﬁc knowledge” (Reategui et al., 1997; Aamodt,
1994b). Speciﬁc knowledge may be deﬁned as part of the knowledge, which cannot be
easily modeled, unlike general knowledge (known as model-based knowledge).
Cases can have different shapes and sizes, for example, they may cover a situation that
evolves over time (such as designing an oil well), or be a snapshot (such as a mud losses
incident during the drilling phase).
The common elements of any case are:
1. a general description of the case;
2. the task of the case (what is to be achieved/accomplished);
3. a problem description (set of ﬁndings);
4. a solution description to reﬂect the task; and
5. the ﬁnal outcome of the solution (the degree of success of the implemented solution).
There is a strong integration between cases and general domain knowledge in the
CREEK system (Aamodt, 2004). Fig. 12.5A illustrates the generic CREEK concept
where the cases are linked to the general domain knowledge. As seen, general knowledge
plays an important role in a CREEK system.
Fig. 12.5B illustrates this structure. The ﬁgure illustrates a complex generalized
episode (GE), with its underlying cases and more speciﬁc GEs. The entire case memory
is a discrimination network where a node is either a GE (containing the norms), an index
name, index value, or a case. Each index value pair points from a GE to another GE or to
a case. An index value may only point to a single case or a single GE. The indexing
scheme is redundant since there are multiple paths to a particular case or GE. This is
illustrated in Fig. 12.5B by the indexing of case 1.
12.1.3 Rule-based systems
Rule-based systems are knowledge-based systems that represent the domain knowledge
with a set of rules and suggest a solution to or conclusion of a problem by using a rule-
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Methods for Petroleum Well Optimization
Figure 12.5A Integration between cases and general domain knowledge. Modiﬁed from Aamodt, A.,
2004. Knowledge-intensive case-based reasoning in CREEK. In: Funk P., González Calero P.A. (Eds.)
Advances in Case-Based Reasoning. ECCBR 2004. Lecture Notes in Computer Science, vol. 3155. Springer,
Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28631-8_1.
Figure 12.5B Structure of cases and generalized episodes (Aamodt and Plaza, 1994).
Case-based reasoning (CBR) in digital well planning and construction
485
based reasoning method. A rule-based system has one more component in addition to
the knowledge base and inference engine, which is known as the working memory. As
Fig. 12.6 shows, the inference engine receives a problem from the working memory and
provides the reasoning result to the working memory. The working memory contains
the description of the problem and updates its content based on the reasoning results
received from the inference engine. The rules in the knowledge base and the reasoning
method used by the inference engine are discussed in the following.
12.1.3.1 Rules
Normally rules represent what to do or not to do if certain situations are met. Similarly,
the application domain knowledge is represented with a set of rules that represent the
facts that would be true when some conditions are met. A typical rule has a format of
If <conditions> Then <conclusion>, where conditions represent premises or facts, and
the conclusion represents associated actions for the premises. The condition might be a
premise or set of premises that are connected with logical operators AND and OR. The
conclusion can be an action to be taken or facts that are inferred from the given premises
(Luger, 2002; Prentzas and Hatzilygeroudis, 2007).
A frequently used means of acquiring rules is interviewing the domain experts. Rules
represent the general knowledge of the application domain. They preserve the natu-
ralness, modularity, and ease of explanation because they are used in a direct fashion as
Figure 12.6 Rule-based reasoning. Modiﬁed from Eshete, A.B., 2009. Integrated Case Based and Rule
Based Reasoning for Decision Support. Thesis for the degree of Master in Information Systems. Norwegian
University of Science and Technology.
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Methods for Petroleum Well Optimization
acquired from experts. The shortcoming of this method is the difﬁculty of acquiring
complete and perfect knowledge of a complex domain because the experts may be
incapable of communicating their knowledge or the appropriate expert may not be
available. Also, it is not always sufﬁcient to represent the domain only with general
knowledge (Luger, 2002; Prentzas and Hatzilygeroudis, 2003, 2007; Leake, 1996).
12.1.3.2 Rule-based reasoning technique
A rule-based reasoning technique represents how a system solves a problem by using
knowledge of the application domain that is represented in the form of rules. There
are two rule-based reasoning methods: forward chaining and backward chaining
(Luger, 2002).
In forward chaining, the system receives a problem description from the working
memory as a set of conditions and tries to derive conclusions as a solution. Once it
receives the conditions, it searches all rules for which the conditions match part or all of
the conditions in the working memory. The search result produces a set of rules that are
applicable to provide a conclusion about the problem; the set is known as a conﬂict set.
Rule-based reasoning technique uses conﬂict resolution strategy to select one rule at a
time from the set. The selected rule is then applied to derive a conclusion about the
problem. The content of the working memory is updated based on the derived
conclusion. Searching applicable rules continues, based on the updated working memory
content, and the reasoning process continues, based on the new matched rules. This
process continues until the desired solution is obtained or there is no rule that has a
condition that matches the current description of the problem in the working memory.
Backward chaining is similar to forward chaining in most processes; the big difference
is that it receives the problem description as a set of conclusions, instead of conditions,
and tries to ﬁnd the premises or causes of the conclusions. It searches the rules where the
conclusion matches with part or all of the conclusions in the working memory. Like
forward chaining, conﬂict resolution strategy is used to select one rule from the set of
applicable rules. The selected rule is used to derive the premises that led to the given
conclusion. The working memory is updated each time a premise(s) is derived, and the
reasoning process continues on the updated content of working memory until the
desired solution is obtained or there is no rule for which its conclusion matches the given
conclusions in the working memory.
Rule-based systems are more applicable to a complete, narrow, limited, and well-
understood application domain due to the difﬁculties of acquiring knowledge for
such systems. A problem is solved from scratch in rule-based systems; the reasoning
process for a problem is always performed even if the same problem has been solved
before by following the same reasoning process.
Case-based reasoning (CBR) in digital well planning and construction
487
12.1.4 Integration of Case-based Reasoning and Rule-based
Reasoning
The ultimate goal of the ﬁeld of AI is to develop systems that exhibit human-like, or even
better, intelligence (Eshete, 2009). Most current knowledge-based systems represent
some aspects of human intelligence. Integrating two or more knowledge-based tech-
niques begets a better simulation of intelligence than would have been gained from one
technique (Marling et al., 2005; Prentzas and Hatzilygeroudis, 2002, 2003).
On the other hand, the reasoning power of a knowledge-based system depends on
the explicit representation and use of different kinds of knowledge about the domain.
There is no one method of knowledge representation that can represent the domain
knowledge as it is in reality. The more knowledge-based techniques are integrated, the
more the domain knowledge is represented, which begets the more efﬁcient system
(Dı
´az-Agudo and Gonzalez-Calero, 2000).
CBR and rule-based reasoning techniques are two alternative ways of problem-
solving in intelligent systems. Their knowledge representation and reasoning methods
are naturally alternatives (Prentzas and Hatzilygeroudis, 2003). We compare the
following techniques based on their knowledge representation and problem-solving
capability.
Cases represent knowledge accumulated from speciﬁc situations, whereas rules
represent general knowledge about the domain. Acquiring rules is much harder than
acquiring cases. Because of that, maintaining or updating rules is also harder than updating
and maintaining cases (Luger, 2002; Prentzas and Hatzilygeroudis, 2002, 2003, 2007).
In the problem-solving process, CBR uses solutions for similar past problems,
whereas rule-based reasoning solves problems from scratch, even if similar problems have
been solved previously. The CBR method plays a greater role than rule-based reasoning
in handling missing or unexpected features in the problem description and selected cases
in problem description and rules. The case-based system tries to ﬁnd the similarity
between the problem and the cases even though there are features that do not match
between them. However, the rule-based system tries to ﬁnd rules that perfectly match
with part or all of the problem description. The rule-based reasoning method is better in
providing the explanation for the given solution than CBR (Luger, 2002; Prentzas and
Hatzilygeroudis, 2002, 2007; Leake, 1996). Due to their interchangeable nature, inte-
grating case-based and rules-based systems provide effective knowledge representation
and effective problem-solving power, and the techniques offset each other’s weaknesses
(Marling et al., 2005; Prentzas and Hatzilygeroudis, 2002, 2003).
12.1.5 Knowledge-intensive case-based systems
In a case-based system, cases represent experiences that are bound to speciﬁc situations
regarding the application domain. New situations are handled based on similar past
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Methods for Petroleum Well Optimization
situations. The similarity is performed by checking the existence of similar descriptive
features in the new case and past cases, and one factor to calculate similarity is the
number of similar features. This is more of a syntactical similarity; it does not consider
the contextual meaning of the features that describe the problem. This limitation can be
solved by integrating the speciﬁc cases with the model of the general domain knowledge.
The general domain knowledge enriches the cases by making it possible to interpret the
features based on the context or the given situation (Aamodt, 2004, 1994; Diaz-Agudo
and Gonzalez-Calero, 2000).
The general domain knowledge represents the model of the application domain in
the real world by providing the concepts and the different relationships between them.
The model is a network of interrelated concepts, which is known as a semantic network.
The relationships between concepts represent the meaning of the concepts in different
situations. Hence each concept has many relationships to other concepts. The reasoning
method that is applied in the semantic network is known as model-based reasoning
(Aamodt, 2004).
Knowledge-intensive case-based systems are systems that integrate case-based tech-
nique with model-based technique. In this case the domain knowledge is represented as
speciﬁc cases and general domain knowledge, which increases the knowledge inten-
siveness of the system. The more the domain knowledge is represented, the greater the
system’s capability in reasoning about the problems (Aamodt, 2004, 1994; Dı
´az-Agudo
and Gonzalez-Calero, 2000).
12.1.6 Ontology engineering
In philosophy, ontology is the study of being or existence. It seeks to describe the
basic categories and relationships of being and existence to deﬁne entities within a
speciﬁc domain. Ontology is an organization and classiﬁcation of domain knowledge. In
recent years, ontological issues have been widely used for the purposes of sharing and
reusing knowledge (Perez and Benjamins, 1999). Ontologies have been adopted in
many business and scientiﬁc communities as a way to share, reuse, and process the
domain knowledge. Ontologies are now central to many applications such as scientiﬁc
knowledge portals, information management and integration systems, electronic com-
merce, and semantic web services. Noy and McGuinness (2000) stated that ontology is
needed to:
1. share common understanding of the structure of information;
2. reuse domain knowledge;
3. make domain assumptions explicit; and
4. analyze domain knowledge.
Case-based reasoning (CBR) in digital well planning and construction
489
The ontology is referred to as a hierarchical structure (entities and relations) representing
the oil well engineering mode model structure as a core of the CBR system. To clarify
the concept of how an ontology is built, we provide an example from a platform in the
North Sea in August 2002. While drilling an 8½00 hole, the driller suddenly noticed a
low rate of penetration (ROP). It was assumed that action needed to be taken due to this
low ROP
. In theory, there are some parameters which cause low ROP as, for example,
low bit weight, low bit rotary speed, high rock strength, and so on. According to Eq.
(12.4),
ROP ¼ K
S2
W
db

Wo
db
2
N
(12.4)
Therefore, some general causal relationships can be derived:
•
Low rotary speed always causes low ROP
.
•
Low ROP can be caused by high compressive rock strength.
Some of the entities can, for example, be deﬁned in the ontology as follows:
•
The bit parameter has a subclass of rotary speed.
•
Rotary speed has a subclass of low rotary speed.
•
The bit parameter has a subclass of ROP
.
•
ROP has a subclass of low ROP
.
The driller therefore checked all related parameters and did not ﬁnd any deviations from
the normal conditions. He also checked rock strength with a geologist, and the rock
parameters were also the same. The driller remembered the same low ROP had been
recorded in a previous well. In that case, low mud viscosity was the reason for low ROP
.
The drill cuttings accumulated around the bit due to low lifting capacity in a low mud
circulation rate. In that situation, the cuttings are redrilled by the bit, and bit balling may
occur. In the previous well, increasing the mud circulation rate increased the ROP
. This
case gives additional information that can be added to the ontology as speciﬁc
knowledge:
•
Low ROP is sometimes caused by a low mud circulation rate.
•
Low mud circulation rate causes poor mud lifting capacity.
•
Poor mud lifting capacity leads to accumulation of cuttings at the drill bit, which
causes bit balling.
Then, the acquired speciﬁc knowledge will be transformed as entities/relationships and
ﬁnally integrated with the general knowledge into the model which is built into an
ontology.
Therefore, the ontology represents the knowledge base in a format which can be used
for the CBR purpose. In any ontology design, the goal is to translate the knowledge from
information (for example, text and graphs) into symbolic elements (similar to indexes in
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Methods for Petroleum Well Optimization
a book) and build it in a hierarchical structure. Fig. 12.7 illustrates the contents of the
ontology, showing that the ontology comprises three elements:
1. theory and general rules (denotes general knowledge);
2. cases or episodes (denotes speciﬁc knowledge); and
3. human reasoning based on a person’s mind and understanding.
According to Uschold and Gruninger (1996), there are three possible processes for
developing an ontology:
1. a top-down development process;
2. a bottom-up development process; and
3. a combined development process.
These three options for developing an ontology are depicted in Fig. 12.8. In a top-down
process, the ontology is developed only by general domain knowledge regardless of the
cases. For example, drilling may be subdivided into two subclasses: vertical drilling and
Figure 12.7 Transformation of general knowledge, cases, and human reasoning behind a hierarchy
structure (called ontology). Modiﬁed from Abdollahi, J., 2007. Analysing Complex Oil Well Problems
through Case-Based Reasoning. Thesis for the degree of Philosophiae Doctor (PhD), Norwegian University
of Science and Technology.
Figure 12.8 Three different process options for building an ontology: the top-down process is only
based on general knowledge; the bottom-up process is only based on case knowledge; and the
combined process is based on both general knowledge and case knowledge. Based on the concept of
Uschold, M., Gruninger, M., 1996. Ontologies: principles, methods and applications. Knowl. Eng. Rev. 11.
Case-based reasoning (CBR) in digital well planning and construction
491
directional drilling. We can further categorize directional drilling into horizontal drilling
and slant well drilling and so on. In a bottom-up process, which is the inverse of the
top-down process, the ontology is only developed based on speciﬁc domain knowledge
appearing in the cases. The most speciﬁc entities are extracted from the cases and extend
the hierarchy with a subsequent grouping of these entities into the more general
concepts. In the combined process, both general knowledge and cases (speciﬁc
knowledge) are used for building the ontology.
12.2 Application of case-based reasoning in digital well
construction planning
This section summarizes the digital well construction planning solution and
provides case study examples of how cross-domain experts plan concurrently in a single
common system. This approach allows a teamwide focus on planning better wells faster
in a single engineering solution. Case studies show how the well planning team was able
to improve cross-discipline collaboration between engineering and geoscience, as well as
the interactions with service companies. Overall, the well planning time was reduced
signiﬁcantly, and the reliability of the well design was ensured through the engineering
validation of each task. The integrated digital well planning solution proved to be a more
cost-effective solution for well planning and ensured the high-quality delivery of drilling
programs.
12.2.1 Case-based architecture for petroleum well design
In general, the design of a well can be deﬁned as an interactive process, involving spe-
cialists and information to set up a plan with enough details to drill the well safely and
economically. The process is characterized by a set of activities, which present a strong
interdependent relationship among them. Due to this interdependence, the activities are
not necessarily developed in sequence. In the majority of the situations, they are
developed simultaneously and interactively. Fig. 12.9 shows the several engineering
objects involved in the construction process of a well. Depending on the level of detail
desired, some of these activities can be subdivided into smaller activities. The well design
is rarely developed by one person alone. Generally, in a company, the activities are
grouped in areas with specialized teams. This division of activities depends on the
processes of the company and on the availability of specialized labor.
To exploit the beneﬁts of CBR for petroleum well design, the architecture with the
main components of a case-based system needs to be deﬁned. Obviously, some of these
components are adapted to the application’s domain. Next, these components are
described, after which the architecture with the acting ﬂow is proposed, as shown in
Fig. 12.10.
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12.2.1.1 Indexing attributes
Indexing attributes are responsible for the identiﬁcation of cases in the retrieval process.
They should adequately characterize the cases so as to help this process. Generally, they
cannot be very speciﬁc; otherwise, it would be difﬁcult for a case to be reused. On the
other hand, if they are too general, the retrieval process would not be able to select the
Figure 12.9 Several engineering objects involved in the process of a development design for
petroleum well drilling.
Figure 12.10 Architecture of a case-based system to help in petroleum well designs. Modiﬁed from
Mendes, J.R.P., Morookaa, C.K., Guilhermeb, I.R., 2003. Case-based reasoning in offshore well design. J.
Petrol. Sci. Eng. 40 7e60. https://doi.org/10.1016/S0920-4105(03)00083-4.
Case-based reasoning (CBR) in digital well planning and construction
493
most relevant cases for a given situation. Excluding any attributes that were too speciﬁc
or too general, all the following indexing attributes are used:
1. ﬁeld
2. water depth
3. average inclination
4. true vertical depth
5. displacement
6. azimuth
In practice, it can be observed that water depth, average inclination, true vertical depth,
displacement, and azimuth take on nonnumerical values, such as words or sentences used
by an engineer in their daily routine. This observation suggests that these attributes be
considered as linguistic terms, rather than be treated as strictly numerical values. The
next section shows how to deal with these attributes using the fuzzy set theory.
The indexing attributes presented in the previous section, those that have numerical
valuesdwater depth, average inclination, true vertical depth, displacement, and
azimuthdwill be dealt with as linguistic variables. The linguistic terms associated with
these indexing attributes are deﬁned by the fuzzy sets in Fig. 12.11.
Figure 12.11 Indexing attributes as linguistic variables. Modiﬁed from Mendes, J.R.P., Morookaa, C.K.,
Guilhermeb, I.R., 2003. Case-based reasoning in offshore well design. J. Petrol. Sci. Eng. 40 7e60. https://
doi.org/10.1016/S0920-4105(03)00083-4.
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Methods for Petroleum Well Optimization
To build this space, at ﬁrst, a set of indexing attributes, which take on numerical
values {Attribute1, Attribute2,., AttributeN}, are considered generically. ui is adopted
as a numerical value of attribute i with ui belonging to the Ui universe. In a traditional
approach, the cases in the S ¼ U1 ✕U2 ✕.. ✕UN space can be represented by the (u1,
u2,., un) vector, and the retrieval process will be carried out in this space. Nevertheless,
it is accepted here that these attributes are better described as linguistic variables and have
the following linguistic terms as values:
TðAttribute1Þ
¼ fT11; T12; .; T1mð1Þg
TðAttribute2Þ
¼ fT21; T22; .; T2mð2Þg
«
«
TðAttributeNÞ
¼ fTN1; TN2; .; TNmðNÞg
(12.5)
where: Tij is the name of the fuzzy set described by a membership function mij(mi); i
indicates the linguistic variabledAttributei; j indicates the variable linguistic term i; and
m(i) is the number of linguistic variables i.
Analogically, another function F:U1 ✕U2 ✕. ✕Un [0,1]n can be deﬁned:
Fðu1; u2; .; uNÞ ¼ ðF1ðu1Þ; F2ðu2Þ; .; FNðuNÞÞ
(12.6)
If the Fi functions are introduced into function F (Eq. 12.5), it will be:
Fðu1; u2; .; uNÞ ¼ ðm11ðu1Þ; m12ðu1Þ; .; m1mð1Þðu1Þ; m21ðu2Þ;
m22ðu2Þ; .; m2mð2Þðu2Þ; .; mN1ðuNÞ;
mN2ðuNÞ; .; mNmðNÞðuNÞÞ
(12.7)
This function F allows the cases in the S space, where the indexing attributes take on
numerical values, to be mapped for a fuzzy n-dimensional unit hypercube, which is
named the fuzzy space of cases (U space).
Introducing the indexing attributes deﬁned in the previous sectionsdexcept the ﬁeld
attribute, which is not dealt with as a fuzzy set theorydthe following F function is
obtained:
Fðu1; u2; u3; u4; u5Þ ¼ ðm11ðu1Þ; m12ðu1Þ; m13ðu1Þ; m14ðu1Þ; m21ðu2Þ;
m22ðu2Þ; m23ðu2Þ; m24ðu2Þ; m31ðu3Þ; m32ðu3Þ;
m33ðu3Þ; m41ðu4Þ; m42ðu4Þ; m43ðu4Þ; m51ðu5Þ;
m52ðu5Þ; m53ðu5Þ; m54ðu5ÞÞ
(12.8)
Case-based reasoning (CBR) in digital well planning and construction
495
where u1, u2, u3, u4, and u5 refer to the numerical values of the water depth, average
inclination, true vertical depth, displacement, and azimuth, respectively. In relation to
the mij(mi) functions, they are fuzzy set membership functions associated with linguistic
variables deﬁned in Fig. 12.11. In this way, in a U space, a case is represented by an
ordered 18-tuple of membership values. It can be noted that in a more general situation,
some attributes may not be described by membership values. In this development, the
ﬁeld attributes case is inserted. This situation suggests the deﬁnition of a U space related
to each ﬁeld. To help comprehension, consider the numerical example illustrated in
Fig. 12.12. In this example, the case can be represented in the S space by the ordered 5-
tuple (130, 49.5, 3060, 2521, 24.8). Based on the previous discussion, applying function
F
, the following mapping for the U space is found:
ð130; 49:5; 3060; 2521; 24:8Þ /
F
ð0:47; 0:12; 0; 0; 0; 0; 0; 0:95;
0; 0:44; 0:37; 0; 0:44; 0:02; 0:72; 0:28; 0; 0Þ
(12.9)
Geometrically, this ordered 18-tuple corresponds to a point in the U space deﬁned by
the Mars ﬁeld. However, the attribute ﬁeld has an important role in the retrieval process;
therefore, it is convenient to deﬁne a more general function, which includes such an
attribute:
Gðv; u1; u2; u3; u4; u5Þ ¼ ðv; Fðu1; u2; u3; u4; u5ÞÞ
(12.10)
where v refers to the ﬁeld attribute value. Thus, a case becomes indexed by an ordered
19-tuple value (Fig. 12.12).
Figure 12.12 Numerical example of the indexing process of a case in the case base. Modiﬁed from
Mendes, J.R.P., Morookaa, C.K., Guilhermeb, I.R., 2003. Case-based reasoning in offshore well design. J.
Petrol. Sci. Eng. 40 7e60. https://doi.org/10.1016/S0920-4105(03)00083-4.
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Methods for Petroleum Well Optimization
The retrieval process is responsible for identifying the most adequate cases for a new
well design. To do so, this process must be capable of “recognizing” how similar a case is
to the new design. This “recognition” can be carried out with a notion of distance
measured in the U space. In this section, a measure of similarity will be established with
the help of a notion of distance. This will allow the retrieval process to identify the cases
with the potential to be reused.
12.2.1.2 Similarity between cases
In general, the distance between two fuzzy sets A ¼ (a1, ., an) and B ¼ (b1, ., bn),
belonging to the same universe, can be deﬁned by Minkowski’s distance:
dðA; BÞ ¼
 X
n
i ¼ 1
jai  bijp
!1=p
; where p  1
(12.11)
Given the two fuzzy sets A and B, the similarity between them can be expressed by
means of a complement of their distance: Similarity(A,B) ¼ 1  d(A,B). In this problem,
to simplify, Hamming’s distance (p ¼ 1) and a normalizing factor in the denominator
were adopted:
SimilarityðA; BÞ ¼ 1 
dðA; BÞ
dðAWB; BÞ
(12.12)
Normalizing factor d(A W B, Ø) uses the union of the fuzzy set concept. This
operation is conveniently represented considering the maximum corresponding mem-
bership degrees of the sets:
SimilarityðA; BÞ ¼ 1 
dðA; BÞ
dðAWB; BÞ
(12.13)
A W B ¼ ðmaxða1; b1Þ; .; maxðan; bnÞÞ
(12.14)
Eq. (12.13) considers that all the dimensions have the same importance. In practice,
this is not what occurs. It is a known fact that some dimensions are more important than
others, or rather that some attributes have a greater relevance in the retrieval process.
Thus, for the similarity calculation to be more realistic, it is necessary to analyze the
dimensions of the hypercube based on the importance of each attribute. In a geometrical
interpretation, each attribute is associated to a subspace in the hypercube, where each
subspace has a weight for the similarity calculation. Mathematically, the expression for
Case-based reasoning (CBR) in digital well planning and construction
497
the similarity calculation between two fuzzy sets A ¼ (a1, ., an) and B ¼ (b1, ., bn)
becomes:
SimilarityðA; BÞ ¼
P
n
i ¼ 1
wi  minðai; biÞ
P
n
i ¼ 1
wi  maxðai; biÞ
(12.15)
where wi is the weight associated to dimension i.
So as to consider the value referring to the ﬁeld attribute in the similarity calculation,
the following alteration of Eq. (12.15) is proposed:
SimilarityðA; BÞ ¼
w1  Gða1; b1Þ þ P
19
i ¼ 2
wi  minðai; biÞ
w1  Gða1; b1Þ þ P
19
i ¼ 2
wi  maxðai; biÞ
(12.16)
where a1 and b1 are names of the ﬁelds that belong to case A and B, respectively, and
function G(a1, b1) is deﬁned as:
Gða1; b1Þ ¼
8
<
:
1
if a1 ¼ b1
0
if a1 s b1
(12.17)
So, to be able to identify the most promising cases for the design of a new well, the
retrieval process is carried out on two levels.
First level: A region of the U space with similar cases to the design is selectedda
similarity ball. The cases contained in this region are considered as case candidates.
Second level: The candidate cases are analyzed in more detail to choose either the
most promising or the set of the most promising cases.
To be able to determine which cases inside the similarity ball are most promising,
more precise knowledge is necessary. When indexing attributes are chosen, more general
characteristics are taken into consideration. This is necessary as a case presents a large
number of aspects that represent it, and the retrieval process cannot analyze all of them,
since the time spent would be too great. When a similarity ball is created for a given
design, and consequently a smaller search space is deﬁned, a more speciﬁc relationship to
deﬁne the most promising cases is necessary. To analyze the cases inside the similarity ball,
more detailed similarities, which are named speciﬁc similarities, were used, for example,
water depth similarity, average inclination similarity, true vertical depth similarity,
displacement similarity, distance similarity, formation similarity, top formation similarity,
formation thickness similarity, and azimuth similarity. Fig. 12.13 shows the membership
functions of some of these similarities.
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Methods for Petroleum Well Optimization
12.2.1.3 Genetic algorithm applied to wells
The initial population is the retrieved case in the retrieval process. The number of these
cases can be controlled, adjusting the minimum similarity required for the design. For
genetic representations, it is suggested that each well from the initial population be
divided into operational intervals, named well pieces. In this way, a design with a piece to
be drilled, similar to one or more well pieces of another well, can inherit information
about how to drill that piece. Each well piece will be related to a gene, and consequently
each well will be related to a chromosome. Each gene contains three pieces of infor-
mation in terms of true vertical depth: case name, top, and bottom. Obviously, the length
of the chromosome will depend on the number of well pieces in which the well was
divided. Fig. 12.14 illustrates this genetic representation. The adaptation measurement of
each individual will be the relevance that the individual has in relation to the well.
Figure 12.13 Examples of some membership functions of similarity attributes from inside the simi-
larity ball used in the case study. Modiﬁed from Mendes, J.R.P., Morookaa, C.K., Guilhermeb, I.R., 2003.
Case-based reasoning in offshore well design. J. Petrol. Sci. Eng. 40 7e60. https://doi.org/10.1016/S0920-
4105(03)00083-4.
Case-based reasoning (CBR) in digital well planning and construction
499
In the case of directional wells, we used to penalize those individuals that present very
abrupt changes in their trajectories. This penalty is applied on the similarity value of the
individual. A good parameter to be used in the calculus of the penalty is the dogleg angle.
The idea is to penalize the individuals that have dogleg angles above or equal to a
maximum value. These angles are calculated between two subsequent well pieces.
The essence of CBR is the use of learning. This learning is restricted, initially, to the
storage of new cases. Therefore, the ability to store a new case performance evaluation is
necessary. In the next section, the use of learning curves as an auxiliary tool in this
evaluation process will be discussed. The question of the growth of the number of cases
in the case base will also be dealt with.
12.2.1.4 Learning curves applied to well evaluation
In petroleum well drilling, the ﬁrst well of a new ﬁeld or area normally takes more time
to drill than the other wells and thus incurs greater costs. A progressive reduction in
drilling time is obtained in subsequent wells until there is no more improvement to be
carried out. Brett and Millheim (1986) observed that the learning curve theory could be
applied in these situations. In this way, the drilling performance of a set of wells in a
determined area could be evaluated. Therefore, they proposed a speciﬁc expression for
applications in petroleum well drilling (Eq. 12.18). Fig. 12.15 illustrates this expression,
showing the typical behavior when drilling is started in a new area.
Figure 12.14 Genetic representation of case A well divided into n well pieces. Modiﬁed from Mendes,
J.R.P., Morookaa, C.K., Guilhermeb, I.R., 2003. Case-based reasoning in offshore well design. J. Petrol. Sci.
Eng. 40 7e60. https://doi.org/10.1016/S0920-4105(03)00083-4.
Figure 12.15 Learning curve proposed by Brett and Millheim. Modiﬁed from Brett, J.F., Millheim, K.K.,
1986. The drilling performance curve: a yardstick for judging drilling performance. In: Annual Technical
Conference and Exhibition of the Society of Petroleum Engineers, New Orleans, SPE 15362, 10.
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Methods for Petroleum Well Optimization
t ¼ C1eð1nÞC2 þ C3
(12.18)
In the expression proposed by Brett and Millheim, the C1 value represents the
additional time to drill the ﬁrst well in a new area in relation to the last one drilled. This
value indicates the ability of the company to be prepared for the difﬁculties that might
occur in such an area; these include drilling problems and availability of technology. The
C2 value represents the speed and efﬁciency with which the organization manages to
adapt to the new drilling environment; in other words, the ability to gain experience
with previous wells and apply it to the next. Finally, C3 represents the ability to maintain
a certain performance level or sometimes improve it.
In the storage process, the learning curve can help in the evaluation of a new well. To
do so, ﬁrst a set of similar wells to the storage candidate must be selected and organized in
the sequence in which they were drilled. This task is essential; however, it is not simple,
especially if a large number of wells are being analyzed. Due to this, the retrieval process
is an important and necessary tool. Later, with the set of adequate wells grouped
appropriately, it is possible to set up the learning curve. The curve can also be broken up
into various subactivities; for example, drilling, tripping, troubles, and casing. Thus, it is
possible to evaluate each activity separately and identify where a problem could be found.
We take as a case study, a prototype system that was implemented so that tests to
investigate the main characteristics of the processes presented could be carried out. For
these tests, seven offshore wells drilled in shallow water depth were used as the sample:
MA1, MA2, MA3, MA4, MA5, MA6, and MA7. They are directional wells where the
production zone is approximately 3000 m deep. All were drilled within a period of
1 year.
MA3 was chosen to represent the new design in the test as full data for the well were
not available, and only its attributes were known. In this situation, the retrieval process
would identify, at a ﬁrst level, the other wells, MA1, MA2, MA4, MA5, MA6, and MA7,
with the similarity values: 0.52, 0.80, 0.68, 0.66, 0.55, and 0.70, respectively. To help
visualization, these values are represented in graphic form in Fig. 12.16. If a minimum
similarity d ¼ 0.6 was deﬁned, all these wells would be candidate cases for the new
design, except for MA1 and MA6 wells, which have just 0.52 and 0.55 similarity,
respectively, with the attributes of the MA3 well. At the second level, Fig. 12.16, the
retrieval process would identify the MA2 and MA7 wells as the most promising, as they
have a 0.82 total similarity with the attributes of the MA3 well. Next would come the
MA4 and MA5 wells with total similarities of 0.74 and 0.72, respectively. In relation to
the adaptation process, the genetic algorithm, described in Section 12.2.1.3, was applied,
considering the MA3 well as a new design and the other wells as initial populations. The
genetic algorithm was adjusted so that the crossover operator could be applied to 90% of
the individuals of the population. The performance obtained with the algorithm is
illustrated in Fig. 12.17. This ﬁgure shows, for each generation, the relevance value of
Case-based reasoning (CBR) in digital well planning and construction
501
the best individual in the population and the average relevance value of all the individuals
of this population. As can be observed, the algorithm converges with 21 generations. In
the initial generation, the best individual is the MA2 well with a 0.89 relevance value.
However, in the last generation, the 21st, there is a signiﬁcant improvement in the best
individual, which has a 0.98 relevance value.
To better illustrate this, the individual created by the genetic algorithm is shown in
Fig. 12.18. It should be noted that despite the ﬁgure representing only trajectory aspects,
each well piece brings all the pertinent information for drilling, such as drilling bits,
Figure 12.17 Genetic algorithm performance. Modiﬁed from Mendes, J.R.P., Morookaa, C.K., Guilher-
meb, I.R., 2003. Case-based reasoning in offshore well design. J. Petrol. Sci. Eng. 40 7e60. https://doi.org/
10.1016/S0920-4105(03)00083-4.
Figure 12.16 Similarity values and total similarity considering the MA3 well as a new design. Modiﬁed
from Mendes, J.R.P., Morookaa, C.K., Guilhermeb, I.R., 2003. Case-based reasoning in offshore well design.
J. Petrol. Sci. Eng. 40 7e60. https://doi.org/10.1016/S0920-4105(03)00083-4.
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Methods for Petroleum Well Optimization
drilling ﬂuid, troubles, pore pressure, and fracture pressure. The next step is the design of
the new well starting from the well created by the genetic algorithm. In this design, the
user can apply computer programs and operational simulators available commercially, or
even algorithms developed by the users themselves. These are well-known and well-
published techniques in the petroleum industry, and you can ﬁnd an introduction to
optimization in Chapter 2. Learning curves are used for the storage process. These curves
help in the macroscopic evaluation, allowing problem points in the wells which are
storage candidates to be identiﬁed. With the identiﬁcation of these problem points, a
deeper analysis can be carried out in a second stage. So, to illustrate this procedure, the
MA7 well was chosen as a storage candidate.
Fig. 12.19 shows the learning curve for the total time required to drill the well. This
ﬁgure also shows the curves for four subactivities and one comprising the rest of the
activities, which are troubles, drilling, running casing, handling BOP
, and other activ-
ities. Fig. 12.19 shows that the MA7 well does not provide learning in the activities dealt
with. It can also point out two critical activities: running casing and handling BOP
.
These activities deserve more detailed studies to identify the causes of loss of “learning.”
In relation to drilling, the well has a satisfactory performance; however, it does not bring
a signiﬁcant improvement, when compared with the well prior to the sequence MA6.
Figure 12.18 Individual created by the genetic algorithm after 21 generations. Modiﬁed from Mendes,
J.R.P., Morookaa, C.K., Guilhermeb, I.R., 2003. Case-based reasoning in offshore well design. J. Petrol. Sci.
Eng. 40 7e60. https://doi.org/10.1016/S0920-4105(03)00083-4.
Case-based reasoning (CBR) in digital well planning and construction
503
The same behavior can be observed in relation to the troubles and other activities. Even
with this superﬁcial evaluation, it can be concluded that the well should not be stored.
This example well illustrates the proposal of the tool. The learning curve allows the
identiﬁcation of problematic points by means of rapid analysis. Once these points are
identiﬁed, another speciﬁc investigation process for the problems pointed out should be
made available.
12.2.2 Case-based reasoning for drilling ﬂuids
12.2.2.1 The lost circulation problem
During normal conditions, the drilling ﬂuid is circulated down into the wellbore
through the pipe, out of the drill bit, and back up to the surface through the annulus.
However, sometimes the drilling ﬂuid ﬂows into the formation instead of back to the
surface, and this is known as lost circulation. This can occur if the formation being
drilled through is fractured or if the hydrostatic pressure of the ﬂuid column is too great,
and therefore, it is dependent on both the drilling parameters and the geological
properties. Fig. 12.20 represents a simpliﬁed depiction of lost circulation.
As the ﬂuid circulating down through the pipe begins to ﬂow into the formation
instead of returning through the annulus, the ﬂuid level at surface will begin to shrink
noticeably. This is often the ﬁrst sign of lost circulation noted on the rig ﬂoor. Also called
“loss of returns,” this indication of lost circulation carries an inherent delay with it, which
adds to the difﬁculty of studying the problem, as well as remediating it quickly. Since
Figure 12.19 Learning curves for speciﬁc activities. Modiﬁed from Mendes, J.R.P., Morookaa, C.K.,
Guilhermeb, I.R., 2003. Case-based reasoning in offshore well design. J. Petrol. Sci. Eng. 40 7e60. https://
doi.org/10.1016/S0920-4105(03)00083-4.
504
Methods for Petroleum Well Optimization
drilling ﬂuid is so critical for safe and efﬁcient operations, losing circulation represents a
major issue that must be addressed. There are three courses of action that drillers can take
when faced with the issue of lost circulation. We will look at the ﬁrst one in a case study
of the Kern River Field.
1. The primary method used in the Kern River Field is the loss of circulation material
(LCM) pill. There are a wide variety of materials used to make these pills, so this
remediation method represents a broad category with new pill types being developed
regularly.
2. The next method utilized involves plugging the thief formation with cement and
then redrilling that section of the wellbore. Doing this is signiﬁcantly more time-
consuming and costly, which means it is less commonly used.
3. The third option, drilling blind, is not a remedial method at all. Drilling blind is the
process of drilling without the knowledge and safety value that the mud provides, and
it must be done at a much slower rate to compensate for this added risk.
12.2.2.2 Data collection and processing
The ﬁrst step in developing a CBR system is to design a database of past cases that can be
called upon and reused to help make future decisions. In the case study, for this system,
information was required from two ﬁelds: the drilling data for each past lost circulation
event and the reservoir models for the Kern River Field. While the latter was a fairly
straightforward data acquisition from previously developed modeling tools, the in-
consistencies in the recording format of the drilling data required the creation of a whole
new database for the past lost circulation cases. Creating the database was a manual,
Figure 12.20 Representation of lost circulation.
Case-based reasoning (CBR) in digital well planning and construction
505
time-intensive process but meant that the drilling reports for each lost circulation event
were reviewed and the information standardized, before ﬁnally being submitted into the
case-based reasoner. In addition to standardizing the recording of information, much of
the more complex information was condensed into a shorthand code to allow the CBR
to process the text information more quickly and smoothly.
The ﬁrst step of the data collection involved extensive review of the Kern River
drilling reports, focusing on a query of all wells that experienced loss of circulation.
Reviewing the drilling reports was the ﬁrst pass at determining what information was
recorded well enough to be utilized and needed to begin developing the database. At this
initial stage, as much information as possible was recorded, since no sensitivity studies
had been done to determine the most important variables and it was not known what
information was going to be the most consistently recorded. The database was initially
populated with a query of all events labeled as lost circulation, but this only recorded the
information as written down in a brief event description. To get the remainder of the
data, the drilling parameters of the operation before lost circulation and after lost cir-
culation, information on the losses, as well as the remedial actions taken, required reading
the drilling report and manually entering the data into the database. While time-
consuming, this process was vital to understanding the limitations of the data, in terms
of consistency and complexity. While the inconsistency of which information was re-
ported and how it was reported created many challenges, understanding what infor-
mation the reasoner would need to make decisions similar to those of an engineer led to
the development of a shorthand notation to break up complex descriptions of the
drilling operation. This shorthand was particularly important for describing the remedial
methods used.
Once all the lost circulation events were recorded in the database with their
accompanying remediation data, the data still had to be processed for use in the study.
The data processing, in contrast to the data collection, was automated by several macros.
These macros were used to clean the data of duplicate events and data fragments caused
by importing the information from the database, as well as to remove events that were in
the lateral section of the wellbore. Since the lost circulation remediation procedure is
different during lateral drilling, the aim was to avoid events that occurred in this part of
the operation. Some of the inconsistency of the data recording was also tackled during
this portion of the analysis. If a case did not have a full set of the variables required by the
CBR, it would not be useable. Since most of the lost circulation events had incomplete
records for some of the drilling parameters that had been collected, parameter estimation
had to be used. The estimated parameters were chosen from cases with full sets of
variables that had the highest degree of similarity, assuming that if most of the drilling
506
Methods for Petroleum Well Optimization
parameters were similar, then the missing parameters would also be similar. If a sufﬁcient
number of similar variables could not be identiﬁed, the case was removed from the
working data set that would be used by the reasoner. With this removal of invalid events,
the ﬁnal case size was left at 112 events.
The next portion of the data to be accounted for came from reservoir models and
well log data. Pulling foot-by-foot resistivity and porosity information, as well as satu-
ration levels, from the models, this information was matched to the lost circulation event
information using the recorded measured depth of the event. The intention was to add
the log data to the list of variables that the reasoner used to compare similarity, but the
uncertainty in the drilling data made this impractical. Even a few feet of uncertainty
could drastically change the reservoir properties, such as whether the event matched
with a vapor sand or a shale formation. However, by accounting for this uncertainty, it
was shown that there is a correlation between the formation and lost circulation.
Fig. 12.21 shows the how the majority of lost circulation events occurred within the
desaturated regions of the formation, which traditional logic would dictate is the case.
It also shows two other key points, that certain sands are more prone to lost circulation
than others and that the uncertainty of the data increases with depth. This is shown by
the increased percentage of events matching with modeled shale and liquid formations
and makes logical sense considering that the delay before loss of returns becomes
apparent would increase with depth. By adding the surface location information to the
depth and frequency data, as shown in Fig. 12.22, the clustering of similar events be-
comes more apparent. This is, however, not completely cut and dry, which is why
additional variables are needed. So while the formation properties could not be used to
Figure 12.21 Histogram of lost circulation by formation in the Kern River Field study, increasing depth
left to right. Blue represents events that matched with modeled desaturated zones, and pink repre-
sents those that did not. Modiﬁed from Reedy, K., Popa, A. S., Cassidy, S.D., 2018. Remediation solutions
for lost circulation using case based reasoning. In: SPE Western Regional Meeting, 22e26 April, Garden
Grove, California, USA. https://doi.org/10.2118/190066-MS.
Case-based reasoning (CBR) in digital well planning and construction
507
show similarity between two lost circulation events, the surface location of the well and
the depth of the event could be used to approximate this. These factors were believed to
be the most critical in determining similarity, which impacted the development of the
similarity function.
In the ﬁnal database of 112 events with 12 variables: eight of the variables related to
the drilling parameters recorded around the lost circulation event, and four were tied
to the location where the lost circulation occurred. In addition, the cases were deﬁned by
the well name, API, and Event ID, none of which were utilized by the reasoner for
calculations. A sample data set is detailed in Table 12.2.
The drilling parameters that remained were ROP
, mud volume, weight on bit
(WOB), and standpipe pressure (SPP) (both before and after). The location parameters
were the measured depth and the X and Y grid coordinates of the well’s surface location.
12.2.2.3 Lost circulation case-based methodology
The creation of the case-based reasoner is broken down into three distinct steps: (1)
information gathering; (2) data analysis; and (3) processing and programming the ﬁnal
product.
Figure 12.22 Map of Kern River Field producers. Color represents depth of lost circulation event, with
dark colors being closer to the surface. The bubble size is the number of lost circulation events.
Modiﬁed from Reedy, K., Popa, A. S., Cassidy, S.D., 2018. Remediation solutions for lost circulation using
case based reasoning. In: SPE Western Regional Meeting, 22e26 April, Garden Grove, California, USA.
https://doi.org/10.2118/190066-MS.
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Methods for Petroleum Well Optimization
The previous section focused on how the data are collected, analyzed, and processed
to be used for decision-making; this section explains how that information can be
utilized and combined with a custom-made tool to create the case-based reasoner.
The end product of this study was constrained by its two primary objectives; it
needed to be a functioning proof of concept that could be expanded upon and improved,
as well as a user-friendly tool that could be used immediately in the ﬁeld to begin
expanding the database of cases to improve accuracy. This is why Excel was chosen as the
medium in which to write the CBR. Writing each portion of the study in Excel macros
allowed for each to be compartmentalized, analyzed, and improved independently of the
others. Additionally, the prevalence of Excel within the industry means that it is more
widely understood than most other software, making it the best choice for rapid
deployment in the ﬁeld. The initially gathered data could be linked to the workbook
using the built-in applications from outside sources and be saved for later use, only
requiring updates with the addition of new cases to the database. The core code of the
case-based reasoner corresponded to the retrieve and reuse portion of CBR logic.
The other two steps, revise and retain, were intended to be done by the user; namely, the
engineer accepting, modifying, or rejecting the reasoner’s recommendation for the ﬁnal
action plan and saving the ﬁnal outcome in the database as a new case. Retrieving the
previous cases required deﬁning similarity and identifying similar cases. Similarity was
deﬁned as the Euclidean distance between the cases, with all variables normalized Eqs.
(12.19) and (12.20) were used:
xi ¼
x  xmin
xmax  xmin
(12.19)
Table 12.2 Dataset of sample cases for the Kern River Field study.
Event ID
123
148
327
373
449
Depth Prior
273
64
107
1173
795
Depth After
273
80
107
1517
1577
ROP Prior
71
80
71
95
63
ROP After
71
80
71
86
24
Mud Volume Prior
350
250
294
349
183
Mud Volume After
350
250
294
320
150
WOB Prior
10,000
10,000
10,000
15,000
10,000
WOB After
10,000
10,000
10,000
10,000
1200
SPP Prior
380
200
450
1123
600
SPP After
380
200
450
850
250
X Pos
1,712,860
1,711,532
1,706,689
1,703,765
1,697,723
Y Pos
701,149
703,098
699,986
711,122
723,961
ROP
, rate of penetration; SPP
, standpipe pressure; WOB, weight on bit.
Reedy, K., Popa, A. S., Cassidy, S.D., 2018. Remediation solutions for lost circulation using case based reasoning. In: SPE
Western Regional Meeting, 22e26 April, Garden Grove, California, USA. https://doi.org/10.2118/190066-MS.
Case-based reasoning (CBR) in digital well planning and construction
509
DisðX; YÞ ¼
 X
n
i ¼ 1
Wi  D
	
Xi; Yj

r
!1=r
DðXi; YiÞ ¼
8
>
>
>
>
>
<
>
>
>
>
>
:
jXi  Yij
Di is continuous
0
Di is discrete; Xi ¼ Yi
1
Di is discrete; Xi s Yi
(12.20)
If r ¼ 2, the Dis (X,Y) is Euler distance. The similarity function between goal case
and source case in this model uses Euler distance.
SimðX; YÞ ¼ 1  Dis ðX; YÞ ¼ 1 
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
X
n
i ¼ 1
WiDðXi; YiÞ2
s
& ðDis ðX; YÞ˛½0; 1Þ
SimðX; YÞ ¼
1
1 þ Dis ðX; YÞ ¼
1
1 þ
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
P
n
i ¼ 1
WiDðXi; YiÞ2
s
& ðDis ðX; YÞ˛½0; NÞ
(12.21)
Since the geological formation in which the lost circulation occurred is the primary
factor for determining the best remedial option, depth and surface location are given the
highest weights, with the individual variables “Surface X” and “Surface Y” having
reduced inﬂuence so as not to double dip. All other pre-loss-circulation variables are
given similar weight values to each other, but lower than location, with mud volume and
SPP having the lowest values since those two are also correlated. Finally, the after-loss-
circulation variables are removed during the weighting process since there is no clear
convention for how they were recorded, but all of the weights are left variable so that
they could be adjusted in future to improve accuracy.
For each new problem run, the reasoner determines the similarity of all cases within
the case database. Then a separate function is run to match the new case with those cases
that are most similar to it. A large enough sample size must be used to obtain accurate
results, but only cases of sufﬁcient similarity should be selected to ensure the reasoner
functions properly.
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Methods for Petroleum Well Optimization
This was a challenge given the small database of 112 cases. The data-matching
function selects the most similar cases, taking a minimum of 10 and up to a maximum
of 20. Since two identical cases would have a distance of 0, and two extremely different
cases would have a maximum of 1, a cut off of 0.45 was set to determine how many cases
between 10 and 20 were selected. Once the list of similar cases has been selected, a
second macro is used to reuse their successes and failures to identify a favorable solution.
This macro is where the system’s rules are implemented. These rules are utilized to
determine not only what is deﬁned as a success or failure during a lost circulation event
but also how these successes and failures are utilized to predict future success. The
program begins by collecting the mediation data that had been previously parsed for the
matched events and determines which option was the ﬁnal, successful method used in
each case.
A simple set of rules is used to make this decision.
•
If only one of the three remedial methods was used, this is the successful method.
•
If one and only one method had “full returns,” this is the successful method.
•
If neither of the previous two conditions is met, and “drilling blind” is used, this is the
successful method.
•
All other outcomes are marked “unclear.”
These initial rules for determining the successful outcome of past cases were intentionally
simplistic. While the outcomes of individual cases could be described in more complex
ways, with events listing partial returns of circulation, and the weight or volume of which
pill was successful and which was not, the inconsistency of reporting meant that attempts
to increase the reasoner’s ability to describe the results would have been complex without
any guarantee of accuracy. Therefore, the success/failure marking was left simple, but all
data and logic behind it are presented for the user to have in the revision stage of the
process. However, the recommendation process involved a few more rules than the
previous step. Since the demarcation of success and failure was simplistic, the decision of
what to recommend had to be more involved to ensure accuracy. To ensure that the most
similar cases were given priority in the recommendation, each case was given a weight
based on its similarity to the new case, and these weighted scores were added up to
determine the ﬁnal outcome.
Wn ¼ Wn1 
Dn1
SD

W1 ¼ 1
(12.22)
Case-based reasoning (CBR) in digital well planning and construction
511
The weight of each individual case decreases proportionally to the increased distance
it has from the new case being tested, while the closest case has a weight set to one. In
addition to the weighting scale, a few more rules have been added to increase the ac-
curacy of the prediction.
•
If the success only resulted in partial returns, the weight of this success is halved.
•
If a cement success was a casing cement rather than a cement plug, the success is
allocated to drilling blind.
•
If a pill provided partial returns before drilling blind occurred, the success is allocated
to a pill with half of the weight.
•
If cement provided partial returns before drilling blind occurred, the success is
allocated to cement with half of the weight, unless it was cement casing rather than a
plug.
•
The success of drilling blind is only allocated to drilling blind if another remediation
was attempted ﬁrst or if partial or full returns occurred.
•
If a remedial method caused lost returns, the weight is subtracted from the ﬁnal score.
This occurs after the ﬁnal success has already been allocated.
•
The average number of pills used in a successful pill case is recorded. If greater than
1.5, a maximum of two pills will be recommended; otherwise, only one will be
recommended.
Finally, all of the scores are added up. The method with the highest score is then the one
that is recommended, with all three scores tallied below the recommendation for
transparency. Due to the sheer prevalence of pill usage, the data set is slightly biased
toward pills, so in the case of pills, the reasoner will recommend the maximum number
of pills as well as the second-highest scoring option.
12.2.2.4 The accuracy of a case-based reasoning system
The accuracy of a CBR system can be checked prior to deploying it in the ﬁeld by
running the cases from the database through the program as if they were new events.
While not a perfect check of the system, it does provide a quick way to determine if the
reasoner is capable of learning from past cases to make correct recommendations based
on the patterns.
When the system described earlier was run on all of its cases, there was an 81% match
between the machine’s output and the most successful method utilized in the real-world
event. Five of the lost circulation events are presented in the following, detailing what
remediation was used in the actual case, what the reasoner predicted, as well as some
additional data about the remedial methods used in each instance. Table 12.3 presents
each case in the same manner in which it is presented when it is matched as a similar case
and used to predict a solution for a new test case. These data are given, along with the
weighted similarity matching and overall recommendation, to help the engineer make
an informed decision from the past cases.
512
Methods for Petroleum Well Optimization
Event 148 represents a case where the reasoner picked the same results as were used
on site for this event. It was a relatively straightforward lost circulation event where losses
were noted between 64 and 80 feet of measured depth. A lost circulation material pill was
immediately attempted, which led to full returns to surface. When this past case was run
through the reasoner, the most similar cases also had success with LCM pills. This led to
the system recommending a pill as the best course of action. However, the reasoner also
noted that a signiﬁcant minority number of similar events had required cement plugs in
the past when the ﬁrst pill was not successful, and the system recommended this as the
follow-up plan. Since Event 148 occurred at a depth of 64 feet, this is consistent with
standard practice, since drilling the rest of the well blind is not practical or safe.
Another relatively straightforward lost circulation case was Event 123, where returns
were lost at a depth of 273 feet but quickly recovered with a single pill. When this case
was run through the reasoner, the majority of the matched cases had required drilling
blind after many failed pill and cement plug attempts, but the three most similar cases had
been successful with one pill each. The reasoner not only accounted for the similarity of
those three cases and recommended that a pill be attempted but also accounted for the
remaining majority of less similar cases by limiting the number of pills recommended to
Table 12.3 Sample results from the case-based reasoner.
Event
ID
Number
of PILLs Success?
Number
of plugs Success?
Footage
drilled
blind
Final
method
used
Actual
success
Recommendation
148
1
Full returns 0
N/A
N/A
Pill
Pill
Max 1 Pill
before trying
CMT.
123
1
Full returns 0
N/A
0
Pill
Pill
Max 1 Pill
before drilling
blind.
373
2
No returns 1
No
returns
344
Drilled
blind
Drilled
blind
Drill blind
327
4
No returns 2
Full
returns
101
Cement CMT
Max 1 Pill
before trying
CMT.
449
4þ
No returns 0
N/A
782
Drilled
blind
Drilled
blind
Max 1 Pill
before trying
CMT.
Reedy, K., Popa, A. S., Cassidy, S.D., 2018. Remediation solutions for lost circulation using case based reasoning. In: SPE
Western Regional Meeting, 22e26 April, Garden Grove, California, USA. https://doi.org/10.2118/190066-MS.
Case-based reasoning (CBR) in digital well planning and construction
513
one before switching directly to drilling blind. While the additional recommendation
would have been unnecessary in this case, having the extra information provides the user
with both an alternative option should the recommendation fail as well as additional
insight into why the recommendation was made in the ﬁrst place.
Event 373, however, is a case where the reasoner could have saved time and money
for the drilling operation. Circulation was lost in this well at a measured depth of 1173
feet leading into the horizontal section of the well. Two pills and a cement plug were
attempted, both failing, in this case event before the decision was ﬁnally made to drill
blind for the remainder of the well. The reasoner identiﬁed correctly that other cases
similar to this one had low success with both pills and cement, recommending that
drilling blind be the method used. This is also consistent with the conventional belief that
pills and cementing become less effective as the wellbore angles off from a vertical
alignment.
Event 327 is a case where the reasoner did not correctly predict the best method.
During this case of lost circulation, four different pills were attempted before the decision
was made to drill blind. However, more losses were incurred, and it became necessary to
try cement plugs. On the second attempt, the plug succeeded. Despite the cases most
similar to Event 327 also requiring the use of cement plugs, the sheer number of less-
similar cases where pills were successful outweighed them. This is the reason why the
secondary recommendation is necessary, to mitigate the effect of the number of pills
without biasing the reasoner away from them. While this case would not have been
predicted perfectly by the reasoner, by limiting the number of pills attempted and
suggesting a cement plug before drilling blind, the recommendation of the system would
have been an improvement over what was actually done.
Similarly, for Event 449, the reasoner recommended that one pill be attempted before
moving on to a cement plug, when the actual case drilled blind for 782 feet. After
circulation was lost at a measured depth of 795 feet, more than four pill attempts were
made before the well was ﬁnally drilled blind for the remainder of the operation. While
this outcome did not match with the recommendation of the case-based reasoner,
utilizing the knowledge base of the system could have potentially saved that drilling
operation time and money. No attempt at setting a cement plug was made, even though a
nearby well that had lost circulation at almost the exact same depth had success reme-
diating the well with a cement job. In fact, the only matched similar event that required
drilling blind also forwent cement in favor of drilling blind in excess of 1000 feet. In this
case, following the recommendation of the system could have been beneﬁcial.
514
Methods for Petroleum Well Optimization
Despite the challenges faced during data acquisition and analysis, this case-based
reasoner can successfully predict the most successful remediation method to alleviate
lost circulation. This proof-of-concept design opens the door for the continued use of
case-based reasoners and other future machine learning solutions to lost circulation.
Improving the way data are collected and managed in drilling operations can only
increase the accuracy of analytical systems and even lead to further development of this
system in particular. With access to an improved database, the system has the ability to
make recommendations about greater details of the remediation process, such as pill
weight, pill volume, or plug depth. Even just utilizing it in the ﬁeld would improve it
over time as the engineers revise the recommendations and retain new cases, imple-
menting the ﬁnal two steps of the CBR process.
12.3 Summary
1. The main objective of this chapter was to demonstrate and analyze some complex
problem cases within the oil well engineering domain through the CBR methodology.
2. This chapter indicates that CBR, particularly when integrated with other reasoning
methods, substantially improves human problem-solving and decision-making.
3. This chapter has tried to formalize the main concepts involved and illustrate the
potential for the application of CBR in the area of well design. It can be stated that
the architecture proposed can help in the design of petroleum wells, as it allows not
only the reuse of previous designs but also prevents potential failures within the new
designs. Besides this, as the architecture works based on situations that have actually
occurred, this allows the designs to address the real requirements of the well as closely
as possible.
4. Despite the challenges faced during data acquisition and analysis, the case-based
reasoner can successfully predict the most successful remediation method to alle-
viate lost circulation. With access to an improved database, the case-based reasoners
are able to make recommendations about more details of the remediation process,
such as pill weight, pill volume, or plug depth. Even just utilizing the reasoner in the
ﬁeld would improve it over time as the engineers revise the recommendations and
retain new cases, implementing the ﬁnal two steps of the CBR process.
12.4 Problems
Problem 1: Learning method
Given the training data set in Table 12.4 with two numeric attributes A and B and a class
attribute class:
Case-based reasoning (CBR) in digital well planning and construction
515
1. We want to use the basic k-NN method with k ¼ 3. An unknown instance is given
with values A ¼ 5 and B ¼ 3. What is its class? Justify your answer.
2. We use the same data set and learning method as in (1), but now we weight the
contribution of each of the three nearest neighbors with a number equal to the in-
verse of the squared distance. What is the class of the same unknown instance? Again,
justify your answer.
Problem 2: A case-based system for mud design and selection of
optimal mud weight
Suppose that the database of a CBR system contains the four cases in Table 12.5:
The system is using the nearest neighbor retrieval algorithm with the following
similarity function: where T is the target case, S is the source case, i is the number of a
feature, and wi are the weights. Cases with smaller values of d(T, S) are considered to be
more similar.
dðT; SÞ ¼
X
m
i ¼ 1
jTi  Sijui
Consider the new (target) case in Table 12.6:
Table 12.4 Training data set.
A
B
Class
9
6
No
2
5
Y
es
3
4
No
3
3
No
3
2
Y
es
6
3
Y
es
6
2
No
7
2
Y
es
8
1
No
Table 12.5 Database for mud design and selection of optimal mud weight.
Case ID attributes Measured depth (mt) Inclination (degree) Pore pressure Mud weight (s.g.)
Case 1
3152
25
1.38
1.68
Case 2
3244.5
30
1.43
1.78
Case 3
3474
35
1.58
1.8
Case 4
3600
45
1.68
1.9
516
Methods for Petroleum Well Optimization
Answer the following questions:
1. Which case will the CBR system retrieve as the “best match,” if all the weights
wi ¼ 1?
2. The solution that the CBR system should propose is the mud weight (s.g.) rating.
Suggest how the solution of the retrieved case should be adapted for the target case.
3. What can be changed in the similarity function to make the feature “pore pressure”
three times more important than any other feature?
4. Will this change inﬂuence the solution?
Problem 3: A case-based system for drilling cost estimation
This chapter has developed a CBR model for cost estimation of drilling wells based on
features of the project of a drilling well (case). Suppose that the database of a CBR system
contains the four cases in Table 12.7:
Consider the new (target) case in Table 12.8:
Answer the following questions:
1. Which case will the CBR system retrieve as the “best match,” if all the weights
wi ¼ 1?
Table 12.7 Cost estimation of drilling wells.
The case
base
Water
depth
(m)
Average
inclination
(degree)
Operation
time (hours)
Nonproductive
time (hours)
Drilling
length
(m)
Well cost
(million
dollars)
Well 1
67
57
1900
130
4100
50
Well 2
75
45
1950
200
3900
45
Well 3
90
55
2000
50
3800
39
Well 4
55
30
1870
40
3800
35
Table 12.8 Well ﬁve.
The
case-
base
Water
depth
(m)
Average
inclination
(degree)
Operation
time (hours)
Non-productive
time (hours)
Drilling
length
(m)
Well cost
(million
dollars)
Well 5
80
45
2000
150
4000
?
Table 12.6 Target case.
Case
Measured depth (mt)
Inclination
Pore pressure
Mud weight (s.g.)
Case 5
3552
49
1.7
?
Case-based reasoning (CBR) in digital well planning and construction
517
2. The solution that the CBR system should propose is the “well cost (million dollars)”
rating. Suggest how the solution of the retrieved case should be adapted for the target
case.
3. What can be changed in the similarity function to make the feature “drilling length
(m)” two times more important than any other feature?
4. Will this change inﬂuence the solution?
Problem 4: Digital experiences in the life cycle well integrity model
Answer the following questions:
1. Describe the characteristics of problems in which it is better to use rule-based expert
systems and of problems where the case-based systems are more appropriate.
2. Predicting failures before they occur is a very important issue in drilling engineering.
How many papers have been published since 2010 on kick detection or downhole
failures detection using case-based systems or ontology engineering?
3. Describe the ﬂow of events leading up to a warning if the process approaches a failure
state, as shown in Fig. 12.23.
Digital experiences are key for “manipulating” the way the life cycle well integrity
model (LCWIM) is running the engineering process and developing the digital plans
(Fig. 12.24).
Figure 12.23 Flow of events leading up to a warning if the process approaches a failure state.
Modiﬁed from Skalle, P., Aamodt, A., 2020. Petrol 18 946: downhole failures revealed through ontology
engineering, J. Petrol. Sci. Eng. 191 107188. https://doi.org/10.1016/j.petrol.2020.107188.
518
Methods for Petroleum Well Optimization
4. Present three applications of the CBR system in LCWIM.
5. Which software is designed to assist drilling engineers to reuse knowledge to diagnose
and avoid costly problems before they escalate?
6. How does this help operators to easily recall experiences and use them to make the
right decision about the situation to lower risks, increase well drilling operation, and
minimize nonproductive time while drilling?
Nomenclature
C1 time difference between the ﬁrst and last well of a series
C2 reﬂects rate of learning
C3 minimum drilling time for an area
Cin the input case
Cre the retrieved case
db bit diameter
K constant of proportionality
m the number of ﬁndings for the retrieved case
N counting of the wells in a series of similar wells
N rotary speed
n the number of ﬁndings for the input case
S compressive strength of the rock
Sim (X, Y) the similarity of the two cases and xi and yi are the same normalized variable in each case
T time required to drill the nth well
W bit weight
Wi a weighting factor that represents the importance of each variable for determining how similar are the
two cases
W
o threshold bit weight
Figure 12.24 Digital well planning and operation. Modiﬁed from Brechan, B., Sangesland, S., 2019.
Digital Well Planning, Well Construction and Life Cycle Well Integrity: The Role of Digital Experience, SPE-
195628-MS, Society of Petroleum Engineers, SPE Norway One Day Seminar, 14 May, Bergen, Norway.
https://doi.org/10.2118/195628-MS.
Case-based reasoning (CBR) in digital well planning and construction
519
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Aamodt, A., Plaza, E., 1994. Case-based reasoning: foundational issues, methodological variations and
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Open-source code
Y
ou can develop your models using open-source code for different tools available
on https://github.com/
Examples include:
•
Case-Based Reasoning Tool.
•
Towards Microservices Oriented Case-Based Reasoning.
•
CBR pattern and fuzzy logic.
Case-based reasoning (CBR) in digital well planning and construction
521

CHAPTER 4
Application of intelligent models
in reservoir and production
engineering
Contents
4.1 Reservoir fluid properties
80
4.1.1 One-phase properties
80
4.1.2 Two-phase properties
115
4.2 Rock properties
140
4.3 Enhanced oil recovery
154
4.3.1 Enhanced oil recovery processes
155
4.3.2 Minimum miscibility pressure
166
4.4 Well test analysis
180
4.5 Formation damage
183
4.6 Asphaltene
192
4.7 Production pipelines
198
4.8 Wax
200
4.9 Other applications
201
References
203
This chapter investigates the applications of artificial intelligence (AI)
techniques in the areas of reservoir and production engineering.
According to the literature, intelligent models have extensive applica-
tions in various fields of reservoir and production engineering. Herein,
the application of intelligent models will be discussed in nine different
topics, namely, Reservoir fluid properties, Rock properties, Enhanced
oil
recovery,
Well
test
analysis,
Formation
damage,
Asphaltene,
Production pipelines, Wax deposition, and Other applications, as
shown in Fig. 4.1.
79
Applications of Artificial Intelligence Techniques in the Petroleum Industry
DOI: https://doi.org/10.1016/B978-0-12-818680-0.00004-7
© 2020 Elsevier Inc.
All rights reserved.
4.1 Reservoir fluid properties
4.1.1 One-phase properties
It is an essential task to accurately determine the pressure-volume-
temperature (PVT) properties of oil and gas due to their high significance for
material balance calculations as these parameters directly affect reservoir per-
formance and evaluation as well as production operation and design. In the
industry, reliable experimental data is desirable; however, in many cases,
experimental data is hardly available. PVT correlations and equation of state
(EOS) are two approaches to estimate PVT properties when the experimen-
tal PVT data is unavailable. The utility of EOS requires the time-consuming
and expensive procedure of determining the detailed compositions of crude
oils. PVT correlations are useful owing to their ability to predict the PVT
properties using only a few parameters such as oil and gas specific gravity
(SG) and reservoir temperature/pressure [1].
The development of conventional PVT correlations with graphical
methods and linear/nonlinear multiple regression dates back to the 1940s.
Figure 4.1 Various areas of reservoir and production engineering considered in this
chapter.
80
Applications of Artificial Intelligence Techniques in the Petroleum Industry
In these correlations, bubble point pressure (Pb) and oil formation volume
factor (Bo) were considered as strong functions of temperature (T), oil/gas
SG (γo,γg), and solution gasoil ratio (Rs) [1]. There are many developed
empirical correlations in the literature [243], and many others assessed
the efficiency of these correlations [4350].
In order to overcome the shortcomings of empirical correlations, intel-
ligent models were developed by many scholars to predict the PVT prop-
erties more accurately. The first application of intelligent models in the
area of PVT properties dates back to the 1990s. In 1997, Gharbi and
Elsharkawy [1] employed artificial neural networks (ANNs) to estimate
the bubble point pressure (Pb) and oil formation volume factor (Bo) of the
Middle East crude oils considering T, Rs, γo, and γg as input parameters.
The authors selected the ANN architectures of 48-4-1 and 46-6-1 for
Pb and Bo as the most accurate models, respectively. In their study, ANNs
could precisely predict the Pb and Bo of the Middle East crude oil with an
average absolute percent relative error (AAPRE) of 6.89% and 2.79%,
respectively. It should be mentioned that AAPRE in some papers is
named average absolute relative error (AARE) or average absolute relative
deviation (AARD). All of these terms have the same meaning.
Afterward, Gharbi [51] proposed another ANN model to estimate the
isothermal compressibility coefficient (Co). They acquired a dataset con-
sisting of reservoir temperature (T), reservoir pressure (P), Co, Rs, γo, and
γg for each data point from 102 different Middle East crude oils. The
developed ANN was able to provide predictions with a correlation coeffi-
cient (R2) of 0.993.
A year later, Elsharkawy [52] investigated the capability of radial basis
function neural network (RBFNN) in predicting various crude oil and
gas parameters. They developed the RBF model to estimate Bo, Rs, grav-
ity of evolved gas (γgr), compressibility of undersaturated oil (Co), density
of saturated oil (ρo), and oil viscosity (μo) as functions of T, P, gravity of
separator gas, and gravity of stock tank oil (°API). The author could suc-
cessfully employ RBFNN to predict Rs, Bo, μo, ρo, Co, and γgr with R2
values
of
0.9897,
0.9824,
0.9788,
0.9762,
0.8634,
and
0.9164,
respectively.
In 1999, Al-Shammasi [53] developed ANNs using a global set of data
to provide correlations for PVT properties (Pb and Bo). They considered
T, Rs, γo, and γg as input variables and compared the performance of the
proposed model with the other correlations. According to the modeling
results,
Al-Shammasi
concluded
that
the
developed
model
could
81
Application of intelligent models in reservoir and production engineering
outperform other correlations, especially for oils with the °API of higher
than 30. They also claimed that the correlation developed by Petrosky
and Farshad [33] was the most accurate global correlation to estimate the
oil formation volume factor. In this year, Gharbi et al. [54] proposed an
ANN model for universal implementation that could predict the Bo and
Pb considering Rs, T, °API, and γg as the inputs. High correlation coeffi-
cients were obtained for the predictions of Pb and Bo as 98.91 and 98.75,
respectively. Later in this year, Varotsis et al. [55] employed ANN to pre-
dict the PVT properties of oils and gas condensates. The oil ANN models
were aimed to predict Bo, Co, Rs, ρo, μo, z-factor, and the relative density
of liberated gas. The authors considered Pb, T, stock tank oil density, and
fluid molecular weight as the inputs of the oil ANN model, whereas dew
point pressure (Pd), T, field gas-oil ratio (GOR), and fluid molecular
weight were the inputs for gas condensate ANN model. The gas conden-
sate ANN models were utilized to predict relative volume, maximum con-
densation, cumulative produced fluid, gas relative density, and z-factor.
The developed ANN models were able to make accurate predictions.
After 2 years, in 2001, Elsharkwy and Gharbi [56] attempted to predict
the viscosity of crude oil with the aid of ANNs. In their study, °API, P,
T, and γg were fed as the inputs, and four training algorithms were con-
sidered to train the network. They reported that all developed ANN
models were efficient in viscosity prediction with a correlation coefficient
of higher than 0.96. Another study was conducted in 2001 by Osman
et al. [57]. They used ANNs to develop a predictive model to estimate
the oil formation volume factor at the bubble point pressure. The authors
utilized 803 data points from different oil fields. The input parameters
were considered as Rs, γg, °API, and T. The proposed model could pre-
dict the Bo at Pb with an AAPRE of 1.789% and R2 of 0.988.
In 2002, Abdel-Aal [58] introduced abductive networks based on the
self-organizing group method of data handling (GMDH) as an alternative
for ANNs to overcome the limitations of ANNs such as the complexity
of the design space. They claimed the outperformance of this predictive
tool over other models. This model was applied to predict the Pb and Bo.
They considered T, Rs, γg, and °API as the inputs of the models, and
reported the values of 5.62% and 0.86% as the AAPRE of the Pb and Bo
models, respectively. Another application of ANNs is reported by Al-
Marhoun and Osman [59] in this year. They employed ANN to estimate
the Pb and Bo at Pb for Saudi crude oils. In their study, T, Rs, γg, and
°API were fed as input variables, while the Pb and Bo at Pb were the
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
outputs of the models. The values of 0.5116% and 5.8915% were
obtained for the AAPRE of the Bo and Pb models, respectively.
In 2003, González et al. [60] were the scholars who applied ANNs
[multilayer perceptron (MLP)] to estimate the dew point pressure (Pd) of
gas condensates. They used a dataset consisted of 802 experimental data
points of a retrograde gas reservoir in Colombia (Cusiana field). SG and
molecular weight of C71 fraction, hydrocarbons and nonhydrocarbon
compositions, and temperature were the input parameters of the model,
which could provide estimations with an AAPRE of 8.742% and the cor-
relation coefficient of 0.9324. Fig. 4.2 shows the accuracy of their pro-
posed model.
The following application of ANNs in PVT properties estimation is
published by Goda et al. [61]. They constructed ANN models based on
180 data points from Middle East oils. Each data point contained Pb, Bo at
Pb, T, Rs, γg, and °API. The models were able to predict Pb and Bo at Pb,
while the other available parameters were introduced as the inputs. The
developed models showed a high accuracy to predict the aforementioned
parameters. In this year, Gharbi and Elsharkawy [62] attempted to con-
struct models for Pb and Bo prediction based on ANNs for worldwide
crude oils. The ANN models were trained using a dataset consisting of
5200 data points, which was claimed to be the largest dataset up to that
date. They used T, Rs, γg, and °API as input parameters. They reported
the values of 6.48% and 1.97% as the AAPRE of the Pb and Bo models,
Figure 4.2 Cross-plot of predicted values versus actual values for Pd. Adapted from
A. González, M.A. Barrufet, R. Startzman, Improved neural-network model predicts
dewpoint pressure of retrograde gases, J. Pet. Sci. Eng. 37 (34) (2003) 183194.
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Application of intelligent models in reservoir and production engineering
respectively. It should be mentioned that generally the models developed
for Bo are more accurate than those ones developed for Pb.
Two years later, ANNs were utilized by Osman and Al-Marhoun [63]
to estimate the brine isothermal compressibility (Cw), formation volume
factor (Bw), density (ρw), and viscosity (μw). The first ANN model (RBF)
could predict the first three parameters (Cw, Bw, and ρw) as functions of
salinity, P, and T. The brine density was estimated through the second
model (MLP) as a function of salinity and T. The RBFNN could predict
the corresponding parameters with a correlation coefficient of higher than
0.997. The accuracy of the MLP model predictions was reported by an
R2 value of 0.99967.
In 2007, the first application of genetic algorithms (GAs) in the area of
PVT properties prediction was reported by Hajizadeh [64]. In their study,
GAs were employed to predict the viscosity of crude oils as a function of
P, T, GOR, and ρo. They gathered 89 data points from different reports.
GAs were able to predict the viscosity with an R2 of 0.99742. They also
concluded that the temperature is the most influential factor in viscosity
prediction. In this year, El-Sebakhy et al. [65] reported the first applica-
tion of support vector machines (SVMs) in PVT properties forecasting.
They considered GOR, T, °API, and γg as input parameters. The authors
claimed that the proposed model could accurately predict the Pb and Bo.
After 2 years, in 2009, El-Sebakhy [66] conducted similar research and
applied SVM to predict PVT properties. El-Sebakhy considered three dif-
ferent published datasets [58,59,63]. The author claimed that the SVM
modeling scheme could predict the Pb and Bo with a high correlation
coefficient of more than 0.96. In addition, El-Sebakhy [67] was the first
scholar who attempted to utilize an adaptive neuro-fuzzy inference system
(ANFIS) to predict the PVT properties. In their study, ANFIS was applied
to predict Bo and Pb as functions of GOR, °API, T, and γg. The modeling
results showed that the ANFIS could precisely predict the PVT properties
with an R2 of higher than 0.95. Again, they used the previous three data-
sets and reported Fig. 4.3 to show the accuracy of the ANFIS scheme in
comparison to empirical models and ANNs.
Nowroozi et al. [68] were the second scholars who attempted to pre-
dict PVT properties using ANFIS. Their neuro fuzzy (NF) system was
based on 110 data points and was aimed to predict the dew point pressure
using nonhydrocarbon and hydrocarbon components, C71 molecular
weight, and reservoir temperature as input parameters. The modeling
results showed high accuracy for the ANFIS model with the root mean
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
Figure 4.3 The accuracy of the ANFIS scheme in PVT properties prediction in comparison with empirical models and ANNs. (A) oil forma-
tion volume factor and (B) bubble-point pressure. ANNs, Artificial neural networks; ANFIS, adaptive neuro-fuzzy inference system.
Adapted from E.A. El-Sebakhy, Data mining in forecasting PVT correlations of crude oil systems based on Type1 fuzzy logic inference systems,
Comput. Geosci. 35 (9) (2009) 18171826.
square error (RMSE) and AAPRE of 4.96 psia and 3.9%, respectively.
They reported the following figure as the results of training and testing
datasets by the ANFIS (Fig. 4.4).
ANNs were employed once again by Omole et al. [69] to predict the
viscosity of Nigerian crude oil. In their study, T, °API, GOR, Pb, and γg
were used as the inputs, and the values of 0.06781 and 0.9989 were
reported as an AAPRE and R2 obtained for the predictions of the pro-
posed ANN model. In this year, SVM was utilized by Dutta and Gupta
[70] in order to provide PVT correlations for Indian crude oil. The devel-
oped SVM-based models were aimed to provide correlations for Pb,
GOR, Bo, and μo. They considered T, Rs, γg, and γo as input variables for
the bubble point pressure model as well as the saturated oil formation vol-
ume factor model. The inputs of the GOR model were considered as T,
Pb, γg, and γo. In order to model the formation volume factor for under-
saturated oil, seven inputs were considered as follows: T, P, Pb, γg, γo, Rs,
and Bo at bubble point pressure. Finally, in the model of saturated and
undersaturated viscosity, P, Pb, γg, γo, μod (dead oil viscosity), and μob (sat-
urated oil viscosity) were the input parameters. Support vector machines
were able to predict the mentioned parameters with an AAPRE of less
than 12.5% and an R2 of higher than 0.98 for testing data.
Further applications of intelligent models in PVT properties estimation
in 2009 were reported by Nagi et al. [71] and Abass [72]. Nagi et al. [71]
attempted to predict the PVT properties using SVMs, and Abass [72] tried
to estimate the shape of the phase envelope of gases through predicting
the key points of the phase envelope (a novel application of ANNs).
Abass [72] considered the compositions of gases, MW of C71, and the SG
of C71 as input parameters to estimate critical point, cricondenbar, and
cricondentherm of natural gases as the outputs. The estimations of devel-
oped ANNs for cricondenbar and cricondentherm showed high accuracy
with the average error of less than 6 4%. Fig. 4.5 illustrates a phase enve-
lope for a gas condensate along with its key points.
In 2010, Dutta and Gupta [74] attempted to develop similar correla-
tions for Pb, GOR, μo, and Bo for crude oils of western India using
ANNs. They considered the same datasets and input variables. The pro-
posed models could accurately predict its corresponding parameter with
an AAPRE of less than 11% and an R2 of higher than 0.98. In this year,
Khoukhi and Alboukhitan [75] constructed a hybrid model, namely,
genetic-NF inference system (GANFIS) to predict the Pb and Bo of crude
oils as a function of Rs, T, °API, and γg. The obtained results showed that
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
Figure 4.4 The results of the ANFIS (A) training and (B) testing phase. ANFIS, Adaptive neuro-fuzzy inference system. Adapted from S.
Nowroozi, et al., Development of a neural fuzzy system for advanced prediction of dew point pressure in gas condensate reservoirs, Fuel
Process. Technol. 90 (3) (2009) 452457.
the GANFIS model could accurately predict Bo and Pb with an RMSE of
0.0159 and 144.818 psia, while these values for standalone ANNs were
0.0176 and 243.675 psia. In another work in 2010, Khoukhi and
Albukhitan [76] employed GANFIS once again to predict Pb and Bo of
crude oils as a function of Rs, T, °API, and γg. GANFIS could make
accurate predictions similar to their previous study [75]. Another applica-
tion of intelligent models in PVT properties estimation in this year was
reported by Olatunji et al. [77]. In their study, type-2 fuzzy logic systems
(T2FLSs) were utilized to predict Pb and Bo using GOR, T, °API, and γg
as the inputs of the fuzzifier. The authors used two published datasets
[58,61]. They reported values higher than 0.93 as the correlation coeffi-
cient of the constructed systems. They claimed that the significant advan-
tage of T2FLS is its capability to generate prediction intervals with no
additional computational cost.
A year later, in 2011, Olatunji et al. [78] applied T2FLS to estimate
PVT properties once again. In their study, a large data bank was gathered
from the literature [25,5759,61,79], and the previous parameters were
fed as the inputs. Again, the developed system could provide prediction
with an R2 of higher than 0.93. Fig. 4.6 shows the performance compari-
son of T2FLS with some other predictive models. In this year, Alimadadi
et al. [80] reported the first application of committee machines (CMs) in
PVT properties prediction. They developed two parallel MLP models,
and their results were recombined by a CM to predict the ρo and Bo. The
authors gathered 190 data points from 19 Iranian reservoirs to train the
Figure 4.5 Phase envelope of a gas condensate sample. Adapted from L. Fan, et al.,
Understanding gas-condensate reservoirs, Oilfield Rev. 17 (4) (2005) 1427 [73].
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
Figure 4.6 The performance comparison of T2FLS with other predictive models. T2FLS, Type-2 fuzzy logic systems. Adapted from S.O.
Olatunji, A. Selamat, A.A.A. Raheem, Predicting correlations properties of crude oil systems using type-2 fuzzy logic systems, Expert Syst. Appl.
38 (9) (2011) 1091110922.
ANN models. In their study, input parameters were °API, T, P, Pb,
solution GOR, and oil composition. The constructed CM could provide
precise estimations with AAPRE of 1.743% and 0.479% for Bo and ρo,
respectively.
The other application of intelligent models in PVT properties predic-
tion was reported by Asadisaghandi and Tahmasebi [81]. They conducted
a comparative study on the performance assessment of ANNs and empiri-
cal correlations in predicting the PVT parameters in Iranian oil fields. The
authors also proposed two models to predict the Pb and Bo at Pb as func-
tions of T, °API, Rs, and γg. The modeling results indicated that ANNs
are the most efficient models to predict the Pb and Bo at Pb with the low-
est AAPRE and the highest correlation coefficient among different empir-
ical models used in the comparison. Al-Dhamen and Al-Marhoun [82]
were among the scholars who employed ANNs to predict the dew point
pressure of retrograde gases. They considered T, γg, GOR, and conden-
sate SG as the input variables. The developed network could predict the
Pd with an AAPRE of 6.5%. In this year, Khoukhi et al. [83] made an
effort on the estimation of viscosity and GOR curve through SVM and
functional networks (FNs).
Another attempt to predict PVT properties was made by Khoukhi
[84] in 2012. They intended to construct two hybrid models, namely, a
genetically optimized NN (GONN) and a GANFIS. The constructed
models were able to make predictions for Pb and Bo using Rs, °API, T,
and γg as input variables. In their study, three datasets were employed
[25,59,63]. The performances of the hybrid models in estimating the PVT
properties were compared to those of standalone ANN, ANFIS, and sev-
eral empirical correlations. The modeling results indicated that the highest
performances belong to GANFIS and GONN models. In another study,
Zendehboudi et al. [85] utilized ANNs and particle swarm optimization
(PSO) algorithm to construct the PSO-ANN model in order to predict
the condensate-to-gas ratio (CGR) as a function of T, Pd, and molecular
weight (MW) of the mixture. They concluded that the MW had the
highest impact on the CGR. The proposed intelligent model was able to
predict the amount of CGR with an R2 of 0.9721.
The second application of CMs in the area of PVT properties estima-
tion is reported by Asoodeh and Bagheripour [86]. In order to estimate
the Pb, they constructed a power-law committee intelligent system to
combine the results of standalone intelligent models, including a NN,
NF, and FL algorithms. The inputs of the individual intelligent models
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
were T, °API, and Rs/γg. The authors claimed that the utilization of CMs
could significantly improve the accuracy of the predictions. Fig. 4.7 shows
the comparison between the performances of various predictive tools uti-
lized to predict bubble point pressure in the study of Asoodeh and
Bagheripour [86].
Ikiensikimama and Azubuike [87] were the other scholars who utilized
ANNs to predict PVT properties in 2012. They modeled the Bo of
Niger-Delta considering T, Rs, °API, and γg as input parameters of the
ANN model. The value of 0.968 was obtained for the R2 of the proposed
model. Naseri et al. [88] investigated the application of ANNs in predict-
ing the viscosity of Iranian dead crude oil. In their study, °API and T
were used as input variables, and the accuracy of the developed ANN was
reported as 16.75% in terms of AAPRE. Another approach toward the
estimation of the crude oil viscosity was proposed by Makinde et al. [89]
using ANNs. They developed a feedforward backpropagation NN
(FFBPNN) to model the Nigerian undersaturated crude oil viscosity as a
function of T, P, Pb, and μo at Pb. The developed FFBPNN showed the
AAPRE of 0.01998 and the R2 of 0.999. Selamat et al. [90,91] con-
structed two hybrid models based on T2FLS and sensitivity-based linear
learning method (SBLLM) to predict Pb and Bo. The input variables were
GOR, T, °API, and γg. Both studies reported high accuracy of the pro-
posed models (T2-SBLLM) in predicting the PVT properties.
In 2013, Rafiee-Taghanaki et al. [92] introduced the first application
of the least square SVM (LSSVM) in the area of PVT properties
Figure 4.7 The comparison of the accuracies of various Pb models. Adapted from M.
Asoodeh, P. Bagheripour, Estimation of bubble point pressure from PVT data using a
power-law committee with intelligent systems, J. Pet. Sci. Eng. 90 (2012) 111.
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Application of intelligent models in reservoir and production engineering
estimation. They implemented the coupled simulated annealing (CSA)
optimization algorithm to optimize the LSSVM model. Developing the
LSSVM models based on GOR, T, °API, and γg as input variables
resulted in an R2 value of 0.977 and 0.943 (for testing dataset) for the Pb
and Bo predictions, respectively. Fig. 4.8 shows the error distribution plot
for the predictions of the LSSVM model. Azubuike and Ikiensikimama
[93] developed an ANN model to predict Bo for a dataset collected from
different parts of the world. The proposed network could accurately pre-
dict the oil formation volume factor (R2 of 0.9939) using GOR, T, °API,
and γg as input parameters.
In this year, Farasat et al. [94] attempted to predict the bubble point
pressure of crude oils using an LSSVM scheme. T, hydrocarbon and non-
hydrocarbon compositions, MW, and SG of C71 were fed as the inputs,
while the saturation pressure was the output of the model. Then, the pre-
diction of the dew point pressure of gas condensate reservoirs was the
concern of the study conducted by Arabloo et al. [95]. They used an
LSSVM scheme along with a CSA optimization method to predict the Pd
as a function of T, hydrocarbon and nonhydrocarbon compositions, MW
and SG of C71. Figs. 4.9 and 4.10 show the accuracy of the proposed
SVM and LSSVM models in the studies of Farasat et al. and Arabloo
et al., respectively.
In another work, Fayazi et al. [96] utilized CSA-LSSVM to model the
gas viscosity as a function of P, T, MW of C71, and gas composition.
They reported the values of 0.26% and 0.99 as the AAPRE and R2 of the
model. In another field of study, Kamari et al. [97] and Chamkalani et al.
[98] focused on the estimation of gas compressibility factor through intel-
ligent models. They both employed the LSSVM framework optimized by
a CSA to predict the Z-factor. In another investigation in 2013,
Numbere et al. [99] applied ANNs to estimate the saturation pressure of
Nigerian crude oils. They attained 1248 data points from the Niger-Delta
region to construct the ANN model. γo, γg, °API, T, and GOR were
considered as influential parameters on Pb. The value of 0.9698 was
reported as the R2 of the proposed network. Moghadasi et al. [100] were
the following scholars to apply ANNs to predict the saturation pressure.
The input variables were °API, GOR, γg, and T collected from southwest
Iranian oil fields. The authors claimed that ANNs could accurately predict
the Pb with an R2 of 0.9821. Kazemi et al. [101] attempted to apply
ANNs in order to predict the bubble point pressure as a function of
hydrocarbon and nonhydrocarbon components, T, SG and MW of C71.
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
Figure 4.8 Error distribution plot for the predictions of the LSSVM model. LSSVM, Least square support vector machine. Adapted from S.
Rafiee-Taghanaki, et al., Implementation of SVM framework to estimate PVT properties of reservoir oil, Fluid Phase Equilib. 346 (2013) 2532.
Figure 4.9 Comparison between measured and predicted data for dew point pres-
sure by the LSSVM framework. SVM, Support vector machine. Adapted from A.
Farasat, et al., Toward an intelligent approach for determination of saturation pressure
of crude oil, Fuel Process. Technol. 115 (2013) 201214.
Figure 4.10 Comparison between measured and predicted data for dew point pres-
sure by the LSSVM scheme. LSSVM, Least square support vector machine. Adapted
from M. Arabloo, et al., Toward a predictive model for estimating dew point pressure in
gas condensate systems, Fuel Process. Technol. 116 (2013) 317324.
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
The values of 2.88 and 0.9714 were obtained as the AAPRE and R2 for
the predictions of the developed model.
In 2014, several papers were reported in the area of viscosity predictive
intelligent models. Hemmati-Sarapardeh et al. [102] constructed three
separate LSSVM models to predict the oil viscosity at different conditions
(P . Pb, P , Pb, and P 5 Patm). The models took T, °API, P, Pb, μod, and
μob as input parameters. The models were developed using more than
1000 experimental data points of Iranian oil fields. The prediction of the
LSSVM models showed a high accuracy of more than 0.95 in terms of
the correlation coefficient. They also stated that the developed models
could properly predict the physical trend of viscosity with the change of
P, T, and °API. In another work, Hemmati-Sarapardeh et al. [103] devel-
oped six CSA-LSSVM models in order to estimate the viscosity of Iranian
saturated crude oils. Each model was based on different input parameters.
Among the proposed models, model 6 showed the highest performance
with an AAPRE of 3.66% and an R2 of 0.996. This model was con-
structed to predict the viscosity using five input variables: °API, P, Pb, μod,
and μob.
Yousefi et al. [104] were the scholars who optimized the LSSVM
framework with a CSA optimization algorithm to construct the CSA-
LSSVM model. The developed model was aimed to predict the viscosity
of pure and impure hydrocarbon gases using MW, ρg, pseudo-reduced
temperature (Tpr), and pseudo-reduced pressure (Ppr) as input parameters.
Fig. 4.11 illustrates the relative importance of each input variable on the
Figure 4.11 The relative importance of independent variables on μg. Adapted from S.
H. Yousefi, et al., Toward a predictive model for predicting viscosity of natural and
hydrocarbon gases, J. Nat. Gas Sci. Eng. 20 (2014) 147154.
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Application of intelligent models in reservoir and production engineering
gas viscosity. The developed model could provide estimations with an
AAPRE of 1.56%.
In the field of predicting the compressibility factor of natural gases,
Fayazi et al. [105] constructed an LSSVM-CSA model to estimate the
Z-factor. The input variables were considered as gas compositions, P,
T, and MW of C71. The developed model showed an accuracy of 0.19%
in terms of AAPRE. In order to predict the compressibility factor of the gas
phase and two-phase gas condensates, Ghiasi et al. [106] developed a hybrid
CSA-LSSVM based on the compositional analysis of more than 1800 gas
condensate samples. The model needed T, hydrocarbon and nonhydrocar-
bon compositions, MW, and SG of C71 as the inputs. The proposed CSA-
LSSVM model could predict the one- and two-phase compressibility factors
with an AAPRE of 0.726% and 1.302%, respectively.
In this year, several works were conducted in the area of gas conden-
sates. Ahmadi and Ebadi [107] put their effort into predicting the dew
point pressure of retrograde gases using the LSSVM framework. They
considered hydrocarbon and nonhydrocarbon compositions, T, MW, and
SG of C71 as input variables as well as RBF as the kernel function. The
developed model showed an R2 of 0.9575. Another study on dew point
prediction was performed by Rostami-Hosseinkhani et al. [108]. They
utilized an RBF network coupled with a GA, which took hydrocarbon
and nonhydrocarbon compositions, T, MW, and SG of C71 as input vari-
ables. The authors reported the value of 0.970 as the R2 of the model
predictions. Majidi et al. [109] were other scholars who attempted to pre-
dict Pd using ANNs. They also employed hydrocarbon and nonhydrocar-
bon compositions, T, MW, and SG of C71 as input parameters. The
results indicated a value of 6.8043% as the AAPRE of the developed net-
work. In this field, Arabloo and Rafiee-Taghanaki [110] employed the
LSSVM scheme to model the constant volume depletion behavior of ret-
rograde gases. A dataset of more than 200 retrograde gas samples was
gathered, and T, P, hydrocarbon and nonhydrocarbon compositions, SG,
and MW of C71 were considered as the inputs, while cumulative pro-
duced gas was the only output. The proposed model could provide pre-
dictions with an AAPRE of 15.9%.
In the field of predicting the Pb and Bo, 11 studies were reported in
2014. Talebi et al. [111] developed an MLP as well an RBF model to
predict saturation pressure as a function of T, °API, GOR, and γg. The
predictions of the MLP network showed an AAPRE of 16.94%, and the
value of 15.53% was reported as the AAPRE for the predictions of
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
the RBF network. The dependency of saturation pressure on the input
parameters was reported in Fig. 4.12. Al-Marhoun et al. [112] were other
scholars in employing ANNs as a predictive tool to estimate saturation
pressure. They attempted to construct a model that is capable of making
predictions for bubble point pressure of black oils using either the compo-
sition of oil or T, γo, GOR, and γg as the input parameters. The AAPRE
values for the ANN model were 5.22% and 12.78% when it was fed with
four input variables and with the oil composition, respectively. As another
research in the area of PVT properties estimation through intelligent
models, Shojaei et al. [113] introduced an ANFIS approach to predict the
saturation pressure as a function of T, °API, GOR, and γg. They gathered
a large database from open literature to construct the ANFIS model. The
modeling results showed the good accuracy of the proposed model with
an AAPRE of 12.18% for its predictions.
Gholami et al. [114] developed a CM comprising an SVM and an
alternating conditional expectation (ACE) to predict the Pb as a function
of T, hydrocarbon and nonhydrocarbon compositions, MW, and SG of
C71. GAs were employed to optimize the CM. The value of 7.632% was
obtained as the AAPRE of CM. Ganji-Azad et al. [115] conducted
another study to predict saturation pressure and oil formation volume fac-
tor using the ANFIS algorithm. The developed models were aimed to
provide prediction through GOR, °API, T, and γg. The proposed models
showed high correlation coefficients of higher than 0.94. In another study,
Ahmadi et al. [116] applied gene expression programing (GEP) to predict
Figure 4.12 The relative importance of each input on Pb. Adapted from R. Talebi,
et al., Application of soft computing approaches for modeling saturation pressure of
reservoir oils, J. Nat. Gas Sci. Eng. 20 (2014) 815.
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Application of intelligent models in reservoir and production engineering
Pb of crude oils. To construct the models, they utilized a data bank,
including the real or experimental data of T, SG and MW of C71 frac-
tion, and oil composition. The developed model could estimate the Pb
with an R2 of 0.9278. Some other applications of AI for prediction of Pb
and Bo were also reported in [117,118].
In 2015, a majority of studies were aimed to predict saturation pressure
and oil formation volume factor. Gholami et al. [119] optimized an ANN
with GAs to model the bubble point pressure as a function of T, the com-
position of crude oils, and specifications of C71 fraction. The developed
model could estimate the saturation pressure with an R2 of 0.9892.
Shokrollahi et al. [120] constructed a CM using three intelligent models,
including ANN-MLP, ANN-RBF, and LSSVM, while GAs were
employed to optimize the CM. The intelligent models were trained to
accept GOR, °API, T, and γg as input variables to provide the inputs of
the CM. The developed CM could provide reliable predictions for Pb and
Bo with the R2 values of higher than 0.97. Fig. 4.13 shows the relative
deviation of the CM predictions. Another approach to predict the satura-
tion pressure of crude oils was introduced by Ahmadi et al. [121]. They
constructed a hybrid model comprising ANN and PSO algorithms. They
considered T, reservoir fluid compositions, and C71 fraction specifications
as input parameters. The proposed hybrid approach could predict the sat-
uration pressure with the correlation coefficient of 0.9944.
In this year, Ansari and Gholami [122] and Bagheripour et al. [123]
utilized SVMs to estimate the bubble point pressure. Ansari and Gholami
[122] attempted to predict the saturation pressure as a function of T, reser-
voir fluid compositions, and C71 fraction specifications, while Rs, T, °
API, and gas SG were considered as input parameters in the study of
Bagheripour et al. [123]. Ansari and Gholami [122] investigated the capa-
bility of various optimization methods and selected the bat-inspired algo-
rithm (BA) as the best optimization method. The SVM model, coupled
with the BA optimization technique, showed an accuracy of 6.26% in
terms of AAPRE. Bagheripour et al. [123] used the RBF as the kernel
function. The developed SVM model showed a high correlation coeffi-
cient of 0.992.
In another study, Olatunji et al. [124,125] conducted two studies to
predict Pb and Bo as functions of T, GOR, °API, and γg using a hybrid
model (T2-SBLLM). They considered three and two datasets in the stud-
ies and stated that the developed models could predict the Pb and Bo with
satisfying accuracy.
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
Figure 4.13 Relative error distribution plots for committee machine predictions of (A) Pb and (B) Bo. Adapted from A. Shokrollahi, A. Tatar,
H. Safari, On accurate determination of PVT properties in crude oil systems: committee machine intelligent system modeling approach, J.
Taiwan Inst. Chem. Eng. 55 (2015) 1726.
In this year, several scholars employed intelligent models to predict
properties of hydrocarbon gases. Rabiei et al. [126] attempted to predict
the dew point pressure of gas condensates using a GONN. They
employed a dataset with 308 gas condensate samples obtained from litera-
ture and an Iranian retrograde-gas field. The proposed approach could
predict the Pd as a function of T, fluid compositions, and C71 specifica-
tions with an AAPRE of 2.46%. In another study, Esfahani et al. [127]
utilized the LSSVM framework to precisely estimate the density of natural
gases. The parameters of the LSSVM model were optimized with the aid
of the CSA algorithm. The proposed approach was able to accurately pre-
dict the density of natural gases as a function of Tpr, Ppr, and apparent
MW of gas over a broad range of gas composition, temperature, and pres-
sure with a low AAPRE of 3.47%.
The other approaches in predicting properties of gases were reported
by Shateri et al. [128] and Mohamadi-Baghmolaei et al. [129]. Both of
the studies were aimed to predict the compressibility factor of gases.
Shateri et al. [128] applied a kind of RBF network, namely, Wilcoxon-
generalized RBF network (WGRBFN), for the first time to predict the
Z-factor of natural gases using a dataset containing 978 data points. The
proposed model could predict the gas compressibility factor as a function
of Tpr and Ppr with an AAPRE of 2.35%. The competency of the pro-
posed model is shown in Fig. 4.14. In another study, various intelligent
models were considered as predictive tools for gas compressibility factor
estimation. They assessed the performance of ANN, FIS, and ANFIS
algorithms in predicting the Z-factor as a function of Ppr, Tpr, and γg. The
results indicated that ANN had the highest performance with an AAPRE
of 0.002% [129].
The prediction of the gasoil ratio was the aim of another work in
this year. Zamani et al. [130] were motivated to predict the GOR of oil
reservoirs at reservoir conditions. They considered Pb, T, γg, and °API as
input variables to the ANFIS model. The developed model showed an
accuracy of 4.631% in terms of AAPRE. In another research, Baarimah
et al. [131] aimed to predict several PVT properties, including Bo, Pb,
solution GOR, oil gravity, and gas SG using FL system and two types of
ANNs. The results showed the high performance of FL-based models.
The values of higher than 0.97 were reported as the R2 for the predictions
of FL approaches.
In 2016, several scholars attempted to utilize intelligent models to
provide accurate predictions for the saturation pressure and formation
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
volume factor of crude oils. To this end, Mahdiani and Kooti [132]
employed four genetic programing (GP) algorithms and a GONN to esti-
mate the oil formation volume factor. The developed models were com-
pared to 15 previously introduced models. Four parameters of GOR,
T, and gravity of oil and gas were introduced as input parameters. In con-
clusion, ANNs were introduced as the most precise model, while the GP
algorithm was the most flexible one. Fig. 4.15 illustrates the accuracy of
the proposed models in terms of median relative error. In the following
study, Tatar et al. [133] intended to apply a genetically optimized RBF
neural network (GA-RBF) to predict the bubble point pressure of crude
oils. The input parameters were considered as crude oil compositions, T,
and C71 specifications. The proposed approach could predict the satura-
tion pressure of crude oils with an AAPRE of 2.141%.
In another communication, Abooali and Khamehchi [134] modeled
saturation pressure and oil formation volume factor using the GP method.
Temperature and solution GOR were considered as input variables as
well as γg and °API. The values of 9.680% and 1.970% were obtained as
the AAPRE of the proposed model for Pb and Bo predictions, respec-
tively. Another approach to predict the Pb and Bo at Pb was introduced by
Figure 4.14 The comparison between the proposed WGRBFN model by Shateri et al.
and other correlations. WGRBFN, Wilcoxon-generalized radial basis function network.
Adapted from M. Shateri, et al., Application of Wilcoxon generalized radial basis function
network for prediction of natural gas compressibility factor, J. Taiwan Inst. Chem. Eng.
50 (2015) 131141.
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Application of intelligent models in reservoir and production engineering
Oloso et al. [135]. They developed two ensemble models (ensemble SVM
and ensemble regression tree) based on 895 data points. The intelligent
ensemble models were constructed to predict Pb and Bo using T, GOR,
°API, and γg as input variables. The predictions of the developed models
showed high correlation coefficients of higher than 0.98.
The application of NNs is reported in two other studies. Adeeyo [136]
and Alakbari et al. [137] were the scholars who employed ANNs to esti-
mate PVT properties; however, Alakbari et al. employed FL algorithm as
well. Adeeyo attempted to model the Bo and Pb using 2024 and 2114 data
points, respectively. Alakbari et al. utilized a dataset of 760 data points to
construct the model of bubble point pressure based on the BP network,
RBF network, and FL algorithm. Adeeyo obtained the values of 7.6169%
and 0.0011% as the AAPRE of the Pb and Bo models. Alakbari et al.
reported values higher than 0.99 as the R2 of the developed models.
Another popular research interest this year was the prediction of dew
point pressure of gas condensate reservoirs. To this end, two studies
focused on developing optimized NNs to predict this property of retro-
grade gases. In one of these studies, Najafi-Marghmaleki et al. [138]
employed GAs to optimize the RBF network. The GA-RBF could
model the Pd as a function of fluid compositions, T, and C71 specifica-
tions. The results revealed the competency of the GA-RBF model in dew
Figure 4.15 The accuracy of the models proposed by Mahdiani and Kooti in terms
of MRE. MRE, Mean relative error. Adapted from M.R. Mahdiani, G. Kooti, The most
accurate heuristic-based algorithms for estimating the oil formation volume factor,
Petroleum 2 (1) (2016) 4048.
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
point pressure estimation with an AAPRE of 7.32%. In another study,
Manshad et al. [139] utilized the PSO algorithm to improve the accuracy
of NNs. The proposed ANN-PSO could make accurate predictions for
Pd as a function of fluid compositions, T, and C71 specifications with the
AAPRE of 3.513%.
In the other investigations, GEP and multigene GP (MGGP) were
employed to model dew point pressure as a function gas composition, T,
and specifications of C71 (MW and SG). Kamari et al. [140] developed
their predictive models using the GEP approach. The authors stated that
the proposed method could outperform three popular empirical correla-
tions used in comparison with an AARD of 7.9%. Fig. 4.16 shows the
accuracy of the GEP algorithm in predicting the dew point pressure. Also,
Kaydani et al. [141] reported the application of MGGP in Pd prediction.
They utilized a dataset of 158 experimental data points. The accuracy of
the proposed approach was reported as 4.44 and 0.95 in terms of AAPRE
and R2, respectively.
In this year, several scholars aimed to predict some other properties of
crude oils and natural gases. Hemmati-Sarapardeh et al. [142] applied the
LSSVM framework optimized with the CSA algorithm to predict the
dead oil viscosity of light and intermediate crude oils. They collected
about 1500 data points from different geological locations. They devel-
oped two separate models for temperatures above and below 313.15K.
Among the two input variables (T and °API), the °API of oil was intro-
duced as the most influential factor in dead oil viscosity. The results
Figure 4.16 The AARD of the proposed model by Kamari et al. in comparison with
some other correlations. AARD, Average absolute relative deviation. Adapted from
A. Kamari, et al., Rapid method for the estimation of dew point pressures in gas
condensate reservoirs, J. Taiwan Inst. Chem. Eng. 60 (2016) 258266.
103
Application of intelligent models in reservoir and production engineering
indicated that the proposed models could predict the target parameter
with the MAPE (mean absolute percentage error) of 17.17%. In addition,
Fig. 4.17 exhibits the performance of the CSA-LSSVM model in compar-
ison with several previously published correlations in terms of MAPE. In
another study, Ghorbani et al. [143] constructed a new multihybrid model
using the GMDH along with GAs to predict the viscosity of Iranian crude
oil for a diverse range of pressure. The developed model could provide
accurate predictions for crude oil viscosity using T, P, Pb, GOR, and °
API of oil as input parameters. The comparison between the proposed
approach with seven other correlations shows the superiority of the hybrid
model with a correlation coefficient of higher than 0.998.
As another approach to predict the viscosity of crude oils, Tatar et al.
[144] utilized the GA-RBF network to estimate the viscosity of heavy oils
in the presence of kerosene. The input parameters were considered as the
mass fraction of kerosene and temperature. The developed GA-RBF
could provide estimations with an AAPRE of 2.14%.
Another application of the CSA-LSSVM algorithm was reported by
Tohidi-Hosseini et al. [145]. They employed a dataset of 1137 data points
to predict solution gasoil ratio as a function of T, Pb, γg, and °API using
the CSA-LSSVM approach. The kernel function used in this study was
Gaussian RBF. The value of 15.94% was reported as the AAPRE of the
Figure 4.17 The MAPE of the proposed model by Hemmati-Sarapardeh et al. in com-
parison with some other correlations. MAPE, Mean absolute percentage error.
Adapted from A. Hemmati-Sarapardeh, et al., A soft computing approach for the deter-
mination of crude oil viscosity: light and intermediate crude oil systems, J. Taiwan Inst.
Chem. Eng. 59 (2016) 110.
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
developed model for the entire database. Fig. 4.18 shows the performance
comparison of the proposed model with several other correlations. An
extensive application of intelligent models was reported in this year by
Kamari [146]. He made a comprehensive study in his PhD thesis in the
area of utilizing intelligent models to develop accurate predictive tools.
He investigated the application of five intelligent models, namely, decision
tree (DT), ANFIS, LSSVM, GEP, and ANNs to predict numerous prop-
erties of reservoir fluids such as Pd, z-factor, oil viscosity, Bo, and satura-
tion pressure.
In 2017, almost all studies focused on predicting the common PVT
properties (Bo and Pb) as functions of usually considered inputs: T, GOR,
γg, and °API. To this end, Heidarian et al. [147] applied GAs to develop
correlations for the saturation pressure of the Middle East crude oils using
286 data points. The proposed correlations showed the accuracy of
0.1624 in terms of mean ARE (MARE). In another attempt, Ramirez
et al. [148] utilized ANNs to predict the values of Pb and Bo. They col-
lected 504 data points from the literature to develop the models. The pre-
dictions of ANNs showed an accuracy of higher than 0.98 in terms of the
correlation coefficient. In another approach, Moussa et al. [149] intended
to predict the saturation pressure as well as gas solubility (Rs) with the aid
of a hybrid model based on ANNs. They excluded the Rs from the input
Figure 4.18 The accuracy of the CSA-LSSVM model proposed by Tohidi-Hosseini
et al. in comparison with other correlations. CSA, Coupled simulated annealing;
LSSVM, least square support vector machine. Adapted from S.-M. Tohidi-Hosseini,
et al., Toward prediction of petroleum reservoir fluids properties: a rigorous model for
estimation of solution gas-oil ratio, J. Nat. Gas Sci. Eng. 29 (2016) 506516.
105
Application of intelligent models in reservoir and production engineering
parameters and considered this variable as an output. The predictions of
the developed models showed a high correlation coefficient of 0.99.
In this year, several scholars focused on predicting the viscosity of res-
ervoir fluids. Hajirezaie et al. [150] collected 601 datasets of different geo-
graphical locations from the literature to correlate the viscosity of
undersaturated oils to reservoir pressure, Pb, and bubble point viscosity
with the aid of GP algorithm. The estimations of the developed universal
correlation showed a good match with actual data with an AAPRE of
4.47%. As a conclusion, the authors claimed that the bubble point viscos-
ity had the most influence on the oil viscosity at undersaturated condi-
tions. In another approach, it had been tried to predict the viscosity of
hydrocarbon gas/vapor by means of a hybrid GMDH-type ANN. In the
study of Dargahi-Zarandi et al. [151], 3800 data points were employed to
develop the model that could predict the viscosity as a function of four
input variables: Ppr, Tpr, MW, and density with an AAPRE of 3.45%. The
authors introduced the density of the mixture as the most influential
parameter.
Further viscosity model was developed by Adeeyo and Saaid [152] to
estimate the viscosity at Pb and viscosity of dead oils. The authors utilized
the LevenbergMarquardt (LM) optimization method to improve the
performance of ANNs. The developed models showed high efficiency in
making predictions.
In another field of study, Ahmadi and Elsharkawy [153] developed a
GEP model to predict the dew point pressure of gas condensates. They
gathered a large data bank from the literature and compared the perfor-
mance of their proposed model with some other correlations. Fig. 4.19
illustrates the performance of the GEP algorithm to estimate the dew
point pressure.
The authors of another study were inspired by the prediction of natu-
ral gas density. Saeedi Dehaghani and Badizad [154] employed ANFIS to
predict natural gas density. The AAPRE of 1.704 was obtained for the
predictions of the proposed model. Fig. 4.20 illustrates a 3D-plot of pre-
dicted normalized densities versus normalized input coordinates (P and T).
In 2018, the prediction of primary PVT properties (saturation pres-
sure and oil formation volume factor) through intelligent approaches
was the concern of several studies. In this way, Bo models were con-
structed to utilize T, GOR, γg, and °API of oil as input variables.
Mahdiani and Norouzi [155] proposed a new methodology to model Bo
based on an evolutionary optimization algorithm (Simulated Annealing).
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
They could estimate the oil formation volume factor with an R2 of
0.976. Fattah and Lashin [156] proposed another approach to provide
correlations for Bo of volatile oils. They implemented the GP algorithm
and could provide a correlation with the accuracy of 0.3252% in terms
of AAPRE. In another work, Elkatatny and Mahmoud [157] applied
ANN, ANFIS, and SVM models to predict Bo. They could develop an
empirical correlation using ANNs, which was able to determine the oil
formation volume factor with an AAE of less than 1%. The accuracy of
the developed correlation is shown in Fig. 4.21 in comparison with
some other correlations.
Figure 4.19 The AARD of the proposed model by Ahmadi and Elsharkawy in com-
parison with some other correlations. AARD, Average absolute relative deviation.
Adapted from M.A. Ahmadi, A. Elsharkawy, Robust correlation to predict dew point pres-
sure of gas condensate reservoirs, Petroleum 3 (3) (2017) 340347.
Figure 4.20 3D surface of predicted normalized density versus normalized P and T.
Adapted from A.H. Saeedi Dehaghani, M.H. Badizad, A soft computing approach for pre-
diction of P-ρ-T behavior of natural gas using adaptive neuro-fuzzy inference system,
Petroleum 3 (4) (2017) 447453.
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Application of intelligent models in reservoir and production engineering
Some other scholars tried to predict the saturation pressure of crude
oils as a function of conventional input variables: T, GOR, γg, and °API.
For this purpose, Hashemi Fath et al. [158] developed ANNs based on
760 datasets collected from various geographical zones. They reported the
value of 14.26% as the AAPRE of the model predictions. Fig. 4.22 shows
the cumulative frequency versus ARE for the developed model in com-
parison with some other models. In another investigation, Wood and
Choubineh [159] compared the performance of ANNs and transparent
Figure 4.21 The accuracy of the empirical correlation by Elkatatny and Mahmoud
with other correlations. Adapted from S. Elkatatny, M. Mahmoud, Development of new
correlations for the oil formation volume factor in oil reservoirs using artificial intelligent
white box technique, Petroleum 4 (2) (2018) 178186.
Figure 4.22 The cumulative frequency versus ARE for the model developed by
Hashemi Fath et al. and other models. ARE, Absolute relative error. Adapted from A.
Hashemi Fath, A. Pouranfard, P. Foroughizadeh, Development of an artificial neural net-
work model for prediction of bubble point pressure of crude oils, Petroleum 4 (3) (2018)
281291.
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
open-box (TOB) learning network in predicting the saturation pressure.
The authors claimed that ANN could outperform its rival with an RMSE
of 66.044 psi. Further application of intelligent models was reported by
Elkatatny and Mahmoud [160]. They applied three intelligent models to
develop correlations for Pb. The ANN model outperformed the other
models with an AAPRE of 7.5%.
In this year, several studies were conducted on the field of predicting
the viscosity of hydrocarbon fluids. One of these studies focused on pre-
dicting the viscosity of natural gases, while others attempted to estimate
the viscosity of oils by means of an intelligent approach. Rostami et al.
[161] made an effort to precisely predict natural gas viscosity as a function
of Ppr, Tpr, and MW of gas with the aid of three intelligent systems (RBF,
MLP, LSSVM) coupled with four optimization algorithms, namely, scaled
conjugate gradient (SCG), Bayesian regularization (BR), CSA, and LM.
They gathered an enormous database of 3800 data points to construct a
universal model. They succeeded in developing an accurate predictive
model (MLP-LM) that could predict the target parameter with an RMSE
of 0.001 cP using only three input parameters (with no density data).
Fig. 4.23 illustrates the performance of the proposed MLP-LM model in
comparison to other models.
Figure 4.23 Cumulative frequency versus ARE for MLP-LM model developed by
Rostami et al. and some other correlations. ARE, Absolute relative error; LM,
LevenbergMarquardt;
MLP,
multilayer
perceptron.
Adapted
from
A.
Rostami,
A. Hemmati-Sarapardeh, S. Shamshirband, Rigorous prognostication of natural gas
viscosity: smart modeling and comparative study, Fuel 222 (2018) 766778.
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Application of intelligent models in reservoir and production engineering
The GP was utilized once again, this time to predict the viscosity of
Arabian crude oils. Alqahtani et al. [162] developed two GP models to
predict saturated oil viscosity as a function of GOR and μo at Pb and
undersaturated oil viscosity as a function of P, Pb, and μo at Pb. The accu-
racy of the models yielded the values of 9.37% and 1.64% in terms of
AARE for saturated and undersaturated oil viscosity, respectively. Another
extensive approach was proposed by Oloso et al. [163] to predict the vis-
cosity of dead, saturated, and undersaturated oil using an ensemble SVM
algorithm. They investigated effect different input parameters to select the
most accurate model for each oil. For dead oil, the model that had two
inputs of T and °API showed the best performance with an AAPRE of
10.32%. For saturated oil, °API, Pb, and viscosity of dead oil were consid-
ered as the inputs of the most accurate model (AAPRE of 7.036%), and
finally, for the undersaturated oil, the most efficient model (AAPRE of
1.189%) could provide predictions using μod, Pb, P, μob, and °API as input
variables. In the study of Dabiri-Atashbeyk et al. [164], dead oil viscosity
was modeled using ANNs (MLP and RBF) optimized with GAs. They
reported the GA-MLP models as the best model with a mean absolute
error (MAE) of 0.082%.
The other applications of intelligent models were reported as follows.
Saghafi and Arabloo [165] aimed to determine the deviation factor of ret-
rograde gases below the Pd. They applied the GP approach to provide
correlations for the gas phase and two-phase Z-factor as a function of Ppr
and Tpr. The developed correlations could estimate the Z-factor with an
AAPRE of 3% and 5.1% for gas phase and two-phase fluids, respectively.
Fig. 4.24 shows the 3D-plot of the predictions of the Z-factor. In another
work, Akinsete and Omotosho [166] utilized ANNs to predict the two-
phase gas compressibility factor. They considered T, P, and γg as input
parameters, and 86 datasets were collected from a Nigerian gas field. The
developed model could estimate the Z-factor with an AAPRE of 1.343%.
Among the other applications of AI in this year, Fath et al. [167]
attempted to predict solution GOR as a function of γg, T, Pb, and °API
using MLP and RBF NNs. They gathered 710 experimental data points
from different oil fields over the world to construct a comprehensive
model. The developed RBF showed a high accuracy of 11.9% in terms of
AAPRE. Fig. 4.25 illustrates the competency of the proposed RBF net-
work in comparison with different correlations. Zhong et al. [168] intro-
duced a mixed kernel functionbased SVM (MKF-SVM) to predict the
dew point pressure of gas condensates using 564 data points. They used
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
Figure 4.24 3D surface of (A) gas phase and (B) two-phase Z-factor as a function of Tpr and Ppr. Adapted from H. Saghafi, M. Arabloo,
Development of genetic programming (GP) models for gas condensate compressibility factor determination below dew point pressure, J. Pet.
Sci. Eng. 171 (2018) 890904.
T, fluid compositions, and specifications of C71 fraction as input para-
meters. The value of 7.01% was obtained as the AAPRE of the predic-
tions made by the MKF-LSSVM model.
In 2019, three studies focused on predicting the GOR of hydrocarbon
fluids. To this end, Khamis and Fattah [169] applied three intelligent
models, namely, ANNs, SVMs, and FNs, to forecast the GOR of volatile
oils and gas condensates. The models were developed using P, T, Pb, con-
densate yield, and oil/gas density at stock tank conditions as input para-
meters. The investigators gathered 1180 and 1850 data points from eight
gas condensate reservoirs and five volatile oil reservoirs, respectively. The
results showed the outperformance of SVMs with an AAPRE of 0.12%.
In another investigation, Cheshmeh Sefidi and Ajorkaran [170] utilized
the MLP to predict the GOR as a function of γg, T, Pb, and °API. The
MLP-ANN was developed and tested using 1137 experimental data
points collected from the literature. The proposed model showed an accu-
racy of 17.93% in terms of AAPRE. It had been tried to predict the
GOR as a function of similar variables (γg, T, Pb, and °API) using ANFIS
in the study of Baghban and Nabipour [171]. The developed ANFIS
model could predict the value of GOR with an AAPRE of 7.25%.
In this year, Saghafi et al. [172] and Wood and Choubineh [173]
attempted to predict the oil formation volume factor as a function of γg, T,
GOR, and °API through various intelligent models. Saghafi et al. [172]
Figure 4.25 The comparison between the networks proposed by Hashemi Fath and
other
correlations.
Adapted
from
A.
Hashemi
Fath,
F.
Madanifar,
M.
Abbasi,
Implementation of multilayer perceptron (MLP) and radial basis function (RBF) neural
networks to predict solution gas-oil ratio of crude oil systems, Petroleum 6 (2018).
112
Applications of Artificial Intelligence Techniques in the Petroleum Industry
applied ANFIS and GP algorithms, while Wood and Choubineh [173]
utilized the TOB learning network. Saghafi et al. [172] gathered about
1200 data points to develop their models. They reported the values of 1.8%
and 2.1% as the AARD for the predictions of the ANFIS model and GP
correlation. The authors also concluded that the °API and GOR had the
most impact on the Bo. Fig. 4.26 shows the cumulative frequency as a func-
tion of AARD for the proposed models and other correlations. Wood and
Choubineh [173] attained three published datasets and could establish a pre-
dictive model with a correlation coefficient of 0.9599.
Yang et al. [174] were the scholars who aimed to estimate the satura-
tion pressure through intelligent models in this year. They utilized three
tree-based algorithms, namely, random forests, LightGBM, and XGBoost,
as well as an MLP. The authors also employed the stacking ensemble as a
super-learner algorithm. They assessed the performance of the intelligent
model in three situations of input variables. Once, for the first time, only
T and fluid compositions were introduced to the models as the inputs, the
Figure 4.26 Cumulative frequency versus AARD for the models developed by
Saghafi et al. and other correlations. AARD, Average absolute relative deviation.
Adapted from H.R. Saghafi, A. Rostami, M. Arabloo, Evolving new strategies to estimate
reservoir oil formation volume factor: smart modeling and correlation development,
J. Pet. Sci. Eng. 181 (2019).
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Application of intelligent models in reservoir and production engineering
super-learner model could predict the Pb with an MARE of 7.162%. In
addition, the super-learner showed an accuracy of 5.146% in terms of
MARE, when GOR, °API, and γg are included in the input parameters
as well as T and fluid compositions.
A comparative study was carried out this year by Razghandi et al.
[175]. They investigated the performance of RBF and MLP networks in
forecasting the viscosity of undersaturated crude oils. The authors utilized
600 data points to develop ANNs. The scholars considered the undersatu-
rated oil viscosity as a function of P, Pb, and viscosity at Pb. In their study,
data points were categorized into four subsets based on μob as the most
influential parameter on viscosity. For each subset, two networks (RBF
and MLP) were developed and tested. The MLP was selected as the more
powerful tool to predict μob compared to the RBF network with an
AAPRE of 3.09%.
Kamari et al. [176] introduced a novel field of application of intelligent
models in this year. They employed four AI techniques, namely, LSSVM,
GEP, ANN (MLP), and DT, to predict the specifications of C71 fraction
(MW, SG, and normal boiling point) for crude oil and gas condensate
fluids. The authors considered bulk MW, bulk SG, and cumulative
weight fractions as three inputs of the developed models. The obtained
results indicated that the DT-based model could outperform the other
models and could provide predictions for normal boiling point, SG, and
molecular weight with an AAPRE of 1.64%, 0.68%, and 5.10%,
respectively.
Three papers focused on forecasting the properties of gas reservoirs this
year. Hemmati-Sarapardeh et al. [177] attempted to predict the Z-factor
of natural gases through a hybrid GMDH. The developed model could
provide predictions for the compressibility factor as a function of Tpr, Ppr,
and gas composition with an AAPRE of 2.88% over a broad range of
input variables. In another study, Zare et al. [178] applied a FIS to predict
the gas density as a function of P, T, and MW of gas. The predictions
showed a correlation coefficient of 0.9980. Haji-Savameri et al. [179]
made an effort to predict the dew point pressure of gas condensates. They
compared the efficiency of various approaches in Pd prediction. First, an
MLP and an RBF network were developed. The MLP network was opti-
mized using LM, BR, and SCG, while GA, BA, and salp swarm algorithm
(SSA) were considered as optimization algorithms for the RBF network.
Four highest performance intelligent models were MLP-LM, MLP-BR,
RBF-SSA, RBF-GA. These models were employed to construct a CM
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
intelligent system (CMIS) accordingly. The authors also employed the
generalized reduced gradient (GRG) method to develop correlations for
Pd as a function of T, fluid compositions, and specifications of C71.
Finally, the performance of the constructed CMIS and GRG-based corre-
lation was compared with some other correlations. Fig. 4.27 illustrates the
outperformance of the CMIS and the developed correlation over the
other models. Table 4.1 summarizes the applications of AI models in pre-
dicting one-phase fluid properties. Some studied, which were not
reported in the main text, are also reported in the table [180198]. Also,
in most of the cases, the ANN in the tables and text means an MLP
ANN model.
4.1.2 Two-phase properties
Interfacial tension (IFT) and minimum miscibility pressure (MMP) are
among the most important two-phase properties of reservoir fluids.
However, IFT is discussed in this part, while MMP is considered as part
of the EOR (enhanced oil recovery) section due to its high importance in
gas injection processes. The significance of IFT is well understood since it
has an important role in many industrial and engineering processes [199].
The application of AI in predicting the IFT value of binary mixtures dates
back to 2009 when Kumar [200] developed a predictive model to esti-
mate the IFT at crystal/solution interface with the aid of ANNs. Since
then, intelligent models have been employed by many researchers in
this field of study. Herein, several approaches to predict the IFT are
mentioned.
In this way, Meybodi et al. [199] employed LSSVMs to predict the
IFT value of pure hydrocarbon and water systems over the temperature
range of 454.4890 °R and pressure range of 0.1300 MPa using 1213
data points. They utilized the CSA algorithm to optimize the model and
applied the RBF kernel function. The proposed approach could predict
the IFT as a function of T/critical temperature of hydrocarbon (Tc) and
ρw 2 ρhc with an AAPRE of 1.45%. Fig. 4.28 shows the graphical com-
parison between the developed CSA-LSSVM model and three previously
published models in terms of R2, ARE, AARE (average ARE), and
RMSE.
In 2016, Najafi-Marghmaleki et al. [201] made an effort to predict the
IFT between hydrocarbon gas and water. They construct three intelligent
models based on 1105 experimental IFT data gathered from the literature.
115
Application of intelligent models in reservoir and production engineering
Figure 4.27 Cumulative frequency versus ARE for the CMIS and the correlation developed by Haji-Savameri et al. in comparison with
other models. ARE, Absolute relative error; CMIS, committee machine intelligent system. Adapted from M. Haji-Savameri, et al., Modeling
dew point pressure of gas condensate reservoirs: comparison of hybrid soft computing approaches, correlations, and thermodynamic models,
J. Pet. Sci. Eng. 184 (2019) 106558.
Table 4.1 Summary of applications of artificial intelligence models in the area of one-phase reservoir fluid properties.
Author(s)
Intelligent
model(s)
Type of study conducted
Input parameters
Error (AARE %)
Gharbi and
Elsharkawy [1]
ANN
Predicting the bubble
point pressure (Pb) and
oil formation volume
factor (Bo)
Temperature, gas relative density, oil SG,
solution gasoil ratio
6.89 (Pb)
2.79 (Bo)
Gharbi[51]
ANN
Prediction of
compressibility factor
(Co)
Temperature, pressure, gas relative density,
oil SG, and solution gasoil ratio
2.147
Elsharkawy [52]
ANN-RBF
Estimation of Bo, Rs, Co,
ρo, μo, and the gravity of
evolved gas (γgr)
T, P, gravity of separator gas, and gravity
of stock tank oil (°API)
0.53 (Bo)
4.53 (Rs)
5.98 (Co)
0.40 (ρo)
8.72 (μo)
3.03 (γgr)
Al-Shammasi
[53]
ANN
Predicting the Bo and Pb
Temperature, gas relative density, oil SG,
solution gasoil ratio

Gharbi et al. [54]
ANN
Predicting the bubble
point pressure and oil
formation volume factor
Temperature, solution gasoil ratio, the
gas relative density, oil SG
6.48 (Pb)
1.97 (Bo)
Varotsis et al.
[55]
ANN
Forecasting the complete
PVT behavior of oils
and gas condensates
Inputs for oil models: fluid composition,
Pb, T, ρo at Pb, μod, flash molar ratio,
and flash liquid and gas densities
Inputs for gas condensate models: fluid
composition, Pd, T, Z-factor at Pd, field
GOR, and separator and tank liquid
densities
, 6.83 (oil
properties)
, 2.2 (gas
condensate
properties)
(Continued)
Table 4.1 (Continued)
Author(s)
Intelligent
model(s)
Type of study conducted
Input parameters
Error (AARE %)
Elsharkwy and
Gharbi [56]
ANN-BP
ANN-ward
ANN-LM
ANN-GRNN
Predicting the viscosity of
crude oil
Produced gas gravity, pressure,
temperature, and
stock tank oil °API gravity
18.67
23.81
23.87
18.45
Osman et al. [57]
ANN
Prediction of Bo at Pb
T, solution GOR, γg, and °API
1.79
Abdel-Aal [58]
ANN
Estimating the Pb and Bo
Inputs for Bo model: Rs and T
Inputs for Pb model: T, Rs, °API, and γg
5.62 (Pb)
0.86 (Bo)
Al-Marhoun and
Osman [59]
ANN-BP
Prediction of Pb and Bo at
Pb
T, Rs, °API, and γg
5.89 (Pb)
0.51 (Bo)
Gonzalez et al.
[60]
ANN
Predicting the dew point
pressure (Pd)
Fluid compositions, T, SG of C71, and
molecular weight (MW) of C71
8.74
Goda et al. [61]
ANN
Prediction of Pb and Bo at
Pb
Pb model: T, GOR, °API, and γg
Bo model: Pb, T, GOR, °API, and γg
3.07 (Pb)
0.72 (Bo)
Gharbi and
Elsharkawy
[62]
ANN
Prediction of Pb and Bo at
Pb
T, Rs, °API, and γg
6.48 (Pb)
1.97 (Bo)
Osman and Al-
Marhoun [63]
ANN-RBF
ANN-BP
Prediction of formation
water properties
RBF model: P, T, and salinity
Viscosity (BP) model: T and salinity
, 1.064 (Bw,
Cw, ρw)
1.908 (μw)
Hajizadeh [64]
GA
Prediction of oil viscosity
P, T, GOR, and oil density
0.99742 (R2)
El-Sebakhy et al.
[65]
SVM-Gaussian
RBF
ANN-BP
Prediction of Pb and Bo
GOR, T, °API, and γg
, 1.37 (Bo)
, 15.11 (Pb)
, 1.789 (Bo)
, 21.02 (Pb)
Shokir [180]
GP-OLS
algorithm
Gas condensate Pd
prediction
Composition of gas condensate, T, and
MW of C71
4.2
El-Sebakhy [67]
ANFIS
ANN-BP
Estimation of Bo and Pb
GOR, T, °API, and γg
, 14.22 (Pb)
, 1.32 (Bo)
, 21.02 (Pb)
, 1.789 (Bo)
El-Sebakhy [66]
SVM
ANN-BP
Estimation of Bo and Pb
GOR, T, °API, and γg
, 15.12 (Pb)
, 1.37 (Bo)
, 1.789 (Bo)
, 21.02 (Pb)
Nowroozi et al.
[68]
ANFIS
Prediction of gas
condensate Pd
Composition of gas condensate, T, and
MW of C71
3.9
Omole et al. [69]
ANN-BP
Prediction of saturated oil
viscosity
GOR, P, T, °API, and γg
6.78
Dutta and Gupta
[70]
SVM-RBF
Predicting the PVT
properties of crude oil
Pb model: T, Rs, γg, γo
GOR model: T, Pb, γg, γo
Saturated Bo model: T, Rs, γg, γo
Undesaturated Bo model: T, P, Pb, γg, γo,
Bo, Rs
Saturated oil viscosity: Pb, μod, γg, γo
Undersaturated oil viscosity: P, Pb, μob
2.447
6.715
0.625
0.063
11.633
3.752
(Continued)
Table 4.1 (Continued)
Author(s)
Intelligent
model(s)
Type of study conducted
Input parameters
Error (AARE %)
Nagi et al. [71]
SVM-RBF
ANN
Predicting the Bo and Pb
GOR, T, °API, and γg
, 1.37 (Bo)
, 15.12 (Pb)
, 1.789 (Bo)
, 21.02 (Pb)
Abass [72]
ANN
Prediction of cricondenbar,
cricondentherm, and
critical points of natural
gases
Gas compositions and C71 specifications
.4
AlQuraishi [181]
GP
Prediction of Pb
Fluid composition, C71 specifications, and
temperature
5.813
Shokir and
Dmour [182]
GP
Prediction of pure and
hydrocarbon gas mixture
viscosity
MW of gas, ρg, Tpr, and Ppr
5.6
Dutta and Gupta
[74]
ANN
Predicting the PVT
properties of crude oil
Pb model: T, Rs, γg, γo
GOR model: T, Pb, γg, γo
Saturated Bo model: T, Rs, γg, γo
Undesaturated Bo model: T, P, Pb, γg, γo,
Bo, Rs
Saturated oil viscosity: Pb, μod, γg, γo
Undersaturated oil viscosity: P, Pb, μob
7.668
8.817
1.779
0.151
10.886
2.988
Khoukhi and
Alboukhitan
[75]
GANFIS
Prediction of Bo and Pb
GOR, T, °API, and γg
10.470 (Pb)
0.8821 (Bo)
Khoukhi and
Alboukhitan
[76]
GANFIS
Prediction of Bo and Pb
GOR, T, °API, and γg

Olatunji et al.
[77]
Type-2 fuzzy
logic
ANN
Prediction of Bo and Pb
GOR, T, °API, and γg
, 1.493 (Bo)
, 20.65 (Pb)
, 1.4592 (Bo)
, 22.68 (Pb)
Olatunji et al.
[78]
Type-2 fuzzy
logic
ANN
Prediction of Bo and Pb
GOR, T, °API, and γg
, 1.493 (Bo)
, 20.65 (Pb)
, 1.4592 (Bo)
, 22.68 (Pb)
Alimadadi et al.
[80]
Committee
machine
(two MLP)
Predicting the ρo and Bo
°API, T, P, Pb, solution GOR, and oil
composition
1.743 (Bo)
0.479 (ρo)
Asadisaghandi
and
Tahmasebi
[81]
ANN
Prediction of Pb and Bo
T, °API, Rs, and γg
2.0309 (Pb)
0.5783 (Bo)
Al-Dhamen and
Al-Marhoun
[82]
ANN
Prediction of gas
condensate Pd
T, γg, GOR, and condensate SG
6.5
Khoukhi et al.
[83]
SVM
Functional
networks
ANN
Estimation of viscosity and
GOR curve
Pb, °API, γg, T, and Bo
9.0757 (GOR)
8.5969 (viscosity)
10.2012 (GOR)
8.551437 (viscosity)
12.701 (GOR)
10.24569 (viscosity)
(Continued)
Table 4.1 (Continued)
Author(s)
Intelligent
model(s)
Type of study conducted
Input parameters
Error (AARE %)
Torabi et al.
[183]
ANN-LM
Predicting the oil viscosity
μod model: T and °APIμob model: μod, Pb,
and GORμo model: P, μod, and μob
3.9
7.6
6.2
Khoukhi [84]
GONN
GANFIS
Prediction of Pb and Bo
Rs, °API, T, and γg
0.10015 (Bo)
0.1013 (Bo)
Zendehboudi
et al. [85]
PSO-ANN
BP-ANN
Prediction of CGR
T, Pd, and molecular weight (MW) of the
mixture
6.9114 (MAAE)
10.6875 (MAAE)
Asoodeh and
Bagheripour
[86]
PLCIS (NN,
ANFIS, and
FL)
Prediction of Pb
T, °API, and Rs/ γg

Ikiensikimama
and Azubuike
[87]
ANN
Prediction of Bo
T, Rs, °API, and γg
1.1904
Naseri et al. [88]
ANN
Dead oil viscosity
estimation
°API and T
16.75
Makinde et al.
[89]
ANN-BP
Undersaturated oil viscosity
prediction
T, P, Pb, and μo at Pb
0.01998
Selamat et al.
[90]
T2-SBLLM
Prediction of Pb and Bo
T, Rs, °API, and γg
, 1.9866 (Bo)
, 3.6273 (Pb)
Selamat et al.
[91]
T2-SBLLM
Prediction of Pb and Bo
T, Rs, °API, and γg
, 1.493 (Bo)
, 20.65 (Pb)
Shokir et al.
[186]
GP
Z-Factor prediction
Ppr, Tpr, gas composition, and SG of C71
0.58
Alarfaj et al.
[184]
ANN-MLP
ANN-GRNN
ANN-RBF
DT
SVM
GEP
ANFIS
Prediction of gas
condensate Pd
Gas composition, T, specifications of C71
fraction

Fattah [185]
GP
Predicting the GOR
P, T, Pb, ρo, condensate yield, and gas
density at standard conditions
,7.73
Rafiee-
Taghanaki
et al. [92]
CSA-LSSVM
Prediction of Pb and Bo
GOR, T, °API, and γg
5.06 (Pb)
1.45 (Bo)
Azubuike and
Ikiensikimama
[93]
ANN
Prediction of Bo
GOR, T, °API, and γg
0.9691
Farasat et al. [94]
SVM
Saturation pressure
estimation
T, fluid compositions, and specifications of
C71
4.7
Arabloo et al.
[95]
CSA-LSSVM
Prediction of gas
condensate Pd
T, fluid compositions, and specifications of
C71
6.30
Fayazi et al. [96]
CSA-LSSVM
Gas viscosity prediction
P, T, MW of C71, and gas composition
0.26
Kamari et al. [97]
CSA-LSSVM
Z-Factor prediction
Ppr and Tpr
2.54
Chamkalani
et al. [98]
CSA-LSSVM
Z-Factor prediction
Ppr and Tpr
0.28
Numbere et al.
[99]
ANN
Saturation pressure
estimation
γo, γg, °API, T, and GOR
17.176
(Continued)
Table 4.1 (Continued)
Author(s)
Intelligent
model(s)
Type of study conducted
Input parameters
Error (AARE %)
Moghadasi et al.
[100]
ANN
Predicting the saturation
pressure
°API, GOR, γg, and T
5.88
Kazemi et al.
[101]
ANN
Estimation of saturation
pressure
Fluid composition, T, C71 specifications
2.88
Chamkalani
et al.[187]
PSO-ANN
GA-ANN
ANN
Gas deviation factor
forecasting
Ppr and Tpr
0.999 (R2)
0.997 (R2)
0.992 (R2)
Lashkenari et al.
[188]
ANN
Oil viscosity estimation
T, P, Pb, GOR, and °API
19.3
Hemmati-
Sarapardeh
et al. [102]
LSSVM
Oil viscosity prediction
μod model: T and °API
μob model: μod and P
μo model: P, Pb, and μob
21.2
13.48
1.4
Hemmati-
Sarapardeh
et al. [103]
CSA-LSSVM
Saturated oil viscosity
prediction
°API, P, Pb, μod, and μob
3.66
Yousefi et al.
[104]
CSA-LSSVM
Prediction of viscosity of
hydrocarbon gases
MW, ρg, pseudo-reduced temperature
(Tpr), and pseudo-reduced pressure (Ppr)
1.56
Fayazi et al.
[105]
CSA-LSSVM
Z-Factor prediction
gas compositions, P, T, and MW of C71
0.19
Ghiasi et al.
[106]
CSA-LSSVM
Z-Factor prediction
T, gas compositions, and C71
specifications
0.726 (one-phase
Z-factor)
1.302 (two-phase
Z-factor)
Ahmadi and
Ebadi [107]
LSSVM
Dew point pressure
estimation
Gas compositions, T, MW, and SG of
C71
5.02
Rostami-
Hosseinkhani
et al. [108]
GA-RBF
Dew point pressure
estimation
Gas compositions, T, MW, and SG of
C71
3.660
Majidi et al.
[109]
ANN
Dew point pressure
estimation
Gas compositions, T, MW, and SG of
C71
6.8043
Arabloo and
Rafiee-
Taghanaki
[110]
SVM
Prediction of CVD
behavior of retrograde
gases
T, P, hydrocarbon and nonhydrocarbon
compositions, SG and MW of C71
15.9
Talebi et al.
[111]
ANN-RBF
ANN-MLP
Predicting the Bo and Pb
T, °API, GOR, and γg
15.53
16.94
Al-Marhoun
et al. [112]
ANN
Pb prediction
T, °API, GOR, and γg
5.22
Shojaei et al.
[113]
ANFIS
Predicting the Pb
T, °API, GOR, and γg
12.18
Gholami et al.
[114]
Committee
machine
Predicting the Pb
T, fluid compositions, MW and SG of
C71
7.632
Ganji-Azad et al.
[115]
ANFIS
Predicting the Pb and Bo
GOR, °API, T, and γg
1.6 (Bo)
12.2 (Pb)
Ahmadi et al.
[116]
GEP
Prediction of Pb
T, SG and MW of C71 fraction, and oil
composition
4.178
Afshar et al.
[117]
AGNFIS
GONN
Prediction of Pb
GOR, °API, T, and γg
0.9903 (R2)
0.9945 (R2)
(Continued)
Table 4.1 (Continued)
Author(s)
Intelligent
model(s)
Type of study conducted
Input parameters
Error (AARE %)
Eslamnezhad
et al. [118]
GA-ANN
(MLP)
Prediction of Bo
Co, ρ, GOR, Rs, μ, T, P, °API, SG of gas,
and SG of oil
1.23
Abooali and
Khamehchi
[189]
GP
Prediction of gas dynamic
viscosity
Tpr, Ppr, apparent MW, and ρg
2.55
Ghorbani et al.
[193]
Hybrid
GMDH-
ANN
Oil viscosity prediction
T, P, Rs, and °API
. 0.96 (R2)
Karimnezhad
et al.[194]
GA
Prediction of Bo
Rs, T, γg, and γo
0.00290 (MSE)
Ahmadi et al.
[190]
LSSVM
Prediction of CGR
T, Pd, and MW of gas
39.5
Fattah [192]
GP
Rs prediction
P, T, °API, SG of gas, and SG of oil
10.54
Al-Wahaibi et al.
[191]
ANN
Developing oil viscosity
correlations
T, Pb, Rsb, SG, and °API
,2.9
Gholami et al.
[119]
GA-ANN
Predicting the saturation
pressure
T, the composition of crude oils, and
specifications of C71 fraction
0.9892 (R2)
Shokrollahi et al.
[120]
Committee
machine
Prediction of Bo and Pb
GOR, °API, T, and γg
1.461 (Bo)
9.67 (Pb)
Ahmadi et al.
[121]
PSO-ANN
Prediction of Pb
T, reservoir fluid compositions, and C71
fraction specifications
 2.5
Ansari and
Gholami [122]
SVM-BA
Pb prediction
T, reservoir fluid compositions, and C71
fraction specifications
6.26
Bagheripour
et al. [123]
SVM
Pb prediction
Rs, T, °API, and gas SG
0.992 (R2)
Olatunji et al.
[124]
T2-SBLLM
Prediction of Pb and Bo
T, GOR, °API, and γg
, 1.013 (Bo)
, 17.65 (Pb)
Olatunji et al.
[125]
T2-SBLLM
Prediction of Pb and Bo
T, GOR, °API, and γg
, 0.51 (Bo)
, 3.63 (Pb)
Rabiei et al.
[126]
GA-ANN
Prediction of Pd
T, gas compositions, and C71 fraction
specifications
2.46
Esfahani et al.
[127]
CSA-LSSVM
Predicting the density of
natural gases
Tpr, Ppr, and apparent MW of gas
3.47
Shateri et al.
[128]
WGRBFN
Z-Factor prediction
Tpr and Ppr
2.35
Mohamadi-
Baghmolaei
et al.[129]
ANN
FIS
ANFIS
Z-Factor prediction
Ppr, Tpr, and γg
0.002
0.719
0.420
Zamani et al.
[130]
ANFIS
Estimation of GOR
Pb, T, γg, and °API
4.631
Hajirezaie et al.
[196]
GEP
Predicting the gas/vapor
viscosity of hydrocarbon
fluids
Tpr, Ppr, gas MW, and ρg
4.9
Baniasadi et al.
[195]
GEP
Prediction of GOR
Pb, γg, and °API
2.29
(Continued)
Table 4.1 (Continued)
Author(s)
Intelligent
model(s)
Type of study conducted
Input parameters
Error (AARE %)
Tatar et al. [198]
ANN-MLP
GA-RBF
Prediction of brine density
P, T, and NaCl concentration
0.002
0.0005
Kaydani et al.
[197]
MGGP
Z-Factor prediction
M-Factor, Tpr, and Ppr
1.592
Mahdiani and
Kooti [132]
GP
GA-ANN
Bo prediction
GOR, T, and gravity of oil and gas

Tatar et al. [133]
GA-RBF
Prediction of Pb
Oil compositions, T, and C71
specifications
2.141
Abooali and
Khamehchi
[134]
GP
Predicting the Bo and Pb
T, GOR, °API, and γg
9.680 (Pb)
1.970 (Bo)
Oloso et al.
[135]
Ensemble
SVM
Ensemble RT
SVM
RT
Predicting the Pb and Bo
T, GOR, °API, and γg
12.437
13.126
12.512
15.039
Adeeyo [136]
ANN-BP
Predicting the Pb and Bo
T, Rs, γo, and γg
7.6169 (Pb)
0.0011 (Bo)
Alakbari et al.
[137]
ANN-BP
ANN-RBF
FL
Predicting the Pb
T, Rs, °API, and γg
8.88
1.97
1.89
Najafi-
Marghmaleki
et al. [138]
GA-RBF
Prediction of dew point
pressure
Fluid compositions, T, and C71
specifications
7.32
Manshad et al.
[139]
ANN-PSO
Prediction of dew point
pressure
Fluid compositions, T, and C71
specifications
3.513
Kamari et al.
[140]
GEP
Prediction of dew point
pressure
Gas composition, T, and specifications of
C71
7.9
Kaydani et al.
[141]
MGGP
Prediction of dew point
pressure
Gas composition, T, and specifications of
C71
4.44
Hemmati-
Sarapardeh
et al. [142]
CSA-LSSVM
Predicting the dead oil
viscosity
T and °API
17.17 (MAPE)
Ghorbani et al.
[143]
GMDH-GA
Predicting the oil viscosity
T, P, Pb, GOR, and °API
,3.77 (MAE)
Tatar et al. [144]
GA-RBF
Predicting the oil viscosity
Mass fraction of kerosene and temperature
2.14128
Tohidi-Hosseini
et al. [145]
CSA-LSSVM
Prediction of solution
GOR
T, Pb, γg, and °API
15.94
Heidarian et al.
[147]
GA
Saturation pressure
estimation
T, GOR, γg, and °API
0.1624 (MARE)
Ramirez et al.
[148]
ANN
Predicting the Pb and Bo
T, GOR, γg, and °API
0.98 (R2)
Moussa et al.
[149]
Hybrid ANN
Prediction of Pb and Rs
T, γg, and °API
0.99 (R2)
Hajirezaie et al.
[150]
MGGP
Predicting the oil viscosity
Reservoir pressure, Pb, and bubble point
viscosity
4.47
Dargahi-Zarandi
et al. [151]
Hybrid ANN
Predicting the gas viscosity
Ppr, Tpr, MW, and density
3.45
(Continued)
Table 4.1 (Continued)
Author(s)
Intelligent
model(s)
Type of study conducted
Input parameters
Error (AARE %)
Adeeyo and
Saaid [152]
ANN-LM
Oil viscosity prediction
T, Pb, ρg, and ρo
,12.6
Ahmadi and
Elsharkawy
[153]
GEP
Dew point pressure
estimation
Gas composition, T, and specifications of
C71
8.1003
Saeedi
Dehaghani
and Badizad
[154]
ANFIS
Prediction of natural gas
density
P and T
1.704
Mahdiani and
Norouzi [155]
SA
Prediction of Bo
T, GOR, γg, and °API
0.976 (R2)
Fattah and
Lashin [156]
GP
Prediction of Bo
T, GOR, γg, and °API
0.3252
Elkatatny and
Mahmoud
[157]
ANN
ANFIS
SVM
Prediction of Bo
T, GOR, γg, and °API
0.99
Hashemi Fath
et al. [158]
ANN
Saturation pressure
prediction
T, GOR, γg, and °API
14.26
Wood and
Choubineh
[159]
ANN
TOB
Saturation pressure
prediction
T, GOR, γg, and °API
66.044 psi (RMSE)
Elkatatny and
Mahmoud
[160]
ANN
ANFIS
SVM
Saturation pressure
prediction
T, GOR, γg, and °API
7.5
11.5
14.9
Rostami et al.
[161]
MLP-LM
MLP-BR
MLP-SCG
RBF
LSSVM
Prediction natural gas
viscosity
Ppr, Tpr, and MW of gas
1.48
1.79
6.99
7.21
6.52
Alqahtani et al.
[162]
GP
Predicting the oil viscosity
P, GOR, Pb, and μo at Pb
9.37 (saturated oil)
1.64
(undersaturated
oil)
Oloso et al.
[163]
Ensemble
SVM
Predicting the oil viscosity
T, Pb, μod, P, μob, and °API
10.32 (dead oil)
7.036 (saturated oil)
1.189
(undersaturated
oil)
Dabiri-
Atashbeyk
et al. [164]
GA-MLP
GA-RBF
Predicting the oil viscosity

0.082 (MAE)
Saghafi and
Arabloo [165]
GP
Z-Factor prediction
Ppr and Tpr
3 (gas phase)
5.1 (two-phase)
Akinsete and
Omotosho
[166]
ANN
Z-Factor estimation
T, P, and γg
1.343
(Continued)
Table 4.1 (Continued)
Author(s)
Intelligent
model(s)
Type of study conducted
Input parameters
Error (AARE %)
Hashemi Fath
et al. [167]
MLP
RBF
GOR prediction
γg, T, Pb, and °API
14.8979
11.9538
Zhong et al.
[168]
MKF-SVM
Pd prediction
T, fluid compositions, and specifications of
C71
7.01
Khamis and
Fattah [169]
ANN
SVM
Functional
network
GOR prediction
P, T, Pb, condensate yield, and oil/gas
density at stock tank conditions
0.12
Cheshmeh Sefidi
and Ajorkaran
[170]
ANN-MLP
GOR prediction
γg, T, Pb, and °API
17.93
Baghban and
Nabipour
[171]
ANFIS
GOR prediction
γg, T, Pb, and °API
7.25
Saghafi et al.
[172]
ANFIS
GP
Bo prediction
γg, T, GOR, and °API
1.8
2.1
Wood and
Choubineh
[173]
TOB
Bo prediction
γg, T, GOR, and °API
0.9599 (R2)
Yang et al. [174]
XGBoost
LightGBM
RF
MLP
Super learner
Estimation the saturation
pressure
T, fluid compositions, GOR, °API, and γg
7.884
8.324
9.661
8.546
7.162
(MARE)
Razghandi et al.
[175]
ANN-MLP
ANN-RBF
Oil viscosity prediction
P, Pb, and viscosity at Pb
3.09
Kamari et al.
[176]
LSSVM
GEP
ANN-MLP
DT
Predicting the C71 MW,
SG, and NBP
Bulk MW, bulk SG, and cumulative
weight fractions
3.20 (NBP)
1.6 (SG)
10.39 (MW)
4.46 (NBP)
2.40 (SG)
12.70 (MW)
3.81 (NBP)
11.43 (SG)
11.85 (MW)
1.64 (NBP)
0.68 (SG)
5.10 (MW)
Hemmati-
Sarapardeh
et al. [177]
Hybrid
GMDH
Z-Factor prediction
Tpr, Ppr, and gas composition
2.88
Zare et al. [178]
FIS
Gas density estimation
P, T, and MW of gas
0.9980 (R2)
(Continued)
Table 4.1 (Continued)
Author(s)
Intelligent
model(s)
Type of study conducted
Input parameters
Error (AARE %)
Haji-Savameri
et al. [179]
MLP-BR
MLP-LM
MLP-SCG
RBF-SSA
RBF-GA
RBF-BA
Committee
machine
Developed
correlation
Predicting the Pd
T, fluid compositions, and specifications of
C71
5.701
5.888
8.139
6.576
6.778
6.643
5.285
7.624
AARE, Average absolute relative error; AGNFIS, adaptive genetic-fuzzy inference system; ANFIS, adaptive neuro-fuzzy inference system; ANN, artificial neural network; BA,
bat-inspired algorithm; BP, backpropagation; BR, Bayesian regularization; CGR, condensate-to-gas ratio; CSA, coupled simulated annealing; CVD, constant volume depletion;
DT, decision tree; FIS, fuzzy inference system; FL, fuzzy logic; GA, genetic algorithms; GANFIS, genetic-neuro-fuzzy inference system; GA-RBF, genetically optimized radial
basis function neural network; GEP, genetic expression programing; GMDH, group method of data handling; GONN, genetically optimized neural network; GP, genetic
programing; GRNN, generalized regression neural network; LM, LevenbergMarquardt; LSSVM, least square support vector machine; MAE, mean absolute error; MAPE,
mean absolute percentage error; MARE, mean absolute relative error; MGGP, multigene genetic programing; MKF, mixed kernel function; MLP, multilayer perceptron;
MSE, mean square error; NBP, normal boiling point; NN, neural network; OLS, orthogonal least squares; PLCIS, power-law committee intelligent system; PSO, particle
swarm optimization; RBF, radial basis function; RF, recovery factor; RMSE, root mean square error; RT, regression tree; SA, simulated annealing; SBLLM, sensitivity-based
linear learning method; SCG, scaled conjugate gradient; SG, specific gravity; SSA, salp swarm algorithm; SVM, support vector machine; TOB, transparent open-box;
WGRBFN, Wilcoxon-generalized radial basis function network.
Figure 4.28 The accuracy of the model developed by Meybodi et al. in comparison with other correlations in terms of (A) R2, (B)
Average relative error, (C) Average absolute relative error, and (D) RMSE. Adapted from M.K. Meybodi, et al., A computational intelligence
scheme for prediction of interfacial tension between pure hydrocarbons and water, Chem. Eng. Res. Des. 95 (2015) 7992.
The intelligent models were CSA-LSSVM, GA-RBF, and conjugate
hybrid-PSO ANFIS (CHPSO-ANFIS) methods. The developed GA-
RBF outperformed the other developed models as well as the several lit-
erature correlations and could predict the IFT with an AARE of 1.26%.
Fig. 4.29 demonstrates the cumulative frequency versus ARE for the con-
structed models and previously published correlations. Ahmadi and
Mahmoudi [202] applied LSSVM algorithm, coupled with GAs, to pre-
dict the IFT between gas and oil. They considered pressure, temperature,
oil drop density and gas density as input parameters. The constructed
model could predict the target value with an AAPRE of 1.6028%.
In another research, Ayatollahi et al. [203] developed a CSA-LSSVM
model to estimate the IFT between the paraffin and CO2. Temperature
and pressure were considered as input parameters as well as MW of paraf-
fin. The proposed model could provide predictions for the IFT with an
AAPRE of 4.7%. They stated that the pressure had the most impact on
the IFT. Fig. 4.30 illustrates the competency of the CSA-LSSVM in pre-
dicting the IFT.
The CSA-LSSVM model was employed once again this year by
Barati-Harooni et al. [204] to predict the IFT value between live oil and
formation water. The IFT was considered as a function of P, T, and
Figure 4.29 Cumulative frequency versus ARE for models developed by Najafi-
Marghmaleki et al. and other correlations. ARE, Absolute relative error. Adapted from
A. Najafi-Marghmaleki, et al., On the prediction of interfacial tension (IFT) for water-
hydrocarbon gas system, J. Mol. Liq. 224 (2016) 976990.
136
Applications of Artificial Intelligence Techniques in the Petroleum Industry
Figure 4.30 The accuracy of the CSA-LSSVM model developed by Ayatollahi et al. in predicting the IFT for the testing subset. CSA,
Coupled simulated annealing; IFT, interfacial tension; LSSVM, least square support vector machine. Adapted from S. Ayatollahi, et al., A rig-
orous approach for determining interfacial tension and minimum miscibility pressure in paraffin-CO2 systems: application to gas injection
processes, J. Taiwan Inst. Chem. Eng. 63 (2016) 107115.
salinity. The CSA-LSSVM model showed an accuracy of 0.76% in terms
of AAPRE.
In 2017, Hemmati-Sarapardeh and Mohagheghian [205] aimed to pre-
dict the IFT between normal alkanes (n-C5 to n-C16) and nitrogen (N2)
using the GMDH system. Three input variables were considered as P, T,
and MW of n-alkane. The modeling results revealed the superiority of
the GMDH approach with an AAPRE of 3.91% (for the testing subset).
In another approach to predict the IFT in water/hydrocarbon systems,
GP was employed by Rostami et al. [206]. The proposed approach was
able to predict the IFT as a function of T, P, the difference between
hydrocarbon and water density, and Tc of hydrocarbon with an AAPRE
of 4.38%.
In the following year, several scholars focused on predicting the IFT in
hydrocarbon/brine systems as a function of P, T, the carbon number of
hydrocarbon, and ionic strength of brine. For this purpose, Darvish et al.
[207], Rouhibakhsh and Darvish [208], and Emami Baghdadi et al. [209]
utilized ANFIS-PSO, fuzzy C-means (FCM), and LSSVM algorithms,
respectively. The values of 0.9957, 0.9309, and 0.9851 were obtained as
the correlation coefficient of the models developed by Darvish et al.,
[207] Rouhibakhsh and Darvish [208], and Emami Baghdadi [209],
respectively. Another study this year was performed to estimate the IFT
between N2 and n-alkanes by Ameli et al. [210]. They utilized ANN-
MLP, ANN-RBF, and LSSVM algorithms to develop IFT models. The
performance of each of these intelligent models was assessed with different
optimization techniques. The MLP network coupled with the LM opti-
mization method (MLP-LM) showed the highest accuracy in predicting
the IFT as a function of dimensionless pressure and temperature with an
AAPRE of 1.38%. The obtained results showed that the dimensionless
temperature had more impact on the IFT value.
Also, in 2019, the prediction of IFT between oil and brine was the
interest of several studies. Kiomarsiyan and Esfandiarian [211], Abooali
et al. [212], and Amar et al. [213] were the scholars who attempted to
forecast the IFT in oil/brine systems with the aid of intelligent models.
Kiomarsiyan and Esfandiarian [211] utilized a grid partitioning based FIS
to predict the IFT in oil/brine systems as a function of P, T, the carbon
number of hydrocarbon, and ionic strength of brine. The model showed
the correlation coefficient of 0.9447 (testing subset). Abooali et al. [212]
could construct their models using the GP algorithm. They used T, P, oil
SG and total acid number (TAN), brine pH, and NaCl equivalent salinity
138
Applications of Artificial Intelligence Techniques in the Petroleum Industry
(Seq) as input parameters. The predictions of the constructed model
showed an accuracy of 3.3932% in terms of AAPRE. Fig. 4.31 shows the
absolute relative deviation versus the number of testing subset. Amar et al.
[213] introduced two novel intelligent models, namely, adaptive boosting
support vector regression (AdaBoost SVR) and gradient boosting DT
(GBDT), to model the IFT between crude oil and brine. Two sets of IFT
models were developed through each of these models. The first models
had six input parameters, including P, T, γo, total acid number (TAN),
brine pH, and NaCl equivalent salinity (Seq), while the second ones were
developed based on four inputs: P, T, Seq, and γo. The modeling results
revealed the outperformance of GBDT that had six input variables with
an AARD of 1.01%. Fig. 4.32 shows the performance of the developed
models in terms of AARD.
Ameli et al. [214] made an effort to forecast the IFT between the
n-alkanes and supercritical CO2 as a function of T, P, and MW of
n-alkane. They constructed three models—ANN-MLP, GA-RBF, and
Figure 4.31 ARD% of predictions of the IFT model developed by Abooali et al. over
the testing dataset. ARD, Absolute relative deviation; IFT, interfacial tension. Adapted
from D. Abooali, et al., A new empirical model for estimation of crude oil/brine interfa-
cial tension using genetic programming approach, J. Pet. Sci. Eng. 173 (2019) 187196.
139
Application of intelligent models in reservoir and production engineering
CHPSO-ANFIS—and reported the values of 2.59%, 1.39%, and 1.81% as
the AAPRE of each model, respectively.
Also, several studies were conducted to determine the IFT between
CO2 and brine using intelligent models [215219] that are summarized
in Table 4.2 along with the abovementioned applications of intelligent
models in IFT prediction.
4.2 Rock properties
Comprehensive knowledge of reservoir characteristics is of great impor-
tance in any reservoir simulation and modeling study. The process of
defining different reservoir properties is referred to as reservoir characteri-
zation, which is a significant part of modern reservoir management.
Porosity, permeability, fluid distributions, and pore/grain size distribution
are among the major characteristics of a hydrocarbon reservoir [220].
Rock samples (cores) are usually available for a limited well location,
which makes the determination of reservoir characteristics a complex
problem [221]. These parameters are conventionally calculated using seis-
mic surveys, production data, well tests, well logs, and core data [220].
Due to the importance of reservoir characteristics and the demand for
their accurate determination, AI techniques are being applied to precisely
estimate these parameters using easily attained data since the early 1990s.
According to the literature, AI techniques have been applied to various
purposes. In the 1990s, different intelligent models were employed by
Figure 4.32 The AARD% comparison between the IFT models developed by Amar
et al. AARD, Average absolute relative deviation; IFT, interfacial tension. Adapted from
M.N. Amar, et al., Modeling oil-brine interfacial tension at high pressure and high salinity
conditions, J. Pet. Sci. Eng. 183 (2019) 106413.
140
Applications of Artificial Intelligence Techniques in the Petroleum Industry
Table 4.2 Summary of applications of artificial intelligence models in the area of IFT (interfacial tension) prediction.
Author(s)
Intelligent
model(s)
Type of study conducted
Input parameters
Error
(AAPRE)
Meybodi et al. [199]
CSA-
LSSVM
Prediction of IFT between oil and
water
T/critical temperature of hydrocarbon (Tc)
and ρw 2 ρhc
1.45
Najafi-Marghmaleki
et al. [201]
CSA-
LSSVM
GA-RBF
CHPSO-
ANFIS
Prediction of IFT between gas and
water
T, gas Tc, and ρw 2 ρhc
1.35
1.26
3.99
Ayatollahi et al. [203]
CSA-
LSSVM
Prediction of IFT between paraffin
and CO2
T, P, and MW of paraffin
4.7
Barati-Harooni et al.
[204]
CSA-
LSSVM
Prediction of IFT between oil and
brine
P, T, and salinity
0.76
Zhang et al. [215]
ANN-MLP
Prediction of IFT between CO2 and
brine
P, T, monovalent cation (Na1 and K1)
molality, bivalent cation (Ca21 and
Mg21) molality, mole fraction of N2, and
CH4
2.70
Hemmati-Sarapardeh
and
Mohagheghian
[205]
GMDH
Prediction of IFT between n-alkanes
and N2
P, T, and
MW of n-alkane
3.91
Rostami et al. [206]
GP
Prediction of IFT between oil and
water
T, P, ρw 2 ρhc, and Tc of hydrocarbon
4.38
Niroomand-Toomaj
et al. [217]
GA-RBF
MLP
LSSVM
Prediction of IFT between CO2 and
brine
T, P, and salinity
2.353
2.543
4.819
(Continued)
Table 4.2 (Continued)
Author(s)
Intelligent
model(s)
Type of study conducted
Input parameters
Error
(AAPRE)
ANFIS
2.6468
Partovi et al. [218]
PSO-RBF
Hybrid
ANFIS
Prediction of IFT between CO2 and
brine
P, T, monovalent cation (Na1 and K1)
molality, bivalent cation (Ca21 and
Mg21) molality, mole fraction of N2,
and CH4
2.07
1.96
Kamari et al. [216]
DT
CSA-
LSSVM
GEP
Prediction of IFT between CO2 and
brine
P, T, monovalent cation (Na1 and K1)
molality, bivalent cation (Ca21 and
Mg21) molality, mole fraction of N2,
and CH4

Darvish et al. [207]
ANFIS-
PSO
Prediction of IFT between oil and
brine
P, T, the carbon number of hydrocarbon,
and ionic strength of brine
0.15799
Rouhibakhsh and
Darvish [208]
FCM
Prediction of IFT between oil and
brine
P, T, the carbon number of hydrocarbon,
and ionic strength of brine
0.9309
(R2)
Emami Baghdadi et al.
[209]
LSSVM
Prediction of IFT between oil and
brine
P, T, the carbon number of hydrocarbon,
and ionic strength of brine
0.27444
Ameli et al. [210]
MLP-LM
MLP-BR
MLP-SCG
RBF-PSO
RBF-GA
LSSVM-
CSA
Prediction of IFT between n-alkanes
and N2
dimensionless pressure and temperature
1.33
1.68
2.74
2.88
2.34
5.83
Dehghani et al. [219]
SGB
Prediction of IFT between CO2 and
brine
P, T, and brine salinity
0.60725
Kiomarsiyan and
Esfandiarian [211]
FIS
Prediction of IFT between oil and
brine
P, T, the carbon number of hydrocarbon,
and ionic strength of brine
0.9447
(R2)
Abooali et al. [212]
GP
Prediction of IFT between oil and
brine
T, P, oil SG and TAN, brine pH, and Seq
3.3932
Amar et al. [213]
AdaBoost
SVR-1
AdaBoost
SVR-2
GBDT-1
GBDT-2
Prediction of IFT between oil and
brine
P, T, γo, TAN, brine pH, and Seq
1.2744
2.3205
1.0188
1.5392
Ameli et al. [214]
ANN-MLP
GA-RBF
CHPSO-
ANFIS
Prediction of IFT between n-alkanes
and supercritical CO2
T, P, and MW of n-alkane
2.59
1.39
1.81
AAPRE, Average absolute percent relative error; AdaBoost SVR, adaptive boosting support vector regression; ANFIS, adaptive neuro-fuzzy inference system; ANN, artificial neural
network; BR, Bayesian regularization; CHPSO, conjugate hybrid particle swarm optimization; CSA, coupled simulated annealing; DT, decision tree; FCM, fuzzy C-means; FIS,
fuzzy inference system; GA, genetic algorithm; GA-RBF, genetically optimized radial basis function neural network; GBDT, gradient boosting decision tree; GEP, genetic expression
programing; GMDH, group method of data handling; GP, genetic programing; LM, LevenbergMarquardt; LSSVM, least square support vector machine; MLP, multilayer
perceptron; PSO, particle swarm optimization; RBF, radial basis function; SCG, scaled conjugate gradient; SG, specific gravity; SGB, stochastic gradient boosting; TAN, total acid
number.
researchers to predict various characteristics of the reservoir rocks. In this
decade, ANNs were the most popular intelligent models among the scho-
lars and were utilized in 10 studies. Various applications of ANNs in this
decade include mineral identification [222], lithology determination [223],
porosity estimation [224226], and permeability prediction [225230].
In addition, Mohaghegh et al. [231] reported extensive applications of
ANNs in predicting several reservoir characteristics such as fluid saturation
distribution, permeability, and porosity. Apart from these applications of
ANNs, several scholars used ANNs along with fuzzy sets to model differ-
ent parameters such as lithology recognition [232] and permeability pre-
diction [233]. In another study, Nikravesh [234] developed numerous
ANNs and NF models to predict a broad range of reservoir parameters.
Most of the mentioned studies used well logs as the inputs of the models.
According to journal papers, in the 2000s, many studies were aimed to
predict the permeability and porosity of reservoirs. To this end, ANNs
were employed in four studies, while both FL and hybrid models were
employed in three works. The scholars who attempted to predict the reser-
voir rock properties through ANNs were Helle et al. [235], Rezaee et al.
[236], and Ahmadi et al. [237]. Helle et al. [235] conducted a study on pre-
dicting the porosity and permeability of Jurassic reservoirs in the Viking
Graben area using well log data as the inputs of ANNs. Rezaee et al. [236]
developed an MLP to estimate the permeability as a function of pore throat
radii and porosity. Ahmadi et al. [237] utilized the BP learning algorithm to
develop the permeability model. Well logs were gathered from an Iranian
offshore gas field and were considered as the inputs of the ANNs. Also,
Lim [220] employed FL and ANN algorithms to estimate the porosity and
permeability from well log data. They validate their models with real data
obtained from an offshore well in Korea. In their study, FL was a tool to
select the corresponding well logs with core permeability and porosity,
while ANNs were employed to construct the predictive tool.
In this decade, Kadkhodaie Ilkhchi et al. [238] employed a FCM clus-
tering technique to classify the rock type through permeability and poros-
ity data. They developed a FIS to predict the permeability of different
Iranian offshore gas fields. Later, Nashawi and Malallah [239] utilized a FL
system to estimate the rock permeability using well log data attained from
reservoirs in the Middle East.
Huang et al. [221] developed a hybrid model, namely, integrated
neural-fuzzy-genetic-algorithm (INFUGA), to predict the permeability of
an offshore oil reservoir in Western Australia. They utilized ANNs to
establish hyper-surface membership functions. They claimed that the
144
Applications of Artificial Intelligence Techniques in the Petroleum Industry
constructed INFUGA model could enhance the estimations by 3%31%
compared to other developed neural-fuzzy models. CMs comprising par-
allel ANNs were constructed by Bhatt and Helle [240] to predict the per-
meability and porosity. The input data for the permeability model was
well log data (neutron porosity, gamma-ray, density, and sonic) obtained
from the Viking Graben area in the North Sea. In the porosity network,
density, resistivity, and sonic logs were the inputs of the ANN. Another
application of hybrid intelligent systems was reported by Saemi and
Ahmadi [241]. They constructed the coactive NF inference system
(CANFIS) to predict the reservoir rock permeability from well log data.
The developed model was optimized using a GA. The proposed approach
could predict the permeability of an Iranian offshore gas field satisfactorily.
In 2010, Al-Anazi and Gates [242] attempted to estimate the permeabil-
ity and classify the electrofacies in heterogeneous reservoirs using the SVM
method. They employed extended fuzzy clustering techniques to extract
clusters from both log and core data. The permeability predictions based on
core data were slightly more accurate. Fig. 4.33 shows the competency of
the SVM framework in lithofacies classification in comparison with linear
Figure 4.33 The comparison between SVM, PNN, and LDA in electrofacies clustering.
LDA, Linear discriminant analysis; PNN, probabilistic neural networks; SVM, support vector
machine. Adapted from A. Al-Anazi, I. Gates, A support vector machine algorithm to
classify lithofacies and model permeability in heterogeneous reservoirs, Eng. Geol. 114
(34) (2010) 267277.
145
Application of intelligent models in reservoir and production engineering
discriminant analysis (LDA) and probabilistic NNs (PNNs), and Fig. 4.34
shows the accuracy of the proposed model in predicting the permeability.
In another study, Labani et al. [243] constructed a CMIS using FL,
NF, and ANN algorithms to predict the parameters of nuclear magnetic
resonance(NMR) log, which provides valuable information for obtaining
permeability and effective porosity of reservoir rock, through conven-
tional well log data. GAs were employed to optimize the CMIS. The
results indicated that the constructed CMIS could accurately determine
the free fluid porosity (FFP) and permeability using neutron porosity,
bulk density, sonic transit time, and effective porosity from high resolu-
tion integrated logging tool as the inputs. Fig. 4.35 shows the accuracy of
the GA-CMIS model in predicting the FFP and permeability.
In this year, FL and ANN algorithms were employed by Afify and
Hassan [244] to predict permeability and porosity for Egyptian reservoirs.
The best related well logs with the target parameters were selected using
FL, and predictive models were developed with the aid of ANNs. The
proposed approach showed an R2 of 0.825 and 0.957 for permeability
and porosity predictions, respectively.
Figure 4.34 The accuracy of the SVM model in predicting the core permeability.
SVM, Support vector machine. Adapted from A. Al-Anazi, I. Gates, A support vector
machine algorithm to classify lithofacies and model permeability in heterogeneous reser-
voirs, Eng. Geol. 114 (34) (2010) 267277.
146
Applications of Artificial Intelligence Techniques in the Petroleum Industry
Helmy et al. [245] and Helmy and Fatai [246] both developed hybrid
predictive tools using type-2 FL, SVM, and FNs to estimate the permeabil-
ity and porosity. In both studies, FNs were applied to choose the most
influential well logs on the outputs. In each of these studies, two hybrid
models were constructed. In the first models (functional networksfuzzy
logicSVM), the well logs selected by FNs were entered into the T2FLS
to remove uncertainties, and SVMs were applied to provide predictions. In
the second ones (functional networksSVMfuzzy logic), SVMs were uti-
lized as a tool to map the inputs (the well logs selected by FNs) to a higher
dimensional space, and the FL algorithm was aimed to predict the target
parameter. Fig. 4.36 shows the comparison between the R2 of the models
developed by Helmy et al. [245] in porosity and permeability prediction.
Figure 4.35 The accuracy of the GA-CMIS in the prediction of (A) FFP and (B) perme-
ability. CMIS, Committee machine intelligent system; FFP, free fluid porosity; GA,
genetic algorithm. Adapted from M.M. Labani, A. Kadkhodaie-Ilkhchi, K. Salahshoor,
Estimation of NMR log parameters from conventional well log data using a committee
machine with intelligent systems: a case study from the Iranian part of the South Pars
gas field, Persian Gulf Basin, J. Pet. Sci. Eng. 72 (12) (2010) 175185.
147
Application of intelligent models in reservoir and production engineering
Figure 4.36 The comparison between the R2 of the models developed by Helmy et al. in predicting the (A) permeability and (B) poros-
ity. Adapted from T. Helmy, A. Fatai, K. Faisal, Hybrid computational models for the characterization of oil and gas reservoirs, Expert Syst.
Appl. 37 (7) (2010) 53535363.
In 2011, Kaydani et al. [247] employed GAs to optimize the ANNs in
order to predict the permeability of a heterogeneous Iranian reservoir
using well logs. They developed separate models for different reservoir
zones. The authors concluded that the reservoir zonation technique could
help the GA-ANN model to provide more precise permeability predic-
tions. Naseryan Moghadam et al. [248] made another effort to estimate
the permeability and porosity of the Darquvain reservoir (southwest of
Iran) using ANNs. They attained real-life well logging data to develop
their models. The developed models could predict the porosity and per-
meability with an R2 of 0.994 and 0.991, respectively.
In this year, the type-2 FL algorithm was employed by Olatunji et al.
[249] to predict the permeability from well logs. The comparison between
the efficiency of the proposed model and other models showed a better per-
formance of type-2 FL in predicting the permeability of five different reser-
voirs. In another study, Mokhtari et al. [250] investigated the applicability of
ANNs in the area of carbonate reservoir permeability estimation. They also
explored the impact of different parameters such as porosity and irreducible
water saturation on the distribution of reservoir permeability. The ANN struc-
ture showed an accuracy of 0.99 in terms of R2 in permeability prediction.
Sadeghi et al. [251] were the other scholars who employed intelligent
systems to predict the reservoir permeability in this year. They constructed
a CMIS using empirical formulas, multiple regression, and NF algorithms
to predict the permeability of a heterogeneous reservoir (Balal oil field)
through conventional well logs. The authors optimized the CMIS using
GAs that led to the R2 value of 0.706. In another work, FN and SVM
schemes were utilized by Anifowose et al. [252] to construct a hybrid
model in order to predict the porosity and permeability.
In 2012 El-Sebakhy et al. [253] introduced the application of FNs in
reservoir permeability prediction. They compared the performance of the
developed FN model with ANNs, ANFIS, and linear/nonlinear regres-
sion methods. The results showed a better outperformance of FNs over
the other predictive tools.
In this year, Kaydani et al. [254] developed an ANFIS model to pre-
dict the reservoir permeability using well-logging data of a heterogeneous
reservoir in Iran. Ghiasi-Freez et al. [255] attempted to construct a CMIS
using ANNs, FL, and ANFIS to forecast the permeability. The authors
employed GAs to optimize the weights of the CMIS. The results showed
the superiority of the developed CMIS in comparison to standalone intel-
ligent systems. Saffarzadeh and Shadizadeh [256] were the other scholars
who applied intelligent models to predict the permeability this year. They
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Application of intelligent models in reservoir and production engineering
utilized the SVM framework to estimate the permeability of an Iranian oil
reservoir from petrophysical logs. A comparison was made between the
SVM model and an ANN model, which revealed the higher performance
of the SVM model in permeability prediction. In another study, Ja’fari and
Moghadam [257] combined the results of ANFIS and ANN algorithms to
construct a CMIS, which was capable of predicting the porosity and per-
meability of Iranian oil reservoirs using conventional well logs. The pro-
posed CMIS was optimized by GAs to provide estimations for porosity and
permeability with an MSE of 0.01385 and 0.01234, respectively.
In 2013, Ahmadi et al. [258] proposed an approach to estimate reser-
voir permeability with the aid of ANNs. They employed a hybrid of GA
and PSO methods to optimize the developed ANNs (HGAPSO-ANN).
The developed model could predict the permeability of an Iranian reser-
voir with an R2 of 0.94206, while the corresponding value for standalone
ANNs was 0.85309. In another investigation, Zargari et al. [259] investi-
gated the competency of two intelligent models in estimating the two
main reservoir characteristics (porosity and permeability). They applied
ANNs and ANFIS algorithm to predict the porosity and permeability of
one of the Iranian carbonate reservoirs using well log data. Between the
two utilized models, ANNs showed higher efficiency in providing predic-
tions for both porosity and permeability. Olatunji et al. [260] employed
the extreme learning machine (ELM) to forecast the permeability of a car-
bonate reservoir in the Middle East. They gathered the well logging data
from five wells to develop the models. Apart from ELM, SVM and
ANNs were used to make predictions for reservoir permeability, and a
comparison was made between the results obtained from different models.
The results indicated that the ELM scheme had the highest performance
with an R2 of higher than 0.79.
In this year, Anifowose et al. [261,262] constructed different hybrid
intelligent systems to predict reservoir rock properties. In the first study,
Anifowose et al. [261] made a benefit from the hybridization of FNs and
the T2FLS. The hybrid models were aimed to predict the permeability
and porosity of different reservoirs located in North America and the
Middle East. In their study, top interval, grain density, grain volume, and
length were the input parameters for porosity models, whereas permeabil-
ity models were developed based on well logging data. The developed
models were able to predict the porosity and permeability with an R2 of
higher than 0.814 and 0.794, respectively. In the other work, Anifowose
et al. [262] evaluated the performance of different intelligent models in
predicting the porosity and permeability. They considered three versions
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of ANFIS and introduced two novel hybrid algorithms. They combined
the FNs with SVM and T2FLS to construct the FN-SVM and FN-
T2FLS models. The results revealed the outperformance of the FN-SVM
model over the other schemes.
In 2014, Shokooh Saljooghi and Hezarkhani [263] attempted to pre-
dict the reservoir porosity using well logging data. To this end, they
employed wavelet theory as the activation function of ANNs. The
authors utilized different wavelets to construct wavelet networks in order
to predict the porosity from conventional well logs. Fig. 4.37 shows the
capability of the proposed wavenet in comparison with conventional
ANNs. In this year, two studies were aimed to predict both reservoir
porosity and permeability. Ahmadi et al. [264] employed FL and LSSVM
algorithms to predict these parameters using petrophysical logs. They opti-
mized the FL and LSSVM models using GAs. The developed GA-FL and
GA-LSSVM models could provide reliable predictions with an R2 of
higher than 0.96 and 0.97, respectively. In another work, Aïfa et al. [265]
Figure 4.37 The accuracy of the wavenet model in predicting the porosity in com-
parison with conventional ANNs. ANN, Artificial neural network. Adapted from B.
Shokooh Saljooghi, A. Hezarkhani, Comparison of WAVENET and ANN for predicting the
porosity obtained from well log data, J. Pet. Sci. Eng. 123 (2014) 172182.
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Application of intelligent models in reservoir and production engineering
tried to predict these reservoir properties using ANFIS structure. The
developed model could estimate the permeability and porosity of a sand-
stone reservoir with an R2 of 0.9879 and 0.9689, respectively.
Several scholars aimed to predict the reservoir permeability with the
aid of intelligent models in this year. Bagheripour [266] constructed a
committee neural network (CNN) using MLP, RBF, generalized regres-
sion NN (GRNN) models. They employed GAs to optimize the devel-
oped CNN. The proposed model could predict the permeability of the
South Pars gas field reservoirs with an R2 of 0.8331. Fig. 4.38 illustrates
the comparison between the developed CNN model and standalone
ANNs. A comparative study was performed by Baziar et al. [267] to
investigate the ability of three AI techniques in reservoir permeability pre-
diction. They considered MLP, CANFIS, and SVM as predictive tools to
provide predictions for the permeability of Mesaverde tight gas sandstones
using well-logging data obtained from three wells. The results revealed
the better performance of the SVM and CANFIS models over the MLP
NNs.
Kaydani et al. [268] were other scholars who utilized intelligent mod-
els to estimate the permeability. They attempted to develop a model using
the MGGP algorithm to predict the permeability of a heterogeneous
Iranian reservoir. The predictions of the developed MGGP were com-
pared to those of ANNs, ANFIS, and GP. The comparison results
Figure 4.38 The MSE and R2 comparison between permeability models developed
by Bagheripour. MSE, Mean square error. Adapted from P. Bagheripour, Committee
neural network model for rock permeability prediction, J. Appl. Geophys. 104 (2014)
142148.
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
indicated the higher accuracy of MGGP with an R2 of 0.907. Also,
Olatunji et al. [269,270] proposed different approaches toward permeabil-
ity predictions using various intelligent models. In one of their studies
[269], they utilized T2FLS coupled with SBLLM to develop a hybrid
model that could predict the permeability using well logs with R2 values
up to 0.9388 for different datasets. In another work [270], they predicted
the reservoir permeability through the hybridization of T2FLS and ELM.
The predictions of the proposed model showed R2 values up to 0.9795
for different datasets.
In 2015 Shokooh Saljooghi et al. [271] employed wavenets once again
to predict the reservoir permeability. The developed scheme could esti-
mate the permeability of reservoir rock with an R2 of 0.92. In this year,
Anifowose et al. [272] focused on predicting the reservoir rock properties
using an ensemble model of SVMs. The comparisons between the effi-
ciency of the proposed model and several other intelligent models showed
the outperformance of the ensemble SVM model in predicting the poros-
ity and permeability.
In 2016 Kamari et al. [273] made an effort to predict the permeability
in a heterogeneous reservoir through CSA-LSSVM algorithm. They gath-
ered more than 700 data points from a Saudi Arabian naturally fractured
reservoir (NFR) to develop the model. The results were compared with
an ANN-MLP model, which showed a higher accuracy of CSA-LSSVM
in permeability estimation with an R2 of 0.98. In this year, three studies
were conducted to predict both permeability and porosity. Dashti and
Sfidari [274] investigated the efficiency of the SVM and ANN-BP models
in predicting the rock properties of a reservoir in Mansouri oil field in
Iran. They gathered the density, interval transit time, and neutron porosity
logs. The ANN-BP could provide more accurate predictions. In another
work, Barati-Harooni et al. [275] applied ANFIS to estimate the para-
meters of the NMR log (permeability and free-flowing porosity) using
well-logging data. Vardian et al. [276] made an effort to predict the per-
meability and porosity of a gas condensate NFR using ANFIS. The pro-
posed method could provide estimations with an R2 of 0.9976.
In 2017, Taghipour et al. [277] utilized the cuckoo optimization algo-
rithm to optimize the LLSVM model to estimate the permeability using
well logs. The predictions of the proposed model showed an accuracy of
0.99602 in terms of the correlation coefficient.
In the following year, Elkatatny et al. [278] attempted to develop a
mathematical correlation to determine the permeability of heterogeneous
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Application of intelligent models in reservoir and production engineering
porous media using mobility index, neutron porosity, and bulk density.
The derived correlation was able to predict the permeability with an
RMSE of 0.28 mD. In another work, Elkatatny et al. [279] employed
intelligent models once again to predict the reservoir rock properties. This
time, they investigated the performance of ANNs, ANFIS, and SVMs to
estimate the porosity of a carbonate reservoir rock. The authors consid-
ered sonic compressional time, neutron porosity, and bulk density as the
input parameters. They also developed a mathematical correlation using
ANNs. The developed correlation could estimate the value of porosity
with an AAPRE of less than 8%.
In 2019, Ahmadi and Chen [280] proposed an LSSVM model coupled
with GAs to predict the porosity and permeability of an Iranian oil reser-
voir using petrophysical logs. The performance of the developed GA-
LSSVM model was compared to that of several other intelligent models.
GA-LSSVM approach showed the highest accuracy of less than 1% in
terms of AAPRE. In another study, Rostami et al. [281] conducted a
comprehensive study, in which various intelligent models were applied to
predict the permeability of heterogeneous reservoirs. They employed the
RBF-ANN, MLP-ANN, LSSVM, ANFIS, GP, and CMIS algorithms as
intelligent models as well as GA, PSO, and LM as the optimization algo-
rithms. The authors introduced the CMIS model as the most precise
model, as well as the effective porosity as the most influential parameter.
An empirical correlation was also derived from GP modeling. Fig. 4.39
shows the accuracy of the constructed CMIS to predict the permeability.
4.3 Enhanced oil recovery
The oil recovery from reservoirs with primary and secondary processes
hardly exceeds 30%50%. The field results have revealed that after the
primary and secondary phase of oil production, two-thirds of the original
oil in place will be left behind inside the porous media. The main reasons
for such recovery efficiency may be listed as macroscopic and microscopic
heterogeneities, different fluid characteristics (densities and viscosities), var-
ious IFTs and wettabilities, and different driving forces. The tertiary meth-
ods of oil recovery, which are known as EOR processes, have attracted
growing attention in recent decades since the oil price and its consump-
tion have risen remarkably [282].
In Section 4.3.1, it has been attempted to review the applications of
AI techniques in the area of EOR processes. Furthermore, a review of
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
several approaches in estimating the MMP for different gases and reservoir
fluids using intelligent algorithms is provided in Section 4.3.2 due to the
high relevance between the MMP concept and gas injection scenarios.
4.3.1 Enhanced oil recovery processes
The application of AI models in the area of EOR processes dates back to
the 1990s. In 1995, Gharbi et al. [283] introduced the first application of
ANNs in this field. They aimed to predict the efficiency of water flooding
(immiscible displacement) scenarios. In another work, an NF intelligent
model was developed by Nikravesh et al. [284] to enhance the manage-
ment of waterfloods in tight fractured reservoirs. In 1998, Elkamel [285]
employed ANNs to predict and optimize the waterflooding efficiency in
heterogeneous reservoirs.
In the following decade, intelligent models were employed for various
purposes in the area of EOR processes. In 2003, ANNs were utilized by
Gharbi [286] once again. This time, predicting the performance of solvent
flooding (miscible displacement) process in heterogeneous reservoirs was
Figure 4.39 Cross-plot of experimental value versus predicted value of permeability
using CMIS developed by Rostami et al. CMIS, Committee machine intelligent system.
Adapted from A. Rostami, et al., Rigorous prognostication of permeability of heteroge-
neous carbonate oil reservoirs: smart modeling and correlation development, Fuel 236
(2019) 110123.
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Application of intelligent models in reservoir and production engineering
the aim of their study. In 2007, Jiang et al. [287] developed a chaotic
NN (CNN) to assess the performance of polymer flooding processes.
The developed model could efficiently predict the water cut and the
amount of the produced oil. In 2009, Vafaei et al. [288] employed an
MLP to estimate the steam distillation recovery of crude oil. They consid-
ered °API,characterization factor of the oil, viscosity, and distillation factor
as the inputs of the network to predict the distillate yield. The proposed
model showed an accuracy of 5.78% in terms of the AAPRE. In another
study, Hou et al. [289] could predict the performance of polymer flooding
through the hybridization of orthogonal design and SVMs.
In 2011, Karambeigi et al. [290] attempted to predict the efficiency of
chemical flooding (polymer and surfactant) using an ANN-MLP. The
developed model was able to predict the recovery factor (RF) and net
present value (NPV: an economic index) using salinity of polymer drive,
Kv/Kh ratio, polymer concentration in polymer drive, polymer drive size,
polymer concentration in surfactant slug, and surfactant slug size as input
variables. The predictions of the MLP network showed an AAPRE of
4.598% and 2.475% for net present value and RF, respectively. Besides,
the authors investigated the capability of the proposed model in optimiz-
ing the performance of the chemical EOR processes. Fig. 4.40 shows the
accuracy of the proposed model in the estimation of RF.
In 2013, Al-Dousari and Garrouch [291] focused on predicting the
performance of surfactant-polymer (SP) floods using ANNs. A sensitivity
analysis was performed on the ANN structure, and the structure of 18-
11-4 was selected as the best structure. Eighteen dimensionless groups
were considered as inputs such as rock wettability, gravity, capillary pres-
sure, relative permeabilities, and IFT. Four considered outputs were oil
recovery at 0.75, 1.5, and 2.25 pore volumes (PVs) injected as well as the
breakthrough dimensionless time. The datasets were obtained from a 3D
compositional simulator. The developed ANN model could predict the
oil recovery with an AAPRE of lower than 3.3% for the testing subset.
In another investigation, Esmaeilnezhad et al. [292] utilized the
ANFIS structure to evaluate the performance of different EOR techni-
ques using seven input parameters. The developed model was aimed to
predict the performance of five EOR scenarios, including one chemical
flooding process (polymer flooding), two gas injection processes (CO2 and
hydrocarbon injection), and two thermal EOR techniques (steam injec-
tion and in situ combustion). They considered initial oil saturation, oil vis-
cosity and density, temperature, permeability, primary oil in place, and
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
porosity as input variables, while the amount of recovered oil through for
each EOR method was regarded as the output. The proposed method
was also implemented on an Iranian oil field that was never under an
EOR treatment. The model introduced the hydrocarbon injection as the
most proper EOR technique in the mentioned oil field.
In 2014, Mohammadi et al. [293], Ahmadi et al. [294], and Amirian
et al. [295] were among the scholars who implemented the AI techniques
in the area of EOR processes. Mohammadi et al. [293] made an effort to
predict the performance of the CO2 injection scenario using ANNs. They
employed the BR algorithm to train the MLP network. In their study, 14
input variables such as the thickness of the reservoir, depth, rock effective
porosity, pressure, and rate of injection were considered. They claimed
that the developed model could predict the RF with an error of about
0.396%. Ahmadi et al. [294] proposed an LSSVM approach to predict the
performance of the in situ combustion process in heavy oil reservoirs.
They gathered a databank using experimental data reported in the litera-
ture. The input parameters of the LSSVM model were temperature,
porosity, fluid saturations, air injection rate, pressure, and °API gravity.
The developed model was able to estimate the RF of the in situ
Figure 4.40 The accuracy of the ANN-MLP model in predicting the RF of a chemical
flooding process. ANNs, Artificial neural networks; MLP, multilayer perceptron; RF,
recovery factor. Adapted from M. Karambeigi, R. Zabihi, Z. Hekmat, Neuro-simulation
modeling of chemical flooding. J. Pet. Sci. Eng. 78 (2) (2011) 208219.
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Application of intelligent models in reservoir and production engineering
combustion process with an AAPRE of 2.2992% for the testing dataset.
In the study of Amirian et al. [295], it had been tried to evaluate the per-
formance of the steam-assisted gravity drainage (SAGD) process in hetero-
geneous formations using ANNs with the aid of cluster analysis.
In 2015, intelligent models were employed by many researchers to be
applied in the field of EOR techniques. Ahmadi et al. [296] aimed to esti-
mate the oil production rate for the VAPEX (vapor-extraction) process
using LSSVMs. The developed LSSVM model was optimized using GAs.
In another work, Ahmadi et al. [297] introduced another application of
the LSSVM scheme in the area of EOR processes. They attempted to
estimate the velocity of the combustion front in the in situ combustion
method. The developed model could predict the target value using °API,
air injection rate, P, T, porosity, and saturation of oil, gas, and water as
input variables with an AAPRE of 0.85312% for the testing dataset.
In this year, Amirian et al. [298] employed cluster analysis along with
ANNs to forecast the performance of the SAGD process in heterogeneous
oil fields. The authors constructed a training databank through numerical
flow simulations. Then, the models were tested using actual field data. In
another study, Amirian and Chen [299] applied ANNs as predictive tools
to estimate the efficiency of the waterflooding scenario in heterogeneous
formations. Kamari et al. [300] focused on predicting the fluctuations of
the producing oil rate with respect to the change of water injection rate
and the ratio of oil and water viscosity. To this end, they utilized the
LSSVM algorithm coupled with the CSA optimization method. In their
study, RBF was used as the kernel function. The developed CSA-LSSVM
model could provide predictions with an AAPRE of 8.06553% for the
total dataset.
In 2016, Ahmadi and Pournik [301] utilized the LSSVM algorithm to
predict the performance of a chemical EOR process so called SP flooding.
They gathered a databank from the literature. They considered seven
input parameters: the salinity of polymer drive, Kv/Kh ratio, polymer con-
centration in polymer drive, polymer drive size, polymer concentration in
surfactant slug, surfactant concentration in surfactant slug and surfactant
slug size, and the two output parameters were RF and NPV. The authors
stated that surfactant concentration and surfactant slug size were the most
influential parameters on the RF, while the corresponding parameters for
the NPV were surfactant concentration and polymer concentration in sur-
factant slug. In their study, GAs were applied to optimize the LSSVM
framework. The developed model could provide predictions for the RF
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
and NPV with a correlation coefficient of higher than 0.993. Fig. 4.41
shows the capability of the constructed LSSVM model in predicting the
RF and NPV.
In another study, Baghban and Bahadori [302] employed the SVM
scheme to find the optimum surfactant structure. The model was con-
structed to predict the mole average weighted carbon number as the out-
put, while salinity, temperature difference from 25°C, the mole average
weighted ethylene oxide, mole average weighted propylene oxide, and
equivalent alkane carbon number were considered as the input variables.
The authors utilized Gaussian function as the kernel function of the SVM.
The accuracy of the developed model was reported as 0.9911 in terms of
R2 for the testing dataset. Pendar et al. [303] applied ANFIS to investigate
the impact of various geometric fracture attributes on the efficiency of the
VAPEX technique. The model was developed to predict the oil RF as a
function of the dimensionless coordinate of fractures center, dimensionless
standard deviation of fracture centers, dimensionless fracture length,
dimensionless standard deviation of fracture lengths, fracture orientations
and intensity, and PV. Helaleh and Alizadeh [304] proposed an approach
to estimate the performance of WAG (water alternating gas) injection
using SVMs and ant colony optimization (ACO) technique. In their
study, ACO was utilized to optimize the parameters of the SVMs, while
SVMs were aimed to predict the performance of the surfactantwater
solution alternating the CO2 injection process. The efficiency of the
ACO technique was compared to PSO and GA algorithms. Also, an
ANN model was developed to be compared with the proposed SVM
models. The kernel function employed by the authors was RBF. The
developed model could predict the fractional oil recovery with an R2 of
higher than 0.98 using 18 input parameters such as solutiongas ratio,
capillary number, buoyancy ratio, and local heterogeneity parameter.
Fig. 4.42 illustrates the accuracy of the ACO-SVM model predictions.
This year, Le Van and Chon [305] utilized ANNs in the way of pre-
dicting the performance of ASP (alkaline-SP) flooding. The developed
model was also applied to optimize the ASP flooding process in terms of
maximizing the NPV through the adjustment of the injection strategy
and chemical concentration. The authors considered 13 input variables in
the ANN structure, including the well distance, slug size of preflushing
water, second polymer slug size, ASP slug size, AS (alkaline-surfactant)
slug size, first polymer slug size, surfactant concentration in ASP slug, sur-
factant concentration in AS slug, polymer concentration in the second
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Application of intelligent models in reservoir and production engineering
Figure 4.41 The accuracy of the LSSVM model developed by Ahmadi and Pournik in
estimating the (A) RF and (B) NPV. LSSVM, Least square support vector machine; NPV,
net present value; RF, recovery factor. Adapted from M.A. Ahmadi, M. Pournik, A pre-
dictive model of chemical flooding for enhanced oil recovery purposes: application of
least square support vector machine, Petroleum 2 (2) (2016) 177182.
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
Figure 4.42 The accuracy of the ACO-SVM model developed by Hellaleh and Alizadeh. ACO, Ant colony optimization; SVM, support vec-
tor machine. Adapted from A.H. Helaleh, M. Alizadeh, Performance prediction model of miscible surfactant-CO2 displacement in porous media
using support vector machine regression with parameters selected by ant colony optimization, J. Nat. Gas Sci. Eng. 30 (2016) 388404.
polymer slug, polymer concentration in the ASP slug, polymer concentra-
tion in the first polymer slug, alkali concentration in the ASP slug, and
alkali concentration in AS slug. Kamari et al. [306] proposed another
approach to estimate the performance of SP flooding using the LSSVM
algorithm. In their work, LSSVMs were employed to predict both RF
and NPV using seven input parameters. The RBF was utilized as the ker-
nel function. The LSSVM scheme showed a high accuracy of 2.3% and
1.5% in terms of AAPRE in predicting NPV and RF, respectively. They
also investigated the impact of each input variable on the value of RF and
NPV, which is shown in Fig. 4.43.
In 2017, the performance of the water flooding process in heteroge-
neous reservoirs was predicted using ANNs in the study of Amirian and
Chen [307]. They considered the residual oil and water saturations,
DykstraParsons coefficient, and mean of porosity and permeability as
input parameters of ANNs. In this year, two studies were conducted to
screen the EOR methods using intelligent models. Ramos and Akanji
[308] developed an ANFIS model to select the most efficient EOR tech-
niques in Block T of the Angolan oil field. The model introduced the
polymer flooding, hydrocarbon injection, and in situ combustion as the
candidate EOR techniques to be implemented in the mentioned field
with respect to parameters such as oil saturation, °API, viscosity, perme-
ability, porosity, and depth.
In this year, Ebaga-Ololo and Chon [309] and Janiga et al. [310] were
among the scholars who attempted to utilize intelligent models in the area
of polymer flooding scenarios. Ebaga-Ololo and Chon [309] utilized
ANNs to predict the performance of this EOR technique considering
two polymer slugs. The authors introduced six input parameters (first
polymer slug size, drive water slug size, second polymer slug size, concen-
tration of first polymer slug size, concentration of second polymer slug
size, and injection rate) and the ANN model was responsible for provid-
ing predictions for three outputs (RF after waterflooding, after the injec-
tion of the first polymer slug, and after the injection of the second
polymer slug). Janiga et al. [310] made an effort to find the optimum
strategy of polymer flooding EOR. They proposed an intelligent
approach to optimize the initiation time, slug size, and polymer concen-
tration with the aid of nature-inspired optimization algorithms.
Besides, two applications of intelligent models were reported by
Jahani-Keleshteri [311] and Siavashi and Doranehgard [312] in the field of
thermal EOR technique this year. Jahani-Keleshteri [311] employed
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
Figure 4.43 The relative importance of each input parameter on the RF and NPV in an SP flooding process. NPV, Net present value; RF,
recovery factor; SP, surfactant-polymer. Adapted from A. Kamari, et al., Integrating a robust model for predicting surfactantpolymer flood-
ing performance, J. Pet. Sci. Eng. 137 (2016) 8796.
LSSVM to predict the steam distillation rate in a steam injection EOR
process. The model was supposed to predict the target value using four
input variables. The developed LSSVM model could estimate the steam
distillation rate with an AAPRE of 2.999%. Siavashi and Doranehgard
[312] attempted to utilize the PSO algorithm in the way of optimizing
the hot water injection process in heavy oil formations. The optimization
process was done with the aim of maximizing the cumulative oil produc-
tion through adjusting the bottom hole pressure of producing wellbores,
and water injection rate and temperature.
In this year, several scholars focused on utilizing intelligent models in
CO2 injection EOR processes. ANNs were employed by Le Van and
Chon [313] to predict the performance of WAG processes using CO2.
ANNs were aimed to estimate five parameters (RF, oil rate, GOR,
cumulative CO2 production, net CO2 storage) using four input variables
(WAG ratio, duration of each cycle, vertical-to-horizontal permeability
ratio, initial water saturation). Three out of five output parameters (RF,
cumulative CO2 production, net CO2 storage) were in good agreement
with simulated results obtained from the CMG software. Also, the techni-
cally and economically optimized injection design was found through the
developed ANNs. In another study, Rostami et al. [314] aimed to
develop novel correlations for the solubility of CO2 in both live and dead
oil systems during CO2 injection using GEP. In their study, the CO2 sol-
ubility in dead oil was considered as a strong function of Rs, γo, MW, T,
and Ps. To model the CO2 solubility in a live oil system, Pb was consid-
ered as another influential parameter in addition to mentioned parameters.
The developed correlations showed a high accuracy of 0.0378 and 0.0376
in terms of AARD in calculating the CO2 solubility for the dead oil and
live oil systems, respectively. Fig. 4.44 shows the cumulative frequency
plot of the developed correlations and previously published correlations in
the literature.
In 2018, Amirian et al. [315] employed the BPNN and LM methods
for performance evaluation of polymer flooding in heavy oil reservoirs.
They proposed two distinct models for the performance prediction of
polymer flooding in laboratory and field scale. To predict the efficiency
of this technique in laboratory scale, eight input parameters were consid-
ered (viscosity ratio, polymer slug size, polymer molecular weight, poly-
mer
concentration,
permeability,
polymer
solution
viscosity,
oil
viscosity, and porosity), while 13 input variables were used in the field
scale model (well spacing, pilot pattern factor, injection water salinity,
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
Figure 4.44 The cumulative frequency plot for the correlations developed by Rostami et al. and previously published ones.(a) dead oil,
and (b) live oil Adapted from A. Rostami, et al., Modeling of CO2 solubility in crude oil during carbon dioxide enhanced oil recovery using
gene expression programming, Fuel 210 (2017) 768782.
polymer molecular weight, polymer concentration, polymer solution
viscosity, viscosity, formation water salinity, °API gravity, reservoir tem-
perature, permeability, porosity, and reservoir depth). The developed
models could predict the target value with R2 values of 0.9482 and
0.9568 for the testing dataset of experimental and real field reservoir
data, respectively.
In 2019, Moosavi et al. [316] and Menad and Noureddine [317] were
among the scholars who employed AI techniques in EOR processes.
Moosavi et al. [316] utilized ANNs to predict the RF and oil production
rate in CO2-foam flooding processes. Two models were considered: an
MLP and an RBF NN. The RBF network was optimized by GAs, while
the MLP network was trained using the LevenbergMarquardt BP train-
ing algorithm. The RBF and MLP models were able to predict the RF
and oil rate as functions of surfactant type, rock porosity and permeability,
core PV, initial oil saturation, and injected PV of foam. The prediction of
both models showed a high accuracy of higher than 0.997 in terms of R2.
Menad and Noureddine [317] constructed a hybrid model based on the
MLP NN and nondominated-sorting GA version II (NSGA-II) to opti-
mize the WAG processes. The LM algorithm was utilized for training the
MLP network.
4.3.2 Minimum miscibility pressure
MMP is an important two-phase property of reservoir fluids. In miscible
gas injection processes, the injection pressure should be high enough
(higher than MMP) to make the injected gas miscible in the reservoir
fluid. Otherwise, the injection process is considered as an immiscible gas
injection. The miscibility can be attained through first contact or multiple
contact processes. The lowest pressure at which the injected gas can attain
miscibility with the reservoir oil is called MMP [318]. The applications of
the AI techniques in MMP prediction could be discussed in the two-
phase fluid properties section. However, the high correspondence of this
parameter to gas injection processes made us categorize this part in the
EOR section.
Since the experimental determination of MMP between different gases
and reservoir oil is a time-consuming and costly procedure, a considerable
amount of research has been conducted to propose rigorous and rapid
approaches to predict the MMP using AI techniques. Intelligent models
have attained increasing attention in the area of MMP prediction since
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
2003. In this year, Huang et al. [318] made the first approach to predict
the MMP of pure and impure CO2 using ANNs. In their study, the
MMP of pure CO2 in live oil systems was considered as a function of T,
MW of C51 fraction, and mole percent of volatile (CH4 and N2) and
intermediate oil fractions (C2C4). Then, a factor was considered for the
MMP of contaminated CO2 using the concentration of the contaminants
in the injected gas. The developed model was also utilized to investigate
the effect of several parameters (solution gas in CO2, the amount of vola-
tile and intermediate fractions, and their ratio) on the CO2 MMP. The
value of 0.939 was obtained for the R2 of the proposed model.
In 2005, Emera and Sarma [319] utilized GAs to correlate the MMP
of CO2-oil systems with T, MW, and the ratio of volatile-to-intermediate
oil fractions. The developed GA-based model showed an average error of
5.86% in predicting the CO2 MMP.
In 2008, it had been tried to predict the MMP of pure and contami-
nated CO2 using ANNs coupled with GAs [320]. The reservoir tempera-
ture and the composition of both reservoir fluid and injected gas were
considered as input variables. The proposed method could provide predic-
tions with an AAPRE of 2.32%. Fig. 4.45 shows a comparison between
the accuracy of the developed model and the previously published
models.
Another approach was proposed toward CO2 MMP prediction by
Ahmadi et al. [321] in 2011. The authors attempted to predict the MMP
through ANNs with the aid of the stochastic PSO algorithm. The devel-
oped model could estimate the output parameter considering nine input
variables such as T and the composition of oil and injected gas with an R2
of 0.99422.
In 2013, numerous scholars aimed to predict the MMP between dif-
ferent gases and crude oils using intelligent models. To this end, LSSVM
was employed by Shokrollahi et al. [322] to predict the CO2 MMP con-
sidering T, injected gas composition (CO2, H2S, N2, and C1C5), MW
of C51 fraction, and the ratio of volatile (C1 and N2) to intermediate
(C2C4, H2S, and CO2) oil fractions as input parameters. The developed
LSSVM model could predict the MMP with an AAPRE of 9.6%. Tatar
et al. [323] utilized an RBF NN to estimate the MMP of CO2 as a func-
tion of oil composition, T, and the purity of injected CO2. They used the
data bank of Shokrollahi et al. [322]. The predictions of the proposed
model showed an AAPRE of 2.26427%. Fig. 4.46 exhibits the cross-plot
of predicted versus experimental MMP values.
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Application of intelligent models in reservoir and production engineering
Figure 4.45 The accuracy of the GA-ANN model developed by Dehghani et al. in
comparison with other models. ANNs, Artificial neural networks; GA, genetic algo-
rithm. Adapted from S.M. Dehghani, et al., Minimum miscibility pressure prediction
based on a hybrid neural genetic algorithm, Chem. Eng. Res. Des. 86 (2) (2008)
173185.
Figure 4.46 The cross-plot of predicted MMP by the RBF neural network developed
by Tatar et al. versus experimental MMP values. MMP, Minimum miscibility pressure;
RBF, radial basis function. Adapted from A. Tatar, et al., Implementing radial basis func-
tion networks for modeling CO2-reservoir oil minimum miscibility pressure, J. Nat. Gas
Sci. Eng. 15 (2013) 8292.
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
In another investigation, Rahimzadeh Kivi et al. [324] applied ANFIS
to correlate the CO2 MMP with 27 independent variables. The devel-
oped ANFIS-based correlation showed an AAPRE of 3.9%. The further
approach toward predicting the CO2 MMP was reported by Chen et al.
[325]. The authors made an effort to estimate the CO2 MMP through T,
MW of C51 fraction, mole percent of volatile (CH4 and N2) and inter-
mediate oil fractions (C2C4), and impurities of the injected CO2 with
the aid of ANNs. The values of 3.46% and 8.77% were obtained as the
AAPRE of the developed model for predicting the pure and impure CO2
MMP, respectively. Zendehboudi et al. [326] utilized ANNs along with
the PSO algorithm to model the CO2 MMP as a function of T and the
composition of reservoir fluid and injected gas. They stated that the reser-
voir temperature is the most influential variable on the value of MMP.
The developed PSO-ANN could efficiently estimate the MMP of CO2
in the Azadegan oil field of Iran, unlike the other methods utilized in
their study.
In 2014, different AI techniques were employed by researchers to pre-
dict the MMP of CO2-oil systems. In all of the studies conducted in this
year, it had been tried to provide accurate predictions for CO2 MMP using
temperature and the composition of oil and injected gas. For this purpose,
Ahmadi and Ebadi [327] used the FL algorithm. The performance of the
FL model was investigated using different membership functions. The
curve-shape membership function could provide the most accurate predic-
tion. The performance of different FL models, along with some other mod-
els, can be seen in Fig. 4.47. Shahrabi et al. [328] proposed their approach
using ANFIS. The developed model showed an AAPRE of 4.6% in pre-
dicting the CO2 MMP. Chen et al. [329] attempted to develop a hybrid
model using BP-ANNs and GAs. The developed GA-BPNN could pro-
vide predictions for pure and impure CO2 with an AAPRE of 5.51%.
Kaydani et al. [330] proposed a method using MGGP, which was able to
estimate the MMP with an AAPRE of 2.15%. Sayyad et al. [331] tried to
optimize ANN using the PSO algorithm to accurately estimate the MMP
of CO2. The developed model showed an accuracy of 2.32% and 3.27% in
terms of AAPRE for pure and impure CO2, respectively.
In another study, different AI techniques were employed by Asoodeh
et al. [332] to predict the CO2-oil MMP through the aforementioned
parameters. The authors investigated the efficiency of ANNs, SVMs, and
CMs. The results indicated that the CM could outperform the other
models, and SVMs showed better performance in comparison to ANNs.
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Application of intelligent models in reservoir and production engineering
In 2015, Ahmadi et al. [333] and Kamari et al. [334] attempted to esti-
mate the CO2 MMP via intelligent models. Ahmadi et al. utilized the
LSSVM framework along with different evolutionary algorithms (EAs) to
predict the MMP of CO2 as a function of T, the ratio of volatile-to-
intermediate oil fractions, and MW of C51 fraction. They employed RBF
as the kernel function. Fig. 4.48 shows the comparison between the
AAPRE of different developed models. Kamari et al. [334] employed the
GEP technique to correlate the MMP of CO2 with the pseudo critical
temperature of pure and impure injected CO2, reservoir temperature, the
ratio of volatile-to-intermediate oil fractions, and MW of C51 fraction.
They used the data bank of Shokrollahi et al. [322]. The value of
10.474% was obtained as the AAPRE of the developed correlation.
In other investigations, ANNs were employed by Kasiri and Akbari
[335] to estimate the MMP of different gases such as N2, CO2, and
hydrocarbon gases through the critical properties of C71 oil fraction, the
composition of reservoir oil and injected gas, and reservoir temperature.
The developed model showed an AAPRE of 2.21%. Fathinasab et al.
[336] utilized the GP method along with constrained multivariable search
methods to develop a correlation for nitrogen MMP as a function of T
Figure 4.47 Comparison between the fuzzy models and conventional MMP models.
MMP, Minimum miscibility pressure. Adapted from M.-A. Ahmadi, M. Ebadi, Fuzzy
modeling and experimental investigation of minimum miscible pressure in gas injection
process, Fluid Phase Equilib. 378 (2014) 112.
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
and thermodynamic properties of oil and injected gas. The proposed cor-
relation could estimate the MMP of N2 with an AAPRE of 10.02%.
Fig. 4.49 shows the cumulative frequency plot for the developed correla-
tion and previously published models.
In 2016, various attempts on the prediction of CO2 MMP using T
and the composition of injected gas and reservoir oil were reported in the
literature. SVMs were employed by two groups of researchers. Bian et al.
[337] developed a hybrid model based on SVMs and GAs. The GA-SVM
model was able to predict pure and impure CO2 MMPs with an AAPRE
of 4.75% and 7.69%, respectively. Zhong and Carr [338] proposed an
SVM approach utilizing MKF toward predicting the CO2 MMP, which
showed an accuracy of 0.9381 in terms of the correlation coefficient.
Hemmati-Sarapardeh et al. [339] and Mollaiy-Berneti [340] developed
an ANN and an ANFIS model to estimate the MMP using the aforemen-
tioned variables, respectively. Hemmati-Sarapardeh et al. [339] compared
the performance of the developed ANNs with different models that are
shown in Fig. 4.50. Hemmati-Sarapardeh et al. [339] used the data bank
Figure 4.48 The comparison between the AAPRE of different models developed by
Ahmadi et al. AAPRE, Average absolute percent relative error. Adapted from M.A.
Ahmadi, et al., Connectionist model for predicting minimum gas miscibility pressure:
application to gas injection process, Fuel 148 (2015) 202211.
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Application of intelligent models in reservoir and production engineering
Figure 4.49 The cumulative frequency plot for the correlation developed by Fathinasab et al. and some other models. Adapted from M.
Fathinasab, S. Ayatollahi, A. Hemmati-Sarapardeh, A rigorous approach to predict nitrogen-crude oil minimum miscibility pressure of pure
and nitrogen mixtures, Fluid Phase Equilib. 399 (2015) 3039.
of Shokrollahi et al. [322]. The developed ANFIS model [340] was able
to provide predictions with an R2 of 0.9823.
Also, Fathinasab and Ayatollahi [341] developed their MMP predictive
models with the aid of the GP algorithm. They employed GP along with
constrained multivariable search methods to develop a correlation, which
could calculate the MMP of CO2 with an AAPRE of 11.76%. Fig. 4.51
illustrates the cumulative frequency plot of the developed correlation and
several previously published ones.
Another approach was proposed by Hemmati-Sarapardeh et al. [342]
toward predicting the MMP of N2-crude oil systems using the LSSVM algo-
rithm. They employed the CSA algorithm to optimize the LSSVM model.
The CSA-LSSVM could estimate the MMP value for pure and impure nitro-
gen with an AAPRE of 5.17%. They used the data bank of Fathinasab et al.
[336]. The efficiency of the developed CSA-LSSVM model in predicting the
MMP for the testing dataset is shown in Fig. 4.52.
Figure 4.50 The AAPRE comparison between the ANN model developed by
Hemmati-Sarapardeh et al. and other correlations. AAPRE, Average absolute percent
relative error; ANNs, artificial neural networks. Adapted from A. Hemmati-Sarapardeh,
et al., Accurate determination of the CO2-crude oil minimum miscibility pressure of pure
and impure CO2 streams: a robust modelling approach. Can. J. Chem. Eng. 94 (2) (2016)
253261.
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Application of intelligent models in reservoir and production engineering
Figure 4.51 Cumulative frequency plot for the correlation developed by Fathinasab and Ayatollahi in comparison to previously pub-
lished ones. Adapted from M. Fathinasab, S. Ayatollahi, On the determination of CO2crude oil minimum miscibility pressure using genetic
programming combined with constrained multivariable search methods, Fuel 173 (2016) 180188.
Figure 4.52 The accuracy of the CSA-LSSVM model developed by Hemmati-Sarapardeh et al. in predicting the MMP of pure and impure
nitrogen. CSA, Coupled simulated annealing; LSSVM, least square support vector machine; MMP, minimum miscibility pressure. Adapted
from A. Hemmati-Sarapardeh, et al., Determination of minimum miscibility pressure in N2crude oil system: a robust compositional model,
Fuel 182 (2016) 402410.
In 2017, Karkevandi-Talkhooncheh et al. [343] and Ahmadi et al.
[344] attempted to estimate the CO2-oil MMP using reservoir tempera-
ture, pseudo critical temperature of injected gas, oil composition, and
MW of C51 oil fraction. Karkevandi-Talkhooncheh et al. [343] utilized
ANFIS to predict the MMP. They used the data bank of Fathinasab and
Ayatollahi [341]. They investigated the performance of various EAs in
optimizing the ANFIS structure. Among ACO, GA, PSO, BP, and differ-
ential evolution (DE) algorithms, PSO showed the highest performance,
which is shown in Fig. 4.53. In the study of Ahmadi et al. [344]
employed GEP to develop an accurate correlation to calculate the MMP.
The developed GEP-based correlation showed a high performance, which
is shown in Fig. 4.54 in comparison to previously published correlations.
In 2018, Karkevandi-Talkhooncheh et al. [345] and Amar and Zeraibi
[346] utilized RBF NNs and SVMs to predict the CO2-oil MMP, respec-
tively. They both considered reservoir temperature, pseudo critical tem-
perature of injected gas, oil composition, and MW of C51 oil fraction as
input parameters. Karkevandi-Talkhooncheh et al. [345] employed differ-
ent EAs to optimize the RBF network, among which the imperialist
competitive algorithm (ICA) showed the highest performance and could
improve the accuracy of RBF predictions to high accuracy of 6.01% in
terms of AAPRE. They used the data bank of Fathinasab and Ayatollahi
[341]. The comparison between the developed RBF networks and previ-
ously published predictive models is shown in Fig. 4.55. Amar and
Zeiraibi [346] constructed a model comprising SVMs and artificial bee
colony (ABC) optimization algorithm. The SVM-ABC showed AAPRE
values of 2.75% and 4.33% in predicting the MMP of pure and impure
CO2 streams, respectively.
In 2019, different attempts were made to estimate the MMP of pure
and impure CO2. To this end, the GMDH scheme was employed by
Huang et al. [347] and Delforouz et al. [348] to develop accurate correla-
tions. In the modified GMDH networks proposed by Huang et al. the
CO2-oil MMP was considered as a function of T and the composition
of crude oil and injected gas. Delforouz et al. utilized T, Ppc and Tpc of
injected gas, the ratio of volatile-to-intermediate oil fractions, and MW of
C51 fraction as input variables. Apart from the modified GMDH correla-
tions, Huang et al. developed three other models based on BPNN, GEP,
and traditional GMDH. The results showed the outperformance of the
modified GMDH network with MAPE values of 13.6% and 13.78%
for predicting the pure and impure CO2 streams, respectively. The
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
Figure 4.53 The performance of ANFIS models developed by Karkevandi-Talkhooncheh et al. in comparison with different models
reported in the literature. ANFIS, Adaptive neuro-fuzzy inference system. Adapted from A. Karkevandi-Talkhooncheh, et al., Application of
adaptive neuro fuzzy interface system optimized with evolutionary algorithms for modeling CO2-crude oil minimum miscibility pressure, Fuel
205 (2017) 3445.
Figure 4.54 The MSE comparison between the correlation developed by Ahmadi et al. and other correlations. MSE, Mean square error.
Adapted from M.A. Ahmadi, S. Zendehboudi, L.A. James, A reliable strategy to calculate minimum miscibility pressure of CO2-oil system in mis-
cible gas flooding processes, Fuel 208 (2017) 117126.
Figure 4.55 The AAPRE comparison between the RBF networks developed by Karkevandi-Talkhooncheh et al. and other existing meth-
ods. AAPRE, Average absolute percent relative error; RBF, radial basis function. Adapted from A. Karkevandi-Talkhooncheh, et al., Modeling
minimum miscibility pressure during pure and impure CO2 flooding using hybrid of radial basis function neural network and evolutionary
techniques, Fuel 220 (2018) 270282.
correlations developed by Delforouz et al. could determine the MMP of
pure and impure CO2 with AAPRE values of 8.35% and 12.89%,
respectively.
In another investigation, ANNs were employed by Dong et al. [349].
They considered T and oil composition as independent predictors. The
developed ANNs could estimate the CO2 MMP with an R2 of 0.948.
Barati-Harooni et al. [350] attempted to predict the N2 MMP with
respect to T, average critical temperature of injected gas, mole fractions of
volatile and intermediate fractions and MW of C71 oil fraction with the
aid of three intelligent models, namely, GA-RBF, PSO-ANFIS, and
MLP. According to the obtained results, the GA-RBF model exhibited
the highest performance with an AAPRE of 1.9%. In the study of
Choubineh et al. [351], ANNs were developed to predict the MMP of
different gases according to T, the ratio of volatile-to-intermediate oil
fractions, MW of C51 fraction, and SG of the injected stream. The ANN
structure of 910-9-1 with logsig (for input layer) and purelin (for hidden
and output layers) activation functions showed the accuracy of 0.95 in
terms of R2.
In 2020, Dargahi-Zarandi et al. [352] developed a new model,
namely, AdaBoost SVR, to predict the MMP of pure and contaminated
CO2 streams through reservoir temperature, pseudo critical temperature
of injected gas, mole fractions of volatile and intermediate fractions and
MW of C51 oil fraction. They compared the performance of the devel-
oped AdaBoost SVR model with the GMDH and MLP-based networks.
The results indicated that the AdaBoost SVR outperformed both GMDH
and MLP models with an AAPRE of 3.09%. Fig. 4.56 illustrates the per-
formance of AdaBoost SVR, GMDH, and MLP models in predicting the
MMP of pure and contaminated CO2.
4.4 Well test analysis
Well testing is a subdivision of reservoir and production engineering,
which is usually considered as a significant step toward qualitatively and
quantitatively characterization of hydrocarbon reservoirs. The identifica-
tion of well test interpretation models and estimation of reservoir para-
meters are two important parts of a well test analysis process [353]. The
information provided by well tests is also important for estimating the res-
ervoir productive capacity and average pressure. The analysis of reservoir
performance and predicting its future production is based on having
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
appropriate information about the reservoir properties and circumstances.
Generally, oil and gas well test analyses are performed to achieve several
objectives [354]:
•
characterizing the reservoir and evaluating the well condition,
•
predicting the skin factor (a measure of formation damage),
•
describing the reservoir through the calculation of reservoir parameters,
and
•
determining the productive zones of a drilled well.
The first applications of AI techniques in the area of well test analysis
date back to the early 1990s. During this decade, ANNs aided many scho-
lars to propose different approaches toward the identification of well test
interpretation models. In this way, Al-Kaabl et al. [355] attempted to ana-
lyze the well test data with respect to pressure versus time data, fluid and
test type (draw-down or build-up test), ρo, μo, Co, ϕ, fluid saturations,
and rate data. In another study, ANNs were employed by Allain and
Horne [356] to automate the process of well test interpretation model
identification. In 1993 Juniardi and Ershaghi [357] utilized ANNs to dis-
tinguish the reservoir type through well-testing data. In this year, other
Figure 4.56 The performance of AdaBoost SVR, GMDH, and MLP models in predict-
ing the MMP of pure and contaminated CO2. AdaBoost SVR, Adaptive boosting sup-
port vector regression; GMDH, group method of data handling; MLP, multilayer
perceptron; MMP, minimum miscibility pressure. Adapted from A. Dargahi-Zarandi,
et al., Modeling minimum miscibility pressure of pure/impure CO2-crude oil systems
using adaptive boosting support vector regression: application to gas injection processes,
J. Pet. Sci. Eng. 184 (2020) 106499.
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Application of intelligent models in reservoir and production engineering
attempts were made to utilize ANNs in this area by Al-Kaabi and Lee
[358] and Ershaghi et al. [359]. In 1995 three studies were conducted in
order to interpret well tests. Kumoluyi et al. [360] utilized higher order
NNs. Sung et al. [361] constructed their model based on ANNs, which
were trained by the BP algorithm. Athichanagorn and Horne [362] pro-
posed their model through the hybridization of ANNs with the sequential
predictive probability method.
In the subsequent decade, ANNs and GAs were the most applied AI
techniques in this area. The application of ANNs was reported in five
studies, and the GA was employed in one research. Deng et al. [353] con-
structed their ANN using the binary number of pressure derivative curves
instead of the data point series of the curves with the aim of identifying
the well test interpretation model. Aydinoglu et al. [363] attempted to
analyze the pressure transient data obtained from the simulation of a
faulted formation with the aid of ANNs. Jeirani and Mohebbi [364] intro-
duced the build-up test data to ANNs as input parameters for estimating
the permeability, skin factor, and initial pressure of oil reservoirs. The
developed models could estimate these parameters for different kinds of
reservoirs with an error of lower than 6.6%, 7.2%, and 0.44%, respec-
tively. The applicability of BP ANNs in well test analysis of fractured for-
mations was investigated in the study of Alajmi and Ertekin [365]. The
authors considered the permeability/porosity of the matrix/fracture in a
dual-porosity formation as the outputs of the ANNs. The inputs of the
ANNs were obtained through the polynomial fit algorithm that was
employed to report the well test data in terms of polynomial coefficients.
Guyaguler et al. [366] were the scholars who employed GAs to select the
reservoir model through pressure transient data automatically.
Since 2010, many scholars attempted to utilize intelligent models in
this field. In this way, ANNs were employed in six studies, while the use
of other AI techniques was reported in two investigations. In 2011, Vaferi
et al. [367] utilized an MLP ANN to select the reservoir model through
well test data automatically. They selected the SCG and 12 hidden neu-
rons as the best training algorithm and the best number of neurons in the
hidden layer, respectively.
In 2014, Ghaffarian et al. [368] and Adibifard et al. [369] were among the
scholars who investigated the applicability of ANNs in this area. Ghaffarian
et al. [368] applied different MLP networks to identify the gas condensate res-
ervoir model using the data points of pseudo pressure derivative plots as the
inputs. In their study, first, 12 reservoir-boundary models were recognized
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
with the aid of a single MLP network. Afterward, the recognized models
were clustered into three clusters, for each of which a distinct MLP network
was developed to increase the accuracy of the model identification.
Adibifard et al. [369] investigated the performance of ANNs in the esti-
mation of reservoir parameters for a naturally fracture reservoir with the
pseudo-steady-state interporosity flow using well test data obtained from
theoretical pressure derivative curves. They considered the coefficients of
the fitted Chebyshev polynomials on the pressure derivative data as the
inputs of the ANNs as well as flow rate, μo, Bo, h, ϕ, Ct, and rw. The
authors employed the LM training algorithm to train the network. In their
study, the Chebyshev polynomials coefficients were replaced by conven-
tional polynomial coefficients and normalized pressure derivative data for
the sake of comparison. The developed network and the comparison made
for different inputs are shown in Figs. 4.57 and 4.58, respectively.
In 2015, Vaferi et al. [370] could develop a recurrent ANN, which
was able to categorize the reservoir models based on pressure transient
data with an accuracy of 98.39%. In this year, Awotunde [371] utilized
three optimization algorithms, namely, PSO, DE, and covariance matrix
adaptation evolution strategy to predict various reservoir parameters such
as drainage radius, wellbore storage coefficient, skin factor, and reservoir
permeability in different reservoir models (fractured, radial composite, and
homogeneous reservoirs) through well test data. The obtained results
revealed the outperformance of the DE algorithm over the other models.
In the subsequent year, Vaferi et al. [372] employed an MLP ANN to
recognize the reservoir model among eight different models through
well-testing data. Their model had 17 neurons in its hidden layer and
could classify the reservoirs with an accuracy of higher than 94%.
In 2017, Ahmadi et al. [373] attempted to identify the well test inter-
pretation model and estimate the reservoir parameters through various
versions of ANNs. The developed MLP, GRNN, and PNN were vali-
dated with seven real build-up tests. In their study, PNN was aimed to
classify the reservoir models, while GRNN and MLP were considered as
predictive tools for predicting the reservoir permeability, wellbore storage
coefficient, skin factor, and storativity ratio.
4.5 Formation damage
An inevitable and undesirable problem in the petroleum industry that
occurs during different steps of an oil field development and production is
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Application of intelligent models in reservoir and production engineering
Figure 4.57 The structure of the ANN model developed by Adibifard et al. ANN, Artificial neural network. Adapted from M. Adibifard, S.
Tabatabaei-Nejad, E. Khodapanah, Artificial neural network (ANN) to estimate reservoir parameters in naturally fractured reservoirs using well
test data, J. Pet. Sci. Eng. 122 (2014) 585594.
referred to as formation damage. Many controllable and uncontrollable
factors may cause this issue. In other words, any factor that can cause the
disturbance of the existing equilibrium (between different phases) inside a
porous medium may potentially be considered as a cause of formation
damage. Formation damage can severely reduce reservoir permeability
and well performance [374]. According to Porter [375], formation damage
cannot be considered as a necessarily reversible issue. Considering this
fact, accurate prediction of formation damage prior to its occurrence can
be a great contribution to the petroleum industry.
AI techniques have been employed to predict or assess the formation
damage since the 1990s. The first employment of these methods was
reported in 1996. Kalam et al. [376] utilized ANNs to evaluate the forma-
tion damage severity. The ANNs were trained using the BP algorithm.
They aimed to assess the formation damage through observing changes in
wettability or relative permeability curves. Hence, the developed ANNs
were supposed to predict relative permeability curves and wettability using
connate water saturation (Swc) and residual oil saturation (Sor) for different
rock types.
Figure 4.58 The MRE comparison for the developed ANN model with different
inputs. ANN, Artificial neural network; MRE, mean relative error. Adapted from M.
Adibifard, S. Tabatabaei-Nejad, E. Khodapanah, Artificial neural network (ANN) to esti-
mate reservoir parameters in naturally fractured reservoirs using well test data, J. Pet.
Sci. Eng. 122 (2014) 585594.
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Application of intelligent models in reservoir and production engineering
In 2002, Zuluaga et al. [377] attempted to predict the impairment of
the permeability owing to the invasion of foreign particles in nonconsoli-
dated formations through ANN and FL approaches. In their investigation,
ϕ, K, flowrate, and the concentration of fines were considered as ANN
model input variables. In addition to these parameters, PV injected was
considered as another input for the developed fuzzy model. The best
ANN structure was selected as 47-17 with sigmoid activation function.
ANNs were supposed to provide a prediction for permeability in 17 points
of the core. The results showed a higher performance of ANNs in com-
parison to the FL model.
In 2010, Rezaian et al. [378] utilized ANNs to predict permeability
diminishing owing to asphaltene deposition. In their research, injection
velocity and duration, as well as initial permeability (Ki) and asphaltene
concentration, were fed to the constructed MLP network to estimate the
permeability variation. They considered 26 neurons in the hidden layer,
and tansig activation function was employed in their network. The model
was trained using the LM algorithm. The developed model showed an
AAPRE of 8.3%.
In 2011, Zabihi et al. [379] proposed an ANN approach toward pre-
dicting permeability impairment as a result of sulfate scaling. In their
study, BP algorithm was employed to train the MLP network in order to
predict the damaged permeability (Kd) as a function of T, ΔP, Ki, volume
of water injected, and the concentration of barium (C21
Ba ) and sulfate
(C22
SO4) ions in the formation water and seawater, respectively. The authors
also compared the capability of different training algorithms (Fig. 4.59).
They selected the structure of 69-3-1 as the most efficient structure for
their MLP network that was trained through the LM algorithm with BR.
They utilized tansig as the activation function of the hidden layers. The
developed network could predict the target parameter with an AAPRE of
1.06% for the testing dataset.
In 2012, an ANN approach was proposed by Bai et al. [380] toward
predicting the water sensitivity index considering montmorillonite, illite,
carbonate, clay, and Arl-Tier content and air permeability as input para-
meters. The BP algorithm, along with the sigmoid activation function,
was employed to develop the network. The developed model had 10
neurons in its hidden layer and could estimate the sensitivity index with
an MAE of 0.03.
In this year, Rezaian et al. [381] utilized ANNs to predict the ratio of
K/Ki considering injection velocity and duration, as well as initial
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
permeability (Ki) and asphaltene concentration as input variables. The
MLP network was trained using the LM algorithm, and tansig was
employed as the activation function. The constructed network had
26 neurons in its hidden layer.
In 2013, Foroutan and Moghadasi [382] made an effort to predict
the relative permeability during a calcium sulfate precipitation process
using ANNs. They introduced T, flowrate, scaling index, and time as
input parameters to a network that was trained using the LM algorithm.
Their constructed model had two hidden layers (each of them had
10 neurons). The authors utilized purlin and tansig transfer functions in
the first and second hidden layers, respectively. The accuracy of the
developed model yielded to 0.97911 in terms of the coefficient of
determination.
Another intelligent approach was proposed toward predicting the scale
deposition in 2014. Kamari et al. [383] utilized LSSVMs that were opti-
mized with the CSA algorithm to estimate the barium sulfate deposition
as a function of NaCl concentration and temperature. The performance
Figure 4.59 The comparison between the training algorithms employed by Zabihi
et al. Adapted from R. Zabihi, et al., Artificial neural network for permeability damage
prediction due to sulfate scaling, J. Pet. Sci. Eng. 78 (34) (2011) 575581.
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Application of intelligent models in reservoir and production engineering
of the developed LSSVM model in predicting the solubility product data
(Ksp) at different temperatures was compared to those of ANNs and sev-
eral other published models (Fig. 4.60).
In 2015, a similar model was developed by Shokrollahi et al. [384] to
predict the impairment of reservoir permeability during a waterflooding
process, which usually occurs due to the incompatibility of the injected
water with native formation water. The developed CSA-LSSVM model
was supposed to predict the permeability reduction factor (Kd/Ki) regard-
ing the injection rate, volume of injected water, T, ΔP, Ki, and solution
ionic components after seawater injection as input parameters. They
employed the RBF as the kernel function. The predictions of the CSA-
LSSVM approach showed an AAPRE of 0.33%. Fig. 4.61 shows the high
accuracy of the developed model in predicting the Kd/Ki ratio.
In another study in 2015, Sun et al. [385] proposed a predictive model
based on quantum NNs to estimate the severity of aqueous phase trapping
(a formation damage mechanism). They considered the initial water satu-
ration (Swi), IFT between oil and water, ϕ, gas permeability, and average
pore diameter as input parameters. The developed model with nine neu-
rons in its hidden layer could predict the target parameter with an
AAPRE of 4.86%.
Two years later, Ahmadi et al. [386] attempted to predict the
impairment of permeability caused by scale deposition in a porous
medium. They investigated the applicability of ANNs along with different
evolutionary optimization algorithms for this aim. Eight input variables
were introduced to the networks, including T, Ki, injection rate, pressure
drop, and the concentration of calcium, strontium, barium, and sulfate.
The hidden layer of the developed model had seven neurons, and sigmoid
transfer function in its structure. The ICA, PSO, and GAs were employed
as well as a hybrid model based on PSO and GAs (HGAPSO) to enhance
the performance of the developed network. The obtained results indicated
that the HGAPSO-ANN model could outperform the other model with
an R2 of 0.9969.
In 2019, Rostami et al. [387] aimed to develop a novel correlation for
evaluating the formation damage owing to mixed sulfate deposition based
on the GEP algorithm. To this end, Kd was considered as a function of T,
ΔP, flowrate, injected volume, and concentrations of the existing ions in
the formation. The developed correlation showed an accuracy of 0.640%
in terms of AAPRE. Fig. 4.62 illustrates the cross-plot of predicted versus
experimental value of the Kd.
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
Figure 4.60 The comparison of Ksp calculated via different approaches. Adapted from A. Kamari, et al., Rigorous modeling for prediction of
barium sulfate (barite) deposition in oilfield brines, Fluid Phase Equilib. 366 (2014) 117126.
Figure 4.61 The capability of the CSA-LSSVM model developed by Shokrollahi et al. in predicting the values of Kd/Ki ratio in the testing
dataset. CSA, Coupled simulated annealing; LSSVM, least square support vector machine. Adapted from A. Shokrollahi, et al., Rigorous
modeling of permeability impairment due to inorganic scale deposition in porous media, J. Pet. Sci. Eng. 130 (2015) 2636.
Figure 4.62 Cross-plot of Kd predicted by GEP-based correlation versus experimental values. GEP, Gene expression programing. Adapted
from A. Rostami, et al., Application of a new approach for modeling the oil field formation damage due to mineral scaling, Oil Gas Sci.
Technol. - Rev. IFP Energ. Nouvelles 74 (2019) 62.
4.6 Asphaltene
Asphaltenes are very complex molecular compounds that exist as a heavy
fraction in petroleum. These heavy macromolecules are insoluble in non-
polar solvents (i.e., n-heptane). However, they dissolve in aromatic sol-
vents such as toluene. Asphaltenes are made up of aromatic rings with
long alkane branches. They are known as the oil fraction with the highest
values of molecular weight, polarity, and aromaticity. A hypothetical
asphaltene molecule is shown in Fig. 4.63. In such a structure, inorganic
components (nitrogen and sulfur) may exist along with metals such as
vanadium and nickel [389].
Asphaltenes have the potential to considerably damage the oil-bearing
formation and production equipment. If the oil system becomes unstable,
asphaltene molecules can flocculate, precipitate, and deposit. The stability
of asphaltenes can be disturbed when the thermodynamic properties of
the mixture (pressure, temperature, composition) change. Asphaltene pre-
cipitation and deposition can lead to catastrophic formation damage
Figure 4.63 A hypothetical asphaltene molecule. Adapted from J.S. Amin, et al.,
Investigating the effect of different asphaltene structures on surface topography and
wettability alteration, Appl. Surf. Sci. 257 (20) (2011) 83418349 [388].
192
Applications of Artificial Intelligence Techniques in the Petroleum Industry
problems in terms of pore throat plugging and wettability alteration
during different phases of field development (drilling, production, and
injection). Therefore, it is essential to predict this phenomenon so that
preventive precautions can be made.
In recent decades, different techniques have been introduced to evalu-
ate
asphaltene
precipitation,
including
refractive-index
evaluator,
asphaltene-solubility evaluator, and rules-of-thumb evaluator. Since 2004,
AI approaches have aided many scholars to predict the asphaltene precipi-
tation prior to its occurrence. In this year, Lababidi et al. [390] could eval-
uate asphaltene precipitation potential using the FL approach. In another
study, in 2009, Zahedi et al. [391] utilized ANNs to predict the amount
the asphaltene precipitation using oil composition, P, T, and the ratio of
the solvent to oil as input parameters. They trained the network through
the LM algorithm and used 15 neurons in the hidden layer. The con-
structed model could predict the target values with high accuracy.
Since then, many researchers investigated the applicability of intelligent
models in predicting the asphaltene precipitation based on various influen-
tial parameters. In 2010, Sayyad Amin et al. [392] constructed a Bayesian
belief network that yielded an AAPRE of 4.6%. Ashoori et al. [393] pro-
posed their model using ANNs considering T, MW of n-alkane, and dilu-
tion ratio as input parameters. Their model had five neurons in its hidden
layer and was constructed using sigmoidal activation functions.
In 2011, Ahmadi [394] and Abedini and Abedini [395] were the scho-
lars who utilized ANNs to predict the precipitation of asphaltene with
regard to temperature, the molecular weight of the precipitant, and the
dilution ratio. They both employed the LM algorithm to train the mod-
els. Ahmadi [349] employed the ICA optimization technique to seek the
optimum initial weights of the ANN. The predictive model developed by
Abedini and Abedini [395] showed an AAPRE of 24.6%, and the ANN-
ICA model showed an R2 value of 0.99367.
Four studies were conducted on the intelligential prediction of asphal-
tene precipitation by different investigators in 2012. Ahmadi [396] devel-
oped another model based on ANNs with the aim of predicting
asphaltene precipitation as a function of temperature, the molecular
weight of the precipitant, and the dilution ratio. In this study, they
attempted to determine the initial weights of the ANNs with the aid of
the unified PSO (UPSO) algorithm. The developed ANN-UPSO model
was trained through the LM algorithm and could predict the amount of
asphaltene precipitates with an R2 of 0.9949.
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Application of intelligent models in reservoir and production engineering
Ahmadi and Golshadi [397] and Ahmadi and Shadizadeh [398]
developed different ANNs to estimate the asphaltene precipitation due to
natural depletion based on pressure and temperature. In both studies, the
LM training algorithm was employed. Ahmadi and Golshadi [397] opti-
mized their ANN using an HGAPSO that lead to an R2 of 0.96463.
Their model had seven neurons in its hidden layer, and the sigmoid trans-
fer function was employed in this layer. In the study of Ahmadi and
Shadizadeh [398], the initial weights of the network were provided using
the PSO algorithm that resulted in an AAPRE of less than 4%. The archi-
tecture of the developed ANN-PSO model was similar to the ANN-
HGAPSO model.
In another study in 2012, asphaltene precipitation was modeled con-
sidering a different set of input parameters. Rasuli Nokandeh et al. [399]
developed an ANN model that was able to take composition, T, P, °API,
GOR, and Pb as input parameters and return the amount asphaltene pre-
cipitation with an AAPRE of 10.2% for the validation set. Their model
had three hidden layers that respectively had 10, 20, and 10 neurons.
Fig. 4.64 shows the accuracy of their model in predicting the weight per-
cent of the asphaltene precipitation for the validation set.
Various intelligent models were employed by scholars in 2013 to
model the amount of asphaltene precipitation with respect to different
independent variables. Hemmati-Sarapardeh et al. [400] introduced the
Figure 4.64 The results of the ANN model developed by Rasuli Nokandeh et al.
ANNs, Artificial neural networks. Adapted from N. Rasuli Nokandeh, M. Khishvand, A.
Naseri, An artificial neural network approach to predict asphaltene deposition test result,
Fluid Phase Equilib. 329 (2012) 3241.
194
Applications of Artificial Intelligence Techniques in the Petroleum Industry
first application of the LSSVM modeling technique in the area of asphal-
tene precipitation prediction. They utilized the SA technique to optimize
the LSSVM model. The developed model was able to predict the asphal-
tene precipitation with respect to P, Pb, °API, T, and SARA fractions
weight percent with an AAPRE of 18.69%. The cross-plot of the devel-
oped LSSVM model is provided in Fig. 4.65. In another approach,
Zendehboudi et al. [401] utilized the ICA optimization technique to opti-
mize their ANN model that was constructed with the aim of predicting
both bubble point pressure and asphaltene precipitation simultaneously
taking T, P, composition, and GOR as input parameters. In order to pre-
dict the Pb, the model needed the GOR as the input, while it used pres-
sure to estimate the asphaltene precipitation. Their model had one hidden
layer consisted of 7 neurons in its structure. The ICA-ANN model was
trained using the LM algorithm and could provide predictions for the
amount of asphaltene precipitates with a maximum ARE of 6.95%.
Salahshoor et al. [402] made their effort toward predicting the asphaltene
deposition in terms of pressure drop (dP) and permeability impairment
(Kd/Ki) as a function of time and the injected PV of oil using the ANFIS
method.
Figure 4.65 The cross-plot of the asphaltene precipitation model developed by
Hemmati-Sarapardeh et al. Adapted from A. Hemmati-Sarapardeh, et al., Asphaltene
precipitation due to natural depletion of reservoir: determination using a SARA fraction
based intelligent model, Fluid Phase Equilib. 354 (2013) 177184.
195
Application of intelligent models in reservoir and production engineering
In 2014, two studies were conducted to predict the amount of asphal-
tene precipitation regarding temperature, the molecular weight of the pre-
cipitant, and the dilution ratio as input parameters. To this end,
Chamkalani et al. [403] developed their predictive model using the
LSSVM algorithms. They optimized their LSSVM model using the PSO
algorithm and utilized the RBF as the kernel function. Their LSSVM-
PSO model showed an accuracy of 8.9% in terms of AAPRE for the
testing dataset. In another approach, Asoodeh et al. [404] attempted to
construct a CMIS through FL and ANNs. They employed the GA-
pattern search technique to optimize both ANN and FL models. The
developed CMIS showed better efficiency in comparison to all individual
models with an R2 of 0.99. Fig. 4.66 illustrates the efficiency of the devel-
oped CMIS in predicting the measured asphaltene precipitation data.
In this year, Zendehboudi et al. [405] proposed a different approach
toward predicting the asphaltene precipitation at static and dynamic con-
ditions. In other words, the models were aimed to predict the amount of
precipitated asphaltene in the presence and absence of the porous
medium. They employed ANNs as the predictive tool and optimize them
Figure 4.66 The capability of the CMIS model developed by Asoodeh et al. in pre-
dicting the amount of asphaltene precipitation. CMIS, Committee machine intelligent
system. Adapted from M. Asoodeh, A. Gholami, and P. Bagheripour, Asphaltene precipi-
tation of titration data modeling through committee machine with stochastically opti-
mized fuzzy logic and optimized neural network, Fluid Phase Equilib. 364 (2014) 6774.
196
Applications of Artificial Intelligence Techniques in the Petroleum Industry
with the PSO and ICA algorithms. The optimized networks showed a
better accuracy in comparison to conventional ANNs with the outperfor-
mance of the ICA-ANN model.
In the subsequent year, Naseri et al. [406]. Ansari and Gholami [407],
and Fattahi et al. [408] were the scholars who utilized intelligent modeling
techniques to model asphaltene precipitation considering temperature, the
molecular weight of the precipitant, and the dilution ratio as input vari-
ables. Naseri et al. [406] construct their model based on the RBF ANNs.
Their model could provide predictions with an AAPRE of 2.46%. In the
study of Ansari and Gholami [407], their SVM model was optimized
through the ICA optimization technique. The Gaussian RBF was
employed in the structure of their SVM-ICA model. The predictions of
the developed model showed an R2 of 0.9921 for the testing dataset.
Fattahi et al. [408] developed their predictive models using the SVM algo-
rithm and employed the harmony search algorithm to find the optimal
value of the SVM model parameters. They used the RBF kernel function
that led to an R2 of 0.9924.
In another study this year, Manshad et al. [409] attempted to construct a
model to predict the precipitation of asphaltene during the CO2 injection
process introducing T, P, oil SG and composition, the amount and molecu-
lar weight of solvent, and asphaltene content as the inputs. Manshad et al.
[409] employed the PSO technique to optimize their MLP network. Their
developed model showed an AAPRE of 12.1%.
In the following years, the precipitation of asphaltene was mostly
modeled as a function of temperature, type of precipitant, and the dilution
ratio using various intelligent models. Gholami et al. [410] developed a
CMIS based on alternating conditional expectation and SVM techniques.
They also utilized GAs to optimize the weights of their CMIS. The
developed CMIS could predict the amount of asphaltene precipitation
with an R2 of 0.9917 in the testing phase. Ghorbani et al. [411] proposed
an SVM model that was optimized by means of the GAs. They employed
the Gaussian RBF as the kernel function. Baghban and Khoshkharam
[412] made another approach using the LSSVM technique. They also
used GAs to optimize the tuning parameters of the LSSVM model. They
reported the value of 1.98% as the AAPRE of their model. In another
study, Hemmati-Sarapardeh et al. [413] utilized the LSSVM technique
coupled with the CSA optimization algorithm. They gathered a large
dataset consisted of 326 data points, and the developed CSA-LSSVM
showed an AAPRE of 9.46%.
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Application of intelligent models in reservoir and production engineering
Later in 2017, Zarei and Baghban [414] employed the MLP-ANN
technique to predict the amount of asphaltene precipitation. In a different
approach in this year, Alimohammadi et al. [415] attempted to predict the
weight percent of precipitated asphaltene considering P, °API, molecular
weight of the precipitant, the dilution ratio, and the ratio of resin-to-
asphaltene as input parameters. Alimohammadi et al. developed an MLP
network using 10 neurons in its hidden layer and utilized logsig as the
transfer function. The model was trained through the LM algorithm and
showed an R2 of higher than 0.98.
Meighani et al. [416], Taherpour et al. [417], Chen et al. [418], and
Sedaghat and Rouhibakhsh [419] proposed different approaches toward
predicting the precipitation of asphaltene with regard to the conventional
input variables (temperature, type of precipitant, and the dilution ratio) in
2018. They respectively employed the SVM, FCM algorithm, ANFIS,
and LSSVM modeling techniques.
Tashayo et al. [420] and Sadi and Shahrabadi [421] made dissimilar
approaches in this year. Tashayo et al. [420] aimed to estimate the reduc-
tion of asphaltene precipitation due to the presence of inhibitors as a func-
tion of molecular weight and concentration of inhibitors, °API, oil
asphaltene content, and the number of carboxylic, hydroxyl and circular
structure groups. Their RBF network could predict the target parameter
with an R2 of 0.9957. In the study of Sadi and Shahrabadi [421], the
GMDH technique was implemented to correlate the precipitation of
asphaltene with P, T, °API, Pb, the concentration of nonhydrocarbon
gases, and SARA fractions. They utilized GAs to find the optimal net-
work structure of the GMDH model. The developed model could pro-
vide predictions with an AAPRE of 3.65%.
Bassir and Madani [422,423] proposed two approaches toward predict-
ing the amount of asphaltene precipitation considering paraffin type, crude
oil colloidal stability index, and the dilution ratio as the inputs of their
models. They employed the LSSVM and the RBF NNs to construct their
models in each study. The LSSVM model was optimized with the CSA
technique. Both models showed an R2 value of higher than 0.99.
4.7 Production pipelines
Pipelines are the most economical and efficient means of oil and natural
gas transportation over long distances in different environments owing to
their lower accident rate compared to highways and railways; however,
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
they are subjected to corrosion and degradation [424]. According to statis-
tics, pipeline networks are spread over 2000 km in more than 60 countries
around the world [425]. Obviously, pipeline accidents result in vast eco-
nomic losses as well as catastrophic environmental effects such as oil spills
[426]. With regard to the report of CONCAWE (Conservation of Clean
Air and Water in Europe), natural hazards, mechanical, operational, corro-
sion, and third-party activities are the most probable causes of oil pipeline
failure [427]. According to the important role of pipelines, it is necessary
to predict their condition before an accident happens.
In 1998, Belsito et al. [428] developed an ANN to locate and detect
the leaks in liquified gas pipelines based on the pressure at 13 points and
flowrates at the inlet and outlet of the pipeline. Their network had a hid-
den layer with 7 neurons with a sigmoidal transfer function. In 2005, Da
Silva et al. [429] made another approach toward detecting the leaks in oil
pipelines with the aid of the FL system. A year later, Carvalho et al. [430]
applied ANNs to classify the pipe weld defects using magnetic flux leak-
age signals. In 2007, ANNs were employed once again to detect faults in
pipeline systems based on stationary and nonstationary status [431]. After
2 years, Singh and Markeset [432] utilized the FL scheme to establish a
risk-based inspection plan for petroleum pipelines. Their FL model was
aimed to predict the rate of corrosion with regard to T, flowrates of gas
and liquid, pH, P, and CO2 partial pressure.
In 2012, Mandal et al. [433] and Ren et al. [434] investigated the
applicability of the SVM and ANN techniques in this regard. Mandal
et al. [433] attempted to optimize the SVM model using the ABC algo-
rithm with the aim of leak detection in pipelines. Their model showed an
accuracy of 95.19%. Ren et al. [434] focused on predicting the maximum
rate of internal corrosion in pipelines considering the natural gas pipeline
mileage, elevation difference, pipe inclination, pressure, and Reynolds
number as input variables.
In 2014, Senouci et al. [426,435] proposed another approach toward
predicting the failure of pipelines. They employed the ANN and FL tech-
niques to predict the type of pipe failure based on the type of product car-
ried by the pipeline, land use, pipeline age, pipeline location, and pipeline
diameter. In another study in this year, ANNs were employed to predict
the condition of offshore petroleum pipelines based on operational, exter-
nal, and physical factors [424]. The developed network had three hidden
layers and could predict the condition of pipelines with an average per-
cent validity of higher than 97%.
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Application of intelligent models in reservoir and production engineering
In a 2019 study by Zabihi et al. [436], DRA (drag-reducing agent)
concentration and boiling temperature, oil flow rate and density, tempera-
ture, inside diameter, relative roughness of pipe, and the coefficients of
Reynolds number were considered as input parameters. They implemen-
ted the MLP and RBF ANNs as modeling techniques. The developed
MLP model had 17 and 2 neurons in its first and second hidden layers,
respectively. The developed MLP network was trained using the BR
algorithm, and showed an AAPRE of 7.42% in the testing phase, while
the corresponding value obtained for the RBF network was 12.49%.
In 2020, Moayedi et al. [437] conducted a study to predict the drag
reduction in petroleum pipelines as a function of temperature, type of
pipe, Reynolds number, and type and concentration of DRAs. They
developed an MLP network that was trained through the ICA algorithm,
which showed an R2 of 0.9791.
4.8 Wax
Wax deposition is a significant problem in the oil and gas industry that
can lead to catastrophic problems such as oil production reduction, pipe-
line plugging, and formation damage [438]. Wax precipitation is a con-
cern in both crude oil and gas condensate fields when the temperature
falls below a specified temperature known as wax appearance temperature
[439]. Therefore several scholars intended to utilize AI techniques in this
field to predict wax deposition or its characteristics prior to its occurrence.
Different approaches were proposed toward predicting the amount of
wax precipitation considering oil composition and SG, pressure, and tem-
perature of the system as input parameters. To this end, Khaksar Manshad
et al. [440], Kamari et al. [441], Eghtedaei et al. [438], and Chu et al.
[442] respectively employed ANN, CSA-LSSVM, RBFNN, and ANFIS
techniques to develop their models.
In recent years, different scholars aimed to predict the wax disappear-
ance temperature (WDT) considering molar mass and pressure as input
variables. For this purpose, Moradi et al. [443] constructed their model
based on ANNs. They developed a three-layer network that was trained
through the LM algorithm. Bian et al. [444] attempted to employ several
AI techniques in this regard. Gray wolf optimizer-based SVM (GWO-
SVM), LSSVM, GA-ANFIS, and PSO-ANFIS techniques were utilized
in their study. The GWO-SVM model showed the highest performance
with an AAPRE of 0.7128%. Benamara et al. [445] focused on predicting
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
the WDT using RBF ANNs. They investigated the performance of two
optimization techniques, namely, GAs and ABC, in order to optimize the
RBF network. In addition, they provided an explicit correlation using
the GMDH technique. The developed RBF-ABC could outperform the
other models with an AAPRE of 0.5402%. In a different approach,
Mansourpoor et al. [446] modeled the WDT using P, the molecular
weight of oil, and its SG. To this end, they developed a three-layer ANN
that had 16 neurons in its hidden layer and was trained through the LM
algorithm. Their model showed an efficiency of 0.273% in terms of
AAPRE.
In other studies, the prediction of wax deposition thickness and the
rate of wax deposition were the aims of the scholars. Jalalnezhad and
Kamali [439] and Saeedi Dehaghani [447] respectively utilized ANFIS and
ANN techniques to predict the wax deposition thickness as a function of
Reynolds number, wax content, oil and pipeline temperature, and deposi-
tion time. ANFIS model could provide predictions with an R2 of 0.9858,
and the developed four-layer ANN could predict wax deposition thick-
ness with an AAPRE of 4.54%. Xie and Xing [448] and Kamari et al.
[449] employed RBF-ANN and LSSVM-CSA techniques, respectively,
to predict the wax deposition rate. Xie and Xing [448] utilized the pipe
wall shear stress, pipe wall wax crystal solubility coefficient, crude oil vis-
cosity, and pipe wall temperature gradient as input parameters. The devel-
oped RBF network showed a relative error of less than 1.5%. In the study
of Kamari et al. [449] the gradient of wax molecular concentration, shear
stress, the dynamic viscosity of crude oil, and temperature difference in
the pipeline system were introduced to the model as the predictors. Their
model could estimate the objective parameter with an AAPRE of 0.49%.
4.9 Other applications
AI techniques have other applications in addition to the aforementioned
fields. In recent decades, these modeling techniques have been employed
in different areas such as artificial lift, inflow performance relationship
(IPR), multiphase flow, pressure gradient, and unconventional reservoirs.
In the following section, a brief summary of the application of AI techni-
ques is presented.
In the area of artificial lift, intelligent models have been utilized to
optimize the lifting process. Hamedi et al. [450] attempted to optimize
the gas-lift allocation problem using the PSO optimization technique.
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Application of intelligent models in reservoir and production engineering
They were able to determine the optimum value of the gas injection rate
for five Iranian oil wells. Rasouli et al. [451] utilized ANNs and GAs to
predict the efficiency of long-time gas-lift processes. In another study,
Mahdiani and Khamehchi [452] employed GAs to optimize the gas-lift
allocation problem considering the instability phenomenon as a constraint.
Ebrahimi and Khamehchi [453] investigated the performance of the SVM
technique to optimize the natural gas lift process. The developed SVM
model was optimized using PSO and GAs.
Several other scholars aimed to utilize intelligent models in order to predict
the well performance. In 1996, Diamond et al. [454] utilized DTs to estimate
the well performance based on core and log data. Their model could predict
well deliverability in a gas condensate reservoir (Britannia Gas Condensate field)
with acceptable accuracy. Later, Sajedian et al. [455] investigated the applicabil-
ity of GP and ANN techniques in predicting the two-phase IPR in vertical
wells. In the introduced RF model, flowing bottom hole pressure, average res-
ervoir pressure, maximum oil flow rate, bubble point pressure, oil formation
volume factor, solution gasoil ratio, and gas viscosity were chosen as the
inputs of the models and oil flow rate as the output. The GP-based model
showed better accuracy in comparison to the MLP-ANN model.
Halali et al. [456] and Fazavi et al. [457] were among the scholars who
attempted to utilize AI techniques in the area of pressure gradient in pipe-
lines. They considered oil and water slip velocity, pipe diameter and
roughness, and oil viscosity as input variables to predict the pressure gradi-
ent in wateroil pipelines. Fazavi et al. [457] employed the LSSVM tech-
nique to construct their model, while the model of Halali et al. [456] was
based on the RBF NNs. The LSSVM and the RBFNN models, respec-
tively, showed an AAPRE of 9.86% and 8.25% in predicting the pressure
gradient. Ebrahimi and Khamehchi [458], Al-Wahaibi and Mjalli [459],
and Osgouei et al. [460] proposed other models toward predicting the
pressure drop in oil systems.
Also, intelligent models have been widely applied in the field of
NFRs. For instance, Adibifard et al. [369] utilized ANNs to predict reser-
voir parameters in NFRs, considering well test data as input information.
In another study, Negara et al. [461] attempted to predict rock properties
of both fractured and conventional reservoirs using the SVM framework.
Many other applications of AI techniques in the field of unconventional
reservoirs were introduced in recent years [462465].
Intelligent models have been widely applied in the area of multiphase
flow. A group of researchers aimed to predict the flow regime of
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Applications of Artificial Intelligence Techniques in the Petroleum Industry
multiphase flows [466469]. Another group of scholars utilized intelligent
models to estimate the bottom hole pressure in vertical wells, experienc-
ing a multiphase flow [470,471]. In addition, other applications of AI
techniques in this regard were introduced by Hasanvand and Berneti
[472] and Al-Naser et al. [473]. Hasanvand and Berneti [472] focused on
forecasting oil flowrate in multiphase flow through an ANN. Al-Naser
et al. employed ANNs to detect the flow pattern in multiphase flows.
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Green Chemistry and
Green Engineering: A
Framework for Sustainable
Technology Development
Martin J. Mulvihill,1 Evan S. Beach,2
Julie B. Zimmerman,2 and Paul T. Anastas2
1Berkeley Center for Green Chemistry, University of California, Berkeley,
California 94720-7360; email: marty_m@berkeley.edu
2Center for Green Chemistry and Green Engineering, Yale University, New Haven,
Connecticut 06520; email: evan.beach@yale.edu, julie.zimmerman@yale.edu,
paul.anastas@yale.edu
Annu. Rev. Environ. Resour. 2011. 36:271–93
First published online as a Review in Advance on
August 19, 2011
The Annual Review of Environment and Resources
is online at environ.annualreviews.org
This article’s doi:
10.1146/annurev-environ-032009-095500
Copyright c
⃝2011 by Annual Reviews.
All rights reserved
1543-5938/11/1121-0271$20.00
Keywords
toxicity, efﬁciency, interdisciplinary, design, safer chemicals,
nanotechnology
Abstract
Green chemistry and engineering seek to maximize efﬁciency and min-
imize health and environmental hazards throughout the chemical pro-
duction process. This review demonstrates how green chemistry prin-
ciples and metrics can inﬂuence the entire life cycle of a chemical from
design through disposal. After reviewing essential metrics and recent ad-
vances in the ﬁeld within this context, we consider the case of nanotech-
nology. As an emerging ﬁeld, nanotechnology provides an instructive
framework to consider the inﬂuence and application of green chem-
istry. Interdisciplinary innovation guides both ﬁelds, and both seek to
transform the nature of technology. The applications and implications
of emerging green technology are discussed, and future opportunities
for interdisciplinary collaborations are highlighted.
271
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Contents
1. INTRODUCTION. . . . . . . . . . . . . . . . 272
1.1. Historical Context . . . . . . . . . . . . . 273
1.2. Drivers for Green
Chemistry Adoption . . . . . . . . . . . . 274
2. APPLYING GREEN
CHEMISTRY. . . . . . . . . . . . . . . . . . . . . 275
2.1. Design Tools for
Green Chemistry . . . . . . . . . . . . . . . 276
2.2. Raw Materials
for Green Chemisry. . . . . . . . . . . . . 280
2.3. Manufacturing with
Green Chemistry . . . . . . . . . . . . . . . 281
2.4. Chemical Use . . . . . . . . . . . . . . . . . 283
2.5. End of Life for Chemicals
and Products . . . . . . . . . . . . . . . . . . . 284
2.6. Need for Collaboration . . . . . . . . 284
3. CASE STUDY: GREEN
NANOTECHNOLOGY
DEVELOPMENT . . . . . . . . . . . . . . . . 285
3.1. Design of Nanotechnology . . . . . 285
3.2. Raw Materials for
Nanotechnology . . . . . . . . . . . . . . . . 286
3.3. Production of
Nanomaterials . . . . . . . . . . . . . . . . . . 286
3.4. Use of Nanotechnology . . . . . . . . 288
3.5. End of Life of
Nanomaterials . . . . . . . . . . . . . . . . . . 288
3.6. Lessons from Green
Nanotechnology . . . . . . . . . . . . . . . . 289
4. FUTURE OF GREEN
CHEMISTRY. . . . . . . . . . . . . . . . . . . . . 289
4.1. Educational Efforts . . . . . . . . . . . . 289
4.2. Concluding Comments . . . . . . . . 289
1. INTRODUCTION
“Green chemistry” is deﬁned as “the design of
chemical products and processes that reduce or
eliminate the use and generation of hazardous
substances” (1, p. 11). Green chemistry seeks to
reinvent the production and use of chemicals in
our society so that they are inherently safer and
more efﬁcient (2, 3). This focus aligns with the
broader sustainability movement and the terms
sustainable/green chemistry are often used in-
terchangeably.1
It has been widely recognized that a
transition to a sustainable society necessitates
signiﬁcant changes in resource and energy con-
sumption. To efﬁciently use limited resources,
both transmaterialization and dematerializa-
tion must occur. Transmaterialization is the
process of shifting away from hazardous and
nonrenewable resources toward safer and/or
renewable or reusable materials. Demateri-
alization seeks to minimize the material and
energy inputs to society while maintaining its
prosperity. These broad shifts seek to avoid the
environmental and human health hazards asso-
ciated with resource and energy consumption.
Future chemicals and processes should have
physical, chemical, and toxicological properties
that allow safe handling and disposal. Green
chemistry aims to accomplish this through the
rational design of chemicals and processes ac-
cording to a set of principles and metrics identi-
ﬁed during the past few decades. To achieve the
full potential of green chemistry, a coordinated
transformation of many social, political, eco-
nomic, and technological factors must occur.
Green chemistry provides an intellectual
and technological framework that advances
both transmaterialization and dematerializa-
tion strategies within the chemical enterprise.
The
principles
of
green
chemistry
along
with other sustainability metrics help identify
opportunities for innovation. Green chemistry
ensures
that
new
technologies
minimize
unintended hazards and provides insights into
the implications that new technologies have.
By considering both the applications and im-
plications for new technology, green chemistry
stands apart from other technological trends
that focus almost exclusively on application.
This perspective promotes interdisciplinary
1The term green chemistry is used commonly by academics
because of the historical development of the ﬁeld. The term
sustainable chemistry is often preferred by industry as a way
to distinguish technological innovation from the potential
political overtones of the word green.
272
Mulvihill et al.
Annu. Rev. Environ. Resour. 2011.36:271-293. Downloaded from www.annualreviews.org
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design and development of new technologies
that embody principles of sustainability.
1.1. Historical Context
The
green
chemistry
movement
started
over two decades ago. Initial motivation for
redesigning chemicals and chemical process
came from the pollution prevention legislation
in the early 1990s authored by the Environ-
mental Protection Agency (EPA) (4). This
legislation clearly articulated a shift toward
inherently safer and sustainable chemicals as
being the best pollution prevention strategy.
EPA: Environmental
Protection Agency
Key early support of green chemistry came
from the U.S. Presidential Green Chemistry
Challenge Awards established in 1995 (5), the
Green Chemistry Institute founded in 1997
(6), and the publication of the inaugural issue
of the Royal Society of Chemistry journal,
Green Chemistry, in 1999 (7). The publication
of Green Chemistry: Theory and Practice (1) in
1998 clearly explained the 12 principles (see
Table 1) of green chemistry and helped pro-
vide a coherent vision for the emerging green
chemistry
movement.
Although
seemingly
intuitive, the formulation of these principles
helped
chemists
and
chemical
engineers
Table 1
The 12 principles of green chemistrya
Number
Principle
1
Prevention: It is better to prevent waste than to treat or clean up waste after it has been created
2
Atom economy: Synthetic methods should be designed to maximize the incorporation of all
materials used in the process into the ﬁnal product
3
Less hazardous chemical syntheses: Wherever practicable, synthetic methods should be
designed to use and generate substances that possess little or no toxicity to human health and
the environment
4
Designing safer chemicals: Chemical products should be designed to effect their desired
function while minimizing their toxicity
5
Safer solvents and auxiliaries: The use of auxiliary substances (e.g., solvents, separation agents,
and others) should be made unnecessary wherever possible and innocuous when used
6
Design for energy efﬁciency: Energy requirements of chemical processes should be
recognized for their environmental and economic impacts and should be minimized. If
possible, synthetic methods should be conducted at ambient temperature and pressure
7
Use of renewable feedstocks: A raw material or feedstock should be renewable rather than
depleting whenever technically and economically practicable
8
Reduce derivatives: Unnecessary derivatization (use of blocking groups,
protection/deprotection, temporary modiﬁcation of physical/chemical processes) should be
minimized or avoided if possible because such steps require additional reagents and can
generate waste
9
Catalysis: Catalytic reagents (as selective as possible) are superior to stoichiometric reagents
10
Design for degradation: Chemical products should be designed so that at the end of their
function they break down into innocuous degradation products and do not persist in the
environment
11
Real-time analysis for pollution prevention: Analytical methodologies need to be further
developed to allow for real-time, in-process monitoring and control prior to the formation of
hazardous substances
12
Inherently safer chemistry for accident prevention: Substances and the form of a substance
used in a chemical process should be chosen to minimize the potential for chemical
accidents, including releases, explosions, and ﬁres
aReprinted and adapted from Reference 1 with permission.
www.annualreviews.org • Green Chemistry
273
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understand how principles of sustainability
could be applied to their research.
1.2. Drivers for Green
Chemistry Adoption
The adoption and development of green chem-
istry has been catalyzed by the formulation of
principles and metrics that guide the design
of sustainable chemicals. Key examples include
the following: atom economy (8, 9), environ-
mental factor (E-factor) (10), the 12 principles
of green chemistry (1), principles of sustain-
able chemistry (11, 12), and 12 more princi-
ples of green chemistry (13). Other design and
engineering metrics were also developed in-
cluding the 12 principles of green engineering
(14), cradle to cradle design (15, 16), natural
capitalism (17, 18), and design for the environ-
ment (19). These metrics share a common vi-
sion that chemistry should be developed in a
manner that seeks to maximize efﬁciency and
minimize health and environmental hazards
throughout every stage of a chemical’s life cycle.
The proliferation of metrics allows re-
searchers, business people, and politicians to
each characterize progress toward meeting
sustainability goals. The various stakeholders
often prioritize aspects of green chemistry
according to their own needs and choose
metrics accordingly. Although enabling the
spread of green chemistry ideas, the diversity
of metrics can confuse the interpretation of
greenness claims. Recent press and marketing
campaigns extolling promises of green jobs
and the green economy complicate matters,
bringing a new wave of greenness claims that
will need to be examined carefully.
Two things should be remembered when
evaluating a greenness claim: (a) There is always
room for improvement, and (b) every metric
needs a baseline. Creating safer, more efﬁcient
technologies is an iterative process, whereby
each innovation improves on previous technol-
ogy. As a result, there can be greener chemi-
cals, but the claims of a green product should
be carefully scrutinized. To lend credibility to
improvement claims, such claims should always
be referenced to a baseline and should be quan-
tiﬁable. Speciﬁc examples of greenness metrics
are discussed in more detail below as they relate
to the design of chemicals.
1.2.1. Economic drivers. Green chemistry
tools and metrics have allowed businesses to
start evaluating their products and processes
within the context of a “triple bottom line,”
which incorporates economic, environmental,
and social factors into the decision making
process (20). Green chemistry provides metrics
to evaluate the efﬁciency, environmental, and
health impacts of new technology. Under con-
ditions of good protection rules for health and
environment, green chemistry clearly makes
economic sense. In spite of the capital barrier
to changes, there are a growing number of
academic and business studies that demonstrate
the proﬁtability of green chemistry initiatives
(21, 22). The cost savings generated by these
initiatives can be explained by decreasing
costs associated with waste removal, protective
equipment, regulatory compliance, decreased
liability, and manufacturing security (3).
The U.S. government recently reported
that the cost associated with safety and en-
vironmental regulation compliance is as high
as 4% of gross domestic product across all
manufacturing sectors (23). Green chemistry
guides the creation of inherently safer chem-
icals that will necessitate fewer manufacturing
safety controls. Further cost savings could be
realized if regulations provided an advantage to
green chemistry solutions used to meet regula-
tory compliance. For example, inherently safer
chemical alternatives could have a fast-track ap-
proval process, making them more attractive
to chemical companies and formulators. The
development of green chemistry and sustain-
able business practices in industry have begun
to challenge the myth that investing in green
technologies is too costly to be competitive.
1.2.2. Health and safety drivers. Green
chemistry has been hailed by politicians and
public interest groups as a solution to chem-
ical hazards in consumer products and the
274
Mulvihill et al.
Annu. Rev. Environ. Resour. 2011.36:271-293. Downloaded from www.annualreviews.org
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environment. These groups see the promise of
“benign by design” as the solution for elimi-
nating the toxic chemicals that are sometimes
found in consumer products. As both the
federal and state governments in the United
States consider chemical policy reform, green
chemistry has been identiﬁed as a key tool for
comprehensively addressing the current weak-
nesses in chemical regulation (24). By placing
green chemistry and safe chemical design at
the center of policy reform, advocates hope to
avoid chemical-by-chemical regulation, which
has led to regrettable substitutions of one
hazardous chemical for another.
1.2.3. Research drivers. Green chemical–
based innovation has produced numerous suc-
cesses for research and development opera-
tions. The rate of patent applications and the
number of new green technologies emerging
have been increasing (25–27). The growth in
green chemistry patents over the past 15 years
has largely been driven by industry, which capi-
talized on the cost savings brought by increased
efﬁciency and the minimization of hazardous
waste. Green chemistry patents have been ﬁled
by a wide array of both chemical producers
and chemical users, demonstrating the need for
innovation throughout the supply chain. The
broad appeal of new cleaner technologies has
spurred academic research as well, and aca-
demic literature describing green chemistry has
seen similar growth.
Many technological breakthroughs guided
by green chemistry have occurred in the past
15 years, and there are a number of literature re-
views that highlight this work (2, 3, 14, 28). The
purpose of this review is to put these technical
breakthroughs in the broader context of inter-
disciplinary work concerning the environment,
energy, and resources. To do so, this review is
organized along the chemical production life
cycle: design, raw materials, manufacturing,
use, and end of life. At each point, signiﬁcant
advances and opportunities are discussed. The
application of green chemistry to the con-
comitantly emerging ﬁeld of nanotechnology
is used to highlight how green chemistry can
inﬂuence technology development.
2. APPLYING GREEN CHEMISTRY
Green chemistry is a design philosophy. The
design stage of a new chemical or process is
the most appropriate and critical stage to en-
gage in green chemistry. During the design
phase of a new chemical or product, the scope
of possible innovation ranges from incremen-
tal or superﬁcial design improvements to com-
pletely redesigning the system of production—
amuchdeeperformofinnovation.Considerthe
following design problem: The electronics in-
dustry wants to remove a toxic ﬂame retardant
from circuit boards without sacriﬁcing perfor-
mance or function (29). See Figure 1.
Drop-in
replacements
like
tetrabromo-
bisphenol-A (TBBA) meet the stated goal but
introduce other safety concerns. The safety
proﬁle could be improved by polymerizing
the TBBA, immobilizing the chemical. This
decreases the exposure potential while contin-
uing to use chemicals of concern during the
manufacturing process.
Deeper innovation, requiring signiﬁcant
technological redesign, could design away the
Substance
Material
Product  
Complexity
Investment
Depth of innovation
Circuit board example
Reduce voltage
Separate high and low voltage
Change board material
Mineral-based retardants
Phosphorus-based retardants
Polymerized TBBA
TBBA to replace PBDE/PBBs
Figure 1
The innovation possibilities for replacement of toxic brominated ﬂame
retardants in circuit boards. This ﬁgure shows the range of possible solutions
available to the electronics industry as they remove pentabromodiphenyl ether
(PBDE) and related polybrominated biphenyls (PBBs) from circuit boards.
There are many potential solutions ranging in complexity, cost, and potential
impact. Reprinted and adapted from Reference 29 with permission.
www.annualreviews.org • Green Chemistry
275
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LCA: life-cycle
analysis
ﬂammability potential in circuit boards. For
example, inherently safer mineral-based ﬂame-
retardant chemicals could have been used.
Unlike the drop-in replacements discussed
in Figure 1, these new materials would have
required signiﬁcant changes in the manufactur-
ing process. Decreasing the operating voltage
of the circuits on the board creates the poten-
tial to remove the source of the ﬂammability
hazard and eliminates the need for an added
ﬂame retardant. It also requires redesigning the
circuits rather than the circuit board. Although
this solution requires the largest investment, it
also has the largest potential payoff. Reducing
the operating voltage of the circuits potentially
removes the ﬂammability hazard, decreases the
energy consumption, and increases the product
durability.
All possible innovation, from superﬁcial
to deep, should be considered during the
design process. There are advantages to each
approach. Often, simple innovations can be
readily adopted on a short timescale, meeting
immediate goals without signiﬁcant research
and development investment. Deeper innova-
tion takes more resources but has potentially
greater economic and sustainability beneﬁts.
The immediate appeal of innovation by simple
material substitution is also complicated by
potential unintended consequences associated
with drop-in replacements. These include
changes in product performance or shifting the
toxicity burden from one pathway to another,
resulting in a different harmful outcome.
Sustainable
innovation
is
catalyzed
by
interdisciplinary
collaboration
during
the
design process. If you ask a chemical engineer
how to solve the ﬂame-retardant problem, the
solutions may all be related to chemical sub-
stance substitutions. Electrical and mechanical
engineers might adopt a heat dissipation tech-
nology, and materials scientists may consider
modifying the resins in the circuit board.
Each idea should be thoroughly considered
before resources are allocated. This is only
possible in multidisciplinary design teams.
The advantage of interdisciplinary teams goes
beyond manufacturing design to the design and
implementation of technology in the academic
setting. A recent study quantiﬁed the productiv-
ity of interdisciplinary research collaboration.
This study demonstrated that interdisciplinary
teams
publish
more
than
research
teams
without interdisciplinary collaboration (30).
The circuit board example demonstrates
the importance of design. The design phase
determines much of a product’s ﬁnal cost and
environmental impacts (Figure 2). In fact,
over 70% of costs are committed during the
initial design of a product (31). These social
and environmental costs are not incurred
until much later in the chemical life cycle.
The majority of costs are incurred during the
manufacture, use, and disposal stages. Figure 2
underscores the importance of considering
environmental and health effects during the
initial design period, despite the fact that these
impacts are associated with the later stages of
commercialization.
2.1. Design Tools for
Green Chemistry
During the design phase, innovators must be
able to rapidly compare competing ideas on the
basisoftheirsustainabilityproﬁles.Anumberof
tools have been introduced to aid the evaluation
of chemicals, processes, and product life cycles.
Many of the early adopters of green chemistry
created metrics that focused on particular as-
pects of the design process. These metrics can
be grouped into three categories: materials ef-
ﬁciency, energy efﬁciency, and toxicity. Such
metrics are most useful for evaluating individ-
ualchemicals,chemicalreactions,orsingle-step
chemical processes. To evaluate product life cy-
cles, systems thinking approaches are needed.
Although many tools exist, two of the most
commonly used frameworks in green chemistry
are the 12 principles of green chemistry (1) and
life-cycle analysis (LCA) (32, 33).
2.1.1.
Materials
efﬁciency. Chemists are
most familiar with materials efﬁciency metrics
(see Figure 3). The ﬁrst metric they learn is re-
action yield, which is calculated by dividing the
276
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Figure 3
The four most common materials efﬁciency metrics, described in Section 2.  refers to the summation of
the components.
amount of the product obtained by the maxi-
mum amount that could have been obtained if
there were complete conversion of the starting
material into product. This metric neglects to
account for any of the excess reagents, solvents,
or auxiliary inputs to the chemical reaction. The
concept of atom economy (8) partially addresses
this issue by considering the total amount of
all reagents (not just the starting material) that
contributes to the product. Atom economy is
calculated by dividing the molecular weight of
the desired product by the summed molecu-
lar weights of all of the reagents. A calculation
of atom economy assumes complete conver-
sions of the reactants into products (i.e., 100%
yield) and neglects to include the contribution
of solvents and auxiliaries. The reaction mass
efﬁciency is a more holistic metric that incor-
porates both atom economy and reaction yield
by dividing the mass of the desired product by
the sum of the masses of all of the reactants.
By using the actual masses of reactants rather
than their molecular weights, this metric cap-
tures the atom efﬁciency as well as the reaction
efﬁciency. Even though the reaction mass efﬁ-
ciency still neglects contributions by solvent or
auxiliaries, it has become widely used by indus-
trial chemists as a quick evaluation for chemical
procedures (34).
The ﬁrst metric to account for the con-
tributions of solvents and auxiliaries was the
E-factor (35). The E-factor is derived by di-
viding the total mass of waste by the mass of
product. The E-factor was ﬁrst used to quan-
tify the large amount of waste being generated
by various sectors of the chemical industry. Un-
like yield and atom economy, the E-factor can
be calculated for an industrial process as a whole
as well as for individual chemical reactions. The
broad scope and simplicity of the E-factor have
made it an oft-cited metric. A similar metric,
mass productivity, expresses the same concept
as a percentage, making it easier to compare
with the other materials efﬁciency metrics.
The utility of the various materials efﬁciency
metrics was evaluated within the context of drug
synthesis (34). Constable et al. (34) concluded
that the reaction mass efﬁciency and mass
productivity were the most valuable metrics for
driving business adoption of greener processes
because they made sense to both scientists and
business leaders.
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2.1.2. Energy efﬁciency. Energy efﬁciency
is a central theme in the sustainability move-
ment as well as one of the central tenets of
green chemical design. The overall energy
consumption in the chemical industry was
recently quantiﬁed. Chemical manufacturing
typically consumes 25%–30% of the total
energy used by the manufacturing sector in
the United States. This represents signiﬁcant
energy consumption, about 5% of all U.S.
energy use. For example, in 1994, chemicals
production consumed 5.1 EJ (Exa joules) of
energy and produced the equivalent of 282 Mt
(megatons) of CO2 emissions (36).
Opportunities for reducing energy use and
CO2 emissions exist in most chemical man-
ufacturing processes. For example, processes
that operate at or near room temperature
and atmospheric pressure require much less
energy. Materials with lower-embodied energy
(i.e., recycled or reclaimed material) and/or
high biological content could be used to reduce
overall energy consumption throughout their
life cycle. This is particularly true when
products are designed so that their embodied
energy can be partially recovered at the end of
life, either through reuse or efﬁcient, nontoxic
incineration coupled with energy generation.
Green chemists have also developed alter-
natives to conventional heating methods for
chemical reactions; these include microwave
irradiation and sonochemical methods. These
methods have the potential to reduce the
amount of energy consumed. They have also
enabled the discovery of new reaction pathways
and processes (37, 38).
Traditionally, the energy efﬁciency of pro-
cesses is optimized after a chemical process has
already been developed. If energy is only con-
sidered after the development phase has been
complete, many of these potential efﬁciency-
improving technologies discussed cannot be
applied. The authors of this review believe
that energy consumption should be considered
during the research and development phase,
and more techniques to improve the energy
use proﬁle of chemical manufacturing are
needed.
2.1.3. Toxicity. Understanding the potential
hazard associated with a chemical or product is
essential for successful green chemical design.
Ideally, chemicals would be comprehensively
screened for potential toxicity using structure-
function relationships before being produced.
The current state of the art is not this advanced,
but recent developments in predictive toxicol-
ogy and quantitative structure activity relation-
ships are making predictive tools more available
to molecular designers (39).
Traditional toxicology relies on a “kill and
count” methodology to assess acute toxicity,
where increasing doses of a signal chemical are
administered to model animals (rat, mouse,
rabbit, and others) until the animals die. The
dose at which 50% of a test population dies is
considered the median lethal dose. Although
the lethal dose may be a useful measure of a
chemical’s acute toxicity, many of the current
concerns in chemical production arise from
other toxicity end points. Current producers
and consumers are concerned with chemicals’
carcinogenic, mutagenic, endocrine disrupting,
persistence, and bioaccumulation potentials.
These end points are species and environ-
ment speciﬁc and require a much greater
understanding of the chemical and biological
processes occurring at the molecular level.
Many of the chemical toxicity resources cur-
rently available to scientists and engineers were
recently reviewed by Voutchkova et al. (40).
The emerging technologies in toxicology are
rapidly producing a large amount of pathway-
speciﬁc toxicological information. The ability
for toxicologists to collect and interpret in-
formation about gene- and protein-level inter-
actions with toxic substances (toxicogenomics)
provides detailed information about many hu-
man and environmental health end points.
Some of this information is freely available on-
line in a variety of public databases including
PubChem (40a) and the EPA’s ACToR (40b).
This information is also being used to develop
more robust predictive tools (41).
In addition to detailed toxicological infor-
mation, there are a number of easily measured
or modeled chemical properties, which can be
278
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used to help predict a chemical’s fate in the
environment. Programs like the EPA’s PBT
proﬁler (42), ToxCast (42a), and CalTOX (43)
use calculated partition coefﬁcients, vapor pres-
sures, solubility, and others to estimate the per-
sistence, bioaccumulation, and toxicity proﬁles
of many organic chemicals. These programs are
easy to use and provide a good starting point to
evaluate and compare hazard proﬁles for related
organic compounds. Another more recent anal-
ysis correlates molecular properties, including
size, shape, ﬂexibility, solubility, and electron-
ics, to molecular toxicity (41). By starting with
readily quantiﬁable molecular attributes, these
methods make it easier for chemists to consider
the hazard potential of a new chemical much
earlier in the design process.
2.1.4.
Comprehensive
design
metrics.
Consumer, regulatory, and business interests
generate a demand to deﬁne particular chem-
icals or products as green or not green. Given
the diversity of metrics available, this is not
a straightforward process. Binary green/not
green categorizations can be misleading be-
cause (a) they discount the necessity of contin-
uous improvement and (b) assume that all end
users value various green criteria in the same
way. A number of comprehensive metrics have
been developed that seek to quantify and/or
compare chemicals using an array of sustain-
ability factors. These include the 12 principles
of green chemistry, LCA, cradle to cradle,
and design for environment. The two most
commonly used by academic researchers are
the 12 principles of green chemistry and LCA.
The 12 principles of green chemistry were
ﬁrst developed by Anastas & Warner (1). Both
were trained as organic chemists, and the
12 principles sought to translate the broad con-
cepts of sustainability into practical advice that
chemists could use while developing new chem-
icals or processes. The 12 principles (Table 1)
address all aspects of chemical reactions from
chemical feedstocks (principle 7) to end-of-life
considerations (principle 10). They give practi-
cal advice concerning the selection of reactants
(principle 9), solvents (principle 5), and reaction
conditions (principle 6), as well as ways to plan
(principles 2, 4, and 8) and monitor (principle
11) chemical reactions. In general, the princi-
pals promote a safer (principles 3, 4, and 12)
and more efﬁcient (principles 1 and 2) approach
to chemistry. Rather than a set of quantiﬁable
metrics, the 12 principles of green chemistry
embody a design philosophy for chemical syn-
thesis most useful to chemists and engineers
creating new products and processes.
In contrast LCA is a well-deﬁned set of
quantiﬁable metrics used to evaluate the en-
vironmental impact of a product. The LCA
methodology is outlined in the International
Standards Organization (ISO) protocol 14040
(44). A LCA consists of four activities: (a) deﬁ-
nition of goals and scope; (b) inventory analysis;
(c) impact assessment; and (d ) interpretation. By
evaluating production processes against many
metrics, LCA helps prevent unintended burden
shifting. LCAs rely on data that are often only
available for high production volume chemi-
cals or for products of concern that have been
thoroughly studied (32). LCA is well suited
for rigorously comparing existing alternatives,
whereas the 12 principals are better applied to
the development of new technologies.
Both of these comprehensive design met-
rics incorporate materials efﬁciency, energy
efﬁciency, and toxicity considerations. They
also address product degradation and various
environmental impacts. To fully evaluate a
chemical or process using either LCA or the 12
principles of green chemistry, large amounts
of data are needed. For many chemicals, some
of the required data are missing, necessitating
assumptions that reduce the accuracy of the
metrics. Although a complete evaluation of
chemicals helps designers to make choices,
each method gives different results.
A recent article compares the results of
LCA to the 12 principles of green chemistry
for 12 different polymers (45). As seen in
Table 2, the ranking of the polymers under
each design metric is strikingly different. Both
design metrics consider a wide range of safety
and efﬁciency metrics. The analysis shows
that LCA favors overall materials efﬁciency,
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Table 2
Rankings for each of the polymers based on the 12 principles green design and the
normalized life-cycle assessment resultsa
Polymer (production process)b
Rank by green chemistry
principles
Rank by life-cycle analysis
PLA (NatureWorks)
1
6
PHA (Utilizing Stover)
2
4
PHA (General)
3
8
PLA (General)
4
9
High-density polyethylene
5
2
Polyethylene terephthalate
6
10
Low-density polyethylene
7
3
Biopolyethylene terephthalate
8
12
Polypropylene
9
1
General purpose polystyrene
10
5
Polyvinyl chloride
11
7
Polycarbonate
12
11
aReprinted and adapted from Reference (43) with permission.
bAbbreviations: PLA, polylactic acid; PHA, polyhydroxyalkonate.
whereas the 12 principles favor biobased ma-
terials. Similar valuation of particular design
traits is present in all comprehensive design
metrics. There are many ways to measure the
impacts of a chemical, and researchers and
companies will often favor particular metrics.
If the various weights and biases in the metric
are made clear and transparent, then end users
can make use of the information intelligently.
2.2. Raw Materials
for Green Chemisry
Traditionally, chemicals have been made from
petroleum feedstocks. Although chemical pro-
duction only accounts for 3%–5% of petroleum
consumption,
petroleum
sources
represent
over 98% of chemical feedstocks (46, 47).
Chemists, chemical companies and consumers
all envision advantages for moving to renewable
feedstocks. The chemists see an opportunity for
new innovation and a chance to take advantage
of nature’s ability to perform exquisitely selec-
tive chemistry. Chemical companies envision
renewable feedstocks providing a ﬁnancially
stable source of starting material. With such
a large portion of starting materials coming
from oil, chemical companies are particularly
vulnerable to ﬂuctuations in crude oil prices.
Finally, consumers are increasingly choosing
naturally derived products because of their
perceived safety and environmental beneﬁts.
Petrochemical feedstocks provide very sim-
ple hydrocarbons, which chemists have learned
to make more complex. Natural feedstocks
are inherently different. They are complex
molecules, and chemists are still developing el-
egant ways to efﬁciently transform them into
useful products (48). The idea of a bioreﬁnery,
which could take biomass and postconsumer
waste and turn it into fuels (49) and other chem-
ical products (46, 50), has been suggested by
many researchers as an important path toward
chemical sustainability. Figure 4 outlines the
potential materials ﬂow through a bioreﬁnery.
Like a traditional petroleum reﬁnery, a biore-
ﬁnery maximizes materials utilization through
many parallel processes. An ideal bioreﬁnery
uses all input mass to produce biofuel or chem-
ical feedstock material.
Future bioreﬁneries are envisioned to inte-
grate the conversion of biomass into both fu-
els and ﬁne chemical products in one facility
(51). The conversion of biomass to products
280
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Figure 4
The materials ﬂow from biomass material to ﬁnal products or chemical feedstocks. Reprinted and adapted
from Reference 51 with permission.
usually proceeds either through a biological
pathway or a thermochemical pathway. Biolog-
ical pathways use fermentation or other natural
forms of biomass conversion to turn raw ma-
terials into simpler chemicals (see Figure 4).
Then, these molecules can be used directly as
fuels or feedstock, or they can undergo further
chemical modiﬁcation to make materials or ﬁne
chemicals (51).
Thermochemical pathways use heat and
controlled amounts of oxygen or steam to pro-
duce syngas. This mixture of carbon monoxide
and hydrogen can be converted into petroleum-
like feedstocks. The gasiﬁcation of biomass can
be accomplished on a wide range of both virgin
and waste streams, making the process attrac-
tive. Because the products are similar to petro-
chemicals, many researchers see this as a viable
option in the short term. Unfortunately, this
method uses a signiﬁcant amount of energy to
produce the syngas, and it also destroys the po-
tential chemical complexity of the feedstock,
two aspects of the process that do not comply
with the 12 principles of green engineering (52).
2.3. Manufacturing with
Green Chemistry
Although green chemistry encourages innova-
tion throughout the chemical supply chain, the
greatest advances in the past 15 years have
been related to chemical production. This is
natural, given that the training of chemists
focuses on techniques for molecular manipula-
tion. The scope, precision, and sustainability of
these transformations continue to rapidly im-
prove. For the sake of this review, we focus on
a few classes of chemicals and processes where
recent research has been inﬂuenced by green
chemistry.
2.3.1. Catalysis. The Nobel Prize in chem-
istry has been awarded three times in the
past 10 years for advances in catalysis. In
2001, it was awarded for chiral hydrogenation
and oxidation reactions. The 2005 award
recognized the discovery metathesis reactions
in organic synthesis and highlighted their
importance for green chemistry. In 2010, the
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Nobel committee recognized the importance
of palladium-catalyzed cross-coupling reac-
tions in chemical synthesis. This recognition
underscores the importance of catalysis. Recent
trends in green catalysis include catalysts that
are recyclable, catalysts made using abundant
nontoxic metals, and biocatalysis.
Catalysts by their very nature improve ef-
ﬁciency, and waste can be further reduced if
the catalysts are immobilized on a solid sup-
port. In addition to reducing waste, these re-
cyclable catalysts present signiﬁcant reductions
in cost and hazard by removing the expensive
and potentially toxic metals from the chemical
waste stream. Catalysts have been successfully
supported on inorganic surfaces (53, 54) as well
as on polymer surfaces (55). Inorganic meso-
porous and nanomaterials have also been shown
to be effective on their own as catalysts for many
reactions (56).
Biological systems rely on catalysts, in the
form of enzymes, for the majority of chemical
transformations. Recent advances in our under-
standing of proteomics have allowed chemists
to harness and tailor many of these enzymes to
perform desired chemical reactions (57). The
use of enzyme-based catalysts in chemical pro-
cesses has been termed biocatalysis. Biocatal-
ysis encompasses whole-organism processes,
such as fermentation, isolated enzymes used in
chemical reactions, and chemically or geneti-
cally modiﬁed enzymes. Computer modeling
(58) and directed evolution (59) are two of the
many techniques that have been used to rapidly
develop efﬁcient enzymes for chemical reac-
tions (57). Biocatalysis is becoming a vibrant
area for the research and development of new
catalysts because of the dual advantages of sus-
tainability and increased selectivity. Biocatal-
ysis exempliﬁes interdisciplinary collaboration
leading to important developments in green
chemistry, and it is expected to grow substan-
tially in coming years (57).
2.3.2. Solvents. Solvents often contribute the
greatest adverse impact to the LCA of chemical
reactions and processes (60). When possible,
the elimination of solvent is the greenest
alternative. The removal of solvents from
reactions can be aided using alternative types
of heating. For example, microwave irradiation
has effectively promoted a number of reactions
without the addition of solvent (37). Solventless
reactions have also been enabled using eutectic
mixtures and ball milling technologies (61).
For reactions requiring solvent, a number of
alternative green solvents have been developed
(61). The major classes of alternative solvents
include ionic liquids, supercritical CO2/gas-
expanded liquids, switchable solvents, and re-
newably sourced solvents. Green solvents are
designed to minimize or eliminate exposures to
volatile organic compounds, which contribute
to air pollution, ﬂammability hazards, and ad-
verse human health effects. To promote the
adoption of alternative solvents in industry,
tools have been developed to aid in the solvent
selection process (62) see Table 3. These tools
help chemists quickly identify safer alternatives
for many of the common hazardous solvents.
Some green solvents have the added beneﬁt
of being optimized for product isolation.
Supercritical CO2 can be removed by changing
the pressure of the reactor, leaving only the
products of the reaction (63). Similarly, the use
of switchable solvent has been shown to aid in
the precipitation of products at the completion
of the reaction (64). By designing a solvent
that functions well for both the reaction and
the separation steps, there is a potential to save
both waste and energy, making these more
exotic solvents more attractive.
2.3.3. Separations. The isolation of a pure
product is paramount for the production of
ﬁne chemical products. Many techniques for
the isolation and puriﬁcation of chemicals use
large quantities of solvent and energy. Devel-
opments in pervaporation membranes have
yielded high-purity chemicals with low energy
consumption (65). Micro- or nanostructured
membranes
have
also
been
developed
to
improve the isolation of catalysis, metals, poly-
mers, and other by-products from reactions
(66). Improvements in membrane technology
will enable greater product isolation, decreased
282
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Table 3
Solvent replacement optionsa
Undesirable solvents
Alternativeb
Pentane
Heptane
Hexane(s)
Heptane
Diisopropyl ether or diethyl ether
2-MeTHF or tert-butyl methyl ether
Dioxane or dimethoxyethane
2-MeTHF or tert-butyl methyl ether
Chloroform, dichloroethane, or carbon tetrachloride
Dichloromethane
Dimethyl formamide, dimethyl acetamide, or
N-methylpyrrolidinone
Acetonitrile
Pyridine
Triethylamine (if pyridine used as base)
Dichloromethane (extractions)
EtOAc, MTBE, toluene, 2-MeTHF
Dichloromethane (chromatography)
EtOAc/heptane
Benzene
Toluene
aReprinted from Reference 62 with permission.
bAbbreviations: EtOAc, ethyl acetate; MTBE, methyl tert-butyl ether; 2-MeTHF, 2-methyltetrahydrofuran.
energy use, and cleaner waste streams, as well
as improve water treatment.
2.4. Chemical Use
Chemical users have a tremendous potential
to drive change in the chemical industry. Most
chemicals are not sold directly to the public;
rather chemicals are essential components
of business supply chains. A single consumer
product often contains or was made using many
different solvents and chemicals. Traditionally,
information about chemical identities and
hazards associated with chemicals are not
effectively shared through the supply chain.
Recent consumer demand for safer and more
sustainable products, coupled with increased
regulatory scrutiny, has caused businesses to
seek greener chemicals and processes through-
out their supply chains. Some of the key drivers
of this shift include decreasing liability, in-
creasing market share, responding to chemical
regulations, responding to voluntary initiatives
or incentive programs, and responding to
public awareness (67).
Because most chemical users do not develop
their own chemicals, their approach to green
chemistry naturally involves assessing alterna-
tives. Like the comprehensive design metrics
discussed above, alternatives assessment meth-
ods evaluate chemicals or design options against
multiple factors. Chemicals need to meet prod-
uct performance and price constraints as well
as perform well against greenness metrics. A
number of tools have been developed to help
businesses and other chemical users evaluate
chemicals and their alternatives (67).
The prerequisite for using any of these tools
is obtaining full ingredient disclosure from all
chemical and product suppliers. Some pro-
grams like the EPA’s Design for the Environ-
ment (67) and CleanGredients (67) have tried to
streamline this process by acting as third-party
accrediting agencies, which evaluate chemicals
under nondisclosure agreements and then make
a summary of the results publicly available.
Although third-party evaluation is valuable
when available, it cannot currently be relied on
to supply all of the information necessary to
evaluate materials throughout a business supply
chain. Changes to regulations regarding conﬁ-
dential business information or greater support
for third-party evaluation are necessary before
many products can be evaluated.
In the face of these constraints, businesses
have adopted two complementary strategies.
The ﬁrst begins by using publically available au-
thoritative lists of hazardous chemicals and by
making a restricted substance list. A restricted
substance list can be distributed to suppliers,
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allowing companies to dictate what chemicals
are not included in their supply streams. Com-
paniescanalsodistributesolicitationsforthein-
clusion of preferred substances. The restricted
and preferred substance lists can inﬂuence sus-
tainability choices throughout the supply chain
(67).
When information is available for a partic-
ular class of chemicals, alternative assessments
can be accomplished. Through a partnership
between the EPA Design for the Environment,
CleanGredients, and the National Science
Foundation,
the
surfactants
and
cleaning
products used by janitors in King County,
Washington, underwent an alternatives analy-
sis. This process resulted in the identiﬁcation
of safer alternatives, which reduced both
health and environmental hazards associated
with cleaning products (68). Although this
evaluation took a signiﬁcant investment in
time and resources, it can now be easily applied
to other municipalities or janitorial companies
with relatively little additional effort.
2.5. End of Life for Chemicals
and Products
Chemicals and products should be created so
that they degrade or can be reused at the end
of their useful life. For most organic chemi-
cals, this means that, at the end of life, naturally
occurring processes, including hydrolysis, pho-
tolysis, and enzymatic degradation, should be
able to break the chemical down. The design
of biodegradable small molecules has been re-
viewed by Boethling and coworkers at the EPA
(69). The review includes rules of thumb that
can qualitatively guide chemical evaluation. In
general, water-soluble compounds break down
faster, especially those containing hydroxyl, es-
ter, carboxylic acid, aldehyde, or ketone groups.
For hydrophobic compounds, it is preferable
to have linear structures, especially >4 carbon
chains, rather than aromatic or branched struc-
tures. Finally, quaternary carbons and halogen
substituents should be avoided because they are
associated with slow biodegradation.
For some inorganic or highly engineered
materials, natural degradation is either imprac-
tical or undesirable. Products with these mate-
rials should be designed so that the materials
can be easily reclaimed and recycled (15, 52).
The proliferation of electronic devices has led
to a signiﬁcant waste disposal issue. The known
toxic materials in electronics have resulted in e-
waste being classiﬁed as hazardous under the
Basel Convention. A closer look at this waste
stream reveals a great opportunity for recycling.
Over half of the material in e-waste is metal,
which has the potential to be recycled. How-
ever, about 2.5% of the material in the waste
stream consists of known toxins, including lead,
cadmium, and brominated ﬂame retardants
(70). Unfortunately, these materials are not eas-
ily separated, and the conditions found in cur-
rent recycling facilities produce unacceptably
high exposures to these toxic compounds. Many
of these residual toxic chemicals are then re-
leased into the air and water, adversely effecting
the surrounding population and environment.
New initiatives and regulations promoting
extended producer responsibility have been
driving innovation in product design to stream-
line the reuse, recycling, and safe disposal of
consumer goods. The European Union di-
rective 2000/53/EC (71) stipulated minimum
reuse and recovery rates for vehicles. This pro-
gram will eventually mandate a 95% reuse and
recovery rate by mass in 2015. Even though ad-
vances in shredding technology have allowed
the current standards to be met without signif-
icant redesign, the 95% standard has spurred
European car companies to design cars for
greater ease of disassembly (71). By designing
products that are more easily disassembled, ex-
posure to toxic chemicals during the recycling
process can be reduced.
2.6. Need for Collaboration
There are many drivers of green chemistry. The
promotion of greener chemicals and processes
necessitates collaboration and communication
between scientists, engineers, business leaders,
politicians, and the public. Designing across
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the life cycle of a chemical product requires
many types of technical knowledge. Enabling
green chemical policies and markets necessi-
tates the inclusion of nontechnical experts and
advocates. Effective communication between
these stakeholders can be challenging because
they use different technical terms and maintain
different vested interests, but the design and
production of greener products depend on the
ability of these groups to communicate.
Collaboration can be promoted with the
creation of interdisciplinary research centers.
Interdisciplinary
research
centers
promote
collaboration by including all of the stake-
holders (industry, government, academic, and
advocacy groups) in discussions concerning the
future of chemical development. By identifying
key issues and then supporting innovation, ap-
plicable technological solutions can be rapidly
developed. The American Chemical Society
pharmaceutical and formulator roundtables
are good examples of innovation hubs where
industry leaders collectively identify challenges
and help fund research that advances green
chemistry solutions. The new technology is
then made available to all of the roundtable
members and eventually to the public through
peer-reviewed publications (72).
Another example of recent success in mul-
tistakeholder collaboration is Green Center
Canada (72a), an interdisciplinary academic
center that evaluates both the technological
and business potential of university inventions.
When promising technologies are identiﬁed it
helps academic researchers partner with indus-
try to pursue the commercialization of green
products and services. The authors believe that
a collaborative model will bolster the market
for green chemistry solutions in the future.
3. CASE STUDY: GREEN
NANOTECHNOLOGY
DEVELOPMENT
Both nanotechnology and green chemistry have
promised to do more with less, which will
advance the dematerialization of technology.
The development of nanotechnology occurred
concurrently with the development of green
chemistry. Innovation and practical techno-
logical applications have been hallmarks of
both ﬁelds. Proponents of both technologies
have promised to help address the challenges
currently facing society. Nanotechnology has
promised advances in catalysis, energy pro-
duction, and human health by harnessing the
novel properties of materials that occur at the
nanoscale. Green chemistry has promised to ad-
dress many of the same challenges by minimiz-
ing the adverse health effects of chemicals while
maximizing the efﬁciency of their production.
From inception, nanotechnology has been
driven by application, whereas green chemistry
has sought to balance the beneﬁts from appli-
cations of new technologies with the adverse
implications that they may have for human and
environmental health. Although nanotechnol-
ogy has garnered signiﬁcantly more govern-
ment and industry support, advances in green
chemistry are now well situated to help en-
sure that nanotechnology realizes its promise
without regrettable implications owing to un-
intended hazards or negative public perception
(73).
It is instructive to consider the current status
of nanotechnology from a life-cycle perspec-
tive. We have highlighted areas where green
chemistry has been integrated into nanotech-
nology as well as areas where the development
of nanotechnology could beneﬁt from a greener
approach.
3.1. Design of Nanotechnology
Research groups and companies around the
world are designing new products and processes
that incorporate nanotechnology. This is the
optimal time to consider how these processes
can be made safe and efﬁcient. Many of the
same metrics that were discussed above can be
applied to processes involving nanotechnology.
Application of even simple design metrics can
be very helpful as nanomaterials are considered
for commercialization. For example, one study
calculated E-factors for ﬁve different synthetic
pathways to make gold nanoparticles. These
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pathways had E-factors ranging from 200 to
96,400 kg waste/kg product (74), highlighting
the need for efﬁcient nanomaterials syntheses,
and the signiﬁcant differences between current
synthetic strategies.
The E-factor does not include any consid-
eration of the potential toxicity of the reagents
used in the synthesis. In the above exam-
ple, the synthesis that used the most benign
reagents, starch and glucose, had one of the
highest E-factors (29,600 kg waste/kg prod-
uct). The most mass-efﬁcient synthesis used
reagents with more signiﬁcant health concerns.
These trade-offs should be carefully consid-
ered through the application of comprehensive
design metrics like the 12 principles. The 12
principles have be adapted for nanosynthesis by
Dahl et al. in their recent review of greener nan-
otechnology (75); see Figure 5.
Although many of the inputs for nanomate-
rial production are evaluated using the metrics
alreadydiscussed,thenanomaterialsthemselves
present new challenges. In particular, concerns
surrounding human and environmental toxicity
are beginning to draw attention from scientists,
lawmakers, and consumers (73, 74). The same
properties that impart novel reactivity could
potentially also result in new hazards.
Evidence has shown that the toxicity of
nanomaterials is dependent on a wide range of
factors that cannot be easily generalized (76).
Toxicity pathways that rely on the production
of reactive oxygen species are often material
dependent, and bioavailability is often dictated
by surface coating and/or surfaces charges (76).
New assays (77) and risk management models
(78) for assessing nanoparticle hazards are being
developed. Further support for research in this
area is needed to guarantee the safety and so-
cial acceptance of nanotechnology in consumer
products.
3.2. Raw Materials
for Nanotechnology
Nanotechnology
researchers
have
already
begun incorporating some alternative chemical
feedstocks into their synthetic strategies. Sci-
entists have used natural products as reducing
agents and nanoparticle coatings during the
synthesis of nanomaterials. Starch, agar, pro-
teins, sugars, tea extract, coffee extract, ascorbic
acid, and even whole organisms have been used
as reducing agents and nanoparticle coatings to
synthesize nanomaterials (75). The diversity of
elements from which nanomaterials have been
fashioned spans most of the periodic table.
The next step in greening nanomaterial feed-
stocks needs to focus on the metal precursors.
The metals used should, when practicable,
be earth abundant and nontoxic. Addition-
ally, care should be taken to use precursors
that minimize hazard and embedded energy
content.
3.3. Production of Nanomaterials
Greening the production of many of the
common nanomaterials (Au, Ag, CdSe, ZnO,
FexOy, TiO2, and carbon nanotubes) has al-
ready occurred. Many of the most acutely toxic
reagents and hazardous solvents have already
been replaced for more benign alternatives (75).
For example, initial reports for the produc-
tion of gold nanoparticles used borane or di-
borane as the reducing agent in benzene as
a solvent (74). With a focus on commercial-
ization, it was clear that less hazardous ma-
terials and methods were needed. A 2011ISI
Web of Science R
⃝search for “gold nanoparti-
cle synthesis” yields over 1,200 hits for the past
15 years. Many of these methods use much safer
chemicals.
A number of synthetic strategies have been
developed to improve nanoparticle production.
These include using molten salt and hydrother-
mal, templated, sonochemical, and microreac-
tor methods (75, 79). Like other green chem-
istry methodologies, the application of these
techniques enables safer, more efﬁcient, and
often novel chemistry.
The
puriﬁcation
of
nanomaterials
re-
mains a signiﬁcant challenge in nanoscience.
The
chemical
and
physical
properties
of
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Figure 5
Showing the relationship between the 12 principles of green chemistry and the design and practice of greener nanoscience. The middle column summarizes the 12
principles as they apply to nanoscience. The numbers in square brackets refer to the relevant green chemistry principles, which are shown in an abbreviated form in the
left-hand column. The right-hand column gives examples of each of the green nanoscience design principles as they are practiced. Reprinted from Reference 75 with
permission.
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nanoparticles
prohibit
many
traditional
methods for chemical puriﬁcation. In general,
nanomaterials cannot be distilled, recrystal-
lized, or puriﬁed by chromatography. Some of
the more common methods for puriﬁcation,
including dialysis, centrifugation, and ﬁltering,
are time and resource intensive. In an effort to
improve puriﬁcation, reduce solvent waste, and
speed the dialysis puriﬁcation, researchers have
used a continuously ﬂowing diaﬁltration in-
strument to improve selectivity and yield (80).
A similar ﬂow technique designed speciﬁcally
for colloidal separations, ﬁeld ﬂow fractiona-
tion, uses centrifugal force to separate various
sized particles. Field ﬂow fractionation has
been used as both an analytical and preparatory
technique for the separation of biological and
inorganic nanoscale materials (81). Given the
similar size scales of proteins and nanoparticles,
techniques for biological separation including
electrophoretic (82) methods have also been
adapted to nanoparticle puriﬁcation. Unfor-
tunately, these separation techniques are not
easily scaled to the volumes needed for produc-
tion. Additional practical and scalable methods
are needed to minimize solvent and energy
use.
The advances in green synthetic techniques
have given scientists many tools. Now the chal-
lenge becomes applying these tools throughout
the discovery and synthesis of the next gen-
eration of nanomaterials. The most efﬁcient
synthesis will be accomplished when the need
for auxiliaries, puriﬁcation, and multiple surface
functionalization steps are eliminated.
3.4. Use of Nanotechnology
The market for nanotechnology continues to
grow, with an average annual growth rate
greater than 30%. Some researchers project
a $1 trillion market for nanotechnology by
the year 2015 (74). As the market contin-
ues to grow, consumers and regulators will
demand a greater understanding of the po-
tential hazards associated with nanomaterials.
Manufacturers also demand high degrees of
product uniformity and reliability. To meet
these demands, new technologies for rapid
characterization and screening of nanoparti-
cles are needed (83). The technology currently
used to characterize nanomaterials relies on
advanced microscopy techniques, which are
time-consuming and often only characterize a
few representative nanostructures. These tech-
niques are expensive, time-consuming, and not
easily adapted to the high-throughput require-
ments of the manufacturing setting. Addition-
ally, microscopy is not suited for the iden-
tiﬁcation of residual molecules or ions that
may actually have a signiﬁcant effect on the
function and hazards associated with the mix-
ture (83). Changes from batch to batch can
affect both product performance and health
hazards. This bottleneck highlights the im-
portance of analytical chemistry in promoting
the adoption of new greener technologies and
will continue to inspire innovation in coming
years.
3.5. End of Life of Nanomaterials
Like their toxicity, the fate of nanoparticles
in the environment is hard to predict. It is
inﬂuenced by material composition and the
nature of the nanomaterial surfaces (84).
The
relationships
between
nanoparticles’
physical properties and features (composition,
shape, size, and coating) and their fate and
degradation in the environment are still poorly
understood. If these relationships could be
understood, then the principles of green chem-
istry could be applied toward the design of safer
nanomaterials.
To design around the current uncertainty,
researchers have targeted biologically inspired
bionanocomposite materials similar to abalone
shells (85). Even though this approach is not
universally applicable, both application and im-
plication beneﬁt from using biology for de-
sign inspiration. The beneﬁts of biological and
biologically inspired materials have inﬂuenced
both green chemistry and nanotechnology over
the past two decades.
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3.6. Lessons from Green
Nanotechnology
Nanotechnology has focused on the develop-
ment of new applications. In our opinion, the
attention to rapid development of technology
by researchers and funding agencies has some-
what overshadowed the need for understanding
the implications of these new technologies.
Application-based
research
has
attracted
signiﬁcantly more funding, with only recent
appropriations being made to understand the
implications of these technologies (73, 74).
By taking a green chemistry approach, some
researchers have proactively demonstrated the
potential for the acceptance and success of
nanotechnology in the market place (75, 86).
The green chemistry framework helps provide
an advantage to emerging technologies that
promote both performance and safety.
Both nanoscience and green chemistry call
for an interdisciplinary approach to research
and development. The far-reaching appli-
cations for both technologies are clear. The
potential synergy can be supported through
the integration of green chemistry into the
already existing interdisciplinary collaborations
in nanoscience. This allows comprehensive
design and analysis of nanomaterials to occur
in collaborative atmospheres and includes an
analysis of the environmental impacts of nano-
technology. The interdisciplinary Center for
Environmental Implications of Nanotechnol-
ogy has begun making this vision a reality (87).
4. FUTURE OF GREEN
CHEMISTRY
Green
chemistry
and
green
engineering
continue to grow, inﬂuencing scientists and
engineers worldwide. The growing interna-
tional community now includes educational
and/or research initiatives in more than 25
countries. New technical journals, numerous
international conferences, and emerging social
networking sites for green chemistry have
helped
practitioners
collaborate.
Many
of
these collaborations are built around educating
chemists about the potential beneﬁts of green
chemistry. In order for green chemistry to
impact the way materials are produced, sus-
tainability concepts need to be incorporated
throughout the educational process.
4.1. Educational Efforts
Educational programs in green chemistry are
being promoted by open-access models for
curriculum dissemination, including many re-
sources that are available on the Web. This is
particularly important for the global dissemi-
nation of green chemistry curricula. The Be-
yond Benign Foundation (89), the American
Chemical Society Green Chemistry Institute
(90), and the Greener Education Materials (91)
Web sites all contain curricular material that
has been developed and tested in classrooms.
Although most of the available resources focus
on chemistry education, new interdisciplinary
programs are being enabled by campus-wide
sustainability efforts. Interdisciplinary collab-
orations and learning opportunities can excite
and engage even more people about the poten-
tial for green chemistry to meet the needs of
society’s pressing resource challenges.
4.2. Concluding Comments
Green chemistry is an iterative process. Apply-
ing the various metrics and principles of green
chemistry helps identify better products, but
there will always be room for improvement.
This means that there are no green chemi-
cals, only greener alternatives. This notion of
continual improvement is natural and appeal-
ing to most scientists and academics but can
cause some confusion among politicians, busi-
ness leaders, and consumers, who often prefer
deﬁnite answers. To avoid ﬂawed policies, poor
investments, and greenwashing campaigns, it is
important for scientists and engineers to reach
out to the public. They must explain both the
applications and implications of emerging tech-
nologies.
Green chemistry takes the ﬁrst step by get-
ting scientists to consider both application- and
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implication-driven questions. The interdisci-
plinary interactions that arise from this holis-
tic approach to technology development should
encourage more effective communication with
the public. We must take this opportunity to
engage all of the public stakeholders because
the success of any new technology ultimately
rests with the public.
SUMMARY POINTS
1. Green chemistry is a design strategy.
2. Green chemistry and engineering seek to maximize efﬁciency and minimize health and
environmental hazards throughout every stage of a chemical’s life cycle.
3. Green chemistry evaluates and designs for both the applications and implications of new
technology.
4. Green chemistry and engineering have the potential to improve the performance and
public acceptance of new technologies.
FUTURE ISSUES
1. Interdisciplinary collaboration and cooperation are needed to realize the full potential
of green chemistry and engineering.
2. Continued development of predictive toxicology models and high-throughput screening
will speed the design of safer chemicals.
3. Education initiatives aimed at the public will help curtail greenwashing and inform con-
sumer activity.
4. Chemical policy reform should make chemical safety data publicly available.
5. Transparency throughout supply chains is needed to improve chemical manufacturing
and product formulation.
DISCLOSURE STATEMENT
The authors are not aware of any biases that might be perceived as affecting the objectivity of this
review.
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72. Andrews I, Dunn P, Hayler J, Hinkley B, Hughes D, et al. 2011. Green chemistry articles of interest to
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Figure 2
Plotting the trends in cost over time with respect to design and use. Typically 70% of costs and
environmental impacts are determined in the design phases, even if they are not incurred until the
manufacturing and use phases. Reprinted with permission of John Wiley & Sons, Inc., c
⃝2009,
Environmental Engineering: Fundamentals, Sustability, Design, by J.R. Mihelcic and J.B. Zimmerman (31).
www.annualreviews.org • Green Chemistry
C-1
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Annual Review of
Environment
and Resources
Volume 36, 2011
Contents
Preface ♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣v
Who Should Read This Series? ♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣vii
I. Earth’s Life Support Systems
Improving Societal Outcomes of Extreme Weather in a Changing
Climate: An Integrated Perspective
Rebecca E. Morss, Olga V. Wilhelmi, Gerald A. Meehl, and Lisa Dilling ♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣1
Ocean Circulations, Heat Budgets, and Future Commitment
to Climate Change
David W. Pierce, Tim P. Barnett, and Peter J. Gleckler ♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣27
Aerosol Impacts on Climate and Biogeochemistry
Natalie Mahowald, Daniel S. Ward, Silvia Kloster, Mark G. Flanner,
Colette L. Heald, Nicholas G. Heavens, Peter G. Hess, Jean-Francois Lamarque,
and Patrick Y. Chuang ♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣45
State of the World’s Freshwater Ecosystems: Physical, Chemical,
and Biological Changes
Stephen R. Carpenter, Emily H. Stanley, and M. Jake Vander Zanden ♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣75
II. Human Use of Environment and Resources
Coal Power Impacts, Technology, and Policy: Connecting the Dots
Ananth P. Chikkatur, Ankur Chaudhary, and Ambuj D. Sagar
♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣101
Energy Poverty
Lakshman Guruswamy ♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣139
Water and Energy Interactions
James E. McMahon and Sarah K. Price ♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣163
Agroecology: A Review from a Global-Change Perspective
Thomas P. Tomich, Sonja Brodt, Howard Ferris, Ryan Galt, William R. Horwath,
Ermias Kebreab, Johan H.J. Leveau, Daniel Liptzin, Mark Lubell, Pierre Merel,
Richard Michelmore, Todd Rosenstock, Kate Scow, Johan Six, Neal Williams,
and Louie Yang ♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣193
viii
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Energy Intensity of Agriculture and Food Systems
Nathan Pelletier, Eric Audsley, Sonja Brodt, Tara Garnett, Patrik Henriksson,
Alissa Kendall, Klaas Jan Kramer, David Murphy, Thomas Nemecek,
and Max Troell ♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣223
Transportation and the Environment
David Banister, Karen Anderton, David Bonilla, Moshe Givoni,
and Tim Schwanen ♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣247
Green Chemistry and Green Engineering: A Framework for
Sustainable Technology Development
Martin J. Mulvihill, Evan S. Beach, Julie B. Zimmerman, and Paul T. Anastas ♣♣♣♣♣271
The Political Ecology of Land Degradation
Elina Andersson, Sara Brogaard, and Lennart Olsson
♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣295
III. Management, Guidance, and Governance of Resources and Environment
Agency, Capacity, and Resilience to Environmental Change:
Lessons from Human Development, Well-Being, and Disasters
Katrina Brown and Elizabeth Westaway ♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣321
Global Forest Transition: Prospects for an End to Deforestation
Patrick Meyfroidt and Eric F. Lambin ♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣343
Reducing Emissions from Deforestation and Forest Degradation
Arun Agrawal, Daniel Nepstad, and Ashwini Chhatre ♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣373
Tourism and Environment
Ralf Buckley ♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣397
Literature and Environment
Lawrence Buell, Ursula K. Heise, and Karen Thornber ♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣417
Religion and Environment
Willis Jenkins and Christopher Key Chapple ♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣441
Indexes
Cumulative Index of Contributing Authors, Volumes 27–36 ♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣465
Cumulative Index of Chapter Titles, Volumes 27–36 ♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣♣469
Errata
An online log of corrections to Annual Review of Environment and Resources articles may
be found at http://environ.annualreviews.org
Contents
ix
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Lawrence Berkeley National Laboratory
Lawrence Berkeley National Laboratory
Title
Energy efficiency improvement and cost saving opportunities for petroleum refineries
Permalink
https://escholarship.org/uc/item/96m8d8gm
Authors
Worrell, Ernst
Galitsky, Christina
Publication Date
2005-02-15
eScholarship.org
Powered by the California Digital Library
University of California
LBNL-56183 
 
 
 
 
 
 
Energy Efficiency Improvement and Cost Saving Opportunities 
For Petroleum Refineries 
 
An ENERGY STAR® Guide for Energy and Plant Managers 
 
 
 
 
 
Ernst Worrell and Christina Galitsky 
 
 
 
 
 
 
Energy Analysis Department 
Environmental Energy Technologies Division 
Ernest Orlando Lawrence Berkeley National Laboratory 
University of California 
Berkeley, CA 94720 
 
 
 
 
 
February 2005 
 
 
 
 
This report was funded by the U.S. Environmental Protection Agency’s Climate Protection 
Partnerships Division as part of ENERGY STAR. ENERGY STAR is a government-backed program 
that helps businesses protect the environment through superior energy efficiency. The work was 
supported by the U.S. Environmental Protection Agency through the U.S. Department of Energy 
Contract No.DE-AC02-05CH11231. 
Energy Efficiency Improvement and Cost Saving Opportunities  
for Petroleum Refineries 
 
® Guide for Energy and Plant Managers 
An ENERGY STAR
 
Ernst Worrell and Christina Galitsky 
Energy Analysis Department 
Environmental Energy Technologies Division 
Ernest Orlando Lawrence Berkeley National Laboratory 
 
February 2005 
 
ABSTRACT 
 
The petroleum refining industry in the United States is the largest in the world, providing 
inputs to virtually any economic sector, including the transport sector and the chemical 
industry. The industry operates 146 refineries (as of January 2004) around the country, 
employing over 65,000 employees. The refining industry produces a mix of products with a 
total value exceeding $151 billion. Refineries spend typically 50% of cash operating costs 
(i.e.,, excluding capital costs and depreciation) on energy, making energy a major cost factor 
and also an important opportunity for cost reduction. Energy use is also a major source of 
emissions in the refinery industry making energy efficiency improvement an attractive 
opportunity to reduce emissions and operating costs. 
 
Voluntary government programs aim to assist industry to improve competitiveness through 
increased energy efficiency and reduced environmental impact. ENERGY STAR®, a 
voluntary program managed by the U.S. Environmental Protection Agency, stresses the need 
for strong and strategic corporate energy management programs. ENERGY STAR provides 
energy management tools and strategies for successful corporate energy management 
programs. This Energy Guide describes research conducted to support ENERGY STAR and 
its work with the petroleum refining industry. This research provides information on 
potential energy efficiency opportunities for petroleum refineries.  
 
This Energy Guide introduces energy efficiency opportunities available for petroleum 
refineries. It begins with descriptions of the trends, structure, and production of the refining 
industry and the energy used in the refining and conversion processes. Specific energy 
savings for each energy efficiency measure based on case studies of plants and references to 
technical literature are provided. If available, typical payback periods are also listed. The 
Energy Guide draws upon the experiences with energy efficiency measures of petroleum 
refineries worldwide. The findings suggest that given available resources and technology, 
there are opportunities to reduce energy consumption cost-effectively in the petroleum 
refining industry while maintaining the quality of the products manufactured. Further 
research on the economics of the measures, as well as the applicability of these to individual 
refineries, is needed to assess the feasibility of implementation of selected technologies at 
individual plants. 
iii 
iv 
Contents 
 
1. Introduction.......................................................................................................................... 1 
2. The U.S. Petroleum Refining Industry ................................................................................ 3 
3. Process Description ............................................................................................................. 9 
4. Energy Consumption ......................................................................................................... 18 
5. Energy Efficiency Opportunities....................................................................................... 25 
5. Energy Efficiency Opportunities....................................................................................... 25 
6. Energy Management and Control...................................................................................... 28 
6.1 Energy Management Systems (EMS) and Programs................................................... 28 
6.2 Monitoring & Process Control Systems ...................................................................... 30 
7. Energy Recovery ............................................................................................................... 34 
7.1 Flare Gas Recovery...................................................................................................... 34 
7.2 Power Recovery........................................................................................................... 35 
8. Steam Generation and Distribution ................................................................................... 36 
8.1 Boilers.......................................................................................................................... 37 
8.2 Steam Distribution....................................................................................................... 40 
9. Heat Exchangers and Process Integration.......................................................................... 43 
9.1 Heat Transfer– Fouling................................................................................................ 43 
9.2 Process Integration....................................................................................................... 44 
10. Process Heaters................................................................................................................ 49 
10.1 Maintenance............................................................................................................... 49 
10.2 Air Preheating............................................................................................................ 50 
10.3 New Burners.............................................................................................................. 50 
11. Distillation ....................................................................................................................... 51 
12. Hydrogen Management and Recovery ............................................................................ 53 
12.1 Hydrogen Integration................................................................................................. 53 
12.2 Hydrogen Recovery................................................................................................... 53 
12.3 Hydrogen Production................................................................................................. 55 
13. Motors.............................................................................................................................. 56 
13.1 Motor Optimization ................................................................................................... 56 
14. Pumps .............................................................................................................................. 59 
15. Compressors and Compressed Air................................................................................... 65 
16. Fans.................................................................................................................................. 70 
17. Lighting............................................................................................................................ 71 
18. Power Generation ............................................................................................................ 74 
18.1 Combined Heat and Power Generation (CHP).......................................................... 74 
18.2 Gas Expansion Turbines............................................................................................ 75 
18.3 Steam Expansion Turbines. ....................................................................................... 76 
18.4 High-temperature CHP.............................................................................................. 77 
18.5 Gasification................................................................................................................ 77 
19. Other Opportunities ......................................................................................................... 79 
19.1 Process Changes and Design ..................................................................................... 79 
19.2 Alternative Production Flows.................................................................................... 79 
19.3 Other Opportunities ................................................................................................... 80 
20. Summary and Conclusions .............................................................................................. 81 
v 
Appendix A: Active refineries in the United States as of January 2003 ............................... 94 
Appendix B: Employee Tasks for Energy Efficiency ........................................................... 99 
Appendix C: Energy Management System Assessment for Best Practices in Energy 
Efficiency........................................................................................................................ 100 
Appendix D: Energy Management Assessment Matrix ...................................................... 102 
Appendix E: Support Programs for Industrial Energy Efficiency Improvement ................ 105 
 
vi 
1. Introduction 
 
As U.S. manufacturers face an increasingly competitive global business environment, they 
seek out opportunities to reduce production costs without negatively affecting product yield 
or quality. Uncertain energy prices in today’s marketplace negatively affect predictable 
earnings, which are a concern, particularly for the publicly traded companies in the 
petroleum industry. Improving energy efficiency reduces the bottom line of any refinery. 
For public and private companies alike, increasing energy prices are driving up costs and 
decreasing their value added. Successful, cost-effective investment into energy efficiency 
technologies and practices meets the challenge of maintaining the output of a high quality 
product while reducing production costs. This is especially important, as energy efficient 
technologies often include “additional” benefits, such as increasing the productivity of the 
company.  
 
Energy use is also a major source of emissions in the refinery industry, making energy 
efficiency improvement an attractive opportunity to reduce emissions and operating costs. 
Energy efficiency should be an important component of a company’s environmental 
strategy. End-of-pipe solutions can be expensive and inefficient while energy efficiency can 
be an inexpensive opportunity to reduce criteria and other pollutant emissions. Energy 
efficiency can be an efficient and effective strategy to work towards the so-called “triple 
bottom line” that focuses on the social, economic, and environmental aspects of a business1. 
In short, energy efficiency investment is sound business strategy in today's manufacturing 
environment. 
 
Voluntary government programs aim to assist industry to improve competitiveness through 
increased energy efficiency and reduced environmental impact. ENERGY STAR®, a 
voluntary program managed by the U.S. Environmental Protection Agency (EPA), 
highlights the importance of strong and strategic corporate energy management programs. 
ENERGY STAR provides energy management tools and strategies for successful corporate 
energy management programs. This Energy Guide describes research conducted to support 
ENERGY STAR and its work with the petroleum refining industry. This research provides 
information on potential energy efficiency opportunities for petroleum refineries. ENERGY 
STAR can be contacted through www.energystar.gov for additional energy management 
tools that facilitate stronger energy management practices in U.S. industry. 
 
This Energy Guide assesses energy efficiency opportunities for the petroleum refining 
industry. Petroleum refining in the United States is the largest in the world, providing inputs 
to virtually all economic sectors, including the transport sector and the chemical industry. 
The industry operates 146 refineries (as of January 2004) around the country, employing 
over 65,000 employees, and produces a mix of products with a total value exceeding $151 
billion (based on the 1997 Economic Census). Refineries spend typically 50% of cash 
                                                 
1 The concept of the “triple bottom line” was introduced by the World Business Council on Sustainable 
Development (WBCSD). The three aspects of the “triple bottom line” are interconnected as society depends on 
the economy and the economy depends on the global ecosystem, whose health represents the ultimate bottom 
line.  
 
1
operating costs (i.e., excluding capital costs and depreciation) on energy, making energy a 
major cost factor and also an important opportunity for cost reduction. 
 
This Energy Guide first describes the trends, structure and production of the petroleum 
refining industry in the United States. It then describes the main production processes. Next, 
it summarizes energy use in refineries along with the main end uses of energy. Finally, it 
discusses energy efficiency opportunities for U.S. refineries. The Energy Guide focuses on 
measures and technologies that have successfully been demonstrated within individual 
plants in the United States or abroad. Because the petroleum refining industry is an 
extremely complex industry, this Energy Guide cannot include all opportunities for all 
refineries. Although new technologies are developed continuously (see e.g., Martin et al., 
2000), the Energy Guide focuses on practices that are proven and currently commercially 
available. 
 
This Energy Guide aims to serve as a guide for energy managers and decision-makers to 
help them develop efficient and effective corporate and plant energy management programs, 
by providing them with information on new or improved energy efficient technologies.  
 
 
2
2. The U.S. Petroleum Refining Industry 
 
The United States has the world’s largest refining capacity, processing just less than a 
quarter of all crude oil in the world. Although the major products of the petroleum refining 
sector are transportation fuels, its products are also used in other energy applications and as 
feedstock for the chemical industries. 
 
0
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Capacity
Actual Input
Figure 1. Capacity and actual crude intake of the U.S. petroleum refining industry 
between 1949 and 2001, expressed in million barrels/day of crude oil intake. Source: 
Energy Information Administration. 
 
The U.S. petroleum refining sector has grown over the past 50 years by about 2%/year on 
average. Until the second oil price shock, refining capacity grew rapidly, but production 
already started to level off in the mid to late 1970s. This was a period where the industry 
started to reorganize.  It was not until after the mid-1980s that refinery production started to 
grow again. Since 1985, the industry has been growing at a somewhat slower rate of 
1.4%/year. Figure 1 shows the developments in installed capacity (expressed as crude intake 
capacity) and actual crude intake in the U.S. refining industry since 1949. 
 
Figure 1 shows that capacity utilization has been pretty steady, with exception of the period 
between the two oil price shocks. Following the first oil price shock, federal legislation 
favoring domestic production and refining subsidized the construction and operation of 
many small refineries (U.S. DOE-OIT, 1998). As shown, this led to a reduced capacity 
 
3
utilization. Figure 2 shows the number of operating refineries in the United States since 
1949. 
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Figure 2. Number of operating refineries in the United States. Source: Energy 
Information Administration. 
 
Figure 2 clearly demonstrates the increasing number of refineries after the first oil price 
shocks in the 1970s. The small refineries only distill products, and are most often inefficient 
and less flexible operations, producing only a small number of products. Increasing demand 
for lighter refinery products, and changes in federal energy policy, have led to a reduction in 
the number of refineries, while increasing capacity utilization (see Figure 1). 
 
These market dynamics will also lead to a further concentration of the refinery industry into 
high capacity plants operating at higher efficiencies. The number of refineries has declined 
from 205 in 1990 to 147 in 2002. The current refineries have a higher capacity utilization 
and are generally more complex, with an emphasis on converting technology. This trend will 
continue to increase the ability to process a wider range of crudes and to produce an 
increasing share of lighter petroleum products.  Also increasing is the need to produce 
cleaner burning fuels to meet environmental regulations (e.g., reduction of sulfur content). 
Appendix A provides a list of operating refineries in the United States as of January 2003. 
 
Petroleum refineries can be found in 32 states, but the industry is heavily concentrated in a 
few states due to historic resource location and easy access to imported supplies (i.e., close 
to harbors). Hence, the largest number of refineries can be found on the Gulf Coast, 
followed by California, Illinois, New Jersey, Pennsylvania, and Washington. Some of the 
 
4
smallest producing states have only very small refineries operated by independent operators. 
These small refineries produce only a very small mix of products, and are ultimately not 
expected to be able to compete in the developing oil market. Figure 3 depicts refining 
capacity by state (expressed as share of total capacity crude intake) in 2002. 
 
0%
5%
10%
15%
20%
25%
30%
Alabama
Alaska
Arkansas
California
Colorado
Delaware
Georgia
Hawaii
Illinois
Indiana
Kansas
Kentucky
Louisiana
Michigan
Minnesota
Mississippi
Montana
Nevada
New Jersey
New Mexico
North Dakota
Ohio
Oklahoma
Pennsylvania
Tennessee
Texas
Utah
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Share Capacity (%)
Total Capacity: 16.8 Million Barrels/Calender day
Figure 3. Refining capacity by state as share of total U.S. refining capacity in 2003. 
Capacity is expressed as capacity for crude intake. Source: Energy Information 
Administration. 
 
The refineries are operated by 59 companies. Although there are a relatively large number of 
independent companies in the U.S. refining industry, the majority of the refining capacity is 
operated by a small number of multi-national or national oil processing companies. The 
largest companies (as of January 2003) are: ConocoPhilips (13% of crude capacity), 
ExxonMobil (11%), BP (9%), Valero (8%), ChevronTexaco (6%), Marathon Ashland (6%), 
and Shell (6%), which combined represent 59% of crude distillation (CDU) capacity. Each 
of these companies operates a number of refineries in different states. Figure 4 depicts 
companies operating over 0.5% of CDU capacity in the United States   
 
The small refineries produce a relative simple mix of products. Small refineries may often 
use high cost feedstocks, which may result in a relatively low profitability. As a result, small 
companies’ share of total industry economic value is smaller than their share of total 
industry production capacity.  
 
5
0%
2%
4%
6%
8%
10%
12%
14%
16%
ConocoPhilips
ExxonMobil
BP
Valero Energy Corp.
ChevronTexaco
Marathon Ashland Petro LLC
Shell
Motiva Enterprises LLC
Sunoco Inc.
Tesoro
Flint Hills Resources LP
Citgo
Premcor Refg Group Inc
Willaims
Lyondell Citgo Refining Co. Ltd.
Chalmette Refining LLC
Atofina Petrochemicals Inc.
PDV Midwest Refining LLC
Sinclair Oil Corp.
Frontier Refg Inc.
Coastal Eagle Point Oil Co.
Murphy Oil U.S.A. Inc.
Farmland Industries Inc.
Crown Central Petroleum Corp.
Giant Refining Co.
NCRA
Ultramar Inc.
Share CDU Capacity (%)
Includes companies operating 0.5% or more of CDU capacity (2003)
In the U.S. a total of 59 companies operated refineries in 2003.
 
Figure 4. Refining capacity (expressed as percentage of CDU capacity) for companies 
operating over 0.5% of CDU capacity in 2003. The depicted companies operate 94% of 
total national capacity. Companies operating less than 0.5% of CDU capacity are not 
depicted. Source: Energy Information Administration. 
 
The further concentration of refineries in the United States has contributed to a reduction in 
operating costs but has also impacted refining margins (Killen et al., 2001). The Western 
United States market is more or less isolated from the other primary oil markets in the 
United States. Although overall market dynamics in the United States and the Western 
United States market follow the same path, the operating margin from Western refineries is 
higher than that in other regions. Between 1995 and 2000, the operating margin of West 
Coast refineries has grown from $3 to a high of $8/bbl crude in 2000 (Killen et al., 2001), 
compared to 1 to 4$/bbl in other U.S. markets.   
 
U.S. refineries process different kinds of crude oil types from different sources. Over the 
past years, overall there has been a trend towards more heavy crudes and higher sulfur 
content (Swain, 2002). These effects vary for the different regions in the United States, but 
overall this trend has been clear over the past 10 years. This trend is likely to continue, and 
will affect the product mix, processing needs, and energy use of refineries. This trend will 
also result in a further expansion of conversion capacity at U.S. refineries. 
 
 
6
0
1000
2000
3000
4000
5000
6000
7000
1949
1951
1953
1955
1957
1959
1961
1963
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
Production (Million bbl/year)
Other
Still Gas
Residual FO
Coke
Gasoline
LPG
Jet Fuel
Distilled FO
Asphalt
Figure 5. Petroleum refining production, by major product categories in the United 
States, 1949 – 2001. Source: Energy Information Administration. 
 
While the type of processed crude oil is becoming increasingly heavier and higher in sulfur, 
the demand for oil products, and hence the product mix of the refineries, is changing 
towards an increased share of lighter products. Figure 5 depicts the past trend in production 
since 1949 by product category. Figure 5 shows an increase in the production and relative 
share of lighter products like gasoline, while the share of heavier fuels like residual fuel oil 
declined over the past 50 years. 
 
Figure 5 does not show the changing quality demands of the product categories. Started in 
California, increased air quality demands in many parts of the United States will result in an 
increased demand for low-sulfur automotive fuels (gasoline, diesel). This will result in an 
increase of hydrotreating capacity at the petroleum refinery, as well as alternative 
desulfurization processes in the future. Small refineries will most likely not be able to invest 
in this type of expansion, and will further lose market share. With limited markets for the 
hydroskimming refineries, a further concentration of refineries is likely to take place over 
the next few years. Expansion of existing refineries will provide the increased demand, as no 
greenfield refineries will likely be built in the next few years within the United States 
 
At the same time, the dynamic development of the petroleum industry faces other new 
challenges and directions. Increasing and more volatile energy prices will affect the bottom 
line of refineries. Commodity markets, like that of most oil products, show smaller and 
smaller margins. Both factors may negatively affect the profitability of petroleum refining. 
 
7
Increased needs to reduce air pollutant emissions from refinery operations as well as 
increased safety demands will drive technology choice and investments for future process 
technology. However, environmental compliance alone has not been the major factor 
affecting profitability (EIA, 1997).  Instead, a combination of the above factors is the driver 
for reduced profitability of refinery operations. This trend is expected to continue, and in the 
future the above challenges combined will affect the industry and technology choice 
profoundly. 
 
The continued trend towards low-sulfur fuels and changes in the product mix of refineries 
will affect technology choice and needs. For example, the current desulfurization and 
conversion technologies use relatively large amounts of hydrogen. As hydrogen is an energy 
intensive product, increased hydrogen consumption will lead to increased energy use and 
operation expenses, unless more efficient technologies for hydrogen production and 
recovery are developed and applied. In the long-term, new desulfurization technologies may 
reduce the need for hydrogen. At the same time, refineries are faced with challenges to 
reduce air pollution and other energy related issues (e.g., regulatory changes of power 
supply). The petroleum refining industry will face many other challenges. Climate change, 
new developments in automotive technology, and biotechnology are posed to affect the 
future structure of refineries. Table 1 summarizes the challenges to the petroleum refining 
industry. 
 
Table 1. Key drivers and challenges for the petroleum refining industry. The order in 
the table does not reflect an order of priorities. 
Challenge 
Key Issues 
Safety 
Safety incidents, refineries now mainly located in urbanized areas 
Environment 
Emissions of criteria air pollutants (NOx, VOC) and greenhouse gases 
Profitability 
Commodity market, further concentration of the industry 
Fuel Quality 
Sulfur, MTBE-replacement 
Feedstock 
Increasing demand for lighter products from decreasing quality crude 
Energy 
Costs of power and natural gas 
 
Katzer et al. (2000) explored the forces of change and the impacts on the future of petroleum 
refining. They see important new development needs in catalysis, optimization and control, 
reaction engineering and reactor design, biotechnology for desulfurization, increased use of 
natural gas as feedstock, and power generation. In the view of Katzer et al., the refinery of 
the future will look more like an automated chemical plant that will maximize high-value 
products (e.g., engineered molecules for specific applications) and integrate into the total 
energy-infrastructure. 
 
8
3. Process Description 
 
A modern refinery is a highly complex and integrated system separating and transforming 
crude oil into a wide variety of products, including transportation fuels, residual fuel oils, 
lubricants, and many other products. The simplest refinery type is a facility in which the 
crude oil is separated into lighter and heavier fractions through the process of distillation. In 
the United States, about 25% of refinery facilities are small operations producing fewer than 
50,000 barrels/day (U.S. DOE-OIT, 1998), representing about 5% of the total industry 
output. The existence of small, simple and relatively inefficient refineries is in part due to 
legislation subsidizing smaller operations following the first oil price shock. These small 
operations consist only of distillation capacity (i.e., no reforming or converting capacities) 
and make a limited number of products. 
 
Modern refineries have developed much more complex and integrated systems in which 
hydrocarbon compounds are not only distilled but are also converted and blended into a 
wider array of products. The overall structure of the refinery industry has changed in recent 
years because of a growing demand for lighter products. This has led to more complex 
refineries with increased conversion capacities. Increased conversion will lead to an increase 
in the specific energy consumption but will also produce a product mix with a higher value. 
These dynamics will continue in the future, as demand for heating (fuel) oil is decreasing. 
 
In all refineries, including small less complex refineries, the crude oil is first distilled, which 
is followed by conversion in more complex refineries. The most important distillation 
processes are crude or atmospheric distillation, and vacuum distillation. Different 
conversion processes are available using thermal or catalytic processes, e.g., using a 
catalytic reformer, where the heavy naphtha, produced in the crude distillation unit, is 
converted to gasoline, and the fluid catalytic cracker where the distillate of the vacuum 
distillation unit is converted. Newer processes, such as hydrocrackers, are used to produce 
more light products from the heavy bottom products. Finally, all products may be treated to 
upgrade the product quality (e.g., sulfur removal using a hydrotreater). Side processes that 
are used to condition inputs or produce hydrogen or by-products include crude conditioning 
(e.g., desalting), hydrogen production, power and steam production, and asphalt production. 
Lubricants and other specialized products may be produced at special locations. 
 
The principal energy using processes in refineries (in order of overall energy consumption in 
the United States) are the crude (or atmospheric) distillation unit, hydrotreaters, reformer, 
vacuum distillation unit, alkylate production, catalytic crackers, and hydrocrackers. 
 
The main production steps in refineries are discussed below, providing a brief process 
description and the most important operation parameters including energy use (see also 
Chapter 4). Figure 6 provides a simplified flow diagram of a refinery. The descriptions 
follow the flow diagram, starting with the intake of the crude through to the production of 
the final products. The flow of intermediates between the processes will vary by refinery, 
and depends on the structure of the refinery, type of crude processes, as well as product mix. 
 
 
9
Figure 6. Simplified flowchart of refining processes and product flows. Adapted from 
Gary and Handwerk (1994). 
 
Desalting. If the salt content of the crude oil is higher than 10 lb/1000 barrels of oil, the 
crude requires desalting (Gary and Handwerk, 1994). Desalting will reduce corrosion and 
minimize fouling of process units and heat exchangers. Heavier crudes generally contain 
more salts, making desalting more important in current and future refineries. The salt is 
washed from the crude with water (3-10% at temperatures of 200-300°F (90-150ºC). The 
salts are dissolved in the water, and an electric current is used to separate the water and the 
oil. This process also removes suspended solids. The different desalting processes vary in 
the amount of water used and the electric field used for separation of the oil and water. The 
efficiency of desalting is influenced by the pH, gravity, viscosity, and salt content of the 
crude oil, and the volume of water used in the process. Electricity consumption of desalting 
varies between 0.01 and 0.02 kWh/barrel of crude oil (IPPC, 2002). 
 
Crude Distillation Unit (CDU). In all refineries, desalted and pretreated crude oil is split 
into three main fractions according to their boiling ranges by a fractional distillation process. 
The crude oil is heated in a furnace to approximately 750°F (390ºC), and subsequently fed 
into the fractionating or distillation tower. Most CDUs have a two-stage heating process. 
First, the hot gas streams of the reflux and product streams are used to heat the desalted 
crude to about 550°F (290ºC). Second, it is further heated in a gas-fired furnace to 400ºC 
(Gary and Handwerk, 1994). The feed is fed is to the distillation tower at a temperature 
between 650 and 750°F (340-390ºC). Energy efficiency of the heating process can be 
improved by using pump-around reflux to increase heat transfer (at higher temperatures at 
lower points in the column). 
 
10
In the tower, the different products are separated based on their boiling points. The boiling 
point is a good measure for the molecule weight (or length of the carbon chain) of the 
different products. Gasoline, with relatively small molecules, boils between 70 and 140ºC, 
while naphtha, which has a larger molecule, has a boiling point between 140 and 180ºC. The 
distillation towers contains 30-50 fractionation trays. The number of trays depends on the 
desired number and purity of product streams produced at the particular CDU. 
 
The lightest fraction includes fuel gas, LPG, and gasoline. The overhead, which is the top or 
lightest fraction of the CDU, is a gaseous stream and is used as a fuel or for blending. 
 
The middle fraction includes kerosene, naphtha, and diesel oil. The middle fractions are 
used for the production of gasoline and kerosene. The naphtha is led to the catalytic 
reformer or used as feedstock for the petrochemical industry.  
 
The heaviest fractions are fuel oil and a bottom fraction, which has the lowest value. Fuel oil 
can be further processed in the conversion unit to produce more valuable products. About 
40% of the products of the CDU (on energy basis) cannot be used directly and are fed into 
the Vacuum Distillation Unit (VDU), where distillation is performed under low pressure. 
 
Because the CDU processes all incoming crude oil, it is a large energy user, although the 
specific energy consumption compared to the conversion process is relatively low. Energy 
efficiency opportunities consist of improved heat recovery and heat exchange (process 
integration), improved separation efficiencies, and other smaller measures. Integration of 
heat from the CDU and other parts of the refinery may lead to additional energy savings. 
  
Vacuum Distillation Unit (VDU) or High Vacuum Unit (HVU). The VDU/HVU further 
distills the heaviest fraction (i.e., heavy fuel oil) from the CDU under vacuum conditions. 
The reduced pressure decreases the boiling points making further separation of the heavier 
fractions possible, while reducing undesirable thermal cracking reactions (and associated 
fouling). The low pressure results in much larger process equipment. In the VDU, the 
incoming feedstream is heated in a furnace to 730-850°F (390-450ºC). 
 
Vacuum conditions are maintained by the use of steam ejectors, vacuum pumps, and 
condensers. It is essential to obtain a very low pressure drop over the distillation column to 
reduce operating costs. 
 
Of the VDU products, the lightest fraction becomes diesel oil. The middle fraction, which is 
light fuel oil, is sent to the hydrocracker (HCU) or fluid catalytic cracker (FCC), and the 
heavy fuel oil may be sent to the thermal cracker (if present at the refinery). 
 
The distillation products are further processed, depending on the desired product mix. 
Refinery gas is used as fuel in the refinery operations to generate heat (furnaces), steam 
(boilers), or power (gas turbines), while some of the refinery gas may be flared. Parts of the 
refinery gas may also be used to blend with LPG or for hydrogen production. Hydrogen is 
used in different processes in the refinery to remove sulfur (e.g., hydrotreating) and to 
convert to lighter products (e.g., hydrocracking). 
 
11
Hydrotreater. Naphtha is desulfurized in the hydrotreater and processed in a catalytic 
reformer. Contaminants such as sulfur and nitrogen are removed from gasoline and lighter 
fractions by hydrogen over a hot catalyst bed. Sulfur removal is necessary to avoid catalyst 
poisoning downstream, and to produce a clean product. The treated light gasoline is sent to 
the isomerization unit and the treated naphtha to the catalytic reformer or platformer to have 
its octane level increased. Hydrotreaters are also used to desulfurize other product streams in 
the refinery. 
 
Although many different hydrotreater designs are marketed, they all work along the same 
principle. The feedstream is mixed with hydrogen and heated to a temperature between 500 
and 800°F (260-430ºC). In some designs, the feedstream is heated and then mixed with the 
hydrogen. The reaction temperature should not exceed 800°F (430ºC) to minimize cracking. 
The gas mixture is led over a catalyst bed of metal oxides (most often cobalt or molybdenum 
oxides on different metal carriers). The catalysts help the hydrogen to react with sulfur and 
nitrogen to form hydrogen sulfides (H2S) and ammonia. The reactor effluent is then cooled, 
and the oil feed and gas mixture is then separated in a stripper column. Part of the stripped 
gas may be recycled to the reactor. 
 
In the hydrotreater, energy is used to heat the feedstream and to transport the flows. The 
hydrotreater also has a significant indirect energy use because of the consumption of 
hydrogen. In the refinery, most hydrogen is produced through reforming (see below). Some 
hydrogen is also produced as a by-product of cracking. 
 
Catalytic Reformer. The reformer is used to increase the octane level in gasoline. The 
desulfurized naphtha and gasoline streams are sent to the catalytic reformer. The product, 
called reformate, is used in blending of different refinery products. The catalytic reformer 
produces around 30-40% of all the gasoline produced in the United States Because the 
catalytic reformer uses platinum as catalyst, the feed needs to be desulfurized to reduce the 
danger of catalyst poisoning.  
 
Reforming is undertaken by passing the hot feed stream through a catalytic reactor. In the 
reactor, various reactions such as dehydrogenation, isomerization, and hydrocracking occur 
to reformulate the chemicals in the stream. Some of the reactions are endothermic and others 
exothermic. The types of reactions depend on the temperature, pressure, and velocity in the 
reactor. Undesirable side reactions may occur and need to be limited. The reformer is a net 
producer of hydrogen that is used elsewhere in the refinery. 
 
Various suppliers and developers market a number of reforming processes. In principle all 
designs are continuous, cyclic, or semi-regenerative, depending on the frequency of catalyst 
regeneration (Gary and Handwerk, 1994).  In the continuous process, the catalysts can be 
replaced during normal operation, and regenerated in a separate reactor. In the semi-
regenerative reactor, the reactor needs to be stopped for regeneration of the catalysts. 
Depending on the severity and operating conditions, the period between regenerations is 
between 3 and 24 months (Gary and Handwerk, 1994). The cyclic process is an alternative 
in between these two processes. The advantage of the semi-regenerative process is the low 
capital cost. The marketed processes vary in reactor design.  
 
12
Fluid Catalytic Cracker (FCC). The fuel oil from the CDU is converted into lighter 
products over a hot catalyst bed in the fluid catalytic cracker (FCC). The FCC is the most 
widely used conversion process in refineries. The FCC produces high octane gasoline, 
diesel, and fuel oil. The FCC is mostly used to convert heavy fuel oils into gasoline and 
lighter products. The FCC has virtually replaced all thermal crackers. 
 
In a fluidized bed reactor filled with particles carrying the hot catalyst and a preheated feed 
(500-800°F, 260-425ºC), at a temperature of 900-1000°F (480-540ºC) the feed is ‘cracked’ 
to molecules with smaller chains. Different cracking products are generated, depending on 
the feed and conditions. During the process, coke is deposited on the catalysts. The used 
catalyst is continuously regenerated for reuse, by burning off the coke to either a mixture of 
carbon monoxide (CO) and carbon dioxide (CO ) or completely to CO
2
2. If burned off to a 
CO/CO -mixture, the CO is combusted to CO
2
2 in a separate CO-burning waste heat 
recovery boiler to produce steam. The regeneration process is easier to control if the coke is 
burned directly to CO2, but a waste heat recovery boiler should be installed to recover the 
excess heat in the regenerator. The cracking reactions are endothermic, while the 
regeneration is exothermic, providing an opportunity for thermal integration of the two 
process steps. 
 
Older FCCs used metal catalysts, while new FCC designs use zeolite catalysts that are more 
active. This has led to a re-design of modern FCC units with a smaller reactor, and most of 
the reactions taking place in the so-called riser, which leads the hot feed and regenerated 
catalysts to the reaction vessel. The different FCC designs on the market vary in the way that 
the reactor and regeneration vessels are integrated. Varying the catalyst circulation rate 
controls the process. 
 
Fluid catalytic crackers are net energy users, due to the energy needed to preheat the feed 
stream. However, modern FCC designs also produce steam and power (if power recovery 
turbines are installed) as by-products. The power recovery turbines can also be used to 
compress the air for the cracker. The recovery turbine is installed prior to the CO or waste 
heat boiler, if the FCC works at pressures higher than 15 psig (Gary and Handwerk, 1994). 
 
Hydrocracker (HCU). The hydrocracker has become an important process in the modern 
refinery to allow for flexibility in product mix. The hydrocracker provides a better balance 
of gasoline and distillates, improves gasoline yield, octane quality, and can supplement the 
FCC to upgrade heavy feedstocks (Gary and Handwerk, 1994). In the hydrocracker, light 
fuel oil is converted into lighter products under a high hydrogen pressure and over a hot 
catalyst bed. The main products are naphtha, jet fuel, and diesel oil. It may also be used to 
convert other heavy fuel stocks to lighter products. The hydrocracker concept was developed 
before World War II to produce gasoline from lignite in Germany, and was further 
developed in the early 1960s. Today hydrocrackers can be found in many modern large 
refineries around the world. 
 
In the hydrocracker, many reactions take place. The principal reactions are similar to that of 
an FCC, although with hydrogenation. The reactions are carried out at a temperature of 500-
750°F (290-400ºC) and increased pressures of 8.3 to 13.8 Bar. The temperature and 
 
13
pressures used may differ with the licensed technology. The reactions are catalyzed by a 
combination of rare earth metals. Because the catalyst is susceptible to poisoning, the 
hydrocracker feed needs to be prepared by removing metallic salts, oxygen, nitrogenous 
compounds, and sulfur. This is done by first hydrogenating the feed, which also saturates the 
olefins. This is an exothermic reaction, but insufficient to provide all the heat for the 
hydrotreating units of the cracker.  The nitrogen and sulfur-compounds are removed in a 
stripper column, while water is removed by a molecular sieve dryer or silica gel. 
 
The prepared feed is mixed with recycled feed and hydrogen, and preheated before going to 
the reactor. The reactions are controlled by temperature, reactor pressure, and velocity. 
Typically the reactor is operated to have a conversion efficiency of 40-50%, meaning that 
40-50% of the reactor product has a boiling point below 400F (205ºC).  The product flow 
(effluent) is passed through heat exchangers and a separator, where hydrogen is recovered 
for recycling. The liquid products of the separator are distilled to separate the C4 and lighter 
gases from the naphtha, jet fuel, and diesel. The bottom stream of the fractionator is mixed 
with hydrogen and sent to a second stage reactor to increase the conversion efficiency to 50-
70% (Gary and Handwerk, 1994).  
 
Various designs have been developed and are marketed by a number of licensors in the 
United States and Western Europe. The hydrocracker consumes energy in the form of fuel, 
steam, and electricity (for compressors and pumps). The hydrocracker also consumes energy 
indirectly in the form of hydrogen. The hydrogen consumption is between 150-300 
scf/barrel of feed (27-54 Nm3/bbl) for hydrotreating and 1000 and 3000 scf /barrel of feed 
(180-540 Nm3/bbl) for the total plant (Gary and Handwerk, 1994). The hydrogen is 
produced as by-product of the catalytic reformer and in dedicated steam reforming plants 
(see below). 
 
Coking. A new generation of coking processes has added additional flexibility to the 
refinery by converting the heavy bottom feed into lighter feedstocks and coke. Coking can 
be considered a severe thermal cracking process. Modern coking processes can also be used 
to prepare a feed for the hydrocracker (see above). 
 
In the Flexi coking process, a heavy feed is preheated to 600-700°F (315-370ºC) and 
sprayed on a bed of hot fluidized coke (recycled internally). The coke bed has a reaction 
temperature between 950 and 1000°F (510-540ºC). At this temperature, cracking reactions 
take place. Cracked vapor products are separated in cyclones and are quenched. Some of the 
products are condensed, while the vapors are led to a fractionator column, which separates 
various product streams.  
 
The coke is stripped from other products, and then processed in a second fluidized bed 
reactor where it is heated to 1100°F (590ºC). The hot coke is then gasified in a third reactor 
in the presence of steam and air to produce synthesis gas. Sulfur (in the form of H2S) is 
removed, and the synthesis gas (mainly consisting of CO, H , CO  and N
2
2
2) can be used as 
fuel in (adapted) boilers or furnaces. The coking unit is a consumer of fuel (in preheating), 
steam, and power.  
 
 
14
Visbreaker. Visbreaking is a relatively mild thermal cracking operation, used to reduce the 
viscosity of the bottom products to produce fuel oil. This reduces the production of heavy 
fuel oils, while the products can be used to increase FCC feedstock and increase gasoline 
yields. This is accomplished by cracking the side chains of paraffin and aromatics in the 
feed, and cracking of resins to light hydrocarbons. Depending on the severity (i.e., time and 
temperature in the cracker) of the reactions, different products may be produced.  
 
There are two main processes: coil (or furnace) cracking and soak cracking. Coil cracking 
uses higher reactor temperatures and shorter residence times, while soak cracking has 
slightly lower temperatures and longer residence times (Gary and Handwerk, 1994). The 
reaction products are pretty similar, but the soaker cracker uses less energy due to the lower 
temperature, and has longer run times (due to reduced coke deposition on the furnace tubes). 
A soaker furnace consumes about 15% less energy than a coil furnace. The visbreaker 
consumes fuel (to heat the feed), steam, and electricity. 
 
Alkylation and Polymerization. Alkylation (the reverse of cracking) is used to produce 
alkylates (used in higher octane motor fuels), as well as butane liquids, LPG, and a tar-like 
by-product. The reactions are catalyzed by either hydrofluoric acid or sulfuric acid.  Several 
designs are used, using either of the catalysts. The most suitable alkylation process for a 
given refinery is determined by economics, especially with regard to the costs of acid 
purchase and disposal (Gary and Handwerk, 1994).  
 
Alkylation processes use steam and power. There are no large differences in energy intensity 
between both processes (Gary and Handwerk, 1994). 
 
Hydrogen Manufacturing Unit or Steam reforming (HMU). There are a number of 
supporting processes that do not produce the main refinery products directly, but produce 
intermediates used in the various refining processes. Hydrogen is generated from natural gas 
and steam over a hot catalyst bed, similar to the processes used to make hydrogen for 
ammonia.  
 
Hydrogen is produced by reforming the natural gas feedstock with steam over a catalyst, 
producing synthesis gas. Synthesis gas contains a mixture of carbon monoxide and hydrogen. 
The carbon monoxide is then reacted with steam in the water-gas-shift reaction to produce CO2 
and hydrogen. The CO2 is removed from the main gas stream using absorption, producing 
hydrogen.  
 
Energy is used in the form of fuel (to heat the reformer), steam (in the steam reforming), and 
power (for compression). Many different licensors supply the technology. Modern variants use 
a physical adsorption process to remove CO2, which uses less energy than chemical absorption 
processes.   
 
Gas Processing Unit. Refinery gas processing units are used to recover C3, C , C  and C
4
5
6 
components from the different processes, and to produce a desulfurized gas which can be 
used as fuel or for hydrogen production in steam reforming (see above). The lighter products 
are used as fuel or for H2 production, while the heavier fraction is recycled in the refinery. 
 
15
The process consists of a number of distillation, absorption, and stripper columns to recover 
the ethane, propane, and butane. The process uses fuel (to heat the incoming gas) and power 
(for compressors and other uses). 
 
Acid Gas Removal. Acid gases such as H S and CO
2
2 need to be removed to reduce air 
pollution (before 1970, they were just burned off) and are produced as a by-product of 
producing higher quality refinery products. These gases are removed by an (chemical) 
absorption process, and then further processed. H2S can be processed into elemental sulfur 
through the Claus process. The process consumes fuel and electricity, but the Claus process 
produces low-pressure steam (1.7 bar). 
 
Bitumen Blower (BBU). Heavy fuel oil of some heavy crude oil is blown with hot air to 
produce bitumen or asphalt. 
 
Other processes may be used in refineries to produce lubricants (lube oil), petrochemical 
feedstocks, and other specialty products. These processes consist mainly of blending, 
stripping, and separation processes. These processes are not discussed in detail here, as they 
are not found in a large number of refineries.  
 
Table 2 and Figure 7 provide an overview of the processing capacities of the different 
processes used in U.S. refineries, based on capacity as of January 1st, 2003. The distribution 
of the processes will vary by state depending on the type of crudes used and products 
produced. For example, California has a much higher capacity (relative to CDU-capacity) of 
hydrocracking and hydrotreating, when compared to the U.S. average. This is due to the 
types of crude processed in California, the relative higher desired output of lighter products 
(e.g., gasoline), and the regulatory demand for lower sulfur content from gasoline to reduce 
air pollution from transport.  
 
16
0
2,000,000
4,000,000
6,000,000
8,000,000
10,000,000
12,000,000
14,000,000
16,000,000
18,000,000
CDU
HVU
Coking
Thermal
FCC
Cat Reforming
Hydrocracker
Hydrotreater
Alkylation
Pol/Dim
Aromatics
Isomerization
Lubes
Oxygenates
Asphalt
Capacity (b/cd)
 
Figure 7. Capacity distribution of the major refining processes in U.S. petroleum 
refineries, as of January 1st, 2003. Source: Oil & Gas Journal (2002). 
 
Table 2. Capacity distribution of the major refining processes in U.S. petroleum 
refineries, as of January 1st, 2003. The distribution is also given as share of CDU 
capacity. Source: Oil & Gas Journal (2002). 
Process 
Capacity 
Distribution 
(Barrel per calendar day) (share of CDU capacity)
Crude Distillation 
16,623,301
100.0%
Vacuum Distillation 
7,347,704
44.2%
Coking 
2,243,947
13.5%
Thermal Operations 
43,500
0.3%
Catalytic Cracking 
5,677,355
34.2%
Catalytic Reforming 
3,512,237
21.1%
Hydrocracking 
1,474,710
8.9%
Hydrotreating 
11,247,745
67.7%
Alkylation 
1,170,019
7.0%
Polymerization/Dim. 
64,000
0.4%
Aromatics 
383,255
2.3%
Isomerization 
644,270
3.9%
Lubes 
167,500
1.0%
Oxygenates 
122,899
0.7%
Asphalt 
471,850
2.8%
Hydrogen 
3,631 MMcfd
-
Coke 
114,387 tpd
-
Sulfur 
27,051 tpd
-
 
17
4. Energy Consumption 
 
The petroleum refining industry is one of the largest energy consuming industries in the 
United States. Energy use in a refinery varies over time due to changes in the type of crude 
processed, the product mix (and complexity of refinery), as well as the sulfur content of the 
final products. Furthermore, operational factors like capacity utilization, maintenance 
practices, as well as the age of the equipment affect energy use in a refinery from year to 
year.  
 
The petroleum refining industry is an energy intensive industry spending over $7 billion on 
energy purchases in 2001. Figure 8 depicts the trend in energy expenditures of the U.S. 
petroleum refining industry. The graph shows a steady increase in total expenditures for 
purchased electricity and fuels, which is especially evident in the most recent years for 
which data is available. Value added as share of value of shipments dipped in the early 
1990s and has increased since to about 20%. Figure 8 also shows a steady increase in fuel 
costs. Electricity costs are more or less stable, which seems to be only partially caused by 
increased cogeneration.  
 
0
1000
2000
3000
4000
5000
6000
7000
8000
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
Energy Costs (Million $/yr)
0%
5%
10%
15%
20%
25%
Share Energy/VA
Fuel
Electricity
Energy (% value added)
Figure 8. Annual energy costs of petroleum refineries in the United States 1988-2001 
for purchased fuels. This excludes the value of fuels generated in the refinery (i.e., 
refinery gas and coke). Purchased fuels can be a relatively small part of the total 
energy costs of a refinery (see also Figure 9). The total purchased energy costs are 
given as share of the value added produced by petroleum refineries. Source: U.S. 
Census, Annual Survey of Manufacturers. 
 
18
0
500
1000
1500
2000
2500
3000
3500
1995
1996
1997
1998
1999
2000
2001
Final Energy Use (TBtu)
Other
Hydrogen
Purchased steam
Purchased electricity
Coal
Natural Gas
Petroleum Coke
Still Gas
Residual Fuel oil
Distillate Fuel Oil
LPG
Crude Oil
 
Figure 9.  Annual final energy consumption of U.S. petroleum refineries for the period 
1995 – 2001. Data for 1995 and 1997 contains estimated values for natural gas, coal, 
electricity, and steam purchases. The order in the legend corresponds with the order of 
fuels in the graph.  Source: Petroleum Supply Annual, Energy Information Administration. 
 
In recent years, energy consumption in refineries peaked in 1998, and has since then slightly 
declined. Based on data published by the Energy Information Administration, energy 
consumption trends are estimated by fuel since 1995.2 In 2001, the latest year for which data 
were available, total final energy consumption is estimated at 3,025 TBtu. Primary energy 
consumption3 is estimated at 3,369 TBtu. The difference between primary and final 
electricity consumption is relatively low due to the small share of electricity consumption in 
the refinery and relatively large amount of self-produced electricity. Figure 9 depicts the 
annual energy consumption of petroleum refineries between 1995 and 2001. Figure 9 shows 
                                                 
2 Data before 1995 are also available. However, for some years (including 1995 and 1997) the data reported by 
EIA is not complete, and interpolations were made by the authors to estimate total energy consumption. For 
example, for 1995 EIA did not report on consumption of natural gas, coal, purchased electricity, and purchased 
steam, while for 1997 it did not report on coal, purchased steam, and other fuels. Furthermore, we use 
electricity purchase data as reported by the EIA, although the U.S. Census reports slightly different electricity 
purchases for most years. The differences are generally small and do not affect overall energy use data. 
 
3 Final energy assigns only the direct energy content to secondary energy carriers like purchased electricity and 
steam to calculate energy consumption. Primary energy consumption includes the losses of offsite electricity 
and steam production. We assume an average efficiency of power generation on the public grid of 32%. Steam 
generation efficiency is supposed to be similar to that of refinery boilers (assumed at 77%). 
 
19
that energy use has basically remained flat, while production volumes and mix have 
changed, strongly suggesting an improvement of the energy efficiency of the industry over 
the same period.  
 
Figure 9 shows that the main fuels used in the refinery are refinery gas, natural gas, and 
coke. The refinery gas and coke are by-products of the different processes. The coke is 
mainly produced in the crackers, while the refinery gas is the lightest fraction from the 
distillation and cracking processes. Natural gas and electricity represents the largest 
purchased fuels in the refineries. Natural gas is used for the production of hydrogen, fuel for 
co-generation of heat and power (CHP), and as supplementary fuel in furnaces.  
 
Petroleum refineries are one of the largest cogenerators in the country, after the pulp and 
paper and chemical industries. In 1998, cogeneration within the refining industry 
represented almost 13% of all industrial cogenerated electricity (EIA, 2001). By 1999 
cogeneration increased to almost 35% of total electricity use. In 2001, the petroleum refining 
industry generated about 13.2 TWh, which represented about 26% of all power consumed 
onsite (EIA, 2002). Figure 10 shows the historic development of electricity generation and 
purchases in oil refineries (generation data for 2000 were not reported by the U.S. Census). 
0
10000
20000
30000
40000
50000
60000
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
Electricity (GWh/year)
0%
5%
10%
15%
20%
25%
30%
35%
40%
Self-Generation (%)
Generated
Purchased
Share
 
Figure 10. Electricity purchases and generation by petroleum refineries from 1988 to 
2001. On the right-hand axis, the share of self-generation is expressed as a function of 
total power consumption. Source: U.S. Census, Annual Survey of Manufacturers. 
 
 
 
20
Table 3. Estimated 2001 energy balance for the U.S. petroleum refining industry. 
Estimates are based on a combination of publicly available data sources. The energy 
balance for an individual refinery will be different due to different process 
configurations. Data sources are given in the text. 
Throughput 
Fuel 
Steam 
Electricity 
Final 
Primary 
Process 
Million bbl/year 1
 
 
 
 
 
TBtu 
TBtu 
GWh 
TBtu 2
TBtu 3
Desalter 
5313.3 
0.2 
0.0 
265.7 
1.1 
3.0 
CDU 
5313.3 
359.2 
243.5 
3613.0 
687.8 
714.0 
VDU 
2416.7 
115.5 
126.1 
845.8 
282.1 
288.3 
Thermal 
Cracking 
723.4 
84.1 
-10.5 
4485.3 
85.8 
118.3 
FCC 
1885.4 
108.2 
0.5 
7013.8 
132.8 
183.7 
Hydrocracker 
507.2 
68.5 
36.9 
5680.7 
135.9 
177.1 
Reforming 
1166.0 
206.1 
101.3 
3416.3 
349.4 
374.1 
Hydrotreater 
3679.8 
253.2 
270.1 
15455.4 
656.6 
768.7 
Deasphalting 
112.5 
16.1 
0.3 
213.8 
17.2 
18.8 
Alkylates 
366.8 
13.1 
121.1 
2640.7 
179.3 
198.5 
Aromatics 
97.2 
11.7 
4.1 
291.5 
18.0 
20.1 
Asphalt 
284.9 
59.6 
0.0 
740.7 
62.1 
67.5 
Isomers 
204.3 
90.3 
39.9 
398.3 
143.5 
146.4 
Lubes 
67.8 
87.5 
2.5 
1247.0 
95.0 
104.1 
Hydrogen 
5,959 
268.2 
0.0 
893.9 
271.2 
277.7 
Sulfur 
9.0 
0.0 
-81.2 
108.5 
-105.1 
-104.3 
0.0 
10.0 
39.0 
13.1 
Other 
 
13.4 
Total Process Site Use 
1741 
865 
47349 
3026 
3369 
Purchases 
 
78.4 
34187 
 
 
Site Generation 
 
786.3 
 
 
 
Cogeneration 4
140.3 
61.8 
 13162 
 
 
Boiler generation 5
 
724.5 
 
 
 
Boiler fuels 
940.9 
 
 
 
 
Total Energy Consumption 
2822 
78 
34187 
3018 
3289 
Notes: 
1. Unit is million barrels/year, except for hydrogen (million lbs/year) and sulfur (million short 
tons/year). 
2. Final fuel use is calculated by estimating the boiler fuel to generate steam used. Electricity is 
accounted as site electricity at 3,412 Btu/kWh. 
3. Primary fuel use includes the boiler fuel use and primary fuels used to generate electricity. Including 
transmission and distribution losses the electric efficiency of the public grid is equal to 32%, 
accounting electricity as 10,660 Btu/kWh. Some refineries operate combined cycles with higher 
efficiencies. For comparison, Solomon accounts electricity at 9,090 Btu/kWh. 
4. Cogeneration is assumed to be in large singe-cycle gas turbines with an electric efficiency of 32%. 
5. Boiler efficiency is estimated at 77%. 
 
A number of key processes are the major energy consumers in a typical refinery, i.e., crude 
distillation, hydrotreating, reforming, vacuum distillation, and catalytic cracking. 
Hydrocracking and hydrogen production are growing energy consumers in the refining 
industry. An energy balance for refineries for 2001 has been developed based on publicly 
available data on process throughput (EIA, 2002), specific energy consumption (Gary and 
Handwerk, 1994; U.S. DOE-OIT, 1998a, U.S. DOE-OIT, 2002), and energy consumption 
data (EIA, 2001; EIA, 2002; U.S. Census, 2003). Table 3 provides the estimated energy 
balance for 2001. The energy balance is an estimate based on publicly available data, and is 
based on many assumptions on process efficiencies and throughputs. The estimated energy 
 
21
balance matches with available energy consumption data for almost 100% on a final energy 
basis, and almost 98% on a primary energy basis. The process energy uses should be seen as 
approximate values to provide a view on important energy using processes in the refinery. 
 
-200
-100
0
100
200
300
400
500
600
700
800
Desalter
CDU
VDU
Thermal Cracking
FCC
Hydrocracker
Reforming
Hydrotreater
Deasphalting
Alkylates
Aromatics
Asphalt
Isomers
Lubes
Hydrogen
Sulfur
Other
Final Energy Use (TBtu)
Electricity
Steam
Fuel
Figure 11. Estimated energy use by petroleum refining process. Energy use is 
expressed as primary energy consumption. Electricity is converted to fuel using 10,666 
Btu/kWh (equivalent to an efficiency of 32% including transmission and distribution 
losses). All steam is generated in boilers with an efficiency of 77%. 
 
The major energy consuming processes are crude distillation, followed by the hydrotreater, 
reforming, and vacuum distillation. This is followed by a number of processes consuming a 
somewhat similar amount of energy, i.e., thermal cracking, catalytic cracking, 
hydrocracking, alkylate and isomer production. 
 
Note that the figures in Table 2 and Figure 11 are based on publicly available data. A similar 
capacity utilization is assumed for all installed processes, based on the average national 
capacity utilization. In reality, the load of the different processes may vary, which may lead 
to a somewhat different distribution. In cracking the severity and in hydrotreating the treated 
feed may affect energy use. An average severity is assumed for both factors. Furthermore, 
 
22
energy intensity assumptions are based on a variety of sources, and balanced on the basis of 
available data. The different literature sources provide varying assumptions for some 
processes, especially for electricity consumption.   
 
Although the vast majority of greenhouse gas (GHG) emissions in the petroleum fuel cycle 
occur at the final consumer of the petroleum products, refineries are still a substantial source 
of GHG emissions. The high energy consumption in refineries also leads to substantial GHG 
emissions. This Energy Guide focuses on CO2 emissions due to the combustion of fossil 
fuels, although process emissions of methane and other GHGs may occur at refineries. The 
estimate in this Energy Guide is based on the fuel consumption as reported in the Petroleum 
Supply Annual of the Energy Information Administration, and emission factors determined 
by the Energy Information Administration and U.S. Environmental Protection Agency. The 
Energy Information Administration provided emission factors for electricity consumption. 
The CO  emissions in 2001 are estimated at 222 million tonnes of CO
2
2 (equivalent to 60.5 
MtCE). This is equivalent to 11.6% of industrial CO2 emissions in the United States. Figure 
12 provides estimates of CO2 emissions (by fuel) for several recent years. Figure 12 shows 
that the main fuels contributing to the emissions are still gas, natural gas, and coke.  
0
10
20
30
40
50
60
70
1995
1996
1997
1998
1999
2000
2001
CO2 Emissions (MtCE)
Other
Hydrogen
Purchased steam
Purchased electricity
Coal
Natural Gas
Petroleum Coke
Still Gas
Residual Fuel oil
Distillate Fuel Oil
LPG
Crude Oil
 
Figure 12. Estimated CO2 emissions from fuel combustion and electricity consumption 
at U.S. petroleum refineries. Data for 1995 and 1997 includes estimates for different 
fuels (i.e., coal, purchased steam, and other fuels). Sources: Energy Information 
Administration, U.S. Environmental Protection Agency.  
 
The Energy Information Administration estimated CO2 emissions at 87.4 MtCE in 1998. 
This is substantially higher than the estimate above. The reason for the differences is 
 
23
unclear. Partially these may be due to different data sources and potentially due to emissions 
from flaring that are not included in the above estimate. 
  
 
 
24
5. Energy Efficiency Opportunities 
 
A large variety of opportunities exist within petroleum refineries to reduce energy 
consumption while maintaining or enhancing the productivity of the plant. Studies by 
several companies in the petroleum refining and petrochemical industries have demonstrated 
the existence of a substantial potential for energy efficiency improvement in almost all 
facilities. Competitive benchmarking data indicate that most petroleum refineries can 
economically improve energy efficiency by 10-20%. For example, a 2002 audit of energy 
use at the Equilon refinery (now Shell) at Martinez, California, found an overall efficiency 
improvement potential of 12% (US DOE-OIT, 2002b). This potential for savings amounts to 
annual costs savings of millions to tens of millions of dollars for a refinery, depending on 
current efficiency and size. Improved energy efficiency may result in co-benefits that far 
outweigh the energy cost savings, and may lead to an absolute reduction in emissions.  
 
Major areas for energy efficiency improvement are utilities (30%), fired heaters (20%), 
process optimization (15%), heat exchangers (15%), motor and motor applications (10%), 
and other areas (10%). Of these areas, optimization of utilities, heat exchangers, and fired 
heaters offer the most low investment opportunities, while other opportunities may require 
higher investments. Experiences of various oil companies have shown that most investments 
are relatively modest. However, all projects require operating costs as well as engineering 
resources to develop and implement the project. Every refinery and plant will be different. 
The most favorable selection of energy efficiency opportunities should be made on a plant-
specific basis. 
 
In the following chapters energy efficiency opportunities are classified based on technology 
area. In each technology area, technology opportunities and specific applications by process 
are discussed. Table 4 summarizes the energy efficiency measures described in this Energy 
Guide, and provides access keys by process and utility system to the descriptions of the 
energy efficiency opportunities. This Energy Guide is far from exhaustive. For example, the 
Global Energy Management System (GEMS) of ExxonMobil has developed 12 manuals - 
containing some 1,200 pages, which describe in detail over 200 best practices and 
performance measures for key process units, major equipment, and utility systems. In 
addition to the strong focus on operation and maintenance of existing equipment, these 
practices also address energy efficiency in the design of new facilities. GEMS identified 
opportunities to improve energy efficiency by 15% at ExxonMobil refineries and chemical 
plants worldwide. This Energy Guide provides a general overview in am easily accessible 
format to help energy managers to select areas for energy efficiency improvement based on 
experiences around the world. 
 
This Energy Guide includes case studies from U.S. refineries with specific energy and cost 
savings data when available. For other measures, the Energy Guide includes case study data 
from refineries around the world. For individual refineries, actual payback period and 
energy savings for the measures will vary, depending on plant configuration and size, plant 
location, and plant operating characteristics. Hence, the values presented in this Energy 
Guide are offered as guidelines. Wherever possible, the Energy Guide provides a range of 
savings and payback periods found under varying conditions.  
 
25
 
26
Although technological changes in equipment conserve energy, changes in staff behavior 
and attitude can have a great impact. Staff should be trained in both skills and the company’s 
general approach to energy efficiency in their day-to-day practices. Personnel at all levels 
should be aware of energy use and objectives for energy efficiency improvement. Often this 
information is acquired by lower level managers but not passed to upper management or 
down to staff (Caffal, 1995). Though changes in staff behavior, such as switching off lights 
or improving operating guidelines, often save only very small amounts of energy at one 
time, taken continuously over longer periods they can have a great effect. Further details for 
these programs can be found in Chapter 6. 
 
Participation in voluntary programs like the ENERGY STAR program, or implementing an 
environmental management system such as ISO 14001, can help companies to track energy 
and implement energy efficiency measures. One ENERGY STAR partner noted that 
combining energy management programs with ISO 14001 has had the largest effect on 
saving energy at their plants.  
 
Companies like BP have successfully implemented aggressive greenhouse gas (GHG) 
emission reduction programs at all their facilities worldwide (including exploration and 
refining). BP has reduced its global GHG emissions to 10% below 1990 levels within 5 
years of the inception of its program; years ahead of its goal, while decreasing operation 
costs. These efforts demonstrate the potential success of a corporate strategy to reduce 
energy use and associated emissions. Yet, other companies used participation in voluntary 
programs to boost energy management programs. Petro-Canada participates in Canada’s 
Climate Change Voluntary Challenge and Registry. Petro-Canada has developed a 
corporate-wide emission reduction and energy efficiency program, and reports the results 
annually. In Europe, various countries have voluntary agreements between industry sectors 
and governments to reduce energy or GHG emission intensity. For example, all refineries in 
the Netherlands participated in the Long-Term Agreements between 1989 and 2000. BP, 
ExxonMobil, Shell, and Texaco all operate refineries in the Netherlands. The refineries 
combined (processing about 61 million tons of crude annually) achieved a 17% 
improvement of energy efficiency. Today, the refineries participate in a new agreement in 
which the refineries will be among most energy efficient refineries worldwide by 2010, 
using the Solomon’s index as a gauge. 
 
Table 4 provides an access key to the Energy Guide. For each of the main processes used in 
a refinery, Table 4 provides the relevant sections describing energy efficiency measures that 
are applicable to that process and may be relevant when assessing energy efficiency 
opportunities for a particular process. Utility measures are summarized in the last row of 
Table 4. While boilers and lighting will be distributed around the refinery, they are only 
designated as utilities. 
 
 
27
Table 4. Matrix of energy efficiency opportunities in petroleum refineries. For each major process in the refinery (in rows) 
the applicable categories of energy efficiency measures are given (in columns). The numbers refer to the chapter or section 
describing energy efficiency. 
Process 
Energy 
Management 
Flare Gas Recovery 
Power Recovery 
Boilers 
Steam Distribution 
Heat Exchanger  
Process Integration 
Process Heaters 
Distillation 
Hydrogen 
Management  
Motors 
Pumps 
Compressed Air 
Fans 
Lighting 
Cogeneration 
Power Generation 
Other Opportunities 
Desalting 
6 
 
 
 
 
 
 
 
 
 
 
14 
 
 
 
 
 
 
CDU 
6 
7.1 
 
 
8.2 
9.1 
9.2 
10 
11 
 
13 
14 
 
16 
 
 
 
 
VDU 
6 
 
 
 
8.2 
9.1 
9.2 
10 
11 
 
 
 
 
16 
 
 
 
 
Hydrotreater 
6 
 
 
 
8.2 
9.1 
9.2 
10 
11 
12 
 
 
 
16 
 
 
 
 
Cat.Reformer 
6 
7.1 
 
 
8.2 
9.1 
9.2 
10 
11 
12 
 
 
 
16 
 
 
 
 
FCC 
6 
7.1 
7.2 
 
8.2 
9.1 
9.2 
10 
11 
 
 
 
 
16 
 
 
 
 
Hydrocracker 
6 
7.1 
7.2 
 
8.2 
9.1 
9.2 
10 
11 
12 
 
 
 
16 
 
 
 
 
Coker 
6 
7.1 
 
 
8.2 
9.1 
9.2 
10 
11 
 
 
 
 
16 
 
 
 
 
Visbreaker 
6 
7.1 
 
 
8.2 
9.1 
9.2 
10 
11 
 
 
 
 
16 
 
 
 
 
Alkylation 
6 
 
 
 
8.2 
9.1 
9.2 
10 
11 
 
 
 
 
16 
 
 
 
 
Light End 
6 
 
 
 
8.2 
9.1 
9.2 
 
11 
 
 
 
 
 
 
 
 
 
Aromatics 
6 
 
 
 
8.2 
9.1 
9.2 
10 
11 
 
 
 
 
 
 
 
 
 
Hydrogen 
6 
 
 
 
8.2 
9.1 
9.2 
10 
 
12 
 
 
 
16 
 
 
 
 
Utilities 
6 
7.1 
7.2 
8.1 
8.2 
9.1 
9.2 
 
 
12 
 
 
15 
16 
17 
18 
18 
19 
 
6. Energy Management and Control 
 
Improving energy efficiency in refineries should be approached from several directions. A 
strong, corporate-wide energy management program is essential. Cross-cutting equipment 
and technologies, such as boilers, compressors, and pumps, common to most plants and 
manufacturing 
industries 
including 
petroleum 
refining, 
present 
well-documented 
opportunities for improvement. Equally important, the production process can be fine-tuned 
to produce additional savings.  
 
6.1 Energy Management Systems (EMS) and Programs  
Changing how energy is managed by implementing an organization-wide energy 
management program is one of the most successful and cost-effective ways to bring about 
energy efficiency improvements.   
 
An energy management program creates a foundation for improvement and provides 
guidance for managing energy throughout an organization. In companies without a clear 
program in place, opportunities for improvement may be unknown or may not be promoted 
or implemented because of organizational barriers. These barriers may include a lack of 
communication among plants, a poor understanding of how to create support for an energy 
efficiency project, limited finances, poor accountability for measures, or perceived change 
from the status quo. Even when energy is a significant cost for an industry, many companies 
still lack a strong commitment to improve energy management. 
 
The U.S. EPA, through ENERGY STAR, has worked with many of the leading industrial 
manufacturers to identify the basic aspects of an effective energy management program.4  
The major elements are depicted in Figure 13. 
 
A successful program in energy management begins with a strong commitment to 
continuous improvement of energy efficiency. This typically involves assigning oversight 
and management duties to an energy director, establishing an energy policy, and creating a 
cross-functional energy team. Steps and procedures are then put in place to assess 
performance, through regular reviews of energy data, technical assessments, and 
benchmarking. From this assessment, an organization is then able to develop a baseline of 
performance and set goals for improvement.  
  
Performance goals help to shape the development and implementation of an action plan. An 
important aspect for ensuring the successes of the action plan is involving personnel 
throughout the organization. Personnel at all levels should be aware of energy use and goals 
for efficiency. Staff should be trained in both skills and general approaches to energy 
efficiency in day-to-day practices. In addition, performance results should be regularly 
evaluated and communicated to all personnel, recognizing high performers. Some examples 
of simple employee tasks are outlined in Appendix B.  
 
                                                 
4 See the U.S. EPA’s Guidelines for Energy Management at www.energystar.gov.   
 
28
 
 
Figure 13. Main elements of a strategic energy management system. 
 
 
Evaluating performance involves the regular review of both energy use data and the 
activities carried out as part of the action plan. Information gathered during the formal 
review process helps in setting new performance goals and action plans and in revealing best 
practices. Establishing a strong communications program and seeking recognition for 
accomplishments are also critical steps. Strong communication and recognition help to build 
support and momentum for future activities. 
 
A quick assessment of an organization’s efforts to manage energy can be made by 
comparing the current program against the table contained in Appendix C. Appendix D 
provides the ENERGY STAR energy management matrix to evaluate and score an energy 
management system. 
 
 
29
6.2 Monitoring & Process Control Systems  
The use of energy monitoring and process control systems can play an important role in 
energy management and in reducing energy use. These may include sub-metering, 
monitoring and control systems. They can reduce the time required to perform complex 
tasks, often improve product and data quality and consistency, and optimize process 
operations. Typically, energy and cost savings are around 5% or more for many industrial 
applications of process control systems. These savings apply to plants without updated 
process control systems; many refineries may already have modern process control systems 
in place to improve energy efficiency.  
 
Although energy management systems are already widely disseminated in various industrial 
sectors, the performance of the systems can still be improved, reducing costs and increasing 
energy savings further. For example, total site energy monitoring and management systems 
can increase the exchange of energy streams between plants on one site. Traditionally, only 
one process or a limited number of energy streams were monitored and managed. Various 
suppliers provide site-utility control systems (HCP, 2001). 
 
Specific energy savings and payback periods for overall adoption of an energy monitoring 
system vary greatly from plant to plant and company to company.  
 
A variety of process control systems are available for virtually any industrial process. A 
wide body of literature is available assessing control systems in most industrial sectors such 
as chemicals and petroleum refining. Table 5 provides an overview of classes of process 
control systems.  
 
Table 5. Classification of control systems and typical energy efficiency improvement 
potentials. 
System 
Characteristics 
Typical energy savings 
(%) 
Dedicated systems for various 
industries, well established in 
various countries and sectors 
Typical savings 4-17%, average 
8% , based on experiences in the 
UK 
Monitoring and 
Targeting 
Computer 
Integrated 
Manufacturing 
(CIM) 
Improvement of overall economics 
of process, e.g., stocks, productivity 
and energy 
> 2% 
Moisture, oxygen and temperature 
control, air flow control 
“Knowledge based, fuzzy logic” 
Process control 
Typically 2-18% savings 
Note: The estimated savings are valid for specific applications (e.g., lighting energy use). The energy savings 
cannot be added, due to overlap of the systems. Sources: (Caffal 1995, Martin et al., 2000). 
 
Modern control systems are often not solely designed for energy efficiency, but rather for 
improving productivity, product quality, and the efficiency of a production line. 
 
30
Applications of advanced control and energy management systems are in varying 
development stages and can be found in all industrial sectors. Control systems result in 
reduced downtime, reduced maintenance costs, reduced processing time, and increased 
resource and energy efficiency, as well as improved emissions control. Many modern energy 
efficient technologies depend heavily on precise control of process variables, and 
applications of process control systems are growing rapidly. Modern process control 
systems exist for virtually any industrial process. Still, large potentials exist to implement 
control systems and more modern systems enter the market continuously. Hydrocarbon 
Processing produces a semi-annual overview of new advanced process control technologies 
for the oil refining industry (see e.g., HCP, 2001).  
 
Process control systems depend on information from many stages of the processes. A 
separate but related and important area is the development of sensors that are inexpensive to 
install, reliable, and analyze in real-time. Current development efforts are aimed at the use of 
optical, ultrasonic, acoustic, and microwave systems, that should be resistant to aggressive 
environments (e.g., oxidizing environments in furnace or chemicals in chemical processes) 
and withstand high temperatures. The information of the sensors is used in control systems 
to adapt the process conditions, based on mathematical (“rule”-based) or neural networks 
and “fuzzy logic” models of the industrial process.  
 
Neural network based control systems have successfully been used in the cement (kilns), 
food (baking), non-ferrous metals (alumina, zinc), pulp and paper (paper stock, lime kiln), 
petroleum refineries (process, site), and steel industries (electric arc furnaces, rolling mills). 
New energy management systems that use artificial intelligence, fuzzy logic (neural 
network), or rule-based systems mimic the “best” controller, using monitoring data and 
learning from previous experiences. 
 
Process knowledge based systems (KBS) have been used in design and diagnostics, but are 
hardly used in industrial processes. Knowledge bases systems incorporate scientific and 
process information applying a reasoning process and rules in the management strategy. A 
recent demonstration project in a sugar beet mill in the UK using model based predictive 
control system demonstrated a 1.2 percent reduction in energy costs, while increasing 
product yield by almost one percent and reducing off-spec product from 11 percent to four 
percent. This system had a simple payback period of 1.4 years (CADDET, 2000).  
 
Although energy management systems are already widely disseminated in various industrial 
sectors, the performance of the systems can still be improved, reducing costs and increasing 
energy savings further. Research for advanced sensors and controls is ongoing in all sectors, 
both funded with public funds and private research. Several projects within U.S. DOE’s 
Industries of the Future program try to develop more advanced control technologies (U.S. 
DOE-OIT, 2000). Sensors and control techniques are identified as key technologies in 
various development areas including energy efficiency, mild processing technology, 
environmental performance and inspection, and containment boundary integrity. Sensors 
and controls are also represented in a cross-cutting OIT-program. Outside the United States, 
Japan and Europe also give much attention to advanced controls. Future steps include 
further development of new sensors and control systems, demonstration in commercial 
 
31
scale, and dissemination of the benefits of control systems in a wide variety of industrial 
applications. 
 
Process control systems are available for virtually all processes in the refinery, as well as for 
management of refinery fuel gas, hydrogen, and total site control.  An overview of 
commercially offered products is produced by the journal Hydrocarbon Processing. The 
most recent overview was published in 2001. Below examples of processes and site-wide 
process control systems are discussed, selected on the basis of available case studies to 
demonstrate the specific applications and achieved energy savings  
 
Refinery Wide Optimization. Total site energy monitoring and management systems 
(Kawano, 1996) can increase the exchange of energy streams between plants on one site. 
Traditionally, only one plant or a limited number of energy streams were monitored and 
managed. Various suppliers provide site-utility control systems (HCP, 2001). Valero and 
AspenTech have developed a plant-wide energy optimization model to optimize the flows of 
intermediates, hydrogen, steam, fuel and electricity use, integrated with an energy 
monitoring system. The optimization system includes the cogeneration unit, FCC power 
recovery, and optimum load allocation of boilers, as well as selection of steam turbines or 
electric motors to run compressors. The system was implemented at Valero’s Houston 
refinery in 2003 and is expected to reduce overall site-wide energy use by 2-8%. Company 
wide, Valero expects to save $7-$27 million annually at 12 refineries (Valero, 2003).  
 
CDU. A few companies supply control equipment for CDUs. Aspen technology has 
supplied over 70 control applications for CDUs and 10 optimization systems for CDUs. 
Typical cost savings are $0.05 - $0.12/bbl of feed, with paybacks less than 6 months. Key 
Control supplies an expert system advisor for CDUs. It has installed one system at a CDU, 
which resulted in reduced energy consumption and flaring and increased throughput with a 
payback of 1 year. 
 
Installation of advanced control equipment at Petrogals Sines refinery (Portugal) on the 
CDU resulted in increased throughputs of 3-6% with a payback period of 3 months. 
 
FCC. Several companies offer FCC control systems, including ABB Simcon, AspenTech, 
Honeywell, Invensys, and Yokogawa. Cost savings may vary between $0.02 to $0.40/bbl of 
feed with paybacks between 6 and 18 months. 
 
Timmons et al. (2000) report on the advantages of combining an online optimizer with an 
existing control system to optimize the operation of a FCC unit at the CITGO refinery in 
Corpus Christi, Texas. The Citgo refinery installed a modern control system and an online 
optimizer on a 65,000 bpd FCC unit. The combination of the two systems was effective in 
improving the economic operation of the FCC. The installation of the optimizer led to 
additional cost savings of approximately $0.05/barrel of feed to the FCC, which resulted in 
an attractive payback (Timmons et al., 2000).  
 
 
32
The ENI refinery in Sanassazzo (Italy) installed in 2001 an optimizer on a FCC unit from 
Aspen Technology. The system resulted in cost savings of $0.10/bbl with a payback of less 
than one year. 
 
Hydrotreater. Installation of a multivariable predictive control (MPC) system was 
demonstrated on a hydrotreater at a SASOL refinery in South Africa. The MPC aimed to 
improve the product yield while minimizing the utility costs. The implementation of the 
system led to improved yield of gasoline and diesel, reduction of flaring, and a 12% 
reduction in hydrogen consumption and an 18% reduction in fuel consumption of the heater 
(Taylor et al., 2000). Fuel consumption for the reboiler increased to improve throughput of 
the unit. With a payback period of 2 months, the project resulted in improved yield and in 
direct and indirect (i.e., reduced hydrogen consumption) energy efficiency improvements. 
 
Alkylation. Motiva’s Convent (Louisiana) refinery implemented an advanced control 
system for their 100,000 bpd sulfuric acid alkylation plant. The system aims to increase 
product yield (by approximately 1%), reduce electricity consumption by 4.4%, reduce steam 
use by 2.2%, reduce cooling water use by 4.9%, and reduce chemicals consumption by 5-6% 
(caustic soda by 5.1%, sulfuric acid by 6.4%) (U.S. DOE-OIT, 2000). The software package 
integrates information from chemical reactor analysis, pinch analysis, information on flows, 
and information on energy use and emissions to optimize efficient operation of the plant. No 
economic performance data was provided, but the payback is expected to be rapid as only 
additional computer equipment and software had to be installed. The program is available 
through the Gulf Coast Hazardous Substance research Center and Louisiana State 
University. Other companies offering alkylation controls are ABB Simcon, Aspen 
technology, Emerson, Honeywell, Invensys, and Yokogawa. The controls typically result in 
cost savings of $0.10 to $0.20/bbl of feed with paybacks of 6 to 18 months. 
 
33
7. Energy Recovery 
 
7.1 Flare Gas Recovery  
Flare gas recovery (or zero flaring) is a strategy evolving from the need to improve 
environmental performance. Conventional flaring practice has been to operate at some flow 
greater than the manufacturer’s minimum flow rate to avoid damage to the flare (Miles, 
2001). Typically, flared gas consists of background flaring (including planned intermittent 
and planned continuous flaring) and upset-blowdown flaring. In offshore flaring, 
background flaring can be as much as 50% of all flared gases (Miles, 2001). In refineries, 
background flaring will generally be less than 50%, depending on practices in the individual 
refinery. Recent discussions on emissions from flaring by refineries located in the San 
Francisco Bay Area have highlighted the issue from an environmental perspective (Ezerksy, 
2002).5 The report highlighted the higher emissions compared to previous assumptions of 
the Air Quality District, due to larger volumes of flared gases. The report also demonstrated 
the differences among various refineries, and plants within the refineries. Reduction of 
flaring will not only result in reduced air pollutant emissions, but also in increased energy 
efficiency replacing fuels, as well as less negative publicity around flaring. 
 
Emissions can be further reduced by improved process control equipment and new flaring 
technology. Development of gas-recovery systems, development of new ignition systems 
with low-pilot-gas consumption, or elimination of pilots altogether with the use of new 
ballistic ignition systems can reduce the amount of flared gas considerably (see also section 
19.3). Development and demonstration of new ignition systems without a pilot may result in 
increased energy efficiency and reduced emissions. 
 
Reduction of flaring can be achieved by improved recovery systems, including installing 
recovery compressors and collection and storage tanks. This technology is commercially 
available. Various refineries in the United States have installed flare gas recovery systems, 
e.g., ChevronTexaco in Pascagoula (Mississippi) and even some small refineries like Lion 
Oil Co. (El Dorado, Arkansas). A plant-wide assessment of the Equilon refinery in Martinez 
(now fully owned by Shell) highlighted the potential for flare gas recovery. The refinery will 
install new recovery compressors and storage tanks to reduce flaring. No specific costs were 
available for the flare gas recovery project, as it is part of a large package of measures for 
the refinery. The overall project has projected annual savings of $52 million and a payback 
period of 2 years (U.S. DOE-OIT, 2002b). 
 
Installation of two flare gas recovery systems at the 65,000 bpd Lion Oil Refinery in El 
Dorado (Arkansas) in 2001 has reduced flaring to near zero levels (Fisher and Brennan, 
2002). The refinery will only use the flares in emergencies where the total amount of gas 
will exceed the capacity of the recovery unit. The recovered gas is compressed and used in 
the refineries fuel system. No information on energy savings and payback were given for 
this particular installation. John Zink Co., the installer of the recovery system, reports that 
                                                 
5 ChevronTexaco commented on the report by the Bay Area Air Quality Management District on refinery 
flaring. The comments were mainly directed towards the VOC-calculations in the report and an explanation of 
the flaring practices at the ChevronTexaco refinery in Richmond, CA (Hartwig, 2003). 
 
34
the payback period of recovery systems may be as short as one year. Furthermore, flare gas 
recovery systems offer increased flare tip life and emission reductions. 
 
7.2 Power Recovery 
Various processes run at elevated pressures, enabling the opportunity for power recovery 
from the pressure in the flue gas. The major application for power recovery in the petroleum 
refinery is the fluid catalytic cracker (FCC). However, power recovery can also be applied to 
hydrocrackers or other equipment operated at elevated pressures. Modern FCC designs use a 
power recovery turbine or turbo expander to recover energy from the pressure. The 
recovered energy can be used to drive the FCC compressor or to generate power. Power 
recovery applications for FCC are characterized by high volumes of high temperature gases 
at relatively low pressures, while operating continuously over long periods of time between 
maintenance stops (> 32,000 hours). There is wide and long-term experience with power 
recovery turbines for FCC applications. Various designs are marketed, and newer designs 
tend to be more efficient in power recovery. Recovery turbines are supplied by a small 
number of global suppliers, including GE Power Systems. 
 
Many refineries in the United States and around the world have installed recovery turbines. 
Valero has recently upgraded the turbo expanders at its Houston and Corpus Christi (Texas) 
and Wilmington (California) refineries. Valero’s Houston Refinery replaced an older power 
recovery turbine to enable increased blower capacity to allow an expansion of the FCC. At 
the Houston refinery, the re-rating of the FCC power recovery train led to power savings of 
22 MW (Valero, 2003), and will export additional power (up to 4 MW) to the grid. Petro 
Canada’s Edmonton refinery replaced an older turbo expander by a new more efficient unit 
in October 1998, saving around 18 TBtu annually.  
 
Power recovery turbines can also be applied at hydrocrackers. Power can be recovered from 
the pressure difference between the reactor and fractionation stages of the process.  In 1993, 
the Total refinery in Vlissingen, the Netherlands, installed a 910 kW power recovery turbine 
to replace the throttle at its hydrocracker (get data on hydrocracker). The cracker operates at 
160 bar. The power recovery turbine produces about 7.3 million kWh/year (assuming 8000 
hours/year). The investment was equal to $1.2 million (1993$). This resulted in a payback 
period of approximately 2.5 years at the conditions in the Netherlands (Caddet, 2003).   
 
 
35
8. Steam Generation and Distribution 
 
Steam is used throughout the refinery. An estimated 30% of all onsite energy use in U.S. 
refineries is used in the form of steam. Steam can be generated through waste heat recovery 
from processes, cogeneration, and boilers. In most refineries, steam will be generated by all 
three sources, while some (smaller) refineries may not have cogeneration equipment 
installed. While the exact size and use of a modern steam systems varies greatly, there is an 
overall pattern that steam systems follow, as shown in Figure 14. 
 
Figure 14 depicts a schematic presentation of a steam system. Treated cold feed water is fed 
to the boiler, where it is heated to form steam. Chemical treatment of the feed water is 
required to remove impurities. The impurities would otherwise collect on the boiler walls. 
Even though the feed water has been treated, some impurities still remain and can build up 
in the boiler water. As a result, water is periodically drained from the bottom of the boiler in 
a process known as blowdown. The generated steam travels along the pipes of the 
distribution system to get to the process where the heat will be used. Sometimes the steam is 
passed through a pressure reduction valve if the process requires lower pressure steam. As 
the steam is used to heat processes, and even as it travels through the distribution system to 
get there, the steam cools and some is condensed. This condensate is removed by a steam 
trap, which allows condensate to pass through, but blocks the passage of steam. The 
condensate can be recirculated to the boiler, thus recovering some heat and reducing the 
need for fresh treated feed water. The recovery of condensate and blowdown will also 
reduce the costs of boiler feed water treatment. For example, optimization of blowdown 
steam use at Valero’s Houston refinery use led to cost savings of $213,500/year (Valero, 
2003). 
 
   Flue Gas
Pressure Reduction Valve
Steam
Cold Feed Water                 Warm Feed
       Water
           Economizer
Steam Trap
         Steam Using
    Steam Using
             Process
        Process
           Steam Trap                                        Steam Trap
          Boiler
    Flue
           Burner
 Blow Valve
     Pump                                   Condensate
 
Figure 14. Schematic presentation of a steam production and distribution system. 
 
The refining industry uses steam for a wide variety of purposes, the most important being 
process heating, drying or concentrating, steam cracking, and distillation. Whatever the use 
 
36
or the source of the steam, efficiency improvements in steam generation, distribution and 
end-use are possible. A recent study by the U.S. Department of Energy estimates the overall 
potential for energy savings in petroleum refineries at over 12% (U.S. DOE, 2002). It is 
estimated that steam generation, distribution, and cogeneration offer the most cost-effective 
energy efficiency opportunities on the short term. This section focuses on the steam 
generation in boilers (including waste heat boilers) and distribution. Table 6 summarizes the 
boiler efficiency measures, while Table 7 summarizes the steam distribution system 
measures. 
 
Steam, like any other secondary energy carrier, is expensive to produce and supply. The use 
of steam should be carefully considered and evaluated. Often steam is generated at higher 
pressures than needed or in larger volumes than needed at a particular time. These 
inefficiencies may lead steam systems to let down steam to a lower pressure or to vent steam 
to the atmosphere. Hence, it is strongly recommended to evaluate the steam system on the 
use of appropriate pressure levels and production schedules. If it is not possible to reduce the 
steam generation pressure, it may still be possible to recover the energy through a turbo 
expander or steam expansion turbine (see section 18.3). Excess steam generation can be 
reduced through improved process integration (see section 9.2) and improved management 
of steam flows in the refinery (see section 6.2 on total site management systems). Many 
refineries operate multiple boilers. By dispatching boilers on the basis of efficiency, it is 
possible to save energy. An audit of the Equilon refinery (now owned by Shell) in Martinez, 
California, found that scheduling of steam boilers on the basis of efficiency (and minimizing 
losses in the steam turbines) can result in annual energy savings equaling $5.4 million (U.S. 
DOE-OIT, 2002b). 
 
8.1 Boilers 
Boiler Feed Water Preparation. Depending on the quality of incoming water, the boiler 
feed water (BFW) needs to be pre-treated to a varying degree. Various technologies may be 
used to clean the water. A new technology is based on the use of membranes. In reverse 
osmosis (RO), the pre-filtered water is pressed at increased pressure through a semi-
permeable membrane. Reverse osmosis and other membrane technologies are used more and 
more in water treatment (Martin et al., 2000). Membrane processes are very reliable, but 
need semi-annual cleaning and periodic replacement to maintain performance. 
 
The Flying J refinery in North Salt Lake (Utah) installed a RO-unit to remove hardness and 
reduce the alkalinity from boiler feedwater, replacing a hot lime water softener. The unit 
started operation in 1998, resulting in reduced boiler blowdown (from 13.3% to 1.5% of 
steam produced) and reduced chemical use, maintenance, and waste disposal costs (U.S. 
DOE-OIT, 2001). With an investment of $350,000 and annual benefits of approximately 
$200,000, the payback period amounted to less than 2 years.  
 
Improved Process Control. Flue gas monitors are used to maintain optimum flame 
temperature, and to monitor CO, oxygen and smoke. The oxygen content of the exhaust gas 
is a combination of excess air (which is deliberately introduced to improve safety or reduce 
emissions) and air infiltration (air leaking into the boiler). By combining an oxygen monitor 
with an intake airflow monitor, it is possible to detect (small) leaks. Using a combination of 
 
37
CO and oxygen readings, it is possible to optimize the fuel/air mixture for high flame 
temperature (and thus the best energy efficiency) and low emissions. The payback of 
improved process control is approximately 0.6 years (IAC, 1999). This measure may be too 
expensive for small boilers. 
 
Reduce Flue Gas Quantities. Often, excessive flue gas results from leaks in the boiler and 
the flue, reducing the heat transferred to the steam, and increasing pumping requirements.  
These leaks are often easily repaired. Savings amount to 2-5% (OIT, 1998). This measure 
consists of a periodic repair based on visual inspection. The savings from this measure and 
from flue gas monitoring are not cumulative, as they both address the same losses. 
 
Reduce Excess Air. The more air is used to burn the fuel, the more heat is wasted in heating 
air. Air slightly in excess of the ideal stoichometric fuel/air ratio is required for safety, and 
to reduce NOx emissions, and is dependent on the type of fuel. For gas and oil-fired boilers, 
approximately 15% excess air is adequate (OIT, 1998; Ganapathy, 1994). Poorly maintained 
boilers can have up to 140% excess air. Reducing this back down to 15% even without 
continuous automatic monitoring would save 8%. 
 
Improve Insulation. New materials insulate better, and have a lower heat capacity. Savings 
of 6-26% can be achieved if this improved insulation is combined with improved heater 
circuit controls. This improved control is required to maintain the output temperature range 
of the old firebrick system. As a result of the ceramic fiber’s lower heat capacity, the output 
temperature is more vulnerable to temperature fluctuations in the heating elements (Caffal, 
1995). The shell losses of a well-maintained boiler should be less than 1%. 
 
Maintenance. A simple maintenance program to ensure that all components of the boiler 
are operating at peak performance can result in substantial savings. In the absence of a good 
maintenance system, the burners and condensate return systems can wear or get out of 
adjustment. These factors can end up costing a steam system up to 20-30% of initial 
efficiency over 2-3 years (DOE, 2001a). On average, the possible energy savings are 
estimated at 10% (DOE, 2001a). Improved maintenance may also reduce the emission of 
criteria air pollutants. 
 
Fouling of the fireside of the boiler tubes or scaling on the waterside of the boiler should 
also be controlled. Fouling and scaling are more of a problem with coal-fed boilers than with 
natural gas or oil-fed ones (i.e., boilers that burn solid fuels like coal should be checked 
more often as they have a higher fouling tendency than liquid fuel boilers do). Tests show 
that a soot layer of 0.03 inches (0.8 mm) reduces heat transfer by 9.5%, while a 0.18 inch 
(4.5 mm) soot layer reduces heat transfer by 69% (CIPEC, 2001). For scaling, 0.04 inches (1 
mm) of buildup can increase fuel consumption by 2% (CIPEC, 2001). Moreover, scaling 
may result in tube failures. 
 
Recover Heat From Flue Gas. Heat from flue gasses can be used to preheat boiler feed 
water in an economizer. While this measure is fairly common in large boilers, there is often 
still potential for more heat recovery. The limiting factor for flue gas heat recovery is the 
economizer wall temperature that should not drop below the dew point of acids in the flue 
 
38
gas. Traditionally this is done by keeping the flue gases at a temperature significantly above 
the acid dew point. However, the economizer wall temperature is more dependent on the 
feed water temperature than flue gas temperature because of the high heat transfer 
coefficient of water. As a result, it makes more sense to preheat the feed water to close to the 
acid dew point before it enters the economizer. This allows the economizer to be designed 
so that the flue gas exiting the economizer is just barely above the acid dew point. One 
percent of fuel use is saved for every 25°C reduction in exhaust gas temperature. 
(Ganapathy, 1994). Since exhaust gas temperatures are already quite low, limiting savings to 
1% across all boilers, with a payback of 2 years (IAC, 1999). 
 
Recover Steam From Blowdown. When the water is blown from the high-pressure boiler 
tank, the pressure reduction often produces substantial amounts of steam. This steam is low 
grade, but can be used for space heating and feed water preheating. For larger high-pressure 
boilers, the losses may be less than 0.5%. It is estimated that this measure can save 1.3% of 
boiler fuel use for all boilers below 100 MMBtu/hr (approximately 5% of all boiler capacity 
in refineries). The payback period of blowdown steam recovery will vary between 1 and 2.7 
years (IAC, 1999).  
 
Table 6. Summary of energy efficiency measures in boilers. 
Payback 
Period (years) 
Other Benefits 
Measure 
Fuel Saved 
Improved Process 
Control 
3% 
0.6 
Reduced Emissions 
Reduced Flue Gas 
Quantity 
2-5% 
- 
Cheaper emission controls 
1% improvement for 
each 15% less excess 
air 
Reduced Excess Air 
- 
 
Improved Insulation 
6-26% 
? 
Faster warm-up 
Boiler Maintenance 
10% 
0 
Reduced emissions 
Flue Gas Heat 
Recovery 
1% 
2 
 
Reduced damage to structures 
(less moist air is less corrosive). 
Blowdown Steam 
Heat Recovery 
1.3% 
1 - 2.7 
Reduces solid waste stream at 
the cost of increased air 
emissions 
Alternative Fuels 
Variable 
- 
 
Reduce Standby Losses. In refineries often one or more boilers are kept on standby in case 
of failure of the operating boiler. The steam production at standby can be reduced to 
virtually zero by modifying the burner, combustion air supply and boiler feedwater supply. 
By installing an automatic control system the boiler can reach full capacity within 12 
minutes. Installing the control system and modifying the boiler can result in energy savings 
up to 85% of the standby boiler, depending on the use pattern of the boiler.  
 
The Kemira Oy ammonia plant at Rozenburg (the Netherlands) applied this system to a 
small 40 t/hr steam boiler, reducing the standby steam consumption from the boiler from 6 
t/hr to 1 t/hr. This resulted in energy savings of 54 TBtu/year. Investments were 
 
39
approximately $270,000 (1991$), resulting in a payback period of 1.5 years at this particular 
plant (Caddet, 1997b). 
 
8.2 Steam Distribution 
When designing new steam distribution systems, it is very important to take into account the 
velocity and pressure drop (Van de Ruit, 2000). This reduces the risk of oversizing a steam 
pipe, which is not only a cost issue but would also lead to higher heat losses. A pipe too 
small may lead to erosion and increased pressure drop. Installations and steam demands 
change over time, which may lead to under-utilization of steam distribution capacity 
utilization, and extra heat losses. However, it may be too expensive to optimize the system 
for changed steam demands. Still, checking for excess distribution lines and shutting off 
those lines is a cost-effective way to reduce steam distribution losses. Other maintenance 
measures for steam distribution systems are described below. 
 
Improve Insulation. This measure can be to use more insulating material, or to make a 
careful analysis of the proper insulation material. Crucial factors in choosing insulating 
material include: low thermal conductivity, dimensional stability under temperature change, 
resistance to water absorption, and resistance to combustion. Other characteristics of 
insulating material may also be important depending on the application, e.g., tolerance of 
large temperature variations and system vibration, and compressive strength where 
insulation is load bearing (Baen and Barth, 1994). Improving the insulation on the existing 
stock of heat distribution systems would save an average of 3-13% in all systems (OIT, 
1998) with an average payback period of 1.1 years6 (IAC, 1999). The U.S. Department of 
Energy has developed the software tool 3E-Plus to evaluate the optimal insulation for steam 
systems (see Appendix E). 
 
Maintain Insulation. It is often found that after repairs, the insulation is not replaced. In 
addition, some types of insulation can become brittle, or rot. As a result, energy can be 
saved by a regular inspection and maintenance system (CIBO, 1998). Exact energy savings 
and payback periods vary with the specific situation in the plant. 
 
Improve Steam Traps. Using modern thermostatic elements, steam traps can reduce energy 
use while improving reliability. The main advantages offered by these traps are that they 
open when the temperature is very close to that of the saturated steam (within 2°C), purge 
non-condensable gases after each opening, and are open on startup to allow a fast steam 
system warm-up. These traps are also very reliable, and useable for a wide variety of steam 
pressures (Alesson, 1995). Energy savings will vary depending on the steam traps installed 
and state of maintenance. 
 
Maintain Steam Traps. A simple program of checking steam traps to ensure that they 
operate properly can save significant amounts of energy. If the steam traps are not regularly 
monitored, 15-20% of the traps can be malfunctioning. In some plants, as many as 40% of 
the steam traps were malfunctioning. Energy savings for a regular system of steam trap 
                                                 
6 The IAC database shows a series of case studies where a particular technology was used.  It gives a wide 
variety of information, including the payback period for each case.  We calculated an overall payback for a 
technology by averaging all the individual cases. 
 
40
checks and follow-up maintenance is estimated at up to 10% (OIT, 1998; Jones 1997; Bloss, 
1997) with a payback period of 0.5 years (IAC, 1999). This measure offers a quick payback 
but is often not implemented because maintenance and energy costs are separately budgeted. 
Some systems already use this practice. 
 
An audit of the Flying J Refinery in North Salt Lake (Utah) identified annual savings of 
$147,000 by repairing leaking steam traps (Brueske et al., 2002). 
 
Monitor Steam Traps Automatically. Attaching automated monitors to steam traps in 
conjunction with a maintenance program can save even more energy, without significant 
added cost. This system is an improvement over steam trap maintenance alone, because it 
gives quicker notice of steam trap malfunctioning or failure. Using automatic monitoring is 
estimated to save an additional 5% over steam trap maintenance, with a payback of 1 year7 
(Johnston, 1995; Jones, 1997). Systems that are able to implement steam trap maintenance 
are also likely to be able to implement automatic monitoring. On average, 50% of systems 
can still implement automatic monitoring of steam traps. 
 
Repair Leaks. As with steam traps, the distribution pipes themselves often have leaks that 
go unnoticed without a program of regular inspection and maintenance. In addition to saving 
up to 3% of energy costs for steam production, having such a program can reduce the 
likelihood of having to repair major leaks (OIT, 1998). On average, leak repair has a 
payback period of 0.4 years (IAC, 1999). 
 
Recover Flash Steam. When a steam trap purges condensate from a pressurized steam 
distribution system to ambient pressure, flash steam is produced. This steam can be used for 
space heating or feed water preheating (Johnston, 1995). The potential for this measure is 
extremely site dependent, as it is unlikely that a producer will want to build an entirely new 
system of pipes to transport this low-grade steam to places where it can be used, unless it 
can be used close to the steam traps. Hence, the savings are strongly site dependent. Many 
sites will use multi-pressure steam systems. In this case, flash steam formed from high-
pressure condensate can be routed to reduced pressure systems. 
 
Vulcan Chemicals in Geismar (Louisiana) implemented a flash steam recovery project at 
one of the processes at their chemical plant. The project recovers 100% of the flash steam 
and resulted in net energy savings of 2.8% (Bronhold, 2000). 
                                                 
7 Calculated based on a UK payback of 0.75 years.  The U.S. payback is longer because energy prices in the 
U.S. are lower, while capital costs are similar. 
 
41
Table 7. Summary of energy efficiency measures in steam distribution systems. 
Measure 
Fuel Saved 
Payback 
Period (years) 
Other Benefits 
Improved Insulation 
3-13% 
1.1 
 
Improved Steam Traps 
Unknown 
Unknown 
Greater reliability 
Steam Trap 
Maintenance 
10-15% 
0.5 
 
Automatic Steam Trap 
Monitoring
5% 
1 
 
8
Leak Repair 
3-5% 
0.4 
Reduced requirement for major 
repairs 
9
Flash Steam Recovery/ 
Condensate Return 
83%
Unknown 
Reduced water treatment costs 
Condensate Return 
Alone 
10% 
1.1 
Reduced water treatment costs 
 
 
Return Condensate. Reusing the hot condensate in the boiler saves energy and reduces the 
need for treated boiler feed water. The substantial savings in energy costs and purchased 
chemicals costs makes building a return piping system attractive. This measure has already 
been implemented in most places where it is easy to accomplish. Care has to be taken to 
design the recovery system to reduce efficiency losses (van de Ruit, 2000). Maximum 
energy savings are estimated at 10% (OIT, 1998) with a payback of 1.1 years (IAC, 1999) 
for those sites without or with insufficient condensate return. An additional benefit of 
condensate recovery is the reduction of the blowdown flow rate because boiler feedwater 
quality has been increased. 
                                                 
8 In addition to a regular maintenance program 
9 Includes flash steam recovery from the boiler.  Although this represents actual savings achieved in a case 
study, it seems much to high to be a generally applicable savings number.  As a result, it is not included in our 
total savings estimate. 
 
42
9. Heat Exchangers and Process Integration 
 
Heating and cooling are operations found throughout the refinery. Within a single process, 
multiple streams are heated and cooled multiple times. Optimal use and design of heat 
exchangers is a key area for energy efficiency improvement. 
 
9.1 Heat Transfer– Fouling  
Heat exchangers are used throughout the refinery to recover heat from processes and transfer 
heat to the process flows. Next to efficient integration of heat flows throughout the refinery 
(see process integration below), the efficient operation of heat exchangers is a major area of 
interest. In a complex refinery, most processes occur under high temperature and pressure 
conditions; the management and optimization of heat transfer among processes is therefore 
key to increasing overall energy efficiency. Fouling, a deposit buildup in units and piping 
that impedes heat transfer, requires the combustion of additional fuel. For example, the 
processing of many heavy crude oils in the United States increases the likelihood of 
localized coke deposits in the heating furnaces, thereby reducing furnace efficiency and 
creating potential equipment failure. An estimate by the Office of Industrial Technology at 
the U.S. Department of Energy noted that the cost penalty for fouling could be as much as 
$2 billion annually in material and energy costs. The problem of fouling is expected to 
increase with the trend towards processing heavier crudes. 
 
Fouling is the effect of several process variables and heat exchanger design. Fouling may 
follow the combination of different mechanisms (Bott, 2001). Several methods of 
investigation have been underway to attempt to reduce fouling including the use of sensors 
to detect early fouling, physical and chemical methods to create high temperature coatings 
(without equipment modification), the use of ultrasound, as well as the improved long term 
design and operation of facilities. The U.S. Department of Energy initially funded 
preliminary research into this area, but funding has been discontinued (Huangfu, 2000; Bott, 
2000). Worldwide, research in fouling reduction and mitigation is continuing (Polley and 
Pugh, 2002; Polley et al. 2002) by focusing on understanding the principles of fouling and 
redesign of heat exchangers and reactors. Currently, various methods to reduce fouling focus 
on process control, temperature control, regular maintenance and cleaning of the heat 
exchangers (either mechanically or chemically) and retrofit of reactor tubes (Barletta, 1998). 
 
A study of European refineries identified overall energy savings of 0.7% by cleaning the 
heat exchanger tubes of the CDU and other furnaces with an estimated payback period of 
0.7 years. 
 
Fouling was identified as a major energy loss in an audit of the Equilon refinery in Martinez, 
California (now owned by Shell). Regular cleaning of heat exchangers and maintenance of 
insulation would result in estimated annual savings of over $14 million at a total expenditure 
of $9.85 million (U.S. DOE-OIT, 2002b). Hence, the simple payback period is around 8 
months. 
 
CDU. Fouling is an important factor for efficiency losses in the CDU, and within the CDU, 
the crude preheater is especially susceptible to fouling (Barletta, 1998). Initial analysis on 
 
43
fouling effects of a 100,000 bbl/day crude distillation unit found an additional heating load 
of 12.3 kBtu/barrel (13.0 MJ/barrel) processes (Panchal and Huangfu, 2000). Reducing this 
additional heating load could results in significant energy savings. 
 
9.2 Process Integration 
Process integration or pinch technology refers to the exploitation of potential synergies that 
are inherent in any system that consists of multiple components working together. In plants 
that have multiple heating and cooling demands, the use of process integration techniques 
may significantly improve efficiencies.  
 
Developed in the early 1970s, it is now an established methodology for continuous 
processes (Linnhoff, 1992; Caddet, 1993). The methodology involves the linking of hot and 
cold streams in a process in a thermodynamic optimal way (i.e., not over the so-called 
‘pinch’). Process integration is the art of ensuring that the components are well suited and 
matched in terms of size, function and capability. Pinch analysis takes a systematic approach 
to identifying and correcting the performance limiting constraint (or pinch) in any 
manufacturing process (Kumana, 2000a). It was developed originally in the late 1970s at the 
University of Manchester in England and other places (Linnhoff, 1993) in response to the 
“energy crisis” of the 1970s and the need to reduce steam and fuel consumption in oil 
refineries and chemical plants by optimizing the design of heat exchanger networks. Since 
then, the pinch approach has been extended to resource conservation in general, whether the 
resource is capital, time, labor, electrical power, water or a specific chemical species such as 
hydrogen. 
 
The critical innovation in applying pinch analysis was the development of “composite 
curves” for heating and cooling, which represent the overall thermal energy demand and 
availability profiles for the process as a whole. When these two curves are drawn on a 
temperature-enthalpy graph, they reveal the location of the process pinch (the point of 
closest temperature approach), and the minimum thermodynamic heating and cooling 
requirements. These are called the energy targets. The methodology involves first 
identifying the targets and then following a systematic procedure for designing heat 
exchanger networks to achieve these targets. The optimum approach temperature at the 
pinch is determined by balancing the capital-energy tradeoffs to achieve the desired 
payback. The procedure applies equally well to new designs as well as to retrofits of existing 
plants. 
 
The analytical approach to this analysis has been well documented in the literature 
(Kumana, 2000b; Smith, 1995; Shenoy, 1994). Energy savings potential using pinch 
analysis far exceeds that from well-known conventional techniques such as heat recovery 
from boiler flue gas, insulation and steam trap management. 
 
Pinch analysis, and competing process integration tools, have been developed further in the 
past several years. The most important developments in the energy area are the inclusion of 
alternative heat recovery processes such as heat pumps and heat transformers, as well as the 
development of pinch analysis for batch processes (or in other words bringing in time as a 
factor in the analysis of heat integration). Furthermore, pinch analysis should be used in the 
 
44
design of new processes and plants, as process integration goes beyond optimization of heat 
exchanger networks (Hallale, 2001). Even in new designs additional opportunities for 
energy efficiency improvement can be identified. Pinch analysis has also been extended to 
the areas of water recovery and efficiency, and hydrogen recovery (hydrogen pinch, see also 
below).  Water used to be seen as a low-cost resource to the refinery, and was used 
inefficiently. However, as the standards and costs for waste water treatment increase and the 
costs for feedwater makeup increase, the industry has become more aware of water costs. In 
addition, large amounts of energy are used to process and move water through the refinery. 
Hence, water savings will lead to additional energy savings. Water pinch can be used to 
develop targets for minimal water use by reusing water in an efficient manner. Optimization 
software has been developed to optimize investment and operation costs for water systems 
in a plant (Hallale, 2001). New tools have been developed to optimize water and energy use 
in an integrated manner (Wu, 2000). Water pinch has until now mainly been used in the 
food industry, reporting reductions in water intake of up to 50% (Polley and Polley, 2000). 
Dunn and Bush (2001) report the use of water pinch for optimization of water use in 
chemical plants operated by Solutia, resulting in sufficient water use reductions to allow 
expansion of production and of the site with no net increase in water use. No water pinch 
analysis studies specific for the petroleum refining industry were found. Major oil 
companies, e.g., BP and Exxon, have applied hydrogen pinch analysis for selected 
refineries.  
 
Total Site Pinch Analysis has been applied by over 40 refineries around the world to find 
optimum site-wide utility levels by integrating heating and cooling demands of various 
processes, and by allowing the integration of CHP into the analysis. Process integration 
analysis of existing refineries and processes should be performed regularly, as continuous 
changes in product mix, mass flows, and applied processes can provide new or improved 
opportunities for energy and resource efficiency.  
 
Major refineries that have applied total site pinch analysis are: Amoco, Agip (Italy), BP, 
Chevron, Exxon (in the Netherlands and UK), and Shell (several European plants). Typical 
savings identified in these site-wide analyses are around 20-30%, although the economic 
potential was found to be limited to 10-15% (Linnhoff-March, 2000). A total-site analysis 
was performed of a European oil refinery in the late 1990s. The Solomon’s EII of the 
refinery was within the top quartile. The refinery operates 16 processes including a CDU, 
VDU, FCC, reformer, coker and hydrotreaters. A study of the opportunities offered by 
individual process optimization of the CDU, VDU, FCC, coker, and two hydrotreaters found 
a reduction in site EII of 7.5%. A total-site analysis including the cogeneration unit 
identified a potential reduction of 16% (Linnhoff-March, 2000). Identified opportunities 
including the conversion of a back-pressure turbine to a condensing turbine, and improved 
integration of the medium-pressure and low-pressure steam networks. The economically 
attractive projects would result in savings of approximately 12-13%.  
 
Site analyses by chemical producer Solutia identified annual savings of $3.9 million (of 
which 2.7 with a low payback) at their Decatur plant, 0.9M$/year at the Anniston site and 
3.6 M$/year at the Pensacola site (Dunn and Bush, 2001). 
 
 
45
Process Integration - Hot Rundown – Typically process integration studies focus on the 
integration of steam flows within processes and between processes. Sometimes it is possible 
to improve the efficiency by retaining the heat in intermediate process flows from one unit 
to another unit. This reduces the need for cooling or quenching in one unit and reheating in 
the other unit. Such an integration of two processes can be achieved through automated 
process controls linking the process flows between both processes. An audit of the Equilon 
refinery in Martinez, California, identified annual savings of $4.3 million (U.S. DOE-OIT, 
2002b). However, the audit results did not include an assessment of investments and 
payback. 
 
Crude Distillation Unit (CDU). The CDU process all the incoming crude and, hence, is a 
major energy user in all refinery layouts (except for those refineries that receive 
intermediates by pipeline from other refineries). In fact, in Chapter 4 it is estimated that the 
CDU is the largest energy consuming process of all refinery processes. Energy use and 
products of the CDU depend on the type of crude processed. New CDUs are supplied by a 
number of global companies such as ABB Lummus, Kellog Brown & Root, Shell Global 
Solutions, Stone & Webster, Technip/Elf, and UOP. An overview of available process 
designs is published as Hydrocarbon Processing’s Refining Processes (HCP, 2000). 
 
Process integration is especially important in the CDU, as it is a large energy consumer 
processing all incoming crude oil. Older process integration studies show reductions in fuel 
use between 10 and 19% for the CDU (Clayton, 1986; Sunden, 1988; Lee, 1989) with 
payback periods less than 2 years. An interesting opportunity is the integration of the CDU 
and VDU, which can lead to fuel savings from 10-20% (Clayton, 1986; Petrick and 
Pellegrino, 1999) compared to non-integrated units, at relatively short paybacks. The actual 
payback period will depend heavily on the layout of the refinery, needed changes in the heat 
exchanger network and the fuel prices. 
 
The CDU at BP’s Kwinana (Australia) refinery was already performing well with limited 
opportunities for further economic process integration. An analysis of the CDU identified a 
significant potential for reduction but with a payback of around 6 years. However, 
integration with the residue cracking unit offered significant opportunities to reduce the 
combined heating demand by 35-40% with a simple payback period of 1.6 years (Querzoli, 
2002). 
 
Fluid Catalytic Cracker (FCC). The FCC is a considerable energy consumer in a modern 
refiner. In this Energy Guide, the FCC energy use is estimated at 6% of total energy use. 
Depending on the design and product mix of a particular refinery, FCC energy use can be 
higher than 6%. There are a large number of FCC designs in use, and many were originally 
built in the 1970s. Today, more energy efficient designs are being marketed by a number of 
suppliers. The designs vary in reactor design, type of catalyst used and degree of heat 
integration. An overview of available process designs is published as Hydrocarbon 
Processing’s Refining Processes (HCP, 2000). The major suppliers are ABB Lummus, 
Kellog Brown & Root, Shell Global Solutions, Stone & Webster, and UOP. The optimal 
design will be based on the type of feed processed and desired product mix and quality. 
 
46
When selecting a new FCC, process energy efficiency should be an integral part of the 
selection process. 
 
In existing FCC units, energy efficiency can be improved by increasing heat integration and 
recovery, process flow scheme changes, and power recovery. A FCC has a multitude of 
flows that need to be heated (sink) and cooled (source). The better the integration of the heat 
sinks and sources, the lower the energy consumption of an FCC will be. Older FCC designs 
often do not have an optimized heat exchange setup, which may especially lead to wasted 
low-temperature heat, which could be used to preheat boiler feed water or cold feed. 
However, by better integrating the sources and sinks, following the principles of pinch 
technology (see above), through improved combinations of temperature levels and 
heating/cooling loads energy use is lowered. Various authors have reported on the 
application of pinch analysis and process optimization of FCCs (Hall et al., 1995; Golden 
and Fulton, 2000). The appropriate combination will depend on the feed processed and 
output produced. Furthermore, economics for the installation of heat exchangers may 
determine the need for less efficient combinations. 
 
Al-Riyami et al. (2001) studied the opportunities for process integration of a FCC unit in a 
refinery in Romania. The FCC unit was originally built by UOP and is used to convert 
vacuum gas oil and atmospheric gas oil. Several design options were identified to reduce 
utility consumption. The study of the FCC identified a reduction in utilities of 27% at a 
payback of 19 months. However, the calculation for the payback period only includes the 
heat exchangers, and, depending on the design of the FCC and layout of the plant, the 
payback period may be longer for other plant designs. 
 
At a refinery in the United Kingdom, a site analysis of energy efficiency opportunities was 
conducted. The audit identified additional opportunities for heat recovery in the FCC by 
installing a waste heat boiler before the electrostatic precipitator, resulting in savings of 
$210,000/year at a payback of 2 years (Venkatesan and Iordanova, 2003). 
 
FCC-Process Flow Changes. The product quality demands and feeds of FCCs may change 
over time. The process design should remain optimized for this change. Increasing or 
changing the number of pumparounds can improve energy efficiency of the FCC, as it 
allows increased heat recovery (Golden and Fulton, 2000). A change in pumparounds may 
affect the potential combinations of heat sinks and sources.  
 
New design and operational tools enable the optimization of FCC operating conditions to 
enhance product yields. Petrick and Pellegrino (1999) cite studies that have shown that 
optimization of the FCC-unit with appropriate modifications of equipment and operating 
conditions can increase the yield of high octane gasoline and alkylate from 3% to 7% per 
barrel of crude oil. This would result in energy savings. 
 
Reformer. At a refinery in the United Kingdom, a site analysis of energy efficiency 
opportunities was conducted. The audit identified opportunities to improve the performance 
of the economizer in the waste heat boilers of two reformer furnaces. The changes would 
 
47
result in annual savings of $140,000 in each reformer at a payback period of 2 years 
(Venkatesan and Iordanova, 2003). 
 
Coker. A simulation and optimization of a coker of Jinling Petrochemical Corp.’s Nanjing 
refinery (China) in 1999 identified a more efficient way to integrate the heat flows in the 
process. By changing the diesel pumparound, they achieved an energy cost reduction of 
$100,000/year (Zhang, 2001). Unfortunately, there is insufficient data to estimate the 
savings for U.S. refineries or to evaluate the economics of the project under U.S. conditions.  
 
48
10. Process Heaters 
 
Over 60% of all fuel used in the refinery is used in furnaces and boilers. The average 
thermal efficiency of furnaces is estimated at 75-90% (Petrick and Pellegrino, 1999). 
Accounting for unavoidable heat losses and dewpoint considerations, the theoretical 
maximum efficiency is around 92% (HHV) (Petrick and Pellegrino, 1999). This suggests 
that on average a 10% improvement in energy efficiency can be achieved in furnace and 
burner design.  
 
The efficiency of heaters can be improved by improving heat transfer characteristics, 
enhancing flame luminosity, installing recuperators or air-preheaters, and improved controls. 
New burner designs aim at improved mixing of fuel and air and more efficient heat transfer. 
Many different concepts are developed to achieve these goals, including lean-premix 
burners (Seebold et al., 2001), swirl burners (Cheng, 1999), pulsating burners (Petrick and 
Pellegrino, 1999) and rotary burners (U.S. DOE-OIT, 2002e). At the same time, furnace and 
burner design has to address safety and environmental concerns. The most notable is the 
reduction of NOx emissions. Improved NOx control will be necessary in almost all 
refineries to meet air quality standards, especially as many refineries are located in non-
attainment areas.  
 
10.1 Maintenance 
Regular maintenance of burners, draft control and heat exchangers is essential to maintain 
safe and energy efficient operation of a process heater. 
 
Draft Control. Badly maintained process heaters may use excess air. This reduces the 
efficiency of the burners. Excess air should be limited to 2-3% oxygen to ensure complete 
combustion. 
 
Valero’s Houston refinery has installed new control systems to reduce excess combustion air 
at the three furnaces of the CDU. The control system allows running the furnace with 1% 
excess oxygen instead of the regular 3-4%. The system has not only reduced energy use by 3 
to 6% but also reduced NOx emissions by 10-25%, and enhanced the safety of the heater 
(Valero, 2003). The energy savings result in an estimated cost savings of $340,000. Similar 
systems will be introduced in 94 process heaters at the 12 Valero refineries, and is expected 
to result in savings of $8.8 million/year. 
 
An audit of the Paramount Petroleum Corp.’s asphalt refinery in Paramount (California) 
identified excess draft air in six process heaters. Regular maintenance (twice per year) can 
reduce the excess draft air and would result in annual savings of over $290,000 (or nearly 
100,000 MBtu/year). The measure has a simple payback period of 2 months (U.S. DOE-
OIT, 2003b). 
 
An audit co-funded by U.S. Department of Energy, of the Equilon refinery (now owned by 
Shell) in Martinez (California) found that reduction of excess combustion and draft air 
would result in annual savings of almost $12 million (U.S. DOE-OIT, 2002b). A similar 
audit of the Flying J Refinery at North Salt Lake (Utah) found savings of $100,000/year 
 
49
through oxygen control of the flue gases to control the air intake of the furnaces (Brueske et 
al., 2002). 
 
10.2 Air Preheating  
Air preheating is an efficient way of improving the efficiency and increasing the capacity of 
a process heater. The flue gases of the furnace are used to preheat the combustion air. Every 
35°F drop in the exit flue gas temperature increases the thermal efficiency of the furnace by 
1% (Garg, 1998). Typical fuel savings range between 8 and 18%, and is typically 
economically attractive if the flue gas temperature is higher than 650°F and the heater size is 
50 MMBtu/hr or more (Garg, 1998). The optimum flue gas temperature is also determined 
by the sulfur content of the flue gases to reduce corrosion. When adding a preheater, the 
burner needs to be rerated for optimum efficiency. The typical payback period for 
combustion air preheating in a refinery is estimated at 2.5 years. However, the costs may 
vary strongly depending on the layout of the refinery and furnace construction. 
 
VDU. At a refinery in the United Kingdom, a site analysis of energy efficiency opportunities 
was conducted. The refinery operated 3 VDUs of which one still used natural draught and 
had no heat recovery installed. By installing a combustion air preheater, using the hot flue 
gas, and an additional FD fan, the temperature of the flue gas was reduced to 470°F. This led 
to energy cost savings of $109,000/year with a payback period of 2.2 years (Venkatesan and 
Iordanova, 2003).  
 
10.3 New Burners 
In many areas, new air quality regulation will demand refineries to reduce NOx and VOC 
emissions from furnaces and boilers. Instead of installing expensive selective catalytic 
reduction (SCR) flue gas treatment plants, new burner technology reduces emissions 
dramatically. This will result in cost savings as well as help to decrease electricity costs for 
the SCR.  
 
ChevronTexaco, in collaboration with John Zink Co., developed new low-NOx burners for 
refinery applications based on the lean premix concept. The burners help to reduce NOx 
emissions from 180 ppm to below 20 ppm. The burners have been installed in a CDU, VDU, 
and a reformer at ChevronTexaco’s Richmond, (California) refinery, without taking the 
furnace out of production. The burner was also applied to retrofit a steam boiler. The 
installation of the burners in a reforming furnace reduced emissions by over 90%, while 
eliminating the need for an SCR. This saved the refinery $10 million in capital costs and 
$1.5 million in annual operating costs of the SCR (Seebold et al., 2001). The operating costs 
include the saved electricity costs for operating compressors and fans for the SCR. The 
operators had to be retrained to operate the new burners as some of the operation 
characteristics had changed. 
 
50
11. Distillation 
 
Distillation is one of the most energy intensive operations in the petroleum refinery. 
Distillation is used throughout the refinery to separate process products, either from the 
CDU/VDU or from conversion processes. The incoming flow is heated, after which the 
products are separated on the basis of boiling points. Heat is provided by process heaters 
(see Chapter 10) and/or by steam (see Chapter 9). Energy efficiency opportunities exist in 
the heating side and by optimizing the distillation column.  
 
Operation Procedures. The optimization of the reflux ratio of the distillation column can 
produce significant energy savings. The efficiency of a distillation column is determined by 
the characteristics of the feed. If the characteristics of the feed have changed over time or 
compared to the design conditions, operational efficiency can be improved. If operational 
conditions have changed, calculations to derive new optimal operational procedures should 
be done. The design reflux should be compared with the actual ratios controlled by each 
shift operator. Steam and/or fuel intensity can be compared to the reflux ratio, product 
purity, etc. and compared with calculated and design performance on a daily basis to 
improve the efficiency. 
 
Check Product Purity. Many companies tend to excessively purify products and 
sometimes with good reason. However, purifying to 98% when 95% is acceptable is not 
necessary. In this case, the reflux rate should be decreased in small increments until the 
desired purity is obtained.  This will decrease the reboiler duties. This change will require no 
or very low investments (Saxena, 1997). 
 
Seasonal Operating Pressure Adjustments. For plants that are in locations that experience 
winter climates, the operating pressure can be reduced according to a decrease in cooling 
water temperatures (Saxena, 1997). However, this may not apply to the VDU or other 
separation processes operating under vacuum. These operational changes will generally not 
require any investment. 
 
Reducing Reboiler Duty. Reboilers consume a large part of total refinery energy use as part 
of the distillation process. By using chilled water, the reboiler duty can in principal be 
lowered by reducing the overhead condenser temperature. A study of using chilled water in 
a 100,000 bbl/day CDU has led to an estimated fuel saving of 12.2 MBtu/hr for a 5% 
increase in cooling duty (2.5 MBtu/hr) (Petrick and Pellegrino, 1999), assuming the use of 
chilled water with a temperature of  50°F. The payback period was estimated at 1 to 2 years, 
however, excluding the investments to change the tray design in the distillation tower. This 
technology is not yet proven in a commercial application. This technology can also be 
applied in other distillation processes. 
 
Upgrading Column Internals. Damaged or worn internals can result in increased operation 
costs.  As the internals become damaged, efficiency decreases and pressure drops rise. This 
causes the column to run at a higher reflux rate over time. With an increased reflux rate, 
energy costs will increase accordingly. Replacing the trays with new ones or adding a high 
performance packing can have the column operating like the day it was brought online. If 
 
51
operating conditions have seriously deviated from designed operating conditions, the 
investment may have a relative short payback.  
 
New tray designs are marketed and developed for many different applications. When 
replacing the trays, it will often be worthwhile to consider new efficient tray designs. New 
tray designs can result in enhanced separation efficiency and decrease pressure drop. This 
will result in reduced energy consumption. When considering new tray designs, the number 
of trays should be optimized  
 
Stripper Optimization. Steam is injected into the process stream in strippers. Steam 
strippers are used in various processes, and especially the CDU is a large user. The strip 
steam temperature can be too high, and the strip steam use may be too high. Optimization of 
these parameters can reduce energy use considerably. This optimization can be part of a 
process integration (or pinch) analysis for the particular unit (see section 9.2).  
 
Progressive Crude Distillation. Technip and Elf (France) developed an energy efficient 
design for a crude distillation unit, by redesigning the crude preheater and the distillation 
column. The crude preheat train was separated in several steps to recover fractions at 
different temperatures. The distillation tower was re-designed to work at low pressure and 
the outputs were changed to link to the other processes in the refinery and product mix of the 
refinery. The design resulted in reduced fuel consumption and better heat integration 
(reducing the net steam production of the CDU). Technip claims up to a 35% reduction in 
fuel use when compared to a conventional CDU (Technip, 2000). This technology has been 
applied in the new refinery constructed at Leuna (Germany) in 1997 and is being used for 
another new refinery under construction in Europe. Because of the changes in CDU-output 
and needed changes in intermediate flows, progressive crude distillation is especially suited 
for new construction or large crude distillation expansion projects. 
 
 
52
12. Hydrogen Management and Recovery 
 
Hydrogen is used in the refinery in processes such as hydrocrackers and desulfurization 
using hydrotreaters. The production of hydrogen is an energy intensive process using 
naphtha reformers and natural gas-fueled reformers. These processes and other processes 
also generate gas streams that may contain a certain amount of hydrogen not used in the 
processes, or generated as by-product of distillation of conversion processes. In addition, 
different processes have varying quality (purity) demands for the hydrogen feed. Reducing 
the need for hydrogen make-up will reduce energy use in the reformer and reduce the need 
for purchased natural gas. Natural gas is an expensive energy input in the refinery process, 
and lately associated with large fluctuations in prices (especially in California). The major 
technology developments in hydrogen management within the refinery are hydrogen process 
integration (or hydrogen cascading) and hydrogen recovery technology (Zagoria and 
Huycke, 2003). Revamping and retrofitting existing hydrogen networks can increase 
hydrogen capacity between 3% and 30% (Ratan and Vales, 2002). 
 
12.1 Hydrogen Integration 
Hydrogen network integration and optimization at refineries is a new and important 
application of pinch analysis (see above). Most hydrogen systems in refineries feature 
limited integration and pure hydrogen flows are sent from the reformers to the different 
processes in the refinery. But as the use of hydrogen is increasing, especially in California 
refineries, the value hydrogen is more and more appreciated. Using the approach of 
composition curves used in pinch analysis, the production and uses of hydrogen of a refinery 
can be made visible. This allows identification of the best matches between different 
hydrogen sources and uses based on quality of the hydrogen streams. It allows the user to 
select the appropriate and most cost-effective technology for hydrogen purification. A recent 
improvement of the analysis technology also accounts for gas pressure, to reduce 
compression energy needs (Hallale, 2001). The analysis method accounts also for costs of 
piping, besides the costs for generation, fuel use, and compression power needs. It can be 
used for new and retrofit studies. 
 
The BP refinery at Carson (California), in a project with the California Energy Commission, 
has executed a hydrogen pinch analysis of the large refinery. Total potential savings of $4.5 
million on operating costs were identified, but the refinery decided to realize a more cost-
effective package saving $3.9 million per year. As part of the plant-wide assessment of the 
Equilon (Shell) refinery at Martinez, an analysis of the hydrogen network has been included 
(U.S. DOE-OIT, 2002b). This has resulted in the identification of large energy savings. 
Further development and application of the analysis method at California refineries, 
especially as the need for hydrogen is increasing due to reduced future sulfur-content of 
diesel and other fuels, may result in reduced energy needs at all refineries with hydrogen 
needs (Khorram and Swaty, 2002). One refinery identified savings of $6 million/year in 
hydrogen savings without capital projects (Zagoria and Huycke, 2003). 
 
12.2 Hydrogen Recovery 
Hydrogen recovery is an important technology development area to improve the efficiency 
of hydrogen recovery, reduce the costs of hydrogen recovery, and increase the purity of the 
 
53
resulting hydrogen flow. Hydrogen can be recovered indirectly by routing low-purity 
hydrogen streams to the hydrogen plant (Zagoria and Huycke, 2003).  Hydrogen can also be 
recovered from offgases by routing it to the existing purifier of the hydrogen plant, or by 
installing additional purifiers to treat the offgases and ventgases. Suitable gas streams for 
hydrogen recovery are the offgases from the hydrocracker, hydrotreater, coker, or FCC. Not 
only the hydrogen content determines the suitability, but also the pressure, contaminants 
(i.e., low on sulfur, chlorine and olefins) and tail end components (C5+) (Ratan and Vales, 
2002). The characteristics of the source stream will also impact the choice of recovery 
technology. The cost savings of recovered hydrogen are around 50% of the costs of 
hydrogen production (Zagoria and Huycke, 2003).  
 
Hydrogen can be recovered using various technologies, of which the most common are 
pressure swing and thermal swing absorption, cryogenic distillation, and membranes. The 
choice of separation technology is driven by desired purity, degree of recovery, pressure, 
and temperature. Various manufacturers supply different types of hydrogen recovery 
technologies, including Air Products, Air Liquide, and UOP. Membrane technology 
generally represents the lowest cost option for low product rates, but not necessarily for high 
flow rates (Zagoria and Hucyke, 2003). For high-flow rates, PSA technology is often the 
conventional technology of choice. PSA is the common technology to separate hydrogen 
from the reformer product gas. Hundreds of PSA units are used around the world to recover 
hydrogen from various gas streams. Cryogenic units are favored if other gases, such as LPG, 
can be recovered from the gas stream as well. Cryogenic units produce a medium purity 
hydrogen gas steam (up to 96%). 
 
Membranes are an attractive technology for hydrogen recovery in the refinery. If the content 
of recoverable products is higher than 2-5% (or preferably 10%), recovery may make 
economic sense (Baker et al., 2000). New membrane applications for the refinery and 
chemical industries are under development. Membranes for hydrogen recovery from 
ammonia plants have first been demonstrated about 20 years ago (Baker et al., 2000), and 
are used in various state-of-the-art plant designs. Refinery offgas flows have a different 
composition, making different membranes necessary for optimal recovery. Membrane plants 
have been demonstrated for recovery of hydrogen from hydrocracker offgases. Various 
suppliers offer membrane technologies for hydrogen recovery in the refining industry, 
including Air Liquide, Air Products and UOP.  Air Liquide and UOP have sold over 100 
membrane hydrogen recovery units around the world. Development of low-cost and 
efficient membranes is an area of research interest to improve cost-effectiveness of 
hydrogen recovery, and enable the recovery of hydrogen from gas streams with lower 
concentrations.  
 
At the refinery at Ponca City (Oklahoma, currently owned by ConocoPhilips), a membrane 
system was installed to recover hydrogen from the waste stream of the hydrotreater, 
although the energy savings were not quantified (Shaver et al., 1991). Another early study 
quotes a 6% reduction in hydrogen makeup after installing a membrane hydrogen recovery 
unit at a hydrocracker (Glazer et al., 1988).    
 
 
54
12.3 Hydrogen Production 
Reformer – Adiabatic Pre-Reformer. If there is excess steam available at a plant, a pre-
reformer can be installed at the reformer. Adiabatic steam reforming uses a highly active 
nickel catalyst to reform a hydrocarbon feed, using waste heat (900°F) from the convection 
section of the reformer. This may result in a production increase of as much as 10% 
(Abrardo and Khurana, 1995). The Kemira Oy ammonia plant in Rozenburg, the 
Netherlands, implemented an adiabatic pre-reformer. Energy savings equaled about 4% of 
the energy consumption at a payback period between 1 and 3 years (Worrell and Blok, 
1994). ChevronTexaco included a pre-reformer in the design of the new hydrogen plant for 
the El Segundo refinery (California). The technology can also be used to increase the 
production capacity at no additional energy cost, or to increase the feed flexibility of the 
reformer. This is especially attractive if a refinery faces increased hydrogen demand to 
achieve increased desulfurization needs or switches to heavier crudes. Various suppliers 
provide pre-reformers including Haldor-Topsoe, Süd-Chemie, and Technip-KTI. 
 
 
55
13. Motors 
 
Electric motors are used throughout the refinery, and represent over 80% of all electricity 
use in the refinery. The major applications are pumps (60% of all motor use), air 
compressors (15% of all motor use), fans (9%), and other applications (16%). The following 
sections discuss opportunities for motors in general (section 13.1), pumps (Chapter 14), 
compressors (Chapter 15), and fans (Chapter 16). When available, specific examples are 
listed detailing the refining process to which the measure has been applied and to what 
success.   
 
Using a “systems approach” that looks at the entire motor system (pumps, compressors, 
motors, and fans) to optimize supply and demand of energy services often yields the most 
savings. For example, in pumping, a systems approach analyzes both the supply and demand 
sides and how they interact, shifting the focus of the analysis from individual components to 
total system performance. The measures identified below reflect aspects of this system 
approach including matching speed and load (adjustable speed drives), sizing the system 
correctly, as well as upgrading system components. However, for optimal savings and 
performance, the systems approach is recommended. Pumps and compressors are both 
discussed in more detail in Chapters 14 and 15. 
 
13.1 Motor Optimization 
Sizing of Motors. Motors and pumps that are sized inappropriately result in unnecessary 
energy losses. Where peak loads can be reduced, motor size can also be reduced. Correcting 
for motor oversizing saves 1.2% of their electricity consumption (on average for the U.S. 
industry), and even larger percentages for smaller motors (Xenergy, 1998).  
 
Higher Efficiency Motors. High efficiency motors reduce energy losses through improved 
design, better materials, tighter tolerances, and improved manufacturing techniques. With 
proper installation, energy efficient motors run cooler and consequently have higher service 
factors, longer bearing and insulation life and less vibration. Yet, despite these advantages, 
less than 8% of U.S. industrial facilities address motor efficiency in specifications when 
purchasing a motor (Tutterow, 1999).  
 
Typically, high efficiency motors are economically justified when exchanging a motor that 
needs replacement, but are not economically feasible when replacing a motor that is still 
working (CADDET, 1994). Typically, motors have an annual failure rate varying between 3 
and 12% (House et al., 2002). Sometimes though, according to a case study by the Copper 
Development Association (CDA, 2000), even working motor replacements may be 
beneficial. The payback for individual motors varies based on size, load factor, and running 
time. The best savings are achieved on motors running for long hours at high loads. When 
replacing retiring motors, paybacks are typically less than one year from energy savings 
alone (LBNL et al., 1998).  
 
To be considered energy efficient in the United States, a motor must meet performance 
criteria published by the National Electrical Manufacturers Association (NEMA). However, 
most manufacturers offer lines of motors that significantly exceed the NEMA-defined 
 
56
criteria (U.S. DOE-OIT, 2001d). NEMA and other organizations have created the “Motor 
Decisions Matter” campaign to market NEMA approved premium efficient motors to 
industry (NEMA, 2001). Even these premium efficiency motors may have low a payback 
period. According to data from the CDA, the upgrade to high efficiency motors, as 
compared to motors that achieve the minimum efficiency as specified by the Energy Policy 
Act, have paybacks of less than 15 months for 50 hp motors (CDA, 2001). Because of the 
fast payback, it usually makes sense not only to buy an energy efficient motor but also to 
buy the most efficient motor available (LBNL, 1998).  
 
Replacing a motor with a high efficiency motor is often a better choice than rewinding a 
motor. The practice of rewinding motors currently has no quality or efficiency standards. To 
avoid uncertainties in performance of the motor, a new high efficiency motor can be 
purchased instead of rewinding one. 
 
Power Factor. Inductive loads like transformers, electric motors and HID lighting may 
cause a low power factor. A low power factor may result in increased power consumption, 
and hence increased electricity costs. The power factor can be corrected by minimizing 
idling of electric motors, avoiding operation of equipment over its rated voltage, replacing 
motors by energy efficient motors (see above) and installing capacitors in the AC circuit to 
reduce the magnitude of reactive power in the system.  
 
Voltage Unbalance. Voltage unbalance degrades the performance and shortens the life of 
three-phase motors. A voltage unbalance causes a current unbalance, which will result 
torque pulsations, increased vibration and mechanical stress, increased losses, motor 
overheating reducing the life of a motor. Voltage unbalances may be caused by faulty 
operation of power correction equipment, unbalanced transformer bank or open circuit. It is 
recommended that voltage unbalance at the motor terminals does not exceed 1%. Even a 1% 
unbalance will reduce motor efficiency at part load operation. If the unbalance would 
increase to 2.5%, motor efficiency will also decrease at full load operation. For a 100 hp 
motor operating 8000 hours per year, a correction of the voltage unbalance from 2.5% to 1% 
will result in electricity savings of 9,500 kWh or almost $500 at an electricity rate of 5 
cts/kWh (U.S. DOE-OIT, 2000b). By regularly monitoring the voltages at the motor 
terminal and using annual thermographic inspections of motors, voltage unbalances may be 
identified. Furthermore, make sure that single-phase loads are evenly distributed and install 
ground fault indicators. Another indicator for a voltage unbalance is a 120 Hz vibration 
(U.S. DOE-OIT, 2000b). 
 
Adjustable Speed Drives (ASDS)/ Variable Speed Drives (VSDs). ASDs better match 
speed to load requirements for motor operations. Energy use on many centrifugal systems 
like pumps, fans and compressors is approximately proportional to the cube of the flow rate. 
Hence, small reductions in flow that are proportional to motor speed can sometimes yield 
large energy savings. Although they are unlikely to be retrofitted economically, paybacks 
for installing new ASD motors in new systems or plants can be as low as 1.1 years (Martin 
et al., 2000). The installation of ASDs improves overall productivity, control and product 
quality, and reduces wear on equipment, thereby reducing future maintenance costs.  
 
 
57
Variable Voltage Controls (VVCs). In contrast to ASDs, which have variable flow 
requirements, VVCs are applicable to variable loads requiring constant speed. The principle 
of matching supply with demand, however, is the same as for ASDs.   
 
58
14. Pumps 
 
In the petroleum refining industry, about 59% of all electricity use in motors is for pumps 
(Xenergy, 1998). This equals 48% of the total electrical energy in refineries, making pumps 
the single largest electricity user in a refinery. Pumps are used throughout the entire plant to 
generate a pressure and move liquids. Studies have shown that over 20% of the energy 
consumed by these systems could be saved through equipment or control system changes 
(Xenergy, 1998).  
 
It is important to note that initial costs are only a fraction of the life cycle costs of a pump 
system. Energy costs, and sometimes operations and maintenance costs, are much more 
important in the lifetime costs of a pump system. In general, for a pump system with a 
lifetime of 20 years, the initial capital costs of the pump and motor make up merely 2.5% of 
the total costs (Best Practice Programme, 1998). Depending on the pump application, energy 
costs may make up about 95% of the lifetime costs of the pump. Hence, the initial choice of 
a pump system should be highly dependent on energy cost considerations rather than on 
initial costs. Optimization of the design of a new pumping system should focus on 
optimizing the lifecycle costs. Hodgson and Walters (2002) discuss software developed for 
this purpose (OPSOP) and discuss several case studies in which they show large reductions 
in energy use and lifetime costs of a complete pumping system. Typically, such an approach 
will lead to energy savings of 10-17%. 
 
Pumping systems consist of a pump, a driver, pipe installation, and controls (such as 
adjustable speed drives or throttles) and are a part of the overall motor system, discussed in 
Section 13.1. Using a “systems approach” on the entire motor system (pumps, compressors, 
motors and fans) was also discussed in section 13.1. In this section, the pumping systems are 
addressed; for optimal savings and performance, it is recommended that the systems 
approach incorporating pumps, compressors, motors and fans be used. 
 
There are two main ways to increase pump system efficiency, aside from reducing use. 
These are reducing the friction in dynamic pump systems (not applicable to static or "lifting" 
systems) or adjusting the system so that it draws closer to the best efficiency point (BEP) on 
the pump curve (Hovstadius, 2002). Correct sizing of pipes, surface coating or polishing and 
adjustable speed drives, for example, may reduce the friction loss, increasing energy 
efficiency. Correctly sizing the pump and choosing the most efficient pump for the 
applicable system will push the system closer to the best efficiency point on the pump curve.  
 
Operations and Maintenance. Inadequate maintenance at times lowers pump system 
efficiency, causes pumps to wear out more quickly and increases costs. Better maintenance 
will reduce these problems and save energy. Proper maintenance includes the following 
(Hydraulic Institute, 1994; LBNL et al., 1999):  
• Replacement of worn impellers, especially in caustic or semi-solid applications. 
• Bearing inspection and repair. 
• Bearing lubrication replacement, once annually or semiannually.  
• Inspection and replacement of packing seals. Allowable leakage from packing seals 
is usually between two and sixty drops per minute.  
 
59
• Inspection and replacement of mechanical seals. Allowable leakage is typically one 
to four drops per minute.  
• Wear ring and impeller replacement. Pump efficiency degrades from 1 to 6 points for 
impellers less than the maximum diameter and with increased wear ring clearances 
(Hydraulic Institute, 1994).  
• Pump/motor alignment check.  
 
Typical energy savings for operations and maintenance are estimated to be between 2 and 
7% of pumping electricity use for the U.S. industry. The payback is usually immediate to 
one year (Xenergy, 1998; U.S. DOE-OIT, 2002c). 
 
Monitoring. Monitoring in conjunction with operations and maintenance can be used to 
detect problems and determine solutions to create a more efficient system. Monitoring can 
determine clearances that need be adjusted, indicate blockage, impeller damage, inadequate 
suction, operation outside preferences, clogged or gas-filled pumps or pipes, or worn out 
pumps. Monitoring should include:  
• Wear monitoring  
• Vibration analyses 
• Pressure and flow monitoring  
• Current or power monitoring  
• Differential head and temperature rise across the pump (also known as 
thermodynamic monitoring) 
• Distribution system inspection for scaling or contaminant build-up 
 
Reduce Need. Holding tanks can be used to equalize the flow over the production cycle, 
enhancing energy efficiency and potentially reducing the need to add pump capacity. In 
addition, bypass loops and other unnecessary flows should be eliminated. Energy savings 
may be as high as 5-10% for each of these steps (Easton Consultants, 1995). Total head 
requirements can also be reduced by lowering process static pressure, minimizing elevation 
rise from suction tank to discharge tank, reducing static elevation change by use of siphons, 
and lowering spray nozzle velocities. 
 
More Efficient Pumps. According to inventory data, 16% of pumps are more than 20 years 
old. Pump efficiency may degrade 10 to 25% in its lifetime (Easton Consultants, 1995). 
Newer pumps are 2 to 5% more efficient. However, industry experts claim the problem is 
not necessarily the age of the pump but that the process has changed and the pump does not 
match the operation. Replacing a pump with a new efficient one saves between 2 to 10% of 
its energy consumption (Elliott, 1994). Higher efficiency motors have also been shown to 
increase the efficiency of the pump system 2 to 5% (Tutterow, 1999).  
 
A number of pumps are available for specific pressure head and flow rate capacity 
requirements. Choosing the right pump often saves both in operating costs and in capital 
costs (of purchasing another pump). For a given duty, selecting a pump that runs at the 
highest speed suitable for the application will generally result in a more efficient selection as 
well as the lowest initial cost (Hydraulic Institute and Europump, 2001). Exceptions to this 
 
60
include slurry handling pumps, high specific speed pumps, or where the pump would need a 
very low minimum net positive suction head at the pump inlet.  
 
Correct Sizing Of Pump(s) (Matching Pump To Intended Duty). Pumps that are sized 
inappropriately result in unnecessary losses. Where peak loads can be reduced, pump size 
can also be reduced. Correcting for pump oversizing can save 15 to 25% of electricity 
consumption for pumping (on average for the U.S. industry) (Easton Consultants, 1995). In 
addition, pump load may be reduced with alternative pump configurations and improved 
O&M practices.  
 
Where pumps are dramatically oversized, speed can be reduced with gear or belt drives or a 
slower speed motor. This practice, however, is not common. Paybacks for implementing 
these solutions are less than one year (OIT, 2002a).  
 
The Chevron Refinery in Richmond, California, identified two large horsepower secondary 
pumps at the blending and shipping plant that were inappropriately sized for the intended 
use and needed throttling when in use. The 400 hp and 700 hp pump were replaced by two 
200 hp pumps, and also equipped with adjustable speed drives. The energy consumption was 
reduced by 4.3 million kWh per year, and resulted in annual savings of $215,000 (CEC, 
2001). With investments of $300,000 the payback period was 1.4 years.  
 
The Welches Point Pump Station, a medium sized waste water treatment plant located in 
Milford (CT), as a participant in the Department of Energy’s Motor Challenge Program, 
decided to replace one of their system’s three identical pumps with one smaller model 
(Flygt, 2002). They found that the smaller pump could more efficiently handle typical 
system flows and the remaining two larger pumps could be reserved for peak flows. While 
the smaller pump needed to run longer to handle the same total volume, its slower pace and 
reduced pressure resulted in less friction-related losses and less wear and tear. Substituting 
the smaller pump has a projected savings of 36,096 kW, more than 20% of the pump 
system’s annual electrical energy consumption. Using this system at each of the city’s 36 
stations would result in energy savings of over $100,000. In addition to the energy savings 
projected, less wear on the system results in less maintenance, less downtime and longer life 
of the equipment. The station noise is significantly reduced with the smaller pump.  
 
Use Multiple Pumps. Often using multiple pumps is the most cost-effective and most 
energy efficient solution for varying loads, particularly in a static head-dominated system. 
Installing parallel systems for highly variable loads saves 10 to 50% of the electricity 
consumption for pumping (on average for the U.S. industry) (Easton Consultants, 1995). 
Variable speed controls should also be considered for dynamic systems (see below). Parallel 
pumps also offer redundancy and increased reliability. One case study of a Finnish pulp and 
paper plant indicated that installing an additional small pump (a “pony pump”), running in 
parallel to the existing pump used to circulate water from the paper machine into two tanks, 
reduced the load in the larger pump in all cases except for startup. The energy savings were 
estimated at $36,500 (or 486 MWh, 58%) per year giving a payback of 0.5 years (Hydraulic 
Institute and Europump, 2001).  
 
 
61
Trimming Impeller (or Shaving Sheaves). If a large differential pressure exists at the 
operating rate of flow (indicating excessive flow), the impeller (diameter) can be trimmed so 
that the pump does not develop as much head. In the food processing, paper and 
petrochemical industries, trimming impellers or lowering gear ratios is estimated to save as 
much as 75% of the electricity consumption for specific pump applications (Xenergy, 1998). 
 
In one case study in the chemical processing industry, the impeller was reduced from 320 
mm to 280 mm, which reduced the power demand by more than 25% (Hydraulic Institute 
and Europump, 2001). Annual energy demand was reduced by 83 MWh (26%). With an 
investment cost of $390 (US), the payback on energy savings alone was 23 days. In addition 
to energy savings, maintenance costs were reduced, system stability was improved, 
cavitation was reduced, and excessive vibration and noise were eliminated.  
 
In another case study, Salt Union Ltd., the largest salt producer in the UK, trimmed the 
diameter of a pump impeller at its plant from 320 mm to 280 mm (13 to 11 inches) (Best 
Practice Programme, 1996b). After trimming the impeller, they found significant power 
reductions of 30%, or 197,000 kWh per year (710 GJ/year), totaling 8,900 GBP ($14,000 
1994 US). With an investment cost of 260 GBP ($400 1993 US), and maintenance savings 
of an additional 3,000 GBP ($4,600 1994 US), this resulted in a payback of 8 days (11 days 
from energy savings alone). In addition to energy and maintenance savings, like the 
chemical processing plant, cavitation was reduced and excessive vibration and noise were 
eliminated. With the large decrease in power consumption, the 110 kW motor could be 
replaced with a 75kW motor, with additional energy savings of about 16,000 kWh per year.  
 
Controls. The objective of any control strategy is to shut off unneeded pumps or reduce the 
load of individual pumps until needed. Remote controls enable pumping systems to be 
started and stopped more quickly and accurately when needed, and reduce the required 
labor. In 2000, Cisco Systems (CA) upgraded the controls on its fountain pumps to turn off 
the pumps during peak hours (CEC and OIT, 2002). The wireless control system was able to 
control all pumps simultaneously from one location. The project saved $32,000 and 400,000 
kWh annually, representing a savings of 61.5% of the fountain pumps’ total energy 
consumption. With a total cost of $29,000, the simple payback was 11 months. In addition to 
energy savings, the project reduced maintenance costs and increased the pumping system’s 
equipment life. 
 
Adjustable Speed Drives (ASDs). ASDs better match speed to load requirements for 
pumps where, as for motors, energy use is approximately proportional to the cube of the 
flow rate10. Hence, small reductions in flow that are proportional to pump speed may yield 
large energy savings. New installations may result in short payback periods. In addition, the 
installation of ASDs improves overall productivity, control, and product quality, and reduces 
wear on equipment, thereby reducing future maintenance costs.  
                                                 
10 This equation applies to dynamic systems only. Systems that solely consist of lifting (static head systems) 
will accrue no benefits from (but will often actually become more inefficient) ASDs because they are 
independent of flow rate. Similarly, systems with more static head will accrue fewer benefits than systems that 
are largely dynamic (friction) systems. More careful calculations must be performed to determine actual 
benefits, if any, for these systems.  
 
62
According to inventory data collected by Xenergy (1998), 82% of pumps in U.S. industry 
have no load modulation feature (or ASD). Similar to being able to adjust load in motor 
systems, including modulation features with pumps is estimated to save between 20 and 
50% of pump energy consumption, at relatively short payback periods, depending on 
application, pump size, load and load variation (Xenergy, 1998; Best Practice Programme, 
1996a). As a general rule of thumb, unless the pump curves are exceptionally flat, a 10% 
regulation in flow should produce pump savings of 20% and 20% regulation should produce 
savings of 40% (Best Practice Programme, 1996a).   
 
The ChevronTexaco refinery in Richmond (California) upgraded the feed pumps of the 
diesel hydrotreater by installing an ASD on a 2,250 hp primary feed pump, as well as 
changing the operation procedures for a backup pump system. The cost savings amount to 
$700,000/year reducing electricity consumption by 12 GWh/year. The pump system retrofit 
was implemented as part of a demand side management program by the local utility. The 
refinery did not have to put up any investment capital as it participated in this program (U.S. 
DOE-OIT, 1999).  
 
Hodgson and Walters (2002) discuss the application of an ASD to replace a throttle of a new 
to build pumping system. Optimization of the design using a dedicated software package led 
to the recommendation to install an ASD. This would result in 71% lower energy costs over 
the lifetime of the system, a 54% reduction in total lifetime costs of the system. 
 
Avoid Throttling Valves. Throttling valves should always be avoided. Extensive use of 
throttling valves or bypass loops may be an indication of an oversized pump (Tutterow et al., 
2000). Variable speed drives or on off regulated systems always save energy compared to 
throttling valves (Hovstadius, 2002).  
 
An audit of the 25,000 bpd Flying J Refinery in Salt Lake City (Utah) identified throttle 
losses at two 200 hp charge pumps. Minimizing the throttle losses would result in potential 
energy cost savings of $39,000 (Brueske et al., 2002). The shutdown of a 250 hp pump 
when not needed and the minimization of throttle losses would result in additional savings 
of $28,000 per year.  
 
Correct Sizing Of Pipes. Similar to pumps, undersized pipes also result in unnecessary 
losses. The pipe work diameter is selected based on the economy of the whole installation, 
the required lowest flow velocity, and the minimum internal diameter for the application, the 
maximum flow velocity to minimize erosion in piping and fittings, and plant standard pipe 
diameters. Increasing the pipe diameter may save energy but must be balanced with costs for 
pump system components. Easton Consultants (1995) and others in the pulp and paper 
industry (Xenergy, 1998) estimate retrofitting pipe diameters saves 5 to 20% of their energy 
consumption, on average for the U.S. industry. Correct sizing of pipes should be done at the 
design or system retrofit stages where costs may not be restrictive.  
 
Replace Belt Drives. Inventory data suggests 4% of pumps have V-belt drives, many of 
which can be replaced with direct couplings to save energy (Xenergy, 1998). Savings are 
estimated at 1% (on average for the U.S. industry) (Xenergy, 1998).  
 
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Precision Castings, Surface Coatings, Or Polishing. The use of castings, coatings, or 
polishing reduces surface roughness that in turn, increases energy efficiency. It may also 
help maintain efficiency over time. This measure is more effective on smaller pumps. One 
case study in the steel industry analyzed the investment in surface coating on the mill supply 
pumps (350 kW pumps). They determined that the additional cost of coating, $1,200, would 
be paid back in 5 months by energy savings of $2,700 (or 36 MWh, 2%) per year (Hydraulic 
Institute and Europump, 2001). Energy savings for coating pump surfaces are estimated to 
be 2 to 3% over uncoated pumps (Best Practice Programme, 1998).  
 
Sealings. Seal failure accounts for up to 70% of pump failures in many applications 
(Hydraulic Institute and Europump, 2001). The sealing arrangements on pumps will 
contribute to the power absorbed. Often the use of gas barrier seals, balanced seals, and no-
contacting labyrinth seals optimize pump efficiency. 
 
Curtailing Leakage Through Clearance Reduction. Internal leakage losses are a result of 
differential pressure across the clearance between the impeller and the pump casing. The 
larger the clearance, the greater is the internal leakage causing inefficiencies. The normal 
clearance in new pumps ranges from 0.35 to 1.0 mm (0.014 to 0.04 in.) (Hydraulic Institute 
and Europump, 2001). With wider clearances, the leakage increases almost linearly with the 
clearance. For example, a clearance of 5 mm (0.2 in.) decreases the efficiency by 7 to 15% 
in closed impellers and by 10 to 22% in semi-open impellers. Abrasive liquids and slurries, 
even rainwater, can affect the pump efficiency. Using very hard construction materials (such 
as stainless steel) can reduce the wear rate.  
 
Dry Vacuum Pumps. Dry vacuum pumps were introduced in the semiconductor industry in 
Japan in the mid-1980s, and were introduced in the U.S. chemical industry in the late 1980s. 
The advantages of a dry vacuum pump are high energy efficiency, increased reliability, and 
reduced air and water pollution. It is expected that dry vacuum pumps will displace oil-
sealed pumps (Ryans and Bays, 2001).  Dry pumps have major advantages in applications 
where contamination is a concern. Due to the higher investment costs of a dry pump, it is not 
expected to make inroads in the petroleum refining industry in a significant way, except for 
special applications where contamination and pollution control are an important driver. 
 
 
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15. Compressors and Compressed Air 
 
Compressors consume about 12% of total electricity use in refineries, or an estimated 5,800 
GWh. The major energy users are compressors for furnace combustion air and gas streams 
in the refinery. Large compressors can be driven by electric motors, steam turbines, or gas 
turbines. A relatively small part of energy consumption of compressors in refineries is used 
to generate compressed air. Compressed air is probably the most expensive form of energy 
available in an industrial plant because of its poor efficiency. Typically, efficiency from start 
to end-use is around 10% for compressed air systems (LBNL et al., 1998). In addition, the 
annual energy cost required to operate compressed air systems is greater than their initial 
cost. Because of this inefficiency and the sizeable operating costs, if compressed air is used, 
it should be of minimum quantity for the shortest possible time, constantly monitored and 
reweighed against alternatives. Because of its limited use in a refinery (but still an inefficient 
source of energy), the main compressed air measures found in other industries are 
highlighted. Many opportunities to reduce energy in compressed air systems are not 
prohibitively expensive; payback periods for some options are extremely short – less than 
one year.  
 
Compressed Air - Maintenance. Inadequate maintenance can lower compression 
efficiency, increase air leakage or pressure variability and lead to increased operating 
temperatures, poor moisture control and excessive contamination. Better maintenance will 
reduce these problems and save energy. Proper maintenance includes the following (LBNL 
et al., 1998, unless otherwise noted):  
 
• Blocked pipeline filters increase pressure drop. Keep the compressor and 
intercooling surfaces clean and foul-free by inspecting and periodically cleaning 
filters. Seek filters with just a 1 psi pressure drop. Payback for filter cleaning is 
usually under 2 years (Ingersoll-Rand, 2001). Fixing improperly operating filters will 
also prevent contaminants from entering into equipment and causing them to wear 
out prematurely. Generally, when pressure drop exceeds 2 to 3 psig replace the 
particulate and lubricant removal elements. Inspect all elements at least annually. 
Also, consider adding filters in parallel to decrease air velocity and, therefore, 
decrease pressure drop. A 2% reduction of annual energy consumption in 
compressed air systems is projected for more frequent filter changing (Radgen and 
Blaustein, 2001). However, one must be careful when using coalescing filters; 
efficiency drops below 30% of design flow (Scales, 2002). 
• Poor motor cooling can increase motor temperature and winding resistance, 
shortening motor life, in addition to increasing energy consumption. Keep motors 
and compressors properly lubricated and cleaned. Compressor lubricant should be 
sampled and analyzed every 1000 hours and checked to make sure it is at the proper 
level. In addition to energy savings, this can help avoid corrosion and degradation of 
the system.  
• Inspect fans and water pumps for peak performance.  
• Inspect drain traps periodically to ensure they are not stuck in either the open or 
closed position and are clean. Some users leave automatic condensate traps partially 
open at all times to allow for constant draining. This practice wastes substantial 
 
65
amounts of energy and should never be undertaken. Instead, install simple pressure 
driven valves. Malfunctioning traps should be cleaned and repaired instead of left 
open. Some automatic drains do not waste air, such as those that open when 
condensate is present. According to vendors, inspecting and maintaining drains 
typically has a payback of less than 2 years (Ingersoll-Rand, 2001).  
• Maintain the coolers on the compressor to ensure that the dryer gets the lowest 
possible inlet temperature (Ingersoll-Rand, 2001).  
• Check belts for wear and adjust them. A good rule of thumb is to adjust them every 
400 hours of operation.  
• Check water-cooling systems for water quality (pH and total dissolved solids), flow 
and temperature. Clean and replace filters and heat exchangers per manufacturer’s 
specifications.  
• Minimize leaks (see also Reduce leaks section, below).  
• Specify regulators that close when failed. 
• Applications requiring compressed air should be checked for excessive pressure, 
duration or volume. They should be regulated, either by production line sectioning or 
by pressure regulators on the equipment itself. Equipment not required to operate at 
maximum system pressure should use a quality pressure regulator. Poor quality 
regulators tend to drift and lose more air. Otherwise, the unregulated equipment 
operates at maximum system pressure at all times and wastes the excess energy. 
System pressures operating too high also result in shorter equipment life and higher 
maintenance costs.  
 
Monitoring. Proper monitoring (and maintenance) can save a lot of energy and money in 
compressed air systems. Proper monitoring includes the following (CADDET, 1997):  
• Pressure gauges on each receiver or main branch line and differential gauges across 
dryers, filters, etc. 
• Temperature gauges across the compressor and its cooling system to detect fouling 
and blockages 
• Flow meters to measure the quantity of air used 
• Dew point temperature gauges to monitor the effectiveness of air dryers 
• kWh meters and hours run meters on the compressor drive 
• Compressed air distribution systems should be checked when equipment has been 
reconfigured to be sure no air is flowing to unused equipment or obsolete parts of the 
compressed air distribution system.  
• Check for flow restrictions of any type in a system, such as an obstruction or 
roughness. These require higher operating pressures than are needed. Pressure rise 
resulting from resistance to flow increases the drive energy on the compressor by 1% 
of connected power for every 2 psi of differential (LBNL et al., 1998; Ingersoll-
Rand, 2001). Highest pressure drops are usually found at the points of use, including 
undersized or leaking hoses, tubes, disconnects, filters, regulators, valves, nozzles 
and lubricators (demand side), as well as air/lubricant separators, aftercoolers, 
moisture separators, dryers and filters.  
 
Reduce leaks (in pipes and equipment). Leaks can be a significant source of wasted 
energy. A typical plant that has not been well maintained could have a leak rate between 20 
 
66
to 50% of total compressed air production capacity (Ingersoll Rand, 2001). Leak repair and 
maintenance can sometimes reduce this number to less than 10%. Overall, a 20% reduction 
of annual energy consumption in compressed air systems is projected for fixing leaks 
(Radgen and Blaustein, 2001).  
 
The magnitude of a leak varies with the size of the hole in the pipes or equipment. A 
compressor operating 2,500 hours per year at 6 bar (87 psi) with a leak diameter of 0.02 
inches (½ mm) is estimated to lose 250 kWh/year; 0.04 in. (1 mm) to lose 1,100 kWh/year; 
0.08 in. (2 mm) to lose 4,500 kWh/year; and 0.16 in. (4 mm) to lose 11,250 kWh/year 
(CADDET, 1997).  
 
In addition to increased energy consumption, leaks can make pneumatic systems/equipment 
less efficient and adversely affect production, shorten the life of equipment, and lead to 
additional maintenance requirements and increased unscheduled downtime. Leaks cause an 
increase in compressor energy and maintenance costs. The most common areas for leaks are 
couplings, hoses, tubes, fittings, pressure regulators, open condensate traps and shut-off 
valves, pipe joints, disconnects, and thread sealants. Quick connect fittings always leak and 
should be avoided. A simple way to detect large leaks is to apply soapy water to suspect 
areas. The best way to detect leaks is to use an ultrasonic acoustic detector, which can 
recognize the high frequency hissing sounds associated with air leaks. After identification, 
leaks should be tracked, repaired, and verified. Leak detection and correction programs 
should be ongoing efforts.  
 
A retrofit of the compressed air system of a Mobil distribution facility in Vernon (CA) led to 
the replacement of a compressor by a new 50 hp compressor and the repair of air leaks in the 
system. The annual energy savings amounted to $20,700, and investments were equal to 
$23,000, leading to a payback period of just over 1 year (U.S. DOE-OIT, 2003b). 
   
Reducing the Inlet Air Temperature. Reducing the inlet air temperature reduces energy 
used by the compressor. In many plants, it is possible to reduce inlet air temperature to the 
compressor by taking suction from outside the building. Importing fresh air has paybacks of 
up to 5 years, depending on the location of the compressor air inlet (CADDET, 1997). As a 
rule of thumb, each 5°F (3°C) will save 1% compressor energy use (CADDET, 1997; 
Parekh, 2000).  
 
Maximize Allowable Pressure Dew Point at Air Intake. Choose the dryer that has the 
maximum allowable pressure dew point, and best efficiency. A rule of thumb is that 
desiccant dryers consume 7 to 14% of the total energy of the compressor, whereas 
refrigerated dryers consume 1 to 2% as much energy as the compressor (Ingersoll Rand, 
2001). Consider using a dryer with a floating dew point. Note that where pneumatic lines are 
exposed to freezing conditions, refrigerated dryers are not an option. 
 
Controls. Remembering that the total air requirement is the sum of the average air 
consumption for pneumatic equipment, not the maximum for each, the objective of any 
control strategy is to shut off unneeded compressors or delay bringing on additional 
compressors until needed. All compressors that are on should be running at full load, except 
 
67
for one, which should handle trim duty. Positioning of the control loop is also important; 
reducing and controlling the system pressure downstream of the primary receiver results in 
reduced energy consumption of up to 10% or more (LBNL et al., 1998). Radgen and 
Blaustein (2001) report energy savings for sophisticated controls to be 12% annually. 
Start/stop, load/unload, throttling, multi-step, variable speed, and network controls are 
options for compressor controls and described below.  
 
Start/stop (on/off) is the simplest control available and can be applied to small reciprocating 
or rotary screw compressors. For start/stop controls, the motor driving the compressor is 
turned on or off in response to the discharge pressure of the machine. They are used for 
applications with very low duty cycles. Applications with frequent cycling will cause the 
motor to overheat. Typical payback for start/stop controls is 1 to 2 years (CADDET, 1997).  
 
Load/unload control, or constant speed control, allows the motor to run continuously but 
unloads the compressor when the discharge pressure is adequate. In most cases, unloaded 
rotary screw compressors still consume 15 to 35% of full-load power when fully unloaded, 
while delivering no useful work (LBNL et al., 1998). Hence, load/unload controls may be 
inefficient and require ample receiver volume.  
 
Modulating or throttling controls allows the output of a compressor to be varied to meet 
flow requirements by closing down the inlet valve and restricting inlet air to the compressor. 
Throttling controls are applied to centrifugal and rotary screw compressors. Changing the 
compressor control to a variable speed control has saved up to 8% per year (CADDET, 
1997). Multi-step or part-load controls can operate in two or more partially loaded 
conditions. Output pressures can be closely controlled without requiring the compressor to 
start/stop or load/unload.  
  
Properly Sized Regulators. Regulators sometimes contribute to the biggest savings in 
compressed air systems. By properly sizing regulators, compressed air will be saved that is 
otherwise wasted as excess air. Also, it is advisable to specify pressure regulators that close 
when failing.  
 
Sizing Pipe Diameter Correctly. Inadequate pipe sizing can cause pressure losses, increase 
leaks, and increase generating costs. Pipes must be sized correctly for optimal performance 
or resized to fit the current compressor system. Increasing pipe diameter typically reduces 
annual energy consumption by 3% (Radgen and Blaustein, 2001). 
 
Heat Recovery For Water Preheating. As much as 80 to 93% of the electrical energy used 
by an industrial air compressor is converted into heat. In many cases, a heat recovery unit 
can recover 50 to 90% of the available thermal energy for space heating, industrial process 
heating, water heating, makeup air heating, boiler makeup water preheating, industrial 
drying, industrial cleaning processes, heat pumps, laundries or preheating aspirated air for 
oil burners (Parekh, 2000). Paybacks are typically less than one year. With large water-
cooled compressors, recovery efficiencies of 50 to 60% are typical (LBNL et al., 1998). 
Implementing this measure recovers up to 20% of the energy used in compressed air 
systems annually for space heating (Radgen and Blaustein, 2001).  
 
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Adjustable Speed Drives (ASDs). Implementing adjustable speed drives in rotary 
compressor systems has saved 15% of the annual compressed air energy consumption 
(Radgen and Blaustein, 2001). The profitability of installing an ASD on a compressor 
depends strongly on the load variation of the particular compressor. When there are strong 
variations in load and/or ambient temperatures there will be large swings in compressor load 
and efficiency. In those cases, or where electricity prices are relatively high (> 4 cts/kWh) 
installing an ASD may result in attractive payback periods (Heijkers et al., 2000). 
 
High Efficiency Motors. Installing high efficiency motors in compressor systems reduces 
annual energy consumption by 2%, and has a payback of less than 3 years (Radgen and 
Blaustein, 2001). For compressor systems, the largest savings in motor performance are 
typically found in small machines operating less than 10kW (Radgen and Blaustein, 2001). 
 
 
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16. Fans 
 
Fans are used in boilers, furnaces, cooling towers, and many other applications. As in other 
motor applications, considerable opportunities exist to upgrade the performance and 
improve the energy efficiency of fan systems.  Efficiencies of fan systems vary considerably 
across impeller types (Xenergy, 1998). However, the cost-effectiveness of energy efficiency 
opportunities depends strongly on the characteristics of the individual system. 
 
Fan Oversizing. Most of the fans are oversized for the particular application, which can 
result in efficiency losses of 1-5% (Xenergy, 1998). However, it may often be more cost-
effective to control the speed (see below with adjustable speed drives) than to replace the fan 
system. 
 
Adjustable Speed Drive (ASD). Significant energy savings can be achieved by installing 
adjustable speed drives on fans. Savings may vary between 14 and 49% when retrofitting 
fans with ASDs (Xnergy, 1998). 
 
An audit of the Paramount Petroleum Corp.’s asphalt refinery in Paramount (California) 
identified the opportunity to install ASDs on six motors in the cooling tower (ranging from 
40 hp to 125 hp). The motors are currently operated manually, and are oversized for 
operation in the winter. If ASDs were installed at all six motors to maintain the cold-water 
temperature set point electricity savings of 1.2 million kWh/year could be achieved (U.S. 
DOE-OIT, 2003b). The payback would vary be relatively high due to the size of the motors 
and was to be around 5.8 years, resulting in annual savings of $46,000.  
 
High Efficiency Belts (Cog Belts). Belts make up a variable, but significant portion of the 
fan system in many plants. It is estimated that about half of the fan systems use standard V-
belts, and about two-thirds of these could be replaced by more efficient cog belts (Xenergy, 
1998). Standard V-belts tend to stretch, slip, bend and compress, which lead to a loss of 
efficiency. Replacing standard V-belts with cog belts can save energy and money, even as a 
retrofit. Cog belts run cooler, last longer, require less maintenance and have an efficiency 
that is about 2% higher than standard V-belts. Typical payback periods will vary from less 
than one year to three years. 
 
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17. Lighting 
 
Lighting and other utilities represent less than 3% of electricity use in refineries. Still, 
potential energy efficiency improvement measures exist, and may contribute to an overall 
energy management strategy. Because of the relative minor importance of lighting and other 
utilities, this Energy Guide focuses on the most important measures that can be undertaken. 
Additional information on lighting guidelines and efficient practices is available from the 
Illuminating Engineering Society of North America (www.iesna.org) and the California 
Energy Commission (CEC, 2003). 
 
Lighting Controls. Lights can be shut off during non-working hours by automatic controls, 
such as occupancy sensors, which turn off lights when a space becomes unoccupied. Manual 
controls can also be used in addition to automatic controls to save additional energy in small 
areas.  
 
Replace T-12 Tubes by T-8 Tubes or Metal Halides. T-12 refers to the diameter in 1/8 
inch increments (T-12 means 12/8 inch or 3.8 cm diameter tubes). The initial output for T-
12 lights is high, but energy consumption is also high. T-12 tubes have poor efficacy, lamp 
life, lumen depreciation and color rendering index. Because of this, maintenance and energy 
costs are high. Replacing T-12 lamps with T-8 lamps approximately doubles the efficacy of 
the former. It is important to remember, however, to work both with the suppliers and 
manufacturers on the system through each step of the retrofit process. There are a number of 
T-8 lights and ballasts on the market and the correct combination should be chosen for each 
system.  
 
Ford North America paint shops retrofitted eleven of their twenty-one paint shops and saw 
lighting costs reduced by more than 50% (DEQ, 2001). Initial light levels were lower, but 
because depreciation is less, the maintained light level is equal and the new lamps last two to 
three times longer. Energy savings totaled 17.5 million kWh annually; operation savings 
were $500,000 per year. The Gillette Company manufacturing facility in Santa Monica, 
California replaced 4300 T-12 lamps with 496 metal halide lamps in addition to replacing 10 
manual switches with 10 daylight switches (EPA, 2001). They reduced electricity use by 
58% and saved $128,608 annually. The total project cost was $176,534, producing a 
payback of less than 1.5 years.  
 
Replace Mercury Lights by Metal Halide or High-Pressure Sodium Lights. In industries 
where color rendition is critical, metal halide lamps save 50% compared to mercury or 
fluorescent lamps (Price and Ross, 1989). Where color rendition is not critical, high-pressure 
sodium lamps offer energy savings of 50 to 60% compared to mercury lamps (Price and 
Ross, 1989). High-pressure sodium and metal halide lamps also produce less heat, reducing 
HVAC loads. In addition to energy reductions, the metal halide lights provide better 
lighting, provide better distribution of light across work surfaces, improve color rendition, 
and reduce operating costs (GM, 2001). 
 
Replace Standard Metal Halide HID With High-Intensity Fluorescent Lights. 
Traditional HID lighting can be replaced with high-intensity fluorescent lighting. These new 
 
71
systems incorporate high efficiency fluorescent lamps, electronic ballasts, and high-efficacy 
fixtures that maximize output to the workspace. Advantages of the new system are many: 
lower energy consumption, lower lumen depreciation over the lifetime of the lamp, better 
dimming options, faster start-up and restrike capability, better color rendition, higher pupil 
lumens ratings, and less glare (Martin et al., 2000). High-intensity fluorescent systems yield 
50% electricity savings over standard metal halide HID. Dimming controls that are 
impractical in the metal halide HIDs save significant energy in the new system. Retrofitted 
systems cost about $185 per fixture, including installation costs (Martin et al., 2000). In 
addition to energy savings and better lighting qualities, high-intensity fluorescents may help 
improve productivity and have reduced maintenance costs.  
 
Replace Magnetic Ballasts With Electronic Ballasts. A ballast is a mechanism that 
regulates the amount of electricity required to start a lighting fixture and maintain a steady 
output of light. Electronic ballasts save 12 to 25% power over their magnetic predecessors 
(EPA, 2001). Electronic ballasts have dimming capabilities as well (Eley et al., 1993). If 
automatic daylight sensing, occupancy sensing and manual dimming are included with the 
ballasts, savings can be greater than 65% (Turiel et al., 1995).  
 
Reflectors. A reflector is a highly polished "mirror-like" component that directs light 
downward, reducing light loss within a fixture. Reflectors can minimize required wattage 
effectively.  
 
Light Emitting Diodes (LEDs) or Radium Lights. One way to reduce energy costs is 
simply switching from incandescent lamps to LEDs or radium strips in exit sign lighting. 
LEDs use about 90% less energy than conventional exit signs (Anaheim Public Utilities, 
2001). A 1998 Lighting Research Center survey found that about 80 percent of exit signs 
being sold use LEDs (LRC, 2001). In addition to exit signs, LEDs are increasingly being 
used for path marking and emergency way finding systems. Their long life and cool 
operation allows them to be embedded in plastic materials, which makes them perfect for 
these applications. Radium strips use no energy at all and can be used similarly. 
 
The Flying J Refinery in North Salt Lake (Utah) replaced exit signs by new LED signs 
saving about $1,200/year.  
 
System Improvements. By combining several of the lighting measures above, light system 
improvements can be the most effective and comprehensive way to reduce lighting energy. 
High frequency ballasts and specular reflectors can be combined with 50% fewer efficient 
high-frequency fluorescent tubes and produce 90% as much light while saving 50 to 60% of 
the energy formerly used (Price and Ross, 1989). An office building in Michigan reworked 
their lighting system using high-efficiency fluorescent ballasts and reduced lighting load by 
50% and total building electrical load by nearly 10% (Price and Ross, 1989). Similar results 
were obtained in a manufacturing facility when replacing fluorescent fixtures with metal 
halide lamps. Often these system improvements improve lighting as well as decrease energy 
consumption.  
 
 
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Reducing system voltage may also save energy. One U.S. automobile manufacturer put in 
reduced voltage HID lights and found a 30% reduction in lighting. Electric City is one of the 
suppliers of EnergySaver, a unit that attaches to a central panel switch (controllable by 
computer) and constricts the flow of electricity to fixtures, thereby reducing voltage and 
saving energy, with an imperceptible loss of light. Bristol Park Industries has patented 
another lighting voltage controller called the Wattman© Lighting Voltage Controller that 
works with high intensity discharge (HID) and fluorescent lighting systems with similar 
energy saving results (Bristol Park Industries, 2002).  
   
 
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18. Power Generation 
 
Most refineries have some form of onsite power generation. In fact, refineries offer an 
excellent opportunity for energy efficient power generation in the form of combined heat 
and power production (CHP). CHP provides the opportunity to use internally generated fuels 
for power production, allowing greater independence of grip operation and even export to 
the grid. This increases reliability of supply as well as the cost-effectiveness. The cost 
benefits of power export to the grid will depend on the regulation in the state where the 
refinery is located. Not all states allow wheeling of power (i.e., sales of power directly to 
another customer using the grid for transport) while the regulation may also differ with 
respect to the tariff structure for power sales to the grid operator. 
 
18.1 Combined Heat and Power Generation (CHP) 
The petroleum refining industry is one of the largest users of cogeneration or CHP in the 
country. Current installed capacity is estimated to be over 6,000 MWe, making it the largest 
CHP user after the chemical and pulp & paper industries. Still, only about 10% of all steam 
used in refineries is generated in cogeneration units. Hence, the petroleum refining industry 
is also identified as one of the industries with the largest potential for increased application 
of CHP. In fact, an efficient refinery can be a net exporter of electricity. The potential for 
exporting electricity is even enlarged with new innovative technologies currently used 
commercially at selected petroleum refineries (discussed below). The potential for 
conventional cogeneration (CHP) installations is estimated at an additional 6,700 MWe 
(Onsite, 2000), of which most in medium to large-scale gas turbine based installations. 
 
Where process heat, steam, or cooling and electricity are used, cogeneration plants are 
significantly more efficient than standard power plants because they take advantage of what 
are losses in conventional power plants by utilizing waste heat. In addition, transportation 
losses are minimized when CHP systems are located at or near the refinery. Third parties 
have developed CHP for use by refineries. In this scenario, the third party company owns 
and operates the system for the refinery, which avoids the capital expenditures associated 
with CHP projects, but gains (part of) the benefits of a more energy efficient system of heat 
and electricity supply. In fact, about 60% of the cogeneration facilities operated within the 
refinery industry are operated by third party companies (Onsite, 2000). For example, in 2001 
BP’s Whiting refinery (Indiana) installed a new 525 MW cogeneration unit with a total 
investment of $250 million carried by Primary Energy Inc. Many new cogeneration projects 
can be financed in this way. Other opportunities consist of joint-ventures between the 
refinery and an energy generation or operator to construct a cogeneration facility. 
 
Optimization of the operation strategy of CHP units and boilers is an area in which 
additional savings can be achieved. The development of a dispatch optimization program at 
the Hellenic Aspropyrgos Refinery (Greece) to meet steam and electricity demand 
demonstrates the potential energy and cost-savings (Frangopoluos et al., 1996). 
 
For systems requiring cooling, absorption cooling can be combined with CHP to use waste 
heat to produce cooling power. In refineries, refrigeration and cooling consumes about 5-6% 
of all electricity. Cogeneration in combination with absorption cooling has been 
 
74
demonstrated for building sites and sites with refrigeration leads. The authors do not know 
of applications in the petroleum refinery industry. 
 
Innovative gas turbine technologies can make CHP more attractive for sites with large 
variations in heat demand. Steam injected gas turbines (STIG or Cheng cycle) can absorb 
excess steam, e.g., due to seasonal reduced heating needs, to boost power production by 
injecting the steam in the turbine. The size of typical STIGs starts around 5 MWe, and is 
currently scaled up to sizes of 125 MW. STIGs have been installed at over 50 sites 
worldwide, and are found in various industries and applications, especially in Japan and 
Europe, as well as in the United States. Energy savings and payback period will depend on 
the local circumstances (e.g., energy patterns, power sales, conditions). In the United States, 
the Cheng Cycle is marketed by International Power Systems (San Jose, California). The 
Austrian oil company OMV has considered the use of a STIG to upgrade an existing 
cogeneration system. The authors do not know of any current commercial applications of 
STIG in an oil refinery. 
 
Steam turbines are often used as part of the CHP system in a refinery or as stand-alone 
systems for power generation. The efficiency of the steam turbine is determined by the inlet 
steam pressure and temperature as well as the outlet pressure. Each turbine is designed for a 
certain steam inlet pressure and temperature, and operators should make sure that the steam 
inlet temperature and pressure are optimal. An 18°F decrease in steam inlet temperature will 
reduce the efficiency of the steam turbine by 1.1% (Patel and Nath, 2000). Similarly, 
maintaining exhaust vacuum of a condensing turbine or the outlet pressure of a backpressure 
turbine too high will result in efficiency losses.  
 
Valero’s Houston refinery constructed a 34 MW cogeneration unit in 1990, using two gas 
turbines and two heat recovery steam generators (boilers). The system supplies all electricity 
for the refinery and occasionally allows export to the grid. The CHP system has resulted in 
savings of about $55,000/day (Valero, 2003).  
 
Even for small refineries, CHP is an attractive option. An audit of the Paramount Petroleum 
Corp.’s asphalt refinery in Paramount (CA) identified the opportunity to install CHP at this 
refinery. The audit identified a CHP unit as the largest energy saving measure in this small 
refinery. A 6.5 MWe gas turbine CHP unit would result in annual energy savings of $3.8 
million and has a payback period 2.5 years (U.S. DOE-OIT, 2003b). In addition, the CHP 
unit would reduce the risk of power outages for the refinery. The investment costs assume 
best available control technology for emission reduction. The installation was installed in 
2002. 
 
18.2 Gas Expansion Turbines 
Natural gas is often delivered to a refinery at very high pressures. Gas is transmitted at high 
pressures, from 200 to 1500 psi. Expansion turbines use the pressure drop when natural gas 
from high-pressure pipelines is decompressed to generate power or to use in a process 
heater. An expansion turbine includes both an expansion mechanism and a generator. In an 
expansion turbine, high-pressure gas is expanded to produce work. Energy is extracted from 
pressurized gas, which lowers gas pressure and temperature. These turbines have been used 
 
75
for air liquefaction in the chemical industry for several decades. The application of 
expansion turbines as energy recovery devices started in the early 1980s (SDI, 1982b). The 
technology has much improved since the 1980s and is highly reliable today. A simple 
expansion turbine consists of an impeller (expander wheel) and a shaft and rotor assembly 
attached to a generator. Expansion turbines are generally installed in parallel with the 
regulators that traditionally reduce pressure in gas lines. If flow is too low for efficient 
generation, or the expansion turbine fails, pressure is reduced in the traditional manner. The 
drop in pressure in the expansion cycle causes a drop in temperature. While turbines can be 
built to withstand cold temperatures, most valve and pipeline specifications do not allow 
temperatures below –15°C. In addition, gas can become wet at low temperatures, as heavy 
hydrocarbons in the gas condense. This necessitates heating the gas just before or after 
expansion. The heating is generally performed with either a combined heat and power 
(CHP) unit, or a nearby source of waste heat. Petroleum refineries often have excess low-
temperature waste heat, making a refinery an ideal location for a power recovery turbine. 
Industrial companies and utilities in Europe and Japan have installed expansion turbine 
projects. However, it is unknown if any petroleum refineries have installed this technology. 
 
In 1994, the Corus integrated steel mill at IJmuiden (the Netherlands) installed a 2 MW 
power recovery turbine. The mill receives gas at 930 psi, preheats the gas, and expands with 
the turbine to 120 psi.  The maximum turbine flow is 1.4 million ft3/hr (40,000 m3/hr) while 
the average capacity is 65%, resulting in an average flow of 0.9 million ft3/hr. The turbine 
uses cooling water from the hot strip mill of approximately 160°F (70 °C), to preheat the gas 
(Lehman and Worrell, 2001). The 2 MW turbine generated roughly 11,000 MWh of 
electricity in 1994, while the strip mill delivered a maximum of 12,500 MWh of waste heat 
to the gas flow.  Thus, roughly 88% of the maximum heat input to the high-pressure gas 
emerged as electricity. The cost of the installation was $2.6 million, and the operation and 
maintenance costs total $110,000 per year.  With total costs of $110,000 per year and 
income of $710,000 per year from electricity generation (at the 1994 Dutch electricity cost 
of 6.5 cents per kWh), the payback period for the project is 4.4 years. 
 
18.3 Steam Expansion Turbines.  
Steam is generated at high pressures, but often the pressure is reduced to allow the steam to 
be used by different processes. For example, steam is generated at 120 to 150 psig. This 
steam then flows through the distribution system within the plant. The pressure is reduced to 
as low as 10-15 psig for use in different process. Once the heat has been extracted, the 
condensate is often returned to the steam generating plant. Typically, the pressure reduction 
is accomplished through a pressure reduction valve (PRV). These valves do not recover the 
energy embodied in the pressure drop. This energy could be recovered by using a micro 
scale backpressure steam turbine. Several manufactures produce these turbine sets, such as 
Turbosteam (previously owned by Trigen) and Dresser-Rand.  
 
The potential for application will depend on the particular refinery and steam system used. 
Applications of this technology have been commercially demonstrated for campus facilities, 
pulp and paper, food, and lumber industries, but not yet in the petroleum industry. The 
investments of a typical expansion turbine are estimated at 600 $/kWe, and operation and 
maintenance costs at 0.011 $/kWh. 
 
76
18.4 High-temperature CHP 
Turbines can be pre-coupled to a crude distillation unit (or other continuously operated 
processes with an applicable temperature range). The offgases of the gas turbine can be used 
to supply the heat for the distillation furnace, if the outlet temperature of the turbine is high 
enough. One option is the so-called `repowering' option. In this option, the furnace is not 
modified, but the combustion air fans in the furnace are replaced by a gas turbine. The 
exhaust gases still contain a considerable amount of oxygen, and can thus be used as 
combustion air for the furnaces. The gas turbine can deliver up to 20% of the furnace heat. 
Two of these installations are installed in the Netherlands, with a total capacity of 35 MWe 
at refineries (Worrell et al., 1997). A refinery on the West Coast has installed a 16 MWe gas 
turbine at a reformer (Terrible et al., 1999). The flue gases of the turbine feed to the 
convection section of the reformer increasing steam generation. The steam is used to power 
a 20 MWe steam turbine. 
 
Another option, with a larger CHP potential and associated energy savings, is “high-
temperature CHP”. In this case, the flue gases of a CHP plant are used to heat the input of a 
furnace or to preheat the combustion air. The potential at U.S. refineries is estimated at 34 
GW (Zollar, 2002). This option requires replacing the existing furnaces. This is due to the 
fact that the radiative heat transfer from gas turbine exhaust gases is much smaller than from 
combustion gases, due to their lower temperature (Worrell et al., 1997). A distinction is 
made between two different types. In the first type, the exhaust heat of a gas turbine is led to 
a waste heat recovery furnace, in which the process feed is heated. In the second type, the 
exhaust heat is led to a “waste heat oil heater” in which thermal oil is heated. By means of a 
heat exchanger, the heat content is transferred to the process feed. In both systems, the 
remaining heat in the exhaust gases after heating the process feed should be used for lower 
temperature purposes to achieve a high overall efficiency. The second type is more reliable, 
due to the fact that a thermal oil buffer can be included. The main difference is that in the 
first type the process feed is directly heated by exhaust gases, where the second uses thermal 
oil as an intermediate, leading to larger flexibility. An installation of the first type is installed 
in Fredericia, Denmark at a Shell refinery. The low temperature remaining heat is used for 
district heating. R&D has to be aimed at making detailed design studies for specific 
refineries and the optimization of furnace design, and more demonstration projects have to 
be carried out.  
 
18.5 Gasification  
Gasification provides the opportunity for cogeneration using the heavy bottom fraction and 
refinery residues (Marano, 2003). Because of the increased demand for lighter products and 
increased use of conversion processes, refineries will have to manage an increasing stream 
of heavy bottoms and residues. Gasification of the heavy fractions and coke to produce 
synthesis gas can help to efficiently remove these by-products. The state-of-the-art 
gasification processes combine the heavy by-products with oxygen at high temperature in an 
entrained bed gasifier. Due to the limited oxygen supply, the heavy fractions are gasified to 
a mixture of carbon monoxide and hydrogen. Sulfur can easily be removed in the form of 
H2S to produce elemental sulfur. The synthesis gas can be used as feedstock for chemical 
processes. However, the most attractive application seems to be generation of power in an 
Integrated Gasifier Combined Cycle (IGCC). In this installation the synthesis gas is 
 
77
combusted in a gas turbine (with an adapted combustion chamber to handle the low to 
medium-BTU gas) generating electricity. The hot fluegases are used to generate steam. The 
steam can be used onsite or used in a steam turbine to produce additional electricity (i.e., the 
combined cycle). Cogeneration efficiencies can be up to 75% (LHV) and for power 
production alone the efficiency is estimated at 38-39% (Marano, 2003). 
 
Entrained bed IGCC technology is originally developed for refinery applications, but is also 
used for the gasification of coal. Hence, the major gasification technology developers were 
oil companies like Shell and Texaco. IGCC provides a low-cost opportunity to reduce 
emissions (SOx, NOx) when compared to combustion of the residue, and to process the 
heavy bottoms and residues while producing power and/or feedstocks for the refinery. 
Potentially about 40 refineries in the United States have a sufficiently large capacity to make 
the technology attractive (Marano, 2003).  
 
IGCC is used by the Shell refinery in Pernis (the Netherlands) to treat residues from the 
hydrocracker and other residues to generate 110 MWe of power and 285 tonnes of hydrogen 
for the refinery. The IPA Falconara refinery (Italy) uses IGCC to treat visbreaker residue to 
produce 241 MWe of power (Cabooter, 2001). New installations have been announced or 
are under construction for the refineries at Baytown (ExxonMobil, Texas), Deer Park (Shell, 
Texas), Sannazzaro (Agip, Italy), Lake Charles, (Citgo, Louisiana) and Bulwer Island (BP, 
Australia).   
 
The investment costs will vary by capacity and products of the installation. The capital costs 
of a gasification unit consuming 2,000 tons per day of heavy residue would cost about $229 
million of the production of hydrogen and $347 million for an IGCC unit. The operating 
cost savings will depend on the costs of power, natural gas, and the costs of heavy residue 
disposal or processing.   
 
 
78
19. Other Opportunities 
 
19.1 Process Changes and Design 
Desalter. Alternative designs for desalting include multi-stage desalters and combination of 
AC and DC fields. These alternative designs may lead to increased efficiency and lower 
energy consumption (IPPC, 2002). 
 
 
 
 
Catalytic Reformer - Increased Product Recovery. Product recovery from a reformer 
may be limited by the temperature of the distillation to separate the various products. An 
analysis of a reformer at the Colorado Refinery in Commerce City, Colorado (now operated 
by Valero) showed increased LPG losses at increased summer temperatures. The LPG 
would either be flared or used as fuel gas. By installing a waste heat driven ammonia 
absorption refrigeration plant, the recovery temperature was lowered, debottlenecking the 
compressors and the unsaturated light-cycle oil streams (Petrick and Pellegrino, 1999). The 
heat pump uses a 290°F waste heat stream of the reformer to drive the compressor. The 
system was installed in 1997 and was supported by the U.S. Department of Energy as a 
demonstration project. The project resulted in annual savings of 65,000 barrels of LPG. The 
recovery rate varies with ambient temperature. The liquid product fraction contained a 
higher percentage of heavier carbon chain (C , C
5
6+) products. The payback period is 
estimated at 1.5 years (Brant et al., 1998). 
 
Hydrotreater. Desulfurization is becoming more and more important as probable future 
regulations will demand a lower sulfur content of fuels. Desulfurization is currently mainly 
done by hydrotreaters. Hydrotreaters use a considerable amount of energy directly (fuel, 
steam, electricity) and indirectly (hydrogen). Various alternatives are being developed, but 
of which many are not yet commercially available (Babich and Moulijn, 2003). New 
catalysts increase the efficiency of sulfur removal, while new reactor designs are proposed 
to integrate some of the process steps (e.g., catalytic distillation as used in the CDTech 
process implemented at Motiva’s Port Arthur (TX) refinery. In the future, designs building 
on process intensification that integrate chemical reactions and separation are proposed. Use 
of any alternative desulfurization technology to produce low sulfur should be evaluated on 
the basis of the sulfur content of the naphtha and diesel streams, and on the applicability of 
the process to the specific conditions of the refinery. 
 
Various alternatives are demonstrated at refineries around the world, including the oxidative 
desulfurization process (Valero’s Krotz Springs, Louisiana) and the S Zorb process at 
Philip’s Borger (TX). The S Zorb process is a sorbent operated in a fluidized bed reactor. 
Philips Petroleum Co. claims a significant reduction in hydrogen consumption to produce 
low-sulfur gasoline and diesel (Gislason, 2001). A cursory comparison of the characteristics 
of the S Zorb process and that of selected hydrotreaters suggests a lower fuel and electricity 
consumption, but increased water consumption.  
 
19.2 Alternative Production Flows 
FCC - Process Flow Changes. The product quality demands and feeds of FCCs may 
change over time. The process design should remain optimized for this change. Increasing or 
changing the number of pumparounds can improve energy efficiency of the FCC, as it 
 
79
allows increased heat recovery (Golden and Fulton, 2000). A change in pumparounds may 
affect the potential combinations of heat sinks and sources.  
 
New design and operational tools enable the optimization of FCC operating conditions to 
enhance product yields. Petrick and Pellegrino (1999) cite studies that have shown that 
optimization of the FCC unit with appropriate modifications of equipment and operating 
conditions can increase the yield of high octane gasoline and alkylate from 3% to 7% per 
barrel of crude oil. This would result in energy savings. 
 
19.3 Other Opportunities 
Flare Optimization. Flares are used to safely dispose of combustible gases and to avoid 
release to the environment of these gases through combustion/oxidation. All refineries 
operate flares. Which, in the majority of refineries are used to burn gases in the case of a 
system upset. Older flare systems have a pilot flame that is burning continuously. This 
results in losses of natural gas. Also, this may lead to methane (a powerful greenhouse gas) 
losses to the environment if the pilot flame is extinguished.  
 
Modern flare pilot designs are more efficient using electronic ignition when the flare is 
needed, have sensors for flame detection and shut off the fuel gas, reducing methane 
emissions. These systems can reduce average natural gas use to below 45 scf/hour. The 
spark ignition systems use low electrical power, which can be supplied by photovoltaic 
(solar cell) system, making the whole system independent of an external power supply. 
Various systems are marketed by a number of suppliers, e.g., John Zink.  
 
Chevron replaced a continuous burning flare by an electronic ignition system at a refinery, 
which resulted in savings of 1.68 million scf/year (or 168 MBtu/year), with a payback off 
less than 3 years. 
  
Heated Storage Tanks. Some storage tanks at the refinery are kept at elevated temperatures 
to control viscosity of the product stored. Insulation of the tank can reduce the energy losses.  
 
An audit of the Fling J Refinery at North Salt Lake (Utah) found that insulating the top of a 
80,000 bbl storage tank that is heated to a temperature of 225°F would result in annual 
savings of $148,000 (Brueske et al., 2002). 
 
80
20. Summary and Conclusions 
 
Petroleum refining in the United States is the largest refining industry in the world, 
providing inputs to virtually any economic sector, including the transport sector and the 
chemical industry. The industry operates 146 refineries (as of 2004) around the country, 
employing over 65,000 employees. The refining industry produces a mix of products with a 
total value exceeding $151 billion. Energy costs represents one the largest production cost 
factors in the petroleum refining industry, making energy efficiency improvement an 
important way to reduce costs and increase predictable earnings, especially in times of high 
energy-price volatility.  
 
Voluntary government programs aim to assist industry to improve competitiveness through 
increased energy efficiency and reduced environmental impact. ENERGY STAR, a 
voluntary program managed by the U.S. Environmental Protection Agency, stresses the need 
for strong and strategic corporate energy management programs. ENERGY STAR provides 
energy management tools and strategies for successful corporate energy management 
programs. This Energy Guide describes research conducted to support ENERGY STAR and 
its work with the petroleum refining industry. This research provides information on 
potential energy efficiency opportunities for petroleum refineries.  
 
Competitive benchmarking data indicates that most petroleum refineries can economically 
improve energy efficiency by 10-20%. This potential for savings amounts to annual costs 
savings of millions to tens of millions of dollars for a refinery, depending on current 
efficiency and size. Improved energy efficiency may result in co-benefits that far outweigh 
the energy cost savings, and may lead to an absolute reduction in emissions.  
 
This Energy Guide introduced energy efficiency opportunities available for petroleum 
refineries. It started with descriptions of the production trends, structure and production of 
the refining industry and the energy used in the refining and conversion processes. Specific 
energy savings for each energy efficiency measure based on case studies of plants and 
references to technical literature were provided. The Energy Guide draws upon the 
experiences with energy efficiency measures of petroleum refineries worldwide. If available, 
typical payback periods were also listed.  
 
The findings suggest that given available resources and technology, there are opportunities 
to reduce energy consumption cost-effectively in the petroleum refining industry while 
maintaining the quality of the products manufactured, underling the results of benchmarking 
studies. Further research on the economics of the measures, as well as the applicability of 
these to different refineries, is needed to assess the feasibility of implementation of selected 
technologies at individual plants. Table 8 summarizes the energy efficiency opportunities.  
 
 
81
Table 8. Summary of energy efficiency opportunities for utilities and cross-cutting 
energy uses. 
Management & Control 
Process Integration 
Energy monitoring 
Total site pinch analysis 
Site energy control systems 
Water pinch analysis 
Power Generation 
Energy Recovery 
CHP (cogeneration) 
Flare gas recovery 
Gas expansion turbines 
Power recovery 
High-Temperature CHP 
Hydrogen recovery 
Gasification (Combined Cycle) 
Hydrogen pinch analysis 
Boilers 
Steam Distribution 
Boiler feedwater preparation 
Improved insulation 
Improved boiler controls 
Maintain insulation 
Reduced flue gas volume 
Improved steam traps 
Reduced excess air 
Maintain steam traps 
Improve insulation 
Automatic monitoring steam traps 
Maintenance 
Leak repair 
Flue gas heat recovery 
Recover flash steam 
Blowdown heat recovery 
Return condensate 
Reduced standby losses 
Heaters and Furnaces 
Distillation 
Maintenance 
Optimized operation procedures 
Draft control 
Optimized product purity 
Air preheating 
Seasonal pressure adjustments 
Fouling control 
Reduced reboiler duty 
New burner designs 
Upgraded column internals 
Compressed Air 
Pumps 
Maintenance 
Operations & maintenance 
Monitoring 
Monitoring 
Reduce leaks 
More efficient pump designs 
Reduce inlet air temperature 
Correct sizing of pumps 
Maximize allowable pressure dewpoint 
Multiple pump use 
Controls 
Trimming impeller 
Properly sized regulators 
Controls 
Size pipes correctly 
Adjustable speed drives 
Adjustable speed drives 
Avoid throttling valves 
Heat recovery for water preheating 
Correct sizing of pipes 
 
Reduce leaks 
Sealings 
Dry vacuum pumps 
Motors 
Fans 
Proper sizing of motors 
Properly sizing 
High efficiency motors 
Adjustable speed drives 
Power factor control 
High-efficiency belts 
Voltage unbalance 
Adjustable speed drives 
Variable voltage controls 
Replace belt drives 
Lighting 
High-intensity fluorescent (T5) 
Lighting controls 
Electronic ballasts 
T8 Tubes 
Reflectors 
Metal halides/High-pressure sodium 
LED exit signs 
 
82
Table 9. Summary of process-specific energy efficiency opportunities. 
Desalter 
Hydrocracker 
Multi-stage desalters 
Power recovery 
Combined AC/DC fields 
Process integration (pinch) 
Furnace controls 
Air preheating 
Optimization distillation 
Coking 
CDU 
Process integration (pinch) 
Process controls 
Furnace controls 
High-temperature CHP 
Air preheating 
Process integration (pinch) 
Furnace controls 
Air preheating 
Progressive crude distillation 
Optimization distillation 
Visbreaker 
VDU 
Process integration (pinch) 
Process controls 
Optimization distillation 
Process integration (pinch) 
Furnace controls 
Air preheating 
Optimization distillation 
Alkylation 
Hydrotreater 
Process controls 
Process controls 
Process integration (pinch) 
Process integration (pinch) 
Optimization distillation 
Optimization distillation 
New hydrotreater designs 
Hydrogen Production 
Catalytic Reformer 
Process integration (pinch) 
Process integration (pinch) 
Furnace controls 
Furnace controls 
Air preheating 
Air preheating 
Adiabatic pre-reformer 
Optimization distillation 
Other 
FCC 
Optimize heating storage tanks 
Process controls 
Optimize flares 
Power recovery 
Process integration (pinch) 
Furnace controls 
Air preheating 
Optimization distillation 
Process flow changes 
 
83
Acknowledgements  
 
This work was supported by the Climate Protection Partnerships Division of the U.S. 
Environmental Protection Agency as part of its ENERGY STAR program through the U.S. 
Department of Energy under Contract No. DE-AC03-76SF00098. 
 
Many people inside and outside the industry provided helpful insights in the preparation of 
this Energy Guide. We would like to thank Brian Eidt and staff at ExxonMobil, F.L. Oaks 
(Marathon Ashland), and Marc Taylor (Shell) for the review of the draft report. We would 
like to thank Susan Gustofson (Valero) and Chaz Lemmon (ConocoPhillips) for providing 
insights into the petroleum refining industry in California.  We also like to thank Gunnar 
Hovstadius (ITT Fluid Technology) for his review and help, as well as Elizabeth Dutrow 
(U.S. Environmental Protection Agency), Don Hertkorn and Fred Schoeneborn for their 
review of earlier drafts of the report. Despite all their efforts, any remaining errors are the 
responsibility of the authors. The views expressed in this paper do not necessarily reflect 
those of the U.S. Environmental Protection Agency, the U.S. Department of Energy or the 
U.S. Government. 
 
84
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93
Appendix A: Active refineries in the United States as of January 2003 
 
Company 
Site 
State 
Capacity 
(b/cd) 
Company 
Share 
Share 
- Total 
company 
(b/cd) 
67,500 
0.4%
Age Refining & 
Marketing 
Big Spring  
Texas 
58,500 
0.3% 
 
 
San Antonio  
Texas 
9,000 
0.1% 
30,000 
0.2%
American 
International Rfy 
Inc 
Lake Charles  
Louisiana 
30,000 
0.2% 
10,000 
0.1%
American Refining 
Group Inc. 
Bradford  
Pennsylvania 
10,000 
0.1% 
175,068 
1.0%
Atofina 
Petrochemicals Inc. 
Port Arthur  
Texas 
175,068 
1.0% 
1,519,200 
9.0%
BP 
Ferndale 
(Cherry Point)  
Washington 
225,000 
1.3% 
 
 
Kuparuk  
Alaska 
16,000 
0.1% 
 
 
Prudhoe Bay 
Alaska 
14,200 
0.1% 
 
 
Toledo  
Ohio 
157,000 
0.9% 
 
 
Whiting  
Indiana 
410,000 
2.4% 
 
 
Texas City  
Texas 
437,000 
2.6% 
 
 
Los Angeles  
California 
260,000 
1.5% 
29,400 
0.2%
Calcasieu Refining 
Co. 
Lake Charles  
Louisiana 
29,400 
0.2% 
67,520 
0.4%
Calumet Lubricants 
Co. LP 
Cotton Valley  Louisiana 
13,020 
0.1% 
 
  
Princeton  
Louisiana 
8,300 
0.0% 
 
  
Shreveport 
Louisiana 
46,200 
0.3% 
55,000 
0.3%
Cenex Harvest 
States Coop 
Laurel  
Montana 
55,000 
0.3% 
182,500 
1.1%
Chalmette Refining 
LLC 
Chalmette  
Louisiana 
182,500 
1.1% 
1,079,000 
6.4%
ChevronTexaco 
El Paso  
Texas 
90,000 
0.5% 
 
  
El Segundo  
California 
260,000 
1.5% 
 
  
Honolulu  
Hawaii 
54,000 
0.3% 
 
  
Pascagoula  
Mississippi 
325,000 
1.9% 
 
  
Perth Amboy  
New Jersey 
80,000 
0.5% 
 
  
Richmond  
California 
225,000 
1.3% 
 
  
Salt Lake City  Utah 
45,000 
0.3% 
510,000 
3.0%
Citgo 
Corpus Christi  Texas 
156,000 
0.9% 
 
  
Lake Charles  
Louisiana 
326,000 
1.9% 
 
  
Savannah  
Georgia 
28,000 
0.2% 
 
94
 
Company 
- Total 
(b/cd) 
Share 
company
Company 
Site 
State 
Capacity 
(b/cd) 
Share 
142,287 
0.8%
Coastal Eagle Point 
Oil Co. 
Westville 
New Jersey 
142,287 
0.8% 
2,263,200 
13.4%
ConocoPhilips 
Arroyo Grande California 
41,800 
0.2% 
 
  
Belle Chasse 
Louisiana 
253,500 
1.5% 
 
  
Billings  
Montana 
60,000 
0.4% 
 
  
Borger  
Texas 
143,800 
0.9% 
 
  
Commerce 
City  
Colorado 
60,000 
0.4% 
 
  
Ferndale 
(Cherry Point)  
Washington 
92,000 
0.5% 
 
  
Linden 
New Jersey 
250,000 
1.5% 
 
  
Ponca City  
Oklahoma 
194,000 
1.2% 
 
  
Rodeo 
California 
73,200 
0.4% 
 
  
Sweeny  
Texas 
213,000 
1.3% 
 
  
Trainer 
Pennsylvania 
180,000 
1.1% 
 
  
Westlake  
Louisiana 
252,000 
1.5% 
 
  
Wilmington 
California 
136,600 
0.8% 
 
  
Wood River 
Illinois 
288,300 
1.7% 
 
  
Woods Cross  
Utah 
25,000 
0.1% 
23,000 
0.1%
Countrymark 
Cooperative Inc. 
Mount Vernon  Indiana 
23,000 
0.1% 
6,800 
0.0%
Cross Oil Refining 
and Mktg, Inc. 
Smackover  
Arkansas 
6,800 
0.0% 
100,000 
0.6%
Crown Central 
Petroleum Corp. 
Pasadena  
Texas 
100,000 
0.6% 
14,000 
0.1%
Edgington Oil Co. 
Long Beach  
California 
14,000 
0.1% 
42,400 
0.3%
Ergon Refining Inc. 
Vicksburg  
Mississippi 
23,000 
0.1% 
 
  
Newell 
(Congo)  
West Virginia 
19,400 
0.1% 
1,823,000 
10.8%
ExxonMobil 
Baton Rouge  
Louisiana 
491,000 
2.9% 
 
  
Baytown 
Texas 
516,500 
3.1% 
 
  
Beaumont  
Texas 
348,500 
2.1% 
 
  
Billings  
Montana 
58,000 
0.3% 
 
  
Joliet  
Illinois 
238,000 
1.4% 
 
  
Mobile Bay 
Alabama 
22,000 
0.1% 
 
  
Torrance  
California 
149,000 
0.9% 
112,000 
0.7%
Farmland Industries 
Inc. 
Coffeyville  
Kansas 
112,000 
0.7% 
524,980 
3.1%
Flint Hills Resources 
LP 
Corpus Christi  Texas 
259,980 
1.5% 
 
  
Saint Paul  
Minnesota 
265,000 
1.6% 
25,000 
0.1%
Flying J Inc. 
North Salt 
Lake  
Utah 
25,000 
0.1% 
5,000 
0.0%
Foreland Refining 
Corp. 
Eagle Springs  Nevada 
5,000 
0.0% 
149,000 
0.9%
Frontier Refg Inc. 
Cheyenne  
Wyoming 
46,000 
0.3% 
 
  
El Dorado  
Kansas 
103,000 
0.6% 
 
95
 
Company 
- Total 
(b/cd) 
Share 
company
Company 
Site 
State 
Capacity 
(b/cd) 
Share 
96,200 
0.6%
Giant Refining Co. 
Bloomfield  
New Mexico 
16,800 
0.1% 
 
  
Gallup  
New Mexico 
20,800 
0.1% 
 
  
Yorktown  
Virginia 
58,600 
0.3% 
880 
0.0%
Haltermann 
Products 
Channelview 
Texas 
880 
0.0% 
35,000 
0.2%
Hunt Refining Co. 
Tuscaloosa  
Alabama 
35,000 
0.2% 
25,000 
0.1%
Kern Oil & Refining 
Co. 
Bakersfield  
California 
25,000 
0.1% 
55,000 
0.3%
La Gloria Oil & Gas 
Co. 
Tyler  
Texas 
55,000 
0.3% 
63,000 
0.4%
Lion Oil Co. 
El Dorado  
Arkansas 
63,000 
0.4% 
8,500 
0.1%
Lunday Thagard 
South Gate  
California 
8,500 
0.1% 
270,200 
1.6%
Lyondell Citgo 
Refining Co. Ltd. 
Houston  
Texas 
270,200 
1.6% 
935,000 
5.5%
Marathon Ashland 
Petro LLC 
Canton  
Ohio 
73,000 
0.4% 
 
  
Catlettsburg  
Kentucky 
222,000 
1.3% 
 
  
Detroit  
Michigan 
74,000 
0.4% 
 
  
Garyville  
Louisiana 
232,000 
1.4% 
 
  
Robinson  
Illinois 
192,000 
1.1% 
 
  
Saint Paul Park Minnesota 
70,000 
0.4% 
 
  
Texas City  
Texas 
72,000 
0.4% 
7,000 
0.0%
Montana Refining 
Co. 
Great Falls  
Montana 
7,000 
0.0% 
879,700 
5.2%
Motiva Enterprises 
LLC 
Convent  
Louisiana 
235,000 
1.4% 
 
  
Delaware City  Delaware 
175,000 
1.0% 
 
  
Norco  
Louisiana 
219,700 
1.3% 
 
  
Port Arthur  
Texas 
250,000 
1.5% 
128,000 
0.8%
Murphy Oil U.S.A. 
Inc. 
Meraux  
Louisiana 
95,000 
0.6% 
 
  
Superior  
Wisconsin 
33,000 
0.2% 
58,000 
0.3%
Navajo Refining Co. Artesia  
New Mexico 
58,000 
0.3% 
81,200 
0.5%
NCRA 
McPherson  
Kansas 
81,200 
0.5% 
50,000 
0.3%
Paramount 
Petroleum Corp. 
Paramount  
California 
50,000 
0.3% 
160,000 
0.9%
PDV Midwest 
Refining LLC 
Lemont 
(Chicago)  
Illinois 
160,000 
0.9% 
68,000 
0.4%
Petro Star Inc. 
North Pole  
Alaska 
18,000 
0.1% 
 
  
Valdez  
Alaska 
50,000 
0.3% 
48,500 
0.3%
Placid Refining Co. 
Port Allen  
Louisiana 
48,500 
0.3% 
416,500 
2.5%
Premcor Refg Group 
Inc 
Lima  
Ohio 
161,500 
1.0% 
 
  
Port Arthur  
Texas 
255,000 
1.5% 
 
96
 
Company 
- Total 
(b/cd) 
Share 
company
Company 
Site 
State 
Capacity 
(b/cd) 
Share 
24,300 
0.1%
San Joaquin Refining 
Co Inc. 
24,300 
0.1% 
Bakersfield  
California 
932,800 
5.5%
Shell 
Anacortes  
Washington 
140,800 
0.8% 
 
  
Bakersfield  
California 
65,000 
0.4% 
 
  
Deer Park  
Texas 
333,700 
2.0% 
 
  
Martinez  
California 
154,800 
0.9% 
 
  
Saint Rose  
Louisiana 
55,000 
0.3% 
 
  
Saraland 
(Mobile)  
Alabama 
85,000 
0.5% 
 
  
Wilmington  
California 
98,500 
0.6% 
19,000 
0.1%
Silver Eagle Refining Evanston  
Wyoming 
3,000 
0.0% 
 
  
Woods Cross  
Utah 
11,000 
0.1% 
150,195 
0.9%
Sinclair Oil Corp. 
Evansville 
(Casper)  
Wyoming 
22,500 
0.1% 
 
  
Sinclair  
Wyoming 
62,000 
0.4% 
 
 
Tulsa  
Oklahoma 
65,695 
0.4% 
5,500 
0.0%
Somerset Refinery 
Inc. 
Somerset  
Kentucky 
5,500 
0.0% 
16,800 
0.1%
Southland Oil Co. 
Lumberton  
Mississippi 
5,800 
0.0% 
 
 
Sandersville  
Mississippi 
11,000 
0.1% 
730,000 
4.3%
Sunoco Inc. 
Marcus Hook  
Pennsylvania 
175,000 
1.0% 
 
  
Toledo  
Ohio 
140,000 
0.8% 
 
  
Tulsa  
Oklahoma 
85,000 
0.5% 
 
  
Philadelphia  
Pennsylvania 
330,000 
2.0% 
2,800 
0.0%
Tenby Inc. 
Oxnard  
California 
2,800 
0.0% 
570,500 
3.4%
Tesoro 
Anacortes  
Washington 
115,000 
0.7% 
 
  
Ewa Beach  
Hawaii 
93,500 
0.6% 
 
  
Mandan 
North 
Dakota 
58,000 
0.3% 
 
  
Martinez 
California 
166,000 
1.0% 
 
  
Salt Lake City Utah 
58,000 
0.3% 
 
  
Kenai  
Alaska 
80,000 
0.5% 
35,150 
0.2%
U.S. Oil & Refining 
Co. 
Tacoma  
Washington 
35,150 
0.2% 
80,887 
0.5%
Ultramar Inc. 
Wilmington 
California 
80,887 
0.5% 
65,000 
0.4%
United Refining Co. 
Warren  
Pennsylvania 
65,000 
0.4% 
 
97
 
Company 
- Total 
(b/cd) 
Share 
company
Company 
Site 
State 
Capacity 
(b/cd) 
Share 
1,416,000 
8.4%
Valero Energy Corp. Ardmore 
Oklahoma 
85,000 
0.5% 
 
  
Benicia 
California 
144,000 
0.9% 
 
  
Corpus Christi Texas 
134,000 
0.8% 
 
  
Denver 
Colorado 
28,000 
0.2% 
 
  
Houston  
Texas 
83,000 
0.5% 
 
  
Krotz Springs  Louisiana 
83,000 
0.5% 
 
  
Paulsboro  
New Jersey 
167,000 
1.0% 
 
  
St. Charles 
Louisiana 
155,000 
0.9% 
 
  
Sunray 
(McKee) 
Texas 
155,000 
0.9% 
 
  
Texas City  
Texas 
215,000 
1.3% 
 
  
Three Rivers 
Texas 
90,000 
0.5% 
 
  
Wilmington 
California 
77,000 
0.5% 
407,513 
2.4%
Williams 
North Pole  
Alaska 
227,513 
1.3% 
 
  
Memphis  
Tennessee 
180,000 
1.1% 
52,500 
0.3%
Wynnewood 
Refining Co. 
Wynnewood  
Oklahoma 
52,500 
0.3% 
12,500 
0.1%
Wyoming Refining 
Co. 
Newcastle  
Wyoming 
12,500 
0.1% 
5,400 
0.0%
Young Refining 
Corp. 
Douglasville  
Georgia 
5,400 
0.0% 
 
 
98
 
99
Appendix B: Employee Tasks for Energy Efficiency 
 
One of the key steps to a successful energy management program is the involvement of all 
personnel. Staff may be trained in both skills and the general approach to energy efficiency 
in day-to-day practices. Personnel at all levels should be aware of energy use and objectives 
for efficiency. By passing information to everyone, each employee may be able to save 
energy every day. In addition, performance results should be regularly evaluated and 
communicated to all personnel, recognizing high performers. Examples of some simple 
tasks employees can do include the following (Caffal, 1995):  
 
• Report leaks of water (both process water and dripping taps), steam and compressed 
air and ensure they are repaired quickly. 
• Check to make sure the pressure and temperature of equipment is not set too high. 
• Carry out regular maintenance of energy consuming equipment.  
• Ensure that the insulation on process heating equipment is effective.  
• Switch off motors, fans and machines when they are not being used and it does not 
affect production, quality or safety. 
• Switch off unnecessary lights and relying on day lighting whenever possible. 
• Use weekend and night setbacks on HVAC in any unused offices or conditioned 
buildings. 
• Look for unoccupied, heated or cooled areas and switch off heating or cooling. 
• Check that heating controls are not set too high or cooling controls set too low. In 
this situation, windows and doors are often left open to lower temperatures instead of 
lowering the heating. 
• Prevent drafts from badly fitting seals, windows and doors, and hence, leakage of 
cool or warm air.  
 
Appendix C: Energy Management System Assessment for Best Practices in Energy Efficiency 
ORGANIZATION 
SYSTEMS MONITORING 
TECHNOLOGY 
O & M 
 
Accountability 
Organization 
Monitoring & 
Targeting 
Utilities 
Management 
Reviews 
Plans 
Operation & 
Maintenance 
No utilities 
consumption 
monitoring. 
No specific 
reviews held. 
No energy 
improvement 
plans published. 
No written 
procedures for 
practices 
affecting energy 
efficiency. 
Energy efficiency 
of processes on site 
not determined. 
Few process 
parameters 
monitored 
regularly. 
No energy 
manager or 
"energy 
champion.” 
0 No awareness of 
responsibility for 
energy usage. 
Energy not 
specifically 
discussed in 
meetings. 
No procedures 
available to 
operating staff. 
Energy 
improvement 
plans published 
but based on an 
arbitrary 
assessment of 
opportunities. 
Energy only 
reviewed as part 
of other type 
reviews 
Utilities (like 
power and fuel 
consumption) 
monitored on 
overall site 
basis. 
Energy efficiency 
of site determined 
monthly or yearly. 
Site annual energy 
efficiency target 
set. Some 
significant process 
parameters are 
monitored. 
Energy manager 
is combined 
with other tasks 
and roles such 
that less than 
10% of one 
person’s time is 
given to specific 
energy 
activities. 
1 Operations staff 
aware of the 
energy efficiency 
performance 
objective of the 
site. 
Weekly 
monitoring of 
steam/power 
balance. 
Infrequent 
energy review. 
Energy 
performance 
plan published 
based on 
estimate of 
opportunities. 
Procedures 
available to 
operators but not 
recently 
reviewed. 
Weekly trend 
monitoring of 
energy efficiency 
of processes and of 
site, monitored 
against targets. 
Process parameters 
monitored against 
target. 
Energy manager 
appointed 
giving greater 
than 10% of 
time to task. 
Occasional 
training in 
energy related 
issues. 
2 Energy efficiency 
performance 
indicators are 
produced and 
available to 
operations staff. 
Periodic energy 
campaigns. 
Intermittent 
energy review 
meetings. 
 
 
 
 
 
100
 
101
ORGANIZATION 
SYSTEMS MONITORING 
TECHNOLOGY 
O & M 
 
Accountability 
Organization 
Monitoring & 
Targeting 
Utilities 
Management 
Reviews 
Plans 
Operation & 
Maintenance 
3 Energy efficiency 
performance 
parameter 
determined for all 
energy consuming 
areas. Operations 
staff advised of 
performance. All 
employees aware 
of energy policy. 
Performance 
review meetings 
held once/month. 
Energy manager 
in place greater 
than 30% of 
time given to 
task. Ad-hoc 
training 
arranged. 
Energy 
performance 
reported to 
management. 
Daily trend 
monitoring of 
energy efficiency 
of processes and 
of site, monitored 
against target. 
Process 
parameters 
monitored against 
targets. 
Daily 
monitoring of 
steam/power. 
Steam & fuel 
balances 
adjusted daily. 
Regular 
plant/site energy 
reviews carried 
out. 
A five-year 
energy 
improvement 
plan is published 
based on 
identified 
opportunities 
from energy 
review. 
Procedures 
available to 
operators and 
reviewed in the 
last three years. 
4 Energy efficiency 
performance 
parameter 
included in 
personal 
performance 
appraisals. All 
staff involved in 
site energy targets 
and improvement 
plans. Regular 
weekly meeting to 
review 
performance. 
An energy 
manager is in 
place giving 
greater than 50% 
time to task. 
Energy training 
to take place 
regularly. 
Energy 
performance 
reported to 
management and 
actions followed 
up. 
Same as 3, with 
additional 
participation in 
energy efficiency 
target setting. 
Process 
parameters 
trended. 
Real time 
monitoring of 
fuel, steam and 
steam/power 
balance. 
Optimum 
balances 
maintained. 
Site wide energy 
studies carried 
out at least every 
five years with 
follow up 
actions 
progressed to 
completion 
A ten year 
energy 
improvement 
plan based on 
review is 
published and 
integrated into 
the Business 
Plan.  
Procedures are 
reviewed regularly 
and updated to 
incorporate the 
best practices. 
Used regularly by 
operators and 
supervisors. 
 
Appendix D: Energy Management Assessment Matrix 
 
ENERGY STAR Guidelines For Energy Management Assessment Matrix 
The U.S. EPA has developed guidelines for establishing and running an effective energy 
management program based on the successful practices of ENERGY STAR partners.  
 
These guidelines, illustrated in the graphic, are 
structured 
on 
seven 
fundamental 
management 
elements that encompass specific activities. 
 
This 
Assessment 
Matrix 
is 
designed 
to 
help 
organizations and energy managers compare their 
energy management practices to those outlined in the 
Guidelines. The full Guidelines can be viewed on the 
ENERGY STAR web site - www.energystar.gov 
 
How To Use The Assessment Matrix 
The matrix outlines the key activities identified in the 
ENERGY STAR Guidelines for Energy Management 
and three levels of implementation: 
 
● No evidence 
● Most elements 
● Fully Implemented  
 
Compare your program to the Guidelines by choosing 
the degree of implementation that most closely match
assign yourself a score in order to help identify areas to focus on for improvement.  
 
es your organization's program. You can 
terpreting Your Results 
 the level of implementation identified in the Matrix should help you 
he total "score" achieved in the matrix is less important than the process of evaluating your 
he U.S. EPA has observed that organizations fully implementing the practices outlined in the 
esources and Help 
 a variety tools and resources to help organizations strengthen their energy 
. Read the Guidelines sections for the areas where you scored lower. 
. Become an ENERGY STAR Partner, if you are not already. 
. Review ENERGY STAR Tools and Resources. 
. Find more sector-specific energy management information at www.energystar.gov. 
In
Comparing your program to
identify the strengths and weaknesses of your program.  
 
T
program's practices, identifying gaps, and determining areas for improvement. 
 
T
Guidelines achieve the greatest results.  Organizations are encouraged to implement the Guidelines 
as fully as possible. 
 
R
ENERGY STAR offers
management programs. Here are some next steps you can take with ENERGY STAR: 
 
1
 
2
 
3
 
4
 
5. Contact ENERGY for additional resources. 
 
102
 
                           ENERGY STAR Guidelines For Energy Management Assessment 
Matrix 
 
0 - Little or no 
evidence 
1 - Some elements/degree 
2 - Fully implemented 
Score 
Make Commitment to Continuous Improvement 
No central corporate 
resource Decentralized 
management 
Empowered corporate 
leader with senior 
management support 
Corporate resource not 
empowered 
Energy Director 
  
Active cross-functional 
team guiding energy 
program 
No company energy 
network 
Energy Team 
Informal organization 
  
Formal stand-alone EE 
policy endorsed by senior 
mgmt. 
Referenced in environmental 
or other policies 
Energy Policy 
No formal policy 
  
Assess Performance and Opportunities 
All facilities report for 
central 
consolidation/analysis 
Little metering/no 
tracking 
Local or partial 
metering/tracking/reporting 
Gather and Track Data 
  
Some unit measures or 
weather adjustments 
All meaningful adjustments 
for corporate analysis 
Normalize 
Not addressed 
  
Standardized corporate 
base year and metric 
established 
Establish baselines 
No baselines 
Various facility-established 
  
Not addressed or only 
same site historical 
comparisons 
Some internal comparisons 
among company sites 
Regular internal & external 
comparisons & analyses 
Benchmark 
  
Some attempt to identify and 
correct spikes 
Profiles identifying trends, 
peaks, valleys & causes 
Analyze 
Not addressed 
  
Technical assessments 
and audits 
Reviews by multi-functional 
team of professionals 
Not addressed 
Internal facility reviews 
  
Set Performance Goals 
Short term facility goals or 
nominal corporate goals 
Short & long term facility 
and corporate goals 
Determine scope 
No quantifiable goals 
  
Estimate potential for 
improvement 
Specific projects based on 
limited vendor projections 
Facility & corporate defined 
based on experience 
No process in place 
  
Specific & quantifiable at 
various organizational 
levels 
Loosely defined or 
sporadically applied 
Establish goals 
Not addressed 
  
 
 
103
 
Create Action Plan 
Define technical steps 
and targets 
Facility-level consideration as 
opportunities occur 
Detailed multi-level targets 
with timelines to close gaps 
Not addressed 
  
Determine roles and 
resources 
Informal interested person 
competes for funding 
Internal/external roles 
defined & funding identified 
Not addressed 
  
Implement Action Plan 
All stakeholders are 
addressed on regular 
basis 
Create a communication 
plan 
Tools targeted for some 
groups used occasionally 
Not addressed 
  
Periodic references to energy 
initiatives 
All levels of organization 
support energy goals 
Raise awareness 
No overt effort made 
  
Broad training/certification 
in technology & best 
practices 
Some training for key 
individuals 
Build capacity 
Indirect training only 
  
Threats for non-performance 
or periodic reminders 
Recognition, financial & 
performance incentives 
Motivate 
Occasional mention 
  
No system for 
monitoring progress 
Regular reviews & updates 
of centralized system 
Track and monitor 
Annual reviews by facilities 
  
Evaluate Progress 
Compare usage & costs 
vs. goals, plans, 
competitors 
Measure results 
No reviews 
Historical comparisons 
  
Revise plan based on 
results, feedback & 
business factors 
Review action plan 
No reviews 
Informal check on progress 
  
Recognize Achievements 
Acknowledge contributions 
of individuals, teams, 
facilities 
Provide internal 
recognition 
Not addressed 
Identify successful projects 
  
Incidental or vendor 
acknowledgement 
Government/third party 
highlighting achievements 
Get external recognition 
Not sought 
  
 
 
 
 
Total Score 
 
104
Appendix E: Support Programs for Industrial Energy Efficiency 
Improvement 
 
This appendix provides a list of energy efficiency supports available to industry. A brief 
description of the program or tool is given, as well as information on its target audience and 
the URL for the program. Included are federal and state programs. Use the URL to obtain 
more information from each of these sources. An attempt was made to provide as complete a 
list as possible; however, information in this listing may change with the passage of time. 
 
Tools for Self-Assessment 
 
Steam System Assessment Tool 
Description: 
Software package to evaluate energy efficiency improvement projects 
for steam systems. It includes an economic analysis capability. 
Target Group: 
Any industry operating a steam system 
Format: 
Downloadable software package (13.6 MB) 
Contact: 
U.S. Department of Energy, Industry Technologies Program 
URL: 
http://www.oit.doe.gov/bestpractices/steam/ssat.html
 
Steam System Scoping Tool 
Description: 
Spreadsheet tool for plant managers to identify energy efficiency 
opportunities in industrial steam systems. 
Target Group: 
Any industrial steam system operator  
Format: 
Downloadable software (Excel) 
Contact: 
U.S. Department of Energy, Industry Technologies Program 
URL: 
 http://www.oit.doe.gov/bestpractices/software_tools.shtml#steamtool 
 
MotorMaster+  
Description: 
Energy efficient motor selection and management tool, including a 
catalog of over 20,000 AC motors. It contains motor inventory 
management tools, maintenance log tracking, efficiency analysis, 
savings evaluation, energy accounting and environmental reporting 
capabilities. 
Target Group: 
Any industry 
Format: 
Downloadable Software (can also be ordered on CD) 
Contact: 
U.S. Department of Energy, Industry Technologies Program 
URL: 
http://www.oit.doe.gov/bestpractices/software_tools.shtml
 
105
ASDMaster: Adjustable Speed Drive Evaluation Methodology and Application 
Description: 
Software program helps to determine the economic feasibility of an 
adjustable speed drive application, predict how much electrical energy 
may be saved by using an ASD, and search a database of standard 
drives. 
Target Group: 
Any industry 
Format: 
Software package (not free) 
Contact: 
EPRI, (800) 832-7322 
URL: 
http://www.epri-peac.com/products/asdmaster/asdmaster.html
 
AirMaster:+ Compressed Air System Assessment and Analysis Software  
Description: 
Modeling tool that maximizes the efficiency and performance of 
compressed 
air 
systems 
through 
improved 
operations 
and 
maintenance practices 
Target Group: 
Any industry operating a compressed air system  
Format: 
Downloadable software 
Contact: 
U.S. Department of Energy, Industry Technologies Program 
URL: 
http://www.oit.doe.gov/bestpractices/software_tools.shtml
 
Fan System Assessment Tool (FSAT) 
Description: 
The Fan System Assessment Tool (FSAT) helps to quantify the 
potential benefits of optimizing fan system. FSAT calculates the 
amount of energy used by a fan system; determines system efficiency; 
and quantifies the savings potential of an upgraded system. 
Target Group: 
Any user of fans 
Format: 
Downloadable software 
Contact: 
U.S. Department of Energy, Industry Technologies Program 
URL: 
http://www.oit.doe.gov/bestpractices/software_tools.shtml
 
Pump System Assessment Tool (PSAT)
Description: 
The tool helps industrial users assess the efficiency of pumping system 
operations. PSAT uses achievable pump performance data from 
Hydraulic Institute standards and motor performance data from the 
MotorMaster+ database to calculate potential energy and associated 
cost savings.  
Target Group: 
Any industrial pump user 
Format: 
Downloadable software 
Contact: 
U.S. Department of Energy, Industry Technologies Program 
http://www.oit.doe.gov/bestpractices/steam/psat.html
URL: 
 
 
106
ENERGY STAR Portfolio Manager 
Description: 
Online software tool helps to assess the energy performance of 
buildings by providing a 1-100 ranking of a building's energy 
performance relative to the national building market. Measured energy 
consumption forms the basis of the ranking of performance.  
Target Group: 
Any building user or owner 
Format: 
Online software tool 
Contact: 
U.S. Environmental Protection Agency, 
URL:  
http://www.energystar.gov/index.cfm?c=business.bus_index
 
Optimization of the Insulation of Boiler Steam Lines – 3E Plus 
Description: 
Downloadable software to determine whether boiler systems can be 
optimized through the insulation of boiler steam lines. The program 
calculates the most economical thickness of industrial insulation for a 
variety of operating conditions. It makes calculations using thermal 
performance relationships of generic insulation materials included in 
the software. 
Target Group: 
 
Energy and plant managers 
Format: 
 
Downloadable software 
Contact: 
 
U.S. Department of Energy, Industry Technologies Program 
URL: 
 
http://www.oit.doe.gov/bestpractices/software_tools.shtml
 
107
Assessment and Technical Assistance 
 
Industrial Assessment Centers 
Description: 
Small- to medium-sized manufacturing facilities can obtain a free 
energy and waste assessment. The audit is performed by a team of 
engineering faculty and students from 30 participating universities in 
the United States and assesses the plant’s performance and recommends 
ways to improve efficiency. 
Target Group: 
Small- to medium-sized manufacturing facilities with gross annual sales 
below $75 million and fewer than 500 employees at the plant site. 
Format: 
A team of engineering faculty and students visits the plant and prepares 
a written report with energy efficiency, waste reduction and 
productivity recommendations. 
Contact: 
U.S. Department of Energy, Industry Technologies Program 
URL: 
http://www.oit.doe.gov/iac/
 
Plant-Wide Audits 
Description: 
An industry-defined team conducts an on-site analysis of total energy 
use and identifies opportunities to save energy in operations and in 
motor, steam, compressed air and process heating systems. The 
program covers 50% of the audit costs. 
Target Group: 
Large plants 
Format:  
 
Solicitation (put out regularly by DOE) 
Contact: 
 
U.S. Department of Energy, Industry Technologies Program 
URL:  
http://www.oit.doe.gov/bestpractices/plant_wide_assessments.shtml
 
 
Manufacturing Extension Partnership (MEP)  
Description: 
MEP is a nationwide network of not-for-profit centers in over 400 
locations providing small- and medium-sized manufacturers with 
technical assistance. A center provides expertise and services tailored to 
the plant, including a focus on clean production and energy efficient 
technology.  
Target Group: 
Small- and medium-sized plants 
Format: 
Direct contact with local MEP Office 
Contact: 
National Institute of Standards and Technology, (301) 975-5020 
URL: 
http://www.mep.nist.gov/
 
108
Small Business Development Center (SBDC) 
Description: 
The U.S Small Business Administration (SBA) administers the Small 
Business Development Center Program to provide management 
assistance to small businesses through 58 local centers. The SBDC 
Program provides counseling, training and technical assistance in the 
areas of financial, marketing, production, organization, engineering and 
technical problems and feasibility studies, if a small business cannot 
afford consultants. 
Target Group: 
Small businesses 
Format: 
Direct contact with local SBDC 
Contact: 
Small Business Administration, (800) 8-ASK-SBA 
URL: 
http://www.sba.gov/sbdc/
 
ENERGY STAR – Selection and Procurement of Energy Efficient Products for Business 
Description: 
ENERGY STAR identifies and labels energy efficient office 
equipment. Look for products that have earned the ENERGY STAR. 
They meet strict energy efficiency guidelines set by the EPA. Office 
equipment included such items as computers, copiers, faxes, monitors, 
multifunction devices, printers, scanners, transformers and water 
coolers. 
Target Group: 
Any user of labeled equipment. 
Format: 
Website 
Contact: 
U.S. Environmental Protection Agency 
URL:  
http://www.energystar.gov/index.cfm?c=business.bus_index
 
 
109
Training 
 
Best Practices Program 
Description: 
The Best Practices Program of the Office for Industrial Technologies of 
U.S. DOE provides training and training materials to support the efforts 
of the program in efficiency improvement of utilities (compressed air, 
steam) and motor systems (including pumps). Training is provided 
regularly in different regions. One-day or multi-day trainings are 
provided for specific elements of the above systems. The Best Practices 
program also provides training on other industrial energy equipment, 
often in coordination with conferences. A clearinghouse provides 
answers to technical questions and on available opportunities: 202-586-
2090 or http://www.oit.doe.gov/clearinghouse/ 
Target Group: 
Technical support staff, energy and plant managers 
Format: 
Various training workshops (one day and multi-day workshops) 
Contact: 
U.S. Department of Energy, Industry Technologies Program 
URL: 
http://www.oit.doe.gov/bestpractices/training/
 
ENERGY STAR 
Description: 
As part of ENERGY STAR’s work to promote superior energy 
management systems, energy managers for the companies that 
participate in ENERGY STAR are offered the opportunity to network 
with other energy managers in the partnership. The networking 
meetings are held monthly and focus on a specific strategic energy 
management topic to train and strengthen energy managers in the 
development and implementation of corporate energy management 
programs. 
Target Group: 
Corporate and plant energy managers 
Format: 
Web-based teleconference 
Contact: 
Climate 
Protection 
Partnerships 
Division, 
U.S. 
Environmental 
Protection Agency 
http://www.energystar.gov/
URL: 
 
110
Financial Assistance 
 
Below the major federal programs are summarized that provide assistance for energy 
efficiency investments. Many states also offer funds or tax benefits to assist with energy 
efficiency projects. 
 
Industries of the Future  - U.S. Department of Energy 
Description: 
Collaborative R&D partnerships in nine vital industries. The 
partnership consists of the development of a technology roadmap for 
the specific sector and key technologies, and cost-shared funding of 
research and development projects in these sectors. 
Target Group: 
Nine selected industries: agriculture, aluminum, chemicals, forest 
products, glass, metal casting, mining, petroleum and steel. 
Format: 
Solicitations (by sector or technology) 
Contact: 
U.S. Department of Energy, Industry Technologies Program 
URL: 
 http://www.eere.energy.gov/industry/technologies/industries.html 
 
Inventions & Innovations (I&I) 
Description: 
The program provides financial assistance through cost-sharing of 1) 
early development and establishing technical performance of innovative 
energy-saving ideas and inventions (up to $75,000) and 2) prototype 
development or commercialization of a technology (up to $250,000). 
Projects are performed by collaborative partnerships and must address 
industry-specified priorities. 
Target Group: 
Any industry (with a focus on energy intensive industries) 
Format: 
Solicitation 
Contact: 
U.S. Department of Energy, Industry Technologies Program 
URL: 
 http://www.eere.energy.gov/inventions/ 
 
National Industrial Competitiveness through Energy, Environment and Economics (NICE³) 
Description: 
Cost-sharing program to promote energy efficiency, clean production 
and economic competitiveness in industry through state and industry 
partnerships (large and small business) for projects that develop and 
demonstrate advances in energy efficiency and clean production 
technologies. Applicants must submit project proposals through a state 
energy, pollution prevention or business development office. Non-
federal cost share must be at least 50% of the total cost of the project. 
Target Group: 
Any industry 
Format: 
Solicitation 
Contact: 
U.S. Department of Energy, Industry Technologies Program 
URL: 
http://www.eere.energy.gov/wip/program/nice3.html
 
111
Small Business Administration (SBA) 
Description: 
The Small Business Administration provides several loan and loan 
guarantee programs for investments (including energy efficient process 
technology) for small businesses. 
Target Group: 
Small businesses 
Format: 
Direct contact with SBA 
Contact: 
Small Business Administration 
URL: 
http://www.sba.gov/
 
112
State and Local Programs 
 
Many state and local governments have general industry and business development 
programs that can be used to assist businesses in assessing or financing energy efficient 
process technology or buildings. Please contact your state and local government to 
determine what tax benefits, funding grants, or other assistance they may be able to provide 
your organization. This list should not be considered comprehensive but instead merely a 
short list of places to start in the search for project funding. Below we summarize selected 
programs earmarked specifically for support of energy efficiency activities. 
  
California – Public Interest Energy Research (PIER) 
Description: 
PIER provides funding for energy efficiency, environmental, and 
renewable energy projects in the state of California. Although there is a 
focus on electricity, fossil fuel projects are also eligible. 
Target Group: 
Targeted industries (e.g., food industries) located in California 
Format: 
Solicitation 
Contact: 
California Energy Commission, (916) 654-4637 
URL:  
http://www.energy.ca.gov/pier/funding.html
 
California – Energy Innovations Small Grant Program (EISG) 
Description: 
EISG provides small grants for development of innovative energy 
technologies in California. Grants are limited to $75,000. 
Target Group: 
All businesses in California 
Format: 
Solicitation 
Contact: 
California Energy Commission, (619) 594-1049 
URL:  
http://www.energy.ca.gov/research/innovations/index.html
 
Indiana – Industrial Programs 
Description: 
The Energy Policy Division of the Indiana Department of Commerce 
operates two industrial programs. The Industrial Energy Efficiency 
Fund (IEEF) is a zero-interest loan program (up to $250,000) to help 
Indiana manufacturers increase the energy efficiency of manufacturing 
processes. The fund is used to replace or convert existing equipment, or 
to purchase new equipment as part of a process/plant expansion that 
will lower energy use. The Distributed Generation Grant Program 
(DGGP) offers grants of up to $30,000 or up to 30% of eligible costs 
for distributed generation with an efficiency over 50% to install and 
study distributed generation technologies such as fuel cells, micro 
turbines, cogeneration, combined heat & power and renewable energy 
sources. Other programs support can support companies in the use of 
biomass for energy, research or building efficiency. 
Target Group: 
Any industry located in Indiana 
Format: 
Application year-round for IEEF and in direct contact for DGGP 
Contact: 
Energy Policy Division, (317) 232-8970. 
http://www.in.gov/doc/businesses/EP_industrial.html
URL: 
 
 
113
Iowa – Alternate Energy Revolving Loan Program 
Description: 
The Alternate Energy Revolving Loan Program (AERLP) was created 
to promote the development of renewable energy production facilities 
in the state. 
Target Group: 
Any potential user of renewable energy 
Format: 
Proposals under $50,000 are accepted year-round. Larger proposals are 
accepted on a quarterly basis. 
Contact: 
Iowa Energy Center, (515) 294-3832 
URL: 
http://www.energy.iastate.edu/funding/aerlp-index.html
 
New York – Industry Research and Development Programs 
Description: 
The New York State Energy Research & Development Agency 
(NYSERDA) operates various financial assistance programs for New 
York businesses. Different programs focus on specific topics, including 
process technology, combined heat and power, peak load reduction and 
control systems. 
Target Group: 
Industries located in New York 
Format: 
Solicitation 
Contact: 
NYSERDA, (866) NYSERDA 
URL: 
http://www.nyserda.org/programs/Commercial_Industrial/default.asp?i=2
 
Wisconsin – Focus on Energy 
Description: 
Energy advisors offer free services to identify and evaluate energy-
saving opportunities, recommend energy efficiency actions, develop an 
energy management plan for business; and integrate elements from 
national and state programs. It can also provide training. 
Target Group: 
Industries in Wisconsin 
Format: 
Open year round 
Contact: 
Wisconsin Department of Administration, (800) 762-7077 
URL: 
http://focusonenergy.com/page.jsp?pageId=4
 
114

R
ecovery is at the heart of oil production from 
underground reservoirs. If the average worldwide 
recovery factor from hydrocarbon reservoirs can be 
increased beyond current limits, it will alleviate a number of 
issues related to global energy supply. Currently the daily 
oil production comes from mature or maturing oil fields and 
reserves replacement is not keeping pace with the growing 
energy demand. The world average recovery factor from 
hydrocarbon reservoirs is stuck in the mid-30 per cent 
range. This challenge becomes an opportunity for advanced 
secondary and enhanced oil recovery (EOR) technologies that 
may mitigate the demand-supply balance.
This paper presents a big-picture overview of EOR 
technologies with the focus on challenges and opportunities. 
The implementation of EOR is intimately tied to the price of oil 
and overall economics. EOR is capital and resource intensive, 
and expensive, primarily due to high injectant costs. The 
timing of EOR is also important: a case is made that advanced 
secondary recovery (improved oil recovery or IOR) technologies 
are a better first option before full-field deployment of EOR. 
Realisation of EOR potential can only be achieved through 
long-term commitments, both in capital and human resources, 
a vision to strive towards ultimate oil recovery instead of 
immediate oil recovery, research and development, and a 
willingness to take risks. While EOR technologies have grown 
over the years, significant challenges remain. Some of the 
enablers for EOR are also discussed in this paper.
EOR/IOR definitions
At this stage, it is important to define EOR. There is a lot 
of confusion around the usage of the terms EOR and IOR. 
Figure 1 shows these in terms of oil recovery, as defined by 
the Society of Petroleum Engineers (SPE)1,2. Primary and 
secondary recovery (conventional recovery) targets mobile oil 
in the reservoir and tertiary recovery or EOR targets immobile 
oil (that oil which cannot be produced due to capillary and 
viscous forces).
Primary, secondary and tertiary (EOR) recovery methods 
follow a natural progression of oil production from the start 
to a point where it is no longer economical to produce from 
the hydrocarbon reservoir. EOR processes attempt to recover 
oil beyond secondary methods, or what is left. Recovery, 
especially EOR, is closely associated with the price of oil and 
overall economics. On average, the worldwide recovery factor 
from conventional (primary and secondary) recovery methods 
is about a third of what was originally present in the reservoir. 
This implies that the target for EOR is substantial (²/³ of the 
resource base). Improving the recovery factor can be achieved 
by deploying advanced IOR technologies using best-in-class 
reservoir management practices, and EOR technologies. 
Worldwide EOR oil production
The total world oil production from EOR has remained relatively 
level over the years, contributing about 3 million barrels of oil 
per day (Figure 2), compared to ~85 million barrels of daily 
production, or about 3.5 per cent of the daily production. The 
bulk of this production is from thermal methods contributing ~2 
million barrels of oil per day. This includes the Canadian heavy 
oil (Alberta), California (Bakersfield), Venezuela, Indonesia, 
Oman, China and others. CO2-EOR, which has been on the 
rise lately contributes about a third of a million barrels of oil per 
day, mostly from the Permian Basin in the US and the Weyburn 
field in Canada. Hydrocarbon gas injection contributes another 
one third of a million barrels per day from projects in Venezuela, 
64 
World Petroleum Council: Official Publication 2010
Enhanced oil recovery: 
challenges & opportunities 
BY SUNIL KOKAL AND ABDULAZIZ AL-KAABI
EXPEC ADVANCED RESEARCH CENTRE, SAUDI ARAMCO
ENVIRONMENT AND SUSTAINABILITY
Figure 1: EOR/IOR definition
Figure 2: Worldwide EOR production rates
2000 
1500 
1000 
500 
0 
Production (KB/d)
Number of Projects Worldwide
>100
<5
>100
<5
<25
Thermal   Chemical   HC Gas     CO2       Others
(Data from Oil & Gas Journal, SPE, and other sources)
the US (mostly Alaska), Canada and Libya. Hydrocarbon gas 
injection is mostly implemented where the gas supply cannot 
be monetised. Production from chemical EOR is practically all 
from China with the total worldwide production of another third 
of a million barrels per day. Other more esoteric methods, like 
microbial have only been field-tested without any significant 
quantities being produced on a commercial scale.
These numbers were taken from the SPE literature, Oil and 
Gas Journal3 and other sources, and probably are a little 
conservative because some of the projects are not reported, 
especially the new ones. A better estimate of the total EOR 
production will be about 10-20 per cent higher than the 
3 million per day figure quoted above.
EOR current status
The global average or aggregate recovery factor from oil 
reservoirs is about a third. This is considered low and leaves 
a substantial amount of oil underground. A global effort has 
been under way for some time to increase this number and 
one reason for its failure is the relationship between oil price 
and resource availability. Figure 3, from the International Energy 
Agency, shows the connection between production cost and oil 
resources and the cost of converting them to reserves. 
The cheapest injectant for producing oil is water. As long as 
companies can produce oil by injecting water, they will continue 
to do so. Another ~2 trillion barrels of oil can be produced with 
the price of oil below US$40 (2008 $) per barrel. Many of the 
EOR technologies kick in when the price of oil is between 
US$20-80 per barrel. In the early 1980s there was tremendous 
interest generated in EOR due to oil price escalation. The 
number of EOR projects and R&D investment peaked in 1986. 
The interest fizzled out in the 1990s and early 2000s with a 
collapse in the price of oil. A renewed and growing interest 
has taken hold during the past 5 years as the price of oil has 
increased again. Figure 4 shows this relationship between EOR 
projects and oil price. There is a lag between the price of oil and 
EOR projects. In the last price escalation, interest was mostly in 
the US but this time the interest in EOR projects is global.
Besides the link of EOR to oil price, the projects are generally 
complex, technology-heavy and require considerable capital 
investment and financial risks. The risks are aggravated with 
the fluctuations in the price of oil. The unit costs of EOR oil are 
substantially higher than those of secondary or conventional 
oil. Another challenge for EOR projects is the long lead time 
required for such projects. Typically, it may take several 
decades from the start of the concept – generating laboratory 
data and conducting simulation studies – to the first pilot and 
finally, full commercialisation. Two examples are given here, one 
each for thermal (Figure 5) and miscible gas injection (Figure 6) 
projects. While there has been some discussion in the literature 
of applying or deploying EOR at an early stage of a reservoir’s 
life, this is generally difficult, and not necessarily the best option, 
due to the risks involved and lack of data availability, that can 
easily be obtained during the secondary stage of recovery.
The two most popular EOR methods as discussed below are 
thermal (steam) and miscible gas injection, which are mature 
technologies. In chemical EOR, polymer injection is reaching 
commercial status (Figure 7). Acid gas injection, in-situ 
combustion (including the newer high-pressure air injection, 
(HPAI)) and combination chemical flooding are still in the 
technology development stage. Microbial, hybrid and other 
novel technologies are in the R&D stage. This compounds 
and restricts the application of EOR for a given field. If thermal 
and miscible gas injection methods are applicable to a given 
reservoir, then the decision to move forward is a little easier. If 
not, the decision is harder, and depends on the availability of 
injectant, economics and other factors previously discussed.
EOR technology matrix
EOR methods are classified by the main mechanism of oil 
displacement4-9. There are really just three basic mechanisms for 
recovering oil from rock other than by water alone. The methods 
are grouped according to those which rely on (a) A reduction of 
oil viscosity, (b) The extraction of the oil with a solvent, and (c) 
The alteration of capillary and viscous forces between the oil, 
injected fluid, and the rock surface. EOR methods are therefore 
classified into the following three categories:
• Thermal methods (injection of heat);
• Miscible gas injection methods (injection of a solvent);
• Chemical methods (injection of chemicals/surfactants).
 Global Energy Solutions       65
TECHNOLOGY AND INNOVATION
Figure 3: Oil price and resources availability
*IEA
SAGD is primarily being applied in Alberta and several hybrid 
technologies (e.g. injection of solvent with steam) are being 
tested. This technology is ripe for being applied in other parts 
of the world. Air injection, if tamed and understood, may also 
have applications in light oil reservoirs as the injectant supply 
is plentiful. Steam flooding too has been tested successfully in 
light oil reservoirs that satisfy certain criteria (depth < 3,000 ft, 
oil saturation-porosity product > 0.1).
Miscible gas EOR
Gas injection, especially CO2, is another popular EOR 
method, and is applicable to light oil reservoirs, in both 
carbonates and sandstones. Its popularity is expected to 
increase for two reasons: increased oil recovery through 
miscibility and disposal of a greenhouse gas. There are 
over 100 commercial CO2-EOR projects, the bulk of them 
concentrated in the west Texas carbonates of the Permian 
Basin in the US. Their success has partially been due to the 
availability of low-cost natural CO2 from nearby fields and 
reservoirs. Another important CO2-EOR project is Weyburn-
Midale in Saskatchewan (Canada) where CO2 is sourced from 
a gasification plant in North Dakota and piped across the 
border. Many other CO2-EOR projects are on the drawing 
board as a result of environmental reasons (sequestration).
Hydrocarbon gas is also an excellent solvent for light oil 
reservoirs, if available. In places where it cannot be monetised 
(no local market), it can be injected into an oil reservoir for 
EOR. This has been the case in Alaska, Venezuela, Libya 
and Canada. Other gases, such as nitrogen (Cantarell field, 
Mexico) and acid or sour gases (Tengiz field, Kazakhstan, 
Harweel field, Oman and Zama field, Canada), have, or will be 
injected, although to a lesser extent than CO2 and hydrocarbon 
gases. The current challenges in gas injection as an EOR method 
are gravity segregation, and most importantly, availability of a 
low-cost gas source.
The future of gas injection lies primarily with CO2. There is 
a concerted effort around the world to reduce carbon capture 
costs. Once this becomes feasible, injection of CO2 may 
become widespread in light oil reservoirs. Hydrocarbon gas 
injection has limited potential except where there is no market 
for it. 
Chemical EOR
In chemical EOR or chemical flooding, the primary goal is to 
recover more oil by either one or a combination of the following 
processes: (1) Mobility control by adding polymers to reduce 
the mobility of the injected water, and (2) Interfacial tension (IFT) 
reduction by using surfactants, and/or alkalis. Considerable 
research and pilot testing was done in the 1980s and a string 
66 
World Petroleum Council: Official Publication 2010
Thermal EOR
Thermal EOR methods are generally applicable to heavy, 
viscous crudes, and involve the introduction of thermal energy 
or heat into the reservoir to raise the temperature of the oil 
and reduce its viscosity. Steam (or hot water) injection and 
in-situ combustion are the popular thermal recovery methods. 
Three common methods involving steam injection are cyclic 
steam stimulation (huff and puff), steam flooding and steam 
assisted gravity drainage (SAGD). In-situ combustion involves 
the injection of air, where the oil is ignited, generates heat 
internally and also produces combustion gases, which 
enhance recovery.
Steam injection has been most popular in heavy oil 
sand reservoirs with ongoing projects in Alberta (Canada), 
Venezuela, California, Indonesia, the former Soviet Union, and 
Oman3. Lesser (small commercial or field trials) have been 
reported in Brazil, China, Trinidad and Tobago, and other 
countries. SAGD has been mostly popular in the oil sands 
and extra-heavy crudes of Alberta, and tested in Venezuela 
with limited success. Several hybrid versions of SAGD have 
been reported but remain at field-trial levels only6. 
In-situ combustion projects, not as popular as steam 
flooding, have been reported in Canada, India, Romania, and 
the US. It has been applied mostly to heavy oil sandstone 
reservoirs. A new version, HPAI, for light crudes has been 
gaining in popularity over the past 10 years and shows 
potential, especially in light oils and low permeability carbonate 
reservoirs. Several projects have been concentrated in the 
north-western US and Mexico is also considering HPAI for 
one of its fields.
The future of thermal methods is perhaps the brightest for 
the more difficult heavy oil and tar sands resources. Currently, 
ENVIRONMENT AND SUSTAINABILITY
Figure 4: EOR projects and oil price correlation
Data from Oil & Gas Journal, EIA and other sources
of projects were implemented during that time, mostly in the 
US. Consequently, none of those projects were successful, at 
least economically. The only place where chemical EOR has 
been successful, especially polymer, is in China over the last 
decade. Based on the success in China and the recent increase 
in oil price, a renewed vigour has come into chemical EOR 
and several field trials and pilots are ongoing, and/or on the 
drawing board. The famous one is the Marmul field in Oman. 
Other projects are in Canada, the US, India, Argentina, Brazil, 
Austria and Argentina. Surfactant injection has not produced 
any successes and remains challenging, especially in a high 
salinity, high temperature environment. Alkalis, although 
cheap, bring along a string of operational headaches (scaling, 
emulsions, plugging, etc.). Nearly all of the polymer floods have 
been implemented in sandstones, and carbonates remain a 
major challenge.
Chemical EOR faces significant challenges, especially in 
light oil reservoirs. One of the reasons is the availability, or 
lack of, compatible chemicals in high temperature and high 
salinity environments. Figure 8 shows the current limitation on 
a salinity-temperature plot. R&D will play a critical role in the 
future of chemical EOR.
Advanced IOR and best practices 
A good ‘first’ option for any reservoir is to maximise secondary 
stage recovery. Advances in technology and the utilisation 
of best-in-class reservoir management practices will enable 
the maximisation of water flooding oil recovery before 
deploying EOR. Saudi Aramco is perhaps the world leader in 
optimising the recovery from its reservoirs through prudent 
reservoir management practices. Some of these include10 
the deployment of maximum reservoir contact wells (MRC), 
intelligent autonomous fields, gigacell simulation, deep 
diagnostics (ability to see inside the reservoir with clarity), and 
advanced monitoring and surveillance technologies. These 
are just a fraction of available technologies that may help 
improve oil recovery and should be considered before full-
scale deployment of EOR. 
Another option to consider before EOR is ‘smart water 
flooding’. Here, the idea is to inject water with an optimised 
composition (in terms of salinity and ionic composition) into 
the reservoir instead of any available water that may currently 
be injected or planned to be injected. Recent research has 
shown11,12 that salinity and/or ionic composition can play a 
significant role in oil recovery during water flooding and may 
yield up to 10 per cent or higher additional oil recoveries when 
compared to unoptimised water injection. This option has 
several advantages compared to EOR:
• It can achieve higher ultimate oil recovery with minimal 
investment in current operations (this assumes that a water-
flooding infrastructure is already in place). The advantage 
lies in avoiding extensive capital investment associated with 
conventional EOR methods, such as expenditure on new 
infrastructure and plants needed for injectants, new injection 
facilities, production and monitoring wells, changes in tubing 
and casing, for example.
• It can be applied during the early life cycle of the reservoir, 
unlike EOR.
• The payback is faster, even with small incremental oil recovery.
Figure 9 shows the results from a BP study11 of incremental 
oil recoveries (over and above water-flooding recoveries) in 
several sandstone reservoirs. 
Smart water flooding is relatively new and in the technology 
development stage, however, the idea of customised water 
for improving oil recovery is very attractive. There have been 
a few field trials and pilots, mostly in sandstones, and fewer 
in carbonates. The initial results are promising and a number 
of questions remain, although R&D has been accelerating in 
 Global Energy Solutions       67
Figure 5: Timing of EOR: Cold Lake – Canada
Figure 6: Timing of EOR: Weyburn project in Canada
TECHNOLOGY AND INNOVATION
is driven by short-term profits. This commitment to a long-term 
view will ensure the optimum exploitation of oil resources by 
keeping depletion rates low, improving secondary oil recovery 
through sustainable development and focusing on long-term 
profits. Appropriate EOR methods can then be deployed to 
maximise ultimate oil recovery. 
Moving towards difficult resources
As the easy and conventional light oil gets depleted, a move 
towards more difficult hydrocarbon resources is already 
well under way. These resources include heavy and extra-
heavy crudes, oil sands, bitumen and shale oil. Typically, the 
conventional oil recovery for these resources is generally low. 
An EOR method has to be implemented relatively early in these 
reservoirs. This has been, and will be, a primary driver for EOR, 
especially thermal, in the more difficult resources worldwide.
Life-cycle planning
A more holistic approach in the life-cycle planning of a 
reservoir is happening across the industry. The motivation 
towards maximising recovery, rather than thinking about short-
term profits, helps in better resource exploitation. Life-cycle 
planning includes thinking about EOR early enough to conduct 
relevant R&D studies, feasibility testing and conducting pilots 
to enable key decisions to be made at the right time. 
R&D
Investment in R&D is essential to generate the right options 
for field development. Often, in a drive to produce oil as fast 
as possible, incorrect strategy is adopted to develop an 
68 
World Petroleum Council: Official Publication 2010
this area. Saudi Aramco, through its upstream arm (EXPEC 
Advanced Research Centre), has initiated a strategic research 
programme in this area to explore the potential of increasing 
oil recovery by tuning the injected water properties. 
Another aspect of water flooding that can be improved is 
the monitoring and surveillance (M&S) of projects. In many 
cases, adequate monitoring is not done because of the cost 
involved. This may, however, be detrimental to the overall 
recovery during water flooding. While an optimum M&S 
plan cannot be predetermined for a given reservoir, some 
of its components include: the time-tested open/cased hole 
logging, coring, flood-front monitoring, single and interwell 
tracer tests, and emerging technologies, such as: borehole 
gravimetry, crosswell and borehole to surface electromagnetic 
(EM), and geophysical methods (crosswell seismic, 4D seismic 
and 4D vertical seismic profiler (VSP)). A good M&S plan is 
essential in optimising oil recovery at the secondary recovery 
stage, and even more important during the EOR phase.
EOR enablers
Significant challenges still remain for the widespread 
deployment of EOR. Ultimately, however, companies will have 
to resort to EOR as the ‘easy oil’ gets depleted. This section 
discusses some of the EOR ‘enablers’5. 
Focus on ultimate oil recovery
There is a concerted move around the world as companies 
(especially the national oil companies, and increasingly the 
international oil companies) realise that they need to focus on 
‘ultimate’ oil recovery and not on ‘immediate’ oil recovery that 
Figure 8: Chemical EOR challenges
Figure 7: IOR/EOR maturity and deployment
miscible with the oil at moderate reservoir pressures. The 
number of projects injecting CO2 for EOR has been steadily 
rising and is anticipated to increase further in the foreseeable 
future. In many ways, this is a win-win situation, sequestering 
CO2 at the same time as producing incremental oil.  
❏
1. Stosur, G.J.: “EOR: Past, Present and What the Next 25 Years May Bring,” SPE 
paper 84864, presented at the SPE IOR Conference in Asia Pacific, Kuala Lumpur, 
Malaysia, October 20-21, 2003.
2. Stosur, G.J., Hite, J.R. and Carnahan, N.F
.: “The Alphabet Soup of IOR, EOR and 
AOR: Effective Communication Requires a Definition of Terms,” SPE paper 84908, 
presented at the SPE International IOR Conference in Asia Pacific, Kuala Lumpur, 
Malaysia, October 20-21, 2003.
3. Moritis, G.: “CO2, Miscible, Steam Dominate EOR Processes,” Oil and Gas J., 
April 2010.
4. Thomas, S., “EOR – An Overview”, Oil and Gas Science and Technology, Rev. 
IFP
, Vol 63, #1, (2008).
5. Schulte, W.M.: “Challenges and Strategy for Increased Oil Recovery,” IPTC paper 
10146, presented at the IPTC, Doha, Qatar, November 21-23, 2005.
6. Manirique, E., Thomas, C., Ravikiran, R., et al.: “EOR: Current Status and 
Opportunities,” SPE paper 130113, presented at the IOR Symposium, Tulsa, OK, 
April 26-28, 2010.
7. Wilkinson, J.R., Teletzke, G.F
. and King, K.C.: “Opportunities and Challenges for 
EOR in the Middle East,” SPE paper 101679, presented at the Abu Dhabi IPTC, Abu 
Dhabi, U.A.E., November 5-8, 2006.
8. Manrique, E.J., Muci, V.E. and Gurfinkel, M.E.: “EOR Field Experiences in 
Carbonate Reservoirs in the US,” SPEREE, December 2007.
9. Awan, A.R., Teigland, R. and Kleppe, J.: “EOR Survey in the North Sea,” SPE 
paper 99546, presented at the SPE IOR Symposium, Tulsa, OK, April 22-26, 2006.
10. Saggaf, M.: “A Vision for Future Upstream Technologies,” JPT, March 2008.
11. Lager, A., Webb, K.J. and Black, J.J.: “Impact of Brine Chemistry on Oil 
Recovery,” Paper A24, presented at the EAGE IOR Symposium, Cairo, Egypt, April 
22-24, 2007.
12. Strand, S., Austad, T., Puntervold, T., Høgnesen, E.J., Olsen. M. and Barstad, 
S.M.: “Smart Water for Oil Recovery from Fractured Limestone: A Preliminary Study,” 
Journal of Petroleum Science and Engineering, 2009. 
TECHNOLOGY AND INNOVATION
oil reservoir. This can lower the overall 
recovery from the reservoir considerably. 
Proper R&D investment, especially early on, 
not only assures a good overall strategy for 
secondary recovery, but for EOR as well. A 
good example is the Marmul field in Oman 
where R&D studies and pilot testing with 
chemicals were done in the 1980s. This 
data and results helped PDO (Oman) and 
Shell to implement chemical EOR with 
little difficulty at a later date. Another good 
example is China, where R&D investment 
in chemical EOR has paid off handsomely 
with successful implementation of full-field 
EOR projects (two examples: Daqing and 
Shengli fields).
Capability development
EOR projects are inherently complex compared to conventional 
recovery methods. These projects are also manpower-
intensive, requiring highly-skilled professionals to run them. 
For companies that nurture, develop and possess these 
competencies, implementation of EOR will be easier. In addition, 
EOR professionals also ensure better IOR implementation 
strategies. 
Stepwise implementation
EOR projects are also facilitated by stepwise implementation 
and integration of R&D, technology, people, and commitment. 
A stepwise implementation involves moving from laboratory 
scale tests, single well tests, pilot tests and on to full-field 
implementation. This will significantly reduce risks associated 
with typical EOR projects, and eventually improve overall 
economics. 
Energy security
EOR implementation may be aided by a company’s or country’s 
need for energy security concerns. The US is a prime example 
of this need and has taken a true leadership role in EOR 
implementation in its fields, in spite of being a free economy. 
Another example is PDO where the dwindling oil production 
rates have forced it to implement EOR projects aggressively. 
Environmental concerns
In recent years, a strong boost to EOR has come from 
environmental concerns. This is especially true for CO2-EOR. 
CO2, a greenhouse gas, has been closely linked to global 
climate change. There are incentives to sequester this CO2. 
It is also a very good solvent for light crudes and is generally 
 Global Energy Solutions       69
Figure 9: Summary of low salinity recovery benefits for various fields

CHAPTER TWO
Petroleum well optimization
Key concepts
1. This chapter presents the fundamentals and terminologies of optimization and
considers the objective function and constraints in a typical optimization program.
Several techniques are presented for solving optimization problems, generally clas-
siﬁed as direct or indirect (derivative-based) search methods. Metaheuristic algo-
rithms are introduced for solving very complex problems using these advanced
optimization tools. These make it possible to achieve the global optimum solution.
2. Geometric programming (GP), multiobjective optimization, and stochastic and
robust optimization (RO) models are important approaches to optimization that we
can follow to improve the formulation of the drilling and production problem.
3. The main drilling engineering formulations presented include rate of penetration
(ROP) optimization, minimum mechanical speciﬁc energy (MSE), path optimiza-
tion, hole cleaning, bottomhole assembly (BHA) conﬁguration, and the minimum
well proﬁle energy model. An appropriate optimization framework for drilling
problem formulation is established in this chapter.
4. We present a general formulation for well placement, the quality map approach, and
optimal closed-loop ﬁeld development under geological uncertainty. This chapter
establishes an approach that addresses the value of time-dependent information in
achieving better decisions in terms of reduced uncertainty and increased probable net
present value (NPV) in the production optimization problem.
2.1 Mathematical optimization
2.1.1 Fundamentals of optimization
Optimization is concerned with selecting the best from among a set of many solutions by
efﬁcient quantitative methods. It has evolved from being a methodology of academic
interest into a technology that has made, and continues to make, a signiﬁcant impact in
industry. Optimization can take place at many levels in a system, ranging from individual
items of equipment to subsystems within a piece of equipment. Optimization problems
can be primarily classiﬁed in terms of continuous and discrete variables. They can also be
classiﬁed as single or multiple variables, linear or nonlinear, convex or nonconvex,
differentiable or nondifferentiable, steady state or dynamic, heuristic or robust, and
Methods for Petroleum Well Optimization
 2022 Elsevier Inc.
ISBN 978-0-323-90231-1, https://doi.org/10.1016/B978-0-323-90231-1.00004-2
All rights reserved.
31j
deterministic or optimization under uncertainty. A typical programming (optimization)
problem can be expressed as:
Minimize f ðxÞ/Objective function
Subject to: hðxÞ ¼ 0/Equality constraints
gðxÞ  0/Inequality constraints
Dimensionfhg ¼ m
Dimensionfxg ¼ n
If n > m, then degrees of freedom are greater than zero, and they must be selected to
optimize the objective function.
The following optimization terminologies deserve mentioning.
•
Feasible solution: a set of variables that satisfy the equality and inequality constraints.
•
Feasible region: the region of feasible solutions.
•
Optimal solution: a feasible solution that provides the optimal value of the objective
function.
•
Convex function: a function f ðxÞ : Rn/R is convex if and only if for any two
different values of x; x1, x2˛Rn lying in the region R:
f ðax1 þ ð1  aÞx2Þ  af ðx1Þ þ ð1  aÞf ðx2Þ
ca˛ð0; 1Þ
(2.1)
•
Convex region: a region R is convex if and only if for any x1, x2 in the region, there
is an X which can be deﬁned as:
f ðxÞ; Linear function
hðxÞ; gðxÞ; Linear functions
X ¼ ax1 þ ð1  aÞx2
ca˛ð0; 1Þ
(2.2)
By implication, X must always lie in the region. Fig. 2.1 is a graphical illustration of the
convex and nonconvex regions concept.
Figure 2.1 Convex (left) and nonconvex (right) regions.
32
Methods for Petroleum Well Optimization
The nature of the solution search region has an important bearing on the potential
for obtaining suitable results in optimization. In other words, it can be a guide to
determining whether a solution is a local optimum or the global optimum. Fig. 2.2
depicts the inﬂuence of the nature of a feasible region on the optimality of solutions. The
generalized optimization problem shown in Eq. (2.2) can be categorized by the form of
the equations and the type of the variables as follows:
I. Linear programming:
(a) f(x), Linear function
h(x), g(x), Linear functions
II. Nonlinear programming:
ðaÞUnconstrained min fðxÞ
f ðxÞ: Nonlinear
ðbÞConstrained model
hðxÞ; gðxÞ: Nonlinear
III. Mixed-integer (linear or nonlinear) programming:
Contains continuous ðxÞ and discrete ðyÞ variables
y ˛Y
x ˛X
Figure 2.2 Optimal solutions of a constrained nonconvex maximization problem indicating how the
nature of the feasible region could affect solution quality.
Petroleum well optimization
33
A number of techniques exist for solving these problems, and these can be generally
classiﬁed into direct and indirect (derivative-based) search methods. We list some
examples in the following.
Indirect search methods: these use derivatives in determining the search direction.
•
Newton method
•
Quasi-Newton
•
Steepest descent
•
Conjugate gradient
Direct search methods: these rely on function evaluation to choose the search direction.
•
Simplex method
•
Random methods (for example, random walk/jump, and simulated annealing)
•
NeldereMead simplex
•
Metaheuristic algorithms (for example, genetic algorithm, evolutionary program-
ming, ant colony optimization, and particle swarm optimization) as shown in Fig. 2.3
Metaheuristic algorithms are problem-solving methods, which try to ﬁnd good-enough
solutions to very hard optimization problems, within a reasonable computation time,
where classical approaches fail or cannot even be applied. Many existing metaheuristic
approaches are nature-inspired techniques, which work by simulating or modeling
different natural processes in a computer.
Figure 2.3 Taxonomy frameworks of metaheuristics.
34
Methods for Petroleum Well Optimization
2.1.2 Geometric programming
GP is a class of optimization problems with the general form as:
min
f0ðxÞ
s:t:
fiðxÞ  1;
i ¼ 1; .; n
hiðxÞ ¼ 1;
i ¼ 1; .; m
(2.3)
if the objective function, f0ð.Þ, and inequality constraint functions, fið.Þ, are posynomials,
and equality constraint functions, hið.Þ, are monomials (Boyd et al., 2007). A monomial
is a function in the form of:
gðxÞ ¼ cxa1
1 xa2
2 .xan
n
(2.4)
where the coefﬁcient c is a positive real number; exponents, a1, a2,., an are real
numbers; and x1, x2, ., xn are nonnegative variables. Then, a posynomial function is
deﬁned as a summation of monomials. The advantage of GP is that it can handle
nonlinear functions encountered in complex engineering problems. Furthermore,
although GP is not a convex optimization problem, it can be converted to a convex form
(Boyd and Vandenberghe, 2004). This conversion is explained in the following. The
monomial function in Eq. (2.4) can be rewritten as:
gðxÞ ¼ elog cea1 log x1.ean log xn
(2.5)
By associating one new yi variable with each xi variable as yi ¼ logðxiÞ, and having a
new constant c0 ¼ logðcÞ, the monomial function is reorganized as:
gðyÞ ¼ ec0ea1y1.eanyn ¼ eaTyþc0
(2.6)
Following a similar transformation, the optimization problem in Eq. (2.3) can be
transformed to:
min
X
L0
k ¼ 1
eaT
0kyþc0
0k
s.t:
X
L0
k ¼ 1
eaT
ikyþc0
ik  1;
i ¼ 1; .; n
ebT
i yþc0
i ¼ 1;
i ¼ 1; .; m
(2.7)
Petroleum well optimization
35
In Eq. (2.7), the number of monomials in the objective posynomial is represented by
L0. In addition, the number of monomials in the ith inequality constraint posynomial is
shown by Li. The coefﬁcients of the objective are shown by a vector a0k. To represent the
coefﬁcients of the ith inequality constraint, aik is used, and bi is used for the equality
constraint. Finally, to convert the GP to a convex form, the logarithms of the objective,
inequality, and equality constraints of Eq. (2.7) are taken (Mireslami, 2018):
min log
 X
L0
k ¼ 1
eaT
0kyþc0
0k
!
s:t: log
 X
Li
k ¼ 1
eaT
ikyþc0
ik
!
 0;
i ¼ 1; .; n
bT
i y þ c0
i ¼ 0;
i ¼ 1; .; m
(2.8)
2.1.3 Multiobjective optimization
In many real-world problems, several objectives need to be minimized simultaneously.
These objectives are usually conﬂicting, where decreasing one may lead to an increase in
the others (Miettinen, 1999). Choosing a trade-off among the objectives depends on the
application and the designer’s desire. However, in many applications, designers need to
be able to provide solutions with a balanced trade-off between the objectives. The
general form of a multiobjective optimization problem is expressed as:
Min½ f1ðxÞ.fNðxÞ
s:t
x ˛X
(2.9)
wheretheaim istoﬁndasolutioninthefeasiblesetX(Fig.2.4)thatminimizestheobjectives
f1ðxÞ,., fNðxÞ.Sincetheseobjectivecomponentsoftenconﬂict,theycannotbeminimized
simultaneously. Thus, there exist several optimal solutions for a multiobjective problem.
All these solutions are optimal but offer different trade-offs among the objectives.
These solutions are called Pareto optimal solutions (Fig. 2.5), which are solutions that
cannot be dominated by any other solution. This means that there is no solution that
achieves lower values for all objectives than the Pareto optimal solution. The set of all
Pareto optimal solutions is called the Pareto front, and this is also known as the optimal
trade-off curve. One of the Pareto optimal solutions may be chosen depending on the
priorities and preferences of the domain expert. However, in most applications, extreme
solutions that largely minimize one objective but signiﬁcantly degrade the others are
not desirable. Often, the solutions with a balanced trade-off between the objectives
are sought.
36
Methods for Petroleum Well Optimization
2.1.4 Stochastic optimization
Although uncertainties exist in the variables and parameters of most optimization
problems, usually the variables and parameters are assumed as deterministic values in the
problem modeling. The classic optimization techniques achieve nominal solutions and
ignore uncertainties. The obtained nominal solutions can lead to infeasibility and
constraint violation because of the parameters’ ﬂuctuations. Stochastic optimization (SO)
is a popular approach in most of the applications that involve uncertainties in parameters
and indeterministic variables. It provides optimal solutions considering the parameters
and variables with uncertainties as random variables. In SO, the uncertainties of the
variables and parameters are characterized by speciﬁc probability distributions. The
general form of a single variable linear programming problem is expressed as an example
of a classic deterministic optimization technique as:
min. hðxÞ
s.t. kðxÞ  0
(2.10)
Figure 2.4 Mapping solution space and objective function space.
Figure 2.5 Representation of the Pareto optimal front.
Petroleum well optimization
37
where the problem objective is hð.Þ, with constraint kð.Þ, and problem variable x. To
consider the uncertainties in variables, a SO formulation can be written as:
min. E½hðe
xÞ
s.t. E½kðe
xÞ  0
(2.11)
where Eð:Þ is the expected value operator. This SO formulation minimizes the expected
value of the objective function by considering the probability density function of the
random variable e
x. Usually in SO, uncertainties are modeled as random variables. These
random variables follow a distribution. One of the well-known distributions is Gaussian
or normal distribution (Fig. 2.6), which is usually represented with a mean and standard
deviation. Gaussian distribution has been used to model a wide variety of uncertain
phenomena. An interesting fact about Gaussian distribution is how optimizing for a
wider range of data improves the prediction accuracy. If an optimization constraint that
includes uncertainty is modeled by a Gaussian distribution, instead of optimizing for the
mean of the distribution, the constraint is optimized for a range of one standard devi-
ation, s, around the distribution’s mean, and 68.2% of the realizations of the un-
certainties will be satisﬁed. Similarly, if the constraint is optimized for a range of 2s
around the distribution’s mean, 95.4% of the realizations of the uncertainties will be
covered (Mireslami, 2018).
2.1.5 Robust optimization
Similar to SO, RO has been developed to deal with the uncertainties in problem pa-
rameters and variables. In RO, the problem is solved while considering uncertainty sets
for problem variables and parameters. Suppose the linear programming problem includes
uncertainties. Then, the RO formulation for this problem is shown as:
min
x max
w˛W hðx; wÞ
s:t:
max
w˛W kðx; wÞ  0:
(2.12)
Figure 2.6 An example of Gaussian distribution.
38
Methods for Petroleum Well Optimization
The robust formulation minimizes the objective function by taking the uncertainties
w ˛ W into account. In many works, for example, Bertsimas et al. (2011), strict
robustness is considered where the objective is minimized for the worst-case realization
of the uncertainties, and the solutions from RO are pessimistic but are guaranteed to be
feasible in practice.
RO can be a useful tool to deal with uncertainties in several business and engineering
ﬁelds such as supply chain management (Bertsimas et al., 2011), circuit design, and
antenna design. Although RO can be used in applications that include uncertainties in
addition to SO, it is a more pessimistic approach that only considers the worst-case
realizations of the uncertainties. However, SO ﬁnds a realistic and general solution
by imposing the probability distributions that the parameters are known to follow.
Therefore, SO is more effective when the uncertainties are probabilistic, and a
distribution is known for them.
Fig. 2.7 illustrates the differences and connections between various optimization
regimes, including deterministic optimization, RO, and SO. Because SO seeks to
optimize the expected value, an exact distribution of uncertainties is required, which
cannot be accurately estimated with empirical data. By contrast, RO asks for the support
of uncertain parameters only.
2.2 Petroleum well optimization
2.2.1 Drilling problem formulation
2.2.1.1 Rate of penetration optimization
Analytical models were developed for ROP prediction based on laboratory experiments
and have been improved to incorporate advances such as bit technology and drilling in
unconventional reservoirs, or the introduction of additional parameters. Most of these
models have empirical coefﬁcients that incorporate changes in lithology, geology, and
other factors that are not readily measured. The empirical coefﬁcients are constrained;
the values utilized in the upper and lower bounds are based on physics and engineering
judgment. We introduce four analytical ROP models in Table 2.1.
Bourgoyne and Y
oung’s method is the most common and detailed drilling ROP
optimization method since it is based on statistical synthesis of past drilling parameters.
This model is also considered as one of the most complete mathematical drilling models
used for ROP optimization. Detailed data analysis is very important to modify the
coefﬁcients used in the proposed mode with available data. Eq. (2.17) gives the general
Figure 2.7 Differences and connections between different optimization schemes.
Petroleum well optimization
39
Table 2.1 Analytical ROP models.
Bingham
Hareland and Rampersad
Motahhari et al.
Winters, Warren, and Onyia
(All bits)
(Drag bit)
(PDC bit)
(Roller-cone bits)
ROP ¼ aRPM
WOB
Db
b
where: ROP is the rate of
penetration (ft/hr); WOB
is the weight on bit (klb);
RPM is the rotary speed of
the drill (revolutions/min);
Db is the bit diameter (in);
and a and b are constants
determined for a given rock
formation.
(Eq. 2.13)
ROP ¼ 14:14NcRPM Av
Db
where: Nc is the number of
cutters; Av is the area of
rock compressed ahead of a
cutter (in2); and the other
variables repeat themselves
from the Bingham model.
Av is set based on the type
of drag bit.
(Eq. 2.14)
ROP ¼ Wf
RPMgWOBa
DbUCS

where: UCS is the unconﬁned rock
strength (psi); Wf is the wear
function; G is the model
coefﬁcient, which represents the
drillability; and a and g are ROP-
related model exponents
calculated by minimizing the least
squares loss.
(Eq. 2.15)
1
R ¼ as2D3ε
NW 2 þ fsD2
NWε þ b
ND þ crmD
Im
where: s is rock compressive strength;
D is bit diameter; ε is rock ductility;
N is rotary speed, W is weight on
bit; Ф is the cone offset coefﬁcient;
a, b, and c are model coefﬁcients; r
is the equivalent mud density which
is deﬁned as the apparent mud
density which results from adding
annular friction to the actual ﬂuid
density in the well; m is mud
viscosity; and Im is the modiﬁed jet
impact force.
(Eq. 2.16)
PDC, polycrystalline diamond compact; ROP
, rate of penetration; RPM, rotations per minute; WOB, weight on bit.
By authors Bingham, M.G., 1964. A New Approach to Interpreting Rock Drillability. The Oil and Gas Journal; Hareland, G., Rampersad, P
.R., 1994. Drag-bit model including
wear. In: SPE Latin America/Caribbean Petroleum Engineering Conference. Society of Petroleum Engineers https://doi.org/10.2118/26957-MS; Motahhari, Hamed Reza,
Hareland, G., James, J.A., 2010. Improved drilling efﬁciency technique using integrated PDM and PDC bit parameters. J. Can. Pet. Technol. 49 (10), 45e52; Roller Bit Model
With Rock Ductility and Cone Offset W
.J. Winters ; T.M. Warren ; E.C. Onyia Paper presented at the SPE Annual T
echnical Conference and Exhibition, Dallas, Texas,
September 1987. Paper Number: SPE-16696-MS https://doi.org/10.2118/16696-MS Published: September 27 1987.
40
Methods for Petroleum Well Optimization
ROP equation which is a function of both controllable and uncontrollable drilling
parameters.
ROP ¼ Exp
 
a1 þ
X
8
j ¼ 2
ajxj
!
(2.17)
The controllable and uncontrollable drilling parameters that affect the ROP are
individually identiﬁed in Fig. 2.8.
2.2.1.1.1 Rate of penetration objective function
A drilling optimization model essentially optimizes an objective function or a metric.
This objective or metric will act as a key performance indicator (KPI) for drilling.
Structured steps toward optimization were introduced in the 1970s by deﬁning an
objective function and optimizing it thereafter (Tansev, 1975). An objective function
represents the entity or function that is being optimized (in this case ROP). The
objective is modeled in terms of controllable parameters that can be changed on
the surface of the rig. Optimal control parameters can be determined to optimize the
objective using an optimization algorithm. For example, when ROP is the objective
function, the aim would be to maximize ROP
. ROP is modeled as a function of
controllable drilling parameters, such as WOB, RPM, and ﬂow rate. An optimization
algorithm is used to determine the optimal control parameters, such as WOB, RPM, and
ﬂow rate, which maximize ROP when implemented ahead of the bit. The vibration
classiﬁcation models (torsional, axial, and lateral) can be used to deﬁne constraints for the
optimization algorithm. A maximum MSE threshold can be set to ensure an adequate
supply of energy for drilling.
2.2.1.1.2 Rate of penetration constraints
Vibrations predominantly occur in the form of torsional, axial, and lateral vibrations and
cause stick-slip, bit-bounce, and whirl, respectively. Stick-slip in drilling is notorious for
Figure 2.8 Rate of penetration equation schematics.
Petroleum well optimization
41
causing drilling dysfunction and making drilling inefﬁcient (Fear et al., 1997). These
concerns are addressed using a metric called the stick-slip index (SSI) that measures the
intensity of torsional vibrations (Eq. 2.18: Arevalo et al., 2010; Ertas et al., 2013).
SSI ¼ Maxðbit RPMÞ  Minðbit RPMÞ
Averageðbit RPMÞ
(2.18)
The SSI serves as a proxy that measures the intensity of torsional vibrations (or stick-
slip) attaining a value of 1 when the bit is in complete stick-slip. SSI should be maintained
at <1 to avoid drilling dysfunction (Arevalo and Fernandes, 2012). Additionally, using
SSI as a metric to avoid excessive vibrations has produced great results in the ﬁeld,
resulting in an improvement of drilling operations (Payette et al., 2015; Sanderson et al.,
2017). The primary aim of modeling SSI is to ensure that the optimal control parameters
determined by the drilling optimization model do not lead to excessive vibrations.
The effect of the other forms of drilling vibrationsdaxial vibrations and lateral
vibrationsdcan be measured using the readings of downhole accelerometers. These give
a good indication of the amount of dysfunction produced by these vibrations (Arevalo
and Fernandes, 2012). Modeling and mitigating axial vibrations will lead to a reduction
in bit-bounce; reduction in lateral vibrations helps control drilling whirl and stick-slip.
The boundary of the bottomhole pressure to ensure a safe and stable drilling oper-
ation should be given as:
Ppore  Pbhp  Pfrac
(2.19)
The limits of WOB and RPM are also assumed as follows:
WOBmin  WOB  WOBmax
(2.20)
ROPmin  ROP  ROPmax
( 2.21)
In addition, the optimization space should be restricted using an MSE threshold.
2.2.1.2 Minimum mechanical speciﬁc energy
The concept of MSE was introduced by Teale in 1965. Teale deﬁned MSE as the me-
chanical energy and the efﬁciency of bits used to remove a unit volume of rock. In Teale,
the MSE model was conducted based on scientiﬁc experimental results and shown as:
MSE ¼ 4W
pD2 þ 480NrT
D2Rr
(2.22)
where T is the surface torque.
In Eq. (2.22), MSE is a function of WOB, RPM, ROP
, torque, and bit diameter. This
relationship provides a guideline for manipulating drilling operational parameters, such
42
Methods for Petroleum Well Optimization
as WOB and RPM, to optimize drilling performance for maximum efﬁciency. The
objective is to minimize MSE; in other words, the optimal criterion is to minimize MSE
by regulating WOB and RPM.
2.2.1.3 Bottomhole assembly conﬁguration
BHA performance can be quantiﬁed using vibration indices, such as the BHA strain
energy and the stabilizer side force. These indices are calculated under the steady-state
dynamics of a BHA. Based on statistical studies, it is found that the BHA with less
index value exhibits less MSE, higher ROP
, and less vibration (Bailey and Remmert,
2010). For the convenience of comparing BHAs of different lengths, the normalized
BHA strain energy is proposed, given by:
SE ¼ 1
L
X
N
i ¼ 1
M2
i li
2ðEIÞi
(2.23)
where: L is the length of the BHA; N is the number of ﬁnite elements to mesh the BHA;
li is the length of the ith element; and Mi is the bending moment of the ith element. In
Eq. (2.23): L, E, and I are determined with the given BHA; li is determined after
meshing the BHA; while the internal bending moment Mi needs to be calculated from
the BHA deformation. For a given element, the internal force vector can be obtained by:
fint ¼

ke;l

þ

ke;n

ue
(2.24)
where fint is the internal force vector, and ue is the displacement vector of an element
under its local coordinate, which can be transformed from the global coordinate after
obtaining the global displacement vector u. The internal axial force, shear force, and
bending moment are components of fint. The stabilizer side force can be determined
from the change of internal shear forces at the stabilizer, given by:
SFk ¼ f k
y;iþ1  f k
y;i
(2.25)
where k is the node number, and i and i þ 1 are element numbers. In this case: node k is
shared by elements i and i þ 1; SFk is the side force of the stabilizer located at node k; f k
y;i
is the internal shear force of node k obtained from element i; and f k
y;iþ1 is the internal
shear force of node k obtained from element i þ 1.
The BHA index values are calculated under different RPM conditions, uniformly
distributed within an operating interval (typically 50e200 RPM). The distribution of
RPM values can also be user-deﬁned when preknowledge of the operating RPM range
is available. To obtain good dynamic performance over the whole operating range, the
Petroleum well optimization
43
sum of squares of the index value at each RPM value is calculated as the cost function
available:
J ¼
X
W
i ¼ 1
VI2
ui
(2.26)
where W is the number of distinct RPM values, and VIui is the index value under ui.
The cost function deﬁned in Eq. (2.26) is to be minimized by optimizing the positions of
the stabilizers. The mechanism of this optimization is to modify the BHA natural
frequencies and move them away from the operating RRM to avoid resonance. The
stabilizers can modify the natural frequencies of a BHA by changing the contact
boundaries. Another method of varying the BHA natural frequency is to increase the
BHA length. However, this may cause the BHA to be overweight. Optimizing stabilizer
positions is free from this problem and involves minimal change to the existing BHA
structure. The BHA strain energy has the highest covariance with drilling MSE (Bailey
and Remmert, 2010; Feng, 2019); therefore, in this chapter, it is used as the index value
to be minimized. In addition, indices such as the BHA strain energy, the stabilizer side
force, the transmitted strain energy, and the end point curvature are positively correlated
with each other. Based on Eq. (2.26), the vibration index over the whole operating
RPM range is minimized by relocating stabilizer positions, as given in Eq. (2.27):
min J ¼
X
W
i ¼ 1
VIui s
ð Þ2; s ¼ ½s1
s2
.
sl
s:t:
0 < s1 < s2 . < sl1 < sl < L
(2.27)
where: s1 is the position of the ﬁrst stabilizer; s2 is the position of the second; sl1 is the
position of the ðl  1Þth; sl is the position of the ðlÞth stabilizer; and L is the length of the
BHA.
2.2.1.4 Path optimization framework
To quantitatively evaluate the well path in the optimization framework, multiple cost
functions are deﬁned. The cost functions considered here reﬂect production loss, drilling
time, completion cost, borehole tortuosity (Zheng, 2017), control authority, and drilling
dysfunctions, such as adverse drill string vibrations. We do not attempt here to achieve a
cost function description that is necessarily complete or optimal; there may be other cost
functions relevant to well path optimization that we have not included, or there may be
better cost functions than those we have chosen. Instead, our aim is to demonstrate the
framework of using cost functions to achieve better well path planning and directional
drilling control.
44
Methods for Petroleum Well Optimization
Prior to drilling, an initial plan is drawn up for the well’s vertical and lateral sections,
and an estimation made of the total production from the well. An undesired deviation
from the planned path will lead to missed sections of the pay zone, which can be
quantiﬁed. It is assumed that the “out-of-zone” sections will not be completed, saving
on completion cost, but also the operator will lose the estimated hydrocarbon
production from those sections and will forego the associated revenue. Inversely, the
more closely the planned path is followed, the more hydrocarbon production will be
possible. Therefore, the production cost can be formulated as follows:
J1ðrÞ ¼ c1  PðrÞ  MD
c1 ¼ Predicted production
feet
 oil Price
(2.28)
where: c1 is a constant predeﬁned in the planning stage that represents the total predicted
revenue per feet; MD is the measured depth of the section; and P(r) is the function
representing how closely the path is followed. It can be deﬁned as a normal distribution
function of the distance from the bit to the planned path:
PðrÞ ¼
1
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
2ps2
p
eðdðrÞmÞ2
2s2
(2.29)
where: s is the standard deviation; m is the mean of the distribution; and d(x) is the
deviation from the planned path. If the lateral section is deﬁned by two points, the
deviation can be calculated as follows:
d1ðrÞ ¼ jðp1  p2Þ  ðr  p2Þj
jp1  p2j
(2.30)
where p1 and p2 are the points deﬁning the line. The planned path is usually deﬁned by
multiple points, in which case we can ﬁnd the distance from the path by comparing the
distance between the bit and each point on the planned path:
d2ðrÞ ¼ minð½kr  p1k; kr  p2k; .;
	
	r  pn
	
	Þ
(2.31)
If the resolution of the points is less than desired (meaning the distance between the
points deﬁning the planned path is too large), Eq. (2.30) can be used to ﬁnd the distance
to the line segment between the two closest points to the bit. A visualization of the
production cost function is presented in Fig. 2.9.
The drilling time is proportional to the drilling cost as the operator will continue to
incur costs for equipment and crew throughout the drilling operation. The drilling cost
function is assumed to be linear and can be formulated as follows:
J2ðtÞ ¼ c2 

tf  ti

(2.32)
Petroleum well optimization
45
where c2 is a constant equal to the drilling cost per hour, and ti and tf are the initial and
ﬁnal times, respectively.
In this framework, we consider cemented completions typically associated with the
fractured completion of shale wells. The required casing and cementing costs are a
function of well depth. The completion cost is assumed to be a linear function of well
depth and can be formulated as follows:
J3ðrÞ ¼ c3  LðrÞ
(2.33)
where c3 is a constant equal to the casing and cementing cost per wellbore distance
and L(r) is the total well depth. Tortuosity, control authority (which is the ability to
control the response of the bit), and costs of drilling dysfunctions, such as harmful drill
string vibrations, are harder to quantify. In a ﬁrst, simpliﬁed approach, we will assume
here that these latter factors correlate with the tortuosity of the wellbore. The tortuosity
cost can be formulated as follows by dividing the path into n intervals (D’Angelo et al.,
2018):
J4ðrÞ ¼ c4 

TIðriÞ  TI

rf

TIðrÞ ¼
n
n þ 1
1
Lc
X
n
i ¼ 1
Lcsi
Lrsi
 1
(2.34)
It is assumed that an increase in the curvature and tortuosity of the wellbore is un-
desirable, as it may excite harmful vibrations, reduce control authority, and lead to a
range of “knock-on” problems. These problems include, among others, difﬁculty
running and properly cementing the casing, fracture realignment during hydraulic
fracturing, and premature wear of production equipment such as submersible pumps set
in tortuous vertical well sections (Shor et al., 2015).
For a downhole motor system, every time there is a change of tool face input, the
drilling operation needs to stop for adjustment. This can be penalized with the following
cost function:
J5ðuÞ ¼
X
kf
i ¼ 0
c5Hðkuðk þ 1Þ  uðkÞkÞ
(2.35)
Figure 2.9 The production cost function.
46
Methods for Petroleum Well Optimization
where H is the Heaviside step function. With this function, a ﬁxed amount of time
penalty c5 is applied every time the control input u changes. The overall cost function
is the summation of the above-deﬁned costs and will be explicitly included in the
optimization formulations described in the following.
2.2.1.4.1 Well path optimization problem formulation
In this section, the well path planning problem described earlier is formulated into an
optimization problem of the following form:
minimize J x
ð Þ
subject to f i x
ð Þ  bi; i ¼ 1; .; m
where: the state vector x ¼ ( r
!, _
r
! q
!, u
!, dt) is the optimization variable; the function
J(x) is the objective or the cost function; and the functions fi, ., fm are the constraint
functions bounded by the constants bi, ., bm.
The system dynamics, which are described by ordinary differential equations, can be
expressed in nonlinear state space form (Chachuat, 2007):
_
x ¼ f ðxðtÞ; uðtÞÞ
(2.36)
For numerical computations, the dynamics should be expressed in discrete time,
which can be accomplished by using Euler’s method with step-size dt (Semmler, 1995):
xðk þ 1Þ ¼ xðkÞ þ dt  f ðxðkÞ; uðkÞÞ
(2.37)
First, we deﬁne the input as:
uðkÞ ¼ ½aðkÞ; TFðkÞ
(2.38)
where: a(k) is the binary action input for sliding and rotating; a(k) ¼ 0 represents the
rotate action; and a(k) ¼ 1 represents the slide action. TF(k) is the desired tool face angle
for the slide actions. The equation of motions for the translation of the bit string can be
deﬁned as follows:
rðk þ 1Þ ¼ rðkÞ þ dt  _
rðkÞ
_
rðk þ 1Þ ¼ ROP  qðkÞe4q1ðkÞ
(2.39)
T
o set the tool face to the desired orientation, the BHA needs to be turned around the
body-z axis. This can be accomplished by ﬁnding the desired change in tool face angle
DTF ¼ TFdesired  TFcurrent, ﬁnding the body-z direction in inertial frame bz ¼
qe4q1, and rotating the quaternion with the desired angle around the body-z axis:
qtf k
ð Þ ¼

cos
DTF k
ð Þ
2

; sin
DTF k
ð Þ
2

qe4q1

q k
ð Þ
(2.40)
Petroleum well optimization
47
The quaternion dynamics are updated to discrete time to accommodate the tool face
input as follows:
_
qðkÞ ¼ 1
2 UðkÞ  qtf ðkÞ
qðk þ 1Þ ¼ qtf ðkÞ þ dt  _
qðkÞ
(2.41)
where the rate matrix U is deﬁned as:
UðkÞ ¼
2
6
6
6
6
6
6
6
6
4
0
u3ðkÞ
u2ðkÞ
u1ðkÞ
u3ðkÞ
0
u1ðkÞ
u2ðkÞ
u2ðkÞ
u1ðkÞ
0
u3ðkÞ
u1ðkÞ
u2ðkÞ
u3ðkÞ
0
3
7
7
7
7
7
7
7
7
5
(2.42)
The ROP will be distinctly different for these two actions, with drill string rotation
leading to signiﬁcantly faster ROP than sliding. The angular velocities will also be
different, with sliding having signiﬁcantly more build rate. These distinctions between
rotating and sliding can be represented with the following equations:
ROPðkÞ ¼ ROProt þ ðROPsilde  ROProtÞaðkÞ
uðkÞ ¼ urot þ ðuslide  urotÞaðkÞ
(2.43)
where ROPslide and uslide are the ROP and the angular velocities for sliding, and ROProt
and urot are the ROP and the angular velocities for rotating. Finally, the problem is
subject to state constraints that are the product of the dynamics and are imposed by the
user. The prescribed initial state x0 that includes the position and orientation of the bit is
given by:
x0 ¼ x0
(2.44)
The desired ﬁnal state variable range bf to gf that is prescribed by the desired
proximity to the pay zone that we want to enforce is given by:
bf  xf  gf
(2.45)
To summarize the optimization problem, there are ﬁve cost function equations:
Eq. (2.28) for production loss; Eq. (2.32) for drilling time cost; Eq. (2.33) for completion
48
Methods for Petroleum Well Optimization
cost; Eq. (2.34) for tortuosity cost; and Eq. (2.35) for the change of input cost. Eq. (2.39)
is the equality constraint representing the equations of motion for translation. Eqs.
(2.41), (2.42), and (2.43) are the equality constraints representing the equations of
motion for orientation. Eqs. (2.44) and (2.45) are the initial and desired ﬁnal states,
respectively. Eq. (2.38) is the integer constraint representing the binary drilling action
input and the tool face input (Pehlivantu
¨rk, 2018).
Slide drilling path optimization
min
x;u J1ðxÞ þ J2ðxÞ þ J3ðxÞ þ J4ðxÞ þ J5ðuÞ
subject to:
(2.28,2.32,2.33,2.34,2.35)
rðk þ 1Þ ¼ rðkÞ þ dt  _
rðkÞ
_
rðk þ 1Þ ¼ ROP  qðkÞe4q1ðkÞ
(2.39)
ROPðkÞ ¼ ROProt þ ðROPsilde  ROProtÞaðkÞ
(2.43)
_
qðk þ 1Þ ¼

cos
DTF k
ð Þ
2

; sin
DTF k
ð Þ
2

q k
ð Þe4q k
ð Þ1

q k
ð Þ þ dt  _
q k
ð Þ
_
q k
ð Þ ¼ 1
2 U k
ð Þ

cos
DTF k
ð Þ
2

; sin
DTF k
ð Þ
2

q k
ð Þe4q k
ð Þ1

q k
ð Þ
(2.41)
UðkÞ ¼
2
6
6
6
6
6
6
6
6
4
0
u3ðkÞ
u2ðkÞ
u1ðkÞ
u3ðkÞ
0
u1ðkÞ
u2ðkÞ
u2ðkÞ
u1ðkÞ
0
u3ðkÞ
u1ðkÞ
u2ðkÞ
u3ðkÞ
0
3
7
7
7
7
7
7
7
7
5
(2.42)
uðkÞ ¼ urot þ ðusilde  urotÞaðkÞ
(2.43)
x0 ¼ x0
(2.44)
bf  xf  gf
(2.45)
uðkÞ ¼ ½aðkÞ; TFðkÞ; aðkÞ˛½0; 1
(2.38)
Petroleum well optimization
49
2.2.1.5 Wellbore proﬁle energy
One of the most important elements of drilling automation is wellbore trajectory
control, especially in the era of extended reach and horizontal drilling. By deﬁnition,
wellbore trajectory control is the process of occasionally restricting the drilling direction
so that the deviation of the actual drilling path from that designed is minimized. Well
path deviation can be described using the deviation vector and trend angle, as shown in
Fig. 2.10. The deviation vector is deﬁned as the vector beginning from the actual drilling
path position A and pointing to position B, which is the cross-point of the designed path
with the deviation plane. The deviation plane is the plane through point A and
perpendicular to the planned trajectory. The drilling trend angle is the angle between
two unit-length tangential vectors Ta and Tb of the two ending spots A and B of the
deviation vector AB.
The concept of well proﬁle energy was introduced to better quantify the
complexity of well paths using mathematical reasoning rather than geometrical
reasoning (Samuel and Liu, 2009). This uses curvature bridging. The criterion used to
select the best curve between deviated point and target point is the minimization of
well path energy. This criterion considers curvature and torsion together, which
supplies an advanced qualiﬁcation of wellbore quality. In the consideration of reducing
wellbore ﬁction, unconventional curves are coupled in the well path design. Catenary,
spline, and clothed curves supply curvature bridging in transition zones as they make
the well path smooth.
Here, the wellbore proﬁle energy is not the borehole-induced strain energy of the
rocks. It is actually a drilling difﬁculty/complexity index, which is based on the “thin
elastic line” analogy of the well path. The relevant real strain energy exists in the drilling
Figure 2.10 Schematic of deviation vector AB, trend angle q, and correction trajectory A-Q-D.
Modiﬁed from Liu, Z., Samuel, R., 2014. Wellbore Trajectory Control Using Minimum Well Proﬁle Energy
Criterion for Drilling Automation. SPE Annual Technical Conference and Exhibition, Amsterdam, The
Netherlands. https://doi.org/10.2118/170861-MS.
50
Methods for Petroleum Well Optimization
string inside the wellbore. Considering the drill string as an elastic beam, its bending
strain energy is a function of curvature.
Ebend ¼
1
2EI
Z L
0
M2dx
(2.46)
where bending momentum is M ¼ EIkðxÞ. Substituting bending momentum in Eq.
(2.46), bending strain energy can be expressed as:
Ebend ¼ EI
2
Z L
0
kðxÞ2dx
(2.47)
where E is the Y
oung’s modulus and I is the area moment of inertia. For a circular
cross-section beam with diameter D:
I ¼
Z A
0
r2dA ¼ pD4
32
(2.48)
Similarly, the torsion strain energy of a beam is:
Etorsion ¼ GI
2
Z L
0
sðxÞ2dx
(2.49)
where G ¼
E
2ð1þvÞ is shear modulus and v is Poisson’s ratio. The strain energy of the
wellbore path is deﬁned as:
E ¼
Z L
0

kðxÞ2 þ sðxÞ2
dx
(2.50)
This states that the wellbore proﬁle energy equals the arc-length integral of the sum of
curvature kðxÞ squared and torsion sðxÞ squared (Samuel and Liu, 2009).
2.2.1.5.1 Wellbore trajectory
The drilling trajectory model to be optimized here was originally presented by
Wang et al. (1993). It is a sidetracking horizontal well trajectory which is composed of
two sections as shown in Fig. 2.11, and the corresponding parameters are explained in
the nomenclature at the end of this chapter. The sidetracking horizontal well trajectory
has its deﬁnite target area, and the coordinates of the sidetracking point are given. As
mentioned in Section 2.1.3, to optimize the trade-off between the length and
smoothness of the drilling trajectory, the length and well-proﬁle energy are considered as
objective functions; the details are described in the following (Huang et al., 2017).
Petroleum well optimization
51
Length of drilling trajectory:
L ¼ L1 þ L2
(2.51)
where L1 is the length of the ﬁrst turn section and L2 is the length of the second turn
section. From Eq. (2.51), we can deduce the expression of L1 and L2 as:
L2 ¼

Da  ka;1
kf;1
Df

ka;2  kf;2
kf;1
ka;1

L1 ¼

Da  ka;2L2

=ka;1
(2.52)
Well-proﬁle energy can be expressed as:
EW ¼
Z L
0

kðxÞ2 þ sðxÞ2
dx
(2.53)
If the wellbore curvature and the wellbore torsion are constant, the well-proﬁle
energy can be expressed as:
EW ¼

k2
1 þ s2
1

L1 þ

k2
2 þ s2
2

L2
(2.54)
where:
8
>
>
>
>
>
>
>
<
>
>
>
>
>
>
>
:
ki ¼
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
ﬃ
k2
a;i þ k2
f;isin2ai1 þ ai
2

r
; i ¼ 1; 2
si ¼ kf;i
 
1 þ
k2
a;i
k2
i
!
cos
ai1 þ ai
2

; i ¼ 1; 2
(2.55)
Figure 2.11 Two-section sidetracking trajectory. Modiﬁed from Huang, W., Wu, M, Cheng, J., Chen, X,
Cao, W., Hu, Y., Gao, H., December 17e20, 2017. Multi-objective Drilling Trajectory Optimization Based on
NSGA-II. 11th Asian Control Conference (ASCC) Gold Coast Convention Centre, Australia.
52
Methods for Petroleum Well Optimization
where ai indicates the average inclination angle of the ith section.
min½ f1ðxÞ ¼ L; f2ðxÞ ¼ Ew
s:t:
8
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
<
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
:
klb
a;i  ka;i  kub
a;i
klb
f;i  kf;i  kub
f;i
g1ðxÞ 
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
ﬃ
ðNB  NTÞ2 þ ðEB  ETÞ2
q
 Hmax
g2ðxÞ  jDB  DTj  Dmax
g3ðxÞ  L1
g4ðxÞ  L2
h1ðxÞ ¼ ðNB  NTÞtN þ ðEB  ETÞtE þ ðDB  DTÞtD
(2.56)
2.2.1.5.2 Wellbore trajectory constraints
1. Limits of control variables: In the drilling problem, the control variables are the
build rate and the turn rate of the two sections of the drilling trajectory. Due to the
limits of dogleg severity and deﬂecting tool, every variable has its bounds.
2. Bounds of target areas constraint: T
o ensure the terminal point hits on the target
window plane, it is necessary to build a constraint. As a point on a plane, the coordinate
of the terminal point will satisfy the equation where ðtN; tE; tDÞ is the coordinate of the
normal vector of the target window plane. In the sidetracking horizontal well, we
assume that the tangent of the curve at the terminal point is perpendicular to the target
window plane. Thus, the normal vector can be calculated as:
8
>
>
>
>
<
>
>
>
>
:
tN ¼ sin aB cos fB
tE ¼ sin aB sin fB
tD ¼ cos aB
(2.57)
Petroleum well optimization
53
To avoid off-target effects, the terminal point should also satisfy the following constraint
functions:
8
<
:
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
ﬃ
ðNB  NTÞ2 þ ðEB  ETÞ2
q
 Hmax
jDB  DTj  Dmax
(2.58)
3. Non-negativity constraint: The length of each section of the drilling trajectory
should be positive.
2.2.1.6 Hole cleaning optimization
2.2.1.6.1 Hole cleaning objective function
When drilling a horizontal well, a cutting bed can form from the accumulation of drill
cuttings at the low side of the wellbore. If the cutting bed height is very large, it can cause
high drag and torque, ﬂuid loss, stuck pipe, and many other problems, which indicates
ineffective hole cleaning. If the cutting bed height is low, the hole is very clean, which
indicates effective hole cleaning. Therefore, the cutting bed height can be regarded as a
criterion on which to assess the hole cleaning of a well. The cutting bed height should be
kept as low as possible to maintain safe drilling operations, which is the main objective of
hole cleaning optimization. The dimensionless cutting bed height can be calculated as
follows (Guan et al., 2016):
Min H ¼ 100Tcutting bed
Dhole
(2.59)
where: H is the dimensionless cutting bed height; Tcb is the cutting bed height; and Dh is
the diameter of the open hole. SI units are adopted for all variables if not speciﬁcally
indicated.
The formulas for calculating the cutting bed height developed by Zhou (Zhou and
Pu, 1998) and Wang (Wang et al., 1993) are combined and rearranged in the following
way to include the drill pipe rotation effect:
Tcb ¼ 0:015Dh

1000me þ 194:48m0:5
e

ð1 þ 0:587εÞðVcr  VaÞ þ Dh

0:0001N2
 0:35468N þ 0:16236N  Va  0:09465N  ε þ 0:00034N  Va  ε

=100
me ¼ K½ð2n þ 1Þ=ð3nÞn
Dh  dpo
1nð12VaÞn1=1000n
Vcr ¼ 40:09


rs  rf

rf
ds
0:667"
1 þ 0:17q þ 0:55sinð2qÞ

rf me
0:333
#
(2.60)
54
Methods for Petroleum Well Optimization
2.2.1.6.2 Hole cleaning variables and constraints
In hole cleaning optimization, there are four constraints that must be considered: the
maximum allowable pressure of the circulating system constraint; maximum supplying
ﬂow rate constraint; minimum jet velocity constraint; and formation condition constraint.
In practice, the maximum allowable pressure of the system is limited by the pump
pressure capacity and the pressure-bearing capacity of the drilling equipment, especially
the surface equipment. The pressure-bearing capacity of the circulating system is the
maximum allowable pressure that the surface equipment (for example, drilling hose and
surface pipelines) can bear. Since these surface pipelines and hoses connect the drilling
pump and the drill pipes, the drilling ﬂuid ﬂows through these pipelines and hoses, and
delivers energy from the drilling pump to the drill bit, and then ﬂows back to the surface.
This means that the surface hoses and pipelines are under high pressure from the drilling
pump. If the total pressure loss in the circulating system exceeds the pressure-bearing
capacity, the surface equipment may fail to work. Therefore, the maximum allowable
pressure of the system is the smaller of the values of the pressure-bearing capacity of the
circulating system and the pump pressure capacity.
The total pressure loss in the drilling system consists of the pressure drop through the
drill bit and parasitic pressure losses in the drill pipes, drill collars, annular space, and
surface equipment, which can be expressed as follows:
Dploss ¼ Dpp þ Dpbit
(2.61)
where: Dploss is the total pressure loss of the circulating system; Dpp is the parasitic
pressure loss in the system; and Dpbit is the pressure drop across the drill bit.
The total pressure loss in the system should be lower than the maximum allowable
pressure of the system pmax. The maximum allowable pressure of the circulating system
constraint can be written as follows:
Dploss < pmax
(2.62)
The maximum supplying ﬂow rate is the largest ﬂow rate that the pump can
provide when the rated pressure mode of the pump is working. The ﬂow rate should
not exceed the maximum supplying ﬂow rate, so the second constraint can be written
as follows:
Q < Qr
( 2.63)
where Q is the ﬂow rate and Qr is the rated ﬂow rate that the pump cylinder can provide.
When rock is broken by the drill bit, the drill cuttings should be removed from the
bottom of the hole as soon as possible. Hydraulic power is needed for this task. The ﬂuid
Petroleum well optimization
55
velocity through the drill bit nozzles should be sufﬁciently high to ﬂush the cuttings from
the bottom of the hole. Thus, the jet velocity should be greater than a minimum jet
velocity. The minimum jet velocity constraint can be expressed as:
Vj > Vj min
(2.64)
where Vj ¼ Q
An, An is the total ﬂow area of the drill bit nozzles, and Vj min is the min-
imum jet velocity.
While performing drilling operations, excessive downhole pressure may fracture the
formation and a large amount of drilling ﬂuid may ﬂow into the formation, resulting in
ﬂuid loss. Conversely, inadequate downhole pressure, meaning the downhole pressure is
lower than the pore pressure, will cause the formation ﬂuids to rush into the wellbore,
which can cause well kick or even a very dangerous blowout. Moreover, if the downhole
pressure is lower than the formation caving pressure, the wellbore rock may slough off
and block the wellbore. All three cases may lead to the failure of drill operations. In
drilling practice, the downhole pressure is transformed into the equivalent circulating
density (ECD) for the convenience of comparing downhole pressure to formation
pressures. The ECD should always be controlled within the formation pressure window,
which means that the downhole pressure should be lower than the formation fracture
pressure and greater than the formation pore pressure and caving pressure.
The formation condition constraint is expressed in the following way where rpore,
rcaving and rfrac are the equivalent densities of formation pore pressure, caving pressure,
and fracture pressure, respectively:
max

rpore; rcaving

< ECD < rfrac
(2.65)
The downhole pressure is the sum of the hydrostatic pressure and the annular pressure
loss when the drilling ﬂuid is circulating. The ECD is calculated by adding the equivalent
density of the annular pressure loss and the static drilling ﬂuid density:
ECD ¼ rf þ DPanu
gHv
(2.66)
where: rf is the drilling ﬂuid density; DPanu is the frictional pressure loss in the annulus; g
is the gravitational acceleration; and Hv is the true vertical depth of the well.
56
Methods for Petroleum Well Optimization
Minimization H ¼ 100Tcutting bed
Dhole
(2.67)
Objective : minH ¼ 100Tcb
Dh
Constraints :
ð1ÞDploss < pmax
ð2ÞQ < Qr
ð3ÞQ=An > Vj min
ð4Þmax

rpore; rcaving

3ECD3rfrac
Variables: Q; An; K:
2.2.2 Production problem formulation
2.2.2.1 The quality map approach
The quality map is a two-dimensional representation of the reservoir’s response to
production or injection. The concept of the quality map approach in well-placement
optimization was introduced by da Cruz et al. (1999). The map’s construction makes
it a measure of “how good” each part of the reservoir is for production or injection.
The parameters governing ﬂuid ﬂow in porous media are complex, and numerical
models are often the best tools for analyzing the various phenomena existing in the
subsurface. While these models are quite reliable, they may need high CPU time when
coupled with an automated optimization algorithm. In the quest for the most proﬁtable
solution to reservoir management questions, numerical models are run perhaps
thousands of times for each development scenario. An example of this is determining the
optimal performance of the reservoir with increasing well count.
In contrast, once the map has been built, the basic quality map approach eliminates
the need for further ﬂow simulations irrespective of the number of wells to be optimized.
The map is built by running a ﬂow simulator with a single well (producer or injector) in
the reservoir. The well location is varied in each run over the active part of the reservoir,
and the quality of each well position is evaluated as either the cumulative oil or NPV
value. Thus, each active cell in the reservoir in the I-J plane has a quality associated with
it. da Cruz et al. (1999) showed that the number of visited points used in building the
quality map could be reduced by kriging. The reason behind the quality concept is that,
because building the map takes into account the interactions between reservoir
Petroleum well optimization
57
heterogeneity and the ﬂow of ﬂuids, the map could serve as a tool for determining the
“good spots” in the reservoir. Thus, the quality map is itself a proxy used in place of the
simulator for multiple well scenarios.
2.2.2.1.1 The quality concept
Determining the quality or the objective function of any given well conﬁguration is
based on a simple inverse-distance weighting process. The method determines each well
quality ðQwÞ by adding the qualities ðQcÞ of all the cells assumed to belong to the well
based on inverse-distance weighting, as shown in Eqs. (2.68) and (2.69).
Qw ¼
X
ncw
c ¼ 1
QCwc
(2.68)
wc ¼
1
a  db
wc
(2.69)
where wc, the inverse-distance weight, is 1 when the wellecell distance dwc is zero. The
quality ðQtÞ for the total number of wells is the sum of all the well qualities ðQwÞ and
that is what the optimization process seeks to maximize as shown in Eq. (2.70):
Qt ¼
X
nw
w ¼ 1
Qw
(2.70)
where ncw is the number of cells belonging to well w, and nw is the total number of wells
to be optimized. Sensitivity studies showed that optimal values for the coefﬁcient a and
the exponent b are 1 and 2, respectively. These values worked best for all the cases
investigated (Badru, 2003).
2.2.2.2 Well placement problem
Well placement decisions made during the early stage of exploration and development
activities have signiﬁcant impacts on the future recovery and proﬁtability of the project.
In addition, these early decisions have the ability to improve the later placement
decisions by providing more information for the decision-making process (greater
certainty). Therefore, recovery and efﬁcient use of information may add value beyond
the amount of oil recovered from any particular well. In this respect, the quality of the
placement decision is dependent upon the amount, quality, and efﬁcient use of the
information at the time of decision. Production data are a component of the information
available (Fig. 2.12).
The objective function measures the quality of alternative solutions in an optimi-
zation problem. In well placement optimization, the NPV is often used as the objective
function. In this case, the aim is to maximize the NPV of a ﬁeld development project.
The NPV is deﬁned as the net cash ﬂow discounted to the present time (often taken to be
58
Methods for Petroleum Well Optimization
the start of the project). In a waterﬂood project, the NPV is obtained from the expected
revenue from the sales of oil and gas, and the costs associated with production and
injection. Capital costs consist of the expenses incurred to put production and injection
wells in place, while the operating costs consist of recurrent expenses incurred for water
injection, produced water treatment/disposal, well remediation, and human resources.
Capital costs are usually incurred at the start of the project and so do not often need to be
discounted to arrive at the present value. However, in some cases, there are items of
capital expenditure, such as the costs of drilling inﬁll wells and putting in place additional
storage and gathering facilities, which may be incurred later in the life of the project. The
budgets for such capital expenses need to be discounted to their present value. Operating
costs are generally recurrent and are usually discounted to the start of the project. A
combination of the cash ﬂows arising from revenues and expenditures gives the NPV
deﬁned as follows (Onwunalu and Durlofsky, 2010):
Net Cash Flow t
ð Þ ¼ Oil Production t
ð Þ  Oil Price t
ð Þ þ Gas Production t
ð Þ
 Gas Price t
ð Þ  Water Production t
ð Þ  Water Handling Cost t
ð Þ
 OPEX  CAPEX
(2.71)
NPVi ¼
X
t
Net Cash FlowiðtÞ
ð1 þ interest rateÞt
(2.72)
2.2.2.2.1 Sequential well placement problem
In this approach, the location decision for each of the wells is made sequentially. In other
words, the decision for Well #3 is made independent of the fact that two more wells are
going to be drilled later. From an optimization point of view, only one optimization is
performed at a time. Accordingly, optimization of Well #3 considering all geological
models is performed ﬁrst (Fig. 2.13). Then, optimization of Well #4 is performed using
the same geological models, and ﬁnally, the decision for Well #5 is made. These three
sequential optimizations are made using the same set of geological models.
Figure 2.12 Decision quality versus state of information.
Petroleum well optimization
59
2.2.2.2.2 Multiplacement approach
Guyaguler and Horne (2001) used the multiplacement approach to ﬁnd the optimum
location of two producers. The multiplacement approach is different from the sequential
approach in that the decision on the location of the three wells is made at the same time
(Fig. 2.14) as opposed to the optimization of the wells’ locations being performed in
three sequential decisions (Fig. 2.13). In this approach, the fact that Well #4 and Well #5
are going to be drilled is considered while making the location decision for Well #3.
Figure 2.13 Sequential placement approach. Modiﬁed from Özdo
gan, U., Horne, R.N., 2004. Optimi-
zation of Well Placement with a History Matching Approach. Society of Petroleum Engineers. https://doi:
10.2118/90091-MS.
Figure 2.14 Multiplacement approach. Modiﬁed from Özdo
gan, U., Horne, R.N., 2004. Optimization of
Well Placement with a History Matching Approach. Society of Petroleum Engineers. https://doi:10.2118/
90091-MS.
60
Methods for Petroleum Well Optimization
2.2.2.3 Closed-loop reservoir management
The optimal continuous operation of existing wells, often referred to as closed-loop
reservoir management (CLRM), has been the subject of signiﬁcant research in recent
years. CLRM, depicted in Fig. 2.15, entails optimizing well settings based on current
geological knowledge, operating the reservoir, collecting data over a time period, and
performing data assimilation (history matching) to update the geological description for
consistency with observed data. This procedure, repeated over the reservoir life, can
provide improved performance relative to heuristic approaches for reservoir manage-
ment (Shirangi, 2017).
2.2.3 Well control optimization
The general ﬁeld development optimization problem involves determination of the well
types, locations, and controls, with the goal of minimizing a cost function. Following
Isebor et al. (2014) and de Brito (2019), the optimization problem can be stated as
follows:
min
x˛X;u˛U;z˛Z Jðp; x; u; zÞ; subject to
8
<
:
gðp; x; u; zÞ ¼ 0;
cðp; x; u; zÞ  0
( 2.73)
The vectors x and u indicate integer (grid-block based) well location variables and
continous well control variables, respectively, while z is a categorical variable, which
indicates whether the well is an injector ðzk ¼ 1Þ; a producer ðzk ¼ 1Þ, or not drilled
at all ðzk ¼ 0Þ. The well location variables can also be treated as real-valued, and this
may be preferable in cases where wells are not centered in grid blocks (for example, with
deviated wells). Here, g ¼ 0 denotes the ﬂow simulation equations, p represents the
solution unknowns, which in this system are the pressure and saturation in every grid
block, and c deﬁnes any nonlinear constraints. The spaces X and U are deﬁned to include
bound constraints, which can be expressed as xl  x  xu and ul  u  uu, where
subscripts l and u denote lower and upper bounds. In this part, the objective is to
maximize NPV
, meaning we set J ¼ NPV, with NPV given by:
NPVðp; x; u; zÞ ¼
X
np
k ¼ 1
X
ns
s ¼ 1
Dts

po; qo
k;sðp; x; uÞ  cpwqpw
k;sðp; x; uÞ

ð1 þ dÞ
ts
365

X
ni
k ¼ 1
X
ns
s ¼ 1
Dtsciwqiw
k;sðp; x; uÞ
ð1 þ dÞ
ts
365

X
nw
k ¼ 1
jzkjcw
ð1 þ dÞ
tk
365
(2.74)
Petroleum well optimization
61
Figure 2.15 Schematic of closed-loop reservoir management. Modiﬁed from Shirangi, M.G., 2017. Advanced Techniques for Closed-Loop Reservoir
Optimization under Uncertainty. PhD Thesis, Department of Energy Resources Engineering, Stanford University, Stanford; Hou, J., Zhou, K., Zhang, X.,
Kang, X., Xie, H., 2015. A review of closed-loop reservoir management. Petrol. Sci. 12, 114e128. https://doi.org/10.1007/s12182.014-0005-6.
62
Methods for Petroleum Well Optimization
where: ni is the number of injection wells; np is the number of production wells;
nw ¼ ni þ np is the total number of wells; ns is the number of simulation time steps; ts and
Dts are the time and time-step size at time step s; and d is the annual discount rate. The
price of oil is po, the cost for handling produced water is cpw, and the cost of injected water
is ciw. The oil and water production rates and the water injection rate for well k at time step
s are denoted as qo
k;s, q pw
k;s , and q iw
k;s. The variable tk represents the time at which well k is
drilled, and the per-well drilling cost is denoted by cw. Eq. (2.73) can be expressed as:
max
u˛U NPVðp; uÞ; subject to
8
<
:
gðp; uÞ ¼ 0;
cðp; uÞ  0
(2.75)
The space U again includes the bound constraints for the continuous well control
variables, and c speciﬁes any nonlinear constraints.
2.3 Summary
1. For many companies, drilling and production optimization is a complex and moving
target. Scattered data sets, lack of consistency among crews and equipment, and
manual data analysis impede real-time optimization. Using an advanced optimization
approach in a real-time system gives us a powerful tool with which to centralize
drilling and production data, implement best practices, and enforce standards. Also,
optimization can be carried out in real time. The result is a loop of continuous
improvement that drives drilling performance higher and reduces ﬁeld costs.
2. In this chapter, we develop different mathematical formulations of petroleum well
optimization for drilling and production in the petroleum industry.
3. The path optimization module is part of the automated directional drilling advisor
framework, which aims to optimize and automate the directional drilling process.
This intelligent multiobjective optimization approach is modeled for a more complex
drilling trajectory. It leads to cost optimization and produces realistic directional
drilling instructions with a consideration of different constraints and tendencies.
4. Closed-loop reservoir management, which consists of automatic history matching
and reservoir production optimization, is an effective technique to improve the
technical and economic effects of reservoir development.
2.4 Problems
Problem 1: Deterministic and stochastic mathematical
formulation
The equations for penetration rate and bit life are incorporated into a drilling cost
equation, and the cost function is minimized over the control variables. These variables
Petroleum well optimization
63
then dictate the optimal drilling of the next bit run, where penetration rate is
P ¼ f 1(W
,N,H), bit life is L ¼ f 2(W
,N,H), and the bounds on the control variables are W
,
N, H.
1. Presentadeterministicmathematicalformulationconsideringthefollowingparameters.
2. Present a stochastic mathematical formulation considering the following parameters.
Cb is bit cost ($); CPF is cost per foot ($/ft.); Cr is rig cost ($/hr.); D0 is initial depth (ft.);
H is bit hydraulic; horsepower; L is bit life (hrs.); M is sample size (total number of bit
runs); N is rotary speed (RPM); and P is penetration rate (ft./hr.)
Problem 2: ROP model with GP format
The ROP is a function of several drilling parameters some of which are manageable by
the drilling engineers (controllable), while others are a fact that must be accepted and
dealt with (uncontrollable). In this problem, only controllable parameters will be treated
to develop the ROP correlation. ROP is a function of WOB, RPM, torque, and ﬂow
rate (Q).
1. Present an ROP model with GP format for this problem.
Problem 3: Multiobjective mathematical formulation
The rate of penetration, the drilling life of bit, and MSE are taken as the optimization
objectives. The fastest rate of penetration, longest bit life, and smallest MSE are expected
to be achieved simultaneously. However, these three objectives are often in conﬂict with
each other. A preferable set of drilling parameters is expected to satisfy all these objects to
some degree or another and to offer a relatively fast rate of penetration, a long bit life and
small MSE. Several constraints are applied in the wellbore drilling parameters optimi-
zation. These include weight on bit, bit rotation speed, value of tooth, and bearing wear.
1. Give an efﬁcient mathematical formulation for objective functions.
2. Give an efﬁcient mathematical formulation for constraints.
Problem 4: Well placement optimization
Consider three constraints that restrict well placement:
1. Maximum well length
2. Minimum inter-well distance
3. Minimum well-to-boundary distance
How can we formulate these three important geometric constraints during well
placement optimization?
Problem 5: Shortcomings of well placement
1. What are the shortcomings of sequential and multiplacement approaches for well
placement? Formulate and present a new approach to well placement that does not
have these shortcomings.
64
Methods for Petroleum Well Optimization
2. How can the time-dependent uncertainty be included in the optimization scheme?
3. What would be the improvements obtained using the time-dependent information
which is uncertain but may be modeled and predicted?
Problem 6: Nonlinear optimization
Consider the following problem:
f x
ð Þ ¼ 5:357847x2
3 þ 0:8356891x1x5 þ 37:293239x1  40792141
subject to;
g1 x
ð Þ ¼ 85:334407 þ 0:0056858x2x5 þ 0:0006262x1x4  0:002205x3x5  0;
g2 x
ð Þ ¼ 85:334407 þ 0:0056858x2x5 þ 0:0006262x1x4  0:002205x3x5  92;
g3 x
ð Þ ¼ 80:51249 þ 0:0071317x2x5 þ 0:0029955x1x2 þ 0:0021813x2
3  90;
g4 x
ð Þ ¼ 80:51249 þ 0:0071317x2x5 þ 0:0029955x1x2 þ 0:0021813x2
3  110;
g5 x
ð Þ ¼ 9:300961 þ 0:0047026x3x5 þ 0:0012547x1x3 þ 0:0019085x3x4  20;
g6 x
ð Þ ¼ 9:300961 þ 0:0047026x3x5 þ 0:0012547x1x3 þ 0:0019085x3x4  25;
78  x1  102;
33  x2  45;
27  xi  45
ði ¼ 3; 4; 5Þ:
1. Use the metaheuristic algorithms, such as genetic algorithm, harmony search, par-
ticle swarm optimization, simulated annealing, ant colony optimization, bat algo-
rithm, and cuckoo search algorithm, to determine an optimal solution to the
problem by writing code in MATLAB software.
2. Plot the convergence of these metaheuristic algorithms to the optimal solution.
3. Perform a sensitivity analysis on the parameters of the algorithms such as population
number and number of generations, the rate of crossover, the rate of mutation, etc.
Which parameter algorithms have the most effect on convergence? A convergence
plot could be best minimum in the population versus the number of function calls or
the best minimum in the population versus generation. Which method is better?
Problem 7: Nonlinear optimization
For optimizing the following functions (Fig. 2.16), compare one or more of the
following algorithms.
Petroleum well optimization
65
1. The NeldereMead downhill simplex
2. Particle swarm optimization
3. Ant colony optimization
4. Simulated annealing
5. Random search
6. Steepest descent
Problem 8: Well placement and number of wells optimization
In this problem, the aim is the optimization of the number, location, and drilling time of
the wells in the single-realization ﬁeld, in which two predrilled wells are planned. A
summary of the problem deﬁnition can be found.
1. Determine the number of wells to be drilled.
2. Determine their location.
3. Determine the timing of drilling the wells.
Figure 2.16 Functions.
66
Methods for Petroleum Well Optimization
Single realization
Objective function: NPV
Field optimization time: 8 years
Well drilling cost: 3 MM USD per well
Oil price: 50 USD per barrel
Water production cost: 6 USD per barrel
Discount rate: 8%
Predrilled well 1 location: Cell 79
Predrilled well 2 location: Cell 447
Properties
Reservoir type 2 phase, black oil
Dimensions: 20  40  1
Grid size: 100 m  100 m  20 m
Top depth: 2700 m
Initial pressure: 2000 psi
Porosity: 0.2
Initial water saturation: 0.25
Fault 1: Between rows 13 and 14, transmissibility: 0.2
Fault 2: Between rows 27 and 28, transmissibility: 0.05
Fetkovich Aquifer properties: PI:5, Volume: 1.0E9, Eastern side of the ﬁeld
Wells: BHP controlled; set be 500 psi (Fig. 2.17)
(The Eclipse DATA ﬁle can be found at page 73 of Nezhadali, 2019)
Figure 2.17 Predrilled wells location. Modiﬁed from Nezhadali, M., 2019. Hybridization of Gradient-Based
and Gradient-free Optimization Techniques for Simultaneous Optimization of Number of Wells, Their
Location and Drilling Time in a Two-Dimensional Reservoir. MSc Thesis, Department of Petroleum Engi-
neering, Stavanger University, Stavanger.
Petroleum well optimization
67
Problem 9: ROP Optimization by SA
Use the simulated annealing algorithm (SA) to ﬁnd the minimum of function ROP in
Table 2.2 using the following cooling schedules:
1. Linearity decreasing
2. Geometrically decreasing
where: D is true vertical depth of drilling (ft.); gp is pore pressure gradient (lbm/gal); rc is
ECD (lbm/gal);
W
db

t is threshold bit weight per inch of bit diameter at which the bit
begins to drill (1000 lbf/in.); W
db is bit weight per inch of bit diameter (1000 lbf/in.); h is
fractional tooth dullness; Fj is hydraulic impact force beneath the bit (lbf); and a1 to a8 are
constants that must be chosen based on local drilling conditions.
Total bit hours (TBH) is measured in working hours, and e is the average efﬁciency, that
is, 120 for rock bits and 550 for polycrystalline diamond compact (PDC) bits. The main
part of the gathered data corresponded to drilling with roller-cone bits. This index was
Table 2.2 Sample of ﬁnal inputs.
X1
X2
X3
X4
X5
X6
X7
ROP
9125
1.59
60
0.35
0.01
9.5
4436
5.36
9181
1.59
60
0.55
0.02
9.5
4463
2.32
9223
1.59
60
0.68
0.02
9.5
4484
2.66
9332
1.59
60
0.88
0.02
9.5
4537
4.51
9469
1.59
60
1.1
0.02
9.49
4604
5.78
9600
1.59
60
1.3
0.01
9.63
744
5.46
9614
1.59
60
1.4
0.01
9.79
4807
1.31
9660
1.59
60
0.12
0.01
9.82
4830
3.28
9738
1.59
60
0.32
0.01
9.79
4870
3.28
9804
1.59
60
0.52
0.01
10.2
5107
2.73
9843
1.59
60
0.65
0.01
10.2
5127
2.46
9850
1.59
60
0.66
0.01
10.3
5131
3.28
Parameter
Range
X1
9125e11,549
X2
0.22e2.57
X3
30e180
X4
0e1.99
X5
0.01e0.34
X6
9.13e12.17
X7
4436e6574
Based on Arabjamaloei, R., Shadizadeh, S., 2011. Modeling and optimizing rate of penetration using intelligent systems in
an Iranian Southern oil ﬁeld (Ahwaz oil ﬁeld). Petrol. Sci. Technol. 29 16, 1637e1648, https://doi.org/10.1080/
10916460902882818.
68
Methods for Petroleum Well Optimization
used instead of data for bit tooth wear, which is not accessible when the bit is working
downhole, and there is no unique measurement method to express the value of bit
tooth wear.
Estimated ROP shows the optimum ROP with respect to available surface and
subsurface equipment. This model is presented in the following:
ROP ¼ ð f1Þ  ð f2Þ  ð f3Þ  .  ð f8Þ
f1 ¼ e2:303a1 ¼ K
f2 ¼ e2:303a2ð10000DÞ;
f3 ¼ e2:303a3D0:69ðgprcÞ;
f4 ¼ e2:303a4DðgprcÞ;
f5 ¼
2
6
6
6
4
W
db


W
db

t
4 
W
db

t
3
7
7
7
5
a5
; f6 ¼
N
60
a6
;
f7 ¼ ea7h;
f8 ¼
 Fj
1000
a8
X1 ¼ DepthðftÞ
X2 ¼
WOB
1000  dn ð1000 lbf =inÞ
X3 ¼ RPMðrev=minÞ
X4 ¼
r  q
350  q  dn
X5 ¼ Iw ¼ TBH
e
X6¼ECD¼MW þ DPann
0:052L ðppgÞ
X7¼Hydrostatic Head ¼ 0:052
 MW  TVDðpsiÞ
Problem 10: ROP model for reamers by GA
Reamers are an integral part of Deepwater Gulf of Mexico (GOM) drilling, and their
performance signiﬁcantly impacts the economics of well construction. This problem
presents a novel programmatic approach to modeling the ROP for reamers and
improving drilling efﬁciency. The proposed objective function and constraints consti-
tuting the optimization problem of selecting operational parameters to efﬁciently drill
Deepwater GOM wells (without positive displacement motorsdPDMs) are presented in
Fig. 2.18.
1. Solve the problem with a genetic algorithm (GA) using your assumed input data for
optimal drilling parameters.
2. Convert the single-objective optimization problem to a multiobjective optimization
problem.
3. The framing of the problem is a geometric program; convert the problem to a SO
problem.
Petroleum well optimization
69
Nomenclature
A terminal point of the ﬁrst section
ACO ant colony optimization
AIA artiﬁcial immune algorithm
B terminal point of the second section
BA bees algorithm
BBO biogeography-based optimization
BFO bacteria foraging optimization
DE differential evaluation
DHTRQ the downhole torque (kft-lb)
Figure 2.18 The rate of penetration (ROP) model for reamers. Modiﬁed from Soares, C., Armenta, M.,
Panchal, N., 2020. Enhancing Reamer Drilling Performance in Deepwater Gulf of Mexico Wells. SPE Drilling
& Completion. https://doi.org/10.2118/200480-PA.
70
Methods for Petroleum Well Optimization
DHWOB the downhole weight on bit (klb)
EA evolutionary algorithm
EP evolutionary programming
FTRQ the frictional torque (kft-lb)
GA genetic algorithm
GEM grenade explosion method
GP genetic programming
GSA gravitational search algorithm
HS harmony search
KPI key performance indicator
O start point of sidetracking trajectory
PSO particle swarm optimization
SA simulated annealing
STRQ the surface torque (kft-lb)
SWOB the surface weight on bit (klb)
T center point of the target window
TLBO teachingelearning-based optimization
TOR the torque on reamer (kft-lb)
TS Tabu search
WFA water ﬂow-like algorithm
WOR the weight on reamer (klb)
a inclination angle ()
f azimuth angle ()
ka;1 build rate of ﬁrst turn section (/30 m)
kf;1 turn rate of ﬁrst turn section (/30 m)
ka;2 build rate of second turn section (/30 m)
kf;2 turn rate of second turn section (/30 m)
References
Arabjamaloei, R., Shadizadeh, S., 2011. Modeling and optimizing rate of penetration using intelligent
systems in an Iranian Southern oil ﬁeld (Ahwaz oil ﬁeld). Petrol. Sci. Technol. 29 (16), 1637e1648.
https://doi.org/10.1080/10916460902882818.
Arevalo, Y
.I., Fernandes, A.J., 2012. Quantiﬁcation of drillstring-integrity-failure risk using real-time
vibration measurements. SPE Drill. Complet. 27, 216e222. https://doi.org/10.2118/147747-PA.
Arevalo, Y
.I., Medina Marquez, Y
., Naslausky, A., 2010. Drilling mechanics challenges and strategies to
reach new exploratory Deepwater frontiers in Brazil. In: SPE Annual Technical Conference and
Exhibition. Society of Petroleum Engineers.
Badru, O., 2003. Optimization Using the Quality Map Approach. Master’s Thesis. Department of
Petroleum Engineering, Stanford University, Stanford.
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Texas At Austin, Austin.
Open-source code
Y
ou can develop your models using open-source code for different tools available
on https://github.com/
Examples include:
Nelder-Mead simplex method
Geometrical Optimization Toolbox
Multiobjective optimization methods
Genetic Algorithms with Python
Optimization with Genetic Algorithm in Matlab
Drilling Rate of Penetration (ROP) prediction
The python tool for well trajectories
Petroleum well optimization
73

TYPE Brief Research Report
PUBLISHED 22 May 2024
DOI 10.3389/feduc.2024.1396377
OPEN ACCESS
EDITED BY
Silvia F. Rivas,
Universidad de Salamanca, Spain
REVIEWED BY
Amira Benabdelkader,
Université Frères Mentouri Constantine 1,
Algeria
Amina Guerriche,
Université Frères Mentouri Constantine 1,
Algeria
*CORRESPONDENCE
Daniela Galatro
daniela.galatro@utoronto.ca
RECEIVED 05 March 2024
ACCEPTED 09 May 2024
PUBLISHED 22 May 2024
CITATION
Chakraborty S, Kalhori SK, Gonzalez Y,
Mendoza J and Galatro D (2024) Student
perception of sustainability in industry:
a case study in an undergraduate petroleum
processing course.
Front. Educ. 9:1396377.
doi: 10.3389/feduc.2024.1396377
COPYRIGHT
© 2024 Chakraborty, Kalhori, Gonzalez,
Mendoza and Galatro. This is an open-access
article distributed under the terms of the
Creative Commons Attribution License
(CC BY). The use, distribution or reproduction
in other forums is permitted, provided the
original author(s) and the copyright owner(s)
are credited and that the original publication
in this journal is cited, in accordance with
accepted academic practice. No use,
distribution or reproduction is permitted
which does not comply with these terms.
Student perception of
sustainability in industry: a case
study in an undergraduate
petroleum processing course
Sourojeet Chakraborty1, Sadafnaz Kashi Kalhori2,
Yris Gonzalez3, Jorge Mendoza3 and Daniela Galatro2*
1Stable Isotope Laboratory, Department of Earth Sciences, University of Toronto, Toronto, ON,
Canada, 2Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto,
ON, Canada, 3Facultad de Ciencias Naturales y Matemáticas, Escuela Superior Politécnica del Litoral
(ESPOL), Guayaquil, Ecuador
Research demonstrates a predominantly negative public perception of the
oil and gas (O&G) industry, regardless of initiatives created to minimize the
environmental impact. This might be attributed to a lack of open communication
and debate spaces where these initiatives are learned and discussed. To test
our hypotheses within a university setting, a major revamp of the course
“Petroleum Processing” in our university was implemented, where sustainability
concepts and open discussion were assimilated into the lecture content. Pre-
and post-surveys were conducted to assess students’ perceptions regarding
sustainability in the O&G industry before and after the course. Perceptions
remained unchanged following course delivery. However, students believe they
are more informed about the sustainability approaches implemented.
KEYWORDS
SDG 9: industry, innovation and infrastructure, course revamp, oil and gas (O&G)
industry, students’ perceptions
1 Introduction
The oil and gas (O&G) industry has been traditionally perceived as incompatible
with transparently addressing and successfully incorporating green technology and
sustainability initiatives into their supply chain operations (United Nations, 2017, 2023;
Dadd et al., 2023), with signiﬁcant discrepancies observed even today among companies
and geographies (Okeke, 2021; CAPP, 2023). An analysis of 150 annual reports of 15
O&G ﬁrms based in Europe, America, and Asia led Okeke (2021) allows us to conclude
that European companies have typically put more emphasis on environmental, social, and
economic components of environmental sustainability than their American and Asian
counterparts. This trend may be attributed to the regulatory pressure on implementing
sustainability measures and initiatives to enhance societal awareness toward fulﬁlling
such core sustainability practices (Okeke, 2021). Nevertheless, public perception of the
O&G industry continues to reﬂect distrust and dislike, which may potentially arise from
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Chakraborty et al.
10.3389/feduc.2024.1396377
its repeated non-compliance regarding environmental and social
issues (Theodori and Jackson-Smith, 2010). A survey conducted by
the consulting ﬁrm Ernst & Young found that over half of the 1,200
surveyed teenagers responded that the O&G produced presently is
“not worth the environmental impact” (Rassenfoss, 2019). When
compared to the public perception of various other industries, the
survey revealed that the respondents distrusted industrial giants
almost at the same level as healthcare; this distrust is only surpassed
by the banking and pharmaceutical sectors.
Despite signiﬁcant contribution toward climate change, the
O&G industry continues to, and likely will for the foreseeable
future, remain a key player in the global energy pool (Dewar
et al., 2022). As such, several major O&G players worldwide
are mandated to develop and implement integrated sustainability
initiatives and strategies to maintain their operating licenses while
global energy trends shift toward greener measures. Such strategies
primarily focus on (i) reducing operations-based emissions by
piloting and deploying commensurate technologies that monitor
waste/exit streams, (ii) research initiatives to develop novel
mitigation technologies, and (iii) diversifying toward the low
carbon energy sector (Dewar et al., 2022). However, commensurate
quantiﬁcation of these measures’ impact(s) remains challenging,
as several more longitudinal indicators are required. Moreover,
the public may also be unaware of novel transformative measures
undertaken by the O&G sector. We hypothesize this occurrence
because of: (i) a lack of open communication, and failure to
adequately highlight the spectrum of “green” initiatives undertaken
by the O&G sector, and (ii) a lack of spaces where people
may debate about the perceived/actual beneﬁts/drawbacks of
these measures.
To
test
our
hypotheses
within
a
university
setting,
a
major revamp of the elective course “Petroleum Processing”
in the Department of Chemical Engineering and Applied
Chemistry at the University of Toronto was implemented,
and key sustainability concepts, such as integration between
bioreﬁneries and petrochemical plants and hydrogen production,
were
assimilated
into
the
lecture
content.
A
succinct
simpliﬁed Life Cycle Assessment (LCA) comparison between
diﬀerent
processes/industries
was
also
performed.
Students
were
provided
with
a
space
to
discuss
these
impactful
environmental/sustainability
initiatives
and
oﬀer
opinions.
Students’
perceptions
were
recorded
using
a
pre-survey
at the beginning of the semester and a post-survey at the
end of the course. This curriculum transformation exercise
supports
preparing
our
engineering
students
and
future
leaders to tackle the challenges in the sustainable development
goals (SDG), particularly SDG 9 (industry, innovation, and
infrastructure), as the revamping of our course aligns with
encouraging students to actively upgrade industries by promoting
innovative sustainable technologies and ensuring their access
to information.
This article is structured as follows: section “2 Materials and
methods” details the methodology used to record and assess
students’ perceptions regarding sustainability in the O&G industry
before and after course delivery. Section “3 Results” presents and
discusses the results we obtained from the conducted surveys.
Section “4 Conclusion” discusses key conclusion that can be
drawn from the survey results as well as future directions for
this research.
2 Materials and methods
This section describes our sustainability integration strategy,
and the framework used to assess students’ perceptions.
2.1 Original course description and
structure
CHE 451 – Petroleum Processing is a fourth-year elective
course oﬀered to chemical engineering undergraduates. Typically,
the course is delivered in-person during the fall semester and has
15–25 students enrolled annually. The course aims to examine
the operations of the oil reﬁning industry from a primarily
macroscopic standpoint via block ﬂow diagrams (BFDs), while
its main learning outcome is for students to obtain a generic
overview of key petroleum processing operations, products, their
economic importance, as well as major safety and environmental
aspects employed in oil reﬁning. Prior to implementing the revamp
described in this work, the course traditionally included 16 lectures
(designated as L), as summarized in Table 1. The grading scheme
included a midterm (worth 25%), two assignments (A1 and A2),
each worth 10%, a ﬁnal project worth 20% (an essay on reﬁneries
for the future), and a ﬁnal assessment worth 35%. The lectures had
only one module discussing the environment and safety aspects
and the course content was predominantly industry focused, rather
than environment/climate-change focused.
2.2 Sustainability integration strategy
The imminent need to incorporate sustainability initiatives into
traditional engineering curricula has been an issue addressed by
several prior researchers. Some novel practices reported in the
literature are (i) the embedding of sustainability-based perspectives
into courses, (ii) careful design of new courses, and (iii) providing
pathways for students to specialize and gain expertise in sustainable
development (Ashraf and Alanezi, 2020). Out of these, the design
and introduction of new courses, which aim to educate and
empower students toward the ever-changing global perceptions
and needs, remains the most popular pedagogical pathway of
choice. Implementation of these strategies has recently revealed
that (i) most such studies neglect the learning process in favor
of assessing learning outcomes at a speciﬁc timeframe (a form
of testing bias); (ii) and students’ perceptions of sustainability
may often approach those of their instructors throughout the
learning experience, which may be viewed as a sign of conformance
(van Mierlo and Beers, 2020). We believe that this convergence
of ideological stance is to be avoided, and suitable learning
environments should be devised, where students may develop their
individual, diﬀerent stances on the subject matter, which may be
diﬀerent from their fellow students and instructors. Thus, we aimed
to develop and execute a course revamp in an unbiased, minimally
disruptive fashion, to assess any tangible shift in student perception
accurately. Therefore, this led to the design of a sustainability
integration strategy to address the following research questions: (i)
what are the current problems students perceive with the O&G
industry? and (ii) how can a teaching team facilitate spaces for
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Chakraborty et al.
10.3389/feduc.2024.1396377
TABLE 1 Original and revamped CHE 451 lectures (L) and assignments (A).
Original CHE 451
Revamped CHE 451
Code
Description
L1
Introduction to petroleum processing
Introduction to petroleum processing
L2
Reﬁnery feedstocks and products
Petroleum products and test methods
L3
Reﬁning processes
Processing operations in a petroleum reﬁnery
L4
Crude distillation
Lubricating oils
L5
Coking and thermal processes
Petrochemicals
L6
Catalytic cracking
Product blending
L7
Hydroprocessing and hydrotreatment
Supporting processes
L8
Catalytic reforming and isomerization
Alberta crude oil
L9
Alkylation
Safety and environmental issues
L10
Product blending
Biofuels in a petroleum reﬁnery
L11
Supporting processes
Hydrogen production
L12
Alberta crude/heavy crude oil
The reﬁnery of the future
L13
Lubricating oils and blending stocks
–
L14
Petrochemical feedstocks
–
L15
Environmental and safety aspects in reﬁning
–
L16
Reﬁnery of the future
–
A1
Mass/volumetric balance in a reﬁnery
Mass/volumetric balance in a reﬁnery
A2
Safety aspects in a reﬁnery
Safety aspects in a reﬁnery
open discussion regarding industry practices and future directions
for the energy transition? As a ﬁrst solution, we executed a
paradigm shift on the learning process by (i) aiming to deliver more
comprehensive lectures that describe the environmental impact
of oil production, reﬁning/petrochemical supply chain operations,
and actions implemented toward remediating it; (ii) facilitating in-
class discussions comparing the LCAs between oil and bioreﬁneries,
as well as blue/green hydrogen production; and (iii) facilitating
open discussions for students to debate the pros and cons of speciﬁc
sustainability-related issues.
2.2.1 Lectures and assignments
The modiﬁed lectures for CHE 451 are summarized in Table 1.
While lectures L1–L3 maintained a similar structure as those
being oﬀered previously (ensuring minimal disruption and some
conformity with previous content), the environmental impact of
oil reﬁning and petrochemical plants operations were incorporated
in L4–L8, accounting for 15% of the course content. L7 was
restructured to have 60% of the content discuss carbon capture
initiatives and hydrogen sulﬁde management in reﬁneries. L9 was
dedicated to describing in detail potential safety issues and risks
related to oil reﬁneries, potential environmental issues, mitigation
paths, and a summary of incidents experienced, and lessons learnt
by the global O&G industry. L10 was a completely new lecture
which introduced bioreﬁneries, highlighting feed characteristics,
operation and maintenance, and integration with existing oil
reﬁneries and/or petrochemical plants. Likewise, another novel
lecture, L11, introduced hydrogen production in its entire color
spectrum (blue and green primarily, but also turquoise, pink,
yellow, gray, etc.). Moreover, a new assignment A2 was formulated
including safety aspects in oil reﬁneries. Students were asked
to complete a fault tree analysis on the side stripper of a
distillation column, perform a simpliﬁed Hazardous Operation
Procedure (HAZOP) analysis on a gasoline storage tank, and size
a pressure relief valve on a pressure vessel containing an ideal
hydrocarbon vapor.
2.2.2 Life cycle analyses discussions in-class
Life cycle analysis (LCA) is a reputed methodological
framework often employed to perform a detailed environmental
impact and feasibility assessment of a process/product through its
ﬁve life cycle stages: raw material extraction, manufacturing and
processing, transportation, usage and retail, and waste disposal. An
LCA analysis typically estimates resource consumption, including
energy or carbon emissions. However, despite several eﬀorts
toward standardization and universalization, LCAs tend to be
speciﬁc, as inventory data may often be limited, and they may
not necessarily estimate which product/process proves to be most
cost-eﬀective or best-performing. Nevertheless, we considered
that by resorting to a structured process that analyzed the life
cycles of bioreﬁneries, oil reﬁneries, and hydrogen production,
LCAs permit for a fair comparative assessment of their individual
environmental impact(s) and by extension, their sustainability.
For the purposes of course discussion, three existing LCAs
from the literature are selected: for oil reﬁning (Liu et al.,
2020), biodiesel (Sajid et al., 2016), and hydrogen production
(Wilkinson et al., 2023). The instructor provided a summary
of these papers during lecture L11, and a comparison table
was provided to the students for analysis. Student discussions
were to be based around comparing the assumptions and total
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emissions generated by each of these processes. Current LCAs
favor bioreﬁneries/hydrogen-based processes over O&G processes
in terms of sustainability, and this is in line with current global
energy market trends. However, the road toward a sustainable
alternative is not straightforward (expectedly), as standardization
eﬀorts are required to accurately evaluate/quantify sustainability
through the LCA framework between these options. This exercise
aimed to elucidate the inconsistencies between the assumptions
employed in the LCAs, and the subsequent challenges in making
a fair comparison between diﬀerent processes.
2.2.3 General discussions
A set of discussion questions was provided to students during
the lectures, including: (i) “Can bioreﬁneries be integrated into
reﬁneries?”, (ii) “How have oil reﬁneries changed over the last
100 years?”, (iii) “Are petrochemicals the future of the O&G
industry?”, (iv) “Do we “need” oilsands?”, and (v) “Is hydrogen
the fuel of the future?”. For each of these prompts, students
discussed in depth the advantages and disadvantages of these
processes and their corresponding technologies, as well as any
foreseeable challenges. These questions were intentionally designed
to engage students in current “hot” topics, such as bioreﬁneries
and hydrogen production, and topics of controversy, such as
petrochemicals/oilsands. We hypothesized that these discussions
would promote changes in the students’ perception of the O&G
industry, which may be inferred from the post-survey results (see
section “2.2.4 Assessment of integration eﬀectiveness”).
In our general discussions we explored the integration of
bioreﬁneries into reﬁneries as an eﬀective path to shift the
knowhow from O&G toward clean energy, while simultaneously
revamping existent oil reﬁneries. We reviewed the environmental
regulations
required
for
upcoming
years,
and
discussed
contradictory reports pointing out that these eﬀorts might or
not be suﬃcient to tackle and/or eradicate the concern of the
emissions. Other “non-conventional” oil extraction/production
processes, such as oil-sands, were also discussed, speciﬁcally
the potential of oil-sands to produce more pollution than its
conventional counterpart, its contribution to Canada’s economy,
and current innovation eﬀorts to reduce environmental impact.
The discussion about petrochemicals and their role as
important raw materials for several processes and ﬁnal products,
was also incorporated in CHE 451. Recycling was the highlight of
the discussion, with particular emphasis on the challenges facing
current recycling practices worldwide.
Finally, a discussion surrounding blue hydrogen as a feasible
alternative to other energy sources, such as coal, petroleum, and
natural gas was incorporated. The beneﬁts of blue hydrogen
technology, such as the maturity of the production process, as
well as the technical challenges around hydrogen storage and
transportation were discussed.
2.2.4 Assessment of integration effectiveness
To eﬀectively identify and measure the extent of the impact
of the implementation of our sustainability integration strategy
in CHE 451, students were asked to anonymously ﬁll out a pre-
survey at the beginning and a post-survey at the end of the fall
semester. The pre-survey (Rassenfoss, 2019), run through Quercus
(the online platform of our university), included three sections. The
ﬁrst section featured two rating-based questions (Q1 and Q2) and
was intended to assess the extent of student knowledge on process
sustainability in the O&G sector. The second section was related to
the perception of the O&G industry and included 15 rating-based
questions (Q3 to Q14) and was intended to assess the student’s
perception of the O&G industry with respect to topics such as
innovation, level of pollution, technology, leadership, economic
importance, among others. The third section of the pre-survey
referred to student’s general industry perception, where seven
rating-based questions were intended to rank the perception of
the following industries: technology, automotive, retail, healthcare,
energy, investment banking, and pharmaceutical. The post-survey
was designed to assess changes in the students’ perception of the
course. This was done through three rating-based questions (Q1,
Q2, and Q14 from the pre-survey). The rating scale for both
surveys was deﬁned from 1 to 5, as shown in Supplementary
Appendices A, B.
3 Results
Figure 1 shows the results of the pre-survey. In Figure 1A, it
can be observed that students believe that they are fairly informed
about the sustainability approach adopted by the O&G (2.6/5.0).
At the same time, students have slightly positive perceptions on
how the industry eﬃciently tackles sustainability issues (3.0/5.0).
In Figure 1B, we can cluster the responses based on rankings,
observing that questions Q3 to Q7 provide the highest-ranking
values (greater than 4.0), followed by Q8/Q9/Q11/Q14 (greater
than 3.0), and Q10/Q12/Q13 (less than 3.0). The ﬁrst cluster
includes economic variables (e.g., importance to the national
economy, it is a major employer, and provides a valuable service)
and ranking the industry based on pollution. The second cluster
encompasses items regarding how innovative and technologically
advanced the industry is and the overall perception of the O&G
industry. Finally, the third cluster includes the students’ perception
of the O&G industry’s long-term importance, and the level of
trust students have in O&G companies. Based on the results of
the pre-survey, we can infer that the students believe the O&G
industry plays an important role in the economy (4.6/5.0) and fairly
rank its contributions to technological advances and innovations
(3.7/5.0); there is an interesting ﬁnding that students neither agreed
or disagreed that the O&G industry is “not worth the impact to the
environment” (2.5/5.0). Finally, the level of trust “to do the right
thing” reveals inconclusive results (2.8/5.0).
Figure 1C shows the students’ general industry perception
(technology, automotive, retail, healthcare, energy, investment
banking, and pharmaceutical). Students cautiously rank their
positive perception of all industries, with all rankings less than
3.1/5.0. The responses can be clustered into Technology and Energy
(“high” positive perception, with 3.1/5.0 average), Pharmaceutical,
Healthcare, and Automotive (“medium” positive perception, with
2.3/5.0 average), Retail and Investment Banking (“low” positive
perception, with 1.6/5.0 average).
Finally, when comparing the results from the pre- and post-
surveys (Q1, Q2, and Q14), Figure 2 reveals that students perceive
that they are better informed about the sustainability approach
adopted by the O&G industry at the end of the semester (+54.8%
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Chakraborty et al.
10.3389/feduc.2024.1396377
FIGURE 1
Pre-survey results: (A) questions 1 and 2, (B) questions 3 to 14, and
(C) general industry perception.
FIGURE 2
Comparison of University of Toronto students’ perception before
and after the course.
compared to the pre-survey). A +9.1% for Q2 reveals that students
have a “more positive” perception of the O&G industry eﬃciently
tackling sustainability issues; nevertheless, there is no substantial
increase in terms of their “positive” overall perception of the
industry (+2.8% for Q14). We believe that by revamping the course
Petroleum Processing, students became more informed about the
current trends and challenges related to the O&G industry and the
energy transition, as was reﬂected in the post-survey results.
Figure 3A shows the distribution of responses in the pre-
and post- surveys for the students. For Q1, a larger variation in
student responses can be seen in the pre-survey in comparison
to the post-survey. In the pre-survey, student responses for all
ﬁve level of perception ratings were reported, with 50.0% of the
students submitting a response of 2 (“disagree”). In comparison,
FIGURE 3
Distribution of responses in the pre- and post-surveys, (A) Q1,
(B) Q2, and (C) Q14.
in the post-survey, only three diﬀerent levels of perception ratings
were reported, with 72.7% of the students submitting a response
of 4 (“agree”). From this result we can infer that students entered
the class with diﬀerent levels of perceived knowledge surrounding
sustainability approaches in O&G and that by the conclusion of the
course, the majority of students reported.
Figure 3B shows that prior to the course delivery, the students’
rankings for Q2 followed a “normal-like” distribution. Following
course delivery, there is a shift in this distribution, with 63.6% of
students reporting a ranking of 3 (“neither agree or disagree”) and
no students reporting a ranking of 1 (“strongly disagree”).
Figure 3C shows minimal change in the distribution pattern of
survey responses for Q14, however there was an increase of 13.6%
in the number of students that reported a ranking of 4 (“agree”).
In order to investigate the generalizability of the pre-survey
ﬁndings, future work will include conducting similar surveys across
diﬀerent higher education institutions in diﬀerent locations. For
example, within Canada, students’ responses in provinces such as
Alberta may diﬀer, as in this province, O&G is a larger contributor
to its GDP, compared to the province of Ontario. Post-surveys
can also be conducted in diﬀerent institutions (including diﬀerent
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Chakraborty et al.
10.3389/feduc.2024.1396377
countries), considering diﬀerent levels of sustainability content,
to understand the impact that it has on students’ perceptions.
For instance, it would be quite interesting to compare students’
perceptions in countries where the O&G industry makes a large
contribution to the country’s GDP in comparison to Canada (The
World Bank, 2021). Most notably, our approach paves the pathway
for a more rigorous curriculum design/development. The role of
sustainability to create more inclusive, well-aware students has been
documented in the construction sector (Hayles and Holdsworth,
2008), and that the predominant aim of environmental education
is to change perceptions, bias, attitudes, to impact collective
behavior change (Cotgrave and Kokkarinen, 2010). The biggest
barriers to the incorporation of sustainability in an existent
curriculum are, perhaps, academic indiﬀerence and approach
toward teaching and assessment, student backgrounds, and lack
of eﬀective communication between the industry and academia
(Cotgrave and Kokkarinen, 2010). This work provides a “middle-
path” that does not shame the existent O&G sector, but instead,
consciously presents sustainability in this sector in an unbiased
fashion to university students, empowering them to critically assess
and take their unique stance and perceptions on this industry.
Our results might have signiﬁcant implications in the context
of curriculum design and sustainability issues, which becomes even
more relevant for future “environmentally conscious” generations.
It is worthwhile to comprehensively understand the drivers and
barriers for/toward curriculum change, to identify and develop
a compatible framework to realize these goals. The instructor’s
ability, as well as the techniques/modes of delivery of the lecture
content is also known to inﬂuence student perceptions (Stubbs
and Schapper, 2011). What is also most reassuring is the fact
that our approach has been shown to work in other scenarios,
such as the comparative study between the UK and Australia, to
develop appropriate curriculum design and promote sustainable
literacy in construction education (Cotgrave and Kokkarinen,
2010). Likewise, attempts in the USA (Vincent and Focht, 2011)
to obtain an ideal view of student curriculum reveal three
curricular models (Systems Science, Policy and Governance, and
Adaptive Management) as being most favored. A review on the
characteristics of a sustainable curriculum (Woo et al., 2012) reveal
that the key characteristics of curriculum structure should be based
on complexity of knowledge (being ﬂexible and permeate at a
given discipline level), contextualization, prospective orientation,
as well having consistency between theoretical concepts and
practical cases. Simultaneously, teaching methods for instructors
are more valued if they incorporate authentic learning experiences,
reﬂection/introspection space, mutual learning, and research. Our
approach to revamp a university elective curriculum builds on these
“best practices” and is likely to pave the way for more pedagogical
revamps across several universities in the future. As learning
competencies become more technology-based (Chakraborty et al.,
2023), it becomes progressively critical to integrate sustainability
initiatives toward a more Artiﬁcial Intelligence (AI) predominated
world; both contributing extensively toward the E.D. 4.0 goal
and the I.D. 4.0 competencies. There is a growing revolution of
Higher Education Institutions (HEI) to integrate curricula with
the UN’s Sustainable Development Goals (Cuevas-Cancino et al.,
2024), and our work serves to clearly demonstrate how such
pedagogical initiatives may be integrated/revamped into existing
university curricula.
4 Conclusion
In this work, an undergraduate course in Petroleum Processing
was revamped to include sustainability-related content, as well as
the facilitation of open discussions to debate the pros and cons
of sustainability approaches adopted by O&G industry. A pre-
survey gathered information regarding the students’ perception
of the O&G industry, O&G industry with respect to other
industries, as well as how well-informed students believe they are
on the sustainability measures currently employed by the O&G
industry. A post-survey was administered following completion
of the course to assess changes in students’ perceptions related
to the pre-survey. We believe that the changes implemented in
the course Petroleum Processing make students more informed
about the current challenges facing the O&G industry regarding
sustainability and trends in approaches taken to contribute to the
energy transition by consciously presenting sustainability in this
sector in an unbiased fashion to university students, to ultimately
empowering them to critically assess and take their unique
stance and perceptions on this industry. Moreover, our results
might have signiﬁcant implications in the context of curriculum
design integrating sustainability issues for future “environmentally
conscious” generations.
Data availability statement
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
Ethics statement
The
studies
involving
humans
were
approved
by
the
University of Toronto, Ethics Protocol 44048. The studies
were conducted in accordance with the local legislation and
institutional requirements. The participants provided their written
informed consent to participate in this study.
Author contributions
SC: Formal analysis, Investigation, Validation, Writing –
original draft. SK: Conceptualization, Data curation, Formal
analysis,
Investigation,
Writing
–
original
draft.
YG:
Conceptualization, Formal analysis, Investigation, Resources,
Writing – review & editing. JM: Formal analysis, Investigation,
Resources, Visualization, Writing – review & editing. DG:
Conceptualization, Data curation, Formal analysis, Investigation,
Methodology, Project administration, Supervision, Validation,
Visualization, Writing – review & editing.
Funding
The author(s) declare that no ﬁnancial support was received for
the research, authorship, and/or publication of this article.
Frontiers in Education
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Chakraborty et al.
10.3389/feduc.2024.1396377
Conﬂict of interest
The authors declare that the research was conducted in the
absence of any commercial or ﬁnancial relationships that could be
construed as a potential conﬂict of interest.
Publisher’s note
All claims expressed in this article are solely those of the
authors and do not necessarily represent those of their aﬃliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/feduc.2024.
1396377/full#supplementary-material
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Frontiers in Education
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energies
Article
Life Cycle Costing and Eco-Eﬃciency Assessment of
Fuel Production by Coprocessing Biomass in Crude
Oil Reﬁneries
Pedro L. Cruz 1,*
, Diego Iribarren 2
and Javier Dufour 2,3
1
Low Carbon and Resource Eﬃciency, R&Di, Instituto de Soldadura e Qualidade, 4415-491 Grijó, Portugal
2
Systems Analysis Unit, IMDEA Energy, 28935 Móstoles, Spain; diego.iribarren@imdea.org (D.I.);
javier.dufour@imdea.org (J.D.)
3
Chemical and Environmental Engineering Group, Rey Juan Carlos University, 28933 Móstoles, Spain
*
Correspondence: plprieto@isq.pt
Received: 11 October 2019; Accepted: 5 December 2019; Published: 8 December 2019


Abstract: Biobased liquid fuels are becoming an attractive alternative to replace, totally or partially,
fossil ones in the medium term, mainly in aviation and long-distance transportation. In this regard,
coprocessing biomass-derived feedstocks in conventional oil reﬁneries might facilitate the transition
from the current fossil-based transport to a biobased one. This article addresses the economic and
environmental feasibility of such a coprocessing strategy. The biomass-based feedstocks considered
include bio-oil and char from the fast pyrolysis of lignocellulosic biomass, which are coprocessed
in ﬂuid catalytic cracking (FCC), hydrocracking, and/or cogasiﬁcation units. The assessment was
based on the standardized concept of eco-eﬃciency, which relates the environmental and economic
performances of a system following a life-cycle approach. Data from a complete simulation of
the reﬁnery process, from raw materials to products, were used to perform a life cycle costing
and eco-eﬃciency assessment of alternative conﬁgurations of the coprocessing strategy, which
were benchmarked against the conventional fossil reﬁnery system. Among other relevant results,
the eco-eﬃciency related to the system’s carbon footprint was found to improve when considering
coprocessing in the hydrocracking unit, while coprocessing in FCC generally worsens the eco-eﬃciency
score. Overall, it is concluded that coprocessing biomass-based feedstock in conventional crude oil
reﬁneries could be an eco-eﬃcient energy solution, which requires a careful choice of the units where
biofeedstock is fed.
Keywords: eco-eﬃciency; life cycle costing; life cycle assessment; coprocessing; biomass
1. Introduction
The increasing global energy demand and the progressive depletion, supply uncertainty, and
environmental issues of fossil fuels have led to a growing interest in alternative and renewable energy
sources [1]. Regardless of the expected upsurge in electromobility [2,3], the use of liquid fuels in
transport will remain important in the medium term [4,5] since subsectors such as long-distance
transportation and aviation cannot yet be eﬀectively powered by electricity. In this sense, liquid biofuels
would arise as the most straightforward alternative to fossil fuels, contributing to the global objectives
of greenhouse gas (GHG) emission savings [6,7] without requiring a signiﬁcant transformation of the
existing infrastructure and vehicle ﬂeets. However, the realization of a full biomass-to-fuel concept is
still far oﬀdue to the huge demand of fuels, the relatively low maturity of the technologies involved,
and their high capital and operating costs [8]. In the short-to-medium term, a realistic scenario could
rely on the concept of coprocessing, especially for the production of drop-in fuels [9,10]. This concept
mainly refers to the introduction of biomass-derived feedstock, in addition to conventional fossil
Energies 2019, 12, 4664; doi:10.3390/en12244664
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sources, in existing petroleum reﬁneries [11,12]. The transition from the current fossil-based transport
to a biobased one would be thereby enabled [13].
Currently in the EU, diesel and gasoline are mixed with biobased fatty acid methyl esters (FAME)
and ethanol, respectively [7]. Nevertheless, these fuels are typically related to ﬁrst-generation biomass
and therefore associated with sustainability concerns on land competition with food production [14].
Hence, second-generation biomass (i.e., biomass from nonfood feedstock, such as lignocellulosic
biomass from dedicated energy crops or agricultural and forestry waste) should be used for the
production of biobased liquid fuels. In this respect, since raw lignocellulosic biomass could not be
easily introduced directly in a reﬁnery, it could be converted into suitable intermediates. In particular,
biomass pyrolysis is often seen as the most likely biobased pathway to be integrated into a conventional
reﬁnery as a ﬁrst step before coprocessing [9]. The raw bio-oil produced through pyrolysis requires a
mild hydrodeoxygenation (HDO) process whose product (usually called HDO-oil) can be mixed with the
typical feed of some reﬁnery conversion units, for example, ﬂuid catalytic cracking (FCC) and catalytic
hydrocracking (HC) units [15–17]. In fact, taking into account product yields and operating conditions,
coprocessing in these reﬁnery units represents the most likely option [10,12,18,19]. Furthermore,
biomass pyrolysis also produces gases, used to heat the pyrolysis reactor and satisfy the energy needs
of the pyrolysis plant, and char, suitable to produce syngas through gasiﬁcation [20–23]. Hence,
in reﬁneries with dedicated coke gasiﬁcation processes, char coprocessing could be appropriate [24],
avoiding the erection of a facility only for char [25–27] while increasing hydrogen production (for
self-consumption and/or as a net product) [26,28–30].
Given the growing interest in coprocessing biofeedstock in conventional reﬁneries, several
works in the literature have focused on deep experimental studies in diﬀerent conversion units on a
laboratory/pilot scale [9,11,16,18,31–42]. In addition, previous works by the authors have evaluated the
eﬀect that coprocessing could have on an oil reﬁnery from a global perspective. To that end, modelling
and simulation of coprocessing units integrated into a reﬁning scheme were performed [17,43],
providing key data to assess and the system’s environmental performance from a life-cycle perspective.
Thus, a life-cycle assessment (LCA) of diﬀerent coprocessing reﬁnery schemes was performed in [44],
ﬁnding that coprocessing strategies could signiﬁcantly reduce the carbon footprint of the reﬁnery.
However, other environmental impacts such as acidiﬁcation, eutrophication, and abiotic depletion of
elements were found to increase, mainly due to the increased use of chemicals (including catalysts)
and the need for fertilizers.
To complete the feasibility assessment of coprocessing schemes, their environmental assessment
should be complemented with a thorough economic analysis. In this sense, the concept of eco-eﬃciency
matches this need for suitability assessment under economic and environmental aspects. It refers
to the delivery of competitively priced goods that fulﬁl human needs while progressively reducing
environmental impacts of products and resource intensity throughout the entire life cycle [45]. In fact,
the standardized eco-eﬃciency concept incorporates this traditional notion while stressing the life-cycle
perspective required for the economic and environmental assessment of product systems [46]. Even
though some eco-eﬃciency studies have been conducted for energy systems [47–50], there is a lack of
this type of study for biobased coprocessing strategies. Hence, this article aims to enrich the feasibility
assessment of coprocessing schemes by complementing the previous LCA [44] with a life cycle costing
(LCC) under the umbrella of an eco-eﬃciency assessment of the coprocessing of bio-oil and char in
conventional reﬁneries.
2. Materials and Methods
2.1. Deﬁnition of Case Studies
The reﬁnery considered to include biofeedstock coprocessing follows a deep conversion scheme
with the aim of reducing low-value byproducts such as fuel oils and asphalts and promoting the
production of the most demanded fractions (gasoline, diesel, and kerosene) [51]. As shown in Figure 1,
Energies 2019, 12, 4664
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it includes fractionation at atmospheric and vacuum conditions, FCC, HC, coking, and coke gasiﬁcation,
hydrotreatment, sulfur recovery, steam production, and product blending. For the sake of simplicity,
catalytic reforming, isomerization, alkylation and further product upgrading were not considered
since they are not especially aﬀected by biofeedstock coprocessing. On the other hand, the system
does include the biomass pyrolysis plant and the HDO process to produce HDO-oil for coprocessing.
The products of the reﬁnery comprise liqueﬁed petroleum gas (LPG), gasoline, kerosene, diesel, and
hydrogen (self-consumed within the reﬁnery and a net output in some cases).
Energies 2019, 12, x FOR PEER REVIEW 
4 of 18 
 
Figure 1. Refining process scheme and case studies of biofeedstock coprocessing (based on [44]). 
2.2. Economic Component 
Life cycle costing (LCC) is a methodology for the analysis of the total cost of a system along its 
entire life cycle. Net present value (NPV) is among the most common life-cycle economic indicators. 
It represents the difference between the present value of cash inflows and the present value of cash 
outflows over the lifetime of the system. In order to calculate the NPV of each of the four case studies, 
the following steps were addressed: 
• 
Cost estimation for standard equipment through size dimensioning and well-established 
correlations. 
• 
Cost estimation for specific equipment through literature correlations and rescaling. 
• 
Estimation of direct, indirect, and other costs to calculate the total investment cost (TIC). These 
costs were estimated as a function of the cost of equipment. 
• 
Estimation of annual variable costs. 
• 
Evaluation of annual cash flows over lifetime and NPV calculation. 
Equations for cost estimation from the literature usually involve different currencies and years. 
In this regard, all monetary values were converted into €2019. Spain was assumed as the reference 
location for the estimation of other costs, for example, feedstock, electricity, services, and land. The 
industrial price index (IPRI) and USD/€ exchange rates from the Spanish National Statistics Institute 
[58] were used to express data in €2019. 
2.2.1. Cost Estimation for Standard and Specific Equipment 
Figure 1. Reﬁning process scheme and case studies of biofeedstock coprocessing (based on [44]).
As done in the LCA study of coprocessing schemes in conventional reﬁneries [44], four case
studies were investigated to explore the feasibility of diﬀerent combinations of biomass feedstock
coprocessing in terms of eco-eﬃciency:
•
Case 1:
This is the base case, where the reﬁnery only processes petroleum crude (100
Mbbl/day). Angolan CLOV (Cravo-Lirio-Orquidea-Violeta oilﬁelds) was considered, which
is an intermediate-to-heavy sweet crude that may represent the future of reﬁning according to the
trend towards deep conversion from heavy crudes [44].
•
Case 2: The reﬁnery coprocesses crude oil (100 Mbbl/day) and HDO-oil in the FCC unit, and char
in the cogasiﬁcation section. HDO-oil accounts for 20 wt% of the riser feed (value reported as
the maximum for cofeeding [52,53]). HDO-oil and char are produced in a dedicated pyrolysis
plant from poplar biomass, and the amount of char corresponds to that coproduced along with
the bio-oil in the pyrolysis plant [54].
•
Case 3: The reﬁnery coprocesses crude oil (100 Mbbl/day) and HDO-oil in the HC unit, and char
in the cogasiﬁcation section. As in Case 2, the amount of HDO-oil corresponds to 20 wt% of the
hydrocracker feed and the amount of char is that coproduced with the bio-oil.
Energies 2019, 12, 4664
4 of 17
•
Case 4: It represents a combination of Cases 2 and 3, increasing the amount of HDO-oil coprocessed
in the reﬁnery. Crude oil (100 Mbbl/day) is fed into the reﬁnery together with HDO-oil in the FCC
and HC units and char in the cogasiﬁcation section. The amount of HDO-oil corresponds to 20
wt% of the riser feed plus 20 wt% of the hydrocracker feed. The amount of char corresponds to
that coproduced with the bio-oil in the pyrolysis section.
The validated models and process simulation of these coprocessing cases were directly retrieved
from previous works [17,43,44], where further details can be found, and used to obtain key data to
perform the eco-eﬃciency assessment. In fact, the environmental component of the eco-eﬃciency
assessment was directly based on the LCA results from [44], whereas the economic component was
speciﬁcally addressed in this article along with the joint economic–environmental interpretation in
terms of eco-eﬃciency. For the life-cycle evaluations, a cradle-to-gate approach was followed, covering
from crude oil extraction and biomass cultivation to fuel production at the reﬁnery. Most of the
inventory data were obtained from process simulation, while additional inventory data were taken from
Iribarren et al. [55] for HDO-oil and char as well as from well-established databases [56]. According to
the original LCA study [44], the environmental component of the study includes six life-cycle indicators
evaluated with the CML method (Institute of Environmental Sciences of Leiden University [57]): abiotic
depletion of elements (ADPe), abiotic depletion of fossil fuels (ADPf), global warming (GWP), ozone
layer depletion (ODP), acidiﬁcation (AP), and eutrophication (EP). ADPe is related to the extraction of
mineral resources and expressed in kg Sb eq, while ADPf is associated with the extraction of fossil fuels
and expressed in energy units (e.g., kJ). ODP refers to stratospheric ozone depletion and is expressed in
kg CFC-11 eq. AP, which is expressed in kg SO2 eq, is related to the emission of acidifying substances
to the air, while EP, which is expressed in kg PO43−eq, refers to nutriﬁcation because of emissions
of nutrients to air, water, and soil. GWP, expressed in kg CO2 eq, is associated with greenhouse gas
emissions to the air over a 100-year time horizon. In this respect, it should be noted that CO2 uptake
during biomass growth was quantiﬁed at the biomass plantation stage, thus avoiding the need for a
distinction between the biogenic and non-biogenic origin of subsequent emissions [44]. Finally, the
economic and eco-eﬃciency components of the study are detailed in Sections 2.2 and 2.3, respectively.
2.2. Economic Component
Life cycle costing (LCC) is a methodology for the analysis of the total cost of a system along its
entire life cycle. Net present value (NPV) is among the most common life-cycle economic indicators.
It represents the diﬀerence between the present value of cash inﬂows and the present value of cash
outﬂows over the lifetime of the system. In order to calculate the NPV of each of the four case studies,
the following steps were addressed:
•
Cost estimation for standard equipment through size dimensioning and well-established
correlations.
•
Cost estimation for speciﬁc equipment through literature correlations and rescaling.
•
Estimation of direct, indirect, and other costs to calculate the total investment cost (TIC). These
costs were estimated as a function of the cost of equipment.
•
Estimation of annual variable costs.
•
Evaluation of annual cash ﬂows over lifetime and NPV calculation.
Equations for cost estimation from the literature usually involve diﬀerent currencies and years.
In this regard, all monetary values were converted into €2019. Spain was assumed as the reference
location for the estimation of other costs, for example, feedstock, electricity, services, and land.
The industrial price index (IPRI) and USD/€ exchange rates from the Spanish National Statistics
Institute [58] were used to express data in €2019.
Energies 2019, 12, 4664
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2.2.1. Cost Estimation for Standard and Speciﬁc Equipment
Cost estimation for standard equipment was mainly based on the principal dimensioning and
design parameters from the simulation. On the other hand, some estimations were made by rescaling
from data reported for similar equipment, assuming a Williams scaling exponent of 0.7. Table 1
summarizes the cost estimation method used for general and speciﬁc equipment.
Table 1. Cost estimation method for general and speciﬁc equipment.
Equipment
Cost Estimation Method
Comments
Vessels (ﬂash separators,
storage tanks, distillation
columns, and some
reactors)
C = 13014·W0.92·
 L
D
−0.15·
 e
8
−0.21
W = 0.0246·D·(L + 0.8·D)·(e + x)
e =
PD·( D
2 )·1000
St·E−0.6·PD + CA
C = cost (€2019); W = weight of material (t); L=
height (m); D = diameter (m); e = thickness (mm);
x = complexity factor (2–4); PD = design pressure
(kg/cm2g); St = constant (1055 for carbon steel); E
= constant (0.85); CA=corrosion addition (3 mm)
Heat exchangers
C = 8500 + 1560·A0.75 for A < 250 m2
C = 418·A
for A ≥250 m2
C = cost (€2019)
A = exchanging area (m2)
Pumps
C = 6900 + 206·Q0.9
C = cost (USD2017)
Q = volume ﬂow (l/s)
Compressors (piston)
C = 22000 + 2300·W0.75
C = cost (USD2017)
W = required power (kW)
Furnaces
C = 0.25·Q
for Q < 6·106 kcal/h
C = 0.18·Q
for Q ≥6·106 kcal/h
C = cost (€2019)
Q = required heat duty (kcal/h)
Distillation columns
Calculated considering:
- Column as a vessel
- Reboiler and/or condenser as heat
exchangers
- Plates: 6000 €2008 each
Stage eﬃciency: 85%
Liquid residence time at the bottom of the
column: 2.5 min
CDU (crude distillation
unit)
Correlated from [59]
Included: side cuts with strippers, all battery
limits (BL) process facilities, heat exchangers
VDU (vacuum distillation
unit)
Correlated from [59]
Included: all facilities, three-stage jet system for
operation of ﬂash zone at 30–40 mmHg, coolers
and exchangers
FCC (ﬂuid catalytic
cracking unit)
Correlated from [59]. Catalyst (Zeolite Y)
initial load of 40 t (1 USD2001 per pound)
Included: product fractionation, gas compression
of lights, complete reactor–regenerator section,
heat exchangers
HC (hydrocracking unit)
Correlated from [59]; catalyst initial load
(Ni–Mo/Al2O3) of 175 USD2005 per barrel of
feed a day
Included: stabilization of gasoline, fractionation,
complete preheat, reaction, hydrogen circulation
facilities, hydrogen sulﬁde removal, heat
exchangers, electric motor-driven hydrogen
recycle compressors
Coking unit
Correlated from [59]
Included: coker fractionator, hydraulic decoking
equipment, coke dewatering, crushing, coke
storage, coke drums designed for 50–60 psig,
blowdown condensation and puriﬁcation of
wastewater, heat exchangers
Gasiﬁer
Rescaled from [60]; material bed (dolomite)
initial load of 5.5 lb per metric ton of feed
Cost of dolomite included in the gasiﬁer cost
Tar reformer
Rescaled from [61]; catalyst (olivine) initial
load rescaled (172.9 USD2014/t)
-
WGS (water–gas shift)
reactors
Calculated considering:
- Shell as a vessel
- High-temperature shift catalyst Fe–Cr,
low-temperature shift catalyst Cu–Zn: 4.67
USD1994 per pound [62]
Catalyst load: calculated considering a gas hourly
space velocity of 600 h−1 (high-temperature shift)
and 1000 h−1 (low-temperature shift)
PSA (pressure swing
adsorption)
Rescaled from [63]
-
HDS
(hydrodesulfurization)
Correlated from [59]
Included: catalyst initial load, product
fractionation, complete preheating, reaction,
hydrogen circulation facilities, heat exchangers
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Table 1. Cont.
Equipment
Cost Estimation Method
Comments
Claus unit
Correlated from [59]
Included: Claus unit, three converters (reactors)
with initial charge of catalyst, incinerator and 150
ft tall stack, sulfur receiving tank, loading pump
and waste heat boiler
Steam boiler
Correlated from [64]
-
Biomass pyrolysis plant
Rescaled from [54]
Included: biomass pre-treatment, pyrolysis
reactor, one-step HDO to reduce oxygen content
to 15%, variable costs considered
2.2.2. Estimation of the Total Investment Cost
TIC involves the sum of the design, construction, and installation costs of the system. It is
composed of ISBL (inside battery limits), OSBL (outside battery limits), contingencies, and EPC
(engineering, procurement, and construction) costs [65], which were calculated as a function of the total
purchased equipment cost (TPEC) as shown in Table 2. ISBL investment represents the purchasing
and installation costs of all the equipment of the process. It includes materials, catalysts, engineering,
construction, and supervision costs. On the other hand, OSBL investment represents costs associated
with general services, interconnections, and commissioning. Moreover, contingencies represent likely
variations in the investment estimation, while EPC costs are those estimated for crude distillation unit
(CDU), vacuum distillation unit (VDU), FCC, HC, coking, hydrodesulfurization (HDS), pyrolysis and
Claus units as ﬁnal installed ones according to Table 1. Finally, some installed units involve the duty to
pay royalties, and thus paid-up royalties were also considered as a cost contributing to TIC [59].
Table 2. Method for the total investment cost (TIC) calculation. TPEC: total purchased equipment
cost; ISBL: inside battery limits; OSBL: outside battery limits; EPC: engineering, procurement,
and construction.
Item
Calculation Method
Equipment (TPEC)
Sum of all process equipment costs
Materials (M)
60% of TPEC
Engineering (En)
20% of (TPEC + M)
Construction (C)
60% of (TPEC + M)
Supervision of construction (SC)
10% of (TPEC + M)
ISBL
TPEC + M + En + C + SC
Services (S)
4% of ISBL
Interconnections (I)
8% of ISBL
Commissioning (Co)
4% of ISBL
OSBL
S + I + Co
EPC
Sum of EPC systems investments
Contingencies (Cont)
10% of (ISBL + I + S)
Paid-up royalties (R)
Estimated from [59]
TIC
ISBL + OSBL + EPC + Cont + R
2.2.3. Estimation of Annual Variable Costs
The operation of the reﬁnery involves costs related to the consumption of feedstock, general
services, pyrolysis plant operation, and others. In this sense, the main annual variable costs of the
system are those detailed in Table 3, which include:
•
Materials: crude oil, natural gas, process water, monoethanolamine (aq.), oxygen, air, and catalyst
replacement. It should be noted that the biomass consumed in the pyrolysis plant was not included
in this group but within the pyrolysis costs.
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•
General services: also known as utilities, including cooling water, heating steams, and electricity.
•
Pyrolysis costs: biomass consumption, electricity, waste disposal, catalysts, and cooling water
according to Peters [54].
•
Other costs: staﬀ, depreciation, insurance, and running royalties. Staﬀwas assumed to be 300
people in the reﬁnery, and 15 people in the pyrolysis plant (assuming a 5% increment in the
original staﬀ), with a mean gross salary of 40,000 €/year. Linear depreciation over 15 years was
considered (6.67% of TIC each year). Insurance was considered to be 1% of TIC. Running royalties
were considered for FCC, HC, and HDS according to [59].
Table 3. Main operating costs of the system.
Item
Cost
Unit
Comment
Materials
Crude
41.63
€/bbl
Mean average spot crude prices [66]
Hydrogen
550.00
€/t
[43]
Natural gas
4.69
USD/MMBtu
UK (Heren NBP Index) [66]
Processed water
0.66
€/t
[43]
Monoethanolamine (aq.)
0.134
€/kg
[43]
Oxygen
0.20
USD/kg
[67]
Replacement of catalysts and beds
FCC (Zeolite Y)
0.25–0.80
USD/bbl
[59], higher value assumed for coprocessing cases
HC (NiMo/Al2O3)
0.08–0.16
USD/bbl
[59], higher value assumed for coprocessing cases
Tar reforming (olivine)
172.9
USD/t
[61], assumed total replacement every 10 years
HTS (Fe–Cr)
4.67
USD/lb
[62], assumed total replacement every 3 years
LTS (Cu–Zn)
4.67
USD/lb
[62], assumed total replacement every 3 years
HDS (Co–Mo/ Al2O3)
0.03–0.06
USD/bbl
[59], 0.03 for HDS-GSLN, 0.05 for HDS-HNAP and
HDS-KERO, and 0.06 for HDS-GO
Services
Cooling water
0.03
€/m3
[43]
High-pressure steam
-
-
Self-produced in the reﬁnery
Low-pressure steam
-
-
Self-produced in the reﬁnery
Electricity
0.07
€/kWh
[43]
Pyrolysis costs
Biomass
110.81
€/t HDO-oil
[54], reference year 2013
Electricity
27.44
€/t HDO-oil
[54], reference year 2013
Waste disposal
0.12
€/t HDO-oil
[54], reference year 2013
Catalysts
1.87
€/t HDO-oil
[54], reference year 2013
Cooling water
1.19
€/t HDO-oil
[54], reference year 2013
2.2.4. Net Present Value Calculation
The net present value (NPV) is the diﬀerence between the present value of cash inﬂows and the
present value of outﬂows over a period of time. This economic indicator represents the proﬁtability
and economic potential of an investment, taking into account inﬂation and discount rates, annual
variable costs, and inﬂows from selling products. It was calculated using Equation (1):
NPV =
t=T
X
t=1
Ct
(1 + r)t
(1)
where Ct is the net cash ﬂow in the year t, r is the discount rate, and T stands for the total number of
years assumed for the project investment. Selling prices (without taxes) of products were assumed to
be 450 €/t for propane and butane; 0.39, 0.30, and 0.40 €/l for gasoline, kerosene, and diesel, respectively;
and 550 €/t for hydrogen [43]. Other general assumptions in the LCC study were 8000 annual operating
hours, 3 years of construction, and 30 years of operation, 10% discount rate, and 1% linear inﬂation.
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2.3. Eco-Eﬃciency Framework
The eco-eﬃciency indicator (or eco-eﬃciency score) was calculated according to Equation (2),
using the selected life-cycle economic indicator (NPV) in the numerator, monetizing the system’s
functional value, and a life-cycle environmental indicator (ADPe, ADPf, GWP, ODP, AP, or EP) in
the denominator. This deﬁnition of the system’s eco-eﬃciency means that the most favorable scores
should involve a high NPV and a low impact (under the speciﬁc environmental indicator considered).
This deﬁnition of eco-eﬃciency is in line with the standardized concept [46] as well as with other
related works [45,50,68].
EEi,j = NPVi
ji
(2)
where EEi,j represents the eco-eﬃciency indicator for the case study i (i.e., 1, 2, 3, or 4) and the life-cycle
environmental indicator j (ADPe, ADPf, GWP, ODP, AP, or EP for the whole lifetime of the reﬁnery).
Furthermore, the benchmarking of each case study against the base case (i.e., case study 1) was
performed through the ratio of the corresponding eco-eﬃciency scores. This ratio, known as factor-X
according to [46], quantiﬁes the relative level of eco-eﬃciency improvement or decline with respect to
the conventional reﬁnery (Equation (3)):
FXi,j =
EEi,j
EEcase 1,j
(3)
where FXi,j represents the factor-X of the case study i for the environmental indicator j.
3. Results and Discussion
3.1. LCC Results
3.1.1. Cost of Standard and Speciﬁc Equipment and Total Investment Cost
The costs of standard and speciﬁc equipment were estimated following the methodology detailed
in Section 2.2.1. Figure 2 shows the breakdown of costs by relevant section of the reﬁnery. When
compared to the base case (case study 1, i.e., conventional reﬁnery without biofeedstock coprocessing),
the introduction of HDO-oil in the reﬁnery (case studies 2–4) increases the cost of HC (cases 3 and
4) and FCC (cases 2 and 4) and adds the cost of the pyrolysis plant. The contribution of these three
sections means approximately half of the equipment costs in all the coprocessing cases. The rest of
equipment remains similar, only showing slight changes due to capacity increments.
Energies 2019, 12, x FOR PEER REVIEW 
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2.3. Eco-Efficiency Framework 
The eco-efficiency indicator (or eco-efficiency score) was calculated according to Equation 2, 
using the selected life-cycle economic indicator (NPV) in the numerator, monetizing the system’s 
functional value, and a life-cycle environmental indicator (ADPe, ADPf, GWP, ODP, AP, or EP) in 
the denominator. This definition of the system’s eco-efficiency means that the most favorable scores 
should involve a high NPV and a low impact (under the specific environmental indicator considered). 
This definition of eco-efficiency is in line with the standardized concept [46] as well as with other 
related works [45,50,68]. 
𝐸𝐸௜,௝= 𝑁𝑃𝑉
௜
𝑗௜
 
(2) 
where EEi,j represents the eco-efficiency indicator for the case study i (i.e., 1, 2, 3, or 4) and the life-
cycle environmental indicator j (ADPe, ADPf, GWP, ODP, AP, or EP for the whole lifetime of the 
refinery). Furthermore, the benchmarking of each case study against the base case (i.e., case study 1) 
was performed through the ratio of the corresponding eco-efficiency scores. This ratio, known as 
factor-X according to [46], quantifies the relative level of eco-efficiency improvement or decline with 
respect to the conventional refinery (Equation 3): 
𝐹𝑋௜,௝=
𝐸𝐸௜,௝
𝐸𝐸௖௔௦௘ ଵ,௝
 
(3) 
where FXi,j represents the factor-X of the case study i for the environmental indicator j. 
3. Results and Discussion 
3.1. LCC Results 
3.1.1. Cost of Standard and Specific Equipment and Total Investment Cost 
The costs of standard and specific equipment were estimated following the methodology 
detailed in Section 2.2.1. Figure 2 shows the breakdown of costs by relevant section of the refinery. 
When compared to the base case (case study 1, i.e., conventional refinery without biofeedstock 
coprocessing), the introduction of HDO-oil in the refinery (case studies 2–4) increases the cost of HC 
(cases 3 and 4) and FCC (cases 2 and 4) and adds the cost of the pyrolysis plant. The contribution of 
these three sections means approximately half of the equipment costs in all the coprocessing cases. 
The rest of equipment remains similar, only showing slight changes due to capacity increments. 
 
Figure 2. Distribution of equipment costs by section of the refinery (HC: hydrocracking; FCC: fluid 
catalytic cracking; HDS: hydrodesulfurization; GasCo: gas concentration unit). 
Figure 2. Distribution of equipment costs by section of the reﬁnery (HC: hydrocracking; FCC: ﬂuid
catalytic cracking; HDS: hydrodesulfurization; GasCo: gas concentration unit).
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As a consequence of the increase in the cost of equipment, and according to the methodology
detailed in Section 2.2.2, the implementation of coprocessing involves an increased TIC (Table 4). This
increase with respect to the base case is +20%, +21%, and +35% for cases 2, 3, and 4, respectively. TIC
also increases when expressed per installed capacity (MW and MWhannual), mainly due to the erection
of the pyrolysis plant. When compared to the base case (24.17 €/MWhannual), coprocessing adds 3.21,
3.71, and 5.17 € to the TIC per annual MWh of products in cases 2, 3, and 4, respectively.
Table 4. Results of the TIC (€) estimation for each case study.
Item
Case 1
Case 2
Case 3
Case 4
Equipment
152,737,685
163,681,797
161,492,770
166,398,964
Materials
91,642,611
98,209,078
96,895,662
99,839,378
Engineering
48,876,059
52,378,175
51,677,686
53,247,668
Construction
146,628,177
157,134,525
155,033,059
159,743,005
Supervision of construction
24,438,030
26,189,088
25,838,843
26,623,834
ISBL
464,322,561
497,592,663
490,938,021
505,852,851
Services
18,572,902
19,903,707
19,637,521
20,234,114
Interconnections
37,145,805
39,807,413
39,275,042
40,468,228
Commissioning
18,572,902
19,903,707
19,637,521
20,234,114
OSBL
74,291,610
79,614,826
78,550,083
80,936,456
EPC
665,637,106
881,094,086
897,721,145
1,051,973,398
Contingencies
52,004,127
55,730,378
54,985,058
56,655,519
Paid-up royalties
9,792,722
11,004,218
11,254,240
12,443,945
TIC
1,266,048,126
1,525,036,171
1,533,448,548
1,707,862,170
TIC (€/MW)
193,348
219,030
223,009
234,713
TIC (€/MWhannual)
24.17
27.38
27.88
29.34
3.1.2. Annual Variable Costs and Inﬂows
Annual variable (operating) costs account for 1521, 1609, 1598, and 1675 MM€ for cases 1, 2, 3, and
4, respectively. Figure 3 shows the contribution of each variable cost per MWh of product. It should be
noted that these costs refer to a normalized year, while inﬂation was considered for cash-ﬂow calculation
in the speciﬁc years. Furthermore, although catalyst replacement in some reactors (gasiﬁer, tar reformer,
water–gas shift (WGS)) occurs in diﬀerent years, the value considered in the analysis was annualized
and added to the rest of the annual costs for catalyst replacement. Nevertheless, the replacement costs
were considered in the expected replacement years for NPV calculation (Section 3.1.3).
As shown in Figure 3, crude oil, the main feedstock of the reﬁnery, was identiﬁed as the main
contributor to variable costs, representing 79–87% (1331 MM€/year and 22.9–25.4 €/MWh) depending
on the case study. It is distantly followed by depreciation (8–10%) and electricity (around 1.5%).
Hence, the ﬂuctuation in the price of crude oil has an important impact on the system’s economic
performance. Even though the absolute annual costs increase due to coprocessing, annual unit costs
slightly decrease (Figure 3): Case 4 shows the lowest annual unit cost (28.77 €/MWh), followed by case
2 (28.89 €/MWh) and case 3 (29.06 €/MWh). The relatively low price of the biomass feedstock and,
consequently, the low cost of the pyrolysis plant operation, in addition to the change in the fuel yields
of the reﬁnery, are behind this ﬁnding. Thus, the costs associated with the increase in amortization due
to the increased capacity of the reﬁnery and the operation of the pyrolysis plant are oﬀset.
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Figure 3. Distribution of annual variable costs in the refinery. 
Regarding the inflows of the refinery, Table 5 presents the economic values attributed to the 
products. In the conventional refinery (case 1), diesel represents 47.3% of the monetary incomes, 
followed by gasoline (26.4%) and kerosene (23.9%), whereas the remaining products only represent 
2.4%. When compared to the base case, the coprocessing scheme in case 2 (characterized by the use 
of HDO-oil in FCC) involves an increase in gasoline (+8%) and kerosene (+57%) revenues, which 
respectively represent 27.6% and 36.6% of the total incomes in case 2. The monetary inflow associated 
with hydrogen also increases (+39%) due to char cogasification. However, diesel revenues decrease 
(–27%) as a consequence of the changes in product distribution. Alternatively, the coprocessing 
scheme in case 3 (characterized by the use of HDO-oil in hydrocracking) was found to lead to an 
increase in the incomes related to both gasoline (+42%) and diesel (+8%) with respect to the base case, 
but at the expense of reducing kerosene revenues (−27%). In this case, hydrogen production does not 
fully meet the system’s hydrogen demand, and therefore hydrogen does not constitute an inflow but 
a net operating cost. Finally, case 4, as a combination of cases 2 and 3, shows increased revenues for 
gasoline (+42%) and kerosene (+45%) at the expense of reduced revenues for diesel (−25%), with 
gasoline, kerosene, and diesel representing 33.8%, 31.3%, and 32.2% of the total incomes, respectively. 
Table 5. Annual inflows (€) for each case study. 
Product 
Case 1 
Case 2 
Case 3 
Case 4 
Propane 
14,508,131 
17,919,320 
19,064,014 
23,264,346 
Butane 
28,138,364 
26,641,174 
30,248,564 
35,407,612 
Gasoline 
527,821,846 
567,469,293 
752,079,644 
747,424,460 
Kerosene 
478,529,206 
752,823,803 
351,254,968 
691,852,252 
Diesel 
945,892,588 
687,674,311 
1,025,693,078 
712,595,180 
Hydrogen 
5,071,940 
7,026,966 
- 
- 
TOTAL 
1,999,962,074 
2,059,554,867 
2,178,340,268 
2,210,543,849 
TOTAL (€/MWh) 
38.18 
36.97 
39.60 
37.97 
3.1.3. Net Present Value 
In order to estimate the NPV, the cash flows over the refinery lifetime were calculated in each 
case study considering costs, inflows, inflation, and discount rate. The resultant NPVs are 3512, 3134, 
4258, and 3776 MM€ for cases 1, 2, 3, and 4, respectively. Figure 4 shows the NPV evolution over the 
refinery lifetime. The NPV was found to follow the same trend in all the case studies considered. As 
stated in Section 2.2.4, a lifetime of 33 years was considered, with 3 years for design and construction 
and 30 years for operation. The following TIC payment distribution was considered during the first 
three years: year 1 for costs related to EPCs, TPEC, materials, engineering, services, interconnections, 
Figure 3. Distribution of annual variable costs in the reﬁnery.
Regarding the inﬂows of the reﬁnery, Table 5 presents the economic values attributed to the
products. In the conventional reﬁnery (case 1), diesel represents 47.3% of the monetary incomes,
followed by gasoline (26.4%) and kerosene (23.9%), whereas the remaining products only represent
2.4%. When compared to the base case, the coprocessing scheme in case 2 (characterized by the use
of HDO-oil in FCC) involves an increase in gasoline (+8%) and kerosene (+57%) revenues, which
respectively represent 27.6% and 36.6% of the total incomes in case 2. The monetary inﬂow associated
with hydrogen also increases (+39%) due to char cogasiﬁcation. However, diesel revenues decrease
(−27%) as a consequence of the changes in product distribution. Alternatively, the coprocessing scheme
in case 3 (characterized by the use of HDO-oil in hydrocracking) was found to lead to an increase in
the incomes related to both gasoline (+42%) and diesel (+8%) with respect to the base case, but at
the expense of reducing kerosene revenues (−27%). In this case, hydrogen production does not fully
meet the system’s hydrogen demand, and therefore hydrogen does not constitute an inﬂow but a net
operating cost. Finally, case 4, as a combination of cases 2 and 3, shows increased revenues for gasoline
(+42%) and kerosene (+45%) at the expense of reduced revenues for diesel (−25%), with gasoline,
kerosene, and diesel representing 33.8%, 31.3%, and 32.2% of the total incomes, respectively.
Table 5. Annual inﬂows (€) for each case study.
Product
Case 1
Case 2
Case 3
Case 4
Propane
14,508,131
17,919,320
19,064,014
23,264,346
Butane
28,138,364
26,641,174
30,248,564
35,407,612
Gasoline
527,821,846
567,469,293
752,079,644
747,424,460
Kerosene
478,529,206
752,823,803
351,254,968
691,852,252
Diesel
945,892,588
687,674,311
1,025,693,078
712,595,180
Hydrogen
5,071,940
7,026,966
-
-
TOTAL
1,999,962,074
2,059,554,867
2,178,340,268
2,210,543,849
TOTAL (€/MWh)
38.18
36.97
39.60
37.97
3.1.3. Net Present Value
In order to estimate the NPV, the cash ﬂows over the reﬁnery lifetime were calculated in each
case study considering costs, inﬂows, inﬂation, and discount rate. The resultant NPVs are 3512, 3134,
4258, and 3776 MM€ for cases 1, 2, 3, and 4, respectively. Figure 4 shows the NPV evolution over
the reﬁnery lifetime. The NPV was found to follow the same trend in all the case studies considered.
As stated in Section 2.2.4, a lifetime of 33 years was considered, with 3 years for design and construction
and 30 years for operation. The following TIC payment distribution was considered during the ﬁrst
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three years: year 1 for costs related to EPCs, TPEC, materials, engineering, services, interconnections,
paid-up royalties, and 1/3 of contingencies; year 2 for 1/2 of construction and 1/3 of contingencies; and
year 3 for commissioning, 1/2 of construction, and 1/3 of contingencies. Hence, as shown in Figure 4,
the ﬁrst years are associated with negative values, and afterwards, once operative, the system starts to
recover the investment.
g
,
,
 
paid-up royalties, and 1/3 of contingencies; year 2 for 1/2 of construction and 1/3 of contingencies; 
and year 3 for commissioning, 1/2 of construction, and 1/3 of contingencies. Hence, as shown in 
Figure 4, the first years are associated with negative values, and afterwards, once operative, the 
system starts to recover the investment. 
Regarding the effect of coprocessing with respect to the base case, case 2 (use of HDO-oil in FCC) 
was found to lead to a decrease in the profit (−11%). On the other hand, case 3 (use of HDO-oil in 
hydrocracking) results in a 21% NPV increase. In this case, despite the costs related to the pyrolysis 
plant and other additional costs such as catalyst replacement, the enhanced amount and distribution 
of products leads to significantly high inflows. Finally, case 4 shows an intermediate behavior, with 
a 7% NPV increase in year 33. 
 
Figure 4. Net present value (NPV) evolution in each case study. 
3.1.4. LCC Summary 
The analysis of TIC, variable costs, and NPV proved that coprocessing could be economically 
feasible. Nevertheless, coprocessing can be performed under different layouts and, therefore, 
different consequences compared to a conventional refinery. Coprocessing in FCC (case 2) involves 
an increase in the investment, mainly related to the FCC unit and the addition of the pyrolysis plant. 
Consequently, the operational costs associated with the pyrolysis plant, FCC catalyst replacement, 
and services grow. Nevertheless, the operating unit cost (per MWh of produced fuel) decreases since 
the production rate increases. Product revenues also increase due to the higher production (mainly 
gasoline and kerosene). However, the NPV of case 2 result is lower than that of the conventional 
refinery (case 1), implying less profit throughout the operation of the process. 
Coprocessing in hydrocracking (case 3) also increases the investment cost because of the HC unit 
and the pyrolysis plant, as well as the operating costs due to hydrogen consumption, HC catalyst 
replacement, services, and the pyrolysis plant. On the other hand, the rise in gasoline and diesel 
production involves an increase in inflows. In fact, the NPV of case 3 result is higher than that of the 
conventional refinery, which means an enhancement of the profit on the investment. 
Coprocessing in both FCC and HC (case 4) shows an intermediate behavior between cases 2 and 
3. It involves a growth of the investment, closely linked to the pyrolysis plant and the FCC and HC 
units, as well as of the operating costs related to catalyst replacement, services, and hydrogen 
consumption. Due to the product distribution achieved, gasoline and kerosene inflows increase, 
while the diesel inflow decreases. The NPV of this case is higher than that of the base case, but lower 
than that of case 3. 
Figure 4. Net present value (NPV) evolution in each case study.
Regarding the eﬀect of coprocessing with respect to the base case, case 2 (use of HDO-oil in FCC)
was found to lead to a decrease in the proﬁt (−11%). On the other hand, case 3 (use of HDO-oil in
hydrocracking) results in a 21% NPV increase. In this case, despite the costs related to the pyrolysis
plant and other additional costs such as catalyst replacement, the enhanced amount and distribution of
products leads to signiﬁcantly high inﬂows. Finally, case 4 shows an intermediate behavior, with a 7%
NPV increase in year 33.
3.1.4. LCC Summary
The analysis of TIC, variable costs, and NPV proved that coprocessing could be economically
feasible. Nevertheless, coprocessing can be performed under diﬀerent layouts and, therefore, diﬀerent
consequences compared to a conventional reﬁnery. Coprocessing in FCC (case 2) involves an increase
in the investment, mainly related to the FCC unit and the addition of the pyrolysis plant. Consequently,
the operational costs associated with the pyrolysis plant, FCC catalyst replacement, and services grow.
Nevertheless, the operating unit cost (per MWh of produced fuel) decreases since the production rate
increases. Product revenues also increase due to the higher production (mainly gasoline and kerosene).
However, the NPV of case 2 result is lower than that of the conventional reﬁnery (case 1), implying less
proﬁt throughout the operation of the process.
Coprocessing in hydrocracking (case 3) also increases the investment cost because of the HC unit
and the pyrolysis plant, as well as the operating costs due to hydrogen consumption, HC catalyst
replacement, services, and the pyrolysis plant. On the other hand, the rise in gasoline and diesel
production involves an increase in inﬂows. In fact, the NPV of case 3 result is higher than that of the
conventional reﬁnery, which means an enhancement of the proﬁt on the investment.
Coprocessing in both FCC and HC (case 4) shows an intermediate behavior between cases 2 and 3.
It involves a growth of the investment, closely linked to the pyrolysis plant and the FCC and HC units,
as well as of the operating costs related to catalyst replacement, services, and hydrogen consumption.
Energies 2019, 12, 4664
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Due to the product distribution achieved, gasoline and kerosene inﬂows increase, while the diesel
inﬂow decreases. The NPV of this case is higher than that of the base case, but lower than that of case 3.
It is important to remark that changes in the crude oil selected and/or in the quality of the
coprocessed HDO-oil and char, strongly dependent on biomass composition and pyrolysis conditions,
could signiﬁcantly aﬀect relevant aspects such as product distribution, and thus economic and
environmental results. However, regardless of speciﬁc implications, the conclusion on the potential
feasibility of coprocessing is not altered, given the validity of the data used in the study.
Finally, it should be noted that no economic penalty or externality was assumed according to
the origin of the fuels (fossil or biobased). If taxes were implemented by policy-makers, for example,
on fossil greenhouse gas emissions, coprocessing could, to a certain extent, contribute to keeping the
existing reﬁneries proﬁtable.
3.2. Eco-Eﬃciency Results
Based on the NPV results (Section 3.1.3) as well as on the LCA results retrieved from [44],
the eco-eﬃciency scores of each case study were calculated for each environmental indicator according
to Equation (2). The corresponding results are presented in Table 6. The higher the scores in each
category, the better the eco-eﬃciency performance achieved. Except for the ADPe-related eco-eﬃciency,
where case 1 involves the highest score, the most favorable eco-eﬃciency scores were found to be
associated with case studies coprocessing biomass-based feedstock. In particular, four of the six highest
eco-eﬃciency scores refer to case 3, while case 4 involves the most favorable result for the carbon
footprint-related eco-eﬃciency. A straightforward identiﬁcation of the most eco-eﬃcient case study is
not possible due to the dependence on the speciﬁc life-cycle environmental indicator considered.
Table 6. Eco-eﬃciency scores of each case study.
Eco-Eﬃciency Indicator (EE)
Case 1
Case 2
Case 3
Case 4
EEi,ADPe (k€/kg Sb eq)
99.38
69.75
90.70
65.61
EEi,ADPf (k€/kJ)
5.16·10−7
4.60·10−7
6.19·10−7
5.53·10−7
EEi,GWP (k€/kg CO2 eq)
3.83·10−5
3.73·10−5
5.05·10−5
5.20·10−5
EEi,ODP (k€/kg CFC-11 eq)
40.91
36.46
49.25
43.80
EEi,AP (k€/kg SO2 eq)
4.01·10−3
3.23·10−3
4.61·10−3
3.81·10−3
EEi,EP (k€/kg PO43−eq)
2.61·10−2
2.06·10−2
2.83·10−2
2.35·10−2
Given the common diﬃculty in understanding the dimensions and units of the eco-eﬃciency
indicators, the factor-X calculation (Equation (3)) was used since it facilitates the report of eco-eﬃciency
results and the benchmarking of the diﬀerent case studies against the reference case (i.e., case 1) [46,50].
Factor-X values above 1 indicate an improvement in eco-eﬃciency with respect to the conventional
reﬁnery, whereas values below 1 point to a decline in eco-eﬃciency.
As shown in Figure 5, the coprocessing of HDO-oil in FCC (case 2) was found to involve a
generalized decline in eco-eﬃciency when compared to the base case, which is closely linked to the
reduced NPV. On the other hand, coprocessing in HC (case 3) was found to lead to an improvement
in eco-eﬃciency for all the environmental categories considered, except for ADPe. For instance,
coprocessing biomass-based feedstock in hydrocracking shows a 32% improvement in the carbon
footprint-related eco-eﬃciency. The favorable (i.e., generally eco-eﬃcient) behavior of case 3 is
linked to the enhanced NPV, which keeps the favorable ADPf, GWP, and ODP results of case 3
while counterbalancing its environmental deterioration in terms of AP and EP (but not suﬃciently to
overcome the environmental decline in ADPe).
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Energies 2019, 12, x FOR PEER REVIEW 
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Figure 5. Factor-X for each case study. 
5. Conclusions 
This article used the LCC methodology and the standardized eco-efficiency concept to discuss 
the feasibility of coprocessing biomass-based feedstock in conventional petroleum refineries. From 
the LCC results, the economic feasibility of coprocessing was proven. In particular, coprocessing in 
hydrocracking and cogasification units was found to significantly improve the economic 
performance of the refinery. In contrast, coprocessing in FCC and cogasification involves a reduction 
in the net present value of the refinery. In between, coprocessing in FCC, hydrocracking, and 
cogasification units involve a moderate increase in the net present value of the refinery, supporting 
the effect of coprocessing in hydrocracking over the effect of coprocessing in FCC. 
The eco-efficiency assessment showed that coprocessing in hydrocracking and cogasification 
units generally improves the eco-efficiency of the refinery, for example, 32% improvement in the 
carbon footprint-related eco-efficiency score. However, opposite findings arose when coprocessing 
in FCC, while the eco-efficiency of coprocessing in FCC, hydrocracking, and cogasification units was 
found to be highly dependent on the specific life-cycle environmental indicator considered. Overall, 
it is concluded that coprocessing biomass-based feedstock in conventional crude oil refineries could 
be an eco-efficient energy solution, which requires a careful choice of the units where biofeedstock is 
fed. 
Author Contributions: P.L.C. performed the economic assessment; P.L.C. and D.I. performed the environmental 
and eco-efficiency assessment; all authors conceived the study, analyzed the data, and contributed to writing the 
article. 
Funding: This research has been partly supported by the Spanish Ministry of Economy, Industry and 
Competitiveness (ENE 2015-74607-JIN AEI/FEDER/UE). 
Acknowledgments: The authors thank Antonio Valente (IMDEA Energy) for valuable scientific exchange. 
Conflicts of Interest: The authors declare no conflict of interest. 
References 
1. 
BP. BP Energy Outlook 2018; BP: London, UK, 2018. 
2. 
A portfolio of power-trains for Europe: A fact-based analysis. The role of Battery Electric Vehicles, Plug-in 
Hybrids 
and 
Fuel 
Cell 
Electric 
Vehicle. 
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European Environment Agency. Electric Vehicles in Europe; European Environment Agency: Copenhagen, 
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Figure 5. Factor-X for each case study.
Finally, since case 4 was deﬁned as a combination of cases 2 and 3, it shows an eco-eﬃciency
improvement for three environmental indicators (ADPf, GWP, and ODP), but an eco-eﬃciency decline
for the remaining environmental indicators (ADPe, AP, and EP). In other words, the moderate NPV
increase when coprocessing biobased feedstock in both FCC and HC does not compensate for the
environmental penalty in several categories.
4. Conclusions
This article used the LCC methodology and the standardized eco-eﬃciency concept to discuss
the feasibility of coprocessing biomass-based feedstock in conventional petroleum reﬁneries. From
the LCC results, the economic feasibility of coprocessing was proven. In particular, coprocessing in
hydrocracking and cogasiﬁcation units was found to signiﬁcantly improve the economic performance
of the reﬁnery. In contrast, coprocessing in FCC and cogasiﬁcation involves a reduction in the net
present value of the reﬁnery. In between, coprocessing in FCC, hydrocracking, and cogasiﬁcation
units involve a moderate increase in the net present value of the reﬁnery, supporting the eﬀect of
coprocessing in hydrocracking over the eﬀect of coprocessing in FCC.
The eco-eﬃciency assessment showed that coprocessing in hydrocracking and cogasiﬁcation units
generally improves the eco-eﬃciency of the reﬁnery, for example, 32% improvement in the carbon
footprint-related eco-eﬃciency score. However, opposite ﬁndings arose when coprocessing in FCC,
while the eco-eﬃciency of coprocessing in FCC, hydrocracking, and cogasiﬁcation units was found
to be highly dependent on the speciﬁc life-cycle environmental indicator considered. Overall, it is
concluded that coprocessing biomass-based feedstock in conventional crude oil reﬁneries could be an
eco-eﬃcient energy solution, which requires a careful choice of the units where biofeedstock is fed.
Author Contributions: P.L.C. performed the economic assessment; P.L.C. and D.I. performed the environmental
and eco-eﬃciency assessment; all authors conceived the study, analyzed the data, and contributed to writing
the article.
Funding:
This research has been partly supported by the Spanish Ministry of Economy, Industry and
Competitiveness (ENE 2015-74607-JIN AEI/FEDER/UE).
Acknowledgments: The authors thank Antonio Valente (IMDEA Energy) for valuable scientiﬁc exchange.
Conﬂicts of Interest: The authors declare no conﬂict of interest.
Energies 2019, 12, 4664
14 of 17
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Index
Note: ‘Page numbers followed by “b” indicate boxes, those followed by “f ” indicate
ﬁgures and those followed by “t” indicate tables.’
A
Absolute weight, 403
Acidizing-water shutoff joint operation
technology, 385, 431
Actuators, digital twin, 2
Adaptive neurofuzzy inference system (ANFIS)
model, 258
architecture, 267e268, 268f
artiﬁcial intelligence models, 263e266
drill-off test data, Surmeh formation, 264t
LevenbergeMarquardt (LM) algorithm, 265
rate of penetration (ROP) prediction model,
266, 267f
two-layered feed-forward backpropagation
algorithm, 265
average percentage error, 273t
drilling rate prediction, 270, 271t, 272f
model performance analysis, 270e274
theoretical basis and justiﬁcation
fuzzy set, 266e267
linguistic variables, 266e267
membership function, 266e267
sequence involved, 268f
Takagi, Sugeno, and Kang (TSK) fuzzy
inference system, 268
Agglomerative clustering, 360
Analytic hierarchy process (AHP), 398
enhanced oil recovery strategy, 432f
hierarchical approach
absolute weight, 403
decision-making, 403
integrated hierarchy breakdown, 404f
principles, 404
relative weights, 403
steps and considerations, 404e406, 405f
high-rate gas well completion designs
absolute weighting matrix, 412f, 412t
AHP criteria, 407
criteria-weighting matrices, 411t
normalized matrices, 410t
pairwise criteria assessment, 409t
priority preference vector matrix, 410t
relative weighting preference vector, 411t
hydraulic fracturing, candidate well selection
consistency index (CI), 423
principles, 422
productivity, 424
random consistency indexes (RI), 422t
results obtained, 424, 424t
step involved, 422e424
sensitivity analysis, 408, 413t
structured hierarchical analysis, 408
Annular capacity, 324
Ant colony optimization (ACO), 125f, 125te126t
Anticollision constraints
ellipse of uncertainty, 117e118
separation factor (SF), 117e118, 117f
unintentional collision, 118
Artiﬁcial bee colony (ABC), 125f, 125te126t
Artiﬁcial neural network (ANN), 137, 137f
kick detection, 376f, 377e378, 378t
rate of penetration (AOP), 289e291, 290f
Attribute space, 397, 397f
Authority for expenditures (AFEs), 308
Automatic drilling systems
digital twins. See Digital twins
integrated real-time operation center, 21e25
Automation
categories, 14, 14f
closed-loop control, 14, 19f
deﬁnition, 13
envelope protection, 18f
envelope protection system, 18
human-automation system, 14
levels, 15
local automation, 14
mechanization, 14
modes, 17e20
multilevel control system, 18e20
semi-automation, 14
523j
Autonomy, automation, 20b
Average-linkage clustering, 362, 362f
Axial and rotational friction, 80f
Axial rotational kinetic energy, 239
Axial vibrations, 196, 197t
B
Backpropagation neural network
(BP-NN), 226
Balanced iterative reducing and clustering using
hierarchies (BIRCH), 363
Bending strain energy, 238
Bhattacharyya coefﬁcient, 462e463
Bit balling, 194e195, 194f, 206f
Bit dulling, 212, 213f
Borehole failure, 457e458, 458f
Bottomhole assembly (BHA)
cost function, 43e44
internal force vector, 43
RPM value, 43e44
stabilizer side force, 43
strain energy, 43
two-dimensional friction modeling, 84
Bottomhole balling, 195, 206e211, 207f
Bottomhole pressure (BHP), 341e342
Bourgoyne and Young (BY) model,
258, 262t
average percentage error, 273t
constant coefﬁcient, 262t
constants, 264t
simulated annealing algorithm, 264f
Build-sail type well, 99f, 100te101t
Buoyancy factor, two-dimensional friction
modeling, 84
C
Carrying capacity index (CCI)
annular velocity, 177
calculation, 177
clearance area, 177
consistency index, 177
consistency index and annular velocity, 178
curve generation, 178, 178f
deﬁnition, 177
model integration, 178e179, 178f
power law index, 177
Case-based reasoning (CBR) system
accuracy, 512e515, 513t
case representation, 482
cases, 480, 484
general domain knowledge, 485f
generalized episodes, 485f
generic CREEK concept, 484
structure, 485f
cyclical process, 480e483, 481f
databases, 479
drilling ﬂuids
data collection and processing, 505e508
lost circulation problem, 504e505, 505f
explanation engine, 482f
functionality, 479e480, 480f
general structure, 478
output parameters, 483
petroleum well design
case-based architecture, 492, 493f
case similarity, 497e498, 499f
engineering objects, 492, 493f
genetic algorithm, 499e500, 500f, 502e503,
503f
indexing attributes, 493e497, 494f
learning curve, 500e504, 500f
prototype system, 501
similarity values and total similarity, 501e502,
502f
storage process, 501
retain step, 482e483
retrieval step, 482
reuse step, 482e483
revise step, 482e483
rule-based reasoning integration, 488
similarity assessment, 483e484
technique, 480e481
Casing depth selection
casing and bit selection, 306f
casing points, 303e304, 305f
casing string conﬁgurations, 304, 305f
conductor pipe, 304
cost efﬁciency, 303e304
geological uncertainty, 303e304
intermediate casing, 304
liner string, 304
mathematical approach, 349
multiple criteria approaches, 349. See also Casing
seat selection method
production casing, 304
production tubing, 304
real-time approach, 349
surface casing, 304
524
Index
technological approach, 341e347
well cost estimation, 306e308
Casing point section (CPS) problem, 311, 311f
Casing seat selection method
burst pressure, 331t
elements, 319
gas-ﬁlled casing
burst loading, 321
deﬁnition, 321f
external load pressure, 321
internal load pressure, 320e321
low-density gas, 320
maximum permissible pore pressure gradient,
322
pore pressure, 321e322
scenario, 328
integrated method, 319
kick margin, 323e324, 329
kick margin and riser margin, 329
leaking tubing, 326e328
mud weight constraints, 322e323, 322f
multiple criteria, 320
numerical example, 330e331
remote real-time pore pressure monitoring
excessive overbalance, 332
formation-pressure-while-drilling (FPWD)
technology, 332
Gulf of Mexico well case study, 335e341
logging-while-drilling (LWD) services, 331
predrill pressure prediction, 333, 333fe334f
pressure prediction, 333e335, 335f
safe mud weight window, 332, 332f
velocity and pore pressure model, 332
riser margin
deﬁnition, 325f
effects, 325f
kick pressure, 326
lower bottomhole pressure, 324
minimum mud weight, 324
pseudo pore pressure, 324
scenario, 328e329
single fracture gradient curve, 330, 330f
weak point, 326, 329e330
well data, 331t
Casing string placement optimization
2D horizontal well trajectory, 309f
full approach methodology, 312e313
RSH oil ﬁeld, Southern Iran
casing setting depth intervals, 319f
drilling plan, 316e318
drilling wells, 314, 315t
optimization plan, 317fe318f, 318t
well paths, 316, 316f
wells plan view, 315f
uncertainty sources, 309e310
utility function, 310e311
Catenary well path
catenary curve, 95e96
conventional well path, 97
drag forces, 98f
freely hanging drill string, 96, 97f
geometry, 96e97
string length, 96e97
Chip hold-down effect, 195f
Choice problems, 387
Classiﬁcation and regression tree (CART)
technique, 278
Closed-loop automation, 14, 19f
Closed-loop reservoir management (CLRM),
61, 62f
Cluster analysis
deﬁnition, 359
hierarchical clustering (HC), 360e363
partition clustering methods, 363e365
Clustering using representatives (CURE), 363
Complete-linkage clustering, 362, 362f
Compressibility effect, 324
Computerization, 14
Computer programs, 477e478
Conﬁdence coefﬁcient, 446e448, 448f
Consistency index (CI), 423
Correlation coefﬁcient, 441, 441f
Coulomb law of friction, 79
CREEK system, 484
Criterion, 387
Cumulative distribution function (CDF),
444e445, 444f
Cuttings transport mechanisms, 168
D
Daily mud logging reports (DMLR), 258
3D analytical model equations, 78t
Data cleaning, 357
Data communication, 16e17
Data ﬂow, digital twin, 5f
Data integration, 357
Data mining, 358
association, 359
Index
525
Data mining (Continued)
categories, 358e359
classiﬁcation, 359, 366
clustering, 359e365
description, 358e359
kick detection
artiﬁcial neural network algorithm, 376f
class and attributes, 374e375
data mining evaluation criteria, 375e377
data points, 372e374
subsurface parameters, 372e374
system architecture, 375, 375f
optimum bit selection, 381e382
outlier detection, 359
prediction, 358e359
rate of penetration (ROP), 371e372, 381
real-time drilling
data aggregation, 367t
data streaming infrastructure, 366e368, 368f
decision support systems, 368
real-time operation centers (RTOCs), 367e368
wellsite information transfer standard markup
language (WITSML), 367e368
regression, 359
tracking patterns, 359
Data related technologies, 3
Data selection, 357
Data transformation, 358
Data warehouse, 369be371b
actions, 369b
algorithm, 369b
data mining process, 369b
decision-making process, 369b
new drilling scenario documentation, 369b
variables, 370b
Decision-maker, 385e386
Decision space, 396e397
Design problems, 387
Deterministic optimization, 39f
Deviation vector, 50, 50f
3D geomechanical model, 115
Diameter method, 362, 362f
Digital drilling optimization
hydromechanical speciﬁc energy (HMSE),
212e214
real-time surveillance
bit balling, 205e206, 206f
bit dulling, 212, 213f
bit mechanical efﬁciency, 202f
bottomhole balling, 206e211, 207f
friction losses, 205
mechanical efﬁciency factor (EFFM), 202
WOB and RPM tests, 204, 204f
Digital model, 4, 4f
Digital shadow, 4, 4f
Digital twins
actuators, 2
analytics techniques, 2
architecture, 3f
data, 1
data ﬂow, 5f
data related technologies, 3
decision-making, 8
deviations, 2
drilling monitoring
advanced mathematical model, 9
2D visualization, 9, 11f
3D visualization, 9, 11f
parameters, 9
real-time mathematical models, 9, 10f
time scales, 13f
well construction, 12, 12f
ﬁve-dimension digital twin model, 4e6
greater efﬁciency and safety, 7
high-ﬁdelity modeling technologies, 3
integration, 1
model-based simulation technologies, 4
modeling basis, 8
objective, 2
predictive maintenance and scheduling, 7
products and services personalization, 8
real-time remote monitoring and control, 7
scenario and risk assessment, 7
sensors, 1
synergies and collaborations, 7
value, 7e8
visualization, 2f
well construction, 12e13, 13f
Distance matrix, 381t
Distribution, 445e446, 449f
normal distribution, 445e446
uniform distribution, 445
Divisive hierarchical clustering (HC), 360
Dogleg severity, 95
Downhole torque (T), 214
Down-up principle, 322
526
Index
Drilling automation system, 14
control levels, 20b
control-time, 16
data communication, 16e17
data resolution and response time, 16
functionality, 15
modeling, 15e16
remote support and decision-making, 15
time-scale analysis, 15
well construction, 15
Drilling blind process, 505
Drilling engineering, data mining
Drilling hydraulics
dual drill string (DDS), 179, 180f
frictional pressure loss calculation, 182
multiple-pump systems, 180e181, 184f
pressure distribution single pump system, 182,
183t
principle layout, 184f
Reelwell drilling method (RDM), 179e184, 180f
single-pump system, 180e181, 184f
Drilling operations centers, 26f
Drilling optimization specialist (DOS), 24e25
Drilling performance optimization strategy, 243f
Drilling problem formulation
bottomhole assembly (BHA) conﬁguration,
43e44
hole cleaning optimization, 54e57
mechanical speciﬁc energy (MSE), 42e43
path optimization framework
completion cost, 46
cost function, 44
desired ﬁnal state variable range, 48
downhole motor system, 46e47
drilling cost function, 45e46
equation of motions, 47
normal distribution function, 45
optimization problem, 47
production cost, 45
quaternion dynamics, 48
slide drilling path optimization, 49b
standard deviation, 45
system dynamics, 47
tortuosity cost, 46
rate of penetration (ROP) optimization
analytical models, 39, 40t
constraints, 41e42
controllable and uncontrollable drilling
parameters, 41, 41f
data analysis, 39e41
objective function, 41
reamers, 70f
wellbore proﬁle energy, 50e54
Drilling system energy
energy ﬂow, 234e236, 236f
energy loss, 233e234, 233fe234f
theory
average translational velocities, 238e239, 239f
axial rotational kinetic energy, 239
dynamic energy system, 236e239
energy loss, 239
ﬁnite element method (FEM), 238
kinetic energy, 238
mechanical energy, 238
motor hydraulic energy input, 237
reaction axial force (DWOB) and torque
(DTOB), 237
strain energy, 238
surface torque, 236e237
surface weight on bit (SWOB), 237
work done, axial forces, 237
Drilling Systems Automation (DSA) Roadmap
Report, 26
Drill MAP, 336
Dual drill string (DDS), 179, 180f
Dual-gradient technique, 346f
Dynamic energy system, 236e239
Dynamic test (DT) methods, 278
E
Elementary models, wellbore friction
Aadnøy and Andersen’s new catenary model, 75
combined axial motion and rotation
backreaming during tripping, 88
casing drilling, 88
drag and tangential friction forces, 91
frictional capacity, 91
maximum inclination, 92
rotating liner, 88
spinning, 91
curved wellbore sections
capstan effect, 82
dogleg severity, 83, 83f
force balance, 81e82, 82f
pipe tension, 83
3D analytical model equations, 78t
forces and geometries, 76, 76f
geometrical projections, 77t
Index
527
Elementary models, wellbore friction (Continued)
straight wellbore sections
axial and rotational friction, 80f
buoyancy factor (BF), 76
Coulomb law of friction, 79
force balance, 79f
maximum wellbore angle, 79
pipe size and torque, 81
torque, 80e81
three-dimensional friction modeling, 87, 90f
two-dimensional friction modeling, 84e87
Energy ﬂow, drill string, 234e236, 236f
Enhanced hierarchical clustering, 362e363
Envelope protection automation, 18, 18f
Equivalent circulating density (ECD), 56,
253e254, 340
Equivalent static density (ESD), 336
Evolutionary algorithm, 142
Exponential utility curve, 313, 313f
F
Feedback control, 20b
Final consistency ratio (CR), 423
Finite element method (FEM), 238
Five-dimension digital twin model, 5f
connections, 6, 7f
data, 6
formula, 4e5
physical entities, 5
services, 6
virtual models, 5e6
FLAC3D software, 142e143
Flowing wellhead pressures (FWHP), 401
Force balance, straight inclined pipe, 79f
Formation drillability prediction
dual optimal problem, 229e230
feature extraction methods, 227e228
kernel function, 230
kernel principal component analysis (KPCA), 226
multidimensional heterogeneous model, 227, 227f
quantum particle swarm optimization-support
vector machine (QPSO-SVM), 226
execution, 230
parameters, 231
predicted and actual formation drillability, 232f
structural diagram, 228f
support vector machine (SVM), 228e229, 229f
Forward chaining, rule-based reasoning
technique, 487
Frictional pressure loss calculation, 182
Friction models
catenary well path, 95e99
dogleg severity, 95
oil well design. See Oil well design
radius of curvature well path model, 92e95
Full approach methodology, 312e313
Furthest-neighbor method, 362, 362f
Fuzzy analytic hierarchy process (FAHP) method,
385
Fuzzy c-means (FCM), 365
Fuzzy complementary matrix, 426
G
Gaussian distribution, 38f
General domain knowledge, 489
Generalized optimization problem, 33f
Generalized stress transformation system, 133f
Genetic algorithm (GA), 125f, 125te126t
Geomechanical constraints, 114e115
Geomechanical modeling, 450e452
Geometric programming (GP), 35e36
Gulf of Mexico (GoM) well, 335e341
casing seats scenarios, 340e341, 341f
composite wellbore hydrodynamics proﬁle,
339e340, 339f
logging-while-drilling (LWD) measurements,
335e336
pore pressure prediction
ﬁrst update, 338f
predrill pore pressure model, 336e338
second pore pressure update, 338e339, 338f
uncertainty, 335e336
H
Hareland-Rampersad (HR) model, 258
Harmony search (HS), 125f, 125te126t
Health, safety, and the environment (HSE),
307e308
Hierarchical clustering (HC)
agglomerative clustering, 360
average-linkage clustering, 362, 362f
balanced iterative reducing and clustering using
hierarchies (BIRCH), 363
CHAMELEON, 363
clustering using representatives (CURE), 363
complete-linkage clustering, 362, 362f
dendrogram, 361f
disadvantages, 362e363
528
Index
divisive clustering, 360
enhanced hierarchical clustering, 362e363
ROCK, 363
single-linkage clustering, 361e362, 362f
tree, 381
High-ﬁdelity modeling technologies, 3
Hole cleaning
angle factors, 166, 167t
carrying capacity index (CCI), 177e179
cuttings transport
area occupied, 173
conﬁgurations, 174f
cuttings bed height, 173
cuttings particle, 171, 172f
frictional force, 171
mechanisms, 168
multidispersed cuttings bed, 173
packing efﬁciency, 171e172
hole cleaning model
ﬂow rate correction factors, 170, 170t
maximum safe ROP, 170
rheology, 169, 169f, 171f
washed-out hole, 171
hydraulic simulators, 166
inclination angle
0e35 degrees, 174e175
35e60 degrees, 175
60e90 degrees, 175
parameters, 167e168, 168f
real-time modeling, 175e176
Hole cleaning optimization
cutting bed height, 54
downhole pressure, 56
drill pipe rotation effect, 54
equivalent circulating density (ECD), 56e57
formation condition constraint, 56
maximum allowable pressure, 55
maximum supplying ﬂow rate, 55
minimum jet velocity constraint, 55e56
objective function, 54
pressure-bearing capacity, 55
total pressure loss, 55
variables and constraints, 55e57
Human-automation system, 14
Hydraulic energy reduction factor, 214
polycrystalline diamond compact (PDC)
bits, 214
roller-cone bits, 215
Hydraulic fracturing (HF)
candidate well section (CWS), 419e420,
422e424
fracture geometry and stress status, 420f
lower permeability reservoirs, 419
Hydraulic optimization
drilling hydraulics. See Drilling hydraulics
elements, 158, 159t
ﬂow rate, 149, 158e159
hole cleaning. See Hole cleaning
hydraulic horsepower, 159
jet impact force, 159
nozzle pressure drop, 159e160
optimization criterion, 159e160, 160t
optimum nozzle and ﬂow rate selection
classical criteria, 161, 163t
earlier bit runs, 160e161, 161t
ﬂow ranges and optimization criteria, 162f
ﬂow rate constraints, 162f
fraction parasitic loss, 163
hydraulic parameters, 162t
parasitic pressure losses, 161, 162f
performance criteria, 160
proposed optimization criteria
cuttings transport analysis, 164
hole section, 164, 164t
minimum ﬂow rate, 165t
pressure losses and ﬂow rates, 165, 165f
three mud pumps, 165
two classical hydraulic optimization criteria,
166
Hydraulic system, 151f
classical hydraulic optimization criteria
carrying capacity, 157, 158f
fraction parasitic pressure loss, 155e156
hydraulic horsepower, 155
impact force, 155
jet impact criterion, 155
parasitic pressure loss, 156e157
shortcomings, 156e158
pressure losses
bottomhole assembly (BHA), 150
depth intervals, 153, 154f
drill string ﬂow velocity, 152
ﬂoating drilling rig, 150
ﬂow rate, 152
incompressible and frictionless system, 152
laminar ﬂow regime, 150
logarithmic relationship, 153
Newtonian ﬂuids, 150
Index
529
Hydraulic system (Continued)
non-Newtonian ﬂuids, 150
parasitic pressure drop, 151
turbulent ﬂow regime, 150
Hydromechanical speciﬁc energy (HMSE)
accelerated ﬂuid entrainment, 214
axial, rotary, and hydraulic energies, 213e214
downhole torque (T), 214
exploratory gas well, Niger Delta basin
dogleg severities (DLS), 216e218
drilling parameters and wellbore pressures,
216e218, 217f
GR-depth and HMSE-depth plots,
218, 219f
total ﬂow area (TFA), 218
lithology prediction
differential pressure, 216
polycrystalline diamond compact (PDC) bits,
214
rock compaction, 216
roller-cone bits, 215
pore pressure prediction
ﬁeld example, 222e225
ﬁxed cutter bits, 220
methodology, 221
pump-off/jet-impact force, 220
I
Indexing attributes, case-based system
indexing process, 496f
linguistic terms, 495
as linguistic variables, 494, 494f
retrieval process, 493e494, 497
Inference engine, 478
Integrated real-time operation center, 21f
collaborative well planning, 21e23
data management and archiving, 25
drilling optimization and detailed technical
analysis, 24e25
onshore collaboration center, 22f
predictive modeling, 24
real-time monitoring (RTM), 23e24
training and mentoring, 25
well engineering and planning, 23
Inverse distribution function, 445
J
Johancsik model, 92
K
Kappa statistic, 377
Kernel principal component analysis (KPCA), 226
Kern River Field study
dataset of sample cases, 509t
drilling blind, 505
drilling reports, 506
loss of circulation material (LCM) pill, 505
lost circulation events, 507e508, 507fe508f
Kick detection
artiﬁcial neural network algorithm, 376f
class and attributes, 374e375
data mining evaluation criteria
accuracy, 376f
artiﬁcial neural network (ANN), 377e378,
378t
comparison models evaluation, 378
confusion matrix, 375e376
error calculation formulae, 378t
kappa statistic, 377
precisionerecall curve (PRC) area plot, 377
receiver operator characteristic (ROC) area,
377
data points, 372e374
subsurface parameters, 372e374
system architecture, 375, 375f
K-means clustering, 364e365, 364f, 379e380
Knowledge-based systems, 477e478
Knowledge discovery in databases (KDD), 357,
358f
Knowledge-intensive case-based systems,
488e489
Knowledge representation, 478
Kurtosis, 442e443
L
Lagrange dual theory, 229e230
Lateral vibrations, 196, 197f, 197t
Learning curves, well evaluation
genetic algorithm performance, 501e502,
502fe503f
learning curve theory, 500e501
offshore wells, 501e502
prototype system, 501
speciﬁc activities, 503e504, 504f
storage process, 501
Learning (descriptive) problems, 387
Leptokurtic distribution, 442
530
Index
Level of coefﬁcient, 446e448, 448f
LevenbergeMarquardt (LM) algorithm, 265
Linear programming, 33
Local automation, 14
Logging-while-drilling (LWD) services, 331
Lost circulation case-based methodology
case-based reasoner, 508
data-matching function, 511
Euler distance, 509e510
geological formation, 510
objectives, 509e510
outcomes, 511e512
prediction accuracy, 512
Lost circulation problem, 504e505, 505f
M
Machine and model interface, automation
drilling, 17
Machine learning (ML)
rate of penetration (ROP) prediction
adaptive neurofuzzy inference system (ANFIS)
model, 263e274
data collection, 253
decision tree model, 274e279
multilayer perceptron neural network
(MLPNN), 279e281, 282t
multiple-linear regression, 257e263
optimization workﬂow, 253e257, 254f
prediction models, 252t
radial basis function (RBF) neural network
(RBFNN), 281e283
statistical and data-driven, 257e289
support vector regression model, 283e289
types, 250e253, 251f
Magnetic survey methods, 119e120
Managed pressure drilling (MPD)
advantages and disadvantages, 347, 348t
conventional drilling, 347
deﬁnition, 345
dual-gradient technique, 346f
principle, 345e347
single gradient drilling ﬂuid, 345f
system arrangements, 348f
Mathematical optimization
convex and nonconvex regions concept, 32, 32f
direct search methods, 34
geometric programming (GP), 35e36
indirect search methods, 34
linear programming, 33
mixed-integer (linear or nonlinear) programming,
33
multiobjective optimization, 36, 37f
optimization problems, 31e32
optimization terminologies, 32
robust optimization, 38e39
stochastic optimization (SO), 37e38
Maximum method, 362, 362f
Maximum number of neurons (MNN), 283
Mean, 440
Measurement while drilling system (MWD), 161
Mechanical earth models (MEMs), 454e455
Mechanical speciﬁc energy (MSE), 42e43
analysis trends
baselining trends, 198e199
bit balling, 199
founder point, 201f
operational considerations, 198e199
postdrill MSE analysis, 198
rock-cutting dysfunction, 199e200, 200f
severe bit dysfunction, 199f
data generation, 241te242t
drilling efﬁciency
bit balling, 194e195, 194f
bottomhole balling, 195
causes, 193e196
“drill-off” curve, 197e198, 198f
inadequate depth of cut (DOC), 197e198
input energy limit, 193e194
regions, 197e198
vibrations, 195e196
drilling system energy. See Drilling
system energy
Mechanization, 14
Mesokurtic distribution, 442
Metaheuristic algorithms, 34, 34f
Minimum mud pressure required (MMPR), 135
Minkowski’s distance, 497
Mixed-integer(linearornonlinear)programming,33
Model-based simulation technologies, 4
Model predictive control (MPC), 20b
Modular dynamic test (MDT) data, 258
MogieCoulomb (MG-C) failure criterion, 134
MohreCoulomb shear failure criterion
collapse and fracture, 468f
deterministic prediction, 461e462,
462f, 468f
failure model, 461
stochastic prediction, 462e469, 467f, 469f
Index
531
Monomial function, 35e36
Monte Carlo simulation (MCS)
general procedures, 450f
multivariate statistics
conﬁdence coefﬁcient, 446e448, 448f
correlation coefﬁcient, 441
covariance, 441
cumulative distribution function (CDF),
444e445, 444f
distribution, 445e446, 447t
kurtosis, 442e443
mean, 440
percent-point function (PPF), 445
probability density function (PDF), 443, 443f
quartiles, 443
skewness, 442
variance, 440e441, 440f
phases, 454f
wellbore stability
borehole failure, 457e458
probability distribution, 458e460, 459f
safe mud weight window (SMWW), 450e453
simpliﬁed Kirsch equation, 456
stress transformation and equations, 455e457
uncertainty propagation, 450
well geomechanical model design, 454e455
Mud weight constraints, 322e323, 322f
Multiattribute decision-making (MADM)
graphical representation, 390f
matrix representation, 389, 389f
multiattribute value theory (MAVT), 391e395
problem solving steps, 389e390
trade-off analysis, 390e391, 391f
Multiattribute value theory (MAVT)
different criteria, 393
intrinsic importance of criteria, 394
linear representation, 393
partial value function, 392, 393f
preference aggregation, 394e395
swing weights, 393e394
value functions, 392
values measure preferences, 391e392
Multicriteria problems
alternative, 388
basic formulation, 388
criterion, 387
types, 387
Multidimensional heterogeneous model, 227, 227f
Multilayer perceptron neural network (MLPNN)
hidden and output layer, 280e281
output, 279e280
properties, 282t
transfer functions, 280
Multilevel control system, 18e20
Multiobjective decision-making (MODM)
attribute space, 397, 397f
decision space, 396e397, 396f
genetic algorithms, 397
mathematical programming, 396
multiobjective problem, 396
Multiobjective genetic algorithm (MOGA)
methodology, 128, 132t
Multiobjective optimization, 36, 37f
Multiplacement approach, 60, 60f
Multiple-criteria decision-making (MCDM), 385,
386f
analytic hierarchy process (AHP), 398
hierarchical structure, 399f
method selection, 399, 400f
multiattribute decision-making (MADM),
389e395
multiobjective decision-making, 396e397
preference ranking organization method for
enrichment evaluations (PROMETHEE),
399
systematic rankings and weightings, 398
technique for order preference by similarity to the
ideal solution (TOPSIS), 398
well completion optimization. See Well
completion optimization
Multiple-linear regression
adaptive-neurofuzzy inference system (ANFIS),
258
Bourgoyne and Young (BY) model,
258, 262t
daily mud logging reports (DMLR), 258
drilling data, 259f, 261f
drilling parameters, 261f
Hareland-Rampersad (HR) model, 258
modular dynamic test (MDT) data, 258
simulated annealing algorithm (SAA), 260e263,
263t
South Pars gas ﬁeld (SP) offshore Iran, 257e258
Multiple-pump systems, 180e181, 184f
parameters, 188t
pressure distribution, 182e184, 183f, 187
principle illustration, 184f
pumpemotor sets, 180e181
532
Index
N
Nano rig, 27f
National-oilwell varco Operating System
(NOVOS), 257f
Nonlinear programming, 33
Nonproductive time (NPT), 308
Normal distribution, 445e446, 449f
Normal probability law, 445e446
O
Offset well constraints, 118
Oil well design
build-and-hold proﬁle
build-sail type well, 99f, 100te101t
static weight, 99e101
well friction, 101, 102f
catenary build proﬁle
build rate, 103
drawbacks, 103
horizontal reach, 102e103
well path construction, 103
2D well path optimization
kick-off depth, 108
minimum lowering force, 107
minimum pull force, 107
minimum torque, 107e108
optimal inclinations, 108
long-reach well
critical sail angle vs. friction coefﬁcient, 104f
drag and torque, 105t
hook loads, 105
maximum sail angle, 104
well trajectories, 103, 104f
ultralong-reach well design
hook load, 106
hydraulics and hole cleaning problems, 106
pipe size, 106
sail angle, 105e106
torque, 106e107, 107t
well path, 106, 106f
Ontology engineering
applications, 489
driller, 490
elements, 490e491
process options, 491e492, 491f
rate of penetration (ROP), 490
Optimal well trajectory
anticollision constraints, 117e118, 117fe118f
constraints affecting, 114f
geomechanical constraints
3D geomechanical model, 114e115
microimages, 114e115
mud weight window, 115, 116f
numerical model, 114e115
wellbore stability improvement, 116f
offset well constraints, 118
prediction models, 113
well control constraints, 118e122
Optimization level, automation, 20b
Overbalanced drilling casing program, 342f
P
Parasitic pressure drop, 151e152, 153t
Pareto optimal solutions, 36, 37f
Partial value function, 392
Particle swarm algorithm (PSO), 125f, 125te126t
Partition clustering methods, 364f
Euclidean distance, 363e364
Fuzzy c-means (FCM), 365
K-means clustering, 364e365, 364f
Percent-point function (PPF), 445
Petroleum Safety Authority (PTIL), 118e119
Petroleum well optimization
drilling problem formulation. See Drilling
problem formulation
production problem formulation, 57e61
well control optimization, 61e63
Physical entities, digital twin, 5
Physical models, digital twin, 3
Platykurtic distribution, 443
Polycrystalline diamond compact (PDC) bit, 194
Pore pressure prediction, hydromechanical speciﬁc
energy
exploratory well, Niger Delta
actual pore pressure measurements, 224e225
bit data summary, 223t
d-exponent plot, 225f
formation bulk density, 222f, 224
normal compaction trend (NCT), 224
overburden pressure/gradient proﬁles,
222f, 224
pore pressure proﬁle, 223f
ﬁeld example, 222e225
ﬁxed cutter bits, 220
methodology, 221
pump-off/jet-impact force, 220
Index
533
Pore pressure prediction method, 333f
Portfolio problems, 387
Postdrill mechanical speciﬁc energy (MSE)
analysis, 198
Power law index, 177
Precisionerecall curve (PRC) area plot, 377
Predrilled wells location, 67f
Predrill pore pressure model, 333fe334f
Preference function, 388
Preference ranking organization method for
enrichment evaluations (PROMETHEE),
399
Probability density function (PDF), 443, 443f
Probability distribution, 458e460, 459f
Production cost function, 46f
Production problem formulation
closed-loop reservoir management (CLRM),
61, 62f
quality map approach
ﬂow simulator, 57
map’s construction, 57
well quality, 58
well placement problem
capital costs, 58e59
multiplacement approach, 60, 60f
net cash ﬂow, 58e59
NPV, 58e59
objective function, 58e59
placement decision quality, 58, 59f
sequential well placement problem, 59, 60f
Proportional integral derivative (PID) controller,
20b
Q
Quantum particle swarm optimization-support
vector machine (QPSO-SVM), 226
Quartiles, 443
R
Radial basis function (RBF) kernel, 230
Radial basis function (RBF) neural network
(RBFNN), 281e283
Radius of curvature well path model
horizontal projection, 93, 94f
inclination angle, 93
straight well sections, 94e95
vertical projected height, 93
vertical projection, 92, 93f
Random consistency indexes (RI), 422t
Ranking problems, 387
Rate of penetration (ROP), 291, 292t
adaptive neurofuzzy inference system (ANFIS)
model, 263e274
correlation analysis stage, 293
data collection, 253
data mining
correlation coefﬁcient (CC), 371e372
data properties, 371t
descriptive data analytics, 372
solid content (SC), 372
statistical and sensitivity analyses, 371
decision tree model
feature selection, 276e278, 278f
input and output parameters, 274e275, 274t
noise reduction, 275e276
regression tree, 278e279, 279f
formation drillability fusion submodel stage, 291
model structure, 293f
multilayer perceptron neural network (MLPNN),
279e281, 282t
multiple-linear regression, 257e263
optimization workﬂow, 253e257, 254f
appropriate bit selection, 254
automation console, 257, 257f
data records, 253
equivalent circulating density (ECD), 253e254
machine learning model, 255, 256f
preprocessing, 255
remote operation center, 255e256
rig control system, 256
sensor, 255
prediction models, 252t
radial basis function (RBF) neural network
(RBFNN), 281e283
simulated annealing algorithm (SA), 68
statistical and data-driven, 257e289
submodel stage, 293e295
support vector regression model, 283e289
Real-time mathematical models, 9, 10f
Real-time mechanical speciﬁc energy (MSE)
surveillance, 198
Real-time monitoring (RTM), 23e24
Real-time operation centers (RTOCs),
367e368
Receiver operator characteristic (ROC) area, 377
Reelwell drilling method (RDM), 179e184, 180f
Regression tree, 278e279, 279f
Reinforcement learning, 250
534
Index
Relative weights, 403
Relief well directional designs, 121f
Reservoir completions methods, 433f
Revolutions per minutes (RPM), 24
Riserless drilling
deep and ultradeep operations, 342e343
pressure margin, 343
riserless dual gradient drilling (DGD), 343, 344f
riserless mud recovery (RMR) system, 343
Riserless dual gradient drilling (DGD), 343, 344f
Riserless mud recovery (RMR) system, 343
Risk-averse decision-maker, 310
Risk-neutral decision-maker, 310
Robust optimization, 38e39, 39f
Rock drillability assessments
drillability d-exponent
abnormal pressure zone detection, 225
basic drillability exponent, 226
corrected d-exponent, 226
limitations, 226
formation drillability prediction, 226e231
Rule-based systems
rule-based reasoning
backward chaining, 487
forward chaining, 487
inference engine, 484e486
working memory, 484e486
rules, 486e487
S
Safe mud weight window (SMWW)
basic stress model, 452
coverage interval, 460f
distribution graph, 453
fracture limit, 452
geomechanical modeling, 450e452
input probability distributions, 452, 452f
MohreCoulomb shear failure criterion
collapse and fracture, 468f
deterministic prediction, 461e462,
462f, 468f
failure model, 461
stochastic prediction, 462e469, 467f, 469f
mud pressure, 450e452
uncertainty estimation, 453t
wellbore pressure, 452
Sand control method, 433e434, 433f
S-curved relief well, 119, 120f
Semantic data models, digital twin, 3
Semi-automation, 14
Sensors, digital twin, 1
Separation factor (SF), 117e118, 117f
Sequential minimal optimization (SMO)
algorithm, 378
Sequential well placement problem, 59, 60f
Shear failure, 458f
Sidetracking horizontal well trajectory, 51, 52f
Similarity index (C), 417
Simple analytical utility function, 310
Simpliﬁed Kirsch equation, 456
Simulated annealing algorithm (SAA), 260e263,
263t
Single-linkage clustering, 361e362, 362f
Single-pump system, 180e181, 184f
formula and calculation, 185t
pressure distribution, 181f, 182, 186t
principle illustration, 184f
pumpemotor sets, 180e181
Skewness, 442, 442f
Slide drilling path optimization, 49b
Smart hub services, 27f
Sorting problems, 387
Stick-slip index (SSI), 41e42
Stick-slip vibration, 196
Stochastic optimization (SO), 37e38, 38fe39f
Strain energy, 238
Stress transformation, 455e457, 455fe456f
Supervised learning, 250
Supervisory control, 20b
Support vector machine (SVM), 228e229
Support vector regression (SVR) model
accuracy, 288
constructive parameters, 285
ﬁnal formulization, 283e284
kernel function, 284
most-used, 284
performance and regression plots,
285e286, 286f
relative deviations, 287f
risk function, 284
RMSE approach, 288
statistical performance indices, 286e288, 288t
Surface-seismic-while-drilling (SSWD)
technology, 120e122, 121f
Surface weight on bit (SWOB), 237
Surrogate models, 137
Swing weights, 393e394
System integration, automation drilling, 16
Index
535
T
Takagi, Sugeno, and Kang (TSK) fuzzy inference
system, 268
t-distribution, 449f
Technique for order preference by similarity to the
ideal solution (TOPSIS), 398
geometric distance matrices, 412e414
high-rate gas well completion designs
alternatives and criteria matrix, 414, 415t
difference matrix, 415, 416t
risk criterion, 414
sensitivity analysis, 417e418, 418t
similarity index (C), 417, 417f
weights, 412e414
Tensile failure, 458f
Tensile strain energy, 238
Three-dimensional friction modeling, 87, 90f
Three-dimensional well path design
single-objective optimization
ant colony optimization (ACO), 127
artiﬁcial bee colony (ABC), 127
comparative study, 127e128
computational times, 123e126
constraints, 124t
metaheuristic algorithms, 123e126,
125te126t
objective function, 122e123
performance trends, 125f
radius-of-curvature method, 122e123
symbols and abbreviations, 122e123
total measured depth (TMD), 123
vertical plane, 124f
two-objective optimization
genetic algorithm (GA) behavioral parameter
values, 130t
multiobjective genetic algorithm (MOGA)
methodology, 128
objective function trends, 129e130, 129f
Pareto frontier, 130e131, 131f
torque calculation, 128e129
Top-hole level tank (THLT), 180e181
Torsional vibration, 196, 197t
Tortuosity cost, 46
Trade-off analysis, 390e391, 391f
Trouble-free time (TFT), 308
Two-dimensional friction modeling
bottom force, 84
bottomhole assembly (BHA), 84
buoyancy factor, 84
drilling torque, 88f
drill string forces, 85, 86t
frictional factors, 85
S-shaped well
geometry, 85f
torque and drag, 85, 87f
string tension reduction, 85e87, 88f
Two-layered feed-forward backpropagation
algorithm, 265
U
Unconditional data exchange, 16
Unconventional drilling methods
contingency casings, 341e342
managed pressure drilling (MPD), 345e347
riserless drilling, 342e343
Uniform distribution, 445
Unintentional collision, 118
Unsupervised learning, 250
Utility function, 310e311
V
Value functions, 392
Vapnik’s statistical learning theory, 228e229
Variance, 440e441
Vibrations, drilling inefﬁciency
axial vibrations, 196
downhole vibrational accelerations, 211, 212f
features, 197t
lateral, 196
stress, 195e196
torsional, 196
types, 196f
vibrational founder, 208e209, 209f
Virtual models, digital twin, 5e6
W
Wellbore friction optimization
advanced models, 92
elementary models. See Elementary models,
wellbore friction
friction models
catenary well path, 95e99
dogleg severity, 95
oil well design. See Oil well design
radius of curvature well path model, 92e95
Wellbore proﬁle energy
536
Index
bending strain energy, 50e51
curvature bridging, 50
deviation vector, 50, 50f
drilling difﬁculty/complexity index, 50e51
drilling trajectory
constant wellbore curvature and torsion,
52e53
control variable limits, 53
length, 52
non-negativity constraint, 54
sidetracking horizontal well trajectory, 51, 52f
target areas constraint, 53
torsion strain energy, 51
wellbore trajectory control, 50
Wellbore stability
Monte Carlo simulation (MCS)
borehole failure, 457e458
probability distribution, 458e460, 459f
safe mud weight window (SMWW). See Safe
mud weight window (SMWW)
simpliﬁed Kirsch equation, 456
stress transformation and equations, 455e457
uncertainty propagation, 450
well geomechanical model design, 454e455
optimization steps, 449f
Well completion optimization
high-rate gas well completion designs
analytic hierarchy process (AHP). See Analytic
hierarchy process (AHP)
big bore (BB), 399e400, 402
conceptual framework of decision,
406e407, 407f
criteria reliability and weighting, 407
large diameter wellbores, 400
monobore (MB), 399e400, 402, 402f
optimized big bore (OBB), 399e400, 403
scenario, 399e400
well completion decision scenarios, 402e403
well design objectives and constraints, 401
hydraulic fracturing (HF)
candidate well section (CWS), 419e420,
422e424
fracture geometry and stress status, 420f
lower permeability reservoirs, 419
production well and layer selection
acidizing-water shutoff joint operation,
430t, 431
case study, 424e425
correlation coefﬁcient, 430t
fuzzy complementary matrix, 426
fuzzy consistent matrix index weight,
426e431, 428t
fuzzy ﬁtness matrix, 425
Pearson correlation coefﬁcient, 426
water cut, 424e425
well’s characteristic parameters, 428t
Well control
constraints
interception points, 120
magnetic survey methods, 119e120
relief well directional designs, 121f
relief-well trajectory, 118e119, 119f
S-curved relief well, 119, 120f
surface-seismic-while-drilling (SSWD)
technology, 120e122, 121f
optimization
net present value (NPV), 61e63
optimization problem, 61
Well cost estimation, 306e308
Well geomechanical model design, 454e455
Well path optimization
three-dimensional well path design
single-objective optimization, 122e128
two-objective optimization, 128e131
trajectory-monitoring models, 131e141
Wellsite Information Transfer Standard Markup
Language (WITSML), 24, 367e368
Well trajectory optimization
algorithm
artiﬁcial neural network (ANN), 137, 137f
deep well drilling, 135
elastoplastic calculations, 135
FLAC3D software, 134e135
global minimum mud pressure required
(GMMPR) determination, 134e135
minimum mud pressure required
(MMPR), 135
proposed algorithm, 136f
proxy model, 137, 137f
random sampling, 137
elastoplastic method, 141
geomechanical parameters, 139t
geometrical approach, 131
inclination and azimuth angle
feedback controller, 140, 141f
generalized stress transformation system, 133f
MogieCoulomb failure criterion, 134
MohreCoulomb (MeC) criterion, 132e133
Index
537
Well trajectory optimization (Continued)
vs. mud pressure, 140f
optimization code, 140, 141f
stress state, 132
mechanical stability, 138e140, 140f
radial cylinder mesh, 138
rock properties, 138
wellbore dimensions, 137e138, 138f
538
Index

Journal of Chemical Technology and Biotechnology
J Chem Technol Biotechnol 82:603–609 (2007)
Perspective
Green chemistry for the second generation
bioreﬁnery – sustainable chemical
manufacturing based on biomass
James H Clark∗
Green Chemistry Centre of Excellence, University of York, York YO10 5DD, UK
Abstract: The material needs of society are reaching a crisis point. The demands of a growing and developing
world population will soon exceed the capacity of our present fossil resource based infrastructure. In particular,
the chemical industry that underpins most industries needs to respond to these challenges. The chemical
manufacturing and user industries face an unprecedented range and intensity of drivers for change, the greatest of
which, REACH (Registration, Evaluation and Authorisation of Chemicals) has yet to bite. In order to address the
key issues of switching to renewable resources, avoiding hazardous and polluting processes, and manufacturing
and using safe and environmentally compatible products, we need to develop sustainable and green chemical
product supply chains. For organic chemicals and materials these need to operate under agreed and strict criteria
and need to start with widely available, totally renewable and low cost carbon – the only source is biomass
and the conversion of biomass into useful products will be carried out in bioreﬁneries. Where these operate at
present, their product range is largely limited to simple materials (e.g. cellulose), chemicals (e.g. ethanol) and
bioenergy/biofuels. Second generation bioreﬁneries need to build on the need for sustainable chemical products
through modern and proven green chemical technologies such as bioprocessing, controlled pyrolysis, catalysis
in water and microwave activation, in order to make more complex molecules and materials on which a future
sustainable society will be based.
2007 Society of Chemical Industry
DRIVERS FOR CHANGE : ACROSS THE WHOLE
LIFECYCLE
The fundamental challenge that we face in this
new century is the conversion of a society based
on consumption controlled only by demand and
market forces into a sustainable society based on more
realistic needs and natural resources; from a wasteful
and destructive attitude towards the environment
to one which respects the Earth as a limited and
sensitive resource. This dramatic reassessment of our
relationship with the planet must be done while we are
facing an unprecedented rate of growth in demand
for resources, as the mega-economies of the East
move inexorably towards the standards and demands
established in the last century in the West.
The challenge for chemical manufacturing is as great
as any faced by industry. The chemical industry that
has been so effective for much of the 20th century
is now under enormous pressure to change in almost
all aspects of how it operates.1 The last years of the
20th century saw an exponential growth in legislation
affecting chemical manufacturing processes.2,3 This
was in part a reaction to high proﬁle chemical disasters
such as Bhopal and to NGO and media attention on
the industry. Manufacturing was also facing costs for
energy and for the disposal of hazardous waste, both of
which were increasing at a rate greater than the price
of their products.
The early years of the 21st century have seen
a
dramatic
increase
in
public
and
government
concern in the human and environmental safety
of products, concerns that are now being voiced
by downstream users. This is largely consequential
of general concerns over the environment and the
atmosphere in particular, and a steady stream of
reports largely from NGOs, over the detection of
synthetic chemicals in animals and humans (as much
a result of improvements in analytical science as any
increase in exposure to chemicals).3 Throughout both
of these periods we have seen a rapid increase in
the primary raw material of the organic chemicals
industry – oil. Increasingly, scientists, industrialists
and politicians are looking for alternatives to oil
for long-term, sustainable chemical manufacturing.4,5
Thus chemical manufacturing faces an unprecedented
degree of pressure, at all stages in the lifecycle or
supply chain of chemical products (Fig. 1).
PRODUCT SUBSTITUTION
Of
all
the
current
pressures
on
the
chemi-
cal
supply
chains
perhaps
the
most
signiﬁcant
∗Correspondence to: James H Clark, Green Chemistry Centre of Excellence, University of York, York YO10 5DD, UK
E-mail: jhc1@york.ac.uk
(Received 3 November 2006; revised version received 27 February 2007; accepted 5 April 2007)
Published online 26 June 2007; DOI: 10.1002/jctb.1710
2007 Society of Chemical Industry. J Chem Technol Biotechnol 0268–2575/2007/$30.00
JH Clark
Figure 1. Pressures on the chemical industry across the lifecycle.
is
new
legislation
on
the
testing
of
chemicals,
most strikingly illustrated by REACH (Registra-
tion, Evaluation and Authorization of Chemicals)
(http://ec.europa.eu/environment/chemicals/reach/-
reach intro.htm) For the ﬁrst time the entire sup-
ply chain is knowingly affected by and concerned with
the long-term availability of chemicals. It is not pos-
sible to predict how many currently used chemicals
will fall foul of REACH or other product-focused
legislation (e.g. ROHS) (http://www.rohs.gov.uk/) but
numbers around 10% have been suggested and at
this level, a very substantial number of ﬁnished prod-
ucts (>3000) will be affected. While the debate rages
over the rights and wrongs of REACH, it is clear
that through REACH and beyond we will need to
discover, develop and apply greener substitutes for
many important chemicals. These may well include
solvents, adhesives, ﬂame retardants, stabilisers, coat-
ings and primers and with applications across a huge
range of industrial and consumer products. Our record
for chemical product substitution is far from perfect.
CFCs were replaced by HCFCs which were then
replaced by HFCs which are now themselves con-
sidered unacceptable; we now ﬁnd ourselves using
products, e.g. ﬂammable hydrocarbons, that we sub-
stituted 40 years ago. Another example is methyl bro-
mide, widely used as a fumigant – and often under a
poor degree of control, yet is a massive ozone depleter.
Repeated attempts to ban this product have been
reversed or delayed due to a lack of a suitable substi-
tute (http://epa.gov/ozone/mbr/). Volatile chlorinated
solvents in dry-cleaning, polybrominated compounds
in ﬂame retardants, aluminium chloride in numerous
organic processes, chromates in aerospace materials
processing, are all known to be less than desirable
for human health and/or environmental reasons yet
we continue to use them in large quantities due to a
lack of suitable substitutes. The list of products for
substitution will quickly get longer as the new legisla-
tion bites – we need to rapidly build up our efforts in
this, through targeted R & D, and ﬁrst and foremost
through determining a mechanism and agreeing crite-
ria for the substitution process. I propose a twin route
approach to the mechanism for product substitution,
with one route being based on existing commercial
chemicals and the other, at least for the substitution of
organic chemicals, being based on chemicals derived
from alternative, renewable resources (Fig. 2).
ACCEPTABILITY CRITERIA FOR JUSTIFICATION
As shown in Fig. 2 we need acceptability criteria to
help us check the credentials of likely alternatives for
substances of concern that we wish to substitute (as a
Figure 2. Routes to the substitution of hazardous chemical products.
604
J Chem Technol Biotechnol 82:603–609 (2007)
DOI: 10.1002/jctb
 10974660, 2007, 7, Downloaded from https://scijournals.onlinelibrary.wiley.com/doi/10.1002/jctb.1710 by Bilkent University, Wiley Online Library on [19/08/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
Perspective
result of legislative, social, market or other pressures).
Alternatives could be based on known chemicals with
existing markets in other areas and which are not likely
to be under threat from external pressures. Suitable
acceptability criteria for such chemicals might include:
- performance not less than 90% of existing;
- no medium-term availability problems;
- cost increase not more than 10% over existing;
- ‘REACH-resistant’ (known and very low toxicity,
persistence and bioaccumulation);
- relatively low environmental impact ‘green’ supply
chain.
We need also to develop a database of chemicals
derived from biomass since these are likely to become
increasingly important in future markets due to their
sustainability and the increasing costs and unreliability
of supply of traditional petroleum-derived chemicals.
Here, suitable acceptability criteria might include:
- biomass feedstock to have excellent long-term
availability
in
the
region
and
not
likely
to
compromise food production;
- maximum three low-environmental impact process
steps;
- process step yields not less than 70% (excluding any
fermentation steps);
- obtainable at not less than 99% purity or in a suitable
formulation (where appropriate).
These ‘molecules of the future’ will be obtained from
the next generation of bioreﬁneries.
Our Green Chemistry Network project ‘‘Green
Chemistry and the Consumer’’ (http://www.chemsoc.
org/networks/gcn/industry.htm#consumer) has been
directed at looking for both types of alternatives,
especially for generic substances of concern and using
technical, patent and scientiﬁc literature. Examples
include:
- the replacement of carcinogenic chromatic surface
treatments with Lawsone, a natural and sustainable
chemical derived from henna;
-
the
use
of
microbially
derived
polysaccharide
adhesives to replace formaldehyde/VOC omissions;
-
alternative
plasticizers
to
phthalates
including
adipates, benzoates and citrates.
THE BIOREFINERY: 1ST AND 2ND
GENERATIONS
While the 20th century saw the emergence of the
petroleum industries as major sources of energy,
chemicals and many materials, it seems likely that
the 21st century will see the gradual handover of these
responsibilities to the biomass industries.4–6 In these,
biological matter derived from trees, grasses, plants
and crops will be utilised for energy and chemicals
production as well as for food. In this way we can seek
a truly sustainable society based on the consumption
of renewable resources, although biomass cannot be
expected to satisfy all of our energy needs. The
bioreﬁnery is the term used to describe the facilities
that will carry out these conversions. In a typical
bioreﬁnery today, a raw material such as trees are
converted into both material and bio-energy products.
In the next generation bioreﬁnery, the feedstock will
be fractionated further into valuable components by
extraction, fermentation and controlled pyrolysis, as
well as by more traditional methods and the chemical
products may be further converted into higher value
products (e.g. esteriﬁcation of fermentation acids)
(Fig. 3).
It is essential that we use green chemical technolo-
gies and apply green chemistry principles throughout
the bioreﬁnery so as to minimise the environmental
footprints of its products.7 At the ﬁrst stage, chemi-
cals can be extracted using, for example, supercritical
carbon dioxide8 and the bulk materials (cellulose,
lignin, etc.) can be separated by ultrasonic tech-
niques. The bulk materials can be broken down into
small molecules by fermentation and by pyrolysis.
There is a superﬁcial similarity between the crack-
ing of biomass and the cracking of petroleum but
the product complexities are signiﬁcantly different.
Petrochemical production requires the fractionation
of mixtures of hydrocarbons; individual hydrocarbons
are separated and we then apply organic chemistry
methods developed over many years to add value to
those petroleum platform molecules through func-
tionalisation including oxidation, halogenation and
nitration. These commodity chemicals are then further
elaborated through further established organic chem-
istry methods (e.g. Friedel Crafts reactions, Heck and
Suzuki reactions, reductions, condensations, etc.) to
Figure 3. Green Chemistry and the Bioreﬁnery.
J Chem Technol Biotechnol 82:603–609 (2007)
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JH Clark
still higher value speciality chemicals which become
the building blocks of the formulations of major indus-
tries including personal and household products, phar-
maceuticals, advanced materials, dyes and coatings.
Biological cracking of biomass is fundamentally more
complex and problematic than the thermal and chemi-
cal cracking of petroleum, although we are still early on
the learning curve for the biotechnology necessary to
give improved selectivity in bioprocesses. The biomass
platform molecules that we are capable of producing
in the quantities necessary to ultimately feed large
volume chemical manufacturing contrast considerably
with their petroleum analogues in that they are already
highly functionalised. Of the 15 or so most promising
biomass platform molecules as suggested by the US
DoE9 (http://www.nrel.gov/docs/fy04osti/35 523.pdf)
many are acids and all are highly oxygenated and
polar. When these molecules are produced from
fermentation processes, they are generated at low
concentrations (typically <10%) in water and in the
presence of other polar molecules. The separation dif-
ﬁculties for such fermentation broths are an order of
magnitude greater than with petrochemicals. We must
also apply signiﬁcantly different chemistries to such
highly functionalised and oxygenated chemicals so as
to build these up into the valuable chemical products
our society is built on (Fig. 4).
In particular, we should learn to do more organic
chemistry in water,10 and with impure compounds; we
must also develop more methods which are selective,
for example towards different hydroxyl groups in
polyhydroxy molecules. Reduction is likely to play a
particularly important role in the chemistry of biomass
platform molecules as will selective esteriﬁcation,
cyclisation, amidation and dehydration. These will
require clever catalysts that are selective and operate
in water. We will also need clever process engineering
that can help to separate complex aqueous systems
by low energy techniques such as membranes. We
cannot afford to expend large amounts of energy to
separate these mixtures even before we do chemistry
on them. Indeed, given that so many of our carefully
isolated, functionalised and puriﬁed chemical products
end up in formulations, it would seem wise to
seek methods that can convert the multi-component
systems we obtain from biomass into multi-component
formulations with the correct set of properties we
require in applications such as cleaning, coating and
dyeing.
A WHEAT BIOREFINERY FOR CHEMICALS,
ENERGY, FOOD AND MATERIALS
A good example of an abundant and successful crop
in the UK and elsewhere is wheat. It also serves as
an illustration of the diverse chemical and materials
potential in agricultural products, and how green
chemical technologies can be used to maximise their
extraction and utilisation (Fig. 5).
While wheat is grown for its food value, a high
proportion of the crop is of low to zero value at source
(wheat straw). In food processing, large volumes of
inferior grain are also discarded, a situation made
worse in certain regions of the world due to over
supply. In France, for example, at the time of writing,
large quantities of wheatgrain are being dumped while
some farmers are burning part of their harvest, since
the caloriﬁc value is a more economical source of
energy than conventional fuels – as much a result of
the rapidly escalating costs of fuel as the oversupply of
food crops.
We have recently shown that the valuable waxes
on the surface of wheatstraw can be extracted
using low environmental impact supercritical ﬂuid
technology.8 This has the immediate advantage over
the traditional use of volatile organic solvents of
avoiding the escape of volatile organic chemicals,
Figure 4. Comparing petroleum and biomass routes to everyday chemical products.
606
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Perspective
Figure 5. Wheat as a source of chemicals and materials through green chemical technologies.
the use of toxic solvents and solvent contamination
of the product extracts. An additional and especially
important advantage is the ability to fractionate the
waxes into more valuable components such as insect
semiochemicals (hydrocarbons) and nutraceuticals
(fatty alcohols). Bulk waxes derived from sustainable,
natural, plant products also have a growing market
for the replacement of both synthetic (petroleum
derived) waxes and animal product derived waxes
even including lanolin (obtained from sheep’s wool).
The structural cellulose and the energy source
starches in plants such as wheat along with chitin are
nature’s largest volume biopolymers – totally sustain-
able, non-hazardous and biodegradable. Our non-food
utilisation of these leading products from nature’s
bounty is limited and unimaginative, especially given
the enormous volume of polymeric products which
we use in modern society, the high proportion of
non-renewable carbon we use to manufacture them,
and the growing public concern over the end-of-life
of these largely non-biodegradable materials. We have
focused our attention on the physical modiﬁcation of
starch and cellulose so as to increase their surface area,
porosity and hence use in more sophisticated applica-
tions than ﬁlling, paper and the other current, largely
mundane applications. For example, ‘expanded’ high
surface area starch and cellulose can be used as a sep-
aration medium,11 for example in chromatography,
to separate complex mixtures of natural products and
synthetic compounds including organometallic species
(Fig. 6).
THE TREE BIOREFINERY : FROM PAPER AND
PULP TO CHEMICALS AND ENERGY
Cellulose is especially abundant in trees, which are
possibly the ultimate renewable chemical and materials
resource. After removing valuable chemical products
from the surface of the bark using supercritical CO2 we
can then separate the cellulosic materials from the glue
that holds them together, lignin, using conventional
Figure 6. Separation of ferrocenes using expanded cellulose.
methods such as heating for long periods with oxidants
such as sodium chlorite, or alkalis, or using more
energy-efﬁcient ultrasonic activation.12
In paper mills and in generation bioreﬁneries
using tree products as the raw material, lignin
(http://www.lignin.org) is often considered to be more
of a problem than an opportunity – its separation from
the cellulosics in the acid hydrolysis stage of bioethanol
manufacture for example, can be problematic and give
rise to intractable solid residues. Typically, it is only
used as a solid fuel. In papermaking industries for
example, lignosulfates are produced which do have
uses, for example, as a dispersant in the manufacture
of concrete (http://www.kmtchem.com/lignin.htm). In
the second generation bioreﬁnery we need to make
better use of this abundant and valuable sustainable
resource.
Lignin is the richest source of aromatics in nature
(http://www.lignin.org) and given the large number of
aromatic compounds embedded in modern society,
most of which are derived from oil, we must learn how
to efﬁciently exploit this natural treasure. In particular,
we use large volumes of aromatic compounds in mod-
ern plastics such as polyethylene terephthalate, PET
and including advanced engineering materials such
J Chem Technol Biotechnol 82:603–609 (2007)
607
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JH Clark
Figure 7. 1st and 2nd Generation Tree Bioreﬁneries.
as polyethersulfone, PES and polyetheretherketone,
PEEK.
Apart
from
developing
more
interesting
and
valuable use for lignin as a complex material product,
we should learn to use it as a source of low molecular
weight aromatic building blocks. The most widely
reported of these is vanillin, a natural product for
which current market demand is soaring. Vanillin is
obtainable in yields of 2–3% from lignin, typically by
heating with caustic for long periods of time. We have
developed improved methods based on oxidations
using environmentally friendly heterogeneous catalysts
(V Budarin, FEI Deswarte, JJE Hardy, AJ Hunt, R
Luque, DJ Macquarrie, K Milkowski, A Rodriguez, O
Samuel, RJ White, AJ Wilson, unpublished results).
These reactions can still be slow if operated under
conventional reactor conditions but we are now
developing rapid and relatively high yielding vanillin
production processes using microwave activation, an
extremely promising energy efﬁcient green chemical
technology for numerous applications from drying
to extraction to chemical manufacturing.13 We now
need to further develop this technology so as to
develop green chemical routes to other small and
useful aromatic molecules starting from lignin. Beyond
that the challenge is to build these aromatic molecules
up into the type of advanced materials our modern
society depends on (Fig. 7).
FUTURE NEEDS AND TRENDS
While the availability of fossil resources for chemical
manufacturing might continue for longer than some
people believe and while the pressures on chemical
manufacturing might not force immediate changes in
their practices, changes to raw material and to process
now seem inevitable. The ‘not in my lifetime’ attitude
to such changes is however, being severely tested by the
new drivers for product substitution. Equally impor-
tant are the massive forces behind the development
of bioenergy which, like petroleum before it, can be
expected to provide a ‘too tempting to resist’ source
of sustainable chemicals. Some new bioenergy and
biofuel companies are already developing downstream
processing to add value to their co-products and to
sell valuable chemical products and intermediates. The
glycerol from biodiesel manufacture is one such ‘‘irre-
sistible’’ resource and Solvays’ recent announcement
of a new chemical plant to convert biodiesel glyc-
erine into epichlorohydrin is a manifestation of this
(http://www.solvaypress.com/pressreleases/0,,38 695
-2-0,00.htm).
Similarly,
new
companies
making
bioethanol as a fuel will ﬁnd it quite easy to also
manufacture and sell products from ethanol such as
ethyl acetate. The winds of change in chemical design
and manufacture are strong and getting stronger – but
we must act quickly to ensure that the new chemicals
for the 21st century are not only from a renewable
resource but also green across the full supply chain
and product lifecycle – only then can we talk with
conﬁdence about a truly sustainable chemical based
society.
REFERENCES
1 Clark JH, Green chemistry today (and tomorrow). Green Chem
8:17–21 (2006).
2 Knight DJ, Regulation of Chemicals, RAPRA Review Report
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Perspective
3 Harrison RE and Hester RE (eds), Chemicals in the Environment.
RSC, Cambridge (2006).
4 Bozell J and Patel MK (eds), Feedstocks for the Future. ACS, New
York (2006).
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Chichester (2004).
7 Azapajic A, Podou S and Clift R (eds), Sustainable Development
in Practice. Wiley, Chichester (2004).
8 Deswarte FEI, Clark JH, Hardy JJE and Rose PM, The frac-
tionation of valuable wax products from wheat straw using
CO2. Green Chem 8:39–42 (2006).
9 Werpy T and Petersen G (eds), Top Value Added Chemicals from
Biomass. DoE, USA (2004).
10 Lindstrom VM, Stereoselective organic reactions in water. Chem
Rev 102:2751–2772 (2002).
11 Budarin V, Clark JH, Deswarte FEI, Hardy JJE, Hunt AJ and
Kerton FM, Delicious not siliceous: expanded carbohydrates
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Chem Commun 23:2903–2905 (2005).
12 Mason TJ and Cintas P, Sonochemistry in Handbook of Green
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Science, Technology and Innovation Studies
Dirk Meissner
Leonid Gokhberg
Ozcan Saritas    
Editors 
Emerging 
Technologies 
for Economic 
Development
Science, Technology and Innovation
Studies
Series Editors
Leonid Gokhberg
Moscow, Russia
Dirk Meissner
Moscow, Russia
Science, technology and innovation (STI) studies are interrelated, as are STI
policies and policy studies. This series of books aims to contribute to improved
understanding of these interrelations. Their importance has become more widely
recognized, as the role of innovation in driving economic development and fostering
societal welfare has become almost conventional wisdom. Interdisciplinary in
coverage, the series focuses on the links between STI, business, and the broader
economy and society. The series includes conceptual and empirical contributions,
which aim to extend our theoretical grasp while offering practical relevance.
Relevant topics include the economic and social impacts of STI, STI policy design
and implementation, technology and innovation management, entrepreneurship
(and related policies), foresight studies, and analysis of emerging technologies.
The series is addressed to professionals in research and teaching, consultancies
and industry, government and international organizations.
More information about this series at http://www.springer.com/series/13398
Dirk Meissner • Leonid Gokhberg •
Ozcan Saritas
Editors
Emerging Technologies
for Economic
Development
Editors
Dirk Meissner
National Research University
Higher School of Economics
Moscow, Russia
Leonid Gokhberg
National Research University
Higher School of Economics
Moscow, Russia
Ozcan Saritas
National Research University
Higher School of Economics
Moscow, Russia
ISSN 2570-1509
ISSN 2570-1517
(electronic)
Science, Technology and Innovation Studies
ISBN 978-3-030-04368-1
ISBN 978-3-030-04370-4
(eBook)
https://doi.org/10.1007/978-3-030-04370-4
Library of Congress Control Number: 2019934100
# Springer Nature Switzerland AG 2019
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the
material is concerned, speciﬁcally the rights of translation, reprinting, reuse of illustrations, recitation,
broadcasting, reproduction on microﬁlms or in any other physical way, and transmission or information
storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology
now known or hereafter developed.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication
does not imply, even in the absence of a speciﬁc statement, that such names are exempt from the relevant
protective laws and regulations and therefore free for general use.
The publisher, the authors and the editors are safe to assume that the advice and information in this
book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or
the editors give a warranty, express or implied, with respect to the material contained herein or for any
errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional
claims in published maps and institutional afﬁliations.
This Springer imprint is published by the registered company Springer Nature Switzerland AG.
The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Contents
1
What Do Emerging Technologies Mean for Economic
Development? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1
Dirk Meissner, Leonid Gokhberg, and Ozcan Saritas
Part I
Materials and Manufacturing
2
New Materials: The Case of Carbon Fibres . . . . . . . . . . . . . . . . . . .
13
Ozcan Saritas, Alexander Sokolov, and Konstantin Vishnevskiy
3
Nanotechnology for High-Tech Industries: Light-Emitting
Diodes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
49
Vitaly Roud, Alexander Sokolov, and Dirk Meissner
4
Technology Use in Traditional Industries: Catalysts for Petroleum
Reﬁning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
77
Dirk Meissner and Pavel Rudnik
Part II
Energy and Transport
5
Renewable Energy Technological Potential Assessment for
Evidence-Based Policy-Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
Boris Ermolenko, Georgy Ermolenko, and Liliana Proskuryakova
6
Global Market Creation for Fuel Cell Electric Vehicles . . . . . . . . . . 131
Alexander Sokolov, Ozcan Saritas, and Dirk Meissner
7
Technology Roadmaps: Emerging Technologies in the Aircraft
and Shipbuilding Industries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
Marina Klubova, Lubov Matich, Vladimir Salun,
and Natalia Veselitskaya
Part III
Living Systems and Environment
8
Water Treatment and Puriﬁcation: Technological Responses
to Grand Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177
Ozcan Saritas and Konstantin Vishnevskiy
v
vi
Contents
9
Emerging Technologies Identiﬁcation in Foresight and Strategic
Planning: Case of Agriculture and Food Sector . . . . . . . . . . . . . . . . 205
Leonid Gokhberg, Ilya Kuzminov, Pavel Bakhtin, Anton Timofeev,
and Elena Khabirova
10
Technology Assessment for Container Closure Integrity Testing
Technology for Biotech Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . 225
Qin Guo, Michael Clark, Mitsutaka Shirasaki, and Tugrul Daim
11
Emerging Technologies, Trends and Wild Cards in Human
Enhancement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243
Ozcan Saritas
Part IV
Security
12
Illuminating the “Dark Side” of Emerging Technologies . . . . . . . . . 263
Aharon Hauptman
13
Defence and Security: New Issues and Impacts . . . . . . . . . . . . . . . . 287
Andrew James
14
Defense 4.0: Internet of Things in Military . . . . . . . . . . . . . . . . . . . 303
Serhat Burmaoglu, Ozcan Saritas, and Haydar Yalcin
Part V
Challenges to STI Policy
15
How to Stimulate Convergence and Emergence of Technologies? . . . .
323
Dirk Meissner, Leonid Gokhberg, and Ozcan Saritas
Editors and Contributors
About the Editors
Dirk Meissner is Deputy Head of the Laboratory for
Economics of Innovation at HSE ISSEK and Aca-
demic Director of the Master Program “Governance
for STI.” Dr. Meissner has 20 years of experience in
research and teaching technology and innovation man-
agement and policy. He has strong background in
policy making and industrial management for STI
with special focus on foresight and roadmapping,
funding of research and priority setting. Prior to join-
ing HSE, Dr. Meissner was responsible for technology
and innovation policy at the presidential ofﬁce of the
Swiss Science and Technology Council. Previously,
he was management consultant for technology and
innovation management with Arthur D. Little. He is
member of OECD Working Party on Technology and
Innovation Policy. He is Associate Editor of Techno-
logical
Forecasting
and
Social
Change,
IEEE
Transactions on Engineering Management, Journal
of the Knowledge Economy, member of Editorial
Review Board at Small Business Economics and Jour-
nal of Knowledge Management. He guest edited Spe-
cial Issues in Industry and Innovation journal, Journal
of Engineering and Technology Management, Techno-
logical Analysis and Strategic Management among
others.
vii
viii
Editors and Contributors
Leonid Gokhberg is First Vice-Rector of the HSE
and also Director of HSE ISSEK. His area of expertise
is statistics and indicators on STI as well as foresight
and policy studies in this area. He has authored over
400 publications in Russian and international peer-
reviewed
journals,
monographs,
and
university
textbooks. Prof. Gokhberg has coordinated dozens of
national and international projects funded by public
agencies, businesses, and international organizations.
He has served as a consultant of the OECD, Eurostat,
UNESCO,
and
other
international
and
national
agencies. Leonid is also a member of the Global
Innovation Index Advisory Board, the OECD Govern-
ment Foresight Network, and OECD and Eurostat
working groups and task forces on indicators for S&T
as well as steering committees of various prestigious
international and national initiatives. Prof. Gokhberg is
Editor-in-Chief of the Scopus-indexed scientiﬁc journal
Foresight and STI Governance and editor of the
Springer academic book series Science, Technology,
and Innovation Studies, and participates on the editorial
boards of several other inﬂuential journals. He holds Ph.
D. and Dr. of Sc. degrees in Economics.
Ozcan Saritas is Professor of Innovation and Strategy
at the HSE and Editor-in-Chief of Foresight—the jour-
nal of future studies, strategic thinking and policy. He
worked as Senior Research Fellow at the Manchester
Institute of Innovation Research, the University of
Manchester. His research focuses on innovation and
policy research with particular emphasis on socio-
economic and technological foresight. With a Ph.D.
from the “Foresight and Prospective Studies Program,”
he introduced the “Systemic Foresight Methodology,”
and has produced a number of publications on the topic.
Dr. Saritas has extensive work experience with the
international
organizations,
including
the
United
Nations, OECD, and the European Commission. He
has been involved in large-scale national, multinational,
and corporate research and consultancy projects on
sectors including energy, climate change, agriculture,
food, water, transportation, and ICT among others;
published a number of articles in respected journals;
and have delivered keynote speeches in more than
50 countries across the world. Besides his research and
publication activities, he designs and delivers academic
and executive education courses on Foresight and Stra-
tegic Planning. He has recently co-authored a book,
entitled
Foresight
for
Science,
Technology
and
Innovation published by Springer, which has become
one of the key readings in the ﬁeld.
Contributors
Pavel Bakhtin Information and Analytical Systems Unit, HSE ISSEK, Moscow,
Russia
Serhat Burmaoglu Faculty of Economics and Administrative Sciences, Izmir
Katip Celebi University, Izmir, Turkey
Michael Clark Portland State University, Portland, OR, USA
Tugrul Daim Portland State University, Portland, OR, USA
Boris Ermolenko National Research University, Higher School of Economics,
Moscow, Russia
Georgy Ermolenko National Research University, Higher School of Economics,
Moscow, Russia
Leonid Gokhberg National Research University, Higher School of Economics,
Moscow, Russia
Qin Guo Portland State University, Portland, OR, USA
Aharon Hauptman Tel Aviv University, Tel Aviv, Israel
Andrew James Manchester Institute of Innovation Research, The University of
Manchester, Manchester, UK
Elena Khabirova Unit for Digital Economy Studies, HSE ISSEK, Moscow,
Russia
Marina Klubova National Research University, Higher School of Economics,
Moscow, Russia
Ilya Kuzminov National Research University, Higher School of Economics,
Moscow, Russia
Lubov Matich National Research University, Higher School of Economics,
Moscow, Russia
Editors and Contributors
ix
Dirk Meissner National Research University, Higher School of Economics,
Moscow, Russia
Liliana Proskuryakova National Research University, Higher School of Economics,
Moscow, Russia
Vitaly Roud National Research University, Higher School of Economics,
Moscow, Russia
Pavel Rudnik National Research University, Higher School of Economics,
Moscow, Russia
Vladimir Salun National Research University, Higher School of Economics,
Moscow, Russia
Ozcan Saritas National Research University, Higher School of Economics,
Moscow, Russia
Mitsutaka Shirasaki Genentech, Beaverton, OR, USA
Alexander Sokolov National Research University, Higher School of Economics,
Moscow, Russia
Anton Timofeev National Research University, Higher School of Economics,
Moscow, Russia
Natalia Veselitskaya National Research University, Higher School of Economics,
Moscow, Russia
Konstantin Vishnevskiy National Research University, Higher School of Econom-
ics, Moscow, Russia
Haydar Yalcin Faculty of Economics and Administrative Sciences, Izmir Katip
Celebi University, Izmir, Turkey
Faculty of Humanities and Social Sciences, Izmir Katip Celebi University, Izmir,
Turkey
x
Editors and Contributors
Abbreviations
Terms
A&F
Agriculture and Forestry
EHS
Environment, Health and Safety
FTA
Future Oriented Technology Assessment
GC
Grand Challenges
GVC
Global Value Chain
HDM
Hierarchical Decision Model
ICT
Information and Communication Technologies
iFORA
Intelligent Foresight Analytics (HSE)
IoT
Internet of Things
PET
Privacy Enhancing Technology
PPP
Public Private Partnership
R&D
Research and Development
RRI
Responsible Research and Innovation
S&T
Science and Technology
SME
Small and Medium Sized Enterprises
STEEP
Social, Technological, Economic, Environmental and Political
STEEPV
Social, Technological, Economic, Environmental, Political and Value
STI
Science, Technology and Innovation
SWOT
Strengths, Weaknesses, Opportunities, Threats
URS
User Requirement Speciﬁcation
Institutions
EC
European Commission
FAO
Food and Agricultural Organization of the United Nations
FDA
Federal Drug Agency
xi
HSE
National Research University Higher School of Economics
xii
Abbreviations
IEA
International Energy Agency
ISSEK
Institute for Statistical Studies and Economics of Knowledge, National
Research University Higher School of Economics
NISTEP
National Institute of Science and Technology Policy
OECD
Organization for Economic Cooperation and Development
UNESCO
United Nations Educational, Scientiﬁc and Cultural Organization
WEF
World Economic Forum
1
What Do Emerging Technologies Mean
for Economic Development?
1
Dirk Meissner, Leonid Gokhberg, and Ozcan Saritas
Economic development of countries and regions is commonly perceived as
associated to innovation which in turn is thought of to be driven by technology
(ies) (Camagni and Capello 2013). In this understanding it follows that technologies
are crucial for economic development which is precondition for employment crea-
tion and social welfare of regions and countries. However, generating economic and
societal value from technologies is not a simple linear process which can be
established easily. The potential values from technologies for different interest
groups closely correlate with the technology development, e.g. life cycle, stage
(Lundvall and Borrás 1997; Proskuryakova et al. 2017; Gokhberg and Meissner
2016). It has been recognized that mature technologies in the late phases of their life
cycle provide less potential for regional and country economic development than
technologies at the beginning of the life cycle. This however holds true only while
considering the long-term value and impact generated; in selected cases where
technologies are mature but take the form of platform technologies with related
impact on industry standards, the economic impact might be reasonably strong and
also sustainable, and in other cases of mature technologies, a short-term impact
might appear possible based on the actual technology with its respective applications
and the technologies’ perception as standard setting throughout industries,
e.g. becoming platform technology (Gertler 2003; Gokhberg et al. 2016). Besides
what has already emerged and near market, it is also very important to recognize the
‘technological emergence’. Recently, more and more efforts are dedicated for
technological emergence to get ready and capitalize on what is likely to emerge.
D. Meissner (*) · L. Gokhberg · O. Saritas
National Research University, Higher School of Economics, Moscow, Russia
e-mail: dmeissner@hse.ru; lgokhberg@hse.ru; osaritas@hse.ru
# Springer Nature Switzerland AG 2019
D. Meissner et al. (eds.), Emerging Technologies for Economic Development,
Science, Technology and Innovation Studies,
https://doi.org/10.1007/978-3-030-04370-4_1
Although it is hard to predict their emergence, once detected more recently
big data and analytics are used to track technologies with potential transfor-
mational impacts. Investigating in emerging and cutting-edge technologies can
inform policy and strategy and reveal new opportunities for social and economic
development. In this respect, opportunities created by emerging and new
technologies are numerous.
2
D. Meissner et al.
Whether emerging or already emerged, technologies in the early stages of life
cycles with already tested and proven applications and market awareness created
appear more promising enablers of sustainable development (Huggins and Johnston
2009; Ketels and Memedovic 2008). Obviously emerging technologies inherit a
strong application and sometimes multiple use potential, but still they are featured by
reasonable uncertainty for completion and broad diffusion, hence providing risks
which cannot be neglected (Laur et al. 2012; Carayannis et al. 2018). However,
frequently the chances and opportunities provided by emerging technologies are
considered to outweigh the respective risks, which is why investment in emerging
technologies is on the rise for long including public investment.
A reasonable number of leading countries have rather precise view of the most
important socio-economic and science and technology (S&T) priorities, which often
include emerging technologies like nanotechnologies and biotechnologies, among
many others. These technologies are often seen as instruments for enabling new
technologies with further socio-economic impacts and addressing grand challenges.
For instance, the development of the ‘graphene’ has brought a number of potential
applications for the development of battery technologies for energy storage. Fast-
charging batteries enabled by graphene can revolutionize how energy is stored and
can be used in a number of socio-economic sectors like information and communi-
cation technologies, transport, agriculture and health. Therefore, when emerging
technologies are focused, their alignment with broader societal, environmental and
all other demand side issues, like price and affordability, equity and accessibility,
among the others, should be taken into consideration. Beyond these, when setting
priorities for new technologies at the national or corporate levels, there are also other
issues to be addressed such as risks and uncertainties associated to those
technologies (Klofsten et al. 2015; Meissner et al. 2017). Thus, technology assess-
ment and also technology and economic foresight need to take the latter much more
into account than previously done (Kuzminov et al. 2018). Therefore, emerging
technologies need to be considered from a holistic point of view, e.g. as systems of
innovation. The systems perspective on innovation, in this book with a strong focus
on emerging technologies, provides a more solid assessment of opportunities and
risks while at the same time requires broader skills and competences by those
assessing the technologies from the different perspectives.
The book gives a comprehensive overview on the variety of emerging
technologies and approaches to assessing emerging technologies and estimating
their contribution to economic development, by considering them in technical
terms as well as their prospective applications. Chapters in the book identify and
describe existing and potential markets for emerging technology-based applications.
Overall, the book stresses the interdisciplinary nature of emerging technologies.
Complementary to the technology and market descriptions, methods like integrated
roadmapping are introduced and explained through real case examples. Moreover,
the book shows integrated roadmaps for nanotechnologies and develops policy
measures and recommendations for companies to further develop these technologies.
These measures are illustrated with the use of policy measures and roadmaps for
implementation.
1
What Do Emerging Technologies Mean for Economic Development?
3
In the book different trends and scenarios are discussed for the development of
emerging technologies based on a number of existing and emerging macroeconomic
trends of the global, and the national, economies. The book gives valuable insights in
the emerging technology development and their applications in different sectors. It is
structured along major application ﬁelds for emerging technologies across ﬁve major
sections. Following the introduction, Part I sheds light on emerging technologies in
materials and manufacturing. Part II highlights the applications in the energy- and
transport-related technologies. Then, Part III focuses on water and agriculture, while
Part IV outlines human- and security-oriented applications. The concluding part
focuses on measuring emerging technologies and the meaning of science, technol-
ogy and innovation (STI) policies for leveraging the impact of emerging
technologies.
Part
I
on
materials
and
manufacturing
starts
with
an
analysis
of
nanotechnologies and new materials, especially carbon ﬁbres. Saritas, Sokolov and
Vishnevski ﬁnd that recent advancements have led to the development of new
materials with improved speciﬁcations and reduced dimensions. Cutting-edge
metals, foams and other substances make buildings, vehicles and gadgets more
energy efﬁcient and environmentally friendly. Due to their strategic importance,
new materials are at the radar of national policies. In recent decades, a number of
developed and developing countries have conducted numerous studies to determine
STI-based development prospects for the sphere of converging technologies with a
particular focus on new materials, nanotechnologies and their production. All carbon
ﬁbre production technologies involve in one way or another pyrolysis of raw
material. No ‘revolutionary’ technologies for carbon ﬁbre production are expected
to emerge in the near future. The carbon ﬁbre industry is currently changing from
custom production (e.g. for the aerospace industry) to general mass market-oriented
production. The most important aspect is believed to be increasing capacities of
individual production lines.
Nanotechnology for high-tech industries especially for light-emitting diodes is
described by Roud, Sokolov and Meissner in the following chapter. They argue that
enhancing energy efﬁciency has been one of the top policy goals in many countries
in the last decade. Innovative lighting solutions and light-emitting diodes (LED) in
particular are among the very much promising opportunities to increase energy
efﬁciency. LED technologies are becoming dominant in a number of application
segments. Demand for economic and energy security makes the development of the
LED industry one of the national priorities in many countries including Canada, the
USA, Japan, China and European countries, among others. The key areas of the LED
industry’s
development
envisage
designing
materials
with
unprecedented
characteristics and using nanoscale components. At the same time, the technology
level of semiconductor industries as such to a large extent is determined by chips
processing—the key stage of LED production chain.
4
D. Meissner et al.
Another application ﬁeld for nanotechnologies is found in traditional industries.
Meissner and Rudnik analyze catalysts for petroleum reﬁning, namely, the potential
for development and the application of nanotechnologies in catalytic oil reﬁning
processes. Catalysts play a key role in most oil reﬁning processes, so improving their
properties and developing new catalyst types are seen as high-priority technological
objectives, the achievement of which would have a major effect on the efﬁciency of
production in the fuel-and-energy complex. Such technologies have the potential for
deeper oil processing; accelerated development of motor, diesel and other fuels’
production; and production of petrochemical materials—provided advanced, high-
performance equipment, innovative efﬁcient catalysts and sorbents are used and the
principle of combining production processes at a single high-capacity installation is
observed. The authors argue that Russia offers signiﬁcant opportunities for
expanding into external markets which requires efforts to acquire knowledge and
experience related to the application of innovative international technologies and
processes to achieve breakthrough in domestic production. Along this way Russia
has a chance to become an international technology development hub, offering
conditions for creating and disseminating far beyond its borders the most advanced
industrial and research technologies, attracting international intellectual capital and
high-tech companies and—most importantly—achieving competitive positions to
secure advantages in a speciﬁc segment of the technology chain, to successfully
integrate into the international division of labour.
Part II focuses on emerging technologies in the energy and transport domains.
The part begins with an analysis of measures to increasing the share of renewables in
the energy mix and assessing the technological potential for evidence-based policy-
making. The authors, Ermolenko, Ermolenko and Proskuryakova, ﬁnd that waves of
innovations in energy technologies led to the paradigm shifts in the industrial and
societal development. They argue that the centuries-long dominant position of fossil
fuels as economic drivers has led to the establishment of major players—multina-
tional companies that would like to preserve their business as long as possible. The
chapter analyzes recent technical developments of wind power and solar power
especially and their contribution to the changing energy mix and environmentally
friendly energy production. Approaches taken in Russia are used to illustrate con-
certed political initiatives to promote environmentally friendly energy production in
a country which is used to fossil fuels-based energy production to a reasonable
extent.
A roadmap for fuel cell electric vehicle (FCEV) Global Market Creation is
presented by Sokolov, Saritas and Meissner. They ﬁnd that in parallel with the
technological development, recent discussions about global warming and climate
change caused by carbon dioxide emissions brought public support for emission-free
vehicles, which are perceived as an asset. Despite of advancements and public
support, the introduction of FCEVs is still not at the desirable levels. Car
manufacturers frequently announce the near-time launch of FCEVs, which are
postponed with the same frequency. Saritas, Meissner and Sokolov aim to make
an attempt of a systemic analysis for a broader and more holistic analysis, which may
portray the bigger picture, and help to understand the industrial dynamics better.
Their key argument is that the stakeholder base in the transportation industry and
thus for FCEVs is broader than usually thought, and there are a number of other
issues to be addressed for a successful and widespread launch of FCEVs. The
chapter illustrates the links between the key technologies for FCEVs, the consumer
properties of existing and advanced FCEVs, the most promising products and their
respective market shares, volumes and growth rates. It highlights the structure of
potential demand for innovative products and outlines their most prospective
markets. The roadmap also provides an assessment of technical capabilities required
for manufacturing of products with the most preferable consumer properties, which
would allow generating the signiﬁcant competitive advantages for FCEVs.
1
What Do Emerging Technologies Mean for Economic Development?
5
In their contribution on emerging technologies in the aircraft and shipbuilding
industries
Klubova,
Veselitskaya,
Matich
and
Salun
investigate
emerging
technologies in aircraft and shipbuilding industries aimed at addressing common
grand challenges. For this purpose, the authors propose a framework for global
trends and technology identiﬁcation in two ﬁelds of the transport sector based on
scenario and roadmapping approaches. The suggested framework supports policy-
makers, companies and other interested parties set priorities, select innovation
projects and implement them based on a vision of a desirable future.
Living systems and environment are the main subjects of Part III. In the ﬁrst
chapter, Saritas and Vishnevski view nanotechnology as a response to grand
challenges with special emphasis on the case of water treatment and puriﬁcation.
Their chapter sheds light on the potential uses of nanotechnologies for supplying
clean water. It shows that new technologies are needed for effective and resource-
efﬁcient water treatment, and nanotechnologies may offer affordable solutions.
Overall, the chapter advocates that supplying drinking water to billions of people,
while at the same time protecting water resources, is the best strategic guideline for
the water supply industry and for applying new techniques and materials including
nanotechnology. Developing new technologies for deep puriﬁcation of water with
nanotechnology will increase healthy water supply by removing both visible and
invisible impurities hazardous to human and animal health. Thus, the chapter
discusses nanotechnology solutions for water treatment and puriﬁcation. How
nanotechnologies can signiﬁcantly increase the efﬁciency of certain traditional
water puriﬁcation processes such as coagulation, sorption and ﬂotation is discussed.
Next, technological, market and institutional aspects are considered regarding nano-
technology solutions. The chapter is concluded with future scenarios and strategic
steps to be taken for the implementation of nano-based water treatment and
puriﬁcation.
Gokhberg, Kuzminov, Bakhtin, Timofeev and Tochilina introduce an approach
towards identifying emerging technologies. They exemplify the methodology in the
case of agriculture and food. The paper discloses a new approach to sectoral
emerging technology identiﬁcation and their future development analysis. Their
proposed research strategy relies on a combination of traditional foresight methods
with big data-augmented science, technology and innovation (STI) landscape
mapping. On the ﬁrst step, the results of text-mining analysis present the ontology
of currently emerging technologies in global agriculture and food (A&F) sector. On
the second step, these technologies with the usage of text-mining techniques were
aggregated: (a) future technological market forecasts and (b) parameters of their
potential to bring answers to sectoral and national challenges. Based on this big data-
augmented analysis, prospective science and technology (S&T) development areas
for the Russian A&F sector were highlighted. The comparative beneﬁts of the
proposed approach for evidence-based STI policy and corporate strategic planning
are shortly summed up in terms of its objectivity and comprehensive information
source coverage.
6
D. Meissner et al.
The part concludes with a chapter on technology assessment, trends and wild
cards in human enhancement. First, Guo, Clark, Shirasaki and Daim propose a solid
approach towards assessing container closure integrity testing technology for the
biotech industry. They develop a framework for assessing technologies in the
biotechnology sector. The proposed framework is applied for evaluating several
container closure integrity testing technology alternatives. Then, in the chapter
Saritas describes emerging technologies, trends and wild cards in human enhance-
ment. He argues that the key question is whether human will be at the centre of
change as it has historically been or will be put aside by smarter machines. Machines
then will become the principal creator of new technologies and race far ahead of
humanity. While the machines are advancing, emerging technologies also offer new
possibilities for humans to remain competitive against machines through the
enhancement of physical and mental capacities. The development and use of, such
as, neuroscience, silicon chips and smart technologies offer new opportunities. A
desirable future is that there would be an ecosystem of humans and machines, where
both complement each other rather than competing with each other. Machines would
continue to support humanity’s well-being and quality of life and offer new
possibilities for advanced ‘human-machine’, ‘human-human’, ‘human-work’ and
human-environment interactions. Following the exploration of technologies, this
chapter reviews and discusses the implications of human enhancement on the future
of work, where the socio-economic impacts of emerging technologies can be well
observed. These technologies are expected to make revolutionary changes in work-
ing environment as people will be able to work harder, longer and smarter. The
chapter will also address some of those ethical issues associated with human
enhancement technologies.
Another promising ﬁeld for applying emerging technologies is the security
sector which is the core of Part IV. Hauptman illuminates the ‘dark side’ of
emerging technologies. He postulates that almost every new technology developed
for the beneﬁt of society has a potential ‘dark side’, manifested, for example, by
security or privacy threats. His chapter presents selected ﬁndings of two European
projects, which by employing foresight methods tried to shed light on these dark
sides: one project assessed the threats of potential abuse (by criminals or terrorists)
posed by selected emerging technologies, while the second focused on the impacts
of emerging technologies on privacy. The chapter pays special attention to new, even
surprising possibilities opened by the convergence of technologies and also deals
with related policy issues. The foresight processes, which help to explore evolving
security or privacy aspects of new technologies, reﬂect a need for continuous
analysis of the unfolding technology landscape for potential, sometimes surprising,
implications.
1
What Do Emerging Technologies Mean for Economic Development?
7
New issues and impacts for defence and security are discussed by James. He
illustrates how after 09/11 foresight studies shifted in security thinking away from a
focus on state-centric threats towards a much broader view of security risks recently.
This expanded perspective includes risks presented by the vulnerability of the
European society to the failure of critical infrastructure, to pandemics, environmental
change and resource-based conﬂicts. The chapter places a particular emphasis on the
treatment of technological change in these defence and security foresight studies and
argues that the growing importance of dual-use technologies is likely to mean that
defence will play a declining role as a sponsor and lead user of advanced
technologies in the future.
Defence 4.0 by means of Internet of Things in Military is analyzed by
Burmaoglu, Saritas and Yalcin. They show how scientiﬁc and technological
developments have been inﬂuencing military concepts and practice, particularly
following the inception of the scientiﬁc revolution in the late sixteenth century.
Their argumentation starts from the fact that a number of technologies have been
developed for defence, found their civilian applications and vice versa. Wherever the
boost for change comes from, the nature of warfare has changed radically both due to
S&T advancements and changing socio-economic and geopolitical contexts. Despite
of the barriers due to strict organizational culture, armies have adapted themselves
into changing characteristics of warfare through new concepts and instruments.
Among S&T developments, recent advancements in information and communica-
tion technologies (ICTs) bring enormous opportunities as well as challenges for
defence. One of the recent phenomena emerged with the rapid development of ICTs
is the Internet of Things which affects every aspect of life with a growing number of
devices communicating with each other. While the possibilities introduced by the
Internet of Things have been providing immense beneﬁts, the increasing number of
connections makes the system ever more complex and vulnerable because of the
difﬁculty of securing huge networks.
To conclude, Part V summarizes the previous chapters and looks especially on
challenges to science, technology and innovation policy. Meissner, Gokhberg and
Saritas consider how to stimulate emergence and convergence of technologies. They
argue that technology development and application are increasingly taking place
under ever more challenging conditions. In their view, technology features such as
complexity, interdisciplinarity and budget as well as time constraints and also
application features such as increased solutions’ reliability, multiple application
potential and direct and immediate economic impact create additional challenges
for technology developers. Especially emerging technologies are frequently consid-
ered to demonstrate features which fulﬁl these expectations and requirements.
Therefore, companies and policy-makers often wish to support the development of
such emerging technologies and leverage their potential for achieving commercial
impact and regional economic development. This requires that aims and goals of
technology support are formulated in a more ﬂexible form as currently practised, and
no deﬁnite ﬁxed indicators and deliverables are described. In doing so governments
are asked to obtain a more entrepreneurial attitude which is not expressed in
standardized public announcements but which is ﬁlled with live by the public sector
itself. The authors conclude that emerging technologies provide signiﬁcant
opportunities for companies and research institutions; however, for regional eco-
nomic development, it requires policy-makers looking beyond the existing policy
measures. This means especially concerted—e.g. consistent and coherent—STI
policy approaches solving the routine policy-maker dilemma which is often to
develop new STI policy measures instead of rethinking and streamlining the existing
STI policy mix.
8
D. Meissner et al.
Acknowledgements The book chapter was prepared within the framework of the Basic Research
Program at the National Research University Higher School of Economics (HSE) and supported
within the framework of the subsidy by the Russian Academic Excellence Project ‘5-100’.
References
Camagni R, Capello R (2013) Regional innovation patterns and the EU regional policy reform:
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Carayannis EG, Grigoroudis E, Campbell DF, Meissner D, Stamati D (2018) The ecosystem as
helix: an exploratory theory-building study of regional co-opetitive entrepreneurial ecosystems
as quadruple/quintuple helix innovation models. R&D Manag 48(1):148–162
Gertler MS (2003) Local knowledge: tacit knowledge and the economic geography of context, or
the undeﬁnable tacitness of being (there). J Econ Geogr 3:75–99
Gokhberg L, Meissner D (2016) Seizing opportunities for national STI development. In: Deploying
foresight for policy and strategy makers. Springer, Cham, pp 267–273
Gokhberg L, Meissner D, Sokolov A (2016) Foresight: turning challenges into opportunities. In:
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and growth. Growth Change 40(2):227–259
Ketels CH, Memedovic O (2008) From clusters to cluster-based economic development. Int J
Technol Learn Innov Dev 1(3):375–392
Klofsten M, Bienkowska D, Laur I, Sölvell I (2015) Success factors in cluster initiative manage-
ment. Ind High Educ 29(1):65–77
Kuzminov I, Meissner D, Lavrynenko A, Tochilina E (2018) Technology classiﬁcation for the
purposes of futures studies WP BRP 78/STI/2018. National Research University Higher School
of Economics
Laur I, Klofsten M, Bienkowska D (2012) Catching regional development dreams: a study of
cluster initiatives as intermediaries. Eur Plann Stud 20(11):1909–1921
Lundvall B, Borrás S (1997) The globalizing learning economy—implications for technology
policy at the regional, national and European level. In: Paper to the EU-TSER workshop on
globalization and the learning economy: implications for technology policy. Brussels
Meissner D, Polt W, Vonortas NS (2017) Towards a broad understanding of innovation and its
importance for innovation policy. J Technol Transf 42(5):1184–1121
Proskuryakova L, Meissner D, Rudnik P (2017) The use of technology platforms as a policy tool to
address research challenges and technology transfer. J Technol Transf 42(1):206–227
1
What Do Emerging Technologies Mean for Economic Development?
9
Dirk Meissner is Deputy Head of the Laboratory for
Economics of Innovation at HSE ISSEK and Academic
Director of the Master Program “Governance for STI”.
Dr. Meissner has 20 years experience in research and teach-
ing technology and innovation management and policy. He
has strong background in policy making and industrial man-
agement for STI with special focus on Foresight and
roadmapping, funding of research and priority setting. Prior
to joining HSE Dr. Meissner was responsible for technology
and innovation policy at the presidential ofﬁce of the Swiss
Science and Technology Council. Previously he was man-
agement consultant for technology and innovation manage-
ment with Arthur D. Little. He is member of OECD Working
Party on Technology and Innovation Policy. He is Associate
Editor of Technological Forecasting and Social Change,
IEEE Transactions on Engineering Management, Journal of
the Knowledge Economy, member of Editorial Review
Board at Small Business Economics and Journal of Knowl-
edge Management. He guest edited Special Issues in Industry
and Innovation journal, Journal of Engineering and Technol-
ogy Management, Technological Analysis and Strategic
Management among others.
Leonid Gokhberg is First Vice-Rector of the HSE and also
Director of HSE ISSEK. His area of expertise is statistics and
indicators on STI as well as foresight and policy studies in
this area. He has authored over 400 publications in Russian
and international peer-reviewed journals, monographs, and
university textbooks. Prof. Gokhberg has coordinated dozens
of national and international projects funded by public
agencies, businesses, and international organizations. He
has served as a consultant of the OECD, Eurostat, UNESCO,
and other international and national agencies. Leonid is also
a member of the Global Innovation Index Advisory Board,
the OECD Government Foresight Network, and OECD and
Eurostat working groups and task forces on indicators for
S&T and, as well as steering committees of various presti-
gious international and national initiatives. Prof. Gokhberg is
Editor-in-Chief of the Scopus-indexed scientiﬁc journal
Foresight and STI Governance and editor of the Springer
academic book series Science, Technology, and Innovation
Studies, and participates on the editorial boards of several
other inﬂuential journals. He holds PhD and Dr. of
Sc. degrees in Economics.
10
D. Meissner et al.
Ozcan Saritas is Professor of Innovation and Strategy at
the HSE and editor-in-chief of “Foresight”—the journal of
future studies, strategic thinking and policy. He worked as
Senior Research Fellow at the Manchester Institute of
Innovation Research, The University of Manchester. His
research focuses on innovation and policy research with
particular emphasis on socio-economic and technological
Foresight. With a PhD from the “Foresight and Prospective
Studies Program,” he introduced the “Systemic Foresight
Methodology”, and has produced a number of publications
on the topic. Dr. Saritas has extensive work experience with
the international organizations, including the United Nations,
OECD, and the European Commission. He has been
involved in large scale national, multinational and corporate
research and consultancy projects on sectors including
energy, climate change, agriculture, food, water, transporta-
tion, and ICT among others; published a number of articles in
respected journals; and have delivered keynote speeches in
more than 50 countries across the world. Besides his research
and publication activities, he designs and delivers academic
and executive education courses on Foresight and Strategic
Planning. He has recently co-authored a book, entitled
“Foresight
for
Science,
Technology
and
Innovation”
published by Springer, which has become one of the key
readings in the ﬁeld.
Part I
Materials and Manufacturing
13
New Materials: The Case of Carbon Fibres
2
Ozcan Saritas, Alexander Sokolov, and Konstantin Vishnevskiy
2.1
Introduction
New materials are one of the key pillars of the convergence in science, technology,
and innovation (STI). Recent advancements in STI have led to the development of
new materials with improved speciﬁcations and reduced dimensions. Cutting-edge
metals, foams, and other substances make buildings, vehicles, and gadgets more
energy efﬁcient and environmentally friendly. Due to their strategic importance, new
materials are at the radar of national STI policies. In recent decades, a number of
developed and developing countries have conducted numerous studies to determine
STI-based development prospects for the sphere of converging technologies with a
particular focus on new materials, nanotechnologies, and their production (Kim et al.
2014).
A number of foresight studies have been undertaken at national (Saritas et al.
2007; Vishnevskiy and Yaroslavtsev 2017), regional (Battistella and Pillon 2016),
and sectoral levels (Aydogdu et al. 2017) to investigate the future of new materials
and nanotechnologies. Russia is one of the countries, which prioritised the domain of
new materials and nanotechnologies for future STI development. Russia’s future
development agenda considers new materials and nanotechnologies among the key
priority areas along with information and communication technologies; life sciences
(biotechnology, medicine, and public health); rational uses of natural resources,
transport, and space systems; and energy efﬁciency (Saritas 2015). In Russia,
strategies are formulated to develop necessary competences in STI domains with a
particular focus on nanotechnology, biotechnology, and power engineering sectors
(Shmatko 2016).
O. Saritas (*) · A. Sokolov · K. Vishnevskiy
National Research University, Higher School of Economics, Moscow, Russia
e-mail: osaritas@hse.ru; sokolov@hse.ru; kvishnevskiy@hse.ru
# Springer Nature Switzerland AG 2019
D. Meissner et al. (eds.), Emerging Technologies for Economic Development,
Science, Technology and Innovation Studies,
https://doi.org/10.1007/978-3-030-04370-4_2
At the regional level, Battistella and Pillon (2016) assert that new materials are
one of the key technological components of regional development and policy. At the
industrial level, new materials and nanotechnologies play a major role. For instance,
Burmaoglu and Saritas (2017) underline the critical role of new materials for the
defence sector. Saritas and Proskuryakova (2017) discuss the use of new materials
and nanotechnologies for water puriﬁcation. International organisations also focus
on new materials and nanotechnologies due to their strategic importance. For
instance, the OECD considers these technologies as a critical component towards
the next production revolution along with the Internet of Things (IoTs), robotics,
industrial biotechnology, and 3D printing (OECD 2017).
14
O. Saritas et al.
Among new materials, carbon ﬁbres (CF) hold a prominent place and are deﬁned
as a nanostructured organic material containing between 92 and 99.99% of carbon
(RCN 2010). CFs and composites based on them attract researchers’ and
manufacturers’ attention due to their properties, such as good electroconductivity
and nearly zero linear expansion coefﬁcient, which make them indispensable for
certain special applications. High binding energy allows CFs to retain their strength
in a wide range of temperatures (up to 2200 ○С). They are the most heat-resistant
among all known ﬁbrous materials, which means they can be used as thermal shields
and heat-insulating materials.
The most important competitive advantages of CFs are believed to be their high
modulus of elasticity and breaking point values, lightness, low friction coefﬁcient,
resistance to atmospheric impact, and chemical reagents. Certain speciﬁc features of
CF materials allow them to be combined with other types of ﬁbres, such as boron,
glass, and organic ones (e.g. Kevlar 49, Armos, SVM). This enables products to be
made that combine the advantages of the two initial materials. Such hybrid
composites are already applied in aerospace, as well as in sporting equipment
manufacturing.
Access to advanced technologies for the mass production of CFs and the scale of
their industrial application are currently seen as indicators of national science,
technology, and production potential levels and help to achieve sustainable develop-
ment in various sectors of the economy. Currently CFs are most commonly used as
reinforcing ﬁllers in composites. They are considered to be a promising construction
material for making various important products.
Thus, the present chapter examines CFs in detail and makes a future-oriented
assessment. Long-term foresight future-oriented technology assessment (FTA) stud-
ies (Miles et al. 2016) and scenario analyses (Erdmann and Schirrmeister 2016;
Amanatidou et al. 2016) have been undertaken to examine emerging trends in
advanced science convergence (Vaseashta 2014). The present study ﬁrst begins a
detailed account of the CFs and CF composites in Sects. 2.2 and 2.3, respectively.
Different types of CFs and composites are presented with their key advantages and
uses. Next, their properties and uses are compared with a SWOT analysis. Compet-
ing technologies and the advantages of CFs and composites are discussed. Following
the production and technology of CFs, Sect. 2.4 examines the demand side. A
detailed market analysis and pricing forecasts are followed by future projections
on demand. Here is a scenario approach employed as one of the most frequently used
methods in foresight (Saritas and Burmaoglu 2015). The chapter is rounded off in
Sect. 2.6 with a set of technology strategies to exploit emerging market opportunities
and broader societal, environmental, and economic and other issues to consider
when innovating in CF technologies.
2
New Materials: The Case of Carbon Fibres
15
2.2
Carbon Fibres (CFs)
Carbon ﬁbre (CF) has increasingly been recognised as a revolutionary material with
desirable properties including strength, rigidity and weight, and environmental
friendliness. These properties made CFs to drive many industries such as wind
power, aerospace, as well as automotive. Therefore, it is important to understand
the nature of this highly demanded material, which is likely to open new horizons for
many industries. The section below begins with an analysis of the types of carbon
and a comparative analysis of them.
2.2.1
Types of CFs and a Comparative Analysis
CFs differ by precursors, which are the raw material used during the synthesis. Up to
90% of the CFs are produced from polyacrylonitrile (PAN), and the remaining are
made of petroleum pitch or rayon. These materials are organic polymers, which
consist of long strings of molecules bounded by carbon atoms. Four groups of CFs
can be distinguished: (1) PAN, (2) pitch (coal, peat, wood, and oil tar distillation
residue), (3) viscose, and (4) gas phase. These are brieﬂy described below.
2.2.1.1 Polyacrylonitrile (PAN)-Based CFs
Fibres of this type are produced by sequential processing of primary PAN ﬁbre (DoD
2005). An analysis of their strengths and weaknesses compared with other kinds of
CF and with alternative products is given in Table 2.1.
2.2.1.2 Pitch-Based Carbon Fibres
There are several varieties of such ﬁbres, the ones based on liquid-crystal
(mesophase) and other regular (isotropic) pitches. Mesophase pitches can be used
to make CFs with a high modulus of elasticity value, while isotropic pitches enable
CFs to be produced relatively cheaply. A SWOT analysis of these types of CFs is
presented in Table 2.2.
2.2.1.3 Viscose-Based CFs
Viscose is an artiﬁcial chemical ﬁbre made of rayon by subjecting it to a chemical
treatment to achieve structural modiﬁcation. Cellulose is one of the most commonly
used natural polymers, constantly reproduced on a major scale (see Table 2.3 for the
SWOT analysis).
16
O. Saritas et al.
Table 2.1 SWOT analysis of PAN-based carbon ﬁbres
Strengths
Weaknesses
Compared with other kinds of carbon ﬁbre
• Possibility to produce long unbroken ﬁbres
• Perfected technology
• Lower modulus of elasticity value than pitch-
based CFs
Compared with alternative products
• Favourable combination of high breaking
point and modulus of elasticity values
• Heat resistance
• High costs of primary PAN ﬁbre
• Low quality of Russian-made carbon ﬁbres
compared with the highest international level,
which negatively affects export opportunities
• The CFs’ strength is limited by discrete
defects in the polymer substrate and on the
ﬁbres’ surface
Opportunities
Threats
• Application of CFs for energy saving and
environmentally neutral production purposes
• Production of ﬁbres with speciﬁc physical
and chemical properties (electroconductivity,
high sorptive capacity) based on PAN bundles
• Wide application in aerospace, defence
industries, and nuclear power engineering
• Risk of pressure by environment protection
organisations due to emission of cyan-hydric
acid during production
Source: HSE
Table 2.2 SWOT analysis of pitch-based CFs
Strengths
Weaknesses
Compared with other kinds of CFs
• High modulus of elasticity (up to 900 hPa) of
mesophase pitch-based CFs
• Low raw materials’ costs of isotropic pitch-
based CFs
• Good electroconductivity
• Low bending strength
Compared with alternative products
• High thermal conductivity
• High production costs
• Low production volume
• ‘Dirty’ production
• Mismatched and unstable raw materials
Opportunities
Threats
• Used in centrifuge bowls, in combination
with PAN-based ﬁbres: pitch-based ﬁbres
provide axial reinforcement, PAN-based
ﬁbres—peripheral reinforcement
• Thick monoﬁlaments are used as cores for
carborundum and other ceramic ﬁbres
• Used in space-based solar batteries and for
spacecraft equipment manufacturing
• Russia lacks the raw materials—pitches
suitable for making ﬁbres
• Russia lacks industrial extrusion (or other
elongation) technologies for making pitch-
based ﬁbres
• Limitations imposed by environment
protection organisations due to pitch’s
carcinogenic properties
Source: HSE
2
New Materials: The Case of Carbon Fibres
17
Table 2.3 SWOT analysis of viscose-based CFs
Strengths
Weaknesses
• Relatively low costs of the raw material—
viscose ﬁbre
• High graphitisability
• Good textile properties
• High electroconductivity
• Heat insulation
• Possibility to produce long ﬁbres
• Low modulus of elasticity values
• Low-strength properties, limiting the scope for
application in composite production
• High ash content
• Mismatched raw materials
Opportunities
Threats
• Good prospects for medical applications
• Application in aerospace industry—as
heat insulation in rocket nozzles
• Lack of raw materials supplies
Source: HSE
Table 2.4 SWOT analysis of gas phase-based CFs
Strengths
Weaknesses
• A simple and inexpensive way to produce
ﬁbres
• Maximum strength (close to
monocrystals)
• Easily adjustable ﬁbre structure
• Electroconductivity
• Environmentally neutral production
• Hard to produce unbroken ﬁbres
• Insufﬁcient reliability due to low cohesion of
ﬁbre layers
Opportunities
Threats
• Extremely high physical and chemical
properties
• Low production costs
• Technology is still at the laboratory prototypes
stage
Source: HSE
2.2.1.4 Gas Phase-Based CFs
Gas phase-based ﬁbres can be produced via catalytic pyrolysis of a hydrocarbon gas
(methane, ethylene, acetylene, carbon monoxide, etc.) at 500–1500 ○C. This tech-
nology is still under development and not used for mass production. Table 2.4
presents a SWOT analysis of the gas-based CFs.
The comparison of above precursors leads to a conclusion that PAN-based CFs
have the biggest market, both for mass and specialised applications. PAN-based
ﬁbres are used as high-strength construction composites. Pitch-based ﬁbres have a
rather limited scope of application, mainly for special purposes (e.g. PAN- and pitch-
based ﬁbres can be used in combination with manufactured gas centrifuges).
Viscose-based CFs supplement replace graphite in high-temperature processes
are used as a carbon material, primarily in medicine. Production costs of this material
are determined by two factors: low product yield (10–15% of the primary viscose
ﬁbre mass) and a complex and expensive extraction process during graphitisation. It
should be noted that since viscose-based CFs were ﬁrst discovered and produced in
Russia (in the 1970s and 1980s), the relevant production technologies and
18
O. Saritas et al.
Table 2.5 Carbon ﬁbres’ characteristics
Carbon ﬁbre type
PAN-
based
Viscose-
based
Pitch-
based
Gas phase-
based
Strength (hPa)
1.8–7.0
0.35–0.70
1.4–4.0
1.0–4.0
Modulus of elasticity (hPa)
200–600
20–60
140–930
200–300
Price (USD per kilo)
40
20
300
NA
Market volume (demand)
■■■
■□□
■□□
■■□
Technological development
■■■
■■□
□□□
□□□
Fibre yield from the raw material
■■■
■□□
■■□
□□□
Availability of raw materials and
production capacities
■■■
■□□
□□□
□□□
Biocompatibility
□
■
□
□
‘■’—presence of property. Levels: ■■■, high; ■■□, medium; ■□□, low; □□□, no such
property
Source: HSE
equipment remained practically unchanged. Accordingly, the quality of the ﬁbres
currently produced remains quite low, with a relatively high costs and prices
(USD60–100 per kilo).
The technology for making CFs from gas phase is still being developed, so these
ﬁbres are not yet on the market. However, gas phase-based CFs have many potential
applications due to expected low production costs and relatively high properties. The
competitive advantages of each type of CFs are described in Table 2.5.
2.2.2
Competing Technologies for CFs
CFs’ growing popularity in various industries is limited by competition from
alternative materials—both traditional (such as steel, aluminium, titanium, etc.)
and new ones, particularly aramid, basalt, boron ﬁbres, silicon carbide, and alumin-
ium oxide ﬁbres. On the whole, CF surpasses the other materials (see Table 2.6), but
the latter are also applied in various industries.
2.2.2.1 Aramid Fibres
The main technical characteristics of aramid ﬁbres, which determine the prospects
and advantages of their application, include a low density (approximately
1400–1500 kg/m3 compared to an average of 1700–1900 kg/m3 for CFs); high
tensile strength; higher impact strength compared with high-modulus CFs; and
high static and dynamic load resistance. The high speciﬁc strength of materials
based on aramid ﬁbres means that the mass of a construction can be signiﬁcantly
reduced, while adhering to all relevant hardness and strength requirements. This
feature of aramid ﬁbre-based materials turns out to be quite efﬁcient in terms of
2
New Materials: The Case of Carbon Fibres
19
Table 2.6 Properties of carbon ﬁbres and alternative products
Material
Strength
(hPa)
Modulus of elasticity
(hPa)
Density
(t/cm3)
Diameter
(microns)
High-strength CF
3.6–7.2
300
1.8
5–10
High-modulus CF
2.5–3.25
500–800
1.8–2.2
5–10
Glass ﬁbre
3.5–4.6
72–110
2.5–2.9
3–25.8
Aluminium oxide
ﬁbre
2.2–2.4
385–420
3.95
10–25
Silicon carbide
ﬁbre
3.1–4.0
410–450
2.7–3.4
100–140
Boron ﬁbre
3.45
400
2.6
100–200
Basalt ﬁbre
1.8–3
90–115
2.2–2.6
10
Steel
2–3
200
7.8
Aluminium
0.3–0.6
70–80
2.6–2.7
Titanium
1–1.2
110
4.5
Source: HSE
improving products’ operational and economic characteristics including, for exam-
ple, in the aerospace industry, ﬁreproof clothing, sporting equipment, etc.
Using aramid ﬁbres as the basis for composite materials can signiﬁcantly increase
their damage resistance properties. Such composites, like metals, have a plastic
deformation potential which reduces their vulnerability to brittle fracture. On the
other hand, a major ﬂaw of aramid ﬁbres—and therefore composite materials based
on them—is very low compression and bending strength, which somewhat limit
their application. In addition, such ﬁbres are hard to colour, which can be seen as
another drawback.
2.2.2.2 Basalt Fibres
Basalt ﬁbres are made of natural minerals by melting and then transforming them
into ﬁbre without using any chemical additives. Currently, two major kinds of basalt
ﬁbres are manufactured: staple and unbroken ﬁbres.
Depending on diameter, staple ﬁbres are divided into several types with different
thermal conductivity, sound absorption, density, and other properties, which deter-
mine their application areas:
•
Microﬁne—less than 0.6 microns in diameter, used in super ﬁne air-gas and liquid
ﬁlters, for making thin paper and special products.
•
Ultraﬁne—0.6–1.0 microns in diameter, used in superlight heat insulation and
sound absorbent products, paper, ﬁne air-gas and liquid ﬁlters.
•
Superﬁne—1.0–3.0 microns in diameter. Special thermal processing of this kind
means microcrystalline material can be produced with an acid resistance 2.5 times
higher than regular ﬁbres and with a hygroscopicity twice as low. The main
advantage of this basalt ﬁbre is zero shrinkage during exploitation. Microcrystal-
line ﬁbre is used to make thermal-resistant heat insulation materials, plates, and
ﬁlters for operation in aggressive high-temperature environments.
20
O. Saritas et al.
•
Fine (9–15 microns) and thickened (15–25 microns) rock ﬁbres are widely used
as ﬁltration base in hydraulic facilities’ drainage systems.
•
Rough ﬁbres 50–500 microns in diameter are corrosion-resistant and can be used
to strengthen construction materials.
Unbroken basalt ﬁbre has such properties as resistance to high-temperature and
aggressive environments, durability, mechanical strength, and low hygroscopicity.
Due to its characteristics, basalt ﬁbre is used in shipbuilding, wind energy genera-
tion, automobile industry, construction, etc.
2.2.2.3 Boron Fibres
The main advantage of boron ﬁbres over carbon ones is high strength combined with
high elasticity: boron ﬁbre has tensile strength of 4300 mPa, modulus of elasticity of
380 hPa, and density of 2.63 g/cm3. Moreover, in terms of hardness, boron ﬁbre is
second only to diamond. Boron ﬁbres’ mechanical properties are in effect the same
as those of CF. However, boron ﬁbre has a bigger cross-section than CF, with
approximately 100 microns diameter (also manufactured are 140 and 200 microns
boron ﬁbres)—compared to a diameter of about 5–6 microns for CF. Boron ﬁbres’
increased diameter determines a higher productivity of their production process,
which is based on chemical deposition over a tungsten wire core or CF.
Boron ﬁbre’s larger diameter has advantages and disadvantages. The advantages
include simple operation, good matrix penetration of interﬁbrillar space due to small
speciﬁc external surface, and high resistance to loss of stability under pressure. The
disadvantages include poor ﬂexibility of the ﬁbre, which limits its application area.
Another signiﬁcant drawback of boron ﬁbres and the composites based on them is
their high costs, due to complex production technology—frequently an order of
magnitude higher than that for other ﬁbres.
2.2.2.4 Silicon Carbide Fibre
Silicon carbide ﬁbre has several speciﬁc features and advantages. First, silicon
carbide ﬁbre can work without oxidation under quite high temperatures (carbon
ﬁbre starts to oxidise in an oxygen environment at 400 ○C). In addition, from a
technological point of view, making composite materials based on a metallic matrix
and silicon carbide ﬁbre is easier than producing CF-based metallic composites—
because their reactivity to metals is low—but wetting of ﬁbres’ surface with melted
metals is quite good.
Unlike electroconductive CF, silicon carbide ﬁbre is a semiconductor, and its
conductive properties are adjustable up to a point. The areas and scope of application
of this kind of ﬁbres and derived composites are determined by their technical
characteristics, which in addition to high temperature resistance also include high
shock resistance, high bending and tensile strength, and high durability.
2.2.2.5 Aluminium Oxide Fibres
The main mechanical properties of aluminium oxide ﬁbres, including strength and
modulus of elasticity, are similar to those of carbon ﬁbres. Aluminium oxide ﬁbres’
advantages over CFs are better electrical insulation properties, colourlessness, and
ability to retain properties under high temperature and during contact with melted
metal in open-air environments. The latter characteristic permits the making of
composite materials based on aluminium oxide ﬁbres by casting, including shaping
them into complex forms. On the other hand, a serious drawback of this kind of ﬁbre
is its relatively high density which somewhat limits its application areas. Aluminium
oxide ﬁbres are successfully applied for strengthening metals. Such composite
materials retain good mechanical properties under high temperature and have high
electric conductivity, etc. The above properties mean that these materials can be used
for long periods of time in high-temperature environments, and they can also be
pressed, rolled, or subjected to secondary shaping under temperatures close to metal
matrix’s melting temperature.
2
New Materials: The Case of Carbon Fibres
21
2.2.3
Advantages of CFs
CFs exceed all known composite ﬁbre ﬁlls in terms of strength and modulus of
elasticity. Accordingly, carbon-ﬁlled plastics based on CFs have much better elas-
ticity and strength properties than aluminium or steel. At the same time, the speciﬁc
weight of CF remains under 2 g/cm3, which means that constructions can be twice as
light as those made from aluminium and ﬁve times lighter than steel. Such materials
are increasingly applied in aircraft construction and in products where the moment of
inertia is crucial (e.g. centrifugal energy storage systems, high-speed centrifuges).
Thus, as shown in Tables 2.7 and 2.8, CFs are superior to all other ﬁbres overall.
Table 2.7 Comparative analysis of the properties of carbon ﬁbres and alternative materials
Fibre material
Strength
(hPa)
Modulus of elasticity
(hPa)
Density
(g/cm3)
Diameter
(microns)
Carbon high-
strength
3.6–7.2
300
1.8
5–10
Carbon high-
modulus
2.5–3.25
500–800
1.8–2.2
5–10
Glass
3.5–4.6
72–110
2.5–2.9
3–25.80
Aluminium oxide
2.2–2.4
385–420
3.95
10–25
Silicon carbide
3.1–4.0
410–450
2.7–3.4
100–140
Boron
3.45
400
2.6
100–200
Basalt
1.8–3
90–115
2.2–2.6
10
Steel
2–3
200
7.8
Aluminium
0.3–0.6
70–80
2.6–2.7
Titanium
1–1.2
110
4.5
Source: HSE
22
O. Saritas et al.
Table 2.8 Comparison of CF and alternative materials
Type
Presence of
“nanocomponent”
Maximum
operational
temperature, ○С
Maximum Young’s
modulus value, hPa
Possible matrix
types
Composite areas of application
Carbon ﬁbre
Yes
3000
700
• Polymeric
• Metallic
• Ceramic
• Carbon
1. Aerospace
2. Construction
3. Energy
4. Medicine
5. Sports
6. Oil and gas production
7. Industry
Boron
Yes
400
450
1. Polymeric
2. Metallic (А1)
Aerospace
SiC/W, SiC/C
Yes
800
420
Metallic (Ti)
Aerospace
SiC, ﬁne
Yes
1300
450
1. Metallic
2. Ceramic
Aerospace, automobiles, high-voltage
cables
Polycrystalline oxides
Yes
1200
350
1. Metallic
2. Ceramic
Aerospace, automobiles, high-voltage
cables, stationary gas turbines
Monocrystalline oxides
and oxide eutectics
Yes/No
1600
450
1. Metallic
2. Ceramic
Aerospace, stationary gas turbines
Source: HSE
2
New Materials: The Case of Carbon Fibres
23
2.3
CF Composites
CF composites are complex structures created by combining CFs (which are used as
reinforcing elements) with a binding matrix. They have high values of three basic
properties: strength, rigidity, and low speciﬁc weight. CF exceeds all known com-
posite ﬁbre ﬁlls in terms of strength and modulus of elasticity. Accordingly, carbon-
ﬁlled plastics based on them have much better elasticity and strength properties
than aluminium or steel. At the same time, the speciﬁc weight of CF remains under
2 g/cm3, which means that CF constructions can be twice as light as aluminium and
ﬁve times lighter than steel ones.
2.3.1
Types of CF Composites and a Comparative Analysis
CF composites consist of CFs and a binding matrix. The matrix, together with the
reinforcing element, is the most important component of composite materials. It is
the properties of the matrix which largely determine the resulting composite’s
properties. Material of the same kind as the reinforcing element is frequently used
as the matrix material, which means that composites can be created using the
maximum permissible parameters of the reinforcing ﬁbre ﬁll. Currently, ceramic
and metallic matrices are widely used. In terms of binding techniques for connecting
ﬁbres with the matrix, all CF composites can be divided into three types:
•
Solid-phase binding techniques for connecting ﬁbres with the matrix are based on
assembling blank packages, i.e. ﬁrst combining reinforcing elements and the
matrix and subsequently binding the components with each other to make the
end product by hot pressing, forging, rolling, diffusion welding, extrusion, etc.
•
Liquid-phase techniques are based on employing various methods of moulding
melted metal matrices to impregnate the preliminary arranged system of ﬁbres
(in vacuum, under normal or increased pressure). An advantage of this technique
is that it enables complex-conﬁguration products to be made with minimum
(or no) subsequent processing and with limited impact on fragile components.
•
Creation of a metallic matrix using deposition techniques amounts to depositing
alternating matrix and reinforcing layers on the substrate: metal layers are placed
on the ﬁbres using various methods (e.g. gas-phase deposition, chemical, electro-
lytic deposition, etc.) to ﬁll the interﬁbrillar space.
Four types of composite materials, believed to have the best application
prospects, are described below:
•
Polymeric matrix CF composites (coal plastics)
•
Carbon-carbonic composites
•
Metallic matrix CF composites
•
Ceramic matrix CF composites
≥
24
O. Saritas et al.
Table 2.9 Main characteristics of carbon-carbonic composites
Property
Value
Density (g/cm3)
>1.70
Thermal conductivity coefﬁcient (kcal/m∙h degree, at 50 ○C):
– by Х, У axes
– by Z axis
5–10
4–8
Linear thermal expansion coefﬁcient by Х, У, Z axes, α∙10–6 1/○C in 20–100 ○С
temperature range
2.5–1.6
Speciﬁc heat, kcal/kg degree at 50 ○С
0.19
Failure stress under stretching, kg-force/cm2
– by Z axis
– by Х, У axes
>200
>300
Failure stress under pressure, kg-force/cm2
– by Z axis
– by Х, У axes
>1100
>1200
Modulus of elasticity under stretching, Е∙10–3, kg-force/cm2
– by Z axis
– by Х, У axes
210
400
Modulus of elasticity under pressure, Е∙10–3, kg-force/cm2
– by Z axis
– by Х, У axes
210
230
Failure stress under shifting, kg-force/cm2
– by Х, У axes
300
Source: HSE
2.3.1.1 Carbon-Carbonic Composite Materials
Carbon-carbonic composite material is a modular carbon-graphite material based on
a 3D carbon structure made of CF and a pyrocarbon matrix. The choice of
technologies applied to make carbon-carbonic composites is determined by the
techniques employed to make the carbon matrix. Currently two techniques are
used most frequently: carbonisation of a polymeric matrix of a previously moulded
coal plastic blank and deposition of pyrocarbon from the gas phase into CF substrate
pores. Frequently, a combination of the above techniques is used to give the
composite the required properties. In turn, a pyrocarbon matrix is made using
isothermal and non-isothermal (thermal gradient) techniques.
Carbon-carbonic composite materials are produced through a pyrocarbon satura-
tion technological process in thermal gradient gas aggregates. Reinforcing the
carbon matrix with CF enables materials with higher thermal characteristics than
modular graphite and glass carbon to be produced. Carbon-carbonic composites
increase products’ strength and durability, reduce the weight of constructions, and do
not require additional reinforcement by metal frameworks or similar, i.e. they are
monocarbon materials. In addition, this material is heat-resistant: when exposed to
high-temperature gas ﬂows, its loss of mass is insigniﬁcant, and the product retains
its original shape (see Table 2.9).
CF-based composite materials are widely used in nonferrous metallurgy to make
fasteners for melting furnaces’ sheathing. Due to their high biocompatibility,
resistance
to
biological
environment’s
impact,
nontoxic
nature,
and
electroconductivity close to human tissues, these materials can be applied in medi-
cine. In mechanical engineering they are used as a nonmetallic, self-lubricating
material in heavy-load frictional units’ slider bearings and in car brakes. Finally,
carbon-carbonic composite materials are used to make tennis rackets.
2
New Materials: The Case of Carbon Fibres
25
Table 2.10 SWOT analysis of carbon-carbonic composites
Strengths
Weaknesses
• High elasticity and strength properties
• High heat resistance
• Chemical stability
• Signiﬁcant reserve of accumulated research from Soviet times;
production technologies comparable with the leading international
level
• Average thermal
oxidation resistance
• Labour-intensive,
complex technology
• Fragility
Opportunities
Threats
• Possibility to adjust functional properties by adding metals
• Application in aerospace and the automobile industry (brake
systems, taking into account the trend to increased speed) and to
help components endure increased thermal load
• Highly specialised
consumption
Source: HSE
In the near future, carbon-carbonic composites that are very high density (similar
to the theoretical density of graphite) will be in demand. No radically new
technologies would be required to meet this demand. The problem could be solved
by combining already available production processes and certain other technological
approaches (such as applying high pressure, combined matrices, etc.).
A SWOT analysis for the aforementioned ﬁbre type is given in Table 2.10.
2.3.1.2 Metallic Matrix CF Composites
Metallic matrices of ﬁbre composites are light metals (with density of 4–5 g/cm3),
such as aluminium, magnesium, and beryllium; high-temperature metals such as
titanium, nickel, and niobium; and various alloys.
Aluminium alloys combining physical, mechanical, and technological properties
are widely used as a matrix material. From a technological point of view, aluminium
matrices can be divided into several types: deformable, casting, and powder. For
magnesium matrices, MA2-1, MA5, and MA8 magnesium alloys are used and
certain others. Titanium matrices are well suited for high-temperature work and
welding, and they can retain their high strength properties (360–1050 mPa) under
increased temperature (300–450 ○С) for long periods of time.
Titanium and magnesium matrices remain highly resistant to deformation even
under increased temperature, which makes it necessary to apply super plastic
deformation modes to make composites with fragile ﬁbres.
A SWOT analysis for the aforementioned ﬁbre type is given in Table 2.11.
2.3.1.3 Ceramic Matrix CF Composites
Ceramic matrix materials’ properties include high melting temperature, compression
strength which is retained under sufﬁciently high temperatures, and resistance to
oxidation. New ceramics based on highly refractory thorium, aluminium, beryllium,
zirconium, magnesium, and vanadium oxides are widely applied in equipment
designed for extreme operational environments.
26
O. Saritas et al.
Table 2.11 SWOT analysis of metallic matrix CF composites
Strengths
Weaknesses
• High modulus of elasticity
• Higher gas resistance compared with coal
plastics
• Can be used under high temperature
• Low resistance to melting of metal
• Low corrosion resistance
• Formation of aluminium carbide at the
junction of ﬁbres and metal, which destroys the
material
• Heavy weight compared with other CF
composites
• Not particularly good radiolocation
properties
• Speciﬁc recycling requirements
Opportunities
Threats
• Small-size design coupled with metallic
elements (e.g. in truss-type load-bearing
designs such as the ones used in space shuttles
and Buran spacecraft)
• Elements operating for long periods of time
under increased temperature (high-pressure
compressor blades, turbine blades) require
‘metallic behaviour type’
• No consumer demand
Source: HSE
Along with refractoriness, ceramics have high tensile and impact strength and are
resistant to vibration and thermal shock. Ceramic-based composite materials have a
high modulus of elasticity combined with low plasticity, which requires an optimal
balance of the properties of the matrix and reinforcing materials. In other words, it is
important to select reinforcing materials with a higher modulus of elasticity than that
of the matrix to make the material sufﬁciently strong.
A SWOT analysis for the aforementioned ﬁbre type is given in Table 2.12.
2.3.1.4 Polymeric Matrix CF Composites (Coal Plastics)
Polymeric matrix CF composites (coal plastics) are a polymeric composite material
made of interlaced CF threads placed into a polymeric resin matrix. Hardened
epoxide, polyether and other thermosetting resins, and polymeric thermoplastic
materials are used as matrices. The main advantages of polymeric matrix composites
include a high speciﬁc strength and modulus of elasticity, resistance to aggressive
chemical environments, low thermal and electrical conductivity, etc. In the course of
making such materials, it is relatively easy to bind the reinforcing elements with the
matrix under moderate temperature and pressure. Both traditional (such as moulding,
contact vacuum, and autoclave forming) and specialised production processes
(e.g. winding, pultrusion, etc.) are used when the material and the product are
made at the same time.
A SWOT analysis for the aforementioned ﬁbre type is given in Table 2.13.
2
New Materials: The Case of Carbon Fibres
27
Table 2.12 SWOT analysis of ceramic matrix CF composites
Strengths
Weaknesses
• Average mechanical properties
• Chemical stability
• Thermal stability
• Increased strength
• Low resistance to thermal oxidation
• Carbon ﬁbre must be specially treated for
the use with such matrixes (e.g. borated)
• Fragility
Opportunities
Threats
• Application in components operating under
high temperatures (internal combustion engines,
rockets, etc.)
• Possibility to make nonconducting materials
conductive
• Highly specialised application areas
Source: HSE
Table 2.13 SWOT analysis of polymeric matrix CF composites
Strengths
Weaknesses
• High elasticity and strength properties
under stretching
• Low production costs
• Relatively simple production technology
• Thermal stability
• Corrosion-resistant
• Average compression strength
• Need to have speciﬁc ratio of ﬁbres to achieve
required technical properties
• Vulnerable to ‘precision’ strikes (e.g. a carbon car
bonnet can turn into a sieve if frequently hit by
small pebbles)
• Hard to restore the original look (unlike metal or
glass ﬁbre components); even after small damage,
the whole component must be replaced
• Carbon components are prone to fading under
sunlight
• Problems with recycling and reuse
Opportunities
Threats
• Possibility to adjust functionality by
adding small amounts of carbon materials
• Wide application in aviation and
consumer goods
• Lagging far behind international producers
Source: HSE
Coal plastics are much superior to regular construction materials (aluminium,
steel, etc.) in strength, modulus of elasticity, mass, etc. Coal plastics also display
chemical and thermal stability, are corrosion-resistant, environmentally neutral, and
do not require expensive utilisation.
Coal plastics’ advantage over glass-ﬁbre plastics is their low density and higher
modulus of elasticity; coal plastics are both very light and strong materials. CFs and
coal plastics have a practically zero linear expansion coefﬁcient.
Black coal plastics are good electrical conductors, which somewhat limit their
application sphere. Coal plastics are used in aircraft construction, rocket building,
mechanical engineering, spacecraft and medical equipment, prostheses, in light
bicycles, and other sporting goods.
Currently technologies for the production of carbon-carbonic and polymeric
matrix CF composites are the most developed. Russia has sufﬁciently high
28
O. Saritas et al.
Table 2.14 Carbon ﬁbre composites’ characteristics
Carbon ﬁbre composites
Polymeric matrix (coal
plastics)
Carbon-
carbonic
Metal
matrix
Ceramic
matrix
Strength
0.9–3.5 hPa
■■□
■□□
■■□
Modulus of elasticity
■■■
■■□
■■□
■□□
Technological
development
■■□
■■■
■□□
□□□
Simple technology
■■■
■□□
□□□
□□□
Chemical and thermal
stability
■□□
■■□
■□□
■■□
Market volume
(demand)
■■■
■■□
■□□
■□□
Source: HSE
technological potential for developing and producing carbon-carbonic composites
and to make products with certain properties above average international values. At
present these expensive materials—which rely on complex technologies for produc-
tion—are only applied in defence, aerospace, and nuclear power industries. Regard-
ing coal plastics, Russian production potential is signiﬁcantly below international
levels. However, the possibility to achieve high elasticity and strength characteristics
as well as relatively simple production technologies combined with comparatively
low costs suggests that this market should be promoted for application in numerous
industries such as construction, energy, automobile industry, shipbuilding, sporting
goods manufacturing, etc. (Table 2.14).
Coal ﬁbre composites based on ceramic and metallic matrices have not yet been
widely applied due to complex production processes and unstable characteristics
(caused by low corrosion resistance and high fragility of ceramic matrices). Coal
aluminium (a metallic matrix coal ﬁbre composite) was developed as early as the
beginning of the Soviet period. However, it was never used on an industrial scale
because of insufﬁcient research.
Thus coal plastics (polymeric matrix composites) have the biggest market poten-
tial due to their high physical and chemical properties as well as simple production
technology. Carbon-carbonic composites could ﬁll the specialised applications
niche, especially in areas which already have the necessary technological potential
(e.g. brake disks). Metal matrix-based composites could be used in aircraft construc-
tion, while ceramic matrix composites could be applied in high-temperature
environments.
2.3.2
Competing Technologies for CF Composites
Along with CF composites, other materials can be used to accomplish similar
objectives (see Table 2.15). Developing a strategy for new materials, one should
take into account the speciﬁc features of competing technologies. Combining
2
New Materials: The Case of Carbon Fibres
29
Table 2.15 Main properties of coal plastics and alternative products
Properties
Coal plastic
Ribbon
Bundle
Organic plastics
Glass-ﬁbre plastics
Breaking point, mPa
900
700
1600
600
Modulus of elasticity, hPa
150
130
80
50
Density, g/cm3
1.5
1.53
1.35
2.2
Source: HSE
various kinds of composites would enable hybrid structures to be made, featuring all
their advantages.
Currently scientists identify several materials which compete with CFs in various
areas: glass, boron, organic, basalt plastics, composite-based silicon carbide ﬁbres,
powder-ﬁlled polymers, and textolites.
2.3.2.1 Glass-Fibre Plastics
Glass-ﬁbre plastics are polymeric composite materials in which thermosetting syn-
thetic resins are usually used as a matrix (e.g. phenolic, phenol-formaldehyde,
epoxy, polyether, polyimide, organosilicon, and thermoplastic polymers such as
polyamides, aliphatic polyamides, polyethylene, polystyrene, etc.). For reinforce-
ment, glass ﬁbres are used in the form of threads, bundles, glass fabrics, and cut
ﬁbres formed out of melted inorganic glass. The ﬁller’s characteristics largely
determine the mechanical properties of glass-ﬁbre plastics. For example, composites
containing particularly positioned unbroken ﬁbres, unidirectional and crossed, have
the highest strength and rigidity.
The main technical
characteristics
which
determine glass-ﬁbre plastics’
advantages are their high strength; relatively low density and thermal conductivity;
good electrical insulation properties; transparency to radio waves; and resistance to
aggressive environments, speciﬁcally water resistance and chemical stability.
Active application of glass-ﬁbre plastics began in the mid-twentieth century,
when this composite material began to be used to make ‘blisters’ (dome-like
constructions housing locator antennas). It should be noted that initially, plastic
ﬁbre was introduced in reinforced glass ﬁbre to neutralise the faults of fragile
matrices, with a small number of ﬁbres. With time the content of ﬁbre in glass-
ﬁbre plastics has increased to 80% of the total composite’s mass. The matrix’s
purpose also changed: it became simply a binding structure connecting strong ﬁbres
to each other.
Due to their technical properties and relatively low production costs, glass-ﬁbre
plastics are currently used quite widely in such areas as construction, shipbuilding,
radio electronics, consumer goods production, sport goods, window frames, etc.
Glass-ﬁbre plastics are also used as construction and heat insulation material to make
boats, launches, rocket engines, car bodies, cisterns, helicopter blades, and
corrosion-resistant equipment and to build pipelines.
30
O. Saritas et al.
2.3.2.2 Boron-Filled Plastics
Boron-ﬁlled plastics are composite materials in which thermosetting resins are used
as a matrix: epoxy, polyimide, and certain other polymers. The matrix’s heat
resistance deﬁnes boron plastics’ thermal properties, which is reﬂected in the
material’s relatively low operational temperatures. As a ﬁller, these composites
contain boron ﬁbres in the form of monothreads, bundles, and ribbons in which
boron threads are interlaced with other threads. Mechanical properties of the
resulting composites are deﬁned by the ﬁller’s characteristics.
Boron ﬁbres have high mechanical properties, including high fatigue strength,
hardness, and the highest compression strength among all other ﬁbres. Materials
made of boron ﬁbres are resistant to aggressive environments. A serious drawback is
their fragility, which complicates the processing of boron plastic products and limits
the range of their shapes. Moreover, due to certain speciﬁc features of boron ﬁbres
production technology (deposition of boron from chloride onto expensive tungsten
substrate), their production costs may be very high at about USD400 per kilo.
Due to high production costs, boron plastics are not widely used. They are mostly
applied in aircraft and spacecraft equipment to reduce the mass of high-load parts
operating in aggressive environments (Rutkovskaya and Koledayev 2010).
2.3.2.3 Organoplastics
Organoplastics are composite materials in which thermosetting resins are used as a
matrix, such as epoxy, polyether, phenolic, and polyimides. Synthetic ﬁbres are used
as ﬁller (which accounts for 40–70% of such composites’ mass) in the form of
threads, bundles, fabrics, felt, paper, etc. Synthetic organic ﬁbres are most com-
monly used, especially aramid ones. Natural or artiﬁcial ﬁbres are applied more
rarely. The mechanical properties of the resulting organoplastics are determined by
the ﬁller’s characteristics. This means that the degree of the ﬁller’s macromolecular
orientation plays a major role in improving them. For example, macromolecules of
poly-paraphenylene terephthalamide (Kevlar) are mostly oriented towards the
fabric’s axis, which ensures a high tensile strength of the ﬁbres.
Generally, organoplastics have a number of advantages over other materials; they
are lighter than glass ﬁbre and coal plastics due to their low density and have
sufﬁciently high tensile density; high fatigue strength; and high resistance to impact
and dynamic load. Furthermore, aramid ﬁbre-based organoplastics can operate for a
long time under increased temperature (about 180–200 ○C). Their major weakness is
low compression and bending strength.
Organoplastics are widely used in many industries such as aerospace, automobile,
shipbuilding, mechanical engineering, chemical, radio electronics, sports goods
manufacturing, etc. Kevlar-based materials are used to make bulletproof armour.
2.3.2.4 Basalt Plastics
Basalt plastics are composite materials in which basalt threads are used as a
reinforcing element, while epoxy, epoxy-phenolic, phenol-formaldehyde, and poly-
amide resins are used as a binder. In terms of their basic technical characteristics,
basalt plastics are certainly not inferior to glass ﬁbre ones. They even surpass them
by such parameters as modulus of elasticity, impact strength, and resistance to
aggressive environments. One of the main advantages of basalt plastics is their
high strength. On this criterion, basalt ﬁbres are close to carbon ﬁbres, so basalt
plastic products are three times stronger and four times lighter than steel ones. In
addition, they are highly durable and can work for 100–200 years.
2
New Materials: The Case of Carbon Fibres
31
Another obvious advantage of basalt plastics is their high dielectric properties and
stability of their main qualitative characteristics during prolonged exploitation.
Application of basalt plastics widens the range of special properties composite
materials which enable them to operate under increased temperature and humidity.
Basalt ﬁbres used to produce basalt plastics are made from the natural material
basalt (which makes up 30% of the Earth’s crust). According to experts, basalt
plastics production has a short technological chain. Thus, due to their properties,
basalt plastics can successfully compete with metal and glass-ﬁbre plastic products,
surpassing them by corrosion resistance including alkali resistance. Basalt plastics’
main drawback is their relatively low compression and bending strength.
The areas of application of basalt plastics include electrical and radio engineering,
production of acoustic materials, communication systems, mechanical engineering,
industrial and civilian construction, and road building. Basalt plastics can be used to
make road slabs, boat hulls, basalt plastic reinforcement rods, fencing panels for
buildings, hot and cold water pipes, hydraulic facilities’ coating, etc. In asbestos
cement and concrete products, it is efﬁcient to replace asbestos and metal
components with basalt plastic reinforcing elements. Several types of basalt plastic
construction reinforcement rods are also available, offering a number of advantages
when building bridges of complex design, various other structures, dams, and
subterranean installations.
The application of rovings made of integrated glass-basalt threads means that
basalt plastic reinforcement elements with a ﬁlling degree of up to 80 weight percent
can be made; basalt textolites based on basalt fabric have a ﬁlling degree of up to
70 weight percent; hardened basalt laminates based on basalt paper are also pro-
duced (Table 2.16). One kilo of basalt plastic reinforcing elements saves 9 kg of
reinforcing steel.
Table 2.16 Characteristics of basalt plastics products
Property
Basalt plastic
reinforcement elements
Basalt
textolites
Hardened basalt
laminates
Breaking point, mPa
Stretching
1080–1380
140–220
Bending
670–780
120–200
Compression
460–490
–
Modulus of elasticity under
stretching, mPa
89,000–93,000
39,000–40,000
12,000–14,000
Water absorption in 24 h
0.01
0.02
0.02
Source: HSE
32
O. Saritas et al.
2.3.2.5 Powder-Filled Polymers
Adding various ﬁllers signiﬁcantly improves composites’ mechanical properties
(e.g. strength, elasticity, heat resistance, etc.) and adds the required visual features.
Moreover, ﬁllers are applied to reduce production costs. For example, calcium
carbonate and kaolin (or china clay) are cheap ﬁllers which have practically unlim-
ited reserves. At the same time, their white colour is optimal for producing paintable
materials.
Filled polymers were ﬁrst produced in the USA where Dr. Leo H. Baekeland
invented a way to synthesise phenol-formaldehyde resin (Bakelite), to which the
scientist added wood ﬂour to make it stronger. Bakelite-based powder-ﬁlled com-
posite production technology amounts to an irreversible hardening, in a special form,
of a mixture of partially hardened polymer and ﬁller under pressure. Filled
thermoreactive polymers are now widely used: there are tens of thousands of ﬁlled
polymer brands available on the market.
Powder-ﬁlled polymers are used to make polyvinylchloride materials for produc-
tion of sufﬁciently strong and elastic pipes, facing tiles, electric insulation materials,
and polyether glass-ﬁbre plastics (AIMS 2008). Adding talc to certain polymers such
as polypropylene signiﬁcantly improves their properties and increases their modulus
of elasticity and heat resistance. Soot is mostly used as rubber ﬁller but is also added
to such polymers as polystyrene, polyethylene, and polypropylene. In addition,
organic ﬁllers such as wood ﬂour, ground nutshells, and various plant and synthetic
ﬁbres are widely used.
2.3.2.6 Textolites
Textolites are laminated plastics based on various thermoreactive and thermoplastic
polymers and thermoreactive resins, such as phenol-formaldehyde, epoxy,
polyimide, organosilicon, etc. Sometimes inorganic binders are used, based on
nonferrous metal silicates and phosphates. Fabrics made of various ﬁbres are used
as ﬁllers—cotton, glass, asbestos, carbon, basalt, etc. Textolite production involves
impregnating the fabric with resin and then pressing it under high temperature.
Despite the fact that this technology was developed in the 1920s, the same principles
are applied now. Currently, textolites are used to make not just slabs but also
complex-shaped products such as bushings, rods, etc. Textolites have properties
such as high strength, high impact resistance, and a broad operating temperature
range (from –65 to 105 ○C). Textolites are applied as electrical and heat insulation
and heat-resistant materials. One of the ﬁrst applications of textolites was to produce
kitchen table surfaces. Due to their hardness, textolites are also used as a construc-
tion material.
2.3.3
Advantages of CF Composites
Comparing various ﬁbre-matrix combinations (Table 2.17), it should be noted that
coal plastics (CFs) have the best balance and offer the best physical and chemical
properties—a high modulus of elasticity and strength.
2
New Materials: The Case of Carbon Fibres
33
Table 2.17 Comparative analysis of composites based on various ﬁbres and matrices
Matrix
type
Carbon ﬁbre
Polymeric ﬁbre
Ceramic ﬁbre
Advantages
Disadvantages
Advantages
Disadvantages
Advantages
Disadvantages
Polymeric
High elasticity and
strength properties under
stretching
Average compression
strength
High
tensile
strength
Low compression
strength
High compression
strength
Average
tensile
strength
Metallic
High modulus of
elasticity value
Low resistance to metal
melting, low corrosion
resistance
–
Fibres unstable
during composite
production
High resistance to metal
melting
–
Carbon
High elasticity and
strength properties
Low resistance to thermal
oxidation
–
Fibres unstable
during composite
production
High mechanical and
thermal oxidation
properties
–
Ceramic
Average mechanical
properties
Low resistance to thermal
oxidation
–
Fibres unstable
during composite
production
High mechanical and
thermal oxidation
properties
–
Source: HSE
34
O. Saritas et al.
Fig. 2.1 Properties of coal plastics and alternative products. Source: HSE
Table 2.18 Main properties of coal plastics and alternative products
Properties
Coal plastics
Ribbon
Bundle
Organic plastics
Glass-ﬁbre plastics
Breaking point, mPa
900
700
1600
600
Modulus of elasticity, hPa
150
130
80
50
Density, g/cm3
1.5
1.53
1.35
2.2
Source: HSE
Figure 2.1 compares the characteristics of coal plastics and alternative materials:
Russian- and foreign-made glass-ﬁbre plastics, titanium, and aluminium alloys.
Thus coal plastics are superior to glass-ﬁbre plastics in terms of modulus of
elasticity, but organic plastics typically have higher tensile strength and lower
density. Nevertheless, organoplastics’ compression strength is 5–10 times lower
than their tensile strength, and their production costs remain quite high (Table 2.18).
2.4
Demand for CF Products
Demand for CF materials has grown strongly across the world in the last few decades
due to their increased application in special-purpose product manufacturing for
nuclear power, aerospace, and other industries and for consumer goods production.
As to global CF production, its volume is expected to reach about 50,000 tons a year
(Ponomarev 2012). Below, some of the key industrial markets for CF products are
presented.
2
New Materials: The Case of Carbon Fibres
35
2.4.1
Markets for CFs
Most of CF demand is expected to come from seven large industries: aerospace,
construction, energy, manufacturing, sports and recreation, medicine, and oil and gas
production and transportation. Table 2.19 shows products to be demanded by each of
these industries.
CF-based composite materials are increasingly applied in aircraft and products for
which the inertia moment is crucial (such as centrifugal energy storage systems and
high-speed centrifuges). CFs can also be usefully applied in deepwater drilling
installations, for shelf development, and to ensure a presence in strategic areas
such as the Arctic. It is becoming obvious that the application of CF in
manufacturing should be extended to make equipment with advanced operational
parameters in terms of their average price, strength, and elasticity. Figure 2.2
illustrates each of these parameters with future forecasts and their particular rele-
vance to the aforementioned industries.
These parameters are keys for the widespread use of CF composite materials—
e.g. in the automobile industry (in particular to reduce cars’ weight) and in ship-
building (primarily, to make hull skins). CFs can also be successfully applied in
medical products (such as medical tissues, wheelchairs) and sports and recreation
goods (Dislife 2008).
2.4.2
Market Pricing of CFs and Composites
The leading countries, primarily the USA, are actively working on reducing CF’s
costs. Several years ago, Russia also began to realise the importance of this issue, but
low current demand from civilian industries does not contribute to dynamic devel-
opment in this sphere. Only special application industries (such as space, nuclear
energy, etc.) can afford to buy expensive CF. In addition to scaling, the following
precursor innovations to reduce production costs are proposed: adopting textile PAN
ﬁbre as a precursor and using lignin as raw material to make CF ﬁbre and
polyoleﬁns.
Other
technological
innovations
suggested
include
advanced
stabilisation, plasma oxidation, MAP carbonization, and advanced surface treatment.
The current misbalance (demand for CF exceeding supply) contributes to the
growth of prices. For example, the price of a kilo of 24K CF in 2010–2011 grew
from 24 to 30 euros (ROSSTS 2011). Future international CF prices should be
comparable with prices for the materials they replace (such as steel). Currently, the
average world price for CF is about USD37.5 per kilo (other estimates say up to
USD50 per kilo). In the future, these prices are expected to decrease. Based on
(continued)
36
O. Saritas et al.
Table 2.19 Industrial demand for carbon ﬁbre products
Aerospace industry
Products
• Airplane, helicopter, and rocket engine
components
• Airplane, helicopter, and glider structural
components
• Stealth-type craft skin
• Space-based antennas’ bodies
• Landing modules’ skins
Demand drivers
Barriers
• Improved mass/dimension product
characteristics
• Improved competitiveness of the industry
• Long-term agreements
• Increased share of composite materials
• Low productivity (long production cycle,
long periods required for R&D)
• High costs and long time required for testing
of materials
• Hard-to-process raw materials
• High costs of carbon ﬁbre
Advantages of alternative products
Glass-ﬁbre plastics
Organoplastics
• Relatively low cost (300–500 roubles per kilo)
• Transparent to radio waves
• Lower density
• Can operate for a long time under increased
temperatures
Silicon carbide ﬁbre-based composites
Steel
• High shock strength, bending strength, and
tensile strength
• Durability
• Relatively low costs
• Repairability
• Perfected technologies
Titanium
Aluminium
• Good price/strength ratio
• Highly corrosion-resistant
• Low costs
• Perfected technologies
Trends
Increased share of composite materials applied to improve products’ operational characteristics
(in Boeing and Airbus aircrafts over 50% of the mass is composites)
Strategy
• Implementation of the full cycle ‘R&D—pilot production—mass production’ in Russia
• Procurement of cutting-edge technologies and equipment
Construction
Products
• Bridge structures
• Reinforced covers and elements for high-rise,
seismic, and coastal construction
• Light mobile shelter supports
• Cases and components of chemically stable
equipment, pipelines, and ﬁxtures
• Concrete reinforcements
• Elements for recovering concrete products
Demand drivers
Barriers
• Number and state of repair of bridges
• Increased production of new materials for the
industry
• Reduced time and costs
• Lack of construction standards and rules
• High costs of materials
• Insufﬁcient availability of information
about materials’ testing
Advantages of alternative products
Glass-ﬁbre plastics
Organoplastics
• Relatively low cost (300–500 roubles per kilo)
• Relatively low cost
• Suitable for application under increased
temperature and humidity
(continued)
2
New Materials: The Case of Carbon Fibres
37
Table 2.19 (continued)
Trends
• Increased share of composite materials applied to improve products’ operational characteristics
• Poor state of bridges (in Russia over 20% of bridges have structural defects)
Strategy
• Implementation of the full cycle ‘R&D—pilot production—mass production’ in Russia
• Development of relevant regulations (construction standards and rules, etc.)
Energy
Products
• Gyroscopes as energy storage systems
• Latticed power line girders
• Carrier high-voltage cable cores
• High-speed rotors
• Next-generation gas centrifuges
• Wind power plants’ blades
Demand drivers
Barriers
• Increased share of applied composite materials
• Energy efﬁciency as a national priority
• Next-generation gas centrifuges for uranium
enrichment
• Growth of global wind power market
• Demand for open sea wind turbines,
lightweight, water-resistant, and high-strength
• High CF costs
• High risks associated with nuclear power
generation
• Vague prospects of wind power generation
in Russia
Advantages of alternative products
Glass-ﬁbre plastics
Silicon carbide ﬁbre-based composites
• Relatively low cost (300–500 roubles per kilo)
• High shock strength, bending strength, and
tensile strength
• High durability
Steel
Aluminium
• Relatively low cost
• Repairability
• Perfected technologies
• Low cost
• Perfected technologies
Trends
• Growth of the global wind energy market (CF in blades); bigger wind turbine blades
• Growth of the nuclear energy market (CF in uranium enrichment centrifuges)
Strategy
• Implementation of the full cycle ‘R&D—pilot production—mass production’ in Russia
• Promoting demand for CF from manufacturers of end products which may contain CF
• Expansion of the global wind energy market
Oil and gas production and transportation
Products
Demand drivers
Barriers
• Presence in strategic areas (the Arctic)
• Increased share of applied composite materials
• Long-term agreements
• High costs of CF
• Investment risks
• Limited number of consumers—companies
using relevant technologies
Advantages of alternative products
Basalt plastics
• Relatively low cost
• Resistant to increased temperatures and
humidity
production volume, relevant prices, and the actual growth rate, three price growth
forecasts can be developed for the global CF market (Fig. 2.3).
38
O. Saritas et al.
Table 2.19 (continued)
Trends
• Increased depth drilling installations are required to reach (over 2.4 km)
• Increased importance of being present in strategic regions (the Arctic shelf)
Strategy
• Active long-term government support due to the segment’s moderate appeal to private investors
• Development of relevant regulations (including national standards, speciﬁcations for end
products containing CF)
Medicine
Products
• Wheelchairs
• Artiﬁcial prostheses
• Medical tissues
• Dialysers
Demand drivers
Barriers
• Development of new materials sector in
medicine
• High share of applied innovations
• High costs of CF
Advantages of alternative products
Titanium
• Good price/strength ratio
• High corrosion resistance
Trends
• Increased application of new materials (CF for prostheses, medical tissues, and wheelchairs)
Strategy
• Supporting Russian CF producers’ participation in the ﬁnal links of technological chains, jointly
with leading companies and countries
• Investing in highly market-ready products
Source: HSE
According to forecasts drawn up using calculations, under the most optimistic
scenario (discovery of a new raw materials source or development of new production
technology), by 2020 the price of CF will be around USD15–20 per kg. It is widely
believed that the industry has already reached the maximum decrease in production
costs. A kilo of raw material (e.g. PAN ﬁbre) currently costs at least USD6, and its
price cannot be expected to drop further. However, hopes associated with scientiﬁc
progress indicate that prices may fall to USD20 (or even 17–18) per kilo of CF.
Based on the above production volume and prices and on the actual growth rates,
three forecasts can be developed for global CF production (see Fig. 2.4).
According to forecasts, a radically different optimistic scenario is possible. It may
be the result of national policy (e.g. introducing limitations for harmful emissions) or
global need (in particular, development of sea-shelf oil ﬁelds). It is difﬁcult to
forecast the effects of such factors at this stage.
The Russian CF market is relatively immature, which results in a higher growth
rate and more diverse scenarios (Fig. 2.5). The actual volume of the Russian carbon
ﬁbre market in 2011 equalled to USD10–15 million (Gavriliuk 2011).
2
New Materials: The Case of Carbon Fibres
39
800
8
20
10
0
2013 2014 2015 2016 2017 2018 2019 2020
Pessimistic
Moderate
Optimistic
6
4
2
0
2015
2020
Long term
World
600
400
200
0
2015
2020
Long term
World
Russia
Russia
Oil and gas production
and transportation
Aerospace industry
Energy
Construction
Other industries
Sports and recreation
Medicine
Note: carbon fibre with relevant
properties will be widely applied in
relevant segments after a certain
period of time
Average price
(USD/kg)
Strength
(GPa)
Modulus of
elasticity (GPa)
Fig. 2.2 Target CF properties for various segments. Source: HSE
15
20
25
30
35
40
2014
2015
2016
2017
2018
2019
2020
Pessimisctic
Moderate
Optimistic
Fig. 2.3 Actual and expected CF prices (USD per kilo). Source: HSE
40
O. Saritas et al.
0
20
40
60
80
100
120
140
160
180
200
2014
2015
2016
2017
2018
2019
2020
Pessimistic
Moderate
Optimistic
Fig. 2.4 Actual and expected volume of the global CF market (million USD). Source: HSE
0
50
100
150
200
2014
2015
2016
2017
2018
2019
2020
Pessimistic
Moderate
Optimistic
Fig. 2.5 Actual and expected volume of Russia’s CF market (million USD). Source: HSE
2.5
Future Prospects and Scenarios for CF Markets
Currently, the production of CF and derived composites is limited to a small number
of Japanese, American, South Korean, Taiwanese, and Chinese high-tech
companies. Leading producers of technological chain include the full production
cycle from PAN ﬁbre to composite materials. Currently, many products used in
almost all areas of human activities are made from—or contain—CF. Below, two
sets of scenarios are presented based on the forecasts of the global and Russian
markets. Each set considers optimistic, moderate, and pessimistic scenarios.
2.5.1
Global Market Scenarios
Three possible scenarios for the global market are presented in Fig. 2.6.
The optimistic scenario assumes that the global economic recession will mani-
fest in a decline of the CF market’s growth rate to 10% (or perhaps remaining at the
current 15%). Subsequently, when global economic growth resumes, the market’s
actual growth rate will return to 20–25% a year. However, due to reduced prices, the
growth rate in monetary terms will be lower, 10–15%. The main growth drivers will
be (1) increased demand for CF (among other things due to introduction of relevant
regulations and standards), (2) development and improvement of technologies, and
(3) reduced CF prices.
2
New Materials: The Case of Carbon Fibres
41
0
20
40
60
80
100
120
140
160
180
200
2014
2015
2016
2017
2018
2019
2020
Pessimistic
Moderate
Optimistic
Fig. 2.6 Actual and expected volume of global CF production (thousand tons). Source: HSE
0
20
40
60
80
100
120
140
160
180
200
2014
2015
2016
2017
2018
2019
2020
Pessimistic
Moderate
Optimistic
Fig. 2.7 Actual and expected volume of Russian CF production (thousand tons). Source: HSE
The moderate scenario indicated a decline in growth rates to 5%, followed by a
fast growth. On average, the growth rate of 10–15% in 2013–2015 was reﬂected as a
5–10% in monetary terms (due to reduced prices). Declining rate of growth is
expected towards 2020. The main growth drivers include the support of strategic
industries, development of innovative industries, and the gradual growth of demand
from the defence industry complex.
The pessimistic scenario is based on the premise that global economic problems
will affect research-intensive industries ﬁrst. In that case, in the next 2 years, market
growth is expected at 2–3%; after that, it would be rather ‘inﬂationary’, i.e. 5–6% a
year. However, experts estimate that the long-term probability of the occurrence of
the pessimistic scenario is relatively low.
42
O. Saritas et al.
2.5.2
Market Scenarios in Russia
In addition to the global scenarios presented above, three scenarios are suggested
regarding alternative development trajectories of the Russian market (Fig. 2.7).
The optimistic scenario assumes that the structure of CF demand in Russia
would match the analogous international scenario, while production would move
on to radically new qualitative, quantitative, and price levels. According to this
scenario, annual growth in the next few years would be on average 48%. The
explosive market growth would increase production volume to 3000 tons of
average-quality material by the end of 2017, fully meeting the economy’s demand.
In 2013–2017, sufﬁciently moderate growth was predicted; after 2017, it is expected
that the global average growth rate will reach about 20–25%, with prospective
exports of about 20% of the total production volume. By 2020, Russia’s carbon
ﬁbre production is expected to reach to 3–5% of the global output. Overall, experts
believed this scenario was possible, although most of them still thought it to be
overly optimistic. In 2019–2020, the growth rate may slow down due to a possible
global economic recession which will affect the Russian economy about 1 year after
other countries.
The moderate scenario assumes that Russian production would amount to 2.5%
of global volume, with exports at about 5–10% of total output. Russia will start
producing CFs with average characteristics for civilian applications, while also
keeping its positions in high-modulus materials production. While increasing at a
growth rate of 35% in 2013–2017, the production levels in the future is expected to
remain at the average international level of about 15–25% a year.
The pessimistic scenario is based on the premise that Russia does not have
resources to produce large volumes of high-quality products. Here, Russia’s share of
global production output may fall to 0.5% of global production output. Currently, the
share of Russian carbon ﬁbre is 0.25% of global output. Accordingly, the export
prospects for CF-based innovative products remain vague. Internal demand for such
products would be met primarily by imports.
Table 2.20 Development scenarios for Russian market
Optimistic
Moderate
Pessimistic
• 3000 tons of CF in the
long term
• Good export prospects
• Production structure
matches the global level
• Annual growth rate of
45–50% by 2013
• After 2017, 10–20%
growth in monetary
terms
• 5% of global CF
production volume
• 600 tons of CF in the
medium term
• Good export prospects
• Production structure
matches the global level
• Annual growth rate of
30–35% by 2013
• After 2017, 10–15%
growth in monetary
terms
• 3% of global CF
production volume
• Russia lacks the resources to produce
large volumes of high-quality products
• Slow production volume growth at
3–5% a year
• 0.5% of global CF production volume
Source: HSE
2
New Materials: The Case of Carbon Fibres
43
The three scenarios for Russia are summarised in Table 2.20.
Thus, the Russian market for CF-based products shows the same trends as the
global market, only with a certain lag. As in the world generally, aerospace industry
and construction offer the highest market potential. By 2020, other industries of the
Russian economy would also have signiﬁcant demand for CF.
2.6
Technology Strategy for the Exploitation of CFs
The results of the aforementioned study indicate that to achieve a signiﬁcant increase
in CF production and improve its quality, a set of measures must be implemented to
solve key technological problems. Efforts should be concentrated primarily on
increasing CF’s strength, reducing production costs, and improving the quality of
CF-based composites. The technological objectives for CF-related production can be
divided into three groups:
1. Development of technologies and equipment to produce high-strength CF
•
Development of PAN precursor to produce high-strength CF using wet
moulding technique
•
Perfecting ‘dry-wet’ PAN production technology
•
Development of technologies to reduce PAN and CF’ defects and impurities
•
Perfecting technological modes for thermal oxidation and carbonisation of
PAN threads and bundles
•
Development of high-performance equipment to produce high-strength CFs in
bundle form
2. Development of technologies and equipment to reduce CF’s production costs
•
Development of high-performance equipment to produce technological PAN
precursor in bundle form
•
Reducing the consumption rate of speciﬁc raw materials
•
Development of equipment to produce PAN bundles and CF based on textile
PAN bundle
•
Development of technologies and equipment for efﬁcient recycling and
utilisation of waste, heat, and other emissions accompanying CF production
•
Development of new precursor compositions, switching to materials with high
linear density
3. Development of technologies to improve the quality of CF-based composites
•
Improving coal plastics’ structure to increase their strength
•
Development of technologies and launching production of advanced binder
materials, including by adding nanoparticles
•
Development of technologies for surface treatment; improving composition of
oilers used in CF production
•
Producing high-quality raw materials, primarily PAN (shown by our analyses
to be the most important element of CF production’s technological chain)
44
O. Saritas et al.
The main CF production technologies are deﬁned by the raw material type.
Accordingly, three groups of CF production technologies can be identiﬁed based
on different materials:
•
Polyacrylonitrile ﬁbres
•
Pitch ﬁbres (oil and coal)
•
Rayon (viscose) ﬁbres
All CF production technologies involve in one way or another pyrolysis of raw
material. No ‘revolutionary’ technologies for CF production are expected to emerge
in the near future. Technologists’ efforts in each of the above groups are
concentrated on producing cheap and high-quality CF. Textile sorts of PAN—
‘thick’ bundles—are becoming increasingly important in the production of
PAN-based CF. Industrial production of relatively cheap CF based on polyﬁlament
PAN bundles is expected to be launched in Russia in the next few years. Another
important objective is to produce high-quality CF with high strength and modulus of
elasticity values and relatively high rupture elongation.
The CF industry is currently changing from custom production (e.g. for the
aerospace industry) to general mass market-oriented production. The most important
aspect is believed to be increasing capacities of individual production lines. Cur-
rently Russian production lines’ output is about 16–20 tons a year, while the average
global ﬁgure is 500 tons. Russian producers should aim to achieve 100 tons output
by 2020. Moreover, the product range is of crucial importance (threads, bundles). An
adequate range would mean market demand can be met. Finally, low-cost CFs are
becoming increasingly popular across the world.
However, besides their technological and market value, it should be borne in
mind that new materials and nanotechnologies in general, and CFs in particular, have
a number of broader impacts on society, economy, environment, politics, values, and
culture (Loveridge and Saritas 2009, 2012; Cagnin et al. 2011). For instance, the
process of manufacturing sheets of CF and plastic polymer resins is a wasteful
process. Stakeholders from scientiﬁc and academic communities, policymakers,
industry, and society should work together for a more responsible innovation in
the domain and achieve increasing recycling and reuse of the material.
Acknowledgements The chapter was prepared within the framework of the Basic Research
Program at the National Research University Higher School of Economics (HSE) and supported
within the framework of the subsidy by the Russian Academic Excellence Project ‘5-100’.
References
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Amanatidou E, Saritas O, Loveridge D (2016) Strategies for emerging research and innovation
futures. Foresight 18(3):253–275
2
New Materials: The Case of Carbon Fibres
45
Aydogdu A, Burmaoglu S, Saritas O, Cakir S (2017) A nanotechnology roadmapping study for the
Turkish defense industry. Foresight 19(4):354–375. https://doi.org/10.1108/FS-06-2017-0020
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Burmaoglu S, Saritas O (2017) Changing characteristics of warfare and the future of military R&D.
Technol Forecast Soc Chang 116:151–161
Cagnin C, Loveridge D, Saritas O (2011) FTA and equity: new approaches to governance. Futures
43(3):279–291
Dislife (2008) From racing bolides to wheelchairs, 20 September. http://www.dislife.ru/ﬂow/theme/
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Erdmann L, Schirrmeister E (2016) Constructing transformative scenarios for research and
innovation futures. Foresight 18(3):238–252
Gavriliuk A (2011) Development of techniques to produce next-generation CFs from coal isotropic
pitch. Kulibin, 26 December. http://kulibin.org/projects/show/2915. Аccessed 10 Sept 2012
Kim K-H, Shium W, Moon Y-H, Kwon O-J, Kim K-H, Son J-K (2014) The structure of bio-
information-nano technology convergence from ﬁrms’ perspective. Foresight 16(3):270–288
Loveridge D, Saritas O (2009) Reducing the democratic deﬁcit in institutional foresight
programmes: a case for critical systems thinking in nanotechnology. Technol Forecast Soc
Chang 76(9):208–1221
Loveridge D, Saritas O (2012) Ignorance and uncertainty: inﬂuences on future-oriented technology
analysis. Tech Anal Strat Manag 24(8):753–767
Miles I, Saritas O, Sokolov A (2016) Foresight for science, technology and innovation. Springer
International, Heidelberg
OECD (2017) The next industrial revolution: implications for governments and business. OECD,
Paris. https://doi.org/10.1787/9789264271036-en
OUSD (AT&L) Industrial Policy (2005) Polyacrylonitrile (PAN) CFs industrial capability assess-
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Accessed 1 Dec 2016
ROSSTS (2011) Carbon Valley will replace Moskvitch factory, 27 July. http://rossts.ru/index.php/
articles/6/4367/. Accessed 10 Sept 2015
Russian Corporation of Nanotechnologies (2010) Technology Roadmap “Nanotechnology Appli-
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upload/OldNews/Files/33822/current.pdf. Accessed 17 Jul 2017
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Vaseashta A (2014) Advanced science convergence based methods for surveillance of emerging
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46
O. Saritas et al.
Ozcan Saritas is Professor of Innovation and Strategy at
the HSE and editor-in-chief of “Foresight”—the journal of
future studies, strategic thinking and policy. He worked as
Senior Research Fellow at the Manchester Institute of
Innovation Research, The University of Manchester. His
research focuses on innovation and policy research with
particular emphasis on socio-economic and technological
Foresight. With a PhD from the “Foresight and Prospective
Studies Program,” he introduced the “Systemic Foresight
Methodology”, and has produced a number of publications
on the topic. Dr. Saritas has extensive work experience with
the international organizations, including the United Nations,
OECD, and the European Commission. He has been
involved in large scale national, multinational and corporate
research and consultancy projects on sectors including
energy, climate change, agriculture, food, water, transporta-
tion, and ICT among others; published a number of articles in
respected journals; and have delivered keynote speeches in
more than 50 countries across the world. Besides his research
and publication activities, he designs and delivers academic
and executive education courses on Foresight and Strategic
Planning. He has recently co-authored a book, entitled
“Foresight
for
Science,
Technology
and
Innovation”
published by Springer, which has become one of the key
readings in the ﬁeld.
Alexander Sokolov is Deputy Director of HSE ISSEK and
Director of HSE International Foresight Center. His main
professional interests are related to Foresight, STI priorities,
indicators and policies. Prof. Sokolov is tenure professor at
HSE, he teaches Foresight for undergraduate and postgradu-
ate students. He authored over 120 publications in Russia
and internationally and managed many Foresight projects,
including: Russian S&T Foresight: 2030; Foresight for
Russian ICT sector (2012); Innovation Priorities for the
Sector of Natural Resources (2008–2010); Russian S&T
Delphi
Study:
2025
(2007–2008);
Russian
Critical
Technologies (2009) et al. Prof. Sokolov is member of sev-
eral high-level working groups at OECD and other interna-
tional organizations, serves for advisory boards at several
international conferences and journals.
2
New Materials: The Case of Carbon Fibres
47
Konstantin Vishnevskiy is Head of the HSE ISSEK
Department for Digital Economy Studies. He holds a PhD
from Moscow State University, Faculty of Economics.
Dr. Vishnevskiy has long-standing experience in the devel-
opment of technology roadmaps, the elaboration of Foresight
methodology
and
corporate
innovation
development
programs, the integration Foresight into government policy
as
well
as
ﬁnancial
and
econometric
modeling.
He
participated in many high-level research projects on S&T
Foresight on national and regional level both in Russia and
abroad.
Dr.
Vishnevskiy
authors
about
70
scientiﬁc
publications
on
long-term
planning
and
Foresight,
roadmapping, digital economy, macroeconomic regulation
and government policy, innovation strategies for businesses.
He presented about 100 reports on academic and profes-
sional conferences and workshops concerning Foresight,
roadmapping and innovations.
49
Nanotechnology for High-Tech Industries:
Light-Emitting Diodes
3
Vitaly Roud, Alexander Sokolov, and Dirk Meissner
3.1
Introduction
Enhancing energy efﬁciency has been one of the top policy goals in many countries
in the last decade. Innovative lighting solutions, and light-emitting diodes (LEDs) in
particular, are among the very much promising opportunities to increase energy
efﬁciency. A modern LED is a multilayer thin-ﬁlm structure with the thickness of
layers in the range of nanometers. LED technologies are becoming dominant in a
number of application segments. Demand for economic and energy security makes
the development of the LED industry one of the national priorities in many countries
including Canada, the USA, Japan, and China and European countries, among
others. These innovative technologies have attracted the attention of major lighting
industrial corporations such as General Electric, Philips, and Osram, among others.
The key areas of the LED industry’s development envisage designing materials with
unprecedented characteristics and using nanoscale components. At the same time,
the technology level of semiconductor industries as such to a large extent is
determined by chip processing—the key stage of LED production chain.
The following chapter on LEDs is based on a foresight study undertaken by
National Research University Higher School of Economics. The study involved
multiple expert interviews and related foresight techniques involving technical
experts and market experts in the ﬁeld of lightening technologies.
V. Roud · A. Sokolov · D. Meissner (*)
National Research University, Higher School of Economics, Moscow, Russia
e-mail: vroud@hse.ru; sokolov@hse.ru; dmeissner@hse.ru
# Springer Nature Switzerland AG 2019
D. Meissner et al. (eds.), Emerging Technologies for Economic Development,
Science, Technology and Innovation Studies,
https://doi.org/10.1007/978-3-030-04370-4_3
3.1.1
LEDs
An LED is a semiconductor light source emitting incoherent light when voltage is
applied to it. LEDs are multilayer thin-ﬁlm structures where the thickness of layers
may potentially be in the range of nanometers. Overall there are two types of LEDs,
namely, inorganic and organic LEDs (OLEDs).
50
V. Roud et al.
An inorganic LED is an LED with a structure consisting of inorganic compounds
only; it was discovered in 1907 and its commercial use commenced in the
mid-1960s. Its competitive advantages are ensured by a number of characteristics
attractive for the customers, such as high energy performance in many applications,
long lifetime, electrical safety due to low-voltage power supply, small sizes of
devices, mechanical resistance, high switching rate, resistance to low temperatures,
and absence of hazardous components (such as mercury). An organic light-emitting
diode (OLED) is an LED whose layers include ﬁlms of organic compounds. OLED
was developed in 1950s; its commercial prospects were outlined in early 1990s.
Meaningful commercial use of OLEDs began in the 2000s. Their competitive
advantages are ensured by the expected decrease in production technology costs
and a number of new opportunities for OLED use in a variety of technological
processes, i.e., the construction of light-emitting panels, the use of polymer organic
materials, development of a technological base for producing ﬂexible light sources
and displays, and its use as a component for hybrid and organic electronic devices.
Solid-state light source technologies are currently believed to be drivers for a
number of industries. The attractive features of LED technologies include their
compact size, high energy efﬁciency compared to alternative technological
solutions, and the opportunity for quick control of light emission. Thanks to these
characteristics, LEDs are used in lighting applications as energy-efﬁcient light
sources suitable for smart lighting schemes and for information display applications
as individual indicators and display panels of both small and large sizes.
One of the most important socioeconomic impacts of the large-scale use of LED
technologies is the prospect of a dramatic decrease in electricity consumption for
lighting, which amounts, by various estimates, to 18–20% of all produced power
consumption.
The future of electronic household appliance markets is also associated with the
development of LED technologies, which are capable, in the long run, of surpassing
a number of information display technologies. LEDs allow for the development of
transparent and ﬂexible display panels, as well as hybrid devices using organic
electronic elements.
LEDs compete with other technological solutions such as incandescent light
bulbs,
low-pressure
gas-discharge
lamps,
high-pressure
and
high-intensity
gas-discharge lamps, as well as non-electric light sources each demonstrating
advantages and disadvantages depending on the ﬁeld of application (Table 3.1).
3.1.1.1 Incandescent Light Bulbs
This traditional lighting technology uses the heating of a wire ﬁlament to transform
electric energy into light. The largest share of emission is not visible light, but the
(continued)
3
Nanotechnology for High-Tech Industries: Light-Emitting Diodes
51
Table 3.1 Lighting and illumination technologies
Advantages
Disadvantages
Prospects
Incandescent light bulbs
Traditional incandescent light bulbs, halogen lamps, halogen lamps with infrared coating, etc.
• Continuous light spectrum
• Low cost
• Small size
• Simple light ﬁttings
• No toxic elements
• DC (with any polarity) and
AC operation
• Manufacture of bulbs with a
broad range of voltage ratings
(from fractions of volt to
hundreds of volts)
• Low luminous efﬁcacy
• Short lifetime
• Luminous efﬁcacy and
lifetime depend on voltage
• Color temperature is
about 2300–2900 K which
gives the light a yellowish
tinge
• Engineering solutions
allowing for increased energy
efﬁciency of incandescent light
bulbs by 40% and lifetime by
100% (IRC-halogen lamps)
expected
• Technology approaching its
limit
• Further advancement not
anticipated
Low-pressure gas-discharge lamps
Fluorescent and compact ﬂuorescent lamps
• Luminous efﬁcacy about
40–60 lm/W may be higher for
cold cathode lamps
• Longer lifetime for
commercial products about
5000–10,000 h
• Low average luminance
• Contain mercury and
require special recycling
• Require time to be
switched on and reach full
capacity
• More complicated
dimming
• Electromagnetic
radiation
• Side ultraviolet radiation
• Mechanical fragility
• Large sizes
• Impossible to use at a low
temperature
• Recycling issues
• Luminous ﬂux ripple
• Line light spectrum
• Expected luminous efﬁcacy
increase by 10–15% and cost
reduction within 10 years
• Challenges of white light
quality and dimming addressed
• Mercury content makes
technology undesirable for
most applications
• Technological limit almost
achieved
Plasma lamps, high-efﬁciency plasma (HEP) lamps, other low-pressure lamps
• High energy efﬁciency
• High luminous ﬂux
• Large size
• Require time to reach
working mode
• Speciﬁc light spectrum
• Increase in energy efﬁciency
forecasted
• Limited use of lamps possible
due to light spectrum
High-pressure and high-intensity gas-discharge (HID) lamps
Gas-discharge (sodium vapor and metal halide) high-pressure and high-discharge lamps, electric
arc-based lamps
• Highest energy efﬁciency
• High luminous ﬂux
• Low quality of light
• Large size
• require time to reach
working mode
• Require time after
switch-off to “cool” before
another switch-on
• Uncertain switch-on at
low temperatures
• Limited use due to low
quality of light (narrow
spectrum, unsuitable color
temperature)
• Improvement of light quality
core of ongoing development
work
• Recycling issues
• Require cooling when
used for a long time
52
V. Roud et al.
Table 3.1 (continued)
Advantages
Disadvantages
Prospects
Electroluminescent lamps with luminophors
• Energy efﬁciency close to
parameters of compact
ﬂuorescent lamps
• Environmentally friendly
• Possibility of dimming;
instant switch-on
• Large size
• Relatively short lifetime
• Limited spread and case
record
• Commercial prospects
unclear
Electroluminescent wires
• Flexibility
• Low electricity consumption
• Relatively low output
• Not suitable for lighting
• Good prospects (and current
use) for decoration of buildings
and vehicles, advertising
Non-electric light sources
Fluorescent materials with prolonged afterglow
• Don’t require electricity for
glowing
• Low brightness
• Limited time of glowing
• Good prospects for road signs
and information displays
• Longer afterglow time
expected
Combustion lamps (kerosene lamps, candles, wick-fed lamp, etc.)
• Don’t require connection to
electric network
• Technologically simple
• Cheap to buy
• “Dirty”
• Fire-prone
• Service costs (fuel
purchase)
• Technological advancement
not envisaged
Inorganic light-emitting diodes
Single LEDs and LED matrixes
• High luminous efﬁcacy
• Long lifetime
• Low source voltage
• No hazardous elements
• Possibility of luminance and
color of spectrum control
• Smart control
• Possibility of operation at low
temperatures (as low as tens of
degrees К)
• Wide range of color
temperatures: 2500–10,000 К
• High current cost
• Heat control issues
• Efﬁciency drop with
increased output and
temperature
• Decrease of costs
• Luminous efﬁcacy increase
• Higher quality of white light
• Better integration into
existing infrastructure
Organic light-emitting diodes
Light-emitting OLED panels
• In future—high luminous
efﬁcacy
• Possibility to develop ﬂexible
and semitransparent light-
emitting panels
• Expected reduction of
production costs
• Low average luminance
• Technology under
development
• Insufﬁcient lifetime (high
degradation speed)
• Signiﬁcant decrease of cost
• Longer lifetime
• Higher energy efﬁciency
Source: HSE
3
Nanotechnology for High-Tech Industries: Light-Emitting Diodes
53
Table 3.2 Alterative lighting technologies: incandescent light bulbs
Advantages
Disadvantages
Prospects
Incandescent light bulbs
Traditional incandescent light bulbs, halogen lamps, halogen lamps with infrared coating, etc.
• Continuous light
spectrum
• Low cost
• Small size
• Simple light ﬁttings
• No toxic elements
• Possible DC (with
any polarity) and AC
operation
• Possibility to vary
input voltage
• Low luminous efﬁcacy
• Short lifetime
• Luminous efﬁcacy and
lifetime depend
considerably on voltage
• Limited color range
2300–2900 K (yellowish
tinge)
• Increasing energy efﬁciency of
incandescent light bulbs by 40% and
lifetime by 100% (IRC-halogen lamps)
• Technology close to limit
• Further advancement not anticipated
Source: HSE
infrared band; thus, the bulb has an extremely low efﬁciency. For an average
temperature of about 2700 ○С, the lifetime of the lamp may be up to 1000 h. The
key factor limiting the lifetime of an incandescent light bulb is the uneven evapora-
tion of the ﬁlament material, resulting in its uneven thinning and subsequent break
off. The key positive peculiarities of the technology are the continuous light spec-
trum, which is close to the solar one; the lack of glimmer that is hazardous for eyes;
high switching rate; and allowing for the use of this technology in display and
decorative applications (Table 3.2). Simple light ﬁttings, the low cost of the device,
and ease of recycling were the reasons for the widespread use of the incandescent
light bulbs, which now is causing problems with the inefﬁcient use of electricity on a
large scale.
The advancement of this traditional technology resulted in the development of
halogen bulbs. The increase in the lifetime and efﬁciency of the bulbs was achieved
through the joint use of inert gas and halogen vapor for ﬁlling the bulb. The lifetime
of such bulbs is extended up to 2000–3000 h, and the temperature of the wire
ﬁlament—up to 3000 ○С. The efﬁciency of halogen bulbs is up to 25–28 lm/W.
Another area of technological development is the reduction of infrared light and to
use it for the additional heating of the ﬁlament. Such devices were named
IRC-halogen lamps (IRC—infrared coating). In the medium term, it is expected
that new engineering solutions would allow increasing their energy efﬁciency by
40% and lifetime by 100%. However, further technological progress is not
envisaged.
3.1.1.2 Low-Pressure Gas-Discharge Lamps (Fluorescent Lamps,
Compact Fluorescent Lamps, Plasma Lamps, etc.)
Fluorescent lamps, falling into the category of low-pressure gas-discharge lamps,
use the glow of gas-discharged mercury vapor modiﬁed into visible light by ﬂuores-
cence. These sources compare favorably with incandescent light bulbs due to their
lower power consumption and longer lifetime (5000–10,000 h) but at the same time
are characterized by unsatisfactory quality of white light, which is only partially
suitable for human eyes. Other negative factors include glimmer that causes higher
eye strain and the complexity and large size of light ﬁttings. The technology has been
continuously improved since the 1930s, and in the 1990s a new generation of the
devices has gone mainstream—compact ﬂuorescent lamps, which are relatively
energy-efﬁcient, compact, and long-lasting light sources. However, a number of
issues make this technology solution only temporary. The most important challenge
is the presence of mercury vapor and other toxic substances, which causes short-term
problems with regard to recycling the waste equipment. Experts note that the
improvement of the characteristics of ﬂuorescent lamps due to the advancement of
technology in the next 20 years would not exceed 10–15% that, in addition to the
aforementioned deﬁciencies, would urge the exploration of alternative lighting
sources.
54
V. Roud et al.
Apart from ﬂuorescent lamps, this category includes plasma (common and high
efﬁciency) and neon lamps. Being energy-efﬁcient they feature speciﬁc spectral
composition, which is extremely uncomfortable for human eyes, and extended
switch-on time and time to reach working mode. Further research to enhance
technology and increase energy efﬁciency is underway in the sector (Table 3.3).
3.1.1.3 High-Pressure and High-Intensity Gas-Discharge Lamps
Lamps within this category demonstrate high capacity (luminous ﬂux) and energy
efﬁciency, but have a very narrow spectrum; besides, they require time to reach
working mode and to “cool” before another switch-on. Such equipment is used in
large lighting systems without high requirements for emission parameters (e.g., in
the illumination of large service areas, tall buildings, etc.). Technological advances
are primarily aimed at improving the quality of light. Due to the wear and tear of
such lamps when frequently switched on and off, they are efﬁcient only when
switched on for a long time (Table 3.4).
3.1.1.4 Electroluminescent Light Sources
Luminescence is known to be used in a number of ways. The most relevant of them
in the context of this paper is LED lighting. Electroluminescent lamps with multi-
color luminophors are used today for outdoor advertising and decorative illumina-
tion. Their distinct advantages are believed to be energy efﬁciency close to the
parameters of compact ﬂuorescent lamps, environmental friendliness, the possibility
of dimming, and instant switch-on. Commercial prospects for this type of lamps are
unclear.
Electroluminescent wires may also be used for decoration purposes. They con-
sume very little power and their brightness and luminous ﬂux are low (Table 3.5).
3.1.1.5 Non-electric Light Sources
The most widespread elements in this category are the traditional combustion lamps,
generating light on the basis of fuel combustion. They play an important role when
there is no access to power networks. However, they are ﬁre-prone and harmful for
3
Nanotechnology for High-Tech Industries: Light-Emitting Diodes
55
Table 3.3 Alterative lighting technologies: low-pressure gas-discharge lamps
Advantages
Disadvantages
Prospects
Low-pressure gas-discharge lamps
Fluorescent and compact ﬂuorescent lamps
• Luminous efﬁcacy of about
40–60 lm/W (may be higher for
cold cathode lamps)
• Longer lifetime for commercial
products (about 5000–10,000 h)
• Low average luminance
• Contain mercury
• Require special
recycling
• Require time to be
switched on and reach
full capacity
• More complicated
dimming
• Electromagnetic
radiation
• Side ultraviolet
radiation
• Fragility
• Large sizes
• Impossible use at low
temperature
• Luminous ﬂux
ripple, discrete light
spectrum
• Luminous efﬁcacy increase by
10–15% within the next decade
• Cost expected to decrease
• Challenges of white light
quality
• Possibilities of dimming
addressed
• Presence of mercury makes
technology undesirable for
majority of applications
• Technological limit almost
reached
Plasma lamps, high-efﬁciency plasma (HEP) lamps, other low-pressure lamps
• High energy efﬁciency
• High luminous ﬂux
• Large size
• Require time to reach
working mode
• Speciﬁc light
spectrum
• Forecasted energy efﬁciency
increase
• Limited use of lamps due to
light spectrum
Source: HSE
Table 3.4 Alterative lighting technologies: high-pressure and high-intensity gas-discharge lamps
Advantages
Disadvantages
Prospects
High-pressure and high-intensity gas-discharge (HID) lamps
Gas-discharge (sodium vapor and metal halide) high-pressure and high-discharge lamps, electric
arc-based lamps
• Highest
energy
efﬁciency
• High
luminous
ﬂux
• Low light quality
• Large size
• Require time to reach working
mode
• Require time after switch-off to
“cool” before another switch-on
• Problematic switch-on at low
temperature
• Recycling issues
• Require cooling when used for a
long time
• Limited use due to low light quality
(narrow spectrum, unsuitable color
temperature)
• Further development for light quality
improvement
Source: HSE
56
V. Roud et al.
Table 3.5 Alterative lighting technologies: electroluminescent light sources
Advantages
Disadvantages
Prospects
Electroluminescent light sources
Electroluminescent lamps with luminophors
• Energy efﬁciency close to
parameters of compact ﬂuorescent
lamps
• Environmentally friendly
• Possibility of dimming
• Instant switch-on
• Large size
• Relatively
short lifetime
• Limited spread and case record
• Commercial prospects unclear
Electroluminescent wires
• Flexibility
• Low electricity consumption
• Relatively
low output
• Not suitable
for lighting
• Good prospects for decoration of
buildings, vehicles, in advertising
Source: HSE
Table 3.6 Alterative lighting technologies: non-electric light sources
Advantages
Disadvantages
Prospects
Non-electric light sources
Fluorescent materials with prolonged afterglow
• Don’t require energy for
glowing
• Low brightness
• Limited time of
glowing
• Good prospects for road signs and
information displays
• Development for longer afterglow
time
Combustion lamps (kerosene lamps, candles, wick-fed lamp, etc.)
• Don’t require connection to
electric network
• Technologically simple
• Cheap to manufacture/
purchase
• “Dirty”
• Fire-prone
• Service costs (fuel
purchases)
• Technological advances not
envisaged
Source: HSE
the environment and require spending to purchase fuel. When choosing non-electric
sources of light, it is preferable to use ﬂuorescent materials, which accumulate
energy of the sun (or other) light and, for an extended period of time, emit a pale
glow suitable for household indication and marking (Table 3.6).
LEDs still occupy a rather small market share indicated by the Russian market
ﬁgures (Fig. 3.1). Compact ﬂuorescent lamps, incandescent, and linear ﬂuorescent
lamps remain the dominating light sources in the market with more than two-thirds
of all lamps. However, LEDs have a signiﬁcant potential for lightening applications
taking account of the ongoing technological progress for LEDs as products and for
the manufacturing process supporting the application diffusion and penetration at
broader scale and speed.
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Nanotechnology for High-Tech Industries: Light-Emitting Diodes
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42.0%
23.0%
22.0%
9.0%
3.0%
1.0%
Compact Fluorescent Lamp
Incandescent
Linear Fluorescent lamps
Light Emitting Diode
Halogen
High Intensity Discharge lamp
Fig. 3.1 Russian light sources market segmentation by types of lamps (%). Source: European
Commission (2014)
3.2
Information Display Technologies
Another application ﬁeld is the information display technologies, in which LEDs
also compete with other solutions, namely, E-ink, EL (electroluminescent) panels,
LCD (liquid crystal display) panels, plasma display panels, projection systems, and
laser
projection
with
each
technology
providing
speciﬁc
advantages
and
disadvantages (Table 3.7).
3.2.1
E-Ink
Technological advancements in this area are aimed at the production of displays with
features similar to traditional paper in optical and mechanical parameters. Usually
this refers to making static pictures or images with low update rate. The technology
is based on microcapsules ﬁlled with transparent liquid and pigment particles of
various colors and charge. The electric charge directs their movement and shapes an
image. The main advantages of such displays are ultralow power consumption
(exclusively at the moment of image change) and reduced eye strain for users;
major disadvantages are the limited number of displayed colors and long response
delay (200–500 ms). This technology is developing rapidly: research is aimed at
shortening response delay and wider color range. Ultimately color displays are
expected (Table 3.8).
(continued)
58
V. Roud et al.
Table 3.7 Information display technologies
Advantages
Disadvantages
Prospects
E-ink
• Energy efﬁciency
• Lower eye strain
• Limited color range
• Limited size
• Low frame rate (for
existing samples)
• Technology under development
• Wider use in mobile devices
• Envisaged
EL panels
• Possibility for
developing thin and
ﬂexible displays
• Potentially low cost
• Low contrast
• Commercial prospects unclear
LCD displays
• Established production
technology
• Contrast ratio control
(LED-backlit displays)
• Wide dynamic range
• Low efﬁciency
• Low quality of picture
in open sunlight
• Pronounced tendency to shift to
LED-backlit displays
• Higher energy efﬁciency
Plasma display panels
• Wider color gamut
(as compared with
LCD—CFFL)
• High luminance
• Suitable for outdoor use
• Large size
• High granularity
• High cost
• Burnout of display
elements
• Degradation of
luminophors
• Leakage
electromagnetic
radiation
• High power
consumption
• Substantial technology improvements
not expected short term
• Application for outdoor information
indication
OLED displays
• Wide color gamut
• Potentially high energy
efﬁciency
• Potentially low price
• Low operating voltage
• Short lifetime
• Limited display size
• High current cost
• Unbalanced
degradation of colors
• Midterm (7–8 years) wider use possible
provided cost and lifetime challenges
solved
• Rapid advancement of operating
parameters
Matrix LED displays
• Mass use of large
images
• Long lifetime
• Energy efﬁciency
• High luminance
• Almost no
disadvantages
• Low resolution
• Wide use of LED displays for alarm
and advertising applications
Projection systems
• Possibility making
large-size images
• Low luminance
• Limited lifetime
• Low contrast ratio
• Size
• Smaller sizes
• Dissemination of LED technologies as
light sources (for mobile applications)
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Nanotechnology for High-Tech Industries: Light-Emitting Diodes
59
Table 3.7 (continued)
Advantages
Disadvantages
Prospects
Laser projectors
• Wide color gamut
• High contrast ratio
• High cost
• Granularity of an
image
• Emission coherence
(unsafe, capacity
restrictions)
• Low deﬁnition
• Limited use
• Cost reduction
• Development of powerful continuous
emission sources in green zone of light
Source: HSE
Table 3.8 Alternative information display technologies: E-ink
Advantages
Disadvantages
Prospects
– Energy
efﬁciency
– Reduced
eye strain
– Limited color gamut
– Limited size
– High response delay
(for existing samples)
The technology is being developed. Wider use in
mobile devices is envisaged. The development of
color displays is expected
Source: HSE
Table 3.9 Alternative information display technologies: LCD displays
Advantages
Disadvantages
Prospects
– Established production
technology
– Contrast ratio control
(LED-backlit displays)
– Wide dynamic range
– Low efﬁciency
– Low quality of picture
in open sunlight
– Pronounced tendency to shift to
LED-backlit displays
– Higher energy efﬁciency
Source: HSE
3.2.2
Liquid Crystal Displays (LCDs)
This category includes a number of products based on various principles of image
generation and backlit mechanisms. A number of basic versions of LCD displays
appeared over the last 40 years, including passive- and active-matrix displays,
ﬂuorescent lamp-backlit displays, and modern LED-backlit displays. The latest
solutions allowed, preserving traditional production technology, for reaching a
new level of image quality through higher contrast ratios and considerably reduced
power consumption by using high brightness, super-efﬁcient LEDs. The commercial
future of display technologies for the next decade is likely to be closely associated
with the production of LED-backlit LCD displays (Table 3.9).
60
V. Roud et al.
Table 3.10 Alternative information display technologies: plasma display panel
Advantages
Disadvantages
Prospects
– Wider color gamut
(as compared with LCD—
CFFL)
– High luminance
– Suitable for outdoor use
– Large size
– High granularity
– High cost
– Burnout of
display elements
– Degradation of
luminophors
– Leakage
electromagnetic
radiation
– High power
consumption
– Substantial technology improvements
are not expected in the short term
– Key area of application is outdoor
information indication
Source: HSE
3.2.3
Plasma Display Panel (Gas-Discharge Screen)
The technology is based on small cells combined in a matrix and ﬁlled with gases
(typically—xenon or neon) with different glow spectrums, placed between glass
plates. When a high voltage is applied across the cell, the resulting plasma emits
ultraviolet (UV) light, which is subsequently transformed into visible light by
phosphor.
The manufacturing of plasma displays is challenging because it is necessary to
use large-size transparent front electrodes. Other barriers in using this product are
strict limits on the sizes of the devices—the lower limit of the diagonal of existing
panels is about 80 сm. Other disadvantages of the technology noted by experts are
required high voltage, side electromagnetic radiation, and extremely high power
consumption. It should be noted that plasma technology is one of the few that are
suitable for use in large-scale outdoor displays. Experts believe that it is in this
segment that plasma displays would be used in future (Table 3.10).
3.2.4
Electroluminescent Panel (EL-Panel or Electroluminescent
Paper)
The electroluminescent panel developed by Ceelite (USA) looks like a thin glowing
sheet of paper. Separate ﬂexible and thin glowing panels (plastic light panels,
glowing plastic) and similar devices are produced using the technology of a light-
emitting capacitor (LEC). Polymer ﬁlm components for stencil screen printing are
used to manufacture Ceelite EL-panels.
The range of sizes of EL-panels is wide: from several centimeters up to a meter.
These light and ﬂexible devices ensure even glow (illumination); they can be
included in any construction, be cut into pieces (it is possible to make glowing
letters or shapes), or be used as an element of design for clothes, cars, or rooms. The
considerable enhancement of this technology is expected in the future (Table 3.11).
3
Nanotechnology for High-Tech Industries: Light-Emitting Diodes
61
Table 3.11 Alternative information display technologies: electroluminescent panel
Advantages
Disadvantages
Prospects
– Possibility for developing thin and
ﬂexible displays
– Potentially low cost
– Low
contrast ratio
– Considerable technology
enhancement is expected
Source: HSE
Table 3.12 Alternative information display technologies: projection systems
Disadvantages
Prospects
– Low luminance
– Limited lifetime
– Low contrast
ratio
– Large size
– Smaller sizes
– Dissemination of LED technologies as light sources (for mobile
applications)
Source: HSE
3.2.5
Projection Systems
Projection systems form an important area of information display technology’s
development. A great variety of technologies for projecting images exists, e.g.,
those employing cathode-ray tubes, combinations of lamps and LCD matrixes, and
LCD matrixes on silicon base, among others. The optic system focuses the resulting
image on a screen, where the dimensions of the image are smaller than, for example,
on traditional LCD displays. Major technological restrictions are attributed to use of
powerful lamps that require timely replacement and efﬁcient cooling. The current
shift to using LED backlit would allow for the reduction of the size and power
consumption of these devices as well as extending their lifetime (Table 3.12).
3.2.6
Laser Projectors
These systems produce images using a laser beam, which “draws” each pixel in
pulse mode. The employment of lasers allows for simplifying the optic system,
which should only control the beam’s direction and shape high-contrast pictures at
long distances. Disadvantages of these projectors include the high cost of equipment,
granularity and the low deﬁnition of an image, as well as emission coherence.
Prospects for the technology depend upon the development of enhanced lasers
(especially in the green zone of light) and the lower cost of the devices (Table 3.13).
62
V. Roud et al.
Table 3.13 Alternative information display technologies: laser projectors
Disadvantages
Prospects
– High cost
– Granularity of an image
– Emission coherence (safety hazards,
capacity restrictions)
– Low deﬁnition
– Limited use
– Cost reduction
– Development of powerful continuous emission
sources in the green zone of light
Source: HSE
3.3
Lighting Applications and the World Market
3.3.1
Fields of Application
Lightening applications are demanded by the corporate sector (in particular assem-
bly and tool engineering), municipalities, and households, as well as by companies
with dedicated sophisticated and partially unique requirements like railways and
aviation companies as well as corporate lighting. LEDs are commonly used in
mobile devices, large-size displays, industrial and household electronic devices,
signaling devices, vehicles, lighting, and outdoor decorative illumination. LED
applications are offered by companies in different industries and markets
(Table 3.14).
The segmentation of demand for LEDs by intended use (ﬁelds of application)
requires special attention. An in-depth analysis of the ﬁrst-level segments is required
for the comparison of LED development potential with the potential of alternative
products, with which they “compete” for the ﬁeld of application, as well as for
revealing leaders in LED production. The status of major ﬁelds of worldwide
application of LEDs in absolute and relative terms is shown, respectively, in
Figs. 3.2 and 3.3.
The current market share of lighting (roughly 30%) as of the main ﬁeld of
application for LEDs will change in the future, with the saturation of the market,
although the scale of LEDs’ use for lighting and in the automotive industry would be
relatively stable (Lux Research 2015).
The world market is so far dominated by “standard” LEDs with a typical
operating current of 20 mА. However, the segment of devices with power intake
of 0.5 W and more has been growing since 2004 by some 50% per year. High-
radiance LEDs (with operating current from 20 mА) have the following market
shares: white LEDs, 48%; blue and green LEDs based on InGaN structures, 28%;
yellow, red, and orange LEDs based on AlGaInP, 17%; and RGB LEDs, 7%. The
rate of LED performance enhancement is higher than it was forecasted at the
beginning of twenty-ﬁrst century. Laboratory samples with a luminous ﬂux of
100 lm/W were produced as early as in 2007.
As for UV band LEDs, more than 90% of the market is used in the medical
industry and in the production of tools—this sphere requires А-type sources (with a
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Nanotechnology for High-Tech Industries: Light-Emitting Diodes
63
Table 3.14 LED application ﬁelds and suppliers
Application ﬁeld
Suppliers
1. Mobile devices
– Organizations specializing in the design and development of
devices: cell phones, mobile messengers, etc.
– Organizations using small-size panels to improve the visual
appearance of products, i.e., package producers
2. Large-size displays
– Manufacturers of outdoor advertising
– Companies specializing in the design and development of
tools
3. Industrial and household
electronic devices
– Manufacturers of various indication devices for industrial and
household purposes
– Manufacturers of household and ofﬁce equipment (printers,
scanner, etc.)
4. Signaling devices,
information signs
– Manufacturers of outdoor advertising
– Highway police
– Organizations ensuring trafﬁc lights maintenance
5. Vehicles
– Organizations selling spare car parts
– Car service enterprises, car dealers, and centers specializing in
automotive tuning
– Car manufacturers and vehicle ﬂeet operators
6. Lighting
– Specialist companies for maintenance of street lighting
systems
– Construction and design organizations
– Manufacturing enterprises—subdivisions of plants, factories,
warehouses, and vehicle ﬂeet operators, responsible for the
maintenance of external and internal lighting on the territories of
the enterprises
– Housing and utility infrastructure services—subdivisions
responsible for the lighting of yards and area surrounding the
building, public, and utility service areas in houses
7. Outdoor decorative
illumination
– Specialist companies for installation and maintenance of the
municipal illumination system
– Private companies using illumination of their buildings for
brand image promotion
– Owners of single-family detached homes (for landscape
design)
Source: HSE
wavelength of 400–315 nm) and В-type sources (315–280 nm)—as well as for
forgery detection. The remaining 10% were used for photocatalytic treatment of
water and air, primarily by UV-А LEDs. Today it is one of the key products on the
UV LED market (Fig. 3.4). This situation will not change in the short term at least.
Medical equipment is an important and dynamic segment of the UV LED market
with a volume of $120 mln and growth rate of about 10%. Here LEDs may compete
with traditional mercury lamps. Beam output power of these LEDs has increased up
to several W/сm2. Many new players have joined this segment over the past few
years.
64
V. Roud et al.
0
2000
4000
6000
8000
10000
12000
14000
16000
2013
2015
2017
Others
Automotive
Lighting
Sign
Mobile
Backlight TV/Monitor
Fig. 3.2 Dynamics of LED use worldwide ($bln). Source: Strategies Unlimited (2013)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2013
2015
2017
Others
Automotive
Lighting
Sign
Mobile
Backlight TV/Monitor
Fig. 3.3 Structure of LED use worldwide (%). Source: Strategies Unlimited (2013)
The introduction of UV-C LEDs (with a wavelength of 280–100 nm) in the
cluster of cleaning and disinfection, which was expected by the experts several years
ago, has not yet commenced due to a number of technical and economic problems:
low output capacity and efﬁciency, short lifetime, and high costs. They have been
primarily used in the sphere of the development of scientiﬁc analytical tools. Growth
of UV-C LED market is ﬁrst of all associated with the need for the mass production
of AlN substrates that may be the basis for LEDs with a beam output power that
3
Nanotechnology for High-Tech Industries: Light-Emitting Diodes
65
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2008
2015
2020
Water and air treatment
Medical equipment and tools
Forgery detection
UV curing
Fig. 3.4 Retrospective structure of UV LED market (%). Source: Yole Development data (2013)
security (examination of
authenticity of banknotes,
identification cards)
water disinfection(on
site)
UV curing (of paints, coatings, inks,
resins, adhesives)
hardbake
sewage water
treatment
indication (to use in
blankophores, fluorescent
labels)
medicine (to detect
billirubin)
sterilization (in
medicine, food
industry)
indication (identification of
biological agents, DNA)
wireless
communication
(with NLOS)
AIN
AI0.5GaN AI0.4GaN
AI0.2GaN
GaN
In0.1GaN
200
250
300
350
400
wavelength,
nm
radiation power
UV - C
UV - B
UV - A
medicine (to treat
psoriasis)
lithography (resist exposure of UV radia-
tion, UV radiation for the production of 
printed circuits
Fig. 3.5 Fields of application of LED sources. Source: HSE
exceeds the current level by 100 times. A certain trend exists for increasing the
production of UV LEDs by AlN producers to get additional proﬁt and wider
presence in the supply chain. Fields of their application are shown in Fig. 3.5.
The production of diode (semiconductor) lasers is based on the technology and
construction, which were invented by Russian scientist Zhores Alferov in 1963.
Currently such emitters are used almost in every industry. The most promising area
66
V. Roud et al.
0
2
4
6
8
10
12
2012
2013
2014
2015
2016
2017
Fig. 3.6 Dynamics of the world laser market ($bln). Source: Strategies Unlimited (2014)
of use is in ﬁber-optic communications (Great Stone 2013). Visible lasers may be
used as a central element of the projection system of a laser display (television).
The market of lasers for projection systems is on the rise, but in absolute terms, it
is not that large and is limited by the lack of red lasers of the required capacity
(Nogee 2014). Market growth is expected due to microprojectors and pico-projectors
using laser LEDs. Intensive market growth is possible in the case of mass production
of powerful RGB lasers for home cinema systems of 100-in. and over category and
digital cinemas. Currently mass production is impeded by the lack of compact lasers
of the required capacity. Vertical cavity surface-emitting lasers (VCSEL) satisfy
these requirements (Figs. 3.6, 3.7, and 3.8).
In Russia, this type of laser suitable for commercial use is being developed by
Spectralaser (St. Petersburg). A photonic crystal laser (PCL) designed by this
company has narrow beam and large aperture. These parameters allow for using
direct photon energy conversion of the PCL, increasing energy efﬁciency and
achieving the dramatic simpliﬁcation and reduction of costs as compared to existing
samples developed by Novalux (USA). The fastest demand growth for VCSEL
components is expected in HDTV and USB 4.0 segments. The primary advantage
of VCSEL as compared to Fabry-Perot and DFB (distributed feedback) lasers is low
power consumption. This point requires special attention taking into account the
increased interest in data transmission at the bit rate of 40 and 100 Gbit/s using
protocols deﬁned in IEEE 802.3ba and the need to solve the complicated technolog-
ical problem of reducing power dissipation for keeping transceivers’ size small. The
10-fold rise of the bit rate (from 10 to 100 Gbit/s) while using widespread types of
lasers causes a substantial increase in power consumption and dissipation, which is
not consistent with the market requirements. Therefore, long-wavelength VCSELs
have become an attractive alternative to conventional lasers in the area of high-speed
3
Nanotechnology for High-Tech Industries: Light-Emitting Diodes
67
32%
17%
10%
8%
7%
7%
6%
4%
4%
3%
2%1%
Communications
KW Materials Processing
Photolithography
Medical
Sensors & Instruments
Micro Materials Processing
Data storage
Military
Marking
R&D
Displays & Light shows
Printing
Fig. 3.7 Structure of use of the lasers (%). Source: Strategies Unlimited (2014)
0
20
40
60
80
100
120
140
160
180
2012
2013
2014
2015
2016
2017
Fig. 3.8 Dynamics of green laser diodes market to be used in projection systems ($mln). Source:
Strategies Unlimited (2014)
technologies. However, the degree of their reliability is unclear as producers have
not yet revealed this information.
The process of manufacturing the VCSEL laser and making the body at the base
level is well-proven and gives accurate and reproducible results. It is widely used for
manufacturing SOI (silicon-on-insulator) microcircuits, MEMS devices, and gallium
arsenide structures for the mass production of red LEDs (with a structure similar to
VCSEL). VCSEL lasers, emitting in wavelength ranges of 1270–1330 nm and
1500–1600 nm, are of utmost interest as an alternative to lasers with emitting cut due
to low power consumption and a potentially more efﬁcient interface with single-
mode optical ﬁber.
68
V. Roud et al.
Built-in projection systems and LD ﬂashlights, backlit for photo and video
equipment, have the best growth potential in the mobile devices segment in the
Russian market. The share of the display sub-segment will also increase in the
medium term, but in the long term, a decline is expected.
The share of LED market in electronic home appliances, including LCD displays,
will not change in the long-term perspective due to the growth of production of these
devices by about 20% per year. In the large-size display segment, experts forecast a
larger share of video screens. The share of “scrolling text” panels would go down.
LED applications in the external light market (parking lights, turn signals, etc.), as
well as of parking systems, are expected to demonstrate signiﬁcant growth which
might turn similar as in the ﬁelds of decorative elements and internal car
illumination.
It is presumed that LEDs have a rather high degree of interchangeability within a
ﬁeld of application. So, market potential for each type of LEDs was assessed for a
situation when a speciﬁc LED type is outgrowing all the others. This may happen,
for example, when projects are selectively funded.
3.3.2
Demand for LED Applications and Competing Technologies
Factors slowing down extensive use of LEDs have technological and market aspects.
Enhancement of LED characteristics involves in-depth studies, design and develop-
ment works, and institutional measures (e.g., implementation of procedures and
criteria for LED products certiﬁcation, studying implications of narrowband light
on health), as well as targeted marketing researches to determine demand for LED
characteristics in all key ﬁelds of application (Table 3.15).
The key advantage of light-emitting diodes is critically higher level of luminous
efﬁcacy as compared to alternative light sources. Their implementation may have
substantial economic and social advantages, with the most important one being the
lower power consumption for lighting, which, by various estimates, amounts to
18–20% of all produced electricity. Today LEDs signiﬁcantly outweigh other light
sources by their efﬁciency and lifespan (Table 3.16).
Analysis of LEDs’ luminous ﬂux growth and cost reduction rate in the last
45 years suggests that every 10 years there is a 20-fold increase of LEDs’ luminous
efﬁcacy and 10-fold decrease of cost of produced light (Fig. 3.9).
Currently LEDs are about 20 times more expensive than bright white lamps and
2.6 times more expensive than compact ﬂuorescent lamps; however, their cost has
gone down considerably in the last years and this trend should preserve. Experts
believe that sharp cost decline of LEDs is expected in a few years due to technology
advancement and this would ensure their competitiveness. Comparative analysis of
costs for various light sources with luminous ﬂux of about 500 lm, used for local
light, shows that in spite of high initial cost, XL7090 and XR7090 LEDs with
¼
¼
¼
¼
¼
¼
¼
¼
3
Nanotechnology for High-Tech Industries: Light-Emitting Diodes
69
Table 3.15 Market potential development for key LED groups in Russia
Market
capacity,
$mln
2008
2020
Phosphide- and arsenide-based LEDs (infrared, red, yellow, and orange)
Phosphide-based color LEDs with enhanced luminous efﬁcacy (η > 100 lm/W)
27
190
Phosphide-based color LEDs: infrared, red, yellow, orange, and green
(η
80 lm/W)
27
190
Phosphide-based color LEDs with substantially enhanced luminous efﬁcacy
(η > 140 lm/W)
27
190
Nitride-based LEDs
Color nitride-based LEDs: blue, green, and UV
27
190
Color nitride-based LEDs with plasmonic effect
27
190
Color nitride-based LEDs with photonic crystals
27
190
Nitride-based warm white glow LEDs with one or two luminophors (η ¼ 80 lm/
W, P < $25/kilolumen, Lt
50,000)
42
350
White light generation
Nitride-based warm white glow LEDs with one or two luminophors
(η
100 lm/W, P
$10/kilolumen)
47
460
Nitride-based warm white glow LEDs with one or two luminophors
(η
126 lm/W, P
$10/kilolumen)
47
460
Nitride-based warm white glow LEDs with one or two luminophors (P < $2/
kilolumen)
47
460
Other inorganic LEDs
Nitride-based UV LEDs with luminophors
33
300
White LEDs with color mixing
33
300
Blue LEDs with ZnO
27
190
OLED: η
25 lm/W, P < $100/kilolumen, Lt
5000 ч
42
350
OLED: η > 45 lm/W
42
350
OLED: P < $30/kilolumen
42
350
Organic LEDs in lighting applications
OLED: η > 100 lm/W
47
460
Source: HSE
η luminous efﬁcacy, P price of light, Lt lifetime
luminous efﬁcacy of 55 lm/W are more cost-effective than incandescent light bulbs
and halogen lamps and only slightly behind the energy-saving lamps. In general light
systems, where a high luminous ﬂux is required, LEDs are at this point much less
economically sound than ﬂuorescent, energy-saving, or metal-and-halogen lamps.
However, if electricity is produced by more expensive sources (petrol or diesel
generators, accumulator batteries, etc.) or tariffs for electricity are commercial
ones, then economic efﬁciency of light-emitting diodes improves considerably
Karasev et al. (2014).
Apart from economic beneﬁts, LEDs have a number of promising features
ensuring their advantages over alternative technologies. First of all, LED
technologies provide for high-quality and electricity- and ﬁre-safe lighting in houses,
70
V. Roud et al.
Table 3.16 Comparative analysis of various light sources
Type of the light
source
Efﬁciency of the light source
(luminous efﬁcacy) (lm/W)
Efﬁciency of a product with
this source of light (lm/W)
Lifetime (h)
Incandescent light
bulbs
8–13
6–10
1000
Halogen lamps
16–37
12–20
50–6000
Compact
ﬂuorescent lamps
50–70
35–50
6000–
15,000
Metal-and-halogen
lamps
60–100
<40
6000–
10,000
Fluorescent
lamps
60–100
55–70
15,000–
32,000
Semiconductor
LEDs (Cree XR-E)
100–110
90–100
>50,000
High-pressure
sodium vapor
lamps
90–130
<50
15,000–
32,000
Source: HSE
10000
1000
100
10
1
0,1
0,01
0,001
1965
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
2020
output flow (lumen)
Price per lumen ($/lumen)
Red $/lumen
White $/lumen
White Flux
Red flux
Expon. (Red flux)
Expon. (White flux)
Fig. 3.9 Luminous ﬂux growth and cost reduction rate of LEDs. Both curves have the same
numerical scale (with different units). Source: HSE
public and industrial premises, on the roads and large public places, resulting in
higher quality of housing and social and utility infrastructure, better health, and
safety conditions. Besides, LED usage would reduce the cost of recycling of
outdated light sources with hazardous components, thus supporting higher level of
environmental safety and better ecology. Advantages of LED lighting used in the
preservation system for especially high-value items are evident. In particular, the low
level of UV band emission (for a combination of “blue” diode plus one luminophor)
is a critical factor for preserving illuminated pictures, photographs, and objects of
virtu. Small sizes of LED lamps allow installing them in places that are hard to reach
for other light sources (except for miniature incandescent light bulbs), making them
less noticeable and energy-efﬁcient. Besides, long lifespan and high vibration
resistance of these sources of light are preferential qualities for manufacturing of
vandal-proof equipment.
3
Nanotechnology for High-Tech Industries: Light-Emitting Diodes
71
Light-emitting diodes are most suitable for usage in extreme conditions. High
light directionality (small beam angle) is a quite satisfactory replacement for com-
plicated systems of directed beaming. Apart from that LED-based lamps have lower
heat radiation than other light sources of the same size.
3.4
Conclusions: LED Development Perspectives
LED are established light sources with multiple application ﬁelds. It shows that the
different applications provide signiﬁcant opportunities for LEDs in the short term
and also long term (Russian Electronics 2013). It appears that LED technology
although reasonably developed in scientiﬁc terms is lacking full application. It has
been frequently observed that there remains a signiﬁcant time interval between
development of a laboratory and an industrial sample which might be shortened if
equipment procurement and development of a production base would go simulta-
neously. In addition, there appears a reasonable need for fundamental researches in
this, which needs to be complementary to ongoing groundwork performed in related
sectors of organic chemistry. It may be used for capacity development in the ﬁeld.
Table 3.17 summarizes the main perspectives for LED and OLEDs in the respective
application ﬁelds.
OLED research and development is particularly aimed at display technologies
advancement. Top-priority technical challenges in this area fundamentally differ
from those in general lighting. Thus, the key point in assessing display quality is
division of pixels, while lifetime is not that crucial. Within the frameworks of
forecasts for the various segments of LED market would develop unevenly. The
organic LEDs market environment requires special consideration. Scientiﬁc research
and development in the sphere of organic LEDs are still at the initial stage and are led
mostly by separate departments of major companies. They so far consider OLED
technology development works as risky projects and insecure investments. In
particular, materials research is a very time-consuming and expensive line of
research for small organizations.
(continued)
72
V. Roud et al.
Table 3.17 LED technologies perspectives by segments
Inorganic LEDs
Organic LEDs
1. Mobile electronic devices
Application
• Color status indicators
• LD ﬂashlights
• Backlit for photo and video equipment
• LCD displays
• Displays
Competing technologies
• No direct competitors in the ﬁeld of indication
• Share of single indicators drops as the number
of integral display panels increases
• Competitor: xenon ﬂashlights, alternative
display technologies
• LCD displays
• E-paper
• EL displays (including ﬂexible displays)
LED technologies perspectives
• Absolute dominance of LEDs
• Small cheap LEDs in demand
• Trend for higher demand for powerful LEDs
(annual growth of demand for LEDs with
power intake >1 W 20%)
• Share of separate indicators drops with
transition to new display technologies
• OLED video screens for mobile devices
would dominate in short- and midterm
perspective
• In long-term perspective are display
technology for hybrid devices (with organic
elements)
2. Large-size displays
Application
• LCD displays backlit
• Large-size LED displays
• Displays
Competing technologies
• Halogen lamps displays
• Alternative LCD displays backlit projection
technologies
• Plasma panels
• OLED displays
• LCD displays
• Plasma panels
• Laser and projection technologies
LED technologies perspectives
• LED displays hold ﬁrm position in the niche
of large-size video screens
• Use of LED backlit expanding in the niche of
LCD displays backlit
• Promotes demand for bright LEDs
• Short-term and midterm development of
OLED technologies for TV and computer
displays
3. Industrial and household electronic devices
Application
• Color and monochromic indication displays
• Used in telecommunication and electronics
(ﬁber-optic communication lines, ﬁber lines for
digital television, etc.)
• Metering equipment
• LCD displays backlit
• Other (UV disinfection of water and premises,
banknotes authentic control units, plant growth
stimulation tools, etc.)
• Displays
(continued)
3
Nanotechnology for High-Tech Industries: Light-Emitting Diodes
73
Table 3.17 (continued)
Inorganic LEDs
Organic LEDs
Competing technologies
• Display technologies in indication application
• Laser technologies in the ﬁelds of
telecommunication and electronics, metering
equipment
• Variety of competitors in niche applications
• Other display technologies
LED technologies perspectives
• Dominance in speciﬁc niches
• Dominance of OLED displays in small-size
devices
4. Signaling devices
Application
• Trafﬁc lights, railway semaphore signals
• Trafﬁc situation displays
• Safety marking and inscriptions
• Control systems, presence sensors, smoke
detectors, including in security systems
• Light-emitting panels of predetermined form
Competing technologies
• Halogen and ﬂuorescent lamps
• Electroluminescent lamps and wires
• Neon lamps
• Halogen and ﬂuorescent lamps
• Electroluminescent lamps and wires
• Neon lamps
LED technologies perspectives
• Promising segment
• Lifespan, high energy efﬁciency, emission
spectrum control, and instantaneous switching
make inorganic LEDs dominant technology in
short-term and long-term perspectives
• Currently application is limited by
insufﬁcient lifetime and low environmental
resistance
5. Transport
Application
• Parking and position lights, turn signals, stop
signals with color LEDs
• Headlights
• Internal car illumination
• Car dashboards
• Built-in electronic systems
• Decorative elements
• Internal car illumination
• Indication on windscreen
Competing technologies
• Incandescent bulbs (halogen lamps)
• Fluorescent and compact ﬂuorescent lamps
• Electroluminescent lamps
• Incandescent bulbs (halogen lamps)
• Fluorescent and compact ﬂuorescent
• Electroluminescent lamps
• Electroluminescent displays
LED technologies perspectives
• LEDs steadily replace alternative technologies
in most applications
• Projects for development of transparent
windscreen built-in displays are underway
• Broad-scale implementation of these
technologies in future
74
V. Roud et al.
Table 3.17 (continued)
Inorganic LEDs
Organic LEDs
6. Outdoor architecture and decorative illumination
Application
• Advertising and decorative billboards
• Architecture
• Decorative and landscape lighting
• Light-emitting panels including ﬂexible and
semitransparent
Competing technologies
• Incandescent bulbs
• Fluorescent lamps
• Electroluminescent lamps and wires
• High-pressure gas-discharge lamps
• Electroluminescent displays
• Electroluminescent wires
LED technologies perspectives
• Durability, lifespan, controlled spectrum,
directionality, small size, high switching speed,
high energy efﬁciency, and smart control
ensure LED leadership in decorative
applications
• Use of smart control systems provides for
more capabilities in architecture illumination,
advertising applications
• OLEDs promising for indoor decoration
• Short-term perspective manufacturing of
commercial equipment is not expected
7. Lighting
Application
• LED lamps and light sources
• Light-emitting panels
Competing technologies
• Incandescent bulbs
• Fluorescent lamps and compact ﬂuorescent
lamps
• Other types of lamps
• Incandescent bulbs
• Fluorescent lamps and compact ﬂuorescent
lamps
• Other types of lamps
LED technologies perspectives
• Universal use of LED lamps and light sources
supported by state programs in numerous
countries
• New type of light sources—light-emitting
panels (sectional at ﬁrst, then one-piece large-
size panels) forthcoming
• Trend for reducing average luminance
Source: HSE
Acknowledgments The book chapter was prepared within the framework of the Basic Research
Program at the National Research University Higher School of Economics and supported within the
framework of the subsidy by the Russian Academic Excellence Project ‘5-100’.
References
European Commission (2014) Update on the status of LED market. http://iet.jrc.ec.europa.eu/
energyefﬁciency/sites/energyefﬁciency/ﬁles/reqno_jrc92971_jrc92971_online.pdf.pdf
Great Stone (2013) Production of LEDs and LED lighting devices. Great Stone. http://
investinbelarus.by/docs/production_of_leds_and_led_lighting_devices.pdf
3
Nanotechnology for High-Tech Industries: Light-Emitting Diodes
75
Karasev O, Sokolov A, Roud V, Doroshenko M, Vishnevskiy K, Veselitskaya N, Afanasiev A,
Hahanov Y, Pashkov V (2014) In: Gokhberg L, Karasev A, Malyshev M (eds) LED Industry:
innovation technologies, products and markets. A Roadmap. Rusnano, HSE, Moscow
Lux Research, Inc. (2015) Scaling up carbon ﬁber: roadmap to automotive adoption. http://
speautomotive.com/SPEA_CD/SPEA2015/pdf/KEY/KEY1.pdf
Nogee A (2014) The worldwide market for lasers market review and forecast 2014. Strategies
Unlimited, 2014. http://cdn-content.opticsinfobase.org/WW%20Laser%202014%20Final.pdf
Russian Electronics (2013) Moscow international LED forum 2013: jeto «konec starogo sveta».
http://www.russianelectronics.ru/engineer-r/review/doc/65459/
Strategies Unlimited (2013) Worldwide LED component market grows 9% with lighting ranking
ﬁrst among all application segments, according to strategies unlimited. http://www.strategies-u.
com/articles/2013/02/worldwide-led-component-market-grew-9%2D%2Dto%2D%2D13-7-bil
lion-with-lig.html
Yole Développement (2013) LED applications. http://www.yole.fr/iso_upload/News/2013/PR_
BulkGaN%20_YOLEDEVELOPPEMENT_Nov.2013.pdf
Vitaliy Roud is Senior Researcher at the HSE ISSEK Lab-
oratory for Economics of Innovation. Vitaliy has participated
in a number of research and policy advice projects initiated
by public agencies, international organization and companies
including several national-level foresight initiatives. His aca-
demic interests include empirical studies of innovation,
evidence-based
innovation
policy,
methodology
of
innovation surveys, STI statistical indicators, STI policy
design and evaluation. Mr. Roud holds a lecturer position
in the Master’s Program ‘Governance of STI’ at HSE.
Alexander Sokolov is Deputy Director of HSE ISSEK and
Director of HSE International Foresight Center. His main
professional interests are related to Foresight, STI priorities,
indicators and policies. Prof. Sokolov is tenure professor at
HSE, he teaches Foresight for undergraduate and postgradu-
ate students. He authored over 120 publications in Russia
and internationally and managed many Foresight projects,
including: Russian S&T Foresight: 2030; Foresight for
Russian ICT sector (2012); Innovation Priorities for the
Sector of Natural Resources (2008–2010); Russian S&T
Delphi
Study:
2025
(2007–2008);
Russian
Critical
Technologies (2009) et al. Prof. Sokolov is member of sev-
eral high-level working groups at OECD and other interna-
tional organizations, serves for advisory boards at several
international conferences and journals.
76
V. Roud et al.
Dirk Meissner is Deputy Head of the Laboratory for Eco-
nomics of Innovation at HSE ISSEK and Academic Director
of the Master Program “Governance for STI”. Dr. Meissner
has 20 years experience in research and teaching technology
and innovation management and policy. He has strong back-
ground in policy making and industrial management for STI
with special focus on Foresight and roadmapping, funding of
research
and
priority
setting.
Prior
to
joining
HSE
Dr. Meissner was responsible for technology and innovation
policy at the presidential ofﬁce of the Swiss Science and
Technology Council. Previously he was management con-
sultant for technology and innovation management with
Arthur D. Little. He is member of OECD Working Party on
Technology and Innovation Policy. He is Associate Editor of
Technological
Forecasting
and
Social
Change,
IEEE
Transactions on Engineering Management, Journal of the
Knowledge Economy, member of Editorial Review Board
at Small Business Economics and Journal of Knowledge
Management. He guest edited Special Issues in Industry
and Innovation journal, Journal of Engineering and Technol-
ogy Management, Technological Analysis and Strategic
Management among others.
77
Technology Use in Traditional Industries:
Catalysts for Petroleum Refining
4
Dirk Meissner and Pavel Rudnik
4.1
Global Technological Development Trends in the Oil
Refining Sphere
National oil reﬁning markets are all integrated into the global petroleum production
and consumption system, and that it is affected by global trends, their dynamics and
consequences, both within and outside industry-speciﬁc processes—in the frame-
work of international paradigms. Therefore, the strategic objectives set in the oil
reﬁning sphere can only be accomplished if the sphere’s development is approached
in a systemic way—i.e. domestic demand and available S&T potential are viewed in
the context of global technological trends and development vectors on the global
catalyst market.
4.1.1
Oil Refining Structure
The technological processes that are applied to extract products from crude oil
required in practically all spheres of modern life demonstrate a complex structure:
the current scheme is based on double processing and includes over 20 technological
processes performed at various installations, using different kinds of catalysts.
During primary processing, crude oil is distilled under atmospheric pressure and
separated into fractions with different boiling temperature. The heavy fraction
(boiling temperature >350 ○С) is often subjected to additional vacuum distillation.
Catalysts are actively applied in secondary oil reﬁning processes, including catalytic
cracking,
reforming,
hydroﬁning,
isomerisation,
alkylation,
hydrocracking,
D. Meissner (*) · P. Rudnik
National Research University, Higher School of Economics, Moscow, Russia
e-mail: dmeissner@hse.ru; prudnik@hse.ru
# Springer Nature Switzerland AG 2019
D. Meissner et al. (eds.), Emerging Technologies for Economic Development,
Science, Technology and Innovation Studies,
https://doi.org/10.1007/978-3-030-04370-4_4
hydrodesulphurisation, hydrodewaxing, deasphalting, and selective treatment of
lubricants.
78
D. Meissner and P. Rudnik
Various processes’ speciﬁc roles vary depending on the ﬂuctuations of prices of
and demand for oil and oil products and are affected by the emergence of new
requirements for oil products’ properties, new technologies, and catalysts. For
example, in regions where only basic oil reﬁning is performed and consumption of
fuel oil is high, the predominant technologies include catalytic reforming, petrol
isomerisation, hydroﬁning of diesel and jet fuels, and hydroﬁning of fuel oil for the
production of commercial-grade fuel. In regions where oil is processed more deeply
and consumption of fuel oil is low, the predominant technologies include destructive
reﬁning (cracking and hydrocracking) and upgrading of oil products (via reforming
and hydroﬁning) and fuel oil. In addition to oil reﬁneries, some of these regions also
have petrochemical synthesis facilities. In that case, pyrolysis and catalytic
reforming processes are applied to maximise lower oleﬁns and aromatic
hydrocarbons output (Krylov 2004). Among these processes the development
prospects for secondary oil processing nanotechnologies involve upgrading six
technological processes, which are believed to have the biggest catalyst markets:
•
Hydroﬁning
•
Catalytic cracking
•
Isomerisation of light gasoline fractions
•
Catalytic reforming
•
Alkylation
•
Hydrocracking
Key oil reﬁning development trends and potential for applying nanotechnologies
to produce catalysts are analysed below in the framework of this technological
processes’ structure.
4.1.2
Oil Refining Trends and Development of Global Catalyst
Market
Recently, developed countries were showing a trend towards abandoning the con-
struction of new crude oil reﬁning facilities and switching to upgrading production
processes by introducing new installations and applying catalytic processes to
upgrade and improve the quality of intermediate primary reﬁning products. Having
more secondary oil reﬁning installations allows for an increase in the output of high-
quality, expensive light oil products, with lower output of cheap fuel oil. Table 4.1
shows that leading global producers of oil products (Western European countries,
the USA, Russia, and Japan) set the technological structure of global oil processing
industry, which comprises ﬁve key production processes: catalytic cracking,
reforming, hydroﬁning, alkylation, and isomerisation.
Thus, light oil products are becoming increasingly important on the global oil
reﬁning market, and catalysts are actively applied in their production. Catalytic
4
Technology Use in Traditional Industries: Catalysts for Petroleum Refining
79
Table 4.1 Current technological processes applied for oil reﬁning in Russia and other countries (%
of total crude oil reﬁning)
Main secondary processes
Russia
USA
Western Europe
Japan
Catalytic cracking
7.1
35.8
15.8
19.8
Hydrocracking
2.6
9.1
7.5
4.0
Thermal cracking and viscosity breaking
4.0
0.2
12.2
0
Coking
1.0
16.2
2.5
23.8
Hydroﬁning of distillates
26.0
41.3
35.3
52.5
Catalytic reforming
10
18.3
12.7
13.9
Alkylation
0.3
5.6
1.4
0.8
Isomerisation
0.9
3.0
2.7
0.3
Source: HSE
Fig. 4.1 Forecasted growth
rates for global catalyst
market. Source: HSE
0
0.5
1
1.5
2
2.5
3
3.5
2015
2020
2030
bn, $
catalytic cracking
hydrofining
reforming
isomerisation
hydrocracking
processes’ capacity is rapidly growing. This trend is expected to continue and will
largely determine the industry’s development vectors. Another global trend, partic-
ularly evident in developed countries—importers of oil products—is the increas-
ingly stringent environmental laws aimed primarily at reducing harmful emissions
from burning fuel and constantly growing requirements for oil products’ quality,
which among other things resulted in the EU member countries’ switching to Euro-5
fuel with extremely low sulphur content. Also, the demand trends of recent years
show that consumption of distilled diesel fuels and high-quality petrol in the EU
countries is rapidly growing. Petrol consumption in the USA and Asia-Paciﬁc region
is also growing. Demand for jet fuel grows less rapidly, while demand for fuel oil is
gradually falling. All these trends contribute to the growth of global catalyst market,
which is estimated at 15–20 billion USD annually, and the range of available
industrial catalysts is annually renewed by 15–20%. Currently the global catalyst
market has the following structure: hydroﬁning catalysts amount to about 40%;
cracking catalysts, 30%; hydrocracking, 7%; reforming, 5%; and other catalysts,
20%. Figure 4.1 shows the forecasted growth rates for main oil reﬁning
technologies’ markets (in value terms).
Overall the global oil reﬁning market is determined by a few key trends noted in
the framework of the ﬁve selected technological processes.
Catalytic Cracking
•
Installations’ total capacity will remain at the current level until 2030.
•
A slight growth of cracking catalysts consumption is expected.
•
In value terms, the global market is expected to signiﬁcantly grow by 2030 due to
considerable growth of catalyst prices.
Hydroﬁning
•
Hydroﬁning installations’ total capacity by 2030 will grow by 25%.
•
Consumption of sulphide hydroﬁning catalysts is expected to grow both in
absolute and value terms.
•
Catalyst prices are generally expected to remain at the current level.
Isomerisation of Light Gasoline Fractions
80
D. Meissner and P. Rudnik
•
Global isomerisation catalysts market is expected to grow by 40–50% by 2030.
•
Low-temperature isomerisation is the most advanced and promising process.
Catalytic Reforming
•
Reforming installations’ capacity is expected to grow by 20% by 2030.
•
Reforming in moving catalytic layer is considered to be the most advanced and
promising way to apply this process.
•
Annual consumption of catalysts is expected to quickly grow, both in value and
absolute terms, due to increased production capacity.
Hydrocracking
•
One of the most promising technologies; by 2030 installations’ capacity is
expected to double.
•
Annual consumption of catalysts is also expected to double.
4.1.3
Russian Oil Refining Industry and Catalyst Market
Major changes were noted in Russian oil reﬁning industry in recent years. The output
grows, and the quality of produced motor fuels is gradually improving. Several
Russian oil reﬁneries are building new facilities for deep oil processing; some of
them are already operating. The backbone of the Russian oil reﬁning industry is
27 large reﬁneries with total designed capacity of 260 million tons of crude oil a year
(which amounts to 95–98% of all processed oil). Lately Russian oil companies have
been signiﬁcantly increasing their investments in oil processing, which resulted in
growing oil reﬁning volume and gradual improvement of motor fuel quality—due to
discontinuing production of leaded petrol, there is a higher share of high-octane
4
Technology Use in Traditional Industries: Catalysts for Petroleum Refining
81
Fig. 4.2 Forecasted trends
for Russian catalyst market.
Source: HSE
0
100
200
300
400
500
600
2015
2020
2030
bn, $
catalytic cracking
hydrofining
reforming
isomerisation
hydrocracking
Table 4.2 Market development trends
Hydroﬁning
• Capacity growth will mostly occur due to wider application of nickel-molybdenum aluminium
oxide-based catalysts
• By 2030 possible industrial application of oxidation technology—a radically new catalyst
production technology is expected
• New carrier types are expected to be developed by 2030, e.g. nanostructured titanium dioxide
Catalytic cracking
• Cracking installations’ total capacity will be growing
• Bead catalyst-based installations are expected to be decommissioned by 2030
• Capacity will grow mostly due to wider application of microspheric silica-alumina zeolite-
containing catalysts with average particle diameters between 10 and 150 microns
Isomerisation of light gasoline fractions
• The number of isomerisation installations in Russia is expected to double by 2030, reaching 30
• Consumption of catalysts will signiﬁcantly grow, both in absolute and value terms
• Catalyst prices are expected to moderately grow, due to growing prices of precious metals
Catalytic reforming
• Total installations’ capacity is expected to grow by 50% by 2030
• Capacity growth will be due to increased number of oxide- and zeolite-based catalyst
installations
• Catalyst prices are expected to signiﬁcantly grow due to growing prices of precious metals
Hydrocracking
• More stringent requirements for oil products, primarily in terms of sulphur content, will prompt
the development of more efﬁcient catalysts, integration with hydroﬁning and de-metallisation
processes, removal of heavy multiring hydrocarbons, hardware improvement, development of
more efﬁcient technological schemes (Kapustin 2007)
• Installations’ total capacity will increase
• By 2030 the number of such installations is expected to reach seven in Russia
• Catalyst prices are not expected to signiﬁcantly change until 2030
Source: HSE
petrol and environmentally clean diesel fuel. Forecasts for major types of oil reﬁning
catalysts’ markets (in value terms) are presented in Fig. 4.2.
82
D. Meissner and P. Rudnik
The development of the Russian market during the time horizon of the study will
be affected by the following key trends (Table 4.2).
Thus an analysis of global and Russian trends revealed vectors affecting the
development of technological processes suitable for the application of catalysts with
the required properties. Improvement of these processes will allow for the produc-
tion of competitive oil reﬁnery products for the Russian and international markets in
the next 15 years.
4.2
Catalytic Refining Technologies’ Characteristics
Oil reﬁning catalysts (further on, catalysts) are nanostructured substances which, in
the course of a cycle of intermediate interactions, speed up oil reﬁning chemical
reactions without being spent in the process. Russian oil reﬁning nanotechnologies
are developed to improve catalysts for cracking heavy crude oil to reﬁne it into light
products, for gasoline fractions’ reforming, for deep hydroﬁning of diesel fuel to
remove sulphur, to process accompanying oil gases, etc. Analysing the structure of
demand for catalysts, the dynamics of key technical and economic properties of
major installation types and the catalysts they use, allows one to identify
opportunities for and barriers hindering technological development of oil reﬁning
industry. The development of oil reﬁning technologies went through several key
stages connected with the introduction of speciﬁc catalytic processes. The 1960s was
the time of major application of catalytic reforming of gasoline fractions
(aromatisation, isomerisation, and de-hydrolysation of original hydrocarbon
compounds), which allowed for the signiﬁcant improvement of the efﬁciency of
internal combustion engines with the spark ignition of air-fuel mixture—carburettor
ones, and currently engines based on fuel injection into cylinders. The increased
efﬁciency of such internal combustion engines is thermodynamically linked to the
increased compression ratio of the air-fuel mixture, which is limited by petrol’s
antiknock quality. The acceptable compression ratio increases with the reduction of
the engine cylinders’ diameter, while the volume power is boosted by increasing the
number of crankshaft revolutions—which complicates adjusting the composition of
the working mixture under varying speeds and loads.
As of 2002, leading automobile manufacturers started the mass production of
petrol internal combustion engines with forced injection of fuel into cylinders and
electronic adjustment of spark ignition’s advance. This solved the problem of
preparing the working mixture and providing optimal conditions for combusting it
in high-speed engines, but made requirements for petrol’s antiknock quality and the
range of its components’ boiling temperatures even more stringent. Traditional oil
reﬁning processes needed novel technological solutions not just abroad but in Russia
as well.
4
Technology Use in Traditional Industries: Catalysts for Petroleum Refining
83
Next, the main characteristics of processes, installations, and catalysts to a greater
or lesser degree represented and demanded on Russian and global markets are
described.
4.2.1
Catalytic Cracking
Catalytic cracking is a major technology for producing a high-quality petrol compo-
nent with octane number of 91–93 (RM). It also produces fractions which can be
used as diesel or jet fuel components. Catalytic cracking produces a signiﬁcant
amount of gas rich with propane-propylene and butane-butylene fractions (raw
materials for making high-octane MTBE ether, alkyl benzene, and other valuable
motor fuel components). Catalytic cracking installations also supply materials for
producing high-quality “needle” coke. Catalytic cracking gas-oils are used to make
carbon-black materials and naphthalene. During the long period of development
(starting in the 1930s), catalytic cracking was signiﬁcantly improved, both in terms
of contact between raw material and catalyst (in the stationary layer, bead catalyst
moving layer, microspheric catalyst “boiling” layer) and the applied catalysts
(tableted catalysts based on natural clays, bead synthetic aluminium silicates includ-
ing nanostructured zeolite-containing ones). These improvements resulted in radical
changes in the technological process as a whole, which allowed for increasing end
product output (petrol component) from 30–40% to 50–55% mass (Melik-
Akhnazarov 1980). The catalytic cracking process is largely determined by the
properties of the applied catalyst. To process heavy oil residue and secondary
products, modern cracking catalysts must provide a high output of high-octane
petrol (over 50% of raw material mass), with minimum required coke content in
the catalyst. A typical modern cracking catalyst is a very complex nanostructured
composite, whose all-important component is 0.2–0.6 micron nanocrystals of ultra-
stable sheet-morphology zeolite (Parmon 2008). Cracking catalysts are heteroge-
neous dispersed multicomponent porous systems, with the following overall func-
tional structure:
•
Active phase (3–20% mass)—zeolite Y in various cation-exchange forms
•
Binder—aluminium silicates, oxides Al, Si
•
Filler—clays of kaolin or montmorillonite type (Gerzeliyev 2008)
To synthesise a cracking catalyst with speciﬁc properties, the components (zeo-
lite, ﬁller, binder) must be combined in the optimal way, taking into account their
physical–chemical interaction during production of the catalyst. Cracking catalyst’s
activity is primarily determined by the quality of the nanostructured zeolite used,
which in turn is adjusted both at the synthesis and processing stages. X and Y
zeolites in rare-earth or ultra-stable forms are highly active and highly selective in oil
fractions’ cracking; they are stable under high-temperature impact in air and water
vapour environments and have adequate entrance window sizes in structural cavity;
accordingly,they arefrequentlyusedto synthesisecrackingcatalysts (Gerzeliyev 2008).
Cracking catalyst’s matrix must ensure the catalyst has the necessary basic
properties: high mechanical strength, primary cracking of the raw material’s heavy
component (the matrix’s adjustable activity to process vacuum gas-oil with high-end
boiling point (up to 600 ○С)), efﬁcient heat removal from zeolite crystals during
the catalyst’s regeneration, and high heat capacity to provide heat for endothermic
cracking reaction; nanostructured zeolite’s surface must be accessible to reacting
molecules (i.e. it must be sufﬁciently porous); and the catalyst must have acceptable
volume weight so it could be retained in the system. Currently two kinds of cracking
catalysts are produced:
84
D. Meissner and P. Rudnik
•
Granulated catalyst in the form of beads or granules (average particle size from
2 to 5 mm), for application in installations with moving catalytic layer
•
Microspheric catalyst (maximum particles diameter up to 120–150 microns), for
application in installations with aerated catalyst layer
The demand for granulated catalyst is estimated at 6–8 thousand tons a year,
depending on installations’ utilisation rate. As to microspheric catalyst, demand for
it, again depending on installations’ utilisation rate, amounts to about 8–9 thousand
tons a year. These catalyst types are used in both traditional and advanced “process-
catalyst” complexes (the catalytic process’s scheme and the applied catalyst type):
1. In lift reactor installations, microspheric (powdered, with average particle diame-
ter of 10–70 microns) silica-alumina zeolite-containing catalysts are used.
2. In double-regeneration cracking installations, microspheric (powdered, with
average particle diameter of 10–70 microns) silica-alumina zeolite-containing
catalysts are used.
3. In X-design technology, microspheric (powdered, with average particle diameter
of 10–70 microns) silica-alumina zeolite-containing catalysts are used.
4. In millisecond cracking installations, microspheric (powdered, with average
particle diameter of 10–70 microns) silica-alumina zeolite-containing catalysts
with optimal rare-earth elements content are used.
5. In moving catalytic layer installations, bead silica-alumina zeolite-containing
catalysts are used.
6. In aerated catalytic layer installations, microspheric (powdered, with average
particle diameter of 50–150 microns) silica-alumina zeolite-containing catalysts
are used.
Lift reactor installations are currently considered the mainline type; they offer
several advantages such as reduced contact time (the catalyst does not have time to
coke which allows for the use of heavier raw materials); increased temperature
(in reactor, 530 ○С; in regenerator, about 700 ○С) and petrol output (52–56%); the
octane number is over 90 and catalyst circulation ratio is 6–7 (the amount of catalysts
per ton of raw material); and high productivity. However, it also shows certain
drawbacks, such as complex operation and high energy consumption. Installations of
this type are operating in several Russian cities; their optimal capacity is up to
2 million tons of raw material a year.
4
Technology Use in Traditional Industries: Catalysts for Petroleum Refining
85
Double-regeneration cracking installations are an analogue of lift reactors. There
are no such installations in Russia, but in foreign countries they amount to 7–10% of
the total number of lift reactor installations. Such installations have several signiﬁ-
cant advantages including the possibility of using heavy raw materials; ﬁner catalyst
cleaning (resulting in higher activity); opportunity to change circulation ratio; more
ﬂexible temperature controls; and more efﬁcient regeneration due to an independent
air supply to each regenerator. There are also certain drawbacks including signiﬁcant
emissions, loss of catalysts, high energy consumption, and problems with retaining
the installation’s thermal balance.
X-design technology is mentioned among the prospective ones due to several
advanced properties, e.g. low energy consumption during regeneration, opportunity
to prolong service life, and more efﬁcient use of catalyst. A drawback is the high
energy requirements for transporting the catalyst.
A millisecond cracking installation is operating in Belarus, but there are no plans
to build one in Russia so far—though the costs are quite comparable with others.
Among its strengths, the experts noted a compact reactor, convenient operation, low
energy consumption, and opportunity to use very heavy raw materials. Weaknesses
mentioned by the experts included ultrashort (in terms of time) contact; cracking of
heavy raw materials; and the opportunity to obtain better characteristics using
vacuum gas-oil and fuel oil mix.
Installations with a moving catalytic layer belonging to an already obsolete type
are no longer built because their weaknesses are well known. These include small
contact surface, low petrol output and low octane number, and quick coking of
catalyst which occurs due to long contact of raw material with catalyst. Other
obvious advantages of this type are the use of air as a transportation agent to move
the catalyst in the installation and the separation of reactor and regenerator.
In the late 1990s, such installations were widely used in Russia, amounting to
about 30% of all installations of the 43–102 type. Currently, 11 such installations
remain in Russia. Each processes about 600 thousand tons a year. In 20 years’ time,
they are expected to become completely obsolete and will be replaced with
new ones.
Aerated catalytic layer installations are also obsolete now. They are large,
difﬁcult to operate, and have high energy requirements. However, they also have
certain advantages such as higher output and octane number (the main advantage)
and possibility of maintaining higher temperatures (which provides a deeper crack-
ing producing more light fractions). Three such installations currently remain in
operation in Russia. In other countries they are no longer in use. This type of
installation uses a microspheric catalyst with a pore diameter between 50–70 and
150 microns. Total contact time is between 2 and 6 min; contact time in aerated
layer, under 2 min; reactor temperature, about 500 ○С; regenerator temperature,
650–670 ○С; catalyst circulation ratio, about 4–5; and petrol output, 48–50%.
86
D. Meissner and P. Rudnik
4.2.2
Catalytic Reforming
Catalytic reforming is a complex chemical process which involves reactions
resulting in formation of aromatic hydrocarbons. It is applied in the petrochemical
industry to perform some of the most important, large-scale, and common processes:
the production of highly aromatic distillates from low-octane straight-run gasoline
fractions, i.e. high-octane petrol components, and separating from them individual
aromatic hydrocarbons such as benzene, toluene, and xylenes (Kapustin 2007).
Aromatic compounds and benzene are now ofﬁcially recognised as harmful
cancerogenic substances, and a decision was made to radically reduce their content
in petrol. However, reducing the reforming petrol’s share in the total pool of petrol
ﬁrstly would be quite expensive for the society, and secondly, reforming is one of the
main sources of cheap hydrogenous gas at oil reﬁneries, which is necessary for
hydrogenation processes (hydroﬁning, hydrocracking).
Reforming’s share in oil processing at global oil reﬁneries is on average 14%.
Commercial petrol produced in the USA on average contains 20–25% of reformate;
in Western Europe the relevant ﬁgure is about 30–40% and in Russia 45–50%.
Therefore, completely abandoning reforming is hardly going to happen in the near
future (Krylov 2004).
Straight-run gasoline fractions (their content in oil is usually 15–20% mass) due
to their chemical composition have low antiknock value (MON-50 and RON-55).
Gasoline fractions of most oil types contain 60–70% of parafﬁn hydrocarbons, about
10% of aromatic, and about 30% naphthenic. However, the process’s product—
reformate—has high antiknock value (MON ¼ 80–90 and RON ¼ 90–100), due to
results of various chemical reactions (the main one being cyclodehydrogenation of
hydrocarbons) leading to changes in its chemical composition (Borodacheva and
Levinbuk 2008).
The process’s evolution during the last 60 years amounted to an increased depth
of raw material processing, the selective aromatisation of parafﬁn hydrocarbons, and
an increased stability of catalysts. Thus output of aromatic hydrocarbons and
hydrogen increased by more than 1.5 times, while catalysts’ inter-regeneration
cycle grew by 4 times. The process’s technological progress amounted to a reduced
working pressure, from 3.0 to 0.35 MPa, due to the development of new highly
stable catalysts and the application of a modiﬁed technology with continuous
catalyst regeneration.
Catalysts used in reforming perform two major functions: dehydrating-hydrating
and acidizing. Catalyst’s dehydrating-hydrating function is normally performed by
metals—platinum, palladium, and nickel. Platinum component has the highest
dehydrating properties. Its function is to speed up dehydration and hydration
reactions, which stimulates formation of aromatic hydrocarbons, continuous hydra-
tion, and the partial removal of intermediate reaction products which lead to coking.
Platinum content in the catalyst is usually at 0.3–0.6% (mass). Lower platinum
content reduces the catalyst’s resistance to poisons, while a higher concentration
reveals a tendency towards stronger demethylation reactions. Another factor limiting
the catalyst’s platinum content is high cost. Platinum-conditioned activity of the
catalyst can be adjusted by changing platinum concentration and the degree of its
dispersion in the carrier, up to values measured in whole nanometres.
4
Technology Use in Traditional Industries: Catalysts for Petroleum Refining
87
Acidizing function is performed by the catalyst carrier—aluminium oxide. The
catalyst’s acidic properties determine its cracking and isomerising activity. A halo-
gen is added to the catalyst to increase its acidizing function. Recently, chlorine was
frequently used for this purpose; a while ago it was ﬂuorine (and occasionally still
is), which stabilises high dispersion of platinum by forming complexes with it and
with aluminium oxide. Chlorine’s advantage is that it promotes cracking reactions to
a lesser extent (this is particularly important under a strict regimen). Chlorine content
is 0.4–2.0% (mass). Loss of chlorine in the catalyst during its oxidising regeneration
is replenished by periodic or ongoing chlorine feed (the amount is 1–5 mg/kg of raw
material). The catalyst’s acidity is very important for achieving a certain depth of raw
material’s processing, for making a product with the required octane number during
the speciﬁed period for staying in the reaction zone, and under the speciﬁed
temperature. Catalysts combining both these functions (dehydrating and acidizing)
are called bifunctional. They are also used for cyclodehydrogenation reactions which
are particularly relevant for processing raw material with high parafﬁn hydrocarbon
content.
The main catalyst evaluation criteria are the raw material’s feed rate; stable
reformate (catalysate) output; product’s octane number or output of aromatic
hydrocarbons; light fractions’ content in the reformate; output and composition of
gas; and catalyst’s service life. An efﬁcient way to increase catalysts’ activity,
selectivity, and stability is to introduce special elements into them—promoters,
which ensure desirable effects. Russian reﬁneries initially used AP-56 aluminium-
platinum catalysts, based on ﬂuorinated aluminium oxide and containing 0.56%
(mass) of platinum. The catalyst was applied without preliminary hydroﬁning of raw
material, to produce catalysate with MON 75. Under such conditions, the main
reaction leading to formation of aromatic hydrocarbons was naphthene dehydroge-
nation. The second stage of the Russian variant of reforming was connected with
development and application of AP-64 chloride-containing aluminium-platinum
catalyst and application of such technologies as raw material hydroﬁning and
dehydration, chlorination of the catalyst during the reaction cycle, and reducing
the process’s pressure from 4.0 to 3.0 MPa. Such intensiﬁcation of the process
allowed the production of up to 40% (mass) of aromatic hydrocarbons in the
reformate through cyclodehydrogenation of parafﬁn hydrocarbons.
However, each of the applied catalytic reforming types has its own advantages
and drawbacks.
1. The process in the stationary catalytic layer with intermediate heating between
reaction zones (several reactors are positioned in a row one after another, with
reaction’s products being heated in between them, on their way from the previous
to the next reactor). At the same time hydrogen is circulating through the system,
which requires high-pressure compressors. The catalyst is platinum (including
other metal additives, i.e. bimetallic) overlaid on aluminium oxide. This catalytic
complex has a number of advantages and currently meets the main consumer
88
D. Meissner and P. Rudnik
requirements; also this technology is believed to be the best-developed one. The
drawbacks are a low volume rate (13 inv.h.); high pressure (35 atm.) (which
increases the costs); high sensitivity to harmful contaminants; and limited regen-
eration of catalyst, which reduces its activity.
2. Process in the stationary catalytic layer with intermediate heating between reac-
tion zones—zeolite platinum-containing (including with other metal additives,
i.e. bimetallic) catalyst. This catalyst’s advantages (compared with platinum on
aluminium oxide) include lower pressure (20 atm.), high volume rate (2–2.5 inv.
h. and potentially 3–3.5), higher octane number of the reformate, and increased
hydrogen output under the same process regimen. This catalyst shares its
drawbacks with platinum on aluminium oxide, though it has fewer conditions
for continuous regeneration (which results in reduced activity), and is sensitive to
harmful contaminants. Another major drawback of the process is high pressure—
higher than in moving layer.
3. Process in moving catalytic layer—platinum (including other metal additives,
i.e. bimetallic) overlaid on aluminium oxide. The attractive properties including
lower sensitivity to harmful contaminants, higher octane number of reformat, and
higher hydrogen output, plus the opportunity to process heavier raw materials. On
the other hand, higher strength is required to improve product quality.
4. Process in moving catalytic layer—zeolite platinum-containing (including with
other metal additives, i.e. bimetallic).
Signiﬁcant strengths of this technology are the highest octane number of the
reformate, lowest pressure (9 atm.), possibility of processing heavier raw materials,
and higher hydrogen output. But at the same time production costs are high,
including high catalyst prices. It’s expected that catalysts’ development moves
towards increasing octane number and reformate output and reducing the content
of aromatics and benzene in it. Overcoming this contradiction requires strengthening
catalysts’ isomerisation function—and the zeolite component does exactly that.
4.2.3
Isomerisation of Light Gasoline Fractions
The isomerisation process is applied to increase C5–C6 oil fractions’ octane number
by transforming the normal structure parafﬁns into isomers with a higher octane
number. Isomerisates’ most important consumer property (unlike reformates) is a
very small difference between their motor and research octane numbers. Accord-
ingly, a high-octane isomerisate can be considered an adequate component for
mixing with reformate, because of the following important reasons:
•
To increase light gasoline fractions’ octane characteristics (overpoint fraction—
100 ○С)
•
To reduce the difference between MON and RON in commercial petrol and
increase octane index
•
To reduce overall aromatic hydrocarbons content, including benzenes
4
Technology Use in Traditional Industries: Catalysts for Petroleum Refining
89
•
To level petrol’s octane numbers for the whole mass of evaporant fuel
In the last decade isomerisation process became one of the most proﬁtable
technologies for the production of high-octane, environmentally clean petrol
components, widely applied in many countries to increase octane numbers of
С5–100 ○С gasoline fraction by regrouping molecular structure of regular parafﬁns
into their isomers with higher octane number.
Isomerisate of С5–С6 light gasoline fraction has a low content of aromatic and
oleﬁnic hydrocarbons with research octane characteristic between 68 and 83 points,
depending on the processed fraction’s quality, isomerisation type, and process
scheme.
It should be stressed that combining isomerisation and reforming processes to
meet the current and prospective requirements for motor petrol is a high-priority and
economically sensible option. If reforming removes heavy low-octane petrols and
thermal processes’ petrols from the overall petrol pool and transforms them into
aromatic high-octane components, isomerisation extracts from the petrol pool
low-octane light straight-run and secondary components, replacing them with
high-octane components—isomers which do not contain benzene and other aromatic
hydrocarbons. The isomerisation process as such has a number of positive techno-
logical and environmental features, such as low costs, 100% product output, and low
hydrogen consumption.
And the end product—isomerisate—is the most valuable component of commer-
cial petrol, because it does not contain benzenes, aromatic hydrocarbons, sulphurous
compounds, and oleﬁnic hydrocarbons and has high RON and MON values. The
purpose of applying catalytic isomerisation in modern oil reﬁning is to produce high-
octane isocomponents of motor petrol or materials for the petrochemical industry,
primarily isopentane, to synthesise isoprene rubber.
There
is
high-temperature,
medium-temperature,
and
low-temperature
isomerisation. Currently low-temperature and medium-temperature processes are
applied most commonly.
Low-temperature isomerisation catalysts are divided into three types:
(a) Chlorinated metal oxides, primarily chlorinated ή- and ά aluminium oxide
(b) Wide-pore zeolites of the faujasite type
(c) Zirconium oxide promoted with sulphate, molybdate, or tungstate ions
To prolong this type of catalyst’s stable action, 0.3–0.5% mass of platinum or
palladium is added to it. The working temperature range is between 100 and 200 ○С,
the process pressure is 10–30 atm., and raw material load is 0.5–4 h–1. Isomerisation
reaction is conducted under presence of hydrogen, with molar ratio to raw material of
0.02–4. Due to the above thermodynamic characteristics of isomerisation process,
this class of catalysts seems to be the most promising one for processing light
parafﬁn fractions from butane to NK-70 fraction. A serious weakness of the
(a) and (b) catalysts is their sensitivity to presence of contaminants such as water,
sulphur, and nitrogen in the raw material, which under low temperatures are
efﬁciently absorbed on the catalyst’s surface, negatively affecting its acidizing and
hydrating functions. For chlorine-promoted aluminium oxide, the acceptable content
of water, sulphur and nitrogen is about 2–5 ppm. For zeolites and promoted
zirconium oxide, acceptable content of contaminates in raw material is much higher
than for aluminium oxide.
90
D. Meissner and P. Rudnik
Medium-temperature isomerisation catalysts are represented by zeolite-based
catalysts of the mordenite type, with a sodium content of about 2–3 ppm and
modiﬁed with 0.4–0.5% mass of platinum. Their working temperature range is
260–300 ○С, process pressure is 10–30 atm., and raw material load is 0.5–1.5 h–1.
Hydrogen/raw material ratio is 0.5–1.5. Mordenite-based isomerisation catalysts are
much more tolerant to raw material contaminants—up to hundreds of ppm. This type
of catalysts is mainly used for isomerisation of parafﬁn fractions; they are not
suitable for the isomerisation of n-butane.
High-temperature isomerisation catalysts include catalysts based on ﬂuorinated
aluminium oxide and medium-pore zeolites such as ZSM-5 type, etc.; their working
temperature range is 320–380 ○С, process pressure is 10–30 atm., raw material load
is 0.5–1.5 h–1, and hydrogen/raw material ratio is 0.5–1.5.
High-temperature isomerisation catalysts can accept contaminant content in raw
material of up to hundreds of ppm. They are applied for the isomerisation of light,
medium, and heavy parafﬁn fractions.
4.2.4
Hydrofining
The development of advanced processes for the hydroﬁning of petrol, kerosene, and
diesel fractions is mainly aimed at reducing concentration of sulphurous, oleﬁnic,
and partially nitrogenic oxygen-containing compounds. This is due to the growing
share of sulphurous oil in the overall oil production, coupled with stricter
requirements for sulphur content in fuel—due to the corrosion of fuel storage
equipment and engines caused by sulphurous compounds and atmospheric pollution
by sulphur oxides in exhaust gases (Kapustin 2007).
Several types of catalysts are currently applied, namely:
•
Molybdena-alumina
catalyst
is
synthesised
by
applying
ammonium
paramolybdate on γ-А12О3 particles’ surface from water solution of a particular
concentration.
•
Cobalt-alumina catalyst is produced by saturating aluminium oxide tablets
γ-А12О3 with Со(МО3)3 water solution at room temperature.
•
Cobalt-alumina molybdenum hydroﬁning catalyst is produced by saturating
aluminium hydroxide with Со and Мо salts solution.
•
Nickel-alumina-molybdenum catalyst is applied for hydroﬁning of diesel fuel and
other oil fractions (e.g. vacuum gas-oil in catalytic cracking installations).
•
Zeolite catalyst is based on gamma-aluminium oxide and РdY and А1РО4 zeolite
types,
using
as
source
material
NY,
γ-А12О3,
РdС12,
zeolites,
4
Technology Use in Traditional Industries: Catalysts for Petroleum Refining
91
ethylenediaminetetraacetic
acid,
NН4ОН
(water-based),
NaОН,
and
sulphuric acid.
Most promising “process-catalysts” complexes are:
1. Process with low (100–50 ppm) residual sulphur content (for standard material
with initial sulphur content of about 1%)—aluminium oxide (carrier), cobalt-
molybdenum, or nickel-molybdenum. The process is regularly performed under
the pressure of 35–38 atm.; the higher the pressure, the purer the fuel. Many
believe that pressure of 45–50 atm. may be applied. However, the higher the
pressure that is used, the more metal would be needed—for thicker walls and use
of different pumping equipment, because equipment suitable for pressure range
35–38 atm. could not be used under 45–48 atm. Switching to high-pressure
equipment would increase energy consumption accordingly.
2. Process with ultralow (50–10 ppm) residual sulphur content—Nebula-type cata-
lyst. After the ﬁrst regeneration, its properties drop to the coated catalysts’ level.
3. Process with ultralow (50–10 ppm) residual sulphur content (with low volume
rate)—aluminium oxide (carrier) and cobalt-molybdenum (active component).
According to the experts, this catalyst is applied at nine out of every ten
installations in the world (and only one of ten uses Nebula-type catalysts).
4. Process NZSD (under 10 ppm)—Nebula type.
5. Process NZSD (under 10 ppm)—aluminium oxide (carrier) and nanomodiﬁed
cobalt-molybdenum (active component) is thought to have very high potential.
4.2.5
Hydrocracking
Hydrocracking is among the most quickly advancing oil reﬁning processes. Sufﬁce it
to say that in the last 20 years, hydrocracking installations’ capacity grew by four
times globally: they process almost 250 million m3 of raw materials a year. The
process allows, through application of appropriate catalysts and operating
parameters, to achieve a high output of a wide range of high-quality components
of mainline oil products from practically any hydrocarbon raw material—such as
liqueﬁed gases, jet and diesel fuel, lubricant components, etc. The difference
between hydrocracking and hydroﬁning is that the former includes processes
where over 10% of the material is subjected to destruction, reducing molecular
size. Depending on the conversion rate, hydrocracking processes are divided into
light (soft) hydrocracking (LHC) and deep (hard) one. Accordingly, in the ﬁrst case
the conversion rate is between 10% and 50% and in the second over 50%.
The ﬁrst group of processes is designed for both raw material treatment (for
subsequent processing) and for increasing “light” oil products’ output. The second
group is applied exclusively to increase “light” oil products’ output. Hydrocracking
of oil distillates and residue to produce “light” oil products is a relatively new
destructive oil reﬁning process and is widely applied in the USA. Hydrocracking’s
main strength is the opportunity to process both distillate and residue materials,
producing high-quality products such as liqueﬁed gases, high-octane petrols, waxy
diesel, and jet fuel. Hydrocracking is the only secondary oil reﬁning process which
allows jet fuel resources to be signiﬁcantly increased. Most of hydrocracking
processes are designed to process distillate material (heavy atmospheric and vacuum
gas-oils, cracking and coking gas-oils, deasphaltisates). Hydrocracking is a highly
selective and ﬂexible technology; slight modiﬁcation of the process’s conditions
signiﬁcantly affects end products’ characteristics. The following types of industrial
hydrocracking processes are currently applied in oil reﬁning industry:
92
D. Meissner and P. Rudnik
•
Light hydrocracking of vacuum gas-oils, to upgrade catalytic cracking materials
while at the same time producing diesel fractions
•
Hydrocracking of vacuum distillates under pressure, to produce motor fuels and
high-index lubricant bases
•
Hydrocracking of oil residue, to produce motor fuels, low-sulphur fuel oil, and
materials for catalytic cracking
Three “process-catalyst” complexes can be identiﬁed at this stage:
•
Single-stage hydrocracking—catalyst based on amorphous aluminium silicates
containing NiWS-phase sulphide nanoparticles
•
Single-stage hydrocracking—catalyst based on crystal aluminium silicates
(zeolites) containing NiWS-phase sulphide nanoparticles
•
Two-stage
hydrocracking—catalyst
based
on
crystal
aluminium
silicates
(zeolites) containing platinum nanoparticles
Single-stage hydrocracking is currently considered to have the second highest
potential for practical application in Russia; it offers a number of advantages
including comparatively low capital investments in installations’ construction, low
running costs, and a very high quality of kerosene (sootless ﬂame height more than
28 mm). But it also has a low conversion rate into target products and low quality
compared with the two-stage process. Single-stage cracking is based on two
catalysts:
1. Catalyst based on amorphous aluminium silicates containing NiWS-phase sul-
phide nanoparticles provides high selectivity for producing motor fuels and low
selectivity of gas formation (few side products). On the other hand, the catalyst is
unstable—it quickly deactivates so its service life is short (1 year), while the
zeolite-based catalyst serves for 2–3 years.
2. Catalyst based on crystal aluminium silicates (zeolites) containing NiWS-phase
sulphide nanoparticles is believed to be the best for the single-stage scheme. It has
higher stability compared with amorphous aluminium silicate.
Selectivity, as a highly relevant consumer characteristic, is currently a factor
contributing to the development of this catalyst’s production the world over. It is
expected to signiﬁcantly improve by 2030. Service life without regeneration is also
expected to increase. The latter parameter deserves special attention in production of
catalysts for moving systems; its importance is quite comparable to selectivity. The
experts also expected signiﬁcant improvement of such catalyst parameters as activity
and strength.
4
Technology Use in Traditional Industries: Catalysts for Petroleum Refining
93
4.2.6
Alkylation
4.2.6.1 Alkylation of Isobutane with Butylenes
In the process of butylene’s and isobutane’s amalgamation in the reactor, butylene
has a tendency to amalgamate with itself—which reduces productivity. This problem
is solved by increasing the amount of isobutane which envelopes butylene and does
not allow other butylene to join it, while the catalyst allows to reduce the number of
isobutanes required to isolate 1 butylene (beta, 10 to 1; mcm22, 6 in total). To
achieve maximum efﬁciency of the process, a catalyst is required which would
create conditions for forming alkylbenzene from 1 isobutane and 1 butylene. In
that case separation becomes unnecessary, and heat release is greatly reduced. There
are two “process-catalyst” complexes available (catalytic process scheme and rele-
vant catalyst):
1. Standard process—hydrogen ﬂuoride- or sulphuric acid-type catalyst. It’s
zeolite catalyst which is not applied at industrial-grade installations.
a
2. Standard process—Y (faujasite)-type catalyst was developed after ﬂuoride- or
sulphuric acid-type catalysts. Its advantage over the latter is that it is non-toxic,
non-corrosive, and does not generate acidic drainage or acidic gases. Catalysts of
this type, due to their high activity, perform the process at a relatively low
temperature and in a liquid-phase reactor. Currently this complex is believed to
have the highest potential.
4.2.6.2 Alkylation of Benzene
Alkylation with Ethylene
In the process of ethylene’s and benzene’s amalgamation in a reactor, ethylene has a
tendency to amalgamate with itself and with ethylbenzene—which reduces produc-
tivity. This problem is solved by increasing the amount of benzene which envelopes
ethylene and does not allow other ethylene to join it, while the catalyst allows for the
reduction of the number of benzenes required to isolate 1 ethylene (pentasil, 40 to 1;
mcm22, 6 in total). To achieve maximum efﬁciency of the process, a catalyst is
required which would create conditions for forming ethylbenzene from 1 benzene
and 1 ethylene. In that case separation becomes unnecessary, and heat release is
greatly reduced.
Y-type catalyst—due to their high activity—can perform the process at a rela-
tively low temperature and in a liquid-phase reactor. zsm5-type catalyst (pentasil) is
the second generation of catalysts (after Y). Its advantage over the Y type is that the
pore size is the same as benzene (raw material). The zsm5 catalyst can work under
higher temperatures and lower pressure than the Y type (which reduces the amount
of steel, etc. needed to build installations). Since zsm5 type can operate under higher
temperatures, its activity is an order of magnitude higher than that of the Y type, so it
is beginning to supplant the latter.
94
D. Meissner and P. Rudnik
On the other hand, zsm5 catalysts have the lower selectivity than the Y type, and
also they can only continuously operate for half a year and require oxidative
regeneration twice a year—which is hardly proﬁtable since the facility must operate
two reactors and switch production between them—thus increasing energy
consumption.
Zeolite beta-type catalyst belongs to the next (third after zsm5 catalysts’) genera-
tion. Its activity is similar to that of zsm5, but it has higher selectivity than zsm5.
This type of catalyst also can work in a liquid-phase reactor under low
temperatures—which according to the experts is an advantage because you only
need one reactor and do not have to switch production from one to another. Also,
zeolite beta-type catalyst has a longer “mileage” without regeneration than zsm5.
The mcm22-type catalyst is the latest-generation catalyst which can work in
liquid-phase reactor under lower temperatures than zeolite beta type. The mcm22
type also has higher productivity and selectivity.
Alkylation with Isopropylene
In the process of propylene’s and benzene’s amalgamation in the reactor, propylene
has a tendency to amalgamate with itself and with isopropylbenzene which reduces
productivity. This problem is solved by increasing the amount of benzene which
envelopes propylene and does not allow other propylene to join it, while the catalyst
reduces the number of benzenes required to isolate 1 propylene (beta, 10 to 1;
mcm22, 6 in total). To achieve the maximum efﬁciency of the process, a catalyst
is required which would create conditions for forming isopropylbenzene from
1 benzene and 1 propylene. In that case separation becomes unnecessary, and heat
release is greatly reduced. It turns out that the following “process-catalyst”
complexes (catalytic process scheme and relevant catalyst) are most promising:
•
Y (faujasite)-type catalyst is developed after aluminium chloride or phosphoric
acid carrier-type catalysts. Its advantage over the aluminium chloride or phos-
phoric acid carrier-type catalysts is that it is non-toxic, non-corrosive, and does
not generate acidic drainage or acidic gases. Catalysts of this type, due to their
high activity, can perform the process at a relatively low temperature and in a
liquid-phase reactor.
•
Zeolite beta-type catalyst belongs to the third after Y catalysts’ generation. Its
activity is similar to that of the Y type, but it has higher selectivity than the Y type.
Also, zeolite beta-type catalyst has a longer “mileage” without regeneration than
the Y type.
•
mcm22-type catalyst is the latest-generation catalyst which can work in liquid-
phase reactor under lower temperatures than zeolite beta type. The mcm22 type
also has higher productivity and selectivity.
4
Technology Use in Traditional Industries: Catalysts for Petroleum Refining
95
An analysis of traditional oil reﬁning processes’ technological characteristics
revealed the most efﬁcient “process-catalyst” complexes with the best short- and
long-term development prospects. These prospects are connected with the applica-
tion of nanotechnologies for the production of next-generation catalysts.
4.3
Future Outlook for Nanotechnologies in Refining
Technologies
Oil reﬁning catalysts (further on, catalysts) are nanostructured substances which, in
the course of a cycle of intermediate interactions, speed up oil reﬁning chemical
reactions without being spent in the process. The development of nanotechnologies
is thought to provide the main solutions to improve catalysts for cracking heavy
crude oil to reﬁne it into light products, for gasoline fractions reforming, for deep
hydroﬁning of diesel fuel to remove sulphur, to process accompanying oil gases,
etc., is the main way to solve numerous oil reﬁning and petrochemical problems.
4.3.1
Catalytic Cracking
Solid and plausible estimates assume that Russian producers of cracking catalysts by
2020 may control up to 80% of the domestic market and achieve the level of product
quality on a par with world leaders’ products. To accomplish strategic objectives, the
country must by 2030 build a powerful production base capable of meeting a
signiﬁcant proportion of domestic demand. Also Russia should become an interna-
tional
technology
development
hub,
offering
conditions
for
creating
and
disseminating far beyond its borders the most advanced industrial and research
technologies, attracting international intellectual capital and high-tech companies,
and—most importantly—achieving competitive positions to secure advantages in a
speciﬁc segment of the technology chain, to successfully integrate into the interna-
tional division of labour. In the context of the set strategic objectives, four “process-
catalyst” complexes appear as having the highest potential for Russian producers: lift
reactor, double-regeneration, millisecond, and X-design cracking installations.
Each catalyst’s production stage may be implemented using one or more alterna-
tive technologies—each of the six stages of producing beads of microspheric
catalysts can involve various technological solutions (2–3). For example, forming
and drying can be conducted in liquid or gaseous environments while burning and
thermo-steam stabilisation in ﬁre or electric furnaces.
It was established that the lowest catalyst production costs can be achieved
through a particular combination of various bead catalyst production techniques.
Given further technological development, production costs are expected to drop
during the next 10 years.
The lowest production costs for microspheric catalysts also can be achieved
through a particular sequence of production processes. Given further technological
development, production costs are expected to drop during the next 20 years. The
highest quality of catalysts also can be achieved by combining the following
granulated catalysts’ production processes. A high level of quality is expected to
be achieved by 2020.
96
D. Meissner and P. Rudnik
Top quality of microspheric catalysts is achieved by combining production
techniques, e.g. obtaining high-quality raw materials and zeolites; ensuring sufﬁcient
number of ion exchanges; using gas drying in the optimal way; and removing as
much moisture and residual compounds as possible, preferably completely (recrys-
tallization quality). Dynamic development of microspheric catalysts’ production
technology is likely to be completed until 2030. Such parameters as capital intensity,
labour intensity, required skill level of personnel, and energy consumption are
expected to rapidly grow. An additional stage of the technology is expected to
emerge—the treatment or puriﬁcation of raw materials, which would allow the
improvement of the catalyst’s properties such as channel size and conﬁguration,
and the number of active centres. This will lead to increased selectivity and activity
of the catalyst. Relevant technological solutions are expected to be implemented
by 2020.
4.3.2
Isomerisation of Light Gasoline Fractions
By 2020 the top Russian producers of isomerisation catalysts may control 50% of the
domestic market, exceeding world leaders in terms of product quality. Especially
low-temperature isomerisation catalysts are the most efﬁcient which enable the
production of products with 88–89 octane number in a raw material’s single pass
through the reactor. Currently the low-temperature catalyst-based complex is not
present on the Russian market, but such complexes can be implemented quite
quickly—which would enable the use of the catalyst in the most efﬁcient way for
the isomerisation of n-butane, pentane-hexane, or pentane-heptane fractions, at the
lowest production costs. Among new unconventional “process-catalyst” complexes
designed recently is the BIMT process—single-stage catalytic technology which
allows for the production of a wide fraction of light oil hydrocarbons (overpoint to
380 ○С) or gas condensates without preliminary distillation of the fraction into three
commercial products, e.g. liqueﬁed propane-butane fraction, Euro-4 petrol, and
Euro-3 and Euro-4 diesel fuel. Compared with existing classic industrial
technologies for production of these fuel types, BIMT technology allows for the
reduction of capital investment and running costs by 6–8 times and energy consump-
tion by 3–4 times. Another advantage is that it can be applied in a commercially
viable way starting on a very low scale (20–50 thousand tons a year)—i.e. it can be
implemented in hard-to-reach Far North areas.
The catalyst for this technology is currently produced at the rate of 150 tons a year
(and there is potential to increase the output by six times). Currently the catalyst’s
production technology involves six sequential stages. Each stage can be
implemented in several alternative ways, i.e. there are various (5–6) available
technological solutions. The lowest production costs of low-temperature catalysts
can be achieved by combining various production technologies for various stages of
the process. If the technology is improved further, production costs are expected to
drop further. Meanwhile it should be noted that the catalysts’ quality is not affected
by speciﬁc combination of production processes and their application. Any of the
above combinations, and any way of applying them, result in the same quality of
catalyst. For isomerisation of wider gasoline fractions, it would make more sense to
use medium- or high-temperature isomerisation catalysts (at least until 2020).
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Technology Use in Traditional Industries: Catalysts for Petroleum Refining
97
4.3.3
Hydrofining
The top Russian diesel fuel hydroﬁning catalyst producers by 2020 may control 50%
of the domestic market and by 2030 60% and reach the level of product quality on a
par with world leaders’. Taking into account the practical aspects of super-low
sulphur content diesel fuel production, the following conclusions can be made.
1. Changes in quality requirements for diesel fuel aim to reduce sulphur content.
Changing octane number, density, boiling temperature for the 95% fraction, etc.
can be a matter of the not so distant future.
2. The potential for restructuring existing installations for the production of super-
low sulphur content diesel fuel (SLSDF) will largely depend on their design and
operational parameters. Many reﬁneries can discover that the major reconstruc-
tion of their existing single-stage installations would not be the most practical
solution for SLSDF production. For many of them, reconstruction coupled with
the integration of the second production stage, or the construction of a new
installation for the second production stage (additional puriﬁcation), would
make more sense economically.
3. A more demanding operating regime required for SLSDF production can nega-
tively affect output and lead to increased energy consumption. Also, SLSDF-
producing oil reﬁneries will in effect have to produce transport fuel identical to
chemically pure products, so operating the installation is going to be much harder.
All of the above will signiﬁcantly affect oil reﬁneries’ operations and distribution
infrastructure. They will need additional reservoirs, extra facilities for waste dis-
posal, loading/unloading of catalyst, new distribution techniques, etc. Within the
next decade, aluminium oxide in these complexes will be replaced with a new
carrier, e.g. nanostructured titanium dioxide coated with cobalt-molybdenum or
platinum (if under 10 ppm), to reduce sulphur content (puriﬁcation rate of
1–5 ppm) and nitrogen, since platinum hydrates nitrogen compounds quite well.
From an environmental point of view, the nitrogen removal problem is not yet very
relevant, but people are beginning to talk about it in Europe. The new puriﬁcation
process will include ﬁve stages: carrier preparation, impregnating solution prepara-
tion, impregnation, drying, and burning. In turn, more thorough puriﬁcation will
allow for meeting the demand of one or several consumer groups more fully.
Relevant technological solutions may be applied by 2030. New ways of
implementing the technological stages of existing catalyst production processes
are new drying technologies, such as microwave drying which can reduce capital
investments and increase productivity and contact drying, i.e. a more uniform
distribution of active components in granules which improves quality, increases
activity, and reduces energy consumption (by 15–20%).
98
D. Meissner and P. Rudnik
4.3.4
Hydrocracking
Hydrocracking is one of the processes with the highest potential for destructive
processing of heavy distillate and residual materials. In recent years the work on
improving residual hydrocracking technologies has been signiﬁcantly stepped
up. The wide application of hydrocracking is hindered by high capital investments
and high running costs, due to the need to apply the process under high pressure, and
with high hydrogen consumption. Accordingly, light hydrocracking processes
(LHC) are widely applied, which provide a sufﬁciently high output of medium
distillates and a signiﬁcant amount of high-quality FCC materials under moderate
pressure (less than 10 MPa). Modern hydrocracking catalysts allow for applying this
process at ordinary vacuum gas-oil hydroﬁning installations, after some minor
reconstruction.
A major direction of distillate materials’ hydrocracking development is designing
highly efﬁcient, stable, and easily regenerated catalysts. Currently, along with
advanced amorphous catalysts, nanostructured zeolite catalysts are widely applied.
Zeolite-containing catalysts provide the highest output of medium distillates, are
highly ﬂexible, and allow the application of the production process under a softer
regimen. Another important area of modernising the hydrocracking process is
ﬁnding better ways to remove heavy multiring aromatics (HMA, i.e. containing
11 or more rings). Experts on the operation of installations agreed that the problem of
removing emerging HMA cannot be solved exclusively by selecting appropriate
catalysts: it requires targeted reconstruction to remove contaminations emerging in
the course of hydrocracking.
In recent years a targeted modular PNA management process was developed,
speciﬁcally for selective adsorptive removal of HMA from recycling hydrocracking
ﬂow. Currently this system is successfully applied at a number of industrial
installations in various regions of the world. It is different from single-stage hydro-
cracking in that it has an adsorber. Adding an adsorptive HMA removal module as
part of a hydrocracking installation’s reconstruction pays off in less than a year.
Removal of heavy multiring aromatics can also be useful in the case of upgrading
recycling products in adjoined systems, e.g. hydrocracking-FCC, which allows for
increasing the overall output of light products (petrol from catalytic cracking and
diesel fuel from hydrocracking), and reducing microspheric catalyst consumption
during FCC.
Modernisation of internal reactor devices recently became an equally important
process development area, in order to, among other things, increase the output of
valuable liquid products by reducing the formation of gases. Generally, in recent
years, hydrocracking technology became signiﬁcantly different from previous
hydro-destructive process models. Taking into account these changes, and new
opportunities to produce cheap hydrogen, hydrocracking can be deﬁnitely expected
to play a leading role in solving major oil reﬁnery problems during the next 20 years,
in particular production of non-sulphurous de-aromatised and waxy diesel fuels. A
modern modiﬁcation of hydrocracking process is catalytic isocracking, which in
addition to removing hetero-compounds and aromatic hydrocarbons performs selec-
tive hydroisomerisation of n-parafﬁns. Due to stricter requirements for oil products,
ﬁrst of all concerning sulphur content, hydrocracking will be developing towards the
application of more efﬁcient catalysts; the combination with hydroﬁning and
de-metallizing; the removal of HMA; the improvement of hardware solutions; and
the development of more efﬁcient technological schemes.
4
Technology Use in Traditional Industries: Catalysts for Petroleum Refining
99
For the next two decades, the complex based on single-stage scheme and catalysts
based on crystal aluminium silicates (zeolites) containing NiWS-phase sulphide
nanoparticles is seen as a technology with the lowest production costs compared
with other complexes and catalysts in this market segment—since it has a longer
operational life without reloading and allows for the processing of cheaper raw
materials. Two-stage hydrocracking with a catalyst based on crystal aluminium
silicates (zeolites) containing platinum nanoparticles is seen as a priority complex
in terms of application by Russian producers; it offers several advantages, the main
ones being deep processing and the opportunity to meet the demand of one or more
consumer groups as fully as possible. On the other hand, the experts noted high
capital intensity and production costs. If productivity, labour intensity, and demand
for skilled personnel remain unchanged, capital intensity is expected to grow by
2030, while energy consumption should decline.
4.3.5
Alkylation
According to certain forecasts, Russian producers of isobutane-butylene alkylation
catalysts by 2020 may control between 80% and 100% of the domestic market if the
ﬁrst installation is built in Russia, and up to 20% if the ﬁrst installation is built
abroad, and achieve the level of product quality on a par with world leaders. Also,
Russian producers of benzene-ethylene-isopropylene alkylation catalysts by 2020
may control 50% and by 2030 80% of the domestic market and achieve the level of
product quality on a par with world leaders. As of 2020 they are expected to hold and
strengthen these leading positions. During 2015–2020 labour intensity, capital
intensity, energy consumption, and the share of faulty products are all expected to
drop. At the same time demand for highly skilled personnel will grow.
Production technology for zeolite-based catalysts for alkylation of isobutane with
butylenes (alkyl benzene production) and alkylation of benzene with ethylene
(ethylbenzene production) and with propylene (isopropylbenzene production)
allows for production of catalysts with a more ordered crystal structure, which
increases their activity and selectivity, and deﬁnitely has development priority for
the period until 2020, leading to reduced labour intensity and increased productivity.
Also, the most promising combinations of processes for each stage of these
complexes’ production technologies have been identiﬁed in recent years, allowing
for a reduction in production costs compared with other complexes and catalysts in
this market segment. It is expected that in the future new ways to implement these
technological stages will be developed; in particular, raw materials will be treated by
ultrasound crushing (alkylation); solutions will be prepared for activating the mate-
rial by ultrasound or magnetic radiation. Continuous crystallisation and ion
exchange techniques will also be applied, as well as granulation with a reduced
share of binder and contactless drying. By 2020 catalyst production technology may
emerge based on the introduction of nanosized precursor in the soaking system with
the subsequent extraction of precursor from reaction products and its recycling. This
technology is based on a trial way of producing platinum salt and offers a distinct
cost advantage: it will allow to reduce production costs of petrol, etc.
100
D. Meissner and P. Rudnik
4.3.6
Catalytic Reforming
Signiﬁcant progress has been made in recent years in developing catalysts and
technologies for reforming gasoline fractions. In the late 1990s to early 2000s, a
breakthrough in the process’s qualitative characteristics was achieved. The depth of
raw material aromatisation came close to the thermodynamic equilibrium value,
while the length of technological cycle in the RON 96–98 reformate production
mode has reached 2 or more years. This progress was largely achieved by applying
latest-generation catalysts. The main directions of the reforming process develop-
ment include the following:
1. The development of dual-purpose process: producing high-octane petrol compo-
nent and aromatic hydrocarbons, which increases the process’s ﬂexibility.
2. The development of new reforming catalysts. In the case of aromatic
hydrocarbons, it is the catalysts that increase benzene and xylene output. In
case of high-octane petrols, it is the catalysts where high octane number is
achieved by increasing the share of isoparafﬁns and reducing the share of
aromatic hydrocarbons.
3. The reconstruction of operating installations with stationary catalytic layer, by
upgrading the continuous regeneration stage (dual forming, octanising) or by
upgrading equipment.
4. Combining the reforming process with various others (ﬁrst of all isomerisation),
to improve product quality.
Currently there is an emerging trend to modernise the existing catalytic reforming
installations (their combined capacity is about 20 million tons a year) to apply
isomerisation (up to 5 million tons a year) and pentaforming (up to 15 million tons
a year) processes, to minimise the need for new construction. The essence of
pentaforming is, using technological scheme of traditional catalytic reforming
installation as a basis, to radically change the process’s chemistry—to make
isomerisation
and
С5-cyclodehydrogenation
the
main
direction
of
parafﬁn
hydrocarbons’ transformations—which increases the octane number of the
non-aromatic part of petrol reforming up to 70. Calculations show that in this case
the total content of aromatic hydrocarbons can be reduced to 40–45% while retaining
reforming petrol’s octane number at the level of 95 (Pashigreva et al. 2007). The
implementation
of
this
approach
requires
developing
technology
for
low-temperature isomerisation of light gasoline fractions and producing solid
catalysts based on nanostructured zeolite materials. Meanwhile the technology
allowing for meeting the demand of the majority of consumers, which provides
reformate output with octane number of up to 105, is frequently considered
possessing the highest potential for the next 20 years: the process in moving catalytic
layer with zeolite-based platinum-containing catalyst, including with other metal
additives, i.e. bimetallic. During the next decade, new technological solutions are
expected to emerge, in particular for producing highly porous carrier with competi-
tive advantages—wider fraction range of raw material. Production technology for
“platinum on aluminium oxide” catalyst will be supplemented with a way of evenly
applying platinum throughout the zeolite and carrier volume, which would allow for
increasing
reformate’s
octane
number
and
reducing
the
requirements
for
contaminants’ content in raw materials. Relevant technological solutions may be
implemented by 2020.
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Technology Use in Traditional Industries: Catalysts for Petroleum Refining
101
4.4
Measures for Spurring Technology and Economic
Development
Obviously, producing competitive oil reﬁnery products (as opposed to supplying
raw materials) requires the signiﬁcant growth of consumption of advanced high-
quality catalysts—which would be able to produce higher-quality end products. As
shown above, supporting the industry primarily involves promoting demand for
catalysts. It needs to be noted that the catalyst is not an end product—which means
that demand for catalysts depends on demand for oil reﬁning products, which in turn
is determined, among other things, by global and local trends. Therefore, several
demand factors may be named. Firstly, the price includes catalyst prices and
engineering services prices (i.e. application of catalysts and equipment mainte-
nance). That means the actual price of the catalyst is not the crucial consumer
decision factor. Also selectivity, i.e. desired products’ output, is noted among the
most important demand-affecting characteristics. In addition, the stability of
characteristics declared at the application stage is very important: varying quality
of end products is unacceptable. The catalyst’s service life is also important, since
replacing it is a complex and highly expensive procedure.
Environmental characteristics become increasingly important, since a major
factor affecting demand for end products, e.g. petrol, is their compliance with
environmental safety standards. Thus demand drivers can be notionally divided
into market-related and partially market-related ones. We say “notionally” because
even environmental aspects are up to a point market-related: the consumer will not
buy petrol which does not meet environmental standards, though obviously this
factor is primarily ecological and social in nature.
102
D. Meissner and P. Rudnik
For the further development of Russian oil reﬁning industry, it requires the
adoption of legislation introducing stricter requirements for oil products’ quality
and a different taxation policy for oil reﬁneries. Accelerated restructuring of the
industry and promoting development and implementation of competitive Russian
technologies involve the restructuring of the design services market, ﬁrst of all by
creating a public research and engineering oil reﬁning and petrochemical centre. In
order to create an advanced catalyst industry in Russia that is competitive both in
Russia and internationally, a whole set of various steps must be taken, including the
following:
1. The development of advanced domestic technologies
2. Building
advanced
catalyst
factories,
possibly
through
public-private
partnerships
3. Providing support to R&D centres, including guaranteed public funding at the
initial stages of research, development, and experimental application
4. Providing economic incentives for major aspects of the industry’s activities, in
particular:
•
Deep and integrated oil processing
•
Deep processing of natural and associated gases
•
The production of high value-added products, to export products meeting
current international standards and those forecasted for the medium term
•
Steps to promote internal demand for high-quality oil reﬁning and petrochem-
ical products, in particular through accelerated development of Russian auto-
mobile industry and other industries
Creating economic conditions need to motivate the industry to export high value-
added, high-quality oil reﬁning and petrochemical products, instead of crude oil.
Following the introduction of Euro-4 and Euro-5 motor fuel standards, new (higher-
quality) diesel fuel and vacuum gas-oil hydroﬁning catalysts became the most
relevant. Further on, to increase oil processing depth, advanced cracking and
hydrocracking catalysts’ role will also increase.
Naturally, Russia, as a leading oil producer, is a major consumer of oil reﬁning
catalysts. Therefore, all international companies—catalyst producers, both the
leaders and the “rank-and-ﬁle” ones—are quite active in Russia. Foreign companies
see the Russian market as constantly growing, with a high potential. At the same
time, it should be stressed that Russia possesses technologies on a par with foreign
ones or even exceeding them, but their large-scale application requires much more
development effort and major investments. Among other things, Russia has serious
research results in the ﬁeld of new catalysts, not yet mass-produced anywhere in the
world. It is important to realise that Russia has practically all conditions for
implementing the full oil reﬁning cycle: cheap raw materials, technological capacity,
and R&D potential. The only inadequate things are the main development factors:
government support of the industry and investments in production.
Acknowledgements The book chapter was prepared within the framework of the Basic Research
Program at the National Research University Higher School of Economics and supported within the
framework of the subsidy by the Russian Academic Excellence Project ‘5-100’.
4
Technology Use in Traditional Industries: Catalysts for Petroleum Refining
103
References
Borodacheva AV, Levinbuk MI (2008) Oil reﬁning industry’s development trends and speciﬁc
features of Russian oil reﬁning. Russ Chem J 6:37–43
Gerzeliyev IM (2008) Review – cracking catalysts
Kapustin VM (2007) Oil processing technology. Part 2. Destructive processes. Kolos, Moscow
Krylov OV (2004) Heterogeneous catalysis. Academkniga, Moscow
Melik-Akhnazarov NK (1980) Catalytic processes for deep oil processing
Parmon VN (2008) Catalysis and nanotechnology: from basic research to major industry in modern
Russia. In: Catalysis in industry. Moscow, p 6–17
Pashigreva AV, Bukhtiyarova GA, Klimov OV, Noskov AS, Polunkin YM (2007) Deep
hydroﬁning of primary and secondary oil distillates with new-generation catalysts. Oil Reﬁn
Petrochem 10:19–23
Dirk Meissner is Deputy Head of the Laboratory for Eco-
nomics of Innovation at HSE ISSEK and Academic Director
of the Master Program “Governance for STI”. Dr. Meissner
has 20 years experience in research and teaching technology
and innovation management and policy. He has strong back-
ground in policy making and industrial management for STI
with special focus on Foresight and roadmapping, funding of
research
and
priority
setting.
Prior
to
joining
HSE
Dr. Meissner was responsible for technology and innovation
policy at the presidential ofﬁce of the Swiss Science and
Technology Council. Previously he was management con-
sultant for technology and innovation management with
Arthur D. Little. He is member of OECD Working Party on
Technology and Innovation Policy. He is Associate Editor of
Technological
Forecasting
and
Social
Change,
IEEE
Transactions on Engineering Management, Journal of the
Knowledge Economy, member of Editorial Review Board
at Small Business Economics and Journal of Knowledge
Management. He guest edited Special Issues in Industry
and Innovation journal, Journal of Engineering and Technol-
ogy Management, Technological Analysis and Strategic
Management among others.
Pavel Rudnik is Director of the HSE ISSEK Centre for
Strategies and Programs. He is the author of academic papers
on STI development published in leading Russian and inter-
national journals, and co-author of monographs on econom-
ics of innovation and S&T development. In 2012–2015
Mr. Rudnik was Deputy Director of the Innovation Develop-
ment Department at the Ministry of Economic Development
of the Russian Federation. His main areas of responsibility
included methodological and organisational support for tech-
nology platforms and innovative territorial clusters, and
designing
and
implementing
innovation
development
programes of state-owned companies.
104
D. Meissner and P. Rudnik
Part II
Energy and Transport
107
Renewable Energy Technological Potential
Assessment for Evidence-Based
Policy-Making
5
Boris Ermolenko, Georgy Ermolenko, and Liliana Proskuryakova
5.1
Introduction
Global energy markets are constantly on the move. Over the centuries we have seen
various energy sources dominating the world economy. Waves of innovations in
energy technologies led to the paradigm shifts in the industrial and societal develop-
ment. The ever-increasing competition among nations over fossil fuels has con-
stantly led to military and political conﬂicts. While some countries earn national
incomes on extracting and exporting hydrocarbons, others have applied efforts to
secure the uninterrupted import of oil, gas, and coal. The centuries-long dominant
position of fossil fuels as economic drivers has led to the establishment of major
players—multinational companies that would like to preserve their business as long
as possible.
However, over the past decade, the situation has been changing: global
investments in renewables surpassed investments in power generation from tradi-
tional sources (IRENA 2016). Annual investments in Europe alone amount to
$0.9–1.6 trn (REN21 2017). In 2014 new capacities based on renewables accounted
to more than one half of all capacities installed worldwide (OECD/IEA 2015a). One
hundred seventy-six countries have recently announced various targets on advancing
the renewables, and 154 countries provide government support in attaining these
targets (REN21 2017). Among the developed countries, Germany has the highest
share of renewables in the total electricity generation (25.2%). Three sources proved
especially dynamic: wind, solar PV, and hydropower. Contemporary wind power,
hydropower, geothermal power, and biomass power facilities prove to be competi-
B. Ermolenko · G. Ermolenko · L. Proskuryakova (*)
National Research University, Higher School of Economics, Moscow, Russia
e-mail: germolenko@hse.ru; lproskuryakova@hse.ru
# Springer Nature Switzerland AG 2019
D. Meissner et al. (eds.), Emerging Technologies for Economic Development,
Science, Technology and Innovation Studies,
https://doi.org/10.1007/978-3-030-04370-4_5
tive with gas-based generation (IRENA 2016). The share of renewables in today’s
energy consumption is over 19%, including over 10% of the “new” renewables
(REN21 2015). In 2000–2014 the number of renewable power plants in Germany
has grown 50 times and reached 1.5 mln units. Germany follows an ambitious goal
to increase the share of renewables in ﬁnal electricity consumption from today’s 28%
to 80% by the year 2050. Italy intends to level off the shares of natural gas and
renewables in electricity production. Kuwait and Oman plan to increase the share of
renewables to 10% by the year 2020, Saudi Arabia to 50% (without hydropower) by
the year 2040. Through scaling up the renewables, the United States could cut
greenhouse gas emissions from power production by up to 78% below 1990 levels
by 2030 while meeting increased demand (MacDonald et al. 2016).
108
B. Ermolenko et al.
The main beneﬁts of renewables are common for all countries including zero cost
of the energy resources and in some cases lower capital costs for construction of
generating facilities (as compared to those working on the fossil fuel). Due to the
increasing vehicle-to-population ratio, producers of alternative motor fuels (i.e.,
biofuels) and producers of vehicles with new engine types (i.e., hybrid, fuel cells)
will strengthen their market positions.
At the same time, there are additional speciﬁc positive effects of renewables. For
a country like Russia, these involve a growing investment attractiveness of end
users’ energy saving projects, modernization of the energy sector through the
introduction of advanced and energy efﬁcient technologies, and a growing demand
for decentralized efﬁcient energy generation. The import substitution policy in the
energy sector that was launched due to sanctions may open new opportunities to the
producers of new energy products and services (including those related to
renewables), technology transfer from Asia (China, Japan, South Korea), and locali-
zation of the full cycle of renewable energy equipment production.
In order to realize the transition to green growth and mitigate the impact of
climate change, IEA suggests to increasing the investments in renewable-based
power production by 1.5 times. The most important policy measure to stimulate
the development of renewables is the stable demand for the produced energy at a
ﬁxed price, assured by long-term energy supply agreements that allow investors and
producers calculating payback period of capital investments. This sectoral policy
instrument has the largest structural, social, and economic effects. However, a
guaranteed connection to the grid is another important condition.
Scholars often analyze the deployment of renewables in different countries and
use various methods to assess their impacts on the society, environment, and the
economy (Bhattacharya et al. 2016; Hua et al. 2016; OECD/IEA 2015b; Kim et al.
2015; Kumar and Agarwala 2016; Atalay et al. 2016; Mondal et al. 2016). However,
the prospects and cumulative effects of renewable energy technology application in
Russia are little researched as compared to some other countries. Among the few
contributions of Russia are studies on the prospects of bioenergy in Russia (Pristupa
and Mol 2015), application of solar energy solutions and the use of other renewables
in rural areas (Shepovalova 2015), analysis of green energy production in the North-
West Russia for further export to the European Union (Boute and Willems 2012),
and production and consumption of wooden pellets (Proskurina et al. 2015).
5
Renewable Energy Technological Potential Assessment for Evidence-Based. . .
109
Studies of technical potential of renewable energy are usually devoted to certain
aspects, such as effects of renewable energy use in power supply systems and CO2
emission reduction (Kumar and Madlener 2016; Boubaker et al. 2016). There are
also attempts to make comprehensive national assessments of one or more renewable
energy sources (Ghafoor et al. 2016; Abanda 2012; Shepovalova 2015). One of the
most detailed studies was performed by Osmani et al. (2013) who presented the
renewable energy potential for electricity generation of the United States, taking into
consideration multiple criteria (including cost, environmental, and social impacts).
This study analyzes the average annual wind speed for onshore power plants, but the
height over the ground for wind turbines and technical features of wind turbines
under consideration (signiﬁcant factors that impact the output) is not speciﬁed.
Moreover, the authors don’t assess the quantities of saved fossil fuels and avoided
greenhouse gas emissions.
A long-term (30 years) wind observations for three locations in Hong Kong based
on the Weibull density function (WDF) was introduced by Lun and Lam (2000).
Weibull PDF is the most commonly used function to describe wind distribution
(Arslan et al. 2014). Another study by Andresen et al. (2015) presents a detailed
32-year-long hourly model of wind power time series for Denmark for the period of
1980–2035 based on a global renewable energy atlas—RE atlas (the US National
Renewable Energy Laboratory computer program). This study aims at illustrating
how current differences in model wind may result in signiﬁcant differences in
technical and economic model predictions.
Previous studies on solar and wind potential are based on data from the National
Center for Environmental Prediction and the National Center of Atmospheric
Research Re-analysis 1 (NCEP/NCAR) (Gunderson et al. 2015) and International
Energy Agency (Ramli et al. 2017) and local measurement stations (Bilir et al. 2015;
Islam et al. 2011; Azad et al. 2015). Other authors use the NASA Surface Meteorol-
ogy and Solar Energy (SSE) as a source of reliable data for their analysis (Fathoni
et al. 2014). However, most studies provide calculations only for selected locations.
Having reviewed the studies on solar and wind technical potential, we conclude
that most studies don’t provide a detailed methodology for calculating this potential
that makes it impossible to validate and further use the outcomes (Khare et al. 2013;
Aslani and Wong 2014; Ahmad and Tahar 2014; Alemán-Nava et al. 2014).
Moreover, none of the research papers reviewed above attempts to systematically
assess avoided greenhouse gas emissions or fossil fuel savings.
The chapter consists of seven sections. First the methodology section features
terms, methods, and information (data) sources followed by a description of the most
recent renewable policy and economic developments in Russia. Then a methodology
for the assessment of the wind energy and solar PV technical potential and operating
conditions for solar PV- and wind power-based generation is introduced. Thereafter,
the authors provide actual calculations of various types of renewables technical
potential in the Russian Federation. Finally, the conclusions sections discusses
policy and social and research implications of the ﬁndings.
The approach used in the chapter involves several steps and methods. First, we
describe the current position of renewables in the Russian energy sector in terms of
capacities, the place of renewables in the energy mix, and the latest developments in
the sector. Second, we identify social and economic preconditions and effects (gains)
stemming from a faster development and deployment of renewable energy in Russia,
including reliability of power supply and the cost of heat and power in different
regions of the country, energy supply as a contribution to the quality of life, as well
as negative impacts on environment and people’s health and conservation of fossil
fuels. Third, we offer the latest calculations of solar PV and wind exploitable
technical potential for Russia.
110
B. Ermolenko et al.
This is done through assessment of the following potentials:
– Fuel potential—renewables potential in tons of oil equivalent.
– Heat energy potential—amount of heat energy that may be produced through
renewable energy conversion into heat and the subsequent economy of thermal
energy, released by the combustion of organic fuels.
– Electrical energy potential—quantity of electricity in kWh, obtained through the
use renewable energy sources and the subsequent decrease of electricity produced
using traditional energy sources.
– Resource saving potential—volume of saved organic fuels (in physical and
monetary terms), which would otherwise be used in the amount that corresponds
to the energy potential of renewables.
– Environmental potential (impact prevention potential)—volume of avoided pol-
lutant emissions in the atmosphere that are typical for combustion of each of the
organic fuel types necessary to obtain energy that corresponds to the energy
potential of renewables. Assessment of the environmental potential may be done
by type of pollutants (emissions) in physical terms, as well as in tons of СО- and
СО2-equivalents. We also assess the prevented environmental-economic damage
emanating from the local and global air pollution.
The sources of information for assessing the electrical energy, heat energy,
environmental potential, and resource saving potential of various types of
renewables are information on the fuel potential of these sources with disaggregation
by region of the country. The assessment of the potential of various types of
renewables is based on the interrelated indicators:
– Consumption of fuel (in oil equivalent) versus electricity production
– Consumption of fuel (in oil equivalent) versus heat production
– Amount of oil equivalent versus equivalent volumes of natural gas, coal, fuel oil,
and diesel
– Amount of combusted natural gas, coal, fuel oil, and diesel versus amounts of
discharged air pollutants
– Amounts of certain air pollutants discharged versus their cumulative reduced
mass in СО- and СО2-equivalents
– Reduced mass of air pollutants discharged versus magnitude of environmental-
economic damage (losses) due to negative impact on the environment
5
Renewable Energy Technological Potential Assessment for Evidence-Based. . .
111
This approach allows making the up-to-date assessments of the exploitable
technical potential of renewables by type and region of Russia with their fuel,
power, heat, resource saving, and environmental effects for the country and its
85 regions based on the data from Kiseleva et al. (2015). The proposed classiﬁcation
by type of potential is linked with several goals of renewable use, including reducing
the fossil fuel consumption for heat and power generation and the subsequent
reduction of air pollution from fuel combustion. In doing so the approach overcomes
methodological limitations of previous country-level assessments, such as incom-
plete or unreliable data sources and absence of hourly, daily, and monthly
assessments among others. The present assessments were done with the use of the
NASA SSE database, the network of Russian state weather bureau and aerological
stations data, ofﬁcial statistical data (by region). Comparison with previous
calculations of renewable energy potentials in Russia, namely, Bezroukikh et al.
(2007) and Nikolaev (2011), allowed identifying and overcoming some of the
previous methodological limitations and to cross-check and verify the outcomes.
The main outcomes of the study are a further elaborated methodological approach
for calculating the technical potential of renewables compared with previous studies,
as well as latest calculations of this potential for Russia. However, the study doesn’t
review the economic potential and possible variations under different future energy
scenarios, i.e., only one “ideal” development option is considered.
5.2
The Development of Renewables in Russia
Russia has been predominantly dependent on fossil fuels, both for domestic use and
as a source of country’s export revenues (Proskuryakova and Filippov 2015).
Mining and quarrying of fossil fuels represents nearly 37% of the gross value
added in the Russian economy (Russian Federal State Statistics Service 2015).
Advancing renewable energy technologies is a challenge for Russia, oriented toward
the use of traditional sources. Natural gas, oil, and coal constitute over 91% of
country’s total primary energy supply (51.8%, 21.8%, and 17.3%, correspondingly),
with nuclear at 6.2%, small hydro at 1.9%, biofuels and waste at 1%, and geothermal
at 0.1% (OECD/IEA 2013). The country’s difﬁculties associated with relying on the
traditional energy mix include the exhaustion of traditional hydrocarbons, negative
impacts on environment and climate, barriers to international energy technology
cooperation, hardly predictable fossil fuel prices, and increasing competition at
traditional energy markets.
In 2009 renewable-based electricity generation in Russia was found as not more
than 1% of the total electricity production (UNDP 2010). The total capacity of
renewable-based electricity generation facilities and power plants a few years ago
was estimated at 2249 MW, 57% of which was produced at bioenergy-based heat
and power plants, mostly located near wood, pulp, and paper processing plants,
followed by small hydropower plants (37%), geothermal power plants at Kamchatka
region (5%), solar power plants (0.5–0.8%), and wind power plants (0.1%)
(UNESCO 2014).
112
B. Ermolenko et al.
Russia’s abundant resource base of renewable energy sources is acknowledged
by the International Renewable Energy Agency (IRENA), REN21, International
Energy Agency (IEA), and the International Finance Corporation (IFC). Legislation
in force and industry support mechanisms would allow Russia achieving its initial
4.5% target of installed renewable electricity generation by 2020 (IFC 2016). One of
the notable barriers for investments and more-precise monitoring measures in Russia
is the lack or insufﬁciency of statistical data on energy production and use (REN21
2016). Over the last decade, renewable energy development in Russia and world-
wide was spurred by the continuous growth of energy consumption and increasing
competitiveness of renewable energy technologies. Development of renewables in
Russia has sped up after the year 2010 (Government of the Russian Federation
2015). The government has set targets for wind and solar energy at the wholesale
market until 2024 and the respective conditions for the development of all
renewables at retail power market and in the off-grid areas. Legislative and regu-
latory documents already helped launch the solar power projects with 400 MW of
total installed capacity (and prospects for increasing up to 1.5 GW by 2024) and
wind power projects of 780 MW (with prospects to increase this ﬁgure up to 3.6 GW
by 2024) (Russian Ministry of Energy 2015). Moreover, decentralized power gener-
ation from renewables is rapidly growing, primarily among manufacturing and
agricultural enterprises.
The Russian Statistical Service reports that of all renewables (excluding large
hydro) the highest volume of installed capacity belongs to publicly owned solar
power plants (227 MW), which is more than double the capacity of wind power
plants (Table 5.1). Comparable volume of electricity is produced by privately owned
off-grid solar power plants.
The main directions of state policy in the sphere of Renewable Energy Sources
(RES) identify the following targets of renewable energy development in Russia:
•
Increasing power sector energy efﬁciency on the basis of renewable energy
sources, necessary for a reliable, sustainable, and long-term energy supply for
economic development of the Russian Federation
•
Involving innovative science-intensive technologies and equipment in the energy
sector and the development of local production of RES-based high-tech
generating and auxiliary equipment
•
Reduction of greenhouse gas emissions as one of the most important activities
related to the implementation of Russia’s international commitments
The local production development of high-tech generating and auxiliary equip-
ment for renewable energy sector is the goal of government measures to support
renewable energy industry.
Energy policy in Russia has recently introduced several tools to support
renewable-based electricity production:
– 2024 target indicators for the development of renewables by type.
–
–
–
–
–
–
–
–
–
5
Renewable Energy Technological Potential Assessment for Evidence-Based. . .
113
Table 5.1 Technical and economic indicators of public-owned power plants (without nuclear plants) (2014)
Installed
capacity,
kW
No. of
hours of
installed
capacity
use
Electricity produced MW/h
Net output
electricity,
MW/h
Thermal output by power station, including boiler house, total, gigacal
Share of electricity consumed
for own power station
consumption
Total
Including heat
energy
(in cogeneration
mode)
Total
Of which
For
electricity
production,
%
For heat
output,
kWh/gigacal
By turbine
units
By peak
water-
heating
boiler
By
desuperheating
and pressure
reducing units
By boiler
house
Russian
Federation
204,281,376
4058
814,465,278
187,620,864
762,609,972
519,760,953
405,256,474
35,088,879
29,794,880
34,047,328
4.2
34.0
Heat and
power plants,
public-owned
153,228,592
4249
639,022,727
187,620,864
587,945,092
519,760,953
405,256,474
35,088,879
29,794,880
34,047,328
5.2
34.0
Hydropower
plants, public-
owned
49,435,295
3532
172,869,476
x
172,145,538
x
x
x
x
x
0.4
x
Hydropower-
pumped
storage plants
1,215,900
1541
1,873,897
x
1,851,140
x
x
x
x
x
1.2
x
Geothermal
power plants
74,000
6035
446,589
–
416,330
–
6.8
x
Wind
power plants,
public-owned
100,308
1235
92,600
x
91,883
x
x
x
x
x
0.8
x
Solar
power plants,
public-owned
227,281
–
159,989
–
159,989
–
x
Source: Russian Federal State Statistics Service (2016). Approximately 19% of Russia’s electricity is generated by large hydropower plants. However the small
hydropower plants’ installed capacity is small—around 250 MW, slightly above solar plants. Tidal energy is used only at one experimental Kyslogubskaya power
plant (RusHydro 2016). None of publicly owned renewable-based power plants produce heat energy in cogeneration mode
x denotes not applicable; – denotes zero
114
B. Ermolenko et al.
– Target indicators for the degree of localized production of the main and auxiliary
power generation equipment based on renewables.
– Trade of installed capacity of renewable-based power generation facilities at the
wholesale market within the target indicators for renewables (by type) and capital
cost limits for the construction of 1 MW of facility’s installed capacity by type of
renewables.
– Return on invested capital and currency ﬂuctuations in Russia are included in the
marginal capital costs.
– Utilities are obliged to buy electricity (that amount to 5% of power grid losses) at
retail market from renewable-based facilities.
– Up to 50% of the cost for connection to the power grid is compensated to the
renewable-based facilities.
These measures may bear fruit in the mid- to long-term perspective should the
general economic conditions be favorable to the establishment and growth of small
and medium energy companies, international renewable technology transfer, and the
diversiﬁcation of business for large energy companies.
The energy policy priorities toward decentralization of power and heat generation
and diversiﬁcation of energy resources in the supply-demand balance may contribute
to a considerable structural shift not just in the energy sector but in the economy
overall. A consistent policy of decentralized energy generation will allow many
companies, which generate power for own needs, to be more ﬂexible in planning
their operations including the choice of production sites and even to cut costs.
Despite some Russian regions offer better conditions for the deployment of
renewables, it is important to assess their prospects across the country at all accessi-
ble territories.
The deployment of renewables in many of Russia’s regions appears economi-
cally, environmentally, and socially justiﬁable (Ermolenko et al. 2013a, b), primar-
ily, for the following territories:
– Regions with decentralized power supply, which account for approximately 70%
of the country’s territory inhabited by 10–20 mln persons. Power production in
these regions is made predominantly with the use of low-power petrol and diesel
generators, which require expensive organic fuel transported from other regions.
The prime cost and commercial tariffs for electricity in such regions could go as
high as RUB 250 ($4.4) per kWh.
– Regions with centralized power supply and high capacity deﬁcit, extremely high
costs and extensive technical difﬁculties associated with connection of consumers
to power grid and heating system, and substantial ﬁnancial losses due to frequent
disconnection of ﬁnal users from the grid.
– Settlements with problematic environmental situation, including high air pollu-
tion from industrial and urban facilities that use fossil fuels.
– Regions with high renewables potential.
– Regions with obsolete and depreciated traditional generating equipment.
– Cities and places of public entertainment and medical treatment.
5
Renewable Energy Technological Potential Assessment for Evidence-Based. . .
115
– Regions that face difﬁculties in power supply of private houses, private farms,
places of seasonal employment, and private garden plots.
5.3
Renewables Technical Potential Assessment
5.3.1
Wind Energy
In order to deﬁne the wind energy potential for investment purposes, we suggest
introducing a conceptual framework with differentiation by the following features:
– Type of potential (resource)
– Potential estimation method
– Territory for which the potential is assessed
– Validity period of particular wind speeds frequency when estimating potential
– Time period for which the energy potential is assessed
– Method for assessing the wind vector
– Wind speed characteristics, determining range of application of wind turbines
– Speciﬁc indicator type of the wind energy potential
The proposed classiﬁcation by type of potential is linked with renewable energy
development objectives, such as decreasing the consumption of heat and electrical
energy, produced by combustion of fossil fuels, and, consequently, decreasing fossil
fuels consumption and volume of air pollutants.
The available technical wind energy potential of a given territory is understood as
part of long-time average annual total wind ﬂow energy (gross natural wind energy
resource) that can be converted into electrical energy by real wind turbines during
the reference time interval on territories available for installation of wind turbines at
the time of assessment, based on the efﬁciency of its use.
Factors for smart selection of the Russian territories for assessing the available
technical wind energy potential are based on the deployment of wind turbines at
distances ensuring minimal power losses associated with the mutual inﬂuence of
wind turbines and the potential to assuring a minimum negative impact on the
environment associated with the creation of acoustic discomfort zones and with-
drawal of lands from the economic turnover.
The technical potential at selected available lands is deﬁned as the quantity of
power that can be generated by real wind turbines without any additional restrictions.
In this chapter we consider the technical potential in the following territories:
federal districts (districts of the Russian Federation) and national (Russian Federa-
tion). The suggested classiﬁcation of wind energy potential allows us to develop a set
of indicators that may be used for a wide array of regional and local tasks associated
with analysis of wind potential, technical, and economic possibilities of its use,
potential resource (including fossil fuels) saving, prevention of local and global
environment pollution, as well as for the development of regional wind energy
programs and decision-making on the investments into centralized and decentralized
116
B. Ermolenko et al.
energy supply systems partly based on renewables.
Assessment of the wind energy technical potential was made in the frame of the
“Atlas of renewable energy resources in Russia” reference book (Kiseleva et al.
2015) and is substantially different in methodological terms from approaches used
earlier in similar studies, i.e., for the development of a “Guide on renewable energy
resources in Russia and local fuels” (Bezroukikh et al. 2007) and the studies
performed by Nikolaev (2011). The analysis performed in 2015 and presented in
this chapter is much more oriented toward practical projections of renewable (wind
and solar, in particular) power plants both for centralized power supply and
distributed hybrid systems that supply power to small consumers. The methodologi-
cal differences between the present study and previous works on assessing renew-
able potential include:
The use of base information on the wind speed PDF at the 50 m height over the
ground level on coordinate grid with 64,800 1○× 1○cells from the database of the
US National Aeronautics and Space Administration “NASA Space Environments
and Effects Program” (NASA SSE) (NASA 2015), instead of information on wind
speed PDF obtained through statistical processing of the data at the very few and
unevenly distributed through Russia, technically imperfect state meteorological
(10–18 m height) and aerologic (100, 200, 300 m heights, and more) stations.
Given the large territory of Russia, most of which is high-latitude, veriﬁcation of
the data is of particular importance. This was done for locations for which national
meteorological stations data is available. Those national stations that are representa-
tive for typical Russia’s regions with high wind potential were chosen for
performing a correlation analysis. Statistical processing of data showed a high
degree of correlation Kcorr ¼ 0.818 between ground-based measurement data and
NASA SSE data. The methodology was validated also through a comparative
analysis of the data obtained in the course of the study with the data of wind
monitoring campaign carried out by a German engineering company CUBE Engi-
neering GmbH in Krasnodar region for 18 months using 70 m masts with wind
sensors located at three levels in line with international standards. This data veriﬁca-
tion proves the validity of the study.
– Wind energy potential was calculated for a year, each of its months, average daily
value for each month, and every 3 h of a day, while the other sources provide
information only about annual potential.
– Mathematical model was developed and tested for identiﬁcation of parameters for
the Weibull wind speed PDF at any given height over the ground level on the
basis of data on wind speed repeatability at a certain basic distance. This allowed
assessing wind energy potential for various heights for the whole territory of
Russia, resolving the problem of identifying wind speed proﬁle for the full range
of heights of rotor axis of existing wind turbines, and, consequently, addressing
the issue of their optimal choice.
5
Renewable Energy Technological Potential Assessment for Evidence-Based. . .
117
– Potential assessment was made both for each cell of the coordinate grid (map) and
for each region of Russia and the whole country, while in previous calculations
the assessments did not go into territories smaller than the country’s regions.
– For this study the wind potential was estimated with a view of actual deployment
of wind turbines on the whole or available part of any given territory, for its space
unit and one wind facility.
One of the key parameters to evaluate the technical potential of wind energy of a
Russia’s region is the potential availability of the territory, at which the wind
turbines may be located given the technical and environmental limitations. In line
with the best international practice on assessing the lands for wind energy use and
the existing Russian classiﬁcation of land stock, the authors consider only those
lands that are suitable and available for wind energy generation purposes, including
certain categories of agricultural lands and pastures.
5.3.2
Solar Energy
For solar energy we introduce the conceptual framework of energy potential and
resources with differentiation along the following features:
– Type of potential (resource)
– Potential estimation method
– Territory for which the potential is assessed
– Time period for which the energy potential is assessed
– Method for assessing position of the sun for assessing the potential
– Availability of unavailability the system to track the position of the sun
– Speciﬁc indicator type of the solar energy potential
Except features 5 and 6, we use the same features in the wind energy potential
assessment.
Total solar radiation directed toward the solar receiver shows highest efﬁciency.
However, as solar receivers are located at a certain angle to the earth’s surface and
cannot be placed immediately adjacent without shading one another, we used annual
sums of total solar radiation on horizontal surface to calculate the technical potential.
The source of data for calculating these ﬁgures, as well as yearly solar radiation, is
the NASA SSE database (NASA 2015).
Analysis of NASA data reliability on solar radiation monthly amounts was
carried out with the use of actinometrical data of Moscow State University Meteo-
rological Observatory (Kiseleva et al. 2015) for the two land observation periods:
1961–1990 and 1983–1993. No major discrepancies in the assessments of monthly
solar radiation distribution by NASA and Moscow State University were detected.
For the majority of points, the difference does not exceed 5% that is considered
satisfactorily for PV plants technical calculations. Differences exceed 10% at only a
few points: Yuzhno-Sakhalinsk (46.9○N.L.), Yeniseisk (58.5○N.L.), Vanavary
(60.3○N.L.), Tura (64.3○N.L.), and Turuhanskaya (65.8○N.L.). Most likely, these
signiﬁcant deviations in solar radiation data are the result of random error or of
inaccurate accounting in mathematical models of NASA local climate features in
these regions.
118
B. Ermolenko et al.
In calculating the solar radiation technical potential (besides the amounts of total
solar radiation), one should take into consideration factors that limit efﬁcient con-
version of solar energy into power and heat:
1. Territory coefﬁcient, which reﬂects the share (size) of each category (and subcat-
egory) of land that may be used for deployment of solar power and heat facilities
2. Technical characteristics of solar energy converters reﬂected by their efﬁciency
coefﬁcient: efﬁciency factor for conversion of solar energy into electricity
5.4
Operating Conditions for Solar PV and Wind-Based Power
Generation
The main difﬁculty associated with operation of solar PV and wind power plants is
that their performance capacity fully depends on the meteorological conditions.
Therefore, the ability to forecast the distribution of wind and solar radiation in
time is of special interest for any system (grid) operator. Daily wind power output
curves for a typical month in certain regions by 3 MW wind turbines at the 100 m
height (typical height of MW-sized power plants, connected to the grid) are shown in
Fig. 5.1. The curves have some similarities, albeit there are considerable shifts by
time of the day and values depending on the time of the year and region.
Republic of Altay
Volgograd region
Murmansk region
Sakhalin region
Chukotka Autonomous
Region
January
February
March
April
May
June
July
August
September
October
November
December
65,00
60,00
55,00
50,00
45,00
40,00
35,00
30,00
25,00
20,00
15,00
capacity factor
Fig. 5.1 Monthly power output curve (unit capacity factor) of a 3 MW wind facility at the 100 m
height in some Russia’s regions (Source: HSE)
For instance, in Murmansk and Sakhalin regions during winter months, we see
sustainable energy production, while in all regions, the lowest output is observed
during summer months with a highly visible minimum during morning hours.
Besides daily ﬂuctuations of power output, monthly uneven distribution of wind
energy should also be taken into account (Fig. 5.1). The latter is as high as 50% in
some regions of the country.
5
Renewable Energy Technological Potential Assessment for Evidence-Based. . .
119
In spring solar power plants in all Russia’s regions can operate for at least 9 h/day,
and in 38 regions they can operate for between 12 and 15 h/day. In summer in 48 out
of 85 Russia’s regions, solar power plants can work 12–13 h/day and in 58 regions
over 15 h/day. In autumn, solar power plants in 7 of Russia’s regions cannot work at
all, and in 20 regions they can work for no more than 3–4 h/day, in 32 regions 6 h/
day, and in 24 regions 9 h/day.
Analysis of hourly changes in solar radiation on the ground surface shows that in
winter in 48 of Russia’s regions, solar power plants cannot work at all, in 36 regions
they can work for no more than 3 h/day, and in 5 regions they can work for around
6 h/day.
The solar energy technical potential, which may be used both for conversion into
electrical and heat energy, is substantial in the Russian Federation. The solar energy
potential is highest in the South-West (Northern Caucasus, Black and Caspian Sea
regions), Southern Siberia, and the Far East of Russia. Good prospects for solar
energy use were found in the following regions: Kalmykia, Stavropol region, Rostov
region, Krasnodar region, Volgograd region, Astrakhan region, and other regions in
the South-West and Altai, Primorsky region, Chita region, Buryatia Republic, and
other regions in South-East. Unexpectedly certain northern regions have higher
technical potential than those situated in the South. This is linked to limited farmland
territories and reserve lands in the northern regions, as well as with more territories
theoretically available for solar receivers. Therefore, the use of solar energy for
electricity and power generation by energy companies depends on the technical-
economic justiﬁcation for each power plant.
Among the areas with the highest wind energy technical potential at a given
territory are Krasnoyarsk region, Republic of Sakha, Yamalo-Nenets, Chukotka,
Nenets, Krasnodar, Altai, Saratov, Rostov, Volgograd, and Orenburg regions.
Meanwhile, Kamchatka, Khabarovsk, Sakhalin, and Magadan regions (despite
seemingly obvious potential) have minimal values due to lack of required categories
of land. In other words, although the technical potential of wind is high in the Far
East and some other Russia’s regions, the land available for deployment of wind
turbines is very limited. The efﬁciency of wind turbine operations also differs
substantially depending on height: at 30 m wind activity drops so that wind turbines
can utilize only 5–10% of their capacity; for higher capacity we recommend
elevating the turbines to 100–140 m.
Maximum cumulative biomass energy potential was found in Irkutsk region,
Krasnoyarsk region (South), Vologda region, Arkhangelsk region, Kirov region,
Perm region, Leningrad region, Komi Republic, and Sverdlovsk region, while
minimum potentials were found in Sakha (Yakutia) Republic (North), Nenets
autonomous region, Chukotka autonomous region, Magadan region, and Murmansk
region. Maximum total energy hydropower potential of small rivers was found at
Siberian, Far Eastern, North-Western, Southern, Ural, and North Caucasus federal
districts.
120
B. Ermolenko et al.
Active thermal waters are found in Buryatia Republic, Chukotka, Yakutia and
Western Siberia, Krasnodar region, and Northern Caucasus.
Tables 5.2 and 5.3 feature the outcomes of the assessment of oil fuel, coal, and
natural gas savings with the replacement of fossil fuels with various types of
renewables, prevention of air pollution calculated as СО- and СО2-equivalents.
The assessment of emissions and greenhouse gas prevention in tons of СО- and
СО2-equivalents through the use of technical potential of renewables instead of heat
and power plants and facilities based on fossil fuel combustion revealed that
replacement of oil products allows preventing the emission of 122,194 mln tons of
СО-equivalent per year and 293,286 mln tons of СО2-equivalent per year: coal,
3,836,579 mln tons of СО-equivalent per year and 523,189 mln tones of СО2-
equivalent per year, and natural gas, 24,125 mln tons of СО-equivalent per year and
231,961 mln tons of СО2-equivalent per year.
Tables 5.4 and 5.5 demonstrate the regional differences of wind energy: the oil,
coal, and natural gas savings if fossil fuels are replaced by wind energy technical
potential and emissions of air pollutants calculated as СО- and СО2-equivalents are
prevented.
Based on the analysis of commercially available wind turbines and information
about wind farm projects (those that are realized, under construction, and planned),
the wind turbine model ENERCON E-101 3050 kW has been selected to assess
technical potential.
Tables 5.6 and 5.7 demonstrate the regional divergences of solar PV: the assessed
savings of oil, coal, and natural gas if fossil fuels are replaced with the technical
potential of solar energy and emissions of air pollutants calculated as СО- and СО2-
equivalents are averted.
5.5
Conclusions
The chapter offers an overview of the major existing and future renewable energy
potentials in Russia that may be explored for achieving the multi-sectoral gains. A
methodology for physical and cost assessment of energy, fuel, resource, and envi-
ronmental potential of various types of renewable energy sources, as well as a
comprehensive multilevel system of wind and solar energy potential assessment, is
suggested. High social and economic as well as environmental potentials of all major
renewable energy sources in Russia are substantiated. This means that transition
from traditional heat and power generation to renewable low-carbon energy
resources will signiﬁcantly reduce the rate of fossil fuel depletion and energy-
related greenhouse gas emissions, as well as local and global negative environmental
impact. The switch to renewables will also make it possible to preserve raw materials
for manufacturing and for future generations.
5
Renewable Energy Technological Potential Assessment for Evidence-Based. . .
121
Table 5.2 Fuel and power: oil fuel potential of renewables
Technical potential
Type of renewables
Fuel and power potential
Oil fuel potential
Power
Heat
Fuel
Resource saving
Environmental
mln kWh/year
mln gcal/year
mln toe/year
mln tons/year
СО-equiv., mln tons/g
СО2-equiv., mln tons/g
Solar energy
87,747,704.0
202,293.0
30,060.7
21,942.1
27,425.7
65,826.4
Wind energy
17,100,856.7
39,645.0
5891.2
4300.2
5374.8
12,900.5
Biomass energy
2,896,910.9
5783.6
859.4
627.3
784.1
1882.0
Geothermal energy
246,592,891.6
571,677.3
84,951.3
62,008.2
77,504.7
186,024.6
Low-grade heat
34,753,230.8
80,568.6
11,972.5
8739.0
10,923.0
26,217.1
Small hydro
584,534.9
1355.1
198.7
145.1
181.3
435.2
Source: HSE
122
B. Ermolenko et al.
Table 5.3 Coal and gas potential of renewables
Technical potential
Type of
renewables
Coal potential
Gas potential
Resource
saving
Environmental
Resource
saving
Environmental
mln tons/year
СО-equiv., mln tons/g
СО2-equiv., mln
tons/g
bln. m3/year
СО-equiv., mln tons/g
СО2-equiv., mln
tons/g
Solar energy
39,141.6
861,099.3
117,424.8
25,969.0
5401.8
51,938.0
Wind energy
7670.9
168,756.6
23,012.7
5105.1
1061.9
10,210.1
Biomass energy
1119.1
24,619.1
3357.2
744.8
154.9
1489.5
Geothermal
energy
110,613.6
2433,455.1
331,840.8
73,614.6
15,312.5
147,229.2
Low-grade heat
15,589.2
342,955.7
46,767.5
10,374.8
2158.0
20,749.5
Small hydro
258.8
5693.0
776.3
172.2
35.8
344.4
Source: HSE
Table 5.4 Technical potential of wind energy at 100 m height
Russia and
federal districts
(FD)
Fuel and energy potential
Oil fuel potential
Electric
energy
Heat
energy
Fuel
5
Renewable Energy Technological Potential Assessment for Evidence-Based. . .
123
Resource
saving
Environmental
mln
kWh/year
mln
Gcal/
year
mln
toe/year
mln tons/
year
СО-equiv,
mln tons/
year
СО2-
equiv., mln
tons/year
Russia
17,100,857
39,645
5891
4300
5375
12,900
Central FD
1,491,619
3458.0
514
375
469
1125
North-West FD
1,481,854
3435.4
511
373
466
1118
South FD
1,980,809
4592
682
498
623
1494
Privolzhsky
FD
2,664,236
6177
918
670
837
2010
Ural FD
2,098,280
4865
723
528
660
1583
North
Caucasus FD
847,660
1965
292
213
266
640
Far East FD
2,708,869
6280
933
681
851
2044
Siberian FD
3,776,910
8756
1301
950
1187
2849
Source: HSE
Table 5.5 Technical potential of wind energy at 100 m height
Russia and
federal
districts (FD)
Coal potential
Natural gas potential
Resource
saving
Environmental
Resource
saving
Environmental
mln tons/
year
СО-equiv.,
mln tons/
year
СО2-
equiv.,
mln
tons/
year
bln m3/
year
СО-equiv.,
mln tons/
year
СО2-
equiv.,
mln
tons/
year
Russia
7671
168,757
23,013
5105
1062
10,210
Central FD
669
14,720
2007
445
93
891
North-West
FD
665
14,624
1994
442
92
885
South FD
889
19,547
2666
591
123
1183
Privolzhsky
FD
1195
26,292
3585
795
165
1591
Ural FD
941
20,707
2824
626
130
1253
North
Caucasus FD
380
8365
1141
253
53
506
Far East FD
1215
26,732
3645
809
168
1617
Siberian FD
1694
37,272
5083
1128
235
2255
Source: HSE
124
B. Ermolenko et al.
Table 5.6 Technical potential of solar energy
Russia and federal districts
(FD)
Fuel and energy potential
Fuel oil
Electrical
energy
Heat energy
Fuel
Resources
saving
Environmental
Mln kWh/year
mln Gcal/
year
mln
toe/year
mln tons/year
СО-equiv., mln tons/
year
СО2-equiv., mln tons/
year
Russia
87,747,704
202,293
30,060.7
21,942.1
27,425.7
65,826.4
Central FD
4,744,733.0
10,999.7
1634.6
1193.1
1491.3
3579.3
North-West FD
13,037,743.3
30,225.5
4491.5
3278.5
4097.8
9835.4
South FD
3,826,115.8
7737.2
1149.8
839.2
1049.0
2517.7
Privolzhsky FD
5,503,378.9
12,758.5
1895.9
1383.9
1729.7
4151.6
Ural FD
16,278,705.4
37,739.0
5608.0
4093.4
5116.4
12,280.3
North Caucasus FD
1,495,762.2
3467.6
515.3
376.1
470.1
1128.4
Far East FD
23,327,681.5
54,080.7
8036.4
5866.0
7331.9
17,597.9
Siberian FD
18,736,228.6
43,436.3
6454.6
4711.4
5888.8
14,134.2
Source: HSE
5
Renewable Energy Technological Potential Assessment for Evidence-Based. . .
125
Table 5.7 Technical potential of solar energy
Russia and federal
districts (FD)
Coal potential
Natural gas potential
Resource
saving
Environmental
Resource
saving
Environmental
mln tons/
year
СО-equiv., mln
tons/year
СО2-equiv., mln
tons/year
bln m3/year
СО-equiv., mln
tons/year
СО2-equiv., mln
tons/year
Russia
39,141.6
861,099.3
117,424.8
25,969
5401.8
51,938
Central FD
2128.3
46,822.6
6385.0
1412.1
293.7
2824.1
North-West FD
5848.3
128,660.7
17,545.0
3880.1
807.1
7760.3
South FD
1497.1
32,935.1
4491.2
993.3
206.6
1986.5
Privolzhsky FD
2468.6
54,309.1
7405.9
1637.9
340.7
3275.7
Ural FD
7302.1
160,643.5
21,906.3
4844.7
1007.7
9689.4
North Caucasus FD
671.0
14,760.7
2012.9
445.2
92.6
890.3
Far East FD
10,464.1
230,205.1
31,392.2
6942.5
1444.1
13,885.0
Siberian FD
8404.5
184,895.2
25,213.4
5576.1
1159.9
11,152.1
Source: HSE
126
B. Ermolenko et al.
Moreover, the assessment of wind (at 50 and 100 m) and solar energy technical
potential in all regions of Russia undertaken with the proposed methodology showed
that these clean energy resources may fully substitute fossil fuels for energy genera-
tion. Wind and solar energy potential surpass the natural gas-based (currently the
main energy source in Russia) power generation several times to order of magnitude
(depending on the region of Russia). The best-suited regions for deployment of
renewables are identiﬁed, and recommendation to overcome the existing challenges
and for effective development of renewable energy in Russia may be of interest to
decision-makers, investors, and companies that work in renewables.
Future implications for research may include the development of mathematical
models, information and software support to assess the public and private
investments in renewables, and design in individual and hybrid power systems
using different types of renewables. The successful development and deployment
of solar and wind power capacities in Russia reply on application of innovative
technologies, energy storage systems, and smart grind elements, and these
interconnections may further be addressed.
The study’s social implications are presented in the chapter devoted to effects
(gains) from renewable energy capacity deployment and include better quality of
life, reduction of emissions and corresponding improvement in health conditions of
population, and reduction of electricity and heat costs for population.
The study outcomes can be used by institutional, private, and foreign investors for
wind and solar power plant investment designing in centralized and distributed
power systems.
Acknowledgments The book chapter combines two earlier published papers on methodology of
renewable potential assessment and the ﬁrst calculations of this potential for Russia:
Ermolenko B.V., Ermolenko G.V., Fetisova Yu., Proskuryakova L.N. (2017) Wind and solar
PV technical potentials: measurement methodology and assessments for Russia. Energy, Vol.
137, pp 1001–1012.
Ermolenko G., Proskuryakova L., Ermolenko B. (2017) Switching to renewables: what will
Russia gain? Foresight, Vol. 19, No. 5, pp. 528–540.
Contributions in this publication were supported within the framework of the Basic Research
Program at the National Research University HSE and were funded within the framework of the
subsidy by the Russian Academic Excellence Project ‘5-100’.
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5
Renewable Energy Technological Potential Assessment for Evidence-Based. . .
129
Boris
Ermolenko is
an
Assistant
Professor
at
D.
Mendeleev University of Сhemical Technology of Russia,
Russian Federation. Dr. Ermolenko specializes in renewable
energy and energy efﬁciency studies: resources, economics,
policy, technologies and innovation. He authored over
50 research publications and took part in multiple national
and international research projects and conferences on these
topics.
Georgy Ermolenko is Head of Center for Renewable
Energy
at
HSE
Institute
of
Energy.
Dr.
Ermolenko
specializes in renewable energy studies: resources, policy,
technologies and innovation. He authored over 30 research
publications and took part in over 15 national and interna-
tional research projects on these topics. Dr. Ermolenko is
Member of the Expert Council of the State Duma of the
Russian Federation Committee on Energy; Deputy Chairman
of the UNECE Expert Group on Renewable Energy.
Liliana Proskuryakova is Leading Researcher at HSE
ISSEK S&T Laboratory. Dr. Proskuryakova specializes in
energy studies, S&T policy and international cooperation in
research and innovation. She authored over 40 research
publications and took part in many national and international
research
projects
on
these
topics.
In
2003–2011
Dr. Proskuryakova served as member of the Board of Directors
and adviser of the Partnership for Transparency Fund. She is
member of the Advisory Board member of the “Lake Baikal”
Foundation for the support of environmental research and
development. Dr. Proskuryakova holds an MA degree in Inter-
national Relations and a PhD in Political Science.
131
Global Market Creation for Fuel Cell Electric
Vehicles
6
Alexander Sokolov, Ozcan Saritas, and Dirk Meissner
6.1
Introduction
Fuel cell electric vehicles (FCEVs) have been considered as a future vision for the
automotive industry to replace the vehicles with petrol engines. Manufacturers in the
industry have been working on concepts and prototypes for more than a decade now
while having advances in addressing the main technical challenges like battery life.
In parallel with the technological development, recent discussions about global
warming and climate change caused by carbon dioxide emissions brought public
support for emission-free vehicles, which are perceived as an asset. Despite of
advancements and public support, the introduction of FCEVs is still not at the
desirable levels. Car manufacturers frequently announce the near-time launch of
FCEVs, which are postponed with the same frequency.
Numerous business models and market projections have been made for the
launch of FCEVs resulting in promising roadmaps. But it is obvious that these
roadmaps reﬂected the technology side quite realistically while lacking a fully-
ﬂedged consideration of the market side. The reason for this may be the fact that
some of the actual and potential stakeholders are overlooked. Commonly market
roadmaps make assumptions of customers’ behaviour, take into account competing
products, etc. but neglect the more or less “hidden” stakeholders, which become
The chapter builds on a previously published article “Saritas, O., Meissner, D. & Sokolov, A. A
Transition Management Roadmap for Fuel Cell Electric Vehicles (FCEVs). J Knowl Econ (2018).
https://doi.org/10.1007/s13132-018-0523-3.”
A. Sokolov · O. Saritas · D. Meissner (*)
National Research University, Higher School of Economics, Moscow, Russia
e-mail: sokolov@hse.ru; osaritas@hse.ru; dmeissner@hse.ru
# Springer Nature Switzerland AG 2019
D. Meissner et al. (eds.), Emerging Technologies for Economic Development,
Science, Technology and Innovation Studies,
https://doi.org/10.1007/978-3-030-04370-4_6
obvious if one analyses the systemic impact of FCEVs as innovations within
industry value chains in relation to the transportation sector in a broader sense.
132
A. Sokolov et al.
The present chapter aims to make an attempt of a systemic analysis for a broader
and more holistic analysis, which may portray the bigger picture and help to
understand the industrial dynamics better. The key argument is that the stakeholder
base in the transportation industry and thus for FCEVs is broader than usually
thought, and there are a number of other issues to be addressed for a successful
and widespread launch of FCEVs. The roadmap presented in the study, entitled
“FCEV Global Market Creation”, undertakes a broader analysis of society, technol-
ogy, economy, environment and politics (STEEP) at two stages. First the STEEP
systems are discussed, and the stakeholders for each item are analysed. From this
analysis, some potential barriers for the diffusion of FCEVs have become clearer. It
is suggested that a more in-depth analysis in a future study will yield more insights
for the implementation of FCEVs.
Thus, the second section of the chapter provides a background for the study and
clariﬁes the objectives of a broader stakeholder and issue analysis for the FCEVs.
Approach and methodology used for analysis will be described in the third section.
Following a systematic and holistic analysis of external drivers and stakeholders will
be presented in the fourth section to uncover broader set of issues affecting the
FCEVs. The ﬁnal section will present a roadmap template to bring together demand
and supply dynamics and strategies to be adopted for successful FCEV
implementation.
6.2
Background and Objectives
Climate change, increasing pressures on natural resources and environmental
hazards are among the indications that humanity is reaching to the end of the reliance
on non-renewable sources of energy like oil, coal and natural gas. The scramble is
now on to ﬁnd renewable energy sources that will keep cars, homes and businesses
running without destroying the environment. Among the alternatives for future
energy sources, hydrogen comes as one of the ﬁrst options. It is an inﬁnitely
renewable and relatively pollution-free fuel that scientists, policy-makers and society
alike see as a viable alternative to fossil fuels.
In the automotive industry, hydrogen is a promising alternative for combustion
engines. A hydrogen fuel cell in a car produces zero emissions with only water
vapour and heat released through the tailpipe. Hydrogen is three times more efﬁcient
and provided that renewable sources are used for generation such as water and
energy, and it does not release any pollution. Hydrogen can be used to power
vehicles as well as to be used in fuel cells to generate and store energy. Both of
these technologies can be used separately or on the same platform, where a car can
be powered by a hydrogen combustion engine and the fuel cell as a source of energy
to supply electric power in place of a conventional alternator.
6
Global Market Creation for Fuel Cell Electric Vehicles
133
On the technical side, the main issues are related to the engine technology and
then related to recovery, storage and transfer of hydrogen into the car itself. How-
ever, there is a broader range of social, technological, economic, environmental and
political (STEEP) issues, which have positive and negative implications for the
widespread implementation of hydrogen in the automotive industry. For instance,
whether hydrogen and fuel cell technologies can be clean and efﬁcient is very much
dependent to how hydrogen is produced. Generation of hydrogen from oil and
natural gas is much cheaper but still puts pressures on natural resources. There are
also struggles with storage and transportation under high pressure, which makes it
bulky and impractical. As hydrogen has no smell, sensors must be used to detect
leaks. In addition, a number of refuelling stations are required for hydrogen-powered
cars. These are among the main barriers to the commercial development of hydrogen
fuel cells.
The next few years appear to be critical for the commercialisation of FCEVs.
There are some promising trends. For instance, total worldwide fuel cell shipments
grew 20% between 2014 and 2015 and 200% between 2011 and 2015
(US Department of Energy 2015a, b). Adequate refuelling infrastructure will be
required for FCEVs to be marketed as a credible and attractive alternative to
conventional vehicles. Once hydrogen refuelling stations (HRSs) are available, the
initial uptake of the FCEVs will be limited by the cost of buying and using the
vehicles. The UK H2 Mobility report (2013) predicts that sufﬁcient early adopters
should generate sales of approximately 10,000 vehicles per annum by 2020. As the
vehicle costs become more competitive and refuelling network develops, FCEV
uptake increases rapidly. The same report distinguishes three phases for
implementing a viable business case for FCEVs:
1. Market seeding (2015–2020) with high capital costs and low number of
stations—small at the beginning but expandable in the future. Revenues are
also low because of low number of FCEVs on roads. Stations are planned to
the nearest places, where a critical mass of consumers exists—such as close to the
feet bases.
2. Investing in growth (2020–2025). The number of FCEVs will grow. New stations
will be built, and the existing ones will be enlarged and upgraded. Revenues will
increase gradually, and HRSs will become more attractive.
3. Developed network and market (2025–2030). Demand is high enough to grow
revenues, and operations become proﬁtable. A full network will be built by this
time. Further HRSs will be built according to market demand.
No quick solutions are expected in the immediate future. At present, the total
number of FCEVs has been estimated to increase by more than a factor of 10 from
2015 to 2020 and by a factor of 100 by 2030 (US Department of Energy 2015a, b).
This is mainly due to a limited number of models on the market, limited infrastruc-
ture and higher costs compared to battery electric vehicles (BEVs) and plug-in
hybrid electric vehicles (PHEVs) (Global EV Outlook 2013). First commercial
introduction of FCEVs was in 2015, and more widespread HRSs are expected by
2020s. HRSs come to break even in the late 2020s. By the 2030s, 20% of vehicles
are expected to be FCEVs and by the 2050s 50%. However, successful
demonstrations in Germany, Scandinavia, Japan and California indicate that there
are great potentials for the deployment and use of FCEVs. Further countries are
expected to make signiﬁcant efforts to exploit the potentials and avoid risks
associated to the STEEP dimensions of the FCEVs. The countries which will prosper
will be the ones, which do not only develop technologies but also implement them,
develop necessary infrastructure, regulations and standards, as well as market and
promote them to a range of consumers for a widespread use. As all these require
skills and capabilities beyond the technical ones, the present study adopts an eagle
eye view with a thorough analysis of overall context and framework conditions as
well as future technologies, which may shape the future of mobility and transport
and thus overhaul the automotive sector. The methodology used for the study is
described in the next section.
134
A. Sokolov et al.
6.3
Approach and Methodology
The process begins with a wider scanning and surveying phase (Miles et al. 2016),
where the implications of key trends and drivers of change are examined with
implications for the future FCEV markets. A framework based on the analysis of
social, technological, economic, environmental and political (STEEP) systems was
used to ensure that the topic is explored from multiple different but also
interconnected lenses (Saritas 2013). The analysis was conducted at two steps.
A two-step STEEP analysis was used in the study process:
1. To identify “external drivers” through the analysis of social, technological,
economic, environmental and political determinants of FCEVs
2. Stakeholder analysis and mapping to identify key players in each STEEP
category
The external drivers (STEEP) are described in brief with emerging key issues
under each of them. Following a basic outline of these sub-items, Wild Cards are
suggested for each category to describe those events and developments with a small
likelihood of occurrence but with big potential impacts on FCEVs. Three or four
exemplary Wild Cards are presented under each category to provoke positive or
negative disruptive thinking in the FCEV domain.
The main stakeholders are then described with an analysis of their potential
inﬂuence on the diffusion and absorption of the FCEVs and a discussion on potential
argumentation lines against FCEVs. At the current stage, this analysis is not exhaus-
tive, and the stakeholders are listed in general terms with no direct relevance to a
speciﬁc country or market.
The chapter concludes with a template of a roadmap to be developed for FCEVs
in the second phase of the project. The roadmap template can be used for the purpose
of developing multilevel and multidimensional strategic roadmaps, as exempliﬁed
by Saritas and Aylen (2010), for different countries or businesses operating in the
FCEV domain.
6
Global Market Creation for Fuel Cell Electric Vehicles
135
6.4
Characteristics of External Drivers
6.4.1
Social Factors
Social factors involve ways of life (e.g., attitudes to work time, use of leisure time,
family living patterns, work-life balance), demographic structures, social inclusion
and cohesion issues (fragmentation of lifestyles, levels of (in)equality, educational
trends). Public attitude towards hydrogen is considered to be one of the key factors
towards the transition to FCEVs. The social perception of FCEVs can be assessed in
terms of:
•
Availability of a critical mass of customers
•
Accessibility to infrastructure
•
Aesthetics and convenience for making the vehicles attractive for the customers
without compromising from quality
Availability
Building a critical mass of FCEV customers is a must for making the technology
economically viable. For customers, initial purchase cost is one of the determinants
of the selection of a vehicle. Without intervention or dedicated mechanisms, in the
early years after market introduction, FCEVs will be signiﬁcantly more expensive
than the conventional vehicles, and refuelling will be limited with a low number of
locations. These seem to be main barriers by the consumers. However, there are
various ways of overcoming these initial barriers, which will be discussed in the
Technology and Economy sections below (Sects. 6.4.2 and 6.4.3).
Accessibility
The accessibility to infrastructure is obviously raised as one of the key points for the
widespread use of FCEVs. Gas stations need to invest in the ability to refuel
hydrogen tanks before FCEVs become practical. Consumers require both local
availability and national coverage for long-distance travels. Detailed spatial
modelling studies can be done to identify those locations to deliver the greatest
consumer beneﬁt. The low number of customers will make it less likely for such
investments at the early phase; however, after providing sufﬁcient initial coverage,
then the network could be developed in line with the demand by the vehicle owners/
users. Accessibility should also be considered in terms of availability of the mainte-
nance infrastructure and associated repair frequencies and costs.
Aesthetics and Convenience
Customers increasingly value new technologies, environmental friendliness and
costs of a new car. Performance and driving behaviour of vehicles are also consid-
ered to be important when purchasing decisions are made. The dependency between
total costs and the availability of hydrogen refuelling stations (HRSs) are the key
determinants of buying a FCEV. A recent survey indicated that some 10% of new
vehicle buyers showed themselves to be potential early adopters of FCEVs, being
receptive to new technology and environmentally motivated (UK H2 Mobility
2013). The expectation is that the cars may cost higher to purchase at the beginning.
However, when lifetime costs are considered, it can be said that the operating costs
will be considerably lower compared to existing internal combustion engines.
136
A. Sokolov et al.
Various advantages of using FCEVs can be emphasised in the process of market
creation, such as:
•
Longer driving range as the electricity is generated in the car.
•
Hydrogen charging time is usually quick.
•
Like other electrical engines, there is no engine noise in the car—though, special
effects can be created for motor fans.
•
No carbon dioxide is pumped into the atmosphere, no noise is made, and the
engine delivers better torque than most petrol cars, which translates to greater
acceleration.
Considering all these factors, consumers appear to be receptive to FCEVs,
especially in terms of vehicle performance and refuelling time. In the ﬁrst 5–8
years of deployment, plug-in hybrid vehicles might be preferred given the fact that
it is possible to use the existing petrol, diesel and electricity networks. However, as
the HRSs expand, the advantage of longer driving range and quick refuelling time
will be more attractive for car owners/users.
The UK H2 Mobility (2013) report identiﬁed seven distinct consumer groups with
deﬁning characteristics under three categories as illustrated in the Fig. 6.1.
Being the 10% of the overall consumers, potential early adopters can be consid-
ered as the ﬁrst target group. This group is willing to pay a premium prize for an
Fig. 6.1 FCEV consumer segments. Source: UK H2 Mobility Report (2013)
FCEV. This makes the introduction of FCEVs possible before cost parity is reached
with existing vehicle technologies. The other groups would still require a discount to
buy an FCEV.
6
Global Market Creation for Fuel Cell Electric Vehicles
137
Wild Card Thinking
Some Wild Card thinking can be applied for the acceleration of the speed of adoption
of FCEVs:
•
Strategies for attracting emerging young “technology-savvy society” would be a
good group to target to expand the potential early adopter group.
•
FCEVs can be presented with a new image by combining sleek design and
technology, which may be used to create a new fashion. People are ready to
pay 10 times higher for an Apple iPhone than others; similarly they may be ready
to pay 1.2 times higher for a stylish car.
•
Recently communications, electronics and photography sectors have converged
to a large extent. It is expected that the automotive industry too will converge with
those sectors. Integration and interoperability with information, communication,
multimedia systems and social networking technologies with large touch screens
will also attract a number of users.
•
Mass media can be used to promote FCEVs. Media attitudes are equally impor-
tant, especially mass media which has strong impact on public opinions. It has
been frequently observed that media tends to report on accidents and failures of
technologies most preferably instead of success stories. Here the impact of
different media channels on consumer attitudes and behaviour could be given
special attention.
•
Society should be convinced about the safety, security and reliability of FCEVs,
without any negative impacts on public and individual health.
6.4.2
Technological Factors
Technological factors focus on rates of technological progress, pace of diffusion of
innovations, problems and risks associated with technology such as security and
health problems. Some of the technological factors associated to FCEVs are:
•
Reliability of supply
•
Equipment (hardware)
•
Vehicle technologies
Reliability of Supply
The supply of hydrogen is an important issue for the creation of FCEV markets.
Necessary infrastructure should be established to ensure sound and stable supply of
energy. Any lack of supply can be easily used as an argument against FCEVs.
When hydrogen is supplied, environmental implications should also be consid-
ered. Pure hydrogen can be industrially derived, but it takes energy. If that energy
does not come from renewable sources, then fuel cell cars are not considered to be as
clean as they seem. Ways should be found to generate hydrogen using renewable
energy sources or integrated with carbon capture and storage (CCS) technologies.
138
A. Sokolov et al.
Fuel storage and transport challenges seem to be solved at least technologically in
principle. However there still remain concerns with respect to logistical issues,
e.g. centralised or decentralised fuel production, infrastructure for fuel distribution
and the appropriateness of the existing infrastructure for upgrading to the respective
fuel distribution.
Equipment (Hardware)
The cost of buying and installing equipment for compressing, storing and dispensing
hydrogen on site and the cost of ﬁnancing the expenditure should be considered at
this point. A hydrogen refuelling station (HRS) is considered to have a 20-year
lifetime. The cost of land should also be factored in the cost calculations. Operation
of the equipment and facilities will incur certain costs too such as the cost of
maintaining the HRS, general operating cost and the cost of administration and
sales and rental charge for the land used by the HRS—if it is not a mobile one.
Network development can be achieved with a number of small but widespread
HRSs, which should be expandable with upgrades to medium- and large-scale
HRSs. This option for expansion should be considered right from the beginning.
Vehicles
The vehicle technology is probably the most advanced and least problematic part of
the overall FCEV business plans. A number of vehicle producers are ready for mass
commercial production. Although FCEVs are more expensive than the cars with
conventional engines at the moment, these costs are expected to go down following
the “market seeding” phase between 2015 and 2020, when investments are made for
growth and networks and markets are developed.
An important issue here may be the maintenance and repair infrastructure for
FCEVs. As FCEVs are equipped with more sophisticated technologies, special
attention will need to be given to repair facilities and equipment, which may look
quite different than current garages for conventional vehicles. The transport and
storage of hydrogen and spare electronic equipment will need special attention in
terms of security and safety. Operating staff training for this new technology should
also be taken into account.
Wild Card Thinking
Possible technology-related Wild Cards:
•
New ways of extracting hydrogen. A team of Virginia Tech researchers has
discovered a way to extract large quantities of hydrogen from any plant, a
breakthrough that has the potential to bring a low-cost, environmentally friendly
fuel source to the world.
•
Mobile hydrogen refuelling stations can be used to provide further access to
hydrogen in remote or congested areas or when a likely power cut starts effecting
supply. Powertech company has pioneered the use of lightweight carbon ﬁbre
composite tanks for the high pressure bulk transportation of hydrogen, for mobile
hydrogen fuelling station applications and for portable self-contained hydrogen
6
Global Market Creation for Fuel Cell Electric Vehicles
139
fuelling units. Transportable compressed hydrogen units can be custom-designed
to meet customer needs, including transport trailers, mobile fuellers and portable
ﬁlling stations.
•
If the technology for BEVs does not develop signiﬁcantly and if the process of
producing hydrogen becomes easier and cheaper, then the case will be much
stronger for FCEVs.
6.4.3
Economic Factors
Levels and distribution of economic growth, industrial structures, competition and
competitiveness and market and ﬁnancial issues are the sorts of factors to be
considered under this category. As one of the key drivers for hydrogen cars,
economic factors in this domain involve a number of points to be considered,
including:
•
Cost of FCEVs
•
Demand
•
Investments
Cost
Fuel cells (FCs) are still very expensive, even when compared to BEVs. FCs still
remain to be ten times more expensive than internal combustion engines; however
progress is made towards cost reduction. Another important aspect is hydrogen
itself. Currently, there is no market price for hydrogen intended for alternative
energy use comparable to that for gasoline. Hydrogen as an alternate energy carrier
is in an early phase of development. Estimates of the cost of hydrogen per gallon of
gas equivalent range from $2.10 to $10. Hydrogen produced from natural gas, the
cheapest available method, is 3–4 times as expensive as gasoline, in terms of
equivalent amounts of energy (US Department of Energy 2015a).
The Department of Energy in the USA aims to reduce the cost of hydrogen to
$2 per gasoline gallon equivalents by 2020 (US Department of Energy 2015a, b). Its
previous goal of $1.50, set before gasoline prices went up, was based on the use of
natural gas as a source for hydrogen. The new goal is independent of the method of
production, in response to questions about the environmental effects of using natural
gas for hydrogen production.
Demand
Overall the market demand has three determinants:
1. Vehicle attributes including the price of the vehicle, performance and range and
hydrogen consumption
2. Consumer attitudes in terms of different user segments presented above (Fig. 6.1)
and the size of each group
140
A. Sokolov et al.
3. Hydrogen refuelling stations with a widespread urban and national coverage and
hydrogen process and emissions generated through the production process
Economic feasibility means a target of 200 people per pump at the annual
depreciation target of 250 euros/year per customer to achieve the cost target of
500K euros over 10 years period (Hasegava 2013). This may be challenging in the
ﬁrst period between 2015 and 2020 but then will gradually become more realistic.
Considering the cost and demand factors, ﬁrst and immediate clients for FCEV
seem to be ﬂeets, such as bus ﬂeets in Europe. Large number of vehicles may create
the economies of scale that justify the cost of building stations. Among the
expectations of the ﬂeet operators from the FCEVs are low emission, long range
and fast fuelling vehicles. Van type of vehicles is more preferred by this group.
Special rates or tax exemptions are expected for low- or zero-emission vehicles.
Fleets are also desirable to begin with as they have more predictable driving patterns
than other users. Filling stations might be located at the ﬂeet base or close to it.
Investments
Venture capital and private equity investments in fuel cells and hydrogen increased
worldwide by 9.2% between 2014 and 2015. Venture capital and private equity
investments in US fuel cell companies grew by 96.2% during the same period
(US Department of Energy 2015b). Coupling with the increasing investments, the
FCEV industry may create further employment across the value chain from vehicle
manufacture, development of new components, fuel production, distribution and
supply, thereby bringing signiﬁcant economic beneﬁt.
The HRSs may not be proﬁtable at the beginning. A break-even point may be
expected in the late 2020s. Business cases should be created for the initial network of
stations to ensure how ﬁrst mover commercial advantage could be secured.
Wild Card Thinking
Possible Wild Cards in this category may include:
•
The nearest-term application for FCs seems to be lift trucks (forklifts). Several
industrial truck companies have announced commercial FC products that can
replace battery-powered forklifts. These have been extensively tested and are
available for commercial purchase today to be used in production plants, logistics
and airports.
•
A possible “electron economy” may replace the “hydrogen economy”. In an
electron economy, most energy would be distributed with highest efﬁciency by
electricity, and the shortest route in an existing infrastructure could be taken. The
efﬁciency of an electron economy is not affected by any wasteful conversions
from physical to chemical and from chemical to physical energy. With an electron
economy, attentions could be quickly turned to the energy storage technologies
and upgrade to smart grids.
•
On the more positive side, UK-based AFC Energy is conﬁdent it has identiﬁed a
low-cost and sustainable source of hydrogen in the form of the waste gases
6
Global Market Creation for Fuel Cell Electric Vehicles
141
produced by the chlorine industry, and following successful trials at a chlor-alkali
plant at Bitterfeld in Germany, the company is now working on a 50 kW
commercial-scale version of its fuel cell technology. It should be considered
that technology and economy should go hand in hand to achieve hydrogen
breakthrough.
6.4.4
Environmental Factors
Pressures connected with sustainability and climate change and more localised
environmental issues (including pollution, resource depletion, associated biodiver-
sity and welfare concerns) are among the environmental factors to be considered.
Environmental impact is one of the key arguments for supporting or contesting the
transition process towards hydrogen-powered FC cars. There are two key factors to
be considered:
•
Emission reductions, which is highly desirable
•
Hydrogen production, which is frequently used as a counter-argument to the
hydrogen economy
Emission Reductions
The environmental beneﬁts of hydrogen are a very positive attribute. When used in a
fuel cell to power an electric vehicle, the emissions include only water and heat. But
hydrogen is produced using energy from natural gas, coal, solar, wind or nuclear
power, each of which has its own environmental effects. The UK H2 roadmap
indicates that hydrogen production mix in the roadmap for 2030 is 51% water
electrolysis (WE), 47% steam methane reforming (SMR) and 2% existing capacities.
WE, using renewable electricity, includes both on-site production at the HRS and
centralised production with distribution to the HRS. In 2030, the roadmap shows that
the UK national demand for hydrogen for FCEVs will be 254,000 tons p.a. FCEVs
will help to meet long-term emission reduction targets by offering a practical mass
market solution to help meet this objective.
Hydrogen Production
Transitional approaches relying on natural gas could facilitate the use of hydrogen
technologies until production methods using other, more environmentally friendly
resources become available. Therefore, one likely early path for the development of
hydrogen could be using the wide availability of natural gas and its distribution
pipelines to create hydrogen for on-site fuelling.
Hydrogen can also be produced from coal reserves. Most analyses show that the
higher efﬁciency of hydrogen applications can result in lower greenhouse gas
emissions, even when the hydrogen is produced from coal.
Other environmental beneﬁts of FCEVs include improved air quality and reduc-
tion in noise pollution from trafﬁc compared with conventional vehicles powered by
conventional engines. These can be used as additional arguments to promote
FCEVs.
142
A. Sokolov et al.
Wild Card Thinking
Some of the associated Wild Cards from the environmental point of view may
include:
•
Hydrogen is widely accepted as a solution to global warming by the UN with a
decree.
•
A breakthrough in electric power storage occurs within a decade involving battery
technology, fuel cells, new chemicals and materials with some nanotechnology
applications. This breakthrough allows the integration of power systems in
mobile equipment (cars and trucks) with stationary energy needs (back-up
power and load management option for homes, ofﬁces and factories). These
storage applications begin in the high-cost and high-value parts of the energy
sector and as the technology matures, costs decline and zero-emission targets
become closer to achieve.
•
High corn prices driven by a bad harvest could hurt corn ethanol producers, which
are suffering from a saturated market for ethanol. This may allow hydrogen to
take off faster than expected as an alternative energy source.
6.4.5
Political Factors
Political factors involve dominant political viewpoints or parties, political (in)stabil-
ity, regulatory roles and actions of governments, political action and lobbying by
non-state actors (e.g. pressure groups, paramilitaries). Regarding the hydrogen ﬁeld,
the following political factors can be considered to be inﬂuential:
•
Reduction in energy dependency
•
Political incentives
•
Joint action
Energy Dependency
The commercialisation of FCEVs and hydrogen has potential beneﬁts in terms of
reduction of carbon emissions, air quality improvements and energy security
enhancements, in addition to wider economic beneﬁts. For instance, switching
from imported fossil fuels to hydrogen may bring £1.3 billion annual beneﬁt to the
UK economy by 2030. Energy cost reduction potential for the transport sector in the
Japanese economy was estimated 23 billion euros for 2010 (Hasegava 2013).
Diversiﬁcation of energy supply through hydrogen could help to reduce the
reliance of imported fossil fuels for transport and thereby increase energy security
in energy importing countries. The local production of hydrogen can also provide
more of the process inputs to be produced locally by reducing the dependency on
external energy markets.
6
Global Market Creation for Fuel Cell Electric Vehicles
143
Political Incentives
There are currently no production incentives for hydrogen. The political support
often is limited to discussion about emission-free vehicles, but practical initiatives
are missing. Limited public funds are available for research and demonstration
projects.
As far as the cost of hydrogen is considered, a national pricing system will need to
be introduced within countries. This will depend on the size of the country, distribu-
tion of hydrogen production facilities across the country and proximity to HRSs.
Tax exemptions are planned for hydrogen vehicles. However, the practical
implementation of such tax exemptions might carry the danger of giving advantages
to national or local manufacturers as was discussed after the crisis in 2008 when
most European countries implemented such measures. Hence national (country-
speciﬁc) measures have to be analysed in detail if these are applicable for fuel
cell-powered cars.
Joint Action
In order to overcome the commercialisation challenge, a close cooperation is needed
between vehicle manufacturers; equipment manufacturers in production of fuel cells,
hydrogen refuelling stations and hydrogen technology components and subsystems;
fuel retailers; hydrogen producers; energy utilities; and the government departments
such as science, technology and innovation, transport and energy.
Wild Card Thinking
Some of the Wild Cards in the political sphere might include:
•
Introduction of large-scale public procurement programmes for FCEVs
•
Governments back zero-interest mortgage plans for hydrogen cars
•
Massive movement of public transport vehicles and large ﬂeets to FCEVs
6.5
Characteristics of Stakeholders
A number of roadmaps have been developed in course of the FCEV history.
Although most roadmaps were professionally developed, they still lack an “eagle
eye view” on the “hidden” stakeholders’ attitudes, inﬂuence and argumentation
lines. Developing effective roadmaps hence require knowledge and information
about actual but also potential stakeholders, which follow their own strategies and
might have the potential to block technologies and innovation diffusion or at least
create obstacle and barriers to delay diffusion. It is important to underline that
stakeholders vary in their inﬂuence on the diffusion of innovation, their power and
their argumentation strategies.
144
A. Sokolov et al.
6.5.1
Stakeholders: Societal
In the societal sphere, mainly customers are considered as stakeholders. However,
they show special characteristics, in particular with reference to FCEVs. In the
transportation, business car owners’ associations also play an important role
(Table 6.1).
Overall the individual stakeholders have only moderate inﬂuence on innovation
diffusion, but associations representing car owners have considerable power at
different levels.
6.5.2
Stakeholders: Technological
From the technological point of view, various stakeholders exist (Table 6.2). These
refer to direct competitors who aim at similar application but using competing
technological solutions and surrounding actors like infrastructure suppliers and
fuel producers.
Table 6.1 Societal stakeholders
Stakeholders
Inﬂuence
Power
Argumentation strategies
• Traditional car
owners
%
%
• Misses typical car features
• Reluctant towards noiseless drive
• Young
generation
"
!
• Wish to differentiate from traditional drivers
• Limited experience with infrastructure
• Car owners
associations
"
"
• Adverse attitudes, mainly dominated by traditional
drivers
• Point on noise, danger of fuel supply, need to train
traditional driver to adjust
Source: HSE
! low,
medium,
%
" high
Table 6.2 Technological stakeholders
Stakeholders
Inﬂuence
Power
Argumentation strategies
• Alternative FC (SOFC,
PAFC, MCFC)
producers
!
%
• Similar application ﬁelds for FCs or at least
potentially similar ﬁelds
• Might point to dangers and environmental
issues of membranes
• Infrastructure suppliers
"
"
• Decentralised infrastructures need to be
build—investment cost
• Existing infrastructure reshaped for fuel
transport—opportunity cost
• Fuel producers
(gasoline)
%
"
• Consequences of lacking demand for
gasoline—reﬁnery closures, job losses,
impact on petrochemical industry
Source: HSE
! low,
medium,
%
" high
6
Global Market Creation for Fuel Cell Electric Vehicles
145
The analysis shows that competing technological solution providers have poten-
tially stronger inﬂuence on the diffusion of innovation. The reason lies in their
technology follower position in the FC development, polymer electrolyte membrane
fuel cells (PEMFCs) have been the main FC type for transport applications for a
while, but still other FC types might be used for mobile applications. Producers of
these FCs might follow a strategy to point strongly on the inherent dangers of
membrane technology, which are still manifold. Infrastructure suppliers have strong
inﬂuence and power on the diffusion per se. As long as the infrastructure for fuel
supply remains insufﬁciently developed, these actors need careful consideration and
treatment. Other often neglected stakeholders are traditional fuel producers, who
belong to downstream oil and gas business, eventually forming part of the petro-
chemical industry. Here the challenge is that the fuel is commonly produced in
reﬁneries from crude oil using different cracking technologies—fuel is only one
product of cracking crudes. So far, the essentials for the chemical industry are
produced together with traditional fuel, there is no either or. It follows that with a
signiﬁcant reduction of fuel demand, reﬁneries need to lower output with respective
impact on the subsequent industries. This gives reasonable arguments for the
industry to block FCEVs.
6.5.3
Stakeholder: Economic
The most progressed FCEV producers are Asia-based companies. National
producers in the Western markets are still lagging behind in technological terms;
thus they will aim at inﬂuencing the national, regional and local communities. In a
similar
way,
the
petrochemical
industry
will
act
(see
also
technological
stakeholders). Last but not least, the repair and maintenance infrastructure lobby is
important. Currently any car can be ﬁxed in emergency cases within the existing
infrastructure, but there remains a challenge to (Table 6.3):
1. Upgrade the existing infrastructure on a broader scale for regular maintenance
2. To upgrade the emergency relief infrastructure
Table 6.3 Stakeholder diversity
Stakeholders
Inﬂuence
Power
Argumentation strategies
• Technology
follower
%
%
• Technological leadership concentrated in Asia
(Japan, South Korea), Europeans lagging, oppose
with lobby work
• Petrochemical
industry
"
"
• Job losses due to either reﬁnery closure or high
investment for new equipment
• Repair and
maintenance
industry
%
%
• Signiﬁcant investment in equipment
• No competences in new technologies, reluctant to
accept dual system
Source: HSE
! low,
medium,
%
" high
146
A. Sokolov et al.
Presumably the strongest opposition might come from the petrochemical indus-
try, which is well aware of the investments needed in their reﬁning facilities to
compensate for the decreasing demand in gasoline and related products. The repair
and maintenance industry will need physical investment in line with extensive
training to assure adequate services to customers.
6.5.4
Stakeholders: Environmental
In technological terms FCEVs are not new at the small-scale production and opera-
tion. However environmental groups and also health- and safety-related interest
groups might raise concerns about the reliability of manufacturing and operation
of FCs in the broadest sense at large scale (Table 6.4).
It can be assumed that the concerns regarding Environmental Health and Safety
(EHS) will not be announced and communicated by the interest groups to the end
user directly; presumably the end user is not aware and interested in the
manufacturing and handling of large-scale FC production units. Thus these interest
groups are likely to inﬂuence the policy level to issue-related regulations and
probably laws which might have reasonable impact on the business models. Also
there is a possibility that EHS-related arguments and interest groups are used by
other parties with the aim of delaying FC diffusion.
6.5.5
Stakeholders: Political
The mass introduction but also the pilot introduction of FCEVs will require physical
infrastructural adjustments of the public transport system. Also there is a need at the
policy level to implement complementary systems, for instance, by enforcing
standards (Table 6.5).
The main policy actors will be at the level of municipalities and regions. National
level policy-makers will have reasonable inﬂuence to design measures, which are
Table 6.4 Environmental stakeholders
Stakeholders
Inﬂuence
Power
Argumentation strategies
• Laws, legal
regulations
"
"
• Especially important for EHS
• Environmental
groups
%
%
• Long-term H2 impact not known
• Health, safety
groups
"
"
• Unknown reliability of new standards and
technologies, potential negative impact on safety
and health of workers in all domains
Source: HSE
! low,
medium,
%
" high
supportive to FCEVs diffusion, still the implementation of any federal measures is at
the regional and the municipalities’ level. Especially municipalities have the respon-
sibility for assigning and licensing respective space for related infrastructure,
whereas regional authorities will be responsible for monitoring and quality/safety
testing and certiﬁcation.
6
Global Market Creation for Fuel Cell Electric Vehicles
147
Table 6.5 Political stakeholders
Stakeholder
Inﬂuence
Power
Argumentation strategies
• Municipalities
"
"
• Responsible for infrastructural decisions
• Regional
"
"
• Financial incentives for municipalities, regional
standards, complementarities of standards between
regions
• Federal
%
%
• Initiator and promoter role but less
implementation power
Source: HSE
! low,
medium,
%
" high
6.6
Development of an Eagle Eye FCEV Roadmap
A roadmap for creation of a market for FCEVs is a tool for long-term complex
planning, which allows setting strategic goals and estimating potential contribution
of new technologies, products and services to build a competitive and sustainable
FCEV market. The roadmap considers alternative ways to achieve the goals and
choose the most efﬁcient products and relevant technology applications. It provides
decision-makers with estimates of future markets and prospects of innovative
products and designs an innovative technological value chain from R&D to market
entry. The FCEV roadmap presents estimated indicators of economic efﬁciency of
the potentially prospective technologies and products, with the high demand poten-
tial and attractive consumer properties of FCEVs within the timescale also taking
into account the elaborated stakeholder views. The proposed roadmap will be
developed on the bases of both qualitative and quantitative methods, the expert
community survey data and evidence-based analyses. In this stage the stakeholder
analysis will be incorporated in the market dimension of the roadmap.
The roadmap template presented here is designed on the basis of a market-driven
and technology-driven approach, which starts from the analysis of a market demand.
The elaboration of the FCEV roadmap covers the analysis of key needs of the
marketplace and customers, possible markets development within several scenarios,
estimation of future demand for particular types of FCEVs and respective
requirements including potential attitudes, measures and activities and argumenta-
tion lines of stakeholders. It will also require comprehensive analysis of technologi-
cal innovation and product development based on identiﬁcation of future dynamics.
Thus the FCEV roadmap allows considering both technological and market sides
providing a combination of market pull and technology push approaches. The
process will generate a roadmap demonstrating new products and technologies,
which are important in achieving the set goals and a business map containing
economic appraisal and comparison of alternative paths of future development. In
addition, the roadmap should provide a detailed analysis of market pull, including:
148
A. Sokolov et al.
•
Areas of product’s application determining the demand for technological
solutions
•
Speciﬁcity of different segments of FCEV markets
•
Balance between technological facilities and consumers’ needs
•
Economic estimation of technology trajectories
•
Analysis of stakeholders
•
Recommendations aimed at support of market-oriented technologies and
products
It will also pay special attention to description of technology push factors:
•
Technologies that provide competitive advantages for FCEVs
•
Technological limitations
•
Priority technological tasks
•
Revealing of technological “forks”
The principal structure of the FCEV roadmap is presented on the Fig. 6.2.
The roadmap template includes four major layers:
1. Technologies. This layer contains the description of the prospective technologies
within the identiﬁed time horizon. It provides a SWOT analysis of these
technologies that summarises beneﬁts and limitations of each technology. It
also provides a forecast of target properties required to satisfy market needs and
a set of the main technological tasks necessary to be done to reach these features.
Finally, it gives an opportunity to estimate prospects for each technology in terms
of readiness for implementation and potential effect.
2. Products. This layer provides a brief description of prospective products in terms
of readiness for commercialisation and potential effects for researched area. It
also estimates potential time of commercialisation and the most prospective
market niches for each product.
3. Markets. There will be elaborated scenarios of potential FCEV market develop-
ment based on the eagle eye view approach. The roadmap will provide a brief
description of main market’s features and possible strategies for each scenario
and each market. Thus, all markets should be ranked from the most prospective
down to the less ones.
4. Alternatives. The roadmap also reveals possible development of alternative
products and solutions. It takes into account the dynamics of the main product
properties, opportunities of export of these products and their cost.
6
Global Market Creation for Fuel Cell Electric Vehicles
149
Target properties
Technologies
Technologies
Technologies
Products
Products
Products
Markets
Markets
Markets
Alternatives
Alternatives
Alternatives
SWOT-analysis
Necessary R&D
Market 1
Challenges
timeline
timeline
timeline
timeline
Goals
Challenges
Goals
Challenges
Goals
Challenges
Goals
Scenario forecast for domestic market
Scenario forecast for world market
Market 2
Market 3
Degree of competition
SWOT-analysis
Alternative 1
Target properties
SWOT-analysis
Necessary R&D
Target properties
SWOT-analysis
Necessary R&D
Target properties
SWOT-analysis
Necessary R&D
Target properties
SWOT-analysis
Necessary R&D
Target properties
SWOT-analysis
Necessary R&D
Technology 1
Technology 2
Technology 3
Product 1
Product 2
Product 3
Scenario forecast for domestic market
Scenario forecast for world market
Scenario forecast for domestic market
Scenario forecast for world market
Degree of competition
SWOT-analysis
Alternative 2
Degree of competition
SWOT-analysis
Alternative 3
Risks, barriers 
and limitations
Risks, barriers 
and limitations
Risks, barriers 
and limitations
Risks, barriers 
and limitations
Fig. 6.2 Structure of roadmap. Source: HSE
150
A. Sokolov et al.
For each layer, it is necessary to consider challenges and a set of relevant goals
taking into account potential risks, the most signiﬁcant particular challenges for
FCEVs markets, to reveal obstacles that could hamper FCEVs market development
and to assess key risks and threats.
The complete roadmap illustrates the links between the key technologies for
FCEVs, the consumer properties of existing and advanced FCEVs, the most
promising products and their respective market shares, volumes and growth rates.
It highlights the structure of potential demand for innovative products and outlines
their most prospective markets. The roadmap also provides an assessment of techni-
cal capabilities required for manufacturing of products with the most preferable
consumer properties, which would allow generating the signiﬁcant competitive
advantages for FCEVs.
Being of practical value, the FCEV roadmap demonstrates optional paths of
building added value chain “technologies—products—markets”. Such paths/
trajectories
are
aimed
at
detailed
description
of
possible
strategies
of
commercialisation on particular markets—what kinds of FCEVs should be pro-
duced; what level of their consumer properties will allow them to compete against
other similar (conventional and new) goods at different time periods; what kind of
new technologies should be introduced to obtain the required product properties.
The FCEV roadmap reveals alternative ways to achieve the market goals and to
choose efﬁcient allocation of resources. The roadmap takes into account
manufacturing and market developments and prospects of technologies, products
and services contributing to the design of complex innovation value chains ranging
from technology to market entrance of FCEVs and allows building strategies for
linking FCEVs development with other related industries (suppliers and consumers
of related products/technologies). It integrates the expert community views on
innovative development ways in FCEV and related areas, provide a set of well-
grounded
trajectories
of
innovation
development
and
indicated
principal
“bifurcations” as points of the key decisions to be made. The roadmap should be
regularly updated to enhance its practical value for decision-making.
Acknowledgements The book chapter was prepared within the framework of the Basic Research
Program at the National Research University Higher School of Economics and supported within the
framework of the subsidy by the Russian Academic Excellence Project ‘5-100’.
References
Global EV Outlook (2013) Understanding the electric vehicle landscape by 2020. https://www.iea.
org/publications/freepublications/publication/GlobalEVOutlook_2013.pdf. Accessed 17 Feb
2017
Hasegava T (2013) Presentation on FCEV market development at Nissan. Higher School of
Economics, Moscow
Miles I, Saritas O, Sokolov A (2016) Foresight for science, technology and innovation. Springer
International Publishing, Switzerland
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Saritas O (2013) Systemic foresight methodology. In: Meissner D, Gokhberg L, Sokolov A (eds)
Science, technology and innovation policy for the future: potentials and limits of foresight
studies. Springer, Berlin, pp 83–117
Saritas O, Aylen J (2010) Using scenarios for roadmapping: the case of clean production. Technol
Forecast Soc Change 77(7):1061–1075
UK H2 Mobility Report (2013.) https://www.gov.uk/government/uploads/system/uploads/attach
ment_data/ﬁle/192440/13-799-uk-h2-mobility-phase-1-results.pdf. Accessed 17 Feb 2017
US Department of Energy (2015a) Hydrogen production, 2015 production section. https://energy.
gov/sites/prod/ﬁles/2015/06/f23/fcto_myrdd_production.pdf. Accessed 17 Feb 2017
US Department of Energy (2015b) Fuel cell technologies market report 2015. Washington. https://
energy.gov/sites/prod/ﬁles/2016/10/f33/fcto_2015_market_report.pdf. Accessed 17 Feb 2017
Alexander Sokolov is Deputy Director of HSE ISSEK and
Director of HSE International Foresight Center. His main
professional interests are related to Foresight, STI priorities,
indicators and policies. Prof. Sokolov is tenure professor at
HSE, he teaches Foresight for undergraduate and postgradu-
ate students. He authored over 120 publications in Russia
and internationally and managed many Foresight projects,
including: Russian S&T Foresight: 2030; Foresight for
Russian ICT sector (2012); Innovation Priorities for the
Sector of Natural Resources (2008–2010); Russian S&T
Delphi
Study:
2025
(2007–2008);
Russian
Critical
Technologies (2009) et al. Prof. Sokolov is member of sev-
eral high-level working groups at OECD and other interna-
tional organizations, serves for advisory boards at several
international conferences and journals.
Ozcan Saritas is Professor of Innovation and Strategy at
the HSE and editor-in-chief of “Foresight”—the journal of
future studies, strategic thinking and policy. He worked as
Senior Research Fellow at the Manchester Institute of
Innovation Research, The University of Manchester. His
research focuses on innovation and policy research with
particular emphasis on socio-economic and technological
Foresight. With a PhD from the “Foresight and Prospective
Studies Program,” he introduced the “Systemic Foresight
Methodology”, and has produced a number of publications
on the topic. Dr. Saritas has extensive work experience with
the international organizations, including the United Nations,
OECD, and the European Commission. He has been
involved in large scale national, multinational and corporate
research and consultancy projects on sectors including
energy, climate change, agriculture, food, water, transporta-
tion, and ICT among others; published a number of articles in
respected journals; and have delivered keynote speeches in
more than 50 countries across the world. Besides his research
and publication activities, he designs and delivers academic
and executive education courses on Foresight and Strategic
Planning. He has recently co-authored a book, entitled
“Foresight
for
Science,
Technology
and
Innovation”
published by Springer, which has become one of the key
readings in the ﬁeld.
152
A. Sokolov et al.
Dirk Meissner is Deputy Head of the Laboratory for Eco-
nomics of Innovation at HSE ISSEK and Academic Director
of the Master Program “Governance for STI”. Dr. Meissner
has 20 years experience in research and teaching technology
and innovation management and policy. He has strong back-
ground in policy making and industrial management for STI
with special focus on Foresight and roadmapping, funding of
research
and
priority
setting.
Prior
to
joining
HSE
Dr. Meissner was responsible for technology and innovation
policy at the presidential ofﬁce of the Swiss Science and
Technology Council. Previously he was management con-
sultant for technology and innovation management with
Arthur D. Little. He is member of OECD Working Party on
Technology and Innovation Policy. He is Associate Editor of
Technological
Forecasting
and
Social
Change,
IEEE
Transactions on Engineering Management, Journal of the
Knowledge Economy, member of Editorial Review Board
at Small Business Economics and Journal of Knowledge
Management. He guest edited Special Issues in Industry
and Innovation journal, Journal of Engineering and Technol-
ogy Management, Technological Analysis and Strategic
Management among others.
153
Technology Roadmaps: Emerging
Technologies in the Aircraft
and Shipbuilding Industries
7
Marina Klubova, Lubov Matich, Vladimir Salun,
and Natalia Veselitskaya
7.1
Introduction
Recently the emergence of technologies and development of industries in the world
have been inextricably linked to the response to grand challenges (EC 2010). In this
regard, a clearly established scientiﬁc and technology policy should promote the
opportunities and ways to overcome threats associated with said grand challenges
(Chaminade and Edquist 2006; Bergek et al. 2008; Georghiou 2011; Keenan et al.
2012; Edquist 2011; Gokhberg 2013).
In this context, the development of the aircraft and shipbuilding industries has
multiplier effects on the economy and society, on a domestic and international level.
The speed and volume of trafﬁc ﬂows are clearly correlated with the development of
the global economy. Furthermore, emerging technologies in the ﬁeld of aircraft and
shipbuilding will effectively respond to various types of global challenges: eco-
nomic, environmental, scientiﬁc and technological, and social. Such technologies
form the basis for the improvement of existing and the emergence of new types of
aircraft and ships. Such improvements and innovations eventually lead to:
– The transformation of global value chains
– An increase in energy efﬁciency through the use of alternative energy sources
– A reduction in negative environmental impacts (carbon dioxide and other harmful
emissions, noise pollution, etc.)
– The prevention of terrorist acts
– An acceleration of knowledge and technology transfer
M. Klubova (*) · L. Matich · V. Salun · N. Veselitskaya
National Research University, Higher School of Economics, Moscow, Russia
e-mail: mklubova@hse.ru; lmatich@hse.ru; vsalun@hse.ru; nveselitskaya@hse.ru
# Springer Nature Switzerland AG 2019
D. Meissner et al. (eds.), Emerging Technologies for Economic Development,
Science, Technology and Innovation Studies,
https://doi.org/10.1007/978-3-030-04370-4_7
An integrated analysis of the emerging technologies in aircraft and shipbuilding is
justiﬁed due to similar features of both industries in their products’ life cycles. In
particular, these industries are characterized by the high duration of technological
and production processes, high capital expenditures, etc.
154
M. Klubova et al.
Despite the wide opportunities that emerging technologies offer, the high capital
intensity of the aircraft and shipbuilding industries makes it necessary to set scien-
tiﬁc and technological priorities. Depending on which global challenges are
recognized as the most signiﬁcant (energy efﬁciency, environmental friendliness,
safety, etc.), alternative variants (forks) of the development of emerging
technologies can be selected and implemented by the country. The forks form the
basis for building scenarios for emerging technologies in the aircraft and shipbuild-
ing industries.
7.2
Background
Global foresight experience demonstrates that for the successful identiﬁcation and
further implementation of industry development priorities, it is important to deter-
mine “the future shape” of those developments. During this process, long-term
scientiﬁc, technological, and socioeconomic developments are considered based
on the factor inﬂuence system, including joker events, global challenges, trends,
threats, drivers, barriers, and restrictions (Saritas and Proskuryakova 2017).
Over the course of the last few decades, state and regional foresight exercises for
the transport system in general, as well as for individual vehicle types, have been
actively carried out. They are initiated by associations of organizations and unions,
large companies producing transport equipment, companies operating on transpor-
tation markets, etc. (Vishnevskiy and Yaroslavtsev 2017).
The information base of this research includes the following studies in the ﬁeld of
aircraft construction and shipbuilding (Fig. 7.1):
– Leading global forecasts and foresight studies
– Technology foresight studies at the national level
– Technology roadmaps
– Strategic documents of leading companies in the aircraft and shipbuilding
industries
– Scientiﬁc publications
During this study, more than 50 documents describing the scientiﬁc, technologi-
cal, and socioeconomic development of the transport sector were analyzed (Fig. 7.1),
and a bibliometric analysis of more than 2000 scientiﬁc articles (the Horizon
2005–2016) on “Emerging technologies” topics was conducted.
Global forecasts and foresight studies call one’s attention to the development of
technologies in the design, production, testing and repair of vehicles (EREA 2012;
EC 2012), global trends and challenges (ACEA 2011; NISTEP 2014; OECD 2012),
2017
2020
2025
2030
2035
2040-2050
2005-2010
Noise roadmap a blueprint for managing noise from aviation sources to 2050
Vision 2050 (International Air Transport Association)
Integration of Civil Unmanned Aircraft Systems (UAS) in the 
National Airspace System (NAS) Roadmap
Global Aviation Safety Roadmap
The International Air Transport Association Technology Roadmap (IATA) 
Roadmap Aeronautics Manufacturing  
and Maintenance, LRN
Aeronautics and Air transport: beyond vision 2020 (towards 2050) 
Canadian Aerospace Environmental Technology Road Map
Sustainable Aviation CO2 Roadmap, Sustainable Aviation Council
Flightpath 2050: Europe’s Vision for Aviation, EREA 
UK Aerospace Technology An Evolution
From Air Transport System 2050 Vision to Planning for Research and Innovation, EREA
HORIZON
NASA Technology Roadmaps 2015, NASA
Russia 2030: Science and Technology Foresight, the Ministry of Education and Science of 
the Russian Federation
Alternative future scenarios for marine ecosystems 
UK Marine Industries Roadmap & Capability Study. Workshop: Shipbuilding and Repair
Global Scenarios of Shipping in 2030 
Green growth opportunities in the EU 
shipbuilding sector
Sea Change – A Marine Knowledge, Research and 
Innovation Strategy sets out a new vision for the marine 
sector in 2020
Foresight Series. Climate change Implications for Ireland’s Marine Environment and Resources
Emissions from international shipping over the last 50 years and future scenarios until 2050
Fig. 7.1 Foresight and roadmaps for the shipbuilding and aircraft industries (examples). Source:
HSE
important problems, and prospects for the development of air and water transport
(IATA 2011; IEA 2012; OECD 2015).
7
Technology Roadmaps: Emerging Technologies in the Aircraft and. . .
155
National technology foresight studies focus on the problems of the energy
efﬁciency of transportation, the reduction of negative environment impacts
(EC 2011, 2015a; Marine Institute 2005; CE Delft 2012; Pinnegar et al. 2006),
transport safety (EC 2015b), and strategy development for improving product
competitiveness (UK Marine Industries Alliance 2011; GenerationBalt 2011). Stud-
ies also refer to trafﬁc management technologies, the interaction of vehicles with one
another and with the existing infrastructure (EC 2014; Federal Ministry of Transport
and Digital Infrastructure of Germany 2015).
International and national roadmaps analyze the trajectories of technology imple-
mentation aimed at reducing emissions (IATA 2013; NRC 2011; Sustainable Avia-
tion Council 2012), noise pollution (Sustainable Aviation Council 2013), the cost of
production and operation of transport equipment (Marine Industries Roadmap &
Capability Study 2011), and security problems (ICAO 2006). Furthermore, there are
roadmaps for individual types of vehicles, which are formulated on the basis of an
analysis and the prioritization of individual technologies and technological solutions
(NASA 2013).
156
M. Klubova et al.
Fig. 7.2 Methodology and process visualization. Source: HSE
Forecasts were also prepared by Boeing (2016), Airbus (Airbus Group 2016),
Bombardier, Embraer, Hindustan Aeronautics (India), Safran (2016), and others.
Companies formulate foresight-based strategies for the development of their own
businesses in order to identify technological priorities, opportunities, and potential
threats that might limit the companies’ future growth.
The list of key shipbuilding companies producing scientiﬁc and technology
forecasts includes Mitsubishi Heavy Industries (2017), Hyundai Heavy Industries
(2016), Daewoo Shipbuilding & Marine Engineering (NVIDIA Corporation 2014),
Samsung
Heavy
Industries
(2012),
and
China
State
Shipbuilding
Corporation (2017).
With a help of the Web of Science database, an analysis of scientiﬁc publications
on emerging technologies was conducted: a signiﬁcant number of studies were
devoted to the development of transportation infrastructure (including the aircraft
and
shipbuilding
industries).
The
most
interesting
research
on
emerging
technologies in the aerospace industry concerns self-renewing materials for aircraft
construction (Yang et al. 2013). Shipbuilding and aircraft engineering papers are
devoted to the management of unmanned vehicles (Wahlstrom et al. 2015;
Vachtsevanos et al. 2016), the design of electric motors for ships (Pestana 2014), etc.
This chapter presents a three-stage approach (Fig. 7.2) involving the parallel and
consistent use of various foresight methods, including a STEEPV analysis of grand
challenges, factors and trends, scenario building, and roadmapping.
The STEEPV analysis involves a comprehensive study of the research object in
six key categories: social, technological, economic, environmental, political, and
value (Table 7.1). This method has been used for over 10 years in Europe (FOREN
2001) and other countries, having proved an effective tool for analyzing the external
environment.
7
Technology Roadmaps: Emerging Technologies in the Aircraft and. . .
157
Table 7.1 STEEPV
Category
Description
Social
Lifestyle (e.g., the use of free time, the trends of family relations), demographic
structure, social integration, and cohesion (disparities in lifestyle, equality/
inequality, education)
Technological
The level of technological progress, the speed of innovation diffusion,
problems and risks associated with technology (including safety and health
risks)
Economic
The level and rates of economic growth, the structure of industries, competition
and competitiveness, market and ﬁnancial aspects
Environmental
Ensuring sustainable development and combating climate change, more
localized issues related to the environment (including pollution, depletion of
resources, loss of biodiversity, etc.)
Political
Dominant political structures, political stability/instability, regulating the roles
and actions of governments, lobbying for the interests of nongovernmental
groups (e.g., advocacy groups, military groups)
Value
Attitude toward work [e.g., entrepreneurship, career development, dependence
on authorities, demand for mobility (work, workplaces)], preferences for
recreation, culture, social relations, etc.
Source: HSE based on Loveridge (2002)
The construction of scenarios provides the option of “crosscutting” promising
areas that have the potential to implement a complete innovation cycle: from basic
research to the commercialization of the ﬁnal product.
The development of roadmaps makes it possible to move from the identiﬁcation
of promising areas to the organization of speciﬁc innovative projects and a list of
activities necessary for their implementation. Roadmaps are often used by large
companies (often on a regular basis).
The roadmap is a generalizing document that reﬂects a multilevel system of
strategic development within a subject area on a single time scale. It contains
indicators of the economic effectiveness of advanced technologies and products
with high demand potential and attractive consumer properties (Matich 2017).
The concept of integrated roadmaps includes two interrelated components: a
technological roadmap reﬂecting the prospective directions of scientiﬁc and techno-
logical development and a business roadmap that provides an economic evaluation
of these areas (Vishnevskiy et al. 2016).
Foresight research involves the integration of two approaches: technology-driven
(technology push) and market-oriented (market pull). Based on the application of
“technology push” methods, the elements of the innovative technological chain are
formed (such as research with the potential for practical application, innovative
technologies, high-tech products, and services). The “market pull” approach implies
an analysis of demand for innovative products and individual technologies used in
their production. The integration of these approaches is particularly effective for
high-tech industries that establish a link between the application of promising
products and the technological capabilities for their production, which are identiﬁed
on the basis of research and development results (Dekhtyaruk et al. 2014).
158
M. Klubova et al.
Fig. 7.3 Grand challenges. Source: HSE
Despite the advantages of roadmaps and scenarios, their isolated application often
yields a fragmented picture of the future.
Therefore, over the last few years, the process of roadmap development has often
been combined with a scenario approach (Saritas and Aylen 2010). Scenarios can be
used to verify the reliability of technological roadmaps. Scenarios assume the
emergence of disruptive innovations and wild cards. An integrated approach includ-
ing both roadmaps and scenarios allows for overcoming the limitations of the
individual methodologies.
7.3
Global Trends and Emerging Technologies
7.3.1
Key Determinants
The identiﬁcation of global trends that determine the future is one of the key
objectives for scientiﬁc, technological, and innovation policies. During this study,
more than 150 grand challenges were revealed. The most signiﬁcant ones are shown
in Fig. 7.3. They are classiﬁed by STEEPV.
The technological development of the shipbuilding and aircraft industries is
inﬂuenced by the following macroeconomic trends:
7
Technology Roadmaps: Emerging Technologies in the Aircraft and. . .
159
•
Sustainable trafﬁc growth
•
Energy efﬁciency as a key factor determining transportation preferences
•
Globalization and the liberalization of transport
•
The uneven development of air transportation according to regions of the world,
the increasing importance of emerging markets
•
Customized, personalized transport services, combined with or supplemented by
information and communication devices
•
The consolidation of the world market: mergers, acquisitions, and alliances
•
The concentration of industries and formation of global corporate leaders
•
The emergence of new large corporate players and regional production centers
Grand challenges include trends in the shipbuilding and aircraft industries and in
related ﬁelds. For example, the emerging trends on the markets for passenger and
cargo transportation determine the prospects for the technological development of
aircraft construction because they create demand for certain technologies and aircraft
components. In general, grand challenges and macroeconomic trends in the devel-
opment of the transportation market create the following effects:
•
An extension of the route network
•
An improvement in ﬂight safety
•
An improvement of air transport’s environmental friendliness (reducing the
environmental impact of air transport)
•
The complication of the operating conditions of aviation equipment
•
The diversiﬁcation of the ground infrastructure
•
A rise in operational efﬁciency and the diversity of aviation business models
•
The increased intermodality of trafﬁc
•
An expansion of operating conditions for ships
•
The development of technologies in maritime areas (e.g., the biotechnology of the
seas)
•
The intensive development of the shelf, particularly the Arctic and the Antarctic
•
The proliferation of new economic models on the goods delivery market
Based on these trends, we can identify the groups of key factors that have the
greatest impact on emerging technologies (Fig. 7.4).
Technological capabilities (primarily horizontal technologies) represent the
factors contributing to the development of emerging technologies on the part of
technology push (for more detail, see Sect. 7.3.2 Global Technology Trends). The
majority of other factors characterize the socioeconomic and environmental needs,
thus forming the requirements for the various parameters of vehicles. Therefore, we
can consider these factors as those from the market pull side. Groups of factors
“efﬁciency,” “mobility,” “individualization,” “ecology,” and “security” are used as a
basis for scenario analysis and are discussed in more detail in Sect. 7.3.3, Scenarios.
160
M. Klubova et al.
s
e
g
n
e
l
l
a
h
C
 
d
n
a
r
G
Social
Technological
Economic
Ecological
Political 
Value
s
r
o
t
c
a
f
 
y
e
K
s
r
o
t
c
a
f
 
y
e
K
Individualisation
a new stage of the global economy technological development including industry structure change
new models of economic development, including the transformation of global value chains, etc.
conflicts intensification in several world regions
the threat of terrorist acts
changing lifestyle
 
б аланс «работа - личная жизнь»
depletion of strategic mineral resources, the search for new sources of energy and energy security
energy prices increase
formation of technological base for the main players of the world market
importance of information technologies for the economic development
aging population
growth of the mobility of
population 
increasing size of the middle
class, etc. 
Better mobility, less congestion, 
more safety and security
Efficiency
Mobility
Sustainable 
environmental 
development
Technological 
capabilities
Traffic
Directions of movement
Infrastructure
Velocity
Types of 
passengers
Distance
Freight cost
Horizontal 
technologies 
New knowledge 
and technologies
TRL
Competence
Capacity
Quality control
Resources
Noise level
Types of 
transport
Multimodality
Payload
Emission level
Depletion of 
natural resources
Climate 
change
Dual use 
relevant for both 
civil and military 
applications
IT spread
Devices
Risk of 
terrorism
Fuel costs
Time value
Production 
costs
Accident rate
Safety rate of 
national transport 
systems
Fig. 7.4 Key factors impacting emerging technologies individualization. Source: HSE
7.3.2
Global Technological Trends
The study of the socioeconomic development from the perspective of global
challenges has led to the development of the concept of emerging technologies,
which are designed to respond to said challenges. A bibliometric analysis of the
above topics conﬁrms this thesis. So, to the query “emerging technologies,” the
following results were obtained; in the last decade, growth periods (2005–2008 and
2011–2015) and a certain decline of interest in the topic have been noted. The total
number of publications during the period under review exceeded 2000 articles
(Fig. 7.5).
A further semantic analysis of the publications allowed us to identify the thematic
categories.
Figure 7.6 presents the key areas of publication activity on “emerging
technologies” (engineering, electronic):
– Engine arrangement
– Control devices
– ICT technologies
– Infrastructure
7
Technology Roadmaps: Emerging Technologies in the Aircraft and. . .
161
0
50
100
150
200
250
300
350
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Fig. 7.5 Publications on “emerging technologies” (engineering, electronic). Source: HSE based on
Web of Science
Fig. 7.6 Semantic analysis of the publications on “emerging technologies” (engineering, elec-
tronic). Legend: for each item, the size of the item’s label and the size of the item’s circle depend on
the prominence (weight) of the item. The distance between two terms provides an indication of the
number of co-occurrences of the terms. The colors divide items into clusters (VOSviewer Manual
2016). Source: HSE based on Web of Science
162
M. Klubova et al.
The bibliometric analysis on the ﬁeld of emerging technologies helps one form an
idea of the key research issues and the areas of their inﬂuence.
Global technology trends have a crosscutting impact on all areas, including the
aviation and shipbuilding industries, by creating opportunities for innovative devel-
opment. The main global technology trends identiﬁed in the study include:
1. Research in the ﬁeld of alternative energy sources
2. The transition to advanced manufacturing
3. The digitalization of production (industrial Internet of things, “factories of the
future,” big data, augmented reality, cloud technologies, etc.)
4. The move to the “Internet of Everything” (Bandyopadhyay and Sen 2011)
5. The development of information analysis technologies
6. The transition to new interfaces (neuro-, bio) and augmented reality
7. The development of materials with new properties
8. The use of additive technologies
For example, we can examine the inﬂuence of additive technologies on the
aircraft, shipbuilding, and other industries in more detail. Additive production
implies direct manufacturing of three-dimensional parts without the use of interme-
diate operations for processing blanks (raw material savings of more than 70%). The
demand for additive production is attributable to industry needs for the simpliﬁcation
and acceleration of the process to create individual parts. Layer-by-layer processing
allows for creating three-dimensional products according to a given individual
pattern. This technology refers to a new industrial stage with a modiﬁed production
chain.
Today, additive production processes (AP processes) are actively used in rapid
prototyping. AP technologies are also used for the rapid production of tools for
printing and molds.
With the improvement of technology quality, the scope of additive production is
substantially expanded. Currently such technologies are used in the production of
goods and products, including elements of mechanisms, parts, and assemblies for the
aerospace, defense, shipbuilding, automotive, and electronics industries.
The simultaneous appearance and complementarity of these trends enhance the
positive effects and contribute to the formation of such scientiﬁc and technological
areas as:
– Robotics
– Sensor systems
– Systems of artiﬁcial intelligence
– 3D printing
– New materials
– ICT
– Microelectronics
– Storage and power converters
– Holographic technology
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Technology Roadmaps: Emerging Technologies in the Aircraft and. . .
163
Table 7.2 Important facts concerning technological areas for the aircraft and shipbuilding
industries
More than 1000 parts of the A350 are now made by 3D printing—more than on any other
commercial aircraft
Parts created by 3D printing are 30–55% lighter and use 90% fewer raw materials than those made
by traditional methods
3D printing will affect the technologies used by the Navy for the modeling and design of ships,
submarines, aircraft carriers, and of everything else that is on board
3D printers produced by the German company BigRep are already used in the production of
screws
The launch in 2018 of the Zephyr T drones by Airbus, powered only by solar energy
Source: Benthien et al. (2015), Hiufu Wong (2016), Airbus (2016)
As presented in Table 7.2, robotics and 3D printing technologies have already
found a wide application in the aircraft and shipbuilding industries. Further, it is
likely that horizontal technologies will be used increasingly often in these industries.
The most popular horizontal technological areas used in different parts of aircraft
and ships and during their production processes include:
– Sensor systems and 3D printing used during equipment, design, and production
processes
– Nanotechnology and new materials used in engine production, materials, and
design
7.3.3
Scenarios
The key factors have a different impact on the development of the transport sector.
An analysis of the possible areas of inﬂuence of separate factors (Fig. 7.8) allows one
to group the emerging technologies in three scenarios.
The impact of key factors (Fig. 7.4) varies across different scenarios of the
transport sector’s development. The construction of the scenarios is based on the
main groups of transport sector development factors: efﬁciency, ecology, security,
and individualization (Table 7.3). An analysis of the separate factors’ possible
inﬂuence (positive or negative) (Fig. 7.8) allows one to group the emerging
technologies into three scenarios:
– Scenario A “National industries”
– Scenario B “Green energy”
– Scenario C “Transport individualization”
For example, the growth of such a factor as “trafﬁc and distance” contributes to the
development of scenario A and scenario B, while, other things being equal, this
development prevents the emergence of scenario C or postpones the time of its
implementation.
+
+
+
+
+
164
M. Klubova et al.
Table 7.3 Application of horizontal technologies in aircraft and shipbuilding industries
Horizontal
technology/
applications
Engine
Equipment
Materials
Design
(construction,
etc.)
Production
technologies
Robotics
+
+
Sensors
+
+
+
Artiﬁcial intelligence
+
+
3D printing
+
+
+
Nanotechnology and
new materials
IT
+
+
Microelectronics
+
+
Accumulators and
energy converters
Source: HSE
Information presented in Table 7.4 is complemented by Fig. 7.7 and gives the
reasoning behind the selection of key technologies for the development of each
scenario. The key factors encompass the requirements for the parameters of future
vehicles.
Expected technological breakthroughs will improve the design and construction
of vehicles, testing, etc. However, the pace of social, environmental, and value
changes poses an ever-increasing number of challenges for the transport industry.
To answer such challenges, advanced technologies are needed. The improvement of
transport efﬁciency by means of optimizing fuel consumption and reducing the cost
of maintenance, etc., may contradict targets for improving environmental impacts,
for example, the reduction of aviation noise levels. In this regard, there are alterna-
tive technological forks, which are distributed between different scenarios.
Scenario A: National Industries
The main tasks under this scenario are balanced economic growth, the increase of
high-tech product exports, and safe operation of the transport system. The key
concept of transport sector development in this scenario is technology push. State
policy incentivizes the development of shipbuilding and aircraft building as system-
forming industries of the economy. The main technological objectives ﬁnanced by
the government are to increase the safety of passenger transportation, cargo trans-
portation, the functioning of transport infrastructure, increasing production efﬁ-
ciency, and competitiveness of products. Main players are state scientiﬁc centers,
large aircraft and shipbuilding corporations, and public authorities.
7
Technology Roadmaps: Emerging Technologies in the Aircraft and. . .
165
Table 7.4 Matrix of main groups of factors/scenarios
Scenario A National
industries
Scenario B Green
energy
Scenario C Transport
individualization
Efﬁciency
■■■
□□□
■■□
Ecology
■■□
■■■
□□□
Security
■■■
■■□
■■□
Individualization
■□□
□□□
■■■
Source: HSE
Fig. 7.7 Matrix key factors/scenarios. Source: HSE
166
M. Klubova et al.
Examples of technologies and technological solutions:
– Open rotor
– Advanced turbofan
– Virtual engineering
– Robotics for smart welding
– Intermetallic alloys for combustion chimneys
– New polymers, thermoplastic
Scenario B: Green Energy
Main tasks under this scenario are improving the ecological situation, preserving
biodiversity, and reducing the likelihood hazardous natural phenomena and
man-made environmental catastrophes. The key concept for transport sector devel-
opment is the search for solutions to environmental problems. The main technologi-
cal areas are the reduction of the level of emissions of CO2, NOx, etc., a decrease in
noise pollution caused by vehicles, and the reduction of the negative impact trans-
port infrastructure has on the ecosystem and solving problems of vehicle utilization.
Main players are public organizations, state scientiﬁc centers, and large aircraft
building and shipbuilding corporations.
Examples of technologies and technological solutions:
– Hybrid vehicles
– Electric engines
– Research on alternative fuels
– Biopolymer membranes
– Floating power plants
– Wind turbine-powered sea vessels
Scenario C: Transport Individualization
The main tasks under this scenario are raising attention to individual needs when
designing vehicles, providing convenience and accessibility of transport for the
public and businesses. The key concept of transport sector development in this
scenario is market pull. The rising need for multimodal transport systems on the
one hand and the emergence of opportunities to overcome the same distance in about
the same time by different vehicles on the other contribute to the emergence of new
types of transport and personalization of services. The management of individual
mobility will be carried out using new information and communication technologies.
Accordingly, the main players are small and medium businesses, vehicle users, and
software developers.
Examples of technologies and technological solutions:
– Flying cars (Toyota 2017)
– Craft-to-craft communication
7
Technology Roadmaps: Emerging Technologies in the Aircraft and. . .
167
– Jetpacks
– Vertical takeoff aircraft
– Bionic structures
– Self-healing, multifunctionality, a new generation of rolled metal ﬁbers, and
smart materials
– Multi-criteria multiparameter optimization and virtual testing technologies
7.3.4
Technology Roadmap “Emerging Technologies in the Aircraft
and Shipbuilding Industries”
Our roadmap was developed on the basis of the approaches of the world’s leading
centers in the ﬁeld of strategic forecasting and planning, taking into account the
results of foreign strategic documents in the ﬁeld of aircraft and shipbuilding
development, and also based on the results of expert analysis.
To specify the format of the visual representation of the roadmap, we created a
hierarchical structure of the roadmap, which deﬁnes the relationships between global
trends and emerging technologies (Fig. 7.8).
Fig. 7.8 Structure of the
technology roadmap
“emerging technologies in the
aircraft and shipbuilding
industries.” Source: HSE
168
M. Klubova et al.
7.4
Conclusions
The aircraft and shipbuilding industries have a multiplier effect on the socioeco-
nomic development of countries. Grand challenges, including technological, social,
economic, and others, affect the vision of the future for these industries. This
inﬂuence is expressed in the emergence of new technologies and products,
modiﬁcations of business models, etc. As a consequence, one of the priority tasks
of states is to determine the directions of development as well as the emerging
technologies in these sectors.
The study also included a comprehensive analysis of the grand challenges
affecting the development of the aircraft and shipbuilding industries in the medium
and long term. The construction of scenarios was based on the main groups of factors
affecting the development of the transport sector (efﬁciency, ecology, security,
individualization), which in their turn are inﬂuenced by grand challenges. According
to the results, the roadmap “Emerging Technologies in the Aircraft and Shipbuilding
Industries” was developed. The strategic documents in the ﬁeld of aircraft and
shipbuilding were used as the background for this paper. The methodology used is
based upon a comprehensive analysis of STEEPV factors, scenarios, and roadmap
development.
As a result of this study, several interesting conclusions may be drawn. First, the
STEEPV analysis of the key factors helped us identify the prospects for the devel-
opment of the aircraft and shipbuilding industries in light of the social, technologi-
cal, economic, environmental, political, and value aspects. During the research, more
than 150 challenges were identiﬁed and classiﬁed by STEEPV. Some of them
created market trends, such as sustainable trafﬁc growth; energy efﬁciency as a
key factor determining transportation preferences; transport globalization and liber-
alization; uneven development of air transportation; customized, personalized trans-
port services combined with or supplemented by information and communication
devices; the consolidation of the world market: mergers, acquisitions, and alliances;
the concentration of industries and the formation of global corporate leaders; and the
emergence of new large-scale corporate players and regional production centers.
These trends signiﬁcantly affect the technological development of the shipbuilding
and aircraft industries.
Second, the study revealed that grand challenges create trends in the shipbuilding
and aircraft industries and in related ﬁelds. For example, the trends emerging on the
passenger and freight transport markets determine the prospects for aircraft techno-
logical development as they create demand for certain aircraft technologies and
components.
Third, the effects of the development of the transport market that were spurred on
by grand challenges and macroeconomic trends were deﬁned in this paper. These
include the extension of the route network; the improvement in ﬂight safety and the
environmental friendliness of aircraft; the increased complexity of aviation
equipment’s operating conditions; and the diversiﬁcation of ground infrastructure.
The bibliometric analysis identiﬁed the main technology trends and nine key
technology areas determining the technological development of the aircraft and
¼
¼
shipbuilding industries which are robotics, sensor systems, artiﬁcial intelligence
systems, 3D printing, new materials, ICT, microelectronics, storage and power
converters, and holographic technology.
7
Technology Roadmaps: Emerging Technologies in the Aircraft and. . .
169
On the basis of the aforementioned results, three scenarios were developed:
Scenario A “National industries,” Scenario B “Green energy,” and Scenario C
“Transport individualization.” Each scenario assumes the need for the priority
development of various groups of technologies addressing grand challenges. The
scenario development is especially important in view of the high capital intensity of
the aircraft and shipbuilding industries.
As a result of the research, we developed the “Emerging Technologies in the
Aircraft and Shipbuilding Industries” roadmap. It demonstrates the relationships
between the impact of grand challenges and the possible directions for the techno-
logical development of the aircraft and shipbuilding industries, which are grouped
according to the three scenarios. In addition, the roadmap shows how each factor
affects the feasibility of implementing each scenario (whether it facilitates or hinders
the implementation of each scenario). The roadmap provides an opportunity to
transform priorities in the aircraft and shipbuilding industries into speciﬁc technol-
ogy development projects.
The results of this study, including the developed roadmap, can be used as an
instrument for priority setting, the selection of innovation projects, and their imple-
mentation by policy-makers and other users of the results of foresight studies. It
stipulates scenario forks within the set of emerging technologies, which contribute to
bringing the industries to a desired future state.
Acknowledgments The book chapter was prepared within the framework of the Basic Research
Program at the National Research University Higher School of Economics and supported within the
framework of the subsidy by the Russian Academic Excellence Project ‘5-100’.
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Marina Klubova is Research Fellow at the International
Foresight Center, HSE ISSEK. She obtained her Master’s
degree in Economics from the Lomonosov Moscow State
University. Ms. Klubova was involved in projects related to
roadmapping and market analysis, including “Technology
Foresight of Civil Shipbuilding to 2030”, “Roadmaps of
S&T Development of Civil Aviation” among others. Her
research interests cover S&T and innovation policy, strategic
planning, roadmapping and scenario analysis.
Lyubov Matich is the Неad of Project Management Unit at
the HSE ISSEK. She obtained her Master’s degree in Eco-
nomics from the Lomonosov Moscow State University.
Ms. Matich prepared a wide number of corporate and
industry-level projects on innovative development programs
for Russian companies; technology roadmaps for the space,
petro, and aircraft industries among others. Her professional
interests refer to company strategies, management of
innovations, and technology roadmapping.
7
Technology Roadmaps: Emerging Technologies in the Aircraft and. . .
173
Vladimir Salun is Director of the Center for Industrial and
Corporate Projects at the HSE ISSEK. The main activities of
the Center are focused on innovative development of the key
sectors of the Russian economy, introduction of advanced
methods in strategy planning and project management at
state-owned and private companies, improvement of their
competitiveness through better selection and implementation
of STI priority objectives. Dr. Salun has an excellent record
of managing numerous consulting assignments for leading
Russian corporate companies, in particular related to devel-
oping their innovation strategies and technology roadmaps.
He has PhD degree in Physical and Mathematical Sciences.
Natalia Veselitskaya is Senior Researcher at the HSE
ISSEK Foresight Center. Natalia holds a PhD in Innovation
Management. She has worked on strategic foresight studies
for the support of research and innovation and for corporate
innovation management. Ms. Veselitskaya focuses on the
implementation of foresight methods for the resolution of
different technological and socio-economic challenges. In
particular,
she
is
engaged
in
developing
technology
roadmaps for companies, regions and industries.
Part III
Living Systems and Environment
177
Water Treatment and Purification:
Technological Responses to Grand
Challenges
8
Ozcan Saritas and Konstantin Vishnevskiy
8.1
Introduction
Nanotechnologies have been increasingly used in wide-ranging application areas.
The present chapter sheds light the potential uses of nanotechnologies for supplying
clean water. Water is one of the Grand Challenges facing humanity. The chapter
begins with a discussion on the increasing demand for fresh water across the globe
and technological challenges and opportunities associated with water supply. It is
shown that new technologies are needed for effective and resource-efﬁcient water
treatment and nanotechnologies may offer affordable solutions.
Overall, the chapter advocates that supplying drinking water to billions of people,
while at the same time protecting water resources, is the best strategic guideline for
the water supply industry and for applying new techniques and materials including
nanotechnology. Developing new technologies for deep puriﬁcation of water with
nanotechnology will increase healthy water supply by removing both visible and
invisible impurities hazardous to human and animal health. Thus, the chapter
discusses nanotechnology solutions for water treatment and puriﬁcation. How
nanotechnologies can signiﬁcantly increase the efﬁciency of certain traditional
water puriﬁcation processes such as coagulation, sorption and ﬂotation is discussed.
Next technological, market and institutional aspects are considered regarding nano-
technology solutions. The chapter is concluded with future scenarios and strategic
steps to be taken for the implementation of nano-based water treatment and
puriﬁcation.
While investigating in nanotechnology and its uses for water treatment and
puriﬁcation, the chapter focuses on the case of Russia. More recently, Russia is
O. Saritas (*) · K. Vishnevskiy
National Research University, Higher School of Economics, Moscow, Russia
e-mail: osaritas@hse.ru; kvishnevsky@hse.ru
# Springer Nature Switzerland AG 2019
D. Meissner et al. (eds.), Emerging Technologies for Economic Development,
Science, Technology and Innovation Studies,
https://doi.org/10.1007/978-3-030-04370-4_8
engaged in improving its energy and water infrastructures, as well as more efﬁcient
use of its natural resources (Saritas 2015). However, the country still experiences a
set of challenges related to the protection and use of water resources, water puriﬁca-
tion and networks, consumption patterns, discharge, treatment and reuse (Saritas and
Proskuryakova 2017).
178
O. Saritas and K. Vishnevskiy
8.2
Background
Grand Challenges are deﬁned as global problems in the long-term that are potentially
capable of changing the world. They are challenges faced by everyone (Saritas and
Miles 2012). Coming up with adequate solutions would increase well-being and
quality of life and improve the environment—and this requires raising awareness
and fostering cooperation among key stakeholders (Miles et al. 2016). Grand
Challenges can emerge in economic, social and research spheres, but for any such
challenge, it requires responses at regional, national and global levels. Furthermore,
the potential consequences of Grand Challenges should be made clear, and scientists
should be able and willing to propose adequate responses which have to meet
stringent criteria, including the practical applicability of relevant research, and a
potential for increasing efﬁciency as well as the availability of adequate economic
and social investments. In order to develop effective technology and innovation-
driven responses, the principles and efﬁciency of interindustrial priorities need to be
analysed which might not be directly connected with a speciﬁc science and technol-
ogy (S&T) area and application of relevant results. Also there is consensus that the
objectives have to be achieved at international and national levels (i.e. those set to
meet global challenges) which will eventually lead to increased well-being and
quality of life, improved environment and stronger internal and external competi-
tiveness of countries and regions. Consequently, these challenges are seen as key
global subject areas thus regularly included in strategies and forecasts developed by
relevant government agencies and international organisations (e.g. environment
protection, information technologies, etc.). It is broadly understood that these
challenges may turn into opportunities associated with applying innovative products
and serve as an initiator for overcoming established ‘bottlenecks’ of innovation
systems.
Water is one of the most tangible and fastest-growing challenges the humanity
faces today. The water security is a grand challenge which is strongly tied to
survival, social well-being and economic growth (Inayatullah and Elouaﬁ2014). It
is also a crucial element of the water-energy-climate-food nexus. As an example
Maggio et al. (2016) discuss the role of water for global food security. Sustainable
energy policies require taking water into consideration (Horner et al. 2016). Moriarty
and Honnery (2014) highlight ongoing climate change and impacts on water. The
world is expected to face a 40% global shortfall between estimated demand and
available supply by 2030 (UNwater 2015).
Water is not just a ‘life source’ but also a source of many diseases. The ‘shortage
of drinking water’ is associated with the growth of sickness rates as well as
environmental pollution. These are among the other global challenges to which
scientists all over the world are seeking adequate responses. Polluted water causes up
to 80% of all human diseases in the world and accelerates ageing by 30%. More than
a billion people experience a shortage of safe drinking water, and 2.4 billion do not
have access to water which meets established sanitary norms. This means that almost
a third of the planet’s population lives in areas experiencing a shortage of pure
drinking water. According to certain forecasts, by 2025 this share will increase to
two thirds (Prüss-Üstün et al. 2008).
8
Water Treatment and Purification: Technological Responses to Grand Challenges
179
The low quality of drinking water supplied is frequently caused by several
natural, anthropogenic and technology-related reasons. In some regions, the quality
of water is affected by natural concentrations of pathogenic microorganisms in the
atmosphere, soil, water, ﬂora and fauna. They can engender epidemics of infectious
and parasitic diseases such as cholera, typhoid fever, salmonellosis, dysentery,
amoebiasis, lambliasis, viral hepatitis or poliomyelitis. There are areas with naturally
increased background radiation level, and in certain regions water has a signiﬁcantly
increased concentration of microelements.
Among the reasons for low water quality are inadequate drinking water puriﬁca-
tion technologies and secondary pollution in the course of treatment and transporta-
tion through pipeline networks. A signiﬁcant trigger of deteriorating water quality in
densely populated areas, and the subsequent growth of oncological and other serious
diseases, is untreated or inadequately treated waste water dumped into natural
reservoirs after industrial, communal or agricultural use. This sewage contains vast
amounts of mineral and organic pollutants (contaminants) ranging from heavy
metals to oil products, which negatively affect the ecosystem. Such pollutants are
a result of faulty industrial waste treatment and sewage systems’ application. They
may also be caused by the dissemination of algae from increased concentration of
phosphorus and nitrogen in waste products, detergent powders and fertilisers.
Dangerous substances are also generated during soil erosion, when pesticides,
chemical reagents and heavy metals enter the water supply. Another cause is
pollution of subterranean waters during intense and inefﬁcient irrigation.
A list of industrial waste water contaminants would number millions. Many of
these substances have carcinogenic and mutagenic properties. For example, benzo-
pyrene causes oncological diseases and is classiﬁed as a Class 1 dangerous sub-
stance. It is a product of burning organic fuels and is commonly present in areas with
heavy automobile trafﬁc; particularly high concentrations are also observed near
fuel-burning power plants.
Substances which can cause serious diseases are widely used in agriculture. These
include mineral fertilisers which on the one hand increase crop yield while on the
other increase concentration of nitrates, phosphates and potassium in soil and
subterranean waters—and thus in vegetables, fruit and drinking water. Up to 65%
of nitrates introduced into the human digestive tract subsequently ﬁnd their way into
blood and tissue, hindering their ability to take in oxygen. Pesticides are also
dangerous. There are approximately a thousand of such substances which do not
degrade for many years. Rather, they accumulate and migrate in the environment and
ﬁnd their way into water and then into the human body.
180
O. Saritas and K. Vishnevskiy
Fig. 8.1 Industry-speciﬁc water supply challenges in Russia. Source: HSE
Thus the current state of water puriﬁcation and treatment industries indicates that
a whole system of challenges exists on their agenda. In summary, these challenges
can be grouped into three main types:
1. Global—due to the growth of industrial and anthropogenic pollution of water
sources, shortage of drinking water, spreading of serious diseases, ageing of
population
2. Regional—due to more stringent requirements for the quality of consumed water
as 50% of surface water sources do not match ofﬁcial standards and only about
30% of subterranean water is treated
3. Industrial—due to depreciation of capital assets of the water supply and sewage
industry
Obsolete and worn-out equipment of the water supply systems increase the
number of accidents. These, in turn, cause loss of water and soil erosion and
damaged roads and building foundations. Figure 8.1 shows the chain of events in
the water supply in the case of the Russia.
It is increasingly clear that meeting these challenges requires radically innovative
S&T solutions (Saritas and Proskuryakova 2017). Past few decades have seen the
emergence of nanotechnologies as one of the key pillars of industrial development.
A number of forward-looking studies have been undertaken at various levels of
governance, including national (Saritas et al. 2007; Vishnevskiy and Yaroslavtsev
2017), regional (Battistella and Pillon 2016) and sectoral levels (Aydogdu et al.
2017) to investigate the ways of exploiting nanotechnologies in various S&T areas.
Other studies have been undertaken to explore the social, economic and environ-
mental aspects of nanotechnologies within the framework of foresight studies
(Loveridge and Saritas 2009, 2012). The present chapter takes a closer look at the
nanotechnology solutions for water treatment and puriﬁcation. With a forward-
looking methodology, the study makes use of frequently used foresight methods
including, reviews, SWOT analysis, forecasting, scenarios and strategy planning
(Saritas and Burmaoglu 2015).
8
Water Treatment and Purification: Technological Responses to Grand Challenges
181
8.3
Nanosolutions and Alternatives for Water Treatment
Water treatment, as well as sensible management and use of water resources, is a
major area of S&T activities. The scientiﬁc community clearly realises the impor-
tance of developing new technologies for deep puriﬁcation of water to remove
visible and invisible impurities hazardous to human health. Supplying drinking
water to billions of people, while at the same time protecting water sources, are
the best strategic guidelines for the water supply industry and for applying new
techniques and materials including nanotechnology (Bottero et al. 2006; Theron
et al. 2008).
Applying nanotechnologies can signiﬁcantly increase the efﬁciency of certain
traditional water puriﬁcation processes such as coagulation, sorption and ﬂotation
(Draginsky et al. 2005). Membrane-based technologies that are used to make
molecular nanostructured materials, including baro-membrane and electromembrane
processes, membrane bioreactors and membrane degassing already exist (Desiatov
et al. 2008; Nikolayev 2008; Poliakov and Vidiakin 2009). Technological solutions
based on nanoproducts are available, including carbon nanotubes and fullerenes,
dendrimers, zeolites and catalysts. At present, the following technologies are in the
process of being launched: extremely efﬁcient carbon nanotube-based sorbents,
organic-mineral composites containing highly selective nanoparticles in an organic
matrix and innovative molecular mesh sorbents, among the others. However, the
priorities and prospects for applying these materials by interested parties remain
rather ambiguous.
There is a broad consensus that it is currently quite challenging to determine
which water treatment and puriﬁcation techniques should be classiﬁed as nanotech-
nology. Sorption technology and the use of catalysts are most frequently named as
solutions based on nanotechnology. All remaining traditional water treatment
techniques are not considered nanotechnology-related. Table 8.1 presents the
advantages and disadvantages of traditional technologies, with potentials of apply-
ing nanotechnologies, and alternative technologies, where nanotechnologies are
largely not applicable.
182
O. Saritas and K. Vishnevskiy
Table 8.1 Advantages and disadvantages of traditional and alternative technologies
Name
Description
Advantages
Disadvantages
Traditional technologies where nanotechnologies can be applied
Sorption
Solid or liquid
substances, with
sorption happening on
the surface of its
particles. Silica gel,
activated carbon, white
carbon, certain oxides,
resins, etc. are used as
adsorbents
An adsorbent can
absorb only one
substance, i.e. they act
selectively (something
membranes are not
capable of). This
technique can be
efﬁciently applied if
water is puriﬁed to a
very low concentration
of pollutant: adsorbent
consumption would not
be high, which makes
perfect economic sense
After a while the
adsorbent becomes
saturated and needs to
be replaced
In certain cases, the
costs of applying this
technology can be quite
high
Catalysis
Substances that
accelerate water
puriﬁcation process
The main advantage of
using catalysts is that
they only accelerate the
process and do not
actually take part in the
process
The catalyst remains in
the water and may harm
people and the
environment
Alternative (non-nano) technologies
Chlorination
Treating water with
chlorine and chlorine
compounds. The most
common technique for
drinking water
disinfection; based on
free chlorine and the
ability of its compound
to suppress microbes’
enzyme systems by
catalysing redox
processes
Efﬁciently kills
pathogenic germs
Chlorination is
necessary; without it,
unchecked breeding of
microorganisms would
make water unsuitable
for drinking
If a certain
concentration is
exceeded, water
acquires an unpleasant
odour
Chlorinated water has
an adverse effect on
human skin
Interaction between
chlorine and certain
organic compounds
produces their chlorine
derivatives which are
hazardous to human
health. Some such
compounds have
carcinogenic properties
Ozonisation
Based on application of
ozone gas. After
interaction with
chemical and
microbiological
pollutants, ozone
transforms into ordinary
oxygen
Ozonisation kills germs
and does not leave an
unpleasant odour. No
hazardous by-products
remain in the water
A large amount of
sediments may
accumulate during
chemical treatment
Ozone production is
energy-intensive;
accordingly, it is quite
an expensive product
Coagulation,
ﬂocculation
Introduction of
aluminium or iron salts
Application of this
technique requires
(continued)
8
Water Treatment and Purification: Technological Responses to Grand Challenges
183
Table 8.1 (continued)
Name
Description
Advantages
Disadvantages
or polyelectrolytes into
treated water. This
enlarges suspension and
makes colloid particles
stick together, become
larger, and sediment
This technique is
relatively inexpensive
and quite efﬁcient
subsequent additional
treatment as traces of
reagents remain in the
water
Flotation
This method is based on
removing impurities
with the help of air
bubbles. Coming to the
surface, they capture
impurity particles
including oils and oil
products, creating a ﬁlm
or a foam layer on the
water surface, which is
subsequently removed
with the help of special
foam-gathering
mechanisms
Can treat large volumes
of water and is quite
inexpensive
Flotation is not
particularly effective
compared with
membranes in terms of
removing impurities. A
certain amount of
impurities remain in the
water. If we try to
achieve better results
through ﬂotation, it
becomes a very lengthy
process
Distillation
Distillation—
evaporation of liquid
with subsequent cooling
and condensation of the
vapour. When water is
evaporated through
phase transition, it is
puriﬁed of almost all
impurities
Distillation is quite a
simple and very
efﬁcient technique
Distilled water in effect
is unsuitable for
drinking
Distillation is quite an
expensive technique
Source: HSE
Alternative technologies are believed to have the following major disadvantages:
•
Use of chemical reagents
•
Short reaction time
•
Saturation of water with traces of reagents
•
High costs
•
Insufﬁcient puriﬁcation of water
•
Need to use bulky equipment
However, nanotechnologies discussed across this chapter should not be seen as
competing alternatives; rather, they should be merged together to develop integrated
technologies. For example, the application of micron-grade carbon powder requires
special devices for its subsequent removal. Therefore, a combination is used
(i.e. carbon powder plus membrane), because submicron-sized particles get through
regular ﬁlters but not through such ﬁne ﬁltering elements as membranes.
Nanoﬁltering and reverse osmosis membranes offer signiﬁcant advantages but
also have some disadvantages (Karelin 1988). The main difﬁculty is that such
puriﬁcation in effect produces a distillate unsuitable for drinking. Therefore, to
produce regular water, membrane-based techniques must be applied in combination
with others. In addition, membrane nanotechnologies generate about 30% waste.
Ion-exchange technology was proposed to utilise this waste. They work together
well, which reduces the amount of water dumped into sewage. Application of
ion-exchange technology to treat waste water generated through membrane
nanotechnologies-based water puriﬁcation signiﬁcantly reduces the cost of
ion-exchange installations (at least threefold). In future, an integrated approach
may be used which takes into account various technologies’ speciﬁc features and
input water properties. It is also believed that in future selective membranes will be
developed, ﬁltering out only speciﬁc components. This would enable water to be
puriﬁed from all kinds of pollutants while retaining all necessary elements. Such
membranes can be self-structuring, working in a similar way to biological analogues.
They would enable natural water resources to be restored, which would ultimately
solve the problem of providing adequate drinking water supply for all.
184
O. Saritas and K. Vishnevskiy
In addition, to the above technologies, promising water treatment technologies
mentioned by experts include electric pulse water treatment. Under certain
conditions, alternative products such as various ﬁlters and sterilisers can also be
used. Their advantages and disadvantages deserve a closer look.
Sediment Filters
Sediment ﬁlters are used primarily to remove mechanical and organic suspensions,
sand, rust particles and colloid substances. The main drawback of this class of ﬁlters
is its relatively low take-up capacity of solids or heavily polluted water as this means
they require frequent cleaning when actively used. In such situations, automated
particulate ﬁltering systems seem more appropriate. The ﬁlter medium is dry alu-
minium silicate, which allows particles above 20 microns in size to be ﬁltered
(Lysov et al. 2002). To remove larger particles (20–50 microns), mesh or disk coarse
ﬁlters are used. To remove small particles (5–20 microns), special ceramic
(Maсrolite) particulate ﬁlters are applied.
De-ironing Filters
The main purpose of this ﬁlter class is to remove dissolved iron and manganese from
water and some other compounds. Certain ﬁlters of this type also quite efﬁciently
remove dissolved hydrogen sulphide. The ﬁlter medium is manganese dioxide-
containing substances such as Birm, Filox, Greensand, etc., which act as catalysts
for the oxidation reaction. As a result of the reaction, unwanted impurities dissolved
in water (iron or manganese) transform into insolubles and sediment into the ﬁltrate
layer; they are then washed out during backwashing. A speciﬁc feature of this
technology is that it does not require special reagents for backwashing. Some of
the ﬁlter media do require regeneration (with potassium permanganate).
Softening Filters
This class of ﬁlters is designed to soften water, i.e. remove hardness salts like
calcium and magnesium (Alekseyev and Gladkov 1994). Using special particulate
agents also removes a certain amount of organic compounds, heavy metal salts,
manganese, iron, nitrates and sulphates. The ﬁltering medium is cation-exchange
resins which have high hardness salt take-up capacity. Such ﬁlters require periodic
regeneration with saline solution, which implies a need for special (saline) vat to
make and use such solutions.
8
Water Treatment and Purification: Technological Responses to Grand Challenges
185
Charcoal Filters
Charcoal ﬁlters are designed to remove dissolved gases, residual chlorine and certain
organic compounds from water, get rid of unpleasant odours and generally improve
the water’s organoleptic properties. The ﬁltering medium here is ﬁbrous activated
charcoal which, due to its high absorptive capacity (1 g of such material can absorb
up to 200 mg of pollutants), means it can efﬁciently absorb dissolved gases and
compounds. To increase service life and prevent ‘volley’ dumping of pollutants into
an output channel, activated charcoal made of coconut shells is used in charcoal
ﬁlters, which has a high absorptive capacity (four times higher than traditional
charcoal). This reduces the need to frequently change the ﬁltering medium (neces-
sary if traditional materials are used because organic compounds accumulating in the
ﬁltrate are quite hard to extract via backwashing). To prevent biologic overgrowing,
special charcoals with bacteriostatic additives are used in charcoal ﬁlters.
Ultraviolet Sterilisers
Ultraviolet sterilisers are used to disinfect water and to neutralise germ pollution—
microbes, bacteria and viruses in water. Ultraviolet irradiation is the most common
way of neutralising germ pollution. This method is different from other methods
(ozonisation or chlorination) because it lacks hazardous impurities. Chambers
containing ultraviolet lamps are used as sterilisers, installed in hard cases. Water
ﬂows through such sterilising chambers where it is constantly treated with ultraviolet
radiation.
The experience of international water puriﬁcation shows that traditional
technologies that incorporate innovative components combined with membrane-
based processes can signiﬁcantly increase the efﬁciency of water treatment and
puriﬁcation.
8.4
The Future of Nanotechnology-Based Water Supply
The future of water treatment and puriﬁcation is currently in the process of being
shaped towards incorporating nanotechnologies into the water supply industry to
signiﬁcantly increase the quality of drinking water and reduce the incidence of
diseases caused by polluted water sources and pipeline networks. The present
section discusses various nano-based technologies, products and processes and
explores their feasibility of application.
186
O. Saritas and K. Vishnevskiy
Fig. 8.2 Possible applications of nanotechnologies in industrial water treatment and puriﬁcation.
Source: HSE
8.4.1
Application Areas for Nanosolutions
At this stage, the largest area of application for nanotechnologies is water treatment
for municipal and industrial use (which constitutes 67% of water use), with a major
share of that (up to 95%) belonging to industry. In the near future (before 2020),
given overall market growth, it is likely that we will see a shift in the share of
different segments in favour of the following three areas: municipal waste water
treatment will grow to 10%, applications in major technological processes to 12%
and treatment of industrial waste water to 25%. These segments will show
accelerated growth, with the remaining areas growing at more modest rates. Fig-
ure 8.2 illustrates the possible applications for nanotechnologies in industrial water
treatment and puriﬁcation.
Membrane-based nanotechnologies have good prospects in industrial, as well as
municipal, water treatment and puriﬁcation areas—including nuclear and fuel-
burning power generation (Sterman and Pokrovsky 1991), radio- and microelectron-
ics and food and chemical industries.
Many industries require water of a particular quality and composition, which
creates stable demand for membrane technologies by various industrial companies.
Thus water treatment and puriﬁcation combine three segments: water for the
general population, water for industrial purposes and wastewater treatment. Each of
these areas can be further divided into smaller segments, with speciﬁc problems and
demand for new technologies, products and components.
8
Water Treatment and Purification: Technological Responses to Grand Challenges
187
8.4.2
Market Prospects for Nanoproducts and Nanotechnologies
in Russia
As already noted, water treatment and puriﬁcation market is growing in Russia quite
quickly (Proskuryakova et al. 2018). There is a high demand for household water
ﬁlters, despite the low quality of some of those products. The growing demand is
driven by greater attention to a healthier lifestyle, including the consumption of only
natural and pure food and high-quality water. Meanwhile the market development is
not uniform in all regions. In some regions, demand is increasing rapidly for new
water treatment technologies: for example, Moscow and other large cities are seeing
greater popularity of membrane-based techniques. At the same time, rural areas
which frequently lack any mains water supply have practically no market for water
treatment and puriﬁcation, e.g. in rural areas which are occupied by about 30% of the
total population. Moreover, industrial characteristics also inﬂuence the water treat-
ment market development such as:
•
Signiﬁcant depreciation of capital assets and need for upgrading and replacement
of obsolete equipment in enterprises
•
Modernisation of municipal installations
•
Numerous newly built production facilities and commercial real estate
•
Active construction of housing and social infrastructure
Water puriﬁcation technologies are actively applied in the mass production of
industrial and household ﬁltration systems and are well represented even in the retail
sector. However, there is practically no R&D in Russia that aims to advance drinking
water puriﬁcation processes; only various components of the water supply system
are manufactured which lack mass demand. Neither are there any companies capable
of implementing all stages of the technological chain in the international market.
Russia does not occupy a leading position. Accordingly, ‘Russian-made’ water
puriﬁcation and treatment systems are in effect just assembled from individual
(imported) components.
Remarkably, the prospects for this market segment depend not just on the level of
public funding but also on private investments. The commercial segment shows the
highest growth rate today, so the long-term private-public partnership (PPP)
mechanisms (including long-term contractual agreements) may play a major role.
Another important condition of the growth in the water puriﬁcation market is a close
interdependency between all stages of the technological life cycle. It is important to
note that this interdependency appears in various production segments individually
and if they are brought together under a single management system. However,
production of water puriﬁcation solutions must be divided between various
participants of this process: research should be done at specialised research centres,
development of technologies and the manufacture of water puriﬁcation system
components by specialised public-private corporations and end products by engi-
neering companies. Furthermore, developed countries demonstrate examples of
efﬁcient production of equipment for water treatment systems by single companies
188
O. Saritas and K. Vishnevskiy
Fig. 8.3 The possibilities of applying nanotechnologies in various market segments. Source: HSE
such as Dow Chemical, whose activities include research, technology development
and production of membranes, ﬁlters, water puriﬁcation systems, etc.
Thus, the rapid growth of this market segment makes any assessments of ‘antici-
patory’ technological, socio-economic and organisational steps even more uncertain.
Therefore, the distribution of players in this market, the potential to produce
innovative products and the long-term demand for such products will be particularly
important. Figure 8.3 gives some estimates of demand for membrane-based
technologies in various market segments and the supply structure of products used
in baro-membrane processes for each segment.
Membranes and installations for microﬁltration and ultraﬁltration and for use in
membrane bioreactors in combination with non-membrane technological solutions
show the highest potential demand (Judd 2006). Next, each of these technologies are
described brieﬂy followed by a SWOT analysis for the feasibility of their
application.
Microﬁltration membranes used in tubular modules installations have proved to
be an efﬁcient rough ﬁltration technique applied at the initial stages of the drinking
water production process. Microﬁltration membranes ﬁlter out small suspensions,
ﬁne and colloid impurities, algae and unicellular microorganisms larger than 0.1
microns. Microﬁltration is widely used in medicine, food industry (alcoholic and soft
drinks, wine, beer, vegetable oil and other products) and water treatment
installations. A SWOT analysis of the aforementioned membrane is given in
Table 8.2.
Ultraﬁltration membrane process which is mostly used in installations with
hollow-ﬁbre, ﬂat, roll and tubular modules is a process of separating solutions and
8
Water Treatment and Purification: Technological Responses to Grand Challenges
189
Table 8.2 SWOT analysis of microﬁltration membranes
Strengths
Weaknesses
(compared with alternative products)
(compared with alternative products)
• Compact equipment
• Easily increased output
• Water treatment process can be automated
• Short service life
• Only some pollutants (within a speciﬁc range)
can be removed
• Require periodic washing and cleaning
Opportunities
(external factors contributing to product
promotion)
Threats
(external factors hindering product promotion)
• Need to modernise existing equipment
• Increased requirements for waste water
treatment
• Extremely fast growth of water
consumption
• Development of special-purpose water
treatment segments
• Conservatism of major consumers—water
supply companies
• Budgetary limitations
Alternative products and processes
Particulate ﬁlters, aeration, chemical treatment, disinfection
Source: HSE
Table 8.3 SWOT analysis of ultraﬁltration membranes
Strengths
Weaknesses
• Efﬁciently remove large organic molecules,
colloid particles, bacteria and viruses, without
ﬁltering out dissolved salts
• No need for preliminary chlorination
• More efﬁcient coagulation and sedimentation
with reduced consumption of coagulants and
incomplete coagulation
• Need to be used in combination with other
membrane methods to efﬁciently remove all
pollutants
• Require washing to clean the membrane
from pollutants
Opportunities
Threats
• Additional puriﬁcation of tap water
• Household application
• Conservatism of major consumers—water
supply companies
• Budgetary limitations
Alternative products and processes
Sand granular ﬁlters
Source: HSE
colloid systems using semipermeable membranes. Ultraﬁltration is applied in the
following areas:
•
Clean water surfaces at water intake stations.
•
Perform additional puriﬁcation of water for city mains.
•
De-iron and improve the quality of subterranean water.
•
Treat water for industrial use.8.3.
A SWOT analysis of the aforementioned membrane is given in Table
190
O. Saritas and K. Vishnevskiy
Table 8.4 SWOT analysis of nanoﬁltration membranes
Strengths
Weaknesses
• High level of puriﬁcation
• Produce physiologically perfect drinking water
with good organoleptic properties
• Require thorough preliminary cleaning
from chlorine and other substances
• Roll modules with nanoﬁlter membranes
are quite unreliable
Opportunities
Threats
• Can be applied in local additional puriﬁcation
in buildings
• High export potential
• Conservatism of major consumers—water
supply companies
• Budgetary limitations
Alternative products and processes
Deioniser water-softening installations
Source: HSE
Nanoﬁltration membrane process for application in roll modules installations is a
fractional membrane process which removes only ions that are multiply charged
(such as calcium, magnesium, iron, etc.) from the water, not all salts. Nanoﬁltration
membranes also efﬁciently remove low-molecular compounds. Due to lower energy
consumption than reverse osmosis, nanoﬁltration is successfully applied abroad as
the most commonly used process to produce drinking water from surface sources.
This process provides particularly pure high-quality water for use in medical,
pharmaceutical, electronic, glass-making, food and other industries. Water passed
through a system of membrane ﬁlters is puriﬁed of hazardous bacteria, viruses,
microorganisms, colloid particles (including pesticides), molecules of heavy metal
salts, nitrates, nitrites and other harmful impurities. A SWOT analysis of the
aforementioned membrane is given in Table 8.4.
Reverse osmosis membranes are applied in ﬂat and roll modules installations. The
reverse osmosis method is based on ﬁltering solutions through semipermeable
membranes which let the solvent’s molecules through but fully or partially ﬁlter
out molecules and ions of dissolved substances. This type of membrane is used in
various industries which need very pure water (galvanic production, printed circuits
manufacturing, instrument making, electronics, applying noble metal coatings,
bottled water and drinks production, food industry, pharmaceuticals, etc.). A
SWOT analysis of the aforementioned membrane is given in Table 8.5.
Ion-exchange membranes used in electrodialysis installations and membrane bipo-
lar electrolysers are based on the application of a particular class of substances—natural
or synthetic ionites. Synthetic ionites (deioniser resins) are ‘mesh’ polymers with
various linkage degrees of the gel micro- or macroporous structure, covalently bonded
with ionogenic groups. There are two ways to apply the ion-exchange method for water
treatment and puriﬁcation: using deioniser resins (ionites) and via ion-exchange
membranes—in electrodialysis and electrodeionisation processes. Unlike baro-
membrane techniques (where pressure is applied to separate mixtures through
membranes), electromembrane processes separate mixtures by applying an electric
ﬁeld. Electrodialysis has more potential applications than reverse osmosis. It can be
used for desalination of sea water and at the ﬁnishing stage of deeply desalted water
production. Its main advantage over reverse osmosis is its ability to treat water with a
8
Water Treatment and Purification: Technological Responses to Grand Challenges
191
Table 8.5 SWOT analysis of reverse osmosis membranes
Strengths
Weaknesses
• Up to 99.9% selectivity
• Unique quality of treated water
• Remove low-molecular humic compounds
• All-purpose: efﬁciently remove pollutant
mixtures such as heavy metals, calcium and
magnesium ions, phosphates, sulphates and
chlorides
• No secondary pollution of water
• Convenient for transportation and
installation
• Long service life of the system, with
periodic backwashing of membranes
• Simple to run, reliable
• Installation can work in automatic mode
• Very environmentally safe
• Expensive
• High energy consumption
• Too much puriﬁcation for drinking water
supply—some of the useful elements are
removed from the water
Opportunities
Threats
• High export potential: desalination is a
relevant objective in many countries
• More stringent requirements for
environmental safety as a market growth
factor
• Desalination is not an issue in most of Russia
• Conservatism of major consumers—water
supply companies
Alternative products and processes
Distillers and evaporator systems
Source: HSE
high concentration of salts, when reverse osmosis becomes unproﬁtable because of the
low degree of conversion. The major spheres of application are in electronics and fuel-
burning power plants. Electromembrane processes are also used in membrane bipolar
electrolysers to produce ‘chlorine water’. A SWOT analysis of the aforementioned
membrane is given in Table 8.6.
Membrane bioreactors (MBR) combine membrane and biological puriﬁcation.
One of the most promising ways to treat waste water involves using activated sludge
bioreactors in combination with ultra- or microﬁltration membrane modules, capable
of processing and utilising signiﬁcant volumes of pollutants. Membrane bioreactors
can be applied at various water treatment stages (e.g. pretreatment before
nanoﬁltration and reverse osmosis and preﬁnishing treatment before the disinfection
stage). Most commonly, they are used to treat sewage. A SWOT analysis of the
aforementioned membrane is given in Table 8.7.
Innovative coagulants include partially hydrolysed salts, aluminium dihydroxy
sulphate, aluminium dihydroxochloride, aluminium pentoxychloride and aluminium
oxychloride sulphate, with the following functionalities (Tkachev et al. 1978, 1988;
Gerasimov 2001; Getmantsev 2003):
•
Highly efﬁcient and effective coagulation process
•
Low reagent consumption
•
High environmental protection properties
192
O. Saritas and K. Vishnevskiy
Table 8.6 SWOT analysis of ion-exchange membranes
Strengths
Weaknesses
• High-quality water treatment with minimum
consumption of reagents
• Stable performance
• Certain kinds of membranes have low
hydrophilic properties
• Require regeneration to restore their
ion-exchange potential
Opportunities
Threats
• Thorough puriﬁcation by removing calcium
and magnesium ions, as well as iron and
manganese ions (in dissolved state)
• No limits for increasing productivity of
ion-exchange membrane-based installations
• Limited application area (desalination,
de-ironing)
• Limited water puriﬁcation from certain
reagents and a wide range of pollutants
• Susceptible to pollution by organic
substances
• Require washing for regeneration because
they can be a breeding environment for
bacteria
Alternative products and processes
Distillation, common oxidation, catalytic oxidation
Source: HSE
Table 8.7 SWOT analysis of membrane bioreactors
Strengths
Weaknesses
• Long life cycle of the activated
sediment
• Create conditions for breeding
speciﬁc bacteria
• No overﬂow of sediment
• High-quality treatment of output
sewage
• More efﬁcient way to treat sewage
than classic bioreactors
• Require pretreatment of sewage containing large
suspension particles
Opportunities
Threats
• No need for additional ﬁltration
and disinfection
• Application area limited to pretreatment of water and
treatment of municipal and industrial waste water
Alternative products and processes
Mechanical ﬁltration and sedimentation, coagulation and ﬂocculation
Source: HSE
•
Soft impact overtreated objects
•
Potential to produce sediments with speciﬁed properties
A SWOT analysis of the aforementioned membrane is given in Table 8.8.
Innovative sorbents (including carbons) offer improved efﬁciency compared with
traditional ones. Innovative sorbents (most of which belong to the nanotechnology
sphere) can be divided into carbon-based and others. New-generation sorbents are used
to remove cations (of copper, iron, ammonium, vanadium, manganese, aluminium,
lead, zinc, phosphates) and anions (including sulphides, ﬂuorides and nitrates) from
water. Innovative sorbents are also used to absorb oil products and ether-soluble
substances, to deeply purify water of various microorganisms (bacteria and viruses),
including for the production of drinking water from swamp water sources. In addition
to the above-mentioned new-generation sorbents, there are innovative sorbents capable
of removing arsenic, cadmium and zinc from water and sewage. A SWOT analysis of
the aforementioned membrane is given in Table 8.9.
8
Water Treatment and Purification: Technological Responses to Grand Challenges
193
Table 8.8 SWOT analysis of innovative coagulants
Strengths
Weaknesses
• Easy to use
• Can be used to treat a wide range of waste waters
• Can be used in a wide range of рН and alkalinity
values; do not change the рН value of treated water
• Coagulant elements (aluminium)
remain in the water after treatment
• Can be applied only at pretreatment
stage; subsequent treatment required
Opportunities
Threats
• Scientiﬁc progress opens up the prospects of
creating even more efﬁcient coagulants
• Development of alternative treatment
techniques
• Need to treat puriﬁed water to get rid of
new pollutants (e.g. radiation)
Alternative products and processes
Pressure ﬂotation, ﬂocculation, ﬁltration
Source: HSE
Table 8.9 SWOT analysis of innovative sorbents
Strengths
Weaknesses
Compared with other carbon-based sorbents
• Suitable for a wide range of industries
• High abrasive resistance
• Activated carbons (AC) must be replaced/
regenerated using chemical, thermal or
biological techniques
Compared with alternative products
• Convenient for loading/unloading, storage
and transportation (do not generate dust)
• Fire-resistant
• Suppress germ growth
• Timber-based AC have lower mechanical
strength than charcoals produced from other
materials
• Alternative products have higher ash content
and offer higher cost-efﬁciency (such as coal
coke, pitch, electrode pitch coke, oil coke)
• Less strong
• More expensive
Opportunities
Threats
• AC can be used to absorb organic substances
of artiﬁcial origin
• Wide range of application areas in the
chemical, food, pharmaceutical, power
generation and metallurgic industries, as well
as in oil and gas production and reﬁnery
• Risk of environmental pollution due to AC’s
ash content
Alternative products and processes
Mechanical ﬁltration, sedimentation, coagulation and ﬂocculation, ﬂotation, chlorination and
ozonisation, plus baro-membrane-based puriﬁcation techniques (depending on pollution rate and
type)
Source: HSE
194
O. Saritas and K. Vishnevskiy
Table 8.10 Innovative water treatment and puriﬁcation products
Key and promising products
Beginning of
mass production
in Russia
Market segment
Membranes
Reduced cost, increased productivity,
reduced energy consumption, increased
temperature and chemical reagents
resistance
Microﬁltration membranes
Short term
Ultraﬁltration membranes
Nanoﬁltration membranes
Reverse osmosis membranes
Ion-exchange membranes
Membrane bioreactors
Medium term
Membranes with dendrimers
Long term
Membranes with fullerenes
Long term
Nanoreactive membranes
Medium term
Nanocomposite membranes
Medium term
Membranes based on molecular
zeolite mesh
Long term
Nanocomponents in traditional technologies
Reduced cost, increased productivity,
reduced energy consumption
Innovative sorbents
Short term
Innovative coagulants
Short term
Activated nanocatalysts inbuilt
into membrane systems
Long term
Nanosize biopolymers with
adjustable properties, for
removing pollutants
Long term
Source: HSE
Table 8.10 provides a summary with a benchmark about the market segments for
promising technologies, as well as the expected start date of their mass production in
Russia.
Next, Fig. 8.4 describes the development prospects for various water treatment
and puriﬁcation technologies incorporating nanocomponents.
8.4.3
Market Segments for Nanotechnology Water Treatment
and Purification
Following the focus on alternative technologies, this section exempliﬁes various
market segments for nanotechnology-based water treatment and puriﬁcation.
8.4.3.1 Water Treatment for Industrial and Municipal Use
Many industries require water with special properties, though each of them has
particular criteria. Furthermore, there is a constant stable demand for water softening
8
Water Treatment and Purification: Technological Responses to Grand Challenges
195
Ultrafiltration
Nanoreactive membranes
Nanocomposite membranes
Pervaporation
Membranes with fullerenes
Membranes with
dendrimers
Membranes based on
molecular zeolite mesh
Microfiltration
Nanofiltration
Reverse osmosis
Ion exchange
Electrochemical activation
Innovative sorbents
Innovative coagulants
Nanosize biopolymers
Biosensor purification
techniques
Nanocatalysts
Distillation
Chlorination
Ozonisation
Thermal method
Ultraviolet
Distillation
Chlorination
Thermal method
Ozonisation
Ultraviolet
Electric pulse treatment
Ultraviolet (light-emitting
diodes)
Traditional technologies
Non-membrane technologies
Membrane technologies
Nanotechnologies
NOW
2020
2025
Fig. 8.4 Development prospects of nanotechnologies for water treatment and puriﬁcation.
Source: HSE
(removal of Са and Mg salts) and desalination (removal of a signiﬁcant proportion of
all dissolved components). Water treatment is also required in nuclear and fuel-
burning power generation, radioelectronics and microelectronics, food industry and
biotechnology, chemical industry, housing and communal services.
8.4.3.2 Drinking Water Production and Quality Improvement
Promising areas in this segment include the puriﬁcation of subterranean and surface
waters, desalination of brackish and sea water and upgrading of water supply
installations (Koroteyev and Desiatov 2003). Dozens of major drinking water
production and municipal and industrial sewage treatment projects have been
implemented across the world. These projects’ installation capacities range from
1000 to 100,000 cubic metres per day. In France, a unique project for a one-step
drinking water production from a river source using a nanoﬁltration membrane-
based system was implemented. The equipment guarantees safety (no chemicals
used to disinfect water) and a high quality of output water, including its softening;
the installation supplies a region with 800,000 residents.
8.4.3.3 Household Membrane Water Purifiers
A membrane module is the basic element of a water puriﬁer. The main consumers
are manufacturers of household water puriﬁcation systems. Water treatment and
drinking water production segment dominates the market across the world.
196
O. Saritas and K. Vishnevskiy
Table 8.11 Structure and dynamics of the provision of nanotechnologies for water supply in
Russia (%)
Segment
2015
2020
Membrane-based technologies—nanoﬁltration and reverse osmosis
85
80
Water treatment (for municipal and industrial use)
50
40
Production of drinking water
40
40
Household water puriﬁers
8
15
Industrial waste water treatment
1
1
Municipal sewage treatment
0
1
Industrial application (specialised)
2
3
Other (and innovative) water treatment technologies
15
20
Source: HSE
However, it is expected that by 2020 its growth in Russia will be slower than the
global average (from 15 to 20% globally, from 39 to 40% in Russia). Nevertheless,
these ﬁgures suggest that the segment has good prospects and looks attractive to
investors (Table 8.11).
The sewage treatment segments still remain at the initial development stages. In
Russia, the application of nanotechnologies for treatment of waste water is very rare.
However, domestic and international developers already offer equipment with
nanostructured corrosion-resistant ﬁltering elements based on steel, titanium, zirco-
nium, nickel and ceramics. Applying this equipment for waste water treatment
means they can stay within the maximum permissible concentration limits. When
it comes to Russian water treatment technologies, we should primarily mention
electric pulse treatment (the equipment is manufactured in Tomsk). In addition,
several other water treatment techniques may be relevant in terms of identifying
various groups of consumers, manufacturers and products in the course of further
research in this ﬁeld. Table 8.12 summarises various water treatment and puriﬁcation
techniques with relevant market segments in Russia.
8.5
Future Scenarios and Strategies for Nanotechnology-
Based Water Treatment and Purification
8.5.1
Forecasts for the Membrane Technologies Market
Membrane technologies are widely perceived a signiﬁcant and promising area of
water treatment and puriﬁcation nanotechnologies. Figures 8.5 and 8.6 show the
likely development prospects for water puriﬁcation nanotechnologies markets in
Russia and the world. The forecast includes three scenarios: optimistic, moderate
and pessimistic.
The optimistic scenario of global market growth suggests an annual growth rate
of about 10–11%. The main growth factors include the increasing shortage of
drinking water, development of membrane technologies and a signiﬁcant increase
8
Water Treatment and Purification: Technological Responses to Grand Challenges
197
Table 8.12 Market segmentation of water treatment technologies in Russia
Drinking water purification
Industrial 
water 
purification
Waste water 
treatment
Centralised 
water 
supply
Collective 
use
Household 
filters
Industrial 
waste 
water 
treatment
Municipal 
sewage 
treatment
Membranes
Baromembrane processes
Ultrafiltration
+
+
+
+
+
Nanofiltration
–/+
+
–/+
Reverse osmosis
–/+
+
–/+
Microfiltration
+
+
+
+
+
+
Electric membrane processes
Electrodialysis
+
Electrodeionization
+
Membrane bioreactors (MBR)
+
+
Membrane degassing
+
Nanosorbents
+
+
+
+
+
+
Nanocoagulants
+
+
+
+
Traditional technologies
Ion exchange
–/+
+
–/+
Chlorination
+
+
+
+
Ozonisation
+
+
+/–
+/–
Coagulation, flocculation
+
+
+
+
flotation
+
+
+
+
Distillation
+
Note: The green pluses (+) strong positive correlation; (±) positive correlation; (∓) negative
correlation
Source: HSE
of membrane technologies’ supply from China, with a large growth of membrane
module production (ITA 2005). The optimistic forecast is supported by the growing
popularity of complex natural and waste water puriﬁcation technologies, coupled
with a growing shortage of water. The emergence of competitive closed-cycle
technologies, and increased public concern in Europe and the USA about the state
of affairs in this ﬁeld, could also be the drivers of the optimistic scenario in the near
future. The pessimistic forecast envisions low market growth rates—no higher than
5–6% a year. The moderate scenario assumes that average market growth rate would
amount to 7–8% a year in the long term.
Russia’s global market share is small (according to various estimates, about 3%
of physical volume and less than 1% in monetary value). This is due to low water
costs and the systemic reasons why Russia is technologically lagging behind.
198
O. Saritas and K. Vishnevskiy
6000
8000
10000
12000
14000
16000
18000
20000
2014
2015
2016
2017
2018
2019
2020
optimistic
moderate
pessimistic
Fig. 8.5 Actual and expected global market growth (million USD). Source: HSE
0
200
400
600
800
1000
1200
1400
2014
2015
2016
2017
2018
2019
2020
optimistic
moderate
pessimistic
Fig. 8.6 Actual and expected Russian market growth (million USD). Source: HSE
The optimistic scenario for Russian market growth is based on a high growth rate
of the overall water treatment market (for industrial and municipal purposes). The
optimistic scenario assumes continued government support and the active
popularisation and promotion of relevant technologies. According to the optimistic
scenario, by 2020, Russia’s market share will reach to 7%, or 1.3 billion USD.
According to the pessimistic scenario where the government does not provide any
support, the annual growth rate of the membrane technologies market will not
exceed 4%. The main barrier hindering the application of nanotechnologies is
traditional water treatment techniques; however, their efﬁciency may be increased
by introducing new designs unrelated to nanotechnologies. The moderate scenario
assumes limited government support, making constrained growth of about 11%
feasible.
8
Water Treatment and Purification: Technological Responses to Grand Challenges
199
8.5.2
Strategic Options
8.5.2.1 Federal Strategies
An aggressive strategy envisages supporting segments with the maximum market
potential and might contribute to achieving the optimistic development scenario. The
need for an aggressive strategy is conditioned by the current situation in the industry
and by the understanding that high-quality water treatment has strategic importance
to preserve the nation’s health and environmental balance. Such a strategy is based
on the assumptions that the prospects for the membrane technologies market are
optimistic assuming that water puriﬁcation nanotechnologies have numerous appli-
cation areas, ranging from industrial waste water treatment and water puriﬁcation to
production of drinking water, and that the demand for membranes will continue to
grow globally, with Russia becoming an exporter of nanomembranes and one of the
industry’s ‘regulators’.
The core of such a strategy is to support the market segments centralised and
decentralised water puriﬁcation and water puriﬁcation for industrial use. For
implementing this strategy, a combination of various measures is required. First,
government support to industry should be provided by means of direct funding at
the initial stage of upgrading technology and equipment, adopting relevant legally
binding documents (including national standards, terms of reference, end product
speciﬁcations which may include nanomembranes) and supporting demand via pref-
erential treatment of consumers who opt for these products. Furthermore, improving
customs rules for the industry and supporting the participation of Russian membrane
manufacturers in technology chains comprising leading companies and countries are
essential measures. A signiﬁcant market breakthrough can be achieved by building an
advanced nanomembrane factory, which would require government participation to go
ahead. Second, market players must adopt a more aggressive approach in areas such as
the active promotion of nanomembrane-based products which has many potential
consumers who’re not planning to switch to such products because of a lack of
awareness of their efﬁciency and performance, the promotion of water puriﬁcation
nanotechnologies among end-product users and extending cooperation between
Russian manufacturers and developers and launching cooperation with foreign
partners (in particular, a more active search for potential consumers).
Implementing an active strategy corresponds with the moderate development
scenario and would be possible under the following conditions:
•
Provision of government support for the industry, including through the targeted
federal programme ‘Pure Water’
•
Active participation of several stakeholders in the programme (such as Rusnano,
Rostechnologies,
etc.)
to
increase
the
production
and
consumption
of
nanoproducts
•
Retaining existing research and human potential and training highly skilled
researchers for this ﬁeld
•
Promoting the application of nanomembranes
200
O. Saritas and K. Vishnevskiy
•
Setting up cooperation with manufacturers and consumers in various countries,
including joint ventures with leading producers
8.5.2.2 Regional Strategies
Strategies to promote innovative water puriﬁcation technologies in various market
segments are determined by the application potential of the new technologies in
centralised and decentralised water supply systems. Making the most of this poten-
tial depends on the growth in water consumption segments, their market shares, the
upgrading or replacement of worn-out pipeline networks and how well water
treatment installation equipment meets ofﬁcial Russian standards for the quality of
source water. To design technology diffusion promotion strategies, we grouped
similar regions together based on similar regional features such as the nature of
problems they face in water treatment and puriﬁcation. Depending on the availability
of a centralised water supply, we classiﬁed regions into two groups: Group A
comprises regions with a higher availability of mains water supply; and Group B
consists of regions with a lower availability of mains water. Figure 8.7 illustrates the
two groups of regions, distinguished by the availability of centralised water supply,
the quality of centralised and decentralised water sources and the quality of ‘output’
water.
The regions included in these groups are characterised by reasonably different
features. Group A-1 and A-2 regions possess relatively high-quality central water
supply resources, but A-1 regions and 7 of 17 regions show relatively low quality of
decentralised water supply sources, whereas А–2 regions feature a low quality of
output water and 6 out of 9 regions have a relatively low quality of decentralised
water supply sources. A-1 regions accordingly need to modernise water treatment
and puriﬁcation equipment and pipeline networks. This might be done by the
application of new technologies which is basically possible because most of these
regions have a relatively high economic development level. However, the relatively
low quality of decentralised water supply sources in some of the regions is a
motivation to promote individual and collective water puriﬁcation technologies.
A-2 regions operate worn-out pipeline networks which makes it ﬁrst priority to
replace them. Introduction of new technologies, including membrane-based ones,
will have no economic or social effect without upgraded pipeline networks. If the
problem is with obsolete water treatment installation equipment, the application of
new technologies may be justiﬁed. The relatively high level of economic develop-
ment in regions with low-quality decentralised water supply sources opens up
opportunities to promote technologies for individual and collective water puriﬁca-
tion. The quality of source water is rather low in A-3 regions, however, a relatively
high quality of water is delivered to end users. This implies a favourable condition of
pipeline networks and equipment at centralised water supply installations. Applying
membrane bioreactors and other nanotechnologies for treating industrial and munic-
ipal waste water is possible, which subsequently would have a positive effect on the
water source. The good prospects of the waste water treatment market offer a high
8
Water Treatment and Purification: Technological Responses to Grand Challenges
201
Group A-1
Group A-3
Group B-1
Group B-3
Group A-2
Group A-4
Group B-2
Group B-4
Fig. 8.7 Region-speciﬁc strategies to promote water treatment and puriﬁcation nanotechnologies.
Source: HSE
demand potential for relevant nanotechnologies. Like in A-3 regions, the source
water and output water is of low quality in A-4 regions, e.g. ﬁve of ten regions have
low levels of economic development. In the other ﬁve regions, a relatively low
quality of decentralised water supply sources is combined with a relatively high
economic development level. Therefore, the ﬁrst priority is upgrading water treat-
ment equipment and pipeline networks. The federal government needs to be actively
involved in solving the problems of those regions with relatively low development
levels. Subsequently, it might be possible to promote nanotechnologies for individ-
ual and collective water puriﬁcation in the more economically developed regions
with low quality of decentralised water supply sources.
Relatively problem-free regions are grouped in B-1, where a high quality of water
in centralised water supply systems is combined with a relatively high quality of
decentralised water sources, but most of the regions in this group have low levels of
economic development. It’s typical for this group that planned replacement of water
treatment
and
puriﬁcation
equipment
is
required
which
potential
for
nanotechnologies application for water treatment and puriﬁcation can be applied
both for water supply and sewage treatment purposes. Depending on the perspec-
tive—medium or long term—these can be supplementary or alternative strategies. In
B-2 regions a relatively high quality of decentralised water sources is combined with
an unfavourable situation with centralised water supply which is also a typical
feature less developed of economic regions (9 out of the 12 regions). Accordingly,
two potential modernisation strategies emerge: (1) extending the centralised water
supply system, while at the same time modernising the equipment and infrastructure,
and (2) a more active application of innovative technologies for individual and
collective water supply. The eventual choice of strategy requires feasibility studies
in any case. The strategies can also be applied in combination and in various
timeframes. Lack of adequate ﬁnancial support can become a major obstacle for
202
O. Saritas and K. Vishnevskiy
implementation of both strategies.
A relatively favourable situation with centralised water supply, and a poor quality
of decentralised water supply together with lower levels of economic development,
is a feature of B-3 regions. Therefore, modernisation of centralised water treatment
installation equipment is required, as well as more aggressive promotion of
technologies for individual and collective water puriﬁcation, membrane bioreactors
and other innovative technologies for waste water treatment. In these constellations
the federal authorities need to be actively involved to successfully deal with these
problems. Finally, Group B-4 possesses a low quality of decentralised water sources
which
is
combined
with
centralised
water
supply
problems.
There,
the
modernisation of water treatment and puriﬁcation equipment is the ﬁrst priority,
and strategies similar to the ones designed for Group B-2 can be applied.
The future of water treatment and puriﬁcation is in the process of being shaped.
The incorporation of nanotechnologies into the water supply solutions would signif-
icantly increase the quality of drinking water and reduce the incidence of diseases
caused by polluted water sources and pipeline networks. Hence, new application
ﬁelds for nanosolutions are emerging. To take advantage of these opportunities,
governments follow different strategies. The present chapter discussed that there are
no uniform strategies by governments both at the federal and regional levels. Instead,
these strategic approaches vary and take regional and national characteristics into
account.
Acknowledgements The book chapter was prepared within the framework of the Basic Research
Program at the National Research University Higher School of Economics and supported within the
framework of the subsidy by the Russian Academic Excellence Project ‘5-100’.
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(2015)
World
Water
Development
Report.
http://www.unwater.org/publication_
categories/world-water-development-report/. Accessed 27 July 2017
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materials. Foresight 19(2):198 217
A photograph of Ozcan Saritas.
Ozcan Saritas is Professor of Innovation and Strategy at
the HSE and editor-in-chief of “Foresight”—the journal of
future studies, strategic thinking and policy. He worked as
Senior Research Fellow at the Manchester Institute of
Innovation Research, The University of Manchester. His
research focuses on innovation and policy research with
particular emphasis on socio-economic and technological
Foresight. With a PhD from the “Foresight and Prospective
Studies Program,” he introduced the “Systemic Foresight
Methodology”, and has produced a number of publications
on the topic. Dr. Saritas has extensive work experience with
the international organizations, including the United Nations,
OECD, and the European Commission. He has been
involved in large scale national, multinational and corporate
research and consultancy projects on sectors including
energy, climate change, agriculture, food, water, transporta-
tion, and ICT among others; published a number of articles in
respected journals; and have delivered keynote speeches in
more than 50 countries across the world. Besides his research
and publication activities, he designs and delivers academic
and executive education courses on Foresight and Strategic
Planning. He has recently co-authored a book, entitled
“Foresight
for
Science,
Technology
and
Innovation”
published by Springer, which has become one of the key
readings in the ﬁeld.
Konstantin Vishnevskiy is Head of the HSE ISSEK,
Department for Digital Economy Studies. He holds a PhD
from Moscow State University, Faculty of Economics.
Dr. Vishnevskiy has long-standing experience in the devel-
opment of technology roadmaps, the elaboration of Foresight
methodology
and
corporate
innovation
development
programs, the integration Foresight into government policy
as
well
as
ﬁnancial
and
econometric
modeling.
He
participated in many high-level research projects on S&T
Foresight on national and regional level both in Russia and
abroad.
Dr.
Vishnevskiy
authors
about
70
scientiﬁc
publications
on
long-term
planning
and
Foresight,
roadmapping, digital economy, macroeconomic regulation
and government policy, innovation strategies for businesses.
He presented about 100 reports on academic and profes-
sional conferences and workshops concerning Foresight,
roadmapping and innovations.
205
Emerging Technologies Identification in
Foresight and Strategic Planning: Case
of Agriculture and Food Sector
9
Leonid Gokhberg, Ilya Kuzminov, Pavel Bakhtin, Anton Timofeev,
and Elena Khabirova
9.1
Introduction
In conditions of global challenges for sustainable development and attempts to
reduce global threats driven by complex issues (such as climate change, ageing
population, natural resource scarcity, water security, human health and wellbeing
threats) (Kallhauge et al. 2005; Keenan et al. 2012), the global and national gover-
nance systems are faced with extremely difﬁcult missions. In particular, the global
food problem is far from solution, as several hundred million people in less devel-
oped countries face undernourishment and even famine (FAO 2009), while global
population growth (United Nations 2015), which is far from plateauing, puts addi-
tional demand pressure on the global food production-distribution systems. The
global food problem is aggravated by clearly expressed negative environmental
trends threatening to decrease gross A&F output in the future (World Bank 2007).
It is admitted that A&F sector is one of the largest greenhouse gas emitters globally
(O’Mara 2011; Tubiello et al. 2013), and the situation grows worse because of global
shift towards consumption of animal products, more resource intensive and environ-
mentally
unsustainable.
The
negative
trends
also
include
degradation
of
bioproductivity of agricultural land, namely, soil erosion (Montgomery 2007), soil
L. Gokhberg · I. Kuzminov (*) · A. Timofeev
National Research University, Higher School of Economics, Moscow, Russia
e-mail: lgokhberg@hse.ru; ikuzminov@hse.ru; Aatimofeev@hse.ru
P. Bakhtin
Information and Analytical Systems Unit, HSE ISSEK, Moscow, Russia
e-mail: pbakhtin@hse.ru
E. Khabirova
Unit for Digital Economy Studies, HSE ISSEK, Moscow, Russia
e-mail: etochilina@hse.ru
# Springer Nature Switzerland AG 2019
D. Meissner et al. (eds.), Emerging Technologies for Economic Development,
Science, Technology and Innovation Studies,
https://doi.org/10.1007/978-3-030-04370-4_9
compaction (Hamza and Anderson 2005), negative net nutrients ﬂaw and fertility
fall (Pimentel 2006). Among the other environmental threats, there are World Ocean
bioproductivity loss due to overﬁshing (Srinivasan et al. 2010), contamination with
harmful anthropogenic substances (Aarkrog 2003), climate change and acidiﬁcation
(Hoegh-Guldberg and Bruno 2010).
206
L. Gokhberg et al.
The solutions to the existing global challenges, as many researches and interna-
tional think tanks see it, lie in wide-scale adoption of new technologies (Omenn
2006). At the same time, many organizational innovations, such as new business
models, citizen vigilance schemes and governance mechanisms, which could possi-
bly alleviate some of the global issues, are also becoming feasible solely because of
the development of some universal, or platform, or enabling technologies (Gokhberg
et al. 2013).
Therefore, governance and management systems have to acquire technology-
awareness capabilities (Spitsberg et al. 2013; Momeni and Rost 2016; Bildosola
et al. 2017). This means that effective STI policy becomes a more crucial success
factor for governance both on global and national levels, as well as for corporate
strategic management. For the policies to become effective, they need to be both
evidence-based (Smith and Haux 2017) and proactive (Mani 2004; Aghion and
Grifﬁth 2008). This, in turn, formulates the necessity of constant monitoring of the
technologies that are emerging, as they are important drivers of efﬁciency of the
human activity.
The concept of emerging technology, its scope and deﬁnition is a highly
discussed topic in social sciences. While a number of impactful publications con-
centrate on conceptualizing the term “emerging technology” (among others see
Rotolo et al. 2015; Halaweh 2013), it is sufﬁcient for the scope of this paper to
understand the emerging technology as a new technology that might have a signiﬁ-
cant impact on the economic activity in certain sectors of the economy.
In the era of explosive growth of diversity of S&T and of quantity of available
information (Hidalgo 2015), technologies identiﬁcation and mapping becomes less
feasible without the use of modern data science techniques. This necessity is caused
not only by the “information push” but also by signiﬁcant drawbacks of human-
performed analytics. While spotting critical technologies and existing and emerging
trends, as well as forecasting future development and setting STI priorities, sectoral
experts often demonstrate subjectivity, incompleteness of their knowledge and bias
of views favouring familiar subject areas (see, e.g. Winkler and Moser 2016).
Reconciliation of ideas among the large expert groups could lead to overextended
periods of foresight studies—one of the main sources of evidence for modern STI
policy. This means that some technologies might already transform from the
emerging stage to the stage of commercially viable products before a dedicated
foresight report on the emerging technology landscape is published. These factors
along with budget limitations drive governments and private companies towards at
least partial automation of foresight and strategic planning activities.
One example of early attempts at solving these problems is the tech mining
(Porter and Cunningham 2004; Madani 2015; Huang et al. 2015; Bakhtin and Saritas
2016). It strongly relies on large expert validation and manual ﬁltering and cleaning
of data outputs. For the purposes of emerging technologies identiﬁcation, the text
mining in combination with semantic analysis tools seems to be most appropriate, as
a task of identiﬁcation of new man-made phenomena of known nature (technologies
in this case) can be reduced to designation of new syntactic constructions
describing them.
9
Emerging Technologies Identification in Foresight and Strategic Planning:. . .
207
To demonstrate the potential of the text-mining techniques for technologies
identiﬁcation and mapping, we conducted the case study of emerging technologies
identiﬁcation in the agriculture and food sector by applying an intelligent Big Data
Text Mining system iFORA, developed at HSE ISSEK featuring semantic analysis
of heterogeneous textual data sources. Moreover, there are proposed new analytical
tools
for
technologies’
sequential
analysis.
For
the
identiﬁed
prospective
technologies with the synthesis of the materials of expert foresight exercises, desk
research and Big Data Text Mining analytics, there were aggregated (1) parameters
of technologies’ potential to bring answers to sectoral and national challenges and
(2) corresponding future markets forecasts. Based on this analysis, prospective S&T
development areas and technological solutions for the Russian A&F sector were
highlighted and STI policy strategies outlined in the context of two scenarios of the
sector development.
9.2
Methodology
Current study is based on a set of quantitative, qualitative and expert-based S&T
Foresight techniques. The main information source for the study is the ample
material S&T Foresight for Agriculture and Food Sector in the Russian Federation
until 2030 (HSE 2017; Gokhberg et al. 2017). With the participation of
representatives of about 400 organizations (leading research organizations, agricul-
tural companies, universities, industrial unions and associations and development
institutes), there were undertaken 25 in-depth expert interviews, and 15 panels and
workshops were organized. These structured discussions were dedicated to the main
S&T, socio-economic and environment trends along with the most promising
technologies and markets’ main directions of future policy in the sector. Understand-
ing the danger of experts’ biased opinions (which is particularly true for lobbyist
business associations), described data collection methods were counterbalanced with
the desk research and specialized texts automatic analysis.
A&F sector-relevant texts were processed with Big Data Text Mining system
iFORA. The composition of data sources of the text-mining system at the time of the
exercise includes:
•
Stratiﬁed random sample of summaries and metadata of 1,885,624 internationally
top-cited research papers for 10-year period from 2005, representing various
research areas and acquired from the open citation indexes and other open data
sources
•
Stratiﬁed random sample of summaries and metadata of 2,277,869 international
patents for 10-year period, acquired through open-access sources of the World
Intellectual Property Organization Patent Cooperation Treaty (WIPO PCT)
208
L. Gokhberg et al.
•
7,007,033 newsfeed items from Alexa and SimilarWeb1 tops of global news
portals with science and technology ﬂavour, for the period since the inception
of the WWW
•
171,247 analytical reports available from international and national organizations
and agencies
At the time of the study, the system featured more than 12 million individual
documents, with several hundred million individual sentences, of which up to
3 million documents were at least partially relevant to A&F sector and adjoining
sectors, such as biotechnology and bioenergy.
Synthesis of the materials of expert foresight exercises, desk research and Big
Data Text Mining analytics allowed preparing and validating of lists of technologies,
markets, trends and challenges for Russian A&F sector. Among all identiﬁed
technologies, the emerging ones were classiﬁed by text-mining analysis of dynamics
of their presence intensity in the discourse during the last years (for details on applied
computational procedures and measures, see in Bakhtin et al. 2017) with visualiza-
tion in the form of trend maps. They consist of two-dimensional plots with one axis
representing the popularity of a term (the so-called signiﬁcance axis) and the other
showing the year-by-year dynamics of the normalized popularity (relative frequency
of use) (the so-called trending axis). Interpretation of the results of this analysis was
done based on the assumption that currently unfolding technology trends (including
the development and adoption of emerging technologies) are characterized by the
growth of interest towards them at least in one of the corpora of documents
processed (patents and news/blogs).
For balanced classiﬁcation of identiﬁed technologies, semantic mapping is
executed with the use of combination of machine learning methods, based on
co-occurrence term graph clustering and regularization of mutual information and
topic monopolism of terms. The principal results of semantic mapping are clusters of
principal terms that give insights into optimal groupings of technologies by semantic
similarity.
After technologies identiﬁcation and mapping stages, it is proposed to implement
the following analytical tools for STI policy evidence gathering by combination of
expert Delphi procedures and text-mining techniques:
•
Assessment of technology potential to bring answers to global and national
challenges through an expert evaluation using special questionnaires and further
validation of the results with computation of textual parameters of the semantic
proximity (for details on measurement, see in Bakhtin et al. 2017) between two
lists: technologies and challenges. These two ontologies were preliminarily
composed during desk research, supplemented with the materials of in-depth
expert interviews and workshops, and ﬁnally veriﬁed with text-mining
instruments on the extensive corpus of A&F related documents.
1URL: https://www.alexa.com/ and URL: https://www.similarweb.com/
9
Emerging Technologies Identification in Foresight and Strategic Planning:. . .
209
•
Aggregation of connected technological markets forecasts on the basis of lists of
prospective markets composed by expert participants of the study and with an
extraction of forward-looking market volume estimates from up to a million
market report press releases relevant to agriculture2 from key global players in
the ﬁeld.
The analysis of these additional parameters for identiﬁed technologies can serve
as an instrument of strategic priorities setting in the ﬁeld of technologies that are
currently emerging through the ever-intensifying global S&T development process.
9.3
Emerging Technologies Extraction and Mapping
As a result of proposed text-mining analysis, the ontology of global agriculture and
food technologies is developed. It consists of several thousands of technologies,
which were classiﬁed by dynamics of intensity of their presence in the discourse
during the last years (see details in Sect. 9.2). The results of this analysis were
visualized by trend maps on media resources and international patents (Figs. 9.1 and
9.2, respectively3). It is suggested that emerging technologies with strong potential
of surviving and upscaling to the global production systems receive an increasing
public awareness in wide audience information resources (media) and growing
presence in intellectual property protection documents (patents). The upper right
quadrant of the trend maps consists of the strongest topics shaping the future agenda
of the sector; they are popular and gaining traction. In media they exempliﬁed by
CRISPR technologies, agroforestry and aquaponic technologies, precision agricul-
ture and microalgae utilization, etc. In patents this group also consists of several
genetic technologies but moreover includes fertigation and hatchery technologies.
The lower right quadrant of maps contains the so-called weak signals: they are
highly trending but underrepresented in discourse yet. They can contain the
emerging technologies. This group presented in media by smart irrigation
technologies, molecular breeding and zinc ﬁnger nuclease technologies, etc. At the
same time, the patent map shows, for instance, that bionematicides, mutagenesis and
synthetic seed technologies are increasingly gaining popularity. Among the popular
topics losing their signiﬁcance in media are fertilization, pruning, antifouling
technologies and many more. In patents, topics with declining popularity are
composting and horticultural technologies, among others.
For dynamic classiﬁcation of identiﬁed technologies, which is an essential task of
technology landscape mapping (Bakhtin and Saritas 2016), semantic map instrument
was used (see details in Sect. 9.2). It is a clustered co-occurrence graph, which
consists of thousands of connected vertices, each of which represents an important
2Identiﬁcation of market reports lexically relevant to agriculture is done by the advanced methods of
topic modelling and term-document biclustering.
3In the illustrative visualization, only few points can be labelled without overlap.
210
L. Gokhberg et al.
Fig. 9.1 Trend map of agricultural technologies on media resources. Source: HSE
term4 (Fig. 9.3). Technologies were automatically grouped in the following large
coloured clusters of the most promising S&T development areas:
•
Urban agriculture technologies (robotic greenhouses, vertical farms, recirculating
aquaculture systems, aquaponics, hydro-/aeroponics, artiﬁcial lighting, sensors
and control systems, etc.)
•
Advanced precision agriculture technologies (geographic information systems,
autonomous vehicles, GNSS and remote sensing, etc.)
•
LEISA (Low External Input Sustainable Agriculture) technologies and waste
processing technologies (waste processing, soil management, organic agriculture,
etc.)
•
Biotechnologies (genetic engineering, vaccines, antibiotics, probiotic production
technologies, embryo transfer, DNA sequencing)
•
Advanced
food
technologies
(biochemical,
enzyme
technologies,
nanotechnologies for food industry, active packaging, nutrient additives)
•
Smart bioenergy convergent technologies (biofuel production, solar energy, etc.)
4In the illustrative visualization, only few of the vertices can be labelled without overlap.
9
Emerging Technologies Identification in Foresight and Strategic Planning:. . .
211
Fig. 9.2 Trend map of agricultural technologies on patent resources. Source: HSE
Fig. 9.3 Semantic map of identiﬁed A&F sector technologies and related topics. Source: HSE
212
L. Gokhberg et al.
•
System integration technologies for managing logistics in agricultural sector (big
data, machine learning, robotic storage facilities, etc.)
•
Advanced technologies for the ﬁsheries sector
Dynamic terms’ classiﬁcation based on clustering mechanisms provides mapping
of the identiﬁed technologies, thematic linkage among them and with non-
technological concepts and topics in the sphere. It provides an ad hoc ontology for
fast analytics and for expert discussions in the absence of ofﬁcial taxonomies. This
allows mapping even emerging ﬁelds, which have not yet been categorized for the
purposes of ofﬁcial statistics or legal regulation. Technologies of identiﬁed thematic
groups were further analysed on the next step to make a conclusion about their
potential from strategic priorities setting viewpoint.
9.4
Implications to Strategic Priorities Setting in the Field
of Emerging Technologies
During the preparation of Science and Technology Foresight for Agriculture and
Food Sector in the Russian Federation until 2030 (HSE 2017) on the basis of the
presented results, prospective S&T areas were highlighted which are supposed to
play a critical role in stabilizing country’s position on traditional markets and
entering the emerging innovative ones.5 Nine prospective S&T areas are envisaged,
comprising 68 speciﬁc innovative technological groups6 (which are presented in
Annex), with 47 related global challenges and 37 markets for advanced production
technologies, equipment and consumer agricultural products.
A list of STEEPV (social, technological, economic, environmental, political and
value) sectoral challenges identiﬁed with the experts’ participation and automatic
knowledge extraction procedures was reviewed by Saritas and Kuzminov (2017).
Through text-mining analysis of their mentions in the contexts of previously
identiﬁed technologies, their semantic proximity was evaluated (for measurement
details, see in Bakhtin et al. 2017). The potential of a technology to bring answers to
global and national challenges can be seen as the evidence that it is a prospective one
that worth a particular attention during strategic priorities setting.
5For profound survey of the status and prospects of S&T development of agriculture and food sector
in Russia, see Gokhberg and Kuzminov (2017).
6Taking into account the broad deﬁnition of technology concept from Oxford English Dictionary,
where it is explained as “the codiﬁed and applicable pieces of knowledge that can be used for
production and distribution of goods and services, other purposeful economic and non-economic,
but socially impactful activities”, the main concept of operationalization problem may occur.
However, deep theoretical questions about adjustable parameters of technology atomicity (what
level of technical knowledge formalization should be seen as a technology) remain out of the scope
of the paper and need additional comprehensive research.
The most promising S&T development areas for the A&F sector are often related
with the emergence of new technological markets. With the means of syntactic
parsing (Socher et al. 2013), we aggregated forward-looking global markets’ volume
estimates for 2030 for previously identiﬁed emerging technologies directly from
market reports’ press releases from key global players in the ﬁeld (for full tables of
A&F markets forecasts, see in HSE 2017). Based on the analysed forecasts, it should
be mentioned that to position Russia on global markets of A&F sector, choose
adequate strategies for market expansion; an understanding of existing and future
image of both traditional commodity markets and perspective unconventional
products is required. Attention should be paid not only to the export of traditional
and modernized means of production and a wide range of products, including
biochemical ones, but also to the export of new technologies, information systems
and smart services for system integration based on domestic information and space
technologies. Platform solutions, which will be to some extent applied in all
branches of A&F sector, are worth special consideration. The largest prospective
markets are emerging markets for agricultural lands with improved characteristics,
unmanned systems in A&F sector, and smart management systems for technological
processes and automated regulation of economic processes. Until 2030, Russia will
be able to achieve strong presence on such prospective global markets as the
new-generation melioration systems market, home hydro- and aeroponic systems,
9
Emerging Technologies Identification in Foresight and Strategic Planning:. . .
213
the market for a new nutrient medium for plants and various biodegradable plastics.
Other prospective niches include the following:
•
Smart agriculture, including high-tech plant growing and animal farming
products, based, among other things, on new technological solutions
•
Functional foods, with unique useful properties
•
New varieties, hybrids, breeds and cross-breeds created through accelerated
selection
•
Balanced standardized forage for high-yield animal farming and aquaculture
•
Highly efﬁcient and safe active ingredients for vaccines, antibiotics, and antivirus
preparations for animal farming and weed and pest killers
•
Biotechnology- and synthetic biology-based food production systems, including
new strains of useful microorganisms, bioreactors and enzyme complexes
•
Climate-adaptive production systems, including next-generation irrigation
complexes
Summing up the two outlined parameters from expert and text-mining
evaluations for identiﬁed technologies—naming connection to global challenges
and related markets forecasts, illustration for one example of prospective technolog-
ical area—advanced precision agriculture technologies are presented in Table 9.1. It
is assumed that these two main characteristics get reliable ﬁrst approximation of
social, political and economic value of the identiﬁed emerging technologies on
national scale. Therefore, it is strongly proposed to consider them in strategic
priorities setting.
214
L. Gokhberg et al.
Table 9.1 Additional parameters’ summary for prospective technological area example
Prospective technologies in a
group
Connection to global
challenges and trends
Global markets forecast
volume in 2030
Advanced precision agriculture technologies
– Big data and Internet of
Things technologies for
agriculture
– New electronic
technologies, wireless
networks and microsensors
– Advanced robotic
technologies based on
artiﬁcial intelligence, swarm
intelligence and machine
learning
– Application of unmanned
aerial vehicles in agriculture
– Application of nano-and
pico-satellites in agriculture
– Fully automated operation
of agricultural machinery
– Technologies for deep
processing of agricultural
materials at the place of their
harvesting (mobile
semiautomated mini-plants)
– Abandoning full-coverage
irrigation in favour of
precision underground
irrigation techniques, to
achieve signiﬁcant water
saving
– Abandoning regular full-
coverage fertilizer application
in favour of dynamically
adjusted techniques
– Replacing conventional
fertilizers with composite
capsular ones
– Development of distributed
production of advanced
hardware based on additive
and telecommunication
technologies
– Moving on from manned
agricultural machinery to
automated equipment, based
on micro-geopositioning and
self-learning robots
– Replacement of traditional
agricultural labour markets
with skills and competencies
markets based on cloud
technologies, controlled
robots and remote
employment
– Unmanned systems in A&F
sector—$250 bln
– Systems for precise
agriculture, telematics and
unmanned technologies in
crop farming—$65 bln
– Robotized systems for
urbanized and industrial crop
farming—$30 bln
– Self-driving machinery
based on micro-
geopositioning—$4 bln
– GPS/GLONASS sensors
and RFID chips for the
logistics in A&F sector—$4
bln
Source: HSE
Vital characteristic of sectoral S&T innovation process—rates of emerging
technologies’ adoption and diffusion—determines, from the one side, and relies
upon the sector economic and institutional development, from the other side.
Depending on the future evolution of global and national economy, oil price
dynamics and investment spending and the most likely policy choices made at key
threshold points—macroeconomic, institutional and political ones—two possible
development scenarios for the period from 2020 to 2030 were developed in S&T
Foresight for A&F sector in the Russian Federation until 2030 (HSE 2017).
In the “local growth” scenario, the gradually strengthening oil prices and the
increasing volume of the export of hydrocarbons and other raw material resources
will be the main drivers for the development of A&F sector technologies. Due to the
expecting economy enhancement, GDP will annually grow by 1.3% between 2017
and 2019. By 2020 this indicator’s growth rate may achieve 3–4% per year in a
medium-term perspective because of the expansionary monetary and ﬁscal policy
aimed primarily at the improvement of the investment environment. Structural and
9
Emerging Technologies Identification in Foresight and Strategic Planning:. . .
215
institutional changes in the economy focused on the sustainable growth will enable
the Russian economy to develop more rapidly than the world economy.
The basic conditions for “Global Breakthrough” scenario realization include the
accelerating growth of the Russian economy in a medium term by 1–2 percentage
points due to the increasing public investment spending. This scenario assumes that
the monetary policy will soften more quickly in the next 2 years and that additional
investments will be made to develop science, support agricultural products’ export
and foster consumer demand for domestic products. Owing to the ﬁscal and mone-
tary policy, investment growth rate in the scenario “Global Breakthrough” may
exceed the ﬁgures of the “local growth” scenario 2–2.5 times after 2020. The average
annual growth rate of consumption in this scenario will be by 0.7 percentage points
higher. GDP will increase faster by 1.6 percentage points than in the “local growth”
scenario.
Russia can become a global supplier of high-value-added products, technologies
and services. This is the goal of the “Global Breakthrough” scenario. Another option
is less ambitious and easier to achieve yet also desirable: this entails saturating the
domestic market with competitive domestic products and technologies—the “local
growth” scenario. Both trajectories are possible and may start at the same point in
time from identical external conditions. The difference between the higher and the
lower trajectories is determined by the quality of the institutional framework and
economic mechanisms by the year 2020; the gap between these trajectories will
grow over time, making the leap from the less favourable scenario to the more
ambitious one more and more resource intensive (Fig. 9.4).
If Russia will heavily invest in the outlined emerging and prospective
technologies, it can become a global supplier of high-value-added products,
technologies and services. Advancing outlined promising S&T areas and putting
into action the innovative “Global Breakthrough” scenario together will help to
increase the Russian agricultural sector’s global competitiveness and contribute to
the following goals:
– Russian producers’ entering prospective food markets and Russia’s integration
into the global agricultural production system
– Signiﬁcant reduction of ready-made food product imports and increased added
value generated by agricultural industries
– Ensuring food security, including efﬁciently meeting demand for high-quality,
affordable foods in line with medically recommended consumption norms
– Creating new highly productive jobs in the A&F sector, increased employment
and improved quality of life in rural areas
Increasing investment appeal of the A&F sector
–
– Saving the country’s hard currency reserves and promoting overall economic
growth
216
L. Gokhberg et al.
Fig. 9.4 STI development scenarios for the Russian A&F sector. Source: HSE
The development of the national technological potential depends on better insti-
tutional solutions for innovation activities and proper support mechanisms for
technology transfer (Thurner and Zaichenko 2015). Research on various technology
sectors that have an agricultural application would require intensiﬁcation and sup-
port measures for knowledge dissemination through technology platforms and
regional clusters. Especially emerging cross-sectoral technological solutions require
a supportive environment for innovative companies, including access to venture
capital funding. The Russian government targets the establishment of spin-offs and
start-ups at leading technical and agricultural universities and research centres.
Naturally, ﬁnancial resources’ allocation during this process should rely upon
identiﬁed structure of prospective technological areas with stronger monitoring
tools at the sectoral ministry.
9.5
Discussion and Conclusions
Developed big-data-augmented approach to technologies identiﬁcation and mapping
allows proposing the ontology of currently emerging technologies in global agricul-
ture and food sector. The raw results of the emerging technologies identiﬁcation
show that the algorithms used (syntactic construction extraction) gain an appropriate
phenomenon indeed: an expert validation of extracted lists of terms shows their
relevance to the sphere of interest. The high results’ accuracy is achieved through the
comprehensive information source coverage and automatic analysis with objective
criteria. Thereafter two additional parameters were evaluated for the list of identiﬁed
technologies: (1) their potential to bring answer to global and national challenges
9
Emerging Technologies Identification in Foresight and Strategic Planning:. . .
217
and (2) future technological market forecasts. Based on such a characterization of
technologies, prospective S&T development areas for the Russian A&F sector were
highlighted.
For deepening the analysis of the emerging technologies’ potential value for
national economies, the current study can be continued with big-data-augmented
approaches to an assessment of Russian comparative scientiﬁc and technological
level in identiﬁed S&T areas. It can be done by an integration of expert evaluation
procedures, bibliometry and estimation of complex parameters on the corpus of
sectoral scientiﬁc papers and patents’ full texts by Russian authors.
The big-data-augmented foresight exercise allows appraising the beneﬁts of the
proposed approach for evidence-based STI policy and corporate strategic planning.
Although the productivity growth of analysts and experts’ work due to technology
identiﬁcation on big-data sources cannot be measured directly, because this type of
information processing, clearly, could not be performed manually altogether, the
inner circle tools of mapping (aimed at preliminary ﬁltering of data and at technol-
ogy classiﬁcation suggestions) help analysts, in our experience, to rise work produc-
tivity by 3–5 times. It takes analysts much less time to review hundreds instead of
thousands of terms due to ﬁltering, choose most important technologies and catego-
rize them with the help of machine-generated suggestions on places of technologies
in sectoral ontology. Furthermore, productivity is improved due to the “single
window” effects of automatic attachment of expansions (of abbreviations),
deﬁnitions and translations of technology-signifying terms. These options decrease
the negative consequences of cognitive ﬂow disruptions produced by switching
between data sources in search of information, which is dominant in traditional
analysts’ working settings.
The outer circle mapping tools (aimed at helping to present the results easily and
effectively), such as semantic map and trend map shown above, can be used by both
the internal analysts of a company conducting a foresight study and the invited
experts. These tools provide very useful information products for structuring expert
interviews and panel discussions or for complex topic brieﬁngs. Therefore, there is a
capacity for an automation of some aspects of the production of evidence on
emerging technologies for supporting decision-making.
Complete mechanization of the foresight process is not a desired outcome, as the
continuous dialogue among stakeholders and expert community is of vital impor-
tance for the sustainable governance. However, the demonstrated tools of big-data
analysis give decision-makers the leverage in the negotiation with the sectoral groups,
scientists and consultants. The latter will have to address practical questions about the
technologies they advertise for government or corporate support based on objectively
collected data on the parameters of these technologies in the current S&T discourse.
The demonstrated text-mining approach will not replace the domain experts as
analysts in the foreseeable future but will transcend the foresight exercises from local
ontology building to the high-level ontology interpretation. Many foresight studies
have been restricted to collective mapping of trends in some domain in hope that the
mapping is precise and full. If mapping is automated, resources are available for
what foresight is really about: building an integrated and balanced vision of the
future based on an intense interpersonal communication of domain experts.
Acknowledgements The book chapter was prepared within the framework of the Basic Research
Program at the National Research University Higher School of Economics and supported within the
framework of the subsidy by the Russian Academic Excellence Project ‘5-100’.
218
L. Gokhberg et al.
Annex: Prospective Technological Areas for the Innovative
Scenario of Russian A&F Sector Development Between
2020 and 2030
Advanced precision agriculture technologies
– Big data and Internet of Things technologies for agriculture
– New electronic technologies, wireless networks and microsensors
– Advanced robotic technologies based on artiﬁcial intelligence, swarm intelligence and machine
learning
– Application of unmanned aerial vehicles in agriculture
– Application of nano- and pico-satellites in agriculture
– Fully automated operation of agricultural machinery
– Technologies for deep processing of agricultural materials at the place of their harvesting
(mobile semiautomated mini-plants)
Urban agriculture technologies
– Urban hothouse complexes including robotic hothouses, cassette planting of vegetable
seedlings, etc.
– Vertical farms and skyscraper farms
– Home aeroponics and hydroponic installations for city apartments
– Recirculation aquaculture and aquaponics technologies
– Aeroponics technologies for food self-sufﬁciency of manned spacecraft, ships, submarines and
other autonomous objects
LEISA technologies (organic agriculture, integrated pest control systems, water- and soil-saving
agriculture, restoring fertility of degraded lands, sustainable ﬁshing and ﬁsh farming)
– No-till farming and mulching technologies
– Tractor-less plant growing technologies (agricultural bridges—stationary installations capable
of working round the clock implementing a speciﬁed programme, without human interference)
– Soil erosion prevention technologies, antierosion farming, soil crumbling and ﬁeld smoothing
technologies
– Salination-preventing irrigation techniques
– Techniques for dealing with desertiﬁcation of agricultural lands and dangerous hardening of soil
– Technologies for preventing nutrients’ washing out from soil and water reservoirs’
eutrophication
– Substituting chemical-based weed killers (herbicides) with agrotechnical approaches
(pre-emergence and post-emergence harrowing of crops, etc.)
– Technologies for rehabilitation of soils damaged by excessive agricultural use, including
depleted, salinated, desertiﬁed, hardened, polluted with toxic substances and radionuclides
– Technologies for rehabilitation of lands used for dumping and disposal of household and
industrial waste and rehabilitation of former industrial enterprises’ grounds and territories
(continued)
– Nanostructured geotextile coatings for soil protection and country road building
– Technologies to produce nanostructured protective coatings for seed material
– Nanoremediation and nanobioremediation systems for soils and groundwaters
– Technologies for preventing solid waste from garbage dumps and garbage ﬁltrate getting on
agricultural lands, into soil and groundwater used for agricultural production
– Organic, biodynamic agriculture technologies, managing organic products’ supply chains and
automated remote control of complying with organic agriculture certiﬁcation requirements
– Land melioration technologies (improving hydrological regime, acidity parameters, soil
fertility), with minimum interference into ecosystemic processes
– “Blue” technologies (sustainable ﬁshing and ﬁsh farming)
– Technologies for minimizing by-catch, non-destructive trawling and other ﬁshing techniques
– Ships with zero emissions and discharge into the environment and low-level noise pollution
– Technologies for biodegrading plastic-based garbage in oceans
– Technologies for cleaning water reservoirs from non-organic and organic pollutants
9
Emerging Technologies Identification in Foresight and Strategic Planning:. . .
219
Technologies for full on-the-spot utilization and recycling of agricultural, ﬁshery and food
industry waste, including production of new valuable chemical and pharmaceutical products
– Technologies for safe application, in environmental, sanitary and epidemiologic terms, of
organic animal farming sewage for irrigation, in a partially closed water cycle
– Technologies for on-the-spot production and compact, environmentally safe, and long-term
storage of high-quality animal and poultry forage, without losing its nutritious properties
– Low-waste and wasteless production technologies for animal farming, including
biotechnologies to accelerate bioconservation and integrated recycling of animal farms’ waste.
Smart bioenergy convergent technologies (local smart grids and biofuels from agricultural waste
for rural settlements’ energy self-sufﬁciency)
– Technologies for production of second-generation biofuel (bio-petrol and bio-diesel, high-
molecular spirits from cellulose-containing plant growing and forestry waste)
– Technologies for manure and dung processing in biogas systems
– Technologies for disposal of food industry’s primary processing, storage and production waste,
to supply energy to agricultural companies and rural areas
– Technologies for on-the-spot processing of agricultural and rural settlements’ waste, to produce
electricity, heat and construction materials
– Efﬁcient and environmentally friendly gas-generator plants to power agricultural machinery
System integration technologies for managing agricultural sector’s logistics, based on
supercomputers, big data and machine learning, robotic storage facilities and transport systems
– Decision-making support systems for the agricultural sector
– Technologies for tracking supply chains and remote automated control of agricultural products’
origins
– Information technologies for agricultural insurance and trading
– Technologies for automated product ﬂow management and dynamic optimisation, real-time
stock control and distribution
– Highly accurate short- and medium-term weather forecasting techniques
– Technologies for predicting and preventing anthropogenic disasters due to water-based
economic activities
Technologies for production of next-generation personalized and functional foods
– Technologies to produce nanodispersed and nanostructured foods with new absorbency
properties (high, selective, delayed)
– Functional albumin pastes
– Technologies to produce enriched dairy products with biﬁdogenic properties and
immunostimulating effects
– Technologies for making edible meat product packaging, including ﬂavoured collagen ﬁlms
– Technologies to make composite materials using grain shell ﬁbres
(continued)
– Technologies for 3D scanning of meat products for total screening to detect alien insertions and
check their hygienic suitability
– Technologies for long-term energy-efﬁcient storage of food materials and products, with
minimal loss of their valuable components, production of aseptic semi-ﬁnished products,
hydrothermal treatment, preservation, microﬁltration, shock freezing, low-temperature vacuum
drying, cold processing, production of ready-to-eat foods in protective packaging with
integrated biocide effect, technologies for long-term room-temperature storage of products
traditionally considered as perishables
220
L. Gokhberg et al.
Technologies for production of synthetic foods
– Technologies for growing meat tissues in artiﬁcial mediums
– Technologies for production of synthetic meat and synthetic eggs from protein materials, with
taste indistinguishable from natural ones
– Technologies for producing milk and dairy products based on yeast culture bioreactors
– Technologies for production of forage and extraction of valuable proteins from food and forage
waste
– Technologies for directly synthesizing nutritious substances from chemical and mineral
materials
– Technologies for production of foods indistinguishable from traditional ones from new
unconventional raw material sources (e.g. algae, insects, etc.)
– Food printing technologies
Advanced technologies for the ﬁsheries sector
– Technologies for complete processing of by-catch
– Recirculation aquaculture and aquaponics technologies
– Phytobiotechnologies to produce forage, fertilizers and biofuel from algae and microalgae
– Phyto- and micro-biotechnologies for protection and cleaning of water and sewage pipes from
biofouling
– Technologies for genetic modiﬁcation of microalgae to produce new bioproducts and
pharmaceutical substances
– Ships with zero emission and discharge into the environment and low-level noise pollution
– Technologies for cleaning water reservoirs from non-organic and organic pollutants
– Technologies for biodegrading plastic-based garbage in oceans
Source: HSE
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Leonid Gokhberg is First Vice-Rector of the HSE and also
Director of HSE ISSEK. His area of expertise is statistics and
indicators on STI as well as foresight and policy studies in
this area. He has authored over 400 publications in Russian
and international peer-reviewed journals, monographs, and
university textbooks. Prof. Gokhberg has coordinated dozens
of national and international projects funded by public
agencies, businesses, and international organizations. He
has served as a consultant of the OECD, Eurostat, UNESCO,
and other international and national agencies. Leonid is also
a member of the Global Innovation Index Advisory Board,
the OECD Government Foresight Network, and OECD and
Eurostat working groups and task forces on indicators for
S&T and, as well as steering committees of various presti-
gious international and national initiatives. Prof. Gokhberg is
Editor-in-Chief of the Scopus-indexed scientiﬁc journal
Foresight and STI Governance and editor of the Springer
academic book series Science, Technology, and Innovation
Studies, and participates on the editorial boards of several
other inﬂuential journals. He holds PhD and Dr. of
Sc. degrees in Economics.
Ilya Kuzminov is Head of the Information and Analytical
Systems Unit at HSE ISSEK. He is also Senior Research
Fellow at the Research Laboratory for Science and Technol-
ogy Studies, and the HSE International Research and Educa-
tional Foresight Center. Dr. Kuzminov is responsible for the
coordination of research activities in the ﬁelds of future-
oriented studies of environmental technologies, sustainable
development, mining, agriculture, and forestry sectors. His
scientiﬁc
interests
include
theory,
methodology,
and
practices of research in global challenges and grand
responses in STI, priority setting, scenario designing,
roadmapping, foresight evaluation and implementation into
policy making, and environmental management, as well as
big data and machine learning approaches to STI data analy-
sis (including text mining, semantic analysis, and knowledge
discovery). Dr. Kuzminov holds a PhD in Economic and
Social Geography.
9
Emerging Technologies Identification in Foresight and Strategic Planning:. . .
223
Pavel Bakhtin is Research Fellow at the Information and
Analytical Systems Unit, Institute for Statistical Studies and
Economics of Knowledge, National Research University
Higher School of Economics (HSE). He is an expert in the
ﬁeld of data mining, text mining and machine learning for
S&T foresight. Pavel plays a key role in the development of
the HSE based big data intelligent foresight analytics system
iFORA. Mr. Bakhtin owns software patents and over a dozen
publications about text data mining for strategic planning
and foresight, including publications in high impact interna-
tional journals. He holds a technical background in the
sphere of business informatics, master of management in
the area of STI governance.
Anton Timofeev is Laboratory Assistant at the HSE ISSEK
Information and Analytical Systems Unit. He is a software
architect and specialist in the ﬁeld of natural language
processing, data visualization, knowledge management
systems and semantic analysis. He leads the development
and design of architecture of big data analytics systems
iFORA. Mr. Timofeev participates in the development of
software solutions and technical support for Foresight meth-
odology, sectoral strategic analytics, horizon scanning, tech-
nology mining and technology landscape mapping. As a
developer he was involved in a number of foresight and
S&T monitoring activities. Mr. Timofeev holds a software
license in the ﬁeld of text mining and contributed to several
publications.
Elena Khabirova is Expert at HSE ISSEK Information and
Analytical Systems Unit. Ms. Khabirova graduated from
HSE with a Master’s degree in Sociology. She participates
in sectoral research projects with the application of big data
foresight analytics system iFORA particularly in the areas of
agriculture and environment.
225
Technology Assessment for Container
Closure Integrity Testing Technology
for Biotech Industry
10
Qin Guo, Michael Clark, Mitsutaka Shirasaki, and Tugrul Daim
10.1
Background
The majority of patients place a high level of trust that their healthcare providers are
administering drug products that have met US Food and Drug Administration (FDA)
product safety standards (Healtfacts 1997). Patients, healthcare providers, and regu-
latory agencies (FDA, et al.) have an interest in ensuring a high level of patient
safety. Pharmaceutical companies meet this expectation by following FDA guide-
lines for drug product sterility (US Department of Health and Human Services
2004). In 2008, the FDA issued a guideline that sterility testing is required but that
container closure integrity (CCI) testing is preferred (US Department of Health and
Human Services 2008). CCI testing’s advantages are conservation of samples,
faster results, and sensitivity that can pinpoint leaks (Brasten et al. 2014).
CCI is the ability of a container closure system to maintain the sterility and quality
of drug products throughout their shelf life (Ewan et al. 2015). The FDA deﬁnes a
container closure system as “the sum of packaging components that together contain
and protect the dosage form. This includes primary packaging components and
secondary packaging components, if the latter are intended to provide additional
protection to the drug product. A packaging system is equivalent to a container
closure system” (US Department of Health and Human Services 1999). Typical
container closure systems include IV bag, ampoule, serum vial, cartridge, and
syringes (White 2012).
Q. Guo · M. Clark · T. Daim (*)
Portland State University, Portland, OR, USA
e-mail: guoqin@pdx.edu; mrclark@pdx.edu; tugrul.u.daim@pdx.edu
M. Shirasaki
Genentech, Beaverton, OR, USA
e-mail: mshira2@pdx.edu
# Springer Nature Switzerland AG 2019
D. Meissner et al. (eds.), Emerging Technologies for Economic Development,
Science, Technology and Innovation Studies,
https://doi.org/10.1007/978-3-030-04370-4_10
Based on a review of the literature, there is not a preferred CCI testing technology
within the pharmaceutical industry (Brasten et al. 2014; Ewan et al. 2015; White
2012). The primary two low-tech options are dye ingress testing and manual visual
inspection. Dye ingress testing involves examining a sample base by a quality
control technician visually inspecting for dye contamination of the product, a time-
consuming process that destroys the samples reviewed. Manual visual inspection
requires trained inspectors visually examining all of the products, a time-consuming
process that can be prone to human error. The four high-tech options are CCD
camera visual inspection, high-voltage leak detection, vacuum decay/pressure lead
detection, and laser headspace analysis. The industry expert panel advised assessing
high-voltage leak detection, vacuum decay/pressure lead detection, and laser head-
space analysis. Automated solutions are preferred due to time, quality, and cost
concerns. Additional information about these options is discussed in the technology
candidates’ section.
226
Q. Guo et al.
10.2
Technology Candidate
10.2.1 Dye Ingress Testing
The dye ingress leak testing is probably the most common leak test method used in
pharmaceutical/biotech industry to ensure the container closure integrity is intact in
various container/package types. In the simplest terms, the methodology works in
three phases: the container is submerged in the bath of dye solution (typically
methylene blue), and vacuum is drawn; the container is placed in ambient atmo-
spheric pressure; a technician inspects the ingress of blue dye into the packaging. In
this study, only tubing vials are considered as the target for primary container type.
Variables
•
Vacuum level and dwell time
•
Atmospheric pressure and dwell time
•
Concentration of dye surfactant
•
Inspector capability
Limitations
•
This methodology can only be applied as sampling method.
•
Sensitivity of the testing is depended on the differential between the vacuum
versus the atmospheric pressure times the dwell time.
•
Accuracy of the testing is depended on the inspector capability to detect the dye
within the container.
•
Some leak within the seal of the vial is not visible to the inspector.
10
Technology Assessment for Container Closure Integrity Testing Technology. . .
227
As this methodology can only be applied as sampling based, this technology is
excluded from further considerations.
10.2.2 Helium Leak
The helium leak testing is said to be the most accurate method of detecting leaks in
any container types. This method uses a vacuum to draw out the helium inside the
container and uses a mass spectrometer to detect the rate of helium leaking from the
container. Because it only detects the helium molecules, it is not affected by the other
gas molecules in the air.
Variables
•
Vacuum level and dwell time
Limitations
•
This method needs vials to be preﬁlled with helium gas.
•
It needs certain amount of headspace.
As this methodology can only be applied to vials preﬁlled with helium in the
headspace, the methodology is not generic enough to be considered in this study.
This technology is excluded from further considerations.
10.2.3 Manual Visual Inspection
The manual visual inspection methodology is the most common 100% inspection
method in the pharmaceutical/biotech industries. This methodology provides ﬂexi-
bility to inspect for all type of visually detectable defects in the container such as
particulates in solution, bruises, scratches, and dented seals including container
closure defects such as cracks, deformed or split stopper, un-crimped seal, etc. An
inspector takes one or more vials (depending on the vial sizes) and places it in front
of black and white background under preset lighting condition. Entire circumference
of the vial is visually inspected including the solution.
Variables
•
Inspector capability
•
Lighting condition
•
Container sizes and types
228
Q. Guo et al.
Limitations
•
This method is highly depended on the inspector capability for accuracy and
repeatability.
•
Lack of focus from inspector due to all other defects being inspected at the
same time.
•
Typical manual visual inspection takes 20 seconds per vial depending on the vial
sizes.
•
This methodology is a highly labor-intensive work, and inspectors need to take a
lot of “micro breaks” to keep effectiveness of inspection.
•
Defect must be “visible” to the inspector. For example, a crack under seal of vial
cannot be detected by the inspector unless it is grossly leaking.
This methodology is often used as the benchmarking method for high-tech
alternatives such as high-voltage leak detection or vacuum decay methods
introduced below. This method is typically used in conjunction with CCI testing
as CCI testing alone does not offer inspection of all the defect types. Therefore, this
technology is excluded from further evaluations.
10.2.4 CCD Camera Visual Inspection
The charge-coupled device (CCD) camera visual inspection is most commonly used
in fully automated visual inspection machines. Several CCD camera stations are
used to inspect full circumference of the container for various defect types including
container closure integrity defect, such as cracks, un-crimped seal, deformed stop-
per, etc. The container is placed in spin stations and presented in front of inspection
stations with different lighting techniques for detection of visually detectable
defects. The CCD camera technology is versatile in detecting different types of
defects as they appear in the images taken by the CCD cameras, and proprietary
image processing software is programmed to detect various types of defects. The
technology has evolved in speed and complexity in logic in recent years, and most
defects that can be detected by manual visual inspection now can be detected by
automated visual inspection.
Variables
•
Size and type of container
•
Lighting condition
Limitations
•
The detection capability is highly dependent on the size of defect.
10
Technology Assessment for Container Closure Integrity Testing Technology. . .
229
•
The defect must be “visible” to the vision system. For example, crack under the
seal of the vial will not be “visible” unless it is grossly leaking.
This technology is typically used in conjunction with CCI testing as CCI testing
alone does not offer inspection of all the defect types. Therefore, this technology is
excluded from further evaluations. This technology can be used in place of or in
conjunction with the manual visual inspection.
10.2.5 High-Voltage Leak Detection
High-voltage leak detection (HVLD) is a nondestructive test method that applies
high-voltage potential to liquid-ﬁlled non-conductive container (glass vial in this
study). This methodology has been successfully validated and in use since the early
1970s in the pharmaceutical industry for detection of pinhole in glass ampoules for
fully automated 100% inspection. The high-voltage potential is applied at multiple
locations of the container covering the entire circumference. If there is a crack or a
pinhole, electrical current is conducted through the liquid, and potential difference
can be detected compared to the “good” vial. The method is fast and highly accurate
and requires no sample preparation. This method is generally safe in all types of
liquid product. However, there is a reported case of ozone generation in the head-
space of liquid container affecting the efﬁcacy of the protein-based liquid drug
product.
Variables
•
Conductivity of liquid solution.
•
Fill volume (must be able to cover the entire length of vial when placed side way).
Limitations
•
This method can only be used for liquid product with conductivity of σ ≥1 μS/
cm.
•
Depending on the headspace content, there is a risk of ozone generation within
the headspace of liquid-ﬁlled container.
10.2.6 Vacuum Decay or Pressure Decay Leak Detection
The vacuum decay or pressure decay leak detection system is a nondestructive test
that can be used on liquid or solid product in rigid or ﬂexible and conductive or
non-conductive containers. The container is placed within the chamber that isolates
the container from atmospheric pressure. Depending on the system, either vacuum or
high pressure is applied to the container, and pressure change within the chamber is
230
Q. Guo et al.
measured. Although the measurement time is short, the sensitivity of this measure-
ment is depended on the dwell time and the amount of headspace present within the
container. The vacuum decay is generally preferred over pressure decay because the
vacuum decay technology can also offer measurement of moisture content drawn
into the chamber.
Variables
•
Amount and level of pressure of headspace
Limitations
•
This method requires individual chamber for different container sizes and shapes.
•
The accuracy of the measurement is depended on the dwell time. So the speed of
the equipment is limited based on the accuracy required.
•
The pressure change must be measured after vacuum or pressure level is
established within the chamber. Therefore, missing a large part of container
may not be detected as there is no change in the pressure.
The principle of vacuum and pressure decay technologies is the same except for
the positive or negative pressure applied within the chamber. Therefore, only
vacuum decay technology is considered within this study.
10.2.7 Laser Headspace Analysis
The laser headspace analysis is a nondestructive test method which uses frequency
modulation spectroscopy. This method can be used on liquid or solid product in
transparent container. Although the technology can be used for liquid-ﬁlled con-
tainer, it requires a clear path for the frequency-modulated light to pass through the
container and the headspace. For this reason, the liquid product is generally avoided
for the application of this technology. The frequency of the laser can be modulated
for different types of molecules of interest such as O2 or water contents. The laser
passes through the headspace, and the amount of light absorbed by the headspace is
proportionate to the concentration of molecules in interest. This method requires a
wait period once the container is closed depending on the level of existing vacuum in
the headspace. The atmospheric air and moisture must ingress to the headspace for it
to be detected.
10
Technology Assessment for Container Closure Integrity Testing Technology. . .
231
Variables
•
Amount and contents of headspace
•
Vacuum level of headspace
Limitations
•
The contents of the headspace must be different from the atmospheric air.
•
The liquid product may produce droplets on the wall of the container that
interferes with the light.
•
Depending on the level of vacuum in the headspace, a longer wait period may be
required for the accuracy desired.
10.3
GAP Analysis
Prior to select the best alternative for the current technologies, there is a need to
identify the current situation and the desired outcome. Gap analysis is a project
management tool that helps identify the gap between the current and future state, as
well as the tasks that need to be done to bridge the gap (Mind Tools Ltd 2015). This
project management tool is useful in the initial stages of a project. The processes of
conducting a gap analysis are as follows (Mind Tools Ltd 2015):
•
State descriptions.
A gap analysis starts with analyzing the current state of the technology and then
identifying the objectives that need to be achieved. The analysis can be quantita-
tive, qualitative, or both. The key thing is to be speciﬁc and factual with an
emphasis on identifying weaknesses for the current situation and improvements
for the future state.
•
Bridge the gap.
This can be done through identifying whether a gap exists between the current
and future state and what is needed to bridge the gap.
In this project, the TOP (technical, organizational, and personal) methodology is
adopted to help understand the current and future states of container closure integrity
testing technologies. This methodology breaks down the overall system and
performs a detailed analysis from three perspectives: technical, organizational, and
personal (Linstone 1999). Technical perspective considers rational factors. Personal
perspective
considers
factors
that
emerge
from
decision-makers’
or
any
stakeholders’ own motivations. Organizational perspective is a collection of all
personal perspectives and can also be similar to inter- or intraorganizational politics.
As shown in Table 10.1, the gap analysis is conducted to identify the gap between
four candidates: manual visual inspection, high-voltage leak detection (HLVD),
232
Q. Guo et al.
Table 10.1 Gap analysis results
Gap analysis
Requirement
Capabilities
Gap
Technical
• 100%
product
inspection
• Reliability
• Accuracy
• Ease of use
• Fast
• Low
maintenance
• Nonlabor
intensive
Low tech
• High reliability
• Slow
• No maintenance
Low tech
• Need to improve accuracy
• Need to reduce labor
High tech
• Fully automated
• High-moderate
accuracy
• Easy to use
• Fast
• Nonlabor intensive
High tech
• Need to minimize maintenance
Organizational
• 100%
product
inspection
• Patient safety
• Accuracy
• Reliability
• Efﬁcacy
• Minimized
cost
• Maintenance
• Labor
• Production
capacity
Low tech
• Low initial cost
• Low to moderate
accuracy
• Low production
speed
• High labor cost
Low tech
• Need to improve patient safety
level
• Need to improve inspector
capability
• Need to increase production
speed
High tech
• High initial cost/low
maintenance cost
• High accuracy
• High production
speed
High tech
• Need to minimize capital
investment costs
• Need to reduce maintenance
costs
Personal
• Patient/
personal safety
• Efﬁcacy
• Reliability
• Easy to
operate
• Easy to
maintain
• Easy to
qualify
Low tech
• Low to moderate
accuracy
• Low initial cost
Low tech
• Need to reduce manual labor
processes
• Need to increase degree of
conﬁdence for patient safety
High tech
• Easy to operate
• Easy to maintain
• Difﬁcult to qualify
High tech
• Need for ongoing training for
engineers
Source: Authors
vacuum decay/pressure leak detection, and laser headspace analysis. Capabilities are
the current situation for the four candidates of CCI testing technology. Requirements
refer to the future state of the four candidates. The gaps are identiﬁed after deﬁning
the current situations and the future states. For example, in the technical aspect, the
speed of the current technology, which is the manual visual inspection, is drastically
slow. Meanwhile, the accuracy of the results varies from people to people. Thus, in
the future situation, the speed of inspection should be raised, and the accuracy needs
to be improved.
10
Technology Assessment for Container Closure Integrity Testing Technology. . .
233
Goal
Criteria
Sub-Criteria
Sub-Criteria
Criteria
Sub-Criteria
Sub-Criteria
Criteria
Sub-Criteria
Sub-Criteria
Criteria
Sub-Criteria
Sub-Criteria
Alternative X
Alternative Y
Alternative Z
Level 1:
Level 2:
Level 3:
Level 4:
Fig. 10.1 HDM structure. Source: Authors
10.4
Hierarchical Decision Model Background/Methodology
In this project, a hierarchical decision model (HDM) is applied to evaluate the CCI
testing technologies for biotech industry. The HDM is an effective tool for dealing
with complex decision-making. By decomposing multi-criteria decisions to a series
of pairwise comparisons, and then synthesizing the results, the HDM helps to
capture both subjective and objective aspects of a decision (Lee et al. 2010).
Additionally, the HDM incorporated a useful technique for measuring the consis-
tency of the decision-maker’s evaluations, thus reducing the bias in the decision-
making process.
The
multiple alternatives among which the best decision is to be made (Turan et al.
HDM consists of a goal, a set of evaluation criteria and sub-criteria, and
2009). The HDM develops a set of weights for the evaluation criteria regarding to the
decision-maker’s pairwise comparisons. The higher the weight, the more important
the corresponding criterion (Triantaphyllou and Mann 1995). Then, a score is
assigned to each alternative, which is usually done by experts. The higher the
score, the better the performance of the alternative regarding to the considered
criterion. The third step is combining the weights and the scores to calculate an
overall score for each alternative, and the one with the highest score is the best
decision.
The HDM is typically structured into a hierarchal tree. The goal of the decision is
represented at the top level of the hierarchal tree. The next level is the criteria and all
possible sub-criteria. All the decision alternatives are placed at the lowest level of the
hierarchal tree. Figure 10.1 below is a general representation of the HDM.
234
Q. Guo et al.
Technology Selection
Quality
Ease of Use
Process Efficiency
Cost
Inspection
Accuracy
Efficacy of
Product
Equipment/
Technology
Reliability
Operation
Qualification
Maintainance
Production
Capacity
Production
Integrability
Laser Headspace
Analysis
HVLD
Vacuum Decay
Leak Detection
Vendor Service
& Training
Capital Cost
Operational
Cost
Maintenance
Cost
Fig. 10.2 HDM model used. Source: Authors
10.4.1 HDM Model
Based on the literature review and discussions with our expert panel, there is not a
preferred CCI testing technology within the pharmaceutical industry. The two
low-tech manual visual inspection technologies (dye ingress testing and manual
visual inspection) are not preferred options due to their need to improve accuracy
and production speeds while decreasing labor-intensive processes. Therefore, an
HDM model was created to evaluate the three high-tech automated inspection
technologies (high-voltage leak detection, vacuum decay/pressure leak detection,
and laser headspace analysis).
The HDM model (Fig. 10.2) criteria and sub-criteria were identiﬁed from the
literature and validated by experts. An online version of the model was sent to
experts to complete the criteria pairwise comparison process. HDM Software was
used for the analyses (Portland State University, Engineering & Technology Man-
agement Department (2015).
Quality
•
Inspection accuracy
•
Equipment/technology reliability
•
Efﬁcacy of product
From the technical, organizational, and personal perspectives, the technology
should ensure an inspection process that yields quality drug products. One hundred
percent of drug products should be inspected with a high degree of accuracy. The
equipment/technology should perform the inspections with a high degree of reliabil-
ity. As a result, patients can be assured of the efﬁcacy of the drug products.
Ease of Use
•
Operation
•
Maintenance
•
Qualiﬁcation
From the technical and personal perspectives, the technology should be easy to
operate, maintain, and qualify. The learning curve and ongoing training time should
be minimal for operators, maintenance staff, and engineers.
Process Efﬁciency
•
Production capacity
•
Production integrability
10
Technology Assessment for Container Closure Integrity Testing Technology. . .
235
•
Vendor service and training
From the technical and organizational perspectives, the technology should
improve the efﬁciency of the drug product inspection process. Faster inspection
results due to automated processes should expand production capacity. The technol-
ogy should integrate seamlessly with existing production processes and potentially
improve workﬂows. The technology vendor should provide reliable, efﬁcient service
and training that avert any process slowdowns and inefﬁciencies.
Cost
•
Capital
•
Operational
•
Maintenance
From the organizational perspective (pharmaceutical company), the acquisition
and ongoing cost of a technology should be minimized without materially impacting
other criteria. Costs include the initial capital investment (equipment, implementa-
tion, and training), operational (equipment and labor), and maintenance (equipment
service and repair).
236
Q. Guo et al.
Technology Selection
Quality
Ease of Use
Cost
Inspection 
Accuracy
Equipment/
Technology 
Reliability
Process Efficiency
Operation
Efficacy of 
Product
Capital Cost
Maintenance 
Cost
Operational 
Cost
Maintainance
Qualification
Production 
Capacity
Vendor Service 
& Training
Production 
Integrability
0.45
0.16
0.27
0.13
0.40
0.24
0.36
0.53
0.24
0.23
0.46
0.26
0.28
0.29
0.40
0.31
0.18
0.1
0.16
0.08
0.04
0.04
0.13
0.07
0.07
0.04
0.05
0.04
Fig. 10.3 Expert feedback results. Source: Authors
10.5
Result/Analysis
10.5.1 Expert Feedback
The result of pairwise comparison by the subject matter experts is shown below in HDM
model. Sixteen experts provided feedback, including 13 experts in the USA, 2 in
Canada, and 1 in Ireland with average experience of 18 years (Figs. 10.3 and 10.4).
There is a clear preference in “Quality” criteria having weight of 0.45, followed
by “Process Efﬁciency” with weight of 0.27. The “Ease of Use” and “Cost”
weighted the least with 0.16 and 0.13, respectively. This result shows the “Cost”
does not have signiﬁcant impact in the decision-making in the selection of CCI
testing technology.
The disagreement among the expert was quite small with score of 0.05 as seen in
the Appendix.
10.5.2 Final Scores
The ﬁnal scores for technology selection were made based partially on user require-
ment speciﬁcations (URS) which had 162 line items. The URS was speciﬁc to the
application for fully automated CCI testing equipment for liquid-ﬁlled glass vials.
Each line item was categorized in the sub-criteria listed above and scored either
“met” or “not met” and score “1” or “0.” The sub-criteria of “Equipment/technology
reliability,” “Ease of use for operation,” “Ease of use for maintenance,” and “Pro-
duction capacity” were scored solely on the URS line items “met” or “not met”
scores. The other sub-criteria were scored based on our expert understanding of the
technology and the equipment (Table 10.2).
10
Technology Assessment for Container Closure Integrity Testing Technology. . .
237
0.18
0.1
0.16
0.08
0.04
0.04
0.13
0.07
0.07
0.04
0.05
0.04
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
Criteria
Expert Weights
Expert Weights
Fig. 10.4 Sub-criteria weights. Source: Authors
Table 10.2 Final score of each alternative
Alternatives
Sub-criteria
Weight
HVLD
Vacuum
decay
Laser headspace
analysis
Inspection accuracy
0.18
100
90
80
Equipment/technology
reliability
0.1
100
100
100
Efﬁcacy of product
0.16
80
100
95
Operation
0.08
100
100
100
Maintenance
0.04
100
100
100
Qualiﬁcation
0.04
100
100
100
Production capacity
0.13
100
100
100
Process integrability
0.07
100
100
50
Vendor service and training
0.07
100
80
80
Capital cost
0.04
100
90
90
Operational cost
0.05
100
100
80
Maintenance cost
0.04
100
90
90
96.8
96.0
88.9
Source: HSE
238
Q. Guo et al.
The research model presented is an initial technology assessment tool for the
pharmaceutical industry. The model could be improved in the following areas:
•
Safety should be added as a criterion distinct from quality/efﬁcacy of product
(patient safety), quality/inspection accuracy (product safety), and ease of
use/operational (operational safety).
•
The three technology alternatives could be combined with CCD camera visual
inspection technology.
•
Future research could forecast what new CCI technologies may develop.
•
The expert panel could be expanded to represent the views of quality control
experts.
•
The experts’ judgments were based on their experiences using the technology
alternatives. Only 58% had experience with laser headspace analysis, a newer
technology.
•
Although cost was a low-weighted criterion, vendor cost estimates for each of the
technology alternatives were not available.
10.6
Discussion and Conclusion
High-voltage leak detection equipment scored the highest of 96.8. HVLD scored
slightly higher than the vacuum decay technology based on inspection accuracy,
vendor service and training, capital cost, and maintenance cost. For the inspection
accuracy, the HVLD can detect pinholes down to 2 μm pinholes. The HVLD
technology has been in use in US pharmaceutical/biotech industry since the 1970s.
The equipment vendor has a strong US presence and has better service and training.
The HVLD equipment generally costs less than the other alternatives because the
principle of the technology is simpler and has less moving parts. The HVLD
equipment costs less to maintain for the same reason. On the other hand, the
HVLD scored less for the efﬁcacy of product as there is a reported issue where
high potential voltage applied to the ambient air-ﬁlled headspace created ozone
within the headspace, and in turn, the ozone oxidized the protein within the liquid
solution degrading the active drug product. This issue can be avoided by placing the
high-voltage prong on the bottom side where there is no air. The disadvantage of the
HVLD is that it can only be used for liquid-ﬁlled container.
Vacuum decay leak detection equipment scored the close second highest of 96.0.
The vacuum decay technology has a disadvantage in the inspection accuracy as the
protein can clog the small pinholes or crack. The vacuum decay has no impact to the
quality of the product and has an advantage over the HVLD. However, the vacuum
decay technology has disadvantage under cost as the equipment is much more
complicated than the HVLD alternative with individual chamber for different size
containers, a need to create vacuum within that chamber and measure pressure
change and moisture content. The equipment vendor is based in Switzerland, and
the technology is relatively new in the industry. The company does not yet provide
strong US local customer support with quick response and knowledgeable service
engineer. The advantage of the vacuum decay is that the technology can offer a
solution to both liquid-ﬁlled and lyophilized products. In this case, it did not translate
to a higher score as the score is based solely on the liquid-ﬁlled product.
Laser headspace analysis scored the third highest based on disadvantage in the
inspection accuracy, impact to the efﬁcacy of product, process integrability, and
vendor service and training. The inspection accuracy for the laser headspace analysis
is impacted by the potential blockage by the droplet on the wall, which absorbs the
10
Technology Assessment for Container Closure Integrity Testing Technology. . .
239
laser and can create inaccurate result. This condition can also cause potential impact
to the efﬁcacy of the product. Although the laser used is low-energy laser, the laser
has potential to alter the protein structure if the liquid solution is in the path of the
laser. This technology has disadvantage in the process integrability as well as due to
the wait time required after ﬁlling is completed. As the ambient air must permeate
through the crack into the headspace of the container, depending on the size of crack
that is desired to be detected, a longer wait period is required. This technology also
does not work for the ambient air-ﬁlled vials as there is no change in the contents of
the headspace. The technology is the newest of the three alternatives, and as with the
vacuum decay technology, the representation in the USA is still weak and scores low
on vendor service and training.
This chapter demonstrated the use of hierarchical decision model and multiple
perspectives to assess technologies in the biotechnology sector through a case
container closures. The model’s strength is its ability to consider all perspectives
that may impact the decision. The weakness of the approach is its reliance on experts
and can be overcome by carefully selection of the right experts.
Appendix
 
Minimum
0.04
0.01
0.03
0.02
0.01
0.01
0.01
0.02
0.01
0.02
0.01
0.01
Disagreement
0.05
240
Q. Guo et al.
Selection
Inspection Accuracy
Equipment/Technology 
Reliability
Efficacy of Product
Operation
Maintainance
Qualification
Production Capacity
Process Integrability
Vendor Service & Training
Capital Cost
Operational Cost
Maintenance Cost
Inconsistency
ER
0.22
0.16
0.06
0.04
0.03
0.03
0.09
0.08
0.23
0.01
0.01
0.04
0.03
ED
0.19
0.06
0.19
0.11
0.05
0.02
0.11
0.12
0.06
0.02
0.06
0.02
0.01
GV
0.16
0.16
0.05
0.05
0.02
0.1
0.09
0.24
0.04
0.02
0.05
0.01
0.03
JL
0.11
0.11
0.21
0.09
0.05
0.06
0.07
0.07
0.13
0.01
0.04
0.04
0.02
JN
0.13
0.07
0.16
0.02
0.02
0.01
0.25
0.14
0.12
0.01
0.05
0.02
0.04
JC
0.16
0.11
0.27
0.06
0.02
0.03
0.1
0.07
0.03
0.06
0.06
0.03
0
JO
0.36
0.12
0.04
0.1
0.04
0.02
0.16
0.03
0.06
0.01
0.04
0.02
0.03
LB
0.04
0
0.26
0.06
0.02
0.01
0.36
0.05
0.07
0.07
0.02
0.03
0.04
MK
0.32
0.05
0.03
0.09
0.02
0.01
0.01
0.03
0.05
0.08
0.18
0.12
0.01
MS
0.29
0.13
0.29
0.04
0.01
0.01
0.08
0.04
0.05
0.01
0.02
0.02
0.01
PM
0.15
0.13
0.11
0.08
0.04
0.09
0.09
0.07
0.05
0.05
0.08
0.06
0.01
RD
0.31
0.13
0.04
0.08
0.02
0.01
0.15
0.03
0.03
0.14
0.01
0.04
0.09
RM
0.08
0.01
0.44
0.12
0.07
0.13
0.09
0.02
0.01
0
0.01
0.01
0.05
RI
0.12
0.15
0.09
0.1
0.1
0.03
0.12
0.06
0.12
0.02
0.04
0.03
0.01
RM
0.13
0.13
0.13
0.12
0.04
0.04
0.09
0.03
0.09
0.07
0.07
0.07
0
TD
0.13
0.11
0.2
0.09
0.04
0.05
0.14
0.05
0.05
0.07
0.03
0.03
0.01
Mean
0.18
0.1
0.16
0.08
0.04
0.04
0.13
0.07
0.07
0.04
0.05
0.04
1
Maximum
0.36
0.16
0.44
0.12
0.1
0.13
0.36
0.24
0.23
0.14
0.18
0.12
Std. Deviation
0.09
0.05
0.11
0.03
0.02
0.04
0.08
0.05
0.05
0.04
0.04
0.03
10
Technology Assessment for Container Closure Integrity Testing Technology. . .
241
References
Brasten L, Jacobs B, Brydzinski A (2014) Container closure integrity testing. Controlled Environ-
ment. p 18, 24 March 2014
Ewan S, Jian M, Stevenson C, Henderson O, Klohr S, Wessel M, Mehta P, Adler S, Lake C,
Walsh J, Polomene T, Everaert V (2015) White Paper: Container closure integrity control versus
integrity testing during routine manufacturing. PDA J Pharmaceut Sci Technol 69:461–465
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Lee HB, Cho WN, Hong YS (2010) A hierarchical end-of-life decision model for determining the
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Linstone HA (1999) Decision making for technology executives: using multiple perspectives to
improved performance, 2nd edn. Artech House, Norwood, MA, pp 560–570
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pages/article/gap-analysis.htm. Accessed 01 Dec 2015
Portland State University, Engineering & Technology Management Department (2015) HDM
Hierarchical Decision Model. http://research1.etm.pdx.edu/hdm2/. Accessed 02 Dec 2015
Triantaphyllou E, Mann SH (1995) Using the analytical hierarchy process for decision making in
engineering applications: some challenges. Int J Indus Eng Appl Pract 2(1):35–44
Turan T, Amer M, Tibbot P, Almasri M, Al Fayez F, Graham S (2009) Use of Hierarchal Decision
Modeling (HDM) for selection of Graduate School for Master of Science Degree Program in
Engineering in PICMET. Portland
U.S. Department of Health and Human Services (1999) Guidance for industry – container closure
systems for packaging human drugs and biologics. Chemistry, Manufacturing, and Controls
Documentation, May 1999
U.S. Department of Health and Human Services (2004) Guidance for industry. Sterile drug products
produced by aseptic processing – current good manufacturing practice, September 2004
U.S. Department of Health and Human Services (2008) Guidance for industry. Container and
closure system integrity testing in lieu of sterility testing as a component of the stability protocol
for sterile products, February 2008
White EK (2012) The aseptic core: container-closure integrity. J Valid Technol Spring:10–16
Qin Guo received Master of Science degree in Engineering
and Technology Management at the Department of Engi-
neering and Technology Management of Portland State Uni-
versity.
She
also
has
a
Bachelor’s
Degree
in
Telecommunications Engineering from Shanghai University
in China. Qin had worked at China Mobile as a network
supervision coordinator. She is also a member of Omega Rho
International Honor Society of Operations Research and
Management Science.
242
Q. Guo et al.
Michael Clark is Adjunct Professor at the College of Engi-
neering, Technology and Management at Oregon Institute of
Technology. He is pursuing a PhD at the Department of
Engineering and Technology Management at Portland State
University. Mr. Clark has over 15 years of experience with
research and grant administration in government and aca-
demic organizations. He received his BS in Management
Information
Systems:
Management
Accounting
from
Oregon Institute of Technology, MIB in International Busi-
ness from Pepperdine University in California, MA in Busi-
ness Administration from Instituto Tecnológico y de
Estudios Superiores de Monterrey in Mexico, and MLS in
Library Science from Southern Connecticut State University.
Mitsutaka Shirasaki is an inspection vision systems engi-
neer at Genentech, a leading biotech company that discovers,
develops, manufactures and commercializes medicines to
treat patients with serious or life-threatening medical
conditions. Previously he had worked at Eisai Machinery
as a technical services director. Mr. Shirasaki recently
completed his Master of Science degree in Engineering and
Technology Management at the Department of Engineering
and Technology Management of Portland State University.
He also has Bachelor of Science Degree in Engineering in
Electrical Emphasis and Mathematics from Texas Christian
University. Mr. Shirasaki is also a member of Omega Rho
International Honor Society of Operations Research and
Management Science.
Tugrul Daim is Professor and Director of the Technology
Management Doctoral Program in the Maseeh College of
Engineering and Computer Science at Portland State Univer-
sity. Prof. Daim is also Director of the Research Group on
Infrastructure and Technology Management and Faculty
Fellow at the Institute for Sustainable Solutions. He is also
a Leading Research Fellow at the Higher School of Econom-
ics in Moscow Russia. He is the Editor-in-Chief of IEEE
Transactions on Engineering Management. He has been at
various editorial roles in journals including International
Journal of Innovation and Technology Management, Tech-
nological Forecasting and Social Change, Technology in
Society, Foresight, Journal of Knowledge Economy and
International Journal of Innovation and Entrepreneurship.
Tugrul received his BS in Mechanical Engineering from
Bogazici University in Turkey, MS in Mechanical Engineer-
ing from Lehigh University in Pennsylvania, MS in Engi-
neering Management from Portland State University, and
PhD in Systems Science: Engineering Management from
Portland State University.
Emerging Technologies, Trends and Wild
Cards in Human Enhancement
11
Ozcan Saritas
11.1
Background
The future role and place of human in the world is a widely debated issue. New
technologies are developed continuously and are presented to markets momentarily.
The key question is whether human will be at the centre of change as it has
historically been or will be put aside by smarter machines. An ongoing concern on
how the middle class is hollowed by technology indicates the global socio-economic
extent of the issue (Saritas 2015). It has been already over 10 years since Kurzweil
mentioned ‘singularity’ as a fateful moment when our technology becomes smarter
than us and able to learn faster than we can (Kurzweil 2006). Machines then will
become the principal creator of new technologies and race far ahead of humanity. As
a result, humans may effectively fall out of the loop and can be eliminated. For
instance, new software has already been developed, which is capable of collecting
data about anything like statistics, ﬁnancial reports, sports and so on and turns it into
articles like this one (AP 2016).
While the machines are advancing, emerging technologies also offer new
possibilities for human to remain competitive against machines through the enhance-
ment of physical and mental capacities. The development and use of, such as,
neuroscience, silicon chips and smart technologies offer new opportunities. A
desirable future is that there would be an ecosystem of human and machine, where
both complement each other rather than competing with each other. Machines would
continue to support humanity’s well-being and quality of life and offer new
possibilities for advanced ‘human-machine’, ‘human-human’, ‘human-work’ and
human-environment interactions.
O. Saritas (*)
National Research University, Higher School of Economics, Moscow, Russia
e-mail: osaritas@hse.ru
# Springer Nature Switzerland AG 2019
D. Meissner et al. (eds.), Emerging Technologies for Economic Development,
Science, Technology and Innovation Studies,
https://doi.org/10.1007/978-3-030-04370-4_11
243
However, the future may bring some wild cards too. Machines may go ahead of
human and take the control in a human-excluded world; a new social divide may
occur between enhanced and non-enhanced human; or with the contribution of
machines, human may move life to a virtual level to dominate the real life. With
these ideas in mind, this article will ﬁrst look at some technologies for human
enhancement, which have uses starting from foetus and then the entire life of
human. Some of these technologies might be biological through medical interven-
tion or with the use of drugs; others maybe implantable through the integration of
human body and electronic chips or externally through wearable technologies or
other smart devices.
244
O. Saritas
Following the exploration of technologies, this chapter will review and discuss
the implications of human enhancement on the future of work, where the socio-
economic impacts of emerging technologies can be well-observed. These
technologies are expected to make revolutionary changes in working environment
as people will be able to work harder, longer and smarter. While the opportunities are
vast, a number of scientists and ethicists have already raised serious concerns about
the trend of technologically augmented humans, such as the identity-affecting
changes mentioned by Delaney (2016). The chapter will also address some of
those ethical issues associated to human enhancement technologies. Then, overall
conclusions will be drawn based on the developments presented in the paper with a
few examples of wild cards which may cause future surprises and shocks.
11.2
Technologies for Human Enhancement
Human enhancement technologies involve broad areas of scientiﬁc and technologi-
cal augmentation, which are fundamentally different from each other in terms of the
ways and timings of use, as well as their impacts. Therefore, it is useful to categorise
them and present the emerging technologies under each category.
Three broad areas can be mentioned where human enhancement will be observed:
1. Medicine/drugs/diet
2. External/wearable technologies
3. Internal/implanted technologies
Below, technologies will be presented in each category and examples will be
given for each.
11.2.1 Human Enhancement Based on Medicine, Drugs and Diet
Technologies for human enhancement based on medicine, drugs and diet can be
applied through the entire life of human and even earlier. These technologies will be
examined under three categories:
11
Emerging Technologies, Trends and Wild Cards in Human Enhancement
245
11.2.1.1 Technologies Related to Fertilisation
The earliest phase of human enhancement can be undertaken at the fertilisation
phase. A new technological procedure called ‘preimplantation genetic diagnosis
(PGD)’ is a screening test used to detect genetic or chromosomal disorders within
embryos created through in vitro fertilisation (IVF) with the aim of preventing
certain diseases or disorders before passing on to the child (APA 2016). The PGD
process begins with an embryo screening. This is done before embryos are located to
the uterus. In this process, couples are able to make decisions about the subsequent
phase in the in vitro fertilisation. Embryos without genetic or chromosomal disorder
are selected and transferred to the uterus. PGD process is recommended in the cases
of:
•
There is a case of heritable genetic disorder in one or both couples.
•
One or both of couples have a chromosomal abnormality.
•
When mother is beyond the maternal age.
•
The mother had previous miscarriages (Penn Medicine 2016).
A large number of clinical preimplantation genetic diagnosis cycles have been
achieved across the world resulting with a number of healthy babies.
11.2.1.2 Body Enhancement Technologies
So far, physical enhancement has largely focused on restoration. In the next few
years to come, this will be extended to cover also healthy individuals (The Academy
of Medical Science 2012). A wide variety of enhancement applications can be
mentioned under this category including technologies for enhancement of physical
functions of human in various ways such as via transfer of organs and tissues; stem
cells; plastic surgery and cosmetic modiﬁcations; and drug and diet-based
enhancement.
Transplantation of Organs, Tissues and Limbs
Transplantation or organs dates back to the earlier twentieth century, when a series of
experimental studies were undertaken. In 1912, Alexis Carrel was awarded the
Nobel Prize for his pioneering scientiﬁc and clinical work. It was after the World
War II, when surgical transplantation of human organs from dead and living donors
started. Since then, the transplantation of human organs, tissues and cells has
advanced dramatically both in terms of quality and quantity across the world and
helped to save thousands of lives. Recent years have witnessed greater progress in
understanding organ and tissue rejection, more demand for organs and tissues as
well as higher number of donations (WHO 2010).
Considerable work is undertaken on tissues, particularly to replace degenerating
tissues through tissue engineering and regenerative medicine. Applications in sim-
pler tissues, e.g. bone and joint tissues, have entered. Progress has also been made on
tissues with higher complexity, such as tracheas. Although organ production is still
difﬁcult, treating speciﬁc parts of an organ is possible. Using stem cell together with
cardiac pumps emerges as a new research area. This may be extended to cover other
organs in human body (Aidil Bin Ahmad 2016).
246
O. Saritas
Beyond organs and tissues, technologies are being developed to improve limb
functions. Devices like bionic limbs and exoskeletons, mimic or surpass the func-
tionality of human limbs are promising to overcome the traditional challenges of user
control, energy efﬁciency and usability as they become more automated and easy-to-
use. While helping disabled and elderly people to gain their physical capabilities
back by restoring mobility (Galle et al. 2017), future applications in limb
technologies are also expected to help healthy individuals, whose jobs involve
manual labour (The Academy of Medical Science 2012).
Hearing and Sight Enhancements
Ageing population is a fact in today’s world. As people live and work longer, or
operate under more and more extreme conditions such as in construction, army or
space work, there is a greater need to maintain and restore the sensory functions of
the body (The Academy of Medical Science 2012). A number of technologies and
products to aid hearing and sight are already available. Advanced hearing aids will
support prolonged auditory abilities. Technologies like retinal implants, gene trans-
fer and replacing photoreceptors in the eye are among the new ways to restore and
maintain vision. Night vision for human is considered to be near (Aidil Bin Ahmad
2016).
Stem Cells
Different from the other cells in human body, stem cells are ‘unspecialised’ and have
the potential to become any type of cell in the body. Moreover, they are able to
divide and multiply themselves for long period of time (NIH 2016). It is possible to
transform stem cells into cells of the blood, bones, skin, muscles, heart and even
brain. Sources of stem cells can be different. However, all stem cells may be
transformed into different types of cells (Health News 2013).
Laboratory studies are underway to understand the properties of cells and what
makes them different from other specialised cells. Stem cells have already been used
to screen new drugs and detect the causes of defects in birth. Stem cell research is
rapidly advancing. Recent work focuses on the development processes of an organ-
ism from one cell and the ways of replacing damaged cells with the healthy ones in
organisms (NIH 2016). With a number of potential further advancements, stem cell
research appears to be one of the most fascinating areas of research as they are
expected to become the future of medicine.
Plastic Surgery and Cosmetic Modifications
Plastic surgery and cosmetic enhancement are in continuous and increasing demand.
Younger appearance is believed to have implications for individuals’ social and
employment relations. Humans have been enhancing their appearance since we
worked out how to use tools. Archaeological evidence suggests cosmetics date at
least 6000 years and cosmetic surgery over 4000 years. At present, cosmetic surgery
can be considered as one of the most common forms of human enhancement.
Enhancement of appearance goes beyond the physical body to extend into digital
world (HEB 2010). As people’s virtual second life is overtaking their real physical
life, it will be more common to see avatars, which will represent not real humans but
an enhanced or a new form of how they wish to look like.
11
Emerging Technologies, Trends and Wild Cards in Human Enhancement
247
11.2.1.3 Mental Enhancement Technologies
Besides physical enhancement, technologies enhancing memory, concentration and
motivation will be covered under the category of mental enhancement technologies.
Some of the most noteworthy technologies in this category are mentioned below.
Neuroscience and Neurotechnology
Neuroscience is concerned with the scientiﬁc study of the nervous system. Today
neuroscience goes beyond being a branch of biotechnology to become an interdisci-
plinary science at the intersection of disciplines like chemistry, computer science,
engineering, linguistics, mathematics and medicine as well as philosophy, physics
and psychology (Bear 2017). Based on the scientiﬁc background, neurotechnologies
under development provide a number of opportunities for the enhancement of
cognitive functions while opening new ways of communication between human
brains and brain and machines. For instance, mind mapping for understanding
brain’s structural and functional connections may help to understand how these
change in the cases of Alzheimer’s and schizophrenia and may provide solutions for
treatment. Brain-like computers may reason, predict and react like human neocortex.
Devices connected to brain are promising both for improving lives of patients with
physical and neurological conditions and opening vast amount of opportunities for
applications in automotive, education, gaming and security industries (Doraiswamy
2015). Moreover, brain-to-brain communication allows the direct linkage of human
brains. Direct brain-to-brain interfaces (BBIs) are technologies that combine neuro-
imaging and neurostimulation methods to exchange information between brains
directly in neural code (Chantel et al. 2015). Transmitting thoughts from one brain
to another without speech or text represent an important ﬁrst step in exploring the
feasibility of complementing or bypassing traditional language-based or motor-
based communication (IFLscience 2016).
Nootropics (Smart Drugs)
Nootropics include smart drugs, neuroenhancers, memory enhancers, intelligence
enhancers and cognitive enhancers. The main functions of nootropics include
improving mental functions such as memory, cognition, intelligence, attention,
concentration and motivation. They are delivered in the forms of drugs,
nutraceuticals, functional foods and supplements. Cognitive enhancement drugs
aim to make people more productive at work, allowing them to perform better and
enjoy their tasks more. Some of the most commonly used smart drugs are:
– Piracetam was the ﬁrst smart drug on the market. It has been used ‘to enhance,
elevate, and stimulate the functions of the (ACh) Acetylcholine receptors
implicated in memory processes and developments’. Because the drug enhances
248
O. Saritas
cognitive functions related to the central nervous system, it is used not only by
individuals with neurodegenerative and central nervous system but also by
healthy individuals (Memolition 2014).
– Modaﬁnil was developed as a treatment for narcolepsy to improve attention and
make tasks more enjoyable, compared with placebo. Lower rate of accidents
among shift workers was observed following the use of the drug (Jha 2012).
– Ritalin is a central nervous system stimulant, which is commonly used to improve
focus. Although legitimately prescribed for the treatment of ADHD, Ritalin has
been misused by individuals for a stimulant effect and to increase academic or
athletic performance (Kroutil et al. 2006).
– Aniracetam aims at improving memory recall and immune function, increasing
intellectual clarity as well as giving well-being and healthy feelings. In essence,
‘Aniracetam is an ampakine and nootropic of the racetam chemical class pur-
ported to be considerably more potent than piracetam. It is lipid-soluble and has
possible cognition-enhancing effects’ (Wikipedia 2017).
– Pramiracetam enhances long-term memory functionality and helps choline trans-
formation into neurons (SDFT 2016).
Drugs developed for brain conditions such as Alzheimer’s and schizophrenia are
increasingly used by healthy individuals to boost their cognitive skills—though the
access to these drugs for healthy individuals, potentials to create addiction and
abuse, and their side-effects on healthy brains are still in question (Keane 2008).
Functional Food
There is an increasing tendency towards healthy eating. This trend brings
discussions on the potential beneﬁts of certain foods and ingredients. Although
there are a lot of hypes, scientiﬁc evidence also supports that some of these food
and ingredients may have positive impact on health and well-being. Recently, foods
have been enhanced to increase their beneﬁts for health and reduce diseases. They
are called ‘functional foods’, which include healthy additives as well as vitamins.
Some of the food enhancements have been made to meet legal regulations, for
instance, through addition of iodine into salt or vitamin D into milk. This sort of
‘invisible fortiﬁcation’ is not considered as functional food (OMICS 2014). On the
contrary, functional food helps to improve physical and mental state by improving:
– cognitive performance
– mood and vitality
– reaction to stress
– short-term memory
– vigilance and attention
– changes in memory and other mental processes during ageing. (EU 2010)
For instance, glucose may improve mental performance, including memory and
time to make decisions. Caffeine may lead to improvement in cognitive performance
with effects on reaction time, vigilance, memory and psychomotor performance.
Sucrose may reduce pain perception. Vitamin B can be used to improve cognitive
performance and maintenance of mental health in older people. The amino acid
tryptophan can reduce the time taken to fall asleep. Tyrosine and tryptophan may
help to recover from jet lag. Ingredients like S-adenosylmethione (SAMe) and folic
and n-3 fatty acids may help to improve depression (EU 2010).
11
Emerging Technologies, Trends and Wild Cards in Human Enhancement
249
Overall, research on nutrition is increasingly concerned with new technologies
such as nutrigenomics, imaging techniques as well as other converging technologies.
A number of beneﬁts can be mentioned such as food development for a certain group
of population suffering from diseases or risk factors such as obesity, diabetes, allergy
or cardiovascular disease. Similarly personalised diets and foods can be developed in
line with individual genetic information to support optimal health for population.
Longevity
A combined consequence of the technologies presented above would be the longev-
ity of human life. In this respect a number of trends can be mentioned in this ﬁeld.
These include (Canton 2006):
1. Within ten years, humans routinely living beyond one hundred will be an
accepted reality
2. Longevity Medicine will postpone aging and promote health, enabling people to
be more active, more productive, and enjoy longer lives
3. Health-enhancement rights, fueled by the wealth of aging baby boomers and the
fusion of nano, bio, IT and neuro innovations, will become ﬁerce social issue
4. Mapping personal DNA proﬁles, and linking that knowledge to prevent illness,
will radically change medicine, making it boldly predictive
5. Health enhancement via biotech, stem cells, and genomic drugs will enhance
human intelligence
6. Supercomputers, artiﬁcial intelligence, and advanced medical information tech-
nology will usher in a new era that will empower doctors to extend the quality of
life
7. Personalized DNA diets will greatly enable longevity as people learn which
foods enhance their health and prevent illness
8. Life-extension treatments, from genetic vaccines and designer DNA “surgery”
to smart drugs and neuro-medical devices, will augment health, improving
intelligence, and maximizing beauty
9. Cognitive brain-science breakthroughs will protect the aging mind, refreshing
vital memories, improving physical agility, and promoting human performance
enhancement
10. The evolutionary transformation of human beings, via emerging breakthroughs
in Longevity Medicine, will provide vast new choices of an astounding and
alarming nature for individuals and society”.
250
O. Saritas
11.2.2 Human Enhancement Technologies Based on Internal
and Implanted Technologies: Towards Transhumanism
Human ICT implants such as cochlear implants and cardiac pacemakers have been in
common clinical use for many years, which helped to form close links between
technology and the body. Recent years have witnessed advancements in implanted
medical devices in their functionality and uses. Some of these are able to modify
behaviour by directly interacting with the human brain and to restore functionality
(Gasson 2012).
Implantable devices can be categorised as ‘medical’ or ‘non-medical’ devices.
These may be passive or active devices. The passive implants are typically those
structural devices such as artiﬁcial joints, artiﬁcial valves and vascular grafts. Active
implantable medical devices are integrated into human body either medically or
surgically. Implantable non-medical devices are usually in the form of electronic
chips. An example of a passive device is the radio frequency identiﬁcation (RFID)
device. Active devices may use electrical impulses to interact with the human
nervous system (EGE 2004).
Bionic eye can be given as an example to such implantable technologies (Monash
2016). A research group at Monash University, Australia, has been developing a
direct-to-brain bionic eye system. This allows blind people to see by wearing a pair
of glasses with a digital camera that captures low-resolution black-and-white images
like a retina. First tests have been undertaken with people who lost their sight in
traumas and then with the others who have been blind since birth. But it is a
precursor of developments that will change the way we deal with disabilities in
society (Kraft 2012).
One of the most striking recent advancements in human enhancement is the use of
DNA as a storage device. As the building block of life, DNA may also be the natural
repository for digital data of all sorts. Scientists have already successfully converted
739 kilobytes of hard drive data in genetic code and then retrieved the content with
100% of accuracy. The only obstacle for the practical application of the technology
is its cost. Sequencing, and especially synthesising the DNA, is a costly process.
Like other new technologies, it is expected that costs will be reduced to affordable
levels in the near future. DNA data storage could be feasible at a large scale by 2023
(Draxler 2013).
A more and intensive use of human enhancement technologies will enhance the
progress towards transhumanism (H+ or h+). Transhumanism is concerned with
fundamentally transforming the human condition through the use of technologies.
The aims are to enhance human physical, psychological and intellectual capacities as
well as to eliminate ageing. There are an increasing number of studies on the beneﬁts
and risks of transhumanism to study their moral and ethical impacts. The extreme
use of technologies would transform humans into a ‘post-human’ state (Peragine
2013). During the Global Future 2045 International Congress in Moscow in 2012,
the
following
developments
were
foreseen
for
the
movement
towards
transhumanism across a timeline (GF2045 2013):
11
Emerging Technologies, Trends and Wild Cards in Human Enhancement
251
– 2012–2013: Emergence of new transhumanist movements and parties amid the
ongoing socio-economic crisis.
– 2014: New centres for cybernetic technologies to radically extend life, where the
‘race for immortality’ starts.
– 2015–2020: Creation of robotic human copy (avatar) and robots to replace human
tasks as well as smart machines like ﬂying cars.
– 2025: Creation of brain-to-brain communication systems, brain transplantation
into avatar bodies, and life extension.
– 2030–2035: Emergence of ‘re-brain’, mapping human brain and a step closer to
‘understanding the principles of consciousness’.
– 2035: The ﬁrst successful transplantation of personality—‘epoch of cybernetic
immortality begins’.
– 2040–2050: The arrival of bodies ‘made of nano-robots’ that can take any shape
and ‘hologram bodies’.
– 2045–2050: Drastic changes to the social structure and sci-tech development with
‘spiritual self-improvement’ takes precedent.
11.2.3 Human Technologies Based on External and Wearable
Technologies
11.2.3.1 Exocortex
Exocortex is ‘a theoretical artiﬁcial external information processing system that
would augment a brain’s biological high-level cognitive processes. An individual’s
exocortex would be composed of external memory modules, processors, IQ devices
and software systems that would interact with, and augment, a person’s biological
brain’. The interaction between human and exocortex is undertaken through a direct
brain-computer interface. This interaction makes the extensions functionally a part
of the individual’s mind, which make them a step closer to cyborgs or transhumans
(Synthetic Telepathy 2012).
A gadget developed by Thalmic Labs, namely, Myo, measures the electrical
impulses produced by muscles using the ‘electromyography’ technique. Myo’s
sensors are sensitive enough to detect gestures, which are then translated into a
digital command for a computer or a mobile device. ‘When people go to move a
hand, they are using muscles in their forearm which, when they contract and activate,
produce just a few microvolts of electrical activity. The sensors on the surface of the
skin amplify that activity by thousands of times and plug it into a processor in the
band, which is running machine learning algorithms. Similar technology is found in
high-tech arm and hand prosthetics, as well as the Necomimi Brainwave Controlled
Cat Ears’. The continuous use of Myo over time provides the system to learn and
develop its accuracy (Mitroff 2013).
11.2.3.2 Wearable Computers
Wearable computing devices are projected to increase in popularity over the next
years as a wave of new gadgets continuously hit the consumer market. ABI Research
forecasts the wearable computing device market will grow to 485 million annual
device shipments by 2018 (ABI 2013). Currently, the largest share of wearable
technologies consists of sports and activity trackers. These have recently been
complemented by a number of other wearable technologies including cameras,
smart glasses, smart watches and 3D motion sensors.
252
O. Saritas
11.2.3.3 Robotics and Smart Devices
A robot is a device or a system, which is capable of undertaking a speciﬁc task such
as welding, surgical operations, drones and exploratory vehicles among the others.
Different than robots, smart devices are equipped with embedded computing
capabilities. Today there are billions of mobile phones in the world as well as
sensors and other computing devices with increasing interconnections and networks
of them (WEF 2012).
Recent advancements in robot technologies mean that it can now move from a
mere focus on technical innovation and applications towards a focus on integration
with other devices in an ecosystem of smart devices, humans, as well as wider
society. Robots and smart devices have traditionally been controlled by humans for
certain functions like production, exploration and defence purposes. With the intro-
duction of the Internet of Things (IoT) systems, an ecosystem will be generated with
intelligent machines, devices and robots. Whether being autonomous, ﬁxed or
mobile, specialised for a speciﬁc task within a speciﬁc environment or generalised,
these devices will be capable of performing a number of general or specialised
services. In all instances, they will need to operate and co-operate with humans and
other robots within a smart ecosystem. Humans, robots and other entities need to
learn, adapt and modify their behaviours to maintain a synergy within the ecosystem
to obtain useful and optimal services. This will lead to an ecosystem of the Internet
of Everything (IoE) to include human at centre of the network system. Thus, the
following areas can be considered as research priorities in this domain:
– Smart device ecology connectivity
– Event and transaction models for the smart device ecology
– Smart device ecology service models, especially adaptive and emergent
paradigms
– Data mining contextual information from live video feeds within an intelligent
environment, with an emphasis on retrieving human behavioural information to
aid the environment to provide better service responses
– Biologically-inspired approaches to smart devices ecologies and robots
– All aspects of robot interaction within the ecology, but especially with humans
– Behavioural paradigms and architectures for intelligent robots, covering tradi-
tional controllers (IQ based) and socially orientated controllers (EQ based)
i.e. exploring various psychometric traits such as the irrational, rational, ego
centric and so on
– Psychological and practical impacts of intelligent robots working alongside
humans, particularly in the envisioned domestic environment, exploring the
11
Emerging Technologies, Trends and Wild Cards in Human Enhancement
253
adaptations of space and human behaviour with case studies and other experi-
mental evidences. (IntEnv 2009)
In the future humans will be able to have the night vision capability and will be
able to hear sounds in a wider frequency spectrum. These technologies will open
new possibilities for new communication channels, networks and technologies.
Unavoidably, all these transformations will have implications for socio-economic
activities of humans. Major changes are expected in social relations, culture, work
environments, skills and employment patterns, among the other spheres of life. The
future may be prone to advancements and opportunities, as well as challenges and
tensions because of these changes. Below, a discussion will be undertaken in relation
to the impacts of human enhancement and the future of work. Further multidisci-
plinary research needs to be undertaken in this domain for broader implications.
11.3
Implications for Human Enhancement on Work, Society
and Ethics
It is expected that the automation of knowledge work will affect US$5–7 trillion of
the world’s economic activity (Saritas 2015). Advancing technologies are already
confronting existing working population. Computers and machines have already
started replacing human in routine jobs. For instance, Amazon is planning to deliver
packages to customers within 30 min with the use of drones (Amazon 2014).
Improvements in productivity, costs and efﬁciency are considered to be the main
drivers for the use of machines. This trend is pushing the employees to take jobs at
the lower or higher ends of occupational skills while affecting the middle class jobs
at a greater extent.
Although human appears to be in a disadvantaged position when it comes to
employment, the enhancement technologies presented above are expected to open
new horizons for the future of work and employment. Enhancement technologies
will help to overcome some of the challenges like ageing workforce and changing,
and more challenging, working practices.
Widespread use of enhancements might inﬂuence an individual’s ability to learn
or perform tasks and might open new opportunities for entering new professions;
inﬂuencing motivation; enabling people to work in more extreme conditions or, into
old age, reduce work-related illness; or facilitating earlier return to work after illness
(The Academy of Medical Sciences 2012). Increased efﬁciency through enhance-
ment would lead to a better work-life balance. Occupational accidents and illnesses
may be reduced with better and more secure working places and practices. More-
over, there might be a new human enhancement sector, which would lead to
multidisciplinary innovations, new markets, high-skilled employment as well as
economic gains for economies.
The availability and access to these technologies will play a major role in their
widespread implementation. High costs, at least at the beginning, would affect the
equality of accessing them. Even if the costs are low, all products on the market may
not be equally beneﬁcial. They need to be regulated, and their beneﬁts and risks
should be well explained to the potential users. This is especially crucial for
technologies such as pharmacological cognitive enhancers.
254
O. Saritas
The use and beneﬁts of enhancements will vary with the context of application.
By maximising beneﬁts from enhancement technologies, the emerging new
occupations and working practices should be understood and involve individuals
in the design and integration of technologies and in the design of any regulatory
frameworks. Further effects like the consequences of delayed workforce retirement
on youth employment may also need to be taken under consideration. Therefore,
continuous monitoring to inform the reassessment of any policy or regulatory
decisions is considered to be crucial.
With the widespread applications of human enhancement technologies, serious
concerns have been raised. Besides opportunities, a number of challenges are
brought in terms of health and safety as well as ethical, social and political
considerations. All the impacts of using enhancement technologies need to be
considered through deliberative dialogue with users as well as wider society includ-
ing working and nonworking populations, elderly, employers, trade unions, policy-
makers as well as scientiﬁc researchers and producers. Scientists including social
scientists, philosophers, ethicists, policy-makers and the public need to be in a
continuous dialogue to discuss the ethical and moral consequences of enhancement
and thus maximise beneﬁts and minimise any harmful effects.
For a more responsible development of human enhancement technologies, a set
of social, technological, economic, environmental, political and value/cultural
(STEEPV) aspects (Miles et al. 2016) need to be considered. These are (WEF 2012):
1. Social dimension to ensure equal access to these technologies by providing
necessary subsidies for the ones, who are in utmost need, such as disabled and
elderly. This requires not only physical but also psychological and sociological
understanding of people’s needs, desires, capabilities and traits and a
neurocognitive understanding of the physical and behavioural dimensions.
2. Technological dimension involves the responsible innovation in the ﬁeld. Besides
engaging users and broader stakeholders in a deliberative process of technology
development, process and products need to address expectations related to physi-
cal and psychological health of the potential users. Technologies would aim to
achieve not only functional but also intelligent devices and applications, which
may interact with humans.
3. Economic dimension is a crucial one as the technologies should be made avail-
able to a broader range of users to ensure that they are accessible not only for
wealthy ones but also for the middle- and low-income users.
4. Environmental dimension is concerned about the ecological impacts of the
products developed for human enhancement. These include not also physical
waste but also chemical and biological ones, which need to be able to be treated
appropriately with no or less environmental impact.
5. Political and legal dimension is concerned with the development of regulatory
structures for machines, humans and human-machine interactions. A set of new
11
Emerging Technologies, Trends and Wild Cards in Human Enhancement
255
issues is likely to arise at the interfaces, which need to be addressed with a more
proactive and predictive assessment.
6. Value/cultural: This dimension also includes developing an ethical understanding
of what is being offered with human enhancement technologies, preventing any
doubts about what it would mean about being human and possible clashes
between humans and machines as well as enhanced and un-enhanced people
due to the fear of losing their jobs, relationships and uniqueness. There is a need
to ensure that technologies function globally, in different cultural contexts by
considering different beliefs, genders, ethnicities, generations as well as social
classes.
11.4
Weak Signals and Wild Cards in Human Enhancement
Human enhancement technologies presented above will certainly introduce funda-
mental changes to human life and society. During this process some intended and
unintended consequences will arise. To conclude this article on these grounds, a list
of weak signals and wild cards are presented below to address potential uncertainties
in this domain:
– Machines may go ahead of human and take the control in a human-excluded
world.
– New social divide: A new social divide may occur between enhanced and
non-enhanced human or with the contribution of machines.
– People will have two or three personalities: Move life to a virtual level to
dominate the real life.
– Future humans will share highly interactive virtual environments with robots and
with advanced IT. Humans may experience states of consciousness that current
human brains cannot access.
– If brain can be provided with unlimited memory, unlimited calculation ability and
instant wireless communication ability, it will be possible to produce a human
with unsurpassable intelligence. This kind of link between brain and machine is
expected to be demonstrated in the future (Baker 2008).
– A neural prosthesis will be implanted in the brains of great apes which may
endow them with ‘synthesised speech’. This would be an unprecedented break-
through in interspecies communication (Saniotis 2009)
– As scientists develop new opportunities for self-improvement through technol-
ogy, society is likely to be faced with choices about accepting, rejecting or
regulating these new kinds of possibilities, which may have implications on the
meaning of democracy and social responsibility.
– As humanity ﬁnds ourselves able to use enhancement technologies to change
more and more of what was formerly understood as ‘given’, in Dworkin’s words,
we experience the disruption of ‘the boundary between chance and choice’
(Honnefelder 2008).
256
O. Saritas
– Human and cognitive enhancement may challenge the long-standing assumptions
about personal identity and human responsibility as well as self-understanding
(Caldera 2008).
– A complete ban may be seen on the use of biotechnology for enhancement
purposes. These arguments revolve around the claim that enhancement
technologies will change the basis of human nature, leading to differences in
equality and legal protection between enhanced and non-enhanced persons.
What is clear is that further scientiﬁc, economic, social and ethical investigations
will be required to better assess the advantages and disadvantages of such
technologies and to decide to what extent to use them.
Acknowledgements An earlier version of this work was published in Saritas, O. (2013).
The book chapter was prepared within the framework of the Basic Research Program at the
National Research University Higher School of Economics and supported within the framework of
the subsidy by the Russian Academic Excellence Project ‘5-100’.
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11
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259
Ozcan Saritas is Professor of Innovation and Strategy at
the HSE and editor-in-chief of “Foresight”—the journal of
future studies, strategic thinking and policy. He worked as
Senior Research Fellow at the Manchester Institute of
Innovation Research, The University of Manchester. His
research focuses on innovation and policy research with
particular emphasis on socio-economic and technological
Foresight. With a PhD from the “Foresight and Prospective
Studies Program,” he introduced the “Systemic Foresight
Methodology”, and has produced a number of publications
on the topic. Dr. Saritas has extensive work experience with
the international organizations, including the United Nations,
OECD, and the European Commission. He has been
involved in large scale national, multinational and corporate
research and consultancy projects on sectors including
energy, climate change, agriculture, food, water, transporta-
tion, and ICT among others; published a number of articles in
respected journals; and have delivered keynote speeches in
more than 50 countries across the world. Besides his research
and publication activities, he designs and delivers academic
and executive education courses on Foresight and Strategic
Planning. He has recently co-authored a book, entitled
“Foresight
for
Science,
Technology
and
Innovation”
published by Springer, which has become one of the key
readings in the ﬁeld.
Part IV
Security
Illuminating the “Dark Side” of Emerging
Technologies
12
Aharon Hauptman
12.1
Introduction
Foresight studies are important in order to anticipate and assess potential impacts—
sometimes quite unexpected and surprising—of new technologies on society.
Although the future is practically unpredictable and inherently uncertain, especially
if human whims, desires, and fears are involved, foresight studies can help us to at
least contemplate about alternative futures and so, hopefully, to increase the
prospects of informed decisions leading to a desired future.
There is probably no need to convince the reader that almost every new techno-
logy, even if developed for the beneﬁt of society, can have a “dark side”. This
potential wicked face of technology can be manifested, for instance, by harmful
effects on the environment or on human health or by other unexpected uninten-
tional—or intentional—consequences. This situation reﬂects a pressing need for
foresight studies which are invaluable for continuous analysis of the unfolding
technology landscape for potential threats, opportunities, and other interesting
implications.
In this chapter we focus the attention on two important categories of
consequences—security threats and implications on privacy. In the security context,
we concentrate on potential abuse of new technologies by negative actors like
terrorists or criminals. In the privacy context, we not only deal with the increased
threat of excessive privacy intrusions but also with potential changes in people’s
perception of privacy—induced by emerging technologies. In some cases, the same
technologies may have implications both on security and privacy. Special attention
is paid to new, even surprising opportunities opened by the convergence of various
technologies.
A. Hauptman (*)
Tel Aviv University, Tel Aviv, Israel
# Springer Nature Switzerland AG 2019
D. Meissner et al. (eds.), Emerging Technologies for Economic Development,
Science, Technology and Innovation Studies,
https://doi.org/10.1007/978-3-030-04370-4_12
263
264
A. Hauptman
12.2
Emerging Technologies and Security Threats
A foresight study on the potential dark side of emerging technologies in terms of
their security threats was carried out within the European project FESTOS1
(conducted
in
2009–2011
under
the
“Security”
theme
of
the
European
Commission’s Seventh Framework Programme, FP7). The goals of this project
were to assess potential abuse threats posed by selected emerging technologies, to
cope with the dilemma of knowledge control versus the freedom of research and to
propose policy guidelines to reduce the likelihood of abuse.
12.2.1 FESTOS Horizon Scanning
The horizon scanning effort in FESTOS focused on ﬁve ﬁelds: information and
communication technologies (ICT), nanotechnologies, biotechnology, robotics, new
materials, and converging technologies (nano-bio-info-cogno). In the ﬁrst stage,
80 technologies were identiﬁed and brieﬂy described, including related threat
indications. Three broad categories of threats were observed:
•
Disruption of certain applications, e.g. cyberattack on intelligent transportation
systems. This conspicuous category is increasingly important with our growing
dependence on technologies.
•
Increased accessibility to technologies that once were conﬁned to the military
sector
or
to
unique
laboratories
and
were
prohibitively
expensive,
e.g. commercial off-the-shelf components that may generate damaging electro-
magnetic pulses (EMP).
•
Surprising malicious uses of new technologies that are being developed for
beneﬁcial purposes, for instance, employing advanced toy robots for terror
attacks or using synthetic biology to engineer bacteria that consume fuel instead
of producing it.
All the three categories are important in addressing emerging threats. FESTOS
concentrated mainly on the third, where we may ﬁnd the most unexpected potential
threats,
signals
to
“wild
cards”
(low-probability
high-impact
events
or
developments). “Wild cards” are increasingly recognized in recent years as an
important foresight method, in light of apparent growing frequency of “strategic
surprises” and the lack of preparedness (Lee and Preston 2012) or even denial of
decision-makers (Schwartz and Randall 2007), and their elicitation can inspire the
envisioning of interesting surprising scenarios.
1FESTOS: Foresight of Evolving Security Threats pOsed by emerging technologieS. The project
partners were Interdisciplinary Center for Technology Analysis and Forecasting (ICTAF), Tel Aviv
University (Israel), Technical University of Berlin (TUB) (Germany), Finland Futures Research
Centre, University of Turku, Foundation for European Scientiﬁc Cooperation (FEWN) (Poland),
and EFP Consulting (UK).
¼
¼
¼
12
Illuminating the “Dark Side” of Emerging Technologies
265
12.2.2 FESTOS Expert Survey
About 280 experts participated in the online survey run by FESTOS, in which their
opinions were elicited concerning the threat potential of 33 selected technologies.
For each technology the experts were asked to assess:
•
When will this technology be sufﬁciently mature2 to be used in practice?
•
How easy will it be to use it for malicious purposes3 (1 ¼ not easy at all, 5 ¼ very
easy)?
•
How severe is the potential security threat posed by this technology (1 ¼ very low
severity, 5
very high severity)?
•
The likelihood that it will actually come to pose a security threat, in different time
frames (1
very unlikely, 5
very likely).
•
To which societal spheres it will pose a security threat.
One striking general ﬁnding was that majority of experts agree that the public is
rather badly informed about dangers of new technologies and that governments tend
to underestimate the potential threats. Main technology-oriented results of the survey
are presented below. We show here only the results for 17 selected technologies
which may be regarded as converging technologies in the broad sense, including
certain nano-/biotechnologies and cognition/brain technologies (for full results, see
Hauptman and Sharan 2013).
12.2.2.1 Maturity Timeframes
Table 12.1 presents the distribution of the estimated time of “sufﬁcient maturity to be
used in practice” for each technology. Naturally, for many new technologies, the
uncertainty regarding their time of maturity is high, and therefore disagreement
between experts is not surprising.
12.2.2.2 Abuse Potential: Severity of Threat and Easiness of Abuse
Table 12.2 presents the experts’ assessments (averages) of the severity of threats
posed by each technology and the easiness of its malicious use. A useful way to
prioritize the technologies is by simply multiplying the easiness of malicious use by
the severity of threat, which can be interpreted as the potential of abuse.
12.2.2.3 Likelihood to Actually Pose a Security Threat in the Future
It should be noted that a technology can be maliciously used even before it is
sufﬁciently mature for regular purposes, e.g. if it is in a phase of prototype testing
2“Sufﬁciently mature” means that the technology was at least demonstrated and validated outside
the laboratory, through testing of prototypes. (This is similar to TRL-5 or higher, on the “technology
readiness scale” used in many technology assessments).
3“Easy” means that the technology is easily available/affordable/adaptable or “disruptable”. “Mali-
cious” refers to terrorism and crime.
Table 12.1 Technologies descriptions and maturity timeframes
Technology
Potential threats
(examples)
Till
2020
(%)
2021–2035
(%)
Later
or
never
(%)
Median
(continued)
266
A. Hauptman
Advanced artiﬁcial
intelligence
Artiﬁcial general
intelligence (AGI) is
aiming at general purpose
systems with intelligence
comparable to the human
mind. According to some
forecasts, desktops may
have the processing
power of human brains
by 2029, and by 2045 AI
could be able to “improve
itself” at a rate that far
exceeds anything
conceivable in the past
New opportunities
for malicious use,
e.g. by interpreting
facial expressions
and human
intentions, design of
“smart” malware for
cyberattacks, or
malicious use of
autonomous robots.
More speculatively,
could it lead to
machines that have
(malicious)
intentions
themselves?
49
34
17.0
2018
AI-based robot-human
interaction and
coexistence
Japan and S. Korea are
preparing for “human-
robot coexistence
society”, foreseen to
emerge before 2030. A
striking feature of this
development is “social
robots” with AI, with
which people will have
emotional and even
intimate interactions
New opportunities
for malicious use of
robots that have close
intimate interactions
with people who trust
them?
36.9
47.4
15.8
2023
Swarm robotics
Coordination of large
numbers of robots,
inspired by the
observation of insects.
Large number of simple
individuals can interact to
create collectively
intelligent systems. Tiny
(millimetre-sized) robots
could be mass-produced
in swarms and
programmed for
applications such as
surveillance, micro-
manufacturing, medicine,
cleaning, and more
Self-adaptation and
self-reprogramming
could be employed
for malicious
behaviour of the
swarm. The ability to
easily mass-produce
tiny robots for
swarms makes the
threat even more
concrete
22.2
77.8
0.0
2030
Technology
Potential threats
(examples)
(%)
2021–2035
(%)
Median
(continued)
12
Illuminating the “Dark Side” of Emerging Technologies
267
Table 12.1 (continued)
Till
2020
Later
or
never
(%)
Molecular
manufacturing
Assembling various
products “bottom-up”
and molecule by
molecule, possibly in
small “nanofactories”
Creation of new
hazardous materials
or new types of
weapons
30
50
20.0
2023
Self-replicating
nanoassemblers
Nanoassemblers,
envisioned as tools for
molecular manufacturing,
could self-replicate
exponentially.
Uncontrolled “runaway
replication” has been
described in speculative
scenarios of futuristic
nanotechnology
Even if uncontrolled
“runaway
replication” is
unlikely, one can’t
rule out an
intentional design for
malicious purposes
16.7
41.6
41.7
2030
Medical nanorobots
Nanoscale robots inserted
in the body are
envisioned to conquer
diseases. A possible
“shortcut” to
bionanorobotics could be
engineered viruses or
bacteria to create
bio-devices
Nanorobots
instructed to harm
humans and/or to
remotely control
human actions
18.2
54.5
27.3
2030
Molecular nanosensors
Sensors with molecular
precision will enable
advanced nano-
diagnostics and will
detect where a person has
been by sampling
environmental clues
Could make people
“molecularly naked”,
with personal
information abused
by criminals.
84.6
15.4
0.0
2018
Nano-enabled brain
implants
Neural interface implants
in the central nervous
system to treat motor
disorders or to control
prosthetic limbs or
external devices. Future
implants could enhance
brain functions.
Thought/behaviour
control of people or
equipping future
perpetrators with
“super mental power”
13.3
60
26.7
2030
Technology
Potential threats
(examples)
(%)
2021–2035
(%)
According to one report,
2035 chips wired directly
to the user’s brain would
make information be
accessible through
cognition and might
include synthetic sensory
perception beamed direct
to the user’s senses
(continued)
268
A. Hauptman
Table 12.1 (continued)
Till
2020
Later
or
never
(%)
Median
Brain-to-brain
communication
Advances in brain-
computer interface (BCI)
may lead to
“radiotelepathy” enabled
by direct conversion of
neural signals into radio
signals and vice versa
Control of people’s
behaviour and
actions
11.1
66.7
22.2
2030
Cyborg insects
Insects controlled
through implanted
electrical stimulators.
Researchers envision
living communication
networks (for sensing,
surveillance, etc.) by
implanting electronics in
insects. Advanced
capabilities could be
offered by micro/nano
technologies
Could be used by
perpetrators for
harming people or
agriculture, for
spying, or for other
malicious activities
46.2
38.4
15.4
2023
Brain-computer
interface; “mind-
reading” gadgets
Systems that enable
people with disabilities to
operate gadgets “by
thought” have been
demonstrated, as well as
systems for the gaming
industry. Further progress
is envisioned
Malicious distortion
of communication
between users and
gadgets. Could
hacking such a
device enable
inﬂuencing user’s
actions?
50
41.7
8.3
2023
Human enhancement
based on NBIC
convergence
Converging technologies
offer unprecedented
Malicious use of
speciﬁc capabilities
by perpetrators and
hacking of the
23.5
64.7
11.8
2030
Technology
Potential threats
(examples)
(%)
2021–2035
(%)
enhancement of human
performance:
augmentation of physical
and mental abilities and
new modes of interaction.
Some experts envision
convergence of human
and machine intelligence
involved (implanted)
devices
(continued)
12
Illuminating the “Dark Side” of Emerging Technologies
269
Table 12.1 (continued)
Till
2020
Later
or
never
(%)
Median
Metamaterials and
optical cloaking
“Cloaks” made of
metamaterials with
negative refraction index
can hide objects from
sight in certain
wavelengths (possibly in
all wavelengths in the
future) or make them
appear as other objects
“Invisibility
cloaking” or perfect
camouﬂage for
malicious purposes
36
12
52.0
2030
Programmable matter
Materials that can be
programmed to self-
assemble, alter their
shape and physical
properties to perform a
desired function, and then
disassemble—in
response to user input or
autonomous sensing
Reconﬁgurable
shape-changing tools
with perfect
performance,
including weapons
(that pass security
checks), adaptable to
changing
requirements
24
28
48.0
2030
3-D printing
Future low-cost printers
could be able to self-copy
and to use a wide variety
of materials for a wide
range of products
“Home-made”
(undetectable?)
weapons or cheap
manufacturing of
fake products
50
23
27.0
2018
Synthetic biology
Building of biological
agents from
interchangeable
bio-components. Could
enable synthetic genome
and then using it to
control a recipient cell.
Could lead to engineering
of living systems to
perform new functions
not found in nature
Accessible tools to
build novel
bioweapons, virulent
pathogens, dangerous
organisms, etc.
50
34.6
15.4
2018
Technology
Potential threats
(examples)
(%)
2021–2035
(%)
Median
270
A. Hauptman
Table 12.1 (continued)
Till
2020
Later
or
never
(%)
Induced pluripotent
stem cells (iPS cells)
Scientists used viruses to
ﬂip genetic switches in
the DNA of skin cells
from adult mice to turn
them into iPS cells that
are functionally
equivalent to embryonic
stem cells
May enable
genetically
engineered traits into
cells to create
“designer embryos”
or camouﬂage at the
cell level?
Reproductive cloning
by “rogue
organizations”?
29.4
41.2
29.4
2023
Source: Author
and doesn’t not comply yet with safety standards. The (average) likelihoods in
different future timeframes, as assessed by the expert survey participants, are
shown in Table 12.3. For most technologies, the likelihood rises with time, but
interestingly in some cases (certain IT technologies not presented in this chapter), the
likelihood declines in later stages, presumably because the experts envision that
preventive means will be effectively applied.
12.2.2.4 The Impact on Society
The experts were asked to assess to which of the following societal spheres each
technology will pose a security threat (multiple choice): people, infrastructures,
economy, environment, political systems, and values. The percentages of
respondents who opted for each societal sphere are shown in Table 12.4. Due to
multiple choices, the sum of percentages across each technology can vary between
0 and 600. This sum (the right column) is interpreted as the so-called overall
intensity of threat.
Evidently, some technologies potentially threaten several societal spheres, while
others affect fewer spheres. Broadly speaking, most technologies pose threats to
people, with signiﬁcant threats to infrastructures and the environment as well.
Interestingly, several technologies are perceived as potentially threatening the politi-
cal systems and values—especially converging technologies related to the human
brain and human body in general.
Some free-text comments submitted by respondents underscored the insufﬁcient
awareness (even by experts!) of potential security threats and hence the importance
of foresight studies like FESTOS. A notable remark of one expert: “Going over your
questions I suddenly realized how smilingly innocent technologies can pose severe
security threats in the near future. Being a relatively experienced individual, yet
unaware of the above, I would say now that the public is not informed about such
threats. . .”.
Table 12.2 Ranking of selected technologies by their abuse potential
Technology
12
Illuminating the “Dark Side” of Emerging Technologies
271
A: Easiness of
malicious use
B: Severity of
threat
Abuse potential
(A × B)
Advanced AI
3.21
3.43
11.01
Synthetic biology
3.16
3.40
10.74
Cyborg insects
3.33
3.08
10.26
AI-based robot-human
interaction
3.00
2.94
8.82
Swarm robotics
2.89
3.00
8.67
Brain implants
2.73
3.07
8.38
Human enhancement
2.63
3.13
8.23
Nanoassemblers
2.75
2.92
8.03
3-D printing
2.89
2.71
7.83
Metamaterials and
cloaking
2.50
2.95
7.37
Programmable matter
2.29
2.79
6.39
Molecular manufacturing
2.50
2.50
6.25
Medical nanorobots
2.27
2.73
6.20
Brain-to-brain
communication
2.25
2.56
5.76
Brain-computer interface
2.33
2.42
5.64
Molecular nanosensors
2.08
1.85
3.85
iPS cells
1.44
1.87
2.68
Source: Author
Table 12.3 Likelihood of posing a threat in different time intervals
Technology
2016–2020
2021–2025
2026–2035
After 2035
Advanced AI
2.56
3.13
3.43
3.71
AI robots
2
2.25
2.65
2.94
Swarm robotics
1.44
1.89
2.56
2.89
Molecular manufacturing
1.88
2.44
3
3
Nanoassemblers
1.1
1.67
2.11
2.73
Medical nanorobots
1.25
1.78
2.44
3.11
Molecular nanosensors
1.88
2.44
3
3
Brain implants
1.64
2.07
2.46
2.85
Brain-to-brain communication
1.11
1.44
2
2.56
Cyborg insects
2.38
2.92
3.08
3.17
Brain-computer interface
1.9
2.11
2.2
2.22
Human enhancement
1.69
2.07
2.67
3.13
Optical cloaking
1.99
2.56
3.33
3.82
Programmable matter
1.95
2.41
3.14
3.40
3-D printing
2.68
3.18
3.53
3.56
Synthetic biology
2.59
3.14
3.64
4.15
iPS cells
1.31
1.69
1.86
2.31
Source: Author
272
A. Hauptman
Table 12.4 Intensity of threats
Technology
Economy
(%)
Environment
(%)
Infrastructures
(%)
People
(%)
Political systems
(%)
Values
(%)
Overall intensity of
threat
Advanced AI
67
38
76
82
51
31
345
Human enhancement
38
6
38
94
69
81
326
Swarm robotics
67
67
78
78
11
22
323
Cyborg insects
42
67
92
92
8
17
318
AI-based robot-human
interaction
41
41
59
88
35
41
305
Programmable matter
45
64
73
73
18
18
291
Brain-to-brain
communication
40
10
10
100
60
70
290
Molecular manufacturing
50
88
38
75
25
13
289
Self-replicating
nanoassemblers
55
82
55
73
18
0
283
3-D printers
75
33
58
75
17
8
266
Metamaterials and optical
cloaking
46
23
85
69
31
8
262
Synthetic biology
23
69
15
100
23
31
261
Nano-enabled brain implants
7
20
13
93
33
67
233
Brain-computer interface
8
8
17
100
33
50
216
Molecular sensors
20
30
20
100
40
0
210
Medical nanorobots
18
46
9
100
9
18
200
iPS Cells
9
18
0
82
0
27
136
Source: Author
12
Illuminating the “Dark Side” of Emerging Technologies
273
Some experts asserted that although the technologies are new, the threats are not,
because similar malicious uses are already possible by existing technologies. In this
context, technologies like nanoassemblers and molecular manufacturing are
regarded as “too speculative” by some respondents, but there is a clear disagreement
about that: While one expert claims that molecular manufacturing is “too speculative
to deserve attention at the moment”, another one says that it is “no more dangerous
than organic chemistry”, and a third one thinks that the same technology “could be
dangerous by scaling down the resources and facilities needed to manufacture other
risky technologies [. . .] enabling creative malefactors to invent entirely new
categories of threats there is no general preparedness or recognition of”. Many
experts stressed the well-known fact that all technologies are inherently prone to
potential misuse and that the necessary regulations usually lag behind the
technologies. Moreover, risk policy is problematic because, as one expert put it,
“Society tends to misestimate risks, and be very over-conﬁdent about its estimates.
Certain minor risks are exaggerated, while others are regarded as silly”.
12.2.2.5 “Wild Card” Threat Scenarios
The selected technologies with their threat indications can be regarded as “weak
signals”, some of them hinting at potential “wild cards”: surprising low-probability
high-impact events (Petersen and Steinmüller 2009). In recent years there is a
growing recognition that the elicitation of potential wild cards as part of a foresight
study is not just an interesting intellectual exercise in imaginative thinking but may
prove as an essential means for preparedness to critical future surprises. As an
occurrence of a wild card has a very high impact on speciﬁc systems/stakeholders,
organizations are usually especially vulnerable to such events, and paying special
attention to wild cards in foresight studies undertaken by these organizations could
alleviate this vulnerability (Hauptman and Steinmüller 2019).
In a special scenario workshop organized by FESTOS, experts were invited to
share ideas about wild cards inspired by selected emerging technologies. These ideas
were subsequently developed into four full-ﬂedged “wild” narrative scenarios (some
would call them science ﬁction stories or outlines for such stories), vividly
displaying the dark side of several technologies. Special attention was given to
potential combinations or convergence of technology trends.
As a case in point, the Internet of Things (IoT) could, in combination with
programmable matter and/or molecular manufacturing, give rise not only to a
revolution in manufacturing but also in the use of “intelligent”, “nano-enabled”
everyday gadgets. Such sophisticated networked future objects (which may also
emerge as outcomes of recent developments in so-called 4-D printing) could be
capable
of
self-healing,
self-reconﬁguration,
self-upgrading,
or
automated
recycling—an interesting development in itself. But what if a malware or a malicious
remote signal transforms self-healing into self-destruction? This wild card,
“disassembling of nano-enabled products by remote signal”, was the basic idea
that evolved into one of the four narrative scenarios, entitled “At the ﬂea market”.
Why ﬂea market? The title hints at a market that ﬂourishes in a world scared of self-
destructing gadgets, with high demand to old “pre-nano” things that did not fall
victim to the virus or malicious signals. Here is an excerpt from this narrative
scenario:
274
A. Hauptman
“Now, it has gotten into the hairdryer!” Sandra cursed loudly. Yesterday, it was sitting on the
shelf and today there was just an unsightly heap. The device had started to ooze like a block
of Camembert. A couple of metallic parts protruded from the heap. A small sign that read
“Made by NanoTrust, Inc. China” was visible. Sandra cleaned up the mess with a hand
broom and dustpan. Yesterday, she had been hoping that the hairdryer wouldn’t be infected.
The disintegration, however, crept into everything, especially the new ones. “Never again
nano!” She swore that to herself weeks ago, when her television stopped working, closely
followed by her espresso machine, her washing machine, her new living room lamp and her
smartphone. At that time, the washing machine company had been accommodating, offering
a life-long guarantee and providing a new machine. Then the plague started to afﬂict the
entire city. One after the other, dealers started going bankrupt, manufacturers were no longer
reachable and the military patrolled the streets to prevent looting. . . .
Due to space limitation, we only brieﬂy mention here the other three narrative
scenarios fully developed in the above-mentioned workshop4:
•
“Cyborg-insects attack!”: Swarms of cyborg insects (insects with implanted
electronics) attack people, animals, and agriculture crops.
•
“The Genetic Blackmailers”: DNA of human individuals is misused for extortion.
•
“We’ll change your mind. . .”: A terrorist group uses a virus to change the
behaviour of a portion of the population for a certain period of time.
For the full narrative scenarios, the reader is referred to the FESTOS reports
(Dienel and Peperhove 2011).
12.3
Emerging Technologies and Privacy
The impact of technology on privacy, including technology-driven changes in
privacy perceptions, is not a new phenomenon, as can be exempliﬁed by the history
of photography: After its invention in the nineteenth century, it was mostly used to
make private portrait photographs of wealthy people in special studios with cumber-
some static cameras. The photographed person had full control of the result, and it
was unheard of that a private photo portrait is reproduced (not to mention distributed
in public) without permission. After a few years, technology advances led to smaller
hand-held cameras, by which a picture could be taken without its subject even
knowing that a camera was present. The rapid spreading of journalistic snapshot
photography, practiced in public (and often in private) locations, heralded in a way
“the end of privacy”, at least for celebrities. No wonder that the ﬁrst (and still
famous) publication advocating the right to privacy in the USA was written by
4The Scenarios Workshop was moderated by the Futurist and Science Fiction writer Dr. Karlheinz
Steinmüller, who also subsequently wrote the narrative scenarios based on the workshop.
Warren and Brandeis5 in 1890 largely in response to this new trend. Prophetically, it
referred not only to photography but also to other “modern devices”:
12
Illuminating the “Dark Side” of Emerging Technologies
275
the existing law affords a principle from which may be invoked to protect the privacy of the
individual from invasion either by the too enterprising press, the photographer, or the
possessor of any other modern device for rewording or reproducing scenes or sounds.
Fast-forward to the twenty-ﬁrst century: The social network phenomenon, with
its photo-sharing and other features, is of course one present-day example of
changing sensitivity to privacy, especially among young people. Incorporation of
face recognition features in photo-tagging and in smartphones has stirred debates.
But this is only the tip of the iceberg—if we think about new technologies that may
be part of our life in the future, such as nano-enabled personalized medicine,
ubiquitous ultrasensitive sensors, or “synthetic telepathy”.
The famous physicist Freeman Dyson speculated about a system of such implant-
able chips and its potential to become a powerful instrument of social change, for
good or for evil purposes (Dyson 2009). Even present-day brain scanning
technologies, still far from “real” synthetic telepathy, already raise non-trivial
questions related to privacy. Consider real-time functional MRI, already in use for
medical research and diagnosis. Although “reading thoughts” by fMRI is impossi-
ble, it is possible to “read” emotional states and to detect lies in a reliability much
higher than the old-fashioned polygraphs. Such advancing capabilities made
researchers think whether this may lead to a new frontier—mental privacy—with
interesting potential implications on what we perceive as “private”:
The foreseeable ability to read the state of a person’s brain and thereby to know aspects of
their private mental experience, as well as the potential to incorrectly interpret brain signals
and draw spurious conclusions, raises important new questions for the nascent ﬁeld of
neurotics. One can imagine the scenario of someone having their private thoughts read
against their will, or having the contents of their mind used in ways of which they do not
approve (deCharms 2008)
Consider some other examples of technologies that may become ubiquitous in the
next decades: Anthropomorphic household robots are people’s companions.
Machines resembling humans, speaking with humans, and behaving almost like
humans are part of daily life. Besides the obvious privacy concerns related to the
surveillance capabilities of robots (the ability to sense, process, and record their
surroundings), there are less obvious social and psychological implications. Would
you feel comfortable to undress in front of a friendly smiling robot? Or would you
speak about your intimate secrets, while the robot is listening? Why not, don’t you
do it in front of your cat? The answer is not trivial.
Nanotechnology advances may signiﬁcantly improve our quality of life, but they
are also likely to enable unprecedented capabilities of surveillance and information
5Warren and Brandeis, “The Right to Privacy”, Harvard Law Review, December 15, 1890 http://
groups.csail.mit.edu/mac/classes/6.805/articles/privacy/Privacy_brand_warr2.html
gathering—with obvious privacy implications. Indeed, surveys have shown that
“losing personal privacy” is the public’s biggest fear about nanotechnology. One
of the emerging nanotechnology developments is ultrasensitive nanosensors, which
will have a dramatic impact on medicine and security—and on privacy. Molecular
nanosensors can detect a single molecule and distinguish between different
molecules. It is expected that such sensors will be able, for example, to detect
drugs from saliva samples of people or to infer where a person has been by quickly
sampling minute environmental clues on clothes. No wonder that researchers have
expressed concerns about so-called nano-panopticism. In an article published in the
prestigious journal Nature, one of the leading scientists, active in this ﬁeld, called the
emerging molecular nanosensors “plenty of eyes at the bottom”6 and expressed his
worries about a “molecularly naked patient” whose insurance company knows more
about his body than himself (Toumey 2007):
276
A. Hauptman
With so many huge databases of personal information, plenty of eyes at the bottom,
molecularly naked patients and more, it is hard to imagine how multiple developments in
nanotechnology will not intrude further into our privacy.
Bearing in mind the above-mentioned reﬂections, special effort was intentionally
made in the European project PRACTIS to look forward beyond the information and
communication technologies (the “usual suspect” in privacy-oriented debates) and to
discuss the privacy aspects (sometimes unexpected and surprising) of new
technologies emerging from other ﬁelds, such as nanotechnology, robotics, or
cognition, including technologies enabled by the convergence of certain ﬁelds.
The goals of PRACTIS7 (conducted in 2010–2013 under the “Science in Society”
theme of FP7) were to assess impacts of emerging technologies on privacy, to
propose means to cope with potential risks to privacy while maximizing the beneﬁts
of these new technologies, and to formulate a framework for thinking about ethical
and legal issues related to privacy in the future when these technologies prevail. In
both projects special attention was paid to new, even surprising opportunities opened
by the convergence of technologies.
The project referred to three main types of impacts: threats to privacy, enhance-
ment of privacy (better protection), and changing our perceptions of privacy. The
ﬁrst type of impact is the most straightforward and includes for instance technologies
that make it easier for information to be collected about people. The second type
refers mainly to innovations that could enable new privacy-enhancing technologies
6Paraphrasing Richard Feynman’s famous visionary lecture “Plenty of Room at the Bottom”
from 1959.
7PRACTIS: PRivacy-Appraising Challenges to Technologies and EthIcS. The project partners
were the Interdisciplinary Center for Technology Analysis and Forecasting (ICTAF); Tel Aviv
University (Israel); University of Lodz (Poland); Research Centre in Informatics and Law of the
University Faculties of Notre-Dame de la Paix (FUNDP) (Belgium); Interdisciplinary Centre for
Comparative Research in the Social Sciences (ICCR) (Austria); Nexus, Berlin (Germany); and
Finland Futures Research Centre, University of Turku.
(PETs), such as methods for anonymization of information. Certain emerging
technologies have both a potential to pose new threats to privacy and to enhance
privacy (sometimes indirectly), depending on the speciﬁc application.
12
Illuminating the “Dark Side” of Emerging Technologies
277
The third kind of impact, namely, the change of perception, is the most complex
and is hard to attribute to any one technology by itself. We already know that
perceptions of privacy (or more precisely the sensitivity to what is perceived as
privacy intrusion) may change over time. Part of the explanation for such change lies
in technologies or, more speciﬁcally, in the way that people may get used to intrusive
technologies such that they no longer consider them a threat to their privacy. People
may be willing to accept certain privacy violations and even not to perceive it at all
as a threat to privacy, if the beneﬁts in terms of better health service or improved
security are perceived as worthwhile. In other words, there is a trade-off between the
(perceived) privacy and the (perceived) beneﬁts: in certain conditions people may be
willing to “sacriﬁce” some of their privacy for concrete beneﬁts such as improved
security, lower insurance costs, better health services, and the like. Privacy seems to
be a sociocultural construct, depending on dominant values of a society, its socio-
cultural heritage, and contemporary technological developments. According to this
understanding, the perceptions of privacy can change in time with technology being
an important driver in shaping our concept of privacy, as it directly inﬂuences our
daily lives and our values (for more details on these issues and other aspects of
project PRACTIS, see Hauptman et al. 2011).
12.3.1 PRACTIS Expert Survey
Two hundred sixty-six experts responded to the PRACTIS survey questions and
submitted their opinions on 39 emerging technologies. For each technology the
respondents were requested to assess the foreseen time frame of its widespread use,
its threat to privacy (on a scale 0–5: 0 ¼ no threat; 5 ¼ very high threat), its inﬂuence
on changing people’s sensitivity about their privacy (on a scale 0–5: 1 ¼ becoming
less sensitive; 3 ¼ no change; 4 and 5 ¼ more sensitive), and, where applicable, its
possible ability also to contribute to privacy enhancement.
In this chapter only some results are presented for selected technologies closely
related to technologies’ convergence, and we omit technologies with obvious pri-
vacy implications such as facial recognition or mobile phone tracking (these were
indeed identiﬁed as near-term threats at the time of the survey—2011).
Highest threat levels are attributed to the following technologies (the years in
brackets indicate the likely time frames of widespread use): cyborg insects (2023),
brain-to-brain communication (2028), mind-reading commercial gadgets (2023),
and reality mining (2018). With regard to change of perception, most technologies
are likely to make people more sensitive about their privacy.
In general, the experts tend to associate higher threats to privacy with larger
increases in sensitivity, especially for technologies associated with the human body.
Top increases in sensitivity are attributed to cyborg insects (2023), invisibility
cloaking (2028), nano-based surveillance (2023), portable full genome sequencing
(2023), brain-to-brain communication (2028), and advanced artiﬁcial intelligence
(2023). Important impacts (threats as well as sensitivity changes) are attributed to
several technologies in the domains of cognition, biology, nanotechnology, and
robotics (Table 12.5).
278
A. Hauptman
Some comments submitted by respondents of the PRACTIS expert survey
reﬂected the opinion that the combination or convergence of several technologies
may have much higher impact on privacy than single technologies, for example, the
combination of advanced sensors with wireless networks and sophisticated software.
In a vivid portrayal of one expert:
Cyborg implants, memory chips, augmented reality, wireless networking, cloud computing,
etc., in conjunction with brain-computer interfaces (BCI) offer amazing post-human
capabilities. . . the potential is there to hack. . .create false memories, provide false
behavior-changing information, perhaps even record and make public individuals thoughts,
emotions and past experiences.
Another conspicuous opinion was that in a modern society, absolute prevention
of privacy intrusion is impossible due to a trade-off between beneﬁts and some
privacy sacriﬁce. Indeed, an important change of perception of privacy, supported by
several experts in the survey, relates to individuals “getting used” to technologies
and their willingness to accept certain privacy violations (or even not to perceive it at
all as such), if the beneﬁts (e.g. better health service or improved security) are
perceived as worthwhile. In other words, there is a trade-off between the (perceived)
privacy and the (perceived) beneﬁts.
An interesting opinion of one expert (probably not shared by many) was that a
future with less privacy should be viewed as a rather positive trend, as less privacy
means more decent behaviour and self-control:
it may be better to get over our recent modern obsession with privacy and accept that any and
every action and thought that we have may be open to investigation and to public
exposure. . . The abolition of privacy (or perhaps a return to a very limited degree of privacy,
similar to the levels in which human social behaviour and morality developed) might make
for a better, fairer world.
One of the observations that stemmed from the PRACTIS study was that many of
the emerging technologies have the potential either to pose a threat to privacy or to
enhance it and in several cases—both at once. In other words, at this point in time,
the technologies have not yet acquired a social meaning, which can develop in either
way (Ahituv et al. 2013). Moreover, many of the emerging technologies and their
operation will be complex and difﬁcult to understand: while the average data subject
will be able to notice some of the technologies in operation (such as visible robots),
other technologies, such as nanotechnologies, are not visible. The complexity affects
privacy in the sense that the data subjects will ﬁnd it more difﬁcult to ﬁgure out how
these technologies process their data and whether the data is aggregated, matched
with other subjects’ data, transferred to third parties, etc. The technological
Table 12.5 Threats and enhancements of selected technologies
Technology
Potential threat/enhancement
(continued)
12
Illuminating the “Dark Side” of Emerging Technologies
279
Threat
level
Sensitivity
change
Nano-enabled personalized
medicine
By providing better diagnostics
and genetic information, nano-
enabled devices may lead to
personalized medicine, based on
comparison of diagnostic
information about one’s body
with similar information about
other people
Threat: Implies storing vast
amounts of personal medical
information in large centralized
systems. People may become
“molecularly naked” in front of
insurance companies and other
interested parties
2.90
3.78
Nano-based surveillance
Nanotech-enabled miniature
highly sensitive surveillance
devices, with increased
computing power and higher
storage capacity. For example,
cheap video cameras with the size
and aerodynamic characteristics
of a mosquito
Threat: Proliferation of devices
which make it much easier and
cheaper to carry out surveillance
could imply a growing threat to
privacy
Enhancement: Could provide also
anti-surveillance monitoring
3.80
4.24
Molecular nanosensors
Sensors with molecular precision
that can detect any number of
molecules and distinguish what
type of molecule is being
detected. For example, such
sensors would extract more
information from ﬁngertips by
quickly detecting drug
metabolites present in sweat
Threat: Deriving “lifestyle
intelligence” from ﬁngertips,
knowing where a person has
visited by sampling
environmental clues on clothes,
etc.
Enhancement: In police use,
thanks to high accuracy and zero
false alarms, such sensors could
help to focus on “real” suspects
and reduce privacy intrusion to
most people
2.70
3.60
Portable full genome
sequencing (FGS)
Complete and rapid DNA
sequencing of a person, by
sampling minute amounts of
biological material, such as a hair
follicle or a drop of saliva. In the
future this technology might be
simple enough even for a
layperson to use
Threat: Combined with micro
sampling devices, this could
enable easy and clandestine
access to personal biological
samples, making genetic data
available to interested bodies
Enhancement: Individuals may be
able to ascertain their own health
status and avoid having legally
proscribed disclosure from being
applied. It could provide a unique
personal identiﬁcation
3.89
4.24
Intelligent medical implants
A combination of miniaturized
electronics, biomaterials, and
signal processing techniques
applied in medical implants such
as sub-retinal implants, auditory
Threat: Patients’ private medical
information could be extracted
and their implants reprogrammed
without the patients’
authorization or knowledge.
Enhancement: With better coded
2.58
3.42
Technology
Potential threat/enhancement
brainstem implants, cardiac
deﬁbrillators, etc.
implants. Privacy might be better
protected in relation to present
data systems
(continued)
280
A. Hauptman
Table 12.5 (continued)
Threat
level
Sensitivity
change
Medical nanorobots
Nanorobots assimilated into the
human body, or residing on the
skin, could revolutionize medical
diagnostics and treatment, to
conquer disease, ill health, and
ageing. Future advances could
include controlled swarms of
molecular nanorobots, reacting
faster than neurons
Threat: If nanorobots can
“intrude” the human body by
food or drinks, it may pose new
risks for privacy intrusion,
including a risks of being used
against someone’s will
Enhancement: Could protect
medical privacy—medications
and procedures that are intrusive
could become more private
2.36
2.33
Advanced artiﬁcial intelligence
Threat: AI-enabled autonomous
robots could be used for target-
oriented surveillance and
“spying”
Enhancement: If appropriate
“computer ethics” is applied in
the intelligent networks to which
all machines are connected.
Intelligent enforcement of rules
deﬁned by the user
2.94
4.07
Robots as social actors
“Anthropomorphized”
robots designed to interact more
socially. Research has shown that
people tend to interact with
sufﬁciently human-like machines
as if they are real humans.
Threat: “Chilling effect”—impact
on our sense of being alone and
able to act with freedom. The
mere presence or a “humanoid”
robot could create the feeling of
being observed
3.30
3.50
“Cyborg insects”
Future miniature ﬂying robots
may be insect-like or even based
on real insects with implanted
devices (“cyborg insects”)
Threat: Unprecedented
capabilities for large-scale
surveillance. Could covertly enter
ofﬁces or houses
Enhancement: Could also be used
as protection from privacy
intrusions of other robots
4.64
4.50
Invisibility cloaking
Specially engineered materials
with tailored refraction index
could enable hiding objects from
sight (in certain wavelengths and
in the future possibly in all
wavelengths) or make them
appear as other objects
Threat: A perfect camouﬂage
could enable spying on people
without being detected
Enhancement: Perhaps could be
used also as a countermeasure
against surveillance
3.13
4.36
Brain-to-brain communication
Advances in brain-computer
interface may lead to
Threat: Ultimate privacy
intrusion, when even one’s
4.73
4.10
Technology
Potential threat/enhancement
“radiotelepathy” enabled by
direct conversion of neural
signals into radio signals and vice
versa
thoughts would not be secret
anymore
complexity affects the transparency, awareness, and, ultimately, the data subjects’
ability to control their personal data (Ahituv et al. 2013).
12
Illuminating the “Dark Side” of Emerging Technologies
281
Table 12.5 (continued)
Threat
level
Sensitivity
change
Reality mining
This term has been coined by
MIT researchers as a new
paradigm in data mining based on
the collection of machine-sensed
data from mobile phones, cellular
tower identiﬁers, GPS signals,
etc., to discover patterns in daily
user activity. Such large-scale
data mining allows the modelling
of large communities of
individuals to provide insight into
the dynamics of individual and
group behaviour
Threat: Massive collection of data
pertaining to human social
behaviour obviously raises
privacy questions, especially if
the anonymization of data is
difﬁcult
Enhancement: Innovative
solutions for privacy-preserving
data mining have been proposed,
possibly handing control back to
the users
4.08
3.74
Source: Author
12.3.2 PRACTIS Privacy Scenarios
Based on the PRACTIS expert survey, interviews with experts, and related analysis,
ﬁve alternative future scenarios of privacy were constructed, reﬂecting different
versions of changing privacy perceptions inﬂuenced by emerging technologies.
The
ﬁve
scenarios
range
from
utopia
to
dystopia,
reﬂecting
different
combinations of attitudes to privacy protection and to new technologies. Their
brief summaries are presented below (Auffermann and Luoto 2011).
12.3.2.1 Scenario 1: “Privacy Has Faded Away”
The majority of people’s perception of privacy has changed along with the “new
rules” posed by emerging technologies. Emerging technologies are readily embraced
and widely accepted. The border between private and public space has vanished
since people have given up their privacy voluntarily. Various technologies have
become more vital than privacy, which is still a value, but people have started to sell
their “moral” for money. People make trade-offs in favour of goods and services
(e.g. health care and safety provided by new technologies) and have given up privacy
voluntarily.
282
A. Hauptman
12.3.2.2 Scenario 2: “People Want to Maintain as Much Privacy
as Possible”
People value their privacy, want to protect it, and do not accept new technologies as
easily as before because of former experiences of privacy intrusions. The effective
use of privacy-enhancing technologies (PETs) is established and ensured by appro-
priate legislation. Most people are worried about the merge of information into one
database covering different areas of life. In order to avoid the creation of such a
database, the state controls the usage of information. The popularity of social media
has decreased, since the majority of people prefer real-world interaction. Once public
exposure had gone to extremities, most people started to value their privacy and
private information once again.
12.3.2.3 Scenario 3: “People Have Lost Their Control of Privacy”
Dependence on new technologies has led to a situation where people are perma-
nently monitored by the state or the private sector both at home and in public places.
Information is gathered constantly about everybody. Citizens have equal access to
others’ information. Without noticing, people have been “sleepwalking” into a
no-privacy world, and suddenly they have no choice but to live with it. People
have no control over their own information (which is actually not their own
anymore) as they have become dependent on new technologies, so they simply
have to accept the public exposure of their private lives in order to use them.
12.3.2.4 Scenario 4: “Segmented Privacy”
Emerging technologies and privacy are perceived differently depending on which
“class” a person belongs to. Privacy has become a market value. Technology
providers have made two versions of their technology applications—one with high
privacy settings and a higher price and another with low privacy settings and a lower
price. Only wealthy people can afford to buy technologies where privacy settings are
considered and highly valued. Instead of a trade-off, privacy and security seem to go
hand in hand. Most wealthy people have moved into gated communities where they
feel safer than in their former residential areas and with their privacy better protected
when they live in these well-demarcated areas outside the rest of the society. Past
developments have led to a situation in which income inequality has grown
extremely high and the society is very prone to conﬂicts. This has increased the
tension between different social classes.
12.3.2.5 Scenario 5: “Tailor-Made Privacy”
Privacy
is
understood
differently
by
each
individual.
Privacy-enhancing
technologies (PETs) are available to everyone. Technology applications allow
people to choose privacy settings which are tailored to their individual needs.
Through education, people have become more aware of the possibilities and
disadvantages of emerging technologies and know how the information gathered
about them is used and for what purposes. Sufﬁcient funding has been directed at
data protection. Private companies and schools have hired supervisors in charge of
data protection, and civil society actors are also active. The majority of people
remain open towards new technologies, since they are perceived to offer major
beneﬁts and people do not have to worry too much about their privacy risks.
12
Illuminating the “Dark Side” of Emerging Technologies
283
The PRACTIS scenarios were analysed in depth in order to recognize the
potential changes in privacy climates and explore their impacts on ethical principles.
The results of the scenario analysis have provided valuable inputs for further
discussion on ethical frameworks and legal considerations for the interface between
privacy and technology (not elaborated in this chapter).
12.4
Conclusions
The foresight processes which helped to assess evolving security threats and privacy
implications of new technologies reﬂect a pressing need for continuous analysis of
the unfolding technology landscape for potential threats, opportunities, and other
interesting implications such as the changes in public perception as exempliﬁed in
the case of privacy. The technologies covered in projects such as FESTOS and
PRACTIS described in this chapter are only the tip of the iceberg; every emerging
technology may embody its potential dark side as well as its bright side. Emerging
technologies, especially in their early stages of development, can be viewed as
conveying signals of change. Such signals may be hardly perceptible at present
but are likely to constitute a strong trend, in some cases even a surprising wild card,
in the future.
In addition to exploring the technology developments, both projects exemplify
how foresight studies can deal with important societal issues. FESTOS put on the
table the dilemma between knowledge control—limiting the access to sensitive
knowledge—and the freedom of scientiﬁc research. The dark side of technology
needs activities from the side of actors and decision-makers balancing between the
security needs of our open democratic societies and the freedom of knowledge and
scientiﬁc research. Probably more promising than top-down measures are bottom-up
approaches: Instead of legislation and coercive measures with rather questionable
outcomes, FESTOS proposed to develop “soft” and optional measures that base ﬁrst
of all on self-regulation, self-control, and education of engineers and scientists.
Codes of conduct, ethical guidelines, and educational measures may be established
on substate levels but develop in the future towards national, European-wide, and
global regimes (Auffermann and Hauptman 2012). These should be complemented
with dedicated R&D efforts which have been proposed in the context of each
potentially threatening technology aiming at adoption of a “security-by-design”
approach.
Similarly, privacy intrusions can be minimized or prevented (if this is indeed
desired by the society) not only by top-down regulatory means (although these are
probably unavoidable) but by increased user awareness, ethical considerations, and
responsible use of speciﬁc technologies. These considerations should be applied in
the early stages of technology development, implying the high importance of the
notion of “privacy by design” (PbD). Implementation of privacy by design can
reduce the risk of privacy intrusion and enable the data subject clear and simple
options as to the collection of personal data, including the option to turn off the
collection of data altogether. PbD can be supported by regulations, which can either
require it or encourage it. Perhaps most importantly, PbD might also be driven by a
market demand—depending on the appropriate awareness of users.
284
A. Hauptman
At any rate, in both privacy and security realms, alarmism is not recommended,
but continuous assessment of the potential dark side of new technologies is always
needed. It is an essential prerequisite to the implementation of the developing notion
of responsible research and innovation (RRI)—an absolute necessity if we wish that
after all the bright side of new technologies prevails.
References
Ahituv N et al (2013) Privacy implications of emerging technologies for stakeholders: research
summary and policy recommendations. PRACTIS Deliverable D6.3
Auffermann B, Hauptman A (2012) EFP Foresight Brief No. 225. http://www.foresight-platform.
eu/wp-content/uploads/2012/10/EFP-Brief-No.-225-FESTOS.pdf
Auffermann B, Luoto L (2011) Integrated security threats report. FESTOS Deliverable D3.3, with
contribution from all partners
deCharms RC (2008) Applications of real-time fMRI. Nat Rev Neurosci 9(9):721
Dienel HL, Peperhove R (2011) Final scenario and indicators report. FESTOS Deliverable D4.3
Dyson F (2009) “Radiotelepathy”, the direct communication of feelings and thought from brain to
brain. Edge 2009. www.edge.org/q2009/q09_3.html
Hauptman A, Sharan Y (2013) Foresight of evolving security threats posed by emerging tech-
nologies. Foresight J Future Stud Strateg Think Policy 15(5):375–391
Hauptman A, Steinmüller K (2019) Surprising scenarios: imagination as a dimension of foresight.
In: Peperhove R et al (eds) Envisioning uncertain futures. Springer, Berlin (forthcoming)
Hauptman A, Sharan Y, Soffer T (2011) Privacy perception in the ICT era and beyond. In: von
Shomberg R (ed) Towards responsible research and innovation in the information and
communication technologies and security technologies ﬁelds. European Commission. http://
ec.europa.eu/research/science-society/document_library/pdf_06/mep-rapport-2011_en.pdf
Lee B, Preston F (2012) Preparing for high-impact, low-probability events – lessons from
Eyjafjallajokull. A Chatham House report
Petersen JL, Steinmüller K (2009) Wild cards. In: Glenn JC, Gordon TJ (eds) Futures research
methodology – V3.0. The millennium project
Schwartz P, Randall D (2007) Anticipating strategic surprises. In: Fukuyama F (ed) Blindside –
how to anticipate forcing events and wild cards in global politics. Brookings Institution Press,
Washington, DC
Toumey C (2007) Plenty of eyes at the bottom. Nat Nanotechnol 2:192–193. https://doi.org/10.
1038/nnano.2007.93
12
Illuminating the “Dark Side” of Emerging Technologies
285
Aharon Hauptman is Senior Researcher at the Unit for
Technology and Society Foresight at Tel Aviv University
and Fellow at Zvi Meitar Institute for Legal Implications of
Emerging Technologies, IDC Herzliya. His research deals
with Technology Foresight, its relations with R&D policy
and the evaluation of trends and impacts of emerging
technologies. Dr. Hauptman has been involved in foresight
in many areas, including Delphi surveys and “wild card”
analysis, for example in EU projects such as FESTOS,
PRACTIS, iKNOW, RACE2050 and FORCE. He holds a
PhD in Engineering from Tel Aviv University and BSc from
the Technion (Israel Institute of Technology).
287
Defence and Security: New Issues
and Impacts
13
Andrew James
13.1
Introduction
In a previous paper (James and Teichler 2014), we observed that public domain
foresight studies in Europe were characterised by a shift away from a focus on state-
centric military threats to a much broader view of security risks. In the intervening
years, much has changed. Many of those changes (the global ﬁnancial crisis, the
growth of authoritarianism and the rise of an increasingly assertive Russia using
hybrid warfare including cyber) were barely mentioned in these foresight studies or
(infamously in the case of the global ﬁnancial crisis) not at all. The focus of today has
returned to state-centric hybrid threats. Events have overtaken foresight studies.
This chapter provides a meta-analysis of some of the main themes emerging from
public domain defence and security foresight studies conducted since the 9/11
attacks on the United States. There are also a number of studies undertaken that—
because of the sensitivity of the subject area—have either not been published or have
only been published in an abridged form. For instance, the UK Science and Tech-
nology Strategy for Countering International Terrorism makes reference to a sce-
nario study conducted by the government’s defence laboratory DSTL on future
technological threats to the United Kingdom (see HM Government 2009). The
chapter focuses mainly on studies undertaken in Europe and argues that these
foresight studies reﬂect a shift in security thinking away from a focus on state-
centric threats towards a much broader view of security risks. This expanded
perspective includes risks presented by the vulnerability of European society to the
failure of critical infrastructure, to pandemics, environmental change and resource-
based conﬂicts. The chapter places a particular emphasis on the treatment of
technological change in these defence and security foresight studies and argues
A. James (*)
Manchester Institute of Innovation Research, The University of Manchester, Manchester, UK
e-mail: Andrew.James@manchester.ac.uk
# Springer Nature Switzerland AG 2019
D. Meissner et al. (eds.), Emerging Technologies for Economic Development,
Science, Technology and Innovation Studies,
https://doi.org/10.1007/978-3-030-04370-4_13
that the growing importance of dual-use technologies is likely to mean that defence
will play a declining role as a sponsor and lead user of advanced technologies in the
future.
288
A. James
The chapter is structured as follows: Sect. 13.2 presents some recent defence and
security foresight exercises, highlighting who has sponsored the studies, their
objectives and participation. The next ﬁve sections identify some of the main themes
identiﬁed from a meta-analysis of these studies. Section 13.3 stresses the core theme
that these foresight studies reﬂect a wider shift in security thinking away from a
focus on state-centric threats towards a much broader view of security risks.
Section 13.4 considers their treatment of the issue of resource-based conﬂicts.
Section 13.5 notes the importance of the topic of technological vulnerability and
the potential misuse of science. Section 13.6 notes how these foresight studies
emphasise the changing nature of knowledge production and use in the future, and
Sect. 13.7 emphasises how in the future defence is likely to be a technological
follower rather than leader in most ﬁelds. Section 13.8 goes beyond James and
Teichler (2014) to consider the meaning of “emerging technologies” in a military
context and what they mean for the future of war. Section 13.9 provides some
conclusions.
13.2
Recent Foresight Exercises
A number of foresight exercises on defence and security matters have been
conducted in recent years and placed in the public domain. These exercises have
been sponsored by agencies of national governments, international organisations as
well as the European Commission.
At the national level, the UK Ministry of Defence’s Defence Concepts and
Doctrine Centre leads an ongoing Global Strategic Trends Programme. In France,
the Ministry of Defence’s Délégation Aux Affaires Stratégiques undertakes an
ongoing programme of foresight studies. In Finland, the FinnSight 2015 programme
on the outlook for science, technology and society undertaken by the Academy of
Finland and the Tekes innovation and technology agency considered security issues.
In the United States, the National Intelligence Council (an institution supporting the
Director of National Intelligence within the US intelligence community) has
conducted a series of Global Trends studies.
Similarly, international organisations and foundations have engaged in foresight
studies contributing to the discussion of future defence challenges. The United
Nations and the Bertelsmann Foundation have supported studies dealing with the
future of a globalised world, which also address security and defence issues (UNO
2004). Finally, NATO has more particularly addressed the defence challenges that
the Atlantic community is likely to face in the coming decades (NATO 2006).
In addition, there have been a series of studies undertaken in Europe that have
been sponsored directly or indirectly by the European Commission. The Seventh
Framework Programme included for the ﬁrst time a security research theme and
within that there was a funding stream on foresight, scenarios and security as an
evolving concept. This aimed at research in broad societal foresight to capture new
and emerging threats as well as other aspects of security as an evolving concept
(e.g. ethical and economic aspects). The Commission has funded projects such as
FORESEC on Europe’s evolving security: drivers, trends and scenarios and
FESTOS which has as its goal “to identify and assess evolving security threats
posed by abuse or inadequate use of emerging technologies and new S&T knowl-
edge, and to propose means to reduce their likelihood”. Within the FP7 Social
Sciences and Humanities funding stream, the Commission has also funded foresight
studies. For instance, the theme Blue Sky Research on Emerging Issues Affecting
European S&T funded Project SANDERA which focused on the future relationship
between the EU science and technology policy strategy to move towards the
European Research Area and those EU policies focused on the security of the
European citizen in the world both through EU defence policies and EU security
policies. This chapter draws on some of the insights from SANDERA which brought
together academics and think tanks from eight European countries.
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The Commission was also heavily involved in ESRIF (the European Security
Research and Innovation Forum). ESRIF describes itself as “a European strategy
group in the civil security research domain”. ESRIF was established in September
2007 by the EU Member States and the European Commission with the objective to
develop a mid- and long-term strategy for civil security research and innovation in
Europe. ESRIF included a working group on foresight and scenarios, and its ﬁnal
report—delivered in 2009—includes scenarios.
In addition the European Union Council has sponsored studies discussing future
defence challenges such as the European Defence Agency’s Long-Term Vision for
Defence and the EU Institute’s for Security Studies The New Global Puzzle
(European Defence Agency 2006; Gnessotto and Grevi 2006).
The purpose of these foresight studies has been to inform policy-makers, provide
a basis for policy priorities and raise awareness amongst key stakeholders. Their
main published outputs have been lists of drivers of change, and some studies have
also developed scenarios (e.g. ESRIF and SANDERA).
In comparison to the studies sponsored by national governments, the EU reports
have addressed the topics from a security rather than defence perspective. Hence,
their principal focus has been on the identiﬁcation of “risks” or “hazards” to security
at either the national or European level. Military threats are addressed only in a
strictly limited way, relating to the Petersberg Tasks and the ﬁght against terrorism.1
Whilst these reports, like other foresight studies, have aimed to simulate public
debate, they have sought to do so in a different manner by involving stakeholders
across the EU and claiming the topic of security as a responsibility of the
Commission.
These defence and security foresight exercises have found it difﬁcult to engage
with civil society. The FORESEC study and ﬁnal conference raised the problem that
1This reﬂects the status of defence matters in the European Union which, prior to the Lisbon treaty,
were strictly intergovernmental and from which the European commission was largely excluded.
European security foresight exercises rely almost exclusively on a community of
security “experts”. By omission or commission, the broader European scientiﬁc
community and civil society have been effectively excluded from such exercises.
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A. James
The remaining sections of this chapter report a meta-analysis of these foresight
exercises undertaken as part of Project SANDERA, and from that we have identiﬁed
ﬁve broad themes:
•
Defence and security foresight studies reﬂect a wider shift in security thinking
away from a focus on state-centric threats towards a much broader view of
security risks.
•
Defence and security foresight studies emphasise that resource-based conﬂicts are
likely to grow in importance in the future.
•
Defence and security foresight studies tend to emphasise the technological
vulnerability of European society and the potential for the misuse of science
•
Defence and security foresight studies emphasise the changing nature of knowl-
edge production and use in the future and how that might increase security risks to
Europe.
•
Defence and security foresight studies emphasise how in the future defence is
likely to be a technological follower rather than a technological leader in most
ﬁelds.
13.3
From “Defence” to “Security”: The Emergence of a New
Security Paradigm
The ﬁrst point from our meta-analysis is that the focus of the defence and security
foresight studies that we have reviewed is less on state-centric threats and the
potential for state-on-state conﬂict and more on the security risks posed by inten-
tional and unintentional human actions, technological change and environmental
factors.
In this way these foresight studies reﬂect a broader shift in academic and policy
thinking from the traditional notion of national security, which puts the emphasis on
the security of the state and military threats to its territorial integrity towards a
thinking in broader terms to include new types of threats (e.g. ecological, economic)
to new objects of security (the human beings or the citizen, society), calling for a new
means to ensure security. Several concepts have been developed to capture these or
parts of these notions such as “human security”, “total defence”, “societal security”,
“security of the citizen” or the “all-hazards” approach [for a discussion see,
e.g. Giegerich and Pantucci (2008)].
Many of these studies assume a shift from traditional state-centric warfare
towards a much broader view of security risks. This perspective is captured in the
UK Ministry of Defence’s Development Concepts and Doctrine Centre (DCDC)
report which observes that “Greater interdependence and intensifying competition
are likely to be a deﬁning feature of the next 30 years. This tension is likely to
13
Defence and Security: New Issues and Impacts
291
heighten preoccupation with risk at every level, from the personal to the interna-
tional” (DCDC 2007).
Almost every one of these foresight studies places an emphasis on the increasing
interdependence of large parts of the world economy. In the future, the world is seen
as one characterised by interdependencies through supply chains, ﬁnancial ﬂows as
well as knowledge ﬂows. These studies are agreed that the twenty-ﬁrst century will
be “the Asian century” (Délégation aux affaires stratégiques 2008; DCDC 2007).
They also emphasise how almost ubiquitous communications technologies are likely
to strengthen that sense of interdependence. What is striking is how the world has
changed in ways not anticipated by these foresight studies. The emergence of
authoritarianism and anti-globalisation, not least with the election of President
Donald Trump, raises profound and worrying questions about these easy
assumptions about growing interdependence that have been commonplace in many
foresight studies (although for balance it ought to be noted that the DCDC future
reports warn of the tensions arising from globalisation).
There is an emphasis on competition in this new globalised and multipolar world.
This competition is seen not only in economic terms although this is recognised as
important. Equally, attention is paid to the nature of political competition in a world
where the United States is likely to face near-peer competitors such as China and
India and in which Europe struggles to retain its position in the world. The competi-
tion is also seen in terms of a competition between the state and the emergence of
new actors and the importance of non-state terrorist groups and disaffected
individuals. Competition also takes the form of competition between competing
identities, ideologies and religious world views.
The emphasis on risk also means that these foresight studies consider the
consequences for European society of unintended dangers such as natural or
man-made disasters. Interdependence means that Europe will become more vulner-
able to pandemics no matter whether a virus is spread by tourists, terrorists or both.
Equally, food security, energy security and so forth are matters of concern as well as
increasingly frequent and violent weather events as a consequence of climate
change.
13.4
The Rise of Resource-Based Conflicts
The second observation from our meta-analysis is that defence and security foresight
studies emphasise that resource-based conﬂicts are likely to grow in importance in
the future.
Climate change is seen as likely to exacerbate existing conﬂict situations by
intensifying already stressed security situations, particularly in regions with weak
institutions that are not able to mitigate or adapt to the changed climatic
circumstances (Academy of Finland and Tekes 2006; NIC 2008). The role of climate
change in spurring an increase in uncontrolled migration is also emphasised not least
from Africa as the sub-Saharan region experiences increasingly prolonged droughts
and famines. There is likely to be an increasing prevalence and frequency of human
2008).
and animal pathogens as a consequence of climate change, international ﬂows of
people and sociocultural change, and this is likely to lead to an increasing number of
292
A. James
global pandemics (Délégation aux affaires stratégiques 2008).
Some of these foresight studies also note that demographic developments may
have security implications. Asia, Africa and Latin America will account for virtually
all population growth over the next 20 years amounting to 1.2 billion more people by
2025 (UNO 2004). In combination with continued economic growth, this will
signiﬁcantly increase demand for energy, food and water resources and amplify
the problem of climate change. In countries with signiﬁcantly more young males
than females (“youth bulge”), economic and social institutions need to develop in
order to avoid that these countries (in particular Afghanistan, Nigeria, Pakistan and
Yemen) continue to be prone to instability and internal conﬂict (DCDC 2007;
National Intelligence Council 2008).
Geopolitical struggles over material and energy resources are also identiﬁed as a
potential source of growing interstate tensions in the future (DCDC 2007; Déléga-
tion aux Affaires Stratégiques 2008). This may take three forms: ﬁrst, states might
use their control over energy resources for political coercion and inﬂuence. Second,
terrorists and pirates might pose threats to transit routes calling for military protec-
tion of those routes, a situation we witness currently at the coast of Somalia. Finally,
domestic instability and conﬂict within strategic energy-producing states could
trigger intervention from outside (National Intelligence Council
13.5
Dangerous Knowledge: Technological Vulnerability
and the Misuse of Science
Another theme that we have identiﬁed is that defence and security foresight studies
tend to emphasise the vulnerability of European society due to its growing depen-
dence on technology and the potential for the misuse of science and technology.
A common theme is that in the future, cyberspace is likely to be a key area for
conﬂict and that security will need to be ensured in this domain. A number of
security foresight studies note how critical infrastructures including energy supply,
transport and water may be vulnerable to deliberate or accidental failure due to their
dependence on networked information and communication technologies (Academy
of Finland and Tekes 2006; NIC 2008).
Another theme is the potential for misuse of science and technology. A number of
the foresight studies emphasise that there is likely to be accelerating convergence
and interaction between major enabling technologies such as information
technologies, biotechnologies, nanotechnologies and neuro- and cognitive sciences
(Institute of the Future 2005; James et al. 2008). These converging technologies
could have a profound transformative impact on European societies and are seen as
presenting signiﬁcant economic, social, cultural and ethical opportunities and
challenges. Military application of nanotechnology is seen by some as having
potentially destabilising effects, and there may be spillovers from the military use
of converging technologies to crime and terrorism (Nordmann 2008). Thus, ESRIF’s
ﬁnal report says:
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Defence and Security: New Issues and Impacts
293
There is no doubt that rapid evolution in ICT/cyber security and its misuse will continue and
ﬁ
even accelerate. Some technologies already identi ed as candidates for misuse are nano-
technology, artiﬁcial intelligence and ‘synthetic biology’ (i.e., the use of DNA technology to
‘engineer’ living organisms). These threats will have to be continually monitored and
countered. (ESRIF 2009: 25)
13.6
Beyond the Military-Industrial-Scientific Complex: The
Changing Nature of Knowledge Production and Use
A further theme emphasised by defence and security foresight studies is the chang-
ing nature of knowledge production and use in the future and how that might
increase security risks to Europe. The globalisation process is likely to further
weaken the ability of states to control research, production and exportation of
sensitive technologies and goods, and even maintaining secrecy about sensitive
technologies and systems will be extremely difﬁcult (DCDC 2007). These studies
suggest that technological innovation will continue to be predominantly commer-
cially led, and commercial dynamics mean that companies will seek to exploit
technologies in as many new applications and markets as possible (DCDC 2007).
Global capital ﬂows and cross-border ownership will mean that the ownership of
companies will become increasingly complex (Délégation aux affaires stratégiques
2008). This may provoke tensions with the security community who are likely to
regard the proliferation of some scientiﬁc and technological knowledge as a threat its
potential to offer terrorists an increasing access to sensitive technologies (National
Intelligence Council 2008).
They also emphasise that we are likely to see the emergence of new scientiﬁc
superpowers. Science in the twenty-ﬁrst century will be more like a network, with
multiple, linked centres of excellence. The United States, Britain and other current
leaders will still be important centres of research and innovation but will be joined by
India and, probably, China. A host of small countries or regions, including South
Korea, Taiwan, Israel and Brazil, will also develop world-class capabilities in
strategic specialties or interdisciplinary areas, building targeted programmes that
fuse global scientiﬁc knowledge with local technical, natural or even cultural
resources (Institute for the Future 2005).
A further element in these studies is that we are likely to see new knowledge
circulation patterns. The globalisation of scientiﬁc and technological activity is
likely to continue. There will be increasing information exchange across borders,
and we are likely to move “from brain drain to brain circulation (Institute for the
Future 2005). This circulation is likely to be driven by the growth of research and
entrepreneurial opportunities in emerging countries, the general lowering of global
barriers to migration and the erosion of the standard career model in business and
academia (Institute for the Future 2005).
These foresight studies also suggest that an increasing body of scientiﬁc and
technological knowledge and technique is likely to be widely available as a conse-
quence of the emergence of new scientiﬁc superpowers and the diffusion of knowl-
edge. This is likely to be facilitated by modern means of communication (mainly, but
not only, the Internet) (Délégation aux affaires stratégiques 2008). The Internet and
information technologies have broadened access to scientiﬁc knowledge and are
starting to lower the barriers to participation in scientiﬁc research. In the next
decades, the spread of pervasive computing technologies, low-cost sensors, ﬂexible
electronics and desktop manufacturing tools, combined with commons-based, peer-
294
A. James
reviewed scientiﬁc production systems, will broaden the range of opportunities for
wider participation in science and technology (Institute for the Future 2005).
These trends and technologies, some of the studies suggest, will lower the barriers
to participation in science for individuals, groups and emerging countries (Institute
for the Future 2005). The result may be the growth of “amateur science”, new
scientiﬁc and technical centres of excellence in developing countries and a more
global distribution of world-class scientists and technologists (DCDC 2007).
There is a concern in the defence and security communities that the pace of
change in many science and technology domains may exceed the speed at which
governments can respond and that the spread of scientiﬁc knowledge and techno-
logical capabilities may become able to outperform states who can be constrained by
their decision-making processes, technological path dependencies and commercial
and political factors (DCDC 2007).
This is an emerging trend, and such developments may be regarded as a threat to
the security community since it has the potential to widen the tools available to both
small terrorist groups and to emerging countries seeking to increase their ability to
wage war by both old and new means (DCDC 2007). The diffusion of the knowledge
underpinning weapons of mass destruction and of dual-use knowledge in the life
sciences that has the potential to be applied to biological weapons is frequently noted
and discussed (DCDC 2007; Délégation aux affaires stratégiques 2008). In addition,
terrorists and/or criminals might abuse new emerging technologies for their
purposes, in particular the output of robotics, nanotechnology in combination with
medicine, cognitive science, sensors, networks and smart materials (Délégation aux
affaires stratégiques 2008). This driver is supported also by a trend that innovation,
research and development will originate from more international and diffuse sources
and will proliferate widely, making regulation and control of novel technologies
more challenging (DCDC 2007).
At the same time, such developments may also be seen as an opportunity by the
defence and security communities, and they will increasingly seek to access the
potential of “democratised innovation” as part of a move towards an open innovation
model for defence. This may stimulate efforts on the part of the defence and security
policy communities to build closer and cooperative relationships with the wider civil
research community although how that civil research community may respond is an
open question as we will see in the next section.
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Defence and Security: New Issues and Impacts
295
13.7
Defence as a Technological Follower Rather than
a Technological Leader
Defence and security foresight studies emphasise how in the future defence is likely
to be a technological follower rather than a technological leader in most ﬁelds.
Defence has played an important role as a sponsor and lead user of some
advanced technologies at some stages in their development. The growing emphasis
of defence science and technology policy on accessing commercial off-the-shelf
technologies as well as a move towards an open innovation model that will depend
upon accessing globally available technological knowledge suggests that defence
may play a declining role as a sponsor of advanced technologies in the future.
Equally, defence procurement has declined in absolute terms in Europe and also
when compared to the size of other (civilian) markets (James et al. 2008). This is a
continuation of past trends, and defence and security science and technology
research may be of declining importance in the European science policy mix.
The rapid pace of nondefence origin technological change and the likelihood that
absolute defence R&D spending in Europe will continue to decline (or at best remain
stable) are likely to impact the way that governments and industry conduct defence
R&D. The role of defence R&D is already shifting from the development of new
technologies in large specialised defence research establishments to partnerships that
can access and exploit technologies that are the product of commercially funded
research. This trend is likely to continue (James et al. 2008).
Speciﬁc national government R&D investment in in-house expertise and tech-
nology development is likely to continue where there is no civilian equivalent and
where there are particular concerns about the need to retain national operational
sovereignty, for example, in some critical defence and security technologies, such as
cryptography, nuclear, counterterrorism and chemical, biological, radiological and
nuclear (CBRN) defence (DCDC 2007). In other ﬁelds, however, governments are
already developing R&D programmes that draw on the increasingly diverse global
science and technology base in industry, SMEs and universities and adapt and apply
that knowledge for military use. This approach is likely to broaden and deepen.
The pace of this move towards an open innovation approach is likely to depend in
the ability of the civil and military research communities to work together. On the
other hand, it will require the willingness of those nontraditional sources of scientiﬁc
and technological knowledge to engage with the defence sector. There are major
cultural differences between universities and the defence sector. On the other,
traditional defence ﬁrms will need to embrace new business models and marketing
practices reﬂecting the different dynamics of value creation in civilian markets and
requirements linked to commercial customers.
¼
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A. James
13.8
Emerging Technologies and the Future of War
So far this chapter has closely followed an earlier journal paper (James and Teichler
2014). This section goes beyond that earlier paper to consider the meaning of
“emerging technologies” in a military context and what they mean for the future
of war.
This question is important because radical technological change is back on the
military’s agenda. In his last major address as Defence Secretary, Chuck Hagel
announced the Defence Innovation Initiative, aimed at fostering a third “game-
changing” offset strategy. Eisenhower’s “New Look” doctrine in the 1950s led to
the development of new types of nuclear weapons, long-range delivery systems and
active and passive defences to offset the Soviet Union’s quantitative force advan-
tage. The offset strategy of the 1970s and 1980s led to leap-ahead capabilities like
standoff precision strike, stealth, wide-area surveillance and networked forces. The
Defence Innovation Initiative calls for “a new Long-Range Research and Develop-
ment Planning Program [that] will help identify, develop and ﬁeld breakthroughs
from the most cutting-edge technologies and systems, especially in robotics, auton-
omous systems, miniaturization, big data and advanced manufacturing, including
3-D printing”.2 At the time of writing, it was not possible to judge the Trump
administration’s view of the Defence Innovation Initiative although the President’s
2017 budget request had included a proposed substantial increase in spending of
defence research and development.
13.8.1 Emerging Technologies in the Military Context
This chapter has already shown that visions of the military future almost always have
a strong technological element. Emerging technologies feature prominently in fore-
sight studies. They identify a host of emerging technologies that may have
implications for security, military capability and—in some cases—the conduct of
future war. These include:
•
Autonomous systems and artiﬁcial intelligence: self-thinking, deciding and
organising partially sentient devices that mimic aspects of human intelligence
and decision-making are being developed and may move/reduce the need for
human input and reduce the manpower burden.
•
“Big data” information analysis and exploitation may lead to better decision-
making and enhanced intelligence analytic capability.
•
Developments in nanotechnology and microsystems promise sensors of small
size and improved performance.
2“Hagel Announces New Defence Innovation, Reform Efforts” http://www.defense.gov/news/
newsarticle.aspx?id 123651 (last accessed 7 February 2015).
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Defence and Security: New Issues and Impacts
297
•
Human enhancement and augmentation: a range of technologies offer consid-
erable scope for enhancing and augmenting the physical and cognitive perfor-
mance of humans, including prosthetics, drugs and genetic manipulation.
•
Synthetic biology: the design and fabrication of biological components and
systems that do not already exist in the natural world with the potential to produce
to create novel threats in the form of “designer” bio-weapons.
•
Social and behavioural sciences: developments are expected to provide addi-
tional insights into the intent and behaviour of individuals and groups leading to
new opportunities to inﬂuence them.
However, before going any further, it is important to deﬁne what is—and what is
not—meant by “emerging technologies”. The UK’s Defence Technology Plan
deﬁnes emerging technologies as follows: “Emerging technologies can be
characterised as: immature technologies in the early proof-of-principle stages;
[and] more mature technologies but where a novel defence application has been
identiﬁed”. Whilst this deﬁnition appears clear and straightforward (and this chapter
will use it), it is the case that a feature of much of the discussion of emerging
technologies is a lack of clarity as to the subject of analysis.
“Emerging” is used variously to examine technologies that analysts regard as
potentially emerging in the far future (e.g. the latest UK MOD DCDC programme
report looks out to 2040 and consciously examines what technological developments
may occur). In contrast, “emerging” is sometimes used to describe technologies that
have reached a stage that we know that they will ﬁnd application in a weapon system
in the near future [e.g. many of the “emerging” IT technologies discussed by Bruce
Berkowitz in his 2003 book are now in military service at least with the US military
(Berkowitz 2003)]. Sometimes analysts conﬂate the far future and the soon to be
ﬁelded as “emerging technologies” giving the impression to the unwary that (true)
emerging technologies on the technological far horizon are as certain to be ﬁelded as
those in late-stage development. This raises important questions about timing that
are critical to discussions about emerging technologies. It also raises issues about
uncertainty. Both issues will be discussed later in this chapter.
There is an important distinction here that is sometimes missed by military
analysts of emerging technologies. The distinction is between technologies and the
weapons, their delivery systems and the infrastructure that supports military capabil-
ity. Technologies underpin weapon systems but are distinct from them. Thus,
nanotechnologies may be important to the military, but only if they ﬁnd application
in weapon systems. Consequently, how emerging technologies and other factors are
combined into military capability should be the critical consideration not the
emerging technologies themselves.
Equally, new or improved classes of weapon rarely (if ever) comprise only new
(“emerging”) technologies but instead combine new technologies with mature
technologies. Thinking inﬂuenced by the economist Joseph Schumpeter emphasises
that innovation can be new combinations of existing technologies and stresses the
potential signiﬁcance of combining existing technologies in a new use. Innovation
that produces modern weapon systems is increasingly based on this kind of dynamic
recombination of generic technologies which are often information technologies
(Hasik 2008). The DCDC Strategic Trends study identiﬁes the rapid asymmetric
insertion and exploitation of widely available commercial technologies—GPS,
low-cost unmanned aerial vehicles, mobile telephones—as a signiﬁcant concern.
Indeed, the experience of Iraq and Afghanistan provides graphic illustrations of how
such tactics can have devastating asymmetric effects. The contrast between the rate
of combinatorial innovation of this kind and the pace of developments in the
traditional defence acquisition has been striking (DCDC 2014).
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A. James
13.8.2 Emerging Technologies and the Future of War
Most emerging technologies represent incremental improvements to what went
before and enhance the competencies of the military along dimensions that they
have traditionally valued. This kind of technological development presents relatively
few challenges to the military, although their insertion into existing platforms can be
difﬁcult. In contrast, it is new technologies that are radical, competence destroying
and create new sources of military advantage along dimensions not traditionally
valued or poorly understood by the military that tend to be the focus of attention and
concern.
Fundamentally, these types of new technologies can change the environment in
which military forces operate. In The Pursuit of Power, William H McNeill (1982)
charts the consequence of technological change on the balance of power. In War and
Power in the 21st Century: The State, Military Power and the International System,
Paul Hirst (2001) analysed how new military technologies change the way that wars
are fought and how power relations change as a result.
A radical new technology can change the balance of power or create new forms of
insecurity. The most dramatic illustration of the impact of new technology was the
Allied development of the atomic and hydrogen bombs during the Second World
War and the subsequent development of similar capability by the Soviet Union. In
turn, the development of inertial navigation technologies added the prospect of
accuracy to devastating lethality.
It is a commonplace that today’s emerging technologies may lead to the prolifer-
ation of novel disruptive threats. Many—most—of the emerging technologies are
not the preserve of the military and governments. Most are emerging out of work
being conducted in universities, ﬁrms and garages across the world. Some require
only modest resources. For example, synthetic biology is an area of S&T that has a
growing and Internet-linked “DIY community”.
New technologies may also inﬂuence the likelihood of conﬂict. The emergence of
the hydrogen bomb arguably reduced the threat of conﬂict. The increased availability
and capability of remotely operated vehicles and their increasingly autonomous
successors may reduce the threshold for their use by reducing the political risk of
military casualties, likewise cyberwarfare. “The anonymity that cyberspace can offer
reduces the risk of retribution, so may increase the attractiveness of making an
attack”. In The Future of War (2004), Christopher Coker talks about the
“re-enchantment of war in the twenty-ﬁrst century”. Coker argues that developed
societies are likely to continue with war in the future because technological
change—not least that associated with the information revolution—may make it
more rational and precise than ever before. Indeed, he says: “if war is seen as merely
one end on a spectrum of violence, death is not essential to it. Killing could be made
redundant (though probably not optional), leaving physical coercion or the will to
power by other means” (p. 141).
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Defence and Security: New Issues and Impacts
299
New technologies can redeﬁne the way that warfare is conducted or create new
types of warfare. Technology and military doctrine are closely coupled and interde-
pendent (Alic 2007). Blitzkrieg, the AirLand Battle and Carrier Strike are but three
examples of how new technologies combined with organisational and doctrinal
change led to new ways of warfare (Williamson and Murray 1996). The Revolution
in Military Affairs provides another example. The Internet and its widespread
application have created the possibility of a new form of warfare—cyberwarfare—
that was hardly imaginable 20 years ago.
Emerging technologies may also pose profound ethical and moral questions.
Many areas of emerging technology will pose ethical challenges. Take the use of
biotechnology, for instance. Christopher Coker (2004: 140) argues:
by enhancing, modifying or altering our genes, we may be able to enhance the things we do
well, and have always done well, as a species. One of the things we have done particularly
well over the centuries has been war, and there is nothing to suggest that we will be going out
of the war business—indeed quite the opposite.
In his latest book, Warrior Geeks, Coker (2013) warns that technological change
is threatening to create a battlespace that has no place for human qualities such as
courage, sacriﬁce or honour and even more fundamental categories such as subjec-
tivity, agency and ethics.
13.9
Conclusion
This chapter has analysed some of the main themes emerging from public domain
defence and security foresight studies conducted since the 9/11 attacks in the United
States. We have emphasised how these foresight studies reﬂect a shift in security
thinking away from a focus on state-centric threats towards a much broader view of
security risks that includes risks presented by the vulnerability of European society
to the failure of critical infrastructure, pandemics, environmental change and
resource-based conﬂicts.
This chapter has also emphasised how emerging technologies may inﬂuence the
future of war. This chapter has noted how the role of defence for the development of
new technologies is likely to change dramatically in the future. In particular, defence
and security foresight studies have emphasised that the growing importance of dual-
use technologies is likely to mean that defence will play a declining role as a sponsor
and lead user of advanced technologies in the future. This can be seen as the
continuation of trend developments over the last two decades. At the same time,
whilst the role of defence in the creation of knowledge has changed along these
dimensions, there are also continuities. “Defence”, i.e. the call to protect the security
of a country, its population and assets against threats or from any harm, can be
expected to remain an accepted justiﬁcation for extraordinary political action and the
channelling of political, ﬁnancial and industrial resources. We have pointed to the
“securitisation” of cyberspace and of critical infrastructure, areas that have formerly
been considered outside the realm of defence, governments and private actors.
300
A. James
This chapter concludes with a ﬁnal reﬂection. Rémi Barré has rightly observed
that “The objective, themes and content of a foresight have speciﬁc meaning and
intention”.3 Nowhere is more true than in the ﬁeld of defence and security foresight.
Defence and security represent distinct epistemic and policy communities. Risk is
the lens through which these policy communities view the world and this is reﬂected
in the often pessimistic character of the visions of the future that emerge from
defence and security foresight exercises. Many of the foresight exercises are essen-
tially closed activities that draw upon expertise from within the policy community
(although in some exercises there have been attempts to “reach out” to broader
expertise). Like all policy ﬁelds, there are strong vested interests, and the proper role
of defence and security in Europe is controversial and contested. The meaning and
intention of security and defence foresight activities are deserving of further aca-
demic scrutiny.
Acknowledgements I wish to acknowledge the contribution of Dr Thomas Teichler to the journal
paper on which parts of this chapter are based. I also wish to thank the participants in the Clements
and Strauss Centers Seminar “Emerging technologies and the future of war” at the University of
Texas at Austin in February 2015. Section 13.8 draws on my comments to that seminar and the
useful discussion that followed. I also wish to express my thanks to all the members of the
SANDERA consortium for their contribution to our thinking on these matters and in particular
the participants in the SANDERA workshops in Manchester and Valencia and the contributors to
the SANDERA discussion papers. All errors and omissions are entirely my responsibility.
References
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Alic JA (2007)Trillions for military technology: how the pentagon innovates and why it costs so much.
Palgrave MacMillan, New York
Berkowitz B (2003) The new face of war: how war will be fought in the 21st century. The Free Press,
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Coker C (2004) The future of war: the re-enchantment with war in the twenty-ﬁrst century.
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Coker C (2013) Warrior geeks: how twenty-ﬁrst century technology is changing the way we think
about war. Hurst & Company, London
3Intervention by Rémi Barré at the AUGUR project conference Sharing Visions on Europe in 2030:
Lessons from Comparative Approaches of Recent Foresight Exercises 2 June 2010, Brussels.
¼
¼
13
Defence and Security: New Issues and Impacts
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DAS (2008) Geostrategic prospectives for the next thirty years. Report made under the direction of
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the_DTIB_in_Europe
McNeill WH (1982) The pursuit of power: technology, armed force and society since AD 1000.
University of Chicago Press, Chicago
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302
A. James
Andrew James is Professor of Innovation Management &
Policy at Alliance Manchester Business School and a former
Director of the Manchester Institute of Innovation Research.
His research focuses on technology and innovation manage-
ment and policy in the aerospace, security and defence
sectors. Between 2009 and 2011, he was the Scientiﬁc Coor-
dinator of SANDERA (The Future Relationship between
Security and Defence Policies and the European Research
Area) funded under the Seventh Framework Program Socio-
Economic Sciences and Humanities theme Blue Sky
Research on Emerging Issues Affecting European S&T.
Defense 4.0: Internet of Things in Military 14
Serhat Burmaoglu, Ozcan Saritas, and Haydar Yalcin
14.1
Introduction
Scientiﬁc and Technological (S&T) developments have been inﬂuencing military
concepts and practice, particularly following the inception of the scientiﬁc revolution
in the late sixteenth century. This interaction has not always been one way. Defense
has traditionally been one of the key drivers of S&T advancements due to large
amount of funding it received particularly by national governments. A number of
technologies have been developed for defense, found their civilian applications, and
vice versa. Wherever the boost for change comes from, the nature of warfare has
changed radically both due to S&T advancements and changing socioeconomic and
geopolitical contexts. Despite of the barriers due to strict organizational culture,
armies have adapted themselves into changing characteristics of warfare through
new concepts and instruments. Examples can be found from the earlier Revolutions
in Military Affairs (Burmaoglu and Saritas 2017). A recent example is NATO’s
changing concepts to tackle with the recent hybrid wars, which is characterized by
the involvement of non-state actors in warfare.
Among S&T developments, recent advancements in Information and Communi-
cation Technologies (ICTs) bring enormous opportunities as well as challenges for
defense. One of the recent phenomena emerged with the rapid development of ICTs
S. Burmaoglu
Faculty of Economics and Administrative Sciences, Izmir Katip Celebi University, Izmir, Turkey
O. Saritas (*)
National Research University, Higher School of Economics, Moscow, Russia
e-mail: osaritas@hse.ru
H. Yalcin
Faculty of Economics and Administrative Sciences, Izmir Katip Celebi University, Izmir, Turkey
Faculty of Humanities and Social Sciences, Izmir Katip Celebi University, Izmir, Turkey
# Springer Nature Switzerland AG 2019
D. Meissner et al. (eds.), Emerging Technologies for Economic Development,
Science, Technology and Innovation Studies,
https://doi.org/10.1007/978-3-030-04370-4_14
303
¼
is the Internet of Things (IoTs), which affects every aspect of life with a growing
number of devices communicating with each other. The number of connected
devices is expected to reach 50 billion connected devices by 2020, and the potential
market value is expected to be between $2.7 and $6.2 trillion per year by 2025
(Mantyika et al. 2013). While the possibilities introduced by the IoT have been
providing immense beneﬁts, the increasing number of connections makes the system
ever more complex and vulnerable because of the difﬁculty of securing huge
networks. If one of the main platforms for warfare is going to be the cyberspace
and if the combatants of the future are going to be irregulars, then digitalization of
warfare and cyberterrorism can be considered as the logical paradigm of future
conﬂict (Rathmell 1997), which should be countered by authorities appropriately.
304
S. Burmaoglu et al.
The aim of this chapter is to discuss how the IoTs will affect the military affairs
and to propose future scenarios for exploring alternative trajectories (Miles et al.
2016) while contributing to the existing literature on the opportunities and threats
brought by new and emerging technologies as well as changing nature of warfare.
Thus, the outline of the chapter is as follows. In the second section, digitalization and
IoT concepts are reviewed from multiple dimensions by considering their social,
technological, economic, environmental, political, and value/cultural (STEEPV)
aspects. Then in the third section, the relationships between defense and IoTs are
investigated with emerging opportunities and threats. In the fourth section, two
scenarios are presented. These predictable and possible scenarios portray alternative
roles the digitalization and IoTs can play in the future warfare. Finally, ﬁndings are
discussed, and future implications for research and policy are discussed in the ﬁfth
section.
14.2
Digitalization and Internet of Things
The Internet of Things (IoTs) can be considered as an important enabler of the
information society by providing advanced services through interconnected physical
and virtual things, based on interoperable information and communication
technologies.1 The term was ﬁrst coined by Ashton (2009):
the Internet has been almost completely dependent on people for its supply of information.
But in the future, things will be able to input data themselves. It will be as though a net is laid
over the physical world, linking up and processing the abundance of data generated by
“smart” things and ubiquitous sensors. This is expected to reveal patterns and make
everything from energy to logistics transparent and potentially open to real-time
optimization.
It is a new term, but not a new process. The precursor operations of IoT were
known as “pervasive computing,” “ubiquitous computing” (ubicomp), and “ambient
intelligence.” Ashton (2009), in his presentation at Procter & Gamble, recommended
1http://handle.itu.int/11.1002/1000/11559-en?locatt format:pdf&auth
the use of RFID technology in the company’s supply chain application. This kind of
operations could not make progress until the early 1990s because of the high
investment costs of database storage technologies. Once the data storage
expenditures became cheaper, a new data storage model, namely, “cloud,” showed
up. The cloud system used from the 2000s enables IoT, because it provides an
infrastructure, which can replace a central server. It is still possible to access a ﬁle or
piece of information, which would normally be hosted by the central server, since
these bytes of information are “distributed” or “replicated” throughout the network.
This kind of applications enables new services like “distributed computing,” where
every device on the network is used as a potential node for storing information. The
IoTs is meant to move these technologies once step further to describe a network
between many and different types of devices.2
14
Defense 4.0: Internet of Things in Military
305
Buildings, cars, consumer products, and people then become information spaces
connecting with each other through “Radio Frequency Identiﬁcation Tags” or
sensors and transmitting all kinds of data through this tags. By connecting the things,
the world has become an interface or a living organism that makes “real-time data
workﬂow of the connected things.” This organism gives a chance to make a smart
ecology in our everyday life. In order to proﬁt the advantages of the real-time
information ﬂows, we must learn how to make sense and use it. We must have an
ability to read data as “data” or “information,” not a noisy or unusable thing (Daim
et al. 2016). In other words, with the gadgets, sensors, and machines that track our
every move in the real world, it is possible to develop apps and infrastructures that
may learn and predict our actions and emotions. Cloud-based apps are the key to
using leveraged data. IoTs does not function without cloud-based applications to
interpret and transmit the data coming from all the stakeholders. The cloud enables
“turning information into action” via linked data. Cloud-based technologies and IoT
focus more on the functionality and the data, not the devices. In other words, IoT is
more about the data than the hardware that serves it. The hardware is just there to
serve the data to the user’s needs for all aspects of everyday life and thus enables
cheaper IT choices that connect everything with each other. Connected devices
could have their own connected channels. Dedicated channels could also serve as
a backbone for things (devices, etc.) to communicate in case of emergencies. That
way, one network can still stay up if the other one becomes overloaded or ofﬂine, for
instance, millions of people streaming a popular video will not bypass an emergency
call or alert.
Through the improvement of the information architecture, IoT technologies not
only improve everyday life of humans but also transform some of the key industries.
Examples are given in Table 14.1 with key changes and potential beneﬁts provided
for the users.
A number of standards and protocols are needed to regulate the IoT systems and
ensure the secure operation of the IoT services. These are discussed in the next
session.
2http://www.theinternetofthings.eu/what-is-the-internet-of-things
306
S. Burmaoglu et al.
Table 14.1 The internet of things: a transformational force
Industry
Key change
Potential beneﬁts
Automotive
and
transportation
Real-time driving behavior, trafﬁc,
and vehicle diagnostics
Improved customer experience,
reduced pollution, increased safety,
and additional revenue streams
Healthcare
Remote monitoring of staff and
patients ability to locate and identify
status of equipment
Improved employee productivity,
resource usage, and outcomes that
result in efﬁciency gains and cost
savings
Manufacturing
Quick response to ﬂuctuations in
demand; maximized operational
efﬁciency, safety, and reliability,
using smart sensors and digital
control systems
Enhanced agility and ﬂexibility and
reduced energy consumption and
carbon footprint
Retail
Stock-out prevention through
connected and intelligent supply
chains
Ability to predict consumer behavior
and trends, using data from video
surveillance cameras, social media,
Internet, and mobile device usage
Supply chain
Real-time tracking of parts and raw
materials, which helps organizations
preempt problems, address demand
ﬂuctuations, and efﬁciently manage
all stages of manufacturing
Reduced working capital
requirements, improved efﬁciencies,
and avoidance of disruptions in
manufacturing
Infrastructure
Smart lighting, water, power, ﬁre,
cooling, alarms, and structural health
systems
Environmental beneﬁts and signiﬁcant
cost savings with better utilization of
resources and preventive maintenance
of critical systems
Oil and gas
Smart components
Reduced operating costs and fuel
consumption
Insurance
Innovative services such as pay-as-
you-go insurance
Signiﬁcant cost savings for both
insurers and consumers
Utilities
Smart grids and meters
More responsive and reliable services;
signiﬁcant cost savings for both
utilities and consumers resulting from
demand-based and dynamic pricing
features
Source: Ericsson, M2M Magazine 2013, Zebra Consulting/Forrester Research, IBM, McKinsey &
Co., Data Informed, ZDNet
14.2.1 Standards, Protocols, and Applications for the IoTs
Besides the standards and protocols set by the Internet Engineering Task Force
(IEFT), there are several other protocols which are also under discussion. For
instance, Message Queue Telemetry Transport (MQTT) is a lightweight publish/
subscribe messaging transport connectivity protocol. Developed by IBM, the proto-
col is integrated with the IBM WebSphere application server. Another IoT solution,
ZigBee (or XBee), is a set of application proﬁles for creating low-rate wireless mesh
networks which has been built upon the 802.15.4-2003 standard. DASH7 Alliance
operated at the 433 MHz frequency range. Among other uses, the system enables
tag-to-tag communications with a range up to 1 km. It is suitable, for instance, for
setting up friendly ﬁre warning applications for soldiers. BACnet is used as a
communication protocol for Building, Automation and Control networks. BACnet
is essentially used in HVAC systems (heating, ventilation, and air-conditioning),
lighting control, and access control. It is also suitable for both in-house and outdoor
smart headquarter applications (Bandyopadhyay et al. 2013).
14
Defense 4.0: Internet of Things in Military
307
With the developments on ICTs, Bluetooth has become a key technology in
computing and product markets. It is a key for wearable products and plays an
important role enabling IoT through smartphones, smartwatches, and other wearable
technologies. There are also new developments about Bluetooth technology such as
Bluetooth Low Energy (BLE) or Bluetooth Smart, which are designed not for ﬁle
transfer but more for small and real-time dataﬂows. It has a major advantage
certainly in a more personal device context over many competing technologies
given its widespread integration in smartphones and many other mobile devices. It
has a huge potential about smart applications and devices. ZigBee is another option
for IoT applications. Proﬁles that produce via ZigBee use the IEEE802.15.4 proto-
col. This is an industry standard for wireless networking technology operating at
2.4 GHz. The technology targets applications with relatively infrequent data
exchanges and at low data rates over a restricted area. This can be military head-
quarters and within a 100 m range or inside a building. ZigBee or RF4CE has some
signiﬁcant advantages. It consumes low power and provides better security, robust-
ness, and scalability. With its high node counts, it takes the advantage of wireless
control and sensor networks, in particular with M2M and IoT applications (Ding
et al. 2009). Z-Wave is a low-power RF communication technology like BACnet . It
is preferable for microenvironmental automation for products such as light
controllers and sensors. It supports full mesh networks without the need for a
coordinator node and is very scalable, enabling control of up to 232 devices. In
other words, it makes an infrastructure for calm technology focusing on data which
focus on a broad mix of information.
Sigfox is an alternative wide-range technology. Its range falls between Wi-Fi and
cellular. The system uses the ISM bands to transmit data to and from connected
objects over a very narrow spectrum. These are free of charge and do not require
licensing. Sigfox uses an Ultra Narrow Band (UNB) technology. The system is
designed to handle low data transfer speeds from 10 to 1000 bits per second. It can be
used for real-time data gathering in areas which need small data transaction, such as
military headquarters. Similarly, LoRaWAN targets wide area network (WAN)
applications. It is designed for low-power WANs to support low-cost mobile secure
bi-directional communication, which is needed by the IoT systems, M2M, as well as
smart city and industrial applications.
Already deployed in tens of thousands of connected objects, the network offers a
robust, power-efﬁcient, and scalable system that is able to communicate with
millions of battery-operated devices across areas of several square kilometers,
making it suitable for various M2M applications such as smart meters, patient
monitors, security devices, street/trafﬁc lighting, and environmental sensors cur-
rently being rolled out in major cities especially across Europe. With these options
there is a necessity for an information system architecture, which makes sense the
data for making decisions. Hadoop is one of the powerful options for this kind of
applications. Hadoop is “a framework that allows for the distributed processing of
large data sets across clusters of computers using simple programming models. It is
designed to scale up from single servers to thousands of machines, each offering
local computation and storage. Rather than relying on hardware to deliver high-
availability, the library itself is designed to detect and handle failures at the applica-
tion layer, so delivering a highly-available service on top of a cluster of computers,
each of which may be prone to failures.”3 It has a big potential in big data age such as
Ambari, which is “a web-based tool for provisioning, managing, and monitoring
Apache Hadoop clusters which includes support for Hadoop HDFS, Hadoop
MapReduce, Hive, HCatalog, HBase, ZooKeeper, Oozie, Pig and Sqoop. Ambari
also provides a dashboard for viewing cluster health such as heat maps and ability to
view MapReduce, Pig and Hive applications visually along with features to diagnose
their performance characteristics in a user-friendly manner.”4
308
S. Burmaoglu et al.
With IoTs, a number of new technologies have been deployed as solutions. These
technologies can be seen in many areas, from the creation of data structures that
enable the continuous use of linked data to the next generation of database types.
Technologies made it possible to develop decision support mechanisms that enable
real-time data mining through machine learning. Some of these technologies with
examples for their customized use are as follows:
•
Avro (a system for data serialization)
•
Cassandra (a scalable multi-master database without single points of failure)
•
Chukwa (a data collection system to manage large-scale distributed systems)
•
Base (a scalable, distributed database, which supports structured data storage for
large tables)
•
Hive (a data warehouse infrastructure to provide data summarization and ad hoc
querying)
•
Mahout (a scalable data mining and machine learning library)
•
Pig (a high-level dataﬂow language and execution framework for parallel
computation)
•
Spark (a general and fast computing engine for Hadoop data)
•
Tez (a generalized dataﬂow programming framework. It has been adopted by
Hive, Pig, and other frameworks in the Hadoop ecosystem as well as by other
commercial software, such as ETL tools)
•
ZooKeeper
(a
high-performance
coordination
service
for
distributed
applications)5
3http://hadoop.apache.org/
4http://hadoop.apache.org/
5http://hadoop.apache.org/
¼
×
14
Defense 4.0: Internet of Things in Military
309
Hadoop is a an effective tool for managing large amounts of unstructured data.
However, it has also some shortcomings in terms of running applications on analyt-
ics, in particularly the ones which integrate unstructured and structured data.6
Conversely, SQL has a long and successful history of enabling heterogeneous data
sources to be accessed with almost identical calls.
Besides all socioeconomic and industrial beneﬁts, IoT technologies pose a num-
ber of effects including individual level (changes skills) and organizational (changes
motivation and resources) and technological capabilities (access, concurrent user
licenses, data trafﬁc). Another impact would be coming with changing business
styles and lifestyles. In other words, with the IoTs (and of course ubiquitous
computing), it is possible to manage the effects of events or phenomena around us
more easily. Objects have the potential to directly affect many areas in the ecosystem
of the Internet and connected devices, including the information system architecture
and indirectly the programming language used in software technology. Technologies
that allow working on top of structured data have triggered the development of new
applications that allow instant use and analysis of unstructured data with the IoTs.
The transition from using SQL to Hadoop is one of the most striking examples that
can be shown in this context. The modiﬁcation of the information system architec-
ture, the software languages, and the database structures has also brought with it the
necessity of the connected world of synchronous data ﬂow and use, which has a great
potential for the meaninglessness of the data which is not structured according to
predetermined procedures. This structure affects a lot of ﬁelds from education to
health, as well as defense at the forefront of the most affected areas. This technology
makes it easy to get to know about the context, and it makes it possible to internalize
the processes and make the service planning so much more convenient and
personalized. Unlike the “one size ﬁts all” era, which leads to Henry Ford’s “Any
customer can have a car painted any color that he wants so long as it is black,” in
today’s world personality and customized services have come to the forefront, and
convergence has come to foreground in every ﬁeld. In parallel to this widespread and
customized use, the present resources used are reaching to the critical point. One of
the most striking examples of potential dangers is the limits of the Internet protocol.
It is evident that the number of IP deployed using the IPv4 infrastructure is likely to
increase in the near future and that the IPv4 table will soon be consumed. The most
basic necessity of starting to use the IPv6 table for this situation is growing
(IPv6
3.4
1038 aka 340 trillion IPs).
Like every aspects of life, the IoTs has impacts for the defense industry too. There
are multidimensional impacts; some of these create new threats, whereas others
provide new opportunities for defense and military. These are discussed in the next
section.
6https://internetofthingsagenda.techtarget.com/feature/Focus-on-wide-data-not-just-big-data-in-
analytics-systems
310
S. Burmaoglu et al.
14.3
Digitalization of War Theaters
Wars have always been in human life from the ancient times to the present.
Motivations, shapes, and sizes of wars have changed drastically over time
(Burmaoglu and Saritas 2017). An earlier deﬁnition from von Clausewitz (von
Clausewitz 1968) highlights some key components of a war such as “opponent,”
“violence,” and “will,” which refer to the “nature of war,” which is “an act of
violence to compel our opponent to fulﬁll our will” (p.2). Although the concept of
“violence” has remained the same, the “means” and “ends” of warfare have changed
dramatically in time. For instance, in a more recent deﬁnition, Kaldor (2010:272)
outlines the key characteristics of present wars: “War is an act of violence involving
two or more organized groups framed in political terms.” This deﬁnition indicates
the increasing number of actors involved in warfare.
From a transformative perspective, it is claimed that when the history of warfare
is analyzed, three generations can be distinguished (Lind et al. 1989; Hammes 2005).
First generation can be described as the “tactics of line and column,” and it reﬂects its
age with the calculation of number of barrels. Quantity was equal to power at that
time, and keeping the line meant maximizing the ﬁrepower. Second generation’s
distinction came with the usage of technology, mobilization, and power of indirect
ﬁres (Artillery). The change of power from manpower to mass power differentiated
these ﬁrst two generations. The third generation of warfare is characterized by
Blitzkrieg. In contrast to second generation’s technology-driven aspect, Lind et al.
(1989) state that the main motivation of the third generation was “ideas,” where
Germany’s superiority in tactics was considered as a superiority. Lind et al. (1989)
expressed this superiority from offensive and defensive viewpoints. From an offen-
sive viewpoint, this was as an “attack relied on inﬁltration to bypass and collapse the
enemy’s combat forces rather than seeking to close with and destroy them” (p.23).
On the other hand, the defensive viewpoint considered this superiority as “the
defense was in depth and often invited penetration, which set the enemy up for a
counterattack.” It is noteworthy that the distinctions between generations were made
based on the dominance of military concepts and technologies (Burmaoglu and
Saritas 2017).
The aforementioned generations are mainly concerned with the historical evolu-
tion of wars, but how about future? The changes and transformations observed today
are broader to include overall changes in society, technology, economy, environ-
ment, politics, and values (STEEPV). Although being criticized, the “fourth-genera-
tion warfare” suggested by Lind et al. (1989) and Hammes (2005) takes into account
these broader changes and considers warfare as a twilight zone—between war and
peace, between civilian and military, and between tactics and strategy.
Another interpretation of the transformations regarding today’s wars comes from
Umberto Eco. Narrated by Lucas (Lucas 2010), after two world wars and the Cold
War, by contrast, Eco believed war could no longer be deﬁned with Clausewitzian
fashion, in terms of the straightforward linear vectors of force operating between
clearly deﬁned rival centers of power. In contrast, Eco quoted that “power is no
longer monolithic and monocephalous: it is diffused, packeted, made of the
continuous agglomeration and breaking down of consensus.” War, based on Eco’s
interpretation, may be characterized more than “two opposing states.” There are a
number of proxy forces on the war theater as well as the controlling governments of
states versus their own internal, opposition parties and religious factions, the media,
and ﬁnancial sector. This interpretation explains the war with an analogy as parallel
processing not serial computing. Finally, Eco states that war is no longer a simple
serial sequence of events but all sorts of events going on at once.
14
Defense 4.0: Internet of Things in Military
311
Another interpretation regarding the future of war comes from Hoffman (2006),
who considers warfare as a world of asymmetric and ethno-political phenomena,
where machetes and Microsoft merge with apocalyptic extremes wearing Reeboks
and Ray Bans dream of acquiring mass destruction. Moreover, these adversaries are
considered to be beyond low-tech. Opponents will be capable of undertaking
“advanced irregular warfare,” with access to encrypted command systems and
other modern lethal systems, such as man-portable air defense missiles. In these
structures, they will not need formal networks and will make use of cellular
structures with greater autonomy. In brief, what he proposed may be considered as
“complex irregular warfare,” which consists of organizations with distributed net-
work structures.
Irregular warfare is also termed as “asymmetric warfare” (Grange 2000), where
the opponent is not a nation and they have limited capabilities. Thus an asymmetry
emerged between the sides involved in the warfare. Arreguín-Toft (2001) considers
asymmetric warfare, as where the weak wins wars. Because of the involvement of
large number of forces with headquarters, bases, and troops and their distributed
organization, operations in asymmetric warfare demand increased communication
and coordination as well as greater demand for ﬂexibility, mobility, and networking
of distributed forces.
Besides changing concepts, the evolution of technologies has also affected the
nature of warfare (Aydogdu et al. 2017). Evolution of military technology was
examined by van Creveld (2010) with four stages. The ﬁrst period from 2000 BC
to 1500 AC emphasizes that most military technology utilized its energy from
muscles of men and animals. Second stage is covering from 1500 to 1830, and
this stage is called as “the age of the machines.” During this period, the military
operations were characterized by mobilization, coordination, and communication,
which raised the need for synchronization and energy dramatically (van Creveld
2010). Third stage is called as “the age of systems” and emphasizes the integration of
technology into complex networks. Moreover, using tanks, railways, and highways
and improving means of logistics made this stage more complex with increasing
integration. Hence, it became more important than before to supply energy to
military units and share the intelligence between units online at that time for
sustaining the on-going operations (Saritas and Burmaoglu 2016).
As of today, rapid technological progress and innovation have increased the
information intensity for running military units and making decisions as well as
carrying out missions and undertaking operations. Collection, processing, and syn-
thesis of this vast amount of information require higher level of “digitalization,”
“computerization,” and a “network structure.” Moreover, there is a greater demand
for the seamless ﬂow and diffusion of information and data between military forces
and other actors in war theater.
312
S. Burmaoglu et al.
Satellite ISR
Satellite GPS
C2 and
Info Systems
Satellite
Communication
Networking
IT
Satellite ISR
Info-C2 Layer
Effector Layer
Fig. 14.1 Network centric warfare demonstration (http://mil-embedded.com/articles/the-internet-
things-the-intelligence-community/ Access Date: 01.06.2016)g. Source: Author
Combining the conceptual and technological transformations observed in war-
fare, it can be asserted that future wars may be characterized by involving non-state
actors, distributed and cellular type forms, asymmetric nature, and high-tech with the
help of information systems and power of social media. Recent years have seen the
emergence of the network-centric warfare concept. This concept was created with
the increasing need for command and coordination. Such a system constitutes an
information grid that connects the elements of all combat soldiers, weapons, military
equipment, and so on by utilizing computers, sensors, and wired/wireless networks.
In this concept the war power is increased by intelligence superiority through
information sharing and integration as connecting intelligence collection systems,
command and control system, and strike system (Yang et al. 2015).
Figure 14.1 demonstrated the layers of the network structure.
As can be seen in the ﬁgure, a central network body is constructed to get the data
from various sources and after analyzing the data transmit it to the theater based on
their levels. This layered structure of network becomes more complicated with
adding more and more data points. Traditional approaches to military doctrine
have been transformed by the network-centric warfare with expanding communica-
tion gateways connecting battleﬁeld assets and headquarters and increasing data
sharing between legacy assets and new deployments.
14
Defense 4.0: Internet of Things in Military
313
An important application of network-centric warfare should be considered as
Command, Control, Communications, Computers, Intelligence, Surveillance, and
Reconnaissance (C4ISR) systems. The electronic understanding of war theater may
be exempliﬁed by the Command, Control, Communication, Intelligence, and Elec-
tronic Warfare (C3IEW) concept, which was ﬁrst introduced by the Defense Science
Board in the USA in the early 1990s and aimed to ensure interoperable and cost-
effective military systems for the Department of Defense. C4ISR is the improved
version of this concept with its ﬁrst version issued in 1996, and the second version is
in 1998 (http://cecomhistorian.armylive.dodlive.mil/2013/03/11/history-of-c4isr/).
In network-centric concept, IoTs may ﬁnd potential application areas some of
which have been applied. For example, IoT can be applied to surveillance and
reconnaissance systems, soldier combat uniform, medical services, logistics
services, precise guided munitions, combat platforms, and so on.
Credencys Solutions Inc. proposed that IoTs may help military with six ways.
These are (1) providing battleﬁeld situational awareness, (2) proactive equipment
maintenance, (3) monitoring warﬁghter’s health, (4) remote training, (5) real-time
ﬂeet management, and (6) efﬁcient inventory management.7 From these six ways, it
can be asserted that intelligence, maintenance, logistics, ﬂeet management, training,
and medical services are the potential sub-working areas for exploiting IoTs in
defense.
Meanwhile, it is clear that application of IoTs with today’s technology level may
cause important security threats such as illegal remote control, information leakage,
false information insert, and signal disturbance. As an issue of vulnerability,
cyberthreats have also become more common and sophisticated. Four areas concern
the security of the IoT environment for military including (1) making sure informa-
tion is reliable and systems are resilient, (2) keeping pace with technology, (3) focus-
ing on the insider threat, and (4) embracing (big and community) data analytics to
minimize cyberthreats (Daly 2016).
Besides vulnerability of information systems, according to retired Marine Corps
General James Cartwright (2015), culture is the most important obstacle for not
applying IoT in military. Based on his speech at CSIS institution, not only the culture
is an obstacle, but also “man-machine partnering” should be focused for
implementing IoT. Last statement should be considered as another discussion
point, because IoT is mostly designed for machine-to-machine communication;
however for war theater, man-machine interaction is another level to be achieved.
Man-unmanned teaming (MUM-T) project may be a special example for
man-machine interaction. Based on Colonel Eschenbach (2016)’s article in Army
Aviation Magazine, the history of this concept can be traced back to the World War
II with the designation of BQ-8 robots in B-17 Flying Fortresses for remote piloting.
Moreover, the concept gained importance in the last decade with controlling
AH-64D Apache and its payload utilizing an add-on system designated MUM-T2.
7https://www.credencys.com/blog/internet-of-things-the-agent-of-change-for-the-defense-system/.
Access Date: 16.04.2016.
According to Colonel Eschenbach, these unmanned systems can be used for situa-
tional awareness and enrooting air transportation forces. He envisioned that future
organizational force structure of Army Aviation has been shaped by MUM-T
advantages. Iriarte (2016) has been supporting Eschenbach’s ideas and narrated
the Unmanned Aircraft Systems (UAS) usage interoperable with AH-64 Apache.
Based on her article, the future of this concept will be with increasing autonomy,
reducing workload, and manpower.
314
S. Burmaoglu et al.
From the healthcare point of view, it can be seen that IoTs have the potential to
change the dynamic of healthcare itself. By networking different devices together on
the battleﬁeld and in garrison, information sharing may be more convenient based on
the ﬁndings of Military Health System Communications Ofﬁce (2015). Moreover,
automated alerts for medical staff are produced by theater mobile computing
applications which can increase the quality of medical decisions. These applications
should help improve the readiness of warﬁghters and perhaps increase their chances
of survivability. IoTs has potential for transformative change in human health. Using
connected devices and wearables, to continuously monitor patients as they live their
lives particularly those with chronic conditions like diabetes, it can improve patient
adherence to prescribed therapies (as procedures that already planned), avoid
hospitalizations (and post-hospitalization complications), and improve the quality
of life for hundreds of millions of patients. McKinsey report predicted this could
have an economic impact of $170 billion to $1.6 trillion per year in 2025.
Finally, it can be said that IoTs has many potential areas in defense sector, but the
obstacles are very hard to overcome in a short time. Hence, it can be interpreted that
the adoption and dissemination of IoT applications need various stages. In the next
section, alternative scenarios are presented to provide alternative narratives on the
context and adoption of IoTs in military.
14.4
Possible Future Scenarios for IoT in Military
As discussed in the previous section, IoT has many advantages and disadvantages
for military forces. However, it should be admitted that for some of the sub-military
ﬁelds such as reconnaissance and logistics, IoT applications will have a great
contribution. These contributions may also be called as force multipliers. In this
section, the future of war contexts and potential usage of IoTs in these contexts are
represented by using alternative scenario trajectories.
There is a consensus on the existing literature that future wars will be
characterized by their distributed, synchronous, and complex nature. The key
uncertainties are about the visibility of war theaters as well as their boundaries in
terms of where they begin and where they end. New technological developments,
which may be called as intelligent systems, work autonomously with varying
controlled and supervision degrees and collect information by sensing, communi-
cating, and collaborating each other in a war theater. They are enabled by machine
learning, perception, and reasoning. They can process information, undertake defen-
sive actions, and unleash a variety of effects on the adversary. These intelligent
systems would be used for carrying equipment, shielding troops, and sensing ﬁelds
at ﬁrst. Hence they can be produced with different physical entities ranging from
insect-sized to mid-size and can move over the ground or air. However, especially
for the physical engagement at war, it should be a requirement to authorize humans
for asking responsibility. The physical entities and virtual counterparts would be the
other side of this environment. These virtual self-operating intelligent systems may
be called as “cyber robots.” They can protect C4ISR systems and energy grids and
warn and prevent about incoming cyber threats. Moreover, these systems should be
thought systemically, and all other physical or virtual entities should be considered
as parts of this big collaborative network. The battleﬁeld of the future will be
populated by fewer humans. But these warriors will be physically and cognitively
augmented. They can interact with the autonomous intelligent systems by using
improved man-machine interaction algorithms.
14
Defense 4.0: Internet of Things in Military
315
However, from the human side, not only the interaction but also the visibility of
war theaters is an important issue. Disinformation and deception will be essential to
survive and operate on the battleﬁeld of the future. The war theater of the future is
expected to feature a crowded and synchronous battleﬁeld. However, physical
entities will play minor role and will be replaced by cyber entities, which will be
even more difﬁcult to detect and track. Therefore, quantity and quality of informa-
tion and availability of communications, as well as rapid decision-making, will be
crucial. Conventional concepts and approaches would not work because of their
hierarchical structure of decision-making environment, which makes the process
slower and inefﬁcient. In this new environment, decision-making will be distributed
as well as the structure of network. Hence, for future wars, military leadership and
the competencies of leaders would become more important than the present. They
can self-organize and collaborate within these dense networks with the aid of
human-machine teams. Within this context, it is considered that that the key scenario
variations would be deﬁned by the different levels and types of human-machine
interaction. The main question here is to what extent human supervision should be
maintained? Two scenarios are proposed to consider the trajectories of “human-
supervised” and “autonomous” systems. The ﬁrst scenario is more predictable
considering the current trends, where the second one, as a probable scenario, also
becomes more and more feasible as the speed of technological development
increases and leads to the real Defense 4.0.
14.4.1 Predictable Scenario: Human-Supervised War Environment
One of the key features of the changing nature of warfare is that the future wars will
take place in contexts, where it will be difﬁcult to distinguish friends and enemy.
Instead of battleﬁelds, future wars are expected to take place much closer to human
settlements and cities and, therefore, will become increasingly scattered in multiple
locations. In these cases, operations will increasingly involve UAVs and thus will be
more efﬁcient with the engagement of smaller but more efﬁcient security personnel.
Within this context, seamless connection, continuous data ﬂow, and synchronization
will be crucial. Armies in human-supervised war environment will set up centers to
manage military operations. As there is no need to set up such centers physically on
site, where the operations take place, they can be established in more secure sites in a
more ﬂexible project-based structure instead of distributed and self-sufﬁcient nodes.
What is important here is to provide continuous energy supply both for headquarters
and operational centers.
316
S. Burmaoglu et al.
Besides energy supply, it is considered that cloud systems will play an important
role to enable and facilitate this process. Current cloud technologies are based on a
single centralized logic. It is currently a technological challenge to set up a more
scattered cloud system for distributed and different types of operations. During the
transition time, a hierarchical cloud system can be designed in-line with the
constraints of the operation sites. Thus, a war theater cloud system can be created
by considering the structure operation command.
At the individual level, the hierarchy in the military leaders will evolve from a
rigid hierarchy to a more functional hierarchy. This will enable a joint decision-
making among the military leaders and shared responsibility. However, it can be said
that the leaders, which are not involved and interact with the war environment, will
be less sympathetic and more relentless in decision-making. In the literature, this
situation is called the “trolley dilemma” (Foot 1967).
The trolley dilemma is an experiment applied in ethics. The general form of the
problem is the following:
There is a runaway trolley barreling down the railway tracks. Ahead, on the tracks, there are
ﬁve people tied up and unable to move. The trolley is headed straight for them. You are
standing some distance off in the train yard, next to a lever. If you pull this lever, the trolley
will switch to a different set of tracks. However, you notice that there is one person on the
side track. You have two options: (1) Do nothing, and the trolley kills the ﬁve people on the
main track. (2) Pull the lever, diverting the trolley onto the side track where it will kill one
person. Which is the most ethical choice? And other scenario is a trolley is hurtling down a
track towards ﬁve people. You are on a bridge under which it will pass, and you can stop the
train by putting something very heavy in front of it. As it happens, there is a very fat man
next to you—your only way to stop the trolley is to push him over the bridge and onto the
track, killing him to save ﬁve. Should you proceed? (Eagleman 2015:126–128)
For the ﬁrst ethical scenario, most of the people are willing to pull the lever.
However, in the second one, no one wants to push a man over the bridge. Even if the
result is the same, personal interaction changes the behavior.
In order to overcome this problem, training programs should be designed and
implemented for the military leaders and users of weapon systems to make them
capable of developing empathy with the opponents and civilians. Virtual reality or
augmented reality systems are considered to be immensely useful for this purpose.
Consequently, it should be born in mind that challenges related to human-
supervised war environments will exist at all levels, from systems to individuals.
Therefore, both infrastructures, including energy, the ICT, and cloud systems, and
future military personnel and leaders should be developed considering the context
and requirements of this new context of warfare.
14
Defense 4.0: Internet of Things in Military
317
Overall, this scenario is considered as an initial phase toward the probable
scenario, “autonomous war environment,” which will be described in the next
section.
14.4.2 Probable Scenario: Autonomous War Environment
Autonomous war environment can be also called as a new machine era, where the
Defense 4.0 concept will reach its true meaning. In this context, operations, logistics,
and intelligence units will talk to each other and plan actions through artiﬁcial
intelligence devices and algorithms. Protocols for authority and responsibility must
be clearly deﬁned in this system, particularly when intelligence is received and
operations activated. Not inferential-based but evidence-based operations should
be authorized. Among the biggest challenges for this scenario are continuous energy
supply, human-machine interaction limitations, and development of cloud comput-
ing and supervised artiﬁcial intelligence algorithms.
From the social and human point of view, this can also be considered as a
catastrophic scenario. Although there are a number of beneﬁts, questions regarding
the reliability of systems and the possibility of losing their control create doubts
about the development of this capability.
At ﬁrst, autonomous systems are expected to be used for the purposes of intelli-
gence and logistics. While cost reductions and efﬁciency increases will be observed
in logistics, systems like drone swarms will help to collect intelligence from a wider
range of geographical areas. The use of biomimetic colonies in operations within
built up areas will reduce the loss and damage of personnel and vehicles.
From the organizational point of view, information will be collected and
processed of undertaken in data centers. Therefore, it can be said that this scenario
also suggests a degree of centralization in decision-making. This will be particularly
observed in logistics and intelligence units. At the individual level, the processes of
human-machine interaction need to be well considered and planned during
operations.
An important aspect in autonomous war is the role of military leadership. New
generation military leaders should be equipped with virtual management and abstract
thinking skills. The assessment of information from autonomous devices and devel-
oping an operation plan through human-operated systems require new capabilities
beyond conventional defense leadership perspectives.
Overall, access to information and cloud systems, accessibility and synchroniza-
tion, and assessment and decision-making will be among the key characteristics of
this scenario. Without doubt, energy supply will play a crucial role for the seamless
operation of the autonomous systems.
318
S. Burmaoglu et al.
14.5
Conclusions and Discussion
Digitalization and its exploitation may have many facets. One of them is the use of
sensors and sensor technologies. Sensors are increasingly becoming an important
part of our daily life and are used for acquiring online data from technological or
biologic subjects that take part in the war theater. In peace times they are used for
training troops, optimizing weapons, vehicles or maintenance systems, and so
on. The other crucial technology is social media. It is mostly used for human
intelligence. One of the successful examples of using social media is UCINET, a
social network analysis software, which was developed by Harvard scholars to ﬁnd
Saddam Hussein. Finally, it can be brieﬂy summarized that these technologies are
collecting data or gathering it, and after analysis in appropriate software, the results
are used even in intelligence, operations, logistics, or whatever else. However, using
IoT in military is one-step forward. There are some similarities, but in the case of
IoT, sensors can talk to each other and can act according to their manufacturing
objective. In both scenarios, it can be asserted that the main challenge that military
authorities face is energy. After solving energy sustainability, human-supervised war
environment may be activated easily, because on-going operations will be strength-
ened by beneﬁting technological capabilities. However, autonomous war environ-
ment is one-step forward from human-supervised war environment.
Both scenarios will have same bottlenecks as ethics and social acceptance. Even
these developments may reveal beneﬁt for community; they may eventually destroy
privacy and freedom of choice. For instance, continuous surveillance of the society
online and ofﬂine reminds George Orwell’s world-famous novel 1984 which may
raise concerns about “privacy.” A ﬁne balance will still be required when collecting
information for intelligence while caring for the private lives of citizens, especially
in autonomous war environment.
Acknowledgments The contributions by Professor Ozcan Saritas in this study were prepared
within the framework of the Basic Research Program at the National Research University Higher
School of Economics and supported within the framework of the subsidy by the Russian Academic
Excellence Project ‘5-100’. The contributions by Asst. Professor Haydar Yalcin in this study was
supported by Scientiﬁc and Technological Research Council of Turkey Postdoctoral Research
Programme (TUBITAK BIDEP 2219) [1059B191700840].
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Serhat Burmaoglu is Associate Professor on Quantitative
Decision Making at Izmir Katip Celebi University and
Visiting Scholar in Science, Technology and Innovation
Policy Studies, School of Public Policy, Georgia Institute
of Technology. His research interest is investigating the
relationship between economic growth, productivity, com-
petitiveness, innovation and knowledge economy in macro-
economic level by using multivariate statistical analysis and
data mining applications for extracting usable patterns to
direct
technology
policy.
Dr.
Burmaoglu
performs
bibliometric and patent analyses on emerging technology to
prepare scenario-based foresight studies. He published many
book chapters and articles in different national and interna-
tional journals.
Ozcan Saritas is Professor of Innovation and Strategy at
the HSE and editor-in-chief of “Foresight”—the journal of
future studies, strategic thinking and policy. He worked as
Senior Research Fellow at the Manchester Institute of
Innovation Research, The University of Manchester. His
research focuses on innovation and policy research with
particular emphasis on socio-economic and technological
Foresight. With a PhD from the “Foresight and Prospective
Studies Program,” he introduced the “Systemic Foresight
Methodology”, and has produced a number of publications
on the topic. Dr. Saritas has extensive work experience with
the international organizations, including the United Nations,
OECD, and the European Commission. He has been
involved in large scale national, multinational and corporate
research and consultancy projects on sectors including
energy, climate change, agriculture, food, water, transporta-
tion, and ICT among others; published a number of articles in
respected journals; and have delivered keynote speeches in
more than 50 countries across the world. Besides his research
and publication activities, he designs and delivers academic
and executive education courses on Foresight and Strategic
Planning. He has recently co-authored a book, entitled
“Foresight
for
Science,
Technology
and
Innovation”
published by Springer, which has become one of the key
readings in the ﬁeld.
Haydar Yalcin is Assistant Professor on Information Man-
agement and Technology. His research interest is measuring
scientiﬁc productivity using big data methods and data
mining applications for extracting usable patterns to direct
information and technology policy. Dr. Yalcin performs
scientometric analysis, bibliometric and social network anal-
ysis on emerging technologies to prepare scenario-based
foresight studies. He published many articles and book
chapters in different national and international journals.
Part V
Challenges to STI Policy
How to Stimulate Convergence
and Emergence of Technologies?
15
Dirk Meissner, Leonid Gokhberg, and Ozcan Saritas
Until the last century, technology development and application, e.g., manufacturing
and commercial application, were often centered in reasonable proximity which led
to the thinking that regional industry absorbs regionally developed technology at
reasonable pace. While this view is certainly true, it neglects that also industry is
developing routines over the years which are often hard to overcome, e.g., although
technology and skills are available locally, industry might postpone modernization
and skills upgrading due to delayed decision-making and limited willingness to
leave the old routines as David (1990) describes for the advance of the “New
Economy.”
From the technology point of view, accelerated diffusion speed brings positive
effects when it comes to the sophistication of technologies and the search for
complementary application ﬁelds and so on. But from the point of view of regional
and local development, this becomes even more questionable because the absorptive
capacity of existing industries in close proximity of the technology’s place of origin
is not necessarily sufﬁcient to take advantage of the next technological wave.
Moreover, it appears that building new industrial zones for economic development
is more comfortable for companies, namely, large companies, than upgrading
existing facilities to master new technologies well. Among the reasons for preferring,
establishing new facilities is the fact that technology intensive manufacturing is
frequently capital intensive which for accounting and controlling reasons might
tempt companies to favor new facilities over existing ones due to the depreciation
of existing assets and also for reasons of public subsidies, loans, grants, or the like
which is often offered to companies investing at green ﬁeld. It therefore cannot be
assumed any longer that the supporting technology development will provide local
D. Meissner (*) · L. Gokhberg · O. Saritas
National Research University, Higher School of Economics, Moscow, Russia
e-mail: dmeissner@hse.ru; lgokhberg@hse.ru; osaritas@hse.ru
# Springer Nature Switzerland AG 2019
D. Meissner et al. (eds.), Emerging Technologies for Economic Development,
Science, Technology and Innovation Studies,
https://doi.org/10.1007/978-3-030-04370-4_15
323
and regional spillovers for the beneﬁt of respective economic and social value with
close proximity to the place of origin. In this respect one might argue that local and
regional development should change the existing paradigm of “investment into
research and technology development creates lasting regional economic advantage”
toward a broader understanding of the resulting spillovers. These spillovers are not
necessarily bound to appear in a region but might appear elsewhere in the world
which lets the question arise why investment was done in one region but the impact
achieved in another region.
324
D. Meissner et al.
Accordingly governments are challenged to justify why public intervention is
actually required to stimulate the emergence and convergence of technology. At ﬁrst
sight any government intervention seems doubtful in light of the market economy
thinking which postulates that the market will balance demand and supply. The
failure in this thinking however is that technologies in many cases don’t generate
new demand but replace existing solutions, e.g., making established technologies
obsolete. Assessing the eventual impact resulting from these replacements on the
respective economy appears difﬁcult for several reasons. First of all challenges arise
in assessing the actual technology-induced economic but also societal impact.
Economic impact is frequently measured by job creation and economic returns of
a technology but still in most cases the impact which a technology has on revenue
generation remains uncertain. For example, frequently products, services, and pro-
cesses involve several technologies which is why solid economic assessment is only
partially possible. Similar is true for the societal impact which evolves. It’s not
always clear and predictable which impact a technology or bundle of technologies
might have on society in advance and also the causality between the emerging
technology and societal impact is difﬁcult to assess. Having said this we ﬁnd that
impact assessment is still characterized by the chicken and egg problem, but for
government interventions, it’s almost essential to know the possible impact. Against
this background numerous reasons for governmental intervention in technology
emergence and convergence appear:
•
Among the governments’ ambitions for technology development is the explicit
aim to enable companies to develop new products and improve processes at
broader scale for manufacturing which eventually results in economic growth
(Cooke and Leydesdorff 2006). This is an obvious intention by policy makers;
however ﬁrms’ competitive advantages result from their presence in the value
chain (Krugman 1995; Porter 1990, 1998), e.g., especially in course of open
markets and global value chains, companies tend to diversify their value genera-
tion activities. Value chain-related activities are typically spread across different
locations, e.g., countries and regions. But this does not imply that each step or
activity of the value chain is established in one region only. Quite on the contrary,
companies, especially large companies, operate different facilities in different
regions for various reasons. Among the motivations are the end consumer
proximity as well as the regional supply chain for selected products and service
features, the servicezation of products for which regional proximity is a plus but
also the human resource dimension in all facets and in some cases national
15
How to Stimulate Convergence and Emergence of Technologies?
325
regional regulations which requires this (Miozzo et al. 2016; Miles 2016; Miles
and Miozzo 2015).
•
Whereas in the early stages of the technology life cycle only few knowledgeable
individuals and entities are involved and competent to apply and further develop
the technology, the number of parties increases considerably with growing
application diffusion. Typically, there is uncertainty about the eventual possible
applications an initial invention might bring over the lifetime which is due to the
small and often closed community of individuals who are engaged. Frequently
these individuals are primarily scientists and dedicated engineers who aim at the
perfect scientiﬁc and/or engineering solution with little or limited attention to
application ﬁelds. Thus discussions about the technologies are mainly limited to
community internal debates at least for a while. Following the involvement of a
broader public audience, namely, a scientiﬁc and engineering audience, more
views and perspectives on the technology under consideration appear which
provides the ground for very early adoption (Helpman and Trajtenberg 1994). It
follows that information about the existence of the invention, hence technology,
diffuses and more actors are becoming aware of the potentials. Increasing aware-
ness of the inventions outside the initial communities also brings spillover effects
which cross regions and countries in addition to the initial technology ﬁeld.
Therefore, an invention might show potential to stimulate activities in other
regions which for some reason provide better framework conditions for the
invention to unfold its economic and application potential than locally only.
•
Technologies typically originate at a dedicated location but show limited appli-
cation potential and economic impact, respectively, in the very early stages. The
challenge arising is that technologies diffuse fast within and beyond communities,
and therefore locations and regions of origin might experience that the merits of
applying and exploiting technologies are grasped at another destination. This
observation is by no means a twenty-ﬁrst century phenomenon but has been
described in the 1980s already (see, e.g., Cooke et al. 1984). Creating measurable
economic value from technology is hardly an issue for scientiﬁc and engineering
work and related competencies only but is extended to the common business,
operations, and maintenance skills which are essential for manufacturing and
production hence for economic value. Economic value here involves local and
regional employment creation but also tax revenues to the local and regional
public budgets. In addition value to society arises through more indirect value,
e.g., by means of employment which in turn contributes to social welfare and less
social tensions among many others. For these reasons technology creation is often
seen by the policy community as a means of long-term value creation. However,
in many cases respective support measures are aimed at initial technology
development in the ﬁrst instance but less on timely providing adequate labor
force competences, which is companies’ absorptive capacities in the broadest
sense. Beyond scientists, engineers, and other related highly qualiﬁed, labor
industry
demands
lower
skilled—but
still
ﬂexible—manpower
for
manufacturing. Again this is hardly a new observation but has been described
by Saxenian (1981) already. What has changed since then is the even faster
326
D. Meissner et al.
diffusion of information thus technology and obviously technology’s complexity
and interdisciplinary nature.
As in any technology development case, completion is always uncertain both
with regard to completion in time and budgeted resources which is why it appears to
be little constructive to talk about emerging technologies if there is no certainty
about completion. Furthermore, users’ technology acceptance might be indicated,
but this again is highly risky and uncertain because competing technologies are
potentially also in the development stages, and the actual technology performance
remains rather vague with much of the potential value being assigned to expectations
and enthusiasm. The latter is also a potential barrier for acceptance and diffusion if
the technology under development eventually doesn’t provide reasonable value to
users to replace existing solutions or to enter new grounds base on the new
technology. Therefore, the psychological dimension is crucial to consider. Against
these arguments the authors understand emerging technologies as:
Solutions of which basic principles and modes of action have been developed and
demonstrated successfully. Initial applications of the technologies are known and at least
partially understood but there are additional yet unexplored application ﬁelds. Emerging
technologies are in principle platform technologies in their early technology life cycle stages.
A technology might be entitled ‘emerging’ if it is in operation for demonstration purposes at
least and multiple application ﬁelds are possible.
Among other features emerging technologies are characterized by their potential
to initiate new discoveries and inventions which are based on their initial invention,
e.g., the level and degree of their multiple usage potential (David 1990; Youtie et al.
2008). This includes that emerging technologies aren’t diffusing a single application
ﬁeld only but provide the basis for complementary technologies which in turn form
signiﬁcant parts of new technological solutions in other ﬁelds. From a technological
point of view, this is closely related to complementary technologies, in other words
platform technologies, which share a common main principle stemming from an
earlier invention.
Like any technology emerging technologies are frequently challenged by the
prevailing uncertainty about potential side effects and less favorable impacts on
society, namely, in the environment, health, and safety (EHS) context. Thus in order
to generate economic beneﬁts, thorough assessments of the technologies are required
which are aimed at society and related impacts in the ﬁrst instance. These
assessments also request a dedicated media and information campaign targeted at
informing society and raising awareness. Experience with various technologies
provides evidence that the emergence of technologies and related economic effects
might suffer from societal resistance if no early-stage awareness and information
campaigns are in place. In such cases technological development progresses at high
speed, but knowledge and information about the technology are trapped in a rather
closed community with selected information pieces being made available to a
broader audience. Consequently, media and interested communities use the available
information pieces to communicate among society but run danger of drawing
misleading pictures of technological impacts which might inﬂuence societal opinion
about the technology at large thus ﬁnally determine acceptance or resistance.
Accordingly, investments into further technology and application development are
at stake as long as investors are confronted with uncertainty about society attitude
toward technologies.
15
How to Stimulate Convergence and Emergence of Technologies?
327
Emerging technologies are characterized by numerous uncertainties including
technological development (achieving reliability and operationality under time and
budget constraints), competitive technologies development, market and user devel-
opment, standards, state regulations and certiﬁcations, among others. All these
features evolve and develop over time with different impact on the technology itself,
and also they inﬂuence each other to some extent. A suitable instrument to visualize
and understand these developments and effects are roadmaps, i.e., roadmaps dedi-
cated to emerging technologies and economic development. However, such
roadmaps are only impactful if building on a series of mini roadmaps under a
common umbrella which enable regional actors at least to build their own targeted
strategies and roadmaps (Walsh 2004).
Another important challenge for achieving economic value from technology
development, namely, the diffusion of emerging technologies, lies with human
resources. It’s frequently argued that labor is mobile but still causing considerable
cost to employers which was postulated 35 years ago by Dorfman (1983). The labor
mobility-related cost has increased considerably over the last years when numerous
countries, regions, and locations (municipalities) have decided to develop local hubs
for technology development at the leading edge. Often these initiatives are
challenged by a shortcoming in available talent with respective competences and
the necessary integration into the appropriate communities and networks. Thus
demand for such talent has grown, while supply remained at the similar level.
Furthermore, it cannot be expected that supply of such talent increases at the same
speed as demand develops.
In many cases technology is non-rival, e.g., it provides multiple application
possibilities which can be developed at marginal costs (Fu et al. 2011). Even the
digital (knowledge) economy technologies ﬁeld remain featured by a signiﬁcant
share of tacit—hence non-codiﬁed—knowledge which provides advantages for
regional and local innovation (technology) ecosystems as postulated by Jaffe et al.
(1993). Such advantage is mainly found in a time advantage which the research (viz.,
research institutions and universities) and also the innovation (namely companies)
communities in a region enjoy over other actors outside these regional communities.
Yet these advantages are hardly of long-lasting nature since reverse engineering and
international labor mobility enable competitors to copy or invent other related
solutions. In this regard labor mobility is especially important as this affects the
tacit knowledge which becomes accessible if skilled labor is moving to other places
and occupations.
Diffusion paths of technologies, including emerging technologies, take a broad
range of shapes, among which are trade of goods and capital by means of inward and
outward foreign direct investment, mobility of people, cross border R&D and
innovation collaboration, media and social network communication, and, last but
not least, the global value chains (Pietrobelli 1996). Global value chains and labor
mobility are becoming more and more central to diffusion as these channels include
the physical transfer of technology (GVC) and the tacit knowledge which is neces-
sary to operate technologies. Information-related channels (social networks and
media) more likely have an awareness and less sophisticated information function.
328
D. Meissner et al.
Regional proximity of actors is an important driver for technologies to diffuse in
the application sphere. It is often found that face-to-face communication of actors is
supportive for technologies to emerge and diffuse at higher speed (Ku et al. 2005).
The rationale behind this observation is that face-to-face communication allows the
actors involved exchanging tacit knowledge, e.g., direct verbal communication
about the technology under question takes different forms than in more structured
and documented communications. Closer regional proximity of communities
demonstrates positive effects on the social relationships of actors which results in
free discussion of ideas leading to positive externalities—i.e., information and
knowledge dissemination—and building of trust among individuals. Social
relationships and resulting trust development provide a clear contribution to the
absorptive capacities of actors, namely, ﬁrms, which also contribute to technology
diffusion and adoption speed (Fu 2008). The latter offers strong potentials for
technological development and thus economic development but at the same time
inherits reasonable threats for entities, namely, commercial entities when it comes to
labor mobility between the actors involved (Ku et al. 2005).
In this respect emerging technologies clearly provide strong opportunities for
generating economic impact at company and at regional level, e.g., at micro- and
macro-level. However, in order to leverage emerging technologies’ economic poten-
tial for the advantage of regional economic development, a much broader approach
to science, technology, and innovation (STI) policy is required with dedicated
features:
•
Standard (common practice) STI policy measures targeting at supporting tech-
nology development but should provide more room for creativity in the design of
projects and application ﬁelds.
•
In order to establish lasting economic impact and provide the respective frame-
work conducive to sustainable technology-based leadership, policies need to look
beyond the initial technology horizon. This implies the active support of related
regional innovation milieus and ecosystems by means of developing and keeping
human resources which are at the front of the technological dimension but which
also possess a broader experience and related soft skills.
•
While there is a reasonable amount of scholarly works done on soft skills, the key
messages haven’t diffused to the national STI policy-making communities.
Related policy measures share the common understanding so that featured
ecosystems evolve in clusters and platform or by attracting talent without any
additional support. But this is only a part of the truth. Clusters, platforms, and the
like certainly play a role and might act as nucleus but hardly involve the potential
to inﬂuence individual’s attitudes which on the other hand is key.
15
How to Stimulate Convergence and Emergence of Technologies?
329
STI policy is hence required to take these challenges into account if it aims at
ﬁnding ways to enhance technology emergence and convergence. Obviously
measures responding to the described challenges are hardly found in the narrow
understanding of STI policy but beyond the common measures. It requires unortho-
dox approaches which provide reasonable space for interpretation of societal
attitudes, legal issues, and other related regulations. Although this might in some
cases contradict the standard rules and procedures of public spending—which is in
almost all countries worldwide strongly regulated—policy should develop models
which allow a more creative and pragmatic support. This involves especially:
– Supporting individuals aiming at extending and broadening their horizon in ﬁelds
other than their current or previous education but always in line with the clear
ambition to use the experiences gathered for the advantage of regional develop-
ment. This is easily said but difﬁcult to implement. Some might argue that such
public support schemes take the form of scholarships which can hardly provide
guarantees of receivers’ return and impact generated. Right on the contrary, if a
reasonable effect is expected, regulations should be as ﬂexible as possible without
putting much administrative burden on the receiver’s side and leaving aside
attempts to assess and quantify the resulting impact.
– It’s appropriate to establish schemes which take the shape of “play money” being
spent and invested with uncertain return.
– Further this requires that aims and goals of public support are formulated in a
more ﬂexible form as currently practiced and no deﬁnite ﬁxed indicators and
deliverables are described. In doing so governments are asked to obtain a more
entrepreneurial
attitude
which
is
not
expressed
in
standardized
public
announcements.
– Establish a system innovation thinking which incorporates user understanding,
e.g., STI policy measures need to account for the requirements and perceptions of
technology and innovation adopters from the initial support phases.
Summing
up,
we
ﬁnd
that
emerging
technologies
provide
signiﬁcant
opportunities for companies and research institutions. However, the widespread
expectation that emerging technologies deliver signiﬁcant regional economic impact
is often fulﬁlled partially only due to economic constraints and global spillovers. In
order to leverage the economic impact in favor of the region of origin, policy makers
need to look beyond the existing policy measures. This said means especially
concerted—e.g., consistent and coherent—STI policy approaches are required. It
is a common policy maker dilemma to develop new STI policy measures which aim
at supporting emerging technologies, but in very few rare cases, the existing STI
policy mix is rethought fully. For technology developers and applicants, however, it
is much more important to experience a seamless and consistent sustainable policy
mix; however, the respective actors are often critical and skeptical against considering
changing framework conditions.
The editors wish to express their gratefulness to all authors who contributed to
this volume. The book proves how emerging technologies are identiﬁed, selected,
and evaluated against economic value and impact. Furthermore, the chapters provide
clear strategic intelligence for exploiting emerging technologies in different ﬁelds.
330
D. Meissner et al.
Acknowledgments The book chapter and the whole book were prepared within the framework of
the Basic Research Program at the National Research University Higher School of Economics
and supported within the framework of the subsidy by the Russian Academic Excellence Project
‘5-100’.
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1080/09595238400185281
David P (1990) The dynamo and the computer: an historical perspective on the modern productivity
paradox. Am Econ Rev 80(2):355–361
Dorfman NS (1983) Route 128: The development of a regional high technology economy. Res
Policy 12:299–316
Fu X (2008) Foreign direct investment, absorptive capacity and regional innovation capabilities:
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Fu X, Pietrobelli C, Soete L (2011) Role of foreign technology and indigenous innovation in the
emerging economies: technological change and catching-up. World Dev 39(7):1204–1212
Helpman E, Trajtenberg M (1994) A time to sow and a time to reap: growth based on general
purpose technologies. NBER Working Paper No. 4854. National Bureau of Economic
Research, Cambridge, MA
Jaffe AB, Trajtenberg M, Henderson R (1993) Geographic localization of knowledge spillovers as
evidenced by patent citations. Q J Econ 108(3):577–598
Krugman P (1995) Development, geography and economic theory. MIT Press, Cambridge, MA
Ku YL, Liau S-J, Hsing W-C (2005) The high-tech milieu and innovation-oriented development.
Technovation 25:145–153
Miles I (2016) Twenty years of service innovation research. In: Service innovation. Springer, Japan,
pp 3–34
Miles I, Miozzo M (2015) The globalization of knowledge intensive services. In: The handbook of
global science, technology, and innovation. Wiley, pp 171–190
Miozzo M, Desyllas P, Lee HF, Miles I (2016) Innovation collaboration and appropriability by
knowledge-intensive business services ﬁrms. Res Policy 45(7):1337–1351
Pietrobelli C (1996) Emerging forms of technological cooperation: the case for technology
partnerships–inner logic, examples and enabling environment. In: Science and technology
issues. UNCTAD-United Nations, Geneva
Porter M (1990) The competitive advantage of nations. The Free Press, New York
Porter M (1998) On competition. Harvard Business School Press, Boston, MA
Saxenian A (1981) Silicon chips and spatial structure: the industrial base of urbanization in Santa
Clara County. California Working Paper 345. Institute of Urban and Regional Development,
University of California, Berkeley, March
Walsh ST (2004) Roadmapping a disruptive technology: a case study. The emerging microsystems
and top-down nanosystems industry. Technol Forecast Soc Change 71:161–185
Youtie J, Iacopetta M, Graham S (2008) Assessing the nature of nanotechnology: can we uncover
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15
How to Stimulate Convergence and Emergence of Technologies?
331
Dirk Meissner is Deputy Head of the Laboratory for Eco-
nomics of Innovation at HSE ISSEK and Academic Director
of the Master Program “Governance for STI”. Dr. Meissner
has 20 years experience in research and teaching technology
and innovation management and policy. He has strong back-
ground in policy making and industrial management for STI
with special focus on Foresight and roadmapping, funding of
research
and
priority
setting.
Prior
to
joining
HSE
Dr. Meissner was responsible for technology and innovation
policy at the presidential ofﬁce of the Swiss Science and
Technology Council. Previously he was management con-
sultant for technology and innovation management with
Arthur D. Little. He is member of OECD Working Party on
Technology and Innovation Policy. He is Associate Editor of
Technological
Forecasting
and
Social
Change,
IEEE
Transactions on Engineering Management, Journal of the
Knowledge Economy, member of Editorial Review Board
at Small Business Economics and Journal of Knowledge
Management. He guest edited Special Issues in Industry
and Innovation journal, Journal of Engineering and Technol-
ogy Management, Technological Analysis and Strategic
Management among others.
Leonid Gokhberg is First Vice-Rector of the HSE and also
Director of HSE ISSEK. His area of expertise is statistics and
indicators on STI as well as foresight and policy studies in
this area. He has authored over 400 publications in Russian
and international peer-reviewed journals, monographs, and
university textbooks. Prof. Gokhberg has coordinated dozens
of national and international projects funded by public
agencies, businesses, and international organizations. He
has served as a consultant of the OECD, Eurostat, UNESCO,
and other international and national agencies. Leonid is also
a member of the Global Innovation Index Advisory Board,
the OECD Government Foresight Network, and OECD and
Eurostat working groups and task forces on indicators for
S&T and, as well as steering committees of various presti-
gious international and national initiatives. Prof. Gokhberg is
Editor-in-Chief of the Scopus-indexed scientiﬁc journal
Foresight and STI Governance and editor of the Springer
academic book series Science, Technology, and Innovation
Studies, and participates on the editorial boards of several
other inﬂuential journals. He holds PhD and Dr. of
Sc. degrees in Economics.
332
D. Meissner et al.
Ozcan Saritas is Professor of Innovation and Strategy at
the HSE and editor-in-chief of “Foresight”—the journal of
future studies, strategic thinking and policy. He worked as
Senior Research Fellow at the Manchester Institute of
Innovation Research, The University of Manchester. His
research focuses on innovation and policy research with
particular emphasis on socio-economic and technological
Foresight. With a PhD from the “Foresight and Prospective
Studies Program,” he introduced the “Systemic Foresight
Methodology”, and has produced a number of publications
on the topic. Dr. Saritas has extensive work experience with
the international organizations, including the United Nations,
OECD, and the European Commission. He has been
involved in large scale national, multinational and corporate
research and consultancy projects on sectors including
energy, climate change, agriculture, food, water, transporta-
tion, and ICT among others; published a number of articles in
respected journals; and have delivered keynote speeches in
more than 50 countries across the world. Besides his research
and publication activities, he designs and delivers academic
and executive education courses on Foresight and Strategic
Planning. He has recently co-authored a book, entitled
“Foresight
for
Science,
Technology
and
Innovation”
published by Springer, which has become one of the key
readings in the ﬁeld.

Optimization of Well Oil Rate Allocations in Petroleum Fields
Vassileios D. Kosmidis, John D. Perkins, and Efstratios N. Pistikopoulos*
Centre for Process System Engineering, Department of Chemical Engineering, Imperial College London,
South Kensington Campus, London SW7 2AZ, U.K.
Currently, optimization of oil production operations in offshore fields is based mainly on heuristic
rules and simulation-like tools with limited optimization features. In this work, we present a
novel mixed-integer optimization formulation and an effective solution strategy for daily well
operation and gas-lift allocation of integrated oil and gas production facilities in offshore fields
considering the nonlinear reservoir behavior, the multiphase flow in pipelines, and surface
capacity constraints. On the basis of (i) a degrees of freedom analysis, (ii) well oil rate upper
and lower bounds, and (iii) the approximation of each well model with piecewise linear functions,
an efficient approximate mixed-integer nonlinear program (MINLP) model is proposed. The
resulting model is then solved as a sequence of MILP problems. The accuracy and robustness of
the method is investigated by comparing the results of the exact and approximate models, while
the performance of the algorithm is illustrated in a number of field examples. The results show
that the proposed method achieves an increase in oil production up to 3% when compared with
heuristic rules typically applied in practice.
Introduction
A typical offshore oilfield is shown in Figure 1 and
consists of (i) the reservoir, which is defined as an
accumulation of oil, gas, and water in porous permeable
rock, (ii) the production wells, which transfer reservoir
fluids to the manifold, (iii) the manifold, where the well
streams are mixed, (iv) the surface flow lines, which
connect the manifolds to the surface facilities, and (v)
the surface facilities where the reservoir fluids are
separated into oil, gas, and water and gas is compressed
and injected back to the reservoir. Each well consists
of two pipe segments: the well tubing and the well flow
line. Between them, there is a valve, the choke, which
is used to control the well flow rate. The region of the
well inside the reservoir is called the well bore. The
paper considers two types of production wells:
(i)
naturally flowing and (ii) gas lift. The first are able to
provide flow naturally to the surface, while the second
require an injection of high-pressure gas to reduce the
pressure drop in the well tubing and therefore facilitate
extraction. Finally, the wells, manifolds, flow lines, and
surface facilities define the pipeline network, as shown
in Figure 1.
In petroleum fields, oil production is often constrained
by the reservoir conditions, flow characteristics of the
pipeline network, and the capacity of the surface
facilities.1-3 Consequently, proper determination of the
daily optimal operating conditions requires simulta-
neous consideration of the interactions between the
reservoir, the wells, and the surface facilities.
Various methods for daily oil production optimization
have been presented in the literature. These can be
divided into three categories: (i) sensitivity analysis
using simulation tools, (ii) heuristic methods, and (iii)
mathematical programming methods.
Traditionally, the petroleum industry has applied
NODAL2 analysis,4 which is a simulation method to
determine the daily optimal operating policy by repeti-
tively varying the optimization variables and simulating
the underlying system. Therefore, NODAL analysis is
by its trial and error nature limited to oilfields with a
small number of wells.
The majority of publications5-7 tackle the daily oil
production optimization using heuristic rules that are
incorporated in software tools known as well manage-
ment routines.8 Well management routines decompose
the pipeline network into levels, usually (i) the well
level, (ii) the manifold level, and (iii) the separator level
as shown in Figure 1, and heuristic rules are applied
sequentially. For instance, at the well level, rules such
as close a well if it violates an upper bound in the gas-
oil ratio (GOR), which is defined as the ratio of gas to
oil volumetric flow rate, are applied. At the manifold
level, upper bound constraints on oil, gas, and water
flow rates are imposed. If any of the upper bounds is
violated, the well production rate is scaled appropriately
until the constraint is satisfied. At the separator level,
oil production targets are imposed. If these are not
satisfied, then decisions such as gas-lift initiation are
enabled. It is obvious that well management routines,
while accounting for network constraints, are formu-
lated in an ad hoc manner and do not systematically
address the maximization of oil production. The current
major commercial reservoir simulators9,10 are based on
similar heuristic rules, and gas-lift allocation is consid-
ered separately from well rate optimization. One of the
most widely applied heuristic rules for allocation of gas-
to-gas-lift wells is known as the incremental GOR
(IGOR) rule. IGOR is defined as the amount of gas
required by a gas-lift well to produce an additional
barrel of oil. It was proposed originally by Redden et
al.,11 but Weiss et al.7 derived a necessary condition for
optimal allocation of gas-to-gas-lift wells. The condition
states that all wells tied to a common manifold must
operate at the same IGOR. The IGOR heuristic rule has
also been applied by Barnes et al.12 and Stoisits et al.13
in the Prudhoe Bay and Kuparuk River fields, respec-
tively. However, it must be noted that the necessary
* To whom correspondence should be addressed. Tel.: +44
20 75946620. Fax: +44 20 75946606. E-mail: e.pistikopoulos@
imperial.ac.uk.
3513
Ind. Eng. Chem. Res. 2004, 43, 3513-3527
10.1021/ie034171z CCC: $27.50
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Published on Web 03/24/2004
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condition was derived by analyzing a pipeline network
where all of the wells are tied directly to a fixed-pressure
separator and, therefore, interactions between the wells
that share a common flow line are not considered.
Mathematical programming techniques have also
been applied in production operations optimization.
Carroll and Horne14 applied gradient-based and poly-
tope optimization techniques to a single well. Fujii and
Horne15 proposed a two-stage optimization strategy
where a pipeline network simulator acted as an evalu-
ation tool for a series of suggested values of control
variables produced by an optimization algorithm. Mar-
tinez et al.,16 Palke and Horn,17 and Wang et al.18
applied genetic algorithms (GAs). Although GAs are
robust, they are especially computationally intensive
when the optimization problem is subjected to nonlinear
equality constraints. Fang and Lo2 proposed a linear
programming (LP) model to optimize gas lift subject to
multiple nonlinear flow-rate constraints. Dutta-Roy and
Kattapuram19 analyzed a gas-lift optimization problem
with two wells sharing a common flow line. They pointed
out that when flow interactions among wells are sig-
nificant, nonlinear optimization tools are needed. They
coupled a pipeline network simulator with an optimiza-
tion tool that is based on the sequential quadratic
programming (SQP) method. However, because of com-
mercial confidentiality, there is no information regard-
ing the optimization model. Handley-Schahler et al.20
developed a commercial optimization tool for optimiza-
tion of production operations. The resulting optimization
problem was solved using the sequential LP (SLP)
method. Their tool was applied to a gas production
network and to gas-lift wells that are tied directly to a
separator. However, because of commercial confidential-
ity, there is no information available about the optimi-
zation model. Wang et al.18 proposed a linear program
(LP) and a mixed-integer LP (MILP) for optimization
of production operations. The LP formulation was
similar to that proposed by Lo et al.,21 which is based
on the premise that, for a pipeline network of naturally
flowing wells, when the well chokes are fully open and
the pipeline network is simulated, the resulting well oil
rates are maximum. These well oil rates were used as
upper bounds to the LP formulation that incorporated
only mass balance and surface capacity constraints. The
MILP formulation extended the work of Fang and Lo.2
Both the LP and MILP formulations do not handle flow
interactions among wells that share a common flow line.
Recently, Wang et al.22 proposed a SQP method that is
able to take into account flow interactions among wells
in a treelike structure pipeline network. Del Rio et al.23
Applied spline interpolation to smooth the pressure
gradient surface when mechanistic models24 are used
to predict the pressure drop in a pipeline network. Next,
they applied polytope methods and GAs to determine
the optimal tubing diameter and well oil rate of a single
well.
Usually, in the petroleum industry, the pressure drop
in a production network is computed by multidimen-
sional hydraulic lookup tables,29 which define the inlet
pressure as a function of the outlet pressure and oil,
gas, and water flow rates.29 Therefore, there is a need
to develop a robust and efficient optimization algorithm
that simultaneously optimizes the well oil rates and gas-
lift rates, is able to be integrated with measured data
or hydraulic tables, and is applicable to any pipeline
structure.
In sections 2 and 3, we present the problem statement
and a model for the production operation optimization.
Section 4 contains a motivating example showing the
need for a more efficient solution method. In section 5,
a novel approximate optimization model is presented.
The resulting optimization model is based on a degrees
of freedom analysis, well upper and lower bounds, and
separability programming techniques and is solved as
a sequence of MILP problems. In section 6, two motivat-
ing examples are used to demonstrate the accuracy and
robustness of the proposed formulation. The perfor-
mance of the algorithm is demonstrated in section 7
with three example problems. Conclusions are discussed
in section 8.
2. General Problem Statement
The production optimization problem is based on a
time period of 1 day and can be stated as follows: given
is a reservoir, a set of naturally flowing wells, a set of
gas-lift wells, a surface pipeline network, a set of
separation facilities, and a compressor plant (see Figure
1). The problem involves the maximization of the profit
from the sales of oil minus the cost of gas compression
subject to a set of constraints. The constraints include
Figure 1. Two-well operation system.
3514
Ind. Eng. Chem. Res., Vol. 43, No. 14, 2004
mass and momentum balances in the wells, minimum
and maximum pressure and flow-rate constraints at the
inlet and outlet of the pipelines, maximum oil, gas, and
water handling capacity constraints, and gas compres-
sion constraints. The following key decisions are con-
sidered: (i) how to control the well oil rates of naturally
flowing wells by manipulating appropriately the well
chokes and (ii) how to distribute gas among gas-lift
wells.
3. Mathematical Model
The mathematical model of the system depends on
(i) the type of reservoir such as dry gas and oil and gas
reservoirs and (ii) the type of well such as naturally
flowing and gas lift. In this section, we present the
mathematical model of the two-well network shown in
Figure 1, which consists of a naturally flowing and a
gas-lift well. The two-well network model contains all
of the components that allow modeling of more complex
pipeline networks. The model is based on the following
assumptions:
(i) The system is under steady-state conditions be-
cause the dynamics of the reservoir is in order of weeks,
that of the pipeline network is in order of minutes, and
the time horizon of the production operation problem
is 1 day.
(ii) The thermodynamic description of the reservoir
fluids is based on the black oil model,24 an empirical
approach widely applied in the petroleum industry.
(iii) A continuous and differentiable multiphase pres-
sure drop model was applied.25
(iv) The energy balance in the pipeline network is
considered, assuming linear temperature profiles along
the pipes.4
For simplicity in the presentation of the paper, we
describe in the following the optimization model of a
two-well network problem as shown in Figure 1.
(i) A well inflow model that describes the multiphase
fluid flow from the reservoir to the well bores of a
production well. In this work, Peaceman’s26 well model
was applied. This model assumes steady-state radial
flow and can be expressed as
where I is the set of wells i, qo,i is the well volumetric
oil rate under stock tank conditions, PIi is the produc-
tivity index of the well, PR,i is the reservoir block
pressure that contains the well and can be assumed
constant for a time period of 1 day, and Pi(L0) is the
bottom hole pressure of the well i. For naturally flowing
wells, the gas flow rate qg,i under stock tank conditions
is given by the relation
where N is the set of naturally flowing wells. For gas-
lift wells, the gas flow rate is given by
where G is the set of gas-lift wells and qg,i
inj is the gas
injection rate. The water qw,i and liquid qL,i flow rates
are given by
where WORi is the water oil ratio of well i defined as
the ratio of water to oil flow rate.
The productivity index PIi, GORi, and WORi are
generally nonlinear functions of the well oil flow rate
qo,i. However, they may be assumed constant for a
period of 1 day, with their values given from a reservoir
simulator.
(ii) The well tubing model simulates the multiphase
fluid flow from the well completion to the wellhead of a
production well and is described by the following
ordinary differential equation (ODE), which is valid at
L ∈[L0, Lch):
(iii) The well choke model is used to control the well
flow rate. The characteristic of the choke valve is the
transition between the critical and subcritical flow
regimes. A generic choke model is as follows:
where di is the choke valve diameter, Pi(Lch
- ) and
Pi(Lch
+ ) are the pressures upstream and downstream of
the choke, yi is the well choke pressure ratio, and yc is
the critical pressure ratio, which depends on upstream
conditions. In this paper, the choke model proposed by
Sachdeva et al.27 is used because it satisfies continuity
at the transition point. The choke model is not dif-
ferentiable because of the existence of the max operator,
which is approximated using the smoothing function
proposed by Samsatli et al.28
(iv) The well flow-line model simulates the multiphase
fluid flow from the wellhead to the manifold and is
described by an ODE similar to eq 6 but valid at L ∈
[Lch, Lm). The combination of the well inflow, well
tubing, choke, and well flow-line models will be refered
to in the rest of the paper as the well model.
(v) The manifold model is where the well streams are
mixed. The mass balance for each phase p is given by
the linear equation
where Im is the set of the wells tied to the manifold m
and qp,m is the volumetric flow rate of phase p in
manifold m. Moreover, any network point must have
unique pressure. Therefore,
where Pi(Lm) is the pressure of the well i at the manifold
level and Pm(Lm) is the manifold pressure.
(vi) The surface flow-line model simulates the multi-
phase flow from the manifold to the surface facilities
and is described by the following ODE valid at L ∈[Lm,
Ls]:
qo,i ) PIi[PR,i - Pi(L0)],
∀i ∈I
(1)
qg,i ) GORiqo,i,
∀i ∈N
(2)
qg,i ) qg,i
inj + GORiqo,i,
∀i ∈G
(3)
qw,i ) WORiqo,i,
∀i ∈I
(4)
qL,i ) qo,i + qw,i,
∀i ∈I
(5)
dPi
dL ) fP(Pi,qo,i,qg,i,qw,i),
∀i ∈I, L ∈[L0, Lch) (6)
qL,i ) fc[di,Pi(Lch
- ),Pi(Lch
+ ),yi,GORi,WORi],
∀i ∈I (7)
yi ) max(yc,Pi(Lch
- )/Pi(Lch
+ )),
∀i ∈I
(8)
∑
i∈Im
qp,i ) qp,m,
∀m ∈M, p ∈{o, g, w, L}
(9)
Pi(Lm) ) Pm(Lm),
∀m ∈M, i ∈Im
(10)
dPm
dL ) fP(Pm,qo,m,qg,m,qw,m),
∀m ∈M, L ∈[Lm, Ls]
(11)
Ind. Eng. Chem. Res., Vol. 43, No. 14, 2004
3515
(vii) The separator s, which operates at pressure Ps
and has capacity Cp,s for each phase p
where Ms is the set of manifold flow lines m that are
connected to separator s, S is the set of separators, and
Pm(Ls) is the pressure of the surface flow line at the
separator level.
(viii) Operational constraints involve (a) well oil rate
lower bounds to satisfy stable flow rate,4 (b) well oil rate
upper bounds to prevent sand production, (c) lower
bound pressure constraints at the inlet of the flow lines
to prevent development of erosional velocities, (d) choke
diameter design constraints, and (e) upper bound con-
straints on gas-lift availability.
(ix) An objective function is defined as maximization
of the profit from the sales of oil minus the cost of gas
compression
where the coefficients wo and wg are the price of oil and
gas compression, respectively.
The mathematical model of the system involves (i)
ODEs that apply over a particular length interval or
stage30 and (ii) boundary conditions between the stages.
Considering, for instance, the two-well network of
Figure 1, then the first stage of the system is defined
by the well tubing model (eq 6) with boundary conditions
described by the well inflow model (eqs 1-5). The second
stage of the system is defined by the well flow-line model
(eq 6) with boundary conditions imposed by the choke
model (eqs 7 and 8). Finally, the third stage of the
system is described by the surface flow-line model (eq
11) with boundary conditions imposed by the separator
pressure (eq 12). Consistent initialization of the system
of ODEs that describe the first stage of a naturally
flowing well involves the solution of the nonlinear
equations shown in Table 1. When the number of
equations and unknowns are counted in Table 1, it is
observed that the first stage of a naturally flowing well
has only 1 degree of freedom. When the well oil rate qo,i
is selected as the degree of freedom and its value is
specified, the nonlinear system of equations in Table 1
becomes well posed and its solution provides initial
conditions for the integration of the ODE for the first
stage. Therefore, the first stage of a naturally flowing
well model can be replaced by the algebraic equation
where n(‚) is a functional relation whose closed form is
unknown but whose value can be determined after
initialization and integration of the corresponding ODE.
Initialization of the second stage involves the system
of nonlinear equations shown in Table 2. When the
number of equations and the number of unknowns are
counted, it is observed that the second stage adds 1
extra degree of freedom to the system. The choke
diameter di is selected as the degree of freedom because,
in practice, this is the control variable of a well. In
summary, when qo,i and di are selected as the degrees
of freedom, the ODEs in the first and second stages can
be integrated from the well bore up to the manifold.
Then, the pressure of the well at the manifold level
Pi(Lm) is calculated from the following algebraic equa-
tion:
Consistent initialization of the system that describes the
first stage of a gas-lift well involves the solution of the
nonlinear equations shown in Table 3. When the
number of equations and unknowns in Table 3 are
counted, it is observed that the first stage of gas-lift well
has 2 degrees of freedom. Consistent initialization of the
second stage involves the solution of the following
Table 1. Consistent Initialization of the First Stage of a
Naturally Flowing Well
equation
unknown
1. qo,i ) PIi(PR,i - Pi(L0))
qo,i, Pi(L0)
2. qg,i ) GORiqo,i
qg,i
3. qw,i ) WORiqo,i
qw,i
4. qL,i ) qo,i + qw,i
qL,i
5.
dPi(L0)
dL
) fP(Pi(L0),qo,i,qw,i,qg,i)
dPi(L0)
dL
no. of equations: 5
no. of unknowns: 6
degrees of freedom: 1
Pm(Ls) ) Ps,
∀s ∈S, m ∈Ms
(12)
∑
m∈Ms
qp,m e Cp,s,
∀p, s ∈S
(13)
qo,i
L e qo,i e qo,i
U ,
∀i ∈I
(14)
Pm
L e Pm(Lm),
∀m ∈M
(15)
0 e di e 1,
∀i ∈I
(16)
∑
i∈G
qg,i
inj e C
(17)
max wo∑
i∈I
qo,i - wg∑
i∈G
qg,i
inj
(18)
Table 2. Initialization of the Second Stage of a Naturally
Flowing Wella,b
equation
unknown
qL,i ) fc[di,Pi(Lch
- ),Pi(Lch
+ ),yi,GORi,WORi]
di, Pi(Lch
+ ), yi
yi ) max[yc,Pi(Lch
- )/Pi(Lch
+ )]
dPi(Lch
+ )
dL
) fP[Pi(Lch
+ ),qo,i,qw,i,qg,i]
dPi(Lch
+ )
dL
no. of equations: 3
no. of unknowns: 4
degrees of freedom: 1
a qo,i, qw,i, qg,i, and yc were calculated from initialization of the
first stage. b Pi(Lch
- ) was calculated from integration of the first
stage.
Table 3. Consistent Initialization of the First Stage of a
Gas-Lift Well
equation
unknown
qo,i ) PIi[PR,i - Pi(L0)]
qo,i, Pi(L0)
qg,i ) GORiqo,i + qg,i
inj
qg,i, qw,i
qw,i ) WORiqo,i
qg,i
qL,i ) qo,i + qw,i
qL,i
dPi(L0)
dL
) fP[Pi(L0),qo,i,qw,i,qg,i]
dPi(L0)
dL
no. of equations: 5
no. of unknowns: 7
degrees of freedom: 2
Pi(Lch
- ) ) n(qo,i),
∀i ∈N
(19)
Pi(Lm) ) n(qo,i,di),
∀i ∈N
(20)
3516
Ind. Eng. Chem. Res., Vol. 43, No. 14, 2004
system of nonlinear equations:
The unknowns of the system of equations (21)-(23) are
di, Pi(Lch
+ ), yi, and dPi(Lch
+ )/dL. However, gas-lift wells
either do not have choke valves or, if they have the
choke, do not operate, and therefore their settings (di)
are fixed. In both cases, the second stage of a gas-lift
well does not add any extra degrees of freedom. In
summary, a gas-lift well model has 2 degrees of free-
dom: one can select the well oil rate and the other the
gas injection rate. Thus, the pressure of a gas-lift well
at the manifold level Pi(Lm) is calculated by the following
algebraic equation:
Initialization of the third stage involves the solution of
the system of nonlinear equations shown in Table 4.
When the number of equations and the number of
unknowns are counted, it is observed that the third
stage has 4 degrees of freedom. Selection of fluid flow
rates qp,m and manifold pressure Pm(Lm) allows the
integration of the ODEs for the third stage from the
manifold to the separator. Then, the pressure of the
surface flow line at the separator level is calculated from
the following relation:
When the above developments are taken into account,
the optimization formulation for the two-well network
can be written as follows:
However, formulation (P1) can cause severe computa-
tional difficulties when the choke operates in the critical
flow regime because in this case the choke upstream
pressure Pi(Lch
+ ) becomes independent of the well flow
rate and the nonlinear system of equations in Table 2
becomes infeasible. Mathematically, this implies that
the Jacobian matrix of the nonlinear system of equa-
tions in Table 2 becomes rank deficient. A way to avoid
this difficulty is to replace the choke model with a
positive pressure drop. The rational behind this is the
following. The choke valve is used to control the well
flow rate by creating a suitable pressure drop in the well
tubing. Therefore, the choke upstream pressure is the
new degree of freedom that can replace the choke
diameter. With this, (P1) becomes
Table 4. Initialization of the Third Stage
equation
unknown
dPm(Lm)
dL
) fP[Pm(Lm),qo,m,qw,m,qg,m]
dPm(Lm)
dL
, Pm(Lm), qo,m, qw,m, qg,m
no. of equations: 1
no. of unknowns: 5
degrees of freedom: 4
qL,i ) fc[di,Pi(Lch
- ),Pi(Lch
+ ),yi,GORi,WORi],
∀i ∈G
(21)
yi ) max[yc,Pi(Lch
- )/Pi(Lch
+ )],
∀i ∈G
(22)
dPi(Lch
+ )
dL
) fP[Pi(Lch
+ ),qo,i,qw,i,qg,i],
∀i ∈G
(23)
Pi(Lm) ) g(qo,i,qg,i
inj),
∀i ∈G
(24)
Pm(Ls) ) f[Pm(Lm),qo,m,qg,m,qw,m]
(25)
max wo∑
i∈N
qo,i - wg∑
i∈G
qg,i
inj
s.t.
Pm(Ls) ) f[Pm(Lm),qo,m,qg,m,qw,m],
∀m ∈M
qp,m ) ∑
i∈Im
qp,i,
∀p, m ∈M
Pm(Lm) ) Pi(Lm),
∀m ∈M, i ∈Im
Pm(Ls) ) Ps,
∀s ∈S, m ∈Ms
Pm
L e Pm(Lm),
∀m ∈M
∑
i∈G
qg,i
inj e C
Pi(Lm) ) g(qo,i,qg,i
inj),
∀i ∈G
qw,i ) WORiqo,i,
∀i ∈I
qL,i ) qo,i + qw,i,
∀i ∈I
qg,i ) GORiqo,i,
∀i ∈N
qg,i ) GORiqo,i + qg,i
inj,
∀i ∈G
qo,i
L e qo,i e qo,i
U ,
∀i ∈I
0 e di e 1,
∀i ∈I
(P1)
max wo∑
i∈N
qo,i - wg∑
i∈G
qg,i
inj
s.t.
Pm(Ls) ) f[Pm(Lm),qo,m,qg,m,qw,m],
∀m ∈M
qp,m ) ∑
i∈Im
qp,i,
∀p, m ∈M
Pm(Lm) ) Pi(Lm),
∀m ∈M, i ∈Im
Pm(Ls) ) Ps,
∀s ∈S, m ∈Ms
Pm
L e Pm(Lm),
∀m ∈M
∑
i∈G
qg,i
inj e C
Pi(Lm) ) n[qo,i,Pi(Lch
+ )],
∀i ∈N
Pi(Lm) ) g(qo,i,qg,i
inj),
∀i ∈G
qw,i ) WORiqo,i,
∀i ∈I
qg,i ) GORiqo,i,
∀i ∈N
qg,i ) GORiqo,i + qg,i
inj,
∀i ∈G
qo,i
L e qo,i e qo,i
U ,
∀i ∈I
(P2)
Ind. Eng. Chem. Res., Vol. 43, No. 14, 2004
3517
Formulation (P2) may again not be robust for the
following reasons:
(i) When a well is weak and cannot flow given the
manifold pressure selected by the optimizer, the well
integration becomes infeasible and (P2) will fail to
converge because the objective function and the con-
straints cannot be evaluated.
(ii) Some gas-lift wells known as noninstantaneous
cannot flow without the appropriate amount of gas lift.
In this case, (P2) will fail to converge because the well
integration becomes infeasible.
(iii) Multiple solutions exist in multiphase network
problems, and not all of them are stable.
The above limitations can be avoided by implementing
a preprocessing step where weak, noninstantaneous
gas-lift wells and unstable solutions are identified and
treated appropriately prior to the solution of the opti-
mization problem.
4. Separable Programming for Optimization of
Production Operations
Fang and Lo2 applied separability programming
techniques to the optimization of production operations.
To simplify the optimization problem, they assumed
that (i) the manifold pressure is constant and known,
(ii) the gas-lift wells respond instantaneously to gas lift,
(iii) the GOR and WOR of each well are constant, and
(iv) the multiphase flow network model has a unique
solution.
Because a naturally flowing well has 2 degrees of
freedom, when the manifold pressure is fixed and the
choke is fully open, the well model equations can be
solved from the well bore up to the manifold. Moreover,
the resulting oil flow rate is the maximum because the
choke was set fully open and any oil flow rate less than
the maximum can be achieved by reducing the choke
diameter. In the case of gas-lift wells, when the manifold
pressure is fixed, the oil flow rate becomes a function
of the gas injection rate, as can be seen by eq 24. Figure
2 shows a graphical representation of the oil and gas
flow-rate relations for a naturally flowing and gas-lift
well. Fang and Lo2 approximated the gas-oil relation
for gas-lift wells with piecewise linear functions. First,
the gas injection rate was discretized at j points for each
well i, qg,i,j
inj,d, and the oil flow rate qo,i,j
d
was calculated
after simulation of each well for each discrete value of
the gas injection rate. Then the gas-oil relation was
approximated with piecewise linear functions using the
following constraints:31
where J is the set of discrete points j, |J| is the
cardinality of the set J, and λi,j are positive variables,
known as a special order set (SOS), where at most two
must be adjacent. The adjacency condition is imposed
with the binary variables yi,j (see Appendix A).
Because of the concavity property of the gas-oil
relations as shown in Figure 2, the adjacency condition
can be automatically satisfied without using binary
variables yi,j and the problem can be formulated as a
LP problem:2
However, the oil-gas relation for a noninstantaneous
gas-lift well is not concave as shown in Figure 3, where
the well starts flowing only after a certain amount of
gas is injected. In this case, the adjacency condition
must be imposed using binary variables and the for-
mulation (P3) becomes a MILP problem, which can be
solved by a standard branch and bound method.32 (P3)
ignores the network constraints from the manifold to
the surface facility, which could lead to suboptimal or
even infeasible solutions depending on the value se-
lected for the manifold pressure. Next, we propose an
optimization formulation, which extends the work of
Fang and Lo,2 takes into account the interactions among
wells when allocating well oil rates and gas-lift rates,
Figure 2. Oil-gas relation for a naturally flowing and gas-lift
well.
qo,i ) ∑
j
λi,jqo,i,j
d
qg,i
inj ) ∑
j
λi,jqg,i,j
inj,d
∑
j
λi,j ) 1
λi,j g 0
λi,j, SOS
}
∀i ∈G
(26)
max wo∑
i∈I
qo,i - wg∑
i∈G
qg,i
inj
s.t.
∑
i
qp,i e Cp,
∀p
qo,i ) ∑
j
λi,jqo,i,j
d
qg,i
inj ) ∑
j
λi,jqg,i
inj,d
∑
j
λi,j ) 1
λi,j g 0
qg,i ) GORiqo,i + qg,i
inj}
∀i ∈G
qo,i e qo,i
max
qg,i ) GORiqo,i}
∀i ∈N
(P3)
qw,i ) WORiqo,i
qL,i ) qw,i + qo,i
qo,i
L e qo,i e qo,i
U }
∀i ∈I
3518
Ind. Eng. Chem. Res., Vol. 43, No. 14, 2004
and overcomes the computational difficulties of formu-
lation (P2). The algorithm is based on (i) well oil rate
upper and lower bounds and (ii) the approximation of
each well with piecewise linear functions.
4.1. Well Oil Rate Upper and Lower Bounds and
Piecewise Linear Approximation. Because a natu-
rally flowing well has 2 degrees of freedom, when the
manifold pressure is discretized at j points Pm,j
d
and the
choke of each well i that is connected to the manifold m
is fully open, the maximum well oil rate qo,i,j
max,d is
determined for each discrete value of the manifold
pressure after solution of the well model. If for a given
value of manifold pressure Pm,j
d
the well model is
infeasible, qo,i,j
max,d is equal to zero. The resulting flow
rate is the maximum because the well was set fully open
and any flow rate qo,i can be achieved by reducing the
choke diameter. Figure 4 shows a typical schematic
representation of the maximum well oil rate qo,i
max as a
function of the manifold pressure. The maximum well
oil rate qo,i
max can be approximated with piecewise linear
functions by applying the following constraints:
Constraint equation (27) sets an upper bound on
the well flow rate for any manifold pressure. Next,
well oil rate lower bounds for stable flow are deter-
mined. Lea and Tighe33 proposed the following criterion
for stability:
Condition equation (28) states that the partial deriva-
tive of the bottom hole pressure of the well tubing with
respect to the well oil flow rate must be nonnegative.
A typical form of the bottom hole pressure as a func-
tion of the well oil rate is shown in Figure 5. The
stability condition is considered by adding a lower bound
in the well oil rate. Because a gas-lift well i has 2
degrees of freedom, by discretization of the manifold
pressure at j points Pm,j
d
and the gas injection rate of
each well at k points qg,i,k
inj,d, the well oil flow rate
qo,i,j,k
d
can be calculated by solving the well model
equations for each grid point (j, k). If the simulation of
the gas-lift well is infeasible, then the corresponding
well oil rate qo,i,j,k
d
is set equal to zero. Therefore, the
well oil flow rate qo,i for each gas-lift well can be
approximated by the following piecewise linear rela-
tions:
Figure 6 depicts the oil flow rate as a function of
the manifold pressure and gas injection rate for a
gas-lift well. When the above developments are taken
into account, the mathematical programming formula-
tion for optimization of production operations can be
Figure 3. Oil-gas relation of a noninstantaneous gas-lift well.
Figure 4. Piecewise linear approximation of the maximum well
oil rate in naturally flowing wells.
qo,i ) ∑
j ∑
k
µi,j,kqo,i,j,k
d
qg,i
inj ) ∑
j ∑
k
µi,j,kqg,i,j,k
inj,d
Pm ) ∑
j ∑
k
µi,j,kPm,j
d
∑
j ∑
k
µi,j,k ) 1
ηi,j ) ∑
k
µi,j,k
êi,k ) ∑
j
µi,j,k
úi,t ) ∑
j
µi,j,j+t
ni,j, êi,k, úi,t, SOS
}
∀m, ∀i ∈Im
(29)
qo,i
max ) ∑
j
λm,jqo,i,j
max,d
Pm ) ∑
j
λm,jPm,j
d
Pm
L e Pm e Pm
U
qo,i e qo,i
max
∑
j
λm,j ) 1
λm,j, SOS
}
∀m, ∀i ∈Im
(27)
∂Pi(Lo)
∂qo,i
g 0,
∀i ∈I
(28)
Ind. Eng. Chem. Res., Vol. 43, No. 14, 2004
3519
written as follows:
(P4) does not ignore any constraint of the original
formulation (P1), and the manifold pressure (Pm) is an
optimization variable and is not fixed to an arbitrary
value.2,18 Thus, (P4) takes into account the interactions
among wells tied to a common manifold. However, the
cost for taking into account the interactions among wells
tied to a common manifold is the approximation of a
gas-lift well model by a 2D curve as shown in Figure 6
instead of a 1D curve as shown in Figure 2. The math-
ematical programming formulation (P4) involves non-
linear equations and 0-1 binary variables; therefore,
it belongs to the class of MINLP34 problems. Moreover,
it has two characteristics: (i) the number of nonlinear
equality constraints is equal to the number of surface
flow lines and independent of the number of wells, and
(ii) the binary variables are used to linearize nonlinear
constraints. The most efficient way to solve (P4) is a
sequence of MILP problems following an SLP method.
5. Solution Procedure
SLP methods solve nonlinear optimization program-
ming problems via a sequence of LP problems. Since
their introduction by Griffith and Stewart,35 many
variants of SLP algorithms have appeared in the
literature.36-38 In this work, the penalty successive LP
algorithm of Zhang et al.37 was applied. The formulation
(P4) can be written in compact form as follows (P5):
max wo∑
i∈N
qo,i - wg∑
i∈G
qg,i
inj
s.t.
Pm(Ls) ) f[Pm(Lm),qo,m,qg,m,qw,m]
qp,m ) ∑
i∈Im
qp,i,
∀p
qp,m e Cp,m,
∀p, m
Pm(Ls) ) Ps,
∀s, m
Pm
L e Pm(Lm),
∀m
∑
i∈G
qg,i
inj e C
qo,i
max ) ∑
j
λm,jqo,i,j
max,d
Pm ) ∑
j
λm,jPm,j
d
qo,i e qo,i
max
∑
j
λm,j ) 1
λm,j, SOS
qo,i e qo,i
max
qg,i ) GORiqo,i }
∀m, ∀i ∈N
qo,i ) ∑
j ∑
k
µi,j,kqo,i,j,k
d
qg,i
inj ) ∑
j ∑
k
µi,j,kqg,i,j,k
inj,d
Pm ) ∑
j ∑
k
µi,j,kPm,j
d
∑
j ∑
k
µi,j,k ) 1
ηi,j ) ∑
k
µi,j,k
êi,k ) ∑
j
µi,j,k
úi,t ) ∑
j
µi,j,j+t
ni,j, êi,k, úi,t, SOS
}
∀m, ∀i ∈G
qw,i ) WORiqo,i
qL,i ) qo,i + qw,i
qo,i
L e qo,i e qo,i
U }
∀i ∈I
(P4)
Figure 5. Well stable region.
Figure 6. Piecewise linear approximation of a gas-lift well.
max f(x)
(30)
s.t.
h(x) ) 0
(31)
Ax + By e c
(32)
xL e x e xU
(33)
3520
Ind. Eng. Chem. Res., Vol. 43, No. 14, 2004
where x is the vector of continuous variables, y is the
vector of binary variables, f(x) is the linear objective
function of (P4), h(x) is the nonlinear equality con-
straints of (P4), which represent the surface flow-line
model, and constraint (32) represents the mixed-integer
linear constraints, which approximate the well models.
The algorithm of Zhang et al.37 involves the following
steps:
1. Initialization. Set iteration counter l ) 1 and select
a starting point xl after deleting the nonlinear con-
straints [h(x)] in (P5) and solving the correspond-
ing MILP problem. Select a large positive weight φ
and scalars 0 < F0 < F1 < F2 < 1 (typically F0 ) 10-6,
F1 ) 0.25, and F2 ) 0.75). Let ∆l be a bound vector on
continuous variables and ∆LB > 0 its lower bound.
2. MILP Subproblem. Solve the following MILP to
obtain a new point x:
where r is the number of nonlinear equality constraints
in (P4) and zr
+ and zr
- are nonnegative variables.
3. The trust region method39 is used to determine
whether the new point x should be accepted or rejected.
First, ∆FE,l and ∆FEL,l are calculated
where
FE(x) is an exact penalty function, and FEL(x) is a first-
order Taylor approximation of FE(x).
If ∆FEL,l ) 0 or
stop; the optimum is found. Otherwise, compute the
ratio Rl ) (∆FE,l/∆FEL,l).
(i) If Rl < F0, reject the current solution x and shrink
∆l+1 to 0.5∆l.
(ii) If F0 < Rl < F1, accept the current solution x, shrink
∆l+1 to 0.5∆l, and set counter l ) l + 1.
(iii) If F1 < Rl < F2, accept the current solution x and
set counter l ) l + 1.
(iv) If Rl > F2, accept the current solution x, ∆l+1 )
2∆l, and set counter l ) l + 1.
Go to step 2.
Remarks
1. The MILP subproblems were solved in ILOG
CPLEX 8.142 on an Intel 4 1.8 GHz machine.
2. To find a near-global optimal solution, the relative
optimality criterion of MILPs was set 0.0001%, while
the convergence criterion for the SLP, which is defined
by eq 38, was set equal to ϵ ) 10-3.
3. The adjacency condition can also be enforced in a
Simplex type LP solver39 with restricted basis entry
rule, which allows only adjacent SOS variables to enter
the Simplex tableau.
4. A similar algorithm was proposed by Bullard and
Biegler38 but in the context of solving nonsmooth
nonlinear systems of equations, where the nonsmooth
equations were handled using binary variables and the
nonlinear system of equations was solved with an SLP
method.
The solution procedure for the optimization of produc-
tion operations involves the following steps:
(i) Preprocessing step, where the reservoir block
pressure PR,i, the productivity index PIi, the GORi, and
the WORi of each well are extracted from a reservoir
simulator and each well model is approximated by
piecewise linear functions. The following method is
developed to construct piecewise linear approximations
of each well model. The method utilizes hydraulic look-
up tables. First, the choke valve model of each naturally
flowing well is set fully open and the well tubing model,
choke model, and well flow-line model are integrated
for different discrete values of the manifold pressure,
oil rate, gas rate, water rate, and gas injection rate, and
the bottom hole pressure is stored in tabular form. Then
using linear interpolation between the well inflow model
and the tabulated bottom hole pressure, the maximum
well oil rate is calculated as a function of the manifold
pressure (eq 27) for the case of naturally flowing wells,
while for the case of gas-lift wells, the oil rate is
calculated for each discrete value of the manifold
pressure and gas injection rate for gas-lift wells (eq 29).
The well oil rate bound for stable flow can be extracted
from the bottom hole table. It must be noted that the
cost of constructing each well model is minimal. How-
ever, the accuracy of the approximation depends on the
number of discrete points used to construct look-up
tables. The computational cost of constructing look-up
tables is not included in the optimization run because
a preprocessing tool has been used for their construc-
tion.
(ii) Processing step, which involves the solution of (P4)
as described previously.
(iii) Postprocessing step, which involves the determi-
nation of each well choke setting. The well choke setting
is determined by fixing the manifold pressure, the well
oil rate, and the gas injection rate at the values
calculated from step ii and solving the corresponding
well model.
6. Accuracy and Robustness of the Formulation
The accuracy and robustness of the optimization
formulation (P4) has been investigated by comparing it
with the exact optimization formulation (P2) in two
example problems. The first example involves the two-
well network shown in Figure 1, where the reservoir
fluid is a dry gas. The corresponding exact optimization
max f(x) - φ[∑
r
(zr
+ - zr
-)]
s.t.
zr
+ - zr
- ) hr(xl) + ∇hr(xl) (x - xl)
Ax + By e c
xL e x e xU
-∆l e x - xl e ∆l
zr
+, zr
- g 0
(P6)
∆FE,l ) FE(xl) - FE(x)
(34)
∆FEL,l ) FE(xl) - FEL(x)
(35)
FE(xl) ) f(xl) + φ[∑
r
|hr(xl)|]
(36)
FEL(x) ) f(xl) + ∇f(xl) (x - xl) + φ∑
r
|hr(xl) +
∇hr(xl) (x - xl)| (37)
|f(x) - f(xl)| < ϵ(1 + |f(xl)|)
(38)
Ind. Eng. Chem. Res., Vol. 43, No. 14, 2004
3521
formulation is given in Appendix B. The reservoir block
pressure, the productivity index, the well design pa-
rameters, and the separator pressure and capacity are
summarized in Table 5. The proposed algorithm was
applied. First, the manifold pressure was discretized
from 1800 to 2300 psia every 100 psia and the corre-
sponding maximum gas well rates are summarized in
Table 6. The objective function was maximization of gas
production, and the resulting MINLP problem involved
one nonlinear equation because the system has one flow
line and five binary variables. The MILP subproblems
were solved using CPLEX,42 and the optimality criterion
was set equal to 0.0001%. Finally, the well choke
settings were calculated by simulating each well after
fixing the manifold pressure and the well gas rate to
the optimal values. The choke settings are presented
in Table 7, where it is observed that both wells have
their chokes fully open. Then the exact optimization
formulation (P2) was solved using the SQP solver of
MATLAB.40 The values of the objective function, mani-
fold pressure, and gas well rates calculated from ap-
plication of both methods are summarized in Table 8.
The results suggest that the proposed method is very
accurate. In Table 9, the computational performance,
of both methods in terms of the number of iterations
and function evaluations are reported. The results
suggest that the SQP method requires more iterations
and function evaluations compared to the proposed
method. This is due to the fact that linearization of
highly nonlinear equations is likely to be poor even
relatively close to the linearization point. This leads to
successive solutions being very close together, which
potentially slows the convergence. The case is depicted
in Figure 7 where the manifold pressure is linearized.
In addition, the increased number of function evalua-
tions can be explained by the fact that the exact optim-
ization formulation has three nonlinear constraints
while the approximate formulation has only one.
The second example involves the pipeline network
shown in Figure 8. The network consists of four dry gas
wells and four flow lines, and the gas deliverability
pressure was set equal to 1300 psia. The reservoir and
well data are summarized in Table 10. The objective
function was maximization of gas production, and the
resulting MINLP problem involved 4 nonlinear equality
constraints and 20 binary variables. The optimality
condition for the MILP subproblems was set equal to
0.0001% and was solved with CPLEX.42 The proposed
method converged to the optimal solution after five
iterations. The optimal point is presented in Table 11,
where it is observed that well 4 is closed. The exact
optimization formulation (P2) failed to converge because
well 4 could not flow to the manifold pressure selected
Table 5. Reservoir, Well, and Separator Data for the
Two-Well Dry Gas Network
well 1
well 2
reservoir pressure (psia)
3660
3000
a, b coefficientsa
793, 0.004
700, 0.003
well vertical length (ft)
5000
5000
well horizontal length (ft)
2000
2000
flow-line length (vertical) (ft)
2000
pressure (psia)
capacity (lbm/s)
separator
1500
10
a a and b reservoir deliverability coefficients.
Table 6. Interpolation Data for the Two-Well Dry Gas
Network
manifold pressure (psia)
well 1 (lbm/s)
well 2 (lbm/s)
1800
4.97
2.80
1900
4.65
2.38
2000
4.30
1.92
2100
3.92
1.43
2200
3.52
0.91
2300
3.10
0.35
Table 7. Choke Settings for the Two-Well Dry Gas
Network
choke setting
(% open)
choke setting
(% open)
well 1
100
well 2
100
Table 8. Optimal Values of Both Methods in the
Two-Well Dry Gas Network
approximate
optimization
exact
optimization
objective value
6.04
6.04
manifold pressure (psia)
2017.4
2017.1
well 1 flow rate (lbm/s)
4.22
4.23
well 2 flow rate (lbm/s)
1.82
1.84
Table 9. Computational Performances of Both Methods
in the Two-Well Dry Gas Network
function evaluations
iterations
exact optimization
180
30
proposed algorithm
12
4
Figure 7. Linearization of a gas well manifold curve.
Figure 8. Four-well dry gas model.
3522
Ind. Eng. Chem. Res., Vol. 43, No. 14, 2004
by the optimizer. It must be noted that the accuracy of
the method has been shown in a number of examples
including naturally flowing and gas-lift wells.
7. Examples
7.1. Naturally Flowing Wells. A field consisting of
10 naturally flowing wells and 2 separators is analyzed.
The pipeline network is depicted in Figure 9, the
reservoir and well data are given in Table 12, and the
operating pressure and capacity of each separator are
given in Table 13. The objective function is maximiza-
tion of oil production and the MILP subproblem involves
20 binary variables, 942 constraints, and 535 continuous
variables. The problem is solved using CPLEX42 with
optimality criterion equal to 0.0001%. The optimal fluid
flow rates in the separators are given in Table 13. The
results of Table 13 suggest that the gas capacity of
separator A is active and the wells with the higher GOR
are choked back, as can be observed in Table 14, where
the GOR and WOR of each well and the corresponding
optimal choke settings are reported. Similarly, the water
capacity of separator B is the bottleneck of the network
as shown by Table 13. The wells with the highest WOR
are operated with choke restrictions, as can be observed
from the results summarized in Table 14. Then the LP
method proposed by Wang et al.18 was applied to solve
the naturally flowing well network. Their algorithm
consists of the following steps:
(i) The well chokes are set fully open, and the
corresponding network is simulated without considering
the separator capacity limits.
(ii) The oil flow rates calculated from the previous step
become upper bounds, which along with separator
handling limits and mass balances on the node consti-
tute the LP formulation whose solution determines the
optimal operating policy.
The results of the LP method are summarized in
Table 13. The results of Table 13 suggest that the
proposed method produces 94 more barrels of oil per day
compared to the LP method. The reason is that
setting the well chokes fully open does maximize oil
production.
7.2. Gas-Lift Well Example. A field consisting of 13
gas-lift wells and 2 separators was analyzed. The
pipeline network is depicted in Figure 10, and the
reservoir, well, and separator data are given in Table
15. The gas-oil relation for each gas-lift well was
constructed by discretizing the manifold pressure be-
tween 300 and 800 psia every 100 psia and the gas
injection rate at 0, 250, 500, 1000, 2000, 3000, 4000,
5000, 6000, 7000, and 8000 Mscf/day. The computa-
tional time for the preprocessing step, which involves
the approximation of all wells, is 7.2 s on an Intel 4 1.8
GHz machine The objective function was maximization
Table 10. Reservoir, Well, and Separator Data for the
Four-Well Dry Gas Model
well 1
well 2
well 3
well 4
flow
lines
reservoir
pressure
(psia)
3660
3000
2500
2300
a, b coefficientsa 793, 0.004 700, 0.003 650, 0.005 600, 0003
well vertical
length (ft)
5000
5000
5000
5000
well horizontal
length (ft)
2000
2000
2000
2000
8000
diameter (in.)
2.5
2.5
2.5
2.5
3.0
pressure (psia)
separator
1300
a a and b reservoir deliverability coefficients.
Table 11. Optimal Values of the Four-Well Dry Gas
Model
well flow rate
(lbm/s)
pressure
(psia)
well 1
4.680
manifold 1
1922.1
well 2
2.446
manifold 2
1817.5
well 3
0.982
manifold 3
1526.9
well 4
0.0
manifold 4
1300.0
Figure 9. Naturally flowing well example.
Table 12. Reservoir and Well Data for the Naturally
Flowing Wells Example
reservoir
pressure
(psia)
PI
(stb/psia)
horizontal
(ft)
vertical
(ft)
diameter
(in.)
well 1
1720
8.0
2000
3400
4.0
well 2
1700
8.0
2000
3000
3.5
well 3
1800
6.0
2000
3000
3.5
well 4
2000
6.0
2500
4000
4.0
well 5
2100
10.0
2500
4000
4.0
well 6
1200
7.0
2500
4000
3.5
well 7
1100
6.9
2500
4000
4.0
well 8
1000
6.0
2000
3000
3.5
well 9
1000
5.0
2000
3000
4.0
well 10
1100
6.0
2500
4000
4.0
flow line A
5000
5.0
flow line B
5000
5.0
Ind. Eng. Chem. Res., Vol. 43, No. 14, 2004
3523
of the profit (eq 18), where the price of the oil was 10$/
barrel and the cost of gas compression was 0.1$/MMscf.
The MILP subproblem involved 348 binary variables,
912 continuous variables, and 1160 constraints, and the
optimality criterion was set equal to 0.0001%. The
algorithm converges to the optimal solution after five
iterations. The optimal well oil rate and gas injection
rate for each well are summarized in Table 16. The
results indicate that the field will produce at total oil
rate of 19904.4 stb/day.
Then the problem is solved by applying the IGOR
heuristic rule as follows:
(i) A realistic manifold pressure is selected, and the
IGOR curve for each manifold is constructed. The
pressure of each manifold was set equal to 575 psia, and
the manifold IGOR curve was constructed as follows.
First, the gas-oil relation for each gas-lift well is
constructed, and then by variation of the value of IGOR,
the manifold oil rate as a function of the gas flow rate
is plotted as shown in Figure 11.
(ii) The optimal IGOR is the one that maximizes oil
production while satisfying separator gas capacity
constraints. From Figure 11, the optimal IGOR is
calculated.
Table 13. Separator Data and Optimal Separator Flow Rates
Sep A
capacity
approximate
method
LP
method
Sep B
capacity
approximate
method
LP
method
oil (barrel/day)
15000
12047.4
11989.1
10000
6738.6
6702.4
water (barrel/day)
5000
2629.3
2446.8
2000
2000
2000
gas (MMscf/day)
25000
25000
25000
18000
8561.8
8658
pressure (psia)
600
300
Table 14. Reservoir and Optimal Well Choke Settings of
the Naturally Flowing Well Example
GOR
(Mscf/stb)
WOR
(stb/stb)
choke settings
(% open)
well 1
2000
0.3
100
well 2
2500
0.1
27.1
well 3
2000
0.7
68.3
well 4
3000
0.3
11.4
well 5
2000
0.01
100
well 6
1000
0.01
100
well 7
1500
0.5
49.5
well 8
2000
0.5
29.4
well 9
1000
0.3
100
well 10
1500
0.5
30.0
Table 15. Reservoir and Well Data for Gas-Lift Well
Example
reservoir
pressure
(psia)
PI
(stb/psia)
horizontal
(ft)
vertical
(ft)
diameter
(in.)
well 1
1920
13
1000
1000
4
well 2
1900
9
1000
2000
4
well 3
1800
6
2500
2700
4
well 4
1720
10
3000
2000
4
well 5
1600
5
2500
2000
4
well 6
1820
3
2500
2500
4
well 7
1420
4
2000
2000
4
well 8
1320
2
2000
3000
4
well 9
1450
3.5
2000
3000
4
well 10
1550
5.5
1500
2000
4
well 11
1720
10
2000
4000
4
well 12
1720
10
2000
1500
4
well 13
1520
8
3000
5000
4
flow line A
5000
6.0
flow line B
5000
6.0
pressure
(psia)
oil
capacity
(stb/day)
gas
capacity
(Mscf/day)
water
capacity
(stb/day)
separator A
300
15000
25000
6000
separator B
300
15000
15000
4000
Figure 10. Gas-lift well pipeline network.
Table 16. Comparison of the Approximate and IGOR
Heuristic Rule in the Gas-Lift Example
approximate method
IGOR method
gas-lift rate
(Mscf/day)
oil rate
(stb/day)
gas-lift rate
(Mscf/day)
oil rate
(stb/day)
well 1
4000
2833.7
2500
2750.2
well 2
3000
2651.1
3000
2734.0
well 3
3000
1539.7
2530
1463.2
well 4
1966
1979.0
2000
1987.2
well 5
2750
1535.33
2500
1550.2
well 6
631
948.4
1200
1153.1
well 7
1500
869.6
1330
761.9
well 8
750
353.2
650
240.87
well 9
500
485.1
1082
710.9
well 10
2250
1584.7
2082
1486.4
well 11
663
1759.3
430
1613.2
well 12
2250
2971.7
1910
2792.6
well 13
500
393.6
560
238.8
total
19904.4
19482.6
3524
Ind. Eng. Chem. Res., Vol. 43, No. 14, 2004
(iii) From the optimal value of the IGOR, the gas-lift
rates are calculated by fixing the IGOR of each well and
the manifold pressure.
(iv) Using the gas-lift rates calculated from the
previous step, the network is simulated and the optimal
well oil rates are determined.
The well oil rates resulting from application of the
IGOR heuristic rule are summarized in Table 16, where
a difference of 422 barrels of oil per day is observed in
favor of the proposed method. The result can be ex-
plained by the fact that the IGOR curves are constructed
by neglecting the interactions between wells sharing a
common flow line because the manifold pressure was
fixed to a rather arbitrary value.
7.3. Treelike Structure Pipeline Network. To
evaluate the computational performance of the algo-
rithm, the pipeline network shown in Figure 12
was solved using the proposed method. The network
consists of 25 wells, where 15 of them are gas-lift wells
(TB01, ..., TB15) and the remaining 10 wells are
naturally flowing (NT01, ..., NT10). The delivery pres-
sure was set equal to 600 psia. Two scenarios of the
problem were solved. In the first scenario, there is an
upper bound on the total gas production, while in the
second, there is an upper bound in the total water
production. The objective in both cases was maximiza-
tion of oil production, and unlimited gas-lift availability
was assumed. The problem involved 612 binary vari-
ables, 1660 continuous variables, and 2133 constraints.
The CPU time for approximating well models is 11.25
s. The total oil production, the number of iterations, and
the CPU time on an Intel 4 1.8 GHz machine are
reported in Table 17. It must be noted that the cost of
solving each MILP subproblem was between 3 and 5 s,
while the rest of the time was consumed in function
evaluations of the pipeline network. In addition, as can
be seen from the results of Table 17, the number of
iterations required by the algorithm to converge was on
the same order of magnitude as that for the previous
examples.
8. Conclusions
In this paper, a new formulation has been proposed
for the optimization of oil and gas production operations.
The new formulation simultaneously optimizes well
rates and gas-lift allocation, is able to handle flow
interactions among wells, and can be applied to difficult
situations where some wells are too weak to flow to the
manifold or require a certain amount of gas lift to flow.
The accuracy, efficiency, and robustness of the formula-
tion have been established by comparison with an exact
optimization formulation that was solved using an SQP
method in a number of examples. The algorithm is
applicable both to treelike structure pipeline networks
and to pipeline networks with loops. Because of the
ability of the algorithm to account for flow interactions,
it will always propose superior operating policies com-
pared to the heuristic rules typically applied in practice.
The proposed optimization method can be used for real-
time production control because all of the variables
required for the construction of a well model can be
measured and the discrete data can be directly incor-
porated in the formulation, a subject currently under
further investigation.
Appendix A
To impose the adjacency condition in the SOS
variables, the following binary variables are intro-
duced:31
where yi,j are binary variables.
Figure 11. Manifold IGOR curves.
Figure 12. Treelike structure pipeline network.
Table 17. Computational Statistics of a Treelike
Structure Pipeline Network
constraint
oil production
(stb/day)
iterarations
CPU
(s)
qg < 110 MMscf/day
55 428
7
47.6
qw < 10 000 stb/day
50 226
5
33.8
λi,1 e yi,1
(A.1)
λi,j e yi,j-1 + yi,j,
j ) 2, ..., |J| - 1
(A.2)
λi,J e yi,J-1
(A.3)
∑
j
yi,j ) 1
(A.4)
Ind. Eng. Chem. Res., Vol. 43, No. 14, 2004
3525
Appendix B: Dry Gas Exact Optimization
Formulation
The dry gas exact optimization formulation is as
follows:
where qg,i is the well gas flow rate and ai and bi are
known as reservoir deliverability coefficients.41
Nomenclature
I ) set of wells i
N ) set of naturally flowing wells i
G ) set of gas-lift wells i
M ) set of manifolds m
S ) set of separators s
di ) diameter of choke i
GORi ) gas-oil ratio of well i
qp,i ) flow rate of phase p in stock tank conditions
qg,i
inj ) gas injection rate of gas-lift well i
PR,i ) reservoir block pressure of well i
Pi(L0) ) well bore pressure of well i
Pm(Lm) ) pressure of the manifold m
Ps ) separator pressure
PIi ) productivity index of well i
WORi ) water oil ratio
yi ) pressure ratio of choke i
yc ) critical pressure ratio
wo, wg ) weighting coefficient
Units
stb/day ) barrels per day
scf/day ) standard cubic feet per day
Literature Cited
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Oil Production and Gas Allocation in Continuous Gas Lift. J. Pet.
Technol. 1981, Oct, 1887-1892.
(2) Fang, W. Y.; Lo, K. K. A Generalized Well-Management
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28467.
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R. Application of Genetic Algorithms on the Distribution of Gas
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and Exhibition, New Orleans, LA, 1994; SPE 26993.
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Production Considering Gas Lift and Phase Behaviour. Presented
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Phase Flow Through Chokes. Presented at the 61st Annual
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Solution of a Class of Multistage Dynamic Optimization Problems
max qg,m
s.t.
Pm(Ls) ) f(Pm,qg,m)
Pm(Ls) ) Ps
∑
i
qg,i ) qg,m
qg,m e C
Pi(Lm) ) g[qg,i,Pi(Lch
+ )],
∀i
PR,i
2 - Pi
2(L0) ) aiqg,i
2 + biqg,i,
∀i
0 e qg,i e qg,i
U ,
∀i
3526
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1. Problems without Path Constraints. Ind. Eng. Chem. Res. 1994,
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Production. Presented at the Production Operation Symposium,
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Received for review October 8, 2003
Revised manuscript received January 9, 2004
Accepted January 13, 2004
IE034171Z
Ind. Eng. Chem. Res., Vol. 43, No. 14, 2004
3527

An online optimization strategy for a ﬂuid catalytic
cracking process using a case-based reasoning
method based on big data technology†
Peng Ni,a Bin Liub and Ge He
*b
Rigorous mechanistic models of reﬁning processes are often too complex, which results in long modeling
times, low model computational eﬃciencies, and poor convergence, limiting the application of
mechanistic-model-based process optimization and advanced control in complex reﬁning production
processes. To address this problem and take advantage of big data technology, this study used case-
based reasoning (CBR) for process optimization. The proposed method makes full use of previous
process cases and reuses previous process cases to solve production optimization problems. The
proposed process optimization method was applied to an actual ﬂuid catalytic cracking maximizing iso-
paraﬃns (MIP) production process for industrial validation. The results showed that the CBR method can
be used to obtain optimization results under diﬀerent optimization objectives, with a solution time not
exceeding 1 s. The CBR method based on big data technology proposed in this study provides a feasible
solution for ﬂuid catalytic cracking to achieve online process optimization.
1
Introduction
In recent years, as crude oil has become increasingly heavy and
inferior, the market demand for clean fuels and low-carbon
olens has been on the rise, and the targets for safety and
environmental protection have been increasingly stringent.
However, given the slowdown in the growth of rened oil
consumption and the challenges brought by the rapid devel-
opment of new energy elds, international competition for
petrochemical products has become increasingly erce, thereby
putting forward new requirements for modern oil rening
production.1
To
address
this
problem,
intelligent
manufacturing provides an eﬀective solution path by opti-
mizing the renery production process and the supply network
through vertical, horizontal, and end-to-end integration.2 This
improves the operational agility of the production process, and,
in the face of internal and external disturbances, allows the
rapid detection of, adaptation to, and handling of new situa-
tions, such as changes in oil properties and product prices. The
development of intelligent manufacturing for renery produc-
tion necessarily includes someasurements,1,3 scheduling and
management,4,5 advanced process control (APC), and real-time
optimization (RTO),6 all of which are based on mathematical
models.7 However, the applications of rigorous mechanistic
models (based on rst principles), especially the industrial
applications of APC, are mainly limited to heat transfer and
separation processes, such as heat exchangers and distillation
columns, while they are rarely applied in the reaction sections
of complex renery production units, such as uid catalytic
cracking and continuous reforming.8 This is because their
reaction mechanisms are too complex and are not yet fully
understood. In addition, the construction of mechanistic
models is time consuming, and the resulting models have a low
computational speed and poor convergence. In addition, the
calibration data used for modeling are likely to deviate from the
original range of model adaptations as the process changes. All
these factors have reduced or even limited the application of
mechanistic model-based process optimization in complex
renery production processes.
Big data technology directly mines patterns from massive
production data and retrieves and extracts useful information.
The analysis process can reduce the reliance on complex
process mechanisms. The application of cutting-edge data
analysis technologies, such as big data and articial intelli-
gence, to the eld of unit optimization can further improve the
unit control, enhance the operating eﬃciency of the unit in the
optimal operating range, increase the target product yield,
improve product quality, reduce the energy consumption and
production costs, improve safety, control environmental indi-
cators, improve production eﬃciency, and increase economic
benets from multiple dimensions. As an alternative to APC
and RTO, extracting reliable solutions from historical data sets
based on mechanistic models is a feasible approach without
any requirements for rst-principles models.8
aChina University of Petroleum, Beijing 102249, China
bLanzhou Petro of PetroChina Company Limited, Lanzhou 730060, China. E-mail:
877157304@qq.com; hege@scu.edu.cn
† Electronic
supplementary
information
(ESI)
available.
See
DOI:
10.1039/d1ra03228c
Cite this: RSC Adv., 2021, 11, 28557
Received 25th April 2021
Accepted 19th August 2021
DOI: 10.1039/d1ra03228c
rsc.li/rsc-advances
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Based on the above discussion and the requirements of
intelligent process manufacturing, we propose a data-model-
based optimization method for uid catalytic cracking units
in this paper. This method is a general strategy of distributed
reasoning based on historical cases, i.e., case-based reasoning
(CBR). CBR mimics human reasoning by reusing data and
solutions from similar problems in the past to solve new
problems. It is an excellent tool for reusing previously acquired
experience and is widely used to build design automation or
decision support systems,9 such as production scheduling,10
processing,11,12 and fault diagnosis.13 In this paper, we rst
process the accumulated data sets of industrial reneries and
high-delity simulation activities to form a case base with
a determined structure. We then use a fuzzy matching method
to extract cases from the case base that are similar to the new
cases. The CBR method proposed in this paper is essentially
a variable correlation algorithm, which can intelligently select
variables that are strongly correlated with the target variables
from a large number of the laboratory information manage-
ment system (LIMS) and distributed control system (DCS)
variables, thereby minimizing the model complexity and
allowing the model to exhibit high computational speeds, fast
convergence, and strong adaptability while ensuring reliability.
Thus, it can be used to guide real-time online optimization.14–16
The rest of this article is organized as follows. Section 2
briey reviews the method and applications of CBR. The
method of CBR in the optimization of the uid catalytic
cracking process is presented specically in Sections 3. Section
4 focuses on the validation of the eﬀectiveness of the model
with cases, and the article is concluded with the main ndings.
2
Research progress of CBR method
in reﬁning and chemical ﬁeld
CBR is a problem-solving and learning method that has
received extensive attention in recent years. Aamodt and Plaza17
overviewed the foundational issues of CBR and described some
of the leading methodological approaches in this eld.
Regarding the case extraction, Kolodner18 proposed three basic
steps of extraction: (1) nd the corresponding features, (2)
calculate the similarity of each feature, and (3) nally multiply
the partial similarity by the corresponding coeﬃcient and
compute
the
sum
to
obtain
the
overall
similarity.
Xia19 developed the dynamic CBR (DCBR) in view of the
dynamics of process operation support systems. Aiming at the
adaptation problem of case reuse, Koiranen et al.20 proposed
a hybrid adaptation system, the main components of which are
based on fuzzy logic and neural networks.
CBR has been used in many aspects of the rening industry,
mainly in chemical process synthesis, design analysis, and fault
diagnosis. (1) The following research progress has been made in
the application of CBR in chemical process synthesis and
design analysis. In 2001, Pajula et al.21 proposed a CBR-based
approach for chemical process synthesis and demonstrated
the application of CBR in separation systems with cases. Avra-
menko et al.22 in 2004 used the CBR method as a design support
tool for the pre-selection of the packing type for reactive distil-
lation columns. In 2005, Seuranen et al.23 presented a new CBR-
based approach for separation process synthesis and selection
of single separations. Lopez-Arevalo et al.24 in 2007 proposed an
approach for managing the complexity in the redesign/
retrotting of chemical processes. This approach uses model-
based reasoning (MBR) to automatically generate alternative
representations of an existing chemical process at multiple
levels of abstraction. In the overall process, the hierarchical
representation leads to sets of equipment and sections orga-
nized according to their functions and purposes. In 2009,
Robles et al.25 proposed an approach for accelerating the
inventive preliminary design for chemical engineering by
coupling CBR with the TRIZ (theory of inventive problem
solving) theory to achieve an extension of the CBR method from
routine design to inventive design. Stephane et al.26 in 2010
attempted to improve the retrieval step of the CBR-based
preliminary design of chemical engineering units. (2) The
following research progress has been made in the application of
CBR in process monitoring and fault diagnosis. Zhao et al.27
integrated CBR and ontology to develop a new learning hazard
and operability analysis (HAZOP) expert system to improve the
learning capability of the expert system. Zhao et al.13 proposed
an improved CBR method to predict the status of the Tennessee
Eastman (TE) process. Yan et al.28 proposed a case retrieval
method based on a learning pseudo-metric (LPM) to replace the
distance measure retrieval method and established a CBR-
based fault diagnosis model for the TE process.
Zhang et al.8 applied the CBR method for the rst time to
optimize the renery production process. First, the accumu-
lated data sets from the industrial plants as well as high-delity
simulation activities were processed to form a case base with
a determined structure. Fuzzy matching was employed to eval-
uate the similarity, and an optimization model was established
for the parameters of the fuzzy membership function. The
application to an industrial uid catalytic cracking unit was
performed as an example for validation.
3
Case-based reasoning (CBR)-based
process optimization model
CBR method is essentially a process of database case extraction
and reuse. The optimization process is to discretize the
continuous operating variables (that represent the operation
conditions), and then interpolate and match the operating
variables to obtain the optimal operating variables. In addition
to the establishment of case base, process optimization using
CBR can be described by the following four processes: case
extraction, case reuse, case revision and retention. Fig. 1 shows
the technical route of the CBR method.
 Retrieve: With the feed composition of the current case,
including wax/residue ratio, residual carbon, sulfur contents,
distillation temperature range and the feeding rate, etc., using
the distance similarity to calculate the similarities between the
operation conditions of the current case with historical cases,
and then perform the comparison on four levels. The target is to
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nd the historical cases with the most similar operation
conditions.
 Reuse: The solution resulting from the retrieved cases is
used as a suggested solution to the target problem. By matching
the feed compositions to nd the candidates from the case
base, and arrange these candidates in descending order
regarding the total liquid yield (or the gasoline yield, the coke
yield). Then the optimal solution is the rst one with the highest
total liquid yield (or the highest gasoline yield, the lowest coke
yield).
 Revise: If the actual product yield and the total liquid yield
under the recommended conditions did not conform to the
calculation results in the corresponding case base, then such
case base would be modied, then the above operation was
repeated to obtain the process optimization results.
 Retain: The process model or the simulation soware was
used to simulate diﬀerent operating conditions, and a large
number of cases representing various operating conditions
were obtained. The generated cases were added to the case base
to complete its expansion. On the other hand, due to the
process transformation, the cases that did not appear were then
eliminated.
3.1
Case base establishment
Before using CBR for process optimization, a historical case
base must be established. First, the historical production data
of DCS and LIMS of the unit are collected to establish a standard
database of production data. The database format is standard-
ized, and indexing rules are established to facilitate querying,
addition, and extraction at a later stage. The variables used in
the modeling are extracted from the standard database for pre-
processing to prepare for the subsequent establishment of the
case base. The pre-processing includes deleting invalid data,
interpolating and tting the missing values, eliminating the
eﬀects of noise signals and outliers with data smoothing tech-
niques, and classifying variables. Linear interpolation is chosen
as the interpolation method, and wavelet decomposition is used
as the data smoothing technique to eliminate the eﬀects of
high-frequency noise signals and outliers during data analysis.
The case base should be established to cover all possible
problems and variable sets in the application eld.29 The vari-
ables are divided into feed variables, inuencing variables, and
product variables. The case model is expressed as follows:
Ck ¼ {(Ik,Pk) / Sk}
(1)
where I is the feed information in the case base, including the
feed wax residue ratio, feed ow rate, and feed properties. The
feed properties refer to the density, residual carbon, sulfur
content, and boiling range temperature of the feed, totaling
nine properties. P is the product information in the case base,
including the product yield, product properties, and product
price, and it is used to evaluate the operating conditions, mainly
considering its contribution to the economic benets. The
expressions of I and P are similar in form to data tables, where
each piece of information corresponds to the column data of
the table, and the diﬀerent case conditions are the row data of
the table. S is the inuencing variable, which represents the
case solution and is the DCS operating parameter to be solved. I
is expressed as follows:
Fig. 1
Technical route of the process optimization method based on the case base.
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Ik ¼ (Ik,1,Ik,2, ., Ik,11) ¼ (r, r, s, x, Tini, T5%, T10%, T30%, T50%,
T70%, Qm)
(2)
where r is the feed wax residue ratio, r is the feed density, s is
the feed residual carbon content, x is the feed sulfur content,
Tini, T5%, T10%, T30%, T50% and T70% are the initial boiling point,
and the 5%, 10%, 30%, 50%, and 70% boiling range tempera-
tures of the feed, respectively. P is expressed as follows:
Pk ¼ (Pk,1,Pk,2, ., Pk,m) ¼ (Ydrygas, YLPG, Ygasoline, Ydiesel, Yslurry,
Ycoke.)
(3)
where m is the number of product attributes, Ydrygas, YLPG, Ydiesel,
Yslurry and Ycoke are the yields of dry gas, liqueed petroleum gas
(LPG), gasoline, diesel, slurry, and coke, respectively.
3.2
Case extraction
Aer the case base is established, the case with the highest
similarity and its corresponding solution are extracted from the
case base by means of case similarity calculations to complete
the case extraction.9 In this study, we adopted the distance
similarity. Assuming that a variable in the case corresponding
to the current operating condition is x, the similarity between x
and the corresponding variable xk of the case in the case base is
calculated as follows:
Simðx; xkÞ ¼ 1 
jx  xkj
max
n ðxkÞ  min
n ðxkÞ
(4)
where k ¼ 1, 2, ., n, with n representing the number of cases.
Four-level matching of the feed information was conducted
based on the established case base using a distributed
reasoning algorithm, which is shown by the steps in the
rounded box in Fig. 1, as described in detail below:
(1) The cases that meet the upper and lower limits of the wax
residue ratio are selected, and the resulting number of cases is
denoted as n1. The similarity Sim(I1,Ik,1) between the wax
residue ratio I1 of the current feed and the wax residue ratio Ik,1
in the case base is calculated using eqn (4). The rst n2(n2 # n1)
cases greater than 0.9 are selected from the results ranked in
descending order as the rst-level case base.
(2) The weighted similarity D1 of the density, residual
carbon, and sulfur content between the current feed and the
feed in the case base in the rst-level case base is calculated,
and they are assigned diﬀerent weights. The rst n3(n3 # n2)
cases greater than 0.85 are selected according to the D1 results
ranked in descending order as the second-level case base. D1 is
calculated as follows:
D1 ¼
X
4
i¼2
ðwi$SimðIi; Ik;iÞÞ
, X
4
i¼2
wi
(5)
where Sim(I2,Ik,2), Sim(I3,Ik,3), and Sim(I4,Ik,4) are the density
similarity, residual carbon similarity, and sulfur content simi-
larity, respectively, which are calculated using eqn (4).
(3) The weighted similarity D2 of the boiling range tempera-
tures (including the initial boiling point and the 5%, 10%, 30%,
50%, and 70% boiling range temperatures) between the current
feed and the feed in the case base in the second-level case base is
calculated, diﬀerent weights are assigned to boiling range
temperatures. The rst n4(n4 # n3) cases greater than 0.8 are
selected according to the D2 results ranked in descending order
as the third-level case base. D2 is calculated as follows:
D2 ¼
X
10
i¼5
ðwi$SimðIi; Ik;iÞÞ
, X
10
i¼5
wi
(6)
where Sim(I5,Ik,5), Sim(I6,Ik,6), Sim(I7,Ik,7), Sim(I8,Ik,8), Sim(I9,Ik,9),
and Sim(I10,Ik,10) are the similarities of the initial boiling point
and the 5%, 10%, 30%, 50%, and 70% boiling range tempera-
tures, respectively, which were calculated using eqn (4).
(4) The similarity Sim(I11,Ik,11) of the ow rate between the
current feed and the feed in the historical base in the third-level
case base is calculated. The rst n5(n5 # n4) cases greater than
0.7 are selected according to the results ranked in descending
order as the fourth-level case base.
3.3
Case reuse
The case reuse of the process optimization is realized by opti-
mizing the objective function.
(1) If the maximization of the total liquid yield Fk is used as
the objective of the process optimization, the operating condi-
tion corresponding to the maximum value of Fk in the fourth-
level case base is the optimal operating condition ST:
ST ¼ argmax
k ðFk;1Þ;
ðk ¼ 1; 2; .; n5Þ
(7)
Fk,1 ¼ Pk,2 + Pk,3 + Pk,4
(8)
(2) If the maximization of the gasoline yield Fk,2 is used as the
objective of the process optimization, the operating condition
corresponding to the maximum value of Fk,2 in the fourth-level
case base is the optimal operating condition ST:
ST ¼ argmax
k ðFk;2Þ;
ðk ¼ 1; 2; .; n5Þ
(9)
Fk2 ¼ Pk,3
(10)
(3) If the minimization of the coke yield Fk,3 is used as the
objective of the process optimization, the operating condition
corresponding to the minimum value of Fk,3 in the fourth-level
case base is the optimal operating condition ST:
ST ¼ argmax
k ðFk;3Þ;
ðk ¼ 1; 2; .; n5Þ
(11)
Fk3 ¼ Pk,6
(12)
3.4
Case revision and retention
If the product yield and total liquid yield actually obtained
under the recommended operating conditions do not match the
product calculation results in the corresponding case base, the
case base is revised, and then the above operations are repeated
to obtain the process optimization results.
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Case expansion provide enough cases to ensure the reliability
of results and broad applicability of model. There are two
methods of case expansion: one is industrial historical data
(actual data), and the other is the prediction results of the process
model or the simulation soware. In addition, regarding some
hidden parameters such as the mass transfer performance of the
unit change slowly with time, so regular updates can ensure the
timeliness of the model. There are two methods of case update:
one is to update the expansion regularly, and the other is to
expand immediately when the process is modied or the oper-
ating conditions continue to change signicantly. During the
case update, the new production data are added to the data set
while part of the oldest data are eliminated, and the new data set
is used to adapt to the new operating conditions.
4
Industrial application
4.1
Description of uid catalytic cracking process
Fig. 2 shows the maximizing iso-paraﬃns (MIP) process ow of
the uid catalytic cracking process. The reaction regeneration
process of the core section is described as follows. The raw
material enters the rst reaction zone of the riser reactor, comes
into contact with the regenerated catalyst from the regenerator,
and
immediately
gasies
and
reacts,
with
the
heat
of
gasication of the raw material and the heat of reaction
provided by the high-temperature regenerated catalyst. The
reaction oil and gas have a short residence time in the rst
reactor and then enter the expanded second reactor with the
catalyst. Aer the oil and gas carries the catalyst through the
cyclone fast separator at the outlet of the riser, the oil and gas
with a small amount of catalyst enters the top cyclone separator
for further separation, and the separated oil and gas goes to the
fractionation column and absorption stabilization system.
The MIP process of a uid catalytic cracking unit in a renery
in northwest China was modeled for industrial validation. This
unit has a production load of 300 million tons per annum, and
its calibrated operating conditions were as follows: wax oil feed
rate, 250 t h1; residue oil feed rate, 140 t h1; catalyst–oil ratio,
6.5; inlet temperature of the second reactor, 502
C; inlet
temperature of the rst reactor, 512 C; outlet temperature of
the second reactor, 501 C; settler top pressure, 0.18 MPa; riser
pressure drop, 50 kPa; and regenerator pressure, 0.22 MPa.
The production data from October 2019 to May 2020 were
collected, and a case base was created according to the method
presented in Section 3.1, including 42 DCS items and 9 LIMS
analysis indices. The description of these variables is given in
Appendix A, Tables S1 and S2.†
Table 1
Feed material property data
No.
Type
Name
Value
Unit
1
General properties
Density (20 C)
913.5
kg m3
2
Residual carbon
3.1
Wt%
3
Slag mixing ratio
0.436
Dimensionless
4
Boiling range temperature
Initial boiling point
219
C
5
5% distilled temperature
324
C
6
10% distilled temperature
348
C
7
30% distilled temperature
400
C
8
50% distilled temperature
435
C
9
70% distilled temperature
489
C
10
Element content
Sulfur
0.35
Wt%
Fig. 2
Flow diagram of the MIP process of the ﬂuid catalytic cracking unit.
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4.2
Discussion of process optimization results under
diﬀerent optimization objectives
The selected feed composition and physical properties were as
follows: wax oil feed rate, 227.90 t h1; residual oil feed rate,
176.53 t h1; and the corresponding physical property data of
the feed are shown in Table 1.
In this study, 22 important DCS items were selected as the
inuencing variables for optimization, and the process opti-
mization calculation was carried out with the selected feed for
diﬀerent optimization objectives, including the maximum
gasoline yield, the maximum total liquid yield, and the
minimum coke yield, to obtain the results of the inuencing
variables. Specically, the product yield distribution of three
diﬀerent working conditions (optimal, suboptimal and third
best) under three diﬀerent optimization objectives is given. See
Table 2 for specic data results. The calculated optimal chem-
ical parameters are given with the best gasoline yield as an
example, as shown in Table 3.
MATLAB 2014b (MathWorks, Inc.) was used to modeling for
process optimization. Computer conguration: processor: Intel
(R) Xeon (R) Gold 5117 CPU @ 2.00 GHz (dual processor);
memory: 32.0 GB; operating system type: 64-bit. The optimiza-
tion calculation process, including case extraction and case
reuse, was controlled to be completed within 1 s.
4.3
Eﬀectiveness validation and benet calculation of
process optimization
A total of 20 sets of operating conditions were taken to validate
the eﬀectiveness of the process optimization. The diﬀerences
between the actual and optimized values calculated by the CBR
method using the maximization of the total liquid yield as the
objective are shown in Fig. 3. Aer optimization, the total liquid
yield,
liqueed
petroleum
gas
yield,
and
gasoline
yield
increased by 1.10%, 0.36%, and 0.57%, respectively, on average,
and the coke yield decreased by 1.17% on average.
The benets were calculated with the following consider-
ations. The energy consumption was based on the relevant
Chinese national standard. The energy consumption for
Table 2
Optimization product distribution for diﬀerent optimization objective
Optimization objective and
value (wt%)
Product
Yield under optimal
condition (wt%)
Yield under second
optimal condition (wt%)
Yield under third
optimal condition (wt%)
Maximization of gasoline
yield, 52.50%
Total liquid
86.01
87.07
85.49
Gasoline
52.5
52.16
52.02
Diesel
16.63
17.36
16.66
Dry gas
3.65
3.77
3.58
LPG
16.88
17.55
16.81
Slurry
3.53
3.81
3.81
Coke
6.81
5.35
7.12
Maximization of total liquid
yield, 87.07%
Total liquid
87.07
86.06
86.01
Gasoline
52.16
51.45
52.5
Diesel
17.36
17.06
16.63
Dry gas
3.77
3.71
3.65
LPG
17.55
17.55
16.88
Slurry
3.81
4.05
3.53
Coke
5.35
6.18
6.81
Minimization of coke yield,
5.35%
Total liquid
87.07
86.06
85.94
Gasoline
52.16
51.45
51.44
Diesel
17.36
17.06
17.01
Dry gas
3.77
3.71
3.7
LPG
17.55
17.55
17.49
Slurry
3.81
4.05
4.08
Coke
5.35
6.18
6.28
Table 3
Operating condition data for the maximum gasoline yield
No.
Item
Optimal condition
Second optimal
condition
Third optimal
condition
1
TI3106B
506.48
503.86
506.34
2
TI3106A
511.82
509.48
511.8
3
TI3111
676.02
679.39
677.46
4
FIC3105
1.53
1.49
1.52
5
FIC3208
168.66
176.88
170.99
6
FIC3209
216.92
237.64
213.65
7
FIC3109
2.25
2.26
2.25
8
PdI3122
69.47
68.11
69.34
9
PdIC3103
52.23
54.45
48.87
10
DI3102
32.47
50.11
31.23
11
TIC3101
496.73
495.49
496.02
12
TIC3204
197.67
193.59
197.5
13
FIC3111
7.5
5.01
7.5
14
FIC3110
3
5.5
3
15
PI3106
0.23
0.23
0.23
16
TIC3125
694.58
694.91
697.21
17
TI3131A
669.51
671.82
669.75
18
TI3126A
699.97
699.98
703.64
19
TIC3102
691.88
693.73
700.38
20
PI3110
0.3
0.31
0.3
21
DI3112
419.34
415.17
437.66
22
FIC3122
2524.2
2629.98
2374.71
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processing heavy raw oil should be less than 85 kg of standard
oil per ton, and the product with increased yield was multiplied
by an energy consumption coeﬃcient of 0.085. Coke was pro-
cessed by the regenerator to provide energy for the unit at no
cost. The maximization of the total liquid yield was the objective
for optimization. The yield change and prot calculation of
each product are shown in Table 4. The prot change was
calculated as follows:
It is estimated that the use of the optimized system can bring
about a prot improvement of approximately 13.52 million
yuan per year for a 3 million ton/year uid catalytic cracking
unit.
5
Conclusion
Modeling, optimization, and control of uid catalytic cracking
processes are all important aspects of the rening industry.
However, the complexity of the process and the uncertainty of
the raw materials and product markets pose a challenge for
achieving timely control optimization based on models. To
address this problem, this study proposes a CBR-based process
optimization method based on big data technology, which
ensures both the accuracy of the optimization results and
satisfactory computational eﬃciency to eﬀectively support
industrial
applications
of
complex
renery
production
processes. Taking the MIP process of the uid catalytic cracking
unit as an example, eﬀective optimization results were obtained
by the CBR method under diﬀerent optimization objectives.
Furthermore, the feasibility of the method was veried through
optimization tests under 20 sets of operating conditions.
Therefore, the optimization of the process operating conditions
by the CBR method based on the big data technology proposed
in this paper can lead to more reasonable product quality
control and product yield distribution in uid catalytic cracking
units, which is signicant for improving the product structure,
performing real-time adjustments to external changes, and
improving renery eﬃciency. The method proposed in this
paper can also be combined with APC systems to achieve ne
optimization and control in real time.
Although the application of real-time operation and optimal
control based on rigorous mechanistic models in complex
production processes is still in its infancy, the CBR method
used in this paper is advantageous in terms of data models. The
fusion of the two technical methods will become an important
research topic and direction in the future. In particular, the
development of plant-wide process optimization in real time
using mechanism-based big data technology may be achieved.
Conﬂicts of interest
There are no conicts of interest to declare.
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133
© 2023. The Author(s). This is an open-access article distributed under the terms of the Creative Commons 
Attribution-ShareAlike International License (CC BY-SA 4.0, http://creativecommons.org/licenses/by-sa/4.0/), 
which permits use, distribution, and reproduction in any medium, provided that the Article is properly cited.
  Corresponding Author: Konstantin Vasilev Kostov; e-mail: kostov_77@abv.bg
1  Department of Mechanical Engineering, Manufacturing and Thermal Engineering, Technical University 
of Sofia, Faculty of Engineering and Pedagogy of Sliven, Bulgaria; ORCID iD: 0000-0001-6134-6783; e-mail: 
kostov_77@abv.bg
2  Department of Mechanical Engineering, Manufacturing and Thermal Engineering, Technical University of Sofia, 
Faculty of Engineering and Pedagogy ff Sliven, Bulgaria; ORCID iD: 0000-0003-1773-6009; e-mail: ivov.ivan@abv.bg
3  Department of Mechanical Engineering, Manufacturing and Thermal Engineering, Technical University of 
Sofia, Faculty of Engineering and Pedagogy of Sliven, Bulgaria; ORCID iD: 0000-0002-9474-3977; e-mail: koycho_
atanasov@abv.bg
POLITYKA ENERGETYCZNA – ENERGY POLICY JOURNAL
2023   Volume 26    Issue 1    133–144
DOI: 10.33223/epj/161625
Konstantin Vasilev Kostov1, Ivan Ivov Ivanov2, Koycho Tonchev Atanasov3
The analysis of the energy index and the 
application of equivalent distillation productivity 
as criteria for identification of the energy efficiency 
of a petroleum refinery
Abstract: As a result of the development of industrial organic synthesis, the output of secondary processes 
in oil processing is becoming increasingly diverse. Production volume is a nodal indicator that is li-
mited by the available production capacity, equipment configuration and the monetary equivalent of 
energy costs. In order to determine the technological potential and cost of produced petroleum pro-
ducts, it is necessary to create a complex that includes all stages of production. The most important 
criterion for evaluating the energy efficiency of an oil refinery is the relative energy consumption, 
which depends on its complexity.  This criterion can be presented as a set of the different types of 
energy resources used in the course of production and applied to the total production. For this pur-
134
pose, the energy resources invested in the given technology should be referred to a finished product 
or raw material. The peculiarity of oil refineries is that, due to the variety of oil derivatives, energy 
consumption, as a set of different installations, is much more appropriate to relate not to individual 
target products but to the amount of processed oil. In practice, all types of energy carriers must be 
converted to an equivalent value.
This paper provides an in-depth analysis of the energy costs of oil refineries. The collection of 
energy flows of different types and dimensions is the subject of the present study. Based on this, 
a method is presented that allows a comparison of the energy efficiency of refineries with different 
capacity and configuration of crude oil processing stages based on the energy index and the equiva-
lent distillation performance.
Keywords: energy index, energy costs, energy efficiency, petroleum refinery
Introduction
In oil refineries, depending on the organization of the technological process, thermal engine-
ering and power systems of different types are used. The classic scheme is through the use of wa-
ter vapor as a heat carrier brought to the individual productions through a heat-transfer network 
(Kostov et al. 2022). In order to perform an energy-efficiency analysis of energy consumption 
(de Lima and Schaeffer 2011; Łebkowski et al. 2015; Wu et al. 2017; Ghadim and Faridzad 
2021), it is important to introduce a single criterion with which to evaluate energy consumption. 
The introduction of such a criterion should represent the total used energy resources related to 
a finished product or raw material. A special feature with regard to oil refineries is that due to the 
variety of oil derivatives, with regard to the energy consumption of the plant as a set of different 
installations, it is much more appropriate to refer not to the individual target products, but to the 
processed raw material (oil) measured in metric tons or barrels. In practice, all types of energy 
carriers, such as different types of fuels, heat supplied by steam and hot water, electrical energy 
of all voltage levels, desalinated and circulating water, technical air and gases creating an inert 
environment and others, must be translated to an equivalent value – tons of oil equivalent (toe) 
or energy (GJ or MWh). 
The collection of energy flows of different types and dimensions is a labor-intensive task, 
but in essence, its realization is not a problem. Formulated in this way, the concept of energy 
consumption has a clear physical meaning, it is easily defined and enables the assessment of 
the dynamics of change and traceability over time. A major drawback of the method is that it 
does not allow comparability with similar oil refineries. The reason for this is that although they 
are similar, the refineries have different productivity, technological configuration and technical 
levels of equipment. On the other hand, the monetary equivalent of energy costs occupies a pri-
mary share (excluding oil purchase costs) in the maintenance of the enterprise and are essentially 
the most important factor in market competition. 
135
Attempts to compare operating oil refineries with different productivity and technology have 
been made in research (Gary et al. 2007; Riazi et al. 2013; Kaiser 2017; Herce et al. 2022). These 
studies are based on the work of Nelson (1976a), who established the proportional relationship 
between the technological complexity of oil-refining processes and the amount of capital invest-
ment required for their implementation. For example, if an atmospheric distillation of a standard 
crude oil unit (SCU) has a complexity factor of SCU = 1.0, a pseudo fluidized-bed catalytic 
cracking (FCC) plant should have a complexity factor of FCC = 8.2. This is because the capital 
investment per ton of feedstock processed at the installation FCC plant is 8.2 times greater. The 
set of technological installations, depending on the specific configuration of each oil refinery, 
forms a characteristic “complex energy index” subsequently named after the discoverer. Nel-
son’s index is an objective criterion for evaluating the technological complexity of refineries, and 
in Nelson (1977) the author rightly suggests using it as a correlation factor in their comparison. 
The main conclusion is that technologically more complex oil refineries, i.e. those with a larger 
index, generally have higher energy costs. At the time he conducted his research (Nelson 1976a) 
and published the results, analysis of the data showed a similar correlation of the US refineries 
studied. However, it should be noted that, with few exceptions, all refineries at the time used low 
temperature processes and were constructed of standard construction materials. The use of the 
original technological complexity coefficients in their original form is inapplicable nowadays 
because three main factors are not taken into account: 
)
) the costs for the oil terminals, the storage of the raw material and the distribution of the fi-
nished product;
)
) the dynamics in the development of the industry, the emergence of new construction mate-
rials, new technical concepts and new technologies;
)
) the sharp and significant change in the structure of operating costs in all categories.
For the last 20 years, for example, the price of energy carriers has increased several times, 
while the costs of labor, chemical reagents, spare parts, etc., despite having increased in absolute 
terms, already occupy a much smaller share in the total amount of maintenance. This is why the 
coefficients for technological complexity have undergone significant development and today si-
gnificantly differ from the original values (Zhang et al. 2001; Bandyopadhyay et al. 2019; Dalei 
and Joshi 2020; Atris 2020).
1. Materials and methods
Solomon Associates’ trusted benchmarking methodology was first implemented in the US 
in 1980. It is still used for commercial purposes, but it takes as its axiom some basic notions of 
performance evaluation, such as:
)
) larger oil refineries have undeniable advantages over smaller ones;
)
) newer refineries are always more efficient;
136
)
) technologically more complex refineries are more profitable;
)
) the most efficiently operating refineries are located near deep-sea ports.
The company’s very first report has cast doubt on some of these widely held beliefs. For 
example, some relatively small oil refineries turned out to be quite efficient, and vice ver-
sa – the indicators of some large and new plants turned out to be below average. Currently, 
the company Solomon Associates, based on comparative analyses of oil refineries around the 
world, has accumulated an extremely rich database of more than 500 refineries. Systematizing 
and comparing these refineries creates a unique opportunity to validate the method and verify 
the results. The analysis is performed for the relevant geographical area, and the oil refineries 
according to the relevant indicators are grouped into four quartiles. The first quartile includes 
the best oil refineries.
To evaluate the energy efficiency of an oil refinery, the company Solomon Associates (SA) 
introduces the correlation parameter “Solomon Energy Intensity Index” or “Solomon EII” or 
“EII”. This benchmark is an oil refinery energy efficiency metric that compares the actual energy 
consumption of a refinery with the “standard” energy consumption of a refinery of similar size 
and configuration. The formula for determining EII looks like this:
	
100
(
)
AECOR
EII
day
ES UEDP
HC
ESWD
=
⋅
⋅
⋅
+
+

(1)
where:
AECOR	 –	 actual energy consumption of an oil refinery,
ES		
	
–	 energy standard,
UEDP		
–	 usable equivalent distillation performance,
HC	
	
	
–	 heat content,
ECWD	
–	 energy consumption for water desalination.
The actual energy consumption of the refinery is:
	
AECOR
RTE
HEE
=
+

(2)
where:
RТЕ	
–	 the required thermal energy,
HEE	 –	 heat equivalent of electricity.
Before analyzing Equation 1, assumptions are introduced that the required thermal energy is 
a sum of the heat obtained from the combustion of the fuels and the heat absorbed by all other 
heat carriers, and that the thermal equivalent of the electricity is assumed to be 9090 Btu per 
1 kW. Essentially, the denominator of Equation 1 represents the standard energy consumption 
of the installation. Obviously, when the actual consumption matches the standard, the index 
EII = 100. The standard energy consumption consists of several multipliers. The first and most 
137
important of these is usable equivalent distillation productivity (UEDP). The determination of 
UEDP takes place in several stages:
1. The so-called stream-day (SD) throughput of the refinery is determined. This is the nomi-
nal performance for a calendar day at 100% utilization during the year, at the maximum possi-
ble sustainable load, without peak overloads. Atmospheric distillation is the first technological 
process along the course of the raw material, which is why the stream-day productivity of the 
refinery and the atmospheric distillation plant match. 
2. The productivity of each subsequent installation in the technological chain decreases with 
the extraction of the target products. This is determined by multiplying the stream day productivity 
of the refinery by a factor taking into account the percentage share of the specific installation in 
the total oil processing. The term “installation” is collective – it means both purely technological 
units and all auxiliary and energy systems related to the production of thermal and electrical energy. 
3. The equivalent distillation productivity (EDP) is determined by multiplying the producti-
vity per calendar day for each installation by the relevant technological complexity factor (KT) – 
(Table 1, Column 3). 
4. The usable equivalent daily productivity (UEDP) is obtained by multiplying the EDP with 
a proportionality factor (KP) taking into account the actual working time of the particular instal-
lation for the studied period and a multiplicity factor (KM). The latter coefficient is entered if two 
or more installations with the same purpose are available in the configuration of the oil refinery. 
Table 1 presents the technological complexity factor (KT) and the standard energy con-
sumption of some typical technological installations of an oil refinery.
Based on the analysis conducted for UEDP, it can be stated that:
	
%
100
T
P
M
SD
UEDP
SD
K
K
K






= Σ
⋅
⋅
⋅
⋅













(3)
where:
SD	–	 stream day,
KT	–	 technological complexity factor,
KP	–	 proportionality factor,
KМ	
–	 multiplication factor.
To determine the UEDP for the entire refinery, it is necessary to determine the equivalent da-
ily productivity of each of the external facilities for the refinery – oil terminals, commodity – raw 
materials bases, state reserve bases, etc.:
	
(
)
C
EDPEO
PEO K
= Σ
⋅

(4)
where:
PEO	 –	 permeability of external objects,
KC		
–	 configuration coefficient.
138
The configuration coefficient (KC) is analogous in a physical sense to the technological 
complexity coefficient (KT) and its purpose is to unify external objects by type and throughput 
(Table 2).
Represented in this way for the entire oil refinery, the usable equivalent distillation produc-
tivity will be equal to the sum of the daily productivity of the process plants and external sites:
	
refinery
installation
UEDP
UEDP
EDPEO
=
+
∑

(5)
Table 1. Technological complexity factor (KT)
Tabela 1. Współczynnik złożoności technologicznej (KT)
Process Type
Process Type ID
KТ
Energy standard
[BTU/barrel of oil]
Atmospheric crude distillation
Standard crude unit 
SCU
1.0
3 + 1.23×°API
Mild crude unit
MCU
0.8
3 + 0.94×°API
Vacuum distillation
Standard vacuum colimm
VAC
1.0
15 + 2.3×°API
Vacuum fractionating columm
VFR
1.2
25 + 2.3×°API
Mild vacuum fractionating 
MVU
0.8
12 + 1.1×°API
Heavvy feed vacuum unit  
HFV
1.0
15 + 1.85×°API
Visbreaking
Vacuum bottoms feed
VBF, VBFS
3.2
140
Atmosspheric resid  
VAR, VARS
3.2
140
Thermal cracking 
3.8
220
Coking
Delayed coking 
DC
7.5
180
Fluid coking  
FC
7.5
400
Flexicoking 
FX
11.0
575
Catalytic cracking
Fluid catalytic cracking 
FCC
8.2
70 +[40 × (coke, % raw material)] 
Mild residual catalytic cracking 
MRCC
9.1
70 +[40 × (coke, % raw material)] 
Residual catalytic cracking
RCC
10.0
70 +[40 × (coke, % raw material)] 
Catalytic reforming
Cyclic
RCY
3.5
[3.65×(C5 +RONC)]-120
Continuous regeneration
RCR
3.6
[3.65×(C5 +RONC)]-133
Note:  1. Density in degrees API for each installation.
           2. RONC octane number according to the motor method.
139
Since UEDP has no real physical meaning, it can be used as a correlation parameter for the 
unification, evaluation and comparison of oil refineries with different configurations.
When determining the energy index (EII), it is necessary to take into account another impor-
tant parameter – the energy standard (ES).
For the requirements of oil refineries, energy standards have been developed for all possible 
combinations of technological processes for the production of fuels and oils. For the purposes of 
this study, a sampling  of the energy standards of some of them are presented in Table 1. Once 
determined, these standards are not of a constant value and need to be periodically updated in 
connection with the implementation of new construction materials and the development of tech-
nologies. The heat content of the crude oil (200°F – 93.3°C) and a proportional part of the energy 
costs necessary for the operation of the general plant economy located outside the limits of the 
specific production must be added to the energy consumption of the installations. 
2. Discussion
The object of the research is the oil refinery located in the city of Burgas, in the Republic of 
Bulgaria. It is the largest oil refinery in the southeast of Europe and the largest industrial enterpri-
se in Bulgaria. It was put into operation in 1963 and is a classic type of refinery with a complex 
Nelson index of 8.9. The principle technological scheme is presented in Figure 1.
The research aims to determine and compare the relative energy consumption and the EII 
energy index, and then to follow and analyse the trend and dynamics of their changes over an 
eight-year period. Achieving the set goal should be considered as a stage in the implementation 
of a system for monitoring key indicators of energy efficiency, to be integrated with appropriate 
software in the management information system (SAP R3) of the refinery. The ultimate goal is 
the creation of conditions for an objective analysis of the achieved results and the determination 
of competitive advantages and restraining factors in the implementation of the energy policy and 
the management of the refinery as a whole.
Table 2. Configuration coefficient (KC)
Tabela 2. Współczynnik konfiguracji (KC)
Type of transportation
Delivery of petrol
Expedition of the product
Railway tanks
0.50
0.50
Tanker trucks
0.40
0.40
Tanker terminal
0.10
0.21
Offshore buoy
0.10
0.10
Barge terminal
0.10
0.15
Pipeline
0.00
0.00
140
The main information required for the study is contained in the monthly and summarized 
annual reports of the technological installations of the refinery. It concerns an energy analysis co-
vering a long period so that the average values of the energy indicators within a calendar month 
are sufficiently representative to eliminate the influence of the inevitable fluctuations in the in-
stantaneous values determined by transitional and technological regimes. The determination of 
the relative energy expenditure is performed on an annual basis, and every two years in the case 
of the complex energy index (EII). The result is shown in Figure 2.
According to the set goal, the change in the energy index EII is compared with the change in 
the energy intensity defined as the consumption of conditional fuel referred to the processed oil 
(toe). On the chart, based on data provided by the SA consultant, the first and fourth quartile li-
mits determined by a study of eighty-nine refineries from eastern and southern Europe are shown 
Fig. 1. Block diagram of the refinery
Rys. 1. Schemat blokowy rafinerii
141
for reference. The change of both indicators is in the direction of improvement with a positive 
downward trend, but there are some peculiarities.
The EII amendment gives an indication that the refinery is implementing reforms in the 
management of energy flows and implementing measures to reduce energy consumption at 
a faster pace than competitors. If all refineries had the same progress and moved in a pack, 
the EII index would be unchanged. The first period (2014–2016), when the decrease was 
14.3% compared to the initial value, is particularly indicative. The explanation is that for 
two years, organisational and technical energy-saving measures were successfully imple-
mented with a short implementation period and a significant effect. Relative energy con-
sumption also decreased by 11.2%. In the next two-year period, the energy index grew and 
reached the limit of the fourth quartile. The reason is rooted in two serious accidents that 
put major installations out of order for a long time at the beginning of 2017 and the middle 
of 2018. The relative energy consumption changed insignificantly because emergency stops 
have no direct relation to its value. 
The 2018–2020 period is characterized by the entry into regular operation of investment sites 
with a higher coefficient of technological complexity aimed at improving the assortment and 
quality of the finished product. Logically, an increase in IEDP and standard energy consumption 
follows, and together with them, a decrease in the EII index. At the same time, the relative ener-
gy consumption increases because the volume of oil processed does not change, but the actual 
energy consumption increases. This trend continued in the next two-year period. The energy 
Fig. 2. Change in the EII energy index and relative energy consumption
Rys. 2. Zmiana wskaźnika energetycznego EII i względne zużycie energii
142
index decreased by 7.5%, while the relative energy intensity remained unchanged. Research for 
2022 determines EII = 98 < 100. The refinery approached the limits of the third quartile, and for 
the first time, the actual energy consumption is below the values of the standard for the specific 
technological configuration. 
Conclusion
The conducted analysis shows indisputable progress regarding the energy policy and effi-
ciency of the considered refinery, but the EII energy index still positions it at the border between 
the third and fourth quartile. To some extent, this is due to the fact that the energy of thermal 
energy flows is not taken into account. The presence of secondary energy carriers, such as low-
caloric gases and low-pressure steam, represent sources of energy, but their simple summation 
as a heat equivalent is incorrect. Despite the stated critical remarks, the proposed method has 
its merits and does not call into question the fundamental regularities on which it is based. The 
possibility to estimate the energy intensity of the refinery by means of an energy index based on 
energy standards and the equivalent distillation capacity is applicable to the comparison of oil 
refineries that have different levels productivity and different configurations.
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ekwiwalentnej wydajności destylacji jako kryteriów identyfikacji 
efektywności energetycznej rafinerii ropy naftowej
Streszczenie
W wyniku rozwoju przemysłowej syntezy organicznej wydajność procesów wtórnych w przetwór-
stwie ropy naftowej staje się coraz bardziej zróżnicowana. Wielkość produkcji to  wskaźnik węzłowy, 
który jest ograniczony dostępnymi zdolnościami produkcyjnymi, konfiguracją urządzeń oraz ekwiwa-
lentem pieniężnym kosztów energii. W celu określenia potencjału technologicznego i kosztu wytwa-
rzanych produktów naftowych konieczne jest stworzenie kompleksu obejmującego wszystkie etapy 
produkcji. Najważniejszym kryterium oceny efektywności energetycznej rafinerii ropy naftowej jest 
względne zużycie energii, które zależy od jej złożoności. Kryterium to można przedstawić jako zestaw 
różnych rodzajów zasobów energetycznych wykorzystywanych w trakcie produkcji i stosowanych w ca-
łej produkcji. W tym celu zasoby energii zainwestowane w daną technologię należy odnieść do goto-
wego produktu lub surowca. Specyfika rafinerii ropy naftowej polega na tym, że ze względu na różno-
rodność produktów ropopochodnych energochłonność, jako zespół różnych instalacji, znacznie bar-
dziej adekwatnie odnosi się nie do poszczególnych produktów docelowych, ale do ilości przerobionej 
ropy. W praktyce wszystkie rodzaje nośników energii muszą być przeliczane na wartości równoważne. 
Artykuł zawiera dogłębną analizę kosztów energii rafinerii ropy naftowej. Przedmiotem niniejszego opra-
cowania jest zbiór przepływów energii różnych typów i wymiarów. Na tej podstawie przedstawiono me-
todę pozwalającą porównać efektywność energetyczną rafinerii o różnej wydajności i konfiguracji etapów 
przerobu ropy naftowej na podstawie wskaźnika energetycznego i ekwiwalentnej wydajności destylacji.
Słowa kluczowe: indeks energetyczny, koszty energii, efektywność energetyczna, rafineria ropy naftowej

Abstract— A study of the optimal real-time management of 
hydrogen H2 networks in an oil refinery has been carried out. 
The paper addresses the main problems related to the 
operation of the networks combining data reconciliation with a 
real-time optimization RTO system for the optimal production 
and redistribution of hydrogen. Coherent and robust results 
have been achieved regarding the data reconciliation stage 
despite all the uncertainty in measurements and process 
operation, according to the off-line validation performed. Once 
the network state has been estimated, a RTO approach was 
followed aiming at the real-time optimal operation of the global 
refinery H2 network; an analysis of the solutions achieved was 
also performed. 
I. INTRODUCTION 
Resource saving is a major concern for process plants in 
general and in particular for oil refineries due to the low 
profit margins and big quantities involved; efficiency in the 
use of hydrogen as a raw material in an oil refinery is 
considered. Hydrogen H2 is an expensive utility required in 
many processes in an oil refinery, which is distributed by 
means of a H2 network from producer to consumer plants. In 
consumer plants, H2 is mainly used as reactant for 
desulfurization, de-nitrification and de-aromatization of 
naphtha and diesel in the presence of other H2 consuming 
side reactions. Desulfurization and de-nitrification reactions 
enable not to generate acid gases (sulphur oxides SOx: SO2, 
SO3; nitrogen oxides NOx: NO, NO2) either when used as 
heating fuel or in combustion engines, thus avoiding 
atmosphere pollution. 
In recent years when heavier fuels are being processed 
and also due to more strict environmental regulations, H2 
requirements have experienced a steady increase, with H2 
gaining significant importance in the refinery global 
economic balance. An efficient use of H2 in the daily 
operation is desired not only for its high production cost, but 
also because the economic penalty is even higher in scenarios 
where H2 production capacity is bottleneck for oil processing 
capacity. Further, decisions related to a H2 management are 
complex as many plants and operating constraints are 
involved in the network operation with a high degree of 
interrelation, not only from an optimality viewpoint but also 
 
*The research leading to these results has received funding from the 
European Union Seventh Framework Programme (FP7/2007-2013) under 
grant agreement n° 604068. Financial support from the Spanish Ministry of 
Science and Education under project DPI2012-37859 is also gratefully 
acknowledged. 
E. G. Sayalero, D. Sarabia, G. Gutiérrez and C. de Prada are with the 
Systems Engineering and Automation Department, University of 
Valladolid, Valladolid, Mergelina building EII, 47011, Spain (e-mail: 
elenags@cta.uva.es). 
S. Marmol, M. Sola, C. Pascual and R. González are with the Process 
Engineering/Advanced Control Departments, Petroleos del Norte Petronor, 
Muskiz, Vizcaya, 48550, Spain (e-mail: rgonzalezm@repsol.com). 
from a practical viewpoint because several operators in 
different control rooms are typically in charge; thus a 
quantitative criterion for decision support can prove very 
valuable. 
The approach to deal with the H2 network optimal 
management is driven by an operational framework where H2 
production must always exceed H2 consumption, because H2 
deficit is extremely damaging for catalysts which are very 
expensive. As H2 accumulation in a buffer vessel is not 
possible, besides the slow dynamic for H2 production in the 
furnaces of the intended steam-reforming plants, the target of 
H2 production minimization has to be achieved by means of: 
1) a good dynamic fit of H2 production to consumption, in 
order to minimize excess sent to the Fuel Gas network under 
pressure control in the headers; 
2) a better H2 redistribution from H2 producer to H2 
consumer plants and good reuse of the non-reacted H2. 
The decisions to be taken in the H2 network optimal 
management are: i) which plants must produce H2 and their 
production rates; ii) which combination of make-up flows 
from each header must provide H2 to each consumer plant; 
iii) the membranes state and in general the high pressure 
purge streams flow rate. One of the main problems to 
perform appropriate decisions regarding the management of 
H2 networks is the lack of information on many variables and 
the uncertainty associated to the existing measurements. 
Because of it, data reconciliation has been used as a way to 
estimate unknown magnitudes and to correct inconsistencies 
in the data, before a model based optimization procedure 
could be applied to determine the best use of H2 in the 
network. 
The optimal management of H2 in oil refineries has also 
been studied mainly from a design viewpoint as in [1], as 
well as integrated with other utility systems in the refinery 
operation as in [2]. 
The paper is organized as follow. The process is 
described in section II. In Section III the main challenges 
regarding data reconciliation are presented along with 
experimental results for validation purposes. In Section IV a 
framework is established for the optimal operation of a H2 
network and the achieved solutions analysed. Finally, 
conclusions are reported in Section V. 
II. HYDROGEN NETWORK DESCRIPTION 
High purity H2 is produced in steam-reforming furnaces 
in two plants (H3 and H4) in the refinery under 
consideration. Another low-purity H2 producer plants exist 
(P1 and P2), not being truly decision variables in operation 
as H2 is a byproduct of the catalytic reforming process. It is a 
low-purity H2 compared with the H2 produced in steam- 
Optimal Management of Hydrogen in a Petrol Refinery 
Elena G. Sayalero, Daniel Sarabia, Gloria Gutiérrez, Sergio Mármol, José Miguel Sola, Carlos 
Pascual, Rafael González and Cesar de Prada 
2016 European Control Conference (ECC)
June 29 - July 1, 2016. Aalborg, Denmark
978-1-5090-2591-6 ©2016 EUCA
1019
Authorized licensed use limited to: ULAKBIM UASL - Bilkent University. Downloaded on August 19,2024 at 07:51:32 UTC from IEEE Xplore.  Restrictions apply. 
 
 
Figure 1.  Schematic of the Petronor hydrogen network.
reforming furnaces. From these plants, H2 is distributed to 
the consumer plants using several interconnected networks 
at different purities and pressures. Excess H2 is partly 
recycled through a Low Purity distribution Header (LPH). A 
schematic of the network can be seen in Fig.1. 
In a general consumer plant (Fig. 2), before entering the 
reactor (∆R) the hydrocarbon (HC) feed is mixed with the 
recycled H2 stream (R) and with a make-up H2 stream from 
the network: e.g. from the two steam-reforming furnaces 
producer plants with high-purity H2 and from the Low Purity 
distribution Header LPH typically for the biggest consumer 
plants. After being separated in a high pressure HP separation 
drum (HPsep), non-reacted H2 is partially recycled (R) and 
can be partially purged to the Low Purity distribution Header 
(LPHHP). Most of the gas from the HP separator is recycled 
into the reactor inlet. A HP purge is usually needed to 
maintain the H2 purity minimum constraint in the HP system, 
while avoiding the accumulation of light ends in the system. 
Light ends are both generated in the reactor and supplied in 
the make-up due to low-purity H2 sources. 
Reacted sulphur turns into hydrogen sulfide (H2S), which 
is removed by absorption on an amine solution. Downstream 
of the high pressure (HP) separator, several separators and 
distillation columns at lower pressure enable the complete 
separation of H2 and light gases from the desulfurized 
hydrocarbon; these streams from the medium and low 
pressure MP/LP separation processes (LPoper) are burnt as 
fuel gas (FGLP), since their H2 purity is not high enough for 
making profitable its recovery and reuse. Regarding losses 
from the MP/LP systems, these purges are not a degree of 
freedom in operation, being separated under pressure control 
with the purpose of the complete removal of gas from the 
liquid hydrocarbon processed. Losses from the HP system are 
of a different nature: these purges are generally manipulated 
or decision variables and are usually sent to Low Purity 
Headers due to their high H2 purity, thus being reused in 
other consumer plants. Whenever available, a membrane unit 
(Z) makes possible to purify and recycle a permeate stream 
(FPRM_Z), at the expense of a purge to fuel gas (FGZ) with 
lower H2 purity. 
 
Figure 2.  Schematic of an H2 consumer plant with make-up from two 
Production Headers (H3, H4) and from one Low Purity Header (LPH). 
The total make-up stream H2 purity can vary depending 
on the ratio of flow rates coming from the different producer 
plants (H3, H4, P1, P2) or distribution headers (low-purity 
header LPH) which are combined, being the cheapest either 
high-pressure HP surplus from other consumer plants or 
byproduct streams from the catalytic reforming units. 
III. HYDROGEN NETWORK DATA RECONCILIATION 
Data reconciliation is a first stage needed for subsequent 
applications such as real-time optimization RTO or key 
performance index KPI computation, both with the purpose 
of decision support or process steering towards a more 
efficient operation. 
1020
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The target is to estimate consistent values of the plant 
variables based on a process model from available on-line 
measurements, 
taking 
advantage 
of 
redundancy 
in 
measurements. Accurate, consistent and robust estimations 
are looked for irrespective of process disturbances, 
measurement noise, measurement drifts, outliers and 
unreliable measurements, which are common in industrial 
practice, while at the same time enabling to update certain 
unknown model parameters. 
A model of the H2 behaviour in the network and 
associated plants is available from previous work reported in 
[3]; the network description and first results were presented 
in [4]. The model comprises mass balances for the considered 
species H2 and light ends LIG (mainly methane, ethane and 
propane), without distinguishing the compounds lumped as 
light ends but just taking into account a single variable 
molecular weight wLIG. All gas streams are thus modelled, as 
well as unit operations like membranes, reactors and 
separators including a simplified solubility model. 
Data reconciliation in the H2 network is a challenging 
problem due to the difficulty of an accurate plant state 
estimation. The uncertainty is mainly caused by: 
1) A great variability in operating conditions and in H2 
consumption in hydrotreating plants HDT, mainly influenced 
by: i) feedstock sulphur content; ii) desired grade for 
naphtha/diesel, with low or high sulphur specification for 
product according to its final use as either transport fuel or 
heating fuel; iii) the type of hydrocarbon processed and 
specially the light-cyclic-oil LCO content due to double-
bounds leading to higher H2 consumption. 
2) The lack of on-line measurements for gas H2 purity yH2 
and molecular weight w. In general, on-line complete quality 
analysis of products is difficult due to mixtures complexity 
and also economically demanding due to installation and 
maintenance costs of analysers, thus not often economically 
justified in process industries. 
3) The use of orifice-plate differential pressure flow 
meters for gas streams in spite of being error prone because it 
is the cheapest available technology, also commonly used for 
similar applications in other process industries. Drift errors 
are present due to: i) process variability, with different 
[pressure P, temperature T, molecular weight w] conditions in 
operation from those corresponding to design/calibration; ii) 
the differential pressure transmitter, when the flow rate is 
up/low in the scale; iii) inaccurate compensations, i.e. 
flowmeters where no pressure measurement is available but 
just downstream or upstream a valve, flowmeters where no 
temperature measurement is available. 
4) 
Furthermore, 
another 
source 
contributing 
to 
uncertainty in a great extent is the fact that gas molecular 
weight w experiences significant variations for small changes 
on the light ends composition, due to the low value of H2 
molecular weight w (2) as compared to those of the main 
impurities methane CH4, ethane C2H6 and propane C3H8, 
whose w are 16, 30 and 44 respectively. This is an important 
difference as opposed to other gas networks as natural gas 
networks, where gas composition can be assumed constant. 
5) In certain cases, improper instrument installation or 
maintenance is also responsible for systematic errors. 
The data reconciliation problem is formulated as the 
solution of the minimization of the sum of square errors of 
the model-measurement deviations, basically flow rates F 
and hydrogen purities yH2 normalized by the instrument span 
fweig_F/y, under the constraints imposed by the model and other 
technical limits as in (1). 
(
)
(
)
∑
∑
=
=
−
+
−
=
purities
i
H
med
i
H
i
i
y
weig
med
i
i
i
flows
i
i
F
weig
w
y
F
y
y
f
F
F
f
J
i
i
i
1
2
2
,
2
,
_
2
,
1
,
_
}
,
,
{
/
min
β
 
 
ope
des
ope
i
des
des
i
T
w
P
w
P
T
·
·
=
β
 
(1) 
subject to: 
       model equations 
LIG
i
LIG
i
LIG
i
H
i
H
i
H
i
i
i
i
w
w
w
y
y
y
F
F
F
max
,
min
,
2
max
,
2
2
min
,
max
,
min
,
≤
≤
≤
≤
≤
≤
 
The reference for model flow rates Fi is not the raw 
measurement Fi,med but a compensated one, where the 
compensation factor βi allows to correct for process 
deviations in operating conditions (ope) from the fixed 
calibration/design 
conditions 
(des) 
in 
pressure 
(P), 
temperature (T) and molecular weight (w) of gas stream. In 
general Pope and Tope are measured whereas wi is estimated in 
the data reconciliation problem. The compensation factor βi 
is derived from mass and energy balances applied to the 
computation of the flow rate for the orifice-plate flowmeter 
with a differential pressure transmiter. The H2 network data 
reconciliation problem includes 190 process measurements, 
the decision variables being gas stream flow rates as well as 
the consumption/generation terms in the reactors and the 
solubility coefficients in the separators. 
Two approaches have been implemented for the solution 
of (1), namely, sequential and simultaneous approaches. The 
sequential one connects a reduced SQP optimization 
algorithm (SNOPT®) with the model simulation in 
EcosimPro®, taking advantage of  a smaller number of 
degress of freedom for the optimization (166) with 143 
explicit inequality constraints. 
One of the main obstacles to obtain adequete solutions in 
data reconciliation is the presence of gross errors, generated 
usually by faulty instruments, that distorts the estimation 
spreading the errors among other variables. Instruments with 
gross errors must be detected by a combination of data 
analysis and repeated execution of the data reconciliation and 
removed. This procedure is slow and implies additional 
difficulties for the industrial implementation.  
An alternative is the use of robust estimators, substituting 
the least squares cost function (1) by another cost function 
which is equal for small errors, but for larger ones grows at 
speeds lower than linear, like the Fair function (2), thus 
limiting the spreading of errors among other variables and 
increasing robustness: 
1021
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∑
∈
















+
−
M
j
j
j
c
c
c
ε
ε
1
log
2
 
(2) 
Here ε stands for the normalized error and c is a tuning 
parameter. The second implementation for the solution of the 
data reconciliation problem is based on a simultaneous 
approach and uses the cost function (2) and IPOPT® as 
optimization algorithm in the GAMS® environment, giving 
robust results against gross errors and helping in the detection 
of the faulty instruments. The implementation involves 
around 4000 variables and constraints, including slack 
variables to guarantee feasible solutions, and takes a few 
CPU minutes. 
A.  Validation 
For the joint validation of the model and the data 
reconciliation, trends have been computed for periods of 
several days in different seasons with industrial real historical 
data. Furthermore, periods covering a change in the operating 
point have been chosen to evaluate the results for two 
different scenarios as well as the transient state between 
them. Allowed range for all the individual flow rates, centred 
in the raw measurement, was also tuned at this stage. 
Next, results for the validation stage with the sequential 
approach will be presented. The off-line validation performed 
is accepted as correct according to the following facts: 
1) Even though the model may suffer from poor 
identifiability at some points due to the lack of on-line 
measurements, consistent results in trends according to 
measured variables are obtained thanks to the allowed ranges 
in certain parameters. At the same time, model flexibility is 
enough to fit any set of real data, especially in the HP system 
which is the most determining and interesting to estimate. 
Consistency checking in trends has been carried out with raw 
measurements for important streams such as producer plants 
total production, make-up streams, HP purges and analysers. 
Flow rates and H2 purities are shown in Fig. 4, 5, 6 and 9 for 
a few of the most important streams, where time scale is 
hours. 
2) Consistency checking in certain important relief-valves 
where no flow measurement is available has been performed 
according to the valve opening, as can be seen in Fig. 7-8. It 
should be noted that in transient states when the relief-valve 
is opened the degree of redundancy decreases by one, 
therefore contributing to reliability of the results in case of 
consistent trends as the ones shown. 
Three flow rates are compared in each figure: the raw 
measurement (*_dtF), the compensated raw measurement 
with pressure, temperature and molecular weight in operation 
(*_Fvc) and the model flow rate (*_Fmdl) solution of the 
data reconciliation problem, whose reference for model 
fitting is the compensated raw measurement (*_Fvc). 
Regarding H2 purities, the raw measurement (*_dty) is 
compared with the reconciled model purity (*_ymdl) only for 
the HP system where on-line measurements are available. 
Model parameters and bounds have been tuned to achieve 
good performance, guaranteeing consistency and robustness 
of results at the expense of a more reduced flexibility than 
expected according to process variability in certain parameter 
ranges. Although unbiased estimates cannot be guaranteed 
with the weighted-least squares WLS technique in the 
presence of non-random errors, consistency according to 
expected trends in important variables have been achieved 
taking advantage of all redundancies. Narrow ranges for 
certain flowmeters FI and analysers AI have proved 
determining, due to multiple local optima and interactions. 
In a similar way, the robust implementation (2) in 
GAMS® allows for an easy detection of potentially faulty 
instruments, as in Fig. 3 that shows two potential faults in 
yellow in a couple of instruments in one consumer plant after 
the data reconciliation has been performed. 
 
 
Figure 3.  Typical display in the Excel® interface showing two faulty 
instruments. 
 
 
Figure 4.  Production of H2 in steam-reforming furnace plant. 
 
 
Figure 5.  Purge to FG from the low-purity header LPH. 
1022
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Figure 6.  Make-up stream from LPH to important consumer plant. 
 
 
Figure 7.  Relief valve opening from H4 header to H3 header. 
 
Figure 8.  Estimated flow rate for relief from H4 header to H3 header. 
 
Figure 9.  HP high-pressure H2 purity for important consumer plant. 
IV. HYDROGEN NETWORK OPTIMAL REDISTRIBUTION 
The optimal redistribution is formulated as the RTO 
problem: 
 
3
3
4
4
·
·
min
H
H
H
H
F
F
p
F
p
J
i
+
=
 
(3) 
s.t.     process model 
      process constraints 
where the aim is to minimize the cost of the fresh H2 
generated in the steam-reforming plants under the constraints 
imposed by the model and operation, with pi the price for the 
produced H2. Decision variables include production of fresh 
H2, make-up streams to consumer plants from the different 
headers, H2 purities, HP purges and membranes state. 
All model parameters and certain variables estimated in 
the data reconciliation stage are assumed as invariable and 
their values fixed in the optimal redistribution problem. That 
is the case for the H2 consumption in reactors, the solubility 
parameters in separators, as well as certain gas flow rates 
without influence over H2 optimal management. The 
underlying hypothesis is that the plants current operation is 
maintained and not disturbed in spite of allowing for margin 
in the production, redistribution and reuse of H2 for a better 
management of this utility. Constraints apply mainly to pipes 
capacity, high-pressure system minimum H2 purity yHP
H2 in 
the consumer plants to guarantee catalyst maintenance, 
minimum ratio H2/hydrocarbon at the reactors inlet, operating 
range for membranes, producer plants capacity and 
reciprocating and centrifugal compressors capacity. 
Again, the problem has been implemented and solved in 
the two environments above mentioned with sequential and 
simultaneous approaches. For the sake of comprehension, 
first the general framework for an improved efficiency in H2 
use will be described and then solutions and optimal 
strategies analysed. 
At the global network scope, two extreme scenarios can 
be distinguished according to the availability of H2 in the 
Low-Purity Header LPH, i.e. low-purity H2 in excess either 
from consumer plants HP purges or from catalytic-reforming 
units and which can be further reused. These two extreme 
cases are excess of LPH H2, when the LPH H2 needs to be 
purged because it is not useful to fulfil the constraints, mainly 
the minimum specification for the HP system purity yHP
H2; 
and deficit of LPH H2, when all of it can be reused as make-
up thus substituting high-purity H2 from the steam-reforming 
furnace producer plants. Scenarios found in industrial 
practice are in between these two, sharing characteristics 
from both of them. These two scenarios, deficit or excess of 
LPH H2, mainly depend on: i) H2 generated as by-product in 
the catalytic-reforming plants; ii) minimum specifications 
yHP
H2 at the consumer plants. Regarding the use of H2 from 
the LPH, these specifications are determining: low 
specifications enable to take advantage of all the low-purity 
H2 in excess, whereas higher specifications imply the need to 
purge at the network level, get rid of low-purity H2 in excess 
because it is no longer useful to fulfil the constraints. 
When yHP
H2 specifications are high there is excess of LPH 
H2 that needs to be purged, because not all of it can be used 
to fulfil the constraints. The specification yHP
H2 is binding 
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constraint in all consumer plants, because the excess of LPH 
H2 enables to adjust its value; in the deficit case, H2 
requirements in consumer plants have to be satisfied with 
high-purity H2 from steam-reforming furnaces units and as a 
result a gap can arise in the yHP
H2 with respect to the 
minimum specified. High-pressure HP purges in consumer 
plants going directly to FG instead of to other headers are not 
minimized in case of excess, which entails maximizing 
make-up from the catalytic-reforming plants. The membranes 
are a particular case of HP purges to FG and consequently 
they are needed in scenarios of LPH H2 excess. For each of 
the previous items, the opposite applies in the extreme case 
with deficit of LPH H2. 
Three categories can be established for consumer plants 
regarding the decision variable HP purge flow rate, ordered 
according to decreasing efficiency in H2 use: i) HP purge to 
the Low Purity Header (LPH), collecting H2 excess from 
different plants; ii) HP purge to the Fuel Gas Header (FG) 
through a membrane unit, thus recovering part of the H2 in 
the permeate. In this case, the purge H2 purity is lower than 
the constraint for yHP
H2; iii) HP purge directly to the Fuel Gas 
Header (FG). Again, the purge H2 purity is the same as the 
constraint for yHP
H2; therefore, this is the case only for plants 
with a low H2 purity constraint in the HP system, thus not 
being profitable the reuse of the excess H2. 
An analysis of solutions for the optimal operation enables 
to identify a clear logical pattern regarding high pressure 
(HP) purges from consumer plants or headers. The solution is 
the logical one, that is, to purge Low Purity Header LPH 
excess (in general low-purity H2), if any, at the network 
scope through the HP purge to FG at the lowest H2 purity 
until it gets saturated, following an increasing order of H2 
purity to purge in consumer plants. Profits of up to 0.5-2% 
are achieved depending on the scenario; the biggest margins 
correspond to scenarios with HP purges to FG with non-zero 
values in split-range pressure control secondary branches. 
The RTO system has been design as decision-support in 
open-loop, for given hydrocarbon loads not modified. At the 
same time, the existence of patterns in the optimal solution 
facilitates its implementation in real-time as a control system, 
taking also into account that the biggest consumer plants are 
those with the highest flexibility regarding make-up and with 
potential for profit increase due to make-up redistribution or 
exchange among the make-ups from the different headers. 
A better control of the multivariable interactions in the 
interconnected headers also allows for a reduced gap in the 
mandatory header purges under pressure control for the 
purpose of disturbance rejection, thus enabling a better 
dynamic fit of H2 production to H2 consumption therefore 
minimizing 
the 
production 
cost. 
This 
approach 
is 
complementary to the RTO one as stated in the introduction. 
Another possible decision with a higher degree of complexity 
is to manipulate hydrocarbon loads, which can provide better 
margins because the remaining production capacity of H2 can 
be used to increase the total refinery processing capacity. 
This lead to the implementation by the personnel of the 
refinery of a MPC layer that manages the H2 use in the six 
most important plants (two producers, four consumers) and 
the main distribution headers, by means of a commercial 
DMC controller with its linear programming local optimizer 
acting on the fresh H2 production, hydrocarbon loads, H2 
make-up to the plants and HP purges. The DMC minimizes 
H2 losses to the fuel-gas network, maximizes hydrocarbon 
loads to the plants and maintains within range other 
important variables like H2 purities or H2/hydrocarbon ratios 
while providing consistent operation results. 
V. CONCLUSION 
The problem of H2 management in oil refineries has been 
addressed and the main features of a system oriented to 
provide support in its operation has been presented. The 
system receives data from the PI software that collects 
information and measurements from the control rooms and 
uses data reconciliation to estimate unmeasured variables 
and to provide consistent compensated measurements. These 
feed a RTO decision-support system giving targets for the 
optimal H2 use in the network.  
The general framework for an improved efficiency in H2 
use was described. Regarding the optimal H2 redistribution, 
solutions could be easily parameterized corresponding to the 
logical optimal operation; trade-offs were identified and 
patterns discovered. A real time implementation as a control 
system commanded with a DMC controller has been 
implemented successfully. Nevertheless, some topics around 
the interaction of the RTO and MPC layers are still open and 
a more integrated approach can be advantageous due to 
frequent changes in scenarios, aiding the operators to save 
time in the identification and implementation of the optimal 
policy. Moreover, explicit consideration of the uncertainty as 
a stochastic optimization problem is planned for future work. 
According to the previous analysis, the most important 
variables to monitor with the aim of suboptimal operation 
identification and subject to changes depending on the 
particular scenario are the following: i) make-up from the 
LPH to the biggest consumer plants, just to assure a roughly 
even distribution; ii) direct HP purges to FG for certain 
medium-size consumer plants; iii) purge to FG from the 
membrane unit in operation, as well as the inlet flow rate, 
i.e. membranes optimal point of operation. 
ACKNOWLEDGMENT 
The cooperation of the Petronor-Repsol group is 
gratefully acknowledged. 
REFERENCES 
[1] G. P. Towler, R. Mann, A. J.-L. Serriere, C. M. D. Gabaude, 
“Refinery hydrogen management: cost analysis of chemically-
integrated facilities,” Ind. Eng. Chem. Res., vol. 35, no. 7, pp. 2378–
2388, 1996. 
[2] J. Zhang, X. X. Zhu, G. P. Towler, “A simultaneous optimization 
strategy for overall integration in refinery planning,” Ind. Eng. Chem. 
Res., vol. 40, pp. 2640–2653, 2001. 
[3] E. Gómez, D. Sarabia, S. Cristea, G. Gutiérrez, C. A. Mendez, J. M. 
Sola, E. Unzueta, R. González, C. de Prada, “Simplified modelling 
and validation of an industrial diesel hydrodesulfurization plant,” 
DYCOPS 9th International Symposium on Dynamics and Control of 
Process Systems, Leuven, Belgium, 2010. 
[4] D. Sarabia, C. de Prada, E. Gómez, G. Gutiérrez, S. Cristea, C. A. 
Mendez, J. M. Sola, R. González, “Data reconciliation and optimal 
management of hydrogen networks in a petrol refinery,” Control Eng. 
Pract., vol. 20, no. 4, pp. 343–354, 2012. 
 
1024
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METHODS FOR PETROLEUM
WELL OPTIMIZATION
Automation and Data Solutions
RASOOL KHOSRAVANIAN
BERNT S. AADNØY

Preface
In today’s world, there are two paths: navigating to a new digital future or being engulfed
by exponential competitive change. For this reason, many companies have started to
focus on digitalization, optimization, and tight control of their core operations while
at the same time innovating.
Optimization and decision-making during the planning and execution of drilling, and
afterwards during well operations, is challenging due to subsurface uncertainty, limited
availability of measurements, and the need for interaction and collaboration between
different disciplines. However, digitalization has driven a radical shift in how we can
manage and run well operations remotely. It is a growing force in the offshore oil and
gas industry too. Its potential to optimize operations, including increasing safety and
quality, and reducing risk, is a strong driver for an industry with ever-rising costs.
The coronavirus (COVID-19) crisis has accelerated the pace of digitalization beyond
anything we could have imagined. Many companies have already boosted their digital
transformation with the use of cloud computing, big data, artiﬁcial intelligence (AI),
and the Internet of Things (IoT). This has made a signiﬁcant contribution to the transfor-
mation toward fully connected and automated systems that will result in high-performance
operations in different industries, including petroleum and energy.
Data mining, metaheuristic optimization algorithms, multiple-criteria decision-
making (MCDM), case-based reasoning (CBR), Monte Carlo Simulation, and machine
learning (ML) are attractive tools in this age of artiﬁcial intelligence. ML algorithms, such
as deep learning, could form one of the fundamental pillars for prediction and optimiza-
tion in petroleum well operations across the industry. This book, the ﬁrst of its kind,
presents a unique, understandable view of optimization, machine learning, and the other
available tools, using many practical examples.
Models do not deliver enough value if there is no direct path to code production, yet,
up to now, there has not been one cohesive resource that bridges between theory and
application, showing how to go from models to code. Therefore, in this book, we
have focused on giving today’s engineers and R&D teams real-time data solutions speciﬁc
to drilling and production assets.
In this book, you will learn how to translate an executable model of your application
into running code. Information on how to access the relevant open-source code has been
given at the end of most chapters.
ixj
The availability of open-source code seems to be helping to promote digitalization.
Also, open discussions about the particular advantages and drawbacks of speciﬁc code
or software will help companies, developers, and users view the trade-offs between the
different software.
Digitalization is the way forward, and we believe it is time for MSc and PhD programs
in universities’ petroleum and energy departments to focus on the knowledge and skills
required to tackle the oil and gas industry’s most challenging problems using digitaliza-
tion. Such a program could be an interdisciplinary course, encompassing a range of
updated petroleum engineering fundamentals, which would produce technically well-
prepared graduates with a sound knowledge of the industry. This book provides support
for such a course, ﬁlling the gaps between theory and practice in earlier text books.
Finally, we think the way forward for companies is neither to adopt a wait-and-see
strategy to get a better picture of how digitalization develops before implementing it
themselves nor to pursue a conservative digitalization strategy. The authors of this
book propose that researchers, and oil and gas companies, their CEOs, managers, and
engineers should understand the signiﬁcant impact that digitalization can have and
accelerate its integration into their business’s core priorities.
Stavanger, July 2021
Rasool Khosravanian
Bernt S. Aadnøy
x
Preface

Open Journal of Energy Efficiency, 2021, 10, 121-135 
https://www.scirp.org/journal/ojee 
ISSN Online: 2169-2645 
ISSN Print: 2169-2637 
 
DOI: 10.4236/ojee.2021.104008  Nov. 19, 2021 
121 
Open Journal of Energy Efficiency 
 
 
 
 
Economic Benefits of Energy Efficiency to the 
Petroleum Refineries in Ghana: A Case of Tema  
Oil Refinery (TOR) 
David Ayo1, Jones Lewis Arthur1,2
, Kwadwo Adinkrah-Appiah3 
1Institute of Distance Learning, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana 
2Faculty of Applied Science and Technology, Sunyani Technical University, Sunyani, Ghana 
3Faculty of Engineering, Sunyani Technical University, Sunyani, Ghana 
 
 
 
Abstract 
This study investigates the economic benefits of energy efficiency to petro-
leum refineries regarding Tema Oil Refinery (TOR), Ghana. The study ex-
plores lessons relating to the cost of production, energy recovery levels and 
economic fortunes of the refinery activities and designs a conceptual frame-
work for improving the energy efficiency of Tema Oil Refinery (TOR). The 
study adopted a descriptive design using a quantitative approach to provide a 
statistical background to investigate the economic benefits of energy efficien-
cy. A sample of 84 was adopted for study from a staff population of 520 
working at Tema Oil refinery. In addition to primary data, secondary data on 
energy supply and consumption values from 2008 to 2019 was gathered. For 
the primary survey, 84 respondents were sampled from TOR and a structured 
questionnaire was used to retrieve information. A correlation analysis at P < 
0.05 was conducted to test the relationship and significance of energy effi-
ciency and economic benefits to the refinery. The study concludes that there 
is a linear trend between energy production (supply) and energy consump-
tion. The energy generated in the entire economy of Ghana far exceeds the 
amount of energy consumed thus raising issues of waste or excesses that calls 
for better policies and management plan to improve EE. The study also iden-
tified that issues of lost energy are critical to the operations of the petroleum 
industry as the situation is compounded by the inability of the refineries to 
explore better ways to reduce and manage the waste. The study concludes 
that a significant and positive correlation between energy efficiency and the 
economic fortunes of Tema Oil Refinery is necessary for its economic for-
tunes. The supply of energy should have equivalence to the public consump-
tion of energy. 
How to cite this paper: Ayo, D., Arthur, 
J.L. and Adinkrah-Appiah, K. (2021) Eco-
nomic Benefits of Energy Efficiency to the 
Petroleum Refineries in Ghana: A Case of 
Tema Oil Refinery (TOR). Open Journal of 
Energy Efficiency, 10, 121-135. 
https://doi.org/10.4236/ojee.2021.104008 
 
Received: September 22, 2021 
Accepted: November 16, 2021 
Published: November 19, 2021 
 
Copyright © 2021 by author(s) and  
Scientific Research Publishing Inc. 
This work is licensed under the Creative 
Commons Attribution International  
License (CC BY 4.0). 
http://creativecommons.org/licenses/by/4.0/   
 
 
Open Access
D. Ayo et al. 
 
 
DOI: 10.4236/ojee.2021.104008 
122 
Open Journal of Energy Efficiency 
 
Keywords 
Efficiency, Refinery, Petroleum, Economic Benefit, Recovery, Production 
Cost 
 
1. Introduction 
This study investigates the economic benefits of energy efficiency to petroleum 
refineries regarding Tema Oil Refinery (TOR), Ghana. The study explores les-
sons relating to the cost of production, energy recovery levels and economic 
fortunes of the refinery activities. The study finds out how increased energy effi-
ciency can reduce the cost of production associated with the refinery activities as 
well as encourage economic buoyancy to provide an economically beneficial en-
vironment to the refinery.  
Energy Efficiency (EE) emphasizes the goal to reduce the amount of energy 
required to provide products and services [1]. Energy efficiency also relates to 
the use of less energy to provide the same level of energy [2]. It is, therefore, one 
method to reduce the adverse impacts of the use of energy such as greenhouse 
gas emissions. Efficient energy use deals with a means to derive more efficient 
technology or process. But, the issue of energy efficiency has attracted much at-
tention in recent literature due to the potential positive impacts it could have on 
reducing energy demand and usage. 
[3] argues that the energy efficiency school of thought proposes end-use 
energy utilization for eight energy items by incorporating end-use vitality profi-
ciency pointers as well as carbon force markers for four areas (private sector, 
administration, industry and transport). These pointers are processed by utiliz-
ing action data for key sectors of the economy. Some literature has quantified 
energy productivity to be unique and linked to many dimensions of energy effi-
ciency [4]. For example, power level consumption changes across nations relying 
upon modes of transport (for example, street, air, water, rail), vehicle types (for 
example traveller vehicles, transports, and so on) and on the normal inhabitancy 
(travellers per vehicle) has diminished over time as individuals progressively 
drive their vehicles as single occupants [4]. On energy consumption in the as-
sembling sectors, the normal assembling energy force in a nation is considered 
to rely upon the general load of the distinctive sub-parts in the assembling blend. 
For instance, the force is especially high in nations like Nigeria, where the paper 
and printing industry-which is vitality serious accounted for about 57% of abso-
lute assembling energy utilization in 2017 [5]. Taking a gander at private struc-
tures, energy proficiency upgrades for space warming is followed by patterns in 
private space warming force characterized as vitality utilization per floor zone 
[3]. 
Improving energy proficiency, by diminishing the amount of devoured vitali-
ty, can upgrade the security of vitality. Vitality productivity implies utilizing less 
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vitality to achieve a similar errand [6] [7]. More proficient utilization of vitality 
across a nation would reduce the expenditure burden on energy users such as 
property holders, schools, government offices, organizations and businesses. The 
cash that would have been expended could rather be spent on different things 
like training, buyer merchandise/items and other benefits. Energy conservation 
through improved effectiveness and preservation has potential impacts on ac-
commodating the objectives of financial turn of events, energy security and nat-
ural assurance [8]. 
Energy effective pointers essentially have diminished in numerous IEA na-
tions including The Netherlands, Portugal, Germany and Ireland who have en-
countered decreases of over 35% since 2000 [3]. Nations with warmer climates 
including Ghana by and large have lower space warming forces, as less vitality is 
required on normal, to keep the temperature inside private structures at com-
fortable levels.  
Ensuring Energy efficiency has the end benefit of reducing the cost at which 
energy is sold to the final consumer as well as improving the longevity of its 
usage. However, [9] has reported that there have been several energy inefficien-
cies in the petroleum sector [9] [10]. The Commission further adds that Tema 
Oil Refinery loses a lot of energy in producing petroleum products such as gaso-
line and diesel. The inefficiencies in the energy system that also influence energy 
recovery levels are mainly blamed on the lack of capital, outdated technology 
and limited skilled persons on oil sites. The implications for energy inefficiencies 
in Ghana’s economy are low revenue accruals as well as instability in employ-
ment in the sector.  
Some literature has indicated that energy efficiency is an important compo-
nent of a company’s environmental strategy, including those in the petroleum 
industries [11] [12] [13]. Although the final points of sale of energy are often 
expensive and inefficient, energy efficiency can be the cheapest opportunity to 
reduce pollutant emissions and have high economic benefits for refineries [14]. 
However, it is important to note that oil refining is one of the biggest vitality ex-
pending parts of energy, devouring around 4 percent of all-out worldwide essen-
tial vitality utilization [15]. Ensuring energy efficiency at the refineries is an im-
portant but daunting task. For example, a fruitful, financially savvy speculation 
into vitality proficient innovations and practices would address the difficulty of 
keeping up the energy yield, decreased production cost and improve energy re-
covery levels [16]. Alternately, higher energy proficiency is frequently connected 
with higher profitability, vitality and creation advancement regularly connected, 
and vitality productivity costs lowered [17] [18]. 
Cost-effective investment is especially important, as energy-efficient technol-
ogies often include additional benefits, such as increasing the productivity of the 
company, reducing the marginal cost of production to a globally acceptable 
minimum and improving energy efficiency and or recovery levels. But these 
globally acceptable standards have barely being achieved in sustaining energy ef-
ficiency in Ghana. As mentioned, the lack of capital and outdated technology are 
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the leading causes of this challenge. But a huge assortment of chances exists in-
side oil treatment facilities to decrease vitality utilization while keeping up or 
improving the efficiency of the plant. Studies by a few organizations in oil refin-
ing including Tema Oil Refinery (TOR) have exhibited the presence of a signifi-
cant potential for vitality effectiveness improvement in practically all offices [16] 
[17]. 
Tema Oil Refinery (TOR) is the chief oil processing plant in Ghana. The 
processing plant was among the initial eight treatment facilities in Africa, as of 
1963. The processing plant produces a 45,000 barrel for each stream day (bpsd) 
limit Crude Distillation Unit and supplies this amount out of the public interest 
of 65,000 bpsd [19]. Many experts argue that the efficiency of the refinery ma-
chinery is weak and as a result plays no direct economic benefit to the refinery 
and Ghana as a whole [11] [20]. Other experts have also argued that obtaining 
new refinery technology will improve efficiency as well as provide consistency 
and also reduce the current high cost of production. 
According to [11], energy efficiency in the oil sector of Ghana has become 
very critical because of the minimal effort being made toward the achievement 
of higher efficiency. Without an efficient energy management practice, there will 
be no form of sustainable energy in Ghana’s petroleum sector. [21] indicated 
that there are approved ways of ensuring energy efficiency in oil-producing 
countries-Ghana is however yet to fully ensure higher efficiency levels and im-
prove sustainability. Ghana generates energy from hydropower, fossil fuel (ther-
mal energy), and renewable energy sources. Energy generation is one of the key 
means to improving the development of Ghana’s economy. The Energy Efficiency 
Policy Review, commissioned by the Energy Commission in 2015 under the 
China-Ghana South-South Cooperation and financed by the Danish government 
identified gaps and Solutions in Ghana’s Report. The partnership identified gaps 
that impact energy efficiency in the petroleum sector and also found that fossil 
energy source is critical at the level of increasing energy consumption.  
The 2010 Ghana National Energy Policy envelops cross-slicing intends to deal 
with the significant test of quickly developing vitality requirements for the public 
advancement plan. These underline the requirement for improved help strategy, 
and for the private sector inclusion to cultivate manageable and productive vital-
ity age. As indicated by the [10], Ghana’s sustainable power source advancement 
will fundamentally concentrate on the huge little hydro capability of the nation. 
Consequently, 21 small scale and medium-hydro power locales, with limits ex-
tending from 4 kW to 325 kW, have just been recognized as reasonable for age. 
Ghana likewise has extraordinary potential for squandering to-vitality and bio-
mass, essentially recovery of woody biomass assets, and the National Energy 
Policy puts more accentuation on biofuel age ventures. Sun-oriented radiation 
additionally gives generous potential to control age, and expanded government 
uphold for the public sun-based assembling segment which framed piece of pub-
lic vitality strategy. Meanwhile, [3] indicates that the implementation of energy 
policies has barely made an impact in ensuring energy efficiency in Ghana’s re-
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fineries. This assertion creates a bigger problem that ought to be investigated. 
Various policy directions have been recommended for the petroleum industry. 
These include the non-parametric data development analysis approach [22] 
achieving EE through reduced consumption [23] and application of new tech-
nology [10]. 
In addition, Ghana Energy Commission argues that issues of inadequate re-
search and development, weak demonstration and deployment as well as poor 
financing of energy investments as key factors undermining energy efficiency in 
the sector. Currently, the crude furnace of Tema Oil Refinery (TOR) is produc-
ing at a very low capacity (25,000 bpsd) but at a very high cost of production per 
barrel (over $30 bp). This raises a mandatory question regarding the economic 
benefits of the refinery amidst minimal production rates and faulty furnaces of 
the refinery. The concern of how energy efficiency can be improved for TOR to 
enjoy some economic benefits in terms of its cost of production. Therefore, this 
study assesses data on energy losses/gains as well as energy supply during the 
operational process of TOR. The study, therefore, examines the trends in Energy 
Efficiency (EE) in the Petroleum Sector of Ghana; assesses the energy recovery 
levels of Tema Oil Refinery (TOR); and examines the relationship between 
Energy Efficiency EE and the economic fortunes of Tema Oil Refinery (TOR). 
2. Methods 
The researcher used a quantitative (descriptive) approach of research to provide 
a statistical background to investigate the economic benefits of energy efficiency. 
These are the parts of the study that have informed the use of the quantitative 
approach. Furthermore, the descriptive design was also adopted to examine per-
ceptions of the key variables being investigated. A descriptive study is useful in 
defining a subject by constructing the profile of people, groups or events through 
tabulation and the collection of data on study variables. An explanatory struc-
ture is utilized in this investigation. An illustrative structure likewise guarantees 
supreme clarification of the situation and ensures that there is no predisposition 
in information assortment, and empowers information assortment from a note-
worthy objective populace or informational collection in a practical way. There-
fore, an explanatory design helped to establish the economic benefits of energy 
efficiency with an examination of profitability and production levels of the Tema 
Oil Refinery.  
[24] defines population as the total of items for which the information is de-
sired. The population of the study is the staff of Tema Oil refinery made up of 
520 current active staff of Tema Oil Refinery (TOR). 
The size of the sample decides the factual exactness of the discoveries. As in-
dicated by [25], when directing examination one can’t consider everyone, all 
over the place, doing everything. As a rule, when directing quantitative explora-
tion, one, as a rule, chooses an example of individuals, settled in their unique 
circumstances and concentrated inside and out. For sampling to be effectively 
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done a handful of respondents who are ready to respond to the data instrument 
was sampled. An estimate of 84 respondents was, therefore, obtained. This sam-
ple size is obtained from the application of [26] formulae for establishing a sam-
ple size.  
(
)
(
)
(
)(
)
2
2
2
2
2
2
520 0.5
84.00
1
0.5
520 1 0.05
NC
n
C
N
e
=
=
=
+
−
+
−
 
where n is the sample size, N is the population, C is the coefficient of variation 
(0.5), e is the level of precision (0.05) [26]. For a total staff of, a sample size of 84 
was obtained for the study.  
The procedure for sampling respondents of TOR to respond to the primary 
data instrument was based on probability sampling. The simple random sam-
pling method was used to sample the staff of TOR who is in charge of the engi-
neering section of the refinery. A simple random sampling technique will then 
be employed to select at least 84 staff across the 520 staff.  
Secondary data from the latest annual published statements from the Tema 
Oil refinery was used in the study. The data on energy recovery levels were ob-
tained for a period of 10 years from the year 2008 to 2017. Data from secondary 
data reports are considered reliable since they are prepared based on standar-
dized accounting/economic/engineering principles. Primary data in the form of 
questionnaires will be used to solicit information from experts within the Tema 
Oil Refinery using well-structured questionnaires.  
The research instrument used was the annual published statements from the 
Tema oil refinery for the period, 2008 to 2017. This instrument represents sec-
ondary data that helped with the analysis of the trends in energy efficiency and 
the recovery levels of the Tema Oil refinery. The primary data instrument in the 
form of a well-structured questionnaire was also used to elicit information from 
staff on the economic benefits of energy efficiency. 
The questionnaire was divided into four sections where demographic infor-
mation of staff will be accessed for section A. Section B inquired about issues 
regarding energy efficiency at TOR. Section D accessed staff energy recovery le-
vels at TOR. The research questionnaire adapted [27] that explored the energy 
efficiency of oil refineries. The questionnaire used a 5 point Likert scale to 
measure/quantify responses. The five-point Likert scale included; 1 = Strongly 
Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.  
The questionnaire was administered using face to face approach and virtual 
means such as Google forms. The research instrument was administered to only 
technical and financial staff of Tema Oil refinery that are willing and ready to 
provide the necessary information needed for the completion of the research. 
Face to face method of administration as well as virtual methods of data gather-
ing was used. Pre-testing was conducted amongst the staff of Tema Oil refinery 
to test the reliability and validity of the research questions. 
Data was collected, edited, sorted for completeness and then analyzed using 
IBM Statistical Product and Service Solutions (SPSS) software version 23 to 
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conduct inferential statistical analysis. 
In the establishment of the objectives of the study variables such as profitabil-
ity and production level of oil were used to assess the economic benefits of EE to 
TOR. Descriptive statistical analysis in the form of Means, Standard Deviations 
and Trends were used as well. At a 95% confidence level, the t and F-test were 
used in the determination of the statistical significance of this research. The sig-
nificance of regression coefficients was tested by t-test whereas the determina-
tion of the significance of regression equation was tested by F-test. Hence the 
Asymptotic or significance of the variables was valued at P < 0.05. 
Reliability was assured by calculating Cronbach’s alpha statistic to estimate re-
liability. [28] recommends 0.7 as the accepted benchmark for Cronbach’s alpha. 
[28] argues that, if the coefficient alpha is too low, the indication is that the items 
measuring the scale have very little in common. Therefore, a Cronbach’s alpha 
of at least 0.70 will be ensured. 
The researcher assured ethical consideration by adhering to a permission 
note/letter on conducting a study at TOR with a positive response from TOR to 
allow this study to be conducted. The sampled respondents were given adequate 
time and space to fill the questionnaire. 
3. Results 
The first part of the results described the trends of energy supplied and con-
sumed whereas the second part explored statistical tools such as mean and stan-
dard deviation. The inferential analysis used correlation statistics to examine the 
relationship between energy efficiency and energy fortunes. In sum, the follow-
ing specific objectives were assessed; examined the trends in Energy Efficiency 
(EE) in the Petroleum Sector of Ghana, assessed the energy recovery levels of 
Tema Oil Refinery (TOR), examined the relationship between Energy Efficiency 
(EE) and economic fortunes of Tema Oil Refinery (TOR), and designed a con-
ceptual framework for improving the energy efficiency of Tema Oil Refinery 
(TOR). 
3.1. Demographic Background of Respondents 
Of the 84 respondents, 69% are males while 31% are females. The presentation 
showed that males are more dominant than female respondents.  
Out of 84 staff of Tema Oil Refinery, 45.3% (38) represent the 30 - 39 years 
age group, 27.4% (23) for 30 - 39 years and 19.0% (16) for 50 - 59 years. The 
least age group was 20 - 29 years with only 8.3% (7) respondents. In all, 60.7% 
(51) respondents have attained Masters’ level education while 39.3% (33) had a 
first degree. The dominant educational level amongst Staff of Tema Oil Refinery 
is the masters’ level.  
3.2. Energy Supplied 
Energy efficiency is indicated by the difference between the energy produced and 
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consumed. Since 2008, energy supplied has been a little above 6000 kilotonnes 
and later dipped in 2009. From 2010 to 2017 the amount of energy produced or 
supplied have steadily increased but later dipped in 2018. In 2019 however, the 
amount of energy supplied exceeded 8000 kilotonnes. Overall, there has been a 
gradual increase in the amount of energy supplied in the Ghanaian economy. 
3.3. Energy Consumed 
The consumption of energy increased from 5187 kilotonnes in 2008 to 7421 ki-
lotonnes in 2019. The yearly difference between energy consumed in 2008 to 
2019 sums up to above 2000 kilotonnes. The consumption patterns have been 
marginally increasing. The total consumption in 2015 and 2016 was marginally 
higher than energy consumption in 2017. The lowest energy consumption was 
experienced in 2015 while the highest was experienced in 2019.  
3.4. Economic Benefits of Energy Efficiency in Tema Oil Refinery 
Table 1 presents descriptive statistics on the economic benefits of energy effi-
ciency. On the issues of factors that improve the economic fortunes of Energy 
Efficiency (EE) for TOR, Higher performance is achieved when TOR is energy 
efficient ranked highest (mean = 4.22, S.D. = 0.574) whilst There exist cost-effective 
energy efficiency measures that improve our operations was least ranked (mean 
= 3.27, S.D. = 0.961). 
3.5. Energy Efficiency of Tema Oil Refinery 
On mean raking for ensuring EE at TOR, the issue of Energy generation is mo-
nitored against consumption was ranked highest (Mean = 4.14, S.D. = 0.541).  
 
Table 1. Descriptive statistics of economic benefits of energy efficiency. 
Statements 
N 
Mean Std. Deviation 
Higher performance is achieved 
when TOR is energy efficient 
84 
4.33 
0.574 
Monetary benefits have been 
accrued out of the efficient use of energy 
84 
4.12 
0.547 
Efficient monitoring of energy 
generation has ensured lasting plants 
84 
4.04 
0.768 
The estimated payback time for 
investing in energy efficiency is relatively short 
84 
3.49 
0.925 
Energy is conserved with the 
application of energy efficiency policy 
84 
3.29 
0.687 
There exist cost-effective energy 
efficiency measures that improve our operations 
84 
3.27 
0.961 
Source: Field Survey, 2020 Mean Scale: 1.00 - 1.49 = Strongly Disagree; 1.50 - 2.49 = Dis-
agree; 2.50 - 3.49 = Neutral; 3.50 - 4.49 = Agree; 4.50 - 5.00 = Strongly Agree; SD < 0.5 = 
Closely spread data. 
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The least ranked variable for EE was Energy performance is high at (mean = 
0.44, S.D. = 0.628). There is a mix of responses regarding the Energy efficiency 
of Tema Oil Refinery. Generally, 3 out of the 6 indicators were ranked above 
men 3.5 showing the key roles those variables played in ensuring EE at TOR 
(Table 2). 
3.6. Energy Recovery Levels of Tema Oil Refinery 
The energy recovery levels are promising for TOR as the majority of the indica-
tors for EE provide better energy recovery levels for the industry. Of the 6 indi-
cators, 5 provided positive input to energy recovery levels of TOR. The high-
est-ranking variable was energy recovered in time is reusable in other parts of 
the refinery (mean = 4.23, S.D. = 0.608) as against the lowest-ranked variable of 
There is accurate control of furnace temperature that makes it easy for recovery 
purposes (mean = 3.55, S.D. = 0.501) (Table 3). 
 
Table 2. Descriptive statistics of energy efficiency. 
Statement 
N Mean Std. Deviation 
Energy generation is monitored against consumption 
84 
4.14 
0.541 
Machines used in generating energy often 
undergo maintenance 
84 
3.64 
0.482 
Efficient Energy is generally recorded with steam 
84 
3.61 
0.491 
Consumption records are adjusted to energy price change 
84 
2.95 
0.727 
Energy performance is high 
84 
2.73 
0.628 
The level of energy generation commensurate 
to the output obtained 
84 
2.44 
0.499 
Source: Field Survey, 2020 Mean Scale: 1.00 - 1.49 = Strongly Disagree; 1.50 - 2.49 = Dis-
agree; 2.50 - 3.49 = Neutral; 3.50 - 4.49 = Agree; 4.50 - 5.00 = Strongly Agree; SD < 0.5 = 
Closely spread data. 
 
Table 3. Descriptive statistics of energy recovery levels. 
Statement 
N 
Mean Std. Deviation 
Energy recovered in time is 
reusable in other parts of the refinery 
84 
4.23 
0.608 
There are periodic audits made on energy recovery levels 
84 
3.88 
0.589 
Staff are aware of techniques used in energy recovery levels 84 
3.81 
0.595 
The energy lost from the plant cannot be recovered 
84 
3.60 
0.594 
There is an explicit policy on energy recovery levels 
84 
3.55 
0.501 
There is accurate control of furnace temperature 
that makes it easy for recovery purposes 
84 
3.31 
0.728 
Source: Field Survey, 2020 Mean Scale: 1.00 - 1.49 = Strongly Disagree; 1.50 - 2.49 = Dis-
agree; 2.50 - 3.49 = Neutral; 3.50 - 4.49 = Agree; 4.50 - 5.00 = Strongly Agree; SD < 0.5 = 
Closely spread data. 
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3.7. Inferential Analysis on the Relationship between Energy  
Efficiency and Energy Fortunes of Tema Oil Refinery 
The inferential analysis for EE of TOR is shown in Table 4. The Pearson correla-
tion of 0.289 showed that the efficiency of energy produced has a weak positive 
correlation with the benefits or fortunes of energy. This further shows that issues 
of energy fortunes are explained by 28.9% EE. The significant value of 0.008 
showed that Energy efficiency has a significant association with energy benefits 
or fortunes. Overall there is a significant and positive correlation between energy 
efficiency and the energy fortunes of Tema Oil refinery. 
4. Discussions 
This section discusses the findings of the study as related to EE and the energy 
fortunes of TOR.  
Trends in Energy Efficiency (EE) in the Petroleum Sector of Ghana 
The secondary data analysis presented a detailed graphical representation of 
trends using data gathered from National Energy statistics. The analysis showed 
that there is a linear trend between energy production (supply) and energy con-
sumption. Energy production increases go with energy supply consumption as 
well. However, the amount of energy generated in the entire economy of Ghana 
far exceeds the amount of energy consumed thus raising concerns on issues of 
waste or excesses. The study results support [3] [4] that argued for effective 
energy productivity to improve EE. This further highlights the need to efficiently 
manage energy further because of out-match consumption levels. Some scholars 
have made similar assertions regarding the poor efficiency of Ghana’s energy 
sector. According to [29], the management of energy in most West African na-
tions is largely poor and has no sustainable plans to effect. The results are similar 
to [30] that indicated that energy produced in most developing nations is not ef-
ficiently managed following consumption rates and patterns but argued that ef-
ficiency programmes are largely absent in most countries. The study further 
corroborates [6] that argued that Energy efficiency is about using less energy to 
accomplish the same task. But, [29] posits that the management of energy in  
 
Table 4. Correlations. 
Variables 
Measured 
Energy Fortunes 
Energy Efficiency 
Energy Fortunes 
Pearson Correlation 
1 
0.289** 
Sig. (2-tailed) 
 
0.008 
N 
84 
84 
Energy Efficiency 
Pearson Correlation 
0.289** 
1 
Sig. (2-tailed) 
0.008 
 
N 
84 
84 
**Correlation is significant at the 0.01 level (2-tailed). 
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most West African nations is largely poor and that many of such states do not 
even have sustainable plans in place. This assertion further supports [30] that 
argued that energy consumption rates and patterns in most developing nations 
are poorly managed.  
4.1. Energy Recovery Levels of Tema Oil Refinery (TOR) 
The results of the study confirm that staff is aware of techniques used in ensur-
ing energy recovery levels at the refinery and that periodic audits are made on 
energy recovery levels. Findings further showed that when energy is recovered in 
time it is reusable in other parts of the refinery. Overall there is a positive asser-
tion regarding the energy recovery levels of Tema Oil Refinery and this affirms 
information from the literature [12] [13]. The results further confirm that even 
though governments over the years have had the provision of energy services 
high on the national development agenda, past and existing policies and plans 
have not delivered effective results to accelerate energy efficiency and recovery 
levels.  
4.2. Relationship between Energy Efficiency and Economic  
Fortunes of Tema Oil Refinery (TOR) 
The inferential statistics were used to determine the relationship between energy 
efficiency and the economic fortunes of Tema Oil Refinery. The inferential anal-
ysis confirmed a significant and positive correlation between energy efficiency 
and the economic fortunes of Tema Oil Refinery. This significant and positive 
relationship showed that energy efficiency is necessary for economic fortunes. 
Tema oil refinery, therefore, stands to benefit economically when the staff can 
ensure efficient transmission and or supply of energy. The supply of energy 
should have equivalence to the public consumption of energy. The results sup-
port [31] Alleyne (2018) that established a relationship between energy efficiency 
and profitability or benefits to oil-producing companies. The study results differ 
from [32] that established the fact that higher energy efficiency is often asso-
ciated with higher productivity, as energy and production technologies are often 
linked. The study further established a converse output with [33] that investi-
gated the prospects of energy efficiency and found that Investments in energy ef-
ficiency entail uncertainty, though it proffers higher benefits.  
4.3. The Conceptual Framework for Improving Energy Efficiency  
of Tema Oil Refinery (TOR) 
The study provides detailed primary and secondary data analysis to confirm the 
need to have in place, a common framework for improving the energy efficiency 
of Tema Oil Refinery through the adoption of the strategy of marching equiva-
lence. This conceptual strategy ensures that both the left and right-hand sides of 
a model are roughly equivalent to project some efficiency in a model. This study 
suggests a working model for Tema Oil Refinery to ensure that the amount of 
energy supplied (in kilotonnes) is largely equivalent to the consumption of 
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energy. As a caveat, the conceptual framework proposes that the energy pro-
duced and supplied should be 10% more than the last consumption of energy, as 
a means of catering for any contingencies. This supports the assertion of [22] 
that suggested a similar working model for ensuring efficiency at the workplace. 
[22] investigated Energy Efficiency Modelling and Estimation in Petroleum Re-
fining Industry—A Comparison Using Physical Data that analyzed the use of the 
non-parametric data development analysis approach with physical data. The 
proposed model for the evaluation of energy efficiency does not require energy 
data in the operational levels for considering the structural effects. The output of 
the study is also supported by [23] that studied Energy Efficiency Improvement 
in the Petroleum Refining Industry in the United States and found that the best 
way of improving energy is reducing consumption. Also, [10] indicated that 
energy efficiency can be enhanced through the application of new technology 
that yields a lower input/output ratio, using the same fuel or an alternative mod-
el.  
5. Conclusions 
The study concludes that there is a linear trend between energy production 
(supply) and energy consumption. Massive supply of energy has resulted in an 
increase in energy consumption for the energy sector. This development sug-
gests that attempts to improve energy supply would be productive because con-
sumption is most likely to rise with increased energy production. However, the 
amount of energy generated in the entire economy of Ghana far exceeds the 
amount of energy consumed raising issues of waste or excesses that call for bet-
ter policies and management plans to improve EE. 
Issues of lost energy are critical to the operations of the petroleum industry 
and the situation is compounded by the inability to explore better ways to reduce 
and manage the waste. This is worsened by the fact that energy lost from plants 
is difficult to recover. However, managers in the petroleum industries such as 
TOR could improve EE by capitalizing on the staff awareness of techniques used 
in recovery levels at the refinery and that periodic audits are needed to help re-
cover energy levels. This would ensure that energy recovered in time is reused in 
other parts of the refinery. More so, energy production activities should be guided 
by good policy guidelines as well as strategic management decisions so that excess 
production of energy is discouraged in order to reduce cost and. therefore, waste 
of energy resources. 
The study concludes that a significant and positive correlation between energy 
efficiency and the economic fortunes of Tema Oil Refinery and necessary for 
economic fortunes. The supply of energy should have equivalence to the public 
consumption of energy. 
An adoption of marching equivalence could be a key conceptual framework 
for improving EE in refineries such as Tema Oil Refinery (TOR). This concep-
tual strategy ensures that both the left and the right-hand sides of a model are 
roughly equivalent to project some level of efficiency in the model. This study 
D. Ayo et al. 
 
 
DOI: 10.4236/ojee.2021.104008 
133 
Open Journal of Energy Efficiency 
 
suggests a working model for Tema Oil Refinery to ensure that the amount of 
energy supplied (in kilotonnes) is brought to par with the consumption of ener-
gy. 
Conflicts of Interest 
The authors declare no conflicts of interest regarding the publication of this pa-
per. 
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Optimization techniques for petroleum
engineering: A brief review
Anuj Kumar, Mridul Vohra, Sangeeta Pant & Sanjeev Kumar Singh
To cite this article: Anuj Kumar, Mridul Vohra, Sangeeta Pant & Sanjeev Kumar Singh (2021)
Optimization techniques for petroleum engineering: A brief review, International Journal of
Modelling and Simulation, 41:5, 326-334, DOI: 10.1080/02286203.2021.1983074
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ARTICLE
Optimization techniques for petroleum engineering: A brief review
Anuj Kumar, Mridul Vohra, Sangeeta Pant and Sanjeev Kumar Singh
Department of Mathematics, University of Petroleum and Energy Studies, Dehradun, India
ABSTRACT
Optimization in the production of oil comprises various processes to measure, analyze, model, 
prioritize, and implementation to enhance productivity of a field, reservoir, well, or surface. It is a 
practice to ensure recovery of developed reserves while maximizing returns. With the advance­
ment of technologies associated with the field of petroleum engineering, there has always been an 
advent of an optimization technique. Albeit not abruptly but still with considerable consistency, 
conventional as well as nature inspired optimization techniques extend their roots in almost all 
complex problems of the world including petroleum engineering. In this article, we have reviewed 
the extensive use of optimization-based procedures and approaches in petroleum engineering 
that are being used to expand, develop, and generate petroleum fields with the appropriate 
designs and operations; reservoir development, planning and management.
KEYWORDS 
Optimization techniques; 
optimization-based 
approaches; reservoir 
development; nodal analysis; 
metaheuristic
1. Introduction
While going through the study and reviewing that how 
most of the refineries are being optimized, we came to a 
particular point that today’s refinery optimization pro­
cesses are largely done manually. They also required 
qualified professionals with in-depth knowledge of the 
refinery process and the appropriate technology for 
optimization who are becoming increasingly rare due 
to demographic change. Linear models are being used 
which have a limited range of validity as well as main­
taining the process simulation, production planning, 
and production scheduling models is time-consuming 
and requires significant expertise. The data reconcilia­
tion process is largely heuristic and simplistic. The key 
constraints are not adequately identified and challenged, 
advanced control and real-time optimization strategies 
are not updated with plan and schedule changes, sche­
dulers focus on finding a feasible solution with limited 
time to minimize the deviation to the plan or optimize 
the schedule, the integration between the various appli­
cations is limited.
Optimization models in the field of petroleum engi­
neering for hydrocarbon production are mainly con­
cerned with reservoir development planning, its 
management to develop designs and operations, and 
the surface facilities capacity. In most of the cases, a 
huge amount of valuable oil gets to stay underground 
since the pressure declines in a reservoir and liquid 
drops out of a solution and, as a result, it causes a 
significant reduction in the productivity of a well. 
Consequently, detecting locations in the system with 
abnormally high-pressure and loss of restrictions and 
hence determining the daily optimal operating condi­
tions that will be required for the concurrently consid­
ering the complex interactions. With these changes, the 
oil industry is facing several challenges in achieving its 
goals of efficiency and production optimization. Also, as 
we see all around that the structure of petroleum engi­
neering is changing worldwide, hence regarding it we 
also paid attention to how to manage it and also how to 
optimize it to be applied in small fields in a better way. 
This article mainly focuses upon the approaches on how 
to better optimize the hydrocarbon production techni­
ques and their simulation involving various aspects like 
drilling, reservoir-related simulation and, planning of 
production. This article presents a brief overview of the 
approach from the viewpoint of applications on how to 
optimize the production systems and further how to 
design them.
2. Algorithms and the tools required for 
Optimization
2.1 Optimization-based on conventional and 
heuristic/metaheuristic techniques
The term ‘production optimizations’ means a systematic 
approach to production development whereas the deci­
sion maker may differ in a formula, various conditions 
and, processing requirements. In recent years, in the 
petroleum industry, production optimization has 
CONTACT Sangeeta Pant 
pant.sangeet@gmail.com 
Department of Mathematics, University of Petroleum and Energy Studies, Dehradun, India, 248007
INTERNATIONAL JOURNAL OF MODELLING AND SIMULATION 
2021, VOL. 41, NO. 5, 326–334 
https://doi.org/10.1080/02286203.2021.1983074
© 2021 Informa UK Limited, trading as Taylor & Francis Group 
become an increasingly valuable tool of growth as it 
reduces time, cost, and the risk involved in the process 
of developing and applying the various methods still 
requires a considerable amount of knowledge and art 
particularly to set up a modelling situation that perfectly 
summarizes the general process and which align indus­
trial practices with academic research [1].
Resource allocation and selection related to service 
activities are viewed as two independent processes. 
Furthermore, most of the studies conducted in past 
highlighted the multi-objective optimization of the 
overall solution, but service activities which are having 
high-importance usually have a bigger impact on the 
solution in comparison to the services having low 
importance. In addition, various approaches based on 
conventional optimization techniques or heuristics/ 
metaheuristics approaches have been progressed from 
academic research to industries having practical field 
implementations. One can visualize that researchers or 
industry experts have not pursued practical applica­
tions of these techniques as vigorously as these algo­
rithms deserve. Fortunately, optimization of complex 
systems based on heuristics/metaheuristics is a highly 
active and advanced area of research both inside and 
outside of the industries related to petroleum [2–5]. 
There is a huge scope for further advancement of these 
techniques so one can achieve widespread field appli­
cations of these algorithms in the petroleum industry 
in the near future. The most powerful effect will be on 
the way of conducting numerical reservoir simulation 
studies. To integrate large-scale reservoir simulations 
with existing optimization models, we must consider 
how to properly and simultaneously perform history 
matching and influencing the required functions to be 
generated, and finally how to do the viable optimiza­
tion. In this view, take note that conventional optimi­
zation techniques like MINLP and NLP are the most 
favoured optimization procedures to apprehend thor­
oughly the non-linear and the non-smooth attributes 
intrinsic to industrial difficulties (e.g. problems that 
arise due to the reservoir development and its working 
or dynamics) as hinged on the significant tasks that 
rest of the paper valuations and sum up them conse­
quently to the solution algorithms, have advanced to 
carry-out the underlying non-linearities collectively 
from the well-known usual Newton-based approaches 
like Generalized Reduced Gradient to derivative-free 
methods like genetic algorithm and Grey wolf optimi­
zer, etc.[6,7].
Simultaneously, earlier as one looked into gratuitous 
large-scale and advanced studies, one calculated lade to 
carry out an optimization method. Also, we are prob­
ably able to better vindicate using the general systems 
approaches by considering the global producing facil­
ities for which we can easily analyse and clarify the 
reservoir description.
Nowadays, due to its simplicity and derivation-free 
mechanism 
heuristic/metaheuristics 
optimization 
approaches are the hotspot for researchers of all fields 
[8,9]. Scientists and researchers are developing and 
applying these techniques to various complex pro­
blems of optimization [10,11]. They are widely applic­
able in all most all areas of sciences and industries [12– 
14]. Heuristic/metaheuristic methods to solve the com­
plex optimization problems provide good and feasible 
solutions rather than exact optimal methods adopted 
for the time-constrained complex difficulties. These 
problems differentiate with the approaches as in the 
mathematical optimizing techniques [15]. The heuris­
tics are generally being applied in the management 
routines of the wells which mortify the pipeline net­
work into discrete levels [16]. Kosmidis et al. [17] 
provide us with examples of such comparable heuris­
tics/metaheuristic-based routines, for example, rules 
and orders that include the closing of a well if it 
contravenes the supremum of the gas–oil ratio (GOR) 
which is being applied and executed at the level of well 
(in which the definition of GOR is given as the ratio of 
gas-to-oil volumetric flow rate). These kinds of several 
competing methods and techniques give us the exact 
ideas and explanations that development of an opti­
mum well planning for field development is not easy 
but challenging and also that the uniform spacing 
development might not be optimum when observed 
from an economic and profitable point of view and 
hence in this article further we indicate a part of as 
how optimization techniques, heuristic/metaheuristic 
approaches can be successfully employed and used so 
as to develop the economically optimal schedules and 
well placements in the most efficient manner. The 
Production scenarios that maximize the complete pro­
ject NPV show that the well spacing, which is non- 
uniform, is required when the changing well costs and 
changing reservoir characteristics are taken into 
consideration.
2.2 Optimization techniques in the establishment, 
organization, and management of reservoir
The management of reservoir mainly concerns and 
focuses upon the organisation of applicable scientific, 
cost-effective and operative resources from discovery 
and abandonment to optimally utilize reservoir in the 
most systematic, secure, responsible and profitable 
manner by making use of the advanced data-driven 
statistical/mathematical models connected with human 
INTERNATIONAL JOURNAL OF MODELLING AND SIMULATION
327
discernment and their expertise [18–20]. The principal 
objective is to maximise the value of hydrocarbon ben­
efits by achieving a set of targets that chiefly consists of:
1 improving the estimates of the hydrocarbons in 
place
2 increasing the recovery, production, and forecasts 
of the oil and gas
3 minimising the capital funding and operating 
expenditures and
4 ultimately, maximising the overall profitability to 
account.
These approaches tend to provide us the most suita­
ble solutions rather than exactly optimum solutions to 
the time-constrained complex industrial problems that 
directly connect to the use of optimization techniques 
[15]. Achieving these goals collectively needs a holistic 
mulling of the full spectrum that vastly spreads out to 
the hydrocarbon benefits, ongoing and the upcoming 
future establishing proposal/projects and numerous 
investments under thought [21–23].
The Establishment and the planning studies needed 
for the management of a reservoir have always been a 
perpetual area and a vast field that has benefited excep­
tionally from the utilization of various recent optimisa­
tion techniques. Constantly various methods, models, 
and progressing sophisticated tools have been imple­
mented to account for and provide the solutions for 
the ever more rigorous and demanding practical limita­
tions which were considered to be avoided. The repre­
sentative difficulties in this field can be broadly classified 
as the following [24,25]:
• Facility location-allocation, which concerns mainly 
the resolution on the location (i.e. placement) and the 
allocation (i.e. number of unit(s)) in the production 
stages and wells to the well platforms
• Production planning and scheduling regarding the 
following aspects [26]:
− Scheduling of reservoir development
− Scheduling of well-drilling operations
− Scheduling of production rate
− Timing for building a compressor in gas field 
development and
− Annual revenue, capital expenditure, and operating 
expenditure of reservoirs
3. Oil production systems- its design and 
working based on optimization approaches
Optimization prototypes for the designing and working 
of a well-integrated system related to oil production 
cover the complete field from the sub-surface structure 
which will consist of the drainage area, reservoir, and 
wells, and well-head assembly, complete up to the 
surface and down facilities. Such enhanced optimization 
prototypes consist mainly the several component mod­
els related to wellbore simulation in the reservoir area, 
well tubing strings models for fluid flow in the pipelines 
from source to destination, models related to the choke 
valve of the well, surface flow line models of the surface 
pipeline network systems, and various separator models 
for separation facility.
An economic objective controls the main model 
objective function since it is the most important point 
and factor which must be seriously paid attention to. 
Hence, there is a need to optimize allocating a con­
trolled injection gas volume in order to maximize the 
rate of oil production [27].
4. Rate allocations-optimization based 
techniques
4.1 Optimization in Gas-lift administration/ 
allocation problems
The gas lift mechanism is quite a vast area to understand 
which generally means that it is having a proper amount 
increase in the production rates of oil but also injects 
and extremely reduces besides increasing its cost due to 
more prices related to the compression and the gas. 
Preferably, if the operators are not limited by the 
amount of injection gas, which is present there, they 
could administer and fix gas by injecting a proper 
amount of it into the particular well until they can 
acquire maximum production as needed [28]. Our ulti­
mate objective is to establish and govern the optimal 
well production and lift gas rates related to the pressure 
constraints and the rate constraints within the nodes of 
the surface-pipeline network and also lift gas amount as 
much as available to us. We traditionally use a technique 
that is mainly concerned with the presenting curve, 
which plots the lit gas rate against the oil rate for a 
well and when the supply of gas is not under any limit 
then the optimal lift in the gas rate corresponds directly 
to the maximum oil production and thus when they 
have limited gas supply, operators usually use formal 
optimization tools and techniques which are generally 
based upon the optimal methods [28–30].
4.2 General problems which are related to the rate 
allocation
The optimization of the production as well as the gas lift 
rates for single wells and concurrently the overall pro­
duction rate of collective reservoirs to attain certain 
operational is an obvious optimization problem which 
attracts the attention of researchers to model the 
328
A. KUMAR ET AL.
situation as a constrained optimization problem so that 
various optimization techniques can be implemented to 
attain optima. The difficulty is one of the prior optimi­
zation implementations dating back to 1970s using 
Linear Programming that proceeded to Non-Linear 
Programming with Mixed-Integer Programming as the 
controlling process now as the new and advanced pro­
gramming/optimization techniques can be of great ben­
efit in providing the optimal solutions for the solutions 
of the problems related to rate allocation operations 
[31–33].
4.3 Applications based on Nodal analysis
Nodal analysis plays a major role in many applications 
that are quite beneficial for the oil and gas extraction 
process. Daily working in the oilfields is planned on the 
basis of nodal analysis as it is one of the most famous 
and reliable methods that help production engineers in 
forecasting the performance of different parameters of 
the reservoir.
What Nodal analysis basically means is making 
changes in the different parameters in order to reach 
the basic and the most important spots that are fit to our 
ultimate goal which is to optimize the production in a 
cost-effective manner and without damaging the reser­
voir. For the past many years, experts have used systems 
analysis to analyze the performance of complex systems 
composed 
of 
multiple 
interacting 
components. 
Researchers and scientists within the petroleum indus­
try have widely adopted this term nodal analysis in spite 
of system analysis over the years so that it becomes very 
famous within the industry [34]. Nodal analysis was first 
introduced by the Gilbert to oil wells but Proano and 
Brown popularized this concept within the petroleum 
industry which is typically referred to as Nodal 
Analysis [35].
To simulate and understand the fluid flow system or 
rather to control the fluid rate system, it is needed to 
‘Break or Divide’ the whole system into different nodes 
that separate system components. Fluid characteristics 
are evaluated locally for the distinct elements.
Nodal analysis is fundamentally being executed on 
the concept of continuity of the pressure, that says, there 
is only one distinctive pressure value at a considered 
node nonetheless of whether the pressure is evaluated 
based on the performance of equipment located either 
upstream or downstream. The main objective is the 
completion of design optimization that suits a reservoir 
and identifying the constraints in the production system 
to improve the efficiency as per the needs. The curve of 
performance, that is, the pressure–rate curve of 
upstream equipment is called ‘inflow performance 
curve’ while the pressure–rate curve of downstream 
equipment is called ‘outflow performance curve.’ [36].
The Operating point is defined as the intersection 
point of these two performance curves. It gives us the 
exact idea about the amount of operating flow-rate and 
pressure at any specific node [37].
4.4 Field expansion and growth
The Oil and gas industry encounters rate-allocation 
difficulties in vast grown fields where the facilities for 
the production are not anymore able to meet the 
required or the sufficient field demands. Well- 
equipped techniques and properly recorded cases on 
the two Alaskan oilfields situated near Kuparuk River 
and the Bay of Prudhoe present that how one can 
control the production of oil by the means of gas 
processing dimensions of surface facilities consisting 
of a gas plant, which is centrally located, and the 
separation units[38,39]. Various tools and techniques 
based on neural networks have also been used to 
accelerate the simulation and the optimization pro­
cesses in the oil and gas industry [40–42]. However, 
the use and training of these tools based on neural 
networks are computationally and financially a big 
budget task as it mainly depends upon how precisely 
the training tools work and how the simulated system 
acts or responds.
5. Optimization-based recommendations for 
the reservoir management, its organization and 
development
The techniques for the reservoir management mainly 
focus upon how to organize the appropriate applied/ 
technical functioning, and the reliable cost-effective 
methods and resources right from the starting to the 
stopping phases that ideally utilize the reservoir in the 
most feasible, secure, responsible, and economical way, 
and what it requires is the data driven statistical/math­
ematical models, which when collaborated with the 
human insight and proficiency will provide us with the 
optimal results. The principal/ultimate goal is to max­
imize the value for Hydrocarbon benefits and their 
collection by carrying out the following objectives 
which are:
1. To improve the estimates of hydrocarbons;
2. To increase the recuperation, manufacturing, and 
prediction of hydrocarbons;
3. To minimize the capital financing and managing 
the expenditures;
4. To maximize the overall profits.
INTERNATIONAL JOURNAL OF MODELLING AND SIMULATION
329
Achieving these targets requires the integrated 
thoughts of the full range that covers the ongoing and 
the upcoming projects, various hydrocarbon assets, and 
of course finances under the consideration [21–23,43]. 
Continuously evolving and increasingly sophisticated 
tools and models and studies related to the planning of 
reservoir management has been a very crucial area that 
has to be benefited from the application of the required 
optimization techniques wherever needed and further 
they can be categorized as in facility location-allocation 
problems and their proper scheduling [24,25]. In gen­
eral, all of these techniques and methods do not apply to 
the well that does not respond instantaneously [35].
After this, we also need to consider the techniques 
which will prove to be the most beneficial for optimizing 
the net value on the present rate for the development of 
the full field. The Approach for this is automated and 
combines a cost-effective package with an optimization 
engine based on recent heuristics/metaheuristics tech­
niques. The novel framing of the scheduling problem 
and well placement as a classic ‘traveling salesman pro­
blem’ is needed prior to the optimization via simulated 
annealing, which can be applied in real life. Using this 
technique for the full field development, an example 
shows that the wells with non-uniform spacings are 
optimal (from an NPV view) when the well interference 
effects and changing reservoir properties are to be con­
sidered. Optimizing field NPV examples show that with 
the changing well costs also the wells with non-uniform 
spacings are optimal. NPV projects increases of 25 to 30 
million dollars were seen using the optimal and non- 
uniform development against the uniform develop­
ments, which were reasonable. The capability of this 
technology to conclude that many potential applications 
get open by these non-linear well spacings would surely 
have an impact upon the economic performance for the 
development of petroleum fields [44].
6. Summary and further research directions
The objective of this article is to present and consider 
the different techniques for the optimal enlargement of a 
petroleum field. The goal of the optimization techniques 
for petroleum engineering that are taken into considera­
tion is to increase the project’s expected present net 
value. To date, very few works in the literature have 
addressed the well placement, infrastructure related to 
surface, design issues related to the network, facility 
scheduling, facility location–allocation, and planning 
simultaneously [45–47]. In this structure, one can 
inspect and work upon the choices that are precisely 
concerned with the platform capacity and to understand 
where, when, and how to drill the wells and production 
strategies that are required for each of the wells. When 
deciding these choices, there is considerable unreliabil­
ity about the reservoir holdings and properties, the oil 
price in the upcoming future, and what are all the 
technical restrictions that can persist. So there is a 
huge scope for researchers to implement optimization 
techniques to these practical problems faced by the 
petroleum industry. So one can mainly focus on how 
to practice and solve the problems of petroleum engi­
neering using optimization techniques. One can in fact 
formulate the appropriate optimization models needed 
using the whole-systems optimization approach, which 
can be used in supply chain modelling and petroleum 
engineering optimization-related problems [48–50].
The difficulties that are being discussed in this article 
lie on the margin line between petroleum engineering 
economics and reservoir development. When taken into 
consideration the problems under an interdisciplinary 
structure and the fields, it is almost impossible to use the 
level of detail that can be engaged in each discipline let 
alone and with validated optimization models. One can 
use advanced various statistical software, machine 
learning tools, and other techniques to elicit relevant 
information and meaning associated with a vast amount 
of exploration and production data [51]. The content 
presented here is therefore on a more aggregated level, 
which is in accordance with operations research as an 
interdisciplinary approach of solving complex planning 
and development problems using the optimal techni­
ques. This allows one to analyze location problems and 
to find how different decision variables interact with 
each other. However, it is important to note that such 
accumulated techniques do not exclude the need for 
completely elaborated analysis in the fields of both engi­
neering and economics.
In our opinion, this article points out the different 
interesting topics for future research. There is a constant 
need for improvised decision support methods based on 
recent optimization techniques in the petroleum indus­
try, and efforts must be made so as to develop models 
and solution methods for more reservoir elaborations, 
which are generally considered to be complex. It will 
also be quite beneficial to study if and how reservoir 
simulators, which are available to us, can be merged 
with the optimization models that are cited here. The 
article also opens for a framework treatment of unrelia­
bility, both traditional choices independent uncertainty 
but also difficulties where the arbitrary segments are 
decision dependent. There will obviously be some chal­
lenges that will be left to formulate a computationally 
more systematic approach that includes analytics within 
the optimization framework that cater the need of solu­
tions of a wide range of problems related to petroleum 
330
A. KUMAR ET AL.
engineering including water management in hydraulic 
fracturing [52–55], well scheduling and its control [56– 
58] and shale gas supply chain design and planning [59– 
62]. Future research should be carried with such opti­
mization models in general and for applications that are 
studied under the petroleum industry. Improvised and 
well-defined reservoir simulators and optimization 
models will also ease and could be of great help in 
further research in the area of petroleum field 
optimization.
7. Conclusion & future scope
In this article, a brief overview of various optimization 
techniques applied in the field of petroleum engineering 
has been presented. Although oil industries also apply 
various optimization techniques to long-term predic­
tions related to petroleum engineering, the impact of 
the decisions taken by the decision-maker based on 
these techniques on long-term oil recovery needs 
further investigation. In spite of the extensive use in its 
downstream field of petroleum engineering for refining 
and petrochemicals (hydrocarbon) production, we con­
tend that optimization has not completely perforated 
the upstream petrochemicals industry of inspection 
and its production. In this article, we reviewed various 
optimisation techniques which can be really beneficial 
to the researcher in the fields of petroleum engineering 
or petroleum industries. To a certain extent, we have 
acquired maximum of it but still, the approach some­
where lacks the connection between real world and 
practical problem dynamics that is increasingly a domi­
nant topic of downstream optimization practice. For 
that, we need to do research based on software techni­
ques which include simulation as well. Nevertheless, we 
hereby note in our standpoint for the future researches 
that these challenges and difficulties will surely form a 
part of future research areas. The above information 
gathered will be providing results on the basis of the 
actions taken that is how the problems can be 
approached for the optimal solutions.
The enormous potential of digitization will also 
prevail in traditional industries, such as refineries. In 
the future, planning and scheduling optimization 
models will use disruptive technologies such as 
machine learning and/or cognitive computer func­
tions using live data. The goal is to achieve a step 
change in the refinement of refinery optimization. 
Machine learning or cognitive computing functions 
should be used which will be giving enormous bene­
fits. Automated data driven optimization using 
Artificial Intelligence lead to processes automated 
data management and application integration. Cloud 
deployment can be there to facilitate collaboration, 
scalability, and rapid development and delivery of 
enhancements automated surfacing of optimization 
opportunities. Automated backcasting to be imple­
mented to close the gap between the plan, schedule, 
actual and optimum. Automated model updates to 
create a self-learning digital twin Automated and opti­
mized production scheduling Multi-unit dynamic 
optimization
However, this is not limited to refinery optimization. 
Similar possibilities exist in the optimization of oil and 
gas production and the production of petrochemicals. 
This technology has the potential to make a major con­
tribution to the human resource and sustainability chal­
lenges facing the industry.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes on contributors
Dr. Anuj Kumar is an Associate Professor of Mathematics at 
University of Petroleum and Energy Studies (UPES), 
Dehradun, India. Before joining UPES, he worked as an 
Assistant Professor (Mathematics) in The ICFAI University, 
Dehradun, India.  He has obtained his Master’s and doctorate 
degree in Mathematics from G. B. Pant University of 
Agriculture and Technology, Pantnagar, India. His area of 
interest is reliability analysis and optimization. He has pub­
lished many research articles in journals of national and 
international 
repute. 
He 
is 
an 
Associate 
Editor 
of International Journal of Mathematical, Engineering and 
Management Sciences. He is also a regular reviewer of various 
reputed journals of Elsevier, IEEE, Springer, Taylor & Francis 
and Emerald.
Mr. Mridul Vohra is pursuing his B.Sc (Hons.) Mathematics 
from University of Petroleum and Energy Studies (UPES), 
Dehradun, India. his area of interest is engineering applica­
tions of nature inspired optimization techniques.
Dr. Sangeeta Pant received her doctorate from G. B. Pant 
University of Agriculture and Technology, Pantnagar, India. 
Presently, she is working with the department of Mathematics 
of the University of Petroleum and Energy Studies, Dehradun, 
as an Assistant Professor. She has published around 23 
research articles in the journals of national/international 
repute in her area of interest and instrumental in various 
other research related activities like editing/reviewing for var­
ious reputed journals and organizing/participating in confer­
ences. Her area of interest is numerical optimization, 
evolutionary algorithms, and nature inspired algorithms.
Dr. Sanjeev Kumar Singh is a Professor of Mathematics at 
University of Petroleum and Energy Studies (UPES), 
Dehradun, India. Before joining UPES, he worked with var­
ious organizations like The NorthCap University, Galgotia 
University and Sanskriti University in various positions.  He 
INTERNATIONAL JOURNAL OF MODELLING AND SIMULATION
331
has obtained his Master’s and doctorate degree in 
Mathematics from G. B. Pant University of Agriculture and 
Technology, Pantnagar, India. His area of interest is 
Numerical simulation, differential geometry and optimiza­
tion. He has published many research articles in journals of 
national and international repute.
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Petroleum Refining 
Process Control and 
Real-Time Optimization
TRANSFORMING OIL BOILING INTO MANAGING MOLECULES
R
efineries are a complex network of processes that convert crude oil into finished
petroleum products such as gasoline, diesel, jet fuel, heating oil, fuel oil, lubricants, asphalt,
coke, wax, and chemical feed stocks. Crude oil and these products are complex mixtures
of hydrocarbon molecules as described in “What Is Crude Oil?” and “What is Gaso-
line?” Examples of simple hydrocarbon molecules are shown in Figure 1. The
boiling point curves in Figure 2 [1] characterize the composition of a typical
crude oil without having to determine the precise molecular composition.
While crude oil naturally contains some gasoline-type molecules,
Figure 2 shows there are not enough gasoline molecules to meet
demand. In simple terms, refineries are processes that convert
the crude oil curve to the product demand curve. Expanding
on this simple idea, refineries separate hydrocarbons into
similar oil fractions, convert low-value molecules into
higher value molecules, and blend hydrocarbon frac-
tions into product streams. Additionally, the con-
version step can include processing to remove
environmentally undesirable components such
as sulfur or hydrogen sulfide (H2S).
REFINERY PROCESS OVERVIEW
Refining processes can be classified into
three major groups: separations, conver-
sions, and blending. Distillation is the
most common separation process in
refining. Conversion includes differ-
ent types of reaction process, such as
cracking, hydroprocessing, reform-
ing, isomerization, and alkylation.
Cracking reactions break large mol-
ecules into smaller ones, while iso-
merization and alkylation reactions
rearrange and combine smaller
molecules into larger, more valu-
able molecules. Reforming reac-
tions rearrange the structure of
molecules without changing the
number of atoms to create higher
ROBERT E. YOUNG
COURTESY OF EXXON MOBIL CORPORATION
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value gasoline blending components. Hydroprocessing
reactions use hydrogen to remove contaminants such as
sulfur and nitrogen from the hydrocarbons. Most of these
reactions require a catalyst and are carried out in a variety
of different reactor configurations including fluidized bed
reactors, fixed-bed reactors, and liquid-contacting reactors.
Refineries use a variety of conversion processes and pro-
cessing capacity to convert crude oil fractions into products.
The ability of a refinery to convert crude oil fractions to
products is its conversion capacity. Refineries that lack con-
version capacity must sell intermediate products to other
refiners instead of converting higher molecular weight oil
fractions into finished products. There are currently about
153 operating refineries in the United States, with the
newest being 25 years old [6]. Over their life span, these
refineries have undergone design modifications reflecting
changing crude supplies and product specifications as well
as new regulatory requirements. Some refineries are capable
of producing lubricants, wax, or other specialty products,
whereas some companies integrate petrochemical complex-
es with refineries to gain economic efficiencies.
The flow sheet in Figure 3 illustrates the network of
process units in a typical fuels refinery. The process vessels
and piping connections at the ExxonMobil Torrance Refin-
ery are shown in Figure 4. U.S. refinery process capacities
are available online from the Department of Energy [7].
Crude unit capacities range from 1.7 KBD (thousand bar-
rels per day) to 580 KBD, where one barrel equals 42 gal.
As an example, the ExxonMobil Torrance Refinery has a
crude unit capacity of 149.5 KBD, fluidized catalytic
cracker (FCC) capacity of 95 KBD, and a hydrocracker
capacity of 20 KBD. 
The first refinery process is the crude unit, the only
purely separations unit. The crude unit often includes two
large distillation columns for separating the crude oil into
fractions that can be further processed. Several smaller dis-
tillation units are also used to separate the lighter hydro-
carbons into purified gas streams. Conversion units
downstream of the crude unit include a reformer, FCC,
coker, hydrocracker, and alkylation unit. Each conversion
unit also possesses the ability to separate the reaction
products into blending component streams.
C
rude oil is a complex mixture of hydrocarbons with varying
small amounts of sulfur, nitrogen, salt, and metals such as nick-
el, vanadium, and copper. Hydrocarbons are molecules that have
carbon atoms as their backbone and hydrogen attached to the car-
bon backbone. The hydrocarbons in crude oil are typically paraf-
fins, napthenes, and aromatics. Olefins are not typically found in
crude oil but are produced in the refining process. Examples of
these hydrocarbon classes are shown in Figure 1, recognizing that
much larger and more complex molecules are found in crude oil.
Crude oil is primarily produced from oil fields in the earth but
is also recovered from sources such as tar sands. Crude oil is
transported from the oil fields to refineries by a combination of
marine tankers and pipelines. Supply-chain optimization is per-
formed by crude traders, transporters, suppliers, and refiners.
The most common characterization of crude oils is based
on American Petroleum Institute (API) gravity, sulfur and nitro-
gen content, and total acid content (TAN), where API gravity is
a petroleum-specific measure of density that is inversely pro-
portional to specific gravity. API gravity increases for lighter
crudes and decreases for denser crudes. The API gravity of
water is 10◦API. Light crudes contain lower molecular weight,
higher value products, while more dense heavy crudes contain
material that requires more processing to become gasoline, jet
fuel, or diesel. Sulfur content generally runs from 0.1% to
greater than 5%. Crude oil with more than 0.5% total sulfur or
high hydrogen sulfide concentration (called sour) requires sig-
nificant additional processing to make clean products that meet
environmental specifications. Sweet crudes contain less sulfur.
High amounts of total nitrogen in crude oil are undesirable
because the nitrogen poisons some catalysts used to convert
less desirable components in the crude oil into more valuable
products. Finally, crudes with higher total acid content are
more difficult to process because the higher acid content
requires exotic metallurgy, more expensive equipment, and
higher maintenance cost to achieve good reliability.
A distillation or boiling-point characterization is also useful
for understanding the composition of a particular crude oil or
hydrocarbon mixture. A boiling-point curve shows the volume
fraction of the crude that boils away as a function of tempera-
ture as shown in Figure 2 [1]. These boiling-point curves indi-
cate the types of molecules present in the crude because the
components in the mixture boil off in the order of their pure
component boiling points. However, because crude oil and
most petroleum products are complex mixtures of thousands
of individual components, a boiling-point curve is insufficient
to describe the molecular structure of the mixture. In the past
when refiners had only this limited information to base deci-
sions on, they earned the nickname “oil boilers.”
While useful in general comparisons, these properties do not
provide a basis for billion-dollar decisions. For example, with
clean fuels as a critical business driver, low-sulfur crude oils that
contain hard-to-remove sulfur-containing hydrocarbons can pro-
duce higher sulfur gasoline [2]. Advances in laboratory assay
analyses along with compositional modeling now permit
improved characterization of crude oils and other petroleum mix-
tures on a molecular level. Access to the molecular fingerprint of
petroleum mixtures including crude oil is changing the practice
of oil boiling to managing molecules.
What Is Crude Oil?
74
IEEE CONTROL SYSTEMS MAGAZINE » DECEMBER 2006
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G
asoline is one of several thousand products produced from
crude oil. Transportation fuels, such as gasoline, jet fuel, and
diesel, account for well over half the products produced by
refineries. In 2004, gasoline alone comprised 46.8% of the prod-
ucts produced [3]. Gasoline, which contains hydrocarbons with
between 5–12 carbon atoms, is a blend of hydrocarbons that
boil between 38–205 °C. 
Early gasoline specifications focused on engine perfor-
mance, specifically, octane number and Reid vapor pressure
(RVP). The octane number is a measure of how well a fuel can
be compressed without igniting prior to introducing a spark. If the
fuel ignites before the spark plug fires, the engine is said to
knock. Heptane, a seven-carbon paraffin, compresses poorly
and was designated an octane number of zero. Conversely, iso-
octane, an eight-carbon branched paraffin, performs much better
and was assigned an octane number of 100. Consequently, a
fuel with 87 octane performs like a mixture of 87% iso-octane
and 13% heptane. Modern engines have sensors to measure
engine performance and are designed not to knock by changing
the ignition control. However, using fuel with a low octane num-
ber can result in the ignition control advancing the timing, lead-
ing to lower horsepower. 
The RVP of gasoline indicates how easily the liquid fuel vapor-
izes at 38 °C. Higher RVP is economically attractive because more
components can be used in gasoline blending, which improves
engine starting. Lower RVP prevents engine vapor lock and
reduces evaporation of gasoline to the atmosphere. RVP specifica-
tions are changed in the summer months because the warmer
temperatures add to vapor lock and evaporation issues, resulting in
summer and winter gasoline specifications. 
More recently, federal and state regulations to improve air
quality and provide cleaner fuel are the primary drivers for gaso-
line specifications, bringing about boutique gasoline products as
shown in Figure A [4], [5]. These regulations limit the amounts of
sulfur, benzene, and other aromatics in gasoline and, in some
locations, mandate or restrict the addition of oxygenated additives
such as MTBE or ethanol. Currently, many refiners are working on
clean fuels projects to reduce the sulfur in diesel fuels.
Finally, another key component in gasoline, not part of the
refining process, is additives. Fuel additives improve engine per-
formance by preventing buildup of combustion products such as
carbon deposits and gum in fuel injectors and engine valves.
Quality base-fuel stock and fuel additives help keep consumers’
vehicles performing well.
FIGURE A  Boutique gasoline fuel specifications. These specifications vary across the United States to meet local environmental
requirements. This variation increases both the cost of producing fuels and the complexity of ensuring an adequate supply of fuels to
each location. (Reprinted with permission from Exxon Mobil Corporation.)
Washington
Oregon
Nevada
Idaho
Utah
Colorado
New Mexico
Texas
Oklahoma
Kansas
Nebraska
South Dakota
North Dakota
Iowa
Wisconsin
Minnesota
Michigan
Ohio
W. Va.
Virginia
North Carolina
South Carolina
Georgia
Florida
Alabama
Mississippi
Loulsiana
RFG w/Ethanol
NV CBG
7.2 RVP
7.0 RVP
7.8 RVP, MTBE-No Increase
7.8 RVP
7.0 RVP, 30 ppm S
300 ppm S
RFG - North
RFG - South
Oxygenated Fuels
CA CBC
RFG/CA CBG
AZ CBG
Oxy Fuels/7.8 RVP
Oxy Fuels/7.0 RVP
Conventional
This Map is not Intended to Provide Legal Advice or to be Used as Guidance for State and/or
Federal Fuel requirements, Including but not Limited to Oxy Fuel or RFG Compliance
Requirements. Exxon Mobil Makes no Representations or Warranties, Express or
Otherwise, as to the Accuracy or Completeness of this Map. 
K.W. Gardner
G010080
Exxon Mobil
As of January, 2005
New York
Vt.
Maine
N.H
Mass.
R.l
Conn.
N.J.
Del.
Md
Pennsylvania
Montana
Wyoming
Arizona
Tennessee
Kentucky
Indiana
Missouri
Arkansas
Illinois
California
What Is Gasoline?
DECEMBER 2006 « IEEE CONTROL SYSTEMS MAGAZINE
75
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76
IEEE CONTROL SYSTEMS MAGAZINE » DECEMBER 2006
The refinery process flow includes several hydropro-
cessing units to remove contaminants either before or after
the conversion units, depending on whether the feed is
being treated to avoid catalyst deactivation or the product
is being treated to meet specifications. Catalysts in the con-
version units may contain significant amounts of expen-
sive rare metals such as platinum or may be sulfuric or
hydrofluoric acid, which require special handling. While
the metals in the catalysts are reclaimed to make new cata-
lyst, replacing the catalyst involves lost processing and
expensive maintenance. Many of these processes are
licensed to refining operating companies by process engi-
neering companies [8], [9].
REFINERY PROCESS CONTROL AND OPTIMIZATION
Refinery control and optimization systems are organized
in a hierarchical structure typical of many large-scale sys-
tems as shown in Figure 5. This hierarchical structure is
common throughout the petroleum and chemicals indus-
try [10]. At the lowest level, the basic flow, pressures, and
temperature controls are implemented in distributed con-
trol systems (DCSs) and programmable logic controllers
(PLCs). These systems are designed to collect and record
sensor measurements from the refinery processes, com-
pute control signals for the manipulated variables such as
control valves and solenoids, and provide the man-
machine interface to the process including generating
FIGURE 1  Common hydrocarbon molecules. These molecules represent various classes of hydrocarbons that make up crude oils and petro-
leum products processed in refineries.
H C
H
H
C
H
H
C
H
H
H
H
C
H
H
C
H
H
C
H
H
H
C
H
H
H
C
H
H
C
H
C
H
H
H
H C
H
H
H C
H
H
C
H
H
C
H
H
H
C
H
H
C
H
H
C
H
H
C
H
H
Propane
n-Butane
Isobutane
n-Heptane
iso-Octane
(2,2,4 Methyl Pentane)
C
H2
C
H2
C
H2
C
H2
C H2
C
H2
C
H2
C
H2
C
H2
C
H
C H3
C H
C H3
C
H
H
C
H
H
H
Ethylene
C
H
H
C
H
H
C
H
H
H
Propylene
Olefins
Cyclopentane
1,2-Dimethylcyclohexane 
Paraffins
C
H
C
H
C
H
C
H
C H
C H
Benzene
C
H
C
H
C
CH3
C
H
C H
C H
Toluene
Para-Xylene
H
C
H
H
C
C
H
H
H
C
C
H
H
H
H
H
C
H
H C
H
H
H C
H
H
C
H
C
CH3
C
CH3
C
H
C H
C H
Aromatics
Naphtenes (Cycloparaffins)
Refining processes can be classified
into three major groups: separations,
conversions, and blending.
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DECEMBER 2006 « IEEE CONTROL SYSTEMS MAGAZINE
77
alarms for abnormal situations. To get a sense of scale, a
moderately large refinery system has on the order of
100,000 sensors and 10,000 actuators. Also implemented
at this level are advanced regulatory controls such as cas-
cade controllers, ratio controllers, constraint controllers,
and sequencing controls. The design of the distributed
control systems allows execution of these controllers
nominally once per second.
At the next level, multivariable predictive controllers
(MPCs) broaden the scope of the control problem. Imple-
mentation of MPCs permit control of highly interactive
multivariable processes that are subject to multiple con-
straints. These controllers require a dynamic process
model, which is often a linear model identified from
process data collected during planned dynamic tests. While
some predictive controllers are implemented on the DCS,
the computing requirements frequently exceed the capabil-
ity of the DCS due to controller size as defined by the num-
ber of controlled and manipulated variables and prediction
horizon length. As a result, predictive controllers are often
implemented using vendor software on general purpose
computers that communicate with the DCS. An overview
of the industrial implementation of predictive controllers in
the process industry is provided in [11].
Another advantage of predictive controllers is that, at
each execution, optimal steady-state control and
FIGURE 2  Boiling-point curves for typical crude oil and refinery products. Refineries convert crude oil molecules into product components
such as those listed above. Hydrocarbons typical of the boiling-point ranges are shown beneath these curves. The conversion process can
operate more effectively with a thorough understanding of the underlying molecular structures. (Reprinted with permission from Exxon Mobil
Corporation.)
S
S
S
S
S
S
S
S
Paraffins
Naphthenes
Benzenes
Benzo-Thiophenes
Naphthalenes
Di-Benzothiophenes
Phenanthrenes
Pyrenes
Chrysenes
Benzo-Pyrenes
Product 
Demand 
Jet Fuel
Gasoline
Diesel and Home Heating Oil
Lubricating Oils 
Crude Supply 
Asphalt
Volume Fraction
180
300
400
500
650
1,000
1,300
Normal Boiling Point (°F)
When the crude oil feed changes composition frequently or the refinery product
demand and pricing fluctuate, a predictive controller based on linear models must
compute targets that trade off the quality of separation for energy savings.
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manipulated variable targets are determined. Since these
controllers usually employ linear dynamic models, a lin-
ear program is used to evaluate the steady-state operat-
ing targets. While linear models are useful for small
regions around the current operating point, the target
calculations over the operating range of most refineries
involve significant nonlinearities. Examples of common
nonlinearities include tiered pricing discontinuities, con-
straints associated with reaction rates, product proper-
ties such as octane number and Reid vapor pressure, and
the thermodynamics that determine the equilibrium in
separation processes. As pointed out [10], these nonlin-
earities can cause the predictive control to target a
steady-state operating point that is not optimal when the
second and third levels of the control and optimization
hierarchy are collapsed into one level. When significant
nonlinearities exist, a nonlinear optimization program is
needed to compute the steady-state predictive control
target values.
When the feed or process changes that excite these
nonlinear process behaviors occur frequently, offline
optimization may not be sufficient to keep the operation
near the most cost-effective operation. In these cases, a
real-time optimizer (RTO) is often implemented to deter-
mine the optimal operating point under changing condi-
tions. RTO implementations make up the third level of
the control structure hierarchy shown in Figure 5. The
RTO is typically a nonlinear program minimizing cost or
maximizing profit subject to constraints derived from
steady-state mass and energy conservation balances,
reaction kinetic relationships, thermodynamic equilibri-
um equations, physical property relationships, and phys-
ical equipment constraints. The mass and energy
conservation balances relate energy requirements to pro-
duction, permitting the value of production to be opti-
mized relative to the cost of energy. 
FIGURE 4  The ExxonMobil Torrance Refinery located in Torrance,
California. The vessels and piping comprise a complex network of
material and energy flows that convert crude oil into products.
(Reproduced with permission from Exxon Mobil Corporation and
Joe Carson.)
FIGURE 3  Process units and interconnecting flows in a typical fuels refinery. The crude unit separates the crude oil into streams to be converted
and purified into blending components. Each process is a complex combination of reaction and separation operations needed to produce fuels
such as gasoline, diesel, jet fuel, fuel oil, and heating oil.
Gas
Plant
Isobutane
Gasoline
Hydrotreater
Reformer
Naphtha
Crude
Unit
Crude Oil
Reformate
Fuel Gas
Liquified Petroleum
Gas (LPG)
Gasoline
(Regular/Premium)
Jet Fuel
Diesel Fuel
Heating Oil
Fuel Oil
Asphalt
Coke
Hydrocrackate
Hydrocracker
Coker
Gas
Gas Oil
Kerosene
Hydrotreater
Propylene/
Butene
Alkylate
Alkylation
Gas
Gas Oil
Gasoline
FCC
Blending
Kerosene
78
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DECEMBER 2006 « IEEE CONTROL SYSTEMS MAGAZINE
79
A typical RTO can be characterized by a large number
of equations and variables with only a few decision vari-
ables. The RTO dimensionality gets large with the number
of balances needed to describe the mass and energy con-
servation in process equipment multiplied by the large
number of components in the process streams. It is com-
mon to find RTO applications with less than 100 decision
variables, more than 10,000 total variables, more than
100,000 constraint equations, and more than several hun-
dred thousand nonzero Jacobian-matrix entries. Since the
underlying models are steady-state models, these applica-
tions operate on the same time
scale as the process steady-state
response time with often several
hours between implemented solu-
tions. The modeling complexity
and difficulties parameterizing
these large models have motivated
some practitioners to develop
methods for augmenting the non-
linear reactor kinetic models with
the linear dynamic models used in
the predictive controller to collapse
the MPC and RTO levels into a sin-
gle application [12].
Finally, at the fourth level of the
hierarchy, refinery-wide planning
and scheduling optimization is
used to determine optimal targets
for the individual refinery process-
ing units to create the pool of
hydrocarbon materials needed to
meet product demand volumes and
specifications. Traditionally, this
optimization is based on a linear
program with a profit objective
function and constraints relating
product yield shifts to changes in
key operating variables. In addition
to planning overall refinery opera-
tion, these linear programs are used
to evaluate both capital-investment
decisions and crude oil and inter-
mediate hydrocarbon feed purchas-
es. The impacts of operating strategies on blending compo-
nents and products are evaluated using multiperiod linear
programs. Because of the broad scope of the refinery plan-
ning optimization application, decisions based on this appli-
cation occur on a day-to-week time scale. Multiperiod
nonlinear solvers can also be applied to optimally schedule
refinery maintenance activities that may occur only once
every several years [13].
Finally, while not included in the control and optimiza-
tion hierarchy, refineries use PLCs to implement safety
and protective systems. While refinery processes operate
FIGURE 5  Control-system hierarchy illustrating how refineries simultaneously achieve control
objectives on multiple time scales. Fast control actions are required to operate the refinery with-
in safe operating limits. Refinery-performance optimization for changes in market conditions,
feed quality, and equipment performance is applied at slower time scales.
Refinery-Wide Planning Optimization
Product Values
Operating Costs
Inventories
Operating Limits
(Often Manual)
Product Values
Operating Costs
Operating Limits
Relative Product Values
and Operating Costs
Dynamic Response
Weightings/Penalties
Performance-Based
Tuning Parameters
Refinery Instrument System
Base Control System
Multivariable Predictive Controllers
Real-Time Optimization
Single- or Multiperiod Linear Program with
Linear Model of Refinery Operation
Executed Offline
High-Dimension Steady-State Fundamental
Mass/Energy Balance Models
Multiple-Hour Execution Period
Identified Models of Interactive
Multivariable Processes over Time Horizon
1-min Execution Period
Flow, Pressure, Temperature, Analyzer
Controllers
PID, Ratio, Cascade Algorithms
1-s Execution Period
With tighter emission requirements on fuels and the changing world supply
of crude oil, more detailed methods for characterizing refinery feed
and product streams are needed to make the complicated decisions
required to control and optimize refinery operations.
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80
IEEE CONTROL SYSTEMS MAGAZINE » DECEMBER 2006
daily within safe operating envelopes, accidents might
occur if an upset or disturbance moves the process outside
the safe operating envelope. Protective systems monitor
process operation and, independent of the process control
system, sequence the safe shutdown of processes when an
excursion from safe operating limits is detected. These sys-
tems prevent the exposure of personnel to unsafe condi-
tions and protect the refinery from large financial losses
resulting from equipment damage.
CONTROL OF A REFINERY CRUDE UNIT
The crude unit is normally two distillation towers that
operate at different pressures with downstream towers to
purify gas streams, as shown in Figure 6. The atmospheric
tower runs just above atmospheric pressure, while the vac-
uum tower operates under a vacuum allowing the heavier
molecules to boil at lower temperatures. A detailed
description of distillation and an introduction to distilla-
tion control schemes is provided in [14].
One characteristic that separates refinery distillation in
general and crude units in particular from other distilla-
tion applications is the use of side draws and pumparound
circuits. Typical distillation applications produce products
only from the top and bottom of the tower. Energy is typi-
cally removed at the top of a common distillation column.
In crude distillation, the towers have several side draws to
produce oil fractions (known as cuts) that are different
from the overhead and bottoms products. Also,
pumparound circuits at various locations in the tower
remove energy from the tower. The crude oil feed is often
preheated with energy transferred from the pumparounds.
A fired-heater burning fuel gas or fuel oil is often used to
further heat the feed before it is charged to the bottom of
the tower.
The energy added to the feed by the preheat exchangers
and fired heater provide the driving force for separating
the crude oil into different hydrocarbon cuts, as shown in
Figure 7. The energy vaporizes the feed entering the tower.
This vapor travels up the distillation tower, contacting
with the liquid flowing back down the tower. The liquid
flow down the tower is the vapor from the tower overhead
condensed by transferring the heat of vaporization energy
to the air or cooling water through more heat exchangers.
The contacting liquid and vapor streams equilibrate ther-
modynamically on each tray in the distillation tower, caus-
ing the molecules with the lower boiling points to move up
the tower and those with higher boiling points to move
down the tower. 
FIGURE 6  Typical crude unit process flow illustrating the two distillation units and the key heat exchangers, fired heaters, and pumparound
circuits. The refinery can trade off energy usage and separation quality by solving the multivariable control and optimization problems.
Crude Oil
Top
Pumparound
Mid
Pumparound
Bottom
Pumparound
Preheat
Exchangers Heater
Steam
Atmospheric
Crude Tower
Vacuum
Crude Tower
Coker Feed
Steam
Heavy Vacuum
Gas Oil
Light Vacuum
Gas Oil
Vacuum
System
Naphtha
Gas
Plant
Cooling
Water
Kerosene
Atmospheric
Gas Oil
750 to
850°F
< 0.1 atm
Heater
750 –
800 °F
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The quality of the separation is measured by the
amount of overlap between product cuts. In Figure 7, the
cut between the kerosene and the gas oil has little or no
overlap because there is good separation. However, the
heavy naphtha molecules overlap significantly with the
light kerosene molecules from a poor separation. A good
separation costs more in energy but results in higher quality
products, creating an optimization opportunity.
In the atmospheric tower, the major manipulated vari-
ables are the feed rate, the furnace outlet temperature, the
tower pressure, the three pumparound flows, and the five
product draw rates. The control objectives are to meet the
separation specifications of the downstream processing
units while minimizing energy costs and staying within
regulatory and environmental limits. These limits include
crude charge rate and furnace heating and emission limits.
Changing any of the draw rates impacts the quality of sep-
aration throughout the tower. For example, drawing more
kerosene pulls more heavy molecules, changing the liquid
flow to the sections below the kerosene draw. Changing
the liquid flow in any section of the tower changes the con-
tacting between the liquid and vapor in the tower as well
as the equilibria throughout the tower, thus changing the
quality of the separation throughout the tower. Changes to
pumparound flows, however, affect only the separations
above the pumparound because the pumparound removes
energy from the tower and puts it into the crude oil feed.
This energy is no longer available higher up in the tower to
effect better product separation. 
Due to the highly interactive and multivariable nature of
the control problem and since the input-output responses for
the crude unit are often close to linear, model predictive con-
trollers are effectively used to control crude units.
When the crude oil feed changes composition
frequently or the refinery product demand and
pricing fluctuate, a predictive controller based
on linear models must compute targets that
trade off the quality of separation for energy
savings. For example, one could choose to
increase a pumparound flow to reduce the fur-
nace fuel usage at the expense of overlap in a
product cut because the downstream require-
ments can be met at lower cost. In this case, real-
time optimization can be used to characterize
the crude oil composition and model the separa-
tion and heat transfer processes to compute tar-
gets and optimize unit profits.
The number of equations and variables in a
crude-unit RTO is a function of the number of
components in the crude oil, the number of sep-
aration stages in the towers, and the number of
heat exchangers. Without using model reduc-
tion, a typical crude-unit RTO is a nonlinear pro-
gram with about 500,000 equations for often less
than 20 decision variables. Without a significant
loss of fidelity, the number of equations can be reduced to
about 100,000. 
In crude unit operation, multiple models are needed
to achieve the objectives at different control and opti-
mization levels. These models include a linear dynamic
model for the model predictive controller, a steady-state
fundamental model in the RTO application, and a
steady-state linear model in the refinery-wide planning
linear program (LP). While derivation of these individ-
ual models from a single parent model could improve
consistency among the solutions, these models are typi-
cally created and maintained separately. Consistency
checks are needed to avoid conflicting model character-
istics that lead to oscillations between solutions found
from each of the different models.
CONTROL OF REFINERY CONVERSION UNITS
As discussed earlier, many of the refinery processes con-
vert heavier less-valuable crude oil molecules into product
molecules. While reforming, isomerization, and alkylation
reactions are essential for modifying the quality or blend-
ing properties of different hydrocarbon molecules, crack-
ing reactions are critical for converting large molecules
into smaller hydrocarbons that can be used in common
fuels. Thermal cracking reactions that use heat and no cat-
alyst are still used with large molecules that do not lend
themselves to catalytic cracking reactions. Catalytic crack-
ing in either fluidized catalytic crackers (FCCs) or hydroc-
rackers is more popular because the catalyst can selectively
produce the desired molecules. The differences in the reac-
tor configurations result in interesting differences in the
control and optimization applications.
FIGURE 7  Distillation curves for the naphtha (green), kerosene (blue), and gas
oil (red) overlayed on a typical crude distillation curve. Significant overlap
between the 95% point of the naphtha and the 5% point of the kerosene indi-
cates poor separation. Negligible overlap between the kerosene and the gas oil
indicates good separation.
0
0
5%
95%
5%
5%
95%
95%
1,200
1,000
800
600
400
200
Crude Oil Fraction
Vacuum Tower Feed
Gas Oil
Kerosene
Naphtha
Smaller Overlap
Better Separation
Overlap
Boiling Point (°F)
100
80
60
40
20
DECEMBER 2006 « IEEE CONTROL SYSTEMS MAGAZINE
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82
IEEE CONTROL SYSTEMS MAGAZINE » DECEMBER 2006
FLUIDIZED CATALYTIC
CRACKER CONTROL AND OPTIMIZATION
Fluidized catalytic cracking units are responsible for most
of the conversion of heavy oils into gasoline [8]. While
there are many FCC designs, a typical unit has a reactor
vessel and a regenerator vessel, followed by a main col-
umn that separates the products, as shown in Figure 8. A
fluidized catalyst stream is circulated between these two
vessels. Depending on the sulfur and nitrogen content,
the gas oil feed may be hydrotreated before being fed to
the FCC to prevent deactivating (also called poisoning)
the catalyst. The gas oil feed stream is added to the cata-
lyst stream flowing out of the regenerator into the reactor
riser. The cracking reactions occur in the riser. The prima-
ry reactions break paraffin, napthene, and aromatic mole-
cules into smaller similar molecules, olefin molecules,
and carbon called coke. The catalysts used for cracking
are subject to deactivation because of the generation of
coke. The coke produced in a FCC coats the catalyst sur-
face, thus blocking access for reactions with other mole-
cules. Secondary isomerization and ring formation
reactions also occur.  Depending on the operating condi-
tions, secondary cracking occurs, producing light hydro-
carbon gas molecules and more coke.
After leaving the riser, all of the hydrocarbons are
stripped away from the catalyst with steam in the reactor.
The catalyst then circulates back to the regenerator, where
the coke is burned off the catalyst, regenerating the cata-
lyst. Cracking reactions in the FCC are endothermic, mean-
ing that energy must be added to crack the hydrocarbons.
This energy is generated in the process of burning off the
carbon on the catalyst particles. The energy in the reactor
products is used to preheat the oil feed to reaction temper-
atures. The primary manipulated variables are the reactor
riser temperature, the catalyst-to-oil ratio in the reactor
feed, air flow to the regenerator to control catalyst activity,
catalyst circulation rate to control the space velocity or
contact time, and reactor pressure. Additionally, the FCC
main column is a distillation tower similar to the atmos-
pheric tower in the crude unit. The control objectives are to
maximize the conversion of the feed oil to hydrocarbons
boiling at less than 220 ◦C (430 ◦F) without excessive over-
cracking to light gaseous hydrocarbons and coke. The con-
trol system must also maintain adequate catalyst inventory
in both vessels and regulate the regenerator temperature to
control the coke burn.
A multivariable predictive control design is often used to
achieve these control objectives with the manipulated
inputs. Gasoline production economics provide a basis for
selecting the controller tuning parameters. Since the regen-
erator maintains stable catalyst activity, linear dynamic FCC
MPC models do not change significantly over time. This fea-
ture makes it unnecessary to re-identify models on a fre-
quent schedule to maintain good controller performance.
FIGURE 8  Simplified flow diagram of a fluidized catalytic cracking unit that converts gas oil into lighter hydrocarbons. The reactions occur in
the riser after the catalyst is mixed with the gas-oil feed. The products are separated from the catalyst in the reactor. The coke produced as
a by-product of the reactions is burned off the spent catalyst in the regenerator providing energy needed for the reactions in the riser. Multi-
variable predictive controllers are often used to control the mass and energy interactions among these vessels and the heat exchange
between main column and the gas-oil feed. Real-time optimizers are often used to account for the nonlinear reaction kinetic effects.
Reactor
Regenerator
Flue Gas
Air
Spent Catalyst
Fresh Catalyst
Gas Oil
Feed
Main Column
Cat Slurry Oil
Heavy Cat Gas Oil
Light Cat Gas Oil
Gasoline
Gas
Plant
Cooling Water
Riser
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DECEMBER 2006 « IEEE CONTROL SYSTEMS MAGAZINE
83
Real-time optimizers are often used on FCC units. The
cracking reaction rates are sensitive to reactor riser temper-
ature, following an exponential Arrhenius expression.
Higher reactor riser temperature results in higher conver-
sion and more preheat energy. Higher riser temperature
also leads to higher reactor temperature and more coke to
burn to maintain catalyst activity, and potentially more
over-cracking of valuable gasoline molecules to light gas
and coke. Because of these nonlinear temperature depen-
dencies, determining the optimal operating conditions
requires a nonlinear program such as a RTO [10]. The
development of a fundamental model for an FCC RTO
based on the molecular structure of the feeds and products
is given in [15]. The dimension of the FCC model can be
reduced by assuming that some of the FCC vessels contain
well-mixed homogeneous mixtures. However, even
though the total number of components is less than the
number of components in a crude oil stream, model reduc-
tion is required to reduce the model dimension for success-
ful FCC RTO applications. The RTO application has the
fidelity to obtain the information needed to trade off crack-
ing conversion and energy to obtain an optimal yield for a
given product demand at the lowest cost.
This same strategy can be applied to the other refinery
conversion units. Hydrocrackers can crack heavier vacuum
tower gas oil and more aromatic gas oils from the FCC and
coker. A coker is a thermal cracking process unit that con-
verts oil from the vacuum tower bottom into coker naph-
tha, coker gas oil, and coke. Unlike the FCC, because
hydrocrackers are fixed-bed reactors, hydrocracker models
cannot be spatially lumped, resulting in significantly
increased model dimensionality. With judicious model
development and model reduction methods, hydrocracker
models can be developed for use with the same control
and optimization structure used in the FCC application.
Cyclic processing and complex thermal cracking reactions
have limited RTO applications for cokers. However, MPC
can be applied in both applications to improve the perfor-
mance of these other conversion units.
CONCLUSIONS
With tighter emission requirements on fuels and the
changing world supply of crude oil, more detailed meth-
ods for characterizing refinery feed and product streams
are needed to make the complicated decisions required to
control and optimize refinery operations. Changes in ana-
lytical measurement technology provide additional data
for fingerprinting the molecular structure of these hydro-
carbon mixtures. Integrating this molecular characteriza-
tion of the materials with advances in compositional
modeling methods, model reduction techniques, optimiza-
tion, and equation-solving methods, along with continued
implementation of model predictive control, improves
operational effectiveness at all levels of the refinery from
the individual process level to the refinery-wide planning
optimization. The examples presented highlight progress
toward capturing the highest value of every molecule at
every point in the refining complex.
AUTHOR INFORMATION
Robert E. Young (robert.earl.young@exxonmobil.com) is  the
advanced control section supervisor at ExxonMobil’s Tor-
rance Refinery in Torrance, California. He received his B.S.
degree in chemical engineering from the University of Texas,
Austin, and his M.S. and Ph.D. degrees from University of
California, Santa Barbara. He has led and implemented
process control projects in the areas of linear and nonlinear
multivariable predictive control, polymerization reactor con-
trol, process control system replacement, and manufacturing
execution systems. He is an active AIChE Computing and
System Technology (CAST) division member, currently serv-
ing as a 2007 meeting program coordinator and a division
director, as well as a member of IEEE and SIAM. He can be
contacted at ExxonMobil Refining and Supply—Torrance
Refinery, 3700 W 190th St., Torrance, CA 90504 USA.
REFERENCES
[1] D. Kushnerick and C. Kennedy, “Application of compositional modeling
in molecule management,” presented at NPRA Plant Automation and Deci-
sion Support Conf., PD-04-191, San Antonio, TX, 2004.
[2] “Staying the course… Yesterday, Today, and Tomorrow,” Exxon Mobil
Investors Meeting, Exxon Mobil Annu. Rep., 2001.
[3] Energy Information Administration, Office of Oil and Gas, U.S. Dept. of
Energy, “U.S. Refinery yield” Mar. 15, 2006 [Online]. Available:
http://tonto.eia.doe.gov/dnav/pet/pet_pnp_pct_dc_nus_pct_a.htm
[4] K.W. Gardner, “U.S. gasoline requirements map,” presented at Exxon
Mobil Fuels Marketing, Fairfax, VA, Jan., 2005.
[5] Office of Transportation and Air Quality, “Study of unique gasoline fuel
blends (“boutique fuels”), effects on fuel supply and distribution and poten-
tial improvements,” U.S. Environmental Protection Agency, Washington
DC, Oct. 2001. [Online]. Available: http://www.epa.gov/otaq/
regs/fuels/p01004.pdf
[6] “NPRA United States refining and storage capacity report,” National
Petrochemical and Refiners Association, Washington, DC, Jan. 2005.
[Online]. Available: http://www.npra.org/publications/statistics/
RC2005.pdf
[7] Energy Information Administration, Office of Oil and Gas, U.S. Depart-
ment of Energy, “Refinery capacity data historical” May 13, 2005 [Online].
Available:  http://www.eia.doe.gov/oil_gas/petroleum/data_publica-
tions/refinery_capacity_data/refcap_historical.html 
[8] J.H. Gary and G.E. Handwerk, Petroleum Refining, Technology and Econom-
ics. New York: Marcel Dekker, 2001.
[9] 2004 Refining Processes Handbook, Hydrocarbon Processing. Houston, TX:
Gulf Publishing, 2004.
[10] O. Rotava and A.C. Zanin, “Multivariable control and real-time 
optimization—An industrial practical view,” Hydrocarbon Process., vol. 84,
no. 6, pp. 61–71, June 2005.
[11] S.J. Qin and T.J. Badgwell, “An overview of industrial model predictive
control technology,” in Chemical Process Control-V, J.C. Kantor, C.E. Garcia
and B. Carnahan, Eds.  Tahoe, CA: AIChe, 1997, pp. 232–256. 
[12] J. Jackson, M. Piatt, C. Timmons, P. Fountain, and R. Wagler, “Integra-
tion of off-line process models with on-line control and optimization to
improve process performance,” in Proc. 2002 Computer Conf, National Petro-
chemical and Refiners Association, Austin, TX, Nov. 2002, paper CC-02-14. 
[13] V. Bizet, I. Grossmann, and N. Juhasz, “Optimal production and sched-
uling of a process with decaying catalyst,” AIChE J., vol. 51, no. 3, 
pp. 909–921, 2005.
[14] H. Kister, Distillation Operation. New York: McGraw-Hill, 1990.
[15] G. Christensen, M. Apelian, K. Hickey, and S. Jaffee, “Future directions
in modeling the FCC process: An emphasis on product quality,” Chem. Eng.
Sci., vol. 54,  no. 13-14, pp. 2753–2764, 1999. 
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Petrol Production Resource-Efficiency Increase with 
the Help of Mathematical Modeling Method  
 
Maria Gyngazova, Emilia Ivanchina, Mikhail Korolenko  
Department of Fuel Engineering and Chemical Cybernetics, National Research Tomsk Polytechnic University, Tomsk, Russia 
maria.gyngazova@gmail.com 
 
 
Abstract— Catalytic reforming is one of the most important 
processes for high octane gasoline manufacture and aromatic 
hydrocarbons production. There are several ways of this process 
modernization. It is effective to use mathematical modeling 
method to examine all possible variants because it requires low 
time and financial investments. In this work we analyzed the set 
of possible reactions, proposed kinetic model for catalytic 
reforming process, calculated thermodynamic and kinetic 
parameters, investigated catalyst layer hydrodynamics. A 
mathematical model for a moving-bed catalytic reforming 
reactor taking into account activity and circulating factor of the 
catalyst was presented. The model allows analyzing various 
variants of process modernization and can help to find optimal 
operation regimes.  
Keywords- naphtha catalytic reforming process; mathematical 
modelling;process optimization 
I. 
 INTRODUCTION 
Nowdays there is a tendency to optimize the use of natural 
resources and there are some efforts to increase the resource-
efficiency of operating manufacture. 
The worldwide experience of petroleum refineries 
modernization shows, that almost all refineries to be modernize 
have the same set of well-known processes and technologies 
like desalting and preliminary distillation, hydrotreating of 
petrol and diesel fractions, light end isomerization and naphtha 
catalytic reforming. These units are often reconstructed during 
modernization, and in some cases new units are built in order 
to 
increase 
technicoeconomic 
and 
environmental 
characteristics of their operation.  
Catalytic reforming is one of the most important processes 
for 
high 
octane 
gasoline 
manufacture 
and 
aromatic 
hydrocarbons production. There are several ways of this 
process modernization. It is effective to use mathematical 
modeling method to examine all possible variants because it 
requires low time and financial investments. A number of 
research papers is devoted to mathematical modeling of this 
process [1-10]. The number of selected pseudo components in 
the mixture is a determinating factor for model design. 
Obviously, the more the number of specified pseudo 
components are the higher the accuracy of the model will be, 
but at the same time this leads to more complicated 
mathematical formulation. There is a need to develop a 
universal mathematical model that could balance this 
contradiction and that could be used for different raw material 
compositions. 
II. 
MATHEMATICAL MODEL OF A MOVING-BED 
CATALYTIC REFORMING PROCESS  
Our mathematical model for monitoring of oil processing 
process is elaborated from the point of view of system 
approach. The essence of the system analysis is that all the 
information about the investigated industrial object is collected 
for the construction of non-stationary catalytic process 
mathematical model. This model adequately reflects physical 
and chemical and technological essence of the plant. 
On the first — molecular — level thermodynamic and 
kinetic problems are solved; on the second — catalyst grain — 
the diffusion influence on process is considered; on the third – 
the industrial device (a reactor, a column, etc.) is modeled; on 
the fourth — the optimum technological scheme of the whole 
plant is designed. The account of these levels interrelation 
allows studying difficult catalytic process on blocks (modules). 
Each module is described, as a rule, by system of the nonlinear 
algebraic differential equations in ordinary and partial 
derivatives.  
The first stage of elaboration of mathematical model of this 
complex process is the analysis of the prior information about 
the reactions occurring in a catalytic reactor. 
The next stage is the formation of kinetic model of 
multicomponent reaction on the catalyst. The aspiration to 
consider in kinetic model the real detailed superficial 
mechanism involves complication of the model and increase in 
number of kinetic parameters. Here the proved formalization of 
the proceeding reactions mechanism is used taking into account 
components reactivity and specificity of used catalysts, but 
without sensitivity loss to change of initial hydrocarbons raw 
material composition. For reduction of dimension of system of 
the equations describing transformations in multicomponent 
mixture, the method of group aggregation (combination of 
certain hydrocarbons groups in one pseudo-component) with 
representation of each group in the form of a component of 
continuous structure is used. Such approach allows taking into 
account the inconstancy of raw materials structure, common 
for considered processes. 
 
978-1-4673-1773-3/12/$31.00 ©2013 IEEE 
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To aggregate the components of straight-run gasoline 
obtaining process we combine hydrocarbons into groups on the 
basis of affinity of their reactivity expressed in magnitude of 
antiknock value. Unlike aggregation on homological groups, 
the basic characteristic of a desired product in manufacture of 
commodity gasolines −octane number is considered. For 
example, octane numbers of 2-methylpentane and 2,4-
dimethylbutane differ in 30 points though these hydrocarbons 
belong to one homological group (isoparaffin hydrocarbons 
С6). 
The scheme of reactions between pseudo-components 
accepted on such basis has been used at construction of 
mathematical model of catalytic reforming of hydrocarbons on 
Pt-containing catalysts. This has allowed foreseeing the 
consequences of raw material structure change. 
The researches executed on the basis of experimental data, 
received on the industrial unit, have allowed accepting the 
following formalized scheme of the mechanism of catalytic 
reforming process in moving-bed reactors (fig. 2). 
 
Figure 1.  Formalized reaction scheme for naphtha reforming 
Investigations 
have 
shown 
that 
alkyl 
benzene 
hydrodealkylation reactions rates increase with rising the 
reforming process temperature. On the other hand the pressure 
decreasing for catalytic reforming process in moving-bed 
reactors 
with 
constant 
catalyst 
regeneration 
reduces 
dealkylation reactions rates. So it is advisable to insert alkyl 
benzene dealkylation reactions in hydrocarbon transformation 
scheme for catalytic reforming process in moving-bed reactors 
in order to control the yield of high-molecular aromatic 
hydrocarbons. 
According to the chemical reaction rate law elementary 
reaction rate at the set temperature is proportional to 
concentration of reacting substances in the degrees showing 
number of particles entering interaction: 
( )
r
k
f
C
=
⋅
 
 
 
( )
1
2 ...
1
2
v
v
vn
f
C
C
C
Cn
=
⋅
⋅
⋅
 
 
Where r is reaction rate; k is rate constant; Ci is initial 
components concentration; νi is stoichiometric coefficient in 
gross-equation of chemical reaction.  
The given equation is true for elementary reactions. 
Complex reactions can have a fractional order. 
Having such level of mechanism specification the change 
of concentration of i -component in reversible j- reaction of the 
first order can be written as a system of the material balance 
equations: 
2
( )
( )
j
l
i
j
i
H
j
dC
k
x C x C
dt =∑
 
where j = 1,..., m is a number of chemical reaction; Сi (x) 
and kj (x) are respectively distributions of hydrocarbons 
concentration and rate constants on number of carbon atoms in 
a molecule х; lj is a reaction order on hydrogen; t is space time. 
For 
the 
naphthenes 
dehydrogenation 
and 
aromatics 
hydrogenation reactions lj=3, for the dehydrocyclization, 
hydrocracking and hydrodealkylation reactions lj=1, for the 
isomerization reactions lj=0. 
In catalytic reforming reactors with stationary layer of 
catalyst activity and selectivity change through the radius of 
catalyst layer (in radial input of raw material) and with time, 
but in moving-bed reactors they change through the radius and 
height of catalyst layer and with time (fig. 2). 
 
Figure 2.  Schematic representation of a radial moving-bed reactor  
Earlier the mathematical model of a catalytic reforming 
reactor with a fixed bed of the catalyst has been created. For 
describing the process in moving-bed reactor it is necessary to 
consider the two-phase flow. The passive phase is the gas flow, 
the active phase is the gas in the pores of the solid catalyst 
particles. The hydrodynamic regime of both phases is close to 
plug flow regime. 
For the mathematical description of hydrodynamic and heat 
model of catalytic reforming reactor some assumptions are 
accepted: 
• 
mass and heat transport occurs by means of convection; 
• 
adiabatic operation; 
• 
the 
formalized 
mechanism 
of 
hydrocarbons 
transformation (fig.1). 
The model of moving-bed catalytic reforming reactor, 
presented by a system of equations of material balance for 
components and the equation of heat balance is the following: 
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1
( )
( )
0
1
( )
( )
0
j
j
j
l
С
C
C
i
i
i
G
u
r l a l dl
z
R
l
l
l
T
T
T
m
m
m
m
cat
cat
C
G
u
C
C
Q
r
l a l dl
p
p
p
j
j
z
R
l
l
ϕ
ρ
ρ
ϕ ρ
⎧
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎩
∂
∂
∂
⋅
= −⋅
−
⋅
+ ⋅∫
∂
∂
∂
∂
∂
∂
⋅
⋅
⋅
= −⋅
⋅
⋅
−
⋅
⋅
⋅
+
⋅⋅
∑
∫
∂
∂
∂
 
(4) 
The conditions are: 
at z=0 Ci= Ci,0; T= Ten; 
at l=0 Ci=Ci,0 (at the reactor entrance); T=Ten; 
at r=0 Ci=Ci,0; Т=Тen. 
where z is a volume of raw material processed from the 
moment when the fresh catalyst (new catalyst, no regenerations 
were done) was loaded, m3; G is a raw material flow rate, m3/h; 
z=G·t (t is a time of catalyst work from the new catalyst load, 
h); Ci is a concentration of i-th component, mol/m3; u is a flow 
rate, m/h; R is a radius of the catalyst layer, m; l is a catalyst 
layer length in the reactor, m; φ is a catalyst flow rate, m/h; r is 
an integral reactions rate for component i, mol/(m3·h); a(l) is a 
catalyst activity distribution through the catalyst layer length in 
the reactor with a moving bed; ρm, ρcat is density of mixture and 
catalyst respectively, kg/m3; Ср
m, Ср
cat is a heat capacity of 
mixture and catalyst respectively, J/(kg·K); Qj is j-th reaction 
heat, J/mol, Qj is assumed to be constant within each 
homological group (normal alkanes, isoalkanes, aromatic 
hydrocarbons, cyclopentanes, cyclohexanes); Т is temperature, 
K; rj is j-th reaction rate, mol/(m3·h).  
In the model the catalyst deactivation is considered. In 
moving-bed reactors catalyst activity changes through the 
radius and height of catalyst layer and with time. In our 
formalized reaction scheme (fig. 1) coke is one of the reactions 
components. The coke concentration can be calculated 
according to the kinetics of coke formation. The catalyst 
activity will decrease with the coke concentration increase. It 
was found that the catalyst activity depends on the catalyst 
circulating factor (hcir) the following way: 
/
0
j
coke
cir
C
h
j
a
A e
α
−
⋅
=
⋅
 
 
where Ccoke is a mass fraction of coke on the catalyst; a is a 
catalyst activity; 
/(
)
m
cat
cir
h
u ρ
ϕ ρ
=
⋅
⋅
; А0 is a linear component 
determining the number of catalyst active centres; α is a 
coefficient of catalyst poisoning - nonlinear component that 
determines different extent of angle and edge atoms 
deactivation due to coking.  
More 
detailed 
information 
about 
the 
proposed 
mathematical model can be found in [11]. 
III. 
RESULTS AND DISCUSSION 
The example of calculation of catalytic reforming process 
with continuous catalyst regeneration for industrial unit is 
given in table 1. 
 
 
 
 
TABLE I.  
SIMULATION RESULTS OF CATALYTIC REFORMING PROCESS 
IN MOVING-BED REACTOR  
Group composition, wt. %  
Calculation 
Experiment 
n- Alkanes 
6.78 
6.84 
i- Alkanes 
14.19 
14.35 
Naphthenes-5 
0.57 
0.34 
Naphthenes-6 
0.78 
0.23 
Aromatic hydrocarbons 
77.67 
77.16 
Catalysate octane number 
102.5 
Product yield 
89.2 
Comparing the values presented in table 1, we see, that 
component composition of reformate calculated on model, 
coincides with experimental data with the set accuracy (in our 
case the calculation error should not exceed an error of 
chromatographic analysis). 
Using mathematical modeling it is also possible to compare 
the different catalytic reforming units work efficiency and 
choose more suitable variant of process optimization for given 
raw material. 
The approach applied to formalization of the reagents 
transformation scheme, allows reflecting the influence of raw 
material composition on the basic values of reformate quality 
for various conditions of process carrying out (fig. 3, 4). 
 
1 2
3
4 56
7
100
101
102
103
104
105
106
515
520
525
530
535
540
545
temperature, °С
research octane number
1 - р=0.6 MPa, alkanes/(naphthenes+aromatic hydrocarbons)=0.96
2 - р=0.7 MPa, alkanes/(naphthenes+aromatic hydrocarbons)=0.96
3 - р=0.85 MPa, alkanes/(naphthenes+aromatic hydrocarbons)=0.96
4 - р=0.6 MPa, alkanes/(naphthenes+aromatic hydrocarbons)=1.23
5 - р=0.7 MPa, alkanes/(naphthenes+aromatic hydrocarbons)=1.23
6 - р=0.8 MPa, alkanes/(naphthenes+aromatic hydrocarbons)=1.23
7 - р=0.9 MPa, alkanes/(naphthenes+aromatic hydrocarbons)=1.23
 
Figure 3.  The dependence of catalysate octane number on temperature in 
catalytic reforming process in moving-bed reactors at different pressures for 
various raw material composition (simulation results) 
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1
2
3
4 5
6
7
89
89.5
90
90.5
91
91.5
92
515
520
525
530
535
540
545
temperature, °С
reformate yield, %
1 - р=0.6 MPa, alkanes/(naphthenes+aromatic hydrocarbons)=0.96
2 - р=0.7 MPa, alkanes/(naphthenes+aromatic hydrocarbons)=0.96
3 - р=0.85 MPa, alkanes/(naphthenes+aromatic hydrocarbons)=0.96 
4 - р=0.6 MPa, alkanes/(naphthenes+aromatic hydrocarbons)=1.23
5 - р=0.7 MPa, alkanes/naphthenes+aromatic hydrocarbons)=1.23
6 - р=0.8 MPa, alkanes/(naphthenes+aromatic hydrocarbons)=1.23
7 - р=0.9 MPa, alkanes/(naphthenes+aromatic hydrocarbons)=1.23
 
Figure 4.  The dependence of reformate yield on temperature of catalytic 
reforming process in moving-bed reactors at different pressures for various 
raw material composition (simulation results) 
Use of raw material with higher naphthenes content allows 
receiving reformate with better antiknock value and high yield. 
In this case (fig. 3,4) use of raw material with the alkanes to 
naphthenes and aromatic hydrocarbons ratio 0.96 is more 
preferable, than with the ratio 1.23. But even at a large content 
of unbranched alkanes in initial raw material catalytic 
reforming process in moving-bed reactors with continuous 
catalyst regeneration allows receiving reformate with high 
octane number and yield in comparison with other catalytic 
reforming technologies. 
IV. 
CONCLUSION  
In this work we have presented a mathematical model for a 
moving-bed catalytic reforming reactor taking into account 
activity and circulating factor of the catalyst. The model allows 
analyzing various variants of process modernization and can 
help to find optimal operation regimes.  
The mathematical model allows solving successfully the 
problem of product quality and quantity rising in operating 
manufacture conditions and also improving technical and 
economical values of industrial process. The peculiarities of 
raw material processed and also the technological peculiarities 
are considered. 
REFERENCES 
[1] R.B. Smith “Kinetic analysis of naphtha reforming with platinum 
catalyst”, Chem. Eng. Prog., 1959, №55(6), pp. 76-80. 
[2]  H.G. Krane “Reactions in Catalytic Reforming Naphtha”, Proceeding of 
the 5th World Petroleum Congress , 1959, pp. 39- 51. 
[3]  Y.M. 
Zhorov 
“Mathematical 
Description 
of 
Platforming 
for 
Optimization of a Process (I)”, Kinetika i Kataliz, 1965, №6(6), pp. 
1092 – 1098. 
[4]  J. Henningsen “Catalytic Reforming”, Chem. Eng., 1970, №15, pp. 
1073 -1087. 
[5] W.S. Kmak “A Kinetic Simulation Model of the Power Forming 
Process”; AIChE Meeting, Houston, TX, 1972. 
[6] G.B. Marin, G.F.Froment “The Development and Use of Rate Equations 
for Catalytic Refinery Processes”, EFCE Publ. Ser., 1983, Vol 2. , №27, 
p. 117. 
[7] G.F. Froment “Modelling of Catalytic Reforming Unit”, Chem.Eng.Sci., 
1987, №42, pp. 1073 – 1087. 
[8] Unmesh Taskar & James B.Riggs “Modelling & Optimization of Semi 
Regenerative Catalytic Naphtha Reformer”, AIChE J., 1997, №43 (3), 
pp. 740 – 753. 
[9]  Yu.V. Sharikov, P.A. Petrov “Universal model for catalytic reforming“, 
Chemical and Petroleum Engineering, 2007, vol. 43, pp. 580-584 
[10] M.S. Gyngazova, A.V. Kravtsov, E.D. Ivanchina, A.L. Abramin 
“Computer modeling of catalytic reforming process in moving-bed 
reactor with continuous catalyst regeneration” Abstracts XVIII 
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Malta, 2008, pp. 127–128 
[11] Gyngazova M. S. , Kravtsov A. V. , Ivanchina E. D. , Korolenko M. V. , 
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176-177 - pp. 134-143 
 
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Review
Not peer-reviewed version
Intelligent Petroleum Processing: A
Short Review on Applying AI/ML to
Petroleum Products Optimization
Abdallah Alzahawy and Hayder Issa *
Posted Date: 5 April 2024
doi: 10.20944/preprints202404.0442.v1
Keywords: Intelligent petroleum processing; petroleum products optimization; machine learning techniques;
gasoline property prediction; process control and operational efficiency
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Review 
Intelligent Petroleum Processing: A Short Review on 
Applying AI/ML to Petroleum Products Optimization 
A. H. Alzahawi 1 and H. M. Issa 2,* 
1 Kirkuk District Governorate, Kirkuk, 36001, Iraq  
2 Department of Chemistry, College of Science, University of Garmian, Kalar, Sulaymaniyah Province, 46021, Iraq 
* Correspondence: author: hayder.mohammed@garmian.edu.krd 
Abstract: Improving the quality of petroleum products and refining processes through the use of artificial 
intelligence and machine learning techniques is the topic of this article. It shows that expert knowledge and 
conventional empirical models can't get you where you want to go in a refining process. To effectively capture 
complex interactions and forecast fuel qualities, machine learning techniques such as principal component 
analysis (PCA), support vector machines (SVM), artificial neural networks (ANN), and partial least squares 
(PLS) are suggested. Gasoline and other petroleum products, as well as property prediction, process control, 
product quality, and operational efficiency in refineries, can all be improved with the help of machine learning 
applied to spectral or distillation curve data. An exciting new direction in optimizing operations, meeting 
environmental norms, and precisely estimating gasoline quality is offered by advanced machine learning 
algorithms. 
Keywords: intelligent petroleum processing; petroleum products optimization; machine learning techniques; 
gasoline property prediction; process control and operational efficiency 
 
1. Introduction 
Before looking into the new area of using machine learning to improve the quality of petroleum 
products, it's important to see how it could help the oil refining business become more efficient, 
profitable, and environmentally friendly (Sircar et al., 2021). When it comes to refining oil, the biggest 
task is to get the best product quality, process efficiency, and use of resources. In the past, this 
optimization process relied a lot on empirical models and expert knowledge (Ma et al., 2024). 
However, because refining processes are complicated and don't work in a straight line, the results are 
often less than ideal. Machine learning (ML) techniques, on the other hand, have changed the oil 
business by giving people powerful ways to get useful information from huge amounts of data (Al 
Jlibawi et al., 2021). 
An application of machine learning to the optimization of petroleum products entails the 
utilization of algorithms and models for the purpose of analyzing complicated datasets that are 
obtained from the various phases of the refining process. Through the utilization of methodologies 
like supervised and unsupervised learning, neural networks, and predictive analytics, refineries have 
the ability to improve their decision-making processes, optimize product yields, increase quality 
specifications, and streamline operations (Al-Jamimi et al., 2022). 
2. Exploring the Application of Various Machine Learning Methods for Predicting Vapor 
Pressure in Sour Natural Gas 
Predicting vapor pressure correctly is very important in the complicated world of natural gas 
handling, especially when working with sour gas streams that contain a lot of hydrogen sulfide and 
carbon dioxide. Usually, old-fashioned computer programs can't fully capture how temperature, 
pressure, and the make-up of a gas interact with each other, which can lead to waste and safety issues 
(H. M. Issa, 2015). Different machine learning methods, like neural networks (ANN) and ensemble 
models, can be used to make accurate predictions about vapor pressure in sour natural gas systems. 
Disclaimer/Publisher’s Note: The statements, opinions, and data contained in all publications are solely those of the individual author(s) and 
contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting 
from any ideas, methods, instructions, or products referred to in the content.
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©  2024 by the author(s). Distributed under a Creative Commons CC BY license.
 
2 
 
These machine learning techniques are able to discover intricate patterns and relationships 
across large datasets that contain a wide variety of gas compositions and operating conditions (H. M. 
Issa, 2016). Because of this, they are able to generate more precise estimates of the vapor pressure. 
The information that was acquired has the potential to assist in the enhancement of the design of 
processes, the acceleration of operations, and the reduction of risks that are associated with variations 
in vapor pressure. In the long run, machine learning would make the natural gas processing sector 
more profitable and sustainable than it is currently experiencing (Nimmanterdwong et al., 2021). 
3. Utilizing Machine Learning for Quantitative Prediction of Reid Vapor Pressure in Crude Oil 
Crude oil volatility and vapor-liquid equilibrium depend on Reid vapor pressure (RVP). Safe 
RVP storage, transportation, and refining require accurate quantitative prediction. It's possible that 
traditional empirical correlations and equations of state are wrong because they can't show the 
complicated relationship between RVP and the complex makeup of light crudes (Nascimento et al., 
2018). Using machine learning to create reliable RVP forecasting models for light and heavy crude 
oils. Using a lot of raw data and advanced algorithms like neural networks, random forests, and 
gradient boosting, it is possible to find complicated patterns and nonlinear connections between RVP 
and API gravity, molecular weight distribution, and compositional analysis. The more accurate RVP 
predictions from the ML models allow for more precise process design, the best ways to blend oils, 
and better management of risks related to vapor pressure in both upstream and downstream light 
crude oil operations (Hayder M. Issa & Albarzanji, 2020). 
The ability to precisely anticipate the RVP in crude oil is of critical importance in the petroleum 
industry, particularly in the refining process, as it plays a significant role in guaranteeing product 
quality, safety, and operating efficiency. While traditional methods frequently rely on simplified 
correlations, it is possible that these correlations do not fully convey the intricate interaction of 
variables that influence vapor pressure. The incorporation of machine learning techniques, on the 
other hand, presents a potentially fruitful route for improving the accuracy and dependability of 
predictions (Kongkiatpaiboon, 2024). 
The application of machine learning approaches for the purpose of quantitatively forecasting 
RVP in light crude oil is being investigated more thoroughly. Exploring a correlation method that is 
both streamlined and accurate makes use of the capacity of sophisticated algorithms to analyze 
complex datasets and create correct predictions (Lamberg, 2021). Through the utilization of machine 
learning, refineries have the potential to enhance their processes, enhance the quality of their 
products, and streamline their operations, thus contributing to the advancement of effective and 
sustainable practices within the petroleum industry. 
4. Utilizing Machine Learning Techniques for Predicting Gasoline Properties 
Predicting accurately the properties of gasoline is one of the most important tasks in optimizing 
petroleum products. This is necessary to ensure the quality of the products and that they meet strict 
industry standards. Usually, old ways of doing things depend on regression analysis or interpreting 
spectral or distillation curve data by hand, which might not give you a lot of information about the 
complex relationships in the dataset. Adding advanced machine learning methods, on the other hand, 
looks like a good way to improve the accuracy of predictions and get useful data from complicated 
data structures (Correa Gonzalez et al., 2021). 
It is crucial to estimate gasoline qualities accurately to optimize operations, maintain product 
quality, and comply with strict environmental standards. Conventional laboratory testing methods, 
although dependable, can be slow and take a lot of resources, which can impede the flexibility needed 
in today's ever changing refining industry. Many works have examined the use of sophisticated 
machine learning methods to quantitatively forecast important gasoline characteristics by utilizing 
easily accessible data sources, including spectral analysis and distillation curves (Hayder Mohammed 
Issa, 2024). 
Spectral or distillation curve data is combined with regression analysis, PCA, and PLS to make 
quantitative predictions about the properties of gasoline. Refineries may be able to better understand 
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3 
 
gasoline composition, maximize production, and meet strict quality standards by using these 
advanced methods. This exploration shows how machine learning may revolutionize the petroleum 
sector's efficiency and creativity (Hayder M. Issa, 2024b). Partial least squares (PLS), regression 
analysis, and principal component analysis (PCA) form a robust ensemble in the suggested 
methodology (Li & Qin, 2021; Wang et al., 2022). They quantitatively established correlations between 
input factors and target gasoline attributes by capturing the underlying latent variables that govern 
the complex interactions between input data and desired outputs. PLS further enhances the 
predictive power, while PCA enables dimensionality reduction and identification of the most salient 
features within the spectral or distillation curve data (Cai et al., 2021). Usually, studies in this trend 
have the overarching goal of creating computationally efficient and very accurate predictive models 
for characteristics like octane rating, vapor pressure, and compositional analysis by integrating 
various methods (Baird & Oja, 2016). 
Combining machine learning with spectral or distillation curve data could improve gasoline 
property prediction. Spectral analysis, which captures gasoline samples' unique molecular 
fingerprint, can be used to feed prediction models quickly and cheaply. Distillation curve data, 
obtainable from refinery operations, also reveals gasoline stream boiling range dispersion and 
composition. These data sources and the synergistic combination of regression, PCA, and PLS 
methods can help refineries monitor and optimize gasoline properties in real time, improving process 
control, product quality, and operational efficiency (Hayder M. Issa, 2024a). 
Artificial neural networks (ANN) and support vector machines (SVM) provide further methods 
for predictive modeling and analysis in the field of petroleum product optimization. ANNs are adept 
at capturing intricate connections in intricate datasets, resembling the human brain's structure. This 
makes them ideal for forecasting gasoline properties. Its capacity to adapt and learn from data allows 
it to reveal complex patterns and relationships that could be missed by conventional methods (Tipler 
et al., 2022). SVM is a robust supervised learning technique that excels in managing high-dimensional 
data and is especially successful when dealing with non-linearly separable data. By integrating ANN 
and SVM techniques into the predictive modeling framework, refineries can improve the precision 
and reliability of gasoline property forecasts (Leal et al., 2020). Advanced machine learning 
algorithms enhance regression analysis, PCA, and PLS techniques to create a comprehensive toolkit 
for optimizing gasoline manufacturing operations and maintaining product quality. 
Conclusions 
The study delves into the topic of how to optimize the quality of petroleum products and 
refining operations using artificial intelligence and machine learning approaches. It shows how 
conventional empirical models and specialized expertise can't guarantee perfection in the refining 
process. We suggest using machine learning approaches including support vector machines (SVM), 
artificial neural networks (ANNs), principal component analysis (PCA), and partial least squares 
(PLS) to capture complicated interactions and forecast fuel qualities. Refineries can improve their 
process control, product quality, and operational efficiency by integrating machine learning with 
spectral or distillation curve data. This can improve petroleum products like gasoline, as well as 
property prediction. To optimize operations, predict gasoline quality accurately, and comply with 
environmental norms, advanced machine learning technologies offer a viable option. 
The study's findings demonstrate that machine learning methods are useful for improving the 
quality of petroleum products, refining operations, and predicting gasoline properties. Figure 1 
shows how these methods enhance operational efficiency, process control, and decision-making in 
refineries. 
Research can move further by investigating and creating more sophisticated machine learning 
algorithms that are optimized for petroleum processing, taking into account the particular features 
and intricacies of the sector. Data availability, model interpretability, and scalability are just a few of 
the constraints and obstacles that need to be better examined and discussed in order to utilize AI/ML 
approaches in the petroleum business. 
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4 
 
 
Figure 1. A mind map illustrates the petroleum processing concepts of an AI-driven refinery. 
Taking into account aspects like implementation costs, maintenance expenses, and potential 
return on investment, future studies might also investigate the economic viability and cost-
effectiveness of using AI/ML systems in petroleum processing optimization. 
References 
1. 
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refinery catalytic desulfurization using Machine learning approach. Fuel, 322, 124088. 
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Al Jlibawi, A. H. H., Othman, M. L., Ishak, A., Noor, B. S. M., & Al Huseiny, M. (2021). Optimization of 
distribution control system in oil refinery by applying hybrid machine learning techniques. IEEE Access, 
10, 3890-3903. 
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3. 
Baird, Z. S., & Oja, V. (2016). Predicting fuel properties using chemometrics: a review and an extension to 
temperature dependent physical properties by using infrared spectroscopy to predict density. Chemometrics 
and Intelligent Laboratory Systems, 158, 41-47. 
4. 
Cai, G., Liu, Z., Zhang, L., Shi, Q., Zhao, S., & Xu, C. (2021). Systematic performance evaluation of gasoline 
molecules based on quantitative structure-property relationship models. Chemical Engineering Science, 229, 
116077. 
5. 
Correa Gonzalez, S., Kroyan, Y., Sarjovaara, T., Kiiski, U., Karvo, A., Toldy, A. I., . . . Santasalo-Aarnio, A. 
(2021). Prediction of gasoline blend ignition characteristics using machine learning models. Energy & Fuels, 
35(11), 9332-9340. 
6. 
Issa, H. M. (2015). An Investigation of the Comparative Performance of Diverse Vapor Pressure Prediction 
Techniques of Sour Natural Gas. Petroleum Science and Technology, 33(13-14), 1443-1448. 
7. 
Issa, H. M. (2016). A new correlation for vapor pressure prediction of natural gas mixture. Petroleum Science 
and Technology, 34(23), 1913-1919. 
8. 
Issa, H. M. (2024a). Parametric and Nonparametric Approaches of Reid Vapor Pressure Prediction for 
Gasoline Containing Oxygenates: A Comparative Analysis Using Partial Least Squares, Nonlinear, and 
LOWESS Regression Modelling Strategies with Physical Properties. Modelling and Simulation in Engineering, 
2024, 8442457. 
9. 
Issa, H. M. (2024). Prediction of octane numbers for commercial gasoline using distillation curves: a 
comparative regression analysis between principal component and partial least squares methods. Petroleum 
Science and Technology, 42(10), 1233-1249. 
10. 
Issa, H. M. (2024b). Streamlining aromatic content detection in automotive gasoline for environmental 
protection: Utilizing a rapid and simplified prediction model based on some physical characteristics and 
regression analysis. Results in Engineering, 21, 101771. 
11. 
Issa, H. M., & Albarzanji, A. A. (2020). Quantitative prediction of Reid vapor pressure for a light crude oil 
using a simplified and proper correlation. Petroleum Science and Technology, 1-9. 
12. 
Kongkiatpaiboon, S. (2024). Improving Offshore Condensate Production Optimization Processes with 
Prediction of Reid Vapor Pressure Using Machine Learning. Paper presented at the Offshore Technology 
Conference Asia. 
13. 
Lamberg, A. (2021). Predicting distillation properties of fuel blends using Machine Learning.    
14. 
Leal, A. L., Silva, A. M., Ribeiro, J. C., & Martins, F. G. (2020). Using spectroscopy and support vector 
regression to predict gasoline characteristics: a comparison of 1H nmr and nir. Energy & Fuels, 34(10), 12173-
12181. 
15. 
Li, B., & Qin, C. (2021). Predictive analytics for octane number: a novel hybrid approach of KPCA and GS-
PSO-SVR model. IEEE Access, 9, 66531-66541. 
16. 
Ma, G., Shi, J., Xiong, H., Xiong, C., Zhao, R., & Zhang, X. (2024). Machine learning assisted molecular 
modeling from biochemistry to petroleum engineering: A review. Geoenergy Science and Engineering, 212770. 
17. 
Nascimento, M. H., Oliveira, B. P., Rainha, K. P., Castro, E. V., Silva, S. R., & Filgueiras, P. R. (2018). 
Determination of flash point and Reid vapor pressure in petroleum from HTGC and DHA associated with 
chemometrics. Fuel, 234, 643-649. 
18. 
Nimmanterdwong, P., Changpun, R., Janthboon, P., Nakrak, S., Gao, H., Liang, Z., . . . Sema, T. (2021). 
Applied artificial neural network for hydrogen sulfide solubility in natural gas purification. ACS omega, 
6(46), 31321-31329. 
19. 
Sircar, A., Yadav, K., Rayavarapu, K., Bist, N., & Oza, H. (2021). Application of machine learning and 
artificial intelligence in oil and gas industry. Petroleum Research, 6(4), 379-391. 
20. 
Tipler, S., D’Alessio, G., Van Haute, Q., Parente, A., Contino, F., & Coussement, A. (2022). Predicting octane 
numbers relying on principal component analysis and artificial neural network. Computers & Chemical 
Engineering, 161, 107784. 
21. 
Wang, H., Chu, X., Chen, P., Li, J., Liu, D., & Xu, Y. (2022). Partial least squares regression residual extreme 
learning machine (PLSRR-ELM) calibration algorithm applied in fast determination of gasoline octane 
number with near-infrared spectroscopy. Fuel, 309, 122224. 
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those 
of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) 
disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or 
products referred to in the content. 
Preprints.org (www.preprints.org)  |  NOT PEER-REVIEWED  |  Posted: 5 April 2024                   doi:10.20944/preprints202404.0442.v1

polymers
Review
Insight into the Sustainable Integration of Bio- and
Petroleum Reﬁneries for the Production of Fuels
and Chemicals
Wegik Dwi Prasetyo 1,2, Zulfan Adi Putra 3, Muhammad Roil Bilad 1,*
,
Teuku Meurah Indra Mahlia 4
, Yusuf Wibisono 5
, Nik Abdul Hadi Nordin 1
and Mohd Dzul Hakim Wirzal 1
1
Chemical Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia;
wegik_16003931@utp.edu.my (W.D.P.); nahadi.sapiaa@utp.edu.my (N.A.H.N.);
mdzulhakim.wirzal@utp.edu.my (M.D.H.W.)
2
Department of Chemical Engineering, Universitas Pertamina, Jl.Teuku Nyak Arief, Simprug,
Kebayoran Lama, Jakarta 12220, Indonesia
3
PETRONAS Group Technical Solutions, Project Delivery and Technology, PETRONAS,
Kuala Lumpur 50050, Malaysia; zulfan.adiputra@petronas.com
4
School of Information, Systems and Modelling, Faculty of Engineering and Information Technology,
University of Technology Sydney, Sydney, NSW 2007, Australia; TMIndra.Mahlia@uts.edu.au
5
Bioprocess Engineering, Faculty of Agricultural Technology, Brawijaya University, Malang 65141, Indonesia;
y_wibisono@ub.ac.id
*
Correspondence: mroil.bilad@utp.edu.my
Received: 14 February 2020; Accepted: 23 March 2020; Published: 11 May 2020


Abstract: A petroleum reﬁnery heavily depends on crude oil as its main feedstock to produce liquid
fuels and chemicals. In the long term, this unyielding dependency is threatened by the depletion of
the crude oil reserve. However, in the short term, its price highly ﬂuctuates due to various factors,
such as regional and global security instability causing additional complexity on reﬁnery production
planning. The petroleum reﬁning industries are also drawing criticism and pressure due to their direct
and indirect impacts on the environment. The exhaust gas emission of automobiles apart from the
industrial and power plant emission has been viewed as the cause of global warming. In this sense,
there is a need for a feasible, sustainable, and environmentally friendly generation process of fuels
and chemicals. The attention turns to the utilization of biomass as a potential feedstock to produce
substitutes for petroleum-derived fuels and building blocks for biochemicals. Biomass is abundant
and currently is still low in utilization. The bioreﬁnery, a facility to convert biomass into biofuels
and biochemicals, is still lacking in competitiveness to a petroleum reﬁnery. An attractive solution
that addresses both is by the integration of bio- and petroleum reﬁneries. In this context, the right
decision making in the process selection and technologies can lower the investment and operational
costs and assure optimum yield. Process optimization based on mathematical programming has
been extensively used to conduct techno-economic and sustainability analysis for bio-, petroleum,
and the integration of both reﬁneries. This paper provides insights into the context of crude oil and
biomass as potential reﬁnery feedstocks. The current optimization status of either bio- or petroleum
reﬁneries and their integration is reviewed with the focus on the methods to solve the multi-objective
optimization problems. Internal and external uncertain parameters are important aspects in process
optimization. The nature of these uncertain parameters and their representation methods in process
optimization are also discussed.
Keywords: biomass; bioreﬁnery; process optimization; mathematical programming deterministic;
stochastics; single-objective optimization; multi-objective optimization
Polymers 2020, 12, 1091; doi:10.3390/polym12051091
www.mdpi.com/journal/polymers
Polymers 2020, 12, 1091
2 of 24
1. Introduction
To achieve sustainable economic growth, industries require safe and sustainable feedstock [1].
While the economy of energy has various resources (water, wind, solar heating and light, geothermal
heat, biomass, nuclear ﬁssion, and fusion), the material bioeconomy depends highly on biomass.
Research and development are necessary to achieve following objectives: (i) increase the scientiﬁc
understanding of biomass resources and valorize the applications of those resources, (ii) improve
sustainable system to develop, harvest, and process biomass resources, (iii) improve eﬃciency and
performance in conversion and distribution processes and technologies for a host of bio-based
products development, and (iv) create the regulatory and market environment necessary for increased
development and use of bio-based products [2].
The paradigm-shift from the oil-based to the renewable resources-based economy must be
supported by the research and developments. Research ensures continuous development of technology
and the valorization of innovation. Nonetheless, the renewable resources-based economy faces many
challenges. The biggest challenge is the conversion technology, which is required to be technically
feasible, economically viable, and environmentally safe.
Processes and technologies have been
developed to utilize biomass as the feedstock for production of fuels and chemicals [3]. The facility
that processes biomass into fuels and chemicals is known as a bioreﬁnery. The bioreﬁnery that
processes the ﬁrst-generation biomass (sugar and lipid) of edible crop plant into fuels have been well
established. However, the conﬂicting interest of fuel vs. food and fuel vs. feed has triggered the
rising price of the ﬁrst-generation biomass commodity. Agricultural crop residue, wood, and manure
belong to the second-generation biomass class, while microalgae is classiﬁed as the third-generation
biomass [4]. The second- and third-generation biomasses are potential sources of bioreﬁnery feedstock.
In the designing of a new process for the utilization of biomass, a process simulation is a powerful
tool. Well-constructed process simulation provides valuable data of the operating condition, material
balance, and energy requirement [5–10]. The data then can be further utilized in process optimization.
The optimization task is not limited to ﬁnding the optimum condition related to a chemical process
but also to ﬁnd an optimum supply chain network.
An eﬃcient supply chain network is also
required in a highly competitive economy due to the scarcity of resources. The logistics costs in the
agricultural sector include material transportation and distribution, inventory, and information process
costs [11]. The high logistics cost in the agricultural sector is proportional to the volume and quantity.
The discontinuous availability of biomass due to spatial fragmentation and seasonal variability also
leads to an increase in the logistics cost. Furthermore, a supply chain network is characterized by a
high degree of uncertainty, where the value of some parameters cannot be controlled by the decision
maker [12]. Uncertainty is deﬁned as the diﬀerence between the amount of required information
and its availability to execute the task. It is related to the decision-making process under incomplete
information. It can be classiﬁed into randomness, epistemic, and deep uncertainty. Randomness arises
from the random nature of the events and concerns with the membership or non-membership of an
element in a set.
The decision-making process in the optimization of a bioreﬁnery should consider several
operational and strategic uncertainties [13]. Many studies in bioreﬁnery supply chain optimization
involve a deterministic approach that ignores the uncertainty. The deterministic approach is commonly
used in plant planning and scheduling, typically resulting in an overly conservative or over-aggressive
decision. An over-conservative decision leads to unnecessary proﬁt loss while an aggressive decision
may result in severe constraints violation [14]. Failure to account for the uncertainty ultimately
causes faulty decisions and inadequate strategies, leading to failure to capitalize on the opportunities.
The non-deterministic approaches that accounted for the uncertainties have attracted great interest in
the ﬁeld of process optimization. The commonly used non-deterministic approaches are stochastic
programming, robust optimization, and fuzzy programming. Stochastics programming is a common
approach to deal with randomness, and thus is suitable to address the issues related to bio- and
petroleum reﬁneries. The epistemic uncertainty is caused by the lack of data or information on the
Polymers 2020, 12, 1091
3 of 24
parameters. Possibilistic programming is widely used to study epistemic uncertainty [15]. The solutions
attained are based on the estimate of the actual condition and thus carry some risk. Deep uncertainty
can be dealt with robust convex programming approaches. If there is a combination of randomness,
epistemic, and deep uncertainty in the model, fuzzy-interval possibilistic programming or full-inﬁnite
fuzzy stochastic programming can also be employed. While the stochastics-based process optimization
is powerful, it is exhaustive when the number of uncertainty and its samples grow larger. The sources
of uncertainties frequently studied in the nondeterministic bioreﬁnery supply chains include biomass
supply, product demand, product price, and technology development [13]. The studies on the
optimization of bioreﬁnery under uncertainty are dominantly multi-objective with the economic
potential and the environmental impact as the objectives. The maximization of the net present value
(NPV), the maximization of proﬁt, and the minimization of the total cost are commonly applied for the
economic analysis, while the environmental impact objective is often quantiﬁed through Eco-indicator
99 or global warming potential.
This work discloses the context of feedstocks in bio- and petroleum reﬁneries. The issue associated
with crude oil supplies and price instability is highlighted to show the importance of feedstock
diversiﬁcation by considering the ones from biomass. Later, the important characteristics of biomass
that allow it as a potential feedstock for reﬁnery are also elaborated, including the potential end-product
chemicals or intermediate that can bridge biomass processing facilities with the reﬁneries. A section is
dedicated to comparing the chemical composition of diﬀerent biomass sources and their characterization.
Such comparison is important because it inﬂuences the choice of process and technology to convert
biomass. The locality-availability aspect of biomass is also considered by presenting the most potential
biomass source in Malaysia. A comprehensive overview of the current progress of mathematical
programming-based process optimization is also presented. It highlights the prominent issues that
arise in the developed model and approached strategies, including the main uncertainty factors that
aﬀect the optimization.
2. The Depleting Reserves and Price Instability of Global Crude Oil
Fuels and petrochemicals are substantive materials that enable our modern-way of life. They are the
derivative products of crude oil and natural gas converted through a reﬁnery and chemical processing
facilities. Recently, some have witnessed the volatility of crude oil prices due to several factors, namely
the depleting crude oil reserves, global and regional geopolitical instability, the ever-increasing demand,
and many others [16] (Figure 1). The nations to suﬀer the most are the ones who depend on importing
crude oil and natural gases. For such countries, it is a necessity to diversify the feedstocks of the fuels
and petrochemical reﬁneries. According to the data from the Organisation for Economic Co-operation
and Development (OECD), the global proven crude oil reserves are 1482.77 bboe (billion barrels of oil
equivalent) [17] and the global production rate of crude oil is about 3.97 million of ktoe (kilotonne of
oil equivalent) or equals to 29,100 million of boe (barrel of oil equivalent) [18]. It follows that the global
crude oil reserves will last roughly within 51 years by assuming that there is no ﬂuctuation in the
annual crude oil production rate and no new reserve is discovered.
The bioreﬁnery concept emerges in response to these challenges. It is deﬁned as a facility with an
integrated, eﬃcient, and ﬂexible conversion processes of biomass feedstocks, through a combination of
physical, chemical, biochemical, and thermochemical processes, into multiple products such as fuels,
chemicals, materials, and/or heat and power [19–22]. Such an approach is not new. Many industrial
materials used at the beginning of the 20th century, such as dyes, solvents, synthetic ﬁbers, and fuel,
were made from trees and crops [23–25]. However, many of the bio-based chemical products had
been displaced by petroleum derivatives by the late 1960s. Shifting the dependence of society away
from petroleum to renewable biomass resources, including microalgae, is viewed as an important
contributor to the development of a sustainable industrial society [22].
Few countries have issued directives to gradually decrease the dependence on petroleum. The U.S.
Department of Energy has set goals to replace 30% of the liquid petroleum transportation fuel with
Polymers 2020, 12, 1091
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biofuels and to replace 25% of industrial organic chemicals with biomass-derived chemicals by 2025 [2].
The revised Renewable Energy Directive of the European Union (EU) requires the countries within the
EU to fulﬁll at least 27% of the EU’s total energy needs with renewable by 2030. It further speciﬁes
that all countries within the EU must also ensure that at least 10% of their transport fuels come from
renewable sources by 2020 [26].
Polymers 2020, 12, x FOR PEER REVIEW 
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specifies that all countries within the EU must also ensure that at least 10% of their transport fuels 
come from renewable sources by 2020 [26].  
1998
2000
2002
2004
2006
2008
2010
2012
2014
2016
2018
20
30
40
50
60
70
80
90
100
110
 OK WTI Price
 Global Crude Oil Production
Year
OK WTI Price (US$/Barrel)
24000
25000
26000
27000
28000
29000
30000
 Global Crude Oil Production (mboe)
 
Figure 1. Crude oil price fluctuation (red line) and the annual global crude oil production rate (bar 
chart). OK WTI: West Texas Intermediate Crude Oil Traded in Oklahoma, adapted from [18,27]. 
The decline of petroleum and natural gas reserves as the current main sources of fuels and 
chemicals has led to the ever-growing commitment to the establishment of a bioresources-based 
economy. 
3. Biomass as a Renewable Biorefinery Feedstock 
Biorefineries require sustainable and renewable resources. An ideal renewable resource is one 
that can be replenished over a relatively short timescale or is abundant [28]. Biomass fits this 
definition. It is a renewable feedstock that can be utilized to produce fuels and chemicals [29]. Biomass 
is any organic matter that is available on a renewable or recurring basis (excluding old-growth 
timber), including dedicated energy crops and trees, agricultural food and feed crop residues, animal 
wastes, and other waste materials [30]. In contrast with the limited nature and composition of petrol 
resources, bioresource and biomass are a collection of gathered compounds of very different natures, 
namely cellulose, hemicellulose, oils, lignin, and so on [16]. 
3.1. Plant Biomass Composition 
Understanding the biomass composition is very important as a basis for bio- and petroleum 
refinery integration. The intermediate products from biomass as feedstock to a petroleum refinery 
should have a composition close to the current refinery feedstock. Plants capture solar energy as fixed 
carbon, converting CO2 and water to sugars. The produced sugar is stored in three different types of 
polymer: cellulose, hemicellulose, and starch [31]. Biomass is typically composed of 65–85 wt % 
sugar-based polymers (principally cellulose and hemicellulose), with another 10%–25% 
corresponding to lignin. Other biomass minor components include triglycerides, sterols, alkaloids, 
resins, terpenes, terpenoids, and waxes (often collectively referred to as lipids), as well as inorganic 
minerals. In the case of seeds and certain algae strains, significant amounts of oil can present, 
corresponding mainly to triglycerides. This is exemplified by soybeans (ca. 20 wt % oil), rapeseed (ca. 
40 wt % oil), and oil-palm fruit (ca. 50 wt % oil), which together account for the majority of the 
feedstock currently used in biodiesel production. Together, cellulose, hemicellulose, and lignin 
constitute lignocellulose, which is the fibrous materials that form the cell walls of plants and trees.  
Figure 1. Crude oil price ﬂuctuation (red line) and the annual global crude oil production rate (bar chart).
OK WTI: West Texas Intermediate Crude Oil Traded in Oklahoma, adapted from [18,27].
The decline of petroleum and natural gas reserves as the current main sources of fuels and chemicals
has led to the ever-growing commitment to the establishment of a bioresources-based economy.
3. Biomass as a Renewable Bioreﬁnery Feedstock
Bioreﬁneries require sustainable and renewable resources. An ideal renewable resource is one
that can be replenished over a relatively short timescale or is abundant [28]. Biomass ﬁts this deﬁnition.
It is a renewable feedstock that can be utilized to produce fuels and chemicals [29]. Biomass is any
organic matter that is available on a renewable or recurring basis (excluding old-growth timber),
including dedicated energy crops and trees, agricultural food and feed crop residues, animal wastes,
and other waste materials [30]. In contrast with the limited nature and composition of petrol resources,
bioresource and biomass are a collection of gathered compounds of very diﬀerent natures, namely
cellulose, hemicellulose, oils, lignin, and so on [16].
3.1. Plant Biomass Composition
Understanding the biomass composition is very important as a basis for bio- and petroleum
reﬁnery integration. The intermediate products from biomass as feedstock to a petroleum reﬁnery
should have a composition close to the current reﬁnery feedstock. Plants capture solar energy as ﬁxed
carbon, converting CO2 and water to sugars. The produced sugar is stored in three diﬀerent types
of polymer: cellulose, hemicellulose, and starch [31]. Biomass is typically composed of 65–85 wt %
sugar-based polymers (principally cellulose and hemicellulose), with another 10–25% corresponding
to lignin. Other biomass minor components include triglycerides, sterols, alkaloids, resins, terpenes,
terpenoids, and waxes (often collectively referred to as lipids), as well as inorganic minerals. In the
case of seeds and certain algae strains, signiﬁcant amounts of oil can present, corresponding mainly
to triglycerides. This is exempliﬁed by soybeans (ca. 20 wt % oil), rapeseed (ca. 40 wt % oil), and
oil-palm fruit (ca. 50 wt % oil), which together account for the majority of the feedstock currently used
Polymers 2020, 12, 1091
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in biodiesel production. Together, cellulose, hemicellulose, and lignin constitute lignocellulose, which
is the ﬁbrous materials that form the cell walls of plants and trees.
The elemental composition of sugars, lignin, and lipids in biomass can be described by the van
Krevelen plot (Figure 2). The energy content of biomass constituents depends on the oxygen content
and the hydrogen to carbon (H:C) ratio. A biomass constituent with lower oxygen content and higher
H:C ratio will have higher energy content. Hence, energy content per unit of mass follows the order of
lipids > lignin > sugars.
Polymers 2020, 12, x FOR PEER REVIEW 
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The elemental composition of sugars, lignin, and lipids in biomass can be described by the van 
Krevelen plot (Figure 2). The energy content of biomass constituents depends on the oxygen content 
and the hydrogen to carbon (H:C) ratio. A biomass constituent with lower oxygen content and higher 
H:C ratio will have higher energy content. Hence, energy content per unit of mass follows the order 
of lipids > lignin > sugars.  
 
 
Figure 2. Elemental Composition of Biomass (van Krevelen plot) [32] – Reproduced by permission of 
The Royal Society of Chemistry. 
Sugar polymers such as cellulose and starch can be readily broken down by hydrolysis for the 
conversion to ethanol or other chemicals. Lignin is harder to be broken down due to its relative 
chemical inertness. Current bioethanol production is mostly based on the fermentation of sugars that 
are readily obtained from the starch in corn grain, in addition to the sugar in sugarcane and sugar 
beets. Even though lipids and sugars are the ideal starting material for biofuels production, the future 
large-scale production of biofuels will have to be based on the utilization of lignocellulose as the 
principal feedstock due to its relative abundance. A further advantage of lignocellulose utilization is 
the avoidance of the conundrum of “foods vs. fuels”. 
3.2. Biomass Characterization 
Biomass can originate from a multitude of sources with high variability. Proper characterization 
is thus important before the selection processing technology. The most important biomass 
characterization comprises of proximate and ultimate composition (typically shown in Figure 3), 
heating value, and the process of biomass production, collection, storing, transporting, and 
processing (crop yields, economic, equipment availability, grinding performance, etc.). 
Proximate analysis is based on the change of the product under controlled heating; it is 
comprised of moisture content, volatile matter, fixed carbon, and ash. On the other hand, the ultimate 
analysis quantifies the elements that constitute the biomass, typically carbon, hydrogen, oxygen, 
nitrogen, sulfur, and chlorine. This analysis also quantifies the moisture content and ash. Figure 3 
shows the ultimate and proximate analysis of several biomass feedstocks. 
Biomass can also be grouped by the energy contained within the chemical bonds in the biomass. 
The higher heating value (HHV) and lower heating value (LHV) are the parameters used to quantify 
the energy content. The term gross calorific value is used interchangeably as HHV, while the net 
calorific value is interchangeably used in describing LHV. HHV is the amount of heat released from 
the combustion including the latent heat of vaporization of water from the sample. LHV is the 
measured heat released excluding the contribution of the latent heat of vaporization.  
Figure 2. Elemental Composition of Biomass (van Krevelen plot) [32] – Reproduced by permission of
The Royal Society of Chemistry.
Sugar polymers such as cellulose and starch can be readily broken down by hydrolysis for the
conversion to ethanol or other chemicals. Lignin is harder to be broken down due to its relative
chemical inertness. Current bioethanol production is mostly based on the fermentation of sugars that
are readily obtained from the starch in corn grain, in addition to the sugar in sugarcane and sugar
beets. Even though lipids and sugars are the ideal starting material for biofuels production, the future
large-scale production of biofuels will have to be based on the utilization of lignocellulose as the
principal feedstock due to its relative abundance. A further advantage of lignocellulose utilization is
the avoidance of the conundrum of “foods vs. fuels”.
3.2. Biomass Characterization
Biomass can originate from a multitude of sources with high variability. Proper characterization is
thus important before the selection processing technology. The most important biomass characterization
comprises of proximate and ultimate composition (typically shown in Figure 3), heating value, and the
process of biomass production, collection, storing, transporting, and processing (crop yields, economic,
equipment availability, grinding performance, etc.).
Proximate analysis is based on the change of the product under controlled heating; it is comprised
of moisture content, volatile matter, ﬁxed carbon, and ash. On the other hand, the ultimate analysis
quantiﬁes the elements that constitute the biomass, typically carbon, hydrogen, oxygen, nitrogen,
sulfur, and chlorine. This analysis also quantiﬁes the moisture content and ash. Figure 3 shows the
ultimate and proximate analysis of several biomass feedstocks.
Biomass can also be grouped by the energy contained within the chemical bonds in the biomass.
The higher heating value (HHV) and lower heating value (LHV) are the parameters used to quantify
the energy content. The term gross caloriﬁc value is used interchangeably as HHV, while the net
caloriﬁc value is interchangeably used in describing LHV. HHV is the amount of heat released from the
combustion including the latent heat of vaporization of water from the sample. LHV is the measured
heat released excluding the contribution of the latent heat of vaporization.
Polymers 2020, 12, 1091
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Polymers 2020, 12, x FOR PEER REVIEW 
6 of 24 
 
 
(a) 
(b) 
Figure 3. Characterization of Biomass: (a) Proximate Analysis and (b) Ultimate Analysis, adapted 
from [32,33]. 
The biomass that has a higher content of cellulose and hemicellulose relative to the lignin is 
preferred for biochemical conversion to ethanol. Meanwhile, the higher content of lignin and 
extractives in the biomass is desirable for the thermochemical conversion process. The heating value 
of lignin and extractives is higher than the cellulose and hemicellulose. Figure 4 summarizes the 
composition of various feedstocks and their HHV. The lignin-based biomass can also be used to 
produce intermediate chemicals (i.e., pyrolysis oil) that can be used as feedstock for the petroleum 
refinery. 
 
 
(a) 
(b) 
Figure 4. (a) Compositions of Biomass Expressed as a Percentage of Bone-Dry Material, and the (b) 
Higher Heating Value, adapted from [33,34]. 
Palm-oil-based biomass is one of the potential resources for sustainable feedstock. A palm oil 
production plant approximately produces 2.3 tonnes of biomass waste for every 1 ton of palm oil 
Figure 3. Characterization of Biomass: (a) Proximate Analysis and (b) Ultimate Analysis, adapted
from [32,33].
The biomass that has a higher content of cellulose and hemicellulose relative to the lignin is
preferred for biochemical conversion to ethanol. Meanwhile, the higher content of lignin and extractives
in the biomass is desirable for the thermochemical conversion process. The heating value of lignin and
extractives is higher than the cellulose and hemicellulose. Figure 4 summarizes the composition of
various feedstocks and their HHV. The lignin-based biomass can also be used to produce intermediate
chemicals (i.e., pyrolysis oil) that can be used as feedstock for the petroleum reﬁnery.
Polymers 2020, 12, x FOR PEER REVIEW 
6 of 24 
 
 
(a) 
(b) 
Figure 3. Characterization of Biomass: (a) Proximate Analysis and (b) Ultimate Analysis, adapted 
from [32,33]. 
The biomass that has a higher content of cellulose and hemicellulose relative to the lignin is 
preferred for biochemical conversion to ethanol. Meanwhile, the higher content of lignin and 
extractives in the biomass is desirable for the thermochemical conversion process. The heating value 
of lignin and extractives is higher than the cellulose and hemicellulose. Figure 4 summarizes the 
composition of various feedstocks and their HHV. The lignin-based biomass can also be used to 
produce intermediate chemicals (i.e., pyrolysis oil) that can be used as feedstock for the petroleum 
refinery. 
 
 
(a) 
(b) 
Figure 4. (a) Compositions of Biomass Expressed as a Percentage of Bone-Dry Material, and the (b) 
Higher Heating Value, adapted from [33,34]. 
Palm-oil-based biomass is one of the potential resources for sustainable feedstock. A palm oil 
production plant approximately produces 2.3 tonnes of biomass waste for every 1 ton of palm oil 
Figure 4. (a) Compositions of Biomass Expressed as a Percentage of Bone-Dry Material, and the
(b) Higher Heating Value, adapted from [33,34].
Palm-oil-based biomass is one of the potential resources for sustainable feedstock. A palm oil
production plant approximately produces 2.3 tonnes of biomass waste for every 1 ton of palm oil
Polymers 2020, 12, 1091
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produced [33]. For example, as one of the largest palm oil producers, Malaysia produces around
20 million tonnes of palm oil in 2018 (Figure 5) [35], which means that 45 million tonnes of biomass
waste were also generated. Palm oil empty fruit bunch (EFB) is the largest percentage of waste of
43 wt %, which accounts for 19.35 million tonnes of the total biomass waste produced in palm oil
production plants [33]. Palm ﬁbers, palm kernels, and palm shells biomass waste were also generated
at 30, 13, and 13 wt % respectively.
Polymers 2020, 12, x FOR PEER REVIEW 
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produced [33]. For example, as one of the largest palm oil producers, Malaysia produces around 20 
million tonnes of palm oil in 2018 (Figure 5) [35], which means that 45 million tonnes of biomass waste 
were also generated. Palm oil empty fruit bunch (EFB) is the largest percentage of waste of 43 wt %, 
which accounts for 19.35 million tonnes of the total biomass waste produced in palm oil production 
plants [33]. Palm fibers, palm kernels, and palm shells biomass waste were also generated at 30, 13, 
and 13 wt % respectively.  
2010
2011
2012
2013
2014
2015
2016
2017
2018
0
10
20
30
40
50
60
70
Malaysia Palm Oil Production (million tonnes)
Year
 Palm Oil Biomass Generation
 Malaysia Palm Oil Production
 
Figure 5. Annual Palm Oil Biomass Waste Generation in Malaysia, adapted from [33,35]. 
In terms of chemical composition, EFB has a high content of cellulose, highlighting its potential 
as a feedstock source for the biorefinery. Converting EFB biomass waste into fuels and chemicals is 
thus an economically and environmentally sound proposition. Due to the variability of biomass 
types, adoption of the available lignocellulosic conversion process and technology is not readily 
applicable.  
4. Potential Chemicals from Biomass 
The characteristics of the modern biorefinery are parallel to the petroleum refinery: an abundant 
raw material consisting primarily of renewable polysaccharides and lignin enters the biorefinery and, 
through an array of processes, is reacted, fractionated, and converted into a mixture of products, 
including engine fuels, biochemicals, and direct energy [36]. The imbalance between commodity 
chemicals and fuels as happened in a petroleum refinery is envisioned to be followed by biorefinery. 
Chemical products account for only ~5% of petroleum refinery output, while transportation fuels and 
energy take most of the output [23].  
Most of the biobased products are the outcome of a direct physical or chemical treatment and 
processing of biomass, such as cellulose, starch, oil, protein, lignin, and terpene. Through 
biotechnological processes and methods, feedstock chemicals, such as ethanol, butanol, acetone, lactic 
acid, and itaconic acid, and amino acids, glutaminic acid, lysine, and tryptophan, can be produced. 
Of the approximately 170 billion tonnes of biomass produced annually by photosynthesis, less than 
0.11% is used for non-food areas, such as chemistry [37], suggesting that there is a huge gap that can 
be exploited. To enhance the coverage of biorefineries, developments of a vast variety of biobased 
products in an efficient construction-set system are required to also include particular products that 
are not accessible in petroleum refineries. There are over 300 possible chemical building blocks that 
can be derived from biomass [38]. The U.S. Department of Energy applied selection protocols based 
on the cost of feedstock, estimated processing costs, current volumes, prices, the technical complexity 
associated with the best processing pathway and the market potential to further screen into 50 top 
chemical candidates. The next selection criteria are the number of functional groups and potential 
Figure 5. Annual Palm Oil Biomass Waste Generation in Malaysia, adapted from [33,35].
In terms of chemical composition, EFB has a high content of cellulose, highlighting its potential
as a feedstock source for the bioreﬁnery. Converting EFB biomass waste into fuels and chemicals is
thus an economically and environmentally sound proposition. Due to the variability of biomass types,
adoption of the available lignocellulosic conversion process and technology is not readily applicable.
4. Potential Chemicals from Biomass
The characteristics of the modern bioreﬁnery are parallel to the petroleum reﬁnery: an abundant
raw material consisting primarily of renewable polysaccharides and lignin enters the bioreﬁnery and,
through an array of processes, is reacted, fractionated, and converted into a mixture of products,
including engine fuels, biochemicals, and direct energy [36]. The imbalance between commodity
chemicals and fuels as happened in a petroleum reﬁnery is envisioned to be followed by bioreﬁnery.
Chemical products account for only ~5% of petroleum reﬁnery output, while transportation fuels and
energy take most of the output [23].
Most of the biobased products are the outcome of a direct physical or chemical treatment
and processing of biomass,
such as cellulose,
starch,
oil,
protein,
lignin,
and terpene.
Through biotechnological processes and methods, feedstock chemicals, such as ethanol, butanol,
acetone, lactic acid, and itaconic acid, and amino acids, glutaminic acid, lysine, and tryptophan, can be
produced. Of the approximately 170 billion tonnes of biomass produced annually by photosynthesis,
less than 0.11% is used for non-food areas, such as chemistry [37], suggesting that there is a huge gap
that can be exploited. To enhance the coverage of bioreﬁneries, developments of a vast variety of
biobased products in an eﬃcient construction-set system are required to also include particular products
that are not accessible in petroleum reﬁneries. There are over 300 possible chemical building blocks that
can be derived from biomass [38]. The U.S. Department of Energy applied selection protocols based on
the cost of feedstock, estimated processing costs, current volumes, prices, the technical complexity
associated with the best processing pathway and the market potential to further screen into 50 top
chemical candidates. The next selection criteria are the number of functional groups and potential
Polymers 2020, 12, 1091
8 of 24
use as a super chemical commodity. Preference is made for chemicals with more than one functional
group. This screening resulted in top candidates and was categorized based on carbon number (C#).
Kohli [39] considered 5-hydroxylmethylfurfural, phenols, and sugar alcohols as potential platform
chemicals for biofuels, biopolymers, and solvent industries (Figure 6). The National Renewable Energy
Laboratory (NREL) has conducted a market assessment of bioproducts with near-term potential.
The bioproducts-assessed were butadiene, butanediol, ethyl lactate, fatty alcohols, furfural glycerin,
isoprene, lactic acid, propanediol, propylene glycol, succinic acid, and xylene [40].
Polymers 2020, 12, x FOR PEER REVIEW 
8 of 24 
use as a super chemical commodity. Preference is made for chemicals with more than one functional 
group. This screening resulted in top candidates and was categorized based on carbon number (C#). 
Kohli [39] considered 5-hydroxylmethylfurfural, phenols, and sugar alcohols as potential 
platform chemicals for biofuels, biopolymers, and solvent industries (Figure 6). The National 
Renewable Energy Laboratory (NREL) has conducted a market assessment of bioproducts with near-
term potential. The bioproducts-assessed were butadiene, butanediol, ethyl lactate, fatty alcohols, 
furfural glycerin, isoprene, lactic acid, propanediol, propylene glycol, succinic acid, and xylene [40]. 
 
Figure 6. Top Chemical Building Block Candidates from Biomass, adapted from [37]. 
5. Optimization of Biorefinery 
Biofuels and biochemicals produced through biorefinery are expected to be the backbone of the 
future sustainable economy. However, there is an impediment to biorefinery facility adoption and 
implementation. The slow development of biorefineries is caused most critically by the low readiness 
level of the process and technology. Investment in the construction of a biorefinery is thus seen as a 
risky business. Most of the existing biorefinery use limited feedstocks and technologies where only a 
relatively small fraction of materials are converted into high added-value chemicals. A typical 
biorefinery solely produces a single product such as ethanol or biodiesel. Price wise, products of the 
biorefinery therefore cannot compete with the petroleum-derived product [41].  
Process integration and optimization are among the strategies to alleviate the competitiveness 
of the biorefinery. The benefit of biorefinery integration mainly lays in the diversification of 
feedstocks and marketable final products. However, the integrated biorefinery still requires 
continuous improvement and advancement in the areas of feedstock, conversion processes 
(biochemical, chemical, and thermochemical), and their integration with robust and proven 
downstream separation processes [42]. The following sub-sections overview the application of 
mathematical tools in assisting the developments of the biorefinery. 
5.1. Mixed-Integer Non-Linear Programming 
Multitudes of available biomass feedstocks, processing technologies, and potential products 
require well-developed formulation to assess and select the most economically, environmentally, and 
technologically sound biorefinery. Kelloway and Daoutidis [43] formulated a biorefinery 
superstructure that can produce both fuels and chemicals from different kinds of feedstocks, as 
illustrated in Figure 7. The biorefinery superstructure involved hydrogenation, gasification, 
fermentation, pyrolysis, and thermochemical process. The products span from gasoline, diesel, 
Fischer-Tropsch fuel products, xylitol, lactic acid, succinic acid, furfural, formic acid, and acetic acid. 
Figure 6. Top Chemical Building Block Candidates from Biomass, adapted from [37].
5. Optimization of Bioreﬁnery
Biofuels and biochemicals produced through bioreﬁnery are expected to be the backbone of the
future sustainable economy. However, there is an impediment to bioreﬁnery facility adoption and
implementation. The slow development of bioreﬁneries is caused most critically by the low readiness
level of the process and technology. Investment in the construction of a bioreﬁnery is thus seen as a
risky business. Most of the existing bioreﬁnery use limited feedstocks and technologies where only
a relatively small fraction of materials are converted into high added-value chemicals. A typical
bioreﬁnery solely produces a single product such as ethanol or biodiesel. Price wise, products of the
bioreﬁnery therefore cannot compete with the petroleum-derived product [41].
Process integration and optimization are among the strategies to alleviate the competitiveness
of the bioreﬁnery.
The beneﬁt of bioreﬁnery integration mainly lays in the diversiﬁcation of
feedstocks and marketable ﬁnal products.
However, the integrated bioreﬁnery still requires
continuous improvement and advancement in the areas of feedstock, conversion processes (biochemical,
chemical, and thermochemical), and their integration with robust and proven downstream separation
processes [42]. The following sub-sections overview the application of mathematical tools in assisting
the developments of the bioreﬁnery.
5.1. Mixed-Integer Non-Linear Programming
Multitudes of available biomass feedstocks, processing technologies, and potential products
require well-developed formulation to assess and select the most economically, environmentally, and
technologically sound bioreﬁnery. Kelloway and Daoutidis [43] formulated a bioreﬁnery superstructure
that can produce both fuels and chemicals from diﬀerent kinds of feedstocks, as illustrated in Figure 7.
The bioreﬁnery superstructure involved hydrogenation, gasiﬁcation, fermentation, pyrolysis, and
thermochemical process. The products span from gasoline, diesel, Fischer-Tropsch fuel products,
xylitol, lactic acid, succinic acid, furfural, formic acid, and acetic acid.
Polymers 2020, 12, 1091
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Polymers 2020, 12, x FOR PEER REVIEW 
9 of 24 
 
Figure 7. Biorefinery Superstructure with Multiple Feedstocks and Products, adapted from [43]. 
The optimum biorefinery superstructure is determined by solving the formulation using Mixed 
–Integer Non-Linear Programming (MINLP) to maximizing NPV (net present value) and carbon 
efficiency. The optimized carbon efficiency resulted in the preferred biorefinery configuration that 
produces bio-oils from the pyrolysis reactor, while the base case analysis (only maximizing NPV) 
favored the choice of biorefinery configuration that produces chemicals such as xylitol, levulinic acid, 
and formic acid.  
Albarelli et al. [44] demonstrated that process optimization can be used to aid the decision 
making of two different proposed processing alternatives. A sugarcane-based bioethanol biorefinery 
integrated with methanol production from sugarcane lignocellulosic residue was compared with the 
stand-alone bioethanol biorefinery. A thermo-economic model was developed to assess the energy 
efficiency as well as the economic impact of the integrated process. Two objectives were used, namely 
the maximization of energy efficiency and the minimization of the investment cost.  
Fattahi and Govindan [45] proposed multi-stage stochastic programming with the Benders 
decomposition approach to evaluate the sustainable design of biofuel supply chain networks under 
biomass supply uncertainty and disruption risk. The biomass supply-seasonal fluctuation was 
modeled as a modified autoregressive. While the disruption risk was related to the storage capacity 
in the face of biomass yield variation. The yield was modeled as a Bernoulli random variable. Three 
objectives were used: economic, environmental, and social impacts. A lower and upper bound were 
set for both environmental and social impacts. The optimum solution from sets of the Pareto optimal 
solution was obtained using the ε-constraints method.  
Bbosa, Mba-Wright, and Brown [41] conducted a study on the techno-economic analysis of a 
corn stover ethanol biorefinery integrated with a lignin hydrothermal liquefaction. Corn stover was 
used because it is the most abundant agricultural residue in the USA and is expected to be the single 
largest lignocellulosic biomass source in the country. The ethanol plant, as it is known, produces 
lignin as a waste. Through hydrothermal liquefaction, lignin can be converted into marketable 
biochemicals. The study was based on an ethanol plant that processed 2000 metric tonnes per day of 
corn stover to produce 61 MMgal/year of ethanol and different yields of lignin-derived biochemicals. 
The hydrothermal liquefaction process was set to utilize 80% of the solid/lignin from the recovery 
section, while the other 20% was used as fuel to generate heat and power for the facility. As a result, 
the minimum ethanol selling price (MESP) was affected by the yields and market price of the 
biochemical products. The MESP of the integrated ethanol plant and lignin-derived biochemical was 
lower than the reference ethanol price (Iowa’s average 2007 ethanol selling price) and much lower 
than the price of ethanol produced in the stand-alone ethanol plant [41].  
Figure 7. Bioreﬁnery Superstructure with Multiple Feedstocks and Products, adapted from [43].
The optimum bioreﬁnery superstructure is determined by solving the formulation using
Mixed–Integer Non-Linear Programming (MINLP) to maximizing NPV (net present value) and
carbon eﬃciency. The optimized carbon eﬃciency resulted in the preferred bioreﬁnery conﬁguration
that produces bio-oils from the pyrolysis reactor, while the base case analysis (only maximizing NPV)
favored the choice of bioreﬁnery conﬁguration that produces chemicals such as xylitol, levulinic acid,
and formic acid.
Albarelli et al. [44] demonstrated that process optimization can be used to aid the decision making
of two diﬀerent proposed processing alternatives. A sugarcane-based bioethanol bioreﬁnery integrated
with methanol production from sugarcane lignocellulosic residue was compared with the stand-alone
bioethanol bioreﬁnery. A thermo-economic model was developed to assess the energy eﬃciency as well
as the economic impact of the integrated process. Two objectives were used, namely the maximization
of energy eﬃciency and the minimization of the investment cost.
Fattahi and Govindan [45] proposed multi-stage stochastic programming with the Benders
decomposition approach to evaluate the sustainable design of biofuel supply chain networks under
biomass supply uncertainty and disruption risk. The biomass supply-seasonal ﬂuctuation was modeled
as a modiﬁed autoregressive. While the disruption risk was related to the storage capacity in the face
of biomass yield variation. The yield was modeled as a Bernoulli random variable. Three objectives
were used: economic, environmental, and social impacts. A lower and upper bound were set for both
environmental and social impacts. The optimum solution from sets of the Pareto optimal solution was
obtained using the ε-constraints method.
Bbosa, Mba-Wright, and Brown [41] conducted a study on the techno-economic analysis of a
corn stover ethanol bioreﬁnery integrated with a lignin hydrothermal liquefaction. Corn stover was
used because it is the most abundant agricultural residue in the USA and is expected to be the single
largest lignocellulosic biomass source in the country. The ethanol plant, as it is known, produces
lignin as a waste. Through hydrothermal liquefaction, lignin can be converted into marketable
biochemicals. The study was based on an ethanol plant that processed 2000 metric tonnes per day of
corn stover to produce 61 MMgal/year of ethanol and diﬀerent yields of lignin-derived biochemicals.
The hydrothermal liquefaction process was set to utilize 80% of the solid/lignin from the recovery
section, while the other 20% was used as fuel to generate heat and power for the facility. As a
result, the minimum ethanol selling price (MESP) was aﬀected by the yields and market price of the
biochemical products. The MESP of the integrated ethanol plant and lignin-derived biochemical was
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lower than the reference ethanol price (Iowa’s average 2007 ethanol selling price) and much lower than
the price of ethanol produced in the stand-alone ethanol plant [41].
Another techno-economic analysis was reported by Ou et al. [46] on corn grain and corn stover
co-located production plants. The co-locating of an ethanol plant from corn grain and corn stover
oﬀers advantages, namely increasing biomass feedstock inventory and reducing production cost.
It promotes the commercialization of cellulosic ethanol and improves the competitiveness of corn
ethanol to fossil fuel. Corn-based ethanol has been heavily criticized since it causes the food/feed
conundrum. The rising price of corn is the result of a competition between food/feed industries and
chemical industries.
Nearly 46% of the US corn crop was used as a feedstock for bioethanol in the year 2011. Despite the
heavy use of corn in that year, bioethanol production only equaled 10% of US gasoline production [46].
The study compared the MESP of ethanol produced by the stand-alone Gen 1 plant (corn-grain
feedstock), the stand-alone Gen 2 plant (corn-stover feedstock), and the co-located plant (corn-grain
and corn-stover feedstock with diﬀerent mass ratio). The MESP of the co-located ethanol plant was
found to be higher than MESP of the Gen-1 plant but lower than the Gen-2 plant. This study, however,
did not show the network integration between Gen-1 and Gen-2 plant. Both plants were located in the
same location but operated separately.
Zondervan et al. [47] developed a model of a bioreﬁnery that produces multiple products
(ethanol, butanol, succinic acid) that were integrated with the supply line of fossil-fuel-based gasoline.
The integrated model facilitated the blending of chemical products with gasoline [47]. The resulting
superstructure was transformed into MINLP with four diﬀerent optimization objectives (maximizing
proﬁt, minimizing costs, minimizing waste, and minimizing ﬁxed costs) [47]. The diﬀerent processing
steps were formulated as intervals divided over several reﬁning stages containing diﬀerent operations
(splitting, solution, and reaction).
Galanopoulos et al. [48] performed techno-economic analysis through the optimization of an
integrated algae bioreﬁnery and wheat straw bioreﬁnery (illustrated in Figure 8) to minimize the total
cost of biodiesel production. The wheat straw bioreﬁnery supplies wastewater and CO2 as the medium
and nutrient to grow the algae. The algae bioreﬁnery utilizes the lipid of algae while the carbohydrate is
sent to the wheat straw bioreﬁnery, which produces bioethanol and levulinic acid. The total production
cost is chosen as the objective since the development of algae bioreﬁnery has not yet cost-competitive
compared to the conventional bioreﬁnery. Henceforth, the possibilities of bioreﬁnery integration
and the optimization of algae conversion routes to alleviate its economic feasibility are of interest.
The optimization result suggests a reduction of biodiesel total production cost up to 80% if algae
bioreﬁnery is integrated with wheat straw bioreﬁnery over a stand-alone algae bioreﬁnery.
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Another techno-economic analysis was reported by Ou et al. [46] on corn grain and corn stover 
co-located production plants. The co-locating of an ethanol plant from corn grain and corn stover 
offers advantages, namely increasing biomass feedstock inventory and reducing production cost. It 
promotes the commercialization of cellulosic ethanol and improves the competitiveness of corn 
ethanol to fossil fuel. Corn-based ethanol has been heavily criticized since it causes the food/feed 
conundrum. The rising price of corn is the result of a competition between food/feed industries and 
chemical industries.  
Nearly 46% of the US corn crop was used as a feedstock for bioethanol in the year 2011. Despite 
the heavy use of corn in that year, bioethanol production only equaled 10% of US gasoline production 
[46]. The study compared the MESP of ethanol produced by the stand-alone Gen 1 plant (corn-grain 
feedstock), the stand-alone Gen 2 plant (corn-stover feedstock), and the co-located plant (corn-grain 
and corn-stover feedstock with different mass ratio). The MESP of the co-located ethanol plant was 
found to be higher than MESP of the Gen-1 plant but lower than the Gen-2 plant. This study, however, 
did not show the network integration between Gen-1 and Gen-2 plant. Both plants were located in 
the same location but operated separately. 
Zondervan et al. [47] developed a model of a biorefinery that produces multiple products 
(ethanol, butanol, succinic acid) that were integrated with the supply line of fossil-fuel-based 
gasoline. The integrated model facilitated the blending of chemical products with gasoline [47]. The 
resulting superstructure was transformed into MINLP with four different optimization objectives 
(maximizing profit, minimizing costs, minimizing waste, and minimizing fixed costs) [47]. The 
different processing steps were formulated as intervals divided over several refining stages 
containing different operations (splitting, solution, and reaction). 
Galanopoulos et al. [48] performed techno-economic analysis through the optimization of an 
integrated algae biorefinery and wheat straw biorefinery (illustrated in Figure 8) to minimize the total 
cost of biodiesel production. The wheat straw biorefinery supplies wastewater and CO2 as the 
medium and nutrient to grow the algae. The algae biorefinery utilizes the lipid of algae while the 
carbohydrate is sent to the wheat straw biorefinery, which produces bioethanol and levulinic acid. 
The total production cost is chosen as the objective since the development of algae biorefinery has not 
yet cost-competitive compared to the conventional biorefinery. Henceforth, the possibilities of 
biorefinery integration and the optimization of algae conversion routes to alleviate its economic 
feasibility are of interest. The optimization result suggests a reduction of biodiesel total production 
cost up to 80% if algae biorefinery is integrated with wheat straw biorefinery over a stand-alone algae 
biorefinery.  
 
Figure 8. Integrated Algae- and Wheat Straw- Biorefinery that Supplies the CO2 and Nutrients in the 
Wastewater, adapted from [48]. 
The significant reduction in the total production cost is caused by the unnecessity to procure 
nitrogen and phosphorus as the nutrient for the algae growth. Furthermore, the integration of algae 
biorefinery and wheat straw biorefinery provides more choices in treatment and conversion process. 
The integration opts technology of lower energy consumption, while the stand-alone algae 
biorefinery opts more energy-intensive processing technology, causing a substantial increase in the 
operating cost.  
Figure 8. Integrated Algae- and Wheat Straw- Bioreﬁnery that Supplies the CO2 and Nutrients in the
Wastewater, adapted from [48].
The signiﬁcant reduction in the total production cost is caused by the unnecessity to procure
nitrogen and phosphorus as the nutrient for the algae growth. Furthermore, the integration of algae
bioreﬁnery and wheat straw bioreﬁnery provides more choices in treatment and conversion process.
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The integration opts technology of lower energy consumption, while the stand-alone algae bioreﬁnery
opts more energy-intensive processing technology, causing a substantial increase in the operating cost.
Sy et al. [49] developed a target-oriented robust optimization (TORO) approach for a multi-objective
techno-economic and environmental feasibility analysis of an algal-based integrated bioreﬁnery.
The TORO resulted in an optimal conﬁguration that was found to be more immune to variation in
product demand relative to the conﬁguration resulted from the deterministic optimization. A target
value for the objective must be set carefully. Setting the appropriate target value is important. Setting
the target value too high or too low or too conservative resulted in a sub-optimal solution. In this study,
a robustness index (θ) was introduced in the formulated mathematical representation to measure
the highest degree of uncertainty of key model parameters that could be tolerated by a solution
before it becomes infeasible. The solution suggested a similar trend of economic criteria (proﬁt)
and environmental criteria (carbon footprint), where decreasing proﬁt was followed by decreasing
carbon footprint. It might be seen that decreasing proﬁt was caused by the lower production rate
and hence lower carbon emission. The optimum integrated algal bioreﬁnery was achieved by a low
robustness index.
Pharmaceuticals is another class of products that can be generated through bioreﬁnery. However,
the studies on seeking optimum chemical processing technologies for biopharmaceuticals are not
as massive as the ones for biofuels and biochemicals. Ng et al. [50] performed a single objective
optimization of pharmaceutical production from palm-oil-based biomass through maximization of
gross proﬁt. The novelty lays in the fact that pharma-industries are identical with low product yield and
high product price in contrary to the fuel reﬁnery. In the low product output, the optimization is forced
to ﬁnd chemical process routes capable of bringing economic gain. In this study, the optimization started
with constructing the chemical reaction pathway map (CRPM) consisting of choices of the treatment
process and conversion process. The CRPM aided the elimination of chemical process, which was not
technologically proven and feasible. The study ﬁnds that conversion is a determining parameter for
the economic performance of the pharmaceuticals bioreﬁnery. The value of the conversion rate of one
product leads the preference to produce other products altogether or the preference of multi-products
bioreﬁnery. The sensitivity analysis result reveals that the gross proﬁt of some products stays constant
upon the deviation of its respective conversion while the proﬁt of other products is ﬂuctuating. It is not
a necessity to invest in the enhancement of the technology to increase the conversion of some products
since the increasing yield fails to increase proﬁt. This can be caused by the low market demand of
products where the overproduction cannot be absorbed by the market.
Bairamzadeh et al. [51] developed robust possibilistic programming based on mixed-integer
linear programming (MILP) for optimization of a lignocellulosic-based bioethanol supply chain
under three-uncertain parameters, namely the demand variation of bioethanol, price variation
of bioethanol and biomass, and uncertainty in the unit environmental impact coeﬃcients in the
environmental objective function.
The uncertainty parameters were treated as fuzzy numbers.
Multi-objective optimization was performed with coupling social impacts’ objective with the economic
and environmental objectives. The social impact was quantiﬁed by the number of job creations.
The optimization was extended beyond obtaining optimum processing routes and production capacity,
but also to determine biomass sourcing, allocation, and location. This study was considered as
operational planning with a time horizon of a year divided into months. Interestingly, the production
of bioethanol was not constrained to fulﬁll the market demand, whereas a penalty cost for unmet
demand was applied in the objective function of proﬁt maximization. The environmental impact
objective function was further expanded into the quantitation of the eﬀect on human health, ecosystem,
and resource.
An interesting study by Singh et al. [52] aimed to capture the dynamics of corn price dues to
competition and interactions among bioreﬁneries, among farmers, and between bioreﬁneries and the
food market. The commodity price is usually modeled based on historical data without further attempt
to understand the extent of corn-user interaction to the price. The accurate prediction of corn price is
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thus critical since the corn price is the largest cost component to produce bioethanol. An agent-based
model was developed, and the resulting corn price’s estimate was returned to the supply chain design
problem. The design optimization problem was then solved by a genetic algorithm to seek the optimum
location and capacity of each bioreﬁnery in the network under the criteria of maximization of the net
present value.
Giuliano et al. [53] performed process optimization of a bioreﬁnery for the production of levulinic
acid, succinic acid, and ethanol to ﬁnd the optimum process pathway under the criteria of maximizing
the net present value or the internal rate of return using a discretization of the MINLP master problem
to MILP. The results show that the product selling price, discount rate, and plant scale are signiﬁcant
factors that aﬀected the result of the process optimization.
López-Díaz et al. [54] employed multi-objective optimization to identify the optimum choice of
feedstocks, cultivation sites, the location of biomass processing facilities, and the selection of conversion
technologies. The study placed great interest in the optimization of water consumption and discharge
during biomass cultivation, pretreatment, and conversion. The bioreﬁnery operation involves a
substantial amount of water, whereas the discharge of wastewater into the surroundings aﬀects the
water quality. The optimization problem was formulated as MINLP in which the only non-linear terms
corresponded to the exponential to consider the economies of scale for the estimation of the capital
cost of the bioreﬁnery. Total annual proﬁt was used as the economic-evaluation parameter, whereas
the watershed's capacity was set as the environmental constraints.
5.2. Mixed-Integer Linear Fractional Programming
Mixed-integer linear fractional programming (MILFP) has also been studied in chemical process
optimization.
The MILFP arises when continuous time formulations are used, such as in the
maximization of the cyclic proﬁt rate or productivity. This objective takes the form of a ratio between
two linear functions with proﬁt as the numerator and the cycle time as the denominator.
Tong et al. [55] used the unit objective for the optimization of an integrated petroleum reﬁnery
and a bioreﬁnery supply chain. In this study, the problem was formulated into MILFP by transforming
the objective of minimizing total cost to the minimization of unit cost. The unit objective refers to
the total proﬁt or total cost divided by the total amount of the functional unit. A robust optimization
technique was employed to solve the optimization problem in which trade-oﬀbetween robustness and
performance became the main issue. The issue arises since the process robustness is often achieved by
sacriﬁcing the performance. In this case, parametric and reformulation-linearization approaches are
adopted to ﬁnd optimum solutions. A comparative study was performed between the deterministic
programming to minimize the total cost and MILFP to minimize the unit cost. MILFP resulted in higher
total cost but lower unit cost compared to the deterministic programming result. As a consequence,
a strategic decision was also diﬀerent between those techniques in terms of preference of technologies
and production capacities. The reduction of complexity through transformation into MILP or the use
of stochastic optimization strategies such as the Monte Carlo can produce a good solution. However,
there is a possibility that the process misses better points to be evaluated in the solution domain.
Salas et al. [56] proposed a stochastic metaheuristics optimization method to optimize a
lignocellulosic-based bioreﬁnery under operational level uncertainties. In the proposed method,
the operating points were intelligently searched rather than randomly searched as in the
Monte-Carlo simulation.
Ng and Maravelias [57] constructed a MILP for the design and operational planning of the
cellulosic biofuel supply chain. An approximation and linearization approach for the calculation
of shipment and transportation distance was adopted to obtain the linear model. Bairamzadeh,
Saidi-Mehrabad, and Pishvaee [11] employed robust optimization programming to solve the MILP
model of the lignocellulosic-based bioethanol supply chain. The model was developed to handle
disparate types of uncertainty (randomness, epistemic, and deep uncertainty). There were three
uncertain parameters, namely conversion rates, biomass yield, and demand. Uncertainty in the process
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was manifested in terms of unprecise conversion rates. Probability-based scenarios were deﬁned to
express this particular uncertainty. Biomass yield was expressed as a fuzzy number, while the demand
was assumed to vary in a known interval.
5.3. Possibilistic Programming
Babazadeh [58] developed a robust optimization method for the optimization of biomass to the
bioenergy system under deep uncertainty. Deep uncertainty arises from the lack of availability of
historical data and the limited information on the input parameters, which means that possibility and
probability distribution are impossible to construct. The deep uncertainty of parameters was modeled
using a polyhedral uncertainty set.
Mousavi Ahranjani et al. [59] proposed robust possibilistic programming (MORPP) for the
multi-objective optimization of a multiperiod multi feedstock lignocellulosic biofuel supply chain
network under epistemic uncertainty.
The robust possibilistic programming was developed to
overcome the drawbacks of stochastics programming. Stochastics programming requires a large
number of scenarios. Furthermore, the size of the stochastics programming is further enlarged by the
increasing number of parameters and probability. In the possibilistic programming, the average value
of uncertain parameters is used to obtain the solutions without control of deviations from the expected
or mean value of the objective function. The model in this study was developed to determine the
optimal location, capacity, conversion technology, transportation modes, material ﬂow, and production
planning of bioreﬁneries. The objectives used in this study were economic, environmental, and
social aspects.
A multi-objective robust possibilistic programming was again adopted by [15] to conduct the
optimization of switchgrass-based bioethanol supply chain network under the epistemic uncertainty.
Three conﬂicting objectives were modeled, namely the economic, environmental, and social impacts.
The proposed approach was able to maximize the mean value of supply chain performance and control
the optimality as well as feasibility robustness. It outperformed the deterministic optimization in terms
of average and standard deviation measures.
Rabbani et al. [60] used multicriteria decision-making methods, TOPSIS, and augmented
ε-constraint, to ﬁnd the Pareto optimal solution of the switchgrass-based bioenergy production
system. It was modeled as MILP with three conﬂicting objectives and was solved via two-stage
algorithms. In the ﬁrst stage, the ε-constraint was used to obtain sets of Pareto optimal solutions.
The next step was to use TOPSIS to rank the solutions with respect to the weight for each objective
function. TOPSIS works by determining the best alternative that has the shortest distance from
positive-ideal solution and the longest distance from the negative-solution. The planning horizon in
this study was short with an extent up to 3 years divided into twelve periods in accordance with the
harvesting periods.
Santibañez-Aguilar et al. [61] performed multi-objective optimization for bioreﬁnery supply chain
with total annual proﬁt and Eco-indicator99 as the economic and environmental objective, respectively.
The uncertainty scenario based on biomass prices was generated using Latin Hypercube. Each MILP
model was then deterministically solved using the Monte-Carlo method.
The model and approach to optimization problems should guarantee optimality robustness and
feasibility robustness of the solution. Optimality robustness is attained when the objective function
value of the solution vector remains close to the optimal value or have a minimum deviation from the
optimal value. Feasibility robustness is achieved when the solution vector stays feasible for almost all
possible values of uncertain parameters.
There are three types of uncertainty prevailing in the process optimization of bioreﬁnery:
randomness, epistemic, and deep uncertainty. The process optimization problem with the randomness
type of uncertainty can be catered by stochastic programming, epistemic by possibilistic programming,
and deep uncertainty by robust convex programming. Realistically, all the uncertainties can present in
the process optimization of bioreﬁnery.
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6. Optimization of Petroleum Reﬁnery
Petroleum reﬁnery conﬁguration, illustrated in Figure 9, comprises of crude distillation units
(CDU), catalytic reforming units (CRU), delayed coking unit (DCU), ﬂuid catalytic cracking units
(FCC), hydrotreating units (HTU), hydrocracking units (HCU), gasoline blender (GB), and diesel
blender (DB) [62]. CDU fractionates the crude oil into heavier and lighter fractions. The fractions are
gas, light straight-run naphtha, heavy straight-run naphtha, straight-run kerosene, straight-run middle
distillate, straight-run gas oil, and vacuum residue [63]. The vacuum residue is further fractionated
in the vacuum distillation unit. The heavy straight-run naphtha with low octane rating is treated in
the catalytic reforming unit to produce higher octane liquids. The heavier fractions from the vacuum
distillation unit are treated in the DCU to generate a higher quality of fuel oil [62]. The straight-run
middle distillate, straight-run gas oil, light vacuum distillate, and heavy vacuum distillate are treated
in the FCC units. The fractions are broken down and rearranged into lighter molecules to increase
the quality and the quantity of the products. The straight-run middle distillate can also be treated
with hydrogen in the HTU to remove the sulfur content. The eﬄuent of the HT is later be blended to
produce diesel fuel oils. The HCU cracks the straight-run middle distillate under hydrogen feed to
generate gasoline and kerosene.
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middle distillate, straight-run gas oil, and vacuum residue [63]. The vacuum residue is further 
fractionated in the vacuum distillation unit. The heavy straight-run naphtha with low octane rating 
is treated in the catalytic reforming unit to produce higher octane liquids. The heavier fractions from 
the vacuum distillation unit are treated in the DCU to generate a higher quality of fuel oil [62]. The 
straight-run middle distillate, straight-run gas oil, light vacuum distillate, and heavy vacuum 
distillate are treated in the FCC units. The fractions are broken down and rearranged into lighter 
molecules to increase the quality and the quantity of the products. The straight-run middle distillate 
can also be treated with hydrogen in the HTU to remove the sulfur content. The effluent of the HT is 
later be blended to produce diesel fuel oils. The HCU cracks the straight-run middle distillate under 
hydrogen feed to generate gasoline and kerosene.  
 
Figure 9. Process Flow Diagram of Modern Petroleum Refinery, adapted from [64]. 
The vast options of available processes in petroleum refining lead to a very large number of 
refinery configurations, such that designing a petroleum refinery involved 200 ready-made heuristic 
plot plans [65]. It is certainly a daunting task to find the optimum design. The complexity is further 
escalated since refineries are subject to numerous uncertainties during operation, namely the 
fluctuation of crude oil price, crude oil availability, and changing the demand level [66]. Shah et al. 
[67] identified three major uncertainties in the refinery production planning, namely (i) market 
demand for products, (ii) the prices of crude oil and the saleable product, and (iii) the production 
yields of crude oil in the primary crude distillation unit. In terms of saleable products, gasoline and 
diesel make up about 60%-70% of the revenue of a refinery [16].  
Most of the previous studies of oil refinery planning were deterministic in which the strategies 
Figure 9. Process Flow Diagram of Modern Petroleum Reﬁnery, adapted from [64].
The vast options of available processes in petroleum reﬁning lead to a very large number of
reﬁnery conﬁgurations, such that designing a petroleum reﬁnery involved 200 ready-made heuristic
plot plans [65]. It is certainly a daunting task to ﬁnd the optimum design. The complexity is further
escalated since reﬁneries are subject to numerous uncertainties during operation, namely the ﬂuctuation
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of crude oil price, crude oil availability, and changing the demand level [66]. Shah et al. [67] identiﬁed
three major uncertainties in the reﬁnery production planning, namely (i) market demand for products,
(ii) the prices of crude oil and the saleable product, and (iii) the production yields of crude oil in the
primary crude distillation unit. In terms of saleable products, gasoline and diesel make up about
60–70% of the revenue of a reﬁnery [16].
Most of the previous studies of oil reﬁnery planning were deterministic in which the strategies
resulted from the deterministic approach show a lack of robustness or become infeasible upon the
realization of uncertainty parameters [66]. Ribas et al. [68] investigated the impact of uncertainty on
investment decisions in the integrated oil supply chain using stochastic and robust programming.
The research attempted to handle the planning problem at the strategic level where the prior studies
focused to treat reﬁnery planning problems at the tactical and operational levels. Three diﬀerent
models were developed, namely a two-stage stochastic model with ﬁxed recourse, a min–max regret
model, and a max–min model.
Al-Qahtani et al. [69] studied the optimization of multisite reﬁnery networks under the uncertainty
of the raw material price, product price, and demand through stochastic programming and robust
optimization. MILP was formulated for the stochastic model, while the MINLP was formulated for the
robust optimization with a single objective of minimization of the total cost. The non-linearity arose
from the modeling the risk components.
A particular aspect of petroleum reﬁnery operation that has gained increasing interest is the
hydrogen networks. The reﬁnery hydrogen networks, in general, comprise of hydrogen consuming and
producing units, hydrogen puriﬁcation units, compressors, and pipeline systems. The pipeline system
in the reﬁnery hydrogen networks facilitates the distribution of low-pressure gas and high-pressure
gas. The gas in the lower pressure pipeline system can be compressed and the gas in the high-pressure
pipeline system can be purged [70]. Hydrogen is needed for the HTU, HCU, FCC, and isomerization
unit. In a reﬁnery, hydrogen is generated from the catalytic reforming unit and the hydrogen plant and
is distributed through a complex distribution network for hydrodesulfurization and hydro-treating
processes [71]. The high purity hydrogen is produced through steam reforming or partial oxidation of
light hydrocarbon fractions, such as reﬁnery-oﬀgas or light naphtha [72]. The lower purity of hydrogen
is generated as the byproduct of the cyclization and dehydrogenation of hydrocarbon to increase the
aromatics content and the octane number of naphtha products in the catalytic reforming unit [72].
As for current practice, the reﬁnery is required to produce fuel with lower aromatic content; thus,
hydrogen generated as a by-product in those processes is also lower [72,73]. Moreover, the petroleum
reﬁnery must process crude oil with a higher content of sulfur and nitrogen [74]. Consequently,
the processing capacity of hydrocracking and hydro-treating units in the reﬁnery will be changing
over time.
The production and distribution systems of hydrogen are required to anticipate the changes in
hydrogen demand and requirement to ensure a sustainable operation. In the production of hydrogen,
there is a tradeoﬀbetween production load, eﬃciency, and emission. A study of a reﬁnery in China
reveals that the low hydrogen production load decreases the exergy eﬃciency but increases the
CO2 emission [75]. The need for the optimum scheduling of hydrogen production and distribution
relies on the gain of lower reﬁnery operating costs, safer operation, and lower environmental impact.
As stringent environmental regulations and policy require the reﬁnery to produce cleaner fuel with
lower sulfur and gasoline of lower aromatics content, the reﬁnery will likely to build new hydrogen
plants. The capital costs of hydrogen production units are high. It was estimated to be equivalent
to more than one-third of the capital costs of hydrocarbon conversion units for upgrading heavy
petroleum fractions [72]. It is in the view of a sustainable process that the management of hydrogen
generation and consumption should be regarded as a critical engineering aspect in the reﬁnery [72].
The hydrogen network integration is at the forefront approach for reﬁnery hydrogen management.
Many integration methods have been studied and can be categorized into the pinch based methodologies
and mathematical programming techniques [73]. Research on hydrogen reﬁnery networks’ optimization
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has shifted from single-period optimization to multi-period optimization to account for the uncertainty
factors present in the operation of hydrogen reﬁnery networks, such as a change in the operating
parameters. Recent studies also consider a multiple-impurity system where the stream of the hydrogen
reﬁnery networks is not limited to only a mixture of hydrogen and methane but also H2S and CO [62].
The multiple impurity system better represents the real hydrogen reﬁnery network.
Petroleum reﬁnery consumes a high amount of energy. Energy saving leads to lower operational
cost, hence more proﬁt can be obtained. Most of the process optimization only considers material
transfer (within the process) to achieve optimum use of materials. While the energy transfer that
might be conceived is not accounted. This will overestimate the utility cost. Chen et al. [76] performed
simultaneous process optimization and heat integration for methanol production process. The result
was compared with the optimized base case without heat integration and sequential optimization
and heat integration. In the sequential optimization, heat integration was performed after the process
was optimized, while the simultaneous approach was solved numerous times depending on the
number of iterations. The simultaneous approach resulted in decreased of utility consumption
and higher proﬁt. Tovar-Facio et al. [77] performed the process optimization of a water network
for oil reﬁneries integrated with an electrocoagulation treatment system. The study resulted in a
reduction in fresh-water consumption and waste generation compares to the water network without
electrocoagulation. The process optimization also showed that the integration of electrocoagulation
substantially reduced the total annualized cost. Those case studies demonstrate that the optimum
use of materials and energy in chemical processing facilities can be determined through mathematical
programming-based process optimization.
7. Sustainability Parameters in Multi-Objective Optimization
Sustainability is the ultimate goal for the development of the economy and future bioreﬁnery
systems. It has become the objective of many studies on the process optimization of the production
system (see Table 1). The sustainability concept comprises not only economic and environmental
aspects but also has widened out into the areas of social, safety, and health. The impression of the
bioreﬁnery system as sustainable due to the renewability of the biomass sources has been refuted
given that sustainability is not founded solely on renewability or the environmental dimension.
All dimensions of sustainability shall be counted in the development of future bioreﬁneries.
The consideration of sustainability parameters in the early stage of bioreﬁnery design could have a
signiﬁcant impact on improving the overall performance and consequently results in alternative design
options. There are several ways to analyze the economy of a chemical process facility: the minimization
of cost, maximization of proﬁt, and the maximization of net present value, etc. The minimization of
cost and maximization of proﬁt are suitable for the optimization of the existing and already running
facilities, while the maximization of net present value is suitable for optimization of the proposed
design of a new facility. In a certain case, the maximization of net present value suggests omitting
some process. Thus, it helps the decision maker on making the right investment [62].
Global warming and environmental pollution, the negative impacts of industrialization, have
made people realize that a more sustainable practice of industrialization should be embraced. Therefore,
the incorporation of environmental objectives alongside economical objective in the process optimization
of a chemical plant is of paramount important. The common factors of addressing sustainability
include life-cycle assessment, carbon dioxide utilization, process safety and social impact, all of which
are addressed in the following section. The inclusion of sustainability parameters during system
optimization requires multi-objective routines. Sustainability factors, together with the already complex
nature of the reﬁnery system, make system optimization challenging.
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Table 1. Summary of Bioreﬁnery Process Optimization Case Studies.
No
Case Study
Sustainability Parameter
Uncertainty
Methods
Ref
Economy
Environment
Safety
Social
Impact
1
Biomass to bioenergy
Total cost
CO2-equivalent
-
-
Deep uncertainty
Robust
Optimization
[58]
2
Multi feedstock
lignocellulosic-based
bioethanol
Total cost
GHG emission
-
Job
opportunities
Epistemic uncertainty
Robust
Possibilistic
Programming
(MILP)
[59]
3
Switch-grass based
bioethanol bioreﬁnery
Total cost
Environmental
impact
(Point-based)
-
Employment
and
economic
development
indicators
Epistemic uncertainty
Robust
Possibilistic
Programming
(MILP)
[15]
4
Lignocellulosic-based
bioethanol supply chain
Total cost
-
-
-
Combined-randomness,
epistemic, and deep
uncertainty.
Robust
optimization
(MILP)
[11]
5
Switch-grass based
bioenergy production
Total cost
GHG emission
-
Jobs creation
-
MILP
[60]
6
Sugarcane-based
bioethanol
Investment
cost
-
-
-
-
Genetic
algorithm
(MINLP)
[44]
7
Cellulosic biofuel
supply chain
Total
annual cost
-
-
-
-
MILP
[57]
8
Bioreﬁnery supply
chain
Net annual
proﬁt
Eco-indicator99
-
-
Stochastic scenarios,
Latin Hypercube,
Monte-Carlo
Deterministic
programming
[61]
7.1. Life-Cycle Assessment (LCA)
Life-Cycle Assessment (LCA) is regarded as the most scientiﬁcally reliable method to assess the
environmental impact of a product or process. It is an objective process for evaluating the environmental
burdens associated with a product, process, or activity [78]. LCA begins with the establishment of
analysis boundaries and followed by inventory analysis and impact assessment. The inventory analysis
quantiﬁes the materials and energy consumed and emissions and waste generated during the operation,
while the impact assessment involves the quantiﬁcation of global warming potential (GWP) and
damage impact.
Wang et al. [78] performed the optimization of hydrocarbon bioreﬁnery via gasiﬁcation with
the objective criteria of LCA for the integrated life cycle assessment and techno-economic analysis.
The study aimed to ﬁnd the Pareto-optimal of minimizing GWP and maximizing NPV. Simapro and
Ecoinvent software are frequently used to perform LCA analysis of a bioreﬁnery system [79,80].
7.2. Process Safety
Inherent risk associated with the operation of chemical process plants should be thoroughly
considered during the design of a chemical process plant. The capability to quantify and to rank risk
helps in determining the risk appetites and the corresponding safety measures. Typically, the safety
aspects are assessed once the design of the chemical process plant has been completed and the operating
parameters have been speciﬁed [81].
Hazard and operability (HAZOP) and quantitative risk assessment (QRA) are the common
methodologies to evaluate the safety aspects. However, both methods are not suitable to assess
safety in the conceptual phase of plant design. HAZOP requires detailed information on the process
that is only available once the design is completed. However, QRA becomes inconvenient for the
safety analysis of complex facilities since it only estimates the failures and consequences of a piece of
equipment or system of a few units with a probabilistic approach [81].
The assessment of inherent safety in the design phase of a chemical process plant requires metrics
that allow direct calculation with limited information. The material and process factors are typically
incorporated into the metrics. There are two of such metrics: (i) Dow ﬁre and explosion index
Polymers 2020, 12, 1091
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(F&EI) (ii) ﬁre and explosion damage index (FEDI). The FEDI is developed to overcome the limitation
of F&EI in which the latter treats material factors as independent to process operating conditions.
The estimation of FEDI comprises the classiﬁcation of units based on the purpose, the evaluation of
energy factor dues to chemical properties and operating conditions, the assignment of penalty, and the
estimation of potential damage [81].
A diﬀerent approach was reported by El-Halwagi et al. [82]. in their study of the multi-objective
optimization of bioreﬁneries with economic and safety objectives. The economic objective was to
minimize the total annual cost associated with the cost of purchasing and transporting the feedstocks,
the processing cost in the central facilities, the transportation cost of biofuels from central facilities to
the users, the processing cost of the biofuels in the industrial facilities, and the capital costs for the
biofuel production. As for the safety objective, the study proposed an approach that relates the number
of fatalities per year and the quantity of biofuels produced/bought per year. The probit approach was
used to determine the number of fatalities where it is used in the quantitative risk analysis to predict
the number of acute fatalities caused by an accident.
7.3. Social Impact
Social impacts are associated with social justice and the rights of stakeholders, including employees,
customers, and local communities [83]. The opted indicators to justify social impacts can be varied.
Regarding the transformation of land usage, the food footprint can be used to measure the area that is
converted from food production to energy production. The number of jobs created is the widely used
social impact indicator since it is a real social beneﬁts indicator. However, the maximization of job
creation is not always the right objective. In a decreasing population, the optimization model might
involve the minimization of job creation. The social impacts should be considered during the process
of selecting the optimum bioreﬁnery location.
Cambero and Sowlati [84] quantiﬁed the overall social beneﬁts of jobs created by implementing
the weighted sum of all the new jobs created across the supply chain. The assigned weight is based on
the preferability of creating jobs in each location.
8. Integration of Petroleum Reﬁnery and Bioreﬁnery
Bio-oil resulting from the pyrolysis of biomass has the potential to be co-processed with petroleum
feeds in the existing reﬁnery unit to produce biofuel [85]. It seems to be the most probable intermediate
product of bioreﬁnery for linkage with the petroleum reﬁnery. In terms of economics, the retroﬁtting
cost of the petroleum reﬁnery to co-process bio-oil is considered economically viable; the integration
of bioreﬁnery into existing petroleum reﬁnery results in considerable capital savings on the biofuel
production facilities construction that leads to higher competitiveness of the biofuel [62].
Research has been conducted on the lab-scale and pilot-scale of co-processing bio-oil and petroleum
feeds in the FCC unit. The parameters being investigated are the type of bio-oil (raw bio-oil, upgraded
bio-oil, hydrodeoxygenated bio-oil), the type of catalysts, the ratio of bio-oil to vacuum gas oil, etc.
However, there are few studies on the optimization of integrated petroleum reﬁnery and bioreﬁnery.
The integration of the petroleum reﬁnery and the petrochemical facility has also been studied.
Al-Qahtani et al. [86] studied the optimization of petrochemical network design in which the uncertainty
parameter was accounted to accommodate the changing of process yield, raw material cost, product
prices, and product demand. The problem was formulated as a two-stage stochastic MINLP where the
non-linearity manifests from modeling the risk components. The results show that the variations in
process yield and product demand more dominantly aﬀect the sensitivity of the petrochemical network.
Al-Qahtani and Elkamel [87] expanded the study to integrate and to optimize multisite reﬁnery
and petrochemical systems. MILP was developed with the objectives of minimizing the annualized cost
over the set time horizon among the reﬁneries and maximizing the added value of the petrochemical
network. The process integration, utility integration (heat/steam/hydrogen/power), and fuel gas
upgrading were among the aspects modeled in this study. The study was in a certain way motivated
Polymers 2020, 12, 1091
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by the previous ﬁndings in which a signiﬁcant energy consumption reduction (up to 60%) was
achievable through the integration of energy sources and sinks of the steam cracking with other
industrial processes. Furthermore, the integration of a gas turbine through gas-exhaust recovery
between petrochemical units and ammonia plants has the potential to reduce energy usage by up to
10%. The integration of the hydrogen network is becoming crucial due to a stringent environmental
regulation requiring reﬁneries to lower the sulfur content in the fuels that are produced. Consequently,
the hydrogen consumption increases to achieve deeper desulfurization.
The developed formulation Al-Qahtani and Elkamel [87] was implemented to study the integration
of petrochemical complex producing poly(vinyl chloride) (PVC) and three complex reﬁneries.
The reﬁneries complex produced liqueﬁed petroleum gas, light naphtha, two grades of gasoline, jet
fuel, military jet fuel, gas oil, diesel fuel, heating fuel oil, and petroleum coke. The feedstock of the PVC
petrochemical complex is the light naphtha and gas oil produced from the reﬁneries. The optimization
was performed separately for reﬁneries complex and integrated reﬁneries complex-petrochemical
complex. The total annual cost for the reﬁneries complex was found to be higher than the total annual
cost of the integrated reﬁneries complex and PVC complex. The model also suggested that gas oil, an
intermediate stream of the reﬁneries, is the preferred feedstock for the ethylene cracker of PVC complex
against the more commonly used light naphtha. The light naphtha was better allocated for maximum
gasoline production. The integration of the petroleum reﬁneries complex and petrochemical complex
increased the capacity utilization up to 100% of the gas oil desulfurization unit for all reﬁneries.
Al-Sharrah et al. [88,89] developed multi-linear programming for the optimization of petrochemical
facilities in Kuwait.
The study aimed to optimize the petrochemical network under two
criteria objectives: the maximization of proﬁt and the minimization of environmental impact.
The environmental objective was quantiﬁed by the health index of the chemicals set by the National
Fire and Protection Agency.
9. Conclusions and Perspectives
Liquid fuel is the largest product consumed from among a wide array of petroleum-reﬁnery
products.
As society becomes sophisticated and more environmentally conscious, the switch
of petroleum-based to renewable feedstock-based bioreﬁnery is inevitable. The ﬁrst-generation
biomass-based bioreﬁnery, which produces bioethanol and biodiesel, has been adopted. However,
the use of edible biomass sources has intensiﬁed the food and feed vs. the fuel industries' contestation.
In the extended term, the ﬁrst generation of biomass will no longer be sustainable in terms of economic
potential. The second-generation biomass and third-generation biomass are attracting great interest as
the substitute for the ﬁrst-generation biomass.
The decision to adopt bioreﬁnery should be taken with great care since there are a plethora of
options for processes and technologies. On top of maximizing proﬁt, sustainability factors shall be
considered, which makes the optimization process challenging, which then requires mathematical
programming as a powerful tool. The uncertainty of internal and external variables are parameters that
aﬀect the sustainability criteria. An example of internal parameter uncertainty is yield or conversion,
both of which depend on the operating variables. External uncertainty arises from the variation in
demand level and price. To add to this complexity of the decision-making process, the bioreﬁnery is
expected to fulﬁll all the multi-criteria of sustainability, namely economic sustainability, environmental
sustainability, safety, and social impacts.
The indicators for economic and environmental sustainability are already established and adopted.
The NPV and cost are the commonly used economic indicator. For the environmental impact, global
warming potential and CO2-equivalent emission are intensively used. The safety aspect is of paramount
importance but is still rarely used as the objective to synthesize a sustainable system. Nevertheless,
it will not be feasible to establish bioreﬁnery that results in high economic beneﬁt, but, at the same
time, it has a high risk of explosion. In this particular topic, the FEDI or F&EI can be used to measure
the safety level of industrial facilities. However, a standard should be established regarding the FEDI
Polymers 2020, 12, 1091
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or F&EI value that is still universally acceptable. Furthermore, a correlation between the FEDI or F&EI
and the required investment for safety improvement should be established. Thus, this safety objective
can be monetized. The multi-objective optimization will be more practical if all the objectives are in the
same unit. It should be noted that the economical objective is money related and the environmental
objective is related to emission level.
Only a few studies have included the competition factor between agents. The agents in the
bioreﬁnery supply chain includes material suppliers, biomass producers, bioreﬁneries, and markets.
This competition is impacting the cost of materials and the price of products. For example, the cost of
biomass will be lower if there are more biomass producers. The same goes for the price of the product;
the price will be lower if there are more bioreﬁneries and will increase if there are more consumers.
For the environmental impact, CO2-equivalent emission can be monetized by the implementation
of a CO2-penalty cost. For the social impact, the FEDI or F&EI should be monetized by calculating the
required investment for safety infrastructure to improve the safety level. In this case, the monetization
of the environmental impact and safety impact should be established to be coherent with the unit of
economic impacts.
The bioreﬁnery prospect in countries such as Malaysia, which generate an abundance of palm
oil biomass waste, needs to be examined. It should comprise of techno-economic, environmental
impact and social impact analyses. Albeit its prospective potential, the willingness of industries to
adopt bioreﬁnery depends on the economic potential. The techno-economic analysis should not only
consider the internal parameters related to the chemical process, but also the external parameters
which are characteristically uncertain. The decision-making process to adopt the bioreﬁnery based on
overly deterministic assumption leads to sub-optimal solution. This sub-optimality leads to a narrow
capability to accommodate the changing of the uncertainties. Mathematical programming-based
process optimization is a powerful technique to address the challenge. However, process optimization
requires comprehensive mathematical formulation that can represent the model as accurate as possible.
The other prominent challenge discussed in this manuscript is the uncertain parameters involved in
the mathematical model. Without fulﬁlling these two requirements, process optimization will lack the
information required to aid the decision-making process.
Author Contributions: Conceptualization, Z.A.P. and M.R.B.; writing—original draft preparation, W.D.P.;
writing—review and editing, T.M.I.M., Y.W., N.A.H.N., M.D.H.W.; supervision, Z.A.P. and M.R.B.; funding
acquisition, Z.A.P. All authors have read and agreed to the published version of the manuscript.
Funding:
This research and APC were funded by Yayasan Universiti Teknologi PETRONAS grant
number: 0153AA-H03.
Conﬂicts of Interest: The authors declare no conﬂict of interest.
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CHEMICAL ENGINEERING TRANSACTIONS Determination of Energy
Efﬁciency Features of Oil Reﬁnery Units and Their Complexes
Article  in  Chemical Engineering Transactions · January 2023
DOI: 10.3303/CET2081048
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 CHEMICAL ENGINEERING TRANSACTIONS  
 
VOL. 81, 2020 
A publication of 
 
The Italian Association 
of Chemical Engineering 
Online at www.cetjournal.it 
Guest Editors: Petar S. Varbanov, Qiuwang Wang, Min Zeng, Panos Seferlis, Ting Ma, Jiří J. Klemeš
Copyright © 2020, AIDIC Servizi S.r.l. 
ISBN 978-88-95608-79-2; ISSN 2283-9216 
Determination of Energy Efficiency Features of Oil Refinery 
Units and Their Complexes 
Leonid M. Ulyev*, Maxim V. Kanischev, Roman E. Chibisov 
RusEnergoProeсt LLC, Dep. Science and Engineering, Volokolamsk highway 2, 125080, Moscow, Russia 
leonid.ulyev@gmail.com 
The index of the process energy efficiency has been introduced to assess the energy efficiency of oil refinery 
units and their complexes. The index is calculated as the ratio of the payload difference of the hot utilities of 
the operational unit and the reference unit to the payload of the operational unit. As a reference unit, the 
examined unit is selected with a thermal energy recuperation system, which has a specified value of driving 
force for vertical heat transfer. This value of the minimum temperature difference of the heat transfer carrier in 
the heat-exchange equipment is selected using the best available technologies in heat transfer. Additionally, 
the energy efficiency index is calculated taking into account the elimination of heat losses to the environment. 
A simplified method for estimating heat losses from heated surfaces of oil refinery equipment has been 
developed for this purpose. It is shown that to determine the energy efficiency of a unit complex, it is possible 
to use the temperature Total Site profile. Indices of energy efficiency for crude oil unit, catalytic reforming with 
preliminary hydrotreating for the production of stable catalysis unit and hydrotreating of diesel fuel and 
kerosene unit, as well as their complex, are determined. Indices for economically optimal integration of these 
processes are decided. The introduced index of the energy efficiency potential of the process is an absolute 
value indicating the grade of the process perfection. The introduced index can be used to compare the energy 
efficiency of both individual units and plants. 
1. Introduction 
In developed countries, a significant part of the primary energy resources in the industry is used for heating 
raw materials for processing and at the same time cooling products for their further use. In EU countries, 73 % 
of all consumed energy in industry is used for heating, and this is a quarter of all consumed energy (Mistry and 
Misener, 2016). It means that these processes are also responsible for a significant part of CO2 emissions. At 
the same time, most of the energy required for industrial processes is eventually released back into the 
environment as heat (Element Energy Limited, 2014). The problem of heat loss is typical for all industrialized 
countries. US industry uses 2.77×1019 J/y, from them 1.41×1019 J/y (about 51 %) of heat is rejected (LLNL 
Flow Charts, 2019). Reducing the specific energy consumption in industrial enterprises will lead to significant 
useful results, such as sustainable development of society, clean environment, and increase of the 
competitiveness of advanced enterprises. It is unlikely to be possible to abandon existing processes, so it is 
necessary to develop a reasonable strategy for investing in their modernisation.  
In the work of Feng et al. (2011) the boundaries of energy saving at two petrochemical production complexes 
for the production of aniline and the aromatic hydrocarbons production are determined. Individual process 
integration was performed for the first complex. For the second one, individual thermal integration was 
supplemented by interprocess integration. The performed case studies showed the percentage of 
achievement of the calculated energy saving values. It would be reasonable to indicate the existing and 
achieved degrees of energy perfection of processes at the complexes. 
A procedure for optimising industrial complexes taking into account individual and Total Site integrations was 
presented in the paper of Nemet et al. (2016). Li et al. (2018) showed that inter-unit integration is more 
profitable than individual integration. However, the values for comparing the energy efficiency of existing 
industrial plants and their complexes were not presented in these studies. 
 
 
 
 
 
 
 
 
 
 
                                                                                                                                                                 DOI: 10.3303/CET2081048 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Paper Received: 10/02/2020; Revised: 26/04/2020; Accepted: 07/05/2020 
Please cite this article as: Ulyev L.M., Kanischev M.V., Chibisov R.E., 2020, Determination of Energy Efficiency Features of Oil Refinery Units 
and Their Complexes, Chemical Engineering Transactions, 81, 283-288  DOI:10.3303/CET2081048 
 
 
283
In the paper of Varbanov et al. (2018), the authors developed the concept of investment planning for the 
retrofit of heat exchange systems based on the NPV (net present value). The authors work of Ke et al. (2013) 
used system methods to compare the energy efficiency for cement industry. In the paper of Sardarmehni et al. 
(2017), process integration methods were used to assess the energy efficiency of power use in chemical 
plants. In the work of Yang et al. (2016) modeling methods were used to select a reference installation with 
specific energy consumption that compared the specific energy consumption of the process under 
consideration.  
It is clear that not all industrial units operate with the same energy efficiency, so in order to select priority areas 
of modernisation, it is necessary to create a method for evaluating the energy efficiency of industrial units, 
which would show simultaneously both the improvement of the process under consideration and the potential 
for reducing specific energy consumption. 
In the paper of Kanischev et al. (2018), the authors propose an indicator for determining the energy efficiency 
index of oil refineries that is calculated based on a Pinch Methodology (Klemeš et al., 2018): 
−
ε
=
H
r
e
a
l
H
b
e
n
c
h
e
f
H
r
e
a
Q
Q
Q
m
i
n
(
)
m
i
n
(
)
m
i
n
(
l
)
 
(1)
where QHmin(real) is the payload for the hot utilities of the process at the moment; QHmin(bench) is the payload for 
the hot utilities of the reference unit. 
As a reference unit, it is proposed to choose a unit with the current production process, but with a minimum 
temperature difference on heat exchangers, which can be achieved under the condition of vertical heat 
exchange (Smith et al., 2000) in an integrated heat exchange system using the best available technologies in 
the field of heat exchange. When performing Pinch retrofit projects for more than fifty different oil-refining 
processes, it was found that the incomplete recuperation temperature on modern heat exchange equipment 
could reach 10 °C. It is clear that the lower the value of εef is, the more energy efficient the refinery is. When 
εef→1, the energy efficiency of the installation is reduced. 
This paper analyses the energy efficiency of the LK-6Us production complex, which consists of three oil-
processing units: the CDU-6 - (U-1), the Catalytic Reforming Unit with Preliminary Hydrotreatment - (U-2), and 
the Diesel Fuel and Kerosene Hydrotreatment Unit - (U-3). 
2. Characteristics of the process 
The CDU (U-1) is the head unit of the combined industrial complex LK–6Us. The unit includes a section for 
electric desalting and dehydrating of crude oil, a section for atmospheric distillation of desalted oil and 
stabilisation of straight-run gasoline. The nominal capacity of the unit at the time of the survey was 7 Mt/y for 
refined oil. The heat exchange network of the unit involves 28 process streams (Kanischev et al., 2019b). 
The catalytic reforming unit with preliminary hydrotreating for the production of stable catalyzate (U-2) of the 
LK-6Us complex processes 1,150 kt of raw materials per year. The raw material is a fraction of 85-100 °C, 
allocated at the CDU. In this heat exchange system, 5 cold and 10 hot process streams are involved 
(Kanischev et al., 2019c). The hydrotreating unit (U-3) of the combined LK-6Us complex includes two 
processes. One of them is hydrotreating diesel fuel from sulfur compounds and dewaxing that increase the 
cold flow of diesel fuel. The capacity of this process is 2 Mt/y for processed raw materials. The second 
process is designed for hydrotreating kerosene from sulfur compounds, and its nominal capacity is 600 kt/y for 
processed raw materials. 17 process streams participate in the heat exchange system of this section 
(Meshalkin et al., 2019). 
3. Determination of the energy efficiency index of the LK-6Us units 
In the paper of Kanischev et al. (2019b) by constructing a Composite Curves for CDU (U-1) the main energy 
characteristics of the process are defined: useful load for the hot utilities (QHmin(real)), cool utilities (QСmin(real)), 
capacity of heat energy recuperation – QREC(real), heat capacity that must be supplied to the cold process 
streams ΔHhot and taken from the hot process streams – ΔHcold (projections of the corresponding Composite 
Curves on the abscissa axis). These units are described in Table 1. In the work of Kanischev et al. (2019c), 
these values are defined for the process of (U-2), and in the paper of Meshalkin et al. (2019) – for the process 
of (U-3). These values are also shown in Table 1. 
Using the construction of Composite Curves for ΔТmin = 10 °С, the main energy characteristics for the 
reference process units are determined (Table 2): the useful loads on hot (QHmin(bench)), cold (QCmin(bench)) 
utilities, and the capacity of heat energy recuperation QREC(bench). It is clear that the values of ΔHhot and ΔHcold  
are constants for processes. Summing up presented values, we obtain energy values for the entire complex of 
LK-6Us without interprocess heat integration. 
284
Table 1: Main energy indicators of the existing processes. 
Units  
QHmin(real), MW QCmin(real), MW QREC(real), MW 
ΔHhot, MW ΔHcold, MW 
U-1 
128.8 
86.5 
124.2 
210.7 
253.0 
U-2 
22.3 
42.7 
78.3 
121.0 
100.6 
U-3 
46.2 
48.3 
97.6 
145.9 
144.4 
 
197.3 
177.5 
300.1 
477.5 
498.0 
Table 2: Main energy indicators of reference units. 
Units  
QHmin(bench), MW 
QCmin(bench), MW 
QREC(bench), MW
U-1 
83.2 
43.1 
167.8 
U-2 
8.2 
28.6 
92.5 
U-3 
21.7 
23.8 
122.1 
 
113.1 
95.5 
382.4 
 
The energy efficiency index of CDU-6 (U-1) was determined with the help of Eq(1) – εef = 0.39. The energy 
efficiency index of a Catalytic Reforming Unit with Pre-hydrotreating (U-2) – εef = 0.63 and the energy 
efficiency index for a Diesel Fuel and Kerosene Hydrotreating Unit (U-3) – εef  = 0.53 were determined 
analogous. The resulting energy efficiency indices were determined without taking into account heat losses to 
the environment. To better account of the potential for improving the energy efficiency of oil refining units, let 
us assume that the reference units eliminate the loss of heat energy from heated surfaces. The estimation of 
heat loss capacity at oil refineries is performed using data from Table 3, which is obtained as a result of 
survey, modeling and statistical processing of data obtained from more than fifty oil refineries. 
Table 3: The value of heat capacity losses from various types of non-isolated equipment at the refinery 
Equipment 
QLosses, kW 
Open sections of pipes, m 
1.3 
Valve 
3.0 
Heat exchanger flange and pipe 
1.2 
Heat exchanger cover 
2.9 
Open parts of the heat exchanger shell 8.0 
Pump (hot) 
3.0 
Furnace 
900.0 
 
The total heat losses from the unit equipment to the environment are estimated, including losses from the 
insulating surfaces of furnaces – QHLossesSF and losses from transfer pipes and the heat exchange network of 
the unit – QHLosses (Table 4). 
Table 4: Capacity of heat losses to the environment. 
Process Unit  
QHlosses, MW 
QHLossesSF, MW Total, MW 
U–1 
4.2 
4.0 
8.2 
U–2 
2.7 
2.7 
5.4 
U–3 
1.4 
1.4 
2.8 
 
8.3 
8.1 
16.4 
 
The possibility of furnace energy efficient retrofit for oil refineries is usually not considered when evaluating 
energy efficiency. Therefore, we will consider the possibility of reducing the specific energy consumption only 
by eliminating heat losses from the heated surfaces of the heat exchange system and transfer pipes. In this 
case, the energy efficiency index is determined by the expression Eq(2): 
(
)
−
−
ε
=
H
r
e
a
l
)
H
b
e
n
c
h
H
L
o
s
s
e
s
e
f
L
H
r
e
a
Q
Q
Q
Q
m
i
n
(
m
i
n
(
)
m
i
n
(
l
)
. 
(2) 
By calculating the energy efficiency index, taking into account the elimination of heat losses from heated and 
not thermally insulated surfaces, we obtain a slightly greater potential for increasing energy efficiency (Table 
5).  
285
Table 5: Energy efficiency indices of the surveyed refineries. 
Process Unit  
εef 
εefL 
U–1 
0.35 
0.39 
U–2 
0.63 
0.75 
U–3 
0.53 
0.56 
4. Determination of the energy efficiency index of the combined industrial complex LK-6Us 
When determining the energy efficiency indices, the main energy characteristics of the units were also found: 
payloads for hot – QHmin(real), cold utilities – QСmin(real), heat recovery capacity – QREC(real), heat power that must 
be brought to the cold process streams of ΔHhot and diverted from the hot process streams ΔHcold of the 
described units (Table 2). Summing up these indicators for all units included in the complex, we find the 
energy characteristics of the entire complex as a whole. 
Using (1), we obtain the energy efficiency index of the combined LK-6Us complex ignoring the elimination of 
heat losses to the environment εefTS = 0.43. 
If we take into account the elimination of heat losses to the environment, then the net capacity of hot utilities 
on the reference complex of units will be QHmin(bench)  ≈ 113.1-8.3 = 104.8 MW, and cold utilities will not change 
much. Note that these values are obtained for a complex without inter-process integration. The energy 
efficiency index, taking into account the elimination of heat losses in the heat exchange system of the 
complex, will be equal – εefTSL = 0.47. 
With the help of stream data obtained for units of combined LK-6Us complex and the Pinch-SELOOP software 
package (Kanischev et al., 2019a) the Grand Composite Curves (GCC) of units included in the combined 
complex (Figure 1) were constructed. GCC shows the possibility of distributed localization of utilities and the 
areas where heat energy is recovered, the so-called "Pockets" of recuperation (Smith et al., 2000). By cutting 
off the pockets and summing up the loads in the temperature intervals that divide the temperature axis of the 
GCC kink coordinates, we obtain the temperature profile of the combined LK–6Us complex (Figure 2), which 
shows the requirements for placing utilities for the entire set of reference units. 
The temperature profile of the complex of reference plants shows the total payload for the hot and cold utilities 
QHtotal and QCtotal. These values correspond to the values from Table 3 for the complex without inter-process 
integration. 
 
 
Figure 1: Grand Composite Curve. 1 – for unit U-1; 2 – for unit U-2; 3 – for unit U-3.  
All units are part of one territorial complex – the combined production complex LK-6Us and they are located 
on the same industrial site and it is possible to carry out thermal integration without intermediate heat carriers. 
To do this, the temperature profile of the hot streams must move to the right until it touches the profile of cold 
streams (Figure 2). We will see the amount by which the payload of hot and cold utilities can be reduced by 
performing interprocess integration. This value in our case is ΔQHmin = ΔQCmin = 11.44 MW. This is possible 
because at the point of contact of the temperature profiles, the temperature difference between the cold and 
hot streams is ΔTmin = 10°C, as GCC units were built with a shift of the temperatures of cold streams by 
286
ΔTmin/2 up and hot streams down (Smith et al., 2000), and ΔTmin for all reference plants is the same and equal 
to 10°C. Therefore, during interprocess integration, the useful power of hot utilities necessary for carrying out 
processes on a complex of reference plants will be reduced to QHtotalbench = 101.65 MW, and of cold utilities to 
QCtotalbench = 84.07 MW. 
The obtained values allow us to calculate the energy efficiency index of the LK-6Us combined complex in the 
case when heat losses to the environment is not taken into account: 
e
f
B
.
.
.
.
1
9
7
3
1
0
1
6
5
0
4
9
1
9
7
3
−
ε
=
=
. 
 
When allowance for heat losses to the environment, the energy efficiency index will be equal to 
e
f
B
L
.
.
.
.
.
1
9
7
3
(
1
0
1
6
5
8
2
5
)
0
5
3
1
9
7
3
−
−
ε
=
=
. 
 
We write the results obtained in the table of energy efficiency indices of the combined complex LK-6Us 
(Table 6). 
Table 6: Energy efficiency indices of the combined complex LK-6Us. 
LK-6Us 
εefB 
εefBL 
Without of the interprocess integration  0.43 
0.49 
With of the interprocess integration 
0.47 
0.53 
 
 
Figure 2: The temperature profile for combine production complex LK-6Us. 1 – for cold technological streams; 
2 – for hot technological streams; 3 – for hot technological streams with the interprocess integration. 
Note that the value of the energy efficiency index can be considered as the value of the energy-saving 
potential of oil refineries. This means that the maximum amount by which the payload for hot utilities of the LK-
6Us combined complex can be reduced is 53 % of the current consumption of hot utilities, or 103.9 MW. The 
value of cold utilities can be reduced by 177.5-84.07 = 93.43 MW. 
Taking into account the cost of hot and cold utilities 120 $ and 10 $ for 1 kW a year respectively, we will get 
energy-saving potential in monetary terms, which is equal to ~ 13.4 million USA dollars a year. 
The monetary values of the energy saving potential for individual integrations are calculated similarly. For U-1 
– 6 M$, for U-2 – 2.1 for U-3 – 3.4, i.e. with additional interprocess integration, profit increases by  1.4 M$ per 
year. The obtained values of energy efficiency indices help the company management to choose plants to 
analyze the possibility of their modernization. It should be noted that the results presented in the article were 
obtained based on data that were collected according to the developed methodology without going to the 
enterprise. To complete the work, it was enough to indicate the regulatory numbers of the sensors from which 
it is necessary to obtain data. The next step is the synthesis of reconstruction projects of selected process 
units in consideration of specific technical, technological and economic constraints (Ulyev et al., 2018) and the 
calculation of NPV and IRR (internal rate of return), based on which the conclusions are drawn about the need 
for investment (Kanischev, et al., 2018). All ideas presented in the article by the authors are implemented in a 
commercial package (Ulyev et al., 2019). 
 
 
 
287
5. Conclusion 
In this paper, based on pinch analysis methods, a comparative method for determining the energy efficiency of 
oil refining industry units and their complexes is proposed. For this purpose, the main characteristics of 
reference units are determined and algorithms for determining comparative indexes are proposed, which show 
the potential for increasing of the unit energy efficiency and their complexes. To determine the energy 
efficiency indices, taking into account the heat capacity losses into the environment from a unit of the 
corresponding equipment was determined too. Energy efficiency indexes are absolute characteristics that 
show the degree of energy perfection of processes. The developed method is used to determine the energy 
efficiency index of crude oil unit without a vacuum block, a catalytic reforming unit with pre-hydrotreating, and 
diesel fuel and kerosene hydrotreating unit for sulfur compounds included in the LK-6Us production complex. 
Thermal integration of units and the Total Site integration of the combined LK-6Us complex show the 
possibility of reducing energy consumption by 53 %. 
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288
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224 (2023) 211579
Available online 23 February 2023
2949-8910/© 2023 Elsevier B.V. All rights reserved.
Multi-objective optimization of petroleum engineering problems using a 
hybrid workflow: Combination of particle swarm optimization, fuzzy logic, 
imperialist competitive algorithm and response surface methodology 
Mohammad Sadegh Karambeigi a, Atefeh Hasan-Zadeh b, Mohammad Saber Karambeigi c,d, 
Seyyed Ali Faal Rastegar b, Masoud Nasiri e, Yousef Kazemzadeh f,g,* 
a Ministry of Education, Hamedan Department of Education, Farhang St., Hamedan, Iran 
b Fouman Faculty of Engineering, College of Engineering, University of Tehran, 43581-39115, Iran 
c Petroleum Industry Innotech Park, , Tehran 1933713173, Iran 
d The Graduate School of Management and Economics, Sharif University of Technology, Tehran, 863911155, Iran 
e Faculty of Chemical, Gas and Petroleum Engineering, Semnan University, Semnan, 35195-363, Iran 
f Enhanced Oil Recovery Research Center, School of Chemical and Petroleum Engineering, Shiraz University, Shiraz, Iran 
g Department of Petroleum Engineering, Faculty of Petroleum, Gas, and Petrochemical Engineering, Persian Gulf University, Bushehr, Iran   
A R T I C L E  I N F O   
Keywords: 
CEOR 
Multi-objective optimization 
PSO 
Fuzzy logic 
Neuro-simulation 
Design of experiments 
A B S T R A C T   
Optimization techniques are used to find the strategies for chemically enhanced oil recovery in a sandstone 
reservoir. This research develops a multi-objective optimization methodology by combining experimental design 
methods and artificial intelligence techniques. The capability of this hybrid artificial intelligence methodology is 
evaluated in the optimal design of control variables to achieve the highest performance of a surfactant/polymer 
injection project into a sandstone reservoir. In the first step, a two-level full factorial design is used to screen 
initial control variables. Thereafter a response surface methodology (RSM) is employed to optimize the RF and 
NPV of a CEOR application. The neuro-simulation technique provides the required outputs for screening and 
RSM designs. The performance of network is improved using the imperialist competitive algorithm (ICA). Having 
precise fitness functions, multi-attribute optimization was performed using particle swarm optimization (PSO) 
and fuzzy logic (FL). This paper discusses the advantages of different perspectives over single-objective ap­
proaches. Using the RF-objective PSO algorithm, RF exceeded 64% of original oil in place (OOIP), while the 
profit of project slumped to $5.90 MM. On the other hand, NPV-attribute PSO increased NPV to $8.48 MM. 
Meanwhile, RF, as the technical success of the project, plunged to less than 53% OOIP. However, the proposed 
multi-objective algorithm increased RF to 57% OOIP with NPV of $8.11 MM, solving the trade-off between 
technical and economic terms. The results of this study indicate the efficacy of proposed hybrid workflow for 
multi-attribute decision-making of CEOR field implementation.   
1. Introduction 
The oil industry is a major player in world economics. Supply chain 
management is one of the upstream industry’s strategic focuses, and oil 
production plays a key role in this area since it helps smooth out market 
swings. Although coronavirus pandemic has reduced economic activity 
and oil price is highly volatile, Energy Information Administration (EIA) 
reported that daily production is still above 90 million barrels in 2021, 
which shows that this fossil fuel is consistently the key player in the 
world energy map. On the demand side, EIA announced that oil 
consumption was increased by more than 5% over the last year ((Energy 
Information Administration, 2021)). 
In general, the efficiency of primary and secondary production sce­
narios is less than 50% in almost reservoirs, and an immense amount of 
oil remains intact that is the main target of tertiary (EOR) recovery 
methods ((Ahmadi and Shadizadeh, 2012; Santanna et al., 2009; 
Thomas, 2007)). 
A wide range of EOR techniques can be classified into three main 
categories: thermal, gas, and chemical methods ((Gharibshahi et al., 
2015)). Chemical-enhanced oil recovery (CEOR) is defined as the 
* Corresponding author. 
E-mail address: yusefkazemzade@aut.ac.ir (Y. Kazemzadeh).  
Contents lists available at ScienceDirect 
Geoenergy Science and Engineering 
journal homepage: www.sciencedirect.com/journal/geoenergy-science-and-engineering 
https://doi.org/10.1016/j.geoen.2023.211579 
Received 7 June 2022; Received in revised form 8 December 2022; Accepted 12 February 2023   
Geoenergy Science and Engineering 224 (2023) 211579
2
process in which a type or collection of chemicals (e.g., alkaline, sur­
factant and polymer) are used to improve interfacial and/or rheological 
properties of displacing fluids ((Sheng, 2010a; Thomas and Farouq Ali, 
2001)). There are great deals of operational (field) experiences, such as 
Daqing, Shengli, Huabei, and Xingjiang fields in China and Yates field in 
West Texas ((Sheng, 2010b)), proving its outstanding performance to 
recover residual oil trapped in the reservoir ((Iglauer et al., 2010; Liu 
et al., 2015; Zhang et al., 2010)). 
However, implementing CEOR is a difficult process since several 
technical, operational, economic, and most importantly environmental 
factors must be taken into account at the same time. This is because each 
oilfield is unique and has unique features. The high cost of chemicals, 
the difficulty of the operation itself, the severe reservoir conditions, and 
potential environmental hazards ((Muggeridge et al., 2014; Stoll et al., 
2011)). To alleviate some negative effects of such challenges, optimi­
zation of the process due to the effective parameters can pave the way 
for the successful application of this method in pilot and field scales 
((Carrero et al., 2007; Fathi and Ramirez, 1984; Zerpa et al., 2005)). In 
such circumstances, optimal decisions need to be taken in the presence 
of trade-offs between two or more conflicting objectives ((Isebor and 
Durlofsky, 2014; Yasari et al., 2013)). It is defined as multi-criteria de­
cision making. Water flooding optimization in petroleum reservoirs 
((Hourfar et al., 2019)), different methodologies were proposed in the 
literature for the optimization of CEOR approaches; such as 
simulation-based analysis ((AlSofi and Blunt, 2014; Wu et al., 1996)), 
surrogate-based optimization ((Carrero et al., 2007), (Zerpa et al., 
2005)) sensitivity analysis of key parameters ((Anderson et al., 2006)), 
and experimental design ((Prasanphanich et al., 2012; Douarche et al., 
2014)). Despite their efficiency, they have efficiently solved the problem 
as single-objective optimization of either net present value (NPV) or 
recovery factor (RF). Conventional single-objective optimization ap­
proaches may ignore the trade-off viewpoint; therefore, multi-objective 
optimization approaches should be utilized. 
To do that, the current paper proposes a new workflow in which 
design of experiments (DOE) and artificial intelligence techniques are 
coupled to develop a hybrid algorithm for multi-objective optimization 
of surfactant/polymer (SP) flooding in a sandstone reservoir in terms of 
simultaneous maximization of RF and NPV. The other methods as a 
trade-off solution in managing conflicting objectives can be found in 
various fields, even outside of petroleum engineering ((Tai Chui and 
Lytras, 2019; Gustafsson et al., 2019; Chamseddine et al., 2020)). 
The general overview of workflow can be described in Fig. 1. The 
paper discusses the workflow, as well as the priority of multiple per­
spectives over single-objective insight in its different stages. 
2. Background 
2.1. Design of experiments (DOE) 
DOE is defined as a systematic rigorous approach to planning reli­
able, reproducible and accurate professional research. When managing a 
process, it is required to understand the meaningful connections among 
the factors affecting the process and their corresponding response(s) to 
that process. DOE methodologies facilitate the systematic determination 
of such cause-and-effect relationships. 
There are different areas in which DOE can be applied to generate a 
collection of valid information regarding the process ((Montgomery, 
2012)). It was used for screening of significant factors and statistical 
modeling of the process. The former was done by two-level design, and 
the latter was carried out using the response surface methodology. 
2.1.1. Two-level factorial design 
Ranking the importance of factors is the preliminary step of experi­
mentation whereby a large number of factors that might be important 
are analyzed, and insignificant factors are determined and ignored from 
the major DOE plan, saving time and budget ((Sellstr¨
om et al., 1992)). 
Factorial two-level design One of the best methods for concurrently 
screening a large number of parameters to determine whether key ones 
warrant further inquiry is to create first-order models. 
2.1.2. Response surface methodology (RSM) 
RSM is comprised of a group of statistical and mathematical tech­
niques. When a response or a set of responses are affected by indepen­
dent control variables, RSM can be applied to find a functional 
relationship among them ((Khuri and Mukhopadhyay, 2010)). RSM 
models the process via the fitting of the polynomial equation(s) to the 
provided data ((Ba and Boyaci, 2007)). 
Compared to one-factor-at-a-time approach of experimental design 
in which only one factor is changed while others are kept at a constant 
level, RSM can vary the levels of factors simultaneously while it keeps 
the number of required runs minimum. Other aspects of RSM applica­
tions are process optimization and representing the interaction of factors 
((Bezerra et al., 2008; Ghaedi et al., 2015)). Therefore, it is known as an 
interesting multivariable statistical protocol for the design of experi­
ments ((Asfaram et al., 2016; Jeirani et al., 2013)). 
2.2. Artificial neural networks (ANN) 
ANN is a nonlinear processing paradigm that mimics the operational 
Fig. 1. General overview of the workflow.  
M.S. Karambeigi et al.                                                                                                                                                                                                                         
Geoenergy Science and Engineering 224 (2023) 211579
3
principles of the human neural network ((Shabanzadeh et al., 2015)). 
Neuro computing was born in 1943 when McCulloch and Pitts published 
an article on how neurons might work ((McCulloch and Pitts, 1943)), 
and from then on it was successfully used to solve a broad spectrum of 
complex problems in many areas of industry e.g. quality control 
((Anderson and Whitcomb, 2004)), science e.g. pattern recognition 
((Widrow et al., 1994)), finance e.g. stock market prediction ((Bah­
rammirzaee, 2010; Wong and Selvi, 1998)), medicine e.g. medical 
diagnosis ((Baxt, 1995; Patel and Goyal, 2007)), data-mining e.g. 
knowledge discovery ((Lu et al., 1996)), and energy e.g. forecast of 
crude oil production ((Kalogirou, 2000)). ANN is a parallel structure 
made up of layers of closely interconnected processors called neurons, 
which perform comparable tasks to the axons of biological neural net­
works. When example data are supplied, it may learn the empirical 
relationship between an input and an output of a process. ANN is of great 
interest in petroleum engineering studies ((Al-Bulushi et al., 2012; 
Mohaghegh, 2000; Talaat et al., 2018)) because of its unique features: It 
is a model-free function without the requirement of any knowledge 
regarding the process. It has a high tolerance to noisy data, the ability to 
learn rather than find a solution or mathematical modeling, and finally 
the ability to generalize ((Ahmadloo et al., 2010; Al-Dousari and Gar­
rouch, 2013; Salahshoor et al., 2013)). 
According to Kolmogorov’s theorem (from 1957), a multi-layer 
perceptron (MLP) network can act as a universal predictor ((Nezama­
badi-Pour, 2015)) MLP algorithm is recognized as one of the most 
popular paradigms for the construction of ANN ((Silva et al., 2007)). The 
architecture of an MLP comprises the input, one or more hidden and 
output layers. There are different numbers of neurons in each layer. 
Direct links with their weights connect the neurons of the current layer 
and those on the proceeding layer. 
The received signals from previous layers are collected by the output 
layer, creating the response of the network. The most effective training 
technique is backpropagation, which introduces a large amount of 
available data to the network and adjusts the weights to achieve a 
training objective that is typically the smallest difference between the 
output(s) of introduced (real) data and the MLP response. Eventually, 
MLP learns the behavior of presented data which can be used for future 
predictions. 
2.3. Particle swarm optimization (PSO) 
PSO is a population-based stochastic search method that was pro­
posed by Kennedy and Eberhart (1995)). Their idea was inspired by 
social behaviors of animals, such as bird flocking, fish schooling and 
bees swarming ((Khulal et al., 2016; Senthamaraikkannan et al., 2016)). 
PSO belongs to the family of swarm intelligence approaches in which 
collective behaviors of living creatures are studied to develop algorithms 
to solve scientific and engineering problems ((Reddy and Kumar, 
2007)). 
PSO algorithm starts with the random generation of a population of 
possible solutions referred to as a swarm of particles, each of which is 
characterized with a position (x) and a velocity (v) ((Li et al., 2015)). The 
position of each individual (particle) denotes its current location in an 
N-dimensional search space. At each iteration, the fitness of particles is 
evaluated using fitness function(s) and compared to the best individual 
fitness achieved so far by the particle. Moreover, the best experiences of 
all particles in a swarm are compared to select the best global solution. 
Thereafter, all particles move to new positions in the search space in the 
next iteration as Eq. (1): 
xi(t + 1) = xi(t) + vi (t + 1)
(1) 
The velocity vector of each individual is updated accordingly: 
vi(t + 1) = ωvi(t) + C1φ1(pbesti −xi(t)) + C2φ2(gbest −xi(t))
(2)  
Where ω is inertia weight factor, C1 and C2 are acceleration coefficients, 
φ1 and φ2 are random weights taken from numbers uniformly distrib­
uted in the interval (0,1), pbesti is personal best of ith iteration and 
defined as the best location found so far by a particle, and gbest is global 
best and denotes the best global solution among all of the pbesti achieved 
so far ((de Pina et al., 2011; Zendehboudi et al., 2014; Zhu et al., 2011)). 
There are three components (terms) in the velocity update equation 
(Eq. (2)). The first term is the inertia component in which the previous 
flight trajectory is considered for the movement of the particle. The 
cognitive element, which serves as the particle’s internal memory, is the 
next concept. It makes an effort to relocate the particle to areas where it 
has shown a high level of individual fitness. The last word is the social 
component, which directs the particle toward the best results of recent 
nearby particles ((Gustafsson et al., 2019), (Ciaurri et al., 2011)). The 
stochastic effect of cognitive and social components is provided by 
random weights (φ1 and φ2). 
The exploring of particles in the search space proceeds until stopping 
criterion is satisfied which can be defined as the maximum number of 
iterations. Eventually, the last gbest is the optimization solution. 
2.4. Fuzzy logic (FL) 
FL is based on fuzzy sets, which were formalized by Zadeh (1965)). It 
was inspired by the process of human thinking and cognition. The notion 
of graded membership is the basis of FL. It mathematically models 
approximate reasoning and is the most suitable approach to deal with 
information that is uncertain, imprecise, and vague with no sharp 
boundaries, such as computational perception ((Lababidi et al., 2004; 
Sedighi et al., 2014; Solo and Gupta, 2007)). 
Traditional (Boolean) logic is built on classical sets in which the 
membership of an element is a bivalent condition (0 or 1): the element 
either belongs to the set (1) or does not (0). By contrast, the elements of 
fuzzy sets have a degree of membership. Fuzzy sets permit a gradual 
transition from membership to non-membership ((Mohaghegh, 2000)). 
It provides via the definition of membership function as Eq. (3) which 
represents the concept of partial truth: 
A = {(x, μA(x))|x ∈X}
(3)  
Where A is fuzzy set, x is an element of X as a domain of points, and μA(x)
is the membership function which quantifies the belonging degree of x 
to the fuzzy set ((Khatami et al., 2008; Nashawi and Malallah, 2009; 
Nowroozi et al., 2009)). 
2.5. Imperialist competitive algorithm (ICA) 
ICA is a new optimization strategy based on human social-political 
evolution ((Atashpaz-Gargari and Lucas, 2007)). It was introduced in 
2007, and from then on it was used to solve many engineering problems 
((Gerist and Maheri, 2019; Hosseini and Al Khaled, 2014)). The results 
of its different applications indicate the success of the proposed algo­
rithm, especially in petroleum engineering ((Ahmadi and Chen, 2019; 
Ameli and Mohammadi, 2018)). 
In fact, imperialism means expanding a country’s domain of power 
and sovereignty beyond its borders. One country may control other 
countries either using direct sovereignty or by more covert methods, 
such as control of markets, commodities, and raw materials, the latter 
being new colonialism. In this algorithm, the politics of assimilation and 
colonial competition form the core. 
To start the algorithm, an array of optimization variables (Eq. (4)) is 
formed which is known as “country": 
Country =
[
p1, p2, p3, …, pNvar
]
(4)  
Where, for example, p1 is language, p2 is economic policy, p3 is religion, 
and so on. We are looking for the best country to solve the optimization 
problem, i.e., the best set of problem parameters, such as Eq. (5): 
M.S. Karambeigi et al.                                                                                                                                                                                                                         
Geoenergy Science and Engineering 224 (2023) 211579
4
cost = f(country) = f
(
p1, p2, p3, …, pNvar
)
(5) 
As a consequence of applicable decisions, the successful imple­
mentation of CEOR approaches in the most efficient and economical way 
depends on process optimization using methodologies supporting mul­
tiple perspectives. For this purpose, a new hybrid workflow was intro­
duced which combined the abilities of experimental design approaches 
as well as artificial intelligence techniques to model and optimized the 
process in the presence of multiple criteria. 
3. Problem statement and methodology 
3.1. Case study 
Benoist sandstone reservoir was used. It is located in Marion County, 
Illinois, USA. The reservoir is sandstone with 50 ft thickness of pay zone. 
The injection pattern of the reservoir is an inverted five-spot. Table 1 
summarizes the reservoir data ((Prasanphanich, 2009)). 
The residual oil trapped in the reservoir was tertiarily recovered 
using CEOR flooding. Water flooding and chemical flooding were 
simulated using UTCHEM simulator ((Delshad et al., 1996; Pope and 
Nelson, 1978)), and recovery factor was calculated. Economic evalua­
tions were performed at 50 USD/bbl of oil price. Other variables to 
calculate NPV are in Table 2. The detailed procedures for reservoir 
simulation and economic analysis were thoroughly discussed elsewhere 
((Prasanphanich, 2009)). 
The process of surfactant/polymer flooding was modeled due to RF 
and NPV as functions of eight control variables which were as follows: 
surfactant slug, surfactant concentration in surfactant slug, polymer 
concentration in surfactant slug, polymer drive, and polymer concen­
tration in polymer drive, the ratio of vertical to horizontal permeability, 
the salinity of polymer drive, and salinity of post-flush water injection. 
The sampling domain of these variables in search space is presented in 
Table 3. 
The analysis of variance (which is mentioned in the following), and 
the mathematical equation obtained from the modeling (Eqs. (13) and 
(14) in the sequel) show the significant effect of all of the parameters A 
to F to determine the outputs. This, along with other technical reasons 
related to the effectiveness of all considered parameters in recovery 
factors and the profit of the CEOR, will justify the selection of the pa­
rameters A to F. 
3.2. Workflow 
Fig. 2 shows the algorithm of the workflow in which different tools 
were utilized. 
In brief, after screening the most significant variables (first stage), 
the algorithm began modeling surfactant/polymer injection using RSM 
and neuro-simulation, providing objective functions. Thereafter, a 
multi-purpose approach optimized the process via coupling of PSO/FL 
or ICA/FL. The final step was decision-making based on the results of 
previous outputs. The outcome of algorithm is the optimum value of 
effective parameters while a trade-off between technical and economic 
objectives is considered. The detailed description of each stage is as 
follows. 
3.2.1. Screening of influential factors 
The surfactant/polymer flooding had eight initial parameters. They 
were ranked by two-level full factorial design. Limiting levels for each 
factor were extracted from high and low values of each variable in the 
sample domain (Table 3). The design required 2k runs that k is the 
number of initial factors ((Ferreira et al., 2007; Valle et al., 2009; 
Ghobadi Nejad et al., 2019)). The response of each run was provided via 
the neuro-simulation approach. Thereafter, responses were fed into 
software (Design Expert 7.0.0) to analyze using a two-level full factorial 
design. 
3.2.2. Development of objective functions 
Precise objective functions are the essential requirement of present 
optimization algorithm. Mathematical relationships between indepen­
dent variables and outputs of the process were established using statis­
tical modeling. For this purpose, RSM was used through a series of n 
experiments was designed. In the next step, the corresponding responses 
(outputs) of each run be determined. Thereafter, quadratic equations 
were fitted to the data. The validation of equations fitted to the data was 
examined via the analysis of variance (ANOVA). 
Among different methodologies of RSM, the central composite 
design (CCD) was selected as one of the most popular RSM designs. In 
Table 1 
The properties of the simulation model ((Prasanphanich, 2009)).  
Parameter 
Value 
Reservoir size (ft) 
1169(i) × 1168(j) × 53(k) 
Number of grid blocks 
41(i) × 41(j) × 6(k) 
Reservoir pore volume (MM bbl) 
2.082 
Reservoir pressure (psi) 
300 
Reservoir temperature (◦F) 
82 ((Ware, 1983)) 
Average porosity (%) 
15.3 
Permeability range (md) 
84–414 
Initial water saturation (%) 
30 
Water viscosity (cp) 
0.89 
Oil viscosity (cp) 
6.8 
Injection wells 
4 
Production wells 
9 
Distance between injection and production wells (ft) 
330  
Table 2 
Input variable of economic analysis ((Prasanphanich, 2009)).  
Category 
Parameters 
Unit 
Value 
INITIAL CAPITAL 
COSTS 
Facilities and equipment 
$ 
500 
Workover, drilling, and leasehold 
costs 
$ 
0 
OPERATING COSTS 
Water flood operating cost 
$/month 
10 
Chemical injection cost 
$/bbl 
0.1 
Produced water cost 
$/bbl 
0.1 
Oil treatment cost 
$/bbl 
0.1 
overhead cost 
% 
10 
COMMODITY 
PRICES 
Oil price 
$/bbl 
50 
Surfactant price 
$/lb 
2 
Polymer price 
$/lb 
1 
TAXATION 
Royalty 
% 
12.5 
Severance & Ad valorem tax rate 
% 
0.046 
Effective income tax rate 
% 
38.25 
EOR tax credit rate 
% 
0 
GENERAL 
Inflation rate 
% 
3 
Escalation (oil and chemical prices 
and operating cost) 
% 
3 
Real discount rate 
% 
10 
Real reinvestment rate 
% 
10  
Table 3 
Range of independent variables for screening design of the process ((Prasan­
phanich, 2009)).  
Factor 
Unit 
Symbol 
Minimum 
Maximum 
Surfactant slug size 
resePV 
A 
0.097 
0.259 
Surfactant concentration 
Vol. 
fraction 
B 
0.005 
0.03 
Polymer concentration in 
surfactant slug 
wt% 
C 
0.1 
0.25 
Polymer drive size 
PV 
D 
0.324 
0.648 
Polymer concentration in 
polymer drive 
wt% 
E 
0.1 
0.2 
kv/kh ratio 
– 
F 
0.01 
0.25 
Salinity of polymer drive 
meq/ml 
G 
0.3 
0.4 
Salinity of water post-flush 
meq/ml 
H 
0.3 
1.03  
M.S. Karambeigi et al.                                                                                                                                                                                                                         
Geoenergy Science and Engineering 224 (2023) 211579
5
this method, three types of experiments are designed: factorial design 
(2k), axial design (2 × k) and several center points by which computing 
error and reproducibility of data are evaluated. The functional rela­
tionship is a polynomial function consisting of linear and quadratic 
terms as Eq. (6): 
Y = α0 +
∑
k
i=1
αiXi +
∑
k
i=1
∑
k
j=1
αijXiXj
(6)  
Where Y is the output, k is the number of screened variables, α0 is the 
constant value, αi is the linear coefficient, αij is the quadratic coefficient, 
Xi is the single factor, and XiXj is the interaction factor. 
Similar to previous stage, neuro-simulation approach was used to 
generate the required responses of each run. 
3.2.3. Neuro-simulation approach 
The routine approach to provide the responses (here RF and NPV) of 
CEOR processes is the application of simulation methodologies (e.g., 
UTCHEM simulator). However, they need reliable static (geological) and 
history-matched dynamic models ((Cavalcante et al., 2019)) of the 
reservoir, which are often not made available to the general public. 
Furthermore, many input data are necessary for a reliable simulation 
model. Instead, an alternative solution is gathering the data of simula­
tion outputs from the open literature to develop a surrogate model by 
which required responses are generated. Hence, data-dependent para­
digms act very well among which the neuro-simulation approach is the 
best where by the proxy model is generated using artificial neural net­
works (ANN). 
The data were collected ((Prasanphanich, 2009)), and the noisy part 
was removed. Then, they were randomly divided into two separate 
datasets: training (80% of the data) and evaluation (remaining 20%) 
Fig. 2. The workflow of multi-objective optimization.  
M.S. Karambeigi et al.                                                                                                                                                                                                                         
Geoenergy Science and Engineering 224 (2023) 211579
6
datasets. Inputs and outputs of each individual data in different datasets 
were normalized using the mean and standard deviation of variables. 
Three steps were included in ANN modeling: training, generalization, 
and operation. 
Between inputs and outputs was in training phase in which the 
network learned. A multilayer backpropagation network composed of 
input, one hidden, and output layers were selected. The default number 
of neurons in the hidden layer was equal to the number of inputs 
(influential factors). Transfer functions of hidden and output layers were 
tangent sigmoid and purelin types, respectively. Levenberg-Marquardt 
was considered the default training algorithm. The stopping criterion 
of the training phase was the mean square error of 1 × 10−5. 
In generalization phase, ANN performance was improved using the 
evaluation dataset. To this end, the number of neurons in the hidden 
layer was changed from 1 to 15. Consequently, 10 training algorithms 
were assessed: Batch training with weight and bias learning rules 
(trainb), BFGS quasi-Newton (trainbfg), Bayesian regularization 
(trainbr), Fletcher-Powell conjugate gradient (traincgf), Gradient 
descent with adaptive learning rate (traingda), Gradient descent with 
momentum (traingdm), Gradient descent with momentum and adaptive 
learning rate (traingdx), Levenberg-Marquardt (trainnlm), Powell-Beale 
conjugate gradient (traincgb), and Polak-Ribiere conjugate gradient 
(traincgp). 
To compare ANN efficiency in different network structures, three 
statistical parameters were used to quantify the computing error. They 
were MAPE (mean absolute percentage error), SMAPE (symmetric 
MAPE) and MSE (mean square error) as Eqs. (7)–(9): 
MAPE = 100
n
∑⃒
⃒
⃒
⃒
yp −yr
yr
⃒
⃒
⃒
⃒
(7)  
SMAPE = 100
n
∑⃒
⃒yp −yr
⃒
⃒
(|yp|+|yr|
2
)
(8)  
MSE = 1
n
∑(
yp −yr
)2
(9)  
Where n is the number of data in the dataset, yp is predicted data by ANN 
and yr is real data. Error analysis based on just one parameter (either 
MAPE or MSE) may be misleading. Therefore, it should be performed by 
a combination of diverse parameters ((Chai and Draxler, 2014; Mathews 
and Diamantopoulos, 1994; Shcherbakov et al., 2013)). 
The essential answers for full factorial and CCD designs were then 
retrieved in the operation phase as the last stage of ANN modeling after 
having an optimal efficient ANN. 
3.2.4. Improvement of ANN using ICA 
The performance of neuro-simulation was improved using ICA. 
Checking out the various situations, algorithm parameters are set as 
follows: number of initial countries = 100, number of initial imperialists 
= 50, number of decades = 50, revolution rate = 0.3, assimilation co­
efficient = 2, assimilation angle coefficient = 0.5, gamma = 0.02 (which 
total cost of empire = cost of imperialist + (zeta × zeta × mean) (cost of 
all colonies)), damp ratio = 0.99, uniting threshold = 0.02. Apparently 
the method is still somehow a black box to the authors. 
3.2.5. Multi-criteria optimization methodology 
Surfactant/polymer flooding was optimized using multi-purpose 
PSO-FL methodology. In the first iteration, a swarm of 25 particles 
was randomly positioned in the search domain. Then, RF and NPV of 
each individual particle were calculated using fitness (objective) 
functions. 
In each iteration, the values of (pbest)i and gbest were determined. In 
so far as the simultaneous presence of two responses (RF and NPV), the 
optimization was a multi-attribute problem. One efficient solution to 
such problems is to combine multiple objectives into a unique function. 
For this purpose, PSO was coupled with fuzzy logic. Domain trans­
formation was done via the fuzzification of mathematical objective 
functions to generate fuzzy membership functions as Eq. (10): 
μf (Fk) =
⎧
⎪
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎪
⎩
0
Fk < Fmin
k
Fk −Fmin
k
Fmax
k
−Fmin
k
Fmin
k
Fmin
k
< Fk < Fmax
k
1
Fk > Fmax
k
(10)  
Where μf(Fk) is fuzzy membership function of k th objective function 
(k = 1 for RF and k = 2 for NPV), Fk is the k th response, Fmax
k 
and Fmin
k 
are maximum and minimum values of each objective function, respec­
tively. Each fuzzy membership function represented a fuzzy region of 
acceptability. Thereafter, a new function called zeta function was 
defined as: 
ζi =
{μf (F1), μf (F2)
}
i
(11)  
Where ζi is satisfaction factor, μf(F1) is RF fuzzy membership function 
and μf(F2) is NPV fuzzy membership function, respectively. The sub­
scribe i denotes the particle number (from 1 to 25). The algorithm 
evolved towards the maximum values of ζi (as close to unity as possible). 
The fitness of particles was evaluated based on ζi. Then, pbesti values 
were determined so pbesti of each particle has experienced its maximum 
fitness (ζi) so far. Thereafter, the multi-criteria solution was selected as 
follows: 
gbest = Max {pbesti}
(12) 
In the next iterations, the positions of particles were updated ac­
cording to Eqs. (11) and (12), and a new swarm was generated. The 
constant inertia (ω) of 0.7298 and acceleration coefficients (C1 and C2) 
of 1.49618 were assigned ((Cai et al., 2019; Van Den Bergh and Engel­
brecht, 2006)) by which the velocity factor components were calculated 
and new placement of particles was identified. The above steps were 
repeated to update values of pbesti and gbest until the algorithm reached 
the stopping criterion. The last gbest is the solution of the problem. 
The coupling of PSO with FL was found to be a powerful multi- 
attribute optimization method whereby the scenario of surfactant/ 
polymer injection into the reservoir was simultaneously optimized in 
terms of the highest oil recovery factor and maximum profit of the 
project. 
4. Results and discussion 
A workflow for multi-objective optimization of petroleum industry 
problems was developed. The performance of the workflow was exam­
ined in a case study of a complex tertiary oil recovery method. The re­
sults are presented and discussed as follows. 
4.1. Screening of the most effective factors 
Among available EOR techniques, CEOR is known as a complicated 
process. On the one hand, its complexity and uncertainty necessitate 
considering parameters when precise modeling or optimization is 
intended. On the other hand, time and budget limitations increase the 
application cost of complex models. A standard strategy is performing a 
screening study to determine the possible influential factors. Factorial 
designs are common for determining the linear influence of initial fac­
tors ((Lundstedt et al., 1998)). To this end, two-level full factorial design 
was used. Having eight initial factors (Table 3), a total of 256 sets of runs 
were planned. The corresponding outputs were provided by 
neuro-simulation methodology. We discuss the validation of this proxy 
model in the next section. 
The full factorial design was then analyzed using ANOVA at the 5% 
significance level (p-value<0.05). Factors A (surfactant slug size), B 
M.S. Karambeigi et al.                                                                                                                                                                                                                         
Geoenergy Science and Engineering 224 (2023) 211579
7
(surfactant concentration), C (polymer concentration in surfactant slug), 
D (polymer drive size), E (polymer concentration in polymer drive), and 
F (kv/kh ratio) were recognized as important factors for both RF and 
NPV. Factor H (salinity of water post-flush) had the influence on neither 
RF nor NPV and factor G (salinity of polymer drive) was an influential 
parameter just for RF. 
The most significant factors affecting NTG and RF are not the same 
(Fig. 3):; factor A (surfactant slug volume) for RF and factor B (surfactant 
concentration) for NPV, respectively. Previous investigations showed 
that surfactant slug size is one of the most important factors affecting oil 
recovery ((Lorenz, 1989)). Moreover, the surfactant cost includes the 
major portion of the chemical cost for any CEOR scenario ((Meyers, 
1981)), and that is why NPV has been substantially influenced by sur­
factant concentration (see Fig. 4). 
This finding highlights the significance of multi-attribute approaches 
compared to single-objective techniques for the optimization of complex 
problems in petroleum engineering where factors affect responses 
differently. More specifically, it is clear that the ordering of effective 
factors for RF and NPV were different since RF evaluates technical 
perspectives while NPV includes economic circumstances. It demon­
strates the superiority of algorithms over single-objective techniques 
when trade-off effects of goals are taken into account. A union of 
important factors regarding each response was taken as multi-attribute 
screened factors. Therefore, factors A to G were selected as the final 
influential parameters on which principal experimental design (CCD 
plan) was focused. 
4.2. Validation of neuro-simulation approach 
The outputs of the full factorial and CCD designs in each run were 
provided using the neuro-simulation technique. ANN was trained and 
validated based on the observations of input-output behaviors of 
available data gathered from the results of previous simulation studies in 
the literature. ANN was first trained with a multi-layer backpropagation 
network by introducing the training dataset. MAPE and SMAPE of RF 
response in the training phase were 18.85% and 16.64%, respectively. 
They were calculated as 10.03% and 9.52% for NPV, respectively. 
The results of training phase were not acceptable, and therefore the 
efficiency of the network should be increased in the generalization phase 
whereby the performance of trained network to estimate newly- 
presented unseen data was examined over the evaluation dataset. For 
this purpose, further attempts were made to select the best training al­
gorithm and the number of neurons in the hidden layer. 
Bets training algorithm was Bayesian regularization back­
propagation by which overfitting is prevented and outstanding gener­
alization performance is provided for regression problems ((Karambeigi 
et al., 2011)). It shows MAPE values of RF and NPV predictions in 
generalization phase were substantially reduced from 51.24% to 
14.95%–6.13% and 3.28% when default training algorithm (Lev­
enberg-Marquardt) was replaced with Bayesian regularization algo­
rithm as the most efficient training algorithm. 
The optimum number of neurons was then selected when it was 
varied from 1 to 15 and the trends of MAPE were followed. Fig. 5 shows 
the best prediction efficiency was achieved when the number of neurons 
was nine. Hence, MAPE decreased further to 3.53% and 1.95%, 
respectively. The prediction MSE of two responses was calculated as 
1.09% original oil in place (OOIP) and 0.04$ MM, respectively. The 
cross-plots of ANN estimation and actual data in training and general­
ization phases are shown in Fig. 6. 
These values indicate that the MPL structure containing three input, 
one hidden and output layers trained with Bayesian regulation back­
propagation algorithm and having nine neurons in the hidden layer was 
the optimal ANN network to be successfully applied for the accurate 
prediction of unknown data in operation (third) stage in which required 
outputs of screening (full factorial) and principal (CCD) designs were 
provided. 
4.3. ICA for ANN improvement 
By default, ICA is an optimization algorithm. In this paper, however, 
it was u to improve the performance of the ANN. Hence, the inputs and 
two outputs are given to the optimal network obtained in subsection 5.2 
and it reaches with ICA to the optimum level of training and testing of 
both outputs simultaneously. The obtained network has the necessary 
performance for multi-objective optimization techniques used in the 
sequel. 
These are the main parameters of the algorithm that we will not 
elaborate on since the full description can be found in the references 
((Xing and Gao, 2014)). Finally, total performance (for both outputs 
simultaneously) by choosing parameters introduced in section 4.2.4., 
was reported to be 96%, which also confirms the low MSE obtained for 
RF and NPV separately. 
Using imperialist competitive algorithm to improve the performance 
of the neural network, it is no longer necessary to manually adjust the 
network and apply the various methods as shown in Fig. 3. 
4.4. Generation of objective functions using RSM 
After initial variables were ranked, the principal experimental plan 
was designed using CCD to find objective functions. Based on seven 
significant factors that remained from the results of the screening stage, 
and according to Eq. (6), CCD proposed a set of 152 runs. Thereafter, the 
corresponding output for each run was generated using the neuro- 
simulation approach. Following this, the obtained outputs were fed 
into software to fit second-order models using RSM. 
Table 4 shows the results of ANOVA composing of a collection of 
various statistical tests to evaluate the quality of fitted models. It shows 
the fitted quadratic functions on the data were statistically significant in 
Fig. 3. Pareto charts of the main effect of initial factors (yellow line is t-Value 
limit = 1.97). 
M.S. Karambeigi et al.                                                                                                                                                                                                                         
Geoenergy Science and Engineering 224 (2023) 211579
8
99.9% confidence level as the F Values were calculated as 171 for RF 
model and 163 for NPV model. Furthermore, the values of Prob > F were 
less than 0.0001 for both models. 
The coefficient of determination (R2) quantifies the proportion of 
variance explained by predictors of the proposed model. R2 values of 
second-order models were 0.981 and 0.980 for RF and NPV responses, 
respectively. Although R2 is a conventional indicator, it alone cannot 
guarantee the goodness-of-fit because it continuously increases as long 
as any predictor is added to the equation. Therefore, artificial 
improvement of model may occur. To compensate for this effect, 
adjusted R-squared is considered as a modified form of R2 by which the 
degree of relationship is evaluated better in the sampling domain. R2
adj 
increases when useful predictors are added to the model and will be 
reduced if unnecessary terms are included in the equation. Hence, a 
more precise estimation of how the model generalizes can be provided. 
R2
adj of quadratic models for two responses were determined as 0.975 and 
0.975 which show high explanatory power of the fitted models. More­
over, the predictive ability of the regression model for future estimations 
is examined when predicted R-squared is considered. R2
pred of proposed 
models for two outputs were calculated as 0.940 and 0.935 which had a 
reasonable agreement with R2
adj. The favorable values of these statistical 
parameters proved the acceptable fitness of second-order equations. 
There are two other statistical parameters in the ANOVA table. The 
reproducibility of model is measured using the coefficient of variance 
(CV) which is defined as the ratio of the standard deviation to the mean. 
It is a useful parameter for the evaluation of model reliability. The values 
of less than 10% are suggestive of a favorable predictor model. The 
proposed models were reproducible as the CV of their second-order 
equations for RF, and NPV outputs were calculated as 1.99% and 
4.10%, respectively. Furthermore, the contrast in predicted response 
relative to its associated error is measured using an adequate precision 
parameter. The adequate precision of regression models can be ensured 
with a signal to noise ratio greater than 4. They were calculated as 
63.159 and 55.197 by which the successful application of fitted models 
to navigate the search space is indicated. 
Different statistical parameters in ANOVA table confirmed RF and 
NPV were efficiently modeled in terms of seven screened factors. 
Eventually, the developed second-order Eqs. (13) and (14) were 
extracted as objective functions for PSO-FL optimization. 
RF = + 28.73447 + 166.55027A + 3127.84554B + 131.82572C
−47.28657D −52.68134E −26.96072F −252.22730G −1539.24953AB
−17.56655AC + 15.34398AD −71.13774AE −14.44152AF
−159.15490AG −2026.35227BC + 526.65048BD −475.31914BE
−160.10240BF −1335.44628BG + 30.26061CD −122.86153CE
+ 1.44758CF −206.94019CG + 39.70949DE + 8.53134DF
+ 35.65359DG + 26.19122EF + 68.10743EG −2.04736FG
−29.94598A2−51966.24377B2+74.23545C2+28.68177D2
+ 366.60976E2+61.01211F2 + 462.02976G2
(13)  
NPV = −5.06951 + 50.50774A + 561.65623B + 34.61332C
−4.88624D6.43122E −2.89839F −13.74147G −1571.95191AB
−7.54189AC + 1.82158AD −18.83260AE −1.81436AF −29.95762AG
−421.44173BC + 102.08395BD −153.49988BE −38.26693BF
−229.78350BG + 6.38777CD −24.47320CE −0.025723CF
−39.92990CG + 7.19773DE + 1.49304DF + 6.70727DG + 4.42405EF
+ 11.68099EG −0.54155FG
−37.41904A2−7557.48052B2−9.31668C2+0.94855D2
+ 21.37747E2+3.35373F2 + 39.05747G2
(14)  
Where A to F are representative of seven control variables (Table 3). RF 
Fig. 4. Performance of different training algorithms for the training of MLP network.  
Fig. 5. Influence of the number of neurons in hidden layer on the prediction 
efficiency of MLP structure. 
M.S. Karambeigi et al.                                                                                                                                                                                                                         
Geoenergy Science and Engineering 224 (2023) 211579
9
as well as NPV are the responses. 
The accuracy of the obtained equations in predicting the output 
values with respect to the given inputs can be seen in Fig. 7. 
One of the most important applications of RSM rather than its ability 
to establish objective functions is to extract the interaction of factors. 
The analysis of interaction between different factors revealed that there 
was an interaction between surfactant slug size (A) and surfactant 
concentration (B) as shown in Fig. 8. It shows their interactions were 
meaningful as the opposite trends of variations for RF and NPV re­
sponses as functions of factor A were observed when factor B was 
changed from its low level (0.01 vol frac.) to the high level (0.025 vol 
frac.). 
In other words, RF and NPV were conflicting objectives, and multi- 
objective optimization should be performed to find the optimal design 
of control variables for the successful application of surfactant/polymer 
flooding. 
4.5. Multi-objective optimization 
Having validated objective functions, multi-purpose optimization 
could be consequently performed. There was a swarm of 25 particles in 
each PSO iteration, and the optimization algorithm was continued to 60 
iterations. Two extremes of each control variable were low and high 
levels of screened factors (Table 3). The optimization goal was set for the 
simultaneous maximization of RF and NPV. 
In each iteration, the placement and velocity of every particle in the 
search domain were randomly determined. Following this, two re­
sponses were calculated using objective functions obtained in Eqs. (13) 
and (14). Thereafter, zeta function (Eq. (11)) was employed to find the 
unique satisfaction index of each particle. 
Furthermore, the values of pbesti and gbest were determined in each 
iteration. Proceeding with PSO-FL optimization methodology, ζ 
increased as demonstrated in Fig. 9 and two responses (outputs) pro­
gressed through their maximum values as plotted in Fig. 10 in which RF 
was replaced with residual oil saturation (ROS) using Eq. (15) for better 
visualization of the variations. The less ROS, the more RF. 
RSO = 100 −RF
(15)  
4.6. Decision making 
Table 5 presents the optimal arrangement of influential variables 
whereby gbest was met as the goal of the optimization problem. 
The PSO-FL algorithm proposed the optimum values of effective 
factors as follows: surfactant slug size of 0.259 PV containing surfactant 
concentration of 0.0088 vol fraction and polymer concentration of 0.25 
wt%, polymer drive size of 0.648 PV composed of 0.2 wt% polymer and 
Fig. 6. The cross-plots of ANN prediction versus actual data: (a) training phase of RF, (b) training phase of NPV, (c) generalization phase of RF, and (d) generalization 
phase of NPV. 
Table 4 
The results of ANOVA for the response surface model.  
Statistical results 
RF response 
NPV response 
Model F value 
171.26 
162.51 
Model prob > F 
<0.0001 
<0.0001 
R-squared 
0.9810 
0.9800 
Adjusted R-squared 
0.975 
0.974 
Predicted R-squared 
0.9401 
0.935 
CV% 
1.99 
4.10 
Adequate precision 
63.159 
55.197  
M.S. Karambeigi et al.                                                                                                                                                                                                                         
Geoenergy Science and Engineering 224 (2023) 211579
10
0.3009 meq/ml salt, and kv/kh ratio of 0.0176. Implementing the CEOR 
scenario by this procedure, the algorithm was estimated RF of 57.21 % 
OOIP and NPV of $8.11 MM. 
For the approval of this decision, the optimal scenario was compared 
with the RSM method which is a conventional approach to be utilized in 
the process optimization ((Rastegar et al., 2016)). Table 5 (column 2) 
shows the optimal scenario proposed by RSM. It predicted the recovery 
factor as 56.99%OOIP and estimated the net present value as $7.94 MM. 
Comparing two approaches, one found that the PSO-FL methodology 
could search more efficiently the sampling domain and has presented 
better results. 
Multi-objective optimization of surfactant-polymer flooding consid­
ered both technical and economic conditions simultaneously which are 
essential for decision-making for the field operations. To highlight the 
priority of this viewpoint, the current workflow was then compared with 
the PSO single-objective algorithm. Table 5 (column 3) demonstrates the 
results of RF-objective PSO algorithms regardless of economic issues and 
Table 5 (column 4) shows the optimum factors to achieve the highest 
NPV regardless of technical conditions. Although the recovery factor 
increased from 57.21% OOIP (PSO-FL methodology) to 64.04% OOIP 
(RF-objective PSO algorithm), NPV was drastically reduced from $8.11 
MM to $5.90 MM. In other words, the recovery factor was improved 
11.94% OOIP when just a technical goal has been considered but the 
profit of CEOR approach has decreased 27.26%. Hence, decisions based 
on just technical affairs regardless of economic conditions may threaten 
the successful implementation of field-scale CEOR projects. 
On the other hand, NPV-objective of PSO algorithm resulted in the 
improvement of project profit as NPV increased from $8.11 MM (PSO-FL 
approach) to $8.48 MM (single objective of NPV) while the oil pro­
duction of the field decreased substantially as recovery factor was 
reduced from 57.21% OOIP to 52.53% OOIP. Comparing the optimum 
scenarios presented by multi-objective and single-objective algorithms 
revealed the outstanding performance and priority of such methodolo­
gies for multi-purpose decision-making in the management of field 
development plans. 
5. Conclusion 
Based on the results of this study, the following conclusions can be 
drawn.  
1. Algorithms which can consider the presence of trade-offs between 
two or more competing technical and economic objectives have 
Fig. 7. Comparison between predicted values and actual values of RF and NPV.  
Fig. 8. The interactive effects of surfactant slug size and surfactant concen­
tration on (a) RF (b) NPV. 
M.S. Karambeigi et al.                                                                                                                                                                                                                         
Geoenergy Science and Engineering 224 (2023) 211579
11
priority over single-objective approaches that may ignore the trade- 
offs so that their solutions can satisfy just one objective while they 
may be unacceptable with respect to the other objective(s).  
2. Four main stages were included in the structure of the workflow: 
screening, modeling, optimization, and decision-making. The case 
study to assess the effectiveness of proposed workflow was a sur­
factant/polymer flooding project in a sandstone reservoir. The ob­
jectives of the problem were RF as the technical aspect and NPV as 
the economic index.  
3. A Two-level full factorial design was used to screen eight initial 
variables. For RF, they were descendingly ranked as surfactant slug 
(A), polymer concentration in surfactant slug (C), polymer concen­
tration in polymer drive (E), polymer drive size (D), surfactant 
concentration (B), kv/kh ratio (F), the salinity of polymer drive (G), 
and the salinity of water post-flush (H). Thus, the descending order of 
their effect on NPV was as follows: B, C, E, D, A, F, G, H. Based on the 
ANOVA, factors A to G as the union of the most significant factors for 
both responses were determined as influential factors. 
4. Neuro-simulation technique was efficient to generate required out­
puts of experimental designs in both the screening and modeling 
stages. In this regard, multilayer perceptron structure with Bayesian 
regularization backpropagation training algorithm and the optimal 
number of neurons in hidden layer showed the best performance of 
artificial neural network.  
5. ICA was to optimize ANN structure automatically compared to 
manual change of training algorithm and the number of hidden layer 
neurons. It improved the performance of training and evaluation 
phases because manual approach was time consuming.  
6. In the modeling stage, CCD as the best design of RSM was used to fit 
second-order equations to the generated data. The evaluation of 
goodness-of-fit for quadratic models using different parameters of 
ANOVA confirmed that the CCD approach could develop highly ac­
curate objective functions. 
7. Comparing PSO-FL methodology with another multi-objective opti­
mization developed by CCD approach as well as single-objective (RF- 
objective or NPV-objective) PSO algorithm indicated the marked 
preference of PSO-FL technique by which the recovery factor 
increased to 57.21% OOIP with NPV of $8.11 MM as the maximum 
profit of the project. 
8. Although this workflow had promising results, the inherent limita­
tion of such hybrid artificial intelligence algorithms is their de­
pendency to the data. Hence, this workflow can be applied to solve 
other petroleum industry problems in which different objectives are 
conflicting. 
Author contribution statements 
Mohammad Sadegh Karambeigi, Atefeh Hasan-Zadeh, Mohammad 
Saber Karambeigi, Seyyed Ali Faal Rastegar, Masoud Nasiri and Yousef 
Kazemzadeh contributed to the design and implementation of the 
research, to the analysis of the results and to the writing of the 
manuscript. 
Declaration of competing interest 
The authors declare that they have no known competing financial 
interests or personal relationships that could have appeared to influence 
the work reported in this paper. 
Data availability 
No data was used for the research described in the article. 
Acknowledgement 
The authors are thankful to Stat-Ease, Minneapolis for the provision 
of the Design Expert package. 
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Factor/ 
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M.S. Karambeigi et al.                                                                                                                                                                                                                         
Geoenergy Science and Engineering 224 (2023) 211579
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M.S. Karambeigi et al.

Model-based decision analysis applied to petroleum ﬁeld
development and management
Denis José Schiozer*, Antonio Alberto de Souza dos Santos, Susana Margarida de Graça Santos,
and João Carlos von Hohendorff Filho
University of Campinas, PO Box 6052, 13083-970 Campinas, SP, Brazil
Received: 20 September 2018 / Accepted: 20 March 2019
Abstract. This work describes a new methodology for integrated decision analysis in the development and
management of petroleum ﬁelds considering reservoir simulation, risk analysis, history matching, uncertainty
reduction, representative models, and production strategy selection under uncertainty. Based on the concept
of closed-loop reservoir management, we establish 12 steps to assist engineers in model updating and production
optimization under uncertainty. The methodology is applied to UNISIM-I-D, a benchmark case based on the
Namorado ﬁeld in the Campos Basin, Brazil. The results show that the method is suitable for use in practical
applications of complex reservoirs in different ﬁeld stages (development and management). First, uncertainty is
characterized in detail and then scenarios are generated using an efﬁcient sampling technique, which reduces
the number of evaluations and is suitable for use with numerical reservoir simulation. We then perform
multi-objective history-matching procedures, integrating static data (geostatistical realizations generated using
reservoir information) and dynamic data (well production and pressure) to reduce uncertainty and thus provide
a set of matched models for production forecasts. We select a small set of Representative Models (RMs) for
decision risk analysis, integrating reservoir, economic and other uncertainties to base decisions on risk-return
techniques. We optimize the production strategies for (1) each individual RM to obtain different specialized
solutions for ﬁeld development and (2) all RMs simultaneously in a probabilistic procedure to obtain a robust
strategy. While the second approach ensures the best performance under uncertainty, the ﬁrst provides
valuable insights for the expected value of information and ﬂexibility analyses. Finally, we integrate reservoir
and production systems to ensure realistic production forecasts. This methodology uses reservoir simulations,
not proxy models, to reliably predict ﬁeld performance. The proposed methodology is efﬁcient, easy-to-use and
compatible with real-time operations, even in complex cases where the computational time is restrictive.
Nomenclature
AC
Abandonment Costs
B
Benchmark return
BHP
Bottom-Hole Pressure
CAPEX
Investments in equipment and facilities
CLRM
Closed-Loop Reservoir Management
CLFDM
Closed-Loop Field Development and
Management
E
Expectation operator
Ei
Specialized production strategy optimized
for RMi
EMR
Robust production strategy
EMV
Expected Monetary Value
EVoF
Expected Value of Flexibility
EVoI
Expected Value of Information
Gp
Cumulative gas production
NCF
Net Cash Flow
Np
Cumulative oil production
NPV
Net Present Value
NQDS
Normalized Quadratic Deviation with Signal
OPEX
Operational Expenditure
ORF
Oil Recovery Factor
Qg
Gas rate
Qo
Oil rate
Qw
Water rate
Qwi
Water injection rate
R
Gross revenue
RM
Representative Model
SB
Lower semi-deviation from B
SB+
Upper semi-deviation from B
* Corresponding author: denis@unicamp.br
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Oil & Gas Science and Technology - Rev. IFP Energies nouvelles 74, 46 (2019)
Available online at:
 D.J. Schiozer et al., published by IFP Energies nouvelles, 2019
ogst.ifpenergiesnouvelles.fr
https://doi.org/10.2516/ogst/2019019
REGULAR ARTICLE
REGULAR ARTICLE
S2
B
Lower semi-variance from B
S2
Bþ
Upper semi-variance from B
Roy
Amount paid in royalties
ST
Amount paid in social taxes
T
Corporate tax rate
Wi
Cumulative water injection
Wp
Cumulative water production
ɛ(NPV)
Economic value of the production strategy
adjusted to the decision maker’s attitude
sdr
Tolerance level to downside risk
sup
Tolerance level to upside potential
Subscript
wi
With information
woi
Without information
1 Introduction
Field development and management decisions involve risks
due to several uncertainties, mainly (1) reservoir, associated
with recoverable reserves and ﬂow characteristics, (2) oper-
ational, related to production system availability, and
(3) economic, such as oil price, capital expenditures, and
operational expenditures. These uncertainties typically
coexist because data is usually acquired indirectly and spar-
sely, and because developing a petroleum ﬁeld is a long-
term, capital-intensive project. Their combined effects must
be assessed to estimate the risks involved in decisions.
Today, due to the challenges of new oil and gas
discoveries, decision makers recognize the shortcomings of
simplistic uncertainty assessments and the importance of
integrated model-based decision analysis. In particular,
current research focuses on improving the decision-making
process in ﬁeld development and management, making
use of new information that arrives as new development
wells are drilled and production begins. In this context,
the Closed-Loop Reservoir Management (CLRM) was pro-
posed (Chen et al., 2009; Jansen et al., 2005, 2009; Nævdal
et al., 2006; Wang et al., 2009), which consists of a contin-
uous update of the geological model accompanied by a
continuous optimization of well-control for existing and
future wells. Based on this concept, the Closed-Loop Field
Development
(CLFD)
was
generalized
(Shirangi
and
Durlofsky, 2015) to include the continuous optimization
of decision variables related to the production strategy
conﬁguration (e.g., type and position of future wells). This
study is based on the CLFD concept.
However, many factors make this a complex, time-
consuming process to model, namely (1) the coexistence
of
multiple
endogenous
and
exogenous
uncertainties,
(2) the large search spaces, and that (3) ﬂow simulation is
time-consuming in itself. In addition, this is a multidisci-
plinary problem, integrating reservoir engineering, produc-
tion engineering, economic evaluation, and statistical
analyses. Thus, simpliﬁcations are often required in analy-
ses that perform a high number of evaluations, such as
uncertainty quantiﬁcation, optimization procedures, and
decision risk analysis. However, these simpliﬁcations may
yield inaccurate results and so must be selected carefully.
Proxy models, which are used to bypass the ﬂow simu-
lator, are a common simpliﬁcation in uncertainty quantiﬁ-
cation, history matching, and probabilistic forecasting
(Douarche et al., 2014; Feraille, 2013; Feraille and Marrel,
2012; Imrie and Macrae, 2016; Osterloh, 2008; Panjalizadeh
et al., 2014; Scheidt et al., 2007; Touzani and Busby, 2014).
However, multiple factors affect prediction accuracy of the
proxy, which is not physics-based: (1) the high nonlinearity
between input variables (reservoir, operational, and eco-
nomic uncertainties) and output variables (production,
injection,
and
economic
forecasts)
complicates
proxy
modeling, and (2) assumptions and approximations when
modeling the proxy may introduce non-negligible errors
(Trehan et al., 2017).
Lower-ﬁdelity models, another class of approximations,
have also been applied in history matching (Lodoen and
Omre, 2008; Subbey et al., 2004) and production strategy
optimization (Aliyev and Durlofsky, 2015; Wilson and
Durlofsky, 2013). Lower-ﬁdelity models entail many simpli-
ﬁcations to increase computational efﬁciency while respect-
ing the physical processes governing the reservoir. In this
approach, high-ﬁdelity models are upscaled through numer-
ical homogenization procedures, prior to ﬂow simulation.
This simpliﬁcation is attractive because upscaling is rela-
tively straightforward to implement (Trehan et al., 2017).
However, its use is not straightforward because upscaling
errors arise from neglecting subgrid heterogeneity effects
(Durlofsky, 1997, 1998; Preux, 2016; Zabalza-Mezghani
et al., 2004).
Computational efﬁciency can also be achieved through
efﬁcient sampling. The Monte Carlo method is often used
in the petroleum industry. However, as the sampling is
purely random, a very high number of samples is necessary
to ensure reliable results (Mishra, 1998), frequently at unfea-
sible levels (Risso et al., 2011). This study uses a simpliﬁed
statistical technique developed in a related work (Schiozer
et al., 2017), the Discretized Latin Hypercube with Geosta-
tistical realizations (DLHG). By incorporating the desirable
features of random sampling and stratiﬁed sampling, the
DLHG ensures minimum computational costs without
requiring proxy models. We tested this technique in several
examples, achieving a good balance of precision and
computational time. This sampling technique was applied
to uncertainty quantiﬁcation (Schiozer et al., 2017), history
matching (Maschio and Schiozer, 2016), and production
strategy optimization (von Hohendorff Filho et al., 2016).
This study also increases the computational efﬁciency of
the CLFD process by using representative models of the
uncertain system. Techniques to select representative
models were the focus of recent research (Jiang et al., 2016;
Meira et al., 2016, 2017; Shirangi and Durlofsky, 2016). This
work applies a method developed in related research (Meira
et al., 2016, 2017) to reduce the number of scenarios for pro-
duction strategy selection and optimization. The method
combines a mathematical function that captures the repre-
sentativeness of a set of models with a metaheuristic opti-
mization algorithm, to ensure full representation of the
D.J. Schiozer et al.: Oil & Gas Science and Technology - Rev. IFP Energies nouvelles 74, 46 (2019)
2
variability of system inputs (uncertain attributes) and out-
puts (production and economic forecasts). Additional
advantages include low computational cost and simplicity
for day-to-day decision making because the method is soft-
ware based. Production strategy optimization is computa-
tionally
consuming
because
of
the
high
number
of
evaluations required. This is particularly challenging for
the Robust Optimization (van Essen et al., 2009; Yang
et al., 2011; Yasari and Pishvaie, 2015), where multiple
scenarios are evaluated simultaneously.
For the methodology to deliver reliable results, some
conditions must be guaranteed:
 The performance of a high-ﬁdelity model must be pre-
served using an accurate simulation model because of
the complex integration between the production strat-
egy and the system performance (production, injec-
tion, and economic forecasts) (Botechia et al., 2018a).
 The
reservoir
simulation
model
must
honor
all
dynamic data and be fast enough to allow analyses
of multiple scenarios.
 The statistical analysis must be carefully performed to
ensure adequate uncertainty representation while
avoiding a high number of evaluations (which is very
time consuming).
 The procedure for production strategy selection must
reﬂect the effects of both the uncertain models and the
production strategy because both highly inﬂuence the
performance of the project and, consequently, the risk
evaluation.
 Production and economic evaluations must be inte-
grated because both of them impact decisions.
This work presents a methodology that resulted from
several case studies and was ﬁrst outlined in Schiozer et al.
(2015). The method comprises the key steps of decision
analysis to ensure good decisions. We integrate key steps
of reservoir characterization, data assimilation, and produc-
tion optimization providing a core basis for specialized
methodologies, as we demonstrate in the results section.
2 Objectives
Despite the growing concern for in-depth, model-based deci-
sion making, we observed that many solutions, presented in
the literature and used in some companies, still entail many
simpliﬁcations, which can be critical in complex cases. This
is because industry professionals value fast, easy-to-apply
techniques because of limited time and highly complex
decisions.
The objective of this work is to improve the decision-
making
process
in
petroleum
ﬁeld
development
and
management. We present a model-based methodology inte-
grating reservoir simulation, risk analysis, history matching,
uncertainty reduction, representative models, and produc-
tion strategy selection under uncertainty.
This method aims to ensure good decisions while being
practical for application in complex reservoirs and at
different stages of the ﬁeld lifetime, both before and after
reservoir development. Speciﬁc objectives of this study
include: (1) practical for day-to-day decisions and based
on reliable production forecasts from numerical reservoir
simulation, (2) probabilistic-based decision-making based
on an adequate representation of uncertainty, and (3) quan-
titative and objective decision-making based on indicators
and automated procedures.
The methodology was applied to UNISIM-I-D, a bench-
mark case based on the Namorado ﬁeld in the Campos
Basin, Brazil.
3 Methodology
The proposed method is based on the concept of Closed-
Loop Field Development and Management, as an extension
of the Closed-Loop Reservoir Management by Jansen et al.
(2009) (Fig. 1). The main components of the process are
divided into colors:
 Green: gathering of all data and uncertainties and
model construction; multiple simulation models are
used in the process so model ﬁdelity (low, medium
or high) is adapted to balance quality of the results
and computational time.
 Blue: model-based, long-term decisions under uncer-
tainties; the best alternative is implemented in the
ﬁeld (with operational noise due to delays, fails,
etc.) generating measured dynamic data (production,
pressure, 4D seismic, etc.).
 Red: data assimilation; all dynamic data must be
within a tolerance range to select models that will
be used in the blue part; data assimilation may
directly change the simulation models or the high
ﬁdelity geologic models (big loop).
 Black: (1) implementation of long-term decisions
(normally
model-based) and short-term decisions
(normally data-driven), (2) deﬁnition of study objec-
tive, and (3) selection of the type of study (past – data
assimilation; or future – decision analysis).
The twelve steps of the methodology are described
below:
3.1 Green steps
1. Reservoir characterization under uncertainties (to
build models, develop scenarios, and estimate proba-
bilities) (Correia et al., 2015, 2018a, 2018b; Mahjour
et al., 2019). This crucial step requires a multidisci-
plinary approach to consider all possible uncertainties:
reservoir, ﬂuid, economic, and operational attributes.
2. Build and calibrate the simulation model: accurate
risk quantiﬁcation requires reliable responses; there-
fore, the simulation model must be calibrated to have
a fast and yet robust response to avoid biased evalua-
tions (Avansi et al., 2019). Decision makers deﬁne the
degree of model precision according to the objective.
We believe that a high-ﬁdelity model should be
preferred over low-ﬁdelity or proxy models because
D.J. Schiozer et al.: Oil & Gas Science and Technology - Rev. IFP Energies nouvelles 74, 46 (2019)
3
of the high nonlinearity between the reservoir model
and the production strategy performance. The cali-
bration is normally done with a Base Case (in this
work, called Base0).
3.2 Red steps
3. Verify inconsistencies in the Base Case and dynamic
well data (ﬂuid rates and BHP measurements) to be
used in the data assimilation procedures. This step
is often simpliﬁed or skipped but it is crucial as it
can identify inconsistencies in the simulation model
and the real data.
4. Generate scenarios considering reservoir uncertain-
ties. In this work, a scenario is a particular combina-
tion of all possible uncertainties. Several sampling
techniques are available in the literature, but we rec-
ommend the efﬁcient DLHG (Schiozer et al., 2017).
5. Data assimilation: history match and reduce the
number of scenarios with dynamic and seismic data.
Several techniques are available (Avansi and Schiozer,
2015a; Bertolini et al., 2015; Costa et al., 2018;
Davolio and Schiozer, 2018; Maschio and Schiozer,
2008, 2015, 2016; Oliveira et al., 2018) depending on
the complexity of the case and the available data.
From the accepted models, a Base Case is selected
for the following steps (Base1). The usual recommen-
dation is to use a model close to P50 in all indicators
to optimize the initial production strategy, represent-
ing an intermediate case. A new Base Case must be
selected only when Base0 fails to honor the dynamic
data or is too optimistic or pessimistic.
3.3 Blue steps
6. Selection of a deterministic production strategy for
the Base Case. As the production strategy selection
strongly affects the risk quantiﬁcation, an iterative
technique is best to select the production strategy.
The ﬁrst production strategy is selected using an opti-
mization procedure (Gaspar et al., 2014, 2016a; Rav-
agnani et al., 2011; von Hohendorff Filho et al., 2016).
7. Initial risk estimate of the ﬁrst production strategy
with all possible scenarios (from Step 5). This risk
curve is often used in projects. Here, we propose
additional analyses (Steps 8–12) to further improve
decisions and reduce risk, showing that the ﬁnal risk
curve can be very different.
8. Selection of Representative Models (RMs) (Costa
et al., 2008; Meira et al., 2016, 2017; Schiozer et al.,
2004) based on multiple system inputs (probability
distribution and range of uncertain attributes) and
outputs
(production,
injection,
and
economic
forecasts).
9. Selection of a specialized production strategy for each
RM, as in Step 6, to provide different solutions for
ﬁeld development.
10. Production
strategy
selection
under
uncertainty
including reservoir, economic, and other uncertainties.
A Robust Optimization procedure (Silva et al., 2016)
can be used, or a risk-return analysis (Santos et al.,
2017a) to select the best strategy from the candidates
obtained in Step 9. If the simulation runtime for the
number of scenarios is unfeasible, the RMs can be used
to represent them.
11. Identiﬁcation of potential changes in the production
strategy (obtained in Step 10) to manage uncertainty
and improve the chance of success based on the value
of information (Botechia et al., 2018b; Santos et al.,
2017b) and value of ﬂexibility analyses (Santos
et al., 2018a; Silva et al., 2017), and integration with
production
facilities
(von
Hohendorff
Filho
and
Schiozer, 2017, 2018). If the simulation runtime for
the number of scenarios is unfeasible, the RMs can
be used to represent them.
Fig. 1. Closed-loop ﬁeld development and management (modiﬁed from Jansen et al., 2009).
D.J. Schiozer et al.: Oil & Gas Science and Technology - Rev. IFP Energies nouvelles 74, 46 (2019)
4
3.4 Black step
12. The black step is dedicated to the decision analysis.
Technical and economic indicators support long-term,
model-based decisions as well as short-term, data-
driven decisions. The objective guides the process:
model quality, need for further data assimilation
(history matching), objective function selection, etc.
The literature provides several methods for each speciﬁc
step of the comprehensive 12 steps of this study. We refer-
enced methods for speciﬁc steps that we have conducted in
related works. The discussion section explores the focus of
our current research to address existing challenges.
4 Application
The 12-step methodology was applied to a benchmark case
study based on the Namorado ﬁeld in the Campos Basin,
Brazil. A synthetic reservoir, UNISIM-I-R (Avansi and
Schiozer, 2015b), was built to provide a reference model
that represents the true reservoir. The uncertain simulation
model UNISIM-I-D (Gaspar et al., 2015) is in the initial
stages of ﬁeld development and has four years of production
data for four production wells (NA1A, NA2, NA3D,
RJS19). The reservoir model is discretized into a corner
point grid with 81  58  20 cells measuring 100 
100  8 m with a total of 36 739 active cells.
We reference results from related studies in the results
section. Note that our focus is not to compare the efﬁcacy
of each method, but to demonstrate that the 12-step
decision-structure allows the development of different
approaches.
5 Results
5.1 Step 1
The uncertainties of the model include:
 Reservoir
attributes:
geostatistical
realizations of
facies, porosity, net-to-gross ratio, and permeability;
structural model (BL), water relative permeability
(Krw), ﬂuid properties in the East block (PVT), depth
of Water-Oil Contact in the East block (WOC), Rock
Compressibility (CPOR), and vertical permeability
multiplier (Kz) (Tab. 1).
 Economic attributes: oil price, operational expendi-
tures, and capital expenditures (Tab. 2).
 Operational attributes: System Availability (SA) and
Well Index Multiplier (dWI) (Tab. 3).
Details of the simulation model, economic model, and
uncertainties
can
be
found
in
Avansi
and
Schiozer
(2015b) and in Gaspar et al. (2015), while open source ﬁles
can be accessed at http://www.unisim.cepetro.unicamp.br/
unisim-i.
5.2 Steps 2 and 3
Step 2 guarantees that the simulation model adequately
represents the reservoir and is fast enough to be included
in a methodology that demands thousands of simulation
runs. In our case, the simulation runtime was around
7 min running in parallel while using four processors in a
cluster. Although our methodology is applicable to cases
with very high runtimes (hours), simpliﬁcations may be
necessary depending on the time available and scope of
the project.
Step 3 ensures compatibility of the initial response of the
Base Case (Base0) (material balance, pressure, and initial
production) with the existing data. Note that the Base0
case corresponds to the most likely value of each uncertain
attribute.
5.3 Step 4
In Step 4, scenarios were generated to start the probabilistic
process using the DLHG (Schiozer et al., 2017), which
applies the efﬁcient Latin Hypercube Sampling (LHS) and
integrates all types of uncertainties in the sampling step,
i.e., continuous attributes are discretized, and then com-
bined with discrete attributes and geostatistical realiza-
tions. In LHS, the range of each variable (xj) is divided
into n disjoint intervals of equal probability, then one value
is selected at random from each interval. The n values
obtained for x1 are randomly paired, not replaced, with
the n values obtained for x2. This process is continued until
a set of n nX-tuples is formed (Helton and Davis, 2003).
Each attribute is treated according to (1) sampling
number, (2) number of discrete levels, and (3) probability
of each discrete level. The sampling number, which is equal
to the number of ﬂow simulation runs, is set at the begin-
ning of the process based on (1) simulation runtime,
(2) importance of the study (i.e., the required precision),
and (3) available work time (Schiozer et al., 2017). Note
that all sampled scenarios are simulated using the ﬂow
simulator while no proxy models are used for production
forecasts.
Santos
et
al.
(2018b)
showed
that
independence
between the precision of the DLHG and the number of sam-
ples is achieved with a few samples (from 50 samples for
UNISIM-I-D). Due to the short simulation runtime and
the available computational resources, we used more
samples to create smoother risk curves. Figure 2 shows risk
curves for sampling numbers 500 and 100. It is possible to
notice that the Base Case is no longer close to P50 in the
risk curve.
5.4 Step 5
In Step 5, we applied a ﬁltering technique to select the
subset of scenarios (from the full set of sampled scenarios)
that matched the four years of production data. We used
the Normalized Quadratic Deviation with Signal (NQDS)
(Avansi and Schiozer, 2015a; Bertolini et al., 2015) as the
matching indicator, which is normalized for each well and
each objective function (Qo, Qw, Qg, and BHP). We consid-
ered NQDS values between 1 and +1 an acceptable misﬁt.
D.J. Schiozer et al.: Oil & Gas Science and Technology - Rev. IFP Energies nouvelles 74, 46 (2019)
5
Figure 3 shows the 214, out of the 500 initial scenarios,
selected (with all objective functions within the tolerance
range). Figure 4 shows water production curves for the four
wells as an example.
As the Base Case, Base0, did not match the production
data, we selected a new Base Case (Base1) as an intermedi-
ate case from the set of ﬁltered models, using an initial
production strategy (E0) (Fig. 5). Note that this initial
strategy does not represent the ﬁnal decision for ﬁeld devel-
opment and it is only used to select the Base1 case.
5.5 Step 6
The ﬁrst important consideration is how to integrate pro-
duction facilities with the ﬁeld production forecast and
modeling. Generally, when production facilities impose
strong restrictions, integrated modeling is used in all of
the blue steps. In other cases, and in this application, inte-
grated modeling is used to establish approximate well
boundary conditions in Step 6 and to conﬁrm these condi-
tions in Step 11.
Table 1. Reservoir uncertainties for the simulation model (Avansi and Schiozer, 2015b). Level zero of each attribute is
the base (most likely).
Attribute
Uncertainty type
Levels (Probability)
Image
Discrete (realization)
500 geostatistical realizations of porosity, permeability,
and net-to-gross ratio (equiprobable)
BL
Discrete (map)
Present (0.7); absent (0.3)
Krw
Discrete (curve)
krw0 (0.2); krw1 (0.2); krw2 (0.2); krw3 (0.2); krw4 (0.2)
PVT
Discrete (table)
PVT0 (0.34); PVT1 (0.33); PVT2 (0.33)
WOC
Continuous discretized (scalar)
woc0 (0.111); woc1 (0.2222); woc2 (0.334); woc3 (0.222); woc4 (0.111)
CPOR
Continuous discretized (scalar)
cpor0 (0.2); cpor1 (0.6); cpor2 (0.2)
Kz
Continuous discretized (scalar)
kz0 (0.4); kz1 (0.1); kz2 (0.1); kz3 (0.2); kz4 (0.2)
Table 2. Economic parameters and uncertainties (Gaspar et al., 2015).
Description
Field units
SI units
Units
Base Optimistic Pessimistic
Units
Base
Optimistic Pessimistic
Oil price
USD/bbl
50
70
40
USD/m3
314.5
440.3
251.6
Discount rate
%
9
9
9
%
9
9
9
Royalties
%
10
10
10
%
10
10
10
Special taxes on
gross revenue
%
9.25
9.25
9.25
%
9.25
9.25
9.25
Corporate taxes
%
34
34
34
%
34
34
34
Cost of oil production
USD/bbl
10.0
13.0
8.0
USD/m3
62.9
81.8
52.4
Cost of water production
USD/bbl
1.0
1.3
0.8
USD/m3
6.3
8.2
5.3
Cost of water injection
USD/bbl
1.0
1.3
0.8
USD/m3
6.3
8.2
5.3
Abandonment cost
(% well investment)
%
7.4
9.2
6.5
%
7.4
9.2
6.5
Drilling and completion
of vertical well
USD Million
61.2
76.5
54.0
USD Million
61.2
76.5
54.0
Drilling and completion
of horizontal well
USD
Thousand/m
21.7
27.3
19.0
USD
Thousand/m
27.3
19.0
21.7
Well-platform
USD Million
13.3
16.7
11.7
USD Million
13.3
16.7
11.7
Probability
0.50
0.25
0.25
0.50
0.25
0.25
Table 3. Operational uncertainties (Gaspar et al., 2015).
Levels
Parameter
Uncertainty type
0 (base)
1
2
SA – Platform
Discrete (scalar)
0.95
1.00
0.90
SA – Group
Discrete (scalar)
0.96
1.00
0.91
SA – Producer
Discrete (scalar)
0.96
1.00
0.91
SA – Injector
Discrete (scalar)
0.98
1.00
0.92
dWI
Discrete (scalar)
1.00
1.40
0.70
Probability
0.33
0.34
0.33
D.J. Schiozer et al.: Oil & Gas Science and Technology - Rev. IFP Energies nouvelles 74, 46 (2019)
6
We optimized deterministically a production strategy
for Base1, considering design (G1) and operation (G2) vari-
ables. The G1 variables were: number, type (vertical or hor-
izontal), placement, and opening-schedule of wells, and
platform capacity constraints (liquid, oil and water produc-
tion, and water injection). The G2 variables were con-
straints
of
maximum
water-cut,
maximum
liquid
production for producers, and water injection for injectors.
The optimization procedure was divided into phases:
(1) number and type of wells, and platform capacity,
(2) well placement and ﬁne-tuning the platform capacity
constraints, (3) well-opening schedule, (4) well operating
and monitoring constraints, and (5) ﬁne-tuning.
We performed phases 1, 2, and 4 on commercial soft-
ware CMOST (CMG), which uses the optimizer method
Designed Exploration Controlled Evolution (DECE) (Yang
et al., 2007). We used the black-oil numerical reservoir sim-
ulator IMEX (CMG).
The objective function for optimization is the Net
Present Value (NPV) (Fig. 6) and is calculated using the
0.0
0.2
0.4
0.6
0.8
1.0
30
40
50
60
70
80
Cumulative Probability
Np (m3 Million)
100 models
500 models
Base0
(a)
0.0
0.2
0.4
0.6
0.8
1.0
-500
0
500
1000
1500
2000
2500
Cumulative Probability
NPV (USD Million)
100 models
500 models
Base0
(b)
Fig. 2. a) Cumulative oil production (Np) and b) Net Present Value (NPV) risk curves for production strategy E0 for 100 and 500
sampled scenarios, highlighting Base0.
-5
-4
-3
-2
-1
0
1
2
3
4
5
NQDS
Deviation - Qo (214/500 models)
500 models
214 models
upper lim.
lower lim.
NA1A
NA2
NA3D
RJS19
(a)
-5
-4
-3
-2
-1
0
1
2
3
4
5
NQDS
Deviation - Qw (214/500 models)
500 models
214 models
upper lim.
lower lim.
NA1A
NA2
NA3D
RJS19
(b)
-5
-4
-3
-2
-1
0
1
2
3
4
5
NQDS
Deviation - Qg (214/500 models)
500 models
214 models
upper lim.
lower lim.
NA1A
NA2
NA3D
RJS19
(c)
-5
-4
-3
-2
-1
0
1
2
3
4
5
NQDS
Deviation - BHP (214/500 models)
500 models
214 models
upper lim.
lower lim.
NA1A
NA2
NA3D
RJS19
(d)
Fig. 3. NQDS for objective functions a) oil rate (Qo), b) water rate (Qw), c) gas rate (Qg), and d) Bottom-Hole Pressure (BHP), for
the four initial wells (NA1A, NA2, NA3D, RJS19) with production data: 500 initial scenarios (gray) and 214 ﬁltered scenarios that
match production data (red).
D.J. Schiozer et al.: Oil & Gas Science and Technology - Rev. IFP Energies nouvelles 74, 46 (2019)
7
most likely economic scenario (Tab. 3) and a simpliﬁed net
cash ﬂow formula based on the Brazilian Royalty & Taxes
ﬁscal regime (eq. (1)).
NCF ¼
R  Roy  ST  OPEX
ð
Þ  1  T
ð
Þ
½

 CAPEX  AC;
ð1Þ
where NCF is the net cash ﬂow, R is the gross revenue,
Roy is the amount paid in royalties, ST is the amount
paid in social taxes, OPEX is the operational expenditure,
T is the corporate tax rate, CAPEX is the investment in
equipment and facilities, and AC are the abandonment
costs.
The production strategy for Base1 is called E1 and
consists of:
 Number and type of wells: 10 horizontal producers,
two vertical producers (existing wells NA1A and
NA3D), six horizontal water injectors, in a total of
18 conventional wells.
 Platform capacity constraints: 16 275 m3/day (liquid
and oil production), 9068 m3/day (water production),
and 23 328 m3/day (water injection).
At the end of the prediction period (10 957 days),
E1 recorded: NPV of USD 2236 million, Np of 65 million m3,
Fig. 4. Water production for the four initial wells (NA1A, NA2, NA3D, RJS19) with production data: 500 initial scenarios (gray)
and 214 ﬁltered scenarios that match production data (red).
Fig. 5. Cross-plots for cumulative water production versus cumulative oil production (Wp  Np), and net present value versus oil
recovery factor (NPV  ORF) for production strategy E0, for the 214 models (red circles), highlighting the intermediate case chosen
as Base1 (blue circle).
D.J. Schiozer et al.: Oil & Gas Science and Technology - Rev. IFP Energies nouvelles 74, 46 (2019)
8
Wp of 50 million m3, Gp of 6892 million m3, Wi of 138 mil-
lion m3, and Oil Recovery Factor (ORF) of 56%. Figure 7
compares the map of total oil per unit area at the beginning
and end of the prediction period, showing the well place-
ment of E1.
5.6 Step 7
We conducted a ﬁrst risk estimate using production strat-
egy E1 and the 214 possible scenarios from Step 5
(Fig. 8). Risk curves are also referred to as descending or
complementary cumulative distribution functions in the
statistics literature, and we constructed them with the pro-
duction forecasts of multiple scenarios from numerical reser-
voir simulation.
One interesting point to highlight here, which is
normally neglected in many analyses, is the relationship
between the uncertainties and the production strategy.
Base1, the intermediate case for E0 (Fig. 5), became an
optimistic case for E1 (Fig. 8). We observed this behavior
in other cases. Once a strategy is optimized for a particular
model, it becomes more optimistic considering the opti-
mized output parameters (NPV for instance).
Note that these risk curves do not reﬂect the ﬁnal risk
assessment for this project, but provide input for Step 8.
5.7 Step 8
We applied the proposal by Meira et al. (2016) to select nine
RMs from the 214 matched models using multiple risk
curves and cross-plots for four objective functions: NPV,
Np, Wp, and ORF (examples in Figs. 9 and 10). Geostatis-
tical realizations are particularly difﬁcult to handle, and
from the set of 214, we used only nine.
The proposal by Meira et al. (2016) ensures that the set
of RMs represents both the probability distribution of the
input variables (uncertain attributes), ensuring that not
only attributes but also uncertain levels are represented,
and the variability of the main output variables (produc-
tion, injection, and economic forecasts). In addition, this
method is applied using RMFinder software, improving
ease-of-use.
As we already had production strategy E1, we took
Base1 as one of the RMs (Base1 = RM1), saving time
and computational costs.
5.8 Step 9
Because production strategy E1 was optimized speciﬁcally
for Base1, we analyzed other possibilities for ﬁeld develop-
ment using the RMs obtained in Step 8. This follows the
800
1000
1200
1400
1600
1800
2000
0
1000
2000
3000
NPV (USD Million)
Process iteration
Phase 2 - Optimization Evolution
Max NPV
(a)
1600
1700
1800
1900
2000
2100
2200
0
100
200
300
400
NPV (USD Million)
Process iteration
Phase 4 - Optimization Evolution
Max NPV
(b)
Fig. 6. Evolution of the NPV with the optimization procedure for a) phase 2 and b) phase 4.
Fig. 7. Map of total oil per unit area at the beginning (left) and end (right) of the prediction period, including well placement for
production strategy E1.
D.J. Schiozer et al.: Oil & Gas Science and Technology - Rev. IFP Energies nouvelles 74, 46 (2019)
9
rationale that, if the set of RMs represents the uncertain
system, their respective production strategies provide differ-
ent ﬁeld development possibilities, including number and
placement of wells and platform processing capacities.
We repeated the optimization procedure (described
in Step 6) for each RM, obtaining a set of specialized
strategies: E2, E3, . . ., E9 (Tab. 4), where Ei is the strategy
optimized for RMi.
A key uncertainty affecting production strategy selec-
tion is the structural model (Fig. 11). The presence of
hydrocarbons in the, still undrilled, East block is uncertain
because the connectivity of the fault separating the
0.0
0.2
0.4
0.6
0.8
1.0
0
1000
2000
3000
Cumulative Probability
NPV (USD million)
E1
Base1
(a)
0.0
0.2
0.4
0.6
0.8
1.0
35
45
55
65
75
85
Cumulative Probability
Np (m3 million)
E1
Base1
(b)
Fig. 8. NPV and Np risk curves for production strategy E1, highlighting Base1 (circle).
30
40
50
60
70
80
90
0
10
20
30
40
50
60
Np (m3 million)
Wp (m3 million)
214 models
RMs
Base1 (RM1)
(a)
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0
1000
2000
3000
ORF
NPV (USD million)
214 models
RMs
Base1 (RM1)
(b)
Fig. 9. Cross-plots for a) Wp  Np and b) NPV  ORF, for production strategy E1, for the 214 models (red circles), highlighting
Base1 (blue diamond) and nine RMs (black squares).
0.0
0.2
0.4
0.6
0.8
1.0
0
1000
2000
3000
Cumulative Probability
NPV (USD million)
214 models
RMs
Base1 (RM1)
(a)
0.0
0.2
0.4
0.6
0.8
1.0
35
45
55
65
75
85
Cumulative Probability
Np (m3 million)
214 models
RMs
Base1 (RM1)
(b)
Fig. 10. a) NPV and b) Np risk curves for production strategy E1, for the 214 models (red circles), highlighting Base1 (blue diamond)
and nine RMs (black squares).
D.J. Schiozer et al.: Oil & Gas Science and Technology - Rev. IFP Energies nouvelles 74, 46 (2019)
10
West and the East blocks is unknown. Of the set of repre-
sentative models, RM3, RM6, and RM7 do not have hydro-
carbons in the East block and their respective production
strategies (E3, E6, E7) are solutions for such scenarios.
5.9 Step 10
In Step 10, we select the production strategy that performs
best under uncertainty, before considering further actions
to manage this uncertainty (addressed in Step 11).
A production strategy is said to be robust when is insen-
sitive to uncertainty and ensures good performance across
multiple scenarios without requiring system modiﬁcations
after production has started (de Neufville, 2004). However,
note that the deﬁnition of robustness also depends on the
company and can be associated with NPV or oil produc-
tion. For a robust project related to NPV, lower invest-
ments can help avoid negative or lower NPV values for
pessimistic scenarios. For a robust project related to oil pro-
duction, higher investments can be made to increase rates
in optimistic scenarios, for instance.
In Step 9, we obtained a set of specialized production
strategies. One possible approach for Step 10 is to select
the strategy that performs best under uncertainty. The
Robust Optimization, an automated optimization prob-
lem formulated under uncertainty, is another approach
that has become increasingly preferred by practitioners.
Alternatively, a robust strategy can be obtained by manu-
ally reﬁning the best specialized strategy, improving the
performance
under
uncertainty
(to
be
addressed
in
Step 11).
Following the concept of Robust Optimization, Silva
et al. (2016) obtained the robust production strategy for
UNISIM-I-D, called EMR, optimized for nine RMs and
three economic scenarios, simultaneously. Thus, the key
difference between the optimization procedure is that
Step 6 simulates each strategy conﬁguration for one reser-
voir scenario (resulting in one NPV), while Silva et al.
(2016) simulate strategy conﬁguration for many scenarios
(resulting in one Expected Monetary Value [EMV]).
The robust EMR consists of:
 Number and type of wells: 15 producers and nine
water injectors.
 Platform capacity constraints: 20 150 m3/day (liquid
and oil production), 13 950 m3/day (water produc-
tion), and 29 295 m3/day (water injection).
The literature provides several techniques for risk-return
analyses to select one of a set of alternatives. Santos et al.
(2017a) followed on from the classic mean-variance model
and proposed a mean-semivariance framework based on
the premise that variance reﬂects only the overall uncer-
tainty in returns, and not necessarily the risk of a project.
The risk (referred to here as downside risk) is the chance
of failure to achieve a targeted or benchmark return (B).
Thus, variability above this target is not perceived as risk,
but as potentially exploitable optimistic scenarios (referred
to here as upside potential) (Fig. 12).
Santos et al. (2017a) combined the expected monetary
value, downside risk, and upside potential (eq. (2)) to deter-
mine the economic value of a production strategy adjusted
to the decision maker’s attitude, ɛ(NPV), while maintain-
ing the same units and dimension as the NPV:
e NPV
ð
Þ ¼ EMV  S2
B
sdr
þ S2
Bþ
sup
;
ð2Þ
where ɛ(NPV) is the economic value of the production
strategy adjusted to the decision maker’s attitude (in
USD); EMV is the expected monetary value, given by
the sum of the NPV of each scenario weighted by its prob-
ability (in USD); S2
B and S2
B are the lower and upper
semi-variance from benchmark B, respectively (in square
USD); sdr and sup are the tolerance (or indifference) levels
to downside risk and upside potential, respectively (in
USD). Note that decisions based on EMV are a particular
case of equation (2), where decision makers are neutral to
downside risk and upside potential (i.e., sdr = sup ? 1).
Table 4. Characteristics of the nine production strategies (E1–E9). Prod.: number of producing wells; Inj.: number of
water injection wells.
Production
strategy
Wells in West block
Wells in East block
Total
wells
Platform (1000 m3/day)
Prod
Inj
Total
Prod
Inj
Total
Ql
Qo
Qw
Qwi
E1
10
5
15
2
1
3
18
16.3
16.3
9.1
23.3
E2
8
5
13
2
1
3
16
16.3
16.3
11.2
22.8
E3
9
5
14
0
0
0
14
14.0
14.0
9.8
19.5
E4
9
5
14
2
1
3
17
18.2
18.2
11.5
25.5
E5
9
5
14
4
2
6
20
17.8
17.8
10.5
23.8
E6
9
6
15
0
0
0
15
14.3
14.3
7.3
20.6
E7
9
6
15
0
0
0
15
13.2
13.2
5.2
19.5
E8
10
5
15
4
2
6
21
21.7
21.7
14.6
29.8
E9
9
5
14
4
2
6
20
20.2
20.2
9.8
28.2
D.J. Schiozer et al.: Oil & Gas Science and Technology - Rev. IFP Energies nouvelles 74, 46 (2019)
11
Equation (2) uses lower semi-deviation from benchmark
return B (eq. (3)) to quantify downside risk, and upper
semi-deviation from B (eq. (4)) to quantify upside potential:
SB ¼
ﬃﬃﬃﬃﬃﬃﬃﬃ
S2
B
q
¼
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
E min
NPV  B
ð
Þ; 0
½
2


q
;
ð3Þ
SBþ ¼
ﬃﬃﬃﬃﬃﬃﬃﬃ
S2
Bþ
q
¼
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
E max
NPV  B
ð
Þ; 0
½
2


q
;
ð4Þ
where SB is the lower semi-deviation from benchmark B;
S2
B is the lower semi-variance from B; SB+ is the upper
semi-deviation from B; S2
Bþ is the upper semi-variance
from B; E is the expectation operator; and NPV is the
net present value.
The benchmark (B) is deﬁned by the decision maker as
it depends solely on his/her deﬁnition of loss and gain.
However, note that all production strategies must use the
same benchmark for impartiality. Here, we set the bench-
mark as the EMV of production strategy E1, optimized
for the Base Case, and deﬁned sdr and sup as a function of
this value. For this synthetic case study, we considered a
ﬁctitious decision maker averse to downside risk and willing
to exploit the upside potential (sdr = sup = 0.4  B 
USD 700 million).
In Table 5 and Figure 13, we evaluate and compare the
nine production strategies (E1–E9) and the robust strategy
(EMR), with and without economic uncertainty. EMR is
the best production strategy under uncertainty. From the
set of nine alone, E9 is the best. Production strategies E3,
E6, and E7, optimized for RMs without hydrocarbons in
the East block, are the least attractive alternatives consid-
ering the 214 scenarios simultaneously.
5.10 Step 11
Step 11 consists of detailed analyses of the best strategy
obtained in Step 10.
5.10.1 Managing uncertainty
The best strategy can be improved to better manage uncer-
tainty, considering the company’s risk attitude. The most
common actions are (1) acquiring information, to reduce
reservoir uncertainty, (2) adding ﬂexibility to the system,
to allow system modiﬁcations as uncertainty unfolds over
time, and (3) increasing robustness (an automated proce-
dure was addressed in Step 10, here we discuss a manual
procedure). Despite reducing risks and increasing the
EMV, these approaches incur investment and costs and
may delay production. Therefore, before making a decision,
their beneﬁts should be quantiﬁed using the Expected
Value of Information (EVoI) and Expected Value of
Flexibility (EVoF).
The petroleum literature provides many methods to
estimate the EVoI and the EVoF. In related works (Santos
et al., 2017b; Santos et al., 2018a), we showed how these can
become fully automated procedures when integrated into
the 12-step decision structure, as exempliﬁed in Figure 14
for EVoI.
The automated EVoI and EVoF analyses use the
following as input: (1) the set of uncertain scenarios that
match production data (obtained in Step 5), (2) the set of
Fig. 11. Map of total oil per unit area at the beginning of the prediction period of RM7 with production strategy E7 (left) and RM9
with production strategy E9 (right).
0
0.2
0.4
0.6
0.8
1
350
400
450
500
550
600
650
Cumulative probability
Net present value (USD million)
EMV
B
Domain of 
upside 
potential
Domain of
downside risk
Domain of
uncertainty
Fig. 12. Hypothetical NPV risk curve, highlighting three
domains of variability: overall uncertainty (blue), downside risk
(red), and upside potential (green). EMV is the expected
monetary and B the benchmark return (modiﬁed from Santos
et al., 2017a).
D.J. Schiozer et al.: Oil & Gas Science and Technology - Rev. IFP Energies nouvelles 74, 46 (2019)
12
specialized production strategies, optimized individually for
each RM (obtained in Step 9), and (3) the robust produc-
tion strategy, optimized probabilistically for all RMs simul-
taneously (obtained in Step 10).
The use of a robust production strategy is optional for
EVoI, where we aim to learn the most-likely reservoir
scenario so that we can implement a specialized produc-
tion strategy. However, considering a robust production
strategy is important for cases with high uncertainty,
where a single source of information may be insufﬁcient
to reduce all uncertainties. In such cases, the use of a
robust production strategy in the EVoF study is highly
0.0
0.2
0.4
0.6
0.8
1.0
-1000
1000
3000
Cumulative Probability
NPV (USD million)
B
E1
E2
E3
E4
E5
E6
E7
E8
E9
EMR
(a)
0.0
0.2
0.4
0.6
0.8
1.0
-1000
1000
3000
5000
Cumulative Probability
NPV (USD million)
B
E1
E2
E3
E4
E5
E6
E7
E8
E9
EMR
(b)
1000
1250
1500
1750
2000
0
250
500
750
1000
EMV (USD million)
Downside risk (USD million)
E1
E2
E3
E4
E5
E6
E7
E8
E9
EMR
(c)
1000
1250
1500
1750
2000
0
250
500
750
1000
EMV (USD million)
Upside potential (USD million)
E1
E2
E3
E4
E5
E6
E7
E8
E9
EMR
(d)
Fig. 13. NPV risk curves for the nine specialized production strategies (E1–E9) and robust strategy (EMR), considering 214
uncertain scenarios with a) deterministic economic scenario, and b) three uncertain economic scenarios. The vertical dashed line is the
benchmark (B). Cross-plots of c) Expected Monetary Value (EMV) versus downside risk, and d) EMV versus upside potential, both
under economic uncertainty.
Table 5. Evaluation of the nine specialized production strategies (E1–E9) and robust strategy (EMR). Values in USD
million.
Production
strategy
Without economic uncertainty
With economic uncertainty
EMV
SB
SB+
ɛ(NPV)
EMV
SB
SB+
ɛ(NPV)
E1
1581
329
308
1561
1720
443
552
1875
E2
1596
343
348
1601
1735
453
578
1919
E3
974
739
34
196
1075
848
178
92
E4
1556
383
361
1532
1696
492
585
1841
E5
1526
437
365
1443
1665
539
590
1747
E6
1142
582
68
665
1253
702
244
635
E7
1265
446
67
987
1382
582
276
1007
E8
1548
441
396
1494
1694
543
629
1838
E9
1675
327
439
1798
1825
435
682
2220
EMR
1739
244
408
1892
1889
379
688
2360
D.J. Schiozer et al.: Oil & Gas Science and Technology - Rev. IFP Energies nouvelles 74, 46 (2019)
13
recommended to deﬁne the rigid attributes of the produc-
tion strategy.
As the set of RMs represents the uncertain system, their
respective strategies quantify the possibilities to develop the
ﬁeld, namely number and placement of wells, platform size,
and ﬂuid processing capacities. Thus, these strategies are
indicators for the degree and type of ﬂexibility required
by the system. The ﬂexible strategy is deﬁned in an itera-
tive procedure that combines features of the robust strategy
(e.g., well placement) and ﬂexible features from the differ-
ences between specialized strategies (e.g., available slots
for connection of additional wells).
5.10.1.1 Determining the EVoI of an appraisal well
We considered drilling an appraisal well to gather informa-
tion on the presence or absence of hydrocarbons in the East
block (BL) and the Water-Oil Contact (WOC), simultane-
ously. We estimated EVoI for perfect information (100%
reliable when interpreting BL and WOC) and imperfect
information (95% reliable when interpreting BL, and 80%
for WOC). Without further information, EMR is chosen
(Tab. 5).
We determined the value of the project with informa-
tion using equation (2) with the EMV of E1 as the bench-
mark, the same used in the case without information.
We considered three economic scenarios.
Using equation (2) for project evaluation, EVoI is given
by equation (5), where ɛ(NPV)wi is the value of the project
with information and ɛ(NPV)woi is the value of the project
without information. Because of the non-linearity of ɛ, the
cost of information must be deduced to the NPV values
stored in the database before estimating ɛ(NPV)wi:
EVoIe ¼ eðNPVÞwi  e NPV
ð
Þwoi:
ð5Þ
We also calculate the EVoI as the expected increase in
EMV (eq. (6)), which is a particular case of equation (5),
where decision makers are neutral to downside risk and
upside potential (sdr = sup ? 1):
EVoIEMV ¼ EMV wi  EMVwoi:
ð6Þ
We determined the EVoI using the pre-existing set of
214 scenarios matching production data, with updated
probabilities using Bayes Theorem. That is, the 214
equiprobable scenarios are used to estimate ɛ(NPV)woi
and the 214 scenarios with updated probabilities for BL
and WOC are used to estimate ɛ(NPV)wi.
In this application, we assumed that information could
be acquired without delaying the development plan and
that the appraisal well would be used for ﬁeld development.
Thus, the cost of this well is already included in the NPV
values of most production strategies (E1, E2, E4, E5, E8,
E9, those with wells planed for the East block). Aiming
for a ﬁrst assessment, we used the NPV values directly in
the automated procedure, meaning that we only update
the probability of occurrence of each scenario. This ﬁrst
approximate is not the true EVoI but provides an initial
diagnosis. If it reveals that the information source has
potential, a more precise EVoI calculation should be
performed.
Table 6 shows EVoI calculated with equations (5) and
(6). Note that equation (5) improved the EVoI estimate
by accounting for all changes in the risk curve, not only
the increased EMV but also the decreased downside risk
and increased upside potential. Thus, equation (6) underes-
timated the EVoI.
Table 7 shows the best production strategy for each
information outcome. We observed that the robust strategy
EMR is the best decision for many information outcomes,
meaning that strong uncertainty remains after information
acquisition (other attributes besides BL and WOC). This
conclusion is only possible because we used the 214 scenarios
to determine the EVoI, meaning that we accounted for the
effects of all mapped uncertainties, and not only BL and
WOC.
5.10.1.2 Reﬁning the best strategy for further improvements
For complex problems with a large search space, automatic
procedures often yield local maxima. As Step 11 is manual,
local minima can be avoided. We can check for misconcep-
tions from previous automated steps, as shown by Santos
et al. (2017c). The authors used several indicators to assess
NPVi,k
εwoi
Uncertain 
scenarios
Production 
strategies
Database
Bayes Theorem
Mm
M1
Prior prob.
Posterior prob.
Mm
M1
εwi
EVoI
Deduce cost c
Fig. 14. Automated procedure for EVoI analysis integrated into the 12-step decision-structure.
D.J. Schiozer et al.: Oil & Gas Science and Technology - Rev. IFP Energies nouvelles 74, 46 (2019)
14
the performance of E9 over the 214 scenarios, aiming to
increase its robustness. The remaining strategies E1
through E8 served as boundaries for the different variables,
such as platform size and number of wells. Ultimately, the
authors observed that many of the horizontal producers
had been placed in a suboptimal layer of the reservoir, fur-
ther improving the automated optimization procedure (pro-
duction strategy R4) (Fig. 15).
5.10.2 Integration with production facilities
As mentioned in Step 6, the integration of reservoir and
production systems can improve production forecasts. As
the integration increases computation time, it is important
to assess the need for this integration and to use a suitable
methodology (in this application, we used approximate
boundary conditions in Step 6 and applied the integration
in Step 11 only).
von Hohendorff Filho and Schiozer (2017) analyzed the
inﬂuence of this integration, evaluating its effects on the
production strategy parameters. The integration
was
applied to E9 for RM9 as an integrated production develop-
ment, resulting in a lower initial NPV.
We re-optimized E9 altering conﬁgurations related to
platform position, pipe diameters, gas lift rates, platform
capacities, and the number, schedule, placement and
shut-in times of wells. Figure 16 shows how NPV and
ORF were affected during the global optimization for the
best integrated production strategy. The optimization
shows that standalone E9 is not the optimal strategy for
integrated RM9.
Comparing
standalone
and
integrated
production
strategies, we observed important changes in number and
placement of wells that indicate the need to integrate reser-
voir and production models. Speciﬁc variables from produc-
tion systems such as pipe diameters and gas lift rates also
had a great impact, indicating the need to couple reservoir
and production system models to reach more robust results.
The optimized integrated systems resulted in signiﬁ-
cantly increased NPVs, maintaining the same oil recovery
factor while requiring a lower initial investment. These
results demonstrate the beneﬁts of integrating reservoir
and production systems to increase robustness for decision
making.
5.11 Step 12 and discussions
Steps 1–11 deliver a robust process to be used in the
decision analysis, yielding an appropriate strategy that hon-
ors the history data and considers all uncertainties. At the
end of the twelve steps, decision makers have technical and
economic indicators to support both long-term, model-
based decisions and short-term, data-driven decisions. This
ﬂexible 12-step procedure is suitable for companies with
different aims as the process is guided by the objective:
model quality, the necessity for further data assimilation
(history matching), objective function selection, etc.
The 12-step procedure must be repeated whenever new
important information is obtained and, therefore, is a con-
tinuous and iterative process. The most critical time is, of
course, when the development is prepared and the produc-
tion facilities selected. The number and placement of wells
are important outputs of this analysis but the procedure
can also be useful at later stages for real-time reservoir man-
agement. Readers interested in the management phase are
referred to the case study UNISIM-I-M (Gaspar et al.,
2016b).
The Robust Optimization, the optimization problem
formulated under uncertainty to maximize a probabilistic
objective function, ensured the best performance under
uncertainty, considering the EMV, downside risk, and
upside potential. However strong in robustness, this single
production strategy gives little information on the different
possibilities of ﬁeld development, such as number and
placement of wells, and platform processing capacities.
Table 6. EVoI for an appraisal well to assess the uncertainties BL and WOC. Values in USD million.
With information
Without information
EMV
SB
SB+
ɛ(NPV)wi
EMV
SB
SB+
ɛ(NPV)woi
EVoIɛ
EVoIEMV
Perfect information
1922
(+1.8%)
376
(1.0%)
728
(+5.8%)
2478
(+5.0%)
1889
379
689
2360
118
34
Imperfect information
(95% bl, 80% wo)
1910
(+1.2%)
378
(0.4%)
716
(+4.1%)
2440
(+3.4%)
1889
379
689
2360
80
22
Table 7. Best production strategy according to informa-
tion outcomes.
Appraisal well
indicates
Well logs indicate the
depth of water-oil
contact
Best
production
strategy is
Hydrocarbons
present in the East
block
3174 m (woc0)
EMR
3074 m (woc1)
EMR
3124 m (woc2)
EMR
3224 m (woc3)
E9
3274 m (woc4)
E9
Hydrocarbons
absent in the East
block
3174 m (woc0)
EMR
3074 m (woc1)
EMR
3124 m (woc2)
EMR
3224 m (woc3)
EMR
3274 m (woc4)
EMR
D.J. Schiozer et al.: Oil & Gas Science and Technology - Rev. IFP Energies nouvelles 74, 46 (2019)
15
Conversely, these data can be retrieved from the production
strategies
optimized
individually
for
each
RM.
This
approach gives decision makers an objective assessment of
how different (or similar) these alternatives are, which
provides valuable insights for EVoI and EVoF studies.
In addition, as extensive optimization procedures were
unnecessary in this ﬁnal step, the analyses were automated.
This automation is particularly important for EVoI, as it
facilitates the assessment of the value of perfect and imper-
fect information, for varying degrees of information reliabil-
ity. Note that, although we aim to automate as many
procedures as possible, we recognize that manual reﬁne-
ment procedures can be valuable to investigate possible
misconceptions or avoid local maxima of optimization
processes with large search spaces.
We showed that specialized (deterministic) optimiza-
tion can be advantageous in decision and risk analyses,
provided that it is part of a probabilistic process (i.e., it is
not limited to the most likely scenario). However, note that
an adequate RM selection must be guaranteed, representing
system inputs (probability distribution of uncertain attri-
butes) and outputs (variability in production, injection,
and economic forecasts). Furthermore, we showed that
robust and specialized optimizations are complementary
approaches in the decision-analysis process, and our current
recommendation is to perform both in a case study. As this
can be computationally demanding, the decision to choose
one type of optimization over the other depends on the
company’s objectives. Future research is planned on meth-
ods to make conducting both approaches computationally
feasible for day-to-day applications.
In our case study, we used the proposal by Meira et al.
(2016) to select the RMs. The attractiveness of this
proposal is that it ensures that the set of RMs represents
0.0
0.2
0.4
0.6
0.8
1.0
-1000
1000
3000
Cumulative Probability
NPV (USD million)
B
E1
E9
R4
EMR
(a)
0.0
0.2
0.4
0.6
0.8
1.0
-1000
1000
3000
5000
Cumulative Probability
NPV (USD million)
B
E1
E9
R4
EMR
(b)
Fig. 15. NPV risk curves for the production strategy of the base case (E1), the best specialized production strategy (E9), the robust
strategy obtained through a robust optimization procedure (EMR), and a robust strategy obtained by manually improving E9 (R4):
a) NPV without economic uncertainty; and b) NPV with economic uncertainty.
Fig. 16. NPV and oil recovery factor variations for best optimized strategy.
D.J. Schiozer et al.: Oil & Gas Science and Technology - Rev. IFP Energies nouvelles 74, 46 (2019)
16
uncertainty in both system inputs and outputs. Ease-of-use
is guaranteed as the method is software based, RMFinder.
At the time of this study, we applied the initial proposal
of RMFinder (Meira et al., 2016), which had some simpliﬁ-
cations, namely the set of RMs were assumed to be
equiprobable and it only considered a maximum of four
ﬁeld indicators (e.g., NPV, Np, Wp, ORF). Meira et al.
(2017) improved RMFinder by assigning probabilities to
each RM. Further improvements are still ongoing, such as
increasing the number of objective functions (up to 50),
including not only ﬁeld indicators, but also well indicators
(such as ﬂuid rates and BHP).
From previous studies for different case studies, we
observed that around nine RMs are sufﬁcient for production
strategy selection. Future research is currently being con-
ducted on the optimal number of RMs and candidate pro-
duction
strategies
applied
to
studies
of
information
acquisition, robustness, and ﬂexibility, and on the possible
loss of precision that this simpliﬁcation may cause.
Some probabilistic analyses of this study used the full
set of hundreds of scenarios matching production data.
Although ensuring the most accurate predictions possible,
this approach is computationally demanding and poten-
tially unfeasible for simulation models with a very high run-
time. Thus, it is important to deﬁne an objective and the
necessary precision in Step 2. We are currently conducting
research on assessing the feasibility of performing all prob-
abilistic-based analyses on a small subset of RMs, each
characterized by a probability of occurrence.
Our case study was in an early development phase,
meaning that model updating was conducted with limited
production data (four years of production data for four pro-
duction wells). However, the 12-step method is not limited
to such cases and can be applied to ﬁelds with many years
of production data from producers and injectors, as demon-
strated by Soares et al. (2018).
Data-driven, short-term decisions were not included in
this work because they are more appropriate to a ﬁeld in
the management phase.
6 Conclusion
We have presented a model-based, closed-loop methodology
based on twelve steps to be used in decision analysis for pet-
roleum reservoir development and management under
uncertainties, covering both model updating and produc-
tion optimization.
Companies often skip several steps to expedite projects
but we believe that, with the simpliﬁcation presented here,
the methodology is applicable to real cases, including
complex cases with long simulation runtimes, and still
ensure reliable decisions. In such a case, it is possible to
decrease the number of simulations in Steps 6, 7 and 9 and
select a smaller number of representative models (Step 8).
Note that further simpliﬁcations can yield suboptimal
decisions. The level of detail of each step is a function of
the importance of the study, the complexity of the case,
and the available resources and time. The most time-
consuming part is the optimization of the production
strategy and the results are a function of the quality of this
process; therefore, it is important to use computationally
efﬁcient optimization processes.
The methodology is ﬂexible enough to be applied to
reservoirs in different stages of their lifetime. We presented
a case in the development phase but it can be used at other
stages. It is also simple enough to be used in practical appli-
cations because it does not require proxy models or complex
tools.
By providing a comprehensive decision structure that
integrates reservoir characterization, data assimilation,
and production optimization, our method works as the core
basis for specialized methodologies for each of these
domains, as our results have shown.
Acknowledgments. This work was conducted with the support of
Energi Simulation and Petrobras within the ANP R&D tax as
“commitment to research and development investments”. The
authors are grateful for the support of the Center of Petroleum
Studies (CEPETRO-UNICAMP/Brazil), the Department of
Energy (DE-FEM-UNICAMP/Brazil) and the Research Group
in
Reservoir
Simulation
and
Management
(UNISIM-
UNICAMP/Brazil). In addition, special thanks to CMG and
Schlumberger for software licenses and technical support.
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20

processes
Article
Optimization Study on Increasing Yield and Capacity of Fluid
Catalytic Cracking (FCC) Units
Yuming Zhang, Ziming Li *, Zeyu Wang and Qibing Jin


Citation: Zhang, Y.; Li, Z.; Wang, Z.;
Jin, Q. Optimization Study on
Increasing Yield and Capacity of
Fluid Catalytic Cracking (FCC) Units.
Processes 2021, 9, 1497. https://
doi.org/10.3390/pr9091497
Academic Editor: Francesco Parrino
Received: 29 July 2021
Accepted: 23 August 2021
Published: 25 August 2021
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Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
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conditions of the Creative Commons
Attribution (CC BY) license (https://
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4.0/).
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100020, China;
2015400132@mail.buct.edu.cn (Y.Z.); wangzeyu@bipt.edu.cn (Z.W.); jinqb@mail.buct.edu.cn (Q.J.)
* Correspondence: 2019200767@mail.buct.edu.cn; Tel.: +86-130-1125-5688
Abstract: The ﬂuidized catalytic cracking (FCC) unit is the primary production unit for gasoline
and oleﬁns in reﬁneries. Improving product yield and processing capacity is closely related to
the economic beneﬁts of reﬁneries. In this paper, we use the Aspen HYSYS software to model the
mechanism of an 800 kt/a FCC unit in China. The simulation of the FCC unit based on the 21-lump
models and the simulation results are in good agreement with the actual factory production data.
Then we ﬁnd out the optimization method to improve the product yield and processing capacity
through the model research. The results of the study indicate the following optimization plans: when
keeping the riser outlet temperature (ROT) in the range of 535~545 ◦C, it can increase the yield of
gasoline and liqueﬁed petroleum gas (LPG) by up to 5%; when keeping the riser outlet temperature
(ROT) in the range of 510~520 ◦C, it can increase the yield of gasoline and diesel by up to 0.5%; the
optimal ROT of control temperature should be 530 ◦C when ensuring the same gasoline yield and
increasing processing capacity, it can increase the feed rate by 14.3% which in turn increases gasoline
production. Through the above plans, the reﬁnery can achieve the production goals of yield and
capacity. Therefore, the reﬁnery can get more proﬁts.
Keywords: ﬂuidized catalytic cracking; mechanism modeling; operation optimization
1. Introduction
The current economic environment, policies and regulations, and environmental
protection pressure on reﬁnery process integration and operation optimization [1,2]. The
FCC unit is the oil reﬁnery’s largest production unit for gasoline and light products [3].
It plays a key role in the operation of all reﬁneries [4]. The yield and output of the staple
products of the FCC unit are the keys to ensuring the economic beneﬁts of the reﬁnery.
Reﬁnery operators can ﬁne-tune the device based on experience to increase the yield
and output of the FCC unit. However, it is necessary to realize the improvement for
the unit by grasping the chemical reaction, the feed characteristics, and the equipment
performance [5,6]. On this basis, it is crucial to use a strict model [7]. In particular, a strict
model validated by unit data can determine the key locations for process improvement.
Previous authors have studied the problems of the FCC units in terms of mechanism
modeling and operational optimization. Especially the efforts of Arbel et al. [8] and
McFarlane et al. [9] in this area merit highlighting. In Arbel’s work, they developed
a 10-lump model. Although this model ﬁts well for simulating industrial production
trends, with the 10-lump model it is difﬁcult to deal with heavy feedstocks (boiling points
above 510 ◦C), so the developed model is not widely applicable. McFarlane provided an
Advanced Continuous Simulation Language (ACSL) program to integrate the FCCU model.
However, this model is highly nonlinear and strongly interacting, making it difﬁcult to
adapt widely to various reﬁneries. Alshamsi [10] subsequently established a four-lump
model and optimized the product by varying the height of the riser reactor. Stratiev
et al. [11] increased the output of the FCC unit by using vortex separation system (VSS)
riser technology to modify the reactor and regenerator. Luo et al. [12] developed a new
Processes 2021, 9, 1497. https://doi.org/10.3390/pr9091497
https://www.mdpi.com/journal/processes
Processes 2021, 9, 1497
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catalyst with a special coke. They found that it can effectively improve tar conversion rates
and gas yields. Wallin et al. [13] achieved the maximization of reﬁnery proﬁts by changing
the catalyst to oil ratio to alter the catalyst’s efﬁciency.
However, most of the early work used very simpliﬁed chemical reactions to express
the process kinetics. In addition, both the reaction regeneration and fractionation systems
are complex and difﬁcult to converge. The early work in the literature did not integrate the
FCC reaction model with its complex fractionation system, which would cause optimization
to deviate from the actual large-scale integration conditions. At the same time, it is time-
consuming and labor-intensive work to reform the catalytic cracking unit, develop new
catalysts or change the properties of raw materials. This research work ﬁlls the gap
between the development of rigorous dynamics models in large reﬁneries and industrial
applications. We have eliminated the barriers among the three FCC systems and combined
them into a complete system. In this way, we can more truly and comprehensively reﬂect
the overall picture of the processing system of the reﬁnery, ﬁnd the laws and change trends
in the system, and then efﬁciently and accurately realize the optimization of the system.
Moreover, we use the FCC process mechanism model established by Aspen HYSYS to
ﬁnd a convenient way to increase the yield and output of the FCC unit by changing the
ROT. This method is conducive to the adjustment of the reﬁnery, not just conﬁned to the
laboratory. Furthermore, there is no need to invest too much in cost, and the danger is also
within the controllable range.
2. Materials and Methods
2.1. Case Study
We take an 800 kt/a FCC reﬁnery in China as the research object. The FCC unit
mainly comprises the reaction regeneration system, fractionation system, and absorption
stabilization System [14]. Figure 1 shows a sketch of the process ﬂow.
Processes 2021, 9, x FOR PEER REVIEW 
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[11] increased the output of the FCC unit by using vortex separation system (VSS) ris
technology to modify the reactor and regenerator. Luo et al. [12] developed a new cataly
with a special coke. They found that it can effectively improve tar conversion rates a
gas yields. Wallin et al. [13] achieved the maximization of refinery profits by changing t
catalyst to oil ratio to alter the catalyst’s efficiency. 
However, most of the early work used very simplified chemical reactions to expre
the process kinetics. In addition, both the reaction regeneration and fractionation system
are complex and difficult to converge. The early work in the literature did not integra
the FCC reaction model with its complex fractionation system, which would cause op
mization to deviate from the actual large-scale integration conditions. At the same time
is time-consuming and labor-intensive work to reform the catalytic cracking unit, devel
new catalysts or change the properties of raw materials. This research work fills the g
between the development of rigorous dynamics models in large refineries and industr
applications. We have eliminated the barriers among the three FCC systems and co
bined them into a complete system. In this way, we can more truly and comprehensive
reflect the overall picture of the processing system of the refinery, find the laws a
change trends in the system, and then efficiently and accurately realize the optimizati
of the system. 
Moreover, we use the FCC process mechanism model established by Aspen HYS
to find a convenient way to increase the yield and output of the FCC unit by changing t
ROT. This method is conducive to the adjustment of the refinery, not just confined to t
laboratory. Furthermore, there is no need to invest too much in cost, and the danger
also within the controllable range. 
2. Materials and Methods 
2.1. Case Study 
We take an 800 kt/a FCC refinery in China as the research object. The FCC unit main
comprises the reaction regeneration system, fractionation system, and absorption stab
zation System [14]. Figure 1 shows a sketch of the process flow. 
 
Figure 1. The FCC process diagram. 
2.1.1. Reaction Regeneration System 
Figure 1. The FCC process diagram.
2.1.1. Reaction Regeneration System
The crude oil is sent from the upstream device to the oil tank and then enters the riser
reactor after preheating and atomization. The spent catalyst recovered by the device enters
the stripping section. It comes into countercurrent contact with steam to strip the oil gas
carried by the catalyst. Most of the catalyst enters the coking tank through the standby
inclined tube. The reacted oil gas needs to remove the catalyst and then enter the lower
part of the fractionation tower through the reaction oil gas pipeline.
Processes 2021, 9, 1497
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2.1.2. Fractionation System
The reacted oil gas from the settler enters the bottom of the fractionation tower and
then meets the circulating oil slurry in the upper return column to wash and remove the
catalyst and excess heat. The oil gas at the top of the fractionation tower is cooled to 40 ◦C
and then enters the phase separator for vapor-liquid separation. The separated rich gas
enters the air pressure machine. The oil pump pumps the remaining crude gasoline and
enters the top of the absorption column as an absorbent. The light diesel oil is extracted
from the fractionation tower’s 12th and 14th ﬂoors, sent to the light diesel oil stripping
tower, and then cooled out of the device after steam stripping. Cool the lean absorption oil
to 40 ◦C. Part of it goes to the tank area, and the rest goes to the top of the absorption tower
as absorbent.
2.1.3. Absorption Stabilization System
At the top of the fractionation tower, the rich gas enters a ﬁrst-stage air compressor for
compression and then cools to 40 ◦C for vapor-liquid separation. The separated rich gas
enters the second-stage air compressor for compression. Then it is washed with acid water
and cooled by the dry air cooler. Then it is mixed with the bottom oil of the absorption
column and the top gas of the desorption column and then cooled to 40 ◦C for vapor-liquid
separation. The separated rich gas enters from the bottom of the absorption column and is
absorbed with crude gasoline and stable gasoline. The lean gas goes to the reabsorption
column and is further absorbed by light diesel oil. After the top dry gas of the absorption
column enters the separator tank, it is divided into two parts, one goes to the product
reﬁning unit for desulfurization, and the other is used as pre-lift dry gas. The separated
condensate oil is pumped and boosted directly into the top of the desorption column to
desorb the components less than C3. The de-ethane gasoline is drawn from the bottom
of the desorption column and sent to the stabilization column for fractionation after self-
pressure and heat exchange. Liquid hydrocarbons distill from the top of the stabilizing
column. Part of it returns to the stabilizing column, and the rest goes to the product reﬁning
unit. Stabilized gasoline ﬂows out from the bottom of the stabilizing column. A part of it
goes to the gasoline hydrogenation unit as a gasoline product. The other part goes to the
absorption column as the supplementary absorbent.
2.2. Simulation Methods
First, we need to develop a strict kinetic model to ﬁnd the reﬁnery optimization
method. This work uses Aspen HYSYS V11 software [15], and the 21-lump kinetic dynamics
model [16] developed by AspenTech [17,18] is used to model the process mechanism based
on the actual production process data of the reﬁnery. The simulation of the whole process
of the FCC unit of the reﬁnery is shown in Figure 2. The model can solve heavy feedstocks
(boiling point above 510 ◦C), which the original 10-lumped model cannot solve. The default
component package selected in this simulation is “FCC Components Celsius.cml”. Since
most FCC systems are virtual components and light hydrocarbons, the thermodynamic
ﬂuid package chooses the “Peng-Robinson” method [19]. For modeling the ﬂuidized bed
reactor in the process, we selected the FCC module that comes with the software [20].
Processes 2021, 9, 1497
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Figure 2. HYSYS process simulation diagram. 
Figure 2. HYSYS process simulation diagram.
First, specify the size of the reactor and the regenerator, then set the heat loss at
different positions to be 0, and then use the default correction factor to establish the initial
model. Next, we should input feed and catalyst information into the software. Then
conﬁgure the FCC operating variables to solve the initial model. After the solution is
converged, based on the current simulation situation, input the yield and performance
of the measurement process and then update the correction factor to achieve the effect of
correcting the initial model. Next, simulate the fractionation, absorption, and stabilization
systems. The fractionation column selects the absorption column model with top reﬂux.
The stripping column selects the absorption column model with a reboiler. The stabilization
column selects the rectiﬁcation column model. The absorption column and the reabsorption
column both select the absorption column model [21].
3. Results and Discussion
3.1. Yield Analysis on the Model
After the previous work, we have obtained the process model and need to verify
its accuracy. First, compare the simulation results of the main product yields with the
steady-state data of the main product yields under actual operating conditions, as shown
in Table 1. We ﬁnd that the deviation between the simulation yield of the main products
(LPG, gasoline, light diesel) and the actual operating conditions is relatively small.
Table 1. Comparison of calibration yield and simulation yield.
Product Name
Calibration Yield (%)
Simulation Yield (%)
Deviance
LPG
22.3
18
0.19
gasoline
37.4
40.6
0.09
light diesel oil
21.3
23
0.08
dry gas
3.5
5.5
0.57
slurry
8.5
4.6
0.46
3.2. Comparative Analysis of the Main Operating Parameters of the Model
Next, compare the main operating parameters of the process simulation model with
the actual design parameter ranges, as shown in Table 2. We ﬁnd that most of the simulation
parameters are within the design parameters. It is not distinct that individual parameters
are beyond the deﬁned range.
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Table 2. Comparison of design value and simulation value of operation parameters.
Project
Unit
Design Control Scope
Simulation Value
Settler pressure
Mpa
0.218~0.318
0.300
Riser outlet temperature
◦C
475~535
518
Preheating temperature of feed oil
◦C
170~240
175
Regenerator top pressure
Mpa
0.248~0.348
0.348
Regeneration dense phase temperature
◦C
660~720
680
Regenerator dense reservoir
t
45~150
80
Fractionator bottom temperature
◦C
320~350
349.8
Fractionator top temperature
◦C
105~130
119.7
Top pressure of stabilizer
Mpa
̸>1.05
1.17
Analysis of tower top pressure
Mpa
1.2~1.5
1.266
Reabsorber top pressure
Mpa
1.1~1.4
1.200
Stabilizer bottom temperature
◦C
150~190
157.5
Bottom temperature of analytical tower
◦C
95~135
115
Absorber top pressure
Mpa
1.1~1.4
1.3
3.3. Comparative Analysis of the Distillation Curves of the Main Products of the Model
Finally, whether we can get qualiﬁed products is a signiﬁcant assessment indicator.
It requires oil product evaluation of the products [22]. We compare the product D1160
distillation data obtained by the process simulation with the D1160 distillation data of the
actual working condition calibration test, as shown in Figure 3. We can see from the ﬁgure
that the simulated data is in good agreement with the actual data. It also further conﬁrmed
that the process simulation model obtains qualiﬁed results.
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3.3. Comparative Analysis of the Distillation Curves of the Main Products of the Model 
Finally, whether we can get qualified products is a significant assessment indicator. 
It requires oil product evaluation of the products [22]. We compare the product D1160 
distillation data obtained by the process simulation with the D1160 distillation data of the 
actual working condition calibration test, as shown in Figure 3. We can see from the figure 
that the simulated data is in good agreement with the actual data. It also further confirmed 
that the process simulation model obtains qualified results. 
 
 
(a) 
(b) 
(c) 
(d) 
■ Calibration value● Simulation value. 
Figure 3. Comparison of the product D1160 distillation data (a) Stable gasoline distillation data; (b) Crude gasoline distil-
lation data; (c) Light diesel distillation data; (d) Deethanized gasoline distillation data.  
3.4. Optimization of Improving Gasoline Yield 
Gasoline yield is a typical complex function related to temperature, pressure, feed 
quality, and catalyst oil ratio [23]. ROT [24] is an operational variable that is relatively easy 
to manipulate to keep the unit feed constant. With the increase of ROT, the cracking of the 
C5+ component and aromatics chain-breaking reaction will increase [1]. So, the yield of 
gasoline is increased. In this paper, we use the Case Studies tool [25] to calculate the 
Figure 3. Comparison of the product D1160 distillation data (a) Stable gasoline distillation data; (b) Crude
gasoline distillation data; (c) Light diesel distillation data; (d) Deethanized gasoline distillation data.
Processes 2021, 9, 1497
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3.4. Optimization of Improving Gasoline Yield
Gasoline yield is a typical complex function related to temperature, pressure, feed
quality, and catalyst oil ratio [23]. ROT [24] is an operational variable that is relatively easy
to manipulate to keep the unit feed constant. With the increase of ROT, the cracking of
the C5+ component and aromatics chain-breaking reaction will increase [1]. So, the yield
of gasoline is increased. In this paper, we use the Case Studies tool [25] to calculate the
gasoline yield of the unit under different ROT. The results are shown in Figure 4a. The
actual operating condition ROT is 518 ◦C, while the maximum gasoline yield ROT is 533 ◦C.
Nevertheless, there are other valuable products to consider. The yield of other valuable
products calculated by the study is shown in Figure 4c.
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gasoline yield of the unit under different ROT. The results are shown in Figure 4a. The 
actual operating condition ROT is 518 °C, while the maximum gasoline yield ROT is 533 
°C. Nevertheless, there are other valuable products to consider. The yield of other valua-
ble products calculated by the study is shown in Figure 4c. 
 
■ Quality yield of gasoline 
■ Quality yield of coke 
(a) 
(b) 
 
■ quality yield of gasoline ● quality yield of LCO 
▲Quality yield of combustible gas ▼Quality yield of LPG 
 
■ Quality yield of gasoline and LPG 
● Quality yield of gasoline and LCO 
(c) 
(d) 
Figure 4. Effect of ROT on product yield (a) Gasoline yield; (b) Coke yield; (c) Important products; (d) Combined important 
products. 
The diesel yield decreases significantly at ROT of 533 °C. At the same time, the yield 
of combustible gas (light gas) will increase rapidly, which indicates that the feed is over-
cracked [26]. Dry gas and fuel gas are not of great value and tend to overload the gas-rich 
compressor at the top of the tower [27]. In addition, as shown in Figure 4b, there is a strong 
correlation between the coke yield of the catalyst leaving the riser and the ROT. The more 
coke, the more utility consumption of regenerated catalysts will increase, so the above 
factors narrow the acceptable range of ROT. We conducted a study based on different 
Figure 4. Effect of ROT on product yield (a) Gasoline yield; (b) Coke yield; (c) Important products; (d) Combined
important products.
The diesel yield decreases signiﬁcantly at ROT of 533 ◦C. At the same time, the yield
of combustible gas (light gas) will increase rapidly, which indicates that the feed is over-
cracked [26]. Dry gas and fuel gas are not of great value and tend to overload the gas-rich
Processes 2021, 9, 1497
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compressor at the top of the tower [27]. In addition, as shown in Figure 4b, there is a
strong correlation between the coke yield of the catalyst leaving the riser and the ROT.
The more coke, the more utility consumption of regenerated catalysts will increase, so
the above factors narrow the acceptable range of ROT. We conducted a study based on
different maximized product production options for the reﬁnery, as shown in Figure 4d.
If the reﬁnery maximizes the production of gasoline and LPG, the ROT will be in the
range of 535~545 ◦C. It can increase the yield of gasoline and LPG by up to 5%. If the
reﬁnery maximizes the production of gasoline and diesel, the ROT will be in the range of
510~520 ◦C. It can increase the yield of gasoline and diesel by up to 0.5%.
3.5. Optimization of Increasing the Capacity of the Unit
Reﬁneries usually want to process more feedstocks as much as possible. Ideally, they
want the quality and yield of high value-added products (such as gasoline) to be stable
at a certain level [28]. The relationship between gasoline yield and unit feed is shown in
Figure 5. The gasoline yield decreased linearly with the increase of feed. The increase in
feed volume leads to a shorter contact time with the catalyst, which reduces the cracking of
the components and thus reduces the gasoline yield.
Processes 2021, 9, x FOR PEER REVIEW 
8
 
3.5. Optimization of Increasing the Capacity of the Unit 
Refineries usually want to process more feedstocks as much as possible. Ideally
want the quality and yield of high value-added products (such as gasoline) to be sta
a certain level [28]. The relationship between gasoline yield and unit feed is show
Figure 5. The gasoline yield decreased linearly with the increase of feed. The incre
feed volume leads to a shorter contact time with the catalyst, which reduces the cra
of the components and thus reduces the gasoline yield. 
■ quality yield of gasoline ● quality yield of LCO 
▲ Quality yield of combustible gas ▼ Quality yield of LPG 
Figure 5. Effect of feed mass flow rate on yield of important products. 
We consider a production plan that can increase or stabilize the gasoline yield
tive to the capacity of the basic unit. We increase the ROT and the feed volume of the
As shown in Figure 6, we can see that gasoline yield also increases as the ROT incr
However, when the ROT reaches 540 °C, the gasoline yield will decrease rapidly d
over-cracking. When the ROT is 530 °C, we can ensure the gasoline yield and increa
feed rate to achieve a higher gasoline production by up to 14.3%. The refinery can a
operating parameters according to the pattern shown to achieve a production sol
that increases output and improves or stabilizes gasoline yields. 
 
 
Figure 5. Effect of feed mass ﬂow rate on yield of important products.
We consider a production plan that can increase or stabilize the gasoline yield relative
to the capacity of the basic unit. We increase the ROT and the feed volume of the unit.
As shown in Figure 6, we can see that gasoline yield also increases as the ROT increases.
However, when the ROT reaches 540 ◦C, the gasoline yield will decrease rapidly due to
over-cracking. When the ROT is 530 ◦C, we can ensure the gasoline yield and increase the
feed rate to achieve a higher gasoline production by up to 14.3%. The reﬁnery can adjust
operating parameters according to the pattern shown to achieve a production solution that
increases output and improves or stabilizes gasoline yields.
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■ ROT = 510 °C ● ROT = 520 °C 
▲ ROT = 530 °C ▼ ROT = 540 °C 
Figure 6. Effect of feed rate on gasoline yield at different temperatures. 
4. Conclusions 
In this paper, we use Aspen HYSYS to model the entire process mechanism of a
unit and compare actual and simulated data to verify the accuracy of the model. A
same time, we conducted a study on operating parameters optimization to increas
yield of the main products and increase the production capacity. Moreover, we obta
the optimized control range of the ROT with the relevant products as the main outpu
increase the total yield of gasoline and LPG, ROT should be within 535~545 °C; for 
line and diesel, ROT should be within 510~520 °C. To increase gasoline yield and ou
at the same time, ROT needs to be adjusted to 350 °C. Through this research, a re
refinery process model has been established and can provide a reliable guiding bas
the actual control and planning of the refinery. 
Author Contributions: Conceptualization, Y.Z.; methodology, Q.J.; software, Y.Z.; validation
Z.L.; formal analysis, Y.Z.; investigation, Z.L.; resources, Y.Z.; data curation, Z.L.; writing—or
draft preparation, Z.L.; writing—review and editing, Z.L.; visualization, Z.W.; supervision
project administration, Q.J. All authors have read and agreed to the published version of the
uscript. 
Funding: This research received no external funding. 
Institutional Review Board Statement: Not applicable. 
Informed Consent Statement: Not applicable. 
Data Availability Statement: Not applicable. 
Conflicts of Interest: The authors declare no conflict of interest. 
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Marafi, A.; Albazzaz, H.; Rana, M.S. Hydroprocessing of heavy residual oil: Opportunities and challenges. Catal. Today
329, 125–134, doi;10.1016/j.cattod.2018.10.067. 
3. 
None. Survey: Global Refining Industry Competes to Supply Growing Asian Market. Oil Energy Trends 2017, 42, 1
Figure 6. Effect of feed rate on gasoline yield at different temperatures.
4. Conclusions
In this paper, we use Aspen HYSYS to model the entire process mechanism of a FCC
unit and compare actual and simulated data to verify the accuracy of the model. At the
same time, we conducted a study on operating parameters optimization to increase the
yield of the main products and increase the production capacity. Moreover, we obtained
the optimized control range of the ROT with the relevant products as the main output. To
increase the total yield of gasoline and LPG, ROT should be within 535~545 ◦C; for gasoline
and diesel, ROT should be within 510~520 ◦C. To increase gasoline yield and output at the
same time, ROT needs to be adjusted to 350 ◦C. Through this research, a reliable reﬁnery
process model has been established and can provide a reliable guiding basis for the actual
control and planning of the reﬁnery.
Author Contributions: Conceptualization, Y.Z.; methodology, Q.J.; software, Y.Z.; validation, Z.W.,
Z.L.; formal analysis, Y.Z.; investigation, Z.L.; resources, Y.Z.; data curation, Z.L.; writing—original
draft preparation, Z.L.; writing—review and editing, Z.L.; visualization, Z.W.; supervision, Q.J.;
project administration, Q.J. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conﬂicts of Interest: The authors declare no conﬂict of interest.
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1575
Korean J. Chem. Eng., 40(7), 1575-1586 (2023)
DOI: 10.1007/s11814-023-1411-5
INVITED REVIEW PAPER
pISSN: 0256-1115
eISSN: 1975-7220
INVITED REVIEW PAPER
†To whom correspondence should be addressed.
E-mail: ashfaq.ahmed@vu.edu.au, ykpark0426@gmail.com
Copyright by The Korean Institute of Chemical Engineers.
Optimization analysis of the absorption-stabilization process
for fluid catalytic cracking unit
Muhammad Saddam Hussain*, Ashfaq Ahmed**,†, Liu Yibin*, Muhammad Nadeem Amin***,
Tahir Zahoor***, Muhammad Afnan Saleem****, Kosan Roh*****, Murid Hussain******,
Muhammad Saifullah Abu Bakar*******, and YoungKwon Park********,†
*Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Beijing Key Laboratory
of CO2 Utilization and Reduction Technology, Department of Energy and Power Engineering,
Tsinghua University, Beijing, 100084, P. R. China
**Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne 8001, Australia
***Department of Chemical Engineering, NFC IET, Multan 66000, Pakistan
****State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University, Xi’an 710049, P. R. China
*****Department of Chemical Engineering and Applied Chemistry, Chungnam National University, Daejeon 34141, Korea
******Department of Chemical Engineering, COMSATS University Islamabad, Lahore Campus,
Defence Road, Off Raiwind Road, Lahore 54000, Pakistan
*******Faculty of Integrated Technologies, Universiti Brunei Darussalam, Jalan Tungku Link BE1410, Brunei Darussalam
********School of Environmental Engineering, University of Seoul, Seoul 02504, Korea
(Received 24 November 2022 • Revised 28 February 2023 • Accepted 2 March 2023)
AbstractThe absorption-stabilization process (ASP), an important part of petroleum refinery used in the end-use
products of petroleum (such as stable gasoline, liquid petroleum gas, and dry gas), is energy-intensive and has low
product quality. Aspen Plus process simulator was used for the development of the ASP process model. The developed
process model was validated with the actual plant data. The validated model was used to optimize to minimize the cost
of the ASP
. This work shows that the optimization analysis of the ASP can further improve the product quality and
reduce thermal energy consumption. In the new process, changing feeding parameters of supplementary absorption oil,
stripping tower intermediate reboiler, and feeding position of stabilization tower reduced the C3 contents of dry gas
considerably and lowered the C2 and lighter contents of LPG. Additionally, the new process saved 1.32 MW of thermal
energy consumption compared with the existing process. The operating cost has been reduced from 10.921 million
USD annually to 9.830 million USD per year. Furthermore, the cost-saving effect of this optimization is about 9.99%
(1.091 million USD per year).
Keywords: Absorption-stabilization Process, Sensitivity Analysis, Process Optimization, Process Simulation, FCC Process
INTRODUCTION
Currently, there is pressure on refinery operation optimization
due to environmental policies and regulations [1,2]. The fluid cat-
alytic cracking unit (FCCU) is the largest unit in the refinery for
the production of light hydrocarbons and gasoline [3]. Absorption-
stabilization process (ASP) is an important part of all the fluid cat-
alytic cracking units. ASP separates the refinery products. ASP is
mainly attached to FCCU, where it separates the mixture from the
fractionation tower into dry gas, liquefied petroleum gas (LPG) and
stabilized gasoline. A typical ASP consists of four towers (absorp-
tion, reabsorption, stripping, and stabilization tower) and two recy-
cle streams. In ASP
, because the absorption tower and stripping
tower provide feed to each other, they have a very close relationship
and play a very important role in the refinery by improving the energy
consumption, enhancing product quality and ensuring the smooth
work operation of stabilization tower. The rich absorption oil from
the absorption tower is used as feed for the stripping tower, and
desorbed gas from the stripping tower is recycled for the absorp-
tion tower. On the other hand, the operating conditions of the two-
tower process are much different, such as temperature, pressure,
number of stages and feed stage. To minimize the thermal energy
requirements and to increase the purity of products, it is essential
to study the effect of feed, and operating parameters of the absorp-
tion tower, reabsorption tower, stripping tower, and stabilization
tower to improve the performance of the entire ASP
.
The ASP has three feeding methods for stripping towers (desorp-
tion column): cold feed, hot feed, and hot and cold [4,5]. The tem-
perature of the cold feed is between 30 to 40 oC. Because the tem-
perature of cold feed is relatively low, so the temperature at the top
of the stripper is low. It makes the temperature of the desorbed gas
low, and the amount of desorbed gas is small. The processing capac-
ity of the absorption tower is reduced, and the low temperature con-
tributes to the absorption. However, this makes the stripping tower
bottom reboiler duty increase [6]. The temperature of the hot feed
is from 75 to 80 oC. Due to the higher temperature of the hot feed,
1576
M. S. Hussain et al.
July, 2023
the temperature at the top of the stripping tower is high, which
causes the temperature of the desorbed gas to be high. As a result,
the amount of desorbed gas becomes large, so the treatment vol-
ume of the absorption tower, therefore the load of the compressed
rich gas condenser is increased. In addition, due to a large amount
of desorbed gas C3, C4 components are in the absorption tower. The
circulation between the stripper and the compressed rich gas con-
denser increases the absorption tower’s processing capacity and
affects the product quality. But the advantage is that the load of the
reboiler at the bottom of the stripper is reduced [7]. To effectively
use the advantages of hot and cold feeds, many refineries use the
hot and cold double-feed method shown in Figs. 3-5. The hot and
cold dual-feed divides the condensed oil into two feeds in this
method. One feed is the cold feed directly from the oil and gas sep-
arator. The other is exchanged with the stable gasoline at the bot-
tom of the stabilizing tower as a hot feed. This method has a sig-
nificant advantage in refining the product quality and reducing energy
consumption [8,9]. Since one of the two feeds is cold feed and the
other is hot feed, they have not only the advantages of cold feeding
but also of hot feeding. But because of the different temperatures
and same composition groups, the same two feeds enter different
positions of the stripping tower, which destroys the reasonable con-
centration distribution of the components in the tower. However,
this method has problems with back mixing and axial mass transfer,
so it still needs improvement [5]. Therefore, to combine the advan-
tage of both cold and hot feed methods, feed splitting (hot and cold
feed method) was adopted to reduce energy consumption and C3
contents in dry gas. In the hot and cold feeding method for strip-
ping tower [5], there is a problem of back mixing of heavier com-
ponents that desorb and are found in the dry gas or increase the
flowrates to the flash tank and stripping tower; as a result, the equip-
ment size also increases and that affects plant economy.
Bandyopadhyay et al. [10] mentioned that having more than one
stream with different compositions and temperatures was benefi-
cial to enhance the mass transfer and separation efficiency of the dis-
tillation column. To avoid the back mixing of liquefied gas com-
ponents and continuous circulation in the equipment, some indus-
tries use the secondary condensation process to provide the hot and
cold feeds to the stripping column with the different compositions
of hot and cold feed. In the secondary condensation process, the
gas from the first condensation tank enters the secondary conden-
sation tank, where the gas phase is sent to the primary absorption
column, and the liquid phases of both condensation tanks feed to
the stripping column, whereas the liquid phase from first conden-
sation tank is used as hot feed while the liquid from secondary
condenser is used as cool feed to the stripping tower. The best feed
location of the distillation column is where it has the same compo-
sition of internal traffic as the feed streams, which can significantly
improve the mass transfer in the separation process [11].
After this, many researchers searched about optimizing the ab-
sorption-stabilization process by using different feeds and different
designs of heat exchanger networks to increase the purity of prod-
ucts and enhance the process economy. Nowadays, industries use
APS having two absorbers and two strippers to effectively separate
components. Sensitivity analysis is also an effective method to opti-
mize the chemical process [12]. It is common for dry gas to have a
high percentage of C3/C3+. A dry gas that has a C3/C3+ level of less
than 2% is required. The C3/C3+ content of dry gas was reduced by
altering the supplementary absorption oil flow rate of the existing
ASP proposed by Li et al. [13]. A stabilization tower feeds the mate-
rial into the stripper, which is heated in the middle reboiler. Rather
than using stabilized gasoline, light gasoline is removed from the
stabilization tower and used as a supplement absorber. A refrigera-
tor (An aluminum bromide refrigerator powered by low-grade hot
water) was used to cool the absorber feed to improve the absorp-
tion phenomenon. For the absorption column, they used computer
simulation to derive the empirical relationship (Eq. (1)) between
C3 and higher component concentrations and temperatures. This
method uses 2,432kW less energy than the original ASP
. In addi-
tion, the C3/C3+ component of dry gas is reduced to less than 2%
of the total dry gas volume. This optimization has resulted in a
17% reduction in energy consumption by operating the absorption
tower at a lower temperature.
y=0.2282 T62.27
(1)
Researchers from ETH Zurich optimized and integrated a de-eth-
anizer into the gas processing unit (GPU) and the FCCU’s absorp-
tion stabilization process to improve performance and lower C2 con-
centration in LPG. When the GPU’s de-ethanizer was removed,
propylene recovery increased from 94.8 to 99.8%. Extra-absorbent
flowrates, stripper feed temperature and C4 content in stabilizer bot-
toms as optimization variables were used by Lu et al. in their study.
LPG’s C2 content was reduced to less than 0.05%, the concentra-
tion of C3 in off-gas was reduced to less than 1.5%, and the concen-
tration of C4 in the stabilizer bottom was 3.5%. Thus, the simulation
results have shown an increase in propylene production of 1,756
tons per year and plant profit [14]. Zhang et al. developed an algo-
rithm that can improve the end quality products of FCCU. The
RVFL structure links the incremental component, mitigating the
inaccuracy brought on by the concept drift. Group lasso and L2
regularizations are used to dynamically assign the expansion layer’s
random parameters based on the incoming data. The RVFLN’
s uni-
versal approximation and rapid convergence qualities allow it to
meet the prediction accuracy and real-time demands of online qual-
ity prediction, while simultaneously sidestepping the overfitting and
catastrophic forgetting issues that plague connectionism models
[20]. Both the TE process and the FCC unit modeling tasks benefit
more from the W
AR-LSTM based technique than the LSTM based
approach, making it promising for industrial use. Contrarily, non-
uniformly collected data is common in chemical processes, although
the model was constructed using consistently sampled data [21].
To the best of our knowledge, maximum work has been done
on optimization of ASP
. However, there has been no such detailed
work on sensitivity analysis of this process. Such as position of sta-
bilization tower feed and intermediate reboiler. In this study, we
used the absorption-stabilization process of Changqing Petrochemi-
cal’s FCCU with 335,000tons per year rich gas separation capacity.
This paper aims to optimize the absorption-stabilization system of
Changqing Petrochemical’
s FCCU by minimizing the thermal energy
requirements and increasing the product purity. For this purpose,
changing effects of the operational parameters such as temperature,
pressure and flowrates of supplementary absorption oil, light die-
Optimization analysis of the absorption-stabilization process for fluid catalytic cracking unit
1577
Korean J. Chem. Eng.(Vol. 40, No. 7)
sel, stripping tower feed, the position of intermediate reboiler at strip-
ping tower, and feed position of stabilization tower were studied.
PROCESS DESCRIPTION
The existing ASP is the process of cold feed plus intermediate
reboiler, as shown in Fig. 1. It absorbs and stabilizes the crude gas-
oline and rich gas produced at the top of the fractionation tower
and separates a small amount of gas hydrocarbons mixed into the
crude gasoline to reduce the gasoline vapor pressure, ensure that it
meets the product specifications, and recover the liquefied gas (the
main components are C3, C4). The absorption stabilization system
is the post-treatment section of the FCC unit, which uses the prin-
ciples of absorption and rectification to separate the rich gas and
crude gasoline from the fractionation system into dry gas (C2 and
C2), liquefied petroleum gas (LPG) includes C3 and C4 components,
and gasoline products with qualified vapor pressure. The system
generally consists of four columns (an absorption tower, stripping
tower, reabsorption tower, and stabilization tower).
The rich gas from the fractionation system is compressed by two
stages of a gas pressure machine (compressor), injecting washing
water into the outlet pipeline to wash the compressed rich gas; after
condensing and cooling, it is mixed with the rich absorption oil
from the bottom of the absorption tower and the desorption gas
from the top of the stripping tower, and then further cooled in a
cooler and enters the balance tank (or oil and gas separator/flash
tank) which performs balanced vaporization. Non-condensable gas
after vapor-liquid equilibrium and condensed oil is sent to the ab-
sorption and stripping towers, respectively. To prevent the corro-
sion of equipment by sulfur, nitrogen, and cyanide, the front and
rear pipelines of the cooler and the crude gasoline are poured into
softened water for washing, and the sewage is discharged from the
balance tank and the crude gasoline washing tank, respectively.
The non-condensable gas from the balance tank enters the bottom
of the absorption tower, and from bottom to top, it comes into
contact with the absorbent crude gasoline; the supplementary ab-
sorption oil (stabilized gasoline in reverse), and most of the rich
gas C3 components and a small amount of C2 components are
absorbed by the liquid phase components. Absorption is an exo-
thermic process, and low temperature favors the absorption oper-
ation. To make the gas and liquid traffic of the whole tower evenly
distributed along with the height of the tower, it is necessary to set
up two intermediate pumps around in the absorption tower. This
lower temperature will absorb more gases and prevent C3 and C+
components from entering lean gas, hence enhancing the quality
of dry gas. The lean gas from the top of the absorption tower car-
ries a small amount of absorbent and passes through the pressure
control valve to the absorption tower to recover the lean gas with
light diesel (fraction of FCC main fractionation column) as the
absorbent. The dry gas from the top of the reabsorption tower is
rich in ethylene, and the rich liquid at the bottom of the reabsorp-
tion tower is returned upstream of the fractionation system.
The role of the stripping tower is to desorb the C2 in the rich
absorption oil components, also known as de-ethanizers. The con-
densate drawn from the bottom of the balance tank (condensed
oil) is pumped into the top of the stripping tower and desorbed
under the action of the reboiler at the bottom of the tower (ethane
enhances the desorption effect). At present, most refineries gener-
ally control the desorption rate at 100%. A large amount of desorp-
tion gas at the top of the tower is not acceptable. By avoiding de-
Fig.1.Typical process flow diagram of the absorption-stabilization process (ASP).
1578
M. S. Hussain et al.
July, 2023
sorption of some C3, and C4 components are led out from the top
of the tower, mixed with compressed rich gas and rich absorption
oil, cooled and entered into the balance tank, and then sent to the
absorption tower. The bottom of the stripping tower is de-etha-
nized gasoline, which exchanges heat with the stable gasoline from
the stabilization tower and enters the middle of the stabilization
tower. The function of the stabilizing tower is to separate C4 from
the de-ethanized gasoline. De-ethanized gasoline enters under the
action of the reboiler at the bottom of the stabilizing tower, the C4
and light components (known as LPG) are steamed out and obtained
at the top of the tower, and the stabilized gasoline at the bottom of
the tower split into two parts, one is used as a product output device
after heat exchange and cooling, and, the other part is further cooled
as a supplementary absorption oil for the absorption tower.
Fig.2.Simulation flow diagram of the absorption-stabilization process.
Table1.Parameters of all feed streams of ASP
Stream
P/MPa
T/oC
F/kg·h1
/Kg·m3
The TBP distillation curve (%)
IBP
10
30
50
70
90
FBP
Rich gas
1.60
42.0
42,230
-
-
-
-
-
-
-
-
Crude gasoline
1.06
42.0
86,000
721
41.5
060.0
083.0
106.0
132.0
168.0
187.0
Light diesel
1.03
27.1
14,000
874.2
59.5
202.5
232.5
258.5
295.5
352.0
360.0
Table2.Composition of rich gas
Component
Vol%
Component
Vol%
Component
Vol%
Component
Vol%
H2
0.018
CO2
0.008
Isobutane
0.140
sis-Butene
0.036
O2
0.002
Ethylene
0.029
n-Butane
0.052
1,3-butadiene
0.004
N2
0.038
Ethane
0.036
Butene-1
0.036
Pentane
0.002
Methane
0.065
Propylene
0.275
Isobutene
0.046
Isopentane
0.078
CO
0.002
Propane
0.112
Trans-butene-2
0.043
Hexane and above carbon 6
0.002
To control the operating pressure of the stabilizing tower, some-
times non-condensable gas must be discharged. Compared with
liquefied gas, the saturated vapor pressure of ethane is higher. Exces-
sive ethane content in the de-ethanized gasoline will reduce the
stability of the stabilizing tower’s operation and increase the load
on the condenser at the top of the tower, which is forced to dis-
charge non-condensable gas and the reduction of C3, and C4 affect
the rectification effect. So, the de-ethanized gasoline C2 contents affect
LPG and gasoline product quality.
PROCESS SIMULATION
ASP is simulated by using Aspen Plus process simulation soft-
ware V10 [15,16]. In the petroleum refining system, PENG-ROB
Optimization analysis of the absorption-stabilization process for fluid catalytic cracking unit
1579
Korean J. Chem. Eng.(Vol. 40, No. 7)
and RK-SOAVE are the most accurate thermodynamic method
for the simulation of the petroleum refinery [17]. RK-SOAVE is
selected as the property method [18], and the RadFrac process model
is selected for the absorption tower, reabsorption tower, stripping
tower and stabilization tower, Flash-2 is used for the flash tank, and
HX-1 is selected for heaters and coolers. This simulation model
has been extensively used in literature [13,14,19]. The simulation
process flowsheet is shown in Fig. 2. The parameters of all the feed
streams, the composition of rich gas, specifications of all towers and
comparison of operating parameters of ASP are given in Tables 1,
2, 3, and 4, respectively.
RESULTS AND DISCUSSION
1. Sensitivity Analysis
This section optimizes the main parameters of the absorption
stabilization system using the validated process model. T
emperature,
pressure and flow are the main decision variables in design and
production operations. In the following analysis, we also focus on
the impact of these important parameters on the product quality
and production process.
1-1. Effect of Supplementary Absorption Oil Flow Rate and Tem-
perature
Through the application of sensitivity analysis, the operating para-
meters of the absorption tower were analyzed. The influence of
the supplementary absorbent flow rate and temperature on the
quality of dry gas and liquefied gas was obtained, as shown in Fig.
3 (specifying that the molar solubility of C2 in the bottom of the
stripping tower and C5 in the liquefied gas is constant) and Fig. 4
to Fig. 5 (specifying that the temperature of the stripping tower is
constant). For the stripping tower, it is the reverse process of the
Table3.Specifications of absorption tower, reabsorption tower, stripping tower, and stabilization tower
Towers
Pressure/MPa
Temperature/oC
Number of stages
Feed stage
Top/Bottom
Top/Bottom
AB1
1.02/1.05
33/41
40
1/6/9/29
AB2
1/1.03
35/42
30
1
ST1
1.12/1.15
40/113
40
1/8
ST2
1.04/1.045
54/164
52
26
Table4.Comparison of operating parameters
Serial
number
Equipment
Simulation parameters of ASP
Existing plant parameters of ASP
Pressure (MPa)
Temperature (oC)
Pressure (MPa)
Temperature (oC)
01
Primary absorption tower top
1.02
33
1.02
34.7
02
Primary absorption tower bottom
1.05
41
1.0
41.5
03
Crud gasoline into the Primary absorber
1.06
42
1.06
42.0
04
Supplementary absorption oil
1.065
27.1
1.06
27.9
05
Rich gas into Primary absorber
1.1
36
1.1
35.1
06
Stripping tower top
1.12
40
1.12
40
07
Stripping tower bottom
1.15
113
1.15
110.6
08
Cond. oil into stripping tower
1.7
36
1.7
35.1
09
Reabsorption tower top
1
35
1.03
33.0
10
Reabsorption tower bottom
1.03
42
1.05
43.0
11
Lean gas into re-absorber
1.02
33
1.06
32.3
12
Light diesel into re-absorber
1.03
27.1
1.03
27.1
13
Stabilization tower top
1.04
54
1.04
57.7
14
Stabilization tower bottom
1.045
164
1.065
163
15
De-ethanized gasoline into stabilization tower
1.045
116
1.045
125
Fig.3.Effect of supplementary absorption oil on dry gas, bottom
reboiler temperature and heat load of stripping tower.
1580
M. S. Hussain et al.
July, 2023
absorption process, and at the same time, it is equivalent to a recti-
fication process with only a distillation section without a rectifica-
tion section.
Dry gas is mostly composed of C1 and C2 components, whereas
the heavier components affect the quality of dry gas, such as C3
and heavier. As the absorption tower is performing a physical absorp-
tion phenomenon, and the quality of dry gas depends on its com-
position. Fig. 3 shows the influence of supplementary absorbent
flow rate on dry gas quality and the effect of supplementary absorp-
tion oil where the content of C3 and above components in the dry
gas will first decrease faster. When the supplementary absorbent
flow rate reaches 52.5t/h, it will change slowly, so the supplemen-
tary absorbent’ flow rate cannot be too high; otherwise, it will sig-
nificantly increase the operating cost. With increasing the flow rate
of the supplementary absorbent while maintaining the same C2
component at the bottom of the stripping tower, the temperature
at the bottom of the stripping tower also needs to be increased,
which will increase the heat load of the stripping tower sharply. It
can be seen from Fig. 3 that when the flow rate of supplementary
absorbent increases, the dry gas production will gradually decrease,
which is also after 52.5t/h. With the supplementary absorbent
flow rate increase, dry gas production will no longer be sufficient.
It can be seen from Fig. 4 that when the temperature of the strip-
ping tower is constant at 114 oC, and the supplementary absorbent
flow rate is about 61.5t/h, after that as the supplementary absor-
bent flow rate further increases, the content of C3 and above com-
ponents in dry gas not change. Comparing Fig. 3 and Fig. 4, we
can find that when the temperature at the bottom of the tower is
controlled at constant, the turning point of the supplementary absor-
bent flow becomes smaller. When we control the bottom tempera-
ture of the stripping tower at 114 oC, the supplementary absorption
oil flow rate’s influence on dry and liquefied gas is shown in Fig. 4.
The content of C2 and CH4 in the liquefied gas gradually increases
with the increase in supplementary absorption oil flowrate, and when
the supplementary absorption oil flowrate reaches 52t/h, after C2
and CH4 content in liquefied gas increases rapidly. This analysis
shows that when the operating conditions of other towers remain
unchanged, the determination of the supplemental absorption oil
flowrate should weigh the quality of dry gas and LPG.
Furthermore, as the temperature of the supplementary absorbent
increases, the content of C3 and heavier components in the dry gas
increases monotonically, indicating that the temperature of the sup-
plementary absorbent has a significant impact on the quality of the
dry gases, so the temperature of the supplementary absorption should
not be too high. On the other hand, as the temperature of the sup-
plementary absorption oil increases, the temperature at the bottom
of the stripping tower also gradually increases, and it is also close
to a linear shape. It shows that as the temperature of the supple-
mentary absorption oil increases, the heat load of the bottom reboiler
of the stripping tower will increase. Fig. 5 also shows that under
the most economically optimal conditions, the temperature of the
supplementary absorbent should be as low as possible.
1-2. Effect of the Temperature at the Bottom of the Stripping Tower
on the Results
In the stripping tower, the operation temperature of the stripping
tower has an important influence on the quality of dry gas and lique-
fied gas. The stripping tower controllable variable is mainly the
heat load of the reboiler, that is, the temperature of the bottom of
the tower. The temperature of the bottom of the tower has a direct
influence on the amount of desorption gas at the top of the tower.
Fig. 6 can be obtained by analyzing the temperature sensitivity at
the bottom of the stripping tower to the amount of desorption gas
produced at the top. Under the condition that other operating param-
eters remain unchanged, it can be seen from Fig. 6 that as the tem-
perature of the bottom of the stripping tower increases, the amount
of desorbed gas at the top of the stripping tower increases slowly,
but after 116 oC the slope of the curve increases sharply. Hence,
desorbed gas at the top of the tower increases faster. This has a
great impact on the load in the tower. If the temperature is too high,
the tower’
s vapor and liquid phase load will be too large. If the maxi-
mum load exceeds, the tower will flood, so the temperature at the
bottom of the stripping tower should be strictly controlled.
As the temperature at the bottom of the stripping tower increases,
the content of C3 and its heavier components in the dry gas increases,
Fig.4.Effect of supplementary absorption oil flowrate on dry gas
and LPG.
Fig.5.Effect of supplementary absorption oil temperature on dry
gas and stripping tower reboiler temperature.
Optimization analysis of the absorption-stabilization process for fluid catalytic cracking unit
1581
Korean J. Chem. Eng.(Vol. 40, No. 7)
because the rise in the temperature at the bottom of the stripping
tower means that the desorption gas at the top of the tower increases.
Adding means that more C3 and C4 will enter the flash tank, and
a cycle is formed between the flash tank and the absorption tower.
The increase of C3 and C4 in the flash tank will inevitably cause
the gas to enter the absorption tower. The content of C3 and C4 in
the feed increases, and for a certain condition of absorbent under
certain operating conditions, the increase in the content of C3 and
C4 in the gas phase feed means that the range of C3 and C4 in the
output will also increase. Therefore, the temperature of the bottom
of the stripping tower should not be too high; otherwise, the dry
gas amount will be low. It can be seen from Fig. 6 that as the tem-
perature of the stripping tower decreases, the C2 content in the liq-
uefied gas will increase sharply. From the stripping tower tempera-
ture simulation results, it can be seen that the bottom temperature
of the tower should be controlled between 110 and 115 oC. At this
time, dry and liquefied gas quality can be acceptable.
As mentioned, the temperature of the absorption tower has a
particularly important impact on the quality of dry gas. The too
high temperature will make the dry gas not dry, so we should try
to reduce the temperature of the absorption tower. Generally, the
temperature in the absorption tower can be reduced by reducing
the temperature of the supplementary absorption oil, and the tem-
perature of the crude gasoline feed can be adjusted. And at the same
time, a middle section of the absorption tower can be built to extract
heat to withstand quality. For non-refining plants, some set up a
mid-stage reflux heat extraction (pump around) in the absorption
tower, some set up two mid-stage reflux heat extraction, and estab-
lish two mid-stage reflux heat extraction, which can optimize the
tower’s internal heat to a certain extent of gas-liquid load. Still, it
needs to add a heat exchanger, and the manufacturing cost will
increase. This refinery uses two mid-stage refluxes. The heat ex-
traction load of the middle-stage reflux is mainly adjusted by chang-
ing the return temperature of the middle-stage reflux. But this will
inevitably put more stringent requirements on the temperature of
the condensed stream. Therefore, the reflux flow is generally used
to adjust the tower temperature, making the operation easier.
1-3. Effect of Pump Around Flow Rate of the Absorption Tower
on the Results
Regarding the specific impact of the pump around flowrate (mid-
dle reflux) of the absorption tower on the quality of dry gas and
liquefied gas, for a more convenient and intuitive understanding,
the desorption gas flow rate controlled and the liquefied gas at the
top of the tower is stabilized. The effect of the pump around flow-
rate in the absorption tower on the quality of dry gas, liquefied gas,
and LPG is shown in Fig. 7. When the pump around in the absorp-
tion tower’s upper section increases, the C3 and heavier compo-
nents in dry gas approximately decrease in a linear relation, and
C2 and heavier contents of LPG increase.
So, in the middle, when selecting the flow rate, the quality of
dry gas and liquefied gas should be weighed. In the case of this
plant, C2 and CH4 in the liquefied gas content are lower than the
standard, so to reduce the content of C3 and its heavier components
in the dry gas, the flowrate in the pump around section of the ab-
sorption tower can between 120 to 140ton/hr. Sensitivity analysis
is used to explore the influence of the pump around flow on the
absorption tower, as discussed in Fig. 7. The temperature of the bot-
tom of the stripping tower gradually decreases with the increase of
the pump around flow. The decrease in temperature means a de-
crease in the load of the stripping tower bottom reboiler, so increas-
ing the flow rate in the absorption tower’
s middle section can reduce
the stripping tower’s energy consumption. This is mainly because
the increase in the flow rate in the pump around the absorption
tower will improve the absorption effect of the tower so that more
light components enter the rich absorption oil. After the rich
absorption oil enters the flash tank, its light component increases,
and the light component in the liquid phase after the final flash
vaporization will also increase. When the liquid phase enters the
stripping tower, it is extracted from the top under the same condi-
tions as the previous operation.
As for the reabsorption tower, its principle is the same as that of
the absorption tower. Its role is to make the dry gas drier. Of course,
according to the principle of mass balance, it is not difficult to find
that after the dry gas dries, the C3 and above components will be
reduced, and will enter the liquefied gas, so that the output of liq-
Fig.6.Effect of the temperature at the bottom of the stripping tower
on the results.
Fig.7.Pump around flowrate of absorption tower.
1582
M. S. Hussain et al.
July, 2023
uefied gas will increase, which will effectively improve economic
benefits. Since it is also an absorption tower, the main factors affect-
ing the process are temperature, pressure and flow. Low tempera-
ture, high pressure and high flow are favorable to the absorption
phenomenon, but selecting these elements requires a balance of
operating costs. Too low temperature will put higher requirements
on the coolant, and too high pressure and too much flow will put
better requirements on the equipment and increase production costs.
Therefore, it is generally possible to establish a zero-standard
function according to the actual situation and find the most suit-
able production operation conditions through optimization to maxi-
mize production efficiency. However, due to the complexity of the
equipment, we mainly analyzed the absorption and stabilization sys-
tem from the perspective of product quality and energy but do not
specifically analyze the equipment.
1-4. Effect of the Light Diesel Temperature and Flow Rate in the
Reabsorption Tower on the Dry Gas
As for how the temperature of the light diesel in the reabsorp-
tion tower affects the quality of dry gas, we will discuss it through
sensitivity analysis. After sensitivity analysis, Fig. 8 and Fig. 9 can
be obtained. It can be seen from Fig. 8 and Fig. 9 that as the tem-
perature of the light diesel increases, the content of C3 and heavier
components in the top dry gas also increases gradually. Therefore,
under the conditions of other processes, the temperature of the re-
absorbent can be minimized or lower. On the other hand, the in-
creased flow rate of light diesel can decrease the C3 and heavier com-
ponents in dry gas.
2. Optimization of the Feed Position of the Stabilization Tower
As far as the stabilization tower is concerned, it is a rectification
tower with a condenser and a reboiler. Generally, for the design of
a rectification tower, the feed position, reflux ratio, number of the-
oretical plates, the operating pressure in the tower, and the feed tem-
perature are several factors that are often considered. What can be
adjusted is the feed position and reflux ratio, feed temperature and
the operating pressure in the tower, etc.; the number of theoretical
plates cannot be changed. The rectification tower generally has sev-
eral spare inlets, for the stabilization tower in the absorption stabi-
lization system generally has three feeds, top, middle and bottom
respectively, which are selected according to the temperature and
the composition of the raw materials.
Choosing the bottom feed can reduce the content of C5 in the
dry gas, but it can make the gasoline vapor pressure of the device
too high. Choosing the top feed can cause too much C5 content in
the liquefied gas. Generally, the middle feed is selected. In the rec-
tification process, there are many methods for selecting the best feed
position, such as maximizing the separation factor method, mini-
mizing the heat load of the heater and condenser, and the material
composition approach method. The separation factor is generally
for the light and heavy key components. The obvious components
method is more suitable. The material composition approach method
is more practical for separating mixtures with fewer components.
It is more practical to use when the discharge composition is spec-
ified to minimize the heat load of the heater and condenser. The
size of the reflux ratio is directly related to the heat load of the con-
denser and reboiler. As the reflux ratio increases, the heat load of
the condenser and reboiler will increase. Of course, an increase in
the reflux ratio will improve the separated product’s purity. But this
is at the expense of energy consumption. The change in reflux ratio
will also change the vapor-liquid load in the tower. An excessive
flow ratio will increase the vapor-liquid load, and in severe cases,
flooding of the tower will occur.
Therefore, when the purity of the top product decreases and the
reflux ratio needs to be adjusted, the reflux ratio should be con-
trolled within a certain range. While improving the product quality,
various factors should be weighed. Maximizing economic benefit
is the most important thing for the company, not the product qual-
ity. The influence of feed temperature on rectification mainly affects
the heat load of heaters and condensers. Superheated steam feed
will reduce the load of the heater but will increase the load of the
condenser. Subcooled feed will cause the load of the heater. The
increase in feed temperature has no effect on the condenser. Whether
the feed temperature should be increased or decreased should be
determined based on the actual conditions of the plant. The oper-
ating pressure in the tower will also affect the heat load. Under the
same design regulations, an increase in pressure will increase the
Fig.8.The effect of light diesel flow rate on C3 & heavier compo-
nent content in dry gas.
Fig.9.The effect of light diesel temperature on C3 & heavier com-
ponent content in dry gas.
Optimization analysis of the absorption-stabilization process for fluid catalytic cracking unit
1583
Korean J. Chem. Eng.(Vol. 40, No. 7)
heat load of the reboiler. It also puts forward better requirements
for the material of the equipment.
Due to the existence of virtual components in petroleum simu-
lation calculations, it is difficult to select key components for analy-
sis. This section analyzes the feed plate’
s position under the conditions
of a certain amount of production at the top of the tower and a
certain reflux ratio using the working condition study in Table 5. It
can be seen from Table 5 that under the conditions of a certain
amount of production at the top of the tower and a certain reflux
ratio, when the feed position is above 16 plates, as the feed posi-
tion moves downward, the content of C3 and C4 in the liquefied
petroleum gas gradually decreases, the content of C5 in liquefied
gas increases.
The content of C3 and C4 in the stable gasoline gradually increase
before and after the 16th tray. When the feed position is below 16
plates, as the position of the feed plate moves downward, the con-
tent of C3 and C4 in the liquefied gas gradually increases, and the
content of C3 and C4 in the liquefied gas gradually decreases. The
content of C5 gradually increased, and the content of C3 and C4 in
the stable gasoline gradually increased. When the 36th plate was
fed, the content of C5 in the liquefied gas was the highest. The con-
tent of C3 and C4 in stable gasoline is the smallest, so the best feeder
position should be the 16th tray.
3. Optimization of Stripping Tower Conditions
3-1. Optimization of the Stripping Tower Feed
For the stripping tower, the feed temperature has a certain im-
pact on the bottom load and product quality. Through Table 6, we
can easily analyze the characteristics of this change. It is obtained
under the condition that the C2 content in the bottom of the strip-
ping tower is not changed.
It can be seen from Table 6 that as the temperature of the feed
increases, the heat load of the reboiler at the bottom of the strip-
ping tower gradually decreases, but the total heat load increases.
When the C2 regulation in the bottom of the tower remains un-
changed, the dry gas output will increase, which means that when
the plant is in a certain time, the control index of the stripping
tower is that the C2 content is constant.
At a certain value, the operation is just at the edge of the opera-
tion, where increasing the feed temperature will cause more C3 and
above components to enter the dry gas, turning into low-value-added
fuel gas, reducing the refining effect. The temperature used in the
feed analysis in this paper is 35 oC.
Table5.Comparison of results at different feeding positions of stabilization tower
Feed
tray
C3 and C4 in LPG
mole%
C5 in LPG
mole%
C2 and CH4 in stable gasoline
mole%
10
97.69
0.41
0.26
12
98.02
0.21
0.06
14
98.10
0.17
0.02
16
98.11
0.17
0.01
18
98.10
0.18
0.01
20
98.09
0.19
0.02
22
98.08
0.20
0.03
24
98.06
0.22
0.04
26
98.04
0.25
0.06
28
98.00
0.29
0.08
30
97.95
0.34
0.11
32
97.88
0.41
0.15
34
97.78
0.50
0.21
36
97.65
0.62
0.29
Table6.The effect of stripping tower feed temperatures on process
Feed temperature
oC
Reboiler heat load
kW
Pre-feed heater load
kW
Dry gas
t/h
C3 & C3+ in dry gas (105)
wt%
C2 and CH4 in LPG
wt%
30
10,199.70
775.17
2.64
4.60
0.99
35
09,829.62
163.42
2.64
4.59
0.99
40
09,463.54
463.67
2.64
4.60
0.99
45
09,102.02
1,108.34
2.64
4.60
0.99
50
08,745.27
1,773.31
2.64
4.60
0.99
55
8,397.6
2,461.98
2.64
4.60
0.99
60
08,057.15
3,178.62
2.64
4.60
0.99
65
7,726.1
3,928.57
2.64
4.60
0.99
70
07,359.82
4,837.88
2.64
4.60
0.99
1584
M. S. Hussain et al.
July, 2023
3-2. Optimization of Stripping Tower with and without Intermedi-
ate Reboiler
In the desorption operation, to alleviate the heat load of the boil
at the bottom of the stripping tower, a reboiler is often installed in
the middle of the stripping tower. For the feed, under the condition
that the content of C3 components is in control, dry gas remains
unchanged. The results of setting the intermediate reboiler and
without the intermediate reboiler are shown in Table 7.
It can be seen from Table 7 that the stripping tower with an inter-
mediate reboiler can effectively reduce the heat load of the bottom
reboiler, but it requires a new heat exchanger, which will increase
the cost of renovation. Therefore, when the heat source of the reboiler
in the stripping tower of the refinery is sufficient and the heat source
is no longer used for other purposes, there should be no interme-
diate reboiler.
When the C2 content in the top of the tower is specified to be
unchanged, the heat extraction load of the intermediate reboiler
has a certain impact on the product quality and the heat load of
the reboiler. The specific impact is shown in Table 8.
It can be seen from Table 8 that when the heat extraction load
of the intermediate reboiler increases if the C2 content at the bot-
tom of the stripping tower remains unchanged, the dry gas out-
put will increase. Because the C2 content entering the liquefied gas
does not change, the dry gas output will increase. The amount of C2
component in the gas remains unchanged, so the increased amount
will be C3 and above components, so the heat extraction of the
intermediate reboiler should not be too large; otherwise, the dry
gas will not dry, and the economic benefits of the entire product
will be affected.
With an intermediate reboiler, it is equivalent to increasing the
stripping tower bottom reboiler temperature. For the desorption
operation, the increase in temperature will facilitate the desorption.
When the C2 content in the bottom of the stripping tower is very
low, by controlling the temperature of the bottom of the stripping
tower, the quality of the dry gas can be effectively adjusted. How-
ever, when the C3 content in the bottom of the stripping tower is
low, and the adjustable variable in operation is only the tempera-
ture of the bottom, reduce the temperature. The content of C3 and
above components in dry gas can indeed be reduced to make the
dry gas quality meet standards, but the lowering of the bottom
temperature of the tower will cause the liquefied gas to contain more
C2 components resulting in the undesired quality of the liquefied
gas.
Therefore, when the content of the C2 component at the bot-
tom of the stripping tower is low, the intermediate reboiler can be
selected, but when the content of the C2 component is high, it should
be carefully considered, because in that case it will be necessary to
adjust the supplementary absorption oil or the lean absorption oil.
The conditions for entering the tower will increase production costs
to a certain extent. As mentioned, the use of an intermediate reboiler
is beneficial to the desorption, mainly due to the increase in the
temperature in the tower.
The position of the intermediate reboiler also has an impact on
the energy consumption of the whole process. Table 9 analyzes the
position of the intermediate reboiler (this analysis stipulates that
the intermediate reboiler heat load remains unchanged and the C2
content in the bottom of the tower) under stable conditions.
It can be seen from Table 9 that as the position of the interme-
diate reboiler moves down, the heat load of the reboiler at the bot-
tom of the stripping tower gradually increases up to 14 trays and
Table7.Comparison of stripping tower with or without intermediate reboiler
Equipment
Cold feed with intermediate
reboiler heat load
kW
Cold feed without intermediate
reboiler heat load
kW
Desorption reboiler
9,822.20
11,319.7
Stabilization tower reboiler
13,287.10
13,288.4
Stabilization tower condenser
9,969.14
9,966.2
Middle reboiler
1,484.86
0
Table8.The influence of the circulation rate in the middle reboilers of the stripping tower on the process
Stripping tower middle
 reboiler flowrate
t/h
Stripping tower middle
reboiler heat load
kW
Stripping tower bottom
reboiler heat load
kW
Dry gas
t/h
C3 and heavier
in dry gas (105)
wt%
20
0,759.95
10,545.9
2.64
4.59
25
0,922.13
10,384
2.65
4.67
30
1,075.00
10,231.4
2.66
4.74
35
1,219.31
10,087.3
2.67
4.88
40
1,355.73
09,951.1
2.69
4.95
45
1,484.89
09,822.19
2.70
5.07
50
1,607.23
09,700.03
2.73
5.19
55
1,723.39
09,584.1
2.74
5.24
60
1,833.75
09,473.96
2.75
5.28
Optimization analysis of the absorption-stabilization process for fluid catalytic cracking unit
1585
Korean J. Chem. Eng.(Vol. 40, No. 7)
then decreases, the output of dry gas gradually increases, and the
content of C3 and above components shows fluctuation. Thus, when
the heating load of the intermediate reboiler is constant, the lower
the intermediate reboiler, the more economical the product can
be. However, as the intermediate reboiler moves down, the same
heat and the required temperature are obtained. The position is
gradually increased, and the addition of an intermediate reboiler is
mainly to use the low-temperature heat source to improve the econ-
omy of the entire energy. When the intermediate reboiler moves
down, the economic advantage in terms of energy will gradually
decrease, so comprehensive consideration is required when select-
ing the location of the intermediate reboiler.
Additionally, after setting all the parameters at optimized condi-
tions (obtained from the above optimization analysis), the new pro-
cess successfully saved 1.32MW of thermal energy consumption
(2.59%) compared with the existing ASP
. The operating cost was
reduced from 10.921 million USD annually to 9.830 million USD
per year. Furthermore, the cost-saving effect of this optimization
was about 9.99% (1.091 million USD per year).
CONCLUSION
A new ASP for petroleum refinery is presented in this article. A
process optimization study was done to improve the product qual-
ity and energy efficiency of the absorption-stabilization process. We
performed an optimization analysis on the ASP
. The following are
the main findings:
The analysis of the absorption tower of the ASP shows that, under
the condition that the product quality control remains unchanged,
the heat load at the bottom of the stripping tower will also increase.
These quantitative analysis results have important guiding signifi-
cance for adjusting the dry gas and liquefied gas problem. So, the
suitable supplementary absorption oil flow value is adjusted at 52.2
t/h. The analysis of the reabsorption tower of the ASP shows that
the temperature of the light diesel oil is reduced, and the flow rate
lean absorption oil is increased, then the flow rate can effectively
reduce the contents of C3 heavier in the dry gas. Under the same
flow rate of light diesel oil, with the increase in temperature of light
diesel oil, the amount of C3 and heavier increased in dry gas. So,
for suitable conditions, temperature and flow rate analysis of light
diesel against C3 and heavier contents in dry gas is very important
and selected as 30 oC and 10t/h, respectively.
The optimization analysis of the stripping tower of the ASP shows
that under different feeding temperatures, thermal efficiency is greater
at 30 oC than that of 35 oC and greater temperatures. The compari-
son shows that the use of an intermediate reboiler can significantly
improve the thermal efficiency of the stripper, but the sensitivity of
the composition without reboiler is also higher. When the C2 con-
tent in the feed changes greatly, it is easier to make the product
unqualified. The optimization analysis of the temperature at the
bottom of the stripping tower shows that the temperature con-
trolled at 110 to 115 oC is the more suitable; the too high tempera-
ture will significantly increase the vapor and liquid load in the tower.
The location sensitivity of the intermediate reboiler has shown that
as the position goes to the bottom side, the C3 and heavier con-
tents in dry gas increase, but between the 7th and 8th plate, the heat
load of the stripping tower is minimum.
Further, more optimization analysis of stabilization tower shows
that feed at 16th plate can reduce C2 contents of LPG. Additionally,
compared with the existing ASP
, the new process successfully saved
1.32MW of energy consumption (2.59%). The operating cost has
been reduced from 10.921 million USD annually to 9.830 million
USD per year. Furthermore, the cost-saving effect of this optimi-
zation is about 9.99% (1.091 million USD per year). Hence, this
research opens new ways for the future optimization of the ASP
further according to energy efficiency and product quality.
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Table9.The effect of middle reboiler location on results
Liquid out
tray number
Liquid in
tray number
Stripping tower bottom
reboiler heat load
kW
Dry gas
t/h
C3 & C3+ in dry gas (105)
wt%
08
07
9,823.06
2.63
6.64
10
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9,831.99
2.64
7.12
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9,833.94
2.65
7.23
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9,834.18
2.65
7.24
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9,833.99
2.65
7.24
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9,833.72
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7.24
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9,833.42
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Artif Intell Rev (2019) 52:2295–2318
https://doi.org/10.1007/s10462-018-9612-8
Application of artiﬁcial intelligence techniques in the
petroleum industry: a review
Hamid Rahmanifard1
· Tatyana Plaksina1
Published online: 16 January 2018
© Springer Science+Business Media B.V., part of Springer Nature 2018
Abstract In recent years, artiﬁcial intelligence (AI) has been widely applied to optimization
problems in the petroleum exploration and production industry. This survey offers a detailed
literature review based on different types of AI algorithms, their application areas in the
petroleum industry, publication year, and geographical regions of their development. For this
purpose, we classify AI methods into four main categories including evolutionary algorithms,
swarm intelligence, fuzzy logic, and artiﬁcial neural networks. Additionally, we examine
these types of algorithms with respect to their applications in petroleum engineering. The
review highlights the exceptional performance of AI methods in optimization of various
objective functions essential for industrial decision making including minimum miscibility
pressure, oil production rate, and volume of CO2 sequestration. Furthermore, hybridization
and/or combination of various AI techniques can be successfully applied to solve important
optimization problems and obtain better solutions. The detailed descriptions provided in this
review serve as a comprehensive reference of AI optimization techniques for further studies
and research in this area.
Keywords Artiﬁcial intelligence · Genetic algorithm · Particle swarm optimization · ANN ·
Fuzzy logic · Differential evolution · Petroleum engineering
Contents
1 Introduction
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2296
2 Evolutionary algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2298
2.1 Genetic algorithm (GA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2298
2.2 Differential evolution (DE) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2301
3 Swarm intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2303
3.1 Particle swarm optimization (PSO) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2304
B Hamid Rahmanifard
hamid.rahmanifard@ucalgary.ca
1
Department of Chemical and Petroleum Engineering, Schulich of Engineering, University of Calgary,
2500 University Dr. NW, Calgary, AB T2N 1N4, Canada
123
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H. Rahmanifard, T. Plaksina
4 Fuzzy logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2306
5 Artiﬁcial neural network (ANN) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2310
6 Conclusions and recommendations
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2313
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2314
Abbreviations
ACO
Ant colony optimization
AI
Artiﬁcial intelligence
ANFIS
Adaptive neuro-fuzzy inference system
ANN
Artiﬁcial neural networks
ARS
Adaptive random search
BP-ANN
Back propagation artiﬁcial neural networks
CCDE
Cooperative coevolutionary differential evolution
CMA-ES
Covariance matrix adaptation evolution strategy
CR
Crossover probability rate
CSOR
Cumulative steam to oil ratio
DE
Differential evolution
E¶
Exploration and production
EA
Evolutionary algorithms
F
The scaling factor
FL
Fuzzy logic
FIS
Fuzzy inference system
GA
Genetic algorithms
gbest
The other’s best experiences
HDE
Hybrid differential evolution
HF
Hydraulic fracturing
ICA
Imperialist competitive algorithm
MMP
Minimum miscibility pressure
NA
Neighborhood algorithm
NAB
Neighborhood approximation Bayes
NP
The size of population
NPV
Net present value
pbest
A particle’s best experience
PSO
Particle swarm optimization
SAGD
Steam assisted gravity drainage
SI
Swarm intelligence
SPSA
Simultaneous perturbation stochastic approximation
UD
Uniform design
VAPEX
Vapor extraction
WAG
Water alternative gas
1 Introduction
Optimization methods were ﬁrst introduced in the petroleum exploration and production
(E&P) industry in the 1940s, and since then have been utilized widely for predicting, estimat-
ing, and determining various operational parameters (e.g., asphaltene precipitation, minimum
123
Application of artiﬁcial intelligence techniques in the...
2297
miscibility pressure (MMP), wettability, well placement, history matching, drilling opera-
tions, pipeline conditions, and etc.) (Wang 2003). These methods are mainly categorized into
three groups including linear, integer, and nonlinear programming techniques.
Linear programing technique is primarily used for the cases in which both the objective
function and the constraints are linear (Carroll Jr and Horne 1992). The simplex algorithm
and the interior point algorithm are two examples of the linear technique. Though very popu-
lar, this approach has one considerable drawback—it requires a large number of iterations to
converge (Klee and Minty 1970). Integer programming technique, on the other hand, is appli-
cable to the problems for which all unknown components are discrete or mixed continuous
and integer (e.g., coupled well control and placement optimization). To tackle these prob-
lems, the scholars usually use two approaches: the cutting plane technique and the branch
and bound method (Gomory 1958; Land and Doig 2010). The main disadvantage of this
approach is its high computational cost and time. The third approach, nonlinear programming
technique, is utilized for the optimization problems for which either the objectives or con-
straints are nonlinear. This method can be divided into two main categories: gradient-based
algorithms and gradient-free optimization algorithms (Mohagheghian 2016). Gradient-based
optimization algorithms search for the steepest descent (or ascent, depending on the type of
optimum required) direction and the function extremes using analytical or numerical objec-
tive functions including numerical ﬁnite difference methods (Taylor series expansions), the
steepest descent method, Newton’s method, quasi-Newton method, and sequential quadratic
programming technique (Watson et al. 1980; Fujii and Horne 1995; Chen 2013). As their
names suggest, gradient-based optimization algorithms require computation of the objective
function derivative and its constraints. However, not all objective functions are differentiable
due to the following reasons:
• The objective function or the regions deﬁned by the constraints are non-differentiable;
• A simulation-based objective function for which the derivative computation requires
access to the simulation code (especially for commercial software);
• The objective function is a result of a physical experiment for which due to the lack of a
precise actuator, the derivative of the function is impossible.
Therefore, the lack of a computable derivative causes the failure of gradient-based optimizers
and requires application of derivative-free techniques. Heuristic or gradient-free optimization
methods tend to be fast, they provide nearly optimal solutions, and solve the problems more
efﬁciently using knowledge of the domain. However, application of these methods cannot
guarantee ﬁnding the actual value of the optimal solution (Kamrani 2010; Mohagheghian
2016). A generic classiﬁcation of gradient-free approaches includes trajectory-based and
population-based methods. A trajectory-based method considers only one solution, while
a population-based heuristic method usually maintains a population of solutions (Kamrani
2010). Figure 1 summarizes the introduction section and shows a classiﬁcation of the opti-
mization techniques with several examples of each method.
Among population-based methods, Artiﬁcial Intelligence (AI) algorithms have been
widely used for solving problems in the oil and gas industry. In general, AI is deﬁned as
the ability of intelligent agents for continuous learning in the corresponding environment
and perceiving certain activities (Jang et al. 1997). AI is mainly comprised of Evolutionary
Algorithms (EA), Swarm Intelligence (SI), Fuzzy Logic (FL), and Artiﬁcial Neural Networks
(ANN) (Wu 2015).
Because to date no comprehensive study of applications of different AI algorithms to
various problems in the oil and gas industries has been conducted, we offer this summary of
the most pertinent literature on the subject. The rest of this review is organized as follows.
123
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H. Rahmanifard, T. Plaksina
Optimization
Techniques
Linear
programming
Integer
programming
Nonlinear
programming
Branch & 
bound
Cutting
plane
Simplex
Interior
point
Heuristic
Derivative-
based
Newton
quasi-Newton
and etc.
Population-based
Trajectory-based
Artificial 
Intelligence
Tabu Search
Simulated 
Annealing
and etc.
Fig. 1 Classiﬁcation of optimization techniques (Mirzabozorg 2015; Mohagheghian 2016)
In the next section, we present a brief description of EA. In Sect. 3, we discuss features of SI
and some relevant applications. In the subsequent Sect. 4, we offer a description of FL and
complete the review of AI with a section on ANN and its applications.
2 Evolutionary algorithms
BasedontheDarwinianevolutionarytheory,overmillionsofyearsmanyspecieshaveevolved
to adapt to different environments. Similarly, the same concept can be applied to numerical
optimization if we consider the environment as a form of the problem and EA as an adaptation
of the population to ﬁt the best environment (Eiben and Smith 2003). The basic idea of EA
is to evolve a population of candidate solutions under a selective process analogous to the
natural selection, mutation, and reproduction until better solutions are obtained. Speciﬁcally,
using effective search algorithms, parent solutions are combined to generate offspring solu-
tions that can be evaluated and may themselves produce offspring (Husbands et al. 2007).
Continuation of the generation cycle leads to better solutions to search, optimization, and
design problems. EA includes many algorithms such as evolutionary programming, genetic
algorithms, evolution strategies, and evolution programs (Mohaghegh 2000). Because of the
outstanding performance of Genetic Algorithms (GA) and Differential Evolution (DE) in
dealing with solutions to a variety of engineering problems, this category of techniques has
become extremely popular in engineering applications and, thus, they are discussed in more
detail in the next section.
2.1 Genetic algorithm (GA)
Holland (1992) introduced GAs as an evolution of biological species in a natural environment
based on the principle of the “survival of the ﬁttest.” This stochastic optimization algorithm is
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Initial
Population
Processing
(New population)
Processing
(Fitness)
Output
(Fittest Chromosomes)
Crossover
Selection
Mutation
Accepting
Fig. 2 Optimization process in GA terminology (Velez-Langs 2005; Chen 2013)
1 
0 
1 
1 
1 
Crossover 
1 
0 
0 
0 
0 
1 
0 
0 
0 
0 
1 
0 
1 
1 
1 
(a) 
1 
0 
1 
1 
1 
Mutation 
1 
0 
0 
1 
1 
(b) 
Fig. 3 a One point crossover example and b mutation example (Lin et al. 2011)
highly efﬁcient, versatile, and suitable for multi-objective optimization problems. Although
GA was initially suggested as an academic tool for investigation of biological processes,
nowadays it is applied in many engineering ﬁelds due to its ability to handle multiple con-
ﬂicting objectives (Güyagüler et al. 2002; Chen et al. 2010).
Application of GA to any problem requires the deﬁnition of three key parameters (Velez-
Langs 2005):
(a) Chromosomes (representations of control vectors with n unknowns) that contain encoded
variable strings (or sometimes called genes). Each gene represents a parameter (an
unknown) and each chromosome represents a trial (or a possible solution).
(b) A large number of chromosomes (genotype), which represents the individuals of a GA
population,
(c) The operations of selection, mutation, and crossover to produce a population from one
generation (parents) to the next (offspring).
The mechanisms of a typical GA and the overall optimization process in the genetic termi-
nology are shown in Fig. 2.
The ﬁrst step of GA optimization is to produce the initial population or genotype as a
partial space solution. The next step which is performed in each evolution cycle (iteration) is
a modiﬁcation of the population and calculation of the ﬁtness of each chromosome (Algosayir
2012). Then, based on the ﬁtness values of chromosomes, two parents with the highest values
are chosen (selection) to exchange parts of their genetic information and produce an offspring
or child (crossover) (Fathinasab and Ayatollahi 2016). According to the number of positions
at which crossover occurs, there are two common types of crossover: one-point crossover and
two-point crossover (Fig. 3a). Next, among the newly generated offspring those chromosomes
with the best calculated ﬁtness values are returned to the initial population (accepting). Note
that before inserting the selected offspring back into the original population, they are mutated
by changing some of their binary digits (genes) to span the search space more thoroughly
and maintain variety in the population as shown in Fig. 3b (mutation) (Filgueiras et al. 2016).
123
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H. Rahmanifard, T. Plaksina
GA advantages include the following:
• It requires limited parameter settings and its initialization starts with a population of
parameters rather than a single parameter (Ab Wahab et al. 2015; Mohagheghian 2016);
• Probabilistic transition rules can be used instead of the deterministic ones (Algosayir
2012);
• A chromosome or a control vector is considered entirely rather than dealing with each
individual parameter (Algosayir 2012);
• Direct function evaluations can be used instead of derivative calculations (Bittencourt
and Horne 1997);
• The ability to be combined with other algorithms to improve optimization performance
(Güyagüler et al. 2002);
• Easy parallelization can be applied for efﬁcient calculation time (Mariajayaprakash et al.
2015).
Some disadvantages of GA include:
• Randomly selected regions among an initial population may lead to selection of inap-
propriate regions. Therefore, the evolution process is strongly dependent on the values
of the initial members (Bittencourt and Horne 1997);
• GA tends to have slow convergence speed for complex optimization problems (Ballester
and Carter 2007).
To improve the performance of GA and enhance the quality of its solutions, several alternative
approaches for crossover and mutation have been proposed. Üçoluk (2002) and Jong and
Spears (1992) recommended segmented and N-point crossover in which two breaking points
and several random breaking points are used, respectively. For mutation, on the other hand,
bitwise inversion has been utilized during which the genes in a chromosome are mutated using
a random mutation rather than some assigned probability (uniform mutation) (Üçoluk 2002).
GA has been applied to multiple problems in the oil and gas E&P industry. Below, we
provide some of the applications of GA in their chronological order.
Saemi et al. (2007) proposed a new algorithm for the auto-design of neural networks based
on GA to predict permeability from well logs in South Pars gas ﬁeld in the Persian Gulf.
Farshi (2008) used continuous GA with the advantages of binary GA to optimize vertical and
directional well placement problems. The results demonstrated that the modiﬁed GA model
obtained higher objective function values in shorter time. To optimize the input variables
of reservoir simulation models, Andersen (2009) used GA, ANN, and a combination of
them with a commercial reservoir simulator software. Comparison of the model output with
the results of other approaches including Matlab fmincon optimization and Hooke-Jeeves
optimization, demonstrated promising performance of the proposed model.
Chen et al. (2010) combined GA with the Tabu search method to optimize the controlling
variables such as water alternative gas (WAG) ratio, cycle time, injection rates and bottomhole
pressures of the oil producers. The proposed model signiﬁcantly improved the convergence
speed, increased the recovery factor, and the Net Present Value (NPV). Edmunds et al. (2010)
optimized a steam and solvent cycling process using GA method, which reduced the physical
cumulative steam to oil ratio (CSOR) to the value close to one.
In the work of Algosayir (2012), GA, simulated annealing, and their hybridization with
the orthogonal arrays and response surface proxy techniques were considered for steam and
solvent applications in oil sands and fractured carbonate reservoirs. The results indicated that
using a proxy saved 95% of the computational time, while utilizing the orthogonal arrays
(with minimax criterion) improved model convergencey behavior for ﬁnding the yoptimaly
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Application of artiﬁcial intelligence techniques in the...
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solution. Chen (2013) designed an optimization tool using GA with the advantages of binary
and continuous encoding and coupled it with a reservoir simulator to optimize the steam
injectionratesofasteam-assistedgravitydrainage(SAGD)processinasaturatedoilreservoir.
Salmachi et al. (2013) built an integrated framework including a reservoir simulator, an
optimization method (GA), and an economic objective function (NPV) to obtain the optimal
locations of inﬁll wells in coal bed methane reservoirs.
Later, Guria et al. (2014) developed a model using binary coded GA to ﬁnd optimal vari-
ables in a drilling operation in one of the Louisiana offshore ﬁelds with abnormal formation
pressure. They used objective functions such as drilling depth, drilling time, and the cost of
drilling, whereas equivalent circulation density, rotary speed of the drill bit, weight on drill
bit, and the Reynolds number in drill bit nozzles were considered as the control variables. The
model output showed that using the optimal values of the control vector minimized drilling
cost and time while maximizing drilling depth.
In the work of Xu et al. (2015), a modiﬁed GA with altered crossover and mutation
rates was developed to history-match the simulation data with the experimental results of
the vapor extraction (VAPEX) heavy oil recovery process. The modiﬁed approach resulted
in 71% reduction of the computational time and an excellent match with the error less
than 1% in comparison to conventional GA. Various novel optimization approaches using
simultaneous perturbation stochastic approximation (SPSA), GA, and covariance matrix
adaptation evolution strategy (CMA-ES) were proposed by Ma et al. (2015) for placement
of horizontal wells and hydraulic fracturing (HF) stages in shale gas reservoirs. Their results
showed the ability of SPSA, GA, and CMA-ES to handle various HF stage spacing intervals
in geologic systems with homogeneous and heterogeneous petrophysical properties, whereas
in large dimensional problems, GA, and CMA-ES had better performance than SPSA. Bian
et al. (2016) recommended a support vector regression model with GA to predict pure and
impure CO2-crude oil MMP.
2.2 Differential evolution (DE)
DE is a population-based method that uses a real-coded GA with an adaptive random search
(ARS) and a normal random generator to ﬁnd the global minimum of the objective function
(Boender and Romeijn 1995; Maria 1998). The main difference between GA and DE is that
GA relies on crossover operation to ﬁnd the optimal solution, while DE is mostly based on
mutation operation (Ab Wahab et al. 2015). Like other evolutionary algorithms, DE consists
of four stages: initialization, mutation, crossover, and selection. During the initialization step,
a population/generation with a ﬁxed number of candidate solutions (NP) using minimum and
maximum values for each deﬁned variable and a uniform random value in the range from
0 to 1 is created (Storn and Price 1995). The next step is to evolve the initial population
in which every solution is mutated by adding the difference of two random solutions from
the current population to a different selected random solution scaled by a factor F. Then,
during the crossover process, diversity is created in the newly generated candidate solutions
by applying the crossover probability rate (CR). There are two main crossover variants for
DE: exponential and binomial. Finally, in the selection step, every solution vector in the trial
population is compared with the corresponding vector in the initial population and depending
on the nature of the problem (minimization or maximization), the one with the lower or higher
objective function value is moved to the next generation. The four-step process repeated until
thestoppingcriteria(reachingtothemaximumnumberofgenerationsorobtainingthedeﬁned
different tolerance between the objective function values in the current generation and the
previous one) are met. Figure 4 shows different steps of the DE algorithm.
123
2302
H. Rahmanifard, T. Plaksina
Start
Input algorithm parameters 
including D, NP, F, and CR
Generate initial population for NP 
times in an array 
(between upper and lower bounds)
Transfer each trial or target 
vector with better performance
index to the next generation
Meet stopping 
criteria?
Stop and print target 
vectors as the best solution 
of optimization
End
Evaluate the objective function 
for individual population
Determine the trial
vector for each target vector in 
the population using mutation 
and crossover operations
Evaluate the 
objective function 
for each trial vector
Is individual 
trial vector within
the bounds?
Assign a value
randomly to trial vector 
within the bounds
Yes
NO
NO
Fig. 4 The ﬂow chart of DE algorithm (Khademi et al. 2010; Ab Wahab et al. 2015)
As mentioned above, there are three parameters that control DE performance: the size of
population (NP), the crossover constant (CR), and the scaling factor (F). According to Price
et al. (2006), 5–10 times the number of variables in a problem is usually suitable for NP,
while a bound of 0.1 to 1 and 0.4 to 1 are recommended for CR and F, respectively.
Although DE has many advantages including robustness, simplicity in its structure, and
enhanced ability for a local search, the algorithm has relatively slow convergence rate (Wu
et al. 2011). Other advantages and disadvantages of DE algorithm are listed below (Khademi
et al. 2010; Ab Wahab et al. 2015; Mirzabozorg 2015):
Advantages
• Ability to handle non-differentiable, nonlinear and multimodal functions.
• Ease of use, i.e. few control variables to steer the optimization.
• Good convergence properties to the global optimal point in consecutive independent
trials.
Disadvantages
• Parameter tuning is necessary.
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Application of artiﬁcial intelligence techniques in the...
2303
In general, the improvement of DE performance could be achieved by increasing the
population size and introducing the elitism, which avoids the destruction of the best solution
during creation of next generation.
Although DE method has been very successful in tackling various engineering problems,
only a few applications of this technique in petroleum engineering are available in the liter-
ature as discussed below.
In their history matching study, Wang and Buckley (2006) used DE algorithm to match
capillary pressures and relative permeabilities with core ﬂooding data. Considering a cost
function as the objective function, Decker and Mauldon (2006) optimized fracture shapes
and sizes by applying this method. Their work indicated that DE algorithm is an appropriate
tool for estimation of fracture characteristics obtained by geological analyses. In the work
of Jahangiri (2007), the inﬂow control devices and DE approach were applied to optimize
production constraints and maximize oil production from smart wells.
DE algorithm had been also successfully utilized to ﬁnd optimal solutions of variogram
properties in geostatistical systems (Zhang et al. 2009). In this study, it was concluded that
DE method is more efﬁcient and stable in comparison to GA technique for matching the
key variogram variables with experimental data. Furthermore, Hajizadeh (2009) performed a
comparison between DE method and the neighborhood algorithm (NA) for history matching
of a black oil model. The results showed that DE technique performed better than NA method
in matching the ﬁeld data. Hajizadeh et al. (2010) also investigated convergence and robust-
ness of different stochastic population-based optimization algorithms including DE, NA,
and ant colony optimization (ACO) as a part of a history matching study. The results showed
that DE algorithm had the fastest convergence rate and the lowest misﬁt value, whereas NA
technique did not perform as well for a problem with a large number of unknown parameters.
To enhance capabilities of DE algorithm, Wang et al. (2011) introduced a new method
called co-operative co-evolutionary differential evolution (CCDE) and applied it for a
waveform inversion of cross-well data. Their results showed better performance of CCDE
technique in comparison to that of conventional DE method.
Zhang et al. (2014) applied DE algorithm to a micro-seismic study to develop a robust
velocity model for more accurate data interpretation. Awotunde and Mutasiem (2014) min-
imized the cost of drilling operation using DE algorithm by optimizing drilling operational
parameters and reducing drilling time. In another work, Mirzabozorg (2015) compared the
performance of DE method with that of PSO algorithm in a history matching study in a
homogeneous 2D and a heterogeneous 3D SAGD reservoir models. The results conﬁrmed
more efﬁcient performance of DE technique in comparison to that of PSO scheme from
various perspectives. Santhosh and Sangwai (2016) coupled a novel algorithm called hybrid
differential evolution (HDE) with the neighborhood approximation Bayes (NAB) algorithm
in a history matching study to predict reservoir production with minimal uncertainty and
simulation runs.
3 Swarm intelligence
Swarm intelligence (SI) is an innovative intelligent optimization technique that mimics social
and collective behavior of swarms of ants, bees, ﬁsh schools, and insects when they are search-
ing for food, communicating with each other, and mingling in their colonies (Abraham et al.
2006; Engelbrecht 2006). Main characteristics of SI models are their self-organization, decen-
tralization, communication, and cooperation behaviors between individuals within the group
123
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H. Rahmanifard, T. Plaksina
Fig. 5 Inertia weight particle
trajectory (Mohagheghian 2016)
i
i
i
Xi
t+1
Xi
t
Pg
t
Pi
t
Vi
t
Vi
t-1
j
j
j
Xj
t+1
Xj
t
Pj
t
Vj
t
Vj
t-1
Original velocity
Velocity toward gbest
Velocity toward Pbest
Resultant velocity
in absence of a central controlling system. Although these individual interactions are simple,
eventually they lead to a complex global behavior, which is the core of SI (Bonabeau and
Meyer 2001). In recent years, a lot of SI-based techniques have been proposed, which cover
various research areas (Edelen 2003; Kamrani 2010; Ganesan et al. 2013; Alam et al. 2014;
Senthilkumar 2014). Among those techniques, two methods became particularly popular
and widely used for solving discrete and continuous optimization problems: Particle Swarm
Optimization (PSO) and Ant Colony Optimization (ACO) methods (Liu and Passino 2000;
Trelea 2003). Here, we discuss the theory of PSO followed by its chronological use in the
petroleum industry.
3.1 Particle swarm optimization (PSO)
Eberhart and Kennedy (1995) proposed PSO technique based on a natural pattern of bird
ﬂocking or ﬁsh schooling. Like GA technique, PSO algorithm starts with a randomly gener-
ated population, uses a ﬁtness function value to evaluate the population, as well as updates
the population and the search with random techniques. However, PSO technique does not use
crossover and mutation operators. It considers particles with two main parameters: a vector
corresponding to a unique position in the search space and a velocity for the motion of the
particle (Kaewkamnerdpong and Bentley 2005). As its starting point, the algorithm randomly
produces the particles’ positions and velocities. Then, each particle updates its position and
the associated velocity until satisfactory solutions are achieved. In other words, using the
velocity function (regularly updated), each particle goes iteratively through the search space
according to its best position and the entire group’s best position (Mohamed et al. 2011).
Therefore, the particle’s experience would be tracking and memorizing the best encountered
positions and the best population size, which is called a swarm. PSO combines a particle’s
best experience (pbest) and the other’s best experiences (gbest) to update the particle’s posi-
tion in each iteration (Mirzabozorg 2015). Figure 5 shows a typical trajectory of a particle
with respect to each term explained above.
A typical workﬂow of PSO method is presented in Fig. 6.
PSO algorithm has several advantages including the need for a small number of param-
eters as the input data, simplicity for implementation, high efﬁciency in a global optimum
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Start
Initialize algorithm 
parameters such as w, C1,
C2, r1, r2, ...
Initialize particles’ position 
xi(t) and velocity vi(t) using 
random functions
Compute cognitive and 
social components of the 
particles
Update position xi(t+1) and 
velocity vi(t+1) of the 
particles
Calculate the objective 
(fitness) function of each 
particle in the swarm
If the fitness 
conditions are 
satisfied or 
t=Tmax
Calculate the pbest and 
gbest values
Iteration Number t
Stop and print gbest as the 
best solution of 
optimization
Set no. of particles
End
NO
Yes
Fig. 6 PSO diagram (Kathrada 2009; Assareh et al. 2010)
search, and ﬂexibility in scaling of design search. However, it has a tendency to converge
slowly (in other words, weak local search ability). Increasing the population size, dynamic
velocity adjustment, and sub-swarm approach are various strategies that have been proposed
to increase the convergence probability towards a global optimum (Atyabi and Powers 2013;
Chang and Yu 2013; Ab Wahab et al. 2015).
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H. Rahmanifard, T. Plaksina
Recently, PSO has been successfully applied in many areas such as continuous function
optimization (Eberhart and Kennedy 1995), neural network training (Shen et al. 2006), static
function optimization (Shi and Eberhart 1999), dynamic function optimization (Blackwell
and Branke 2004), multimodal optimization (Brits et al. 2002), and data clustering (Cohen
and de Castro 2006). However, its application in oil industry goes back for about six years.
Kathrada (2009) studied PSO implementation for history matching of ﬁnite difference
simulation models. He showed that even with a highly efﬁcient algorithm like “Flexi-PSO”,
there was no guarantee that the obtained predictions would cover the true reservoir behavior
range. Based on socio-economic indicators including population, GDP (gross domestic prod-
uct), import and export data and the NPV as the objective function, Onwunalu and Durlofsky
(2010) applied GA and PSO methods to determine optimal well type (including vertical,
directional, and dual-lateral) and location. Onwunalu and Durlofsky (2010) developed PSO
and GA demand estimation models (PSO-DEM and GA-DEM) in exponential and linear
forms to estimate future oil demand values up to year 2030. They reported that the models
based on PSO algorithm had lower average relative errors.
To optimize oil recovery of a heavy oil reservoir, Wang and Qiu (2013) investigated con-
vergence behavior and performance of three different PSO algorithms. The results indicated
that conventional PSO yielded the highest objective function. Humphries et al. (2014) linked
PSO and the generalized pattern search (GPS) in a simultaneous and sequential manner to
optimize well placement and control problems. The results showed better performance of
the sequential approach in comparison to that of the simultaneous approach. By integrating
uniform design (UD) into the initialization process of PSO, Zhou et al. (2016) developed
a hybrid method to maximize the NPV of a cyclic steam stimulation project. The results
showed that integration of the initialization process of PSO with UD increased the quality
of the initial population and, consequently, the convergence rate. Additionally, utilizing the
recommended hybrid techniques led to the improvement of technical and economic scenarios
for heavy oil reservoirs development. In another study, considering squeeze lifetime, total
injected squeeze volume, and injected water volume as the objective functions, Vazquez
et al. (2016) utilized PSO algorithm to determine the most effective chemical scale inhibitor
squeeze designs in two ﬁeld cases. The proposed model was successful in identifying the
most cost-effective scenario among all chemical scale inhibitor squeeze cases.
Mohagheghian (2016) used GA and PSO algorithms to optimize hydrocarbon WAG per-
formance in the E-segment of Norne ﬁeld. In comparison to the reference cases, the results
showedthatthebestoverallvaluesofNPVfoundbyGAandPSOwere13.8and14.2%higher,
respectively, while for incremental recovery factor as the objective function, he obtained an
increase of 14.2% in the case of GA and 16.2% in the case of PSO.
4 Fuzzy logic
Fuzzy logic (FL) is a powerful mathematical tool for modeling the uncertainty of information
in the real world by generalizing any speciﬁc theory from a crisp (discrete) to a continuous
(fuzzy)form(Zadeh1965).EachvariableofFLcommonlyconsistsofatruthvaluethatranges
in a degree between 0 and 1 and between completely true and completely false (Novák et al.
2012). As shown in Fig. 7, FL generally consists of three essential components including
Fuzziﬁcation unit, Knowledge base (database and rule base) and Reasoning mechanism, and
Defuzziﬁcation (Kar et al. 2014).
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Fuzzifier
(Crisp to Fuzzy)
Membership Functions
Knowledge Base
(Rule base – Data base)
Defuzzification
(Fuzzy to Crisp)
Inference Engine
(Max, Min, etc)
Output
Input
Fig. 7 Fuzzy inference systems (Zoveidavianpoor et al. 2012; Wu 2015)
Table 1 The major function used in fuzzy logic system (Zoveidavianpoor et al. 2012)
Function name
Parametric representation
Graphical representation
Triangular function
μ (x, a, b, c) =
⎧
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎩
0
(x−a)
(b−a)
(c−x)
(c−b)
0
x ≤a
x ∈(a, b)
x ∈(b, c)
x ≥c
⎫
⎪
⎪
⎪
⎬
⎪
⎪
⎪
⎭
Trapezoidal function
A =
⎧
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎩
0
(x−a)
(b−a)
1
(d−x)
(d−c)
x ≤a
x ∈(a, b)
x ∈(b, c)
x ∈(c, d)
⎫
⎪
⎪
⎪
⎬
⎪
⎪
⎪
⎭
Normalized Gaussian function
μ (x, σi, ci) =
e
−(x−ci)2
2σ2
i
Two Sigmoidal functions
μ (x, α1, ξ1, α2, ξ2) =
	
1 + e−α1(x−ξ1)
−1
−
	
1 + e−α2(x−ξ2)
−1
Generalized Bell function
μ (x, α1, β, ξ) =

1 +


 x−ξ
α



2β−1
In Fuzziﬁcation step, crisp inputs are converted into fuzzy (continuous) values using fuzzy
sets. The fuzzy set is a set of ordered pairs for which the membership function allows each
input element to have its membership grade between 0 and 1 (Jang et al. 1997). In general,
the membership function is used to characterize its corresponding fuzzy set. There are ﬁve
basic membership functions as presented in Table 1.
In fuzzy reasoning step, using fuzzy values from previous step and standard operations, a
fuzzy engine executes a fuzzy inference procedure according to the pre-determined rule base
and data base to obtain a reasonable output or conclusion. Rule base and data base, which are
categorized in knowledge base section, are the core of Fuzzy Inference System (Wu 2015).
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H. Rahmanifard, T. Plaksina
Rule base contains a reasonable and careful selection of fuzzy rules and accomplishes the
procedure of mapping fuzzy rules of the inputs to fuzzy values of the outputs, while data base
deﬁnes the membership function of input and output elements (Freuder and Wallace 2005).
Table 2 presents three types of standard operations in FL systems including intersection,
union, and complement where their selection depends solely on a problem. Furthermore, the
intersection and union operations are based on min/max operations, while the complement
is an algebraic operation (Zoveidavianpoor et al. 2012).
In Defuzziﬁcation step, consequents given by fuzzy reasoning step are converted into crisp
values (Mardani et al. 2015).
The beneﬁts of FL can be summarized as follows (De Reus 1994; Kar et al. 2014):
• FL is simple, fast, robust, and insensitive to changing environments
• FL describes systems as a combination of numeric and symbolic
• FL addresses the problems with very restricted conditions or without exact solutions.
• The algorithm could be described with little and/or approximate data.
Although the advantages of FL are numerous, there are certain situations in which FL does
not perform well (De Reus 1994):
• Cases for which good mathematical descriptions and solutions exist, the use of FL might
be sensible only when computing power restrictions are too severe for a complete math-
ematical implementation.
• It is difﬁcult to prove the characteristics of fuzzy systems in most cases due to the lack
mathematical descriptions (e.g., in the area of stability of control systems).
Nowadays, there are numerous applications of FL in the oil and gas industry. Using a fuzzy
neural network and a backpropagation neural network, Ouenes (2000) evaluated factors
affecting rock fracturing and measured their relationship with fracture intensity. The author
found that the use of FL method and a stochastic framework can minimize the risks associated
with data driven techniques and assist the interpretation process. Some studies such as Yang
(2009) and Yin and Wu (2009), described the use of FL for stimulation candidate-well
selection. In Yang’s study, to determine fuzzy variables, the author conducted an analysis
over the factors affecting oil well fracturing, and then selected target wells and formations for
hydraulic fracturing using a fuzzy mathematics model (Yang 2009). To choose a candidate
well for fracturing, Yin and Wu (2009) designed a fuzzy judging mathematical model by
analyzing different effective parameters, determining the relation between fracturing effects
and the parameters, and dividing the grade intervals of each inﬂuencing parameter.
Yetilmezsoy et al. (2011) developed an approach using adaptive neuro-fuzzy system
(ANFIS) for modeling water-in-oil emulsion formation in terms of percentage of SARA (sat-
urates, aromatics, resins, asphaltene), viscosity, and density data. Attia et al. (2013) described
a model using FL and neural networks to predict the multiphase ﬂow pressure drop in surface
pipelines for oil ﬁelds. The results showed that ANFIS outperformed Beggs and Brill, Dukler
Flannigan, Dukler Eaton Flannigan correlations, and neural networks.
Afshar et al. (2014) employed FL and neural network models to calculate bubble point
pressure as a function of gas speciﬁc gravity, oil gravity, solution gas oil ratio, and reservoir
temperature. To avoid trapping in local minima and increase the accuracy, they utilized
models optimized with GA. The results showed that optimization with GA could prevent their
neural network and neuro-fuzzy models from trapping in local minima, which is a common
case for a back-propagation algorithm. Ravandi et al. (2014) generated two new optimized
models using GA and ANFIS for porosity calculation and water saturation determination.
Comparison between the obtained results and the outputs of other methods showed that the
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Table 2 Standard operations in FL system (Zoveidavianpoor et al. 2012)
Fuzzy operator
name
Algebraic
operator name
Symbol
Description
Equation based on
Algebraic
Fuzzy
Intersection
AND
∩
Applied to two fuzzy sets
A and B with the
membership functions
µA(x)and µB(x)
μA∩B = {μA(x), μB(x)},
x ∈X
μA∩B = min{μA(x), μB(x)},
x ∈X
Union
OR
∪
Applied to two fuzzy sets
A and B with the
membership functions
µA(x)and µB(x)
μA∪B = {μA(x), μB(x)},
x ∈X
μA∪B = max{μA(x), μB(x)},
x ∈X
Complement
NOT
NOT
Applied to fuzzy sets A
with the membership
functions µA(x)
μA = 1 −μA(x), x ∈X
μA = 1 −μA(x), x ∈X
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H. Rahmanifard, T. Plaksina
accuracy of the recommended models for estimating porosity and water saturation improved
considerably (mean standard error of 0.00007 and 0.00033, respectively). In another work,
Ahmadi and Ebadi (2014) designed FL method with different types of membership functions
(e.g., curve shaped, triangular, and trapezoidal shape) to specify MMP of injected gas and
reservoir oil. They concluded that a curve-shaped membership function demonstrated a better
match with experimental results unlike other tested types of membership function. Rammay
and Abdulraheem (2014) coupled ANFIS with DE method to reproduce production data of
an arbitrary reservoir model.
Wu (2015) showed the feasibility of Fuzzy Inference System (FIS) and ANN techniques in
pipeline risk assessment by developing two hybrid pipeline risk assessment systems including
FIS and ANN with an expert risk assessment methodology. Olatunji et al. (2015) proposed a
hybrid system through a combination of FL systems and a sensitivity-based linear learning
method to model both permeability and PVT properties of oil and gas reservoirs. In their work,
FL system was used to manage uncertainties in the reservoir data and training the output. In
another research work, conducting different natural depletion tests at various temperatures,
Mohammadi et al. (2015) developed a FL model to predict asphaltene precipitation in the
range of experiment temperatures. Comparative studies were carried out between the model
results and the output of the WinProp module (computer modelling group (CMG) software),
which showed an acceptable performance of their FL model.
Bakyani et al. (2016) designed a model based on ANFIS and optimized it by PSO algo-
rithm to forecast carbon dioxide diffusivity in oils at different reservoir temperatures and
pressures. Zhou et al. (2016) designed an analytic model using FL to assess corrosion fail-
ure likelihood (CFL) in a natural gas pipeline. They considered corrosion thinning factor,
corrosion cracking factor, inspection effectiveness, and inspection times as the key factors
and determined the fuzzy rules between the key factors and CFL. The results demonstrated
success in application of the proposed model and feasibility of its use as a reference for
pipeline inspection and maintenance plans. Later in 2016, Jalalnezhad and Kamali (2016)
presented a novel method using an experimental dataset and ANFIS to model the thickness
of wax precipitation in single-phase turbulent ﬂow. According to their results, ANFIS model
demonstrated a promising performance for oil production optimization and wax deposition
thickness prediction in single-phase turbulent ﬂow.
5 Artiﬁcial neural network (ANN)
An artiﬁcial neural network (ANN) consists of a pool of simple processing units, which
communicate by sending signals to each other over a large number of weighted connections.
A list of its characteristics can be summarized as follows (Rumelhart et al. 1986; Cybenko
1989):
• a set of processing units (neurons),
• a state of activation yk for every unit which is equivalent to the output of the unit,
• connections between units; generally, each connection is deﬁned by a weight w jk which
determines the effect that the signal of unit j has on unit k,
• a propagation rule that determines the effective input sk of a unit,
• an activation or transfer function Fk, that determines the new level of activation based
on the effective input sk(t),
• an external input (bias, offset) θk for each unit used for a better match of the neural
network model to the real one.
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Fig. 8 A simple neuron model
(Mohaghegh 2000)
w1k
w2k
wnk
sk
θk
Fk
X1
X2
Xn
Figure 8 illustrates the basics for a neuron. Each unit performs a relatively simple job:
receiving input from its neighbors or external sources and using this input to compute an
output signal which is then propagated to other units. Apart from this processing, the second
task is adjustment of the weights. The system is inherently parallel in the sense that many
units can carry out their computations at the same time. In most cases, it is assumed that each
unit provides an additive contribution to the input of the unit with which it is connected. As
shown in Fig. 8, the total input (sk(t)) to the unit k is a simple weighted sum of the separate
outputs from each of the connected units plus a bias or offset term θk (Ramadhas et al. 2006):
Then, a rule that calculates the effect of the total input on the output of the unit is required.
Hence, a function (Fk) that takes the total input sk(t) and produces a new value of the
activation of the unit k (activation or transfer function) is needed (Mohanty 2005). The
transfer functions mainly serve as a type of ﬁlter or a gate that allows some signals to move
forward and to stop others as they progress from the input nodes to the output ones. The most
commonly used activation functions are logarithmic sigmoid, hyperbolic tangent sigmoid,
and linear functions (Mohanty 2005).
ThereareseveralpatternsofANNssuchasfeed-forwardnetworks,recurrentnetworks,etc.
Conventional feed-forward networks are the most common networks for function approxima-
tion (Eslamloueyan and Khademi 2009). A multi-layer feed-forward network which consists
of groups of interconnected nodes arranged in layers corresponding to input, hidden, and
output nodes, is shown in Fig. 9.
The advantages of ANNs with respect to other models include the following (Mohaghegh
2000; Dumitru and Maria 2013):
• ANNs are a relatively simple learning algorithm;
• They have an ability to outperform other models when high-quality data are available;
• They can approximate any function, regardless of its linearity;
• ANNs can be used in problems for which it is difﬁcult or impractical to formulate a
non-linear relationship.
ANNs also have some signiﬁcant drawbacks (Groth 2000; Dumitru and Maria 2013):
• It is hard to understand and interpret ANN models, because they are “black box” predic-
tion engines; however, with the new tools on the market, this problem has been alleviated.
• ANNs are susceptible to overtraining meaning that they just memorize their training
data and are not capable of generalization. Note that in recent years, commercial-grade
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H. Rahmanifard, T. Plaksina
Fig. 9 Schematic of a multi
layer feed-forward neural
network model (Ahmadi 2011)
Input
layer
Hidden
layer(s)
Output
layer
neural networks have eliminated overtraining by monitoring test versus training errors
and through “bootstrapping holdout (test) samples;”
• Their predictions are not acceptable over small data sets.
Below we provide some applications of ANN in the oil industry reviewed in a chronological
order.
Yilmaz et al. (2002) described a model based on back-propagation ANN and fractal geo-
statistics to solve the optimal bit selection problem in terms of real rock bit data, gamma ray
and sonic log data for several wells in a carbonate ﬁeld. In the work of Huang et al. (2003),
an ANN model was presented to predict MMP of pure and impure CO2 and oil systems. This
approach used the molecular weight of the C5+ fraction, reservoir temperature, and con-
centrations of volatile (methane) and intermediate (C2–C4) fractions in the oil. Comparison
between the results predicted by the ANN model and other statistical methods demonstrated
that the ANN model accurately estimated CO2 MMP in oil reservoirs. Chapoy et al. (2007)
presented a feed-forward ANN model for estimating hydrate dissociation pressures of natural
gases in the presence/absence of inhibitor aqueous solutions with 19 input variables.
To predict asphaltene precipitation in an oil reservoir, Ahmadi (2011) utilized a hybrid
model combining a feed-forward neural network and the imperialist competitive algorithm
(ICA). He evaluated the performance of this ICA-ANN model in comparison to a scaling
model and a conventional ANN model and showed the effectiveness of the former model.
To predict asphaltene precipitation due to natural depletion, Ahmadi and Golshadi (2012)
developed a feed-forward ANN optimized by hybrid GA and PSO (denoted as HGAPSO)
techniques. Zendehboudi et al. (2012) used a feed-forward ANN optimized with PSO to
predictthecondensate-to-gasratioinagascondensatereservoir.Basedontheirerroranalyses,
the proposed PSO-ANN outperformed conventional ANN and empirical correlations.
Ahmadietal.(2013)describedamethodologyusingafeed-forwardANNmodeloptimized
by GA and PSO methods to examine real ﬁeld data for forecasting reservoir permeability.
Good performance of the model in comparison to those of conventional ANN validated the
accuracy of the hybrid model. Zendehboudi et al. (2014) proposed a hybrid model of ANN and
PSO for predicting the production performance of steam assisted gravity drainage (SAGD)
in heavy-oil fractured reservoirs. The error analysis indicated a good agreement between the
output of PSO-ANN model and the actual data. To determine the conditions of offshore oil
and gas pipelines, El-Abbasy et al. (2014) developed an ANN model using the datasets from
three existing offshore oil and gas pipelines in Qatar. The authors considered different factors
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including diameter, material, type of the carried product, anode wastage, support condition,
joint condition, free spans, and corrosion.
In another work, Ahmadi et al. (2014) developed a correlation using multivariable regres-
sion, back propagation ANN (BP-ANN) and GA-ANN to predict the recovery rate of vapor
extractioninheavyoilreservoirs.Theyconductedcomparativestudiesbetweenthemodelout-
puts and experimental data. From the statistical errors, they found that the proposed GA-ANN
outperformed the conventional BP-ANN and regression correlation. They also demonstrated
the ability of GA-ANN to search in different directions simultaneously, which increased
the probability of ﬁnding the global optimum. Xue et al. (2014) proposed a GA-BP-ANN
model to identify the fracture in terms of deep-shallow laterolog curves and micro-electrode
logging curves. The model prediction was in good agreement with the reservoir production
performance, which proved high accuracy of the utilized method.
The work of Chiroma et al. (2015) described the implementation of GA and neural network
(GA-NN) to predict the West Texas Intermediate (WTI) crude oil price. In the proposed
technique, GA was used to optimize the weights, bias, and topology of the neural network.
The comparison of the model output with those of ten back-propagation algorithms indicated
better performance and higher computational efﬁciency of the GA–NN model. Azizi et al.
(2016) used ANN to estimate the water hold up in a two-phase ﬂow vertical and inclined
pipeline (90◦, 75◦, 60◦, and 45◦from horizontal). In their work, the input parameters were
the pipe inclination angle and water and oil superﬁcial velocities, whereas water holdup
values of two-phase ﬂow were considered as the output parameters. The predicted water
hold-up by the proposed ANN model matched well with the experimental water holdup data.
Zhang et al. (2016) predicted the wettability and modelled nonlinear relationship between
wettability, rock, and ﬂuid properties by developing a general regression neural network
model containing nine inﬂuence factors. Kim et al. (2017) used ANN to address the storage
efﬁciency of CO2 sequestration in deep saline aquifers. The evaluation of the ANN model
with the ﬁeld scale data indicated a very good match (determination coefﬁcient (R2) of 0.96).
6 Conclusions and recommendations
This review has presented a comprehensive summary of different optimization methods in the
ﬁeld of AI and their applications in the petroleum E&P industry. We reviewed and categorized
the pool of algorithms with respect to different types of AI, application areas, and publication
year. We presented a general resource allocation for AI algorithms by dividing it into four
groups, i.e., EA, SI, FL, and ANN and speciﬁed the most popular techniques for the ﬁrst two
categories.
The research shows that the application of AI methods has demonstrated outstanding
performance in prediction, estimation, and optimization of different objective functions (e.g.,
minimum miscibility pressures, oil production rate, asphaltene precipitation around wellbore,
well placement, and reservoir characterization). In general, faster convergence has been
reported for PSO algorithm in comparison to DE and GA methods, whereas DE yielded
superior optimal solutions with respect to GA and PSO approaches. On the other hand,
hybridization of FL and ANN methods with other optimization algorithms (e.g., GA and
neural network (GA-NN), Fuzzy Inference System and Artiﬁcial Neural Networks (FIS-
ANNs), and the combinations of DE and ANFIS) has been shown to be more efﬁcient and
has obtained better solutions in comparison with the conventional FL and ANN models.
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H. Rahmanifard, T. Plaksina
The focus of this review is on the use of GA, DE, and PSO techniques among Evolutionary
computation and SI algorithms, while a future work can be expanded to include other methods
such as Ant Colony Optimization.
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123

Potential for reducing air pollution from oil refineries
A. R. Karbassi & M. Abbasspour &
M. S. Sekhavatjou & F. Ziviyar & M. Saeedi
Received: 16 February 2007 /Accepted: 30 October 2007 /Published online: 8 December 2007
# Springer Science + Business Media B.V. 2007
Abstract Islamic Republic of Iran has to invest 95
billion US$ for her new oil refineries to the year
2045. At present, the emission factors for CO2, NOx
and SO2 are 3.5, 4.2 and 119 times higher than British
refineries, respectively. In order to have a sustainable
development in Iranian oil refineries, the government
has to set emission factors of European Community as
her goal. At present CO2 per Gross Domestic
Production (GDP) in the country is about 2.7 kg
CO2 as 1995's US$ value that should be reduced to
1.25 kg CO2/GDP in the year 2015. Total capital
investment for such reduction is estimated at 346
million US$ which is equal to 23 US$/ton of CO2. It
is evident that mitigation of funds set by Clean
Development Mechanism (3 to 7 US$/tons of CO2)
is well below the actual capital investment needs.
Present survey shows that energy efficiency promo-
tion potential in all nine Iranian oil refineries is about
165,677 MWh/year through utilization of more
efficient pumps and compressors. Better management
of boilers in all nine refineries will lead to a saving of
273 million m3 of natural gas per year.
Keywords Emission factor. Global warming .
Oil refinery. Pollutants . Iran
Introduction
Since industrialization began, enormous amounts of
greenhouse gases have been released into the envi-
ronment as result of fossil fuel combustion. This has
led to a drastic increase in greenhouse gas concen-
trations in the atmosphere and has been a major
reason for the climate changes predicted for the near
future that we already observe now. Most of the rise
(0.6°C) in average global temperature measured in the
twentieth century can be attributed to human impact
on the composition of the atmosphere; a much higher
Environ Monit Assess (2008) 145:159–166
DOI 10.1007/s10661-007-0025-4
A. R. Karbassi
Faculty of Environment, University of Tehran,
Tehran, Iran
M. Abbasspour
Faculty of Mechanical Engineering, Sharif University,
Tehran, Iran
M. S. Sekhavatjou (*)
Department of the Environmental Engineering,
Graduate School of the Energy and Environment,
Science and Research Campus, Islamic Azad University,
Tehran, Iran
e-mail: msa_sekhavat@yahoo.com
F. Ziviyar
University of Tehran,
Tehran, Iran
M. Saeedi
Department of Hydraulics and Environment,
College of Civil Engineering,
Iran University of Science and Technology,
Tehran, Iran
increase (1.4 to 5.8°C) is predicted for the future
(IPCC 2001a, b). Low prices of oil and gas in some
countries has lead to inefficient use of energy and
such trend of consumption may lead to deterioration
of environment (Campbell and Laherrere 1998). Oil
refineries can play an important role in the emission
of pollutants. Commitment to cleaner air has been the
main driver for various measures although, recently,
commitment to the Kyoto Protocol has become an
additional driver to reduce emissions from vehicles by
reducing sulphur in the fuel. The United Kingdom,
Denmark and Finland also have used economic
instruments to accelerate the reduction of sulphur in
diesel to 50 ppm several years in advance of
regulation. The primary driver cited for reducing
sulphur in diesel is the improvement of air quality
for health and environmental benefits while another
benefit is the introduction of improved vehicle and
pollution abatement technologies. Pricing for an
economic instrument in advance of a regulation seems
to be a case of encouraging refiners to advance
spending of capital costs that would ultimately be
incurred by all refiners to meet the regulation. The UK
and Danish example are the most instructive in this
respect (Olivastri and Williamson 2001; Wright
2003). In the U.S. economy, transportation is second
only to electricity generation in terms of the volume
and rate of growth of GHG emissions. In terms of
carbon dioxide, which accounts for 95% of trans-
portation's GHG emissions, transportation is the
largest and fastest growing end-use sector. Today,
the U.S. transportation sector accounts for one-third
of all U.S. end-use sector CO2 emissions, and if
projections hold, this share will rise to 36% by 2020.
U.S. transportation is also a major emitter on a
global scale (Bernow and Dougherty 2000; ACEEE
2005).
The oil and gas sector is the backbone of the
Iranian economy. The share of this sector in GDP is
currently about over 23% (Shafipour and Farsiabi
2007). The magnitude of value added in the oil and
gas sector and its share in GDP is sensitive to crude
oil prices and crude production volume. The share of
oil and gas in GDP was only 14.5% in 1998 when
prices and crude productions were lower. The oil
sectors' share in GDP has been very variable during
Table 1 Percentile of nominal to operational capacity of oil refining in 2004 in Iran
No. Refinery
Nominal capacity (thousand bbl/
day)
Percentile of nominal to operational
capacity
Names of oil fields
1
Abadan
350
103
Ahvaz Asmari, Central areas
2
Arak
150
119.1
Gachsaran
3
Isfahan
200
182.2
Maroon, Shadegan
4
Tehran
220
94.7
Maroon, Shadegan, Ahvaz Asmari
5
Tabriz
110
87.8
Maroon, Shadegan, Ahvaz Asmari
6
Kermanshah
25
93.1
Ahvaz Asmari, Serkan, Male Kooh,
Naft shahr
7
Shiraz
40
134.1
Gachsaran
8
Lavan
20
146.8
Resalat, Reshadat
9
Bandar
Abbass
232
113
Sarkhoon
10
Total
1,347
117.1
–
Source: Ministry of Energy (2003)
Table 2 Share of various sectors in the emission of pollutants
in year 2003 in Iran
Pollutant
NOx
SO2
CO2
Sector
Domestic/commercial/public
8.7
12.8
30
Industries
10.5
29.9
15.1
Transportation
64.3
29.3
17.5
Agriculture
5.3
5.8
3
Power plants
11.2
22.6
24.4
Source: Ministry of Energy (2003)
160
Environ Monit Assess (2008) 145:159–166
past 32 years. For instance in 1974 the share of oil
sector in GDP rose to nearly 50% and in 1986, when
crude oil prices collapsed to about 7 US$ per barrel,
the share of oil in GDP decreased to 5%. In general,
oil export accounts for nearly 85% of total export
earnings. The oil and gas sector provides for over
60% of governmental budgetary revenue. To maintain
the vital share of oil and gas sector to the national
economy, large capital investments are required. Thus
economically it is highly necessary to expand the
production capacity and introduce energy efficiency
measures in petrochemisty and refinery sector of the
country (Ministry of Oil 2005).
At the present, there are nine refineries in Iran that
refine crude oil and produce other by-products of oil
with nominal capacity of about 1,347 bbl/day. Table 1
shows percentile of nominal to operational capacity of
oil refining in 2004. All the country's refineries
(except for Bandar Abbass refinery) are designed for
production of high quality and light crude oil. In the
year 2006, about 59.1%, 31.2% and 9.7% of by-
products are attributed to light and semi-condensates,
fuel oil and heavy oil respectively. It should be
pointed out that present capacity of production of
gasoline and fuel jet does not meet country's demand.
In the very beginning days of year 2007 the sum of
gasoline import was about 32 million liters per day.
Thus, it is evident that Iran must expand her refining
capacity within short span of time.
According to US EPA information, during 1970 to
1995, amounts of NOx and SO2 emissions were
respectively 20.8 and 21.1 million tons per year for
NOx and 31.2 and 18.3 million tons per year for SO2
(EPA website 2002).
The share of various sectors in the emission of
pollutants is shown in Table 2. Figure 1 depicts the
trend of NOx, SOx and CO2 emission during the years
1971 to 2003.
According to the results of greenhouses gases
emissions calculations, total GHGs emission in oil
industries in Iran has been 67,107,497 tons CO2
equivalent in the year 2003.Share of oil refineries in
there emissions is about 12,964,690 tons CO2 equiv-
alent. Gas sector, petrochemical industries and upper
oil industries contribute to GHGs emission amounts
equal to 6,637,259; 7,173,708 and 40,331,840 tons
CO2 equivalent per year, respectively. Share of GHGs
emissions of different sub-sectors in Iranian oil sector
is shown in Fig. 2 (CEERS 2003).
In general, GHGs emission from oil refineries is
about 19% of total GHGs emission of oil sector in
Iran. In the present investigation, we intent to
compute the emission of pollutants from Iranian oil
refineries and subsequently compare them with
international standards. This will provide us with
0
50000000
100000000
150000000
200000000
250000000
300000000
350000000
400000000
1971
1976
1981
1986
1991
1996
2001
2003
Year
Tons per year
CO2
0
200000
400000
600000
800000
1000000
1200000
1400000
1971
1976
1981
1986
1991
1996
2001
2003
Year
Tons per year
NOX
SO2
Fig. 1 Trend of NOx, SOx and CO2 emission during the years 1971 to 2003
Petrochemical 
industries
11%
Gas sector
10%
Oil refineries
19%
Upper industries
60%
Fig. 2 Share of GHGs emissions of different sub-sectors in
Iranian oil sector
Environ Monit Assess (2008) 145:159–166
161
enough data to know about the deviation between
national and international emissions' standards. By
knowing such deviations, appropriate measures and
costs of implementation have been proposed to reduce
emission from Iranian oil refineries. The various
methods energy management will be discussed.
Materials and methods
In order to estimate greenhouse gases (GHGs) and
pollutants such as NOx and SO2 emission factors in
Iranian oil refineries. We first compiled emission
factors. In this direction, energy consumption and its
Table 3 Sources of air pollution in Tabriz oil refinery
No.
Source of air pollution
Abbreviation index
1
Hydrogen unit
H-701
2
Isomax (boiler)
H-604
3
Isomax (separator tower furnace)
H-603
4
Isomax (butane separator)
H-602
5
Isomax (pre heater of reactor feed)
H-601 (A)
6
Isomax (heater of reactor feed)
H-601 (B)
7
Liquid gas unit
H-501 (1PG)
8
Support furnace of distillation unit
H-402
9
Catalytic reforming
H-401
10
Visbreaking unit
H-301
11
Catalytic reforming (naphtha desulphurization unit)
H-202
12
Incineration
Incinerator
13
Atmospheric distillation furnace
H-101 (A)
14
Atmospheric distillation furnace
H-101 (B)
15
Catalytic reforming (butane separator)
H-255
16
Catalytic reforming (heater of feed to forth reactor)
H-254
17
Catalytic reforming (heater of feed to third reactor)
H-253
18
Catalytic reforming (heater of feed to second reactor)
H-252
19
Catalytic reforming (heater of feed to first reactor)
H-251
20
Vacuum distillation furnace
H-151 (A)
21
Vacuum distillation furnace
H-151 (B)
22
Boiler
B-2101
23
Boiler
B-2102
24
Boiler
B-2103
25
Boiler
B-2104
26
Boiler
B-2105
Table 4 Characteristics of liquid fuels in Tabriz oil refinery
No.
Fuel
Viscosity
(kg/l)
Viscosity (cst)
Total Nitrogen
(ppm)
Sulphur percent (%)
Share of total fuel
consumption (%)
1
Heavy liquid (slop wax)
0.944
35 (210°F)
185 (140°F)
2,800
1.7
50
2
Light liquid (low quality
petrol)
0.735
–
4,986
0.75
7.2
3
Kerosene
0.82
–
nila
0.07
6.3
a Below detection limit
162
Environ Monit Assess (2008) 145:159–166
variety were measured in different seasons and under
different loads. The stack gases were analyzed using
Testo 342 model gas analyzer. All industrial processes
were also examined closely to know about possible
implementation of energy efficiency measures. Final-
ly energy balance was computed. Tabriz oil refinery
was selected as the case study. Table 3 indicates
Sources of air pollution in Tabriz oil refinery.
Characteristics of liquid and gas fuels are respectively
presented in Tables 4 and 5. In the present investiga-
tion the emissions from Tabriz oil refinery were
computed based on hydrocarbon composition by:
A¼
X
n
i¼1
xi  ai
 
!,
α
B¼
X
n
i¼1
xi  bi
 
!,
α
Where:
A
number of carbon atoms
B
number of hydrogen atoms
α
overall hydrocarbon percentile in fuel was
assumed as 0.948
xi
percentile of fuel constituents
ai
number of carbon xi
bi
number of hydrogen of xi
Subsequently, the chemical compositions of hydro-
carbons were computed and chemical balances were
obtained.
In this paper, first of all, types and amounts of fuel
consumption in Tabriz oil refinery were determined
and then sources of air pollutants in this refinery were
detected. Subsequently, amount of air pollutant
emissions in Tabriz oil refinery was calculated. Also,
emission factors of air pollutants including NOx, SO2
and CO2 were computed. There are three different
emission factors based on various heating value of
fuel in Tabriz refinery. Finally, with extension of
these factors to other refineries in Iran, the emission
amount of two pollutants (NOx and SO2) were
computed and compared with amounts of Tabriz oil
refinery. Finally these emission factors are compared
with England oil refineries emission factors. Indus-
trial infrastructures in Iran are very similar to
England and it is the main reason that we have
selected England as a means of comparison. Besides,
various energy sectors in England have already
lowered their pollution emission below the 1990s
base-line that is in accordance with Kyoto protocol.
Table 5 Characteristics of gas fuels in Tabriz oil refinery
No.
Gas consumption
Heat value (kcal/kg)
Molecular weight
H2
CH4
C2H6
C3H8
C4H10
C5H12
H2S
N2
CO2
1
Natural gas
11,600
17.5
10
88.1
4.3
1.5
0.7
0.2
0
5.1
0.1
2
Refinery gas
12,195
15.76
26.2
59.7
7.7
3.6
1.9
0.9
10
0
0
Table 6 Overall fuel consumption in Tabriz oil refinery
Fuel
Daily consumption
(kg)
Monthly consumption
(ton)
Total percent of fuel
consumption
Heat value (kcal/kg)
Natural gas
200
6,000
17.6
11,600
Refinery
gas
222,105
6,663.15
19.6
12,196
Fuel oil
558,588
16,757.64
49.3
9,866
Naphtha
81,745
2,452.4
7.2
10,458
Kerosene
71,455
2,143.7
6.3
10,328
Total
1,133,893
34,016.8
100
10,700 (average of heat value)
Environ Monit Assess (2008) 145:159–166
163
Results
Base on this study, the overall fuel consumption in
Tabriz oil refinery is shown in Table 6. It is evident
that mean heat value of various fuels is about 10,700
kcal/kg. Accordingly pollution emissions were com-
puted and presented in Table 7. Finally the emission
factors for CO2, SO2 and NOx per kcal ton of crude
oil, kg of refined oil were determined (Table 8;
Abbaspour and Karbassi 2006). In order to extend
emission factor determined for Tabriz oil refinery to
other refineries, an error coefficient was computed.
Error coefficient was obtained through measurement
of NOx and SO2 in other refineries of the country
(than Tabriz). Thus error coefficient is computed
amongst measurement of three refineries with three
different ages. Finally, the emissions were calculated
and presented in Table 9.
Table 10 compares emission factors computed for
Iranian oil refineries with England oil refineries
emission factors.
The potential of NOx, SO2 and CO2 emission
reduction along with the cost of reduction is comput-
ed and shown in Table 11. Such computation is
derived at through calculation of fuel consumption
and its saving when compared with goals of reduc-
tion. It is necessary to mention that England has very
strict plans for reduction of air pollutants and GHGs
emissions amounts in all sectors. Therefore, effective
reduction of air pollutants and GHGs emission in
England oil sector is expected. Such effective meas-
ures can be set as target for Iranian oil refineries. It
should be pointed out that the average age of Iranian
oil refineries is about 28 years. In this direction,
Iranian government should use new technologies for
establishment of new refineries. The new technologies
have lower emission and therefore can reduce the
over all share of Iranian oil refineries in the
production of GHGs and other pollutants. The result
of present study shows that Iran needs to establish 38
new refineries till the year 2045 to meet with gasoline
demand in the country. The necessary investment for
such establishments is shown in Table 12. It can be
calculated that during installation of new refineries,
the government of Iran should carefully consider
utilization of new and more efficient technology. In
the recent years, the government of Iran has tried to
introduce both liquefied natural gas (LNG) as well as
compressed natural gas (CNG) into the energy basket
Table 7 Computed pollution emissions from Tabriz oil
refinery
Pollutant
CO2
SO2
NOx
Pollutant amount
Ton per year
1,245,000
10,963
6,150
Table 8 Air pollutants emission factors computed for Tabriz
oil refinery
Pollutants
CO2
SO2
NOx
Emission factor
kg emission/kcal
of fuel
0.2852×10−3 2.51×10−6
1.408×10−6
kg emission/ton
fuel consumption
(equivalent crud
oil)
2,995
26.35
14.78
kg emission/kg
refined oil
0.21
1.749×10−3
1.054×10−3
Table 9 Measurement of pollutants (NOx and SO2) emissions
in some oil refineries in Iran (ppm)
Pollutants refinery
SO2
NOx
Tabriz
55
54
Abadan
39
45
Isfahan
43
42
Bandar Abbass
41
51
Table 10 Emission of CO2, SO2, NOx from Iranian oil
refineries compared with England oil refineries
Parameter
Emission from
Iranian oil
refineries
(1,000 tons/
year)
Emission rate
for Iranian oil
refineries
kg/ton of fuel
consumption
Emission rate for
U.K oil refineries
kg/ton of fuel
consumption (4)
CO2
20,960
2,995
850
SO2
184
26.35a
0.22
NOx
103
14.78
3.47
a The very high SO2 emission rate could be due to higher
sulphur contents of Iranian heavy oil that is about 3.5%
164
Environ Monit Assess (2008) 145:159–166
of transport sector. In 2001, only two CNG stations
were constructed at Tehran city. There were about 211
CNG refueling stations in service with over 300 under
construction in the year 2007. By the end of project
the number of CNG stations will be increased to about
500 stations in the year 2020. These stations can
provide service to about 500,000 vehicles. Therefore,
considering the number of total fleet which is about 9
million in 2007 and 32 million in 2045 (Table 12), one
can notice the very negligible share of CNG in the
overall energy consumption of transport sector. It
should be pointed out that LNG project is totally kept
aside and only CNG program is being followed.
Discussion and conclusion
In the present investigation, we found higher pollu-
tants and CO2 emission rates from Iranian refineries
when compared with England (Table 10). As shown
in the table, the emission rate of CO2, SO2 and NOx
from Iranian oil refinery is about 3.52, 119.7 and 4.25
times higher than refineries in England (Abbaspour
and Karbassi 2006; EC 2003).
In order to have a sustainable development in
Iranian oil refineries, the government has to set
emission factors of U.K as her goal. The costs of
reduction of 1 ton of CO2, SO2 and NOx vary between
11–35, 320–530 and 2165–2930 US$ respectively
(EC 2003). In Table 11, capital investments for the
reduction of CO2, SO2 and NOx from Iranian oil
refineries are given.
To attain a better emission factors in Iranian oil
refineries, minimum and maximum investment re-
quirement are 396 and 852 million US$, respectively.
Mean investment is about 624 million US$. Now that
more remittance has been gained as a result of raise in
crude oil prices, the government of Iran is provided
with more opportunity to spend more investment on
this issue. Maximum investment for reduction of 15
million tons of air pollutants and GHGs (per year) is
about one billion US$ that can be supplied on a
4-year basis. Such investment is affordable for
government of Iran. Such an investment not only will
result in reduction of GHGs but also will lead to
conservation of natural resources.
In addition to the above mentioned investment, a
number of other measurements must be considered.
We emphasis on energy efficiency measures as they
are cost effective and the pay back period is generally
less than 5 years. Usually, the effectiveness of energy
efficiency measures last for about 10 to 12 years.
Followings are the most important measures that
should be considered in oil refineries of the country:
–
Reduction of flare gases by setting up refineries
with necessary equipment and implementing
better services.
Table 11 Capital investment needs for reduction of CO2, SO2, NOx from present Iranian oil refineries
Parameter
Reduction (thousand tons/
year)
Capital investment requirement
US$ million
Mean capital investment requirement
US$ million
CO2
14,980
167–525
346
SO2
182.4
59–97
78
NOx
78.7
170–230
200
Total
15,241.1
396–852
624
Table 12 Capital investment needs for the construction of refineries in Iran
Year
Population
(×106)
No. of vehicles
(×103)
Gasoline consumption
(×106 l/day)
No. of required
refineriesa
Capital investment requirement
(US$ billion)
2015
81.44
8,954
80.856
10
25
2021
94.4
23,595
184.08
20
50
2045
102.5
32,800
213.2
8
20
a The oil refinery product capacity is considered as 4,000 m3 /day
Environ Monit Assess (2008) 145:159–166
165
–
Installation of gas recovery systems to compress
them and redirecting them to fuel network of
refinery.
–
Removing the operational problems to improve
combustion of gases.
–
Establishment of vapor collection in bitumen
units.
–
Improvement of different units to increase effi-
ciency of recovery.
–
Changing and reclaiming insulations of furnaces,
storage tanks and pipes.
–
Increasing boilers and furnaces efficiency using
air pre-heaters.
–
Heat recovery with convection heat transfer in
furnaces using fins surfaces.
–
Improving the quality of fuels before consumption.
–
Improving towers' operation.
–
Using mechanical sluices in compressors to
prevent gas leakages.
–
Installation of air pollutants control systems such
as wet scrubbers.
–
Establishing energy management system in dif-
ferent units of refineries.
–
Install combined heat and power (CHP) systems
to recover lot heat.
Acknowledgments
The authors are most grateful to the
Strategic Studies Center, Science and Research Campus, IAU,
for the financial support of this research.
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Environ Monit Assess (2008) 145:159–166

Optimization methods for petroleum ﬁelds development
and production systems: a review
Cheng Seong Khor1,2 • Ali Elkamel3 • Nilay Shah4
Received: 16 September 2015 / Revised: 15 March 2017 / Accepted: 27 August 2017 /
Published online: 19 September 2017
 Springer Science+Business Media, LLC 2017
Abstract In this review, we survey the widespread use of numerical optimization or
mathematical programming approaches to develop and produce petroleum ﬁelds for
design and operations; lift gas and rate allocation; and reservoir development, plan-
ning, and management. Early applications adopted linear programming alongside
heuristics. With continuous advancements in computing speed and algorithms, we
have been able to formulate more complex and meaningful models including non-
linear programs and mixed-integer linear and nonlinear programs. Various formu-
lations and solution strategies have been used including continuous and discrete
optimization, stochastic programming to handle uncertainty, and metaheuristics such
as genetic algorithms to increase solution quality while reducing computational load.
Keywords Numerical optimization  Production optimization  Petroleum
ﬁelds  Reservoir planning  Mixed-integer linear programming (MILP)  Mixed-
integer linear programming (MINLP)
& Cheng Seong Khor
khorchengseong@xmu.edu.my; khorchengseong@gmail.com
Ali Elkamel
aelkamel@cape.uwaterloo.ca
Nilay Shah
n.shah@imperial.ac.uk
1
Present Address: Xiamen University Malaysia, Jalan Sunsuria, Bandar Sunsuria, 43900 Sepang,
Selangor Darul Ehsan, Malaysia
2
Engineering Global Support Ofﬁce, ExxonMobil Research and Engineering Company, Kuala
Lumpur City Centre, 50088 Kuala Lumpur, Malaysia
3
Department of Chemical Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
4
Centre for Process Systems Engineering, Imperial College London, South Kensington Campus,
London SW7 2AZ, UK
123
Optim Eng (2017) 18:907–941
DOI 10.1007/s11081-017-9365-2
1 Introduction
Oil and gas production from petroleum ﬁelds is a widely reported problem mainly
constrained by the reservoir conditions, pipeline network ﬂow characteristics, and
surface facilities capacity. Consequently, determining the daily optimal operating
conditions requires that we simultaneously consider the complex interactions
involving activities of the subsurface reservoir, wells, and surface facilities. The
literature contains various approaches on problems broadly concerning production
systems design, operations, and control. In general, we can categorize these methods
as follows (Vasantharajan et al. 2006; Kosmidis et al. 2004):
•
sensitivity analysis by employing simulation tools;
•
intuition-driven techniques and heuristics based on rules and learnings derived
from past experience and analogies; and
•
formal optimization or mathematical programming techniques.
This work emphasizes the optimization approach, which has been applied in
virtually all aspects of the hydrocarbon industry. However, review articles on
applying optimization to problems in the upstream petroleum industry sector of
exploration, development, and production are scant (Dougherty 1972) consid-
ering how important the E&P activities are to the hydrocarbon enterprise and
to global energy systems and the economy as a whole. Moreover, an early
optimization technique commercially applied in the petroleum industry was
linear programming (LP) although this was more so for its downstream sector
of
oil
reﬁning
(Ranyard
1988).
In
this
article,
we
survey
optimization
applications
in
production
systems
design
and
operations;
lift
gas
and
production rate allocation; and reservoir development, planning, management,
and optimization. The literature often collectively refers to these problems as
petroleum production systems optimization (Beggs 1991). We do not only
intend to give a detailed review; we also want to shed light on the major ways
in which optimization has been applied in this area and how such practice is
likely to grow in the future.
2 Techniques and tools for production systems optimization
2.1 Intuition-based and heuristic approaches
The literature contains publications that handle daily oil production optimization
problems by intuition-based rules and heuristic algorithms in computer software
codes and tools, e.g., see Stackel and Brown (1979), Wallace and van Spronsen
(1983) and Weiss et al. (1990). Heuristic approaches seek good feasible solutions
rather than exact optimal solutions to complex or time-constrained problems
(Rardin and Uzsoy 2001), which contrasts with the use of mathematical optimiza-
tion approaches. The heuristics are typically applied sequentially in well manage-
ment routines that decompose a pipeline network into several levels (Mattax 1990).
908
C. S. Khor et al.
123
Kosmidis et al. (2004) give examples of such heuristics-based routines, e.g., rules
such as closing a well if it violates an upper bound of the gasoil ratio (GOR) that is
implemented at the well level (in which GOR is deﬁned as the ratio of gas-to-oil
volumetric ﬂow rate).
Consequently, while they account for pipeline network constraints, well
management routines are formulated in an ad hoc manner that does not
systematically or holistically address the oil production maximization problem.
Commercial reservoir simulators such as Schlumberger’s ECLIPSE (Schlumberger
2012) and Landmark’s VIP (Landmark 2001) use similar heuristic rules that
consider gas-lift optimization separately from well rate allocation optimization.
One of the most widely applied heuristics to allocate gas to gas-lift wells is
incremental GOR (IGOR), deﬁned as the amount of gas required by a gas-lift well
to produce an additional barrel of oil (Redden et al. 1974). All wells tied to a
common manifold must operate at the same IGOR. This set of rules has been
applied to the operations of the Prudhoe Bay and Kuparuk River ﬁelds (see Barnes
et al. 1990 and Stoisits et al. 1992, respectively).
However, a possible drawback to these heuristic GOR approaches is that the
necessary condition may be derived by assuming all the wells to be tied directly to a
ﬁxed-pressure separator in a pipeline network (Kosmidis et al. 2004). Thus, this
practice does not explicitly account for the highly signiﬁcant and complex nonlinear
interactions between the wells that share a common ﬂow line, primarily the pressure
gradients inﬂuence. In fact, this disadvantage underscores the need to adopt a more
robust approach such as optimization, the focus of this article.
2.2 Optimization-based approaches
Numerical optimization or mathematical programming tools and techniques have
been used in many aspects of the upstream petroleum industry to support activities
and decisions that include designing production systems and facilities, scheduling
resources, history matching to determine reservoir parameters, well location and
trajectory, well control (i.e., production optimization of existing wells), estimating
and setting production parameters, and optimizing recovery factors. These
optimization initiatives typically involve ofﬂine-generated models based on ﬁrst
principles or semi-empirical models using ﬁeld data.
The use of economics-driven optimization methods to develop oil and gas
prospects began in the early 1950s, and signiﬁcant progress was made in the
1960–1980s (Aronofsky 1983, 1988). Nonetheless, applying the methods still
requires considerable art and ingenuity particularly to set up a modeling situation.
Table 1 summarizes the general progress as evidenced from academic research and
industrial practice.
Overall, we can conclude that optimization-based approaches have progressed
from academic research to practical ﬁeld implementations limited by a need for
informed careful use. We believe we have not pursued practical applications
as vigorously as these techniques deserve. Fortunately, optimization is a highly
fertile and active research area both inside and outside the petroleum industry, so we
Optimization methods for petroleum ﬁelds development...
909
123
can be optimistic about how to further advance and achieve widespread ﬁeld
applications of these techniques in the future.
A most important impact is the way we conduct numerical reservoir simulation
studies. To develop optimization models with integrated large-scale reservoir
simulations, we must consider how to appropriately perform history matching,
inﬂuence functions generation, and economic optimization. In this regard, note that
NLP and MINLP are the preferred optimization methods to capture the highly
nonlinear and possibly nonsmooth characteristics inherent to industrial problems
(e.g., due to reservoir dynamics) as based on considerable work that the rest of the
paper surveys and summarizes. Accordingly, the solution algorithms have evolved
to handle the underlying nonlinearities ranging from classical Newton-based
approaches (e.g., generalized reduced gradient (GRG)) to derivative-free methods
(e.g., surrogate model-based and direct search). The latter approach includes
stochastic methods such as those based on genetic algorithms and particle swarm
optimization (PSO).
On the other hand, before we are locked into unnecessary large-scale studies, we
may consider balancing between modeling the complex physical reservoir behavior
and the computational load to execute an optimization procedure. Also, we are
likely able to better justify using an overall systems approach by considering global
production facilities for which we can simplify the reservoir description.
3 Optimization-based approaches to design and operations
Optimization models for the design and operations of an integrated oil production
system cover an entire span from the subsurface structure comprising the drainage
area, wells, and wellhead assembly, up to the surface facilities. Such detailed
optimization models comprise several component models for: reservoir simulation
of well bore, well tubing strings for multiphase ﬂuid ﬂow in pipelines from well
bores to well heads, well choke valve, well ﬂow lines from wellheads to well pad
manifolds, surface ﬂow line for the surface pipeline network systems, and separator
for separation facility. An economics goal governs the model objective function.
3.1 Nodal analysis
Conventionally, the hydrocarbon industry has used the trial-and-error-based
simulation tool of NODAL production systems analysis, which Schlumberger
originally developed, to optimize production systems design and operation.
Practitioners have widely adopted the term over the years so that it became close
to a household name within the industry, and they refer to it simply as nodal
analysis (Beggs 1991). Dating back to papers as early as 1954 that propose the use
of this ‘‘what-if’’ analysis technique (Brown and Lea 1985), nodal analysis in its
general form refers to the systems approach for optimizing the production
operations of oil and gas wells by thoroughly evaluating the complete well
production system but without implementing a formal mathematical program. It
uses correlations to predict multiphase ﬂow behavior through pipes, well
910
C. S. Khor et al.
123
completions, restrictions, and reservoir to analyze ﬂow behavior in an entire
production (Brill 1987). It performs this analysis by repetitively varying the
associated variables to simulate and optimize the underlying system.
Ultimately, nodal analysis determines the daily operating policy by forecasting
the performance of the various elements that make up a completion and production
system. The objective is to optimize the completion design to suit the reservoir
deliverability and identify restrictions or limitations in the production system so as
to improve production efﬁciency (Schlumberger 2006).
However, nodal analysis is largely limited to oilﬁelds with a few wells due to its
trial-and-error nature (Kosmidis et al. 2004). For instance, nodal analysis requires
that by holding all other parameters ﬁxed, we vary a single variable to inspect its
optimal value. For multiple variables, such a procedure is bound to fail due to the
many function evaluations required to cover the search region. This is precisely one
of the main reasons to apply optimization techniques.
3.2 Optimization
Optimization involves applying mathematical programming over a large set of
variables. Within the realm of production systems design and operation, we have
found a complete range of optimization methods applied that focusses on NLP and
MINLP techniques to address the inherent nonlinearities typical of practical
problems. Table 1 in the Appendix summarizes such applications, which include
initiatives under intelligent or smart ﬁelds programs. In fact, a standard industrial
practice is to equip commercial reservoir simulators with optimization engines.
Some of these simulators are: ECLIPSE by Schlumberger Information Solutions
(2012); VIP by Landmark (2001); NETOPT by Simulation Sciences (1999); CMG-
STARS by Computer Modeling Group (CMG) (2006); Codeon Multiﬂo Simulator
by Codeon (2007); MRST by SINTEF (Lie 2016; Krogstad et al. 2015); PROSPER,
GAP, and MBAL by Petroleum Experts (2017); and PumaFlow by Beicip-Franlab
(2017).
4 Optimization-based approaches to rate allocation
4.1 Gas-lift allocation problems
We typically pose a gas-lift optimization problem as maximizing the daily
hydrocarbon production. Our goal is to determine the optimal well production and
lift gas rates subject to pressure and rate constraints in the surface pipeline network
nodes as well as the available lift gas amount. The gas lift mechanism is elaborate:
having an appropriate amount increases the oil production rate but injecting
excessively reduces it besides costing more due to high gas prices and compression
costs. Ideally, if we are not restricted by how much injection gas there is, we could
inject gas into an individual well until we attain maximum production. But in most
cases, we do not have enough gas to maximize each well production especially in
depleted ﬁelds since the lift gas required would have increased as compared to the
Optimization methods for petroleum ﬁelds development...
911
123
speciﬁed initial facility design. Hence we need to optimize allocating a limited
injection gas amount to maximize the oil production rate (Wang and Litvak 2004;
Buitrago et al. 1996).
We conventionally use a tool based on a performance curve that plots oil rate
against lift gas rate for a well. When gas supply is unlimited, the optimal lift gas rate
corresponds to maximum oil production on the performance rate; when limited, we
typically use a formal optimization routine. We traditionally apply an economics
heuristic of equal slope allocation, i.e., that the gas-lift performance curve slopes are
equal for all wells at optimality. But in general, this method does not apply to wells
that do not respond instantaneously to gas injection (Kanu et al. 1981). Table 4 in
the Appendix lists representative papers applying optimization to allocating lift gas
to wells.
4.2 General rate allocation problems
In this problem, we simultaneously allocate both production and lift gas rates of
single wells or total production rate of multiple reservoirs to attain certain
operational goals. The problem is one of the earliest optimization applications
dating back to the 1960s using LP that progressed to NLP with mixed-integer
programming as the dominant method now. Table 5 in the Appendix focuses on the
model characteristics of representative work for this application.
4.3 Mature ﬁeld developments
We frequently encounter rate allocation problems in mature ﬁelds where production
facilities are no longer able to meet conventional ﬁeld demand. Well-documented
cases on the two Alaskan oilﬁelds at Prudhoe Bay and Kuparuk River show how oil
production is constrained by gas processing capacities of surface facilities
comprising separation units and a central gas plant. Table 6 lists representative
work addressing the problem using optimization in these actual ﬁeld developments.
From Table 6 in the Appendix, neural networks appear as the central tool to
accelerate simulation and optimization procedures in the studies by Stoisits et al.
(1992, 1994, 1999). Nonetheless, we note that training neural networks is
computationally expensive; moreover they mainly rely on how accurate the training
tools are and how the simulated system behaves.
5 Optimization-based approaches to reservoir development, planning,
and management
Reservoir management concerns organizing the relevant technical, operational, and
business resources from discovery to abandonment phases that optimally exploit a
reservoir in an efﬁcient, safe, responsible, and proﬁtable manner. It employs data-
driven mathematical models coupled with human insight and expertise. The main
goal is to maximize the value of a hydrocarbon asset or a collection of assets by
achieving the following objectives: (1) improve estimates of hydrocarbons in place;
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123
(2) increase recovery, production, and forecasts of hydrocarbons; (3) minimize
capital investment and operating expenditures; and (4) maximize overall proﬁtabil-
ity. Accomplishing these objectives requires a holistic consideration of the full
spectrum that spans the hydrocarbon assets, ongoing and future development
projects, and various investments under consideration (Vasantharajan et al. 2006;
Saputelli et al. 2005, 2006; Al-Hussainy and Humphreys 1996).
Development and planning studies in reservoir management has been a perennial
area that has beneﬁted enormously from the application of optimization techniques.
Continuously evolving and increasingly sophisticated tools and models have been
employed to handle more stringent and demanding practical limitations. The
representative problems in this ﬁeld can be categorized as facility location–allocation
and production planning and scheduling (Iyer et al. 1998; Ierapetritou et al. 1999).
5.1 Facility location–allocation problems
Facility location–allocation problems pertain to decisions on locating or placing and
allocating the number of units of production platforms and wells to well platforms.
NLP is the chief method used for this class of problem in reservoir development and
planning studies; some recent work has also attempted MINLP formulations.
Table 7 summarizes major work on this application.
5.2 Optimization-based approaches to production planning and scheduling
5.2.1 Early applications of linear programming
Production planning and scheduling is a broad class of problems encompassing
reservoir development, well-drilling operations, production rate, and compressor
building (the latter is in gas ﬁeld developments). We have featured simple reservoir
models in early optimization studies that applied LP techniques as shown in Table 8.
5.2.2 Applications of nonlinear programming
LP model formulations, as exempliﬁed in Table 8, require the objective function
and constraints to be linear, often by approximating via linearizating the actual
nonlinear production systems. Using LP limits applying more representative
nonlinear reservoir models and simulators to couple with increasingly efﬁcient and
sophisticated optimization methods for reservoir development. Fortunately, com-
puting speed and algorithmic techniques have advanced such that we are now able
to model various NLP formulations and compute using suitable solution strategies.
We can broadly classify the progress as general applications of NLP techniques
(Table 9) and applications of NLP-based optimal control for dynamic optimization
that involve differential-algebraic equations (Table 10). In addition, we apply
variants of metaheuristic techniques with stochastic search such as genetic
algorithms (GA) to handle models with nonconvex non-smooth functions as
discussed in Table 11.
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5.2.3 Sequential versus simultaneous optimization-based approaches to design,
planning, and scheduling
To ease the computational burden involved, most earlier works address the
following decisions separately: capacities of production platforms (PPs) and well
platforms (WPs), drilling schedules, and production proﬁles. A sequential approach
entails ﬁxing a well drilling schedule to determine the corresponding production
proﬁle typically by solving an LP, then reverses the procedure by solving the
converse problem of ﬁxed production proﬁle to schedule drilling as done by Attra
et al. (1961) and Aronofsky and Williams (1962). But by not simultaneously
considering all design and planning decisions, we face a major drawback that we do
not properly account for important interactions between these variables; conse-
quently, we compute and implement suboptimal or even infeasible solutions.
Table 12 surveys major works that use a simultaneous MILP approach.
5.2.4 Recent advances in mixed-integer nonlinear optimization-based approaches
Capturing nonlinearities in reservoir performance is pertinent while addressing
challenges of high computational load and quality of solution. In this regard, the
modeling and algorithmic capability of MINLP techniques have beneﬁted from
progress made in discrete optimization and combinatorics (Grossmann 2002; Belotti
et al. 2013). Table 13 in this section highlights such advances and achievements.
5.2.5 Optimization-based approaches to planning and scheduling under uncertainty
We generally have uncertain knowledge of petroleum reservoirs, thus we have to
face numerous uncertain factors each with multiple highly unpredictable future
outcomes in reservoir management (Haugen 1996). Such signiﬁcantly varied or
random parameters include resource availability, ﬁeld size, reservoir properties,
development cost, production deliverability, oil price, product price, or project cost.
Uncertainty is a chief concern in decision making processes that affect proﬁtability
and thus requires risk management to avert negative outcomes particularly in
reservoir production forecasting. Table 2 reports approaches to mitigate uncertainty.
Sources of uncertainty can be either exogenous (i.e., externally imposed) or
endogenous (internally imposed) that give rise to two classes of optimization problems
under uncertainty. We may (although not always) address them differently in
optimization and optimal control of petroleum ﬁelds; recent work presents methods
which are tailored to a speciﬁc problem. To illustrate their differences, consider the
exogenous uncertainty of market demand that is independent of decisions taken in the
course of a project and contrast that with the endogenous uncertainty of oil reserves
that is dependent on (or is resolved by) project decisions made at various design or
operation stages. We can treat endogenous uncertainty using multistage stochastic
programming with a decision-dependent structure of the scenario tree of uncertain
parameters (Goel and Grossmann 2004; Tarhan et al. 2009). A recent review of this
approach (Gupta and Grossmann 2017) poses a deterministic version of such a model
that is adequately detailed yet computationally efﬁcient.
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6 Potential future research directions
This section presents our perspective of future directions in optimization research of
petroleum ﬁeld development and production.
1. Develop increasingly integrated and meaningful models. There is a greater
effort to integrate decisions that we have treated as separate problems such as jointly
optimizing well location and well control (Forouzanfar and Reynolds 2014; Aliyev
and Durlofsky 2015; Humphries and Haynes 2015). Improving solution algorithms
(Bixby and Rothberg 2007; Bixby 2012; Bertsimas et al. 2014) aided by ever
increasing computing power allowed us to capture many more details in models
with affordable computation that integrate decisions at well surface and subsurface
levels. To date, few works have simultaneously addressed well placement, surface
infrastructure network design, facility location–allocation and planning and
scheduling (Tavallali et al. 2013, 2014; Tavallali and Karimi 2016a). We are also
now better able to model and solve increasingly meaningful formulations that
include actual ﬁeld dynamics besides typical reservoir uncertainty, which causes
unexpected performance such as cost in rapidly changing economic conditions and
new development opportunities.
We can formulate appropriate models using the whole-systems optimization
approach, which has been used in supply chain modeling and optimization, e.g.,
Dunnett et al. (2008) and Samsatli et al. (2015a, b). Such models include spatial and
temporal (time) as well as economics aspects. It is valuable for us to incorporate
environmental concerns such as to address greenhouse gas emissions (e.g., methane
from gas ﬁelds) by applying life cycle and environmental impact assessments, e.g.,
Nagurney et al. (2010), Liu et al. (2010), Khor and Elkamel (2010), Zhang et al.
(2014), Kang et al. (2016); Vaﬁand Brandt (2016), Pascual-Gonzlez et al. (2016)
and Gao and You (2017). We are poised to unveil potential design alternatives and
improve operational decisions using such a holistic systematic modeling approach,
thus leading to optimal overall performance.
2. Devise efﬁcient algorithms that need fewer computational resources. We
envision that future work will design algorithmic approaches and components with
the following goals: (a) Tailor the algorithm to speciﬁc decisions and uncertain
factors; (b) Combine the algorithmic components to reduce the computational
burden; and (c) Optimize the decisions with the least computational cost. Similar
aims have also been reported in other areas that beneﬁt from optimization research,
e.g., see Varma et al. (2007), Hou et al. (2015) and Nagurney and Li (2016). Due to
the complex and speciﬁc nature of E&P problems, we have incentives to consider
heuristic-based methods that cater to a particular problem structure and allow
a customized solution strategy (Khor and Varvarezos 2016). In this regard, Isebor
et al. (2014b) explores formal heuristics involving stochastic methods such as
genetic algorithms and proposes the use of the PSO algorithmic procedure. We
expect mathematical programming techniques particularly MILP and MINLP will
continue to be used on a wide range of problems with researchers contributing in
both theory and applications, including those from outside the upstream petroleum
fraternity, e.g., see Gupta and Grossmann (2012a, b), Khor et al. (2012) and Cozad
Optimization methods for petroleum ﬁelds development...
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123
et al. (2014). Tavallali et al. (2016b) gives a viewpoint of the process systems
engineering (PSE) community on this area.
3. Optimize unconventional oil and gas exploitation. As we become more
interested in using big data analytics in exploration and production including for
shale and deepwater resources, such emphasis has paved the way for us to apply
optimization techniques to support decision-making. In tandem with formal
optimization models, we can use advanced statistical analysis, machine learning,
and other methods to elicit information and meaning buried within a vast amount of
exploration and production data (Rassenfoss 2016). There is a growing body of
work especially by PSE researchers e.g., a recent survey discusses the relatively
nascent but considerably extensive application of optimization on shale gas
exploitation (Gao et al. 2017) that addresses a wide range of problems including
well control and scheduling (Knudsen and Foss 2013; Knudsen et al. 2014a, b),
water management in hydraulic fracturing (Yang et al. 2014, 2015; Gao and You
2015b; Lira-Barragan et al. 2016), and shale supply chain design and planning
(Cafaro and Grossmann 2014; Gao and You 2015a, c; Drouven and Grossmann
2016). A challenge remains to develop a computationally efﬁcient approach that
incorporates analytics within an optimization framework that models important
decisions meaningfully and simultaneously accounts for uncertainty.
7 Concluding remarks
Most model-based strategies to optimize petroleum ﬁelds development and
production have largely focussed on reservoir simulation that are limited to speciﬁc
problems. Fortunately, with improved solution algorithms and increased computa-
tional power, we see an explosion of activities and effort within both academic
research and industrial practice to adopt optimization-based approaches. The
models, techniques, and tools developed consider important practical features,
factors, and issues to tackle various problems in meaningful, integrated, and
cohesive ways. Further, these methods often incorporate simulators to generate
reservoir models as a component within the optimization framework developed.
Despite widespread use in its downstream sector of petroleum reﬁning and
petrochemicals production, we argue that optimization has not fully penetrated the
upstream hydrocarbon industry of exploration and production. To the extent we
have adopted it, the approach lacks connection with real world problem dynamics
that is increasingly a mainstay of downstream optimization practice. Nonetheless,
we note in our perspective on future directions (see the previous section) that these
challenges will form part of the research agenda the community will address going
forward.
Appendix
See Tables 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 and 13.
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Table 1 Examples of optimization application to design and operation in production systems of pet-
roleum ﬁelds
References
Type
Model formulation
Solution strategy
Carroll and
Horne
(1992)
NLP
Single-well system
Variables: separator pressure,
tubing diameter
Newton-type algorithm
Polytope method: function-
value-based
Fujii and
Horne
(1995)
NLP
Multiwell system
Two-stage optimization
strategy: use pipeline network
simulator to evaluate values of
control variables computed by
optimization algorithm
Newton-type algorithm
Polytope method
GA
Palke and
Horne
(1997)
NLP
Fully compositional model
Single-well system
Variables: tubing diameter
(caused highly irregular
objective function surface),
separator pressure, gas injection
volume
Newton-type algorithm: failed
for nonsmooth objective
function
Polytope method: performance
depends on good starting point
GA: more robust but
computationally intensive
Wang et al.
(2002a)
MINLP
Based on Fang and Lo (1996)
Simultaneous optimization of
allocation of well production
rates, gas-lift rates, and well
connections to surface pipeline
systems
Separable programming
Genetic algorithms (GA)
Wang et al.
(2002b)
MINLP
Considers ﬂow interactions
among wells in gathering
system via pipeline network
modeled using tree-like
structure
Sequential quadratic
programming (SQP) algorithm
Queipo et al.
(2003)
NLP
Integrated oil production
systems at design and
operational levels comprising
reservoir, tubing, choke, and
separator
Variables: oil ﬂow rate;
diameters of tubing, choke, and
pipeline
SQP
DIRECT global optimization
algorithm
Kosmidis
et al.
(2004)
MINLP
Single reservoir, two-well
system
Constraints: nonlinear reservoir
behavior, multiphase ﬂow in
pipelines, surface capacity
Uses binary variables to impose
adjacency condition for special
order set (SOS) variables and to
linearize nonlinear constraints
Piecewise linear function
approximation of original
MINLP resulting in a sequence
of MILP problems
Optimization methods for petroleum ﬁelds development...
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Table 1 continued
References
Type
Model formulation
Solution strategy
Sarma et al.
(2005)
Optimal
control
Adjoint-based dynamic
optimization
Real-time model-based
’’closed-loop’’ reservoir
management approach
Standard gradient-based
algorithm
Sarma et al.
(2006a)
Optimal
control
Adjoint-based dynamic
optimization
Approximate feasible direction
algorithm combined with
feasible line search to treat
nonlinear path constraints
Sarma et al.
(2006b)
Optimal
control
Combines dynamic optimization
using adjoint equations with
model updating for history
matching
Gradient-based optimization
algorithm
Schulze-
Riegert and
Ghedan
(2007)
Stochastic
optimization
History matching with uncertainty
consideration in reservoir data
Implements algorithmic
approaches including
probabilistic forecasting within
a distributed computing
framework to reduce solution
time
Sarma et al.
(2008a)
NLP
KPCA-based parameterization for
multipoint geostatistical models
Gradient-based optimization
algorithm
Sarma et al.
(2008b)
NLP
General adjoint equations-based
optimization technique to model
closed-loop systems
Applies kernel principal
component analysis (KPCA) to
handle Karhunen-Loeve
expansions representation of
input random ﬁelds
AbdulKarim
et al.
(2010)
NLP
Presents Saudi Aramco’s
experience in intelligent ﬁeld
approach by using real-time
data from ﬁelds for continuous
monitoring and response during
development phase via intra-
process optimization and
detailed modeling
Applies strategies for integration
and implementation in real-time
operation manner
van den Berg
et al.
(2010)
NLP
Presents Shell’s experience in
intelligent ﬁeld approach
through retroﬁts and improved
design by means of real-time
well monitoring, optimization,
and virtual metering; data
acquisition and security; and
model-based production
optimization and forecasting
including for pipelines and
exception-based surveillance
Modeling of smart snake wells
which connect reservoirs in
fully remotely controlled wells
and unmanned platforms as well
as of ﬁsh-hook wells drilling
from coast which curved
upwards into offshore ﬁeld
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Table 1 continued
References
Type
Model formulation
Solution strategy
Ravalec
et al.
(2012)
NLP
Integrated workﬂow for
reconciliation of reservoir
models with both production
and inverted 4D seismic data;
incorporates geological
modeling, upscaling, ﬂow
simulation, downscaling, and
petroelastic modeling
Simulation and nonlinear
optimization to perform history
matching
Horesh et al.
(2015)
General
optimization
scheme
Develops a reduced space
hierarchical clustering method
for aggregation of geo-statistical
prior sampling of dynamic ﬂow
indicators
Applies extended techniques
based on numerical linear
algebra such as singular value
decomposition to realistic case
studies
Table 2 Examples of optimization application to planning and scheduling of reservoir operations under
uncertainty
References
Type
Model formulation
Solution strategy
Jornsten (1992),
Haugen (1996)
and Jonsbraten
(1998)
MILP
Stochastic programming
Uncertainty in demand,
production ﬁeld size, and oil
price
Uses expected value of
perfect information (EVPI)
techniques for scenario tree
generation and sampling
Progressive hedging algorithm
(Rockafellar and Wets 1991);
uncertainty in demand,
production ﬁeld size, and oil
price
Jonsbraten et al.
(1998)
Integer LP
(ILP), Integer
nonlinear
program
(INLP)
Provides underpinning theory
for stochastic program
formulation in which
distribution of uncertain
parameters is not independent
of the decisions made
Bounds are given for the case of
discrete distributions and
variables with an associated
branch-and-bound-based
implicit enumeration
algorithm
Dempster et al.
(2000)
LP
Two-stage and multistage
stochastic linear programming
with continuous recourse
decisions
Uncertainty in demands and
spot market supply costs for
products
Uses LP solvers through
implementation in Xpress
algebraic modeling system
Lund (2000)
Stochastic
dynamic
programming
Handles different ﬂexibility
strategies (particularly on
capacity) for marginal ﬁelds
information, and capacity
ﬂexibility
Considers uncertainty in: (1)
reservoir properties and
volume, and (2) market, e.g.,
oil prices
Follows a Markov decision
process
Optimization methods for petroleum ﬁelds development...
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Table 2 continued
References
Type
Model formulation
Solution strategy
Cullick et al.
(2003)
MINLP
Multiple oil ﬁelds with
uncertainty in reservoir
volume, ﬂuid quality,
deliverability, and costs
Represents uncertainty via
scenarios
Uses ﬁnite difference-based
reservoir simulation for
nonlinear production proﬁle
with surface pipeline network
Economics-based model
Metaheuristics: Tabu search,
scatter search with LP and
NN
Monte Carlo (MC) or quasi-
MC sampling with
correlations
Dispatcher for distributed
computing
Gu
¨yagu
¨ler and
Horne (2004)
NLP
Uses utility theory to
quantify uncertainty in
reservoir simulation forecasts
to evaluate well-placement
conﬁguration
Incorporates risk attitude of
decision-maker through utility
functions
Uses hybridized genetic
algorithms (HGA) of
Gu
¨yagu
¨ler (2002)
Considers random
realizations of reservoir
properties for each evaluation
of well conﬁguration, then
performs simulation to
evaluate objective function
Provides approximate
feasible solution
Aseeri et al.
(2004)
MINLP
Stochastic programming with
ﬁnancial risk management for
optimal operating sequence of
platform building, drilling,
and production
Considers uncertainty in oil
price and productivity index
Decisions on selection of
reservoir and well sites,
capacities of wells and
production platforms, and
ﬂuid production rates from
wells
Includes budgeting
constraints to handle project
cash ﬂow, distribution of
proceeds, and possibility of
taking loans against some
built equity
Uses sample average
approximation for evaluating
expected recourse cost
function
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C. S. Khor et al.
123
Table 2 continued
References
Type
Model formulation
Solution strategy
Dias (2004)
MINLP
Presents models for real
options approach to evaluate
investments in exploration
and production under market
and technical uncertainties
Considers market uncertainty
(e.g., in oil price and rig rate)
and technical uncertainty
(e.g., in petroleum existence,
volume, and quality) using
Monte Carlo simulation
(Details not provided)
Kalligeros
(2004)
LP
Compares proposed real
options-based optimization
formulation with stochastic
linear programming
Handles valuation and
ﬂexibility in engineering
project systems design
Introduces special matrices
(e.g., switching cost matrix)
to achieve customized
formulation that is more
amenable to efﬁcient solution
Ligero et al.
(2005)
Combined
optimization
with
simulation
Uses Value of Information
(VOI) theory to quantify
uncertainty and perform
economic evaluation in
appraisal and development of
petroleum ﬁelds
Considers geological
uncertainty using geological
representative models (GRM)
and reservoir simulation
Optimizes production
strategies as based on GRM
Applies uncertainty reduction
procedure based on physical
processes such as well
perforation
Uses parallel computing to
execute the simulations
Goel and
Grossmann
(2004, 2006)
MINLP
Investment and operational
planning of gas ﬁelds
development
Multistage stochastic
programming with mixed-
integer recourse variables,
disjunctive constraints, and
non-anticipativity constraints
Decision-dependent scenario
tree (i.e., uncertain parameters
are dependent on optimization
decisions)
Uncertainty (endogenous) in
reserves sizes and initial
deliverabilities of ﬁelds
Decisions on production rate
Linear reservoir models
(adequate approximation for
gas ﬁelds)
Decomposition-based heuristics
for constructing
approximation algorithm
using a moving-shrinking
horizon approach
Optimization methods for petroleum ﬁelds development...
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Table 2 continued
References
Type
Model formulation
Solution strategy
Ozdogan and
Horne (2006)
NLP
Considers time-dependent
uncertainty in production
proﬁles that are obtained as
wells are drilled
Uncertainty is represented
using scenarios generated
from a probabilistic
forecasting model
Uses recursive probabilistic
history matching steps
through pseudohistory
concepts to simulate and
optimize well placement
decisions
Uses hybridized genetic
algorithm (HGA) method;
mainly applicable to ﬁelds
with small numbers of wells
because of the
computationally expensive
history matching procedure
Tarhan et al.
(2009, 2013)
MINLP
Design and planning of
offshore oil/gas ﬁeld
infrastructure and synthesis of
process networks under
uncertainty
Nonconvex scenario-based
multistage stochastic MINLP
with continuous recourse
variables and disjunctive
constraints
Nonlinear reservoir models
Considers endogenous
decision-dependent
uncertainty and gradual
uncertainty resolution in
parameters of initial
maximum ﬂowrate,
recoverable volume, and
water breakthrough time of
reservoir
Uncertain parameters follow
discrete distributions
Discrete investment-based
decisions on number of wells
to drill and facilities to build;
continuous operational-based
decisions on oil production
rate; decisions are made on
discrete time horizon
Nonanticipativity constraints
are not pre-speciﬁed but
change as a function of
decisions made
Duality-based branch-and-
bound algorithm; combines
global optimization and outer
approximation that performs
better than the former
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123
Table 2 continued
References
Type
Model formulation
Solution strategy
Busby and
Sergienko
(2010)
NLP
Addresses multiobjective
optimization under
uncertainty for new
development plan to
maximize net present value
with risk considerations
Proposes Gaussian-based
response surface method with
adaptive sampling to solve
history matching and
probabilistic inverse problems
with reduced number of
simulations
Wang et al.
(2011)
NLP
Applies retrospective
optimization approach that
sequentially solves
subproblems with increasing
numbers of reservoir
realizations under uncertainty
in subsurface geology
Implements k-means clustering
technique for sampling of
realizations to reduce
computational burden for
reservoir simulations
Busby et al.
(2014)
NLP
Robust optimization on
expected value and risk
considering scenarios for
different ﬁeld development
plans
Considers uncertainty in well
position and management
parameters of mature ﬁelds
Implements Gaussian (kriging)-
based response surface
method with adaptive design
and experimental design using
Latin hypercube sampling
Gupta and
Grossmann
(2014)
MINLP
Simultaneously considers
decisions with ﬁscal rules (on
production sharing
agreements) and endogenous
uncertain ﬁeld parameters
(i.e., ﬁeld size, oil
deliverability, gas-oil ratio,
and water-oil ratio) using
scenario-based multistage
stochastic programming
Presents a Lagrangean
decomposition approach with
parallel computing to handle
scenario subproblems
Shirangi and
Durlofsky
(2015)
NLP
Applies robust optimization
approach to address optimal
closed-loop ﬁeld development
under geological uncertainty
Optimizes ﬁeld development
plan for the number, type,
locations, and controls of new
wells by using PSO-MADS
hybrid technique
Maximizes expected net
present value (NPV) over
multiple scenarios
(realizations) of overall
project
Performs history matching by
adjoint-gradient-based
randomized maximum
likelihood; performs
multilevel optimization and
increases number of scenarios
if statistical criterion is not
met Chang et al. (2015)
Optimization methods for petroleum ﬁelds development...
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Table 2 continued
References
Type
Model formulation
Solution strategy
NLP
Addresses multiobjective
optimization oilﬁeld
development under geological
uncertainty by maximizing
mean and minimizing
variance of net present value
(NPV) of each geological
scenario
Applies Pareto-based
evolutionary technique of
nondominated sorting genetic
algorithm II (NSGA-II) to
enable decision-making based
on risk attitude
Table 3 Progress in optimization application to exploration, development, and production of petroleum
ﬁelds
Period
Progress
Representative work
1950–1960s
Development of LP models
Reservoir behaviour (ﬂow rates vs.
pressures) takes on a very simple form
Lee and Aronofsky (1958), Charnes and
Cooper (1961) and Attra et al. (1961)
1970s
Formulate LP models combined with
reservoir simulation data from numerical
solution of partial differential equations on
material ﬂows from reservoir for well
development
Typically involves several stages as follows
1. Construct reservoir simulator based on
preliminary estimate of reservoir parameters
for each block
2. Use LP model and preliminary
reservoir simulator results to compute
improved parameters that best match
production history
3. Reuse reservoir simulator to generate
a set of inﬂuence functions describing well
pressure changes with time when ﬂow rate
abruptly changes from zero to a constant
value, based on improved reservoir
parameters
4. Construct new LP model with the
improved inﬂuence functions for economic
optimization
Bohannon (1970) and Devine and Lesso
(1972)
1980–1990s
Mixed-integer linear programming (MILP)
models with use of discrete variables for
improved representation of the hitherto
simpliﬁed representation of nonlinear
variables of reservoir parameter
Sullivan (1982) and Haugland et al.
(1988)
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Table 4 Examples of optimization application to gas-lift allocation problem
References
Type
Model formulation
Solution strategy
Nishikiori
et al. (1989)
NLP
Uses gas-lift performance curve
Gradient projection with quasi-
Newton method
Buitrago et al.
(1996)
NLP
No restriction in number of wells and
well response
Global optimization-based
multistart algorithm using
stochastic domain exploration
Uses heuristic to determine
descent direction
Results indicate proposed
approach could not properly handle
the constraints
Dutta-Roy and
Kattapuram
(1997)
NLP
Multiphase ﬂow via pressure
balance-based pipeline network
simulator
Flow interactions among wells in
gathering network systems sharing
a common ﬂow line
SQP to handle ﬂow interactions
Camponogara
and
Nakashima
(2006)
MINLP
Integer decisions on oil wells
activated to produce with
continuous variables on gas
compression capacity allocated
Considers uncertainty in oil
outﬂow and precedence constraints
on well activation
Implements a dynamic
programming-based approach to
handle various instances of the
precedence constraints
Table 3 continued
Period
Progress
Representative work
1990–2000s
Adopting the systems approach leads to
increasingly simpliﬁed yet adequately
representative models to identify decision
variables that model essential reservoir
behavior incorporating economic
optimization aspect
Models are efﬁciently structured to allow
analytical solution or straightforward
computational procedure
Iyer et al. (1998)
2000s–
present
Mixed-integer nonlinear programming
(MINLP) models
Complex objective functions
Models consider exogenous uncertainty
(e.g., oil prices) and endogenous uncertainty
(e.g., sizes and initial deliverabilities of
ﬁelds)
Heever et al. (2001), Goel and
Grossmann (2004), Goel et al. (2006)
and Belotti et al. (2013)
Optimization methods for petroleum ﬁelds development...
925
123
Table 4 continued
References
Type
Model formulation
Solution strategy
Wang and
Litvak
(2008)
NLP
Considers well management problem
for long-term reservoir simulation
to maximize production through
optimal well production and lift-
gas rates by obeying pressure and
rate constraints in surface pipeline
network and lift gas availability
Solves optimization model at
selected iterations of reservoir
simulation timestep and performs
multiobjective optimization to
handle rate oscillations between
adjacent iterations
Camponogara
and Conto
(2009)
MINLP
Considers distribution of high
pressure lift gas at limited rate by
obeying injection bounds and
activation precedence constraints
Performs piecewise linearization
of nonlinear functions to obtain
MILP model
Strengthens model formulation by
applying integer programming
technique of deriving valid
inequalities that leads to reduced
computational time in a branch-
and-bound procedure
Lu and
Fleming
(2012)
NLP
Implemented within a black-oil and
compositional reservoir simulator
incorporating surface pipeline
network constraints comprising
connection, perforation, and mass
balance equations
Uses GRG method
Performs two function evaluation
methods (for the objective,
constraints, and derivatives)
applicable at different timesteps:
(1) the more rigorous full-network
solution and (2) the
computationally faster proxy
functions obtained from partial-
network solution, which is
parallelized
Camponogara
et al. (2012)
MINLP
Uses a family of linear inequalities
to represent discharge pressure
constraints for lift-gas compressors
Reformulates model as MILP by
piecewise linearization using
binary variables and specially
ordered set variables
Presents valid inequalities with
integer programming techniques of
separation and lifting procedures to
model compressor capacity
Investigates effect of cutting plane
generation on computational
performance
Table 5 Examples of optimization application to general rate allocation problem
References
Type
Model formulation
Solution strategy
Attra et al.
(1961)
LP
Objective function: maximize daily
income of multireservoir operation
system
Constraints: well production capacities;
reservoir injection requirements; gas
compressor capacity limits; gas-lift and
pressure maintenance gas requirements;
sales contracts
Decision variable: production rate of
each well
Sequential optimization of design
and planning decisions
926
C. S. Khor et al.
123
Table 5 continued
References
Type
Model formulation
Solution strategy
Lo and
Holden
(1992)
LP
Objective function to maximize daily oil
rate
Proposes two well management schemes
for rate forecasting of production
streams-second scheme reduces
computational time, thus suitable for
coupling with reservoir simulator
Simplex algorithm
Fang and
Lo (1996)
NLP
Admits multiple nonlinear ﬂow rate
Assumes common ﬂow lines, i.e.,
ignores signiﬁcant ﬂow and pressure
interactions among wells, leading to
possible suboptimal solutions; concave
gas-lift performance curve; and constant
well water-cut and GOR with variable oil
rates
Modiﬁable to handle other constraints,
e.g., capacity of water treatment and
separator units
Disadvantages: piecewise linearization
algorithm requires ad hoc treatment of
nonconcave well proﬁt functions and
heuristic decisions of whether to
temporarily interrupt certain well
operations
4-step algorithm that involve
NLP, reformulating into separable
program, piecewise linearizing oil
outﬂow into MILP, and
algorithmic simplex and
separable programming
Implements on reservoir
simulator for several ﬁeld case
studies
Demonstrates that dynamic
optimization of gas-lift problems
give proﬁt gains in oilﬁeld
exploration
Hepguler
et al.
(June
1997)
NLP
Couples standalone commercial surface
pipeline network optimizer (SimSci’s
NETOPT 1.0) with commercial reservoir
simulator (Schlumberger’s ECLIPSE
100)
SQP for optimizing both general
design and operations
Davidson
and
Beckner
(2003)
MILP
Integrated facility and reservoir model
Multiobjective formulation: maximize
oil production rate and minimize water
production
SQP
Detailed procedure on handling
infeasible conditions
Wang and
Litvak
(2004)
MILP
Multiobjective formulation: maximize
daily oil production and minimize lift
gas rate oscillations
Decisions on well production and lift
gas rates
Constraints on pressure and rate in
surface pipeline network nodes and lift
gas amount
Applicable to long-term reservoir
development studies
Stepwise procedure involving
selected iterations of a reservoir
simulation time step
Optimization methods for petroleum ﬁelds development...
927
123
Table 6 Examples of optimization application to general rate allocation problems for mature ﬁeld
developments of Prudhoe Bay and Kuparuk River
References
Type
Model formulation
Solution strategy
Barnes et al.
(1990)
(Prudhoe
Bay)
NLP
Oil rate vs. gas rate curve deﬁnes
optimized oil rate target, which is
then broken down to targets for
production facilities, ﬂowlines, and
individual production wells
Heuristics on incremental GOR
Stoisits et al.
(1992)
(Kuparuk
River)
NLP
Adaptive modeling via neural
network
Multiple gas constraints
Evaluates effect of additional lift gas
compression capacity on production
Trains NN using single-well
simulation results
Results show accuracy over
nonlinear regression of ﬁeld data
Stoisits et al.
(1994)
(Kuparuk
River)
NLP
Gas lift allocation
Selection of non-gas-lifted
production well
Production allocation to central
processing facilities
Load balancing between centrifugal
and reciprocal compressors
Heuristics on incremental GOR and
formation GOR
Litvak et al.
(1997)
(Prudhoe
Bay)
NLP
Integrated reservoir and surface
facility gathering systems
Allocation based on gas-lift tables of
GOR, liquid well rate, and water-cut
Heuristics to optimize well
connections to manifolds
Simultaneous solution of reservoir
and surface pipeline network ﬂow
equations
Stoisits et al.
(1999)
(Kuparuk
River)
NLP
Well performance, surface line, and
facility models
NN for simulating pressure drop
through surface pipeline network to
overcome expensive computation
via hydraulics simulation
Genetic algorithms
Table 7 Examples of the optimization application to facility location–allocation problems in reservoir
development and planning
References
Type
Model Formulation
Solution Strategy
Devine and
Lesso
(1972)
MILP
Platform capacity, piping cost
Omits well-drilling scheduling and
production planning
Two-stage algorithm: ﬁxed well
allocation to solve for platform
location and its converse
Rosenwald
and Green
(1974)
MILP
Production targets from a
predetermined subset of possible
locations for new wells
Assumes a speciﬁed reservoir
production versus time relation
Selects a speciﬁc number of wells
from candidate solutions and
computes the optimal sequence of
production rates from the chosen
wells
928
C. S. Khor et al.
123
Table 7 continued
References
Type
Model Formulation
Solution Strategy
Grimmett and
Startzmann
(1988)
ILP
Simultaneous selection, sizing, and
placement of major production
facilities and assignment of wells to
these facilities
Binary implicit enumeration–
guarantees global optimal but
demands excessive computational
time and memory even for
moderate-size problems
Garcia-Diaz
et al.
(1996)
ILP
Total number of facilities, various
technology constraints
Branch-and-bound
Lagrangean relaxation
Ierapetritou
et al.
(1999)
MILP
Vertical well completions
Iterative decomposition with model
reformulation at each iteration
Onwunalu
and
Durlofsky
(2010)
NLP
Optimization of well placement
(location and type) for vertical,
deviated, and dual-lateral wells
over single and multiple reservoir
realizations
Demonstrates that PSO is comparable
or superior to the conventionally
used genetic algorithm mainly in
terms of fewer function evaluations
Bouzarkouna
et al.
(2011)
NLP
Nonconventional well placement and
trajectories
Applies derivative-free method of
Covariance Matrix Adaptation—
Evolution Strategy incorporating
adaptive penalization with rejection
and metamodel based on locally-
weighted regression to reduce
number of reservoir simulations
required
Forouzanfar
and
Reynolds
(2014)
NLP
Joint optimization of well placement
(including number of wells and
their locations) and control
problems
Two-stage approach using adjoint-
based gradient projection that
initially estimates total reservoir
production (or injection) rate by
maximizing NPV of operational life
and subsequently considers drilling
cost of wells
Isebor et al.
(2014b)
MINLP
General oil ﬁeld development for
simultaneous optimization of
decisions related to new wells
comprising categorical variables on
their number, type, and drilling
sequence, integer-valued variables
on locations, and real-valued
variables on time-varying controls
Noninvasive derivative-free
techniques encompassing branch
and bound, mesh adaptive direct
search (MADS), PSO, and hybrid
of PSO and MADS
Aliyev and
Durlofsky
(2015)
MINLP
Joint optimization of well placement
and control problems
PSO-MADS hybrid as proposed by
Isebor et al. (2014a, b)
Humphries
and Haynes
(2015)
MINLP
Joint optimal well placement and
optimal control problems for
nonconventional well types to
optimize total oilﬁeld production
Applies simultaneous and multistage
sequential approaches based on
PSO and MADS with increasing
well model complexity
(computational results favor the
latter)
Optimization methods for petroleum ﬁelds development...
929
123
Table 8 Examples of early linear programming application to planning and scheduling of petroleum
ﬁelds
References
Model formulation
Solution strategy
Lee and
Aronofsky
(1958)
Single- and multi-well system
Time-dependent linear pressure inﬂuence
function of reservoir performance surface
pressure constraints and scheduling of well-
drilling
Considers linear relations of well
pressure, production rate, and time
Aronofsky and
Williams
(1962)
Maximizes nonlinear investment rate of
return approximated by truncated Taylor
series
Rounding to approximate non-integer
solutions to reasonable integer values
Parametric programming
Fix well-drilling schedule to
determine production schedule and
vice versa
Rounding to approximate non-
integer solutions to reasonable
integer values
Charnes and
Cooper
(1961)
Simpliﬁed form of Lee and Aronofsky
(1958)
One-reservoir water injection problem
Assumes ﬁxed production schedule
Specialized semi-manual LP
technique
See and Horne
(1983)
Experimental design of model uses
multivariable regression analysis for data
ﬁtting
Production schedule under ﬁxed producer or
injector well locations
Optimal control (shown to be
applicable cheaply and accurately)
Lang and
Horne (1983)
Injection rates; downhole ﬂowing pressure
Surrogate model of reservoir performance
Dynamic programming (shown to be
more efﬁcient than LP)
Table 9 Examples of early nonlinear programming application to petroleum ﬁelds planning and
scheduling
References
Model formulation
Solution strategy
Lasdon
et al.
(1986)
Single-phase 2-D reservoir simulation
Demand schedules and operation constraints
Reduced gradient using interior
penalty functions
Huppler
(1974)
Multireservoir gas ﬁeld
Constraint on gas sales contract
Considers risk by stipulating that any investment
yield a minimum incremental proﬁt-to-investment
ratio
Iterative sequential quadratic
programming
Computes Hessian matrix based
on Fletcher–Powell procedure
930
C. S. Khor et al.
123
Table 10 Examples of NLP-based optimal control application to planning and scheduling of petroleum
ﬁelds
References
Model formulation
Solution strategy
Rowan and
Warren
(1967)
Linearizes a single differential equation describing
rate of average reservoir pressure drop as a function
of total water inﬂux and total production rate
Parameter estimation via least squares ﬁtting to
known data
Solve for production rates by
ﬁxing well-drilling schedule
and vice versa
McFarland
et al.
(1984)
Single dimensional tank-type reservoir models to
describe reservoir dynamics for both gas reservoir
with water drive and three-phase oil reservoir
Incorporates spatial variation in reservoir using
grid reservoir models
Decisions on platform size, no. of wells to drill in
each time period, production rates, and
abandonment time
GRG
Fathi and
Ramirez
(1984)
Optimal injection policy of surfactant slug for
enhanced oil recovery (EOR) in 1-D chemical
ﬂooding
Multiphase ﬂow in porous medium
Optimal control of distributed
parameter systems
Steepest descent gradient
method
Table 11 Examples of metaheuristic method application to nonconvex non-smooth problems in planning
and scheduling of petroleum ﬁelds
References
Model formulation
Solution strategy
Bittencourt
and Horne
(1997)
Integrates economic analysis, simulation, and
project design by evaluating objective
function from cash ﬂow analysis for
production proﬁle obtained from simulation at
each iteration
Optimal relocation of proposed new wells for
oilﬁeld development
Hybridized GA with polytope method
Pan and
Horne
(1998)
Field development scheduling and waterﬂood
issues
Uses sample point distribution design to
generate combination of different levels of
decision variables for simulation runs
Multivariate interpolation algorithms
of least squares and kriging—
achieves signiﬁcant reduction in
simulations
Gu
¨yagu
¨ler
(2002)
Waterﬂood issues
Decisions on placement of water-injection
wells and injection rate
Adapts HGA of Bittencourt and
Horne (1997) with kriging proxy
(very efﬁcient) and neural network
proxy (less efﬁcient), resulting in
hill climber feature–achieves
signiﬁcant reduction in simulation
runs
Yeten et al.
(2003)
Multilateral well placement problem
Decisions on well locations, no. of laterals,
and trajectory of laterals
GA with hill climber
Neural network
Optimization methods for petroleum ﬁelds development...
931
123
Table 12 Examples of MILP-based simultaneous optimization application to design, planning, and
scheduling of petroleum ﬁelds
References
Model formulation
Solution strategy
Bohannon
(1970)
Multireservoir pipeline system (i.e., many
reservoirs producing into one or more
gathering system)
Redetermines linear production rate
decline with cumulative oil
produced
Sullivan
(1982)
Optimizes gas ﬁelds system
Constraints on time-staged platform
placement and well drilling, pipeline and
compressor capacity installation, and
production scheduling
Linearized model results in
signiﬁcant increase in no. of discrete
variables and solution time
(inefﬁcient for real-world problems)
Piecewise linear interpolation via
binary variables for approximating
nonlinear reservoir performance
equations
Haugland
et al.
(1988)
Constraints on platform capacity, drilling
schedule, and production planning
Temporal and spatial discretization
Applicable to only small model size
due to expensive computational time
Scaling to overcome numerical
difﬁculties
Currie et al.
(1997)
Redevelopment program and drilling
schedule for Ekoﬁsk ﬁeld in North Sea part of
Norway
Product-processing requirements, drilling-rig
use, well-platform relationships, other
physical facilities
Applies linear interpolated production proﬁle
Iterative-based procedure in
conjunction with reservoir
simulation
Eeg and
Herring
(1997)
Executes reservoir simulations to obtain
production proﬁles for each well
Linearizes the production proﬁles
into discrete differences to be model
inputs
Iterative scheme of ﬁxing drilling
schedule to solve for production
proﬁle and vice versa
Iyer et al.
(1998)
Multiperiod planning and scheduling of
investment and operations of offshore oilﬁeld
infrastructure
Approximate nonlinear reservoir behavior
using piecewise linear interpolation with
integer variables
Bilevel sequential decomposition
Aggregation disaggregation
techniques of time periods and wells
Generates good feasible solutions
and upper bounds although no
guarantee of optimal solution
Nygreen
et al.
(1998)
Long-term production planning for new
ﬁelds and transportation pipelines in Norway
Allows timing for initiation of ﬁelds
Simultaneous design of pipeline systems
Uses the SCICONIC optimizer on
MGG platform (Scicon 1991)
Carvalho and
Pinto
(2006a, b)
Infrastructure planning in offshore oilﬁelds
Discrete decisions on existence of platforms
and their connections with wells
Continuous decisions on timing of extraction
and production rates
Incorporates reservoir pressure and
investment constraints
Bilevel decomposition of
assignment and planning
subproblems
Heuristic search procedure that
exploits the distance between
platforms and wells to reduce search
space
932
C. S. Khor et al.
123
Table 12 continued
References
Model formulation
Solution strategy
Ulstein et al.
(2007)
Tactical planning of Norwegian petroleum
production involving regulation of
production levels from wells, splitting of
production ﬂows into chemical processing,
further processing of gas, and transportation
in pipeline network
Considers multicomponent ﬂows, regulation
alternatives in production, nonlinear splitting,
and linear quality constraints
Uses 0–1 variables in production and
splitting constraints
Uses LP solvers through
implementation in Xpress algebraic
modeling system
Table 13 Examples of MINLP application to planning and scheduling of petroleum ﬁelds
References
Model formulation
Solution strategy
Dawson and
Fuller
(1999)
Represents reservoir behaviour as log-
linear relationship between oil fraction
and cumulative oil produced
Neglects nonlinear surface pressure
constraints
Assumes ﬁxed conﬁguration of PPs,
WPs, and wells
Generalized Benders decomposition
Problem size limited to only 3 wells
Barnes et al.
(2007)
Design stage (MILP):
Determines optimal production capacity
of main ﬁeld using adjacent satellite ﬁeld
and well drilling schedule
Continuous decisions on costs of wells,
jacket, and topsides
Discrete decisions on selection of
individual wells
Operation stage (MINLP):
Nonlinear equations on pressure drops
in pipes and wells for multiphase ﬂow
Nonlinear production cost equations
considering length, production rate, and
maintenance
Decisions (continuous) on oil ﬂowrates,
operation or shut-in of wells, and
pressures for each point in piping
network
Uses GAMS/CPLEX in a sequential-
based procedure
Optimization methods for petroleum ﬁelds development...
933
123
Table 13 continued
References
Model formulation
Solution strategy
Lin and
Floudas
(2003)
Bilevel formulation and solution
framework
Long-term planning of gas ﬁeld
development
Decisions on production design,
transportation network structure design,
and ﬁeld operations over several time
periods
Incorporates complex economic
calculations
Continuous-time event-based
formulation (with event point concept)
Substantially reduced computational
time compared to discrete-time
formulation() and able to solve
previously intractable problems
Heever et al.
(2001)
Extension of Heever and Grossmann
(2000) for application in multiperiod
long-term design and planning of
gasﬁeld infrastructure development
Objective function considers complex
economic metrics of tax, tariff, royalty
Heuristic iterative scheme with feasible
solutions postulated from Lagrangean
decomposition while Langrange
multipliers are updated through a
subgradient-based method
Heever and
Grossmann
(2000)
Multiperiod formulation extended from
Iyer and Grossmann (1998)
Uses nonlinear representation of
cumulative oil produced for modeling
reservoir pressures, gas-to-oil ratio
(GOR, and cumulative gas produced
Constraints on investment of PPs, WPs,
and wells; drilling schedule; and
periodic production proﬁles
Iterative aggregation-disaggregation
technique
Logic-based outer approximation
algorithm
Bilevel decomposition comprising
upper level for design and lower level
for operation and capacity expansion
planning
Dynamic programming-based
aggregation of time periods
Heever et al.
(2000)
Multiperiod design and planning of
offshore oilﬁeld infrastructures
Considers complex economic rules in
NPV-based objective function
Adopts disjunctive programming
instead of big-M formulation that
reduces solution time
Camponogara
et al. (2011)
Addresses compressor scheduling for
gas-lift allocation problem
Performs piecewise linearization of
compressor performance curves to
reformulate as MILP
Proposes valid inequalities for the
convex hull of the integer feasible
solutions using integer cuts
Obtains stronger formulation with
reduced computational burden
934
C. S. Khor et al.
123
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Petroleum Science and Technology
ISSN: 1091-6466 (Print) 1532-2459 (Online) Journal homepage: www.tandfonline.com/journals/lpet20
Refinery Planning Optimization Integrating
Rigorous Fluidized Catalytic Cracking Unit Models
J. Long, M. S. Mao & G. Y. Zhao
To cite this article: J. Long, M. S. Mao & G. Y. Zhao (2015) Refinery Planning Optimization
Integrating Rigorous Fluidized Catalytic Cracking Unit Models, Petroleum Science and
Technology, 33:17-18, 1587-1594, DOI: 10.1080/10916466.2015.1076846
To link to this article:  https://doi.org/10.1080/10916466.2015.1076846
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Petroleum Science and Technology, 33:1587–1594, 2015
Copyright C
⃝Taylor & Francis Group, LLC
ISSN: 1091-6466 print / 1532-2459 online
DOI: 10.1080/10916466.2015.1076846
Reﬁnery Planning Optimization Integrating Rigorous
Fluidized Catalytic Cracking Unit Models
J. Long,1 M. S. Mao,1 and G. Y. Zhao1
1Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of
Education, East China University of Science and Technology, Shanghai, China
Reﬁneries’ production plan modeling with simple planning models such as the ﬁxed yield planning
models fail to reﬂect the operating conditions of the processing units. In this work, an integrated
optimization strategy including production planning optimization, search of operating parameters and
the operation optimization of ﬂuidized catalytic cracking (FCC) units was proposed. The solution of
optimal integration was validated by case studies of a monthly plan for a whole reﬁnery, which included
three FCC units. The results indicated that the optimization strategy was efﬁcient in determining a
practically executed optimal plan and corresponding operating conditions of FCC units. The integrated
reﬁnery planning model predicted higher proﬁt and could provide the feasible or optimal FCC operation
conditions.
Keywords:: reﬁnery production plan, operating parameters, ﬂuidized catalytic cracking unit, integrated
optimization, maximum beneﬁt
1. INTRODUCTION
Production planning is a vital part of production management in the petroleum reﬁning industry.
The decision level for production plans determines the economic proﬁtability. Although accurate
results for processing units can be obtained by using rigorous models, their complexity and the
length of the solution time prevent them from being regularly applied in reﬁnery planning (Li et al.,
2007). So linear programming (LP) has been widely used because of its quick convergence and
implementation (Aguilar et al., 2012). By the application of swing-cut technology for distillation
units and delta-base model for secondary processing units, LP-based commercial software such as
process industry modeling system (PIMS) is accepted by reﬁneries (Alattas et al., 2011).
However, the production plan established by the software is separated from process operation and
not constrained by the operating conditions of the process. The product yields of units, especially
the secondary processing units, even established by the delta-base structure, are not the optimum
and maybe deviate from the operating constraints. A production plan obtained by them is uncertain
whether the plan is feasible and difﬁcult for the scheduling and operation of unit. Recently, some
researchers utilize simpliﬁed empirical nonlinear process models with considerations for crude
characteristics as well as products’ yields and qualities (Li et al., 2005). But detailed important
operation conditions cannot be obtained. Some researches utilize the process simulator to judge
whether a set of operating conditions could implement a production plan, revise the production
Address correspondence to J. Long, Key Laboratory of Advanced Control and Optimization for Chemical Processes, Min-
istry of Education, East China University of Science and Technology, Shanghai 200237, China. E-mail: longjianryh@163.com
1587
1588
J. LONG ET AL.
FIGURE 1
Flow diagram of PIMS optimization.
planning model to receive a practically executed plan and corresponding operating conditions (Wang
et al., 2008). However, the yields of units may not be the optimal, as they are always calculated from
the statistic data.
The main products of the secondary units after reﬁning are sent to product-blending facilities.
The optimization of the secondary units will have less impact on the other secondary units in the
whole plan model. Therefore, by their optimization, the increased proﬁt will be reﬂected in gross
margin of the whole reﬁnery. FCC unit is one of the most important petroleum reﬁning processes.
PIMS is a ﬂexible planning tool used for economic planning. In order to ensure that the plan is
practically feasible and optimal, process operating constraints and optimized product yield of FCC
units were added to the PIMS model to propose a production planning optimization.
2. PIMS MODEL
The objective of PIMS model is to maximize the beneﬁt of a production cycle (Eq. [1]). Optimizing
variables are processing quantity or output of the units. Constraints such as processing capacity,
balance of material and inventory, the limit of components proportion in feedstocks, products
demand, raw materials supply, product yield of units, and quality index of inﬂows and outﬂows are
given in PIMS tables. Flowchart of PIMS optimization is shown in Figure 1.
Max (OBJ) =

Xij·Pij−

Wi·Ci−

Um·Cm
(1)
Wi in Eq. (1) is equal to the value of inﬂows multiplied by the yield value of outﬂows in production
units. However, the yield values of units are determined by empirical data, even ﬁxed values,
which strongly affect the plan. The yield values do not consider constraints for process operations
INTEGRATING RIGOROUS FLUIDIZED CATALYTIC CRACKING UNIT MODELS
1589
FIGURE 2
Modeling procedure of FCC model.
and process optimization, which results in the plan cannot be ensured feasible and optimized in
allowable ranges of operating conditions of production units. In this work, a PIMS model for the 15
million tons per year reﬁnery was chosen, in which there are three ﬂuid catalytic cracking units (1.2,
1.5, 3.5 million tons/year).
3. FCC PROCESS MODEL
The FCC involves the conversion of heavy oil feedstock into a range of hydrocarbon products
such as gasoline, light diesel and so on. It is signiﬁcant to optimize the operation of industrial
FCC units for their huge throughputs. The simulation work of the three industrial FCC units was
implemented using HYSYS FCC model in HYSYS 8.4 (21-lump model). The FCC model includes a
feed characterization system, riser(s), a reactor, regenerator(s), and a product fractionating tower. The
riser, reactor, and regenerator are rigorous, kinetic-based models. The reactions themselves are all
based on well-understood ﬁrst-order kinetics. FCC template provides reactor factors and advanced
tuning parameters for user deﬁned. Reactor factors were calibrated by the calibration function
of HYSYS with measured plant data. Advanced tuning parameters involved were optimized by
differential evolution algorithm with FCC industrial data. The FCC models were obtained based
on the modeling procedure in Figure 2. With the goal of minimum the sum of squared differences
between the actual yield values and predicted value, FCC modes for three FCC units were established.
1590
J. LONG ET AL.
FIGURE 3
Flowchart of integration of production planning, process operations and optimization of FCC units.
Comparison of calculated results and plant data, the model relative errors on the yield predictions
of dry gas (<20%), liqueﬁed petroleum gas (<10%), gasoline (<5%), diesel (<6%), FCC slurry
(<10%), and coke (<15%) are the acceptable results.
4. OPTIMIZING STRATEGY
After the yields that either from the history data of FCC units or calculated from the delta-base
structure of PIMS were assigned to PIMS plan model, the Yp
FCC,j was obtained. But the production
plan optimized by PIMS according to these data is an optimal plan that has not to be veriﬁed the
correctness. In order to ensure that the plan is practically feasible, with FCC models, the Vopt
k
was
obtained by control sum of squares of deviations in the yield values of predicted and planned as
the minimum. If the Vopt
k
of FCC units at the yield Yp
FCC,j was within the range of normal FCC
INTEGRATING RIGOROUS FLUIDIZED CATALYTIC CRACKING UNIT MODELS
1591
TABLE 1
Lower and Upper of Eight Operating Parameters and the Operating Conditions According to the Executable
Initial Plan
Item
FCC-I
FCC-II
FCC-III
(V opt
k )l
(V opt
k )u
Operating
Value
(V opt
k )l
(V opt
k )u
Operating
Value
(V opt
k )l
(V opt
k )u
Operating
Value
Equilibrium MAT
60
65
62.5
60
70
63.2
60
70
64.5
Feed temperature,
◦C
150
300
201
150
300
200
150
300
219
Lift gas mass, kg/h
2400
2800
2698
9000
11000
10330
50000
80000
76500
p between
regenerator and
reactor, kPa
20
30
25.48
20
40
31
30
50
40.42
Reactor pressure,
kPa
160
200
185
150
250
207
200
300
259
Dense bed
temperature
(regenerator),
kg/m3
700
760
728
700
750
708
650
720
691
Riser outlet
temperature, ◦C
520
530
525
515
535
521
505
520
514.5
Dispersion steam
mass ﬂow
(riser), kg/h
4000
5000
4520
4500
5500
5197
15000
30000
23960
operation conditions, production tasks in Yp
FCC,j (=Ym
FCC,j) were the practically optimal plan and
Vopt
k was corresponding FCC operating condition. If not, optimization of FCC product yields (Ym′
FCC,j)
was conducted by the built-in optimizer function of the FCC model with the goal of maximum the
economic beneﬁts of FCC units (=output multiplied by their price). Modiﬁed yields (Yp′
FCC,j) were
assigned to PIMS model for revising the optimized planning model. The method can be seen in
Figure 3. Based on the mentioned steps, an integration of production planning optimization, FCC
process operating operations constraints and FCC operation optimization was proposed.
5. OPTIMIZATION DISCUSSIONS
Cases are analyzed where three FCC units is processing vacuum gas oil, vacuum residue, unconverted
oil from hydrocracker, hydrogenation modiﬁed residue, and so on. The objective is to determine
their output and corresponding operating conditions at maximizing reﬁnery proﬁt of one month. The
summary of the different yields of FCC products used in the cases was conducted by the previously
mentioned optimizing strategy. A case where no corresponding operating conditions to the initial
production plan of FCC units was found. The operating conditions of the FCC units searched by
HYSYS FCC models at the corresponding FCC products yields used in the planning model were
within the upper-lower limit range of operating conditions. The eight important operating parameters
of three FCC units were selected in the cases. Their presupposed value ranges and the executed
values calculated by FCC models according to the primal product yields of PIMS plan model are
listed in Table 1. The product yields comparison of initial production plan and ﬁnal implementary
plan is shown in Figure 4. The PIMS model predicts that the gross margin (one month) increases
1592
J. LONG ET AL.
FIGURE 4
Executable initial plan and ﬁnal plan. Gross margin per month (yuan): 726,623,840 (a) and 72,775,6120
(b); proﬁts change (yuan): +1,132,280.
INTEGRATING RIGOROUS FLUIDIZED CATALYTIC CRACKING UNIT MODELS
1593
TABLE 2
Optimized Plan at the Maximum of the FCC Units’ Proﬁt
Initial Production Plan
Final Feasible Plan
FCC-I
FCC-II
FCC-III
FCC-I
FCC-II
FCC-III
Processing load, kg·h−1
150.00
120.84
399.99
150.03
120.84
399.99
Product mass ﬂow, kg·h−1
Dry gas
6.75
5.52
16.44
6.91
4.96
16.44
LPG
27.22
18.48
72.84
27.88
17.12
68.84
Gasoline
60.79
54.97
162.33
64.56
60.95
182.33
Light diesel
36.75
25.88
98.12
32.3
24.21
86.12
Slurry
9.19
7.98
21.86
8.75
6.18
21.86
Coke
9.15
7.89
28.2
9.6
7.42
24.4
Loss
0.15
0.12
0.2
Gross margin per month,
yuan (product Sales –
feedstock purchases)
72,132,9930
76,555,5310
Proﬁt per month, yuan
+44,225,380
1,132,280 yuan in the ﬁnal implementary plan. The practically feasible production plan of FCC units
with corresponding operating condition can be executed.
In another case, the corresponding operating conditions of FCC units at the yields of initial
production plan outranged normal FCC operation conditions. It indicated that the FCC product
yields in the planning model was unreasonable, which would be adjusted by optimization of FCC
product yields. Modiﬁed yields obtained by FCC models were assigned to PIMS model for revising
the planning model. A new optimal plan is given in Table 2 and corresponding operating conditions
to the new optimal plan of FCC units are listed in Table 3. The FCC unit plan was in the new product
yields and it was the ﬁnal feasible plan. Taken the initial plan as a reference, it can be seen from
the table that the total proﬁt (gross margin for a month) of the whole reﬁnery increases 44,225,380
yuan.
6. CONCLUSIONS
For an optimized process for the reﬁnery production planning operation, integrating the advantage
of an LP model, detailed operating conditions of FCC units and operation optimization of FCC units
were proposed by combining the PIMS model and HYSYS FCC model. The feasibility of PIMS
TABLE 3
Optimized Operating Conditions at the Maximum of the FCC Units’ Proﬁt
Corresponding Operating Value
Item
FCC-I
FCC-II
FCC-III
Equilibrium MAT
61.5
65.5
61.3
Feed temperature
200.5
219
205
Lift gas mass
2698
9739
72154
p between regenerator and reactor
25.5
35
34.9
Reactor pressure
188
221
225
Dense bed temperature (regenerator)
756.7
700
663
Riser outlet temperature
522.7
525.5
519.5
Dispersion steam mass ﬂow (riser)
4577
5331
24731
1594
J. LONG ET AL.
production plan was validated by searching corresponding operating conditions to the planning
yields of FCC units. The PIMS plan can be successfully implemented and eight important operating
parameters required for FCC units operation are obtained. The effectiveness of maximizing the proﬁt
of FCC units in the reﬁnery optimization circulation was successfully demonstrated when the FCC
units plan was infeasible.
In the case study, the ﬁnal feasible plan given by proposed optimization method had obtained
higher proﬁts than the intimal production plan based on historical production statistics. The results
indicate that reﬁnery production plan can be more effective and proﬁtable by improving the accuracy
of the process units in the reﬁnery planning models with HYSYS FCC model. Integration of PIMS
model, FCC process operations, and FCC process optimization can contribute to FCC production
and operations and improve proﬁt of reﬁnery production planning.
FUNDING
Financial support was provided from fundamental research funds for postdoctoral research in Shang-
hai (no. 14R21410600)
REFERENCES
Aguilar, R. A., Ancheyta, J., and Trejo, F. (2012). Simulation and planning of a petroleum reﬁnery based on carbon rejection
processes. Fuel 100:80–90.
Alattas, A. M., Grossmann, I. E., and Palou-Rivera, I. (2011). Integration of nonlinear crude distillation unit models in
reﬁnery planning optimization. Ind. Eng. Chem. Res. 50:6860–6870.
Li, W. K., Hui, C. W., Karimi, I. A., and Srinivasan, R. (2007). A novel CDU model for reﬁnery planning. Asia-Paciﬁc J.
Chem. Eng. 2:282–293.
Li, W. K., Hui, C. W., and Li, A. X. (2005). Integrating CDU, FCC and product blending models into reﬁnery planning.
Comput. Chem. Eng. 29:2010–2028.
Wang, R. Q., Li, C. F., He, X. R., and Chen, B. Z. (2008). A novel close-loop strategy for integrating process operations of
ﬂuidized catalytic cracking unit with production planning optimization. Chinese J. Chem. Eng. 16:909–915.
NOMENCLATURE
Max(OBJ)
the maximum target value
Xij
amount of the product
Pij
price of the product
Wi
quality of the raw material
Cj
price of the raw material
Um
purchase amount of the utilities
Cm
purchase price of the utilities
i
name of production unit
j
name of product
Yij
yield
α
properties of raw material
β
equipment parameters
Y0
ij
initial yield
Yp
FCC,j
initial plan yield in FCC unit
Ym
FCC,j
predicted yield of FCC model
Yactual
FCC,j
actual yield of j in FCC unit

Index
Note: Page numbers followed by “f” and “t” refer to figures and tables, respectively.
A
Absolute relative error (ARE), 108109,
109f
cumulative frequency vs, 108109, 116f,
136f
AdaBoost SVR, 180, 181f
in geochemistry, 280
Adaptive boosting support vector
regression (AdaBoost SVR),
138139
Adaptive neuro-fuzzy inference system
(ANFIS), 3033, 84, 177f, 238
antecedents and consequences, 32
CO2-oil MMP estimation, 176
detection of drilling parameters, 234
in geophysics, 282283
layers
consequent nodes, 32
input nodes, 32
normalization nodes, 32
output nodes, 33
rule nodes, 32
MMP of CO2-oil systems, prediction of,
169
oil formation volume factor, prediction
of, 112113
in PVT properties estimation, 84
rate of penetration (ROP) prediction,
263264, 268
reservoir permeability, prediction of,
149150
rule-based adaptive models, 3031
saturation pressure, prediction of, 9697
schematic illustration of, 31f
TakagiSugenoKang, 3031
training and testing phase, 87f
Aggregation of data, 3
Alternating conditional expectation (ACE),
9798
Annealing, 65
Ant colony optimization (ACO), 5961
ACO-SVM model, accuracy of, 159,
161f
Gaussian mixture probabilistic model,
5960
pheromone trail, 59
real ant colony capability, 5960
selection of shortest path, 59f
steps
generation of solution, 60
initialization, 60
mean and standard deviation
calculation, 6061
probabilistic model, 60
sampling, 61
selection, 61
weight attribution, 60
Applicability domain of a model, 1921
Artificial bee colony (ABC), 6162, 176
role of employed bees, 6162
sequences of
employed bees, 61
initialization, 61
onlooker bees, 62
scout bees, 62
Artificial neural networks (ANNs), 2328,
81
applications of, 2324, 8283,
201203
asphaltene precipitation, prediction of,
192198
bubble point pressure, prediction of,
9295
CO2 injection in EOR processes,
prediction of, 164
CO2 MMP with an R2 of 0.948,
estimation, 180
condensate-to-gas ratio (CGR),
prediction of, 90
for cricondenbar and cricondentherm
estimation, 86, 88f
303
Artificial neural networks (ANNs)
(Continued)
crude oils of western India, Pb and Bo
correlations, 8688
dew point pressure of retrograde gases,
prediction of, 90, 94f
differential pipe sticking, prediction
of, 248
efficiency of the waterflooding
scenario, assessment of, 158
estimation of brine parameters, 84
formation damage, assessment of,
183191
gasoil ratio, prediction of, 100
Iranian oil fields, estimation of PVT
parameters in, 90
to locate and detect leaks in liquified
gas pipelines, 199
MMP of pure and contaminated CO2
streams, prediction of, 180
Pb and Bo prediction based on,
8284, 8688, 98
performance of ASP (alkaline-SP)
flooding, assessment of, 159162
performance of surfactant-polymer
(SP) floods, assessment of, 156
performance of water flooding
process, assessment of, 162
permeability impairment, prediction
of, 186
in PVT properties estimation, 8384,
85f, 102
ratio of K/Ki considering injection
velocity and duration, 186187
reservoir characteristics, prediction of,
140144
reservoir rock properties, prediction
of, 144
RF and oil production rate in CO2-
foam flooding processes, prediction
of, 166
saturation pressure, prediction of,
108109
soil mapping, 280
summary, 117t
in viscosity of Nigerian crude oil, 86
water sensitivity index, prediction of,
186
wax deposition, prediction of,
200201
wax precipitation, prediction of,
200201
well testing, 180183
basic elements, 24
flowchart of, 25f
gas condensate, 8182
multilayer perceptron neural network,
2426
with PSO algorithms, 98
PSO-ANN model, 90
radial basis function (RBF) neural
network, 2628
structure, 184f
use for correlations for PVT properties,
8182
in viscosity prediction, 82
Artificial probabilistic neural network
(PNN), 248
Asphaltenes, 192198, 192f
Average absolute percent error (AAPE),
231
Average absolute percent relative deviation,
6
Average absolute percent relative error
(AAPRE), 67, 7t, 82, 9596,
114, 138139, 179f
gas compressibility factor, estimation of,
100, 114115
predictions using, 9697
dependency of saturation pressure,
9697, 97f
3D-plot of predicted normalized
densities vs normalized input
coordinates (P and T), 106, 107f
gasoil ratio, 104105
NPV and RF values, 159162
viscosity of reservoir fluids, 106
Average absolute relative deviation
(AARD), 103, 103f, 107f, 138139
vs cumulative frequency, 113f
Average absolute relative error (AARE),
112f
304
Index
for saturated and undersaturated oil
viscosity, 110
Average percent relative error (APRE),
67, 7t
Average relative deviation, 6
B
Back propagation ANN (BPANN),
285286
Bat-inspired algorithm (BA), 98
Bayesian regularization (BR) algorithm, 75
Boltzmann distribution, 65
Brittleness index, AI approaches in
creating, 285286
C
Case-based reasoning (CBR), 4647, 47f
C7 1 fraction specifications, prediction of,
98
for crude oil and gas condensate fluids,
114
Coactive NF inference system (CANFIS),
144145
Coefficient of determination (R2), 89
Committee machine intelligent system
(CMIS), 4748, 114115
Committee machines (CMs) predictions,
8891, 99f
Conjugate hybrid-PSO ANFIS (CHPSO-
ANFIS) model, 115136
CO2-oil MMP estimation, 169, 176
Coupled simulated annealing, 6667
Crossover operation, 4243, 44f
Crossplots, 13f, 1418, 14f, 15f, 16f, 17f,
191f
Crude oils and natural gases, prediction of
properties of, 103104
CSA-LSSVM models, 115136
accuracy of, 105f, 135f, 136138, 137f,
175f
capability of, 190f
compressibility factor of natural gases,
prediction of, 96
crude oils and natural gases, prediction
of properties of, 103104
estimation of IFT values
between live oil and formation water,
136138
between paraffin and CO2, 136
gasoil ratio, prediction of, 104105
Iranian oil fields, estimation of PVT
parameters in, 95
permeability reduction factor, prediction
of, 188
viscosity of pure and impure
hydrocarbon gases, prediction of,
9596, 95f
Cuckoo optimization algorithm (COA),
6870
egg-laying domain, 6869
evaluation of quality of eggs, 69
motion coefficient, 69
Cuckoo optimization algorithm (COA),
268
Cumulative frequency plots, 17f, 1819
D
Data cleaning, 2
Data statistics
kurtosis, 45
skewness, 4
Decision trees (DTs), 3740, 238
Differential evolution (DE), 5556
mutation operator, 5556, 55f
procedure of optimization in, 5556
crossover, 56
initialization, 56
mutation, 56
selection, 56
stopping condition, 56
Differential pipe sticking (DPS) problems,
246
Dimensional analysis, 3
Discretization of data, 4
3-D plots, 19
Drilling engineering, applications of
artificial intelligence techniques in,
270272, 272t
aspects of, 230f
brine density, calculation of, 231232
flow patterns (FP) and pressure losses
prediction, 251253, 255t
305
Index
Drilling engineering, applications of
artificial intelligence techniques in
(Continued)
fluids, 229232, 235t
lost circulation problem, 232238
mud density, prediction of, 232
rate of penetration (ROP) prediction,
253270, 257t
ANFIS model, 263264, 268
committee support vector regression
based on an ICA (CSVRICA)
model, 265266
evaluation of predictive models, 266f
extreme learning machine (ELM), 265
hybrid fuzzy-based intelligent
approach, 263
imperialist competitive algorithm
(ICA)-based committee machine,
264265
monotone MLP (MONMLP), 266
SVM-based model, 264265
upper layer solution aware (USA),
265
rheological parameters of water-based
drilling fluid, 232
stuck pipe prediction, 238250, 249t
viscosity of non-Newtonian fluids,
prediction of, 232
E
Elitism, 5254
Enhanced oil recovery, 154180
AI techniques, 156158, 166
to predict MMP of CO2-oil systems,
166180, 168f
processes, 155166
ANN-MLP model in predicting RF
of chemical flooding process, 156,
157f
CNN model in predicting
performance of polymer flooding
processes, 155156
CO2 injection, 164
minimum miscibility pressure,
166180
performance of surfactant-polymer
(SP) floods, 156
WAG, 164
Error analysis techniques, 12
Error distribution curve, 913, 10f, 11f,
12f, 13f
Euclidean distance, 27
Evolutionary algorithms (EAs), 42
Exploration engineering, application of
intelligent models in
geochemistry, 279282
geo-mechanical characterization of
organic-rich shales, 284285
geophysics, 282283
petro-physics, 283284
shear wave velocity, 288289
total organic carbon (TOC), 286288
Extra trees (ETs), 40
Extreme learning machine (ELM), 150,
289
F
Facies identification from well log, AI
approaches to identifying, 290292
Feedforward backpropagation NN
(FFBPNN), 91
Feed-forward neural network (FNN),
282283
Firefly algorithm (FFA), 6263
absorption coefficient, 6263
equalization of movement of firefly,
6263
randomization coefficient, 6263
Fluid flow properties, AI approaches in
predicting, 289290
Formation damage, assessment of,
183191
Functional networks (FNs), 90
Fuzzy inference system (FIS), 232
Fuzzy logic systems, 2829
defuzzification stage, 29
in drilling fluid engineering, 230
equations, 29
fuzzy inference process, 29
general algorithm for, 30f
membership functions, 30t
G
GA-ANN model, accuracy of, 167, 168f
306
Index
GA-CMIS model, 145146, 147f
GA-MLP models, 110
GA-RBF model, 115136, 180
Gaussian RFB, 28
Gene expression programming (GEP),
4446
of three-gene chromosome, 46f
Generalization of data, 3
Generalized reduced gradient (GRG)
method, 114115
General regression neural network
(GRNN), 252
Genetic algorithm (GA), 5255, 84,
9798, 230231, 282
binary and permutation encodings, 52
crossover operator, 5255, 54f
fitness function, 5254
fundamental genetic operators, 52, 54f
neural networks with, 98
process of operators, 55
pseudocode of, 55
selection of individuals, 5254
ranking selection, 54
roulette wheel selection, 54
tournament selection, 54
Genetically optimized NN (GONN), 90
Genetic expression programing (GEP),
9798
Genetic-NF inference system (GANFIS),
8688, 90
Genetic programming (GP), 4244,
100101
correlations for gas phase and two-phase
Z-factor, 110
oil formation volume factor, prediction
of, 112113
saturation pressure and oil formation
volume factor using, 101102
viscosity of Arabian crude oils,
prediction of, 110
Geochemistry, application of intelligent
techniques in, 279282
to categorize mineral chemistry,
280281
to create accurate geochemical maps,
281, 281f
to distinct porphyry Cu-related
geochemical anomalies, 280
for geochemical data pattern recognition,
280
to identify geochemical distribution
patterns of light rare earth elements,
281
for identifying anomalies in mining, 280
to predict element concentrations, 281
soil mapping in Sirppujoki river
catchment area, 280
Geo-mechanical characterization of
organic-rich shales, application of
intelligent techniques in, 284285
determination of lithofacies, 285
Geophysics, application of intelligent
techniques in, 282283
to establish predictive paradigm for P-
and S-wave impedances, 283
for identifying and eliminating the
multiple reflections in seismic data,
282
to interpret spontaneous potential
(self-potential) anomalies, 283
for modeling horizontal deformation
field, 283
to model synthetic gravity data and
Seferihisar gravity data, 282
to predict shape factor, depth, and
amplitude coefficient parameters,
283
GMDH-based network, 180
Gradient boosting DT (GBDT), 138139
Graphical error analyses, 12, 919
of AAPRE values, 9f
Gravitational search algorithm (GSA),
6768
gravitational constant, 6768
law of motion in, 6768
optimization process, 67
velocity and position of particle, 6768
Gray wolf optimization (GWO), 7071
initialization step, 70
mechanism of updating omega, 71, 72f
repositioning of positions of wolves, 71
Group error, 19
307
Index
Group method of data handling (GMDH),
4042, 8283
prediction of IFT value between normal
alkanes and nitrogen, 138
H
HerschelBulkley drilling fluids, 231
prediction of pressure loss using GRNN
model, 252, 253f
HGAPSO-ANN model, 188
Hybrid group method of data handling,
4042
Hybridization, 5152
Hybrid ML-optimization techniques,
5152
Hyper-surface membership functions,
144145
I
Ifthen rules, 29, 3132
Imperialist competitive algorithm (ICA),
6365
colonies among imperialists, 6364
colony competitive, 6365
generating initial empire, 6364
imperialist competitive, 6365
movement process, 64, 65f
total cost of an empire, 64
Imperialist competitive algorithm (ICA),
176
Integrated neural-fuzzy-genetic-algorithm
(INFUGA), 144145
Integration of data, 23
Intelligent models
applications in oil and gas industry,
2122
effect of number of actual data,
298299
vs empirical models, 297298
vs theoretical models, 295297, 296f
validation of developed models,
299300
Interfacial tension (IFT), 115
AARD% of predictions, 140f
ARD% of predictions of, 139f
artificial intelligence models in
predictions, 141t
in hydrocarbon/brine systems, 138
in oil/brine systems, 138139
value of binary mixtures dates, 115
value of pure hydrocarbon and water
systems, 115
in water/hydrocarbon systems, 138
Iranian oil fields, estimation of PVT
parameters in, 90, 95
using CSA-LSSVM models, 95
Isothermal compressibility coefficient (Co),
81
K
Kohonen neural network (KNN), 280
Kurtosis, 45
L
Lagrangian multipliers, 37
Least-squares support-vector regression
(LS-SVR), 285286
Least-squares SVM (LSSVM), 3537
accuracy of, 160f
applications of
brine density, prediction of, 231232
dew point pressure of retrograde gases,
prediction of, 96
differential and mechanical pipe
sticking, prediction of, 248
MMP of N2-crude oil systems,
prediction of, 173
NPV and RF, prediction of, 159162
Pb and Bo predictions, 9192
rate of penetration (ROP) prediction,
267f, 269, 270f
Z-factor predictions, 9295
definition of error, 3637
error distribution plot, 93f
method of Lagrangian multipliers, 37
safe error margin, 35
tuning parameter of, 3637
LevenbergMarquardt algorithm (LMA),
7375, 284, 289, 296297
approximation by, 74
of Hessian, 7475
308
Index
objective function (error function), 74
recommended steps, 75
LevenbergMarquart learning algorithm,
231
LightGBM, 113114
Lost circulation problem, 232238
application of intelligent models in
predicting, 240t
determination of circulation loss
during underbalanced drilling
(UBD), 238
feedforward backpropagation neural
network to predict, 234
two modular neural networks,
237238
using nongeomechanical and
geomechanical variables, 237238
M
Mean absolute error (MAE), 110
Mean absolute percentage error (MAPE),
102104, 104f
Mean ARE (MARE), 105106, 113114
Mean impact value (MIV), 289
Mechanical sticking problems, 246247
Median relative error, accuracy of,
100101, 102f
Minimum miscibility pressure (MMP), 115,
166180
Mixed kernel function-based SVM (MKF-
SVM), 110112
MLP-LM model
predictions of
hydrocarbon fluids, viscosity of, 109
IFT values, 138
natural gases, viscosity of, 109
Model for Pb and Bo predictions, 8284,
8688, 9192, 98, 105106
for crude oils of western India, 8688
Multigene genetic programming (MGGP),
4344, 45f
Multigene GP (MGGP), 103
Multilayer feed-forward network (MLFN),
290
Multilayer perceptron (MLP) neural
network, 2426, 83, 112114,
231, 252, 296297
in geophysics, 282
hidden layers, 2426
output, 26
two-hidden-layer, 26
usable activation functions, 2426
N
Negative skewness, 4
Neural networks, 102
application of, 8283
limitations of, 8283
Newton’s law of gravity, 67
Nondominated-sorting GA version II
(NSGA-II), 166
Normal distribution, 45
Normalization of data, 3
O
Outliers, 2
detection of, 1921
P
Particle swarm optimization (PSO), 5658,
176, 232, 268, 270f, 285
components
cognitive, 57
physical, 57
social, 57
concept of self-organization and
collaboration, 5657
motion equations of particle, 5758
principles, 57
displacement of particles, 57, 57f
random initialization of swarm, 58
value of objective function, 57
Particle swarm optimization (PSO)
algorithm, 90
Pb models, accuracies of, 91f
Petroleum industry, intelligent models in,
295
Petro-physics, application of intelligent
techniques in, 283284
to predict shear wave velocity from
well-log data, 284
to predict water saturation and porosity,
284
309
Index
Petro-physics, application of intelligent
techniques in (Continued)
to predict well log response, 284
to study clastic facies prediction in
Lower Pannonian sediments, 284
Pipeline accidents, 198199
Pipeline networks, 198200
Population-based optimization techniques,
52
Positive skewness, 4
Postprocessing of data
average absolute percent relative error,
67
average percent relative error, 6
coefficient of determination (R2), 89
crossplots, 13f, 1418, 14f, 15f, 16f, 17f
cumulative frequency plots vs absolute
percent relative error, 17f, 1819
3-D plots, 19
error distribution, 5f, 913, 10f, 11f, 12f,
13f
graphical error analysis, 919
group error, 19
root mean square error, 78
standard deviation, 8
statistical analysis methods, 69
Power-law committee intelligent system,
9091
Predictive models, 12
Preprocessing of data, 15
cleaning of data, 2
discretization of data, 4
integration of data, 23
reduction of data, 3
statistics of data, 45
transformation of data, 3
Pressure-volume-temperature (PVT)
correlations, 8384
application of intelligent models in, 86,
90
for Indian crude oil, 86
using ANNs, 91
using committee machines (CMs),
8890
using SVMs, 86
Probabilistic neural network (PNN), 290
Probabilistic NNs (PNNs), 145
Probability distribution, 45
Processing of data
training stage, 5
validation and testing of data, 56
Pseudocritical values, 23
Pseudo-linear MGGP model, 44f
PSOANN model, 232
R
Radial basis function (RBF) neural
networks (RBFNN), 24, 2628,
81, 110112, 114, 166, 176, 232,
282, 290
distance between inputs and centers, 27
feedforward three-layer network, 27
input layer, 27
output layer, 27
schematic illustration, 26f
spread coefficient, 28
transfer functions, 2728
cubic function, 2728
Gaussian function, 2728
generalized multiquadratic function,
28
inverse multiquadratic function, 28
linear function, 27
multiquadratic function, 27
thin plate spline function, 28
Random forest (RF), 3840, 39f
differences between ET and, 40
in geochemistry, 280281
Random forests, 113114
Reduction of data, 3
Relevancy factor analysis, 21
Reproduction, 43
Reservoir characterization, 140
formation damage, 183191
lithofacies classification, 145, 145f, 146f
lithology determination, 140144
mineral identification, 140144
permeability and porosity prediction,
140145, 148f, 152153
cross-plot of experimental values vs
predicted values, 155f
mathematical correlation for
determining, 153154
MSE and R2 comparison, 152f
310
Index
using ANFIS model, 149150
using committee neural network
(CNN) model, 152
using CSA-LSSVM model, 153
using fuzzy clustering techniques, 145
using FL systems, 144
using GA-ANN model, 147149
using GA-CMIS model, 144145,
147f, 149
using GA-LSSVM model, 151152
using HGAPSO-ANN model, 150
using hybridization of FNs and
T2FLS, 150151
using LLSVM model, 153
using MGGP algorithm, 152153
using type-2 FL algorithm, 149
wavenet model, 151f
Reservoir fluid properties
areas of reservoir and production
engineering, 80f
equation of state (EOS), 80
graphical methods and linear/nonlinear
multiple regression, 8081
one-phase properties, 80115
PVT correlations and properties, 8082
shortcomings of empirical correlations,
81
two-phase properties, 115140
Resilient backpropagation algorithm, 76
Rock properties, 140154
Root mean square error (RMSE), 78, 42,
231, 239f
S
Salp swarm algorithm (SSA), 114115
Scaled conjugate gradient algorithm,
7576
Sensitivity analysis, 21
Sensitivity-based linear learning method
(SBLLM), 91
Shear wave velocity, application of
intelligent techniques in, 288289
Simulated annealing (SA), 6566
calculation of P, 6566
functioning of the Metropolis criterion,
66
principle of, 6566
Skewness, 4
Standard deviation (SD), 8
Statistical error analyses, 12
Steam-assisted gravity drainage (SAGD)
process, 157158
Stuck pipe prediction, 238250
support vector machine (SVM) model,
247248
using three-layer feedforward network
and backpropagation learning
algorithm, 247
Subtree mutation, 42, 43f
Support vector machine (SVM), 176, 280
depletion behavior of retrograde gases,
analysis of, 96
in geochemistry, 280
in geo-mechanical characterization of
organic-rich shales, 285
kernel function, 37
least-square, 3537
ordinary, 3334
in permeability prediction, 149150
in predicting stuck piperelated
problems, 247248
structure, 34f
T
Testing of data, 56
Total organic carbon (TOC), AI
approaches in predicting, 286288
Transformation of data, 3
Transparent open-box (TOB) learning
network, 108109
Type-2 fuzzy logic systems (T2FLSs),
8690, 89f
U
Undefined skewness, 4
Unimodal distributions, 4
V
Validation of data, 56
Viscosity predictive intelligent models, 82,
95
of Arabian crude oils, 110
of gas, 23
311
Index
Viscosity predictive intelligent models
(Continued)
of Nigerian crude oil, 86
of non-Newtonian fluids, 232
of pure and impure hydrocarbon gases,
95f
of reservoir fluids, 106
W
Wax appearance temperature, 200
Wax disappearance temperature (WDT),
200201
Well test analysis, 180183
Whale optimization algorithm (WOA),
7173
encircling process of, 73
equations, 7273
update process, 73
Wilcoxon-generalized radial basis function
network, 100, 101f
X
XGBoost, 113114
Z
Zero-error line, 911
Z-factor predictions, 9295, 110, 111f
of natural gases, 114115
312
Index

Applied 
Optimization 
in the Petroleum 
Industry 
Hesham K. Alfares
Applied Optimization in the Petroleum Industry
Hesham K. Alfares 
Applied Optimization 
in the Petroleum Industry
Hesham K. Alfares 
Department of Systems Engineering 
King Fahd University of Petroleum 
and Minerals 
Dhahran, Saudi Arabia 
ISBN 978-3-031-24165-9
ISBN 978-3-031-24166-6 (eBook) 
https://doi.org/10.1007/978-3-031-24166-6 
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature 
Switzerland AG 2023 
This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether 
the whole or part of the material is concerned, speciﬁcally the rights of translation, reprinting, reuse 
of illustrations, recitation, broadcasting, reproduction on microﬁlms or in any other physical way, and 
transmission or information storage and retrieval, electronic adaptation, computer software, or by similar 
or dissimilar methodology now known or hereafter developed. 
The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication 
does not imply, even in the absence of a speciﬁc statement, that such names are exempt from the relevant 
protective laws and regulations and therefore free for general use. 
The publisher, the authors, and the editors are safe to assume that the advice and information in this book 
are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or 
the editors give a warranty, expressed or implied, with respect to the material contained herein or for any 
errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional 
claims in published maps and institutional afﬁliations. 
This Springer imprint is published by the registered company Springer Nature Switzerland AG 
The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
To my dear parents, my father Kamal Alfares 
and my mother Batool Alawwami, with 
utmost respect and gratitude for their love, 
guidance, support, and prayers.
Preface 
This book addresses a topic that is highly signiﬁcant from both a theoretical and a 
practical point of view. The petroleum industry is of vital importance to the world’s 
energy, economy, and technology. It is a large and critical business sector, which is 
the main source of the world’s energy and the raw materials for a wide variety of 
industrial and consumer products. The petroleum industry is an international, multi-
billion-dollar businesses, which is among the biggest and most inﬂuential industries 
in the world today. Natural oil has been the main source of the world’s energy for the 
last 70 years, making the petroleum industry a key factor in international affairs. The 
enormous importance of the petroleum industry is not due only to its economic scale 
and geopolitical inﬂuence. It is also due to its numerous products and petrochemical 
derivatives that interact with all industries and all economic sectors and touch our 
daily lives in countless ways. 
Currently, the petroleum industry is facing growing challenges. These challenges 
include depleting natural resources, restraining environmental regulations, emerging 
renewable energy alternatives, and increasing international competition. In order to 
effectively deal with these challenges, it is necessary to maximize the utilization 
of the industry’s limited production resources. Maximizing the beneﬁts obtained 
from the available resources requires solving complex decision problems with many 
interacting variables. Operations research optimization techniques provide a proven 
scientiﬁc approach to ﬁnd the best alternatives in such complex decision situations. 
The book provides a broad coverage of optimization models and techniques used 
to solve large-scale problems in the petroleum industry. Optimization refers to math-
ematical and computer models and techniques that are used to ﬁnd the best possible 
solutions for many types of real-life problems. In order to apply optimization tech-
niques, the ﬁrst step is to identify the speciﬁc type of optimization model required for 
the given problem. Optimization models can be either constrained or unconstrained, 
stochastic or deterministic, and linear or nonlinear. After identifying the optimization 
model type, the problem needs to be formulated and then solved. The book provides 
the background on optimization model types, solution method categories, as well as 
real-life illustrative examples.
vii
viii
Preface
This book approaches optimization in the petroleum industry from a practical, 
application-oriented point of view. The book presents several actual large-scale appli-
cations of optimization models in the petroleum industry. All of these applications are 
based on the author’s own experience with real-life case studies and applied projects 
for the local petroleum industry. For each application, alternative heuristic solution 
techniques are provided when optimum solutions are difﬁcult to obtain. Different 
chapters cover a wide spectrum of relevant petroleum industry activities, including 
drilling, producing, production planning, maintenance, and distribution. 
This book has two main objectives. The ﬁrst objective is to demonstrate the 
advantages of using optimization techniques to solve complex problems in the 
petroleum industry. These advantages include reducing the cost, time, and emis-
sions and increasing the quality, safety, productivity, and proﬁtability. The second 
objective is to help both the industry’s decision-makers and academic researchers to 
solve real optimization problems. The knowledge gained from the book will help the 
readers to formulate and solve their own relevant optimization problems. This will 
allow them to maximize economic beneﬁts, ensure operational safety, and reduce 
environmental impact. 
Intended Audience 
Prior knowledge of either the petroleum industry or optimization techniques is not 
necessary to beneﬁt from the book. The book is intended for readers from both the 
academia and the petroleum and petrochemical industries. In the academic domain, 
this book is relevant to senior undergraduate students, graduate students, and instruc-
tors and researchers in industrial, petroleum, and chemical engineering departments, 
as well as in computer science departments. In the industrial domain, the book is rele-
vant to industrial, systems, petroleum, and chemical engineers working on industrial 
optimization problems. Moreover, the book should also be useful for process and 
production planners and plant engineers working in the petroleum industry’s various 
facilities. 
The Book’s Contents 
The chapters of the book introduce the readers to three related topics: the petroleum 
industry, optimization concepts and models, and optimization applications in the 
petroleum industry. First, the readers are provided with essential knowledge of the 
history, classiﬁcations, and main functions and processes of the petroleum and petro-
chemical industries. Second, they are introduced to the main concepts and types of 
optimization models and solution techniques. Third, the readers are exposed to a
Preface
ix
variety of real-life optimization applications in the petroleum industry. In each appli-
cation area, the readers will become aware of the current problem categories, model 
formulation approaches, alternative solution techniques, and future research trends. 
One important aspect that is emphasized throughout the book is the model formu-
lation process of the petroleum industry optimization problems. This process involves 
translating the verbal description of the given real-life optimization problem into a 
set of mathematical expressions. Therefore, the components of mathematical opti-
mization models, i.e., the decision variables, objective function, and constraints, are 
explicitly deﬁned for each real-life problem considered in the book. Moreover, several 
modeling techniques are used in the different chapters of the book, including linear 
programming, integer programming, nonlinear programming, and simulation-based 
optimization. 
The chapters are arranged in the order of the petroleum industry’s value-chain 
sequence, starting from upstream activities (drilling) and ending with downstream 
activities (export of reﬁned products). The contents of individual chapters are 
described in more detail below. 
Chapter 1: Introduction to Petroleum and Petrochemical 
Industries 
This chapter provides a historical background of both the petroleum industry and its 
extension, i.e., the petrochemical industry. The chapter also presents the main stages, 
processes, and products of the two interrelated industries. Particular attention is given 
to the petroleum industry’s ﬁve main processes: exploration, production, upstream, 
midstream, and reﬁning (downstream). Moreover, an overview of previous relevant 
literature reviews is given, highlighting the different problem categories, solution 
approaches, and future trends. 
Chapter 2: Introduction to Optimization Models 
and Techniques 
In this chapter, optimization concepts, models, and techniques are introduced and 
classiﬁed. The different optimization categories are presented and described. These 
categories include unconstrained optimization, mathematical programming, meta-
heuristic algorithms, and simulation-based optimization. Several types of mathe-
matical programming methods are discussed, including linear programming, integer 
programming, goal programming, dynamic programming, stochastic programming, 
and nonlinear programming. Due to its special signiﬁcance, particular emphasis is 
given to linear programming.
x
Preface
Chapter 3: Optimum Locations of Multiple Drilling Platforms 
This chapter describes the optimization of multiple platform (rig) locations and well-
platform assignments for drilling multiple offshore oil and gas wells. The objective is 
to minimize the total cost of drilling all wells in a given offshore ﬁeld. The drilling cost 
of each well is assumed to be a function of both the distance to the drilling platform 
and the platform’s individual cost. Integer programming-based exact methods and 
heuristic algorithms are developed for solving two alternative problem versions. 
Chapter 4: Simulation-Based Optimization of Reﬁnery Valve 
Inspection Frequency 
This chapter presents a simulation-based optimization approach to determine the 
best dynamic inspection policy of relief valves in a large petroleum reﬁnery. A 
simulation model is used to minimize the total inspection, repair, and failure risk 
cost. The model is used to determine the optimum inspection frequency for each 
valve, i.e., the intervals between successive inspections, depending on the valve’s 
individual characteristics and previous inspection results. Simulation is used because 
this optimization problem involves uncertainty and multiple interacting factors such 
as valve pressure, temperature, medium, age, and size. 
Chapter 5: Operations and Workforce Scheduling for Reﬁnery 
Turnaround Maintenance 
This chapter considers workforce assignment and job scheduling for turnaround 
(shutdown) maintenance in a large oil reﬁnery. The maintenance workforce must be 
divided into several teams that work in parallel on different sets of maintenance tasks. 
The objective is to minimize the total shutdown period, assuming the duration of each 
task is a function of the size of the assigned team. Optimization models and solution 
algorithms are developed for this scheduling problem. The model determines the 
number of maintenance teams, the size of each team, and the set and sequence of 
tasks assigned to each team. 
Chapter 6: Simulation-Based Scheduling of Pipeline 
Maintenance Crews 
This chapter presents a simulation-based optimization methodology for scheduling 
multi-specialization pipeline maintenance crews. The simulation model considers
Preface
xi
stochastic daily labor demands of each specialization for the oil and gas pipeline 
maintenance crews. The model determines the optimum days-off scheduling assign-
ments of the employees belonging to each maintenance specialization. The objective 
is to minimize the average throughput time, i.e., order completion time, for each 
maintenance specialization. The simulation model succeeds in reducing throughput 
times for all maintenance specializations without increasing the number or the cost 
of the employees. 
Chapter 7: Optimum Gasoline Blending in Petroleum Reﬁning 
This chapter addresses the optimum gasoline blending for an oil reﬁnery. The objec-
tive is to maximize the proﬁts while satisfying the constraints on demands, supplies, 
speciﬁcations, costs, capacities, and other applicable restrictions. A nonlinear 
programming (NLP) model is formulated to represent the problem, and a two-stage 
heuristic solution procedure is developed to solve it based on linear approximation. 
Given a set of raw materials (components) and their availabilities and speciﬁcations, 
and a set of gasoline products (blends) and their demands and speciﬁcations, the 
model determines amount of each component used to make each product, in order 
to satisfy the demands and meet the speciﬁcations with the minimum total cost. 
Chapter 8: Employee Scheduling in Remote Oil Industry Work 
Sites 
This chapter considers the days-off scheduling of oil company employees working 
in remote locations. During their day-off breaks, the employees are transported by 
company aircraft to and from their work sites. The company has two objectives: 
minimizing the number of employees to reduce the labor cost and minimizing the 
number of assigned days-off breaks to reduce the transportation cost. A bi-objective 
integer programming model is formulated to represent the problem, and an optimum 
analytical procedure is developed to obtain the optimum solution. 
Chapter 9: Optimum Planning of a Distribution Supply Chain 
for Reﬁned Oil Products 
In this chapter, the objective is to design and plan a minimum-cost distribution 
network for reﬁned oil products that consists of supply centers (reﬁneries), distribu-
tion centers (bulk plants), and demand centers (cities). A multi-period mixed-integer
xii
Preface
programming model is used to develop the optimal long-range plan for the distri-
bution system’s conﬁguration and operations. The model is used to determine the 
best annual product distribution plan, as well as the best long-term network conﬁg-
uration plan. The network conﬁguration plan involves annual decisions on possible 
establishments or expansions of new or existing network facilities. 
Chapter 10: Berth Allocation for Loading Tankers at an Oil 
Export Terminal 
In this chapter, an optimization model is used for berth allocation and tanker 
sequencing at an oil products export terminal. Each incoming tanker ship must be 
assigned to either one or two berths in order to load one or more products. The 
objective is to minimize the total demurrage (delay) cost incurred by the terminal 
for keeping the tankers waiting for berths to be assigned. A mixed-integer linear 
programming (MILP) model is formulated and used to optimally solve this problem. 
The model determines the assignment of each tanker to the berths, the products to 
load for each tanker from each berth, and the tanker loading sequence at each berth. 
Dhahran, Saudi Arabia
Hesham K. Alfares
Acknowledgments 
The author duly acknowledges the research support and facilities provided by King 
Fahd University of Petroleum and Minerals (KFUPM), especially the Systems 
Engineering (SE) Department. 
The author gratefully thanks Saudi Aramco for its permission to use photos from 
its archives as ﬁgures in the book. In particular, thanks are due to Mr. Fayez Al-Bishi, 
Head of Domestic Media Relations in Saudi Aramco, for the kind cooperation. 
Moreover, the author sincerely appreciates and acknowledges the partial contri-
bution in Chap. 9 by the following colleagues from the Systems Engineering 
Department at KFUPM: Dr. Hany Osman, Prof. Shokri Selim, and Prof. Salih 
Duffuaa. 
Finally, the author appreciates the assistance in data collection for individual 
chapters provided by the following former KFUPM students: Mr. Wail Abu 
Al-Khair, Mr. Salman Al-Dawood, Mr. Khalid Al-Khodhairi, Mr. Ahmad Al-Saati, 
Mr. Mushaileh Al-Shammari, Mr. Abdullatif Ba-Isa, and Mr. Mohamed Osman.
xiii
Contents 
1 
Introduction to Petroleum and Petrochemical Industries . . . . . . . . . .
1 
1.1
Historical Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1 
1.1.1
History of the Petroleum Industry . . . . . . . . . . . . . . . . . . .
1 
1.1.2
History of the Petrochemical Industry . . . . . . . . . . . . . . . .
2 
1.2
Main Processes of the Petroleum Industry . . . . . . . . . . . . . . . . . . . .
5 
1.2.1
Exploration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5 
1.2.2
Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5 
1.2.3
Upstream . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6 
1.2.4
Midstream . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6 
1.2.5
Reﬁning (Downstream) . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7 
1.3
Main Processes of the Petrochemical Industry . . . . . . . . . . . . . . . .
7 
1.3.1
Feedstock Preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8 
1.3.2
Upstream Petrochemical Industry . . . . . . . . . . . . . . . . . . .
8 
1.3.3
Intermediate Petrochemical Industry . . . . . . . . . . . . . . . . .
8 
1.3.4
Downstream Petrochemical Industry . . . . . . . . . . . . . . . . .
9 
1.4
Main Oil Products and Their Uses . . . . . . . . . . . . . . . . . . . . . . . . . .
9 
1.4.1
Crude Oil Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9 
1.4.2
Types of Natural Gas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11 
1.4.3
Types of Reﬁned Oil Products . . . . . . . . . . . . . . . . . . . . . .
11 
1.5
Types of Petrochemical Products . . . . . . . . . . . . . . . . . . . . . . . . . . .
12 
1.5.1
Oleﬁn Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
13 
1.5.2
Aromatics Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
13 
1.5.3
SynGas Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14 
1.6
Integrated Petroleum and Petrochemical Industrial System . . . . .
15 
1.7
Literature on Optimization in the Petroleum Industry . . . . . . . . . .
16 
1.7.1
Optimization in the Petroleum Industry at Large . . . . . . .
16 
1.7.2
Optimization in the Exploration and 
Production Stage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
17 
1.7.3
Optimization in the Reﬁning Stage . . . . . . . . . . . . . . . . . .
18 
1.7.4
Optimization in the Transportation Stage . . . . . . . . . . . . .
19 
1.7.5
Future Trends in the Petroleum Industry . . . . . . . . . . . . . .
19
xv
xvi
Contents
1.8
Summary and Conclusions
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
21 
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
21 
2 
Introduction to Optimization Models and Techniques . . . . . . . . . . . . .
25 
2.1
Introduction to Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
25 
2.2
Unconstrained Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
27 
2.3
Linear Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
29 
2.3.1
LP Graphical Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
30 
2.3.2
The Simplex Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
31 
2.4
Other Mathematical Programming Techniques . . . . . . . . . . . . . . . .
36 
2.4.1
Integer Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
36 
2.4.2
Goal Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
38 
2.4.3
Network Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
40 
2.4.4
Dynamic Programming (DP) . . . . . . . . . . . . . . . . . . . . . . .
42 
2.4.5
Stochastic Programming . . . . . . . . . . . . . . . . . . . . . . . . . . .
44 
2.4.6
Nonlinear Programming . . . . . . . . . . . . . . . . . . . . . . . . . . .
45 
2.5
Meta-heuristic Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
47 
2.5.1
Genetic Algorithms (GA) . . . . . . . . . . . . . . . . . . . . . . . . . .
49 
2.5.2
Simulated Annealing (SA) . . . . . . . . . . . . . . . . . . . . . . . . .
49 
2.5.3
Tabu Search (TS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
49 
2.5.4
Particle Swarm Optimization (PSO) . . . . . . . . . . . . . . . . .
50 
2.6
Simulation-Based Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . .
50 
2.7
Summary and Conclusions
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
52 
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
52 
3 
Optimum Locations of Multiple Drilling Platforms . . . . . . . . . . . . . . .
55 
3.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
55 
3.2
Relevant Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
57 
3.3
The Offshore Drilling Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
59 
3.3.1
Problem Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
59 
3.3.2
Model Costs and Parameters
. . . . . . . . . . . . . . . . . . . . . . .
61 
3.4
Case 1: Fixed Rig Locations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
63 
3.4.1
Case 1a. Equal Number of Wells Per Platform . . . . . . . .
65 
3.4.2
Case 1b. Unrestricted Number of Wells Per Platform . . .
65 
3.5
Case 2: Optimum Platform Locations . . . . . . . . . . . . . . . . . . . . . . .
67 
3.5.1
Case 2 Optimum Solution Model . . . . . . . . . . . . . . . . . . . .
68 
3.5.2
Case 2a. Solution with Equal Number of 
Wells Per Rig . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
73 
3.5.3
Case 2b. Solution with Unrestricted Number 
of Wells Per Rig . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
73 
3.6
Case 2 Heuristic Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
74 
3.6.1
Case 2 Heuristic Solution: Stage 1 . . . . . . . . . . . . . . . . . . .
75 
3.6.2
Case 2 Heuristic Solution: Stage 2 . . . . . . . . . . . . . . . . . . .
76 
3.6.3
Case 2a. Heuristic Solution with Equal Number 
of Wells Per Rig . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
77
Contents
xvii
3.6.4
Case 2b. Heuristic Solution with Unrestricted 
Number of Wells Per Rig . . . . . . . . . . . . . . . . . . . . . . . . . .
77 
3.6.5
Evaluation of the Heuristic Solution . . . . . . . . . . . . . . . . .
78 
3.7
Summary and Conclusions
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
79 
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
81 
4 
Simulation-Based Optimization of Reﬁnery Valve Inspection 
Frequency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
83 
4.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
83 
4.2
Review of Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
85 
4.2.1
Analytical Inspection Interval Models . . . . . . . . . . . . . . .
85 
4.2.2
Simulation Inspection Interval Models . . . . . . . . . . . . . . .
86 
4.2.3
Reasons for Using a Simulation Approach . . . . . . . . . . . .
87 
4.3
Data Collection and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
88 
4.3.1
Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
88 
4.3.2
Factors Affecting Valve Failure Rates . . . . . . . . . . . . . . . .
90 
4.3.3
Determining Probability Distributions . . . . . . . . . . . . . . .
91 
4.4
Modeling and Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
94 
4.5
Output Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
98 
4.6
Summary and Conclusions
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
102 
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
103 
5 
Operations and Workforce Scheduling for Reﬁnery 
Turnaround Maintenance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
105 
5.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
105 
5.2
Relevant Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
108 
5.3
Model Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
110 
5.3.1
Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
111 
5.3.2
Input Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
111 
5.3.3
Decision Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
111 
5.3.4
The Integer Linear Programming Model . . . . . . . . . . . . .
112 
5.3.5
Values of G and T and Bounds on N and Z . . . . . . . . . . .
113 
5.3.6
Size of the Optimum ILP Model . . . . . . . . . . . . . . . . . . . .
114 
5.4
Heuristic Solution Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
116 
5.4.1
Heuristic Stage 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
116 
5.4.2
Heuristic Stage 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
118 
5.4.3
Size of the Heuristic ILP Model . . . . . . . . . . . . . . . . . . . . .
118 
5.5
Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
119 
5.5.1
Optimum Solution of the Case Study . . . . . . . . . . . . . . . .
121 
5.5.2
Heuristic Solution of the Case Study . . . . . . . . . . . . . . . . .
122 
5.6
Evaluation of the Heuristic Method . . . . . . . . . . . . . . . . . . . . . . . . .
123 
5.7
Summary and Conclusions
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
129 
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
131
xviii
Contents
6 
Simulation-Based Scheduling of Pipeline Maintenance Crews . . . . .
133 
6.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
133 
6.2
Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
136 
6.3
Problem Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
138 
6.4
Data Collection and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
141 
6.5
The Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
142 
6.5.1
Model Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
143 
6.5.2
Model Deﬁnitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
144 
6.5.3
Model Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
144 
6.5.4
Duration of the Simulation Runs . . . . . . . . . . . . . . . . . . . .
146 
6.5.5
Model Veriﬁcation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
147 
6.5.6
Model Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
147 
6.5.7
Number of Replications
. . . . . . . . . . . . . . . . . . . . . . . . . . .
147 
6.6
Optimizing Days-Off Schedules . . . . . . . . . . . . . . . . . . . . . . . . . . . .
148 
6.6.1
Performance of Current Schedules . . . . . . . . . . . . . . . . . .
148 
6.6.2
Optimum Machinist Schedules
. . . . . . . . . . . . . . . . . . . . .
148 
6.6.3
Optimum Schedules of the Other Specializations . . . . . .
150 
6.7
Summary and Conclusions
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
151 
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
151 
7 
Optimum Gasoline Blending in Petroleum Reﬁning . . . . . . . . . . . . . .
153 
7.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
153 
7.2
Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
154 
7.3
The Gasoline Blending Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . .
156 
7.3.1
Reﬁning and Blending Processes . . . . . . . . . . . . . . . . . . . .
156 
7.3.2
Blending Process Inputs and Outputs . . . . . . . . . . . . . . . .
157 
7.3.3
Gasoline Speciﬁcations . . . . . . . . . . . . . . . . . . . . . . . . . . . .
158 
7.4
Calculating Blend Properties from Component Properties . . . . . .
160 
7.4.1
Calculating the E70 of the Blend . . . . . . . . . . . . . . . . . . . .
160 
7.4.2
Calculating the RVP of the Blend . . . . . . . . . . . . . . . . . . .
161 
7.4.3
Calculating the VLI of the Blend . . . . . . . . . . . . . . . . . . . .
161 
7.4.4
Calculating the RON of the Blend . . . . . . . . . . . . . . . . . . .
162 
7.5
Gasoline Blending Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
163 
7.6
Nonlinear Programming Optimization Model . . . . . . . . . . . . . . . . .
166 
7.6.1
Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
166 
7.6.2
Objective Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
168 
7.6.3
Production Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . .
168 
7.6.4
Speciﬁcation Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . .
169 
7.7
Solution Process and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
171 
7.7.1
Stage 1: LP Solution of Linearized Approximation . . . .
171 
7.7.2
Stage 2: NLP Solution Using LP Solution 
as the Initial Point . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
173 
7.7.3
Case Study Solution Results . . . . . . . . . . . . . . . . . . . . . . . .
174 
7.8
Summary and Conclusions
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
176 
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
176
Contents
xix
8 
Employee Scheduling in Remote Oil Industry Work Sites . . . . . . . . .
179 
8.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
179 
8.2
Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
182 
8.3
Problem Deﬁnition and Formulation . . . . . . . . . . . . . . . . . . . . . . . .
184 
8.3.1
Problem Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
184 
8.3.2
Model Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
186 
8.3.3
Model Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
186 
8.3.4
Model Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
188 
8.4
Determining the Workforce Size . . . . . . . . . . . . . . . . . . . . . . . . . . . .
190 
8.4.1
Minimum W for the Simpliﬁed Model . . . . . . . . . . . . . . .
190 
8.4.2
Minimum W for the Full Model . . . . . . . . . . . . . . . . . . . . .
192 
8.5
Determining the Days-Off Assignments . . . . . . . . . . . . . . . . . . . . .
193 
8.5.1
Four Active Days-Off Patterns . . . . . . . . . . . . . . . . . . . . . .
194 
8.5.2
Seven Active Days-Off Patterns . . . . . . . . . . . . . . . . . . . . .
197 
8.5.3
Eight Active Days-Off Patterns . . . . . . . . . . . . . . . . . . . . .
199 
8.5.4
Ten Active Days-Off  Patterns . . . . . . . . . . . . . . . . . . . . . . .
201 
8.5.5
Eleven Active Days-Off Patterns . . . . . . . . . . . . . . . . . . . .
204 
8.5.6
The Days-Off Scheduling Procedure . . . . . . . . . . . . . . . . .
206 
8.6
Summary and Conclusions
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
207 
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
208 
9 
Optimum Planning of a Distribution Supply Chain for Reﬁned 
Oil Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
209 
9.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
209 
9.2
Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
211 
9.3
Problem Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
214 
9.4
Model Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
216 
9.4.1
Model Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
216 
9.4.2
Model Indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
217 
9.4.3
Given Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
217 
9.4.4
Decision Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
219 
9.4.5
Objective Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
220 
9.4.6
Supply, Demand, and Material Balance Constraints . . . .
221 
9.4.7
DC Capacity Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . .
222 
9.4.8
SC to DC Transportation Link Constraints . . . . . . . . . . . .
222 
9.4.9
DC to DM Transportation Link Constraints . . . . . . . . . . .
223 
9.4.10 
DC to DC Transportation Link Constraints . . . . . . . . . . .
224 
9.5
Given Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
226 
9.6
Model Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
230 
9.7
Summary and Conclusions
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
232 
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
234
xx
Contents
10 Berth Allocation for Loading Tankers at an Oil 
Export Terminal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
237 
10.1 
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
237 
10.2 
Review of Relevant Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
241 
10.3 
Problem Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
244 
10.4 
Berth Allocation Optimization Model . . . . . . . . . . . . . . . . . . . . . . .
246 
10.4.1 
Model Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
246 
10.4.2 
Model Indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
247 
10.4.3 
Given Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
247 
10.4.4 
Decision Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
248 
10.4.5 
Objective Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
248 
10.4.6 
Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
248 
10.4.7 
Calculating the Value of S . . . . . . . . . . . . . . . . . . . . . . . . . .
253 
10.5 
Berth Allocation Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
253 
10.6 
Summary and Conclusions
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
257 
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
258 
Appendix A: Bibliography: Optimization in the Petroleum Industry . . . .
261 
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
273
About the Author 
Hesham K. Alfares has been a member of the Shura Council (Consultative Parlia-
ment) of Saudi Arabia since October 2020. Until January 2021, he was the chairman 
in the Systems Engineering (SE) Department at King Fahd University of Petroleum & 
Minerals (KFUPM), Dhahran, Saudi Arabia. Dr. Alfares joined the SE Department 
at KFUPM as a lecturer in 1984, became a full professor in 2004, and the chairman 
in 2015. 
Dr. Alfares obtained a B.S. in Electrical and Computer Engineering from the 
University of California, Santa Barbara, in 1982. He obtained an M.S. in Indus-
trial Engineering from the University of Pittsburgh in 1984. He obtained a Ph.D. in 
Industrial Engineering from Arizona State University in 1991. 
Dr. Alfares spent a full-year sabbatical leave in 1999–2000, in addition to ﬁve 
summer terms, working in Saudi Aramco, the national oil company of Saudi Arabia. 
He spent the summer 2010 term as a visiting Fulbright scholar at the University of 
North Carolina, Charlotte. He spent the spring 2012 term as a visiting scholar at 
Massachusetts Institute of Technology. He spent four summer terms as a visiting 
British Council scholar at four UK universities: Warwick in 1993, Nottingham in 
1998, Loughborough in 2003, and East Anglia in 2013. 
Dr. Alfares research interests are in the areas of production and inventory control, 
scheduling, maintenance, simulation, and applied optimization, with a focus on 
modeling and optimization of petrochemical systems. At the time of printing this 
book, he has more than 130 publications, including 61 journal papers and 4 book 
chapters, in addition to 3 US patents and more than 3300 Google Scholar citations. 
Dr. Alfares has been a member of the editorial boards of the Arabian Journal for 
Science and Engineering,the Journal of Industrial Engineering, and the International 
Journal of Applied Industrial Engineering. He served in the guest editorial board of 
a special issue of the European Journal of Operational Research. He has been a 
member of the scientiﬁc and organizing committees of 35 scientiﬁc conferences. 
Dr. Alfares won grants for 12 funded research projects and 6 industrial consulting 
projects. He won an out-of-state graduate student tuition-waiver scholarship from 
Arizona State University for the 1986–1987 academic year. He won KFUPM Excel-
lence in Research Award three times, in the years 2003, 2008, and 2020. He won
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About the Author
Almarai Prize for Scientiﬁc Innovation in Industrial Engineering in 2014. He has 
been a fellow of the Industrial Engineering and Operations Management Society 
International since 2020.
Chapter 1 
Introduction to Petroleum 
and Petrochemical Industries 
1.1 
Historical Background 
The petroleum and petrochemical industries are international multi-billion-dollar 
businesses, which are among the biggest and most important industries in the world 
today (IBIS World, 2020). Natural oil has been the main source of the world’s energy 
for the last 70 years (Deutsche Bank, 2013). Therefore, the enormous importance 
of the petroleum and petrochemical industries is due to their economic scale and 
geopolitical inﬂuence. It is also due to their numerous products and derivatives that 
interact with all industries and all economic sectors and touch our daily lives in so 
many ways. The two industries are interdependent and closely related. The petroleum 
industry is the starting point of the value chain, and it has a longer history than the 
petrochemical industry. 
1.1.1 
History of the Petroleum Industry 
Crude oil has been known and used by people for thousands of years. Natural oil 
ﬂowed out of the Earth in several places around the world, forming fountains and 
tar pools (Devold, 2013). Prehistoric people used natural oil as a source of ﬁre for 
cooking, lighting, and heating purposes. Prophet Noah is reported to have used oil to 
waterproof the insides and outsides of his Ark in preparation for the Flood. Around 
4000 B.C. the ancient civilizations of Mesopotamia (modern-day Iraq) found several 
uses for oil. They used oil varieties, especially asphalt, as a medical treatment and 
also as an adhesive in making tools and constructing buildings (Nawwab et al., 1995). 
The ancient Egyptians used oil in preserving mummies, while the early Chinse and 
Indians used it for medical purposes (Deutsche Bank, 2013). Later, the Greeks used 
oil for military purposes, especially to burn enemy ships in naval battles. 
Besides the simple use of oil that made its way out of the Earth, there were several 
historical efforts at the drilling and distillation of crude oil. According to Ali (2019),
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 
H. K. Alfares, Applied Optimization in the Petroleum Industry, 
https://doi.org/10.1007/978-3-031-24166-6_1 
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Introduction to Petroleum and Petrochemical Industries
the ﬁrst oil wells were drilled in China around 347 A.D. Also, around 2500 years 
ago, the Chinese drilled for natural gas, which they burned and used to dry rock 
salt (Clark & Tahlawi, 2006). In the ninth century, the Muslim scientist Abu Bakr 
Al-Razi developed a process for oil distillation. Arab scholars at that time proposed 
theories on the origin of Naft, the Arabic word for crude oil, from which the word 
Naphtha is derived (Nawwab et al., 1995). 
The industrial revolution in the nineteenth century led to a huge increase in the 
demand for oil for fueling, lighting, and lubrication. Simple drilling for oil started 
in Azerbaijan in 1846, and by 1884 there were almost 200 small reﬁneries around 
Baku. In the 1850s, basic oil distillation facilities were constructed in Europe and 
North America, sowing the seed for the modern-day oil reﬁneries (Nawwab et al., 
1995). In 1848, Scottish chemist James Young invented a process to distill kerosene 
from oil. In 1850, Young teamed up with geologist Edward Binney to establish the 
ﬁrst commercial oil reﬁnery in the world, manufacturing oil and parafﬁn wax from 
locally mined coal (Ali, 2019). Subsequently, Samuel Kier built the ﬁrst oil reﬁnery 
in the USA in Pittsburgh in 1853. 
In 1859, Edwin Drake drilled the ﬁrst modern American oil well in Pennsylvania, 
which produced 15 barrels a day (Deutsche Bank, 2013). This was followed by a 
big rush for the “black gold” that led to the digging of many commercial oil wells in 
Pennsylvania, starting the petroleum industry that we know today. The ﬁrst pipeline 
was built in Pennsylvania in 1965, transporting 2000 barrels of oil per day for a 
distance of ﬁve miles. In 1865 John Rockefeller established Standard Oil, the world’s 
ﬁrst major oil company, which dominated oil production and reﬁning in the US for 
decades. 
The invention of the gasoline-powered automobile in Germany in 1886 started a 
new era for the petroleum industry and signaled the start of the automobile industry 
(Nawwab et al., 1995). The soaring demand for both the car industry and the oil 
industry transformed petroleum reﬁneries from simple manual distilleries to highly 
sophisticated facilities based on scientiﬁc and engineering principles. Figure 1.1 
shows a typical section of a large modern oil reﬁnery.
The ﬁrst oil tanker was built in Sweden and used in Azerbaijan in 1878, which 
made it possible to transport crude oil overseas. Figure 1.2 shows two modern large 
oil tankers. The discovery of thermal cracking in 1913 doubled the amount of gasoline 
produced from each barrel of oil and launched a new era for the petroleum industry 
(Clark & Tahlawi, 2006). In 1937, the ﬁrst offshore oil well was drilled by Pure 
Oil Company one mile away from the shore in Louisiana. The two world wars 
tremendously increased the demand for petroleum and gas products and led to huge 
advancements in oil exploration, drilling, storage, reﬁning, shipping, and distribution.
1.1.2 
History of the Petrochemical Industry 
The petrochemical industry applies chemical processes on petroleum and natural gas 
to derive a variety of industrial products called petrochemicals. It is a relatively young
1.1 Historical Background
3
Fig. 1.1 A modern oil reﬁnery. Courtesy of Saudi Aramco, copyright owner
Fig. 1.2 Two large oil tankers. Courtesy of Saudi Aramco, copyright owner
industry, although some basic petrochemicals have been known since antiquity. The 
ancient Egyptians produced ethylene and polyethylene from gas and plants. They also 
used bitumen (tar, or natural asphalt) as a construction material for building the pyra-
mids (Syntor Fine Chemicals, 2019). In the eighth century, Muslim alchemist Jabir
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Introduction to Petroleum and Petrochemical Industries
Ibn Hayyan used a chemical process to derive sal ammoniac (ammonium chloride) 
from organic materials (Wikipedia, 2020). 
The industrial revolution in the nineteenth century created a large demand for 
synthetic materials and led to the invention of many new petrochemicals. For 
example, polyvinyl chloride was discovered in 1835, polystyrene in 1839, and the 
ﬁrst synthetic dye in 1856. Production of petrochemicals from natural gas started in 
1872 with the production of carbon black which was used to make synthetic rubber 
(Syntor Fine Chemicals, 2019). 
The modern-day petrochemical industry was born in the USA in 1920, when 
Standard Oil opened the ﬁrst petrochemical plant in New Jersey to produce isopropyl 
alcohol from propylene. Also in 1920, Union Carbide opened another petrochemical 
plant in West Virginia, using thermal cracking of natural gas to produce oleﬁns. 
Another Union Carbide plant in West Virginia started to produce ethyl glycol in 
1924 (Aftalion, 2001). This was followed by several important developments. For 
example, polyethylene was invented in 1935, nylon in 1937, polyester in 1946, and 
polypropylene in the early 1950s. Figure 1.3 shows a large, modern petrochemical 
plant in Saudi Arabia. 
The petrochemical industry developed quickly during World War II and became 
a major industrial and economic sector. As natural raw materials became scarce, 
synthetic materials were used as a substitute. Therefore, the war caused a big surge 
in demand for synthetic materials for both military and civilian applications. Subse-
quently, the industry was transformed from trial-and-error approaches to scientiﬁ-
cally based efﬁcient practices. New catalysts were discovered, and several types of 
catalytic cracking and catalytic reforming were invented, allowing new and improved 
petrochemical processes. Consequently, a large variety of petrochemical products
Fig. 1.3 A modern petrochemical plant. Courtesy of Saudi Aramco, copyright owner 
1.2 Main Processes of the Petroleum Industry
5
started to appear to serve the needs of the manufacturing and service sectors, such 
as synthetic rubber, plastic, and chemical solvents. Later, additional products were 
developed for individual consumers, such as textiles, kitchen appliances, sports shoes, 
and personal hygiene items (Devold, 2013). 
1.2 
Main Processes of the Petroleum Industry 
The main function of the petroleum industry is to produce crude oil, natural gas, 
and reﬁned oil products. This requires several activities before, during, and after 
production. According to Devold (2013), the petroleum and gas industry processes 
are broadly classiﬁed into the following categories: 
1. Exploration. 
2. Production. 
3. Upstream. 
4. Midstream. 
5. Reﬁning (downstream). 
1.2.1 
Exploration 
The objective of exploration is to ﬁnd, evaluate, and prepare sites where oil and gas is 
located. Exploration is done both on land and offshore, and it includes seismic tests, 
exploratory drilling, and ﬁeld development. As shown in Fig. 1.4, seismic tests use 
the reﬂection of sound waves by different underground layers to create an image of 
those subsurface layers (Deutsche Bank, 2013). If an image points to the existence of 
oil beneath the surface, then this particular area is considered a “prospect.” Drilling 
oil wells is very costly, especially offshore. Therefore, exploratory drilling is done 
only in high-probability prospect areas. If oil is found, then additional information 
is collected on the well’s capacity and quality in order to make the right decisions 
on the ﬁeld’s development (Devold, 2013).
1.2.2 
Production 
Oil and gas production is done onshore on offshore, at different depths, reservoir 
capacities, and geological structures. However, it typically involves several common 
features and facilities (Devold, 2013). Outputs from the production wellheads feed 
into the gathering system, and then into the gas-oil separation plant (GOSP). Typi-
cally, the wellheads’ output contains a mix of crude oil, natural gas, and other unde-
sirable components such as water, carbon dioxide, sulfur, and sand. The function of
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Introduction to Petroleum and Petrochemical Industries
Fig. 1.4 A seismic testing truck used for oil exploration. Courtesy of Saudi Aramco, copyright 
owner
the GOSP is to separate and clean the useful desirable components, namely oil and 
natural gas. 
1.2.3 
Upstream 
Upstream activities include well completion, gathering, separation, storage and 
exporting, and utilities (Devold, 2013). Well completion activities include casing 
to strengthen the well hole and installing the wellhead to control the ﬂow of oil or 
gas. Gathering is done by a network of manifolds and pipelines to feed oil and gas 
into the production facilities. Separation is done when the well produces a mix of 
oil and gas, where gravity is usually used to allow gas to bubble up on top and oil 
to settle at the bottom. Crude oil is usually stored in large tank farms, and exported 
via tankers and pipelines, while metering systems are used to transfer ownership. 
Utilities are support systems for upstream facilities and personnel, and they include 
electricity, water, ﬁreﬁghting, and HVAC. 
1.2.4 
Midstream 
Midstream activities include gathering, transportation, and storage of crude oil and 
natural gas. The gathering stage involves the use of short, local pipeline networks 
to connect oil and gas producing wells to the long, main pipeline grid or to the 
processing facilities. The transportation process involves transporting oil and gas
1.3 Main Processes of the Petrochemical Industry
7
from wells to processing facilities, and from there to end users. This can be done by 
several means, such as pipelines, tankers, trucks, and railways. Midstream storage 
involves providing oil and gas storage facilities at terminals at various stages of the 
distribution system, for example near reﬁneries, LNG plants, and export terminals. 
Crude oil is kept in storage tanks, while natural gas is usually stored in underground 
facilities. 
Midstream facilities include gas plants, liqueﬁed natural gas (LNG) plants, and 
pipeline transportation systems. Gas plants separate natural gas (mostly methane) 
from crude oil and other gas components, such ethane, propane, and butane. Separated 
natural gas is then compressed in order to be transported though gas pipelines and 
then ﬁnally loaded on export tankers. When pipelines are not available or economical 
for long-distance transportation, cooling systems transform natural gas to the liquid 
state as LNG, to be transported by specialized insulated LNG tankers. 
1.2.5 
Reﬁning (Downstream) 
Reﬁneries basically heat crude oil to the evaporation point, and then use distillation 
columns to condense the vapors and separate them into different reﬁned products 
demanded in the market, such as gasoline, kerosene, and diesel fuel. The quantity 
and the quality of reﬁned products depend on the mix of feedstock crude types 
and quantities. The reﬁnery’s products and their costs also depend on the reﬁning 
processes used, which include cracking, reforming, and blending. Many reﬁneries 
also include tank farms and distribution terminals for storing reﬁned products and 
transporting them to customers. In addition to reﬁning, downstream processes include 
the marketing and distribution of reﬁned oil and gas products. 
The reﬁning stage of the petroleum industry has pioneered the large-scale indus-
trial use of optimization models and software. Reﬁnery optimization models have 
been applied in all phases of the reﬁnery processes and the associated supply chain 
in order to maximize proﬁts. For example, these models are used in determining and 
procuring the best mix of input crude oils, in operations planning and scheduling, in 
gasoline blending, and in determining and distributing the best mix of reﬁned prod-
ucts. For short-term process planning, optimization models are used to determine 
reﬁnery unit throughputs, distillation cut points, and rates of the conversion units. 
1.3 
Main Processes of the Petrochemical Industry 
Petrochemicals are non-fuel chemical products whose main raw materials (feedstock) 
are obtained from crude oil and natural gas. All petrochemicals are hydrocarbons, 
i.e., organic compounds whose molecules are composed of hydrogen and carbon 
atoms. The four classes of hydrocarbons, namely parafﬁns, oleﬁns, naphthenes, and 
aromatics, differ in the ratio of hydrogen atoms to carbon atoms and also in their
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Introduction to Petroleum and Petrochemical Industries
molecular bonding structure. According to Leingchan (2017), the petrochemical 
industry processes can be divided into the following main stages: 
1. Feedstock preparation. 
2. Upstream petrochemical industry. 
3. Intermediate petrochemical industry. 
4. Downstream petrochemical industry. 
1.3.1 
Feedstock Preparation 
This is an in-between stage, which can be considered either as the last stage for the 
petroleum industry or the ﬁrst stage for the petrochemical industry. The feedstocks, 
i.e., raw material inputs for the petrochemical industry, are the outputs produced 
by the petroleum industry. The main raw materials are natural gas, naphtha, and 
condensates. Natural gas is obtained directly from gas wells and also from crude oil 
after separating it in GOSP facilities. Natural gas is mostly composed of methane, 
but it can also include other hydrocarbon gases such as ethane, propane, and butane. 
Naphtha is obtained from crude oil in reﬁneries, while condensates are obtained from 
natural gas in separation plants. 
1.3.2 
Upstream Petrochemical Industry 
The upstream petrochemical sector uses the raw feedstocks (natural gas, naphtha, 
and condensates) to make primary (base) petrochemical products. These products 
are used as raw materials for producing the intermediate and downstream products. 
Base products are produced in high quantities and in big plants. Base petrochemical 
products are usually divided based on their molecular structure into three categories: 
1. Oleﬁns. Their molecules have a chain of carbon atoms, and they include ethylene, 
propylene, and butadiene. 
2. Aromatics. Their molecules have a ring of carbon atoms, and they include 
benzene, toluene, and xylene (Deutsche Bank, 2013). 
3. Synthesis gas (SynGas). It is a mixture of carbon monoxide and hydrogen, and 
it is used to make ammonia and methanol (Devold, 2013). 
1.3.3 
Intermediate Petrochemical Industry 
The intermediate sector of the petrochemical industry uses upstream products, i.e., 
base products Oleﬁns, Aromatics, and SynGas, as raw materials for producing inter-
mediate petrochemical products. These intermediates are either further processed 
to make downstream products or converted into goods that can be directly used
1.4 Main Oil Products and Their Uses
9
by customers. Intermediate products can be classiﬁed according to their upstream 
feedstock categories into Oleﬁn intermediaries, Aromatics intermediaries, and 
SynGas intermediaries. For example, Oleﬁn intermediates include ethylene dichlo-
ride (EDC) and oxo alcohol, Aromatic intermediates include ethyl benzene (EB) and 
styrene monomer (SM), while SynGas intermediates include methyl alcohol and 
formaldehyde. 
1.3.4 
Downstream Petrochemical Industry 
The downstream sector of the petrochemical industry uses the outputs of the upstream 
and intermediate sectors as raw materials to make end products for sale to consumers 
and other industries. Nowadays, more than 200 main downstream petrochemical 
products are manufactured by companies across the globe (Devold, 2013). Each 
product can be made by several alternative processes that are mostly based on 
polymerization, which is the process of binding hydrocarbon molecules together 
to form larger molecules. Downstream end products of the petrochemical industry 
are classiﬁed into the four following groups (Leingchan, 2017): 
1. Plastic resins. Plastics have numerous uses for industries and consumers. The 
most important plastic products are polyethylene, polypropylene, polyvinyl 
chloride (PVC), ABS, PET, and polystyrene. 
2. Synthetic ﬁbers. Uses of these items include textiles and packaging. The most 
important synthetic ﬁbers are polyester and nylon ﬁber. 
3. Synthetic rubbers. These items are used in making car tires and parts and 
many consumer goods. They include styrene–butadiene rubber (SBR) and 
polybutadiene. 
4. Synthetic coating and adhesive materials. These items are used in construction 
and in many industrial and consumer products. They include polycarbonate and 
polyvinyl acetate. 
1.4 
Main Oil Products and Their Uses 
The main products of the petroleum industry are crude oil and natural gas, in addi-
tion to reﬁned oil products. These products come in several types and varieties, as 
described below. 
1.4.1 
Crude Oil Types 
Natural crude oil comes in several varieties with different physical and chemical 
characteristics, and hence different economic values. Unreﬁned crude oil can be
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Introduction to Petroleum and Petrochemical Industries
very thin with a light color or very thick with a black color. Lower-density crude 
oil is more valuable because it has a higher gasoline content and a lower sulfur 
content. Crude oil is classiﬁed into several types according to several factors, the 
most important of which are viscosity or density, volatility, and toxicity. Based on 
these factors, crude oil is classiﬁed into the following types: 
1. Light crude. Light crude oil has lower viscosity and thus a higher ability to ﬂow. 
Therefore, it is easier and cheaper to produce, transport, and reﬁne. The majority 
of reﬁned products in the global market are produced from light crude. 
2. Heavy crude. This type of crude is characterized by high viscosity and low ability 
to ﬂow. This is due to the higher concentrations of sulfur and heavy metals. Heavy 
crudes usually need to be diluted to be transported through pipelines, and they 
are generally more difﬁcult and expensive to produce, transport, and reﬁne. 
3. Sweet crude. This type of crude contains a low percentage of sulfur, and therefore 
it is less corrosive and toxic. Lower corrosion means longer lives and lower 
maintenance costs for the production and transportation facilities. Sweet crude 
also contains a higher proportion of the more desirable reﬁned products such as 
gasoline. 
4. Sour crude. Sour crude has a sulfur content higher than 0.5%, which is undesir-
able for both processing cost and end-product quality. Sulfur must be removed 
before reﬁning, and toxic hydrogen sulﬁde must be removed before tanker trans-
portation. Therefore, sour crude is less valuable and less desirable than sweet 
crude. 
Another classiﬁcation of crude oils according to market benchmarks and 
geographical locations is used for oil trading purposes. Currently, more than 280 
distinct types of crude oil are traded on the international market. Well-known crude 
oil varieties include Arab Light from Saudi Arabia, West Texas from the USA, Brent 
from the North Sea, OPEC Basket from several OPEC countries, Bonny Light from 
Nigeria, and Urals from Russia. To make it easier to keep track of the different crude 
varieties, several market benchmarks have been established as pricing references for 
both buyers and sellers. The three main benchmarks are West Texas Intermediate, 
Brent Blend, and OPEC Basket, whose characteristics are described below (OPEC, 
2020): 
1. West Texas International (WTI) crude. This crude is produced and reﬁned in 
the USA, and therefore it is sometimes referred to as US crude. It is both light 
and sweet, and hence considered the best quality benchmark crude. Therefore, 
WTI Crude has a higher price than both Brent crude and OPEC Basket crude. 
It is primarily used for making gasoline, because it produces more and better 
gasoline per barrel than other benchmark crudes. 
2. Brent crude. This is a blend of crudes produced from 15 ﬁelds in the North Sea, 
and it is reﬁned mostly in Northwest Europe. However, Brent blend serves as the 
main benchmark for crude oils produced in Europe and Africa. Brent is a light 
and sweet crude, but to a lesser degree than the West Texas crude. It is used for 
making gasoline and middle distillates, such as jet fuel, kerosene, and diesel.
1.4 Main Oil Products and Their Uses
11
3. OPEC Basket crude. OPEC is the Organization of Petroleum-Exporting Coun-
tries, which was established in 1960 to coordinate the policies of member states 
on the production levels and sale prices of crude oil. OPEC Basket benchmark is 
a mix of various crudes produced by OPEC member states. Currently, the Basket 
contains 13 crudes produced in the Middle East, Africa, and South America. 
OPEC Basket crude is less light and sweet than West Texas crude and Brent 
crude, and therefore it is slightly cheaper. 
1.4.2 
Types of Natural Gas 
Natural gas is primarily composed of methane. It is classiﬁed into two main cate-
gories: associated (wet) gas and non-associated (dry) gas. Non-associated gas is 
produced from pure gas ﬁelds and also from coal beds. Associated gas is extracted 
from oil ﬁelds, where it is separated from crude oil in GOSP plants. In addition 
to methane, associated gas usually contains natural gas liquids, including ethane, 
butane, propane, and pentane. Due to the economic value of these gas liquid contents, 
associated gas is commercially more valuable than non-associated gas. However, 
these liquids and non-hydrocarbon contents must be removed before natural gas is 
transported, sold to customers, or used as a feedstock. The increasing demand and 
improving technology are allowing the gas industry to produce more gas from uncon-
ventional sources, adding newer gas types such as sour gas, tight gas, biogas, and 
shale gas. 
1.4.3 
Types of Reﬁned Oil Products 
In oil reﬁneries, distillation columns are used to separate crude oil into several reﬁned 
products. Lighter-weight products rise up to the higher levels of the columns, while 
heavier ones descend to the lower levels. The reﬁned oil products, ordered from 
lightest to heaviest, are listed below (Devold, 2013). For these products, the given 
percentage yields are typical Western Europe values provided by Deutsche Bank 
(2013): 
1. Petroleum gas. On average, this product is 3% of the crude oil weight. It is a 
gas product made up of methane, ethane, propane, and butane, which can be 
converted to liqueﬁed petroleum gas (LPG). It is either directly used for heating 
and cooking, or as a major feedstock for the petrochemical industry. 
2. Naphtha. This product is on average 6% of the crude oil weight. It is a light clear 
liquid which is easily vaporized. It can be used either directly as a solvent and 
diluent or further processed to make gasoline. However, it is most commonly 
used as a petrochemical feedstock, especially for making oleﬁns. 
3. Gasoline. On average, gasoline constitutes 21% of the crude oil weight. It is 
a volatile liquid fuel that evaporates quickly at room temperature. Gasoline is
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Introduction to Petroleum and Petrochemical Industries
mainly used as a fuel for internal combustion engines that run most passenger 
cars. Gasoline is rated by its octane number, which indicates its ability to burn 
evenly without “knocking” under high pressures and temperatures. To run modern 
car engines, aromatics and other additives are added to gasoline to improve its 
octane rating and volatility. 
4. Kerosene. This product is on average 6% of the crude oil weight. It is a liquid 
fuel mainly used in aviation for jet engines. Kerosene is also used for lighting 
and heating purposes, and also as a raw material for making other products. 
5. Diesel oil. This product is on average 36% of the crude oil weight. It is a liquid 
product, which is also called gasoil, diesel fuel, and petro-diesel. It is mainly 
used for diesel engines in cars, trucks, ships, trains, and large machinery. It is 
also used for home heating, and as a raw material for making other products. 
6. Fuel oil. Fuel oil is on average 19% of the crude oil weight. It is a liquid product, 
which is also called heavy gas, and it has six different grades. Lighter grades 
(1–3) are used in industrial heating and power generation. Heavier grades (4–6), 
called bunker oil, are highly polluting, and therefore they are used as fuel for 
ships in international waters. 
7. Lubricating oil. Lubricating oil combined with residuals constitute on average 
9% of the crude oil weight. Lubricating oil, also called mineral base lubricating 
oil, is a liquid that has different degrees of thickness, which does not evaporate 
at room temperature. It is used to make motor oil, gear oils, grease, Vaseline, 
and other lubricants. Additives are used to modify lubricant properties, such as 
viscosity, color, and smell. 
8. Residuals. These are heavy solid products that include coke, asphalt, tar, bitumen, 
and waxes. These are generally low value as end items, but they can be used as 
raw materials for making other products. After processing, coke can be used to 
make electric anodes in the metal industry. Asphalt and bitumen are used for 
sealing roofs and paving roads. 
1.5 
Types of Petrochemical Products 
By further processing the petroleum products, the petrochemical industry can be 
considered as a continuation of the petroleum industry. As stated in Sect. 1.3, the  
primary raw materials for the petrochemical industry are natural gas, naphtha, and 
condensates. 
These raw materials are used to successively manufacture three levels of petro-
chemical products, namely upstream (basic), midstream (intermediary), and down-
stream (end) products. The product varieties within these three product levels are 
described below, classiﬁed according to the three categories of upstream basic 
petrochemical products: oleﬁns, aromatics, and synthesis gas.
1.5 Types of Petrochemical Products
13
1.5.1 
Oleﬁn Products 
Oleﬁns are mainly used to produce plastics and industrial solvents and as raw 
materials for making midstream and downstream products. Key oleﬁn products are 
ethylene, propylene, and butadiene, whose derivatives are described below: 
1. Ethylene (C2) products. Ethylene is the most signiﬁcant basic petrochemical, 
as it is used as a raw material for approximately 60% of other petrochemicals 
(Deutsche Bank, 2013). The most important ethylene derivatives are:
. Ethanol (ethyl alcohol, EtOH).
. Ethanolamines: monoethanolamine (MEA), diethanolamine (DEA), and 
triethanolamine (TEA).
. Polyethylene (PE).
. Polyvinyl chloride (PVC). 
2. Propylene (C3) products. Propylene is generally not usable as an end consumer 
product. It is primarily used as a petrochemical feedstock for making ﬁbers, 
textiles, plastics, paints, and other products. The main derivatives of propylene 
are:
. Polypropylene (PP).
. Polyurethanes.
. Acrylonitrile–butadiene–styrene (ABS).
. Polyacrylonitrile (PAN).
. Cumene.
. Methyl methacrylate (MMA). 
3. Butadiene (C4) products. Butadiene is primarily used as a raw material for 
manufacturing various forms of rubber, latex, and plastics. The main derivatives 
of butadiene are:
. Pyrolysis gasoline (Pygas).
. Styrene–butadiene rubber (SBR).
. Methyl methacrylate (MMA).
. Polybutadiene.
. Polyisobutylene (PIB).
. Polybutylene (PB-1).
. Methyl-tert-butyl-ether (MTBE). 
1.5.2 
Aromatics Products 
Aromatics are hydrocarbon substances that are characterized by distinctive perfumed 
smells. Aromatics are extensively extracted from crude oil, but small quantities are 
made from coal. Aromatics are raw materials for a wide range of products in medicine,
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Introduction to Petroleum and Petrochemical Industries
transport, telecommunications, fashion, and sports. The main aromatic products are 
benzene, toluene, and xylene, whose derivatives are described below: 
1. Benzene (C6) products. Benzene is the most common aromatic. It is primarily 
used as raw material to make polystyrene used in insulation, molding, and 
packaging. It is also used to make nylon, resins, acrylics, furniture, and auto 
components. The main derivatives of benzene are:
. Styrene-acrylonitrile (SAN).
. Acrylonitrile–butadiene–styrene (ABS).
. Styrene–butadiene rubber (SBR). 
2. Toluene (C6) products. Toluene is a colorless aromatic liquid that is also called 
methylbenzene. Toluene is mostly used as an industrial feedstock and also as a 
solvent in paint thinners, permanent markers, contact adhesives, and some types 
of glue. The main derivatives of toluene are:
. Toluene diisocyanate (TDI).
. Nylon.
. Phenol.
. Phenolic resins.
. Epoxy resin.
. Polycarbonates. 
3. Xylene products. Xylenes are colorless liquid aromatics that are also called 
dimethylbenzene. Xylenes consist of three isomers: ortho-xylene, meta-xylene, 
and para-xylene. They are used as raw materials to make other products, and they 
have wide industrial and laboratory applications as solvents and cleaning agents. 
The main derivatives of xylene are:
. Polyethylene terephthalate (PET).
. Glass reinforced plastics (GRP).
. Polyvinyl chloride (PVC).
. Alkyd resins. 
1.5.3 
SynGas Products 
Synthetic gas (SynGas) is mainly used as a fuel in electricity generation. It is a 
mixture of hydrogen, carbon monoxide, and usually some carbon dioxide. In the 
petrochemical industry, SynGas is used to produce methanol or ammonia and to 
make synthetic diesel and gasoline fuels. The main SynGas products are methanol 
and ammonia, whose derivatives are described below: 
1. Methanol products. Methanol is a colorless liquid, which is also called methyl 
alcohol. It can be used directly as fuel and solvent, or as raw material for making 
many products. The main derivatives of methanol are:
1.6 Integrated Petroleum and Petrochemical Industrial System
15
. Melamine resin.
. Urea–formaldehyde.
. Phenol formaldehyde.
. Polyoxymethylene (POM).
. Methyl-tert-butyl-ether (MTBE).
. Methyl methacrylate.
. Dimethyl terephthalate (DMT). 
2. Ammonia products. Ammonia (NH3) is a colorless, alkaline gas which is mainly 
used for the production of fertilizers. It is also an important raw material for 
making many pharmaceuticals, explosives, and cleaning products. The main 
derivatives of ammonia are:
. Urea.
. Ammonium nitrate.
. Methylamine. 
1.6 
Integrated Petroleum and Petrochemical Industrial 
System 
The petroleum industry and the petrochemical industry can be viewed as two stages in 
one larger value-chain system, which is the integrated petroleum and petrochemical 
industrial system. As shown in Fig. 1.5, the petroleum industry is the ﬁrst (upstream) 
stage in this system, while the petrochemical industry is the second (downstream) 
stage. Essentially, several outputs from the petroleum industry (natural gas, naphtha, 
and condensates) are used as inputs (feedstock) for the petrochemical industry. Petro-
chemical plants add value to these petroleum industry’s outputs by processing them 
into a large variety of more valuable petrochemical products. 
Fig. 1.5 The integrated petroleum and petrochemical industrial system
16
1
Introduction to Petroleum and Petrochemical Industries
In many real-life cases, the close relationship between the petroleum industry 
and the petrochemical industry has resulted in both a physical and an organiza-
tional integration of the two industries. Many petrochemical plants are built near 
crude oil reﬁneries or even integrated within large petroleum–petrochemical indus-
trial complexes. The addition of petrochemical products to their business activi-
ties provides oil companies with wide opportunities for additional proﬁtability and 
growth. Therefore, many oil companies have either launched their own petrochem-
ical branches or acquired other petrochemical companies. This is the case for several 
major international petroleum companies such as Exon Mobil, Shell, and Saudi 
Aramco. 
1.7 
Literature on Optimization in the Petroleum Industry 
Due to the complexity and signiﬁcance of the relevant decisions, optimization is 
especially useful in all segments of the petroleum industry. Therefore, there is a 
long and active history of applying optimization models in the petroleum industry. 
This massive amount of literature cannot be covered in detail in any single review. 
However, there is a large number of literature reviews that cover several speciﬁc 
applications of optimization within this area. This section presents an overview of 
the most relevant of these literature reviews, focusing on the most recent ones. The 
aim is to highlight the most active, important, and recent topics of research in this 
area, and to identify relevant trends and future directions. 
Previous reviews of literature on optimization applications in the petroleum 
industry are broadly classiﬁed into ﬁve categories that are presented below: (1) 
the industry at large, (2) exploration and production stage, (3) reﬁning stage, (4) 
transportation stage, and (5) future trends. 
1.7.1 
Optimization in the Petroleum Industry at Large 
Bodington and Baker (1990) review the diverse history of applying different math-
ematical programming optimization techniques in the petroleum industry from the 
1940s to the 1990s. The use of linear programming (LP) models in operations plan-
ning is the earliest and most well-known application of optimization in the petroleum 
industry. The review shows, however, that a variety of other optimization techniques 
have been successfully applied in all aspects of the petroleum industry, ranging from 
strategic planning through process control. 
Shakhsi-Niaei et al. (2013) survey the literature on optimization applications in 
oil-and-gas upstream and midstream sectors. The optimization techniques are clas-
siﬁed according to application area into three types: exploration and development, 
production, and transportation. The techniques are also classiﬁed according to time-
frame as either strategic, tactical, or operational. DiCarlo et al. (2019) provide a
1.7 Literature on Optimization in the Petroleum Industry
17
comprehensive review of operations research (OR) applications in the petroleum 
industry. The focus is on linear, nonlinear, integer, and mixed-integer optimization 
methods. The review is used to identify and discuss the difﬁculties in applying OR 
in the petroleum industry, such as nonlinearity and uncertainty. 
Rahmanifard and Plaksina (2019) review the use of artiﬁcial intelligence (AI) 
algorithms for solving optimization problems in the oil and gas industry. Four main 
types of AI algorithms are considered, namely evolutionary algorithms, swarm intel-
ligence, fuzzy logic, and artiﬁcial neural networks. All four types demonstrate supe-
rior performance in solving a variety of important petroleum industry problems, such 
as minimum miscibility pressures, oil production rate, well placement, and reservoir 
characterization. 
1.7.2 
Optimization in the Exploration and Production Stage 
Durrer and Slater (1977) present an early review of literature on the use of optimiza-
tion approaches in the production of petroleum and natural gas. Their review covers 
the problems of drilling, reservoir simulation, production planning, and enhanced 
recovery. The main challenges in applying optimization models for petroleum and 
natural gas production are identiﬁed as nonlinearity, dimensionality, and uncertainty. 
Velez-Langs (2005) reviews the applications of one optimization approach, 
namely genetic algorithms (GA), in the oil industry’s exploration and production 
stages. The review is focused on GA applications in reservoir characterization, but it 
also includes GA models for gas storage, seismic inversion, engine oil development, 
oil ﬁeld development, and production scheduling. 
Nasrabadi et al. (2012) review well placement optimization techniques, which are 
used to determine the optimum number, type, trajectory, and location of wells in oil 
and gas ﬁelds. The reviewed models are classiﬁed into several categories, including 
optimization algorithms, reservoir response models, uncertainty handling methods, 
and well placement optimization techniques in gas ﬁelds. 
Khor et al. (2017) survey optimization models in petroleum production systems. 
These models are applied in production systems design and operations, lift gas 
allocation, ﬁeld development, facility location–allocation, and production planning 
and scheduling. The models are used for continuous and discrete optimization, 
and they include nonlinear programming, mixed-integer programming, stochastic 
programming, and meta-heuristic algorithms. 
Islam et al. (2020) review different optimization techniques used in well place-
ment in oil and gas ﬁelds for maximizing the economic return. Classical gradient-
based methods are compared with nature-inspired gradient-free optimization meta-
heuristics such as particle swarm optimization, genetic algorithms, evolution strategy, 
and differential evolution. Nature-inspired meta-heuristic techniques are shown to 
outperform classical techniques, and hybrid techniques integrating two or more meta-
heuristic algorithms perform even better than stand-alone algorithms. Kumar et al.
18
1
Introduction to Petroleum and Petrochemical Industries
(2021) survey conventional and nature-inspired (meta-heuristic) optimization algo-
rithms for petroleum engineering, focusing on the problems of reservoir and ﬁeld 
development, planning, and management. These algorithms are used to increase 
reservoir productivity and resource recovery, and ultimately to maximize the net 
proﬁts. 
1.7.3 
Optimization in the Reﬁning Stage 
Bengtsson and Nonås (2010) review reﬁnery planning and scheduling literature 
under three problem categories: planning and scheduling of crude oil unloading 
and blending, production planning and process scheduling, and product blending 
and recipe optimization. Optimization models for solving these problems are based 
on either mixed-integer linear programming (MILP) or mixed-integer nonlinear 
programming (MINLP). However, nonlinear relations are approximated by linear 
ones in order to simplify the models and facilitate the solution of large real-world 
problems. 
Shah et al. (2011) review optimization models for scheduling, planning, and 
supply chain management of oil reﬁnery operations. The reviewed topics are clas-
siﬁed into three categories: petroleum supply chain management, reﬁnery planning, 
and reﬁnery scheduling. The two main challenges are identiﬁed as improving the 
models for reﬁnery operations and developing models to integrate reﬁnery planning 
with supply chain management. Opportunities for improving reﬁnery proﬁtability 
and performance include simultaneous reﬁnery operations and product distribu-
tion scheduling, incorporating nonlinear process and real-time control parameters, 
and coordinated multiple-site production decision making. Based on the review, a 
trend of shifting from simulation-based reﬁnery optimization models to mathemat-
ical programming-based models is observed, and it is expected to continue in the 
future. 
Khor and Varvarezos (2017) provide a multi-dimensional review and analysis 
of petroleum reﬁnery optimization models. Multiple perspectives are addressed, 
including academia versus industry, optimization versus heuristics, short-term 
versus long-term, and linear versus nonlinear. Various solution approaches are 
covered, including mathematical programming, constraint programming, simulated 
annealing, and genetic algorithms. In addition, different time scales are considered, 
i.e., years in strategic planning, months in tactical planning, weeks in operational 
planning, and days and hours in scheduling. 
Al-Jamimi et al.  (2021) review multi-objective optimization (MOO) methods for 
petroleum reﬁnery catalytic processes, namely hydrotreating, desulfurization, and 
cracking. When several conﬂicting objectives are simultaneously pursued, MOO 
methods are used to identify the set of undominated Pareto-optimal solutions. These 
methods include MOO versions of genetic algorithms, particle swarm optimization, 
differential evolution, and simulated annealing.
1.7 Literature on Optimization in the Petroleum Industry
19
1.7.4 
Optimization in the Transportation Stage 
Huang and Seireg (1985) survey the literature on optimization techniques for oil 
and gas pipeline engineering. The relevant papers are classiﬁed into optimal design, 
optimal expansion, optimal operation, and optimal control of pipeline systems, as 
well as offshore pipeline optimization. Out of the three types of gas pipeline systems, 
gathering, transmission, and distribution, Ríos-Mercado and Borraz-Sánchez (2015) 
provide a focused review of optimization models for natural gas transmission lines. 
Problem categories include the line-packing, pooling, and fuel cost minimiza-
tion problems. Solution techniques include dynamic programming, gradient search, 
geometric programming, linearization, and mixed-integer nonlinear programming. 
An et al. (2011) review and compare the literature on biofuel and petroleum-based 
fuel, as well as generic supply-chain models. The papers are classiﬁed according to 
both the decision level (strategic, tactical, operational, and integrated) and the process 
stage (upstream, midstream, and downstream). The following future trends in generic 
supply chain management are identiﬁed: international facility location, increase in 
IT use, enhanced sustainability, and control of product perishability. For biofuels 
supply chains, the expected transition from small-scale pilot plants to large-scale 
commercial production will call for additional and more sophisticated optimization 
models to maximize the economic returns. 
Sahebi et al. (2014) review mathematical programming models for crude oil 
strategic and tactical supply chain management. The models are classiﬁed according 
to the following characteristics: supply chain structure, decision level, modeling 
approach, purpose, shared information, solution technique, uncertainty features, 
environmental impacts, and global issues. 
Arya et al. (2022) review optimization tools used by the oil and gas industry to 
reduce pipeline costs at all stages, from pipeline design to operations. The relevant 
issues in optimizing pipeline operations are identiﬁed by reviewing three key appli-
cations of optimization in the pipeline industry: (1) minimizing fuel consumption and 
maximizing throughput in compressors, (2) maximizing the beneﬁt objective func-
tion, and (3) minimizing the risk of gas supply shortage and the power consumption 
in compressors. 
1.7.5 
Future Trends in the Petroleum Industry 
At the time of authoring this book, several trends are taking place that are expected 
to signiﬁcantly inﬂuence the future of optimization in the petroleum industry. These 
trends, which are highlighted in very recent review papers, are related to information 
technology and supply chain sustainability. The future trends in the area of opti-
mization in the petroleum industry are big data analytics, digital twin technology, 
artiﬁcial intelligence, and green petroleum supply chains.
20
1
Introduction to Petroleum and Petrochemical Industries
Mohammadpoor and Torabi (2020) identify big data analytics as a major force 
that will contribute to shaping the future of the oil and gas industry. Big data is the 
new software and hardware technology used to collect and analyze large datasets that 
have six main attributes: volume, variety, velocity, veracity, value, and complexity. 
Data recording sensors used in all stages of the industry, such as exploration, drilling, 
and production, produce massive amounts of useful data. Proper analysis of this data 
will improve optimization models used to make all the important decisions in the oil 
and gas industry. 
Wanasinghe et al. (2020) review the literature to determine the trends of applying 
digital twin (DT) technology in the oil and gas (O&G) industry. DT technology is 
part of the fourth industrial revolution, which is known as Industry 4.0, and it is used 
to improve performance and minimize costs. DT refers to the construction of digital 
twins, i.e., computer/virtual models, to accurately represent the physical industrial 
assets. The review identiﬁes integrity monitoring, project planning, and life cycle 
management as the main DT application areas in the O&G industry. On the other 
hand, security, lack of standardization, and uncertainty are identiﬁed as the main 
challenges against the effective use of DT in the O&G industry. 
Koroteev and Tekic (2021) review artiﬁcial intelligence (AI) applications in the 
oil and gas industry to identify trends, challenges, and future scenarios. The review is 
focused on the upstream stage because it has the highest amount of capital expenditure 
and also the highest level of uncertainty and risk. AI upstream applications are 
increasing because they are producing signiﬁcant beneﬁts in many real-life situations 
in O&G exploration, ﬁeld development, and production. 
Kuang et al. (2021) also describe the current and future applications of artiﬁcial 
intelligence (AI) in the petroleum industry’s upstream stage. Speciﬁc types of AI 
activities used in this stage include machine learning, computer vision, deep learning, 
optimization, and data mining. Future AI applications in petroleum exploration and 
development include intelligent production equipment, automatic processing, and 
professional software platforms. 
Li et al. (2021) perform a more focused review of AI applications in oil and gas 
ﬁeld development. Within this scope, speciﬁc problems include dynamic prediction 
of production, optimization of the ﬁeld development plan, identiﬁcation of residual 
oil, identiﬁcation of fractures, and enhanced recovery of oil. The future transition 
from the traditional oil ﬁeld to the AI oil ﬁeld is expected to extend the ﬁeld’s life 
cycle, improve efﬁciency and quality, reduce cost, and maximize economic value. 
Abdussalam et al. (2021) discuss the increasing importance of sustainable supply 
chain management (SSCM) optimization models to petroleum companies. A clas-
siﬁcation of SSCM models is proposed based on all relevant factors, including the 
three fundamental aspects of sustainability: economic, environmental, and social. 
Optimization models that consider all three aspects are rare, and the social aspect in 
particular has received the least amount of attention in the published models. Future 
research directions are suggested, in order to cover this gap and other gaps in litera-
ture, and to incorporate the latest technological and economic developments in this 
area.
References
21
1.8 
Summary and Conclusions 
The petroleum industry has an enormous global economic and political importance 
due to two reasons. First, this industry is the biggest supplier of energy to the world 
and the main source of income for several countries on different continents. Moreover, 
the petroleum industry and its natural extension, i.e., the petrochemical industry, 
provide the raw materials for a countless number of essential industrial and consumer 
products. The petroleum industry involves several stages that involve substantial 
ﬁnancial investments, high risks, and complicated decisions. This makes the use of 
optimization models both necessary and highly beneﬁcial for the petroleum industry. 
There is a long and active history of successfully applying optimization tools in the 
petroleum industry. This history illustrates the advantage of using optimization tech-
niques in every stage of the industry, including exploration, production, reﬁning, and 
transportation. There are still numerous opportunities for applying new and improved 
optimization models and techniques to solve new and previously considered problems 
in the petroleum industry. 
Subsequent chapters of this book present a diverse sample of real-life, large-scale, 
and unique applications of optimization in the petroleum industry. These applications 
cover various stages in the industry such as exploration, reﬁning, and transportation; 
different optimization techniques such as simulation and integer programming; and 
different functional areas such as maintenance, scheduling, and production planning. 
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Chapter 2 
Introduction to Optimization Models 
and Techniques 
2.1 
Introduction to Optimization 
Optimization refers to mathematical and computer models and techniques that are 
used to ﬁnd the best possible solution for many types of real-life problems. The 
concept of ﬁnding the best solution implies that multiple solutions are possible. 
This is indeed the case for almost all practical problems, in which we can decide 
or control the values of one or several variables. In such cases, each possible set of 
values for these variables is an alternative solution. Typically, most decision problems 
in industry involve an inﬁnite number of solutions. This is possible because real-life 
problems are characterized by inequality constraints that usually represent resource 
limitations. For example, the equality constraint (x + 2 = 5) has only one solution 
(x = 3). However, the inequality constraint (x + 2 ≤ 5) has an inﬁnite number of 
solutions (x ≤ 3). 
Quite often, optimization problems in the petroleum industry have uncertain 
aspects, and hence they must be analyzed and solved by stochastic optimization 
techniques. When the problem is characterized by multiple, interacting, and dynam-
ically changing random variables, simulation-based optimization models are used. 
Figure 2.1 shows the graphical output of a simulation model, displaying real-time 
3D behavior of an oil ﬁeld’s reservoir layers.
The following deﬁnitions are used to describe and classify optimization models 
and solutions: 
Decision variables 
These are the variables we can control, i.e., decide their values within some limits. 
In the petroleum industry, examples of the decision variables might be the drilling 
locations, or the reﬁnery feedstock quantities. 
Objective function 
This is a function of the decision variables that we aim to optimize (minimize or 
maximize) by ﬁnding the best values of the decision variables. The usual objectives
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 
H. K. Alfares, Applied Optimization in the Petroleum Industry, 
https://doi.org/10.1007/978-3-031-24166-6_2 
25
26
2
Introduction to Optimization Models and Techniques
Fig. 2.1 Simulation model representation of an oil ﬁeld’s reservoir layers. Courtesy of Saudi 
Aramco, copyright owner
are minimizing the total cost and maximizing the net proﬁt. Many other objectives 
are sought, however, such as maximizing the yield of a given reservoir. 
Constraints 
This is the set of restrictions expressed as functions of the decision variables. 
Resource limitations (e.g., feedstock availability) are usually expressed as less-than 
constraints, while many performance speciﬁcations (e.g., purity level) are expressed 
as greater-than constraints. 
Feasible solution 
This is any solution (set of speciﬁc values of decision variables) that satisﬁes all the 
constraints. If a solution is feasible, then it is achievable and acceptable, as it satisﬁes 
all the required conditions. However, a feasible solution may or may not give the 
best value for the objective function. Usually, there is an inﬁnite number of feasible 
solutions for any given optimization problem. 
Optimum solution 
This is the set of values of the decision variables that gives the best (optimum) value 
of the objective function. An optimum solution must also be feasible, i.e., it is the 
best among all feasible solutions. 
Heuristic solution 
This is a very good feasible solution, but not necessarily the best solution. Heuristic 
solution methods are used when optimum solutions are too difﬁcult or too time-
consuming to obtain. These methods use approximations and logical rules of thumb
2.2 Unconstrained Optimization
27
to quickly produce high-quality feasible solutions (that are not guaranteed to be 
optimum). 
Optimization approaches can be classiﬁed according to the characteristics of both 
the given problem and the solution method into the following general types: 
1. Unconstrained optimization. 
2. Linear programming. 
3. Other mathematical programming techniques. 
4. Heuristic algorithms. 
5. Simulation-based optimization. 
2.2 
Unconstrained Optimization 
Unconstrained optimization is applicable to problems that have no constraints. These 
problems are rare in practice, because it is very uncommon to have real-life decisions 
that are not bounded by restrictions, limitations, or regulations. In reality, constraints 
always exist, but their range can be so wide as to render them practically irrelevant. 
In general, the objective is to optimize (minimize or maximize) a single nonlinear 
function f left parenthesis x right parenthesis, where x may either denote a single decision variable or a vector of 
multiple variables. When the objective function has no constraints, we can simply 
use differential calculus. As an example, we can use simple calculus to optimize the 
single-variable objective function f left parenthesis x right parenthesisshown in Fig.  2.2. Since the slope (derivative) 
is equal to zero at the optimum (minimum or maximum) point, we simply need to 
solve the following equation: 
f  prime le
ft parenthesis x right parenthesis equals 0 period
The solutions to the above equation are not all necessarily optimum. A given 
function may have several points (called stationary points) in which the slope is equal 
to zero. However, only the best of these is the optimum point. To ensure optimality 
for a stationary point x*, the following conditions must hold:
Fig. 2.2 A single-variable 
function with several 
stationary points 
-80
-30 
20 
70 
120 
0
5
10
15
20 
28
2
Introduction to Optimization Models and Techniques
f  do
ubl
e prime lef t pa renthes is x Sup erscr
ipt asterisk Baseline right parenthesis greater than 0 comma x Superscript asterisk Baseline is the global minimum point
f  do
ubl
e prime lef t pa renthes is x Sup erscri
pt asterisk Baseline right parenthesis less than 0 comma x Superscript asterisk Baseline is the global maximum point period
For some stationary points, it may happen that both derivatives are equal to zero, 
i.e., f  prime l e ft parenthesis x Superscript asterisk Baseline right parenthesis equals f double prime left parenthesis x Superscript asterisk Baseline right parenthesis equals 0. In this case, we need to take higher-order derivatives 
until we have a nonzero derivative at x*. Let us assume that n is the order of the ﬁrst 
nonzero derivative. If n is an odd number, then x* is an inﬂection point, i.e., a point 
is which the function is temporarily ﬂat as the slope changes sign from negative to 
positive or vice versa. On the other hand, if n is an even number, then: 
f Supe
rsc
ript left p aren thesis n right paren
thesis Baseline left parenthesis x Superscript asterisk Baseline right parenthesis greater than 0 comma x Superscript asterisk Baseline is the global minimum point
f Supe
rsc
ript left p aren thesis n right parent
hesis Baseline left parenthesis x Superscript asterisk Baseline right parenthesis less than 0 comma x Superscript asterisk Baseline is the global maximum point period
To optimize an unconstrained function of several variables f left par e n t h e
sis x 1 comma x 2 comma ellipsis comma x Subscript n Baseline right parenthesis, we  
need to ﬁnd the gradient na
bl a f left  p a r e
nthesis x 1 comma x 2 comma ellipsis comma x Subscript n Baseline right parenthesis. The gradient na
bla f is a column vector 
consisting of the partial derivatives of f left par e n t h e
sis x 1 comma x 2 comma ellipsis comma x Subscript n Baseline right parenthesiswith respect to all the vari-
ables. The element of the gradient in row i is deﬁned by: nabl a f Subscript i Baseline equals StartFraction partial differential f Over partial differential x Subscript i Baseline EndFraction
na
bla f Subscript i Baseline equals StartFraction partial differential f Over partial differential x Subscript i Baseline EndFraction . The gradient 
can be considered as the multi-dimensional slope of the function f left par e n t h e
sis x 1 comma x 2 comma ellipsis comma x Subscript n Baseline right parenthesis. 
Setting the gradient equal to zero, we need to solve the following system of equations 
to determine all the stationary points: 
StartFraction partial differential Over partial differential x Subscript i Baseline EndFraction f left parenthesis x 1 comma x 2 comma ellipsis comma x Subscript n Baseline right parenthesis equals 0 i equals 1 comma ellipsis comma n period
Star
tF raction p a r t i
al diffe re ntia l O v e r
 p
artial differential x Subscript i Baseline EndFraction f left parenthesis x 1 comma x 2 comma ellipsis comma x Subscript n Baseline right parenthesis equals 0 i equals 1 comma ellipsis comma n period
As with the single-variable case, not all stationary points are optimal. To determine 
the optimal point, we need to evaluate the Hessian matrix upper H le f t  p a
renthesis x 1 comma x 2 comma ellipsis comma x Subscript n Baseline right parenthesisat each 
stationary point. The Hessian matrix up p er H equals n a b l a  
squared f left parenthesis x 1 comma x 2 comma ellipsis comma x Subscript n Baseline right parenthesiscan be considered 
as the multi-variable equivalent of the second derivative. The Hessian matrix H is 
a squared n × n matrix, in which the element in row i and column j is deﬁned as 
follows: 
up p e r
 H Subscript i j Baseline equals StartFraction partial differential squared f Over partial differential x Subscript i Baseline partial differential x Subscript j Baseline EndFraction i equals 1 comma ellipsis comma n comma j equals 1 comma ellipsis comma n period
upper H  S
ub scri p t
 i
 j
 B
aseli ne e qu a l s  
St
artFraction partial differential squared f Over partial differential x Subscript i Baseline partial differential x Subscript j Baseline EndFraction i equals 1 comma ellipsis comma n comma j equals 1 comma ellipsis comma n period
At each stationary point
l
ef
t pare
nth e s i s  
x 
1
 Superscript asterisk Baseline comma x 2 Superscript asterisk Baseline comma ellipsis comma x Subscript n Superscript asterisk Baseline right parenthesis
, we need to evaluate the leading principal 
determinants of the Hessian matrix H. The  kth leading principal determinant (LPDk) 
of a squared n × n matrix is the determinant of the ﬁrst k rows and the ﬁrst k columns 
of matrix H, where k = 1,…, n. For each stationary point
l
ef
t pare
nth e s i s  
x 
1
 Superscript asterisk Baseline comma x 2 Superscript asterisk Baseline comma ellipsis comma x Subscript n Superscript asterisk Baseline right parenthesis
, there are 
three possibilities: 
1. If all LPDk > 0,  k = 1, …, n, then H is positive deﬁnite, and
l
ef
t pare
nth e s i s  
x 
1
 Superscript asterisk Baseline comma x 2 Superscript asterisk Baseline comma ellipsis comma x Subscript n Superscript asterisk Baseline right parenthesis
is 
a minimum point.
2.3 Linear Programming
29
2. If the sign of LPDk is (−1)k, k = 1, …, n, then H is negative deﬁnite, and
l
ef
t pare
nth e s i s  
x 
1
 Superscript asterisk Baseline comma x 2 Superscript asterisk Baseline comma ellipsis comma x Subscript n Superscript asterisk Baseline right parenthesis
is a maximum point. 
3. If the signs of LPDk do not follow the two patterns above, then H is indeﬁnite, and
l
ef
t pare
nth e s i s  
x 
1
 Superscript asterisk Baseline comma x 2 Superscript asterisk Baseline comma ellipsis comma x Subscript n Superscript asterisk Baseline right parenthesis
is a saddle point. A saddle point is a point of temporary ﬂatness 
in which the gradient changes signs, i.e., it is a multi-dimensional inﬂection point. 
In addition to the above exact analytical methods, numerical approximate solu-
tions are used for unconstrained optimization. These solutions are especially useful 
in many practical applications in which the objective functions are too complicated 
or discontinuous and hence cannot be differentiated. In such cases, many numer-
ical solution methods exist for unconstrained optimization. These methods start with 
rough initial estimates of the optimum point and then continually increase the accu-
racy in successive iterations. In general, these methods are classiﬁed into two main 
types: 
1. Direct search methods. These methods are used for single-variable functions 
that have no local optimum points. These methods start with a given interval that 
contains the optimum point, and iteratively reduce the width of the interval until 
an acceptable accuracy is reached. These methods include the bisection method 
and the golden-section method. 
2. Gradient-based methods. These methods are used for multi-variable functions 
that are twice differentiable. These methods iteratively search for the optimum 
point in the direction of the slope, which is the gradient nabla f . These methods 
include the Newton–Raphson method and the steepest-ascent method. 
2.3 
Linear Programming 
Linear programming (LP) is the cornerstone of operations research (OR) and the 
foundation of mathematical programming. Linear programming is by far the most 
important and the most frequently used technique of mathematical programming in 
particular and of operations research (OR) in general. LP involves minimizing or 
maximizing a linear objective function of the decision variables, subject to a set of 
linear constraints (linear equations or inequalities). Other mathematical program-
ming techniques can be considered as variations of LP that are applicable to certain 
special cases. Because of the fundamental role of LP in mathematical programming, 
it is covered in more detail in this chapter. 
In general, any linear programming (LP) model can be represented in the following 
form: 
Maximize up pe
r
 
Z eq
u als sig
ma summation Underscript j equals 1 Overscript n Endscripts c Subscript j Baseline x Subscript j Baseline period
Subject to:
30
2
Introduction to Optimization Models and Techniques
s
i
gma 
su m mat io n Und
er scri pt  j  e
q
uals 1 Overscript n Endscripts a Subscript i j Baseline x Subscript j Baseline less than or equals b Subscript i Baseline i equals 1 comma ellipsis comma m
x Su bscr
ip t j B a
s e
l i
n e
 g
reater than or equals 0 j equals 1 comma ellipsis comma n period
In above LP model, the decision variables are x1, …,  xn, the objective function is Z, 
and the constraints are the inequalities (2.9) and (2.10). To determine the optimum 
solutions of LP models, two methods are generally used. The ﬁrst method is the 
graphical solution, which is used for problems with only two decision variables. The 
second method is the simplex algorithm, which was developed by George Dantzig 
in 1947. 
2.3.1 
LP Graphical Solution 
Although the graphical solution is limited in capability and application, it is partic-
ularly useful for illustrating many fundamental concepts of linear programming. 
Basically, two-dimensinal space (a ﬂat surface such as a sheet of paper) is used to 
represent and solve 2-variable LP problems, by assigning one dimension to each vari-
able. The ﬁrst variable, x1, is represented by the horizontal x-axis, while the second 
variable, x2, is represented by the vertical y-axis. While the two variables are repre-
sented by the two axes, constraints are represented by straight lines that indicate the 
boundaries of the feasible region. The objective function Z is represented by parallel 
straight lines corresponding to different values of Z, out of which one feasible line 
gives the best value for Z and intersects with at least one feasible point. This point 
of intersection is the optimum point, and its coordinates (x1, x2) are the optimum 
values of the two decision variables. 
Example 2.1 
To illustrate the graphical solution method, the following two-variable LP model is 
used as an example: 
Maximize Z = 2x1 + 3x2 
Subject to 
x1 + x2 ≤ 8 
x1 + 2x2 ≤ 12 
x1, x2 ≥ 0. 
The above model is illustrated and solved in Fig. 2.3. The constraints are indicated 
by the solid lines, the objective function is indicated by the dotted line, and the 
feasible region is indicated by the shaded area. Moving the objective function line 
in the increasing direction, we stop at the last point in the feasible region. This is the 
optimum point C, at which the optimum solution is x1 = 4, x2 = 4.
The above graphical illustration and solution of Example 2.1 is helpful to make 
two important observations about the properties of LP models and their solutions. 
First, the feasible region is always a convex set, in which the corner points are also 
extreme points of the feasible region. Second, as a consequence, the optimum solution
2.3 Linear Programming
31
Fig. 2.3 Graphical 
illustration and solution for 
Example 2.1
is always a feasible corner point. These properties are utilized in the simplex method, 
which is presented in the following section. 
2.3.2 
The Simplex Method 
The simplex method is a mathematical technique that can be used to solve LP prob-
lems with two or more variables. Similar to the graphical solution, simplex considers 
only feasible corner points. Simplex calculations are performed in a sequence of iter-
ations, where each iteration corresponds to a new and better feasible corner point. 
Starting from a default feasible corner point, which is usually the origin, the method 
moves from each point to the next (adjacent) corner point if such a move improves 
the objective function. If moving to another point cannot improve the objective, 
then the current point is declared the optimum point. The simplex method uses 
tableau or matrix calculations, where each tableau represents one iteration, i.e., 
one feasible corner point. The simplex method allows for sensitivity analysis and 
economic interpretation of the optimum solution. 
2.3.2.1
LP Standard Form 
In order to solve an LP problem by the simplex method, it must be put in the LP 
standard form, which is speciﬁed by the following conditions: 
1. Decision variables. All the decision variables must be non-negative. If any vari-
able x Subscript j is unrestricted in sign, i.e., it can take negative values, then it must be 
replaced by the difference of two variables
l
eft
 p arent
h
esis x Subscript j Superscript plus Baseline minus x Subscript j Superscript minus Baseline right parenthesis
. In the optimum solution, 
at least one of these two variables will be equal to zero. If x S
ub script j Superscript plus Baseline greater than 0 then x S
ub script j Superscript minus Baseline equals 0
and x Subscript j is positive, and if x S
ub script j Superscript minus Baseline greater than 0 then x S
ub script j Superscript plus Baseline equals 0 and x Subscript j is negative.
32
2
Introduction to Optimization Models and Techniques
2. The objective function. The objective function Z is written with all the variables 
on the left-hand side (LHS) and the constant—usually zero—on the right-hand 
side (RHS). For example, the objective function (maximize Z = 2x1 + 3x2) is  
rewritten as (Z − 2x1 − 3x2 = 0). 
3. Constraints. All the constraints are converted to equations with variables on the 
LHS and non-negative constants on the RHS. If the RHS is negative, then the 
whole constraint is ﬁrst multiplied by −1. Each less-than inequality constraint is 
converted to an equation by adding a slack variable to the LHS. For example, the 
constraint (x1 + 2x2 ≤ 12) is rewritten as (x1 + 2x2 + s1 = 12). Similarly, each 
greater-than inequality constraint is converted to an equation by subtracting an 
excess (surplus) variable from the LHS. For example, the constraint (2x1 + 5x2 
≥ 20) is rewritten as (2x1 + 5x2 − e1 = 20). 
2.3.2.2
Basic Solutions 
After putting the LP model in the standard form, it will have n variables (including the 
slack and excess variables), and m equations (equality constraints). Because either 
a slack or an excess variable is added to each constraint, the number of variables 
is greater than the number of constraints (n > m). Since the number of variables is 
greater than the number of equations, there is no unique solution to the system of 
linear equations. 
A basic solution is obtained by setting n − m variables equal to 0 and solving for 
the remaining m variables. The m variables that are kept are called the basic variables, 
while the n − m variables that are set equal to 0 are called the non-basic variables. 
To obtain a solution, the coefﬁcient columns of the basic variables must be linearly 
independent. 
If the solution obtained this way has no negative basic variables, then it is called 
a feasible basic solution. Each basic solution corresponds to a corner point, and a 
feasible basic solution corresponds to a feasible corner point. If we replace only one 
basic variable by a non-basic variable, the resulting basic solution corresponds to 
an adjacent (neighboring) corner point. The basic variable that became non-basic is 
called the leaving variable, while the non-basic variable that became basic is called 
the entering variable. 
2.3.2.3
Simplex Algorithm Steps 
In each iteration, the simplex method moves from one feasible corner point to the 
next, stopping if the current point is optimal, as described below. 
1. Convert the given model to the LP standard form. 
2. Determine the initial feasible basic solution from the LP standard form. The 
usual initial point is the origin, at which all the original variables are non-basic, 
i.e., equal to zero. With less-than constraints, the starting basic variables are the 
slacks that have been added to all constraints.
2.3 Linear Programming
33
3. Determine whether or not the current point is optimal, i.e., whether or not there 
is an entering variable. 
a. For maximization problems, the entering variable is the non-basic variable 
with the most negative Z coefﬁcient. 
b. 
For minimization problems, the entering variable is the non-basic variable 
with the most positive Z coefﬁcient. 
c. If no entering variable is found, stop; the solution is optimal. 
4. Determine the leaving variable, i.e., the basic variable to become non-basic. 
Suppose the entering variable will enter in column k. Deﬁne RHSi = right-hand 
side of row i, and aik = entering variable k coefﬁcient in row i. The leaving 
variable is the basic variable with the minimum ratio Ri, which is calculated as 
follows only when the denominator aik is positive: 
up er R Subscript i Baseline equals StartFraction RHS Subscript i Baseline Over plus a Subscript i k Baseline EndFraction period
upper
 R Subscript i Baseline equals StartFraction RHS Subscript i Baseline Over plus a Subscript i k Baseline EndFraction period
5. Suppose that the leaving variable is in row p. Interchange the entering variable 
in column k with the leaving variable in row p to ﬁnd a new solution using the 
following Gauss–Jordan row operations. To use these operations, we need to 
deﬁne two terms. The pivot equation is the leaving variable row p, while the 
pivot element apk is the coefﬁcient at the intersection of the leaving variable row 
p (pivot equation) and the entering variable column k. The new pivot equation p 
is calculated by: 
EQ Sub
sc
ri
pt p S
uperscript new Baseline equals StartFraction EQ Subscript p Superscript old Baseline Over a Subscript p k Baseline EndFraction period
EQ S
ubscript p Superscript new Baseline equals StartFraction EQ Subscript p Superscript old Baseline Over a Subscript p k Baseline EndFraction period
New equations for basic variables in each row i (i /= p), and the new Z equation, 
are calculated as follows: 
EQ Sub
sc
ript i S
up erscr ipt new 
Ba seli ne e q u a l s
 EQ  Subscript i Superscript old Baseline minus a Subscript i k Baseline asterisk EQ Subscript p Superscript new Baseline comma i equals 1 comma ellipsis comma m left parenthesis i not equals p right parenthesis
upper  Z Supersc ript new
 B aseline equals upper Z Superscript old Baseline minus z Subscript k Baseline asterisk EQ Subscript p Superscript new Baseline period
6. Go back to Step 3. 
When simplex calculations are performed manually, they are usually done using 
tableaus (calculation tables). Simplex calculations can also be performed using 
matrix operations, which is how they are performed in computer software packages. 
When matrices are used instead of tableaus, the method is called the revised simplex 
method. No matter which approach is used, the same calculations are performed. 
Each tableau and each matrix represent one iteration, i.e., one feasible basic solution 
(a feasible corner point). A typical starting simplex tableau is shown in Fig. 2.4, in  
which the pivot equation (leaving variable row p) and the entering variable column 
k are highlighted.
34
2
Introduction to Optimization Models and Techniques
Fig. 2.4 Starting simplex tableau for LP models with (≤) constraints 
Example 2.2 
To illustrate the simplex method calculations, Example 2.1, previously solved graph-
ically, is solved again using the simplex method. The solution steps are presented 
below. 
Standard LP form 
The LP model is converted to the standard LP form by moving all variables to the 
LHS of the objective function and by adding slacks to the two constraints. 
Z − 2x1 − 3x2 = 0 
Subject to 
x1 + x2 + s1 = 8 
x1 + 2x2 + s2 = 12 
x1, x2 ≥ 0. 
Iteration 1 
↓ 
Basic
x1
x2
s1
s2
RHS
Ratio 
Z
– 2
 
– 3
 
0
0
0
 
s1
1 
1
 
1
0
8
  8/1 = 8
 
←
s2
1
2
0
1
12
12/2 = 6 
At this point, x1 = 0, x2 = 0. The entering variable is x2, as it has the most negative 
Z coefﬁcient (−3). The leaving variable is s2, as it has the minimum ratio (6).
2.3 Linear Programming
35
Iteration 2 
↓ 
Basic
x1
x2
s1
s2
RHS
Ratio 
Z
-0.5
0
0
1.5
18 
←
s1
0.5
0
1
-0.5
2
  2/0.5 = 4 
x2
0.5
1
0
0.5
6
6/0.5 = 12 
At this point, x1 = 0, x2 = 6. The entering variable is x1, as it has the only negative 
Z coefﬁcient (−0.5). The leaving variable is s1, as it has the minimum ratio (4). 
Iteration 3 
Basic
x1
x2
s1
s2
RHS 
Z
0
 
0
 
1
 
1
 
20
 
x1 
1
0
 
2
-1
 
4
 
x2 
0
1
-1
 
1
 
4
 
At this point, x1 = 4, x2 = 4. Since there are no negative Z coefﬁcients, this is the 
optimum solution. The corresponding value of the objective function is Z = 20. 
It is important to note that the three above iterations correspond in sequence to 
points A, B, and C in Fig. 2.3. This illustrates that the simplex method moves in each 
iteration from one feasible corner point to an adjacent one. These moves are made 
as long as they improve the objective function, and they are stopped at the optimum 
point when no further improvement is possible. 
2.3.2.4
Advanced LP Topics 
The aim of this section is only to give a preliminary introduction to the main concepts 
of LP models and the simplex method. For most practitioners interested in applied 
optimization, it is sufﬁcient to have a basic understanding of what optimization means 
and some familiarity with how it is done. In real-life applications, the solution of LP 
models is obtained by specialized optimization software. Therefore, the coverage of 
the simplex method is intentionally brief, simpliﬁed, and limited. 
There are many additional topics in LP theory and the simplex method that are 
covered is specialized operations research books. These topics include LP models 
with greater-than and equality constraints, sensitivity analysis, duality theory, opti-
mality conditions, the dual simplex method, and the revised simplex method. The 
interested reader may refer to the books of (Bazaraa et al., 2011, 2013; Taha, 2017; 
Winston & Goldberg, 2004). It is also worth noting that, besides simplex, there 
are other LP solution methods such as the ellipsoid algorithm and the Karmarkar 
interior-point algorithm.
36
2
Introduction to Optimization Models and Techniques
2.4 
Other Mathematical Programming Techniques 
Mathematical programming refers to a large family of constrained optimization 
techniques. It should be noted that this term was coined in the 1940s before the 
word programming became associated with computer programming. This term actu-
ally refers to the mathematical modeling and solution of optimization problems. 
Of course, computer programs nowadays play a big part in mathematical program-
ming, as many optimization software packages are used to solve large-scale, real-life 
optimization problems. 
All mathematical programming models use mathematical expressions to accu-
rately represent constrained optimization problems, and mathematical procedures 
to optimally solve these problems. A mathematical programming model consists of 
three basic components, namely the decision variables, the objective function, and 
the constraints. Mathematical programming techniques are classiﬁed according to 
the characteristics of the given problem and the solution method used. In addition to 
linear programming (LP), the main types of mathematical programming techniques 
are: 
1. Integer programming. 
2. Goal programming. 
3. Network models. 
4. Dynamic programming. 
5. Stochastic programming. 
6. Nonlinear programming. 
Linear programming has been covered in the previous section. The above 
techniques are presented in the following sections. 
2.4.1 
Integer Programming 
An integer programming (IP) model is a mathematical programming model in which 
some or all of the decision variables must take integer values only. Integer program-
ming models can be classiﬁed according to the type of variables into the following 
categories: 
1. Pure integer programming model: an IP model in which all variables are required 
to be integers. 
2. Mixed-integer programming (MIP) model: an IP model in which some of the 
variables are required to be integers. 
3. Binary (0–1) programming model: an IP model in which all the variables must 
be equal to either 0 or 1. 
Many applied optimization problems are formulated as integer programs. Obvi-
ously, this is often the case because some variables must be integer by nature (e.g.,
2.4 Other Mathematical Programming Techniques
37
the number of employees). Moreover, there is another important reason for formu-
lating binary integer programs, which is their use to express many practical logical 
conditions. Binary (0–1) variables are used to represent various logical conditions 
such as yes–no, either–or, and if–then decisions. 
The solution of integer programming models depends on whether they are linear or 
nonlinear. Integer nonlinear programming models are solved by nonlinear program-
ming (NLP) search methods, which are discussed in Sect. 2.4.6. Methods for solving 
integer linear programming (ILP) models are brieﬂy described in this section. The 
ﬁrst step in the solution of ILP models is to solve them as continuous LP models using 
the simplex method, initially ignoring integrality constraints. The presence of integer 
variables in either linear or nonlinear optimization models adds a signiﬁcant degree 
of difﬁculty, as the solution techniques become more complex, and the computation 
times become longer. 
All the techniques for solving integer linear programming (ILP) models start by 
relaxing the integrality constraints and ﬁnding the optimum continuous LP solution. 
If the relaxed LP solution is integer, then the solution process ends. If the solution is 
not integer, then additional steps are required to obtain integer solutions. In general, 
techniques for solving all ILP models are classiﬁed into the three main types: 
1. Branching search methods. These methods enumerate a speciﬁc, relatively 
small subset of all integer solutions. These methods include the branch-and-
bound technique and the additive algorithm for 0–1 implicit enumeration. 
2. Cutting plane methods. These techniques add extra constraints to ensure inte-
grality, thus cutting from the feasible region. These methods include the fractional 
(pure integer) cut algorithm and the mixed (mixed-integer) cut algorithm. 
3. Hybrid methods. These techniques combine features from branching and cutting 
solution approaches. These include the branch-and-cut algorithm that combines 
branch-and-bound with cutting planes, and the branch-and-price algorithm that 
combines branch-and-bound with column generation. 
The branch-and-bound (B&B) algorithm is the most successful and frequently 
used algorithm for solving both pure and mixed-integer linear programs. Thus, this 
algorithm is brieﬂy described here. By branching and bounding to consider a small 
subset of integer solutions, the B&B method implicitly enumerates all feasible integer 
solutions. 
Branching means solving two branches (subproblems) for a given integer variable 
that currently has a non-integer value. The two branches are created by rounding the 
current non-integer value up and down to the nearest integers. For example, if x1 = 
4.7, then the constraint x1 ≤ 4 is added to one subproblem, and the constraint x1 ≥ 
5 is added to the other subproblem.
38
2
Introduction to Optimization Models and Techniques
Fig. 2.5 Branch-and-bound tree for solving Example 2.3 
Bounding means using the best integer solution (best value of the objective func-
tion Z that has been obtained so far) as a limit to eliminate any branch that cannot 
lead to a better value of Z. 
Example 2.3 
The branch-and-bound algorithm is used for solving the integer programming model 
below. 
Maximize Z = x1 + 2x2 
Subject to 
– x1 + x2 ≤ 10 
15x1 + 16x2 ≤ 240 
x1, x2 ≥ 0 and integer. 
First, ignoring integrality constraints, the model is solved as a continuous linear 
program using simplex. The optimum continuous solution is: x1 = 2.58, x2 = 12.58, 
and Z = 27.58. Since this solution is not integer, the branch-and-bound method is 
applied using the branching tree shown in Fig. 2.5. The optimum integer solution is: 
x1 = 3, x2 = 12, and Z = 27. 
The bound Z = 27 is used to exclude the (x1 ≥ 4) branch from consideration. 
Since the non-integer Z value before branching is 27.2, the best possible integer Z 
value is 27. As this value is already achieved, this is the optimal solution, and there 
is no need to consider other branches. 
2.4.2 
Goal Programming 
Goal programming (GP) can be considered as an extension of linear program-
ming (LP) to allow multiple objectives. While LP models have a single objec-
tive function, GP models have multiple, often conﬂicting, objective functions. In 
almost all real-life decisions, multiple criteria and multiple objectives are taken 
into consideration. In business, the objectives usually include proﬁtability, growth,
2.4 Other Mathematical Programming Techniques
39
technological leadership, customer satisfaction, and social/environmental responsi-
bility. With conﬂicting maximization and minimization objectives, it is impossible 
to simultaneously optimize all the objectives. 
In order to deal with multiple objectives, they are ﬁrst converted into ﬂexible 
constraints. Minimization objectives Zi are converted to less-than constraints (Zi ≤ 
T i), while maximization objectives Zi are converted to greater-than constraints (Zi 
≥ T i). Goal programming models may also include other ﬂexible constraints that 
are not derived from minimization and maximization objectives but are speciﬁed 
directly by the decision makers. In such cases, it is possible to have equality ﬂexible 
constraints (Zi = T i) expressing the desire for a certain performance measure to 
be equal to a speciﬁc value. The right-hand side values of these ﬂexible constraints 
(T i) are called target values or aspiration-levels, and they represent the limits on the 
acceptable values of the given objectives. 
Next, two deviational variables are introduced in each ﬂexible constraint i: a slack 
variable si is added, and an excess variable ei is subtracted. This is done for both 
inequality and equality constraints, and it is used to convert inequality constraints 
into equations. In the LP standard form described in Sect. 2.3, only slack variables 
are added to less-than constraints and only excess variables are subtracted from 
greater-than constraints. However, since GP constraints are ﬂexible, both deviational 
variables are added to each constraint, to allow the actual value of each Zi to be either 
above or below the target value T i. It must be noted that one of the two deviational 
variables (either si or ei) must be equal to zero in the ﬁnal solution, because Zi cannot 
be above and below T i at the same time. 
For the three types of constraints, the two deviational variables have different 
implications: 
• For a less-than (≤) constraint, the slack variable is natural, and the amount of 
violation is equal to the value of the excess variable ei. 
• For a greater-than (≥) constraint, the excess variable is natural, and the amount 
of violation is equal to the value of the slack variable si. 
• For an equality (=) constraint, the amount of violation is equal to the sum of values 
of the slack variable and the excess variable (ei + si). 
The goal now becomes the minimization of the constraint violations, i.e., the 
deviational variables that cause violation. For example, suppose we have the three 
ﬂexible constraints below. Their transformation to three goals is done as follows: 
Sta rtLay out 1 st Ro w up er Z 1 less than  or  equa
ls upper  T 1 right  arro w up er Z 1 plus  s 1 min
us e 1 e quals  uppe r T 1  righ t arrow Min imi ze up per G 1 equals e 1 2nd Row upper Z 2 greater than or equals upper T 2 right arrow upper Z 2 plus s 2 minus e 2 equals upper T 2 right arrow Minimize upper G 2 equals s 2 3rd Row upper Z 3 equals upper T 3 right arrow upper Z 3 plus s 3 minus e 3 equals upper T 3 right arrow Minimize upper G 3 equals s 3 plus e 3 period EndLayout
After formulating the goal programming model, it can be solved using one of the 
two methods that are described below.
40
2
Introduction to Optimization Models and Techniques
2.4.2.1
The Weighted Sum Method 
First, each goal Gi is given a weight coefﬁcient wi that reﬂects its relative importance, 
where wi > 0 and higher wi values indicate higher goal priority. Next, all multiple 
goals are combined into a single objective function, which is to minimize the weighted 
sum of individual goals (violations) 
Minimize up pe
r
 
W eq
uals si
gma summation Underscript i equals 1 Overscript n Endscripts w Subscript i Baseline upper G Subscript i Baseline period
Since the model now has a single objective function, it is solved as a linear program 
using the simplex method. The weighting method has several disadvantages and 
therefore it is not recommended for practical applications. First, the weights are not 
determined systematically, but they are somewhat arbitrary. Second, the goals have 
different units, and therefore the objective function W is a weighted sum of apples 
and oranges. However, several methods exist for normalizing the different goals to 
make them unit-free, so they can be properly combined. 
2.4.2.2
The Pre-emptive Method 
Instead of weights, goals are ranked in the order of priority, such that goal G1 has 
the highest priority and goal Gn has the lowest priority. The problem is then solved 
in n steps of LP, where the objective in step i is goal Gi, i = 1, …, n. Higher goals 
must not be affected by the solution of lower goals. Therefore, before proceeding 
with step i, the results obtained for the previous steps (1, …, i − 1) must be ﬁxed. 
There are three different approaches to guarantee that higher-priority results are 
not affected by subsequent lower-priority solutions. The ﬁrst approach is to add 
constraints to ﬁx the optimum values obtained in each step before proceeding to 
the next (lower priority) step. The second approach, which is better, is to substitute 
the variables determined in each step by their optimum constant values. The third 
approach is the column-dropping rule, which is an implementation of the pre-emptive 
method on a multi-objective simplex tableau. 
2.4.3 
Network Models 
Several optimization problems can be represented and solved using network graphs. 
Such problems can be formulated and solved by conventional techniques, such as 
integer programming, but their special structure makes them easier to solve graphi-
cally. All of these problems are considered as minimum-cost network ﬂow problems 
that can be solved by the network simplex method. As shown in Fig. 2.6, a network is
2.4 Other Mathematical Programming Techniques
41
Fig. 2.6 A simple project 
network showing task 
sequences and durations 
deﬁned by two types of symbols: nodes (circles) and arcs (arrows). Network models 
are used for the following optimization problems: 
1. CPM project-scheduling model. The critical-path method (CPM) is used to 
determine the minimum duration of a given project, which is the length of the 
critical (longest) path in the network. Any activity in the critical path is critical, 
i.e., its delay will lead to delaying the project’s completion time. 
2. PERT project-scheduling model. The Project Evaluation and Review Tech-
nique (PERT) is similar to CPM, but it considers uncertainty in job and project 
durations. The time needed to complete each job is given by three estimates based 
on the triangular probability distribution. 
3. The minimum-spanning tree problem. In this problem, the objective is to select 
the set of arcs with minimum total length that connects all nodes in the network. 
4. The shortest-path problem. The objective is to ﬁnd the shortest path from the 
ﬁrst node to the last node in the network. Each arc in the network directly connects 
two nodes, and its length represents the distance between these two nodes. 
5. Maximum-ﬂow problem. The objective is to determine the ﬂow in each indi-
vidual arc, in order to maximize the total amount of ﬂow in the network from the 
ﬁrst node (source) to the last node (sink). 
6. The assignment problem. The assignment problem aims to ﬁnd the minimum-
cost pairing (one-to-one assignment) of n sources to n destinations. 
7. The transportation problem. The transportation problem aims to minimize 
the total cost of transporting a given quantity of an item from m supply points 
(sources) to n demand points (destinations). The unit shipping cost is different 
for each source–destination pair. Source capacities cannot be exceeded, and 
destination demands must be satisﬁed. 
8. The transshipment problem. The transshipment problem is similar to the trans-
portation problem. However, unlike the transportation problem, indirect routes 
are considered by allowing shipments through intermediate (transit) points.
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Introduction to Optimization Models and Techniques
2.4.4 
Dynamic Programming (DP) 
Dynamic programming (DP) divides a large, complicated optimization problem into 
a sequence of smaller, simpler subproblems (called stages). Although the name 
dynamic programming implies change over time, the smaller subproblems do not 
necessarily correspond to consecutive time periods. While the different subproblems 
are optimized one at a time, the stages are recursively linked to each other to ensure an 
optimum solution for the original, overall problem. Dynamic programming models 
have several distinguishing features that are described below. 
2.4.4.1
DP Stages 
The stages represent the subproblems that the larger problem is broken into, and 
each stage can be solved as a single optimization problem. Frequently, the stages 
represent time periods, as in the problem of multi-period inventory control. However, 
it is quite possible to have stages with no time association, such as different steps in 
a petrochemical process. 
2.4.4.2
DP States 
Each stage has a number of associated states. States convey the possible conditions 
of the current stage, i.e., possible values of the decision variable(s). For the inventory 
control problem, as an example, the states could be the possible number of units in 
inventory at the start of the current time period. The deﬁnition of the states is the 
most challenging part in DP, as it requires some creativity and a degree of art plus 
science. In order to properly deﬁne the DP states for a given problem, we need to 
focus on two questions: (1) how are the stages linked together? and (2) how can we 
ensure feasibility of the current stage without having to check the previous stages? 
2.4.4.3
Recursive Relationships 
These relationships link the current stage to the previously considered stage. Based on 
these relationships, the local optimal solutions obtained in each stage are guaranteed 
to produce a global optimum solution for the full original problem. Recursive rela-
tionships can be based on forward computations, where the recursive process starts 
from the ﬁrst stage and moves forward to the last stage. More frequently, especially 
for time-based stages, backward computations are used, where the process starts 
from the last stage (i.e., time period), and moves backward to the ﬁrst stage. Either 
way, the recursive process adds one stage at a time, until all stages (subproblems) 
are included, and the complete problem is optimally solved.
2.4 Other Mathematical Programming Techniques
43
2.4.4.4
The Principle of Optimality 
This principle is also called the Markovian property. It can be stated as follows: given 
the current state, an optimal policy for the remaining stages is independent of the 
policy chosen in the previous stages. Drawing an analogy from the shortest-route 
problem, which is a classical DP problem, this principle can be stated as follows: 
given my current location, the best remaining route to my destination is independent 
of the route I took to reach my current location. This condition must be applicable to 
the given problem in order for the recursive equations to properly work and produce 
globally optimal results. 
2.4.4.5
Network Representation 
As long as the number of states is ﬁnite, any DP model can be represented as a 
network. In the network model, the states are represented by nodes and the allowed 
recursive links are represented by arcs, while the stages are distinct sections of the 
network containing their respective states. Many well-known network optimization 
problems, such as the shortest-route problem and the critical-path method (CPM), 
are actually simple DP problems. 
2.4.4.6
Problem of Dimensionality 
This problem means that DP computation time and memory signiﬁcantly increase 
with moderate increases in the problem size. This is a common phenomenon in many 
areas of optimization and computation. Despite this problem, DP is a particularly 
useful tool for reducing the solution time and effort for many large optimization 
problems. This is not only due to dividing the problem into smaller subproblems, 
but also due to the use of information about previously considered combinations to 
eliminate infeasible and inferior alternatives. 
2.4.4.7
DP Limitations 
Although dynamic programming is a powerful approach for solving large, multi-
stage optimization problems, it has two major limitations. The ﬁrst limitation of 
DP is the problem of dimensionality, which has been discussed in the previous 
section. The second limitation of DP is the lack of a standard way to formulate 
and solve DP models. Therefore, DP models come in a large variety of unique 
structures involving the stages, states, and recursive relations. Among other varieties, 
DP models can be either linear or nonlinear, integer or continuous, and deterministic 
or stochastic. DP solution techniques depend on the characteristics of the given 
model, namely the deﬁnitions of the states and recursive relations. Therefore, there 
is no standard solution technique. Except for the common use of recursive relations,
44
2
Introduction to Optimization Models and Techniques
speciﬁc solution procedures should be uniquely tailored to efﬁciently solve each DP 
problem. 
2.4.5 
Stochastic Programming 
Stochastic programming is a mathematical programming modeling and solution 
framework for optimization problems that involve uncertainty. In deterministic math-
ematical programming approaches, all the given parameters are assumed to be known 
with certainty. However, there is always some degree of randomness and uncertainty 
in real-world problems. To deal with such issues, stochastic programming models 
incorporate data uncertainty into the objective function or the constraints. Usually, 
data uncertainty is speciﬁed by probability distributions of the given input parameters. 
While stochastic programming refers to randomness in the given problem, 
stochastic optimization refers to randomness in either the given problem or the solu-
tion method itself. Stochastic programming is closely related to decision analysis, 
discrete-event simulation, Markov decision processes, and dynamic programming. 
2.4.5.1
Stochastic Programming Models 
Stochastic programming modeling approaches to deal with uncertainty include the 
following (Philpott, 2020): 
1. Robust optimization. This approach is generally used when probability distri-
butions are not known, but the bounds on the values of the parameters are known. 
The aim in such distribution-free cases is to ﬁnd a solution that is feasible for all 
possible data values and also optimal under the worst-case scenario. 
2. Expected-value models. This approach is used when the probability distributions 
of the parameters are known. The aim is to ﬁnd a solution that is expected to 
be feasible (i.e., satisﬁes constraints on average, or with a high probability) and 
optimal (i.e., minimizes/maximizes the objective function on average, or with a 
high probability). 
3. Chance-constrained models. This approach is used when the probability distri-
butions of the parameters are known. In general, the aim is to ﬁnd a solution 
that minimizes the expected cost while ensuring that a certain set of random 
constraints is satisﬁed with a given minimum probability. 
4. Multi-stage recourse models. These models consider multiple decisions over 
multiple stages (time periods), where each decision is followed by a random 
event. The purpose of these models is to determine the optimal decision for the 
ﬁrst stage and the best recourse (corrective action) to take in reaction to each 
random event in the subsequent stages. The two-stage recourse model is the 
most frequently used and analyzed stochastic programming problem.
2.4 Other Mathematical Programming Techniques
45
2.4.5.2
Stochastic Programming Solutions 
Stochastic programming solution techniques include the following (Philpott, 2020): 
1. Scenario optimization. This technique is used when the probability distributions 
of the parameters are discrete. Hence, it is possible to deﬁne a limited number of 
scenarios for the different random outcomes (scenarios) with associated proba-
bilities. These scenarios can be combined in a large LP model, which is called 
the deterministic equivalent model. 
2. Bounding Techniques. When the probability distributions of the problem param-
eters are continuous, or the number of discrete random parameters is large, the 
number of scenarios is inﬁnite or too large. In this case, this technique focuses 
on the two extreme scenarios that represent the upper and the lower bounds on 
the solution space and on the value of the objective function. 
3. Monte Carlo sampling techniques. These techniques are also used when 
scenario optimization is not possible. The random variables, which may be 
continuous or too many, are replaced by randomly generated values from their 
respective probability distributions. The stochastic programming problem is 
then solved as a deterministic mathematical programming model, similar to 
the scenario optimization approach. It is possible to take one sample (one set 
of random variable values) or several samples to increase the accuracy of the 
results. 
4. Other techniques. There are many other stochastic programming techniques, 
including stochastic approximation, stochastic gradient descent, and ﬁnite-
difference stochastic approximation. 
2.4.6 
Nonlinear Programming 
Nonlinear programming (NLP) is a wide class of modeling and solution methods that 
apply to optimization problems in which the objective function, the constraints, or 
both are nonlinear. NLP models differ from LP models in terms of the feasible region 
and the optimal solution. For NLP models, the feasible region is not necessarily a 
convex set, and the optimal solution is not necessarily a corner or even a boundary 
point. Therefore, the simplex method, which searches for feasible corner points, is not 
useful for solving NLP problems. NLP problems are much harder to optimize than 
LP problems. While the LP simplex method iterations are based on exact analytical 
solutions, NLP solution methods are usually based on numerical and heuristic search 
approximations. 
2.4.6.1
Karush–Kuhn–Tucker Conditions 
Due to the general non-convexity of NLP models, it is possible for an NLP search 
algorithm to converge to a local optimum point. In order for a given point to be
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2
Introduction to Optimization Models and Techniques
considered a global optimum point, it must satisfy certain optimality conditions 
called the Karush–Kuhn–Tucker (KKT) conditions. These conditions are based on 
the ﬁrst partial derivatives of the NLP objective function in addition to the Lagrange 
multipliers. 
The KKT conditions are necessary conditions for a point to be optimum, but 
they are not sufﬁcient to guarantee optimality. In other words, any optimal point 
is a KKT point, but a KKT point is not necessarily an optimal point. Any point 
satisfying the KKT conditions is an optimal solution to the NLP model only if the 
constraints are convex and the objective function is concave for maximization (convex 
for minimization). 
2.4.6.2
NLP Model Types 
NLP models include several types of special cases (Bradley et al., 1977), and they 
can be classiﬁed into the following categories: 
1. Unconstrained problems. Unconstrained optimization problems have nonlinear 
objective functions and no constraints. They are discussed in Sect. 2.2. 
2. Quadratic programming. The objective function is quadratic, i.e., a second-
degree polynomial, and the constraints are linear. A popular solution technique 
is Wolfe’s method, which is a modiﬁcation of the two-phase simplex method. 
Other solution techniques include the augmented Lagrangian method, the conju-
gate gradient method, and the gradient projection method. Special cases of 
quadratic programming include quadratic programming with equality constraints 
and quadratic programming with quadratic constraints. 
3. Linear-constrained NLP. The objective function is general nonlinear, and the 
constraints are linear. Solution techniques include the Frank–Wolfe algorithm 
based on linear approximations. The algorithm uses several linear segments to 
approximate the nonlinear objective function. It also performs a search along each 
segment to make sure the optimum point is not missed due to the approximation. 
The feasible directions method is another method that can be used to solve NLPs 
with linear constraints. It is a modiﬁcation of the steepest-ascent method used to 
solve unconstrained NLP problems. 
4. Equality-constrained NLP. The objective function, the constraints, or both are 
nonlinear, and all the constraints are equations. The Lagrange multiplier method 
is commonly used to solve NLPs with equality constraints. In this method, 
a Lagrange multiplier λi, which is a penalty coefﬁcient, is multiplied by the 
constraint violation of each constraint i, and the penalty is added to the objective 
function. To minimize the augmented objective function, all its partial derivatives 
are set equal to zero, and the equations are solved to determine the values of the 
decision variables and all Lagrange multipliers. 
5. Separable programming. The objective function, the constraints, or both are 
nonlinear. Moreover, the objective function is a sum of one-variable terms, and 
each constraint is a function of only one variable. Therefore, the problem can be
2.5 Meta-heuristic Algorithms
47
separated into several NLP models, one for each decision variable. Usually, linear 
approximation is used to solve separable NLP problems. The objective function 
and all nonlinear constraints are approximated by piecewise-linear curves. The 
linearized problem is solved by a customized version of the simplex method, 
in which the entering variable criterion is modiﬁed to incorporate the linear 
approximations. 
6. Convex programming. The objective function is nonlinear and convex (for mini-
mization) or concave (for maximization). This means that any local optimum 
point must be a global optimum point. In general, convex NLP problems are 
considerably simpler and faster to solve than non-convex problems. Many 
solution algorithms are available, including the bundle, subgradient projection, 
interior-point, ellipsoid, and subgradient methods. 
7. Generalized NLP. The objective function and the constraints are nonlinear, and 
they are not restricted by any of the conditions of the above special cases. One of 
the techniques used to solve generalized NLP models is the Method of Approxi-
mation Programming (MAP). It is an extension of the Frank–Wolfe algorithm in 
which the constraints as well as the objective function are approximated by linear 
segments. Many other methods are available for solving general NLP models, 
including interior-point methods, sequential quadratic programming, sequential 
convex programming, and the generalized reduced gradient algorithm. 
2.5 
Meta-heuristic Algorithms 
For many optimization problems, optimum solutions are too difﬁcult to obtain. This is 
the usual case when the optimization models are too large, complicated, stochastic, 
highly nonlinear, non-convex, or have many integer variables. In such cases, the 
only alternative is to use heuristic approaches to obtain good feasible solutions that 
are not necessarily optimal. Heuristic solution algorithms use simpliﬁed rules and 
approximations that are based on the properties of the given problem. They are 
used to quickly produce very good, but usually suboptimal, solutions. Heuristic 
algorithms are usually evaluated based on two criteria. The ﬁrst criterion is the opti-
mality gap, which is the relative difference between the heuristic objective function 
and the optimal objective function. The second criterion is the computation time 
of the heuristic algorithm, especially as compared to that of the optimum solution 
algorithm. 
There are numerous specially designed individual heuristic algorithms for most 
types of optimization problems. Therefore, it is not possible or useful to try to cover 
all of these individual heuristics. Instead, this section will focus on large families or 
classes of heuristic structures, which are called meta-heuristics. A meta-heuristic is 
a higher-level heuristic, or a problem-independent framework, which is designed to 
generate speciﬁc heuristics that follow a given overall search strategy and structure.
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Introduction to Optimization Models and Techniques
In other words, a meta-heuristic must be custom-tailored to generate a problem-
speciﬁc heuristic algorithm that ﬁts the needs and properties of a given optimization 
problem. 
Meta-heuristics have demonstrated excellent performance in solving many large-
scale optimization problems. They achieve near-optimal results in short computa-
tional times by using various randomized search techniques to avoid getting trapped 
in local optimal points. Meta-heuristic algorithms utilize different control mecha-
nisms to balance two major search components: diversiﬁcation and intensiﬁcation 
(Yang, 2011). 
Diversiﬁcation emphasizes global search and avoids falling into local optima by 
exploring new wide-spread areas in the overall search space. On the other hand, 
intensiﬁcation emphasizes local search by exploring the nearby neighborhood of the 
current solution. Extensive numerical experiments have been performed to compare 
the distinct types of meta-heuristics, and they generally show mixed results for 
different optimization problems. 
Glover and Kenneth Sörensen (2015) classify meta-heuristics into the following 
main types: 
1. Local search meta-heuristics. These meta-heuristic algorithms make small 
changes (local moves) to a single current solution, in order to explore adja-
cent (neighboring) solutions. Such meta-heuristics include simulated annealing, 
tabu search, iterated local search, guided local search, and variable neighborhood 
search. 
2. Constructive meta-heuristics. These meta-heuristics add individual compo-
nents one at a time to a partial solution, until a complete solution is obtained. Often 
the construction phase is followed by an improvement phase using local search. 
These meta-heuristics include the greedy randomized adaptive search procedure 
(GRASP), large neighborhood search (LNS), and ant colony optimization (ACO). 
3. Population-based meta-heuristics. These meta-heuristics operate on a popu-
lation (group) of solutions in each iteration, using combinations and modi-
ﬁcations of current solutions (current population) to produce the next popu-
lation of solutions. This type of meta-heuristics includes genetic algorithms 
(GA), particle swarm optimization, evolutionary programming, evolutionary 
computation, evolution strategies, scatter search, and path relinking. 
The number of meta-heuristics has been steadily increasing, and many new meta-
heuristics have been proposed in the last few years. However, there is a growing 
concern that most of the newer meta-heuristics are not sufﬁciently novel, not really 
different, and not even competitive with the original well-established meta-heuristics. 
Therefore, this section will focus only on the following popular and well-tested 
meta-heuristics: 
1. Genetic algorithms. 
2. Simulated annealing. 
3. Tabu search. 
4. Ant colony optimization. 
5. Particle swarm optimization.
2.5 Meta-heuristic Algorithms
49
2.5.1 
Genetic Algorithms (GA) 
Genetic algorithms, proposed by Holland (1975), are the most popular type of meta-
heuristics (Yang, 2011). Genetic algorithms are also the most intuitive because they 
relate the gradual improvement of the solution to the natural biological evolution 
process. The GA process starts with an initial set (generation) of randomly generated 
initial solutions. In GA, each solution must be represented as a vector of numbers or 
characters (chromosomes). At each step, different random operations are applied to 
randomly selected current (“parent”) solutions to produce new (“children”) solutions 
that form the new generation. GA random operations include the selection operator 
that selects individuals as parents, the crossover operator that combines two parents 
to produce one child, and the mutation operator that creates a child as a slightly 
modiﬁed copy of the parent. 
The probability of selecting any solution to be a parent is based on its ﬁtness 
(objective function). Therefore, better solutions are more likely to produce children 
(subsequent solutions). Consequently, after several generations, the solutions gradu-
ally evolve, and their improving objective functions approach the optimal value. At 
the end, the best solution obtained in the process is selected as the ﬁnal solution of 
the optimization problem. 
2.5.2 
Simulated Annealing (SA) 
The SA algorithm was developed by Kirkpatrick et al. (1983). It is inspired by 
the annealing process in metallurgy, which involves heating a material and slowly 
cooling it down to reduce defects. At each iteration, a potential new point (solution) 
is randomly generated. The distance from the current point to the new point, and the 
probability of accepting the move, have probability distributions that are proportional 
to the temperature. Moves to worse points (solutions) can be accepted to diversify 
the search space and avoid getting trapped in local minima. As the temperature 
decreases, so does the probability of making long moves or accepting inferior solu-
tions. Therefore, as the temperature cools down, the search becomes less global and 
more local. 
2.5.3 
Tabu Search (TS) 
Tabu search is an intelligent and improved local search procedure developed by 
Glover (1986). Compared to local search, TS has several signiﬁcant advantages. 
First, TS avoids being stuck in local optima by allowing moves that lead to a worse 
solution. Second, to avoid going back to previously visited points, TS puts them in a 
tabu (forbidden) list to prevent them from being considered again. Third, TS allows
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2
Introduction to Optimization Models and Techniques
the user to introduce logical rules to guide the search, by requiring all potential 
solutions that violate a given rule to be listed as tabu. Finally, TS has several types of 
memory structures to improve the search: short term for the tabu list, intermediate 
term for intensiﬁcation (local search) rules, and long term for diversiﬁcation (global 
search) rules. The success of TS is mainly attributed to the use of tabu lists, which 
have been shown to produce signiﬁcant savings in computation time. 
2.5.3.1
Ant Colony Optimization (ACO) 
This meta-heuristic was developed by Dorigo (1992), and it utilizes the process that 
ants use to search for food in order to search for the optimum solution. In ACO, 
ﬁnding the optimum solution is equivalent to ﬁnding the optimum path on a graph, 
which is the shortest path to the best food source. Initially, ants search randomly 
for food in all directions. When an ant ﬁnds food, it marks the path by leaving 
biological scents (pheromones) on its way back to the colony. Other ants tend to 
follow the marked paths, leaving their own pheromones. With time, shorter routes 
leading to better food sources become more populated and gain stronger pheromone 
scents. Since the pheromones evaporate with time, longer routes, and routes leading 
to poor or depleted food sources become less populated and lose their scents. While 
wandering round the popular paths, ants may discover new and better paths. This is 
equivalent to performing a local search in order to improve the solution. 
2.5.4 
Particle Swarm Optimization (PSO) 
This is a group-based algorithm, proposed by Kennedy and Eberhart (1995), that 
mimics the movement of a ﬂock of birds or a school of ﬁsh. The method uses moving 
particles whose positions represent changing solutions in the search space. The algo-
rithm starts with a group (swarm) of randomly generated particles. Subsequently, 
each particle searches for better positions in the search space using stochastic rules 
based on the particle’s position and velocity. Particle movements are partly random, 
but each particle is attracted to move towards two points: its own best previous loca-
tion, and the global best-known location for the whole swarm. After each movement 
(iteration), the individual best positions are updated as well as the global best position. 
Continuing this way gradually moves the whole swarm towards the best position, 
i.e., the optimum solution. 
2.6 
Simulation-Based Optimization 
Computer simulation is based on developing dynamic computer programs to mimic 
the long-term, stochastic, and time-varying behavior of real systems. It is used to
2.6 Simulation-Based Optimization
51
model and evaluate systems that are complex, stochastic, and dynamic. Those systems 
cannot be analyzed by analytical models such as mathematical programming. Simu-
lation allows for experimentations to be performed in the computer model, which 
cannot be done on real systems, because experiments on the real system are time-
consuming, risky, impossible, or expensive. Simulation models are mainly classiﬁed 
into the following types: 
1. Continuous simulation: system behavior is modeled over a continuous time 
variable. 
2. Discrete-event simulation: system behavior is modeled over discrete-time steps. 
3. Stochastic simulation: the model has random variables, and hence Monte Carlo 
techniques are used to take several random samples. 
4. Deterministic simulation: the model has no random variables. 
Computer simulation models are deﬁned by several concepts and components. 
Entities refer to the items that ﬂow through and get processed within the system. 
Input variables refer to the given values, such as the ﬂow rate. Performance measures 
are the long-term output statistics used to evaluate the system. Functional relation-
ships deﬁne the interrelationships among the various parts of the model. Probability 
distributions are used to represent the random behavior of stochastic variables. An 
event is a certain occurrence within the system, such as the arrival of a new entity. 
Finally, a scenario refers to a certain feasible set of values for the decision variables. 
Therefore, each scenario represents a possible system condition. 
There are several steps in developing and applying simulation models, including 
collecting the data, developing the model, running the model, and analyzing the 
results. In addition, there are some simulation-speciﬁc steps. For example, analyzing 
the data usually involves hypothesis testing to identify the probability distributions. 
Developing the model means deﬁning the logic and the structure involving the ﬂows 
among different processes. Coding the model means inputting it according to the 
speciﬁc requirements of the simulation software used. Testing the model is done by 
both veriﬁcation and validation. Veriﬁcation means comparing the model’s output 
to logically expected outputs. Validation means comparing the model’s output to the 
actual historical data. During and after the simulation runs, a graphical interface with 
animation is usually used to visualize the run and to display the sequence of results. 
Simulation is primarily used for analyzing and predicting the long-term perfor-
mance of a given system or process. If used for these usual purposes, simulation is 
not considered to be an optimization method. Optimization is by deﬁnition prescrip-
tive, i.e., it is used to improve a given system and to transform it to the best possible 
condition. On the other hand, simulation is mostly considered descriptive, i.e., it is 
usually used to describe an actual system and evaluate its as-is performance under 
the current condition. 
However, simulation is highly effective for stochastic optimization when the 
number of possible system conditions (scenarios) is limited. In those cases, the 
different conditions (scenarios) represent the alternative solutions. The simulation 
model is run under each scenario, and the scenario that gives the best long-term 
expected performance is chosen as the optimum solution.
52
2
Introduction to Optimization Models and Techniques
If the number of possible solutions (scenarios) is too large, other simulation-based 
optimization methods are used, including the following: 
1. Statistical ranking and selection methods. 
2. Response surface methodology. 
3. Heuristic methods. 
4. Ordinal optimization. 
5. Stochastic approximation. 
6. Sample path optimization. 
2.7 
Summary and Conclusions 
A variety of optimization models and solution methods are available, which can 
be classiﬁed into two main categories: constrained optimization and unconstrained 
optimization. Constrained optimization approaches, which are the most relevant for 
the petroleum industry, include mathematical programming, meta-heuristic algo-
rithms, and simulation-based optimization. Mathematical programming techniques, 
especially linear programming, are the most important optimization methods for the 
petroleum industry. 
Selecting a particular method to use in any petroleum industry application depends 
on the characteristics of the given optimization problem. For example, integer 
programming is used to represent integer variables and logical conditions, goal 
programming is used to represent multiple objectives, while stochastic program-
ming is used to represent uncertainty. Moreover, meta-heuristic algorithms are used 
to deal with large, nonlinear, and non-convex problems that cannot be optimally 
solved. Finally, simulation-based optimization models are used to optimize highly 
complex stochastic systems with multiple interacting components. 
References 
Bazaraa, M. S., Jarvis, J. J., & Sherali, H. D. (2011). Linear programming and network ﬂows. Wiley.  
Bazaraa, M. S., Sherali, H. D., & Shetty, C. M. (2013). Nonlinear programming: Theory and 
algorithms. Wiley.  
Bradley, S. P., Hax, A. C., & Magnanti, T. L. (1977). Applied mathematical programming. Addison-
Wesley. 
Dorigo, M. (1992). Optimization, learning and natural algorithms. Ph.D. thesis, Politecnico di 
Milano, Italy. 
Dorigo, M. (2007). Ant colony optimization. Scholarpedia, 2(3), 1461. http://www.scholarpedia. 
org/article/Ant_colony_optimization. Accessed July 2020. 
Encyclopaedia Britannica. Mathematical optimization, https://www.britannica.com/science/optimi 
zation 
Glover, F. (1986). Future paths for integer programming and links to artiﬁcial intelligence. 
Computers and Operations Research, 13(5), 533–549.
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Glover, F., & Sörensen, K. (2015). Metaheuristics. Scholarpedia, 10(4), 6532. http://www.schola 
rpedia.org/article/Metaheuristics. Accessed July 2020. 
Holland, J. H. (1975). Adaptation in natural and artiﬁcial systems. University of Michigan Press. 
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of IEEE 
international conference on neural networks (Vol. IV, pp. 1942–1948). 
Kirkpatrick, S., Gelatt, C. D., Jr., & Vecchi, M. P. (1983). Optimization by simulated annealing. 
Science, 220(4598), 671–680. 
Philpott, A. (2020). Introduction to stochastic optimization, https://www.stoprog.org/what-stocha 
stic-programming? Accessed July 2020. 
Taha, H. A. (2011). Operations research: An introduction (9th ed.). Pearson/Prentice Hall. 
Winston, W. L., & Goldberg, J. B. (2004). Operations research: Applications and algorithms. 
Thomson/Brooks/Cole, Belmonte, California. 
Yang, X. S. (2011). Metaheuristic optimization. Scholarpedia, 6(8), 11472. http://www.scholarpe 
dia.org/article/Metaheuristic_Optimization
Chapter 3 
Optimum Locations of Multiple Drilling 
Platforms 
3.1 
Introduction 
Drilling is an important part of the exploration stage in the petroleum industry, and 
its objective is to ﬁnd, test, and prepare sites where oil and gas are located. Drilling is 
done both for exploratory purposes and for long-term oil and gas production. Typi-
cally, many wells have to be drilled for the exploration and production of each inland 
or offshore oil ﬁeld. Because oil and gas ﬁelds exist inland as well as underwater, 
drilling for oil and gas wells is done both onshore and offshore. However, the cost 
of drilling offshore is signiﬁcantly higher than the cost of drilling on land. 
As shown in Fig. 3.1, multiple offshore drilling platforms (rigs) are used to drill 
the wells for a given offshore ﬁeld. Offshore drilling activities start by determining 
the locations of the wells to drill in the ﬁeld and also the number of rigs to drill them, 
as each rig is usually used to drill several wells. This chapter presents an application 
of integer programming (IP) techniques to optimize the locations of multiple rigs in 
order to minimize the total cost of offshore drilling.
Offshore drilling technologies have two main types: subsea templates placed on 
the seabed, and rigs (platforms) positioned above the sea level as shown in Fig. 3.1. 
Each subsea template is connected with ﬂow lines to processing platforms and has 
several slots from which a well can be drilled. Modern drilling technology is not 
limited to vertical drilling, but it also allows horizontal drilling both onshore and 
offshore. Therefore, a ﬁxed-location rig can be used to drill several wells around it 
within a given horizontal distance. For a typical subsea oil ﬁeld with more than 100 
wells to drill, a few rigs are used to horizontally drill different subset of these wells. 
For each offshore well, the drilling time typically ranges from 20 to 150 days, with 
an average cost of roughly $100 million if the drilling time is 100 days (Haugland & 
Tjøstheim, 2015). The cost of drilling each well depends on two factors. The ﬁrst 
factor is the type and cost of the drilling rig. The second factor is the well’s drilling 
time, which is a function of the distance between the rig and the well. The total cost 
of drilling for an offshore ﬁeld consists of the costs of drilling individual wells as 
well as the ﬁxed costs associated with the drilling rigs. Therefore, determining the
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 
H. K. Alfares, Applied Optimization in the Petroleum Industry, 
https://doi.org/10.1007/978-3-031-24166-6_3 
55
56
3
Optimum Locations of Multiple Drilling Platforms
Fig. 3.1 Two offshore drilling rigs (platforms). Courtesy of Saudi Aramco, copyright owner
number of drilling rigs, their locations, and the set of wells assigned to each rig will 
establish the total cost of drilling for an offshore oil ﬁeld. Minimizing this total cost 
is a challenging and important optimization problem in the petroleum industry. 
In this chapter, mathematical programming models and solution algorithms are 
presented to determine the optimum locations of the rigs and the assignment of wells 
to each rig. The unique feature of the problem under consideration is that each rig 
has a different drilling cost rate per mile. Therefore, the drilling cost of each well 
depends not only on its distance to the drilling rig, but also on which particular rig is 
assigned to drill this well. Two real-life cases of this problem are considered in this 
chapter. In the ﬁrst case, the locations of the rigs are assumed to be predetermined and 
ﬁxed. In the second case, rig locations are considered as unknown variables that need 
to be optimized. Integer programming (IP) models are constructed to represent the 
two cases. For the second case, the optimum solution by IP is difﬁcult. Therefore, an 
efﬁcient heuristic solution method is proposed for the second case, and computational 
experiments are performed to conﬁrm the effectiveness of this heuristic solution 
method. 
The remaining sections of this chapter are organized in the following sequence. In 
Sect. 3.2, recent related literature is reviewed and classiﬁed. In Sect. 3.3, the offshore 
drilling location problem under study is deﬁned, the background of the problem is 
presented, and the applicable costs and given parameters are described. In Sect. 3.4, 
the integer programming (IP) model and the optimum solution are presented for Case 
1, in which rig locations are assumed to be known and ﬁxed. In Sect. 3.5, the IP model 
and its optimum solution are described for Case 2, in which rig locations are assumed
3.2 Relevant Literature
57
to be unknown decisions variables. In Sect. 3.6, a heuristic solution procedure for 
solving larger-size Case 2 instances is presented and evaluated. Finally, relevant 
conclusions and suggestions are provided in Sect. 3.7. 
3.2 
Relevant Literature 
The main emphasis in this section is on previously published models for optimizing 
the locations of offshore drilling platforms and optimizing the assignment of wells 
to each platform. Related problems, such as well location and ﬁeld development, are 
also discussed when they are integrated with the models developed for the platform 
location optimization problem. Platform location models have two possible well 
location assumptions: either well locations are assumed to be known and ﬁxed, or 
well locations are assumed to be unknown decision variables. 
The conventional assumption in platform location models is that well locations 
are already predetermined. Aiming to minimize the total drilling cost, Devine and 
Lesso (1972) use a two-stage algorithm to locate drilling platforms and assign wells 
to each platform. The algorithm iterates between an assignment stage, in which wells 
are assigned to known-location platforms, and a location stage, in which platform 
locations are determined to minimize the distances to the assigned wells. Hansen et al. 
(1992) develop a model to optimally locate drilling platforms and assign wells to 
them for an offshore ﬁeld in Brazil. Two solution methods are proposed to minimize 
the cost: an optimal mixed-integer programming (MIP) method and a heuristic tabu 
search method. Rosing (1994) evaluates the applicability of the assumptions on which 
the model of Hansen et al. (1992) is based. 
Kabadi et al. (1996) combine clustering, integer programming, and network-ﬂow 
models in a two-stage procedure for offshore platform location and well assign-
ment. Alfares et al. (2019) consider a different real-life offshore drilling problem in 
which the cost of drilling each well is a function of both its distance to the drilling 
platform and the platform’s individual cost. An integer programming model and a 
heuristic solution algorithm are developed to determine platform locations and the 
wells assigned to each platform. Zhang et al. (2022) develop a digital elevation model 
to optimize platform locations for multi-well shale gas drilling in mountainous areas. 
A genetic clustering algorithm is used to minimize the total cost. 
Other assumptions have been incorporated in the offshore drilling problem. 
Almedallah and Walsh (2019) consider drilling new wells using existing and new 
platforms in an offshore oil ﬁeld that contains existing wells and platforms. To mini-
mize the total cost, the model determines the optimum number, locations, and sizes 
of the new platforms. Rosa and Ferreira Filho (2012) determine the optimum loca-
tions of offshore drilling platforms to maximize the revenue and minimize the cost. 
Distances between the wells and the drilling platforms are assumed to affect the cost 
of drilling, the cost of pipelines, and the productivity of the wells. 
A more general version of the offshore drilling problem is one in which the 
locations of both the drilling platforms and the wells to be drilled are unknown
58
3
Optimum Locations of Multiple Drilling Platforms
decision variables. Dogru (1987) formulates a mixed-integer nonlinear programming 
(MINLP) model to determine the optimal selection of wells and platform locations. 
The objective is to maximize the total production while minimizing the total drilling 
cost. Graph theory and a location/allocation algorithm are used to sequentially solve 
this problem. Barnes and Kokossis (2007) formulate integer programming models 
for the design and operations of oil ﬁelds and propose a two-stage solution process. 
First, the locations of the wells and the drilling center are selected, and then a drilling 
schedule is determined to meet given time-varying demands. 
Cristancho et al. (2010) use 3D visualization to determine the optimal location 
of a new offshore platform. Well planning software is used to simultaneously deter-
mine well locations and well paths while considering geological hazards, obstacles, 
and uncertainties. García et al. (2012) optimize the locations of both the wells and 
platforms, as well as the pipeline network between the wells and the platforms. The 
problem is formulated as a dense causal bidirectional graph, and the solution starts by 
breaking the graph into several subgraphs and solving them individually. The overall 
optimum solution is obtained by combining two heuristic approaches: divide-and-
conquer and active design document. Rodrigues et al. (2016) use a binary linear 
programming (LP) model to minimize the overall cost of developing an offshore oil 
ﬁeld. The binary LP model is used to determine the number, locations, and capacities 
of the platforms; the number and locations of wells; and the required links between 
the wells and the platforms. 
The most general version of the offshore drilling problem has a wider scope, 
involving not only well and platform locations, but also the optimal time-phased 
schedule of ﬁeld development activities and operations. Frair and Devine (1975) 
develop an offshore ﬁeld development model that determines the following: (1) 
number and locations of platforms, (2) assignment of wells to drill by each platform, 
(3) ﬁeld development and drilling sequence, and (4) reservoir production schedule. A 
decomposition approach is used to solve the large nonlinear model whose objective 
is to maximize the discounted cash inﬂows. Haugland et al. (1991) use reservoir 
simulation and economic analysis to construct a mixed-integer programming model 
of offshore ﬁeld development with moveable platforms. The model determines the 
time and location of platform moves, well assignments to drilling platforms, and the 
oil production schedule. 
Iyer et al. (1998) construct a multi-period mixed-integer linear programming 
(MILP) model to represent the ﬁeld development problem. A sequential decom-
position algorithm is developed to solve it based on aggregation of time periods 
and wells followed by successive disaggregation. Van Den Heever and Grossmann 
(2000) improve the model of Iyer et al. (1998) by replacing linear approximations 
by exact nonlinear functions of reservoir pressure, gas-to-oil ratio, and cumulative 
gas production. Carvalho and Pinto (2006) enhance the model of Iyer et al. (1998) 
by adding investment constraints and incorporating the effect of reservoir pressure 
on well production levels and revenues. 
Campozana et al. (2008) use reservoir simulation to optimize the locations of the 
production platforms and the diameters of the production pipelines in an offshore 
oil ﬁeld. Sahebi and Nickel (2014) use an MIP model to optimize platform locations
3.3 The Offshore Drilling Problem
59
and well allocations, as well as the corresponding transportation and pipeline plan-
ning decisions. Rosa et al. (2018) formulate a mixed-integer LP (MILP) model to 
determine the optimum number, locations, and well assignments of the platforms, in 
addition to the pipeline network structure and pipeline diameters. 
3.3 
The Offshore Drilling Problem 
3.3.1 
Problem Description 
This section presents the details of a real-life platform location and well assignment 
optimization problem in offshore oil drilling. Figure 3.2 shows the drilling compo-
nents of a typical offshore drilling rig. The unique feature of this real-life problem 
is that the cost of drilling each well depends on two factors: 
Fig. 3.2 Drilling 
components of a drilling rig. 
Courtesy of Saudi Aramco, 
copyright owner
60
3
Optimum Locations of Multiple Drilling Platforms
1. which platform is used for drilling the well, and 
2. the distance from the well to the drilling platform. 
In this case study, the number and the locations of the wells to be drilled are given. 
In certain cases, the locations of the drilling platforms are also given and ﬁxed. In 
such cases, the problem is to divide the wells into speciﬁc sets containing up to 
25 wells and to assign each set to one of the drilling platforms. In other cases, the 
drilling platform locations are not already ﬁxed, and hence we also need to determine 
their optimum locations. In both cases, the objective is to minimize the total cost of 
drilling all the wells in the offshore ﬁeld. Due to the high cost of offshore drilling, 
this is a signiﬁcant optimization problem with a huge ﬁnancial impact. The cost of 
drilling each well depends mainly on the distance between the well and the drilling 
platform. 
The problem presented here is a case study for drilling wells an offshore oil ﬁeld 
located 60 miles off the Eastern coast of Saudi Arabia. This particular offshore oil 
ﬁeld has a total area of 15 × 8 miles and a total of 90 potential well locations to be 
drilled. Several stages have been planned for development and production operations 
in this ﬁeld. At the time of the study, four jacked-up drilling platforms of different 
types were already assigned to this ﬁeld. In the ﬁrst stage, ﬁeld development activities 
will be limited to the four assigned platforms and to an initial area of 5 × 5 miles 
around these four platforms shown in Fig. 3.3. This 5  × 5-mile area contains 36 
wells to be drilled, numbered 1–36, whose locations are already ﬁxed. All the 36 
well locations are within drilling distance to at least one of the current positions of 
the four assigned platforms. 
Fig. 3.3 5 × 5-mile initial area of interest in the offshore ﬁeld
3.3 The Offshore Drilling Problem
61
3.3.2 
Model Costs and Parameters 
Given 4 rigs (platforms) and 36 wells, the following parameters are deﬁned for 
formulating the optimization model: 
i = well number, i = 1, …, 36; 
j = rig (platform) number, j = 1, …, 4; 
Lij = length (distance in miles) between well at location i and rig in position j; 
cij = cost ($) of drilling well number i by rig in position j; 
D = maximum possible distance between any well and its drilling rig = 5 miles; 
wj = maximum number of wells that platform j can drill; 
Rj = daily rate of rig j, i.e., rig cost ($ per day). 
All the above parameter values are readily available, except the drilling cost cij, 
which is a function of the distance Lij between well i and drilling rig j. It is not easy to 
determine this cost as an exact function of the distance, and hence it is estimated from 
the historical data of previous drilling operations. The drilling cost cij is estimated 
only for feasible (i, j) pairs, which are the pairs in which the distance Lij does not 
exceed the maximum possible distance D. Based on the available data from past 
drilling operations, the drilling cost cij is composed of several cost components. 
Each of these cost components can be expressed as a linear function of the distance 
Lij. Using past drilling cost data from similar offshore ﬁelds, the average values of 
the drilling cost (cij) components are listed below. 
1. Daily rig rate cost. In the petroleum industry, drilling rigs are usually owned 
by drilling contractors. The daily rate is the amount that an oil company pays 
to a drilling contractor for operating a given rig per day. In offshore drilling, rig 
daily rates vary according to the rig capacity, operating conditions, and market 
supply/demand factors at the time of making the agreement with the drilling 
rig contractor. The four rigs on location in the ﬁeld under study are owned by 
four different contractor companies. The four rigs have similar capacities, but 
different daily rates, Rj, which are shown in Table 3.1. 
The number of days needed to drill each well is a function of the distance 
Lij. Using historical data, the average number of days required to drill a well at 
a distance of Lij miles is expressed by Eq. (3.1). Multiplying by the individual 
platform rate Rj, the drilling cost rate for each platform is given by Eq. (3.2):
Number of days equals 77 p e riod 616
 upper L Subscript i j Baseline plus 1 0.0 81
Rig rate cost equa l
s
 upper R  S ubscript
 
j Ba seli ne  l e f
t 
parenthesis 77 period 616 upper L Subscript i j Baseline plus 1 0.0 81 right parenthesis comma j equals 1 comma ellipsis comma 4 period
62
3
Optimum Locations of Multiple Drilling Platforms
Table 3.1 Daily cost rates of the 4 rigs ($/day). Reprinted from Alfares et al. (2019), by permission 
from KIIE(2) 
Rig no. (j)
1
 
2
 
3
 
4
 
Rate (Rj)
170,000
168,000
209,900
160,000
2. Cementing cost. Cement barriers are used to hold the casing in place and 
to prevent ﬂuid migration between subsurface formations. The cost of well 
cementing varies according to the additives used in mixing, well depth, well 
geometry, and the ﬁeld’s rock characteristics. Based on historical data, the average 
cost of cementing is a linear function of the distance Lij, which is given by: 
Cementing cost equals 75 c o mma 493 u
pper L Subscript i j Baseline plus 16 comma 833 period
3. Drilling ﬂuid cost. Drilling ﬂuids are also referred to as drilling mud, and they 
are added to the wellbore to ease the drilling process. These ﬂuids help in drilling 
by controlling the pressure, stabilizing exposed rock, providing buoyancy, and 
by cooling and lubricating while drilling. The cost of these ﬂuids depends on the 
type of ﬂuids used, rock characteristics, and the length of the well. The average 
cost of drilling ﬂuids is expressed below as a linear function of the distance Lij: 
Drilling fluid  cost  equals 625  c omma 31 0
 upper L Subscript i j Baseline plus 94 comma 553 period
4. Transportation, services, and overhead cost. There are other operating 
expenses associated with offshore drilling that can be divided into three parts. The 
ﬁrst part is the cost of transportation of equipment, tools, and personnel to and 
from the platform. The second part is the cost of services such as the spare parts 
and the iron tubes used to run the completion. The third part is the company’s 
drilling overhead expenses such as ofﬁce work, personnel, insurance, fuel, water, 
logistics, and third-party vendor services. The bigger the operation, the higher the 
overhead cost that is incurred and the more third-party vendors that are involved. 
Combining these three parts together, the average cost of transportation, services, 
and overhead is given by: 
Transportation comma service s comma a mpers and overhead c o st equals 2
 comma 353 comma 317 upper L Subscript i j Baseline plus 36 0 comma 176 period
The total drilling cost is obtained by summing up the four components: (i) rig 
rate cost, (ii) cementing cost, (iii) drilling ﬂuid cost, and (iv) transportation, services, 
and overhead cost. Adding Eqs. (3.2)–(3.5), the total drilling cost for each well is 
approximated by the following equation: 
c S u bscr i
p
t i j Ba s e line equ
a
ls upper R Su b s cript j Ba
seline left parenthesis 77 period 616 upper L Subscript i j Baseline plus 1 0.0 81 right parenthesis plus 3 comma 0 54 comma 121 upper L Subscript i j Baseline plus 471 comma 562 period
As shown in Eq. (3.6), the total cost of drilling for well i is a function of two 
variables:
3.4 Case 1: Fixed Rig Locations
63
1. the distance Lij in miles between well i and platform j, and 
2. the daily rate of the drilling platform Rj. 
In the following sections, two cases are presented for the optimal assignment of 
wells to the drilling platforms in the offshore oil ﬁeld. In Case 1, the four platforms 
are assumed to be ﬁxed in their initial locations. Therefore, the problem in Case 1 is 
simply to divide the wells into four sets and assign each set to one of the platforms. 
In Case 2, the platform locations are assumed to be not ﬁxed, and hence we need 
to determine both the optimum platform locations and the optimum assignment of 
wells to the platforms. 
3.4 
Case 1: Fixed Rig Locations 
Case 1 deals with the problem of drilling wells in speciﬁc locations by platforms in 
their current and ﬁxed locations. The objective is to determine the optimum assign-
ment of wells to the four platforms in order to minimize the total cost. The individual 
drilling cost cij of well i depends on both the platform used j and the distance Lij 
to that platform. This cost is calculated by Eq. (3.6) only for feasible (i, j) pairs of 
wells and platforms, i.e., well-platform pairs whose in-between distances are within 
the maximum, Lij < D. 
Since there are 4 platforms and 36 wells to drill in this ﬁeld, the set of feasible 
pairs is deﬁned as follows: 
up p
e
r F equals St artSet le ft pa ren th e s is i co mm a  j  right  p a re n
t
h
esis is a feasible pair if upper L Subscript i j Baseline less than upper D comma i equals 1 to 36 comma j equals 1 to 4 EndSet period
For Case 1, the decision variables in the optimization model for are xij, which are 
deﬁned below only for (i, j) ∈ F as follows: 
x S u b
script i j B as eli ne equal s S tart Layout E nla
rge d left bra
ce 1st Row 1 comma if well i is drilled by rig j comma left parenthesis i comma j right parenthesis epsilon upper F 2nd Row 0 comma otherwise EndLayout
The individual drilling costs cij of feasible (i, j) pairs are calculated by Eq. (3.6). 
The objective function (3.9) for Case 1 is to minimize the sum of these costs: 
upper M i  n  
i
 m i z e u
pp e r Z  eq
uals sigma summation Underscript left parenthesis i comma j right parenthesis element of upper F Endscripts c Subscript i j Baseline x Subscript i j Baseline period
64
3
Optimum Locations of Multiple Drilling Platforms
The objective function (3.9) is optimized subject to constraints (3.10)–(3.12) 
below. Constraint (3.10) allocates each well to only one drilling platform. Constraint 
(3.11) ensures that the number of wells to be drilled by each rig j is no more than 
the speciﬁed limit wj. Finally, constraint (3.12) imposes binary value and feasibility 
restrictions on the decision variables: 
s
i
gma 
su m m ation  U nder s c r i p
t 
j equals 1 Overscript 4 Endscripts x Subscript i j Baseline equals 1 comma i equals 1 comma ellipsis comma 36
si
g
ma s
um m a tion
 Un
de rscr i p t  i
 
equals 1 Overscript 36 Endscripts x Subscript i j Baseline less than or equals w Subscript j Baseline comma j equals 1 comma ellipsis comma 4
x S u bscript i j Baseline el
ement of left parenthesis 0 comma 1 right parenthesis comma for all left parenthesis i comma j right parenthesis element of upper F period
The binary (0–1) integer programming model speciﬁed by expressions (3.7)– 
(3.12) includes 4 × 36 decision variables xij. The model has the structure of a 
linear minimum-cost network-ﬂow problem. Consequently, constraint (3.12) can 
be replaced by xij ≥ 0 for all (i, j) ∈ F, and the resulting solution will be binary, 
i.e., the solution will have 0–1 values for all xij variables. This means that the integer 
programming model can be optimally solved by any relevant software as a minimum-
cost network-ﬂow problem or as a continuous linear programming (LP) problem. 
All the optimization problems presented in this chapter have been solved by a free 
optimization software called OpenSolver (Mason, 2012). 
Initially, platforms 1, 2, 3, and 4 are respectively located at well locations 21, 22, 
11, and 2. The locations of the 36 wells and the current locations of the 4 platforms 
are shown in Fig. 3.4.
Table 3.2 shows the distances in miles Lij between the 36 wells and the 4 platforms 
in their current locations, and the corresponding drilling costs Cij. All the distances 
Lij are within the feasibility threshold D = 5 miles. Therefore, all of the 36 wells can 
be drilled by any of the 4 platforms, and the set F includes all 4 × 36 xij variables. 
Equation (3.6) is used to calculate the drilling costs Cij shown in Table  3.2 for all 
well–platform pairs.
Constraint (3.11) ensures that the number of wells assigned to each platform does 
not exceed the platform’s drilling capacity (wj). In reality, modern horizontal drilling 
allows offshore platforms to drill up to 50 wells. It is preferable to allocate an equal 
number of wells to the 4 platforms for better workload balance, but the ultimate 
objective is to minimize the total drilling cost. Hence, two alternative values of (wj) 
are considered. First, in alternative a, the 36 wells are equally divided among the 4 
platforms by setting wj = 9 for  j = 1 to 4. Second, in alternative b, platform capacity 
limits are effectively removed by setting wj = 36 for j = 1 to 4.
3.4 Case 1: Fixed Rig Locations
65
Fig. 3.4 Well locations and the current locations of the four rigs. Reprinted from Alfares et al. 
(2019), by permission from KIIE(2)
3.4.1 
Case 1a. Equal Number of Wells Per Platform 
This alternative is pursued for the sake of load balancing among the 4 platforms, and 
it is obtained by setting wj = 9 for  j = 1 to 4 in constraint (3.11). The optimization 
model in this case is equivalent to a transportation problem with 36 sources (wells) 
and 4 destinations (platforms), where the capacity of each source is 1, and the demand 
of each destination is 9. Using OpenSolver (2018) Excel add-in to solve the problem, 
the optimal solution is shown in Table 3.3. Table 3.3 shows the well numbers assigned 
to each platforms and the total distance TDj from each platform j to all its assigned 
wells. For this alternative, the total drilling cost is $862,994,588, and the sum of 
distances between the platforms and their assigned wells is equal to 46.61 miles. The 
platform symbols shown in Table 3.3 indicate the well sites at which the platforms 
are currently located.
3.4.2 
Case 1b. Unrestricted Number of Wells Per Platform 
This alternative is pursued to achieve the least possible total cost. This is done by 
either setting wj = 36 in constraint (3.11) or completely eliminating (3.11) from the  
model. The optimal solution for this alternative is shown in Table 3.4, and it has a 
total cost of $773,468,103 and a total distance of 42.76 miles between the platforms 
and their assigned wells. The number of wells assigned to each platform ranges from
66
3
Optimum Locations of Multiple Drilling Platforms
Table 3.2 Distances and drilling costs of wells from current platform locations. Adapted from 
Alfares et al. (2019), by permission from KIIE(1) 
i 
Li1
Li2
Li3
Li4
Ci1
Ci2
Ci3
Ci4 
1
3.31 
3.24 
3
0.57 
55,968,996 
54,308,463 
60,624,722 
10,903,950 
2
3.2 
3.17 
2.63 
0
54,181,623 
53,181,911 
53,466,806 
2,084,522 
3
2.46 
2.47 
2.18 
0.77 
42,157,481 
41,916,384 
44,761,232 
13,998,486 
4
2.5
2.5
2.1
0.8 
42,807,435 
42,399,193 
43,213,575 
14,462,667 
5
2.4 
2.47 
1.78 
0.88 
41,182,550 
41,916,384 
37,022,944 
15,700,481 
6
1.94 1.86 1.62 1.34 
33,708,084 
32,099,283 
33,927,629 
22,817,915 
7
2.17 2.16 
1.04 1.73 
37,445,317 
36,927,365 
22,707,112 
28,852,260 
8
1.32 
1.28 
1.21 1.88 
23,633,802 
22,764,990 
25,995,884 
31,173,162 
9
1.52 
1.48 
1.01 
1.9 
26,883,570 
25,983,711 
22,126,740 
31,482,616 
10 
0.97 
0.93 
0.72 2.41 
17,946,708 
17,132,226 
16,516,482 
39,373,683 
11 
1.44 
1.46 
0
2.63 
25,583,663 
25,661,839 
2,587,564 
42,777,673 
12 
1.96 
1.7 
2.52 2.11 
34,033,060 
29,524,305 
51,338,777 
34,731,879 
13 
1.98 
1.93 
2.62 2.08 
34,358,037 
33,225,835 
53,273,349 
34,267,698 
14 
2.03 
1.98 
2.5 
2
35,170,479 
34,030,516 
50,951,862 
33,029,884 
15 
2.24 
2.28 
3
2.42 
38,582,736 
38,858,599 
60,624,722 
39,528,410 
16 
1.16 1.15 
2.04 2.61 
21,033,988 
20,672,820 
42,052,831 
42,468,219 
17 
0.87 
0.87 
1.76 2.59 
16,321,824 
16,166,610 
36,636,030 
42,158,766 
18 
0.96 1.27 
2.09 
2.6 
17,784,219 
22,604,053 
43,020,117 
42,313,493 
19 
0.24 
1.78 
1.51 3.63 
6,085,054 
30,811,794 
31,799,600 
58,250,354 
20
0.1 
0.25 
1.4 
3.06 
3,810,216 
6,188,572 
29,671,571 
49,430,926 
21
0
0.1 
1.44 
3.2 
2,185,332 
3,774,531 
30,445,400 
51,597,101 
22
0.1 
0
1.46 3.17 
3,810,216 
2,165,170 
30,832,314 
51,132,921 
23 
1.57 
1.56 
2.69 
3.1 
27,696,012 
27,271,200 
54,627,549 
50,049,833 
24 
1.55 
1.6 
2.7 
3.2 
27,371,036 
27,914,944 
54,821,006 
51,597,101 
25 
1.36 
1.31 
2.56 3.14 
24,283,756 
23,247,798 
52,112,606 
50,668,740 
26 
1.79 
1.78 
3.08 3.62 
31,270,757 
30,811,794 
62,172,380 
58,095,627 
27
1.4 
1.39 
2.73 3.54 
24,933,709 
24,535,287 
55,401,378 
56,857,813 
28
1.4 
1.4 
2.78 3.73 
24,933,709 
24,696,223 
56,368,664 
59,797,622 
29 
1.08 
1.08 
2.51 4.01 
19,734,080 
19,546,268 
51,145,320 
64,129,973 
30 
1.57 
1.76 
2.99 4.18 
27,696,012 
30,489,922 
60,431,265 
66,760,329 
31 
2.23 
2.22 
3.66 4.64 
38,420,247 
37,892,982 
73,392,897 
73,877,762 
32 
1.86 
1.86 
3.29 4.75 
32,408,176 
32,099,283 
66,234,981 
75,579,757 
33
1.8 
1.7 
3.2 
4.6 
31,433,246 
29,524,305 
64,493,866 
73,258,855 
34
2
1.86 
3.4 
4.75 
34,683,014 
32,099,283 
68,363,010 
75,579,757 
35 
1.47 
1.47 
2.85 4.56 
26,071,128 
25,822,775 
57,722,864 
72,639,947 
36
2.4 
2.41 
3.84 
5
41,182,550 
40,950,768 
76,875,126 
79,447,927 
3.5 Case 2: Optimum Platform Locations
67
Table 3.3 Case 1a: well assignments and total distances for the 4 platforms. Adapted from Alfares 
et al. (2019), by permission from KIIE(1) 
1
2
3
4 
19
22
6
1 
20
23
7
2 
21 
25
8
3 
24
26
9
4 
28
27
10
5 
29
31
11 
12 
30
32
16
13 
35
33
17
14 
36
34
18
15 
TD1 = 9.81
TD2 = 13.68
TD3 = 11.49
TD4 = 11.63
Table 3.4 Case 1b: well assignments and total distances for the 4 platforms. Adapted from Alfares 
et al. (2019), by permission from KIIE(1) 
1
2
3
4 
15
8
27
7
1 
18
12
28
9
2 
19
13
29
10
3 
20
16
31
11 
4 
21
17
32
5 
24
22 
33
6 
30
23
34
14 
25
35 
26
36 
Σ = 7
Σ = 18
Σ = 4
Σ = 7 
TD1 = 6.66
TD2 = 26.97
TD3 = 2.77
TD4 = 6.36 
4 to 18, leading to a highly uneven distribution of workload among the platforms. 
However, this alternative reduces the total drilling cost by $89,526,485, which is a 
savings of 10% relative to the equal-well-assignment alternative. 
3.5 
Case 2: Optimum Platform Locations 
Case 2 considers the same set of 4 platforms of different types and daily cost rates. 
However, the platforms are now assumed to be moveable to any well location within 
the ﬁeld. Therefore, optimization in Case 2 is used to solve a location and alloca-
tion problem in order to minimize the total drilling cost. The optimization model
68
3
Optimum Locations of Multiple Drilling Platforms
now needs to simultaneously ﬁnd the best locations of the 4 platforms and the best 
allocation of wells to each platform. 
As in Case 1, each platform will be located at one of the well positions to avoid 
unnecessary expenses. If the platform is placed at one of the well locations, then it will 
drill that well vertically, without any need for costly horizontal drilling. Therefore, 
the choice of possible locations for the platforms will be limited to the 36 locations 
of the wells to drill. Similar to Case 1, the index i, i = 1–36, is used to indicate the 
well number, while the index j, j = 1–36, is used to indicate the platform location 
number. If the numbers i and j are equal for a certain decision variable, then this 
indicates that the platform is located at the same position as the well number j. 
3.5.1 
Case 2 Optimum Solution Model 
A binary integer programming model is presented below to optimally solve Case 
2 problem. The decision variables in the model for this case are listed below. The 
decision variable xijk (for i = 1–36, j = 1–36, and k = 1–4) is deﬁned only for feasible 
(i, j) pairs, i.e., (i, j) ∈ F, indicating the distance Lij between well site i and platform 
site j is <D: 
x Su b s
cript i  j k Ba sel ine equa ls St artLayout  E nla rged  left b ra
ce 1st Row 1 
comma if well i is drilled from location j by rig number k 2nd Row 0 comma otherwise EndLayout
y  Su b
script j k B as eli ne  equals S tart Layo ut Enla rg
ed left brace 
1st Row 1 comma if site j is a location for rig number k 2nd Row 0 comma otherwise period EndLayout
The objective function for Case 2 is to minimize the total drilling cost of the 
offshore oil ﬁeld, which is expressed as follows: 
upper M i  n  i
 
m
 i 
z
 e uper Z
 e qu als  si
gma summation Underscript k equals 1 Overscript 4 Endscripts sigma summation Underscript left parenthesis i comma j right parenthesis element of upper F Endscripts c Subscript i j k Baseline x Subscript i j k Baseline comma
where 
c Su b scri
p
t i j k B a seline e
q
uals upper R S u b script k B
aseline left parenthesis 77 period 616 upper L Subscript i j Baseline plus 1 0.0 81 right parenthesis plus 3 comma 0 54 comma 121 upper L Subscript i j Baseline plus 471 comma 562 period
The objective function (3.15) is optimized under the following constraints. 
Constraint (3.17) guarantees that each well is assigned to one and only one drilling 
platform. Constraint (3.18) is a logical constraint that relates the values of the binary 
decision variables xijk and yjk. This constraint ensures that a well can be drilled 
from a given location j only if there is a platform in location j. At the  same  time,  
constraint (3.18) makes sure that no more than wk wells are assigned to each platform 
of type k. Constraint (3.19) avoids locating more than one platform in any given site
3.5 Case 2: Optimum Platform Locations
69
j. Constraint (3.20) guarantees that each well is drilled by one and only one platform. 
Constraint (3.21) ensures that a total of 4 platforms are located for drilling in the 
ﬁeld. Finally, constraint (3.22) imposes binary values and feasibility restrictions on 
the decision variables: 
si
g
ma s
u
m
mati
on  U n ders
cr ipt j  e q u
al
s 1 Overscript 36 Endscripts sigma summation Underscript k equals 1 Overscript 4 Endscripts x Subscript i j k Baseline equals 1 comma i equals 1 comma ellipsis comma 36
si
g
ma s
um ma t ion U n der
sc ript  i  e q
uals  1  Ove r s c r i
p
t 36 Endscripts x Subscript i j k Baseline less than or equals w Subscript k Baseline y Subscript j k Baseline comma j equals 1 comma ellipsis comma 36 comma k equals 1 comma ellipsis comma 4
s
i
gma 
s umm atio
n Unde r s c r i
pt
 k equals 1 Overscript 4 Endscripts y Subscript j k Baseline less than or equals 1 comma j equals 1 comma ellipsis comma 36
si
g
ma s
u mma tion Un ders c r i p t
 
j equals 1 Overscript 36 Endscripts y Subscript j k Baseline equals 1 comma k equals 1 comma ellipsis comma 4
si
g
ma s
u
m
mati
o n U nde
rscript j equals 1 Overscript 36 Endscripts sigma summation Underscript k equals 1 Overscript 4 Endscripts y Subscript j k Baseline equals 4
x Subsc rip t i j k Baseline comma y Subs c r i p t
 j
 k Baseline element of left parenthesis 0 comma 1 right parenthesis comma for all left parenthesis i comma j right parenthesis element of upper F comma k equals 1 comma ellipsis comma 4 period
The distances in miles Lij between each pair (i, j) of the 36 wells are shown in 
Table 3.5. Since all these distances are within the feasibility limit D = 5 miles, then 
all 4 × 36 × 36 xijk variables deﬁned by the range (i = 1–36, j = 1–36, and k = 1–4) 
are feasible. For each possible combination of ijk indices, the drilling cost cijk used 
in the objective function (3.15) is calculated by substituting the values of Rk from 
Table 3.1 and Lij from Table 3.5 in Eq. (3.16).
Similar to Case 1, two alternatives are considered for Case 2: 
1. The ﬁrst, alternative a, is to assign an equal number of wells to the 4 platforms 
by setting wk = 9 for  k = 1–4. 
2. The second, alternative b, is to remove the restriction on the number of wells per 
platform by setting wk = 36. 
OpenSolver is used to obtain the optimum solutions for the two alternatives that 
are presented below.
70
3
Optimum Locations of Multiple Drilling Platforms
Table 3.5 Distance matrix between the different wells. Reprinted from Alfares et al. (2019), by 
permission from KIIE(2) 
1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
11 
12 
1 
0 
0.57 0.85 0.9 1.31 1.51 2.2 2.05 2.08 3.1 
3 
1.93 
2 
0.57 
0 
0.77 0.8 0.88 1.34 1.73 1.88 1.9 2.41 2.63 2.11 
3 
0.85 0.77 
0 
0.2 0.69 0.64 1.5 1.2 1.4 1.78 2.18 1.35 
4 
0.9 0.8 0.2 
0 
0.7 0.7 1.6 1.3 1.3 1.7 2.1 1.4 
5 
1.31 0.88 0.69 0.7 
0 
1.2 0.88 1.15 1.2 1.6 1.78 1.92 
6 
1.51 1.34 0.64 0.7 1.2 
0 
1.17 0.58 0.6 1.16 1.62 1.25 
7 
2.2 1.73 1.5 1.6 0.88 1.17 
0 
1.19 1.2 1.23 1.04 2.41 
8 
2.05 1.88 1.2 1.3 1.15 0.58 1.19 
0 
0.2 0.61 1.21 1.37 
9 
2.08 1.9 1.4 1.3 1.2 0.6 1.2 0.2 
0 
0.81 1.01 1.57 
10 
3.1 2.41 1.78 1.7 1.6 1.16 1.23 0.61 0.81 
0 
0.72 1.79 
11 
3 
2.63 2.18 2.1 1.78 1.62 1.04 1.21 1.01 0.72 
0 
2.52 
12 1.93 2.11 1.35 1.4 1.92 1.25 2.41 1.37 1.57 1.79 2.52 
0 
13 
2 
2.08 1.38 1.35 1.92 1.24 2.43 1.35 1.55 1.86 2.62 0.1 
14 1.93 
2 
1.4 1.38 1.92 1.35 2.4 1.36 1.56 1.82 2.5 0.2 
15 2.21 2.42 1.75 1.8 2.36 1.72 2.89 1.83 2.03 2.27 
3 
0.8 
16 2.52 2.61 1.8 1.9 2.11 1.39 2.26 1.14 1.34 1.32 2.04 0.87 
17 2.64 2.59 1.83 1.8 2.06 1.34 2.09 0.94 1.14 1.03 1.76 1.11 
18 2.56 2.6 
2 
2.2 2.2 1.3 2.29 1.1 1.3 1.03 2.09 0.87 
19 3.15 3.63 2.32 2.2 2.36 2.53 3.44 1.19 1.39 0.92 1.51 1.57 
20 3.24 3.06 2.39 2.3 2.38 1.78 2.1 1.25 1.45 0.87 1.4 1.88 
21 3.31 3.2 2.46 2.5 2.4 1.94 2.17 1.32 1.52 0.97 1.44 1.96 
22 3.24 3.17 2.47 2.5 2.47 1.86 2.16 1.28 1.48 0.93 1.46 1.7 
23 2.99 3.1 2.35 2.3 2.73 1.99 2.93 1.78 1.98 1.96 2.69 1.07 
24 2.96 3.2 2.35 2.35 2.73 2.01 2.96 1.81 2.01 1.97 2.7 1.1 
25 3.06 3.14 2.38 2.38 0.74 1.99 2.87 1.71 1.91 1.83 2.56 1.19 
26 3.48 3.62 2.87 2.87 3.27 
2 
3.43 2.27 2.47 2.38 3.08 1.55 
27 3.46 3.54 2.77 2.7 3.09 2.36 3.15 2.02 2.22 2.05 2.73 1.58 
28 3.65 3.73 2.95 2.98 3.24 2.52 3.26 2.15 2.35 2.13 2.78 1.79 
29 4.03 4.01 3.24 3.25 3.43 2.7 3.22 2.23 2.43 1.99 2.51 2.32 
30 3.97 4.18 3.28 3.28 3.55 2.86 3.54 2.45 2.65 2.38 2.99 2.22 
31 4.53 4.64 3.87 3.87 4.22 3.48 4.22 3.12 3.32 3.06 3.66 2.61 
32 4.76 4.75 3.9 3.8 4.17 3.49 3.99 3.03 3.23 2.79 3.29 2.93 
33 
4.7 4.6 4.2 3.8 4.2 3.5 3.8 
3 
3.2 2.6 3.2 
3 
34 
4.5 4.75 
4 
4.1 4.2 3.49 4.1 3.1 3.3 2.85 3.4 2.93 
35 
4.6 4.56 3.82 3.82 3.89 3.25 3.63 2.72 2.92 2.42 2.85 2.88 
36 4.91 
5 
4.23 4.23 4.54 3.8 4.49 3.41 3.61 3.29 3.84 
3
(continued)
3.5 Case 2: Optimum Platform Locations
71
Table 3.5 (continued)
13 
14 
15 
16 
17 
18 
19 
20 
21 
22 
23 
24 
1 
2 
1.93 2.21 2.52 2.64 2.56 3.15 3.24 3.31 3.24 2.99 2.96 
2 
2.08 
2 
2.42 2.61 2.59 2.6 3.63 3.06 3.2 3.17 3.1 
3.2 
3 
1.38 1.4 1.75 1.8 1.83 
2 
2.32 2.39 2.46 2.47 2.35 2.35 
4 
1.35 1.38 1.8 
1.9 
1.8 
2.2 
2.2 
2.3 
2.5 
2.5 
2.3 2.35 
5 
1.92 1.92 2.36 2.11 2.06 2.2 2.36 2.38 2.4 2.47 2.73 2.73 
6 
1.24 1.35 1.72 1.39 1.34 1.3 2.53 1.78 1.94 1.86 1.99 2.01 
7 
2.43 2.4 2.89 2.26 2.09 2.29 3.44 2.1 2.17 2.16 2.93 2.96 
8 
1.35 1.36 1.83 1.14 0.94 1.1 1.19 1.25 1.32 1.28 1.78 1.81 
9 
1.55 1.56 2.03 1.34 1.14 1.3 1.39 1.45 1.52 1.48 1.98 2.01 
10 1.86 1.82 2.27 1.32 1.03 1.03 0.92 0.87 0.97 0.93 1.96 1.97 
11 2.62 2.5
3 
2.04 1.76 2.09 1.51 1.4 1.44 1.46 2.69 2.7 
12 
0.1 
0.2 
0.8 0.87 1.11 0.87 1.57 1.88 1.96 1.7 1.07 1.1 
13
0 
0.05 0.48 0.84 1.1 0.84 1.73 1.9 1.98 1.93 1.06 1.06 
14 0.05 
0 
0.53 0.89 1.15 0.89 1.78 1.95 2.03 1.98 1.11 1.11 
15 0.48 0.53 
0 
1.15 1.46 1.17 2.07 2.31 2.24 2.28 1.08 1.08 
16 0.84 0.89 1.15 
0 
0.31 0.02 0.92 1.1 1.16 1.15 0.69 0.68 
17 
1.1 1.15 1.46 0.31 
0 
0.35 0.63 0.8 0.87 0.87 0.93 0.93 
18 0.84 0.89 1.17 0.02 0.35 
0 
0.72 0.89 0.96 1.27 1.02 1.02 
19 1.73 1.78 2.07 0.92 0.63 0.72 
0 
0.19 0.24 1.78 0.52 1.34 
20 
1.9 1.95 2.31 1.1 
0.8 0.89 0.19 
0
0.1 0.25 1.51 1.55 
21 1.98 2.03 2.24 1.16 0.87 0.96 0.24 0.1
0
0.1 1.57 1.55 
22 1.93 1.98 2.28 1.15 0.87 1.27 1.78 0.25 0.1
0 
1.56 1.6 
23 1.06 1.11 1.08 0.69 0.93 1.02 0.52 1.51 1.57 1.56 
0
0.1 
24 1.06 1.11 1.08 0.68 0.93 1.02 1.34 1.55 1.55 1.6 
0.1
0 
25 1.17 1.22 1.27 0.63 0.82 0.91 1.16 1.33 1.36 1.31 0.22 0.23 
26 1.56 1.61 1.46 1.17 1.37 1.46 1.63 1.8 1.79 1.78 0.51 0.52 
27 1.59 1.64 1.61 0.99 1.09 1.18 1.25 1.42 1.4 1.39 0.54 0.54 
28 1.79 1.84 1.82 1.15 1.22 1.31 1.27 1.42 1.4 
1.4 0.75 0.75 
29 2.29 2.34 2.44 1.5 1.42 1.51 1.09 1.14 1.08 1.08 1.38 1.38 
30 
2.1 2.15 2.11 1.46 1.51 1.6 1.48 1.6 1.57 1.76 1.15 1.03 
31 2.61 2.66 2.53 2.09 2.19 2.28 2.16 2.26 2.23 2.22 1.55 1.56 
32 2.92 2.97 
3 
2.23 2.18 2.27 1.89 1.93 1.86 1.86 1.91 1.89 
33 
2.8 2.85 2.8
2
2.1 2.19 1.8 1.85 1.8 
1.7
2
2.1 
34
3 
3.05 3.2 
2.5 1.25 1.34 2.1
2
2 
1.86 1.91 1.95 
35 2.91 2.96 3.04 2.09 1.96 2.05 1.55 1.55 1.47 1.47 1.97 1.95 
36 3.04 3.09 2.97 2.43 2.49 2.58 2.37 2.46 2.4 2.41 1.94 1.94
(continued)
72
3
Optimum Locations of Multiple Drilling Platforms
Table 3.5 (continued)
25 
26 
27 
28 
29 
30 
31 
32 
33 
34 
35 
36 
1 
3.06 3.48 3.46 3.65 4.03 3.97 4.53 4.76 4.7 4.5 4.6 4.91 
2 
3.14 3.62 3.54 3.73 4.01 4.18 4.64 4.75 4.6 4.75 4.56 
5 
3 
2.38 2.87 2.77 2.95 3.24 3.28 3.87 3.9 4.2 
4 
3.82 4.23 
4 
2.38 2.87 2.7 2.98 3.25 3.28 3.87 3.8 3.8 4.1 3.82 4.23 
5 
0.74 3.27 3.09 3.24 3.43 3.55 4.22 4.17 4.2 4.2 3.89 4.54 
6 
1.99 
2 
2.36 2.52 2.7 2.86 3.48 3.49 3.5 3.49 3.25 3.8 
7 
2.87 3.43 3.15 3.26 3.22 3.54 4.22 3.99 3.8 4.1 3.63 4.49 
8 
1.71 2.27 2.02 2.15 2.23 2.45 3.12 3.03 
3 
3.1 2.72 3.41 
9 
1.91 2.47 2.22 2.35 2.43 2.65 3.32 3.23 3.2 3.3 2.92 3.61 
10 1.83 2.38 2.05 2.13 1.99 2.38 3.06 2.79 2.6 2.85 2.42 3.29 
11 2.56 3.08 2.73 2.78 2.51 2.99 3.66 3.29 3.2 3.4 2.85 3.84 
12 1.19 1.55 1.58 1.79 2.32 2.22 2.61 2.93 
3 
2.93 2.88 
3 
13 1.17 1.56 1.59 1.79 2.29 2.1 2.61 2.92 2.8 
3 
2.91 3.04 
14 1.22 1.61 1.64 1.84 2.34 2.15 2.66 2.97 2.85 3.05 2.96 3.09 
15 1.27 1.46 1.61 1.82 2.44 2.11 2.53 
3 
2.8 3.2 3.04 2.97 
16 0.63 1.17 0.99 1.15 1.5 1.46 2.09 2.23 
2 
2.5 2.09 2.43 
17 0.82 1.37 1.09 1.22 1.42 1.51 2.19 2.18 2.1 1.25 1.96 2.49 
18 0.91 1.46 1.18 1.31 1.51 1.6 2.28 2.27 2.19 1.34 2.05 2.58 
19 1.16 1.63 1.25 1.27 1.09 1.48 2.16 1.89 1.8 2.1 1.55 2.37 
20 1.33 1.8 1.42 1.42 1.14 1.6 2.26 1.93 1.85 
2 
1.55 2.46 
21 1.36 1.79 1.4 1.4 1.08 1.57 2.23 1.86 1.8 
2 
1.47 2.4 
22 1.31 1.78 1.39 1.4 1.08 1.76 2.22 1.86 1.7 1.86 1.47 2.41 
23 0.22 0.51 0.54 0.75 1.38 1.15 1.55 1.91 
2 
1.91 1.97 1.94 
24 0.23 0.52 0.54 0.75 1.38 1.03 1.56 1.89 2.1 1.95 1.95 1.94 
25 
0 
0.56 0.39 0.59 1.17 0.91 1.49 1.74 1.9 
2 
1.74 1.87 
26 0.56 
0 
0.4 0.52 1.26 0.72 1.08 1.58 1.65 1.7 1.76 1.48 
27 0.39 0.4 
0 
0.21 0.9 0.51 1.11 1.36 1.5 1.4 1.44 1.46 
28 0.59 0.52 0.21 
0 
0.73 0.31 0.96 1.16 
1 
1.2 1.28 1.29 
29 1.17 1.26 0.9 0.73 
0 
0.68 1.23 0.81 
1 
1.2 0.59 1.34 
30 0.91 0.72 0.51 0.31 0.68 
0 
0.68 0.87 0.9 0.85 1.07 0.97 
31 1.49 1.08 1.11 0.96 1.23 0.68 
0 
0.87 0.84 0.88 1.34 0.41 
32 1.74 1.58 1.36 1.16 0.81 0.87 0.87 
0 
0.06 0.1 0.56 0.74 
33 
1.9 1.65 1.5 
1
1 
0.9 0.84 0.06 
0 
0.19 0.6 0.8 
34
2 
1.7 1.4 1.2 1.2 0.85 0.88 0.1 0.19 
0 
0.6 0.9 
35 1.74 1.76 1.44 1.28 0.59 1.07 1.34 0.56 0.6 0.6 
0 
1.27 
36 1.87 1.48 1.46 1.29 1.34 0.97 0.41 0.74 0.8 0.9 1.27 
0
3.5 Case 2: Optimum Platform Locations
73
Table 3.6 Case 2a: well assignments and total distances for the 4 platforms. Reprinted from Alfares 
et al. (2019), by permission from KIIE(2) 
1
2
3
4 
13
8
28
1 
14
10
29
2 
15
11
30
3 
16
17
31
4 
23 
18
32 
5 
24
19
33
6 
25
20 
34
7 
26
21
35
9 
27
22
36
12 
TD1 = 5.31
TD2 = 5.75
TD3 = 5.17
TD4 = 7.40  
3.5.2 
Case 2a. Solution with Equal Number of Wells Per Rig 
First, the integer programming model speciﬁed by expressions (3.13)–(3.22)issolved 
after setting wk = 9 for  k = 1–4 in constraint (3.18). For this equal-well-assignment 
alternative, the optimum solution is shown in Table 3.6. The platform symbols in 
Table 3.6 indicate the wells at which the platforms are optimally located. The optimal 
locations of platforms numbered 1, 2, 3, and 4 are respectively the sites of wells 
numbered 23, 20, 32, and 3. The total cost of drilling the 36 wells is Z = $474,538,097, 
and the sum of the distances between the wells and their assigned drilling platforms 
is 23.63 miles. 
In Case 1a, previously considered in Sect. 3.4.1, equal-well-assignment is also 
considered, but the locations of platforms 1, 2, 3, and 4 are respectively ﬁxed at wells 
21, 22, 11, and 2. By allowing the platforms to move to their optimum locations, 
Case 2a leads to a much better solution. Compared to Case 1a, Case 2a reduces the 
total cost by 45% and the total distance by 49%. 
3.5.3 
Case 2b. Solution with Unrestricted Number of Wells 
Per Rig 
For this alternative, the limits on the number of wells per platform are removed 
by setting wk = 36 for k = 1–4 in constraint (3.18). Solving the binary integer 
programming model deﬁned by expressions (3.13)–(3.22) by OpenSolver leads to the 
optimum solution shown in Table 3.7. Platforms number 1, 2, 3, and 4 are respectively 
located at the sites of wells number 32, 10, 3, and 23. These optimum platform 
locations are indicated by the platform symbols in Table 3.7. The number of wells 
assigned to each of the four platforms ranges from 6 to 14. The total drilling cost of 
the 36 wells is Z = $452,962,073, while the sum of all distances from the wells to
74
3
Optimum Locations of Multiple Drilling Platforms
Table 3.7 Case 2b: well assignments and total distances for the 4 platforms. Reprinted from Alfares 
et al. (2019), by permission from KIIE(2) 
1
2
3
4 
29
7
1
12
19 
30
8
2
13
23 
31
9
3
14
24 
32
10 
4
15
 
25
 
33
11
5
16
26 
34
20
6
17
27 
35
21
18
28 
36
22 
Σ = 8
Σ = 8
Σ = 6
Σ = 14 
TD1 = 4.01
TD2 = 6.14
TD3 = 3.15
TD4 = 9.60  
their assigned platforms is 22.9 miles. Compared to the solution of the corresponding 
alternative for Case 1, i.e., Case 1b, the solution of Case 2b reduces the total cost by 
41% and the total distance by 46%. 
3.6 
Case 2 Heuristic Solution 
For the general offshore drilling optimization problem, let W denote the total number 
of wells to be drilled and G denote the total number of platforms to be used for drilling. 
In the integer programming model for Case 2, deﬁned by expressions (3.13)–(3.22), 
the number of xijk variables is G × W × W and the number of yjk variables is G × 
W. Hence, the total number of binary variables is G × W × (W + 1). For the case 
application under study, with G = 4 and W = 36, the total number of variables is 
equal to 5328, which is quite large for such a small problem. 
As the values of W and G increase, the number of variables G × W × (W + 
1) increases rapidly. Even for a relatively minor increase in the problem size, the 
model can become too large and difﬁcult to solve optimally. Therefore, a two-stage 
heuristic solution method is proposed in order to solve larger instances of the Case 
2 problem. In the ﬁrst stage, initially ignoring the differences in the platform daily 
rates Rk, a simpliﬁed model is used to ﬁnd platform locations and well assignments. 
In the second stage, the G platforms are allocated to the selected platform locations 
based on an inverse distance-daily rate relationship. The two stages of the heuristic 
solution method are described in detail below.
3.6 Case 2 Heuristic Solution
75
3.6.1 
Case 2 Heuristic Solution: Stage 1 
Ignoring the differences in daily rates among the four platforms, the following 
simpliﬁed model of Case 2 is solved. 
Decision variables: 
x S u b
script i j B as eli ne equal s Sta rt Layo ut Enlarged le
ft brace 1st 
Row 1 comma if well i is drilled from a rig in location j 2nd Row 0 comma otherwise EndLayout
y  S u
bscript  j Ba se lin e equa ls StartL
ayo ut Enlarged
 left brace 1st Row 1 comma if site j is a rig location 2nd Row 0 comma otherwise period EndLayout
Objective function: 
upper M i  n i 
m 
i
 z e
 u
p
per 
Z S
u p ers c ri
pt 1 Baseline equals sigma summation Underscript i equals 1 Overscript 36 Endscripts sigma summation Underscript j equals 1 Overscript 36 Endscripts c Subscript i j Superscript 1 Baseline x Subscript i j Baseline period
The objective is to minimize Z1, the total drilling cost without the rig daily rates 
Rk. In (3.25), c S
u b script i j Superscript 1 is the drilling cost after removing the rig daily rates Rk from the 
cost function (3.16), and it is given by: 
c S
u b s cript i j Sup e r script 1 B
aseline equals 3,054,121 upper L Subscript i j Baseline plus 471,562 period
Constraints: 
Constraint (3.27) ensures that one and only one drilling platform is assigned to 
each well. The logical constraint (3.28) relates the decision variables xij and yj and 
imposes an upper bound of wj wells per platform. Constraint (3.29) makes sure the 
4 platforms are assigned, Finally, constraint (3.30) imposes feasibility and binary 
value restrictions on the decision variables: 
si
g
ma s
um m a tion Un ders c r i p t
 j
 equals 1 Overscript 36 Endscripts x Subscript i j Baseline equals 1 comma i equals 1 comma ellipsis comma 36
si
g
ma s
um m a tion
 U n der
sc ript  i  e q
ua
ls 1 Overscript 36 Endscripts x Subscript i j Baseline less than or equals w Subscript j Baseline y Subscript j Baseline comma j equals 1 comma ellipsis comma 36
si
g
ma s
u m ati
on Underscript j equals 1 Overscript 36 Endscripts y Subscript j Baseline equals 4
76
3
Optimum Locations of Multiple Drilling Platforms
x S ubsc ri pt i j Bas eline comma y
 Subscript j Baseline element of left parenthesis 0 comma 1 right parenthesis comma for all left parenthesis i comma j right parenthesis element of upper F period
The simpliﬁed integer programming (IP) model used in stage 1 of the heuristic, 
which is deﬁned by expressions (3.23)–(3.30), has only 1332 variables. This is only 
one-fourth of the number of variables used in the optimal model deﬁned by expres-
sions (3.13)–(3.22). For the general offshore drilling problem, the simpliﬁed IP model 
for stage 1 of the heuristic will have 1/G of the number of variables in the exact opti-
mization model, where G is the number of platforms. Therefore, it is much easier to 
solve the simpliﬁed stage 1 heuristic model than the exact optimization model. 
3.6.2 
Case 2 Heuristic Solution: Stage 2 
The optimal solution of the simpliﬁed model speciﬁes the locations of the four 
platform and the number of wells (NWj) assigned to each platform in site j. From  
the solution of stage 1 model, the total distance (TDj) from each platform in site j to 
all the wells assigned to it is calculated as follows: 
u p per  T
 u
p
per 
D S ubs c ri
pt j Baseline equals sigma summation Underscript i equals 1 Overscript 36 Endscripts upper L Subscript i j Baseline x Subscript i j Baseline period
Next, the values of the cost components that are dependent on the platform daily 
cost rates Rj are calculated. For each platform in location j, these costs are given by: 
c S
ub s
c
ript j Supe rscript 2 B a
s
e
line equals left parenthesis 77 period 616 upper T upper D Subscript j Baseline plus 1 0.0 81 upper N upper W Subscript j Baseline right parenthesis period
Ranking the cost component cj 2 in increasing order, the different platforms are 
assigned to these locations in decreasing order of their daily rates Rj. In other words, 
an inverse cost-rate (cj 2 − Rj) relationship is used to assign the platforms. Therefore, 
the platform with the highest rate Rj is assigned to the location with the least cost cj 2, 
and the platform with the lowest rate Rj is assigned to the location with the greatest 
cost cj 2. 
Finally, the total drilling cost Z is determined by adding the rigs’ daily costs to 
Z1 as shown below. It must be noted that Eq. (3.33) is equivalent to Eq. (3.16), as it 
simply adds the two cost components deﬁned in Eqs. (3.25), (3.26), and (3.32): 
up per Z  e
q
u
als 
up per Z Superscr ipt 1 Basel ine 
plus sigma summation Underscript j equals 1 Overscript 4 Endscripts upper R Subscript j Baseline left parenthesis 77.616 upper T upper D Subscript j Baseline plus 10.081 upper N upper W Subscript j Baseline right parenthesis period
3.6 Case 2 Heuristic Solution
77
In the following section, the heuristic method is applied to the two alternatives 
considered for Case 2. 
3.6.3 
Case 2a. Heuristic Solution with Equal Number 
of Wells Per Rig 
Starting with alternative a, an equal number of wells is assigned to each platform 
by setting the value wj = 9 for  j = 1–36 in constraint (3.28). The simpliﬁed stage 
1 model deﬁned by expressions (3.23)–(3.30) is solved by OpenSolver, obtaining a 
partial cost of drilling is Z1 = $89,145,111. The optimum locations of the 4 platforms 
are the well sites numbered (3, 20, 23, and 32), with corresponding distances to their 
assigned wells equal to TDj = (7.4, 5.75, 5.31, and 5.17 miles), respectively. Given 
the equal number of wells (NWj = 9) for all platforms, using (32) gives the following 
platform-dependent costs: cj 2 = (665.09, 537.02, 502.87, 492.00). 
Assigning the platforms in the reverse order of cost cj 2 to rate Rj, platforms 
numbered 1, 2, 3, and 4 are assigned to the sites of wells numbered 23, 20, 32, and 
3, respectively. Using Eq. (3.33), the total drilling cost is Z = $474,538,097. The 
sum of the distances from all of the wells to their assigned drilling platforms is 23.63 
miles. The ﬁnal heuristic solution for Case 2a is the same as the optimum solution 
for this case obtained in Sect. 3.5.2 and displayed in Table 3.6. 
3.6.4 
Case 2b. Heuristic Solution with Unrestricted Number 
of Wells Per Rig 
To remove the limits on the number of wells per platform, we set the value wj = 
36 for j = 1–36 in constraint (3.28). The optimal solution of the simpliﬁed stage 1 
model deﬁned by expressions (3.23)–(3.30) speciﬁes the well sites numbered (3, 20, 
23, and 32) as platform locations, with corresponding number of wells equal to NWj 
= (9, 8, 11, 8), and distances to their assigned wells equal to TDj = (7.25, 4.5, 7.13, 
and 4.01 miles), respectively. Using (3.32), the following platform-dependent costs 
are given by: cj 2 = (653.45, 429.92, 664.29, 391.89). Assigning the platforms to the 
4 locations in an inverse cost-rate (cj 2-Rj) relationship, platforms number 1, 2, 3, and 
4 are assigned to the sites of wells number 20, 3, 32, and 23, respectively. The ﬁnal 
solution of the heuristic method is shown in Table 3.8. Without imposing limits on 
the number of wells per platform, the number of wells assigned to each of the four 
platforms ranges from 8 to 11.
78
3
Optimum Locations of Multiple Drilling Platforms
Table 3.8 Case 2b: heuristic well assignments and distances to the 4 platforms. Reprinted from 
Alfares et al. (2019), by permission from KIIE(2) 
1
2
3
4 
10
1
29
12 
11
2
30
13 
17
3
31
14 
18
4
32 
15 
19
5
33
16 
20 
6
34
23 
21
7
35
24 
22
8
36
25 
9
26
27 
28 
Σ = 8
Σ = 9
Σ = 8
Σ = 11  
TD1 = 4.5
TD2 = 7.25
TD3 = 4.01
TD4 = 7.13  
The partial cost of drilling is Z1 = $86,885,062, while the sum of the distances 
from all the wells to their drilling platforms is 22.89 miles. Using Eq. (3.33), the 
total drilling cost of the 36 wells is equal to Z = $458,294,439. This is only a 1% 
increase in total cost above the optimum solution obtained in Sect. 3.5.3, whose total 
cost is Z = $452,962,073. 
3.6.5 
Evaluation of the Heuristic Solution 
The offshore drilling case study described in the previous sections is somewhat 
limited in size, with only 36 wells, 4 rigs, and 5328 variables. Therefore, it was not 
so difﬁcult to ﬁnd the optimum solution for the Case 2 integer programming model in 
which the platform locations are decision variables. However, most actual offshore 
drilling problems are larger in size, and therefore their Case 2 models are too difﬁcult 
to solve optimally. For such larger problems, the only choice is to use the two-stage 
heuristic algorithm presented in Sect. 3.6. This makes it necessary to make sure this 
heuristic method is reliable for the general use in solving larger offshore drilling 
problems. To assess general reliability, the performance of the heuristic method has 
to be thoroughly evaluated under a large variety of conditions, i.e., problems with 
varied sizes and properties. 
To judge the quality of the two-stage heuristic solutions of Case 2 models, exten-
sive numerical experiments have been performed. Overall, 30 randomly generated 
Case 2 test problems were solved by two methods: the optimum (Opt.) solution,
3.7 Summary and Conclusions
79
and the two-stage heuristic (Heur.) solution. The 30 test problems are divided into 3 
sizes: 40, 50, and 60 wells, respectively involving 6560, 12,750, and 21,960 binary 
variables in the Case 2 model deﬁned by expressions (3.13)–(3.22). Each problem 
size has 5 alternative-a (equal number of wells) problems, and 5 alternative-b (unre-
stricted number of wells) problems. For each problem, well locations were generated 
randomly using uniform distributions to generate the (x, y) coordinates of each well 
location. After that, the straight-line distances were calculated between all pairs of 
wells. 
The results of the computational experiments for evaluating the heuristic solution 
are summarized in Table 3.9. The notation used in the column headers of Table 3.9 
is explained below: 
#
problem number; 
W
number of wells; 
G
number of platforms; 
Size
size of the ﬁeld in miles; 
Za/Zb Opt.
optimum cost for alternative a/b; 
Za/Zb Heur. heuristic cost for alternative a/b; 
a%/b%
% heuristic cost increase above optimum cost for alternative a/b.
The two-stage heuristic algorithm shows a consistently excellent performance for 
all problem sizes. The heuristic algorithm produced optimum solutions for 9 out 
of the 30 test problems (30%) and very near-optimum solutions for the remaining 
problems. Overall, the total cost objective values obtained by the heuristic were on 
average only 0.38% above the optimal costs. The heuristic’s average cost increase 
above optimum is 0.12% for type-a problems and 0.64% for type-b problems. These 
results prove that the two-stage heuristic algorithm is an applicable and reliable 
method for solving actual large-size drilling platform location and well assignment 
problems. 
3.7 
Summary and Conclusions 
This chapter presented optimization and heuristic approaches for horizontal drilling 
of wells in an offshore oil ﬁeld using several platforms. Two realistic cases of 
assigning wells to each drilling platform have been considered. In Case 1, the loca-
tions of the different platforms are assumed to be given and ﬁxed. In Case 2, the 
locations of the platforms are assumed to be unknown decision variables whose 
optimum values need to be determined. Case 2 is thus considered a location–allo-
cation problem, involving locating the platforms and allocating the wells among 
them. For each case, two different alternatives were considered: alternative a imposes 
equal-well assignment among platforms, while alternative b imposes no limits on 
the number of wells assigned to each platform.
80
3
Optimum Locations of Multiple Drilling Platforms
Table 3.9 Computational evaluation of the heuristic solution. Reprinted from Alfares et al. (2019), by permission from KIIE(2) 
# W G Size
Za Opt.
Za Heur.
a% 
#
Zb Opt.
Zb Heur.
b% 
1a 40 4 
5×5 
713,051,789 
713,898,506 0.12 1b 686,449,920 
697,391,633 1.59 
2a 40 4 
5×5 
728,089,084 
728,089,084 
0 
2b 668,193,823 
669,985,712 0.27 
3a 40 4 
5×5 
691,716,190 
691,716,190 
0 
3b 617,945,094 
620,156,752 0.36 
4a 40 4 
5×5 
740,124,338 
742,630,017 0.34 4b 690,151,341 
694,398,156 0.62 
5a 40 4 
5×5 
717,074,740 
717,074,740 
0 
5b 710,039,879 
714,578,389 0.64 
6a 50 5 
7×7 1,200,466,087 1,200,466,087 
0 
6b 1,030,860,877 1,050,631,134 1.92 
7a 50 5 
7×7 1,202,986,074 1,208,318,119 0.44 7b 1,114,819,801 1,114,819,801 
0 
8a 50 5 
7×7 1,173,905,999 1,178,085,065 0.36 8b 1,105,046,816 1,110,187,189 0.47 
9a 50 5 
7×7 1,168,362,445 1,168,362,445 
0 
9b 1,064,513,910 1,073,142,886 0.81 
10a 50 5 
7×7 1,111,746,205 1,111,746,205 
0 
10b 988,940,246 
988,940,246 
0 
11a 60 6 10×10 1,580,552,006 1,582,445,148 0.12 11b 1,480,626,163 1,490,003,625 0.63 
12a 60 6 10×10 1,878,483,655 1,878,483,655 
0 
12b 1,626,530,237 1,630,972,977 0.27 
13a 60 6 10×10 1,834,522,336 1,840,530,503 0.33 13b 1,640,019,503 1,646,001,516 0.36 
14a 60 6 10×10 1,464,173,184 1,465,685,144 0.1 14b 1,362,627,426 1,374,194,529 0.85 
15a 60 6 10×10 1,790,770,791 1,790,955,517 0.01 15b 1,647,762,503 1,661,739,413 0.85 
References
81
Although the offshore drilling problem has been widely analyzed in the literature, 
the problem presented in this chapter has a unique feature. The drilling platforms 
have different daily rates, and hence different costs per mile. Accordingly, the drilling 
cost of each well depends not only on the distance to the platform, but also on which 
platform is used for drilling. 
Integer programming (IP) models have been developed to represent and optimally 
solve the two cases of this important practical problem. In addition to the optimum IP 
solution, a two-stage heuristic procedure has been proposed to handle larger model 
sizes for the Case 2 optimization problem. Based on numerical experiments with 
30 test problems of varying sizes, the heuristic procedure has shown excellent near-
optimum performance for Case 2. The heuristic solution matched the optimum cost 
in 9 problems and exceeded the optimum cost by an average of only 0.38%. 
Several interesting directions for future extensions of this work are possible. The 
new offshore drilling problem with cost dependence on both the distance and the 
choice of the drilling platform can be integrated with other relevant offshore ﬁeld 
optimization problems. For example, the problem considered in this chapter can be 
integrated with the well location and well path problem (Cristancho et al., 2010), the 
offshore transportation problem (Sahebi & Nickel, 2014), and the pipeline network 
design problem (Rosa et al., 2018). 
Acknowledgements 
1. Adapted from H. K. Alfares, K. G. Murty, A. A. Al-Saati, Application of clus-
tering models in offshore drilling for crude oil and natural gas, Industrial Engi-
neering and Management Systems, Vol. 18, No. 1, March 2019, pp. 104–116, 
with permission from the Korean Institute of Industrial Engineers. 
2. Reprinted from H. K. Alfares, K. G. Murty, A. A. Al-Saati, Application of clus-
tering models in offshore drilling for crude oil and natural gas, Industrial Engi-
neering and Management Systems, Vol. 18, No. 1, March 2019, pp. 104–116, 
with permission from the Korean Institute of Industrial Engineers. 
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drilling for crude oil and natural gas. Industrial Engineering & Management Systems, 18(1), 
104–115. 
Almedallah, M. K., & Walsh, S. D. (2019). A numerical method to optimize use of existing assets in 
offshore natural gas and oil ﬁeld developments. Journal of Natural Gas Science and Engineering, 
67, 43–55. 
Barnes, R. J., & Kokossis, A. (2007). A mathematical programming approach to the analysis, 
design and scheduling of offshore oilﬁelds. In Computer Aided Chemical Engineering (Vol. 24, 
pp. 503–508). Elsevier. 
Campozana, F. P., Dos Santos, R. L., Madeira, M. G., Sousa, S. H. G., & Spinola, M. (2008, January). 
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coupled with an integrated asset optimizer—An application to an offshore deep water oil ﬁeld in 
Brazil. In International petroleum technology conference. 
Carvalho, M. C. A., & Pinto, J. M. (2006). An MILP model and solution technique for the planning of 
infrastructure in offshore oilﬁelds. Journal of Petroleum Science and Engineering, 51(1), 97–110. 
Cristancho, J. H., Peters, R., Denham, W. W., Algu, D., Daley, P., & Njoku, C. (2010). SS-
visualization and collaboration: Keys to optimal platform placement. Offshore Technology 
Conference, 3–6 May, Houston, Texas. 
Devine, M. D., & Lesso, W. G. (1972). Models for the minimum cost development of offshore oil 
ﬁelds. Management Science, 18(8), B-378–B-387. 
Dogru, S. (1987). Selection of optimal platform locations. SPE Drilling Engineering, 2(04), 382– 
386. 
Frair, L., & Devine, M. (1975). Economic optimization of offshore petroleum development. 
Management Science, 21(12), 1370–1379. 
García, A., Guerra, B., Bentes, C., & Simonetti, L. (2012). An intelligent design model for offshore 
petroleum production elements layout. Advances in Artiﬁcial Intelligence—IBERAMIA, 702–711. 
Hansen, P., de Luna Pedrosa Filho, E., & Ribeiro, C. C. (1992). Location and sizing of offshore 
platforms for oil exploration. European Journal of Operational Research, 58(2), 202–214. 
Haugland, D., Jörnsten, K., & Shayan, E. (1991). Modeling petroleum ﬁelds with movable platforms. 
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Haugland, D., & Tjøstheim, B. P. (2015). Optimal intake and routing of ﬂoating oil rigs in the north 
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Iyer, R. R., Grossmann, I. E., Vasantharajan, S., & Cullick, A. S. (1998). Optimal planning and 
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Zhang, J., Hu, N., & Li, W. (2022). Rapid site selection of shale gas multi-well pad drilling based 
on digital elevation model. Processes, 10(5), 854.
Chapter 4 
Simulation-Based Optimization 
of Reﬁnery Valve Inspection Frequency 
4.1 
Introduction 
Relief valves are vital safety devices used in many industrial facilities, such as 
reﬁneries and petrochemical plants. Figure 4.1 shows a typical relief valve in an 
oil facility. By releasing excessive pressure, relief valves prevent process failure, 
equipment damage, explosions, and ﬁres. Therefore, regular inspections and repairs 
of relief valves are necessary preventive maintenance measures to identify and repair 
potentially failing valves. Scheduled inspections of relief valves are used to guarantee 
the continuing safety of the operations, employees, and the facilities.
Inspections of relief valves, however, are expensive and potentially destructive. 
The frequency of valve inspections must provide an optimum balance between safety 
and expenditure. As valve failures are random, determining the optimum inspection 
frequency is a complex stochastic optimization problem. This chapter uses simulation 
to determine the best inspection scheduling policy for relief valves in a large oil 
reﬁnery. 
The optimum valve inspection policy depends on many interacting factors such 
as the pressure, temperature, medium, failure history, size, and type of each valve. In 
addition, stochastic aspects such as the valve’s failure rate and previous inspection 
history must be taken into account. 
Relief valves, also called safety valves and pressure relief valves (PRV), are safety 
devices used in all equipment and facilities with ﬂuids that are subjected to very high 
pressures. These facilities include petrochemical plants, crude oil reﬁneries, and 
large ships. The main function of relief valves is to limit the pressure in a system 
by providing an outlet to release excess pressure. Each valve has its preset threshold 
to open at a speciﬁc pressure limit. When the pressure limit is exceeded, the valve 
opens to release some of the pressurized ﬂuids. The released ﬂuids are usually directed 
through pipes to a high ﬂare location to be burned into the air. When the ﬂuids are 
released, the pressure inside the system starts to decrease until it reaches the level 
required for the valve to close again (Wikipedia, 2020).
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 
H. K. Alfares, Applied Optimization in the Petroleum Industry, 
https://doi.org/10.1007/978-3-031-24166-6_4 
83
84
4
Simulation-Based Optimization of Reﬁnery Valve Inspection Frequency
Fig. 4.1 A relief valve in an oil pipe. Courtesy of Saudi Aramco, copyright owner
Relief valves are designed to work even if the control system fails, in order 
to protect the facility from being over pressurized. The most common causes of 
overpressure in petroleum processes are blocked discharge, gas overﬂow, and acci-
dental ﬁre. According to Malek (2016), relief valves are generally classiﬁed into the 
following types: 
1. Conventional spring-loaded. 
2. Balanced bellows. 
3. Pilot operated. 
4. Power actuated. 
5. Temperature and pressure actuated. 
Guy (2013) provides several guidelines for testing relief valves. He also describes 
several good practices in relief valve maintenance to avoid recurrent issues, reduce 
costs, and improve safety. These practices include having spare part inventory, under-
standing typical repair issues, applying asset management procedures, and following 
manufacturers’ recommendations. 
In this chapter, a simulation-based optimization approach is used to determine 
a dynamic inspection policy for relief valves in a large petroleum reﬁnery. This 
work was motivated by the desire of the reﬁnery’s management to reduce excessive 
inspection costs without compromising safety. This was due to the observation by the 
management that relief valve inspection costs are increasing and becoming exces-
sive. Therefore, a simulation model was developed to represent the current valve 
inspection, failure, and repair process. The model was used to experiment with all 
alternative feasible valve inspection policies and to compare them with the current 
policy. Based on this comparison, an alternative policy was suggested to minimize 
the expected total cost of inspection, repair, and failure risk. The suggested policy is 
expected to reduce the overall cost of inspection, repair, and failure risk by 26%. The
4.2 Review of Related Work
85
annual cost of inspection and repair alone is expected to be cut by approximately 
49%. 
The remaining sections of this chapter are organized as follows. A review of rele-
vant literature on relief valve inspection and maintenance is presented in Sect. 4.2. A  
description of the data collection and analysis process is given in Sect. 4.3. The  valve  
inspection simulation model is presented in Sect. 4.4. The results of the simulation 
model are discussed and analyzed in Sect. 4.5. Finally, a summary and applicable 
conclusions are provided in Sect. 4.6. 
4.2 
Review of Related Work 
Numerous models and techniques have been developed to optimize maintenance 
policies, in order to make the best inspection, repair, and replacement decisions. 
Several wide-scope surveys of the literature on maintenance optimization models 
have been performed. For example, Sharma et al. (2011) provide a comprehensive 
literature review on maintenance optimization, and an outline of future research 
directions. Prajapati et al. (2012) and Alaswad and Xiang (2017) review the literature 
on condition-based maintenance models. Iqbal et al. (2017) give a focused review of 
inspection and maintenance policies for oil and gas pipelines. The literature review 
presented in this section primarily covers techniques for determining the optimum 
inspection intervals for items that are subject to stochastic failures. These techniques 
will be classiﬁed into two types: analytical models and simulation models. 
4.2.1 
Analytical Inspection Interval Models 
Several analytical models have been developed to determine the optimum inspection 
frequency or inspection intervals. The objective of these models is to minimize the 
total cost, which is the sum of the cost of inspection and the cost of failure (or risk). 
In general, these models are used to solve simpler versions of the problem, utilizing 
simplifying assumptions such as constant inspection intervals. Barlow et al. (1963) 
propose one of the earliest and simplest analytical models to determine inspection 
intervals. The model aims to minimize the total maintenance cost per cycle, TC, which 
has two components: (1) the cost of each inspection c1 and (2) the cost per unit time 
of an undetected failure c2. The decision variables are the number of inspections (n) 
and their times i = (i1, i2, …  in) that minimize TC, as expressed below: 
upper T up per C left par enthe
sis t comma bold italic i right parenthesis equals c 1 n plus c 2 left parenthesis i Subscript n Baseline minus t right parenthesis comma
where 
t = failure time, in− 1 ≤ t ≤ in.
86
4
Simulation-Based Optimization of Reﬁnery Valve Inspection Frequency
Baker (1990) determines the optimum inspection period to maximize the proﬁt 
of a machine subject to an exponential failure rate. Schall and Chandra (1990) use  
a process control model to determine the period between inspections/maintenance 
operations. Dias (1990) presents an approximate solution for the inspection period, 
which is nearly optimal when the failure rate is exponentially distributed. Yeh (1997) 
derives the optimal inspection and replacement policies for a semi-Markovian model, 
using acyclic phase-type distributions to approximate the general sojourn time distri-
butions. Shyur et al. (1996) use neural networks to predict inspection and replacement 
times for aircraft components. 
Hariga and Al-Fawzan (2000) incorporate the time value of money in models for 
determining the inspection schedule for a single machine. Different problem types 
are considered, namely deterministic versus stochastic, ﬁnite versus inﬁnite plan-
ning horizon, and continuous versus discrete inspection periods. Hauge (2002) uses  
the Reliability Centered Maintenance (RCM) Tool Box to determine the optimum 
inspection, preventive maintenance, and failure-ﬁnding intervals. The RCM Tool 
Box includes statistical analyses, risk analysis, age exploration, and Monte Carlo 
simulation. Andrawus et al. (2008) combine Failure Modes and Effect Criticality 
Analysis (FMECA) and Delay-Time Maintenance Model (DTMM) to analyze wind 
turbine component failures and determine their inspection intervals. 
Golmakani and Fattahipour (2011) develop a condition-based maintenance model 
to optimize both the inspection interval and the replacement threshold. The model 
minimizes the average maintenance cost per unit time. Zhong et al. (2016) develop 
an integrated inspection and failure model for maintenance of electrical breakers and 
transformers. The optimum inspection interval is determined to minimize the total 
cost of maintenance. Rezaei (2017) formulates a reliability model to determine the 
optimal inspection interval for a steam turbine system, assuming minimal and perfect 
repairs. Ruschel et al. (2020) use process mining techniques on shop-ﬂoor data to 
develop an integrated probabilistic Bayesian Network and a predictive maintenance 
model. Functions relating maintenance inspection costs and equipment availability 
(uptime) are used to ﬁnd the best interval duration. 
4.2.2 
Simulation Inspection Interval Models 
Analytical inspection models are useful only for simpler versions of the inspec-
tion policy optimization problem. Usually, they are used to determine static inspec-
tion policies in which the inspection intervals are constant. In order to consider 
more complex, multi-factor problems and to determine dynamic inspection policies, 
simulation models should be used. 
Saﬁe and Hage (1991) use a simulation model to analyze the risk and reliability 
of alternative inspection policies for the Space Shuttle auxiliary power unit turbine 
wheels. Each inspection policy is deﬁned by the inspection interval, the rejection 
crack size, and the wheel life limit. Mathew and Rajendran (1993) present a simula-
tion model of a sugar plant to determine the frequency of periodic shutdowns used
4.2 Review of Related Work
87
for overhauling, maintenance, and inspections. Wang and Christer (1997) develop a 
procedure to determine the optimal inspection intervals over a ﬁnite time horizon. 
They use simulation to show the validity of approximating the exact objective 
functions by asymptotic formulations. 
Alfares (1999) presents a simulation model for optimizing the inspection policy 
of valves in a petrochemical plant, considering multiple factors that affect failures. 
Alfares (2002) constructs a simulation model to optimize preventive maintenance 
inspection intervals for the major components of a detergent packing line. Guo and 
Wang (2005) develop a simulation model to analyze the mechanical parameters of a 
relief valve under varying conditions, which can serve as an online inspection tool. 
Van Volsem et al. (2007) use simulation to model a multi-stage production process 
subject to inspection. Moreover, an Evolutionary Algorithm (EA) is used to optimize 
the inspection strategy, which is deﬁned by the inspection location, type, and limits. 
Eshlaghy et al. (2011) use graphical simulation software to optimize the number 
of preventive inspections, and hence the interval between inspections, for banking 
ATMs. Yun et al. (2014) ﬁnd the optimal inspection intervals for a one-shot system, 
which is a system that performs its function only once, such as a missile and an 
airbag. They use heuristics to determine the inspection interval and simulation to 
estimate the cost and availability performance. Mendes and Lorenzoni (2018) apply 
simulation to optimize the inspection interval for a standby (backup) system, aiming 
to maximize the availability and minimize the total cost. Golbasi and Turan (2020) 
apply discrete-event simulation to optimize the maintenance of a production system 
from the mining industry. The model compares corrective maintenance, preventive 
maintenance by inspections, and opportunistic maintenance in terms of availability 
and cost. 
4.2.3 
Reasons for Using a Simulation Approach 
The above literature review shows that there are many analytical and simulation 
approaches to optimize the inspection frequency. However, most of these analytical 
and simulation models produce static inspection policies that determine the best ﬁxed 
value for a constant inspection interval. The individual valves in the reﬁnery have a 
high variability in their properties and their inspection/failure histories. Therefore, 
specifying a ﬁxed, uniform inspection cycle for all valves would not be practical. 
Rather, the reﬁnery needs to consider dynamic inspection policies that specify an 
individual and varying inspection interval for each valve. This policy should allow 
the inspection interval to be continuously updated depending on the valve’s individual 
inspection history. 
In contrast to the ﬁxed inspection frequency proposed in the literature, practical 
rules for varying the frequency are usually used in practice. A survey of reﬁneries 
and petrochemical plants in the area identiﬁed two similar inspection policies, based 
on the inspection history of each valve. In one policy, the initial inspection interval 
is 6 years, but it is reduced to 3 years if the valve fails inspection even once. In
88
4
Simulation-Based Optimization of Reﬁnery Valve Inspection Frequency
the other policy, the initial interval is 2 years, but it is increased to 3 years with a 
good inspection history. In addition to these two policies, other practical inspection 
policies based on common sense include ﬁxed (constant) inspection periods, and 
periods that are reduced as the age of the given valve increases. 
In order to analyze dynamic inspection policies where the inspection interval 
changes according to the inspection history, two modeling approaches can be consid-
ered. The solid scientiﬁc approach is to use a simulation model to compare different 
policies. The other, heuristic, approach that could be used for determining the inspec-
tion policy is the decision tree technique. Each node in the tree would denote an 
instance of inspection. Out of any node, there are two branches (possibilities) with 
different costs and probabilities: either the valve passes or fails inspection. If the 
valve passes, the only cost incurred is the inspection cost, and the inspection interval 
is increased. If the valve fails, failure and repair costs are added to the inspection 
cost, and the inspection interval is reduced. One decision tree must be constructed 
for each of the feasible inspection policies. The objective would be to ﬁnd the policy 
(tree) with the minimum expected total cost. 
The decision tree approach is less sophisticated than simulation, however, since 
it is based on a single valve with an average failure rate. The simulation model to 
be presented next replicates all the valves in the reﬁnery, considering their indi-
vidual properties and varying failure rates. Due to the large number of variables, the 
complexity of their interrelationships, and the probabilistic nature of the processes, 
simulation is clearly the best approach for tackling this problem. 
4.3 
Data Collection and Analysis 
A large amount of data is needed in order to build the simulation model of the 
valve inspection process. Therefore, the modeling process was initiated by collecting 
and analyzing the necessary data. The ﬁrst step in understanding the problem was 
getting familiar with current relief valve inspection processes and policies. This was 
accomplished by interviewing the concerned employees and observing their valve 
inspection and repair work in the Relief Valve Shop. 
4.3.1 
Data Collection 
This step involves identifying and collecting the necessary data for the model. Out 
of 2416 relief valves in the reﬁnery, data was collected on a 10% random sample 
(240 valves). Data on valve characteristics and repair history was initially obtained 
from the soft ﬁles in the valve inspection database, and then veriﬁed by checking 
individual paper ﬁles kept in the Relief Valve Shop. Whenever discrepancies were 
observed between the computer and the manual data, the more accurate manual data 
was used. In addition to the 240-valve general sample, data was also collected on all
4.3 Data Collection and Analysis
89
0% 
5% 
10% 
15% 
20% 
25% 
30% 
35% 
40% 
Chemicals
Air
HC Liq
HC Gas
Steam
Water 
Actual 
Sample 
Fig. 4.2 Actual versus sample percentages of valve service (medium) groups 
122 active valves on the crude service (medium). This was done in order to focus on 
one major group of valves by eliminating service as a source of variation. 
The size of the sample is sufﬁcient to include all the different features and varieties 
of valves in the reﬁnery, but not too large to make the data analysis too difﬁcult and 
time consuming. The composition of the sample was found to be in close agreement 
with the actual valve variety in the reﬁnery. For each category of valve size, service 
(medium), type, and inspection interval, the percentages in both the sample and the 
reﬁnery closely match. As an example, Fig. 4.2 shows the percentages of different 
valve service (medium) groups in both the random sample and the whole reﬁnery. 
After selecting the sample, data was collected on the total cost of inspection for 
each valve size. This data includes the costs of labor, materials, and equipment for 
removal, installation, testing, and minor repair. Moreover, data was also collected 
on the out-of-service inspection time, and the pass/fail results of inspections for two 
calendar years. The pass and fail criteria used in the current inspection policy are 
deﬁned as follows: 
1. Pass: Clean (C), or Routine Work (W). 
2. Fail: Minor Repair (M), Major Repair (J), Replace (K). 
For each valve in the sample, data was collected on the total inspection history, 
including the number of inspections, as well as the results and the costs of each 
inspection. Inspection results are classiﬁed into either pass or fail. If a valve passes 
inspection, it may need cleaning only or cleaning plus some minor routine service. 
If a valve fails, it may require minor unexpected repair, major repair, or replacement 
with a new valve. Labor is required with all possible inspection results, while material 
is required only with failure results, i.e., minor repair, major repair, and replacement. 
In general, replacement is the costliest inspection result, and it is necessary only if 
the valve is found to be damaged beyond repair. For the two-year data history period, 
the total number of inspections for the sample is 2909, and the average cost of each 
inspection (c1) is $430.
90
4
Simulation-Based Optimization of Reﬁnery Valve Inspection Frequency
Table 4.1 Summary of the sample data. Adapted from Alfares (1999), with permission from 
Elsevier(1) 
Item
Value 
Number of major repairs (J) 
329 
Number of replacements with a new valve (K)
34
 
Number of minor repairs (M) 
982 
Total number of failures (J+K+M)
1,345
Total number of inspections
2,909 
% of failures to inspections
46.2% 
The data collected was extensively analyzed to identify signiﬁcant factors, deter-
mine relationships, and ﬁt data into probability distributions. First, all the relevant 
general statistics pertinent to the whole data were computed as displayed in Table 
4.1. 
The relief valves are used with more than 100 services (operating mediums) in 
the reﬁnery. Because of the large number of services, it was necessary to group them 
into a reasonable number of categories in order to make the analysis practical. Thus, 
a simple scheme was used to classify all services into six categories: hydrocarbon 
(HC) liquids, hydrocarbon (HC) gases, chemicals, water, air, and steam, as shown in 
Table 4.2. 
4.3.2 
Factors Affecting Valve Failure Rates 
The impact of each factor on valve failure rates was analyzed separately. The effects 
of valve size, service (medium), type, temperature, and pressure on the average time 
between failures (ATBF) are shown in Table 4.3. Obviously, the various categories 
within each factor correspond to different failure rates. To show the effect of the 
valve type factor, for example, the average number of failures per year for each valve 
type is shown in Fig. 4.3.
The inspection data was analyzed to determine the effect of all pertinent factors on 
the results of valve inspections. For each valve, the six factors that affect reliability 
were identiﬁed as the following: (1) valve size, (2) valve type, (3) valve failure history, 
(4) operating temperature, (5) operating pressure, and (6) valve service (medium).
One key factor affecting valve reliability is the valve’s own pervious failure history, 
i.e., its performance in previous inspections. This factor is not included in Table 4.3 
because it requires a separate analysis. The data required to perform this analysis is 
shown in Table 4.4.
Similar to all physical systems, older relief valves are expected to be less reliable 
than newer ones. This is a natural phenomenon due to wear and tear and long-term 
exposure to harsh operating conditions, namely high pressure, high temperature, and
4.3 Data Collection and Analysis
91
0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1 
CV
BE
TK
BB
BA
PI 
0.22 
0.51
0.5
0.48
0.45 
0.95 
Fig. 4.3 Average number of failures per year according to valve type
corrosive substances. Therefore, the speed of valve failure tends to increase as the 
number of previous failures increases. In other words, the time to the next failure 
becomes shorter as the relief valve becomes older and less reliable. To show the 
functional relationship between valve failure history and failure speed, a plot of the 
mean time between failures versus the number of previous failures is displayed in 
Fig. 4.4.
As expected, Table 4.4 and Fig. 4.4 show that the time between the (m − 1)th and 
the mth failure decreases as the number of failures m increases. Figure 4.4 shows a 
roughly linear decreasing trend. Therefore, the least-squares method was used to ﬁt 
the data into a linear function relating failure speed to the number of previous failures. 
Let t(m) denote the average time to the mth failure in months, i.e., the average time 
between the (m − 1)th failure and the mth failure. Applying linear regression to the 
data in Table 4.4, the following function is obtained: 
t lef t parenthe sis m righ t 
parenthesis equals 25.0811 minus 0.62125 m period
4.3.3 
Determining Probability Distributions 
The current inspection policy in the reﬁnery is based on monthly time units. There-
fore, one month was considered as the minimum time unit in data analysis, and all the 
time values in the collected data were rounded to integer number of months. Next, the 
statistical software package UniFit® II was used to determine the appropriate proba-
bility distributions. The probability distribution of the time between two successive 
failures t was found to be negative binomial. The probability distribution for the time 
between the creation (introduction) of new valves was found to be geometric. The
92
4
Simulation-Based Optimization of Reﬁnery Valve Inspection Frequency
Table 4.2 Classiﬁcation of valve services into service groups 
HC Liquids 
Chemicals 
HC Gas
Water 
Air
Steam 
LPG
LSRN
Acid
Tetraethyl 
Lead 
Crude 
vapor 
Water
Air
Steam 
Abusafa 
crude 
Mercaptans Adip
Transport 
Crude & 
vapor 
Condensate Air & HC 
gas 
Asphalt 
Mist oil
Amine
Transport 
Plus 
Disulfide gas Flashing 
water 
Air & 
vapor 
Blended 
Mogas 
Mogas
Ammonia 
gas 
Lube Oil
Fuel gas
Salt water 
Inst. air 
Butane
Naptha
Anti-icing 
Magni-
Form 
Gas
Sea water 
Liquid 
dxygen 
Butane/ 
propane 
Naphtha 
heavy 
Anti-foam 
Magni-
Form INJ 
H.C. Vapor
Water raw 
Nitrogen 
Crude
NG
Anti-static 
Orange 
Dye 
H.C.
Water. AC Oxygen 
Crude & 
vapor 
NG to plt 
10 
Antifoam 
Oxygen 
Inhibitor 
Hydrocarbon 
gas 
Water. 
chilled 
Crude oil NGL
Caustic
PC Solvent Hydrocarbons Water. hot 
Crude 
desalter 
NGL. raw 
Caustic INJ Phosphate Hydrogen
Water. 
treated 
Desulph. 
oil 
Pentane
Chloride
R-12 Freon Kerosene 
vapor 
Water/ 
merox II 
Diesel
Pentene
Clean 
Caustic 
R-12 Freon LPG vapor 
Diesel 
black 
Propane
Coagulant 
Red Dye
Naphtha 
vapor 
Diesel. 
black 
Reformate 
Corrosion 
Inhibitor 
Rich Adip DFF gas 
Ethane
Residue
Demulsifier Sulphite
Sour gas 
Fuel oil 
RJBD-1 
Butane 
Freon
Inhibitor
Sour HC 
Gasoline S.R.  
naphtha 
Freon 12
Lean Adip 
Gasoline 
motor 
SLOP
Freon-12 
Gasoline 
nat 
SLOP oil
creation of new valves is different from replacing damaged old valves with new ones. 
The creation of new valves means installing valves in newly built expansions, thus 
increasing the total number of valves in the reﬁnery. 
Figure 4.5, produced by the statistical package UniFit® II, shows the actual 
(sample) and the ﬁtted (probability distribution) frequencies of the times between 
successive introductions of new valves. For each set of data, UniFit® II speciﬁed
4.3 Data Collection and Analysis
93
Table 4.3 ATBF in months by valve pressure, service, size, temperature, and type. Adapted from 
Alfares (1999), with permission from Elsevier(1) 
Pressure (psi)
< 0
0-100 
101-200 201-300 301-400 401-500 501-600 601-700 > 700 
ATBF
22.25 
22.00 
26.83 
23.25 
16.79 
26.38 
19.95 
18.36 
28.23 
Service 
Water Steam Chemical 
Air
HC gas HC liquid 
ATBF 
21.02 
20.60 
20.26 
41.11 
23.13 
22.88 
Size (Inch)
0.25 0.5-0.75
1.0 
1.5-2.0 3.0-4.0
6.0 
8.0-10.0 18.0 
ATBF 
41.00 
21.99 
24.67 
20.68 
22.58 
29.41 
24.33 
25.20 
Temp. (C°) 
1-99 100-199 200-299 300-399 400-499 600-699 900-999 
ATBF
31.89 
22.97 
21.94 
24.13 
19.73 
27.80 
24.29 
Type* 
CV
BE
TK
BB
BA
PI 
ATBF 
22.76 
23.67 
32.27 
22.92 
18.80 
13.82 
* Valve types 
CV = Conventional
BE = Bellows
TK = Tank 
BB = Balanced Bellows
BA = Balanced
PI  = Pilot operated
Table 4.4 Average time between the (m − 1)th and the mth failure in months t(m). Adapted from 
Alfares (1999), with permission from Elsevier(1) 
Failure number    
m 
No. of failures    
in sample  Fm 
Cumulative time   
to failure Tm 
Ave time for mth 
failure t(m) 
1
238
6644
27.92 
2
211
4487
21.27 
3
188
4040
21.49 
4
170
3845
22.62 
5
143
3036
21.23 
6
120
2391
19.93 
7
89
 
1913
 
21.49
 
8
69
 
1348
 
19.54
 
9
52
 
1196
 
23.00
 
10
35
653
18.66 
11
18
363
20.17 
12
9
167
18.56 
13
3
41
13.67 
Overall
1345
30124 
22.40
94
4
Simulation-Based Optimization of Reﬁnery Valve Inspection Frequency
0.00 
5.00 
10.00 
15.00 
20.00 
25.00 
30.00 
0
2
 
4
 
6
 
8
 
10
 
12
 
14
 
Failure number m 
Months 
Actual 
Fitted Line 
Fig. 4.4 Average time between the (m − 1)th failure and the mth failure t(m). Reprinted from 
Alfares (1999), with permission from Elsevier(2)
the applicable probability distributions and their parameter values. The statistical 
package UniFit® II is associated with the SLAMSYSTEM® simulation software. 
Therefore, UniFit® II conveniently provides the input statements required to deﬁne 
the ﬁtted probability distributions in the simulation model, which is constructed using 
the SLAMSYSTEM® software. Additional probabilities associated with valve fail-
ures were calculated from the sample inspection data. These include the probabilities 
of valve repair and replacement, as well as the probability of immediately noticing 
valve failure in the ﬁeld.
After completing the data analysis and determining all the required parameters, 
statistics, and probability distributions, a valid methodology must be used to deter-
mine the optimum valve inspection policy. Since all the analytical techniques are 
not applicable for this complex stochastic optimization problem, a simulation-based 
optimization approach is used. The simulation model is described in the following 
section. 
4.4 
Modeling and Simulation 
The 2416 valves in the reﬁnery differ in several aspects such as the size, type, and 
medium, and hence they have different failure rates and inspection histories. The 
inspection policy must take these variations in reliability into consideration. There-
fore, the inspection policy must assign longer inspection intervals to valves that pass 
inspections, and shorter intervals to valves that fail inspections. 
Using the SLAMSYSTEM® software, a simulation model was constructed to 
analyze the valve inspection process, which is composed of inspections, failures,
4.4 Modeling and Simulation
95
Fig. 4.5 Frequency histogram for times between introduction of new valves. Reprinted from 
Alfares (1999), with permission from Elsevier(2)
repairs, replacements, and individual valve characteristics. A simpliﬁed ﬂowchart of 
this model is shown in Fig. 4.6, in which the symbols used are deﬁned below:
A
starting inspection interval (months); 
B
number of successive passes needed to increase interval; 
C
number of successive failures needed to decrease interval; 
D
maximum inspection interval (months); 
cft
cumulative failure time; 
f
number of successive failures; 
p
number of successive passes; 
i
inspection interval (months); 
npf 
number of previous failures; 
t
time to the next failure (months). 
As deﬁned above, the parameters A, B, C, and D specify all the characteristics 
of any inspection policy. As an example, the current inspection policy is speciﬁed 
by the following values: A, B, C, D = {12, 2, 1, 36}. The reﬁnery’s management 
established certain bounds on the values of these parameters: A is either 6, 12, or 24; 
B and C are either 1 or 2; and D is either 36, 48, or 60. Moreover, the management 
conﬁrmed the company’s emphasis on safety by asserting that the value of C cannot 
exceed the value of B. By changing the four parameters within the speciﬁed bounds, 
16 feasible alternative inspection policies were generated. The experimental design 
of the simulation model is simply exhaustive enumeration of the 17 policies that
96
4
Simulation-Based Optimization of Reﬁnery Valve Inspection Frequency
Fig. 4.6 Simpliﬁed ﬂowchart of the inspection simulation model. Reprinted from Alfares (1999), 
with permission from Elsevier(2)
4.4 Modeling and Simulation
97
include the existing policy and the 16 new ones. The simulation model is used to run 
each policy individually, in order to evaluate its long-term performance in terms of 
both cost and safety. 
As shown in Table 4.4 and Fig. 4.4, the average time between two successive valve 
failures, i.e., between failure number (m − 1) and failure number (m), decreases as 
the total number of failures, m, increases. Due to valve deterioration over time, older 
valves tend to fail faster than newer valves. The number of previous failures npf was 
used to keep track of the inspection history of each valve. Hence, the value of npf 
was increased by 1 if a valve failed inspection and got repaired. Alternatively, npf 
was reset to zero if a valve failed inspection and got replaced by a new valve. On the 
other hand, npf was not changed if a valve passed inspection. 
As Fig. 4.6 is a simpliﬁed ﬂowchart illustration of the actual simulation model, 
only one box is shown for valve repairs. Nonetheless, the actual simulation model 
considers two types of valve maintenance, minor repair, and major repair. The model 
handles the two types of repair separately, as they have different costs and prob-
abilities. The attributes (characteristics) considered in the model for each entity 
(individual valve) are listed in Table 4.5. 
Values of the time to the next failure t were randomly generated from the negative 
binomial probability distribution. For each valve, the mean of this distribution was 
adjusted according to the individual valve characteristics such as size, type, and 
medium, and also according to the number of previous failures npf . The simulation 
model included 2400 entities (simulated valves), in the same proportions that exist 
in the reﬁnery of varied sizes, services, types, temperatures, pressures, and ages. 
The model was run for 240 time units (months), which is equivalent to 20 years 
of simulated time. This speciﬁc length of 20 years was chosen primarily for two
Table 4.5 Valve attributes considered in the model 
# 
Attribute of each valve 
1 
Time of creation (installation of a new valve). 
2 
Number of previous failures. 
3 
Total inspection cost (depends on valve size). 
4 
Factor for time between failures for the valve's particular size. 
5 
Factor for time between failures for the valve's particular service. 
6 
Factor for time between failures for the valve's particular type. 
7 
Factor for time between failures for the valve's particular temperature. 
8 
Factor for time between failures for the valve's particular pressure. 
9 
Number of successive failures. 
10 
Number of successive inspection passes. 
11 
Assigned inspection interval. 
12 
Probability distribution of time to failure as a function of number of pre-
vious failures. 
13 
Indicator variable = 1 for fail, = 2 for pass. 
14 
Failure time distribution, adjusted by all factors. 
98
4
Simulation-Based Optimization of Reﬁnery Valve Inspection Frequency
Table 4.6 Comparison of actual and simulation values for the current policy. Reprinted from 
Alfares (1999), with permission from Elsevier(2) 
Number of inspections/year
1374
1325 
% of major failures/inspections
12.50%
13.66% 
Average inspection interval (months)
17.5
18.3 
Item
Actual value
Model output 
reasons. First, this time is more than sufﬁcient for the system to reach the steady 
state, at which all long-term statistics and performance measures become stable and 
semi-constant. Second, the oldest inspection record in the sample data is 20 years 
old. 
For each of the 17 simulated inspection policies, ﬁve runs were made with different 
random numbers. In order to verify the results for each policy, 95% conﬁdence inter-
vals were calculated for two performance measures: the total number of valve inspec-
tions, and the cumulative failure time cft. For all 17 inspection policies, the precision 
of each estimate, represented by the half-width of the corresponding conﬁdence 
interval, was within 1% of the average values for these two measures. 
For validation of the simulation model, the model’s output statistics for the current 
policy were compared to the statistics obtained from the actual data based on the 
current policy. Running the model under the current policy, the output statistics were 
in close match with the actual data statistics. The comparison is shown in Table 4.6. 
The output summary for all 17 policies is shown in Table 4.7. It should be noted that 
the model’s values in Table 4.6 are taken from the policy 9 (current policy) row in 
Table 4.7.
4.5 
Output Analysis 
Table 4.7 shows a summary of the simulation model results for the all the valve 
inspection policies considered. Each of the 17 policies is evaluated on the basis of 
two primary performance measures: expected cost and expected risk. The expected 
cost includes the total cost of inspection, replacement, and repair. The expected risk is 
measured by the cumulative failure time cft, which is the sum of times from failure to 
repair for all valves during the entire run. In order to compare the different inspection 
policies, a speciﬁc monetary cost had to be assigned to the cumulative failure time 
cft. 
There is no prior history of any major accident directly caused by a relief valve 
failure in the company. However, the cost of a related accident was obtained from 
the insurance claims for reference. One major accident took place in the reﬁnery 
because a relief valve was tightly blinded and had a total cost of $10 million. This 
single accident clearly was not caused by a relief valve failure. Nonetheless, it will 
be assumed that the very unlikely scenario of a major accident occurring can result
4.5 Output Analysis
99
Table 4.7 Simulation output summary of all policies. Reprinted from Alfares (1999), with 
permission from Elsevier(2) 
#
A 
B 
C
D 
cft/yr. 
month 
Insp. 
/yr. 
Avg. in-
terval 
month 
% fail 
Insp. 
cost
a 
$M/yr 
Risk 
cost 
$M/yr 
Total 
cost 
$M/yr 
1
6
1
1
36
0.84 
1033
21.92 
14.86 
0.56 
0.38 
0.94 
2
6
1
1
48
0.91 
1038
22.7 
15.04 
0.56 
0.42 
0.98 
3
6
1
1
60
0.91 
1038
22.7 
15.04 
0.56 
0.42 
0.98 
4
6
2
1
36
0.65 
1519
16
11.61 
0.82 
0.3
1.12 
5
6
2
2
36
0.71 
1371
17.6 
12.60 
0.74 
0.32 
1.06 
6
12
1
1
36
0.85 
915
24.2 
16.63 
0.49 
0.39 
0.88 
7
12
1
1
48
0.95 
930
25.3 
16.84 
0.5
0.43 
0.93 
8
12
1
1
60
0.95 
930
25.3 
16.84 
0.5
0.43 
0.93 
9
b 
12
2
1
36
0.70 
1325
18.28 
13.66 
0.72 
0.32 
1.04 
10 
12
2
2
36
0.52 
1165
20.6 
15.17 
0.63 
0.35 
0.98 
11
c 
24
1
1
36
0.87 
682
29.5 
19.43 
0.37 
0.4
0.77 
12 
24
1
1
48
1.10 
733
32.2 
19.84 
0.4
0.5
0.90 
13 
24
1
1
60
1.09 
730
32.1 
19.83 
0.39 
0.5
0.89 
14 
24
2
1
36
0.93 
966
25.1 
17.80 
0.52 
0.42 
0.94 
15 
24
2
1
48
0.93 
963
25.1 
17.76 
0.52 
0.42 
0.94 
16 
24
2
2
36
1.00 
840
28.5 
19.25 
0.45 
0.46 
0.91 
17 
24
2
2
48
1.01 
841
28.54 
19.28 
0.45 
0.46 
0.91 
a 
Inspection cost, including repair and replacement cost 
b 
Current policy 
c 
Proposed policy
from relaxing our inspection policy and stretching cumulative failure time (cft) to  
the maximum. 
For the 20-year runs, the maximum cft of any policy is 1.10 months per valve 
per year. As shown in Table 4.7, this maximum cft value corresponds to inspection 
policy number 12. Setting the risk cost of policy number 12 to be $10 million for 
20 years, i.e., $0.5 million per year, the risk cost per month c2 can be calculated as 
follows: 
StartL ayou t 1s t Row T otal c f t le ft pare nthes is 2 0 years com ma 24 0
0 valves  righ t paren thesi s e quals 1 period 1 0 times 2 0 times 24 00 equals 52 comma 800 months 2nd Row Failure time left parenthesis risk right parenthesis cost c 2 equals 1 0 Superscript 7 Baseline divided by 52,800 equals normal dollar sign 189 slash month period EndLayout
As stated in Sect. 4.3, the average inspection cost c1 is $430 per valve. This cost 
also includes the costs of cleaning, preventive maintenance, and minor repair. Based 
on work regulations, the inspection process includes items such as cleaning all valves
100
4
Simulation-Based Optimization of Reﬁnery Valve Inspection Frequency
and replacing all gaskets during inspection. To calculate the total cost of each policy, 
we need to add the costs of valve replacements and major repairs. Adding up the 
costs of inspection, failure risk, major repairs, and replacements with new valves, 
the total cost of each inspection policy is given by: 
upp er T up per C equa ls
 430
 
n pl
us 1 89 c f t pl
us sigma summation Underscript v equals 1 Overscript 2400 Endscripts c Baseline 3 Subscript v Baseline j Subscript v Baseline plus c Baseline 4 Subscript v Baseline k Subscript v Baseline comma
where 
c3v
cost of a major repair for valve v; 
c4v
cost of replacing valve v with a new valve; 
jv
number of major repairs for valve v during the simulation run; 
kv
number of times valve v is replaced with a new valve during the simulation 
run. 
Naturally, the major repair cost c3v and the replacement cost c4v depend on both 
the type and the size of the given valve v. Equation (4.3) is used to convert cumulative 
failure time cft into a risk cost, which is added to the inspection, major repair, and 
replacement costs. The total cost of each inspection policy is shown in Table 4.7. 
The annual inspection and failure costs of the different valve inspection policies are 
displayed in Fig. 4.7. 
Figure 4.7 shows that inspection policy number 11 has the lowest total cost. 
Therefore, this policy is chosen for implementation in the reﬁnery. According to 
the selected policy, the starting inspection interval of each new valve is 24 months. 
The inspection interval is increased by 6 months with each inspection pass, without 
exceeding the maximum limit of 3 years. On the other hand, the inspection interval
0 
0.2 
0.4 
0.6 
0.8 
1 
1  2  3  4  5  6  7  8  9  10 11 12 13 14 15 16 17  
Policy number 
$M 
Cost of Failure 
Cost of Inspection 
Fig. 4.7 Failure and inspection costs for each inspection policy 
4.5 Output Analysis
101
is decreased by six months with each inspection failure, without reducing below the 
minimum limit of 6 months. 
The values in Table 4.7 can be used to compare the new inspection policy with the 
existing inspection policy. For the proposed policy, the estimated annual inspection 
and repair cost is $370,000 and the overall annual cost is $770,000. For the existing 
policy, the annual inspection and repair cost is $720,000 and the overall annual cost 
is $1,040,000. Therefore, the new policy is expected to reduce the annual inspection 
and repair costs by $350,000, which is a saving of 49%. Moreover, the new policy 
will reduce the overall annual cost by $270,000, which is a saving of 26%. On the 
other hand, the average cumulative failure time cft per valve per year is expected to 
increase by only 25%, from 0.70 to 0.87 months. 
The proposed valve inspection policy has several additional advantages over the 
existing policy: 
1. The cost ﬁgures in Table 4.7 include only the direct cost of inspection labor, 
materials, and equipment. Of course, other indirect costs are involved, for 
example: material procurement times, planner times at the relief valve shop, 
removal/installation unit, and crane unit, plus other miscellaneous costs. If these 
indirect costs are considered, the actual savings will be much higher. 
2. Reducing the total number of inspections by about 50% will speed up the 
processing of valves in the Relief Valve Shop and consequently reduce out-
of-service time. 
3. Reducing the number of inspections reduces the total number of removals, instal-
lations, and movements. Vibrations during transportation can be damaging to the 
valves. The fewer of these movements and operations, the smaller the chance of 
something going wrong with the valves. 
4. Starting each valve with an initial interval of 24 months takes full advantage of 
the high reliability of new valves. Not yet subjected to the effects of age, they 
are expected to last a long time before the ﬁrst failure. Indeed, the average time 
to the ﬁrst failure is almost 28 months, which is quite longer than the 24-month 
initial interval. 
5. From a safety point of view, the new policy also allows a maximum of only 
three years for the inspection interval. Therefore, the inspection interval is not 
stretched beyond the current maximum limit. 
To be on the safe side, it is not recommended to apply the new policy instanta-
neously and in full force to the whole reﬁnery. A more cautious approach is recom-
mended, which is to start by implementing this policy on a trial basis. For example, the 
new policy can be applied initially in only one of the plants, or it may be applied only 
to selected valves, with speciﬁc services, manufacturers, and history, while contin-
uing with the existing policy for the remaining valves. Since the proposed initial 
inspection interval is two years and the maximum interval is three years, the recom-
mended length of the trial period is three years. After its applicability is conﬁrmed 
in the ﬁeld, full-scale implementation of the new policy can be safely started.
102
4
Simulation-Based Optimization of Reﬁnery Valve Inspection Frequency
4.6 
Summary and Conclusions 
Determining the optimum inspection interval for reﬁnery relief valves is a complex 
and sensitive task. The inspection frequency must be high enough to eliminate any 
threat to the safety of the plant; while on the other hand, this frequency should be low 
enough to avoid unnecessary waste of resources and disruption of operations. This 
chapter presented a simulation-based approach to optimize the inspection policy 
of relief valves in a large petroleum reﬁnery. The objective of this approach is to 
evaluate all feasible inspection policy alternatives, in order to choose the policy that 
minimizes the total inspection, repair, and risk costs. 
In total, the reﬁnery has 2416 relief valves. In order to collect data needed for the 
simulation model, a random sample of 240 relief valves was selected, representing 
10% of the total number of valves. For these selected valves, complete historical 
data on their previous inspections, repairs, failures, and replacements was collected 
and thoroughly analyzed. For each valve, the statistical software package UniFit® 
II was used to analyze the data and to determine the probability distributions of the 
times between failures and the times between successive introductions of new valves. 
Using these probability distributions, a simulation model was developed and coded 
into the SLAMSYSTEM® simulation software developed by Pritsker (1986). 
The current inspection policy consists of four rules to determine the inspection 
interval for each valve: (1) the initial inspection interval for a new valve is one year, 
(2) if a valve passes two successive inspections, its inspection interval is increased 
by 6 months, (3) if a valve fails inspection, its inspection interval is increased by 
6 months, and (4) the minimum inspection interval is 6 months, and the maximum is 
3 years. By changing each of these rules, many alternative inspection policies were 
generated. After discussions with the valve maintenance technicians and obtaining 
the reﬁnery’s management approval, only 16 new inspection policies were selected 
for further evaluation in the simulation model. 
In addition to the current valve inspection policy, the 16 new policies were tested 
using the simulation model. Each policy was run for the equivalent of 20 calendar 
years in order to assess its expected long-term, steady-state performance. Two main 
performance measures were used to evaluate each inspection policy: the cumulative 
number of inspections, and the cumulative failure time (cft). The total risk cost of 
each policy was calculated by determining the dollar-value equivalent of cft. Based 
on the total cost of inspection, repair, and risk, a new policy was proposed to replace 
the current policy. According to the new inspection policy, the ﬁrst two rules used 
to determine the inspection interval are revised as follows: (1) the initial inspection 
interval for a new valve is 2 years, (2) if a valve passes any inspection, its inspection 
interval is increased by 6 months. 
The proposed inspection policy is estimated to produce a reduction of 49% in 
inspection and repair costs, amounting to $350,000 per year. On the other hand, the 
proposed inspection policy is expected to increase the average cumulative failure 
time (cft) per valve by only 5 days per year.
References
103
The simulation-based optimization approach described in this chapter can be 
generalized to similar inspection applications, where the individual failure rates are 
stochastic and dependent on multiple variables such as pressure and temperature. It 
can also be extended by considering other rules to determine the inspection interval, 
in addition to the four rules used in this reﬁnery. Moreover, in situations where 
items are classiﬁed into different sets according to their characteristics, a different 
inspection policy can be generated for each set. 
Acknowledgements 
1. Adapted from Computers and Industrial Engineering, 36/3, H. Alfares, A simu-
lation model for determining inspection frequency, 685–696, Copyright (1999), 
with permission from Elsevier. 
2. Reprinted from Computers and Industrial Engineering, 36/3, H. Alfares, A simu-
lation model for determining inspection frequency, 685–696, Copyright (1999), 
with permission from Elsevier. 
References 
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Chapter 5 
Operations and Workforce Scheduling 
for Reﬁnery Turnaround Maintenance 
5.1 
Introduction 
Oil reﬁneries and petrochemical plants are very large industrial complexes that use 
both physical processes and chemical reactions to process substantial amounts of 
ﬂuids in several inter-connected sections, such as oil distillation columns and petro-
chemical cracking units. Oil reﬁneries and petrochemical plants operate at high 
temperatures and pressures, process corrosive substances, and therefore require 
regular maintenance. Figure 5.1 shows a maintenance operator performing work 
in an oil reﬁnery.
Several types of maintenance activities are performed in oil plants and reﬁneries. 
These include preventive, predictive, and failure repair maintenance. These relatively 
smaller-scale activities can be carried out while the plant is in operation. Major 
maintenance and renovation tasks, however, can be performed only if production is 
stopped and the plant is completely shut down. 
Turnaround maintenance, also called planned shutdown maintenance, is the most 
comprehensive and expensive planned maintenance activity in large industrial facil-
ities such as oil reﬁneries and petrochemical plants. Turnaround maintenance is used 
to perform extensive preventive and corrective maintenance tasks that cannot be 
performed while the plant is in operation. During the shutdown period, a complete 
plant or a large section of the plant is closed off, and all major components are thor-
oughly maintained. Tasks performed during turnaround (planned shutdown) mainte-
nance include inspections, repairs, replacements, and upgrades, as well as additions 
of new systems or components. 
In oil reﬁneries, turnaround maintenance projects are usually scheduled every 
three to ﬁve years in order to thoroughly maintain and revamp facilities. Each 
turnaround project usually lasts from a few weeks to a few months, depending on 
the scope of the project and the number of equipment problems found during the 
execution. Turnaround maintenance projects require the coordination of specialized 
tools, materials, and workforce to perform a set of speciﬁc maintenance tasks. In
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 
H. K. Alfares, Applied Optimization in the Petroleum Industry, 
https://doi.org/10.1007/978-3-031-24166-6_5 
105
106
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Operations and Workforce Scheduling for Reﬁnery Turnaround …
Fig. 5.1 Employee performing maintenance in an oil reﬁnery (Courtesy of Saudi Aramco, 
copyright owner)
industry, turnaround maintenance is also called shutdown maintenance and testing 
and inspection (T&I). 
Turnaround maintenance is extremely costly, and consequently, it is implemented 
only for a limited set of maintenance tasks that cannot be carried out while the 
plant is running. This expensive type of planned maintenance is used to prevent 
more costly and destructive plant failures and outages. The ﬁrst step in planning 
turnaround maintenance is to identify all the components that need maintenance, 
repair, or replacement. This step speciﬁes the set of planned tasks to be performed 
during the turnaround maintenance project. The required resources for each task are 
then determined, including equipment, materials, and personnel. Subsequently, the 
time required for completing each task is estimated as a function of the number of 
maintenance personnel assigned to the task. This deﬁnes the scope of the turnaround 
maintenance project and its expected time duration. 
The high cost of turnaround maintenance is the sum of two main cost components. 
The ﬁrst component is the direct cost of tools, materials, and labor, which is deﬁnitely 
high. The second component, which is usually much larger, is the indirect cost of 
lost revenue due to the shutting down of production. Both cost components are 
directly dependent on the duration of the shutdown interval. Therefore, the primary 
objective in planning turnaround maintenance activities is to minimize the length of 
the shutdown time duration. Other important objectives include minimizing the total 
cost and maximizing safety and reliability.
5.1 Introduction
107
Turnaround maintenance is especially expensive because it involves the stoppage 
of production and hence the loss of revenue. Therefore, optimization models are 
essential to minimize the maintenance time duration, i.e., the total downtime of the 
reﬁnery. Given a speciﬁc set of maintenance tasks and a limited-size maintenance 
workforce, the technicians are divided into distinct work teams who work concur-
rently on different subsets of maintenance tasks. The problem is to determine the 
number of maintenance teams, the size of each team, and the corresponding team-task 
assignment to minimize the shutdown time. The optimum schedule must consider 
several relevant factors: (1) dependence of individual task durations on the assigned 
team size, (2) differing task arrival times, and (3) sequencing relations among subsets 
of maintenance tasks. 
This chapter presents a real-life application of optimization models in the 
scheduling of reﬁnery turnaround maintenance operations and personnel. Speciﬁ-
cally, an actual problem is presented of scheduling tasks and labor for a large-scale 
turnaround maintenance project in a large oil reﬁnery. Labor is outsourced for this 
project, and hence, a limited-size turnaround maintenance workforce is supplied by 
a local contractor at a ﬁxed daily rate per technician. The turnaround maintenance 
workforce is divided into several work teams of differing sizes. Each work team 
is assigned to a subset of the turnaround maintenance tasks, and all teams work in 
parallel on their respective tasks. Only a limited set of team sizes is acceptable, and 
the given team size (number of technicians in the team) determines the duration of 
each maintenance task assigned to the team. 
Finding the best task and labor schedule for the above-described reﬁnery mainte-
nance situation is a difﬁcult optimization problem. In order to solve this scheduling 
problem, integer linear programming (ILP) models are formulated and solved. These 
optimization models are used to determine the number of work teams, the size 
(number of technicians) of each team, the task-team assignments, the start time 
of each maintenance task, and the duration of each task. The objective is to mini-
mize the total shutdown cost, i.e., sum of lost revenue and the daily cost of contract 
workers, by minimizing the shutdown duration. The solution of this problem has to 
satisfy several constraints, including the total workforce size, task sequencing rela-
tions, team size restrictions, and task arrival times. Due to the integer programming 
model’s size and complexity, it can solve only relatively smaller problem sizes. For 
larger practical problems, an effective heuristic method is developed and veriﬁed by 
solving a large set of randomly generated problems. 
Subsequent sections of this chapter are arranged as follows. A review of up-to-
date literature on turnaround maintenance scheduling is provided in Sect. 5.2. The  
mathematical optimization model is presented in Sect. 5.3. The heuristic solution 
algorithm is described in Sect. 5.4. The actual reﬁnery turnaround maintenance case 
study is presented in Sect. 5.5. The computational analysis to assess the heuristic 
algorithm is presented in Sect. 5.6. The summary and the conclusions are provided 
in Sect. 5.7.
108
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Operations and Workforce Scheduling for Reﬁnery Turnaround …
5.2 
Relevant Literature 
In this section, recent literature is reviewed on optimization models for maintenance 
task and workforce scheduling, focusing on mathematical programming approaches 
for plant turnaround maintenance. 
Plant turnaround maintenance is an important practical problem and a well-studied 
area of research. Hey (2019) proposes using a project management approach to 
optimize plant turnaround maintenance for the oil, gas, and process industries. Al-
Turki et al. (2019) provide a recent comprehensive survey of literature on turnaround 
maintenance trends and approaches. They classify turnaround maintenance literature 
into four major areas: (1) management and planning, (2) scope and risk analysis, (3) 
execution, and (4) performance measurement, learning, and reporting. Maintenance 
scheduling and stafﬁng, which is the focus of this section, is a subtopic within the 
major area of turnaround maintenance management and planning. 
Schulz et al. (2006) develop a mixed-integer nonlinear programming (MINLP) 
model for the cyclic scheduling of planned shutdown maintenance in an ethylene 
plant. Their model includes binary variables for shutdown decisions and nonlinear 
functions of raw material, process, production, and storage variables. Castro et al. 
(2014) formulate a generalized disjunctive programming (GDP) model for the 
optimal shutdown maintenance scheduling of a power plant. Work is performed 
by a single maintenance team, which is available only at certain times. Given the 
seasonal variations in electricity prices, the model is used to maximize the electricity 
sales revenue by minimizing shutdowns during high-rate periods. Ghaithan (2020) 
uses a mixed-integer linear programming (MILP) model to simultaneously optimize 
operational and turnaround maintenance scheduling decisions in oil and gas supply 
chain networks. The objective is to maximize net proﬁt subject to constraints on 
labor availability, plant capacity, and storage space. The model tries to schedule 
plant maintenance during low demand periods to maximize demand satisfaction and 
minimize lost sales. 
Plant maintenance task scheduling is frequently integrated with maintenance 
workforce stafﬁng and scheduling. Alfares et al. (2007) compare three weekly days-
off work schedules for the maintenance workforce of a power generation plant. Using 
integer programming models to optimize each schedule, one of the alternative sched-
ules is chosen to minimize the labor cost while meeting the varying maintenance 
workload. Siu et al. (2013) develop a simulation-based methodology to optimize 
skilled labor schedules in turnaround maintenance of an oil and gas reﬁnery. The 
objective is to minimize the expected total shutdown duration, subject to resource 
limitations, shift scheduling constraints, and working area restrictions. 
Safaei et al. (2011) formulate a workforce-constrained maintenance scheduling 
problem for a steel plant as a bi-objective mixed-integer programming model. 
The objectives are to maximize equipment availability and to minimize workforce 
demands. Maintenance technicians have different trades and skill levels, and they 
are classiﬁed into internal, external, regular time, overtime, and contract workers. 
Equipment availability is measured by the total weighted ﬂow time, which is affected
5.2 Relevant Literature
109
by both preventive maintenance and failure repair downtime. Ighravwe and Oke 
(2014) construct a bi-objective nonlinear integer goal programming model to opti-
mize maintenance scheduling and stafﬁng for a detergent plant. The model aims to 
simultaneously minimize the size of the maintenance workforce and maximize its 
productivity. Given the ratio of full- and part-time workers, the goal programming 
model is solved using the branch-and-bound algorithm. 
Turnaround maintenance activities can only be performed by teams of multi-craft 
maintenance crews. Therefore, several maintenance task and workforce scheduling 
models involve team building, i.e., the allocation of technicians to speciﬁc work 
teams. Kovacs et al. (2012) consider the service technician routing and scheduling 
problem with and without team building. Assuming time windows, multiple work 
sites, and multiple skill levels, adaptive large neighborhood search is used to minimize 
the sum of the routing and the outsourcing costs. Fırat and Hurkens (2012) consider a 
resource-constrained maintenance task and workforce scheduling problem for France 
Telecom. A limited number of multi-skilled technicians must be assigned to teams 
for each workday to process a set of required maintenance tasks. Tasks are assigned 
only to teams with matching skill levels, and they must be performed one at a time, in 
the right sequence, without interruption, and fully within one workday. A two-stage 
combinatorial approach based on mixed-integer programming is used to minimize 
the total cost. 
Several optimization models have been proposed for scheduling maintenance 
tasks and personnel in petroleum processing and transportation facilities. Alfares 
and Abu Al-Khair (2003) use simulation to determine the optimum days-off work 
schedules for multi-craft oil pipeline maintenance employees. The simulation model 
determines the number of technicians of each craft to assign to each days-off 
schedule, considering limited workforce availability, stochastic workload variability, 
and policy restrictions on the employees’ work schedules. The objective is to mini-
mize the average throughput (waiting plus processing) time of both planned and 
failure maintenance work orders. 
Awad and Ertem (2017) propose a stochastic modeling approach to schedule 
preventive maintenance activities for an oil reﬁnery by a limited number of mainte-
nance technicians with multiple skill levels. The model considers the uncertainties 
of maintenance times, work permit delays, weather disruptions, and site availability 
restrictions. The objective is to minimize the total weighted tardiness, subject to 
limits on labor, time, and budget availability. Leite and Vellasco (2020) apply a 
nonlinear optimization model for constrained combinatorial problems in the main-
tenance planning for the oil and gas industry. The model optimizes the schedule of 
offshore maintenance tasks and the corresponding maintenance staff allocation in 
order to maximize overall proﬁtability. 
Redutskiy (2018) integrates safety systems design, maintenance planning, and 
employee scheduling into one optimization model for remote oil and gas facilities. 
The integer programming model determines the optimum design of safety systems 
and the frequency of facility shutdown maintenance. Taking the cost of personnel 
transportation into account, the model also determines the size of the maintenance 
crews and schedules their work shifts and transportation trips. Considering a similar
110
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Operations and Workforce Scheduling for Reﬁnery Turnaround …
problem, Akbar (2018) formulates a MILP model for maintenance task and employee 
scheduling in remote oil and gas facilities. The model ﬁnds the best trade-offs between 
capital and operating costs and potential risk costs due to hazardous processes and 
harsh environments in remote locations. The objective is to minimize the present 
value of all the cost cash ﬂows. 
Seif et al. (2021) formulate a MILP model to group similar maintenance tasks 
for oil and gas plants into small short-term maintenance campaigns. The objective is 
to minimize the shutdown cost while meeting deadlines and applicable maintenance 
and labor constraints. Alfares (2022) considers a similar plant maintenance problem, 
where the individual tasks are grouped and assigned to several work teams. An integer 
LP model is constructed, and a heuristic solution algorithm is developed to minimize 
the shutdown cost. 
This chapter considers a task scheduling and employee assignment problem for 
turnaround maintenance in a large oil reﬁnery. The objective of the problem is to 
determine the number and the size of maintenance teams, as well as the tasks assigned 
and their sequence for each team, in order to minimize the total shutdown period. 
In the following sections, optimization models and heuristic solution algorithms are 
presented for solving this real-life scheduling problem. 
5.3 
Model Formulation 
In this section, the oil reﬁnery maintenance task and workforce scheduling problem 
is deﬁned, and its optimization model is formulated. The reﬁnery has a limited-
size maintenance workforce consisting of multi-craft technicians. These technicians 
must be allocated to several work teams (groups) in order to perform a speciﬁc set 
of shutdown maintenance tasks. The different teams work concurrently (in parallel), 
as each team separately performs its own set of assigned maintenance tasks. Due 
to operational requirements or structural layout, some maintenance tasks are not 
available at the start of the shutdown period, and some of them must be preceded by 
other tasks. 
Solving the above-described reﬁnery shutdown maintenance scheduling problem 
means determining the following: the number of teams, the number of technicians 
assigned to each team, the tasks assigned to each team, the sequence of tasks for each 
team, and the start time of each task. The objective is to minimize the cost of lost 
production by minimizing the total shutdown duration, i.e., the completion time of the 
last maintenance task. Below, the reﬁnery maintenance scheduling problem is further 
deﬁned, and its optimization model is formulated. The binary integer programming 
model is used to represent the problem and also to obtain its optimum solution.
5.3 Model Formulation
111
5.3.1 
Assumptions 
1. The maintenance workforce is uniform, and it consists of multi-skilled techni-
cians who are equally qualiﬁed to work on any maintenance task. 
2. The team size, i.e., the number of technicians assigned to any work group, is 
limited to a given set of standard values. Typically, two standard team sizes are 
given: a smaller (regular) team size and a larger (rush) team size. 
3. The processing time of each maintenance task depends on the size of the group 
(team) assigned to perform the task. Typically, two processing time durations are 
possible for each task: a longer (normal) time for the smaller team size and a 
shorter (crash) time for the larger team size. 
4. Precedence relations exist between certain pairs of tasks, preventing any task 
from starting unless all of its predecessor tasks have been completed. 
5. A task can be started only after its arrival time. Some maintenance tasks cannot be 
performed at the start of shutdown period, but have a given arrival delay period, 
after which they become available for maintenance. 
6. Without loss of generality, maintenance tasks are numbered in an increasing order 
of their arrival times. 
5.3.2 
Input Values 
W
workforce size, i.e., total number of available maintenance technicians; 
sk
standard team size number k, k = 1,…, K, where s1 < s2 < … <  sK, therefore 
smin = s1, smax = sK; 
aj
arrival delay time of task j, i.e., number of days from the start of shutdown 
maintenance during which task j is still not available for maintenance, j  = 1,…, 
J, where a1 ≤ a2 ≤ … ≤ aJ; 
Pj
set of predecessors (preceding tasks) of task j, j = 1,…, J. 
djk 
duration (time) of task j if it is performed by a group (team) of size sk, j = 1,…, 
J, k = 1,…, K, where dj1 > dj2 > … >  djK, 
thus dj,min = djK, dj,max = dj1. 
5.3.3 
Decision Variables 
Z 
shutdown duration, i.e., time from starting the ﬁrst maintenance task to 
completing the last maintenance task; 
N 
number of work groups (teams);
112
5
Operations and Workforce Scheduling for Reﬁnery Turnaround …
Sta
r
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ubsc
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j = 1,…, J, g = 1,…, G, k = 1,…, K, t = 1,…, T. 
5.3.4 
The Integer Linear Programming Model 
The reﬁnery maintenance task and workforce scheduling problem is represented by 
the integer linear programming (ILP) model presented below. The objective function 
(5.1) is to minimize the total shutdown duration Z 
Minimize upp
er Z period
The above objective function is optimized subject to the following constraints. 
Constraints (5.2) guarantee that each task j has a unique schedule, i.e., it is assigned 
to one work group (team) g that has one speciﬁc size sk and only one start time t. 
Constraints (5.3) prevent the assignment of more than one task to any work group 
during the same time period. Constraints (5.4) enforce the applicable precedence 
relations, by allowing each task to start only after all its preceding tasks have been 
completed. Constraints (5.5) ensure the duration of the shutdown period Z is greater 
than or equal to the completion time of any task. Logical constraints (5.6) relate the 
binary variables Xjgkt and Y gk, by guaranteeing that tasks are assigned only to the 
appropriate active work groups. Capacity constraint (5.7) ensures that the number 
of technicians assigned to all work groups does not exceed the total workforce size 
W. Constraints (5.8) ensure that each active work group has only one designated 
size. Constraint (5.9) is used to calculate the total number of active work groups, N. 
Finally, constraints (5.10) are used to number all active groups consecutively, from 
1 to  N: 
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i
gma 
summ ation Un ders c r i p t
 
k equals 1 Overscript upper K Endscripts upper Y Subscript g k Baseline equals upper V Subscript g Baseline comma g equals 1 comma ellipsis comma upper G
s
i
gma 
sum mat
ion Underscript g equals 1 Overscript upper G Endscripts upper V Subscript g Baseline equals upper N
up er V Subsc ript  g  B a
se line
 greater than or equals upper V Subscript g plus 1 Baseline comma g equals 1 comma ellipsis comma upper G minus 1 period
5.3.5 
Values of G and T and Bounds on N and Z 
To completely deﬁne the ILP model expressed by (5.1–5.10), the values of the param-
eters J, G, K, and T must be speciﬁed for each given problem. Out of these, the number 
of maintenance tasks J and the number of allowed group sizes K must be speciﬁed 
as given input values. On the other hand, the maximum number of work groups G 
and the maximum time interval T are not directly given. The values of G and T can 
be considered as part of the initial solution and hence must be calculated to complete 
the model setup. Obviously, G is an upper bound on the number of groups N, and T 
is an upper bound on the shutdown interval Z. 
Accordingly, the values G and T respectively correspond to the maximum possible 
values of N and Z. It is advantageous to have the smallest possible values of G and 
T in order to minimize the size and the difﬁculty of the ILP model. Moreover, it is 
important to have the tightest possible bounds on the decision variables N and Z in 
order to minimize the search space. 
The approximate number of work groups N is roughly equal to the workforce 
size W divided by the group size sk. Therefore, the following bounds on N are
114
5
Operations and Workforce Scheduling for Reﬁnery Turnaround …
logically established to reduce the search space while maintaining the feasibility and 
the optimality of the solution: 
St artLayo
ut 1st
 Row u
p
p
e
r N 
g
reater than or equals upper N Subscript min Baseline 2nd Row where 3rd Row upper N Subscript min Baseline equals left ceiling upper W divided by s Subscript max Baseline right ceiling EndLayout
St artLayo
ut 1st
 Row u
p
p
e
r N 
l
es
s than or equals upper N Subscript max Baseline 2nd Row where 3rd Row upper N Subscript max Baseline equals left floor upper W divided by s Subscript min Baseline right floor period EndLayout
The approximate length of the shutdown interval Z is roughly equal to the sum of 
all task durations, sig
ma s u mmation Underscript j equals 1 Overscript upper J Endscripts d Subscript j k Baseline commadivided by the number of work groups N. Of course, 
task arrival delay times aj also inﬂuence the shutdown duration Z, but these times are 
ignored in calculating the approximate bounds on Z. This temporary dismissal of the 
aj values slightly reduces the values of bounds on Z; however, such a minor reduction 
is desirable because it decreases the model size and the feasible search space, and 
it also focuses the search toward smaller (and better) values of Z. Using the above 
bounds on N, the following bounds are established on the shutdown duration Z: 
St artLayo
ut 1st
 Row u
pper
 Z g r eater than or equals upper Z Subscript min Baseline 2nd Row where 3rd Row upper Z Subscript min Baseline equals left ceiling StartFraction sigma summation Underscript j equals 1 Overscript upper J Endscripts d Subscript j comma min Baseline Over upper N Subscript max Baseline EndFraction right ceiling EndLayout
Star
t
Layout 1st Row upper Z greater than or equals upper Z Subscript min Baseline 2nd Row where 3rd Row upper Z Subscript min Baseline equals left ceiling StartFraction sigma summation Underscript j equals 1 Overscript upper J Endscripts d Subscript j comma min Baseline Over upper N Subscript max Baseline EndFraction right ceiling EndLayout
St artLayo
ut 1st
 Row u
pper
 Z l e ss than or equals upper Z Subscript max Baseline 2nd Row where 3rd Row upper Z Subscript max Baseline equals left floor StartFraction sigma summation Underscript j equals 1 Overscript upper J Endscripts d Subscript j comma max Baseline Over upper N Subscript min Baseline EndFraction right floor period EndLayout
Star
t
La
yout 1st Row upper Z less than or equals upper Z Subscript max Baseline 2nd Row where 3rd Row upper Z Subscript max Baseline equals left floor StartFraction sigma summation Underscript j equals 1 Overscript upper J Endscripts d Subscript j comma max Baseline Over upper N Subscript min Baseline EndFraction right floor period EndLayout
The bounds on N and Z expressed by (5.11–5.14) need to be incorporated in the 
integer linear programming (ILP) model in two ways. First, the bounds (5.11–5.14) 
are added as constraints in the ILP model speciﬁed by (5.1–5.10). Second, the upper 
bounds in (5.12) and (5.14) are used to specify the values of G and T by setting G = 
Nmax and T = Zmax. 
5.3.6 
Size of the Optimum ILP Model 
The size of the ILP model deﬁned by expressions (5.1–5.14), i.e., the total number 
of variables and constraints, is a function of the values of the parameters J, G, K,
5.3 Model Formulation
115
and T. The number of tasks J and the number of group sizes K are given, while 
the maximum number of groups G and the maximum number of time periods T are 
respectively determined by (5.12) and (5.14). The total number of decision variables 
Xgjkt and Y gk in the ILP model is given by equation (5.15), while the total number of 
constraints is given by equation (5.16): 
Number of decision variables eq u a ls  u pp er G up pe r
 J upper K upper T plus upper G upper K plus upper G plus 2
Number of constraints eq ual s upp er  G up per  K  p lu
s
 
uppe
r G u pper T plus 2 upper G plus 2 upper J plus 5 plus sigma summation Underscript j equals 1 Overscript upper J Endscripts upper P Subscript j Baseline period
Equations (5.15) and (5.16) indicate that the ILP model is very large, especially 
in terms of the number of decision variables. Moreover, the size of this model grows 
very quickly in response to small increases in the problem size, i.e., the values of 
parameters J, G, K, and T. 
In order to illustrate the large size and the fast growth of the ILP model, the case 
study to be presented in Sect. 5.5 is used as an example. For this real-life reﬁnery 
maintenance scheduling problem, the input values to ILP model are J = 42, G = 9, 
K = 2, T = 35, and sig
ma s umation Underscript j equals 1 Overscript upper J Endscripts upper P Subscript j = 9. Using equations (5.15) and (5.16), the number 
of variables is 26,489 and the number of constraints is 449. For a problem with 
only 42 maintenance tasks, 26,489 is a disproportionately large number of decision 
variables. Generally, it is exceedingly difﬁcult to optimally solve ILP models with 
so many binary integer variables. 
To illustrate the fast growth in the ILP model size, let us consider the increase 
in the size of the ILP model corresponding to a 10% increase in the values of the 
case study’s input parameters. Starting with the original values (J = 42, G = 9, K 
= 2, T = 35, and sig
ma s umation Underscript j equals 1 Overscript upper J Endscripts upper P Subscript j = 9), increasing each value by 10% and rounding up 
to the nearest integers leads to following values (J = 47, G = 10, K = 3, T = 39, 
and sig
ma s umation Underscript j equals 1 Overscript upper J Endscripts upper P Subscript j = 10). Using equations (5.15) and (5.16), the number of ILP variables 
increases from 26,489 to 55,032 (108% increase), while the number of constraints 
increases from 449 to 549 (22% increase). 
Optimal solutions of such large ILP models are practically impossible to obtain. 
However, there is a real need to solve larger ILP models. The company has to repeat-
edly solve many similar ILP problems of varying sizes in order to schedule turnaround 
maintenance projects of different plants within and outside the reﬁnery. Many of those 
plants involve a higher number of maintenance tasks and work groups (teams) and 
hence need larger ILP optimization models. An alternative solution procedure must 
be used to obtain fast and near-optimal solutions for those large-scale plant mainte-
nance problems. To achieve this objective, a two-stage heuristic solution method is 
presented in the following section.
116
5
Operations and Workforce Scheduling for Reﬁnery Turnaround …
5.4 
Heuristic Solution Method 
The optimum solution of the ILP model speciﬁed by (5.1–5.14) is very difﬁcult, 
especially for larger realistic-size problems. Therefore, a two-stage heuristic solu-
tion procedure is presented in this section. This heuristic procedure utilizes the 
problem properties to quickly produce near-optimal solutions for large instances 
of this problem. 
In stage 1, task start times and task precedence constraints are temporarily ignored 
to reduce the size and complexity of the ILP model. However, arrival delay times are 
considered in stage 1 only for a set of tasks initially selected to be the ﬁrst tasks to be 
processed by each team. Stage 1 determines the number of work teams and the set of 
tasks allocated to each team. In stage 2, task arrival and precedence constraints are 
incorporated and used to modify the initial solution obtained from stage 1. Therefore, 
stage 2 determines the task sequence and task start times for each work team. The 
steps of the two-stage heuristic solution process are described in detail below. 
5.4.1 
Heuristic Stage 1 
1. For each given size sk, calculate the maximum possible number of work teams, 
up e
r
 
N
 S
ubscript k Baseline equals left floor upper W divided by s Subscript k Baseline right floor
, k = 1,…, K. 
2. Deﬁne G as the maximum total number of work teams, i.e., G = sig
ma s ummation Underscript k equals 1 Overscript upper K Endscripts upper N Subscript k. 
Number the teams sequentially such as the ﬁrst N1 teams are of size s1 and the 
next N2 teams are of size s2, and so on. 
3. Deﬁne E as the initial (stage 1) set of tasks to be processed ﬁrst by each team. 
Construct E by selecting the Nmax = N1 earliest-arrival tasks, excluding any task 
that has a set of preceding tasks Pj. Whenever there is a tie in arrival delay times 
aj, choose the task with the least processing time dj1. Add arrival delay times to 
the durations of all tasks in the set E. 
4. Temporarily ignoring task sequencing and start times, allocate tasks j = 1,…, J 
to work teams g = 1,…, G by solving the simpliﬁed ILP model below. 
Parameters 
E
set of the N1 (N1 = Nmax) earliest-arrival tasks that have no predecessors Pj; 
djg 
dj1 for g = 1,…,N1, 
djg 
dj2 for g = N1 + 1,…,N1 + N2, 
djg 
dj3 for g = N1 + N2 + 1,…,N1 + N2 + N3, 
djg 
…; 
sg
s1 for g = 1,…,N1, 
sg
s2 for g = N1 + 1,…,N1 + N2, 
sg
s3 for g = N1 + N2 + 1,…,N1 + N2 + N3, 
sg
…;
5.4 Heuristic Solution Method
117
de
lta  
Su bsc ript  j g  Ba selin e equa
l s S
tartLayout Enlarged left brace 1st Row d Subscript j g Baseline plus a Subscript j Baseline comma if task j element of upper E 2nd Row d Subscript j g Baseline comma otherwise period EndLayout
Decision Variables 
Z 
shutdown duration; 
St art L
ay ou t 1s t  R ow 1st Colum n u pper X  S
ub script j g 
Bas e
lin e e quals 2n d C olumn S
ta rtLayout Enlarged left brace 1st Row 1 if task j is assigned to group g 2nd Row 0 otherwise semicolon EndLayout 2nd Row 1st Column upper Y Subscript g Baseline equals 2nd Column StartLayout Enlarged left brace 1st Row 1 if group g is active 2nd Row 0 otherwise period EndLayout EndLayout
Objective Function 
The objective function (5.17) is to minimize the total shutdown duration, which is 
the time taken to complete all maintenance tasks: 
Minimize upp
er Z period
Constraints 
The above objective function is subject to the following constraints. Constraints 
(5.18) ensure that each task is assigned to one and only one work team. Constraints 
(5.19) set the shutdown duration equal to the maximum completion time, i.e., the 
sum of task processing times, among all work teams. Constraints (5.20) assign at 
least one early-arrival task from set E to each active work team. Logical constraints 
(5.21) ensure that tasks are assigned only to active work teams. Constraints (5.22) 
and (5.23), which are similar to constraints (5.11) and (5.12), establish the lower and 
the upper bounds on the number of work teams. The last constraint (5.24) assures that 
the total number of assigned maintenance technicians does not exceed the number 
of available technicians: 
s
i
gma 
su mma tion
 U nder s c r i p
t
 g equals 1 Overscript upper G Endscripts upper X Subscript j g Baseline equals 1 comma j equals 1 comma ellipsis comma upper J
up pe
r
 
Z gr
ea
ter t han
 o r eq u a l s  
s
igma summation Underscript j equals 1 Overscript upper J Endscripts delta Subscript j g Baseline upper X Subscript j g Baseline comma g equals 1 comma ellipsis comma upper G
118
5
Operations and Workforce Scheduling for Reﬁnery Turnaround …
s
igma
 s umm ation
 U nder s c r i p
t
 j element of upper E Endscripts upper X Subscript j g Baseline greater than or equals upper Y Subscript g Baseline comma g equals 1 comma ellipsis comma upper G
s
i
gma 
su mma tion U
nd ersc r i p t  
j
 equals 1 Overscript upper J Endscripts upper X Subscript j g Baseline less than or equals upper J upper Y Subscript g Baseline comma g equals 1 comma ellipsis comma upper G
s
i
gma 
sum mation
 Underscript g equals 1 Overscript upper G Endscripts upper Y Subscript g Baseline greater than or equals upper N Subscript min
s
i
gma 
sum mation
 Underscript g equals 1 Overscript upper G Endscripts upper Y Subscript g Baseline less than or equals upper N Subscript max
s
i
gma 
summa tion
 Underscript g equals 1 Overscript upper G Endscripts s Subscript g Baseline upper Y Subscript g Baseline less than or equals upper W period
It must be noted that constraints (5.13) and (5.14) must be added to the ILP model 
of stage 1 deﬁned above by expressions (5.17)-(5.24). Adding these two bounds on 
the shutdown duration Z reduces the feasible search space and hence decreases the 
ILP computation time. 
5.4.2 
Heuristic Stage 2 
The solution of stage 1 determines the number and the size of work teams and the 
set of tasks assigned to each team. In stage 2, the set of tasks assigned to each team 
is sequenced, and their start times are determined in the following steps: 
1. Sequence all tasks assigned to the given team in increasing order of their arrival 
delay times aj. Each team has at least one assigned task from the set E that can 
serve as the ﬁrst task(s) for the team. If all task precedence and arrival constraints 
(5.4) are satisﬁed, stop. Otherwise, continue to step 2. 
2. Move the predecessor tasks (that belong to a set Pj) forward in the sequence to 
start as early as possible. 
3. Move the follower tasks (preceded by tasks in the set Pj) backward in the sequence 
to start as late as possible. 
5.4.3 
Size of the Heuristic ILP Model 
The ILP model of the heuristic stage 1, speciﬁed by (5.17–5.24) combined with 
(5.13–5.14), is a basic task-team assignment model. This simpliﬁed model ignores 
precedence relationships, task arrival times, and task start times. Therefore, it is much
5.5 Case Study
119
simpler and smaller than the optimum ILP model deﬁned by (5.1–5.14). The number 
of variables and constraints in the heuristic stage 1 model is given by: 
Number of decision variables eq u a l s up pe r
 G upper J plus upper G plus 1
Number of constraints eq ua ls  up pe r 
J plus 3 upper G plus 5 period
To illustrate the size of the stage 1 model, let us consider the case study to be 
presented in the following section, for which the relevant input values are J = 42 
and G = 16. Using equations (5.25) and (5.26), the heuristic stage 1 model has only 
689 variables and 95 constraints. This is a much smaller size than the optimum ILP 
model that has 26,489 variables and 449 constraints. 
5.5 
Case Study 
Both the optimum ILP model and the two-stage heuristic solution were applied 
for shutdown maintenance scheduling in a large oil reﬁnery that includes multiple 
integrated plants. The case study presented here is applicable to two similar and 
neighboring plants located in the northern part of the reﬁnery. Similar shutdown 
maintenance projects have to be regularly scheduled in all plants of the reﬁnery. 
Several steps have been taken in order to prepare the scope and collect the required 
data for this turnaround project. The ﬁrst step is to identify the required turnaround 
maintenance tasks and classify them as either repetitive or non-repetitive. Repetitive 
maintenance items correspond to standard types of large reﬁnery components such as 
columns, drums, heat exchangers, ﬁn fans, furnaces, piping, and rotating equipment. 
Non-repetitive maintenance items include several types of repair/improvement tasks 
such as alterations to equipment, pipe work modiﬁcations, and valve replacements 
and repairs. The second step is to carefully examine all identiﬁed maintenance tasks 
to conﬁrm they are indeed shutdown items. All maintenance tasks that can be carried 
out while the plant is in operation must be removed from the scope of the turnaround 
maintenance project. 
For each task that is included in the turnaround maintenance project scope, 
several pieces of data are collected. The ﬁrst piece of required data is the processing 
time, which is estimated from three sources of input: historical data, equipment 
maintenance manuals, and estimates by experienced maintenance supervisors. Task 
processing time is a function of the work team size, i.e., the number of technicians 
performing the task. Therefore, the processing time of each task must be estimated 
for each allowed team size. The second piece of required data is the arrival delay time 
of each task, indicating the time after which the task becomes available for techni-
cians to work on. The last piece of required data is the set of immediate predecessors 
for each task, if any. This information is needed to ensure that all maintenance tasks 
are processed in the right logical and physical sequence.
120
5
Operations and Workforce Scheduling for Reﬁnery Turnaround …
For the oil reﬁnery turnaround maintenance case study, the project scope includes 
42 main maintenance tasks. Table 5.1 shows the relevant data for each task, which 
is the arrival delay time aj, the processing time djk, and the set of preceding tasks Pj. 
The following additional data is applicable to the whole project, which is the total 
workforce size and the set of allowed team sizes: 
St a rtLa
yo u t 
1st  Row 1st Co lumn upper W equals 2nd Column 11 0 2nd Row 1st Column upper K equals 2nd Column 2 3rd Row 1st Column s Subscript 1 Baseline equals 2nd Column 12 comma s Subscript 2 Baseline equals 14 period EndLayout
The upper and the lower bounds on the number of work groups N are calculated 
by (5.11) and (5.12) as shown below: 
Start L
a
you
t
 1
s
t Ro
w upp e
r
 N 
S
ub
s
cr ipt min Baseline equals left ceiling 110 divided by 14 right ceiling equals 8 2nd Row upper N Subscript max Baseline equals left floor 110 divided by 12 right floor equals 9 period EndLayout
Therefore, 
St artL
ay out 1st Row upper N greater than or equals 8 2nd Row upper N less than or equals 9 period EndLayout
Table 5.1 Data of the reﬁnery turnaround maintenance tasks (Reprinted from Alfares (2022), with 
permission from Springer(2)) 
6
1
 9
 8
20 
2
9
8
34 
5
3
3 
7
1
 5
 5
21 
2
9
8
35 
6
7
6
3 
8
1
 9
 8
 5
 22 
3
7
6
36 
7
4
4 
9
1
 5
 5
23 
3 
11 
10
4
37 
7
4
4 
10 
1
5
5
6
24 
3
8
7
38 
7
4
4 
11 
1
5
5
25 
3
3
3
39 
8
1
1 
12 
1
4
4
26 
3
9
8
40 
8
3
3
33 
13 
1
4
4
27 
4
8
7
41 
8
8
7 
14 
1
4
4
28 
4
1
1
42 
8
5
5 
∑
130 280 258 
Minimum shutdown duration Z = 32 days 
Required number of work teams N = 9 
j 
aj 
dj,1 
dj,2 
Pj
j 
aj 
dj,1 
dj,2 
Pj
j
aj 
dj,1 
dj,2 
Pj 
1 
0 
15 
13
15 
1
5
5 
29
4
3
3
 
2 
0 
15 
13
16 
1
5
5 
30
4
6
6
 
3 
0 
15 
13
17 
2
5
5 
31
4
4
4
 
4
0
 9
 8
 
18 
2
7
6 
1, 2 
32 
4 
11 
10 
5
1
 9
 8
 
19 
2 
13 
12 
33
5
4
4
 
5.5 Case Study
121
Similarly, the upper and the lower bounds on the shutdown duration Z are 
calculated by (5.13) and (5.14) as shown below: 
Start L
a
you
t
 
1
st  Ro
w upp e
r
 Z 
S
u
b
sc ript min Baseline equals left ceiling 258 divided by 9 right ceiling equals 29 2nd Row upper Z Subscript max Baseline equals left floor 280 divided by 8 right floor equals 35 period EndLayout
Therefore, 
St ar tLa
yo ut  1st Row upper Z greater than or equals 29 2nd Row upper Z less than or equals 35 period EndLayout
5.5.1 
Optimum Solution of the Case Study 
For the case study, the integer linear programming (ILP) optimization model 
expressed by (5.1–5.14) has the following parameters: J = 42, K = 2, G = Nmax = 
9, and T = Zmax = 35. The model is completely deﬁned by inputting the values given 
in Table 5.1 and specifying the above bounds on N and Z in constraints (5.11–5.14). 
To solve this model, OpenSolver (2020) add-in for Excel was used. To speed up the 
solution, automatic scaling was activated, and the Integer Optimality Allowance was 
increased to 5%. 
Running on a laptop with an Intel® Core™ i7, 2.90 GHz processor, and 8.00 
RAM, OpenSolver produced the optimum solution shown in Fig. 5.2 in 941.07 s. 
The solution speciﬁes the following values: 
Minimum shutdown duration Z = 32 days 
Required number of work teams N = 9.
Fig. 5.2 Optimum reﬁnery turnaround maintenance task and team schedule (Adapted from Alfares 
(2022), with permission from Springer(1)) 
122
5
Operations and Workforce Scheduling for Reﬁnery Turnaround …
Table 5.2 Heuristic stage 1 assignment of 42 tasks to 9 work teams (Reprinted from Alfares (2022), 
with permission from Springer(2)) 
g 
sg Jobs 
g 
sg Jobs 
g 
sg Jobs 
1 
12 4, 17, 22, 27
2 
12 10, 13, 23, 24, 29
3 
12 7, 16, 18, 32, 40 
4 
12 1, 11, 26, 28, 39
5 
12 3, 8, 41
6 
12 2, 19, 36 
7 
12 14, 20, 25, 30, 38, 42 8 
12 12, 21, 33, 34, 35, 37 9 
14 5, 6, 9, 15, 31 
5.5.2 
Heuristic Solution of the Case Study 
To solve the reﬁnery turnaround maintenance scheduling problem, the steps of the 
two-stage heuristic algorithm are applied as follows: 
5.5.2.1
Heuristic Stage 1 
1. 
up er N 1 e
q
ual
s
 u
p
per N Su b
s
cri
p
t 
m
ax Baselin e
 
equ
a
ls
 
left floor 110 divided by 12 right floor equals 9 comma upper N 2 equals left floor 110 divided by 14 right floor equals 7 comma upper N Subscript min Baseline equals left ceiling 110 divided by 14 right ceiling equals 8 period
2. G = 9 + 7 = 16, where g = 1, …, 9 for s1 = 12, and g = 10, …, 16 for s2 = 14. 
3. Set E has Nmax = 9 tasks. Choosing all 4 tasks with aj = 0 and the shortest 5 
tasks with aj = 1, E = {1, 2, 3, 4, 7, 9, 12, 13, 14}. Since tasks 8 and 10 are 
preceded by tasks in set Pj, they are excluded from the set E. 
4. The ILP model of stage 1 expressed by (5.17–5.24) plus (5.13–5.14) was opti-
mally solved using OpenSolver. Activating automatic scaling and setting Integer 
Optimality Allowance to 5%, the solution shown in Table 5.2 was obtained in 
only 2.21 s. The number of work groups is 9, and the initial shutdown duration 
is 32 days. 
5.5.2.2
Heuristic Stage 2 
1. For each work team, the assigned maintenance tasks are sequenced in an 
increasing order of their arrival delay times aj. This is already done in Table 
5.2. The scheduled start times satisfy arrival time restrictions for all tasks. 
2. Except for task 33, all predecessor tasks (numbers 1, 2, 3, 4, 5, 6, and 33) are 
ﬁrst in the sequence for their work teams. Task 33, which is a predecessor for 
task 40, is in the third position for team 8, while task 40 is in the ﬁfth position 
for team 3. Considering cumulative processing times, however, task 33 will be 
surely ﬁnished before task 40 is started. Therefore, it is not necessary to move 
task 33 forward to an earlier position in the sequence for team 8. 
3. Out of the follower, i.e., preceded tasks (numbers 8, 10, 18, 23, 35, and 40), only 
task 10 is scheduled in the ﬁrst position. Since task 10 is preceded by task 6, it 
is moved back to the last position in the sequence for work team 2. Likewise, 
since task 18 is preceded by both tasks 1 and 2, it is moved back from the third 
position to the fourth position in the task sequence for team 3.
5.6 Evaluation of the Heuristic Method
123
Team sk 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 
1 
12         4             17           22               27             
2 
12       13           24         29             23               10     
3 
14       7         16               32                 18         40   
4 
12               1                   11             26         28 39   
5 
12               3                       8                 41       
6 
12               2                           19                 36   
7 
12       14           20           25         30         38       42     
8 
12       12           21             33     34         35           37   
9 
12           5               6           9         15         31     
Fig. 5.3 Team assignment and job schedule obtained by the heuristic method (Adapted from Alfares 
(2022), with permission from Springer(1)) 
After performing the three above steps of stage 2 of the heuristic, we obtain the 
solution shown in Fig. 5.3. 
The heuristic solution’s ﬁnal shutdown duration is 32 days, which is equal to the 
optimal duration obtained by ILP. Therefore, for the reﬁnery maintenance case study, 
the two-stage heuristic procedure produces an alternative optimal solution which is 
equivalent to the ILP solution. 
5.6 
Evaluation of the Heuristic Method 
The overall performance of the two-stage heuristic algorithm has to be tested in 
order to determine how well it can solve similar turnaround maintenance scheduling 
problems in the same reﬁnery or in any other plant. Producing the optimal solution 
for the reﬁnery maintenance case study represents performance in a single problem 
only. This cannot be used to make valid conclusions regarding the overall perfor-
mance of the two-stage heuristic method. To make such conclusions, the heuristic’s 
performance must be evaluated in solving many realistic problems of varied sizes 
and characteristics. This evaluation is done by comparing the heuristic’s solutions 
with the optimum ILP solutions in terms of two criteria. The ﬁrst criterion is the 
solution quality, i.e., the length of the shutdown duration. The second criterion is the 
solution speed, i.e., computation time. 
For this purpose, an extensive computational analysis was conducted using a 
broad variety of test problems. In total, 30 test problems were randomly generated 
and then solved by the two methods: the optimum ILP model and the two-stage 
heuristic method. Since the optimum ILP solution is not possible for excessively 
large problems, the size of the test problems was limited to allow solution by both 
methods. To assess the effect of the problem size, two problem sizes were considered: 
30 tasks for 15 test problems and 35 tasks for 15 test problems. The parameter values 
of all the test problems were randomly generated but were designed to be similar to 
those of the reﬁnery maintenance case study. For example, the longer (normal) task 
processing times dj1 were randomly generated from the discrete uniform distribution
124
5
Operations and Workforce Scheduling for Reﬁnery Turnaround …
U(1,12), while the arrival delay times aj were generated from the discrete uniform 
distribution U(0, 6). 
Table 5.3 shows task arrival and processing times for the 30-task test problems, 
while Table 5.4 shows the same values for the 35-task test problems. Table 5.5 shows 
the precedence relationships for all test problems. Table 5.6 shows the workforce size 
W, the standard team size s1, and the rush team size s2, as well as all the results for 
the 30-task test problems, while Table 5.7 shows the corresponding values for the 
35-task test problems.
Each test problem was solved by the optimum using ILP model and the two-
stage heuristic algorithm. For each problem, the two solutions were compared in 
terms of two performance measures: shutdown duration and computation time. For 
both methods, OpenSolver was used on a laptop with an Intel® Core™ i7, 2.90 GHz 
processor, and 8.00 RAM. To reduce computation times of both methods, OpenSolver 
was run under the following settings: activated automatic scaling, 1% Constraint 
Precision Tolerance, and 5% Integer Optimality Tolerance. 
Table 5.6 displays the results of the computational runs for the 30-task test prob-
lems, while Table 5.7 shows the results for the 35-task test problems. In Tables 5.6 
and 5.7, the symbol ZILP denotes the optimal ILP Z value, i.e., shutdown duration, 
while tILP denotes the ILP computation time in seconds. Similarly, the symbols ZHEUR 
and tJEUR denote the corresponding Z value and computation time for the two-stage 
heuristic algorithm. Additionally, the symbol Z% represents the heuristic method’s 
percentage increase in Z value above the optimum ILP Z value. 
Furthermore, the symbol t÷ represents the ratio of the solution times of the two 
methods, which is deﬁned as t÷ = tILP/tHEUR. Actually, t÷ can be considered as the 
ratio of the heuristic method’s computational speed over the optimum ILP method’s 
computational speed. 
The results in Table 5.6 show that the two-stage heuristic method has superior 
performance in terms of both solution quality and solution speed for the 30-task 
problems. With respect to solution quality, the heuristic method produced optimal 
solutions in 11 out of the 15 test problems (73%) and increased the Z values (shutdown 
durations) by only 1.25% on average above the optimum ILP Z values. With respect 
to solution speed (computation time), the heuristic method is on average 1,041 times 
faster than the optimum ILP method. 
The results in Table 5.7 show that the two-stage heuristic method also has superior 
performance in terms of the two performance criteria for the 35-task problems. With 
respect to solution quality, the heuristic method produced optimal solutions in 10 out 
of the 15 test problems (67%) and increased the Z values (shutdown durations) by 
only 1.24% on average above the optimum ILP Z values. With respect to solution 
speed (computation time), the heuristic method is on average 2,213 times faster than 
the optimum ILP method. 
In order to judge the effect of problem size on the heuristic performance, results 
in Tables 5.6 and 5.7 should be compared. Starting with the solution quality, the 
performance of the heuristic relative to ILP is practically the same for 30-task and 
35-task problems. For both sizes, the heuristic produces optimum shutdown durations 
for about 70% of the problems. For all problems, the average increase in shutdown
5.6 Evaluation of the Heuristic Method
125
Table 5.3 Task arrival and processing times for the 30-task test problems 
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(continued)
duration is 1.25% and the maximum increase is only one day. Clearly, the heuristic’s 
near-optimal quality is not affected by the size of the problem. 
Turning the attention to solution speed, the relative advantage of the heuristic 
method over the optimum ILP solution becomes greater as the problem size increases. 
While the heuristic is 1,041 times faster than ILP for the 30-task problems, it is 2,213 
times faster for the 35-task problems. This is due to the fast increase in the optimal ILP 
computation time, whose average increases from 1,175 s for the 30-task problems to
126
5
Operations and Workforce Scheduling for Reﬁnery Turnaround …
Table 5.3 (continued)
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conﬁrms that the heuristic is the only viable solution alternative for large real-life 
problems. For such problems, ILP is clearly not a viable solution alternative.
5.6 Evaluation of the Heuristic Method
127
Table 5.4 Task arrival and processing times for the 35-task test problems (Adapted from Alfares 
(2022), with permission from Springer(1)) 
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7
4
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3
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11
 
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31
 
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2
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11
 
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32
 
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6
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4
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 1
1
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3
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9
7
 
33
 
6
 
10
 
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 6
 
12
 
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5
4
 4
6
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4
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2
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9
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6
 
11
 
9
 
34
 
6
 
12
 
10
 
6
5
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5
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6
8
 7
6
 3
3
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5
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5
 4
6
 
12
 
10
 
35
 
6
 3
3
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4
 4
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10
 
8
6
3
 3
6
 3
3
 6
9
 7
 6
 
11
 
9
6
 
10
 
8
(continued)
128
5
Operations and Workforce Scheduling for Reﬁnery Turnaround …
Table 5.4 (continued)
#
9
10
11
12
13
14
15 
j 
a 
d1 d2 a 
d1 d2 a 
d1 d2 a 
d1 d2 a 
d1 d2 a 
d1 d2 a 
d1 d2 
1
0 11
 
9
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3 0 6 
5 0 9 
7 0 11 9 0 9 
7 0 1 
1 0 12 10 0 4 
4 
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0
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0
9
 7
0
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0
 
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9
 
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8 1 2 
2 2 2 
2 1 9 
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6 1 12 10 1 9 
7 
9 1 11 9 2 5 
4 1 7 
6 1 12 10 2 1 
1 1 12 10 1 4 
4 
10 2 11
 
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11 2
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2 12 10 2 12 10 2
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12 2 12 10 2 12 10 2
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13 2
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14 2 10  8  2 11  9  2 12 10  
2  4  4  2  7  6  3  6  5  2  3  3  
15 3
4
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3 12 10 2
 
9
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18 3 11  9  3 10  8  3 12 10  
3 11  9  2  3  3  4  
10  8  2  3  3  
19 3
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3 12 10 3
 
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33 6
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6 12 10 6 12 10 6
 
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 6
5.7 Summary and Conclusions
129
Table 5.5 Tasks with precedence relations for the two sets of test problems (Adapted from Alfares 
(2022), with permission from Springer(1)) 
# 
30-task problems
# 
35-task problems 
1 
j 
22
17 
26 
16 
29
1 
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5.7 
Summary and Conclusions 
In this chapter, optimization models have been presented to solve an actual reﬁnery 
turnaround maintenance task scheduling and workforce allocation problem. For this 
problem, we are given a set of maintenance tasks and a limited number of maintenance 
technicians. Tasks have arrival time and precedence constraints, and their durations 
depend on the number of technicians assigned to perform them. To solve the problem, 
we need to divide the technicians into distinct work teams to work in parallel on
130
5
Operations and Workforce Scheduling for Reﬁnery Turnaround …
Table 5.6 Results of computational experiments with the 30-task test problems 
No.
W
s1
s2
ZILP
tILP 
ZHEUR 
tHEUR  
Z%
t÷
1
 
90
 12
15
26
 
510.06
 26
 
0.54
 0
 
944.56
 
2
100 
14 
17 
29
1442.36 
29
2.25
0
641.05 
3
 
80
 12
16
26
 
573.87
 28
 
0.28
 7.69
 
2049.54
 
4
90
10 
12 
26
460.05
27
1.89
3.85 
243.41 
5
80
13 
16 
28
1498.70 
28
1.97
0
760.76 
6
100 
13 
17 
29
1788.81 
29
2.07
0
864.16 
7
90
12 
15 
28
1268.34 
28
0.83
0
1528.12 
8
80
10 
12 
27
613.05
28
1.44
3.70 
425.73 
9
100 
14 
18 
27
1397.82 
27
1.35
0
1035.42 
10
80
12 
16 
27
1550.03 
27
2.04
0
759.82 
11
90
12 
15 
29
1702.25 
30
1.92
3.45 
886.59 
12
100 
12 
16 
28
1143.12 
28
0.50
0
2286.24 
13
80
10 
12 
26
774.27
26
0.72
0
1075.38 
14
90
12 
16 
28
1893.93 
28
1.44
0
1315.23 
15
100 
12 
15 
26
1006.46 
26
1.25
0
805.17 
Ave
27.33 
1174.87 
27.67 
1.37
1.25 
1041.41 
Table 5.7 Results of computational experiments with the 35-task test problems (Adapted from 
Alfares (2022), with permission from Springer(1)) 
No. 
W
s1
s2
ZILP
tILP 
ZHEUR 
tHEUR  
Z%
t÷
1
110 
12 
15 
22
690.74
22
0.68
0
1015.79 
2
100 
12 
16 
28
1499.15 
28
1.00
0
1499.15 
3
110 
13 
16 
29
2731.15 
29
2.32
0
1177.22 
4
100 
12 
16 
29
2256.85 
30
0.38
3.45
5939.08 
5
110 
13 
16 
27
1507.87 
28
0.82
3.7037 
1838.87 
6
120 
13 
16 
27
1929.04 
28
0.87
3.7037 
2217.29 
7
120 
13 
15 
25
1495.68 
25
14.55 
0
102.80 
8
100 
12 
15 
27
1529.05 
28
0.83
3.70
1842.23 
9
110 
12 
14 
25
1671.00 
26
0.56
4.00
2983.93 
10
110 
12 
15 
27
2913.47 
27
0.71
0
4103.48 
11
120 
13 
17 
28
2489.01 
28
0.87
0
2860.93 
12
100 
12 
15 
29
2476.74 
29
1.24
0
1997.37 
13
100 
12 
15 
27
1866.14 
27
1.14
0
1636.96 
14
120 
13 
17 
30
3450.75 
30
0.87
0
3966.38 
15
120 
13 
16 
24
1167.32 
24
079
0
14.78 
Ave
26.93 
1978.26 
27.27 
7.06
1.24
2213.08
different sets of tasks. For each team, we must assign a set of maintenance tasks 
and determine their sequence and start times. The objective is to minimize the total 
maintenance duration while satisfying all applicable constraints. 
In order to analyze and solve the turnaround maintenance task and workforce 
scheduling problem, an integer linear programming (ILP) model was constructed.
References
131
The ILP model was used to solve an actual turnaround maintenance scheduling case 
study in a large oil reﬁnery. Although the model successfully produced the optimum 
solution for the case study, it required an exceedingly long computation time. Conse-
quently, an efﬁcient two-stage heuristic method was developed for solving larger real-
life turnaround maintenance scheduling problems. To evaluate the heuristic method, 
an extensive numerical analysis was conducted using a large set of randomly gener-
ated test problems. The numerical experiments conﬁrmed the effectiveness of the 
heuristic method and its ability to produce fast and near-optimal solutions to the 
turnaround maintenance scheduling problem. 
Future extensions of this work include adding paid overtime as an option to 
reduce maintenance task durations. Different skill levels or skill sets can also be 
considered for maintenance tasks and technicians. Moreover, the workforce size can 
be considered a decision variable instead of a given limit. 
Acknowledgements 
1. Adapted with permission from Springer Nature Customer Service Centre GmbH: 
Springer, Journal of Scheduling, Plant shutdown maintenance workforce team 
assignment and job scheduling, H.K. Alfares, Copyright 2022. 
2. Reprinted with permission from Springer Nature Customer Service Centre 
GmbH: Springer, Journal of Scheduling, Plant shutdown maintenance workforce 
team assignment and job scheduling, H.K. Alfares, Copyright 2022. 
References 
Alfares, H. K., & Al-Khair, W. S. A. (2003). Simulation-based days-off scheduling of pipelines 
maintenance workforce. In: Proceedings of the 1st Multidisciplinary International Conference 
on Scheduling: Theory and Applications (MISTA) (Vol. 2, pp. 524–525), Nottingham, UK. 
Alfares, H. K., Lilly, M. T., & Emovon, I. (2007). Maintenance staff scheduling at Afam power 
station. Industrial Engineering & Management Systems, 6(1), 22–27. 
Akbar, A. (2018). Risk Management Perspective on Employee Scheduling for Maintenance of 
Automated Safety Systems for Remotely Located Oil & Gas Facilities. Master’s thesis, Molde 
University College, Molde, Norway. 
Alfares, H. K. (2022). Plant shutdown maintenance workforce team assignment and job scheduling. 
Journal of Scheduling, 25, 321–338. 
Al-Turki, U., Duffuaa, S., & Bendaya, M. (2019). Trends in turnaround maintenance planning: 
Literature review. Journal of Quality in Maintenance Engineering, 25(2), 253–271. 
Awad, M., & Ertem, M. (2017). Stochastic scheduling of workforce-constrained preventive mainte-
nance activities in petroleum plants. In: 2017 Annual Reliability and Maintainability Symposium 
(RAMS) (pp. 1–5). IEEE. 
Castro, P. M., Grossmann, I. E., Veldhuizen, P., & Esplin, D. (2014). Optimal maintenance 
scheduling of a gas engine power plant using generalized disjunctive programming. AIChE 
Journal, 60(6), 2083–2097. 
Fırat, M., & Hurkens, C. A. J. (2012). An improved MIP-based approach for a multi-skill workforce 
scheduling problem. Journal of Scheduling, 15(3), 363–380.
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Ghaithan, A. M. (2020). An optimization model for operational planning and turnaround 
maintenance scheduling of oil and gas supply chain. Applied Sciences, 10(21), 7531. 
Hey, R. B. (2019). Turnaround Management for the Oil, Gas, and Process Industries: A Project 
Management Approach. Gulf Professional Publishing. 
Ighravwe, D. E., & Oke, S. A. (2014). A non-zero integer non-linear programming model for 
maintenance workforce sizing. International Journal of Production Economics, 150, 204–214. 
Kovacs, A. A., Parragh, S. N., Doerner, K. F., & Hartl, R. F. (2012). Adaptive large neighborhood 
search for service technician routing and scheduling problems. Journal of Scheduling, 15(5), 
579–600. 
Leite, G. A., & Vellasco, M. M. B. R. (2020). Development of offshore maintenance service 
scheduling system with workers allocation. In: 2020 IEEE Congress on Evolutionary Computa-
tion (CEC) (pp. 1–6). 
Redutskiy, Y. (2018). Pilot study on the application of employee scheduling for the problem of 
safety instrumented system design and maintenance planning for remotely located oil and gas 
facilities. Engineering Management in Production and Services, 10(4), 55–64. 
Safaei, N., Banjevic, D., & Jardine, A. K. (2011). Bi-objective workforce-constrained maintenance 
scheduling: A case study. Journal of the Operational Research Society, 62(6), 1005–1018. 
Schulz, E. P., Bandoni, J. A., & Diaz, M. S. (2006). Optimal shutdown policy for maintenance 
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Chapter 6 
Simulation-Based Scheduling of Pipeline 
Maintenance Crews 
6.1 
Introduction 
Pipelines are used in the oil industry for transporting crude oil, natural gas, and 
reﬁned products, both onshore and offshore (subsea). Pipelines are one of the safest 
and cheapest means of transporting crude oil as compared to trucks, tanker ships, and 
trains. Pipelines are generally classiﬁed into three main types: (1) gathering pipelines 
for short-distance transport of crude oil from production wells to treatment facilities, 
(2) transportation pipelines for long-distance transport of crude or reﬁned products 
across several regions or countries, and (3) distribution pipelines for short-distance 
transport of reﬁned products from reﬁneries to consumers. Transportation pipelines, 
the focus of this chapter, are used for long-haul cross-country transport of crude oil 
to consumer markets or export terminals. Figure 6.1 shows a typical pipeline used to 
transport crude oil through the desert in Saudi Arabia.
Transportation pipelines are complex systems that are composed of large-diameter 
pipes, valves, motors, pumps, turbines, meters, sensors, and compressor or pump 
stations. Figure 6.2 shows some of the common components of an oil pipeline system. 
These systems involve moving parts, and they deal with high pressures and harsh 
materials. Consequently, pipelines require extensive inspection and regular mainte-
nance according to strict government and industry standards. Pipeline maintenance 
activities include leak detection, leak location, pressure balancing, and predictive 
maintenance. Typical pipeline maintenance activities include cleaning, repairing, or 
replacing pipeline components such as pipes. An important maintenance activity that 
is speciﬁc to pipelines is pigging, which is using devices called “pigs” to perform 
various cleaning, inspection, and maintenance operations inside the pipes.
Due to the complexity and variety of the pipeline components, pipeline main-
tenance work requires several types of technicians with multiple specialized skills. 
Therefore, pipeline maintenance is performed by multi-specialization crews of tech-
nicians with skills in different maintenance areas (crafts). Usually, most pipeline 
maintenance tasks are unplanned failure repairs. Therefore, the pipeline maintenance 
workloads are stochastic, i.e., random variables that can be described by probability
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 
H. K. Alfares, Applied Optimization in the Petroleum Industry, 
https://doi.org/10.1007/978-3-031-24166-6_6 
133
134
6
Simulation-Based Scheduling of Pipeline Maintenance Crews
Fig. 6.1 Crude oil pipeline across the desert. Courtesy of Saudi Aramco, copyright owner
Fig. 6.2 Employee inspecting oil pipeline components. Courtesy of Saudi Aramco, copyright 
owner
distributions. This means that the optimum planning of pipeline maintenance activi-
ties requires stochastic techniques for scheduling groups of maintenance technicians 
with multiple specializations. 
In this chapter, a simulation-based optimization technique is used for stochastic 
days-off scheduling of multi-specialization maintenance crews. This technique
6.1 Introduction
135
is effectively employed to determine the optimum days-off schedules of multi-
specialization pipeline maintenance crews for a large oil company in the Middle 
East. The maintenance crews belong to the Pipelines Department, which is fully 
responsible for all the company’s long-distance pipelines used for transporting oil, 
gas, and reﬁned products. Within this Department, the Pipelines Maintenance Unit 
has the responsibility for planned maintenance and failure repairs of all the company’s 
pipelines in a speciﬁc geographical area. 
The Pipelines Maintenance Unit employs a workforce consisting of 19 main-
tenance technicians classiﬁed into ﬁve different specializations. The labor regula-
tions applicable to pipeline maintenance technicians allow them to be assigned to 
only three alternative days-off work schedules. The majority of work orders issued 
by the Pipeline Maintenance Unit are unplanned, and each order requires multiple 
specializations. 
Each maintenance specialization has a different workload, and all the workloads 
are stochastic random variables. Given the probabilistic workload of each specializa-
tion, the Pipelines Maintenance Unit needs to determine the work schedules for all 
maintenance technicians, i.e., the optimum assignment of each technician to one of 
the three permissible days-off schedules. The objective is to best match the various 
probabilistic workloads by minimizing the average completion time (waiting time 
plus processing time) of work orders for each maintenance specialization. 
Obviously, deterministic models such as integer programming are not applicable 
to the problem described above. Since the problem is stochastic with complex 
relationships among multiple interacting factors, a simulation-based optimization 
approach should be used. Therefore, a simulation model is developed to represent 
and improve the maintenance work order system in order to attain the objectives of 
the Pipelines Maintenance Unit. For the technicians of each maintenance specializa-
tion, the simulation model compares all possible days-off scheduling assignments, 
including the existing ones. Consequently, the model identiﬁes the best days-off 
schedules for the crew of each maintenance specialization. The model estimates that 
switching to the recommended alternative schedules will reduce the average work 
order completion time by 28%. This signiﬁcant improvement is obtained simply by 
rearranging the days-off schedules of the current technicians, without any increase 
in the size or the cost of the workforce. 
The remaining sections of this chapter are organized as follows. Relevant literature 
is reviewed in Sect. 6.2. The problem and its background are described in Sect. 6.3. 
The collection and preliminary analysis of data are presented in Sect. 6.4. The  main-
tenance simulation model is described in Sect. 6.5, where the model’s objectives, 
assumptions, design, setup, and other aspects are presented. The optimum days-off 
scheduling alternatives for each maintenance specialization are presented in Sect. 6.6. 
Finally, a summary and appropriate conclusions are provided in Sect. 6.7.
136
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Simulation-Based Scheduling of Pipeline Maintenance Crews
6.2 
Literature Review 
Workforce scheduling models are used to assign work and off periods to each 
employee in order to satisfy time-varying labor demands over a given planning 
horizon. The objective is to minimize either the total number of assigned employees 
or their total labor cost. In addition to satisfying the required stafﬁng levels for 
each time period, workforce schedules must also satisfy several applicable labor 
regulations pertaining to safety, fairness, and quality of life. In general, workforce 
scheduling problems are classiﬁed into three main categories: shift (hours-of-day) 
scheduling, days-off (days-of-week) scheduling, and tour (hours-of-day and days-
of-week) scheduling. Workforce scheduling problems are also classiﬁed as either 
deterministic or stochastic. This chapter addresses a stochastic days-off scheduling 
problem for a multiple-specialization pipeline maintenance workforce. Since the 
pipeline maintenance workforce scheduling problem is stochastic and complex, a 
simulation model is used to tackle this problem. Therefore, this section focuses on 
simulation models for maintenance workforce scheduling. 
Simulation models are frequently used for scheduling maintenance personnel 
in manufacturing environments. Mjema (2002) uses a simulation model to deter-
mine the optimum stafﬁng level and stafﬁng policies for a maintenance department 
with multiple units and multiple-specialization personnel. The model shows that the 
average work order throughput time is minimized under the policies of location ﬂex-
ibility and specialization ﬂexibility, i.e., allowing technicians to move across units 
and across specializations. Another simulation model is used by Langer et al. (2010) 
to analyze dispatching policies for a maintenance workforce in a manufacturing envi-
ronment. Three policies are compared: ﬁrst-come ﬁrst-served, constant bottleneck, 
and dynamic bottleneck. The best policy, with the least average throughput time, is 
found to be the dynamic bottleneck policy, in which maintenance priority is given to 
the current bottleneck machine. 
Siu et al. (2013) apply what they call the Simpliﬁed Simulation-based Scheduling 
(S3) methodology to plan resource-constrained, location-based reﬁnery shutdown 
maintenance activities. The S3 methodology produces task and workforce sched-
ules to minimize the shutdown duration and the total cost. Specialist maintenance 
technicians are allocated to times and tasks subject to availability limits, scheduled 
break times, and other shift constraints. A simulation-based optimization model is 
used for a repair facility by Turan et al. (2020) to jointly determine the values of 
spare parts inventory, workforce capacity, and repair scheduling priority. Taking 
the decision-maker’s risk-averseness into consideration, a discrete-event simulation 
model is combined with a variable neighborhood search meta-heuristic to minimize 
the sum of inventory holding and backordering costs. 
Several simulation models have been used to optimize workforce planning and 
scheduling for companies providing maintenance as a service to customers. Visser 
and Howes (2007) construct a simulation model to determine the optimum number of 
maintenance technicians for a company that provides on-sight maintenance services 
of medical equipment at hospitals in various locations. Considering the probability
6.2 Literature Review
137
distributions of maintenance frequencies, task durations, and traveling costs, the 
model is used to maximize the company’s expected proﬁt. To analyze workforce 
schedules, Alwadood et al. (2010) develop a simulation model of the maintenance 
department of an information technology company. The objective is to minimize the 
average customer service time, in order to avoid exceeding the time limits and hence 
paying the penalties stated in the Service Level Agreement. The simulation model 
determines the optimum workforce work schedule to reduce the average service 
time and increase the number of completed jobs. Choudhari and Gajjar (2018) use  
a simulation model to analyze the trade-off between crew utilization and customer 
waiting time for an electrical maintenance department that repairs electrical faults in 
several residential buildings. The model is used to determine the appropriate stafﬁng 
level in each shift. The objective is to meet stochastic maintenance demands at the 
desired customer service level while maintaining a high workforce utilization. 
Applications of simulation modeling for workforce stafﬁng and scheduling 
include optimizing the maintenance services of public facilities. To optimize the 
maintenance of a city’s drainage network, Agbulos et al. (2006) combine simula-
tion analysis, work measurement, lean production theory, and methods improve-
ment techniques. The aim is to develop new productivity standards and alterna-
tive work procedures to improve the productivity of drainage maintenance crews. 
Chen et al. (2010) compare three alternative optimization models (basic deterministic 
model, basic stochastic model, and integrated stochastic model) for mass rapid transit 
(MRT) carriage maintenance stafﬁng and shift scheduling. The models are formu-
lated as mixed-integer linear programs (MILP), where the objective is to minimize 
the supplied maintenance man-hours, assuming mixed deterministic and stochastic 
labor demands. First, decomposition techniques are used to simplify the solution 
of the NP-hard models, and then the solutions are evaluated by a simulation-based 
assessment method. 
Simulation is quite often used in workforce shift scheduling for aircraft main-
tenance. A simulation model is presented by Bazargan and Jiang (2010) to opti-
mize maintenance workforce planning for an airport aircraft maintenance station. 
The model considers realistic aspects such as gate assignment, unscheduled main-
tenance, and ground time. The model is used to determine the optimum number of 
maintenance mechanics and their shift schedules to match the workload to the avail-
able workforce. Beliën et al. (2012) integrate simulation with mixed-integer linear 
programming (MILP) to optimize workforce stafﬁng and scheduling for aircraft line 
maintenance. Considering several scenarios and alternative schedules, the model 
suggests the best schedule to reduce labor cost and improve efﬁciency. 
Van den Bergh et al. (2013) propose a three-stage approach to optimize workforce 
scheduling for aircraft line maintenance. The ﬁrst stage is generating multiple alter-
native schedules using MILP, the second stage is evaluating the alternative schedules 
using simulation, and the third stage is ranking these schedules using data envelop-
ment analysis. A model enhancement (ME) heuristic approach is presented by De 
Bruecker et al. (2015) to optimize robust aircraft maintenance workforce schedules. 
The schedules are robust in the sense that, even if there are stochastic ﬂight arrival 
delays, the percentage of ﬂights with late maintenance work does not exceed a given
138
6
Simulation-Based Scheduling of Pipeline Maintenance Crews
threshold. The ME heuristic has three phases: a simulation model, an enhancement 
process, and a MILP model. 
Few models have been developed speciﬁcally for workforce scheduling in pipeline 
maintenance. Aiming to minimize the average work order throughput time, Alfares 
(2007) presents a simulation model for stochastic employee days-off scheduling 
of a pipeline maintenance workforce. Given probabilistic daily labor demands, the 
model is used to select the optimum days-off schedules for multi-skilled maintenance 
technicians. Lee et al. (2017) consider a system for the inspection and repair of oil 
and gas pipelines consisting of small Unmanned Aerial Systems (sUASs), ﬁxed 
sensing systems, and ground-based maintenance crews. To minimize the repair time 
of multiple failures, two mixed-integer programming (MIP) models are integrated 
and simultaneously used to optimally route the sUASs for identifying the failures 
and the maintenance vehicles for repairing those failures. 
Park et al. (2020) use a simulation-based approach to analyze the trade-off between 
maintenance costs and failure risks for an underground pipeline network. Considering 
the relevant costs, the desired reliability, and the failure probability, the approach is 
used to make decisions on inspection, replacement, and repair of pipeline defects. 
6.3 
Problem Description 
The problem of concern in this chapter is the maintenance of oil and gas pipelines 
by The Pipelines Maintenance Unit has a Unit (PMU) of a large oil company. PMU 
supervises, administers, and coordinates the overall work performed by the pipeline 
maintenance workforce, including the management of the crew shift schedules. 
Moreover, PMU directly supervises the pipeline maintenance technicians concerned 
with the execution of day-to-day preventive and failure maintenance jobs. PMU also 
plans, schedules, and assures completion of the assigned pipeline maintenance work 
orders. PMU recommends the optimum workforce balance for daily and weekly 
scheduling in the Unit. Therefore, PMU analyzes the current and future workloads 
for the Unit and forecasts overall stafﬁng requirements. 
The Pipelines Maintenance Unit is responsible for the planning, coordinating, 
and supervising of a multi-specialization group of pipeline technicians within an 
assigned geographical area. It has the responsibility to provide the labor, equipment, 
and materials requirements of the pipeline maintenance work orders. PMU supervises 
the technicians assigned to the maintenance work orders of the producing, utilities, 
processing, or terminal pipeline facilities. PMU assures the technicians’ compliance 
with maintenance speciﬁcations and standards and evaluates their actual performance 
against these standards. PMU is responsible for scheduling the technicians who 
perform maintenance work on the pipelines.
6.3 Problem Description
139
The Pipelines Maintenance Unit has a multi-skilled workforce consisting of 19 
technicians classiﬁed into ﬁve maintenance specializations: air conditioning (AC), 
digital (DG), electrical (EL), machinist (MA), and metal (ME). For each pipeline 
maintenance specialization, the tasks and responsibilities of each technician are 
described below: 
1. HVAC and refrigeration (AC) technician. Performs the installation, diag-
nosis, and repair of all types of large heating, ventilation, air conditioning, and 
refrigeration units in the pipeline facilities. 
2. Digital systems (DG) technician. Performs fault diagnosis, repair, testing, and 
preventive maintenance on digital equipment including mini-computers, periph-
erals, and other digital/data processing equipment in real-time process control, 
sophisticated digital switching systems, and other applications. 
3. Electrical (EL) technician. Performs specialized installation or diagnosis and 
repair on all types of electrical systems and equipment including industrial 
electronics. 
4. Machinist (MA) technician. Performs specialized diagnosis and repair on all 
levels of equipment, machinery, and components with moving mechanical parts. 
5. Metals (ME) technician. Performs specialized maintenance work in the ﬁelds 
of metallurgy, critical coating, welding, and fabrication. 
According to the company’s labor regulations, only three alternative days-off 
schedules are approved for pipeline maintenance technicians. Table 6.1 shows the 
number of technicians of the ﬁve different maintenance specializations and their 
current assignment to each of these three schedules. 
The three days-off schedules that are applicable for the pipeline maintenance 
technicians are described below: 
1. The 5/2 schedule: 5 consecutive workdays followed by 2 consecutive off days 
(weekend) per one-week cycle. 
2. The 14/7 schedule: 14 consecutive workdays followed by 7 consecutive off days 
per three-week cycle. 
3. The 7/3–7/4 schedule: two work stretches each of 7 consecutive workdays sepa-
rated by two breaks of 3 consecutive off days and 4 consecutive off days, per 
three-week cycle.
Table 6.1 Number of technicians for each specialization and schedule (Adapted from Alfares 
(2007), with permission from Taylor & Francis(1) 
Specialization
5/2
14/7
7/3-7/4
Total 
AC
1
1
2 
DG
6
6 
EL
 
3
1
1
5
 
MA
1
2
3 
ME
2
1
3 
140
6
Simulation-Based Scheduling of Pipeline Maintenance Crews
All pipeline maintenance work is performed according to an ofﬁcial work order 
(W/O) process. This process is started when work is required to perform either 
planned (preventive) or unplanned (failure) maintenance. Based on the speciﬁc reason 
for its initiation, any work order is classiﬁed into one of the following categories: 
1.
Minor maintenance. 
2.
Preventive maintenance. 
3.
Testing and inspection. 
4.
Routine work. 
5.
Emergency. 
6.
New expense. 
7.
Major work. 
8.
Capital work. 
9.
Safety and environment. 
10. Special order. 
As soon as a maintenance work order is initiated, materials, tools, and equip-
ment needed are speciﬁed according to its respective type, i.e., initiation category. 
Moreover, the labor requirements, i.e., number of technicians of each maintenance 
specialization, are determined. Naturally, most work orders require a group of tech-
nicians with multiple maintenance specializations. Subsequently, the total cost of the 
work order is estimated, and ofﬁcial approval is requested to start the work. After the 
work order is approved, it is prioritized into one of the three following categories: 
1. Priority A. Highest priority, critical work order, to be scheduled immediately. 
On average, only 10% of pipeline maintenance work orders are given priority A. 
2. Priority B. High priority, urgent work order, to be scheduled as soon as possible. 
On average, only 8% of the work orders are given priority B. 
3. Priority C. Normal priority, regular work order, to be scheduled according to 
the usual practice. On average, 82% of the work orders are given priority C. 
Each work order is scheduled according to its priority level, ﬁrst by assigning it to 
a given week and later by assigning it to a speciﬁc day within this week. Assuming 
ofﬁcial approval has been obtained and the material has been procured, work is started 
on the work order according to the schedule. Pipeline maintenance technicians can 
work for up to 12 h each workday, from 6:00 a.m. to 6:00 p.m. As soon as the work is 
completed on the given work order, a report is prepared and sent to the proponent. The 
proponent of the work order may either approve the order completion report or send 
it back with comments that may necessitate additional work. When the completion 
report is approved by the proponent, the work order is closed. Figure 6.3 shows a 
simpliﬁed ﬂowchart of the work order processing system.
The above description of the work order processing system indicates that all work 
orders must go through four stages of processing in the following order:
1. Hold (HLD) stage. The work order is waiting to receive the materials or approval 
to start. 
2. Work (WRK) stage. The work order is being processed, as it is undergoing 
maintenance work.
6.4 Data Collection and Analysis
141
W/O 
initialized 
Material & 
labor listed 
Cost 
estimated 
W/O daily 
scheduled 
Work 
finished 
W/O 
weekly 
scheduled 
Work 
(re)started 
Start 
approved? 
W/O 
closed 
W/O 
priority 
assigned 
5/2 
7/3-
7/4 
14/7 
AC 
MA 
ME 
DG 
EL 
Yes 
Finish 
approved? 
No 
No 
Yes 
Fig. 6.3 Simpliﬁed ﬂowchart of the maintenance work order process (Reprinted from Alfares 
(2007), with permission from Taylor & Francis(2))
3. Finish (FIN) stage. The work order is completed, but it is still waiting for 
approval to be closed. 
4. Close (CLS) stage. The work order is completed, approved, and entered in the 
database. 
6.4 
Data Collection and Analysis 
Constructing the simulation model requires a great deal of data collection and anal-
ysis. Extensive data was collected that covers a period of seven months of historical 
records of the pipeline maintenance work orders. For each work order started during 
the 7-month period, the following data was collected: starting time, closing time (if 
applicable), the actual work time in hours, and the number of technicians that are 
required from each specialization. The raw data was obtained from the Planning 
Unit. 
To make the given data usable for developing the model, the raw data required 
the following steps of sequential processing:
1. The starting times for all work orders were collected by hours and calendar dates. 
2. Assuming 12 work hours per day, the work order start times were converted to 
cumulative hours from the start of the 7-month data collection period. 
3. The hourly work order starting times were sorted in increasing order.
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6
Simulation-Based Scheduling of Pipeline Maintenance Crews
Table 6.2 Processing time distributions and statistics for all specializations (Adapted from Alfares 
(2007), with permission from Taylor & Francis(1)) 
Maintenance 
specialization 
type 
Number of 
technicians 
available 
Avg. no. of 
technicians 
required 
Specialization 
probability 
(%) 
Total processing hours 
probability distribution 
(by all technicians) 
AC
2
1.28
18.5
Weibull (7.03, 0.83) 
DG
6
1.41
25.0
Weibull (12.77, 0.76) 
EL
5
1.59
21.3
Lognormal (1.48, 1.07) 
MA
3
1.33
23.2
Exponential (23.78) 
ME
3
1.95
12.0
Weibull (11.92, 0.89) 
4. The differences between consecutive start times were calculated to determine the 
order inter-arrival time, i.e., the time between successive orders in hours. 
5. The differences between the ﬁnish time of each specialization and the start time of 
each work order were calculated to determine the work order processing (service) 
times in hours for each maintenance specialization. 
After processing the data, it was used to identify the appropriate probability distri-
butions of work order inter-arrival times as well as the individual processing times 
of the ﬁve maintenance specializations. The ﬁrst step was to plot the processed data, 
in order to help identify the candidate probability distributions that can ﬁt the data. 
Second, for each set of data, the relevant general statistics were calculated, such as 
the mean and the standard deviation. Next, the Chi-square goodness-of-ﬁt test was 
applied to test the candidate distributions at a 5% level of signiﬁcance. 
The probability distribution of the inter-arrival time between consecutive work 
orders was found to be exponential with a mean of 9.79 h: 
Inter hyphen a rrival distri bu tion equals Exp left parenthesis 9.79 right parenthesis period
Table 6.2 displays the probability distributions of the work order processing 
(service) times for each of the ﬁve maintenance specializations. Table 6.2 also shows 
additional pertinent statistics for each specialization, namely the average number of 
technicians required on each work order and the probability of requiring the given 
specialization on any work order. Table 6.3 shows the empirical discrete proba-
bility distributions for the number of technicians required per work order for each 
maintenance specialization.
6.5 
The Simulation Model 
A large part of maintenance work is required due to random failures, and hence, 
maintenance demands are stochastic by nature. Therefore, the daily demands for 
pipeline maintenance are stochastic, and ﬁnding the best days-off schedules of the
6.5 The Simulation Model
143
Table 6.3 Percent probabilities of team sizes required for each specialization 
Team size
1
2
3
4
5
6 
AC
71.7
28.3 
DG
72.8
19.8
3.7
2.5
0
1.2 
EL
59.4
27.5
8.7
2.9
1.5 
MA
74.7
17.3
8.0 
ME
38.5
28.2
33.3
concerned technicians is a stochastic optimization problem. In general, stochastic 
optimization problems can be handled either by simulation models or by analytical 
stochastic methods such as queueing theory or stochastic programming. However, 
the pipeline maintenance workforce scheduling problem is too complicated to be 
solved by analytical methods. Therefore, simulation modeling is the only practical 
approach for solving this complex stochastic problem. 
For the Pipeline Maintenance Unit, the main performance measure is the comple-
tion time taken for the maintenance work orders, which is equal to the service 
/processing time plus the waiting time for the technicians. Therefore, the objective 
of the simulation model is to minimize the average work order completion time for 
each maintenance specialization. This objective is achieved by ﬁnding the optimum 
combination of days-off schedules for the technicians in each specialization. For each 
specialization, alternative combinations of these days-off schedules are considered as 
different simulation scenarios. The simulation model is used to evaluate and compare 
these scheduling alternatives, in order to choose the one with the least average work 
order completion time. Since the number of days-off scheduling alternatives for 
each specialization is ﬁnite and reasonably small, simulation-based optimization is 
suitable for solving this stochastic workforce scheduling problem. 
Next, the simulation model’s features are described, including its assumptions, 
deﬁnitions, design, veriﬁcation, validation, and number of replications. 
6.5.1 
Model Assumptions 
1. Some work orders require technicians from several maintenance specializations. 
Based on historical data, the percentage of time that each specialization is needed 
by a given work order is shown in Table 6.2. 
2. Completion time of each work order includes only the processing (service) time 
plus waiting time for technicians because they are either busy or off duty. The 
processing time per work order for each specialization varies according to the 
probability distributions shown in Table 6.2. 
3. The number of technicians required for each maintenance specialization varies 
randomly. The number of technicians from each specialization assigned to the
144
6
Simulation-Based Scheduling of Pipeline Maintenance Crews
work orders is calculated from empirical probability distributions, whose values 
are shown in Table 6.3. 
4. For the same work order, technicians of different maintenance specializations 
work independently of each other. Therefore, the processing (service) time of 
each maintenance specialization is not affected by the work schedule of the other 
specializations. 
5. At the beginning of the simulation period, there are no previous work orders in 
progress. Hence, only work orders that are started within the simulation period 
are considered in the model. 
6. Maintenance technicians of all specializations work at an average pace during 
their work times. 
7. Maintenance technicians are fully available during the simulation period. Other 
than their off days, they take no regular vacations or emergency leaves during 
the simulation period. 
6.5.2 
Model Deﬁnitions 
1. Entity: maintenance work order. 
2. Creation time: ofﬁcial opening time of the work order. 
3. Service time: the actual work time (number of hours) by the maintenance 
technicians assigned to the work order. 
4. Servers: the maintenance technicians of the relevant specializations assigned to 
work on each work order, i.e., the assigned AC, DG, EL, MA, and ME technicians. 
5. Waiting time: the time from the ofﬁcial opening of the work order to the start 
of actual work on it by the maintenance technicians. This does not include the 
holding time, i.e., the time needed for the materials to be received or the formal 
approval to be obtained. 
6.5.3 
Model Design 
A simulation model was constructed to represent the pipeline maintenance workforce 
days-off scheduling problem. Basically, the conceptual simulation model replicates 
the maintenance work order processing system whose simpliﬁed ﬂowchart is shown 
in Fig. 6.3. In order to run the model, it was coded using AweSim! simulation software 
developed by Symix Systems and Inc. (1999). Therefore, the simulation will be 
described in terms of AweSim! terminology. It must be noted, however, that the 
conceptual model is independent of the simulation package. Therefore, it can be 
coded or programmed in any suitable simulation package available to the interested 
reader. 
Using AweSim! simulation software, the simulation model was run for a period 
of 210 simulated days (7 work months). This runtime period is equal to the previ-
ously mentioned 7-month data collection period, and it is more than sufﬁcient for
6.5 The Simulation Model
145
the model to reach steady state. During each run, the CREATE node generates new 
entities (maintenance work orders) using the probability distribution EXPON (9.79) 
for the time between successive arrivals (generation of new work orders). The gener-
ated work orders sequentially pass through the four stages (HLD, WRK, FIN, and 
CLS). Entities that pass through the WRK (work) node are also assigned to the ﬁve 
maintenance specializations (AC, DG, EL, MA, and ME) based on their respective 
probabilities. 
After the entities (maintenance work orders) are generated and assigned to 
different maintenance specializations, they queue up in the AWAIT node. The enti-
ties wait in AWAIT node for the servers (specialized technicians) only if they are 
either busy processing other work orders or off duty (not working) on their break 
days according to their assigned day-off work schedules. The servers start serving 
new work orders waiting in the AWAIT node immediately after they ﬁnish serving 
previous entities and get freed by the FREE node. As soon as the service of any 
wok order is completed, the completion time is recorded by the COLLECT node. 
Finally, the completed entities (work orders) depart from the system through the 
TERMINATE node. 
As stated in Sect. 6.3, each pipeline maintenance technician can be assigned to one 
of only three applicable work schedules. Table 6.1 shows the current assignment of 
the 19 technicians to the 3 days-off schedules. The simulation model’s representation 
of these 3 schedules is described below: 
1. The 5/2 schedule. Currently, 13 maintenance technicians (1 AC, 6 DG, 3 EL, 1 
MA, and 2 ME) are assigned to the 5/2 days-off schedule. This schedule has a 
one-week cycle, during which technicians work for 5 consecutive days and are 
off for 2 consecutive days. The 5 workdays are Saturday to Wednesday, and the 2 
off days are Thursday and Friday. Figure 6.4 shows AweSim! software network 
representation of this schedule. 
Fig. 6.4 Simulation model of the 5/2 days-off schedule (Reprinted from Alfares (2007), with 
permission from Taylor & Francis(2))
146
6
Simulation-Based Scheduling of Pipeline Maintenance Crews
Fig. 6.5 Simulation model of the 14/7 days-off schedule (Reprinted from Alfares (2007), with 
permission from Taylor & Francis(2)) 
Fig. 6.6 Simulation model of the 7/3–7/4 days-off schedule (Reprinted from Alfares (2007), with 
permission from Taylor & Francis(2)) 
2. The 14/7 schedule. With respect to the 14/7 days-off schedule, four pipeline 
maintenance technicians (1 AC, 1 EL, and 2 MA) are currently assigned. This 
schedule has a three-week cycle, during which technicians work for 14 consecu-
tive days, and then they are off for 7 consecutive days. Figure 6.5 shows AweSim! 
network representation of this schedule. 
3. The 7/3/7/4 schedule. Currently, only two technicians (1 EL and 1 ME) are 
assigned to the 7/3–7/4 days-off schedule. This schedule has a three-week cycle, 
which has a sequence of 7 workdays followed by 3 off days, and then 7 workdays 
followed by 4 off days. The AweSim! network representation of this schedule is 
shown in Fig. 6.6. 
6.5.4 
Duration of the Simulation Runs 
In simulation analysis, the simulation model has to be run long enough to reach 
steady state. The duration of each run of the model is seven (7) simulated months, 
i.e., 210 workdays. The duration of each run is equal to the time interval for which the 
input data has been collected. The time duration of each run is sufﬁciently long for 
the simulation model to be well in the steady state, in which the long-term averages 
of all output values and performance measures become stable and almost constant.
6.5 The Simulation Model
147
6.5.5 
Model Veriﬁcation 
The simulation model is veriﬁed by comparing its performance to the conceptual 
model. The purpose of veriﬁcation is to conﬁrm that the assumptions of the concep-
tual model are satisﬁed in the simulation model. This has been done for the model 
presented here in the standard way, which is testing how the outputs of the model 
change in reaction to changes in the input parameters. By making pilot runs and 
trying various combinations of input parameter values, the model’s outputs showed 
that model behaves as designed and expected. By conﬁrming the satisfaction of all 
the stated assumptions, the veriﬁcation process was completed. 
6.5.6 
Model Validation 
The validation of the simulation model is done by comparing the model’s results 
(outputs) with the actual data. A complete comparison between all these values has 
been made but cannot be fully shown here due to limited space. 
To illustrate the validation process, comparison of the completion time in hours 
for the air conditioning (AC) specialization work orders is presented as an example. 
First, the simulation model was run ﬁve times, with ﬁve replications per run, and 
the average AC order completion time was calculated for each run. Next, ﬁve actual 
values of AC order completion times were randomly collected. Subsequently, the 
ﬁve differences between the simulated and the actual values were taken. The mean 
and the standard deviation of the differences were calculated, and they were found 
to be 0.423 h and 1.687 h, respectively. Finally, the 95% conﬁdence interval of 
the difference (between each system observation and each simulation run average) 
was determined. Since this 95% conﬁdence interval [– 1.67, 2.52] contains zero, the 
simulation model is considered as a valid (accurate) representation of the actual work 
order system. 
6.5.7 
Number of Replications 
In order to obtain statistically valid results from the simulation model, it has to be 
run multiple times (replications). The number of replications (replication size) is 
the number of simulation runs with different random number seeds. The purpose 
of running the replications is to generate a sufﬁciently large sample size of random 
values to allow the desired statistics to be estimated with acceptable accuracy. Deter-
mining the minimum number of replications is a key component of the experimental 
design required for simulation models. For the work order simulation model, the 
number of replications was determined by using the conﬁdence interval approach.
148
6
Simulation-Based Scheduling of Pipeline Maintenance Crews
The conﬁdence intervals approach compares the conﬁdence intervals for a given 
simulation output statistic assuming several possible values of the number of repli-
cations (replication size). Out of these possible values, the replication size that gives 
the smallest common interval is chosen. To illustrate the process, the air conditioning 
(AC) specialization order completion time is used again as an example output statistic. 
It is common practice to limit the replication size to a small number. Therefore, 
the conﬁdence intervals for the AC order completion time were calculated for each 
value of the replication size in the range (1, …, 15). The optimum replication size, 
which has the smallest common interval, was found to be 10. Therefore, the number 
of replications is set equal to 10 runs for each scenario of the simulation model. 
6.6 
Optimizing Days-Off Schedules 
After setting up the simulation model and conﬁrming its soundness, it is run and used 
to optimize the days-off schedules of all maintenance technicians. Running the model 
provides various statistics and performance measures of the work order system. For 
each maintenance specialization, these measures include the number of completed 
work orders, average waiting time per work order, average queue length, i.e., average 
number of orders waiting to be served, and average utilization of technicians. Out of 
these, the most important performance measure is the average work order completion 
time, whose minimization is the main objective of the simulation model. 
6.6.1 
Performance of Current Schedules 
First, the model was run under the current days-off schedules for all maintenance 
specializations. Running the model under the existing days-off schedules serves 
two purposes: (1) validating the model by comparing its output values to actual 
values and (2) evaluating the current performance in order to assess the effect of 
schedule changes on this performance. Under the current days-off work schedules, 
the model provides the average work order completion times shown in Table 6.4 for 
each maintenance specialization.
6.6.2 
Optimum Machinist Schedules 
The main objective of using the simulation model is to reduce the average work order 
completion time without increasing the number of technicians, but only by altering 
their work schedules. From Table 6.4, it is clear that the machinist (MA) average 
completion time per order is much higher than all the other maintenance specializa-
tions. This means that higher priority in reducing the work order completion time must
6.6 Optimizing Days-Off Schedules
149
Table 6.4 Current work order completion time in hours (Adapted from Alfares (2007), with 
permission from Taylor & Francis(1)) 
Specialization
Average
Stand. dev.
95% Confidence interval 
AC
9.07
2.24
[7.77, 10.37] 
DG
16.85
1.70
[15.86, 17.84] 
EL
7.47
1.43
[6.64, 8.3] 
MA
24.43
3.29
[22.53, 26.33] 
ME
9.14
2.65
[7.61, 10.68]
be given to the machinist specialization. Consequently, the process of minimizing 
the machinist work order completion time by optimizing the machinist technicians’ 
days-off schedules will be described ﬁrst, and in greater detail. This process involves 
trying all possible days-off scheduling combinations for the machinist technicians 
in order to select the best one. 
Table 6.1 shows that the Pipelines Maintenance Unit has 3 machinist technicians. 
Out of these, 1 machinist technician is assigned to the 5/2 schedule and 2 are assigned 
to the 14/7 schedule. With 3 technicians and 3 possible days-off schedules, there 
are 10 possible scheduling combinations for the machinists, including the current 
combination. By making the necessary adjustments in the simulation model, the 10 
scheduling combinations are considered as 10 simulation scenarios. After running 
each scenario for 10 replications, the results obtained are summarized in Table 6.5. 
Table 6.5 shows the average work order completion times for all scenarios, 
including the current scenario which is designated as alternative 10. From Table 6.5, 
the best days-off scheduling combination for machinists is alternative 4 (one tech-
nician on 5/2 schedule, one on the 14/7 schedule, and one on the 7/3–7/4 schedule). 
This schedule will reduce the average work order completion time for machinists 
from 24.43 h to 20.41 h. This is a 16.4% time saving which is obtained without hiring 
additional technicians or incurring any extra cost.
Table 6.5 Days-off scheduling alternatives for 3 MA technicians (Reprinted from Alfares (2007), 
with permission from Taylor & Francis(2)) 
Alternative
No. assigned to days-off schedules
Ave. completion 
number
5/2
14/7
7/3-7/4
time (hrs) 
1 
3 
30.68 
2
3
 
43.58
3
2
1
23.14 
4 
1 
1 
1 
20.41 
5
2
 
1
 
23.60
 
6
2
1
24.54 
7 
1 
2 
24.39 
8
1
2
24.33 
9
3
37.22
10
1 
2 
24.43 
150
6
Simulation-Based Scheduling of Pipeline Maintenance Crews
6.6.3 
Optimum Schedules of the Other Specializations 
Following the same process used for the machinists, the simulation model was used to 
determine the optimum days-off schedules for the other maintenance specializations. 
The same simulation-based optimization approach was applied to air conditioning, 
digital, electrical, and metal specializations. For each specialization, all possible 
days-off scheduling combinations were represented as scenarios in the simulation 
model. The scenarios were then run and compared in order to determine the best days-
off scheduling combination for the technicians of each specialization. The results are 
displayed in Table 6.6 which shows the optimum days-off scheduling combinations 
of each specialization and the associated savings in average work order completion 
times. These savings, ranging from 4 to 66.9% and averaging 28%, are obtained 
without hiring more technicians or incurring any additional cost. 
From Table 6.6, it is easy to observe the high variations among different specializa-
tions in the reductions of average work order completion times. These high variations 
can be explained by considering some relevant facts associated with the different 
specializations. To illustrate this point, let us only look at the two specializations 
with the minimum and the maximum reductions, namely digital systems (DG) and 
air conditioning (AC). 
The maximum reduction in completion time (66.9%) is obtained for the digital 
systems (DG) specialization because of two logical reasons. First, the DG special-
ization has the maximum number (6) of technicians and thus the largest number of 
feasible scheduling combinations (27). Second, the existing assignment of all 6 DG 
technicians to only one schedule 5/2 is a very poor scheduling choice, with a big 
room for improvement. 
On the other extreme, the minimum reduction in completion time (4%) is obtained 
for the air conditioning (AC) specialization because it has the minimum number (2) 
of technicians and thus the minimum number of feasible scheduling combinations 
(6).
Table 6.6 Summary of best days-off schedules for all specializations (Reprinted from Alfares 
(2007), with permission from Taylor & Francis(2)) 
Specialization 
5/2
14/7
7/3-7/4 From (hr) 
To (hr)
% Reduction 
AC
1
1
9.07
8.71
4.0 
DG
1
2
3
16.85
5.57
66.9 
EL
2
2
1
7.47
6.44
13.8 
MA
1
1
1
24.43
20.41
16.4 
ME
1
1
1
9.14
5.59
38.8 
References
151
6.7 
Summary and Conclusions 
In this chapter, a simulation-based optimization methodology has been presented to 
determine the best days-off schedules for a multiple-specialization pipeline mainte-
nance workforce. The objective is to determine the days-off schedules of all techni-
cians in order to minimize the average work order completion time for each special-
ization. A simulation model is developed and run to represent and solve this stochastic 
scheduling optimization problem. For each maintenance specialization, the model 
takes into consideration the stochastic labor demands for technicians, as well as the 
stochastic durations of different maintenance work orders. 
The simulation model assumptions include a limited number of technicians in ﬁve 
maintenance specializations and a speciﬁc set of three possible days-off schedules. By 
enumerating all feasible scheduling combinations for each specialization, the simu-
lation model ﬁnds the best scheduling assignment for the technicians. By optimizing 
the days-off scheduling assignments, the model reduced work order completion times 
by an average of 28%. This signiﬁcant improvement in the speed and efﬁciency of 
pipeline maintenance is obtained without additional hiring or an increase in cost. 
The simulation model and the associated methodology presented in this chapter 
can be generalized to other stochastic employee scheduling problems in the petroleum 
and petrochemical industries. In particular, this methodology is especially appro-
priate to workforce scheduling problems for maintenance applications in these indus-
tries, such as the maintenance of reﬁneries, GOSPs, reactors, and petrochemical 
plants. An interesting extension to the model would be to consider sequential/serial 
processing of each work order by the different maintenance specializations (instead 
of concurrent/parallel processing). Other extensions include considering different 
work order processing priorities and different technician skill levels. 
Acknowledgements 
1. Adapted by permission from Taylor & Francis, Copyright 2007. From H.K. 
Alfares (2007). A simulation approach for stochastic employee days-off 
scheduling, International Journal of Modelling and Simulation, 27(1), 9–15, 
https://www.tandfonline.com/doi/abs/10.1080/02286203.2007.11442393 
2. Reprinted by permission from Taylor & Francis, Copyright 2007. From 
H.K. Alfares (2007). A simulation approach for stochastic employee days-off 
scheduling, International Journal of Modelling and Simulation, 27(1), 9–15, 
https://www.tandfonline.com/doi/abs/10.1080/02286203.2007.11442393 
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crews. Journal of Construction Engineering and Management, 132(3), 291–299.
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Alfares, H. K. (2007). A simulation approach for stochastic employee days-off scheduling. 
International Journal of Modelling and Simulation, 27(1), 9–15. 
Alwadood, Z., Kassim, I., & Rani, R. M. (2010). Maintenance workforce scheduling using arena 
simulation. In2010 Second International Conference on Computer Research and Development 
(pp. 517–521). IEEE. 
Bazargan, M., & Jiang, B. (2010). A simulation approach to airline maintenance manpower planning. 
InProceedings of the 2010 Summer Computer Simulation Conference (pp. 556–564). 
Beliën, J., Cardoen, B., & Demeulemeester, E. (2012). Improving workforce scheduling of aircraft 
line maintenance at Sabena Technics. Interfaces, 42(4), 352–364. 
Chen, C.-H., Yan, S., & Chen, M. (2010). Short-term manpower planning for MRT carriage mainte-
nance under mixed deterministic and stochastic demands. Annals of Operations Research, 181(1), 
67–88. 
Choudhari, S., & Gajjar, H. (2018). Simulation modeling for manpower planning in electrical 
maintenance service facility. Business Process Management Journal, 24(1), 89–104. 
De Bruecker, P., Van den Bergh, J., Beliën, J., & Demeulemeester, E. (2015). A model enhancement 
heuristic for building robust aircraft maintenance personnel rosters with stochastic constraints. 
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Langer, R., Li, J., Biller, S., Chang, Q., Huang, N., & Xiao, G. (2010). Simulation study of 
a bottleneck-based dispatching policy for a maintenance workforce. International Journal of 
Production Research, 48(6), 1745–1763. 
Lee, A., Dahan, M., & Amin, S. (2017). Integration of sUAS-enabled sensing for leak identiﬁcation 
with oil and gas pipeline maintenance crews. In2017 International Conference on Unmanned 
Aircraft Systems (ICUAS) (pp. 1143–1152). IEEE. 
Mjema, E. A. M. (2002). An analysis of personnel capacity requirement in the maintenance depart-
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service company. South African Journal of Industrial Engineering, 18(2), 169–185.
Chapter 7 
Optimum Gasoline Blending 
in Petroleum Reﬁning 
7.1 
Introduction 
This chapter presents an application of optimization models and solution techniques 
in gasoline blending at a mid-size crude oil reﬁnery. Gasoline is the most important 
product in crude oil reﬁneries, as it can generate up to 60–70% of the reﬁnery’s total 
revenue (Hussain et al., 2018). Different grades of gasoline are made by blending 
several raw materials in order to meet certain quality speciﬁcations. These raw mate-
rials are mostly produced in the reﬁnery, but they also include small amounts of 
externally made additives. The objective of reﬁnery gasoline blending is to maxi-
mize the total proﬁt by determining the quantities of gasoline products to produce 
and the amounts of raw materials to use. This must be done while considering the 
demands, speciﬁcations, and selling prices of gasoline grades, as well as the costs 
and availabilities of raw materials and other operational and regulatory restrictions. 
The gasoline blending problem affects millions of consumers who ﬁll their car tanks 
at gas stations around the world. Figure 7.1 shows a typical gas station in Saudi 
Arabia.
In this chapter, a gasoline blending optimization problem in a crude oil reﬁnery is 
considered. The problem involves multiple gasoline products and multiple nonlinear 
gasoline blending properties. The objective is to maximize the reﬁnery’s proﬁt, 
subject to speciﬁcation, demand, supply, capacity, and storage constraints. A mixed-
integer nonlinear programming (MINLP) model of the problem is formulated, and a 
two-stage solution procedure is developed. 
Subsequent sections of this chapter are organized as follows. First, relevant recent 
literature is reviewed in Sect. 7.2. The reﬁnery’s gasoline blending problem and its 
context are introduced in Sect. 7.3. The procedures for calculating the properties 
of gasoline blends are described in Sect. 7.4. The reﬁnery gasoline blending case 
study is presented in Sect. 7.5. The nonlinear programming model used to optimize 
gasoline blending is formulated is Sect. 7.6. The two-stage solution technique is 
developed in Sect. 7.7. Finally, a summary and conclusions are provided in Sect. 7.8.
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 
H. K. Alfares, Applied Optimization in the Petroleum Industry, 
https://doi.org/10.1007/978-3-031-24166-6_7 
153
154
7
Optimum Gasoline Blending in Petroleum Reﬁning
Fig. 7.1 Typical urban gas station (Courtesy of Saudi Aramco, copyright owner)
7.2 
Literature Review 
A number of introductory papers provide a general description of the overall gasoline 
blending optimization problem and its main components. For example, Demirbas and 
Bamuﬂeh (2017) describe the objectives of optimizing the process of reﬁning crude 
oil to obtain various fuel products, the most important of which is gasoline. They 
divide the reﬁning process in an oil reﬁnery into three main phases: (1) separation 
into various components by distillation, (2) conversion of heavier hydrocarbons into 
lighter, more useful products, and (3) treatment by blending to make fuels that meet 
the required speciﬁcations. Gasoline is produced in the treatment phase by blending 
four types of hydrocarbons, namely oleﬁns, aromatics, parafﬁns, and napthenes. 
Gasoline blends also contain small quantities of many other additives including 
lubricants, detergents, and dyes, as well as anti-knocking, anti-oxidation, and anti-
rust agents. 
Barsamian and Curcio (2016) present another general description of the gasoline 
blending system, and they apply it in a real reﬁnery. Their general gasoline blending 
optimization framework consists of ﬁve building blocks: (1) input block containing 
the given data, (2) required gasoline speciﬁcations database, (3) blend components 
property database, (4) nonlinear optimization solver, and (5) output block providing 
the optimum solution. The fourth block is a nonlinear programming model, whose 
objective is to maximize the proﬁt subject to material balance constraints and quality 
balance constraints. Applying the framework to a case study at the Bayway reﬁnery
7.2 Literature Review
155
in Linden, New Jersey, USA, they use it to evaluate several options for producing 
only one gasoline grade at a time. 
Several papers describe real-life applications of gasoline blending optimization 
models and techniques. DeWitt et al. (1989) describe a decision support system that 
Texaco started using in 1983 for planning and scheduling blending operations in all 
of its US reﬁneries. The system, called Optimization Method for the Estimation of 
Gasoline Attributes (OMEGA), is used for online interactive gasoline blending opti-
mization. Assuming linear and nonlinear relations between the characteristics of the 
blend and those of its components, OMEGA uses nonlinear programming to deter-
mine the optimum gasoline blending decisions. In 1990, Texaco started to replace 
OMEGA by StarBlend, which is a newer Microsoft Windows-based decision support 
system for optimizing reﬁnery blending operations. Rigby et al. (1995) describe 
the features and advantages of StarBlend, which uses a multi-period extension of 
OMEGA’s nonlinear programming optimization model. 
As will be shown in Sect. 7.4, determining the properties of gasoline blends 
from those of the components generally involves complicated nonlinear relation-
ships. Therefore, simpliﬁed optimization models based on linear approximations are 
common. Curcio (2019) discusses the advantages of using nonlinear models to calcu-
late the properties of gasoline blends as functions of the properties of the components. 
Although most of the 30-plus properties of gasoline are nonlinear, more than 60% 
of US reﬁneries use approximate linear models to calculate the properties of the 
blends. This approximation leads to costly property give away, which is lost revenue 
resulting from unnecessarily exceeding the speciﬁcations. In the USA, an average 
0.5 octane number give away and a 0.3 psi Reid Vapor Pressure (RVP) give away 
are estimated to cost approximately $3 billion a year. 
Different approaches are used to improve the accuracy of the linear optimiza-
tion models of the gasoline blending problem. Jia and Ierapetritou (2003) formu-
late a mixed-integer linear programming (MILP) model of the gasoline blending 
problem integrated with reﬁnery receiving, producing, and distribution decisions. The 
continuous-time model determines the optimal gasoline blending, product storage, 
and the customer delivery schedule. Li et al. (2010) formulate a ﬂexible MILP model 
of the gasoline blending problem with many realistic features including multi-product 
tanks, non-identical blenders, and variable feed rates. Instead of solving a single 
mixed-integer nonlinear programming (MINLP) model, a faster and better algorithm 
is used to solve successive MILP models. 
Naturally, blend speciﬁcations and scheduling constraints make gasoline blending 
a non-convex MINLP problem. Chen and Wang (2010) develop a hybrid approach 
for short-time gasoline blending nonlinear optimization. First, a DNA-based genetic 
algorithm (DNA-GA) is used in global search to efﬁciently identify the feasible 
region and to ﬁnd a promising starting solution. In the second stage, sequential 
quadratic programming (SQP) is applied in local search to improve the starting solu-
tion. Castillo et al. (2013) deﬁne an inventory pinch as the point where the cumu-
lative average production curve is tangent to the cumulative demand curve. Using 
the inventory pinch concept, they develop an algorithm for multi-period gasoline
156
7
Optimum Gasoline Blending in Petroleum Reﬁning
blend planning using a single-period nonlinear model to minimize the number of 
multi-period blend changes. 
Cerdá et al. (2016) develop a continuous-time MINLP model to simultaneously 
optimize blend quantities and the schedules of the blending and distribution oper-
ations. The multi-period model considers several realistic aspects including non-
identical blenders, multi-product tanks, and sequence-dependent changeover costs. 
The MINLP model can be either solved directly or after using an approximate MILP 
model to ﬁnd a good initial solution. Considering a similar problem, Li et al. (2016) 
formulate a continuous-time MINLP model. The model is near-optimally solved by 
a hybrid iterative algorithm that combines mixed-integer programming, nonlinear 
programming, and deterministic global optimization. Hussain et al. (2018) enhance 
the MINLP model of Li et al. (2016) by considering nonlinear functions of the gaso-
line blend’s characteristics. A global optimization algorithm is developed to produce 
solutions that are nearer to optimality than those of Cerda et al. (2016) in shorter 
computation times. 
Gasoline blending literature also includes other solution techniques and other 
problem aspects. Bayu et al. (2020) develop a discrete-time graphical genetic algo-
rithm (GGA) model for single-objective and multi-objective scheduling of the gaso-
line blending and distribution (SGBD) problem. In the single-objective case, only 
the production cost is minimized. In the multi-objective case, both the production 
cost and the sum of squared ﬂuctuations in the blending rate are minimized. Jiang 
et al. (2021) discuss the advantages of adding bio-blend stocks to gasoline to reduce 
emissions and improve the fuel properties. Optimization models are presented to 
maximize the economic value considering nonlinear equations for determining the 
properties of the blends. 
7.3 
The Gasoline Blending Problem 
In this section, the reﬁnery gasoline blending problem and its context are outlined. 
A brief description is provided of the gasoline blending system components, 
including the reﬁning and blending processes, inputs and outputs, and key gasoline 
speciﬁcations. 
7.3.1 
Reﬁning and Blending Processes 
Oil reﬁning is basically the process of converting crude oil into separate reﬁned 
consumer products such as gasoline, kerosene, diesel fuel, jet fuel, and asphalt. 
Fahim et al. (2009) classify reﬁning processes in modern reﬁneries into two main 
types:
7.3 The Gasoline Blending Problem
157
1. Physical separation processes. These processes include distillation, extraction, 
de-asphalting, and de-waxing. 
2. Chemical conversion processes. These processes are further classiﬁed into two 
subcategories: 
a. Catalytic chemical conversion. These processes include reforming, alkyla-
tion, and isomerization. 
b. 
Thermal chemical conversion. These processes include ﬂexicoking and 
visbreaking. 
Blending is a process that takes place after reﬁning, using the reﬁned products as 
components (raw materials) in the blended products. Blending is used in reﬁneries to 
produce several products such as gasoline, diesel fuel, and jet fuel. The objective of 
product blending optimization is to maximize the net proﬁt using the limited avail-
ability of raw materials while ensuring all product speciﬁcations and demands are 
satisﬁed. First, gasoline is isolated by reﬁning, and then non-gasoline components are 
converted into gasoline, and reformation is used to improve the gasoline properties. 
Blending is the ﬁnal step of making gasoline, during which several components (raw 
materials) are mixed into a ﬁnished gasoline product. The blended gasoline products 
must satisfy international standard speciﬁcations, local marketing and regulatory 
requirements, internal capacity limitations, and business economic considerations. 
7.3.2 
Blending Process Inputs and Outputs 
The main input in the reﬁning process is crude oil, which comes in two main types: 
light crude and heavy crude. Light crude is thinner than heavy crude, and it has lower 
density but higher American Petroleum Institute (API) gravity. Light crude also has 
greater gasoline content and lower sulfur content, and thus, it is easier to reﬁne than 
heavy crude. 
In order to make different streams of gasoline products, the main components 
used as inputs in gasoline blending processes include the following types of raw 
materials: 
1. Reformate: also known as platformate, is a high-octane gasoline component 
obtained from the catalytic reforming process in the reﬁnery. 
2. Isomerate: is a gasoline component with a medium-octane value that is produced 
by converting low-octane light naphtha using the isomerization process. 
3. Light hydrocarbons: these are primarily composed of butane and its derivatives 
(C4) and light naphtha (C5 and C6). 
4. Alkylate: this is a mixture of high-octane hydrocarbons (mostly isoheptane and 
isooctane) produced by the alkylation process. As a gasoline component, alkylate 
has excellent anti-knock and clean burning properties.
158
7
Optimum Gasoline Blending in Petroleum Reﬁning
5. MTBE: Methyl Tert-Butyl Ether is a ﬂammable liquid component, which is used 
to increase octane and oxygen levels and reduce pollution emissions of unleaded 
gasoline. 
According to Fahim et al. (2009), gasoline is usually classiﬁed according to its 
octane rating into three types: regular, midgrade, and premium. Regular gasoline has 
an octane rating greater than or equal to 85 and less than 88. Midgrade gasoline has 
an octane rating greater than or equal to 88 and less than 90. Premium gasoline has 
an octane rating greater than or equal to 90. 
In addition to the differences in octane ratings, the distinct gasoline grades also 
differ in other speciﬁcations that affect several aspects of engine performance. These 
speciﬁcations are discussed next. 
7.3.3 
Gasoline Speciﬁcations 
As an automobile fuel, gasoline is a product that is designed to deliver good driving 
performance. For example, gasoline is required to provide high power, good fuel 
economy, low polluting emissions, and smooth engine starts and runs in all weather 
conditions. Gasoline driving performance is dependent on several of its chemical 
and physical properties, including volatility, anti-knock rating, and composition. 
To guarantee good driving performance, there are several standards that specify 
the range of acceptable values for certain indices of these relevant properties. For 
example, volatility is controlled by speciﬁcations on evaporated at 70, Reid vapor 
pressure, distillation proﬁle, vapor lock index, and vapor–liquid ratio. Moreover, 
anti-knocking is controlled by octane number speciﬁcations, while power and fuel 
economy are controlled by the heating value speciﬁcations. 
There are many other gasoline speciﬁcations, such as the boiling point, density, 
oxidation stability, corrosion rating, and distillation residue. Several other speciﬁ-
cations impose limits on the proportions of certain contents such as lead, sulfur, 
hydrocarbons, and oxygen. 
For the reﬁnery gasoline blending case study to be presented in Sect. 7.5, the  
relevant gasoline speciﬁcations are: evaporated at 70, Reid vapor pressure, vapor 
lock index, and Research Octane Number. These four speciﬁcations are described 
below: 
1. Evaporated at 70 (E70). E70 is the percentage of gasoline volume that has 
evaporated at 70°C (158°F). E70 is a measure of gasoline volatility caused by 
the presence of light components in the gasoline blend. A higher proportion of 
light substances in the blend leads to a higher vapor pressure and hence a higher 
E70. It also leads to a higher Reid Vapor Pressure (RVP) and a higher Vapor 
Lock Index (VLI). Both RVP and VLI properties are discussed next. The usual 
range of gasoline E70 values is from 20% to 50%.
7.3 The Gasoline Blending Problem
159
2. Reid Vapor Pressure (RVP). RVP is the pressure required to keep a liquid from 
evaporating at 100°F. RVP measures the effect of pressure resulting from gasoline 
evaporation on the supply of liquid fuel to the engine. RVP indicates how easily 
the engine starts in hot and cold weather, and it measures the explosion and 
the evaporation potential of the fuel. Since driving in colder weather requires a 
higher RVP than driving in warmer weather, gasoline RVP requirements depend 
on the average temperature in the driving location. Therefore, the RVP gasoline 
speciﬁcation is established according to the local climate, and it usually varies 
according to the season of the year. Reid vapor pressure is measured either in 
kilopascals (kPa) or in pounds per square inch (psi), and its typical range is 
48–103 kPa (7–15 psi). 
3. Vapor Lock Index (VLI). VLI is a measure of the gasoline starting and driving 
performance when the engine is hot. VLI is used outside the USA to gauge vapor 
lock and other engine hot-fuel problems. Vapor lock is caused by overheated or 
excessively volatile gasoline. It occurs when excessive gasoline vapors decrease 
or disrupt fuel supply to the engine, stopping the engine and making it difﬁcult 
to restart. The value of VLI is a linear combination of the E70 and the RVP 
gasoline indices that is expressed as VLI = 10(RVP) + 7(E70), where RVP is 
measured in kPa units. Since VLI is related to RVP, it is also affected by the 
ambient temperature, and hence, it varies with the geographical location and the 
seasonal changes in the weather. The usual VLI range is 800–1250. Lower VLI 
values are needed to prevent vapor lock in hot weather, while higher VLI values 
are needed to have easier engine starts in cold weather. 
4. Research Octane Number (RON). The octane number of a gasoline fuel is a 
measure of its anti-knocking performance in spark ignition engines. Knocking 
is the sound of abnormal combustion resulting from spontaneous fuel igni-
tion, which causes excessive pressure and overheating. The higher the gaso-
line’s resistance to auto-ignition (without a spark) under high pressure, the 
higher the gasoline’s octane number. Three octane number indices are used 
to measure various aspects of gasoline anti-knocking performance. The Motor 
Octane Number (MON) measures high-speed highway driving performance. The 
Research Octane Number (RON), which is always greater than MON, measures 
low-speed city-driving performance. The Posted Octane Number (PON), also 
called the anti-knock index (AKI), is simply the average of the MON and RON 
values. It is important to prevent octane give away, i.e., exceeding the octane 
speciﬁcation in the gasoline blend, in order to avoid huge economic losses. 
According to Emerson Process Management (2008), the cost in the USA per 
0.1 octane give away is approximately $1,000,000 per 100,000 barrels per day 
(bpd) crude capacity. 
The European Committee for Standardization sets the EN-228 standards for auto-
motive unleaded fuels. These standards are based on six volatility classes (A to F) 
that are assigned to different countries for three weather seasons: winter, summer, 
and transition period. The EN-228 speciﬁcations for E70, RVP, and VLI values are
160
7
Optimum Gasoline Blending in Petroleum Reﬁning
Table 7.1 EN-228 speciﬁcations for unleaded gasoline (oxygen content ≤ 3.7%) 
Specification
Class A
Class B Class C/C1 Class D/D1 Class E/E1 Class F/F1 
E70 min
20
20
22
22
22
22 
E70 max
48
48
50
50
50
50 
RVP kPa min
45
45
50
60
65
70 
RVP kPa max
60
70
80
90
95
100 
RVP psi min
6.5 
6.5 
7.3 
8.7 
9.4 
10.2 
RVP psi max
8.7
10.2
11.6
13.1
13.8
14.5 
VLI max
–
–
– /1050
– /1150
– /1200
– /1250 
shown in Table 7.1. Additional gasoline standards are established by the Worldwide 
Fuel Charter Committee (2019). 
7.4 
Calculating Blend Properties from Component 
Properties 
Gasoline products come in different grades, and each grade has its own set of speci-
ﬁcations. All gasoline products are blends of different components (raw materials). 
The properties of gasoline blends are mostly nonlinear functions of the properties of 
the individual components. The properties of the gasoline blend must be calculated 
based on its composition to determine whether a given gasoline product meets spec-
iﬁcations. For the gasoline blending case study discussed in this chapter, the main 
speciﬁcations are applicable to the following properties: 
1. Percent Evaporated at 70 degree Celsius (E70). 
2. Reid Vapor Pressure (RVP). 
3. Vapor Lock Index (VLI). 
4. Research Octane Number (RON). 
For the ﬁrst gasoline speciﬁcation (E70), the blend properties are simple linear 
function of the properties of individual components. For the three remaining spec-
iﬁcations (RVP, VLI, and RON), however, the blend properties involve nonlinear 
functions and speciﬁc calculation procedures. 
Suppose that a certain gasoline blend (product) is made by mixing I components 
(raw materials). Given the volume fraction in the blend (f i) of each individual compo-
nent i (i = 1,…, I), the procedures used to calculate the four relevant blend properties 
are explained below. 
7.4.1 
Calculating the E70 of the Blend 
The E70 value is the percentage of the material that evaporates at 70 degree Celsius 
(158 degree Fahrenheit). No mathematical procedures or formulas were found in the
7.4 Calculating Blend Properties from Component Properties
161
literature to relate the E70 value of the blend to the E70 values of the individual 
components. Therefore, a simple linear relationship is assumed here for E70. Given 
the E70 value of each component i (EVci, i = 1,…, I), the E70 value of the gasoline 
blend (EVb) is given by: 
upper  E
 
u
pper
 V b equal
s sigma summation Underscript i equals 1 Overscript upper I Endscripts f Subscript i Baseline upper E upper V c Subscript i Baseline period
7.4.2 
Calculating the RVP of the Blend 
Given the RVP value of each component i (RVci, i = 1,…, I), the RVP value of the 
gasoline blend (RVb) is obtained as follows: 
upper R u pp
e
r
 V b
 Su perscript
 
1
.25 Baseline equals sigma summation Underscript i equals 1 Overscript upper I Endscripts f Subscript i Baseline upper R upper V c Subscript i Superscript 1.25 Baseline comma
or 
upper  
R
 
u
pper
 V b equals 
l
eft b
r
acket sigma summation Underscript i equals 1 Overscript upper I Endscripts f Subscript i Baseline upper R upper V c Subscript i Superscript 1.25 Baseline right bracket Superscript 0.8 Baseline period
7.4.3 
Calculating the VLI of the Blend 
The VLI values of both the components and the blend are functions of the corre-
sponding RVP and E70 values. RVP values are given in units of either pounds per 
square inch (psi) or kilopascals (kPa), where 1 psi = 6.89476 kPa. Given the indi-
vidual and the blend’s RVP values in psi units (VLci, i = 1,…, I), the VLI values of 
the individual components and the blend are given by: 
u pper  V  upper  L c S ub script i
 B asel i n e  eq
uals 68.95 upper R upper V c Subscript i Baseline plus 7 upper E upper V c Subscript i Baseline comma i equals 1 comma ellipsis comma upper I
u pp e r V uppe r L b  e qu als 6
8.95 upper R upper V b plus 7 upper E upper V b period
If the RVP values are given in kPa units, then the coefﬁcient 68.95 changes to 
10 in Eqs. (7.4) and (7.5). In Eq. (7.5), the blend’s input values EVb and RVb are 
obtained from Eqs. (7.1) and (7.3), respectively.
162
7
Optimum Gasoline Blending in Petroleum Reﬁning
7.4.4 
Calculating the RON of the Blend 
Given the Research Octane Number (RON) value of each individual component i 
(ONci, i = 1,…, I), the RON value of the gasoline blend (ONb) is calculated by the 
following procedure described by Riazi (2005). This method is highly accurate, as 
it has an average absolute error of only 0.06%. 
First, for each component i, the individual octane number value ONci is converted 
to an octane blending index OIci. Depending on the range in which each ONci value 
falls, one of the three functions below is used for the conversion: 
If ONci ≤ 76, then OIci = θ 
1(ONci), where: 
StartLayout  1 st Row  t heta 1  left parenthesis upper O upper N c Subscript i Baseline right parenthesis equals 36.01 plus 38.33 StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction minus 99.8 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis squared 2nd Row plus 341.3 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis cubed minus 507.02 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis Superscript 4 Baseline 3rd Row plus 268.64 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis Superscript 5 Baseline period EndLayout
Star
tL ayou
t  1st Row theta 1 left parenthesis upper O upper N c Subscript i Baseline right parenthesis equals 36.01 plus 38.33 StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction minus 99.8 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis squared 2nd Row plus 341.3 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis cubed minus 507.02 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis Superscript 4 Baseline 3rd Row plus 268.64 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis Superscript 5 Baseline period EndLayout
Sta
rtL
ayout 1
s t Row theta 1 left parenthesis upper O upper N c Subscript i Baseline right parenthesis equals 36.01 plus 38.33 StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction minus 99.8 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis squared 2nd Row plus 341.3 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis cubed minus 507.02 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis Superscript 4 Baseline 3rd Row plus 268.64 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis Superscript 5 Baseline period EndLayout
Sta
rtL
ay out 1s
t  Row theta 1 left parenthesis upper O upper N c Subscript i Baseline right parenthesis equals 36.01 plus 38.33 StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction minus 99.8 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis squared 2nd Row plus 341.3 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis cubed minus 507.02 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis Superscript 4 Baseline 3rd Row plus 268.64 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis Superscript 5 Baseline period EndLayout
Sta
rtL
ayout 1s
t  Row theta 1 left parenthesis upper O upper N c Subscript i Baseline right parenthesis equals 36.01 plus 38.33 StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction minus 99.8 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis squared 2nd Row plus 341.3 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis cubed minus 507.02 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis Superscript 4 Baseline 3rd Row plus 268.64 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis Superscript 5 Baseline period EndLayout
Sta
rtL
ay
out 1st Row theta 1 left parenthesis upper O upper N c Subscript i Baseline right parenthesis equals 36.01 plus 38.33 StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction minus 99.8 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis squared 2nd Row plus 341.3 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis cubed minus 507.02 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis Superscript 4 Baseline 3rd Row plus 268.64 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis Superscript 5 Baseline period EndLayout
If 76 ≤ ONci ≤ 103, then OIci = θ 
2(ONci), where: 
StartLayout  1st Row  t heta 2 left parenthesis upper O upper N c Subscript i Baseline right parenthesis equals negative 299.5 plus 1272 StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction minus 1552.9 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis squared 2nd Row plus 651 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis cubed period EndLayout
Sta
rt Layout
 1st Row theta 2 left parenthesis upper O upper N c Subscript i Baseline right parenthesis equals negative 299.5 plus 1272 StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction minus 1552.9 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis squared 2nd Row plus 651 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis cubed period EndLayout
Sta
rtL
ayout
 1st Row theta 2 left parenthesis upper O upper N c Subscript i Baseline right parenthesis equals negative 299.5 plus 1272 StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction minus 1552.9 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis squared 2nd Row plus 651 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis cubed period EndLayout
Sta
rtL
ay
out 1st Row theta 2 left parenthesis upper O upper N c Subscript i Baseline right parenthesis equals negative 299.5 plus 1272 StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction minus 1552.9 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis squared 2nd Row plus 651 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis cubed period EndLayout
If ONci ≥ 103, then OIci = θ 
3(ONci), where: 
theta 3 lef t parenth es is upper  O uper N c Subscript i Baseline right parenthesis equals 2206.3 minus 4313.64 StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction plus 2178.57 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis squared period
the
ta  3 left
 parenthesis upper O upper N c Subscript i Baseline right parenthesis equals 2206.3 minus 4313.64 StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction plus 2178.57 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis squared period
the
ta 
3
 left parenthesis upper O upper N c Subscript i Baseline right parenthesis equals 2206.3 minus 4313.64 StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction plus 2178.57 left parenthesis StartFraction upper O upper N c Subscript i Baseline Over 100 EndFraction right parenthesis squared period
Substituting the two ONci interval border values (76 and 103) in the above func-
tions gives the corresponding two OIc border values (56.3 and 74.5). Hence, the 
octane index OIci values obtained by eqs. (7.6–7.8) will fall into one of the three 
ranges below: 
Sta r
t
La yout 1 st  Row 
1st  
Colum
n upper 
R Subsc
rip t
 1 Ba
se line c
o lon 2nd Column 0 less than or equals upper O upper I c Subscript i Baseline less than or equals 56 period 3 2nd Row 1st Column upper R Subscript 2 Baseline colon 2nd Column 56 period 3 less than or equals upper O upper I c Subscript i Baseline less than or equals 74 period 5 3rd Row 1st Column upper R Subscript 3 Baseline colon 2nd Column 74 period 5 less than or equals upper O upper I c Subscript i Baseline less than or equals normal infinity period EndLayout
Next, the octane index of the blend, OIb, which is the weighted average of the 
octane indices of individual components, is calculated as follows:
7.5 Gasoline Blending Case Study
163
upper  O
 
u
pper
 I b equal
s sigma summation Underscript i equals 1 Overscript upper I Endscripts f Subscript i Baseline upper O upper I c Subscript i Baseline period
In the last step, we need to ﬁnd out the range (R1, R2, or  R3) in which the value of 
OIb falls. If OIb is in the octane index range Rk, (k = 1,.., 3), then we must use the 
corresponding θ 
k function from (7.6, 7.7, or  7.8) to solve the following equation in 
order to ﬁnd ONb, the overall RON value of the gasoline blend: 
theta Sub sc ript 
k Base line le ft 
parenthesis upper O upper N b right parenthesis equals upper O upper I b comma where upper O upper N b element of upper R Subscript k Baseline period
7.5 
Gasoline Blending Case Study 
This section presents an actual case study of optimizing gasoline blending in a mid-
size crude oil reﬁnery located in the Middle East. The reﬁnery has been using a 
manual planning approach, where gasoline blending plans are developed based only 
on the senior planner’s long experience and best judgment, without using mathemat-
ical optimization models. During several previous occasions, the reﬁnery’s blended 
gasoline products were off speciﬁcations, which affected the gasoline supply to the 
customers. The reﬁnery’s management plans to utilize optimization models to mini-
mize these interruptions, aiming to improve performance by relying on scientiﬁc 
decision-making techniques. 
The reﬁnery’s management has requested the use of a mathematical programming 
model of gasoline blending to optimize the blending components mix for gasoline 
production. The model must be customized to ﬁt the speciﬁc requirements of the 
reﬁnery’s ofﬂine blending operations. Moreover, the model must be implemented 
in a user-friendly software tool to be utilized effectively in the reﬁnery’s continuing 
gasoline blending operations. The management prefers to avoid the use of special-
ized optimization software, in order to minimize the costs of software purchase 
and employee training. In particular, the reﬁnery’s management speciﬁed Microsoft 
Excel as the preferred software platform for implementing the gasoline blending 
optimization tool. This is because Excel is already being used to process and store 
all the reﬁnery’s gasoline blending data. Moreover, Excel is a user-friendly software 
that all the reﬁnery’s employees are familiar with. 
The reﬁnery produces two types of gasoline that are subject to the four speci-
ﬁcations discussed in the previous sections (E70, RVP, VLI, and RON). To adjust 
gasoline performance to seasonal changes in the weather, the speciﬁcations of the 
two products change according to the season of the year. The two gasoline blends 
(products), whose speciﬁcations for each weather season are shown in Table 7.2, are  
listed and numbered below:
1. Special unleaded 91-octane (PG-91) gasoline. 
2. Super unleaded 95-octane (PG-95) gasoline.
164
7
Optimum Gasoline Blending in Petroleum Reﬁning
Table 7.2 Required speciﬁcations of gasoline products per season 
Product
PG-91
PG-95 
Season
Summer 
Intermediate 
Winter Summer 
Intermediate 
Winter 
E70 Min
20
22
24
20
22
24 
E70 Max
48
50
52
48
50
52 
RVP (psi) Min 
6.5
6.5
6.5
6.5
6.5
6.5 
RVP (psi) Max
9.1
10
11.5
9.1
10
11.5 
VLI Max
875
915
1160
875
915
1160 
RON Min 
91.0
91.0
91.0
95.0
95.0
95.0
Figure 7.2 shows typical fuel-ﬁlling pumps for the two grades of gasoline, 
91-octane and 95-octane, in a gas station in Saudi Arabia. 
The two gasoline types are made by blending (mixing) ﬁve components (raw 
materials). Table 7.3 shows the relevant data for these components, including their 
speciﬁcations, costs, and bounds on their percentages in the blend. The price/cost, 
inventory, and supply data for each product and raw material are shown in Table 7.4. 
The ﬁve components are listed and numbered below:
1. Platformate, also known as reformate. 
2. C5/C6. 
3. Isomerate. 
4. C4. 
5. MTBE.
Fig. 7.2 Gas station pumps for 91-octane and 95-octane gasolines (Picture is personally provided 
by Nasser Alfares) 
7.5 Gasoline Blending Case Study
165
Table 7.3 Speciﬁcations and proportion limits of the components 
Component 
E70 
RVP" 
(psi) 
RVP" 
(KPA) 
VLI
RON 
PG-91 
min 
PG-91 
max 
PG-95 
min 
PG-95 
max 
Platformate
12
5
34.49 
428.9
95
28%
57%
28%
57% 
C5/C6
63.5
14
96.58 1410.3
80
0%
72%
0%
72% 
Isomerate
63.5
12
82.79 1272.4
80
0%
87%
0%
87% 
C4
100
45
310.45 3104.5
95
0%
16%
0%
16% 
MTBE
87.5
8
55.19 1164.4 
121
0%
15%
0%
15% 
Table 7.4 Material price, inventory, and supply data 
Material
PG-91 PG-95 Platformate 
C5/C6 Isomerate
C4
MTBE 
Price/cost ($/m
3 ) 581.33 621.33
477.99 
402.52 
377.36 
229.56 
528.93 
Storage Capacity 
(m
3 )
111,500 84,240 
12,361 
12,361 
13,725 
14,962 
26,467 
Current 
Inventory (m
3 )
0
0
863
521
8,786 
6,035 
17,420 
Supply Limit 
(m
3 /week)
0
0
43,680 
19,992 
21,840 
14,952 
20,328 
The gasoline blending process is done ofﬂine, and the production planning period 
is one week, or 168 h. The reﬁnery’s combined weekly production capacity of both 
gasoline types is 94,000 m3. The reﬁnery produces gasoline primarily for consump-
tion in the local (national) market, but any excess production is exported to meet the 
demands of neighboring countries. In general, demand for the cheaper PG-91 gaso-
line is much higher than that for the costlier PG-95 gasoline. Due to both demand 
considerations and capacity limitations, the production of PG-95 cannot be more than 
35% of the reﬁnery’s total gasoline production. Usually, the local daily demand is 
around 30,000 BBL (barrels) of PG-91 and 7,000 BBL of PG-95. Using the conver-
sion factor (1 m3 = 6.29 BBL), the typical weekly local demand for each gasoline 
type is approximately given by: 
PG-91 demand = 33,400 m3/week. 
PG-95 demand = 7,800 m3/week. 
Given the above problem description, the reﬁnery’s management aims to deter-
mine the optimum quantities to produce of each gasoline type and the quantities 
(proportions) of all raw materials to blend in order to make each type. The objective 
is to maximize the total weekly proﬁt while meeting all applicable speciﬁcation, 
demand, storage, and supply restrictions. For this purpose, a nonlinear programming 
(NLP) model is formulated to represent and solve this optimization problem. The 
NLP model for optimizing the reﬁnery’s gasoline blending plans is presented in the 
following section.
166
7
Optimum Gasoline Blending in Petroleum Reﬁning
7.6 
Nonlinear Programming Optimization Model 
As discussed in the previous section, a customized model is required by the reﬁnery’s 
management to determine the optimum weekly production quantities of two prod-
ucts: (1) 91-octane gasoline and (2) 95-octane gasoline. In addition, the model must 
determine the amounts (fractions or volumes) of all the ﬁve blending components 
to produce each type of gasoline: (1) platformate, (2) C5/C6, (3) isomerate, (4) 
C4, and (5) MTBE. The quantities of the products and the components determined 
by the model must maximize the weekly proﬁt while meeting all speciﬁcations as 
well as material supply, storage, and production capacity constraints. A nonlinear 
programming (NLP) optimization model is formulated and solved to achieve this 
aim. 
The mathematical model is presented below for a general set of I components 
(raw materials) and J gasoline blends (products). The model can be customized to 
the case study presented in this section by substituting the given numerical values 
in the model. The indices, parameters, decision variables, objective function, and 
constraints of the gasoline blending NLP model are presented below. 
7.6.1 
Notation 
Indices 
i Component (raw material) index (i = 1,…, I), I = number of components 
(i = 1: platformate, i = 2: C5/C6, i = 3: isomerate, i = 4: C4, i = 5: MTBE); 
j 
product (gasoline type) index (j = 1,…, J), J = number of components 
(j = 1: PG-91, j = 2: PG-95); 
k 
Octane blending index’s range index (k = 1, 2, 3). 
Parameters 
Bci
beginning (initial) inventory of component i (m3); 
Bbj
beginning (initial) inventory of gasoline product j (m3); 
Ci
cost per unit ($/m3) of producing or buying component i; 
Dj
demand (m3) of product j during the planning horizon; 
EVci
Percent Evaporated at 70 °C (E70) value of component i; 
ELj
E70 lower speciﬁcation limit (psi) of product j; 
EUj
E70 upper speciﬁcation limit (psi) of product j; 
FLij
lower bound of fraction of component i in product j, 0  ≤ FLij ≤ 1; 
FUij
upper bound of fraction of component i in product j, 0  ≤ FUij ≤ 1; 
GU
maximum gasoline production capacity of the reﬁnery; 
Ici
inventory (maximum storage) capacity (m3) of component i; 
Ibj
inventory (maximum storage) capacity (m3) of gasoline blend j;
7.6 Nonlinear Programming Optimization Model
167
OIci
octane blending index of individual component i; 
OLj
Research Octane Number (RON) lower speciﬁcation limit of product j; 
Pj
selling price per unit ($/m3) of product j; 
RLj
Reid Vapor Pressure (RVP) lower speciﬁcation limit (psi) of product j; 
RUj
Reid Vapor Pressure (RVP) upper speciﬁcation limit (psi) of product j; 
RVci
Reid Vapor Pressure (RVP) value of component i; 
ONci 
Research Octane Number (RON) value of component i; 
SUi
maximum additional supply or production capacity of component i; 
VLci
Vapor Lock Index (VLI) value of component i; 
VUj
Vapor Lock Index (VLI) upper speciﬁcation limit of product j. 
Decision Variables 
ONbj 
RON value of gasoline blend (product) j; 
OIbi
octane blending index of gasoline blend j; 
Sci
additional amount of component i supplied or produced (m3); 
Xij
amount of component i used to make product j (m3); 
u ppe r
 Y S ubs crip t j k  Ba seline  eq
ual s StartLayout Enlarged left brace 1st Row 1 comma if upper O upper I b Subscript j Baseline is in range upper R Subscript k Baseline 2nd Row 0 comma otherwise colon EndLayout
R1
0 ≤ OIj ≤ 56.3; 
R2
56.3 ≤ OIj ≤ 74.5; 
R3
74.5 ≤ OIj ≤∞. 
Figure 7.3 displays a simpliﬁed representation of the overall gasoline blending 
system. The ﬁgure shows the speciﬁc decision variables associated with different 
material ﬂows in the network. 
Fig. 7.3 Illustration of the blending process and the decision variables
168
7
Optimum Gasoline Blending in Petroleum Reﬁning
7.6.2 
Objective Function 
The objective is to maximize the total proﬁt Z, which is the revenue from the sale of 
gasoline products minus the total cost of raw materials: 
Maximize up pe
r
 
Z eq
u
a
ls 
s
igm a su
m
ma t io
n Underscript i equals 1 Overscript upper I Endscripts sigma summation Underscript j equals 1 Overscript upper J Endscripts left parenthesis upper P Subscript j Baseline minus upper C Subscript i Baseline right parenthesis upper X Subscript i j Baseline period
7.6.3 
Production Constraints 
Gasoline product demand constraints. For each gasoline blend (product) j, the  
beginning inventory plus the total produced quantity must be sufﬁcient to satisfy the 
demand: 
up pe r 
B
 
b Su
bs c r ip t j B
as el in e  p l u
s 
sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline greater than or equals upper D Subscript j Baseline comma j equals 1 comma ellipsis comma upper J period
Product storage capacity constraints. The ending inventory of each gasoline blend 
j cannot exceed the maximum storage capacity Ibj: 
up pe r 
B
 
b Su
bs c r ip t j Ba se lin
e pl us  s i g m
a 
summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline minus upper D Subscript j Baseline less than or equals upper I b Subscript j Baseline comma j equals 1 comma ellipsis comma upper J period
Raw material consumption constraints. For each component i, the total amount of 
raw material used cannot exceed the initial inventory plus the additional raw material 
supply: 
s
i
gma 
su m m at ion Un dersc
ri pt  j  e q ual
s 1 Overscript upper J Endscripts upper X Subscript i j Baseline less than or equals upper B c Subscript i Baseline plus upper S c Subscript i Baseline comma i equals 1 comma ellipsis period upper I period
Raw material storage capacity constraints. For each component (raw material) i, 
the ending inventory cannot exceed the maximum storage capacity: 
uppe r B c Su
b
s
crip
t i  Ba selin
e pl us  u p per
 S c Subscript i Baseline minus sigma summation Underscript j equals 1 Overscript upper J Endscripts upper X Subscript i j Baseline less than or equals upper I c Subscript i Baseline comma i equals 1 comma ellipsis period upper I period
Raw material supply capacity constraints. For each component i, the additional 
supplied quantity cannot exceed the maximum additional supply or production 
capacity:
7.6 Nonlinear Programming Optimization Model
169
uppe r S c S
ub sc ri p t  i B
aseline less than or equals upper S upper U Subscript i Baseline comma i equals 1 comma ellipsis period upper I period
Gasoline type allocation constraints. The amount produced of gasoline type 
PG-95 cannot exceed 35% of the total production quantity: 
s
i
gma 
sum at ion U
n
d
ersc
r
i
pt i
 e q ua
ls 1 Overscript upper I Endscripts upper X Subscript i Baseline 2 Baseline less than or equals 0.35 sigma summation Underscript i equals 1 Overscript upper I Endscripts sigma summation Underscript j equals 1 Overscript upper J Endscripts upper X Subscript i j Baseline period
Reﬁnery production capacity constraints. The total amount produced of both 
types of gasoline cannot exceed the reﬁnery’s maximum weekly production capacity: 
s
i
gma 
s
u
mmat
io n  Un der
script i equals 1 Overscript upper I Endscripts sigma summation Underscript j equals 1 Overscript upper J Endscripts upper X Subscript i j Baseline less than or equals upper G upper U period
Raw material minimum and maximum fraction constraints. The volume frac-
tion of each component must be within the minimum and maximum bounds speciﬁed 
for the given gasoline blend: 
up p e r X S u b
s
c
ript
 i  j 
Ba se li n e  g rea
te r th a n  o r 
equals upper F upper L Subscript i j Baseline sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline comma i equals 1 comma ellipsis comma upper I comma j equals 1 comma ellipsis comma upper J
up p e r X S u b
s
c
ript
 i  j 
Ba se li n e  l ess
 t ha n o r  e qua
ls upper F upper U Subscript i j Baseline sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline comma i equals 1 comma ellipsis comma upper I comma j equals 1 comma ellipsis comma upper J period
7.6.4 
Speciﬁcation Constraints 
The following constraints, numbered (7.21) to (7.31), enforce the four performance 
speciﬁcations (E70, RVP, VLI, and RON) on the gasoline products. Properties of 
the products (blends) are functions of the fractions and the properties of the compo-
nents used in the blend. The blend properties, calculated according to the functions 
and procedures presented in Sect. 7.4, must satisfy the required speciﬁcations. To 
represent the fraction of component i in gasoline blend j, the symbol f i, used in equa-
tions (7.1–7.9), is replaced by the equivalent ratio
l
ef t
 par
enth es i
s upper X Subscript i j Baseline divided by sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline right parenthesis
in the constraints 
below: 
Evaporated at 70 (E70) Speciﬁcation Constraints. 
s
i
gma 
summat io n  Un dersc
r
i
pt i
 e q ua
ls  1  O v e r s cr
ipt upper I Endscripts upper E upper V c Subscript i Baseline upper X Subscript i j Baseline greater than or equals upper E upper L Subscript j Baseline sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline comma j equals 1 comma ellipsis comma upper J
170
7
Optimum Gasoline Blending in Petroleum Reﬁning
s
i
gma 
summat io n  Un dersc
r
i
pt i
 e q ua
ls  1  O v e r s cri
pt upper I Endscripts upper E upper V c Subscript i Baseline upper X Subscript i j Baseline less than or equals upper E upper U Subscript j Baseline sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline comma j equals 1 comma ellipsis comma upper J period
Reid Vapor Pressure (RVP) Speciﬁcation Constraints. 
s
i
gma 
summation
 
Un d e rs cript i
 e
q
u
als 
1 O ve
rs cr ip t  u p pe
r I Endscripts upper R upper V c Subscript i Superscript 1.25 Baseline upper X Subscript i j Baseline greater than or equals upper R upper L Subscript j Superscript 1.25 Baseline sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline comma j equals 1 comma ellipsis comma upper J
s
i
gma 
summation
 
Un d e rs cr ipt i
 e
q
u
als 
1 O ve
rs cr ip t  u p per
 I Endscripts upper R upper V c Subscript i Superscript 1.25 Baseline upper X Subscript i j Baseline less than or equals upper R upper U Subscript j Superscript 1.25 Baseline sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline comma j equals 1 comma ellipsis comma upper J period
Vapor Lock Index (VLI) speciﬁcation constraints. These constraints ensure that 
the VLI upper speciﬁcation limit, which is simply expressed as: VLbj ≤ VUj, is  
satisﬁed for each blend (product) j. From eq. (7.5), this inequality can be written as: 
68.95 RVbj + 7 EVbj ≤VUj. Substituting the blend’s EVb and RVb values from (7.1) 
and (7.3), respectively, the following is obtained: 
68.95
 lef
t br acket Sta
r
tF raction sigma summation Underscript i equals 1 Overscript upper I Endscripts upper R upper V c Subscript i Superscript 1.25 Baseline upper X Subscript i j Baseline Over sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline EndFraction right bracket Superscript 0.8 Baseline plus 7 StartFraction sigma summation Underscript i equals 1 Overscript upper I Endscripts upper E upper V c Subscript i Baseline upper X Subscript i j Baseline Over sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline EndFraction less than or equals upper V upper U Subscript j Baseline comma j equals 1 comma ellipsis comma upper J period
68.
95 l ef t
 brac
ke t
 St
artF ractio n sigma summation Underscript i equals 1 Overscript upper I Endscripts upper R upper V c Subscript i Superscript 1.25 Baseline upper X Subscript i j Baseline Over sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline EndFraction right bracket Superscript 0.8 Baseline plus 7 StartFraction sigma summation Underscript i equals 1 Overscript upper I Endscripts upper E upper V c Subscript i Baseline upper X Subscript i j Baseline Over sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline EndFraction less than or equals upper V upper U Subscript j Baseline comma j equals 1 comma ellipsis comma upper J period
68.
95 l ef t  
br acket 
St ar tF r a c t ion 
sigma summation Underscript i equals 1 Overscript upper I Endscripts upper R upper V c Subscript i Superscript 1.25 Baseline upper X Subscript i j Baseline Over sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline EndFraction right bracket Superscript 0.8 Baseline plus 7 StartFraction sigma summation Underscript i equals 1 Overscript upper I Endscripts upper E upper V c Subscript i Baseline upper X Subscript i j Baseline Over sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline EndFraction less than or equals upper V upper U Subscript j Baseline comma j equals 1 comma ellipsis comma upper J period
Research Octane Number (RON) speciﬁcation constraints. First, depending on 
the range in which each component’s ONci value falls, the applicable θ 
k function 
from either equations (7.6, 7.7, or  7.8) is used to convert  ONci to an octane index 
value OIci. Next, the following constraints are used to combine the octane indices 
of individual components into an octane index for the resulting gasoline blend j: 
uppe r O
 up
per I b Su bs cript j Baseline equals StartFraction sigma summation Underscript i equals 1 Overscript upper I Endscripts upper O upper I c Subscript i Baseline upper X Subscript i j Baseline Over sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline EndFraction comma j equals 1 comma ellipsis comma upper J period
up
er O  u p p
e
r I b S u b s cri
pt j Baseline equals StartFraction sigma summation Underscript i equals 1 Overscript upper I Endscripts upper O upper I c Subscript i Baseline upper X Subscript i j Baseline Over sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline EndFraction comma j equals 1 comma ellipsis comma upper J period
The following set of constraints, (7.27–7.29), is based on the formulation proposed 
by Alfares (2015) for modeling discontinuous functions. These constraints are used 
to ensure that, for each product j, one and only one binary Y jk variable is set equal to 
1, which is the one corresponding to the correct Rk range over OIbj. The symbol M 
in constraints (7.28) is a very large (big-M) constant value representing the “inﬁnity” 
upper bound of range R3: 
0 up er  Y Su bsc ri pt j Bas el ine 1 B
as el in e  p l us
 56.3 upper Y Subscript j Baseline 2 Baseline plus 74.5 upper Y Subscript j Baseline 3 Baseline less than or equals upper O upper I b Subscript j Baseline comma j equals 1 comma ellipsis comma upper J
56.3 upp er  Y Su bsc ri pt  j Ba seli ne 
1 Ba se l i n e  p
lus 74.5 upper Y Subscript j Baseline 2 Baseline plus upper M upper Y Subscript j Baseline 3 Baseline greater than or equals upper O upper I b Subscript j Baseline comma j equals 1 comma ellipsis comma upper J
7.7 Solution Process and Results
171
u ppe r Y  Su bs c rip t j 
Ba se li n e  1  Ba
seline plus upper Y Subscript j Baseline 2 Baseline plus upper Y Subscript j Baseline 3 Baseline equals 1 period j equals 1 comma ellipsis comma upper J period
The following constraints implement eq. (7.10) using only one of the three θ 
k func-
tions deﬁned in eqs. (7.7–7.9). These constraints ensure that only the correct θ 
k 
function is selected by the model to calculate ONbj, the actual RON value for each 
gasoline blend (product) j: 
uppe r O 
u
p
per 
I 
b
 Sub s
c
r ipt
 j  B as e l i n e e
quals sigma summation Underscript k equals 1 Overscript 3 Endscripts theta Subscript k Baseline left parenthesis upper O upper N b Subscript j Baseline right parenthesis upper Y Subscript j k Baseline period j equals 1 comma ellipsis comma upper J period
Finally, the constraints below enforce the applicable RON speciﬁcations for each 
product j: 
uppe r O upper 
N b Su b s c r ipt
 j Baseline greater than or equals upper O upper L Subscript j Baseline comma j equals 1 comma ellipsis comma upper J period
7.7 
Solution Process and Results 
The NLP model expressed by (7.11–7.31) is difﬁcult to solve because it has highly 
complicated nonlinear relations as well as binary integer variables. In fact, Excel 
Solver could not directly solve the NLP model of the reﬁnery gasoline blending 
case study. This is because it is generally too difﬁcult to solve non-convex NLP 
models from scratch, i.e., without starting from an initial feasible solution. In order 
to facilitate the solution of the NLP model, it is necessary to provide an initial 
feasible solution. To maximize the effectiveness of the optimization process, the 
initial solution should be as close as possible to the optimal solution. 
Therefore, a two-stage process is developed to solve the gasoline blending 
problem. In the ﬁrst stage, linear programming (LP) is used to solve a set of simpli-
ﬁed linear approximations of the model. In the second stage, NLP is used to solve 
the original nonlinear model starting from the best LP solution. The details of both 
stages are described below. 
7.7.1 
Stage 1: LP Solution of Linearized Approximation 
To obtain an initial solution of the model, a simpliﬁed linearized version is solved 
by linear programming (LP). From the original model, the linearized model keeps 
the Xij and Sci decision variables, but the decision variables Y jk, OIbj, and ONbj 
are removed. Moreover, the linearized LP model keeps the objective function (7.11)
172
7
Optimum Gasoline Blending in Petroleum Reﬁning
and the linear constraints (7.12–7.24), while nonlinear constraints (7.25–7.31) are  
replaced by linear approximations as follows. 
First, the VLI constraints (7.25) are replaced by constraints (7.32) shown below: 
68.95 a
l
pha
 Sub script  j  Baseline StartFraction sigma summation Underscript i equals 1 Overscript upper I Endscripts upper R upper V c Subscript i Baseline upper X Subscript i j Baseline Over sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline EndFraction plus 7 StartFraction sigma summation Underscript i equals 1 Overscript upper I Endscripts upper E upper V c Subscript i Baseline upper X Subscript i j Baseline Over sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline EndFraction less than or equals upper V upper U Subscript j Baseline comma j equals 1 comma ellipsis comma upper J
68.
95 a lp h a
 S u
bsc
ript  j Bas el ine StartFraction sigma summation Underscript i equals 1 Overscript upper I Endscripts upper R upper V c Subscript i Baseline upper X Subscript i j Baseline Over sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline EndFraction plus 7 StartFraction sigma summation Underscript i equals 1 Overscript upper I Endscripts upper E upper V c Subscript i Baseline upper X Subscript i j Baseline Over sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline EndFraction less than or equals upper V upper U Subscript j Baseline comma j equals 1 comma ellipsis comma upper J
68.
95 a lp h a
 S ubscri
pt  j  B a s e l ine
 StartFraction sigma summation Underscript i equals 1 Overscript upper I Endscripts upper R upper V c Subscript i Baseline upper X Subscript i j Baseline Over sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline EndFraction plus 7 StartFraction sigma summation Underscript i equals 1 Overscript upper I Endscripts upper E upper V c Subscript i Baseline upper X Subscript i j Baseline Over sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline EndFraction less than or equals upper V upper U Subscript j Baseline comma j equals 1 comma ellipsis comma upper J
where 
al
ph a
 Sub
scri pt j Base
l
in e equals left bracket StartFraction sigma summation Underscript i equals 1 Overscript upper I Endscripts upper R upper V c Subscript i Superscript 1.25 Baseline upper X Subscript i j Baseline Over sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline EndFraction right bracket Superscript 0.8 Baseline divided by StartFraction sigma summation Underscript i equals 1 Overscript upper I Endscripts upper R upper V c Subscript i Baseline upper X Subscript i j Baseline Over sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline EndFraction comma j equals 1 comma ellipsis comma upper J period
alp
ha S ub s
cript  j 
Base line e qu als left bracket StartFraction sigma summation Underscript i equals 1 Overscript upper I Endscripts upper R upper V c Subscript i Superscript 1.25 Baseline upper X Subscript i j Baseline Over sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline EndFraction right bracket Superscript 0.8 Baseline divided by StartFraction sigma summation Underscript i equals 1 Overscript upper I Endscripts upper R upper V c Subscript i Baseline upper X Subscript i j Baseline Over sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline EndFraction comma j equals 1 comma ellipsis comma upper J period
alp
ha S ub s c
r
ip t j B a s e lin
e equals left bracket StartFraction sigma summation Underscript i equals 1 Overscript upper I Endscripts upper R upper V c Subscript i Superscript 1.25 Baseline upper X Subscript i j Baseline Over sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline EndFraction right bracket Superscript 0.8 Baseline divided by StartFraction sigma summation Underscript i equals 1 Overscript upper I Endscripts upper R upper V c Subscript i Baseline upper X Subscript i j Baseline Over sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline EndFraction comma j equals 1 comma ellipsis comma upper J period
If the constant αj in (7.32) is ignored (i.e., αj = 1 is assumed), the solution is usually 
infeasible, as the original constraint (7.25) is not satisﬁed. Since the nonlinear term
left bracket sigma summation Underscript i equals 1 Overscript upper I Endscripts upper R upper V c Subscript i Superscript 1.25 Baseline upper X Subscript i j Baseline divided by sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline right bracket Superscript 0.8
i
is approximated by sig
ma summation Underscript i equals 1 Overscript upper I Endscripts upper R upper V c Subscript i Baseline upper X Subscript i j divided by sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j 
, 
the constant αj is needed in (7.32) to make sure (7.25) is satisﬁed. Using (7.33), the 
exact value of αj cannot be ﬁxed in advance, as it depends on the ﬁnal optimal values 
of the Xij decision variables. Therefore, extensive numerical simulations have been 
conducted to estimate the appropriate range of values of αj for the gasoline blending 
case study. Using the given RVci values from Table 7.2, randomly generated Xij 
values were substituted into eq. (7.33) to estimate the applicable αj value. Based on 
a large number of randomly generated test instances, the value of αj roughly ranges 
from 1.01 to 1.14 with an average of approximately 1.07. 
While constraint (7.32) keeps VLbj ≤ VUj to ensure feasibility, making VLbj as 
close as possible to VUj ensures nearness to optimality. To achieve both feasibility and 
near-optimality, the value of αj should be adjusted to obtain the maximum possible 
value of VLbj not exceeding the upper bound VUj. For each product j, the appropriate 
value of the factor αj is determined as follows: 
1. Set αj = 1.00. 
2. Given αj, solve the linearized model by LP, and then calculate the actual 
(nonlinear) VLbj as the left-hand side of (7.25). 
3. If (7.25) is violated, (i.e., VLbj > VUj), then increase αj by 0.01 and go to step 2. 
4. Otherwise, if VLbj ≤ VUj, then ﬁx the value of αj and stop. 
Similarly, nonlinear RON constraints (7.26–7.31) in the original model are 
replaced by constraint (7.34), which is a simpliﬁed linear approximation: 
be
t
a S
ubsc ript j  B aseline StartFraction sigma summation Underscript i equals 1 Overscript upper I Endscripts upper O upper N c Subscript i Baseline upper X Subscript i j Baseline Over sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline EndFraction greater than or equals upper O upper L Subscript j Baseline comma j equals 1 comma ellipsis comma upper J
bet
a Su bs c r
ip t j Ba
se li ne  S t a rt
Fraction sigma summation Underscript i equals 1 Overscript upper I Endscripts upper O upper N c Subscript i Baseline upper X Subscript i j Baseline Over sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline EndFraction greater than or equals upper O upper L Subscript j Baseline comma j equals 1 comma ellipsis comma upper J
where
7.7 Solution Process and Results
173
be
ta  S ubsc r
ipt
 j B as eline equals StartFraction upper O upper N b Subscript j Baseline sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline Over sigma summation Underscript i equals 1 Overscript upper I Endscripts upper O upper N c Subscript i Baseline upper X Subscript i j Baseline EndFraction comma j equals 1 comma ellipsis period upper J period
bet
a Su bscrip t j  
Ba se li ne  e q u als
 StartFraction upper O upper N b Subscript j Baseline sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline Over sigma summation Underscript i equals 1 Overscript upper I Endscripts upper O upper N c Subscript i Baseline upper X Subscript i j Baseline EndFraction comma j equals 1 comma ellipsis period upper J period
To estimate the appropriate range of values for βj using (7.35), a large set of numer-
ical simulations was used to compare the linear term
left
 par enthes is  
sigm
a su mm a
tion Underscript i equals 1 Overscript upper I Endscripts upper O upper N c Subscript i Baseline upper X Subscript i j Baseline divided by sigma summation Underscript i equals 1 Overscript upper I Endscripts upper X Subscript i j Baseline right parenthesis
with the true RON value (ONbj). Using the ONci values given in Table 7.2, randomly 
generated values were substituted into eq. (7.35) to estimate the appropriate βj values 
for the case study. Since the ONbj values of both gasoline products (with ONb1 = 
91 and ONb2 = 95) fall within the second ONb interval R2 = [76, 103], the func-
tion (θ 
2) deﬁned in (7.7) was used in the simulations. Based on a large number of 
randomly generated test instances with OIbj values in the range R2, the values of βj 
approximately range from 1.02 to 1.15 with a mean of about 1.08. 
A feasible solution is usually obtained without using the constant βj, i.e., by 
assuming βj is equal to 1 in constraints (7.34). However, the linear approximation 
with βj equal to 1 tends to produce inﬂated RON values (ONbj) of the gasoline blends. 
This octane give away is not desirable, because it makes the initial LP solution far 
from the optimum NLP solution. As previously noted, the nearer the (starting) LP 
solution to the optimum point, the better the ﬁnal solution of the NLP model. There-
fore, the use of βj is necessary to improve the quality of the linear approximation. 
Hence, the value of βj must guarantee ONbj ≥ OLj to ensure feasibility, as well as 
make ONbj as close as possible to OLj to ensure near-optimality. In other words, we 
must use a value of βj to minimize ONbj without violating the lower bound OLj. 
For each gasoline product j, the factor βj is determined by the following process: 
1. Set βj = 1.00. 
2. Given βj, solve the linearized model by, and then calculate the actual (nonlinear) 
ONbj. 
3. If (7.31) is satisﬁed (i.e., ONbj ≥ OLj), then increase βj by 0.01 and go to step 2. 
4. Otherwise, if ONbj < OLj, then decrease βj by 0.01 and stop. 
7.7.2 
Stage 2: NLP Solution Using LP Solution as the Initial 
Point 
After running the linearized model with the best values of αj and βj, the LP solution 
explicitly speciﬁes the values of the Xij and Sci variables as well as the linearized 
model’s objective function ZLP. From the LP solution, the values of all OIbj and ONbj 
variables are calculated. Based on the ranges in which the different ONbj values fall, 
the corresponding values of the binary Y jk variables are determined. Inputting the 
values of all these decision variables as the initial solution, the original nonlinear 
model is then solved by NLP. In order to improve the speed and the quality of the 
NLP solution, the LP solution’s objective function value is used as a lower bound for 
the NLP solution. This is done by adding the following constraint to the NLP model 
deﬁned by (7.11–7.31):
174
7
Optimum Gasoline Blending in Petroleum Reﬁning
s
i
gma 
s
u
mma
t
ion  U nd
e
rs c r ip t i  e
quals 1 Overscript upper I Endscripts sigma summation Underscript j equals 1 Overscript upper J Endscripts left parenthesis upper P Subscript j Baseline minus upper C Subscript i Baseline right parenthesis upper X Subscript i j Baseline greater than or equals upper Z Subscript upper L upper P Baseline period
7.7.3 
Case Study Solution Results 
The two-stage solution process has been applied to the reﬁnery gasoline blending 
case study described in Sect. 7.5. Using the data given in Tables 7.2, 7.3, 7.4 for the 
summer season, the results of the two stages are presented below. 
Stage 1: Solution 
Solving the simpliﬁed linear model by LP starting with the initial values α1 = α2 = 
β1 = β2 = 1.00 gives ZLP = 14,191,416, VLb1 = 915.9, VLb2 = 900.9. Clearly, this 
solution is not feasible since the VLI values of both products exceed the maximum 
summer VLI limit of 875. Moreover, the true nonlinear RON values are 95.1 for 
the 91-octane gasoline and 102.5 for the 95-octane gasoline. These values indicate 
costly octane give away, which is wasting resources by selling gasoline with octane 
that is much higher than the required speciﬁcations. 
Following the search process of stage 1, the best feasible solution of the simpliﬁed 
linear model was obtained with the VLI factors equal to α1 = α2 = 1.10 and the 
RON factors equal to β1 = 1.03 and β2 = 1.07. Using these values, the optimum LP 
solution of the linearized stage 1 model is shown in Table 7.5. 
The initial LP solution, whose objective value is equal to ZLP = $13,980,940, 
directly provides the E70 values of the two gasoline products. Using the values in 
Table 7.5, the remaining speciﬁcations are calculated, and they are shown in Table 
7.6. 
Table 7.5 Stage 1 production and supply quantities of the 5 components 
Component
Platformate C5/C6 Isomer-
ate 
C4
MTBE
Total 
PG-91
Xi1 (m
3 )
29,573
10,173 
16,164
0
711
56,621 
%
52.2
18.0
28.5
0
1.3
100 
PG-95
Xi2 (m
3 )
14,970
 
0
 14,462
 0
 1,056
 
30,488 
%
49.1
 
0
47.4
0
3.5
 
100 
Supply
Sci
43,680
9,652 
21,840
0
0
75,172 
Table 7.6 Stage 1 speciﬁcations of the two gasoline products 
Specification
E70
RVP
VLI 
RON 
PG-91
36.90 
8.87
870.2
91.91 
PG-95
39.04
8.60
866.4
95.05
7.7 Solution Process and Results
175
Stage 2: Solution 
Using the objective function value of the LP solution as a lower bound, constraint 
(7.36) is added as (Z ≥ 13,980,940) to the NLP model deﬁned by (7.11–7.31). Since 
the RON and OIb values of both products fall in range 2, then Y 12 and Y 22 are set 
equal to 1, while all the other Y jk values are set equal to zero. Next, the values of all 
decision variables (Xij, Y jk, Sci, OIbj, and ONbj) obtained by LP from the linearized 
model are inputted as initial solution for the NLP model. Starting from this initial 
solution, solving the original NLP model leads to the optimum NLP solution shown 
in Tables 7.7 and 7.8. The ﬁnal objective function value, representing the reﬁnery’s 
weekly proﬁt, is Z = $14,295,291. 
Two observations can be made based on the above results. First, simple linear 
approximation (with α1 = α2 = β1 = β2 = 1.00) does not work at all for this problem. 
Due to the high nonlinearity of the functions describing blend characteristics, simple 
linear approximation produces solutions that are infeasible and far from optimal. 
Therefore, adjustment of the linear approximation by searching for the best values 
of the correction parameters (α1, α2, β1, β2) is necessary. Second, although the best 
adjusted linear approximation gives feasible and near-optimal solutions, it is not 
sufﬁcient. A second stage using NLP is needed to maximize the proﬁt. For the case 
study, the second-stage NLP solution increased the weekly proﬁt from $13,980,940 
to $14,295,290. Although this is an increase of only 2.25%, it is a net additional 
proﬁt of $314,350 per week, amounting to $16,346,200 per year.
Table 7.7 Final production and supply quantities of the 5 components 
Component
Platformate C5/C6 Isomer-
ate 
C4
MTBE
Total 
PG-91
Xi1 (m
3 )
29,782
11,881 
15,471
0
457
57,590 
%
51.7
 
20.6
26.9
0
 
0.8
 
100 
PG-95
Xi2 (m
3 )
14,761
0
15,155
0
1,094
31,010 
%
47.6
0.0
48.9
0
3.5
100 
Supply
Sci
43,680
11,360 
21,840
0
0
76,880 
Table 7.8 Final speciﬁcations of the two gasoline products 
Specification
E70
RVP
VLI 
RON 
PG-91
37.06
8.987
875
91 
PG-95
39.83
8.703
875
95 
176
7
Optimum Gasoline Blending in Petroleum Reﬁning
7.8 
Summary and Conclusions 
This chapter presented models and solution techniques for optimizing gasoline 
blending decisions in crude oil reﬁneries. In the gasoline blending problem, several 
input components (raw materials) are blended (mixed) to produce several grades 
of gasoline. The objective is to maximize the weekly proﬁt, given supply, demand, 
inventory, capacity, and speciﬁcation constraints. Since the relations between the 
properties of the blend and the properties of the components are nonlinear, a nonlinear 
programming (NLP) model is formulated to represent and optimally solve the 
problem. The NLP model determines the optimum quantities to produce of each 
gasoline product and the amounts of each raw material to use either from the existing 
stock or from additional supply. 
Without providing a good initial solution, a direct solution by NLP is not possible. 
Moreover, due to the highly nonlinear nature of the problem, simple linear approxi-
mation is not viable, as it produces infeasible and low-quality solutions. Therefore, 
a two-stage solution procedure is proposed. In the ﬁrst stage, an adjusted linear 
approximation model is solved by LP. A simple search is used with LP to ﬁne-tune 
the correction parameters and ﬁnd the best feasible starting solution. In the second 
stage, the best solution of stage 1 is used as an initial solution, and the NLP model 
is then solved to ﬁnd the best ﬁnal solution. To illustrate this approach and conﬁrm 
its practical value, it has been successfully applied to a real gasoline blending case 
study in a mid-size reﬁnery. 
The optimization model discussed in this chapter can be extended in several 
directions. For example, the problem can be expanded by including multiple blending 
facilities with shipping network, allowing the exchange of streams between facilities. 
Multiple time periods can also be considered, with time-varying demands, supplies, 
and speciﬁcations, as well as variable inventory levels and associated holding costs. 
The selling prices of gasoline products can be considered as decision variables, 
assuming demand is price sensitive. Uncertainty about the demand, supply, and 
component characteristics can be incorporated. Finally, other realistic features can 
be included, such as multi-product tanks, non-identical blenders, and variable feed 
rates. 
References 
Alfares, H. K. (2015). Maximum-proﬁt inventory model with stock-dependent demand, time-
dependent holding cost, and all-units quantity discounts. Mathematical Modelling and Analysis, 
20(6), 715–736. 
Barsamian, A., & Curcio, L. E. (2016). Gasoline blend optimization via linear & non-linear 
programming optimizers. In: AIChE NJ Seminar. 
Bayu, F., Panda, D., Shaik, M. A., & Ramteke, M. (2020). Scheduling of gasoline blending and 
distribution using graphical genetic algorithm. Computers & Chemical Engineering, 133, 106636.
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Castillo, P. A. C., Mahalec, V., & Kelly, J. D. (2013). Inventory pinch algorithm for gasoline blend 
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Cerdá, J., Pautasso, P. C., & Cafaro, D. C. (2016). Optimizing gasoline recipes and blending oper-
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7782–7800. 
Chen, X., & Wang, N. (2010). Optimization of short-time gasoline blending scheduling problem 
with a DNA based hybrid genetic algorithm. Chemical Engineering and Processing: Process 
Intensiﬁcation, 49(10), 1076–1083. 
Curcio, L. E. (2019). Linear vs. nonlinear: calculate gasoline component properties. In: Hydrocarbon 
Processing, pp. 1–3. 
Demirbas, A., & Bamuﬂeh, H. S. (2017). Optimization of crude oil reﬁning products to valuable 
fuel blends. Petroleum Science and Technology, 35(4), 406–412. 
DeWitt, C. W., Lasdon, L. S., Waren, A. D., Brenner, D. A., & Melhem, S. A. (1989). OMEGA: 
An improved gasoline blending system for Texaco. Interfaces, 19(1), 85–101. 
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Hussain, N., Li, J., Sun, L., Xiao, X., & Cao, C. (2018). An improved approach to scheduling 
gasoline blending and order delivery operations. Computer Aided Chemical Engineering, 44, 
1615–1620. 
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and distribution scheduling. Industrial & Engineering Chemistry Research, 42(4), 825–835. 
Jiang, Y., Phillips, S. D., Singh, A., Jones, S. B., & Gaspar, D. J. (2021). Potential economic 
values of low-vapor-pressure gasoline-range bio-blendstocks: Property estimation and blending 
optimization. Fuel, 297, 120759. 
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blending operations. AIChE Journal, 56(2), 441–465. 
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Part I. short-term scheduling and global optimization for single and multi-period operations. 
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OMEGA to StarBlend. Interfaces, 25(5), 64–83. 
Worldwide Fuel Charter Committee. (2019). Worldwide Fuel Charter for Gasoline and Diesel Fuel 
(6th ed.).
Chapter 8 
Employee Scheduling in Remote Oil 
Industry Work Sites 
8.1 
Introduction 
A large proportion of oil and gas reservoirs around the world are located in remote 
and non-populated areas. For example, most oil and gas ﬁelds in the Middle East 
and North Africa are located in isolated desert locations. In Russia, most of these 
resources are located in far-away Siberian regions. Moreover, Russia, the United 
States of America, Norway, Denmark, and Canada all have some oil and gas resources 
in the harsh Arctic environment (Akbar, 2018). In addition to remote inland loca-
tions, there are many offshore oil and gas ﬁelds in diverse locations across the globe. 
Oil companies take care of transporting employees working in facilities located in 
those remote sites, usually by expensive aircraft ﬂights. Therefore, special arrange-
ments must be taken to coordinate employee work schedules with their transporta-
tion ﬂights. Figure 8.1 shows a helicopter used by an oil company for transporting 
employees to offshore oil facilities.
Oil and gas production and processing facilities and operations span huge 
geographical areas. During each stage of the petroleum industry, work has to be 
continuously and physically performed in numerous facilities located in far-away 
sites both inland and offshore. Examples of those remote temporary and permanent 
work facilities include exploration sites, oil wells, offshore platforms, gas–oil separa-
tion plants, and oil and gas pipelines. Figure 8.2 shows the Shaybah oil ﬁeld facilities 
located at the remote Empty Quarter desert in Saudi Arabia.
When oil facilities are located in remote non-populated areas, special arrange-
ments must be made for the transportation and work assignments of the employees. 
Usually, employees are transported back and forth to remote work sites using heli-
copters or small ﬁxed-wing airplanes. Therefore, special work schedules are used 
to reduce the number of transportation ﬂights by reducing the frequency of days-off 
breaks. To minimize the labor cost and reduce the transportation cost, transportation 
considerations must be incorporated in developing employee work schedules. 
The problem considered in this chapter is a real-life problem for a large oil 
company in the Middle East that has a small ﬂeet of aircraft to transport employees
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 
H. K. Alfares, Applied Optimization in the Petroleum Industry, 
https://doi.org/10.1007/978-3-031-24166-6_8 
179
180
8
Employee Scheduling in Remote Oil Industry Work Sites
Fig. 8.1 Helicopter transporting employees to an offshore oil rig (Courtesy of Saudi Aramco, 
copyright owner)
Fig. 8.2 Shaybah oil ﬁeld in the remote Empty Quarter desert of Saudi Arabia (Courtesy of Saudi 
Aramco, copyright owner)
8.1 Introduction
181
to remote work locations. Obviously, the number of round-trip ﬂights corresponds 
to the number of days-off breaks. Special work schedules are thus used, in which 
remote-area employees do not get the usual one days-off break per week. Instead, 
the company uses several types of special work schedules with lower frequency of 
day-off breaks. 
One of the common remote-area employee work schedules is the 14/7, also called 
the (14, 21), days-off schedule described by Alfares (2002). Under the 14/7 schedule, 
each employee works for 14 consecutive days and then takes one break of 7 consec-
utive days every three weeks. Another remote-area schedule used by the company 
is the 10/4, also called the (10, 14), days-off schedule described by Alfares (2014). 
Under the 10/4 schedule, each employee works for 10 consecutive days and then 
takes one break of 4 consecutive days every two weeks. 
In the actual problem considered in this chapter, the 10/4 schedule is used for 
remote-area employees. By reducing the number of breaks to one in every two-week 
cycle, the 10/4 schedule reduces the number of breaks and hence the number of 
ﬂights by 50%. Obviously, to keep the facilities running, the employees cannot all 
take their four-day breaks at the same time. Work must continue every day of the 
year to maintain oil production and sales revenue and to ensure uninterrupted supply 
to the local and international markets. To maintain work continuity, employees must 
take days-off breaks at different times to ensure that a sufﬁcient workforce remains 
on duty to satisfy the seven-day-a-week labor demands. 
Each different days-off break time assigned to employees requires one round-trip 
transportation ﬂight, regardless of the number of employees. Therefore, the number 
of different days-off break times assigned to employees must be minimized to reduce 
the number of trips and hence the cost of transportation. Consequently, the company 
has two objectives to pursue in optimizing the remote-site employee work scheduling 
problem. The ﬁrst (major) objective is to minimize the labor cost by minimizing the 
workforce size. The second (minor) objective is to minimize the transportation cost by 
minimizing the total number of ﬂights. The secondary objective is indirectly achieved 
by minimizing the number of active days-off break times, i.e., the number of different 
days-off break times assigned to employees. It is also preferred to maximize the set 
of common days-off break times for all labor demand variations, to allow common 
ﬂights for employees in different remote locations. Based on the 10/4 days-off work 
schedule, a bi-objective optimization model and a solution technique are developed 
for representing and solving this problem. 
Subsequent sections of this chapter are organized as follows. Section 8.2 presents 
a brief up-to-date review of relevant literature. Section 8.3 provides the problem 
deﬁnition and the bi-objective integer programming model formulation. Section 8.4 
describes the procedure for calculating the minimum feasible workforce size, i.e., 
the total number of employees required. Section 8.5 introduces the methodology for 
assigning the workforce to different days-off break times and illustrates the appli-
cation of the optimization procedure using numerical examples. Finally, Sect. 8.6 
provides relevant conclusions and suggestions for future research.
182
8
Employee Scheduling in Remote Oil Industry Work Sites
8.2 
Literature Review 
Employee scheduling is a very important practical problem due to its signiﬁ-
cant inﬂuence on labor cost as well as on employee satisfaction and produc-
tivity. The problem is also theoretically challenging, as it involves integer vari-
ables and various constraints expressing work regulations, multiple skills, seniority 
levels, and employee preferences. Therefore, there is a wealth of literature in this 
highly active area of research. Numerous optimization models have been proposed 
for the employee scheduling problem to represent the unique requirements of 
different scheduling situations. These models are generally classiﬁed into three main 
types: shift (hours-of-day) scheduling, days-off (days-of-week) scheduling, and tour 
(hours-of-day and days-of-week) scheduling. 
Due to the large number of publications on workforce scheduling models, there are 
many comprehensive literature reviews. Özder et al. (2020) provide the most recent 
survey and classiﬁcation of personnel scheduling literature, highlighting the latest 
trends and future research directions. Castillo-Salazar et al. (2016) provide a more 
focused survey of literature on workforce scheduling and routing problems, which 
integrate employee scheduling and routing to ensure the arrival of employees to their 
respective work locations. This chapter addresses a days-off scheduling problem for 
remote-site employees of a large oil company with transportation considerations. 
Therefore, this section provides a survey of relevant employee scheduling literature, 
with a focus on recent days-off scheduling models with remote-site, transportation, 
and oil industry aspects. 
To reduce the number of ﬂights to remote work sites, the frequency of days-
off breaks is reduced, resulting in extended work stretches for the employees 
and extended periods of absence away from the family. Langdon et al. (2016) 
analyze the physical and psychosocial issues affecting employees who work in rural 
and remote locations in Australia based on ﬂy-in, ﬂy-out and drive-in, drive-out 
arrangements. They also consider work scheduling aspects for these employees, 
including compressed working schedule design and working hours. They conclude 
that long working hours and extended periods of absence from families are impacting 
employees’ personal relationships, safety performance, psychological wellbeing, job 
satisfaction, and turnover rates. 
Quite a few models have been proposed for personnel scheduling in different 
modes of transportation. Mesquita et al. (2015) formulate three mixed-integer linear 
programming (MILP) models to optimize days-off scheduling of drivers for a 
public transit company. The objective is to minimize cost while meeting demands 
and complying with legal regulations and labor agreements. The three models, 
Assignment/Covering (AC), Multi-commodity Flow (MF), and Multi-commodity 
Flow/Assignment (MFA), are evaluated and compared. An effective decompose-and-
ﬁx heuristic is developed to solve the problem by solving a set of subproblems in a 
hierarchical order. Considering a similar problem, Er-Rbib et al. (2021) address the 
duty assignment problem with group-based driver preferences (DAPGDP) for cyclic 
bus driver scheduling. A MILP model is formulated to minimize violations of soft
8.2 Literature Review
183
driver-preference constraints while satisfying hard workload-balance constraints. 
Two heuristics are proposed to obtain near-optimal solutions of larger instances: 
Preassigning Duties to Rosters (PDR) heuristic and the Partitioning Roster Positions 
(PRP) heuristic. 
Lorenzo-Espejo et al. (2021) formulate two mixed-integer programming models 
for days-off scheduling of maritime pilots who navigate visiting ships inside 
congested harbors. Both models produce days-off schedules for a fourteen-week 
planning cycle that satisfy labor regulations, pilot preferences, and fairness measures. 
The ﬁrst model is used to assign two breaks to each pilot while minimizing the differ-
ence among the workloads of individual pilots. The second model is used to maximize 
the length of pilot breaks while minimizing the difference in break durations among 
individual pilots. Managers can compare the two solutions in light of the employees’ 
preferences and choose the schedule to implement. 
Several personnel scheduling approaches have been proposed speciﬁcally for 
applications in the oil and gas industry. Awad and Ertem (2017) develop a stochastic 
modeling approach to schedule preventive maintenance (PM) work in an oil reﬁnery 
considering uncertain maintenance task times. Uncertainty is assumed for both direct 
times spent on actual PM work and indirect times spent on non-PM work such 
as paperwork, delays, and failure repairs. The model considers limitations on the 
number of multi-skilled maintenance employees, the allocated budget, and the work 
completion time. 
Akbar (2018) presents a mixed-integer linear programming (MILP) model 
to simultaneously optimize employee schedules and maintenance decisions for 
remotely located oil and gas facilities. The MILP model incorporates risk manage-
ment issues in the oil and gas industry, including the design and maintenance of auto-
mated hazard-prevention safety systems. Redutskiy et al. (2021) consider a similar 
problem, focusing on designing safety instrumented systems, planning their main-
tenance, and scheduling the workforce. A multi-objective model is developed for 
scheduling employees to perform the required maintenance tasks in remotely located 
oil and gas industrial facilities. The model balances the capital investment cost in the 
safety system, the operational cost of the maintenance workforce, and the expected 
loss due to failure hazards. 
A number of published studies address personnel scheduling problems with both 
transportation and oil industry aspects. Koubaa et al. (2017) conduct a study to 
determine the decision and modeling needs for scheduling employees who transport 
crude oil by tanker trucks. The speciﬁc management requirements, as well as the 
particular objectives and constraints, are used to customize a mathematical model 
for this employee scheduling problem. Leggate et al. (2018) develop mixed-integer 
programming models for ship crew scheduling and rescheduling for a large interna-
tional company. The crew operates supply vessels providing services mainly to the 
oil and gas industry’s offshore facilities. The models are shown to generate real-time, 
feasible, and low-cost crew schedules. 
Nafstad et al. (2021) optimize helicopter ﬂight scheduling to transport employees 
to, from, or between onshore and offshore facilities for the oil and gas industry. This 
vehicle routing problem is represented by two mathematical models: an arc-ﬂow
184
8
Employee Scheduling in Remote Oil Industry Work Sites
model and an extended set partitioning model. To speed up the solution, the trips are 
generated a priori  and combined into feasible routes. The constraints are gradually 
added to the model using delayed constraint generation during the branch-and-bound 
search. 
Few papers explicitly consider transportation costs in workforce scheduling for 
companies that provide transportation for their employees. Örmeci et al. (2014) 
analyze employee scheduling for a banking call center that provides shuttle pick-
and-drop transportation service for the employees. A mixed-integer programming 
model is used to integrate agent shift assignments with shuttle demands at the start 
and end of each shift. The model considers three objectives, namely total cost (of 
labor and transportation), customer service, and employee satisfaction. The three 
objectives are balanced subject to satisfying work regulations, stafﬁng demands, and 
required employee skills and preferences. 
Alfares (2002) presents an integer programing model for optimizing the schedules 
of remote-area employees of an oil company who are assigned to the (14, 21) days-off 
schedule. The model has two prioritized objectives, the ﬁrst is to minimize the total 
number of workers, while the second is to minimize the number of active days-off 
breaks. Utilizing the special structure of the model, the primal–dual relations are 
used to obtain direct closed-form solutions of the days-off assignments. Considering 
the same two objectives, a similar problem is addressed by Alfares (2014) for  the  
(10, 14) days-off schedule. Assuring a minimum proportion of weekends is assigned 
off, a rotation schedule is developed to guarantee fairness among employees. 
The remote-site employee scheduling problem considered in this chapter is 
described and formulated in the following section. A mathematical optimization 
model is developed to minimize the labor cost and also the transportation cost by 
reducing the number of ﬂights. 
8.3 
Problem Deﬁnition and Formulation 
8.3.1 
Problem Description 
An oil company in the Middle East has many remote temporary and permanent oil 
and gas facilities. These facilities are operated and maintained by employees who are 
ﬂown to these sites to work and who are housed in the company’s accommodations 
during their workdays. These employees are assigned to the 10/4 days-off schedule, 
which allows only one days-off break of four consecutive days during every two-
week cycle. Obviously, different groups of employees take their four-day breaks on 
different days to make sure there is always a sufﬁcient number of employees on 
duty to satisfy the daily workload. For each days-off break time assigned, a round-
trip ﬂight is scheduled to take the employees home (the company’s headquarters’ 
location) at the start of the break and then ﬂy them back to the work site at the end 
of the break.
8.3 Problem Deﬁnition and Formulation
185
The 10/4 schedule provides the advantage of reducing the number of ﬂights by half, 
thus providing signiﬁcant transportation cost savings for the company. However, this 
schedule has three disadvantages of special concern to the employees. The ﬁrst issue 
is reducing the frequency of day-off breaks and increasing the stretch of consecutive 
work hours. This issue, as pointed out by Langdon et al. (2016), can lead to serious 
morale and emotional effects over the long term. Managerial approaches used to 
deal with this issue include: (1) limiting remote-site employee assignments to a few 
number of years, (2) giving the employees longer annual vacations, (3) being ﬂexible 
in rescheduling off day breaks to allow the employees to attend special family events. 
The second disadvantage of the 10/4 days-off schedule from the employees’ point 
of view is that the off days are not always on weekends. This is especially important 
because the employees have a chance to meet with their families only once every 
other week. Naturally, they would much prefer to have this chance at the weekend, 
when all their family members are also free from work. To deal with this concern, 
management guarantees that at least half the weeks will include a weekend day-off. 
Finally, the third concern the employees have about the 10/4 schedule is possible 
lack of fairness. Since employees have to take their four-day breaks on different 
days, some breaks will have more weekend days than others. Obviously, it is not 
fair for some employees to have no weekend days-off while some others have one 
or two weekend days-off. For the sake of fairness, the off day break assignments 
must be rotated among employees, to make sure that each employee has the same 
sequence of off day breaks during the rotation cycle. As employees go through the 
rotation, they keep switching from one days-off break for a given two-week cycle to 
another break in the next two-week cycle. Therefore, it becomes possible for them to 
work continuously for a stretch of more than 10 days spanning two cycles. In order 
to reduce fatigue and avoid morale issues, management limits the maximum work 
stretch to 14 consecutive days. 
For the management, two objectives should be optimized while satisfying the 
above conditions. The ﬁrst (major) objective is to minimize the total number of 
employees in order to reduce the labor cost. The second (minor) objective is to mini-
mize the number of active (assigned) days-off break patterns in order to reduce the 
transportation cost. Management also prefers to use the same set of active days-off 
break patterns as much as possible to satisfy all possible labor demand variations. 
This will help the company to use the same ﬂights to transport employees to and from 
different remote-area locations. Finally, the management requests the use of special-
ized optimization software to be avoided. As remote-site employees do not have 
sufﬁcient IT and optimization backgrounds, a simple spreadsheet-based employee 
scheduling tool is required.
186
8
Employee Scheduling in Remote Oil Industry Work Sites
8.3.2 
Model Assumptions 
The problem described above is further deﬁned by the following assumptions: 
1. The scheduling cycle is two weeks according to the 10/4 days-off schedule. 
2. During each cycle, each employee is assigned 10 successive workdays and 4 
successive off days. 
3. The maximum work stretch is 14 successive workdays. 
4. During a complete rotation cycle, the average number of weekend off days is at 
least one in every two-week cycle. 
5. The primary objective is to minimize the total workforce size. 
6. The secondary objective is to minimize the number of active days-off patterns, 
i.e., different sets of off day breaks assigned to employees. 
7. Daily demand for employees does not change from week to week. Daily labor 
demand has one constant level for all ﬁve regular workdays and another (lower) 
constant level for the two weekend days. 
8. A cyclic rotation schedule is used, in which all employees take turns on all the 
assigned days-off patterns (breaks). 
8.3.3 
Model Notation 
Decision Variables 
xj 
number of employees assigned to days-off break pattern j; 
number of employees whose ﬁrst off day is day j, j = 1, …, 14; 
W 
workforce size, i.e., total number of assigned employees = sigma summation Underscript j equals 1 Overscript 14 Endscripts x Subscript j 
; 
St ar t
Layo ut 1s t Ro w 
1st  Co lu mn  u p er  Q  S ub s c r i p
t j Baseline 2nd Column equals StartLayout Enlarged left brace 1st Row 1 comma if x Subscript j Baseline greater than or equals 1 2nd Row 0 comma if x Subscript j Baseline equals 0 EndLayout comma j equals 1 comma ellipsis comma 14 period EndLayout
Given Parameters 
ε small positive fraction (0 < ε < <  
1);  
M 
a large number, M ≥ W; 
ρ
actual proportion of weeks that include a weekend day-off; 
left-hand side of constraints (8.5a); 
D number of employees required on each weekday; 
number required daily on days 1–5 and 8–12;
8.3 Problem Deﬁnition and Formulation
187
Table 8.1 Days-off matrix A = {aij} for the 14 days-off work patterns (Adapted from Alfares 
(2014)(1)) 
i 
j 
1
2
3
4
5
6
7
8
9 
10 
11 
12 
13 
14 
1
 0
1
1
 
1
1
 
1
 
1
 
1
1
 
1
 
1
0
 
0
 
0
 
2
 0
0
1
 
1
1
 
1
 
1
 
1
1
 
1
 
1
1
 
0
 
0
 
3
 0
0
0
 
1
1
 
1
 
1
 
1
1
 
1
 
1
1
 
1
 
0
 
4
 0
0
0
 
0
1
 
1
 
1
 
1
1
 
1
 
1
1
 
1
 
1
 
5
 1
0
0
 
0
0
 
1
 
1
 
1
1
 
1
 
1
1
 
1
 
1
 
6
1
1
0
0
0
0
1
1
1
1
1
1
1
1 
7
1
1
1
0
0
0
0
1
1
1
1
1
1
1 
8
 1
1
1
 
1
0
 
0
 
0
 
0
1
 
1
 
1
1
 
1
 
1
 
9
 1
1
1
 
1
1
 
0
 
0
 
0
0
 
1
 
1
1
 
1
 
1
 
10
 
1
1
1
 
1
1
 
1
 
0
 
0
0
 
0
 
1
1
 
1
 
1
 
11
 
1
1
1
 
1
1
 
1
 
1
 
0
0
 
0
 
0
1
 
1
 
1
 
12
 
1
1
1
 
1
1
 
1
 
1
 
1
0
 
0
 
0
0
 
1
 
1
 
13
1
1
1
1
1
1
1
1
1
0
0
0
0
1 
14
1
1
1
1
1
1
1
1
1
1
0
0
0
0 
E 
number of employees required on each weekend day (E ≤ D); 
number required daily on days 6–7 and 13–14; 
ri 
number of employees required on each day i; 
r Su b
s cri pt j  Baseline eq ua ls  S ta rtLa yo ut  E nl arg
ed left  brace 1st  R ow  u pp er  D c om ma  fo r workdays comma i equals 1 minus 5 and i equals 8 minus 12 2nd Row upper E comma for weekends comma i equals 6 minus 7 and i equals 13 minus 14 semicolon EndLayout
Jk = set of days-off patterns with k weekend days-off per cycle, k = 0, 1, 2: 
J0 = {1, 2, 8, 9};  
J1 = {3, 7, 10, 14}; 
J2 = {4, 5, 6, 11, 12, 13}; 
a S u b
s cri pt i j Ba sel in e equals  Sta rtLay ou t En larged l e
f
t
 b ra c e  1 s
t R
ow 1 comma i
f da y i  i s  
a workday for days minus off pattern j i equals 1 comma ellipsis comma 14 2nd Row 0 comma otherwise j equals 1 comma ellipsis comma 14 period EndLayout
Table 8.1 shows matrix A = {aij, i = 1, …, 14, j = 1, …, 14}, where 
i = 1–5 and i = 8–12 correspond to regular weekdays and 
i = 6–7 and i = 13–14 correspond to the weekends.
188
8
Employee Scheduling in Remote Oil Industry Work Sites
8.3.4 
Model Formulation 
The following integer linear programming (ILP) model is formulated to represent 
and optimally solve the remote-site employee scheduling problem described above. 
Based on the given assumptions and notation relevant to the 10/4 days-off schedule, 
the ILP model is expressed as follows. 
Objective Function 
The objective function (8.1) includes the primary objective of minimizing the number 
of employees and the secondary objective of minimizing the number of active days-
off patterns. 
The two objectives are linearly combined in the weighted sum Z shown below 
in (8.1), where the small fraction ε represents the lower weight of the secondary 
objective: 
Minimize up pe
r 
Z
 equ
al s si gm
a 
s
umma
ti on 
Underscript j equals 1 Overscript 14 Endscripts x Subscript j Baseline plus epsilon sigma summation Underscript j equals 1 Overscript 14 Endscripts upper Q Subscript j Baseline period
Constraints 
The objective function (8.1) is minimized subject to constraints (8.2–8.8) below. 
Constraints (8.2) ensure that a sufﬁcient number of employees are assigned to satisfy 
the daily labor demand D for each regular workday: 
si
g
ma s
um m ati on  Unde rs cr ipt j eq ual s 1 Ov er script 14 E
ndscripts a Subscript i j Baseline x Subscript j Baseline greater than or equals upper D comma i equals 1 comma period period period comma 5 comma and i equals 8 comma period period period comma 12 period
Constraints (8.3) guarantee that a sufﬁcient number of employees are assigned to 
satisfy the daily labor demand E for each weekend day: 
si
g
ma s
um m ati on  U nde rs cr ipt  j equa ls  1  Ove rsc
ript 14 Endscripts a Subscript i j Baseline x Subscript j Baseline greater than or equals upper E comma i equals 6 comma 7 comma and i equals 13 comma 14 period
Constraints (8.4) enforce the logical relationship between the values of Qj and xj 
(i.e., Qj = 1 if  xj ≥ 1, and Qj = 0 if  xj = 0): 
x Su bs c r ip
t j Baseline le
ss than or equals upper M upper Q Subscript j Baseline j equals 1 comma period period period comma 1 4 period
Weekend-off frequency constraints (8.5) ensure the average assignment of 
weekend off days in 50% of the weeks. That is, out of all the days-off scheduling 
assignments during a full rotation, an average of at least one weekend off day is given 
every two weeks. The set J1 has a coefﬁcient of 0.5 because it contains one weekend
8.3 Problem Deﬁnition and Formulation
189
off day every two weeks, while the set J2 has a coefﬁcient of 1 because it contains 
two weekend off days every two weeks: 
Sta r
tFrac ti on  0
.5 si gm a summation Underscript j element of upper J 1 Endscripts x Subscript j Baseline plus sigma summation Underscript j element of upper J 2 Endscripts x Subscript j Baseline Over sigma summation Underscript j equals 1 Overscript 14 Endscripts x Subscript j Baseline EndFraction greater than or equals 0.5 period
Star
tFra ct io
n 0.5 
sigma summation Underscript j element of upper J 1 Endscripts x Subscript j Baseline plus sigma summation Underscript j element of upper J 2 Endscripts x Subscript j Baseline Over sigma summation Underscript j equals 1 Overscript 14 Endscripts x Subscript j Baseline EndFraction greater than or equals 0.5 period
The denominator in the above constraint is equal to: 
si
g
ma s
um ma t
i
on Un
de rs c
r
ipt j
 e qu a
l
s 1 O
ve rscript 14 Endscripts x Subscript j Baseline equals sigma summation Underscript j element of upper J 0 Endscripts x Subscript j Baseline plus sigma summation Underscript j element of upper J 1 Endscripts x Subscript j Baseline plus sigma summation Underscript j element of upper J 2 Endscripts x Subscript j Baseline period
After multiplying both sides of (8.5a) by the denominator and rearranging, the 
weekend-off frequency constraint can be expressed in the following explicit form: 
m
i
nus s
ig ma  
s
ummat
io n Un de
rscript j element of upper J 0 Endscripts x Subscript j Baseline plus sigma summation Underscript j element of upper J 2 Endscripts x Subscript j Baseline greater than or equals 0 period
Constraints (8.6) express the speciﬁed limit on the work stretch, which is 14 
successive workdays. During each rotation cycle, employees switch from one days-
off pattern to another. To prevent the total number of successive workdays belonging 
to two consecutive cycles from exceeding 14, certain pairs of adjacent days-off 
patterns cannot be allowed. In other words, some days-off patterns cannot be imme-
diately followed by some other patterns, because that would create a sequence of 
more than 14 consecutive workdays. 
Actually, days-off patterns (7, …, 14) end with sequence of 4 or less consecutive 
workdays. Therefore, patterns (7, …,14) can be immediately followed by any pattern. 
However, days-off patterns (1, …, 6) end with sequence of 5 or more consecutive 
workdays. Therefore, days-off patterns (1, …, 6) can be immediately followed only 
by a certain subset of patterns. Speciﬁcally, days-off pattern j, j ∈ {1, 2, …, 6} can 
be immediately followed only by patterns (1, 2, …, j + 4) as well as patterns (12, 13, 
14). Therefore, constraints (8.6) ensure that if any of the patterns (1, …, 6) is active, 
then a pattern that can immediately follow it must be also active: 
sig
m
a su
mma ti
on
 
Under
scr ip t n e
qu al s 1 Oversc
ript j plus 4 Endscripts upper Q Subscript n Baseline plus sigma summation Underscript n equals 12 Overscript 14 Endscripts upper Q Subscript n Baseline greater than or equals upper Q Subscript j Baseline comma j equals 1 comma period period period comma 6 period
Finally, constraints (8.7) and (8.8) restrict the decision variables xj and Qj to 
integer and binary values, respectively: 
x Su bs cr ipt j Baseli
ne  g reater tha
n or equals 0 and integer comma j equals 1 comma period period period comma 14
up pe r Q  S ub
sc ri pt j Baseli
ne equals 0 or 1 comma j equals 1 comma period period period comma 14 period
190
8
Employee Scheduling in Remote Oil Industry Work Sites
8.4 
Determining the Workforce Size 
The above integer linear programming (ILP) model is solved in two main steps. First 
the minimum number of employees W is determined, and then the W employees are 
assigned to different days-off patterns. 
8.4.1 
Minimum W for the Simpliﬁed Model 
The minimum workforce size W is found by temporarily reducing the ILP model 
in order to focus only on minimizing the number of employees. This is done by 
removing the secondary objective and all related variables and constraints from the 
model expressed by (8.1–8.8). First, the bi-objective function (8.1) is replaced by the 
following single-objective function to minimize the workforce size: 
Minimize up pe
r 
W
 equ
al s s
igma summation Underscript j equals 1 Overscript 14 Endscripts x Subscript j Baseline period
Subsequently, the binary decision variables Qj and associated constraints (8.4, 
8.6, and 8.8) are removed from the ILP model. Therefore, the reduced ILP model 
is deﬁned by the decision variables xj, the objective function (8.9), and constraints 
(8.2, 8.3, 8.5, and 8.7). 
The dual model of the reduced ILP model is then constructed. Applying the cyclic 
enumeration procedure proposed by Alfares (2001, 2012), four dominant solutions of 
the dual model are found. Using primal–dual relations, the dominant dual solutions 
are represented by the following bounds on the workforce size W: 
1. The number of employees must be sufﬁcient to satisfy the daily labor demand 
on any single day of the week, hence: 
up pe r W greate
r than or equals max left parenthesis upper D comma upper E right parenthesis period
2. Each 14-day cycle has 10 workdays whose daily labor demand is D and 4 weekend 
days whose daily labor demand is E. Therefore, the total number of required 
employee-days per cycle is equal to: 10D + 4E. Within the same cycle, the 
number of assigned employees is W and each employee has to work for 10 days. 
Therefore, the total number of assigned employee-days per cycle is equal to: 
10W. To ensure the assigned employee-days are sufﬁcient to meet the required 
employee-days in each cycle, we need to include the constraint 10W ≥ 10D + 
4E, or:  
up pe r W great
er than or equals upper D plus 0 period 4 upper E period
8.4 Determining the Workforce Size
191
3. Another bound on W is obtained by summing up four daily labor demand 
constraints expressed by (8.2) and (8.3). In order to develop this bound, 
constraints (8.2) and (8.3) are combined into the following generalized form 
covering both workdays and weekends: 
si
g
ma s
um m ati on  U nder
sc ri pt j equ als
 1 Overscript 14 Endscripts a Subscript i j Baseline x Subscript j Baseline greater than or equals r Subscript i Baseline comma i equals 1 comma period period period comma 14 period
Since the days-off scheduling problem has a cycle of 14 days, the third bound 
on W has a cyclic combinatorial property. Speciﬁcally, there are 14 cyclic sets 
of four daily constraints that can be used to obtain this bound. Denoting these 
sets by T 1, …,  T 14, each set T i is deﬁned as follows: T i = mod 14{i, i + 4, 
i + 7, i + 11}. The mod 14 notation indicates that each set has a modularity 
(circularity) of 14 due to the 14-day scheduling cycle. Adding up the above daily 
labor constraints for a given set i is equivalent to summing up the rows of matrix 
A shown in Table 8.1 belonging to T i, and this summation produces the following 
constraint: 
3 
si
g
ma s
um ma t
i
on Un
ders
cr ip t j  equa ls 1 Overscript 14 Endscripts x Subscript j Baseline greater than or equals sigma summation Underscript i element of upper T Subscript i Baseline Endscripts r Subscript i Baseline comma i equals 1 comma period period period comma 14 period
For any value of i (i = 1, …, 14), the left-hand side of the above constraints is 
always equal to 3W. However, the right-hand side (RHS) has two possible values 
according to the speciﬁc value of i. For example, if i = 1, then RHS is equal to 
(r1 + r5 + r8 + r12) = 4D. However, if i = 2, then the RHS side is equal to (r2 
+ r6 + r9 + r13) = 2D + 2E. Combining the two RHS possibilities into one 
constraint, i.e., 3W ≥ max(4D, 2D + 2E), leads to the following bound on W: 
up pe r W
 greater than or equals max left parenthesis StartFraction 4 upper D Over 3 EndFraction comma StartFraction 2 upper D plus 2 upper E Over 3 EndFraction right parenthesis period
up per W  greater than or equals max left parenthesis StartFraction 4 upper D Over 3 EndFraction comma StartFraction 2 upper D plus 2 upper E Over 3 EndFraction right parenthesis period
u
p
p
er W greater than or equals max left parenthesis StartFraction 4 upper D Over 3 EndFraction comma StartFraction 2 upper D plus 2 upper E Over 3 EndFraction right parenthesis period
4. The last bound on W is required to simultaneously satisfy the weekend labor 
demand constraints (8.3) and the weekends-off frequency constraints (8.5). This 
bound is simply obtained by summing up the four weekend labor demand 
constraints (8.3) for  i = 6, 7, 13, and 14 plus the weekend-off constraint (8.5b). 
This summation produces the constraint 3  sig
ma s um ma ti on Underscript j equals 1 Overscript 14 Endscripts x Subscript j Baseline greater than or equals 4 upper E period, or 3W ≥ 4E, leading 
to the following bound on W: 
up per W greater than or equals StartFraction 4 upper E Over 3 EndFraction period
up p
er W greater than or equals StartFraction 4 upper E Over 3 EndFraction period
In order to determine the minimum value of W, it is necessary to satisfy all the 
four above bounds. Comparing the four bounds expressed by (8.10, 8.11, 8.13, 
and 8.14) reveals the following:
192
8
Employee Scheduling in Remote Oil Industry Work Sites
• The bound in (8.10) is dominated by the bounds in (8.11) and (8.14). 
Therefore, the bound in (8.10) can be ignored without affecting the solution. 
• The second bound in (8.13) is the average of the ﬁrst bound in (8.13) and the 
bound in (8.14). Therefore, this bound is dominated and should be removed. 
From the remaining bounds, the highest value is selected and then it is rounded 
up to the nearest integer. Accordingly, the minimum workforce size W is given 
by the following equation: 
up p
e
r W
 equals left ceiling max StartSet StartFraction 4 upper D Over 3 EndFraction comma StartFraction 4 upper E Over 3 EndFraction comma upper D plus 0.4 upper E EndSet right ceiling comma
up per W equals left ceiling max StartSet StartFraction 4 upper D Over 3 EndFraction comma StartFraction 4 upper E Over 3 EndFraction comma upper D plus 0.4 upper E EndSet right ceiling comma
up per W equa
ls
 
left ceiling max StartSet StartFraction 4 upper D Over 3 EndFraction comma StartFraction 4 upper E Over 3 EndFraction comma upper D plus 0.4 upper E EndSet right ceiling comma
where
left ceiling c right ceiling = ceiling function, i.e., c rounded up to the nearest integer. 
The expression in (8.15) is the general formula for obtaining the minimum 
workforce size W for all possible values of D and E. In most practical situations, 
however, weekend labor demand E is less than or equal to workday labor demand 
D. This is actually the case for all remote work locations in which the oil company 
uses the 10/4 days-off schedule. Taking this fact into consideration, the second 
bound in (8.15) is dominated and should be removed to obtain the following 
equation for the special case in which D ≥ E: 
up p
e
r W
 equals left ceiling max StartSet StartFraction 4 upper D Over 3 EndFraction comma upper D plus 0.4 upper E EndSet right ceiling comma for upper D greater than or equals upper E period
up pe r W equa
ls
 l eft ce ilin
g max StartSet StartFraction 4 upper D Over 3 EndFraction comma upper D plus 0.4 upper E EndSet right ceiling comma for upper D greater than or equals upper E period
8.4.2 
Minimum W for the Full Model 
The minimum workforce size W speciﬁed in (8.16) is developed for the simpliﬁed 
ILP model deﬁned by objective function (8.9) and constraints (8.2, 8.3, 8.5, and 8.7). 
This value of W is guaranteed to satisfy the weekend-off frequency constraint (8.5), 
because this constraint is explicitly used to develop the lower bound on W in (8.14). 
Now, the value of W speciﬁed in (8.16) must be checked to ﬁnd out if it is valid 
for the full bi-objective ILP model deﬁned by (8.1–8.8). In particular, we need to 
conﬁrm the value of W found by (8.16) is sufﬁcient for satisfying the work stretch 
constraints. 
Due to the cyclic nature of the problem and the constant labor demand levels for 
weekdays (D) and weekends (E), it is possible to have multiple optimum solutions for 
the full ILP model. For example, it is possible to obtain the bound 4D/3 in (8.13) from  
several cyclic sets T i as previously discussed. The existence of multiple optimum 
solutions with the minimum W provides the opportunity to choose the solution(s) 
with the desired additional features. From the alternative optimum solutions, it is 
possible to select solutions that (1) have the minimum number of active days-off
8.5 Determining the Days-Off Assignments
193
patterns, (2) satisfy work stretch constraints, and (3) maximize common active days-
off patterns for all labor demand variations. This means that the minimum value of 
W obtained from (8.16) is valid for the full ILP model with the two objectives and 
all constraints. 
As previously noted, rotation schedules are used, in which employees take turns 
on all active (assigned) days-off patterns. Rotation schedules are primarily used to 
guarantee fairness among employees, but they also ensure that the work stretch and 
the weekend-off constraints are satisﬁed. As each employee switches from one two-
week assignment to another, the total number of successive workdays continuing 
from the previous assignment should not exceed 14. This can be ensured simply 
by the proper sequencing of the different bi-weekly days-off assignments. Feasible 
sequences are developed to make sure that each pattern is immediately followed by 
one of the acceptable patterns to avoid extending the work stretch beyond 14 days. 
The alternative solutions with minimum W always contain days-off patterns that can 
be feasibly sequenced. Therefore, work stretch constraints do not affect the minimum 
workforce size W. 
The optimum solution of the ILP model deﬁned by (8.1–8.8) speciﬁes the number 
of employees assigned to each days-off pattern (x1, …,  x14) and the total workforce 
size up
p
er W equals sigma summation Underscript j equals 1 Overscript 14 Endscripts x Subscript j 
. Given these values, a proper sequence of active days-off 
assignments is found that satisﬁes work stretch constraints. Since the length of each 
days-off assignment for the 10/4 schedule is two weeks, each employee is assigned 
to pattern j for 2xj weeks, and the length of the complete rotation cycle is 2W weeks. 
The employees follow the same rotation cycle but start the cycle in different weeks. 
Employee w starts the cycle in bi-weekly slot w, speciﬁcally in week Sw which is 
deﬁned below: 
up er S Subs
cr ip t w B a s eli
ne equals 2 w minus 1 period w equals 1 comma ellipsis comma upper W period
8.5 
Determining the Days-Off Assignments 
After calculating the minimum number of employees W by (8.16), they must be 
assigned to speciﬁc days-off patterns in order to satisfy the daily labor demands. The 
two functions in (8.16) correspond to two different solutions of the ILP model. For 
each of the two cases, multiple optimum solutions exist, out of which the most suit-
able solutions are selected. Selection of the most suitable solutions is based on mini-
mizing the number of active patterns, using the same set of active days-off patterns, 
and satisfying the work stretch and other applicable constraints. For each possible 
solution, primal–dual complementary-slackness relationships are applied to identify 
the non-basic (zero-value) variables and the redundant (dominated) constraints. 
In order to consider all possible labor demand variations, extensive numerical 
simulations have been performed with various values of labor demand levels D and 
E. This has resulted in identifying ﬁve possible demand cases in which the number
194
8
Employee Scheduling in Remote Oil Industry Work Sites
of active days-off patterns is equal to either 4, 7, 8, 10, or 11. For each of these ﬁve 
cases, which depend on the relative values of D and E, the best solutions are derived 
and illustrated by numerical examples below. 
8.5.1 
Four Active Days-Off Patterns 
This solution is applicable when up p
e
r 
W
 
equals left ceiling 4 upper D divided by 3 right ceiling
in (8.16) and when certain other 
conditions to be presented below are satisﬁed. In this case, it is possible to assign 
employees to only four active days-off patterns (4, 6, 10, 14). Setting (x4 = x6), and 
(x10 = x14), then W = 2x4 + 2x10. Ignoring dominated constraints, the active daily 
labor demand constraints are given by: 
x 4  p lus 2  x  
Subscript 1 0 Baseline greater than or equals upper D
2 x 4 plus  x  
Subscript 1 0 Baseline greater than or equals upper D
2 x S ub sc
ript 1 0 Baseline greater than or equals upper E comma
where 
2 x 4 plus 2 x 
Subscript 1 0 Baseline equals upper W period
The linear combination of constraints left parenthes is 8 .18 b right parenthesis minus 0 period 5 times left parenthesis 8 .18 d right parenthesisgives: 
x 4  g re ater th
an or equals upper D minus 0 period 5 upper W period
The linear combination of constraints left parenthesis 8 .18 d right parenthesis minus left parenthesis 8 .18 a right parenthesisgives: 
x 4  l es s th
an or equals upper W minus upper D period
The linear combination of constraints 0 period 5 left bracket left parenthesis 8 .18 d right parenthesis minus left parenthesis 8 .18 c right parenthesis right bracket gives: 
x 4  l ess t han or 
equals 0 period 5 upper W minus 0 period 5 upper E period
A feasible four-pattern solution exists if the lower bound on x4 in (8.19a) is not 
greater than the upper bounds in (8.19b) and (8.19c). Setting the lower bound in 
(8.19a) less than or equal to the upper bound in (8.19b): 
up per D m in us  0 period 5 upper W less than or equals upper W minus upper D period
We obtain 
4 
u
p er  D divided by 3 less than or equals upper W period
8.5 Determining the Days-Off Assignments
195
This bound is redundant since up p
e
r 
W
 
equals left ceiling 4 upper D divided by 3 right ceiling
. Therefore, (8.19b) is redundant 
and can be ignored. 
Setting the lower bound in (8.19a) less than or equal to the upper bound in (8.19c): 
up per D m in us 0 period 5 upper W less than or equals 0 period 5 upper W minus 0 period 5 upper E period
We obtain 
up pe r D p lu s 0 period 5 upper E less than or equals upper W period
Therefore, the conditions for assigning four active days-off patterns (4, 6, 10, and 
14) are given by: 
up p
e
r 
W
 
e
quals left ceiling 4 upper D divided by 3 right ceiling
up pe r D p lu s 
0 period 5 upper E less than or equals upper W period
If the two conditions in (8.20) are satisﬁed, then from (8.19a) and (8.19c) the  
bounds on x4 are given by: 
up per D m in us 0 perio d 5 upp
er W less than or equals x 4 less than or equals 0 period 5 upper W minus 0 period 5 upper E period
The bounds in (8.21) are used to determine the value of x4, i.e., the number 
of employees assigned to days-off pattern 4. Assignments to all four patterns are 
calculated as follows: 
x 4  e quals m ax Start Set le ft ceili
ng upper D minus 0 period 5 upper W right ceiling comma left floor 0 period 5 upper W minus 0 period 5 upper E right floor EndSet
x 6  e qu als uppe
r W minus upper E minus x 4
x Su b
s
c
r
i
p
t 1 0 Baseline equals left ceiling upper E divided by 2 right ceiling
x 14  e qu als up
per E minus x Subscript 1 0 Baseline comma
where
left floor c right floor ﬂoor function, i.e., c rounded down to the nearest integer. 
Days-off patterns 10 and 14 belong to set J1, while patterns 4 and 6 belong to set 
J2. Using  (8.5a) to check if the weekend-off frequency constraints are satisﬁed, the 
actual proportion of weeks with a weekend day-off is given by: 
rh o equals S ta rtFra ct ion 0. 5 left parenthesis x 10 plus x 14 right parenthesis plus left parenthesis x 4 plus x 6 right parenthesis Over upper W EndFraction period
rh
o
 equals StartFraction 0.5 left parenthesis x 10 plus x 14 right parenthesis plus left parenthesis x 4 plus x 6 right parenthesis Over upper W EndFraction period
196
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Employee Scheduling in Remote Oil Industry Work Sites
To estimate ρ, up p
e
r 
W
 
equals left ceiling 4 upper D divided by 3 right ceiling
is approximated by up pe r 
W
 equals 4 upper D divided by 3 Since (8.22d) 
speciﬁes x Su bs crip t 1 0 Baseline plus x 14 equals upper E, then x 4  p lus  x  6  equ al s 
u
p er W minus upper E equals 4 upper D divided by 3 minus upper E Substituting 
these values in (8.22e) gives the following approximation: 
rh o almos t
 
eq
u
al s S
tartFraction 0.5 upper E plus left parenthesis 4 upper D divided by 3 minus upper E right parenthesis Over 4 upper D divided by 3 EndFraction equals StartFraction 8 upper D minus 3 upper E Over 8 upper D EndFraction period
rh
o
 
al mos t equals StartFraction 0.5 upper E plus left parenthesis 4 upper D divided by 3 minus upper E right parenthesis Over 4 upper D divided by 3 EndFraction equals StartFraction 8 upper D minus 3 upper E Over 8 upper D EndFraction period
rho
 almost equals StartFraction 0.5 upper E plus left parenthesis 4 upper D divided by 3 minus upper E right parenthesis Over 4 upper D divided by 3 EndFraction equals StartFraction 8 upper D minus 3 upper E Over 8 upper D EndFraction period
Since E ≤ D, the maximum possible value of E (E = D) is used to ﬁnd the 
minimum value of ρ, leading to the lower bound ρ ≥ 0.625. As this approximate 
lower bound is greater than 0.5, weekend-off frequency constraints are satisﬁed. 
Finally, we need to ensure the four days-off patterns (4, 6, 10, 14) can be sequenced 
to satisfy work stretch constraints. As long as pattern 4 is not immediately followed by 
pattern 10, all cyclic rotation sequences are feasible for the four patterns. For example, 
the sequence (4–6-10–14) is feasible for satisfying work stretch constraints. 
Example 8.1 
Given: 
D = 17, E = 12. 
Using (8.16): 
up p
e
r W
 eq uals left ceiling max StartSet StartFraction 4 times 17 Over 3 EndFraction comma 17 plus 0.4 times 12 EndSet right ceiling equals left ceiling StartFraction 4 times 17 Over 3 EndFraction right ceiling equals 23 period
up
per W  equal s le
ft
 
cei ling max StartSet StartFraction 4 times 17 Over 3 EndFraction comma 17 plus 0.4 times 12 EndSet right ceiling equals left ceiling StartFraction 4 times 17 Over 3 EndFraction right ceiling equals 23 period
u
p
per W equals left ceiling max StartSet StartFraction 4 times 17 Over 3 EndFraction comma 17 plus 0.4 times 12 EndSet right ceiling equals left ceiling StartFraction 4 times 17 Over 3 EndFraction right ceiling equals 23 period
Checking 4-pattern condition (8.20b): 
17 pl us 0 per io d 5  l eft parenthesis 12 right parenthesis equals 23 less than or equals 23 (condition is satisﬁed). 
Using the solution system (8.22) gives:  
Sta rtLayout 1 st Row 1st Column x Subscript 4 
Bas eline equals  2nd Co lu mn
 ma x St artS et le ft  ce iling  17 
ne ga
tive  
0
 
p
e
r
i
o
d 
5
 
l
e f t
 par enth esis 2 3 rig ht p ar en
th esis rig ht  ce il ing  c omma left floor 0 period 5 left parenthesis 23 right parenthesis minus 0 period 5 left parenthesis 12 right parenthesis right floor EndSet 2nd Row 1st Column x 4 equals 2nd Column max StartSet left ceiling 5.5 right ceiling comma left floor 5.5 right floor EndSet equals 6 3rd Row 1st Column x Subscript 6 Baseline equals 2nd Column upper W minus upper E minus x Subscript 4 Baseline equals 23 minus 12 minus 6 equals 5 4th Row 1st Column x Subscript 1 0 Baseline equals 2nd Column left ceiling upper E divided by 2 right ceiling equals left ceiling 12 slash 2 right ceiling equals 6 5th Row 1st Column x Subscript 14 Baseline equals 2nd Column upper E minus x Subscript 1 0 Baseline equals 12 minus 6 equals 6 6th Row 1st Column rho equals 2nd Column StartFraction 0.5 left parenthesis 6 plus 5 right parenthesis plus left parenthesis 6 plus 6 right parenthesis Over 23 EndFraction equals 0.76 period EndLayout
St
ar tLayout 1st Row 1st Column x Subscript 4 Baseline equals 2nd Column max StartSet left ceiling 17 negative 0 period 5 left parenthesis 23 right parenthesis right ceiling comma left floor 0 period 5 left parenthesis 23 right parenthesis minus 0 period 5 left parenthesis 12 right parenthesis right floor EndSet 2nd Row 1st Column x 4 equals 2nd Column max StartSet left ceiling 5.5 right ceiling comma left floor 5.5 right floor EndSet equals 6 3rd Row 1st Column x Subscript 6 Baseline equals 2nd Column upper W minus upper E minus x Subscript 4 Baseline equals 23 minus 12 minus 6 equals 5 4th Row 1st Column x Subscript 1 0 Baseline equals 2nd Column left ceiling upper E divided by 2 right ceiling equals left ceiling 12 slash 2 right ceiling equals 6 5th Row 1st Column x Subscript 14 Baseline equals 2nd Column upper E minus x Subscript 1 0 Baseline equals 12 minus 6 equals 6 6th Row 1st Column rho equals 2nd Column StartFraction 0.5 left parenthesis 6 plus 5 right parenthesis plus left parenthesis 6 plus 6 right parenthesis Over 23 EndFraction equals 0.76 period EndLayout
Following the cyclic days-off pattern sequence 4–6–10–14, a rotation cycle whose 
length is 2 × 23 = 46 weeks is developed to satisfy work stretch constraints. The 
assignment of the 23 employees to the four days-off patterns during the rotation cycle 
is shown in Fig. 8.3. The number in each cell indicates the assigned days-off pattern 
for the given employee in the applicable week.
8.5 Determining the Days-Off Assignments
197
Fig. 8.3 Cyclic rotation schedule for Example 8.1 
8.5.2 
Seven Active Days-Off Patterns 
If any of the conditions in (8.20) is not satisﬁed, then assignment to four days-off 
patterns is not possible. Under certain conditions presented below, assignment to 
seven days-off patterns is feasible. 
Assignment to seven days-off patterns (2, 4, 6, 8, 10, 12, and 14) is conditionally 
possible for both functions of W deﬁned in (8.16). Assuming (x4 = x6 = x10 = x14) 
and (x2 = x8 = x12) and ignoring redundant constraints, the active daily labor demand 
constraints (8.2) and (8.3) are shown below: 
3 x 4 plus  2  
x 2 greater than or equals upper D
2 x 4 plus  3  x
 2 greater than or equals upper E comma
where 
4 x 4 plus  3  x
 2 equals upper W period
The linear combination of constraints 3 ti mes left par en thesis 8 .23 a right parenthesis minus 2 times left parenthesis 8 .23 c right parenthesisgives:
198
8
Employee Scheduling in Remote Oil Industry Work Sites
x 4  g rea ter t
han or equals 3 upper D minus 2 upper W period
The linear combination of constraints 0 pe ri od 5 times lef t parenthesis 8 .23 c right parenthesis minus 0 period 5 times left parenthesis 8 .23 b right parenthesisgives: 
x 4  l es  tha
n
 o
r equals left parenthesis upper W minus upper E right parenthesis divided by 2 period
Setting the lower bound in (8.24a) less than or equal to the upper bound in (8.24b), 
we obtain the following condition for assigning seven days-off patterns: 
1 per io d 2 u pp er
 D plus 0 period 2 upper E less than or equals upper W period
If (8.25) is satisﬁed, then the interval deﬁned by (8.24) is used to determine the 
values of x4 = x6 = x10 = x14. The remaining employees are assigned to patterns 2, 
8, and 12, giving priority to pattern 12 as it has a full weekend off. Assignments to 
all seven patterns are calculated as shown below: 
x 4  e qua ls  x 6  e qual s x Subsc ript 1 0 Ba seline e
quals x 14 equals max StartSet 3 upper D minus 2 upper W comma left floor 0 period 5 upper W minus 0 period 5 upper E right floor EndSet
x 12  
e
qua ls lef
t
 
c
eiling left parenthesis upper W minus 4 x 4 right parenthesis divided by 3 right ceiling
x 2  
e
qua ls lef t ceil
i
n
g
 left parenthesis upper W minus 4 x 4 minus x 12 right parenthesis divided by 2 right ceiling
x 8  e qu als up per W minus
 4 x 4 minus x 12 minus x 2 period
Using (8.5a), the percentage of weeks containing a weekend day-off can be shown 
to be at least 57% (ρ ≥ 0.57). There are many feasible cyclic rotation sequences for 
the seven days-off patterns. For example, the rotation sequence (2–4–6–8–10–12–14) 
ensures that the work stretch constraints are not violated. 
Example 8.2 
Given: 
D = 16, E = 13. 
Using (8.16): 
up p
e
r W
 eq uals left ceiling max StartSet StartFraction 4 times 16 Over 3 EndFraction comma 16 plus 0.4 times 13 EndSet right ceiling equals left ceiling StartFraction 4 times 16 Over 3 EndFraction right ceiling equals 22 period
up
per W  equal s le
ft
 
cei ling max StartSet StartFraction 4 times 16 Over 3 EndFraction comma 16 plus 0.4 times 13 EndSet right ceiling equals left ceiling StartFraction 4 times 16 Over 3 EndFraction right ceiling equals 22 period
u
p
per W equals left ceiling max StartSet StartFraction 4 times 16 Over 3 EndFraction comma 16 plus 0.4 times 13 EndSet right ceiling equals left ceiling StartFraction 4 times 16 Over 3 EndFraction right ceiling equals 22 period
Checking 4-pattern condition (8.20b): 
16 pl us 0 per io d 5 l ef t parenthesis 13 right parenthesis equals 22 period 5 greater than 22 (condition is not satisﬁed). 
Checking 7-pattern condition (8.25): 
1 period  2  left pa re nthes is  16 right parenthesis plus 0 period 2 left parenthesis 13 right parenthesis equals 21 period 8 less than or equals 22 (condition is satisﬁed).
8.5 Determining the Days-Off Assignments
199
Using the solution system (8.26) gives:  
Sta rtLay ou t 1s t Row 
1st Column x 4 equal s 2nd Column x 6 equa
ls x
 Sub s
c
ript 1 0 
B
a
s
el in
e e q
u
als x 14 2n
d
 
R
ow  1
st Colum n equals 2n d Co lu mn
 m ax Start Se t 3  l eft  p ar en thesis 16 right parenthesis minus 2 left parenthesis 22 right parenthesis comma left floor 0 period 5 left parenthesis 22 right parenthesis minus 0 period 5 left parenthesis 13 right parenthesis right floor EndSet 3rd Row 1st Column equals 2nd Column 4 4th Row 1st Column x 12 equals 2nd Column left ceiling left bracket 22 minus 4 left parenthesis 4 right parenthesis right bracket divided by 3 right ceiling equals 2 5th Row 1st Column x 2 equals 2nd Column left ceiling left bracket 22 minus 4 left parenthesis 4 right parenthesis minus 2 right bracket divided by 2 right ceiling equals 2 6th Row 1st Column x 8 equals 2nd Column 22 minus 4 left parenthesis 4 right parenthesis minus 2 minus 2 equals 2 7th Row 1st Column rho equals 2nd Column StartFraction 0.5 left parenthesis 4 plus 4 right parenthesis plus left parenthesis 4 plus 4 plus 2 right parenthesis Over 22 EndFraction equals 0.64 period EndLayout
St
ar tLayout 1st Row 1st Column x 4 equals 2nd Column x 6 equals x Subscript 1 0 Baseline equals x 14 2nd Row 1st Column equals 2nd Column max StartSet 3 left parenthesis 16 right parenthesis minus 2 left parenthesis 22 right parenthesis comma left floor 0 period 5 left parenthesis 22 right parenthesis minus 0 period 5 left parenthesis 13 right parenthesis right floor EndSet 3rd Row 1st Column equals 2nd Column 4 4th Row 1st Column x 12 equals 2nd Column left ceiling left bracket 22 minus 4 left parenthesis 4 right parenthesis right bracket divided by 3 right ceiling equals 2 5th Row 1st Column x 2 equals 2nd Column left ceiling left bracket 22 minus 4 left parenthesis 4 right parenthesis minus 2 right bracket divided by 2 right ceiling equals 2 6th Row 1st Column x 8 equals 2nd Column 22 minus 4 left parenthesis 4 right parenthesis minus 2 minus 2 equals 2 7th Row 1st Column rho equals 2nd Column StartFraction 0.5 left parenthesis 4 plus 4 right parenthesis plus left parenthesis 4 plus 4 plus 2 right parenthesis Over 22 EndFraction equals 0.64 period EndLayout
8.5.3 
Eight Active Days-Off Patterns 
If up p
e
r 
W
 
equals left ceiling 4 upper D divided by 3 right ceiling
and assignment to four or seven days-off patterns is not feasible 
because conditions (8.20b) and (8.25) are not satisﬁed, then it may be possible to 
assign the W employees to eight days-off patterns (3, 4, 6, 7, 10, 11, 13, 14). Setting 
(x4 = x6 = x10 = x14) and (x3 = x7 = x11 = x13) and ignoring redundant constraints, 
the active daily labor demand constraints (8.2) and (8.3) are given by: 
3 x 4 plus  3  
x 3 greater than or equals upper D
2 x 4 plus  3  
x 3 greater than or equals upper E
3 x 4 plus  2  
x 3 greater than or equals upper E
where 
4 x 4 plus  4  x
 3 equals upper W period
The linear combination of constraints left parenthes is  8 .27 c right parenthesis minus 0 period 5 times left parenthesis 8 .27 d right parenthesisgives: 
x 4  g re ater th
an or equals upper E minus 0 period 5 upper W period
The linear combination of constraints 1 pe riod 5 times left parenthesis 8 .27 d right parenthesis minus left parenthesis 8 .27 a right parenthesis minus left parenthesis 8 .27 b right parenthesisgives: 
x 4  l ess t han or e
quals 1 period 5 upper W minus upper D minus upper E period
200
8
Employee Scheduling in Remote Oil Industry Work Sites
The ﬁrst condition for a feasible assignment with eight days-off patterns is: 
up p
e
r 
W
 
e
q
uals left ceiling 4 upper D divided by 3 right ceiling period
Setting the lower bound in (8.28a) less than or equal to the upper bound in (8.28b), 
we obtain the following second condition for an eight-pattern solution: 
0 per io d 5 up
per D plus upper E less than or equals upper W period
If (8.29) is satisﬁed, then the interval deﬁned by (8.28) is used to determine the 
values of x4 = x6 = x10 = x14. After that, patterns 3, 7, 11, and 13 are assigned, 
with higher priority to patterns 11 and 13 because they contain full weekends off. 
Assignments to the eight days-off patterns are calculated as follows: 
x 4  e qua ls  x 6  e qual s x Subsc ript 1 0  Basel ine equal
s x 14 equals max StartSet left ceiling upper E minus 0 period 5 upper W right ceiling comma left floor 1 period 5 upper W minus upper D minus upper E right floor EndSet
x 11  
e
qua ls lef
t
 
c
eiling left parenthesis upper W minus 4 x 4 right parenthesis divided by 4 right ceiling
x 13  
e
qua ls lef t ceil
i
n
g
 left parenthesis upper W minus 4 x 4 minus x 11 right parenthesis divided by 3 right ceiling
x 3  
e
qua ls lef t ceil ing le
f
t
 
parenthesis upper W minus 4 x 4 minus x 11 minus x 13 right parenthesis divided by 2 right ceiling
x 7  e qu als up per W minus 4 x 4
 minus x 11 minus x 13 minus x 3 period
Four days-off patterns (3, 7, 10, 14) belong to set J1, while four patterns (4, 6, 
11, 13) belong to set J2. Therefore, the proportion of weeks containing a weekend 
day-off, ρ, can be shown to be approximately equal to 0.75. To satisfy work stretch 
constraints, there are many feasible cyclic rotation sequences, such as the sequence 
(3–4–6–7–10–11–13–14). 
Example 8.3 
Given: 
D = 17, E = 14. 
Using (8.16): 
up p
e
r W
 eq uals left ceiling max StartSet StartFraction 4 times 17 Over 3 EndFraction comma 17 plus 0.4 times 14 EndSet right ceiling equals left ceiling StartFraction 4 times 17 Over 3 EndFraction right ceiling equals 23 period
up
per W  equal s le
ft
 
cei ling max StartSet StartFraction 4 times 17 Over 3 EndFraction comma 17 plus 0.4 times 14 EndSet right ceiling equals left ceiling StartFraction 4 times 17 Over 3 EndFraction right ceiling equals 23 period
u
p
per W equals left ceiling max StartSet StartFraction 4 times 17 Over 3 EndFraction comma 17 plus 0.4 times 14 EndSet right ceiling equals left ceiling StartFraction 4 times 17 Over 3 EndFraction right ceiling equals 23 period
Checking 4-pattern condition (8.20b): 
17 pl us 0 per io d 5  l eft parenthesis 14 right parenthesis equals 24 greater than 23 (condition is not satisﬁed).
8.5 Determining the Days-Off Assignments
201
Checking 7-pattern condition (8.25): 
1 period  2  left pa re nthes is  17 right parenthesis plus 0 period 2 left parenthesis 14 right parenthesis equals 23 period 2 greater than 23 (condition is not satisﬁed). 
Checking 8-pattern condition (8.29b): 
0 period  5  le ft  pare nt hesis 17 right parenthesis plus 14 equals 22 period 5 less than or equals 23 (condition is satisﬁed). 
Using the solution system (8.30): 
Sta rtLay ou t 1s t Row 1s t Column  x 4 equals  2nd Column x 6 equal s x 
Subs c
r
ipt 1 0 Bas
e
l
i
ne  e
qual s
 x
 14  equals ma
x
 
St
ar tS
et l
e
ft c eiling 14 m inus
 
0
 
pe ri
od 5 lef t parenthes is 2 3 ri gh t 
pa renthesi s ri gh t ce ili ng  co mm a le ft  f loor 1 period 5 left parenthesis 23 right parenthesis minus 17 minus 14 right floor EndSet equals 3 2nd Row 1st Column x 11 equals 2nd Column left ceiling left bracket 23 minus 4 left parenthesis 3 right parenthesis right bracket divided by 4 right ceiling equals 3 3rd Row 1st Column x 13 equals 2nd Column left ceiling left bracket 23 minus 4 left parenthesis 3 right parenthesis minus 3 divided by 3 right bracket right ceiling equals 3 4th Row 1st Column x 3 equals 2nd Column left ceiling left bracket 23 minus 4 left parenthesis 3 right parenthesis minus 3 minus 3 right bracket divided by 2 right ceiling equals 3 5th Row 1st Column x 7 equals 2nd Column 23 minus 4 left parenthesis 3 right parenthesis minus 3 minus 3 minus 3 equals 2 6th Row 1st Column rho equals 2nd Column StartFraction 0.5 left parenthesis 3 plus 2 plus 3 plus 3 right parenthesis plus left parenthesis 3 plus 3 plus 3 plus 3 right parenthesis Over 23 EndFraction equals 0.76 period EndLayout
St
ar tLayout 1st Row 1st Column x 4 equals 2nd Column x 6 equals x Subscript 1 0 Baseline equals x 14 equals max StartSet left ceiling 14 minus 0 period 5 left parenthesis 23 right parenthesis right ceiling comma left floor 1 period 5 left parenthesis 23 right parenthesis minus 17 minus 14 right floor EndSet equals 3 2nd Row 1st Column x 11 equals 2nd Column left ceiling left bracket 23 minus 4 left parenthesis 3 right parenthesis right bracket divided by 4 right ceiling equals 3 3rd Row 1st Column x 13 equals 2nd Column left ceiling left bracket 23 minus 4 left parenthesis 3 right parenthesis minus 3 divided by 3 right bracket right ceiling equals 3 4th Row 1st Column x 3 equals 2nd Column left ceiling left bracket 23 minus 4 left parenthesis 3 right parenthesis minus 3 minus 3 right bracket divided by 2 right ceiling equals 3 5th Row 1st Column x 7 equals 2nd Column 23 minus 4 left parenthesis 3 right parenthesis minus 3 minus 3 minus 3 equals 2 6th Row 1st Column rho equals 2nd Column StartFraction 0.5 left parenthesis 3 plus 2 plus 3 plus 3 right parenthesis plus left parenthesis 3 plus 3 plus 3 plus 3 right parenthesis Over 23 EndFraction equals 0.76 period EndLayout
8.5.4 
Ten Active Days-Off Patterns 
When up p er W equals left ceiling upper D plus 0 period 4 upper E right ceilingand a seven-pattern solution is not feasible, i.e., condition 
(8.25) is not satisﬁed, it could be possible to assign employees to 10 days-off patterns. 
In that case, assignment to patterns (2, 3, 4, 6, 7, 9, 10, 11, 13, 14) is possible subject 
to conditions to be presented below. Setting (x4 = x6 = x10 = x14), (x3 = x7 = x11 
= x13), and (x2 = x9), the active daily labor demand constraints (8.2) and (8.3) are  
given by: 
3 x 4 plus  3  x
 3  
plus x 2 greater than or equals upper D
2 x 4 plus  3  x 
3 p
lus 2 x 2 greater than or equals upper E
3 x 4 plus  2  x 
3 pl
us 2 x 2 greater than or equals upper E comma
where 
4 x 4 plus  4  x 
3 pl
us 2 x 2 equals upper W period
The linear combination of constraints 2 ti mes left  p arenthesis 8  .31 a right parenthesis plus left parenthesis 8 .31 c right parenthesis minus 2 times left parenthesis 8 .31 d right parenthesis
gives: 
x 4  g rea te r than 
or equals 2 upper D plus upper E minus 2 upper W period
202
8
Employee Scheduling in Remote Oil Industry Work Sites
The linear combination of constraints minus left parenthes is  8 . 31 a right parenthesis minus left parenthesis 8 .31 b right parenthesis plus 1 period 5 times left parenthesis 8 .31 d right parenthesis
gives: 
x 4  l ess t han or e
quals 1 period 5 upper W minus upper D minus upper E period
The ﬁrst condition for a 10-pattern solution is: 
up p er W equals
 left ceiling upper D plus 0 period 4 upper E right ceiling period
Setting the lower bound in (8.32a) less than or equal to the upper bound in (8.32b) 
gives the second condition: 
left  p are
n
th es is
 6 upper D plus 4 upper E right parenthesis divided by 7 less than or equals upper W period
If (8.33) is satisﬁed, then the interval deﬁned by (8.32) is used to determine the 
values of (x4 = x6 = x10 = x14). After that, μ3, the average value of (x3 = x7 = x11 
= x13) is calculated in terms of the x4 value. The bounds on μ3 can be shown to be 
given by: 
mu 3 gr eater t han 
or equals upper D minus 0 period 5 upper W minus x 4
mu 3 les s th an or
 
eq
uals left parenthesis upper W minus upper E minus x 4 right parenthesis divided by 2 period
The bounds in (8.34) are used to assign employees to days-off patterns 3, 7, 11, 
and 13, giving higher priority to patterns 11 and 13 to maximize weekend days-off. 
Assignments to all 10 patterns are calculated by the following system of equations: 
x 4  e qua ls  x 6  e qual s x Subscr ip t 1 0 Ba seline  equ als x 
14 equals max StartSet left ceiling 2 upper D plus upper E minus 2 upper W right ceiling comma left floor 1 period 5 upper W minus upper D minus upper E right floor EndSet
mu 3 equ
a
ls  max St artSet  upper D
 
m
i
nus 0 period 5 upper W minus x 4 comma left parenthesis upper W minus upper E minus x 4 right parenthesis divided by 2 EndSet
x 3  equa
ls left ceiling mu 3 right ceiling
x 13  
e
quals le
f
t
 
ceiling left parenthesis 4 mu 3 minus x 3 right parenthesis divided by 3 right ceiling
x 11  
e
quals  left  ceili
n
g
 
left parenthesis 4 mu 3 minus x 3 minus x 13 right parenthesis divided by 2 right ceiling
x 7  e qual s 4 m u 3 mi nus x
 3 minus x 11 minus x 13
x 9  
e
qua ls lef t ceil
i
n
g
 left parenthesis upper W minus 4 x 4 minus 4 mu 3 right parenthesis divided by 2 right ceiling
8.5 Determining the Days-Off Assignments
203
x 2  e qu als up per W minus
 4 x 4 minus 4 mu 3 minus x 9 period
The minimum proportion of weeks containing a weekend day-off for the 10-
pattern days-off schedule can be shown to be roughly equal to 0.64, ensuring that the 
weekend-off frequency constraints are satisﬁed. Work stretch constraints are easily 
satisﬁed by many rotation sequences, including the sequence (2–3–4–6–7–9–10–11– 
13–14). 
Example 8.4 
Given: 
D = 27, E = 24. 
Using (8.16): 
up p
e
r W
 eq uals left ceiling max StartSet StartFraction 4 times 27 Over 3 EndFraction comma 27 plus 0.4 times 24 EndSet right ceiling equals left ceiling 27 plus 0.4 times 24 right ceiling equals 37 period
up
pe r W  e qual s le
ft
 ceil in g ma x Sta rt Set StartFraction 4 times 27 Over 3 EndFraction comma 27 plus 0.4 times 24 EndSet right ceiling equals left ceiling 27 plus 0.4 times 24 right ceiling equals 37 period
Checking 4-pattern condition (8.20b): 
27 pl us 0 per io d 5  l eft parenthesis 24 right parenthesis equals 39 greater than 37 (condition is not satisﬁed). 
Checking 7-pattern condition (8.25): 
1 period  2  left pa re nthes is  27 right parenthesis plus 0 period 2 left parenthesis 24 right parenthesis equals 37 period 2 greater than 37 (condition is not satisﬁed). 
Checking 8-pattern condition (8.29b): 
0 period  5  le ft  pare nt hesis 27 right parenthesis plus 24 equals 37 period 5 greater than 37 (condition is not satisﬁed). 
Checking 10-pattern condition (8.33b): 
left br ac ket 6 left parenthesis 27 right parenthesis plus 4 left parenthesis 24 right parenthesis right bracket divided by 7 equals 36 period 9 less than or equals 37]
(condition is satisﬁed). 
Using the solution system (8.35) gives:  
Sta rtLay ou t 1s t Row 1s t Column x 4 equ als 2nd C olumn x 6 equa ls x Subscr
ipt  1 0 
B
ase line equals x 1 4 eq uals max 
S
t
a
rt Set 
lef t  ceil ing 
2 le f
t
 parenthesis
 
2
7
 r ig
ht p a
r
enthesis plu s 24
 
m
i
nu s 
2 l eft parenthes is 3 7 ri gh t 
par e
n
thes is right ceiling
 
c
o
mm a 
lef t flo or 1 period 5 left p ar en
th esis 37 ri gh t pa re nth es is mi nu s 27  m inus 24 right floor EndSet equals 4 2nd Row 1st Column mu 3 equals 2nd Column max StartSet 27 minus 0 period 5 left parenthesis 37 right parenthesis minus 4 comma left parenthesis 37 minus 24 minus 4 right parenthesis divided by 2 EndSet equals 4 period 5 3rd Row 1st Column x 3 equals 2nd Column left ceiling 4 period 5 right ceiling equals 5 4th Row 1st Column x 13 equals 2nd Column left ceiling left bracket 4 left parenthesis 4 period 5 right parenthesis minus 5 right bracket divided by 3 right ceiling equals 5 5th Row 1st Column x 11 equals 2nd Column left ceiling left parenthesis 4 left parenthesis 4 period 5 right parenthesis minus 5 minus 5 right parenthesis divided by 2 right ceiling equals 4 6th Row 1st Column x 7 equals 2nd Column 4 left parenthesis 4 period 5 right parenthesis minus 5 minus 5 minus 4 equals 4 7th Row 1st Column x 9 equals 2nd Column left ceiling left bracket 37 minus 4 left parenthesis 4 right parenthesis minus 4 left parenthesis 4 period 5 right parenthesis right bracket divided by 2 right ceiling equals 2 8th Row 1st Column x 2 equals 2nd Column 37 minus 4 left parenthesis 4 right parenthesis minus 4 left parenthesis 4 period 5 right parenthesis minus 2 equals 1 9th Row 1st Column rho equals 2nd Column StartFraction 0.5 left parenthesis 5 plus 4 plus 4 plus 4 right parenthesis plus left parenthesis 4 plus 4 plus 4 plus 5 right parenthesis Over 37 EndFraction equals 0.69 period EndLayout
St
ar tLayout 1st Row 1st Column x 4 equals 2nd Column x 6 equals x Subscript 1 0 Baseline equals x 14 equals max StartSet left ceiling 2 left parenthesis 27 right parenthesis plus 24 minus 2 left parenthesis 37 right parenthesis right ceiling comma left floor 1 period 5 left parenthesis 37 right parenthesis minus 27 minus 24 right floor EndSet equals 4 2nd Row 1st Column mu 3 equals 2nd Column max StartSet 27 minus 0 period 5 left parenthesis 37 right parenthesis minus 4 comma left parenthesis 37 minus 24 minus 4 right parenthesis divided by 2 EndSet equals 4 period 5 3rd Row 1st Column x 3 equals 2nd Column left ceiling 4 period 5 right ceiling equals 5 4th Row 1st Column x 13 equals 2nd Column left ceiling left bracket 4 left parenthesis 4 period 5 right parenthesis minus 5 right bracket divided by 3 right ceiling equals 5 5th Row 1st Column x 11 equals 2nd Column left ceiling left parenthesis 4 left parenthesis 4 period 5 right parenthesis minus 5 minus 5 right parenthesis divided by 2 right ceiling equals 4 6th Row 1st Column x 7 equals 2nd Column 4 left parenthesis 4 period 5 right parenthesis minus 5 minus 5 minus 4 equals 4 7th Row 1st Column x 9 equals 2nd Column left ceiling left bracket 37 minus 4 left parenthesis 4 right parenthesis minus 4 left parenthesis 4 period 5 right parenthesis right bracket divided by 2 right ceiling equals 2 8th Row 1st Column x 2 equals 2nd Column 37 minus 4 left parenthesis 4 right parenthesis minus 4 left parenthesis 4 period 5 right parenthesis minus 2 equals 1 9th Row 1st Column rho equals 2nd Column StartFraction 0.5 left parenthesis 5 plus 4 plus 4 plus 4 right parenthesis plus left parenthesis 4 plus 4 plus 4 plus 5 right parenthesis Over 37 EndFraction equals 0.69 period EndLayout
204
8
Employee Scheduling in Remote Oil Industry Work Sites
8.5.5 
Eleven Active Days-Off Patterns 
This solution is applicable when up
p er W equals left ceiling upper D plus 0 period 4 upper E right ceiling and assignment to 7 or 10 
patterns is not feasible because conditions (8.25) and (8.33b) are not satisﬁed. In this 
case, employees can be assigned to 11 days-off patterns (1, 3, 4, 5, 6, 7, 9, 10, 11, 
13, and 14). Setting (x4 = x6 = x10 = x14), (x3 = x7 = x11 = x13), and (x1 = x5 = 
x9), and removing redundant constraints, the active daily labor demand constraints 
(8.2) and (8.3) are shown below: 
3 x 4 plus  3  x 
3 p
lus 2 x 1 greater than or equals upper D
2 x 4 plus  3  x 
3 p
lus 2 x 1 greater than or equals upper E
3 x 4 plus  2  x 
3 pl
us 3 x 1 greater than or equals upper E comma
where 
4 x 4 plus  4  x 
3 pl
us 3 x 1 equals upper W period
The linear combination of constraints 6 times left  p arenthesis 8  .36 a right parenthesis plus left parenthesis 8 .36 c right parenthesis minus 5 times left parenthesis 8 .36 d right parenthesis
gives: 
x 4  g rea te r than 
or equals 6 upper D plus upper E minus 5 upper W period
The linear combination of constraints nega tive 6 times left pa re nt hesis 8 .36 b right parenthesis minus left parenthesis 8 .36 c right parenthesis plus 5 times left parenthesis 8 .36 d right parenthesis
gives: 
x 4  l es s than 
or equals upper W minus 1 period 4 upper E period
Setting the lower bound in (8.37a) less than or equal to the upper bound in (8.37b), 
we obtain: 
up pe r D p lu s 0 period 4 upper E less than or equals upper W period
This condition is redundant since up p er W equals left ceiling upper D plus 0 period 4 upper E right ceiling. After ﬁxing the values of 
(x4 = x6 = x10 = x14) within the bounds in (8.37), the bounds on x3 can be calculated 
as functions of x4. The bounds on (x3 = x7 = x11 = x13) are given by: 
x 3  g rea ter t han 
or equals 3 upper D minus 2 upper W minus x 4
x 3  l es  tha n or 
e
qu
als left parenthesis upper W minus upper E minus x 4 right parenthesis divided by 2 period
8.5 Determining the Days-Off Assignments
205
After determining the values of (x4 = x6 = x10 = x14) and (x3 = x7 = x11 = 
x13), employees are assigned to days-off patterns 1, 5, and 9, with higher priority to 
pattern 5 because it has a full weekend off. Calculations used to assign employees 
to the 11 days-off patterns are shown below: 
x 4  e qua ls  x 6  e qual
s x Subsc ri pt  1 0 Baseline e
quals x 14 equals max StartSet 6 upper D plus upper E minus 5 upper W comma left floor upper W minus 1 period 4 upper E right floor EndSet
x 3  e qua ls  x 7  e qual s x 1
1
 eq uals x 13 
e
quals ma
x
 
St
artSet 3 upper D minus 2 upper W minus x 4 comma left floor left parenthesis upper W minus upper E minus x 4 right parenthesis divided by 2 right floor EndSet
x 5  
e
qua ls lef t ceil
i
n
g
 left parenthesis upper W minus 4 x 4 minus 4 x 3 right parenthesis divided by 3 right ceiling
x 1  
e
qua ls lef t ceil ing l
e
f
t
 parenthesis upper W minus 4 x 4 minus 4 x 3 minus x 5 right parenthesis divided by 2 right ceiling
x 9  e qu als up per W minus  4 x 
4 minus 4 x 3 minus x 5 minus x 1 period
It can be shown using (8.5) that the 11-pattern schedule satisﬁes the weekend-off 
constraints, as the minimum proportion of weeks with weekend days-off is approx-
imately equal to 0.57. Work stretch constraints are easily satisﬁed by many rotation 
sequences, such as the sequence (1–3–4–5–6–7–9–10–11–13–14). 
Example 8.5 
Given: 
D = 17, E = 15. 
Using (8.16): 
up p
e
r W
 eq uals left ceiling max StartSet StartFraction 4 times 17 Over 3 EndFraction comma 17 plus 0.4 times 15 EndSet right ceiling equals left ceiling 17 plus 0.4 times 15 right ceiling equals 23 period
up
per W  equal s le
ft
 ceil in g ma x Sta rt Set StartFraction 4 times 17 Over 3 EndFraction comma 17 plus 0.4 times 15 EndSet right ceiling equals left ceiling 17 plus 0.4 times 15 right ceiling equals 23 period
Checking 4-pattern condition (8.20b): 
17 pl us 0 per io d 5 l ef t parenthesis 15 right parenthesis equals 24 period 5 greater than 23 (condition is not satisﬁed). 
Checking 7-pattern condition (8.25): 
1 period  2  left pa re nthes is  17 right parenthesis plus 0 period 2 left parenthesis 15 right parenthesis equals 23 period 4 greater than 23 (condition is not satisﬁed). 
Checking 8-pattern condition (8.29b): 
0 period  5  le ft  pare nt hesis 17 right parenthesis plus 15 equals 23 period 5 greater than 23 (condition is not satisﬁed). 
Checking 10-pattern condition (8.33b): 
left br ac ket 6 
l
ef t paren th esis 17 right parenthesis plus 4 left parenthesis 15 right parenthesis right bracket divided by 7 equals 23 period 1 greater than 23 (condition is not satisﬁed). 
Using the solution system (8.39) gives:  
Sta rt Lay ou t 1s t Row 1s t Column x  4  2n d Column  equ als x 6 equ a l
s x Subscript 1 0 Baseline equals x 14 equals max StartSet 6 left parenthesis 17 right parenthesis plus 15 minus 5 left parenthesis 23 right parenthesis comma left floor 23 minus 1 period 4 left parenthesis 15 right parenthesis right floor EndSet equals 2 2nd Row 1st Column x 3 2nd Column equals x 7 equals x 11 equals x 13 equals max StartSet 3 left parenthesis 17 right parenthesis minus 2 left parenthesis 23 right parenthesis minus 2 comma left floor left parenthesis 23 minus 15 minus 2 right parenthesis divided by 2 right floor EndSet equals 3 3rd Row 1st Column x 5 2nd Column equals left ceiling left bracket 23 minus 4 left parenthesis 2 right parenthesis minus 4 left parenthesis 3 right parenthesis right bracket divided by 3 right ceiling equals 1 4th Row 1st Column x 1 2nd Column equals left ceiling left bracket 23 minus 4 left parenthesis 2 right parenthesis minus 4 left parenthesis 3 right parenthesis minus 1 right bracket divided by 2 right ceiling equals 1 5th Row 1st Column x 9 2nd Column equals 23 minus 4 left parenthesis 2 right parenthesis minus 4 left parenthesis 3 right parenthesis minus 1 minus 1 equals 1 6th Row 1st Column rho 2nd Column equals StartFraction 0.5 left parenthesis 3 plus 3 plus 2 plus 2 right parenthesis plus left parenthesis 2 plus 1 plus 2 plus 3 plus 3 right parenthesis Over 23 EndFraction equals 0.70 period EndLayout
206
8
Employee Scheduling in Remote Oil Industry Work Sites
Sta rt Lay ou t 1s t Row 1s t Column x 4 2nd Colum
n
 equ als x  6 e
q
u
a
l s  
x 
Sub s
c
ript  1 0 Baseline 
e
q
u
al s 
x 1 4
 
equa ls max StartSet 6 
l
e
f
t pa
ren th esi s 17 right parenth esis  p lu
s 15  minus  5 l eft paren th esi s 23  rig ht p arenthesis comma left floor 23 minus 1 period 4 left parenthesis 15 right parenthesis right floor EndSet equals 2 2nd Row 1st Column x 3 2nd Column equals x 7 equals x 11 equals x 13 equals max StartSet 3 left parenthesis 17 right parenthesis minus 2 left parenthesis 23 right parenthesis minus 2 comma left floor left parenthesis 23 minus 15 minus 2 right parenthesis divided by 2 right floor EndSet equals 3 3rd Row 1st Column x 5 2nd Column equals left ceiling left bracket 23 minus 4 left parenthesis 2 right parenthesis minus 4 left parenthesis 3 right parenthesis right bracket divided by 3 right ceiling equals 1 4th Row 1st Column x 1 2nd Column equals left ceiling left bracket 23 minus 4 left parenthesis 2 right parenthesis minus 4 left parenthesis 3 right parenthesis minus 1 right bracket divided by 2 right ceiling equals 1 5th Row 1st Column x 9 2nd Column equals 23 minus 4 left parenthesis 2 right parenthesis minus 4 left parenthesis 3 right parenthesis minus 1 minus 1 equals 1 6th Row 1st Column rho 2nd Column equals StartFraction 0.5 left parenthesis 3 plus 3 plus 2 plus 2 right parenthesis plus left parenthesis 2 plus 1 plus 2 plus 3 plus 3 right parenthesis Over 23 EndFraction equals 0.70 period EndLayout
St
ar tLayout 1st Row 1st Column x 4 2nd Column equals x 6 equals x Subscript 1 0 Baseline equals x 14 equals max StartSet 6 left parenthesis 17 right parenthesis plus 15 minus 5 left parenthesis 23 right parenthesis comma left floor 23 minus 1 period 4 left parenthesis 15 right parenthesis right floor EndSet equals 2 2nd Row 1st Column x 3 2nd Column equals x 7 equals x 11 equals x 13 equals max StartSet 3 left parenthesis 17 right parenthesis minus 2 left parenthesis 23 right parenthesis minus 2 comma left floor left parenthesis 23 minus 15 minus 2 right parenthesis divided by 2 right floor EndSet equals 3 3rd Row 1st Column x 5 2nd Column equals left ceiling left bracket 23 minus 4 left parenthesis 2 right parenthesis minus 4 left parenthesis 3 right parenthesis right bracket divided by 3 right ceiling equals 1 4th Row 1st Column x 1 2nd Column equals left ceiling left bracket 23 minus 4 left parenthesis 2 right parenthesis minus 4 left parenthesis 3 right parenthesis minus 1 right bracket divided by 2 right ceiling equals 1 5th Row 1st Column x 9 2nd Column equals 23 minus 4 left parenthesis 2 right parenthesis minus 4 left parenthesis 3 right parenthesis minus 1 minus 1 equals 1 6th Row 1st Column rho 2nd Column equals StartFraction 0.5 left parenthesis 3 plus 3 plus 2 plus 2 right parenthesis plus left parenthesis 2 plus 1 plus 2 plus 3 plus 3 right parenthesis Over 23 EndFraction equals 0.70 period EndLayout
8.5.6 
The Days-Off Scheduling Procedure 
The results presented above for the ﬁve days-off scheduling cases are summarized 
in Table 8.2. To coordinate and reduce transportation ﬂights to different remote 
locations, the number of common days-off breaks is maximized, although daily labor 
demands may vary from one location to another. Hence, the solutions are designed 
to assign the same set of days-off patterns as much as possible for all labor demand 
variations. This is clear from Table 8.2, where one set of active patterns (4, 6, 10, 
and 14) is used consistently for all ﬁve days-off scheduling cases. Moreover, the set 
of patterns (3, 7, 11, and 13) is used to satisfy labor demands for three out of these 
ﬁve cases. 
A user-friendly spreadsheet-based tool has been developed to automate the days-
off scheduling solutions presented above for the ﬁve cases. By entering the daily labor 
demands for regular workdays D and weekends E, the tool automatically determines 
the workforce size W, the number of active days-off patterns, and the assignment of 
the W employees to these respective patterns. This tool eliminates the need to use 
specialized optimization software, and hence, it saves the costs of software purchase 
and employee training. The tool is very easy to use, even for people with low IT skills. 
The quality of the days-off work schedules obtained from the tool is considered to 
be excellent by both employees and management. Therefore, the use of this tool has 
been welcomed by remote-site employees and supervisors.
Table 8.2 Summary of results for the ﬁve cases of days-off assignments (Reprinted from Alfares 
(2014)(2)) 
No. of 
patterns 
W = 
⎡term⎤ 
Condition: 
term  ≤ W
Active days-off patterns  
Solution 
system 
4
4D/3
D + 0.5E
4, 6, 10, 14
8.22 
7 
4D/3, or 
D + 0.4E 
1.2D + 0.2E 
4, 6, 10, 14,    2, 8, 12
8.26 
8
4D/3
0.5D + E
4, 6, 10, 14,    3, 7, 11, 13
8.30 
10
D + 0.4E 
(6D + 4E)/7 4, 6, 10, 14,    3, 7, 11, 13,   2, 9
8.35 
11
D + 0.4E
D
 
+ 0.4E
4, 6, 10, 14,    3, 7, 11, 13,   1, 5, 9
8.39 
8.6 Summary and Conclusions
207
8.6 
Summary and Conclusions 
This chapter presented the two-objective optimum scheduling of employees of an 
oil company that provides their transportation ﬂights to their remote work sites. The 
ﬁrst objective is to minimize the labor cost by minimizing the workforce size. Since 
a round-trip ﬂight is needed for each active days-off break, the second objective is 
to minimize the transportation cost by minimizing the number of assigned days-off 
patterns. Other objectives include using a common set of active days-off patterns for 
all demand scenarios, allocating scheduling assignments fairly among employees, 
and avoiding the need to use specialized optimization software. These objectives 
are optimized subject to satisfying the constraints on labor demands, weekend-off 
frequency, and work stretch duration. 
Using the 10/4 days-off schedule, a bi-objective integer programming model was 
formulated. Utilizing the model’s properties, an analytical solution procedure was 
developed to optimally solve this unique real-life scheduling problem. Applying 
cyclic enumeration, lower bounds were found and used to determine the minimum 
workforce size. Subsequently, extensive numerical analysis identiﬁed ﬁve possible 
cases that correspond to either 4, 7, 8, 10, or 11 active days-off patterns. For each case, 
primal–dual relationships were applied to eliminate zero-valued variables and redun-
dant constraints and determine the complete days-off scheduling solution. Simple 
equations were developed to obtain the full solution of each case, eliminating the 
need to use integer programming solvers. Finally, a rotation scheme was proposed to 
allocate the days-off scheduling assignments fairly among employees while ensuring 
the work stretch constraints are satisﬁed. 
There are several possibilities for extending the work presented in this chapter. One 
promising extension is to consider multiple-location joint optimization of employee 
days-off scheduling and ﬂight transportation scheduling. Another possibility is to 
change the assumption of two constant levels of daily demands for workdays and 
weekends, D and E, to variable daily demands. Finally, the model presented in this 
chapter can be extended by adding practical aspects such as overtime, multiple skills, 
and varying employee seniority/skill levels. 
Acknowledgements 
1. Adapted from H.K. Alfares, Multiobjective scheduling of remote-area employees 
with minimum cost of transportation, Journal of Industrial Engineering, 2014; 
released under a CC BY 3.0 license; https://doi.org/10.1155/2014/978347 
2. Reprinted from H.K. Alfares, Multiobjective scheduling of remote-area 
employees with minimum cost of transportation, Journal of Industrial 
Engineering, 2014; released under a CC BY 3.0 license; https://doi.org/10. 
1155/2014/978347
208
8
Employee Scheduling in Remote Oil Industry Work Sites
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University College, Molde, Norway. 
Alfares, H. K. (2001). Efﬁcient optimization of cyclic labor days-off scheduling. OR-Spektrum, 23, 
283–294. 
Alfares, H. K. (2002). Optimum workforce scheduling under the (14, 21) days-off timetable. Journal 
of Applied Mathematics and Decision Sciences, 6(3), 191–199. 
Alfares, H. K. (2012). A dual-based combinatorial algorithm for solving cyclic optimization 
problems. Recent Patents on Computer Science, 5(3), 188–196. 
Alfares, H. K. (2014). Multiobjective scheduling of remote-area employees with minimum cost of 
transportation. Journal of Industrial Engineering, 2014. 
Awad, M., & Ertem, M. (2017). Stochastic scheduling of workforce-constrained preventive mainte-
nance activities in petroleum plants. In: 2017 Annual Reliability and Maintainability Symposium 
(RAMS) (pp. 1–5). IEEE. 
Castillo-Salazar, J. A., Landa-Silva, D., & Qu, R. (2016). Workforce scheduling and routing 
problems: Literature survey and computational study. Annals of Operations Research, 239(1), 
39–67. 
Er-Rbib, S., Desaulniers, G., Elhallaoui, I., & Munroe, P. (2021). Preference-based and cyclic bus 
driver rostering problem with ﬁxed days off. Public Transport, 13, 251–286. 
Koubaa, M., Dhouib, S., & El Mhamedi, A. (2017). A proposition of a decision support system for 
staff scheduling in the road transportation of crude oil sector. In: 2017 6th IEEE International 
Conference on Advanced Logistics and Transport (ICALT) (pp. 150–156). IEEE. 
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on communities, safety, workers and their families. Work, 55(2), 413–427. 
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offshore supply vessels. Journal of the Operational Research Society, 69(6), 959–970. 
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Chapter 9 
Optimum Planning of a Distribution 
Supply Chain for Reﬁned Oil Products 
9.1 
Introduction 
Reﬁned oil products such as gasoline, kerosene, and diesel are produced by reﬁning 
crude oil in reﬁneries. These products must be delivered to end customers via highly 
complex, ﬂexible, and evolving supply chains or distribution networks. Distribu-
tion networks of reﬁned oil products consist of multiple stages, including reﬁneries, 
pipelines, pump stations, and storage tanks. Distribution networks are used to deliver 
multiple reﬁned products using multiple transportation modes such as pipelines, 
trucks, trains, and ships. 
As the supplies, demands, and capacities change over time, different distribution 
plans must be developed for different points in time. The plans must specify the 
amounts and transportation means of various products to be transported through 
alternative links in the network. In order to keep up with ever-increasing demands, 
long-term plans must also be made to add new facilities or expand existing ones 
in the distribution network. As shown in Fig. 9.1, specialized computer programs 
with sophisticated graphical interfaces are used to monitor and control global supply 
chains of crude oil and reﬁned products.
Distribution of reﬁned petroleum products is part of the overall oil and gas supply 
chain management (SCM) system. In general, SCM deals with the movement of 
materials through all processing and storage stages, from the raw materials provided 
by the original suppliers to the ﬁnal products received by the end customers. The 
materials move through multiple stages, via multiple links, with multiple facilities 
at each stage. Therefore, the multiple-ﬂow SCM system is actually a network, not 
a chain. Hence, supply chain management can be deﬁned as the control of material 
ﬂow through a network consisting of multiple suppliers, manufacturers, distributors, 
and customers. The objective of SCM is to deliver the right product, at the right time, 
quality, quantity, and cost. The petroleum supply chain is unique due to its large size, 
high complexity, and global economic signiﬁcance. It is also more difﬁcult to model 
due to the enormous number of facilities and stages, which include oil production, 
storage, reﬁning, and distribution.
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 
H. K. Alfares, Applied Optimization in the Petroleum Industry, 
https://doi.org/10.1007/978-3-031-24166-6_9 
209
210
9
Optimum Planning of a Distribution Supply Chain for Reﬁned Oil Products
Fig. 9.1 Digital display of an oil supply chain system. Courtesy of Saudi Aramco, copyright owner
The distribution network of reﬁned products is the last (downstream) stage of 
the oil and gas supply chain. This stage starts from the reﬁneries, where the reﬁned 
products are produced, and it ends with the delivery of these products to the end 
customers. In between, the reﬁned products are moved through several intermediary 
locations using pipeline, trucks, trains, and tanker ships. For each move between 
two locations in the network, one or more modes (means) of transportation can be 
used. Selection from the alternative means of transportation is primarily based on the 
overall cost, but it also depends on the quantity of the products, the distance of the 
movement, and the physical topography of the region. Another factor of increasing 
importance is the pollution level and the environmental impact of accidental risk 
associated with each mode of transportation. 
Each transportation mode that is used to move reﬁned products through the distri-
bution network has its own advantages and disadvantages. Pipelines are considered 
as the safest and least expensive means of transporting oil on land. Although pipelines 
have low operational and labor costs, their construction is generally expensive and 
time consuming. Rail cars present a high-capacity, long-distance, and cost-effective 
alternative for moving oil products between locations where pipelines are not avail-
able. Tank trucks are usually used in the ﬁnal step of distribution for delivering reﬁned 
products to the ﬁnal consumers. Trucks offer a low-capacity, short-distance means to 
deliver fuels to gas stations, where pipelines and trains cannot be used. Offshore, oil 
tankers are used for high-capacity, long-distance transport of crude oil across oceans, 
while barges are used for low-capacity, short-distance transport of reﬁned products 
across bounded seas and waterways. 
Huge expenditures are required for setting up and running distribution networks 
of reﬁned oil products. Therefore, long-term plans must be carefully developed to
9.2 Literature Review
211
minimize both the facility construction cost and the distribution expenses while 
matching the varying supplies and demands during the planning horizon. These 
plans must minimize the distribution cost by determining the optimum amount of 
each product moved by each transportation mode across each link in the network 
during each time period. On the other hand, these plans must also minimize the 
construction cost by determining the optimum amounts and times of investments 
made to increase the capacity, by either expanding or adding components in the 
distribution network. These components include diverse types of oil facilities in the 
network such as reﬁneries, pipelines, tanks, and distribution centers. 
This chapter addresses the optimum strategic planning of the reﬁned products 
distribution network for a large oil company in the Middle East. The network consists 
of three main types of facilities (stages), and it is used to distribute two reﬁned 
products using three modes of transportation. The company needs to make a ﬁve-year 
plan to optimize both the annual distribution of products and the annual investments 
in new and existing network facilities. A mixed-integer linear programming (MILP) 
model with multiple products, time periods, and transportation modes is developed to 
solve this optimization problem. The objective of the model is to minimize the sum of 
the material transportation cost and the facility construction/expansion cost. The total 
cost is minimized subject to several types of demand, capacity, and material balance 
constraints. The model has two unique features: ﬁxed and variable components for 
the costs of transportation and facility development (construction and expansion), 
and bounds on the amounts of transported materials and capacity additions. Using 
real data from the company, the model is effectively used to develop a strategic plan 
for the reﬁned products distribution network. 
The remaining sections of this chapter are organized as follows. A survey of 
recent literature on oil products distribution supply chains is presented in Sect. 9.2. 
The speciﬁc distribution network optimization problem is described in Sect. 9.3. The  
mixed-integer linear integer programming (MILP) model is formulated in Sect. 9.4. 
The given data for the case study is presented in Sect. 9.5, and the near-optimum 
solution obtained from the model is described in Sect. 9.6. Finally, conclusions and 
suggestions are provided in Sect. 9.7. 
9.2 
Literature Review 
This chapter addresses the optimum strategic planning and design of the distribution 
network of reﬁned petroleum products. As previously noted, distribution of reﬁned 
products is the last (downstream) stage of the petroleum supply chain. This stage 
starts from the reﬁneries, where these reﬁned products are made. In this chapter, a 
single-objective deterministic optimization approach is used for the strategic plan-
ning of the distribution network. Consequently, the main focus of this section in on 
single-objective, deterministic, and downstream models of petroleum supply chain 
optimization.
212
9
Optimum Planning of a Distribution Supply Chain for Reﬁned Oil Products
A number of previous literature reviews cover different areas within petroleum 
supply chains. An et al. (2011) review and classify the literature on biofuel, agri-
product, petroleum-based, and generic supply chains. Future trends are identiﬁed, 
gaps in the literature are found, and future research topics are suggested. Sahebi et al. 
(2014) review mathematical programming models of crude oil supply chain optimiza-
tion. They classify papers in this area based on the supply chain structure, decision 
level, modeling approach, purpose, and shared information. Each reviewed paper is 
also analyzed in terms of the solution technique, uncertainty features, environmental 
impact, and global issues. 
Emenike and Falcone (2020) survey the literature on optimization-based 
approaches for energy supply chain resilience. These approaches are designed to 
maximize energy security and demand satisfaction by minimizing supply chain 
downtime and the shortages caused by sudden major disruptions. The literature 
survey most relevant to this chapter is provided by Lima et al. (2016), who review the 
applications of mathematical programming models to reﬁned products distribution 
problems in the downstream oil supply chain. Lima et al. classify the reviewed models 
primarily as either strategic, tactical, or operational, and they also consider them 
under the subcategories of uncertainty, risk, resilience, sustainability, integration, 
and inventory management. 
Several deterministic, single-objective, linear programming (LP) models have 
been proposed for the strategic planning of the downstream segment of the petroleum 
supply chains. Fernandes et al. (2013) develop a mixed-integer linear programming 
(MILP) model to optimize the strategic planning and design of downstream petroleum 
supply chains networks. The multi-ﬁrm, multi-echelon, multi-product, and multi-
mode-transportation model is used to maximize the total proﬁt for all the companies 
in the supply chain. The model determines the optimum facility locations and capac-
ities, as well as the quantities, routes, and transportation modes of the transported 
products. Another multi-echelon, multi-product, multi-mode MILP model is formu-
lated by Kazemi and Szmerekovsky (2015) for the strategic planning of downstream 
petroleum supply chain networks. The model minimizes the total cost by optimizing 
facility locations and capacities, as well as the transportation modes and transfer 
quantities. 
A few researchers integrate deterministic approaches for strategic supply deci-
sions with stochastic approaches for tactical and operational decisions. For example, 
Ghezavati et al. (2015) propose a two-stage hierarchical approach for minimizing the 
total cost of designing the downstream segment of a petroleum supply chain. In the 
ﬁrst stage, a mixed-integer linear programming (MILP) model is used to optimize 
strategic decisions, such as the depot (distribution center) locations and capacities 
and the annual production quantities. In the second stage, a simulation model is used 
to make tactical and operational decisions, such as the number of loading platforms 
and the volumes of individual orders. 
A number of petroleum supply chain models combine and simultaneously opti-
mize the upstream, midstream, and downstream segments. A MILP model to integrate 
planning and scheduling decisions in a petroleum and petrochemical supply chain is 
developed by Kuo and Chang (2008). The model maximizes the total proﬁt during
9.2 Literature Review
213
the planning horizon by determining the optimal procurement, production, inventory, 
and transportation decisions for all products and units in the supply chain. Nasab and 
Amin-Naseri (2016) formulate a MILP model for the integrated strategic and tactical 
planning of a petroleum supply chain (PSC) aiming to maximize the proﬁt. The model 
covers all PSC stages, considering ﬁve echelons: crude oil ﬁelds, crude oil storage 
tanks, reﬁneries, distribution centers, and customers. The model optimizes facility 
locations, capacities, and expansions, in addition to inventory, production, routing, 
and transportation decisions. 
Short-term operational planning of downstream petroleum supply chains (PSC) is 
the focus of several models. Two MILP models are constructed by Relvas et al. (2013) 
for the operational planning of a downstream PSC. The objective is to minimize the 
weighted sum of three operational goals: excess production, ﬂow rate, and variation 
in end inventories among the various products. Moradi and MirHassani (2015) opti-
mize the daily operational planning of the downstream PSC using a MILP model 
to integrate inventory control and pipeline transportation scheduling for multiple 
reﬁned products. The objective function is the weighted sum of three operational 
terms and three economic terms. Zaghian and Mostafaei (2016) formulate a MILP 
model for short-term transportation planning of reﬁned oil products. The products 
are distributed to several serially located distribution centers through a multi-product 
pipeline. The objective is to minimize the total cost while satisfying the demands for 
all products. 
Nonlinear programming (NLP) models are sometimes used to accurately represent 
nonlinear cost and process relationship in PSC. Sinha et al. (2011) use multi-agent 
technology to develop an NLP operational model of the overall petroleum supply 
chain, from upstream to downstream. A co-evolutionary particle-swarm optimiza-
tion heuristic is used to solve the model, whose objective is to minimize the total 
cost. Azadeh et al. (2017) develop a mixed-integer nonlinear programming (MINLP) 
model of the upstream and the midstream stages of the PSC. The objective of their 
multi-product and multi-period model is to maximize the net present value of all the 
cash ﬂows. The model is used to optimize multiple decisions in PSC: oil ﬁeld devel-
opment (facility construction), transformation (reﬁning), transportation (of crude 
oil), and distribution (of reﬁned products). 
Quite often, multiple objectives are pursued in optimizing petroleum supply 
chains. Ghaithan et al. (2017) construct a multi-period, multi-product, and multi-
objective linear programming (MOLP) model for downstream medium-term tactical 
planning of integrated oil and gas supply chains. The model has three prioritized 
objectives: minimizing the cost, maximizing the revenue, and maximizing the service 
level. Using the augmented ε-constraint algorithm, Pareto optimal solutions are 
generated and used to analyze the trade-offs among the different objectives. Attia 
et al. (2019) consider a similar problem, but for the upstream stage of the oil and gas 
supply chain. An MOLP model is formulated for intermediate-term tactical planning 
of the oil and gas supply chain with three prioritized objectives: minimizing the cost, 
maximizing the revenue, and minimizing the depletion rate of oil and gas reserves 
to ensure sustainability.
214
9
Optimum Planning of a Distribution Supply Chain for Reﬁned Oil Products
Siddiqui et al. (2018) develop a tactical planning bi-objective MILP model for 
the integrated transportation and inventory control of reﬁned petroleum products. 
The downstream model considers two transportation modes (ships and pipelines) 
between reﬁneries and distribution centers, and it minimizes the total cost and the 
environmental risk of transportation. A component-based approach is used to formu-
late the integrated MILP model, and a time-based decomposition heuristic is used 
to obtain the solution. A multi-objective MILP model is developed by Zarei et al. 
(2020) for making strategic and tactical decisions for natural gas supply chains. Three 
objective functions are considered and combined by fuzzy programming: maximum 
proﬁt, minimum emissions, and minimum water consumption. 
This chapter presents a mixed-integer linear programming (MILP) model for 
optimal planning of the distribution network of reﬁned oil products. The model is 
used by a large oil company to optimize strategic facility expansion decisions and also 
tactical product transportation decisions. The MILP model considers multiple years, 
transportation modes, and products, as well as the addition of new transportation links 
and the expansion of existing facilities in the distribution network. The objective of 
the model is to minimize the total ﬁxed and variable costs of facility development 
and product transportation. In comparison with the previous literature, the proposed 
PSC model is distinguished by several features. Material transportation amounts 
have minimum thresholds, and their costs include a ﬁxed part and a variable part. 
Facility construction and expansion magnitudes have lower and upper bounds, limited 
frequencies, and both ﬁxed and variable costs. The speciﬁc problem is described, and 
the model is presented in the following sections. 
9.3 
Problem Description 
The problem under consideration in this chapter is the optimization of the reﬁned 
products domestic distribution network for a large oil company in the Middle East. 
This company manages a large-scale complex supply chain that extends over a 
vast geographical area. This supply chain network contains many highly integrated 
facilities, including oil and gas wells, gas–oil separation plants, reﬁneries, bulk 
plants, pipelines, and storage tanks. The distribution network to be optimized is the 
last (downstream) segment of this comprehensive supply chain. This downstream 
segment begins with the production or importation of the reﬁned oil products, and it 
ends with the delivery of these products to the ﬁnal customers. 
A schematic view of the reﬁned products distribution network is shown in Fig. 9.2. 
The downstream supply chain network consists of three successive stages (echelons), 
which are represented by nodes in the network. The three echelons correspond to the 
three following types of facilities.
1. Supply centers (SC): These are the reﬁneries and import terminals where the 
reﬁned products are respectively made or imported.
9.3 Problem Description
215
Fig. 9.2 Typical conﬁguration of the distribution network 
2. Distribution centers (DC): These are the bulk plants where the reﬁned products 
are regionally stored before ﬁnal distribution. Distribution centers are supplied 
by either supply centers or by other distribution centers. 
3. Demand markets (DM): These are the cities and towns where the reﬁned 
products are delivered to the ﬁnal customers. Demand markets are supplied by 
distribution centers. 
The domestic distribution network is used to transport two kinds of reﬁned oil 
products: (1) gasoline and (2) diesel. The two products can be transported using 
three alternative modes of transportation: (1) pipelines, (2) trucks, and (3) ships. 
Transportation is allowed only between successive echelons: i.e., SC to DC, DC to 
DC, and DC to DM. Even for these successive links, it is possible that some or all 
modes of transportation are not available between certain pairs of facilities. Each 
existing (available) direct transportation link between a pair of successive facilities 
is represented by an arc (arrow) in the network. 
The concerned oil company needs to develop a strategic ﬁve-year plan for the 
operation and the development of the distribution network. The plan must determine 
the optimum strategic decisions such as the times and the amounts of investments in 
possible facility development (construction and expansion) projects. The plan must 
also determine the optimum tactical decisions such as the annual quantities, routes, 
and transportation modes of the two products. 
All available relevant data and information must be used as inputs for developing 
the optimum plan for the reﬁned products distribution network. The given information 
includes the following: (1) the current network structure, specifying the existing 
facilities and their capacities, (2) existing transportation links, with their respective 
capacities for each transportation mode, (3) customer demands for each product and 
each city, (4) all the relevant costs, and (4) all the applicable operational limitations.
216
9
Optimum Planning of a Distribution Supply Chain for Reﬁned Oil Products
The objective of the distribution plan is to minimize the total product distribution 
cost, which is the sum of the facility development cost and the product transportation 
cost. To achieve this objective, a mixed-integer linear programming (MILP) model is 
formulated and solved to obtain the optimum distribution network plan. The details 
of the MILP model for optimizing the distribution network are presented in the 
following section. 
9.4 
Model Formulation 
To optimize the distribution network plan of the reﬁned products, a multi-period, 
multi-echelon, multi-product, and multi-transportation-mode MILP model is formu-
lated. The downstream petroleum supply chain model has three echelons (SC, DC, 
and DM), two products (gasoline and diesel), and three transportation modes (trucks, 
pipelines, and ships). The model minimizes the total distribution network cost, i.e., 
the sum of ﬁxed and variable costs, over a planning horizon of ﬁve years. To mini-
mize the total cost, the model optimizes the strategic investment plan for expanding 
distribution centers and expanding or establishing new transportation links, as well as 
the tactical transportation plan for distributing the two reﬁned products. To be more 
speciﬁc, the model is used to optimize the following distribution network planning 
decisions: 
1. Times (years) and amounts of added capacities for distribution centers. 
2. Times (years) and amounts of added or newly established capacities for 
transportation links. 
3. Quantities of each product transported through alternative transportation links 
each year. 
9.4.1 
Model Assumptions 
1.
Direct transportation of the reﬁned products can be done only from SC to DC, 
from DC to DC, and from DC to DM. 
2.
Even when transportation is allowed, transportation links by one or more modes 
of transportation may or may not currently exist between two nodes. 
3.
Up to three modes of transportation (trucks, pipelines, and ships) can be 
simultaneously used to move the products between two nodes. 
4.
Each existing mode of transportation is considered as a separate transportation 
link between any two nodes. 
5.
The quantity transported through each active (used) transportation link cannot 
be less than the minimum economic threshold and cannot be more than the 
link’s maximum transportation capacity.
9.4 Model Formulation
217
6.
The transportation cost of each active transportation link consists of two parts: 
a ﬁxed (constant) cost to operate the link and a variable cost that depends on 
the transported quantity. 
7.
For each transportation link, the number of capacity expansions during the 
planning horizon is limited, and the amounts of individual and total capacity 
expansions are limited by speciﬁc bounds. 
8.
New transportation links can be established, and additional transportation modes 
can be started during the planning horizon. 
9.
The cost of establishing a transportation link or expanding the capacity of an 
existing one consists of two parts: a ﬁxed construction cost and a variable cost 
that depends on the magnitude of new/added capacity. 
10. For each distribution center, the number of capacity expansions during the 
planning horizon is limited, and the amounts of individual and total capacity 
expansions are limited by speciﬁc bounds. 
11. The cost of expanding the capacity of a DC consists of two parts: a ﬁxed 
construction cost and a variable cost that depends on the magnitude of added 
capacity. 
12. Capacity expansions of distribution centers and transportation links are perma-
nent. 
13. New facilities (SC, DC, DM) cannot be established during the planning horizon. 
14. The annual demands of each DM must be satisﬁed on time; hence, shortages 
and backorders are not allowed. 
9.4.2 
Model Indices 
i
supply center (reﬁnery or import terminal), i = 1, …, I; 
j
distribution center (bulk plant), j = 1, …, J; 
k
demand market (city), k = 1, …, K; 
l
distribution center (DC), l = 1, …, J (l /= j); l denotes a DC other than j; 
m transportation mode (trucks, pipelines, ships), m = 1, …, M; 
p
product (diesel, gasoline), p = 1, …, P; 
t
time period (year), t = 1, …, T. 
9.4.3 
Given Parameters 
Si
annual capacity of SCi; 
Rj
current annual capacity of DCj; 
Dkpt
demand of DMk for product p during year t; 
WAijm
current capacity of transportation link between SCi and DCj by mode m; 
WBjkm
current capacity of transportation link between DCj and DMk by mode m; 
WCjlm
current capacity of transportation link between DCj and DCl by mode m;
218
9
Optimum Planning of a Distribution Supply Chain for Reﬁned Oil Products
LRj
minimum possible additional capacity per expansion of DCj; 
LAijm
minimum possible new/additional capacity per establishment/expansion of 
link between SCi and DCj by mode m; 
LBjkm
minimum possible new/additional capacity per establishment/expansion of 
link between DCj and DMk by mode m; 
LCjlm
minimum possible new/additional capacity per establishment/expansion of 
link between DCj and DCl by mode m; 
URj
maximum total expansion (additional capacity) of DCj during the planning 
horizon; 
UAijm
maximum total new/additional capacity of link between SCi and DCj by 
mode m during the planning horizon; 
UBjkm
maximum total new/additional capacity of link between DCj and DMk by 
mode m during the planning horizon; 
UCjlm
maximum total new/additional capacity of link between DCj and DCl by 
mode m during the planning horizon; 
QAijmp 
minimum quantity of product p that can be transported per year by mode m 
between SCi and DCj; 
QBjkmp 
minimum quantity of product p that can be transported per year by mode m 
between DCj and DMk; 
QCjlmp 
minimum quantity of product p that can be transported per year by mode m 
between DCj and DCl; 
CAijm
cost per unit of transportation by mode m from SCi to DCj; 
CBjkm
cost per unit of transportation by mode m from DCj to DMk; 
CCjlm
cost per unit of transportation by mode m from DCj to DCl; 
ARj
cost per unit of added capacity in DCj; 
AAijm
cost per unit of new/added transport capacity by mode m between SCi and 
DCj; 
ABjkm
cost per unit of new/added transport capacity by mode m between DCj and 
DMk; 
ACjlm
cost per unit of new/added transport capacity by mode m between DCj and 
DCl; 
FRj
ﬁxed cost of expanding capacity of DCj per expansion; 
FAijm
ﬁxed cost of establishing/expanding transport capacity by mode m between 
SCi and DCj per establishment/expansion; 
FBjkm
ﬁxed cost of establishing/expanding transport capacity by mode m between 
DCj and DMk per establishment/expansion; 
FCjlm
ﬁxed cost of establishing/expanding transport capacity by mode m between 
DCj and DCl per establishment/expansion; 
TAijmp
ﬁxed cost of transporting product p between SCi and DCj by mode m; 
TBjkmp 
ﬁxed cost of transporting product p between DCj and DMk by mode m; 
TCjlmp
ﬁxed cost of transporting product p between DCj and DCl by mode m; 
NL
maximum number of expansions of a transportation link during the planning 
horizon; 
NR
maximum number of expansions of a distribution center during the planning 
horizon.
9.4 Model Formulation
219
9.4.4 
Decision Variables 
XAijmpt 
amount of product p transported by mode m from SCi to DCj in year t; 
XBjkmpt 
amount of product p transported by mode m from DCj to DMk in year t; 
XCjlmpt 
amount of product p transported by mode m from DCj to DCl in year t; 
StartLay o
ut 1st Row uppe r Y 
upper A Subscr ipt i j m p t Baseline equals StartLayout Enlarged left brace 1st Row 1st Column 1 comma 2nd Column if upper X upper A Subscript i j m p t Baseline greater than 0 2nd Row 1st Column 0 comma 2nd Column if upper X upper A Subscript i j m p t Baseline equals 0 semicolon EndLayout EndLayout
upper Y u
pper B Subscrip t j 
k m p t Baseli ne equals StartLayout Enlarged left brace 1st Row 1st Column 1 comma 2nd Column if upper X upper B Subscript j k m p t Baseline greater than 0 2nd Row 1st Column 0 comma 2nd Column if upper X upper B Subscript j k m p t Baseline equals 0 semicolon EndLayout
upper Y u
pper C Subscrip t j 
l m p t Baseli ne equals StartLayout Enlarged left brace 1st Row 1st Column 1 comma 2nd Column if upper X upper C Subscript j l m p t Baseline greater than 0 2nd Row 1st Column 0 comma 2nd Column if upper X upper C Subscript j l m p t Baseline equals 0 semicolon EndLayout
XERjt
added capacity in DCj at year t; 
XEAijmt 
new/added transport capacity by mode m between SCi and DCj in year t; 
XEBjkmt 
new/added transport capacity by mode m between DCj and DMk in year t; 
XECjlmt 
new/added transport capacity by mode m between DCj and DCl in year t; 
upper Y
 upper E uppe r R 
Subscr ipt j t Baseline equals StartLayout Enlarged left brace 1st Row 1st Column 1 comma 2nd Column if upper X upper E upper R Subscript j t Baseline greater than 0 2nd Row 1st Column 0 comma 2nd Column if upper X upper E upper R Subscript j t Baseline equals 0 semicolon EndLayout
upper Y u
pper E upper A Subs
cript i j m t Baseline equals StartLayout Enlarged left brace 1st Row 1st Column 1 comma 2nd Column if upper X upper E upper A Subscript i j m t Baseline greater than 0 2nd Row 1st Column 0 comma 2nd Column if upper X upper E upper A Subscript i j m t Baseline equals 0 semicolon EndLayout
upper Y u
pper E upper B Subs
cript j k m t Baseline equals StartLayout Enlarged left brace 1st Row 1st Column 1 comma 2nd Column if upper X upper E upper B Subscript j k m t Baseline greater than 0 2nd Row 1st Column 0 comma 2nd Column if upper X upper E upper B Subscript j k m t Baseline equals 0 semicolon EndLayout
upper Y u
pper E upper C Subs
cript j l m t Baseline equals StartLayout Enlarged left brace 1st Row 1st Column 1 comma 2nd Column if upper X upper E upper C Subscript j l m t Baseline greater than 0 2nd Row 1st Column 0 comma 2nd Column if upper X upper E upper C Subscript j l m t Baseline equals 0 period EndLayout
Figure 9.3 illustrates the relationship between the decision variables and various 
parts of the distribution network. 
Fig. 9.3 Decision variables for various parts of the distribution network
220
9
Optimum Planning of a Distribution Supply Chain for Reﬁned Oil Products
9.4.5 
Objective Function 
The objective of the MILP model is to minimize the total cost of operating and 
expanding the distribution network of the reﬁned products during the planning period. 
This total cost is composed of several components, representing the variable and 
ﬁxed costs of transportation, DC expansion, and transportation link expansion and 
establishment. 
The ﬁrst cost component is the total variable cost of transportation Z1, which is 
expressed in Eq. (9.1). This cost is the sum of the costs of transporting the reﬁned 
products from supply centers (SC) to distribution centers (DC), from distribution 
centers (DC) to demand markets (DM), and between different distribution centers 
(DC): 
up er
 
Z
 1 e
q
u
als 
s
i
gma 
s
u
mmat
io
n 
U
n
ders
cript j equal s 
1
 
Over
script upper J 
E
n
dscripts s
igma summatio
n 
Und erscript m equals 1 Overscript upper M Endscripts sigma summation Underscript p equals 1 Overscript upper P Endscripts sigma summation Underscript t equals 1 Overscript upper T Endscripts left parenthesis sigma summation Underscript i equals 1 Overscript upper I Endscripts upper C upper A Subscript i j m Baseline upper X upper A Subscript i j m p t Baseline plus sigma summation Underscript k equals 1 Overscript upper K Endscripts upper C upper B Subscript j k m Baseline upper X upper B Subscript j k m p t Baseline plus sigma summation Underscript l equals 1 left parenthesis l not equals j right parenthesis Overscript upper J Endscripts upper C upper C Subscript j l m Baseline upper X upper C Subscript j l m p t Baseline right parenthesis period
The second cost component is Z2, expressed in Eq. (9.2), which is the total ﬁxed 
cost of transportation, i.e., the cost of operating all active transportation links: 
up er
 
Z
 2 e
q
u
als 
s
i
gma 
s
u
mmat
io
n 
U
n
ders
cript j equals  1
K
O
vers
cript upper J En
d
s
cripts sig
ma summation U
nd
ers cript m equals 1 Overscript upper M Endscripts sigma summation Underscript p equals 1 Overscript upper P Endscripts sigma summation Underscript t equals 1 Overscript upper T Endscripts left parenthesis sigma summation Underscript i equals 1 Overscript upper I Endscripts upper T upper A Subscript i j m p Baseline upper Y upper A Subscript i j m p t Baseline plus sigma summation Underscript k equals 1 Overscript upper K Endscripts upper T upper B Subscript j k m p Baseline upper Y upper B Subscript j k m p t Baseline plus sigma summation Underscript l equals 1 left parenthesis l not equals j right parenthesis Overscript upper J Endscripts upper T upper C Subscript j l m p Baseline upper Y upper C Subscript j l m p t Baseline right parenthesis period
The third cost component is Z3, shown  in  Eq. (9.3), which is the total variable 
cost of expanding the distribution centers: 
up er
 
Z
 3 e
q
u
als 
sigma sum
mation Underscript j equals 1 Overscript upper J Endscripts sigma summation Underscript t equals 1 Overscript upper T Endscripts upper A upper R Subscript j Baseline upper X upper E upper R Subscript j t Baseline period
The fourth cost component is Z4, shown  in  Eq. (9.4), which is the total ﬁxed cost 
of expanding the distribution centers: 
up er
 
Z
 4 e
q
u
als 
sigma sum
mation Underscript j equals 1 Overscript upper J Endscripts sigma summation Underscript t equals 1 Overscript upper T Endscripts upper F upper R Subscript j Baseline upper Y upper E upper R Subscript j t Baseline period
The ﬁfth cost component is Z5, shown  in  Eq. (9.5), which is the total variable 
cost of establishing and expanding all the transportation links, from SC to DC, from 
DC to DM, and from DC another DC: 
up er
 
Z
 5 e
q
u
als 
s
i
gma 
su
mm
a
t
ion 
Underscript j  e
q
u
als 
1 Overscript up
p
e
r J Endscr
ipts sigma su
mm
ati
on Underscript m equals 1 Overscript upper M Endscripts sigma summation Underscript t equals 1 Overscript upper T Endscripts left parenthesis sigma summation Underscript i equals 1 Overscript upper I Endscripts upper A upper A Subscript i j m Baseline upper X upper E upper A Subscript i j m t Baseline plus sigma summation Underscript k equals 1 Overscript upper K Endscripts upper A upper B Subscript j k m Baseline upper X upper E upper B Subscript j k m t Baseline plus sigma summation Underscript l equals 1 left parenthesis l not equals j right parenthesis Overscript upper J Endscripts upper A upper C Subscript j l m Baseline upper X upper E upper C Subscript j l m t Baseline right parenthesis period
9.4 Model Formulation
221
The ﬁnal cost component is Z6, expressed in Eq. (9.6), which is the total ﬁxed 
cost of establishing and expanding all the transportation links, from SC to DC, from 
DC to another DC, and from DC to DM: 
up er
 
Z
 6 e
q
u
als 
s
i
gma 
su
mm
a
t
ion 
Underscript j  e
q
u
als 
1 Overscript up
p
e
r J Endscr
ipts sigma su
mm
ati
on Underscript m equals 1 Overscript upper M Endscripts sigma summation Underscript t equals 1 Overscript upper T Endscripts left parenthesis sigma summation Underscript i equals 1 Overscript upper I Endscripts upper F upper A Subscript i j m Baseline upper Y upper E upper A Subscript i j m t Baseline plus sigma summation Underscript k equals 1 Overscript upper K Endscripts upper F upper B Subscript j k m Baseline upper Y upper E upper B Subscript j k m t Baseline plus sigma summation Underscript l equals 1 left parenthesis l not equals j right parenthesis Overscript upper J Endscripts upper F upper C Subscript j l m Baseline upper Y upper E upper C Subscript j l m t Baseline right parenthesis period
The objective function of the MILP model is to minimize the total cost of the 
distribution network, as expressed in (9.7): 
Minimize up per Z  equa ls up per Z  1 pl us up
per Z 2 plus upper Z 3 plus upper Z 4 plus upper Z 5 plus upper Z 6 period
9.4.6 
Supply, Demand, and Material Balance Constraints 
The total quantity of all products transported out from SCi in a given year cannot 
exceed its annual production capacity: 
s
i
gma 
s
u
mmat
i
o
n Un
derscrip t j equals 1 
Overscript upper J Endscripts sigma summation Underscript m equals 1 Overscript upper M Endscripts sigma summation Underscript p equals 1 Overscript upper P Endscripts upper X upper A Subscript i j m p t Baseline less than or equals upper S Subscript i Baseline comma for all i comma for all t period
The total amount of product p transported into DCj (from both SCi and other 
DCl) must be equal to the total amount transported out from DCj (into both DMk 
and other DCl): 
s
i
gma 
su
mm
a
t
ion 
Underscr i
p
t m e
quals 1 
Ov
ersc
r
i
pt u
pp
er
K
M
 End
scripts l
e
ft pa
renthesi
s 
sigma summation
 Underscript i equals 1 Overscript upper I Endscripts upper X upper A Subscript i j m p t Baseline plus sigma summation Underscript l not equals j Endscripts upper X upper C Subscript l j m p t Baseline right parenthesis equals sigma summation Underscript m equals 1 Overscript upper M Endscripts left parenthesis sigma summation Underscript k equals 1 Overscript upper K Endscripts upper X upper B Subscript j k m p t Baseline plus sigma summation Underscript l not equals j Endscripts upper X upper C Subscript j l m p t Baseline right parenthesis comma for all j comma for all p comma for all t period
The total amount of product p transported into DMk during year t must be at least 
equal to the annual demand of the DM in that year: 
s
i
gma 
s
u
mmat
ion Unde rscript j equals 1 
Overscript upper J Endscripts sigma summation Underscript m equals 1 Overscript upper M Endscripts upper X upper B Subscript j k m p t Baseline greater than or equals upper D Subscript k p t Baseline comma for all k comma for all p comma for all t period
222
9
Optimum Planning of a Distribution Supply Chain for Reﬁned Oil Products
9.4.7 
DC Capacity Constraints 
The total quantity of all products transported into DCj cannot exceed the extended 
capacity of DCj: 
s
i
gma 
s
u
mmat
i
o
n Un
derscrip t 
i
 
equals 1 
O
v
ersc
r
i
pt u
pper I E ndscr ip
t
s
 s
igm
a summat ion Und
erscript m equals 1 Overscript upper M Endscripts sigma summation Underscript p equals 1 Overscript upper P Endscripts upper X upper A Subscript i j m p t Baseline plus sigma summation Underscript l equals 1 comma l not equals j Overscript upper J Endscripts sigma summation Underscript m equals 1 Overscript upper M Endscripts sigma summation Underscript p equals 1 Overscript upper P Endscripts upper X upper C Subscript l j m p t Baseline less than or equals upper R Subscript j Baseline plus sigma summation Underscript tau equals 1 Overscript t Endscripts upper X upper E upper R Subscript j tau Baseline comma for all j comma for all t period
The total amount of all capacity expansions of DCj cannot exceed the maximum 
additional capacity allowed during the planning horizon: 
s
i
gma 
summat ion Unders
cript t equals 1 Overscript upper T Endscripts upper X upper E upper R Subscript j t Baseline less than or equals upper U upper R Subscript j Baseline comma for all j period
If the capacity of DCj is expanded, the amount of each expansion must be at least 
equal to the given lower bound: 
upper X upper E upper R S
ubscript j t Baseline greater than or equals upper L upper R Subscript j Baseline upper Y upper E upper R Subscript j t Baseline comma for all j comma for all t period
While constraint (9.13) imposes a lower bound on the amount of each DC expan-
sion upper X upper E upper R Subscript j t, it also ensures that that upper Y upper E upper R Subscript j t is equal to 0 if upper X upper E upper R Subscript j t is equal to 0. The 
following logical constraint is not meant to impose an upper bound on upper X upper E upper R Subscript j t, but  it  
is used to ensure that upper Y upper E upper R Subscript j t is equal to 1 if upper X upper E upper R Subscript j t is positive: 
upper X upper E u
pper R
 Subscript j t Baseline less than or equals upper U upper R Subscript j Baseline upper Y upper E upper R Subscript j t Baseline comma for all j comma for all t
The number of capacity expansions of DCj during the planning horizon cannot 
exceed the maximum limit: 
s
i
gma 
summat ion Un der
script t equals 1 Overscript upper T Endscripts upper Y upper E upper R Subscript j t Baseline less than or equals upper N upper R comma for all j period
9.4.8 
SC to DC Transportation Link Constraints 
The total amount of all products transported from SCi to DCj by mode m must not 
exceed the mode m (extended) capacity of link i-j: 
s
i
gma 
summatio n Unders cr
i
p
t 
p e
quals 1 O
verscript upper
 P Endscripts upper X upper A Subscript i j m p t Baseline less than or equals upper W upper A Subscript i j m Baseline plus sigma summation Underscript tau equals 1 Overscript t Endscripts upper X upper E upper A Subscript i j m tau Baseline comma for all i comma for all j comma for all m comma for all t period
9.4 Model Formulation
223
If transportation mode m is used to transport product p on link i-j, then the 
transported quantity must be at least equal to the minimum economic threshold: 
upper X upper A Subscript  i j m p t Baseline
 greater than or equals upper Q upper A Subscript i j m p Baseline upper Y upper A Subscript i j m p t Baseline comma for all i comma for all j comma for all m comma for all p comma for all t period
Constraint (9.18) below ensures that YAijmpt is equal to 1 if XAijmpt is positive. 
Constraints (9.17) and (9.18) together ensure that the value of YAijmpt is correctly 
related to the value of upper X upper A Subscript i j m p t: 
upper X u
p
per A Subscri
p
t i j m p t Baseline less th
an or equals left parenthesis upper W upper A Subscript i j m Baseline plus upper U upper A Subscript i j m Baseline right parenthesis upper Y upper A Subscript i j m p t Baseline comma for all i comma for all j comma for all m comma for all p comma for all t period
The total amount of all mode m established/expanded capacity at link i-j during 
the planning horizon cannot exceed the maximum limit: 
s
i
gma 
summatio n Underscript t equals 1
 Overscript upper T Endscripts upper X upper E upper A Subscript i j m t Baseline less than or equals upper U upper A Subscript i j m Baseline comma for all i comma for all j comma for all m comma for all t period
If mode m transportation capacity of link i-j is established/expanded, the amount 
of each individual new or added capacity must be greater than or equal to the allowed 
lower bound: 
upper X upper E upper A Subscript i j m
 t Baseline greater than or equals upper L upper A Subscript i j m Baseline upper Y upper E upper A Subscript i j m t Baseline comma for all i comma for all j comma for all m comma for all t period
The following logical constraint is used to ensure that upper Y upper E upper A Subscript i j m t is equal to 1 if 
upper X upper E upper A Subscript i j m t is positive: 
upper X upper E upper A Subscript i j m
 t Baseline less than or equals upper U upper A Subscript i j m Baseline upper Y upper E upper A Subscript i j m t Baseline comma for all i comma for all j comma for all m comma for all t period
The number of capacity expansions (including possible capacity establishments) 
of mode m transportation at link i-j cannot exceed the maximum limit: 
s
i
gma 
summatio n Underscript t e
quals 1 Overscript upper T Endscripts upper Y upper E upper A Subscript i j m t Baseline less than or equals upper N upper L comma for all i comma for all j comma for all m period
9.4.9 
DC to DM Transportation Link Constraints 
The amount of all products transported by mode m between DCj and DMk cannot 
exceed the extended mode m capacity of link j-k: 
s
i
gma 
summatio n Unders cr
i
p
t p 
equals 1 Overscript upper
 P Endscripts upper X upper B Subscript j k m p t Baseline less than or equals upper W upper B Subscript j k m Baseline plus sigma summation Underscript tau equals 1 Overscript t Endscripts upper X upper E upper B Subscript j k m tau Baseline comma for all j comma for all k comma for all m comma for all t period
224
9
Optimum Planning of a Distribution Supply Chain for Reﬁned Oil Products
If transportation mode m is used to transport product p on link j-k, then the 
transported quantity must be at least equal to the minimum economic threshold: 
upper X upper B Subscript j k m p t Baseline
 greater than or equals upper Q upper B Subscript j k m p Baseline upper Y upper B Subscript j k m p t Baseline comma for all j comma for all k comma for all m comma for all p comma for all t period
The following logical constraint ensures that YBjkmpt is equal to 1 if XBjkmpt is 
positive: 
upper X u
p
per B Subscri
p
t j k m p t Baseline less th
an or equals left parenthesis upper W upper B Subscript j k m Baseline plus upper U upper B Subscript j k m Baseline right parenthesis upper Y upper B Subscript j k m p t Baseline comma for all j comma for all k comma for all m comma for all p comma for all t period
The total amount of all mode m established/expanded capacity at link j-k during 
the planning horizon cannot exceed the maximum limit: 
s
i
gma 
summatio n Underscript t equals 1
 Overscript upper T Endscripts upper X upper E upper B Subscript j k m t Baseline less than or equals upper U upper B Subscript j k m Baseline comma for all j comma for all k comma for all m comma for all t period
If mode m transportation capacity of link j-k is established/expanded, the amount 
of each individual new or added capacity must be greater than or equal to the allowed 
lower bound: 
upper X upper E upper B Subscript j k m
 t Baseline greater than or equals upper L upper B Subscript j k m Baseline upper Y upper E upper B Subscript j k m t Baseline comma for all j comma for all k comma for all m comma for all t period
The following logical constraint is used to ensure that upper Y upper E upper B Subscript j k m t is equal to 1 if 
upper X upper E upper B Subscript j k m t is positive: 
upper X upper E upper B Subscript j k m
 t Baseline less than or equals upper U upper B Subscript j k m Baseline upper Y upper E upper B Subscript j k m t Baseline comma for all j comma for all k comma for all m comma for all t period
The number of capacity expansions (including possible capacity establishments) 
of mode m transportation at link j-k cannot exceed the maximum limit: 
s
i
gma 
summatio n Underscript t e
quals 1 Overscript upper T Endscripts upper Y upper E upper B Subscript j k m t Baseline less than or equals upper N upper L comma for all j comma for all k comma for all m period
9.4.10 
DC to DC Transportation Link Constraints 
The total amount of all products transported from DCj to DCl by mode m must not 
exceed the mode m (extended) capacity of link j-l: 
s
i
gma 
summatio n Unders cr
i
p
t 
p e
quals 1 Overscript  upper P Endsc
ripts upper X upper C Subscript j upper L m p t Baseline less than or equals upper W upper C Subscript j upper L m Baseline plus sigma summation Underscript tau equals 1 Overscript t Endscripts upper X upper E upper C Subscript j upper L m tau Baseline comma for all j comma for all l left parenthesis l not equals j right parenthesis comma for all m comma for all t period
9.4 Model Formulation
225
If transportation mode m is used to transport product p on link j-l, then the 
transported quantity must be at least equal to the minimum economic threshold: 
upper X upper C Subscript j l m p  t Baseline greate
r than or equals upper Q upper C Subscript j l m p Baseline upper Y upper C Subscript j l m p t Baseline comma for all j comma for all l left parenthesis l not equals j right parenthesis comma for all m comma for all p comma for all t period
Logical constraint (9.32) ensures that YCjlmpt is equal to 1 if XCjlmpt is positive: 
upper X u
p
per C Subscri
p
t j l m p t Basel ine less than or e
quals left parenthesis upper W upper C Subscript j l m Baseline plus upper U upper C Subscript j l m Baseline right parenthesis upper Y upper C Subscript j l m p t Baseline comma for all j comma for all l left parenthesis l not equals j right parenthesis comma for all m comma for all p comma for all t period
The total amount of all mode m established/expanded capacity at link j-l during 
the planning horizon cannot exceed the maximum limit: 
s
i
gma 
summatio n Underscript t e quals 1 Ov
erscript upper T Endscripts upper X upper E upper C Subscript j l m t Baseline less than or equals upper U upper C Subscript j l m Baseline comma for all j comma for all l left parenthesis l not equals j right parenthesis comma for all m period
If mode m transportation capacity of link j-l is established/expanded, the amount 
of each individual new or added capacity must be greater than or equal to the allowed 
lower bound: 
upper X upper E upper C Subscrip t j l m t Base
line greater than or equals upper L upper C Subscript j l m Baseline upper Y upper E upper C Subscript j l m t Baseline comma for all j comma for all l left parenthesis l not equals j right parenthesis comma for all m comma for all t period
The following logical constraint is used to ensure that upper Y upper E upper C Subscript j l m t is equal to 1 if 
upper X upper E upper C Subscript j l m t is positive: 
upper X upper E upper C Subscrip t j l m t Base
line less than or equals upper U upper C Subscript j l m Baseline upper Y upper E upper C Subscript j l m t Baseline comma for all j comma for all l left parenthesis l not equals j right parenthesis comma for all m comma for all t period
The number of capacity expansions (including possible capacity establishments) 
of mode m transportation at link j-l cannot exceed the maximum limit: 
s
i
gma 
summatio n Underscript  t equals 
1 Overscript upper T Endscripts upper Y upper E upper C Subscript j l m t Baseline less than or equals upper N upper L comma j comma for all l left parenthesis l not equals j right parenthesis comma for all m period
Finally, the following constraint restricts all the non-binary decision variables to 
non-negative values: 
StartLayout 1st Row 1st Column Blank 2nd Column upper X uppe r A S
ubscript i j m p  t Baseline comma 
upper X upper B Subscript j k m p t Baseline comma upper X upper C Subscript j l m p t Baseline comma upper X upper E upper R Subscript j t Baseline comma upper X upper E upper A Subscript i j m t Baseline comma upper X upper E upper B Subscript j k m t Baseline comma upper X upper E upper C Subscript j l m t Baseline greater than or equals 0 comma 2nd Row 1st Column Blank 2nd Column for all i comma for all j comma for all k comma for all l left parenthesis l not equals j right parenthesis comma for all m comma for all p comma for all t period EndLayout
226
9
Optimum Planning of a Distribution Supply Chain for Reﬁned Oil Products
9.5 
Given Data 
According to the problem description given in Sect. 9.3, the distribution network 
is used to transport two reﬁned products (P = 2: p = 1 for gasoline, p = 2 for  
diesel). The two products are transported from reﬁneries to customers/cities by three 
transportation modes (M = 3: m = 1 for trucks, m = 2 for pipelines, m = 3 for  
ships). The network consists of six supply centers (SC), i.e., reﬁneries (I = 6), 17 
distribution centers (DC), i.e., bulk plants (J = 17), and 20 demand markets (DM), 
i.e., cities (K = 20). The strategic plan of the distribution network must be developed 
for a planning horizon of ﬁve years (T = 5). 
The 20 demand markets considered in this case study are the cities in the country 
where the company operates whose population is at least 100,000 inhabitants. Cities 
meeting this criterion that are within a 50-km distance of each other are combined 
as one demand market. The size of the problem, which is deﬁned by the values of I, 
J, K, M, P, and T, determines the size of the MILP model. The MILP model of the 
case study is quite large, as it has 79,954 constraints and 64,441 decision variables, 
including 32,215 binary variables. 
All 17 distribution centers have the same initial capacity, which is equal to 50 
million barrels (BBL) per year for the sum of the two products (Rj = 50 million 
BBL/year, ∀j). The annual capacities of the 6 supply centers are displayed in Table 
9.1. The annual gasoline demands in the next 5 years of the 20 demand markets 
are given in Table 9.2. The corresponding demands for diesel are displayed in Table 
9.3. These future demand values are based on forecasted annual growth of 7% for 
gasoline and 5% for diesel. 
Distances in kilometers between supply centers (SC) and distribution centers 
(DC) are given in Table 9.4. Distances between distribution centers (DC) and demand 
markets (DM) are given in Table 9.5. Distances between different distribution centers 
(DC) are given in Table 9.6. The unit cost of transportation by each transportation 
mode is linearly related to these distances. Speciﬁcally, CAijm values are proportional 
to distances in Table 9.4, CBjkm values are proportional to distances in Table 9.5, while 
CCjlm values are proportional to distances in Table 9.6.
Truck transportation of reﬁned products is available between all locations where 
the distances are given in Tables 9.4, 9.5, and 9.6. The pipeline transportation mode 
is assumed if a pipeline currently exists between any two locations. Marine trans-
portation mode by tanker ships is assumed available between any two locations that 
are both on the coasts. 
The remaining data is given below. Unless otherwise speciﬁed, all the values 
below are applicable to:
Table 9.1 Supply center (SC) capacities (million BBL/year) 
i
1
2
3
4
5
6 
Si1 = Si2
200.75
259.15
146
381.43
45.26
32.12
9.5 Given Data
227
Table 9.2 Gasoline demands (million BBL/year) in the next 5 years 
k
Dk11
Dk12
Dk13
Dk14
Dk15 
1
2.68
2.87
3.07
3.28
3.51 
2
9.51
10.18
10.89
11.65
12.47 
3
5.58
5.97
6.39
6.83
7.31 
4
2.15
2.30
2.46
2.63
2.82 
5
46.93
50.21
53.73
57.49
61.51 
6
4.35
4.66
4.99
5.33
5.71 
7
5.40
5.78
6.18
6.62
7.08 
8
12.50
13.37
14.31
15.31
16.38 
9
4.18
4.47
4.79
5.12
5.48 
10
87.85
94.00
100.58
107.62
115.16 
11
59.89
64.09
68.57
73.37
78.51 
12
27.65
29.58
31.65
33.87
36.24 
13
8.17
8.75
9.36
10.01
10.71 
14
12.20
13.05
13.97
14.94
15.99 
15
11.09
11.86
12.69
13.58
14.53 
16
26.83
28.70
30.71
32.86
35.16 
17
8.88
9.51
10.17
10.88
11.64 
18
5.67
6.07
6.50
6.95
7.44 
19
4.96
5.30
5.67
6.07
6.50 
20
3.10
3.32
3.55
3.80
4.07
for all i comma for all j comma for all k comma for all l left parenthesis l not equals j right parenthesis comma for all m comma for all p comma for all t period
Transportation link capacities: 
WAij1 = WBjk1 = WCjl1 = 100 million BBL/year if a road exists; = 0 otherwise. 
WAij2 = WBjk2 = WCjl2 = 100 million BBL/year if a pipeline exists; = 0 otherwise. 
WAij3 = WBjk3 = WCjl3 = 50 million BBL/year if a sea route exists; = 0 otherwise. 
Transportation costs: 
CAij1 = CBjk1 = CCjl1 = $2500/million BBL/km. 
CAij2 = CBjk2 = CCjl2 = $1250/million BBL/km. 
CAij3 = CBjk3 = CCjl3 = $1250/million BBL/km. 
TAijmp = TBjkmp = TCjlmp= $2,50,000. 
Establishment/expansion costs:
228
9
Optimum Planning of a Distribution Supply Chain for Reﬁned Oil Products
Table 9.3 Diesel demands (million BBL/year) in the next 5 years 
k
Dk21
Dk22
Dk23
Dk24
Dk25 
1
2.66
2.79
2.93
3.08
3.23 
2
9.43
9.90
10.40
10.92
11.46 
3
5.53
5.81
6.10
6.40
6.72 
4
2.13
2.24
2.35
2.47
2.59 
5
46.52
48.85
51.29
53.86
56.55 
6
4.32
4.53
4.76
5.00
5.25 
7
5.35
5.62
5.90
6.20
6.51 
8
12.39
13.01
13.66
14.35
15.06 
9
4.14
4.35
4.57
4.80
5.04 
10
87.09
91.45
96.02
100.82
105.86 
11
59.38
62.35
65.46
68.74
72.17 
12
27.41
28.78
30.22
31.73
33.32 
13
8.10
8.51
8.93
9.38
9.85 
14
12.09
12.70
13.33
14.00
14.70 
15
10.99
11.54
12.12
12.73
13.36 
16
26.59
27.92
29.32
30.79
32.32 
17
8.81
9.25
9.71
10.19
10.70 
18
5.62
5.91
6.20
6.51
6.84 
19
4.91
5.16
5.42
5.69
5.97 
20
3.08
3.23
3.39
3.56
3.74
Table 9.4 Distances in kilometers between SCi and DCj 
1
2
3
4
5
6
7
8
9 
10 11 
12 
13 
14 
15 
16 17 
1 140 170 120 100 140 160 130 140 150 45 140 180 80 
20 
10 
5 
20 
2 140 160 120 90 140 160 130 140 150 45 140 180 80 
20 
10 
8 
20 
3 120 80 140 100
-
85 100 75 
20 100 15 
65 
75 140 140 140 140 
4 100 65 130 110 20 100 120 90
-
100 35 
50 
70 130 145 145 140 
5 140 130 130 50 100 110 90 
95 100
-
95 130 45 
30 
35 
35 50 
6 140 100 150 85 15 
70 
90 
60 
30 95
-
80 
90 130 130 130 150 
i j
ARj = $1,250,000/million BBL. 
FRj = $5,000,000. 
AAijm = ABjkm = ACjlm = $500,000/million BBL. 
FAijm = FBjkm = FCjlm = $1,250,000. 
Transportation and expansion limits: 
QAijmp, QBjkmp, QCjlmp = 250,000 BBL.
9.5 Given Data
229
Table 9.5 Distances in kilometers between DCj and DMk 
1 
2 
3 
4 
5 
6 
7 
8 
9 
10 11 12 13 14 15 16 17 18 19 20 
1
-
35 40 80 100 200 200 170 90 90 120 125 65 130 120 140 135 80 125 25 
2 35
-
65 55 65 160 190 160 65 120 100 100 95 160 110 160 140 120 160 60 
3 40 70 80 15 120 220 220 200 120 120 150 160 100 140 160 140 140 80 120 25 
4 150 150 120 180 90 70 40 50 110 55 85 20 90 85 70 95 50 100 100 160 
5 100 80 70 120 30 90 100 75 20 100 15 20 75 140 30 140 100 110 150 125 
6 200 160 150 210 110
-
30 20 100 110 70 90 135 150 70 150 120 160 160 210 
7 200 190 155 220 125 30
-
30 125 90 90 70 130 130 75 130 90 145 140 200 
8 170 160 135 200 100 20 30
-
100 100 65 35 110 125 60 135 100 140 140 200 
9 90 65 60 115 20 100 125 100
-
100 30 85 65 135 45 145 115 100 150 120 
10 90 120 65 145 80 110 90 100 100
-
100 85 40 30 80 40 10 50 45 105 
11 120 100 83 150 40 70 90 65 30 100
-
10 85 125 15 135 100 130 140 140 
12 55 20 90 75 65 180 170 145 50 130 80 85 100 165 90 155 145 130 165 80 
13 65 95 25 90 45 135 130 110 65 40 85 80
-
70 70 80 50 40 85 80 
14 130 160 90 150 115 150 130 125 135 30 125 120 70
-
110 15 35 60 25 110 
15 140 160 100 145 125 150 130 135 145 40 135 125 80 15 120 5 
45 50 10 100 
16 140 160 100 145 125 150 130 135 145 40 135 125 80 15 120
-
45 50 10 100 
17 110 150 80 125 115 165 150 145 135 50 150 140 70 40 130 25 60 35 20 85 
j k 
Table 9.6 Distances in kilometers between DCj and DCl (l /= j) 
1 
2 
3 
4 
5 
6
7
8
9 
10 
11 
12 
13 
14 
15 
16 
17 
1
-
35 40 150 100 200 200 170 90 
90 120 55 
65 130 140 140 110 
2 
35
-
70 150 80 160 190 160 65 120 100 20 
95 160 160 160 150 
3 
40 70
-
180 140 220 220 200 120 120 150 90 100 140 140 140 100 
4 
150 150 180
-
100 70 40 
50 110 55 
85 150 90 
85 
95 
95 100 
5 
100 80 140 100
-
90 100 75 
20 100 15 
65 
75 140 140 140 140 
6 
200 160 220 70 90
-
30 
20 100 110 70 180 135 150 150 150 165 
7 
200 190 220 40 100 30
-
30 125 90 
90 170 130 130 130 130 150 
8 
170 160 200 50 75 20 30
-
100 100 65 145 110 125 135 135 145 
9 
90 65 120 110 20 100 125 100
-
100 30 
50 
65 135 145 145 135 
10 
90 120 120 55 100 110 90 100 100
-
100 130 40 
30 
40 
40 
50 
l
j 
11 120 100 150 85 15 70 90 
65 
30 100
-
80 
85 125 135 135 150 
12 
55 20 90 150 65 180 170 145 50 130 80
-
100 165 155 155 145 
13 
65 95 100 90 75 135 130 110 65 
40 
85 100
-
70 
80 
80 
70 
14 130 160 140 85 140 150 130 125 135 30 125 165 70
-
15 
15 
40 
15 140 160 140 95 140 150 130 135 145 40 135 155 80 
15
-
5 
25 
16 140 160 140 95 140 150 130 135 145 40 135 155 80 
15 
5
-
25 
17 110 150 100 100 140 165 150 145 135 50 150 145 70 
40 
25 
25
-
230
9
Optimum Planning of a Distribution Supply Chain for Reﬁned Oil Products
LAijm, LBjkm, LCjlm = 500,000 BBL. 
UAijm, UBjkm, UCjlm = 100 million BBL. 
LRj = 10 million BBL. 
URj = 100 million BBL. 
NL = 2. 
NR = 2. 
9.6 
Model Solution 
Using the given data shown above, the MILP model was coded in GAMS version 
24.7.4, and it was solved using the CPLEX solver. A near-optimal solution was found, 
whose objective function (total cost Z) is equal to $661.3 million. The solver took 
900 s (15 min) of computation time, and it speciﬁed an optimality gap of 2.27%. 
The solution of the model speciﬁes the values of all the decision variables, which are 
presented next. 
The solution of the MILP model speciﬁes the amounts of each product to transport 
through each link by each transportation mode during each year of the planning 
horizon. The ﬁrst stage of distribution is transporting the reﬁned products from 
the 6 supply centers (SC) to the 17 distribution centers (DC). The annual gasoline 
transportation amounts from each SCi to each DCj (XAijm1t values) are given in Table 
9.7. The annual diesel transportation amounts from each SCi to each DCj (XAijm2t 
values) are given in Table 9.8.
The second stage of distribution is transporting the reﬁned products from the 
17 distribution centers (DC) to the 20 demand markets (DM). The annual gasoline 
transportation amounts from each DCj to each DMk (XBjkm1t values) are given in 
Table 9.9. For diesel, the annual transportation amounts from each DCj to each DMk 
(XBjkm2t values) are given in Table 9.10.
The ﬁve-year distribution plan of both products does not include any product 
transportation between different distribution centers, i.e., from DCj to another DCl. 
Therefore, for both gasoline (p = 1) and diesel (p = 2), the associated transportation 
decision variables are all equal to zero: 
upper X upper C Su bscript j l m  Baseline 1 t Baseline equals upper X upper C Subscript j l m Baseline 2 t Baseline equals 0 comma for all j comma for all l left parenthesis l not equals j right parenthesis comma for all m comma for all t period
The ﬁve-year plan does not involve the establishment of any new transportation 
link or the expansion of any existing link. Therefore, 
upper X upper E up per A Subs cript i j m t Baselin e equals upper X upper E upper B Subscript j k m t Baseline equals upper X upper E upper C Subscript j l m t Baseline equals 0 comma for all i comma for all j comma for all k comma for all l left parenthesis l not equals j right parenthesis comma for all m comma for all i period
9.6 Model Solution
231
Table 9.7 Gasoline transportation in million BBL from SCi to DCj 
i 
j 
m t = 1 t = 2 t = 3 t = 4 t = 5 
i
j
 
m
t
 
= 1 t = 2 t = 3 t = 4 t = 5 
1 
4 
1
4.29
3 
6 
3 22.26 17.84
13.51 
1 10 
2
50
3 
7 
1
6.62 7.08 
1 15 
2
18.57
3 
8 
1
15.31 15.46 
1 16 
3 30.73 28.7 
50
50 49.33 3 11 3
38.46 37.88 33.9 
2 
3 
1
2.82 4 
1 
1
13.92 25.22 
2 
4 
1
4.74 4 
2 
1 14.34 18.67 23.7 21.78 29.09 
2 10 
2
50
50 
4 
6 
3
11.17
44.75 
2 14 
1 27.46 22.56 36.67 25.81 20.58 4 
9 
2
43.2 
50
50 44.94 
2 15 
2 
50
50
4 11 3
9.43 
2 15 
3
49.3 
50 
4 12 3 
50 
4.47 48.17 50 
2 16 
2
12.04 
4 13 1 20.47 21.03 28.1 40.62 10.71 
2 17 
3 
41.3
5 
4 
1 17.85 16.48 27.36 44.75 11.94 
3 
5 
1 
50
6 
8 
1
13.37 14.31
32.12 
3
5
 
2
50
 
Table 9.8 Diesel transportation in million BBL from SCi to DCj 
i 
j 
m t = 1 t = 2 t = 3 t = 4 t = 5 i
j
 
m
t
 
= 1 
t = 2 
t = 3 
t = 4 
t = 5 
1 10 2
50
3 
7 
1
6.2 
32.28 
1 14 1
24.19
3 
8 
1
17.88 
1 16 2 
50
50 49.33 37.3 
50 
3 11 3 24.84 5.81
16.1 40.57 
1 16 3 18.6 20.63
4 
1 
1
19.55 20.53 
1 17 2
50
4 
2 
1 14.22 18.16 20.36 27.82 20.91 
1 17 3
48.74
50 
4 
6 
3
36.49 5.25 
2 
3 
1
2.59 4 
9 
1 
50 
48.85
42.04 47.1 
2 10 2 
50
4 
9 
3 42.04
42.04 
2 14 1 20.9 27.1 13.33
29.42 4 11 3
12.12 
2 15 2
31.43
4 12 3
45.53 1.83
50 
2 17 2 
8.7
50
4 13 1 13.63 20.22 21.23 9.38 39.29 
3 
5 
1
50
5 
4 
1 27.41 28.78 17.9
33.32 
3 
5 
3
50
6 
8 
1
13.01 17.81 32.12 
The distribution plan involves the capacity expansion of only two distribution 
centers, namely DC9 and DC16. Both of these DC expansions are required in year 1, 
and their additional capacity amounts are given below. The values of all other XERjt 
decision variables are equal to zero: 
XER9,1 = 42.04 million BBL/year. 
XER16,1 = 49.33 million BBL/year.
232
9
Optimum Planning of a Distribution Supply Chain for Reﬁned Oil Products
Table 9.9 Gasoline transportation in million BBL from DCj to DMk 
j 
k 
m t = 1 t = 2 t = 3 t = 4 t = 5 
j
k
 
m
t
 
= 1 t = 2 t = 3 t = 4 t = 5 
1 
1 
1
3.28 3.51 10 10 1
50
50
50 
1 
3 
1
6.83 7.31 11 11 1
13.49 25.19 
1 10 
1
2.89 11 11 3 14.07
20.32 
1 18 
1
7.44 11 12 1
13.1 
1 20 
1
3.8 
4.07 11 15 1 11.09 11.86 12.69 13.58 9.43 
2 
1 
1 
2.68 2.87 3.07 
12 9 
3 4.18 4.47 4.79 5.12 
2 
2 
1 
9.51 10.18 10.89 11.65 12.47 12 11 3 45.82
43.39 44.88 
2 
4 
1 
2.15 2.30 2.46 2.63
13 3 
1 5.58 5.97 6.39 
2 
5 
1
3.73 7.49 16.62 13 5 
1 6.72 0.25 
2 20 
1
3.32 3.55
13 10 1
23.65 
3 
4 
1
2.82 13 13 1 8.17 8.75 9.36 10.01 10.71 
4 12 
1 17.85 16.48 31.65 33.87 5.04 13 17 1
5.86 
4 17 
1
10.88 11.64 13 18 1
6.07 6.50 6.95 
5 
5 
1 
40.2 49.96
44.89 14 10 1 6.38 
18.39 10.86 4.59 
5 12 
1 
9.8
14 14 1 12.2 13.05 13.97 14.94 15.99 
5 15 
1
5.11 14 17 1 8.88 9.51 4.31 
6 
6 
1 
4.35
4.99 5.33 5.71 15 10 2 48.95 44.0 12.90 43.93 43.5 
6 
6 
3
4.66
15 16 3 1.05 
6 
7 
1 
5.4 
5.78 6.18
15 19 2
5.3 
5.67 6.07 
6.5 
6 
8 
1 
12.5
16 10 2
19.29 29.18 14.17 
6 11 
3
7.4
8.18 39.04 16 16 1
30.71
35.16 
7 
7 
1
6.62 7.08 16 16 2 25.78 28.7
32.86 
8 
8 
1
13.37 14.31 15.31 16.38 16 19 2 4.96 
8 12 
1
31.2 17 10 2 32.52 
9 
5 
1
50
50
17 18 1 5.67 
9 
9 
3
5.48 17 20 1 
3.1 
9 11 
3
43.2
39.46
9.7 
Summary and Conclusions 
This chapter presented the long-term optimum planning and design of a large network 
for the distribution of reﬁned oil products. The plan allows for network development 
by establishing new transportation links, as well as expanding existing transporta-
tion links and distribution centers. This is a downstream supply chain optimization 
problem that involves both tactical and strategic decisions. The tactical decisions are 
the amounts, routes, and transportation modes of reﬁned products delivered to end 
customers each year. The strategic decisions are the times, locations, and amounts 
of establishing and expanding distribution facilities and links. 
A real-life problem has been considered, which involves multiple products (gaso-
line and diesel), multiple stages/echelons (supply centers, distribution centers, and
9.7 Summary and Conclusions
233
Table 9.10 Diesel transportation in million BBL from DCj to DMk 
j 
k 
m t = 1 t = 2 t = 3 t = 4 t = 5 
j 
k 
m t = 1 t = 2 t = 3 t = 4 t = 5 
1 
1 
1
3.08 3.23 9 11 3 41.38
37.48 37.25 
1 
3
1
 
6.4
 
6.72
 
10 10 1 
50
50 
1 18 
1
6.51 6.84 11 11 1 13.85 
1 20 
1
3.56 3.74 11 11 3
27.21 
2 
1 
1 
2.66 2.79 2.93
11 12 1
13.95 
2 
2 
1 
9.43 
9.9 
10.4 10.92 11.46 11 15 1 10.99 11.54 12.12 2.15 13.36 
2 
4 
1 
2.13 2.24 2.35 2.47
12 9 
3
4.35
5.04 
2 
5 
1
1.29 14.43 9.45 12 11 3
41.17 1.83
44.96 
2 20 
1
3.23 3.39
13 3 
1 5.53 5.81 
6.1 
3 
4 
1
2.59 13 10 1
29.44 
4 12 
1 27.41 28.78 8.19
33.32 13 13 1 
8.1 
8.51 8.93 9.38 9.85 
4 17 
1
9.71
13 18 1
5.91 
6.2 
5 
5 
1
50 39.42
14 10 1
14.72 
5 15 
1
10.58
14 14 1 12.09 12.7 13.33 14.00 14.7 
6 
6 
1 
4.32 4.53
14 17 1 8.81 9.25
10.19 
6 
6 
3
4.76 
5.0 
5.25 14 19 1
5.16 
6 
7 
1 
5.35 5.62 
5.9
15 10 2
26.01 
6 
8 
1 12.39
15 19 2
5.42 
6 11 
3 
4.15 21.17 26.16 31.49
16 10 2 37.09 42.71 20.01 0.82 17.68 
7 
7
1
 
6.2
 
6.51
 
16 16 1
30.79 
7 
8 
1
15.06 16 16 3 26.59 27.92 29.32
32.32 
7 17 
1
10.7 16 19 2 4.91
5.69 
8 
8 
1
13.01 13.66 14.35
17 10 2
48.74 50
50 44.03 
8 12 
1
22.03 17.77
17 18 1 5.62 
9 
5 
1 46.52 48.85
47.1 17 19 3
5.97 
9 
9 
2 
4.14
4.57 
4.8
17 20 1 3.08
demand markets), multiple transportation means (trucks, pipelines, and ships), and 
multiple time periods (ﬁve years). Costs consist of ﬁxed and variable parts, and thresh-
olds apply to material transportation and capacity addition amounts. The objective is 
to minimize the total cost, which is the sum of the variable and ﬁxed costs of trans-
portation and facility development. This objective is optimized subject to several 
economic, operational, and logical constraints. A mixed-integer linear programming 
(MILP) model is formulated to represent the problem. Using the given data, the 
model is solved and a near-optimal solution is obtained. 
For future research, there are several directions for extending the work presented 
in this chapter. For example, stochastic aspects can be introduced in the model in 
order to deal with possible uncertainties in supply, demand, or available capacities. 
Another direction is to consider alternative objectives such maximizing total proﬁt, 
or multiple objectives such as maximizing customer satisfaction and minimizing 
environmental impact in addition to minimizing cost. A third direction is to enlarge 
the problem by including additional products, stages/echelons, and transportation
234
9
Optimum Planning of a Distribution Supply Chain for Reﬁned Oil Products
modes. Another relevant future research direction is to increase the accuracy of cost 
calculations by considering nonlinear cost functions and taking the time value of 
money into account. 
Acknowledgement 
1. Partial contribution to this chapter is provided by Dr. Hany Osman, Dr. Shokri 
Selim, and Dr. Salih Duffuaa, in addition to Mr. Mohamed Osman. 
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Chapter 10 
Berth Allocation for Loading Tankers 
at an Oil Export Terminal 
10.1 
Introduction 
Exporting crude oil, reﬁned oil products, and petrochemical products to customers 
overseas is the last stage of the petroleum industry’s value chain. Globally, 3.2 billion 
metric tons of crude oil, reﬁned oil products, gas, and petrochemicals have been 
transported via waterways in 2018 (UNCTAD, 2019). This is done by tanker ships 
that carry massive amounts of products over long distances across oceans and seas. 
Oil tankers are ships that are designed for the mass transportation of crude oil and its 
products. Tanker ships vary widely in terms of size, capacity, and load type. Tankers 
are loaded with oil and associated products in specialized port facilities that are called 
oil export marine terminals. 
Oil export marine terminals consist of several quays (docks) equipped with storage 
tanks, pipelines, pumping facilities, and ship loading gear. These terminals contain 
a limited number of berths, which are speciﬁc locations in the quays that are used 
for anchoring the incoming tankers. Berths are designed to provide safe mooring 
and efﬁcient loading of products into the vessels. Figure 10.1 shows an oil export 
terminal with several oil tankers.
Tanker ships are very cost efﬁcient in transporting oil and reﬁned products, and 
they are only second to pipelines in terms of the average transportation cost per cubic 
meter. According to their functions, oil tankers are classiﬁed into two main types: 
crude tankers that are used to move unreﬁned oil over longer distances and product 
tankers that are used to move smaller amounts of reﬁned petroleum and petrochemical 
products. In terms of body size and load capacity, oil tankers are classiﬁed into ﬁve 
categories: general-purpose tankers, medium-range tankers, long-range tankers, very 
large crude carriers, and ultra large crude carriers. These categories range in size from 
200 to 400 m in body length and from 10,000 to 550,000 metric tons in load capacity. 
In addition to oil tankers, other types of tankers include gas tankers and chemical 
tankers. 
An oil export terminal contains a limited number of berths at which tankers can 
be moored. The different berths usually vary in their sizes, loading (pumping) rates,
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 
H. K. Alfares, Applied Optimization in the Petroleum Industry, 
https://doi.org/10.1007/978-3-031-24166-6_10 
237
238
10
Berth Allocation for Loading Tankers at an Oil Export Terminal
Fig. 10.1 Oil export marine terminal. Courtesy of Saudi Aramco, copyright owner
and the products they can load. When tankers arrive at an export terminal, they have 
to be assigned to speciﬁc berths as soon as possible. The assignment of a tanker to 
one or more berths depends on many factors, such as the availability of berths and 
the size of the tanker, as well as the types and quantities of the products required 
by the tanker. In the Port of Houston, for example, a typical petrochemical tanker 
may load up to 30 different types of liquid products, and this often means visiting 
up to four different berths in the port (Cankaya et al., 2019). In addition to time for 
loading products, an arriving tanker spends time at the port for security clearance, 
inspections, repairs, refueling, and immigration procedures. A tanker may also need 
to undergo one or more tank cleaning operations, the durations of which depend on 
the current product to load and the previous product loaded into the given tank. 
The assignment of incoming tankers to speciﬁc berths in a marine port is called 
the berth allocation problem (BAP), which is one of the most important and chal-
lenging real-life optimization problems. In general, the berth allocation problem 
deals with a number of berths with speciﬁc properties such as sizes, available prod-
ucts, and loading/pumping rates. Moreover, there is a set of vessels with known 
characteristics such as arrival times, sizes, and product demands. Practical consid-
erations in assigning vessels to berths include the vessel’s loading times, the size 
of the berthing areas, and the vessel’s promised turnaround time. Solving the berth 
allocation problem means not only assigning each ship to a speciﬁc berth, but also 
determining the sequence of serving the ships assigned to each berth. Different time-
based objectives are usually sought in solving the BAP, such as minimizing the total 
delay time and the total service time, but the ultimate objective is actually to mini-
mize the total cost. The berth allocation problem is an NP-complete optimization 
problem, which is very difﬁcult to solve.
10.1 Introduction
239
Over the years, the berth allocation problem has been well studied using various 
models and solution techniques. According to Bierwirth and Meisel (2010, 2015), 
berth allocation models in the literature can be classiﬁed based on four main criteria: 
spatial properties, temporal properties, handling time, and performance measures. 
Spatial properties refer to the berths’ physical layout, which can be classiﬁed into 
four types: discrete, if berths are placed at ﬁnite speciﬁc positions; continuous, if 
berths can be placed on any point on the quay; hybrid; or vessel draft constrained. 
Temporal properties refer to the applicable time aspects of the vessels, such as the 
due dates and the type of arrival times (static, dynamic, or stochastic). Handling 
time properties refer to the service times of vessels at the berths, and they can be 
either ﬁxed constants, dependent on berthing positions, dependent on the assigned 
cranes, or stochastic. Finally, performance measures refer to the different objective 
functions to be optimized. These include, for example, minimizing ship waiting, 
handling (service), and tardiness times. All of these objectives are indirect measures 
of the cost, and hence, they ultimately lead to minimizing the total cost. 
Most prior approaches to the berth allocation problem are focused on container 
ships rather than tanker ships. However, berth allocation problems for the two types of 
ships are different because container ships are loaded by cranes, while tanker ships are 
loaded by pumps. In fact, berth allocation of container ships is often combined with 
both the quay crane assignment problem and the quay crane scheduling problem. 
Moreover, Bierwirth and Meisel (2010, 2015) include the number and the work 
schedules of cranes assigned to each vessel within the handling properties used to 
classify berth allocation problems. The main difference between pumps and cranes is 
that cranes are general-purpose tools that can be used to load any cargo, while pumps 
are special-purpose tools that are dedicated for speciﬁc ﬂuid products. Moreover, 
container ships can mix various products in the same space, while tanker ships have 
separate tanks for storing individual liquid products. Figure 10.2 shows crude oil 
tankers at berths in an oil export terminal.
In this chapter, a real-life berth allocation optimization problem is considered for 
loading tanker ships in a large oil export terminal. The terminal is operated by a 
specialized port management company that provides bulk handling, terminal, and 
material management services to several nearby oil and petrochemical companies. 
The terminal contains a limited number of berths that are dedicated for exporting 
many oil and petrochemical products to international customers overseas. The 
terminal is used for export purposes only, and hence, no off-loading takes place 
at the terminal. Each berth has a certain subset of the products available for loading, 
and a certain pumping rate (loading speed) for each available product. Occasionally, 
the set of products required by a given tanker cannot be supplied from a single berth. 
Therefore, arriving tankers are served by loading the required products from either 
one or two applicable berths. 
The capacity of the export terminal is considered sufﬁcient to meet the require-
ments of the current volume of incoming tankers. However, the terminal has expe-
rienced congestions and lengthy delays for tanker ships waiting to be loaded with 
the required products. This problem has led to signiﬁcant ﬁnancial losses for the 
terminal due to the heavy penalties (demurrage charges) paid for delayed tankers.
240
10
Berth Allocation for Loading Tankers at an Oil Export Terminal
Fig. 10.2 Crude oil tankers at berths at an oil export terminal. Courtesy of Saudi Aramco, copyright 
owner
This problem is mainly attributed to the lack of an efﬁcient method to assign tankers 
to different berths and to sequence the assigned tankers on each berth. In order to 
avoid service delays and the resulting ﬁnancial losses, the terminal’s management 
aims to improve berth allocation and tanker scheduling. The speciﬁc goals are to 
minimize the waiting time of tanker ships at the anchorage area and to maximize the 
speed of loading the required products at the assigned berths. 
In order to achieve the above-stated goals, a mixed-integer linear programming 
(MILP) model is formulated to optimize tanker berth allocation at the terminal and 
tanker sequencing at each berth. The objective is to minimize the total delay duration 
of incoming tankers, which is the time delay from tanker arrival to the start of service. 
Compared to previous berth allocation models, the new MILP model has several 
advantages and unique additional features. First, this model deals with tanker ships 
loaded by pumps instead of container ships loaded by cranes. Second, the MILP 
model deals with multiple oil and petrochemical products, with different product 
demands for each tanker and different product supplies at each berth. An important, 
realistic, and unique feature of the model is allowing each tanker with multiple-
product demands to be assigned to two berths, in order to sequentially load two 
different sets of the demanded products. 
The remaining sections of this chapter are organized in the following order. In 
Sect. 10.2, a brief overview is presented of the relevant literature on berth allocation 
and ship loading. In Sect. 10.3, a general description is given of the export terminal’s 
current setup and its berth allocation problem characteristics. In Sect. 10.4, the mixed-
integer linear programming (MILP) model for optimizing berth allocation and tanker 
sequencing is presented. In Sect. 10.5, the given data and the details of the export
10.2 Review of Relevant Literature
241
terminal’s berth allocation case study are speciﬁed, and the optimum solution by 
the MILP model is described. Finally, a summary and relevant conclusions and 
recommendations are provided in Sect. 10.6. 
10.2 
Review of Relevant Literature 
There is a large body of literature on berth allocation and related problems in marine 
terminals. Consequently, several literature surveys have been performed that focus 
on various aspects of this active area of research. Steenken et al. (2004) provide 
an early wide-scope review and classiﬁcation of operations research models for 
optimizing container terminal operations. The berth allocation problem is reviewed as 
one of three functions of the ship planning process, namely berth allocation, stowage 
planning, and crane split. Later, Stahlbock and Voß (2008) update this general review 
to include papers published until 2007. 
A number of review papers also cover the berth allocation problem within the 
wider domain of seaside operational problems in container terminals. These papers 
cover three related problems: berth allocation problem (BAP), quay crane assignment 
problem (QCAP), and quay crane scheduling problem (QCSP). Bierwirth and Meisel 
(2010, 2015) survey and classify the literature on BAP, QCAP, and QCSP up to 
2014, while Carlo et al. (2015) review and classify the literature on these three 
related problems between 2004 and 2012. These review papers use the previously 
mentioned BAP classiﬁcation proposed by Bierwirth and Meisel (2010), which is 
based on spatial, temporal, handling times, and performance measure characteristics. 
The only review of literature that is dedicated solely to the berth allocation 
problem (BAP) in container terminals is provided by Theofanis et al. (2009). The 
BAP models are classiﬁed according to the following criteria: (a) discrete versus 
continuous berthing space, (b) static versus dynamic vessel arrivals, and (c) static 
versus dynamic handling (service) time. Rodrigues and Agra (2022) perform a recent 
survey of literature on the berth allocation problem (BAP) under uncertainty. Their 
survey covers stochastic models and algorithms for three versions of BAP: pure BAP, 
BAP integrated with QCAP, and BAP integrated with QCCP. 
The literature survey presented in this section is concerned with the berth alloca-
tion problem (BAP) for tanker ships in oil and petrochemicals export terminals. This 
survey has three points of focus. First, while all the previous literature reviews focus 
on BAP models for container vessels that are loaded by cranes, this survey focuses 
on BAP models for tanker ships that are loaded by pumps. Second, since previous 
reviews cover the literature up to 2014, this survey is mainly directed toward recent 
literature published since 2015. Finally, as the main objective of the case study 
presented later in this chapter is to minimize the total demurrage (delay costs) of 
arriving ships, the literature survey in this section emphasizes models for minimizing 
the delay time and associated demurrage penalty costs. 
The basic berth allocation problem (BAP) considers the assignment of a certain 
number of vessels to a speciﬁc number of berths, given the vessel arrival and handling
242
10
Berth Allocation for Loading Tankers at an Oil Export Terminal
times. In the basic BAP, each vessel can visit any berth, and it must be assigned to 
only one berth. The basic BAP does not consider multiple-product vessel demands or 
multiple-product berth availabilities. Buhrkal et al. (2011) compare three important 
MILP models of this problem, whose objective is to minimize the total handling 
(service) and waiting times of all vessels. These models are the dynamic berth allo-
cation problem (DBAP) model developed by Imai et al. (2001) and improved by 
Monaco and Sammarra (2007), the heterogeneous vehicle routing problem with 
time windows (HVRPTW) model of Cordeau et al. (2005), and the generalized set-
partitioning problem (GSPP) model of Christensen and Holst (2008). Based on a 
computational study, Buhrkal et al. (2011) show that the GSPP model signiﬁcantly 
outperforms the two other models. 
More recent mathematical optimization models have been proposed for berth 
allocation of container ships. For example, Ribeiro et al. (2016) present a mixed-
integer linear programming (MILP) model for a unique berth allocation problem 
(BAP) in an iron ore port. This BAP model is based on three main assumptions: Berth 
maintenance activities are performed during the planning period, tardiness penalties 
(demurrage) are paid, and earliness rewards (despatch) are earned. An adaptive large 
neighborhood search (ALNS) heuristic is used to minimize the overall net cost, i.e., 
total demurrage minus total despatch. 
Perez and Jin (2020) develop a mathematical optimization model to simultane-
ously assign the berth and the handling equipment for each vessel. Based on the 
cargo type, specialized equipment such as conveyors and pipelines are assigned to 
each vessel in order to minimize the total stay (turnaround time) at the port. A 
MILP model is formulated by Liu et al. (2021) for the integrated berth allocation and 
vessel sequencing for entering and leaving a port with a one-way navigation channel. 
An adaptive large neighborhood search (ALNS) heuristic is used to minimize the 
weighted dwelling time of all vessels. 
Several recent mixed-integer linear programming (MILP) models speciﬁcally 
address berth allocation for tanker ships in oil and petrochemical terminals. For 
example, Rodrigues et al. (2016) develop a MILP model for routing and scheduling 
tankers used in the pickup of oil from offshore platforms and its delivery to coastal 
terminals. The objective is to minimize the sum of variable fuel costs, ﬁxed berthing 
costs, and the costs of assigning individual ships to multiple berths in the same 
terminal visit. The model has several realistic features such as product inventory 
levels at the terminals, time windows, heterogeneous ﬂeet, and speciﬁc terminal 
berthing rules. However, the model only considers the total tanker capacity, without 
assuming individual capacities for the various products. Since the optimum solution 
is difﬁcult, two heuristics based on relax-and-ﬁx and time decomposition techniques 
are developed to efﬁciently solve the MILP model. 
Yamashita et al. (2019) analyze the same actual problem addressed by Rodrigues 
et al. (2016). However, they propose an effective multi-start heuristic to solve larger 
realistic instances. The new heuristic alternates between two phases until a stopping 
criterion is satisﬁed: (1) a constructive phase to generate a feasible solution and (2) 
an improvement phase using dispatching, insertion, and exchange moves to ﬁnd a 
better solution. Ye et al. (2017) consider the integrated scheduling and routing of
10.2 Review of Relevant Literature
243
heterogeneous vessels for shipping reﬁned oil products from one export terminal to 
many demand locations. The objective is to minimize the total shipping cost while 
satisfying the demand, supply, safety, and berth allocation constraints. A two-step 
solution approach is proposed. First, a continuous-time MILP model is used to assign 
tasks to vessels, and then a discrete-time MILP model is used to assign times to tasks. 
Cankaya et al. (2019) develop two models to optimize the scheduling of chemical 
tankers in the port of Houston: a mixed-integer programming (MIP) model and 
a constraint programming (CP) model. The objective is to minimize the overall 
makespan, assuming each tanker can visit multiple berths for loading and unloading 
several chemical products. However, there is no explicit consideration of multiple 
products in the two models. Although each tanker ship is allowed to visit multiple 
berths, this is not explicitly related to satisfying the ship’s demands for the different 
products. The MIP model is better for the short-term horizon, while the CP model is 
better for the long-term horizon, and both are much better than the simple ﬁrst-come, 
ﬁrst-served (FCFS) rule which used to be applied in the port. 
Cruz et al. (2019) construct a MILP model for an integrated berth allocation, 
ﬂeet composition, and periodic routing problem. The model is used to manage a 
heterogeneous ﬂeet of vessels that supplies offshore platforms used in oil and gas 
exploration and production operations in Brazil. To minimize the total ﬁxed costs 
and routing costs of the vessels, a heuristic is developed that sequentially solves 
decreasingly simpliﬁed versions of the model. Vieira et al. (2021) extend the work 
of Cruz et al. (2019) by removing the assumptions of having platform clusters that 
must be served together and by considering a cyclical planning horizon. An exact 
branch-and-cut algorithm is proposed for solving smaller problems, and an adaptive 
large neighborhood search heuristic is proposed for solving larger problems. Chagas 
et al. (2022) improve the work of Vieira et al. (2021) by considering time windows, 
using a three-stage procedure to balance the time interval between weekly visits. 
Ladage et al. (2021) formulate a MILP model of the single-ship pickup and 
delivery problem with pickup time windows, tank allocations, and changeovers. 
In this problem, a multi-compartment chemical tanker picks up multiple chemicals 
from several ports and delivers them to different ﬁnal destinations. Assuming the 
chemical tanker is allocated to a single berth in each port, the model maximizes 
the proﬁt by determining the ports to visit and the arrival times and the amounts of 
products to load for each port. 
When the data is not fully known with certainty, given input values are represented 
by probability distributions, and stochastic optimization techniques are used to solve 
the berth allocation problem. Zhai et al. (2019) apply a two-step procedure combining 
optimization and simulation to schedule tanker operations in multiple terminals. 
For simplicity, multiple products and multiple-terminal visits are not considered. 
Zolkeﬂey et al. (2021) build a simulation model to evaluate different tanker berth 
allocation policies in a Malaysian oil port. The ﬁrst-come ﬁrst-served policy, with no 
regard to vessel size or cargo type, is found to be the most effective, and it is expected 
to reduce the waiting time by 17% and the demurrage cost by 21%. Yao et al. (2021) 
use a simulation model to analyze tanker trafﬁc ﬂow and evaluate alternative layouts 
for 8 new crude oil berths in a large marine terminal in China.
244
10
Berth Allocation for Loading Tankers at an Oil Export Terminal
In this chapter, a unique real-life berth allocation problem is considered, and 
a new mixed-integer linear programming (MILP) model is formulated to solve this 
problem. Compared to the above-reviewed previous literature, the model presented in 
this chapter is mainly distinguished by the following features: (1) multiple-product 
demands are explicitly considered for each tanker, (2) multiple-product supplies 
(availabilities) are explicitly considered for each berth, and (3) each tanker can be 
allocated to either one or two berths. The real-life berth allocation problem for the 
oil export terminal is described in the following section. 
10.3 
Problem Description 
The berth allocation problem under consideration pertains to a large marine terminal 
in the Middle East, which is used for exporting crude oil as well as reﬁned products 
and petrochemicals to international customers in various parts of the world. During 
the years 2014–2016, more than 54 million tons of oil and petrochemical products 
have been exported through this terminal. The terminal is operated by a specialized 
port management service company, which also operates another export terminal in 
the same country. At this particular terminal, the company provides bulk handling, 
terminal, and material management services for 18 petroleum and petrochemical 
companies located nearby. 
The export terminal under consideration contains 8 berths that can supply a total 
of 42 different products from 99 storage tanks. Each berth has between 20 and 25 
different product outlets, except for one berth that is dedicated to only 5 liquiﬁed 
petroleum gas (LPG) products (propane, butane, isobutane, and 2 mixtures of these 
gases). The other (liquid) products are distributed among the 7 remaining berths based 
on their relative demands and shipment frequencies. Therefore, some of the products 
are available in most of the berths, while some others are available only in a single 
berth. Furthermore, the 8 berths are divided into types: 3 single-cargo berths and 
5 multi-cargo berths. This division is based on the berth’s structure, which allows 
it to accommodate either single-cargo tankers ships or multi-cargo tanker ships. 
Obviously, the combination of product demands by each tanker is a primary factor 
for allocating it to the relevant berths. 
Given the above physical setup, the terminal has sufﬁcient capacity to efﬁciently 
and timely handle the current ﬂow of visiting tankers. However, the terminal in 2015 
lost approximately $100 million due to non-efﬁcient operations. These losses resulted 
mainly from demurrage and lost opportunity costs, as the berths were practically 
always busy. This congestion at the terminal forced most incoming tankers to queue 
up and wait for their turns to be loaded with oil and chemical products. Besides the 
terminal’s direct losses resulting from delay penalties (demurrage), the terminal’s 
customers also incur indirect losses due to their delays. 
In order to avoid or at least reduce these losses, extensive data has been collected 
and analyzed to ﬁnd the causes of these delays. The amount of data that has been 
analyzed for the terminal’s operations is quite large, as it contains more than 8200
10.3 Problem Description
245
product loading services during the years 2014, 2015, and 2016. During these three 
years, 698 tankers made 3009 visits to the terminal, and they were loaded with more 
than 54 million tons of products. The raw data was extracted from the Terminal 
Management System, which is used to keep and manage the records and schedules 
of the shipments. 
Based on a careful analysis of the terminal’s data for the years 2014–2016, it 
is clear that the demurrages occurred because of the long waiting time to assign a 
tanker to a speciﬁc berth. The average waiting times for the 8 berths are shown in 
Fig. 10.3, and they range from 2.9 days to 5.4 days. These long waiting times are 
primarily due to the long loading and non-loading times for each tanker. While at the 
berth, the services provided to each tanker ship are classiﬁed into two main activ-
ities: non-loading services and loading services. Non-loading services include port 
authority ship inspection, customs clearing procedures, ship tank cleaning, quality 
check, and after-loading inspection. Loading services consist mainly of the transfer 
of the required products from the terminal’s tanks to the ship’s tanks. 
The long service time is the essential problem for the terminal, and it leads to high 
occupancy rates that range from 43% to 90% for the 8 berths. Berth occupancy time 
is calculated from the time a tanker moors at the berth until it unmoors and leaves the 
berth. Since the current tanker scheduling method is the ﬁrst-come ﬁrst-served rule, 
each tanker will only moor at the berth if the berth is vacant and ready to offer service. 
This means that the waiting time of the tankers at the terminal is directly affected by 
the berths’ occupancy. Clearly, berth occupancy rates should be minimized in order 
to reduce the long waiting times and the associated delay costs of arriving tankers. 
The non-loading time is considered as non-operational time, as it is the time taken 
by the concerned government agencies such as the port authority, customs, and the
Fig. 10.3 Average waiting time in days per tanker for each berth 
246
10
Berth Allocation for Loading Tankers at an Oil Export Terminal
coast guards. Obviously, the terminal management does not have full control over 
the non-loading time due to the involvement of other parties. On the other hand, the 
loading time is considered operational because it is directly related to the operations 
and fully under the control of the terminal’s management. The loading time and the 
waiting time can be reduced by improving berth allocation and tanker sequencing. 
Based on the above observations, the scheduling and allocation of the tankers 
to the berths must be improved in order to optimize the tanker loading process. 
The simple ﬁrst-come ﬁrst-served rule cannot efﬁciently handle the high number of 
visiting tankers and the large volume of exports at the terminal. A more effective 
berth allocation and tanker sequencing technique should be used. Therefore, an opti-
mization model is constructed to minimize the waiting time of the assigned tankers. 
Minimizing the waiting time will reduce the delay time, and hence minimize the total 
demurrage cost. The mathematical optimization model for berth allocation at the oil 
export terminal is presented in the following section. 
10.4 
Berth Allocation Optimization Model 
This section presents a new mixed-integer linear programming (MILP) model for 
optimizing berth allocation and tanker scheduling at the oil export terminal. This 
unique model has two main advantages over previous berth allocation models in the 
literature. First, the new model explicitly considers different demands for multiple 
products by each tanker and different supplies of multiple products at each berth. 
Second, the model allows each tanker to be assigned to either one or two berths. 
Naturally, the proposed MILP model determines the sequence of tankers for each 
berth. Moreover, if any tanker is assigned to two berths, then the model also speciﬁes 
the sequence of berths for the tanker and the set of products to load at each assigned 
berth. 
The components of the MILP model are described below, including the assump-
tions, indices, given parameters, decision variables, objective function, and applicable 
constraints. 
10.4.1 
Model Assumptions 
1.
Each tanker vessel has different given demands for a speciﬁc set of multiple 
products. These demands must be fully satisﬁed. 
2.
Each tanker can be assigned either to one berth to load all demanded products, 
or to two berths to load two different subsets of products. 
3.
A tanker’s demand for each individual product must be fully loaded in a single 
berth, i.e., one product loading cannot be split between two berths. 
4.
Each tanker has a given loading (intake) rate for each product. 
5.
Each tanker has a given arrival time at which it reaches the terminal.
10.4 Berth Allocation Optimization Model
247
6.
Each berth has different given supplies for a speciﬁc set of multiple products. 
7.
Each berth has a given loading (pumping) rate for each product. 
8.
Each berth has a given starting (availability) time at which it can receive 
incoming tankers. Before that time, the berth is either undergoing maintenance 
or is occupied by previously scheduled tankers. 
9.
Each berth can accommodate only one vessel at a time, regardless of the size 
of the vessel. 
10. For each tanker-berth assignment, the loading rate of each product is the 
minimum of the tanker’s intake rate and the berth’s pumping rate. 
11. For each tanker-berth assignment, all the applicable products are loaded 
simultaneously in different compartments (tanks) within the tanker. 
12. For each tanker-berth assignment, there is a ﬁxed non-loading time of 10 h. 
13. The demurrage penalty is charged per hour, starting from 6 h after arrival at the 
terminal until the tanker is moored at a berth. 
14. If a tanker is assigned to a second berth, then a second demurrage penalty is 
charged per hour, starting from the end of loading on the ﬁrst berth until the 
tanker is moored at the second berth. 
10.4.2 
Model Indices 
v 
Vessel (tanker), v = 1, …, V; 
b Berth, b = 1, …, B; 
p Product, p = 1, …, P; 
s 
Sequence position, s = 1, …, S. 
10.4.3 
Given Parameters 
Ib
Starting (initial availability) time of berth b; 
Av
Arrival time of vessel v, A1 ≤ A2 ≤ … ≤ AV ; 
Rvp
Demand (tons) by vessel v for product p; 
Gvbp 
Pumping rate of product p from berth b to vessel v (tons/hr); 
Bv
Set of berths that supply the products demanded by vessel v; 
Pvb
Set of products demanded by vessel v and supplied by berth b; 
Lvbp 
Loading time of product p in vessel v at berth b, Lvbp = Rvp/Gvbp; 
C
Demurrage cost ($/hr) per hour for each vessel; 
B
Total number of berths; 
P
Total number of products; 
S
Maximum number of vessels assigned to a single berth; 
V
Total number of vessels arriving during the planning period.
248
10
Berth Allocation for Loading Tankers at an Oil Export Terminal
10.4.4 
Decision Variables 
StartL a
yout
 1s t Row u p er X Subscri pt italic vb ps  Ba seline  e qua ls St artLayou t 
Enl
arged left brace 1st Row 1 comma if vessel v is assigned to position s at berth b to load product p 2nd Row 0 comma otherwise semicolon EndLayout EndLayout
Start L
ayou
t 1 st Row up per  U Subscr ipt  italic v bs  Ba seline  e
qua
ls StartLayout Enlarged left brace 1st Row 1 comma if vessel v is assigned to position s at berth b 2nd Row 0 comma otherwise semicolon EndLayout EndLayout
Start L
ayou
t 1 st Row  u ppe r Q 1 Sub script it alic v b Ba seline eq
ual
s StartLayout Enlarged left brace 1st Row 1 comma if berth b is the first assigned berth for vessel v 2nd Row 0 comma otherwise semicolon EndLayout EndLayout
Start L
ayou
t 1 st Row  u ppe r Q 2 Subsc ript ital ic vb Base line eq ua
ls 
StartLayout Enlarged left brace 1st Row 1 comma if berth b is the second assigned berth for vessel v 2nd Row 0 comma otherwise semicolon EndLayout EndLayout
Hvbs
Holding (loading plus non-loading) time of vessel v if assigned to berth b at 
sequence position s; 
T vbs
Start time of vessel v if it is assigned to berth b in sequence position s; 
Evb
Finish time of vessel v on the ﬁrst assigned berth b; 
W1vb 
Waiting time of vessel v for the ﬁrst berth b (beyond 6 h); 
W2vb 
Waiting time of vessel v for the second berth b; 
Dv
Delay (total waiting) time of vessel v; 
Y vbs
Product term of decision variables: Y vbs = Uvbs × ∑
j/=v(T jb,s−1 + Hjb,s−1). 
10.4.5 
Objective Function 
The objective function (10.1) of the MILP model is to minimize the total demurrage 
cost for all vessels: 
Minimize up per 
Z
 
equa
ls 
upper C sigma summation Underscript v equals 1 Overscript upper V Endscripts upper D Subscript v Baseline period
The above objective function is minimized subject to several applicable constraints 
that are presented below. The constraints are used to keep the model linear and to 
ensure the solution is feasible. 
10.4.6 
Constraints 
The constraints of the berth allocation optimization model can be classiﬁed into 
two main types: general constraints and two-berth assignment constraints. The latter 
type of constraints constitutes the main distinguishing feature of the berth allocation 
MILP model presented in this chapter. These constraints are required to ensure that
10.4 Berth Allocation Optimization Model
249
vessels can be feasibly assigned to two berths, by allowing any vessel to sequentially 
visit two berths in order to load two different sets of the required products. 
Constraints (10.2) ensure that each vessel-product pair is uniquely assigned to 
one and only one berth-position pair, i.e., to only one berth and one position in the 
sequence of vessels on that berth:
s
igma 
s
u
mmat
ion Un dersc
ript b elemen
t of upper B Subscript v Baseline Endscripts sigma summation Underscript s equals 1 Overscript upper S Endscripts upper X Subscript italic vbps Baseline equals 1 comma for all v comma for all p element of upper P Subscript italic vb Baseline period
Constraints (10.3) guarantee that no more than one vessel is assigned to each 
berth-position-product combination: 
s
i
gma 
summat ion U nde rscrip t v  equals  1 
Overscript upper V Endscripts upper X Subscript italic vbps Baseline less than or equals 1 comma for all b element of upper B Subscript v Baseline comma for all p element of upper P Subscript italic vb Baseline comma for all s period
Constraints (10.4) and (10.5) are used to make sure that the binary decision vari-
ables Uvbs behave as deﬁned. Constraints (10.4) ensure that Uvbs takes the value of 
1 if vessel v is assigned to berth b in sequence position s, while constraints (10.5) 
ensure that Uvbs takes the value of 0 otherwise:
s
igma s
ummati on U nderscri
pt p el ement of
 upper P Subscript v b Baseline Endscripts upper X Subscript italic vbps Baseline less than or equals upper M times upper U Subscript italic vbs Baseline comma for all v comma for all b element of upper B Subscript italic v Baseline comma for all s
s
igma s
ummati on Under
script p elem ent
 of upper P Subscript italic vb Baseline Endscripts upper X Subscript italic vbps Baseline greater than or equals upper U Subscript italic vbs Baseline comma for all v comma for all b element of upper B Subscript italic v Baseline comma for all s period
Constraints (10.6) relate the binary decision variables Uvbs, Q1vb, and Q2vb, by  
dividing berth assignments for a given vessel into a ﬁrst berth and a possible second 
berth: 
s
i
gma 
summa tion Un derscrip
t s equ als 1
 Overscript upper S Endscripts upper U Subscript italic vbs Baseline equals upper Q 1 Subscript italic vb Baseline plus upper Q 2 Subscript italic vb Baseline comma for all v comma for all b element of upper B Subscript italic v Baseline period
The following constraints are used to prevent assigning more than one vessel to 
the same position on the same berth: 
s
i
gma 
summa tion
 Un derscr ipt
 v equals 1 Overscript upper V Endscripts upper U Subscript italic vbs Baseline less than or equals 1 comma for all b element of upper B Subscript italic v Baseline comma for all s period
The following constraints ensure that vessels are assigned to berths in natural 
order, i.e., to sequence position 1, and then to 2, and last to position S:
250
10
Berth Allocation for Loading Tankers at an Oil Export Terminal
s
i
gma 
summa ti
o
n
 Und
erscript 
v e quals 1 Over s c r i pt uppe
r V Endscripts upper U Subscript italic vbs Baseline greater than or equals sigma summation Underscript v equals 1 Overscript upper V Endscripts upper U Subscript italic vb comma s plus 1 Baseline comma for all b element of upper B Subscript v Baseline comma s equals 1 comma ellipsis comma upper S minus 1 period
Constraints (10.9) ensure that the ﬁrst berth and the second berth for any vessel 
v are not the same. It should be noted that constraints (10.9) impose a limit of 1 on 
the right-hand side of constraints (10.6). Therefore, constraints (10.9) also guarantee 
that each vessel v is assigned no more than one position s on the same berth b: 
upper  Q 1 Su bscri
pt ital ic vb
 Baseline plus upper Q 2 Subscript italic vb Baseline less than or equals 1 comma for all v comma for all b element of upper B Subscript italic v Baseline period
Constraints (10.10) ensure that each vessel v is assigned to one and only one ﬁrst 
berth:
s
igma 
summa tion 
Und
erscript b element of upper B Subscript italic v Baseline Endscripts upper Q 1 Subscript italic vb Baseline equals 1 comma for all v period
The following constraints guarantee that no more than one second berth is assigned 
to any vessel v:
s
igma 
summa tion
 Un
derscript b element of upper B Subscript italic v Baseline Endscripts upper Q 2 Subscript italic vb Baseline less than or equals 1 comma for all v period
Constraints (10.12) relate vessel start times to their berth assignments and posi-
tions in the loading sequence on the given berth. If vessel v is not an assigned to berth 
b at sequence position s, then its corresponding start time for this berth-sequence pair 
is set equal to 0: 
upper  T S ubscript  italic  vbs 
Bas
eline less than or equals upper M times upper U Subscript italic vbs Baseline comma for all v comma for all b element of upper B Subscript italic v Baseline comma for all s period
The precedence constraints shown below ensure the correct sequencing of all 
vessels assigned to each berth. For berth b, the start time of vessel v at sequence 
position s must be after the ﬁnish time of the previous vessel j (j /= v) at sequence 
position s − 1. The ﬁnish time of vessel j is equal to its start time T jb,s−1 plus its 
holding time Hjb,s−1, which is loading time plus 10 h of non-loading time: 
upper  T Subs
c
r
ipt i talic
 
vbs sase line grea
t
er
 than o r equa ls  upp e r  U  Subscript italic vbs Baseline sigma summation Underscript j equals 1 left parenthesis j not equals v right parenthesis Overscript upper V Endscripts left parenthesis upper T Subscript italic jb comma s minus 1 Baseline plus upper H Subscript italic jb comma s minus 1 Baseline right parenthesis comma for all v comma for all b element of upper B Subscript italic v Baseline comma s equals 2 comma ellipsis comma upper S period
Obviously, the above constraints are not linear due to the presence of the second-
order term Ujb,s−1
∑(T jb,s−1 + Hjb,s−1), which is the product of the binary variable 
Uvbs and the sum of continuous variables ∑(T jb,s−1 + Hjb,s−1). To remove nonlin-
earity, this product term is replaced by a new variable, Y vbs = Uvbs
∑
j/=v(T jb,s−1 + 
Hjb,s−1), as shown in the revised constraints below:
10.4 Berth Allocation Optimization Model
251
upper  T Subsc
ript it alic v bs  Bas e l i n e g
reater than or equals upper Y Subscript italic vbs Baseline comma for all v comma for all b element of upper B Subscript italic v Baseline comma s equals 2 comma ellipsis comma upper S period
Moreover, the three following linear constraints are added to ensure that the value 
of the new variable Y vbs is indeed equal to the product of the two terms: Uvbs and
∑
j/=v(T jb,s−1 + Hjb,s−1): 
upper  Y S ubscript
 italic  vbs B as elin e  l e ss
 than or equals upper M times upper U Subscript italic vbs Baseline comma for all v comma for all b element of upper B Subscript italic v Baseline comma s equals 2 comma ellipsis comma upper S
upper  Y
 
S
ubscr ipt i
t
alic vbs  Baseline
 
l
ess tha n or e qu als s i g m a 
summation Underscript j equals 1 left parenthesis j not equals v right parenthesis Overscript upper V Endscripts left parenthesis upper T Subscript italic jb comma s minus 1 Baseline plus upper H Subscript italic jb comma s minus 1 Baseline right parenthesis comma for all v comma for all b element of upper B Subscript italic v Baseline comma s equals 2 comma ellipsis comma upper S
upper  Y
 
S
ubscr ipt i
t
alic vbs  Baseline
 
greate r than or
 equals  sigma  s umma t i o n  Und
erscript j equals 1 left parenthesis j not equals v right parenthesis Overscript upper V Endscripts left parenthesis upper T Subscript italic jb comma s minus 1 Baseline plus upper H Subscript italic jb comma s minus 1 Baseline right parenthesis minus upper M left parenthesis 1 minus upper U Subscript italic vbs Baseline right parenthesis comma for all v comma for all b element of upper B Subscript italic v Baseline comma s equals 2 comma ellipsis comma upper S period
Assuming parallel (simultaneous) loading of the different products on each vessel, 
constraints (10.17) set the holding time for any vessel equal to the maximum product 
loading time plus 10 h of non-loading time: 
upper H S
u
bscri pt i
t
alic jb co
mma s  minus 1  Ba sel ine gr eater t ha n or  e q u als 
left parenthesis upper L Subscript italic jbp Baseline plus 10 right parenthesis upper X Subscript italic jbp comma s minus 1 Baseline comma for all j left parenthesis j not equals v right parenthesis comma for all b element of upper B Subscript j Baseline comma p element of upper P Subscript italic jb Baseline comma s equals 2 comma ellipsis comma upper S period
The starting time of each vessel assigned to a given berth must be after the initial 
availability (opening) time of the berth. Constraints (10.18) enforce this restriction 
for the ﬁrst vessel in the berthing sequence on the berth. Vessels in later sequence 
positions on the given berth will automatically start later due to the transitive effects 
of precedence constraints (10.13): 
upper  T Subscr
ipt ita lic v
b 1 Baseline greater than or equals upper I Subscript italic b Baseline upper U Subscript italic vb 1 Baseline comma for all v comma for all b element of upper B Subscript italic v Baseline period
The following constraints guarantee that the starting time of each vessel v, at  
position s on berth b, is after the vessel’s arrival time: 
upper  T Subscri
pt ital ic vbs  sa
seline greater than or equals upper A Subscript italic v Baseline upper U Subscript italic vbs Baseline comma for all v comma for all b element of upper B Subscript italic v Baseline comma for all s period
Excess waiting time for the ﬁrst berth W1vb is counted if an arriving vessel is 
not moored at a berth within 6 h of its arrival, thus W1vb = max(0, T vbs − Av − 
6). The constraints below ensure that the value of W1vb is correctly calculated, and 
that this value is counted only if vessel v is assigned to berth b. Since W1vb ≥ 0, 
excess waiting time is zero if the vessel is moored within 6 h of its arrival, i.e., if the 
right-hand side is negative. The term M(1 − Q1vb) is added on the left-hand side of 
(10.20) to make sure these constraints are active only if berth b is the ﬁrst assigned 
berth for vessel v:
252
10
Berth Allocation for Loading Tankers at an Oil Export Terminal
uppe r M left  parenth e
s i
s
 1 m
inus
 
upper Q 1 Subsc
ript it alic 
vb Baseline right parenthesis plus upper W 1 Subscript italic vb Baseline greater than or equals left parenthesis sigma summation Underscript s equals 1 Overscript upper S Endscripts upper T Subscript italic vbs Baseline right parenthesis minus left parenthesis upper A Subscript italic v Baseline plus 6 right parenthesis upper Q 1 Subscript italic vb Baseline comma for all v comma for all b element of upper B Subscript italic v Baseline period
For the second berth, the waiting time W2vb is the time duration from the end of 
loading on the ﬁrst berth Evb to the start of loading on the second berth T vbs. This is  
expressed in the constraints below, where the term M(1 − Q2vb) is added to ensure 
that these constraints are active only if berth b is actually the second berth assigned 
to vessel v: 
uppe r M left  parenth e
s i
s
 1 m
inus
 
upper Q
 2 Subs cript
 italic vb Baseline right parenthesis plus upper W 2 Subscript italic vb Baseline greater than or equals left parenthesis sigma summation Underscript s equals 1 Overscript upper S Endscripts upper T Subscript italic vbs Baseline right parenthesis minus upper E Subscript italic vb Baseline comma for all v comma for all b element of upper B Subscript italic v Baseline period
The ending time of vessel v on the ﬁrst berth Evb is equal to its start time T vbs plus 
its holding time Hvbs on that berth. This is expressed in constraints (10.22) below. 
Similar to constraints (10.20), the term M(1 − Q1vb) is added on the left-hand side 
of (10.22) to make them active only if berth b is the ﬁrst assigned berth for vessel v: 
uppe r M left  paren th
e
s
is 1
 minus  upper Q 
1 Subsc ript 
italic vb Baseline right parenthesis plus upper E Subscript italic vb Baseline greater than or equals sigma summation Underscript s equals 1 Overscript upper S Endscripts left parenthesis upper T Subscript italic vbs Baseline plus upper H Subscript italic vbs Baseline right parenthesis comma for all v comma for all b element of upper B Subscript italic v Baseline period
If a vessel is assigned to a second berth, then its start time on the second berth 
T vbs must be after its end time on the ﬁrst berth Evb. This is expressed in constraints 
(10.23) below. As in the case of constraints (10.21), the addition of the term M(1 − 
Q2vb) ensures that these constraints are active only if vessel v is actually assigned to 
a second berth: 
uppe r M left  p
a
r
enth
esis 1 minus
 upper Q 2 S
ubscript italic vb Baseline right parenthesis plus sigma summation Underscript s equals 1 Overscript upper S Endscripts upper T Subscript italic vbs Baseline greater than or equals upper E Subscript italic vb Baseline comma for all v comma for all b element of upper B Subscript italic v Baseline period
Vessel delay time Dv, which is used to determine the demurrage cost, is the sum 
of the excess waiting times at all berths: 
up e
r
 D Su
bscrip t italic 
v B
aseline equals sigma summation Underscript b element of upper B Subscript italic v Baseline Endscripts upper W 1 Subscript italic vb Baseline plus upper W 2 Subscript italic vb Baseline comma for all v period
Finally, the following constraint speciﬁes the ranges of feasible values for all the 
decision variables: 
StartLayout 1 st Row  1st Colum n u pper X  Subsc ript italic vbps Ba seli ne co
mma 
upper U  Subsc rip t itali c v
bs Baseline comma upper Q 1 Subscript italic vb Baseline comma upper Q 2 Subscript italic vb Baseline 2nd Column equals 0 comma 1 comma upper T Subscript italic vbs Baseline comma upper H Subscript italic vbs Baseline comma upper E Subscript italic vb Baseline comma upper W 1 Subscript italic vb Baseline comma upper W 2 Subscript italic vb Baseline comma upper D Subscript italic v Baseline comma upper Y Subscript italic vbs Baseline 2nd Row 1st Column Blank 2nd Column greater than or equals 0 comma for all v comma for all b element of upper B Subscript italic v Baseline comma for all p element of upper P Subscript italic vb Baseline comma for all s period EndLayout
10.5 Berth Allocation Case Study
253
10.4.7 
Calculating the Value of S 
In order to effectively utilize the above MILP model expressed by (10.1)–(10.25), it 
is necessary to start by calculating the value of S, the maximum number of vessels 
per berth, as described below. 
First, the given data for any speciﬁc berth allocation problem must include the 
number of vessels V, the number of berths B, and the number of products P. However, 
the given data does not include the maximum number of sequence positions S. In  
order to use the model, the value of S, which is equal to the maximum number of 
tankers that can be assigned to a single berth, must be calculated from the given data. 
The value of S must be large enough to allow including all possible feasible solutions. 
At the same time, S must be kept as small as possible to avoid increasing the problem 
size and complexity. To achieve this balance, the following equation is proposed to 
heuristically calculate the minimum (initial) value of S. Numerical experiments have 
shown that this minimum value of S is quite sufﬁcient for obtaining the optimum 
solution in a variety of test problems: 
up p
er S  greater than or equals left ceiling StartFraction upper V 1 plus 2 upper V 2 Over upper B EndFraction right ceiling plus 1 comma
u
p
per 
S greater than or equals left ceiling StartFraction upper V 1 plus 2 upper V 2 Over upper B EndFraction right ceiling plus 1 comma
where 
V 1 
number of single-product-demand vessels that need only one berth and 
V 2 
number of multiple-product-demand vessels that may need two berths. 
10.5 
Berth Allocation Case Study 
The above MILP model has been applied for optimizing berth allocation and tanker 
scheduling in the export terminal described in Sect. 10.3. Obviously, the inputs of 
the model constantly change according to the changing number and demands of the 
incoming tankers. Therefore, the model must be periodically solved using new data 
for a limited future time duration, which is called the planning period. The input 
data on the next set of arriving tankers must be periodically updated to obtain a new 
optimum solution for the current planning period. With a total of 3009 tanker arrivals 
in three years, the average number of arrivals is 2.75 tankers per day or 19.2 tankers 
per week. However, the actual rate of arrivals varies with the seasons of the year. In 
order to have up-to-date schedules and keep the problem size manageable, a planning 
period of three days (72 h) is adopted for the terminal berth allocation problem. 
Next, a sample of one of the instances of the MILP model is presented for a speciﬁc 
planning period of three days (July 25–27, 2016) at the export terminal. During this 
planning period, nine tankers were scheduled to arrive at the export terminal, whose 
data is given in Table 10.1.
Four of the tankers have multiple-product demands, while ﬁve tankers have 
single-product demands. Overall, the nine tankers collectively demand a total of
254
10
Berth Allocation for Loading Tankers at an Oil Export Terminal
Table 10.1 Data on arriving tankers during the planning period 
Tanker 
number     
v 
Arrival 
time Av 
(hr) 
Required 
Products 
Pv 
Required 
quantity Rvp 
(tons) 
Pumping 
rate Gvbp 
(tons/hr) 
Loading 
time Lvbp 
(hr) 
1
11
Methanol
7,745
759
10.2 
2
15
 
Styrene
 
7,825
 
588
 
13.3 
N-Butanol
2,105
219
9.6 
LAB
1,955
275
7.1 
3
16
Benzene
15,620
331.6
47.1 
4
24
Methanol
23,905
816
29.3 
5
39
MTBE
29,940
1,682
17.8 
6
45
Styrene
15,980
410
39 
EDC
10,460
375
27.9 
7
64
 
Styrene
 
6,300
677
9.3 
LAB
1,040
236
4.4 
8
70
Benzene
4,965
247
20.1 
9
72
 
DEG
 
830
177
4.7 
MEG
12,390
666
18.6
nine different petrochemical products, which are listed and numbered (p = 1, …, 9) 
as shown below: 
1. Benzene. 
2. Diethylene Glycol (DEG). 
3. Ethylene Dichloride (EDC). 
4. Linear Alkyl Benzene (LAB). 
5. Mono-Ethylene Glycol (MEG). 
6. Methanol. 
7. Methyl Tert-Butyl Ether (MTBE). 
8. Normal-Butyl Alcohol (N-Butanol). 
9. Styrene. 
The loading times for the various products Lvbp, which are shown in Table 10.1, 
are calculated by dividing the required quantities Rvp by the pumping rate, i.e., Lvbp 
= Rvp/Gvbp. The loading (pumping) rate for each product Gvbp is the minimum of the 
berth’s pumping capacity and the tanker’s intake capacity. For each product, however, 
the pumping capacities at the different berths are practically the same. Therefore, the 
loading time for each vessel’s given product quantity is equal for all the applicable 
berths in the terminal. 
Different subsets of the 9 required products are available in each berth in the 
terminal. Out of the 8 berths, one single-cargo berth does not supply any of the 9 
required products, and thus, it is excluded from consideration in this case study. 
For the 7 remaining berths, the time of initial berth availability Ib and the set of 
available products are given in Table 10.2. As availability times Ib are not available
10.5 Berth Allocation Case Study
255
Table 10.2 Initial times and available products at the different berths 
Berth
p
1
2
3
4
5
6
7
8
9 
b
Ib 
Benzene DEG EDC LAB MEG Methanol MTBE N-Butanol Styrene 
1
87
1
1
1
1
1
 1
 1
0
1 
2
34
1
1
1
1
1
 1
 0 
1
1
 
3
16
0 
1
1
 
0 
1
1
 1
0
1 
4
47
1
1
1
0 
1
1
 1
0
1 
5
123
1
1
1
1
1
 1
 1
0
1 
6
65
0
0
0
0
0
1
1
 0
0 
7
108
0
0
0
0
0
1
1
 0
0 
in historical records, random values were generated that are similar to typical values 
in the terminal. A clear cell with a value of “1” in Table 10.2 indicates that product 
p is available in berth b, while a bold cell with a value of “0” indicates that product p 
is not available in berth b. In any berth where a given product is available, the supply 
is more than sufﬁcient to satisfy the demands of all incoming tankers. Therefore, no 
limits are imposed on the product supplies at the associated berths. 
As previously noted, the terminal has 5 multi-cargo berths (numbered 1–5) and 3 
single-cargo berths (numbered 6–8). Berth 8 is excluded from consideration in this 
example, because it is dedicated to LPG products and does not supply any product 
required by the 9 incoming tankers. This means that multi-cargo tankers (numbered 
2, 6, 7, and 9) cannot be assigned to the remaining single-cargo berths (numbered 6 
and 7). 
According to the data given above, V = 9, B = 7, P = 9, V 1 = 5, and V 2 = 4. 
Using Eq. (10.26), the minimum value of S is given by: 
up p
er S greater than or equals left ceiling StartFraction 5 plus 2 left parenthesis 4 right parenthesis Over 7 EndFraction right ceiling plus 1 equals 3 period
u
p
per S greater than or equals left ceiling StartFraction 5 plus 2 left parenthesis 4 right parenthesis Over 7 EndFraction right ceiling plus 1 equals 3 period
Assuming S = 3 and using all the given data, the MILP model expressed by 
(10.1)–(10.25) was solved by OpenSolver for Excel, Version 2.9.3. The optimum 
solution was obtained in a few seconds, and it is given in Table 10.3 and Fig. 10.4.
The optimum objective function (minimum total delay for all tankers) is 105.1 h. 
Table 10.3 shows the optimum berth allocation and sequencing for each tanker, while 
Fig. 10.4 displays the time spans of the tanker assignments for each berth. The black 
parts in Fig. 10.4 indicate the times of initial unavailability for each berth. 
From the optimum berth allocation solution, several interesting results are 
observed. First, the two berths with late availability, namely berths 5 and 7, are 
not allocated any tankers. Second, it is obvious from Fig. 10.4 that allocating more 
than 3 tankers to any single berth will not lead to improving the solution. Therefore, 
the value of S = 3 is quite sufﬁcient for this example, and there is no need to try higher 
values of S. This conﬁrms that the lower bound on S obtained by (10.26) is quite 
effective for this particular berth allocation problem. Third, a single-cargo tanker,
256
10
Berth Allocation for Loading Tankers at an Oil Export Terminal
Table 10.3 Berth allocation and sequencing for each tanker 
Vessel 
v 
Berth 
b 
Sequence 
position 
Arrival 
time 
Start 
time 
Load 
duration 
Finish 
time 
Delay 
duration 
1
3
1
115
16
20.2
36.2
0 
2
2
1
15
34
23.3
57.3
13 
3
4
 
1
 
16
 
47
 
57.1
104.1
25
 
4
6
 
1
 
24
 
65
 
39.3
104.3
35
 
5
3
2
39
39
27.8
66.8
0 
6
3
 
3
 
45
 
66.8
 
49
115.8
15.8
 
7
2
2
64
64
19.3
83.3
0 
8
2
3
70
83.3
30.1
113.4
7.3 
9
1
 
1
 
72
 
87
 
28.6
115.6
 
9
 
Fig. 10.4 Tanker allocation and sequencing for each berth
namely tanker 4, is assigned to the single-cargo berth number 6. Finally, for this 
example, no tanker is allocated to two berths. Although visiting two berths to load 
different sets of required products is allowed, it is very costly in terms of loading and 
waiting time and the resulting demurrage cost. Therefore, the optimization model 
considers two-berth assignments for tankers as a last resort, trying to avoid them 
unless they are necessary. 
The above-described optimum solution speciﬁes new berth initial availability 
times for the future. For each berth, the new initial availability time corresponds 
to the load completion time of the last assigned tanker in the current batch. These 
values will be used for scheduling the next batch of tankers arriving after the current 
three-day planning period. To schedule the next batch of tankers, the clock must be 
reset to start from the ﬁrst hour on day 4. Therefore, the revised Ib values, shown 
in Table 10.4, are obtained by subtracting the current 3-day (72-h) planning period 
from the berth ﬁnish times.
10.6 Summary and Conclusions
257
Table 10.4 Berth initial availability times for the next batch of tankers 
Berth b 
1
2
3
4
5
6
7
 
Finish time 
115.6
113.4
115.8
104.1
123
104.3
108 
Next Ib
43.6
41.4
43.8
32.1
51
32.3
36 
10.6 
Summary and Conclusions 
This chapter presented an application of optimization models for berth allocation 
and tanker sequencing at an oil products export marine terminal. The terminal has 
multiple berths, each of which can accommodate one tanker at a time. Each tanker 
needs a speciﬁc set of required products, and each berth can provide a speciﬁc set 
of available products. Each tanker can be assigned to either one or two berths to be 
loaded with the required products. If assigned to two berths, then a tanker will visit 
them separately to load two different sets of the required products. After arrival at 
the terminal, a tanker may wait a while before it is moored at the ﬁrst berth. After 
ﬁnishing product loading at the ﬁrst berth, a tanker that is assigned to two berths may 
also wait for another while before it is moored at the second berth. The objective is 
to minimize the total delay (waiting times) for all tankers. A mixed-integer linear 
programming (MILP) model is formulated to represent and optimally solve this real-
life problem. The model is unique because it allows tankers to visit two berths, while 
explicitly considering different sets of multiple products in both tanker demands and 
berth supplies. 
The MILP model allows the terminal management to determine the optimum berth 
allocation for each tanker. The model also determines the sequence of tanker loading 
at each berth. The model has been effectively used to minimize the total delay times 
of all arriving tankers and hence signiﬁcantly reduce the total demurrage cost at the 
terminal. Although a three-day planning period is used in the case study presented 
in Sect. 10.5, the model can be used with other time periods. The planning period 
to use in other berth allocation situations depends on the number of berths and the 
frequency of tanker arrivals. In general, it is recommended to use a planning period 
of a few days in order to keep the model fairly simple to solve and also to keep the 
berthing schedules adequately updated. Once the planning period is ﬁxed, the model 
needs to be solved periodically. This is done by inputting the new data of the next 
batch of tankers scheduled to arrive during the upcoming planning period. 
In order to extend the model presented in this chapter, several logical alternatives 
are possible. For example, serial (sequential) loading of the different products on 
each tanker can be considered instead of parallel (simultaneous) loading. Another 
possibility is to introduce uncertainty regarding tanker arrival time and loading times 
into the model. Moreover, the berth allocation model can be integrated with other 
relevant problems such as tanker maintenance and berth maintenance scheduling. 
Alternatively, the berth allocation problem can be integrated with tanker routing 
and trip planning. Another interesting extension is to consider both product pickup 
(loading) at the export terminal and delivery (unloading) at import terminals. Other
258
10
Berth Allocation for Loading Tankers at an Oil Export Terminal
promising extensions include the incorporation of berth supply limits, time windows, 
and speciﬁc berthing restrictions. 
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For additional reading, the interested reader may refer to the following list of publi-
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reﬁned products. 
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for transporting multiple petroleum products using pipelines. In International 
Conference on Services Systems and Services Management (ICSSSM11) (pp. 1–6). 
IEEE.
Index 
A 
Additive algorithm, 37 
Adhesive materials, 9 
Alkylate, 157 
Ammonia, 4, 8, 14, 15, 92 
Ammonia products, 15 
Ant Colony Optimization (ACO), 48, 50 
Arab Light crude, 10 
Aromatics, 7–9, 12–14, 154 
Arrival time, 107, 111, 118, 122, 129, 142, 
238, 239, 243, 246, 247, 251, 254, 
256, 257 
Artiﬁcial Intelligence (AI), 17, 19, 20 
Asphalt, 1, 3, 12, 92, 156 
Assignment problem, 41, 79, 110, 182, 239, 
241 
Associated (wet) gas, 11 
Average time between failures, 90 
AweSim!, 144–146 
B 
Basic solutions, 32, 33 
Basic variables, 32, 33 
Benzene products, 14, 254, 255 
Berth, 237–257 
Berth-Allocation Problem (BAP), 238, 241, 
242 
Berth occupancy, 245 
Big data analytics, 19, 20 
Binary (0-1) programming, 36 
Bio-blend, 156 
Biofuels, 19, 212 
Blending, 7, 18, 153–158, 160, 162–167, 
171, 172, 174, 176 
Bounding techniques, 45 
Branch-and-bound technique, 37 
Brent crude, 10, 11 
Bulk plant, 214, 215, 217, 226 
Butadiene, 8, 9, 13, 14 
Butadiene products, 13 
Butane, 7, 8, 11, 92, 157, 244 
C 
Capacity, 5, 41, 58, 61, 64, 65, 108, 112, 
136, 153, 157, 159, 165–169, 176, 
209–213, 215–219, 221–227, 231, 
233, 237, 239, 242, 244, 254 
Car industry, 2 
Casing, 6, 62 
Catalyst, 4 
Cementing, 62 
Chance-constrained models, 44 
Coal, 2, 11, 13 
Coating materials, 9 
Computational experiments, 56, 79, 130 
Condensates, 8, 12, 15, 92 
Condition-based maintenance, 85, 86 
Constrained optimization, 27, 29, 36, 46, 52 
Constraint programming, 18, 243 
Constraints, 18, 25–27, 29, 30, 32, 34–40, 
44–47, 58, 64, 65, 68, 69, 73, 75, 77, 
107, 108, 110, 112, 114–119, 121, 
124, 129, 130, 136, 153–155, 166, 
168–173, 175, 176, 182–184, 186, 
188–201, 203–205, 207, 211, 213, 
221–226, 233, 243, 246, 248–252 
Constructive metaheuristics, 48 
Container ship, 239, 240, 242 
Convex programming, 47 
Corrosion, 10, 92, 158
© The Editor(s) (if applicable) and The Author(s), under exclusive license 
to Springer Nature Switzerland AG 2023 
H. K. Alfares, Applied Optimization in the Petroleum Industry, 
https://doi.org/10.1007/978-3-031-24166-6 
273
274
Index
Cost of lost revenue, 106 
Cracking, 2, 4, 7, 18, 105 
Crane, 101, 239–241 
Critical-Path Method (CPM), 41, 43 
Crude oil, 1, 2, 5–13, 16, 18, 19, 83, 92, 
133, 134, 153, 154, 156, 157, 163, 
176, 183, 209, 210, 212, 213, 237, 
239, 240, 243, 244, 261 
Cutting plane methods, 37 
D 
Days-off break, 179, 181, 184–186, 206, 
207 
Days-off scheduling, 134–136, 138, 143, 
144, 149–151, 182, 183, 188, 191, 
206, 207 
Decision variables, 25–27, 29–31, 36, 42, 
46, 47, 51, 57, 58, 63, 64, 68, 69, 75, 
78, 79, 85, 111, 113, 115, 117, 119, 
131, 166, 167, 171–173, 175, 176, 
186, 189, 190, 219, 225, 226, 230, 
231, 246, 248, 249, 252 
Decomposition algorithm, 58 
Demand market, 215, 217, 220, 226, 230, 
233 
Demurrage, 239, 241–248, 252, 256, 257 
Despatch, 242 
Deterministic model, 135, 137 
Deviational variables, 39 
Diesel, 7, 10, 12, 14, 92, 156, 157, 209, 
215–217, 226, 228, 230–233 
Digital twin technology, 19 
Direct search methods, 29 
Distillation, 1, 2, 7, 11, 105, 154, 157, 158 
Distribution center, 211–218, 220, 226, 
230–232 
Distribution network, 209–211, 214–216, 
219–221, 226, 270 
Downstream, 5, 7–9, 12, 13, 15, 19, 
210–214, 216, 232 
Downstream petrochemical products, 9 
Downtime, 107, 109, 212 
Drilling, 1, 2, 5, 17, 20, 25, 55–69, 73–79, 
81 
Drilling ﬂuid, 62 
Dual model, 190 
Dynamic Programming (DP), 19, 36, 42–44 
E 
Echelon, 212–216, 232, 233 
Entering variable, 32–35, 47 
Equality-constrained NLP, 46 
Equipment availability (uptime), 86 
Ethane, 7, 8, 11, 92 
Ethylene, 3, 8, 9, 13, 108, 254 
Ethylene products, 13 
Evaluation of the Heuristic, 78, 80, 123 
Evaporated at 70 (E70), 158, 160, 166, 169 
Evolutionary programming, 48 
Expansion, 19, 92, 211, 213–215, 218, 220, 
222–225, 227, 228, 230, 231 
Expected-value models, 44 
Exploration stage, 55 
Exporting, 6, 11, 237, 239, 244 
Export terminal, 7, 133, 237–241, 243, 244, 
246, 253, 257 
F 
Facility expansion, 214 
Failure history, 83, 87, 90, 91 
Failure rates, 83, 86, 88, 90, 94, 103 
Feasible solution, 26, 27, 47, 171, 173, 174, 
242, 253 
Feedstock, 7–9, 11, 13–15, 25, 26 
Fibers, 9, 13 
Field development, 5, 17, 18, 20, 57, 58, 
60, 213, 261 
First-come ﬁrst-served, 136, 243–246 
Fixed cost, 55, 218, 220, 221, 223, 243 
Fuel oil, 12, 92 
Future trends, 16, 19, 212 
Fuzzy programming, 214 
G 
Gas-Oil Separation Plant (GOSP), 5, 6, 8, 
11, 151, 179, 214 
Gasoline, 2, 7, 10–14, 92, 153–176, 209, 
215–217, 226, 227, 230–232 
Gasoline speciﬁcations, 154, 156, 158–160 
Gathering system, 5 
Generalized disjunctive programming, 108 
Genetic Algorithms (GA), 17, 18, 48, 49, 
155 
Goal programming, 36, 38, 39, 52, 109 
Gradient, 17, 19, 28, 29, 45–47 
Graphical LP solution, 30, 31 
Graph theory, 58 
H 
Heavy crude, 10, 157 
Hessian matrix, 28 
Heuristic solution, 26, 47, 56, 57, 74–81, 
107, 110, 115, 116, 119, 122, 123
Index
275
Hierarchical, 182, 212 
Horizontal drilling, 55, 64, 68, 79 
HVAC, 6, 139 
Hybrid methods, 37 
Hydrocarbons, 7–9, 11, 13, 90, 92, 154, 
157, 158 
I 
Industrial revolution, 2, 4, 20 
Inspection policy, 83, 84, 86–89, 91, 94, 95, 
98–103 
Integer programming, 17, 21, 36–38, 40, 
52, 55–58, 64, 68, 73, 74, 76, 78, 81, 
107–110, 135, 138, 156, 181, 183, 
184, 207, 211, 243 
Inventory, 42, 84, 136, 155, 164–166, 168, 
176, 212–214, 242 
Isomerate, 157, 164–166, 174, 175 
K 
Karush–Kuhn–Tucker conditions, 45, 46 
Kerosene, 2, 7, 10, 12, 92, 156, 209 
Knocking (engine), 12, 159 
L 
Labor cost, 108, 136, 137, 179, 181, 182, 
184, 185, 207, 210 
Large Neighborhood Search (LNS), 48, 
109, 242, 243 
Layout, 110, 239, 243 
Leading principal determinant, 28 
Leaving variable, 32–35 
Light crude, 10, 157 
Light hydrocarbons, 157 
Linear approximation, 46, 47, 58, 155, 
171–173, 175, 176 
Linear combination, 159, 194, 197–199, 
201, 202, 204 
Linear-constrained NLP, 46 
Linear Programming (LP), 16, 27, 29, 
30–40, 45, 52, 58, 64, 110, 171–176, 
212 
Liqueﬁed Natural Gas (LNG), 7 
Liqueﬁed Petroleum Gas (LPG), 11, 92, 
244, 255 
Loading time, 238, 246, 247, 250, 251, 254, 
257 
Local search metaheuristics, 48 
Location/allocation algorithm, 58 
Logical constraints, 68, 75, 112, 117, 
222–225, 233 
LP standard form, 31, 32, 39 
Lubricating oil, 12 
M 
Maintenance, 10, 21, 83–87, 97, 99, 102, 
105–113, 115, 117, 119–123, 
129–131, 133–146, 148–151, 183, 
242, 247, 257 
Maintenance teams, 107, 108, 110 
Makespan, 243 
Mathematical programming, 16, 18, 19, 27, 
29, 36, 44, 45, 51, 52, 56, 108, 163, 
212 
Maximum-ﬂow problem, 41 
Metaheuristic algorithms, 17 
Methane, 7, 8, 11 
Methanol products, 14 
Methyl-Tert-Butyl-Ether (MTBE), 13, 15, 
158, 164–166, 174, 175, 254, 255 
Midstream, 5–7, 12, 13, 16, 19, 212, 213 
Minimum-spanning tree, 41 
Mixed-integer linear programming, 18, 58, 
108, 137, 155, 182, 183, 211, 212, 
214, 216, 233, 240, 242, 244, 246, 
257 
Mixed-integer nonlinear programming, 18, 
19, 58, 108, 155, 213 
Model size, 81, 114, 115 
Model veriﬁcation, 147 
Monte Carlo sampling techniques, 45 
Multi-objective optimization, 18 
Multiple objectives, 38, 39, 52, 213, 233 
Multi-stage recourse models, 44 
N 
Naphtha, 2, 8, 11, 12, 15, 92, 157 
Naphthenes, 7 
Natural gas, 2, 4–9, 11, 12, 15, 17, 19, 133, 
214, 261 
Net present value, 213 
Network models, 36, 40, 41, 43 
Non-associated (dry) gas, 11 
Nonbasic variables, 32, 33 
Nonlinear Programming (NLP), 17–19, 36, 
37, 45–47, 58, 108, 153–156, 165, 
166, 171, 173, 175, 176, 213 
Non-loading time, 245–248, 250, 251 
NP-complete, 238 
Number of replications, 143, 147, 148 
Nylon, 4, 9, 14
276
Index
O 
Objective function, 19, 25–27, 29–32, 
34–36, 38, 40, 44–47, 49, 63, 64, 68, 
69, 75, 87, 112, 117, 166, 168, 171, 
173, 175, 188, 190, 192, 213, 214, 
220, 221, 230, 239, 246, 248, 255 
91-octane gasoline, 166, 174 
95-octane gasoline, 164, 166, 174 
Octane number, 12, 155, 158, 159, 162 
Offshore, 2, 5, 19, 55–64, 68, 74, 76, 78, 
79, 81, 109, 133, 179, 180, 183, 210, 
242, 243 
Oil storage, 213 
Oil transportation, 6, 135, 210, 237 
Oleﬁns, 4, 7–9, 11–13, 154 
OPEC, 10, 11 
OPEC Basket crude, 10, 11 
OpenSolver, 64, 65, 69, 73, 77, 121, 122, 
124, 255 
Operational planning, 18, 213 
Operations Research (OR), 17, 29, 35, 241 
Optimization models, 7, 16–21, 25, 37, 47, 
52, 61, 63, 65, 67, 76, 85, 107–110, 
115, 121, 129, 136, 137, 153–156, 
163, 166, 176, 181, 182, 184, 242, 
246, 248, 256, 257, 261 
Optimum solution, 26, 30, 31, 35, 42, 47, 
50, 51, 56, 58, 68, 69, 73, 77–79, 
110, 116, 121, 131, 154, 192, 193, 
211, 241, 242, 253, 255, 256 
Overhead, 62 
P 
Parafﬁns, 2, 7, 154 
Pareto-optimal solutions, 18, 213 
Particle swarm optimization, 17, 18, 48, 50, 
213, 261 
Performance measures, 39, 51, 98, 102, 
108, 124, 143, 146, 148, 239, 241 
Petroleum gas, 11, 244 
Pipeline, 2, 6, 7, 10, 19, 57–59, 81, 85, 109, 
133–136, 138–146, 149, 151, 179, 
209–211, 213–217, 226, 227, 233, 
237, 242, 261 
Planning horizon, 86, 136, 166, 211, 213, 
216–218, 222–226, 230, 243 
Plastic, 5, 9, 13, 14 
Platform, 20, 55–69, 73–79, 81, 163, 179, 
212, 242, 243, 261 
Platformate (reformate), 157, 164–166, 
174, 175 
Platform location, 57–60, 63, 66–68, 73, 
74, 77–79, 261 
Polyethylene, 3, 4, 9, 13, 14 
Polymerization, 9 
Polypropylene, 4, 9, 13 
Polystyrene, 4, 9, 14 
Population-based metaheuristics, 48 
Precedence relations, 111, 112, 118, 124, 
129 
Pre-emptive method, 40 
Pressure, 12, 17, 58, 62, 83, 84, 90, 93, 97, 
103, 105, 133, 155, 158–160, 167, 
170 
Preventive maintenance, 83, 86, 87, 99, 
109, 139, 140, 183 
Primal-dual relations, 184, 190, 207 
Primary (base) petrochemical products, 8 
Principle of optimality, 43 
Probability distribution, 41, 44, 45, 49, 51, 
90–92, 94, 97, 102, 142–145, 243 
Problem of dimensionality, 43 
Production stage, 16, 17 
Project Evaluation & Review Technique 
(PERT), 41 
Propane, 7, 8, 11, 92, 244 
Propylene products, 13 
Pumping rate, 237–239, 247, 254 
Q 
Quadratic programming, 46, 47, 155 
R 
Recursive relationships, 42 
Redundant constraints, 197, 199, 204, 207 
Reﬁned products, 7, 10, 11, 133, 135, 157, 
209–216, 220, 226, 230, 232, 237, 
244, 261 
Reﬁnery, reﬁning, 2, 3, 5, 7, 10, 16, 18, 21, 
25, 83, 84, 87–92, 94, 97, 98, 
100–103, 105–110, 112, 115, 
119–123, 129, 131, 136, 153–158, 
163, 165, 166, 169, 171, 174, 176, 
183, 209, 213, 217, 261 
Reﬁning stage, 7, 16, 18 
Reforming, 4, 7, 157 
Reid Vapor Pressure (RVP), 155, 158–161, 
163–165, 167, 169, 170, 174, 175 
Reliability, 78, 86, 90, 94, 101, 106, 138 
Reliability centered maintenance, 86 
Relief valves, 83–85, 87, 88, 90, 91, 98, 
101, 102 
Remote areas, 181, 184, 185 
Repair cost, 88, 100–102 
Replacement cost, 99, 100
Index
277
Research Octane Number, 158–160, 162, 
167, 170 
Reservoir, 5, 17, 18, 25, 26, 58, 179 
Residuals, 12, 20 
Rig, 55, 56, 59, 61–65, 68, 73, 75–78, 180 
Rig daily rate, 61, 75 
Rig location, 56, 63, 75 
Risk cost, 99, 100, 102, 110 
Robust optimization, 44 
Rotation cycle, 185, 186, 189, 193, 196 
Rotation schedule, 184, 186, 193, 197 
Route, 41, 43, 50, 138, 184, 212, 215, 227, 
232 
Routing, 109, 182, 183, 213, 242, 243, 257 
Rubber, 4, 5, 9, 13, 14 
S 
Safety, 83, 84, 95, 97, 101, 102, 106, 109, 
136, 140, 182, 183, 243 
Saudi Arabia, 4, 10, 60, 133, 153, 164, 179, 
180 
Saudi Aramco, 3, 4, 6, 16, 26, 56, 59, 84, 
106, 134, 154, 180, 210, 238, 240 
Scenario optimization, 45 
5/2 schedule, 139, 145, 149 
7/3-7/4 schedule, 139, 149 
10/4 schedule, 181, 185, 193 
14/7 schedule, 139, 146, 149, 181 
Scheduling, 7, 17, 18, 21, 83, 107–110, 
112, 115, 119, 122, 123, 129–131, 
134–138, 143, 144, 149–151, 155, 
156, 181–186, 188, 191, 206, 207, 
212, 213, 239–243, 245, 246, 253, 
256, 257 
Seismic tests, 5, 6 
Separable programming, 46 
Sequencing, 107, 116, 193, 240, 242, 246, 
250, 255–257 
Service level, 137, 213 
Shift scheduling, 108, 137 
Ship, 1, 12, 133, 183, 209, 210, 214–217, 
226, 233, 237–245 
Shortest-path problem, 41 
Shut-down maintenance, 105, 106, 
108–111, 119, 136 
Simplex method, 31–35, 37, 40, 45–47 
Simulated annealing, 18, 48, 49 
Simulation-based optimization, 25, 27, 50, 
52, 84, 94, 103, 134–136, 143, 150, 
151 
Simulation model, 25, 26, 51, 84–88, 
94–98, 102, 109, 135–138, 141–151, 
212, 243 
Solution quality, 123, 124 
Solution speed, 123–125 
Solvents, 5, 11, 13, 14, 92 
Sour crude, 10 
Stafﬁng, 108, 109, 136–138, 184 
Stages (DP), 42 
Standard Oil Company, 2, 4 
States (DP), 42 
Stationary point, 27, 28 
Steady state, 98, 102, 145, 146 
Stochastic model, 109, 137, 183, 241 
Stochastic programming, 17, 36, 44, 45, 52, 
143 
Storage, 2, 6, 7, 17, 108, 153, 155, 165, 
166, 168, 209, 213, 214, 237, 244 
Strategic planning, 16, 18, 211, 212 
Sulfur, 5, 10, 157, 158 
Supply center, 214, 215, 217, 220, 226, 
230, 232 
Supply chain management, 18–20, 209, 261 
Sustainability, 19, 20, 212, 213 
Sweet crude, 10 
Synthesis gas (SynGas), 8, 9, 12, 14 
T 
Tabu search, 48, 49, 57 
Tactical planning, 18, 213, 214 
Tanker, 2, 3, 6, 7, 10, 133, 183, 210, 226, 
237–247, 253–257 
Tank farm, 6, 7 
Tardiness, 109, 239, 242 
Task-team assignments, 107, 118 
Temperature, 11, 12, 49, 83, 84, 90, 93, 97, 
103, 105, 159 
Testing and Inspection (T&I), 106, 140 
Textiles, 5, 9, 13 
Threshold, 64, 83, 86, 138, 214, 216, 
223–225, 233 
Throughput time, 136, 138 
Time value of money, 86, 234 
Time windows, 109, 242, 243 
Toluene products, 14 
Tour scheduling, 136, 182 
Train/rail, 210 
Transportation cost, 179, 181, 184, 185, 
207, 211, 216, 217, 227, 237 
Transportation mode, 209–212, 214–217, 
223–226, 230, 232 
Transportation problem, 41, 65, 81 
Transportation stage, 16, 19 
Transshipment problem, 41 
Truck, 6, 7, 12, 133, 183, 209, 210, 
215–217, 226, 233
278
Index
Turnaround maintenance, 105–110, 115, 
119–123, 129–131 
U 
Unconstrained optimization, 27, 29, 46, 52 
Union Carbide, 4 
Upstream, 5, 6, 8, 9, 12, 15, 16, 19, 20, 212, 
213 
V 
Validation, 51, 98, 143, 147 
Vapor Lock Index (VLI), 158–160, 167, 
170 
Variable cost, 214, 216, 217, 220 
Vessel, 183, 237–239, 241–243, 246–254, 
256 
Viscosity, 10, 12 
W 
Waiting time, 135, 137, 143, 144, 148, 240, 
242, 243, 245, 246, 248, 251, 252, 
256, 257 
Weighted sum method, 40 
Well completion, 6 
Wellhead, 5, 6 
Well location, 57, 58, 60, 64, 65, 67, 68, 79, 
81 
Well placement, 17 
West Texas International (WTI) crude, 10 
Workforce size, 107, 111–113, 120, 124, 
131, 181, 186, 190, 192, 193, 206, 
207 
Work order, 109, 135, 136, 138, 140–145, 
147–151 
Work stretch, 139, 182, 185, 186, 189, 192, 
193, 196, 198, 200, 203, 205, 207 
X 
Xylene products, 14

CHAPTER TEN
Well completion optimization by
decision-making
Key concepts
1. Optimizing completion designs to enhance well productivity and to minimize costs,
completion time, future intervention, and maintenance requirements.
2. Application of the multicriteria decision-making (MCDM) techniques, analytic
hierarchy process (AHP), and technique for order preference by similarity to the ideal
solution (TOPSIS) in evaluating gas well completion design alternatives.
3. In the case study described in this chapter, 14 effective parameters were recognized by
a decision-making committee for choosing the candidate wells for hydraulic
fracturing (HF). Then the analytical hierarchy process was applied for assigning the
quantitative weight of each parameter on candidate well selection. In this procedure,
productivity is deﬁned as the most signiﬁcant factor while water cut and production
method are the least inﬂuencing parameters in the candidate well selection.
4. Acidizing-water shutoff joint operation technology is an effective method to control
water cut and increase oil production. For a thick, multilayer, and heterogeneous
reservoir, there are many factors affecting production well and layer selection for the
acidizing-water shutoff joint operation, and the relationships among various factors
are complex and nonlinear. The fuzzy analytic hierarchy process (FAHP) method is
reliable and simple; it can guarantee an improved success rate of the acidizing-water
shutoff joint operation for production wells.
10.1 Basic concepts
The MCDM process is a decision aid that was described by Belton and Stewart
(2002) as laid out in Fig. 10.1.
Virtually all decision aid theories were developed to help decision-makers. The term
decision-maker designates the person (or persons) confronted with a problem and charged
with solving that problem and making a decision regarding it. Decision-maker(s) can be, as
described by Belton and Stewart (2002), as follows:
•
A single individual with sole responsibility for a personal decision or for a decision
that might affect others (companies, organizations, etc.)
•
A relatively small and homogeneous group of individuals sharing more or less
common goals.
Methods for Petroleum Well Optimization
 2022 Elsevier Inc.
ISBN 978-0-323-90231-1, https://doi.org/10.1016/B978-0-323-90231-1.00012-1
All rights reserved.
385j
•
A larger group representing different points of view within the same organization.
•
Highly diverse interest groups with very different agendas. This group may share
corporate responsibility for a decision, it may have the task of investigating an issue
with the goal of making a recommendation to a decision-making authority, or it may
have been assembled for the explicit purpose of exploring alternative perspectives
without any executive power.
In theory, the decision aid should start with the identiﬁcation and structuring of the
complexity that exists in a decision process. In other words, all the important aspects of a
decision should be identiﬁed and clariﬁed. The core goals and values of the decision-
maker should be the main factors in the identiﬁcation of the key issues and the avail-
able alternatives with respect to all constraints, uncertainties, divergent goals, values, and
other issues related to the external environment and other stakeholders.
Next, the model building phase must reﬂect a more convergent mode of thinking, a
process of extracting the essence of the problem from the complex representation in a
way that supports a more detailed and precise evaluation of possible ways to move
forward. The model will then be used to synthesize the information and to inform the
decision-maker of their options. Sensitivity and robustness analyses may challenge the
decision-maker to identify or create new alternatives. The ultimate goal of the process is
to help the decision-maker develop the action plan to be implemented. It is possible that
Figure 10.1 The multiple-criteria decision-making process. Modiﬁed from Belton, V., Stewart, T.J., 2002.
Multiple Criteria Decision Analysis: An Integrated Approach. Kluwer Academic Publishers.
386
Methods for Petroleum Well Optimization
the outcome of each of these phases would be a return to the previous phase or even a
return to divergent thinking, as a result of the need to think creatively about other
options or aspects of the decision situation.
10.1.1 Multicriteria problems
A multicriteria problem reﬂects a decision situation where the available options have to
be judged against several criteria. Roy ﬁrst deﬁned four types of multicriteria problems
(Roy, 1993):
•
Choice problems: when a simple choice must be made from a set of possible actions
(or decision alternatives).
•
Sorting problems: when actions must be sorted into classes or categories such as
“deﬁnitely acceptable”, “possibly acceptable but needing more information”, and
“deﬁnitely unacceptable”.
•
Ranking problems: when actions must be ranked according to some sort of
preference order, which might not necessarily be complete.
•
Learning (descriptive) problems: when actions and their consequences must be
described in a formalized manner so that decision-makers can evaluate them. These
are essentially learning problems (Belton and Stewart, 2002) in which the decision-
maker seeks simply to gain a greater understanding of what may or may not be
achievable.
To these categories, Belton and Stewart (2002) have added two more types:
•
Design problems: which imply searching, identifying, or creating new decision
alternatives to meet the goals and aspirations identiﬁed through the MCDA process.
Keeney (1992) also supports this way of thinking, which he calls “value focused
thinking” and claims to be the most appropriate for real-life decision situations.
•
Portfolio problems: when a subset of alternatives must be chosen from a large set of
possibilities, taking into account not only the characteristics of the individual alter-
natives but also the manner in which they interact and the positive or negative
synergies between them.
10.1.1.1 What is a criterion?
The dictionary deﬁnes a “criterion” as “a means or standard for judging” (Belton and
Stewart, 2002). A criterion can also be seen as a tool constructed for evaluating and
comparing potential actions according to a well-deﬁned (as much as possible) point of
view (Roy, 2005).
When referring to the same concept, different MCDA schools of thought often use
other terms for the methodologies that they develop. These terms are goal, objective,
and attribute.
Well completion optimization by decision-making
387
10.1.1.2 What is an alternative?
Alternatives or more generally, potential actions, designate the object of the decision or
that which the decision aiding is directed toward (Roy, 2005). An action is qualiﬁed as
potential when it is possible to implement or when it is relevant in a speciﬁc decision
context. Usually, the term alternative is used to denote actions that are mutually exclusive.
Alternatives may be explicitly deﬁned and discrete, or implicitly deﬁned and
continuous as described in mathematical programs.
10.1.2 The basic formulation of a multicriteria problem
A decision-maker needs to select from a set of feasible alternatives, A, an alternative, a,
that complies best with their set of criteria, C. The levels of achievement in all the
criteria considered, compared with the set of alternatives, can be measured, and these are
Ck, where k is the number of criteria considered, k˛[1, . ,n].
The basic decision problem can then be formulated as follows, where F is the
decision-maker’s unknown preference function (Bogetoft and Pruzan, 1997):
Max
s:t:a˛AF½C1ðaÞ; C2ðaÞ; .CnðaÞ
(10.1)
The assumption that a preference function can be estimated is central to a multicriteria
analysis. What this function actually does is to bring all criteria to a common mea-
surement scale (“sum up”) through the perception of a decision-maker. Then, what
remains is to analyze the different alternatives according to where they are situated on this
scale.
It is important to remember that such a function does not (necessarily) exist in the
mind of a decision-maker. Moreover, it is not necessary to explicitly deﬁne the function
for a decision-maker to make decisions that are consistent with their underlying values
(Bogetoft and Pruzan, 1997). Such a function can represent some of the subconscious
preferences a decision-maker has regarding the different criteria in the problem analyzed.
It is, in a way, a measure of the awareness and understanding gained by the decision-
maker during the decision-making process. This preference function is what concep-
tually distinguishes multicriteria methods from other methods because it explicitly
introduces the decision-maker’s contribution into the analysis.
10.1.3 Classiﬁcation of methods
The set of alternatives in a decision problem may be explicitly deﬁned and discrete, or
implicitly deﬁned and continuous as described in mathematical programs. Accordingly,
methods and methodologies for MCDM can be divided into two groups:
•
Multiattribute decision-making (MADM): methods dealing with problems in which
the set of alternatives is discrete (and ﬁnite).
•
Multiobjective decision-making (MODM): methods dealing with problems in
which the set of alternatives cannot be explicitly deﬁned or given.
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Methods for Petroleum Well Optimization
10.1.3.1 Multiattribute decision-making
Methods for solving multiattribute problems require the decision-maker to analyze a set
of discrete, ﬁnite (predeﬁned) alternatives A ¼ {A1, A2, . Am}. The problem may be to
choose, rank, or sort alternatives according to a set of criteria C ¼ {C1, C2, . Cn} that
best reﬂects the decision-maker’s concerns.
A multiattribute problem can be easily represented in a matrix format, as illustrated in
Fig. 10.2. In this matrix, aij are attributesdlevels of achievement in each criterion,
corresponding to each alternativedwhich are supposed to be known (possible to
estimate).
In the case of small problems, this matrix can be also translated into a graphical
representation. For example, consider a two-criterion problem, where four alternatives
must be evaluated according to two measurable (minimizing in this case) criteria, for
example, cost and emissions. This problem can be represented graphically as shown in
Fig. 10.3.
There are several steps in solving MADM problems. First, the idea is to reduce the set
of alternatives to the efﬁcient ones. This task consists in eliminating dominated alter-
natives by checking how alternatives perform simultaneously in different criteria. In the
case of small problems, this matrix can be also translated into a graphical representation.
For example, consider a two-criterion problem. The point closest to the origin could
represent the best, ideal alternative. In practical applications, such alternatives do not
exist, but an ideal point may help in classifying the alternatives available.
An alternative is dominated if another alternative exists (in the same set), which is at
least as good in all criteria and strictly better in one. For example, in Fig. 10.3, alternative
2 is dominated by alternative 3.
There might be still alternatives left to analyze after the dominated ones are identi-
ﬁed. These are the efﬁcient alternatives (called also noninferior or Pareto-optimal) which
are not dominated by any other feasible alternative. In this example, alternatives 1, 3, and
4 are efﬁcient. Note that at this point, the differentiation of alternatives is independent of
the decision-maker’s preferences.
Figure 10.2 Matrix representation of a multiattribute decision-making problem.
Well completion optimization by decision-making
389
The second step in solving multiattribute problems is when the decision-maker has to
evaluate and make further selections from the set of efﬁcient alternatives. In large,
complex decision problems, the selection can be facilitated by some sort of modeling or
quantiﬁcation of the decision-maker’s values and preferences regarding the criteria that
are speciﬁc to the decision problem. There are several methodological concepts for
solving multiattribute problems or modeling the decision-maker’s contribution. Two
methods, commonly used in practice, the trade-off analysis and MAVT (multiattribute
value theory), will be discussed here.
10.1.3.1.1 Trade-off analysis
Trade-off analysis is a simple, straightforward method to help decision-makers analyze
the set of efﬁcient alternatives.
No efﬁcient alternative is better than any of the other efﬁcient alternatives: when
choosing one of the alternatives, the decision-maker will gain in one criterion but at the
same time lose in another one. For instance, alternative 3 is better than alternative 4 in
criterion i but worse in criterion j and vice versa. If the decision-maker is mostly
concerned with alternatives that perform well in criterion i, then they will choose
alternative 3 (or alternative 4 for criterion j) (Fig. 10.4).
Making a trade-off means deciding which of the criteria is preferred and, in essence,
how much the differences in attribute levels (when moving from one efﬁcient solution to
another) matter for the decision-maker. If the decision-maker’s preferences are constant
in a given problem setting, then the trade-off can be represented linearly as shown in
Fig. 10.5 or mathematically through a formula:
f

Ci; Cj

¼ Ci þ aCj
(10.2)
For instance, if in a given problem context a decision-maker can specify a constant
trade-off between two criteria (the red or the blue lines in Fig. 10.5), then the preferred
Figure 10.3 Graphical representation of a multiattribute decision-making problem (Catrinu, 2006).
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Methods for Petroleum Well Optimization
alternative(s) will be the ones that ﬁrst meet the indifference curve; that is, the horizontal
translation of the trade-off indifference curve. (In this example, it is assumed that the
marginal rate of substitution depends on criterion Cj and not on Ci.) Thus, alternative 3
will be chosen given the “red” trade-off, while alternative 4 will be chosen given the
“blue” trade-off (Catrinu, 2006).
10.1.3.1.2 Multiattribute value theory
Compared with the trade-off analysis, multiattribute value theory (MAVT) is a more
advanced approach. This approach assumes that it is possible to construct a means of
associating a real number with each alternative to produce a preference order for the
Figure 10.4 Making trade-offs.
Figure 10.5 Representing trade-offs (Catrinu, 2006).
Well completion optimization by decision-making
391
alternatives, consistent with the decision-maker’s value judgments (Bogetoft and Pruzan,
1997). In other words, each alternative, A, has value, V(A), for the decision-maker, and
this value can be expressed numerically. In principle, values measure preferences when
taking all criteria into account. Then, based on these values, the alternatives can be
differentiated. For example, if alternative A1 is preferred to A2 (A1 > A2), then V(A1) >
V(A2). Additionally, when the decision-maker is indifferent to the difference between
alternatives A1 and A2 (A1 ~ A2), then V(A1) ¼ V(A2). The existence of such values
stems from the following assumptions regarding the decision-maker’s preferences.
•
Preferences are complete: for any pair of alternatives, either one is strictly preferred to
the other or there is indifference to the choice of either of them.
•
Preferences and indifferences are intransitive: for any three alternatives A1, A2, and
A3, if A1 > A2 (or A1 ~ A2) and A2 > A3 (or A2 ~ A3), then A1 > A3 (or A1 ~ A3).
Value functions are in particular appealing for quantitatively oriented managers or
management scientists because the functions give a sense of objectivity to the decision-
making process and help to focus the decision process on those aspects that matter. In
principle, once determined, value functions automatically lead to the optimal alternative.
A value function can be constructed by using different procedures/methods. All these
methods seek, in one way or another, to synthesize preference information, reﬂecting:
•
the values a decision-maker would assign to the performance of each alternative in
each of the criteria considered, or intracriterion evaluations; and
•
the relative importance of criteria for the decision-maker, or intercriteria evaluations.
The ﬁrst step in traditional value function methods is the assessment of the “marginal”
(or “partial”) value functions, vk(a), or scores. A partial value function can be estimated
for each criterion, k, and it measures, theoretically, the relative importance a decision-
maker assigns to different performance levels (attributes) in that speciﬁc criterion (aik).
The partial value function can be deﬁned in the same way as a value function; that is,
in terms of preservation of preference ordering. Such a function “translates” each of the
criteria analyzed, measured on its own scale, into value scales (usually normalized). A
partial value function may be linear or not, as shown in Fig. 10.6.
The shape of the partial value function should reﬂect the way a decision-maker thinks
in terms of an attribute. Theoretically, the accuracy of partial value function estimations
improves considerably if the decision problem is well structured; that is, if the criteria are
clearly represented and measured to reﬂect, incite, and trigger the right-thinking strategy
“inside” the decision-maker (Bogetoft and Pruzan, 1997). The usual procedure for
estimating the shape of a partial value function is ﬁrst to check if certain assumptions are
valid in a given problem setting. This is done through a preliminary questionnaire that
emphasizes the essential characteristics of the decision-maker’s values, such as:
•
whether the partial value function is monotonically increasing or decreasing against
the “natural” scale; that is, if the highest value of the attribute is preferred against
lower levels or vice versa;
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Methods for Petroleum Well Optimization
•
whether the partial value function is nonmonotonic; that is, an intermediate point on
the scale deﬁnes the most preferred or least preferred point.
After verifying these properties, the analyst can assume a certain shape for the partial
value function. A linear representation is commonly used in practical applications. It has
been demonstrated that the linearity assumption is usually valid in well-structured
decision problems. However, experimental simulations cited by Bogetoft and Pruzan
(1997) warn against the oversimpliﬁcation of the problem by the inappropriate use of
linear value functions. It has been shown that the results of a multicriteria analysis may be
very sensitive to such assumptions, thus leading to bad recommendations.
The second step in building value models is the assessment of the relative importance of
different criteria considered in the decision process. When multiple criteria are considered
in decision-making, not all of them are equally preferred, judged in the same way, or have
the same weights. As is true for partial value functions, theoretically, these weights, wk,
corresponding to each criterion, Ck (k ˛ [1, . ,n]), can be estimated through a new
questionnaire. The purpose of this questionnaire is to establish an order of criteria, in
terms of importance or indifference (equal preference). Ideally, the decision-maker
should be also able to characterize their preferences; that is, how much more (and
why) they prefer one criterion than another.
Many methods for weight elicitation focus on swing weights, meaning weights that
“compensate” for values against criteria. Swing weights can be determined only when
the scales for measurement in each criterion are clearly deﬁned. On these scales, a worst
and a best value in each criterion can be identiﬁed, and the decision-maker is asked to
assess which swing (interval step) from the lowest levels (usually) gives the greatest in-
crease in value. For instance, if a swing from worst to best on the highest-rated criterion
is assigned a value of 100, what would be the value of a swing from worst to best in the
second-ranked criterion? In practical applications, swing values can be derived using any
Figure 10.6 Partial value functions (Catrinu, 2006).
Well completion optimization by decision-making
393
two reference points on a criterion scale. Thus, instead of the worst and the best levels,
“neutral” and “good” reference points can be deﬁned if the decision-maker considers
that this helps in making comparisons.
In practical applications, it is important to know that weights are dependent on the scales
used for scoring as well as on the intrinsic importance of criteria (swing weights capture these
issues very well). For instance, if an important criterion does not differentiate much
between alternatives, meaning if the minimum and maximum points on the value scale
correspond to similar achievement levels, then that criterion might be ranked quite low
(Bogetoft and Pruzan, 1997).
Another issue to be emphasized is that in practical applications, where decision
problems are deﬁned over hierarchies of criteria, the determination of weights can
become difﬁcult (Poyhonen, 1998; Poyhonen and Hamalainen, 2001). In these situa-
tions, the simplest way out would be to consider only the criteria in the last level of the
tree for the weights-revealing questionnaires. However, many methods have been
developed for dealing with hierarchical value tree analysis (Poyhonen and Hamalainen,
2001).
10.1.3.1.3 Preference aggregation in multiattribute value theory
So far, the main steps in the construction of multiattribute value functions have been
discussed. The purpose in determining the scores (partial values) and weights is to
contribute to good approximations of the overall value functions, V(A), according to
which the alternatives can be evaluated.
Overall value functions can be constructed by some type of aggregation of scores and
weights. In practical applications, the additive aggregation is mostly used. Thus,
supposing that for any alternative, Ai (i ˛ [1, . ,m]), and criterion, Ck (k ˛ [1, . ,n]),
the scores, vk(aik), and the weights, wk, can be assessed, then the overall value function can
be written as:
VðAiÞ ¼
X
n
k ¼ 1
wkvkðaikÞ
(10.3)
This additive aggregation form is widely used in practice because it is easily explained
and understood by decision-makers from a wide variety of backgrounds (Belton and
Stewart, 2002). The use of additive value functions is, however, restricted by several
conditions that must be veriﬁed before every application.
The ﬁrst requirement is that the criteria should be preferentially independent. This means
that the decision-maker is able to compare alternatives in terms of a speciﬁc set of
criteria, without thinking about how these alternatives would perform with respect to
the rest of the criteria. Moreover, theoretically, the existence of an additive represen-
tation is also implied by three main properties: the corresponding trade-offs, the interval
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Methods for Petroleum Well Optimization
scale property, and the property that weights can be interpreted as scaling constants for values.
For a detailed discussion and illustrative examples of these issues, the reader can consult
Keeney and Raiffa (1993).
In cases when the additivity conditions are not valid, the common advice is to ﬁrst go
back to the problem identiﬁcation and structuring process (Belton and Stewart, 2002).
The alternative would be to use other forms of aggregation of preferences (Dyer and
Sarin, 1979), as for instance multiplicative value functions:
VðaÞ ¼
Y
n
k ¼ 1
½vkðaÞwk
(10.4)
To conclude this discussion, Fig. 10.7 shows an illustration of the main procedural
steps in modeling multiattribute value functions. For instance, suppose that a decision-
maker has to analyze and choose between a ﬁnite and clearly deﬁned set of efﬁcient
alternatives that must be compared in terms of several, relevant criteria. This choice
depends on the underlying values the decision-maker has in this decision situation, and
in principle, the alternative with the highest value should be chosen.
The theory provides us with the means of constructing models for the decision-
maker’s preference values. The main components in a value model are the scores and
the weights. The scores reﬂect the preferences that a decision-maker has for different
achievement levels under each criterion considered (achievements in different alterna-
tives), while the weights reﬂect the preferences for the different criteria. The scores result
from comparisons of attribute levels in each criterion, while weights result from inter-
criteria comparisons.
When aggregating the scores and the weights (additive, multiplicative, or any other
form of aggregation), overall values for each of the alternatives considered are obtained,
and the alternative with the highest value is recommended.
Figure 10.7 The steps in assessing value functions (Catrinu, 2006).
Well completion optimization by decision-making
395
10.1.3.2 Multiobjective decision-making
In multiobjective decision problems, alternatives are not explicitly known in advance.
These problems reﬂect situations where practically a nearly inﬁnite number of options
are feasible. Mathematical programming is used to model this type of problem, and
solutions can be determined through a set of mathematically deﬁned constraints. The
scope for modeling here is to seek for solutions rather than extracting and interpreting
the decision-maker’s preferences (Ehrgott, 2005).
A multiobjective problem can be formulated as follows, where: X is the vector of
decision variables (may include integer or binary variables); F(x) is the vector of objective
functions; G(x) is the set of equality constraints; and H(x) is the set of inequality
constraints:
max FðxÞ
s.t. : GðxÞ ¼ 0
HðxÞ  0
x  0
(10.5)
The concept of a solution in multiobjective problems will be illustrated through an
example. As in the MADM case, consider a simple problem with two objectives to
minimize, F1 and F2, and ﬁve constraints, as deﬁned and represented graphically in
Fig. 10.8. The colored area in this ﬁgure represents the space (the set) of feasible solu-
tions, which are the solutions (pairs of decision variables) that satisfy all constraints in this
problem. The problem can be further translated into the attribute (or criteria) space
(Fig. 10.9) to show how alternative solutions perform in terms of the two objectives
Figure 10.8 Decision space.
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Methods for Petroleum Well Optimization
chosen. In fact, the MODM problem representation in Fig. 10.9 is equivalent to the
MADM representation in Fig. 10.3 (Catrinu, 2006).
One can clearly observe how the space of solutions is continuous (and inﬁnite) in the
MODM case, as compared with the MADM case. As with MADM, the next step is to
select the efﬁcient solutions (nondominated solutions, pareto-optimal solutions)
belonging to this set. This set of efﬁcient solutions for the MODM problem is found at
the frontier of the feasible set (the red lines in Figs. 10.8 and 10.9), since both objectives
must be maximized. There are different mathematical procedures for solving multi-
objective problems (Evans, 1984). Brieﬂy, current practices can be divided into exact and
heuristics. The simplex algorithm is, for example, an exact procedure. Then, heuristic
procedures are usually used in large combinatorial problems (large mix-integer prob-
lems), where the conventional (exact) solution algorithms do not lead to a ﬁnal solution.
In essence, an heuristic search procedure associates heuristics with a search algorithm for
exploring the space of feasible solutions. Genetic algorithms, tabu search, and simulated
annealing are examples of heuristic procedures for solving large combinatorial problems.
Many optimization packages and decision-support software applications have been
developed to allow for different practices (Korhonen, 2005). Depending on the nature
(and the size) of the problem analyzed, different methods for MODM may require
speciﬁc procedures for ﬁnding the efﬁcient solution. The purpose of this chapter is not
to go into the details of mathematical optimization but to give an idea of what kind of
resources MODM requires in practical applications.
MODM methods differ in the way in which objectives are assessed, and when and
how the intervention of a decision-maker is needed. The classiﬁcation continues further
with four main groups of MODM methods: aggregation methods, generation methods,
interactive methods, and goal programming.
Figure 10.9 Attribute space.
Well completion optimization by decision-making
397
10.1.4 Selecting an appropriate multicriteria decision-making method
MCDM methods can be usefully applied to well completion decision-making scenarios.
MCDM consists of a set of techniques that allows a range of criteria and component
issues related to a complex decision to be assigned semiquantitative scores and combined
with weightings. The approach enables the decision alternatives to be systematically
ranked by the appropriate technical experts. In the case of well completions, the experts
who rank the criteria might include all, or some, of the following: production engineers,
reservoir engineers, drilling engineers, facilities engineers, geologists, and petroleum
economists. Such systematic rankings and weightings have the potential to improve the
reliability, transparency, repeatability, and efﬁcacy of complex decisions, provided that
structured information is assessed and compared using appropriate tools and algorithms,
such as those associated with MCDM. The tools that should be applied depend on the
type of decision under consideration: some decisions involve multiple attributes; some
decisions involve multiple objectives; and some decisions involve both, such as well
completion decisions. Some scenarios may be distinguished as being either MADM or
MODM situations.
The criteria for MADM scenarios are typically expressed in data/information
matrices that then serve as the inputs for the MCDM analysis techniques, such as AHP
,
and TOPSIS.
AHP is a structured technique for organizing and analyzing complex decisions to be
made by a group of decision-makers. It was originally developed by Saaty in the 1970s
(see, for example, Saaty, 1980) and combines mathematical and psychological techniques
that have remained highly relevant to industrial decision-making (Saaty, 2008a,b). It is
now widely used across many industries including various wellbore applications in the oil
and gas industry (see, for example, Okstad, 2006). AHP does not seek to identify the
correct decision; rather its approach is to identify a decision that is most appropriate for
the goals (that is, the strategic priorities) that the stakeholders are striving to achieve.
AHP considers qualitative and quantitative criteria simultaneously and provides the
ability to check the inconsistency in judgments, which is useful when planning and
prioritizing well completion projects. It also provides a method for analyzing and
transforming the key criteria to make them easier to compare and manipulate numer-
ically, so that the planner/decision-maker can evaluate the alternatives based upon the
structured criteria in a systematic and consistent way (see, for example, Ekmekc
¸io
glu
et al., 2010) and convert verbal/linguistic assessments into semiquantitative scores.
TOPSIS was developed by Hwang and coauthors in the 1980s (Hwang and Y
oon,
1981; Hwang and Lin, 1987). Its premise, in mathematical terms, is that the chosen
alternative in a decision should have the shortest geometric distance from the “positive
ideal” solution and the longest geometric distance from the “negative ideal” solution.
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Methods for Petroleum Well Optimization
A review of the applications of these, and other, MCDM techniques in environ-
mental sciences (Huang et al., 2011) provides more background on and insight into the
development trends and the signiﬁcance of these techniques in decision analysis. The
preference ranking organization method for enrichment evaluations (PROMETHEE) is
another popular MCDM, with reviews of its applications provided by Behzadian et al.
(2010). Such methods are particularly useful where multidiscipline and/or multi-
company teams of technical experts are working on complex problems, especially
in situations that involve high levels of uncertainty. The methods allow different per-
spectives and the differing expert judgments of individuals, professional disciplines, or
corporate entities to be taken into account (Fig. 10.10).
The decision tree gives guidance for selecting an MCDA model according to the
information that is available in the decision problem. However, it is also possible to use a
combination of the methods, with the purpose of solving or structuring the problem
(Fig. 10.11).
10.2 Well completion optimization by decision-making
10.2.1 Selection of high-rate gas well completion designs
The well completion scenario to be evaluated using AHP and TOPSIS methodologies
here is for the development of a giant, shallow-water, natural gas ﬁeld using large
wellbore completions able to accommodate high initial gas ﬂow rates (e.g., in the order
of 300 MMscfd per well). This is a scenario relevant to the industry today and has
recently been considered for the Middle East Gulf giant gas ﬁeld discoveries involving a
Figure 10.10 Hierarchical structure of MCDM methods. MCDM, multicriteria decision-making.
Modiﬁed from Aruldoss, M., Lakshmi, T.M., Venkatesan, V.P., 2013. A survey on multi criteria decision
making methods and its applications. Am. J. Inf. Syst. 1 (1), 31e43.
Well completion optimization by decision-making
399
95/800 liner and production tubing (see, for example, Al-Baqawi et al., 2013, for offshore
Saudi Arabia). However, the decision that has to be made here is how that completion
should be conﬁgured. Three alternatives could be considered: (1) monobore (MB) as
proposed by Simonds and Swan (2000) and Al-Baqawi et al. (2013), and used historically
offshore Qatar (Clancy et al., 2007); (2) big bore (BB) as deployed in the Arun ﬁeld
offshore Sumatra, Indonesia (Cannan et al., 1993); and (3) optimized big bore (OBB) as
installed in the development of the North Field offshore Qatar (Clancy et al., 2007).
For more than 15 years, large diameter wellbores with simpliﬁed completions (that is,
monobore, which is favored when possible) have been deployed around the world to
avoid ﬂow restrictions for high-volume gas wells (see, for example, Offshore, 2001).
Initially, large wellbore completions were considered to be those with tubulars which
were 65/800 or larger, but over the past decade, technology has led to the diameter of the
tubulars increasing, and they are now typically 95/800. Larger production tubing provides
increased ﬂow area within the wellbore. In addition, monobore tubular designs (that is,
tubulars with a single internal diameter [ID] across the full length of the wellbore) reduce
potential ﬂow restrictions and ease access to the reservoir zones.
Figure 10.11 Selection of methods in multicriteria decision analysis. Modiﬁed from Sen, P., Yang, J.B.,
1998. Multiple Criteria Decision Support in Engineering Design. Springer London.
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Methods for Petroleum Well Optimization
Other beneﬁts of large diameter completions in offshore gas ﬁeld developments
(some of which were noted in Offshore, 2001) are:
1. fewer constraints on production;
2. ease of use of well intervention tools/maximum workability;
3. minimized pressure drops/maximize ﬂowing pressures;
4. maximized production and earlier return on investment;
5. exploitation of the reservoir through fewer wells and fewer slots on a platform;
6. reduction of turbulent gas ﬂow in the wellbores;
7. possible elimination of one or more platforms;
8. lower long-term operating costs;
9. more rapid depletion of the resource (fewer years of production);
10. fewer total wellbores to be drilled;
11. less complex topside facilities with less weight; and
12. lower maintenance costs.
It was with the aforementioned beneﬁts in mind that Clancy et al. (2007) compared the
three well design alternatives for achieving large monobore completions (MB, OB, and
OBB) as part of the detailed planning for a phase of development of the North Field,
taking into account well design, drilling challenges, critical equipment speciﬁcations,
and evolving optimization developments, such as those associated with the ongoing
development of the Arun ﬁeld and the OBB alternative (Benesch et al., 2006).
The summary descriptions of these three alternative well designs are provided below
as drawn from the speciﬁcations provided by Clancy et al. (2007) and meet the well
design objectives and constraints.
•
The selected design should not pose greater health, safety and environmental risks
than the alternatives at any stage of drilling or production.
•
Completion should be able to accommodate large-volume acid-fracture stimulations
at surface pressures as high as 5000 psi.
•
Each wellbore should be capable of producing at initial rates of greater than 150
MMscfd.
•
Flowing wellhead pressures (FWHP) should be capable of sustaining production for
25 years of plateau production.
•
Completions should not lead to the requirement to install compression early to
compensate for low FWHP
.
•
Design life reliability for the well completion should be 25 years.
The alternative designs are shown in three simpliﬁed well completion diagrams/tubular
conﬁgurations (Fig. 10.12), and their key characteristics are described in the following.
Well completion optimization by decision-making
401
10.2.1.1 Well completion decision scenarios evaluated
10.2.1.1.1 Monobore (Fig. 10.12A, left)
•
All internal speciﬁcations including surface-controlled subsurface safety valves
(SCSSV) are constant
•
700 throughout the wellbore
•
Limited production ﬂow capacity versus BB and OBB
•
Cheaper to drill than BB and OBB
•
Reduced operating costs, repairs, and maintenance during operating life
•
Easier to operate and access than BB and OBB
10.2.1.1.2 Big bore (Fig. 10.12B, center)
•
A 95/800 BB design could achieve signiﬁcant overall well cost reduction compared to
MB (e.g., the Arun ﬁeld)
•
Cost reduction comes with higher associated risk of failure
•
Larger drilling rigs required to deliver 95/800 diameter in the reservoir with tapered
casing
•
Reduced directional drilling capabilities due to larger diameter well bore
•
Higher torque, drag and pump requirements while drilling
•
Higher drill cuttings volume and handling costs
•
Complex 95/800 production-tubing tieback
•
Challenges to provide full-bore access through the SCSSV
•
Tubing less gas completion with drill-in liners can be used in some cases (e.g., the
Arun ﬁeld)
•
Completed open hole in low pressure shallow reservoirs
Figure 10.12 Wellbore tubular designs for big bore, monobore and optimized big bore
conﬁgurations.
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Methods for Petroleum Well Optimization
10.2.1.1.3 Optimized big bore (Fig. 10.12C, right)
•
A larger 95/800 by 75/800 by 700 ﬂow path than the MB design
•
700 diameter in reservoir versus 95/800 for BB
•
Signiﬁcantly higher ﬂow capacity than the 700 MB
•
Same hole sizes through the reservoir zones as the MB design so minimum additional
drilling risk
•
Achieved by tapered casing design
•
About six additional days (compared with MB) to set 95/800 by 75/800 tapered pro-
duction tubing
•
Potential risk of casing wear during the drilling phase
10.2.1.2 The hierarchical approach of the analytic hierarchy process
In the AHP
, decisions are based on pairwise comparisons (that is, comparing entities in
pairs to judge which of the two entities is preferred). Decision-making begins by
providing a hierarchical tree. The hierarchical decision tree shows the factors (criteria) to
be compared, and they provide the basis for evaluating competing alternatives in making
decisions. Weights (or priorities) are typically applied to the deﬁned AHP criteria to help
distinguish their relative importance in making a selection from among the alternatives.
Weights need to be applied transparently and consistently, and the basis on which they are
selected must be clearly documented. Once the hierarchy is constructed, the decision-
makers systematically evaluate its various elements by comparing them with one
another, two at a time (namely, a pairwise comparison), with respect to their impact on
an element above them in the hierarchy. The results of such comparisons are expressed
numerically in matrices, such that a high-performing alternative can be selected. Each
level of the AHP hierarchy is related to a higher level, and relative weights are calculated
and displayed in a matrix. The combination of the relative weights in the hierarchy
enables the ﬁnal weight of each alternative to be determined, which is referred to as the
absolute weight. The comparison of absolute weights identiﬁes the optimal decision
(meaning, the alternative with the highest absolute weight).
The AHP method as proposed and developed by Saaty (see, for example, Saaty, 1994)
integrates three hierarchical levels: (1) the preferred decision; (2) criteria contributions to
the decision; and (3) alternative method contributions to the criteria (Fig. 10.13).
The attractions of the approach are that the preferred decision can be presented in
both qualitative and quantitative terms using hierarchical displays, and the quality of the
decision can be analyzed with sensitivity cases.
Well completion optimization by decision-making
403
To establish these three hierarchical levels involves the application of three funda-
mental principles (see, for example, Saaty, 1994).
1. Decomposition: breaking a decision down into smaller components.
2. Comparative judgment: assessing elements against each other in pairs to build a
pairwise comparison matrix.
3. Synthesis of priority: priority setting by ranking the pair comparisons to establish
local and global priorities.
The third principle typically involves the calculation of eigenvectors to establish local
priorities. Fig. 10.14 shows a ﬂow diagram illustrating the components of the generic
AHP process.
Several more detailed calculation steps are required to apply the fundamental prin-
ciples of the AHP method (see, for example, Taylor, 2002). These typically involve
constructing and evaluating the hierarchy in the following sequence:
A. Semiquantify the contribution to each alternative method of each criteria using
pairwise comparisons. A standard preference scale is typically used to document each
expert’s view on a semiquantitative scale (e.g., 1 to 9, see Table 10.1).
B. Form criteria comparison matrices collating the criteria comparisons from step A.
C. Derive criteria priority values from the data in the criteria comparison matrices
(i.e., divide the sum of each matrix column by the total of that column) to establish
column values on scales of zero to one.
D. Create normalized criteria matrices, adding a ﬁnal column (i.e., the preference
vector) that lists the average of each row of priority values.
E. Create a priority preference vector matrix that combines the ﬁnal columns of each of
the normalized matrices for each criterion into a single matrix.
F
. Construct a criteria relative weighting matrix, using the standard preference scale
(e.g., Table 10.1), to document the relative signiﬁcance of each of the criteria to the
speciﬁc decision objective. This matrix is used to establish the relative weights to be
Figure 10.13 Integrated hierarchy breakdown applied by analytic hierarch process MCDM method-
ology. MCDM, multicriteria decision-making. From Khosravanian, R., Wood, D., 2016. Selection of high-
rate gas well completion designs applying multi-criteria decision making and hierarchy methods. J. Nat.
Gas Sci. Eng. http://www.elsevier.com/locate/jngs.
404
Methods for Petroleum Well Optimization
Figure 10.14 Flowchart illustrating the sequence of steps and considerations in a typical analytic
hierarchy analysis.
Well completion optimization by decision-making
405
applied to each of the criteria through the calculation of a relative weighting pref-
erence vector, a similar approach to that involved in steps C and D.
G. Calculate the absolute weighting matrix by multiplying the elements of the priority
preference vector matrix by the relative weighting preference vector. The rows of
the absolute weights matrix are summed to establish an absolute weight value for
each alternative method.
H. Rank the alternatives according to the magnitude of the absolute weight value
determined in step G.
I. Make a decision selecting the alternative with the highest absolute weight.
10.2.1.3 Conceptual framework of decision to select from gas well completion
alternatives
The AHP conceptual framework for the decision to select high-rate gas well completion
alternatives is shown in Fig. 10.15. There are four criteria used to assess the three
alternative completion methods. The four criteria form the third layer of the hierarchy
beneath the completion alternatives that make up the middle layer. Note that the four
criteria inﬂuence each of the completion alternatives. The decision that selects the most
appropriate of the three alternatives based upon the criteria assessments forms the top
level of the hierarchy.
In this and other generic AHP frameworks, each level of the hierarchy is related to
the level above it, and a “weight” of inﬂuence of each component of the underlying layer
on the layer above it is calculated. The weights applied between the lower layers of the
Table 10.1 An example of a semiquantitative preference scale used by experts for making pairwise
comparisons built upon linguistic variables. Note: Such scales rarely involve more than nine
preference levels to accommodate the human limitations of comparing large numbers of grouping
consistently (Saaty, 1994).
Preference scale to apply for pairwise comparisons
Preference level
Numerical value
Equally preferred
1
Equally to moderately preferred
2
Moderately preferred
3
Moderately to strongly preferred
4
Strongly preferred
5
Strongly to very strongly preferred
6
Very strongly preferred
7
Very strongly to extremely preferred
8
Extremely preferred
9
From Zadeh, L.A., 1975. The concept of a linguistic variable and its application to approximate reasoning. Inf. Sci. 8,
199e249; After Ariﬁn, A.Z., 2011. The analytical hierarchy process (AHP) method for stocks rank. In: 2011 Cambridge
Business & Economics Conference, June 27e28, ISBN 9780974211428, p. 26.
406
Methods for Petroleum Well Optimization
hierarchy are called relative weights. The combination of the relative weights of each
component in the second level of the hierarchy is used to extract a ﬁnal weight for each
option (in this case the three gas well completion methods) which is referred to as the
absolute weight.
10.2.1.4 Reliability of criteria, and weighting the criteria
The study of the reliability of criteria is functional, and the library method can be used to
collect data. To weight the criteria, experts can be surveyed with a questionnaire. The
results of weighting the criteria and ranking the options are presented (see Tables 10.5
and 10.6). The weights have also been applied for sensitivity analysis using ranking
multiple objectives in well completion programming (see Table 10.7).
10.2.1.5 Applying analytic hierarchy process to aid the decision to select one of the
well completion alternatives available for high-rate gas wells: monobore,
big bore, or optimized big bore
The four AHP criteria used for the well completion scenario considered are:
1. time to run completion (days)dreferred to as “time” in subsequent ﬁgures and tables;
2. initial gas production rate achieved from the completed well (MMscfd)dreferred to
as “production” in subsequent ﬁgures and tables;
3. ﬁnal development cost including drilling, completion, and subsequent well operating
costs (US$ millions)dreferred to as “costs” in subsequent ﬁgures and tables; and
4. the complexity and availability/supply of the equipment required to run each
completion. The more specialist equipment required and the complexity of running
a speciﬁc completion adds to the risk of cost and time overruns. These criteria are
referred to as “equipment” in subsequent ﬁgures and tables.
Note that for the four criteria considered, low values are good for criteria 1, 3, and 4,
whereas a high value is good for the production criterion.
Figure 10.15 Conceptual framework for hierarchical analysis of the key attributes for high-rate gas
well completion alternatives. From Khosravanian, R., Wood, D., 2016. Selection of high-rate gas well
completion designs applying multi-criteria decision making and hierarchy methods. J. Nat. Gas Sci. Eng.
http://www.elsevier.com/locate/jngs.
Well completion optimization by decision-making
407
These four criteria are comparatively assessed in pairs using the preference scale and
the weight of each well completion alternative is determined in relation to these four
speciﬁed criteria. For example, the pairwise comparison of the “cost” criteria requires
three comparisons to be made and is conducted as follows:
•
costs of MB are compared with costs of BB;
•
costs of MB are compared with costs of OBB; and
•
costs of BB are compared with costs of OBB.
For each of these criteria comparisons, the decision-maker/planner assigns a linguistic
assessment from a deﬁned list of alternatives (for example, those on the left side of
Table 10.1), and then those linguistic descriptions of priority are converted into a
semiquantitative numerical scale (such as on the right side of Table 10.1). Such a pairwise
assessment is illustrated for the well completion scenario in Table 10.2.
The criteria comparison matrices are transformed into normalized matrices for each
criterion by summing the columns and dividing the value in each column cell by that
sum (Table 10.3). The preference vector is calculated by averaging each row in the
normalized matrix, and it reveals the priorities for each criterion with respect to the
completion alternatives.
The preference vectors for each criterion are arranged in a priority preference vector
matrix (Table 10.4).
The right-hand column of the normalized matrix is the relative weighting preference
vector used in the calculation of absolute weights.
The criteria preference vectors in Table 10.4 are multiplied by the relative weighting
preference vectors in Table 10.5 to calculate the absolute weighting matrix shown in
Table 10.6.
The structured hierarchical analysis provided by AHP and the decision preferences
that it reveals is a useful way of combining technical analysis (that is, the relative criteria
assessments for the alternatives provided by the multidisciplined team of expert engi-
neers, economists, etc.) with the strategic priorities of the decision-makers (for example,
whether it is more appropriate for the organization to be maximizing production or
minimizing costs in certain circumstances). The AHP structure is conducive to sensi-
tivity analysis being conducted at two levels; at the technical level on the relative criteria
assessments (Tables 10.2 and 10.3), and at the strategic level on the relative weightings
applied (Table 10.5). Making changes in the assumptions/analysis at either of those two
levels is likely to yield a different ranking of priorities in Table 10.6 and Fig. 10.16.
To illustrate this for the well completion scenario, a sensitivity analysis is presented in
Table 10.7 for the different strategic assumptions, changing the pairwise relative
weighting in Table 10.5.
What becomes clear from the sensitivity analysis is that if the base case is prioritized to
maximize production, it selects OBB, and if the priority shifts to any of the other criteria,
then the decision changes to MB. In none of the sensitivity cases run is BB selected; it is
408
Methods for Petroleum Well Optimization
always outperformed by either the MB or the OBB completion alternatives. Also, when
production has less than a moderate weighting, then the MB alternative is selected (for
example, compare the AHP results for cases 5 and 6). These sensitivity case outcomes are
consistent with the calculations of the priority preference vector matrix (Table 10.4),
from which it is clear that the criteria time, cost, and equipment are all prioritized by the
MB alternative. It is only production that is prioritized by the OBB alternative, and none
of the criteria are optimized by the BB completion alternative. Hence, it is not surprising
that weighting schemes favoring time and/or cost and/or equipment would lead to an
Table 10.2 Pairwise criteria assessment for the three gas well completion alternatives based on four
criteria.
Time criteria
MB
BB
OBB
MB
1
3
5
BB
1/3
1
3
OBB
1/5
1/3
1
Cost criteria
MB
BB
OBB
MB
1
5
7
BB
1/5
1
3
OBB
1/7
1/3
1
Production criteria
MB
BB
OBB
MB
1
1/6
1/9
BB
6
1
1/5
OBB
9
5
1
Equipment criteria
MB
BB
OBB
MB
1
2
7
BB
1/2
1
5
OBB
1/7
1/5
1
Note that the number refers to the relative assessment of the row alternative versus the column alternative. Hence, a “5” in
a cell for row MB and column OBB means that the criteria is “strongly preferred” on the preference scale for MB versus
OBB. The value for row OBB and column MB then must be “1/5.”
BB, big bore; MB, monobore; OBB, optimized big bore.
From Khosravanian, R., Wood, D., 2016. Selection of high-rate gas well completion designs applying multi-criteria
decision making and hierarchy methods. J. Nat. Gas Sci. Eng. http://www.elsevier.com/locate/jngs.
Well completion optimization by decision-making
409
Table 10.3 Normalized matrices for the gas well completion alternatives based on four criteria with
the preference vector revealing the priorities.
Drilling and completion time
Preference vector average
of each row
MB
BB
OBB
MB
0.6522
0.6923
0.5556
0.633
BB
0.2174
0.2308
0.3333
0.260
OBB
0.1304
0.0769
0.1111
0.106
Total
1.0000
1.0000
1.0000
1.000
Gas production
Preference vector average
of each row
MB
BB
OBB
MB
0.0625
0.0270
0.0847
0.058
BB
0.3750
0.1622
0.1525
0.230
OBB
0.5625
0.8108
0.7627
0.712
Total
1.0000
1.0000
1.0000
1.000
Cost (capex and opex)
Preference vector average
of each row
MB
BB
OBB
MB
0.7447
0.7895
0.6364
0.724
BB
0.1489
0.1579
0.2727
0.193
OBB
0.1064
0.0526
0.0909
0.083
Total
1.0000
1.0000
1.0000
1.000
Equipment required
Preference vector average
of each row
MB
BB
OBB
MB
0.6087
0.6250
0.5385
0.591
BB
0.3043
0.3125
0.3846
0.334
OBB
0.0870
0.0625
0.0769
0.075
Total
1.0000
1.0000
1.0000
1.000
BB, big bore; MB, monobore; OBB, optimized big bore.
From Khosravanian, R., Wood, D., 2016. Selection of high-rate gas well completion designs applying multi-criteria
decision making and hierarchy methods. J. Nat. Gas Sci. Eng. http://www.elsevier.com/locate/jngs.
Table 10.4 Priority preference vector matrix derived from Table 10.3.
Priority Preference Vector Matrix For Gas Well Completion Scenario
Time
Production
Costs
Equipment
MB
0.6333
0.0581
0.7235
0.5907
BB
0.2605
0.2299
0.1932
0.3338
OBB
0.1062
0.7120
0.0833
0.0755
Total
1.0000
1.0000
1.0000
1.0000
BB, big bore; MB, monobore; OBB, optimized big bore.
From Khosravanian, R., Wood, D., 2016. Selection of high-rate gas well completion designs applying multi-criteria
decision making and hierarchy methods. J. Nat. Gas Sci. Eng. http://www.elsevier.com/locate/jngs.
410
Methods for Petroleum Well Optimization
MB decision. Quite extreme weightings toward production are required for OBB to be
the appropriate completion to select. The value of AHP is that its structure and tabular
input/output can help to highlight how technical and strategic assumptions might
change such a decision.
In the analysis presented earlier, all input assumptions are crisp deterministic
assumptions. In situations in which there is large uncertainty surrounding the input
assumptions, a fuzzy AHP analysis can be performed. This replaces crisp single input
Table 10.5 Criteria-weighting matrices and the calculation of the relative weighting preference
vector.
From Khosravanian, R., Wood, D., 2016. Selection of high-rate gas well completion designs applying multi-criteria
decision making and hierarchy methods. J. Nat. Gas Sci. Eng. http://www.elsevier.com/locate/jngs.
Well completion optimization by decision-making
411
numbers with fuzzy sets, typically of three numbers (namely, low, medium, and high),
and performs fuzzy set analysis and arithmetic (see, for example, Junior et al., 2014). This
approach is not considered here, although such an approach is likely to provide further
insight into more complex decisions.
10.2.1.6 Using TOPSIS to aid completion selection in high-rate gas wells
The TOPSIS method is an alternative MCDM to AHP that can provide complementary
insight into decision problems and typically is easier and quicker to compute. The
method compares a number of alternatives (such as gas well completion techniques) in
Table 10.6 Absolute weighting matrix calculated to provide the absolute weight value for each well
completion alternative.
The right-hand column displays the absolute weight values for the alternatives, with the highest ranked value highlighted,
indicating the decision to select based upon the criteria and weighting assumptions made.
BB, big bore; MB, monobore; OBB, optimized big bore.
From Khosravanian, R., Wood, D., 2016. Selection of high-rate gas well completion designs applying multi-criteria
decision making and hierarchy methods. J. Nat. Gas Sci. Eng. http://www.elsevier.com/locate/jngs.
Figure 10.16 Absolute weights calculated for the gas well completion scenario based upon the as-
sumptions and calculations shown in Tables 10.2e10.6. From Khosravanian, R., Wood, D., 2016. Se-
lection of high-rate gas well completion designs applying multi-criteria decision making and hierarchy
methods. J. Nat. Gas Sci. Eng. http://www.elsevier.com/locate/jngs.
412
Methods for Petroleum Well Optimization
either semiquantitative and/or quantitative scores. It establishes the difference between
each alternative score and the best assessed score for each criterion. Those differences are
normalized, typically using a linear square root sum of squares formula or nonlinear
vector formula (Huang et al., 2011), to establish a normalized matrix of alternatives and
criteria. Weights (that is, priorities) are then applied to each criterion to derive a
weighted normalized matrix. Best-case and worst-case values are identiﬁed among the
alternatives for each criterion. The differences between each matrix element and the best
and worst cases are calculated to establish two geometric distance matrices: (1) the
geometric distance between each alternative and a best assessed case (often referred to as
the “positive ideal”) for each criterion alternative and (2) the geometric distance
between each alternative and least attractive or worst case (often referred to as the
Table 10.7 Sensitivity analysis of relative priority weightings and their impact on the gas well
completion alternative selected.
Input relative weightings pairwise criteria assessments for sensitivity cases
PvT
CvT
EvT
CvP
EvP
EvC
Strategic objectives/priorities
Base case
5.000
3.000
1.000
0.143
0.111
0.500
Maximize production
Case 1
1.000
1.000
1.000
1.000
1.000
1.000
Equal weight to all criteria
Case 2
1.000
5.000
1.000
2.000
1.000
0.333
Minimize costs
Case 3
0.333
1.000
0.200
1.000
1.000
1.000
Minimize time
Case 4
1.000
1.000
5.000
1.000
3.000
2.000
Minimize equipment
Case 5
3.000
1.000
1.000
0.333
0.500
1.000
Moderate bias toward production
Case 6
2.000
1.000
1.000
0.500
1.000
1.000
Slight bias toward production
PvT refers to cell value in: row ¼ production; column ¼ time
CvT refers to cell value in: row ¼ costs; column ¼ time
EvT refers to cell value in: row ¼ equipment; column ¼ time
CvP refers to cell value in: row ¼ costs; column ¼ production
EvP refers to cell value in: row ¼ equipment; column ¼ production
EvC refers to cell value in: row ¼ equipment; column ¼ costs
Calculated absolute weightings for sensitivity case assumptions
Case
Strategic objectives/priorities
MB
BB
OBB
Sum
Base case
Maximize production
0.2607
0.2345
0.5048
1.0000
Case 1
Equal weight to all criteria
0.5014
0.2544
0.2442
1.0000
Case 2
Minimize costs
0.5657
0.2329
0.2013
1.0000
Case 3
Minimize time
0.5510
0.2510
0.1980
1.0000
Case 4
Minimize equipment
0.5342
0.2800
0.1858
1.0000
Case 5
Moderate bias toward production
0.3730
0.2487
0.3783
1.0000
Case 6
Slight bias toward production
0.4423
0.2542
0.3035
1.0000
Notes: (1) Base case assumptions are ones used in Tables 10.2e10.5. (2) Alternative selected is highlighted in each case.
BB, big bore; MB, monobore; OBB, optimized big bore.
From Khosravanian, R., Wood, D., 2016. Selection of high-rate gas well completion designs applying multi-criteria
decision making and hierarchy methods. J. Nat. Gas Sci. Eng. http://www.elsevier.com/locate/jngs.
Well completion optimization by decision-making
413
“negative ideal”) for each criterion. Two square root sums of square vectors for all
criteria for each alternative are then extracted from the two geometric-distance matrices,
and these are used to establish a similarity index (expressed on a 0 to 1 scale or, sometimes
more conveniently, on a 0%e100% scale). The similarity index is a ratio between the
distance (separation) from the least attractive case and the sum of the distances from the
least attractive and best cases. The alternatives are then ranked according to their simi-
larity index; the best alternative (rank 1) has the highest similarity index and is recom-
mended as the alternative to select.
From the aforementioned description, it is clear that there are both similarities and
differences between the TOPSIS and AHP methods. To be effective, the value of each
criterion should be progressively increasing or decreasing across the alternatives. If the
variation across the alternatives for a criterion is highly nonlinear, then a nonlinear vector
formula for normalization improves the accuracy of the TOPSIS calculations. For both
AHP and TOPSIS, it is important that the criteria are independent of each other and that
key criteria are not excluded or inadvertently omitted from the calculation. To illustrate
this point, and how the gas well completion scenario might be addressed with the
TOPSIS method, a slightly different set of criteria are used based upon a mixture of
absolute quantitative values for three criteria (time, production, and cost, which are the
same criteria as used in the AHP analysis earlier, but with a different assessment scale) and
a semiquantitative score assessment for the “risk” criterion, which replaces the
“equipment” criteria used in the AHP analysis.
The “risk” criterion is measured on a 0e10 scale. 0 is equivalent to no risk, whereas
10 is equivalent to certain failure, with other grades of risk on this scale expressed
numerically and linguistically as follows: 1 ¼ very low; 3 ¼ low; 5 ¼ moderate;
7 ¼ high; and 9 ¼ extreme. The risk criteria as used in the well completion scenario
address the risk of not being able to successfully land the completion in the wellbore
without encountering technical problems. High-risk alternatives are more likely to also
involve cost and time penalties. A case could therefore be argued that cost, time, and risk
are not fully independent of each other, although that is the assumption made here.
Table 10.8 shows the alternatives and criteria matrix for the gas well completion
scenario evaluated here by the TOPSIS method. For three of the criteria, low values are
better than high values, but for the production criterion, high values are better than low
values. The best and the worst cases (the positive ideal and negative ideal) are extracted
from the alternatives and criteria matrix and are listed in Table 10.8. These values form
the basis of subsequent difference/distance calculations.
414
Methods for Petroleum Well Optimization
It is clear from Table 10.8 that the MB alternative has the best-case scores for two of
the criteria (time and costs) and the OBB alternative has the best-case scores for the other
two criteria (production and risk). On the other hand, the BB alternative has inter-
mediate scores for all four criteria.
The next steps of the TOPSIS calculation are to calculate differences between each
alternative value and the negative ideal, normalize those differences, and weight the
normalized differences. These three steps are shown in the upper, middle, and lower
matrices displayed in Table 10.9, respectively.
The differences in the upper matrix in Table 10.9 are expressed in absolute terms
to avoid negative numbers in the difference matrices. The normalization metric in
Table 10.9 is the square root sum of squares vector of each column of the upper matrix,
which is used as the quotient to establish the values in the middle matrix. Once weights
are applied, the values of the lower matrix (Table 10.9) are again assessed for the best-case
and worst-case values for each criterion, and those values are listed in the lower two rows
in Table 10.9.
The next steps are to establish the differences between each of the values in the lower
matrix of Table 10.9 and the best- and worst-assessed cases, to derive the top two
matrices displayed in Table 10.10.
Table 10.8 Alternatives and criteria matrix for the three gas well completion alternatives based on
four criteria with the weights applied to each criterion listed.
Time (days)
Production
(mmscf/day)
Costs
($ millions)
Risk (1 ¼ low;
9 ¼ extreme)
MB
71
105
11
7
BB
87
168
14
5
OBB
112
315
24
3
Low
High
Low
Low
Criteria objectives
Good
Good
Good
Good
Best assessed case:
71
315
11
3
Worst assessed case:
112
105
24
7
Criteria weights (w):
0.40
0.20
0.10
0.30
Note: This is the starting point for TOPSIS analysis. Note the mixture of quantitative and semiquantitative assessment
scores and that the weights of all the criteria should sum to 1.
BB, big bore; MB, monobore; OBB, optimized big bore; TOPSIS, technique for order preference by similarity to the ideal
solution.
From Khosravanian, R., Wood, D., 2016. Selection of high-rate gas well completion designs applying multi-criteria
decision making and hierarchy methods. J. Nat. Gas Sci. Eng. http://www.elsevier.com/locate/jngs.
Well completion optimization by decision-making
415
Table 10.9 Difference matrix (expressed in absolute terms) from the worst case (upper), normalized
difference matrix (middle), and weighted normalized difference matrix (lower).
Time (days)
Production
(mmscf/day)
Costs
($ millions)
Risk (1 ¼ low;
9 ¼ extreme)
MB
41
0
13
0
BB
25
63
10
2
OBB
0
210
0
4
Square root of the sum of the squared differences used for normalization
Normalization metric
48.0
219.2
16.4
4.5
Normalized matrix
MB
0.8538
0.0000
0.7926
0.0000
BB
0.5206
0.2873
0.6097
0.4472
OBB
0.0000
0.9578
0.0000
0.8944
Weighted normalized matrix
MB
0.3415
0.0000
0.0793
0.0000
BB
0.2082
0.0575
0.0610
0.1342
OBB
0.0000
0.1916
0.0000
0.2683
Best assessed case:
0.3415
0.1916
0.0793
0.2683
Worst assessed case:
0.0000
0.0000
0.0000
0.0000
BB, big bore; MB, monobore; OBB, optimized big bore.
From Khosravanian, R., Wood, D., 2016. Selection of high-rate gas well completion designs applying multi-criteria
decision making and hierarchy methods. J. Nat. Gas Sci. Eng. http://www.elsevier.com/locate/jngs.
Table 10.10 The differences (i.e., weighted distances) from the best- and worst-assessed cases for
each alternative with respect to each criterion.
Difference in weighted matrix from the best-assessed case
Time
Production
Costs
Risk
MB
0.0000
0.1916
0.0000
0.2683
BB
0.1333
0.1341
0.0183
0.1342
OBB
0.3415
0.0000
0.0793
0.0000
Difference in weighted matrix from the worst-assessed case
MB
0.3415
0.0000
0.0793
0.0000
BB
0.2082
0.0575
0.0610
0.1342
OBB
0.0000
0.1916
0.0000
0.2683
Dþ ¼ square root of sum of squares of each alternative difference from best case
D ¼ square root of sum of squares of each alternative difference from worst case
C ¼ similarity index (%). Highest C value between zero and 100% is the best
Dþ
D
C (%)
Rank (1 ¼ best)
MB
0.3297
0.3506
51.5%
2
BB
0.2325
0.2615
52.9%
1
OBB
0.3506
0.3297
48.5%
3
Note: These are the top two matrices from which are extracted, row by row, the Dþ and D vectors, by taking the square
root sum of squares of each row in the top two matrices.
BB, big bore; MB, monobore; OBB, optimized big bore.
From Khosravanian, R., Wood, D., 2016. Selection of high-rate gas well completion designs applying multi-criteria
decision making and hierarchy methods. J. Nat. Gas Sci. Eng. http://www.elsevier.com/locate/jngs.
416
Methods for Petroleum Well Optimization
The similarity index (C) is shown in the lower part of Table 10.10 with a ranking that
is based upon it. C ¼ D/(Dþ plus D), and it identiﬁes the alternative which has the
greatest geometric distance from the worst case. For the criteria assumptions and the
weights applied in Tables 10.8e10.10, it is the BB alternative that is ranked highest, but
only just. These results are also shown graphically in Fig. 10.17. In fact, the three al-
ternatives show quite similar values of C for these “base case” assumptions. It is inter-
esting that BB is the highest-ranked completion method, because, as noted earlier, it does
not possess the best-case assessment for any one of the individual criteria. However,
because the other two alternatives both possess two best and two worst cases for the
criteria set, essentially pulling in opposite directions, they counteract each other,
allowing the BB alternative to be favored as an effective compromise between the other
two extremes.
To illustrate the effect of different assumptions for the well completion scenario, a
sensitivity analysis is presented in Tables 10.11 and 10.12 for different strategic as-
sumptions, by changing the criteria weighting (that is, the strategic priorities) in
Table 10.8.
The base case weightings prioritize time and risk, which are criteria that pull in
opposite directions, with time favoring MB and risk favoring OBB. The sensitivity
analysis results show that as strategic priority shifts to other criteria, the completion
option that is ranked number 1 also changes, even though the technical assessments for
Figure 10.17 Similarity index calculated for the gas well completion scenario based upon the as-
sumptions and calculations shown in Tables 10.8e10.10. From Khosravanian, R., Wood, D., 2016. Se-
lection of high-rate gas well completion designs applying multi-criteria decision making and hierarchy
methods. J. Nat. Gas Sci. Eng. http://www.elsevier.com/locate/jngs.
Well completion optimization by decision-making
417
each criterion are unchanged. Equal weighting across all criteria (case 1) favors OBB, but
the other two completion options also achieve high similarity indexes, especially the BB
alternative. Priority weightings in favor of costs select the BB alternative, with MB not
far behind (case 2). Priority weightings in favor of risk and production select the OBB
alternative and do so by a wide margin in the case of production priority (cases 3e5).
The only sensitivity case shown that selects the MB alternative (Tables 10.11 and 10.12)
involves the criteria of time and cost being prioritized by the weightings (case 6).
Table 10.11 Sensitivity analysis cases run for a range of criteria weightings (priorities) based upon
speciﬁc strategic objectives and their impact on the gas well completion alternative selected by
TOPSIS analysis.
w(time)
w(production) w(cost)
w(risk)
Sum of
weights
Strategic objectives/
priorities
Base case
0.400
0.200
0.100
0.300
1.000
Prioritize time
Case 1
0.250
0.250
0.250
0.250
1.000
Equal weight to all
criteria
Case 2
0.200
0.200
0.400
0.200
1.000
Prioritize costs
Case 3
0.200
0.200
0.200
0.400
1.000
Prioritize risk
Case 4
0.100
0.700
0.100
0.100
1.000
Strongly prioritize
production
Case 5
0.150
0.450
0.200
0.200
1.000
Moderately prioritize
production
Case 6
0.400
0.100
0.400
0.100
1.000
Prioritize time and
costs
TOPSIS, technique for order preference by similarity to the ideal solution.
From Khosravanian, R., Wood, D., 2016. Selection of high-rate gas well completion designs applying multi-criteria
decision making and hierarchy methods. J. Nat. Gas Sci. Eng. http://www.elsevier.com/locate/jngs.
Table 10.12 Sensitivity analysis case results run for a range of criteria weightings (priorities) based
upon speciﬁc strategic objectives showing their impact on the gas well completion alternative
selected by TOPSIS analysis.
Case
Strategic objectives/priorities
MB C%
BB C%
OBB C%
Base case
Prioritize time
51.54%
52.93%
48.46%
Case 1
Equal weight to all criteria
47.06%
51.91%
52.94%
Case 2
Prioritize costs
57.88%
60.17%
42.12%
Case 3
Prioritize risk
36.47%
51.13%
63.53%
Case 4
Strongly prioritize production
14.69%
31.85%
85.31%
Case 5
Moderately prioritize production
30.40%
39.99%
69.60%
Case 6
Prioritize time and costs
78.05%
65.39%
21.95%
BB, big bore; MB, monobore; OBB, optimized big bore; TOPSIS, technique for order preference by similarity to the ideal
solution.
From Khosravanian, R., Wood, D., 2016. Selection of high-rate gas well completion designs applying multi-criteria
decision making and hierarchy methods. J. Nat. Gas Sci. Eng. http://www.elsevier.com/locate/jngs.
418
Methods for Petroleum Well Optimization
Collectively, the sensitivity analysis highlights that with certain strategic priorities, it
could be reasonably justiﬁable to select either one of the three high-rate gas well
completion alternatives evaluated. The TOPSIS model also allows further sensitivity
analysis of the criteria assessments to be easily conducted (for example, by changing the
values in the top row of Table 10.8). Ultimately, decision-makers can be guided by their
preferred criteria assumptions and weightings preferences to select the highest-ranking
alternative. When making such decisions, it often helps to be aware of what assump-
tions and priorities might justify the selection of a different alternative.
10.2.2 Candidate well selection parameters for hydraulic fracturing
10.2.2.1 Hydraulic fracturing treatment
HF is one of the well-stimulation techniques, which is the most common primary
engineering tool for improving well productivity, particularly in low and moderate
permeability reservoirs. However, HF treatment is one of the most complex production
procedures performed on wells, because of the high rates and pressures, large volume of
materials injected, and continuous blending of materials, and so it needs lots of equip-
ment. In general, HF is the process of pumping a ﬂuid at a high enough pressure into the
wellbore to break the formation. Once the formation breaks down, a fracture is formed,
and by moving the injected ﬂuid into the fracture, the fracture is propagated (Econo-
mides and Nolte, 2000). There are several applications for HF which are stated in the
literature. HF treatment was used for the ﬁrst time in 1947, which was to bypass near-
wellbore drilling ﬂuid damage (Gidley, 1989), but nowadays the main goal is increasing
the ﬂow rate of oil and gas from a low permeability reservoir. Clearly, lower permeability
reservoirs have a corresponding greater beneﬁt in terms of productivity increases from
larger treatment sizes (Bellarby, 2009). Regarding the operational complexities and the
large capital investment, the candidate well selection should be considered as a vital step
in the whole HF treatment process. Fracture geometry and the stress status are illustrated
in Fig. 10.18.
10.2.2.2 Selecting effective parameters for candidate well selection
Although several effective criteria for candidate well section (CWS) have been suggested
by different authors and researchers, various studies have shown that wells with high
deliverability have high potential to be the best candidates (Green et al., 2006). The
history of recognizing the effective criteria for CWS has its roots in 1970, when Howard
and Fast described eight criteria for candidate well selection:
1. depletion state of the reservoir,
2. formation permeability,
Well completion optimization by decision-making
419
3. previous stimulation treatments,
4. well productivity history,
5. offset production history,
6. location of watereoil and gaseoil contact,
7. fracture conﬁnement, and
8. degree of consolidation.
It is noteworthy that some of the main criteria have subparameters, which are listed in
Table 10.13. Some criteria in Table 10.13 need more explanation:
•
The operational parameters refer to any extra essential repreparation of wells before
HF treatment and the length of wellbore to the target formation.
•
The completion process, and speciﬁcally perforation orientation that may affect the
direction in which fractures naturally tend to grow, must be considered.
•
Fracture twisting (tortuosity) can be a common event in wells through the HF
treatment of which the main cause is the orientation discrepancy between perforation
and horizontal stress (Fig. 10.19).
Figure 10.18 Fracture geometry and the stress status regarding the fracture initiation and
propagation.
420
Methods for Petroleum Well Optimization
Table 10.13 Effective criteria in hydraulic fracturing and their dimensions.
No.
Criteria
Unit
1.
Water cut
%
2.
Formation thickness
m
3.
Operational parameters
Qualitative
4.
Productivity
STB/day/
psi
5.
Permeability
mD
6.
Well direction
•
Horizontal
•
Directional
•
Vertical
Qualitative
7.
Height conﬁnement
Qualitative
8.
Well completion method
•
Open hole
•
In same direction
•
In opposite direction
•
Intermediate
Qualitative
9.
Damaged depth
m
10.
Sand production
Lb/bbl
11.
Production method
•
Naturally Flow
•
Artiﬁcial lift
•
Dead wells
Qualitative
12.
Reservoir pressure
Psi
13.
Porosity
%
14.
Field type
•
Offshore
•
Onshore
Qualitative
Figure 10.19 Fracture twisting due to the perforation orientation in comparison with stress
anisotropy.
Well completion optimization by decision-making
421
10.2.2.3 Candidate well selection for hydraulic fracturing treatment based on
analytic hierarchy process
The AHP (Saaty, 2008a) is probably the best known, and most broadly used, multi-
criteria attribute method. The AHP method provides a procedure for determining the
relative importance of a set of actions in a multicriteria decision problem. This approach
makes it possible to incorporate judgments on imperceptible qualitative criteria as well as
perceptible quantitative criteria. The AHP method generally is based on three principles:
•
construction of a hierarchy;
•
comparative judgment of the alternatives and criteria; and
•
synthesis of the priorities.
10.2.2.3.1 The ﬁrst step
AHP initially breaks down a complex MCDM problem into a hierarchy of interrelated
decision elements (subobjectives, attributes, criteria, alternatives, etc.). With AHP
, the
objectives, criteria, and alternatives are arranged in a hierarchical structure similar to a
family tree: the overall goal of the problem at the top, multiple criteria that deﬁne al-
ternatives in the middle, and decision alternatives at the bottom (Albayrak and Erensal,
2004).
10.2.2.3.2 The second step
Once the problem has been decomposed and the hierarchy is constructed, the priori-
tization procedure starts to determine the relative importance of the criteria within each
level (Dagdeviren, 2008). The relative “priority” given to each element in the hierarchy
is determined by comparing the pairwise contribution of each element at a lower level in
terms of the criteria (or elements) with a causal relationship (Macharis et al., 2004). In
AHP
, multiple pairwise comparisons are based on a standardized comparison scale of
nine levels (Table 10.14; Figueira et al., 2005). Let C{Cj|j ¼ 1,2,., n} be the set of
demanding criteria. The result of the pairwise comparison on criteria can be summa-
rized in an (n n) evaluation matrix A, in which every element aij(i, j ¼ 1, 2,., n) is the
quotient of the weights of the criteria, as shown in Eq. (10.5):
Table 10.14 Random consistency indexes (RI).
R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
RI 0.00 0.00 0.58 0.9 1.12 1.24 1.32 1.41 1.45 1.49 1.51 1.48 1.56 1.57 1.59
From Saaty, T.L., 2008a. Decision Making for Leaders: The Analytic Hierarchy Process for Decisions in a Complex
World. R
WS Publications, Pittsburgh, Pennsylvania.
422
Methods for Petroleum Well Optimization
A ¼
0
B
B
B
B
B
B
B
B
@
a11
a12
.
a1n
a21
a22
.
a2n
.
.
.
.
an1
an2
.
ann
1
C
C
C
C
C
C
C
C
A
aii ¼ 1; aji ¼ 1
aij; aijs0
(10.6)
10.2.2.3.3 The third step
The mathematical process commences to normalize and ﬁnd the relative weights for
each matrix. The relative weights are given by the right eigenvector (W) corresponding
to the largest eigenvalue (lmax), as:
0
B
B
B
B
B
B
B
B
@
a11
a12
.
a1n
a21
a22
.
a2n
.
.
.
.
an1
an2
.
ann
1
C
C
C
C
C
C
C
C
A
0
B
B
B
B
B
B
B
B
@
W1
W2
.
Wm
1
C
C
C
C
C
C
C
C
A
¼
0
B
B
B
B
B
B
B
B
@
lmaxW1
lmaxW2
.
lmaxWn
1
C
C
C
C
C
C
C
C
A
0AW ¼ lmaxW
(10.7)
In the case that the pairwise comparisons are completely consistent, the matrix A has
rank 1 and lmax ¼ 1. In that case, weights can be obtained by normalizing any of the rows
or columns of A (Wang and Yang, 2007).
It is noteworthy that the quality of the output of the AHP is strictly related to the
consistency of the pairwise comparison judgments (Dagdeviren, 2008). In the case that
the inconsistency of the pairwise comparison matrices is limited, (lmax) slightly deviates
from n. This deviation (lmax  n) is used as a measure for inconsistency. This measure is
divided by n e 1. This yields the average of the other eigenvectors (Forman, 1998).
Hence, the consistency index (CI), is given by Eq. (10.8).
CI ¼ ðlmax  nÞ=n  1
(10.8)
The ﬁnal consistency ratio (CR), which can conclude whether the evaluations are
sufﬁciently consistent, is calculated as the ratio of the CI and the random index (RI), as
indicated in Eq. (10.9).
CR ¼ CI=RI
(10.9)
The random CIs (given in Table 10.14) correspond to the degree of consistency that
automatically arises when completing random reciprocal matrices with the values on the
1e9 scale. The number 0.1 is the accepted upper limit for CR. If the ﬁnal CR exceeds
Well completion optimization by decision-making
423
this value, the evaluation has to be repeated to improve consistency. The measurement of
consistency can be used to evaluate the consistency of decision-makers as well as the
consistency of all the hierarchy (Wang and Yang, 2007).
With the help of the AHP procedure, the weight of each criterion is computed.
Table 10.15 shows the ﬁnal weight of each criterion and some other parameters related
to the consistency of the pairwise comparison matrix. As revealed in this table, the CR of
the pairwise comparison matrix is 0.064, which is smaller than the value 0.1; therefore,
the weights obtained from AHP computations were shown to be consistent (Wang and
Yang, 2007). These weights have been approved by decision managers (DMs).
Analysis showed that productivity has the highest weight (13.9%) of the 14 criteria,
while water cut and production method have the lowest weights, both (1.4%). The
descending order of 14 effective parameters based on their weights is as follows: pro-
ductivity, height conﬁnement, operational parameter, well completion method, for-
mation thickness, permeability, reservoir pressure, ﬁeld type, sand production, well
direction, porosity, water cut, and production method (Mehrgini et al., 2014).
10.2.3 Production well and layer selection for acidizing
We now take a case study from China (Xian et al., 2017). For a thick, multilayer, and
heterogeneous reservoir, there are many factors affecting the production well and layer
selection for the acidizing-water shutoff joint operation, and the relationships among
various factors are complex and nonlinear. To control water cut and increase oil pro-
duction, water shutoff and acidizing are often performed (Liu et al., 2014). However,
most of the acid will enter the high permeability layer when acidiﬁcation occurs, and a
Table 10.15 Results obtained from the AHP method calculation.
Criteria
Weight (%)
lmax, CI, CR, and RI
Productivity
13.9
lmax ¼ 7.5061
CI ¼ 0.08435
RI ¼ 1.32
CR ¼ 0.0639
Height conﬁnement
12.5
Operational parameters
12.5
Damaged depth
11.1
Well completion method
9.7
Formation thickness
8.3
Permeability
6.9
Reservoir pressure
5.6
Field type
5.6
Sand production
4.2
Well direction
4.2
Porosity
2.8
Water cut
1.4
Production method
1.4
AHP
, analytic hierarchy process; CI, consistency index; CR, consistent ratio; RI, random index.
424
Methods for Petroleum Well Optimization
small amount of acid will enter the low permeability layer, which will not achieve the
desired results. To show how to reach the appropriate solution for a well, the inﬂuence
factors of water shutoff and acidizing, and the parameters’desirability are analyzed, based
on the physical properties of reservoir and the development of the production well. Nine
factors are selected as the index system for production well and layer selection for the
acidizing-water shutoff joint operation: average effective porosity; average effective
permeability; perforated thickness; water cut; coefﬁcient of permeability variation; rate
of water cut; current formation pressure; remaining reserves; and skin factor. The bigger
the evaluation index is, the better the result is. Water cut showed S-type growth with the
development time, so it can be described by the logistic cycle model (Eq. 10.10) where fw
is the water cut, (fw)max is the limited water cut (here it is equal to 0.98), t is the
development time, and a and b are the coefﬁcients.
fw ¼
ðfwÞmax
ð1 þ aebtÞ
(10.10)
By evaluating the logarithm of Eqs. (10.10 and 10.11) can be obtained.
ln
ðfwÞmax
fw
 1

¼ lna  bt
(10.11)
According to the data for the water cut in the production process, a and b can be
obtained by using linear regression analysis. Then the water cut at different times can be
calculated, and the rate of water cut can be calculated by the derivation of water cut. The
nine evaluation indexes are standardized by Eq. (10.12) where xi is the membership of
index x, xmin is minimum value of x, and xmax is maximum value of x.
xi ¼
xi  xmin
xmax  xmin
(10.12)
Fuzzy ﬁtness matrix A can be obtained by Eq. (10.13):
A ¼
2
6
6
6
6
6
6
6
6
6
6
6
6
4
C
index 1
index 2
.
index n
Well1
x11
x12
.
x1n
Well2
x21
x22
.
x2n
.
.
.
.
.
Welln
xn1
xn2
.
xnn
3
7
7
7
7
7
7
7
7
7
7
7
7
5
(10.13)
Well completion optimization by decision-making
425
The linear correlation between each factor and the objective function is measured by
the Pearson correlation coefﬁcient. The equation for this calculation is as follows, where
xi is the membership of index x, yi is the membership of index y, x is the mean value of x,
and y is the mean value of y:
Cxy ¼
Sðxi  xÞðyi  yÞ
ð
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
Pn
i¼1ðxi  xÞ2
q
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
Pn
i¼1ðyi  yÞ2
q
Þ
(10.14)
10.2.3.1 The establishment of a fuzzy complementary matrix
The fuzzy complementary matrix (R ¼ (rij)n  n) represents the relatively important
degree between the indexes on the universe of discourse U. Element rij represents the
relatively important degree between index xi and index xj, and the bigger rij is, the more
important xi than xj. Here, the 0.1e0.9 numerical scale is used to quantitatively describe
the relative importance between two indexes, as shown in Table 10.15. xi is more
important than xj when rij > 0.5, and xj is more important than xi when rij < 0.5. In
addition, they are equally important when rij ¼ 0.5. In the past, the value of rij was
obtained by experience, but this potentially leads to erroneous results. To solve this
problem, the correlation coefﬁcient is divided into ﬁve intervals: [0,0.2], [0.2,0.4],
[0.4,0.6], [0.6,0.8], and [0.8,1]. The important degree of the indexes increased in
accordance with the interval, and indexes are equally important when they are in the
same interval. Based on this, the relative importance between two indexes can be
decided.
Following Table 10.15, the fuzzy complementary judgment matrix R is obtained:
R ¼
2
6
6
6
6
6
6
6
6
4
r11
r12
.
r1n
r21
r22
.
r2n
.
.
.
.
rn1
rn2
.
rnn
3
7
7
7
7
7
7
7
7
5
(10.15)
Matrix A has the following main properties: rij is equal to 0.5 while i ¼ j; rji ¼ 1  rij;
rij ¼ rik  rjk þ 0.5, i,j,k ¼ 1, 2, ., n.
10.2.3.2 Determination of fuzzy consistent matrix index weight
For the fuzzy complementary matrix, Eq. (10.16) can be applied:
rij ¼ 0:5 þ a

wi  wj

i; j ¼ 1; 2; .; n
(10.16)
426
Methods for Petroleum Well Optimization
So, the index weight can be calculated by Eq. (10.17), where wi is the weight of the
index i, and to ensure the weight of wi  0, the conditions a  (n  1)/2 must be met.
Here, a ¼ (n  1)/2:
Wi ¼ 1
n  1
2a þ 1
na
Xn
k¼1rik
(10.17)
Here, let wij ¼ wi, and we can get the index weight matrix W:
W ¼
2
6
6
6
6
6
6
6
6
4
w11
w12
/
w1n
w21
w22
/
w2n
/
/
/
/
wn1
wn2
/
wnn
3
7
7
7
7
7
7
7
7
5
(10.18)
We multiply matrix A by matrix W (Eq. 10.18) and that can give us the judgment
vector of each well.
VðiÞ ¼
Xn
j¼1wij  Aij
(10.19)
According to the value of V
, the oil well can be sorted for the acidizing-water shutoff
joint operation. Then, the layer of the well for the acidizing or water-shutoff operation is
selected. The layer that has high permeability should be selected for the water-shutoff
operation, and the layer that has low permeability or large skin factor should be acidiﬁed.
This is a very complex reservoir due to its multilayer and heterogeneity structure. It is
not possible to both control water cut and increase oil production by carrying out
acidizing or water shutoff separately. Therefore, the acidizing-water shutoff joint
operation has been adopted. There are many factors that affect the effectiveness of the
work, and well and layer selection must be carried out properly. In the example shown
here, nine wells from X ﬁeld are used as a sample database to evaluate the effectiveness of
the other ﬁve candidate wells. The typical characteristic parameters of each well are
shown in Table 10.17.
The ﬁrst nine parameters in Table 10.16 were standardized by Eq. (10.12), and then
the fuzzy ﬁtness matrix A was established as follows.
Well completion optimization by decision-making
427
Table 10.17 Typical characteristic parameters of 14 wells.
Well
H,m
f
K,mD
KV
fw
WV
Unp
Pp,MPa
S
PR
A1
21
0.27
318.1
0.5
0.7
0.6
0.6
12
5
0.65
A2
25
0.29
1079.7
0.9
0.5
0.4
0.8
10
18
4.2
A3
29
0.28
885.4
1
0.7
0.8
0.8
11
20
5
A4
31
0.30
1675.3
0.9
0.4
0.6
0.8
14
37
10
A5
33
0.25
298.6
0.8
0.3
0.5
0.8
12
29
10
A6
34
0.27
672.3
0.9
0.4
0.5
0.6
12
15
8.2
A7
29
0.24
290.2
0,7
0.5
0.5
0.8
13
28
7.2
A8
18
0.22
489.2
0.9
0.9
0.8
0.7
11
10
2.4
A9
20
0.25
510.9
0.9
0.1
0.4
0.8
14
15
4.5
A10
25
0.28
891.2
0.5
0.7
0.8
0.8
15
15
6.5
A11
28
0.19
765.9
0.6
0.9
0.9
0.6
12
39
7.5
A12
25
0.21
909.3
0.7
0.4
0.6
0.6
15
15
2.2
A13
16
0.25
1350.6
0.8
0.5
0.6
0.7
14
12
3.5
A14
19
0.29
597.3
0.5
0.4
0.5
0.5
14
22
3.2
Table 10.16 Numerical scale of 0.1e0.9 of fuzzy complementary judgment matrix.
Scale
Deﬁnition
Illustration
Corresponding
correlation
coefﬁcient
0.5
Equally
important
One factor is equally important
as the other factor
[0,0.2]
0.6
Weakly
important
One factor is weakly more
important than the other factor
[0.2,0.4]
0.7
Essentially
important
One factor is essentially more
important than the other factor
[0.4,0.6]
0.8
Strongly
important
One factor is strongly more
important than the other factor
[0.6,0.8]
0.9
Extremely
important
One factor is extremely more
important than the other factor
[0.8,1]
0.1,0.2,0.3,0.4
Opposite
comparison
If the result of comparing factor i to
factor j is rij, the result of comparing factor
j to factor i is rji ¼ 1  rij
428
Methods for Petroleum Well Optimization
A ¼
2
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
4
0:32
0:73
0:02
0
0:75
0:40
0:33
0:40
0
0:53
0:91
0:57
0:80
0:50
0
1
0
0:38
0:74
0:82
0:43
1
0:75
0:80
1
0:20
0:44
0:84
1
1
0:80
0:38
0:40
1
0:80
0:94
0:95
0:55
0:01
0:60
0:25
0:20
1
0:40
0:71
1
0:73
0:28
0:80
0:38
0:20
0:33
0:40
0:29
0:74
0:45
0
0:40
0:50
0:20
1
0:60
0:68
0:16
0:27
0:14
0:80
1
0:80
0:67
0:20
0:15
0:26
0:55
0:16
0:80
0
0
1
0:80
0:29
0:53
0:82
0:43
0
0:75
0:90
1
1
0:29
0:68
0
0:34
0:20
1
1
0:33
1
0:29
0
0:18
0:45
0:40
0:38
0:40
0:33
1
0:29
0:05
0:55
0:77
0:60
0:50
0:40
0:67
0:80
0:21
0:21
0:91
0:22
0
0
0:20
0
0:80
0:50
3
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
5
(10.20)
Then the correlation coefﬁcients between each index and objective function
(production-increasing ratio) were calculated using Eq. (10.13). The results are shown in
Table 10.18.
Using Table 10.17, the fuzzy complementary judgment matrix R was obtained:
Well completion optimization by decision-making
429
R ¼
2
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
4
C
H
f
K
KV
fw
Wv
Unp
Pp
S
H
0:5
0:9
0:9
0:8
0:8
0:9
0:7
0:9
0:6
f
0:1
0:5
0:5
0:4
0:4
0:5
0:3
0:5
0:2
K
0:1
0:5
0:5
0:4
0:4
0:5
0:3
0:5
0:2
KV
0:2
0:6
0:6
0:5
0:5
0:6
0:4
0:6
0:3
fw
0:2
0:6
0:6
0:5
0:5
0:6
0:4
0:6
0:3
WV
0:1
0:5
0:5
0:4
0:4
0:5
0:3
0:5
0:2
Unp
0:3
0:7
0:7
0:6
0:6
0:7
0:5
0:7
0:4
Pp
0.1
0.5
0.5
0.4
0.4
0.5
0.3
0.5
0.2
S
0:4
0:8
0:8
0:7
0:7
0:8
0:6
0:8
0:5
3
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
5
(10.21)
Then, we can get the judgment vector of each well using Eq. (10.19), and the results
are shown in Table 10.19.
Based on the index weight in Table 10.19, the judgment vector of the last ﬁve wells in
Table 10.17 was calculated, and the results are shown in Table 10.20.
Table 10.19 The weight of each index for acidizing-water shutoff joint operation in X ﬁeld.
Index
H
f
K
KV
fw
WV
Unp
Pp
S
Weight
0.17
0.08
0.08
0.11
0.11
0.08
0.13
0.08
0.15
Table 10.18 Correlation coefﬁcient between each index and objective function.
Index
H
f
K
KV
fw
WV
Unp
Pp
S
Correlation coefﬁcient
0.86
0.12
0.17
0.24
0.22
0.02
0.42
0.02
0.76
430
Methods for Petroleum Well Optimization
Acidizing-water shutoff joint operations have been carried out in the last ﬁve wells in
Table 10.17, and the production-increasing ratio was also obtained. As shown in
Fig. 10.20, the production-increasing ratio increased with the increase of judgment
results. Therefore, the index weight calculated by this method is reasonable and can be
used to help select the wells for the acidizing-water shutoff joint operation.
10.3 Summary
1. The MCDM methods such as AHP and TOPSIS provide useful insight into gas well
completion decisions, particularly when evaluated with a series of sensitivity cases.
For the scenarios being evaluated, namely the selection of MB, BB, and OBB
completions for high-rate gas wells, both AHP and TOPSIS reveal the basis on which
each alternative might be selected, based on key criteria assessments and the priority
weightings applied to them.
2. The key step before a successful HF operation is candidate well selection. In this
chapter, one of the common MCDM approaches, the AHP method, is implemented
to recognize quantitatively the most effective parameters for HF CWS. The 14
criteria deﬁned by combination of DMs and AHP showed that productivity is the
Table 10.20 Judgment result of each candidate well.
Well
A10
A11
A12
A13
A14
Judgment result
0.60
0.59
0.34
0.45
0.33
Figure 10.20 Judgment results and increasing injection ratio of candidate wells. Adapted from Xian,
C., Zhao, L., Luo, Z., Lan, X., 2017. Candidate production well and layer selection method for acidizing-
water shutoff joint operation. Electron. J. Geotech. Eng. 22, Bund. 11.
Well completion optimization by decision-making
431
most signiﬁcant factor (weight is 13.9%), while water cut and production method
(weights are 1.4%) are the least inﬂuencing parameters on candidate well selection.
The quantitative weights of each criteria can be a good input for ranking the target
wells in fractured carbonate reservoirs.
3. Based on the Pearson correlation analysis for production well and layer selection for
acidizing, the primary and secondary relation between the factors is determined, and
the blindness and subjectivity are eliminated. Then FAHP is introduced to determine
the weights of each factor, which makes the decision result more objective and
practical. The method can effectively guide the demodulation of the production well
and can greatly improve the efﬁciency of the acidizing-water shutoff joint operation.
10.4 Problems
Problem 1: Enhanced oil recovery decision-making using the
TOPSIS method
A decision manager wants to start a new strategic project for enhanced oil recovery
(EOR) that depends on proper evaluation, rational understanding of complex re-
lationships, and quantitative assessment of multiple categories, including economic
considerations, dynamic ﬁeld production, production dynamics, risk appraisal, and soft
issues. Use the data given in the paper Novel Enhanced-Oil-Recovery Decision-Making Work
Flow Derived From the Delphi-AHP-TOPSIS Method: A Case Study (https://doi.org/10.
2118/176444-PA) (Fig. 10.21).
1. Present the AHP and TOPSIS excel ﬁle or MATLAB code for analyzing the EOR
projects.
2. Lower progressively the weight of the production dynamics, and observe the changes
in the AHP and TOPSIS rankings.
Figure 10.21 The AHP structure of the decision regarding enhanced oil recovery strategy. AHP,
analytic hierarchy process.
432
Methods for Petroleum Well Optimization
3. Increase progressively the weight of the economic considerations criterion, and
observe the changes in the AHP and TOPSIS.
4. Software has potential for supporting multicriteria decisions. Use at least two of the
below software packages for analyzing the EOR projects.
Software
Web site
1000Minds
http://www.1000minds.com
Analytica
http://www.lumina.com/why-analytica/
Criterium Decision Plus 3.0
http://www.infoharvest.com/ihroot/infoharv/products.asp
DecideIT
http://www.preference.nu/?l ¼ decideit&amp;lan ¼ en
Decision Tools
http://www.palisade.com/decisiontools_suite/
D-Sight
http://www.d-sight.com/
GMAA
http://www.dia.ﬁ.upm.es/wajimenez/GMAA
Hiview 3
http://www.catalyze.co.uk/
Logical Decisions
http://www.logicaldecisions.com
M-MACBETH
http://www.m-macbeth.com
MakeItRational
http://makeitrational.com/multi-criteria-evaluation
OnBalance
http://www.quartzstar.com/
Promax
http://www.cogentus.co.uk/products/
PUrE2
http://www.pureintrawise.org/
TESLA
http://www.quintessa.org/software/tesla.html
The Decision Deck project
http://www.decision-deck.org
Web-HIPRE
http://www.hipre.hut.ﬁ
WINPRE
http://sal.aalto.ﬁ/en/resources/downloadables/winpre
VIP Analysis
http://www.uc.pt/en/feuc/ldias/software/vipa
Problem 2: Optimum selection of sand control method
In this problem, select an optimal sand control method in Fig. 10.22 using a comparison
approach including AHP
, TOPSIS, ELECTRE, PROMETHEE II, and MAVT. The
Figure 10.22 Reservoir completions methods.
Well completion optimization by decision-making
433
typical effective variables for choosing an optimum sand control method include
geological, technical, and economic variables. The well completion engineer has
decided to design a sand control method. Five designs have been proposed by the
company, and eight evaluation criteria have been deﬁned by the experts to select the best
design:
1. production rate,
2. sand control,
3. availability,
4. completion reliability,
5. contractor capability,
6. well completion complexity,
7. capex, and
8. well completion time.
Use your assumption or your case study data for different approaches to build the ranking
for this problem.
Problem 3: Optimum selection of drill bits for drilling operations
Bit selection is an MCDM problem. There are four types of bit candidates (namely, 517,
527, 537, and 617) in speciﬁc formations. They are prioritized and ranked in accordance
with effective criteria, such as speciﬁc energy, formation drillability, cost per foot, and
rate of penetration. What is the ELECTRE I, TOPSIS, and FTOPSIS ranking for this
problem?
Use your assumption or your case study data for different approaches to build the
ranking for this problem.
Nomenclature
fw water cut
H perforated thickness
K average effective permeability
KV coefﬁcient of permeability variation
Pp current formation pressure
PR production-increasing ratio
S skin factor
Unp remaining reserves of oil well
WV rate of water cut
F average effective porosity
434
Methods for Petroleum Well Optimization
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Open-source code
Y
ou can develop your models using open-source code for different tools available
on https://github.com/.
Examples include:
•
Analytic Hierarchy Process (AHP) Tool
•
Fuzzy Analytic Hierarchy Process (also known FAHP or Fuzzy AHP) method
implementation in Python for multi-criteria decision-making
•
Fuzzy TOPSIS MATLAB Code
•
PROMETHEE II Python
•
A Python MCDA (multi-criteria decision-making analysis) Library
•
Fuzzy MCDM d multi-criteria decision-making methods for fuzzy data
Well completion optimization by decision-making
437

www.technology.matthey.com
JOHNSON MATTHEY 
TECHNOLOGY REVIEW
http://dx.doi.org/10.1595/205651317X695776	
Johnson Matthey Technol. Rev., 2017, 61, (3), 207–221
207	
© 2017 Johnson Matthey
Highlights of the Impacts of Green and Sustainable 
Chemistry on Industry, Academia and Society 
in the USA
Impacts of green and sustainable chemistry on US industries, analysis of green 
chemistry resources available in academia (higher education) within the USA, and 
a perspective on the role of green chemistry in US society over the past ten years
Anne Marteel-Parrish*
Department of Chemistry, Washington College, 
Chestertown, MD 21620, USA
*Email: amarteel2@washcoll.edu 
Karli M. Newcity**
Department of Chemistry, Washington College, 
Chestertown, MD 21620, USA
**Email: knewcity2@gmail.com 
Trends such as population growth, climate change, 
urbanisation, resource scarcity, conservation of energy 
and water, and reduction of waste and toxicity have 
led to the development of sustainable practices in 
industry, education and society. The desire to improve 
ways of living, the need for performance materials, 
and the urgency to close the gap between developed 
and emerging nations have propelled creative and 
innovative solutions based on green and sustainable 
chemistry to the forefront. This article provides an 
overview of the main impacts of green chemistry 
on industry, academia and society in the USA in the 
past ten years, as well as a summary of the drivers 
and barriers associated with the adoption of green 
chemistry practices. It also describes how researchers, 
policy makers, educators, investors and industries 
can work together to “build innovative solutions that 
transform and strengthen the chemical enterprise” (1) 
while addressing environmental and social challenges. 
The goal of this article is to understand why green 
chemistry is still primarily viewed as Joel Tickner, 
Director of Green Chemistry and Commerce Council 
(GC3), University of Massachusetts, Lowell, USA, puts 
it: as “an environmental activity rather than one that, 
as experience shows, yields economic benefit, and it 
has yet to be integrated into the fabric of the chemical 
enterprise, educational systems, or government 
programs” (1).
1. Historical Perspective: Paving the Way to 
Green Chemistry
The practice of green chemistry began in 1990 when 
the creation of the Pollution Prevention Act was seen 
as the USA’s initiative to become directly involved in 
pollution prevention at the source (2). In 1995, former 
President Bill Clinton introduced the Presidential Green 
Chemistry Challenge Awards based on five (later 
changed to six) award categories: Greener Synthetic 
Pathways, Greener Reaction Conditions, the Design 
of Greener Chemicals, Small Business, Academic, 
208	
© 2017 Johnson Matthey
http://dx.doi.org/10.1595/205651317X695776	
Johnson Matthey Technol. Rev., 2017, 61, (3)
and a new category created in 2015 based on a 
specific environmental benefit: Climate Change (for 
the reduction of greenhouse gas emissions). These 
awards are used as a marketing tool to communicate 
how green chemistry’s contributions have impacted 
the world. 
In 1998, John Warner and Paul Anastas published the 
book “Green Chemistry: Theory and Practice” providing 
tools, resources and applications of the 12 Principles 
of Green Chemistry (3). In 2001, the Green Chemistry 
Institute decided to join forces with the American 
Chemical Society (ACS) to become advocates of a 
more sustainable environment. In 2009, President 
Obama appointed Paul Anastas to the leadership of the 
US Environmental Protection Agency (EPA)’s Office of 
Research and Development. Anastas resigned from 
this position in 2009 and chose to pursue his career 
at the Center for Green Chemistry at Yale University 
in 2012. 
Following the 12 Principles of Green Chemistry 
provides a way to approach environmental challenges. 
The 12 Principles of Green Chemistry cover the topics 
of: pollution prevention; atom economy; less hazardous 
chemical synthesis; design of safer chemicals; the use 
of safer solvents and auxiliaries; design for energy 
efficiency; use of renewable feedstocks; reduction 
of derivatives; catalysis; design for degradation; 

real-time 
analysis 
for 
pollution 
prevention; 
and 
inherently 
safer chemistry for accident prevention, as mentioned 
in “Green Chemistry: Theory and Practice” (3). The 
philosophy of green chemistry is to produce substances 
in a way that does not harm the environment, health 
and society. A wise way to introduce green chemistry 
to future generations is to define it from a sustainable 
development point of view (4).
The concept of sustainable development began during 
the 1970s when the post-war environmental movement 
highlighted negative effects such as the direct impacts 
of pollution on the environment and health. In 1987, the 
desire to address sustainable development at a global 
scale became important to the United Nations. Through 
the Brundtland Commission, sustainable development 
was defined in the commission’s report entitled 
‘Our Common Future’ (5). This report encouraged 
individuals to become aware of the environmental 
and social issues. It was influential in discovering new 
approaches to protect future generations. In 1992, 
the United Nations Conference on Environment and 
Development, known as the ‘Earth Summit’ or the ‘Rio 
Convention’, was held by the United Nations in Rio de 
Janeiro, Brazil. Its focus was for the world to commit to 
a more sustainable development. 
In 
2002 
the 
World 
Summit 
on 
Sustainable 
Development (WSSD) in Johannesburg, South Africa, 
led to a commitment to reduce global greenhouse gas 
emissions and to a suggestion that all governments 
around the world become unified in taking action 
towards sustainable development (5).
More recently it was devised by W. Cecil Steward, 
the President and CEO of the Joslyn Institute for 
Sustainable Communities, Lincoln, Nebraska, USA, 
to represent sustainable development using five 
domains of sustainability, which include the original 
three domains (environmental, economic and socio-
cultural) and the domains of technology and public 
policy (Figure 1) (6).
The first domain, environmental sustainability, is based 
on assuming that the present environmental processes 
provide a way to keep society as stable as possible 
based on ideal-seeking behaviour. This domain relies 
on making the public aware of the limited amount of 
natural resources. Knowledge of the existence of 
renewable resources is another crucial tool that the 
human race must acquire to continue to thrive (6).
Properly 
harnessing 
and 
utilising 
the 
earth’s 
natural resources is a key goal involving economic 
sustainability. The term ‘economic’ from a business 
Environmental
Public 
policy
Economic
Technological
Socio-
cultural
Sustainable 
communities
Fig. 1. Five domains of sustainable development (6). 
ECOStep: The Five Domains of Sustainability is a concept 
of W. Cecil Steward, FAIA, © 2017 Joslyn Institute for 
Sustainable Communities 
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standpoint takes into account the value of resources (7). 
Ideally compatibility should emerge between improving 
the utilisation of natural resources more efficiently and 
making a profit from the end products. These strategies 
are defined as economic sustainability, which facilitates 
responsible usage of natural and manmade resources 
with no or minimal negative impact on the world. 
Observing sustainability from an economic perspective 
allows businesses to capitalise on the positive effects 
of change within society. 
The socio-cultural domain pictures the necessity 
for a viable and sustainable future due to continued 
world population growth. Rising consumption levels 
undesirably impact environmental sustainability. In 
order to improve the standard of living, implementing 
strategies to educate society is vital to the foundation 
of a more sustainable future. 
Technological advances have a direct impact on 
policymaking. Governments use policies to regulate 
industries and ensure their practices are not detrimental 
to the environment (6). As a society, implementing fit-
for-purpose policies is vital to becoming sustainable.
When these five domains are considered in a 
harmonious way, the development of a society, a 
business or a nation willing to take steps towards a 
more sustainable future should be achieved. These 
domains provide an ideal platform as to how to 
structure a sustainable environment. Examples on how 
these domains have been exploited to impact industry, 
academia and society in the USA over the past ten 
years are detailed in the next section. The limitations 
on an article of this size mean that it focuses on the 
reduction or elimination of pollution and environmental 
toxics and on finding ways to reduce the consumption 
of nonrenewable resources, although this is only one of 
many areas where green chemistry can have an impact. 
Additionally, the geographical scope is also specific to 
the USA due to the limited length of this review. 
2. Overview of the Impacts of Green and 
Sustainable Chemistry Initiatives
2.1 In US Industries 
Before green chemistry became “a framework to 
do chemistry” (8), the US Congress passed the 
Emergency Planning and Community Right-to-Know 
Act (EPCRA) in 1986, which aimed “to support and 
promote emergency planning and to provide the public 
with information about releases of toxic chemicals in 
their community” (9). One of the outcomes of EPCRA 
was the establishment of the Toxics Release Inventory 
(TRI), which: 
“tracks the management of certain toxic 
chemicals that may pose a threat to human 
health and the environment. U.S. facilities in 
different industry sectors must report annually 
how much of each chemical is released to the 
environment and/or managed through recycling, 
energy recovery and treatment”. 
(A “release” of a chemical means that it is emitted to the 
air or water, or placed in some type of land disposal).
As mentioned in the 2015 TRI National Analysis, 
21,849 facilities reported to TRI that they managed 
27.2 billion pounds (12.2 million tonnes) of toxic 
chemicals related to production-related wastes through 
recycling, combustion for energy recovery, treatment 
or disposal (10). As shown in Figure 2, quantities of 
toxic chemicals released decreased while quantities of 
recycled toxic waste increased. As stated in the 2015 
TRI National Analysis, “87% of toxic chemical waste 
managed was not released into the environment due to 
the use of preferred waste management practices such 
as recycling, energy recovery, and treatment”. 
The 2015 TRI National Analysis also highlights 
the total quantities of TRI chemicals disposed of or 
otherwise released by industrial sector (Figure 3).
About 3.4 billion pounds (1.5 million tonnes) of toxic 
chemicals were released, mostly by three sectors: 
metal mining (37%), chemical manufacturing (15%) 
and electrical companies (13%). Unfortunately the 
chemical manufacturing sector is among the leading 
sectors in both production-related waste managed 
(49%) as well as total releases (15%). 
Throughout the development of the concept of green 
chemistry over the past 25 years, there have been 
many considerations on how green chemistry can help 
minimise toxic waste production and therefore prevent 
pollution. One way to manage and control toxic waste 
production is to continuously enforce a set of rules and 
regulations in order to keep our society and environment 
safe. These rules require many businesses and 
corporations to follow strict guidelines in order to meet 
environmental safety requirements that include “waste 
handling, treatment, control, and disposal processes” 
(11). However, these approaches are a costly factor 
for many businesses and corporations. Companies 
spend about $1.00 per pound (approximately 0.45 
kg) to manage waste (8), which is a direct cost to the 
business. The major challenge faced by both industries 
and societies is to expand technological advances in 
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order to achieve more sustainable ways to improve 
the economy and the environment. As Paul Anastas 
defines it: 
“we wanted to begin a shift away from regulation 
and mandated reduction of industrial emissions, 
toward the active prevention of pollution through 
the innovative design of production technologies 
themselves. And we placed an emphasis on 
both the environmental and economic value, 
because we knew the concept would not be 
viable otherwise.” (8) 
The adoption of green chemistry principles could 
be seen as a wise means to reduce costs. The 
businesses and corporations that have implemented 
green chemistry within their design and manufacturing 
of chemical products and processes have seen major 
results on lowered environmental costs and increased 
sales and revenues. Examples of success stories 
on how some of the main chemical-based industries 
have benefited from the adoption of green chemistry 
principles are highlighted below. The examples 
given here are based on selecting some of the most 
Fig. 2. Production-related waste managed by facilities reporting to TRI over 2005–2015 (10). US EPA’s 2015 TRI National 
Analysis 
30000
25000
20000
15000
10000
5000
0
Millions of pounds
30000
25000
20000
15000
10000
5000
0
Number of facilities
2005	 2006	 2007	 2008	 2009	 2010	 2011	 2012	 2013	 2014	 2015
Year
Recycled
Energy recovery
Treated
Disposed of or 
otherwise released
Reporting facilities
All others: 10%
Food: 4%
Paper: 5%
Hazardous waste 
management: 6%
Primary metals: 10%
Electric utilities: 13%
Chemicals: 15%
Metal mining: 37%
Fig. 3. Total disposal or releases by industrial sector in the USA in 2015: 3.36 billion pounds (1.5 million tonnes) (10). US EPA’s 
2015 TRI National Analysis
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recent winners of the Presidential Green Chemistry 
and Engineering Challenge Awards, selecting a 
variety of industrial sectors where a green chemistry 
alternative was successful, and ensuring that most of 
the categories of awards are represented. The authors 
are not affiliated with any of the industries mentioned 
below, nor did they receive funding from any of these 
companies. 
Chemical giants and other large companies, such 
as The Dow Chemical Company, (now merged with 

E. I. du Pont de Nemours and Company (DuPont)), SC 
Johnson & Son, Shaw Industries Group Inc, Merck & 
Co Inc and Pfizer Inc have paved the way to define 
best industrial practices in green chemistry. Smaller 
companies such as Patagonia Inc, the Warner Babcock 
Institute for Green Chemistry LLC, Solazyme Inc 
(now TerraVia Holdings, Inc) and Verdezyne Inc have 
also engaged in the application of green chemistry 
principles. 
According to the EPA and based on the winning 
technologies developed by the Presidential Green 
Chemistry Challenge Awardees (12):
“Through 2016, our 109 winning technologies 
have made billions of pounds of progress, 
including:
•	 
826 million pounds [375,000 tonnes] of 
hazardous chemicals and solvents eliminated 
each year – enough to fill almost 3,800 
railroad tank cars or a train nearly 47 miles 
[75 km] long
•	 
21 billion gallons [95 billion litres] of water 
saved each year – the amount used by 
820,000 people annually
•	 
7.8 billion pounds [3.5 million tonnes] of 
carbon dioxide equivalents released to 
air eliminated each year – equal to taking 
810,000 automobiles off the road.”
The main industrial sectors where green chemistry has 
made an impact over the past ten years include but are 
not limited to: bulk and commodity chemicals, plastics, 
paints, coatings, pesticides, fuels and pharmaceuticals. 
Several companies exemplify what green chemistry at 
work is about. Taking some of the Presidential Green 
Chemistry and Engineering Challenge Awards winners 
as examples: 
•	
Representing the bulk and commodity chemicals 
sector: Solazyme Inc (now TerraVia Holdings, Inc) 
based in South California developed the production 
of vegetable oils via the fermentation of microalgae. 
After finding out that microalgae have an inherent 
ability to produce oils, they used genetic engineering 
to develop an unlimited variety of triglycerides. 
Using Solazyme’s triglycerides results in lower 
emissions of volatile organic compounds (VOCs), 
reduces the quantity of waste produced and lowers 
the environmental impact compared to traditional 
petroleum-based oils. Solazyme won the 2014 
Presidential Green Chemistry Challenge Award in 
the Greener Synthetic Pathways category (13). 
•	
In the biodegradable plastics sector: Verdezyne Inc, 
also based in Southern California, relies on “using 
the power of biology to make a positive impact on 
your products”. Verdezyne took advantage of the 
well-known process of fermentation, using yeast to 
produce everyday products at a lower cost. Their 
yeast fermentation process works with renewable 
feedstocks such as low cost plant-based oils, 
which act as substitutes for petroleum-based 
products. Their products, such as adipic acid, are 
intermediates used in the production of nylons and 
plastics. Verdezyne won one of the Presidential 
Green Chemistry Challenge Awards in 2016 in the 
Small Business Award category (14). They recently 
diversified their ‘green’ products by partnering with 
Aceto Corporation to design FerroshieldTM HC, 
which is a nitrate-free mixture with anti-corrosion 
properties useful in several applications such as 
metal cleaners, engine coolants and aqueous 
hydraulic fluids. 
•	
In 2016, the winner of the new Presidential Green 
Chemistry Challenge Award in the Specific 
Environmental Benefit: Climate Change category 
was Newlight Technologies who developed a 
low-cost thermoplastic named AirCarbonTM from 
methane, a potent greenhouse gas. Several 
well-known companies such as Hewlett-Packard 
Company, IKEA and Sprint have already adopted 
AirCarbonTM in the production of their packaging 
bags, furniture and cell phone cases (15).
•	
Representing the paint industry: one of the issues 
in the paint and coatings industry is the emission 
of large amounts of VOCs when oil-based ‘alkyd’ 
paints dry and cure. The well-known paint company 
Sherwin-Williams developed water-based acrylic 
alkyd paints with low VOCs that can be made 
from recycled soda bottle plastic (polyethylene 
terephthalate (PET)), acrylics and soybean oil. 
These paints exhibit the same properties as alkyd 
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and acrylic paints but with low VOC content, low 
odour, and non-yellowing properties. In 2010, 
Sherwin-Williams claimed that the manufacture 
of this high-performance paint helped to eliminate 
over 800,000 pounds (360 tonnes) of VOCs (16). 
Sherwin-Williams won the 2011 Presidential Green 
Chemistry Challenge Award in the Designing 
Greener Chemicals Award category.
•	
Dow AgroSciences LLC participated in the 
improvement of many pesticides over almost 
two decades. In the 1990s they developed a 
biopesticide called spinosad to repel insect pests 
on vegetables. However, spinosad was not 
effective for insect-pest control in tree fruits and 
tree nuts. In 2008 they received the Presidential 
Green Chemistry Challenge Award in the Designing 
Greener Chemicals 
Award category for the design of 
spinoteram which is a high-performance insecticide 
efficient when applied to tree fruits, tree nuts, small 
fruits and vegetables. Spinetoram exhibits the 
same environmental benefits as spinosad while 
being less persistent in the environment compared 
to 
traditional 
organophosphate 
insecticides. 
Furthermore the toxicity to non-target species is low 
as well as its use rate (17). 
•	
Two companies, Albemarle Corporation and CB&I 
Corporation, have developed a greener solid acid 
catalyst for the production of alkylate, which is 
a blending component for motor gasoline. The 
AlkylClean® technology replaces liquid acid, typically 
hydrofluoric acid or sulfuric acid, with an optimised 
zeolite-based catalyst. This catalyst eliminates the 
production of acid-soluble oils and spent acids and 
bypasses the need for product post-treatment. 
These two companies were the recipient of the 2016 
Presidential Green Chemistry Challenge Award in 
the Greener Synthetic Pathways category (18). 
•	
Several collaborators developed a greener 
synthesis of drugs for the treatment of high 
cholesterol. The latest to date was a collaboration 
between Codexis Inc and Professor Yi Tang of the 
University of California, Los Angeles, who used 
an engineered enzyme and a natural product to 
manufacture simvastatin, originally sold by Merck 
under the trade name Zocor® (19). Their efficient 
biocatalytic process avoids the use of several 
hazardous chemicals while eliminating waste 
and, most importantly, meeting the needs of the 
customers. Codexis and Professor Tang received 
the 2012 Presidential Green Chemistry Challenge 
Award in the Greener Synthetic Pathways 
category. 
Communicating these success stories to the next 
generation of scientists, the students of today, and 
incorporating these real-world case scenarios in the 
K-12 curriculum and beyond is the key to generate a 
systemic interest in the field of green and sustainable 
chemistry (20, 21). 
2.2 In Academia
As mentioned by Haack and Hutchison in a review 
article titled ‘Green Chemistry Education: 25 Years 
of Progress and 25 Years Ahead’ published in 2016, 
green chemistry was first depicted as a possible 
solution to improve laboratory safety, to address issues 
of inappropriate ventilation in laboratories and obsolete 
laboratory space, and to modernise the chemistry 
curriculum (22). Nowadays it seems essential for future 
citizens and leaders of the 21st century to be educated 
about the concepts of green and sustainable chemistry 
to participate in the creation of sustainable societies. 
Supporters of green chemistry in academia have 
followed in the footsteps of leading societies such 
as the ACS, the US EPA and the Royal Society of 
Chemistry in the UK, to create reliable educational 
materials and programmes based on the application 
of green chemistry (2). Some of the educational 
green chemistry resources available for educators 
are textbooks, laboratory 
experiments, 
summer 
programmes, workshops, and more recently, the 
opportunity to continue training and research in green 
chemistry by enrolling into specialised Masters and 
PhD programmes.
The goal of this section is not to present an exhaustive 
list of all initiatives pursued in the academic world but 
to highlight the main current resources and to share 
some of the newest initiatives in academia in the past 
ten years in the USA. Literature and online resources 
dedicated to green chemistry have grown during this 
period, especially targeting undergraduate students. 
Ten years ago, to discover how much content 
related to green chemistry was inserted in chemistry 
textbooks, two surveys were completed as a baseline 
by publishers’ representatives (23). The first survey 
took place in 2006 at the ACS National Meeting 
and Exposition, and the second survey was in 2007 
at the University of Scranton. For the first survey, 
nine publishers whose focus is the publication 
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of undergraduate textbooks were chosen. These 
publishers were Benjamin Cummings, Prentice Hall, 
Houghton Mifflin Company, McGraw-Hill Publishing 
Company, W. W. Norton & Company, Thomson 
Corporation, W. H. Freeman and Company, John 
Wiley and Sons, and Jones & Bartlett Learning. A list of 
these publishers’ undergraduate chemistry textbooks 
for both chemistry majors and non-chemistry majors 
was compiled. For the second survey, they gathered 
information from the same publishers above except 
for W. W. Norton & Company and Jones & Bartlett 
Learning. After analysing the data from the two 
surveys, it was determined that only 33 out of 141 
textbooks examined from all of the publishers 
contained a mention of green chemistry. 
Ten 
years 
later, 
textbooks 
dedicated 
to 
green 
chemistry 
occupy shelves at most college and university libraries 
(24–27). While these textbooks target science majors, 
several textbooks incorporating chemistry in the 
context of sustainability suitable for non-majors were 
recently published (28, 29). The wide dissemination of 
textbooks facilitated the development of single green 
chemistry-based courses as well as the infusion of 
green chemistry into typical major courses such as 
general, organic, inorganic, biochemistry, analytical 
and physical chemistry (30). Courses may be modified 
by choosing greener alternatives as replacements 
to traditional examples. For instance, in an organic 
chemistry laboratory, procedures can use renewable 
reagents, apply the metrics of atom economy instead of 
percent yield, limit the amount of organic solvents and 
use alternative energy sources such as a microwave. 
For inorganic chemistry, these alternatives can 
consist in highlighting reusable catalysts and reagents 
anchored on inorganic solid supports, decreasing the 
use of heavy metals and of solid acids and bases. 
For biochemistry, these alternatives can focus on 
biocatalysis, biosynthesis and the use of raw materials 
from renewable resources. For analytical chemistry, 
reducing the use of column chromatography or high-
energy distillations is a step in the direction of green 
chemistry principles. For physical chemistry, a lesson 
on the thermochemistry of biodiesel, the use of kinetics 
and catalysis, and the benefits to using computational 
studies can be introduced. 
However the most prominent place for green 
chemistry to be taught is still in a laboratory setting. 
The design of green chemistry laboratory exercises, 
mostly in organic chemistry, created a successful draw 
to this ‘metadiscipline’ (31). Several organic chemistry 
laboratory manuals are being used to ‘green’ the 
curriculum at many US undergraduate and graduate 
institutions (32–34). 
Articles describing the implementation of green 
chemistry tools and strategies in the classroom or 
laboratory have seen exponential growth. Some 
journals publishing this type of content include the 
Journal of Chemical Education, Science and Education 
and Chemistry Education Research and Practice as 
well as ACS Sustainable Chemistry and Engineering. 
The number of articles devoted to examples on how to 
implement green chemistry in education has doubled 
since 2007 (22).
There are many online teaching resources that have 
emerged based on collaborations between advocates 
for green chemistry. The following resources do not 
represent an exhaustive list of tools and only a few 
examples are mentioned here. Some examples include: 
a database called Greener Educational Materials 
for Chemists (GEMs) which contains laboratory 
exercises, course syllabi and multimedia content and 
was created by the University of Oregon (35). The 
University of Oregon also created the Green Chemistry 
Education Network, allowing educators to continue 
their professional development through collaborating 
and fostering the integration of green chemistry 
in education. The non-profit organisation Beyond 
Benign, based in Wilmington, Massachusetts, USA, is 
“dedicated to providing future and current scientists, 
educators and citizens with the tools to teach and learn 
about green chemistry in order to create a sustainable 
future”. It is focused on K-12 curriculum development 
and educator training, community outreach and 
workforce development (36). Another example is the 
iSUSTAIN™ Green Chemistry Index which is an online 
tool used to assess the sustainability of products and 
processes (37). 
Mentoring and the creation of a green and sustainable 
chemistry community of practice is also taking place at 
conferences and workshops. National and international 
conferences on sustainability are bringing researchers 
together from all over the world. Examples of well-
known conferences involving presentations of green 
chemistry educational materials are the national and 
regional ACS meetings, the Annual Green Chemistry 
and Engineering Conference and the Biennial 
Conference on Chemical Education in the USA, 
as well as international conferences such as the 
International IUPAC Conference on Green Chemistry, 
the International Symposium on Green Chemistry and 
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the International Conference on Green and Sustainable 
Chemistry. 
To foster critical thinking skills and engage students 
and faculty, workshops and awards are available. 
Each year the Green Chemistry Institute at the ACS 
offers workshops designed for students at the Annual 
Green 
Chemistry 
and 
Engineering 
Conference; 
Beyond Benign designed workshops for K-12 teachers’ 
training as well as online courses for educators; 
the University of Oregon was one of the pioneers 
in offering weeklong Green Chemistry Education 
workshops for educators. Besides the Presidential 
Green Chemistry and Engineering Challenge Awards 
for professional chemists, students can also be actively 
challenged and participate in design competitions 
such as the People, Prosperity and the Planet (P3) 
Student Design Competition launched by the EPA in 
2002 (38). The goal of this competition is to expand the 
breadth of participation by involving interdisciplinary 
teams of students interested in not only chemistry but 
also engineering, architecture, art and business. The 
University of Berkeley’s Greener Solutions programme 
gathers both undergraduate and graduate students 
with local businesses and governmental agencies to 
come up with greener chemistry solutions in a real-
world context (39). 
Students have the opportunity to earn awards such 
as the Ciba Travel Awards in Green Chemistry, which 
are used for a student to travel to an ACS conference 
focused on green chemistry; the Joseph Breen 
Memorial Fellowship, which is for a student to present 
research on green chemistry at an international green 
chemistry conference; and the Kenneth G. Hancock 
Memorial Award, which recognises “outstanding 
student contributions to furthering the goals of green 
chemistry through research and/or studies”. 
Finally, it is possible for undergraduate and graduate 
students to specialise in the study of green chemistry 
and earn a degree in this discipline. While most 
institutions endorse some type of green chemistry 
programming (courses, laboratory curricula focused on 
green chemistry, workshops), some universities such 
as the University of Toledo, Ohio, are offering a BS 
or an MS degree with a minor in green chemistry and 
engineering, and Chatham University offers an MS in 
green chemistry focused on entrepreneurial skills. The 
University of Massachusetts at both Boston and Lowell 
offer a PhD in Green Chemistry.
Although progress has been made, it is important to 
keep in mind that the implementation of green chemistry 
in the curriculum needs to be tailored to the specific 
mission and type of institutions involved (four-year 
undergraduate institutions, community colleges, large 
research universities). One approach does not fit all. 
It is also essential that all stakeholders from academia 
and industry are involved in addressing emerging 
needs for new content related to toxicology as well as 
for metrics and best educational practices. To attempt to 
fill in the gaps, a Green Chemistry Education Roadmap 
Visioning Workshop took place in September 2015 to 
delineate “the Roadmap Vision and the set of green 
chemistry core competencies that every student with a 
bachelor’s degree in chemistry, chemical engineering 
and allied sciences should attain by graduation” (40). 
While the roadmap vision is well established as follows: 
“Chemistry education that equips and inspires chemists 
to solve the grand challenges of sustainability”, the 
“transformative potential of green chemistry” on society 
has not been explored yet since the societal impacts 
are often not taken into account when assessing the 
entire life cycle of newly designed green chemicals 
and processes (40). The next section attempts to give 
examples of how green chemistry is expected to play 
a role in addressing environmental and human health 
issues in a social justice context. 
2.3 In Society 
Even if the field of green chemistry inspires scientists 
to tackle sustainability-related issues on a global 
scale, the limited knowledge about the global 
risk associated with exposure of the human body 
to chemical pollution is leading to “an emerging 
perspective that addresses the confluence of social 
and environmental injustice, oppression for humans 
and nature, and ecological degradation” (31). Since 
the development of chemistry has left unintended 
marks on humans, especially in non-white and low-
income communities, it is essential to consider the 
social consequences of high levels of environmental 
pollution by hazardous chemicals. 
The US EPA defines ‘environmental justice’ as: 
“the fair treatment and meaningful involvement 
of all people regardless of race, color, 
national origin, or income, with respect to the 
development, implementation, and enforcement 
of 
environmental 
laws, 
regulations, 
and 
policies. EPA has this goal for all communities 
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and persons across this nation. It will be achieved 
when everyone enjoys:
•	 
The 
same 
degree 
of 
protection 
from 
environmental and health hazards, and
•	 
Equal access to the decision-making process 
to have a healthy environment in which to live, 
learn, and work.” (41)
The EPA recently decided to implement plan EJ 
2020 which accounts for “improving the health and 
environment of overburdened communities”. By 2020, 
they will:
•	
“Improve on-the-ground results for overburdened 
communities 
through 
reduced 
impacts 
and 
enhanced benefits
•	
Institutionalize environmental justice integration in 
EPA decision-making
•	
Build robust partnerships with states, tribes, and 
local governments
•	
Strengthen 
our 
ability 
to 
take 
action 
on 
environmental 
justice and cumulative impacts
•	
Better address complex national environmental 
justice issues.” (42)
Environmental justice and social justice are mutually 
inclusive as demonstrated in the following definitions of 
social justice as: 
“A 
state 
or 
doctrine 
of 
egalitarianism 
(Egalitarianism defined as 1: a belief in human 
equality especially with respect to social, political, 
and economic affairs; 2: a social philosophy 
advocating the removal of inequalities among 
people)” 
according to the  Merriam-Webster Dictionary (43). 
The National Association of Social Workers states 
that “Social justice is the view that everyone deserves 
equal economic, political and social rights and 
opportunities” (44). 
It has been stated that green chemistry “is one of 
the tools for improving the quality of human life and 
welfare”. Consequently it seems appropriate to refer 
to the green chemistry philosophy as the spring board 
to change the negative perception associated with the 
chemical enterprise and to “reduce the level of social 
burden on the personnel and people living nearby” 
(45). 
The successful implementation of green chemistry 
in industry, the role of green chemistry in increasing 
public well-being and sustainability leadership across 
disciplines, sectors, and cultures are essential to 
promote environmental and social justice. To help 
achieve this goal, the EPA created an interactive 
environmental justice online map across the USA 
called EJSCREEN which: 
“highlights low-income, minority communities 
across the country that face the greatest health 
risks from pollution. The analysis combines 
demographic and environmental data to identify 
where vulnerable populations face heavy 
burdens from air pollution, traffic congestion, 
lead paint, hazardous waste sites and other 
hazards.” (46)
Applying the 12 principles of Green Chemistry to 
address social disparities affecting underprivileged 
populations can lead to many benefits such as the 
delineation of methodologies to provide:
i.	
cleaner air through decreasing the emission 
of hazardous chemicals during use (such as 
pesticides) or the unintentional release (during 
manufacturing or disposal) of toxic chemicals 
leading to health issues but also global warming, 
ozone depletion and smog formation
ii.	
cleaner water by preventing the contamination of 
drinking water with hazardous chemical wastes
iii.	 increased safety for workers using chemicals as 
part of their profession so that the use of toxic 
materials is minimised and the need for personal 
protective equipment is lessened
iv.	 safer consumer products such as the production 
of pharmaceutical drugs with less waste and the 
replacement of cleaning products and pesticides 
with safer alternatives
v.	
safer food based on the reduction of the amount of 
persistent toxic chemicals present in pesticides or 
as endocrine disruptors (47). 
Aligned with the leadership approach of the EPA, 
scientists are motivated to determine that chemical 
exposures fluctuate with social disparities. The 
following section highlights examples where social 
injustices stemming from chemical exposure have been 
the subject of peer-reviewed research. An attempt to 
demonstrate how green chemistry principles can help 
address these social disparities is also presented. 
2.3.1 Pesticides Exposure and Farmworkers
The population of farmworkers in the USA is severely 
affected by pesticides exposure. It is estimated that of 
the 2.5 million farmworkers in the USA, 60% of them 
and their dependents live in poverty (48). About 88% 
of all farmworkers are Hispanic and more than 78% of 
them are foreign-born without legal documentation and 
no higher education (49). 
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Issues associated with the language barrier and the 
lack of health insurance coverage were brought up in a 
report titled ‘Exposed and Ignored. How Pesticides are 
Endangering our Nation’s Farmworkers’ in 2013 (48). A 
study conducted by Washington State Department of 
Health showed that only 29% of pesticide handlers were 
able to read in English and to some extent in Spanish. 
Analysis of the blood work of pesticide handlers who 
could not read English showed significantly greater 
pesticide exposure compared to those who could read 
English to some degree.
Pesticide poisoning or exposure causes farmworkers 
to suffer more chemical-related injuries and illnesses 
than any other workforce in the USA (48). Worldwide, 
25 million agricultural workers experience pesticide 
poisonings each year (50). Protective clothing does 
not provide adequate protection against pesticide 
exposure, especially when handling organophosphate 
and N-methyl carbamate pesticides. Since pesticide 
residues are often invisible and odourless, only a blood 
test would be useful to monitor exposure to these toxic 
chemicals. 
In a thorough report titled ‘Green Chemistry and 
Sustainable Agriculture: The Role of Biopesticides’ 
by Peabody O’Brien et al. in 2009, the role of 
green chemistry applied to the agricultural world 
and biopesticides in particular was validated (51). 
Biopesticides are derived from plants or from microbial 
pesticides. They are less toxic, more pest specific, 
they biodegrade more quickly and do not affect the 
ecological balance.
Another approach is outlined in the Green Chemistry 
Principle #7: “Chemists should, whenever possible, 
use raw materials and feedstocks that are renewable”. 
Green chemists are currently using agricultural waste 
products as renewable feedstocks and are synthesising 
biocatalysts to increase the “conversion of agricultural 
materials into high value products, including novel 
carbohydrates, polysaccharides, enzymes, fuels and 
chemicals” (3). As explicitly mentioned in the Peabody 
O’Brien report:
“Green Chemistry and sustainable agriculture 
are inherently intertwined; farmers need green 
chemists to make safe agricultural chemical 
inputs. Green chemists need farmers practicing 
sustainable agriculture to provide truly “green” 
bio-based raw materials to process into new 
products.” (51) 
Additionally, as defined in the Green Chemistry 
Principle #10: “Chemical products should be designed 
so that at the end of their function they break down into 
innocuous degradation products and do not persist 
in the environment” (3). The challenge to remove 
pesticide residues in the soil, water and air has led 
scientists at Carnegie Mellon University to develop 
specific TAML® catalysts targeting the degradation 
of pollutants from water without presenting endocrine 
disrupting activity (52). 
Another example based on the control of pests 
affecting vineyards, the goal of research conducted 
by Jocelyn Millar at the University of California at 
Riverside was to “identify less-toxic pesticides that may 
be effective alternatives to organophosphates”. Instead 
of using heavy loads of pesticides, the group developed 
a pheromone to control the vine mealybug population 
based on mating disruption. Their pheromone was not 
only successful in trapping the vine mealybugs but was 
also beneficial to attract the vine mealybugs’ predators, 
which was an unexpected benefit to the preservation 
of the ecological balance and of the natural predator 
populations (51).
2.3.2 
Exposure 
to 
Endocrine 
Disruptor 
Bisphenol A and Children
Bisphenol A (BPA), a synthetic organic compound 
used to make plastics and epoxy resins for a variety 
of common consumer goods, has been under scrutiny 
since 2008 when several governmental agencies 
investigated its safety, especially with respect to its use 
in baby bottles and ‘sippy’ cups. BPA and polyfluoroalkyl 
chemicals (PFCs) are oestrogen-like chemicals found 
to “disrupt reproductive development, body weight 
and metabolic homeostasis, and neurodevelopment, 
and to cause mammary and prostate cancer.” Many 
comprehensive reviews regarding the impacts of BPA 
on health have been published (53–55). 
While concerns about the potential hazards of 
endocrine-disrupting chemicals  such as BPA are still 
debated, and after several countries have banned 
its use, a study published by Nelson et al. in 2012 
addressed the population disparities in exposure to 
these chemicals. Their findings demonstrated that:
“people with lower incomes, who may be more 
likely to suffer from other disparities in health and 
exposures, have a greater burden of exposure 
to BPA. The results for children are especially 
troubling. Children overall had higher urinary 
BPA concentrations than teenagers or adults, 
but children whose food security was very low 
or who received emergency food assistance - in 
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other words, the most vulnerable children - had 
the highest levels of any demographic group. 
Their urinary BPA levels were twice as high 
as adults who did not receive emergency food 
assistance. Concerns about health effects from 
BPA exposure are strongest for young children 
and neonates because they are still undergoing 
development. Results for BPA by race/ethnicity, 
adjusting for income, revealed that Non-Hispanic 
Whites and Blacks had similar urinary levels, and 
being Mexican American appeared to be highly 
protective.” (56) 
It is thought that: 
“eating more fresh fruits and vegetables is likely 
to be associated with eating less canned foods, 
which may explain the lower urinary BPA levels 
seen in Mexican Americans compared to other 
groups.” (57)
Several companies are now selling BPA-free products 
but do not always inform what substitute is being 
used. It is even considered that some of the BPA-
free alternatives may actually not be safer than their 
BPA-containing counterparts. Karen Peabody O’Brien, 
former Executive Director of the scientific foundation 
Advancing Green Chemistry, and John Peterson Myers, 
CEO and Chief Scientist at Environmental Health 
Sciences, both located in Charlottesville, Virginia, have 
suggested using green chemistry tools to create: 
“a new generation of non-petroleum-based 
materials from scratch, simultaneously protecting 
public and environmental health while reducing 
dependence on foreign oil” (58). 
In 2014, Richard Wool and his research group at the 
University of Delaware achieved that by converting 
lignin fragments, a waste product of the papermaking 
and other wood-pulping processes, to a compound 
called bisguaiacol-F (BGF). BGF has the same shape 
as BPA, but does not interfere with hormones and 
retains the desirable thermal and mechanical properties 
of BPA (59).
2.3.3 Contaminated Drinking Water and Air in 
Poor Communities 
The most recent example of social injustice was the 
water crisis in Flint, Michigan. When the town of Flint 
switched the source of water for its residents in 2014, 
corrosion inhibitors were forgotten to be added to the 
new water source, which caused lead levels to raise 
to 25 ppb (above the maximum level of 15 ppb set 
by the EPA). Residents complained numerous times 
about the strange taste and colour of the water but 
no further investigation was conducted. Thousands of 
children among the majority of the African American 
population of Flint were exposed to lead without being 
properly informed since this information was not made 
public. It was not until January 2016 that a federal state 
of emergency was declared (60). In March 2017, the 
EPA awarded US$100 million to the State of Michigan 
to upgrade Flint water infrastructure, especially lead 
service lines (61).
While the cause of the increased level of lead in Flint’s 
potable water was due to corrosion in the lead and 
iron pipes that distribute water to city residents, green 
chemistry has been at work to provide environmentally 
friendly alternatives to chemical water treatment such 
as the use of nanomaterials (62), the use of ‘green 
additives’ (63) or the use of photocatalysts (64). 
Some green chemistry advocates are concentrating 
their efforts to address the social and environmental 
(in)justice of chemical exposure using the concept of 
sustainable chemistry as framework in their academic 
research and outreach efforts. This has become a 
priority at academic institutions such as Bridgewater 
State University where Professor Ed Brush is starting a 
Participatory Action Research programme (65). In this 
programme the community will be involved in research 
projects targeting social injustice. His research 
students are interested in assessing the impacts of 
diesel particulate matter emissions on populations 
with a high risk of developing asthma such as females, 
children, people of colour and people of mixed race as 
well as those living in poverty or with low incomes. The 
plan is for students to collect data using air collectors 
and then report their findings related to diesel exhaust 
pollutants’ impact on health. The ultimate goal is to 
delineate how green chemistry principles can be put to 
work to decrease the exposure of minorities to diesel 
exhaust pollution. It is expected that studies related to 
biofuels will inspire their green chemistry proposal to 
reduce social disparities due to exposure to emissions 
exhaust (66–68). 
3. Conclusions
Advances in chemical knowledge and research have 
brought great progress to the field of green and 
sustainable chemistry. As mentioned earlier this article 
was written in the context of attracting attention to 
problems related to chemical pollution and resource 
depletion and it also proposes some alternatives related 
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to the application of green chemistry. The overall goal 
was to demonstrate that the significant development 
of green and sustainable chemistry has opened up 
a new way of performing and teaching chemistry, 
demonstrating that green chemistry is applicable to all 
fields of research and that it should not be a tradeoff 
between cost and environmental impact. In industry, 
while the implementation of green chemistry is driven 
by government regulations, consumer awareness 
and higher demand for more environmentally benign 
products, the rate of adoption is slow. In 2015, T. 
Fennelly & Associates, Inc identified some possible 
accelerators of green chemistry adoption such as (69):
•	
Collaborative efforts relying on establishing price 
and performance trade-offs where transparency 
is addressed and where “open innovation” is 
welcome. The word “coopetition” has been used 
“as a model to drive competition and innovation” 
while simultaneously enabling the growth of green 
chemistry
•	
Compromise is a step in the right direction. 
When companies shift away from regulations 
and mandated reduction of industrial emissions 
towards active pollution prevention, continuous 
improvement of a product will be justified for its 
economic and environmental value 
•	
Finally, continued and enhanced education in green 
and sustainable chemistry is crucial among the 
work force. 
Even if the implementation of green chemistry 
practices in industry face adversity, strategies have been 
identified to accelerate the adoption of green chemistry 
such as: continued research and communication 
among all stakeholders; support for ‘smart’ policies that 
enhance green chemistry innovation and adoption; 
fostering collaboration; dissemination of information 
to the marketplace; and tracking of progress using 
metrics (1).
With educators passionate about the green and 
sustainable chemistry field, not only are institutions 
taking an interest in promoting the ‘green’ concept 
to their students, there are also plenty of resources 
available to encourage them to make a difference. The 
incorporation of green chemistry-based courses and 
the design of academic degrees in green chemistry 
is vital to establishing awareness and knowledge of 
environmentally benign chemistry. Students, who gain 
insight about how green chemistry can positively impact 
local communities as well as the entire world, enter 
the work force with a head start and a sense of ethical 
empowerment on how to solve existing challenges 
using green and sustainable chemistry principles. 
Although many educational materials are available, 
challenges remain for academia, such as (22): 
•	
The slow implementation of green chemistry in the 
undergraduate and graduate curriculum based on a 
“lack of uniform demand”, which can be perceived 
as curricular conservatism from academic and 
industrial stakeholders
•	
“The resistance to infuse green chemistry into the 
main general and organic chemistry textbooks or 
the ACS standardized exams” which does not 
motivate departments to make changes in their 
curriculum
•	
The lack of expertise and confidence from 
inexperienced educators to help students learn 
about green and sustainable chemistry, and 
•	
Finally, the presence of key gaps in terms of content 
such as the introduction of toxicology and metrics 
as well as well-defined curricular objectives and 
assessments.
Through the applications of green chemistry in industry 
and academia, it has been shown how green chemistry 
can make a difference in the sustainable development 
of human civilisation. While this article described some 
of the efforts undertaken in the USA, the scope of 
this article could be expanded by highlighting efforts 
outside the USA such as the commitment of the United 
Nations to develop 17 sustainable development goals 
to transform our world (70). Additionally chemical 
companies around the world such as Dow Chemical 
Company designed their own set of sustainability goals 
to help “redefine the role of business in society” (71). 
Recognised as a means to aid society to live longer 
and better, green chemistry’s focus on the humanistic 
level will drive modern society in the direction of global 
sustainability. 
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15th April, 2015
The Authors
Anne Marteel-Parrish grew up in the North of France and got her Engineering degree in 
Materials Science from the Ecole Polytechnique de Lille, France, in 1999. She received a 
PhD in Chemistry with concentration in Materials Science from the University of Toledo, 
Ohio, USA, in May 2003. Shortly after, she was hired as Assistant Professor in Chemistry 
at Washington College in Chestertown, Maryland, USA. Anne received tenure and was 
promoted to the rank of Associate Professor in 2009. She achieved full professorship in 
2016. She was the Chair of the Chemistry Department at Washington College from 2010 
to 2016. In 2011 she was invested as the Inaugural Holder of the Frank J. Creegan Chair 
in Green Chemistry.
Karli Newcity received her BS in Chemistry at Washington College, Maryland, in 2013. 
Before completing her studies, she took an internship with the Domestic Nuclear Detection 
Office at the US Department of Homeland Security where she trained in Radiochemistry 
and Nuclear Forensics. Currently, she is a Chemist supporting the Detection Branch of the 
Engineering Directorate of the Edgewood Chemical Biological Center, Edgewood, MD, 
USA, where she performs a wide variety of laboratory operations in a test and evaluation 
laboratory.
Credit: Shane Brill, Washington College

processes
Article
Integration of RTO and MPC in the Hydrogen
Network of a Petrol Reﬁnery
Cesar de Prada 1,*, Daniel Sarabia 2, Gloria Gutierrez 1, Elena Gomez 1, Sergio Marmol 3,
Mikel Sola 3, Carlos Pascual 3 and Rafael Gonzalez 3
1
Department of Systems Engineering and Automatic Control, University of Valladolid, Valladolid 47011,
Spain; gloria@autom.uva.es (G.G.); elenags@cta.uva.es (E.G.)
2
Department of Electromechanical Engineering, University of Burgos, Burgos 09006, Spain; dsarabia@ubu.es
3
Petroleos del Norte, S.A, Petronor, Muskiz 48550, Spain; smarmol@repsol.com (S.M.),
jmsola@repsol.com (M.S.); cpascual@repsol.com (C.P.); rgonzalezm@repsol.com (R.G.)
*
Correspondence: prada@autom.uva.es; Tel.: +34-983-423-164
Academic Editor: Dominique Bonvin
Received: 13 November 2016; Accepted: 29 December 2016; Published: 7 January 2017
Abstract: This paper discusses the problems associated with the implementation of Real Time
Optimization/Model Predictive Control (RTO/MPC) systems, taking as reference the hydrogen
distribution network of an oil reﬁnery involving eighteen plants. This paper addresses the main
problems related to the operation of the network, combining data reconciliation and a RTO system,
designed for the optimal generation and redistribution of hydrogen, with a predictive controller
for the on-line implementation of the optimal policies. This paper describes the architecture of the
implementation, showing how RTO and MPC can be integrated, as well as the beneﬁts obtained in
terms of improved information about the process, increased hydrocarbon load to the treatment plants
and reduction of the hydrogen required for performing the operations.
Keywords: real-time optimization; model predictive control; petrol reﬁneries; hydrogen networks
1. Introduction
Process industries, like other industrial sectors, are compelled by the market and the regulatory
norms to operate more and more efﬁciently. This means better product quality, higher production,
fulﬁlment of environmental legislation, etc., with better use of resources and minimum cost.
Achievement of all these aims requires, among other things, proper use of the resources and assets
as well as better production management. This is why, increasingly, the topics and methods related
to production optimization are gaining attention in industry. Once the basic automation layer is in
operation in a factory, so that production reaches a certain degree of stability and adequate information
from the process is available, the next logical step is to move up in the management of the process.
This can be done, ﬁrst, by enhancing the control layer to take care of interactions, constraints and
future consequences of current actions in the operation of the process units, that is, incorporating
Model Predictive control (MPC), and, in a second step, trying to ﬁnd out the best operating conditions,
considering not only technical aspects, but also the economy of the process. This requires a more global
vision than considering in isolation the operation of the individual process units, because what can be
good from the point of view of a speciﬁc process may not be so good for the whole factory. Real-Time
Optimization (RTO) uses, such as global view, try to incorporate different aspects and interrelations
among processes in a model in order to compute operational decisions that optimize process efﬁciency
and economy.
Both layers, MPC and RTO, have different targets and normally they use different types of models,
linear dynamic models in the case of MPC and non-linear ﬁrst principles ones in the RTO, but they are
Processes 2017, 5, 3; doi:10.3390/pr5010003
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not independent. Normally, RTO is placed on top of MPC, computing optimal values of key variables
that later on are passed as set points to the MPC in a cascade structure. This architecture assumes that
there exists an optimum steady state that the MPC must follow, which can be a sensible assumption in
many cases. Nevertheless, it also happens quite often that, due to the plant scale or complex dynamics
involved or because of the presence of signiﬁcant disturbances, the plant is rarely at steady state,
so some alternatives have to be used. Of course, in many cases, one can formulate a dynamic RTO,
or an MPC with an economic target, merging dynamic control with economic target optimization as
in [1,2], and in fact they are examples of very large dynamic optimization problems solved efﬁciently
with state-of-the-art software and methodology [3]. However, this formulation may present stability
problems and, in other cases, the computational load and the difﬁculties of estimating properly the
process states may render the approach not very adequate for real-time operation.
In addition, other factors that contribute to the difﬁculties of industrial implementation of the
RTO/MPC architecture are the fact that large RTO, based on rigorous models, are difﬁcult to keep
up-to-date and there is a lack of resources in industry to maintain these applications, due to limited
number of qualiﬁed personnel, to the relatively frequent changes and revamping in the process and to
the intrinsic difﬁculties of the task that is time consuming and rather specialized. The reference [4]
gives a good summary of these problems. However, the beneﬁts of RTO normally repay the efforts,
not only in terms of the gains obtained through its on-line implementation, but because of other
side beneﬁts, such as improved process information, detection of groups of constraints that limit the
possibility of reaching the targets, reliable feedback to upper production layers (planning), etc.
All these elements have to be taken into account when deﬁning the approach and the
implementation of the system, exploring, besides the traditional cascade RTO/MPC, other ﬂexible
alternatives in which the possibilities offered by available commercial technology have to be considered,
as they provide integrated local Linear Programming (LP) based optimization with predictive
multivariable controllers [5]. In the same way, one should consider the options that sometimes
appear for carrying out similar solutions as the ones computed by the RTO with a specially designed
control system, in line with the self-optimizing approach [6].
Besides integration of RTO with the lower layer represented by MPC, successful implementations
of real-time optimization systems have to take into account that RTO normally only covers certain
aspects of the operation of a large-scale process plant. In fact, there are many elements related to what
should be produced or when it should be produced and at what price, that concern to the upper layer of
plant planning. Then, RTO should operate in the framework and global aims deﬁned by the planning
layer of the company, receiving production aims, prices and constraints imposed by other parts of the
process, that have to be considered by the RTO and MPC. This aspect concerns the information ﬂow
from top to lower layers of the control hierarchy, but additional beneﬁts can be obtained by considering
also the opposite ﬂow by better feedback of the results of the operation to the planning layer, helping
to correct gaps between what is planned and what is achieved in practice and detecting the active
constraints whose removal can improve production and efﬁciency in a signiﬁcant way.
This paper deals with these topics, presenting a large-scale optimization problem related
to the management of the hydrogen distribution network of an oil reﬁnery and discussing its
real-time implementation.
Hydrogen has become an important and expensive utility required in many new processes in
oil reﬁneries for breaking long hydrocarbon chains into lighter and more valuable products and for
removal of Sulphur and aromatics in order to comply with environmental legislation. Hydrogen, either
imported or produced in-house, is distributed by means of a network from producer to consumer
plants. In consumer plants, hydrogen is mainly used as a reactant for desulfurization, de-nitriﬁcation
and de-aromatization of naphtha and diesel, in order to avoid generating acid gases when used as
heating fuel or in combustion engines, thus avoiding atmosphere pollution.
In recent years, when heavier fuels are being processed and also due to more strict environmental
regulations, hydrogen requirements have experienced a steady increase, gaining signiﬁcant importance
Processes 2017, 5, 3
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in the reﬁnery global economic balance. An efﬁcient use of H2 in the daily operation is desired not only
for its high production cost, but also because the economic penalty is even higher in scenarios where
hydrogen production capacity is the bottleneck for oil processing capacity. Nevertheless, decisions
related to hydrogen management are not easy as there are many interrelated plants and constraints
involved in the operation of the network, not only from a modelling and optimization perspective,
but also from a practical point of view because, quite often, several operators in different control rooms
are in charge.
The approach to deal with the hydrogen network optimal management is driven by an operational
framework where hydrogen production must always exceed consumption, with reactors operating
with excess hydrogen, because hydrogen deﬁcit is extremely damaging for catalysts which are very
expensive and accumulation in a buffer vessel is not sensible. One of the main problems to perform
appropriate decisions regarding the management of hydrogen networks is the lack of information on
many variables and the uncertainty associated with the existing measurements. Because of this,
data reconciliation has been used as a way to estimate unknown magnitudes and to correct
inconsistencies in the data, before a model based optimization procedure could be applied to determine
the best use of hydrogen in the network.
The optimal management of hydrogen in oil reﬁneries has been studied mainly from a design
viewpoint as in [7], as well as integrated with other utility systems in the reﬁnery operation, as in [8],
but has received less attention from the perspective of real-time operation and control.
This paper tries to contribute to the automated optimal operation of the hydrogen networks in
oil reﬁneries and is organized as follows: First, the process is described in Section 2, showing the
architecture, operation and targets of the main plants and the functioning of the network as a whole.
Next, Section 3 presents a Data Reconciliation and Real-Time Optimization system developed with the
purpose of achieving the optimal management of the hydrogen network. Then, Section 4 is devoted to
discussing the ways in which the system is implemented in the reﬁnery, in particular through the use of
a predictive Dynamic Matrix Controller (DMC) operating on several H2 production and consumption
plants. Finally, Section 5 presents and evaluates the results achieved, as well as the integration of
the operation of RTO and MPC layers as a decision Support System (DSS) supervising the network
performance. To conclude, the Discussion section centers on the future perspectives and challenges of
process optimization.
2. Process Description
The process taken as reference is the reﬁnery of Petronor, a company of the Repsol group located
in Muskiz, in northern Spain. The reﬁnery processes crude oil in standard distillation circuits but, as
with many other modern installations, incorporates additional ones as well. Among them, conversion
units transform heavy hydrocarbons into more valuable light ones, besides other units dedicated
to the removal of Sulphur from the processed products in order to comply with the environmental
legislation. Both conversion and desulphurization processes use hydrogen as raw material of the
chemical reactions involved, so this product has become one of the most important utilities in a
petrol reﬁnery.
High purity hydrogen is produced in steam-reforming furnaces in two plants, named H3 and
H4, in the reﬁnery under consideration. Additionally, two platformer plants (P1 and P2) generate
lower purity hydrogen as a byproduct of the catalytic reforming process, which increases the octane
number of naphtha. From these four plants, hydrogen is distributed to the consumer ones using
several interconnected networks at different purities and pressures, as can be seen in the schematic of
Figure 1. The network interconnects a total of eighteen plants, four producers and fourteen consumers.
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Figure 1. Schematic of the hydrogen network of the Petronor refinery. Dark grey boxes represent producer plants, while light grey ones refer to hydrogen consumer units. 
 
Figure 1. Schematic of the hydrogen network of the Petronor reﬁnery. Dark grey boxes represent producer plants, while light grey ones refer to hydrogen
consumer units.
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A simpliﬁed schematic of a typical consumer plant can be seen in Figure 2, in this case a
hydrodesulphurization one (HDS) dedicated to the removal of Sulphur from its hydrocarbon feed.
Before entering the reactors, the hydrocarbon feed (HC) (black lines) is mixed with hydrogen (blue
lines) coming from the distribution network: in the diagram from two producer plants (H4 and H3)
and the Low Purity distribution Header (LPH), as well as with recycled hydrogen streams (R, FOUT_Z).
This mixture reacts endothermically under high temperature in the reactors where sulfur is converted
into hydrogen sulﬁde H2S, which can be removed later on by absorption on an amine solution.
It is important to remark that the feed to the reactors must contain an excess of hydrogen required to
prevent shortening the life of the expensive catalysts. As a consequence of this, the reactor output has
still surplus hydrogen. In addition, other light end gases generated in the reactor are also present in
the mixture (a mixture of CH4, C2H6, C3H8 and other gases) which are considered as impurities.
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A simplified schematic of a typical consumer plant can be seen in Figure 2, in this case a 
hydrodesulphurization one (HDS) dedicated to the removal of Sulphur from its hydrocarbon feed. 
Before entering the reactors, the hydrocarbon feed (HC) (black lines) is mixed with hydrogen (blue 
lines) coming from the distribution network: in the diagram from two producer plants (H4 and H3) 
and the Low Purity distribution Header (LPH), as well as with recycled hydrogen streams (R, FOUT_Z). 
This mixture reacts endothermically under high temperature in the reactors where sulfur is 
converted into hydrogen sulfide H2S, which can be removed later on by absorption on an amine 
solution. It is important to remark that the feed to the reactors must contain an excess of hydrogen 
required to prevent shortening the life of the expensive catalysts. As a consequence of this, the 
reactor output has still surplus hydrogen. In addition, other light end gases generated in the reactor 
are also present in the mixture (a mixture of CH4, C2H6, C3H8 and other gases) which are considered 
as impurities. 
 
Figure 2. Simplified schematic of a typical desulphurization plant showing the hydrocarbon and 
hydrogen feeds, the reactor, separation units, membranes and main streams. 
Hydrogen and light ends are separated from the treated hydrocarbon stream in high-pressure 
separation units (SepHP). Most of the hydrogen rich gas from the HP separator, with purity XHP, is 
recycled (R) into the reactor inlet, but a certain HP purge is usually needed to avoid the 
accumulation of light ends in the system, either to the Fuel-gas (FGHP) network of the refinery, where 
the gases are burnt in furnaces, or to the Low Purity distribution Header (LPH) to be reused later on. 
Also, some of the recycled hydrogen (FIN_Z) can be fed to a set of membranes (Z) in order to increase 
its purity, with the low purity retentate flow (FGZ) being sent to the fuel gas network. Referring to 
Figure 2, the hydrocarbon outflow of the high-pressure separators still contains hydrogen that is 
further removed in medium or low pressure separation units (SepLP), but this hydrogen is sent to the 
fuel gas network (FGLP) due to its low purity (XLP) that prevents it from being reused in the reactors 
in a profitable way. 
The hydrogen purity at the reactors’ input depends on the ratio of flow rates coming from the 
different producer plants (H3, H4, P1, P2), distribution headers (e.g., low-purity header LPH) and 
recycles, with the mixture having to satisfy several operational constraints, that must be achieved by 
proper management of the plant. 
Thus, from the point of view of the hydrogen network, these plants operate with a feed of 
hydrogen from different sources that is partially consumed in the reactors, partially sent to the Fuel 
Gas (FG) network and partially reused, either internally or recycled from the low purity header 
LPH. 
The global operation of the network outside the plants can be better explained using Figure 3, 
which is a simplified representation where only a small number of producer and consumer plants 
 
Figure 2. Simpliﬁed schematic of a typical desulphurization plant showing the hydrocarbon and
hydrogen feeds, the reactor, separation units, membranes and main streams.
Hydrogen and light ends are separated from the treated hydrocarbon stream in high-pressure
separation units (SepHP). Most of the hydrogen rich gas from the HP separator, with purity XHP,
is recycled (R) into the reactor inlet, but a certain HP purge is usually needed to avoid the accumulation
of light ends in the system, either to the Fuel-gas (FGHP) network of the reﬁnery, where the gases are
burnt in furnaces, or to the Low Purity distribution Header (LPH) to be reused later on. Also, some
of the recycled hydrogen (FIN_Z) can be fed to a set of membranes (Z) in order to increase its purity,
with the low purity retentate ﬂow (FGZ) being sent to the fuel gas network. Referring to Figure 2,
the hydrocarbon outﬂow of the high-pressure separators still contains hydrogen that is further removed
in medium or low pressure separation units (SepLP), but this hydrogen is sent to the fuel gas network
(FGLP) due to its low purity (XLP) that prevents it from being reused in the reactors in a proﬁtable way.
The hydrogen purity at the reactors’ input depends on the ratio of ﬂow rates coming from the
different producer plants (H3, H4, P1, P2), distribution headers (e.g., low-purity header LPH) and
recycles, with the mixture having to satisfy several operational constraints, that must be achieved by
proper management of the plant.
Thus, from the point of view of the hydrogen network, these plants operate with a feed of
hydrogen from different sources that is partially consumed in the reactors, partially sent to the Fuel
Gas (FG) network and partially reused, either internally or recycled from the low purity header LPH.
The global operation of the network outside the plants can be better explained using Figure 3,
which is a simpliﬁed representation where only a small number of producer and consumer plants
are represented. As mentioned above, the producer plants are of two types: the ones that generate
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controllable ﬂows of fresh high purity hydrogen (H3 and H4) and the ones that generate hydrogen
of lower purity as a by-product (P1 and P2) so that their ﬂows can be considered as non-controllable
disturbances to the network. The generated hydrogen is distributed to the consumer plants through
the corresponding headers. The hydrogen demand of every plant depends on the amount and quality
of the hydrocarbons being treated, which may experience strong changes every two or three days
according to the crude that is being processed. Excess hydrogen from these plants is partially collected
in the low purity header and recycled back to the consumer plants, while the rest goes to the fuel gas
network, where it is mainly burnt in furnaces.
Processes 2017, 5, 3 
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are represented. As mentioned above, the producer plants are of two types: the ones that generate 
controllable flows of fresh high purity hydrogen (H3 and H4) and the ones that generate hydrogen 
of lower purity as a by-product (P1 and P2) so that their flows can be considered as non-controllable 
disturbances to the network. The generated hydrogen is distributed to the consumer plants through 
the corresponding headers. The hydrogen demand of every plant depends on the amount and 
quality of the hydrocarbons being treated, which may experience strong changes every two or three 
days according to the crude that is being processed. Excess hydrogen from these plants is partially 
collected in the low purity header and recycled back to the consumer plants, while the rest goes to 
the fuel gas network, where it is mainly burnt in furnaces. 
 
Figure 3. Schematic of producer and consumer plants with the main hydrogen distribution headers 
and fuel gas network. 
2.1. Process Operation 
Both plants and networks are operated from control rooms equipped with Distributed Control 
Systems (DCS) implementing basic controls (flow, pressure, …) and several MPCs (DMC) in charge 
of more complex multivariable tasks, such as sulfur removal in the plants. In the past, operators 
decided on key variables such as hydrocarbon inflow, use of membranes and fresh hydrogen feed to 
the plants in a largely decentralized way with the overall operation relying on the experience of the 
production managers. This provides flexibility in the operation, but limits the possibilities of 
implementing coordinated functioning and optimization. 
The main network operation aims are: 
• 
Distribute the available fresh hydrogen and the recycled hydrogen (including internal plant 
recycles) so that the requirements of hydrogen at the reactors’ inputs in all plants are satisfied. 
• 
Be as close as possible to the production targets of hydrocarbon feeds to the plants established 
by the refinery planning system. 
• 
Balance the hydrogen that is produced and the hydrogen that is consumed so that the hydrogen 
losses to fuel gas are minimized. 
They are listed in order of importance: proper distribution of hydrogen fulfilling operation 
constraints is a must for the operation of the plants, so it goes on top. Then, production should be 
increased as much as possible and this target should be achieved with minimum hydrogen losses, or 
equivalently, with minimum fresh hydrogen production. These aims, far from being independent, 
are linked together: good distribution and reuse of hydrogen allows reduced losses to fuel gas, 
increasing the hydrogen available for a further increase in hydrocarbon production when hydrogen 
capacity is a limiting factor or for reducing fresh hydrogen production if the production targets were 
already met. 
 
Figure 3. Schematic of producer and consumer plants with the main hydrogen distribution headers
and fuel gas network.
2.1. Process Operation
Both plants and networks are operated from control rooms equipped with Distributed Control
Systems (DCS) implementing basic controls (ﬂow, pressure, . . . ) and several MPCs (DMC) in charge
of more complex multivariable tasks, such as sulfur removal in the plants. In the past, operators
decided on key variables such as hydrocarbon inﬂow, use of membranes and fresh hydrogen feed
to the plants in a largely decentralized way with the overall operation relying on the experience of
the production managers. This provides ﬂexibility in the operation, but limits the possibilities of
implementing coordinated functioning and optimization.
The main network operation aims are:
•
Distribute the available fresh hydrogen and the recycled hydrogen (including internal plant
recycles) so that the requirements of hydrogen at the reactors’ inputs in all plants are satisﬁed.
•
Be as close as possible to the production targets of hydrocarbon feeds to the plants established by
the reﬁnery planning system.
•
Balance the hydrogen that is produced and the hydrogen that is consumed so that the hydrogen
losses to fuel gas are minimized.
They are listed in order of importance: proper distribution of hydrogen fulﬁlling operation
constraints is a must for the operation of the plants, so it goes on top. Then, production should be
increased as much as possible and this target should be achieved with minimum hydrogen losses,
or equivalently, with minimum fresh hydrogen production. These aims, far from being independent,
are linked together: good distribution and reuse of hydrogen allows reduced losses to fuel gas,
increasing the hydrogen available for a further increase in hydrocarbon production when hydrogen
capacity is a limiting factor or for reducing fresh hydrogen production if the production targets were
already met.
Processes 2017, 5, 3
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In order to achieve these targets, the main decision variables are the fresh hydrogen production
of H3 and H4 plants, the hydrocarbon feed to the fourteen consumer plants and the hydrogen
distribution and reuse in the network, including the use of membranes where available. Hydrogen
from the platformer plants P1 and P2, being a by-product of their operation, can be considered a
disturbance more than a decision variable.
The overall operation is framed by the speciﬁc production targets given by the planning system of
the reﬁnery that change according to the market conditions and crudes available, and it is constrained
by the physical and operational limitations imposed by the equipment.
3. Data Reconciliation and RTO
Safe and optimal operation of this system is a difﬁcult problem due not only to its large scale,
complexity and interrelated aims, but also because of the presence of signiﬁcant disturbances that
affect the process and the fact that the information available about many key variables is limited
and unreliable.
Uncertainty is mainly caused by:
•
The changes in hydrogen consumption in the reactors of the hydrotreating plants, which depend
not only on the amount of hydrocarbons being processed, but also on its sulphur content;
the sulphur product speciﬁcation; and the type of hydrocarbon processed, in particular its
light-cyclic-oil (LCO) content.
•
The use of oriﬁce-plate differential pressure ﬂow meters for gas streams is common in the process
industry, but creates particular problems when installed in hydrogen streams. These meters
provide volumetric ﬂow measurements. In order to be converted to mass ﬂow, they need to be
compensated in temperature, pressure and molecular weight, as the operating conditions normally
differ from the calibration ones of the instruments. Pressure and temperature are normally
available, but molecular weight of the streams is not, which prevents proper computation of
the mass ﬂow, normally expressed as Nm3/h. In addition, few hydrogen purity analysers are
installed in the process and the molecular weight of the streams experience signiﬁcant variations
for small changes in purity or light ends composition (which is unknown). This is due to the low
value of H2 molecular weight, 2 g/mol, as compared to those of the main impurities, CH4, C2H6,
and C3H8, which are 16, 30 and 44 respectively. This is an important difference as opposed to other
gas networks, such as the networks of natural gas, where composition can be assumed constant.
3.1. Data Reconciliation
In spite of these difﬁculties, decisions about the operation of the process can be improved if a
model is available and better process information can be obtained from plant measurements.
A ﬁrst principles model of the hydrogen behavior in the network and associated plants is available
from previous work [9,10]. It was developed to provide support in process optimization and it is based
on mass balances of hydrogen and light ends (considered as a single pseudo-component) in the pipes
and units. In addition, it incorporates other equations for compressors, membranes, separation units
(including a solubility model), etc., some of which are reduced order models ﬁtted to experimental data
or with some adjustable parameters. Taking into account the much faster dynamics of the hydrogen
compared to the dynamics of the hydrogen of the reactors, the hydrogen distribution model is static
and contains ﬂows, purities, molecular weights of hydrogen and light ends of all streams and hydrogen
consumption in the reactors as main variables.
When data present signiﬁcant uncertainty, data reconciliation is the ﬁrst step to be applied in a
model based approach to process optimization. The target is to estimate consistent values of all plant
variables from available on-line measurements based on a process model. Data reconciliation requires
redundancy in measurements, taking advantage of the fact that the core of the model, being based
on mass balances, does not present structural errors. Accurate, consistent, and robust estimations
Processes 2017, 5, 3
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are looked for, irrespective of process disturbances, measurement noise, etc., while at the same time
enabling the update of certain unknown model parameters.
The beneﬁts of implementing data reconciliation are three-fold:
•
It provides information about unknown important variables such as hydrogen consumption in
reactors, molecular weights, purities, etc.
•
It allows for reliable computation of Key Performance Indicators (KPIs) and Resource Efﬁciency
Indicators (REIs) to perform process supervision.
•
It provides consistent measurements and a model to be used in process optimization.
Data reconciliation is formulated as a large optimization problem searching for the values of
variables and parameters that satisfy the model equations and constraints and that, simultaneously,
minimize a function of the deviations (e) between model and measurements, properly normalized.
When a sum of squared errors is used as the cost function to be minimized in data reconciliation,
one of the main obstacles to obtain adequate solutions is the presence of gross errors, generated usually
by faulty instruments, which may distort the estimation, spreading the errors among other variables.
Instruments with gross errors can be detected by a combination of data analysis and repeated execution
of the data reconciliation and removed [11]. Nevertheless, this procedure is slow and implies additional
difﬁculties for industrial implementation. An alternative is the use of robust estimators that substitute
the least squares cost function with another cost function that coincides with it for small errors, but for
larger ones grows at lower speeds, such as the Fair function [12] ((Equation 1a), ﬁrst term of the sum),
limiting the spread of errors among other variables and increasing robustness. In our case, the robust
data reconciliation has been formulated as:
min
{Fi,Xi,MWi,εi,pi} ∑
j∈M
αjc2
"
ej


c −log
 
1 +

ej


c
!#
+ ∑αiε2
i + ∑αkRk
(1a)
ej = ηj(Fi −βiFi,mea)
ej = ηj(Xi −Xi,mea)
βi =
q
Td+273
(Pd+1)MWi
q
(Pi+1)MWd
Ti+273
s.t.
model equations
operational and range constraints
Fi,min −εFi ≤Fi ≤Fi,max + εFi
εFi ≥0
Xi,min −εXi ≤Xi ≤Xi,max + εXi
εXi ≥0
MWimp
i,min −εWi ≤MWimp
i
≤MWimp
i,max + εWi
εWi ≥0
(1b)
The cost Equation 1a includes three terms: the sum of the Fair functions of the normalized errors
e, the sum of penalty terms of possible range violations ε of variables to help to assure a feasible
solution and the sum of regularization terms R to favor smooth changes over time of some model
parameters. The coefﬁcients α are possible weighting/removal terms and c is a tuning parameter
of the Fair function. In the equations, e represents errors between the model and measurements of
ﬂows F and hydrogen purities X, with η normalization factors and β compensation factors for ﬂows.
MW refers to molecular weights, and P and T to pressures and temperatures, with the sub-index
d indicating design values and imp impurities. Finally, p represents model adjustable parameters.
The minimization of Equation 1a is performed under the constraint of the network model and a set
of operational and range constraints Equation 1b which includes slack variables ε to help avoiding
infeasibilities. Main decision variables are ﬂows, purities and molecular weights of all streams and
hydrogen consumption rates in the reactors.
The data reconciliation problem is a large Non-Linear Programming (NLP) problem that is
formulated and solved with a simultaneous approach in the General Algebraic Modeling System
(GAMS) environment using the Interior Point Optimizer (IPOPT) as the optimization algorithm.
The implementation involves more than 4400 variables and 4700 equality and inequality constraints.
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It takes less than ﬁve Central Processing Unit (CPU) minutes in a PC with i7 processor and 8 Gb RAM,
giving robust results against gross errors and helping to detect faulty instruments.
3.2. Real-Time Optimization (RTO)
After the data reconciliation step, once the model incorporates the estimated parameters and
reliable estimations of variables are available, it is possible to search for the best way of operating
the process according to the aims speciﬁed in Section 2.1, regarding feasible hydrogen distribution,
achievement of hydrocarbon production targets and minimization of fresh hydrogen generation or
losses. The formulation of the optimization incorporates additional constraints oriented to keep
the operation of the control rooms as undisturbed as possible. Because of that, it assumes as ﬁxed
quantities many speciﬁc values related to the current operation of the units, such as speciﬁc hydrogen
consumption or speciﬁc generation of light ends and its molecular weight in reactors; speciﬁc reactor
quench ﬂows for temperature management or separation factors; and speciﬁc purge ﬂows and its
properties in separation units. In the same way, the state of functioning or stopping the plants and
the current structure of the network are respected, assuming that they are mainly imposed either by
maintenance or global production planning reasons.
Under these assumptions, the optimal redistribution is formulated as the RTO problem:
maxJ = ∑
i
pHCiHCi −∑
j
pHiFHi −∑
k
pRkRk
(2a)
s.t.
Process model
Process constraints
Reﬁnery planning speciﬁcations
where the three terms of the cost Equation (2a) aim to maximize the hydrocarbon load (HCi) to
consumer plants, minimize the use of fresh hydrogen generated in the steam reforming plants (FHj)
and minimize the internal recycles of hydrogen (Rk) in the consumer plants, which is linked to
the operation of the recycle compressors. Here, pHC, pH and pR stand for prices associated with
hydrocarbons, fresh hydrogen and compressors in order to provide an economic meaning to the
cost function.
The problem has to be solved under the constraints imposed by the model and operation of
the units, taking also into account the targets coming from the reﬁnery planning. Constraints apply
mainly to pipes’ capacity, recycle purity in the consumer plants, ratio hydrogen/hydrocarbon at the
reactors’ input, operating range of membranes, producer plants’ capacity, reciprocating and centrifugal
compressors’ capacity, etc. Main decision variables include production of fresh hydrogen, feeds to
consumer plants, hydrogen ﬂows and recirculation, purges, purities and membranes operation.
Processes 2017, 5, 3 
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implementation involves more than 4400 variables and 4700 equality and inequality constraints. It 
takes less than five Central Processing Unit (CPU) minutes in a PC with i7 processor and 8 Gb RAM, 
giving robust results against gross errors and helping to detect faulty instruments. 
3.2. Real-Time Optimization (RTO) 
After the data reconciliation step, once the model incorporates the estimated parameters and 
reliable estimations of variables are available, it is possible to search for the best way of operating the 
process according to the aims specified in Section 2.1, regarding feasible hydrogen distribution, 
achievement of hydrocarbon production targets and minimization of fresh hydrogen generation or 
losses. The formulation of the optimization incorporates additional constraints oriented to keep the 
operation of the control rooms as undisturbed as possible. Because of that, it assumes as fixed 
quantities many specific values related to the current operation of the units, such as specific 
hydrogen consumption or specific generation of light ends and its molecular weight in reactors; 
specific reactor quench flows for temperature management or separation factors; and specific purge 
flows and its properties in separation units. In the same way, the state of functioning or stopping the 
plants and the current structure of the network are respected, assuming that they are mainly 
imposed either by maintenance or global production planning reasons. 
Under these assumptions, the optimal redistribution is formulated as the RTO problem: 
HCi
Hi
Rk
max
p
p
p
i
Hi
k
i
j
k
J
HC
F
R
=
−
−
∑
∑
∑
 
(2a) 
s.t. 
Process model 
Process constraints 
Refinery planning specifications 
 
where the three terms of the cost Equation 2a aim to maximize the hydrocarbon load (HCi) to 
consumer plants, minimize the use of fresh hydrogen generated in the steam reforming plants (FHj) 
and minimize the internal recycles of hydrogen (Rk) in the consumer plants, which is linked to the 
operation of the recycle compressors. Here, pHC, pH and pR stand for prices associated with 
hydrocarbons, fresh hydrogen and compressors in order to provide an economic meaning to the cost 
function. 
The problem has to be solved under the constraints imposed by the model and operation of the 
units, taking also into account the targets coming from the refinery planning. Constraints apply mainly 
to pipes’ capacity, recycle purity in the consumer plants, ratio hydrogen/hydrocarbon at the reactors’ 
input, operating range of membranes, producer plants’ capacity, reciprocating and centrifugal 
compressors’ capacity, etc. Main decision variables include production of fresh hydrogen, feeds to 
consumer plants, hydrogen flows and recirculation, purges, purities and membranes operation. 
 
Figure 4. Block diagram of the data reconciliation and RTO showing the information flows and main 
components. 
 
Figure 4. Block diagram of the data reconciliation and RTO showing the information ﬂows and
main components.
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Again, the problem is a NLP one and has been formulated in the GAMS environment above
mentioned. It involves nearly 2000 variables and more than 1800 equality and inequality constraints
and is solved with a simultaneous approach and the IPOPT algorithm in less than one minute CPU
time. The execution follows that of the data reconciliation according to the block diagram of Figure 4,
running every two hours, and its results are available in the Excel HMI and through the Osisoft
PI system.
4. System Implementation
The implementation of the hydrogen network optimal management system in the reﬁnery
followed three stages. In the ﬁrst one, data reconciliation and process optimization run off-line,
following the schematic of Figure 4. The system is built around an Excel application in a dedicated
PC that performs different tasks: Process data are read at regular intervals from a real-time data
base connected to the process (PI system) and then analyzed and treated to eliminate inconsistencies,
outliers, prepare ranges of variables, etc. One important part of the analysis concerns the state of
functioning of the plants and possible structural changes in the network. Rules for detecting if a plant
is operating or stopped exist, as well as other ones based on valve openings that identify different
ways of operating the hydrogen network using different paths, activated manually. This means that
the model has to be adjusted automatically to the structural changes. For this purpose, the model
includes a set of binary variables that activate or deactivate groups of equations corresponding to
different operating modes. Nevertheless, these binary variables are ﬁxed by the data analysis before
the computation of the data reconciliation takes place, so that the type of optimization problem solved
is NLP and not Mixed Integer Non Linear Programming (MINLP).
The application reads 171 ﬂows and 18 purity measurements, plus other variables and
conﬁguration parameters from the PI (temperatures, pressures, valve openings, etc.) totaling around
1000 variables, averaging them in two-hour periods to smooth the effects of transients and disturbances.
Once data are analyzed and ﬁltered, they are passed to the GAMS environment, which runs the data
reconciliation problem and gives back estimations of all model variables consistent with the model and
constraints and as close as possible to the process measurements. These estimations can be visualized
in the Excel Human Machine Interface (HMI) in different formats.
Notice that no steady state detector is normally in operation.
This is due to two reasons:
on one hand, pressure controllers in headers and consumer plants help to maintain mass balances
fairly well, operating with time constants no greater than a few minutes, which is small compared
with the two-hour average of the data. On the other hand, due to the large scale of the process,
it is very unlikely that all variables are sensibly constant for reasonable time periods, so waiting for the
green light of steady state detectors will lead to not running the system, except for short time intervals.
A more ﬂexible approach has been taken assuming that the fast system dynamics above mentioned
and data averaging allow the obtainment of sensible results.
After the data reconciliation step, the system calls GAMS again to perform the network
optimization as presented in Section 3.2. The optimal values of the process variables can be seen as
well in the Excel HMI.
4.1. Validation and Implementation Problems
The data reconciliation system has been validated analyzing trends for periods of several days in
different seasons with the technical staff of the reﬁnery. Consistency in the estimated values of the
variables, stability of the solutions and correspondence with the measured values, were some of the
criteria used. During the validation, faulty instruments were detected and corrections and updates in
the model were made. Particular attention was devoted to the rules that analyze the raw data from the
PI and convert them into useful information for the model and the data reconciliation constraints.
In the same way, the results of the open loop execution of the RTO problem (2) were studied,
which provide clues and directions on how to run the network optimally. The analysis of the way in
which the network operates by the reﬁnery team and the results of the RTO, lead to the identiﬁcation
of several action patterns and partial aims required for an optimal management of the process.
The most important ones can be summarized as follows:
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•
Losses from the HP separators of a plant to fuel gas, required to avoid light ends accumulation,
should be made at the lowest hydrogen purity compatible with the one required at the reactor
input and the H2/HC minimum ratio, see Figure 2. This implies controlling the HP separators’
purity XHP at these minimums, sending the gas to the FG purge from those plants that operate
with the lowest purity, while, in the others, the excess hydrogen is sent to the LPH for recycle.
•
As excess hydrogen is recycled to the Low Purity Header (LPH), hydrogen unbalance in the
network, that is, hydrogen generated minus hydrogen consumed in the reactors, reﬂects in the
LPH pressure (see Figure 3). This pressure is maintained with a pressure controller venting
gases to the fuel gas network. Then, production of fresh hydrogen could be modiﬁed so that the
adjustments of the unbalance performed by the LPH pressure controller are made with minimum
average valve opening compatible with non-saturated pressure control. This is similar to the
so-called valve-position control. In this way, losses to fuel gas from LPH are minimized while
guaranteeing that enough hydrogen to the consumer plants is provided to cover the demand,
as the pressure is maintained.
•
Maximization of the hydrocarbon load to the consumer plants, which is the most important target,
can be made until either maximum hydrogen capacity is reached or another technical constraint
is faced.
•
Sending higher purity hydrogen (H4) to lower purity header (H3) should be minimized as
purity degrades.
At the same time, the automated implementation of the RTO calculations to the plant control
system is not easy and presents several important problems:
•
The models used in the data reconciliation and RTO are static, with results updated every two
hours, but the implementation of the optimal values has to be applied to the process taking into
account the time evolution of variables. In particular, HC load and hydrogen production have to
be changed dynamically at a higher frequency to balance hydrogen production and consumption.
•
In the same line, due to the presence of disturbances, changing aims, etc., constraints’ fulﬁlment
requires dynamic actions to be performed at a higher rate.
•
Possible changes in hydrogen ﬂows interact among them so that a proper implementation of the
RTO solution would require multivariable control to take care of the interactions.
These requirements of dynamic and multivariable actions lead in a natural way to the
implementation of a MPC layer between the RTO and the basic control system of the network and
plants implemented in the control room DCS, as in Figure 5a. Nevertheless, this architecture does not
solve the problem of a fast update of the optimization targets and requires maintaining and operating
in real-time the large-scale system composed by the data reconciliation and RTO. A dynamic RTO
executed with a shorter sampling time or an economic MPC merging the economic and production
targets Equation 2a with dynamic MPC control could be more appropriate but it is not realistic due to
the large scale of the system.
Processes 2017, 5, 3 
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of several action patterns and partial aims required for an optimal management of the process. The 
most important ones can be summarized as follows: 
• 
Losses from the HP separators of a plant to fuel gas, required to avoid light ends accumulation, 
should be made at the lowest hydrogen purity compatible with the one required at the reactor 
input and the H2/HC minimum ratio, see Figure 2. This implies controlling the HP separators’ 
purity XHP at these minimums, sending the gas to the FG purge from those plants that operate 
with the lowest purity, while, in the others, the excess hydrogen is sent to the LPH for recycle. 
• 
As excess hydrogen is recycled to the Low Purity Header (LPH), hydrogen unbalance in the 
network, that is, hydrogen generated minus hydrogen consumed in the reactors, reflects in the 
LPH pressure (see Figure 3). This pressure is maintained with a pressure controller venting 
gases to the fuel gas network. Then, production of fresh hydrogen could be modified so that the 
adjustments of the unbalance performed by the LPH pressure controller are made with 
minimum average valve opening compatible with non-saturated pressure control. This is 
similar to the so-called valve-position control. In this way, losses to fuel gas from LPH are 
minimized while guaranteeing that enough hydrogen to the consumer plants is provided to 
cover the demand, as the pressure is maintained. 
• 
Maximization of the hydrocarbon load to the consumer plants, which is the most important 
target, can be made until either maximum hydrogen capacity is reached or another technical 
constraint is faced. 
• 
Sending higher purity hydrogen (H4) to lower purity header (H3) should be minimized as 
purity degrades. 
At the same time, the automated implementation of the RTO calculations to the plant control 
system is not easy and presents several important problems: 
• 
The models used in the data reconciliation and RTO are static, with results updated every two 
hours, but the implementation of the optimal values has to be applied to the process taking into 
account the time evolution of variables. In particular, HC load and hydrogen production have to be 
changed dynamically at a higher frequency to balance hydrogen production and consumption. 
• 
In the same line, due to the presence of disturbances, changing aims, etc., constraints’ fulfilment 
requires dynamic actions to be performed at a higher rate. 
• 
Possible changes in hydrogen flows interact among them so that a proper implementation of 
the RTO solution would require multivariable control to take care of the interactions. 
 
 
 
(a) 
(b) 
Figure 5. (a): traditional RTO/MPC implementation; (b): implementing patterns of the optimal 
solution by means of the DMC software. 
These requirements of dynamic and multivariable actions lead in a natural way to the 
implementation of a MPC layer between the RTO and the basic control system of the network and 
plants implemented in the control room DCS, as in Figure 5a. Nevertheless, this architecture does not 
 
Figure 5. (a): traditional RTO/MPC implementation; (b): implementing patterns of the optimal
solution by means of the DMC software.
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4.2. Implementation in the DMC Environment
In view of the existence of the problems and solution patterns of the optimal network management
mentioned in the previous sub-section, an alternative approach that combines these patterns, offering
a simpler implementation, is presented next.
It can be considered as stage two of the system
implementation and it is shown schematically in Figure 5b. It takes advantage of the extended
functionality of the commercial MPC used in the reﬁnery, DMCPlus (Dynamic Matrix Control) from
AspenTech [5], that mixes local LP optimizers and a predictive controller to implement the patterns that
deﬁne the optimal operation and to perform multivariable control of several plants simultaneously.
In a certain way, it follows the path of the self-optimizing control [6], which substitutes the
on-line optimization layer by a control system such that maintaining, in their set points, the so-called
self-optimizing variables, keeps the system close to its economic optimum in spite of disturbances.
Nevertheless, the formulation mentioned above is different because here there are no self-optimizing
variables but the selection of targets that deﬁne the optimal operation in cascade with a standard DMC
controller. Yet, the basic idea is to implement, as a control system, as much as possible of the optimal
management solutions and keep its implementation as simple as possible. The commercial DMC is
composed of two layers: an unconstraint DMC controller, which uses a linear step response model of
the process linking controlled variables with manipulated ones and disturbances; and a LP optimizer
that constitutes the second layer, as in Figure 6a. The LP uses the same model as the controller but in
steady state, and includes a linear cost function of the manipulated variables that is minimized at every
sampling time under a set of constraints. Both layers are executed at the same rate and the results of
the LP are passed as future Set Point (SP) targets to the DMC controller, as can be seen in Figure 6b.
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solve the problem of a fast update of the optimization targets and requires maintaining and 
operating in real-time the large-scale system composed by the data reconciliation and RTO. A 
dynamic RTO executed with a shorter sampling time or an economic MPC merging the economic 
and production targets Equation 2a with dynamic MPC control could be more appropriate but it is 
not realistic due to the large scale of the system. 
 
 
(a) 
(b) 
Figure 6. (a): Control layers showing the two components of the DMC: Local optimizer and MPC 
acting on the basic control system; (b): Predictions of controlled and manipulated variables with set 
points and the final targets set by the LP optimizer. 
4.2. Implementation in the DMC Environment 
In view of the existence of the problems and solution patterns of the optimal network 
management mentioned in the previous sub-section, an alternative approach that combines these 
patterns, offering a simpler implementation, is presented next. It can be considered as stage two of the 
system implementation and it is shown schematically in Figure 5b. It takes advantage of the extended 
functionality of the commercial MPC used in the refinery, DMCPlus (Dynamic Matrix Control) from 
AspenTech [5], that mixes local LP optimizers and a predictive controller to implement the patterns 
that define the optimal operation and to perform multivariable control of several plants 
simultaneously. 
In a certain way, it follows the path of the self-optimizing control [6], which substitutes the 
on-line optimization layer by a control system such that maintaining, in their set points, the so-called 
self-optimizing variables, keeps the system close to its economic optimum in spite of disturbances. 
Nevertheless, the formulation mentioned above is different because here there are no 
self-optimizing variables but the selection of targets that define the optimal operation in cascade 
with a standard DMC controller. Yet, the basic idea is to implement, as a control system, as much as 
Figure 6. (a): Control layers showing the two components of the DMC: Local optimizer and MPC
acting on the basic control system; (b): Predictions of controlled and manipulated variables with set
points and the ﬁnal targets set by the LP optimizer.
The optimal action patterns deﬁned in Section 4.1 can be implemented in the LP layer of a DMC
in terms of partial aims in the LP cost function. At the same time, as they involve the joint on-line
manipulation of several process plants, and in order to keep the implementation as simple of possible,
only the most important ones from the point of view of hydrogen consumption and hydrocarbon
processed were included in the design of the DMC. At present, it controls the operation of six plants:
two hydrogen producers H3 and H4 and four consumers G1, G3, G4 and HD3, as can be seen in
Figure 7.
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Figure 7. Diagram of the DMC controlling the operation of two hydrogen producers H3 and H4 and 
four consumers G1, G3, G4 and HD3, with the main controlled hydrogen flows and HC loads. 
The optimal action patterns defined in Section 4.1 can be implemented in the LP layer of a DMC in 
terms of partial aims in the LP cost function. At the same time, as they involve the joint on-line 
manipulation of several process plants, and in order to keep the implementation as simple of possible, 
only the most important ones from the point of view of hydrogen consumption and hydrocarbon 
processed were included in the design of the DMC. At present, it controls the operation of six plants: two 
hydrogen producers H3 and H4 and four consumers G1, G3, G4 and HD3, as can be seen in Figure 7. 
 
 
Figure 8. Dynamic matrix of the DMC controller. 
The controller was developed and implemented by the refinery team and is based on linear 
models obtained by identification using data from step-tests that forms a dynamic matrix such as the 
one in Figure 8, involving 12 manipulated variables and 29 controlled ones. The main manipulated 
variables refer to the set points of hydrocarbon loads to the consumer units, fresh hydrogen 
production, hydrogen feed to the consumers from the high purity collector and supply of hydrogen 
from one of the platformer plants. The main controlled variables are hydrogen partial pressure in the 
reactors of the consumer plants, losses to fuel gas from the Low Purity Header (valve opening), 
recycle purity and HP losses to FG from some plants, hydrocarbon loads and valve openings to 
avoid control saturation. They are organized in four sub-controllers, so that each one can be 
disconnected without affecting the rest of them in case it is required due to process conditions or 
maintenance actions. 
 
Figure 7. Diagram of the DMC controlling the operation of two hydrogen producers H3 and H4 and
four consumers G1, G3, G4 and HD3, with the main controlled hydrogen ﬂows and HC loads.
The controller was developed and implemented by the reﬁnery team and is based on linear
models obtained by identiﬁcation using data from step-tests that forms a dynamic matrix such as the
one in Figure 8, involving 12 manipulated variables and 29 controlled ones. The main manipulated
variables refer to the set points of hydrocarbon loads to the consumer units, fresh hydrogen production,
hydrogen feed to the consumers from the high purity collector and supply of hydrogen from one of
the platformer plants. The main controlled variables are hydrogen partial pressure in the reactors of
the consumer plants, losses to fuel gas from the Low Purity Header (valve opening), recycle purity and
HP losses to FG from some plants, hydrocarbon loads and valve openings to avoid control saturation.
They are organized in four sub-controllers, so that each one can be disconnected without affecting the
rest of them in case it is required due to process conditions or maintenance actions.
Processes 2017, 5, 3 
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Figure 7. Diagram of the DMC controlling the operation of two hydrogen producers H3 and H4 and 
four consumers G1, G3, G4 and HD3, with the main controlled hydrogen flows and HC loads. 
The optimal action patterns defined in Section 4.1 can be implemented in the LP layer of a DMC in 
terms of partial aims in the LP cost function. At the same time, as they involve the joint on-line 
manipulation of several process plants, and in order to keep the implementation as simple of possible, 
only the most important ones from the point of view of hydrogen consumption and hydrocarbon 
processed were included in the design of the DMC. At present, it controls the operation of six plants: two 
hydrogen producers H3 and H4 and four consumers G1, G3, G4 and HD3, as can be seen in Figure 7. 
 
 
Figure 8. Dynamic matrix of the DMC controller. 
The controller was developed and implemented by the refinery team and is based on linear 
models obtained by identification using data from step-tests that forms a dynamic matrix such as the 
one in Figure 8, involving 12 manipulated variables and 29 controlled ones. The main manipulated 
variables refer to the set points of hydrocarbon loads to the consumer units, fresh hydrogen 
production, hydrogen feed to the consumers from the high purity collector and supply of hydrogen 
from one of the platformer plants. The main controlled variables are hydrogen partial pressure in the 
reactors of the consumer plants, losses to fuel gas from the Low Purity Header (valve opening), 
recycle purity and HP losses to FG from some plants, hydrocarbon loads and valve openings to 
avoid control saturation. They are organized in four sub-controllers, so that each one can be 
disconnected without affecting the rest of them in case it is required due to process conditions or 
maintenance actions. 
Figure 8. Dynamic matrix of the DMC controller.
The LP layer minimizes a cost function that plays with four aims:
•
Maximize hydrocarbon loads to the consumer plants
•
Minimize losses from the LPH to FG
•
Minimize hydrogen purity in the recycles of the consumer plants
•
Minimize hydrogen transfers from higher to lower purity headers
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These four objectives are combined in a single linear cost function assigning different weights to
the variables associated with them that reﬂect their relative importance and priority. This cost function
is optimized under the constraints imposed by the dynamic matrix model and range constraints in
the model variables. The solutions are given as targets to the predictive controller, as in Figure 6,
which computes the corresponding dynamic actions and passes them to the lower basic control layer
of the control room.
4.3. Planning, RTO and DMC Integration
The DMC is operating in the reﬁnery, giving consistent improvements for several months.
Its implementation represented a big step forward in the automation and optimal management
of the hydrogen network of the reﬁnery. Nevertheless, it covers only a subset of the total number of
process plants and headers involved in the hydrogen network and it does not consider all possible
hydrogen management strategies or non-linear effects. This is why further beneﬁts can be obtained by
additional use of the global information obtained from the data reconciliation and use of the network
wide RTO solutions in a Decision Support System (DSS), which corresponds to stage three of the
system implementation.
Main aims of the DSS can be summarized as:
•
Provide reliable and full information about the process functioning
•
Supervise the operation of the DMC and the hydrogen network
•
Identify ways in which the operation of the hydrogen network can be improved
•
Suggest changes that improve the DMC operation
•
Report to the planning system on the achievable targets and limiting constraints
Notice that both RTO and DMC have to operate in the framework of the reﬁnery planning system,
which sets production and quality targets for the various reﬁnery products every two or three days
according to the market and crude to be processed. This information is read from the PI system by the
RTO and DMC and it is used in both to ﬁx many operation ranges, targets and priorities. One example
is the allowed range for the hydrocarbon load of a certain diesel HDS plant that should be maximized
within that range, and the indication that increasing it has higher priority than the hydrocarbon load of
other naphtha HDS. Nevertheless, that information does not cover all parameters involved in the RTO
and DMC optimization problems. In particular, hydrocarbon prices in Equation (2a) or the weights
of the four aims involved in the LP cost function of the DMC are not given explicitly. The reason is
that assigning proper prices to intermediate hydrocarbon streams is not an easy task, mainly when the
crude and outcomes of the reﬁnery change frequently. Because of that, the HC prices in Equation (2a)
and the weights of the LP have been considered as weighting factors reﬂecting the relative priorities of
the products and aims involved. The way in which they are tuned includes extensive off-line tests in
simulation to ﬁnd sets of parameters that respond to different priorities of the several aims involved,
combined with the on-line use of the priorities read from the planning system, which are associated
with the selection of a speciﬁc set of parameters. The control room operators can activate buttons in
the consoles of the DCS that modify the priorities and the cost function accordingly. Notice that, in this
way, the cost functions themselves do not have a real economic meaning, but meaningful economic
interpretation can be obtained from the values of the process variables proposed by the optimization.
The proposed system therefore operates taking into account the modules and interrelations
displayed in Figure 9. More than in a hierarchy, RTO and DMC operate in parallel, with the aim of
using the RTO calculations to improve the operation of the DMC and to be a guide for other corrections.
The PI real-time information system is at the core of the information ﬂow, allowing the results of the
Data Reconciliation/RTO to be used on-line by the different departments involved.
Processes 2017, 5, 3
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Processes 2017, 5, 3 
25 of 30 
corrections. The PI real-time information system is at the core of the information flow, allowing the 
results of the Data Reconciliation/RTO to be used on-line by the different departments involved. 
 
Figure 9. Block diagram of the main elements involved in the hydrogen network management. 
The software has been tested off-line extensively, incorporating updates, corrections and 
improvements, and it is now functioning on-line in the refinery. 
5. Results 
The benefits obtained from the implementation of the system can be classified in four types: 
• 
Improved network information 
• 
Increased hydrocarbon production 
• 
Better use of hydrogen 
• 
Integrated network management 
Improved network information is the result of data reconciliation that provides reliable values 
of all process variables. This is important as a help to daily operation, because many of them were 
not available previously, e.g., hydrogen consumption in the reactors or purity of many streams. The 
estimated values of the main variables are now accessible to all staff in a dedicated application in the 
PI system in different formats, e.g., the one in Figure 10. A significant part of the success of the data 
reconciliation step is due to the incorporation of robust estimators in the formulation of the problem 
that allow the obtainment of sensible solutions in spite of the presence of faulty instruments, as it is 
very difficult to not have something wrong in the plant instrumentation. Several estimators were 
tested, for instance, the Redescending, Fair and Welsh estimators, [12] with the Fair function selected 
finally for its simplicity and good behavior. 
However, the data reconciliation results are also important for other purposes, such as 
helping in the detection and correction of faulty instruments, as they allow one to focus attention on 
those instruments that present consistent deviations between what is measured and what is 
estimated. At the same time, the data reconciliation provides coherent values for other computations, 
among them, the possible revamping of the network structure and the updated model for RTO 
calculations or the computation of efficiency indicators in the network management. This last point 
is particularly important.  
Supervision of the network operation is made using Resource Efficiency Indicators (REIs) that 
were defined in the MORE project [13]. They are computed thanks to the availability of process 
values provided by the data reconciliation. Among them, the most useful ones are those that relate 
the actual value of a resource to the optimum one computed from the RTO solution, as they measure 
how well the process is reaching the targets computed by the optimization. 
 
Figure 9. Block diagram of the main elements involved in the hydrogen network management.
The software has been tested off-line extensively, incorporating updates, corrections and
improvements, and it is now functioning on-line in the reﬁnery.
5. Results
The beneﬁts obtained from the implementation of the system can be classiﬁed in four types:
•
Improved network information
•
Increased hydrocarbon production
•
Better use of hydrogen
•
Integrated network management
Improved network information is the result of data reconciliation that provides reliable values
of all process variables. This is important as a help to daily operation, because many of them were
not available previously, e.g., hydrogen consumption in the reactors or purity of many streams.
The estimated values of the main variables are now accessible to all staff in a dedicated application
in the PI system in different formats, e.g., the one in Figure 10. A signiﬁcant part of the success of
the data reconciliation step is due to the incorporation of robust estimators in the formulation of the
problem that allow the obtainment of sensible solutions in spite of the presence of faulty instruments,
as it is very difﬁcult to not have something wrong in the plant instrumentation. Several estimators
were tested, for instance, the Redescending, Fair and Welsh estimators, [12] with the Fair function
selected ﬁnally for its simplicity and good behavior.
However, the data reconciliation results are also important for other purposes, such as helping
in the detection and correction of faulty instruments, as they allow one to focus attention on
those instruments that present consistent deviations between what is measured and what is
estimated. At the same time, the data reconciliation provides coherent values for other computations,
among them, the possible revamping of the network structure and the updated model for RTO
calculations or the computation of efﬁciency indicators in the network management. This last point is
particularly important.
Supervision of the network operation is made using Resource Efﬁciency Indicators (REIs) that
were deﬁned in the MORE project [13]. They are computed thanks to the availability of process values
provided by the data reconciliation. Among them, the most useful ones are those that relate the actual
value of a resource to the optimum one computed from the RTO solution, as they measure how well
the process is reaching the targets computed by the optimization.
Processes 2017, 5, 3
16 of 20
Processes 2017, 5, 3 
26 of 30 
 
Figure 10. Schematic of a consumer plant showing the tags of measured, reconciled and optimized 
key variables and REIs. They can be further displayed graphically. 
Two of them are displayed for one week of operation in Figure 11 and refer to the ratio between 
the optimal and actual fresh hydrogen production (in blue) and the ratio between the actual and 
optimal hydrocarbon load to the consumers (in red), as shown in Equation 3. They are defined in 
such a way that they indicate better operation when they are close to one. 
REI1 = optimal H2 production/actual H2 production  
REI2 = actual HC load/optimal HC load 
(3) 
 
Figure 11. Two REIs showing the distance to the optimal achievable targets for one week of operation. 
In the figure, we can observe that, for that week, the HC load is very close to the optimal value 
that can be attained according to the network conditions, while some savings of hydrogen are still 
possible. Notice that the hydrogen index in day one exceeds unity in day one because the hydrogen 
index on hydrocarbon has dropped, so both indicators have to be analyzed jointly. The graph also 
shows a short stop on day six. 
 
Figure 10. Schematic of a consumer plant showing the tags of measured, reconciled and optimized key
variables and REIs. They can be further displayed graphically.
Two of them are displayed for one week of operation in Figure 11 and refer to the ratio between
the optimal and actual fresh hydrogen production (in blue) and the ratio between the actual and
optimal hydrocarbon load to the consumers (in red), as shown in Equation 3. They are deﬁned in such
a way that they indicate better operation when they are close to one.
REI1 = optimal H2 production/actual H2 production
REI2 = actual HC load/optimal HC load
(3)
Processes 2017, 5, 3 
26 of 30 
 
Figure 10. Schematic of a consumer plant showing the tags of measured, reconciled and optimized 
key variables and REIs. They can be further displayed graphically. 
Two of them are displayed for one week of operation in Figure 11 and refer to the ratio between 
the optimal and actual fresh hydrogen production (in blue) and the ratio between the actual and 
optimal hydrocarbon load to the consumers (in red), as shown in Equation 3. They are defined in 
such a way that they indicate better operation when they are close to one. 
REI1 = optimal H2 production/actual H2 production  
REI2 = actual HC load/optimal HC load 
(3) 
 
Figure 11. Two REIs showing the distance to the optimal achievable targets for one week of operation. 
In the figure, we can observe that, for that week, the HC load is very close to the optimal value 
that can be attained according to the network conditions, while some savings of hydrogen are still 
possible. Notice that the hydrogen index in day one exceeds unity in day one because the hydrogen 
index on hydrocarbon has dropped, so both indicators have to be analyzed jointly. The graph also 
shows a short stop on day six. 
 
Figure 11. Two REIs showing the distance to the optimal achievable targets for one week of operation.
In the ﬁgure, we can observe that, for that week, the HC load is very close to the optimal value
that can be attained according to the network conditions, while some savings of hydrogen are still
possible. Notice that the hydrogen index in day one exceeds unity in day one because the hydrogen
index on hydrocarbon has dropped, so both indicators have to be analyzed jointly. The graph also
shows a short stop on day six.
Processes 2017, 5, 3
17 of 20
Other REIs are also computed, but the information provided by them suffers from the fact that
they depend on the type of hydrocarbons being processed or the product speciﬁcations, which make
them less useful for supervising the efﬁciency of the operation. As an example, Figure 12 displays two
of them: the speciﬁc use of makeup hydrogen (in blue) and the speciﬁc hydrogen consumption in the
reactors (in red) of a certain HDS, for six days of operation. As can be seen, they experience stronger
changes coinciding with the change of hydrocarbon processed around days two and ﬁve than during
daily operation. However, the distance between the two curves gives useful information about the
efﬁciency of the operation in the plant regarding the use of the hydrogen.
Processes 2017, 5, 3 
27 of 30 
Other REIs are also computed, but the information provided by them suffers from the fact that 
they depend on the type of hydrocarbons being processed or the product specifications, which make 
them less useful for supervising the efficiency of the operation. As an example, Figure 12 displays 
two of them: the specific use of makeup hydrogen (in blue) and the specific hydrogen consumption 
in the reactors (in red) of a certain HDS, for six days of operation. As can be seen, they experience 
stronger changes coinciding with the change of hydrocarbon processed around days two and five 
than during daily operation. However, the distance between the two curves gives useful information 
about the efficiency of the operation in the plant regarding the use of the hydrogen. 
 
Figure 12. Two REIs showing specific use of hydrogen and specific hydrogen consumption in 
reactors of a HDS plant. Figures given in % of a certain scale. 
Improvements in the amounts of hydrocarbon processed and the use of hydrogen are also 
important benefits of the implementation of the system. Nevertheless, quantifying them is not easy. 
A sensible evaluation implies measuring something (costs, resources, …) before and after the 
implementation of a new system and comparing results in both situations to compute the gains. 
However, this procedure requires performing the comparison in the same conditions, i.e., setting a 
base line. The problem when evaluating the system described in this paper is that the raw material, 
targets and operating conditions change quite often, as seen in the example of Figure 12, and it is 
very difficult to find similar situations in the eighteen plants involved in the hydrogen network for 
sensible periods of time. This is due mainly to the change in the crude being processed every few 
days, which may imply a noticeable variation in its properties, in particular hydrogen demand. 
Keeping this in mind, it is possible to perform evaluations of the results, disconnecting and 
connecting again the DMC for short periods of time when the operating conditions do not change 
significantly, and comparing the values before and after. Based on this procedure, it is possible to 
estimate a saving of 2.5% in the hydrogen production, while the increment of hydrocarbon loads is 
more difficult to estimate by this procedure, because the operators tend to be kept constant. In any 
case, there has been a clear improvement of the operating conditions since the online DMC started 
functioning, in the sense that hydrogen availability is no longer a bottleneck for production. 
Nevertheless, this period coincided with an average supply of lighter crudes to the refinery, so it is 
difficult to assign numbers to both factors. At the same time, indicators such as REI2 in Equation 3 
provide values close to one for long periods of time, as in Figure 11, indicating that hydrocarbon 
production, which is the most valuable target, approached the maximum feasible according to the 
operating conditions and the targets fixed by the refinery planning. 
In addition to providing indicators to supervise the behavior of the network under the DMC, 
improvements to the network operation can be obtained by the analysis of the results of the RTO 
optimization compared to the actual operating conditions. At present, the teams involved in the 
analysis are the ones that have developed the system, but the trend is to move it to the personnel 
responsible for the network management in the control rooms. For this purpose, the main results are 
displayed in the PI information system for every plant as in Figure 11, where, by clicking on the 
different tags, one obtains displays of trends of the measured, estimated, optimized variables and 
REIs, facilitating the analysis to the staff involved in decision making in the refinery. 
Below are two of the main points where the analysis is focused: 
• 
One is the hydrogen distribution strategies. Notice that the RTO considers the whole network, 
while the DMC only considers a subset of the plants and headers and does not manipulate 
 
Figure 12. Two REIs showing speciﬁc use of hydrogen and speciﬁc hydrogen consumption in reactors
of a HDS plant. Figures given in % of a certain scale.
Improvements in the amounts of hydrocarbon processed and the use of hydrogen are also
important beneﬁts of the implementation of the system. Nevertheless, quantifying them is not
easy. A sensible evaluation implies measuring something (costs, resources, . . . ) before and after
the implementation of a new system and comparing results in both situations to compute the gains.
However, this procedure requires performing the comparison in the same conditions, i.e., setting a
base line. The problem when evaluating the system described in this paper is that the raw material,
targets and operating conditions change quite often, as seen in the example of Figure 12, and it is very
difﬁcult to ﬁnd similar situations in the eighteen plants involved in the hydrogen network for sensible
periods of time. This is due mainly to the change in the crude being processed every few days, which
may imply a noticeable variation in its properties, in particular hydrogen demand.
Keeping this in mind, it is possible to perform evaluations of the results, disconnecting and
connecting again the DMC for short periods of time when the operating conditions do not change
signiﬁcantly, and comparing the values before and after. Based on this procedure, it is possible to
estimate a saving of 2.5% in the hydrogen production, while the increment of hydrocarbon loads
is more difﬁcult to estimate by this procedure, because the operators tend to be kept constant.
In any case, there has been a clear improvement of the operating conditions since the online DMC
started functioning, in the sense that hydrogen availability is no longer a bottleneck for production.
Nevertheless, this period coincided with an average supply of lighter crudes to the reﬁnery, so it is
difﬁcult to assign numbers to both factors. At the same time, indicators such as REI2 in Equation (3)
provide values close to one for long periods of time, as in Figure 11, indicating that hydrocarbon
production, which is the most valuable target, approached the maximum feasible according to the
operating conditions and the targets ﬁxed by the reﬁnery planning.
In addition to providing indicators to supervise the behavior of the network under the DMC,
improvements to the network operation can be obtained by the analysis of the results of the RTO
optimization compared to the actual operating conditions. At present, the teams involved in the
analysis are the ones that have developed the system, but the trend is to move it to the personnel
responsible for the network management in the control rooms. For this purpose, the main results
are displayed in the PI information system for every plant as in Figure 11, where, by clicking on the
different tags, one obtains displays of trends of the measured, estimated, optimized variables and REIs,
facilitating the analysis to the staff involved in decision making in the reﬁnery.
Processes 2017, 5, 3
18 of 20
Below are two of the main points where the analysis is focused:
•
One is the hydrogen distribution strategies. Notice that the RTO considers the whole network,
while the DMC only considers a subset of the plants and headers and does not manipulate
certain elements as e.g., the membranes. This means that sometimes there is room for further
improvements, implementing a different hydrogen redistribution policy, as the feasible set of
actions is larger in the RTO. For instance, in the case of the membranes, as the DMC does not
manipulate them, the operators ﬁx their behavior according to local needs, while the RTO can
compute the best way of operating them according to the global aims.
•
The second refers to the identiﬁcation of persistent active constraints in the optimization that stop
further changes in some variables that could improve the attainment of the targets. Examples
include the maximum compressor capacity in a recycle or minimum hydrogen purity in a
high-pressure separator. The limiting values of the constraints can be structural or operational,
and should be analyzed individually to see the convenience of changing them. In order to
select the important ones, the value of the associated Lagrange multipliers can be used as they
provide the sensitivities of the cost function w.r.t. the constraints, indicating the beneﬁts that
could be obtained by every unit change in the value of the constraint. Referring to the above
examples, the compressor capacity is a structural decision that is linked to a unit revamping,
but the minimum hydrogen purity is operational and could be relaxed, for instance, at the end of
life of the reactor catalyzer.
The results of the analysis can be implemented or not considering the efforts involved and
expected beneﬁts, which require familiarity with the process and global views of the problems. This is
not a problem with the technical staff, but further training is required with the constraint analysis and
the interpretation of the Lagrange multipliers.
The implementation of the results of the analysis follows two paths:
•
The application of the hydrogen redistribution strategy, deciding, for instance, on a different use
of the membranes or proportions in the hydrogen feed sources. Notice that changes in the global
strategy of hydrogen distribution can help the DMC to reach its own aims. At the same time,
this can help to evaluate the convenience of extending the DMC to other plants or controlled and
manipulated variables not included within its scope.
•
The possible changes on some DMC constraints. As an illustrative example, we will mention the
ratio hydrogen/hydrocarbon in a plant. Data reconciliation estimates its current value, which
is imposed as a lower bound to the RTO as a way to protect catalyst life. However, the DMC
may use other limiting expressions, e.g., the linearized model of the hydrogen partial pressure
obtained experimentally in a certain operating point. If the last one is consistently active and the
ratio hydrogen/hydrocarbon is not, one may decide to change the DMC constraint accordingly,
obtaining more space for improvements, while keeping a safe operation.
Finally, the forth beneﬁt obtained from the system refers to the implementation of feedback to the
upper planning layer. The identiﬁcation of gaps between the targets given by the planning system of
the reﬁnery and what the RTO/DMC compute as feasible targets according to the current condition is
valuable information for better tuning and improvements of models in the planning layer. In the same
way, the detection of active constraints and sensitivity analysis complement this information that can
be relevant when deciding changes in the elements of the plant, such as the compressors’ capacity that
was mentioned above.
Processes 2017, 5, 3
19 of 20
6. Discussion
Development and implementation of the system described above have been the outcome of a
fruitful cooperation between the industrial and academic teams over several years which is giving
clear beneﬁts in terms of better process information, increased production, and savings in the use
of hydrogen and smoother operation. Overall, the system implemented in the reﬁnery is a clear
improvement in the efﬁciency of the use of resources and represents a signiﬁcant step forward to
further integration with other advanced systems in the reﬁnery and enhancements of its functionality.
Nevertheless, the project is still under development, and several problems are open to further
research. Among them, model maintenance appears as a key one to maintain the system alive for a
long period of time. Revamping or major changes are not infrequent in all process plants, and this
requires model (and optimization) adaptation, which should be generated automatically from some
type of schematic.
The RTO system could also be improved in two directions: One is by incorporating a measure of
the uncertainty present in the process, either using stochastic optimization of the modiﬁer adaptation
approaches, as we are aware that the two-step approach of data reconciliation and RTO can lead to
suboptimal targets in the presence of structural errors. The other one is considering plant dynamics at
this level, so that the non-linear effects could be better taken into account.
Regarding the analysis of the RTO solutions and decisions about their implementation,
the development of on-line tools, such as predictive simulation, could help to better evaluate and
increase the conﬁdence in the results.
Finally, the current model could also be used as the base for studies on the convenience of larger
structural changes in the hydrogen network, using superstructures and MINLP software to discover
possible optimal solutions not considered at present.
Acknowledgments:
The authors wish to express their gratitude to project DPI2015-70975P of Spanish
MINECO/FEDER UE, as well as to the EU FP7-NMP project MORE under GA 604068, for the ﬁnancial support
for this study. They also wish to thank Petronor management for their involvement and help.
Author Contributions: S.M. and M.S. implemented the DMC, E.G. worked in the network modelling, G.G.
performed the GAMS implementation, D.S. performed the Excel implementation, C.P. provided operation
requirements, R.G. and C. de P. directed the project. All people participated in the analysis of data and results.
Conﬂicts of Interest: The authors declare no conﬂict of interest.
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© 2017 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access
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(CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

SUSTAINABLE INDUSTRIAL AND ENVIRONMENTAL BIOPROCESSES
Treatment of wastewater from petroleum industry: current
practices and perspectives
Sunita Varjani1
& Rutu Joshi2 & Vijay Kumar Srivastava3 & Huu Hao Ngo4 & Wenshan Guo4
Received: 13 January 2019 /Accepted: 26 February 2019
# Springer-Verlag GmbH Germany, part of Springer Nature 2019
Abstract
Petroleum industry is one of the fastest growing industries, and it significantly contributes to economic growth in developing
countries like India. The wastewater from a petroleum industry consist a wide variety of pollutants like petroleum hydrocarbons,
mercaptans, oil and grease, phenol, ammonia, sulfide, and other organic compounds. All these compounds are present as very
complex form in discharged water of petroleum industry, which are harmful for environment directly or indirectly. Some of the
techniques used to treat oily waste/wastewater are membrane technology, photocatalytic degradation, advanced oxidation pro-
cess, electrochemical catalysis, etc. In this review paper, we aim to discuss past and present scenario of using various treatment
technologies for treatment of petroleum industry waste/wastewater. The treatment of petroleum industry wastewater involves
physical, chemical, and biological processes. This review also provides scientific literature on knowledge gaps and future
research directions to evaluate the effect(s) of various treatment technologies available.
Keywords Petroleumhydrocarbons .Membranetechnology .Photocatalyticdegradation .Wastebiorefinery .Resourcerecovery
Introduction
Water is necessary for life on earth as it is a basic need of all
organisms. Rapid industrialization and economic develop-
ment have led to exponential growth in population and urban-
ization (Zafra et al. 2015; Chen 2018). The world is
witnessing an increase in urbanization and industrialization
due to the consumerist approach (Li and Yu 2011; Zhang
et al. 2015). The domestic and industrial sectors continuously
generate large amount of waste/wastewater at an alarming rate
and usually dispose the waste without proper management
and treatment (Varjani et al. 2019). Petroleum industries and
refineries are important from economic growth point of view
(Li and Yu 2011; Li et al. 2014; Abdulredha et al. 2018).
Highlights
• This manuscript reviews technologies used for treatment of petroleum
industry wastewater
• Integration of available technologies play vital role for treatment of oily
wastewater
• Wastewater treatment process in petroleum industry is briefly discussed
• More studies are required to close existing knowledge gaps prior to
engineering application
• Recovery of resources from petroleum industry wastewater shall be
explored.
Responsible editor: Philippe Garrigues
* Sunita Varjani
drsvs18@gmail.com
1
Gujarat Pollution Control Board, Gandhinagar, Gujarat 382010,
India
2
School of Biological Sciences and Biotechnology, Indian Institute of
Advanced Research, Gandhinagar, Gujarat 382007, India
3
Sankalchand Patel Vidyadham, Sankalchand Patel University,
Visnagar, Gujarat 384315, India
4
Centre for Technology in Water and Wastewater, School of Civil and
Environmental Engineering, University of Technology Sydney,
Sydney, NSW 2007, Australia
https://doi.org/10.1007/s11356-019-04725-x
/ Published online: 13 March 2019
Environmental Science and Pollution Research (2020) 27:27172–27180
Petroleum refineries and petrochemical plants are facing prob-
lem of disposing the waste/wastewater generated. Wastewater
released by petroleum industries contains different types of
organic and inorganic pollutants such as BTEX, sulfides, hy-
drocarbons, phenol, and heavy metals (He and Jiang 2008;
Usman et al. 2012; Varjani 2017a; Raza et al. 2018). Large
quantities of toxic substances are released by activities of pe-
troleum industry such as production process of oil, transpor-
tation, oil refinery, petrochemical product, storage, and distri-
bution. These are harmful for environment and human health
(Perera et al. 2012; Viggi et al. 2015; Raza et al. 2018).
The treatment of oily wastewater generated from petroleum
industry involves different processes such as (1) physical, (b)
chemical, and (c) biological processes. Various processes
available for treatment of petroleum hydrocarbons polluted
water ranges from applied methods to emerging methods
(Usman et al. 2012; Li et al. 2014; Viggi et al. 2015; Varjani
2017b). Characteristics of oily wastewater and effect of petro-
leum hydrocarbons on environment and human health have
been emphasized in this paper. Sections of this paper also
discusses about the current treatment process used in petro-
leum industry and treatment methods used for treating oily
wastewater. The current review focuses to generate critical
review about the different practices used for wastewater treat-
ment in petroleum industries. This review also focuses on
information pertaining to the knowledge gaps and future re-
search directions in this field.
Characteristics of oily wastewater
Petroleum means Brock oil,^ and this word is derived from a
Latin word Bpetra^ and Greek word Boleum^ (Jafarinejad
2016). Petroleum industry generates large amount of oily
waste either solid or liquid due to upstream and downstream
operations (Varjani and Upasani 2017). Upstream process in-
cludes extracting, transporting, and storing crude oil, and
downstream process includes refining of crude oil (Al-
Futaisi et al. 2007; Hu et al. 2013; Thakur et al. 2018).
Depending on a ratio of water and solids waste is categorized
into simple wastewater crude oil or sludge. The pH value of
oily sludge is usually ranging from 6.5 to 7.5, but it varies
depending on sources of crude oil, processing method, re-
agents used, etc. (Hu et al. 2013; Jasmine and Mukherji
2015). Wastewater from petroleum industry contains oil im-
purities, high level of biochemical oxygen demand (BOD) and
chemical oxygen demand (COD), high total solids, hydrocar-
bons, and other waste. This waste includes oily sludge, waste
catalyst, heavy metals, volatile organic compounds, oil and
grease content, high total dissolved salts, ammonia, nitrates,
sulfides, etc. (Jasmine and Mukherji 2015). Oily wastewater
contains mainly four major types of petroleum hydrocarbons
such as aliphatic, aromatic, asphaltenes, and compounds
containing oxygen, nitrogen, and sulfur. It consists organo-
metallic complexes with cadmium, lead, nickel, and vanadium
(Honse et al. 2012; Varjani and Upasani 2017; Thakur et al.
2018). Table 1 summarizes the characteristics of oily
wastewater/effluents and standards for their discharge in envi-
ronment. These pollutants typically disperse, emulsify, or dis-
solve within the oily wastewater. In general, aromatic and
aliphatic compounds counts up to 75% of petroleum hydro-
carbons in oily wastewater (Ward et al. 2003; Perera et al.
2012; Jasmine and Mukherji 2015; Varjani et al. 2018).
Effect on environment/human health
Petroleum industry discharge huge amount of pollutants in
environment. Wastewater released by the petroleum industries
contain large quantities of hydrocarbons, heavy metals, phe-
nols, and other toxic chemicals (Perera et al. 2012; Jasmine
and Mukherji 2015; Thakur et al. 2018; Varjani et al. 2018).
Different activities of petroleum industry such as transporta-
tion, storage, and drilling take part in soil contamination by
oil. Depending on type of soil, lighter oil can move quickly
instead of heavier oil through the soil layers (Fakhru'l-Razi
Table 1
Characteristics of oily wastewater/effluents and environmental
standards for their discharge
Sr. no.
Parameter
Limiting value for
concentration*
1
pH
6.0–8.5
2
Chemical oxygen demand
125.0
3
Biological oxygen demand 3 days, 27 °C
15.0
4
Oil and grease
5.0
5
Suspended solids
20.0
6
Total Kjeldahl nitrogen
40.0
7
Sulfides
0.5
8
Phenols
0.35
9
Cyanide
0.20
10
Benzene
0.1
11
Benzo [a]pyrene
0.2
12
Metal contents
Hexavalent chromium
0.1
Total chromium
2.0
Lead
0.1
Mercury
0.01
Nickel
1.0
Zinc
5.0
Copper
1.0
Vanadium
0.2
Source: G.S.R 186(E), dated 18 March 2008
*Limiting value for concentration is in mg/L, except for pH
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et al. 2009; Varjani et al. 2017). Due to ineffectiveness of
treatment system, industrial wastewater becomes harmful to
ecosystem and other life forms (Poulopoulos et al. 2005;
Veyrand et al. 2013; Varjani 2017b; Al-Hawash et al. 2018).
Oily wastewater can affect different components of environ-
ment such as human health, drinking water and groundwater
resources, air, crop production, and aquatic life (Zafra et al.
2015; Varjani et al. 2017). Accumulation of toxic products in
the water bodies leads serious consequences on the ecosystem
and living organisms either long term or short term which may
be chronic or acute (Poulopoulos et al. 2005; Usman et al.
2012; Al-Hawash et al. 2018; Varjani et al. 2018).
Current treatment process in petroleum
industry
Different technologies adsorption, coagulation, anaerobic
treatment, reverse osmosis, ultrafiltration, chemical desta-
bilization, flocculation, dissolved air flotation (DAF), mem-
brane process, etc. have been used to treat wastewater from
petroleum industry (Bennett and Peters 1988; Kriipsalu
et al. 2008; Sonune and Ghate 2004; Usman et al. 2012; Li
et al. 2014; Padaki et al. 2015). Adsorption method, com-
pared to other techniques, has many advantages such as cost
benefit, simplicity, and adaptability (Sabah et al. 2007;
Ahmad et al. 2016). Depending upon concentration and
source of contamination, different type of treatment tech-
nique is required to reduce the toxic level of pollutants
(Sonune and Ghate 2004; Hanafy and Nabih 2007;
Bennett and Peters 1988; Farajnezhad and Gharbani 2012;
Li et al. 2014; Padaki et al. 2015). Mainly the treatment
process has been differentiated into three categories: (a)
primary, (b) secondary, and (c) tertiary treatment (Fig. 1).
Generally, effluent of petroleum industry is passed through
different stages for reducing toxicity level which is shown
as an schematic diagram in Fig. 2.
Primary treatment
Primary treatment is usually used for physical operation in
petroleum refinery wastewater treatment plant (Ahmad et al.
2016). It is important step as it allows waste for the further
secondary treatment unit. Mostly gravity separation applies to
classify the floating and settle down material from wastewater.
The primary treatment step includes an oil/water separator
which can separate oil, water, and solids. Gravity separation
followed by skimming is carried out for removal of oil from
wastewater (Al-Shamrani et al. 2002; Hanafy and Nabih
2007). Oil–water separator such as API oil–water separator
is widely used because of its low cost and high effectiveness
for primary treatment step. API separators work on phenom-
ena of difference in specific gravity to allow heavy material to
settle below lighter liquids. Hydrocarbons that float on the
surface and the sludge settle down to the bottom (Al-Futaisi
et al. 2007; Ahmad et al. 2016). API separator is not very
much applicable for removal of smaller oil droplets and emul-
sions (Abdulredha et al. 2018). Dissolved air flotation (DAF)
is a water treatment process that uses air to increase the capac-
ity of smaller oil droplets and enhance the separation process.
DAF unit typically consists of chemicals to promote coagula-
tion and increase floc size to make easy separation. In this
stage, the heterogeneous components of the effluent such as
suspended solid colloids or dispersion and immiscible liquids
are reduced significantly (Al-Shamrani et al. 2002; Hanafy
and Nabih 2007). Colloids and dispersion also delay and dam-
age equipment during proceeding stage (Renault et al. 2009).
In induced air flotation (IAF) system, air is induced by rotor
disperse mechanism, the spinning rotor work as a pump and
forces to the fluid. The advantages of the IAF process are
compact size, low cost, and effective removal of free oil and
suspended material (Bennett and Peters 1988; Bennett and
Shammas 2010).
Secondary treatment
The main purpose of this stage is to reduce contamination
level of effluent and make it in permissible limit for discharge
into water bodies. Secondary treatment consists coagulation,
flocculation, and further biological treatment to reduce toxic-
ity of petroleum wastewater (Xu and Zhu 2004; Viggi et al.
2015; Changmai et al. 2017). Petroleum effluent contains
large number of refractory components. Polyaluminum chlo-
ride is more effective rather than ferric chloride in coagulation
process for treatment of petroleum wastewater (Farajnezhad
and Gharbani 2012; Hosny et al. 2016). Coagulation-
flocculation is a process in which chemical product is added
to accelerate the sedimentation in clarification tank (Kriipsalu
et al. 2008; Moulai-Mostefa and Tir 2004; Hosny et al. 2016).
The coagulants are organic or inorganic components such as
aluminum hydroxide chloride and aluminum sulfate or high
molecular weight cationic polymer. The aim of addition of
coagulant is to remove 90% of the suspended solids from
the wastewater (Lin and Wen 2003; Changmai et al. 2017).
Renault et al. (2009) have reported chitosan for efficient
coagulation/flocculation process to treat petroleum industry
wastewater (Renault et al. 2009). Biological treatment is the
most widely used method for removal of organic compounds
in the oil industry wastewater. Biological treatment is mostly
classified in to two categories: (a) suspended growth process
and (b) attached growth process (Chavan and Mukherji 2008;
Srikanth et al. 2018). Suspended growth process includes aer-
ated lagoon, membrane bioreactor technology, sequencing
batch reactor (SBR), and activated sludge treatment. In the
activated sludge process, the wastewater enters in the aeration
tank where microorganism was brought in contact with
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Environ Sci Pollut Res  (2020) 27:27172–27180
contaminated wastewater. Microorganisms use the organic
material as food and decompose organic matter (Srikanth
et al. 2018). N:P ratio has been reported very important pa-
rameter for treatment of oily wastewater by using oil-
degrading bacteria (Chavan and Mukherji 2008).
Tertiary treatment
Tertiary treatment process includes sand filtration, activated
carbon process, and chemical oxidation. It is applicable for
removal of total suspended solids, dissolved and suspended
matter, COD, and trace organics such as PAHs (He and Jiang
2008; Li et al. 2014). After secondary treatment process, ef-
fluent contains suspended solids depending on operating con-
ditions in the clarifier. Metals and fine solids, which could not
be settle down in sedimentation process, can be removed by
sand filtration (Zahrim and Hilal 2013; Ahmad et al. 2016).
This process involves passing the wastewater through a filter
bed comprised of a filter media. Generally, chemical oxidation
is used for reduction of residual COD, trace organic com-
pounds, and nonbiodegradable compounds. This method uses
different types of oxidation reagents like hydrogen peroxide,
ozone, and chlorine dioxide (Usman et al. 2012; Srikanth et al.
2016).
Treatment methods for oily wastewater
It is important to dispose of effluent in a proper manner, and it
should maintain a minimum concentration level of
chemical(s) which is suitable for the environment. Thus, in-
novative research is required to develop new technologies
which degrade the complex molecules into simpler forms,
which is reliable to maintain water quality (Sonune and
Fig. 1 Petroleum industry
wastewater treatment
Fig. 2 Treatment of wastewater
of petroleum industry: schematic
diagram
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Environ Sci Pollut Res  (2020) 27:27172–27180
Ghate 2004; Ani et al. 2018). Petroleum industries have made
few effective technologies for improving treatment capacity
through different methods which are mentioned below and
also have also been summarized in Table 2.
Membrane separation technology
Membrane separation processes are dependent on difference
in (a) pressure, (b) concentration of pollutants, (c) tempera-
ture, and (d) electrical potential (Al-Obaidani et al. 2008;
Jamaly et al. 2015; Adhama et al. 2018). Depending upon
the pore size of utilized membrane process is categorized as
a (a) microfiltration (MF), (b) nanofiltration (NF), (c) ultrafil-
tration (UF), and (d) reverse osmosis (RO), which are mostly
applied to treat oily wastewater (Bruggen et al. 2003;
Tomaszewska 2007; Jamaly et al. 2015). This separation
method play role in physical removal of way of the trapped
particle size of contaminants (Hilal et al. 2004). According to
membrane pore size, ultrafiltration membranes are more ef-
fective than microfiltration membranes. Nanofiltration and re-
verse osmosis can be also used for separating oil from water
especially for high-salinity water (Zhu et al. 2014; Jamaly
et al. 2015). Based on the oil dispersion, oily water can be
classified into three types: (a) free-floating oil, (b) unstable
oil–water emulsions, and (c) stable oil–water emulsions
(Srijaroonrat et al. 1999; Hilal et al. 2004; Qiu et al. 2009).
Generally, free-floating oil can be easily removed by mechan-
ical techniques and also unstable emulsions can be removed
mechanically, but sometimes specific chemical additives are
required (Salahi et al. 2013; Abdulredha et al. 2018).
Membrane separation efficiency is normally identified by oil
rejection coefficient (Ro), and it is defined as:
R0 ¼ Oil concentration in feed−Oil concentration in permeate
Oil concentration in feed
 100
Usually, an effective membrane has high rejection coeffi-
cients for total organic carbon (TOC), total surface charge
(TSC), and COD (Karhu et al. 2013). Another important pa-
rameter in membrane separation is the permeate flux.
Permeate flux is defined by:
J ¼ Vp
At
where J is the permeate flux, Vp is the permeate volume, A is
the membrane effective area, and t is the permeation time
(Rezvanpour et al. 2009). The permeate flux also depends
on membrane properties such as porosity, pore size, and hy-
drophilicity (Mohammadi et al. 2003; Changmai et al. 2017).
Due to small pore size of membrane, oil rejection coefficient is
higher, and use of large pore size membrane leads to high
permeate flux (Kocherginsky et al. 2003; Colle et al. 2009;
Han et al. 2017).
Advanced oxidation process
The main function of advanced oxidation process is to gener-
ate high reactive free radicals. Hydroxyl radicals are effective
in destroying organic chemicals because they are reactive
Table 2 Petroleum industry
wastewater treatment
technologies
Sr. no.
Name of the
techniques used
Treatment details
1
Electrochemical technologies
To remove turbidity, COD, phenol, hydrocarbon, and grease by
using electrocoagulation and electroflotation from petroleum
wastewater
2
Ozonation and biological
activated carbon (BAC)
To enhance biodegradation process of bio refractory and the
growth of a biofilm have been noticed during laboratory
scale pre-ozonation and biological activated carbon (BAC)
3
Anaerobic treatment
Anaerobic digestion method has been used for treatment of
hydrocarbon pollutants
4
Aerobic sequencing batch
reactors
Observer-based time-optimal control: control strategy regulates
and maintains the substrate concentration and feed ratio in
the reactor. An extended Kalman filter has been used as a
nonlinear observer in a petrochemical wastewater treatment
5
Autotrophic denitrification
To remove sulfide from petroleum industry waste water, new
alternative treatment for removal of H2S by combination of
the biological method and existing stripping CO2
7
Wetlands
Some large-scale project and pilot-scale studies have been
conducted for extraction and pumping stations of oil and gas
for treatment using wetland for oil refineries
Source: Dimoglo et al. 2004; Lin et al. 2001; Macarie 2005; Vargas et al. 2000; Vaiopoulou et al. 2005; Knight
et al. 1999
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Environ Sci Pollut Res  (2020) 27:27172–27180
electrophiles (Oller et al. 2011; Jamaly et al. 2015). Hydroxyl
radical is strong oxidant to destroy compound that cannot be
oxidized by conventional oxidant. Hydroxyl radical possesses
faster oxidation rate compared to H2O2 or KMnO4 (Gogate
and Pandit 2004; Usman et al. 2012; Tijani et al. 2014).
Generated hydroxyl radicals can attack organic chemicals by
radical addition, hydrogen abstraction, and/or electron transfer
(Gogate and Pandit 2004; Tijani et al. 2014). Generation of
hydroxyl radical is commonly accelerated by combining O3,
H2O2, TiO2, UV radiation, electron-beam irradiation, and ul-
trasound (Usman et al. 2012; Li et al. 2014; Varjani 2017b).
Electrochemical catalysis
Electrochemical catalysis process is related to oxidation of
hydroxyl radical with a highly organic matter between substi-
tution, addition, and electron transfer process (Zhang et al.
2015; Mohan et al. 2018). It leads to the degradation of pol-
lutants, mineralization, and easy to build airtight circulation
with no secondary pollution (Lin et al. 2001; Dimoglo et al.
2004; Koper 2005; Bajracharya et al. 2015; Changmai et al.
2017). Electrolysis process is applied to treat oily wastewater
which leads to time-dependent reduction in COD. The process
consists of three steps for effective reduction of pollutants in
oily wastewater and does the remediation. The first step is the
direct oxidation of oil components at the electrode, by the
metal oxide itself or by hydroxyl radicals available at elec-
trode surface. The second step is indirect oxidation of oil com-
ponents by intermediate oxidizing agents formed, and the
third step is aggregation of suspended oil droplets by
electroflotation (Santos et al. 2006a, b; Jamaly et al. 2015).
Electrochemical catalytic treatment is a more effective treat-
ment of oily wastewater (Mohan et al. 2018). Ma and Wang
(2006) have performed research with pilot-scale plant having
double anodes and cathodes. Anode contained active metal
and graphite; however, cathode contained iron and a noble
metal content catalyst with big surface. They have reported
approximately 90% reduction of BOD and COD and 99%
reduction in suspended solids by using the electrochemical
process.
Photocatalytic degradation
Photolytic degradation is very well researched in the last de-
cade and conventionally a method for treating petroleum
wastewater (Twesme et al. 2006; Do et al. 2010; Yen et al.
2011; Varjani 2017b). Photocatalysis is a beneficial process
used to oxidize persistent compounds which cannot be oxi-
dized during biological treatment (Vodyanitskii et al. 2016).
TiO2 and ZnO are commonly used photocatalysts in the treat-
ment of petroleum industry wastewater (Santos et al. 2006a, b;
Akpan and Hameed 2009; Vodyanitskii et al. 2016). Benefits
of TiO2/UV technique are low cost, faster reaction rates, no
sludge production, and easy operation at ambient temperature
and pressure with complete mineralization of petroleum in-
dustry wastewater (Twesme et al. 2006; Akpan and Hameed
2009; Varjani 2017b). Park and Choi (2005) have reported
photocatalytic hydroxylation of aromatic ring in the presence
of platinum-loaded TiO2, where they have used water as an
oxidant. Photocatalytic degradation of naphthalene present in
petroleum industry waste using TiO2 in presence of inorganic
anions had been reported by Lair et al. (2007).
Knowledge gaps and future perspectives
Petroleum hydrocarbon pollutants are classified as priority
pollutants. The pollutants which are present in petroleum in-
dustry wastewater can be effectively remediated using differ-
ent technologies (Yen et al. 2011; Vodyanitskii et al. 2016).
Research on study of innovative technologies with minimum
environmental and economical influence indicates that it is
thrust area of research (Lin et al. 2001; Santos et al. 2006a,
b; Viggi et al. 2015; Vodyanitskii et al. 2016). This review
article focuses on improving different technologies used for
wastewater treatment generated by petroleum industry activi-
ties. Petroleum industry wastewater contains different toxic
substances such as toluene, xylene, benzene, ethylbenzene,
phenols, and PAHs (Hanafy and Nabih 2007; Renault et al.
2009; Varjani et al. 2017). These pollutants are very difficult
to be directly removed by using single method specifically
biological treatment which is considered as green approach.
Hence, advanced chemical or physical treatments in combina-
tion with biological treatment are required. Apart from this,
biological treatment to identify the successful species which
can work ex situ and in situ conditions is a very important task
(Varjani 2017b; Chen 2018). It has been reported that integra-
tion of various processes may give better results than individ-
ual process used for treatment of oily wastewater. But knowl-
edge of integration of technologies is still at its infancy which
needs to be explored by researchers. Recent developments are
mostly more expensive, require maintenance, and a time-
consuming process. Hence, all industries do not participate
to reduce toxicity of effluent. Future technology needs easy
operating systems which are suitable for small, medium, and
big industries.
Huge amount of solid and liquid waste is produced due to
petroleum industry activities. Management and handling of
waste generated is nowadays is a big issue for local authorities
not only in urban areas, i.e., municipalities, but also other
regions in any country (Santos et al. 2006a, b; Li and Yu
2011; Tijani et al. 2014; Jamaly et al. 2015; Varjani and
Sudha 2018). Due to increased urbanization and industrializa-
tion generation of wastewater, appropriate disposal, treatment,
and/or recycling is posing more challenges as treatment and
disposal costs large amount in terms of money. However, if we
27177
Environ Sci Pollut Res  (2020) 27:27172–27180
consider that Bwaste^ word is placed wrongly and if that be
used as a Bresource^ then resource recovery from wastes is an
emerging as thrust area of research and management because
it offers environment and social sustainability potentials.
Many research groups are focusing their work on recovery
of various resources such as energy, bioproducts, nutrients
(nitrogen and phosphorous), and metals from wastewater gen-
erated by anthropogenic activities (Li and Yu 2011; Honse
et al. 2012; Varjani and Upasani 2017; Mohan et al. 2018;
Thakur et al. 2018; Varjani et al. 2019). Valuable pollutants
are present in petroleum industrial wastewaters, which can be
regarded as resources after recovery (Al-Futaisi et al. 2007;
Hu et al. 2013; Jasmine and Mukherji 2015). On one side,
efficient resource recovery and reuse can create sustainable
livelihood, whereas on the other side, it supports green econ-
omy by reducing waste and improve environmental health and
cost of recovery. Hence, there is a need to recycle and reuse
the waste produced from activities of petroleum industry in an
efficient manner. Feasible techniques to produce pollution less
products create a new way for environmental and economic
sustainability.
To optimize the exploitation of petroleum industry waste/
wastewater and by-products, there is a need to develop sus-
tainable technologies. Focus of the research shall also be
thrown on biorefinery concept for development of innovative
biobased industries because waste biorefineries may open up
new market opportunities for biobased products and achieve
efficient resource utilization.
Conclusion
Petroleum industry wastewater can be treated by various phys-
ical, chemical, and biological treatment processes. There are
many hazardous components present in waste/wastewater re-
leased by the activities of petroleum industry. Successful reme-
diation strategy should be tailored considering environment and
human health. Bioremediation is one of the technologies
gaining a global interest for cleanup of petroleum hydrocar-
bons. However, integration of various processes gives better
results than individual process used for waste/wastewater treat-
ment. The current practice of industrial wastewater treatment is
focused to remove pollutants from wastewaters to meet the
discharge standards. To resolve hazards associated with petro-
leum components, a suitable technology that treats waste and
generates value addition would be a promising option. Study
regarding the recovery of value-added products from waste/
wastewater with special reference to different techniques, either
separately or by integration, tailoring distinct features of pro-
cesses is a thrust area of research with respect to waste charac-
teristics for production of biobased nontoxic by-products.
Waste biorefinery concept using latest developments in biotech-
nological and bioengineering options pertaining to recovery of
resources from petroleum industry waste/wastewater shall also
be explored.
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ORIGINAL PAPER - PRODUCTION ENGINEERING
Journal of Petroleum Exploration and Production Technology
https://doi.org/10.1007/s13202-024-01848-x
By employing innovative technologies and implementing 
robust reservoir management practices, the industry can 
unlock untapped potential and extend the lifespan of exist­
ing oil fields. Such strategies not only contribute to meet 
the growing energy demands but also promote sustainable 
resource utilization and minimize the need for exploration 
in environmentally sensitive areas.
Mega oilfield development projects must consider a 
variety of objectives given the interdependence of various 
factors and the need to strike a balance between financial, 
environmental, and sustainability concerns. By consider­
ing multiple objectives, comprehensive decision-making 
can be achieved, thus minimizing potential trade-offs and 
maximizing overall project outcomes. Integrating diverse 
objectives enhances the overall viability of the project, sat­
isfies the interests of stakeholders, and contributes to long-
term success. By carefully balancing financial gains with 
environmental responsibility, oilfield development can be 
carried out in a sustainable and responsible manner. This 
Introduction
The global surge in oil demand has created a pressing need 
to optimize the production of existing oil fields, given the 
decreasing number of new discoveries. Due to the maturity 
of most of these large fields, it is essential that reservoir man­
agement and development strategies are implemented with 
care to maximize recovery factor (RF) and economic return, 
and minimize the negative impact on the environment. 
	
 Auref Rostamian
a223191@dac.unicamp.br
1	
School of Mechanical Engineering, Center for Energy and 
Petroleum Studies (CEPETRO), Universidade Estadual de 
Campinas, PO Box 6052, Campinas, São Paulo 
13083–970, Brazil
2	
Bureau of Economic Geology, Jackson School of 
Geosciences, The University of Texas at Austin, Austin, 
TX 78757, USA
Abstract
Oilfield development optimization plays a vital role in maximizing the potential of hydrocarbon reservoirs. Decision-
making in this complex domain can rely on various objective functions, including net present value (NPV), expected 
monetary value (EMV), cumulative oil production (COP), cumulative gas production (CGP), cumulative water produc­
tion (CWP), project costs, and risks. However, EMV is often the main function when optimization is performed under 
uncertainty. The behavior and performance of different objective functions has been investigated in this paper, when EMV 
is the primary criterion for optimization under reservoir and economic uncertainty. One of the goals of this study is to 
provide insights into the advantages and limitations of employing EMV as the sole objective function in oil field develop­
ment decision-making. The designed optimization problem included sequential optimization of design variables including 
well positions, well quantity, well type, platform capacity, and internal control valve placements. A comparative analysis 
is presented, contrasting the outcomes obtained from optimizing the EMV-based objective function against traditional 
objective functions. The study underscores the importance of incorporating multiple objective functions alongside EMV 
to guide decision-making in oilfield development. Potential benefits in minimizing CGP and CWP are revealed, aiding in 
the mitigation of environmental impact and optimization of resource utilization. A strong correlation between EMV and 
COP is identified, highlighting EMV’s role in improving COP and RF.
Keywords  Expected monetary value · Field development · Objective function · Net present value · Optimization
Received: 28 November 2023 / Accepted: 5 July 2024
© The Author(s) 2024
Analysis of different objective functions in petroleum field 
development optimization
Auref Rostamian1
 · Marx Vladimir de Sousa Miranda1 · Abouzar Mirzaei-Paiaman1,2 · Vinicius Eduardo Botechia1 · 
Denis José Schiozer1
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Journal of Petroleum Exploration and Production Technology
approach ensures that the economic benefits derived from 
the project are not achieved at the expense of environmen­
tal degradation. Through careful consideration of multiple 
objectives, the oil and gas industry can substitute develop­
ment practices that harmonize economic gain, environmen­
tal protection, and long-term sustainability.
This study aims to investigate how prioritizing the opti­
mization of EMV as the primary objective function impacts 
other objective function s in the oilfield development and 
management plan, with a specific focus on evaluating the 
behavior of other objective functions across various stages 
of the development process. Furthermore, an analysis of the 
NPV response for each RM is undertaken during the opti­
mization process, considering economic uncertainties. This 
approach contributes significantly to enhancing the under­
standing of the importance of integrating diverse objective 
functions into the entirety of the field development optimi­
zation plan.
Background
The optimization of oil and gas field development involves 
challenges posed by subsurface uncertainty, integrating 
multiple objectives, and balancing financial gains with envi­
ronmental responsibility. Since the late 1970s, scholars have 
been constantly pursuing approaches to effectively meet the 
needs of various oilfield development projects. Over the 
course of extensive research on production optimization, 
numerous approaches have been proposed to assess the per­
formance of different development programs. For instance, 
Rosenwald and Green (1974) aimed to minimize the dis­
crepancy between the production-demand curve and the 
actual flow curve. Babayev (1975) focused on achieving the 
minimum total cost per unit of output. Similarly, Lasdon et 
al. (1986) sought to maximize the deliverability of a gas res­
ervoir within a specified timeframe, minimize the shortfall 
in total gas withdrawal between the demand schedule and 
the actual deliverable gas amount on a monthly basis, and 
explore the optimization of weighted combinations of these 
objectives. These early studies were the first to demonstrate 
the wide range of objectives considered in the pursuit of 
effective oilfield development optimization strategies.
In recent years, the field of production optimization 
has witnessed a surge in research interest, with particular 
emphasis on optimizing the NPV using various optimization 
approaches (Brouwer and Jansen 2004; Sarma et al. 2005; 
Bangerth et al. 2006; Kraaijevanger et al. 2007; Zandvliet 
et al. 2008; Suwartadi et al. 2009; Onwunalu and Durlof­
sky 2010; Afshari et al. 2011; Capolei et al. 2012; Forou­
zanfar and Reynolds 2013; Oliveira and Reynolds 2014; 
Bukshtynov et al. 2015; Jesmani et al. 2016; Naderi and 
Khamehchi 2017; Karkevandi-Talkhooncheh et al. 2018; 
Bertini Junior et al. 2019; Santos(D.R.) et al., 2020; Ng et al. 
2021; Bertini et al. 2022; Santos(D.R.) et al., 2023. However, 
it is essential to acknowledge the inherent uncertainty asso­
ciated with characterizing reservoirs, leading to the adoption 
of multiple geological realizations to account for geological 
uncertainties within the reservoir model. This approach is 
usually referred to as robust optimization (Mirzaei-Paiaman 
et al. 2021). Research endeavors aim to develop optimiza­
tion strategies that consider uncertainties and variations in 
reservoir characteristics, operational parameters, and eco­
nomic factors to ensure more reliable and resilient decision-
making processes in the context of oilfield development and 
management (Jansen et al. 2009; Wang (H.) et al., 2010; 
Ogunyomi et al. 2011; Dilib and Jackson 2012; Wang (H.), 
2013; Valestrand et al. 2014; Shirangi and Durlofsky 2015; 
Morosov and Schiozer 2016; Gupta and Grossmann 2017; 
Jahandideh and Jafarpour 2018; Annan Boah et al. 2019; 
Loomba et al. 2021; Mirzaei-Paiaman et al. 2022, 2023). In 
the context of robust production optimization, the primary 
objective is to maximize the expected NPV (i.e., EMV) by 
considering a set of reservoir realizations, with the expected 
NPV estimated through the averaging of NPV values.
Various objective functions have been explored in 
research studies pertaining to the optimization of oil pro­
duction, apart from commonly used measures like NPV and 
EMV. Some alternative objectives include maximizing COP, 
RF, profit, and others (Ariadji et al. 2014; Rahim and Li 
2015; Zhang et al. 2016; Borzouie and Borzouie 2016; Ran­
jith et al. 2017; Ogbeiwi et al. 2018; Simonov et al. 2019; 
Jia et al., 2020; Silva & Guedes Soares, 2021; Loomba et al. 
2022a; Koray et al. 2023). Each method of optimization has 
its advantages and disadvantages. NPV-based optimization 
is widely used in the industry as it provides a measure of the 
project’s economic viability. However, it is not directly pro­
portionate to elements such as RF, COP, or environmental 
impacts, which are crucial for a sustainable project. Maxi­
mizing COP as an objective function can be advantageous 
as it directly targets the extraction of the maximum amount 
of oil from the reservoir. However, it may neglect economic 
considerations and fail to account for project profitability. 
Optimizing the RF focuses on enhancing the efficiency of 
hydrocarbon recovery. Nevertheless, it might not capture 
other important factors like economic risks or uncertainties 
associated with NPV.
More recently, researchers have focused on considering 
multiple objective functions simultaneously instead of rely­
ing on a single objective function. However, the predomi­
nant objective function considered in such multi-objective 
optimization studies has been NPV, along with the reduc­
tion of risk and uncertainty associated with NPV (Pinto 
et al. 2019; Alpak et al. 2022). In addition to NPV, some 
1 3
Journal of Petroleum Exploration and Production Technology
researchers have considered other objectives in conjunction 
with NPV, such as RF, COP, and water production (Rosta­
mian 2017; Rostamian et al. 2019a a, b; You et al. 2020; 
Wang (L.) et al., 2021; de Moraes and Coelho 2022; de 
Moraes et al. 2023). Considering multiple objective func­
tions simultaneously offers a more comprehensive approach 
to optimization. It allows decision-makers to balance eco­
nomic, technical, and environmental aspects. Incorporating 
NPV, RF, COP, and other relevant factors is an approach 
that provides a more holistic view of the project. However, 
it can be more challenging to implement due to the com­
plexity of simultaneously optimizing multiple objectives 
and managing trade-offs between conflicting goals. In this 
paper, the relationship between different objective functions 
in a case where the primary objective function in the robust 
optimization is EMV, is investigated.
Recent advancements in reservoir management optimi­
zation have seen a shift from traditional objective functions, 
with researchers exploring streamline-based approaches for 
enhanced field development strategies. Streamlines, depict­
ing the convective flow trajectory between injection and 
production points, offer comprehensive insights into the 
time-of-flight, phase distribution, and static characteristics 
of traversed cells. Taware et al. (2012) and Roshandel and 
Siavashi (2023) utilized streamlines and total time of flight 
to identify regions of un-swept and undrained oil. Safarza­
deh et al. (2015) introduced a novel objective function, Well 
Assessment based on Time-of-Flight (WATOF), leveraging 
multi-objective genetic algorithms and streamline simula­
tions to optimize water injection rates. Siavashi et al. (2016) 
emphasized the efficiency of WATOF, contrasting it with 
traditional COP calculations. Naderi et al. (2021) proposed 
the proportionally distributed streamlines (PDSLs) objec­
tive function as an alternative to COP or NPV functions for 
waterflooding projects. Chen et al. (2020) tested PDSLs in a 
real field at Mangala Field, India.
Moreover, in recent years, there has been a notable 
advancement in field development and well placement 
techniques through the application of artificial intelligence 
and machine learning. Mousavi et al. (2024) systemati­
cally evaluated various advanced computational techniques, 
including extreme gradient boosting (XGBoost), light gra­
dient boosting machine (LightGBM), gradient boosting 
with categorical features support (CatBoost), support vec­
tor regression (SVR), and multilayer perceptron (MLP), for 
estimating NPV in reservoir engineering. Similarly, Esfandi 
et al. (2024) explored the use of boosting algorithms for 
precise well placement in Carbon Dioxide-Enhanced Oil 
Recovery (CO2-EOR) in light oil carbonate reservoirs.
This study addresses the shortcomings of prior research 
on petroleum asset management by considering a broader 
range of objectives, including economic and environmental 
factors. A systematic approach to optimizing field devel­
opment decisions that considers the interplay of these 
objectives is proposed. The results demonstrate that a mul­
tidimensional approach can lead to more sustainable and 
profitable field development strategies.
Methodology
In this study, the optimization results from Mirzaei-Paia­
man et al. (2023) are used, where the goal was to obtain an 
optimal field development plan in a water-alternating-CO2 
(CO2-WAG) operation. In their work, an integrated optimi­
zation system was utilized, comprising the iterative discrete 
Latin hypercube (IDLHC) algorithm, a reservoir simulator 
(IMEX by CMG), and in-house software to calculate the 
economic indicators. The primary objective function that 
was maximized in their robust optimization workflow was 
EMV, where a set of nine RMs and three economic scenar­
ios were considered. Their optimization process was struc­
tured into four sequential stages.
1.	 In the initial stage, the focus was on optimizing the 
quantity, position, and type of wells.
2.	 The second stage involved fine-tuning the well loca­
tions to further enhance the optimization process.
3.	 The third stage of optimization was dedicated to finding 
the optimal platform capacity.
4.	 The fourth stage dealt with optimizing the placement of 
internal control valves (ICV).
Knowing that the primary objective function in the robust 
optimization performed byMirzaei-Paiaman et al., (2023) 
was EMV, the behavior and changes of the other aforemen­
tioned objective functions, namely COP, RF, CWP, CGP, 
and NPV of each RM, are investigated under different eco­
nomic scenarios. For a more comprehensive and detailed 
understanding of this workflow, it is highly recommend 
referring to the research conducted by Mirzaei-Paiaman et 
al. (2023).
Application
Reservoir Model description
The optimization study of Mirzaei-Paiaman et al. (2023) 
employed the open-access UNISIM-II-D benchmark model. 
In this benchmark, uncertainties are based on the prelimi­
nary stage of field development, which uses different data 
sources (well logs, well testing, seismic, etc.) derived 
from a reference or ground truth model referred to as the 
1 3
Journal of Petroleum Exploration and Production Technology
systematic approach to select and rank RMs for ensemble-
based production optimization in carbonate reservoirs sub­
ject to WAG injection. Nine RMs were selected based on 
criteria focused on maximizing the well economic indicator 
for producers and minimizing it for injectors. The selection 
process involved evaluating various system outputs, includ­
ing field indicators and well indicators, using a probabilis­
tic optimization method executed multiple times to ensure 
robustness. A higher weight was given to parameters related 
to risk curve representation, and the representativeness of 
RMs was assessed by comparing errors between RM risk 
curves and ensemble risk curves. The authors considered 
different approaches for RM selection, analyzing errors, 
and conducting qualitative analyses of risk curves to assess 
uncertainty representation. Furthermore, they evaluated the 
impact of using a high number of wells and system outputs 
on RM selection and production optimization. Figure 1 dis­
plays the porosity maps corresponding to the 15th layer of 
the RMs. It serves as a sample to illustrate the distinction 
among these nine RMs. Table 2 provides the mean and stan­
dard deviation of the nine selected RMs. The arrangement 
of entries in this table progresses from the most optimistic 
to the most pessimistic case.
In this study, all wells except a Wildcat well are intel­
ligent (i.e., equipped with ICV). Producers and injectors are 
equipped with 3 and 2 ICVs, respectively. When the ratio of 
gas to oil exceeds 2,000 m3/m3, an open valve is switched 
to a shut-off setting. Producers operate under a maximum 
production rate of 3,000 m3/day of liquid and a minimum 
bottomhole pressure of 275 Kgf/cm2. A water-cut above 
0.95 is used as a closing constraint. Maximum injection 
rates of 5,000 m3/day water and 2 million m3/day gas, and 
a maximum bottomhole pressure of 480 Kgf/cm2 are used. 
Six months was used as WAG half-cycle duration. In this 
benchmark, the maximum size of a platform has process­
ing capacities of 28,618, 28,618, and 19,079 m3/day for the 
produced oil, liquid, and water, respectively. The maximum 
allowed processing capacities for water and gas injection 
are 38,157.0 and 8 million m3/day, respectively.
Optimization algorithm
The optimization algorithm employed was IDLHC. This is 
an optimization method used to efficiently explore and search 
a high-dimensional parameter space to find the optimal or 
near-optimal solutions for a given problem. This method­
ology adequately treats posterior frequency distributions of 
discrete random variables and maximizes non-necessarily 
monotonic objective functions within discontinuous search 
spaces and many local optimums. It is particularly useful 
when evaluating the objective function is computationally 
UNISIM-II-R model. The UNISIM-II-R model, constructed 
based on real data from Brazilian pre-salt reservoirs (Cor­
reia et al. 2015), addresses the complexities posed by light 
oil fractured carbonates containing substantial amounts of 
CO2-rich associated gas, thereby presenting various gas 
treatment and recovery challenges. Table  1 presents an 
overview of the characteristics of a UNISIM-II-R reservoir. 
These characteristics include factors such as reservoir depth, 
initial pressure, temperature, oil viscosity, and associated 
gas CO2 content. Analyzing and understanding these attri­
butes is crucial for obtaining the best recovery mechanism.
The UNISIM-II-D model is based on a corner-point 
grid consisting of 46 × 69 × 30 cells, with average dimen­
sions of 100 × 100 × 8 m and comprising a total of 65,000 
active cells. There are two UNISIM-II-D versions, namely 
UNISIM-II-D-BO (representing black-oil simulation) and 
UNISIM-II-D-CO (representing compositional simula­
tion), which utilize distinct fluid models. UNISIM-II-D-BO 
employs a PVT table, whereas UNISIM-II-D-CO employs 
a Peng-Robinson Equation of State (EOS), incorporating 
seven pseudo-components.
Considering the objective of field development optimi­
zation, UNISIM-II-D-BO was used. The selection of UNI­
SIM-II-D-BO as the low-fidelity model was based on its 
favorable simulation runtime. Employing the DLHG (dis­
cretized Latin hypercube combined with geostatistical real­
izations) sampling technique, an ensemble of models was 
generated to account for geological and reservoir engineer­
ing uncertainties (Schiozer et al., 2017). A comprehensive 
range of uncertainties were taken into consideration, includ­
ing matrix porosity, matrix permeability, fracture porosity, 
fracture permeability, fracture spacing, net-to-gross ratio, 
rock type, reservoir top and bottom, relative permeability, 
and rock compressibility. By assimilating 1.5 years of pro­
duction data from a wildcat well, an initial ensemble of 500 
scenarios was reduced to 199 models. Incorporating opera­
tional uncertainties through DLHC led to an additional 
set of 199 simulation models, considering factors such as 
group, platform, producer, injector availability, and well 
index multiplier (Santos (S.) et al., 2020).
For efficient production optimization in a CO2-WAG 
project, a method introduced by Meira et al. (2020) was 
employed by Santos (S.) et al., (2020), which further 
reduced the ensemble to nine RMs. The authors employed a 
Table 1  UNISIM-II-R Reservoir characteristics
Characteristic
Value
Reservoir depth
5000 –5500 m
Initial pressure
560 kgf/cm²
Temperature
59 °C
Oil viscosity
1.14 mPa.s (cP)
Oil density
28°API
Associated gas CO2 content
8.24%
1 3
Journal of Petroleum Exploration and Production Technology
expensive or time-consuming (Sheikholeslami and Razavi 
2017).
The method combines the principles of discrete Latin 
hypercube sampling (DLHS) and iterative optimization 
techniques. IDLHC has been successfully applied in the oil 
industry, where production strategy optimization problems 
are characterized by many discrete random variables in dis­
continuous search spaces with non-necessarily monotonic 
objective functions, usually NPV or RF, with many local 
maximums within a maximization problem (von Filho et 
al. 2016). Population-based optimization using DLHS best 
suits this methodology, with consistent convergence to the 
global optimum, few objective function evaluations, and 
simultaneous multiple numeric reservoir simulation runs. 
This easy-to-use, reliable methodology with low computa­
tional time and costs is an interesting option for optimization 
Table 2  Mean and Standard deviation of 9 selected RMs
Representa­
tive
Model
Permeability, mD
Porosity, fraction
Mean
Standard 
Deviation
Mean
Standard 
Deviation
RM9
351.38
1553.45
0.162
0.091
RM1
297.05
1144.75
0.125
0.091
RM2
294.01
1515.3
0.092
0.081
RM6
273.28
838.05
0.115
0.09
RM7
235.35
1121.5
0.127
0.088
RM8
219.97
758.48
0.134
0.089
RM5
213.18
1079.75
0.115
0.083
RM3
192.01
1406.29
0.102
0.073
RM4
175.23
1584.66
0.113
0.088
Fig. 1  The porosity maps corresponding to the 15th layer of the RMs (The black cells represent the faults in these models)
 
1 3
Journal of Petroleum Exploration and Production Technology
unexplored grids are examined in a second stage. In well 
location optimization, the vector of decision variables con­
tains 2Nw integer variables, where Nw is the number of wells 
undergoing location optimization in a given stage. In stage 
three, the decision vector to be optimized contains four opti­
mization attributes, each of which is represented by a set 
of discretized real values. Platform capacity was optimized 
for the produced water and liquid, as well as the injected 
water and gas. In stage four, each well is given a set of fea­
sible ICV configurations. Each configuration represents a 
default valve location arrangement. 10 and 6 configurations 
are considered for each producer and injector, respectively. 
As a result, the length of the decision vector is equal to the 
number of intelligent wells, and each optimization attribute 
associated with a producer has ten discrete categorical lev­
els. Similarly, each optimization attribute associated with 
an injector has six discrete categorical levels. The search 
space is 10Np × 6Ni, where Np and Ni are the number of 
intelligent producers and injectors, respectively. The num­
ber of sample evaluations in optimization of well quantity, 
well location, well location fine-tuning, platform capac­
ity, and internal control valve placement were 1314, 1396, 
884, 389, and 1014, respectively. Since each sample was 
tested on nine reservoir models, the corresponding number 
of simulations is 11,826, 12,564, 7956, 3501, and 9126, 
respectively. The simulation time for each RM ranged from 
124 s to 158 s. In this study, we use the UNISIM group’s 
cluster. The UNISIM cluster system boasts an approximate 
combined capacity, including 9 TFlops of processing power, 
30 TB of storage, and 3 TB of memory. Additionally, the 
system automatically allocates CPU and memory resources 
for simulations based on various factors, such as the level 
of congestion within the cluster due to other users’ activi­
ties. For a more comprehensive and detailed understanding 
of this optimization algorithm and sequential procedure, it 
is highly recommend referring to Mirzaei-Paiaman et al. 
(2023).
Objective functions
NPV and EMV
In this research, the primary objective is to maximize the 
EMV as the key performance indicator. The EMV repre­
sents the weighted average of potential NPVs associated 
with the project. NPV, in turn, signifies the NPV of future 
cash flows. This study extensively examines the influence 
of economic and geological uncertainties in a comprehen­
sive manner. Specifically, three economic scenarios catego­
rized as most-likely, pessimistic, and optimistic, alongside 
the integration of nine diverse geological reservoir models 
based on UNISIM-II-D is investigated. Each economic 
methods in problems of production strategy design related 
to the oil industry (von Filho et al. 2016).
At the beginning of a given optimization stage, the opti­
mization attributes were defined for the decision variable, 
such as well location, and corresponding optimization levels 
were established with prior user-defined probabilities. To 
initiate the optimization algorithm on the desired problem, 
a Latin hypercube sampling (LHS) should be performed on 
the discrete parameter space. LHS ensures that the param­
eter space is evenly and randomly sampled, preventing any 
clustering or bias in the initial sample points. Each point 
in the LHS represents a set of parameter values, forming a 
population of potential solutions. The objective function, in 
this study, EMV, is then evaluated for each of these points 
to determine their fitness. 100 samples in each iteration of 
the algorithm are evaluated. In the iterative phase, IDLHS 
refines the initial LHS sample by selecting the top 20% of 
best-performing solutions and creating a new population 
based on them. This process continues for a specified num­
ber of iterations or until a convergence criterion is met. The 
refinement of the sample is done using various optimiza­
tion algorithms like genetic algorithms, simulated anneal­
ing, or particle swarm optimization, which guide the search 
towards better solutions in the parameter space. The com­
bination of LHS and iterative optimization ensures a more 
efficient exploration of the high-dimensional space, lead­
ing to the identification of better solutions while reducing 
the computational cost compared to traditional exhaustive 
search methods (Loomba et al. 2022b). Once an optimiza­
tion stage is completed (i.e., an optimal value of a decision 
variable is found), the next stage starts, which addresses the 
optimization of the next decision variable. This sequential 
optimization framework continues until all decision vari­
ables are optimized. As mentioned earlier, this study deals 
with four optimization stages, where in each stage one, well 
quantity, location, and type are optimized. In stage two, 
further tuning of well locations is made. In stage three, the 
capacity of the platform is optimized, and finally, in stage 
four, the placement of ICVs is optimized.
In stage one, well type is implicitly optimized as the 
number and location of wells are optimized, meaning that 
it does not need to be regarded as an individual optimiza­
tion variable. For optimizing the well quantity, the vector 
of optimization attributes has 55 elements, which is equal 
to the number of candidate wells. Each attribute is of the 
binary categorical type, with levels of 0 or 1 (0: do not drill, 
1: drill). The search space for optimizing the well quantity is 
255 in size. After finding the optimal well quantity, the well 
location is optimized. A relatively large individual region 
around each well is considered as the search space. Aiming 
at reducing computations, for wells with large search spaces, 
only a fraction of the grids is explored. Then, if necessary, 
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Journal of Petroleum Exploration and Production Technology
NPVeco =
 k
k = 1pjNPVi,k
(2)
The NPV is a financial metric used to evaluate the profitabil­
ity of an investment or project. It represents the sum of the 
present values of all expected net cash flows (NCF) associ­
ated with the investment over a specific time period. The 
NPV calculation considers the time value of money, which 
accounts for the fact that money received in the future is 
worth less than the same amount received today due to infla­
tion and opportunity costs.
NPV =
 Ts
s=1
NCFs
(1 + b)ts
(3)
NPV is calculated using net cash flow (NCFs), discount 
rate (b), and time period (ts). NCFs is the cash flow dif­
ference during each period, covering revenues, expenses, 
etc. Discounting NCFs with a rate (b) adjusts for the time 
value of money. (b) represents the required return or cost 
of capital. (ts) is the duration from investment to a point in 
time. NPV sums discounted cash flows over the total time of 
the project, indicating the investment profitability. Positive 
NPV means favorable returns, while negative NPV suggests 
potential loss. NPV aids investment decisions and accounts 
for the time value of money.
The NCF is given by:
NCF = ((R −Roy −ST −OC) × (1 −T)) −Inv −AC
(4)
where R is gross revenue, Roy is royalties, ST is social taxes, 
OC is operational costs, T is the corporate tax rate, Inv is the 
platform and wells investment, and AC is the abandonment 
cost. The platform investment (IP) is calculated as:
scenario represents a unique set of economic conditions 
that may influence the project’s outcomes. The optimistic 
economic scenario embodies a favorable projection, indi­
cating an environment with potential for high economic 
growth and positive market conditions. The likelihood of 
this scenario materializing is captured by its assigned prob­
ability. Conversely, the pessimistic economic scenario por­
trays a more adverse outlook, characterized by economic 
challenges and potential downturns. Its occurrence most-
likely quantifies the likelihood of facing such unfavorable 
conditions. Additionally, the most-likely economic scenario 
reflects a balanced and moderate outlook, acknowledging 
the inherent uncertainty in economic conditions. Its prob­
ability represents the likelihood of experiencing this inter­
mediate scenario. Table 3 provides the data related to each 
economic scenario.
Mathematically, the EMV is formulated as the summa­
tion of NPVi multiplied by the probability of occurrence (pi) 
for each RMi (Eq. (1)(Mirzaei-Paiaman et al. 2023).
EMV =
 m
i = 1
 k
j=1pi × pk × NPVi,k
(1)
In Eq. (1), m represents the number of RMs, pi denotes the 
likelihood of each RM’s occurrence. Additionally, pk refers 
to the probabilities of optimistic, pessimistic, and the most-
likely economic scenarios. Finally, NPVi, k represents the 
NPV associated with a specific RM(i) for each economic 
scenario (k). In this study an objective function known as the 
NPV considering economic uncertainty (NPVeco), which is 
associated with each RM is investigated. Equation 1 can be 
reformulated to incorporate NPVeco as follows:
Table 3  Data for three economic strategies
Parameter Type
Parameter
Optimistic
Most Likely
Pessimistic
UNITS
Probability of Occurrence
0.25
0.5
0.25
--
Market Variables
Oil Price
412
257.9
151.8
(USD/m3)
Annual Discount Rate
9
9
9
(%)
Taxes
Royalties
10
10
10
(%)
Social Taxes
9.25
9.25
9.25
(%)
Corporate Taxes
34
34
34
(%)
OPEX
Oil Production Cost
82.41
48.57
30.37
(USD/m3)
Water Production Cost
7.76
4.85
2.86
(USD/m3)
Gas Production Cost
0.16
0.013
0.06
(USD/m3)
Water Injection Cost
7.76
4.85
2.86
(USD/m3)
Gas Injection Cost (Recycled Gas)
0.02
0.014
0.0082
(USD/m3)
Abandonment Cost (% of inv. -CAPEX)
8.2
8.2
8.2
(%)
CAPEX
Drilling of Vertical Well
37.39
23.4
13.78
(USD millions)
Completion of Vertical Well
43.04
26.94
15.86
(USD millions)
Well-platform Connection
21.25
13.3
7.83
(USD millions)
Additional Inv. for Each WAG Inj
2.03
1.63
1.43
(USD millions)
1st ICV for Each Well
1.6
1
0.59
(USD millions)
2nd and 3rd ICV for Each Well
0.48
0.3
0.18
(USD millions/ICV)
1 3
Journal of Petroleum Exploration and Production Technology
In this study, the objective functions were investigated, 
with a primary focus on the EMV as the main objective 
function. The changes and behaviors of each objective were 
studied during different stages of field development optimi­
zation. Throughout the research, the variations and interac­
tions of each objective function, including the oil RF, CGP, 
COP, and CWP, were examined.
Results and discussion
Stage A: well quantity, type, and placement 
optimization
In this section, we investigate the impact of different objec­
tive functions along with the EMV within the context of 
optimizing the number and location of wells. In this case, 
where the number and location of wells are considered as 
decision variables, the solution space for the EMV becomes 
substantially larger compared to situations where the num­
ber of wells is fixed. The optimization procedure is detailed 
in the work of Mirzaei-Paiaman et al. (2023). The resulting 
optimal well arrangement consists of 11 production wells 
and 10 injection wells.
It is essential to highlight that the EMV experienced sig­
nificant growth during the optimization process, starting 
from an average value of 0.01 billion dollars in the prelimi­
nary iterations and reaching the optimal value of 1.17 bil­
lion dollars. This increase in the EMV demonstrates the 
effectiveness of the proposed methodology.
Figure 2A and B present the RF and COP trends through­
out the 13 iterations, respectively, concerning the EMV opti­
mization. Notably, Fig. 3 demonstrates a strong consistency 
between EMV, RF, and COP, indicating that improvements 
in EMV result in simultaneous enhancements in RF and 
COP. In other words, optimizing the EMV in this specific 
case leads to concurrent optimizations of the RF and COP.
This observed correlation among EMV, RF, and COP 
carries significant scientific implications. By evaluating and 
optimizing the EMV, improvements in both the COP and 
the RF are inherently driven. The achieved best COP value 
of 104.8 million m3 and the corresponding 42.2% RF high­
light the effectiveness of the optimization process in maxi­
mizing oil production while enhancing the overall recovery 
efficiency. Such consistent and interrelated behavior among 
the key metrics reinforces the validity of this approach and 
underscores the significance of EMV as a robust objective 
function in decision-making for well placement and produc­
tion strategies.
Figure  3(A) illustrates the relationship between EMV 
and CGP over 13 optimization iterations. The figure clearly 
demonstrates the substantial variation in the solution space 
IP = 417 + 12.2 × Cpo + 3.15
× (Cpl + Cpw + Ciw) +
9.61 × Cpg + 0.1 × nw

(5)
Here, Cpo, Cpl, Cpw, and Ciw are processing capacities, 
and Cpg is the gas processing capacity, all in specific units 
per day. nw denotes the number of well slots (Hayashi 
2006). Table 3 presents the economic parameters associated 
with each economic uncertainty.
Production based objective function
Production-based objective functions in reservoir engi­
neering play a crucial role in optimizing oil and gas field 
development. These functions include the maximization of 
oil RF, CGP, and COP and the minimization of CWP. Each 
objective serves as a valuable indicator of reservoir perfor­
mance and environmental impact. The oil RF is a funda­
mental metric that assesses how efficiently the reservoir can 
produce oil over time. It represents the percentage of origi­
nal oil in place that can be recovered. A higher RF indicates 
a more productive and sustainable reservoir. COP is a key 
factor directly related to the recovery factor. It indicates the 
total volume of oil extracted from the reservoir over time. 
High COP signifies efficient reservoir management and suc­
cessful extraction techniques, contributing to the overall 
recovery factor.
CGP measures the total amount of gas produced from 
the reservoir over its lifetime. While gas production can be 
economically beneficial, it may also pose environmental 
challenges. If not utilized or re-injected efficiently, excess 
gas may need to be flared, leading to harmful emissions and 
environmental consequences. Managing gas production to 
minimize flaring and optimize utilization is vital for sustain­
able operations.
CWP represents the total volume of water produced 
alongside oil and gas. Water production is often an unavoid­
able byproduct of hydrocarbon extraction. If not managed 
properly, it can lead to environmental contamination due to 
the potential toxic material and chemical content. Effective 
water management practices, such as water re-injection into 
wells or water treatment, are essential to mitigate environ­
mental impacts and maintain sustainable operations. It is 
necessary to emphasize that these production-based objec­
tive functions are critical in reservoir engineering as they 
provide valuable insights into reservoir performance, recov­
ery efficiency, and environmental impact. By addressing 
the economic and operational limitations associated with 
gas and water production, reservoir engineers can optimize 
production strategies while ensuring environmental respon­
sibility and sustainable resource management.
1 3
Journal of Petroleum Exploration and Production Technology
Figure 3(B) presents the CWP alongside the evolution of 
the EMV throughout the optimization process. The genera­
tion of excess water during petroleum production has con­
sistently posed considerable challenges. These challenges 
encompass various issues, including infrastructure deterio­
ration both on the surface and subsurface, elevated levels 
of toxicity that require costly specialized treatment, and the 
need for robust pumps to reinject the water into the reser­
voir with substantial energy consumption. Analyzing Fig. 3 
(B) reveals that the peak EMV corresponds to a CWP of 
5.67 × 107 m3. Point B (the minimum achievable CWP at the 
highest EMV) in Fig. 3 (B) is characterized by an EMV of 
1.104 billion dollars and a CWP of 2.95 × 107 m3. This con­
figuration achieves a 47.9% reduction in water production 
at the cost of a 6.2% reduction in EMV. Crucial determi­
nants in selecting between these alternatives include com­
pany policies and adherence to environmental regulations. 
These points merely represent a sampling of the multitude 
of solutions attainable through the optimization process. 
Consequently, informed decision-making hinges on careful 
during the optimization process. CGP ranges from a mini­
mum of 3.22 × 1010 m3 to a maximum of 6.24 × 1010 m3. 
Several crucial factors must be considered by decision-
makers when evaluating the feasibility of gas production 
in the studied field. These factors include available surface 
and subsurface equipment, global gas prices, the need for 
gas production, and compliance with environmental regu­
lations. It is essential to note that the CGP correspond­
ing to the highest EMV obtained in the last iteration was 
5.77 × 1010 m3. In cases where there is no facility to re-inject 
gas into the reservoir or when the gas price is not substan­
tial enough for export, the goal should be to minimize gas 
production. Point A is the minimum achievable CGP at the 
highest EMV. This point in the 9th iteration reveals the low­
est CGP value of 4.12 × 1010 m3, coupled with a reason­
ably high EMV of 1.1 billion dollars. Decision makers may 
choose to sacrifice 6.2% of the EMV value (1.1 billion dol­
lars) to reduce the total gas production (4.12 × 1010 m3) by 
29%, which holds significant importance, particularly due 
to its potential impact on vulnerable environments like off­
shore fields or those near preservation areas.
Fig. 3  (A) CGP versus EMV, (B) CWP versus EMV (Stage A)
 
Fig. 2  (A) Oil RF versus EMV (B) COP versus EMV (Stage A)
 
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Journal of Petroleum Exploration and Production Technology
trend. In other words, an increase in EMV corresponds to an 
elevation in NPVeco. Conversely, in panels Fig. 4-RM3 and 
RM4 (the two most pessimistic scenarios for UNISIM-II-
D), the relationship between NPVeco and EMV is conflict­
ing. In these cases, an upswing in EMV does not necessarily 
entail a parallel increase in NPVeco.
Given the perpetual uncertainty surrounding the true 
reservoir model and its associated properties, it becomes 
crucial to incorporate the pessimistic reservoir model sce­
nario in the context of NPVeco. Embracing this worst-
case scenario as a RM within this optimization process is 
imperative. Such an approach bolsters one’s confidence 
in the potential outcomes of the optimization endeavors. 
Consequently, the strategy of maximizing outcomes under 
the worst-case scenario, which is inherently pessimistic, 
ensures a comprehensive exploration of potential outcomes. 
consideration of these facets to strike a balance between 
economic gains and environmental responsibilities.
In this study, as mentioned earlier, robust optimization is 
conducted by employing nine distinct reservoir models that 
represent the total sub-models of the UNISIM-II-D reservoir. 
These models capture the intricate geological complexities 
inherent in reservoirs and, as a result, yield differing NPVs. 
It is worth noting that each of these reservoir models is sub­
jected to three distinct economic scenarios. Figure 4 graphi­
cally presents the NPV values under economic uncertainty, 
termed NPVeco. As illustrated in the figure, the behavior of 
NPVeco for each reservoir model in response to EMV show­
cases intriguing divergence. This divergence is attributable 
to the unique geological attributes of each reservoir model. 
In panels Fig. 4-RM1, RM2, RM5, RM6, RM7, RM8, and 
RM9 the reservoir model consistently aligns with the EMV 
Fig. 4  NPV of each RM considering economic uncertainty (Stage A)
 
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Journal of Petroleum Exploration and Production Technology
the EMV leads to favorable outcomes for other objectives. 
By delving into these interactions, the broader implications 
and potential benefits of maximizing the EMV can be better 
assessed.
Figure 5 illustrates the relationship between EMV and 
both RF and COP. Notably, both objective functions exhibit 
consistent behaviors with respect to EMV variations. It is 
important to note that, due to the fixed number of wells, the 
range of EMV observed in this figure is not as expansive as 
in the previous step. This reduction in range consequently 
leads to a more constrained solution space for both objec­
tive functions.
Analyzing Fig. 5(A) and 5(B), the optimal values for RF 
and COP, specifically 41.67% and 9.47 × 107 m3, respec­
tively, are identified.
The CGP is illustrated in Fig.  6(A). As discussed ear­
lier, in accordance with the production company’s policy, 
one potential strategy involves minimizing gas production 
while achieving the highest EMV. In Fig. 6(A), Point A (the 
minimum achievable CGP at the highest EMV) highlights 
the reasonable minimum of CGP at 5.58 × 1010, yielding an 
EMV of 1.34 billion dollars. The corresponding CGP value 
for the EMV-maximizing point is 5.69 × 1010 m3. Opting for 
the minimum gas production strategy results in a 2.1% EMV 
reduction, translating to a 2.2% decrease in gas production. 
The decision-making process should be meticulous, particu­
larly when this strategy is chosen.
CWP, illustrated in Fig. 6(B), holds intrinsic importance 
in oil and gas projects due to its environmental and opera­
tional implications. While focusing on optimizing EMV, 
minimizing CWP emerges as another important objective, 
considering its far-reaching effects on surface facilities, 
environmental stability, and project sustainability. Fig­
ure 6(B) demonstrates that, at the maximum EMV strategy, 
CWP stands at 3.62 × 107 m3. However, an alternative route 
considering Point B, the minimum achievable CWP at the 
highest EMV (3.29 × 107 m3), triggers an 8.9% reduction in 
This includes ensuring that the NPVeco of the worst-case 
reservoir models is also maximized. Consequently, one of 
the primary focal points for future studies is the optimiza­
tion of NPVeco for this worst-case reservoir model scenario, 
concurrently considering the EMV. Illustrated in Fig. 4, the 
worst-case reservoir models (RM3 and RM4) present the 
highest NPVeco, reaching 0.826 and 0.508 billion dollars, 
respectively. Meanwhile, the EMV corresponding to this 
optimal value is 1.027 billion dollars for RM3 and 1.037 bil­
lion dollars for RM4. It is noteworthy that when exclusively 
focusing on EMV, the NPVeco value corresponding to the 
worst-case scenarios diminishes by 25% for RM4 and 15% 
for RM3.
The decision-making process becomes notably intricate 
in this scenario. Decision-makers are confronted with the 
challenge of choosing between agreeing upon the final EMV 
value and embracing the highest NPVeco value derived 
from the worst-case scenario. Each value holds its own dis­
tinct significance within the context of substantial projects, 
and both offer unique insights. In essence, this situation 
poses a profound scientific and strategic challenge. The bal­
ance between the quantitative metrics of EMV and NPVeco 
requires careful consideration and deliberation.
Stage B: well placement optimization (fine-tuning)
In the preceding stage, the optimal number of wells required 
to maximize the EMV was determined. The position of 
each well was established using the Mirzaei-Paiaman et 
al. (2023) methodology. The optimization process in this 
stage focused on refining the position of 21 production and 
injection wells. The outcomes of this optimization revealed 
a notable 16.4% increase in the final EMV, which conse­
quently surged to $1.370  billion. This stage involves a 
comprehensive analysis of the behavior exhibited by each 
individual objective in relation to the EMV. This explora­
tion aims to determine whether the pursuit of maximizing 
Fig. 5  (A) Oil RF versus EMV; (B) COP versus EMV (Stage B)
 
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Journal of Petroleum Exploration and Production Technology
project will be profitable if a maximum value for the pes­
simistic case is available.
Stage C, platform liquid capacity
In this stage, four platform capacity parameters were taken 
into consideration in relation to produced and injected flu­
ids, encompassing liquids and gases, for the purpose of 
optimization. To perform the optimization, the liquid pro­
duction capacity was systematically divided into a range of 
21,118 to 28,618 cubic meters per day. In a similar man­
ner, water production capacity was discretized between 
10,079 and 19,079 cubic meters per day. For water injec­
tion capacity, the discretization spanned 20,157 to 38,157 
cubic meters per day, while gas injection capacity was dis­
cretized from 6 × 106 m3/day to 8 × 106 m3/day. The com­
prehensive optimization procedure is outlined in the work 
by Mirzaei-Paiaman et al. (2023). Compared to the earlier 
location optimization stage, the EMV increased by 8.01% to 
$1.48 billion dollars in this phase.
Since the type, number, and location of the wells remain 
constant, the fluctuations in EMV do not appear to be of sig­
nificant magnitude. Examining Fig. 8(A) and 8(B), it is evi­
dent that the upward trajectory of the RF and COP surpasses 
that of EMV variations. This trend is similarly reflected in 
Fig. 8(C) and 8(D), where instances of peak gas and water 
production coincide with high points in the EMV.
When considering a strategy to decrease gas production, 
Fig. 8(C) offers valuable insights. To achieve a reduction 
of more than 13.6%, reducing it from 5.64 × 1010 m3 to a 
minimum of 4.87 × 1010 m3, a concession of just over 5% of 
the EMV is required, as illustrated by Point A (the minimum 
achievable CGP at the highest EMV) in Fig. 8(C). Nota­
bly, opting for point B (the minimum achievable CWP at 
the highest EMV) in Fig. 8(D) results in a substantial 37% 
reduction in CWP, from 3.78 × 107 m3 to 2.38 × 107 m3. This 
CWP and lowers EMV by 1.6%, transitioning from 1.37 bil­
lion dollars to 1.35 billion dollars.
This intricate interplay between CWP and EMV requires 
meticulous evaluation to discern the superior strategy for 
overall project advancement. Crucial variables involve the 
ecological ramifications of water extraction, the economic 
load of water treatment and disposal, the toll on surface 
infrastructure, and the financial benefits resulting from 
higher EMV. Additional points of consideration involve 
potential environmental degradation from water contami­
nation and habitat disturbance, the considerable financial 
burden associated with water treatment and disposal, and 
the potential corrosive and scaling impact on surface facili­
ties due to increased water production. The strategic choice 
between minimizing CWP or maximizing EMV constitutes 
a multifaceted decision, contingent on specific project cir­
cumstances. By undertaking a comprehensive assessment 
of all pertinent factors, decision-makers can aptly navigate 
this choice, steering the project toward optimal outcomes 
for stakeholders and the environment.
As discussed in the previous section, the NPV of each 
RM was examined relative to the EMV, considering eco­
nomic uncertainty. This is shown in Fig. 7 for stage B. It 
is evident that only the two most pessimistic RMs (RM3 
and RM4) exhibit conflicting behavior, while the other RMs 
(RM1, RM2, RM5, RM6, RM7, RM8, and RM9) exhibit 
consistent behavior towards EMV. As shown in Fig. 7, if 
EMV is maximized, the value of RM3 and RM4 does not 
necessarily maximize, as is the case for the other RMs.
More importantly, given the unknown and complex 
behavior of the true reservoir, full confidence can be placed 
in the accurate representation of the reservoir by all the other 
RMs. Therefore, it is crucial to obtain the highest value for 
these two pessimistic RMs while also achieving the maxi­
mum EMV. This decision may improve the reliability of the 
selected strategy. In other words, it can be certain that this 
Fig. 6  (A) CGP versus EMV, (B) CWP versus EMV (Stage B)
 
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Journal of Petroleum Exploration and Production Technology
now being noted during this stage (Fig. 9). It is worth not­
ing that, as EMV exhibits decreased variation in this stage, 
a corresponding reduction in NPV variation is seen in each 
RM. This trend is illustrated in Fig. 10, except for RM3 and 
RM1. Out of the nine RMs analyzed, seven display a con­
sistent pattern of behavior aligning with EMV optimization. 
However, RM3 and RM1 stand apart in this context. Their 
NPV values do not undergo as significant alterations as the 
other RMs. This suggests that adjustments in the liquid pro­
duction capacity of the platform have a relatively minor 
impact on NPV within these two reservoir sub-models. This 
phenomenon can be attributed to the unique characteristics 
and properties of these reservoirs.
strategic adjustment only requires a marginal EMV conces­
sion, approximately 4.7%, which means a decrease from 
1.48 billion to 1.41 billion dollars.
The significance of adopting a gradual decision-making 
approach is underscored by the impact observed. A modest 
reduction of 5% in the peak EMV results in substantial cas­
cading effects. Specifically, a reduction of over 37% in CWP 
and an approximate 13.6% decrease in CGP are brought 
about. This highlights the effectiveness of a deliberate and 
selective strategy, wherein minor adjustments to the EMV 
lead to considerable gains in resource conservation. This 
outcome reflects a management approach characterized by 
astuteness and informed decision-making.
In contrast to the initial two stages, where a relatively 
high degree of variation in the EMV and NPV of each RM 
was observed, a dissimilar trend in the NPV of each RM is 
Fig. 7  NPV of each RM considering economic uncertainty (Stage B)
 
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Journal of Petroleum Exploration and Production Technology
a modest 3.3% sacrifice in EMV, transitioning from 1.51 
to 1.46  billion dollars can be achieved. This results in a 
substantial 13.8% reduction in water production, decreas­
ing it from 3.96 × 107 m3 to 3.42 × 107 m3 (Fig. 10(D)-Point 
B (minimum achievable CWP at the highest EMV)). This 
reduction in both water and CGP holds significant implica­
tions as it mitigates environmental impact, reduces mainte­
nance costs, and curtails water treatment expenses.
As expected, the variation of NPV in each RM is insignif­
icant compared to the previous three stages, given the small 
variation in EMV. In Fig.  11, it becomes evident that all 
NPV solutions are clustered closely around a single solution 
across all RMs. Therefore, no interpretation of the variation 
of NPV can be established in this case.
Based on the results, investigations indicate that there is a 
trade-off between EMV and CGP. In the initial stage (Stage 
A), a judicious reduction of 6.2% in the maximum achiev­
able EMV can result in a substantial reduction of 29% in 
CGP. In the subsequent stage (Stage B), a more modest 
Stage D, ICV location optimization
For optimization of ICV location, each well is given a set 
of feasible ICV configurations, each of which represents a 
predetermined ICV location arrangement. More intricate 
details regarding the optimization procedure can be found 
in the work of Mirzaei-Paiaman et al. (2023). Notably, even 
less variation in EMV is observed in this stage compared to 
the prior three stages, as depicted in Fig. 10. The EMV range 
spans from a minimum of 1.21 to a maximum of 1.51 billion 
dollars. Like stage C, the RF and COP exhibit consistent 
behaviors with EMV during optimization (Fig. 10(A) and 
(B).
Once again, for CGP, by accepting a reduction in EMV 
to 1.50 (a 0.6% reduction from 1.51 billion dollars), a 4% 
decrease in gas production, equating to 5.51 × 1010 m3 from 
5.69 × 1010 m3 (Fig.  10(C)-Point A (minimum achievable 
CWP at the highest EMV) can be achieved. Similarly, in 
this stage, notable reductions in water production by making 
Fig. 8  (A) Oil Recovery Percentage versus EMV (Stage C), (B) COP versus EMV (Stage C), (C) CGP versus EMV (Stage C), (D) CWP versus 
EMV (Stage C)
 
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Journal of Petroleum Exploration and Production Technology
that an alternative strategy in Stage C results in a sacrifice of 
only 4.7% in EMV, yielding a substantial 37% reduction in 
CWP. Finally, in Stage D, a nuanced strategy that entails a 
minor 3.3% reduction in EMV leads to a substantial 13.8% 
reduction in CWP (Table 5).
Conclusions
This study underscores the importance of incorporating 
multiple objective functions alongside EMV to provide a 
comprehensive framework for guiding decision-making 
processes in oilfield development. By examining various 
objective functions in conjunction with EMV as the primary 
reduction of 2.1% in EMV yields a 2.2% reduction in CGP. 
Moving forward to Stage C, a 5% reduction in EMV is asso­
ciated with a noteworthy 13.7% decrease in CGP. Finally, 
in Stage D, a nuanced approach to decision-making allows 
for a minimal 0.6% reduction in EMV to be coupled with 
a consequential 4% reduction in CGP. Table 4 provides a 
summary of the reductions in EMV and CGP at each stage.
This study underscores the significant findings across the 
four stages, highlighting their importance in the context of 
CWP. In Stage A, analysis reveals that by implementing a 
strategic reduction of merely 6.2% in EMV, it can be real­
ized a remarkable 47.9% reduction in CWP. Transitioning to 
Stage B, a more modest 1.6% reduction in EMV is associ­
ated with an appreciable 8.9% reduction in CWP. It is shown 
Fig. 9  NPV of each RM considering economic uncertainty (Stage C)
 
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Journal of Petroleum Exploration and Production Technology
CGP (ranging from 4 to 29%) were achieved across all 
stages with minimal EMV reduction (between 0.6% and 
6.2%). Similar to CGP, substantial reductions in CWP 
(ranging from 8.9 to 47.9%) were obtained with minor 
EMV sacrifices (between 1.6% and 6.2%).
	
●
Insight is provided into the potential environmental ben­
efits of minimizing gas and water production, including 
reductions in greenhouse gas emissions and the preser­
vation of groundwater resources.
For future studies, one can consider multi-objective opti­
mization, which is a more robust and sustainable approach 
to oilfield development than solely relying on EMV. Multi-
objective optimization allows decision-makers to consider 
multiple objectives simultaneously, such as maximizing 
EMV, maximizing the NPV of the worst-case reservoir 
model, and minimizing water and gas production.
criterion, we gain valuable insights into the complexities of 
well placement and production strategies. We underscore 
the importance of balancing economic gains with envi­
ronmental responsibilities to ensure sustainable practices. 
Furthermore, findings emphasize the potential benefits 
of minimizing CGP and CWP to mitigate environmental 
impacts and optimize resource utilization. The results can 
be summarized as follows:
	
●
This study examines the sensitivity of objective func­
tions to optimization variables across different stages of 
the optimization process.
	
●
A strong correlation is identified between EMV and 
COP, emphasizing EMV’s role in driving improvements 
in COP and RF.
	
●
The study advocates for a balanced approach that con­
siders both economic benefits and environmental re­
sponsibilities, proposing modified strategies to reduce 
CGP and CWP. The study demonstrates that strategical­
ly prioritizing CGP and CWP reduction can be achieved 
with minimal sacrifice to EMV. Significant reductions in 
Fig. 10  (A) Oil Recovery Percentage versus EMV (Stage D), (B) COP versus EMV (Stage D), (C) CGP versus EMV (Stage D), (D) CWP versus 
EMV (Stage D)
 
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Journal of Petroleum Exploration and Production Technology
Table 4  Reduction of EMV and CGP in each stage based on alternative strategy
Stage
EMVmax
Billion Dollars
CGPEMVmax
× 1010 m3
CGPmin
× 1010 m3
EMVCPGmin
Billion Dollars
Reduction
in EMV (%)
Reduction
in CGP (%)
A
1.17
5.76
4.12
1.10
6.2
29
B
1.37
5.69
5.58
1.34
2.1
2.2
C
1.48
5.64
4.87
1.40
5
13.6
D
1.51
5.69
5.51
1.50
0.6
4
Table 5  Reduction of EMV and CWP in each stage based on alternative strategy
Stage
EMVmax
Billion Dollars
CWPEMVmax
× 107 m3
CWPmin
× 107 m3
EMVCWPmin
Billion Dollars
Reduction
in EMV (%)
Reduction
in CWP (%)
A
1.17
5.67
2.95
1.10
6.2
47.9
B
1.37
3.62
3.29
1.35
8.9
1.6
C
1.48
3.78
2.38
1.41
4.7
37
D
1.51
3.96
3.42
1.46
3.3
13.8
Fig. 11  NPV of each RM considering economic uncertainty (Stage D)
 
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Journal of Petroleum Exploration and Production Technology
Campinas (UNICAMP) and sponsored by Equinor Brazil and FAPESP 
– São Paulo Research Foundation (2021/04878- 7). Acknowledge­
ments are extended to the Center for Petroleum Studies (CEPETRO) 
and School of Mechanical Engineering (FEM).
Author contributions  Auref Rostamian: Conceptualization, methodol­
ogy, validation, formal analysis, investigation, resources, writing of 
the original draft. Marx Vladimir De Sousa Miranda: Conceptualiza­
tion, methodology, validation, formal analysis, investigation, resourc­
es, writing of the original draft. Abouzar Mirzaei-Paiaman: Conceptu­
alization and methodology. Vinicius Eduardo Botechia: Validation and 
conceptualization, review and editing. Denis José Schiozer: Validation 
and conceptualization, supervising, review and editing, funding acqui­
sition.
Funding  This work was funded by Equinor Brazil and FAPESP – São 
Paulo Research Foundation (Grant Number 2021/04878-7.
Declarations
Conflict of interest  The authors declare no competing interests.
Open Access   This article is licensed under a Creative Commons 
Attribution 4.0 International License, which permits use, sharing, 
adaptation, distribution and reproduction in any medium or format, 
as long as you give appropriate credit to the original author(s) and the 
source, provide a link to the Creative Commons licence, and indicate 
if changes were made. The images or other third party material in this 
article are included in the article’s Creative Commons licence, unless 
indicated otherwise in a credit line to the material. If material is not 
included in the article’s Creative Commons licence and your intended 
use is not permitted by statutory regulation or exceeds the permitted 
use, you will need to obtain permission directly from the copyright 
holder. To view a copy of this licence, visit http://creativecommons.
org/licenses/by/4.0/.
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Abbreviation
b	

Discount Rate (%)
Ciw	

Water injection capacity (103 m3/day)
Cpl	

Liquid processing capacity (103 m3/day)
Cpg	

Gas processing capacity (106 m3/day)
Cpo	

Oil processing capacity (103 m3/day)
Cpw	

Water processing capacity (103 m3/day)
Inv	

Investment (US $ millions)
k	

number of economic scenarios
m	

number of representative models
nw	

Number of Wells
pi	

probability of representative model i
pj	

probability of economic scenario j
Roy	

Royalties (%)
ST	

Social Tax (%)
Ts	

Total simulation time (days)
ts	

time period (days)
AC	

Abandonment Cost
CAPEX	

capital expenditures
CGP	

Cumulative Gas Production
COP	

Cumulative Oil Production
CWP	

Cumulative Water Production
DLHS	

Discrete Latin HyperCube
EMV	

Expected Monetary Value
ICV	

internal control valves
IDLHC	

Iterative Discrete Latin HyperCube
Inv	

investment
IP	

Investment of Platform
LHS	

Latin HyperCube Sampling
NCF	

net cash flow
NPV	

Net present Value considering economic 
uncertainty
OC	

Operational Costs
OPEX	

operational expenditures
PVT	

Pressure, Volume, Temperature
R	

gross Revenue
RF	

Recovery Factor
RM	

Representative Model
ROI	

Return on Investment
Roy	

Royalties
ST	

Social Taxes
T	

Tax rate
WAG	

Water assisted Gas
o	

Oil
l	

Liquid
g	

Gas
w	

Water
i	

representative model i
j	

Economic scenario j
eco	

based on economic uncertainty
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1 3

Petroleum reﬁnery optimization
Cheng Seong Khor1 • Dimitrios Varvarezos2
Received: 30 August 2015 / Revised: 11 June 2016 / Accepted: 19 September 2016 /
Published online: 3 November 2016
 Springer Science+Business Media New York 2016
Abstract In the face of lower margins, stiffer competition, and ever more stringent
product and environmental speciﬁcations, petroleum reﬁneries have increasingly
relied on optimization approaches to maintain their survival and competitive edge.
In this paper, we present a comprehensive overview of the current state of the art
role of optimization methods in reﬁneries for wide-ranging multiscale applications
and activities spanning the traditional planning linear programming to supply chain
that extends to outside-the-fence considerations. The paper aims to provide an
integrated treatment of techniques and tools, and a survey of representative work in
the burgeoning literature of this ﬁeld with an emphasis on comparisons between
industrial practices and academic research.
Keywords Optimization  Petroleum reﬁnery  Modeling  Reﬁnery supply chain 
Reﬁnery planning  Real-time optimization
Abbreviations
APC
Advanced process control
CDU
Crude distillation unit
DRTO
Dynamic real-time optimization
EMPC
Economic model predictive control
FCC
Fluid catalytic cracking
& Cheng Seong Khor
chengseong.khor@exxonmobil.com; khorchengseong@gmail.com
Dimitrios Varvarezos
dimitrios.varvarezos@aspentech.com
1
Engineering Global Support Ofﬁce, ExxonMobil Research and Engineering Company,
Kuala Lumpur City Centre, 50088 Kuala Lumpur, Malaysia
2
Aspen Technology, Houston, TX 77042, USA
123
Optim Eng (2017) 18:943–989
DOI 10.1007/s11081-016-9338-x
LP
Linear programming
MILP
Mixed-integer linear programming
MINLP
Mixed-integer nonlinear programming
MPC
Model predictive control
NLP
Nonlinear programming
RLT
Reformulation–linearization technique
RTO
Real-time optimization
1 Introduction
1.1 Role of optimization in petroleum reﬁning industry
In this paper, we attempt to present a brief yet comprehensive view of the role of
optimization in petroleum reﬁneries. There are many aspects in the design and
operation of reﬁnery processes that are amenable to optimization. Over time,
numerous technologies and work processes have been deployed to serve this
purpose. In fact, one of the ﬁrst applications of optimization is the use of linear
programming for reﬁnery planning (Charnes et al. 1952; Garvin et al. 1957; Manne
1956, 1958; Symonds 1955).
In a larger context, the petroleum reﬁning industry has pioneered both the
adoption of operations research technologies and the early deployment of software
solutions to emerging computing platforms of the late twentith century. The
ultimate goal was to optimize the reﬁnery operations along with the associated
supply chain to maximize proﬁt margins. The supply chain of a typical petroleum
reﬁning company involves a wide spectrum of activities, encompassing crude oil
evaluation, selection, procurement and transportation to the site, continuing with the
scheduling of reﬁning operations, followed by the transportation of ﬁnished
products to intermediate terminals or other storage facilities, and ﬁnally delivery of
the products to end users such as gas stations or other consumer outlets.
Optimization is applied at various levels in the supply chain of the reﬁning
industry covering the scope of planning and scheduling activities as well as process
unit modeling and control. A reﬁnery supply chain typically extends to outside of
the reﬁnery gates, encompassing both inside-the-fence and outside-the-fence
considerations as shown in Fig. 1. The former covers all entities within the reﬁnery
gates from crude oil storage tanks to end product tanks, while the latter include
everything else accounting for the jetties (ship docks) and the shipping points.
From the foregoing description, it is obvious that the economics around an entire
reﬁning supply chain is rather complex and all the associated elements are highly
coupled or inter-related. As an example, the optimum selection of crude oils is a
function of the following factors: a reﬁnery’s location and conﬁguration, seasonal
constraints on processing and product speciﬁcations, capabilities of a reﬁnery’s
assets, projected product forecast and price ﬂuctuations, cost of crude oils and
products, availability and cost of other crude oils in the market, transportation cost,
other logistical constraints such as availability of pipelines and other transportation
944
C. S. Khor, D. Varvarezos
123
Crude oil distillation 
units (CDUs)
Other crude oil
processing units
Blender 1
Blender 2
Component stock 
tanks
Blend
headers
Finished
product
tanks
Distribution
points
Optimization of units and processes
Planning and scheduling of product blending and distribution
 Crude oil
marine vessels
Single-parcel
service vessels
Single-buoy 
mooring 
& jetties
Crude oil
storage/blend
tanks
Charging
tanks
Planning and scheduling of crude oil movement
Crude oil blending
Fractionation & reaction processes
Gasoline blending
Inside-the-fence
Terminals
Gas stations
Product distribution
Outside-the-fence
Outside-the-fence
Crude oil unloading
Crude oil supply
SBM
Blend scheduling
Fig. 1 Different application levels of optimization in a reﬁnery supply chain
Petroleum reﬁnery optimization
945
123
infrastructures, and several other considerations. As evident from the aforemen-
tioned, the scope of reﬁnery optimization is vast, and it has contributed to early use
of the optimization technology in achieving short-, medium-, and long-term
operational plans.
In a nutshell, the use of optimization software at the planning level aims to
achieve two important yet distinct goals. The ﬁrst goal is to optimize purchasing of
the best mix of crude oils for future planning cycles in the long term, covering
duration of a few months to a year. The second goal is to optimize in the short term,
for one week to one month, an entire reﬁnery operation that include unit
throughputs, distillation cutpoints, and severity of the conversion units. As will be
clear from discussions on individual units and reﬁnery-wide optimization in later
sections, the main focus of optimization for a majority of reﬁners has largely been at
the planning level, also commonly referred to within the industry as the ‘‘planning
LP’’. Applications of operational optimization on process unit modeling also
involves the following: (1) real-time optimization (RTO), e.g., ﬁrst-principles-based
modeling of units; (2) design, e.g., using a process simulator with optimization
capabilities such as Aspen Plus; and (3) advanced process control, e.g., based on
linear approximated empirical models.
To analyze and appreciate the scope of reﬁnery optimization, we approach the
subject from a few angles. The main focus is on a synthesis of topics aiming at
continuity and synergies, concerning modeling and optimization approaches and
elucidation of practical industrial challenges in the ﬁeld. The multiple viewpoints
can be better described in terms of axes or dipoles that dissect the subject along an
axis with well-known and understood end-points. The dipoles serve as the
multifaceted foundation for this work. For example, we can consider the academic
and industrial perspectives on reﬁnery optimization as forming such a dipole. In
some areas, academic research is well ahead of industrial practice (e.g., in reﬁnery
scheduling) whereas the opposite is true in a few others (e.g., large-scale MINLP
solvers for reﬁnery planning). It is thus important to realize, analyze, and understand
reasons behind the gaps between the two communities. There is also difference
between what is published and what is actually commercially available and used for
reﬁnery optimization.
In terms of the organization of the paper, we feel that it is imperative to provide
multiple dimensions of analyses. We mainly consider optimization from a process
unit modeling perspective in terms of the variables optimized (e.g., the cutpoints).
Nonetheless, the problem of reﬁnery optimization can be viewed and analyzed
across different functional areas (such as the different operations) as well as the
different analysis timeframes.
We now describe the area and the approach in terms of the multiple viewpoints
and dimensions explored, which help explain and understand both the evolution and
the state-of-the-art in this ﬁeld. Scope of the analyses is provided in terms of units
and processes that are important for optimization.
946
C. S. Khor, D. Varvarezos
123
1.2 Historical perspective of reﬁnery optimization
In this paper, we describe the evolution of reﬁning planning. In the last 60 years or
so, there have been many attempts to apply optimization technology using both
formal mathematical techniques (e.g., linear programming (LP), nonlinear program-
ming (NLP), and mixed-integer programming (MIP)) as well as heuristics (e.g., rule-
based, neural networks, simulated annealing) in many if not all of the different
aspects of reﬁnery operations ranging from crude oil evaluation and acquisition,
planning, scheduling, blending, control, to end-product distribution planning.
Table 1 provides a brief history of major milestones related to reﬁnery optimization
initiatives, which are revisited throughout this paper. A point of possible interest is
that RPMS is claimed to be the ﬁrst software to be sold as a separate standalone
product, whose ﬁrst customer was Chevron—up until that time, all software was
delivered custom-made for a speciﬁc project.
1.2.1 Early developments: linear programming
As early as the 1950s, soon after the development of the simplex algorithm by George
Dantzig, the emerging and constantly evolving LP techniques have been applied in
optimizing reﬁnery planning operations. The ﬁrst models were pure LP models that
represented the basic crude distillation operations as yield vectors and all the other
reﬁnery processes as ﬁxed-yield production patterns. The obvious limitations of this
modeling approach became apparent later, but it took two decades to change the
underlying solution algorithm to accommodate the underlying nonlinearities.
Table 1 A brief history of
reﬁnery optimization
(Bodington and Baker 1990; M
Bernstein 2013, personal
communication, 11 January)
Year
Milestone
1950s
Dantzig invents the simplex algorithm that gives birth
to LP
1950s
First FORTRAN LP code is written for reﬁnery
optimization
1957
Bonner and Moore (B&M) is formed and became a
premier reﬁnery LP software provider
1960s
Research and in-house codes are developed alongside
mainframe computers
1960s–
1970s
RPMS is written for mainframes and sold by B&M
1970s
Model management tools are introduced e.g.,
ExxonMobil’s PLATOFORM)
1980s
Joffe leaves B&M to write PIMS for PC; Haverly
systems introduces GRTMPS
1981
Aspen Technology is formed
1997
Aspen Technology acquires PIMS through Basil
associates
2003
The ﬁrst general purpose MINLP solver is used in a
major commercial planning application (Aspen PIMS
advanced optimization PIMS-AO)
Petroleum reﬁnery optimization
947
123
1.2.2 The evolution to nonlinear programming
A common misconception in both industrial and academic circles is the notion that
reﬁnery planning is still based on linear models. This confusion stems from the fact
that for historical reasons, reﬁnery planners refer to the models as ‘‘LP’’ although
since the late 1980s, the models have actually been nonlinear (NLP) and often
mixed-integer nonlinear (MINLP) in nature.
Original planning models are purely linear formulations until the middle of the
1970s. The transition from linear to nonlinear planning models started in the late
1970s. In 1974, during Clarence ‘‘Larry’’ A. Haverly’s visit to the Chevron El
Segundo reﬁnery in Los Angeles, California, USA, a discussion took place between
Haverly and Douglas C. White, a senior Chevron LP expert modeler-planner. This
discussion led to recognizing the limitation of linear approximations in reﬁnery
planning and a ﬁrst attempt at solving the underlying nonlinear problem recursively.
Indeed, practitioners had noted that linear approximations of the inherently
nonlinear characteristics of optimizing the blending of crude oils and intermediate
products had caused the optimal LP solutions to be ‘‘over-optimistic’’ and that the
difference in the valuation of crude oils were largely exaggerated. The solution
approach involves a customized form of successive linear programming (SLP)
(Lasdon and Joffe 1990), a technique commonly referred to as recursion that is ﬁrst
developed by Haverly in the late 1970s (Haverly 1978, 1979, 1980).
The emergence of the fundamentals for a nonlinear optimization methodology
for reﬁnery planning is believed to have taken place in year 1977 during an LP
training session at the same El Segundo reﬁnery (M Sorensen 2013, personal
communication, 21 January). At that time, William ‘‘Bill’’ Hart, a senior
mathematician from Haverly Systems, was working with Don White. The latter
observed that existing models that included multiple yields for different crude oils
and different operating modes tended to over-optimize the processing and blending
of individual crude oils and their associated side-cuts throughout the reﬁnery (as
indicated earlier). As a result, the linear model frequently overstated the difference
in value between individual crude oils. During that session, Hart developed the
underlying mathematics for the recursion technique (Hart 1978). Hart’s work
provided a starting point that led to its further development by Basil Joffe to the
technique commonly called distributive recursion (Lasdon and Joffe 1990). Until
then, the attempts involve using a simpler recursion technique based on direct
substitution. Distributive recursion provides the critical connection between
property or quality changes at the crude oil distillation level with that at the
downstream blending level.
Early versions of recursion were slow and difﬁcult to work with (Haverly 1980).
But over the course of the subsequent decade, distributive recursion has become
faster and more reliable. The technique became popular in the 1990s when Joffe (at
Bonner and Moore Associates originally, then at Basil Associates before it was
acquired by Aspen Technology around 1997–1998) further developed it to become
accepted as a standard in LP modeling systems for reﬁnery planning.
948
C. S. Khor, D. Varvarezos
123
1.2.3 Local optima and their role in the evolution of planning optimization
technology
Haverly realized that just like any local nonlinear optimization algorithm,
distributive recursion is susceptible to ending up with computing a local optimum.
Haverly published his ﬁndings in a series of articles by considering a pooling
problem with sulfur quality in the streams, which involved three input streams with
different sulfur contents (Haverly 1978, 1979, 1980). In distributive recursion,
stream quality is not expressed as an explicit variable (in the sense of a ﬂow variable
multiplied with a quality variable). This gives rise to the term ‘‘distributive’’
recursion, in which the algorithm serves to initialize and update the variable values
at each iteration. Haverly reported that if one initializes with a high sulfur quality
value, the algorithm would not arrive at an attractive solution. After a few iterations,
the optimizer may realize that this would not be an attractive solution, but the
algorithm is not able to move away from getting trapped in that local optimal
solution. On the other hand, the algorithm may run into the possibility of not
considering a stream to be an optimal solution although it is actually the global
optimal solution. Haverly concluded that for a problem with multiple streams with
different sulfur contents (or any other property, for that matter), depending on the
initial guess, this will determine the resulting optimal solution.
Later, in disregarding the local solutions, Joffe pioneered an improved approach
to the problem by initializing the stream qualities away from zero values. Joffe was
a PhD student at Imperial College London (1968–1971) with Professor Roger W.
H. Sargent, a pioneering ﬁgure in process systems engineering (PSE). Joffe realized
that the problem is due to the presence of nonconvexity. At the time (around 1980s),
he was an employee at Bonner and Moore. Allegedly, he suggested to the
company’s management that a version of RPMS be written for the newfangled
personal computer (PC) invention, but was told that PCs would never be serious
business tools. Joffe decided to leave, and he built what eventually became the
PIMS (Process Industry Modeling System) system of today. The PC-based
architecture solidiﬁed PIMS and eventually gave the lion’s share of the reﬁnery
planning market to the package now known as Aspen PIMS.
Joffe rode on the PC wave successfully by capitalizing on a ﬁrst-mover
advantage that enabled him to dominate the market, aided by his talents and
capability at improving the algorithms employed. In contrast, RPMS and the other
planning systems that existed at that time relied on big mainframe systems (e.g.,
VAX and VMS). Eventually, Haverly adopted the PC platform too in developing
the GRTMPS system (Haverly Systems 2012). In the late 1990s, AspenTech started
efforts leading to the PIMS Advanced Optimization or PIMS-AO package that move
away from the distributive recursion paradigm. That is, unlike distributive recursion
in which both the modeling and solution parts are tightly intertwined, PIMS-AO
decouples these two elements so that they can be exposed to algebraic systems and
are thus amenable to the implementation of global optimization techniques
(Varvarezos 2008).
Petroleum reﬁnery optimization
949
123
1.3 Present developments
The nonlinear characteristics of reﬁnery planning models have always been implicit
in nature. In fact, a nonlinear model was not constructed as a formal nonlinear
algebraic model until later in the form of AspenTech’s PIMS-AO (Advanced
Optimization) (Joffe et al. 2005a; Varvarezos 2008). Even 30 years later, there are
still papers that interpret the term ‘‘LP planning models’’ literally as linear
programming (LP) models in the operations research or mathematical programming
sense of the term. Some articles even advocate the use of NLP in reﬁnery planning,
which is clearly a consequence of not comprehending that NLP has been used in the
reﬁning industry all along. This lack of awareness of the state-of-the-art of reﬁnery
planning optimization is partly a result of the inherent challenges of using existing
commercial reﬁnery planning optimization systems (e.g., Aspen PIMS and
Haverly’s GRTMPS). It is noteworthy that the latest versions of these tools (e.g.,
PIMS-AO) allow for the explicit addition of many classes of declarative nonlinear
algebraic models from a variety of modeling sources (e.g., AspenTech’s Aspen
Custom Modeler (ACM; Aspen Technology 2011a)).
In terms of systems development, the advent of personal computers and
workstations motivated reﬁners to develop integrated systems combining database
management with graphical functions for scheduling, enabled by the solution
engines of LP- and SLP-based algorithms (Bodington and Baker 1990). The system
known as MIMI (‘‘Manager for Interactive Modeling Interfaces’’), originally
conceived by Thomas E. Baker at Chesapeake Decision Sciences, played an early
role in this regard, particularly in integrating reﬁnery planning and scheduling.
While still at Exxon (now ExxonMobil), Baker developed an LP-based system that
eventually led to MIMI (Jones and Baker 1996). In 1998, AspenTech acquired
Chesapeake, and MIMI is now part of Aspen Chemical Supply Chain, which rely on
a combination of LP-based techniques and relaxation algorithms. As MIMI is highly
conﬁgurable, it is suitable for modeling petroleum and chemical processes.
However, a weakness of MIMI for planning applications lies in not providing NLP
capabilities. As a result, users have to write their own programs and algorithms, as
opposed to, for instance, a package like Aspen PIMS in which an NLP is ‘‘wrapped
around’’ the main code.
Until today, scheduling applications extensively employ a rule-based approach
besides
adopting
expert
systems-type
formal
heuristics-based
scheduling
approaches (e.g., simulated annealing and genetic algorithms) that may involve
solving NLPs. It is worth noting that most commercial codes utilize customized
heuristics—this is another dichotomy between academia and industry because in
reality, actual industrial practice does need to employ heuristics in order to solve a
practical problem successfully. However, these heuristics are not usually published
in the academic literature mainly because they may not necessarily be grounded in
fundamental technical background and may lack of scientiﬁc advances.
For the sake of being complete, other related system solutions include PROFIT,
which was an equation-oriented system based on the GAMS platform developed at
the Shell Research Center in Houston in the 1990s. Although its development was
abandoned in favor of PIMS, it is worthy to note that there were efforts to develop
950
C. S. Khor, D. Varvarezos
123
planning tools using the GAMS platform. Another initiative worth mentioning is the
IMPACT system developed largely in secrecy during the 1980s at DMC
Corporation or DMCC (which is responsible for developing the dynamic matrix
control (DMC) technology as surveyed in Table 8).
1.4 Remaining open challenges for reﬁnery planning optimization
1.4.1 Problem size and methodology
Reﬁnery planning is an activity that concerns the operations. While we can afford
not to be detailed in the past, as our capabilities in solving nonlinear problems grow,
we can no longer remain at that paradigm. In this regard, an important issue is that
the size of a typical reﬁnery planning optimization problem could easily be of the
order of one-to-two million variables (Fernandes et al. 2013). An RTO problem on a
single crude distillation unit (CDU) typically involves 150,000 variables. In general,
it is common nowadays to handle optimization models for RTO and model
predictive control (MPC) involving 100,000–200,000 equations with 30–40
decision variables (Darby et al. 2011).
The primary functions of reﬁnery planning include to perform crude oil feedstock
evaluation and to prepare a monthly operating plan. In the former, an evaluation
problem often leads to a multiperiod formulation in which as more time periods are
involved, the greater its complexity, hence the resulting decision-making process.
For the latter, in preparing a monthly operating plan, we cannot assume it is made
up of block operations. This is because in reality, a reﬁnery does not process a
steady diet of crude oils throughout a certain month—instead the operation changes
from the early part of a month to the end based on how a reﬁnery operates and
responses to market conditions.
Nonetheless, it is vital to have planning optimization in place for important units
besides the crude distillation unit (CDU), particularly the ﬂuid catalytic cracker
(FCC) as it is a chief producer of gasoline, a main reﬁning product especially in the
USA. In terms of methodology, to the best of our knowledge, current state-of-the-art
involves a separate RTO envelope each around a CDU, an FCC, and a
hydrocracker-hydrotreater. In other words, these individual RTO envelopes are
not integrated, and the reason is because the steady state assumption, which is
paramount in RTO, breaks down due to an intermediate storage acting as a buffer
tank for an FCC. The intermediate storage tank causes a time-lag and a buffer in
between an RTO implementation and the FCC, thus accounting for these issues
would have to include assumption of dynamics in modeling the operations. This
implies that we cannot have a steady state assumption for an entire reﬁnery in
attempting to develop and successfully run a plantwide RTO since an FCC is not
operated in such a way due to the presence of a buffer tank. It is believed that there
have been attempts at addressing this problem although without a success story as
yet (Adams and Biroli 2002; Fatora and Adams 1998).
Several work have been reported in the academic literature on the integration of
dynamic models in RTO, called dynamic RTO (D-RTO) with direction optimization
of economics in MPC, called economic MPC (EMPC), see, e.g., Kadam and
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123
Marquardt (2007), Engell (2007), and Pontes et al. (2015). Consequently, theoretical
developments in DRTO and computational efﬁciency can potentially enable
optimization on dynamic models of reﬁnery processes and therefore overcome the
limitations of requiring steady-state models.
1.4.2 Local optima
Reﬁnery optimization problems are almost always nonlinear and nonconvex, which
mainly arise from pooling and blending of streams and from process characteristics
as introduced by reformulated gasoline and other regulatory constraints that often
involve nonlinear and even discontinuous correlations. The presence and number of
local optima seems to depend (among other things) on the nature and extend of the
nonlinearity and nonconvexity of the algebraic constraints (e.g. bilinear and
trigonometric). The majority of the nonconvexities in reﬁnery models stem
primarily from the pooling constraints that typically involve a large number of
bilinear and trilinear terms. Large reﬁnery planning models can have equations on
the order of over a hundred thousand and several million nonzeros, in which a large
number are of bilinear and trilinear forms. Virtually all modern reﬁnery planning
models exhibit multiple local optima for which some forms of global optimization
techniques are required to determine the best solution. Despite the progress attained
in global optimization techniques, it is arguable that there is still a gap between
theory and practice, thus giving rise to the use of ad-hoc and stochastic methods
(more details in Sects. 2.10, 5.2). It is noteworthy that the source of nonlinearity is
not due to the solution technique.
1.4.3 Analytics
Analytical and robust solutions are of the utmost importance in reﬁnery planning as
it is used for decision-making in problems involving several hundreds of millions of
dollars. A customer involved in crude oil procurement would be tempted to ask if at
least some forms of Monte Carlo simulation has been used in the analysis leading up
to a decision on procuring a certain crude. Therefore, the challenge for our PSE
community is to contribute tools to enable robust decision-making in reﬁnery
planning—clearly, the more varied a problem is, the more complex the decision-
making process becomes. As an illustration, while reﬁneries in the Midwestern USA
region are used to operate on a steady diet of crude oil processing, such a situation is
no longer true as these reﬁneries are now also processing heavy oils produced from
Canada, which include shale oil from hydraulic fracturing operations.
Crude oils are purchased based on the total acid number (TAN) property. In
practice, if a reﬁnery is customarily fed with low-TAN crudes, the introduction of
high-TAN crudes may possibly corrode the heat vents and even cause to blow them
up. Thus, a strategy is to blend the low-TAN crudes with those of high-TAN
(assuming that the latter crude types have been considered in the reﬁnery design), in
which such a blending operation calls for the use of analytics such as optimization-
based tools.
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C. S. Khor, D. Varvarezos
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1.5 Academia versus industry, past versus future
The development of reﬁnery models and solution techniques has been driven
signiﬁcantly by industrial applications. Although the initial spark for reﬁnery
optimization was provided by the development of LP-based techniques, the driving
forces of innovation in the area for many decades have come initially from operating
companies in the likes of major integrated petroleum corporations such as Chevron,
Shell, and ExxonMobil, and later from specialized software and systems companies
such as Aspen Technology, Honeywell, and Invensys (now Schneider Electric). It is
therefore unsurprising that most publications in this area until the 1990s are from the
practitioners community as made up of the two said factions (Haverly 1980; Lasdon
and Joffe 1990). Nonetheless, on the whole, a lack of open literature in the area during
that time is in part because companies refrain from publishing due to commercial
sensitivity. This is because they consider, for instance, a faster optimization solver as a
strategic advantage with potentially strong impact on company proﬁtability which still
holds true today. Comparatively, reserves of a certain crude oil is not deemed as
valuable now as it was in the past in terms of a company’s assets. As alluded to, it used
to be the case that reﬁneries in the Gulf Coast of the United States are capable of
handling the processing of almost any crude oil type. However, the availability of high
quality sweet crude oils (i.e., with low sulfur content) are becoming rarer nowadays as
lower quality heavier and sourer crude oils (i.e., with high sulfur content) are
increasingly produced. This has led to an even greater emphasis on modeling as based
on TAN and other properties nowadays as explained earlier.
In the last 20 years or so, there have been signiﬁcant academic interests in
addressing some of the fundamental modeling and optimization problems associated
with reﬁning operations. As a result, there has been an increase in publications
addressing many aspects of reﬁnery operations covering the extended supply chain
(see Table 2) and involves planning (see Table 3), logistics (see Table 4) and
scheduling (see Tables 5, 6), blending (see Table 7), real-time optimization (see
Table 8), and control applications. The issue of integration of models with different
granularities and multiple scales has also been investigated (see Fig. 3). Important
considerations encompassing detailed rigorous nonlinear models, global optimality,
and solution robustness have received attention. Despite increased academic
research in this area, there still remain numerous open problems that need further
work, mainly related to effective global optimization techniques, robust MINLP
solvers, and tactical reﬁnery-wide optimization.
2 Modeling and solution strategies for reﬁnery optimization
To provide an understanding of the past, present, and future challenges of reﬁnery
optimization, we will attempt to systematically analyze reﬁnery operations along
several axes that provide differing perspectives of the modeling and optimization
aspects involved in reﬁnery operations. We begin by discussing the academic versus
industrial perspectives in addressing the problems. We then present the problems
from different optimization approaches, ranging from: (1) formal mathematical
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programming techniques, e.g., LP, MILP, NLP, and MINLP; to (2) methods from
the computer science community, e.g., constraint programming (Mouret et al.
2009); to (3) formal heuristics, e.g., simulated annealing and genetic algorithms; to
(4) customized heuristics and empirical methods, e.g., neural networks and the
various ﬂavors of expert systems.
Subsequently, we discuss the different time scales involved in modeling and
optimizing reﬁnery operations. The time scales typically involve years in strategic
planning (e.g., Fernandes et al. 2013), months in tactical planning (e.g., Khor et al.
Table 2 Representative publications on reﬁnery supply chain
References
Model formulation
Solution strategy
Pitty et al.
(2008), Koo
et al. (2008)
Combined simulation and linear dynamic
optimization framework (with binary
variables)
Considers crude oil supply-transportation
and selection-purchasing; scheduling;
operations management; safety stock;
stochasticity in prices, yields, and
transporation
Decisions on capacity expansion and cycle
times for crude oil procurement and
production
Non-dominated sorting genetic algorithm
with heuristics
Exploits parallel computing for
computational efﬁciency
Rocha et al.
(2009)
MILP on planning of daily crude oil
shipments via tankers or pipelines from
oil platforms to reﬁneries
Fixed charge network ﬂow with discrete
time and space representation
Cuts to handle poor relaxation due to
large optimality gap in changeover
costs
Heuristic for platform ofﬂoading
scheduling to obtain feasible upper
bounds
Local branching to improve branch-and-
bound search
Adhitya et al.
(2007a, b)
MILP with rescheduling of crude oil
operations when original schedule
becomes infeasible due to disruptions
(e.g., delay in arrival) using heuristics
Minimizes rescheduling time and
conﬁguration changes to original
schedule, thus avoids time-consuming
total rescheduling
Decomposes original schedule into
operation blocks to generate a new
feasible schedule
Neiro and
Pinto (2004)
Large-scale multiperiod MINLP
Includes multiple reﬁneries, storage tanks,
and terminals connected by pipeline
network
Continuous variables on ﬂowrates,
properties, and operating variables
(e.g., temperature deviation)
Binary variables on inventory and facilities
assignment and transportation modes
Outer approximation algorithm in
GAMS/DICOPT with CONOPT2 (for
NLP) and OSL (for MILP)
Assesses different scenarios
954
C. S. Khor, D. Varvarezos
123
Table 3 Representative academic publications on reﬁnery planning
References
Model formulation
Solution strategy/remark
Moro et al. (1998),
Pinto et al. (2000),
Joly et al. (2002)
MINLP with example on diesel
planning
Feasible path-based generalized
reduced gradient method in GAMS/
CONOPT
Zhang et al. (2001a)
Considers changes in feed properties
and operating conditions using linear
constraints
Swing cut model for CDU
Models ﬁxed volume per weight
transfer ratios (i.e., percentage of a
CDU fraction over the overall CDU
feed)
Linear models may cause CDU
cutpoints and FCC conversion not to
be optimized rigorously, hence
cannot guarantee product properties
to meet speciﬁcations
Zhang et al. (2001b)
Optimizes the subsystems of the main
material processing system (LP) with
hydrogen network (NLP) and utility
system (MILP) that gives rise to an
overall MINLP model
Considers MILP approximation of the
original MINLP formulation due to
unavailable commercial solver
Demonstrates higher proﬁt for
simultaneous optimization of
individual subsystems versus
sequential approach
Neiro and Pinto
(2005)
Large-scale multiperiod MINLP
Continuous variables on ﬂowrates,
properties, and production and
inventory levels
Binary variables on selection of crude
oil types and facilities assignment for
transportation and distribution
Linear models for crude oil and
product terminals as well as pipelines
Nonlinear models for process units and
properties (e.g., sulfur)
Uses outer approximation algorithm in
GAMS/DICOPT with CONOPT2
(for NLP) and CPLEX (for MILP)
Li et al. (2005)
NLP with single-site empirical models
for CDU, FCC, and product blending
Optimizes CDU cutpoints based on
weight transfer ratios of CDU
fractions
Considers crude characteristics;
product yields and qualities
Regression models based on crude
properties for API gravity, pour
point, and octane number of CDU
distillates
Uses local solver GAMS/MINOS5
Sensitivity analysis on CDU cutpoints,
FCC conversion levels, weight
transfer ratios of FCC and CDU
fractions, and CDU fraction
properties
Li et al. (2007b)
Models consider weight transfer ratio
ranges of CDU fractions
More accurate than traditional linear
models
Faster than rigorous simulations
Petroleum reﬁnery optimization
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2008a, b), weeks in operational planning (e.g., Alattas et al. 2012), days and hours
in scheduling (e.g., Lee et al. (1996)), minutes in real-time optimization (Zanin et al.
2000, 2002), and seconds in advanced and regulatory control systems (Iancu et al.
2013).
For applications in reﬁnery planning and real-time optimization, the models are
typically in steady-state mode. On the other hand, models in scheduling and
advanced process control (APC) typically involve the time element in a discrete
fashion explicitly (as in scheduling) or implicitly (as in APC); continuous-time
scheduling models have also been proposed in the academic literature (Floudas and
Lin 2004; Pinto and Grossmann 1995). From a detailed modeling viewpoint,
reﬁnery optimization applications encompass a wide range of practices ranging
from ﬁrst principles to empirical models, and possibly everything in between.
2.1 Academia versus industry
Apart from applications in blending, there is actually little optimization used in the
industry. Hence there is a signiﬁcant disconnect between the theory developed in
Table 3 continued
References
Model formulation
Solution strategy/remark
Elkamel
et al.
(2008)
Single-site reﬁnery-wide NLP and MINLP
planning models with constraints on CO2
emissions reduction target
Incorporates CO2 emissions mitigation
options of ﬂow rate balancing, fuel
switching, and CO2 capture
Considers a scenario-based approach to
evaluate several combinations of the
mitigation options and various levels of
CO2 emissions reduction targets
Applies a sequential strategy to deduce an
optimal mitigation strategy
Mouret
et al.
(2011)
MINLP that integrates multiperiod reﬁnery
planning with crude oil scheduling using
Lagrangian decomposition
Planning solution determines processing
decisions for assumed aggregated batch
from crude scheduling
Adopts an iterative primal-dual algorithm
to solve Lagrangian dual problem that
generates discrete solution on optimal
selection and sequencing as upper bound
on the global optimal
Alattas
et al.
(2011)
Single period NLP using fractionation index
(FI) for CDU operations
Uses Heaviside function for formulating FI
Uses local solver GAMS/CONOPT3.14c
Compares against CDU models using ﬁxed
yield and swing cut solved using CPLEX
Alattas
et al.
(2012)
Multiperiod extension of Alattas et al. (2011)
MINLP with binary variables on sequencing,
changeovers, and processing times of
crudes oil
Uses traveling salesman problem constraints
to generate crude oil processing sequences
Uses subtour elimination constraints to
address subcycles
Mixed-integer disjunctions for representing
FI
Uses local solver GAMS/DICOPT with
CONOPT (for NLP) and CPLEX (for
MILP)
956
C. S. Khor, D. Varvarezos
123
academia and actual practice in industry. The situation arises largely as a result of
the complexity of daily reﬁnery operations—even today’s state of the art in
simulation techniques are not able to capture an entire reﬁnery’s operations. A case
in point pertains to reﬁnery production scheduling problems in which MILP models
have been proposed (e.g., see Shah and Ierapetritou 2011) but the largely linear
formulations adopted are open to criticisms as regards their practical value.
Optimization within the realm of reﬁnery planning has enjoyed more contribu-
tions from academia, e.g., Neiro and Pinto (2004) and Li et al. (2005). There is also
a collaboration between practitioners and academics involving Aspen Technology
(AspenTech) and Professor Christodoulos A. Floudas of Princeton University, USA
on global optimization of reﬁnery planning problems (Joffe et al. 2005b). But in
reality, the real problem is simply too hard to be solved. According to this paper’s
author (Varvarezos), AspenTech has invested a lot of time in this initiative, but the
Table 4 Representative publications on reﬁnery logistics and distribution planning
References
Model formulation
Solution strategy
Sear (1993)
LP on crude oil purchasing and
transportation, product processing and
distribution, and depot operation
Proposes risk management strategies to
balance short-term cost-savings with
longer-term potential drawbacks of
solution obtained
Escudero et al.
(1999)
Two-stage stochastic LP (with partial
recourse) on multiperiod multiproduct
single-site reﬁnery
Adopts discrete scenarios to represent
uncertain parameters of product demand
and product spot market cost
Employs a splitting variable approach for
the nonanticipativity constraints
Proposes variants of Lagrangean
decomposition and Benders
decomposition on sequential and
parallel computing implementations
Kong and
Shah (2001),
Kong (2002)
MILP with generalized assignment
problem formulation
Assigns terminals to drop zones
representing aggregated customers,
which link secondary distribution
demand to primary distribution level
Integrates crude oil supply with primary
distribution
Heuristic on preprocessing rules to reduce
size of generalized assignment problem
Linearizes original MINLP models for
integrating time representation in
scheduling of reﬁnery production with
distribution
Persson and
Gothe-
Lundgren
(2005)
(See entry in Table 6 for details)
Kunt et al.
(2008)
NLP on multiperiod multisite reﬁnery
integrated with MILP on supply and
distribution network with aggregate
product demands
Adopts sequential and simultaneous
optimization approaches
Uses Aspen PIMS, PIMS Multi-Plant
System (MPIMS), PIMS-AO, Aspen
DPO, and Aspen XSLP
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Table 5 Representative publications on reﬁnery crude oil scheduling
References
Formulation and solution strategy
Remark
Shah (1996)
Discrete time MILP that minimizes heel of
crude left in tank after transfer to CDU
Decomposes into upstream subproblem on
ofﬂoading and storage in portside tanks and
downstream subproblem on charging tanks
and CDU
Restricts a tank to feed only one
CDU at a time and a CDU is fed
by only one tank at a time
Divides scheduling horizon into
equal intervals, so each activity
must start and ﬁnish within
boundaries of intervals
Considers only one dock
Lee et al. (1996)
Discrete time MINLP minimizing operating cost
Employs single-crude storage tanks and mixed-
crude charging tanks
Linearizes bilinear terms resulting from crude
blending using reformulation–linearization
technique (RLT)
Linearization may lead to
composition discrepancy
Disregards feeding to multiple
CDUs or multiple tanks feeding
one CDU
Preﬁxes crude mix ranges in
charging tanks
Li et al. (2002)
Discrete time MILP
Considers: multiple crude types; multiple
berths/jetties; two tanks feeding a CDU
simultaneously
Reduces decisions by disaggregating tri-indexed
into bi-indexed binary variables
Adopts iterative MILP-NLP algorithm to handle
composition discrepancy
Includes heuristics to reduce solution time
Algorithm may not solve the NLP
although solution exists
Joly et al. (2002)
MINLP with discrete and continuous time
Considers settling time for brine removal;
nonlinear crude properties
Continuous-time models offer
ease of representing the
different processing times of
various operations
Kelly and Mann
(2003a, 2003b)
Discrete time model that decomposes into
logistics and quality subproblems (but without
detailed mathematical formulation provided)
Solves logistics MILP using
branch and bound-based
methods to provide initial guess
for solving quality NLP using
successive LP with interior
point and simplex (primal and
dual) methods
Magalha
˜es and
Shah (2003)
Continuous time MILP that considers: crude oil
segregation; non-simultaneous receipt and
delivery of crude by tank
Disregards demurrage and crude
changover
Jia et al. (2003)
Continuous time event-based MILP
Considers crude oil inventory control
Requires fewer variables and constraints than
discrete-time MILP
Approximation of inventory cost
shows good agreement with
actual values
Moro and Pinto
(2004)
Continuous time MINLP with nonlinear crude
oil properties
Minimizes operating cost of crude tank farm
Discretizes continuous variables on crude
amounts in storage units to handle bilinear
terms, resulting in MILP
Discretization leads to
approximate solutions and
increases problem size and
solution time
958
C. S. Khor, D. Varvarezos
123
Table 5 continued
References
Formulation and solution strategy
Remark
Jia and Ierapetritou
(2004)
(See entry in Table 6 for details)
Reddy et al. (2004b)
Discrete time MINLP
Considers: multi-CDUs receiving crude
in multiparcels from very large crude
carriers (VLCCs) via high-volume
single-buoy mooring pipeline and
single-parcel tankers through multiple
jetties; multiple sequential crude
transfers; tank-to-tank transfers
Avoids composition discrepancy and
nonlinearity (MINLP) by solving exact
MILP relaxation repeatedly when
crude composition is constant
Reddy et al. (2004a)
Continuous time MILP for similar
features and assumptions as Reddy
et al. (2004b)
Avoids composition discrepancy by
using iterative solution of a series of
MILPs
Li et al. (2007a)
Discrete time MINLP
Considers blending without charging
tanks of speciﬁed compositions,
resulting in bilinear terms
Incorporates nonlinear crude properties
using linearly additive indices
Iterative-based enhancements to Reddy
et al. (2004b) with partial relaxation
strategy to increase solution speed
Furman et al. (2007)
MINLP with event-based continuous
time
Considers mapping of material balances
with timing constraints
Nonconvex bilinear in component
fractions for blending and pooling
Disregards simultaneous input and
output ﬂow to tank for
multicomponent streams
Reduces number of time events and
binary variables by modeling input
and output within same time event
Karuppiah et al.
(2008)
Applies outer-approximation-based
global optimization algorithm to
Furman et al. (2007)
Solves MILP relaxation of nonconvex
MINLP to obtain lower bound with aid
of cutting planes derived from spatial
decomposition of network to reduce
the solution time
Mouret et al. (2009)
Continuous time MINLP with priority
slots on number of tasks
Considers a schedule as a single
sequence of crude transfer operation
Adopts sequential MILP-NLP solution
procedure
Allows breaking of degeneracies or
symmetries to ease solution
Saharidis and
Ierapetritou (2009);
Saharidis et al.
(2009)
MILP with event-based discrete time
Minimize setup costs for ﬂow among
ports, tanks, and CDUs
Optimizes CDU mixture preparation
type by considering several blending
modes and recipe preparation
alternatives
Proposes linearization approach for
bilinear terms
Adopts valid inequalities on operational
features
Proposed approach requires more
solution time compared to heuristics
based on engineering experience
Adopts valid inequalities based on
operational features to reduce solution
time
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Table 5 continued
References
Formulation and solution strategy
Remark
Li et al.
(2012a)
MINLP with unit speciﬁc event-based
continuous time for similar features and
assumptions as Reddy et al. (2004b)
Adopts piecewise-afﬁne underestimators for
bilinear terms in proposed branch and
bound-based global optimization algorithm
Reduces number of bilinear terms and
problem size compared to Reddy et al.
(2004b) and Li et al. (2007a)
Guarantees integer feasible solutions to be
within 2 % of global optimality
Li et al.
(2012b)
MINLP similar to Li et al. (2012a) addressing
demand uncertainty using deterministic
robust counterpart optimization approach
Convert demand equality constraints to
inequalities
Extends algorithm in Li et al. (2012a) to
obtain preventive schedules that are more
robust
Chen et al.
(2012)
Presents and compares multigrid continuous-
time MINLPs for event-based, unit slot, and
multi-operation sequence (MOS)
formulations
Focusses on inland reﬁneries instead of
coastal reﬁneries
Adopts linearization approach of Lee et al.
(1996) and MILP-NLP procedure of
Mouret et al. (2009); the latter, intended to
study effects of composition discrepancy,
might lead to suboptimality or infeasibility
MOS model is generally superior in terms of
solution time and LP relaxation strength but
results in larger size
Hamisu
et al.
(2013)
Discrete time MILP for similar features and
assumptions as Lee et al. (1996)
Considers: limiting volume variation between
intervals to avoid ﬂuctuating CDU charging
rate; penalty for shutdown within
scheduling horizon; penalty deterring
unnecessary swtiching during crude transfer
from storage to charging tanks
Allows ﬁxing of closing stocks at end of
horizon to allow stretching schedules due to
changes in CDU mixed crude demand from
charging tanks
Allows ﬂexibility through demand violation
when exact demand causes infeasibility
960
C. S. Khor, D. Varvarezos
123
efforts seem to be hitting a hard wall. Although present efforts seem to have stalled,
there are always possibilities that these initiatives might be revisited in the future.
A similar situation is encountered in reﬁnery control, for which there appears to
be limited progress in ongoing research on applications of optimization methods. On
the other hand, despite substantial work in nonlinear process control and adaptive
control, reﬁnery optimization can be largely handled with linear MPC.
Techniques such as the reformulation–linearization technique (RLT) proposed by
Sherali and coworkers (e.g., see Sherali and Alameddine 1992) are still unable to
solve practical models of industrial size. At best, the available techniques and
technologies can handle about 500–1000 decision variables (Meyer and Floudas
2006; Misener and Floudas 2014).
Table 6 Representative academic publications on reﬁnery production scheduling
References
Formulation and solution strategy
Remark
Gothe-
Lundgren
et al. (2002)
Discrete time MILP for scheduling a CDU
with two hydrotreaters to produce bitumen
and naphthenic special oils
Decisions (discrete) on operating mode
Considers: variable run lengths, variable
yields, tank capacities, robustness, and
resource availability
Proposes integration with shipment
planning (see later work by Persson
and Gothe-Lundgren (2005))
Jia and
Ierapetritou
(2004)
Continuous time MILP that decomposes into
three subproblems: (1) crude oil scheduling,
(2) downstream processing and intermediate
tanks, and (3) ﬁnished product blending and
lifting (shipping)
Adopts component balance of Lee et al.
(1996) which may cause composition
discrepancy
Disregards changeover costs
Disregards certain operational
features to likely obtain a problem
size amenable for solution
Persson and
Gothe-
Lundgren
(2005)
Discrete time MILP integrating production
scheduling and shipment planning to
simultaneously optimize routing of ships
with transportation planning of products
Uses column generation, valid
inequalities, and constraint branching
with CPLEX
Shah et al.
(2009)
Continuous time MILP minimizing total
makespan
Decomposes a full space so-called centralized
problem into decentralized subsystems at
intermediate storage tanks
Integrates solution of decentralized
subsystems to obtain solution to original
centralized problem
Decomposition strategy involves
fewer variables and constraints,
thus lowers solution time
Shah and
Ierapetritou
(2011)
Continuous-time MILP with integrated
production scheduling and product blending
Considers minimum run-lengths; ﬁll-draw-
delay; one-ﬂow out of blender; sequence-
dependent switchovers; maximum heel
quantity; product quality downgrading
Includes valid inequalities to
improve solution convergence
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123
Table 7 Representative publications on reﬁnery product blending
References
Model formulation
Remark
Pinto et al.
(2000)
Discrete and continuous time MINLP for
planning and scheduling with blending
Considers blending relations based on
simple yield relations, conservation
principles, and constitutive relations
Uses linear blending correlations
Disregards most nonlinear product
properties (e.g., sulfur)
Considers transitions in blending pipelines
but disregards constant blend rates and
minimum run lengths
Glismann and
Gruhn
(2001)
Two-level decomposition of NLP on
product recipes and quantities; MILP on
blend scheduling for the ﬁxed recipes
MILP solution is used to impose new
constraints on NLP to allow recipe
changeovers
Joly and Pinto
(2003)
MINLP for fuel oil/asphalt scheduling
Similar features and assumptions as Pinto
et al. (2000)
Jia and
Ierapetritou
(2003)
Continuous time event-based MILP for
simultaneous gasoline blend scheduling
and order delivery
Considers: multipurpose product tanks;
one product tank delivering multiple
orders; multiple product tanks delivering
one order
Disregards multiple parallel nonidentical
blenders; variable recipes; product
speciﬁcations
Mendez et al.
(2006)
MILP with discrete time and slot-based
continuous time for gasoline blend
scheduling and recipe optimization
Considers parallel identical blenders;
Does not consider: constant blend rates,
multipurpose tanks, setup times for
blender switching, order delivery time
windows
Uses nonlinear correlations that increases
complexity
Solves linearized MILPs iteratively using
linear correlations
Li et al.
(2010)
Continuous time MINLP with process
slots for simultaneous optimization of
recipe, blending, storage, and order
scheduling
Considers: multipurpose product tanks;
parallel nonidentical blenders; constant
blend rates; one blender charging at most
one product tank at a time; minimum run
lengths
Uses linear property indices (instead of
nonlinear correlations)
Adopts piecewise constant component
ﬂows and qualities via multiperiod
formulation
Solves reformulated MILPs assuming
constant blend rates using a schedule
adjustment procedure
Li and Karimi
(2011)
Multigrid continuous-time MINLP using
unit slot formulation
Similar features and assumptions as Li
et al. (2010)
Considers: simultaneous receipt-delivery
by product tanks; blender setup times;
limited component inventories
Employs revised schedule adjustment
heuristic of Li et al. (2010) for unit slots
Unit slots can handle larger problems and
more computationally efﬁcient than
process slots
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2.2 Optimization approaches: mathematical programming
versus heuristics
Figure 2 qualitatively illustrates a spectrum of approaches for reﬁnery optimization
problems. Progressing along the spectrum from left to right corresponds to an
increase in the formality of an approach as regards to amenability to mathematical
proofs. On the left extreme are rule-based informal heuristics such as expert
Table 8 Commercial software for reﬁnery applications with real-time optimization capabilities (Ma-
halec and Marlin 2006)
Software
Developer/vendor
Remarks
Opera
Shell
Released in 1986 for implementation in
ethylene cracking plants
Aspen DMO (Dynamic
Matrix Optimization)
Suncor
First application in 1988 for a hydrocracker
in the Sunoco Sarnia Reﬁnery in Ontario,
Canada
A precursor to linear model predictive
control (MPC)
ROMeo (Rigorous Online
Modeling with equation-
based optimization)
Schneider electric
(formerly Invensys
and SimSci-Esscor)
Uses a single-model concept for process
simulation, data reconciliation, and
optimization, which minimizes costs of
model building, implementation, and
maintenance
Currently used at ExxonMobil reﬁneries
Aspen DMCplus
Aspen Technology
Employed for model predictive control
(MPC) application (not for RTO)
Originally developed as DMC (Dynamic
Matrix Control) by DMC Corporation
(DMCC) Cutler and Ramaker (1979),
Cutler and Ramaker (1980)
Incorporated in Aspen Plus (release 11),
which has an MINLP solver Aspen
Technology (2013a)
Aspen plus optimizer
Aspen Technology
Employs equation-based closed-loop
optimization
Originally developed by Dow chemicals for
application in multi-train ethylene plants
Resulted from the merging of DMCC’s
DMO and AspenTech’s RTOpt (Real-
Time Optimizer) systems
Provides a comprehensive framework for
CLRTO (closed-loop real-time
optimization)
Encompasses several operating modes of the
same model (simulation, optimization,
parameter estimation, data reconciliation)
NOVA optimization and
modeling system
Honeywell
Originally developed by dynamic
optimization technology (DOT), Inc.
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systems. On the right extreme are the formal traditional types of pure mathematical
programs such as LP, NLP, MILP, and MINLP, which are largely mathematically-
provable. This category also includes generalized disjunctive programming (GDP)
and decomposition-based approaches such as Benders (1962) and outer approxi-
mation (Belotti et al. 2013; Viswanathan and Grossmann 1990). It is noteworthy
that a limitation of mathematical programming is in part due to a lack of a
comprehensive MINLP solver.
On the other hand, formal heuristics mainly involve stochastic methods such as
simulated annealing and genetic algorithms. They contain some stochastic elements
of which some are provable. Next, constraint logic programming (CLP) (Hooker
et al. 1994; Jain and Grossmann 2001; Maravelias and Grossmann 2004; Mouret
et al. 2009; Raman and Grossmann 1994) can be considered to occupy a middle
position in the spectrum between the two extremes. But it is worth noting that CLP
may not work well with the continuous domain, as shown by Hooker (2005).
Stochastic programming can be placed one position to the right of CLP since for
most cases, it can be viewed as mathematical programming with probabilistic
elements.
On the whole, the left-hand side of the spectrum tends toward methods that are
more speciﬁc to a particular problems structure with tailored solution strategy. On
the other hand, methods toward the right-hand side are more abstract, hence they are
more widely applicable as general purpose types of solver. In terms of reﬁnery
optimization problems, reﬁnery planning is more amenable towards the implemen-
tation of the more general mathematical programming-based techniques as
compared to scheduling problems. By nature, scheduling is inherently a more
discrete problem relative to planning. The difference has to do more with the
formulation rather than the solution, and as a result, scheduling is a more speciﬁc
problem with relatively greater complexity while planning assumes a higher
abstraction level. This is in part because scheduling involves many aspects of
sequencing, so the use of pure mathematical programming techniques such as MILP
and MINLP becomes necessary. It is noteworthy that simulation is a generally
applicable method for scheduling. Although reﬁnery scheduling is by and large done
with little formal optimization methods, there are a few notable exceptions of formal
(MINLP) optimization in some particular areas of reﬁnery scheduling. The most
prominent is product blending whereby MINLP optimization methods are routinely
applied in practice. As an industrial example, Aspen MBO is a multiperiod
multiproduct MINLP-based event-oriented blending optimization system widely
used by most major oil companies to address complex blending problems
Formal heuristics (e.g., 
simulated annealing, 
genetic algorithms)
Constraint logic 
programming
Stochastic
programming 
Informal heuristics/rule-
based (e.g., expert 
systems)
Formal mathematical programming 
(e.g., LP, NLP, including GDP and 
decomposition-based approaches)
Amenability to 
mathematical proofs
Fig. 2 A spectrum of approaches for reﬁnery optimization
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C. S. Khor, D. Varvarezos
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(Varvarezos and Janak 2012). In addition to blending, other functional areas in a
reﬁnery scheduling setting that utilize mathematical programming techniques to aid
scheduling are crude oil blending and dock scheduling (Varvarezos 2013b).
2.3 Short-term versus long-term temporal consideration
Figure 3 shows a hierarchy of standard optimization-based decision support systems
in reﬁnery operations.
An important aspect in optimization-based support systems for decision-making
in reﬁnery operations is temporal consideration for the time horizon in various
application scales
Information control
Regulatory control, HSE 
& equipment protection
Real-time database software (e.g., PI, Honeywell PHD, Aspen InfoPlus); plant 
instrumentation (e.g., DCS, PLC, analyzers, real-time database); emergency 
shutdown; sensor and actuator validation; limit checking for fault detection
For single and multiple units: control loop performance monitoring; alarm 
management (HSE: health, safety, and environment)
Multivariable & 
constraint control
Multivariable control; model predictive control; advanced process control (APC) 
(e.g., DMC Plus, RMPCT)
Real-time optimization 
(RTO)
For plantwide and individual unit: supervisory control; parameter estimation; 
data reconciliation
Supply chain management: Planning & scheduling of raw materials and 
products; demand forecasting
Site-wide
optimization
Scheduling
Measurement & actuation
PID (proportional, integral, & derivative) control loops, cascades, inferred 
properties
Time scale:
a: execution frequency
b: modeling granularity
c: moving horizon size
a: weekly–monthly
b: weeks–months
c: 1–3 months
Tactical Planning
a: daily
b: hours
c: 2–4 weeks
Strategic Planning
a: yearly
b: years
c: 3–5 years
a: hourly–daily
b: hours
c: hours
a: minutely–hourly
b: seconds–hours–days
c: 10 minutes–1 hour
a: secondly–minutely
b: minutes
c: minutes
a: secondly
b: seconds
c: seconds
a: decisecondly
b: deciseconds
c: deciseconds
a: milisecondly
b: miliseconds
c: miliseconds
Process
Fig. 3 A hierarchy of optimization-based decision support systems in reﬁnery operations (Sources
include Aspen Technology 2011b)
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As depicted in Fig. 3, the time scales involved mainly account for the following
aspects:
1.
the frequency of execution, which measures time intervals at which decisions
are taken by running a system;
2.
the modeling granularity, which concerns intervals of the time period variables
considered in a system;
3.
size of the moving horizon, which pertains to how far one looks into the future
in terms of the slices of time periods under consideration.
2.4 Steady-state versus dynamic operating condition
Reﬁnery optimization is still largely carried out using steady-state models
particularly for applications revolving around planning and scheduling activities.
Nonetheless, full-space dynamic models are increasingly considered for use with
RTO applications (e.g., see Sildir et al. 2012).
2.5 First-principles versus empirical model types
There are two broad types of models as that based on ﬁrst principles and on
empirical approaches. First-principles models account for multicomponent mass and
energy balances, reaction kinetics, vapor-liquid equilibrium expressions. On the
other hand, empirical models are adopted to describe some effects that are not easily
modeled, e.g., detailed reaction kinetics and hydraulic effects. Pantelides and
Renfro (2013) provides a review on the use of ﬁrst-principles models in online
applications (the latter subject is discussed in Sect. 3.8).
2.6 Design versus operations
Reﬁnery design (or design in general, for that matter) is not usually performed using
optimization although rigorous MINLP-based approach for process synthesis has
been proposed (e.g., see Kocis and Grossmann 1989; Daichendt and Grossmann
1998). Design is mostly carried out using a case study approach involving the use of
multiple case studies from past design projects. Nonetheless, such a standard
industrial practice should not be an indication that design cannot be effectively
addressed via an optimization approach as has been demonstrated in a number of
works (e.g., see Khor et al. 2010; Khor and Elkamel 2010).
2.7 Whole versus subsection optimization
Most previous work on reﬁnery optimization up to recently still deal with the
subsections instead of an entire reﬁnery such as crude oil scheduling (e.g., see
Yadav and Shaik 2012), production planning (e.g., Park et al. 2010), gasoline
blending (e.g., Li and Karimi 2011), and crude oil evaluation and selection (e.g.,
Pitty et al. 2008; Koo et al. 2008). Optimization of the whole reﬁnery systems or the
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overall supply chain is still lacking in the literature mainly due to limitations
imposed by the size of the resultant large-scale optimization problems.
2.8 Online versus ofﬂine optimization
At present, reﬁnery-wide optimization is still largely performed on an ofﬂine basis
although RTO utilizes online optimization. It is worth noting that efforts are
ongoing to formalize implementation of online optimization to operating proce-
dures, fault tree analysis, startup and safety procedures as well also to process
monitoring.
2.9 Linear versus nonlinear models
It is arguable that linear (mixed-integer) models are simply more straightforward
and more robust than nonlinear ones although they lack rigor in capturing details
such as physical properties of crude oils and reﬁned products, which are often
nonlinear. On the other hand, nonlinear (mixed-integer) models provide more
detailed representation and are clearly the way forward where modeling strategies
are concerned. Hence, there remains a need to strike a balance with respect to the
accuracy desired that comes at the expense of the resulting computational burden.
2.10 Pooling problems and global optimization
Pooling problems occur in almost all reﬁnery models in which the streams are co-
mingled or mixed. The problem mainly arises because a reﬁnery has only a limited
number of component storage tanks. Materials of different compositions are usually
mixed before being used further. Typical examples include pooling of naphtha for
reformer feed, mixing of components prior to gasoline blending, and pooling of low
sulfur gasoil components. The streams are characterized by a set of qualities such as
speciﬁc gravity and parafﬁn, oleﬁn, naphthene, and aromatic (PONA) content that
blend linearly with ﬂow rates.
Pooling can have a signiﬁcant effect on reﬁnery operations, therefore it is
desirable to represent this tank allocation issue in a planning optimizer. However, it
is not a trivial problem because the composition is not known beforehand, hence
giving rise to a nonlinear and nonconvex bilinear problem (Adhya et al. 1999;
Quesada and Grossmann 1995).
If pooling is not included in a planning optimizer, skewed results can be obtained
that cannot be implemented in practice. On the other hand, sometimes it is faulty to
incorporate pooling because it may generate too pessimistic a solution, which is a
problem reported as early as the paper by Haverly (1978). A guideline is to model
what can be achieved in reality in an operational setting. To illustrate, assume that a
single pool is set up for all reformate, and that reformate is used to make gasoline at
various octane levels. This means that the LP is forced to use the same reformate for
low- and high-octane gasoline that can lead to the optimizer reporting giveaway on
the low-octane-grade product. If the reﬁnery is able to vary the reformate octane
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depending on the ﬁnal destination, this situation should be modeled using two
separate pools: one for low-octane and another for high-octane gasoline.
Global optimization techniques can be mainly categorized as deterministic and
stochastic or statistical methods (Tawarmalani and Sahinidis 2002). In general,
stochastic methods can handle larger problems, while deterministic methods are
more computationally expensive and is thus limited by practical barriers (notably
problem size). Nevertheless, a possible drawback is the lack of theoretical
fundamentals with respect to the stochastic methods as compared to its deterministic
counterpart.
3 Modeling and optimization of units and processes
At the unit level and the associated reﬁning processes, rigorous ﬁrst-principle
models are employed as optimizers to improve operations and maximize
proﬁtability of reﬁneries. To achieve reﬁnery-wide optimization (RWO), it is
imperative to ensure model consistency across the activities of planning, scheduling,
and RTO in order to integrate them by linking the respective optimization models to
the planning LP (Mudt et al. 2006). Efforts toward this direction are largely carried
out through open equation-based modeling (Darby et al. 2011).
As indicated, the key to reﬁnery-wide optimization (RWO) lies in the proper
integration of the three main components which currently exist as separate
applications:(1) the feed forward information required by the model predictive
controllers (at the time scale of minutes); (2) the steady state information as inputs
to and outputs from the online optimizers (in minutes); and (3) the higher level
information as inputs to and outputs from the reﬁnery LP (in days). It may be
noteworthy that intermediate tanks can present particular problems due to time scale
complications (Mudt et al. 2006).
Crude distillation unit (CDU) is one of the most important units from both
operation and optimization viewpoints. In general, the primary units of crude and
vacuum distillation units are modeled using the cutpoint scheme as based on stream
true boiling points (TBP). Crude oil assay management system software (e.g.,
Haverly’s H/CAMS and H/COMET) (Haverly Systems 2013a) uses TBP cuts and
TBP curves of crude oils from various assay database to generate crude oil-wise
yields and properties. A single physical crude oil distillation unit may be modeled as
several logical units depending on a reﬁnery’s speciﬁc requirements; for instance, a
reﬁnery processing high sulfur (HS) and low sulfur (LS) crude oils may be
respectively modeled as two separate logical units.
At the planning level, the most common CDU representation is the one referred
to as the ‘‘heart and swing cut’’ formulation besides the classical ﬁxed-yield
approach. In this approach, the original crude oil assay data is expressed in terms of
a number of main streams, or heart cuts, with associated swing cuts that facilitate
incremental yield adjustments to achieve optimization of the cutpoints for the main
products from the CDU. Representative work in this area include Li et al. (2004),
and Zhang et al. (2001a). Recent papers by the group of Ignacio Grossmann at
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Carnegie Mellon University, USA proposes the use of fractionation index as an
alternative approach (Alattas et al. 2011, 2012).
On the other hand, the secondary units are modeled by employing delta-yield
vector modeling or mode-wise yield vectors. For example, a catalytic cracker is
generally modeled by setting up base yield vectors with yield-controlling delta
vectors for the UOP K-factor, mean average boiling point, and severity, i.e.,
conversion from one primary component to another. The static input data for
determining the delta vectors can be generated from kinetic models, test runs, and
standard correlations, while relevant capacity and quality constraints on the feed and
product side are conﬁgured.
In this regard, Mudt et al. (2006) describes the application of rigorous models in
Aspen FCC, an open-equation ﬂowsheet-based optimizer, for closed-loop RTO at
Suncor’s Sarnia reﬁnery in Canada. The optimization results inform how Suncor
should operate its Houdriﬂow catalytic cracker, and are used in conjunction with the
reﬁnery (LP) planning models, over a next time period (typically 3 months) to
minimize the overall reﬁnery’s cost while meeting customer demand. In essence,
single-unit rigorous models—and combinations of models similar to those being
used in Sarnia—can be used ofﬂine to quantify nonlinear relationships between
important reﬁnery units, such as the FCC and its associated feed pretreater. These
relationships are important particularly in the planning for clean fuels production,
particularly in selecting and designing processes for hydrogen production and deep
desulfurization.
4 Supply chain
There are two major approaches for representing a reﬁnery supply chain. First, the
reﬁnery is represented as a nonlinear model which renders the overall model weaker
at addressing the logistical details (see for example, Neiro and Pinto 2004, 2005;
Aspen XPIMS 2005). Second, the reﬁnery is represented as a simple linear model,
but the model as a whole has richer information on the logistics (see for example,
Aspen Petroleum Supply Chain Planner 2013d).
There are in general many instances in which multiple reﬁnery and petrochemical
sites are modeled as multisite complexes with nonlinear modeling structures
pertaining to these facilities. However, if the primary distribution modeling is
extended to cover detailed transportation modes (such as marine and rail) and
includes all the associated costs and constraints instead of aggregated material
transfer representation, then the resultant reﬁnery models tend to become simpliﬁed
and take on linear forms (Kunt et al. 2008). In such cases, linear programs are
employed chieﬂy because the planning tools used do not model the transportation
details adequately and less so due to inherent limitations of the optimization
technology.
In the context of enterprise-wide optimization, the role of reﬁnery models has
been traditionally limited to linear abstractions too (see, e.g., Rocha et al. 2009).
Only subsystems of the reﬁnery supply chain have been investigated at a level of
detail that is practically signiﬁcant due to the resulting complexity and intractability
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of simultaneously considering the entire supply chain (or multiple parts of it) via
detailed often nonlinear representation. Nonetheless, as evident from the work cited
in Table 2, MINLP has also been adopted in academia to model reﬁnery supply
chains. Despite extensive studies on various subsystems of the reﬁnery supply chain,
it is arguable there still exist gap in the literature on optimization of the overall
supply chain.
Commercial packages in general adopt a similar approach of either to expand
nonlinear models for the components of a supply chain using a bottom-up approach,
or to adopt linear models (using a top-down approach that covers the supply chain to
the extent the models can be possibly solved. Aspen XPIMS, which is a multiperiod
multisite version of Aspen PIMS, is capable of optimizing the transportation
decisions using simpliﬁed linear representations with lower ﬁdelity models for
reﬁnery planning purpose (Aspen Technology 2005). On the other hand, a package
such as Aspen Distribution Planning Optimization (or Aspen DPO) considers
whether to include tanker transportation in a model (Aspen Technology 2011c). It is
also known (from the authors’ personal experience) that major petroleum companies
such as ExxonMobil and Shell have employed linear models for entire supply chain.
Although the reason for such uses has more to do with historical practice rather than
technical reasons, LPs still offer the advantages of being more straightforward and
more robust.
5 Planning
A reﬁning company typically prepares a number of different plans throughout a
planning cycle that vary in scope in two important ways: (a) time horizon; and
(b) the actual decisions made (degrees of freedom). A common breakdown of the
typical plans is as follows (Khor and Elkamel 2013):
•
strategic planning: 5-year plans involving expansion projects;
•
annual planning: plans to support several key activities and decisions such as
annual budgeting, crude oil term contracts, and major maintenance planning
(i.e., shutdowns);
•
monthly planning: rolling horizon plans to support opportunity and/or crude oil
spot purchases and to align reﬁnery operations with product demands. The plans
often span multiple periods in weeks or months;
•
weekly planning: plans that are optimized operating strategies for process units
on a weekly basis and are sometimes viewed as scheduling activities. Under
such a scenario, the crude oils are known and decisions revolve around which
crude oil mix to run and for how long in order to meet speciﬁc product delivery
commitments. The plans often span multiple periods in days or weeks;
•
engineering planning: plans that are mainly directed towards proﬁtability
improvement involving plant-level modiﬁcations and revamp projects. There is
no explicit timeframe associated with this activity.
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C. S. Khor, D. Varvarezos
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As described earlier, reﬁnery planning models have evolved from single-site LP to
multisite multiperiod MINLP formulations. Table 3 provides a summary of some
representative work addressing this problem in academic publications.
5.1 Tools and workﬂow process
Planning is a multifaceted activity as depicted in a generic workﬂow process and
methodology in Fig. 4. The ﬁrst step of data assembly automates data sourcing from
multiple enterprise systems to create optimization cases. The latter second step,
typically involving LPs, receives information that includes demands, prices, costs,
operating parameters, capacities, and blend speciﬁcations. The results generated are
then analyzed (step 3), incorporating multiple users and perspectives, by using
advanced analytics to highlight and address problems such as imbalance in the
inventory supply and demand for a location-speciﬁc material pool. Finally, the plan
is published or synthesized as a new case for reoptimization (step 4), hence the
whole cycle is iterative in nature (particularly steps 2 and 3 as shown in Fig. 4). The
overall cycle typically may take one week up to 30 days and can be expedited by
automating several steps with available technology (Thomas et al. 2009).
5.1.1 Strategic planning
The goal of reﬁnery long-term strategic planning is to identify the optimal timing,
location, and extent of additional investments in a reﬁning network over a ﬁve-year
time horizon, resulting in capacity expansion of production and/or distribution. The
optimization model developed typically requires approximate and aggregated data.
5.1.2 Tactical planning
Reﬁnery medium-term (midterm) tactical planning addresses planning horizons
involving months in a typical aggregation of three-to-six months. It consolidates
features from both strategic and operational scheduling models, including in terms
1. Data assembly for case creation
2. Optimization
3. Information analysis
4. Publishing of plan
Reoptimization
Fig. 4 Workﬂow process and
methodology for reﬁnery
planning
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of the quanta and accuracy of data required, but with different degrees of freedom.
For instance, it accounts for the carryover of crude oil inventory over time and
various key resource limitations, much like the short-term scheduling models—an
example is in deciding the type of crude oils to buy and the timing. On the other
hand, similar to strategic planning models (and unlike the operational models), it
accounts for multiple production sites in a supply chain. However, with their
typically large and complex manufacturing facilities, reﬁneries may also have a
tactical planning process for each manufacturing site to coordinate activities across
major units. The tactical planning models derive their value from this overlap and
integration of modeling features. It may be noteworthy that crude oil evaluation lies
between strategic and tactical planning.
5.1.3 Model building
Some of the major considerations in model building for reﬁnery optimization
include the level of detail required for the type of decisions desired. An example
concerns the modeling of an FCC. Available approaches include using nonlinear
empirical models (Guerra and Le Roux 2011; Li et al. 2005) and a lumping
technique for feedstock and product characterization to reduce problem size, which
are adopted in the simulators Aspen FCC (Aspen Technology 2012a) and FCC-SIM
(KBC Advanced Technologies 2013a). Another example is the different types of
formulations that can be employed to model a CDU, which include swing cut, heart
cut, and shortcut formulations (see also Li et al. 2004).
In representing physical reﬁning units, standard commercial reﬁnery planning
systems such as RPMS typically employ a base plus delta type of linear formulation
that utilizes correction vectors (or also known as shift vectors). A more general
consideration involves deciding whether to adopt polynomial-based relations or
more rigorous models. An example of the latter is the integration of external
nonlinear models from the Aspen PIMS Simulator Interface software in the Aspen
PIMS-AO package (Aspen Technology 2013e).
5.1.4 Optimization under uncertainty
To address uncertainty in the model data and parameters, reﬁnery optimization is
executed by case analysis involving hundreds of examples and scenario evaluations.
For instance, in crude oil evaluation, uncertainty is handled by considering different
pricing scenarios to account for price sensitivity. However, uncertainty is not
addressed systematically through formal methods, but it is handled in a more
empirical manner. For example, simultaneous optimization incorporating the use of
Monte Carlo simulation and/or other more advanced analytics is not typically
practiced. It is noteworthy that the general procedure of managing this issue is quite
similar to the way reﬁnery design is performed, i.e., by using a case analysis
approach.
Academic publications in reﬁnery planning under uncertainty have largely
involved a similar approach in which the two-stage stochastic programming
framework is adopted and reformulated as multiscenario models. Discrete scenarios
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are considered as obtained from historical data (e.g., see Khor et al. 2008a) or
randomly generated by statistical approaches such as sample average approximation
(e.g., see Pongsakdi et al. 2006; Al-Qahtani and Elkamel 2010).
Khor et al. (2008b) and Khor (2010) address single-site reﬁnery planning under
uncertainty in demands, yields, and prices coupled with risk management. A range
of statistical measures is adopted as proxies for risk, namely variance, mean-
absolute deviation, and conditional value-at-risk. The formulation entails a two-
stage multiobjective stochastic LP with weighted sums of the objective function
cost- and risk-based terms. The results indicate that plans with increased robustness
are obtained although at the expense of a higher expected total cost.
Similarly, Pongsakdi et al. (2006) presents a stochastic reﬁnery LP to handle
uncertainty in demand and product prices for a real-world operating site in Thailand.
The model largely adopts the formulation of Pinto et al. (2000) and is extended with
the ﬁnancial risk management approach (in the sense of Barbaro and Bagajewicz
2004). The authors assert that the model allows a plan with lower risk to be
generated without signiﬁcant loss in the expected proﬁt or the upside potential of
higher proﬁts.
The work of Al-Qahtani and Elkamel (2010) proposes integration and
coordination strategies for multisite reﬁneries under uncertainty in demands and
prices. The authors employ two-stage stochastic MINLP with nonlinearity
introduced by the use of variance for risk management, giving rise to a robust
mean-variance formulation (in the sense of Mulvey et al. 1995). Efﬁcient frontier
plots are utilized to analyze the results, which show that plans are relatively more
sensitive towards variability in prices (of crude and products) rather than demand
variations.
5.2 Global optimization
The possibility of local optima has been a concern to reﬁnery planners because of
the risk of obtaining an unreliable solution leading to an unfavorable economic
decision. However, there is no systematic way of knowing a priori the existence of
better solutions than the ones achieved unless a particular model is solved from
different starting points and yields different solutions.
Most reﬁnery planning packages use two or three starting points and then select
the resulting best solution computed. On the other hand, Aspen PIMS employs three
levels of global optimization (Joffe et al. 2005b) in which the ﬁrst level entails use
of proprietary heuristics that exploit the particular mathematical structure of bi- and
trilinear terms in conjunction with the physical meaning of those variables. The
second level starts with the heuristic methods aforementioned and applies stochastic
methods involving statistical multi-start techniques with merit and proximity ﬁlters
to solve an NLP from multiple randomly generated initial points (Ugray et al. 2009).
The third level is a combination of heuristics, stochastic methods, and convex
relaxations. The latter relaxed LP problem (see, e.g., Akrotirianakis and Floudas
2004, 2005) is often augmented with additional linear reformulation–linearization
technique (RLT) constraints (Sherali and Alameddine 1992) to compute improved
upper bounds of the global optimum. The approach is basically a deterministic
Petroleum reﬁnery optimization
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method but leverages on other techniques as offered by heuristics and stochastic
methods. However, the approach is limited by problem size, which is not an
uncommon hurdle.
Due to challenges encountered with the deterministic methods, AspenTech relies
primarily on the custom heuristics explained earlier with the augmentation of the
stochastic multi-start methods. This combination seems to address the majority of
local optima in practice.
6 Logistics and distribution planning
Another extension of reﬁnery optimization involves the logistical issue of
distribution of the reﬁned products in the downstream segment of the petroleum
industry. Primary and secondary distribution planning considers ﬂows of products
from reﬁneries to terminals and thereafter to gasoline ﬁlling stations (or ‘‘gas
stations’’) or convenience stores, as illustrated in Fig. 5. Primary distribution uses
bulk modes of transportation, e.g., pipelines, barges, and ship tankers, including rail
lines or railcars for specialty products, with typical transit times of several days.
Secondary distribution mainly involves trucks and typically involves transit times in
the order of hours. Therefore, in reality, primary distribution is a different problem
from its secondary counterpart with each addressed using a different algorithm.
Decisions in primary distribution planning pertain to which terminals are needed
and their optimal locations, as well as allocation of the terminals to reﬁneries and
the gas stations to terminals, hence giving rise to MILPs. On the other hand,
secondary distribution is mainly concerned with scheduling of trucks, allocation of
compartments in trucks, and sequence of unloading of trucks. As such, secondary
distribution deals with smaller volumes but tends to involve hundreds of thousands
of points on the network, thus it is mainly driven by heuristics with a fairly small use
of LP (Kong 2002; Kunt et al. 2008). It is arguable that the current state-of-the-art in
this aspect of reﬁnery optimization mimics industrial reality. As summarized in
Table 4, model formulations in representative work cover crude oil supply
(purchasing and transportation), reﬁnery processing, and distribution to customers.
Primary
Distribution
Gas station
Terminal
Refinery
Secondary
Distribution
Fig. 5 General scheme of primary and secondary distribution planning in the downstream petroleum
industry
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C. S. Khor, D. Varvarezos
123
7 Scheduling operations
7.1 Crude oil scheduling
Crude oil scheduling (or blend scheduling) is employed for both strategic and
tactical planning purposes. Processed crude oil compositions have the largest impact
on a reﬁnery’s proﬁt margin (Reddy et al. 2004b). Thus, there is incentive in
optimizing the crude oil blend scheduling problem to control the quality of the crude
oil charge for processing in the CDUs.
The economic beneﬁts and operability improvement expected from optimizing
this front-end operations, as espoused by Reddy et al. (2004b), include: improving
the options of using less expensive as well as premium crude oil stocks, reducing
demurrage costs, enhancing consumption and throughput, decreasing yield and
quality giveaways, and augmenting the control and predictability of downstream
processing.
The problem comprises: (1) crude oil unloading from pipelines and/or tankers
(vessels) to storage tanks; (2) crude oil transfer from storage tanks to charging tanks;
and (3) charging of various crude oil mixtures to the CDUs subject to limitations on
ﬂow, composition, properties, and capacity. The main issues that have been
addressed include:
•
the discrepancy in computing the concentration arising from crude oil blending
due to a mismatch between the composition delivered by the storage tanks and
that actually received by a CDU;
•
changeover costs due to crude oil class or tank changes, which results in
production losses and creation of off-speciﬁcation products known as slops; and
Real-time data
Steady-state condition
Data reconciliation (update parameters) 
Optimization
To APC systems
Fig. 6 Stages in an online
closed-loop real-time
optimization cycle
Petroleum reﬁnery optimization
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•
operational features such as multiple jetties for considering several tankers
simultaneously, multiple tanks (typically at most two) feeding one CDU, one
tank feeding multiple CDUs (typically at most two), and settling time for brine
removal after crude oil receipt.
Representative publications on reﬁnery crude oil scheduling are summarized in
Table 5. It is worth noting the problem has gained the interest of major academic
research groups in process systems engineering. Global optimization has also been
applied in crude oil scheduling using outer approximation-based methods (Karup-
piah et al. 2008) and MILP-based piecewise afﬁne relaxation techniques (Li et al.
2012a, b).
7.2 Reﬁnery production scheduling
The focus of reﬁnery production scheduling is on key units at a single site that
encompass the fractionators and reactors including the thermal, catalytic, and
hydrocracking units to convert heavy into light hydrocarbons, as well as the
hydrotreating, hydrodesulfurization, continuous catalytic reforming, alkylation, and
delayed coking units. Scheduling of the units involve handling continuous
processes, intermediate component storage, and recycle streams (Shah et al.
2011). It is relatively less important overall given the cost-beneﬁt analysis in terms
of its complexity and maintainability, which is somewhat evidenced by a smaller
number of academic publications as exempliﬁed in Table 6. Published work largely
involve MILP models (not MINLP) mainly due to challenges presented by the
problem size and as demonstrated for crude oil scheduling (in the foregoing
section), continuous time formulations have been found to be more efﬁcient than
discrete time in modeling real-life events without unnecessary discretization.
7.3 Product blending
Blending is the most mature and evolved yet still complex aspect in reﬁnery
optimization. It is optimized to ensure component blending meets blend targets
across the scheduling period, is on time to avoid consequential ship demurrage, and
done without quality giveaway with reduced transitions and minimum slop
generation. Blending optimization is performed by taking into account constraints
on operations and logistics to meet demand requirements in terms of order delivery
and increased customer satisfaction, so as to improve asset utilization and
productivity while exploiting inferior quality distillation fractions.
Early work addresses gasoline blending planning as in the development of
Texaco’s NLP-based OMEGA and Starblend decision support system (Dewitt et al.
1989; Rigby et al. 1995). The focus is to produce optimal blends of various reﬁnery
intermediate products with some additives to satisfy end product quality speciﬁ-
cations and demand requirements. More recent work has incorporated real-life
operational features such as multipurpose product tanks, parallel nonidentical
blenders, constant rates during blending runs, and minimum run lengths. Table 7
summarizes representative academic publications in this area.
976
C. S. Khor, D. Varvarezos
123
Finished products have many speciﬁcations but it is not necessary to attempt to
model all of the speciﬁcations via the planning LP. Those that must be included are
speciﬁcations that could potentially limit the reﬁnery operations either via unit
operations (e.g., control of gasoil desulfurization) or blending (e.g., fuel oil
viscosity). Particularly for low volume grades, the optimizer solution will
sometimes show very skewed and unrealistic blends, often with only a small
increase in objective function. To prevent such a behavior, it is suggested to put
some wide bounds on the overall blend composition, e.g., a minimum–maximum of
25–60 % for platformate in gasoline. Such an approach enables the blends generated
by the optimizer to be more useful in scheduling.
Recently in modern commercial packages, such as Aspen Multi-Blend Optimizer
(Aspen MBO) version 7, this problem has been addressed more elegantly and
without the need to impose arbitrary recipe constraints by simultaneously solving
the minimum recipe deviation problem superimposed to the traditional cost- or
revenue-driven objective function. The resulting penalty-based formulation has
been shown to stabilize the recipes of the same grade across the entire campaign
with minimal (and user-controlled) impact to the overall blend campaign economics
(Aspen Technology 2012b). However, it is important to monitor the ﬁnal blend: if
the constraints show high values or the overall blend shows giveaways on a
normally limiting speciﬁcation, then the bounds need to be revised.
Blend constraints are also required to model physical constraints in the blending
process such as maximum liqueﬁed petroleum gas (LPG) content; to handle non-
modeled properties by limiting the amount of suspect streams; to model scheduling
constraints, e.g., limitations on component tankage; when downgrading components
(e.g., fuel or bitumen) due to transition pipe cleaning; and when a certain
speciﬁcation is not modeled within the optimizer. Where appropriate, linear blend
indices should be used to handle nonlinearity in the blends, e.g., see Li et al. (2010).
8 Real-time optimization
RTO problems are essentially NLPs that mainly rely on SQP-based dynamic matrix
optimization solver capable of handling a few millions of variables in a few
seconds. If a problem does not involve discrete decisions, then it can readily use an
RTO solver. It is noteworthy that the last decade’s developments of interior-point
methods for NLPs, particularly IPOPT, may potentially render these methods to be
more suitable for such large-scale RTO problems (e.g., see Wa
¨chter and Biegler
2006; Wang et al. 2011).
The scope of real-time optimization (RTO) is typically limited to a single major
unit in a reﬁnery application. In some applications, RTO may cover multiple units
although not to the extent of an entire reﬁnery—the limitation is evident when
reﬁnery-wide optimization is considered (as elucidated in Sect. 12).
The two major uses of RTO for reﬁneries are to perform: (1) scenario analysis by
executing so-called ‘‘ad-hoc’’ optimization; and (2) online closed-loop RTO
(CLRTO) (Mudt et al. 2006; Pedersen et al. 1995) and ofﬂine open-loop RTO (in
advisory mode). The major challenges of RTO largely reside within CLRTO, in
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which a typical feature is the presence of a rigorous ﬁrst-principles-based equation-
oriented model with nonlinear characteristics that is solved as a steady–steady
model using, for instance, sequential quadratic programming (SQP)-based algo-
rithms (Darby et al. 2011). There are several hundred measurements modeled as
well as those that are connected to online measurements for use in calibrating an
RTO model.
There are two optimization executions in every CLRTO cycle: (1) the parameter
estimation or data reconciliation case; and (2) the economic optimization case. The
ﬁrst case has as many as three variations depending on the degree of modeling and
workﬂow sophistication, as explained in the following:
1.
parameter estimation: no degree of freedom involved, instead a number of
measurements is used, directly or using biases, to calculate a corresponding set
of parameters. The problem is posed and solved as a set of nonlinear equations;
2.
data reconciliation: the degree of freedom involved is given by a number of
optimization variables. A set of measurements, typically large, is used to
calibrate the model. An optimization case with a weighted form of least squares
as the objective function is employed to minimize the errors between measured
and calculated values;
3.
data reconciliation with gross error detection: this is a variant of the former with
an additional set of terms in the objective function responsible for effectively
‘‘rejecting’’ gross errors by virtue of the dynamic weights on the errors
becoming signiﬁcantly smaller than the rest of the objective terms.
A CLRTO cycle typically spans 10 min to 1 h and involves the following stages:
(1) predicting and detecting steady-state condition (e.g., by bound checks or if
signals pass speciﬁed tests); (2) obtaining or retrieving the real-time data; (3) data
reconciliation for updating the parameters by a least squares method or through
parameter estimation; (4) optimization to compute the decision variables; and (5)
writing to pass targets to the underlying advanced process control (APC) systems at
the unit level (Fig. 6).
The two most important reﬁning units are crude distillation unit (CDU) and ﬂuid
catalytic cracking (FCC). For the same reasons, they are also the most important
units for RTO applications in terms of both optimizing vertically when considering
the units per se and optimizing horizontally when considering the intersection of
RTO with planning. Other units that are important from an RTO perspective include
hydrocracking, hydrotreating, and reforming as well as the aromatics.
Table 8 provides a list of commercial RTO software. It is noteworthy that Aspen
DMO is the ﬁrst commercial software package developed for optimization
applications without degree of freedom, which is needed because it quickly
becomes impractical for a user to deﬁne all of the thousands of variables that make
up the degrees of freedom for an RTO problem.
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C. S. Khor, D. Varvarezos
123
9 Software for reﬁnery planning and scheduling
Decision support systems and software have been developed for various reﬁnery
optimization applications from the inception of RPMS in the 1960s (as introduced
earlier in this paper). The underlying architecture of most software largely is a
combination of modeling language and matrix generator with a database orientation.
The following is a partial list of software for reﬁnery planning and scheduling
mainly developed by commercial entities, provided in some semblance of
chronological order:
•
RPMS (Reﬁnery and Petrochemical Modeling System) by Honeywell Process
Solutions (originally developed by Bonner and Moore): Provides multisite
reﬁnery-wide planning using automatic recursion SLP; multiperiod model with
capacity availability and seasonal quality speciﬁcations via intermediate storage;
multi-plant model including petrochemical plants in a single production and
supply model with sharing of raw materials and product pools; considers
transportation including distribution logistics (Bonner and Moore 1979);
•
Aspen PIMS and Aspen PIMS-AO (Process Industry Modeling System—
Advanced Optimization) by Aspen Technology (originally developed by Basil
Associates): Features enterprise-wide linear and mixed-integer nonlinear
planning; multiperiod multi-plant models for feedstock selection, product slates,
plant design, and operational planning; employs successive LP; interfaces with
rigorous process simulator models; PIMS-AO employs a proprietary MINLP
solver (Aspen XSLP) (Aspen Technology 2011c);
•
Aspen Petroleum Scheduler by Aspen Technology: Addresses scheduling and
optimization of crude oil receipts using simulation, LP, and expert systems;
crude oil and feedstock scheduling, process operations, product blending, and
product shipping (Aspen Technology 2013c);
•
Aspen MBO (Multi-Blend Optimizer) by Aspen Technology: Performs ofﬂine
blend scheduling and optimization of gasoline, distillates, fuel oils, and other
products using MINLP (solver is Aspen XSLP); optimizes recipes for individual
blends and aggregates blends into time partitions; scheduling of short- and long-
term campaigns using event-based multiperiod multi-blending models; consid-
ers nonlinear blending correlations, tank constraints, discrete volume and recipe
constraints, discrete tank line-up constraints, events (e.g., blends, product
shipments, intermediate component receipts, and tank-to-tank transfers); auto-
matic generation and simultaneous solution of blending processes (Aspen
Technology 2012b);
•
Aspen Fleet Optimizer (formerly Aspen Retail) by Aspen Technology:
Optimizes order management, demand forecasting, fuels inventory management,
replenishment planning, transportation scheduling, and delivery execution
management using LP and heuristics (Aspen Technology 2013b);
•
aspenONE Planning and Scheduling for Reﬁning and Marketing by Aspen
Technology:
Comprises
all
of
the
aforementioned
software
by
Aspen
Technology;
Petroleum reﬁnery optimization
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•
OmniSuite by Haverly Systems: Supports supply chain management of
integrated planning and scheduling covering crude oil supply, reﬁning, product
blending, and product distribution using distributive recursion; multiperiod
multireﬁnery multilocation formulation with recipe blending; enables trans-
portation, inventory, and investment planning; comprises OMNI model man-
agement system, GRTMPS (Generalized Reﬁnery-Transportation-Marketing
Planning System) optimization system, H/SCHED scheduling system, and
H/CAMS (Haverly Crude Assay Management System) (Haverly 2001; Haverly
Systems 2012, 2013a, b);
•
MIMI (Manager for Interactive Modeling Interfaces) by Aspen Technology
(originally developed by Chesapeake): Consists of a family of supply chain
solutions using SLP; CDU models operate on modes (not swing cuts)
representing ﬁxed operations in terms of crude oils and cutpoints (Jones and
Baker 1996);
•
PETRO Linear Programming by Invensys Production Management (originally
developed by Chevron in 1970s for their reﬁneries): Enables reﬁnery-wide
planning using distributive recursion (AllBusiness 2013);
•
PLATOFORM by ExxonMobil: Performs reﬁnery-wide planning and product
sales using LP; deploys single reﬁnery and chemical plant models as well as
regional models for coordinating several reﬁneries or chemical plants; considers
product distribution through intermediate facilities, distribution facility invest-
ment, drilling rig scheduling, portfolio investment, block operations scheduling,
truck ﬂeet sizing, and vessel scheduling (Palmer et al. 1984);
•
RefSim by Shell: Features reﬁnery production planning using LP; modeling of
blending operations has limited operational details (Pantelides and Renfro
2013);
•
ProPlan and ProSched by Ingenious (a Wood Group Mustang Company):
Handle reﬁnery-wide planning and scheduling using optimization and genetic
algorithms (Ingenious 2016a, b);
•
SCHED LP by OMV Vienna, Austria: Performs reﬁnery-wide scheduling based
on continuous time formulation using mixed-integer programming and dynamic
programming-based recursion (Hofferl and Steinschorn 2009; Steinschorn and
Hofferl 1997);
•
REFOPT by Centre for Process Integration, University of Manchester: Supports
reﬁnery-wide planning and scheduling using LP, NLP, simulation; also covers
analysis and design of hydrogen source and sink network (Centre for Process
Integration 2013);
•
VisualMesa Petroleum Reﬁning and Terminals Solution by Soteica Visual
Mesa: Addresses reﬁnery planning using LP and scheduling that encompasses
production accounting, crude oil feeds and fuel products blending, pipeline and
marine scheduling, and terminal scheduling (Soteica Visual Mesa 2015);
•
PrincepsLP Reﬁnery Planning Solution and ﬂowers Reﬁnery Scheduling Solution
by PRINCEPS: Offers multisite and multiperiod planning NLP models (using
SLP) with more accurate logistics and stream pooling representation; provides
simulation-based scheduling application with local optimization around a process
unit for crude oil scheduling and blend scheduling (PRINCEPS 2016a, b);
980
C. S. Khor, D. Varvarezos
123
•
IMPL (Industrial Modeling and Programming Language) by Industrial Algo-
rithms: Enables reﬁnery planning and scheduling optimization including the
coordination of logistics and quality aspects through discrete time representation
by using LP, NLP [particularly quadratic programming (QP)], and heuristics
with capabilities for handling MILP and MINLP formulations (Industrial
Algorithms 2016; Menezes et al. 2015).
Several major oil and gas companies and consultancies have developed their own
proprietary reﬁnery simulators, mainly in sequential modular mode and for steady-
state applications, with ongoing efforts to incorporate optimization capabilities. A
few examples in this regard include PETROX by Petrobras (Niederberger et al.
2005) and Petro-SIM Reﬁning by KBC Advanced Technologies (2013b).
10 Advanced process control
Since the early 1980s, linear optimization techniques have been applied to process control
problems aiming to stabilize dynamic disturbances while at the same time optimizing key
processperformancecharacteristicssuchasmaximizingthroughput.Thepioneeringwork
of Charles ‘‘Charlie’’ Cutler that started at Shell Oil (Cutler and Ramaker 1979) and
resulted in the creation of the DMC controller (Cutler and Ramaker 1980) and the DMC
Corporation, set the stage for advanced process control for the following decades and to a
great extent even up to today. While numerous improvements and modiﬁcations to the
originalalgorithmhavebeenproposedandimplemented,thebasicpremiseistoformulate
the problem as a linear program (LP) or a quadratic program (QP) to optimize the steady-
state process model. The resulting model is empirical in nature and is typically derived
from step test techniques, whereby a key process input variable is moved up and/or down
(i.e., stepwise) from a base operating condition and the resulting effects on other variables
are measured. This technique leads to the creation of the steady-state gains, a matrix that
mimics the partial derivatives of output variables with respect to input variables.
Provisions for uncertainty are handled by heuristics to make the LP or QP more robust.
Despite the success of nonlinear controllers in the chemical and polymer industries, the
linear model predictive control (MPC) system is at the heart of almost all reﬁnery
controllers(DarbyandNikolaou2012).Thissituationisprimarilyduetothelongtradition
and successful track record of applying linear MPC (for example, the Aspen DMCplus
technology has been successfully deployed to over 5000 controllers in reﬁneries
throughout the world). The above development combined with the particular patterns of
reﬁning processes (for instance, the lack of highly nonlinear dynamics) make the need for
a nonlinear approach unnecessary.
11 Reﬁnery-wide optimization: the Holy Grail
Throughout the history of reﬁning operations, it is arguable the drive for
comprehensive optimization on both a meaningful scope and timeframe has re-
mained elusive. Despite progress on the three fronts of mathematical modeling
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techniques, solution algorithms, and computing resources, reﬁnery-wide optimiza-
tion (RWO) is still largely compartmentalized and fragmented. The most likely
platform and starting point for RWO is the scheduling level, which contains both the
required detail and scope for this task. Yet to date, reﬁnery-wide scheduling is
primarily done through simulation and case analysis with isolated pockets of
optimization mainly around crude oil blending operations and ﬁnished products
blending operations [as partly evidenced in a commercial software package such as
Aspen Reﬁnery Multi-Blend Optimizer (MBO) (Aspen Technology 2012b)]. As
alluded to, these activities have been utilizing multiperiod multi-event mixed-
integer nonlinear optimization for the last decade. For product blending in
particular, these developments have been driven by complex speciﬁcations on fuels
(e.g., gasoline and diesel) as mandated by regulatory bodies such as the U.S.
Environmental Protection Agency (EPA), the California Air Resources Board
(CARB), and the European Commission in managing greenhouse gas emissions, as
well as due to increased competition in the global commercial arena.
RWO has long been successfully done only at the planning level using simple
models that preserve mass balances on all the important streams, with fair
representations of the most important stream properties in terms of bulk qualities.
The tracking and use of these qualities vary signiﬁcantly depending on the level of
modeling sophistication, with the most common ones involving sulfur, octane
number, and viscosity. Nonetheless, RWO remains a challenging problem that is yet
elusive on a daily basis, particularly with respect to addressing the differences in
time scales, inventory, dynamics, and modeling details required for all ‘‘optimized’’
aspects of reﬁnery operations. Although it appears that the problem has been
addressed in some publications, there is no known case that this has been
implemented in actual practice within a production capacity. Hence, it is fair to
assert that simulation-based scheduling remains the status quo as largely carried out
through tools such as spreadsheets developed in-house, while optimization is still
most commonly performed at the process unit level as is done via RTO, APC, and
blending schedules.
12 Concluding remarks
In the last ﬁfty years, progress in modeling and optimization techniques in tandem
with advances in computing has made a profound difference in the way reﬁneries
are designed and operated. Reﬁneries were early adopters of information
technology, and they have utilized it to a large extent despite the inherent
conservative nature of the overall business that stems from the large capital
involved coupled with the inherent complexity of operating such capital-intensive
assets. There is a basis for an optimistic view of a continuous role for reﬁnery
optimization in the coming decades.
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123
Appendix
See Tables 2, 3, 4, 5, 6, 7, and 8.
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Citation: Oliveira, M.S.d.; Lizot, M.;
Siqueira, H.; Afonso, P.; Trojan, F.
Efﬁciency Analysis of Oil Reﬁneries
Using DEA Window Analysis,
Cluster Analysis, and Malmquist
Productivity Index. Sustainability
2023, 15, 13611. https://doi.org/
10.3390/su151813611
Academic Editors: Francesco Nocera
and Gerardo Maria Mauro
Received: 21 July 2023
Revised: 5 September 2023
Accepted: 7 September 2023
Published: 12 September 2023
Copyright:
© 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed
under
the
terms
and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
sustainability
Article
Efﬁciency Analysis of Oil Reﬁneries Using DEA Window
Analysis, Cluster Analysis, and Malmquist Productivity Index
Maiquiel Schmidt de Oliveira 1
, Mauro Lizot 2
, Hugo Siqueira 3
, Paulo Afonso 4
and Flavio Trojan 3,*
1
Department of Physics, Statistics and Mathematics, Federal University of Technology,
Francisco Beltrão 85601-970, Brazil; msoliveira@utfpr.edu.br
2
Department of Management of Organizations, Leadership and Decisions, Federal University of Parana,
Curitiba 80060-000, Brazil; mauro.lizot@unochapeco.edu.br
3
Department of Industrial Engineering, Federal University of Technology, Curitiba 84017-220, Brazil;
hugosiqueira@utfpr.edu.br
4
Algoritmi Research Centre, Department of Industrial Engineering and Systems, University of Minho,
4800-058 Guimarães, Portugal; psafonso@dps.uminho.pt
*
Correspondence: trojan@utfpr.edu.br
Abstract: Oil and gas reﬁneries play a key role in the economies of countries by providing energy to
various industrial sectors. A lack of an integrated efﬁciency analysis procedure, in many industries,
could signiﬁcantly impact the planning of sustainable industrial structures and operations. It also can
inﬂuence company performance and competitiveness, and, eventually, negatively compromise the
fuel supply process. All these problems taken together might negatively impact the environment and
sustainable practices. Studies of efﬁciency in the oil industry can help to reduce its environmental
and social impact and to achieve long-term green transition goals. In this work, the data envelopment
analysis (DEA) method was used to present improvement goals for production units, based on
efﬁciency indexes. Furthermore, the DEA window analysis model, integrated with the Malmquist
index and cluster analysis, was used to evaluate efﬁciency and the factors that explain the differences
between reﬁneries in a number of timeframes. A numerical analysis was carried out with data
collected from 12 Brazilian oil reﬁneries between 2012 and 2020, using DEA window analysis, cluster
analysis, and the Malmquist index.
Keywords: oil refineries; DEA window analysis; Malmquist productivity index; production
efficiency analysis
1. Introduction
Reﬁnery efﬁciency is extremely important in the oil and gas supply chain because
petroleum products are needed by a diverse range of sectors as well as for industrial and
commercial activities worldwide that depend on reﬁned products [1]. Thus, an efﬁciency
analysis of oil and gas reﬁneries is also essential because it directly affects the building
of projects and planning of operations, helping reduce their environmental impact and
avoiding a lack of supplies. Managers need to know how efﬁciency levels change over
time in order to plan sustainable building projects and to avoid losses of performance and
problems with fuel supply.
According [2] improvements in the efﬁciency of the oil industry can reduce envi-
ronmental and social impacts. It consequently helps countries that have oil-dependent
economies to achieve goals regarding long-term green transition. In addition, good efﬁ-
ciency analysis allows managers to know how the reﬁneries are being operated, compared
to expected productivity goals. However, the current techniques used to evaluate oil and
gas reﬁnery efﬁciency are applied individually, without ﬂexibility to analyze variables and
scenarios for long-term planning.
Sustainability 2023, 15, 13611. https://doi.org/10.3390/su151813611
https://www.mdpi.com/journal/sustainability
Sustainability 2023, 15, 13611
2 of 19
Data Envelopment Analysis (DEA) is a technique commonly used to analyze efﬁciency;
it helps in evaluating performance and in the promotion of benchmarking in this sector.
Several studies have highlighted the importance and applicability of this technique [3–6].
The main advantage of the DEA model is that it reveals inefﬁciencies in production unit
targets, initially identiﬁed by levels of inefﬁciency. Improving these targets can lead to
corrective actions that in turn enable the elimination of the causes of inefﬁciency. Another
aspect that can be exploited in this context is an analysis of technical inefﬁciency aspects,
which shows how a given product could have its efﬁciency increased without the addition
of new inputs or technologies, thus leading to an opportunity for low-cost improvement [6].
In this context, DEA window analysis (DEA-WA), which is derived from the DEA
technique, can help to analyze the efﬁciency of reﬁneries in different periods, associated
with indicators that can inﬂuence the models, in order to know the differences in reﬁnery
structures between countries [7]. Also, a complete efﬁciency analysis on this topic should
consider aspects of global structured buildings and relationships between reﬁneries and
their groupings.
Thus, this study analyzed the efﬁciency of 12 Brazilian oil reﬁneries between 2012 and
2020 using DEA window analysis (DEA-WA), cluster analysis (CA), and the Malmquist
index (MI), in order to demonstrate the advantages of the integrated use of these techniques,
mainly to provide information for long-term production planning.
The research hypothesis of these experiments was related to the application of data
envelopment window analysis, the Malmquist index, and cluster analysis in an output-
oriented approach to enable the identiﬁcation productivity and efﬁciency behaviors in
different periods, incorporating an analysis of the technological progress of the units.
Also, this study was expected to support the decision-making process for the sustainable
management of reﬁneries.
2. Literature Review
The models commonly used to evaluate efﬁciency, in general, are based on the DEA
methodology, and its applications are related to existing DEA models, i.e., CCR [8] and
BCC [9], which consider the constant return of scale (CRS) and variable return of scale
(VRS), respectively. The majority of studies found in the literature and used to analyze
efﬁciency in reﬁneries have also used DEA, but they have applied it individually or to
speciﬁc periods. They have provided just a partial or punctual view of efﬁciency analysis
and may not show a complete analysis of this topic.
Some of these studies can be cited, such as that of [2] which analyzed the efﬁciency of
nine oil reﬁneries in Iran, using two-stage DEA-SBM (slacks-based measure) between 2011
and 2015. The results showed a low efﬁciency of the analyzed reﬁneries and pointed to the
necessity of production improvement through the acquisition of new technologies. The
study of [6] evaluated the efﬁciency of twelve Indian oil reﬁneries from 2011 to 2016 using
input-oriented DEA-BCC and the Tobit model. In this study, no reﬁnery was fully efﬁcient,
and just three reﬁneries had efﬁciency rates over 95%. The factors pointed out as potential
solutions were a feasibility of renewable energy sources and a reduction in the production
of oil with high sulfur content.
According [1], was veriﬁed the operational performance of 696 units in oil and gas
reﬁneries from 2008 to 2017, divided into four global clusters (USA and Canada, Europe,
Asia-Paciﬁc and Africa and the Middle East), using an input-oriented DEA and DEA-DA
(discriminant analysis). The results showed that the USA and Canada cluster outperformed
the other three clusters and that this performance was close to the operations of American
oil companies with vertical integration, increased proﬁts, and low risks.
In the work of [10] was analyzed the environmental efﬁciency of 50 oil companies in
the USA from 2012, separating them into independent and integrated companies. This
approach contributed to verifying the corporate sustainability of companies, given that
integrated companies outperformed independent companies in terms of corporate sustain-
ability [11,12].
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In the paper of [5] was proposed two efﬁciency measures (operational and environ-
mental) for seven integrated oil companies and twenty-seven independent oil companies
in the USA using the UEN (uniﬁed efﬁciency under natural disposability) method, which
prioritizes environmental efﬁciency, and the UEM (uniﬁed efﬁciency under managerial
disposability) method, which prioritizes operational efﬁciency, as well as the Kruskal–
Wallis test for the two types of uniﬁed measures in the period between 2011 and 2015. The
results showed that independent companies had a lower efﬁciency index when compared
to integrated companies, since the latter operate around the world, needing to meet global
environmental protection standards and measures. In the work of [13] was analyzed the
efﬁciency of seven Indian oil reﬁneries from 2010 to 2018 using DEA-BCC besides ordinary
least squares (OLS), generalized least squares random effect (GLS), and the Tobit model to
explain the determining factors in the variation of efﬁciency indexes. The results showed
that two reﬁneries were the most efﬁcient in the analysis and that three variables could
explain the variations in the indexes: reﬁnery structure, utilization rate, and distillate yield.
In the study of [14] was created an index to analyze the environmental efﬁciency
of ten Brazilian reﬁneries using the DEA-CCR and BCC models. The inputs used were
“percentage of idleness of the plant in operation” and “amount of water consumed”,
and the products were “reﬁnery production volume” and “efﬂuents generated, desirable
and undesirable”.
Although several authors have already used these techniques to analyze oil reﬁneries
or for other applications [3,15,16], and with these techniques only used for uninterrupted
periods [17]. The way to improve these analyses is to integrate other technologies like the
Malmquist index (MI) (Malmquist, 1953), which is used to analyze technological efﬁciency
(catch-up effect) and progress (frontier shift effect). Thus, carrying out studies that include
MI together with DEA will allow us to ﬁll the gaps in both models.
In the study of [18] were applied DEA and the Malmquist index to assess the efﬁ-
ciency of seven Indian oil reﬁneries between 1996 and 2011. Their study revealed that all
reﬁneries showed productivity improvements and that the effect of economic reforms on
total factor productivity (TFP) was 8.6 per cent. In [19] was carried out an environmental
and operational performance assessment combining DEA with the Malmquist index for
the period from 2005 to 2009, considering 17 oil companies from different regions of the
world. In general, these companies improved their environmental performance through
ecotechnological progress and/or managerial innovation, but not operational performance,
showing that one type of performance had no effect on the other.
In the paper of [17] was evaluated the technological innovation efﬁciency (TIE) of
ten reﬁnery plants of the Daqing Petroleum Company from 2012 to 2015 using an input-
oriented DEA-BCC model and the Malmquist index. The results showed that the company
had a high level of TIE and its TFP had fallen each year. Furthermore, it was found
that technological progress decreased more than comprehensive technological efﬁciency,
showing that the decline in TFP was mainly due to insufﬁcient technological progress.
In [20] was found that, despite the growing number of publications involving DEA,
there are few publications using this technique in the petroleum sector. Our literature
review only found 33 publications in this area from 1992 to 2015.
This study used DEA-WA integrated with MI and cluster analysis, which offer the
possibility of analyzing DMUs (decision-making units) in speciﬁc periods, unlike what
happens with the models currently used. No study was found combining these two
techniques to look at oil reﬁneries. The development of the DEA-WA technique is attributed
to [9], whose proposal aimed to analyze the variations of relative technical efﬁciency,
considering each DMU over time, as a distinct unit. This technique allowed for the analysis
of efﬁciency stability as well as of the sensitivity of technical efﬁciency scores and the
efﬁciency trends of DMUs.
In [10] was studied the performance of the USA oil industry and analyzed independent
companies using DEA-WA from an energy and environmental point of view, using three
business implications. In the ﬁrst one, under environmental disposability, the efﬁciency of
Sustainability 2023, 15, 13611
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oil companies showed an inability to reduce drilling and production operations. The second
one showed pressure from regulations and stakeholders in relation to air pollution, with the
growth rates of efﬁciency in relation to managerial disposability not showing great changes
over time. The implications of business concerns were conﬁrmed by regression analysis.
In addition, the grouping (or clusters) of reﬁneries according to their efﬁciency indexes
can improve the analysis and trend deﬁnitions for the sector. In the study of [21] was
performed an analysis of the changing trends in total factor productivity (TFP), technical
efﬁciency (TE), and technological progress (TP), based on eight clusters from China’s oil
and gas industry, measuring their capacity for technological innovation. Some ﬁndings
were that TFP growth rates in three provinces (Heilongjiang, Gansu, and Shanxi) decreased
signiﬁcantly; the TFP growth rates in other provinces and regions increased for a long
period of time, followed by a slight decrease in the last two cycles; in Heilongjiang, Gansu,
and Shaanxi, where the growth rate of TFP decreased signiﬁcantly, the efﬁciency index
increased and the technical progress index decreased notably; and clusters based on mineral
resources had to constantly work on technological innovation.
Brazilian Oil Reﬁneries: Contextualization
According to data from the National Agency of Petroleum, Natural Gas and Biofuels
(ANP), in 2020 Brazil was the highest-ranking oil producer in South America. The proven
reserve of oil in the world reached the mark of 1.7 trillion barrels in 2020, with Brazil
having a proven reserve of 11.9 billion barrels in that year. The Brazilian reﬁning capacity
represents 2.3% of the world capacity, with South America representing 6.2% of this total,
as illustrated in Figure 1:
the analysis of eﬃciency stability as well as of the sensitivity of technical eﬃciency scores 
and the eﬃciency trends of DMUs. 
In [10] was studied the performance of the USA oil industry and analyzed inde-
pendent companies using DEA-WA from an energy and environmental point of view, 
using three business implications. In the ﬁrst one, under environmental disposability, the 
eﬃciency of oil companies showed an inability to reduce drilling and production opera-
tions. The second one showed pressure from regulations and stakeholders in relation to 
air pollution, with the growth rates of eﬃciency in relation to managerial disposability 
not showing great changes over time. The implications of business concerns were con-
ﬁrmed by regression analysis. 
In addition, the grouping (or clusters) of reﬁneries according to their eﬃciency in-
dexes can improve the analysis and trend deﬁnitions for the sector. In the study of [21] 
was performed an analysis of the changing trends in total factor productivity (TFP), 
technical eﬃciency (TE), and technological progress (TP), based on eight clusters from 
China
s oil and gas industry, measuring their capacity for technological innovation. Some 
ﬁndings were that TFP growth rates in three provinces (Heilongjiang, Gansu, and Shan-
xi) decreased signiﬁcantly; the TFP growth rates in other provinces and regions increased 
for a long period of time, followed by a slight decrease in the last two cycles; in Hei-
longjiang, Gansu, and Shaanxi, where the growth rate of TFP decreased signiﬁcantly, the 
eﬃciency index increased and the technical progress index decreased notably; and clus-
ters based on mineral resources had to constantly work on technological innovation. 
Brazilian Oil Reﬁneries: Contextualization 
According to data from the National Agency of Petroleum, Natural Gas and Biofuels 
(ANP), in 2020 Brazil was the highest-ranking oil producer in South America. The proven 
reserve of oil in the world reached the mark of 1.7 trillion barrels in 2020, with Brazil 
having a proven reserve of 11.9 billion barrels in that year. The Brazilian reﬁning capacity 
represents 2.3% of the world capacity, with South America representing 6.2% of this total, 
as illustrated in Figure 1: 
 
Figure 1. Oil-reﬁning capacity in the world (million barrels/day) (adapted from ANP, 2021). 
In Brazil, oil reﬁneries are managed by Petrobras, the main Brazilian company in this 
ﬁeld. According to the ANP statistical yearbook (2020), there are 18 oil reﬁneries in Brazil 
with the capacity to process 2.4 million barrels/day, 13 of which are managed by 
Petrobras, which corresponds to 98% of the country
s total reﬁning capacity. One of these 
reﬁneries, called Replan (SP), has the largest installed capacity, at 434 thousand bar-
Figure 1. Oil-reﬁning capacity in the world (million barrels/day) (adapted from ANP, 2021).
In Brazil, oil reﬁneries are managed by Petrobras, the main Brazilian company in this
ﬁeld. According to the ANP statistical yearbook (2020), there are 18 oil reﬁneries in Brazil
with the capacity to process 2.4 million barrels/day, 13 of which are managed by Petrobras,
which corresponds to 98% of the country’s total reﬁning capacity. One of these reﬁneries,
called Replan (SP), has the largest installed capacity, at 434 thousand barrels/day or 18%
of the national capacity. Others, called Manguinhos (RJ), Riograndense (RS), Univen (SP),
and Dax Oil (BA), are private reﬁneries. The volume of oil produced in the world in 2020
fell by 6.9% compared to the previous year, representing a total of 88.4 million barrels/day.
Brazil had a production of approximately 3 million barrels/day in 2020, occupying the
ninth position in the world [22].
Sustainability 2023, 15, 13611
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3. Materials and Methods
To deﬁne the efﬁciency index formulation, the following inputs were considered: area
(km2), production capacity (barrel/day), number of entry points, production units, oil
storage capacity (m3), and derivatives storage capacity (m3). The product considered was
production (barrel/day).
To perform the selection of variables, the Pearson correlation coefﬁcient was calculated
between all inputs and the product. After that, a model was deﬁned representing the
relationship between the product and inputs with the highest correlation coefﬁcient. One
new input was added at a time until average efﬁciency indicated an increase. Subsequently,
a new model was deﬁned considering inputs with the second-highest correlation coefﬁcient.
After that, the output and new inputs were added until average efﬁciency increased. The
model was tested for all inputs whose correlation coefﬁcient with the output was greater
than 0.5. Finally, the model with the highest average efﬁciency was considered in the
analysis. Efﬁciencies were calculated for the period from 2012 to 2020, using the DEA-
WA model formulations, proposed by [23], as presented in equation (12). Subsequently,
the values were used for the Malmquist index (MI) to evaluate technical efﬁciency and
technological progress, based on the formulations proposed by [24] and expanded by [25]
as a theoretical concept of production analysis, allowing for productivity measurement
with variations over time and for evaluating changes in DMU efﬁciency.
In order to present the results of the Malmquist indexes, the reﬁneries were organized
into clusters, using the Ward linkage method and Euclidean distance. This procedure was
performed based on a study by [26] which presented Ward’s method (proposed by [27]) as
a hierarchical cluster analysis procedure. With this method, production units were grouped
and added to the analyzed variables. A summary of the applied methods is presented in
Figure 2:
rels/day or 18% of the national capacity. Others, called Manguinhos (RJ), Riograndense 
(RS), Univen (SP), and Dax Oil (BA), are private reﬁneries. The volume of oil produced in 
the world in 2020 fell by 6.9% compared to the previous year, representing a total of 88.4 
million barrels/day. Brazil had a production of approximately 3 million barrels/day in 
2020, occupying the ninth position in the world [22]. 
3. Materials and Methods 
To deﬁne the eﬃciency index formulation, the following inputs were considered: 
area (km²), production capacity (barrel/day), number of entry points, production units, 
oil storage capacity (m³), and derivatives storage capacity (m³). The product considered 
was production (barrel/day). 
To perform the selection of variables, the Pearson correlation coeﬃcient was calcu-
lated between all inputs and the product. After that, a model was deﬁned representing 
the relationship between the product and inputs with the highest correlation coeﬃcient. 
One new input was added at a time until average eﬃciency indicated an increase. Sub-
sequently, a new model was deﬁned considering inputs with the second-highest correla-
tion coeﬃcient. After that, the output and new inputs were added until average eﬃciency 
increased. The model was tested for all inputs whose correlation coeﬃcient with the 
output was greater than 0.5. Finally, the model with the highest average eﬃciency was 
considered in the analysis. Eﬃciencies were calculated for the period from 2012 to 2020, 
using the DEA-WA model formulations, proposed by [23], as presented in equation (12). 
Subsequently, the values were used for the Malmquist index (MI) to evaluate technical 
eﬃciency and technological progress, based on the formulations proposed by [24] and 
expanded by [25] as a theoretical concept of production analysis, allowing for produc-
tivity measurement with variations over time and for evaluating changes in DMU eﬃ-
ciency. 
In order to present the results of the Malmquist indexes, the reﬁneries were orga-
nized into clusters, using the Ward linkage method and Euclidean distance. This proce-
dure was performed based on a study by [26] which presented Ward
s method (proposed 
by [27]) as a hierarchical cluster analysis procedure. With this method, production units 
were grouped and added to the analyzed variables. A summary of the applied methods 
is presented in Figure 2: 
 
Figure 2. Summary of the integration of the methods. 
3.1. DEA-Window Analysis Model (DEA-WA) 
The purpose of window analysis is to evaluate the eﬃciency of DMUs over a period 
of time. In this model, eﬃciency is compared to each deﬁned speciﬁc period of time 
(window analysis). It provides a deeper and more speciﬁc temporal analysis and can 
show variations in the eﬃciency of each DMU. These “time windows” are deﬁned based 
on moving averages, wherein a new period is considered and the oldest one is removed. 
In the work of [28] was stated that biased results can be generated if DMUs are 
compared in a single period of time. They may also require a more comprehensive eﬃ-
ciency analysis. Therefore, the use of the window analysis model can be promising in 
their case. 
Figure 2. Summary of the integration of the methods.
3.1. DEA-Window Analysis Model (DEA-WA)
The purpose of window analysis is to evaluate the efﬁciency of DMUs over a period
of time. In this model, efﬁciency is compared to each deﬁned speciﬁc period of time
(window analysis). It provides a deeper and more speciﬁc temporal analysis and can show
variations in the efﬁciency of each DMU. These “time windows” are deﬁned based on
moving averages, wherein a new period is considered and the oldest one is removed.
In the work of [28] was stated that biased results can be generated if DMUs are
compared in a single period of time. They may also require a more comprehensive efﬁciency
analysis. Therefore, the use of the window analysis model can be promising in their case.
In [9] the authors were pioneers in the use of the window analysis technique. They
analyzed variations in relative technical efﬁciency, which considered each DMU over time
as a distinct unit. This technique allows for the analysis of efﬁciency stability and of the
sensitivity of the technical and trending aspects of DMUs.
In the study of [29] was highlighted that window analysis does not consider the
nature of technological progress or the ability of DMUs to increase their efﬁciency with
the inputs, which is conventionally known as “frontier shift”. Nor does it show relevant
information on changes in productivity. So, it is necessary to integrate window analysis
with another method.
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An important measure to be considered is an understanding of the temporal evolution
of DMUs as continuous, deﬁned as “pairing”. Thus, as MI incorporates this continuity
concept, it can be considered as an extension of DEA-WA.
In [30] was claimed that this integration between DEA-WA and MI allows for the
incorporation of the idea that there are changes in technology in the period being ana-
lyzed. This approach allows for a comparison between time-constrained, and therefore
technologically similar, DMUs. The formulation of the DEA-WA model was proposed by
Asmild et al. (2004) and is as follows:
Consider N DMUs (n = 1, 2, . . . , N) and T periods (t = 1, 2, . . . , T) using r outputs
and s inputs. With that, the sample has N × T remarks, n observations in period t, DMUn
t ,
a dimensional input vector r xn
t =
 xn
1t, xn
2t, . . . , xn
rt

, and a dimensional product vector s
yn
t =
 yn
1t, yn
2t, . . . , yn
st

. The windows initialize in the instant k, 1 ≤k ≤T and when the
window size is w (1 ≤w ≤T −k), is denoted by kw, and has N × w observations. The
input matrix for this DEA-WA is described by Equation (1):
Xkw =

x1
k, x2
k, . . . , xN
k , x1
k+1, x2
k+1, . . . , xN
k+1, . . . ., x1
k+w, x2
k+w, . . . , xN
k+w

(1)
and the product matrix is denoted by Equation (2):
ktw =

y1
k, y2
k, . . . , yN
k , y1
k+1, y2
k+1, . . . , yN
k+1, . . . ., y1
k+w, y2
k+w, . . . , yN
k+w

(2)
These inputs and outputs adapted to the BCC model [9] and to the model proposed
by [28], generate the following formulation in (3):
max θ
subject to :
θ′Xt −λ′Xtw ≥0
λ′Ytw −Yt ≥0
n
∑
n=1
λn = 1
λn ≥0 (n = 1, 2, . . . , N × w)
λn′ ≥1 (n = 1, 2, . . . , N × w)
(3)
The number of windows considered for this study was three (3), which was the
number suggested by the original proponents of this technique [31]. And [28] stated that
an output-oriented measure of technical efﬁciency of k −th DMU, denoted by TEk, can be
computed by (4):
TEk = 1
θk
(4)
3.2. Malmquist Index (MI)
The MI, initially proposed by [24] and expanded by [25], as a theoretical concept of
production analysis, proposes a measure of productivity that varies over time and allows
for the evaluation of changes in DMU efﬁciency. It was presented as an empirical index
by [32], and is deﬁned as a linear programming model based on DEA, according to [33],
Equation (4):
M

xt, yt, xt+1, yt+1
=
"
Dt xt+1, yt+1
Dt(xt, yt)
· Dt+1 xt+1, yt+1
Dt+1(xt, yt)
# 1
2
(5)
In Equation (4) y represents the output vector and x the input vector. Dt xt, yt
is
deﬁned as a function of distance results and M as the total change in productivity between
period t and period t + 1.
Sustainability 2023, 15, 13611
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MI scores allow for the decomposition of changes in total productivity factors into two
terms, called changes in technical efﬁciency (catch-up effect) and technological progress
(frontier shift effect). The ﬁrst one is an important measure of the temporal evolution
of DMUs.
The second one shows the evolution of the technical efﬁciency of production units and
corresponds to a shift towards an efﬁcient frontier (frontier shift effect), according to [30],
Equation (5):
MI = TE·TP
(6)
where:
MI = Malmquist Index;
TE = Technical Efﬁciency;
TP = Technological Progress.
According to [30], the MI presents results such as MI > 1 showing progress, MI < 1
showing regression, or MI = 1 showing no change in the productivity of a given DMU in
the period under analysis. The interpretation of the values of TE and TP is similar, namely
TE > 1 shows progress in the relative efﬁciency from period t to period t + 1, TP > 1 shows
progress in the technological frontier for the DMU from period t to period t + 1, TE < 1 and
TP < 1 show setbacks, and TE = 1 and TP = 1 show that there was no change.
In [34] was highlighted that, with the calculation of the Malmquist index, it is possible
to separate the frontier shift effect for all possible combinations of periods, including the
next unobserved periods through forecast. This allows us to know the trends in the progress
of technical efﬁciency, regression of efﬁciency, progress of frontier technology, and tendency
to regress in frontier technology. This also allows operational decisions to be made before
efﬁciency drops.
3.3. Cluster Analysis (CA)
Cluster analysis brings together different techniques designed to assess categorized
alternative sets to analyze the similarities between alternatives or units, according to
predeﬁned criteria. In [35] was mentioned that one of the most used distances in cluster
analysis is Euclidean distance, and that this distance between two cases (i and j) is the
root of the squared sums of the difference between i and j for all variables (v = 1, 2, . . ., p).
Equation (6) presents the formulation:
dij =
v
u
u
t
p
∑
v=1
 Xiv −Xjv
2
(7)
where:
Xiv = the value of variable v of element i;
Xjv = the value of variable v of element j;
p is the number of variables.
In [26] was presented how Ward’s method (proposed by [27]) is a hierarchical cluster
analysis procedure. In this method, productive units are grouped by calculating the square
sums between two groups and then added to the analyzed variables.
4. Results
The window analysis technique has gained prominence in the literature in recent
years, and some authors have studied this technique, not always integrating it with the
Malmquist index [28,36,37]. Thus, based on information from these studies, the efﬁciency
index used in this study was deﬁned in Equation (8):
Efﬁciency Index =
Production (barrels/day)
Access numbers + Annual Production capacity (barrels/day)
(8)
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In order to deﬁne the ideal efﬁciency index for this application, some average efﬁciency
tests were carried out on all variables considered in the analysis. The highest average of
the efﬁciency index was found by Equation (8), in which the variable “Number of entry
points” represents the annual number of entry points to ensure an efﬁcient ﬂow of reﬁnery
production, considering terrestrial entry points and gas pipelines, among others. So,
efﬁciency tends to decrease if the number of entry points is high. The variables were
normalized from 0 to 100 for data standardization, where 0 represents the lowest value and
100 is the highest value.
Thus, this index was calculated using a DEA output-oriented BCC model in order
to deﬁne data inputs for DEA-WA analysis. And then, the window analysis model was
applied for the period between 2012 and 2020, considering seven speciﬁc window periods.
This study collected data from the statistical yearbooks of the ANP (National Agency
for Petroleum, Natural Gas and Biofuels) for 12 Brazilian oil reﬁneries, named in Table 1
Column 1. The numerical results of this analysis are shown in Table 1.
The efﬁciency index averages calculated for the windows under analysis found the
lowest efﬁciency (0.81) at Refap reﬁnery (RS) and the highest efﬁciency (0.97) at Regap reﬁn-
ery (MG). Table 2 shows the average efﬁciency per year of each Brazilian reﬁnery analyzed.
Table 1. Window analysis applied to Brazilian oil reﬁneries.
Reﬁneries
2012
2013
2014
2015
2016
2017
2018
2019
2020
Window
Average
Replan (SP)
Window 1
0.82
0.96
1.00
0.94
Window 2
0.89
0.93
1.00
Window 3
0.93
1.00
0.97
Window 4
1.00
0.97
0.93
Window 5
1.00
0.95
0.81
Window 6
1.00
0.85
0.83
Window 7
1.00
0.97
0.86
Rlam (BA)
Window 1
0.97
0.89
0.88
0.90
Window 2
0.86
0.85
0.97
Window 3
0.83
0.93
1.00
Window 4
0.93
1.00
0.87
Window 5
1.00
0.87
0.77
Window 6
0.92
0.83
0.77
Window 7
0.94
0.88
0.88
Revap SP
Window 1
1.00
0.99
1.00
0.96
Window 2
0.99
1.00
0.95
Window 3
0.95
0.90
1.00
Window 4
0.90
1.00
0.95
Window 5
1.00
0.95
0.85
Window 6
1.00
0.89
0.86
Window 7
1.00
0.96
0.98
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Table 1. Cont.
Reﬁneries
2012
2013
2014
2015
2016
2017
2018
2019
2020
Window
Average
Reduc (RJ)
Window 1
0.95
0.93
0.97
0.88
Window 2
0.87
0.91
0.98
Window 3
0.91
0.97
0.95
Window 4
0.97
0.95
0.77
Window 5
0.96
0.78
0.76
Window 6
0.82
0.80
0.73
Window 7
0.89
0.82
0.88
Repar (PR)
Window 1
0.80
0.91
0.99
0.89
Window 2
0.86
0.93
0.91
Window 3
0.92
0.90
0.95
Window 4
0.90
0.95
0.89
Window 5
0.95
0.90
0.77
Window 6
0.96
0.82
0.78
Window 7
0.90
0.86
0.92
Refap (RS)
Window 1
0.82
0.77
0.80
0.81
Window 2
0.74
0.77
0.98
Window 3
0.76
0.96
0.93
Window 4
0.96
0.93
0.78
Window 5
0.93
0.78
0.67
Window 6
0.82
0.71
0.65
Window 7
0.79
0.72
0.71
RPBC (SP)
Window 1
0.97
0.92
0.95
0.93
Window 2
0.86
0.89
1.00
Window 3
0.88
0.99
1.00
Window 4
0.99
1.00
0.89
Window 5
1.00
0.89
0.80
Window 6
0.96
0.86
0.88
Window 7
0.93
0.95
0.94
Regap (MG)
Window 1
1.00
0.91
1.00
0.97
Window 2
0.89
0.98
1.00
Window 3
0.98
1.00
1.00
Window 4
1.00
1.00
0.96
Window 5
1.00
0.96
0.95
Window 6
1.00
0.98
0.93
Window 7
1.00
0.94
0.96
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Table 1. Cont.
Reﬁneries
2012
2013
2014
2015
2016
2017
2018
2019
2020
Window
Average
Recap (SP)
Window 1
0.69
0.81
1.00
0.86
Window 2
0.78
0.96
0.97
Window 3
0.95
0.95
0.97
Window 4
0.95
0.97
0.61
Window 5
0.97
0.61
0.82
Window 6
0.67
0.89
0.83
Window 7
0.96
0.89
0.88
Reman (AM)
Window 1
0.94
0.96
0.81
0.82
Window 2
0.94
0.79
0.85
Window 3
0.78
0.84
0.90
Window 4
0.84
0.90
0.76
Window 5
0.92
0.79
0.69
Window 6
0.84
0.74
0.69
Window 7
0.75
0.70
0.71
Riograndense RS
Window 1
0.84
0.90
0.96
0.82
Window 2
0.86
0.91
0.89
Window 3
0.88
0.87
0.74
Window 4
0.87
0.74
0.54
Window 5
0.74
0.54
0.77
Window 6
0.62
0.88
0.92
Window 7
0.93
0.97
0.96
Lubnor (CE)
Window 1
1.00
0.88
0.99
0.92
Window 2
0.83
0.93
1.00
Window 3
0.88
0.94
1.00
Window 4
0.94
1.00
0.83
Window 5
1.00
0.84
0.81
Window 6
1.00
0.96
0.82
Window 7
1.00
0.85
0.90
Table 2. Mean efﬁciency of Brazilian oil reﬁneries per year.
Reﬁneries
2012
2013
2014
2015
2016
2017
2018
2019
2020
Replan (SP)
0.82
0.92
0.95
1.00
0.98
0.96
0.89
0.90
0.86
Rlam (BA)
0.97
0.87
0.85
0.94
1.00
0.89
0.85
0.83
0.88
Revap (SP)
1.00
0.99
0.98
0.91
1.00
0.97
0.91
0.91
0.98
Reduc (RJ)
0.95
0.90
0.93
0.97
0.95
0.79
0.82
0.78
0.88
Repar (PR)
0.80
0.88
0.95
0.91
0.95
0.92
0.83
0.82
0.92
Refap (RS)
0.82
0.75
0.78
0.97
0.93
0.79
0.72
0.69
0.71
RPBC (SP)
0.97
0.89
0.91
0.99
1.00
0.91
0.87
0.91
0.94
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Table 2. Cont.
Reﬁneries
2012
2013
2014
2015
2016
2017
2018
2019
2020
Regap (MG)
1.00
0.90
0.99
1.00
1.00
0.97
0.98
0.93
0.96
Recap (SP)
0.69
0.79
0.97
0.96
0.97
0.63
0.89
0.86
0.88
Reman (AM)
0.94
0.95
0.79
0.85
0.90
0.79
0.73
0.69
0.71
Riograndense (RS)
0.84
0.88
0.92
0.87
0.74
0.57
0.86
0.95
0.96
Lubnor (CE)
1.00
0.85
0.93
0.96
1.00
0.89
0.92
0.84
0.90
Global average
0.90
0.88
0.91
0.94
0.95
0.84
0.85
0.84
0.88
The best efﬁciency average occurred in 2016, with an index of 0.95. The lowest average
efﬁciency in the analyzed period was in 2020, with 0.88.
Riograndense (RS) reﬁnery had the lowest efﬁciency index in the analysis, 0.57, in
2017. Other reﬁneries with the lowest indexes were Recap (SP), with 0.63 and 0.69 in 2017
and 2020, respectively; Reman (AM), with 0.69 in 2019; and Refap (RS), with 0.69 in 2019. In
2013 and 2014 and in the period between 2017 and 2020, no reﬁnery showed full efﬁciency,
which can be explained by a higher level of competition at the national and/or international
level and also by variations in the product considered for analysis, production (barrel/day).
There were also variations in annual production capacity (barrel/day). Table 3 describes
average efﬁciencies per window.
Table 3. Efﬁciency of Brazilian oil reﬁneries per window.
Reﬁneries
2012–2014
2013–2015
2014–2016
2015–2017
2016–2018
2017–2019
2018–2020
Regap (MG)
0.97
0.96
0.99
0.99
0.97
0.97
0.97
Revap (SP)
1.00
0.98
0.95
0.95
0.93
0.92
0.98
Replan (SP)
0.93
0.94
0.97
0.97
0.92
0.89
0.94
RPBC (SP)
0.95
0.92
0.96
0.96
0.90
0.90
0.94
Lubnor (CE)
0.96
0.92
0.94
0.92
0.88
0.93
0.92
Rlam (BA)
0.91
0.89
0.92
0.93
0.88
0.84
0.90
Repar (PR)
0.90
0.90
0.93
0.92
0.87
0.85
0.90
Reduc (RJ)
0.95
0.92
0.94
0.90
0.83
0.78
0.87
Recap (SP)
0.83
0.90
0.96
0.85
0.80
0.80
0.91
Riograndense (RS)
0.90
0.89
0.83
0.71
0.68
0.81
0.95
Reman (AM)
0.90
0.86
0.84
0.83
0.80
0.75
0.72
Refap (RS)
0.80
0.83
0.88
0.89
0.79
0.73
0.74
Average
0.91
0.91
0.93
0.90
0.86
0.85
0.89
The 2014–2016 window had the best average efﬁciency, 0.93, while the 2017–2019 win-
dow had the worst rate, 0.85. Only Revap (SP) reﬁnery showed efﬁciency in its operations
in the 2012–2014 window. In the other windows, no reﬁnery was efﬁcient. Riograndense
(RS) reﬁnery had the lowest efﬁciency rates, 0.68 in the 2016–2018 window and 0.71 in the
fourth window, 2015–2017.
Two other reﬁneries, along with Riograndense reﬁnery (RS), had the lowest efﬁciency
indexes in the windows: Reman (AM) reﬁnery, with 0.72 in the 2018–2020 window and
0.75 in the 2017–2019 window, and Refap (SP) reﬁnery, with 0.73 in the 2017–2019 window
and 0.74 in the 2018–2020 window.
Considering the average efﬁciency data per year as input data for cluster analysis,
the standard cluster technique of Euclidean distance was applied, which is expressed in
Table 4.
According to [26] the creation of clusters can provide a grouping of reﬁneries according
to their average efﬁciency indexes per year without data distortion. However, despite the
fact that the average indexes per year were considered, it was perceived that the clusters
were organized according to the global average efﬁciency index. The ﬁrst cluster, C1,
brought together eight reﬁneries that presented the best average efﬁciency rates, equal to
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or greater than 0.89 with an average of 0.92, which were Regap (MG), Revap (SP), RPBC
(SP), Lubnor (CE), Replan (SP), Rlam (BA), Repar (PR), and Reduc (RJ).
Table 4. Cluster analysis based on average efﬁciency indexes, per year.
Cluster
Reﬁneries
Average
Average per Cluster
C1
Regap (MG)
0.97
0.92
Revap (SP)
0.96
RPBC (SP)
0.93
Lubnor (CE)
0.92
Replan (SP)
0.92
Rlam (BA)
0.90
Repar (PR)
0.89
Reduc (RJ)
0.89
C2
Recap (SP)
0.85
0.85
Riograndense (RS)
0.84
C3
Reman (AM)
0.82
0.81
Refap (RS)
0.80
Cluster C2 contained two reﬁneries with indexes of 0.84 and 0.85 and an average of
0.85, Recap (SP) and Riograndense (RS), while cluster C3 had two reﬁneries with the worst
average indexes, 0.80 and 0.82, and an average of 0.81, Reman (AM) and Refap (RS).
To verify the changes in productivity, a decomposition of the Malmquist index was
calculated based on its two effects, matching (catch-up effect) and technological progress
(frontier shift effect), besides the general Malmquist index, which allows for an analysis
of total factor productivity [17]. The data referring to the pairing effect are presented in
Table 5. A signiﬁcant variation can be seen in the results presented. If the pairing results
in an index greater than 1, it means that technical efﬁciency increased in period t + 1 in
relation to period t. If this index is equal to 1, technical efﬁciency remained the same, and
if it is less than 1, it worsened [28,38]. This index was equal to 1 in the case of Recap (SP),
Revap (SP), Lubnor (CE), and Replan (SP) reﬁneries, all belonging to the ﬁrst cluster, which
shows that there were no signiﬁcant changes in their technical efﬁciency during the period.
Table 5. Effect of pairing efﬁciency of Brazilian oil reﬁneries between 2012 and 2020.
Reﬁneries
2012–2014
2013–2015
2014–2016
2015–2017
2016–2018
2017–2019
2018–2020
Average
Regap (MG)
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
Revap (SP)
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
RPBC (SP)
0.98
1.07
1.05
0.96
0.93
1.05
1.05
1.01
Lubnor (CE)
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
Replan (SP)
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
Rlam (BA)
0.88
1.11
1.13
0.92
0.94
0.99
1.00
1.00
Repar (PR)
1.23
0.99
0.96
1.05
0.94
0.95
1.05
1.03
Reduc (RJ)
1.02
1.05
0.99
0.84
0.93
1.04
1.01
0.98
Average C1
1.01
1.03
1.02
0.97
0.97
1.00
1.01
1.00
Recap (SP)
1.43
1.14
0.97
0.69
0.99
1.40
0.96
1.08
Riograndense (RS)
1.12
0.92
0.77
0.69
1.26
1.62
1.08
1.06
Average C2
1.28
1.03
0.87
0.69
1.12
1.51
1.02
1.07
Reman (AM)
0.86
0.85
1.14
0.98
0.82
0.90
1.00
0.94
Refap (RS)
0.98
1.26
1.16
0.84
0.84
0.92
0.93
0.99
Average C3
0.92
1.06
1.15
0.91
0.83
0.91
0.97
0.96
Maximum
1.43
1.26
1.16
1.05
1.26
1.62
1.08
1.08
Minimum
0.86
0.85
0.77
0.69
0.82
0.90
0.93
0.94
Average/clusters
1.07
1.04
1.01
0.86
0.97
1.14
1.00
1.01
SD/clusters
0.14
0.01
0.08
0.14
0.09
0.26
0.01
0.10
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Cluster C1 presented an average of very close to 1 in all analyzed periods, with values
varying by 3% above or below this value. Cluster C2 showed signiﬁcant variations in
technical efﬁciency. Some periods showed strong declines in this index, while other periods
showed a considerable improvement. At Riograndense reﬁnery (RS), for example, there
was an improvement in technical efﬁciency in the 2012–2014 and 2017–2019 periods. Cluster
C3 also showed variation in the data, but in most periods, there was a drop in technical
efﬁciency. It is worth noting that there was an improvement in the period 2014–2016 and a
drop in the technical efﬁciency index, in both reﬁneries, in another four periods.
Changes in the technical efﬁciency indexes, calculated through the pairing effect, are
due to variations in the values of the product or inputs during the years 2012 to 2020.
The minimum value found in this analysis was 0.69, for reﬁneries Recap (SP) and
Riograndense (RS), in the period 2015–2017. Recap (SP) showed a 15% increase in produc-
tion capacity in this period, but the considered product, production (barrel/day), did not
increase considerably.
Riograndense reﬁnery (RS) kept its inputs constant but showed a reduction of 37.5%
in production (barrel/day), which determined a drop in efﬁciency index and a low value
of the pairing effect.
The general averages between the clusters showed considerable variability in most
periods, with a standard deviation of approximately 0.104. Analyzing the periods, there
was a great variability between the averages of the clusters in 2012–2014, 2015–2017, and
2017–2019, with values of the standard deviation of, respectively, 0.14, 0.14 and 0.26. Data
referring to technological progress (frontier shift effect), or frontier shift, are presented in
Table 6.
Table 6. “Technological progress” effect of the Malmquist index decomposition of the efﬁciency of
Brazilian oil reﬁneries between 2012 and 2020.
Reﬁneries
2012–2014
2013–2015
2014–2016
2015–2017
2016–2018
2017–2019
2018–2020
Average
Regap (MG)
1.00
1.12
1.03
0.97
0.95
0.93
0.96
0.99
Revap (SP)
1.00
0.95
1.06
1.06
0.85
0.86
0.98
0.97
RPBC (SP)
1.00
1.08
1.07
0.94
0.86
0.88
0.96
0.97
Lubnor (CE)
0.99
1.21
1.14
0.94
0.90
0.90
0.90
1.00
Replan (SP)
1.23
1.12
1.03
0.91
0.81
0.83
0.86
0.97
Rlam (BA)
1.03
1.02
1.06
1.01
0.82
0.85
0.94
0.96
Repar (PR)
1.00
1.07
1.06
0.94
0.85
0.86
0.97
0.97
Reduc (RJ)
1.00
1.07
1.06
0.94
0.85
0.86
0.98
0.97
Average C1
1.03
1.08
1.06
0.96
0.86
0.87
0.94
0.97
Recap (SP)
1.02
1.09
1.05
0.93
0.86
0.88
0.96
0.97
Riograndense(RS)
1.01
1.13
1.09
0.90
0.83
0.92
0.96
0.98
Average C2
1.01
1.11
1.07
0.92
0.84
0.90
0.96
0.97
Reman (AM)
1.00
1.07
1.00
0.92
0.92
0.91
0.95
0.97
Refap (RS)
1.00
1.05
1.06
0.98
0.86
0.86
0.97
0.97
Average C3
1.00
1.06
1.03
0.95
0.89
0.88
0.96
0.97
Maximum
1.23
1.21
1.14
1.06
0.95
0.93
0.98
1.00
Minimum
0.99
0.95
1.00
0.90
0.81
0.83
0.86
0.96
Average/clusters
1.02
1.08
1.05
0.94
0.86
0.89
0.95
0.97
SD/clusters
0.01
0.02
0.01
0.02
0.01
0.02
0.01
0.01
Subsection
Regarding technological progress (frontier shift effect), in product-oriented efﬁciency
analysis models, if the index is greater than 1, the DMU shows technological progress, if it is
equal to 1, there was no technological change, and if it is less than 1, it shows technological
regression [17,30,38].
Overall, our results show that there was a technological regression in most periods.
The mean and standard deviations between the cluster means were approximately 0.97
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and 0.01, respectively. This shows homogeneity between these averages. There are also
fewer signiﬁcant technological changes when compared to changes in technical efﬁciency.
The ﬁrst three periods show an average greater than 1 among all clusters, which shows
technological progress. Clusters 1, 2, and 3 are illustrated in Figures 3–5.
Sustainability 2023, 15, x FOR PEER REVIEW 
14 of 20 
 
Overall, our results show that there was a technological regression in most periods. 
The mean and standard deviations between the cluster means were approximately 0.97 
and 0.01, respectively. This shows homogeneity between these averages. There are also 
fewer signiﬁcant technological changes when compared to changes in technical eﬃcien-
cy. The ﬁrst three periods show an average greater than 1 among all clusters, which 
shows technological progress. Clusters 1, 2, and 3 are illustrated in Figures 3–5. 
 
Figure 3. Eﬀect of technological progress (cluster C1). 
 
Figure 4. Eﬀect of technological progress (cluster C2). 
Figure 3. Effect of technological progress (cluster C1).
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Overall, our results show that there was a technological regression in most periods. 
The mean and standard deviations between the cluster means were approximately 0.97 
and 0.01, respectively. This shows homogeneity between these averages. There are also 
fewer signiﬁcant technological changes when compared to changes in technical eﬃcien-
cy. The ﬁrst three periods show an average greater than 1 among all clusters, which 
shows technological progress. Clusters 1, 2, and 3 are illustrated in Figures 3–5. 
 
Figure 3. Eﬀect of technological progress (cluster C1). 
 
Figure 4. Eﬀect of technological progress (cluster C2). 
Figure 4. Effect of technological progress (cluster C2).
Replan reﬁnery (SP) remained at a value close to 1, as did the other reﬁneries. In
the last ﬁve periods, average values among the clusters were lower than 1, indicating a
technological regression. All reﬁneries in the three clusters showed technological regression
in the last three periods (from 2016–2018 to 2018–2020). The average for 2015–2017 was
also less than 1, but there was variation in the values. Except for Lubnor (CE), all other
reﬁneries, on average, showed technological regression. The Malmquist index (MI), in its
general form, shows changes in total factor productivity (TFP), which is the multiplication
between the effects of technical efﬁciency and technological progress, as mentioned above.
Table 7 presents the values of the Malmquist index, and Figures 6–8 illustrate this scenario
in clusters 1, 2 and 3.
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Figure 5. Eﬀect of technological progress (cluster C3). 
Replan reﬁnery (SP) remained at a value close to 1, as did the other reﬁneries. In the 
last ﬁve periods, average values among the clusters were lower than 1, indicating a 
technological regression. All reﬁneries in the three clusters showed technological regres-
sion in the last three periods (from 2016–2018 to 2018–2020). The average for 2015–2017 
was also less than 1, but there was variation in the values. Except for Lubnor (CE), all 
other reﬁneries, on average, showed technological regression. The Malmquist index (MI), 
in its general form, shows changes in total factor productivity (TFP), which is the multi-
plication between the eﬀects of technical eﬃciency and technological progress, as men-
tioned above. Table 7 presents the values of the Malmquist index, and Figures 6–8 illus-
trate this scenario in clusters 1, 2 and 3. 
Table 7. Malmquist index of Brazilian oil reﬁneries. 
Refineries 
2012–2014 
2013–2015 
2014–2016 2015–2017 2016–2018 2017–2019 2018–2020 Average 
Regap (MG) 
1.00 
1.12 
1.03 
0.97 
0.95 
0.93 
0.96 
0.99 
Revap (SP) 
1.00 
0.95 
1.06 
1.06 
0.85 
0.86 
0.98 
0.97 
RPBC (SP) 
0.98 
1.16 
1.13 
0.90 
0.80 
0.92 
1.01 
0.98 
Lubnor (CE) 
0.99 
1.21 
1.14 
0.94 
0.90 
0.90 
0.90 
1.00 
Replan (SP) 
1.23 
1.12 
1.03 
0.91 
0.81 
0.83 
0.86 
0.97 
Rlam (BA) 
0.90 
1.13 
1.20 
0.93 
0.77 
0.84 
0.94 
0.96 
Repar (PR) 
1.23 
1.06 
1.03 
0.99 
0.81 
0.82 
1.02 
0.99 
Reduc (RJ) 
1.02 
1.12 
1.05 
0.79 
0.79 
0.89 
0.99 
0.95 
Average C1 
1.04 
1.11 
1.08 
0.94 
0.83 
0.87 
0.96 
0.98 
Recap (SP) 
1.46 
1.24 
1.02 
0.64 
0.85 
1.24 
0.92 
1.05 
Riograndense 
1.13 
1.04 
0.83 
0.63 
1.04 
1.48 
1.04 
1.03 
Average C2 
1.30 
1.14 
0.93 
0.63 
0.94 
1.36 
0.98 
1.04 
Reman (AM) 
0.86 
0.91 
1.14 
0.90 
0.75 
0.82 
0.95 
0.90 
Refap (RS) 
0.98 
1.32 
1.23 
0.82 
0.72 
0.79 
0.91 
0.97 
Average C3 
0.92 
1.12 
1.19 
0.86 
0.74 
0.81 
0.93 
0.94 
Maximum 
1.46 
1.32 
1.23 
1.06 
1.04 
1.48 
1.04 
1.05 
Minimum 
0.86 
0.91 
0.83 
0.63 
0.72 
0.79 
0.86 
0.90 
Average/clusters 
1.09 
1.12 
1.07 
0.81 
0.84 
1.01 
0.95 
0.98 
Figure 5. Effect of technological progress (cluster C3).
Table 7. Malmquist index of Brazilian oil reﬁneries.
Reﬁneries
2012–2014
2013–2015
2014–2016
2015–2017
2016–2018
2017–2019
2018–2020
Average
Regap (MG)
1.00
1.12
1.03
0.97
0.95
0.93
0.96
0.99
Revap (SP)
1.00
0.95
1.06
1.06
0.85
0.86
0.98
0.97
RPBC (SP)
0.98
1.16
1.13
0.90
0.80
0.92
1.01
0.98
Lubnor (CE)
0.99
1.21
1.14
0.94
0.90
0.90
0.90
1.00
Replan (SP)
1.23
1.12
1.03
0.91
0.81
0.83
0.86
0.97
Rlam (BA)
0.90
1.13
1.20
0.93
0.77
0.84
0.94
0.96
Repar (PR)
1.23
1.06
1.03
0.99
0.81
0.82
1.02
0.99
Reduc (RJ)
1.02
1.12
1.05
0.79
0.79
0.89
0.99
0.95
Average C1
1.04
1.11
1.08
0.94
0.83
0.87
0.96
0.98
Recap (SP)
1.46
1.24
1.02
0.64
0.85
1.24
0.92
1.05
Riograndense
1.13
1.04
0.83
0.63
1.04
1.48
1.04
1.03
Average C2
1.30
1.14
0.93
0.63
0.94
1.36
0.98
1.04
Reman (AM)
0.86
0.91
1.14
0.90
0.75
0.82
0.95
0.90
Refap (RS)
0.98
1.32
1.23
0.82
0.72
0.79
0.91
0.97
Average C3
0.92
1.12
1.19
0.86
0.74
0.81
0.93
0.94
Maximum
1.46
1.32
1.23
1.06
1.04
1.48
1.04
1.05
Minimum
0.86
0.91
0.83
0.63
0.72
0.79
0.86
0.90
Average/clusters
1.09
1.12
1.07
0.81
0.84
1.01
0.95
0.98
Sustainability 2023, 15, x FOR PEER REVIEW 
16 of 20 
 
 
Figure 6. Malmquist index (cluster C1). 
Figure 6. Malmquist index (cluster C1).
Sustainability 2023, 15, 13611
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Figure 6. Malmquist index (cluster C1). 
 
Figure 7. Malmquist index (cluster C2). 
Figure 7. Malmquist index (cluster C2).
Sustainability 2023, 15, x FOR PEER REVIEW 
17 of 20 
 
 
Figure 8. Malmquist index (cluster C3). 
When the Malmquist index (MI) is MI > 1, it shows that productivity is improved, 
and with MI < 1, productivity is reduced, as presented by [18,30]. 
5. Discussion 
The maximum improvement value in this productivity index occurred in Riogran-
dense (RS) reﬁnery, 1.48, in the period from 2017 to 2019, while the worst index was 0.63, 
in the same reﬁnery in the period from 015 to 2017. 
This sharp drop in productivity is because of the decrease in the product used in the 
analysis. Production decreased by approximately 4,000 barrels/day in this period, while 
inputs remained constant. 
There were signiﬁcant variations in the values of total factor productivity between 
the reﬁneries and between the analyzed periods, mainly because of the signiﬁcant 
changes in technical eﬃciency, since technological changes presented little variation in 
their values. There was a ﬂuctuation in the Malmquist index, showing that productivity 
varied in the analyzed period. 
As for the predeﬁned hypothesis and the ﬁndings expected from this study, it was 
possible to perceive that DEA-WA and the Malmquist index, when used concomitantly, 
can provide a clear view of the productivity and eﬃciency behaviors of every studied 
reﬁnery and their technological changes in diﬀerent periods. As for the other analysis, 
cluster analysis provided a global study scenario for the studied period 2012–2020. With 
this integrated analysis, it is possible to plan all potential decision-making and reﬁnery 
management actions following our methodology. 
Brazilian oil reﬁneries play an important role in the production of oil and natural gas 
in Brazil, which is the largest oil-reﬁning country in South America, with approximately 
2734 million barrels/day. According [22], there are currently eighteen oil reﬁneries in 
Figure 8. Malmquist index (cluster C3).
When the Malmquist index (MI) is MI > 1, it shows that productivity is improved, and
with MI < 1, productivity is reduced, as presented by [18,30].
5. Discussion
The maximum improvement value in this productivity index occurred in Riograndense
(RS) reﬁnery, 1.48, in the period from 2017 to 2019, while the worst index was 0.63, in the
same reﬁnery in the period from 015 to 2017.
This sharp drop in productivity is because of the decrease in the product used in the
analysis. Production decreased by approximately 4000 barrels/day in this period, while
inputs remained constant.
There were signiﬁcant variations in the values of total factor productivity between the
reﬁneries and between the analyzed periods, mainly because of the signiﬁcant changes in
technical efﬁciency, since technological changes presented little variation in their values.
There was a ﬂuctuation in the Malmquist index, showing that productivity varied in the
analyzed period.
As for the predeﬁned hypothesis and the ﬁndings expected from this study, it was
possible to perceive that DEA-WA and the Malmquist index, when used concomitantly,
can provide a clear view of the productivity and efﬁciency behaviors of every studied
Sustainability 2023, 15, 13611
17 of 19
reﬁnery and their technological changes in different periods. As for the other analysis,
cluster analysis provided a global study scenario for the studied period 2012–2020. With
this integrated analysis, it is possible to plan all potential decision-making and reﬁnery
management actions following our methodology.
Brazilian oil reﬁneries play an important role in the production of oil and natural gas
in Brazil, which is the largest oil-reﬁning country in South America, with approximately
2734 million barrels/day. According [22], there are currently eighteen oil reﬁneries in Brazil,
thirteen of which are managed by Petrobras and ﬁve are managed by the private sector. In
this study, 12 reﬁneries were considered based on their size and foundation time.
The application of the DEA window analysis model to oil reﬁneries enabled us to
monitor the performance of reﬁneries over several periods of time. In [28] was stated
that comparing DMUs for a single period can generate biased results, and a more realistic
efﬁciency analysis should be performed over a period of time. When a window efﬁciency
analysis is performed on Brazilian oil reﬁneries and none of them show efﬁciency through-
out the entire period, it can be worrying for managers, since they are not achieving the
maximum production possible with the inputs used. In order to compare different periods,
the Malmquist general index and its decomposition into its two effects, matching and
technological progress, were also veriﬁed. In most of the analyzed periods, there was
variation between technical efﬁciency data, with gains and losses between periods in each
reﬁnery. Technological progress presented more discrete changes in values.
Environmental and social aspects were not considered in this analysis, because they
would require subjective assessment from decision makers and specialists and the deﬁnition
of relevant criteria to make their development more reliable. In reality, these aspects are
relevant, but they must be developed and combined with a multicriteria analysis.
6. Final Remarks
Integrating data envelopment analysis, the Malmquist index, and cluster analysis
enabled us to identify the reﬁneries’ productivity and efﬁciency behaviors in different
periods and incorporate an analysis of the technological progress of the units.
To the best of our knowledge, the combination of these three techniques had not
previously been applied to oil reﬁneries, which opened the possibility of carrying out this
study and contributing to the literature, in addition to presenting comparisons between
Brazilian and international reﬁneries.
This analysis contributes to the current research in different aspects, as it enables the
use of the combination of these techniques for the analysis of productivity and efﬁciency
and to support managers in their decision making.
It also opens up avenues to new developments that could involve multicriteria analy-
ses with environmental, social, and economic aspects integrated into the efﬁciency aspect
developed in this work. Knowledge of the performance of reﬁneries by time periods in a
given country is the ﬁrst step to thinking about sustainable actions.
A reﬁnery cannot have environmental responsibility without a minimum of efﬁciency
in operations. Thus, this work provides an overview of efﬁciency in various time windows
in order to promote a more robust analysis for future sustainable actions.
This study can signiﬁcantly contribute to the theory in aspects related to efﬁciency
calculation, showing a more viable way to determine operational efﬁciency in reﬁneries
and an initial analysis to help manage these enterprises.
The integration carried out can be highlighted as a practical contribution, since it
separates unwanted periods or speciﬁc problems that occurred within these periods. It also
groups reﬁneries into clusters in order to know the best and worst performances in the
studied periods. The measurement of the necessary technological changes is also evident
with the determination of the Malmquist index.
Sustainability 2023, 15, 13611
18 of 19
Author Contributions: Conceptualization, M.S.d.O. and F.T.; methodology, M.S.d.O.; validation,
M.S.d.O., M.L. and F.T.; formal analysis, H.S., F.T. and P.A.; investigation, M.S.d.O.; resources, M.L.
and H.S.; data curation, M.S.d.O. and F.T.; writing—original draft preparation, M.S.d.O. and F.T.;
writing—review and editing, H.S., F.T. and P.A.; visualization, M.L. and F.T.; supervision, H.S., F.T.
and P.A.; project administration, F.T.; funding acquisition, H.S. and F.T. All authors have read and
agreed to the published version of the manuscript.
Funding: The APC was funded in part by Universidade Tecnologica Federal do Parana, Brazil.
Conﬂicts of Interest: The authors declare no conﬂict of interest.
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Applications of Artificial
Intelligence Techniques
in the Petroleum
Industry
Applications of Artificial
Intelligence Techniques
in the Petroleum
Industry
ABDOLHOSSEIN HEMMATI-SARAPARDEH
Department of Petroleum Engineering, Shahid Bahonar
University of Kerman, Kerman, Iran
AYDIN LARESTANI
Department of Petroleum Engineering, Shahid Bahonar
University of Kerman, Kerman, Iran
MENAD NAIT AMAR
Département Etudes Thermodynamiques, Division
Laboratoires, Sonatrach, Boumerdes, Algeria
Laboratory of Hydrocarbons Physical Engineering,
Faculty of Hydrocarbons and Chemistry, University of
M’Hamed Bougara Boumerdes, Boumerdes, Algeria
SASSAN HAJIREZAIE
Department of Civil and Environmental Engineering,
Princeton University, NJ, United States

Walden University 
 
 
 
College of Management and Technology 
 
 
 
 
This is to certify that the doctoral study by 
 
 
Lionel Bryan Small 
 
 
has been found to be complete and satisfactory in all respects,  
and that any and all revisions required by  
the review committee have been made. 
 
 
Review Committee 
Dr. Cheryl Lentz, Committee Chairperson, Doctor of Business Administration Faculty 
 
Dr. Susan Fan, Committee Member, Doctor of Business Administration Faculty 
 
Dr. Marilyn Simon, University Reviewer, Doctor of Business Administration Faculty 
 
 
 
 
 
Chief Academic Officer 
Eric Riedel, Ph.D. 
 
 
 
Walden University 
2017 
 
 
 
 
 
Abstract 
 
Sustainability Practices That Influence Profitability in the Petroleum Industry 
by 
Lionel Bryan Small 
 
MS, University of the West Indies, 1998 
BS, University of the West Indies, 1989 
 
 
Doctoral Study Submitted in Partial Fulfillment 
of the Requirements for the Degree of 
Doctor of Business Administration 
 
 
Walden University 
June 2017 
 
 
Abstract 
Petroleum industries in the U.S. attract increased scrutiny from governmental bodies, 
businesses, and the civil society for their lack of sustainability practices, such as air 
emissions control, the use of cleaner fuels, and water pollution mitigation. Although the 
short-term cost of implementing these practices may be high as stated by a sample of the 
industry’s leaders, long-term benefits include lower business costs and a reduction of the 
adverse impacts on society, the environment, and the economy. This multiple-case study 
highlighted the practices of several petroleum industry leaders who demonstrated an 
exception to these practices—who have been clear thought leaders in the delivery of both 
environmental sustainability and profitability. Data collection included in-depth 
interviews with 16 purposively selected petroleum business participants supplemented by 
a review of archival records containing annual sustainability reports. The participants 
were experts who practiced sustainability as part of their work-related activities. Data 
saturation occurred when no new data or patterns emerged. Methodological triangulation 
occurred as evidenced by the convergence of data from the different sources. Yin’s 5-step 
analysis, which guided the coding process, yielded 3 main themes: environmental air 
quality, fuel, and water. These themes aligned with practices identified from the review 
of 20 archival reports across a 5-year period. Key practices identified from the archival 
records included flaring reduction, natural gas utilization, and water re-cycling. The 
implications for positive social change include the potential for the preservation of 
resources for present and future generations when all companies operating in the 
petroleum industry embrace sustainability. 
 
 
Sustainability Practices That Influence Profitability in the Petroleum Industry 
by 
Lionel Bryan Small 
 
MS, University of the West Indies, 1989 
BS, University of the West Indies, 1998 
 
 
Doctoral Study Submitted in Partial Fulfillment 
of the Requirements for the Degree of 
Doctor of Business Administration 
 
 
 
Walden University 
June 2017 




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Dedication 
I dedicate this study to Almighty God, who provided the strength, motivation, and 
patience to complete this study and achieve this pinnacle in my life. I also dedicate this to 
my late parents who guided me to embrace education to achieve success. I wanted to 
become a medical doctor, but I hope my parents are proud of my becoming a doctor of 
business. I hope my parents accept the fact that the nature of my doctorate will also 
change lives in a positive manner. Last, I dedicate this achievement to my Aunt Edlin de 
Gourville who was always my support mechanism while I was a youth.  
 
 
 
 
Acknowledgments 
With immense appreciation, I acknowledge my chair, Dr. Chery Lentz. 
Through your masterful guidance and exceptional advice, I committed to complete my 
doctoral study. Secondly, I sincerely appreciate the commitment and kind support from 
my committee members, Dr. Susan Fan and Dr. Marilyn Simon. I was motivated and 
educated by your wisdom. I acknowledge my wife, Mrs. Shaliza Ali for her love, 
patience, and understanding that reinforced my resolve to complete this academic 
journey. I want to honor the motivation and guidance of my mother, Mrs. Marjorie Small, 
to always aim for the continuous improvement that continues to contribute to my success 
and the success of those around me. Finally, I hope this study motivates my three 
children, Fyze Anderson, Bre-Anne Samantha, and Rohan Quinlin to achieve excellence 
and change the world positively in their ways.  
  
 
 
i 
 
Table of Contents 
Section 1: Foundation of the Study 
......................................................................................1 
Background of the Problem ...........................................................................................1 
Problem Statement .........................................................................................................2 
Purpose Statement 
..........................................................................................................3 
Nature of the Study ........................................................................................................4 
Research and Survey Questions 
.....................................................................................6 
Conceptual Framework 
..................................................................................................7 
Definition of Terms........................................................................................................9 
Assumptions, Limitations, and Delimitations 
..............................................................10 
Assumptions 
.......................................................................................................... 10 
Limitations ............................................................................................................ 11 
Delimitations 
......................................................................................................... 12 
Significance of the Study .............................................................................................13 
Contribution to Business Practice 
......................................................................... 13 
Implications for Social Change 
............................................................................. 13 
A Review of the Professional and Academic Literature 
..............................................14 
Summary and Transition 
..............................................................................................41 
Section 2: The Project 
........................................................................................................43 
Purpose Statement 
........................................................................................................43 
Participants 
...................................................................................................................46 
Research Method and Design ......................................................................................47 
 
ii 
 
Research Method .................................................................................................. 47 
Research Design.................................................................................................... 49 
Population and Sampling .............................................................................................51 
Ethical Research...........................................................................................................52 
Data Collection ............................................................................................................54 
Instruments 
............................................................................................................ 54 
Data Collection Technique ................................................................................... 57 
Data Organization Techniques 
.............................................................................. 59 
Data Analysis ...............................................................................................................60 
Reliability and Validity 
................................................................................................64 
Reliability 
.............................................................................................................. 64 
Validity ................................................................................................................. 65 
Summary and Transition 
..............................................................................................66 
Section 3: Application to Professional Practice and Implications for Change ..................68 
Introduction 
..................................................................................................................68 
Presentation of the Findings.........................................................................................69 
Applications to Professional Practice ..........................................................................91 
Implications for Social Change 
....................................................................................95 
Recommendations for Action ......................................................................................97 
Recommendations for Further Research 
......................................................................99 
Reflections .................................................................................................................102 
Conclusion .................................................................................................................103 
 
iii 
 
References 
........................................................................................................................105 
Appendix A: Main Environmenal Themes from NVivo 11 ................................................ 
139 
 
1 
 
 
Section 1: Foundation of the Study 
 
Introduction  
 
Sustainability means living in the present in ways that allow others to exist in the 
future (Epstein & Buhovac, 2014a). Sustainability practices are an important aspect of 
doing business in any industry (Merriman & Sen, 2012). However, the leadership of U. S. 
petroleum companies hesitates to implement practices that support environmental 
sustainability. According to Du, Pan, and Zuo (2013) the perception is that sustainability 
practices increase operating costs and thus affect organizational profitability. 
The purpose of this study was to determine the influence, if any, of practices that 
support environmental sustainability while improving profitability in the U. S. petroleum 
industry. By explaining the influence of such practices, company leaders will be able to 
make better-educated decisions about implementing such practices (Stoddard, Pollard, & 
Evans, 2012). An understanding of these influences may add value to the bottom line for 
the businesses (Hollos, Blome, & Foerstl, 2012). 
Background of the Problem 
Ascertaining the influence of sustainability practices on profitability is difficult 
for business leaders, employees, and community members in the business world (Heffes, 
2011). Although some business leaders are doubtful (Rettie, Burchell, & Riley, 2012), 
sustainability practices may lead to profits (Chakraborty, 2010; Jang, 2010).  
The belief in some business circles is that profitability reduction occurs when 
leaders implement sustainability practices (Butler, Henderson, & Raiborn, 2011). Other 
successful companies recognized the importance of the alignment between social and 
2 
 
 
environmental responsibility and financial performance (Epstein & Buhovac, 2014a). The 
conflicting views presented in these and other studies indicate the need for more research 
on how sustainability increases profitability (Du et al., 2013).  
Costs associated with sustainability can affect profitability outcomes during 
production lifecycles (Williams et al., 2014). The importance of sustainability practices is 
an open question (Heffes, 2011). This ambiguity creates a business problem for 
petroleum executives of organizations uncertain about implementing sustainable 
practices. Some industry leaders believe that sustainability practices decrease profitability 
(Stocchetti, 2012). The goal of this research study was to explore the influence of 
sustainability on profitability in the U. S. petroleum industry. The aim was to determine 
whether sustainability practices could add value to these organizations and improve 
profits.   
Problem Statement 
The lack of sustainability initiatives in the petroleum industry could shorten the 
survival of the human race (Hashmi & Al-Habib, 2013). Some of these initiatives include 
(a) material reduction, (b) recycling, (c) waste reduction, and (d) spill avoidance (De 
Giacomo et al., 2014). British Petroleum (BP) paid over $21 billion in civil damages and 
suffered a loss to its reputation because of sub-standard sustainability practices that 
caused a major spill in the Gulf of Mexico (Baker, 2015). The general business problem 
is that the management of some petroleum companies does not have a holistic 
understanding of profitability in petroleum extraction with long-term effects on the 
environment (Nwagbara & Brown, 2014). The specific business problem is that some 
3 
 
 
petroleum industry leaders lack knowledge about how sustainable environmental 
practices affect profitability.  
Purpose Statement 
The purpose of this qualitative, multiple-case study was to explore sustainable 
environmental practices that petroleum industry leaders can use to affect profitability. 
Sustainability practices and profitability can be balanced benefit petroleum companies 
(Du et al., 2013; Parast & Adams, 2012). The expectation is for this research to explore 
sustainable environmental practices that petroleum industry leaders can use to affect 
profitability.  
The target population was operations personnel and leaders in the petroleum 
industry who had experience in the United States. Front line leaders attained 
sustainability by bridging any gaps between their superiors and followers (Merriman & 
Sen, 2012). Targeting the leaders allowed for changes in these business practices that 
may contribute to sustainability implementation (Epstein & Buhovac, 2014a).  
The implication for positive social change is that leaders in private and public 
organizations might use the findings to improve profitability. Profit and sustainability can 
and must go together because a social benefit exists (Voser, 2012). These benefits may 
include the protection of human lives while creating the economic requirements for social 
comforts (Epstein & Buhovac, 2014a). Sustainable practices are a requirement to enhance 
business outcomes, including reputation and financial performance (Petrovich, 2014).  
4 
 
 
Nature of the Study 
Topics covered in this sub-section are the three main research methods and the 
justification for the choice of method. Quantitative research entails data collection that 
focuses on precise and objective measurements that use numerical and statistical analysis 
to support or refute a hypothesis (Campbell, 2014). In this study, the use of precise and 
objective measurements is not a requirement because the researcher aims to capture the 
experiences and beliefs of participants.  
The approach of mixed-methods methods entails the use of a combination of 
qualitative and quantitative techniques, approaches, concepts and language (Yin, 2014). 
Plans to use oil price data as a variable for this study were absent. The planned approach 
for this study excluded the use of mixed-methods. The key reason for this approach was 
to avoid learning two research methods because I am a new researcher. 
Qualitative research and the results generated are of relevance to the management 
issues experienced, where the research may provide practical solutions to business 
problems (Guercini, 2014). Qualitative researchers strive throughout the research process 
to ensure methodological alignment by thinking about the aspects of the research that 
included the selected methods, and the data analysis (Kramer-Kile, 2012). The focus of 
this study—the influence of sustainability practices on profitability indicators—aligned 
with a qualitative approach because of the need to consider the views of participants. 
Design options for qualitative methodology included (a) ethnography, (b) 
phenomenological, (c) narrative, (d) grounded theory, and (e) case study (Yin, 2014). 
Ethnography entails prolonged observation of a group within their environment 
5 
 
 
(Compton-Lilly, 2015). Phenomenological studies entail the generation of meaning from 
the lived experiences of individuals to achieve an understanding of a phenomenon 
(Finlay, 2012). The narrative approach focuses on stories of lived experiences of an 
individual (Compton-Lilly, 2015). Grounded theory aims at theory creation based on data 
capture from studies (Staller, 2012).  
The use of designs aligned with ethnography, phenomenology, narrative, and 
grounded theory were not the preference because they did not present the opportunity of 
investigating the contemporary phenomenon of sustainability and the relationship with 
profitability in the real-world context. Ethnography was not under consideration because 
this design type aligns with behavior and social interactions (Staller, 2012). The narrative 
approach did not align with this study requirements because the lives of individuals were 
not the focus. I did not select grounded theory for this research topic because of the lack 
of focus on theory creation.  
Whenever the focus is on why more than an organization or act, a multiple-case 
study is the preference (Yin, 2014).The use of a multiple-case study facilitated this 
research because the boundaries between the phenomenon and context were not evident. 
Elements of a successful case study include (a) planning, (b) designing, (c) preparing (d) 
collecting, (e) analyzing, and (f) sharing of results (Yin, 2009).  
For this research, a multiple-case study enabled an exploration of the 
sustainability environmental practices that influence profitability in the petroleum 
industry. Bone (2014) stated that case study design could facilitate the exploration of the 
link between sustainability and profitability. The use of a qualitative methodology 
6 
 
 
encompassing a multiple-case study design allows for a quality study outcome (Yin, 
2014). Multiple-case studies can enable rigorous research and allow for the triangulation 
of data from different sources (Manning, 2016). The goal of this research was to explore 
the sustainable environmental practices that petroleum industry leaders use to affect 
profitability in the petroleum industry. For this study, the multiple cases involved four 
well-known petroleum industry corporations: BP, Chevron, ExxonMobil, and Shell. 
Research Question and Survey Questions 
The online questionnaire used 14 questions to help answer the research question: 
What sustainable environmental practices do petroleum industry leaders use to affect 
profitability? 
1. What, if any, sustainability practices does your employer execute? 
2. Describe how your employer implements sustainability environmental 
practices in the execution of business? 
3. How does your employer behave to illustrate their embrace of sustainability 
practices? 
4. What practices does your employer use to ensure all elements of the 
organizational system and structures benefit by the use of sustainability 
initiatives? 
5. What evidence leads you to believe that your employer focuses on 
environmental protection in alignment of sustainability? 
6. What are the thoughts of your employer about the influence of sustainability 
environment practices on profitability?  
7 
 
 
7. Which sustainability practices does your employer believe has a positive 
influence on profitability? 
8. Which practices does your employer believe negatively affects profitability? 
9. Which practices are beneficial to the environment and affects profitability 
neutrally? 
10. Are you of the belief that sustainability practices have an influence on 
profitability and can you justify reasons for your answer? 
11. What metrics does your employer use to determine how sustainable practices 
affect profitability? 
12. Can you discuss how aligned you are to the importance of sustainability based 
on your job responsibilities within the petroleum industry? 
13. How can you as a professional contribute to the implementation of 
sustainability practices as part of your professional responsibilities? 
14. Why do you think embracing sustainability is important and how does each 
part of the system including operations, finance, human resources, legal, and 
suppliers and customers contribute and benefit?  
Conceptual Framework 
This conceptual framework for this study was systems theory based on Von 
Bertalanffy’s (1972) theory. The definition of the word system from Greek history means 
reunion, conjunction or assembly (Soojin, Miso, & Joonhwan, 2011). In 1950, the 
theorist of this approach asserted systems theory as the necessity of investigating not only 
parts but also the relationships of organizations that resulted from the dynamic interaction 
8 
 
 
to understand behaviors. In systems thinking, applies nonlinear causal thinking 
to planning and management problems (Mirchi, Madani, Watkins, & Ahmad, 2012). 
Understanding a system may be of importance in the implementation of sustainability 
practices. 
Sustainability is a leading model for societal development (Christen & Schmidt, 
2012). A symbiotic relationship exists between sustainability and societal 
development (O’Conner & Gronewold, 2012). Senge et al. (2010) stated that the 
importance of a systems approach to implementing sustainability practices. For success, 
this systems approach entails consideration of all the components including (a) ecological 
systems, (b) fossil fuels, (c) global production, (d) waste, (e) standardization, and (f) the 
maximization of income (Senge et al., 2010). Thus, if all stakeholders of the societal 
system agreed to the importance of sustainability, implementation of this concept may 
become easier (Epstein & Buhovac, 2014a). 
Systems thinking is a theory for better understanding the interrelationships of the 
components within systems (Stacey, 2011). Because business and society have an 
interrelation, balancing business needs and societal needs creates a purposeful system 
(Kassel, 2012). By understand the relationship between elements, the behavior of the 
whole can be determined (Mingers & White, 2010).  
This paragraph outlines the elements considered in the creation of the online 
questions. The formulation of the online questions took into consideration the 
interrelationships between the different elements that contribute to the acceptance or non-
acceptance of sustainability as an important aspect of business. The formulation of the 
9 
 
 
questions followed a holistic appreciation of the need to understand what sustainability 
practices were in use, how organizations and the participants perceived these practices, 
why the practices were important, and how these practices facilitated the creation of 
themes that needed integration to deliver sustainability outcomes. The formulation of the 
questions also took into consideration the need to understand the relationship, if any, 
between the sustainability practices and profitability.  
For sustainability to work, social, environmental, and economic impacts must be 
managed (Epstein & Buhovac, 2014a). By using a systems thinking approach to guide 
this study, an understanding of the interrelations between the different elements was a 
possibility (Fischer & Zink, 2012). This understanding provided reasons why 
stakeholders embraced sustainability practices (Senge et al., 2010).  
Definition of Terms  
Biodegradable:  Material that is biodegradable has undergone conversion from 
harmful products to harmless end products for re-use (Adekunle, Igbuku, Oguns, & 
Shekwolo, 2013). 
Complexity. Complexity is the interrelatedness between chaos and order, 
communications, and organizations (Bouchier, 2012). 
Stakeholders. Stakeholders are those with a vested interest and include 
employees, business owners, suppliers, partners, the community, and the natural 
environment (Latham, 2014). 
Sustainability. Sustainability is the creation of long-term financial performance by 
addressing opportunities originating from economic, social, and environmental 
10 
 
 
performance inclusive of pollution reduction, recycling, waste management, and 
decreases in fuel consumption while facilitating the livelihood of future generation 
(Epstein & Buhovac, 2014b). 
Assumptions, Limitations, and Delimitations 
Assumptions 
This study was based on 4 assumptions. Researchers’ assumptions shape the 
research they undertake (Kirkwood & Price, 2013). These are the facts that the researcher 
assumes to be true, but cannot verify. Assumptions can enable interpretations of results to 
enhance conclusions (Bennell, Snook, Macdonald, House, & Taylor, 2012). 
 The expectation that the participants who participated in this study had 
experience and familiarity with their employer’s sustainability practices was a 
reality. This expectation held true because the participants included 
experienced engineers and technical/operations employees immersed in the 
sustainability activities of their organizations aimed at environmental 
protection (Williams & Dunwoody, 2012). 
 The participants would respond truthfully to the interview questions (Yin, 
2014).  
 The participants would be familiar with the profitability outcomes of their 
employers and could provide responses that aligned sustainability efforts to 
changes if any profitability.  
 The last assumption was that the participants recognized the importance of 
sustainability activities as a business practice (Jooh, Pati, & Roh, 2011). 
11 
 
 
Participants’ responses to the research questions did clarify this assumption as 
expected of studies of this nature (Khankeh, Ranjbar, KhorasaniǦZavareh, 
ZarghamǦBoroujeni, & Johansson, 2015).  
These assumptions bounded the study. Participants’ education and experience 
aligned with the expectation that truthful answers were an expectation and would 
contribute to bias reduction goals because of confidentiality. In support of the last 
assumption, participants did recognize the importance of sustainability as a necessary 
business practice for long-term viability. The idea that these participants were willing to 
contribute to the study indicated the importance of this issue, and the willingness to 
provide honest answers. 
Limitations 
Researchers need to be aware of their study’s limitations (Seeber, 2013), that is, 
constraints or boundaries that of potential weaknesses in the study (Kirkwood & Price, 
2013). A potential weakness of the study is the plan to use participants on a limited 
geological environment despite the global nature of the petroleum industry. The 
presentation of issues related to bias during research adds to validity and reliability 
outcomes (Yin, 2013). The use of sustainability reports written internally by petroleum 
companies as a form of improving data reliability may also introduce bias. The multiple-
case study approach may display what happens in the United States, but the results may 
not be valid for other geographical areas in the petroleum industry.  
Data collection during a period of depressed oil prices may be a limitation of the 
study. Oil and gas companies focus on operational cost cutting during a period of falling 
12 
 
 
oil prices (Pierce, 2014). It was possible that sustainability initiatives aligned with 
operational cost and yielded negative outcomes (Epstein & Buhovac, 2014a). Further 
research on this topic should have the ability to show that environmental protection can 
yield economic prosperity (Beckmann, Hielscher, & Pies, 2014). 
Delimitations  
Delimitations are entitlements or boundaries (Huang & Liao, 2014). In this study, 
participants were limited to 20 petroleum industry participants who had at least 5 years’ 
experience working for a single petroleum exploration and production company. The 
participants had experience working in the United States may not be aware of 
sustainability practices elsewhere within the company or the industry. Petty, Thomson, 
and Stew (2012) stated that the importance of using experienced sustainability 
practitioners to understand sustainability outcomes. The sample of participants included 
engineers who had experience in, and responsibility for, sustainability environmental 
practices in the production of petroleum fluids.  
The participants would describe, without bias, their perceptions of current 
sustainability practices and how, if at all, they think these practices affected the 
profitability of the employer with which they had U.S experience. Ienciu, Popa, and 
Ienciu (2012) stated the importance of independent thinking in the evaluation of 
sustainability practices. Outside the scope of this study are the sustainability practices of 
other companies, and practices in other geographical regions.  
13 
 
 
Significance of the Study 
Contribution to Business Practice 
Sustainability involves living in the present in ways that do not jeopardize the 
future (Senge et al., 2010). Threats to sustainability include (a) ineffective leadership and 
strategy; (b) absence of cultural alignment; (c) absence of performance management, 
evaluation, and reward systems; and (d) perceptions of non-value-adding costs (Epstein 
& Buhovac, 2014a). These threats require mitigating actions to facilitate the ability to 
deliver business goals (Harvard Business School, 2005). 
Sustainable development (SD) is necessary to enhance business practices (Lion, 
Donavan, & Bedggood, 2013). Organizational leaders have the ability to implement 
practices that will enhance the sustainability of the environment, society, and economics 
(Abdulrahman, Huisingh, & Hafkamp, 2015). Companies focusing on sustainability 
could experience win-win scenarios at an ecological, social, and governance level 
(Beckmann et al., 2014). According to Epstein and Buhovac (2014a), achieving 
sustainability requires concentration on the triple bottom line: (a) the environment, (b) 
social issues, and (c) economic outcomes. Sustainability aligns with improvements in 
business performance (Haanaes, Michael, Jurgens, & Rangan, 2013). The outcome may 
result in a competitive advantage for the petroleum industry against the competition of 
energy renewable organizations. 
Implications for Social Change 
Social change was also the intention of this study. Attaining sustainability 
outcomes in the petroleum business must include social elements (Schneider, 2013). The 
14 
 
 
purpose of this study was to determine the influence, if any, of practices that support 
environmental sustainability while improving profitability in the U. S. petroleum 
industry. An objective was for stakeholders aligned with petroleum companies to 
embrace sustainability practices as a solution for value creation to improve business 
performance. This objective may improve the lives of individual communities within the 
petroleum industry, which is a social outcome, and avoid the negative financial outcomes 
experienced by the Exxon Valdez Alaska spill, British Petroleum’s Macondo blowout, 
and Occidental Petroleum Piper Alpha environmental incidences (Dittrick, 2013; 
Weaver, 2014). These unfortunate incidents resulted in the loss of (a) human and animal 
lives; (b) ecological capital; and (c) billions of dollars (Dittrick, 2013; Weaver, 2014). 
The expectation was for sustainability efforts to increase the ability of petroleum 
companies experience profitability while providing social benefits to the communities in 
which the companies operate via jobs, improved health benefits, schooling and superior 
quality of life. The expectation is for long-term sustainability to be a success (Petrovich, 
2014). 
A Review of the Professional and Academic Literature  
Introduction 
Literature reviews can help develop sharper and more insightful questions about a 
topic (Yin, 2014). Reviews can also relate to larger ongoing dialogue in the literature by 
showing gaps that justifies extending prior studies (Cooper, 1984; Marshall & Rossman, 
2006). Literature reviews should (a) indicate how the literature allows for the creation of 
the research question; and (b) provide connectivity to the doctoral study (Yin, 2014).  
15 
 
 
The organization of the review. The purpose of this review was to evaluate the 
literature on sustainability and its influence on profitability. A brief description of the 
content and organization of the review previews a description of the search strategy. The 
literature review itself includes a critical analysis of the conceptual framework and an in-
depth discussion of eight themes on (a) business; (b) petroleum industry; (c) practices; (d) 
profitability; (e) leadership; (f) change management; (g) policy and planning; and (h) 
self-regulation. These themes help in the identification, extraction, and synthesis of 
information that can help answer the research question (Foster, 2013). 
Critical analysis and synthesis of various sources.  
This study focuses on the influence of sustainability practices on profitability in 
the petroleum industry. An exhaustive review of the literature using electronic databases 
can reveal a problem (Foster, 2013). In reviewing the literature, the focus was on the 
elements of SD, sustainability, sustainability practices, and the positive and negative 
perceptions of these issues.  
Strategy in searching the literature. Web searches are an important activity for 
data capture (Maloney & Yoxtheimer, 2012). This approach is in alignment with Dixon-
Fowler et al. (2013) who stated the importance of manual searches when executing 
research. Thus, the three key aspects of the study are sustainability, profitability, and 
petroleum. The searches were in focus on these three elements because this approach 
allows for specification searches that can improve research outcomes in sustainability 
studies (Hajmohammad & Vachon, 2014). The strategy in searching the literature 
16 
 
 
centered on the Thoreau database that facilitates access to multiple databases in the 
Walden University system.   
The following keywords—singly or in combination— were in use; (a) 
sustainability, (b) sustainability practices, (c) SD, and (d) environmental practices. Other 
words and phrases included (a) profitability, (b) profit, and (c) success. Additional 
keywords and phrases included (a) petroleum and (b) oil and gas. As per Walden 
University, the use of peer-reviewed articles published within 5 years of graduation, 
choosing the peer-reviewed option, and bracketing the search between 2012 and 2017 
was a requirement. This approach generated a 100% rate of articles published within the 
required 5-year period.  
Summary of the peer-reviewed literature. The literature review contains 103 
references of which, 11 (11%) are outside the 3–5 -year window as required by Walden 
University and 88 (85.4%) are peer-reviewed, which aligns with Walden University 
requirements. Five textbooks were also of use for the literature review. Additional 
sources of information included a review of the annual sustainability reports of petroleum 
companies including BP, Chevron, ExxonMobil, and Shell. The obtainment of these 
reports was from the company public websites. Additionally, a review of the public 
website of the Environmental Protection Agency (EPA) allowed for guidance in focusing 
the study towards key environmental concerns and practices. 
Application to the Applied Business Problem 
The purpose of the study. The purpose of this qualitative multiple-case study 
was to explore the sustainable environmental practices petroleum industry leaders use to 
17 
 
 
affect profitability. It is possible to balance sustainability practices and profitability 
outcomes to benefit petroleum companies (Du et al., 2013; Parast & Adams, 2012). The 
expectation is for this research to unveil the contribution of these practices to the 
petroleum business. 
The target population for this study was operations personnel and leaders in the 
petroleum industry who had experience in the United States. Front line leaders attained 
sustainability by bridging any gaps between their superiors and followers (Merriman & 
Sen, 2012). Targeting the leaders allowed for changes in these business practices that 
may contribute to sustainability implementation (Epstein & Buhovac, 2014a).  
The implication for positive social change is that leaders in private and public 
organizations might use the findings to improve profitability. Profit and sustainability can 
and must go together because a social benefit exists (Voser, 2012). Sustainable practices 
are a requirement to enhance business outcomes including reputation and financial 
performance (Petrovich, 2014). Additional outcomes included (a) improved employee 
morale, (b) increased employee loyalty, and ultimately, (c) more-efficient business 
operations (Florea, 2012). 
Conceptual framework. The conceptual framework for this study was one 
aligned with systems theory. A system is a set of interdependent components organized 
by design to accomplish one or more objectives (Garrity, 2012). Mirchi, Madani, 
Watkins, and Ahmad (2012) stated that the use of systems theory provides methods and 
techniques to apply non-linear causal thinking to planning and management problems. 
Systems may comprise of and organize by subsystems, and each of these may interact 
18 
 
 
with each other as well as with their environment and share information (Garrity, 2012). 
Systems theory also points to the limits of predictability by introducing circular causality, 
which makes for difficulty in the determination of what causes what, or what precedes 
what (Stacey, 2011). With humans using the resources of nature for existence, harm 
comes to our habitat (Stevens, 2012). In the case of this study, the questions arise whether 
what environmental practices may have an influence on profitability or whether being 
profitable may cause the implementation of sustainability in the petroleum business. 
 
Society depends on sustainable approaches for development (O’Conner & 
Gronewold, 2012). A systems approach is a requirement for the implementation of 
sustainability practices (Senge et al., 2010). Davidson and Venning (2011) stated that the 
application of systems thinking contributes to an integrated decision-making framework 
because such approaches encourage a thorough examination and evaluation of (a) goals 
and objectives; (b) the relationships between inputs, throughputs, and outputs; (c) 
processes for evaluation, feedback, and review; and (d) the context or environment. The 
failure to take advantage of systems thinking results in decision-making processes being 
less effective than they could be. With the need for the application of systems theory to 
decision-making in the implementation of sustainability practices, the examination and 
evaluation of the elements listed above may be of critical importance. 
 
A comprehensive systems approach is a requirement for effective decision 
making regarding global sustainability (Fiksel, 2012). Garrity (2012) stated the 
importance of the use of systems theory in the analysis of the overuse of the natural 
resources. Thinking systematically also enables a review of the literature to unveil items 
19 
 
 
of interest for research (Foster, 2013). Similarly, in the petroleum industry, systems 
thinking may facilitate an understanding of why the management of some companies 
exploits natural resources to extinction for the benefit of their companies and at the same 
time may or may not have an unwillingness to embrace sustainability practices. 
An eco-social relationship exists between the environment and humanity 
(Stevens, 2012). This relationship creates situations where humans make decisions and 
implement changes that are harmful to the environment (Stevens, 2012). Inadequate 
knowledge and tools hinder the successful implementation of sustainability into 
organization systems (Gallo, 2012). Understanding the interrelationships among the main 
factors that contribute to acceptance of sustainability requirements was possible by the 
development of a conceptual model using systems thinking (Gonzalez, Sandoval, Acosta, 
& Henao, 2016). This result indicated the importance of systems thinking in 
understanding the interrelationships that are important in the implementation of 
sustainability. 
The use of system thinking in a greenhouse gas emissions reduction initiative 
aimed to reduce school emissions by 10 tonnes (metric tons) in one year, was a success 
(Lewis, Mansfield, & Baudains, 2014). The achievement of the goal was a reality 
because of the systems thinking approach used to implement a variety of environmental 
and social actions undertaken by the parents, students, teachers, and community partners 
(Lewis et al., 2014). A link between system thinking and sustainability unfolded when 
college students Hedmark University College used a pervasive game to teach 
sustainability while giving elementary students a real world experience (Nordby, 
20 
 
 
Oygardslia, Sverdrup, & Sverdrup, 2016). The game allowed for a collaborative 
environment. Collaboration while working together promoted the use of a systems 
thinking approach in the solution of problems related to the environment (Westphal & 
Zajac, 2013). 
A systems thinking approach was important in understanding and describing 
fishers’ ways of knowing and dealing with complexity in ecosystems (Garavito-
Bermudez, Lundhold, & Crona, 2016). Elements identified as being important included 
(a) feeding interactions; (b) populations’ dynamics over time; (c) climate change, water 
quality, and overfishing; and (d) the ecosystem (Garavito-Bermudez et al., 2016). 
Systems thinking theory was arguably part of fishers’ professional skills and significant 
for sustainable natural resource management. Systems thinking also facilitated the 
successful development of a causal model to understand what were the different factors 
and factor relationships in wind energy at the system level related to sustainability 
(Tejada & Ferriera, 2014). Systems thinking allowed for a holistic understanding of the 
system and provided a better understanding of its behavior (Stacey, 2011). 
Themes and phenomena. An investigation of sustainability dilemmas can 
provide the ability to benchmark global sustainability best practices (Jayanti & Gowda, 
2014). The focus of the literature review was to discuss different dilemmas that may 
influence sustainability. The themes of the literature review entailed eight topics. These 
topics were (a) sustainability in business, (b) sustainability challenges in the petroleum 
industry, (c) sustainability practices, and (d) influence on profitability. A review of the 
relationships between organizations, the environment, and society is of importance when 
21 
 
 
researching sustainability (Linnenluecke & Griffiths, 2013). Other themes, which may 
allow for an understanding of sustainability challenges, included (a) sustainability 
leadership; (b) change management for sustainability; (c) policy and planning issues; and 
(d) self-regulation vs. legal requirements. These themes displayed items in the literature 
concerning sustainability that aligns with this doctoral study. The presentation of gaps in 
the literature allowed for showing how this study can bridge those existing gaps where 
possible and display personal mastery of the research topic (Yin, 2014). 
 
Sustainability in business. Much disagreement about the idea of sustainability 
exists and this situation results in the unsatisfactory outcome (Christen & Schmidt, 2012). 
The perceptions that the concept of sustainability is arbitrary allow for the loss of action-
guiding power (Christen & Schmidt, 2012). Dixon-Fowler, Slater, Johnson, Ellstrand, 
and Romi (2013) studied whether the use of sustainability was profitable. The variables 
of their study included return on assets (ROA), stock price, sales growth, and market 
share. In this study, the topic of discussion was that small firms benefit from 
environmental performance as much or more than large firms and environmental 
performance seems to have the strongest influence on market measures of financial 
performance (Dixon-Fowler et al., 2013).  
In addition, Issa, Attalla, Rankin, and Christian (2013) completed a detailed 
statistical analysis of the costs related to upgrading Toronto schools to a sustainable 
status by the use of greener practices. The results of the quantitative study indicated no 
statistically significant difference between upgrade costs in conventional, energy-
retrofitted, and green schools (Issa et al., 2013). Research concerning the ability of 
22 
 
 
sustainable supply chains to generate profitability outcomes indicated that waste 
elimination in conjunction with profitability improvement is a possibility (Kumar, 
Teichman, & Timpernagel, 2012).  
Completed research entailed the challenges technology organizations faced in 
balancing SD with conventional profit-driven development (Du et al., 2013). The case 
study involved China Mobile that launched a Green-IT program with impressive results 
in 2007. The study results indicated the importance of collaboration amongst the key 
stakeholders including (a) top management, (b) business units, (c) supplier networks, and 
(d) customer networks. By collaborating, a balance between sustainability and profitable 
business outcomes is a possibility (Du et al., 2013). The study highlighted the importance 
of the key constructs of ambidexterity including (a) strategic renewal, (b) organizational 
configuration, (c) ecosystem redefinition, and (d) market renormalization.  
One of the key observations of businesses that embrace sustainability approaches 
is the use of reporting on sustainability outcomes (Lynch, Lynch, & Casten, 2014). 
Stakeholders can use these reports to understand the value added in businesses. Drohan et 
al. (2012) successfully documented their findings from the Goddard Forum on the oil and 
gas impacts on forest ecosystems. By reporting their findings, stakeholders gained a 
comprehensive prospective on the management challenges and research needs for 
evaluating the impacts and generating possible solutions to mitigate environmental risks 
(Drohan et al., 2012). Sustainability efforts requires managing planet Earth as a 
corporation (Mac Cormac & Haney, 2012). The statements in the section are in alignment 
23 
 
 
with the understanding that focusing efforts on sustainability, review use of commas as a 
business problem may allow for appropriate creating of measures to provide solutions. 
 
 
The aim of this doctoral study was to study the business problem concerning the 
influence of sustainability environmental practices on organizations involved in the 
petroleum business. The literature indicated that judgment on the benefits of 
sustainability practices is still pending conclusion (Heffes, 2011). The expectation was 
for this study to provide more clarity on this issue. 
Sustainability opportunities in the petroleum industry. Even though petroleum 
fuel is a requirement for human activities, the burning of such fuels produces gases that 
are harmful to humanity (Liu, Lin, & Sagisaka, 2012). Petroleum companies are liable for 
damage to the environment as per the case of Shell in the Nigerian Niger Delta 
(Hennchen, 2014). Because of increasing evidence of climate change and harm to the 
environment, businesses embraced an active role in the preservation of resources for 
future generations (Jayanti & Gowda, 2014). The need exists for sustainability outcomes 
in the petroleum business (Voser, 2012). Attempts at implementing sustainability in the 
petroleum industry do come with challenges (Aaron, 2012). This conclusion was the 
result of a study to assess the impact of new models of corporate-community 
engagements in response to the failings of older models by petroleum industry trans-
nationals. When Chevron and Shell attempted to implement SD practices in host 
communities in Nigeria’s petroleum producing regions, setbacks were the outcome 
(Aaron, 2012). The question arises as to what practices petroleum companies should 
implement to attain sustainability goals. 
24 
 
 
Companies in the petroleum industry should embrace the practice of educating 
stakeholders in their sphere of operation on the environmental impacts of executing their 
businesses (Willits, Luloff, & Theodori, 2013). Shell (2013) focused on local 
stakeholders wherever they do business to avoid conflicts and ensure the dissemination of 
knowledge about environmental protection efforts occurs. The use of sustainability 
indicators enhances the ability to provide information and knowledge that educates the 
public on sustainability performance (Singh, Murty, Gupta, & Dikshit, 2012).  
The importance of aligning sustainability with research and development efforts 
for continued profitability in the global petroleum industry (Jooh et al., 2011). 
Sustainable environmental practices in the petroleum industry can (a) minimize land 
utilization; (b) reduce waste and air pollution; (c) reduce pollution of underground water 
(Yi et al., 2012). An important sustainability challenge in the petroleum industry was the 
management of produced water to avoid negative public health effects or associated 
environmental issues (Wilson & VanBriesen, 2012).  
Environmental reporting and the practice of astute corporate governance can 
facilitate sustainability outcomes (Ienciu, et al., 2012). This objective is possible by the 
establishment of committees focused on (a) safety, and (b) the environment that monitor 
and transparently report on the environmental impact of the company (Ienciu et al., 
2012). This approach can deliver an improvement in environmental compliance 
performance. Ingelson and Nwapi (2012) stated that environmental compliance is a 
possibility with the use of environmental impact assessments (EIAs). These EIAs should 
be in alignment with the laws to ensure environmental sustainability outcomes and should 
25 
 
 
entail the phases of (a) project proposal, (b) screening, (c) scoping, (d) report submission 
to authorities, (e) decision-making, and (f) project implementation (Ingelson & Swapi, 
2012). 
O’Connor and Gronewold (2012) stated that by embracing the need of corporate 
social responsible as a practice can allow for sustainability outcomes in the petroleum 
industry. This responsibility may entail changes in the products and services in use. The 
use of biofuels as a fuel stock product is an opportunity for the reduction of harmful gases 
produced from fossil fuel consumption (Liu et al., 2012). Ullah, Bano, and Nosheen 
(2014) agreed with Liu et al. (2012) by stating that the pollution and non-sustainable 
outcomes associated with fossil fuel guided global interest towards the use of biofuels as 
a sustainability practice. The expectation was for this practice to continue (Ullah et al., 
2014). 
In the planning phase of hydrocarbon exploration, prospectors need to evaluate 
and determine the impact on the environment (Shell, 2015). The issues of importance 
include (a) the footprints results of energy development; (b) the integration of best 
practices; and (c) impacts on other industries (Drohan et al., 2012). Successful petroleum 
industry operations should also include (a) the need to align operations with regulatory 
requirements; (b) how to protect sensitive environmental resources; and (c) land 
reclamation (Drohan et al., 2012). Astute project management is a necessity to achieve 
sustainability outcomes in the petroleum industry (Hopkins, 2011). 
Any form of mineral extraction is an unsustainable enterprise (Pierce, 2014). 
Drilled wells eventually need plugging and abandoning and operation practices should 
26 
 
 
involve (a) economics; (b) adequate funding for abandonment; (c) taxation incentives to 
achieve environmental sustainability; (d) focus on reservoir development as a unit 
(Pierce, 2014). Companies in the petroleum industry should also focus on pollution 
management during operations (Tatoglu, Bayraktar, Sahadev, Demirbag, & Glaister, 
2014). Waste management, in general, provides an opportunity for sustainability outcome 
successes (Senge et al., 2010). Maloney and Yoxtheimer (2012) stated that an imperative 
practice was to manage the waste products for oil and gas extraction to minimize the 
negative economical outcomes from hydraulic fracturing in unconventional petroleum 
operations. 
Avoiding the flaring of produced gas is an opportunity for reducing emissions. 
Abdulrahman, Huisingh, and Hafkamp (2015) stated that when a reduction in the burning 
of hydrocarbons occurs, the negative impact on the environment reduces. Shell (2015) 
stated that carbon management is a necessity to avoid harmful gases pollution. Additional 
enhancement of this approach entails the use of solar energy to provide power in the 
petroleum industry (Pinkse & Van Den Buuse, 2012). Companies’ willingness to 
embrace solar energy is a function of their ability to integrate this energy type into the 
supply chain, and economics (Pinkse & Van Den Buuse, 2012). 
Shell (2015) stated that their key practices that enhance environmental 
sustainability included (a) risk identification and mitigation; (b) recycling; (c) waste 
management and disposal; and (d) energy conservation. Senge et al. (2010) proposed the 
waste reduction is a possibility by the use of (a) natural nutrients that are biodegradable 
products of industrial processes, and (b) technical nutrients that can circulate back into 
27 
 
 
the creation of new products. Fluid waste disposal in the petroleum industry should 
incorporate the use of (a) recycling, (b) re-injection, and (c) disposal at sewage treatment 
plants (Maloney & Yoxtheimer, 2012). 
BP focused on environmental sustainability in their petroleum operations by the 
use of (a) innovative well control systems; (b) water table isolation; (c) fluids recycling; 
(d) regulated disposal; and (e) leakage protection systems (BP, 2015b). Other practices 
for environmental sustainability in the petroleum industry includes (a) carbon 
management, (b) cleaner burning fuels, (c) alternative energy use, (d) drinking water 
protection, and (e) green building for office space (Shell, 2015). The aims of these 
practices are to (a) preserve biodiversity, (b) minimize deforestation, (c) maintain health, 
and (d) achieve economic viability (Nickel, 2014). 
For drilling of new wells, the use of slim hole techniques and coiled tubing, 
resulted in (a) cost reduction; (b) lower waste volumes; (c) smaller environmental 
footprints; and (d) reduced noise and fuel consumption (Rocca & Viberti, 2013). 
Companies must focus on data and process traceability at every step on the petroleum 
industry supply chain (Bureau Veritas, 2015). By the use of astute reservoir 
characterization and data capture, the possibility exists for an understanding of the fluid 
types that requires management to prevent environmental issues (Rocca & Viberti, 2013). 
This approach will allow for effect air emissions and carbon capture management (Rocca 
& Viberti, 2013). The expected result is a reduction of environmental harm as required by 
stakeholders (Yi et al., 2012). 
28 
 
 
A review of the literature did unveil some of the sustainability challenges in the 
petroleum industry yet documentation in a single study is absent. In addition, authors of 
the literature failed to review the direct influence of sustainability environmental 
practices on profitability, which this study aims to accomplish. Key petroleum industry 
sustainability practices unveiled by a review of the literature surrounds (a) waste 
reduction, (b) recycling, (c) use of cleaner fuels, (d) stake holder engagement, and (e) 
project planning. 
Sustainability practices. Hashmi and Al-Habib (2013) studied sustainability and 
carbon management practices in Saudi Arabia. Von Hirshhausen, Holz, Gerbaulet, and 
Lorenz (2014) stated the importance of decarbonization as a sustainability practice in the 
energy sector in Europe. Responses to surveys completed by Saudi enterprise managers 
indicated the importance of the practice of carbon emission management as a 
sustainability practice (Hashmi & Al-Habib, 2013). Findings from this study can assist 
policy makers and leadership of public and private sector enterprises to formulate future 
sustainability and carbon management policies (Hashmi & Al-Habib, 2013). A similar 
focus on the importance of carbon capture as a practice that can facilitate the expansion 
of Chinese oil companies into global markets to ensure energy security is in execution 
(Vermeer, 2015).  
Managing the risks associated with oil and gas development is an important 
practice (Brasier et al., 2013). The perception is that hydraulic fracturing causes harm to 
the environment and human existence so that all associated activities require scrutiny 
(Brasier et al., 2013). Drohan et al. (2012) stated the importance of aligning activities 
29 
 
 
with regulatory requirements. The development of best practices guidelines is also of 
critical importance (Finer, Jenkins, & Powers, 2013). 
The use of conceptual plans is a best practice for petroleum development in South 
America for all phases of such projects (Finer et al., 2013). These plans should be 
available before commencement of any work. Other best practices include (a) the use of 
computer modeling, (b) horizontal drilling, and (c) the prohibition of new access roads 
(Finer et al., 2013). Additional practices include (a) no permanent camps in the jungle 
interior, (b) allowance for only air and river transport; and (c) limitation on the right of 
way construction for pipelines (Finer et al., 2013). These practices fulfilled the focus of 
executing business in environmentally sensitive industries (Bachoo, Tan, & Wilson, 
2013). 
Additional proposals included (a) astute pipeline construction to prevent ruptures 
and leaks; (b) funds set aside for site abandonment and ecosystem remediation; and (c) 
consideration as to whether the pursuit of oil and gas development is a necessity (Finer et 
al., 2013). Practices that can also have a positive impact on sustainability goals included 
(a) an understanding of the footprints results of energy development; (b) how to protect 
sensitive environmental resources; and (c) land reclamation (Drohan et al., 2012). The 
reduction of (a) heavy equipment traffic, (b) air emissions, and (c) population growth are 
also appropriate practices in environmental protection (Brasier et al., 2013). 
Before embarking on a petroleum oilfield development project the following are a 
necessity (a) a detailed analysis of existing and planned hydrocarbon activities and 
infrastructure; (b) an evaluation of the planned activities; and (c) infrastructure 
30 
 
 
requirements with accompanying expected environmental impacts (Finer, Jenkins, & 
Powers, 2012). The ability to measure and report issues aligned with sustainability 
initiatives is critical (Ienciu et al., 2012). This practice entails astute corporate 
governance whereby reporting practices illustrate transparency, attract appropriate 
oversight, and critique (Ienciu et al., 2012). 
Maloney and Yoxtheimer (2012) proposed best practices for the disposal of waste 
products from drilling activities in the Marcellus Shale in Pennsylvania. These practices 
include (a) the use of landfills, and (b) the use of interstate and inter-basin transport. 
Other practices included flow-back water reuse and injection into disposal wells and 
industrial waste treatment plants (Maloney & Yoxtheimer, 2012). In avoiding spills from 
petroleum development operations, the following practices are a requirement: (a) 
improved prediction of worse case scenarios; (b) planning ahead to prevent damage to the 
environment; and (c) determination and presentation of a risk profile so that mitigating 
actions are available should future problems occur (Michaels, 2010). Wilson and 
VanBriesen (2012) stated the importance of water treatment management to ensure water 
by-products from petroleum development operations do not create environmental 
concerns. Spacing remains a major concern. 
The use of environmental impact assessments (EIAs) proved successful in the 
determination of the success of sustainability practices (Ingelson & Nwapi, 2014). Yip et 
al. (2012) also presented the importance of EIAs in the management of sustainability 
initiatives. Konne (2014) noted the importance of monitoring and enforcement to deliver 
sustainability in the Nigerian petroleum industry. Times existed when lawsuits in 
31 
 
 
international courts are a requirement for national governmental compliance. In Konne’s 
study, more astute monitoring of international petroleum companies was the practice 
required to get the results expected by local tribes in the delivery of profits. 
Best practices are those that focus on (a) inputs, (b) processes, (c) outputs, and (d) 
outcomes (Epstein & Buhovac, 2014b). British Petroleum focused on the generation of 
environmentally friendly energy sources (Quisenberry, 2012). General Electric realized 
$16 Billion in profits by the implementation of practices entailing (a) strategic thinking, 
(b) optimizing managerial processes, and (c) operations efficiency improvement 
(Quisenberry, 2012). Stochetti (2012) stated the importance of practicing a micro level 
approach to sustainability. In addition, having a green supply chain can also deliver 
sustainability outcomes (Timpernagel, 2012). 
Well known petroleum industry super-major Shell includes the following in their 
environmental sustainability practices: (a) risk identification and mitigation; (b) 
recycling; (c) waste minimization and disposal; and (d) energy conservation (Shell, 
2015). ExxonMobil (2015) focused on (a) environmental emergency preparedness, (b) 
energy efficiency, and (c) environmental protection compliance. Additionally, Shell 
(2015) focused on (a) carbon emissions management, (b) cleaner burning fuels, (c) 
alternative energy use, and (d) drinking water protection. Practices for environmental 
cleanup include (a) proactive minimization of site disturbance; (b) the design of 
complementary site re-use infrastructure; and (c) maintenance of the natural setting 
(EPA, 2013).  
32 
 
 
In summarizing the sustainability environmental practices, studies indicated 
numerous options for the prevention of environmental incidences (Finer, Jenkins, 
&Powers, 2012; Ingelson & Nwapi, 2014; Maloney & Yoxtheimer, 2012). A lack of 
clarity exists on how these practices can directly influence the financial performance of 
organizations and so increase the value of a firm. The goal of this study was to bridge this 
gap and deliver a clearer understanding of the alignment between sustainable 
environmental practices and profitability results. 
 
Influence on profitability. The question asked was how does the implementation 
of sustainability practices influence profitability? Profitability aligns with the ability to 
generate positive net cash flows (Ross, Westerfield, & Jaffee, 2013). The question of how 
profitability emerges and evolves is a central issue in strategic management and industrial 
organization research (Jacobides, Winter, & Kassberger, 2012). Sustainable profits may 
represent a small part of the total wealth created over time by a firm and industry 
(Jacobides et al., 2012). A challenge for managers was an understanding of the complex 
interrelationships between social, environmental, economic, and financial performance 
(Epstein & Buhovac, 2014a). 
 
Epstein and Buhovac (2014b) stated that increased profits are possible upon the 
implementation of sustainability practices. This statement supports Baumgartner (2013) 
who stated sustainability could create success, innovation, and profitability for 
companies. Sustainability can allow for cutting overhead costs that can have positive 
outcomes on profitability (Richerson, 2013). For the realization of profitability to occur 
key elements include (a) management commitment, (b) a sustainability strategy, (c) 
33 
 
 
reduced environmental impact, (d) reputation improvement, and (e) lower costs (Epstein 
& Buhovac, 2014b). Of importance was the ability to implement cost-effective value-
added sustainability improvements that will add to the bottom line (Klein, 2015a). 
 
The need exists to blend the triple bottom line of people, planet, and profits to 
achieve sustainability (Klein, 2015b). This approach was a necessity because 
sustainability offers companies opportunities for cost savings, efficiency improvement, 
and the attraction of new customers and suppliers (Kumar, Teichman, & Timpernagel, 
2011). The resultant competitive advantage allows for the generation of profits (Kumar et 
al., 2012). This outcome was also possible by engaging the loyalty of workers in the 
range of knowledge protection (Mazur & Kulczyk, 2013).  
 
Focusing on sustainability in the long-term can give an extended competitive 
advantage (Epstein & Buhovac, 2014a). The protection of a long-term competitive 
advantage was more complicated than the protection of current profits (Mazur & 
Kulczyk, 2013). An option for accomplishing this objective was to increase efficiency by 
(a) the reduction of the use of raw materials; and (b) waste elimination (Kumar et al., 
2012). 
 
Often organization leaders and scholars assumed the need exists to sacrifice 
profits in their pursuit of sustainable activities (Quisenberry, 2012). Questions asked of 
business leaders often include making false choices between being profitable and being 
environmentally sustainable (Scherer, 2013). Improved accounting was a practice that 
can facilitate the guidance of leaders to examine the possibility of waste reduction to 
result in improved profitability outcomes (Scherer, 2013). The focus should be on 
34 
 
 
focusing the opportunities available to link environmental issues to financial ones. The 
common sense of sustainability was to minimize the impact of the company’s activities 
on the environment and on social discomfort without sacrificing profitability (Stocchetti, 
2012). 
 
In summary, the implementation of sustainability practices was possible while 
achieving profitability outcomes (Quisenberry, 2012). Achievement of the required 
outcomes necessitates understanding the linkages between sustainability and profitability 
(Epstein & Buhovac, 2014b). A crucial role exists with the involvement of leadership and 
control systems that emphasizes the interdependencies between an organization’s 
processes (Stocchetti, 2012). 
Leadership role in sustainability. In the creation of a competitive advantage for 
their organizations, leaders in the world’s largest petroleum companies use 
communication as a vehicle to show the benefits of environmental sustainability 
(O’Connor & Gronewold, 2012). Singh, Murty, Gupta, and Dikshit (2012) noted the 
importance of leaders in strategy formulation in the delivery of sustainability initiatives. 
To illustrate corporate social responsibility, leaders must focus on issues such as global 
warming, and the effects of this phenomenon on business outcomes (Bell & Lundblad, 
2011). 
 
Leaders must explore the effects of non-technical risks to understand their impact 
on sustainability initiatives (Hopkins, 2011). Treating the environment with respect was a 
requirement for sustainability (Hopkins, 2011). Having the ability to ensure an optimal 
balance between the environment and the use of human capital was a requirement for 
35 
 
 
effective leadership (Mezher, Tabbara, & Hosani, 2010). Effective leaders ensure than 
while managing their organizations, the use of environmental strategies was equally 
important (Delmas, Hoffmann, & Kuss, 2011). 
 
Leading human capital development aids in sustainability by the enhancement of 
business performance (Monday, 2015). For SD to be a success, scaling up of 
sustainability leadership was a requirement (Christopoulous, 2012). Key elements in 
leading an approach towards sustainability include (a) the integration of different modes 
of governance; (b) disclosure of information; and (c) knowledge and institutional learning 
(Christopoulos et al., 2012). Other elements include (a) empowerment of weaker players; 
(b) deliberation on appropriate choices in governance and policymaking; and (c) enabling 
appropriate local practices in cross-border work activities (Christopoulos et al., 2012). 
 
The literature has documentation concerning the importance of astute leadership 
as a practice that influences the positive aspects of sustainability (Makipere & Yip, 2008; 
Mirchandani & Ikerd, 2008). Gaps exist with how different leadership activities may 
affect the management of sustainability environmental practices. This issue was an area 
of study via the use of the online questionnaire questions documented in Section 1. 
Change management for sustainability. Organizational sustainability may 
require change management approaches (Brannmark & Benn, 2012). This statement may 
be true for organizations that do not presently embrace sustainability as a means of doing 
business. Change management aligns with diversity, leadership, and sustainability 
initiatives (VanEyde, Maes, VanEyde, & Unyzeitig, 2013). Vora (2012) stated the 
importance of change management to sustainability. Elements of change management 
36 
 
 
include (a) leadership, (b) sustainability, (c) strategic planning, (d) culture, (e) learning, 
and (f) negotiation (Van Eyde et al., 2013). Francis-Jennings and Lewis (2014) stated the 
use of two approaches to facilitate sustainable change as (a) content neutral facilitation 
(CNF), and (b) positive defiance (PD). Key items of the CNF and PD approaches include 
(a) defining a sustainability strategy, (b) considering the needs of stakeholders, (c) 
communication among managers, supervisors, and subordinates, (d) ownership of the 
justification for change, and (e) engaging of the top talent (Francis-Jennings & Lewis, 
2014).  
Corporate sustainability drivers catalyze change from the unsustainable status quo 
(SQ) towards more sustainable activities (Lozano, 2013). Change processes that result in 
sustainability should (a) be systemic; (b) encourage open discussion of barriers to 
effective strategy implementation and adaptation; and (c) develop a partnership among all 
relevant stakeholders (Fukukawa, Spicer, Burrows, & Fairbrass, 2012). Other change 
elements that facilitate sustainability include (a) replication of successful training and 
successes; (b) defining success; (c) building new departments where necessary; and (d) 
the use of evidence-based data for decision-making (Francis-Jennings & Lewis, 2014). 
Additional elements include (a) practical experimentation; (b) collaborative decision-
making; (c) over-coming constraints; and (d) acting accordingly to the perspectives of 
customers (Francis-Jennings & Lewis, 2014). 
Chi Cong, Perry, and Loh (2014) identified seven core elements for the 
implementation of successful change to maximize sustainability. These elements are (a) 
vision, (b) the identification of key challenges, (c) objectives, (d) measurement, (e) 
37 
 
 
strategy, (f) initiative, and (d) outcome (Chi Cong et al., 2014). Failure of effective 
change management can compromise sustainability and financial outcomes (Epstein & 
Buhovac, 2014a). The main factors for failure center on (a) goals metrics and rewards; 
(b) bureaucracy and politics; (c) knowledge transfer; (d) process; and (e) leadership 
(Decker et al., 2012). Other issues which lead to failure in the implementation of change 
initiatives include (a) decision-making and planning; (b) culture; (c) alignment; and (d) 
low motivation and ability to change (Decker et al., 2012). 
For the innovative changes that enhance sustainability to attain acceptance, the 
requirement to design them with stakeholders in mind was a requirement (Kravul & 
Bruton, 2013). Change management was of importance in the implementation of 
sustainability practices because such management facilitates these practices to become 
the norm in organizations (Brannmark & Benn, 2012). To implement sustainability using 
change management requires the use of programs that (a) provides direction, and (b) has 
an implementation strategy (Brannmark & Benn, 2012). Fukukawa et al. (2013) found 
that successful implementation of sustainability initiatives at the tertiary education level 
was dependent on change management strategies aligned with policy, and the 
implementation of such policies.  
In summary, for situations where organizations need to change to embrace 
sustainability, the following are imperative; (a) active ownership of the sustainability 
initiatives; (b) professional steering; (c) competent leadership; and (d) engaged 
participants  (Brannmark & Benn, 2012). Change management approaches to implement 
sustainability entail (a) awaking, (b) pioneering, and (c) transformation (Newman, 2012). 
38 
 
 
The ability to apply change management in different organizational structures and to 
enhance multiple types of initiatives including sustainability exists (Vora, 2012). 
Examples of the change management of behaviors that reduce the consumption of wood 
fuel to contribute to the preservation of the environment and health exist in places such as 
Guatemala and Zanzibar (Kravul & Bruton, 2013). These examples highlight the 
importance of change management to sustainability. 
Policy and planning issues. Organizations increasingly need to consider 
environmental issues because of (a) stricter governmental regulation; and (b) growing 
pressures from a broad range of stakeholders (Hofmann, Theyel, & Wood, 2012). 
Planning and policy issues may be critical in the ability of firms to gain a competitive 
advantage in business. Siiskonen (2013) presented the importance of planning as a 
precondition and key practice for efficiency and profitability in the forestry industry in 
Finland and Sweden. The planners focused on continuous yield, and economic and social 
sustainability (Siiskonen, 2013). Planning for sustainability should include ways to 
overcome the inner obstacles that inhibit a sustainability mindset while implementing 
linkages with practical action (Hoppe, 2014). These barriers may encompass global 
sustainability goals and local action (Hoppe, 2014). Planning for the implementation of 
sustainability requires elements aligned with (a) organizational structure, (b) costing, 
capital investment, and the integration of risks; and (c) performance, evaluation, and 
rewards systems (Epstein & Buhovac, 2014a).  
Planning and policy managers should focus on how to improve stakeholders’ 
commitment and cooperation in the changes required for successful sustainability 
39 
 
 
practices implementation (Hoppe, 2014). Research showed that those with visions of 
achieving sustainable outcomes would benefit from more dialogue with planners rather 
than simply seeking to subvert planning processes (Adams, Scott, & Hardman, 2013). 
This argument should be of importance when taking planning into consideration when 
trying to make the cultural changes towards sustainability. The perception of 
unresponsive planning would undergo improvement, while the act of learning by doing 
would also create an effective evidence base on which to base future policy discussions 
(Inch, 2011).  
Planners are typically part of a regulatory system where all developmental 
decisions are subject to legal precedent, appeal and sometimes costly and lengthy judicial 
reviews (Adams et al., 2013). DuBois and DuBois (2012) stated that for environmental 
sustainability to become norm in and organization, new policies, and rules are a 
requirement. Senior management attracts the view as being primarily responsible for the 
implementation of sustainability initiatives (DuBois & Dubois, 2012). This statement 
indicates the importance of policy and planning for sustainability outcomes. In planning 
environmental sustainability, importance elements are (a) use of a centralized strategy; 
(b) coordination plans across local and long distances; (c) focusing on the reduction of 
negative environmental impacts; and (d) safety (Hoppe, 2014). 
Three elements that can have an influence on the attitude of business owners to 
environmental sustainability are those that are (a) regulatory, (b) cognitive, (c) normative 
(Roxas & Coetzer, 2012). Of importance are laws, which facilitate policy adoption, that 
result in environmental sustainability outcomes. As such, the nature of institutional 
40 
 
 
environment and accompanying business policies can also guide leaders to embrace 
sustainability (Roxas & Coetzer, 2012). Idoko, Nkamnebe, and Amobi, (2013) found that 
the absence of enabling environmental state laws that complement federal environmental 
laws allow constraints to environmental sustainability behavior adoption among SMEs in 
Nigeria. Dylan (2012) supported this statement by informing of the importance of 
national and international practices regarding environmental conventions and laws. When 
focus on local laws occurs, the possibility for environmental sustainability increases 
(Dylan, 2012).  
 
 
Self-regulation vs. legal requirements. Siiskonen (2013) conducted research in 
Finland and Sweden where forested areas are of importance to their national economies. 
The results of the research indicated the importance of government regulation in the 
removal of differences between private forest owners and the forestry authority 
(Siiskonen, 2013). When government regulations are mandatory, this approach creates an 
environment of transparency that allows businesses to care for planet Earth while 
achieving profits (Frynas, 2012). By contrast, Hashmi and Al-Habib (2013) found that 
government regulators are not doing enough to regulate carbon emissions in Saudi 
Arabia. The perception was that the Saudi Arabian government was not serious about 
environmental protection. Primary data revealed private sector enterprises were better 
prepared to deal with sustainability and carbon management problems compared to 
public sector enterprises and as such embraced self-regulation (Hashmi & Al-Habib, 
2013). Surveyed Saudi managers reported hope that their employers would start 
41 
 
 
rewarding positive carbon management actions and focus on educating managers about 
carbon management practices (Hashmi & Al-Habib, 2013). 
Institutional language attains use in the description of governmental regulations 
and laws, industry standards for SD reporting and expenditures, and the type of SD 
initiatives and partnerships (O’Connor & Gronewold, 2012). Pierce (2014) stated the 
importance of regulation, policy implementation, and planning as requirements to enable 
sustainability in the petroleum industry. Achieving sustainability is mainly a matter of 
communication, commitment and cooperation (Hoppe, 2014). By implementing these 
practices in self-regulation, sustainability was achievable (Hoppe, 2014). 
Summary and Transition 
Section 1 was an introduction to the study and included (a) background of the 
problem, (b) problem statement, (c) purpose statement, and (d) research question. The 
subject of the study was an exploration of the sustainability practices that improve profits 
in the petroleum industry. The study method and design was a qualitative multiple-case 
study. The target audience was all stakeholders in the petroleum industry. The goal of this 
study was to inform petroleum industry leaders of the elements of sustainability that may 
affect their business outcomes.  
Highlighted in Section 1 are the challenges accompanying the non-
implementation of sustainability practices in the petroleum industry. Organizations 
hesitate to implement sustainability practices when there is a cost and a benefit may not 
result (Epstein & Buhovac, 2014b). Without the implementation of sustainability 
practices harm to the environment was possible (Senge et al., 2010). With environmental 
42 
 
 
deprivation, the creation of a problem occurs whereby a reduction in economic value can 
occur in the petroleum industry Anosike (2014). The purpose of the study was to explore 
what sustainability practices influence profitability in the petroleum industry.  
A qualitative method and a single case study design can provide clarity to the 
understanding of problems in business (Yin, 2014). The research question was as follows:  
What sustainable environmental practices do petroleum industry leaders use to affect 
profitability? A review of the literature is a necessity when executing research (Foster, 
2013). The focus of the review was on the sustainability in business in general and in 
particular the petroleum industry. Additionally, the literature review provided 
background sustainability practices in the petroleum industry and accompanying issues 
and challenges. 
Section 2 includes a detailed description of the (a) purpose statement; (b) 
participants; (c) data collection; (d) data analysis; and (e) reliability and validity issues. 
Section 3 includes (a) an introduction, (b) presentation of findings; (c) implications for 
social change; and (d) recommendations for action. Section 3 includes with (a) 
recommendations for further research, (b) reflections, and (c) a conclusion.  
43 
 
 
Section 2: The Project  
Section 2 of this study covers the following topics: (a) purpose statement; (b) role 
of researcher; (c) participants; and (d) research method and design. Additionally covered 
are (a) population and sampling; (b) ethical research; and data collection. Last, the 
section closes with (a) the data analysis; (b) reliability and validity; and (c) a transition 
and section summary.  
Purpose Statement 
The purpose of this qualitative, multiple-case study was to explore sustainable 
environmental practices that petroleum industry leaders can use to affect profitability. 
Sustainability practices and profitability can be balanced benefit petroleum companies 
(Du et al., 2013; Parast & Adams, 2012). The expectation is for this research to explore 
sustainable environmental practices that petroleum industry leaders can use to affect 
profitability.  
The target population was operations personnel and leaders in the petroleum 
industry who had experience in the United States. Front line leaders attained 
sustainability by bridging any gaps between their superiors and followers (Merriman & 
Sen, 2012). Targeting the leaders allowed for changes in these business practices that 
may contribute to sustainability implementation (Epstein & Buhovac, 2014a).  
The implication for positive social change is that leaders in private and public 
organizations might use the findings to improve profitability. Profit and sustainability can 
and must go together because a social benefit exists (Voser, 2012). These benefits may 
include the protection of human lives while creating the economic requirements for social 
44 
 
 
comforts (Epstein & Buhovac, 2014a). Sustainable practices are a requirement to enhance 
business outcomes, including reputation and financial performance (Petrovich, 2014).  
Role of the Researcher 
One of the results of research can be the creation of paradigm shifts in research 
activities (Carr, 2013). The tasks related to academic research are (a) networking, (b) 
collaboration, (c) managing research, (d) doing research, (e) publication of results, and  
(f) evaluation of the research (Kyvik, 2012). The role of the researcher in this doctoral 
study aligns with the need to improve business practice and facilitate the implementation 
of positive social change (Lunde, Heggen, & Strand, 2013). My primary role was for data 
collection, analysis and the dissemination of results. 
Sustainability initiatives to reduce pollution, minimize waste, and recycle material 
are part of my profession as a petroleum engineer. My work on environmental protection 
has increased over the past 20 years. I had no influence on the respondents because the 
data collection was via an anonymous online questionnaire. Moreover, I work in the 
Middle East rather than in the United States and the geographical distance inhibited one-
on-one contact.  
Yin (2014) stated the importance of using multiple sources of data when 
conducting qualitative case study research. With the reviews of sustainability reports of 
four petroleum companies that operate in the United States and the use of data collected 
from online-administered questionnaires, completed were the plans to have a 
comprehensive understanding of how sustainability environmental activities affect 
profitability. Even though data analysis was via the use of NVivo 11 qualitative analysis 
45 
 
 
software, the role of the researcher is a necessity because of the need to answers 
questions about (a) what data needs assessment; and (b) what different terms are 
requirements for data assessment (Moon, 2015).  
In conducting this research, familiarity with the topic and participants was a 
reality because of ongoing employment in the petroleum industry of both researcher and 
participants. As such, the need to mitigate against bias was a reality. Avoiding bias 
entailed (a) interpreting answers fairly; (b) avoiding entrapment by existing ideologies 
and preconceptions; and (c) being sensitive to contrary evidence (Yin, 2014). This 
approach requires the use of effective questioning so that participant responses enable the 
achievement of reliable data interpretation (Sather, 2012). In addition to the use of an 
online questionnaire to capture participants’ responses, also completed was a review of 
the annual sustainability reports four petroleum industry companies as the secondary data 
source. Assurance of reliability and validity of this case study is a possibility because of 
the use of multiple data collection procedures (Yin, 2013). 
The U.S. Department of Health & Human Services (HHS) prescribed ethical 
guidelines and principles for the protection of human subjects of research. Ethical 
research adds to the quality of the study (Yin, 2014). The role of the researcher aligned 
with the requirements of the Belmont Report for ethical research (HHS, 2016). Basic 
ethical principles required included (a) respect for persons, (b) beneficence, and (c) 
justice (HHS, 2016). In addition, informed consent, assessment of the risk and benefits, 
and the selection of subjects is a requirement (HHS, 2016). These requirements were in 
effect during the research process.  
46 
 
 
Participants 
The study attracted responses from 16 participants from a prescribed survey pool 
who responded to a Survey Monkey administered online questionnaire. Choice of the 
participants was via purposive sampling. Purposive sampling focuses on the attraction of 
experts with knowledge of a known phenomenon (Trotter II, 2012). In addition, 
purposive sampling ensured voluntary participation (Anosike, 2014). For this study, of 
the 30 plus participants asked, 16 responded. 
Bowen et al. (2012) stated the importance of having study participants who have 
experience in the industry under research. The approach of Bowen et al. (2012) aligns 
with having operations, engineering, and middle-level participants with experience in the 
petroleum industry whose daily activities encompass sustainability. This preference 
attained validation by requesting these characteristics in the informed consent letter. Low 
response rates when using questionnaires is a possibility (Thomson & Naoya, 2014). 
Mitigating this outcome is possible by requesting the response of more participants than 
the requirement to attain a quality study (Yin, 2014). The need to mitigate the low 
response rate justified why invitation e-mails to over 30 participants were the reality. 
Qualitative methodology requires capturing participants’ experiences and 
perceptions to develop a greater understanding of their experiences within the required 
context (Thyer, 2012). Of importance is the presentation of human perspectives through 
multiple data collection (Fairweather & Rinne, 2012). The use of questionnaires and the 
review of companies’ sustainability reports enabled this approach. The relationship with 
47 
 
 
participants was via the use of the online questionnaire responses to the questions asked. 
Absent was any direct contact with participants.  
Data preservation will occur over a 5-year period as per Institutional Review 
Board (IRB) regulations after which destruction will occur. Each participant reviewed an 
enclosed informed consent form before commencing the interviews. The form (a) stated 
the study’s purpose, (b) indicated confidentiality and risk issues, and (c) showcased the 
focus on trust. Also included on the form were participants’ rights in withdrawing from 
the data collection process. Responding to the questionnaire carried the assumption that 
the participants read the informed consent form.  
Research Method and Design 
Rigorous scholarly research allows for closing gaps in undeveloped research 
fields (Kieser, Nicolai, & Seidl, 2015). The three main research methods are (a) 
qualitative, (b) quantitative, and (c) mixed methods that include both qualitative and 
quantitative approaches (Zachariadis, Scott, & Barrett, 2013). Whereas quantitative 
research requires numerical methods, qualitative research entails the use of experiences to 
generate results (Upjohn, Attwood, Lerotholi, Pfeiffer, & Verheyen, 2013). In the 
ensuing discussion, I aim to justify the choice of a qualitative methodology for this 
doctoral study.  
Research Method 
Qualitative methodology. Qualitative approaches allow for exploration of 
complex issues in new territory (Parry, Mumford, Bower, & Watts, 2014). Qualitative 
methodologies are participatory in nature in that they facilitate an inclusion rather than 
48 
 
 
the extraction of the views of others (Upjohn et al., 2013). Yin (2014) stated the 
requirements of a good qualitative study as having (a) a core research problem, (b) proper 
framing of the study with assumptions, (c) having the characteristics of the research 
approach and (c) inclusion of the researcher’s role and biases. 
Qualitative methodologies require the use of (a) perception, (b) interpretation, and 
(c) representation (Sather, 2012). These elements align with rigorous data collection, data 
analysis and report writing (Yin, 2014). I completed the plans for this study to attain the 
perception of experienced professional with respect to the influence of sustainability on 
organizational profitability. This achievement was possible by use of a qualitative 
approach because of the alignment with qualitative methodologies.  
Quantitative methodology. Quantitative approaches provide an objective 
measurement of problem frequency (Upjohn et al., 2013). When executing this research 
method, assessing and reporting validation is a critical requirement compare to qualitative 
research (Venkatesh, Brown, & Bala, 2013). To study the influence of sustainability 
practices on profitability in the petroleum industry the focus, as allowed in qualitative 
research, was on the perceptions of the study participants. A focus on the frequency of 
these perceptions was not a requirement. For quantitative research the development of 
correlations, which relate to causation, was a requirement (Zachariadis et al., 2013). 
Based on the expected outcomes of a qualitative methodology, a quantitative approach 
was not the method of research for this study.  
Mixed-methods research (MMR). Three important aspects of conducting 
mixed-methods research (MMR) include (a) appropriateness of mixed methods approach, 
49 
 
 
(b) development of meta-inferences (i.e., substantive theory) from MMR, and (c) 
validation of MMR (Venkatesh, Brown, & Bala, 2013). MMR studies are difficult to do 
well (Latham, 2014). In addition, Plowright (2014) stated that MMR causes confusion as 
to whether the main method was qualitative and quantitative. Being a new researcher, and 
with the ability of the use of a qualitative methodology to complete the research, MMR 
was not the preference at this time.  
Research Design 
I planned to use a multiple-case study design for this research. Multiple-case 
studies enable the collection of data from participants in their environments (Yin 2014). 
Case studies are important for evaluating the success of learning and experimental studies 
(Earley, 2014). The requirements in the execution of cases studies include a focus on (a) 
study design, (b) data collection, (c) data analysis, and (d) a review of the comparative 
literature (Cronin, 2014). I used each of these steps in the execution of the research topic. 
Data collection included (a) participant response to online questionnaires, and (b) reviews 
of the company reports. Case study as a research method is one that is an in-depth 
investigation of life within the real-world context of meaning-making (Compton-Lilly et 
al., 2015). The data collection method aimed at the completion of the in-depth 
investigation of the influence of sustainability practices on profitability in the petroleum 
industry.  
The United States oilfield operations were the defined geographical area. I 
assured that the participants were familiar with the United States oilfield operations by 
requesting that only participants with this background were to respond to the 
50 
 
 
questionnaire. The objections to the use of such methodologies include validity, and 
reliability concerns (Mariotto, Pinto Zanni, & De Moraes, 2014). Yin (2014) supported 
the use of multiple-case studies as a valid form of research. Qualitative research design 
could (a) create thick descriptions that include detail, complexity, context, subjectivity, 
and multifaceted nature of human knowledge; (b) capture the narrative structures of 
human knowledge; and (c) enable the ability to derive generalizations from the bottom up 
(Fishman, 2013). This statement aligns with all aspects of case study research including 
the single-case study. The expected result of the use of a multiple-case study was the 
execution of a focused examination to deliver the analysis required (Yin, 2014). 
Successful legitimization of case study research in the literature included (a) the 
rejection of the positivist criteria; (b) enhancing the value of uniqueness, (c) the dismissal 
of representativeness as a criterion; and (d) disciplining the transfer of knowledge 
between cases (Mariotto et al., 2014). The complexity of sustainability requires a holistic 
examination of one environment via the capture of the experiences of experts (Tracy, 
2010). This holistic approach facilitates results that align with leadership elements that 
contribute to sustainability outcomes (Strand, 2014).  
In a review of the other possible qualitative research designs, options were (a) 
ethnography, (b) phenomenological, (c) narrative, and (d) grounded theory (Yin, 2014). 
Ethnography requires the prolonged observation of a group within their environment 
(Compton-Lilly, 2015). Ethnography was not an appropriate option because of the focus 
on behavior and social connections (Staller, 2012). A phenomenological study focuses on 
generating meaning from individual lived experiences to attain an understanding of a 
51 
 
 
phenomenon (Finlay, 2012). For this study, sustainability does not undergo the treatment 
as a phenomenon, and so this approach does not apply. The narrative approach focuses on 
stories of lived experiences of an individual (Compton-Lilly, 2015). The narrative 
approach does not align with this study requirements. Grounded theory aims at theory 
creation based on data capture from studies (Staller, 2012). I did not select grounded 
theory for this research topic, because of the lack of focus on theory creation. Based on 
these justifications, a single-case study design was the choice. 
Population and Sampling 
Sixteen experienced petroleum company operations personnel, engineers, and 
middle-level managers were the population for this study. Survey Monkey is the online 
website preferred for the execution of the questionnaire. Small numbers of participants 
allow for intensive studies (Cleary, Horsfall, & Hayter, 2014). This claim supports 
justification for the sample size chosen. The aim was for data saturation, which is an 
assumption when little new data becomes available after review of all collected data 
(Issacs, 2014). The choice of population attributes for a study should depend on (a) 
demographics, (b) geographical, (c) physical, (d) psychological, and (e) life experiences 
(Robinson, 2014). For this study, the focus was on the life experiences of the participants, 
based on their observations in the geographical setting of the United States. The 
experience of the participants enabled an increased understanding of the sustainability 
environmental activities that influence profitability in the petroleum industry. The 
variation in participant expertise was a proposal to provide a deeper understanding of the 
study topic (Petty et al., 2012). This sample allowed for attaining multiple perspectives 
52 
 
 
about sustainability across disciplines. The challenge in using multiples disciplines was 
the ability to generate consistent terminology from the research participants (Trotter, 
2012).  
Sampling is central to the practice of qualitative methods (Robinson, 2014). 
Options for sampling may include (a) purposive, (b) theoretical, (c) convenience, or (d) 
snowball (Petty et al., 2012). The sample size was purposive in nature, because of the 
belief that this size was relevant to the study. Purposive sampling is a non-random way of 
ensuring that particular categories of cases within a sampling universe experience 
representation in the final sample of a project (Robinson, 2014). Qualitative sampling 
allows for focusing on a small sample of participants to attain a large data set on issues 
(Trotter, 2012). Face-to- face interview of participants was absent. This approach was 
because of the aim to use only an online questionnaire and the review of the annual 
sustainability reports of petroleum companies that operate in the United States as data 
sources. This approach improved the quality of the data collected (Yin, 2014). 
 
Ethical Research 
 
Ethics is a branch of philosophy that deals with values and the alignment of these 
values in the execution of behaviors (Finch & Mc Fee 2012). Yin (2014) stated the 
importance of ethical behavior for the completion of a quality case study. While 
executing this study, protection of the participants occurred by following the guidelines 
of the Institutional Review Board (IRB). The IRB approval number is 8-17-16-037404. 
The IRB requires protection of participants against issues aligned with (a) risk, (b) 
physicality, (c) psychology, (d) social, and (e) economics. These guidelines to ensure 
53 
 
 
protection involves (a) gaining informed consent from all persons who may be part of the 
study; (b) avoiding deception; alignment with privacy and confidentiality concerns; (c) 
special precautions for the vulnerable; and (d) ensuring an equitable selection of 
participants (Yin, 2014).  
McNamara (2011) presented the importance of having plans to protect 
participants before the IRB grants approval for the execution of any study. The 
requirement was possible via the execution of the informed consent process (Yin, 2014). 
The informed consent process involves providing each participant with an informed 
consent form for his or her review.  
No compensation to the study participants as per IRB approval. The non-material 
benefit for the participants in this study was in helping the researcher identify how 
environmental sustainability actions, where implemented, affect the profitability of 
petroleum companies operating in the United States. The participation may also generate 
a sense of pride in the knowledge of helping an improvement in the understanding of 
environmental sustainability outcomes. 
Each participant had to review the informed consent form that provided the 
necessary information about the planned research process including participants’ rights 
and confidentiality issues. This form was ahead of the research questions in the 
SurveyMonkey questionnaire. This approach aligns with ethical behavior that leads to 
successful research outcomes (Lin-Hi & Muller, 2013).The form had the IRB approval 
number and the date of expiration of the IRB approval. In addition, the form had (a) study 
background and purpose, and (b) contact information. The goal was to avoid unethical 
54 
 
 
behavior that could occur because of the ruthless pursuit of self-interest (Yukl, George, & 
Jones, 2010). Providing as much information as possible, concerning the study, showed 
participants that self-interest was not the only focus. This approach contributed to 
building a trusting relationship with participants that will aid in their willingness to 
provide the required information for the study (Thomson & Naoya, 2012). Participants 
had contact information for Walden University. Before starting interviews with the 
participants, their signatures were a requirement, and each participant received a copy of 
the signed consent forms. Participants can withdraw from the study at will. The 
preference would be for written request for withdrawal.  
The administrative elements of research are of importance (Kyvik, 2012). These 
elements also align with ethics in the need to gain access to, and the protection of 
participants (Yin, 2014). The requirement to protect participants also entails non-
disclosure of participants’ identities especially for controversial topics (Yin, 2014). 
Because of the ongoing discussion on the well-known harm to the environment aligned 
with petroleum industry operations, the necessity existed to ensure participants remain 
anonymous. The informed consent form and collected data are in storage for a 5-year 
period as per IRB requirements. This period allows the opportunity for any required data 
review and interpretation. After this period, data destruction will occur.  
 
 
Data Collection 
Instruments 
The use of computer-assisted tools is a possibility for case study research (Yin, 
2014). For this study, I was the primary data collection instrument even though also in 
55 
 
 
use for facilitating the process was online software. Electronic questionnaires provide a 
quick, inexpensive manner to capture data and previously noted disadvantages are no 
longer valid (Hunter, 2012). Data collection for this study included the use of the online 
tool Survey Monkey as the instrument to capture information over a one-month period. 
The use of Survey Monkey allowed for the quick and inexpensive distribution of 
questionnaires across geographical areas (Brandon, Long, Loraas, Mueller-Phillips, & 
Vansant, 2014). Yin (2014) stated the use of Survey Monkey as a data collection 
instrument. Participants concluded that online questionnaires are relevant, enable quick 
responses, and easy to complete (Minnaar & Heystek, 2013). Based on these statements, 
the use of Survey Monkey as a data collection instrument attained justification. 
Qualitative researchers strive to ensure methodical alignment by thinking about 
the relationship between (a) their theoretical position, (b) selected methods, and (c) 
emerging analysis of the data (Kramer-Kile, 2012). Based on this requirement, of 
importance was the data collection process. Venkatesh et al. (2013) presented the 
importance of data collection in the execution of research activities. Building a study 
within an authentic setting requires in-depth and varied data collection (Snyder, 2012). 
With the need to collect data to execute qualitative research, a presentation of the 
instrument used for data capture was a requirement.  
Rogers (2015) stated that the use of online questionnaires to capture quality data 
during research activities was a possibility. Focus groups are an option for valuable data 
capture during qualitative research (Ullrich & Schiek, 2014). Survey Monkey is an online 
website that allows for the execution of questionnaires and facilitates responses from 
56 
 
 
focus groups (SurveyMonkey, 2015). For this study case, participants were petroleum 
industry professionals with experience, chosen via purposive sampling, and involved with 
sustainability activities in the United States.  
Questionnaire types at Survey Monkey included those that were (a) academic, (b) 
customer satisfaction, (c) education related, and (d) employee related (SurveyMonkey, 
2015). The possibility existed that based on experiences with previous online 
questionnaires, the use of Survey Monkey can add value to attracting participants for my 
study. The keys to engaging participants to respond to online questionnaires lie in the 
ability to reduce the burdens of (a) lengthy questionnaires; (b) topic salience; and (c) 
poorly written or hard to answer questions (Downes-Le Guin, Baker, Mechling, & Ruyle, 
2012). Short data capture instruments in maximizes the numbers of participants who 
respond (Yin, 2014). The limited number of questions on the questionnaire allowed 
participants to provide detailed responses to personal thoughts and experiences aligned 
with the influence of sustainability and environmental practices on profitability in the 
petroleum industry. NVivo 11 Pro allows for the coding and categorizing of large 
volumes of data and can assist in the qualitative data analysis process (Yin, 2014). The 
use of coding software NVivo 11 Pro facilitated the analysis of participants’ responses 
and allowed me to complete the interpretation and assessment for themes creation.  
Yin (2014) stated the importance of multiple sources of data for qualitative 
studies. Multiple sources of data enable quality research (VanEyde et al., 2013). In 
addition to the use of Survey Monkey, I reviewed and captured data from the 
sustainability reports of BP, Chevron, ExxonMobil, and Shell to determine if their focus 
57 
 
 
on sustainability initiatives affects profitability. The use of multiple sources of data was 
of importance in case study research because of the perception that limited applications 
of the results existing beyond one firm or sector (Salzmann, Ionescu-Somers, & Steger, 
2005).  
Other than the online questionnaire tool, SurveyMonkey, I created data tables to 
capture the sustainability practices in use over the years 2011 to 2016 at BP, Chevron, 
ExxonMobil, and Shell. The completion of data capture and analysis was manual. This 
process entailed reading 20 reports. These reports consisted of yearly reports over the 5-
year period of the four companies under study. There were eighteen practices identified 
across the companies even though not all the companies utilized all the practices. 
Appendices L, M, N, and O have the results of the sustainability reports as a data source. 
Data Collection Technique 
A carefully devised strategy of (a) sample specification and selection; (b) data 
processing; (c) screening; and (d) editing can boost the quality of online questionnaire 
responses (Chang & Vowles, 2013). The use of a Survey Monkey online questionnaire 
allowed the capture responses of 16 participants. By use of the strategy outlined earlier, 
the capture of quality data via online methods was a possibility. Baskarada (2014) stated 
the importance of data triangulation to improve data validity. By capturing data from 
separate unique sources via the use of Survey Monkey, I had the ability to review the data 
to observe similarities and differences in thoughts from the participants. The questions 
are in Section 1. For the execution of case studies, multiple sources of data can 
collaborate and validate data captured from other sources (Yin, 2014). Of importance 
58 
 
 
during data collection was the capture of any relevant rival or contrasting theories takes 
place (Baskarada, 2014).  
Distribution of the questionnaire to participants was via the use of Survey 
Monkey. Whitehead (2011) stated that online questionnaires (Internet) offer wider access 
to participants in research studies and the advantages include (a) the ability to reach 
traditionally inaccessible groups; and (b) widened geographical range and less time 
constraint. Guidance to Survey Monkey for the execution of the questionnaire would 
entail specification and classification of the intended participants. Researchers should set 
aside an appropriate amount of time to reflect upon administrative, population, and data 
collection considerations when using the Internet for data collection (Wilkerson, Iantaffi, 
Grey, Bockting, & Rosser, 2014). With the use of Survey Monkey to execute the 
questionnaire, completed actions entailed (a) the placement of the questionnaire on-line; 
(b) Survey Monkey as the platform for data capture from participants; (c) data capture 
and storage; and (d) the ability to download captured data into data analysis software 
NVivo Pro 11. 
Additional sources of data were the annual sustainability reports of BP, Chevron, 
ExxonMobil, and Shell over a 5-year period from 2011 to 2015. This approach aligned 
with data collection using triangulation, which allowed for data validation (Zazhariadis, 
Scott, & Barrett, 2013). Marais (2012) stated the importance of cross-validation using 
triangulation. Attaining data from different sources allowed for data validity and 
reliability (Mariotto, Pinto Zanni, & De Moraes, 2014). The review of these companies’ 
sustainability reports aided in the completion of the data collection requirements. 
59 
 
 
Documentary information is relevant to every case study topic (Yin, 2014). The 
data from documents are useful in corroborating and augmenting evidence from other 
sources (Yin, 2014). An evaluation of the data from the annual sustainability reports was 
a necessity. This process entailed (a) reading the entire reports, (b) forming an opinion of 
the value of the data related to sustainability practices, (c) taking notes as the review was 
ongoing, and (d) placing indication of yes (Y) or no (N) into the spreadsheets listed in 
Appendices J, K, L, or M. The process allowed for an indication whether those particular 
practices were in use at the companies under study during the period 2011 to 2015. The 
data collected from the annual sustainability reports did corroborate the responses of 
participants of the online questionnaire. 
Data Organization Techniques 
Soergel (2015) stated the importance of data organization to (a) learning, (b) 
understanding, (c) inference making, (d) discovery by people, and (e) discovery by 
computer programs. The approaches used to organize the data included placement of the 
responses of each question into separate files, while the manual note-taking allowed for 
separate classification of sustainability practices into manually written spreadsheets. To 
re-cap, the sources are questionnaire responses via Survey Monkey, and raw data 
captured from the review of annual sustainability reports of the four subject companies. 
The use of archival sustainability reports aligns with the need to use archival data to 
complement other data sources (Adler & Hiromoto, 2012). Clear identification of the 
collected data is a requirement for research (Ritzholz et al., 2011). The focus was to 
organize the data for easy access, references, and analysis.  
60 
 
 
Data are a source of knowledge (Mahdzur & Salim, 2015). Transcripts of the data 
are also of importance for the completion of research activities (Yin, 2014). The storage 
of transcripts of each of the responses into separate labeled files occurred. Thus, in 
organizing the data, my notes allowed for making data organization a reality. By taking 
personal notes during the review of online data and sustainability reports, the ability to 
reference the captured data may be easy. These notes entailed summarization of data 
captured from both the online questionnaires and the petroleum companies’ sustainability 
reports. Challenges exist with the ability to handle large amounts of data (Ireland & Hitt, 
2005). If the amount of data is too large, an option for storage after the organization is the 
use of the cloud-based storage (Griffith, 2013). In addition to the use of a cloud-based 
system, data storage will include the use of a password protected hard drive and kept in a 
safe deposit for a 5-year period. Data destruction will occur after the 5-year storage 
requirement via shredding of hard copies or erasure of soft copies. 
Data Analysis  
Ritholz et al. (2011) stated the importance of coding and theme development to 
data analysis. The study approach includes multiple reading of the participants’ responses 
to interpret the presence of any specific patterns. The approach aligns with Tsang (2013) 
who stated the importance of a thorough examination of the data to generate realistic 
interpretation. Yin (2014) stated the importance of interpretive techniques to the 
generation of patterns. Data analysis included planning a manual review of responses to 
develop emerging themes to describe the elements of the data that occurred. Strategies 
stated by Yin (2014) for data analysis included (a) relying on theoretical propositions; (b) 
61 
 
 
working the data from the ground up; (c) the development of a case description; and (d) 
the examination of plausible rival explanations. For this case study, data analysis 
included the strategy of relying on theoretic propositions concerning the influence of 
sustainability practices on profitability.  
These theoretic propositions require taking into account systems theory that was 
the conceptual framework for this study. Under review was the theory that a systems 
approach facilitates the successful implementation of sustainability (Epstein & Buhovac, 
2014a). The propositions aforementioned require attaining an understanding of how each 
of the elements related to sustainability outcomes influence each other (Fischer & Zink, 
2012). For successful data analysis, required was an understanding of how each 
parameter captures from the questionnaires relate to each other (Yin, 2014).  
A key element of qualitative research is the presentation of a prescription for the 
data analysis (Issacs, 2014). Yin (2014) stated the importance of the use of software for 
data analysis. After initial review, transcription, note taking, coding, and theme 
generation, the use of NVivo 11 Pro software facilitated data organization and analysis. A 
detailed analysis of the data should present trends that can allow for and understanding of 
the effectiveness of environmental practices (Maloney & Yoxheimer, 2012). By using 
NVivo 11 Pro software, the expectation was for the emergence of themes aligned with 
the participants’ responses.  
Purposive sampling entailing the focus on experts to provide data saturation is a 
result in qualitative research (Trotter II, 2012). During the course of data collection, when 
little or no new data comes out of the participants, data saturation occurs (Issacs, 2014). 
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In this study, after the review of responses for the 16 participants, and a review of the 18 
sustainability environmental practices of the 4 petroleum companies, the belief was data 
saturation occurred. Across all sources, no new data observation occurred. 
Data triangulation is an important aspect of the analysis process (Yin, 2014). 
Triangulation entails cross-validation of the data captured (Marais, 2012). Triangulation 
can facilitate an improvement in internal validity (Baskarada, 2014). This approach 
requires the use of more than one data source that provides multiple sources of evidence 
to validate research results (Yin, 2014). Data triangulation should entail the collection of 
data from multiple sources aimed at collaborating the same findings (Patton, 2002). For 
this study, data triangulation occurred whereby the responses collected from 16 
participants triangulated with the 20 annual sustainability reports across the 4 petroleum 
companies under study.  
Member checking is of importance in executing qualitative research (Harper & 
Cole, 2012). This process entails the vigorous process of data coding and interpretation 
(Carlson, 2010). Refinement of the coded data was a possibility by the use of additional 
queries to ensure clarity and further elaboration that may of value (Snyder, 2012). This 
objective requires the maintenance of responses and contact information of all 
participants of this study. 
For this research, a multiple-case study enabled an exploration of what 
sustainability environmental practices influence profitability in the petroleum industry. 
Sixteen participants responded to the 14 questions in the questionnaire. Online survey 
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conduit, SurveyMonkey allowed for sending the questionnaire to the sample participants 
who experienced selection by the use of purposive sampling.  
A summary spreadsheet of all responses to the 14 questions was of use in 
capturing the responses from SurveyMonkey into Microsoft Excel. Sixteen individual 
files that represented the individual responses of each sample participants together with 
another format of fourteen question response summaries (all 16 responses for each of the 
14 questions) formed the database of data captured. Saving of the responses was in a pdf 
format.  
Using NVivo 11 Pro, data analysis of the downloaded qualitative data began. The 
process began with the downloading of the Excel files into NVivo 11 Pro. The 16 
participants (individual responses to each question) attained conversion into casefiles in 
NVivo 11 Pro. Then, the 14 question summaries (of all participants) experienced 
placement into nodes in NVivo 11 Pro. Therefore, each item of the questionnaire had a 
corresponding node in the NVivo 11 Pro project. This procedure allowed the placement 
of all the given answers, of each question, into a common area so the researcher could 
conveniently examine and analyze them. I then created additional nodes, for each 
question to hold only those direct quotes that specifically address the research question 
asked. All the participants’ quotes, from the questions, were gathered and examined for 
common themes and/or common sub-themes where applicable. The results of the coding 
process are in Appendices A through I. The presentation of these results is in Section 3 of 
this study. 
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Reliability and Validity 
Reliability 
 Data dependability is the ability to be reasonably sure that the replications of 
findings are a possibility upon a repeat of the study with the same participants in the same 
context (Marshall & Rossman, 2006). Data dependability is a possibility when the 
researcher takes into consideration the changes in the setting of the study that occurs and 
how these changes affected the approach to research process (Venkatesh et al., 2013). 
The ability to increase the creditability of a research study lies with the consistency of the 
research method that removes doubt (Svensson & Doumas, 2013). By using online tools, 
consistency in the administration of the questionnaire was the expectation. Greater 
reliability of the data is possible by administering questionnaires to experts in the field 
(Siti-Nabiha et al., 2014). Siti-Nabiha et al. (2014) were in agreement with Trotter II 
(2012) who stated that the possibility to increase data reliability exists by using a sample 
of participants who have the experience as sustainability practitioners. 
Data reliability is of importance for case study research (Mariotto et al., 2014). 
Gordon (2012) stated that the practices of proper recording and transcription of the data 
improve the reliability of the study. During the recording and transcription process, of 
importance is an unbiased approach while documenting the data as per participants’ 
responses (Yin, 2014). The focus on the reliability of the data was to maximize the ability 
of others to repeat the data capture, organization, and analysis to generate similar 
conclusions. 
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Validity 
The three classifications for validity research include those that align with (a) 
measurement validity, (b) design validity, and (c) inferential validity (Zachariadis et al., 
2013). External and internal data validity increases reliability (Mariotto et al., 2014). 
External validity is how aligned findings from a case study are with others situations that 
were not part of the original study, whereas internal validity is the strength of any cause-
effect links (Yin, 2014). Craig et al. (2013) stated that for online data capture, an issue 
with data validity is possible when participants inaccurately claim they have expertise in 
a particular area. Consideration that the participants’ claims of expertise in the area under 
study are untrue was a necessity. With purposive sampling, knowledge of the 
participants’ expertise was available. 
Data transferability and confirmability are of importance in qualitative research 
(Venkatesh et al., 2013). Transferability included using participants with adequate 
experience and education such that their responses may be applicable in similar 
environments. This outcome will also add to the confirmability of the data whereby 
others can confirm similar outcomes because of the unbiased mode to data capture and 
analysis. Incomplete or no ascertainment of data may negatively influence or bias the 
final study results, threaten internal and external validity, or limit the ability to draw 
accurate inferences (Udtha, Nomie, Yu, & Sanner, 2015). To increase data validity, I (a) 
used two sources of data capture, namely online questionnaires and company archives; 
(b) maintained a chain of evidence storage; and (c) exercised care in the use of data that 
may come from electronic sources, which may not be reliable (Yin, 2014). This approach 
66 
 
 
included the triangulation of the data that improves the validity of the findings (Synder, 
2012). The expected result was a study of higher quality. 
Summary and Transition 
The aim of this qualitative case study was to explore, understand, describe, and 
document what influence, if any, sustainability practices have on profitability in the 
petroleum industry. The geographical area of focus was the petroleum exploration and 
production business in the United States. A deeper understanding of environmental 
sustainability was the preference. By understanding the issues and drivers why 
management of some petroleum companies do or do not embrace sustainability 
approaches may lead to ways to create sustainable value. 
The data collection process planned entailed the use of the online questionnaire 
vehicle Survey Monkey. The primary source of data was to be from responses online of 
experienced sustainability practitioners who work in the United States oil industry. 
Sixteen individuals with in-depth knowledge of sustainability while working in 
engineering, middle-level managers, and operations functions participated. The 
questionnaires involved the use of open-ended questions, and my personal notes and 
NVivo 11 Pro software aided in data analysis and coding to generate the required 
understanding of the research problem. Additionally, the use of annual sustainability 
reports of BP, Chevron, ExxonMobil, and Shell to compare and contrast responses of the 
questionnaires to improve study creditability and validity, and establish whether the 
conclusions converge completed this study. 
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For Section 3, findings from the data capture are enclosed. In addition, I present 
the implications of the findings for business practice and positive social change. Also 
included are a summary of recommendations for action and recommendations for future 
research. Section 3 also contains a discussion of personal reflections, together with a 
summary, and conclusions. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Section 3: Application to Professional Practice and Implications for Change 
Introduction 
Section 3 includes the findings of this qualitative, multiple-case study of U. S. 
employees of BP, Chevron, ExxonMobil, and Shell: leaders and operations personnel. 
Data capture was from two sources: an online questionnaire about employees’ 
experiences and 20 annual sustainability reports from these firms. Appendices J, K, L, 
and M include these reports. 
The purpose of this study was to explore sustainable environmental practices that 
petroleum industry leaders use, if any, to affect profitability. The target population for 
this study included participants in the petroleum industry based in the United States. The 
targeting of this group allowed for possible changes in business practices that could 
contribute to the implementation of sustainability as an element of corporate strategy.  
The findings provided an in-depth understanding of the effects of sustainability 
practices on profitability. The results indicated that sustainability environmental practices 
inter-relate with the profitability outcomes of the companies under study. These 
companies generated multi-billion dollar net revenues during the study period and 
continued to be among the world largest oil companies (Forbes, 2016). In general, 
participants believed that sustainability environmental practices would improve business 
outcomes. A small sample suspected that these practices could have a neutral impact on 
profitability. This perception is of importance to illustrate the possibility of maintaining 
profits while executing sustainability environmental practices to protect the environment 
for the use of future generations (Epstein & Buhovac, 2014; Senge et al., 2010).  
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Data analysis is a critical part of any research (Hajmohammad & Vachon, 2013). 
The process entails the analysis of words to build details views of informants (Isaacs, 
2014). I used Yin’s (2014) five-step process for data analysis: (a) compiling, (b) 
disassembling, (c) reassembling (and arraying), (d) interpreting, and (e) concluding. 
Themes identified resulted from the analysis of the participants responses to the questions 
in the Survey Monkey questionnaire. The analysis involved all the data from the online 
questionnaire and the 20 annual sustainability reports. Attending the data entailed reading 
and making notes on all information related to the central research question. Second, a 
review of plausible rival interpretations occurred to determine whether alternative 
conclusions were possible. The analysis took into consideration the most significant 
aspects of the multiple-case study, including the sustainability environmental practices 
that petroleum industry leaders use to affect profitability. Finally, I used my expert 
knowledge as a petroleum engineer who works on sustainability initiatives to help 
validate the findings. These steps allowed for a complete analysis of the data to generate 
the unbiased outcomes as listed in the following section. 
Findings  
The research question was: What sustainability environmental practices do 
petroleum industry leaders use to affect profitability? Data collection entailed (a) the use 
of SurveyMonkey to capture responses of participants and (b) a review of the annual 
sustainability reports of the companies in this multiple-case study. The use of purposeful 
sampling facilitated the attraction of those who met specific inclusion criteria.  
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The criteria entailed those who are either (a) field operations personnel, (b) 
engineers, or (c) middle-level managers. Also included were participants working or with 
prior work experience within the U.S. petroleum industry during the past 5 years or over 
a 5-year period. In addition, the criteria included those who are (a) of legal working age 
of 18, (b) native English speakers, and (c) have experience with one of the top petroleum 
industry companies within the United States to include BP, Chevron, ExxonMobil, and 
Shell.  
Preliminary Review of Participants’ Responses 
A sample of 16 participants provided responses pertinent to addressing the central 
research question. Data collection entailed a 1-month period. Participants responded to 
the 14 questions within the online questionnaire focused on an understanding of the 
sustainability environmental practices of the companies with which they had petroleum 
industry experience. All participants included working experience with at least one of the 
four companies inclusive of BP, Chevron, ExxonMobil, and Shell. A preliminary review 
of participants’ responses follows. Paraphrases of the participants’ responses to the 
research questions 1-14 are below. The questions are in Section 1. These statements 
include a summary of each participant’s responses, completed by manually analyzing the 
responses to specific questions into a single paragraph format. 
The initial review of participant’s responses resulted in the identification of six 
themes that participants believed are sustainability environmental practices that affected 
profitability. The identified themes resulted from reading the responses and highlighting 
71 
 
 
key words. The themes unveiled using this manual method included (a) the environment, 
(b) fuel, (c) human resources, (d) recycling, (e) mitigation, and (f) water. 
The environment. Every participant stated the importance of environmental 
protection. The focus should be at a community level with the aim to minimize air 
pollution, and activities to improve air quality where possible but burning less 
hydrocarbons. P8 mentioned the importance of returning the environment to original 
status via the use of decommissioning activities. 
Participant 1 (P1) stated, 
 
The focus was on activities that prevent environmental disaster. 
Participant 2 (P2) noted, 
The use of each sustainability environmental practice gives a license to operate 
and does have a positive influence on profitability when implemented. 
Participant 4 (P4) asserted, 
The company embraced sustainability by (a) focusing on environmental safety, 
(b) ensuring the needs of host communities are under consideration, and (c) 
executing business in a manner that considers future benefits for all. 
Fuel. The second theme I recognized was that of fuel. Presented were the 
importance of cleaner fuel, biofuels, and the need to use more natural gas elements. The 
results indicated the desire for organizations to use less fossil fuel where possible and 
include higher percentages of alternative energies, including wind and solar. The main 
goal uncovered was the reduction of the carbon footprint that harms the environment. 
P4 stated, “Shell’s focus was on the use of renewable sources of fuel inclusive 
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of hydrogen and biofuels for transportation.” 
Participant 8 (P8) asserted that “Of importance is (a) the reduction of greenhouse gas 
(GHG) emissions, and (b) focus on renewable resources such as solar and wind 
energy during operations. 
Human Resources. The third theme identified was human resources (HR). The 
participants stated the importance of hiring employees who are sensitive to the 
importance of sustainability while training those who are veteran employees and may not 
heed sustainability as a license for continued operations. One employer linked 
sustainability outcomes with compensation. Participants 8, 9, and 11 mentioned that by 
maintaining a safe environment for employees, employers illustrated leadership in their 
sustainability practices. P 8 mentioned: “Focusing on training employees on the 
importance of sustainability environmental practices and linking compensation to 
individual contribution was part of the business culture.” 
Participant 11 (P11) asserted, 
Training of staff as to the importance of sustainability is a necessity. 
 
Recycling. Recycling was the fourth theme. Employers use products specifically 
designed for recycling. Waste management practices align with the focus on recycling. 
Participant 5 (P5) and Participant 7 (P7) both stated the importance of recycling. 
Participant 14 (P14) stated,  
Recycling was also of value as a sustainability practice. 
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Participant 15 (P15) mentioned as other participants that key sustainability environmental 
practices include (a) recycling, (b) use of LNG and natural gas, (c) spill management, and 
(d) waste management. 
Mitigation. Mitigation against environmental harm is a key practice of protection. 
The reduction of hydrocarbon gas flaring is a preference. This approach reduces the 
production of the greenhouse gases that harm the environment. Another mitigation 
strategy is the implementation of zero tolerance of oil or gas leaks and emissions. P3 
stated that emissions control was a key activity in use as a sustainability practice 
P5 contributed that, 
Employer’s environmental practices included (a) flaring reduction, and (b) 
greenhouse gas emission. 
 
Water Management. The final theme identified was water and the management 
of this resource. Participant 6, 10, 13, and 14 were specific in noting the importance of 
water management as a sustainability practice. 
Participant 9 (P9) mentioned that, 
Water fingerprinting was of use in determining if hydraulic fracturing operations 
in the petroleum are harming local water supplies.  
Participant 11 (P11) asserted that, 
Water management is important to avoid pollution and requires trained staff for 
implementation. Having environmental policies and plans aimed at compliance is 
also critical. 
Participant 13 (P13) contributed that used by the employer were, 
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Metrics to determine sustainability outcomes include statistics about spills and 
fresh water withdrawal frequencies. 
Data Analysis of SurveyMonkey® Questionnaire Using NVivo 11  
The following sub-section contains a summary of each theme revealed upon the 
use of NVivo 11. Using the inductive reasoning process, the researcher analyzed the 
direct quotes from the participants and searched for sub-themes using NVivo 11 
qualitative data software package. Subsequently, further categorization of the sub-themes 
into major themes occurred. In some cases, the varied nature of the quotations did not 
allow for placement into a common category. The use of the NVivo 11 software 
facilitated the confirmation of the six themes already determined namely (a) the 
environment, (b) fuel, (c) HR, (d) recycling, (e) mitigation, and (f) water.  
Research question 1, What, if any, sustainability practices does your employer 
execute? was important in identifying all the practices in use by the four petroleum 
industry companies under study. The analysis allowed for the place of the responses in 
six themes and twenty-one sub-themes. Analysis for this question is in Appendix A. 
According to the participants, their employers executed six types of sustainability 
practices that identified as relating to the following (a) the environment, (b) fuel, (c) 
human resources, (d) recycling, (e) mitigation, and (f) water. Also included in this section 
is a summary of the participants’ responses on their employers’ perception of the 
practices’ influence on profitability. Also included are the personal thoughts of the 
participants themselves. 
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Environment. For elements entailing the environment, as determined by the use 
of the NVivo 11 software, P1, P4, P5, P6, P7, P8, and P9 to P16 all stated their 
employers’ focus on air quality, the community, and general environmental protection 
issues. P1 stated the importance of low-cost energy, whereas P2 mentioned the use of 
employee participation for sustainability implementation.  
The sustainability practices analyzed into sub-themes accordingly as presented in 
Appendix A in response to Question 1. Under the theme of the environment, three sub-
themes unfolded. These were (a) community, (b) air quality, and (c) general comments 
concerning environmental protection. P1, P3, P4, P8, P9, P12, and P13 stated the 
importance of community engagement in the implementation of sustainability outcomes. 
Although all participants’ responses implied the importance of ensuring air quality, P9 
was specific in the mention of the employer’s focus on all efforts to minimize harm to the 
air, land, and water resources that can be a result of petroleum industry operations.  
Fuel. The second theme unveiled was fuel. P4, P6, P8, and P16 stated the 
importance to reduce the use of fossil fuel where possible. Sustainability practices 
involving fuels sub-themed into alternative fuels, biofuels, the use of cleaner fuels, lower 
carbon footprint generation, and the use of natural gas. P16 said that an employer focused 
on the use of cleaner burning fuels in petroleum industry operations. P3, P5, and P15 
mentioned the use of natural gas as the preferred cleaner fuel. P4, P5, and P12 stated that 
biofuel, which reduces the resultant hydrocarbon footprint is in use as an alternative fuel 
at their companies. 
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The captured data in Appendix B indicated that for the theme of air, actions 
ongoing included (a) the need to shift to cleaner fuels, (b) the use of natural gas, and (c) 
projects that reduced GHG emissions. A consensus existed amongst P3, P4, P6, P8, P9, 
P11, P14, P15, and P16 that reduction in the use of hydrocarbon based fuels can improve 
profits. The responses indicated the perceived linkage between the use of fuels and 
positive sustainability outcomes. 
Human resources. The third major theme generated using the NVivo 11 software 
was practices involving HR. See Appendix A. The four subthemes generated were (a) 
training, (b) engagement, (c) employment, and (d) safety. P 4, P6, P8, P11, P15, and P16 
presented the importance of training personnel to understand the value of sustainability 
efforts, and the activities and compliance issues of importance in the successful 
implementation. HR engagement is also a necessity in relating compensation to 
sustainability outcomes as P1 stated. P2, P8, P9, P12, and P14 mentioned how their 
employers kept their workers engaged in sustainability outcomes by the transfer on 
information of results realized.  
P12 stated that previous sustainability experience is a consideration for 
prospective employees. Last, for this theme, P1, P4, P6, P8, P9, P11, P13, and P14 
responded that safety is a core focus area for the implementation of sustainability 
environmental practices. The aim for their employers was to minimize loss or damage to 
people, the environment, and reputation to continue having a license to operate. P1 stated 
that this approach might preclude environmental disasters and avoid negative profitability 
outcomes.  
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The participants claimed that their employers implemented sustainability 
environmental practices aligned with HR practices using the following (a) business 
practices; (b) partnerships; (c) personnel training; and (d) structural and physical changes. 
Sub-themes under business practices included (a) employee guidelines, (b) general 
guidelines, and (c) specific procedures. P13 mentioned that employee guidelines included 
a comprehensive set of codes, policies, and assurance processes that define how 
companies operated. P1, P3, P4, P8, P9, P12, and P13 stated that their employers spend 
time to engage the community and stakeholders at a formal level. For the community and 
stakeholders, the petroleum industry companies hold numerous town hall meetings to 
create awareness and maintain reputation. 
P2, P9, and P12 stated that general HR guidelines included the need to 
incorporate sustainable practices into business affairs. P11 and P14 opinioned that the 
implementation of mandatory standards by the creation of operating manuals, and the 
sharing of learnings among different operating units as an HR policy ensured the business 
benefited from the use of sustainability initiatives. The focus was on ensuring employees, 
customers, and suppliers entertained sustainability in their decision-making and daily 
activities. 
As stated by P12, P13, and P16, the subtheme of partnerships included (a) 
partnering with local governments, (b) working with renewable energy institutes and 
academics; and (c) the allocation and deployment of employees to have consistent and 
continuous engagement with key stakeholders. Personnel training requirements entailed 
(a) formal; and (c) experiential as P9 and P14 stated respectively. P3 and P11 stated the 
78 
 
 
training to include the exposure of a sustainability code for all staff. In addition, P7 
mentioned the need to encourage employees to join volunteer organizations involved 
with environment/sustainability activities while P4 responded on the importance of 
having a HSE department focused on the environment.  
P4 and P14 responded on the importance of structural and physical changes 
focused on the consolidation of office space to reduce the environmental footprint. P14 in 
response mentioned new office buildings should meet the Leadership in Energy and 
Environmental Design (LEED) criteria. P12 asserted that his employer uses solar steam 
generation and research new technologies to deduce GHG emissions as a priority. 
Recycling. Recycling was the fourth theme that came to the forefront. P14 
mentioned the importance of the link between waste management and recycling. P9 
stated that efforts exist at his employer whereby the design and use of products are in 
alignment with recycling outcomes. P15 mentioned that an awareness exists that his 
employer recycles steel and offshore equipment. P4, P5, P7, P9, and P10 noted that 
company policies exist for the recycling of paper and other waste products in their 
operations.  
P6, P10, P13, and P14 stated the importance of spill and water management. 
Again, in their responses, P4, P5, P7, P9, P10, and P14 mentioned their employers 
embrace waste management and recycling as activities to illustrate their implementation 
of sustainability environmental practices. P6 mentioned activities specifically aimed at 
the prevention of water contamination by effective post usage chemical treatment before 
recycling or disposal. 
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Mitigation. P1, P3 through P9, P11, and P13 stated that the mitigation activities 
experienced in their place of employment included those aligned with (a) flaring; (b) 
greenhouse gases; and (c) oil and gas-leaks and emissions. P5, P6, and P7 mentioned 
flaring reduction as mitigating activities to prevent environmental harm. P5 and P8 stated 
that their employers aimed to reduce greenhouse gas emissions during their petroleum 
industry operations. This goal of this approach was to minimize harm to air quality. Last, 
P1, P3, P4, P6, P8, P9, P12, and P13 stated the efforts apace to minimize oil and gas 
leaks and emissions. These efforts are a necessity because of the known harm to the 
environment when hydrocarbon leaks occur (Shell, 2015). 
Mitigating efforts included ensuring the integrity of oil and gas wells, efficient 
and optimal decommissioning of infrastructure and equipment, and closing any gaps 
unveiled in environmental impact assessments. P6 and P11 maintained that ensuring well 
integrity, spills, and leaks may not occur and this avoidance will enhance profitability. 
P6, P8, P9, and P13 experienced employers willing to ensure astute decommissioning 
activities to prevent environmental harm and loss of reputation that can inhibit revenue 
generation and profitability outcomes.  
Water. The last practice detailed using NVivo software was water. The theme 
divided into three sub-themes. The sub-themes analyzed included (a) disposal, (b) water, 
and (c) treatment. First, P13 stated the importance of disposal of water that may be 
damaging to the environment. P6, P10, and P14 disclosed the importance of water 
management for environmental conservation and sustainability outcomes. Companies 
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that produce hazardous waste material should treat such material before disposal (Epstein 
& Buhovac, 2014).  
For the water theme as a practice, P6 alluded that linkages exist between 
management, treatment, and disposal to avoid the contamination of fresh water sources. 
P10 and P13 stated that their employers pay attention to appropriate (a) sourcing, (b) 
storage, (c) transportation, and (d) recycling of produced water. These are activities 
aimed at the effective management of water usage for their petroleum industry 
operations. 
 
P9 stated that water finger printing is under consideration as a new technology 
application that can provide proof that groundwater protection is a reality. See Appendix 
B. P6, P13, and P14 contributed the importance of new technology to reduce the need and 
use of water in the petroleum industry. With employers focusing on new technology 
applications to improve water management outcomes, conclusions are possible as to the 
importance of protecting this resource. 
Influence on profitability. Questions 6, 7, 8, and 9 facilitated responses 
concerning the influences of the themes identified on profitability outcomes. The 
analyses for these questions are in Appendices D, E, F, and G. Even though the first 
question yielded six sustainability practices themes, according to the participants, their 
employer believed that only five of them had a positive influence on profitability. P3, 
P13, and P15 surmised that their employers’ thoughts were that even though short-term 
profitability may suffer the resultant license to operate generated long-term profitability. 
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Themes analyzed based on responses included (a) environment, (b) fuel, (c) human 
resources, (d) recycle, and (e) mitigation.  
None of the participants indicated that sustainability practices targeting water had 
a positive influence on profitability. Similarly, none of the participants was of the belief 
that water had a negative influence. Each participant stated that practices aligned with 
avoiding discharge and pollution of the environment aids in environmental protection.  
P16 stated that ignoring the current energy structure and supply chain while 
demanding society to use renewables prematurely can have a negative effect on the 
employer’s profitability. P12 mentioned that substandard health, safety, and 
environmental practices generally create negative profitability outcomes. This possibility 
exists due to the increased cost of associated corrective actions. Another participant, P15, 
proposed that times exist whereby the most sustainable practice may be too expensive. 
These comments possibly hint at the use of cheaper practices that may be equally 
effective where applicable. 
 
P1 was generic in proposing that the employer was of the belief that any practices 
that result in leaks, spills, or environmental disaster would infringe on profitability due to 
clean-up costs and eventual loss of reputation. The loss of reputation can affect share 
pricing and erode value. P1 also mentioned that minimum safe designs of wells are a 
questionable practice often proposed when the petroleum industry experiences lower oil 
prices and should not occur. P7 stated that the employer believed that any practices that 
required new R&D would have a negative effect on profitability. These comments hinted 
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towards the use of practices that may already be common and accepted as standard in the 
petroleum industry. 
For Question 10, the participants’ beliefs on their perception of the influence on 
profitability generated three sub-themes. The results are in Appendix G. These themes 
were (a) affirmative, (b) neutral, and (c) negative. A lack of a negative response to this 
question occurred. The justifications for the beliefs analyzed into three sub-themes: (a) 
conjecture, (b) evidence, and (c) examples.  
When asked about their personal perceptions, P1, P2, P3, P4, P5, P6, P8, P10, 
P12, P13, P14, and P16 responded affirmatively that sustainability practices do have an 
influence on profitability. In responding neutrally, P11 stated that the influence depends 
on the situation whereas P15 mentioned not having enough information on this issue to 
make a judgment. P7 surmised the belief that influence existed but sustainability 
practices were expensive to develop and implement. P9 hinted that the influence existed, 
but may be negative with the flawed implementation of the practices. 
The justification given by P13 aligned with conjecture included guessing that the 
need exists to develop more technologies to preserve the environment. P14 provided 
conjecture to the belief that the influence of sustainability practices are positive to 
profitability by stating that stakeholders may incline more to allow additional lands for 
petroleum industry operations. P2 stated evidence existed whereby the employer took the 
time to engage with community stakeholders that avoided project inhibition. P6 proposed 
that the employer had evidence that the practice reduction of GHG from hydrocarbon 
usage had a positive effect on profitability.  
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P3, P6, and P13 gave examples to justify their beliefs where sustainability 
environmental practices may profitability. P3 stated the additional costs in the short-term 
necessary in capture emissions. P6 mentioned the cost to capture water. Similarly, P12 
gave the example of governments enforcing carbon regulation that may not be cost-
effective now. The belief is that these aforementioned activities may create long-term 
value for their respective organizations. 
Participants also contributed that there may be elements, of which they are 
unaware, that are beneficial to the environment and affects profitability neutrally. P1, P2, 
P3, P5, P6, P7, P8, P9, P13, and P15 stated that a lack of clarity exists on the practices 
that have a neutral effect on profitability. P14 and P15 responded that their belief was that 
environmental impact assessments are of benefit to the environment but may have a 
neutral effect on profitability. P10 believed that waste management and recycling is 
beneficial to the environment yet affects profitability neutrally. P11 had similar thoughts 
about biofuels and alternative energy.  
Contributions as a professional. Interview Question 13 was How can you as a 
professional contribute to the implementation of sustainability practices as part of your 
professional responsibilities? Based on the responses, the possibility existed to create 
three themes. See Appendix H for details of the analysis for this question. As 
professionals, participants could contribute to the implementation of sustainability 
practices as part of their professional responsibilities by (a) joining the corporate efforts, 
(b) having personally responsibility, or (c) being a vocal advocate.  
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For the theme join corporate efforts, P9 stated that employees should become 
knowledgeable and better equipped to promote the company’s sustainability goals. P11 
divulged that following the rules could contribute to sustainability practices as part of his 
professional responsibilities. P4 was of the belief that following environmental standards 
and practices also aligned with joining corporate efforts.  
The theme of personal responsibility entailed an appreciation of how a project 
that can affect the environment as P13 stated. This approach results in mitigating against 
actions that may inhibit sustainability outcomes. P8 and P11 contributed responses that 
personal responsibility also entails assigning sustainability as an element of every 
employee’s function to facilitate an understanding of how the environmental harm can 
occur. This understanding can allow employees to include a review of the environmental 
impacts of their petroleum industry projects.  
The last sub-theme generated from the responses to this question was vocal 
advocate. P6 and P8 highlighted the importance of vocal advocacy. This approach 
entailed (a) an explanation to stakeholders of the importance of sustainability practices, 
and (b) assuming a role for the dissemination of sustainability plans, results, and 
proposed changes going forward. P8 proposed that for professionals, the assumption of a 
leadership role for the implementation of sustainability practices is of importance.  
Importance of Sustainability. Question 14 was the last item in the online 
questionnaire. The question was; Why do you think embracing sustainability is important 
and how does each part of the system including operations, finance, human resources, 
legal, and suppliers and customers contribute and benefit? Appendix I contains the 
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analysis for this question. In their response to this question, participants’ answers 
facilitated the creation of four themes. All of the participants believed that embracing 
sustainability was important. The main theme generated based on responses were because 
of (a) economic benefit, (b) comfort of life for future generations, (c) need to save the 
environment, and (d) present day life itself. P8, P12, and P16 responded that 
sustainability was important because of economic benefits. P4, P10, and P14 mentioned 
the specific importance of sustainability activities for the benefit of future generations. 
P11 and P13 disclosed the need to protect life by the use of sustainability practices. P4, 
P14, and P15 stated the importance of saving the environment as an expectation of living 
in the present. 
The second part of question 14 only two responses from the participants resulted. 
P8 summarized that organizations could realize positive financial impacts while 
experiencing a reduction in litigations costs. In addition, P14 stated that the perception is 
for more clarity on the economic benefits of each part of the system will result from 
embracing sustainability. This result may provide the granularity required to optimize the 
operations of each organizational function to generate the profitability outcomes required. 
Data Analysis Conclusions by use of NVivo 11 
For this research, a multi-case study enabled an exploration of the sustainability 
environmental practices that influence profitability in the petroleum industry. The 
participants to the 14 items questionnaire comprised of a convenience sample of 
employees who work in the industry. Choice of these participants was via purposeful 
sampling. These 16 participants have several levels of responsibilities in the area of 
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sustainability within their company. The questionnaire sought their perceptions, 
knowledge, and expertise on numerous issues. The primary purpose was to attain 
common responses to the questions that seek to identify what sustainability 
environmental practices the participants believe influence profitability whether 
positively, neutral, or negatively. 
First, the participants presented a host of sustainability environmental practices 
that allowed placement into six themes and 21 sub-themes. Based on the responses of all 
the participants, their four petroleum industry employers with which they had work 
experience executed multiple sustainability practices. These practices generated six 
themes namely (a) environment, (b) fuel, (c) HR, (d) recycle, (e) mitigation, and (f) 
water. Implementing sustainability practices would definitely present challenges to any 
company (Epstein & Buhovac, 2014). There would be logistical, cost, and personnel 
issues that may be barriers to implementation (Senge et al., 2010). Nevertheless, eight of 
the participants, namely, P2, P9, P10, P11, P12, P13, P14, and P16 claimed that their 
employers implemented sustainability environmental practices using elements involving 
(a) business practices; (b) partnerships; (b) personnel training; and (d) structural and 
physical changes.  
In embracing sustainability practices, the employers used a combination of (a) 
direct action; (b) incentives; (c) indirect action and rules, reports and regulations. All 
participants except P5, P15, and P16 stated that these practices were present in their 
organizations. P4, P9, P8, P11, P13, and P14 further indicated that to maximize the 
possibility of all components of the organizational system and structures benefiting from 
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the use of sustainability initiatives, their employers initiated (a) global appeals, (b) local 
appeals, and (c) mandatory standards. Looking at the participants’ perceived thoughts of 
their employers regarding the influence of sustainability environment practices on 
profitability, P3, P7, P9, P10, P12, P13 and P14 specifically claimed their employers 
were either: supportive, neutral, or wait-and-see; no participant provided a negative 
response to this question. 
Even though there were six sustainability practices themes generated, according 
to an analysis the responses 14 of 16 participants concluded that their employers believed 
only five of the practices have a positive influence on profitability. The exceptions to this 
conclusion were P5 and P16. The themes stated as having a positive influence on 
sustainability align with (a) the environment, (b) fuel, (c) human resources, (d) recycle, 
and (e) mitigation. Sustainability practices targeting water did not factor as having a 
positive influence on profitability. Although sustainability practices that target water 
resources were not of the belief to have a positive influence on profitability, the 
participants did not list them as having a negative influence. There were no sustainability 
environmental practices identified as having a negative effect on profitability. 
Additionally, no sustainability practices identified as having a neutral effect on 
profitability.  
Findings from Annual Sustainability Reports  
 
Another requirement for this doctoral study was a review of the annual 
sustainability reports of four petroleum industry companies operating in the United 
States. The years of the reports were 2011 to 2015. This approach facilitated the ability to 
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capture data from other sources beyond the online questionnaire data presented 
previously. As required for qualitative study, data capture from multiple sources allows 
for improving the quality of the study via triangulation (Yin, 2014). 
Triangulation is the convergence of data collected from different sources (Yin, 
2014). To confirm or disconfirm the results of the online questionnaire I reviewed the 
annual sustainability reports of four petroleum industry companies who operate in the 
United States. The companies included BP, ExxonMobil, Chevron, and Shell. These 
companies are reputable brands in the United States among the top petroleum businesses 
globally (Forbes, 2015).  
 
A review of these reports over the years 2011 to 2015 unveiled 18 environmental 
practices that these companies collectively perform. The 18 practices in the following 
two paragraphs are in alignment with those presented by the participants in the online 
questionnaire responses. These environmental practices within the realm of sustainability 
include (a) environmental policy and planning; (b) impact assessments; (c) flaring 
reduction; (d) greenhouse gas (GHG) mitigation; and (e) waste management. Also within 
the reports are the practices of (a) recycling, (b) biofuels usage, (c) natural gas usage, (d) 
using liquefied natural gas (LNG), and (d) carbon capture and storage.  
 
Additional practices observed included the use of (a) renewable energy, (b) 
hydrogen technology for electricity, and (c) spill management techniques. The list of 
practices closes with the inclusion of (a) biodiversity protection; (b) water management; 
(c) performance measurement and reporting; (d) decommissioning of equipment and 
restoration of land assets to original status; and (e) research and development targeted at 
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environmental sustainability. These 18 practices are within the collective sustainability 
reports across all four companies. Some of the companies do not have them all in 
practice. Following are the results of the review of the annual sustainability reports. 
 
The review of the annual reports entailed capturing the sustainability 
environmental practices implemented in the company’s operations. Appendices J, K, L, 
and M show the results in tabular format. Each Appendix has results for one company, 
BP, Chevron, ExxonMobil, and Shell respectively. The list of 18 environmental practices 
is in the first column. The subsequent five columns capture whether any mention existed 
as to the use of that practice as a corporate requirement, which includes the United States 
or reference to the use of that particular practice in the United States existed. The data 
capture is in a chronological manner over the years 2011 to 2015 (see Appendices L, M, 
N, and O).  
For the presence of mention of a particular practice in any particular year, the use 
of alphabet letter Y acknowledges a positive outcome. Similarly, when that practice was 
non-existent, the letter N highlighted a negative outcome. The last column named 
Comments captures any special attributes observed in relation to that particular practice 
as observed in the reports. 
 
BP. The results for BP are available in Appendix J. Of the 18 practices unveiled, 
BP implemented 16. No mention of carbon capture and storage of the use of hydrogen 
technology for electricity generation existed in the reports (BP, 2015). Consequently, the 
findings indicated that BP implements the use of sustainability practices in a similar 
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majority as per the other companies investigated inclusive of Chevron, ExxonMobil, and 
Shell (BP, 2015). 
 
Chevron. Results for Chevron Corporation are in Appendix K. The company 
executed 14 of the 18 environmental practices identified. The practices not mentioned in 
the annual reports are (a) the use of biofuels, (b) LNG usage, (c) carbon capture and 
storage; and (d) hydrogen technology for electricity generation. No mention existed for 
practices focused on decommissioning and restoration during the earlier review years of 
2011 to 2015. This practice is in place in the later years during 2014 to 2015 (Chevron, 
2015). 
 
ExxonMobil. Mention of all 18 practices existed in the annual reports throughout 
the 5-year period with the exception being no mention of recycling in 2011. Most notable 
is that ExxonMobil focuses on energy conservation, and leads industry efforts to 
minimize and mitigate spills associated with their operations. The company embraces the 
use LNG as a clean burning fuel, and leads this area of the business by owning and 
operation the largest LNG terminal in the world in Louisiana in the United States 
(ExxonMobil, 2015). The results are in Appendix L.  
 
Shell Oil Company. In reviewing the results of data captured from the Shell 
annual reports, noted were two practices not in full implementation over the 5-year 
period. The practice of carbon capture and storage was not ongoing in the United States 
operations (Shell, 2015). Research and development studies and the planned 
implementation of carbon sequestration exist for other countries globally (Shell, 2015). 
The possibility exists that this practice may undergo implementation in the United States. 
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Last, no mention existed on the practice of decommissioning and restoration until 2015 
(Shell, 2015). The results are in Appendix M.  
Applications to Professional Practice  
The focus of this study was an understanding of how sustainability environmental 
practices influenced profitability. This sub-section contains an appreciation of the 
application of the study to professional practice. Professional practice entails 
competences aligned with individual motives, traits, skills, aspects of self-image or 
bodies of knowledge applied during work to generate a certain level of performance 
(Lindberg & Rantatalo, 2015). Leaders in the petroleum industry have an opportunity to 
improve performance by embracing sustainability environmental practices (Stocchetti, 
2012). The results of this study indicated which practices could add value to business 
performance and should attain inclusion within professional practices in the petroleum 
industry.  
Practices identified that should attain inclusion in to improve business 
performances are those that (a) protect the environment from harm, (b) engages; informs, 
trains, and compensates human resources (HR) to implement, monitor and focus on 
environmental protection; and (c) execute mitigating activities to ensure air quality by the 
reduction of flaring and the production of greenhouse gases. The use of cleaner burning 
fuels such are biofuels, natural gas, and alternative energy such as solar and wind can 
also be of value. Where possible, recycling of waste products generated during petroleum 
industry operation is a necessity. Water management approaches including minimizing 
the use of fresh water, and the treatment of water to remove harmful products before 
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disposal can also add value to business performance. Practices aimed at decommissioning 
of old infrastructure and the restoration of equipment, petrochemical plants and site to 
original states can reduce any harmful footprints from petroleum industry operations. 
Last, continuous research and development that facilitates the deployment of improved 
sustainability practices continued to be a goal of the four companies studied and should 
be a practice other companies can imitate to create value. 
The responses of all the participants support the understanding that policies that 
incorporate the importance of sustainability in business operations are a requirement. An 
appreciation of sustainability with the skillset to mitigate against associated negative 
outcome should be one of the skills for any employment opportunities (Edirisinghe & 
Fraser, 2015). The performance of employees, teams, and business units should include a 
sustainability performance component where appropriate (Epstein & Buhovac, 2014). 
Employees should have the ability to display their sustainability actions by having the 
ability to incorporate activities associated with this mindset into daily activities at work 
(Rashash, Elliott, & Madhosingh-Hector, 2015). Corporate policies and plans should 
incorporate sustainability as a standard part of business practice (Baumgartner, 2013). 
The expectation is for the improvement in the delivery of professional practices goals. 
Education and training of sustainability initiatives and the understanding of the 
benefits of these initiatives to business should be a requirement of professions across all 
disciplines (Edirisinghe & Fraser, 2015). Companies need to communication the practices 
that can ensure sustainable outcomes (Baumgartner & Winter, 2014). The results of the 
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questionnaire indicated that the participants do not all identify the same practices as being 
of importance and relevant. 
All the participants agreed that environmental protection was importance. Ten 
participants agreed that the practices of ensuring that the human resources align with 
sustainability initiatives are of importance. Ten participants stated that practices aimed at 
mitigating environmental harm are necessary. Eight participants noted that the use of 
cleaner burning fuels or the use of alternative sources of energy could create 
sustainability outcomes. Six participants mentioned the importance of recycling whereas 
four experienced water management as an important practice at their employer. 
The results are in contrast with the four company’s annual sustainability reports 
whose authors captured similar practices over the 5-year period across all companies. 
These results indicated the need for companies to disseminate information to their 
employees more effectively with respect to all sustainability activities ongoing in their 
petroleum operations. Employee training is a tool that may be of use in the dissemination 
of the value and competencies required for a sustainability culture (Epstein & Buhovac, 
2014a). Based on the results of the study, professional practice improvement is a 
possibility by the incorporation of education and training of the sustainability practices 
available. 
Participants’ responses indicated the perception of the importance of working 
together to achieve sustainability. These responses are in alignment with Baumgartner 
(2013) who stated the need for inclusiveness and integration in the governance of 
sustainability initiatives. Balancing the needs of business and society facilitates win-win 
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scenarios (Kassel, 2012). This approach is a possibility when professionals of different 
disciplines work together as part of the same team to formulate plans for sustainability 
activities. Collaboration across different professions and stakeholders is of importance for 
sustainability to become a reality (Senge et al., 2010). With this goal in mind, teams with 
employees across technical, operation, legal, loss prevention, and financial sub-
disciplines should work together to ensure the delivery of sustainability goals as a 
requirement in professional practice. 
The findings of this multi-case study indicated that the four globally recognized 
petroleum industry companies under study use sustainability environmental practices. 
BP, Chevron, ExxonMobil, and Shell generate billions of dollars in yearly profits 
(Forbes, 2016). These companies are profitability (Forbes, 2016). One can infer that with 
the drive for financial success and their willingness to embrace their sustainability 
environmental programs, these programs contribute to their profitability. Participants’ 
responses to the online questionnaire support these conclusions.  
A limited body of research focused on sustainability and associated business 
outcomes exist (Muja, Appelbaum, Walker, Ramadan, & Sodeyi, 2014). These findings 
can be of value to business stakeholders within the petroleum industry. The findings 
display useful insights to the practices in use by four of the largest petroleum industry 
companies in the world. These findings may guide the leadership of other companies 
towards embracing sustainability as a core part of doing business. The benefits to 
humanity include pollution reduction (Tsai, 2013). An improvement in the life of 
humanity, while creating profitability outcomes results can occur (Senge et al., 2010). 
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Implications for Social Change  
Businesses and organizations should operate in a socially responsible manner 
(Epstein & Buhovac, 2014a). Value for organizations is possible when organizations 
integrate sustainability practices into their core business strategy (Micah & Umobong, 
2013). The possibility exists for positive social change when businesses (a) invest in local 
and global communities, (b) interact with stakeholders, and (c) respond to their needs 
(Epstein & Buhovac, 2014a). To achieve these goals, activity planning should be a key 
vehicle in the implementation of social changes (Adams, Scott, & Hardman, 2013). 
Achieving social change is possible by the use of the results of this study to guide the 
leaders of petroleum industry companies to appreciate that the use of sustainability 
environmental practices does have a positive effect on profitability. 
Mounting pressure from key stakeholders in industry made sustainability 
imperative for business organizations (Chakrabarty & Wang, 2012). The results of the 
study provided evidence to conclude that the implementation of sustainability 
environmental practices can create a competitive advantage by improving profitability 
outcomes. These results can have the ability to change the focus of leaders and other 
stakeholders within petroleum industry business to embrace sustainability environmental 
practices as part of the status quo in business operations in the petroleum industry. 
Communities may also benefit, because harm to the environment can be minimum during 
operations, and the focus should be on the restorative efforts once operations of the 
business are complete (Bone, 2014). These results may further enhance the reputation of 
the business involved and may enhance business relations with the host society and 
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ultimately profitability. With the knowledge that the harm to the environment may be 
minimal, a permanent behavioral change is possible in the culture of executing business 
in the petroleum industry whereby all organizations align their operations with the focus 
on the use of these environmental practices where possible (Anosike, 2014). 
Participants’ responses indicated that social change is possible because humans 
may have a cleaner environment for their existence. This conclusion was possible 
because the three elements of sustainability do include (a) social, (b) economic, and (c) 
environmental elements (Kiron, Kruschwitz, Hannaes, & Velken, 2012). A symbiotic 
relationship exists between these three elements (Epstein & Buhovac, 2014a). This 
conclusion is in evidence from the annual sustainability reports where discussion around 
sustainability included the inter-relationship between the three elements (BP, 2015; 
Chevron, 2015; ExxonMobil, 2015; Shell, 2015). The benefit to the host community and 
associated stakeholders may include an improved standard of living including one of 
comfort and longevity (Konne, 2014). 
A review of the annual sustainability reports of the four companies under study 
also indicated a focus on the research and development of new practices and technologies 
that can affect the way humans live. Renewable energy options can aid in sustainability 
efforts (Miskinis, Baublys, Konstantinaviciute, & Lekavicius, 2014). These options 
include wind, solar, and geothermal (Shell, 2015). Social change is a requirement to 
embrace these options because of the need for different equipment, and modes of tapping 
into these energy sources. Depending on the option and the availability of the renewable 
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energy technology, a reduction in environmental impact is possible, and this achievement 
becomes a benefit to humanity. 
Recommendations for Action 
To drive sustainability initiatives through an organization, various actions 
including product costing, capital budgeting, information transfer, and performance 
evaluation are necessary (Epstein & Buhovac, 2014a). Any call to action sustainability 
requires a vision for the future (Senge et al., 2010). Sustainability environmental 
practices are beneficial to organizations because they allow for (a) cost cutting; (b) 
reputation enhancement that attracts employees and customers, and (c) minimal use of 
natural resources that can result in long-term viability (Richerson, 2013).  
Recommendations for action logically follow from conclusions from the study, 
states who needs to pay attention to results and how leaders and other stakeholders in the 
petroleum industry can attain the motivation to focus on sustainability solutions. 
Shunning the call for sustainability would be a missed economic opportunity, and deliver 
a death sentence to large portions of the world’s population (Ross, 2010). Therefore, the 
implementation of sustainability action plans is a necessity (Kiron et al., 2012). Action 
plans must include (a) agreeing on goals, (b) performance measures, (c) a determination 
of human and physical resources, (d) interlocks required, and (e) financial estimates 
(Harvard Business School, 2005). Creating, implementing and monitoring the outcomes 
of these plans are dependent on the leadership traits in every stakeholder (Epstein & 
Buhovac, 2014a). 
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The results of the study indicated an alignment between the sustainability 
practices the participants believe are of importance and those actually in practice based 
on the data within the companies’ annual sustainability reports. Conclusions from the 
participants’ responses indicated that the use of sustainability environmental practices is 
present in the companies researched. More than 90% of the participants agreed that these 
practices should be an integral part doing business in the petroleum industry, and 
facilitated a license to operate (see Appendix C). In addition, the perception exists that 
profitability results when sustainability practices are in use in the companies reviewed in 
this doctoral study.  
Participants indicated that sustainability environmental practices do influence 
profitability. The perception is that the influence is positive for the majority of the 
practices. The results of the study indicated that gaps exist within the practices in actual 
existence in the four companies under study, and the knowledge of the participants who 
have working experience with these organizations. In planning for the removal of any 
knowledge gaps that exist, training of personnel is a requirement (Panford, 2012). This 
approach should be within a comprehensive policy. When corporate leaders integrate of 
sustainability training into corporate plans, a competitive advantage may result (Muja et 
al., 2014). 
Leaders in petroleum industry companies, middle-level management, and 
individual contributors should pay attention to these results. The implementation of 
sustainability is dependent on all (Senge et al., 2010). The use of a chief sustainability 
officer to plan, implement, and monitor sustainability initiatives can give a competitive 
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advantage (Kiron et al., 2012). To incorporate sustainability as one of the must do 
elements in the execution of business, change management is a requirement (Lozano, 
2011). 
For each of the sustainability practices identified, implementation in other 
organizations where gaps exist is a possibility. The call to action requires nine key steps 
to sustainability practices implementation. These steps include (a) getting support from 
corporate management, (b) engaging all stakeholders, (c) creating a plan, and (d) setting 
goals (Richerson, 2013). Other steps include (a) execution, (b) measurement, (c) sharing 
progress, and (d) conducting reviews (Richerson, 2013). For all practices, the focus must 
be on recycling activities and decreasing the rate of on consumption of non-renewable 
energy (Marques & Machado, 2013). 
Plans to disseminate this study are through the Society of Petroleum Engineers 
(SPE). SPE is the largest individual-member organization serving managers, engineers, 
scientists and other professionals worldwide in the upstream segment of the oil and gas 
industry (Society of Petroleum Engineers [SPE], 2016). Membership of SPE stood at 
160,000 globally in 2015. The organization also has a magazine, named the Journal of 
Petroleum Technology (JPT), read for millions of industry professionals on a monthly 
basis. Plans are to publish an extract of this paper in JPT to display the results of this 
study to the petroleum industry. 
Recommendations for Further Research 
This research allowed for the exploration of the sustainability environmental 
practices that affect profitability in the petroleum industry. A review of the literature did 
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not unveil a similar study from any part of the world (Bone, 2014). Not enough is 
available on sustainability research (Muja et al., 2014). The results of the study indicated 
the need for further research. 
As previously stated, the debate about the importance of sustainability 
environmental practices and how they affect profitability is absent in certain business 
environments (Schaltegger et al., 2011). Additional research may provide further deliver 
evidence to understand the affect these practices have on profitability. Sustainability 
initiatives should be at a global level (Senge, 2010). Taking the global concerns of 
sustainability into consideration is a necessity (Carroll & Shabana, 2011).  
Based on statements in the previous paragraphs, an additional area of research 
proposed includes expanding the study to developed and developing nations. The focus 
of this expanded study area can allow researchers to understand the importance of 
sustainability environmental initiatives to the government and citizenry within these two 
national classifications. Such understanding may provide ways forward to ensure the 
implementation of practices that can protect the environment (Ingelson & Nwapi, 2014). 
Other keys areas of interest include research aligned with (a) national and 
publically owned petroleum industry companies; and (b) land operations versus marine 
operations. Nationally owned petroleum companies may or may not operate in a manner 
that takes environmental protection seriously (Vermeer, 2015). This approach is possible 
because of the ability to operate above the law when a company experiences national 
ownership. Ownership structures influence sustainability outcomes (Vermeer, 2015). 
Researching how the different ownership structures affect profitability may provide an 
101 
 
 
understanding of reasons for action or non-action on environmental sustainability issues. 
Similar research on different approaches, if any exists, between land-based and marine 
petroleum industry operations may also provide information to understand where issues 
exist and how to implement appropriate sustainability environmental practices.  
A limitation of this study is the focus on responses from participants associated 
with four large petroleum industry companies operating in the United States. A 
recommendation for further research entails the use of participants who have experience 
of employment with mid-sized and small independent companies. This approach may 
facilitate a more complete sample spectrum whereby researchers can interpret and 
determine the practices across different sized petroleum industry organizations.  
The results may provide answers to the measures necessary to ensure that the 
petroleum industry embraces sustainability as a means of doing business. In summary, 
the expanse of this research across (a) type of ownership, (b) land-based versus marine 
and, (c) different sized companies may generate solutions, where necessary, towards a 
more global holistic approach to the important issues in business. Companies such as 
Coca-Cola embraced sustainability to attain a financial competitive advantage (Kumar et 
al., 2011). Sustainability environmental practices can become an inherent part of business 
in the global petroleum industry and generate the profits required. 
The most harmful result of petroleum industry operation is the creation of harmful 
waste released into the environment in quantities that are un-natural (Rocca & Viberti, 
2013). A mitigating action is the use of green chemicals that are non-damaging (Jones & 
Lubinski, 2013). A complete knowledge of the limitations of green chemicals is 
102 
 
 
unavailable (Marques & Machado, 2013). The possibility exists for the use of greener 
chemicals to be of value in curbing the harmful effects of waste production of petroleum 
industry operations. As such, further research on green chemical as an opportunity for 
environmental protection is a requirement. 
Reflections 
The journey through this doctoral study allowed for a focus on business 
administration, and the manner in which to focus on providing solutions for associated 
issues. Three areas were in focus during this doctoral study process. These areas include 
an appreciation of how organizations lead in the implementation of sustainability 
environmental practices. In addition, an understanding of how successful and renowned 
firms use sustainability practices as part of their business model. The third area is a 
thorough understanding of qualitative research process. 
Businesses must make decisions that give a competitive advantage, and that 
advantage is a possibility with the use of sustainability practices (Quisenberry, 2012). 
Sustainability in organizations requires strategic leadership (Strand, 2014). I was not 
aware what sustainability entailed and the importance of such practices in creating 
opportunities for future generations. Without a skilled a motivated workforce, 
implementation of sustainability practices is impossible (Milliman, 2013). An attainment 
of an appreciation of the importance of human capital in the delivery of sustainability 
practices also resulted from this study.  
103 
 
 
Conclusion 
With this qualitative multiple-case study, I explored, identified, and attained an 
understanding of the sustainability environmental practices that affect profitability in the 
petroleum industry. The focus of this study was four reputable petroleum industry 
companies operating in the United States. Data capture from study participants generated 
five sustainability environmental practices themes perceived to have positive influences 
on profitability. These practice themes were (a) the environment, (b) fuel, (c) HR, (d) 
recycle, and (e) mitigation. The practice theme aligned with water, which was the sixth 
theme identified, attracted the perception as having a neutral effect on profitability.  
To improve study validity and data reliability, I studied the annual sustainability 
reports of the four companies that were the focus of the study. Eighteen actual 
sustainability practices aligned with the six sustainability environmental practices themes 
identified from participant data capture are in use across all four companies. Findings 
from the study uncovered similar practice results whether they are perceptions of the 
participants of the positive outcomes from sustainability practices or the companies 
embracing the practices to deliver value and maintain their competitive advantage. 
Conclusions from data captured indicated that embracing sustainability environmental 
practices should be a necessity to maintain a license to operate.  
Noted from the study, was the need to have policies in place to embrace 
sustainability practices while integrating the involvement of all stakeholders. Having a 
holistic view of how each of the sustainability practices in place affect each other gives 
credence to the need for a systems approach in the delivery of sustainability solutions. 
104 
 
 
This approach attained the perception of being possible by the continued display of the 
results of the sustainability environmental practices so ensure awareness of all parties. 
Accountability, and continued measurement of the impact of sustainability environmental 
practices attained the view as a key element of the petroleum industry business. Where 
necessary, there should a drive to change the culture of organization to one of 
sustainability delivery. This approach can deliver comfort, success, and longevity for 
present and future generations.  
 
105 
 
 
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139 
 
 
Appendix A: Main Environmental Themes generated from NVivo 11 
 
 
 
 
 
 
140 
 
 
Appendix B: Evidence of Environmental Protection generated from NVivo 11 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
141 
 
 
Appendix C: Employer’s Thoughts on Influence of Practices on Profitability 
 
 
 
 
 
 
142 
 
 
Appendix D: Participant’s Determination of Positive Influences 
 
 
 
 
 
 
143 
 
 
Appendix E: Identification of Negative Practice Themes 
 
 
 
 
 
144 
 
 
Appendix F: Identification of Neutral Practices Themes 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
145 
 
 
Appendix G: Participants Personal Beliefs with Justification 
 
 
 
 
 
 
146 
 
 
Appendix H: Contributions to Sustainability as a Petroleum Industry Professional 
 
 
 
 
 
 
 
 
 
147 
 
 
Appendix I: Importance of Sustainability 
 
 
 
 
 
 
148 
 
 
Appendix J: British Petroleum Annual Sustainability Reports (2011-2015) 
Year 
2011 2012 2013 2014 
2015 
Comments 
      Environmental Practices 
 
 
 
 
 
 
Policy & Planning 
Y 
Y 
Y 
Y 
Y 
ongoing updates 
Impact Assessments 
Y 
Y 
Y 
Y 
Y 
always required  
Flaring Reduction 
Y 
Y 
Y 
Y 
Y 
Ongoing challenges 
GHG Mitigation 
Y 
Y 
Y 
Y 
Y 
Carbon costs 
considered 
Waste Management 
Y 
Y 
Y 
Y 
Y 
A focal point 
Recycling 
Y 
Y 
Y 
Y 
Y 
 
Biofuels 
Y 
Y 
Y 
Y 
Y 
15% ethanol in gas 
Natural Gas 
Y 
Y 
Y 
Y 
Y 
Rebranded as gas 
producer 
Liquefied Natural Gas 
Y 
Y 
Y 
Y 
Y 
Texas operation sold 
Carbon Capture/Storage 
N 
N 
N 
N 
N 
Not mentioned 
Renewable Energy 
Y 
Y 
Y 
Y 
Y 
Primarily R&D 
Hydrogen Electricity 
N 
N 
N 
N 
N 
Not mentioned 
Spill Management 
Y 
Y 
Y 
Y 
Y 
Area of focus 
Biodiversity Protection 
Y 
Y 
Y 
Y 
Y 
Within policy goals 
Water Management 
Y 
Y 
Y 
Y 
Y 
Technology in 
development 
Performance 
Measurement/Reporting 
Y 
Y 
Y 
Y 
Y 
Ongoing  
Decommissioning/Restoration 
Y 
Y 
Y 
Y 
Y 
Since GOM spill  
Targeted R & D 
Y 
Y 
Y 
Y 
Y 
 
 
 
 
 
 
 
 
 
 
 
 
149 
 
 
Appendix K: Chevron Corporation Annual Sustainability Reports (2011-2015) 
 
 
Year 
2011 2012 2013 2014 
2015 
Comments 
      Environmental Practices 
 
 
 
 
 
 
Policy & Planning 
Y 
Y 
Y 
Y 
Y 
Corporate policy 
present 
Impact Assessments 
Y 
Y 
Y 
Y 
Y 
Always performed 
Flaring Reduction 
Y 
Y 
Y 
Y 
Y 
33% reduction over 
time 
GHG Mitigation 
Y 
Y 
Y 
Y 
Y 
An ongoing goal 
Waste Management 
Y 
Y 
Y 
Y 
Y 
8% reduction  
Recycling 
Y 
Y 
Y 
Y 
Y 
Reduction of water 
usage 
Biofuels 
N 
N 
N 
N 
N 
No evidence of use 
Natural Gas 
Y 
Y 
Y 
Y 
Y 
Focuses on clean 
fuel 
Liquefied Natural Gas 
N 
N 
N 
N 
N 
No evidence of use 
Carbon Capture/Storage 
N 
N 
N 
N 
N 
Not in reports 
Renewable Energy 
Y 
Y 
N 
N 
N 
mention disappeared 
over time 
Hydrogen Electricity 
N 
N 
N 
N 
N 
No mention 
Spill Management 
Y 
Y 
Y 
Y 
Y 
Efforts ongoing 
Biodiversity Protection 
Y 
Y 
Y 
Y 
Y 
Part of policy 
Water Management 
Y 
Y 
Y 
Y 
Y 
Primarily recycling 
Performance 
Measurement/Reporting 
Y 
Y 
Y 
Y 
Y 
yearly 
Decommissioning/Restoration 
N 
N 
N 
Y 
Y 
Area of focus 
Targeted R & D 
Y 
Y 
Y 
Y 
Y 
Projects ongoing 
 
 
 
 
 
 
 
 
 
 
150 
 
 
 
Appendix L: ExxonMobil Corporation Annual Sustainability Reports (2011-2015) 
Year 
2011 2012 2013 2014 2015 
Comments 
      Environmental Practices 
 
 
 
 
 
 
Policy & Planning 
Y 
Y 
Y 
Y 
Y 
Ongoing updates 
Impact Assessments 
Y 
Y 
Y 
Y 
Y 
 
Flaring Reduction 
Y 
Y 
Y 
Y 
Y 
 
GHG Mitigation 
Y 
Y 
Y 
Y 
Y 
 
Waste Management 
Y 
Y 
Y 
Y 
Y 
 
Recycling 
N 
Y 
Y 
Y 
Y 
Recycling of used 
oil/water 
Biofuels 
Y 
Y 
Y 
Y 
Y 
Focus on algae 
biofuels 
Natural Gas 
Y 
Y 
Y 
Y 
Y 
 
Liquefied Natural Gas 
Y 
Y 
Y 
Y 
Y 
Largest terminal in 
world in Louisiana 
Carbon Capture/Storage 
Y 
Y 
Y 
Y 
Y 
CO2 sold for oil 
recovery in Wyoming 
Renewable Energy 
Y 
Y 
Y 
Y 
Y 
Opposes renewables 
Hydrogen Electricity 
Y 
Y 
Y 
Y 
Y 
Ongoing research 
Spill Management 
Y 
Y 
Y 
Y 
Y 
Leadership taken 
industry-wide 2011 
Biodiversity Protection 
Y 
Y 
Y 
Y 
Y 
Vegetation, wildlife, 
fisheries 
Water Management 
Y 
Y 
Y 
Y 
Y 
Water use reduction 
is focus 
Performance 
Measurement/Reporting 
Y 
Y 
Y 
Y 
Y 
Works alongside 
communities 
Decommissioning/Restoration 
Y 
Y 
Y 
Y 
Y 
corporate policy 
Targeted R & D 
Y 
Y 
Y 
Y 
Y 
Focus on energy 
conservation 
 
 
 
 
 
 
 
 
 
151 
 
 
Appendix M: Shell Oil Company Annual Sustainability Reports (2011-2015) 
Year 
2011 2012 2013 2014 
2015 
Comments 
      Environmental Practices 
 
 
 
 
 
 
Policy & Planning 
Y 
Y 
Y 
Y 
Y 
Policy advisor in 
place 
Impact Assessments 
Y 
Y 
Y 
Y 
Y 
Always done 
Flaring Reduction 
Y 
Y 
Y 
Y 
Y 
Aims to minimize 
flaring ongoing 
GHG Mitigation 
Y 
Y 
Y 
Y 
Y 
Resulted in ongoing 
reduction 
Waste Management 
Y 
Y 
Y 
Y 
Y 
Focuses on both 
hazardous and non-
hazardous 
Recycling 
Y 
Y 
Y 
Y 
Y 
Focused on water 
Biofuels 
Y 
Y 
Y 
Y 
Y 
In use at gas stations 
Natural Gas 
Y 
Y 
Y 
Y 
Y 
Nationwide use for 
fuel where possible 
Liquefied Natural Gas 
Y 
Y 
Y 
Y 
Y 
LNG and GTL in use 
where possible 
Carbon Capture/Storage 
N 
N 
N 
N 
N 
Ongoing research for 
mass deployment 
Renewable Energy 
Y 
Y 
Y 
Y 
Y 
Plans for up to 30% 
usage by Year 2050 
Hydrogen Electricity 
Y 
Y 
Y 
Y 
Y 
Filling stations 
available 
Spill Management 
Y 
Y 
Y 
Y 
Y 
Inconsistent results 
Biodiversity Protection 
Y 
Y 
Y 
Y 
Y 
Always considered 
Water Management 
Y 
Y 
Y 
Y 
Y 
Focused on use 
minimization 
Performance 
Measurement/Reporting 
Y 
Y 
Y 
Y 
Y 
Corporate 
requirement 
Decommissioning/Restoration 
N 
N 
N 
N 
Y 
EPA regulations 
Targeted R & D 
Y 
Y 
Y 
Y 
Y 
Eco-Marathons

172 
                                                                                                                                                                              TECHNICAL JOURNAL 17, 2(2023), 172-178 
ISSN 1846-6168 (Print), ISSN 1848-5588 (Online) 
Original scientific paper 
https://doi.org/10.31803/tg-20220604112718 
Received: 2020-06-04, Accepted: 2022-08-10 
 
 
Quick Review: Uncertainty of Optimization Techniques in Petroleum Reservoir Management 
 
Amir Naser Akhavan*, Seyed Emad Hosseini, Mohsen Bahrami 
 
Abstract: The notable increase in petroleum demand, together with a decline in discovery rates, has highlighted the desire for efficient production of existing oil wells worldwide. 
Mainly, the productivity of the existing large oil fields makes us consider the principles of managing reservoirs to make the most of extraction. At the same time, many different 
uncertainties in the course of the developing oil field, including geological, operational, and economic uncertainties, have a detrimental impact on the reservoir's effective production, 
which is why dealing with uncertainty is crucial for maximizing output. There is a broad variety of studies on managing oil reservoirs under uncertainty information in the literature. 
In this study a short review of earlier works has been done on optimization strategies and management of uncertainty in reservoir production. 
 
Keywords: management of reservoir; management of uncertainty; optimization approaches 
 
 
1 
INTRODUCTION  
     
For many years, oil has defined the world economy, this 
trend will most likely continue in the coming years. Demand 
for oil has increased with the population growth, and this 
demand needs to be met with proper optimization. 
Optimization management between the two factors of oil 
field development and oil production methods should be 
done that overall demand does not face problems. Proper 
planning for optimization requires ground and underground, 
data these data always have uncertainties that should be 
considered. 
Uncertainty is due to incomplete and imprecise 
knowledge as a result of limited sampling of the subsurface 
heterogeneities. Well data and seismic data have incomplete 
coverage and finite resolution. Sub ground and oil Reservoirs 
are heterogeneous and difficult to predict away from wells or 
seismic data. Ignoring uncertainty and locking in important 
model parameters and choices amounts to an assumption of 
perfect knowledge and is generally an unacceptable 
approach. One way to reduce the effects of uncertainty on 
optimizations is to model them. Understanding the (1) 
sources of uncertainty, (2) methods to represent uncertainty, 
(3) the formalisms of uncertainty, and (4) uncertainty 
modeling methods and workflows were essential for the 
integration of all reservoir information sources and providing 
good models for decision making in the presence of 
uncertainty and improvement of optimization. 
  Geophysical prospecting consists of making a 
quantitative inference about subsurface properties from 
geophysical measurements. Due to many ineluctable 
difficulties, observed data are almost always insufficient to 
uniquely specify the rock properties of interest. Hence, 
inevitable uncertainty remains after the estimation. The 
sources of the uncertainty arise from many factors: 
inconsistency in data acquisition conditions, insufficient 
available data as compared to the subsurface complexities, 
limited resolution, imperfect dependence between observed 
data and target rock properties, and our limited physical 
knowledge. While the uncertainty has been identified for a 
long time, quantitative framework to discuss the uncertainty 
has not been well established.  
In this study we examine several sources of uncertainty 
in the development of the oil field make the estimation of the 
future productivity of a reservoir inaccurate. In general, 
uncertainty in information about the management of 
reservoirs falls into four categories. 
 
2  LITERATURE REVIEW 
2.1 Conceptual Model 
 
Based on field studies the following conceptual model 
has been proposed to explain the subject. 
 
 
Figure 1 Conceptual model of the present research 
 
Amir Naser Akhavan, et al.: Quick Review: Uncertainty of Optimization Techniques in Petroleum Reservoir Management 
TEHNIČKI GLASNIK 17, 2(2023), 172-178 
 
    
173 
2.2 Uncertainty 
2.2.1 Engineering Data Uncertainty 
 
Methods such as gas-injection, intermittent water and 
gas injection (WAG), smart water, and thermal and 
polymeric methods are commonly used to increase oil 
recovery. Injecting water into reservoirs is one of the oldest 
oil recovery methods that has been used for many years. Over 
time, this method has undergone many alterations and 
improvements, which has led to its use as the most popular 
oil recovery method in sandstone and carbonate reservoirs. 
Establish a relationship between different types of oil 
production techniques that consider all factors on oil 
production 
for 
proper 
optimization 
is 
necessary. 
Uncertainties of Geological, fluid mobility, laboratory, and 
field heterogeneity are the engineering uncertainties that we 
will examine in the following. 
 
2.2.2 Uncertainty of Geophysical and Geological Information 
 
Two main types of uncertainty affect our confidence in 
the results from numerical models:  parametric uncertainty 
and structural uncertainty. Parametric uncertainty arises 
because of incomplete knowledge of model parameters such 
as empirical quantities, defined constants, initial conditions, 
and boundary conditions. Structural uncertainty in models 
arises because of inaccurate treatment of dynamical, 
physical, and chemical processes, inexact numerical 
schemes, and inadequate resolutions. Uncertainty from 
geology is usually related to seismic data, which is classified 
as Structural uncertainty. 
Seismic data used in the construction of a reservoir 
system are unclear. These uncertainties relate to data 
collection, analysis, and statistical explanation. Typically, 
uncertainties occur as a result of errors in data collection, 
conflicting explanations, error in converting depth data, error 
in preliminary explanation, and the wavelength map error 
that has to do with the crest of the reservoir. 
Probably uncertainties are mostly geological. In 
geological 
information, 
uncertainties 
arise 
due 
to 
sedimentation, rock nature (lithology), rock extension region, 
and rock physical properties, which leads to the following 
uncertainties: 
• 
of the reservoir's gross volume 
• 
of the size and direction of sedimentation 
• 
of difference in extension of rock type 
• 
of porosity data 
• 
of the net/gross ratio 
• 
of contact between fluids. 
 
These uncertainties have an impact on the assessment of 
on-site hydrocarbons and the movement of fluid across the 
reservoir. 
 
2.2.3 Uncertainty of Dynamic Information 
 
Petroleum reservoirs are very heterogeneous and it is 
difficult to predict the movement of fluid in them and always 
cause uncertainties that prevent proper and practical 
optimization. To reduce these uncertainties, various models 
and simulation methods should be used to perform the correct 
optimization to compensate for global oil demand. For 
example one of this method is grey bootstrap is proposed to 
resolve some problems about evaluation of the uncertainty in 
the process of dynamic. This method can evaluate the 
uncertainty without any prior information about probability 
distribution of random variables, separating trends with 
known and unknown law. 
At this point, uncertainties of all variables that have an 
impact on the flow of a fluid through the reservoir are 
addressed. These variables include absolute, vertical-
horizontal, and relative permeability (the measurement of a 
rock's ability, to transmit fluids), fault transmissibility, rate 
of injection, productivity index, pores, and skin (around a 
wellbore), capillary pressure curve, aquifers (water-bearing 
portions). Such uncertainties influence both the calculation 
of the reserve and the change of flow rates with time. 
 
2.2.4 Uncertainty of PVT Information 
 
PVT data are known to be the least uncertain data. Lack 
of certainty of PVT data affects the capacity of process 
units, hydrocarbon transportation, and marketing. Among 
the uncertainties in this category are as follows: 
• 
Uncertainty of fluid tests 
• 
Uncertainty of fluid structure 
• 
Uncertainty of the calculation of PVT properties 
• 
Uncertainty of interfacial tension. 
 
2.2.5 Uncertainty of Field Performance Information 
 
The well drilled in Pennsylvania was the first example of 
a well that showed the earth's layers, but with the drilling of 
other wells, a variety of different layers of the earth emerged, 
and engineers concluded that the earth's layer was different 
in each basin. Therefore, information of different layers and 
surface data should be used to minimize uncertainties 
Furthermore, data on field performance might as well be 
swayed by the following uncertainties: 
• 
The cost of oil production is usually calculated 
systematically and accurately; nevertheless, calculations 
of the water-oil ratio (WOR) and the gas-oil ratio (GOR) 
are occasionally performed; 
• 
The rate of production fluctuation is normally evened out 
as it can occur at short durations; the rate of gas is not 
calculated correctly, especially if parts of it is burned; 
• 
Injection information is less accurate than production 
information as a result of errors in the calculation stage, 
loss of fluid at a different time because of leakage in the 
skin or flow behind the piping system; and 
• 
Pressures gauged at a certain phase of the flow analysis 
are generally less reliable than those acquired during 
shut-in. 
 
2.2.6 Uncertainty of Economic Information 
 
In all different parts of human life, the economy is the 
most effective factor. In the oil industry, drilling a well or 
Amir Naser Akhavan, et al.: Quick Review: Uncertainty of Optimization Techniques in Petroleum Reservoir Management 
174 
                                                                                                                                                                              TECHNICAL JOURNAL 17, 2(2023), 172-178 
using an oil production method or using new technology is 
only used when it has an economic benefit. The main purpose 
of optimization is to reduce the risks that may impede the 
economic benefits of an operation. By accurately recognizing 
the uncertainties, the most optimal model for an economic 
operation can be used. For example, the use of nanoparticles 
to wettability Alteration and reduce sand production is a 
technique that has received much attention from researchers 
in the past few years, but since its economic uncertainty is 
very high, it has not been widely used in industry. 
Production management is challenged by some uncertain 
risks of going up or down in oil prices. Conventional 
production enhancement approaches concentrate on net 
present value over time. The lack of reliability of long-term 
predictions is the key problem of many strategies. Given the 
time-dependent nature and the instability of oil prices, more 
often than not, it makes oil risky production [1, 2]. 
 
2.2.7 Uncertainty of Political Information 
 
The uncertainty of the political system is a key feature 
affecting the local investment climate, which firms and 
entrepreneurs must consider when deciding to start, expand, 
or contract their businesses. Given the impact of oil on the 
economy and relations between countries and its key role in 
determining world powers, it is governed by very complex 
policies. Investors and entrepreneurs engaged in oil trade in 
different countries must act in accordance with that country's 
policy and also consider the impact of factors such as 
sanctions. 
 In order to predict the future of their business, these 
investors must pay close attention to the behavior of 
countries and world powers in order to avoid destructive 
global policies. Many countries have a major source of oil 
revenue, which, given the lifespan of their reservoirs, which 
are in the semi-finals, is forced to use techniques that reduce 
uncertainties and are able to meet demand. Therefore, their 
policy should be formulated in such a way as to give the 
leading companies investment security so that they do not 
face any problems. 
 
2.2.8 Uncertainty of Environmental Information 
 
Coping with the above uncertainty would be a serious 
factor in the production of the oil field. Being less sensitive 
to uncertainty along with the implementation of calculations 
are two methods that have been found to be exceedingly 
contradictory in some research. In this study, however, we 
present a small report of certain optimization techniques in 
the development of the petroleum field, based on the data on 
uncertainty to be able to both reduce uncertainty and 
sensitivity to its data. 
In this study, we present an up-to-date analysis of 
optimization methods used to solve these problems by first 
analyzing the cause of uncertainty and then reviewing some 
practical optimization techniques against technical problems. 
 
 
 
3  RESEARCH METHOD 
 
The principles of robust optimization have been 
introduced primarily in the research papers of engineering 
design. 
The first approach to deal with uncertainties is probably 
stochastic (linear) programming in the form of a risk factor 
to manage robustness, whereas robust optimization is known 
to be more useful in engineering fields, according to the study 
by Mulvey and Bai [3, 4]. 
The person who discussed the great implications 
regarding Robust Optimization in Engineering [5] was 
Taguchi, who is well recognized for developing a leading 
design strategy and has gained a lot of interest in the last few 
years. 
Risk management should be addressed, too, when we 
deal with oil project investments. The recovery of oil is 
severely affected by geological, financial, and technical risks 
of exploration & production operations. The major elements 
of risk reduction [6] are the collection of relevant data and 
flexibility. 
As a result of risk quality dynamics and the amount of 
risk exposure (RE) that threaten risk management's 
efficiency, it has been recommended that dynamic risk 
assessment in oil production and robust optimization 
programs be examined [7]. 
Van Essen et al. (2009) introduced the Robust 
Optimization (RO) approach to minimize the risk of 
geological uncertainties inherent in the development stage of 
the oil field, by implementing a series of discoveries that 
explain a range of possible geological systems to address 
geological uncertainty data [8]. 
NPV included a single objective with fixed oil pricing, 
and it was the related objective function.  They also used a 
standard gradient-based optimization strategy wherein they 
access the gradients through an adjoin formulation. 
Alhuthali et al. (2010) evened up the time of arrival of 
waterfronts across all producers with the aid of several 
geological realizations and implemented two optimization 
parameters of expected value and standard deviation, in 
linear form with a risk aversion coefficient; actually, they 
have employed the gradient and the analytical form of 
Hessian calculation of the objective function [9]. 
Almeida et al. (2010) attempted, under technological and 
geological uncertainties, to generate a pro-active approach 
and specify a project using a genetic algorithm to optimize 
the single objective NPV [10]. 
Chen and Hoo (2012) present a link between the Markov 
chain Monte Carlo (MCMC) and the Kalman filter ensemble 
(EnKF) in trying to collect changes of certain variables to 
monitor the amount of water pumped to a reservoir entitled 
the water-flooding program. They accomplished this by 
employing an efficient model-based system involving the 
uncertain parameter changes and a specific low-order model 
developed from a first principle model [11]. 
Oil production increased (by 9.0 percent and 8.2 percent 
with EnKF and MCMC adjustments, respectively), and water 
production decreased in the final total net present value in the 
parameter update model. The findings also indicated that 
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TEHNIČKI GLASNIK 17, 2(2023), 172-178 
 
    
175 
maximizing the reservoir's oil production had an effect on the 
amount of water added to drive the contained oil out, so it is 
of utmost importance to change the uncertain geological 
variables (porosity and permeability) to optimize the 
reservoir's oil production. 
In water flooding modeling, Capolei, et al. (2013) also 
included an open-loop modeling scenario with no input and 
a closed-loop modeling scenario with geological uncertainty. 
To bolster the RO technique, they developed an updated 
robust proposed methodology (modified RO) with larger 
profits and less risk. The gains were calculated according to 
the predicted NPV, while the risk was calculated according 
to the normal NPV deviation [12]. Yasari, et al. (2013) put 
forth an interesting theory to minimize uncertainty sensitivity 
while no measurement data were expected to be available 
[13]. As such, by using a derivative-free Evolutionary Multi-
objective Optimization (EMO) technique in the context of an 
updated 
Non-dominated 
Sorting 
Genetic 
Algorithm 
(NSGA), known as NSGA-Il, they established the robust 
optimization technique to generate several Pareto-optimal 
alternatives without theoretical deduction of the dynamic 
reservoir systems. And in 2015, in an effort to obtain the 
optimal - yet robust - waterflood strategies, they offered 
multi-objective optimization formulations. Two multi-
objective, Pareto-based robust optimization models have 
been tested to overcome the permeability uncertainties. 
The test studies showed that the proposed approach 
delivered better performance in providing a robust optimal 
alternative(s based on Pareto (injection policies) against 
permeability uncertainties that were accurate for the original 
group of realizations [14]. 
In contrast with the alternative equivalence of certainty 
and robust optimization techniques, the mean-variance 
parameter's potential to help minimize the significant 
inherent geological uncertainties has been suggested for 
production optimization. 
Through their study, it became clear that maximizing 
certainty equivalence and robust optimization remain to be 
risky solutions. Still, the efficiency of mean-variance 
optimization in risk management and reducing the degree of 
uncertainty in optimizing efficiency is quite remarkable. 
Siraj, et al. (2015) suggested a multi-objective 
optimization question that takes into account financial and 
model uncertainties to subsidize the adverse effects, that is, 
the risk of these uncertainties on production output [15]. 
Without significantly sacrificing the main priority of 
economic life-cycle efficiency, they established improved 
robustness. In order to describe the financial and geological 
uncertainty domain, they also provided a set of different oil 
price possibilities and geological system realizations. An 
average NPV among these groups is the main priority. 
Their second goal was to optimize the pace of oil 
production to minimize risk since the risk of uncertainty 
grows with time. The multi-objective model was applied 
separately in a dynamic or lexicographic fashion for both 
types of uncertainties. 
Geological uncertainty greatly affects the optimum well 
placement strategy and has to be considered in the question 
of optimization of well placement. A geological realization 
control mechanism for well placement against geological 
uncertainty was established by Rahim and Li (2015) [16]. 
Hanssen and Foss (2015) framed the question of 
optimization as a two-level stochastic programming 
question, and the outcome was a technique to run the wells, 
rather than a single set point acquired by the deterministic 
problem. The principles of risk theory are super beneficial as 
a result of high degrees of uncertainty in model-based 
financial modeling of the water-flooding mechanism in oil 
reserves. They proposed an inverted risk management system 
in another study to optimize the lower tail (worst instances) 
of the distribution of the economic objective function but 
without seriously sacrificing the upper tail (best instances). 
Within geological uncertainty, they found the worst robust 
optimization scenario and Conditional Value-at-Risk 
(CVaR) measure to optimize the worst problem(s). Also, a 
deviation method of semi-variance was included in 
geological and economical uncertainty defined by a set of 
geological system realizations and a set of different oil price 
scenarios to optimize the worst cases [17, 19]. 
Foroud, et al., and Siraj, et al. (2016, 2017) stressed the 
geological system as a primary cause of uncertainty in the 
simulation of petroleum reservoirs that can lower the 
reliability of optimization process outcomes of simulation. 
The clustering algorithms such as the Kernel K-means 
Method (KKM) were suggested to pick a generic subset of 
geological systems and reduce the overall calculation cost 
during the process of simulation. 
The strategy of some researchers is to control uncertainty 
in the field design. They proposed new methodologies to 
quantify and minimize risks based on an efficient production 
plan and decision-making procedure. It is called risk 
management. In the construction of elaborate petroleum 
fields, Santos, et al. (2017), for instance, considered the 
robust risk assessment strategy by introducing resilience to 
the production mechanism and developing a robust 
production plan. 
The proposed method is based on the performance 
evaluation of all possibilities of an algorithmically optimized 
production plan, which aims to further evolve the 
optimization procedure and minimize risk. Multi-Attribute 
Utility Theory (MAUT) through multiple objectives 
(technological and financial indicators) is the essence of this 
system [22]. They recommended systematic, objective 
methods in subsequent work to quantify the expected value 
of flexibility (EVF). In the production process, this approach 
applies to complex reservoirs with several uncertainties 
shaping the selection of the production strategy [23]. 
Silva, et al. proposed (2017) a five-stage approach to 
estimate the importance of flexibility under exogenous and 
endogenous uncertainties in oil production operations, and 
each stage is split into certain secondary stages to identify the 
specifics of problem analysis and strategy development [24]. 
Measuring the uncertainties of reservoir aquifer response 
by conducting a complete simulation of fluids flow on a wide 
range of models also assumes prohibitive intractable 
calculation of costs and time. Some methods suggest an 
estimated solution (flow proxies) to address this challenge 
[25] or organize the realizations inside a multidimensional 
sphere depending on the flow results received through an 
estimated (computationally cheaper) design [26]. 
In order to measure the uncertainties for a broader class 
of variables, Bardy 2019 employed both methods and 
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176 
                                                                                                                                                                              TECHNICAL JOURNAL 17, 2(2023), 172-178 
combined the complex performance of the entire group of 
models [27]. 
Olalotiti-Lawal provided (2018) a novel technique for 
calibration of the subsurface system and quantification of 
uncertainty through Markov chain Monte Carlo (MCMC), 
wherein proper mixing is improved by contact among 
parallel Markov chains. This approach substantially 
increases the convergence of the sampling performance 
without loss [2]. 
In 2018, Zanbouri and Salahshoor proposed a novel 
robust modeling approach to specify a group of robust 
surrogate systems with unorganized uncertainty for 
economical performance estimation of an uncertain 
petroleum reservoir during the water flooding phase, based 
on geological uncertainty as a serious hurdle in the 
development of petroleum fields. In this process, the MIMO 
surrogate system combined with the desired nonlinear NPV 
objective function was recognized to produce a new updated, 
robust surrogate system in a configuration form of multi-
input single-output (MISO) and allow direct economic 
quality evaluation calculation [5]. 
The robust optimization method was developed by 
Mudhafar et al. in 2018 to evaluate the optimal intervals of 
gas injection, soaking, and oil processing in diverse 
reservoirs within geological uncertainties [9].  
In his analysis, the robust optimization method within 
geological uncertainties showed higher recovery of oil and 
NPV than nominal realization optimization, providing the 
decision-maker with a degree of freedom to substantially 
reduce the plan's risk. 
The redevelopment of Brownfield is a highly valued 
answer to manage the drop in production and to optimally 
position the infill well to optimize recovery and reduce 
operating costs given the unstable climate of oil price [11]. 
A new procedure for robust and efficient well placement 
optimization within geological uncertainty was suggested by 
Hutahaean 
2019. 
Multi-objective 
aided 
background 
matching, 
Bayesian 
posterior 
estimation, 
and 
well 
positioning optimization were incorporated into the multi-
objective environment through various geological systems in 
their conceptual workflow. The proposed workflow provides 
robust and reliable optimal decisions in placing the infill well 
over multiple history match models [12]. 
In oilfield production and reservoir operations, well 
placement efficiency is a serious hurdle because reservoir 
asymmetries generate deeply non-smooth, discontinuity, 
non-convex cost functions comprising several local 
optimums. It is also important to run a massive number of 
simulations on the reservoir. 
Several optimizing strategies can be categorized into two 
classes of approaches to assess the location of the well: 
gradient-based ex, and derivative-free ex approaches [23, 
24]. 
Optimization 
techniques 
have 
recently 
been 
implemented to design and reduce the computational 
problem of well-placement optimization within uncertainty 
[27, 3]. 
In order to find an infill drilling scheme for vertical or 
horizontal well positioning optimization, Jesmani 2020 used 
the Simultaneous Perturbation Stochastic Approximation 
(SPSA) method, a local optimization technique, which would 
reduce computation significantly [13]. 
 
3.1 Propose an Optimal Model of Integrated Reservoir 
Management 
 
Basically, there are five main factors in integrated 
reservoir management: well design and management, 
reservoir properties, reservoir modeling, surface facility 
design and economy. The first three cases are surveyed 
below. The integration of these three makes it possible to 
propose and design efficient and economical ways to enhance 
production from petroleum reservoirs (as schematically 
shown in Fig. 2). 
 
 
Figure 2 Integrated Reservoir Management 
 
Reservoir properties use two main methods: structural 
modeling and stratigraphy modeling. In relation to structural 
modeling, interactive modeling software now helps to 
examine the compatibility between horizons and seismic 
faults and observations made in wells. In stratigraphic 
modeling, core information is used in an integrated manner 
to extract rock types based on geological and petrophysical 
criteria. In practice, a multivariate statistical experiment and 
analysis of well logs is performed, and the resulting cross-
Amir Naser Akhavan, et al.: Quick Review: Uncertainty of Optimization Techniques in Petroleum Reservoir Management 
TEHNIČKI GLASNIK 17, 2(2023), 172-178 
177 
plots are analyzed jointly with petrophysical core data to 
identify rock types. 
The advantages of this approach are twofold: 1. Using 
the information available in all wells; 2. Calibration of 
geological facies in terms of information flow characteristics 
in several wells, Therefore, the identified rock types remain 
significant for both the sedimentologist and the reservoir 
engineer. 
The purpose of describing the reservoir is to improve the 
geological modeling of the reservoir, thereby reducing 
subsequent uncertainties in the reservoir model and assigning 
dynamic properties to network blocks on a good scale to 
reduce uncertainties in production forecasting. These studies 
include performing the required laboratory tests (mainly 
measuring relative permeability and capillary pressure) in 
real reservoir conditions to observe fluid properties, 
wettability conditions, and saturation endpoints. Although 
much more complex and time consuming than conventional 
laboratory studies, SCAL results are much more reliable for 
calibrating the reservoir model. Special methods and 
equipment have been developed for this purpose. The role of 
flow units defined in the scale of the reservoir model can be 
easily related to the distribution of rock types in the exact 
geological model [27]. 
In order to adapt to different development plans, the 
repository simulator must be implemented with a number of 
options. Thus, the reservoir simulator must consider the 
mechanical effects of the rock. Optimization process to 
simulate hybrid effects such as gas injection and management 
Due to the complex well adaptation and modeling of 
heterogeneities, the simulator should be run with 
unstructured networking facilities. Finally, in order to be able 
to perform heavy calculations and use it to make quick 
decisions, the simulator must be able to run on parallel 
machines. 
In addition, the integration of dynamic data greatly 
contributes to the reliability of the geological model for 
subsequent reservoir applications. This integration can be 
done in the early stages of field development using well test 
resources. Later, new dynamic information from the wells 
will allow the geological model to be updated. In practice, 
advanced mathematical methods are now available to model 
the geological model with good experimental results [19]. 
They include: Inversion techniques of simulated well 
experiments, such as the gradient method, to adjust the 
petrophysical properties. And a gradual deformation 
technique to adjust the geological model itself, the 
distribution of facies, reservoir boundaries and fault position. 
In the field of reservoir properties, there are complete 
software lines. This is the case for IFP with Reservoir 
Modeling Line (LMR). 
4 
CONCLUSION 
Uncertainty is due to incomplete and imprecise 
knowledge as a result of limited sampling of the subsurface 
heterogeneities. In this study, optimization strategies in 
petroleum reservoir planning are introduced. It has been 
shown the output of each method independently. It can be 
seen that for reservoir management priority, many 
optimization studies have so far focused on production 
optimization. Mainly, the efficiency of the existing large 
petroleum fields causes us to consider the principles of 
reservoir management in order to increase EOR. There are 
several sources of uncertainty in the oil field development 
that misrepresent the future reservoir productivity. In 
general, 
uncertainty 
in 
information 
about reservoir 
management is divided into four categories. At the same 
time, many different ambiguities in the course of the 
developing oil field, including geological, operational and 
economic uncertainties, have a devastating effect on the 
effective production of this reservoir. 
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[24] Santos, S. M. G., Gaspar, A. T. F. S., & Schiozer, D. J. (2018). 
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[26] Bardy, G., Biver, P. G. Caumon, P., Renard, V., Corpel, V., & 
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[27] Josset, L. & Lunati, I. (2013). Local and Global Error Models 
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Authors' contacts: 
 
Amir Naser Akhavan, Assistant Professor 
(Corresponding author) 
Management, Technology, and Science Departments, 
AmirKabir Technology University Tehran,  
No. 350, Hafez Ave, Valiasr Square,  
1591634311 Tehran, Iran 
E-mail: akhavan@aut.ac.ir 
 
Seyed Emad Hosseini 
Management, Technology, and Science Departments, 
AmirKabir Technology University Tehran, 
No. 350, Hafez Ave, Valiasr Square,  
1591634311 Tehran, Iran 
E-mail: seyedemadh@yahoo.com 
 
Mohsen Bahrami 
Mechanical Engineering Solid Design Department, 
AmirKabir University of Technology Tehran,  
No. 350, Hafez Ave, Valiasr Square,  
1591634311 Tehran, Iran 
E-mail: mbahrami@aut.ac.ir

Petroleum Reﬁning Operations: Key Issues, Advances, and Opportunities
Nikisha K. Shah, Zukui Li, and Marianthi G. Ierapetritou*
Department of Chemical and Biochemical Engineering, Rutgers UniVersity, Piscataway,
New Jersey 08854, United States
Enterprise-wide optimization for the petroleum reﬁning industry involves optimization of the supply chain
involving manufacturing and distribution with emphasis on integration of the different decision making levels.
The key manufacturing operations include crude oil loading and unloading, mixing of crude oil, production
unit operations of conversion and separation, operations of blending, and distribution of products. Other
components of the petroleum supply chain network include oil explorations, crude oil procurement, and sales
and distribution of products. The main issues present in the petroleum industry across various decision levels
(strategic, tactical, and operational) and within oil reﬁnery operations are discussed. This paper presents an
extensive literature review of methodologies for addressing scheduling, planning, and supply chain management
of oil reﬁnery operations. An attempt is also made to identify the future challenges in efﬁciently solving
these problems.
1. Introduction
The petroleum reﬁning industry is the largest source of energy
products in the world and is supplying about 39% of total U.S.
energy demand and 97% of transportation fuels. Oil reﬁnery
transforms crude oil into gasoline, diesel, jet fuel, and other
hydrocarbon products that can be used as either feedstocks or
energy source in chemical process industry. Petroleum reﬁning
has grown increasingly complex in the last 20 years as a result
of tighter competition, stricter environmental regulations, and
lower-margin proﬁts. Furthermore, in the modern global economy,
most oil industries involve multipurpose, multisite facilities
operating in different regions and countries and servicing
international clientele. For such a global industry to remain
competitive in a dynamic global marketplace, it is essential to
achieve enterprise-wide optimization. Enterprise-wide optimiza-
tion involves optimizing the operations of supply, manufactur-
ing, and distribution in a enterprise, and integration of these
different decisions levels leads to creating substantial value to
the process.1,2 The key operational activities in supply chain
management are (1) supply-chain design, (2) supply-chain
planning and scheduling, and (3) supply-chain control (real-
time management).3 Supply-chain design is a long-term strategic
level decision to determine the optimal infrastructure (assets
and network), planning is a tactical level decision, and schedul-
ing is an operational level decision in a supply chain. Typically,
much of the decision-making in a supply chain is focused across
solving subproblems as an entity, but from the enterprise-wide
performance viewpoint, local improvements at any sublevel do
not necessary lead to an overall improvement. Therefore, a
comprehensive integrated approach to enterprise-wide optimiza-
tion is desired. However, an integrated decision-making problem
is signiﬁcantly challenging and computationally intensive, and
literature on the solution approach for integrated optimization
of different levels is very sparse. Hence, decomposition,
simulation, and heuristic techniques are necessary tools to
address the challenges posed by supply chain management
problems in the petroleum industry.
A typical petroleum industry supply chain is composed of
an exploration phase at the wellhead, crude procurement and
storage logistics, transportation to the reﬁneries, reﬁnery opera-
tions, and distribution and delivery of its products (Figure 1).
Oil reﬁnery production operation is one of the most complex
chemical industries, which involves many different and com-
plicated processes with various connections. Instead of tackling
a comprehensive large-scale reﬁnery operations optimization
problem, decomposition approaches are generally exploited. Oil
reﬁnery manufacturing operations can be decomposed into three
problems: (1) crude-oil unloading, mixing, and inventory control,
(2) scheduling of production units, and (3) ﬁnish products
blending and distribution.4
Substantial work in the literature has been devoted to the oil
reﬁnery planning and scheduling problem. However, in the past
few years, focus is shifting to an integrated approach to address
the problem of enterprise-wide optimization.5-11 The objective
of this paper is to provide an analysis of the complexity present
in the oil reﬁnery supply chain and present a literature review
on existing approaches that address the problem of enterprise-
wide optimization in the petroleum industry. The paper con-
cludes with the challenges that still need to be addressed.
2. Issues in Petroleum Industry Supply-Chain
Management
Key issues in petroleum enterprise-wide optimization span a
large spectrum in a supply chain, from the strategic through
the tactical to the operational level and over various functions
in the supply-chain network, from purchasing of the raw
materials through the manufacturing to the distribution and sales.
Since the emphasis of enterprise-wide optimization is on
manufacturing control, scheduling, and planning, the models
required for optimization are usually nonlinear models. In this
section we organize the issues that are present in the reﬁnery
supply chain by ﬁrst presenting the different components of oil
reﬁnery supply chain and providing a detailed description of
front end of the supply chain, from crude oil procurement to
oil reﬁnery operations.
Integrated and coordinated decision making across various
geographically distributed reﬁnery manufacturing and storage
sites offers an additional challenge to reﬁnery operations
optimization. While manufacturing facilities management is an
integral part of enterprise-wide optimization, transportation
* To whom correspondence should be addressed. E-mail: marianth@
soemail.rutgers.edu. Tel: 732-445-2971. Fax: 732-445-2581.
Ind. Eng. Chem. Res. 2011, 50, 1161–1170
1161
10.1021/ie1010004
2011 American Chemical Society
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logistics and ﬁnished product distribution management remain
important parts of the reﬁnery supply chain.
2.1. Petroleum Industry Supply-Chain Components. As
shown in Figure 1, a typical petroleum supply chain involves
the exploration phase, crude procurement, storage logistics,
transportation to the oil reﬁneries, reﬁnery operations, distribu-
tion, and transportation of the ﬁnal products. The problem of
investment and operations in offshore oil ﬁeld development is
characterized by a long planning horizon (typically 10 years)
where a large number of choices in platforms, wells, ﬁeld
locations, and pipeline infrastructure is present.12 The explora-
tion phase involves signiﬁcant investment costs; thus economical
drive exists to maximize the return on investment. Rights for
extracted crude oil are then purchased for the production of
ﬁnished products to satisfy market demand, while respecting
product quality speciﬁcations. The nature of the petroleum
supply chain is such that its economics are extremely complex
and heavily linked. For example, the quality and quantity of
the ﬁnished products are impacted by different crude-oil mixes
and by oil reﬁnery conﬁguration, capacity, and yield constraints.
As mentioned before, oil reﬁnery production operations are the
most complex components of an oil reﬁnery supply chain, and,
to effectively tackle scheduling and planning of single site or
multisite optimization, it is necessary to decompose the reﬁnery
production operations into smaller manageable subproblems.
Generally, oil reﬁnery operations are decomposed into three
subproblems based on the structure of the reﬁnery conﬁguration
as shown in Figure 2. These problems are (1) crude-oil
unloading, mixing, and inventory control, (2) scheduling of
production units, and (3) ﬁnish products blending and distribu-
tion.4 The crude oil procurement problem and oil reﬁnery
operations subproblems are described in detail next. Although
the upstream operations including the safety issues associated
with wellhead operation should not be underestimated,13 it is
not the focus of this review.
2.1.1. Crude Oil Procurement. A typical crude oil procure-
ment process in a reﬁnery involves three phases: crude selection
and purchase, crude transportation and storage, and crude
processing, and these phases are explained in detail by Julka
et al.5 The crude selection is an extremely important step in
reﬁnery supply chain since it has direct impact on ﬁnished
product quality, quantity, and it accounts for about 90% of the
reﬁnery input cost. Crude oil selection and purchase is done
periodically at ﬁxed or variable intervals and is purchased for
production over procurement cycle (of few days to a month) in
advance. After the crudes are selected and purchased, the focus
shifts to optimizing delivery costs from the suppliers to the
reﬁnery jetties. The crude oil is carried to the port by tankers,
where it is unloaded into tanks when the jetty becomes available
for docking. This crude oil unloading problem is limited by
tank capacity, pumping rate, setup costs, and demurrage. Care
should be taken when transporting high-fusion-point crude oil
to avoid large residency time in pipelines.14 The crude oil
loading and unloading problem is further described next.
2.1.2. Crude Oil Operations. Crude oil scheduling problem
is one of the most challenging problems in an oil reﬁnery plant.
An optimal crude oil scheduling is predetermined based on crude
arrival data, production targets, and operational constraints. Once
crude oil is loaded into the storage tanks, crude oil mixtures
are prepared to charge crude distillation units (CDU) for
production. Generally, there are two different types of mixture
preparation: (a) in charging tanks and (b) using pipelines in a
place called manifold just before the CDU. The complexity in
the scheduling problem arises from a bilinear term (ﬂow and
inventory) present in mass balance constraints where the
intensive properties of the crude oil mixtures charging the CDU
Figure 1. Supply chain in the petroleum industry.
Figure 2. Graphic overview of a standard reﬁnery system.
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Ind. Eng. Chem. Res., Vol. 50, No. 3, 2011
must be regulated to meet the downstream product quality
speciﬁcations. Saharidis and Ierapetritou15 solved the crude oil
scheduling problem using discrete time presentation where they
address both types of crude mixture preparation conﬁgurations.
The economic and operability beneﬁts of the crude oil blend
scheduling problem in a reﬁnery is discussed in detail by Kelly
and Mann.16,17
2.1.3. Production Unit Operations. Once the crude prepared
mixture is prepared, it is charged to CDUs for distillation. The
distillation cuts from CDU are then sent to other production
units for fractionation and reaction to produce blend components
for ﬁnished products. The most common reﬁnery process
includes catalytic, hydro, and thermal cracking units to convert
heavy hydrocarbons into light hydrocarbons. They also include
other process units like continuous catalytic reforming, hy-
drotreating, and hydrodesulfurization units. This problem is
characterized by continuous processes, intermediate component
storage, and a recycle stream. The production units scheduling
problem is addressed by Jia and Ierapetritou4 and Shah et al.18
where they applied spatial decomposition to solve the scheduling
problem. Gary, Handwerk et al.19 give a comprehensive
reference for reﬁnery production unit operations.
2.1.4. Blending and Distribution Operations. Product
blending is a crucial step in reﬁnery operations, for example,
gasoline can yield 60-70% of a reﬁnery’s proﬁt. The ﬁnished
product blending process is a mixing of various intermediate
products, along with some additives, to produce blends with
certain qualities in order to minimize cost and maximize
performance while satisfying product quality and demand
requirements. The ﬁnished blend products are stored in storage
tanks, and then the products are lifted during a speciﬁc time
period to satisfy demand orders. While blending is a critical
step in reﬁnery operations, it is also the most complex of the
subproblems. The blend scheduling problem gives rise to a
nonlinear mixed integer model due to bilinear terms. The
problem is further complicated by including some important
logistics details. These logistics details are sequence dependent
switchovers, multipurpose product tanks and blender units,
minimum run-length requirements, ﬁll-draw-delay, constant
blend rate, one-ﬂow out of blender, maximum heel quantity,
and downgrading of product to lower quality. The variables
associated with logistics details are combinatorial variables and
have an exactly one-to-one correspondence with quantity sizing
variables. This combinatorial characteristic of the logistics
problem makes the optimization problem NP hard. Kelly20
emphasized the importance of logic details in the blending and
distribution problem and proposed a decomposition technique
to deal with complexity present in the model due to logistics
and bilinear terms. They decompose the problem into two
subproblems: quality and logistic. A simultaneous production
and blending and distribution scheduling problem for large scale
reﬁnery with logistics details is addressed by Shah and Iera-
petritou,21 in which they assume linear blend property relations
to avoid any nonlinearity. They propose valid inequalities to
reduce the computational effort of a large scale mixed integer
programming model.
Overall, the problem of reﬁnery operations is very compli-
cated and if simultaneous optimization of all the components
of a reﬁnery supply chain is attempted, the resulting scheduling
and planning models will be highly intractable. Thus, in practice
each of the subproblems is addressed individually, and tech-
niques are proposed to integrate the solutions of the subprob-
lems. An overview of the existing work in the area of enterprise-
wide optimization for reﬁnery operations is given in next section
after a brief discussion of different decision levels in an oil-
reﬁnery supply chain.
2.2. Integration of Different Decision-Making Levels in
the Supply Chain. Management of the petroleum supply chain
is a complex task due to the large size of the physical supply
network dispersed over vast geography, complex reﬁnery
production operations, and inherent uncertainty involved. There
are three main decision levels in a traditional supply chain: (1)
strategic, (2) tactical, and (3) operational. Strategic level
decisions are made for a few years, tactical level decisions range
from several months to a few days, and control level decisions
range from a few minutes to seconds. Different decision levels
in a traditional supply chain and their complexity are described
in detail by Simchi-Levi, Kaminsky et al.22 The main issues in
decision making at different levels are related to temporal
integration, where they involve coordination across different
time scales.
The goal of supply-chain design is to determine optimal
infrastructure (location, number, and capacity of wellheads and
reﬁneries, and ﬂow of material through logistics network). The
strategic level decisions are made for several years in a highly
uncertain environment. In many instances, the supply-chain
network design can have extensive impact on the proﬁtability
and risk uncertainty of the global supply chain. In the oil reﬁnery
industry, the supply-chain network is composed of shipping via
vessel, oil tankers, and pipelines that may run across multiple
countries. This network is used to transport crude from wellhead
to reﬁnery for processing, to transport intermediates between
multisite reﬁning facilities, and to transport ﬁnished products
from product storage tanks to distribution centers and ﬁnally to
the customers. Any disruptions arising in the global supply chain
can have tremendous adverse effects in achieving operational
efﬁciency, maintaining quality, proﬁtability, and customer
satisfaction. The adverse events may happen due to uncertainty
in supply of crude, demand, transportation, market volatility,
and political climate. To effectively model a supply-chain design
problem, the dynamics of the supply chain ought to be
considered and data aggregation techniques for the extensive
data set should be employed.
As in the other chemical industries, production planning in
the petroleum reﬁning industry relies on the following sources
of information: process topology, plant capacity, operating
constraints, market demand, and costs involved in the processes.
Planning level decisions are made based on the strategic level
decisions. The objective of planning the production is to
efﬁciently take advantage of the company infrastructure so that
its products may be competitively offered to customers. Specif-
ically, the reﬁning industry is under immense pressure to
produce cleaner products but faces low economic margins
because of stricter environmental regulations and depressed
market demand. In this situation, reﬁnery planning becomes very
important as it can exploit all potential opportunities to push
the economic margin to the maximum limit. A planning problem
determines how to best allocate the production, distribution, and
storage resources in the chain to respond to market demand
orders and forecasts in an economically efﬁcient manner, and a
scheduling problem determines the detailed schedule for shorter
period (typically 10 days) by taking into consideration the
operational constraints of the system. Scheduling models use
small bucket time-periods which means that only one activity,
task, job, or operation can be performed at any given time on
a piece of equipment. Scheduling models are in general order-
driven, which means that scheduling problems are driven by
real product demand and typically accompanied by a customer
Ind. Eng. Chem. Res., Vol. 50, No. 3, 2011
1163
purchase order. Owing to the complexity of reﬁnery operations,
commercial tools for production scheduling are few and these
do not allow a rigorous representation of plant particularities.
Uncertainty also persists at the planning and scheduling level
due to machine breakdown, rush orders, and price ﬂuctuation.
A real time optimization model deals with process level
optimization to determine the product yields, temperatures, and
pressures at each process units. These real times level decisions
are made for very short times (seconds), and the process
operational complexities give rise to nonlinear dynamic process
models.
An integrated optimization of strategic, tactical, operational,
and inventory planning and scheduling level decisions under
uncertainty gives an intractable computationally intensive large-
scale nonlinear model. A further complexity in implementing a
globally optimal enterprise solution is the conﬂicting objectives
employed by different components of the supply chain. The best
way to address decisions at different levels in an integrated
manner is through decomposition techniques, heuristics, and
simulation-based optimization methods.
3. Literature Review
3.1. Petroleum Supply-Chain Design and Planning. The
petroleum industry can be characterized as a typical supply chain.
All levels of decisions (strategic, tactical, and operational) arise in
such a supply chain. In the literature, optimization models deal
with planning and scheduling of several subsystems of the
petroleum supply chain such as oil ﬁeld infrastructure, crude oil
supply, reﬁnery operations, and product transportation. In what
follows, developments on the petroleum supply-chain design and
planning are reviewed on the basis of the following two classiﬁca-
tions: oil ﬁeld infrastructure investments and operations and
petroleum supply-chain planning including crude oil worldwide
transportation and multisite distribution planning.
3.1.1. Oil Field Infrastructure Design and Operations. Oil
ﬁeld development represents a complex and expensive under-
taking in the oil industry. The problem is characterized by long
planning horizons and a large number of choices of platforms,
wells, and ﬁelds, and their interconnecting pipeline infrastruc-
ture. The main challenges in resolving this problem is the
complexity of the models usually characterized by uncertainty
and the large dimensionality of the problem due to large time
horizon and spatial domain.
Van den Heever and Grossmann23 presented a multiperiod
mixed-integer nonlinear programming model for offshore oil
ﬁeld infrastructure planning, where nonlinear reservoir behavior
is incorporated directly into the formulation. Goel and Gross-
mann24 considered the optimal investment and an operational
planning of gas ﬁeld developments under uncertainty in gas
reserves, where the shape of the scenario tree associated with
the problem depends on the investment decisions. They
presented a stochastic programming model that incorporates the
decision-dependence of the scenario tree and proposed a
decomposition-based approximation algorithm for the solution
of this model. Carvalho and Pinto25 proposed an optimization
model for the planning of an infrastructure in offshore oil ﬁelds.
The proposed model determines the existence of a given set of
platforms and their potential connection with wells, as well as
the timing of extraction and production rates. Tarhan et al.26
proposed a multistage stochastic programming model that
captures the economic objectives and nonlinear reservoir
behavior and simultaneously optimizes the investment and
operating decisions over the entire planning horizon. The
uncertainties considered are in the initial maximum oil or gas
ﬂow rate, the recoverable oil or gas volume, and the water
breakthrough time of the reservoir, and those uncertainties are
assumed to be gradually revealed as a function of design and
operation decisions.
3.1.2. Multisite Supply-Chain Planning. In a typical pe-
troleum supply chain, a set of crude oil suppliers and reﬁneries
are interconnected by intermediate and ﬁnal product streams
and a set of distribution centers. Thus multisite supply and
distribution planning is an essential part of an oil-reﬁnery
supply-chain design and has also received lots of attention.
Considering the details of operations at different sites and
integrating the interconnectivity between sites and the appropri-
ate logistics are the main issues on addressing this problem.
Julka et al.5,27 proposed an agent-based framework for supply-
chain decision support systems (DSSs) and demonstrated its
application through a prototype system for crude procurement.
The system serves as a central DSS through which all processes
of a reﬁnery can be studied and enables integrated decisions
with respect to the overall reﬁnery business. Rejowski and
Pinto28 proposed a general framework for modeling petroleum
supply chains. Nodes of the chain are considered as grouped
elementary entities that are interconnected by intermediate
streams. The supply-chain topology is then built by connecting
the nodes representing reﬁneries, terminals, and pipeline
networks. Decision variables include streamﬂow rates, proper-
ties, operational variables, and inventory and facilities assign-
ment. Persson and Gothe-Lundgren29 suggested an optimization
model which integrates the shipment planning and the process
scheduling at the reﬁneries. This problem concerns the simul-
taneous planning of how to route a ﬂeet of ships and the
planning of which products to transport in these ships. Nishi
et al.30 proposed a framework for distributed optimization of
supply-chain planning using an augmented Lagrangian decom-
position and coordination approach. The proposed method is
applied to supply-chain planning problems for a petroleum
complex and a midterm planning problem for multiple compa-
nies, and a near-optimal solution was derived. MirHassani31
showed how the capacitated network may be used to analyze
possible long-term transportation of oil derivatives by pipeline,
truck, railway, and ship and reduce the distribution cost. The
capacitated network can deal with the scheduling of a multi-
product, multidepot system receiving a number of petroleum
products from different reﬁneries and distributes them among
several depots and market areas while the demand is an
uncertain parameter. Pitty et al.32 presented a dynamic model
of an integrated reﬁnery supply chain. The model explicitly
considers the various supply-chain activities such as crude oil
supply and transportation along with intrareﬁnery supply-chain
activities such as procurement planning, scheduling, and opera-
tions management. Stochastic variations in transportation, yields,
prices, and operational problems are also considered in the
proposed model.
Al-Qahtani and Elkamel33 addressed the design and analysis
of multisite integration and coordination strategies within a
network of petroleum reﬁneries using different crude combina-
tion alternatives. Recently, they extended the work to address
the design of optimal integration and coordination within a
multisite reﬁnery and petrochemical system.34 The reﬁnery and
petrochemical systems were modeled as a mixed-integer prob-
lem with the objective of minimizing the annualized cost over
a given time horizon among the reﬁneries and maximizing the
added value of the petrochemical network. Carneiro et al.35
analyzed the strategic planning of an oil supply chain. To
optimize this chain, a two-stage stochastic model with ﬁxed
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Ind. Eng. Chem. Res., Vol. 50, No. 3, 2011
recourse and incorporation of risk management was developed.
The model took a scenario-based approach and addressed three
sources of uncertainty.
Although signiﬁcant progress has been made in the supply-
chain design and planning for petroleum reﬁning industry, the
production plan of an industrial supply chain is in general
created ﬁrst and a compatible schedule is then identiﬁed
accordingly. Because the detailed scheduling constraints are
often ignored in the planning model, there is no guarantee that
an operable schedule can be obtained with this hierarchical
approach. To address this issue, it is necessary to propose an
efﬁcient formulation to coordinate various planning and sched-
uling decisions for optimizing the supply-chain performance.36
Solving this kind of model can yield the proper procurement
scheme for crude oils, the schedules for producing various
petrochemical products, and the corresponding logistics. The
appropriate sources (suppliers) of raw materials, the economic
order quantities, the best purchasing intervals, and also the
transportation schedules can be identiﬁed accordingly.
Finally, it is worth mentioning that most of the oil industry
companies still operate their planning, central engineering,
upstream operations, reﬁning, and supply and transportation
groups as complete separate entities. Therefore, systematic
methods for efﬁciently managing the petroleum supply chain
as one entity must be exploited. In the next sections the work
that has appeared in the literature to cover these separate
problems are reviewed.
3.1.3. Pipeline and Transportation Scheduling. The pipe-
line network plays a key role in the petroleum business. These
operational systems provide connections between ports and/or
oil ﬁelds and reﬁneries (upstream), as well as between these
and consumer markets (downstream). Transportation is among
the basic challenges in a reﬁnery supply chain with the
dimensionality of the problem and the speciﬁcities of each
individual implementation to be the main issues.
The system discussed by Rejowski and Pinto37 is composed
of a petroleum reﬁnery, a multiproduct pipeline connected to
several depots, and the corresponding consumer markets that
receive large amounts of gasoline, diesel, LPG, and aviation
fuel. An MILP optimization model that is based on a convex-
hull formulation is proposed for the scheduling system. In their
later work, Rejowski and Pinto38 divided the pipeline into
segments that connect two consecutive depots and packs that
contain one product and that compose the segments. Recently,
Rejowski and Pinto39 proposed a MINLP formulation based on
a continuous time representation for the scheduling of multi-
product pipeline systems that must supply multiple consumer
markets. The proposed continuous time representation is
compared with the previously developed discrete time repre-
sentation of Rejowski and Pinto28 in terms of solution quality
and computational performance.
Cafaro and Cerda40 studied the scheduling of a multiproduct
pipeline system receiving a number of liquid products from a
single reﬁnery source to distribute them among several depots.
The problem of scheduling a transmission pipeline carrying
several petroleum products from a single oil reﬁnery to a unique
distribution center over a monthly horizon is studied in their
later work.41 Recently, they further introduced a continuous
formulation for the scheduling of multiple-source pipelines
operating on the fungible or segregated mode.42
MirHassani and Ghobanalizadeh43 presented an integer
programming approach to oil derivative transportation schedul-
ing. The system reported is composed of an oil reﬁnery, one
multibranch multiproduct pipeline connected to several depots
and also local consumer markets which receive large amounts
of reﬁnery products. Herran et al.44 also proposed a discrete
mathematical approach to solve short-term operational planning
of multipipeline systems for reﬁned products. Jetlund an
Karimi45 developed an optimization model to obtain the
maximum-proﬁt scheduling of a ﬂeet of multiparcel tankers
engaged in shipping bulk liquid chemicals.
Comillier et al.46,47 studied the petrol station replenishment
problem with time windows (PSRPTW) which aims to optimize
the delivery of several petroleum products to a set of petrol
stations using a limited heterogeneous ﬂeet of tank-trucks. They
described two heuristics based on arc preselection and on route
preselection. Extensive computational tests on randomly gener-
ated instances conﬁrm the efﬁciency of the proposed heuristics.
Abraham and Rao48 made a study on the existing practices of
production planning, scheduling and prevailing constraints in
the six plants of a lube oil section in a petroleum reﬁnery. On
the basis of the data collected from these plants, some generative
and evaluative models were developed. The generative models
developed were ﬂow network optimization model and binary
integer linear programming model. The evaluative model
developed was simulation.
3.2. Reﬁnery Planning. Planning models, or more generally
time incremented microeconomic models, are in general mul-
tiperiod and traditionally use what are known as big-bucket time-
period models compared to small-bucket time-period models
for scheduling operations. Planning models are forecast-driven,
which means that the quantity and quality speciﬁcations of the
demanded products are only forecasted or estimates for the time-
periods into the future.
3.2.1. Production Planning Model. The availability of LP-
based commercial software for reﬁnery production planning,
such as RPMS49 and PIMS,50 has allowed the development of
general production plans of the whole reﬁnery, which can be
interpreted as general trends. However, inaccuracy caused by
nonrigorous linear models may reduce the overall proﬁtability
or sacriﬁce product quality. Major advances in this area will be
on model reﬁnement, notably through the use of nonlinear
programming.
As pointed out by Kelly,51 there are four major driving-forces
pressing us to formulate planning models with nonlinearities:
complex government regulations, increasingly expensive raw
materials of poorer quality, new and more sophisticated produc-
tion processes, and higher energy, chemical, and utility costs.The
reﬁnery planning model uses the nlp process models and blend
relations. Pinto and Moro52 developed a nonlinear planning
model for reﬁnery production. The model allows the imple-
mentation of nonlinear process models as well as blending
relations. Joly et al.53 presented a nonlinear planning model that
represents a general reﬁnery topology and allows implementation
of nonlinear process models as well as blending relations. The
optimization model is able to deﬁne new operating points, thus
increasing the production of the more valuable products and
simultaneously satisfying all speciﬁcation constraints. Neiro and
Pinto54 proposed a nonlinear petroleum production planning
model, which incorporates multiple planning periods and the
selection of different crude oil types. Uncertainties related to
petroleum and product prices as well as demand are also
included as a set of discrete probabilities. Li et al.55 presented
a reﬁnery planning model that utilizes simpliﬁed empirical
nonlinear process models with considerations for crude char-
acteristics, product yields, and qualities, etc.
Reﬁnery production planning is usually associated with the
crude oil unloading and product distribution problems. These
Ind. Eng. Chem. Res., Vol. 50, No. 3, 2011
1165
problems have traditionally been solved separately because an
overall optimization of the problems together could render the
solution of the planning problem intractable. However, with
the development of computation algorithm and hardware, the
integrated solution received lots of attention in the recent past.
Gao et al.56 addressed a production planning optimization
problem of overall reﬁnery using a mixed integer linear
programming model, which considers the main factors for
optimizing the production plan of overall reﬁnery related to the
use of run-modes of processing units. The aim of this planning
is to decide which run-mode to use in each processing unit in
each period of a given horizon, to satisfy the demand, while
the total cost of production and inventory is minimized.
Guyonnet et al.57 explored beneﬁts of the integration of
production planning with these two models. Alabi and Castro58
presented a mathematical model of the reﬁnery operations
characterized by complete horizontal integration of subsystems
from crude oil purchase through product distribution. To avoid
complexity of the overall planning problem, after the identiﬁca-
tion of the nonzero structure of the constraints matrix, structure-
exploiting techniques such as the Dantzig-Wolfe technique and
block coordinate-descent decomposition were applied.
Generally, a reﬁnery complex consists of a process system
and a utility system. The process system not only produces
liqueﬁed petroleum gas, gasoline, diesel, and so on, but also
some byproducts, such as fuel gas and residual fuel oil, which
supply the utility system as fuel. The utility system converts
fuel gas and fuel oil to high pressure or medium pressure steam
and electricity to meet the energy demand of the process system.
The integrated process system and utility system optimization
has also received attention recently. Zhang and Hua59 presented
an approach to address the integration of the process system
and utility system for better energy utilization. A plant-wide
multiperiod planning mathematical model is proposed with the
process unit energy-consumed model embedded in the plant-
wide model to gain the overall optimization and for better energy
efﬁciency. Zhang et al.60 presented a method for overall reﬁnery
optimization through integration of the hydrogen network and
the utility system with the material processing system. To make
the problem of overall optimization solvable, they adopted a
decomposition approach, in which material processing is
optimized ﬁrst using linear programming (LP) techniques to
maximize the overall proﬁt, and supporting systems, including
the hydrogen network and the utility system, are optimized next
to reduce operating costs for the ﬁxed process conditions
determined from the LP optimization.
The capacity of the world petroleum reﬁning industry has
increased rapidly during the past decades. On the one hand, it
plays a very important role in international economics and in
our daily life, and on the other hand environmental regulations
and risks of climate change are pressuring the reﬁnery industry
to minimize its greenhouse gas emissions. Elkamel et al.61
proposed a mixed-integer nonlinear programming (MINLP)
model for the production planning of reﬁnery processes to
achieve maximum operational proﬁt while reducing CO2 emis-
sions to a given target through the use of different CO2
mitigation options. The objective of the MINLP model is to
determine suitable CO2 mitigation options for a given reduction
target while meeting the demand of each ﬁnal product and its
quality speciﬁcations and simultaneously maximizing proﬁt.
Zhang et al.62 proposed a model-centered approach with an
eight-step procedure for the early planning and design of an
eco-industrial park around an oil reﬁnery, considering technical
and environmental factors.
3.2.2. Uncertainty in Reﬁnery Planning. Uncertainty arises
in realistic decision making processes and has huge impact on
the reﬁnery planning activities. Three major uncertainties that
should be considered in reﬁnery production planning include
(1) market demand for products; (2) prices of crude oil and the
saleable products; and (3) product (or production) yields of crude
oil from chemical reactions in the primary crude distillation unit.
Dempster et al.63 applied stochastic programming modeling
and solution techniques to planning problems for a consortium
of oil companies. A multiperiod supply, transformation, and
distribution scheduling problem is formulated for strategic or
tactical level planning of the consortium’s activities. This
deterministic model is used as a basis for implementing a
stochastic programming formulation with uncertainty in the
product demands and spot supply costs. Li et al.64 presented an
approach to address reﬁnery planning under uncertainty. They
proposed “loss function” to calculate the expectation of plant
revenues. The decision maker’s service objectives: conﬁdence
level and ﬁll rate are applied to handle possible unmet customer
demands. Neiro and Pinto65 presented a stochastic multiperiod
model for representing a petroleum reﬁnery. Uncertainty is taken
into account in parameters such as demands, product sale prices,
and crude oil prices. Lagrangean decomposition was applied to
exploit the block-diagonal structure of the problem and to reduce
solution time by decomposing the model on a temporal basis.
Khor et al.66 proposed a two-stage stochastic programming
model with ﬁxed recourse via scenario analysis with incorpora-
tion of risk management for an optimal midterm reﬁnery
planning that addresses three factors of uncertainty: prices of
crude oil and saleable products, product demands, and product
yields. Li et al.67 developed a stochastic programming model
for reﬁnery production planning under demand uncertainty with
uniform distribution assumption, and then a hybrid programming
model incorporating the linear programming model with the
stochastic programming model by a weight factor is proposed.
Subsequently, piecewise linear approximation functions are
derived and applied to solve the hybrid programming model
under a uniform distribution assumption.
In the literature, several studies also addressed the issue of
uncertainty and studied the ﬁnancial risk aspects in reﬁnery
operations planning. Pongsakdi et al.68 studied the problem of
determining what crude to purchase and to decide on the
production level of different products given forecasts of
demands. The proﬁt is maximized taking into account revenues,
crude oil costs, inventory costs, and cost of unsatisﬁed demand.
Lakkhanawat and Bagajewicz69 addressed the similar problem.
Park et al.70 developed an integrated model based on two-stage
stochastic programming for operational planning and ﬁnancial
risk management of a reﬁnery. Downside risk, which rationally
quantiﬁes ﬁnancial risk, is selected as the objective function to
be minimized. Subsequently, the contract sizes and the opera-
tional plan are optimized on the basis of the developed model
and the price scenarios.
In summary, with the development in mathematical program-
ming and computation facility, the reﬁnery planning operations
tend to consider more accurate and complex nonlinear process
models, implementing overall optimization among different
subsystems in the whole plant, and also tend to systematically
address the various uncertainties, with ﬁnancial risk and ﬁnally
environmental aspect considerations.
Furthermore, in the past work, uncertainty is in general
considered as a set of discrete scenarios, each representing a
possible shifting of market expectations. Every environment is
weighted through an expected probability of occurrence. Previous
1166
Ind. Eng. Chem. Res., Vol. 50, No. 3, 2011
work revealed that the computational effort of uncertain multiperiod
reﬁnery production planning models grows exponentially with the
number of time periods and scenarios. Therefore, in order to reduce
the computational effort over uncertain long-planning horizons,
special techniques must be employed.
3.3. Reﬁnery Scheduling. The literature generally addressed
the reﬁnery scheduling in the form of smaller subproblems:
crude oil operations from unloading to the charging into a crude
distillation unit; blending of intermediate products from the CDU
into ﬁnished products; lifting or delivery of the ﬁnished products,
etc. In what follows, we review literature in the above scheduling
applications.
3.3.1. Crude Oil Scheduling. The scheduling of crude oil
operations is crucial to petroleum reﬁning, which includes
determining the times and sequences of crude oil unloading,
blending, and CDU feeding. The infrastructure that serves to
facilitate crude oil loading and unloading operations usually
comprises (a) one or more jetties, (b) crude storage tanks at
port and reﬁnery, (c) intermediate tanks between port and
reﬁnery, and (d) pipeline infrastructure connecting port tanks
to reﬁnery. In the past decade, many approaches have been
proposed for solving this problem.
Shah71 presented one of the earliest mathematical formula-
tions for the crude oil scheduling problem. Their model is based
on a discrete time presentation. Not in their earlier studies,72-74
but in a recent work of Pinto et al.75 were the scheduling
problems in oil reﬁneries that are formulated as mixed integer
optimization models and rely on both continuous and discrete
time representations discussed. Chryssolouris et al.76 addressed
primarily the scheduling of a reﬁnery importing various types
of crude oil. Karuppiah et al.77 presented an outer-approximation
algorithm to obtain the global optimum of a nonconvex mixed-
integer nonlinear programming (MINLP) model for the crude
oil scheduling problem. The model relies on a continuous time
representation making use of transfer events. The proposed
algorithm focuses on effectively solving a MILP relaxation of
the nonconvex MINLP to obtain a rigorous lower bound (LB)
of the global optimum. The solution of this relaxation is used
as a heuristic to obtain a feasible solution to the MINLP which
serves as an upper bound (UB). The lower and upper bounds
are made to converge to within a speciﬁed tolerance in the
proposed outer-approximation algorithm. Pan et al.78 considered
the coastal and marine-access reﬁneries with simpliﬁed work-
ﬂow and proposed a mixed integer nonlinear programming
formulation for crude oil scheduling. Finally, well scheduling
in petroleum ﬁelds is a very important activity related to crude
oil production, where decisions include the operational status
of wells (open or closed), the allocation of wells to manifolds
or separators, and the allocation of ﬂow lines to separators.
However, the work in that area is not reviewed in this paper,
but it can be referred to in Kosmidis et al.79
3.3.2. Reﬁnery Production and Blend Scheduling. The
production unit scheduling problem is addressed by Jia and
Ierapetritou.4 Shah et al.80 presented a decomposition strategy
for solving a large scale reﬁnery scheduling problem. They
proposed a spatial decomposition scheme that generates smaller
subsystems that can be solved to global optimality instead of
formulating a huge one for the centralized problem. Gothe-
Lundgren et al.81 describe a production planning and scheduling
problem in an oil reﬁnery company. The aim of the scheduling
is to decide which mode of operation to use in each processing
unit at each point in time in order to satisfy the demand while
minimizing the production cost and taking storage capacities
into account.
The product blending process involves mixing various stocks,
which are the intermediate products from the reﬁnery, along
with some additives, such as antioxidants and corrosion inhibi-
tors, to produce blends with certain properties. A variety of
support systems have been also developed to address the reﬁnery
blending operations. Jia and Ierapetritou82 studied the problem
of gasoline blending and distribution. The problem involves the
optimal operation of gasoline blending, the transfer to product
stock tanks, and the delivering schedule to satisfy all of the
orders. An efﬁcient mixed-integer linear programming formula-
tion is developed based on continuous representation of the time
domain and on the assumption of a ﬁxed recipe. Joly and Pinto83
developed mixed-integer programming (MIP) models for a real-
world fuel oil and asphalt production scheduling problem.
Mendez et al.84 presents a MILP-based method that addresses
the simultaneous optimization of the off-line blending and the
short-term scheduling problem in oil-reﬁnery applications.
Depending on the problem characteristics as well as the required
ﬂexibility in the solution, the model can be based on either a
discrete or a continuous time domain representation. To preserve
the model’s linearity, an iterative procedure is proposed to
effectively deal with nonlinear gasoline properties and variable
recipes for different product grades. Lee et al.85 addressed a
naphtha feeding problem for the Naphtha Cracking Center
(NCC). The naphtha feeding problem involves two key opera-
tions: delivering naphtha from reﬁneries to NCC and blending
naphtha in storage tanks before feeding it to NCC. This paper
considers both issues simultaneously by transforming them into
a single mixed linear integer programming problem of minimiz-
ing the cost function of naphtha prices, shipping expenses, and
unloading costs, etc. In a recent publication Li et al.86 developed
a slot-based MILP formulation for an integrated treatment of
recipe, speciﬁcations, blending, and storage, considering real-
life features such as multipurpose product tanks, parallel
nonidentical blenders, minimum run lengths, changeovers,
piecewise constant proﬁles for blend component qualities, and
feed rates, etc.
In most of the existing reﬁnery scheduling studies, the
problem data are assumed to be deterministic. However, under
the current situations of unsteady supply of crude oil, variations
of feedstock qualities and yield levels, pressure of intense time,
low inventory ﬂexibility, etc., it is very important for reﬁnery
companies to assess the extra cost of these changes during
making their short-term crude oil scheduling operations. Cao
et al.87 proposed chance constrained mixed-integer nonlinear
stochastic and fuzzy programming models for reﬁnery short-
term crude oil scheduling problems under demand uncertainties
of distillation units. The scheduling problem studied has the
characteristics of discrete event and continuous event coexist-
ence, multistage, multiproduct, nonlinear, uncertainty, and large
scale.
In summary, a reﬁnery scheduling problem is more complex
than a planning problem since in general more operational
constraints should be considered and many combinatorial
decisions need to be optimized; thus it has been hard to
implement an overall scheduling for the whole reﬁnery opera-
tions. An efﬁcient decomposition-based solution strategy is still
necessary to implement overall reﬁnery scheduling optimization.
Finally, efﬁcient reactive scheduling, rescheduling methodolo-
gies are also imperative for reﬁnery scheduling operations.8,88
4. Future Challenges
This article has shown that a very large amount of work was
undertaken to address the problems in reﬁnery supply-chain
Ind. Eng. Chem. Res., Vol. 50, No. 3, 2011
1167
management, especially in the areas of the scheduling and
planning of reﬁnery operations, crude oil procurement, and
petroleum supply-chain design and planning. However, a large
number of issues still exist which provides challenges for
ongoing research.
4.1. Improved Models for Reﬁnery Operations. The crude
oil scheduling and planning problem has been traditionally
addressed using linear relations for CDU yield prediction as a
function of the crude feed. Improvements in the swing cut model
for CDU yield in recent years provide an opportunity to develop
nonlinear planning models that capture the nonlinearity of the
process. Implementing nonlinear process model equations for
planning problems using the latest NLP algorithms provides true
optimal and accurate solutions to the planning problem.
Efﬁcient models and solution algorithms for the blending and
distribution problem are needed to simultaneously address the
optimization of blend recipe and blend logistics. The bilinear
terms in the quality problem are a result of nonlinear property
relations, while the bilinear term in the logistics problem is a
result of a constant blending rate where blend duration and
amount can vary. Some research has been done to address the
simultaneous optimization of quality, quantity, and logistics.20,86
4.2. Integrated Models for Supply-Chain Management.
There is a great economic potential in enterprise-wide optimiza-
tion; however, a lack of comprehensive optimization models
and computational tools are one of the major issues that must
addressed.
(1) Simultaneous reﬁnery operation scheduling (production
unit, blend, and distribution scheduling) models are very
sparse,89,21 and they do not incorporate accurate process models
(e.g., assumptions of linear blending rules). Moreover the
consideration of detailed dynamics and feedback loops will
provide a better representation of the realistic problem.
(2) Integrated scheduling planning models with nonlinear
scheduling process operations complexity and coordinated
control/real time and scheduling decisions making models are
still not adequately explored. Efﬁcient decomposition algorithms
and computational models that integrate the decision making
at different time scales are required.
(3) Coordinated decision making between multisite production
facilities and the design of the supply chain is vital for improving
proﬁt margin while maintaining customer satisfaction. The
challenge in this area is to develop optimization models and
computational tools that capture inherent complexities and
uncertainties present across modern supply chains.
New comprehensive models and algorithms can signiﬁcantly
improve the economic performance of reﬁnery operations by
reducing costs and increasing proﬁts while providing solutions
for real world applications. Moving away from simulation-based
approaches to optimization models will likely occur in the next
years. However there are still many issues, including issues of
interfaces and communication barriers among different reﬁnery
units.
Acknowledgment
The authors gratefully acknowledge ﬁnancial support from
National Science Foundation under Grants CBET 0625515 and
0966861.
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in Reﬁneries. Ind. Eng. Chem. Res. 2007, 46, 3656–3668.
ReceiVed for reView May 1, 2010
ReVised manuscript receiVed December 1, 2010
Accepted December 1, 2010
IE1010004
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Ind. Eng. Chem. Res., Vol. 50, No. 3, 2011

Catalytic Cracking of Crude Oil: Mini Review of Catalyst
Formulations for Enhanced Selectivity to Light Oleﬁns
Abdulkadir Tanimu,* Gazali Tanimu, Hassan Alasiri, and Abdullah Aitani
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ABSTRACT: The direct conversion of crude oil to light oleﬁns is considered
one the cheapest and most reliable sources of petrochemical primary
feedstocks. Unlike in the past when reﬁneries operated to produce mainly
transportation fuels, such as gasoline and diesel, many reﬁneries worldwide are
considering tandem production of both fuels and chemicals (particularly, light
oleﬁns). To achieve this, reﬁning technology, process optimization, and
catalyst formulations may have to be reconﬁgured. Developing active and
selective catalysts for crude oil cracking that ﬁt into the current reﬁnery system
will go a long way in saving cost and time. In this review, catalyst formulations
for the conversion of crude oil to light oleﬁns have been discussed under the
classiﬁcations: zeolite components, tuning of zeolite porosity, and matrix
materials. USY has been the common zeolite that is used in the cracking of
hydrocarbons to gasoline fractions, and ZSM-5 has the desired shape
selectivity for cracking of paraﬃns in gasoline fractions to light oleﬁns. At the
same time, its low hydrogen transfer activity does not consume a large amount
of generated light oleﬁn, resulting in improvement of light oleﬁn production.
Various modiﬁcations of ZSM-5 composition have shown improvement in the
light oleﬁn yield. The wide range of hydrocarbons in crude oil makes pore size tuning of the zeolite especially important. Matrix
materials generally increase the attrition resistance, hydrothermal and chemical stability, metal entrapment ability, coke resistivity,
and ﬂuidizable catalyst formation. However, they can also aﬀect (positively or negatively) catalyst arrangement and active site
properties, which makes careful selection very important.
1. INTRODUCTION
The global demand for petrochemicals has shown a steady
increase with a compound annual growth rate (CAGR) of
5.1% for the 2021−2030 forecast period, as presented in Figure
1. From 2010 to 2020, the demand for petrochemicals has
increased by more than 50%, with a global market size of about
USD 453 billion.1 By the year 2030, it is estimated that the
market size of global petrochemicals will reach about USD 729
billion.2 This rapid growth in the demand for petrochemicals is
a result of the increasing demand for polymer materials in
households, hospitals, transportation, telecommunications, etc.
and other primary chemicals that consume raw materials in the
production of cosmetics, medicine, and fertilizers. The
petrochemical industry has the responsibility to meet the
global market demand through expanding their capacity for the
production of these primary chemicals. However, the grand
challenge lies in sourcing of these cost-eﬀective petrochemical
feedstocks.
Interestingly, this is happening when the global energy
sector is undergoing an energy transition from fossil fuel to
cleaner alternative energy sources, such as solar, wind, and
hydrogen energy. This upcoming energy transition will impact
Received:
February 27, 2022
Revised:
April 21, 2022
Published: May 4, 2022
Figure 1. Summary of the petrochemical market size according to
regions and products, sourced from Precedence Research.1
Review
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the reﬁnery in unprecedented ways as the transportation and
electricity sectors reinvent themselves and the demand for fuels
no longer supports the current reﬁnery setup. Thus, trans-
formative technology that oﬀers greater reﬁning proﬁtability
through direct crude oil conversion will be necessary to keep
the reﬁneries in business.
As identiﬁed by Zhou et al.,3 the basic petrochemical
feedstocks are light oleﬁns (ethylene, propylene, and butene)
and benzene, toluene, and xylene (BTX) aromatics. In the past,
some of these feedstocks have been considered as byproducts
of most petroleum reﬁning processes that form transportation
fuels, such as gasoline, kerosene, and diesel. Interestingly, the
emergence of petrochemical-based products has created great
potentialities in the use of these byproducts, and this has
attracted the attention of researchers in the last 2 decades.
Consequently, the steam cracking of naphtha was developed as
a convenient industrial mechanism of producing light oleﬁns
for the petrochemical applications.4−7 Additionally, catalytic
reforming of naphtha was adopted for the production of BTX
aromatics.8−10 While this is a great milestone in the global
petrochemical demand, it is considered as the most consuming
energy industrial process characterized with a large CO2 gas
emission.11,12 It also restricted the boom in the petrochemical
market to the availability of naphtha, which is a major product
of crude oil reﬁning. However, this can be circumvented, thus
producing the feedstock of the petrochemical directly from
readily available cheap feed, the crude oil, which will save cost
and accord the petrochemical market the desired freedom to
grow based on the market demand. Hence, the drive for the
groundbreaking direct crude oil to chemical (crude-to-
chemical) research is considered a promising solution.
Literally, this route reconﬁgures a reﬁnery to produce
maximum chemicals along with fuels and, thus, merges reﬁnery
and petrochemical plants into one. This transformative
technology will keep the reﬁnery in its high-proﬁtable state.
The steam cracking of crude oil often results in large coke
formation and poor selectivity to light oleﬁns.13 Nonetheless,
modiﬁcation of crude oil steam cracking technology, such as
crude oil fractionation, where the steam cracker is charged with
only the light and clean component of the crude oil and the
remaining components are ejected through controlled vapor-
ization or dilution of the crude oil by mixing with low-density
hydrocarbons, such as gas condensate, has been adopted by
several companies.14−16 The complexity may include the
addition of the pretreating chamber to hydrotreat the crude oil,
solvent deasphalting, demetallizing, etc., thus resulting in
upgraded stream composed of mainly paraﬃns that can steam
crack to light oleﬁns. The complex processes usually involve
utilization of special catalysts, such as modiﬁed zeolites, Al2O3,
molecular sieves, mixed oxides, etc. Moreover, the heavy cut
can be further processed to produce the desired light oleﬁns.
The continuous modiﬁcations of the crude oil cracking
technology to yield light oleﬁns translate to the adoption of
ﬂuidized catalytic cracking (FCC) for a light oleﬁn yield,
especially propylene. Generally, the FCC has a track record of
being used in the cracking of heavy feeds, such as vacuum gas
oil (VGO) and residues.17 Similarly, light feeds, such as light
naphtha and gas condensate, have been successfully cracked in
the FCC unit.18 With excellent conversion in both light and
heavy petroleum cuts, the FCC is considered the right
technology for some reﬁneries that are switching to propylene
production. Advanced FCC technology, such resid FCC,
conveniently cracks the heaviest crude oil cuts to naphtha and
light oleﬁns.19
Typically, kinetics and thermodynamics of the crude oil
cracking signiﬁcantly aﬀect the oleﬁn yield, and this is why
process parameter optimization, such as the residence time, is
important in enhancing the light oleﬁn yield. However, the
modiﬁcation of the reactor design and process optimization
usually come with huge capital investment and may consume a
lot of time. A more important factor in the crude-to-chemical
shift is the catalyst that does the cracking of crude oil to
selectively yield oleﬁn, and this catalyst must be carefully
selected, irrespective of the reactor type (whether it is a ﬁxed
bed platformer, ﬂuidized bed, or FCC). The right catalyst must
have the desired chemical composition, active sites (acidity,
valence electrons, vacancies, etc.), particle size, porosity,
mechanical strength, and hydrothermal stability to optimize
the light oleﬁn yield without much compromise on the high-
octane gasoline yield.
A recent review by Vogt and Weckhuysen identiﬁed that the
Brønsted acid sites in zeolite-based catalysts are the active sites
in the heavy crude oil cracking to chemical (propylene) in the
FCC unit.20 These intrinsic catalyst properties are achieved
through both the catalyst composition and the synthesis
approach. Therefore, this review is intended to cover various
catalyst formulations, their catalytic properties, and speciﬁc
roles in the direct crude oil-to-oleﬁn conversion. A clear
understanding of the nexus between catalyst formulations to
the formation of active sites, catalyst porosity and particle sizes,
and how these contribute to a higher light oleﬁn yield will
further enrich the crude oil-to-chemical research literature.
2. CATALYST FORMULATIONS
The catalyst formulations for the catalytic cracking of
hydrocarbons, including crude oil, comprise zeolite compo-
nents, tuning of zeolite porosity, and matrix materials. It is
well-established that the zeolite synthesis approach largely
determines its properties, such as the crystal structure, shape,
and size, silica/alumina ratio, acidity, surface area, and
porosity.21 Similarly, the type of matrix materials (clay and
binders) and their mixing composition, procedure, and
pretreatment conditions signiﬁcantly impact the catalyst
strength, resistance to attrition, heat conductivity, and catalytic
activity. Although the crude oil conversion to light oleﬁns is at
the infant stage, it is pertinent to have some idea about the
diﬀerent approaches that have been reported for controlling
catalyst properties in a conventional hydrocarbon cracking
process, having known quite well that the crude oil is a very
complex mixture of hydrocarbons. These approaches will be
covered under the following subheadings.
2.1. Zeolite Components. The early catalytic cracking
catalysts were based on natural clay, which generally give low
performance in terms of activity, selectivity, and thermal and
structural stability.22−24 Later, the use of metal oxide catalysts
mainly based on synthetic silica and alumina, such as KVO3−
Al2O3, SiO2−Al2O3, and SiO2−MgO, was considered a great
advancement in the area of catalyst development for cracking
of hydrocarbons.25 However, these catalysts suﬀered fast
deactivation, especially at elevated temperatures, and a high
rate of coke formation that makes regeneration diﬃcult and
consumes a huge amount of steam.26 Indeed, the great
milestone in the FCC process was the discovery of zeolite
ultrastable Y (USY), which was used to crack VGO to gasoline
as a result of its high activity and shape selectivity.27 ZSM-5
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was later used as a good additive as a result of its shape
selectivity cracking of paraﬃns in gasoline fractions. At the
same time, its low hydrogen transfer activity does not consume
a large amount of the generated light oleﬁn.28 Buchanan29
reported that the addition of 25% ZSM-5 as an additive
resulted in an improved octane rating of gasoline, reduction in
gasoline (mainly C5−C12 hydrocarbons), and great improve-
ment in the yield of light oleﬁns as a result of the enhancement
of C5+ oleﬁn isomerization and cracking on the active sites.
Similar trends in terms of light oleﬁn (propylene and butylene)
yield, gasoline yield, and dry gas and coke yield with respect to
ZSM-5 addition to zeolite Y, during VGO cracking at reaction
temperatures of 500−600 °C, were reported by Aitani et al.30
Figure 2 shows the sequence for the evolution of FCC catalysts
from clay up to the zeolites.
The zeolites are a class of crystalline microporous materials
with a well-deﬁned framework that encloses uniform cavities
and channels.31−33 Because of the ease of tuning their chemical
composition and pore structure, this class of materials has
found numerous industrial applications, especially as catalysts
in the reﬁning and petrochemical processes.34−38 Their
chemical composition is particularly important in determining
their acidity, active sites, structure, and stability.39,40 The
common and well-studied zeolites are the aluminosilicates,
which are characterized by their Brønsted acid sites as a result
of the available proton that balances the negative charges
generated as a result of the insertion of trivalent Al atoms into
a silica tetrahedral environment.41 The Brønsted acid sites are
particularly important in zeolite selectivity to light oleﬁns
during catalytic cracking of various ranges of hydrocarbons.20
In addition, the acid strength of zeolite, which is correlated to
its Si/Al ratio, aﬀects the cracking eﬃciency of the catalyst.42
Usman et al.43 explored ZSM-5 zeolites with diﬀerent Si/Al
ratios (30, 280, and 1500) as additives in the cracking of
several light crude oils and discovered that, regardless of the
type of crude oil, ZSM-5(280) zeolites characterized with
relatively low acidity present a high yield of light oleﬁns,
although with compromise on conversion. The light oleﬁn
yield slightly decreases in the case of ZSM-5(1500), which
indicates that there is an optimum Si/Al ratio for ZSM-5 that
increases the production of light oleﬁns. Similarly, three
medium pore size zeolites, SVR, ITH, and MFI, with diﬀerent
Si/Al ratios showed that a lower Si/Al ratio zeolite,
characterized with a high number of acid sites, presents a
high gasoline overcracking and low hydrogen transfer reaction
that led to a high yield of light oleﬁns in the cracking of VGO,
whereas a small-crystal-size MFI zeolite lowers the light oleﬁn
yields.44
2.1.1. Zeolite Composites. The modiﬁcation of zeolite with
silica and alumina or blending of two diﬀerent zeolites is an
eﬀective strategy to improve catalyst performance45−49 or
minimize the formation of the coke deposit.50 Li et al.51
studied the catalytic cracking of Fushun VGO in a micro-
reactor unit using ZSM-5/Al2O3 and USY/Al2O3 catalysts.
Interestingly, optimizing the severity of the reaction conditions
maximized the yield of liqueﬁed petroleum gas (LPG) and
propylene while limiting coke and dry gas yields. The ZSM-5
and MCM-41 blend formed a micro−meso ZSM-5/MCM-41
composite molecular sieve that possessed a secondary building
unit, characterized with diﬀerent acidity and textural properties
that favored oleﬁn selectivity in the cracking of VGO.52 High
oleﬁn catalytic cracking of Brazilian gas oil at 600 °C using H-
FER/HZSM-5 blends mixed with a commercial FCC catalyst
showed an improved cracking activity and higher yield of C2−
C4 light oleﬁns than when H-FER alone is mixed with a
commercial FCC catalyst. This is attributed to the HZSM-5
shape selectivity for cracking of paraﬃns to light oleﬁns and
the lower hydrogen transfer ability of both HZSM-5 and H-
FER that restricts excess hydrogen transfer, aromatization, and
cyclization reactions.53
2.1.2. Heteroatom-Modiﬁed Zeolites. The stability, acidic
properties, and overall performance of ZSM-5 additives can be
tuned via modiﬁcation with phosphorus for lattice Al ion
stabilization, rare earth metal cations for ZSM-5 basicity
modiﬁcation, alkali or alkali earth metals for reducing acid
sites, and transition metals for producing new Lewis sites,
leading to a dual functioning catalyst.54−56 Phosphorus-
impregnated ZSM-5 was used as an additive to USY in VGO
cracking. The addition of phosphorus stabilized the framework
of aluminum, which increased its hydrothermal stability57 and
lowered its acidity, thus preventing over-reaction of primary
products. Catalyst hydrothermal stability is particularly
Figure 2. Evolutional trend of FCC catalysts. This ﬁgure was reprinted with permission from ref 26. Copyright 2019 Taylor & Francis.
Figure 3. ZSM-5 stabilization by phosphorus using synchrotron-based powder XRD and neutron diﬀraction: (a) pure ZSM-5, (b) 0.8 P-ZSM-5
obtained by Fourier map analysis, and (c) 0.8 P-ZSM-5 derived with the rigid-body technique, in which PO4 tetrahedra were introduced as rigid
bodies. This ﬁgure was reprinted with permission from ref 61. Copyright 2020 American Chemical Society.
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important in the cracking of crude oil because the reaction
temperature may be well above 650 °C. These led to a
signiﬁcant increase in the cracking activity and yields of
propylene, butene, and isobutene.58−60 The oleﬁnic ratios were
also higher with P-ZSM-5 compared to only the ZSM-5
additive.57 The activation and stabilization eﬀects of
phosphorus in the ZSM-5 framework as a FCC catalyst
additive were conﬁrmed using synchrotron X-ray diﬀraction
(XRD) and neutron diﬀraction structure reﬁnement by
Louwen and co-workers.61 As shown in Figure 3, it was
observed that the aluminum atoms in zeolite became more
secured to their original framework position by forming stable
immobile Si−O−Al−O−P structures.
The eﬀect of metal incorporation in ZSM-5 has been studied
by the addition of chromium, copper, and gallium to ZSM-5 as
an additive for VGO cracking.62 Cu partial ion exchange with
ZSM-5 suppressed the dealumination of the zeolite framework,
resulting in a signiﬁcant improvement in the light oleﬁn yield.
Cr and Ga also improved the selectivity to light oleﬁns, relative
to unmodiﬁed ZSM-5. Notably, the transition-metal-modiﬁed
zeolite serves as a dual functional catalyst, with Lewis and
Brønsted acid sites serving the dehydrogenation of paraﬃn and
cracking of higher oleﬁn roles, respectively.63 The addition of
lanthanum to P-ZSM-5 showed a decrease in acidity, while the
basic sites of the zeolite increased with the increase in
lanthanum. Interestingly, an optimum loading of lanthanum
minimized hydrogen transfer activity and, thus, enhanced the
light oleﬁn yield in the cracking of C5 raﬃnate with a minimal
coke deposition.64 Likewise, alkali earth metals, such as
calcium, enhanced ZSM-5 activity for the catalytic cracking
of crude oil. The addition of Ca regulated the pore size
distribution, surface area, and acidity of ZSM-5, thereby
enhancing dehydrogenation at Ca sites and cracking at ZSM-5
sites, resulting in a high yield of light oleﬁns and aromatics.65
2.2. Tuning Zeolite Porosity. Zeolite pore dimensions
and topology are other properties that are crucial in catalytic
cracking of crude oil.66−68 In general, crude oil is composed of
a wide range of hydrocarbons from low-molecular-weight
volatile hydrocarbons to large-molecular-weight resins and
asphaltenes.69,70 Cracking of these wide-range hydrocarbons
will certainly be challenging, considering the fact that most
catalytic reactions are carried out on the surface of the catalyst
active sites, which are usually within the internal pores or
cavities of the zeolites. Thus, on one hand, the small-pore-size
zeolite limits the diﬀusion of large-molecular-weight hydro-
carbon into the zeolite cavities and prevents them from
accessing the active sites of zeolites. This results in large coke
formation and fast cast deactivation, which are highly
undesirable. On the other hand, the large and extra-large
pore size zeolites may not be able to keep the hydrocarbons in
the zeolite cavity to allow for complete cracking as a result of
the available exit routes that facilitate easy diﬀusion of large
molecular products out of the zeolite cavities. Additionally, the
large cavity may encourage the excessive hydrogen transfer
reaction, oligomerization, and aromatization of the light oleﬁn
products. One solution to this problem is to adopt a
“molecular traﬃc control” approach, as identiﬁed by Derouane
and Gabelica71 about a half century ago.
Therefore, zeolites with diﬀerent pore sizes are prepared to
allow for preferential diﬀusion of reactants and products
through various channels of the zeolite system. This idea of
multipore zeolite synthesis was clearly presented in a recent
review by Moliner et al.72 and has demonstrated to be eﬀective
for numerous catalytic processes that require special control of
the zeolite cavities. Similarly, the combination of two or more
zeolites of diﬀerent pore sizes and crystal structures have been
considered as an eﬀective strategy for developing cracking
catalyst formulation that can handle a wide range of
hydrocarbon mixtures. Ferrierite (FER) is an example of one
of the most attractive two-dimensional zeolites that has small
and medium pores characterized with 8-ring [3.5 × 4.8 Å
opening along the (001) direction] and 10-ring [4.2 × 5.4 Å
opening along the (010) direction] channels of interconnected
pores (Figure 4).73−75 Because of its interesting pore structure,
often referred to as FER cavities, that are accessible through
only the 8-ring channel, the zeolite ﬁnds application in the
chemical conversions of small molecules, such as the cracking
of light naphtha to light oleﬁns, with better selectivity than the
ZSM-5 zeolite. However, FER small pore sizes hindered
eﬀective diﬀusion of reactants to and from active sites of the
zeolites, which results in low catalytic conversion.
Bastiani et al.53 reported that the distinction in catalytic
cracking activity and product selectivity of H-FER and HZSM-
5 zeolites in the cracking of n-hexane is attributed to their
diﬀerent pore structures, which aﬀect active site accessibility by
n-hexane. ZSM-5 showed higher catalytic activity as a result of
its relatively larger pore size, although it has lower acid site
density than H-FER. The conversion of Arabian light crude oil
to light oleﬁns using a blend of a large-pore-size equilibrium
catalyst (E-Cat) and small-pore-size commercial MFI (ZSM-5)
catalyst results in a higher yield of light oleﬁns than what was
obtained with either of the zeolites.76 Similarly, the selectivity
to oleﬁns of the E-Cat/ZSM-5 blend in both advanced catalytic
evaluation (ACE) and microactivity test (MAT) units77 and
when diﬀerent crude oil feeds were used was discovered to be
higher than when either E-Cat or ZSM-5 was used as the
catalyst.78 Interestingly, the large-pore-size E-Cat zeolite
conviniently cracked the complex hydrocarbon molecules in
the diﬀerent crude oil feeds to paraﬃns, which were selectively
cracked to light oleﬁns by the small-pore-size ZSM-5 zeolites.
2.2.1. Tuning Porosity by the Bottom-Up Approach.
Introducing porosity to zeolites is usually performed through
two major approaches: the bottom-up and top-down
approaches.80 The bottom-up approach may be considered
Figure 4. FER structure showing the (a) medium pore 10-ring
channel view along the c axis, (b) 8-ring channel view along the b axis,
and (c) FER cavity view along the c axis. This ﬁgure was reprinted
with permission from ref 79. Copyright 2007 American Chemical
Society.
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as a single-step synthesis of a mesoporous zeolite, in which the
porosity is introduced directly during the mixing of the zeolite
precursors, by the addition of a templating agent. It may,
however, not be considered as a single-step synthesis if the
template is synthesized in-house, which is usually the case for
most novel zeolite design and development.81,82 Most of these
templates are organic molecules with various functionalities,
such as amines and quaternary ammonium compounds and are
often referred to as soft templates. The use of a double
template was also considered as an interesting approach for
tuning the porosity of zeolites. Zhang et al.83 used (3-
aminopropyl)triethoxysilane as a secondary template that
generates intercrystalline mesoporosity via inhibition of the
crystal growth in the direction of the 3-aminopropyl moiety,
while the amount of TPAOH was regulated to control the
nucleation rate. The summary of various structure-directing
agents that were used to synthesize zeolites of diﬀerent pore
sizes is presented in Table 1.
Solid materials, such as carbon (carbon ﬁber, carbon black,
carbon nanotubes, etc.) and polymer materials (latex,
polystyrene spheres, etc.), have been considered as hard
templates during the synthesis of large-pore-size zeolites.84−86
These approaches of introducing large pores to zeolites may be
of interest in the catalytic cracking of crude oil, where large
porosity is desired to minimize diﬀusion limitations. Both soft
and hard templates are later removed from the zeolite
framework by calcination at a high temperature. Recently,
there has been growing concern about the toxicity, non-
ecofriendliness, and costliness of using these templates for
zeolite synthesis. This has resulted in the consideration of what
is often termed as a green route for the zeolite synthesis, which
literarily eliminates the addition of toxic and often expensive
templates in the zeolite synthesis.87−92 Therefore, the adoption
of green routes in the synthesis of catalytic cracking zeolite
catalysts will add value to the FCC process in terms of
operational cost and environmental safety. For example, a non-
toxic and inexpensive component of shampoo (polyquater-
nium-6) has been used as a template to synthesize a large-pore
EMT-rich faujasite zeolite, which showed higher catalytic
activity than the USY zeolite in the cracking of cumene.93 The
comprehensive review by Meng and Xiao94 provides in-depth
analysis of the forms of green routes for zeolite synthesis, with
their advantages and challenges.
2.2.2. Tuning Porosity by the Top-Down Approach. When
an already synthesized zeolite undergoes post-synthesis
treatment to expand its porosity, it is referred to as a top-
down approach.102,128−130 This approach involves selective
destruction through extraction of the framework atom from the
zeolite structure. The atoms that are commonly removed from
the zeolites are aluminum and silicon. Therefore, the process is
commonly described as dealumination and desilication,
respectively.131 Dealumination is largely used in the reﬁning
industry to tune zeolite porosity in addition to controlling the
acid strength and acid sites by varying the Si/Al ratio.132
Zeolite dealumination has been carried out via several
methods, such as calcination,133 steaming,134 acid leaching,135
and chemical treatment.136 After dealumination, the extracted
aluminum atoms stay outside the zeolite framework to form
what is described to as aluminum extra-framework. Thus, the
post-treated zeolite maintains relatively the same Si/Al ratio as
the parent zeolite and may not present an appreciable increase
in porosity as a result of probable pore blockage by the
discharged aluminum species. This is why a mild acid leaching
often accompanies the dealumination process by calcination or
steaming. Recent studies have shown that the combination of
mild acid leaching followed by calcination or steaming extracts
nearly 70% of aluminum from the beta zeolite framework, and
the location and state of extracted aluminum depend upon the
mild acid calcination/steaming conditions.137
The combined chemical treatment of HZSM-5 with
aluminum ﬂuoride by mechanical mixing and calcination
results in simultaneous dealumination and realumination of the
ZSM-5 zeolite.138 The dealumination gives an appreciable
increase in the zeolite porosity, whereas realumination
increases the medium acid sites, thus compensating for the
lost acid sites as a result of dealumination. Nasser et al.139
discovered that dealumination of mordenite by acid leaching
using 1.0 M solution of nitric acid expands zeolite porosity and
increases its selectivity toward propylene (propylene/ethylene
ratio of 1.26) in the cracking of n-hexane. The combination of
chemical treatment using ethylenediaminetetraacetate (EDTA)
Table 1. Pore Architecture of Zeolites and Their Respective
Structure-Directing Agents
zeolite
pore channels
structure-directing agent
reference
Small and Medium Pores
FER
10 × 8
template-free
95
TMA+ + 1-BMP
79
TMA+ + PYR and PYR alone
73
ZSM-57
10 × 8
N3N′3-HEPDA
96
TNU-10
10 × 8
1,4-BMPB
97
SSZ-75
10 × 8
TM-1,4-BMP
98
Al-ITQ-13
10 × 10 × 9
N3N′3-HMHMDA
99
ITQ-34
10 × 10 × 9
P-1,3-BTMP
100
RUB-41
10 × 8
DMDPA
101
IPC-4
10 × 8
octylamine
102
Small and Large Pores
SAPO-40
12 × 8
TPA
103
SSZ-51
12 × 8
4-DMAPy
104
EMM-8
12 × 8
4-DMAPy
105
MAPO-50
12 × 8 × 8
n-Pr2NH
106
STA-6
12 × 8 × 8
TMTACTD
107
UZM-4
12 × 8 × 8
TMA+ + TEA+
108
OFF
12 × 8
BTMA
109
ZSM-10
12 × 12 × 8
1,4-DM-DABCO
110
MOR
12 × 8
template-free
111
Medium and Large Pores
SSZ-26
12 × 12 × 10
N3N′3-HMBCDA
112
SSZ-33
12 × 12 × 10
TCDA
113
ITQ-24
12 × 10 × 10
HMT
114
ITQ-47
12 × 10
phosphazenes
115
MCM-68
12 × 10 × 10
N3N′3-TEBCODP
116
SSZ-56
12 × 10
N2-EDMDHQ
117
SSZ-82
12 × 10
1,6-BCHPH
118
SSZ-57
12 × 10 × 10
N-Bu-N-CHPy
119
ITQ-22
12 × 10 × 8
1,5-BMPyP
120
ITQ-39
12 × 10 × 10
DCPD
121
ITQ-38
12 × 10 × 10
DCPD
122
Extra-Large Pores Interconnected with Smaller Pores
ITQ-15
12 × 14
1,3,3-TMATCD
123
ITQ-33
18 × 10 × 10
HMT
124
ITQ-40
16 × 15 × 15
DEDPP
125
ITQ-44
18 × 12 × 12
DMSIP
126
ECR-34
18 × 10 × 10
TEA+
127
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and acid treatment in the dealumination of Y zeolite under
microwave irradiation was found to be more eﬀective than
using acid treatment under microwave irradiation.140 Unlike
HCl treatment that forms extra-framework alumina without
signiﬁcant expansion of porosity, EDTA forms a complex with
aluminum, which is easily removed by sequential alkaline
treatment and, hence, results in expanding the porosity.
Desilication is another approach for tuning zeolite porosity
and Si/Al ratio, where silicon atoms in a zeolite framework are
selectively detached using alkaline treatment, thereby creating
a signiﬁcant amount of empty space in the zeolite without
compromising its structural integrity. However, the use of
strong alkali solution has been linked to an uncontrollable pore
size distribution and structural collapse.84,141 Groen et al.142
reported that the desilication approach is particularly eﬀective
at a certain Si/Al ratio within the range of 25−50 for the ZSM-
5 zeolite. Similarly, controllable desilication of ZSM-5 was
achieved by mild alkaline treatment using NaHCO3 to form
hierarchical ZSM-5 with desired micro- and mesopores that
minimize diﬀusion limitations and adjust the zeolite acidity,
thus promoting n-pentane cracking to light oleﬁns.143
Ahmed et al.144 discovered that, during the desilication of
ZSM-48 in 0.1 M NaOH, some aluminum dissolved into the
alkaline solution, which was deposited as a layer of amorphous
alumina on the zeolite framework. This layer decreased the
crystallinity and porosity of the zeolite, and after it was
removed by acid treatment (0.2 M HNO3), both crystallinity
and porosity were greatly enhanced. Similarly, the active sites
were increased, which result in high activity and propylene
selectivity in the cracking of light naphtha. Desilication of the
BEA zeolite using NaOH has also been carried out to create
large mesopores as vacant sites, which were used for controlled
incorporation of nickel and cobalt metals into the displaced
silicon tetrahedral position in the zeolite framework without
blocking the zeolite porosity (Figure 5).145 This approach has
demonstrated to be eﬀective in the cracking of heavy naphtha
with high selectivity to light oleﬁns.
The use of aqueous solutions of organic bases, such as
tetrapropylammonium hydroxide (TPAOH) and tetrabuty-
Figure 5. Controlled incorporation of Ni and Co metals into BEA zeolite after creating vacant sites by desilication. This ﬁgure was reprinted with
permission from ref 145. Copyright 2017 American Chemical Society.
Figure 6. Scanning electron microscopy (SEM) images of hierarchical ZSM-5 treated in (a) 0.1 M HF, (b) 0.2 M HF, (c) 0.5 M HF, and (d) 1 M
HF and transmission electron microscopy (TEM) images of hierarchical ZSM-5 treated in (e) 0.1 M HF, (f) 0.2 M HF, (g) 0.5 M HF, and (h) 1 M
HF. This ﬁgure was reprinted with permission from ref 151. Copyright 2018 Elsevier.
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lammonium hydroxide (TBAOH), in dissolving silicon from
zeolites to create mesostructures has also been considered,
even though the desilication performance is considerably low
because the bases are weak.146−148 Notably, the low
desilication using organic bases may be of an advantage
because it means that the degree of desilication can be
controlled easily. In addition, desilication using organic bases
yields the protonic form of the zeolite, unlike the inorganic
base treatment that requires additional treatment for metal ion
exchange.
The desilication performance could be enhanced by
supplying external energy through heating of the solution to
high-temperature, microwave, or ultrasonication treatment.
However, the desilication in organic bases has been observed
to be accompanied by Al leaching in some zeolites, and this
may likely upset the desired Si/Al ratio.132 The microwave-
assisted alkaline treatment of the microporous one-dimensional
ZSM-23 zeolite has proven to be eﬀective in increasing the
zeolite porosity, stability, and activity in the cracking of n-
hexane to propylene, with a propylene/ethylene ratio of 2.5.149
The combination of inorganic alkali, such as NaOH, with an
aqueous organic base solution has also been reported as an
eﬀective strategy in the desilication of zeolites. Ahmedpour and
Taghizadeh150 reported the formation of narrow and uniform
intracrystalline mesoporosity after desilication of high-silica
ZSM-5 in an alkaline mixture of NaOH and TPAOH with
about 40% TPAOH. Both the ZSM-5 crystal structure and
intrinsic acidity were retained, and because of the improved
porosity, high propylene selectivity was recorded in the
cracking of methanol. Chemical etching of ZSM-5 using
various concentrations of HF or NH4F also forms large
channel structures and meso- and macropore openings, as
shown in Figure 6. The 0.5 M HF-modiﬁed ZSM-5 provided
the desired pore structure and intrinsic acidity required for the
cracking of n-hexane to light oleﬁns.151
The tuning of zeolite porosity via dealumination or
desilication requires an extra step for removing dissolved
aluminum or silicon from the modiﬁed zeolite to prevent
blocking of the created pores or generation of the amorphous
layer of alumina/silica around the zeolite framework. In
addition, the solution containing extracted alumina/silica ends
up being a waste to the environment. Recently, there has been
growing interest in using these dissolved alumina/silica as
partial nutrients for the formation of a crystalline mesoporous
shell around the zeolite core.152,153 This idea is an improve-
ment of the previous method of generating mesopores around
the zeolites by forming an amorphous shell of various
mesoporous silica, such as SBA-15, MCM-41, and TUD-
1.154−158 However, this approach suﬀers some drawbacks, such
as poor thermal and hydrothermal stability as a result of the
amorphous nature of the shell, uncontrollable chemical
composition (Si/Al ratio), and acidity. Therefore, the use of
dissolved alumina/silica as partial nutrients for the formation
of a crystalline mesoporous shell is considered a better
approach because it has addressed the challenges as a result of
amorphousness and minimization of waste generation. Pan et
al.152 wrapped Y zeolite with a nano ZSM-5 crystal shell that is
formed using the dissolved alumina/silica during Y zeolite
dealumination/desilication to form a highly thermally and
hydrothermally stable meso- and macroporous core−shell Y
zeolite (Figure 7). The nano ZSM-5 crystal shell facilitates the
cracking of large molecules in heavy oil into relatively small
molecules that can easily diﬀuse into the micropore of the Y
zeolite core for further selective cracking to form the desired
products. Consequently, the heavy oil conversion was
increased by 1.8%, and the yield of gasoline was increased by
8.9%. The isoparaﬃns and oleﬁns in the gasoline fraction
increased by 5.21 and 6.31 wt %, respectively.
2.3. Matrix Materials. The development of a cracking
catalyst involves special mixing of zeolite and matrix materials,
which provide the desired attrition resistance, hydrothermal
and chemical stability, metal entrapment ability, minimum
coke formation, and ﬂuidizability.159 The matrix materials
consist of clay and silica/alumina binder. The clay,
characterized with large heat capacity, plays the role of
transmitting heat from the regenerator to the reactor and is
also considered as an important balancing (ﬁller) material in
the catalyst development.160 Thus, the clay ﬁller dilutes the
zeolite activity, which prevents excessive cracking and
formation of a large volume of dry gas products.161,162
Additionally, the wettability and swelling properties of clay
have been shown to inﬂuence the catalyst parking arrangement
after spray drying of the catalyst slurry. Dependent upon the
clay composition, it may increase the catalytically active sites
that will contribute to the performance of the catalyst, as
observed in the methanol-to-oleﬁn reaction.163
Alabdullah et al.164 reported the eﬀect of diﬀerent types of
clay in catalyst formation for the catalytic cracking of crude oil.
Among the ﬁve types of clays used, the kaolin-formulated
composite, characterized with a higher amount of exposed
strong Brønsted acid sites, exhibited the highest cracking
activity and light oleﬁn selectivity. The other clays, especially
talc [Mg3Si4O10(OH)2] and bentonite [(Na/Ca)x(Al/
Mg)2(Si4O10)(OH)2], have mobile cations, such as Ca2+,
Mg2+, and Na+, that partially deactivate the zeolite component
in the catalyst and lead to high coke deposition.165,166 The
alumina/silica binders are used to produce a meso- and
macroporous matrix with Lewis acid sites that allows access to
the zeolite and pre-cracks the larger molecules in the
feedstocks. In addition, these components are used to bind
the system together in various shapes for enhanced resistance
to attrition and eﬃcient ﬂuidization. The addition of a binder
Figure 7. Scheme showing the transition from an amorphous silica/
alumina shell to a crystal zeolite shell wrapping the Y zeolite core.
This ﬁgure was reprinted with permission from ref 152. Copyright
2019 Elsevier.
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has also been linked to a drastic decrease in the rate of coke
formation during cracking processes by trapping and cracking
coke precursors, such as polyaromatics, using their available
acid sites.159 Velthoen et al.167 discovered that, under the FCC
riser reactor conditions, both Brønsted and Lewis acidity, pore
accessibility, and zeolite structures were considerably changed.
These changes were ascribed to the signiﬁcant interaction
between the zeolite and matrix (often referred to as the matrix
eﬀect), which allows for migration of labile aluminum from the
binder to the zeolite structural defects, as shown in Figure 8.
More recently, it was discovered that, for a speciﬁc catalytic
reactor concept consisting of a multi-zone ﬂuidized bed, the
addition of silicon carbide in the catalyst formulation increases
the catalyst physical, mechanical, and heat-transport properties
during a direct cracking of crude oil to oleﬁns.168
3. CHALLENGES AND PERSPECTIVES
The crude oil to oleﬁn conversion using various catalyst
formulations appears to be a cost-eﬀective approach for easy
transition of reﬁneries from producing basic transportation
fuels to a blend of fuels and petrochemicals. The FCC catalyst
formulation comprises zeolites, clay, and binders. The zeolite
composition, hydrothermal stability, pore size structure, and
acidity determine its catalytic cracking activity. The high
acidity and pore size structure of USY zeolite warrant cracking
of large-molecular-weight hydrocarbons to gasoline-range
hydrocarbons. ZSM-5 has the desired shape selectivity for
cracking of paraﬃns in gasoline fractions to light oleﬁns. At the
same time, its low hydrogen transfer activity does not consume
a large amount of the generated light oleﬁn, resulting in
improvement of light oleﬁn production. Although the activity
of the ZSM-5 additive in crude oil to oleﬁn cracking could be
improved through modiﬁcation with metal oxides, which will
greatly increase the zeolite surface area and porosity, a balance
needs to be achieved, in which the zeolite framework stability
and active sites are not compromised.
Similar modiﬁcations, such as incorporation of phosphorus
for lattice Al ion stabilization, rare earth metal for ZSM-5
basicity modiﬁcation, alkali earth metals for reducing acid sites,
and transition metals for producing new Lewis sites, have
proven to be eﬀective in increasing the oleﬁn selectivity in the
cracking of crude oils. However, it has been established that
mobile cations, such as Ca2+, Mg2+, and Na+, can deactivate the
zeolite component in the catalyst and lead to high coke
deposition. These mobile cations can be minimized through
proper selection of clay material because most clay samples
have some proportion of these mineral elements.
Additionally, considering the wide range of hydrocarbons in
crude oil, the “molecular traﬃc control” model may be of great
relevance in the design of multipore zeolites to allow for
preferential diﬀusion of large-molecular-weight hydrocarbons
to zeolite active sites for cracking and light oleﬁn products
away from the zeolite before they undergo an excessive
hydrogen transfer reaction, oligomerization, and aromatization
through various channels of the zeolite system. However, the
development of these multipore zeolites is characterized with
complicated synthesis procedures and expensive structure-
directing agents. In addition, a large percentage of the reported
multipore zeolites has a grand challenge of hydrothermal
instability, which is due to the extra-large pore sizes in their
framework. These extra-large pores are mostly generated by
inserting a germanium atom into the Si/Al framework, and this
results in sudden framework collapse under high hydrothermal
conditions. Although post-synthetic isomorphic substitution of
germanium with Si or Al has proven to be eﬀective, only few
zeolites were successfully obtained by this procedure. Perhaps
the small atomic sizes of Si and Al are the reason for the low
success in the germanium substitution. Therefore, atoms of
close atomic size with germanium may be considered to
achieve desirable results. Additionally, the combination of two
or more zeolites of diﬀerent pore sizes and crystal structures
may be considered as an alternative to the multipore zeolites in
the development of cracking catalyst formulation that can
handle a wide range of hydrocarbon mixtures.
Similarly, post-synthesis modiﬁcation of zeolite porosity by
dealumination, desilication, or both has yielded the desired
micro- and mesopores that minimize diﬀusion limitations and
adjust the zeolite acidity for eﬃcient conversion of crude oil to
light oleﬁns. The recent approach of wrapping the core zeolite
with a nanocrystal shell formed from dissolved alumina and
silica will deﬁnitely complement the role of matrix materials in
pre-cracking of larger molecular weight hydrocarbons, which
Figure 8. Transitional changes in catalyst formulation under FCC conditions were discovered with the help of advanced characterization tools,
such as magic angle spinning nuclear magnetic resonance (MAS NMR), TEM, and carbon monoxide Fourier transform infrared spectroscopy (CO-
FTIR). This ﬁgure was reprinted with permission from ref 167. Copyright 2020 John Wiley & Sons.
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will otherwise cause a lot of coke deposition and catalyst
deactivation. However, this approach needs to be further
improved to achieve a uniformly ordered crystalline meso-
porous shell, because this is expected to boost its hydrothermal
stability and cracking eﬃciency. The role of matrix materials in
the catalytic cracking of crude oil to oleﬁns needs to be further
investigated. Until recently, the matrix materials have been
considered simply as ﬁllers, heat carriers, and binders.
However, their roles in catalyst parking rearrangement, catalyst
active site modiﬁcation, Brønsted and Lewis acid site
adjustment, and overall catalyst textural properties need to
be deeply investigated.
One interesting area to consider in the future for optimizing
the FCC catalyst formulation toward achieving a maximum
yield of light oleﬁns and gasoline is the incorporation of
artiﬁcial intelligence (AI) in the catalyst formulation
process.169 AI is already being deployed in optimizing the
highly complicated FCC processes and maximizing light oleﬁn
production.170 With the complexity in achieving an excellent
combination of zeolite composition, surface acidity, porosity,
hydrothermal stability, and matrix eﬀect, AI will be a great tool
in advancing the frontiers of FCC catalyst formulations.
4. CONCLUSION
The growing demand for petrochemicals and increasing
alternative energy sources has driven the reﬁneries toward
producing more light oleﬁns along with transportation fuels.
To achieve a maximum yield of light oleﬁns in a FCC process,
it has been identiﬁed that the reﬁning technology, process
optimization, and catalyst formulations may have to be
reconﬁgured. Modifying the catalyst formulations to achieve
the maximum light oleﬁn yield is considered cost-eﬀective and
time-saving. The catalyst formulations comprise of zeolite
components, which are the active components, tuning the
zeolite porosity to minimize diﬀusion limitations and matrix
materials to provide the desired attrition resistance and
hydrothermal and chemical stability, among others. The large
internal cavities and strong acid sites of the USY zeolite made
it highly active and selective in the cracking of heavy feed, such
as VGO, to gasoline in the FCC process, and the addition of
the relatively small-cavity ZSM-5 zeolite successfully sup-
presses hydrogen transfer and bimolecular reactions of
carbenium ions, resulting in improved light oleﬁn productions,
mainly propylene. Thus far, the maximum light oleﬁn yield has
been achieved with a reasonable addition of the ZSM-5 zeolite.
Incorporation of heteroatoms, such as phosphorus, to ZSM-5
increases its hydrothermal stability and propylene selectivity.
Similarly, the addition of a small amount of alkali earth metals
or transition metals has been connected to increase in light
oleﬁn selectivity. The Si/Al ratio and porosity of ZSM-5 have
shown a signiﬁcant eﬀect on the light oleﬁn yield. Both the Si/
Al ratio and porosity of ZSM-5 have been modulated by post-
synthesis treatment with acid or base commonly described as
dealumination and desilication, respectively. The lower Si/Al
ratio ZSM-5 zeolite has a higher number of acid sites, which
increases the cracking eﬃciency of the catalyst; however, a
higher number of acid sites also facilitates hydrogen transfer
reactions that result in higher dry gas formation. Therefore,
there has to be a balance in the number of acid sites to prevent
excessive hydrogen transfer reactions. The zeolite porosity is
also generated by the bottom-up approach; however, synthesiz-
ing extra-large pore zeolites that have hydrothermal stability is
still a challenge. The matrix materials have been essential in
developing a catalyst formulation with desired attrition
resistance, hydrothermal and chemical stability, metal entrap-
ment ability, minimum coke formation, and ﬂuidizability. The
composition of matrix materials, method of blending matrix
materials with the zeolite components to form the catalyst
formulations, and the transformational changes that the
catalyst formulations undergo in actual FCC conditions have
been shown to aﬀect the overall activity and selectivity of the
FCC catalyst.
■AUTHOR INFORMATION
Corresponding Author
Abdulkadir Tanimu −Center for Reﬁning & Advanced
Chemicals, King Fahd University of Petroleum & Minerals,
Dhahran 31261, Saudi Arabia;
orcid.org/0000-0002-
7541-0042; Phone: +966-13-860-2946;
Email: abdulkadir.tanimu.1@kfupm.edu.sa
Authors
Gazali Tanimu −Center for Reﬁning & Advanced Chemicals,
King Fahd University of Petroleum & Minerals, Dhahran
31261, Saudi Arabia;
orcid.org/0000-0002-5019-0151
Hassan Alasiri −Center for Reﬁning & Advanced Chemicals
and Department of Chemical Engineering, King Fahd
University of Petroleum & Minerals, Dhahran 31261, Saudi
Arabia;
orcid.org/0000-0003-4043-5677
Abdullah Aitani −Center for Reﬁning & Advanced Chemicals,
King Fahd University of Petroleum & Minerals, Dhahran
31261, Saudi Arabia;
orcid.org/0000-0001-5071-4034
Complete contact information is available at:
https://pubs.acs.org/10.1021/acs.energyfuels.2c00567
Notes
The authors declare no competing ﬁnancial interest.
Biographies
Abdulkadir Tanimu is a research scientist at the Center for Reﬁning
and Advanced Chemicals, King Fahd University of Petroleum &
Minerals (KFUPM), Dhahran, Saudi Arabia. He received his Ph.D.
degree in chemistry from KFUPM and B.Sc. degree in chemistry from
the Ahmadu Bello University, Zaria, Nigeria. His research interest is in
the area of catalyst development for application in desulfurization,
hydroprocessing, and catalytic cracking of heavy hydrocarbon feeds.
Gazali Tanimu is a research engineer at the Center for Reﬁning and
Advanced Chemicals at King Fahd University of Petroleum &
Minerals (KFUPM), Dhahran, Saudi Arabia. He obtained his M.Sc.
and Ph.D. degrees both in heterogeneous catalysis at KFUPM, Saudi
Arabia, and B.Eng. degree in chemical engineering at Ahmadu Bello
University, Zaria, Nigeria. His major research interests are catalytic
upgrading of hydrocarbons to high-value petrochemical products,
molecular-level kinetic modeling, and thermodynamic analysis.
Hassan Alasiri is the director of the Center for Reﬁning and Advanced
Chemicals and assistant professor in the Chemical Engineering
Department at King Fahd University of Petroleum & Minerals
(KFUPM), Dhahran, Saudi Arabia. He received his Ph.D. degree from
Rice University and M.Sc. and B.Sc. degrees from KFUPM, all in
chemical engineering. His research interests are catalysis, kinetic
reaction and modeling, molecular simulations, interphase properties,
and phase behavior. He has published numerous research papers in
International Scientiﬁc Indexing (ISI) journals and is an inventor of
several patents.
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5160
Abdullahi Aitani is a research scientist at the Center for Reﬁning and
Advanced Chemicals of the Research Institute at King Fahd
University of Petroleum & Minerals (KFUPM). He received his
Ph.D. degree in industrial chemistry from City University, London,
U.K., and a B.Sc. degree in applied chemical engineering with high
honors from KFUPM. He has participated in various catalytic
cracking projects, such as the development of a high-severity ﬂuid
catalytic cracking (HS-FCC) process and the development of FCC
additives for reducing gasoline sulfur and enhancing propylene
production.
■ACKNOWLEDGMENTS
The authors express thanks to King Fahd University of
Petroleum & Minerals (KFUPM), Dhahran, Saudi Arabia, for
the support in publishing this review under the Project
INRC2214.
■NOMENCLATURE
International Zeolite Association Codes
USY
ultrastable Y
ZSM-5 Zeolite Socony Mobil-5
ITQ
Instituto de Tecnologia Quimica
REY
rare-earth Y zeolite
SVR
SSZ-74
FER
ferrierite
MOR
mordenite
OFF
oﬀretite
MAPO magnesioaluminophosphate
EMM
ExxonMobil material
Organic Structure-Directing Agents
TMA
tetramethylammonium
TEA
tetraethylammonium
TPA
tetrapropylammonium
TBA
tetrabutylammonium
PYR
pyrrolidine
1-BMP
1-benzyl-1-methylpyrrolidium
N3N′3-HEPDA
NNNN′N′N′-hexaethylpentanediammo-
nium cation
1,4-BMPB
d i v a l e n t
l i n e a r
1 , 4 - b i s ( N -
methylpyrrolidinium)butane cation
TM-1,4-BMP
tetramethylene-1,4-bis(N-methylpyrrolidi-
nium) dication
N3N′3-HMHMDA N,N,N,N′,N′,N′-hexamethylhexamethyle-
nediammonium
P-1,3-BTMP
propane-1,3-bis(trimethylphosphonium)
cation
DMDPA
dimethyldipropylammonium hydroxide
BTMA
benzyltrimethylammonium cation
4-DMAPy
4-dimethylaminopyridine
n-Pr2NH
di-n-propylamine
TMTACTD
1,4,8,11-tetramethyl-1,4,8,11-tetraazacy-
clotetradecane
DABCO
1,4-diazabicyclo[2.2.2]octane
1,4-DM-DABCO
1,4-dimethyl-1,4-diazabicyclo[2.2.2]-
octane
N3N′3-HMBCDA
N,N,N,N′,N′,N′-hexamethyl-8,11-
[4.3.3.0]dodecane diammonium
TCDA
tricyclodecane quaternary ammonium ion
HMT
hexamethonium cation
N3N′3-TEBCODP N,N,N′,N′-tetraethyl bicyclo[2.2.2]oct-7-
ene-2R,3S:5R,6S-dipyrrolidium
N2-EDMDHQ
trans-fused ring isomer of N,N-diethyl-2-
methyldecahydroquinolinium
1,6-BCHPH
1,6-bis(N-cyclohexylpyrrolidinium)-hex-
ane dication
N-Bu-N-CHPy
N-butyl-N-cyclohexyl-pyrrolidinium
1,5-BMPyP
1,5-bis(methylpyrrolidinium)-pentane
1,3,3-TMATCD
1,3,3-trimethyl-6-azonium-tricyclo-
[3.2.1.46,6]dodecane
DCPD
dicationic piperidine derivative
DMSIP
(2′-(R),6′-(S))-2′,6′-dimethylspiro-
[isoindole-2,1′-piperidin-1′-ium]
DEDPP
diethyldiphenylphosphonium
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5166

C-1
C
Integer Programming: 
The Branch and 
Bound Method
C-2
Module C
Integer Programming: The Branch and Bound Method
The Branch and Bound Method
The branch and bound method is not a solution technique specifically limited to integer
programming problems. It is a solution approach that can be applied to a number of differ-
ent types of problems. The branch and bound approach is based on the principle that the
total set of feasible solutions can be partitioned into smaller subsets of solutions. These
smaller subsets can then be evaluated systematically until the best solution is found. When
the branch and bound approach is applied to an integer programming problem, it is used
in conjunction with the normal noninteger solution approach. We will demonstrate the
branch and bound method using the following example.
The owner of a machine shop is planning to expand by purchasing some new
machines—presses and lathes. The owner has estimated that each press purchased will
increase profit by $100 per day and each lathe will increase profit by $150 daily. The num-
ber of machines the owner can purchase is limited by the cost of the machines and the
available floor space in the shop. The machine purchase prices and space requirements are
as follows.
Required
Machine
Floor Space (ft2)
Purchase Price
Press
15
$8,000
Lathe
30
4,000
The owner has a budget of $40,000 for purchasing machines and 200 square feet of
available floor space. The owner wants to know how many of each type of machine to pur-
chase to maximize the daily increase in profit.
The linear programming model for an integer programming problem is formulated in
exactly the same way as the linear programming examples in chapters 2 and 4 of the text.
The only difference is that in this problem, the decision variables are restricted to integer
values because the owner cannot purchase a fraction, or portion, of a machine. The linear
programming model follows.
maximize Z  $100x1  150x2
subject to
8,000x1  4,000x2  $40,000
15x1  30x2  200 ft2
x1, x2  0 and integer
The branch and bound method is
a solution approach that parti-
tions the feasible solution space
into smaller subsets of solutions.
where
x1  number of presses
x2  number of lathes
The decision variables in this model are restricted to whole machines. The fact that both
decision variables, x1 and x2, can assume any integer value greater than or equal to zero is
what gives this model its designation as a total integer model.
We begin the branch and bound method by first solving the problem as a regular linear
programming model without integer restrictions (i.e., the integer restrictions are relaxed).
The linear programming model for the problem and the optimal relaxed solution is
maximize Z  $100x1  150x2
subject to
8,000x1  4,000x2  $40,000
15x1  30x2  200 ft2
x1, x2  0
and
x1  2.22, x2  5.56, and Z  1,055.56
The branch and bound method employs a diagram consisting of nodes and branches as
a framework for the solution process. The first node of the branch and bound diagram,
shown in Figure C-1 contains the relaxed linear programming solution shown earlier and
the rounded-down solution.
The Branch and Bound Method
C-3
A linear programming model solu-
tion with no integer restrictions is
called a relaxed solution.
The branch and bound method
uses a tree diagram of nodes and
branches to organize the solution
partitioning.
Figure C-1
The initial node in the branch
and bound diagram
1
1,055.56
UB = 1,055.56 (x1 = 2.22, x2 = 5.56)
LB = 950 (x1 = 2, x2 = 5)
Notice that this node has two designated bounds: an upper bound (UB) of $1,055.56 and
a lower bound (LB) of $950. The lower bound is the Z value for the rounded-down solu-
tion, x1  2 and x2  5; the upper bound is the Z value for the relaxed solution, x1  2.22
and x2  5.56. The optimal integer solution will be between these two bounds.
Rounding down might result in a suboptimal solution. In other words, we are hoping
that a Z value greater than $950 might be possible. We are not concerned that a value lower
than $950 might be available. Thus, $950 represents a lower bound for our solution.
Alternatively, since Z  $1,055.56 reflects an optimal solution point on the solution space
boundary, a greater Z value cannot possibly be attained. Hence, Z  $1,055.56 is the upper
bound of our solution.
Now that the possible feasible solutions have been narrowed to values between the
upper and lower bounds, we must test the solutions within these bounds to determine the
best one. The first step in the branch and bound method is to create two solution subsets
from the present relaxed solution. This is accomplished by observing the relaxed solution
value for each variable,
x1  2.22
x2  5.56
The optimal integer solution will
always be between the upper
bound of the relaxed solution and
a lower bound of the rounded-
down integer solution.
Branch on the variable with the
solution value with the greatest
fractional part.
C-4
Module C
Integer Programming: The Branch and Bound Method
Figure C-2
Solution subsets x2
1
1,055.56
2
3
x2     5

x2     6

UB = 1,055.56 (x1 = 2.22, x2 = 5.56)
LB = 950 (x1 = 2, x2 = 5)
and seeing which one is the farthest from the rounded-down integer value (i.e., which vari-
able has the greatest fractional part). The .56 portion of 5.56 is the greatest fractional part;
thus, x2 will be the variable that we will “branch” on.
Because x2 must be an integer value in the optimal solution, the following constraints
can be developed.
x2  5
x2  6
In other words, x2 can be 0, 1, 2, 3, 4, 5, or 6, 7, 8, etc., but it cannot be a value between
5 and 6, such as 5.56. These two new constraints represent the two solution subsets for our
solution approach. Each of these constraints will be added to our linear programming
model, which will then be solved normally to determine a relaxed solution. This sequence
of events is shown on the branch and bound diagram in Figure C-2. The solutions at nodes
2 and 3 will be the relaxed solutions obtained by solving our example model with the
appropriate constraints added.
Create two constraints (or subsets)
to eliminate the fractional part of
the solution value
First, the solution at node 2 is found by solving the following model with the constraint
x2  5 added.
maximize Z  $100x1  150x2
subject to
The optimal solution for this model with integer restrictions relaxed (solved using the
computer) is x1  2.5, x2  5, and Z  1,000.
Next, the solution at node 3 is found by solving the model with x2  6 added.
maximize Z  $100x1  150x2
subject to
8,000x1  4,000x2  40,000
15x1  30x2  200
x2  6
x1, x2  0
The optimal solution for this model with integer restrictions relaxed is x1  1.33,
x2  6, and Z  1,033.33.
 
x1, x2  0
 
x2  5
 
15x1  30x2  200
 
8,000x1  4,000x2  40,000
These solutions with x2  5 and x2  6 reflect the partitioning of the original relaxed
model into two subsets formed by the addition of the two constraints. The resulting solu-
tion sets are shown in the graphs in Figure C-3.
The Branch and Bound Method
C-5
Figure C-3
Feasible solution spaces for
nodes 2 and 3
2
6
10
14
18
2
6
10
14
18
x1
x2
x1 = 2.5
x2 = 5
Feasible solution space
x2 = 5
Z = $1,000
Node 2
2
6
10
14
18
2
6
10
14
18
x1
x2
x1 = 1.33
x2 = 6
Feasible solution space
x2 = 6
Z = $1,033
Node 3
Figure C-4
Branch and bound diagram
with upper and lower bounds at
nodes 2 and 3
1
1,055.56
3
1,033
2
1,000
x2     5

x2     6

UB = 1,055.56 (x1 = 2.22, x2 = 5.56)
LB = 950 (x1 = 2, x2 = 5)
UB = 1,033 (x1 = 1.33, x2 = 6)
LB = 950 (x1 = 2, x2 = 5)
UB = 1,000 (x1 = 2.5, x2 = 5)
LB = 950 (x1 = 2, x2 = 5)
Notice that in the node 2 graph in Figure C-3, the solution point x1  2.5, x2  5
results in a maximum Z value of $1,000, which is the upper bound for this node. Next, notice
that in the node 3 graph,the solution point x1  1.33,x2  6 results in a maximum Z value of
$1,033. Thus, $1,033 is the upper bound for node 3. The lower bound at each of these
nodes is the maximum integer solution. Since neither of these relaxed solutions is totally
integer, the lower bound remains $950, the integer solution value already obtained at node
1 for the rounded-down integer solution. The diagram in Figure C-4 reflects the addition
of the upper and lower bounds at each node.
Since we do not have an optimal and feasible integer solution yet, we must continue to
branch (i.e., partition) the model, from either node 2 or node 3. A look at Figure C-4
reveals that if we branch from node 2, the maximum value that can possibly be achieved is
$1,000 (the upper bound). However, if we branch from node 3, a higher maximum value
of $1,033 is possible. Thus, we will branch from node 3. In general, always branch from the
node with the maximum upper bound.
Now the steps for branching previously followed at node 1 are repeated at node 3. First,
the variable that has the value with the greatest fractional part is selected. Because x2 has an
integer value, x1, with a fractional part of .33, is the only variable we can select. Thus, two
new constraints are developed from x1,
C-6
Module C
Integer Programming: The Branch and Bound Method
Figure C-5
Solution subsets for x1
1
1,055.56
3
1,033
2
1,000
5
4
x2     5

x2     6

UB = 1,055.56 (x1 = 2.22, x2 = 5.56)
LB = 950 (x1 = 2, x2 = 5)
UB = 1,033 (x1 = 1.33, x2 = 6)
LB = 950 (x1 = 2, x2 = 5)
UB = 1,000 (x1 = 2.5, x2 = 5)
LB = 950 (x1 = 2, x2 = 5)
x1     1

x1     2

x1  1
x1  2
This process creates the new branch and bound diagram shown in Figure C-5.
Next, the relaxed linear programming model with the new constraints added must be
solved at nodes 4 and 5. (However, do not forget that the model is not the original, but the
original with the constraint previously added, x2  6.) Consider the node 4 model first.
maximum Z  100x1  150x2
subject to
8,000x1  4,000x2  40,000
15x1  30x2  200
x2  6
x1  1
x1, x2  0
The optimal solution for this model with integer restrictions relaxed is x1  1, x2 
6.17, and Z  1,025.
Next, consider the node 5 model.
maximize Z  100x1  150x2
subject to
8,000x1  4,000x2   40,000
15x1  30x2  200
x2  6
x1  2
x1, x2  0
However, there is no feasible solution for this model. Therefore, no solution exists at
node 5, and we have only to evaluate the solution at node 4. The branch and bound dia-
gram reflecting these results is shown in Figure C-6.
The Branch and Bound Method
C-7
Figure C-6
Branch and bound diagram
with upper and lower bounds at
nodes 4 and 5
1
1,055.56
3
1,033
2
1,000
5
∞
4
1,025.50
x2     5

x2     6

UB = 1,055.56 (x1 = 2.22, x2 = 5.56)
LB = 950 (x1 = 2, x2 = 5)
UB = 1,033 (x1 = 1.33, x2 = 6)
LB = 950 (x1 = 2, x2 = 5)
UB = 1,000 (x1 = 2.5, x2 = 5)
LB = 950 (x1 = 2, x2 = 5)
UB = 1,025.50 (x1 = 1, x2 = 6.17)
LB  = 950 (x1 = 2, x2 = 5)
Infeasible
x1     1

x1     2

Figure C-7
Solution subsets for x2
1
1,055.56
3
1,033
2
1,000
5
∞
4
1,025.50
7
6
x2     5

x2     6

x2     6

x2     7

UB = 1,055.56 (x1 = 2.22, x2 = 5.56)
LB = 950 (x1 = 2, x2 = 5)
UB = 1,033 (x1 = 1.33, x2 = 6)
LB = 950 (x1 = 2, x2 = 5)
UB = 1,000 (x1 = 2.5, x2 = 5)
LB = 950 (x1 = 2, x2 = 5)
UB = 1,025.50 (x1 = 1, x2 = 6.17)
LB  = 950 (x1 = 2, x2 = 5)
Infeasible
x1     1

x1     2

The branch and bound diagram in Figure C-6 indicates that we still have not reached an
optimal integer solution; thus, we must repeat the branching steps followed earlier. Since
a solution does not exist at node 5, there is no comparison between the upper bounds at
nodes 4 and 5. Comparing nodes 2 and 4, we must branch from node 4 because it has the
greater upper bound. Next, since x1 has an integer value, x2, with a fractional part of .17, is
selected by default. The two new constraints developed from x2 are
x2  6
x2  7
This creates the new branch and bound diagram in Figure C-7.
The relaxed linear programming model with the new constraints added must be
solved at nodes 6 and 7. Consider the node 6 model first.
C-8
Module C
Integer Programming: The Branch and Bound Method
Figure C-8
The branch and bound 
diagram with optimal solution
at node 6
1
1,055.56
3
1,033
2
1,000
5
∞
4
1,025.50
7
∞
6
1,000
x2     5

x2     6

x2     6

x2     7

UB = 1,055.56 (x1 = 2.22, x2 = 5.56)
LB = 950 (x1 = 2, x2 = 5)
UB = 1,033 (x1 = 1.33, x2 = 6)
LB = 950 (x1 = 2, x2 = 5)
UB = 1,000 (x1 = 2.5, x2 = 5)
LB  = 950 (x1 = 2, x2 = 5)
UB = 1,025.50 (x1 = 1, x2 = 6.17)
LB = 950 (x1 = 2, x2 = 5)
Infeasible
UB = 1,000 (x1 = 1, x2 = 6)
LB = 1,000 (x1 = 1, x2 = 6)
Infeasible
x1     1

x1     2

An optimal integer solution is
reached when a feasible integer
solution is achieved at a node that
has an upper bound greater than
or equal to the upper bound at any
other ending node.
maximize Z  100x1  150x2
subject to
8,000x1  4,000x2  40,000
15x1  30x2  200
x2  6
x1  1
x2  6
x1, x2  0
The optimal solution for this relaxed linear programming model is x1  1, x2  6, and 
Z  1,000.
Next, consider the node 7 model.
maximize Z  100x1  150x2
subject to
8,000x1  4,000x2  40,000
15x1  30x2  200
x2  6
x1  1
x2  7
x1, x2  0
However, the solution to this model is infeasible and no solution exists at node 7. The
branch and bound diagram reflecting these results is shown in Figure C-8. This version of
the branch and bound diagram indicates that the optimal integer solution, x1  1, x2  6,
has been reached at node 6. The value of 1,000 at node 6 is the maximum, or upper bound,
integer value that can be obtained. It is also the recomputed lower bound because it is the
maximum integer solution achieved to this point. Thus, it is not possible to achieve any
higher value by further branching from node 6. A comparison of the node 6 solution with
those at nodes 2, 5, and 7 shows that a better solution is not possible. The upper bound at
node 2 is 1,000, which is the same as that obtained at node 6; thus, node 2 can result in no
improvement. The solutions at nodes 5 and 7 are infeasible (and thus further branching
will result in only infeasible solutions). By the process of elimination, the integer solution at
node 6 is optimal.
In general, the optimal integer solution is reached when a feasible integer solution is
generated at a node and the upper bound at that node is greater than or equal to the upper
bound at any other ending node (i.e., a node at the end of a branch).
In the context of the original example, this solution indicates that if the machine shop
owner purchases one press and six lathes, a daily increase in profit of $1,000 will result.
The steps of the branch and bound method for determining an optimal integer solution
for a maximization model (with  constraints) can be summarized as follows.
1. Find the optimal solution to the linear programming model with the integer restric-
tions relaxed.
2. At node 1 let the relaxed solution be the upper bound and the rounded-down integer
solution be the lower bound.
3. Select the variable with the greatest fractional part for branching. Create two new
constraints for this variable reflecting the partitioned integer values. The result will
be a new  constraint and a new  constraint.
4. Create two new nodes, one for the  constraint and one for the  constraint.
5. Solve the relaxed linear programming model with the new constraint added at each
of these nodes.
6. The relaxed solution is the upper bound at each node, and the existing maximum
integer solution (at any node) is the lower bound.
7. If the process produces a feasible integer solution with the greatest upper bound
value of any ending node, the optimal integer solution has been reached. If a feasible
integer solution does not emerge, branch from the node with the greatest upper
bound.
8. Return to step 3.
For a minimization model, relaxed solutions are rounded up, and upper and lower
bounds are reversed.
Mixed integer linear programming problems can also be solved using the branch and
bound method. The same basic steps that were applied to the total integer model in the
previous section are used for a mixed integer model with only a few differences.
First, at node 1 only those variables with integer restrictions are rounded down to
achieve the lower bound. Second, in determining which variable to branch from, we select
the greatest fractional part from among only those variables that must be integer. All other
steps remain the same. The optimal solution is reached when a feasible solution is gener-
ated at a node that has integer values for those variables requiring integers and that has
reached the maximum upper bound of all ending nodes.
The 0–1 integer model can also be solved using the branch and bound method. First, the
0–1 restrictions for variables must be reflected as model constraints, xj  1. As an example,
consider the following 0–1 integer model for selecting recreational facilities following from
chapter 5 in the text.
A community council must decide which recreation facilities to construct in its com-
munity. Four new recreation facilities have been proposed—a swimming pool, a tennis
The Branch and Bound Method
C-9
The steps of the branch and bound
method.
The branch and bound method
can be used for mixed integer
problems, except only variables
with integer restrictions are
rounded down to achieve the ini-
tial lower bound and only integer
variables are branched on.
Solution of the Mixed 
Integer Model
Solution of the 0–1 
Integer Model
C-10
Module C
Integer Programming: The Branch and Bound Method
center, an athletic field, and a gymnasium. The council wants to construct facilities that will
maximize the expected daily usage by the residents of the community subject to land and
cost limitations. The expected daily usage and cost and land requirements for each facility
follow.
Expected Usage
Land Requirements
Recreation Facility
(people/day)
Cost ($)
(acres)
Swimming pool
300
35,000
4
Tennis center
90
10,000
2
Athletic field
400
25,000
7
Gymnasium
150
90,000
3
The community has a $120,000 construction budget and 12 acres of land. Because the
swimming pool and tennis center must be built on the same part of the land parcel, how-
ever, only one of these two facilities can be constructed. The council wants to know which
of the recreation facilities to construct in order to maximize the expected daily usage. The
model for this problem is formulated as follows.
maximize Z  300x1  90x2  400x3  150x4
subject to
$35,000x1  10,000x2  25,000x3  90,000x4  $120,000 (capital budget)
4x1  2x2  7x3  3x4  12 acres (space available)
x1  x2  1 facility
x1, x2, x3, x4  0 or 1
where
Z  expected daily usage (people per day)
x1  construction of a swimming pool
x2  construction of a tennis center
x3  construction of an athletic field
x4  construction of a gymnasium
In this model, the decision variables can have a solution value of either zero or one. If a
facility is not selected for construction, the decision variable representing it will have a value
of zero. If a facility is selected, its decision variable will have a value of one.
The last constraint, x1  x2  1, reflects the contingency that either the swimming pool
(x1) or the tennis center (x2) can be constructed, but not both. In order for the sum of x1
and x2 to be less than or equal to one, either of the variables can have a value of one, or both
variables can equal zero. This is also referred to as a mutually exclusive constraint.
To apply the branch and bound method, the following four constraints have to be added
to the model in place of the single restriction x1, x2, x3, x4  0 or 1.
x1  1
x2  1
x3  1
x4  1
The only other change in the normal branch and bound method is at step 3. Once the
variable xj with the greatest fractional part has been determined, the two new constraints
The branch and bound method
can be used for 0–1 integer prob-
lems by adding “  1” constraints
for each variable.
Problems
C-11
Problems
developed from this variable are xj  0 and xj  1. These two new constraints will form
the two branches at each node.
Another method for solving 0–1 integer problems is implicit enumeration. In implicit
enumeration, obviously infeasible solutions are eliminated and the remaining solutions are
evaluated (i.e., enumerated) to see which is the best. This approach will be demonstrated
using our original 0–1 example model for selecting a recreational facility (i.e., without the
xj  1 constraints).
The complete enumeration (i.e., the list of all possible solution sets) for this model is as
follows.
1. Consider the following linear programming model
maximize Z  5x1  4x2
In implicit enumeration all fea-
sible solutions are evaluated to see
which is best.
Solution
x1
x2
x3
x4
Feasibility
Z Value
1
0
0
0
0
Feasible
0
2
1
0
0
0
Feasible
300
3
0
1
0
0
Feasible
90
4
0
0
1
0
Feasible
400
5
0
0
0
1
Feasible
150
6
1
1
0
0
Infeasible

7
1
0
1
0
Feasible
700
8
1
0
0
1
Infeasible

9
0
1
1
0
Feasible
490
10
0
1
0
1
Feasible
240
11
0
0
1
1
Feasible
550
12
1
1
1
0
Infeasible

13
1
0
1
1
Infeasible

14
1
1
0
1
Infeasible

15
0
1
1
1
Infeasible

16
1
1
1
1
Infeasible

Solutions 6, 12, 14, and 16 can be immediately eliminated because they violate the third
constraint, x1  x2  1. Solutions 8, 13, and 15 can also be eliminated because they violate
the other two constraints. This leaves eight possible solution sets (assuming that solution
1—i.e., choosing none of the recreational facilities—can be eliminated) for consideration.
After evaluating the objective function value of these eight solutions, we find the best solu-
tion to be 7, with x1  1, x2  0, x3  1, x4  0. Within the context of the example, this
solution indicates that a swimming pool (x1) and an athletic field (x3) should be con-
structed and that these facilities will generate an expected usage of 700 people per day.
The process of eliminating infeasible solutions and then evaluating the feasible solu-
tions to see which is best is the basic principle behind implicit enumeration. However,
implicit enumeration is usually done more systematically, by evaluating solutions with
branching diagrams like those used in the branch and bound method, rather than by sort-
ing through a complete enumeration as in this previous example.
C-12
Module C
Integer Programming: The Branch and Bound Method
subject to
3x1  4x2  10
x1, x2  0 and integer
a. Solve this model using the branch and bound method.
b. Demonstrate the solution partitioning graphically.
2. Solve the following linear programming model using the branch and bound method.
minimize Z  3x1  6x2
subject to
7x1  3x2  40
x1, x2  0 and integer
3. A tailor makes wool tweed sport coats and wool slacks. He is able to get a shipment of 150 square
yards of wool cloth from Scotland each month to make coats and slacks, and he has 200 hours of
his own labor to make them each month. A coat requires 3 square yards of wool and 10 hours to
make, and a pair of pants requires 5 square yards of wool and 4 hours to make. He earns $50 in
profit from each coat he makes and $40 from each pair of slacks. He wants to know how many coats
and slacks to produce to maximize profit.
a. Formulate an integer linear programming model for this problem.
b. Determine the integer solution to this problem using the branch and bound method. Compare
this solution with the solution without integer restrictions and indicate if the rounded-down
solution would have been optimal.
4. A jeweler and her apprentice make silver pins and necklaces by hand. Each week they have 80 hours
of labor and 36 ounces of silver available. It requires 8 hours of labor and 2 ounces of silver to make a
pin, and 10 hours of labor and 6 ounces of silver to make a necklace. Each pin also contains a small
gem of some kind. The demand for pins is no more than six per week. A pin earns the jeweler $400
in profit, and a necklace earns $100. The jeweler wants to know how many of each item to make
each week in order to maximize profit.
a. Formulate an integer programming model for this problem.
b. Solve this model using the branch and bound method. Compare this solution with the solu-
tion without integer restrictions and indicate if the rounded-down solution would have been
optimal.
5. A glassblower makes glass decanters and glass trays on a weekly basis. Each item requires 1 pound
of glass, and the glassblower has 15 pounds of glass available every week. A glass decanter requires
4 hours of labor, a glass tray requires only 1 hour of labor, and the glassblower works 25 hours
a week. The profit from a decanter is $50, and the profit from a tray is $10. The glassblower wants
to determine the total number of decanters (x1) and trays (x2) that he needs to produce in order to
maximize his profit.
a. Formulate an integer programming model for this problem.
b. Solve this model using the branch and bound method.
c. Demonstrate the solution partitioning graphically.
6. The Livewright Medical Supplies Company has a total of 12 salespeople it wants to assign to
three regions—the South, the East, and the Midwest. A salesperson in the South earns $600 in
profit per month for the company, a salesperson in the East earns $540, and a salesperson in the
Midwest earns $375. The southern region can have a maximum assignment of 5 salespeople.
The company has a total of $750 per day available for expenses for all 12 salespeople. A sales-
Problems
C-13
person in the South has average expenses of $80 per day, a salesperson in the East has average
expenses of $70 per day, and a salesperson in the Midwest has average daily expenses of $50. The
company wants to determine the number of salespeople to assign to each region to maximize
profit.
a. Formulate an integer programming model for this problem.
b. Solve this model using the branch and bound method.
7. Helen Holmes makes pottery by hand in her basement. She has 20 hours available each week to
make bowls and vases. A bowl requires 3 hours of labor, and a vase requires 2 hours of labor. It
requires 2 pounds of special clay to make a bowl and 5 pounds to produce a vase; she is able to
acquire 35 pounds of clay per week. She sells her bowls for $50 and her vases for $40. She wants to
know how many of each item to make each week in order to maximize her revenue.
a. Formulate an integer programming model for this problem.
b. Solve this model using the branch and bound method. Compare this solution with the solu-
tion with integer restrictions and indicate if the rounded-down solution would have been
optimal.
8. Lauren Moore has sold her business for $500,000 and wants to invest in condominium units
(which she intends to rent) and land (which she will lease to a farmer). She estimates that she will
receive an annual return of $8,000 for each condominium and $6,000 for each acre of land. A con-
dominium unit costs $70,000, and land is $30,000 per acre. A condominium will cost her $1,000
per unit and an acre of land $2,000 for maintenance and upkeep. Lauren wants to know how much
to invest in condominiums and land in order to maximize her annual return.
a. Formulate a mixed integer programming model for this problem.
b. Solve this model using the branch and bound method.
9. The owner of the Consolidated Machine Shop has $10,000 available to purchase a lathe, a press,
a grinder, or some combination thereof. The following 0–1 integer linear programming model has
been developed for determining which of the three machines (lathe, x1; press, x2; grinder, x3)
should be purchased in order to maximize the annual profit.
maximize Z  1,000x1  700x2  800x3 (profit, $)
subject to
5,000x1  6,000x2  4,000x3  10,000 (cost, $)
x1, x2, x3  0 or 1
Solve this model using the branch and bound method.
10. Solve the following mixed integer linear programming model using the branch and bound method.
maximize Z  5x1  6x2  4x3
subject to
5x1  3x2  6x3  20
x1  3x2  12
x1, x3  0
x2  0 and integer
11. Solve problem 9 using the implicit enumeration method.
12. Consider the following linear programming model.
maximize Z  20x1  30x2  10x3  40x4
C-14
Module C
Integer Programming: The Branch and Bound Method
subject to
2x1  4x2  3x3  7x4  10
10x1  7x2  20x3  15x4  40
x1  10x2  x3  10
x1, x2, x3, x4 = 0 or 1
a. Solve this problem using the implicit enumeration method.
b. What difficulties would be encountered with the implicit enumeration method if this problem
were expanded to contain five or more variables and more constraints?

Journal of Physics: Conference
Series
     
PAPER • OPEN ACCESS
Catalyst to oil mass ratio optimization on fluid
catalytic cracking process in green gasoline
production
To cite this article: E Styani et al 2020 J. Phys.: Conf. Ser. 1450 012012
 
View the article online for updates and enhancements.
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iCAST-ES 2019
Journal of Physics: Conference Series
1450 (2020) 012012
IOP Publishing
doi:10.1088/1742-6596/1450/1/012012
1
 
 
 
 
 
 
Catalyst to oil mass ratio optimization on fluid catalytic 
cracking process in green gasoline production 
E Styani1, Foliatini1, R Ekananda2, S R Tandaju1  
1 Polytechnic of AKA Bogor, Industrial Human Resource Development Board, West 
Java, Indonesia  
2  PT Pertamina Research and Technology Center Pulogadung, East Jakarta, Indonesia 
 
E-mail: erna-styani@kemenperin.go.id  
Abstract. Palm oil has potential as raw material for green gasoline as renewable energy source. 
One of the most important variables to support the achievement optimum operating condition in 
the Fluid Catalytic Cracking (FCC) process was the mass ratio of catalyst to oil (c/o). The FCC 
process simulation was carried out by using an Advance Cracking Evaluation (ACE) unit at mass 
ratio c/o (4.00, 5.00, 5.56, 6.00, 6.52) w/w ASTM D3907. The quality test of the FCC product 
were distribution of boiling point by using Refinery Gas Analyzer (RGA) Chromatography 
ASTM D2504 and using high temperature distillation simulating gas chromatography ASTM 
D7169, octane number by using Detailed Hydrocarbon Analysis (DHA) Chromatography ASTM 
D6730 and coke formation by using CO Analyzer ASTM D6316. The study shown that the most 
optimum c/o ratio in FCC process was at ratio 6.00 w/w with the amount of dry gas product 2.48 
percent w/w, LPG 27.36 percent w/w, gasoline 41.81 percent w/w, LCO 11.54 percent w/w, 
HCO 0.00 percent w/w, bottom 3.71 w/w, and coke 12.99 percent w/w with octane number was 
84.693. It can be concluded that FCC process of the palm oil mixture feed at this ratio has better 
quality than PT Pertamina’s FCC feed. 
1. Introduction 
The need of fuel oil in Indonesia reached 1.3 million barrels per day (bpd) in 2013, so it was necessary 
to import 600 thousand bpd of fuel oil with a value of more than 14 thousand trillion rupiahs per day 
[1]. These problems prompted the government to stipulate Regulation of the President of the Republic 
of Indonesia Number 5 Year 2006 concerning national energy policy to develop alternative energy 
sources as a substitute for fuel oil. This is the country's effort in developing renewable fuels to reduce 
crude oil imports. One alternative material that has the potential to substitute for petroleum is vegetable 
oil. 
Palm oil is one of the vegetable oils that has the potential to be used as an alternative fuel in Indonesia. 
Indonesia is one of the largest palm oil producing countries in the world. Indonesia's total crude palm 
oil production reached 21.0 million tons in 2010 and continued to increase to 22.2 million tons in 2011 
[2]. In addition, the use of palm oil as an alternative fuel is more environmentally friendly, which is free 
of sulphur and nitrogen. Palm oil has a long hydrocarbon chain that has the potential to be used as biofuel 
[3]. This background encourages the fuel industry to use palm oil as a feed mixture in the FCC process 
in the production of green gasoline. Before large-scale production is held, a small-scale trial is needed 
first, to find out the right FCC operating conditions in an effort to obtain the most optimum green 
iCAST-ES 2019
Journal of Physics: Conference Series
1450 (2020) 012012
IOP Publishing
doi:10.1088/1742-6596/1450/1/012012
2
 
 
 
 
 
 
gasoline production. One of the most important variables that support the achievement of the most 
optimum operating conditions in the FCC process is the catalyst, so in this experiment the optimization 
of the FCC process was carried out by varying the catalyst to oil (c/o) mass ratio. The mass ratio (c/o) 
is the ratio of the weight of the catalyst circulated to the reactor with the weight of the feed entering the 
reactor. Generally, this c/o ratio can vary between 3-7 (w/w) [4]. In the FCC process, this c/o ratio affects 
cracking reactions, coke formation, heat formation and whether the heat balance of the reactor and its 
regenerator is achieved. Excessive cracking reaction will cause the formation of products dominated by 
very mild fractions, such as dry gas, which is excessive. However, if the cracking reaction condition is 
not achieved, it can cause the formation of products dominated by heavy fractions, such as HCO and 
bottom [4]. These various effects indicate the need for optimization of the mass ratio c/o in the FCC 
process in the production of green gasoline. 
This study aims to find out the optimum c/o mass ratio in the FCC process in the production of green 
gasoline, which is seen from the amount of dry gas products, liquid petroleum gas (LPG), gasoline, light 
cycle oil (LCO), heavy cycle oil (HCO) , bottom and coke and the value of the octane number. In 
addition, to find out the quality of FCC products from palm oil mixed feeds, the product yields are 
compared with products produced from PT Pertamina's FCC feed. 
2. Methodology 
This study consists of five stages, namely the characterization of palm oil (specific gravity ASTM D1298 
[5], carbon residues ASTM D4530 [6], pour points ASTM D97 [7],  and viscosity D445 [8]),  feed 
preparation, catalyst preparation, the FCC process simulation stage, and the FCC product quality test 
stage. The Scheme of the experiment can be seen in Figure 1. 
 
 
Figure 1. The scheme of the experiment. 
The FCC process simulation stage is carried out at the mass ratio c/o (4.00, 5.00, 5.56, 6.00, 6.52) 
w/w. The simulation of the FCC process was carried out by using an Advance Cracking Evaluation 
(ACE) unit, which refers to ASTM D3907 [9]. The quality test of the FCC product includes the 
distribution of boiling points, octane numbers and coke formation. The boiling point distribution test 
was performed by using Refinery Gas Analyzer (RGA) chromatography to determine the amount of dry 
gas, LPG and light gasoline products that refer to ASTM D2504 [10] and using high temperature 
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Journal of Physics: Conference Series
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distillation simulating gas chromatography to determine the amount of gasoline products, LCO, HCO 
and bottom which refers to ASTM D7169 [11]. Boiling point distribution can provide information about 
the estimated mass percent of the product based on its boiling point. The higher amount of gasoline and 
LPG products, followed by the lower number of dry gas products, LCO, HCO and bottom can improve 
the quality of FCC products. The octane number test was carried out by using Detailed Hydrocarbon 
Analysis (DHA) chromatography which refers to ASTM D6730 [12]. Octane numbers were numbers 
that indicate the ability of the fuel to resist spontaneous combustion during compression, before the 
spark. Easy spontaneous combustion occurs in fuel during compression, will cause knocks that 
potentially damage the vehicle engine. The higher-octane number, the better quality of the fuel product 
[13]. Testing for the formation of coke products was carried out by using CO2 analyser that refers to 
ASTM D6316 [14]. This test was done to determine the amount of coke formed from the FCC process. 
The FCC process requires coke products, to meet the heat balance of the reactor and regenerator in the 
cracking process. In this optimization it was expected to know the c/o ratio with the best criteria, namely 
the formation of lower coke products, but not far from the value of the formation of coke from 
commercial feed used in PT Pertamina's FCC unit.  
3. Results and discussion 
3.1. Characterization of palm oil 
In this study the optimization of the mass ratio c/o in the FCC process in the production of green 
gasoline was simulated by using the ACE unit. Before the optimization process was carried out, palm 
oil was characterized first. The results of testing the characteristics of palm oil can be seen in Table 1. 
 
Table 1. The characteristics of palm oil test result. 
Test 
Parameters 
Standards 
Palm Oil 
VGO 
Commercial 
(JECHURA, 
2014) 
Unit 
Expected Results 
Obtained 
Results 
Specific 
gravity 
ATSM D1298 
0.91 
0.93 
 
< Commercial VGO 
As expected 
Carbon 
residue 
ASTM D4530 
0.1 
0.26 
% (w/w) < Commercial VGO 
As expected 
Viscosity 
ASTM D445 
40 
48 
cSt 
< Commercial VGO 
As expected 
Pour Point 
ASTM D97 
9.0 
48.0 
0C 
< Commercial VGO 
As expected 
Through various parameters that have been tested, it can be seen from Table 1.  that palm oil has the 
dominant components of paraffin or isoparaffin, compared to olefin. The paraffin and isoparafin 
components are needed to improve the characteristics and quality of the feed [15]. Based on Table 1. it 
appears that the specific gravity, Carbon residue, viscosity, and pour point value of the palm oil have 
expected value.  The specific gravity of the palm oil sample was 0.91. Generally atmospheric residues 
such as VGO, have specific gravity values reaching 0.93. These results indicate that by adding palm oil 
to VGO, the specific gravity value is estimated to decrease, meaning that by adding palm oil to VGO 
can improve product quality. Low specific gravity shows high paraffinity. Conversely, if the specific 
gravity value is high, the olefinity oil is more dominant [16]. While Carbon residue from palm oil 
samples was 0.10% w/w. Generally, atmospheric residues such as VGO, have a carbon residue amount 
of 0.26% w/w. These results indicate that by adding palm oil to VGO, the amount of carbon residue was 
expected to decrease, meaning that by adding palm oil to VGO can improve product quality. Testing of 
carbon residues is used to give an indication of the tendency for the formation of coke from oil when 
processed in the FCC process [4]. The viscosity of the palm oil sample was 40 cSt. Generally, the 
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atmospheric residues, such as VGO, have viscosity values up to 48 cSt. These results indicate that by 
adding palm oil to VGO, the viscosity value was estimated to decrease, meaning that by adding palm 
oil to VGO can improve product quality. The pour point of palm oil sample was 9.0 
C. Atmospheric 
residues such as VGO, have a pour point value of up to 48.0 C. These results indicated that by adding 
palm oil to VGO, the pour point value is expected to decrease, meaning that adding palm oil to VGO 
can improve product quality. Through some testing of these parameters, it can be seen that palm oil has 
the dominant components of paraffin or isoparaffin, compared to olefin. The paraffin and isoparafin 
components were needed to improve the characteristics and quality of the feed. 
3.2. FCC process stimulation 
The simulation stage of the FCC process using the ACE unit, referring to ASTM D3907 [9], is carried 
out at the c / o ratio used in this experiment (4.00, 5.00, 5.56, 6.00, 6.52) w/w. The flow diagram of the 
FCC process simulation on the ACE unit can be seen in Figure 2. 
 
Figure 2. The scheme of the FCC process simulation at ACE unit. 
 
In the FCC process, coke products are formed which are attached to the catalyst [17]. Coke is 
removed by burning the catalyst at 715C, the process is called the regeneration process. Catalysts with 
high carbon content are burned with air, as a source of oxygen, becoming active catalysts with high 
oxygen content. The regenerated catalyst is accommodated in a spent catalyst bottle. Gas products from 
the regeneration process will flow to the catalytic converter and analysed the formation of its coke 
products by using a CO2 analyser.  
 
 
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3.3. Optimization of Catalyst to oil mass ratio on FCC process for green gasoline production 
The most optimum c / o mass ratio in the FCC process in green gasoline production can be seen from 
several parameters, namely the amount of dry gas products, liquid petroleum gas (LPG), gasoline, light 
cycle oil (LCO), heavy cycle oil (HCO), bottom and coke and the octane number of the product. These 
parameters can be identified through testing the boiling point distribution, analysis of octane numbers 
and analysis of coke formation. In addition, to find out the quality of FCC products from palm oil mixed 
feeds, the product yields are compared with products produced from commercial feeds used in PT 
Pertamina's FCC unit. 
Boiling point distribution testing for FCC products was carried out by a distillation simulation 
method by gas chromatography. This test refers to ASTM D7169 [11]. The data of product conversion 
results based on their fractions from testing the boiling point distribution with gas chromatography 
simulation of high temperature distillation can be seen in Table 2. 
Table 2. Optimization results of catalyst to oil ratio in FCC process in green gasoline production. 
Fraction FCC 
Commercial 
Product 
Catalyst to Oil (c/o) (w/w) 
Pertamina’s 
VGO 
Expected 
Result 
Test Result 
4.02 
5.00 
5.56 
6.00 
6.52 
Dry Gas 
3.72 
2.28 
2.40 
2.40 
2.48 
2.40 
2.48 
<Pertamina’s 
VGO 
Not as expected 
LPG 
19.11 
26.94 27.59 27.20 27.36 27.21 
22.92 
>Pertamina’s 
VGO 
As expected 
Gasoline 
40.66 
41.76 41.77 41.35 41.81 41.17 
40.74 
>Pertamina’s 
VGO 
As expected 
LCO 
21.89 
14.50 12.70 12.61 11.54 11.68 
13.68 
<Pertamina’s 
VGO 
As expected 
HCO + 
Bottom 
5.22 
4.83 
4.25 
4.21 
3.71 
3.63 
7.05 
<Pertamina’s 
VGO 
As expected 
Cokes 
- 
9.70 11.29 12.25 12.99 13.92 
13.55 
<Pertamina’s 
VGO 
Only c/o 6 w/w 
almost expected 
3.4.  FCC quality product test 
Octane numbers are numbers that show how much pressure can be applied before the gasoline ignites 
spontaneously. In engines, a mixture of air and gasoline (in the form of gas) pressed by the piston up to 
a very small volume and then burned by the spark produced by the spark plug. This amount of pressure 
can cause the mixture of air and gasoline to ignite spontaneously, before sparks from the spark plug 
come out. The ability of the fuel to withstand spontaneous combustion during compression, prior to 
sparks, gives it a high-octane number [13]. If this gas mixture ignites due to high pressure and is not due 
to sparks from the spark plug, knocking will occur inside the engine. Knocking is an event when the 
temperature and pressure of the fuel mixture in the cylinder reaches the temperature that causes the 
explosion (explosion), not a perfect combustion process. This knocking can cause the engine to become 
faster damaged, so it is expected that the FCC process with the appropriate c/o ratio can increase the 
octane number [18]. The FCC product from the VGO mixed feed with palm oil which produces the 
highest-octane number is at the c/o ratio of 6.56, which is 85.559 can be seen in Figure 3. 
 
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Figure 3. Relation between c/o ratio and octane number. 
 
The octane number and PIONA test result by using DHA gas chromatography can be seen in  
Table 3. 
 
Table 3. Octane numbers and PIONA elements test result of palm oil and VGO blend product. 
Elements 
Octane Number and PIONA Elements 
catalyst to oil (c/o)(w/w) 
Pertamina’s  
VGO 
Expected 
Result 
Test 
Result 
4.02 
5.00 
5.56 
6.00 
6.52 
Paraffin 
2.848 
2.797 
2.744 
2.146 
2.547 
0.930 >Pertamina’s 
VGO  
As 
expected 
Iso paraffin 
17.101 
17.292 
16.761 
17.877 
17.760 
16.580 >Pertamina’s 
VGO  
As 
expected 
Olefin 
9.903 
10.443 
10.042 
10.786 
8.515 
15.270 <Pertamina’s 
VGO  
As 
expected 
Naphthene  
3.491 
3.625 
3.819 
3.799 
3.806 
2.240 >Pertamina’s 
VGO  
As 
expected 
Aromatic 
47.156 
47.011 
47.567 
50.085 
52.931 
50.610 >Pertamina’s 
VGO  
Not as 
expected 
 
Octane 
Number 
80.499 
81.168 
80.934 
84.693 
85.559 
85.630 >Pertamina’s 
VGO  
Not as 
expected 
 
Table 3 showed that the addition of palm oil can increase the content of paraffin and isoparaffin, and 
reduce the content of olefins. This change has a good effect on product quality. The addition of paraffin 
and isoparafin can increase the crackability of the feed. This is because compared to other types of 
hydrocarbons such as olefins, naphthenic and aromatic, paraffin and isoparafin are more easily cracked. 
Another advantage is that paraffin and isoparaffin generally produce large quantities of gasoline 
products and small amounts of fuel gas products [4]. 
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Formation of coke generally comes from the residue of the feed, that is, from the heavy fractions of 
the feed. Formation of coke in the catalytic cracking process usually occurs constantly. The FCC 
produces enough coke products to be fulfilled heat balance of the reactor and its regenerator [19]. 
Combustion coke in the regenerator can release heat to be supplied to the reactor, this heat is needed in 
the cracking process. Coke is needed in the commercial FCC process only in small quantities. Formation 
of excessive coke will reduce the activity of the catalyst, it can cause cracking reactions and decreased 
product conversion [4]. In this optimization it is expected to know the ratio of c/o the most fulfilling 
criterion is the formation of lower coke products, but not far from the value of coke formation from 
commercial feeds used in PT Pertamina's FCC unit. The most fulfilling criterion is the formation of 
lower coke products, but not far from the value of coke formation from commercial feeds used in PT 
Pertamina's FCC unit. The cracking process of the VGO mixed with palm oil produces coke which 
increases with increasing c/o ratio as shown in Figure 4. 
 
 
Figure 4. Relations between the ratio of c/o with total coke. 
 
Based on various parameters that have been tested, it can be concluded that the ratio c/o 6.00 w/w is 
the most optimum ratio in the optimization of the FCC process from the production of green gasoline 
with this ACE unit. Table 2 and 3 shown that the cracking process with  c/o ratio of 6.00 w/w had the 
most amount of gasoline products and the most significant decrease in the amount of LCO products 
when compared to the LCO products at other c/o ratios, products from standard VGO and FCC products 
commercial. In addition, the product at a ratio c/o 6.00 w/w  has a high number of LPG products and a 
high-octane number compared to products from other c/o ratios. In this experiment it is also expected 
that the number of coke products formed is lower, but not too far from the VGO standard that has been 
used at the refinery, it can be seen in Figure 10, that the number of coke at the c/o ratio of 6.00 b / b 
meets these criteria. That is because the presence of coke products is needed to maintain the equilibrium 
of the reactor and regenerator in the cracking process at commercial FCC refineries [4]. 
4. Conclusions 
The  most optimum c/o ratio in the FCC process in the production of green gasoline that have been 
studied was at  ratio of 6.00 w/w  with the amount of dry gas product 2.48% w/w, LPG 27.36% w/w, 
gasoline 41.81% w/w, LCO 11.54% w/ w, HCO 0.00% w/w, bottom 3.71% w/w, and coke 12.99% w/w 
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and octane number 84.693. In addition, the FCC process of the palm oil mix feed at this ratio has better 
quality than products from PT Pertamina's FCC feed, which was indicated by the production of higher 
amounts of gasoline and LPG products, also lower LCO products, bottom and coke products. Based on 
this result study it can be concluded that the c/o ratio of 6.00 w/w can be used as mixture feed for green 
gasoline production as renewable energy sources that environmentally friendly. 
5. References 
[1] 
Direktorat Jendral Minyak dan Gas Bumi 2015 Rencana Strategis 2015-2019 (Jakarta: 
Kementrian Energi & Sumber Daya Mineral) 
[2] 
Gegner J 2013 Tribology-Fundamentals and Advancemen  in Tech (Germany: Hamburg) 
[3] 
Nugroho, Anindita P N, Fitriyanto, Dwi and Roesyadi A 2014 Jurnal Teknik POMITS 3 117 
[4] 
Sadeghbeigi R 2000 Fluid Catalytic Cracking Handbook (United States: Butterworth-
Heinemann) p 182-205 
[5] 
American Society for Testing and Materials 2017 Standard Test Method for Density, Relative 
Density, or API Gravity of Crude Petroleum and Liquid Petroleum Products by Hydrometer 
Method (United States: ASTM International) p 1-2 
[6] 
American Society for Testing and Materials 2007 D4530 Standard Test Method for 
Determination of Carbon Residue-Micro Method (United States: ASTM International) p 1-2 
[7] 
American Society for Testing and Materials 2005 D97 Standard Test Method for Pour Point of 
Petroleum Product (United Stated: ASTM International) p 1-2 
[8] 
American Society for Testing and Materials 2017 D445 Standard Test Method for Kinematic 
Viscosity of Transparent and Opaque Liquids and Calculation of Dynamic Viscosity (United 
States: ASTM International) p 1-2 
[9] 
American Society for Testing and Materials 2008 D3907 Standard Test Method for Testing Fluid 
Catalytic Cracking (FCC) Catalysts by Microactivity Test (United States: ASTM 
International) p 1-5 
[10] American Society for Testing and Materials 2004 D2504 Standard Test Method for 
Noncondensable Gases in C2 and Lighter Hydrocarbon Products (United States: Gas 
Chromatography ASTM International) p 1–2 
[11] American Society for Testing and Materials 2005 D7169 Standard Test Method for Boiling Point 
Distribution of Samples with Residues Such as Crude Oils and Atmospheric and Vacuum 
Residues by High Temperature Gas Chromatography (United States: ASTM International) p 
1-2 
[12] American Society for Testing and Materials 2006 D6730 Standard Test Method for 
Determination of Individual Components in Spark Ignition Engine Fuels by 100–Meter 
Capillary (with Precolumn) High Resolution Gas Chromatography (United States: ASTM 
International) p 1–2 
[13] Fahim M A, Al-Sahhaf T A and Elkilani A S 2010 Fundamentals of Petroleum Refining 
(Amsterdam: Elsevier) p 26, 35-36, 200-202 
[14] American Society for Testing and Materials 2016 D6316 Standard Test Method for 
Determination of Total, Combustible and Carbonate Carbon in Solid Residues from Coal and 
Coke (United States: ASTM International)  
[15] Jechura J 2014 Refinery Feedstocks & Products-Properties & Spesification (United States: 
Colorado School of Mines) p 21-22 
[16] Wiyantoko B 2016 Kimia Petroleum (Indonesia: Yogyakarta) p 5-10 
[17] Ramachandran R 2006 Data Analysis, Modelling and Control Performance Enhancement of an 
Industrial Fluid Catalytic Cracking Unit Chemical Engineering Science vol 62 (Amsterdam: 
Elsevier) p 1958-1973 
[18]  Scherzer J 1990 Octane – Enhancing Zeolite FCC Catalyst vol 42 (New York: Marcel Dekker) 
[19]  Occelli M L 2007 Fluid Catalytic Cracking (Amsterdam: Elsevier B.V) p 2-3, 7-15, 221-225 
 
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Acknowledgments 
This research was supported by Polytechnic of AKA Bogor, West Java and PT Pertamina Research & 
Technology Center Pulogadung, East Jakarta, Indonesia.

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Energy Sources, Part A: Recovery, Utilization, and
Environmental Effects
ISSN: 1556-7036 (Print) 1556-7230 (Online) Journal homepage: www.tandfonline.com/journals/ueso20
The Development of Techniques for the
Optimization of Water-flooding Processes in
Petroleum Reservoirs Using a Genetic Algorithm
and Surrogate Modeling Approach
M. Haghighat Sefat, K. Salahshoor, M. Jamialahmadi & H. Vahdani
To cite this article: M. Haghighat Sefat, K. Salahshoor, M. Jamialahmadi & H. Vahdani
(2014) The Development of Techniques for the Optimization of Water-flooding Processes in
Petroleum Reservoirs Using a Genetic Algorithm and Surrogate Modeling Approach, Energy
Sources, Part A: Recovery, Utilization, and Environmental Effects, 36:11, 1175-1185, DOI:
10.1080/15567036.2010.538803
To link to this article:  https://doi.org/10.1080/15567036.2010.538803
Published online: 04 Apr 2014.
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Energy Sources, Part A, 36:1175–1185, 2014
Copyright © Taylor & Francis Group, LLC
ISSN: 1556-7036 print/1556-7230 online
DOI: 10.1080/15567036.2010.538803
The Development of Techniques for the Optimization of
Water-ﬂooding Processes in Petroleum Reservoirs Using
a Genetic Algorithm and Surrogate Modeling Approach
M. Haghighat Sefat,1 K. Salahshoor,2 M. Jamialahmadi,1 and H. Vahdani3
1Petroleum Department, Petroleum University of Technology, Ahwaz, Iran
2Petroleum University of Technology, Tehran, Iran
3Iranian Central Oil Fields Company, Tehran, Iran
Recent progresses in computer science and parallel-processing have opened new frontiers in reservoir
simulation applications. New powerful computers can run full ﬁeld reservoir models faster and
with higher accuracy, making reservoir simulator-based optimization feasible. In this study, genetic
algorithm is used to estimate the optimal values for design variables to maximize the net present
value in a water-ﬂooding project. Surrogate-based optimization has shown promising results in all
ﬁelds of science. In this work, multiple artiﬁcial neural network-based surrogate models, having the
capability of on-line recursive adaptation, are presented for optimization purposes. Several genetic
algorithm-based approaches have been developed to execute the necessary optimization tasks. A set
of simulation test studies are conducted on a synthetic reservoir model to evaluate comparatively the
performance of different approaches.
Keywords: artiﬁcial intelligence, artiﬁcial neural network, genetic algorithm, net present value, online
adaptive artiﬁcial neural network, optimization, proxy model, reservoir simulation, surrogate model
1.
INTRODUCTION
Water ﬂooding is the most common secondary recovery method and responsible for high rate
of oil production within the U.S. and Canada. The most important factors which make water
ﬂooding popular are availability of water, ease of the injection process due to hydrostatic head
of water, efﬁciency in displacing oil and relatively simple equipment required for the injection
process (Forrest, 1993).
Prediction shows that due to economic and population growth energy demand in 2030 will be
approximately 35% higher than 2005 (ExxonMobil, 2008). Oil and natural gas provided 60% of
global energy demand in 2005 and prediction shows that they will remain as signiﬁcant contributors
Address correspondence to Morteza Haghighat Sefat, Petroleum Department, Petroleum University of Technology,
Ahwaz, Iran. E-mail: morteza.haghighat@gmail.com
Color versions of one or more of the ﬁgures in the article can be found online at www.tandfonline.com/ueso.
1175
1176
M. HAGHIGHAT SEFAT ET AL.
to world energy in the future decades. Despite this increasing demand, ﬁelds are getting mature
around the world and oil production is declining in many of them. The number of discoveries of
the conventional ﬁelds is decreasing and unconventional ﬁelds are not as easy for developing and
production. At this condition, optimum management of the ﬁelds to increase oil production and/or
reduce production of unwanted ﬂuid becomes crucial. Close-loop reservoir management (Jansen
et al., 2005, 2009) is proposed to address this problem. In closed-loop reservoir management, the
cycle is consisting of two major parts which are, model updating and production optimization.
These two parts are running recursively to ensure optimum operation of the reservoir. The focus
of this paper is on the production optimization. The goal in the production optimization is to
improve production by increasing an objective function which can be cumulative oil production
or net present value (NPV). This objective is achieved by manipulating control parameters of the
injection and production wells which can be rates or bottom-hole pressures (BHPs). A numerically
simulated reservoir model evaluates the objective function having control variables as inputs.
As a result there would be a highly non-linear relationship between the control variable and
the objective function resulting in a challenging optimization problem. Moreover, usually there
are constraints deﬁned by facilities and ﬁeld operation. Thus, a high-dimensional constrained
optimization problem need to be solved (Isebor, 2009).
Numerically simulated reservoir model are usually used to predict the future performance of
the reservoir under deﬁned well control conditions. Although these models are not perfect, they
are considering the maximum information available and under closed-loop reservoir management
assumption model will be updated regularly to reduce the prediction error. Optimization algorithms
need to run these numerical models hundreds or thousands of time searching for the scenario
with the maximum value of the objective function (Guyaguler et al., 2000). This process be-
comes impractical in large and computationally demanding numerical models. The surrogate-based
optimization is an approach to make these computationally demanding optimization problems
practical. This approach reduces the computation time by making an alternative fast model
(surrogates) and using this fast model instead of the actual objective function (Zerpa et al., 2005).
The surrogate-based optimization has been successfully used in several computationally expensive
optimization in aerospace (Giunta et al., 1997; Balabanov et al., 1998), automotive (Craig et al.,
2002; Kurtaran et al., 2002) and oil industries (Queipo et al., 2002, Zerpa et al., 2005).
In this study, a water ﬂooding project is simulated using an Eclipse-100 reservoir simulator.
Genetic algorithm (GA) is used to search and locate the optimum set of parameters that will
maximize the net present value (NPV) of the water ﬂooding project. In order to reduce the
computational cost of running reservoir simulator, multiple surrogates are developed to work as
fast substitutes for the actual reservoir simulator. Online artiﬁcial neural networks (ANNs) are
used for developing surrogate models where they are adapted recursively during optimization
process to improve their prediction performances. Several optimization techniques are developed,
validated and compared.
2.
THEORETICAL BACKGROUND
2.1.
Gradient-free Optimization Methods
The gradient-free methods do not require explicit gradient information of the objective function
versus control variables. They optimize the objective function using just the information available
by function evaluation which will be simulated reservoir model or surrogate model plus economic
model. The gradient-free methods can be classiﬁed into deterministic (e.g., simplex, polytope)
and stochastic (e.g., Genetic Algorithm (GA), Particle SWARM optimization (PSO), simulated
annealing (SA)) (Isebor, 2009). GA is used in this study.
OPTIMIZATION OF WATER-FLOODING PROCESSES
1177
2.2.
General Description of GA
Genetic algorithm (GA) is a gradient-free, stochastic and heuristic optimization algorithm based
on the principal of natural evolution and genetic recombination. The search technique is based
on the survival of the ﬁttest where information is exchanged between best performing individuals
(i.e. cross-over). An occasional alteration of the ﬁtted solution (i.e. mutation) helps to explore
the search space and avoid trapping in local optima (Mitchell, 1996). More information about the
GA application is available in Abukhamsin, 2009. GA is an appropriate algorithm for production
optimization problem due to the following reasons:
 Global optimization, GA is capable of ﬁnding a global solution for the optimization problem
after enough number of iterations
 Parallel processing, the population based nature of the algorithm makes it suitable for parallel
processing and consequently faster optimization process
 Large number of control variables, GA is efﬁcient in handling large number of control
variables happening during production optimization process
 Complex objective function, GA is a suitable choice when the objective function is complex,
non-linear, discontinuous and gradients are not available (Abukhamsin, 2009).
2.3.
Surrogate Model
Numerical simulation of the complicated real world phenomena usually is computationally de-
manding which can take hours or even days to complete. Design optimization, sensitivity study,
design exploration and what-is analysis become almost impossible when the objective function is
computationally demanding as they need thousands or millions of simulation evaluations. The idea
of surrogate modeling is to make a cheap-to-evaluate alternative of the computationally demanding
objective function which can emulate the response of the actual objective function with reasonable
accuracy. Surrogate modeling is looking at the objective function as a black-box and trying to
mimic its operation by investigating input-output relationship (Forrester et al., 2008).
Considering F.x/ as the objective function deﬁned by a k-vector of control variables x 2 D 2
RK (D is the search space). Under general assumption of not continuous objective function, the
only information available for F is discrete observations, indicating as follows:
xi ! yi D F.xi/ji D 1; 2; : : :; n:
(1)
These data are expensive to generate. The goal is to reduce the number of data points required and
efﬁciently use the limited set of samples available to construct a cheap alternative of the actual
objective function with reasonable accuracy (Forrester et al., 2008).
3.
METHODOLOGY
Binary GA is suited to the optimization problem formulated in this study. Three approaches can
be considered for this purpose using an actual reservoir model or/and a surrogate model.
1. GA on an actual reservoir model: GA is applied directly to an actual reservoir model. For
this purpose, reservoir simulator should be run to evaluate each objective function. This
is the basic method of optimization which is used for validation of the developed GA in
ﬁnding optimum solutions. Optimum well location can be found using this approach on the
basis of an actual reservoir model.
1178
M. HAGHIGHAT SEFAT ET AL.
TABLE 1
GA and ANN Parameters
GA Parameters
ANN Parameters
Population size
7
Number of layers
2
Data structure
Integer
Input nodes
1
Crossover probability
0.6
Hidden nodes
4
Mutation probability
0.6
Output nodes
1
Selection method
Ranked based
Learning rate
0.9
Fitness power
2
Training iterations
1,000
Number of elitists
1
2. GA on a surrogate model: In this approach, a surrogate model is ﬁrst developed using
online-adaptive ANNs with intelligent sample selection algorithm (Haghighat Sefat et al.,
2010). It should be noted that reservoir simulator runs only in this step and then goes
off-line afterwards. Hence, GA is used to optimize the developed surrogate model. In this
approach, surrogate models are trained on-line to reduce the modeling error to some user
deﬁned value. But, once optimization procedure is started, they do not change any more.
3. GA on a combination of surrogate model and actual reservoir model: In this approach, sur-
rogate model is ﬁrst developed using online-adaptive ANN with intelligent sample selection.
But, the reservoir simulator is on-line. The GA starts optimization by developed surrogate
model and reservoir simulator is run after each generation for the ﬁttest individual of that
generation. These data are used to recursively update the online-adaptive surrogate model.
It should be noted that in all of the presented approaches it has not been aimed to develop a
general and multi-output proxy to mimic all behaviors of the actual reservoir simulator. Instead
of this complex and non-trivial work, multi proxies have been developed where each predicts one
property of the actual reservoir model. For instance, to evaluate objective function in optimization,
cumulative oil production and cumulative water production of the reservoir are required after
10 years of production. Thus, two proxies are developed to individually predict the required
cumulative oil and water productions.
4.
GA AND ANN PARAMETERS
In order to fairly compare the three developed approaches, a GA with a similar set of parameter
settings is used in all of the conducted optimization studies. Furthermore, ANNs are employed to
develop the necessary surrogate model. Table 1 summarizes the GA and ANN parameters’ values.
5.
OBJECTIVE FUNCTION
In this work, NPV is considered as the objective function. For each individual the reservoir model
is simulated using the deﬁned control variables then the objective function is evaluated by reading
the simulation outputs. NPV is considering all ﬂuid productions and takes into account economics
of the project. NPV is calculated using the following formula (Abukhamsin, 2009):
NPV D
 Y
X
nD1
X
pDo;g;w
1
.1 C i/n Qn
p:Cp
!
  Cd;
(2)
OPTIMIZATION OF WATER-FLOODING PROCESSES
1179
where Qn
p indicates the production rate of phase p during the year n, Cp denotes the unit proﬁt
or cost associated with this phase, i is the annual percentage rate (APR), Y is the total number of
discount years, and Cd is drilling and completion cost determined by:
Cd D
wellcount
X
iD1
CCAPEX C Ltot;iCdrill C .Latcount:Cmill/;
(3)
where CCAPEX is a capital expenditure cost per well including platform cost and the cost of drilling
to the top of the reservoir, Cdrill is the unit drilling cost per feet, and Cmill is the cost of milling a
new junction (Abukhamsin, 2009).
6.
VALIDATION OF THE GA
In order to validate the GA approach in ﬁnding optimum solution, a test study is conducted on the
basis of a simple homogenous reservoir model, simulated using Eclipse-100 reservoir simulator.
The properties of the reservoir are shown in Table 2. Four oil production wells are located in
corners of the reservoir. The schematic diagram of the reservoir has been illustrated in Figure 1.
GA is used to ﬁnd the optimum location of one injection well in this model. So, each individual
in the GA consists of two variables as x and y to indicate the two-dimensional location of the
injection well .2 < x; y < 10/. After each individual is simulated, the relevant objective function
(NPV) can be calculated. Economic parameters used to calculate NPV have been summarized in
Table 3. Cd is neglected in this single well optimization study.
The GA searches the design space to ﬁnd the optimum location of the injection well. NPV
approaches to its maximum value as new generations are sequentially developed. The well location
and the corresponding NPV versus generation number are shown in Figures 2 and 3, respectively.
The ﬁrst developed approach is used for this optimization problem in which the reservoir simulator
should be run for calculation of each individual.
As shown, the GA has found the optimum location for the injection well after 20 generations,
yielding the well coordinates at x D 6 and y D 6 (i.e., center of reservoir) and hence conﬁrming
the maximum performance of 5-spot pattern. Therefore, the ability of GA to ﬁnd the optimum
solution is empirically validated.
TABLE 2
Reservoir Properties
Property
Value
Property
Value
X Dimension
11
Porosity (Fraction)
0.25
Y Dimension
11
Permeability Ave. (mD)
30
Z Dimension
10
Swi
0.22
Dx (ft)
300
Sor
0.2
Dy (ft)
300
Rock compressibility
4e-5
Dz (ft)
10
OWC depth (ft)
6,080
Top (ft)
6,000
Oil density (lb/ft3)
45
Well bore diam. (ft)
0.5
Water density (lb/ft3)
63.02
Inj. well completion (ft)
6,080–6,100
Prod well completion (ft)
6,000–6,050
Active phases
Dead oil, water
1180
M. HAGHIGHAT SEFAT ET AL.
FIGURE 1
Schematic diagram of the reservoir.
7.
RESULTS AND DISCUSSIONS
The three proposed approaches are used to ﬁnd the optimum injection rate of the single injector
in the homogenous 5-spot pattern. In order to calculate the corresponding error of each approach,
a full factorial search is done to ﬁnd the maximum NPV and corresponding injection rate.
7.1.
Full Factorial Search
The objective of this optimization study is to ﬁnd the injection rate of one injector that will
maximize NPV over 10 years of production. For this purpose, a full factorial search can be
employed easily since there exists only one variable. The injection rate varies from 1,000 to 8,000
stb/day. A program is written to change the injection rate in this range with 500 stb/day increments.
The NPV is calculated for each injection rate so as to ﬁnd the maximum NPV and its corresponding
TABLE 3
Economic Parameters Used to Calculate the NPV
Economic Parameter
Value
Annual percentage rate, %
0
Oil selling price, $/bbl
80
Water production cost, $/bbl
10
Water injection cost, $/bbl
5
Cd
0
OPTIMIZATION OF WATER-FLOODING PROCESSES
1181
FIGURE 2
Well location during optimization.
injection rate. The resulting plot of NPV versus injection rate is shown in Figure 4. As illustrated,
the injection rate of 5,000 stb/day has the maximum NPV of approximately 180 MM$.
7.2.
GA on the Actual Reservoir Model Approach
Full factorial search becomes impossible when number of variables is increased. In this approach,
GA is applied to the actual reservoir model so the reservoir simulator is run for calculation of
each individual. The injection rate and the resulting NPV versus generations have been shown in
Figure 5. Running this optimization test study takes 517 sec for running reservoir simulator and
30 generations of GA.
7.3.
GA on the Surrogate Model Approach
In this approach, the reservoir simulator is run and the online adaptive ANNs (i.e., surrogate
model) are trained recursively until their prediction error reduces to 10% (Haghighat Sefat et al.,
2010). Then, the reservoir simulator goes ofﬂine and the GA optimizes the developed proxy. The
resulting injection rate and NPV versus generations have been depicted in Figure 6. Running this
optimization takes 39 sec for running simulator, training ANN and 30 generations of GA.
1182
M. HAGHIGHAT SEFAT ET AL.
FIGURE 3
NPV during well location optimization.
7.4.
GA on the Combination of Surrogate Model and Actual Reservoir
Model Approach
In this approach, the surrogate model is developed similar to the previous approach. The GA starts
optimization using the developed surrogate model. After each generation the reservoir simulator
is run for the ﬁttest individual of that generation. These data are used for recursive updating
of the online-adaptive surrogate model. The resulting injection rate and NPV versus consecutive
generations have been illustrated in Figure 7. Running this optimization takes 135 seconds for
running reservoir simulator, training ANN and 30 generations of GA.
FIGURE 4
Full factorial search of NPV versus injection rate.
OPTIMIZATION OF WATER-FLOODING PROCESSES
1183
FIGURE 5
GA on the actual reservoir model (Approach-1).
8.
CONCLUSIONS
Optimization issue of a water ﬂooding project has been addressed in this paper using three GA-
based approaches. The developed approaches have been formulated based on the actual reservoir
model and/or a set of online adaptive ANNs as surrogate models. The accuracy of each approach,
speciﬁed in terms of the resulting prediction error together with its computation time has been
comparatively demonstrated in Figure 8. As illustrated, the approach due to the GA on the actual
FIGURE 6
GA on the surrogate model (Approach-2).
1184
M. HAGHIGHAT SEFAT ET AL.
FIGURE 7
GA on the combination of surrogate model and actual reservoir model (Approach-3).
reservoir model represents the most accurate and yet the time consuming one. Evaluating the
obtained results conﬁrms that the GA approach based on the combination of multi-surrogates
and actual reservoir model gives the best outcome due to its inherent structural balance between
computation time and prediction error measures. It should be noted that this study includes a simple
reservoir model with only one variable to be optimized. As the complexity rises, the difference
between three developed approaches becomes more obvious. This implies that the computation
FIGURE 8
Comparison of three approaches.
OPTIMIZATION OF WATER-FLOODING PROCESSES
1185
time of the ﬁrst approach increases and the error of the second approach become higher. Therefore,
a combination of these two schemes is able to present an approach incorporating a reasonable
accuracy together with lower computation time.
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The Open Petroleum Engineering Journal, 2016, 9, (Suppl-1, M8) 137-149
137
1874-8341/16
2016  Bentham Open
The Open Petroleum Engineering
Journal
Content list available at: www.benthamopen.com/TOPEJ/
DOI: 10.2174/18748341016090100137
REVIEW ARTICLE
Applying  Sustainable  Development  Principles  and  Sustainable
Operating Practices in Shale Oil and Gas Production
Deborah J. Shields
*
Colorado State University, Fort Collins, CO 80523, USA
Received: July 23, 2015
Revised: September 22, 2015
Accepted: August 12, 2015
Abstract: Shale oil/gas is one of the most rapidly growing types of unconventional fossil fuel development and the abundance of this
resource has postponed peak oil and gas. Physical scarcity of hydrocarbons is now less likely to occur in the near future; however,
the likelihood of social scarcity is increasing. Despite the clear economic benefits of production in terms of jobs, tax revenue, and the
provision of energy resources and industrial feedstocks, there is hostility toward shale oil/gas extraction in many parts of the world.
This is due to concerns about how environmental, social, and economic impacts are managed and mitigated,and how risks and
benefits are distributed among industry, governments and civil society.The application of sustainable development principles and
sustainable operating practices is recommended as a partial remedy for this situation. Sustainability accounting frameworks based on
criteria and indicators of sustainability and best practice codes of conduct represent two possible approaches for tracking how
sustainable a firm’s practices are. These also provide a foundation for corporate social responsibility and can assist firms in gaining
social license to operate. Also needed are estimates of a given operation’s net contribution to sustainable development. Possible
methods include benchmarking against industry standards,achieving mature business conduct, gaining sustainability certification,
demonstrated use of both design for environment and shared value creation methodologies, and integrated sustainability assessment.
Conceptual progress has been made in applying sustainability to shale oil/gas; however, significant progress in applying these tools
and methods in the field is needed because the sector tends to be judged by the behavior of the least responsible firm. Moreover, if
best practices and shared value creation are set aside during the current or a future downturn, public cynicism about the sector will
increase, and social license may be lost and even more difficult to regain.
Keywords: Economics, Energy, Shale, Sustainability.
INTRODUCTION
Systems need energy to be ordered and functional. For example, in the absence of continuous inputs of energy in the
forms of plowing, planting, weeding, fertilizing, and harvesting, agricultural lands will revert to their prior, natural state,
be that forest, shrubland, or steppe. The same is true of economies, which have over many centuries developed into
complex ordered systems of machines, buildings, products and services through continuous and increasing inputs of
energy.
Initially, most of the energy utilized by societies came from wind, water, animals and humans themselves. But fossil
fuel-based energy became widespread during the Industrial revolution, and according to Deirdre McCloskey [1], was in
no small part responsible for the ‘Great Enrichment’ occurring during that period and thereafter. Her views are echoed
by other writers [2 - 4] .
Today it is recognized that hydrocarbons are vital to human well-being - integral parts of developed, modern
societies. They are essential components of economic systems; and are or could be the driving force for some local,
regional, and national economies, providing local employment opportunities and industry expenditures, as well as tax
revenues [5]. Measures of  economic  well-being, such as  Gross  Domestic Product, increase when  access to  energy  is
* Address correspondence to this author at the Colorado State University, Fort Collins, CO 80523, USA; E-mail: Deborah.shields@colostate.edu
138   The Open Petroleum Engineering Journal, 2016, Volume 9
Deborah J. Shields
increased because it is key to development. This is one reason why the United Nations has created the Sustainable
Energy for All program [6]. As UN Secretary -General Ban Ki-moon stated, “Energy is the golden thread that connects
economic growth, increased social equity, and an environment that allows the world to thrive”[7]. Moreover, it is
estimated that an additional 3 billion people will join the global middle class by 2050 [8]. Middle class life is heavily
energy dependent; when people can afford to do so they buy cell phones, TVs, computers, cars, etc all of which require
energy.
Much of this needed energy is currently generated by burning hydrocarbons. Despite desire to and efforts toward
increasing  the  use  of  renewable  energies,  the  percent  provided  by  fossil  fuels  has  remained  remarkably  steady  -
approximately 85 percent between 2003 and 2013 [9]. Given that alternative energy technologies, such as nuclear and
renewables, will be unable to fully meet society’s needs in the short to medium terms, there is naturally concern about
depletion of hydrocarbon resources. Maggio and Cacciola [10] predicted that conventional oil would peak between
2009 and 2021 and conventional natural gas between 2024 and 2046. A simplified explanation of Hubbert Peak oil and
gas is that it represents a point in time when half of the ultimate stock of a resource has been produced [11]. Thereafter
production  will  inevitably  decline.  Exact  peak  dates  are  thought  to  be  less  important  than  the  reality  of  eventual
depletion and the consequent need to shift to alternative energy sources.
Moreover, even assuming society could quickly shift to a low carbon energy future, hydrocarbons would still be
essential because they are core industrial feedstocks. Natural gas condensate is a raw material, the basic input for the
organic chemicals industry (ethylene, propylene, which are the basis for plastics), hydrogen production, fertilizers, and
liquid fuels [12, 13]. Businesses locate their manufacturing facilities where they have efficient and low cost access to
raw materials, which means that developing shale oil/gas directly leads to economic development opportunities in
downstream industries where these resources are utilized [14, 15].
Fortunately, peak oil/gas appears to have been postponed, not least because of technical advancements that enable
economic production of unconventional resources. Conventional oil and gas deposits consist of porous reservoirs in
geologic formations, capped by an impervious rock ‘trap’ within which migrating fluids such as oil, natural gas and
water accumulate. The distribution of oil or natural gas throughout a geologic formation over a wide area, but not in a
discrete reservoir, is called an unconventional deposit [16].
Shale  oil/gas  is  one  of  the  most  rapidly  growing  types  of  unconventional  fossil  fuel  development.  These  are
heterogeneous deposit types in which oil or natural gas is distributed throughout low-permiability shale formations.
Burning natural gas yields fewer greenhouse gas emissions than does burning wood, coal or petroleum. As a result,
conventional or shale gas can be substituted for these other fuels, assisting nations in achieving their goals of reducing
GHG emissions . Some authors now see abundant shale gas as either a transition fuel to renewable energy sources, or a
complementary component of power systems and a hedge against the intermittent nature of renewables [17].
As the International Energy Agency [18] has pointed out, “the boost that [shale gas] would give to gas supply would
bring a number of benefits in the form of greater energy diversity and more secure supply in those countries that rely on
imports to meet their gas needs, as well as global benefits in the form of reduced energy costs. “ This reality is clearly of
interest in the United States [5], Europe [19], and China [20] among other countries and regions. The geopolitical
ramifications of shale alluded to in the previous quote are playing out in Poland, which is largely dependent on gas and
oil imports from Russia [21]. Public reporting on shale gas production and subsequent public debate have been framed
mostly  in  geopolitical  terms,  with  relatively  less  attention  paid  to  technical  issues.  Public  support  for  shale  gas
production is high [22].
Although shale resources are thought to be very large and widely distributed, their recovery is complex and often
expensive. Occurrences of this type require special production techniques that often involve horizontal drilling into the
gas or oil-bearing formation, followed by hydraulic fracturing of the rock to release the hydrocarbon from the rock.
These  extraction  techniques  are  deeply  controversial  [23].  There  are  serious  concerns  about  the  possibilities  for
environmental degradation, as well as the potential for negative impacts on human health and communities [24 - 31].
The concerns stem in part from public perceptions that the hydrocarbons industry does not:
manage risk adequately,
act transparently, or
create benefits for society.
There is also concern that abundant shale resources will actually postpone a shift to a post-carbon economy and
Sustainability and Shale
The Open Petroleum Engineering Journal, 2016, Volume 9   139
result in increased GHG emissions [32]. France, Bulgaria, the Czech Republic and the  Netherlands,  among others,
have  banned  hydraulic  fracturing  [33].  Controversies continue  in the United  States, the  United  Kingdom,
 Argentina,  and other  countries  [34 - 36].
Thus, a dynamic tension exists between potential benefits and costs of hydrocarbons, and specifically with respect
to expanding shale oil/gas production. Industry has increasingly tried to address the concerns of civil society and
governments through the use of best practices, but nonetheless resistance to shale development persists. Hodge [37]
addresses what he sees as a seeming paradox: company environmental and social performance is definitely improving,
but rather than leading to reduced conflict, opposition is increasing in many locations. He suggests that there needs to be
a foundation of sustainability practice and sustainable development (SD) within the sector, combined with efforts to
achieve consensus among industry, governments and stakeholders.
This paper reviews the current literature on sustainability, corporate social responsibility, and the social license to
operate in the context of shale development. The purpose is not to catalogue specific concerns and practices, but rather
to  provide  an  overview  of  the  current  understanding  of  how  SD  principles  and  sustainable  practices  apply  to
nonrenewable resources in general, and shale specifically. The practices are in large part responses to public and
governmental concerns about environmental, social and economic impacts. Approaches for estimating shale oil/gas’s
contribution to sustainable development are then presented. Conclusions about the state of sustainability practice in
shale oil/gas are drawn and steps forward proposed.
SUSTAINABILITY AND SHALE
Sustaining Physical Availability
There are many ways to think about shale oil/gas and sustainability. One is the ability of industry and governments
to sustain the supply of resources to societies and economies. An inability to do so leads to scarcity. Resources become
scarce due to depletion of stocks or disruption of flows. There are two perspectives on stock depletion: the fixed stock
and opportunity cost paradigms [38]. The fixed stock paradigm is based on physical measures and statistical estimates
of resources. Given that shale oil/gas resources are finite, continued drilling and production will lead first to scarcity
and eventually to exhaustion. However, as noted above, recent technological advances have pushed the time of ultimate
depletion into the future.
The  opportunity  cost  paradigm  takes  an  economic  perspective.  Fewer  hydrocarbon  discoveries  would  lead  to
decreased availability, which would in turn result in rising prices. Increasing price would make previously uneconomic
shale  plays  commercially  viable  thus  increasing  supply,  but  at  some  point  rising  price  would  not  lead  to  more
discoveries. Proponents of the opportunity cost paradigm argue that the true physical exhaustion of a nonrenewable
resource is not possible because before that could happen, the price would rise to such a level that substitutes would be
found. From this perspective, the availability of a resource will be determined by what people are willing to give up for
it, i.e., its opportunity cost [38]. In reality, energy markets currently face the opposite situation, abundant resources
driving prices down.
The third type of scarcity addresses flow through the economic system; it is distinct from the preceding measures of
scarcity in that they address the relative abundance of resource stocks. Shields and Šolar [39] define flow disruption in
terms of such situations as a temporary cessation of production, political actions such as embargoes, or an unwillingness
by civil society to allow resource production to take place. They categorize these as situational, political, and social
scarcity respectively. All of these are relevant for shale gas. Situational scarcity is caused by one or more of a broad set
of circumstances that act to limit the flow of resources to markets. Potential causes include demand in excess of current
production capacity or a lack of infrastructure, such as no pipelines leading to gas flaring rather than productive use of
the resource, inadequate LNG port capacity or import capacity when export capacity is needed, or a shortage of LNG
ships.
Political scarcity occurs when the flow of a resource is halted or restricted due to choices made and actions taken by
governments. The most obvious example of economically motivated political scarcity is an embargo. One producer or a
cartel of producers acts to restrict the flow of natural gas to one or more consuming countries with the goal of punishing
an opponent, increasing the market price, or collecting transit fees. Concerns in Europe about disruption of gas supply
from Russia in recent years has increased interest in developing indigenous energy resources, but this may not be
possible given public opposition [33]. The result could be social scarcity, i.e., limited availability of a resource because
citizens believe that the environmental and social costs of production are too great to bear. Giurco et al. [40] redefined
140   The Open Petroleum Engineering Journal, 2016, Volume 9
Deborah J. Shields
peak resources to account for such situations, arguing that a resource peak is caused by more than reduced discoveries
and decreasing availability; the peak is reached not necessarily when half the resource has been extracted, but when
increasing social, economic and environmental costs of production contribute to a decline in production rates, i.e.,
resource availability decreases because the full costs associated with production are seen as increasingly unacceptable.
Frameworks for Sustainable Development
Placing shale in the broader context of SD requires a perspective that looks beyond physical availability to address
both the costs and benefits of production. This is the case because SD necessitates integrating environmental policies
and development strategies so as to satisfy current and future human needs, improve peoples’ quality of life, and protect
the environment, which we depend on for life support services. However, a distinction needs to be made between the
contributions that shale oil/gas resources can make to SD and the sustainability practices undertaken to ensure that the
contribution of a specific operation is a net positive over its life of the operation. Moran and Kunz [41] write that how
the monetary worth/wealth generated by mineral and hydrocarbon supply, demand and use is distributed across people,
places and over time is indicative of the resource’s contribution to sustainable development. They recommend assessing
SD with respect to energy and minerals in terms of equity, with equity
i in turn assessed based on four criteria: the
degree to which future needs will be met, the enhancement of prosperity of the supplying country, the satisfaction of the
receiving country, and the acknowledgement and assurance of profitability for business.
This comprehensive, high-level perspective is not found in other frameworks linking hydrocarbons and sustainable
development. Most widely found is the triple bottom line (TBL) approach. It acknowledges the interconnected and
overlapping nature of social, economic, and environmental realms (sometimes expanded to include governance or other
areas). The IPIECA [42], for example, has described the overlapping domains of the TBL with specific application to
the oil and gas sector, but this was done not to provide an overarching SD framework or to determine the industry’s
contribution to sustainable development. Rather, the purpose was to assist current and future oil and gas companies in
improving the quality and consistency of voluntary reporting on their environmental, health and safety, social and
economic performance. In addition to the areas of economic growth, social progress, and environmental stewardship,
they described the socio-economic, socio-environmental and eco-efficiency overlap areas.
Another commonly used framework is based on the maintenance of capital. Here, SD is defined as managing
resources in a way that is conducive to long-term wealth creation and the maintenance of natural, social, human,
economic/financial, and manufactured/built physical/engineered capitals [43].This perspective extends naturally to
shale oil/gas, which is a form of endowed, natural wealth that is an important source of monetary wealth creation and is
the raw material for many manufactured and built products. The five capitals approach is widely used, mostly in a
descriptive manner, but there is potential to use it as an integrating framework [44].
One area of continued debate within the 5 capitals framework for sustainability is the degree to which different
forms of capital can be substituted for each other. Two alternative perspectives are presented in the literature, although
in reality they exist at the ends of a continuum. The strong sustainability view is that the opportunities for trade-offs are
quite limited, and at the extreme that no form of capital (and especially natural capital) can decrease over time. Under
weak sustainability, trade-offs among forms of capital are permitted, with the caveat that overall capital should remain
constant  or  increasing  [45].  At  its  extreme  all  substitutions  among  capitals  are  acceptable,  even  in  cases  where
irreplaceable cultural heritage sites or environmental assets are comprised or lost. But, assuming that trade-offs are
deemed acceptable, there remains significant disagreement about the relative importance that should be assigned to
different forms of capital, i.e., how much built physical capital is required to offset the loss of an ecosystem or the
depletion of a nonrenewable resource? These are value
ii judgments, which differ across individuals, groups within
society, and between nations, making consensus difficult to reach [46].
A related concern is the degree to which societies should assume that technological advancements will continue to
expand opportunities for substitution. To date, this  has  been  the  case. Societies’ access to  resources  has been  greatly
i In this context, the term equity refers to fairness. Many economists use the term equity as a synonym for justice, with justice in turn being respect for
people’s rights. D. Hausman and M. McPherson, Economic Analysis and Moral Philosophy. Cambridge, UK: Cambridge Univefrsity Press, 1996.
ii Value judgements are statements based on subjective views of right/wrong or good/bad that cannot be described as true or false on objective
grounds.  This  leads  to  the  possibility  of  a  pluralistic  system  of  ethics.  Hence,  one  person  may  believe  that  a  specific  state  (of  for  example
environmental health) is better than another and so place greater value on its attainment than someone who holds a different ethical view about nature.
K. Rothschild, Ethics and Economic Theory. Brookfield, VT: Edward Elgar, 1993.
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enhanced, horizontal drilling and fracturing being obvious cases in point, with shale oil/gas being substituted for
conventional   resources.  Should  continued  breakthroughs  be  presumed?  If  so,  this  would  lead  to  a  set  of  policy
prescriptions vis a vis shale resources predicated upon weak sustainability. Conversely, the pace of technical progress
might slow, which would imply alternative decisions with a bias toward strong sustainability. Also, as Mulder, Ferrer
and van Lente [47] point out, each technological advance will likely come with its own set of contributions, but also
paradoxes. This suggests that advances will have unintended, and unanticipated, consequences. If this is the case, the
logical conclusion is that shale best practices and policies should be adaptable rather than rigid so as to be responsive to
inevitable unexpected circumstances.
Parallel  is  the  question  of  technology’s  role  in  and  contribution  to  sustainability.  Advances  that  lead  to  more
efficient, lower cost production are necessary, particularly in given current low oil and gas prices, but that is no longer
deemed sufficient by society.
An activity is deemed efficient if the monetary value of the activity, its benefits, exceeds the monetary value of
economic resources allocated to perform the activity, i.e., its costs. This is a rational approach, with the caveat that not
all benefits or costs can either be monetized or are considered worthy of inclusion in a benefit – cost calculation by
some persons or groups doing assessments. Each exploration and production opportunity comes with its own mix of
environmental, social, cultural and institutional context and constraints, and societies expect the extractive industries to
be sensitive to and respectful of the place and people where they plan to work. So, in addition to the complexity of the
technological system, there is increasing complexity of designed and cultural information systems, and of accelerating
integration of human, natural, and built systems [48]. The milieu in which engineers work has become more complex
and the expectations placed on them and the systems they design and run have changed.
In 2009, a select international group of petroleum, mining, metallurgical, civil, and chemical engineers convened to
discuss resources and sustainability. They agreed upon the essential components of sustainable engineering [49]:
Economic: The engineered system is affordable.
Environmental: The external environment is not degraded by the system.
Functional: The system meets users’ needs-including functionality, health and safety- over its life cycle.
Physical: The system endures the forces associated with its use and accidental, willful, and natural hazards over
its intended service life.
Political: The creation and existence of the system is consistent with public policies.
Social: The system is and continues to be acceptable to those affected by its existence.
This intersection of the technical aspects of resource exploitation with the human, environmental and governance
aspects has received considerably more thought and emphasis than have conceptual overarching frameworks. A variety
of sustainability accounting frameworks have been created that typically comprise a list of items that should (or must in
the case of certification) be measured and reported upon on a regular basis, or a set of actions or best practices that
should be followed, often accompanied by measures to demonstrate that the best practice has in fact been followed [50].
This process is an extension of goals laid out in Agenda 21, the report of the UN Conference on Environment and
Development (Rio Earth Summit) in 1992. It recommended countries develop criteria and indicators of sustainability
[51]. Criteria describe what it means to be sustainable. They serve as basis for evaluation, comparison or assessment,
and achievement is judged against relevant indicators, which are pieces of information that help people understand
where they are with respect to their sustainability goals or in the achievement of best practices, how far they are from
where they want to be, and whether they are moving toward or away from their goals. Each indicator is a parameter (a
property that is measured or observed) that provides information about the state of a phenomenon, environment, or area
with a significance extending beyond that directly associated with the parameter measurement [52]. In the years after
the Earth Summit numerous sets of criteria and indicators of SD were proposed at various spatial scales (global,
regional, national, community) [53]. Sectoral indicators followed, starting with forestry and then extending to other
resource and industrial sectors, including to the extractive industries.
One of the most widely used sets of oil and gas sustainability criteria and indicators are those published by the
Global Reporting Initiative [54]. GRI has created reporting guidelines, the most recent version being the G4. Their
purpose is to provide a standardized and internationally agreed set of disclosures and metrics for sustainability reporting
that are widely applicable across sectors. Sector supplements clarify how the basic reporting should be adapted to the
particularities of an industry, in this case oil and gas. The first Oil and Gas Sector Supplement (OGSS) was published in
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Deborah J. Shields
2012 [55] and was based on the G 3.1 reporting guidelines. It was developed collaboratively with industry and is cross
referenced to the previously mentioned IPIECA reporting guidelines. GRI sets out 9 reporting principles, whereas
IPIECA has 5 partially overlapping principles and their approach is more general and flexible. A revised version of the
GRI OGSS was produced when the G 4 guidelines were published in 2013 [56]. Shale is mentioned, but largely with
respect to reporting of resources and reserves and of various GHG or toxic emissions from operations.
In December of 2013 the International Association of Oil and Gas Producers (OGP) and IPIECA published a
separate set of good practice guidelines for shale oil and gas that are substantially more detailed than the original
reporting  guidelines.  Issues  of  concern  with  respect  to  shale  oil/gas  production  are  identified  and  best  practices
recommended for subareas [57]:
Worker safety, health and emergency response
1.
Safety, health and emergency response
a.
Hiring and training
b.
Stakeholder engagement and community impacts
2.
Open communication and collaboration
a.
Noise and visual impacts
b.
Traffic and road use
c.
Community health
d.
Local sourcing and economic development
e.
Water sourcing and efficient use
3.
Planning process
a.
Operations
b.
Reuse and recycle
c.
Groundwater and surface water protection
4.
Detailed site assessment
a.
Fracturing fluids and disclosure
b.
Cementing and well integrity
5.
Overall well intengrity
a.
Cementing
b.
Well integrity
c.
Operational water management
6.
Well site design and construction
a.
Spill prevention and emergency response
b.
Operations
c.
Produced water disposal
d.
Air emissions
7.
Planning process
a.
Monitor and reduce
b.
Land use
8.
Site selection and planning
a.
Drilling and operations
b.
Reclamation and restoration
c.
Biodiversity and ecosystems
9.
Open communication and collaboration
a.
Opportunity-screening, site selection, project development, operations and decommissioning
b.
Prevention and rehabilitation
c.
Integrate, adapt and improve
d.
Induced seismicity
10.
Assessing potential for induced seismicity
a.
Monitoring
b.
While these are not specifically intended as the basis for reporting and are not part of the GRI OGSS, indicators for
each practice could be developed or actions in each area described, which could be the basis for shale sustainability
reporting. The report acknowledges that adaptation to circumstances found in the field should be expected. Given the
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heterogeneous nature of shale plays this is both appropriate and necessary.
In 2012 the International Energy Agency published a report titled “The Golden Rules for a Golden Age of Gas”
[58]. They too identified the major environmental and social concerns related to shale gas development and proceeded
to list a series of best practices that they developed as a set of “Golden Rules” guiding shale development and decisions
by policymakers, regulators, and operators. The focus is on environmental best practice and risk management, and the
use of environmental indicators is suggested. The IEA also recommends that the number and type of indicators of best
practice must grow as the number of wells drilled increases. This is an acknowledgement of the potential for cumulative
impacts.  Their  idea  was  that  by  accepting  and  implementing  these  Rules,  the  level  of  environmental  and  social
performance would improve and thus public acceptance of shale development would earn the industry a “social license
to operate”.
Social license refers to the willingness of civil society to accept the presence of an industry in their community. It is
not  granted  by  government,  but  by  the  impacted  communities,  a  level  of  trust  that  must  be  earned,  and  is  both
challenging to maintain and extraordinarily difficult to regain if lost [59]. Loss of social license has significant financial
consequences for firms, from public opposition to their operations, questions from lenders, financial institutions that
have signed on to the Equator Principles for social and environmental risk management [60], pressure for adverse
legislation, and at the extreme violent behavior. Companies report on their operations, i.e., engage in transparency and
information sharing, and more broadly in community engagement activities, so as to gain and retain social license to
operate. Seeley [61] states that the shale industry must work actively to gain and maintain a social license given
widespread public distrust and skepticism that environmental and social issues are being adequately managed.
Assessing Progress Toward Sustainability
Reporting on indicators is useful, but assessing whether progress is actually being made toward sustainability goals
is more complex than simple or minimalistic reporting. There are multiple approaches to assessing effectiveness of
sustainable best practices in the shale oil/gas sector. The base level is the completeness of reporting. GRI has created an
Application Level protocol under which a firm’s reporting under the GRI Framework is graded as C, B, or A based on
completeness, with A representing disclosure in every category. Application Level can be self-disclosed, GRI audited or
third party audited. When labeled A + the report is stated to have been externally reviewed and assured [54].
Raufflet et al. [62] identified best-in-class companies from the mining and oil and gas sectors with Application
Levels of A+, A and (in one case) B+. Based on data from and interviews with representatives of these companies, the
authors identified 29 corporate social responsibility (CSR) institutional expectations (best practices) in the areas of
ethics and governance, environment, community relations, and social, health and safety issues. They recommend that
these extractive sector best practices be used as industry standards against which firms should judge their sustainability
behavior and their contributions to SD – more best practices indicating greater contribution.
CSR is a form of business behavior that leads firms to voluntarily contribute to a better society and a cleaner
environment. Businesses take on commitments beyond common regulatory and conventional requirements, which they
would have to respect in any case [63]. The goal of CSR is to raise standards of social development, environmental
protection, and respect of fundamental rights by embracing open governance, reconciling the interests of stakeholders,
and taking an overall approach to quality. The World Business Council for Sustainable Development describes CSR in
the following way [64]:
a coherent CSR strategy, based on integrity, sound values
iii and a long-term approach, offers clear business
benefits to companies and a positive contribution to the well-being of society;
a CSR strategy provides the opportunity to demonstrate the human face of business;
such a strategy requires engagement in open dialogue and constructive partnerships with government at various
levels, IGOs (inter-governmental organizations), NGOs (nongovernmental organizations) other elements of civil
society and, in particular, local communities, and;
in implementing their CSR strategies, companies should recognize and respect local and  cultural differences,
whilst  maintaining  high and  consistent  global  standards  and  policies; and  finally, being  responsive to  local
iii Values here refers to a business’s core principles that drive corporate decisions and behavior. Values can set a company apart from the competition
by clarifying its identity. They can be strong and meaningful or bland and meaningless. P. Lencioni, Make your values mean something. Harvard
Business Review, July, 2002. Available at: https://hbr.org/archive-toc/BR0207?cm_sp=Article-_-Links-_-MagazineIssue, Accessed July 23, 2015
144   The Open Petroleum Engineering Journal, 2016, Volume 9
Deborah J. Shields
          differences means taking specific initiatives.”
Moran and Kunz define operating sustainability as how well the activities that create value
iv, the measure of an
operation’s contribution to sustainable development, are undertaken [41]. Operating sustainability is assessed in terms
of the maturity of the activities undertaken. They identify four stages of maturity, from least mature (solely profit
maximizing) through efficiency focused (eco-efficiency) to integration (taking a more holistic, systems approach) to
most mature (adaptable and resilient). The first two of these are straight forward. Firms are initially concerned with
short term profit maximization, but eventually become concerned with waste minimization, setting targets for water,
chemical  and  energy  usage  in  stage  two.  In  the  third  stage  the  firm  takes  a  more  interdisciplinary  approach  and
considers not only local activities, but also the value chain
v, the life cycle of the operation and resource, their broader
footprint and their modes of information sharing. At the highest level firms develop flexible processes that enable them
to be more resilient, i.e., able to withstand externally imposed shocks and return to normal operational/functional mode.
It is at this stage that stakeholder preferences become critical.
Equitable Origin (EO) created the EO100 Standard for oil and gas in 2012 [65]. EO is a for-profit social enterprise
that promotes best practices in oil and gas operations. They have identified 6 Principles for environmental and social
policies:
Corporate governance, Accountability and Ethics
1.
Human Rights, Social Impact & and Community Development
2.
Fair Labor & Working Conditions
3.
Indigenous People’s Rights
4.
Climate Change, Biodiversity & Environment
5.
Project Life Cycle Management
6.
EO has recently published a Technical addendum of performance standards for Shale Oil and Gas Operations [66].
There are additional standards added under principles 2 - 6. As with the OGP - IPIECA and IEA best practices, these
address the particularities of shale operations and they have points in common with both, for example recommending
consideration of cumulative impacts, but they are not identical to either.
EO100 is a verifiable standard and third-party auditors certify projects’ performance against the principles and
correlated  performance  standards.  The  implication  is  that  certified  operations  make  a  positive  contribution  to
sustainable development. This approach is analogous to the certification of forest sustainability carried out by groups
such as the Forest Stewardship Council (FSC), although they are a not-for-profit organization [67]. The firm must pay
EO and the certifier to determine if they meet the standards laid out for certification of their oil or gas operation, which
means there needs to be a clear economic benefit to doing so. This has proven to be the case in forestry because
purchasers of final goods made of wood want the products they buy to be made from sustainably managed forests, e.g.,
many manufacturers of doors, tables, paper, garden furniture, prominently display the FSC label on their products
because they know this will increase sales. As yet there does not appear to be a clear parallel in hydrocarbon-based
consumer products because it would be difficult or impossible to label gasoline at the pump or specific plastic bottles as
having been made from sustainable produced hydrocarbons. Alternatively, firms may decide to certify operations to
gain social license to operate or to demonstrate CSR. Thus, adoption of the EO100 Standard may be slow; however, it
may nonetheless occur if firms believe that having certification will increase their ability to gain or keep social license
to operate.
The Lowell Center for Sustainable Production [68] has created a five level indicator framework. In their definition
sustainable products are those products being economically viable, healthy for consumers (e.g. not causing injuries),
environmentally sound (e.g. considering material efficiency), having production process safe for workers, and being
iv The traditional meaning of the phrase to create value is to change business inputs into business outputs in such a way that they have greater
monetary value than the original cost of creating those. It is the economic logic for the existence of a company. Moran and Kunz have slightly
redefined the phrase for use in a sustainability context, arguing that it is not only how much value is created through business activities, but how that
value is distributed across people, places and over time that determines whether a firm is contributing to sustainable development..
v Porter defines the value chain as a series of sequential business activities: inbound logistics, operations, outbound logistics, marketing and sales, and
service, all taking place under an overarching set of support activities: firm infrastructure, human resource management, technological development,
and procurement. M. Porter, Competitiven Advantage: creating and sustaining superior performance. NY: Free Press, 1985.
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beneficial to local communities. This framework was developed as a tool usable by companies to evaluate the
effectiveness of their sustainability indicator system. It could contribute to an assessment of SD contribution. Eltayeb
and Zailani [69] propose something conceptually similar, though in their case more narrow and applied to supply
chains. The process starts with defensive compliance, followed by waste minimization, eco-efficiency, and design for
the environment. The focus is industrial ecology and does not consider social issues, reporting, adaptation or resilience.
Reaching the highest level means a greater contribution to sustainable development.
Longo [70] proposed that the Lowell approach and Etayeb and Zailani’s could be combined with set of social stages
to form a more comprehensive picture of company sustainability practices and contribution to SD in the gas sector. In
her social hierarchy the first stage is again legal compliance, followed by first corporate philanthropy, then CSR, and
ending with shared value creation
vi.
Compliance with the law is a clear concept followed by all responsible businesses. The reason that it resides at the
bottom  of  these  hierarchies  is  that  it  represents  a  minimum  standard.  In  many  cases  the  law  neither  requires  nor
encourages actions that address issues of risk management, community engagement, transparency, or other fundamental
aspects of sustainability. Corporations have engaged in philanthropy for many years, though often in ways that do not
link to strategic aims [71]. More recently, Porter and Kramer recommended that companies use philanthropy in a way
that aligns economic and social goals [72].
Consistent with this view, as oil and gas firms began to engage with civil society in hopes of gaining a social license
to operate, they offered grants or gifts in various forms in hopes of gaining acceptance by local governments and civil
society. These might be as simple as providing a new fire truck or building a medical clinic, but can be much more
substantial. As Morgan [73] points out, “The advantage of a philanthropic approach is that it is simple to provide and
usually does not take long to disburse. Moreover, sometimes grants are the only sensible way to support a community.
However, donations may reduce incentives for the community to be independent; asking for more social investment has
no cost for them. This not only reduces the capacity of the community to deal with their own needs, but also increases
costs for companies. And, because social challenges are rarely seen as solved, additional requests normally follow.” The
results are two-fold: communities want more, but more does not necessarily lead to acceptance or social license.
Philanthropy has since been incorporated as a component of CSR, as discussed above. The most recent extension of
corporate social practice is the embrace of shared value creation as an operating principle. As with philanthropy and
CSR, the underlying motivation is to earn a social license to operate in the face of growing public distrust of the oil and
gas industry, which is accompanied by a failure to understand either how resources are produced or the role of those
resources in a complex society.
Shared value in this context means aligning the business interests of extractives companies with community needs
and  priorities.  The  goal  is  to  create  value  in  a  way  that  benefits  shareholders,  but  also  creates  value  for  society,
expanding total value as opposed to redistributing it. Creating shared value in the extractives sectors is not a new
concept, but the public perceptions listed above demonstrate that current practices fall short of potential. Hidalgo et al.
propose that companies change the existing mindset that sees projects in local communities only as a cost to the
business and instead align business interests with community needs and priorities [74].
According to the International Finance Corporation, the shared value process should start with a comprehensive,
participatory baseline study of the impacted community’s socioeconomic, cultural heritage, and socio-environmental
context before project development, after which agreement on joint objectives for the project’s community programs is
sought. Firms should seek activities that can benefit the project and the community, for example, through imaginative
local staff recruitment and training, finding synergies in the provision of infrastructure between development and wider
community/country needs, and nurturing local supplier networks for lower cost and better local impact [75]. To the
degree that communities are actively engaged, that they experience tangible SD progress that occurs because of the
presence of the oil and gas sector, they will be willing to grant a social license to operate. Moreover, if communities
welcome companies that practice shared value creation, those firms will gain a competitive advantage. If, on the other
hand, business proceeds as usual cloaked in the new language of shared value, communities may well consider this new
language to be nothing more than a new version of green wash.
vi Shared value creation is in no way related to the idea of convincing people to hold similar moral or ethical beliefs. Rather, it refers to value in terms
of social and economic benefits that accrue to the firm and to communities or society in general as a result of business activities. The goal of shared
value creation is to undertake business activities in such a way that they generate additional benefits beyond profits and return to shareholders.
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Deborah J. Shields
In addition to assessing the sector’s contribution to SD in terms of CSR behaviors and shared value creation it is
also possible to conduct quantitative integrated sustainability assessments (ISA). An ISA is a process through which the
 costs  and  benefits of an  oil  and  gas  project  are  assessed  in  the  context of  sustainability  principles [76]. An  ISA
framework should deal with multiple stakeholders with unique objectives, preferences, and levels of risk tolerance, and
a decision context with quantitative and qualitative aspects [77]. After problem definition and stakeholder engagement,
technical  aspects  are  detailed  and  economic  analyses  conducted.  The  information  is  brought  together  using  a
quantitative tool such as multi-criteria decision modeling and trade-off analysis conducted. The results of the analysis
should be communicated to stakeholders with the goal of increasing trust, which would in turn increase the likelihood of
gaining social license to operate, but only to the degree that the development approach taken is responsive to the
concerns and preferences of civil society [78].
The ISA approach has two weaknesses worth noting. First, not all costs and benefits are easily monetized, and
stakeholders may consider putting a monetary value on cultural or spiritual resources offensive. Second, ISA’s often use
present value analysis
vii to the estimate monetary value of a parameter. This method minimizes the impact of costs or
benefits accruing in the far future if a positive interest rate is used.
CONCLUSION
As this chapter has shown, many authors and organizations are writing about the range of concerns evinced by
stakeholders and governments with respect to shale oil/gas extraction. Multiple sets of best practices have been created
to guide sustainable operating practices. The sets are overlapping, but not identical because the goals and perspectives
of their authors differ. That caveat not withstanding, the core elements are very consistent, suggesting that a consensus
on the basic elements of what needs to done in the areas of environmental, social, and economic best practice within the
shale sector is emerging. Moreover, there is increasing commitment to corporate social responsibility by companies
working in the industry. A variety of methods for estimating the contribution of shale operations to the sustainable
development of the regions and communities where they work are available.
Challenges, however, remain. First is the fact that while the major oil and gas firms are adopting these best practices
at a high rate, the juniors lag substantially. When a project is initiated by a company that does not use best practices and
is then sold to a major, the larger firm may find that social license to operate is limited or even absent and that
relationships need to be built or rebuilt with communities. Second, the petroleum sector has always been a boom and
bust industry. While communities with a history of extractive industry activity may be willing to accept the cyclic
nature of the industry, some shale plays are in areas far removed from historic oil and gas activity. Best practices can
help ameliorate the impacts of booms, but the willingness of communities in new areas to tolerate the difficulties
associated with busts and still welcome the industry back when markets recover is yet to be determined.
The shale industry is facing a slow-down currently, and prices for oil and gas have dropped precipitously in the past
two years. Producers are cutting costs, which has the potential to lead to a third challenge. Sustainable operating
practices are not costless; firms will have to decide what they can continue to afford to spend on operations and on
social programs. If actions presented as shared value creation or for environmental protection are halted during this or a
future downturn, there is a real possibility that stakeholders will suspect that the promises made by industry were not
really sincere. In the extractive industries, the sector tends to be judged by the behavior of the least responsible firm.
This may be unfair, but it is a reality exacerbated by the industry’s lack of willingness to condemn firms that pollute,
focus purely on short term profits, or disrupt communities. If best practices and shared value creation are set aside
during the current or a future downturn, public cynicism about the sector will increase, and social license may be lost
and even more difficult to regain.
CONFLICT OF INTEREST
The author confirms that this article content has no conflict of interest.
ACKNOWLEDGEMENTS
Declared none.
vii The current worth of a future sum of money or stream of cash flows given a specified rate of return. Future cash flows are discounted at the
discount rate, and the higher the discount rate, the lower the present value of the future cash flows. Available at: http://www.investopedia.com/
terms/p/presentvalue.asp, Accessed July 23, 2015.
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1 
 
 
 
 
A study in the financial valuation of a topping oil refinery 
 
Thesis submitted for the degree of PhD 
 
Birkbeck College, University of London 
 
May 2016 
 
by Patrick J O'Driscoll 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2 
 
 
Declaration 
 
I declare that the work in thesis is my own only, Patrick J. O'Driscoll. 
Signed................................................... 
Date:  ...................................May 2016. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3 
 
 
Abstract 
 
 
Oil refineries underpin modern day economics, finance and engineering – without their refined 
products the world would stand still, as vehicles would not have petrol, planes grounded without 
kerosene and homes not heated, without heating oil.  In this thesis I study the refinery as a financial 
asset; it is not too dissimilar to a chemical plant, in this respect.  There are a number of reasons for 
this research; over recent years there have been legal disputes based on a refiner's value, investors 
and entrepreneurs are interested in purchasing refineries, and finally the research in this arena is 
sparse.  In this thesis I utilise knowledge and techniques within finance, optimisation, stochastic 
mathematics and commodities to build programs that obtain a financial value for an oil refinery.  In 
chapter one I introduce the background of crude oil and the significance of the refinery in the oil 
value chain.  In chapter two I construct a traditional discounted cash flow valuation often applied 
within practical finance.  In chapter three I program an extensive piecewise non linear optimisation 
solution on the entire state space, leveraging off a simulation of the refined products using a set of 
single factor Schwartz (1997) stochastic equations often applied to commodities.  In chapter four I 
program an optimisation using an approximation on crack spread option data with the aim of 
lowering the duration of solution found in chapter three; this is achieved by utilising a two-factor 
Hull & White sub-trinomial tree based numerical scheme; see Hull & White (1994) articles I & II 
for a thorough description.  I obtain realistic and accurate numbers for a topping oil refinery using 
financial market contracts and other real data for the Vadinar refinery based in Gujurat India. 
 
 
 
 
 
4 
 
 
Declaration ......................................................................................................................................... 2 
Acknowledgements......................................................................................................................................... 13 
Introduction..................................................................................................................................................... 15 
Background and main contribution................................................................................................................. 15 
Assumptions ..................................................................................................................................... 18 
Current state of research ................................................................................................................. 20 
Chapter 1......................................................................................................................................................... 23 
1.0 Introduction to crude oil and its refined products.................................................................................... 24 
(Figure 1.1: A hundred years history of crude oil prices) ................................................................. 26 
1.1 Trading crude and its products.................................................................................................................. 27 
(Figure 1.2: US Refinery capacity and locations in Jan 2012 - EIA)................................................... 28 
(Figure 1.3: A quadratic fit to US oil extraction projection from 2015 onwards)............................. 30 
1.2 OPEC .......................................................................................................................................................... 31 
(Figure 1.4: OPEC production’s relationship with the WTI crude price over ten years) .................. 32 
1.3 The IEA....................................................................................................................................................... 33 
1.4 The refinery’s role...................................................................................................................................... 34 
(Figure 1.5: Typical historical GRM for a Coking Refinery over ten years)....................................... 35 
(Figure 1.6: Dependency of the US on crude imports)..................................................................... 36 
(Figure 1.7:  The leading role of Western Europe in petroleum exports to the US) ........................ 37 
(Figure 1.8: The US problem of growing consumption and reduced production) ........................... 38 
(Figure 1.9: Count on active US refineries)....................................................................................... 39 
(Figure 1.10:  Amount of product yield from a barrel of crude in the US (Retrieved 08/01/2013)) 40 
(Figure 1.11: Sales volumes of gasoline over time (Retrieved 08/01/2013))................................... 41 
(Table 1.1:  Summary of UK Refining Capacity, (2010)).................................................................... 42 
1.5 A brief history of petroleum...................................................................................................................... 43 
1.5.1 The modern oil industry .......................................................................................................... 44 
(Figure 1.12: The oil and gas value chain) ........................................................................................ 45 
(Table 1.2: Basic Features of Benchmark Crudes, Q1 2010 averages by Argus) .............................. 46 
5 
 
 
1.6. Statistics of crude oil and petroleum products over the last 10 years..................................................... 47 
1.6.1 WTI Crude Oil........................................................................................................................... 48 
(Figure1.13: NYMEX Futures price on the front month contract over 28 years)............................. 48 
(Table 1.3.1: Descriptive Statistics, WTI Oklahoma Contract  and the European Brent Spot Price FOB, 
($/bbl) over 20 years)....................................................................................................................... 49 
1.6.2 Brent Crude Oil........................................................................................................................ 50 
(Figure 1.14: Spot FOB European Brent over 25 years, ($/bbl))....................................................... 50 
1.6.3 Gasoline................................................................................................................................... 51 
(Figure 1.15:  NYMEX Futures price of front month Gasoline contract, ($/gallon))......................... 51 
(Table 1.3.2: New York Harbour Conventional Gasoline Regular Spot Price FOB ($/Gallon)........... 52 
1.6.4 Heating Oil............................................................................................................................... 53 
(Figure 1.16: NYMEX Futures price of front-month Heating Oil contract, ($/gallon)) ..................... 53 
(Table 1.3.3: New York “Harbour” No. 2 Heating Oil Spot Price FOB ($/Gallon))............................ 54 
1.6.5 Kerosene.................................................................................................................................. 55 
(Figure 1.17:  Spot prices of Kerosene FOB, ($/Gallon), Gulf Coast)................................................ 55 
(Table 1.3.4:  U.S. Gulf Coast Kerosene-Type Jet Fuel Spot Price FOB ($/Gallon))........................... 56 
1.6.6 Diesel Fuel ............................................................................................................................... 57 
(Figure 1.18: Spot prices of Diesel, ($/gallon), New York)................................................................ 58 
(Table 1.3.5: Los Angeles, CA Ultra-Low Sulphur Diesel Spot Price ($/Gallon))............................... 58 
1.6.7 Naphtha................................................................................................................................... 59 
(Table 1.3.6:  OSN Naphtha CFR Japan Front month Contract ($/tonne))....................................... 60 
1.6.7 Fuel Oil..................................................................................................................................... 61 
(Figure 1.19: Refiner prices of fuel oil, ($/gallon)) ........................................................................... 62 
(Table 1.3.7:  US residual fuel oil ($/gallon)).................................................................................... 62 
1.7 Refinery Optimisation................................................................................................................................ 63 
(Figure 1.20: Futures curve expectations)........................................................................................ 67 
(Figure 1.21:  WTI/Brent spread over the last five years) ................................................................ 68 
1.4.1 Refinery Risks........................................................................................................................... 69 
Chapter 2......................................................................................................................................................... 71 
6 
 
 
2.1 A traditional valuation of an oil refinery ................................................................................................... 72 
2.2 Refinery cash margin, RCM........................................................................................................................ 77 
2.3. Vadinar gross refining margin calculation................................................................................................ 79 
(Table 2.1: Reconciling Revenue from the refining part of the business to GRM, for the period 1 May 
2008 – 31 March 2009 and the 9 month period to 31 December 2009)..................................... 79 
(Table 2.2: Operating Costs per barrel at the Vadinar refinery) ................................................. 80 
(Table 2.3: Production of the refinery broken down by refined product )................................... 80 
(Table 2.4: Revenue generated by each refined product during periods shown )....................... 81 
2.4 Valuation risks to the Oil Refinery............................................................................................................. 83 
2.5 Discounted cash flow value (DCF) ............................................................................................................. 84 
2.6 Weighted Average Cost of Capital (WACC) ............................................................................................... 87 
2.7. Free Cash Flow Method (FCFF)................................................................................................................. 90 
Step 1 - Calculate the free cash flow........................................................................................... 90 
Step 2 - Forecast FCF and the terminal value.............................................................................. 90 
Step 3 - Calculate the weighted average cost of capital (WACC)................................................ 91 
Step 4 - Discount the free cash flows at WACC and aggregate to obtain value of the firm ....... 91 
Step 5  - Calculate the equity value............................................................................................. 91 
2.8. Oil refinery FCF calculation....................................................................................................................... 91 
(Table 2.5: FCF Calculation)......................................................................................................... 92 
(Table 2.6: Indian refined product demand, -’000 bbl/d)............................................................ 94 
2.9 Real Option Approach ............................................................................................................................... 96 
2.10. 
Problems with simple real option approach.................................................................................... 99 
Chapter 3....................................................................................................................................................... 101 
3.1 INTRODUCING COMMODITY LINKED ASSET VALUATION........................................................................ 102 
3.1. The Refiner’s Optionality......................................................................................................... 104 
3.1.1 Real data from an Indian Oil Refinery.............................................................................. 105 
3.1.2. The refinery’s risk ................................................................................................................. 110 
3.1.3. Introducing refinery planning............................................................................................... 112 
3.1.4. Multi-period refinery planning............................................................................................. 113 
7 
 
 
3.2. STOCHASTIC PROGRAMMING FOR OIL REFINERIES............................................................................... 114 
(Figure  3.1: Crude oil distillation and fractionation) ................................................................ 115 
3.2.1. Refinery physical flow constraints........................................................................................ 115 
(Figure 3.2: Fractionation of crude oil)...................................................................................... 116 
3.2.2. Refined oil products market data......................................................................................... 118 
(Figure  3.3: WTI crude front month contract historical price series)........................................ 119 
(Figure  3.4: Diesel fuel low sulphur historical price series )...................................................... 120 
(Figure  3.5: NY RBOB gasoline front month contract historical price series)........................... 121 
(Figure  3.6: Daily jet fuel retail historical price series) ............................................................. 122 
(Figure  3.7: New York Harbor No. 2 daily heating oil historical price future contract series).. 123 
(Figure  3.8: Propane front month future historical price series).............................................. 124 
3.2.3. The producer midterm planning framework........................................................................ 125 
3.2.4. Decision Variables ................................................................................................................ 125 
3.2.4.1. Fixed Yields................................................................................................................... 128 
3.2.4.2. Fixed Blends ................................................................................................................. 130 
3.2.4.3. Unrestricted balances .................................................................................................. 130 
3.2.4.4. Raw material availability constraints ........................................................................... 131 
3.2.5. Deterministic objective function.......................................................................................... 132 
3.2.6. Solving the deterministic objective function........................................................................ 133 
3.3 MULTI-PERIOD OPTIMISATION................................................................................................................ 135 
3.3.0.3. N-period (N+1 dates) optimisation .............................................................................. 136 
3.3.2. Foundation of the oil refinery intrinsic value....................................................................... 139 
(Figure  3.12: N period optimisation) ........................................................................................ 140 
3.3.2.1. Optimisation data flow chart ....................................................................................... 142 
(Figure 3.13: Flow chart process behind the refinery optimisation) ......................................... 143 
3.3.3. Stochastic commodity behaviour......................................................................................... 144 
(Figure 3.14: NYMEX future price data on refinery commodities) ............................................ 145 
3.3.3.1. The Ornstein Uhlenbeck process ................................................................................. 146 
8 
 
 
3.3.3.2. The Samuelson effect, mean reversion and positivity................................................. 147 
3.3.4. Two period - three dates, refinery optimisation.................................................................. 151 
3.3.4.1. Discrete process for crude oil....................................................................................... 153 
3.3.4.2. Discrete process for naphtha ....................................................................................... 153 
3.3.4.3. Discrete process for kerosene ...................................................................................... 154 
3.3.5. Transitional Probabilities...................................................................................................... 155 
3.3.5.1. Crude price probability conditions............................................................................... 156 
3.3.5.2. Naphtha price probability conditions........................................................................... 156 
3.3.5.3. Kerosene price probability conditions.......................................................................... 157 
(Figure 3.15 Node transitioning on the trinomial tree)............................................................. 158 
3.3.6. Steps for a two period dynamic optimisation ...................................................................... 158 
3.3.7. Refinery valuation using the backward recursion methodology.......................................... 160 
3.4. A CONTINUOUS TIME MODEL OF COMMODITY SPOT PRICE PROCESS EVOLUTION............................. 162 
3.4.0.1. The calibration of the spot price process..................................................................... 162 
3.4.1. Generating correlated commodity forward price series...................................................... 163 
3.4.1.1. Approximation for generating a symmetric positive definite matrix .......................... 165 
(Table 3.1: Correlations of commodity price return series – 10 years of futures prices NYMEX)166 
(Table 3.2: Correlations of commodity price return series – positive definite) ......................... 167 
3.4.2. The discrete-time spot price process ................................................................................... 169 
(Figure 3.16 Node transitioning on the trinomial tree with probability inputs)........................ 171 
3.4.2.1. Trinomial tree building procedure: Stage one ............................................................. 172 
(Figure 3.17: Branching for the stochastic process along the trinomial tree)........................... 173 
3.4.2.2. Trinomial tree building procedure: Stage two............................................................. 176 
3.4.3. Forward curves calibration procedure using Levenberg-Marquardt (1944)........................ 177 
3.4.3.1. Results of forward curves calibration .......................................................................... 180 
(Table 3.3: The calibration results of the stochastic one factor model over ten years using crude 
future contract prices from NYMEX.)........................................................................................ 180 
(Figure 3.18: Crude oil model prices versus futures market prices, NYMEX)............................. 181 
9 
 
 
(Figure 3.19: Four period (months) trinomial tree of spot crude oil market prices.) *72 periods were 
calculated in practice................................................................................................................. 182 
3.4.3.2. Delta of the oil refinery................................................................................................ 183 
3.5. NUMERICAL PROCEDURE FOR THE VALUATION .................................................................................... 185 
3.5.1. Numerical Approximation using stages to obtain a valuation ............................................. 190 
3.5.2. Methods available to solve the dynamic program............................................................... 190 
3.5.3. Mathematical issues with the valuation .............................................................................. 193 
3.5.4. Pseudo Code for the static refinery valuation...................................................................... 193 
3.5.5. Pseudo Code for the dynamic refinery valuation................................................................. 195 
3.6. DYNAMMIC PROGRAMMING USING NODES ......................................................................................... 199 
3.6.1.0. Set notation for optimisation....................................................................................... 199 
3.6.1.1. Decision Variables ........................................................................................................ 199 
3.6.1.2. Sets and Indices............................................................................................................ 200 
3.6.1.3. Parameters................................................................................................................... 200 
3.6.2. Expected profit maximisation ......................................................................................... 201 
3.6.3. Maximisation of dynamic objective function.................................................................. 201 
3.6.4. Solving the multi-period refinery portfolio model.......................................................... 202 
3.6.5. Maximising of final wealth.............................................................................................. 204 
3.7. Numerical Results................................................................................................................................... 206 
(Table 3.6:  Summary of stochastic trinomial tree optimisation of a Topping refinery with risk values, 
including the number of scenarios, and the CPU solution times) *All values were calculated 1000 
times and then averaged........................................................................................................... 208 
3.8. Graphical results..................................................................................................................................... 208 
(Figure  3.20: As the number of stages input into the program is increased the solution time increases 
in a non linear way)................................................................................................................... 208 
(Figure  3.21: As the number of years increases towards six the value begins to stabilise) ..... 209 
(Figure  3.22:  The refinery owner chooses an amount of crude to refine at each monthly period over 
the lifetime of the refinery) ....................................................................................................... 210 
(Figure  3.23: At different periods the valuation increases more than others as the refinery manager 
optimises the decisions along the trinomial tree)..................................................................... 211 
3.9. Out of sample stability analysis.............................................................................................................. 212 
10 
 
 
(Table 3.7: Stability associated with scenario generation procedure decision variables.  *Trees were 
generated and optimised 1000 times) ...................................................................................... 212 
3.10 Analysis of the Valuation Model............................................................................................................ 212 
3.11. CONCLUSION ........................................................................................................................................ 213 
Chapter 4....................................................................................................................................................... 216 
A CRACK SPREAD OPTION REFINERY VALUATION......................................................................................... 216 
INTRODUCTION ............................................................................................................................................. 217 
4.1.1 Crack spread effects .............................................................................................................. 222 
(Table 4.1: Factors affecting the crack spread value, assuming the other refined products remain the 
same value) ............................................................................................................................... 222 
4.1.2. Calculating the crack spread value with traded contracts ................................................... 224 
4.1.3. Descriptive statistics for commodity future price returns ................................................... 226 
(Table 4.2: Descriptive statistics for future price returns)......................................................... 227 
4.1.4. Assumptions underlying the crack spread valuation ........................................................... 228 
(Figure 4.2: Daily calculated crack spread prices, Gulf Coast 3-2-1 underlying in $/barrel)*Prices for 
the crack spread are calculated as standard with the 3-2-1 contract; 1 heating oil barrel spot price + 2 
gasoline barrels - 3 crude barrels, all divided by 3.(remembering to multiply gallon prices by 42).
................................................................................................................................................... 230 
(Figure 4.3: Daily log return crack spread prices, Gulf Coast 3-2-1 underlying)........................ 231 
4.1.5. Crack spread modelling ........................................................................................................ 231 
4.1.6. Types of crack spread options listed on the NYMEX exchange............................................ 233 
(Table 4.3: Crack spread options listed on the NYMEX exchange*There are days when open interest 
and volumes on all of these products is extremely high)(Data accessed on 05/03/2013)........ 234 
Valuing the refinery....................................................................................................................................... 235 
4.2.1. Data ...................................................................................................................................... 235 
(Table 4.4: Sample crack spread option prices)......................................................................... 236 
(Table 4.5: Gasoline Crack Spread Option prices)...................................................................... 236 
4.2.2. A strip of European crack spread options ............................................................................ 237 
4.2.3. The Linear program valuation construction......................................................................... 239 
4.2.4. Crack spread option pricing methods................................................................................... 242 
4.2.4.1 The Bachelier method applied to the crack spread option................................................ 242 
11 
 
 
4.2.4.2. Kirk’s Approximation applied to the crack spread option................................................. 244 
(Figure 4.4: Calculated crack spread prices, versus Kirk approx. of the option value $/barrel, strike at 
$2.44 per barrel)........................................................................................................................ 245 
(Figure 4.5: Calculated option price difference between Bachelier and Kirk $/barrel)............. 246 
4.2.4.3. Alexander and Venkatramanan’s approximation.............................................................. 246 
4.2.4.4. Schwartz and Smith(2000) estimation applied to the crack spread option...................... 248 
4.2.5. Hull and White Two factor model ........................................................................................ 249 
(Figure 4.6: Crude oil trinomial price series $/barrel) ............................................................... 251 
(Figure 4.7: Gasoline trinomial price series $/Gallon)............................................................... 251 
(Figure 4.8: Heating oil trinomial price series $/Gallon) ........................................................... 252 
(Table 4.6: European call crack spread option price errors)...................................................... 252 
4.3. The refinery valuation results................................................................................................................. 253 
(Table 4.7: Oil refinery valuation methods with 100,000 simulations, values in million $.  Standard 
error in valuation in brackets.* NYMEX call crack spread option prices over ten years) ......... 253 
4.3.1. The Updated linear program valuation results applied to the refinery............................... 254 
(Table 4.8: All methods with an updated oil refinery valuation, values in million $ Standard errors of 
MC in brackets.   *NYMEX call option crack spread prices)...................................................... 255 
(Figure 4.9: Daily crack spread value ($ per barrel) versus the crude oil purchase decisions (tons); (the 
left y axis is $ per barrel, the right y axis is tons/100)............................................................... 256 
4.3.2. Analysis................................................................................................................................. 256 
4.3.3. Comparison of all oil refinery valuation methods................................................................ 258 
(Table 4.9:  Alternative valuation approaches to the oil refinery: five methods from chapter 4, three 
from chapter 3, one from chapter I and one from chapter II)................................................... 258 
4.3.4. Refinery valuation research extensions ............................................................................... 258 
4.4. Conclusion .............................................................................................................................................. 262 
Thesis Conclusion .......................................................................................................................................... 263 
APPENDIX....................................................................................................................................................... 269 
A.1.1. Calibration of gasoline .................................................................................................... 269 
A.1.2. Calibration of naphtha .................................................................................................... 269 
A.1.3. Calibration of heating oil................................................................................................. 270 
12 
 
 
A.1.4. Calibration of kerosene................................................................................................... 270 
A.1.5. Calibration of cracker feed.............................................................................................. 271 
A.1.6. Calibration of diesel ........................................................................................................ 271 
(Figure A.2 Naphtha model prices versus market residential prices, EIA)................................. 273 
(Figure A.3 Heating oil model prices versus market futures prices, NYMEX)............................ 273 
(Figure A.4. Diesel model prices versus futures market prices, NYMEX)................................... 274 
A.2.0. Calibration of Alexander and Venkatramanan (2011) ........................................................................ 274 
(Figure: A.5. m as a function of strike K on the 12/02/2014, for option data from NYMEX).... 275 
A.2.1. Calibration of Hull and White two factor stochastic process.............................................................. 275 
A.2.2. Trinomial tree construction for Hull and White two factor process.................................... 276 
A.2.3. Calibration results for Hull and White two factor process................................................... 278 
Table A.2.3.1: The results of the calibration to crack-spread futures volatility on 12/02/2014278 
Table A.2.3.2: At-the-money European crack spread options-volatility quotes on 12/02/2014279 
Table A.2.3.3: At-the-money Hull & White two factor implied volatilities................................ 279 
A.2.4. Sub-tree calibration for Schwartz and Smith (2000) two-factor process ............................ 280 
(Table A.2.4.1: Resultant Calibration results for Schwartz and Smith (2000) on WTI crude oil for Dec 
2000 - Dec 2010) ....................................................................................................................... 281 
(Table A.2.4.2: Resultant Calibration results for Schwartz and Smith (2000) on No.2 heating oil for Dec 
2000 - Dec 2010) ....................................................................................................................... 282 
(Table A.2.4.3: Resultant Calibration results for Schwartz and Smith (2000) on RBOB NYH Gasoline for 
Dec 2000 - Dec 2010)................................................................................................................. 282 
(Figure A.2.4.1  WTI Crude Oil calibration results using Schwartz and Smith (2000)): ............. 284 
(Figure A.2.4.2  RBOB Gasoline calibration results using Schwartz and Smith (2000)):............ 285 
(Figure A.2.4.3  No.2 Heating Oil NYMEX calibration results using Schwartz and Smith (2000)):):286 
A 3.0 Comparison of forward curves for the one factor and two factor models.......................................... 287 
Published Paper.............................................................................................................................. 288 
REFERENCES................................................................................................................................................... 315 
 
13 
 
 
Acknowledgements 
 
I would firstly like to thank my supervisor, Professor Helyette Geman, for providing me with the 
original idea and the environment in which to work undistracted and relentlessly.  Secondly, I 
would like to thank my second supervisor, Professor Raymond Brummehlius, who has provided the 
support of my chapters to ensure that they have passed at each stage of the PhD process.  I would 
also like to thank Professor Ron Smith, for his general advice.  I hope that the practical approach to 
real option pricing in this thesis will be a contribution to the literature, that at least, sets some 
threshold for others in this very exciting and practical area to overcome or leverage from. 
 
I would like to thank Reflection, a small statistics company based in Soho, for providing me with 
the statistical forecasting practical paid work; using R for regressions and other relevant statistical 
exercises.  This work enabled me to continue the PhD well past the 3rd year after the government 
funding ran out; I wish them well in their endeavors.  I would also like to thank the financial 
accountancy team at Hermes Investment Management for their contribution and help with 
accountancy techniques for chapter two.  Thanks also to Spiral software in Cambridge for their 
suggestions and input on the optimisation coding carried out in chapters three and four. 
 
Originally this thesis was supposed to be unfinished business from 2003, whence I quit a PhD in 
astrophysics to join the great befallen Merrill Lynch – although I never rue decisions, this path was 
certainly the harder route; there have been many lessons, from extreme patience, managing the 
nuances of an environment that should be rewarding meritocracy, to knowing what is in ones 
control and what is not.  As the original thesis was to be dedicated to my mother Patricia Johnson 
O'Driscoll, who I owe it all to – a very eloquent and sophisticated woman who knew that life is all 
about being happy; it is not too far that I have to look to find the next person to thank, my father 
Patrick O'Driscoll, without his parenting and disciplinary lessons I would not have succeeded at 
14 
 
 
UCL or in the dojo – a true practical person who epitomises work ethic and practicality; and under 
whatever strenuous circumstance one is under he always taught: “one cannot keep a winning man 
down, in the end he will get there”. 
 
I would also like to thank my fellow PhD students for providing support and the necessary adversity 
when required; without the competition and the great discussions with Georg Dettmann, Tobias 
Grasl, Alexander Karalis and Marco Pellicia, the interest for this thesis would have left me a long 
time ago. 
 
Finally, I would like to thank my wife Ying Wu-O'Driscoll, for her constant supervision, advice and 
focus, she has truly been inspirational.  This thesis is dedicated to her; without her love I would also 
have been nowhere, to quote a very famous individual: “we are lost in the water, we must close the 
door, we are done for” - luckily I was never close to feeling this way, but was looking for fun with 
this thesis and creating something that others may be able to use somewhere, somehow.  
 
 
 
 
 
 
 
 
 
 
 
15 
 
 
Introduction 
Background and main contribution 
 
Without the flow and refining of petroleum, planes would be grounded, plastic containers 
ridiculously expensive, butane for lighters non existent, the heating of homes all over the US would 
be impossible to maintain. Without this multi-billion dollar industry, many would be unemployed.  
It is without doubt one of the most significant and interesting sectors of the global economy and 
refineries themselves are the foundation of the flow from raw crude to the final products that the 
market purchases.   
 
In recent years many refineries have gone under due to the difficult climate; in the seven 
years up to January 2015, the four main EU countries in refining lost a total of 2M barrels per day, 
b/d, of operable refining capacity, that is approximately 23%, IEA (2009-2015).  The UK has closed 
two refineries in the last five years, with France closing four.  The WTI and Brent crude oils have 
hit huge lows, with Brent going below $30 per barrel in Jan 2016 and WTI at $31 per barrel in the 
same period.  This benefit to the consumer has been contrasted by a volatile environment and oil 
organisations altering their business and investment strategies.  However, refineries will be required 
as long as there is a demand for kerosene, butane, gasoline, and others, but the processes need to be 
more efficient, more environmentally friendly, and the complexes themselves, will not survive 
unless their complexity increases inline with the most advanced refiners.  The decision to alter 
output product prices needs to be done in a way that means, volumetrically, profits are maximised 
within applicable constraints.   
 
High tech refineries can refine very heavy raw crude; regarded as lower quality due to the 
waste inherent in this type of oil but it is in a businesses ability to adapt that ensures its survival.  A 
measurement named the Nelson Complexity factor measures the technology level of a refinery; a 
number of 9 is regarded as a high level; yet the highest currently is the former BP Texas refinery, 
16 
 
 
acquired by Marathon Petroleum with an index of 15.3.  To expand the complex up to this level can 
be expensive, but in the right local or market region it can be extremely profitable – enabling the 
refinery to serve overseas customers with inexpensive crude input.  There are many types of 
software packages in use at a refinery complex, from health and safety reasons to optimising the 
quality within the  crude distillation unit, or CDU, and the catalytic cracker; in this thesis we 
investigate how optimisation of the volumetric decisions affects the profit for the owner, who can 
buy crude oil on the market, and choose to produce as much as physically possible of the best 
performing petroleum products.  This, to our knowledge, is yet to be achieved in the literature.  It is 
important to note that we are in a setting of incomplete markets as we cannot replicated the value of 
the refinery with available assets in the market - the refinery is an illiquid asset in a part of the 
world without relevant available financial contracts.  We attempt to produce a credible range of real 
asset values; in that they are realistic in the sense that the approach to represent value is logical and 
representative of the oil refinery's business incorporating the underlying commodities.  Whilst using 
a risk neutral assumption on the price return series of the commodities the embedded optionality of 
choosing an alternative refined product is captured by the numerical method presented.  There is in 
fact no available method to remove the uncertainty in the refinery cash flows; with this in mind we 
include a value at risk figure on the final wealth of the refinery applicable to the horizon 
represented.  Due to this real option valuation considering all seven refined products it means that 
the owner has the embedded optionality of many more products than usually considered in refinery 
approaches.  If the correlated commodity price trees are constructed correctly it means that we have 
a numerical method that can value the refinery based upon this choice of refining more or less of a 
particular product at a certain point in time. 
 
The spine of the thesis is the topping refinery itself - one of four standard types of refinery 
and considered the most basic; this category of refiner has been chosen despite the cracking 
refinery, a very advanced refinery, being ubiquitous, as the linear programs used in industry are 
very difficult to obtain for investigative purposes, whereas for the topping programs there are many 
17 
 
 
open source code bases which can be accessed.  Throughout the thesis a standard linear program for 
a topping oil refinery, which is detailed in the appendix is used as the foundation for each valuation.  
In addition to providing a valuation, the optimisations provide a number of other useful outputs not 
explored in the literature.  It is also important to note that wherever in the thesis there is a 
calibration present it is the returns of the prices that have been input into any program and not the 
absolute prices - despite this as frequently evident in the older papers like Schwartz (1997) we 
graph the price series and not the returns to display the movements of the commodities in reality. 
 
In this thesis we firstly introduce the industry and describe the characteristics of the products 
in a statistical analysis; petroleum product moments and events in the time series are analysed along 
with an introduction to the mathematical programming literature available in the refining space. 
 
In chapter two, we describe a standardised discounted cash flow valuation, which is 
representative of the financial market's way to value a real asset.  Many auditors and finance 
professionals will use an income based approach to value an asset.  In this chapter we discuss the 
various approaches and the lag to the more advanced methods within the industry, like the real 
option approach. 
 
In chapter three we go beyond standard calculations and introduce a real option valuation; 
that is more complex and computationally more demanding, but proves to be a worthwhile 
investigation, and in terms of the uncertainty within the prices, continuous stochastic equations are 
chosen.  It is trivial to discretise them onto a relevant simulation tree to enable a dynamic program 
to be optimised at each node.  The choice of equations is based on models required that would 
enable a number of aspects to be included for a realistic and dynamic commodity price series - the 
valuation is higher than DCF reveals and despite being an incomplete market settings gives a range 
of values that represent the optionality for the refinery owner.   
 
Finally, in chapter four we find ways to value the complex, using a dimensional shortcut 
based on using the crack-spread rather than seven prices, which can be compared to other real 
option valuations within an oil refinery linear program.  Due to the simplification introduced in 
18 
 
 
chapter four we can elaborate on the complexity of the underlying stochastic processes.  To our 
knowledge, the valuation procedure introduced in chapter four is the only real option valuation of 
an oil refinery in the current literature including the option pricing analyses.  The method we use is 
also a unique combination of stochastic mathematics to represent the commodity prices over time 
and numerical methods to obtain a value. The program created is able to manage the extensive state 
space due to the use of dynamic set assignment; to our knowledge not applied in the refinery 
literature.  Too many studies, theoretical or numerical, avoid implementing a model if solving over 
a colossal dimensional space.   
 
We provide various alternative but realistic valuations in chapters two to four and the paper 
published in the appendix. These procedures open the door to further areas of research worth 
studying.  The main contribution of this thesis is to provide a valuation for a refinery complex. An 
additional contribution is the combination of the stochastic simulation, with the discretisation onto a 
trinomial tree, into the eventual dynamic program implemented in GAMS, which introduces a 
number of computational shortcuts to find the valuation - including the dynamic set construction. 
 
 
 
Assumptions 
 
The main assumptions in obtaining a valuation in this thesis are: investors are rational and we are 
making calculations in a risk neutral setting; despite being in incomplete markets with no available 
financial contracts for replication.  Many refinery papers in this area assume the same, as it allows a 
depth of equations and techniques for option pricing that can be manipulated without additional 
extraneous tampering, and they can be justified in a real market. 
19 
 
 
 
One of the common issues within pricing is finding a market price of risk, which is 
representative in various scenarios.  The market price of risk is the return expected above the risk 
free-rate that the market demands as compensation for taking the specified risk.  In classic 
economics no rational investor would commit their own resources without expecting to earn above 
the risk free rate.  The market price of risk is usually measured as a ratio of the expected return for 
the asset over the risk-free rate divided by the standard deviation of returns.  In option theory we 
often attempt to model the state variables as random.  This stochasticity leads to risk, how much 
should we expect to earn for taking this risk?  Is this quantity directly tradable in the market? 
 
If the quantity is traded directly in the market then we can hedge away the risk in an option 
by dynamically buying and selling a particular quantity of the underlying.  The refinery does not 
have this luxury due to it being an illiquid real asset in a remote and deregulated market.  Despite 
the issues we aim to construct a risk neutral valuation that takes into these practical considerations 
and provides a statistically correct value.  Throughout we assume no financial trading transactions 
costs, as is commonly applied in option pricing as it is not the focus of our problem.  We do not 
consider stochastic interest rates however, but apply a deterministic risk free curve whenever 
discounting is required, built using standard products for treasuries and swaps using appropriate 
data from Bloomberg.   
 
Where we use specific techniques, we explicitly state the assumptions made, for example in 
chapters three and four we use a Hull and White trinomial tree (1994). There are very standard 
assumptions for this tree, like the size of the step relative to the volatility, along with others 
described in detail later in the thesis.   
 
The major assumption inherent in the final two chapters, which carry out unique valuations 
and comparisons respectively is that the oil refinery asset value can be represented as a daily strip of 
monthly European call options on the crack spread.  This is described in depth in chapters three and 
four, but simplistically means the owner has optionality on each day to defer or execute production 
at the refinery – the advantages and disadvantages of our approach is discussed in depth.  In terms 
20 
 
 
of the refinery itself we ignore taxes and transaction costs, along with preventative maintenance and 
assume start up and shutdown costs are considered insignificant.   
 
 
Current state of research  
 
As mentioned there is no current oil refinery valuation methodology within either the engineering or 
finance literature, whereas in practice, a very basic and flawed method, DCF, is commonly applied 
to real assets; see Brandao (2002 & 2005) for a detailed discussion on replacing DCF with real 
option approaches.  There is an extensive literature on refinery planning and on balancing the fluid 
flows in chemical engineering; Neiro and Pinto (2003), Pongsadki (2005) and Nan and Marc (2013) 
all provide refinery planning optimisations at different timescales. There is also a literature on the 
financial valuation of real assets utilising real option approaches, but the relevant techniques are yet 
to be applied to oil refineries. This is the first such study.   
 
Benyoucef (2010) considers a mixed integer non linear program applied to a network of 
refineries, but not an individual complex and without a financial valuation being the objective.  
Pindyck and Dixit (1994) develop the theory, especially in the dynamic programming space for real 
option valuations, but do not implement the methods to actual plants or any real assets.  Hull and 
White (1994-1995) provide many numerical and mathematical tools applied to real options and the 
stochastic simulation of the relevant equations for forecasting purposes, yet no valuation for a 
refinery is provided.   There are a number of engineering papers: Lin et al (2009) and Cao et al 
(2009), and others, that consider the oil refinery owner's decisions over one period (commonly 
known as two stage stochastic programming), although very similar in terms of the objective 
function, and also being extremely helpful, the formulations are basic when compared to a multi-
period solution, hence used as a foundation for this thesis.  Ribas et al (2012), examine the detailed 
21 
 
 
fluid movements within a Brazilian refinery; the authors optimise at a granularity that is extremely 
detailed and useful in terms of the mathematical program constructed; yet we are interested in the 
financial ramifications.  Leiras et al (2011) collect refinery planning papers and provide a thorough 
comparison of methods and results; very few authors in these studies attempt to capture the 
uncertainty of petroleum prices, especially using continuous stochastic equations, which are adept at 
capturing the relevant bespoke commodity behaviour.  In this study there are many two stage 
programs, discrete normal simulations but rarely are there financial objectives.   
 
We leverage off these previous studies, that use linear programming for financial goals; 
Aldaihani and Al-Deehani (2010) examine the efficiency of the Kuwaiti stock exchange using an 
optimisation for an equally weighted basket of stocks – despite its simplicity in terms of the 
dimension of the problem, the authors find a way to lower risk for investors for a threshold of 
specific return.  Elkamel and Al-Qahatani (2011) use discrete scenario generation, where an 
assumption of the probability of each scenario is applied to a PVC complex on top of a refiner – a 
sample average approximation is applied; despite being non-deterministic, the method leaves space 
for stochastically fluctuating uncertainty.  The type of problem we are developing comes under the 
bracket of tactical midterm refining planning; this is very complex and can be optimising different 
units in the refinery for example, efficient catalytic cracking – in our case we concentrate on the net 
present value over multiple periods.   
 
Decision based systems at refiners aim to aid the process; PIMS (AspenTech) is a complex 
system designed to perfect the chemical flows within the parts of the refinery to create the best 
product possible.  It utilises mathematical programming and this is the most common system 
implemented at refineries in the world (Kelly and Mann, 2003).  Wang (2013) manages the 
optimisation by using a finite planning horizon with periodic preventative maintenance, and random 
failure; however, his optimisation is discrete and cannot manage the dimensional problem.  An 
extensive literature review is provided at the start of each chapter relevant to that chapter's 
objective.  Before moving onto the first attempt at valuing the refinery we provide some context by 
22 
 
 
introducing the refining industry and lay the foundations for the mathematical models introduced in 
later chapters. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
23 
 
 
Chapter 1 
 
An introduction to the oil industry and its refineries 
 
 
“It is wise to apply the oil of refined politeness to the mechanism of friendship.” Sidonie Gabrielle 
Colette. 
 
In this first chapter we begin with a discussion of the petroleum industry and its most important 
products, i.e. the refined outputs from a typical cracking or topping oil refinery.  In recent months  
prices on the WTI and Brent products have dropped dramatically – the effects in the outer economy 
are widespread, but within the oil industry itself it is unprecedented.  We leave the fundamental 
drivers of this market within the macro economy to others but emphasise the detailed refinery 
processes and their importance.  Next, we give a grounding in the basics of the technical indicators 
of crude and its by products; along with some historical context and applications of modelling 
within the oil industry – highlighting statistical moments and graphing typical behaviour of the spot 
prices.  The refining sector is a multi billion dollar industry with huge impacts on the global 
environment, global economy and the global energy industry, hence the significance of the prices of 
the products derived from crude and its refining.  Oil refineries are studied in detail within 
chemical engineering, but analysis of the optimisation of decisions of a refinery owner and its 
knock on effects to the market products is sparse – the financial impacts are even more rare; 
leaving space for investigation and discovery in linking financial and oil refinery analytics.  Here 
we discuss the individual refined products, their behaviour and their significance in the refiner's 
24 
 
 
decision process.  We conclude with a discussion of the risks associated with refineries, and the 
main products utilised to managed these risks.  
 
 
 
 
The goal in this thesis is to investigate the details of an optimisation model that will maximise the 
profit of an oil refinery, subject to a standard set of Topping refinery constraints.  The final model is 
required to be optimal from a statistical perspective in that probability distributions are obtained for 
the commodity prices, and their financial risk can then be included if required.  There are a number 
of standard models derived in the 1950 and 1960s that form the foundation of the current 
mathematical programming models developed for refinery optimisation software.  These ideas 
underpin the optimisation model throughout the thesis. 
 
1.0 Introduction to crude oil and its refined products 
 
The subject of crude oil and its products is so rich and varied that it cannot be fully covered; hence a 
number of issues will not be discussed in this work, for instance the technicalities of oil production 
and exploration or the details of crude transportation across the globe and its oceans.  However, 
ideas based upon why the world has depended on oil for centuries, and will continue to do so for a 
long time yet, are described in this chapter.  Secondly, what constitutes crude oil and its refined 
products, and what practical uses they have is exemplified.  Finally, the refined product price series 
characteristics are analysed through statistics applied to the data over the last ten years. 
The price of crude oil affects us all; it has direct and indirect effects on inflation, cost of 
production in corporations, mining activities and agricultural farming.  Its price volatility moves the 
25 
 
 
value of some of the largest funds heavily invested in the petroleum family, and it is often imputed 
as one of the causes for Governments to go to war.  There are many variations of raw crude oil and 
its refinement upholds the entire transport industry due to the gasoline product.  The diverse 
products refined from petroleum are classified by a number of properties, the most important ones 
being: American Petroleum Institute (API) gravity (a measure of how heavy or light a petroleum 
fuel is relative to water, most values fall between 10 and 70 API gravity degrees); the “cetane” 
number (a measure of light distillate diesel oil’s ignition delay); octane number (a measure of 
resistance to detonation of the fuel) and sulphur content.  Crude oil is considered light if it has a low 
density, and sweet if it contains little sulphur.  There are a profuse number of factors that influence 
the type of products that a refinery will create, the primary ones being the modernity of the refinery 
and the distilled products’ prices in particular.  Most refineries are at ports due to the expensive 
costs of the raw crude transportation1.  The most abundantly refined output product is petrol 
(gasoline in the US) for use in the transportation industry.  A higher yield of petrol comes from light 
crude and there are fewer environmental problems with the sweet type; hence, light sweet types of 
crude are more expensive.  The benchmark in the US and all over the world until 2009 was the 
West Texas Intermediate (WTI) crude.  It is held, after extraction, in huge facilities before travelling 
across America’s pipelines.  It is a high quality, sweet, light oil delivered at Cushing, Oklahoma as 
far as the NYMEX Futures contracts are concerned (this financial contract trades in units of 1,000 
barrels).  The U.S. Governments’ decontrol of oil prices, starting in January 1981, influenced the 
volumes and transparency of the spot markets in oil.  The decontrolling of oil prices created the 
circumstances for the development of the WTI contract in 1983 priced on crude at Cushing.  More 
recently, the Brent contract has replaced WTI, in that it is used as a benchmark for over two thirds 
of the world’s crude oil.  The differential between WTI and the Brent oil contract is a large area of 
research, and many experts believe this shift is only temporary until WTI is not landlocked.  The 
Brent blend in comparison, entails 15 oils from different fields in the East Shetland Basin.  
                                                 
1 The Port of Antwerp provides an example of a refinery cluster: http://www.portofantwerp.com/en/chemical-cluster 
26 
 
 
Benchmarks for crudes were fashioned as there are globally many different varieties of the raw fuel 
demanded.  The Brent crude is regarded as the benchmark crude in Europe; Dubai-Oman is used as 
the benchmark for the Middle East.  In this recent decade the Platts Dubai benchmark has become 
the reference for deliveries of crude out of the East Siberian port of refineries: Kozmino; its price on 
Sep 28th 2015 was $44.5 per barrel.  The physical is underpinned by several million barrels per day 
derivative spread trades on the Brent/Dubai difference.  In practise, the physical trade is miniscule 
in comparison to the paper market; paper contracts have provided investors with the instrument to 
buy and sell this versatile commodity in a liquid, hence reliable way.  Figure 1.1 captures a history 
of crude oil and some significant events. 
 
 
(Figure 1.1: A hundred years history of crude oil prices) 
 
27 
 
 
1.1 Trading crude and its products 
 
The first Futures contracts can be traced to European trade fairs in the 12th century.  In this 
period, travelling with large quantities of goods was perilous.  Vendors resorted to taking the road 
with simple samples and sold Futures for quantities to be delivered at future dates.  These contracts 
have become the primary mechanism for trading crude oil and other fossil fuels in the modern 
financial arena.  Crude oils, as discussed, can be of various compositions, enabling a medley of 
products to be processed.  Trading of these various oil and oil related products, is carried out all 
over the world at exchanges with specific contracts.  The exchanges transacting the largest volumes 
are the New York Mercantile Exchange (NYMEX) and the Intercontinental Exchange (ICE), which 
absorbed the International Petroleum Exchange (IPE) in June 2001.  Dubai, Mumbai and Tokyo 
have become the other large trading platforms for crude oil.  These financial contracts enable oil 
companies, refiners and investors to buy or sell crude oil for future dates.  Trading of these 
contracts on Exchanges directly determines the market prices for crude and its products.  Refineries 
are interested in buying crude in physical form, and selling the distilled products back to the market.  
However, due to the growth of the financial markets in the oil industry, derivative contracts related 
to an asset “on paper” have seen a meteoric rise in transaction volume.  Theretofore, paper contracts 
are frequently utilised to hedge the refinery’s exposure to the volatility of crude prices. 
Refineries create a variety of products using processes known as distillation, cracking and 
purification.  Refinery products demanded by the market are liquid petroleum gas (LPG), gasoline, 
fuel oil, heating oil, naphtha, residues and many others.  Therefore, crude oil needs to be first 
refined to be valuable, hence useful to the end customer.  For example, gasoline is refined in the 
largest cut of the slate due to its use as a fuel for vehicles; retail prices at the “pump” in the UK in 
mid 2012 were around 149 pence per litre2.  There are different grades and combinations of 
products that can be distilled to produce over 4,000 petrochemical products for the market.  Once 
                                                 
2  BP ultimate 28/06/2012 
28 
 
 
refined, the uses of crude throughout industries all over the world are phenomenal.  In the US and 
Canada, the transportation industry leads all others in volumes of crude processed by a large 
margin.  Elsewhere, it is exploited for energy production and is globally a key ingredient to the 
agricultural industry that feeds the world’s population.  
 
  
 
(Figure 1.2: US Refinery capacity and locations in Jan 2012 - EIA) 
 
The survival of refineries is dependent upon the type of crude they can buy and the 
technology available at that particular refinery - the raw crude and its quality can make or break the 
local region's refinery; see figure 1.2 for density of refineries on the Gulf Coast of the US.  Market 
data on crude oil has an indispensable impact: prices, reserve levels and volumes traded on the 
financial markets all act as significant indicators of the worldwide economy and global trade levels.  
Current estimates from the Energy Information Administration (EIA) state that 30% of the world’s 
energy needs are being met by crude oil.  On average, a substantial proportion of a country’s GDP 
is directly accounted for by the energy sector - for example, the UK’s energy sector was 4.4% of 
GDP in 20113.  Products from petroleum account for huge proportions of a number of countries 
                                                 
3  DECC UK Government research, 2011 
29 
 
 
export's value.  For example, the UK exported 38 million tonnes of petroleum-related products in 
2011, roughly equal in value to $26 billion.   
  Environmentalists are concerned about the crude oil sulphur content, and therefore try to 
ensure governments increase regulatory specifications.  The mechanism that Governments usually 
employ to meet these specifications is to introduce a specific taxation based on the amount of 
sulphur contained in the crude - for example, in 2012 the UK government was attempting to 
introduce increases in taxes on refined products4, but not without fierce opposition from the 
Grangemouth refinery in Scotland.  As this wave of opposition has grown, the oil industry has 
adapted.  Many companies now have entire budgets devoted to green technology and corporate 
responsibility.  For instance, advanced technology based car companies are attempting to reduce 
transportation reliance upon crude oils due to its diminishing reserve numbers.  The Gas Journal 
(Dec 21st 2009) states that Saudi Arabia holds the world’s largest reserves at 262.4 billion barrels, 
but these are rapidly declining.       
 
 
                                                 
4  http://www.falkirkherald.co.uk/news/local-headlines/grangemouth-meeting-gets-the-message-across-1-2537090 
30 
 
 
(Figure 1.3: A quadratic fit to US oil extraction projection from 2015 onwards) 
 
A number of bespoke companies now invest in research that attempts to obtain crude oil 
reserve numbers to predict depletion rates.  Crude oil reserve forecasting is usually calculated using 
Hubbert curves.  Recent research predicts that at the current pace of exploration and development, 
the world has less than 50 years left of traditional oil5; see figure 1.3 for a standard quadratic fit to 
EIA crude extraction figures.  In 1956, Hubbert formulated a model for estimating future production 
for fossil fuels.  His main assumption was that after reserves of coal, oil and other fossil fuels are 
discovered, the production curve follows a distinct shape.  Production at first increases 
exponentially; at some point a peak is reached, and finally production brings an exponential decline.  
Hubbert curves on crude take into account current oil reserves; forecasted usage; forecasted 
discoveries and copious other factors.  In response to the data on depletion rates, unconventional oil 
extraction techniques have sprung up in the last ten years.  For example, oil sands – an 
unconventional extra heavy petroleum deposit - represent 97% of Canada’s oil reserves.  Despite 
the fact that it will be many years before reserves have noticeably diminished, investment from a 
proportion of companies has noticeably shifted towards those product areas that could replace or 
substitute refined products.  In the short term however, a sudden supply blockage, unusual weather 
or other logistical issues will dramatically impact the pricing of refined products.  This statement 
holds true only if an entire region important to the refining market is impacted, for example the Gulf 
Coast of the United States - in 2005 when hurricane Katrina hit the Gulf Coast, Florida and Texas, 
its effects had a major impact on gasoline prices, amongst the other disastrous environmental 
effects.  WTI oil Futures reached record highs of $70 per barrel and gasoline prices more than 
tripled in areas such as Louisiana.  On August 31st, eight of the Gulf of Mexico refineries remained 
shut down.  By September the 7th, Gulf oil production had returned to 42% of normal levels.  Out of 
                                                 
5  Gas Journal (Dec 21st 2009) 
31 
 
 
ten refineries that were closed, four were back at full capacity within a week, whereas the other 
refineries were out of commission for months.   
In the midterm, under normal market circumstances, it is the cost of refining that has the 
largest impact on the market price of oil products.  In what is regarded as the standard oil market 
model, spare capacity at refineries used to be common; these days, it has reduced dramatically.  In 
2003 spare capacity had dropped from 3 million barrels per day (bpd) to 1 million barrels per day.  
During the gasoline season (April to August), the price of gasoline can reach highs of $3.406 per 
Gallon to consumers6.  In general, demand increase occurs every year in the summer period.  There 
are innumerable other factors that can impact oil prices, including wars, political interventions, 
government policy announcements and news from organisations such as OPEC. 
 
1.2 OPEC 
The Organisation of the Oil Exporting Countries, OPEC, was founded in Baghdad, Iraq, in 
1960.  It has its’ headquarters in Vienna and is an inter-governmental group of 12 oil producing 
states, ranging from Algeria to the United Arab Emirates.  It was known as the Organisation of 
Arab Petroleum Exporting Countries or OAPEC (consisting of the Arab members of OPEC, plus 
Egypt, Syria and Tunisia) in the early 1970s.  One of its principal goals is the safeguarding of the 
governments involved in the organisation.  Another is stabilisation of the oil price within 
international markets; ensuring a predictable income for those nations involved, and predictable 
costs for importing countries.  Despite these published goals, OPEC was blamed for the 1973 oil 
crisis by implementing oil embargoes in response to the U.S. decision to re-supply the Israeli 
military during the Kippur war. 
                                                 
6  EIA website April 2012 
32 
 
 
 
(Figure 1.4: OPEC production’s relationship with the WTI crude price over ten years) 
 
OPEC was the key provider to the West for oil and in the past, maintained control of the prices.  
Since the discoveries in Alaska and the Gulf of Mexico, the control of prices has loosened 
somewhat, nevertheless the members of OPEC still own somewhere near 80% of proven world 
crude oil reserves7. 
In 1974, after the end of the Bretton Woods agreement, OPEC announced that they would 
price a barrel of oil against gold8.  Following these events, the volatility and price of oil both 
increased and have remained in and around this new threshold since.  The overall effect of the Arab 
oil embargo in economic terms was to increase the real price of crude oil to the refineries.  In 1972, 
the price of a barrel (bbl) of crude oil was $3; by the end of 1974 it had quadrupled to $12 per 
barrel.  In recent times, the price of WTI crude has hovered around $85 a barrel (after a peak to 
                                                 
7  OPEC’s annual statistical bulletin (2012). 
8  Cohen, Benjamin. "Bretton Woods System", 28/06/2001. 
33 
 
 
$140 in July 2008 and a collapse to $38 in December 2008).  In 1988 OPEC decided to use Brent, 
underlying the IPE/ICE Futures contracts, as the reference price of crude oil in its trading activities. 
Note that only 25% of the physical market at anytime constitutes the commercial crude oil market. 
The price variations depend highly on the choices made at the heart of the value chain, i.e. the 
refinery.  As figure 1.4 shows, oil price changes are correlated with OPEC production levels.  Oil 
related businesses, investors and the general public are the parties that can suffer from high or 
volatile oil prices.   
 
1.3 The IEA 
The International Energy Association is an autonomous Paris based intergovernmental 
organisation established in 1974 in response to the 1973 oil crisis9.  It acts as a policy advisor to 28 
member states.  Its aims are to ensure that oil policies are focused on energy security, economic 
development and environmental protection.  It comes under the umbrella of the Organisation for 
Economic Co-Operation and Development (OECD) – it originated in 1961 to stimulate economic 
progress and world trade.  The OECD was first established to help administer the Marshall Plan for 
reconstruction of Europe after World War II.  It is also famous for promoting climate change related 
policies and alternate energy sources.  In 2011, the IEA stated that the $409 billion of fossil fuels 
subsidies were the cause of wasted energy and a viable policy to address this problem would be to 
cut subsidies.    
The influence of organisations like OPEC is certainly significant for the price of crude; the 
IEA is less influential in constructing environmental and economic policies, but can act on 
preponderant short-term issues: for instance, it intervened on the oil markets three times by 
releasing oil stocks in 1991 during the Gulf War; in 2005 for a month after Hurricane Katrina, and 
                                                 
9  http://www.iea.ma/fr/ 
34 
 
 
recently in 2011 to offset the Libyan civil war.  IEA member countries are currently required to 
maintain stock levels equivalent to 90 days of the previous year’s net imports.   
 
1.4 The refinery’s role 
There are currently around 655 oil refineries in the world, located in 116 countries, with a 
daily total capacity of 88 million barrels per day10.  The annual throughput is roughly 75 million 
barrels a day with an average utilisation rate of 85%.  The refinery is built around the process of 
acquiring crude oil and ensuring that converting it into the market products is as smooth and 
profitable a process as possible.  The equipment is very expensive and the technology and 
complexity of most refineries lag behind the top performers within the industry.  There is a notable 
correlation between greater profits and refineries with a high Nelson complexity index11.  This 
index was created by Wilbur Nelson in 1960 to quantify the relative costs of the components of a 
refinery.  It is associated with equipment that can refine even the heavier crudes effectively and 
extract the products that are in high demand. The increased regulation surrounding the sulphur 
content of the raw crude has meant that only the most complex refineries, capable of refining both 
the heaviest and the lightest oils, are able to manage a volatile Gross Refining Margin (GRM): 
equal to the total value of the petroleum products from one barrel of crude after cracking, minus the 
cost of one barrel of crude oil.  This measure is often reported within the industry as an indicator 
over time of refinery profit.  In a regulated business environment, the GRM is most likely set by an 
agency, but in open markets like the US and Europe it is determined openly without regulatory 
interference.  In tough economic conditions, the GRM can become negative, i.e., -$1.6/bbl12.  In 
times of high demand with very little spare capacity a GRM of $20/bbl is not uncommon.  Shown 
                                                 
10  Source: BP Statistical Review of World Energy, June 2011   
11  Reliance Industries: http://www.ril.com/downloads/pdf/business_petroleum_refiningmktg_lc_ncf.pdf 
12  ftp://ftp.eia.doe.gov/pub/oil_gas/petroleum/analysis_publications/petroleum_issues_trends_1996/CHAPTER7.PDF 
35 
 
 
below in figure 1.5 is the evolution of the GRM of a typical coking refinery in the US over ten 
years.  A topping refinery is a basic form of refinery, usually unable to produce gasoline.  A hydro-
skimming refinery is slightly more complex, and produces gasoline and excess fuel that is often in 
low demand.  A cracking refinery is even more complex and allows the cracking of residual fuel 
into the middle distillates.  The middle distillates comprise a wide range of products stemming 
from, jet fuel and kerosene to diesel.  Finally, a coking refinery has an additional coking unit 
enabling the secondary unit to turn residues into high quality products.  The Topping category is a 
small share of global refineries as the cracking type is the most prevalent - yet we focus in this 
thesis on a Topping type that can produce gasoline due to the ease of access to Topping linear 
programs. 
 
 
(Figure 1.5: Typical historical GRM for a Coking Refinery over ten years) 
 
If the refinery does not have the inherent flexibility to meet the demand in its location, then it may 
import the required products.  In recent times, conversion refineries run at full utilisation, but if the 
36 
 
 
market prices of the refined products dip dramatically, and the refining margin goes below some 
pre-specified threshold, the operator can switch the refining equipment off.  Utilisation rates of 
refineries highlight the market conditions, which have seen much lower spare capacity over the last 
ten years.  To recognise the state of the oil market, one can observe the market prices of crude 
versus the cracked products. 
The location and complexity of a refinery determine which strategy it will choose to operate 
on the crude assay, as the markets across the globe demand alternate variations on the typical crude 
slate.  In the US, the main domestic demand in summer is for gasoline, and switches to heating oil 
in winter.  Both of these product’s demand is not met by domestic refiners and large amounts are 
therefore imported.   
 
 
(Figure 1.6: Dependency of the US on crude imports) 
 
After the sophisticated refineries in the Middle East were destroyed in 1990, the US invested in 
light and middle distillate refineries to supply Europe and the Far East.  Consequently, the US 
37 
 
 
became a net exporter of distillates whilst remaining a huge importer of gasoline.  Shipping a 
product reduces profits dramatically; hence if a refiner can meet demand in its local market, it will 
focus its efforts there.  The refineries in Northern Europe, especially in Amsterdam and parts of 
Italy, supply the majority of their gasoline to the US.  Shell’s Pernis Refinery in the Netherlands is 
one of the company’s largest, with pipelines serving the Schiphol airport in Amsterdam – it has a 
crude oil processing capacity of 416,000 barrels per day (bpd).   
 
 
(Figure 1.7:  The leading role of Western Europe in petroleum exports to the US) 
 
Refined products are traded all over the globe; hence the possibilities for geographical arbitrage by 
trading houses can be huge.  In 2006, 30 million tonnes of gasoil/diesel were traded from the CIS 
(Commonwealth of Independent States) region to Europe; 28 million tonnes of naphtha were 
exported from the Middle East to Asia, and 26 million tonnes of gasoline from Europe to the US.  
In Western Europe, there are a large number of sophisticated refineries able to import low quality 
38 
 
 
feedstock from Russia and the Middle East and turn them into high quality gasoline and kerosene 
for exporting mostly to the US. 
The US is likely to continue to be a net importer of gasoline for some time, although the 
investment in more complex refineries is gathering traction as shown by figure 1.7 below.  The US 
is increasingly able to manage oil supply disruptions after the government in 1980 implemented a 
policy that ensured at least 1 billion barrels of crude were stored to hedge against volatile imports13.  
This was a reaction to the Yom Kippur War and the Arab Oil Embargo’s effects.  Consumption in 
the US has not slowed and more refineries are closing due to production having fallen.  Recently, 
the US government has acted to change this situation with more refinery upgrades and lower 
taxation on gasoline production requirements.  This is in direct contrast to the increase in taxation 
on the European refineries. 
 
 
(Figure 1.8: The US problem of growing consumption and reduced production) 
                                                 
13  Carter energy: http://www.pbs.org/wgbh/americanexperience/features/primary-resources/carter-energy/ 
39 
 
 
Despite the recent trend in general production, gasoline production seems to be rising, the number 
of refineries has been falling for a long time due to increased strains economically and 
operationally.  In 1980, not long after the oil embargo, there were around 250 oil refineries in the 
United States capable of supplying petroleum products demanded by the market.  Now there are 
only 150, as shown in figure 1.8.  Industry experts speculate that the reason why this number is so 
low is that this is a rigorous effort by the oil majors to squeeze the market14.  Others state that the 
planning restrictions in the US have increased and not allowed the growth demanded by energy 
politicians.  The report released in March 2011 from the US department of energy, stated that 
federal regulations were a significant factor in the closing of 66 US refineries15. 
 
 
(Figure 1.9: Count on active US refineries) 
 
Various economic and industrial changes have occurred over the past 30 years, yet, the refining 
product yield has stayed more or less constant as displayed in figure 1.10.  Gasoline is usually the 
                                                 
14  Wall Street Journal: http://online.wsj.com/news/articles/SB10001424052702304591604579291432462690714 
15  http://www.instituteforenergyresearch.org/2012/05/03/over-regulation-of-the-nations-refineries/ 
40 
 
 
highest yield from a barrel of crude oil and is on average 47% of the barrel.  In the US, Liquid 
Petroleum Gas (LPG) is at a lower yield per barrel whereas in the Middle East, due to 
petrochemicals, LPG is in higher demand.  Since the demand for middle distillates has grown, and 
the capacity for its refining in the US has recently increased, there has been a growing trend in 
fewer imports. 
 
 
(Figure 1.10:  Amount of product yield from a barrel of crude in the US (Retrieved 08/01/2013)) 
 
Capacity over the last 30 years in the US has stayed approximately constant and the trends in sales 
remain coupled with the state of the economy.  After the financial recession hit the US in 
2008/2009, the sales of gasoline by refineries to retail outlets fell dramatically to 25 billion gallons 
per day as depicted in figure 1.11.  This was close to levels seen in the early 1980s and a clear strain 
on the revenues of the oil majors.  The retailing of petrol and gasoline is the lion's share of the profit 
for oil majors but also a significant contributor to the world economy. 
41 
 
 
 
(Figure 1.11: Sales volumes of gasoline over time (Retrieved 08/01/2013)) 
 
The entrance of retail stores into the domestic oil market has meant the closure of many smaller 
filling stations.  In spite of this, sales had been increasing until the global recession of 2008.  The 
large demand of unleaded and leaded petrol in the UK at the pump is the main driver for UK 
refineries.  In mid 2012 the price of unleaded petrol was approximately 149 pence per litre; with 75-
90% being taxes, VAT and costs - leaving well under 40 pence for the refiner per litre16.  Globally, 
as constraints on refiners has grown the number of refiners has decreased. 
 
 
                                                 
16  Source: Wood / Mackenzie, http://www.ukpia.com/files/pdf/understanding-pump-prices-april-2012.pdf 
 
42 
 
 
 
(Table 1.1:  Summary of UK Refining Capacity, (2010)) 
Refinery 
Owner 
Primary Distillation 
Capacity (Million tonnes 
per annum, MTA) 
Primary 
Distillation 
Capacity 
(Thousands of 
barrels per day, 
KB/D) 
Nelson 
Complexity 
Factor 
Fawley 
ExxonMobil 
15.9 
326 
9.1 
Stanlow  
Shell 
14.4 
296 
7.4 
South 
Killingholme 
ConocoPhillips 
10.8 
221 
11.3 
Lindsey 
Total 
10.8 
221 
5.9 
Pembroke  
Chevron 
10.2 
210 
8.6 
Grangemouth 
Ineos 
10.0 
205 
7.9 
Coryton 
Petroplus 
8.4 
172 
8.3 
Milford Haven 
Murphy 
5.2 
106 
8.0 
Eastham 
Shell/Nynas 
1.3 
27 
3.5 
Dundee 
Nunas 
0.6 
12 
3.5 
Total 
 
87 
1796 
 
 
 
Table 1.1 illustrates that the UK market is much smaller than that of the US.  The US has a refining 
capacity of approximately 15 million bbl/day, whereas the UK processes 1.796 million bbl/day.  
However, the UK capacity has grown in the last 30 years despite increased regulation and refinery 
closures.  The most complex refinery in the UK is the one at South Killingholme owned by 
ConocoPhillips.  The largest capacity is at the Fawley refinery owned by ExxonMobil, with a 
primary distillation capacity of 326,000 barrels per day.   
 
Crude and its derivatives are of prime importance to many areas of local and global 
economies, yet alternative energy sources are continuously being researched and examined.  The 
transition has begun, with many groups of people interested in the emergence of climate friendly 
fuel sources and a removal of polluting fuels.  It will take many years for these changes to have a 
practical impact on the energy industry, but it is inevitable according to senior scientists and 
43 
 
 
petroleum practitioners17.  The economy’s dependency on crude oil is known ubiquitously and this 
reliance has stemmed from a number of significant events.   
 
1.5 A brief history of petroleum 
 
Crude oil has been in use for thousands of years.  It is mentioned in the Bible for building purposes 
in Babylon: oil pits near Ardericca are described as having great quantities on the banks of the river 
Issus.  The “black sludge” is mentioned in the literature all over the Middle East.  The oil wells 
drilled in China in 347AD were some of the earliest known.  In Japan, petroleum was known as the 
burning water in the 7th century.  In the 9th century the first oil fields were exploited in Azerbaijan, 
mainly used for lighting purposes.  In 1745 the first oil well and refinery was built in Ukhta18.  In 
1745 Russia, most households still relied upon candles for lighting.  The Russians realised that 
through distillation of “rock oil” or petroleum, a type of kerosene could be obtained.  This technique 
was then used by Russian churches in oil lamps.  It was only after Lukasiewicz had improved 
Gesner’s19 method to develop a means of refining kerosene from “rock oil” that the first oil 
extraction well was built in central Poland.  This knowledge diffused around the world and by 1861, 
the first modern Russian refinery was built in Baku.  Momentum gained and at some point Russia 
was producing 90% of the world’s oil.  From data records in 1842 from the Caspian Chamber in the 
Department of State Property Ministry, it was found that around 23,000 barrels per year were 
extracted and most frequently sent to Persia.  The first recorded economic oil boom was in 1871, 
when an Armenian entrepreneur in Baku built the first wooden oil derrick.  Consequently, the 
                                                 
17  Raymond Kopp: http://www.rff.org/Publications/Resources/Pages/Replacing-Oil.aspx 
18  Roman Ambramovitche's home town: http://www.themoscowtimes.com/beyond_moscow/ukhta.html 
19  Inventor of Kerosene: http://en.wikipedia.org/wiki/Abraham_Pineo_Gesner 
44 
 
 
industry exploded and in 1901 Baku’s share of production fell to only half of the world’s oil at 
212,000 barrels per day.  
        The modern uses of crude oil really began in the era when the internal combustion engine was 
invented in 1889 by Gottlieb Daimler20.  Before this, engines ran on steam.  With the turn of the 
century came Ford’s implementation of the assembly line for car production; hence, Texas and 
Oklahoma became the epicentres of US crude production.  Even with the American and British 
“Seven Sisters” (the seven great oil corporations that dominated the international oil industry from 
the 1920s to the 1970s), the Middle East began to gain oil concessions.  After World War II, the 
Middle East became a major supplier of oil and joined the list of already profitable fuel producing 
countries. 
 
1.5.1 The modern oil industry 
 
Within the modern oil industry, there are three levels in the supply chain: upstream, midstream and 
downstream.  Upstream is the drilling, exploration and production of the crude oil.  The midstream 
is the transportation and trading of the crude oil to refineries.  Refining the raw crude into its 
marketable products is the downstream business; this includes storage and marketing of the refined 
products.  The collection of these segments constitutes the oil value chain. 
 
                                                 
20  The main precursor to today's modern cars: 
http://inventors.about.com/od/dstartinventors/a/Gottlieb_Daimler.htm 
45 
 
 
     
(Figure 1.12: The oil and gas value chain) 
 
From procurement of the oil out of the fields, the crude is then delivered through products into the 
engines or hands of the consumers.  Shown in figure 1.12 are the stages of the oil value chain: 
development, production, processing, transportation and marketing of the products. 
The level of production is driven by the demand of customers.  The major companies in the 
oil and gas business are: ExxonMobil, Shell, BP, Chevron, Total, ConocoPhillips, Petrobras and 
many others.  These companies are often structured to be vertically integrated.  This means having 
economies of scale: owning the infrastructure within the entire value chain.  This can greatly reduce 
risks that are not easily avoidable by companies only involved in one part of the chain.  Most of the 
companies mentioned above dominate the oil market globally, and are either international oil 
companies (IOCs) or national oil companies (NOCs).  Currently, the industry is in a transition, 
moving from investment in its refineries to focusing on the upstream parts of the business.  The 
expansion of the oil reserves remains at the core of the business: the more crude found in the 
underground reserves, the safer the revenues in the future will be.  Although many refineries have 
closed in the last decade, the ones that are left are utilising capacities at much higher rates to meet 
market obligations. Nationally owned companies are regularly seen in the top rankings of oil 
companies by revenues and this is primarily because they have access to expansive oil reserves.  
Natural 
Gas 
Crude Oil 
46 
 
 
Table 1.2 shows key financial trading figures published in the press of the oil industry’s benchmark 
crudes.  
(Table 1.2: Basic Features of Benchmark Crudes, Q1 2010 averages by Argus) 
 
ASCI 
(Argus 
Sour 
Crude 
Index) 
WTI CMA + 
WTI P-Plus 
(Calendar 
Month 
Average & 
Postings Plus) 
Forties 
(First 
substantial 
oil field 
discovered 
in British 
Territory) 
BFOE (Average of 
21 days in Brent, 
Forties, Oseberg 
and Ekofisk) 
Dubai  
Oman  
Production 
*(MBPD)  
736  
300-400  
562  
1,220  
70-80  
710  
Volume Spot 
Traded 
(MBPD)  
579  
939  
514  
635  
86  
246  
Number of 
Spot Trades 
per Cal Month  
260  
330  
18  
98  
3.5  
10  
Number of 
Spot Trades 
Per Day  
13  
16  
<1  
5  
<1  
<1  
Number of 
Different Spot 
Buyers per 
Cal Month  
26  
27  
7  
10  
3  
5  
Number of 
Different Spot 
Sellers per Cal 
Month  
24  
36  
6  
9  
3  
6  
Largest 3 
Buyers % of 
Total Spot 
Volume  
43%  
38%  
63%  
72%  
100%  
50%  
Largest 3 
Sellers % of 
Total Spot 
Volume  
38%  
51%  
76%  
56%  
100%  
80.00% 
Source: Argus  
*Million Barrels per day 
 
 
As is evident in table 2.1, the crude produced in the largest volume is Brent at 1.22 billion bpd and 
WTI is the most spot traded crude at 939 million bpd.  
The current financial trading of petroleum products is a trillion dollar business 
predominantly carried out in electronic form.  It is a complex network of buyers and sellers with 
differing positions, agendas, algorithms and complex software to reach their profit targets.  Black 
47 
 
 
box trading21, carried out at large investment banks, aims to buy and sell different commodities 
without the market observing the trade.  Products like calendar swaps allow trading of one 
commodity for another at a future date, with the trade being protected by the exchange upon which 
it occurs.  The market has grown more dynamic and complicated but the petroleum economic 
fundamentals still underpin and constitute the petroleum business.  Capturing the underlying 
behaviour using sophisticated models and statistics is the cutting edge trading mechanism.   
 
1.6. Statistics of crude oil and petroleum products over the last 10 years 
 
Prices quoted on oil related markets are usually stated as either FOB (free on board, which means 
that the buyer pays for shipment and loading costs) or CIF (Cost, Insurance and Freight, a term 
expressing that the seller arranges for insurance and transportation of goods to a port and provides 
the buyer with documentation) for imports of cargoes.  These costs are important for refiners that 
need to export outside their domestic targets.  As Indian and other Asian refiners come on line at an 
ever increasing pace, the European gasoline export business is expected to experience a stiffer 
competitive climate.  The Middle East will find it increasingly more efficient to import gasoline 
from Asian refineries.  According to the EIA, in Europe there is limited growth expected until 2020 
for gasoline - 120 million tonnes, compared to 330 million tonnes for diesel.  Demand for diesel has 
been growing within Europe due to the number of diesel engine cars sold in the last decade.  This 
can also be explained by taxation: while gasoline is being taxed as a consumer “luxury” good, 
diesel is being taxed at a lower rate, due to its use in the construction and transportation industries. 
 
Experts in the analysis of time series data, rigorously search historical prices for  a grasp on 
at least four key characteristics: the trend, level, noise and seasonality components of the data.  
                                                 
21  Definition and example: https://www.quantopian.com/posts/black-box-trading-sample-algorithm-explanation 
48 
 
 
Prior to the construction of a trading model, an analysis of each commodity refined and processed is 
required. 
 
1.6.1 WTI Crude Oil  
 
In the US, one barrel of oil is equivalent in units to 42 gallons.  For the WTI future contract 
the underlying oil, if settled physically, is for 1,000 barrels.  Contracts on the Chicago Mercantile 
Exchange (CME) are the widest listed of options and futures of any exchange on commodities in 
the world.  Crude oil futures are generally listed nine years forward using a particular schedule.   
 
 
(Figure1.13: NYMEX Futures price on the front month contract over 28 years) 
 
49 
 
 
Shown here are descriptive statistics of the price series for the two most popular crude contracts in 
the world.  The statistics are analysed within three different time periods and show the large swings 
in price between the two contracts. 
(Table 1.3.1: Descriptive Statistics, WTI Oklahoma Contract 22 and the European Brent Spot Price 
FOB, ($/bbl) over 20 years) 
Jan 1983 – Dec 2001:  
WTI 
Brent 
Mean 
21.50015 
19.24116 
Standard Error 
0.080589 
0.081241 
Median 
20.1 
18.43 
Mode 
18.67 
18.48 
Standard Deviation 
5.525491 
4.947685 
Sample Variance 
30.53105 
24.47959 
Kurtosis 
-0.48625 
1.858936 
Skewness 
0.552815 
1.172789 
Range 
30 
32.35 
Minimum value 
10.42 
9.1 
Maximum value 
40.42 
41.45 
Number of observations 
4701 
3709 
Jan 2002 – Dec 2008: 
WTI 
Brent 
Mean 
54.74521871 
52.86125 
Standard Error 
0.645720219 
0.641014 
Median 
53.555 
50.86 
Mode 
26.86 
25.51 
Standard Deviation 
26.19746695 
26.28938 
Sample Variance 
686.3072745 
691.1314 
Kurtosis 
1.084630188 
0.959895 
Skewness 
1.084774994 
1.064137 
Range 
127.32 
125.78 
Minimum value 
17.97 
18.17 
Maximum value 
145.29 
143.95 
Number of observations 
1646 
1682 
Jan 2009 – Dec 2012: 
WTI 
Brent 
Mean 
80.5739154 
85.25954 
Standard Error 
0.620011611 
0.798837 
Median 
81.245 
80.115 
Mode 
87.81 
93.52 
Standard Deviation 
18.82631338 
24.20358 
Sample Variance 
354.4300753 
585.8135 
                                                 
22  Data are split into three  sub periods: up to end of 2001, up to August 2008 and after August 2008  
50 
 
 
Kurtosis 
-0.217778406 
-1.00624 
Skewness 
-0.37987559 
-0.0736 
Range 
91.23 
94.41 
Minimum value 
33.87 
33.73 
Maximum value 
125.1 
128.14 
Number of observations 
922 
918 
 
1.6.2 Brent Crude Oil 
 
 
(Figure 1.14: Spot FOB European Brent over 25 years, ($/bbl)) 
 
Dated Brent, the 15 day contract, collapsed a number of energy trading firms in 1986; the resultant 
replacement Brent crude futures contract (IPE 1988) transformed the market from a physical one to 
a purely financial market;  this is a cash settled contract, figure 1.14 is for the underlying’s price.   
 
51 
 
 
1.6.3 Gasoline 
 
Gasoline exists as premium leaded and regular unleaded for the automotive industry, and the 
US is globally the largest importer.  Gasoline is different from most other products as its price is 
posted outside gas stations and there are very few substitutes.  The retail price of gasoline is usually 
two to three times higher in Europe than in the US due to various factors.  For instance, on Mar 13th 
2012 the cost of filling the same 39-gallon tank on a Chevrolet Suburban in the US was $2.26, in 
Norway it was $9.97.  Across the globe, the $/gallon price of gasoline is generally closely inline 
before taxes; following taxation the oil prices widely vary from region to region.  In Italy for 
example, the taxes are approximately three times higher than those in the US. 
 
 
(Figure 1.15:  NYMEX Futures price of front month Gasoline contract, ($/gallon)) 
 
 
52 
 
 
(Table 1.3.2: New York Harbour Conventional Gasoline Regular Spot Price FOB ($/Gallon) 
Jan 1983 – Dec 2001: 
New York Harbour Conventional Gasoline 
Mean 
0.587061 
Standard Error 
0.002235 
Median 
0.559 
Mode 
0.516 
Standard Deviation 
0.140056 
Sample Variance 
0.019616 
Kurtosis 
1.045828 
Skewness 
0.975132 
Range 
0.813 
Minimum value 
0.29 
Maximum value 
1.103 
Number of observations 
3927 
Jan 2002 – Dec 2008: 
 
Mean 
1.498597 
Standard Error 
0.016161 
Median 
1.407 
Mode 
1.235 
Standard Deviation 
0.655859 
Sample Variance 
0.430152 
Kurtosis 
-0.13778 
Skewness 
0.691086 
Range 
2.909 
Minimum value 
0.507 
Maximum value 
3.416 
Number of observations 
1647 
Jan 2009 – Dec 2012: 
 
Mean 
2.214921 
Standard Error 
0.019357 
Median 
2.1235 
Mode 
2.862 
Standard Deviation 
0.587775 
Sample Variance 
0.345479 
Kurtosis 
-0.74927 
Skewness 
-0.17852 
Range 
2.542 
Minimum value 
0.788 
Maximum value 
3.33 
Number of observations 
922 
 
 
 
 
 
53 
 
 
1.6.4 Heating Oil 
 
Heating oil commonly refers to No.2 Fuel Oil, often used in the US as distillate home 
heating oil.  Its old names included: range oil, stove oil and coal oil.  It is the fraction from 
distillation regarded as a residue.  Heating oil has a seasonal pricing series23, with demand growing 
in the winter.  In the UK it is the office of fair trading who deal with adverse price reactions for 
customers.  Weather and logistical issues often hit the price of heating oil, which some customers 
rely upon, along with LPG, for hot water and heating.  Futures contracts are usually employed to 
trade heating oil on the markets. 
 
 
(Figure 1.16: NYMEX Futures price of front-month Heating Oil contract, ($/gallon)) 
 
 
 
                                                 
23  http://commodities.about.com/b/2010/09/06/is-there-a-seasonal-trade-in-heating-oil-this-year.htm 
54 
 
 
(Table 1.3.3: New York “Harbour” No. 2 Heating Oil Spot Price FOB ($/Gallon)) 
Jan 1983 – Dec 2001: 
No. 2 Heating Oil Spot Price 
Mean 
0.566397 
Standard Error 
0.002291 
Median 
0.538 
Mode 
0.551 
Standard Deviation 
0.143515 
Sample Variance 
0.020597 
Kurtosis 
3.591524 
Skewness 
1.408758 
Range 
1.481 
Minimum value 
0.284 
Maximum value 
1.765 
Number of observations 
3925 
Jan 2002 – Dec 2008: 
 
Mean 
1.518494 
Standard Error 
0.018653 
Median 
1.506 
Mode 
0.747 
Standard Deviation 
0.75698 
Sample Variance 
0.573018 
Kurtosis 
1.110434 
Skewness 
1.066993 
Range 
3.576 
Minimum value 
0.507 
Maximum value 
4.083 
Number of observations 
1647 
Jan 2009 – Dec 2012: 
 
Mean 
2.308702 
Standard Error 
0.020148 
Median 
2.155 
Mode 
3.048 
Standard Deviation 
0.611789 
Sample Variance 
0.374286 
Kurtosis 
-1.25683 
Skewness 
0.012403 
Range 
2.314 
Minimum value 
1.121 
Maximum value 
3.435 
Number of observations 
922 
 
 
 
 
55 
 
 
1.6.5 Kerosene 
 
The word kerosene is derived from the Greek word for “wax”.  A Persian scholar named 
Razi (Rhazes)24 was the first to distil this fuel in the 9th Century.  Its major use in industry began in 
1854 in New York.  Throughout Europe, it was firstly used in lamps and then became a fuel for 
engines after they were invented.  In the UK there are 2 popular basic grades: premium kerosene 
class C1 which is used for lanterns and in some combustion engines; and class 2, a heavier distillate 
used in domestic heating oil.  Its main role on the market and real value is for its use as a fuel for jet 
engines and rockets.  In the UK in 2006, jet fuel demand grew heavily due to low cost airlines.  Post 
the 2008 financial recession this demand dipped and declined 5.2% in 2009 to 11.5 million tonnes.  
Demand slightly increased in 2011 and growth is expected in the near future because of plans for 
two major new airports in the UK. 
 
(Figure 1.17:  Spot prices of Kerosene FOB, ($/Gallon), Gulf Coast) 
                                                 
24http://elementsunearthed.com/tag/kerosene/ 
56 
 
 
(Table 1.3.4:  U.S. Gulf Coast Kerosene-Type Jet Fuel Spot Price FOB ($/Gallon)) 
Jan 1983 – Dec 2001: 
Kerosene-Type Jet Fuel 
Mean 
0.588439 
Standard Error 
0.002863 
Median 
0.551 
Mode 
0.496 
Standard Deviation 
0.155811 
Sample Variance 
0.024277 
Kurtosis 
2.840876 
Skewness 
1.359925 
Range 
1.149 
Minimum value 
0.282 
Maximum value 
1.431 
Number of observations 
2961 
Jan 2002 – Dec 2008: 
 
Mean 
1.575769 
Standard Error 
0.019791 
Median 
1.557 
Mode 
0.68 
Standard Deviation 
0.803197 
Sample Variance 
0.645126 
Kurtosis 
0.689352 
Skewness 
0.949616 
Range 
3.702 
Minimum value 
0.505 
Maximum value 
4.207 
Number of observations 
1647 
Jan 2009 – Dec 2012: 
 
Mean 
2.350438 
Standard Error 
0.021219 
Median 
2.184 
Mode 
3.079 
Standard Deviation 
0.643952 
Sample Variance 
0.414674 
Kurtosis 
-0.93374 
Skewness 
0.114147 
Range 
3.703 
Minimum value 
1.111 
Maximum value 
4.814 
Number of observations 
921 
 
 
 
57 
 
 
1.6.6 Diesel Fuel 
 
Diesel is used as fuel for diesel engines; the word is derived from the name of the German 
inventor, Rudolf Diesel, who invented the diesel engine in 1892 and unveiled it to the public at an 
exhibition in Paris in 190025.  Diesel engines are lean burn engines, burning the fuel in more air than 
is necessary for the sparking reaction.  Due to the high compression of the engine and it having no 
throttle, diesel engines are more efficient than standard spark-ignited (SI) engines.  In many parts of 
the world, diesel will be priced higher than petrol.  This is due to demand where diesel cars are 
more popular.  Diesel has a slightly higher density than that of ethanol free petrol (gasoline), and it 
offers the same net heating value.  The polluting emissions of diesel are lower than those of petrol, 
with most SI engines producing ten times the amount of CO and slightly more C02.  The demand 
for this fuel usually rises in the colder months, corresponding with rises in heating oil.  The reason 
for less production in recent years on the slate compared to petrol is increases in the sulphur 
controls introduced in the US.  A consequence of these controls, is that the diesel in the US has a 
lower “cetane number” (a measure of ignition quality and the primary measure of diesel quality) 
than European diesel; resulting in worse cold weather performance and some increase in emissions.  
The taxation levied on each refined product varies and is a component of the refinery’s crude slate 
decision process, in that it is incorporated into the market price of the product.  The taxes applied to 
diesel fuel are generally lower than those applied to gasoline but are higher on the diesel vehicles 
themselves.  
 
 
 
 
 
 
                                                 
25  http://www.tested.com/tech/454861-inventions-debuted-worlds-fair/item/diesel-engine-1900/ 
58 
 
 
 
(Figure 1.18: Spot prices of Diesel, ($/gallon), New York) 
 
(Table 1.3.5: Los Angeles, CA Ultra-Low Sulphur Diesel Spot Price ($/Gallon)) 
 
Jan 1983 – Dec 2001: 
Diesel Spot Price 
Mean 
0.725304 
Standard Error 
0.00507 
Median 
0.72 
Mode 
0.875 
Standard Deviation 
0.191914 
Sample Variance 
0.036831 
Kurtosis 
0.029947 
Skewness 
0.435905 
Range 
0.905 
Minimum value 
0.375 
Maximum value 
1.28 
Number of observations 
1433 
Jan 2002 – Dec 2008: 
 
59 
 
 
Mean 
1.679812 
Standard Error 
0.019436 
Median 
1.683 
Mode 
0.83 
Standard Deviation 
0.788793 
Sample Variance 
0.622194 
Kurtosis 
0.41353 
Skewness 
0.78469 
Range 
3.62 
Minimum value 
0.513 
Maximum value 
4.133 
Number of observations 
1647 
Jan 2009 – Dec 2012: 
 
Mean 
2.378716 
Standard Error 
0.02106 
Median 
2.23 
Mode 
2.129 
Standard Deviation 
0.639467 
Sample Variance 
0.408918 
Kurtosis 
-1.16353 
Skewness 
0.01105 
Range 
2.428 
Minimum value 
1.099 
Maximum value 
3.527 
Number of observations 
922 
 
 
1.6.7 Naphtha 
 
Naphtha is a product resulting from the distillation process applied to petroleum, coal tar or 
peat.  It is an all encompassing term, as it refers to the lightest and most volatile fractions of the 
hydrocarbons in petroleum.  In the US, refined Naphtha is primarily used as feedstock for high 
octane gasoline.  It can also be used as a solvent.  It boils between 30 and 200 degrees Celsius; has a 
specific gravity of 0.7 and is volatile and flammable.  There are a number of categories for Naphtha, 
these can generally be split into less dense lighter Naphtha and more dense heavier Naphtha.  The 
lighter Naphtha is referred to as paraffinic Naphtha26.  These are mainly used as feedstock of 
olefins; sometimes these are also called “Light Virgin Naphtha”, LVN, or “straight run gasoline”, 
                                                 
26  http://www.petroleumhpv.org/docs/gasoline/052003_gasoline_robustsummary_pnaphthas_revisedfinal.pdf 
60 
 
 
SRG.  The heavier or denser types are richer in aromatics and naphthenes, known as “N&A s” or 
“straight run benzene”, SRB.  N&As are used in the reformate part of the refining process, where 
the heavy Naphtha is cracked into butane. 
(Table 1.3.6:  OSN Naphtha CFR Japan Front month Contract ($/tonne)) 
Jan 1983 – Dec 2001: 
Naphtha 
CFR 
Japan 
Front 
month 
Contract 
Mean 
188.3587698 
Standard Error 
0.743143425 
Median 
180 
Mode 
177 
Standard Deviation 
46.49251614 
Sample Variance 
2161.554057 
Kurtosis 
2.052506241 
Skewness 
1.167322624 
Range 
345 
Minimum value 
87.5 
Maximum value 
432.5 
Number of observations 
3914 
Jan 2002 – Dec 2008:  
 
Mean 
493.3758978 
Standard Error 
5.65536246 
Median 
460.5 
Mode 
245.5 
Standard Deviation 
226.49709 
Sample Variance 
51300.93179 
Kurtosis 
0.614234028 
Skewness 
0.934071984 
Range 
1087.5 
Minimum value 
167 
Maximum value 
1254.5 
Number of observations 
1604 
Jan 2009 – Dec 2012: 
 
Mean 
739.5837099 
Standard Error 
7.038602064 
Median 
738 
Mode 
735 
Standard Deviation 
204.1194598 
Sample Variance 
41664.75389 
Kurtosis 
-0.622241093 
Skewness 
-0.393942553 
Range 
862.5 
Minimum value 
244 
Maximum value 
1106.5 
61 
 
 
Number of observations 
841 
 
 
1.6.7 Fuel Oil  
 
This is a term used to refer to only the heaviest commercial fuel that can be obtained from 
petroleum distillation: heavier than that of gasoline and Naphtha.  In the US there are six grades of 
fuel oil and its uses vary from stove oil to heating oil for the home.  Due to the recent widespread 
penetration of natural gas, the heating oil has had less use in homes and sales over the last decade or 
so have decreased dramatically.  There are many areas in the US where it is however still copious.  
Residual fuel oil is less useful due to its viscosity, it must be heated with a special system before 
use and it contains a high level of sulphur.  However, it is very cheap and it can be used as fuel on 
boats or small ships and as fuel for power plants.  Fuel oil is transported worldwide by super tankers 
from ports all over the world including: Houston, Singapore and Rotterdam.  In Europe, the Rhine 
is used for the transportation of fuel oil.  Throughout the thesis Fuel oil will refer to No.4 Fuel Oil 
alias Residual Fuel Oil, it is frequently utilised in the US as commercial heating oil for burner 
installations. 
 
62 
 
 
 
(Figure 1.19: Refiner prices of fuel oil, ($/gallon)) 
 
(Table 1.3.7:  US residual fuel oil ($/gallon)) 
Jan 1983 – Dec 2001: 
US residual fuel oil 
Mean 
0.438219 
Standard Error 
0.008312 
Median 
0.402 
Mode 
0.42 
Standard Deviation 
0.125513 
Sample Variance 
0.015754 
Kurtosis 
-0.73377 
Skewness 
0.69265 
Range 
0.451 
Minimum value 
0.259 
Maximum value 
0.71 
Number of observations 
228 
Jan 2002 – Dec 2008: 
 
Mean 
1.049215 
Standard Error 
0.052855 
Median 
0.978 
Mode 
1.252 
Standard Deviation 
0.469787 
Sample Variance 
0.2207 
Kurtosis 
1.72599 
Skewness 
1.279405 
63 
 
 
Range 
2.291 
Minimum value 
0.433 
Maximum value 
2.724 
Number of observations 
79 
Jan 2009 – Dec 2012: 
 
Mean 
1.827756 
Standard Error 
0.078414 
Median 
1.692 
Mode 
2.473 
Standard Deviation 
0.502092 
Sample Variance 
0.252096 
Kurtosis 
-1.09027 
Skewness 
0.122145 
Range 
1.673 
Minimum value 
1.021 
Maximum value 
2.694 
Number of observations 
41 
 
 
1.7 Refinery Optimisation 
 
Crude oil by itself is not of any practical use; to use the hydrocarbon energy the chains must 
be broken.  Hydrocarbons are practical for two reasons; the first is that the chains contain a 
significant amount of energy, and the second is that they take on many different structures.  Why 
does a refiner need an optimisation model?  The scheduling of crude oil unloading, inventories, 
blending and feed to oil refineries is a complicated decision process requiring optimising.  There are 
a number of exogenous and endogenous factors that affect the choices being made by the refinery 
owner.  All these separate areas can and are in fact modelled with linear programs in practise and 
within academia.  In this thesis we have a financial objective for a refinery owner - with the 
objective of maximising the profit: we construct a mathematical model for the optimisation of 
chemical processes under uncertainty in the medium term.  In spite of the fact that many refineries 
have closed down due to decreasing gross refining margins (GRMs), there are those recording 
bumper profits.  This is sometimes due to location, where a refinery has closed and another is left 
servicing the same demand.  In the Midwest US where the imports from Canada are easily 
64 
 
 
transported, the refineries are very profitable.  However, there are certainly cases where demand in 
a location is at its lowest in recent times yet the refinery is running at full capacity and recording 
strong profits27.  This is not due to new capacity or increasing demand.  Western Europe and the 
USA have not seen refinery capacity increases in the last 20 years due to the environmental 
difficulties.  India is however a different case, growth in petroleum products consumption has been 
growing for a long time and refineries are constantly expanding to meet the new demand.  We seek 
a model that works for a refinery within India but the results are globally applicable.  Despite the 
increase in demand, a number of refineries closed in the early 2000s due to the intense competition.  
A set of competitive optimisation decisions allows a refiner to survive: they need alternative 
strategies and more efficient operations than their competitors.  Within the industry a GRM of 
$4/barrel is historically profitable: many refinery owners argue it should be closer to $9/barrel.  To 
reduce the effect of operating costs eating into the fluctuating margins the only choices the refinery 
has is either to refine more efficiently, or expand using newer technology, which emanates in a 
higher GRM.  If a refiner concentrates on optimising one unit, for example the Coker in a blending 
optimisation, it does not guarantee a complete set of optimal decisions.  This is due to there being 
many integrated units and a web of nested decisions that need to be made to produce an optimal 
result.  Consequently, refiners often concentrate on two key areas when optimising: 
 
1 – Raw input crude selection – if the choices or decisions made early on are the correct ones over 
time, it saves huge effort changing them later.  Once the crude slate is chosen, the opportunity for 
optimisation is much more limited. 
 
2 – Feedstock quality management – ensuring that the right crude mix, i.e. specification of the oil, is 
accurately produced by the refinery along the processes reaching yield levels within the individual 
units.   
                                                 
27  http://uk.reuters.com/article/2013/07/19/usa-fuel-exports-idUKL1N0FO1HG20130719 
65 
 
 
Linear Programming techniques are relatively mature and many commercial software systems are 
implemented onsite at most refineries including: Aspen PIMS (AspenTech), GRTMPS (Haverely 
Systems), TRIOS (UOP Limited) and many others.  In the short term, the problem for a refiner is 
the mechanical day to day decisions: operational optimisation, i.e. analysing units for elementary 
composition mixes have great affects on the physical properties and processing required for 
producing saleable products.  This is a large area of research in chemical engineering.  In practise, 
there is a lack of systematic integration between intertemporal planning and operational blending 
optimisation - this is the gap we will exploit in this thesis.  The software listed above can be utilised 
to focus on the medium term decision sets.  The midterm planning arena is starting to be examined 
using ideas from mathematical finance as the price series dynamics of the crude and the refined 
products are vital to the mathematical programming problem at this scale.  This is still a very open 
area, and the model formulation in this thesis seeks to address this problem. 
Dependent on the market that the particular refinery is serving, it may be more profitable to 
invest in cutting edge refining equipment.  This could enable the refining of heavier crude oil and 
allow products to be sold that can only originate from the cheaper, less pure and more common 
crudes.  An example, would be the Vadinar refinery owned by Essar Energy, in Gujarat, India - we 
use data for this refinery in our linear program optimisation.  Recent investment for an additional 
CDU and Cracker has introduced an extra 100,000 barrels per day throughput.  The denouncement 
from an expansion usually more than offsets the investment in the equipment that allows heavier 
crudes to be processed within the first year of operation.   
In the UK, ever since the 1990s, the refining margin has been squeezed.  This is due to the 
increased burden of regulation on the refinery’s operations and the increase in competition from 
emerging markets.  The UK government has increased mandates looking for cleaner processing and 
customers have demanded higher quality refined products, laterally the supply has become heavier.  
This had made life for the refiners strenuous, and demand for huge improvements and adaptations 
to the optimisation software has increased.  This software has to cope with the stricter regulation on 
66 
 
 
the removal of sulphur and nitrogen; the choice of crude oil to purchase and the decision yields to 
maximise in the profit function in the face of all the various constraints present at the refinery. 
The refiner must make the correct set of decisions at each refining period based on the 
internal and external factors that affect the price of petroleum and its constituent products.  There 
are restrictions on, for instance, the physical constraints present at the facilities in the refinery.  The 
capacity constraints state the maximum volumes permitted to be stored by the refinery, typically the 
storage containers split this 50:50 between crude oil and its products.  The mass balance constraints 
are applied at each unit used to process the crude within the refinery, for example the crude 
distillation unit (CDU).  Internally, the mass balance equations have to ensure that the amounts of 
fuel entering and exiting any refinery unit are balanced.  Externally, the local and global market 
prices of the refined products impact the decision maker’s choices of amounts to produce and 
consume.  It is generally assumed within optimisation models, that these decisions do not however 
have an impact on the exogenous prices.  This can easily been seen if one imagines the impact of 
one refinery out of the world’s 700 - it can only be a price taker.  The cost of the raw crude, is 
influenced by: product quality, shipping costs, global petroleum prices, local taxes and logistics.   
The market prices of refined products can have a huge effect on the cost of raw crude.  For 
example, in 2009 there was a major blockage in getting jet fuel to destinations across the US from 
bottlenecked Cushing, Oklahoma.  Consequently, not only did the price of kerosene go up but the 
cost of crude to the refineries dropped.  Optimisation models can use market data as input, hence if 
calibrated correctly, will price in these external fundamental factors for accurate and realistic 
modelling.   
Refining crude, not only means acquiring a raw fuel and processing it, but decision makers 
must attempt to manage the uncertainty in the prices of the end products to lock in value.  The 
volatility of gross refining margins is a sign of the complications within the industry.  Where is this 
volatility coming from?  One of the driving factors is the crude oil price.  The crude futures curve, 
67 
 
 
which depicts the expectations of the spot price, dramatically affects the future prices of the refined 
products.   
 
 
(Figure 1.20: Futures curve expectations) 
 
 
In the US refiners are holding less spare capacity; therefore, any negative event on the refining 
cycle has a larger impact than has previously been the case.  The fundamentals that affect this price 
volatility have shifted, and recently, the spread between sweet and sour crude oil was at an 
historical high.  The spread between Brent and WTI has gone from an average of $4 to $25 per 
barrel in Jan 2013, to only ~ $1 per barrel in September 2014.  
  
68 
 
 
 
(Figure 1.21:  WTI/Brent spread over the last five years) 
 
In terms of managing this spread, the refiner usually purchases the cheapest crude, obviously 
dependent upon its refining complexity, and hedges using a relevant contract on an exchange. 
Market prices of both the crude and the refined products change continuously every day, the 
more volatile these prices relative to each other become, the more uncertainty introduced into the 
decision process.  The refinery revenues over a year must exceed its operating costs, asset 
depreciation and corporate taxes.  To maximise this return on investment, the refinery manager, 
intends to pay the lowest price for crude oil, and sell the yield of the products at the highest market 
value, whilst controlling operating and regulatory costs.  During the 1980s and 1990s, there was 
huge refining capacity over the globe and GRMs were low.  After the demise of the majority of 
these refineries, the GRMs began to increase again up to the year 2000.  Knowing that the GRM can 
drop so low, the largest US oil majors slowed investment and this has by and large remained the 
case ever since.  However, outside the OECD regions there have been many refinery expansions 
despite for instance, the high capital costs required, the increase in the costs of steel and the 
restrictions placed on the refined products quality.  The solicitation of optimisation models has thus 
boomed – the risks needing representation in these models, in practise, is vast. 
 
69 
 
 
1.4.1 Refinery Risks  
 
There are a number of key risks that impact the refinery owner’s profits.  Price Risk relates 
to the fluctuation of crude oil prices and refined petroleum prices affecting margins.  Operating 
efficiency and access to crude oil of the required quantity, quality and price has a significant impact 
on the refinery’s performance.  While refined products normally track the changes in the feedstock 
prices, there is a lag which can impact short-term working capital requirements.  
Foreign Exchange Risk relates to the foreign exchange fluctuations that impact the refinery 
due to its imports and exports as a part of its general operations.  This is an area where refiners’ risk 
management teams can learn from professional foreign exchange analysts, as often huge amounts of 
capital can be lost due to this hedging risk exposure28. 
Reputational Risk relates to the potential commercial and reputational damage that could 
result from health, safety or environmental incident or conflicts with local communities, terrorism, 
or the geo-political location of the refinery29. 
“Crack risk” refers to the oil refiner’s crack spread.  This is a term used in the industry for 
the difference between the price of crude oil and the products extracted from it; or in other words, 
the profit the refinery can expect to make by “cracking” the crude.  The oil majors are vertically 
integrated, ergo they own the supply chain from exploration, to production, to retail; in this case 
there is already a natural hedge in place.  For independent refiners however, like those renting the 
refinery’s equipment, adverse price movements provide the bulk of the risk, whereas oil majors 
already own the crude.  A refiner can utilise the futures market and purchase relevant contracts to 
lock in fixed prices.  To  hedge the crack spread exposure a DM could remain neutral by longing 
crude futures and shorting the refined products contracts separately.  However, instead of 
purchasing so many contracts, the basis risk is usually managed by using a “3-2-1 crack spread 
option”; or some constructed alternative based on the specific products refined in the refiners’ 
                                                 
28  http://people.stern.nyu.edu/igiddy/fxrisk.htm 
29  http://www.ft.com/cms/s/0/84d6a0cc-9977-11e3-91cd-00144feab7de.html#axzz308BTG7mI 
70 
 
 
region.  The “Chicago 3-2-1” is the most popular quoted crack spread contract in terms of volumes 
traded on the NYSE.  The 3-2-1 refers to the volume ratio of the commodities being hedged; from a 
petroleum refinery one would expect two barrels of gasoline and one barrel of heating oil from three 
barrels of crude.  For a premium, a refiner can then execute the option to alleviate the uncertain 
crack spread.  Hedging strategies require the refiner to tie up funds in a margin account to trade 
options or futures.  Alternatively, swaps and more complicated OTC trades can also be executed by 
the refiner to manage its price risk. 
Liquidity is usually in excess in the futures market for crude contracts on the near month 
contract: the issue for refiners is the availability of contracts for each market product at future dates.  
This is can be implied by the prices for the contracts on the financial exchanges.  Some refiners will 
tie in a futures price by entering into an OTC transaction, or use a future contract for example, to 
sell gasoline up to 1 year ahead.  If the price increases above the agreed contracted level, then the 
trader has lost profit, managing the expectations of these future prices is the foundation of the 
hedging business.  Hurricanes and weather events are risks that remain very significant.  This is 
certainly more significant for refineries in the US on the Gulf Coast.   
The following chapters detail a refining optimisation model including relevant constraints 
and financial risks.  The primary goal of such a model is to aid the refinery decision maker in 
maximising profitable choices, however, with a number of alterations the model can be 
implemented to obtain a financial valuation of the refinery as an asset, whilst still outputting the 
decision set for the entire crude assay. 
 
 
 
 
 
 
71 
 
 
Chapter 2 
 
Valuing an oil refinery with discounted cash flows  
 
 
“Thus the art and science of valuation has seen a constant debate between what something is worth 
versus what the market thinks it’s worth and versus what a strategic or motivated buyer thinks it’s 
worth.”30 
 
Financial valuation relies upon one of three pillars: comparable company analysis, discounted 
cash flow calculation or transaction analysis.  Auditors generally apply traditional accountancy 
calculations rather than out of the box financial option related approaches as provided by 
professional valuers of businesses and assets.  In this chapter we provide a standard calculation to 
set the foundation for more complex and accurate approaches in the following chapters; this  
valuation is done as standard within industry, it is practically useful for many reasons from tax and 
litigation to mergers and financial reporting.  In traditional financial theory it is assumed that 
investors are rational and risk averse; hence requiring some additional return to accept risk – the 
time value of money, means that we can discount expected future cash flows (DCF) to value the 
refinery asset today, using an applicable rate of interest for the investor in question.  Finally, we 
collect standard financial values from an oil refinery in Gujurat India, and using these ratios 
                                                 
30  Keith Spence, Hons. B.Sc., MBA 
     Export Development Corporation 
    Co-Chair, CIM Valuation Committee (CIMVal) 
72 
 
 
produce a DCF calculation that gives a present value as a standard auditor would within industry – 
this calculation has been verified by a fully qualified ACA accountant of the ICAEW.   
 
 
2.1 A traditional valuation of an oil refinery 
 
Valuing a project or a company is a vital process for investors and the company itself; the three most 
common applied approaches are: the asset-based approach, the market approach and the income method.  
The asset-based method, values the business on the sum of the parts of the business, the market approach 
compares the company to be valued on companies within the same industry or a comparable entity, whereas 
the income based method, attempts to calculate the benefit stream of the business.  Combinations of the 
above do exist, the traditional Discounted Cash Flow (“DCF”) analysis, capitalisation of earnings, and the 
excess earnings method are the most common utilised to value a financial entity where the economic 
principle of expectation is suitable: valuing a company relying upon the expected economic benefit subject to 
a relevant level of risk.  Surveys have been carried out on the FTSE 100 companies in the UK which 
consistently show that for a project’s NPV, Net Present Value is the most commonly applied business 
valuation method by far31.  There are many valuation methods and some more relevant depending on the 
nature of the company or project.  The refinery is an illiquid asset, hence prices are not observable; the DCF 
calculation needs to address this.  The two most acceptable valuation methods in the UK, dependent upon the 
type of company, are asset based methods and income based methods.  Most often the valuation is calculated 
using DCF analysis, an income based method.  Corporations generate profits in various ways; yet generally 
projects are undertaken after some amount of analysis has indicated that they are worthwhile, i.e. there needs 
to be evidence of profitability.  This is a simple method which takes into account the time value of money 
and the cost of capital to the company in question; the cost of capital is a open and difficult to calculate.  The 
                                                 
31  http://www.rics.org/site/download_feed.aspx?fileID=2369&fileExtension=PDF, Royal Institute of Chartered 
Surveyors 
73 
 
 
capital asset pricing model (CAPM) is one way to determine the discount rate to apply in a business 
valuation.  NPV itself, is a tool that dates back at least to the 19th century, Karl Marx wrote: 
 
 
“The forming of a fictitious capital is called capitalising.  Every periodic repeated income is 
capitalised by calculating it on the average rate of interest, as an income which would be realised by a capital 
at this rate of interest. “ 
 
Why is it important to be able to value a company, for example an oil refinery?  Here are two examples from 
the financial news: 
 
A)       A private investor Gary Klesch was close to buying Total’s (TOTF.PA) British Lindsey Refinery 
for as much as £2 billion, as reported by the Sunday Telegraph on April 2010.  This refinery 
produces 223,000 barrels per day and employs 500 people. 
 
      B)      In the US on October 16th 2009 there was an important dispute about the method to value a Dutch 
 
     Oil refinery Lyondell Basell, which claimed its refinery was worth $3.43 billion where as an   
 
     alternative value by auditors gave it a value of $950 million.  This refinery produces 105,000    
 
     barrels a day.  
  
Clearly the methodology to value is vital for many reasons whether it be as a fair price for investors or a 
dispute in the courts. 
 
The financial statements that apply to a number of years of operation of an oil refinery based in India will be 
examined to value it with the traditional method of DCF analysis.  This will give a fair value, according to 
UK accountancy rules, and allow a comparison to be made of other methods used to value the refinery in the 
future chapters using statistics and more extensive mathematical models. 
 
For an oil refinery there are a number of figures on the financial statements that will be different to 
other businesses.  Officially under International Financial Reporting Standards (IFRS) rules, a valuation 
needs to be carried out using an official method: 
74 
 
 
 
• 
Absolute Value models determine the present value of an asset’s expected future cash flows, these 
are either multi-period like DCF or single period like the Gordon Growth model. 
• 
Relative Value models obtain a price by using market prices of similar assets. 
• 
Option pricing models are used less frequently but are used for certain type of assets, most often 
derivatives.  Black-Scholes (1973) and lattice models are the most common. 
 
The choice between these methods is made when there is no active market in which to value the asset.  The 
IFRS has a key role to play within the world of valuation, in IAS 39 fair value accounting is described.  IFRS 
13 was adopted in May 12, 2011 and provides guidance on performing fair value measurement under IFRS 
but it is not defined in which situations these measurements should be done.  Generally they state that a 
financial statement should reflect the true and fair view of the business affairs of an organisation.  The 
financial reports are seen by various constituents of society and need to reflect the true value and financial 
position of an organisation at a point in time.  FAS 157, issued on September 2006, states that the fair value 
on an asset is the value that it could be sold at to a willing party.  This conflicts with the historical cost 
method that says for example, if a piece of land was bought in 1980 for $1 million, it would still be recorded 
on the balance sheet as a historical cost basis.  The fair value tries to capture the current price, in the Efficient 
Market Hypothesis, the market incorporates new information quickly and so the market price is assumed the 
fair price - yet there are strong and weak forms of this hypothesis, and it assumes that prices are observed.    
Within behavioural finance there are anomalies, as agents are influenced by irrationality and so prices in the 
market are believed to diverge from fair value.  The IFRS authorise three underlying assumptions, which 
define how professionals should incorporate the concepts behind valuation: 
 
1 – Going concern: An organisation will continue for the foreseeable future under the historical cost 
paradigm and using units of constant purchasing power paradigm. 
2 – Stable measuring unit assumption: Accountants should consider the changes in purchasing power of the 
functional currency up to 26% per annum for 3 years in a row as immaterial.  
3 – Units of constant purchasing power: considering remedies for an indefinite time period the erosion 
caused by the historical cost accounting of the real values of constant real value non-monetary items. 
75 
 
 
 
Under these three assumptions the framework under FAS 157 has three levels for inputs to a calculation 
when valuing: 
 
1. The first level prefers inputs to valuation that are “quoted prices in active markets for identical assets 
or liabilities”, for example a stock traded on the New York Stock Exchange.  This is based on direct 
observations of transactions. 
2. The second level is based on market observables, acknowledging that active markets for identical 
assets and liabilities are relatively uncommon and even if they exists there may be little liquidity.  
An example would be the price of an illiquid option based on the Black-Scholes model (1973) using 
implied volatility techniques.  Here fair value is estimated using a valuation technique, the use of 
inputs on the markets is sometimes required. 
3. This level is regarded as “unobservable”, if one and two inputs are not available a valuation 
technique is required, however, significant assumptions which are not observable on the market are 
required, this is known as mark to management. 
 
IFRS 13 gives guidance on carrying out the measurement of valuation, these show where new methodologies 
could be of benefit: 
 
[IFRS 13:11] An entity should consider the properties of the asset or liability being measured that an investor 
would consider when pricing at a particular date. 
 
[IFRS 13:15] Fair value measurement conjectures that a transaction is carried out orderly between buyer and 
seller under the current market conditions. 
 
[IFRS 13:24] Fair value measurement conjectures that the transaction takes place in the most advantageous 
market at that period in time. 
 
[IFRS 13:27] A fair value measurement of a non-financial asset considers the highest and best application. 
76 
 
 
 
[IFRS 13:34] A transaction of an entity’s own equity products is given to an investor without settlement, 
extinguishment or cancellation at the valuation date. 
 
[IFRS 13:42] The fair value of a liability represents the credit and non-performance risk before and after the 
transaction. 
 
[IFRS 13:48] An exception is applied for financial assets and financial liabilities with positions or 
counterparty credit risk that offsets the original transaction, with disclosure. 
 
Annual reporting periods beginning after the 1st January 2013 can apply IFRS 13.  A previous accounting 
time period can make use of these valuation definitions but it must be stated on the financial reports. 
 
Most of the value of a business is in its ability to generate profit.  Within business the term Gross Margin is 
often used as an input to EBITDA which gives a guideline to a company’s value when inserted into a DCF 
analysis.  EBITDA is earnings before interest, tax, depreciation and amortisation.  A significant reporting 
figure calculated in the refining industry is the gross refining margin (“GRM”), this represents the difference 
in the cost of crude oil and the selling prices of the refined products sold.  Another is the Gross Marketing 
Margin (GMM), this is a figure quoted heavily by managers within the industry; the GMM represents the 
price from the refinery to selling to the retail customers, for example petrol stations.  The GRM can be 
calculated differently dependent upon the company and that is why the IFRS does not recognise it as an 
official measure32, but management and investors within the industry consider it an important figure 
representative of the profitability of an oil refinery.  The issue with the GRM is it does not take into account 
the operating expenses.  Another significant financial figure is the refinery cash margin. 
 
                                                 
32   FAS 130 
77 
 
 
2.2 Refinery cash margin, RCM  
 
RCM ($/bbl) = product price ($/bbl) – cost of crude ($/bbl) – operating expenses ($/bbl)  
 
(1) 
 
Here we assume that the RCM multiplied by crude throughput (the capacity for refining crude oil over a 
given period of time) is roughly equal to EBITDA.  It is a measure of cash earnings or profit / (loss) in 
accrual accounting.  This is another figure that the IFRS does not recognise but it is utilised within industry 
by investors comparing the performance of companies in the same sectors as credit periods, which will be 
broadly similar.  EBITDA may not be representative of the Company’s historical operating results and it is 
not meant to be forecasted.  However, it is a measure commonly sought by investors to get a handle on a 
company’s performance.  The RCM is then a convenient calculation to represent the performance of the 
refinery in annual terms.  We also assume that if the throughout annually is multiplied by the RCM for a 
reporting period it will represent a valuation figure.  This is primarily due to the fact that an oil refinery 
generates profits on the difference between the prices of the refined product slate and the cost of crude oil, 
therefore, the RCM will encapsulate most of its business value.  This will not include all capital expenditures 
and some other important financial figures, like the associated debt, but we argue that it is a relevant estimate 
of how much profit is being intrinsically generated.  There are however many types of refinery in different 
geographical complexes.  The aim was to set up a standard valuation for an average oil refinery in terms of 
size and complexity; laying the groundwork for others.  A useful measure within the industry to represent a 
refinery’s complexity is the Nelson Complexity Index. 
 
The Nelson Complexity Index is a useful figure to gage the level of refining complexity being 
carried out at a particular refinery.  It assigns a factor to each piece of refining machinery that stems from its 
complexity and cost compared to the basic process of crude distillation (a complexity factor of 1.0).  
Multiplying this factor by the throughput ratio as a fraction of the distillation capacity gives the complexity; 
aggregating these final values together for each part of the refinery gives the final Nelson Complexity Value.  
It was developed by Wilbur L. Nelson as described in the Oil and Gas journal (1960).  The greater this index 
number, the greater the cost of the refinery, and the higher the value of its products.  US refineries on 
average rank the highest in complexity index with a value of 9.5, Europe at 6.5; the Jamnagar refinery owned 
78 
 
 
by Reliance Industries Ltd is one of the most complex in the world at 14; the most being the 
Texas City Refinery at 15.3.     
 
We sought a refinery that has a Nelson Complexity Index above 5 in order for an optimisation to be 
worthwhile; the less complex refineries, on the whole, do not generate enough revenue for a comparison of 
alternative decisions within the program to be valid.  A Nelson Complexity Index lower than this, means that 
only a few products are refined and the selection choices for optimisation are simplified.  Those refineries 
that exist with a lower complexity index are finding the economic environment much more strenuous, due to 
their smaller range of products, giving them less flexibility and a fraction of the revenue of the major 
refineries. 
 
It is important to obtain realistic financial data to test the methods of valuation that are being 
researched and calculated.  In this chapter the following refinery’s financial statements are examined with 
the aim of creating an additional optimisation analysis based on available company information from 
Companies House UK or on company web pages: 
 
• 
Grangemouth, Scotland 
• 
Coryton (Owned by Petroplus), England 
• 
Humber (Owned by ConcoPhillips), England 
• 
Lindsey Oil (Owned by Total), England 
• 
Fawley (ExxonMobil), England 
• 
Pembroke (was sold to Valero for £750 million in 2009), England 
• 
Milford Haven, England 
• 
Stanlow (Owned by Essar energy), England 
• 
Vadinar, India 
 
We decide that the Vadinar refinery in Gujurat is the most appropriate to analyse and value, due to its huge 
expansion ambitions and its slightly greater than mid level complexity. 
 
79 
 
 
The Vadinar Refinery owned by Essar energy is a relatively new and medium sized refinery in India, yet it 
has plans to be one of the biggest in the next couple of years.  Inserting numbers from the Vadinar oil 
refinery in India into a formula, the Nelson Complexity Index of the refinery is equal to 6.1 and is supposed 
to increase to 11.8 following completion of the Phase I Refinery Project and to 12.8 after Phase II of the 
refinery capacity expansion project.  The following is a calculation of the Vadinar refinery’s GRM: 
2.3. Vadinar gross refining margin calculation  
 
For example, the financial statements for the Vadinar refinery for the year ended 31 March 2009 enable a 
calculation of the GRM: 
(Table 2.1: Reconciling Revenue from the refining part of the business to GRM, for the 
period 1 May 2008 – 31 March 2009 and the 9 month period to 31 December 2009) 
 
 
 
 
 
 
 
 
Period Ended 
Period Ended 
 
 
 
 
 
 
 
31 Mar 2009 
31 Dec 2009 
Revenue – Refined Petroleum Products ………..       7,689.8    
4,965.3 
Cost of crude oil     ……………………………….      
(7,331.2) 
(4,771.9)  
Sales tax incentives ……………………………….         256.3      
168.4 
Commodity hedging gains/losses …….………….         78.9 
   
(40.0)   
GRM (including sales tax incentives) ...………....         693.8      
321.8 
GRM (excluding sales tax incentives) ...………....         437.5     
153.4 
 
Number of barrels (in millions)………………….  
 87 
   
72 
GRM per barrel (including sales tax incentives)…       7.97 
   
4.46 
GRM per barrel (excluding sales tax incentives)…       5.02 
   
2.12 
 
[The IEA benchmark was US$2.33 per barrel in the same period.] 
[All values are in US$ millions] 
 
 
Revenue above is defined as the cash inflows in the period for producing petroleum products.  The cost of 
crude oil is the cash outflow for the barrels of the raw crude needed to produce the refined products.  Sales 
tax incentives are allowances made by the state of Gujurat that may not allow tax breaks to continue and may 
not allow a direct comparison of GRMs with other refineries. 
 
The Vadinar refinery processed 11.95 million metric tonnes (mmt) of crude oil between 1 May 2008 
and 31st March 2009 and 9.90 mmt of crude oil between 1 April 2009 and 31st December 2009.  Hence in 
80 
 
 
this period, the throughput was approximately 75.24 million barrels, see above, approximately 72 million 
barrels (see calculation below). 
 
In this period the refinery sold 9.26 mmt of refined petroleum products. 
 
The on average conversion factor used is: 
1 metric tonne = 7.6 barrels of crude oil.  
 
The Vadinar refinery’s current refining operating costs are $1 lower than the average across the industry, 
below in table 2.2 are the operating costs per barrel, based on data from their financial statements made 
public, for the 9 months ended 31 December 2009. 
(Table 2.2: Operating Costs per barrel at the Vadinar refinery) 
 
Cost Item 
 
 
 
 
 
 
 
 
US$/barrel 
Asset Management Costs1 …………………………………………………. 
0.30 
Manpower Costs ………………………………………………………….. 
0.15 
Purchased energy …………………………………………………………. 
0.31 
Other overhead Costs ……………………………………………………... 
0.39 
Operational variable Costs ………………………………………………... 
0.19 
Total Costs ………………………………………………………………... 
1.34 
 
(1) Includes costs of shutdowns, turnarounds, maintenance and repair and other routine maintenance 
 
The company in this period also had corporate and marketing expenses of US$ 0.96 per barrel. 
In total, operating costs were $US 2.29 per barrel.  Therefore an estimate of the refinery’s profitability for 
one year is: 
 
RCM = [($4.46 – $2.29)*(72e6 barrels)]*(12/9) = US$ 208.32 million (With tax incentives) 
 
Below table 2.3 shows the value each refined product adds to the refinery complex. 
(Table 2.3: Production of the refinery broken down by refined product ) 
 
         
 
 
 
 
 
 
1 May 2008 to    1 April 2009 to 
         
 
 
 
 
 
 
31 March 2009    31 December 2009 
                           
 
 
 
 
 
Percentage          Percentage 
81 
 
 
                                  
         
 
 
 
of total                of total 
 
                
 
 
 
 
            mmt production  mmt production 
Production: 
Liquefied petroleum gas . . . . . . . . . . . . . . . . . . . . . . . . 0.46    3.8% 
   0.40    4.1% 
Naphtha . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.03    0.3%  
   0.11    1.1% 
Motor spirit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  2.10    17.6%  
   1.67    16.8% 
Kerosene/Aviation turbine fuel . . . . . . . . . . . . . . . . . . .  0.57    4.8%  
   0.66    6.7% 
High speed diesel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.10     42.7%  
   4.02    40.6% 
Fuel oil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2.72     22.7%  
   1.97    19.9% 
Sulphur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .0.07     0.6%  
   0.06    0.6% 
Bitumen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.13     1.1%  
   0.40    4.1% 
Fuel loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .0.77     6.4%  
   0.61    6.1% 
Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.95   100%  
   9.90    100% 
[Motor Spirits is Gasoline and Petrol] 
 
 
The above Vadinar Refinery figures meant that the revenue could be broken down into the value generated 
per refined product.  Even though the price of each refined product will have been fluctuating over this 
period, using an average price we can depict the value per product sold during this period. 
(Table 2.4: Revenue generated by each refined product during periods shown ) 
         
 
 
 
 
 
 
   1 May 2008 to  
1 April 2009 to 
         
 
 
 
 
   31 March 2009 
 
31 Dec 2009 
                            
 
 
 
 
   Percentage        
Percentage 
                                  
         
 
 
 
   US$ millions of total 
US$ millions of                
 
 
 
 
 
 
    
   revenue   
 
total revenue 
     
Production: 
Liquefied petroleum gas . . . . . . . . . . . . . . . . . . . . . . . .      $292.21     3.8% 
$203.56    4.1% 
Naphtha . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    $23.07       0.3%  
$54.62      1.1% 
Motor spirit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .   $1353.45   17.6% 
$834.17  16.8% 
Kerosene/Aviation turbine fuel . . . . . . . . . . . . . . . . . . .      $369.11     4.8% 
$332.68    6.7% 
High speed diesel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    $3283.55   42.7%  
$2015.92  0.6% 
Fuel oil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . $1745.58    22.7% 
$988.09    9.9% 
Sulphur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .   $46.14       0.6%  
$29.79      0.6% 
Bitumen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .$84.59      1.1%  
$203.58    4.1% 
Fuel loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .$492.15    6.4%  
$302.88    6.1% 
Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . $7689.8    100%  
$4965.3   100% 
 
 
Growing a company through project value is considered under investment decisions; typically this is 
analysed with the hegemonic discounted cash flow analysis.  Net Present Value (NPV), the internal rate of 
return (IRR), and payback period, are key methods to evaluate the attractiveness of investments within the 
finance industry.  This is true whether it is an acquisition of a company or an asset that is to be purchased. 
82 
 
 
 
The goal of discounted cash flow analysis is to determine: 
• 
The net present value of a stream of expected future cash revenues and expenditures 
• 
The rate of return which the expected future cash flows will yield on a given level of initial 
investment 
 
The oil refinery presented here has fixed operational costs, hence the analysis of costs in the refinery will not 
be as involved as the valuation of the commodity prices of crude bought and refined products sold - this is 
the greatest source of financial risk the refinery has.  Applying income based methods is industry consistent; 
the calculation here is in 2 parts: 
 
• 
Calculation of each cash flow for some future number of years 
• 
The aggregation of the present value worth of each cash flow 
 
A refinery is generating its profits from the net difference between the price of a barrel of crude oil and the 
value of the refined products.  On top of this, the cost of operating the refinery (fuel and staff costs) and the 
costs of freight to the port of the refinery must be injected into the calculation.  Example figures are given 
below: 
 
The Vadinar Gross refining margin in US$ for December 2009 for 11 months is: US$2.12 per barrel.  On 
average it takes the Vadinar refinery 20-30 days to process crude oil from the day it receives the crude oil, 
hence producing an estimate for the refining process sold timeline.  Each month’s price series can be 
forecasted, in terms of optimising the profits of the process with a mathematical model.  The higher the GRM 
the more profitable the refinery will be.  The way to increase the GRM is to refine as efficiently as possible 
the particular crude assay into the highest priced market products for each time period.  In general 
transportation fuels are the most valuable, however, one particular barrel of crude yields approximate 
volumes of each product, barring yield uncertainty in the CDU and an amount due to CDU reactions that can 
increase the production of particular products.  After we obtain the DCF valuation of the refinery with 
83 
 
 
traditional methods, the goal for the next chapter is to create a model to optimise the refinery’s processes and 
maximise its GRM each month over a number of years.   
 
Any valuation should take into account the risk of an investment, and although the refinery’s greatest 
risks are the prices of the commodities involved, other valuation risks are investigated.   
 
2.4 Valuation risks to the Oil Refinery  
 
Risks for a typical oil refinery would be as follows: 
 
• 
Fluctuation of crude oil and refined petroleum product prices and refining margins 
• 
Inability to enter into term contracts for mid to long term crude oil purchases 
• 
Contract dependence on more favourable pricing of its refined petroleum product sales to the 
markets and companies the refinery is located in 
• 
Ability to find, develop and commercially exploit resources and reserves 
• 
Foreign exchange currency fluctuation risk 
 
Most oil refineries purchase third party software to determine how to optimise the refinery’s product slate 
due to the many factors that impact the chemical engineers decision.  In the following chapter the uncertainty 
from a financial aspect based upon choices of which products to market (planning) are considered in a 
model, but there is also uncertainty present in crude delivery time (scheduling), yield uncertainty processed 
in the CDU (blending), quality specifications and these decisions adhere to all the physical, mass balance and 
financial constraints on the refinery.  This decision process is far too complex for a human to completely 
manage no matter how experienced the refiner.  Some companies creating optimisation industry software 
that deal with some or all of these complexities are: IBM, Deliotte and Touche, Aspens Process Industry 
Modelling Systems, PetroSim (KBC, kinetic based models used in over 100 refineries worldwide) and Spiral 
software optimisation (claimed in person that they can save an average complexity refinery $8.5 million per 
year through blend optimisation).  Optimising the buying and selling of the products is vital to the 
84 
 
 
performance of the refinery in a very competitive business that has recently seen many smaller refineries 
close down and pushed the majors into shrinking their refining interests.  It is vitally important that the risks 
of the refinery are taken into account in its financial valuation. 
   
A more accurate valuation methodology implemented less often due to its slight increase in complexity is the 
Real option valuation approach.  In practise, however, DCF and NPV are applied ubiquitously.  
2.5 Discounted cash flow value (DCF) 
 
The real option approach utilises an option pricing equation in the investment decision.  A real option is one 
in which the user has the right, but not the obligation, to commit to a decision or not, to purchase a real asset 
at a particular fixed price, named the strike price.  For example, the option to invest in a gold mine or not, 
would be a real call option approach to valuing the mine, where the gold value is the state variable and the 
strike price would be the investment cost.  It is a logical approach with which to value an investment, whilst 
taking into account the uncertainty often present in financial decisions, that are missed by static income 
based methods.  Industry usually deliberates between using the standard methods for an investment decision, 
NPV, DCF or the more advanced Real option approach.  The DCF approach has to be calculated for at least 
6 to 10 years into the future, dependent upon the type of company, to estimate a fair value of the company or 
asset; for instance, all UK/US courts in a financial prosecution regard it as an official method with which to 
value a company33.  It is not sensible for certain types of businesses and so the real options approach can be 
adopted instead.  There are arguments within the literature that claim option pricing is over valued.  
Trigeorgis and Mason, 1987 argue that the existence of a ‘twin security’ is implicitly assumed in NPV for 
purposes of estimating the required rate of return.  The assumptions underlying the application of option 
pricing to real assets are no stronger than the assumptions underlying the application of NPV to real assets.  
The accuracy of using NPV relies however on the assumption of market completeness; the underlying asset 
itself for the refinery here is not traded.  The DCF analysis is an approximation of the value of the refinery if 
                                                 
33  http://www.nybusinessdivorce.com/2012/08/articles/delaware/recent-fair-value-cases-in-the-delaware-chancery-
court/ 
85 
 
 
it were traded.  The random behaviour of the underlying assets that must be applied with real options is an 
approximation of the future behaviour that the refinery would have if it were traded.  Consequently, the real 
option value is an estimate of the value the option would have if the underlying assets were traded and 
behaved in the manner that the random prices are depicted.  The majority of real asset option-pricing 
applications are where the option value depends on the market price of a commodity such as crude oil.  
Simulating the random prices and solving with a lattice approach, however, enables the valuer to solve a 
much wider set of valuations than ever before, in a manner that ensure managerial support due to its 
simplicity. 
 
The cash flows generated in the future for the DCF are estimated from industry wide figures like 
forecasted macro-economic figures or relevant demand figures published by respected organisations (KBC 
regularly carry out in depth reports on these exact figures).  The issue with the DCF is that it is a static 
measure due to the growth rate of projected cash flows is already ignoring the instability or behaviour of 
asset prices in financial markets and neglecting management’s right to alternate strategies.  The real option 
approach deals with these issues.  
 
In the DCF approach, only the most likely outcomes are considered, and the flexibility of the 
decisions available to management is ignored, a statistical approach to the random variables in question are 
not considered.  The future cash flows are discounted back to present value, but the rate at which this is done 
leaves much room for error.  The company that is being valued usually cannot borrow and lend at the risk 
free rate and has risk attached to its operations.  Trying to capture this risk by a number called the cost of 
capital is an intricate and often inaccurate calculation.  The NPV approach assumes a capital investment once 
committed is left to play out and so is fixed.  With a varying cost of capital calculations do not properly 
account for the changes in risk over the lifecycle of the business.  Whereas, the real option approach assumes 
that the investment can be modified contingent upon the various scenarios that can happen, this is its 
advantage.  Many methods of valuation require an estimation of uncertain cash flows; an example would be 
the Gordon Growth Dividend model.   
 
86 
 
 
“If future outcomes are uncertain, then any estimation of growth and risk may not be sufficiently well-
informed that the assumptions underlying the simple dividend discount model can be rejected.”34 
 
In the real option approach the underlying concept is that of risk adjusted valuation, or applying the risk 
neutral measure.  Typically the real options approach gives a greater value than the NPV; attaching an 
amount of value to alternative strategic decisions.  The initial assumptions inherited for a DCF or a NPV 
approach is the same for a real options approach.  Complete markets and absence of arbitrage are formally 
required for the calculation to hold.  The aim here is to calculate a DCF value for the Essar Oil refinery and 
then show that the real option approach could be more representative.  The real option approach is a difficult 
calculation in comparison to DCF, hence a method with a one period stochastic program is developed to 
value the refinery whilst accepting the inherent uncertainty.  In the process of understanding NPV analysis it 
is assumed that all future cash flows are certain for the next six to ten years, based on historical figures, 
which removes the uncertainty inherent in the generated cash flows of a business.  The exact assumptions 
behind NPV analysis are listed below: 
 
Assumption 1) any new cash flow stream can be exactly replicated by a combination of securities that 
already exist in financial markets.  (Complete markets) 
 
Assumption 2) the financial markets do not allow arbitrage. 
 
Any NPV valuation that is positive is considered to be adding value, meaning it should be undertaken.  The 
advantage of DCF over other traditional methods are numerous, for instance often P/E ratios and multiples 
are forecasted and implemented in a valuation.  These multiples are not of much use if an entire sector is 
over or undervalued, whereas DCF will give a fair value in comparison to this approach, but it can over or 
under value to an extent if the economic environment becomes different to the historical values injected as 
inputs.  For a NPV calculation, the vital component is the discount rate; an input to the valuation formula, 
and it is generally calculated with the method named Weighted Average Cost of Capital (WACC) or CAPM.  
                                                 
34  The idea is that the stock is valued by using predicted dividends and discounting them back.  It has no relevance for 
companies that do not pay dividends. 
87 
 
 
If the asset being valued is illiquid then the discount rates must be adjusted; there are three typical ways in 
which this can be done: 
 
• Estimate the value of the refinery as if it were a liquid asset and then discount that value for 
 
illiquidity  
 
• Adjust the discount rates and use a higher discount rate for the refinery  
 
• Estimate the illiquidity discount by looking at comparable refineries and recording how much 
 
their values are impacted by illiquidity 
 
As carried out by KBC we value the Vadinar refinery using a cost of capital using WACC, which is adjusted 
for the industry we are in; the petroleum sector. 
 
2.6 Weighted Average Cost of Capital (WACC) 
 
Weighted average cost of capital, WACC, is the rate of return a company pays on average to all its 
shareholders.  The company must earn this rate on its’ investments and services in order to satisfy all 
shareholders and interested parties.  The WACC takes into account the capital structure of the company and 
averages all the relative weights of value generated by the different sources of capital within it.  What should 
this discount rate be for the oil refinery?  The challenges that arise in applying CAPM or WACC to illiquid 
real assets, such as a chemical plant or a refinery raise a fundamental issue.  How do we assess risk in illiquid 
asset 
classes 
where 
market 
prices 
cannot 
be 
observed 
(as 
is 
the 
case 
for an Indian refinery) and hence subject to substantial uncertainty?  
 
A simple version of this discount rate calculation is shown below: 
 
WACC = ke*(E/(E+D)) + kd*(D/(E+D)) 
 
 
 
 
 
 
 
 
(2) 
 
Where:  D = the value of debt 
88 
 
 
 
 E = the value of equity 
 
(Note that kd is the cost of debt after tax) 
 
Evaluating the WACC for a company is different than evaluating the WACC for an individual energy 
project.  When WACC for a company is evaluated, we are often trying to determine (under imperfect 
information) what a company's costs of capital are.  In this case we would utilise as much financial data as 
possible to estimate the various terms in the WACC equation.  For an individual energy project, the various 
terms in the WACC equation are determined in large part by the type of investment being made, the type of 
market (regulated versus unregulated) in which the investment is occurring and the individual making the 
investments.  Historically, (looking at KBC reports)35 the petroleum industry has financed around 15-20% of 
its activities through debt (this can be seen on the "cost of capital by sector" web page 36, there are sector 
numbers available for India and KBR reports support the numbers within 2 decimal places; see the second to 
last column in the petroleum rows - showing the ratio of debt to equity).  Hence, we can estimate that Essar 
energy (the owners of the Vadinar refinery) would finance 15% of its activities with debt.  Oil company 
bonds have historically high ratings, so we assume that Essar energy's bonds are AAA; we see that the 10 
year AAA corporate bond would have a yield of 3.08% (at the time of this writing) - the cost of debt 
financing.  Turning now to the cost of equity financing, we need the return on the safe asset; the market risk 
premium; and the Beta for the petroleum industry.  The yield on the 30 year treasury bond was 3.78%.  We 
can assume a risk premium of 5% and from the cost of capital web page we see beta for the petroleum 
industry is between 1.17 and 1.45.  Thus, the cost of equity for Essar energy would be 0.0378 + (1.45 x 0.05) 
= 11%.  The WACC represents the discount rate that a company should use in conducting a discounted cash 
flow analysis of a given energy project.  We use the cost of equity of 3.08% and a cost of debt as 11%, which 
is very close to KBC's calculations for the same period. 
 
In a state of the economy where a payoff is less likely, investors are willing to pay more for it.  This 
is where the confusion of the discount rate can arise.  Each future state of nature has its own unique discount 
                                                 
35 http://www.essar.com/upload/pdf/EOL_AR_2012-13.pdf 
36 http://people.stern.nyu.edu/adamodar/New_Home_Page/datafile/wacc.htm 
89 
 
 
rate, but the DCF approach does not take this into account.  As an example consider a power plant; this can 
be switched on and off based on demand - a DCF calculation ignores this embedded optionality in this real 
asset, there is value attached to being able to switch strategy.  An important real option approach was created 
by Cox, Ross and Rubinstein (CRR) (1979), they apply the binomial method.  This is due to Harrison and 
Kreps (1979), where the risk neutral probabilities are actually state prices for securities scaled by the risk 
free factor.  If markets are complete and free of arbitrage opportunities, then there exists a unique set of ‘risk-
neutral probabilities’ that can be found.  Usually option pricing techniques can only be applied if markets are 
complete and free of arbitrage opportunities. 
 
This is enough to claim that if a manager is willing to use DCF analysis on an illiquid new 
investment, a gold mine, factory or structured product, then the assumptions behind real option analysis have 
already been assumed and the benefits should not be ignored.  Valuation of the refinery is similar to asking 
the following: if the refinery was bought and then immediately sold to the financial markets, what would it 
be worth?  The present value of the cash flows generated by the refinery are those that the capital markets 
would pay for now.  The NPV is the difference between the price actually paid for the new real asset (the PV 
of the outflows) and the price that would be received for the cash flows on the refinery in the financial 
market (the PV of the inflows).  Therefore the capital budgeting method of valuation is figuring out what the 
cash flows of the refinery are worth to the financial markets.  A static approach to valuation is applied first to 
gage the result and consequently uncertainty is introduced.  Surveys completed by corporate finance 
practitioners have continuously found that real options are only used at most in 25% of cases37. 
 
A DCF method generally relied upon in the FTSE 100 and S&P 200, is the Free Cash Flow 
approach, FCF - see Graham and Campbell (2001) for an example and analysis of various valuations.  
Investment analysts compute the free cash flow:  Free Cash Flow is equal to Operating cash flow, less 
expenditures to maintain assets (not including increases in working capital) less the interest charges.  It is the 
actual amount of cash that the company has left from its operations that could be invested to pursue 
profitable projects to enhance shareholder value.  The method itself has less vagueness in its calculation than 
the net income approach, hence manipulation is trickier.  For companies with stable capital expenditures, 
                                                 
37  Graham, J. and H. Campbell, “Theory and Practice of Corporate Finance: Evidence from the Field,” Journal of 
Financial Economics, 60 (2001): 187-243. 
90 
 
 
free cash flow will be approximately equal to earnings, see Jensen C, (1986).  The steps for the Vadinar 
refinery in Gujurat are shown below. (This is the free cash flow method to the firm, FCFF). 
2.7. Free Cash Flow Method (FCFF) 
Step 1 - Calculate the free cash flow 
The free cash flow is defined as: 
FCF = Earnings before interest and taxes (EBIT) 
Less: Tax on EBIT 
Add back: Non-cash charges 
Less: Capital Expenditure 
Less: Net Working Capital Increases 
Plus: Net Working Capital Decreases 
Plus: Salvage Values received 
 
Step 2 - Forecast FCF and the terminal value 
For a time horizon of six years the following values are estimated: FCF1 → FCF6, the terminal value.  Six 
years is chosen as Vadinar refinery's owner is a large company with expansion plans for the next five to ten 
years, values beyond this are generally discounted into much smaller values, hence considered irrelevant for 
our purposes.  When forecasting these cash flows many factors should to be taken into account as they are 
estimated by obtaining a growth rate.  The economic environment globally, locally, the industry the company 
is in, the company’s current status amongst its plans and its leverage level are all relevant in estimates of 
future cash flows. 
  
 
FCF1 FCF2 FCF3 FCF4 FCF5 FCF6   
 
91 
 
 
Step 3 - Calculate the weighted average cost of capital (WACC) 
 
The WACC is calculated using the cost of equity (ke) and the cost of debt (kd) 
(See equation (2) above) 
Step 4 - Discount the free cash flows at WACC and aggregate to obtain value of the 
firm 
Step 5  - Calculate the equity value 
Firm value = Equity + Debt 
 
 
 
 
 
 
 
 
 
(3) 
 
2.8. Oil refinery FCF calculation 
Step 1  
FCF = NI + NCC + [ Int * (1- tax rate)] – FCInv – WCInv 
 
 
 
 
 
(4) 
Where: 
NI = net income 
NCC = non-cash charges 
Int = interest expense 
FCInv = fixed capital investment (capital expenditures) = Change in Gross PP&E 
WCInv = working capital investment = Change in working Capital Accounts = Change in (AcctsRec + 
Inventory – AcctsPay) 
 
We can also start with earnings before interest, taxes, depreciation, and amortization (EBITDA) to arrive at 
FCF: 
 
FCF = [EBITDA * ( 1- tax rate)] + (Dep * tax rate)- FCInv – WCInv 
 
 
 
 
(5) 
Where: 
92 
 
 
EBITDA = earnings before interest, taxes, depreciation, and amortization 
FCF can also be estimated by starting with cash flow from operations (CFO) from the statement of cash 
flows: 
 
FCF = CFO + [Int * (1- tax rate)] – FCInv 
 
 
 
 
 
 
 
(6) 
 
Where: CFO = cash flow from operations. 
Indian corporation tax rate as December 2009 = 33.99% 
Depreciation for 31st March 2009 to 31st December 2009 = US$ 89.9 million 
WCInv = - US$ 80.10 million 
 
In table 2.5 the calculation for the free cash flows for the company Essar Energy are obtained and by 
making an assumption on how much value is generated by refining, the value of the refinery follows: 
 
(Table 2.5: FCF Calculation38) 
 
1) Calculate the free cash flow 
 
 
 
 
 
 
 
All figures in US$millions 
 
 
$m  
 
 
 
 
 
 
 
 
 
 
 
FCF = Earnings before interest and taxes (EBIT) 
           
363.00  
 
 
 
 
 
 
 
 
 
 
 
EBITDA  Dec 09 
 
     
433.10  
 
 
 
 
 
Power  
     
120.80  
 
 
 
 
 
 
Oil  
     
312.30  
 
 
 
 
 
 
 
     
433.10  
 
 
 
 
 
 
 
 
 
 
 
 
 
Less tax on EBIT 
 
 
- (27.60)  
 
 
 
 
 
 
 
 
 
 
 
Add back non-cash charges 
 
 
              
89.90  
 
 
 
 
 
 
 
 
 
 
 
Less cap expenditure  
 
 
-   (405.50)  
Cash flow statement 
 
 
 
 
 
 
 
 
                                                 
38  Obtained by using official material from the ICAEW, The Institute of Accountants for England and Wales 
93 
 
 
 
Less net working cap increases 
 
 
-   (656.60)  
Cash flow statement 
 
 
 
 
 
 
 
 
 
Plus net working cap decreases 
 
 
736.7 
Cash flow statement 
 
 
 
 
 
 
 
 
 
FCF 
 
 
99.9 
 
 
 
 
 
 
 
 
 
 
 
Per (12/9months) 
 
 
133.2 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
(WACC = Ke*E/(E+D) + Kd*D/(E+D)) 
 
10.77% 
 
 
 
 
 
 
 
 
 
 
 
 
Main Assumption for valuation: 72% of profits and costs are due to the oil refinery the rest are due to the 
company’s power business; therefore as an estimate, numbers generated above representing the free cash 
flow value are multiplied by 72%: 
 
FCF Oil Refinery: 0.72*133.2 = US$ 95.9 million 
 
Step 2 
 
There are 2 methods generally applicable to forecasting the cash flows, one is to calculate the 
historical free cash flow and apply a growth rate, under the assumptions that the growth will be constant and 
fundamental factors will be maintained.  The second method, is to forecast the underlying components of 
free cash flows: costs, revenues, tax changes for each year in the future and calculate each year separately.  
The simpler first method is utilised here as a first draft calculation.  How does one estimate growth?  This is 
entirely down to discretion and a full in depth economic report of predicted oil demand and prices within the 
Indian region by a company called KBC39 is  referenced for growth figures in this region. 
 
Gross Profits for the refinery in US$ millions for the following years were: 
2007 
2008 
2009 
40 
74.9 
681.7 
                                                 
39  KBC’s (2011) Outlook for the World Refining Industry 
94 
 
 
 
The last figure is a big jump and is down to the refinery profits being realised and some derivative contracts 
maturing. 
 
If in 2011, the phase I project is realised for the refinery, the capacity will double meaning twice the 
products can be refined.  These figures were difficult to estimate due to the current economic climate. 
 
Instead we apply KBC forecasts for the oil demand for the next 6 years going from 3107kbpd to 
4262kbpd, due to the growth in the region, a growth rate per year is applicable to the refinery of: 
3107 *(1+g) 7 = 4262, therefore g = 4.62% 
(KBC are a company that carry out extensive research in the commodities space and have been hired by 
Essar Energy). 
 
Global oil demand in Europe and the US will most likely decline and the majority of demand in the 
next decade will be from the Asia-Pacific region as shown below in table 2.6: 
 
(Table 2.6: Indian refined product demand, -’000 bbl/d) 
2008  2009  2010  2011  2012  2015  
LPG . . . . . . . . . . . . . .  406  
416  
432  
450  
468  
526  
Naphtha . . . . . . . . . . .  336  
352  
352  
366  
380  
426  
Gasoline . . . . . . . . . . .  260  
299  
332  
363  
394  
478  
Kerosene . . . . . . . . . .   303  
301  
316  
329  
344  
395  
Gas/Diesel . . . . . . . . .   1050  
1112  1184  1246  1314  1553  
Fuel Oil . . . . . . . . . . .   390  
404  
402  
401  
400  
398  
Others . . . . . . . . . . . .   362  
379  
395  
412  
429  
487  
Total . . . . . . . . . . . . .   3107  
3264  3414  3568  3729 
4262  
Source: KBC 
 
 
Step 3 
WACC was calculated as approximately equal to 10.77% 
95 
 
 
Step 4 
FCF1 = 96 *(1.0462)   = 100.33 
FCF2 = 96 *(1.0462)2 = 105 
FCF3 = 96 *(1.0462)3 = 110 
FCF4 = 96 *(1.0462)4 = 115 
FCF5 = 96 *(1.0462)5 = 120.20 
FCF6 = 96 *(1.0462)6 = 126 
 
Step 5 
Fair Value of the Vadinar Refinery = ($100.33/ 1.1077) + ($105/ 1.1077 2) + ($110/ 1.10773) + ($115/ 
1.10774) + ($120.20/ 1.10775) + ($126/ 1.10776) = US$ 569.75 million 
 
With the refining capacity of Vadinar currently at around 267 kbpd this is on the lower side of estimates, but 
looking at the latest share price for Essar Energy it looks like a reasonable estimate of terminal value.  (Share 
Price of Essar on June 2011 is [ESSR on LSE] 405 pence). 
 
The problem with the above value is that it does not take into account the variation in the profits of 
the oil refinery; due to the estimates of the economic variables being static.  If a weather event for example 
hits India, this could seriously affect profits for a number of business quarters.  This method states that 
growth will be consistent with estimates of oil demand over the next decade.  Another effect ignored is that 
of management changing strategic decisions, they could for example decide to change output products due to 
various economic effects that there is no control over.  This would drastically alter the nature of the cash 
flows over time.  Another issue is the calculation of the cost of capital for the company, which is always an 
equivocal calculation.  An alternative would be to use a cost of capital that is industry wide accepted, 
sometimes printed by various financial organisations, to take account of the capital structure of the company.  
Again although DCF is a reliable and tractable method, it has many pitfalls.  The FCF method gave an 
approximate value of $570 million for the refinery complex; in contrast a financial market data calculation 
which is applied in the next chapter. 
 
96 
 
 
 
2.9 Real Option Approach 
 
Most refinery related contracts are traded on NYMEX; where there are numerous contracts that 
expose quantities and values related to the refinery market.  These exchanges trade what is referred 
to as “light-sweet” crude oil and a single contract, or “lot”, refers to the purchase or sale of 1,000 
barrels of oil.  A large fraction of the activity on oil Futures markets is concentrated on delivery for 
the next three months.  In our mathematical model we will specify the delivery to be one month 
ahead, or 30 days.  The quoted Futures price is the price at which the owner of the contract will 
buy/sell crude or the refined products during this delivery period per 1000 barrels.  For each buying 
or selling of the contract, we do not consider transaction fees or a bid-ask spread.  “Fair value” of an 
asset or liability should be based on the current market price, this is known as marking-to-market.  
This type of accounting can change values on the balance sheet as the financial markets evolve.  It 
supersedes the historical cost account based on past revenues.  A book value should always be 
compared to a fair value.  In substitution of the above method, companies often apply marking-to 
model, where a market for the asset or product is not available.  Models can give an artificial 
illusion of liquidity but are absolutely necessary for illiquid or complex assets.  Any advanced 
model dealing with valuation should attempt to capture market stress whence bid-ask spreads 
widen.  In 2003 Warren Buffet was famous for saying “mark-to-myth” when referring to the 
methods applied by financial institutions to value their derivative positions, a statement that 
emphasises the difficulty in making fair a valuation. 
 
Distillation of a liquid can occur almost instantaneously, in the refinery however the 
production is not taking place instantaneously; imagine the process time line present for the oil 
refiner: 
 
97 
 
 
 
 
 
(Figure 2.1: Timeline for refinery decision maker) 
 
Contracts listed on an exchange that manage the differential risk between crude and its products, 
can be purchased by the refiner to lock in a refining margin and ensure that either long term 
contractual obligations are met or the crack risk is hedged accordingly.  Chicago board of trade 
(CBOT) offers a number of contracts on refinery spreads that are relevant to refineries throughout 
the US.  Initially, before production and sales are decided, the DM can assess the market dynamics 
and consider trading financial contracts to manage the inherent risk.  The table below defines 
factors that are significant to the decision maker considering hedging or speculating on the crack 
spread’s value.   
 
This data shows that the financial contracts listed can be indicative of the profit that can be 
extracted from the crack spread, as they enable traders to manage the factors impacting the refiner’s 
profit margin. 
 
A standard real option comparison was the next stage in assessing the financial worth of the 
refinery.  An oil refinery is similar to a chemical plant in many ways and can be considered a 
producer with exposure to a differential spread.  The real difficulty in an option approach is the 
pricing of the choices embedded within it.  The choices available to real assets usually fall into one 
of the following categories: the option to expand, the option to abandon and the option to defer.  
Specific to the oil refinery is the choice available to the decision maker to vary purchasing and 
production decisions of the crude and refined products.  The risk-neutral valuation approach can be 
extended to allow real option valuation of land, plant, equipment and assets.  This approach does 
not require risk adjusted discount rates but it does need market price of risk parameters for all 
t0              
Hedging 
Decision 
t1 
Commitment 
to production 
t2         
Purchase of 
crude oil 
t3                 
Sale of outputs 
98 
 
 
stochastic variables.  Many investments are underpinned by uncertainty connected to future prices.  
These future prices can be used explicitly to estimate the risk-neutral stochastic processes involved 
in the calculation and avoid the requirement to obtain a market price of risk for the random 
variables exemplar set. 
 
Based on the decision maker's (DM) initial set of decisions the other choices available 
would have been altered due to the knock on effects of the limitations on the particular crude assay 
being refined.  For example, producing more gasoline means less heating oil can be produced.  The 
CDU must maintain the mass balances at all times, therefore some choices are not possible, e.g. 
producing all kerosene or no Naphtha at all.  If for instance the refiner knew that residual fuel oil 
would be the highest valued product in one month time, then as much of this fuel oil as could be 
physically stored could be processed.  The refiner has an embedded option that is strictly 
constrained due to the chemical processes that are viable.   
 
This is investigated as a cacophony of commodity future prices and correlations in the 
following chapter.  Trivially this value can be captured by the 3-2-1 crack spread.  There are a 
number of spreads available on financial exchanges, an example of a few follow: 
 
• 1:1 
one crude oil versus one gasoline or one heating oil  
• 2:1:1 two crude oil versus one gasoline and one heating oil 
• 3:2:1 three crude oil versus two gasoline and one heating oil 
• 5:2:1 five crude oil versus two gasoline and one heating oil 
 
The 3-2-1 contract is the most frequently traded spread contract and represents the amount of value 
in a barrel of WTI crude refined into its two most valuable products, gasoline and heating oil.  
Utilising data from CBOT on the 3-2-1 crack spread an option approach of valuation is built.  If the 
refining cash margin is positive the DM should refine, if negative, the refinery should be switched 
off.  
99 
 
 
 
This idiosyncrasy of pricing would be of interest in the current commodity climate, where 
independent traders, private equity firms and even airlines are invested heavily in the refining 
industry40.  This is due to companies either yearning to be vertically integrated or investing to 
capture diversification opportunities.  Delta airlines for instance bought the Trainer refinery, 
185,000 bpd, in June 2012 and reconfigured it to produce more jet fuel.    
 
At any period in time, without purchasing additional financial contracts, the refiner is short 
crude oil, as it needs buying, and is long the refined products, as the DM has the ability to produce 
them.  Therefore the refiner is long the crack spread.  In practise refinery traders will construct 
trades on the constituents of the crack spread based on their estimation of the spread being under or 
overvalued.  If for instance a dispersion trader feels the crack spread is overvalued then it will be 
sold short, and vice versa.  The hedging replication argument allows the refinery to be valued using 
a strip of crack spread options; it is then compared to the DCF valuation previously calculated. 
 
  “The discounted cash flow (DCF) criterion induces investment too early (i.e., when prices are too 
low), but the real options approach induces investment too late (i.e. when prices are too high) when 
it neglects mean reversion in prices”.   
 
2.10. Problems with simple real option approach 
 
In practice, the valuation of the refinery will involve a mixture of positions in each commodity in 
forward contracts to secure future revenues whilst allowing for maximisation in the next time 
period.  These contracts are however correlated and this is one of the difficulties with a commodity 
pricing model.  This valuation is calculated whilst always taking into account the physical 
constraints; if for example there is a surplus of crude oil from one period to the next, the model 
                                                 
40  http://www.ft.com/cms/s/0/e1c96862-9516-11e1-ad72-00144feab49a.html#axzz2V9JYyX17 
100 
 
 
needs to address this as there is less available to then refine in the following period.  The valuation 
is more difficult than the strip of options as the choice on one option affects the next, and the option 
is not as simple as a call; it is in fact an option with choices on the weights of each refined product 
minus the crude cost.  There are two underlying stochastic processes that represent the commodities 
that constitute a spread, Si,j. 
 
The Si and S1 are correlated, and the above spread is subject to the physical constraints of the 
refinery.  There are several methods that can be envisioned to solve the corresponding optimisation 
problem described. 
 
• The most elementary one consists in using the current forward curve to find the optimal 
portfolio of long and short forward contracts attached to the refining period of one 
month up to 10 years for a legitimate financial valuation.  The corresponding values are 
the intrinsic value of the refinery facility. 
• Stochastic optimisation on a tree or through Monte Carlo simulations: the quantity of 
cumulative production denoted Qt and one can work back in time to solve the 
optimisation problem, this is a dynamic programming approach. 
 
This former method is precisely the approach that is considered in the following chapter that to 
investigate the possibilities of obtaining a much more accurate representation of the uncertainty 
facing the refinery DM.  In the next chapter an accurate way to value the refinery, the prevalence of 
the financial market risks and the uncertain behaviour of the commodity price series are analysed 
extensively. 
 
 
 
101 
 
 
 
Chapter 3 
 
A multi-period dynamically consistent valuation of a topping 
oil refinery, with mass balance and capacity constraints 
 
 
 
“The only thing that makes life possible is permanent, intolerable uncertainty; not knowing 
what comes next.” Ursula K. Le Guin.  
 
Valuing an oil refinery is similar but not the same as obtaining a market price for a chemical plant 
or a hydropower station; it is a real asset that generates a commodity product/s sold on the market 
that enable it to continue to operate.  A real option approach can capture the optionality of the 
owner's decision process, which can be deferred or executed, and if the future returns of the 
commodities are simulated accurately, enable a realistic and robust valuation.  We present the 
issues surrounding the modelling of the underlying commodity prices and design a calculation for 
optimising the volumes of crude to purchase and refined outputs to be sold on the market – all 
whilst adhering to the capacity and fluid flow constraints present at a topping oil refinery.  The 
valuation relies upon the assumptions in dynamic programming and maximising profit as a rational 
102 
 
 
and risk adverse agent; a trinomial tree is utilised along with mean reverting stochastic equations 
to simulate the prices and produce the volumetric decisions over the lifetime of the oil refinery.  To 
our knowledge this is the first application of dynamic set assignment to the valuation of a refinery 
in the literature.  The valuation program is created within GAMS and the optimisation is convex, 
non-linear and therefore it can be shown that the value is unique locally and globally.    
 
 
 
 
3.1 INTRODUCING COMMODITY LINKED ASSET VALUATION 
 
Commodity linked investment provides investors with a way to diversify beyond traditional stocks 
and bonds, whether they are related to food, energy or precious metals.  Historically, investors 
wishing to purchase commodities had many barriers to entry; significant amounts of money, 
expertise and time were required.  There are now a number of avenues enabling investors to 
participate in this dynamic asset class, from specific products to investing in the real assets 
themselves.  Consequently, there has been increased activity in commodity related investment, risk 
management and asset valuation.  The uncertainty associated with commodities has a particular 
impact on commodity linked asset valuation.  The aim is to investigate how a stochastic one factor 
model impacts the investment decisions and valuation associated with an oil refinery.  In 
constructing improved models for commodity prices we have a two sided problem; increased 
realism through extra factors, such as jumps, and the difficulty of solving for the contingent claims 
that rely upon them.  This chapter searches for a tractable model that captures the main features of 
the refined commodity processes and can be incorporated into the real option valuation of the 
refinery.  This will contribute to the understanding of the valuation of commodity linked 
103 
 
 
investments applying optimal decisions where the commodity prices have active futures markets 
available.   
        The real option valuation technique has been used extensively over the last decade to improve 
upon the errors apparent in discounted cash flow analysis (DCF).  As mentioned in chapter two, the 
problem with DCF valuation is that it fails to account for the value of managerial flexibility inherent 
in many types of projects and assets.  The options derived from managerial flexibility are 
commonly called “real options” to reflect their association with real assets rather than financial 
assets see Brandao, Dyer and Hahn (2008) for details.  In Eydeland and Geman (1999), the authors 
construct a model to value electricity derivatives; a particularly onerous calculation due to the 
specific properties of electricity prices.  It is noted however, that operating a merchant power plant 
is financially equivalent to owning a portfolio of daily options between electricity and fuel (spark 
spread options).  Therefore, obtaining a model to price these options enables the power plant itself 
to be valued as a real asset.  We will use a similar approach in chapter four, where we use a strip of 
crack spread options to value the refinery.   
Valuation of options on real assets has been limited by the mathematical complexity of the 
approach, the restrictive theoretical assumptions required, and by the lack of its intuitive appeal.  
This complexity stems from the fact that the problem requires a probabilistic solution to a firm’s 
optimal investment decision policy, not only at present but also at all instances in time up to 
maturity of its options.  To solve this problem of dynamic optimisation, the evolution of uncertainty 
in the value of the real asset over time is first modelled as a stochastic process.  Then the value of 
the firm’s optimal policy is represented, if possible, by a partial differential equation - obtained as a 
solution to a value function represented by Bellman’s principle of optimality, see Bellman (1957), 
where appropriate boundary conditions reflect the initial conditions and terminal payoff 
characteristics.  When closed-form mathematical solutions are not possible, which is usually the 
case for more complex problems, where the asset may be subject to several sources of uncertainty 
and more than one type of option, numerical methods and discrete dynamic programming must be 
104 
 
 
applied to obtain a solution.  Generally, a discrete approximation to the underlying stochastic 
process can be achieved enabling a computationally efficient model of the valuation problem.  The 
first example of this was a binomial tree that converges weakly to a lognormal diffusion of stock 
prices as shown in Cox et al. (1979).  The binomial tree can be implemented to accurately 
approximate solutions from the Black-Scholes-Merton continuous-time valuation model for 
financial options – it provides solutions for the early-exercise American options and enables the 
pricing of exotic options in a tractable manner.  However, the use of traditional option-pricing 
methods and the replicating portfolio approach is complicated by the fact that, for most real assets, 
no such replicating portfolio of securities exists, so markets are incomplete.  With this in mind, 
Pindyck and Dixit (1994) suggest the use of dynamic programming, using a subjectively defined 
discount rate.  An oil refinery is impacted by a number of uncertain market prices, and although it 
can be approached as an option on these sources of volatility, it gives the owner a complicated set 
of knock on decisions to make over the refinery’s lifetime.            
 
 
 
 
 
3.1. The Refiner’s Optionality 
 
Fundamentally, the refining plant’s aim is to produce a high quality product sold in local and global 
markets while taking into account efficiency and regulatory requirements.  The decision makers 
recognise that they possess vast “optionality” but do not correctly extract it.  At each point in time 
the refiner can decide to refine or not based on whether it is profitable to do so.  If the price of the 
raw crude spikes producing a gross refining margin that is negative then choosing not to refine is 
the most sensible decision.  In fact, this is just one of the decisions available to the refinery 
105 
 
 
manager.  In standard market conditions it is the volumes of each refined product that constitute the 
majority of the decision making.  If the price of jet fuel increases enough, the shift in the refinery 
processes should be towards producing more of it, whilst adhering to the strict physical, mass 
balance and blending constraints inherent in the refinery’s processing.  This is a complex and 
dynamic set of decisions that can be augmented by the optimisation calculations.  In this work we 
construct a model to aid the refiner in deciphering the optimum amounts of petroleum products to 
purchase and sell.  To our knowledge, this modelling and quantifying of the optionality is yet to 
have been achieved in the literature.  The goal of this work is to quantify and better understand the 
optionality of oil refineries without access to liquid crack spread products, under a model of optimal 
refinery management.  We have three major findings: 
 
(a) the stochastic model for the asset prices fits historical prices of crude and refined 
petroleum product’s futures well 
(b) the actual optionality that could have been extracted allows a valuation much higher than 
DCF reveals 
(c) the model can be run in a reasonable time 
 
We show that the mean reversion present in the commodity price series, which is exploited by the 
embedded refinery optionality, appears to increase the value of the refinery to a much higher price 
than DCF indicates.  One reason for this is the opportunity for the owner to switch to another 
product if one is not performing well.  The model is calibrated on real financial and refinery data, 
and results obtained after using a large enough simulation indicate that a statistically consistent 
valuation is accomplished - despite the strong assumptions present. 
 
3.1.1 Real data from an Indian Oil Refinery 
 
106 
 
 
India currently has over 22 refineries and has accomplished much in a short period through 
expanding its capacity and complexity to compete on a global scale.  It is emerging as a refinery 
hub and with capacity clearly exceeding demand; the country’s refining capacity has grown from 62 
million metric tonnes per annum (MMTPA) in 1998 to 215 MMTPA in 2012 – 4.4% of global 
capacity.  Out of the 22 refineries – 17 are public sector, three are private sector and two are joint 
venture.  Since August 2009, India is the largest exporter of petroleum products in Asia (Platts 
assessment).  The Vadinar refinery in Gujurat, has an installed capacity of 10.5 MMTPA, 
equivalent to 210,000 barrels per stream day (BPSD) of crude along with secondary processing 
units like: fluidised catalytic cracking, naphtha and diesel hydrotreater, vis breaker and product 
treating units.  Capacity was increased to 20 MMTPA on June 5th 2012.  We use this refinery in our 
analysis to analyse the model’s capabilities. 
       Owners of refineries buy crude and have the right to refine for an indefinite period of time – 
assuming financial solvency.  There are of course daily minimum and maximum refining rates as 
the different products induce various choices to the owner.  As the owner refines, two types of 
transaction costs are present: the financial transaction cost if the financial market is used to sell the 
products, a percentage charge, and the commodity charge, a dollar amount associated with the 
operational costs of refining a barrel of crude.  We choose to focus on the uncertainty in the gross 
refining margin (GRM) costs, simply the sale price minus the overall cost of purchasing and 
refining the crude; rather than the detailed operational costs of refining.  This is achieved by 
modelling the financial decision set.   
As posited in Carmona and Ludkovski (2010), the storage of a commodity that has 
optionality when being sent to market, derives from the ability to exploit the mean reverting trends 
in the crude and refined products markets.  The authors value a gas dome storage facility and a 
hydroelectric pump using optimal switching and stochastic optimal control.  The authors use a 
numerical method to deal with the path dependency, which is based on an old quasi-variational 
inequality approach.  The difference with a refining complex in contrast to a natural gas dome 
107 
 
 
storage complex, is the ability to “shift” to an alternative product if the price is more favourable on 
the market.  Oil majors, for example, have financial traders working within their refineries doing 
just this: capturing the optionality with hedging and speculating transactions.  In the short term, 
current forward/future prices on crude and the refined products provide information regarding the 
expected spot price mean reversion.  This mean reversion derives from demand and supply 
characteristics on the market; refined petroleum products are a major part of the developed world’s 
GDP.  In the next section, we introduce the one period program and show how the model would 
capture value in this simplified setting, laying the foundation for the multi-period optimisation. 
No such models, to our knowledge, have been developed to capture this refining value; this 
is mainly due to the huge dynamic programming “curse-of-dimensionality” that exists.  A global 
auditing firm, for example KPMG, value oil refineries as financial assets, like the one in Gujurat – 
US GAAP of UK ICAEW rules are commonly applied due to their extensive application.  To obtain 
a valuation under UK accountancy standards: a typical DCF is employed over six years, with seven 
random variables representing the decision variables, taking years in monthly blocks, and assuming 
a deterministic optimisation, a state space of 7 x 72 exists.  However, commodity prices are in 
constant flux on the market and a representation of this uncertainty is required for the model to be 
considered realistic.  Even a small increase in uncertainty can alter a valuation “materially”; see 
Pindyck and Dixit (1994), this is because it increases the upside potential payoff of the refiner’s 
option, leaving the downside potential unchanged at zero – assuming a downside decision to switch 
off the refinery and incur no operational costs.  Stochastic modelling is a minefield, and this 
addition to the model increases the state space further.  This is a huge computation for any optimiser 
– here we choose to discretise the stochastic prices onto a trinomial tree to alleviate the complexity; 
such as those used by Hull and White (1997).  By including the uncertainty in decision trees the 
investment problem is much more effective, as there are now alternative investment strategies that 
can be evaluated.  The value of the investment option must be calculated for each possible rule and 
is a function of the number of paths on the tree.  The difficulties associated with generating accurate 
108 
 
 
discretisation processes is discussed in Zenios (1995).  The author applies a discrete space binomial 
process for generating interest rate scenarios for asset liability management of fixed income 
securities.  The generation of return scenarios concentrates on fixed income securities with 
contingent claims, and it is emphasized that the future prices are Markov.  The focus in this part of 
the model is to generate a scenario tree; decision analysis and stochastic programming rely heavily 
upon its accuracy.  Another method commonly applied to capture uncertainty is via a Monte Carlo 
simulation as shown in Carmon and Ludkovski (2010).  For reasons explained later, discrete trees 
are more realistic within this framework.  Alternatively, stochastic optimal control as seen in Chen 
and Forsyth (2007) can be applied.  The authors use a regime switching price model with storage 
characteristics - these models capture the uncertainty by finding a control function that represents 
the refinery manager’s set of choices over time.  At the time of writing, no model utilising optimal 
refining management, optimal control theory applied to the refinery decision problem, has been 
constructed or back tested on historical data to quantify its optionality - ultimately providing a 
financial valuation. 
For our valuation model we examine a one-factor tree model of optimal refining 
management that can be back tested, meaning we focussed on: (a) developing a realistic price 
model (mean-reverting) of spot and forward price processes, and (b) developed a methodology for 
capturing the embedded optionality in a refining complex, given all the physical and mass balance 
constraints and the uncertain price processes.  Ensuring that the price model is calibrated to the 
market and constructing a tree that enables each commodity to be optimised is difficult due to the 
state space.  Strong assumptions must be made to implement this model calibration due to being in 
an unregulated market without access to liquid futures contracts and having no certain cost of 
capital.  As described in Schwartz (1997) the mean reversion behaviour of the commodities is an 
important feature to capture with a representative model - the coefficients of calibration are shown 
in the results.  Risk-neutrality is an important assumption when using dynamic programming in real 
options valuation; where it is common to use a constant discount rate. 
109 
 
 
In back testing the model over the same period that our DCF value was calculated with, we 
found it was very successful at consistently capturing vast amounts of optionality.  In chapter four 
we tested the model using crack spread data over the same period of ten years using US contracts 
available on the Chicago board of trade (CBOT).  The sensitivity of the model to investor risk 
aversion, mean reversion level of the commodity prices and the granularity of decision making were 
investigated.  Inventory of petroleum products on site at the refinery is significant.  Having zero 
inventory means we cannot choose to refine immediately; only crude can be bought, whereas being 
partly filled means refining can happen or be delayed if the price is not trending high enough.  A 
fully stocked refinery gives us optionality to refine, but not to buy crude, hence if the products 
move much lower and crude much higher, this crude trade cannot physically be made.  This is 
indicative of the embedded option available to the owner.  An interesting computational extension 
would be to consider how the optionality is affected by the refinery inventory level at the beginning 
and end of the horizon – as these values clearly impact dramatically the value obtained for this 
asset. 
     The optionality in this problem is similar to an American option valuation.  An American 
call option gives the investor the right to exercise the contract at any point in time up to maturity.  
The underlying will be bought at a specified strike, stated in the contract, enabling the owner of the 
option to observe market prices and execute if the momentum moves into favour, see Longstaff and 
Schwartz (2001) for a least squares Monte Carlo valuation approach.  The owner can decide to 
remain dormant allowing the contract to expire if conditions are not profitable; one difference in 
comparison to the refinery is that there is a continuous batch of contracts, each day the decision to 
refine and in what proportion can be executed.  Further, there is not just one underlying as is the 
standard case with an American option, where a stock or a bond represents the intrinsic value – the 
refinery has seven underlyings and each one is correlated with one another.  Finally, the underlyings 
are not stocks, where features such as geometric Brownian motion and dividends are significant, 
they are commodities, which exhibit particular properties and idiosyncratic qualities that 
110 
 
 
differentiate them from other asset classes.  For instance, the futures on commodities display a 
fundamental difference to those on stocks as they approach maturity – their at the money volatility 
decreases in comparison with shorter dated contracts rather than increasing at any given date, this is 
known as the Samuelson  effect; hence each futures volatility itself increases as it approaches its 
own maturity date - see Geman (2005) for a detailed discussion.  To construct a realistic model, we 
investigate these peculiarities and the specifics of the refinery that underpin our construction.  In the 
next section, we describe the risks facing the owner and introduce the refinery planning literature.   
 
 
3.1.2. The refinery’s risk 
 
Price, safety, operational, regulatory and economic risks are just a few of the concerns facing the 
owner of the refinery.  Recently, as mentioned in chapter one, the refining environment has become 
particularly competitive.  Hence risk management becomes a more pressing issue for refiner 
consumers and producers, and knowledge of the product prices can help reduce this risk.  The 
refiner's portfolio includes his own production and a set of sales and purchases over periods of time.   
The goal of using such an approach is to reduce the economic risk and ultimately obtain a 
value for the asset.  This is connected to the fact that the oil spot price may be highly volatile due to 
the various different unpredictable reasons; geopolitical factors, OPEC decisions, wars and to the 
possibility of unknown market factors.  When the refinery buys or sells the commodities, it is 
exposed to the risk that the spot price and the futures price do not converge on expiration of the 
future contract; this is known as basis risk.  The basis risk factors here are: the wholesale spot crude 
price and the refined products spot prices.  The refiner is a price taker with respect to the crude and 
refined products’ prices.  Since we want to concentrate on the financial risks; the detailed 
operational refinery flows (known as system planning), the operational uncertainties, like yield 
111 
 
 
uncertainty in the crude distillation unit (CDU), are ignored.  The data generated by the mid-term 
production planning problem, however, can be used as input data to the scheduling problem, which 
determines detailed crude oil processing schedule, process unit schedule, blending and shipping 
schedules. 
There are two mathematical terms that explain the decision that confronts the refinery 
owner.  At the start of the first period, the decision maker chooses the production variables - the 
volumes of crude to buy and refined products to sell, whereas, the second term considers the risk 
that the profit induces.  Due to the nature of the Vadinar refinery, the hedging decisions will be 
volumes of the refined products to buy or sell when the uncertain prices have unfolded at the end of 
the period.  The literature on this subject differs in the way the expectation and the risk is 
considered when optimising over one time period.  Rarely considered in the literature, is to 
approach the problem as a multi-period sequence of decisions over some fixed time horizon.  This 
approach enables a financial valuation to be obtained, as the cash flows can be modelled over time 
and discounted back to the present day.  This area is considered significant in finance as it impacts 
valuation risk; the risk that the asset is overvalued and is worth less than expected when it matures 
or is sold, as investigated in Mun (2006).  There are two main contributors to this valuation risk: 
 
(a)  The risk that valuations are incorrect due to control, operational and management errors. 
(b)  The valuation uncertainty due to model risk and assumptions underpinning a valuation 
calculation. 
 
Dependent upon the asset being valued, factors such as tail risk, credit risk and many others can 
have differing and substantial effects on a value.  The ubiquitous DCF approach in the case of an oil 
refinery gives erroneous values and is often the cause of financial value disputes.  In the next 
section we discuss the current planning literature.   
 
112 
 
 
3.1.3. Introducing refinery planning 
 
There are four common types of refinery: coking, cracking, topping and hydro-skimming.  Due to 
the detailed analysis in the literature available for the topping type, its granular construction was 
considered parsimonious and representative enough for a real refinery model.  Ravi and Reddy 
(1996) present a one period model to capture the set up of a modern topping refinery, which we 
utilise here but extend it over all time periods until maturity.  They present a fuzzy (a form of many-
valued logic; approximate rather exact sets) fractional programming approach to the optimisation of 
operations at an oil refinery.  This is optimising using a ratio within the objective function, in this 
case, very similar to the Sharpe ratio for the refinery.  The authors set as an objective function the 
mean profit divided by a function representing risk – it is named fuzzy as it is based on simple 
fuzzy logic.  This term applies when the objectives cannot be expressed as “true” or “false” but 
rather as “partially true”.  The authors have 22 constraints to represent mass balances, and two 
fuzzy goals, which maximise profit relative to capacity; here the objective goals are between zero 
and one.  They elicit a very small increase in profit over previous deterministic studies.  The authors 
avoid the inclusion of uncertainty and relevant risk functions in their optimisation.    
 
A great introduction to stochastic programming is discussed in Sen and Higle (1999).  
Historically, optimisation models of chemical plants were mostly linear due to the computational 
issues: Gao et al (2008) use a mixed integer linear programming model for the midterm planning 
problem of a fuel-oil plant in China; Micheletto et al (2007) use a MILP model for a refinery over 
multiple periods of time where demand satisfaction is the focus.  Neiro and Pinto (2003) present the 
problem of managing multiple refinery operations using a mixed integer non linear program.  Their 
objective function maximises the net present value under raw material and product inventory 
constraints.  These include mass balance and operating constraints for each refinery in their network 
and demand scenarios are used to include uncertainty.  Although the authors claim advantages over 
single refinery optimisation by considering the supply chain, the risk preferences of the decision 
113 
 
 
maker are not explored. 
 
A number of studies in the chemical engineering literature approach the refinery as a blendshop 
problem (considering the results from blending of the fractionated products on a very short time 
scale), see Grossman and Zamora (1998), which is clearly necessary in short term planning, but 
exogenous effects or the financial side of the problem are usually not integrated at this time horizon.  
Pongsadki (2005) presents a mathematical program that maximises the profit whilst minimising the 
risk of the refinery.  Their model uses data from the Bangchak Petroleum refinery to decide on the 
production level for given forecasts of demand.  The scenario generation is drawn from normal 
distributions and incorporates constraints of cost payments for the decision maker not meeting the 
client demand.  They plot the expected gross refining margin against value at risk; the authors 
emphasise the dramatic improvement of the optimisation when replacing variance.  Three 
independent scenarios are generated and the optimisation summed over one-period at a time; where 
one period is separate to another, hence a static optimisation is represented; computational details of 
the mathematical program are not provided but no alternative to procedural programming is 
described. 
   
3.1.4. Multi-period refinery planning 
 
As mentioned in the previous section, Mathematical programming (MP) models applied to 
refineries are mostly deterministic; although, often the one-period approaches mentioned are used in 
practice by the oil majors for decision making.  A more realistic model for valuation is a multi-
period program.  The multi-period approach is much more difficult due to the computational issues 
that arise.  Authors consider the entire supply chain or a network of refinery capacities rather than 
approaching the difficulties with the state space or the stochastic nature of the commodity prices.  
114 
 
 
Benyoucef (2010) applies a multi-period stochastic program to refinery capacity management up 
until the year 2030.  Random commodity price variables are not included, and the author optimises 
a network of refineries; algebraic modelling language (AML) details are again avoided.  Marianthi 
et al. (1997) provide a multi period optimisation with uncertainty for short term scheduling, it is not 
at the granularity we require but is useful in the way the authors introduce risk to the refinery 
scheduling.  Bernardo (1999) et al introduce a specialised cubature technique for solving high 
dimensional problems but it is only suitable for Gaussian distributions.  The authors claim 
computational performance gains over efficient sampling techniques and is applied to engineering 
processes.  This paper highlights even more the problems faced by researchers when there is a large 
state space but no alternative approach to optimising over such a state space using an AML is 
provided.                         
 
In the next section, we introduce the physical constraints present in this particular refinery 
model, along with the relevant data.  In section three we discuss the multi-period construction.  In 
section four, we introduce uncertainty via the random spot commodity prices and derive how they 
are related to the forward curve.  In section five, we solve the optimisation refinery model using a 
unique AML representation.  In section six, we discuss the results and their implications, and finally 
in chapter four we compare the previous refinery valuations to a strip of crack spread options. 
 
3.2. STOCHASTIC PROGRAMMING FOR OIL REFINERIES 
 
The construction in Ravi and Reddy (1998) is utilised to formulate the foundations of a 
linear deterministic program.  As described in chapter one, the petroleum flows and streams in a 
refinery plant are complicated movements through each of the units present at a particular category 
of refinery.  Figure 3.1 below depicts the raw crude being pumped into the distillation tower after 
being desalted, and on the right the exit, producing a typical set of marketable products.   
 
115 
 
 
                      
 
(Figure  3.1: Crude oil distillation and fractionation)41 
 
In any oil refinery there are a number of restrictions on the flow of crude and refined products that 
move through the crude oil distillation units (CDUs).  Strict limits are present on how much 
hydrocarbon liquid can be held in storage within the refinery and the amounts that can be released 
onto the refined markets; see Moro and Pinto (2000) for a detailed description.  The following 
section describes the structure of the linear program. 
 
3.2.1. Refinery physical flow constraints 
 
It is necessary to provide some background on the processes that occur when refining is carried out 
on crude oil to precursor the construction of the refinery model.  This illustrates how in the real 
world the refiner’s decisions come to affect the marketable products.  Crude oil is refined into 
                                                 
41  https://rbnenergy.com/complex-refining-101%E2%80%93distillation-no-test-and-no-math-guaranteed 
116 
 
 
useable products such as gasoline; hence gasoline has a higher cash value on the market.  Extracting 
this monetary value is a complicated process that involves isolating mixtures according to their 
boiling point range.  Gasoline boils from 40 to 200 degrees Celsius; figure 3.2 shows the boiling 
point range of the various fractions.  Crude oil distillation separates the hydrocarbons into the 
fractions; the separation occurs in a large tower operated at atmospheric temperature.  Light 
materials like naphtha are removed in the upper section; heavier materials such as residual fuel oil 
are withdrawn from the lower section.  Residual fuel oil can be further separated into vacuum gas 
oil and vacuum residua - the vacuum gas oil goes to the catalytic cracking unit.  In a modern 
refinery, the cracker carries out the most significant process; it cracks long chain hydrocarbons into 
shorter chain molecules.  It enables the refiner to convert raw material into gasoline and distillate; if 
one compares the prices of raw fuel oils to those of gasoline, the advantage is conspicuous.  
 
 
 
(Figure 3.2: Fractionation of crude oil)42 
 
The refined products are obtained through blending of various fractions of crude within the refinery 
units.  Petroleum products have many specifications; some linear programs are constructed to 
                                                 
42 http://www.ril.com/downloads/pdf/about_jamnagar.pdf 
117 
 
 
optimise only the blending process in detail, rather than optimising the cash flows generated 
through the refining process.  The linear program in this case would need mathematical terms for 
the properties of the hydrocarbon fluids.  For instance, gasoline has the following specifications: 
 
• Octane - a measure of harmful components, too high and it wastes power in a      
combustion engine.  
 •   Viscosity - how easily the fuel flows.  
 •   Sulphur - important for regulation reasons 
 
In Ravi and Reddy (1996), the variation in the types of crude that can be purchased are not 
considered, one type of crude is assumed, this can be unrealistic for some refineries and the option 
for the decision maker to select from a number of crudes would not be a difficult extension.  We 
adhere to the most suitable crude to elicit value from, in a model capturing cash flow value rather 
than yields from blending error. 
           The aim of the following section is to depict the dynamics of the commodity spot prices and 
forward curves to insert into the revenue refining model.  It is standard practise to use current spot 
and forward prices to mark-to-market a book of physical and/or financial contracts; the forward 
curve providing information about the market perception of future spot prices.  However, the prices 
observed today can be wildly inaccurate when used for decisions about future activity; a set of 
future prices are however required for valuation purposes. 
The goal is not to provide a forecasting method but to apply a fundamental model of 
commodities to obtain information of possible future prices to be used within an optimisation 
model.  In applying real market data to represent the spot price, S, of each refined product, our 
concern is threefold: 
 
• To find the most appropriate mathematical structure for S, i.e. the type of process 
118 
 
 
• Once we have chosen S(t), a stochastic process, there will be parameters that can be 
estimated from liquid markets and clean data 
• From a trajectories standpoint, the Monte Carlo methods and/or trees, on average must 
generate data that looks like the observed ones; from a statistical standpoint - the moments 
of the distribution of S(T) (for T>t) must coincide with the empirical moments, at least for 
the first four  
 
In comparison to stock prices, which grow on average, commodity prices do not exhibit trends over 
long periods.  The evolution of the futures and spot prices in the next section may show sharp rises 
during short periods, over a long period they revert to “normal levels”.  This is a consequence of 
mean reversion with spikes caused by shocks in the supply/demand balance.  In creating a realistic 
model of the stochastic process it is important to observe the time series over the required time 
period to analyse the specific features and details important to the particular commodity.  The 
refined products future prices from NYMEX used in this chapter and the notation for formulating 
the mathematical model are presented in the following section.  
 
 
 
 
 
 
 
 
 
 
3.2.2. Refined oil products market data 
The following data was obtained using the EIA data repository. 
119 
 
 
 
 
(Figure  3.3: WTI crude front month contract historical price series) 
 
 
 
 
 
 
 
 
 
 
 
 
120 
 
 
 
(Figure  3.4: Diesel fuel low sulphur historical price series ) 
 
 
 
 
 
 
 
 
 
 
 
 
121 
 
 
 
(Figure  3.5: NY RBOB gasoline front month contract historical price series) 
 
 
 
 
 
122 
 
 
 
(Figure  3.6: Daily jet fuel retail historical price series)  
 
 
 
123 
 
 
 
(Figure  3.7: New York Harbor No. 2 daily heating oil historical price future contract series) 
 
124 
 
 
 
 (Figure  3.8: Propane front month future historical price series) 
  
The above figures depict the mean reversion despite the noted increase in price series value 
as the commodities approach 2006; there is a increasing trend which then dips aggressively in 2008 
due to the financial credit crunch.  There is clear random variation and the correlation amongst the 
different petroleum products above is conspicuous.  The figures for correlation and mean reversion 
are shown in the following sections.  There is a clear difference in behaviour in comparison to stock 
price movements and it is this characteristic commodity behaviour that must be accurately captured 
in the model; see Schwartz (1997) for a thorough description. 
In the next section, we describe the foundation of the refinery model, including the 
relevance of the optimisation time period. 
 
125 
 
 
3.2.3. The producer midterm planning framework 
 
Refining is modelled at the level of detail common in tactical mid-term refinery planning, with a 
granularity of one month at its finest and the start-up and shut-down costs considered not 
significant.  The model’s decisions are considered in monthly periods consequently, a set of 
decisions remain constant for a month’s refining – this is known as a refinery run in the market; 
each review period corresponds to a futures price maturity.  The refinery consists of a number of 
units, the Crude Distillation Unit (CDU), the cracker and the Vacuum Distillation Unit (VDU).  For 
example, Neiro and Pinto (2003) model the units of the refinery and their flows, using nodes and 
arcs, constructing a one-period model to capture the uncertainty.  This requires that the uncertainty 
in the yields that are transferred from the CDU to the rest of the refinery are modelled using the data 
inside the CDU; there is an uncertain amount of error associated with the volumes chosen by the 
refinery owner. 
In this work however, the uncertainty is attached to the prices of the outputs rather than the 
volumes from the refining process itself and the producer must schedule the production of each 
refined product.  These decision variables of the refinery scheduling problem are stated below.  
Each of the variables represents the flow rate of a potential product within the refinery in units of 
tons/day over a refining run.   
 
3.2.4. Decision Variables 
 
Xi,t
43 [tonnes/day] : volume of product i bought or sold on this particular day 
 
• X1,t: Crude Oil 
                                                 
43  In the multi-period setup these decision variables become per node and per time period: X1(n, t) 
126 
 
 
• X2,t: Gasoline  
• X3,t: Naphtha (after the splitter) 
• X4,t: Jet Fuel 
• X5,t: Heating Oil 
• X6,t: Fuel Oil 
• X7,t: Naphtha stream exiting the PDU 
• X8,t: Gas Oil 
• X9,t: Cracker Feed 
• X10,t: Residuum 
• X11,t: Gasoline after splitting of Naphtha exiting the PDU 
• X12,t: Gas Oil after the splitter 
• X13,t: Gas Oil stream entering the fuel oil blending facility 
• X14,t: Cracker Feed after the Splitter 
• X15,t: Cracker Feed stream entering the fuel oil blending facility 
• X16,t: Gasoline stream exiting the cracker unit 
• X17,t: Stream exiting the cracker unit into the splitter 
• X18,t: Heating oil stream after splitting of cracker output 
• X19,t: Cracker output stream 
 
In the final model’s objective function, only the first six and the 14th decision variables defined 
above are included - the rest are auxiliary variables required to represent the structure of a real 
refinery and present within the mass balance or constraint equations.  As the variables in the 
127 
 
 
objective function are the volumes that can be sold, the other variables aid in the representation of 
the flows within the refinery and contribute to the mass balance (weight into a refinery unit must 
equal the weight out), fixed blends (assuming fixed cuts from a barrel of crude) and unrestricted 
balance equations (without the auxiliary variables, an unrealistic set of volumes from the cracking 
process would be represented).  The set of constraint equations that represent the flows shown in the 
next section, take on a variety of forms dependent on whether the structure is that of a topping, 
cracking, hydro skimming or coking refinery.  The main reason why the mathematical framework 
applied here was selected is that there are a large number of studies on the topping refinery within 
the engineering literature that can be used for comparison purposes; additionally, it is trivial to alter 
these equations given a different type of refinery complex.  The values assigned to the decision 
variables must satisfy the following constraints that describe the physical restrictions on this 
particular refinery:  
 
• The capacity constraints  
• Raw Material Availability  
 
The mass balance constraints are required for the following units: 
 
• Primary Unit 
• Cracking unit 
 
The increasing oil price has over the years encouraged refiners to search for solutions to extract 
more from the dregs of the barrel.  Refiners are increasingly using solvent deasphalting and 
debottlenecking of existing vacuum and coking units.  For example, the ROSE process, invented by 
KBR uses special internals and designs that permit extraction of maximum high-quality oils and 
128 
 
 
fuels from vacuum residues and other heavy petroleum feedstock.  Without discussing the internal 
mechanics, this would alter the yield specification on the model, hence impacting the optimal 
volumes of refined products; additional blending technology in the modelling process is usually 
implemented in a finer granularity refining model than required in midterm planning. 
In this model, we represent the yields emanating from each separate units: the CDU, the 
VDU and the cracker, as fixed according to the equations below, (the below set-up is taken from 
Ravi and Reddy (1996) where the X variables are defined as above in section 3.2.4. as the volumes 
of the commodities, but here the objective function is converted to a multi-period problem where 
each node is, n, and each time period, t) - for another type of refinery, e.g. a Coking refinery, the 
following equations would be still be required but would consist of different coefficients. 
The equations below essentially capture the primary distillation unit and a middle distillation 
cracker.  The primary unit splits the crude oil into the following refined products: naphtha (13% 
yield), jet fuel (15%), gas oil (22%), cracker feed (20%) and residue (30%).   
 
3.2.4.1. Fixed Yields 
 
Primary unit:   
 
-0.13 X1(n, t) + X7 (n, t) = 0     
      
n
N
∀∈
                                         
(3.1) 
 
 
 
-0.15 X1 (n, t) + X4 (n, t) = 0                     
n
N
∀∈
                                         
(3.2) 
 
 
-0.22X1 (n, t) + X8 (n, t) = 0                      
n
N
∀∈
                                        
(3.3) 
 
 
 
-0.20X1 (n, t) + X9 (n, t) = 0                      
n
N
∀∈
                                        
(3.4) 
129 
 
 
 
 
 
-0.30 X1 (n, t) + X10 (n, t) = 0                    
n
N
∀∈
                                       
(3.5) 
 
Cracker:  
 
-0.05X14 (n, t) + X20 (n, t) = 0                  
n
N
∀∈
                                         
(3.6) 
 
 
 
-0.40X14 (n, t) + X16 (n, t) = 0                 
n
N
∀∈
                                        
(3.7) 
 
 
 
-0.55X14 (n, t) + X17 (n, t) = 0                 
n
N
∀∈
                                        
(3.8) 
 
 
With the higher utilisation of heavy crude oils, refiners often encounter higher residual loads with 
higher levels of contaminants, increased aromatics content, and more often than not, higher acids 
content in their feeds.  On the other hand, the gas oil content of the new feeds will be lower, 
creating a potential loss of feed downstream such as in the FCC units.  Due to the chemical 
composition of the petroleum crude, there is a limit on how much of each product can be blended 
within the complicated production of refinery outputs.  We state below the equations that define 
these fixed blending compositions: 
 
All the constraints below are in the form of equalities; there are three types of constraint: 
fixed yields, fixed blends and unrestricted balances.  Unless there is a shutdown of the plant or 
adverse storage movements the right hand side of the balance constraint is always zero.  Naphtha 
and jet fuel products are straight run, heating oil is a blend of 75% gas oil and 25% cracked oil.  
Fuel oil can be blended from the primary residue, cracked feed, gas oil and cracked oil in any 
proportions.  Yields for the cracker are flared gas 5%, gasoline blend stock, 40%, and cracked oil 
55%.  This information along with the flow describes the physical system.   
130 
 
 
3.2.4.2. Fixed Blends 
 
Gasoline blending:  
 
0.5X2 (n, t) - X11 (n, t) = 0                      
n
N
∀∈
                                            
(3.9) 
 
 
 
0.5X2 (n, t) - X16 (n, t) = 0                      
n
N
∀∈
                                          
(3.10) 
 
 
 
Heating oil blending:  
 
0.75X5 (n, t) - X12 (n, t) = 0                      
n
N
∀∈
                                        
(3.11) 
 
 
 
0.25X5 (n, t) - X18 (n, t) = 0                      
n
N
∀∈
                                       
(3.12) 
 
 
 
3.2.4.3. Unrestricted balances 
 
Naphtha:   
  
X7 (n, t) + X3 (n, t) + X11 (n, t) = 0                      
n
N
∀∈
                            
(3.13) 
 
 
 
Gas Oil:     
 
-X8 (n, t) + X12 (n, t) + X13 (n, t) = 0                      
n
N
∀∈
                       
(3.14) 
 
Cracker feed:     
-X9 (n, t) + X14 (n, t) + X15 (n, t) = 0                      
n
N
∀∈
                       
(3.15) 
 
 
 
131 
 
 
Cracked oil:     
-X17 (n, t) +X18 (n, t) + X19 (n, t) = 0                        
n
N
∀∈
                    
 
(3.16) 
 
 
Fuel oil:       
   
-X10 (n, t) + X13 (n, t) + X15 (n, t) + X19 (n, t) + X6 (n, t) = 0   
n
N
∀∈
       
 
(3.17) 
 
 
Each refinery complex has a maximum amount of petroleum that it can physically store.  These 
containers are at a size that is economically optimal for the region the refinery operates in.  The 
below equations represent the limits on this refinery’s production in tons per petroleum product per 
node and per month: 
3.2.4.4. Raw material availability constraints 
 
Gasoline:   
 
X2 (n,t) ≤ 2700 / month            
n
N
∀∈
                 
             
 
(3.18) 
 
 
 
Naphtha:   
 
X3 (n,t) ≤ 1100 / month            
n
N
∀∈
                          
  
 
(3.19) 
 
 
 
Jet fuel:   
 
X4 (n,t) ≤ 2300 / month            
n
N
∀∈
                                  
 
(3.20) 
 
 
 
Heating oil:    
X5 (n,t) ≤ 1700 / month            
n
N
∀∈
                                      
 
(3.21) 
 
 
 
132 
 
 
 
Fuel oil:   
  
      
X6 (n,t) ≤ 9500 / month                       
n
N
∀∈
                       
 
 
(3.22) 
 
The refinery owner is interested in maximising the net profit, which is simply the costs subtracted 
from the revenues; this objective is a simple function over one period. 
 
3.2.5. Deterministic objective function 
 
The deterministic objective function for profit of Ravi and Reddy (1996) represents the profit in 
dollars over one time period, where the decision variables are as explained in section 2.4 and are in 
tonnes: 
 
Maximise 
Profit = - 8.0 X1 + 18.5 X2 + 8.0 X3 + 12.5 X4 + 14.5 X5 + 6.0 X6 – 1.5 X14           
(3.23) 
 
One serious issue with this function in the chemical engineering literature, the random commodity 
prices are replaced with their mean prices using a set of normal distributions, when in fact it is 
known that commodity price series are not normally distributed, see Moro et al. (2007) for details.  
Further, the above model is deterministic and does not consider the risk or the uncertainty of the 
commodity prices in the next period when they are actually sold.  The move to a multi-period model 
introduces the time series themselves and the analysis of the refined products statistically.  We leave 
for others the examination of the cointegration via an Engle-Granger test; we aim to model the 
commodities from continuous stochastic equations rather than approaching them via econometrics 
using a Euler discretisation of integrating the separate commodities.  Our approach is along the lines 
of Schwartz (1997) where the author discretises the continuous mean reverting equations and finds 
133 
 
 
the parameters via the Kalman Filter. 
        In practice many refiners use Futures contracts or other derivatives to hedge their price risk.  
We use data for our oil refinery that does not use crack spread contracts to hedge, as is common in 
the US; in the region where this refinery is located there is an absence of liquid crack spreads.  An 
oil Future contract is a standardised contract between two parties to buy or sell a specified type of 
oil of standardised quantity and quality for a price agreed today.  In the case of crude oil, the main 
Futures exchanges are the New York Mercantile Exchange (NYMEX) and the Intercontinental 
Exchange (ICE) where the West Texas Intermediate (WTI) and North Sea Brent crude oil are 
traded.     
The delivery periods in the model are fixed to each of the 12 calendar months (M1, M2,…, 
M12) for each year.  We ignore differences in trading and delivery period in the model and assume 
each lot sold is for one month ahead using the financial market rather than the physical spot market.  
To capture the market risk we introduce a number of risk measures within our optimisation, with the 
goal of finding the most effective measure whilst retaining tractability – a valuation however does 
not require this addition.  In terms of decision making over time this is a significant step for a 
number of reasons; one is that optimising with variance is computationally inefficient and is also 
not used in practice as it is now considered an unrealistic representation of financial risk.  Secondly, 
this enables an investigation of the most realistic risk-reward model when we extend to a multi-
period setting.  The first step was to consider how to introduce the uncertainty in the price series 
into the static linear program.  
 
3.2.6. Solving the deterministic objective function 
 
Adopting the deterministic objective model in (3.23) using today’s futures prices in tons, as shown 
in Ravi and Reddy, and not the returns of the price series of crude and the refined products gives:  
134 
 
 
 
Maximise  
 Profit    
-385X1 + 726X2 + 385 X3 + 471X4 + 520X5 +251X6 -72X14  
         (3.24) 
 
The negative values are the purchasing and operational costs, the positive values are the saleable 
product prices.  This includes the same constraints as model (3.23), shown above for one time 
period.  This is a static linear optimisation problem, and so the CPLEX algorithm was used to solve 
it within the optimisation program, General Algebraic Modelling System (GAMS).  The solution to 
equation (3.24), the deterministic objective function, under the constraints defined in section 3.2.5, 
is given below: 
Model 1: Objective Value = $285,418 per day; optimal crude purchased: 1500 tons; time to solve: 
negligible  
 
The solution is trivial computationally:  there are 22 constraints, all equations including the 
objective are linear and there are a total of 18 decision variables.  Now we extend the static 
optimisation incorporating the uncertainty in the commodity price series.  This can be done in a 
number of ways; forecasting any random variable in practise is a vast area of research.  In practice it 
is done by drawing a random number from a recommended probability distribution for the data 
generating process.  When cash flow forecasting, the chemical engineering literature applies 
deterministic values, which misses the range, sensitivity and behaviour of the cash flows – we 
discuss this issue in detail in section four where we describe our choice of the set of stochastic 
processes.  In the following section we detail how the one period model can be reformulated and 
consolidated with future periods to build the multi-period model including commodity price 
uncertainty. 
 
135 
 
 
3.3 MULTI-PERIOD OPTIMISATION  
 
The legacy of Markowitz Portfolio Theory (MPT)44 means that optimisation problems are often 
categorised by how the efficient frontier can be drawn; this was added to MPT in 1958 by James 
Tobin45.  Each of the underlying assumptions has been challenged in various papers; delving into all 
of these is beyond our scope.  The three main assumptions that are investigated or utilised in this 
refinery model are: 
 
• Investors are price takers, and their actions do not influence external market prices 
• The correlations between assets are always fixed and constant 
• Returns on assets are normally distributed 
 
There have been various extensions to deal with these assumptions; studies have, for example, 
managed to capture fat tails and asymmetry and included these within their optimisations to address 
assumption (c).  In this model, ultimately we wish to maximise risk expected return for a given 
level of risk.  The statistical concept of covariance captures this risk by implying that an investor 
should choose assets that do not crash together.  In terms of the refinery this is difficult, due to all 
assets being heavily correlated, hence exposure to the crack spread46 is the main risk to manage.  In 
portfolio optimisation, we often consider variances and covariance matrices in our decisions, i.e. a 
multivariate joint probability density function (pdf) of returns is required; this proxy for risk is valid 
if asset returns are jointly normally distributed, however there are problems.  If returns are not 
                                                 
44 Markowitz, H.M. (March 1952). "Portfolio Selection". The Journal of Finance 7 (1): 77–91.  
45 Tobin, James (1958). "Liquidity preference as behaviour towards risk". The Review of Economic Studies 25 (2): 65–
86.  
46  The crack-spread is a term used in the industry for the differential between the price of the refined products and 
the raw crude oil; indicating the profit margin that can be extracted from “cracking” crude. 
136 
 
 
normal then variance is no longer a valid risk measure.  Despite this issue, a mean-variance model 
used to maximise returns and minimise risk, is a good place to begin.  A modern portfolio 
optimisation approach is thus characterised by the following desirable features: firstly, it enables for 
realistic return distributions, secondly, it builds on a realistic risk measure, thirdly, it is 
computationally tractable, and finally, it enables for a parsimonious robust formulation.  We 
contribute to the literature by proposing a refinery portfolio model that encompasses all of these 
features.  Our approach is based on derived distributions from a discretised set of stochastic 
differential equations; it can rely upon a quantile risk measure, and leads to, after an approximation, 
a convex optimisation problem, enabling robustness checks that confirm its stability.   
             To build our argument, we start with a simple example using future cash flows to define 
optionality.  The example requires that cash flows should be discounted using (a) a constant risk-
free rate of interest of 5% (which would be unrealistic for a period of one month but is used to 
illustrate the value of optionality over a period of time) and assumes (b) a constant cost of crude oil, 
for example, crude is priced at $74 per barrel; these will both be made stochastic in the final 
model’s implementation.  Although in our final results we will use continuous compounding for the 
sake of simplicity we show here the calculations using simple compounding.  In the next section, to 
extract optionality from this behaviour, we analyse how one period’s refining decisions are related 
to the next.   
 
 
3.3.0.3. N-period (N+1 dates) optimisation 
 
By adding one more period of price uncertainty, our refining problem becomes more complicated.  
There are now five different investment strategies that can be implemented.  Optimally, we could 
(a) refine now; (b) wait a month and refine if it has risen, but never refine if it has decreased; (c) 
wait a month and invest if the price has risen, but if it decreases wait another month and then refine 
if it has risen; (d) wait two months and only refine if it has risen twice in a row; or (e) never refine.  
137 
 
 
Which strategy to undertake depends on the initial cost of a barrel of crude and the initial set of 
refined products’ prices, and the value of the refining option should be calculated for each path.    
             The other complicating factor here is that while we can still compute the value of the 
investment option by constructing a risk-free portfolio, the makeup of that portfolio will not be 
constant over the two months; we will have to alter the number of crack-spread contracts in the 
short position after the price of the refined petroleum products changes at t=1.  Keeping the refining 
portfolio riskless by changing its composition is known as dynamic hedging.  Without calculating 
the option value explicitly we state that the value of the option to invest as function of initial 
products prices has a piecewise-linear function.  The, F0, is always a convex function of the RPS0, 
and is greater than or equal to the net payoff from exercising the option today, RPS0 – Costs0.  This 
is a powerful result as we allow investment to fluctuate in continuous time and this means we 
require stochastic processes, see section 3.3. 
138 
 
 
 
 
      t = 0                 t = 1                          t=2                                t=3                  … 
                      
 
                
                                                           
  RPS3uuu = $337 
 
                                                                RPS2uu = $225 
                                                                                               
                                RPS1u = $150                                             RPS3duu = $150 
                    q 
RPS0 = $100        
 
 
    RPS2ud = $100 
 
 
        1- q       
                                RPS1d = $50                                             RPS3ddu = $50 
 
                                                               RPS2dd = $25 
 
 
 
 
 
 
 
 
 
RPS3ddd = $17.50 
(Figure  3.11: Three period optimisation) 
 
Approaching this valuation using option pricing methods; we wish to calculate the option to refine 
at t=0, F0, as a function of the initial RPS0, as well as the optimal refining rule.  To do this we can 
work backwards solving two separate investment problems looking forwards from t=1, first for 
RPS1u and then for RPS1d.  In each case we determine F1, the value of the option at t=1, by 
calculating a risk free portfolio and calculating its return.  Given two possible values for F1, we then 
move to t=0 and determine, F0 by calculating a risk free portfolio.  The descriptions above 
139 
 
 
exemplify the fact that the refinery owner has optionality and that the value of this real option 
contains path dependence.  The terminal value depends on the value of the underlying commodity 
prices, not only at this time, but also at prior points in time.  Specifically, the refinery option’s 
terminal value depends upon the “path” taken by all the underliers over the life of the refinery.  This 
refinery has another condition, as the value of the option to refine today depends on the quantity of 
crude available, which itself depends on the last time the owner refined.   
                 In the next section, we describe a deterministic trading strategy to define the intrinsic 
value of the oil refinery, and follow this by describing a stochastic dynamic program that captures 
the optionality.  
 
3.3.2. Foundation of the oil refinery intrinsic value 
 
We now define the intrinsic value as the expected value of the refinery assuming risk-neutral 
dynamics, conditional on following the best initial deterministic set of decisions.  We start out with 
the set of initial forward curves and find today’s intrinsic plan.  From this decision set we lock in the 
refining decision set today.  If the decision is to purchase an amount of crude, the cost of purchasing 
follows from the current crude spot price and the volume of crude in its storage container.  
Additionally, if there are decisions to sell, the refining units are activated and the revenue flows 
from the current forward curves on the market for the petroleum products.  If the decision is to not 
refine, there is no cash flow and the refinery is unaltered. 
                  Next we simulate a realisation for tomorrow’s forward curve conditional on today’s 
forward curve and the given appropriate price dynamics.  For this new forward curve, tomorrow’s 
intrinsic plan is determined.  This problem is updated for the refining decision set that is already 
locked in as well as the passage of time (one day closer to the terminal date).  This plan is used to 
lock in the storage decision regarding tomorrow.  We continue this procedure until we reach the 
140 
 
 
terminal date of our refining problem.  This procedure gives us one possible realisation of daily 
cash flows from refining over the relevant time horizon.  We then repeat the procedure above to 
obtain the desired number of possible cash flow realisations.  The value of the refinery is given by 
the average net present value of simulated daily cash flows.  The problem with this intrinsic 
approach is that it misses the full flexibility available at each decision date being a deterministic 
approach; to capture this flexibility we need to also consider the fact that the financial markets 
provide us with alternative stochastic decisions at each point in time. 
               The stochastic dynamic programming approach is similar, but simulates possible spot 
price path realisations for each commodity.  Each path is mapped into a discrete spot price state 
space.  Based on the discrete spot price path realisations, we can solve the problem to optimality by 
stochastic dynamic programming.  We consider the problem with a six-year horizon and our 
objective is to obtain the value of the cash flow from operating the refinery. 
               With the approach chosen, we now describe this method for N periods, including 
definitions for the refinery owner’s specific choice variables and conditional random variables. 
        Adopting a discrete time setting, with a finite horizon, the decision periods are denoted θi, 
i=1,….., T (months): 
 
 
 
                          θ1                                                         θ2         …                                          θT                        
(Figure  3.12: N period optimisation)  
 
On each decision date, spaced apart by one month, a number of decisions are required: q1 – q7, these 
are the volumes in tons of the crude to buy, and refined products to sell both decided on the same 
date.  We can represent the vector of qs as a control vector, U.  To model the uncertainty over these 
future time periods a stochastic equation representative of the uncertain prices is selected.  The 
Period 1 
Period 2 
Period T 
141 
 
 
refinery’s current status is described by a state variable x, the current value, xt, being known, but 
future values, xt+1, xt+2, random variables, are not.  Assuming this process is Markov, all the 
information relevant to the determination of the probability distribution of future values are 
summarised in the current state, xt.   
 
At each period t, the volumes of commodities are available as choices to the refinery, and we 
represent them by a control vector U.  The value, Ut, of the control at time, t, must be chosen only 
using the information that is available.  The state and control at time, t, affect the refinery’s profit 
flow, which we define as, πt (xt, Ut).  We let Φ(xt+1 | xt, Ut) be the cumulative probability distribution 
function of the state next period, conditional upon current information (state and control variables). 
 
The discount factor between any two periods is, 1/(1+ρ), where, ρ, is the discount rate.  The 
ultimate goal is to choose the set of controls, {Ut}, over time that maximise the expected net present 
value of the payoffs; where the termination payoff function is defined by πT (xT). 
 
At date t, the refinery owner chooses control variables Ut, the immediate profit flow is, πt (xt, 
Ut).  In the following period, (t+1), the state will be xt+1.  Optimal decisions onwards will extract 
value, Vt+1(xt+1).  This is random as viewed from time t, so the expected value is required, 
Et[Vt+1(xt+1)].  This is known as the continuation value, formally we have: 
 
1
1
1
1
1
[
(
)]
(
)
(
|
,
)
t
t
t
t
t
t
t
t
t
E V
x
V
x
d
x
x U
+
+
+
+
+
=
Φ
∫
  
 
 
 
 
 
 
(3.31) 
 
Discounting this back to period t, the sum of the immediate profit from refining and the 
continuation value is: 
 
Vt (xt) = πt (xt, Ut) + 1/(1+ρ) Et[Vt+1(xt+1)] 
 
 
 
 
 
 
(3.32) 
 
The manager will choose the control vector, Ut, to maximise the sum of these two functions, and the 
result will be the value Vt(xt). 
142 
 
 
  Hence, we write the value of the refinery at date t as: 
 
1
1
1
( )
max{
( ,
)
[
(
)]}
1
t
t
t
t
t
t
t
t
t
U
V x
x U
E V
x
π
ρ
+
+
=
+
+
  
 
 
 
 
 
(3.33) 
 
This is also known as the Bellman equation; see Bellman (1957) for a more rigorous derivation.  
Here we have a fixed horizon of six years, and knowing that we have an independent optimisation 
at the maturity, we start at the end of the horizon and optimise backwards.  We assume that at the 
end of the horizon, the refinery gets a termination payoff, πT(xT).  Therefore, the value in the period 
previously is: 
 
(
1)
1
1
1
1
1
1
(
)
max{ (
,
)
[
(
)]}
1
T
T
T
T
T
T
T
T
U
V
x
x
U
E
V
x
π
ρ
−
−
−
−
−
−
=
+
+
  
 
 
 
 
(3.34) 
 
Knowing the value function at, T-1, enables us to solve the maximisation problem for, UT-2, leading 
to the value function, VT-2(xT-2), and we continue with this process until the present, t=0.     
 
In equation (3.34) the value process does not consider the risk, in the published paper within the 
appendix we combine the mean-risk approach into the value process for decision making purposes.  
In this work we have left to others, the rigorous mathematical proofs of existence and uniqueness of 
solutions; we treat the limit to continuous time in a heuristic way; see Fleming and Rishel (1975) 
for a detailed background.  In the following section we describe the data flow required to capture a 
financial valuation or create a decision process considering the risk. 
 
     
3.3.2.1. Optimisation data flow chart 
 
143 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
(Figure 3.13: Flow chart process behind the refinery optimisation) 
 
As the figure above depicts, the ultimate goal is to have the optimum amounts of decision variables; 
in a multi-period setting, this is known as the policy function.  Not only is variance a symmetric risk 
measure but it is also time inconsistent, we discuss this property in section 5.2., see Geman and 
Ohana (2008) for a proof.  In referring to the literature, when including random product prices into 
an optimisation model, it is done in one of three ways: 
  
    • Mean values replace the stochastic variables  
    • Continuous distributions are utilised  
    • Scenario generation (using discrete distributions)  
 
Other moments are replaced using the historical data but most literature in this area assumes a 
normal distribution.  The prices of refined products are made stochastic and the problem solved 
using scenario generation, type (3) above.  However, the refinery model is required over N time 
periods, and therefore a stochastic differential equation (SDE) is more realistic, enabling a dynamic 
probability distribution to be modelled.  In section four we discuss why we use SDEs, and describe 
Expected Return  
Model 
 
Volatility & Correlation 
Estimates 
 
REFINERY  
OPTIMISATION 
 
Optimal Refinery 
Portfolio  
 
Constraints on Refinery 
Portfolio Choice 
 
Robustness 
Checks 
 
Repeat for variance 
of optimal result 
144 
 
 
the spot price process used to capture uncertainty in the commodity prices.  In the following section 
we introduce the valuation methodology in a simplified setting. 
 
3.3.3. Stochastic commodity behaviour 
 
A model should be robust, in that the parameters should not move erratically from one day to the 
next, leading to a complete change in the refinery portfolio value.  We make a distinction between: 
(a) the probability that the price of oil, S(T), in for example, July 2014 goes over $120.  This 
number represents the probability, p, of that event, ω, under the real measure denoted as, P, and (b) 
the price market practitioners are willing to pay today to receive $1 if the market price of oil at date 
T=1, July 2014, if the market price does go over $120.  We refer to this number as the probability of 
the event, ω, under the pricing measure denoted as,ℚ.  This is clear if one considers for example, a 
market player considering now whether to purchase for, July 2014 a virtual oil refinery, getting the 
refined products without owning or running the complex; the player is interested in the probability 
of the same event under the pricing measure, ℚ; this number includes the risk aversion component 
as well as human judgement about how much the market is willing to pay to secure refined products 
for July 2014.       
         Crude and refined product prices are intertwined in a complex way, as seen in figure 3.14 
below, which shows daily spot prices for crude and the refined product futures prices quoted on 
NYMEX for the last decade.   
 
 
145 
 
 
 
(Figure 3.14: NYMEX future price data on refinery commodities) 
 
In this figure prices seem to revert towards a long term mean in the first half; after the financial 
recession in 2008, this mean seems to exist again but at a higher level.  This mean reversion is often 
evidenced in the literature (see, for instance, Geman and Nguyen, (2003), for the case of 
agricultural commodities; and Pindyck (2000), for energy commodities).  Specifically, these 
commodity prices fluctuate with a daily volatility of over 3.5% on average, which is high; yet, it is 
clear that they are not emanating a random walk as present in stock prices evolution.  Furthermore, 
there is no clear seasonality here as shown in natural gas prices for example; evidence from simple 
tests shows that there are strong and weak months of the year for crude and gasoline for example, 
but high year-to-year volatility by month inhibits exploitation.  Any acceptable commodity-linked 
valuation model must (a) account for mean reversion and (b) value the optionality given the volume 
and other refinery constraints.  An additional feature that is often evidenced is the fact that the 
returns series exhibit tails that are fatter than those of a normal distribution; this easily seen by 
obtaining a QQ-plot of the level or log returns.  If attempting to model the fat tails, one can consider 
146 
 
 
jumps within the stochastic process or use GARCH modelling introduced by Bollerslev (1986).  
Our strategy is to develop a one factor tree model, since the one factor here has constant volatility it 
does not replicate some movements of the futures curve, this is not perfect, however since the state 
space is colossal; this one factor approach is more tractable.  This will sidestep the dynamic 
volatility structure considerations; yet, one factor tree structures still capture optionality.  Valuing 
contingent claims using multifactor models indicates that the more complexity the less likely an 
analytical solution exists.  In the next section, we examine a continuous time price model of spot 
and forward prices.  The chosen process is then extended into a discrete time model to be used in a 
pricing tree.   
 
3.3.3.1. The Ornstein Uhlenbeck process 
 
To model the commodity spot price we require a continuous time stochastic formula that exhibits 
mean reverting behaviour, for example:   
 
S(t + dt) = S(t) + a(b – S(t))dt + σdWt  
 
 
          
 
(3.3.1) 
 
Which, at date t, is an affine function of dWt; this is also known as the Ornstein Uhlenbeck process, 
where the: a, b and σ are positive constants, for a thorough explanation see Vasicek (1995). 
 
Where: 
 
b : the long term mean level – all future trajectories will evolve around a mean in the long run 
a : the speed of mean reversion – it characterises the velocity at which such trajectories will regroup 
around b in time. 
σ : instantaneous volatility – measuring instant by instant the amplitude of randomness entering the 
147 
 
 
system. 
The nice feature here is that the model can represent upward-sloping, downward-sloping or slightly 
humped shapes of the future price term structure.  Hence, S(t+dt), is like dWt, normally distributed, 
but it may take negative values, which is obviously an undesirable feature for commodity prices.  
The above is a one factor equilibrium model; a no-arbitrage model would fit the initial term 
structure.  Gibson and Schwartz (1990) describe a two factor stochastic model where the spot price 
of the commodity and the instantaneous convenience yield are assumed to follow a joint stochastic 
process; with Brownian motions under the objective measure and correlated in time.  This not only 
enables parallel but also twisting movements of the forward curve to be captured.  This is more 
realistic, and as applied and discussed in the case of crude oil, gold and copper by Schwartz (1997), 
two factor models exhibit much difference in the long term over one factor models, when used for 
pricing contingent claims or the valuation of real assets; they are also more accurate in depicting the 
term structure of the future prices.  A single factor model has very different implications about the 
volatility of future price returns as the maturity of the contracts increase, and is incapable of 
describing the volatility of the futures data.  This has important knock-on effects for valuation when 
the models are applied for longer term real assets.  The author’s analysis includes stochastic interest 
rates on futures price data, calibrated with the Kalman filter; despite the improvements over a single 
factor model, for shorter maturity contracts the difference is minimal, considering we require a 
valuation, the extra complication associated with the additional stochastic convenience yield was 
considered adverse to our objective; a two factor model is introduced in chapter four as an 
enhancement to the valuation method used here.  In the next section we discuss the relevant 
properties of the stochastic one factor model.   
 
3.3.3.2. The Samuelson effect, mean reversion and positivity 
 
Futures prices can be used as a proxy for spot prices, however, they can be unavailable for various 
148 
 
 
reasons - please note where we construct a continuous future time series we apply the standard 
Panama approach to manage rolling effects47.  The Schwartz (1997) model is a popular framework 
for energy commodity futures prices that resembles the geometric Brownian motion (GBM), whilst 
introducing mean-reversion to a long-term value θ, and ensuring positivity by using a logarithm 
function on the spot price.  Due to commodity prices neither growing nor declining on average over 
time; they tend to mean-revert to a level which may be viewed as the marginal cost of production.  
Unfortunately, it is non-trivial to define the price of petroleum commodities exhibiting mean 
reversion or showing non-stationarity, see Geman (2000) for a discussion.  Many tests exists, none 
of which dominate to conclude that mean reversion is representative.  Newman and Yin (1996) 
improve upon stationarity tests supporting stationarity and auto regression using monthly data on 
oil.  Based on these analyses we define the commodity process as: 
 
d(InSt) = a[θ - lnSt]dt + σdWt  
 
 
          
 
 
(3.3.2) 
 
Where St is the asset price process, t is time, and Wt, a standard Brownian motion; a, is the force of 
mean reversion, θ the long-run equilibrium level  - the above model prevents negative values of the 
commodity price.  The above is a one factor stochastic differential equation: in terms of dynamics, 
the level moves are captured.  A higher number factor model would allow the volatility or 
convenience yield to be captured and ensue in more accurate dynamics; however this would also 
increase the complexity of building a valuation from a discrete tree to represent the correlated 
commodity series.  Changing variables we have: 
 
 
 
Zt = In St 
 
 
 
 
 
 
 
 
 
(3.3.3) 
 
Applying Ito's lemma leads to: 
                                                 
47 https://www.quantstart.com/articles/Continuous-Futures-Contracts-for-Backtesting-Purposes 
149 
 
 
 
 
 
2
(
)
2
t
t
t
dZ
a
Z dt
dW
a
σ
θ
σ
=
−
−
+
  
 
 
 
 
 
 
(3.3.4) 
 
Now introduce the variable 
at
t
t
X
e Z
=
 and use Ito's lemma again to obtain: 
 
 
at
at
t
t
t
dX
e dZ
ae Z dt
=
+
   
 
 
 
 
 
 
 
(3.3.5) 
 
 
2
(
)
2
at
at
t
t
dX
a
e dt
e dW
a
σ
θ
σ
=
−
+
  
 
 
 
 
 
 
(3.3.6) 
 
Integrating this equation between dates t and T (where T > t) provides: 
 
 
2
2
2
( )
( )
(
)(
)
(
)
2
2
aT
at
aT
at
e
e
X T
X t
e
e
W T
t
a
a
σ
θ
σ
−
=
+
−
−
+
−
   
 
 
(3.3.7) 
 
At date t, X(t) is observed and X(T) is an affine function of the normal variable W(T-t).   
Hence: 
 
 
2
2
2
[
( ) |
]
(
( )
(
)(
);
)
2
2
aT
at
aT
at
t
e
e
L X T
F
X t
e
e
a
a
σ
θ
σ
−
= Ν
+
−
−
   
 
 
(3.3.8) 
 
And the law of 
( )
( )
aT
Z T
e
X T
−
=
 is immediately derived as a normal variable with an adjustment of 
the mean and variance parameters of X (T): 
 
 
2
2 (
)
(
)
(
)
1
[ ( ) |
]
(
( )
(
)(1
);
)
2
2
a T
t
a T
t
a T
t
t
e
L Z T
F
e
Z t
e
a
a
σ
θ
σ
−
−
−
−
−
−
−
= Ν
+
−
−
 
 
 
150 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
(3.3.9) 
 
Forward prices, F(t,T) = E[S(T)|Ft], if we assume that the rational expectations hypothesis holds or 
that computations were, conducted under the pricing measure ℚ:   
 
F(t,T) = exp{e-a(T-t)InSt + (1-e-a(T-t))( θ -σ2/2a) + σ2/4a(1- e2a(T-t))} 
 
 
(3.3.10) 
 
The above equation is key in the building the forward curve in the numerical approach introduced 
later in the chapter.  It is clear that as T goes to infinity only the second term in (3.3.10) remains: 
 
 
2
( ,
)
exp
2
F t
a
σ
θ



+∞=
−





  
 
 
 
 
 
 
 
(3.3.11) 
 
Differentiation provides the volatility of F(t,T): 
 
 
(
)
( , )
a T t
F t T
e
σ
σ
−
−
=
   
 
 
 
 
 
 
 
(3.3.12) 
 
Here we state the mean and variance of the above continuous process at date 0: 
 
 
2
0[ ( )]
(0)
(1
)(
)
2
aT
aT
E Z T
e
Z
e
a
σ
θ
−
−
=
+
−
−
  
 
 
 
 
 
(3.3.13) 
 
 
2
2
0[ ( )]
(1
)
2
aT
Var Z T
e
a
σ
−
=
−
   
 
 
 
 
 
 
(3.3.14) 
 
This is a single factor model, which can still introduce different volatilities as contracts mature at 
different dates.  We use equations (3.3.13) and (3.3.14) extensively in our calibration procedure in 
151 
 
 
section 4.5.  Furthermore, equation (3.3.11) suggests that forward contract volatility decreases for 
longer maturity contracts, which is in agreement with the Samuelson effect - where he argues that 
futures should exhibit increased volatility as they approach maturity but at any point in time 
volatility should decline with time-to-maturity.  An issue with this model is that the volatility goes 
to zero for long times to maturity, a property which is clearly a limitation, since it is not observed in 
practice.  Introducing a second factor for stochastic volatility would better capture the properties of 
the forward curve; conversely, the state space would be increased by another dimension, enhancing 
the complexity of the optimisation problem, see Steiglitz (1998) for a discussion.  Forward curves 
under the one factor model will exhibit level shifts, but cannot capture twists or inversions as 
commonly found by researchers using principal components analysis; despite this, correlation 
between forward curves can still be portrayed.  A balance that was important to consider when 
implementing the model, was tractability.  The tree, upon which the optimisation was applied, was 
calibrated on seven correlated forward curves.  Using the equation above ensured all commodity 
prices were mean reverting, and log normal in distribution, whilst making calibration less strenuous; 
enabling the successful implementation of the optimisation calculation over many time periods.  In 
the next section we discuss the calibration of the above SDE.   
 
3.3.4. Two period - three dates, refinery optimisation  
 
We have shown in the previous section how to simulate correlated continuous commodity prices, 
and we now discuss how we use these prices in our refinery model.  If we use only the information 
available at date 0 to optimise the value over the entire lifetime of the refinery we have solved a 
static problem.  We would choose the amount of crude to refine at each date by using today’s 
scenario tree, an intrinsic value calculation; a dynamic optimisation requires that we rebuild our 
scenario set at each date until maturity, hence capturing extrinsic value.  We begin with a 
152 
 
 
description of the static problem and then describe the steps to ensure a dynamic set of decisions are 
made. 
To simplify ideas, we concentrate on just two refined products, where at each date i, the spot 
price of a commodity is, Sj,i , for instance, naphtha - S2,i, and kerosene – S3,i, are refined from raw 
crude oil - S1,i.  We are ultimately interested in a valuation and hence interested in pricing the 
refinery.  Therefore, we are in a risk neutral setting – at date 0 the crude price is known, whereas the 
refined product prices in one month’s time and on into the future, are not.  We can make this 
assumption despite using market data as we can assume that producers and consumers balance out 
the market premiums on either side of a transaction, see Parsons (2013) for details including a two-
factor calibrated tree; in other words for the sake of model building the risk-neutral and objective 
measures are assumed equal.  We firstly look at the decision process over two decision steps by 
constructing a simple recombining trinomial scenario tree - recombining is computationally simpler.  
This is not always used with trinomial trees but it greatly simplifies the complexity of the numerical 
scheme: it leads to a recombining tree, which has the number of nodes growing polynomially with 
the number of levels rather than exponentially.  Boyle (1986) originally constructed the trinomial 
over the binomial to enhance the speed and accuracy for pricing purposes; as shown in the authors 
description the probabilities are specified so as to ensure that the price of underlying evolves as a 
martingale, while the moments - considering node spacing and probabilities, are matched to those of 
the distribution of the process.  At date 0, we decide the optimal volumes of crude to refine, and 
products to sell forwards for date 1 – we use today’s, i, price of crude and the prompt month prices 
to sell forwards, i+1, for the refined products.  When we next move to date 1, i+1, the spot price of 
crude will again be known, but the product prices at date 2, i+2, will not, so we again sell forwards 
using the i+2 prices.  Upon reaching the end of the horizon, date 2, we no longer decide to refine, 
but sell all remaining products on the spot market – the prices will be known.  In summary, our 
refining profit at each date where random prices are denoted by,
,
j i
S
ɶ
, known commodity prices as, 
153 
 
 
,
j i
S
, and decision variables by, Xj,i,z, - the volume of product, j, to buy or sell at date, i, decided on 
date, z, is: 
 
,0
3,2
3,2,1
,2,1
,
3,1
3,
1,1
3,2
3,2
1,0
2,
,2
1
2,1,0
1,0
1,0
2,2
2
1,1
1
2,2
2,2,2
 0:   
 
 
 1:    
 
 2: 
  
 
 
Date
S X
S X
S X
Date
S
X
S
X
S X
Date
S
X
S
X
+
−
−
+
+
ɶ
ɶ
ɶ
ɶ
 
 
 
 
 
 
(3.3.15) 
 
Previously, we created a continuous-time model of spot and forward petroleum prices that exhibited 
realistic properties.  This section describes the discrete-time version of that model for a simplified 
case of only three stochastic variables to be used in a pricing tree for valuing the refinery and the 
component optionality.  The simplified discrete-time price model is as follows: 
 
3.3.4.1. Discrete process for crude oil 
 
2
,
1,
1
(
)
(
)
(1
)(
)
2
a t
a t
t
t
t
t
t
t
In S
e
In S
e
B
θ
α
−∆
−∆
+∆
∆
∆
=
+
−
−
+ ɶ
   
 
 
 
(3.3.16) 
 
3.3.4.2. Discrete process for naphtha 
 
2
2
2
2,
2,
2
,
2,
1
(
)
(
)
(1
)(
)
2
a
t
a
t
t
t
t
t
t
t
In S
e
In S
e
B
θ
β
−
∆
−
∆
+∆
∆
∆
=
+
−
−
+ ɶ
 
 
 
 
(3.3.17) 
 
154 
 
 
3.3.4.3. Discrete process for kerosene 
 
3
3
2
3,
3,
3
,
3,
1
(
)
(
)
(1
)(
)
2
a
t
a
t
t
t
t
t
t
t
In S
e
In S
e
B
θ
γ
−
∆
−
∆
+∆
∆
∆
=
+
−
−
+ ɶ
 
 
 
 
(3.3.18) 
 
Where: 
 
 
∆t = the time step in years, 
 
 
2 (
)
2
2
,
,
(1
) ,
2
a
t
t
t
S t
e
a
α
σ
−
∆
∆
−
=
  
 
 
 
 
 
 
 
(3.3.19) 
 
2
2
2
(
)
2
2
,
,
2
(1
) ,
2
a
t
t
t
S t
e
a
β
σ
−
∆
∆
−
=
 
 
 
 
 
 
 
 
(3.3.20) 
 
3
3
2
(
)
2
2
,
,
3
(1
) ,
2
a
t
t
t
S t
e
a
γ
σ
−
∆
∆
−
=
 
 
 
 
 
 
 
 
(3.3.21) 
 
2
1,
(0,
),
t
t
B
N
α
∆
∆
ɶ
∼
  
 
 
 
 
 
 
 
 
(3.3.22) 
 
2
2,
(0,
),
t
t
B
N
β
∆
∆
ɶ
∼
 
 
 
 
 
 
 
 
 
(3.3.23) 
 
2
3,
(0,
),
t
t
B
N
γ
∆
∆
ɶ
∼
 
 
 
 
 
 
 
 
 
(3.3.24) 
 
1,
2,
3,
t
t
t
B
B
B
∆
∆
∆
⊥
⊥
ɶ
ɶ
ɶ
 .  
 
 
 
 
 
 
 
(3.3.25) 
 
155 
 
 
The above discretisation of the continuous process is achieved by ensuring that the correlation 
property is correctly represented.  The independent Brownian motions are obtained by a Cholesky 
decomposition of the correlated Brownian motions; explained in section four, see Geman (2005) 
chapter 12 for a thorough description.  In the next section we describe how the probabilities on the 
tree are obtained to construct a consistent pricing tree. 
 
3.3.5. Transitional Probabilities 
 
Formally we let: 
 
(t, i, j, k) = a quadruple of indexes in the tree spanning time, spot price level of crude and 
spot price level of two refined products, 
 
St, i = the crude spot price associated with being at spot price level index i for node (t, i, j, k), 
 
St, j = the naphtha spot price associated with being at spot price level index j for node (t, i, j, 
k), 
 
St, k = the kerosene spot price associated with being at spot price level index k for node (t, i, 
j, k), 
 
i’ε  { the set of indexes for the three successor crude spot price levels emanating from node 
(t, i, j, k) }, 
 
j’ ε { the set of indexes for the three successor naphtha spot price levels emanating from 
node (t, i, j, k) }, 
 
k’ε { the set of indexes for the three successor kerosene spot price levels emanating from 
node (t, i, j, k) }, 
 
p(St+∆t,i’| t, i, j, k) = the transition probability to crude spot price level St+∆t,i’ conditional on 
being at node (t, i, j, k) 
 
p(S2,t+∆t,j’| t, i, j, k) = the transition probability to naphtha spot price level S2,t+∆t,j’ 
conditional on being at node (t, i, j, k) 
 
p(S3,t+∆t,k’| t, i, j, k) = the transition probability to kerosene spot price level S3,t+∆t,k’ 
conditional on being at node (t, i, j, k) 
 
156 
 
 
When transitioning to successor nodes whilst developing the tree, the transition probabilities are 
solved, and must satisfy the following for the crude and refined product prices, we therefore have 
three sets of conditions, (in this simplification we have four dimensions i, j, k and t, but in the full 
model we will need eight dimensions, one for time and the rest for the stochastic petroleum product 
prices): 
 
3.3.5.1. Crude price probability conditions 
 
, '
, '
'
(
(
) | , , , )
(
| , , , )(
(
))
t
t
t
t i
t
t i
i
E In S
t i j k
p S
t i j k
In S
+∆
+∆
+∆
∀
= ∑
 , 
2
,
, '
'
2
, '
(
| , , , )
 (In(S
)
(
(
) | , , , ))
t
t
t
t i
i
t
t i
t
t
p S
t i j k
E In S
t i j k
α ∆
+∆
∀
+∆
+∆
=
×
−
∑
  
, '
'
1.0
(
| , , , ),
t
t i
i
p S
t i j k
+∆
∀
= ∑
   
 
 
 
 
 
 
(3.3.26) 
 
 
3.3.5.2. Naphtha price probability conditions 
 
2,
2,
, '
2,
, '
'
(
(
) | , , , )
(
| , , , )(
(
))
t
t
t
t j
t
t j
j
E In S
t i j k
p S
t i j k
In S
+∆
+∆
+∆
∀
= ∑
 
   
2
,
2,
, '
'
2
2,
, '
2,
(
| , , , )
 (In(S
)
(
(
) | , , , ))
t
t
t
t j
j
t
t j
t
t
p S
t i j k
E In S
t i j k
β ∆
+∆
∀
+∆
+∆
=
×
−
∑
 
2,
, '
'
1.0
(
| , , , ),
t
t j
j
p S
t i j k
+∆
∀
= ∑
  
 
 
 
 
 
 
(3.3.27) 
 
157 
 
 
3.3.5.3. Kerosene price probability conditions 
 
3,
3,
,
3,
, '
'
(
(
) | , , , )
(
| , , , )(
(
))
t
t
t
t k
t
t k
k
E In S
t i j k
p S
t i j k
In S
+∆
+∆
+∆
∀
=∑
 
   
2
,
3,
, '
'
2
3,
, '
3,
(
| , , , )
 (In(S
)
(
(
) | , , , ))
t
t
t
t k
k
t
t k
t
t
p S
t i j k
E In S
t i j k
γ
∆
+∆
∀
+∆
+∆
=
×
−
∑
 
3,
, '
'
1.0
(
| , , , ),
t
t k
k
p S
t i j k
+∆
∀
=∑
  
 
 
 
 
 
 
(3.3.28) 
 
Following Hull & White (1997) and using the probability condition equations as shown above it is 
trivial to build a consistent scenario tree.  The author’s provide the probability equations for 
transition to an up, middle or down node in a risk neutral setting – in section four we build the tree 
consistent with all seven refined products. 
The figure below depicts the simplified price processes for two stochastic variables moving 
to nine successor nodes from date 0; at node, (t, i, j), in this case three dimensions.  If there are three 
stochastic processes as shown above then for node (t, i, j, k) there are, 27 successor nodes, therefore 
in the complete refinery model we will have 3^7 successor nodes, as there are seven refined 
products, this gives 2,187 successor nodes – rendering a valuation computationally intractable, 
based on standard solving times on trees as stated in Hull & White (1997); hence we introduce an 
appropriate approximation in section five. 
 
158 
 
 
 
(Figure 3.15 Node transitioning on the trinomial tree) 
 
Implementing a relevant approximation, with a constant interest rate, r, and, a change in the time 
increment, ∆t equal to 1/12, as it takes one month to refine the crude, we can solve the above 
optimisation problem using standard non-linear program solvers.  We now define the steps required 
for a dynamic solution to the above problem. 
 
3.3.6. Steps for a two period dynamic optimisation  
 
159 
 
 
• At time 0, build an event tree with three decision steps and three sequential branchings 
representing the possible evolution of the forward curve at times 1 and 2. (See figure 3.15 
above) 
 
• Optimise the criterion (maximise the profit over the scenario tree) and implement the date 0 
optimal decision.  The profit has to be calculated over all possible paths through the tree 
from root to leaf. 
 
• At time 1, build an event tree whose first node is information of date 1; this tree has two 
decision steps and represents the possible evolution of the forward curve over one time 
period to date 2. 
 
• Optimise the criterion at date 1 and implement the date 1 optimal decision. 
 
• Continue until date 2; here at maturity sell the remaining products on the spot market. 
 
The above procedure is carried out 1,000 times to obtain robust optimal decision values, further, 
these steps can be applied to an infinite period problem.   
In section five we will add three components to the above set up: (i) we will increase the 
number of periods from two to 72, (ii) we will increase from three state variables to seven, and 
finally (iii) we will apply an approximation to the model to ensure a valuation is obtainable on a 
standard quad core machine.  In the following section we consider the optimisation problem along 
the nodes of the pricing tree. 
 
160 
 
 
3.3.7. Refinery valuation using the backward recursion methodology 
 
In this section we discuss the backward recursion used to value the complicated embedded 
optionality in a refinery complex. 
 
Considering optimal trading and price-taking actions at each node on the pricing tree, we 
begin at the terminal nodes.  We proceed by initially calculating two values for each node whilst 
adhering to the specific refinery mass balance and capacity constraints already defined: 
 
• The value of immediately trading a volume of crude oil at that node’s spot price, ensuring 
that the volume choice is within the mass balance and capacity constraints present at that 
particular node 
• The continuation value of holding the new crude oil inventory going into the following 
period 
 
The optimal spot trade for each node is the one that maximises the sum of these two values.  The 
preceding discussion implies that the optimal values at each node must be calculated for the seven 
sets of possible inventories held and spot volumes traded, which are continuous.   
 
We now state this optimisation mathematically.  Let each node in our one-factor tree be 
denoted by a octuplet of indexes spanning time, a spot price level of crude and a spot price level of 
all refined products as previously introduced.  We use the following notation: 
 
 
L = the set of hydrocarbon liquid inventory levels, 
 
 
Ql = the permissible set of trades at the given inventory level l, 
 
161 
 
 
qj = a set of spot trade volumes that is positive for buys and negative for sales aggregated for all 
products, 
 
rt = the risk-free rate over time, (for simplicity assumed constant) 
 
The optimal value at node (t, i, j, k, l, m, n, o) and inventory, l, is: 
 
 
( , , , , ,
, , )
(
)
( , , , , , , , )
(
, ', ', ', ',
', ', ')
( )
max[
( )
(
(
) | , , , , ,
, , )]
t
t
t i j k l m n o
l
r
t
spot
t i j k l m n o
t
t i j k l m n o
q Q
V
l
V
q
e
E V
l
q
t i j k l m n o
+∆
−
∆
+∆
∈
=
+
+
  
(3.3.29) 
 
Where: 
( , , , , , , , )( )
t i j k l m n o
V
l =  the optimal value at node (t, i, j, k, l, m, n, o) with current inventory l  L, 
7
( , , , , , , , )
,
1
,1
2
( )
spot
t i j k l m n o
rp
t rp
t
rp
V
q
q F
q S
=
=
−
∑
 ,          ignoring transaction costs  
 
 
The dynamic program above ensures that the refinery owner weighs different actions against one 
another with the aim of maximising the direct payoff plus expected future payoffs.  On average, an 
action with a high direct payoff (selling all the gasoline) has a lower expected future payoff (lower 
inventory of gasoline for the next period).  The refinery owner needs to make a decision whilst 
considering the continuation values; see Boogert and De Jong (2006) for an example.  In terms of 
solving, the recursion starts at the terminal period nodes, where all values in the expectation term 
above are zero; thus, the optimised value for each inventory that is possible at each of these final 
nodes is the value of selling as much of the refined products and left over crude oil inventory as is 
possible.  To complete the refinery valuation, we have to calculate the above equation recursively, 
going backward through each node in the tree, until the initial period is reached.  The value of our 
refinery is the value corresponding to the current inventory level in the initial period.  This exact 
162 
 
 
same recursion is described in the Clewlow-Strickland (1999) - a one-factor natural gas storage 
model, and is here present in the form of a model of seven oil products within a whole valuation 
model reliant upon a different set of restrictions - the authors also have a much smaller state space 
to manage and avoid the computational implementation discussion.  Additionally, this approach is 
an extension of the recursion for a similar but less complex set of contracts, named swing options, 
used in Jaillet et al (2004) - again the authors do not discuss the difficult implementation when the 
state space is large.  In the next section we investigate how the price series data is used to obtain a 
valuation. 
 
3.4. A CONTINUOUS TIME MODEL OF COMMODITY SPOT 
PRICE PROCESS EVOLUTION 
 
3.4.0.1. The calibration of the spot price process 
 
Heretofore, we have not detailed the choice of the price model with respect to any measure context, 
objective or risk-adjusted, and the spot and forward prices must be related in this model for 
calibration purposes.  In the risk-neutral world forward prices are expected spot prices.  For crude 
oil and the refined products, storable commodities, in which the standard no-arbitrage arguments do 
not apply, we assume such behaviour exists in the real world as well.  If there were a premium in 
forward prices over spot prices, it should be miniscule; since, consumers and producers are 
influenced by the contango or backwardation in the market.  In backwardation, producers are selling 
refined products at high prices and consumers are willing to pay the premium due to the scarcity of 
the commodity.  In contango, the discount benefits the consumer and the producer is willing to sell 
at the reduced price.  Since both groups are well-dispersed and highly competitive there is no 
163 
 
 
bargaining power over the other in the long run; under risk aversion and large refining frictions, if 
forward prices are martingales in the real world, then the real and risk-neutral world coincide.  This 
makes real world historical data for parameter estimation extremely conducive to calibrating the 
model.  In the next section we discuss how the correlation information was implemented on the 
scenario tree. 
 
3.4.1. Generating correlated commodity forward price series 
 
It is well known that the flexibility value of an option to exchange one asset for another is zero if 
the two asset prices are perfectly correlated, see Margrabe (1978).  Despite the fact that the refined 
petroleum products are highly correlated it is not perfect.  With the chosen continuous time price 
model of spot prices, we have a single factor mean reverting equation for each commodity: 
 
Gasoline: 
2
2
2
2
2
2
(
)
[
]
t
t
t
d InS
a
InS
dt
dW
θ
σ
=
−
+
  
 
 
  
 
(3.4.1) 
Naphtha: 
3
3
3
3
3
3
(
)
[
]
t
t
t
d InS
a
InS
dt
dW
θ
σ
=
−
+
  
 
 
  
 
(3.4.2) 
Fuel oil: 
4
4
4
4
4
4
(
)
[
]
t
t
t
d InS
a
InS
dt
dW
θ
σ
=
−
+
  
 
 
  
 
(3.4.3) 
Heating Oil: 
5
5
5
5
5
5
(
)
[
]
t
t
t
d InS
a
InS
dt
dW
θ
σ
=
−
+
  
 
 
  
 
(3.4.4) 
Jet Fuel: 
6
6
6
6
6
6
(
)
[
]
t
t
t
d InS
a
InS
dt
dW
θ
σ
=
−
+
  
 
 
  
 
(3.4.5) 
Cracker Feed: 
7
7
7
7
7
7
(
)
[
]
t
t
t
d InS
a
InS
dt
dW
θ
σ
=
−
+
  
 
 
  
 
(3.4.6) 
 
Suppose as above, that a commodity, i, has a particular spot price, Si, and spot dynamics described 
by the following: 
 
(
)
[
]
it
i
i
it
i
it
d InS
a
InS dt
dW
θ
σ
=
−
+
   
 
 
  
 
(3.4.7) 
164 
 
 
Also, Fi, is the forward price of the ith asset, i = 2…,7, and ai and σi are the speed of mean reversion 
and volatility of that asset respectively and dWi is the increment in Brownian motion.  We can also 
think of dWi as a random number drawn from a Normal distribution with mean zero and standard 
deviation dt1/2 so that: 
 
 
 
E(dWi) = 0 
and  
E(dWi
2) = dt  
 
 
  
 
(3.4.8) 
 
And the random numbers dWi and dWj are correlated according to: 
 
 
 
E[dWidWj] = ρijdt, 
 
 
 
 
 
  
 
(3.4.9) 
 
We use for example, dW1dW2 = ρ12dt, and dW2dW3 = ρ23dt, and so on until the seventh commodity.   
An interesting extension here would be to carry out the entire valuation with a comparison of 
alternative underlying stochastic processes.  Here we have used a version of the one factor model of 
Schwartz (1997) to represent price uncertainty, but how would the valuation results differ in terms 
of robustness, if this process were to be changed to a GBM or a multivariate normal or even an 
affine stochastic set of equations? 
 
Continuing, we state, ρij, as the correlation coefficient between the ith and jth random walks.  
The symmetric matrix with ρij as the entry in the ith row and jth column is named the correlation 
matrix.  For example, here we have n = 7 and the correlation matrix is as follows: 
 
 
12
13
14
15
16
17
21
23
24
25
26
27 
31
32
34 
53
36 
37
41
42
43
45
46 
47
51
1       
    
     
    
    
   
   
    1       
    
    
    
   
  
    
    1       
   
    
  
  
 D = 
    
    
    1      
    
  
    
ρ
ρ
ρ
ρ
ρ
ρ
ρ
ρ
ρ
ρ
ρ
ρ
ρ
ρ
ρ
ρ
ρ
ρ
ρ
ρ
ρ
ρ
ρ
ρ
ρ
52
53
54
56
57
61
62
63
64
65
67
71
72
73
74
75
76
     
   
     1      
   
    
    
    
    
    1      
   
    
    
   
    
    
    1     
ρ
ρ
ρ
ρ
ρ
ρ
ρ
ρ
ρ
ρ
ρ
ρ
ρ
ρ
ρ
ρ
ρ






















   
 
 
 
        (3.4.10) 
165 
 
 
 
Using NYMEX data the correlation matrix is calculated, but is not positive definite.  The matrix is 
made positive symmetric definite using an approximation to ensure that, yTDy ≥ 0.  This is 
described in the following section. 
 
3.4.1.1. Approximation for generating a symmetric positive definite matrix 
 
We use the correlation structure inherent in the prices to construct the seven dimensional trinomial 
tree.  However, the historical correlation matrix shown below was not in a form that could be used 
to build the scenario tree. 
 
A covariance matrix in the following form was derived: 
 
2
1
12
1
2
13
1
3
14
1
4
15
1
5
16
1
6
17
1
7
2
21
2
1
2
23
2
3
24
2
4
25
2
26
2
6
27
2
7
2
31
3
1
32
3
2
3
(
)         
     
    
     
     
    
   
    (
)          
   
     
5   
    
   
    
     (
)      
 
 = 
σ
ρ σ σ
ρ σ σ
ρ σ σ
ρ σ σ
ρ σ σ
ρ σ σ
ρ σ σ
σ
ρ σ σ
ρ σ σ
ρ σ σ
ρ σ σ
ρ σ σ
ρ σ σ
ρ σ σ
σ
∑
34
3
4
35
3
5
36
3
6
37
3
7
2
41
4
1
42
4
2
43
4
3
4
45
4
5
46
4
6
47
4
7
2
51
5
1
52
5
2
53
5
3
54
5
4
5
56
5
6
57
5
7
61
  
     
    
   
  
    
     
    (
)        
    
    
    
     
    
     (
)       
    
ρ σ σ
ρ σ σ
ρ σ σ
ρ σ σ
ρ σ σ
ρ σ σ
ρ σ σ
σ
ρ σ σ
ρ σ σ
ρ σ σ
ρ σ σ
ρ σ σ
ρ σ σ
ρ σ σ
σ
ρ σ σ
ρ σ σ
ρ σ
2
6
1
62
6
2
63
6
3
64
6
4
65
6
5
6
67
6
7
2
71
7
1
72
7
2
73
7
3
74
7
4
75
7
5
76
7
6
7
    
     
    
    
    (
)        
   
    
    
    
    
    
    (
)      
σ
ρ σ σ
ρ σ σ
ρ σ σ
ρ σ σ
σ
ρ σ σ
ρ σ σ
ρ
σ σ
ρ σ σ
ρ σ σ
ρ σ σ
ρ σ σ
σ
























   
(3.4.11)   
 
 
     Table 3.1 below, shows the correlations of the futures front month prices using historical 
data from NYMEX; these are proxies for the spot price.  The log returns of the prices are used 
between two consecutive days to calculate the correlation; these are found using the Pearson 
moment correlation coefficient.  To generate the correlated forward prices tree, an approximation is 
applied to the correlations; the below matrix is not positive definite.  The standardised t-statistic is: t 
= (r - ρ)/(√(1-r2)/(n-2)), where r is the correlation coefficient, sample size n, and ρ is the true 
166 
 
 
correlation coefficient, with a null hypothesis that this is zero.  All t-stats are between 3 and 10 
showing significance at the 5% level.  
(Table 3.1: Correlations of commodity price return series – 10 years of futures prices 
NYMEX) 
 
  
 WTI  
 
Gasoline  
 
Naphtha  
 Jet 
Fuel  
 Heating 
Oil  
 Fuel 
Oil  
 Cracker 
Feed  
 WTI  
1 
  
  
  
  
  
  
 Gasoline  
0.6862 
1 
  
  
  
  
  
 Naphtha  
1 
0.7862 
1 
  
  
  
  
 Jet Fuel  
0.767 
0.885 
0.767 
1 
  
  
  
 Heating Oil  
0.8723 
0.885 
0.8723 
0.843 
1 
  
  
 Fuel Oil  
0.837 
0.843 
0.834 
0.984 
0.8901 
1 
  
 Cracker 
Feed  
1 
0.8862 
1 
0.827 
0.8723 
0.837 
1 
 
 
Generating the set of correlated trinomial trees is done by following Iman and Conover (1982).  The 
authors describe a method for inducing a desired rank correlation matrix on a set of multivariate 
random variables for use in a simulation study such as the one present for the refinery.  The method 
is simple to apply and is distribution free, and preserves the marginal distribution of the stochastic 
commodity variables.  The authors apply their method to study the geologic disposal of radioactive 
waste.  Without re-describing the author's construction the main idea is the following: suppose that 
a random row vector X has a correlation matrix I.  The elements of X are uncorrelated.  Let C be the 
desired correlation matrix of some transformation of X.  Due to C being positive definite and 
symmetric, C may be written as C =PP' where P is a lower triangular matrix (Scheuer and Stoller, 
1962).  Then the transformed vector XP' has the desired correlation matrix C.  This is the theoretical 
foundation for the trinomial tree simulation.   
 
We apply a method to convert the above matrix into the following, which is positive 
symmetric definite; enabling the scenario tree to be constructed - including correlation information.  
This is done using an algorithm that minimises the root mean squared error between the current 
167 
 
 
matrix and the nearest positive definite matrix obtained by increasing and decreasing the correlation 
values by a very small amount; a matrix is found that is the nearest positive definite one available.  
The matrix used in the valuation is shown below: 
 
(Table 3.2: Correlations of commodity price return series – positive definite) 
 
  
 WTI  
 
Gasoline  
 
Naphtha  
 Jet 
Fuel  
 Heating 
Oil  
 Fuel 
Oil  
 Cracker 
Feed  
 WTI  
1.0000 
  
  
  
  
  
  
 Gasoline  
0.6932 
1.0000 
  
  
  
  
  
 Naphtha  
1.0000 
0.8932 
1.0000 
  
  
  
  
 Jet Fuel  
0.7520 
0.8593 
0.7520 
1.0000 
  
  
  
 Heating Oil  
0.8570 
0.8587 
0.8570 
0.7989 
1.0000 
  
  
 Fuel Oil  
0.8471 
0.8604 
0.8471 
0.7671 
0.8524 
1.0000 
  
 Cracker 
Feed 
1.0000 
0.8932 
1.0000 
0.8520 
0.8570 
0.8471 
1 
 
 
For an in depth description of this algorithm, see Higham (2002).  We next describe the framework 
and motivations behind the process of discretising a particular stochastic model onto a trinomial 
tree. 
 
We first diagonalise the above covariance matrix above and write it as: 
 
 
1
2
7
      0...
cos      -sin ...
cos      -sin ...
0        
...
sin        cos ...
sin        cos ...
                  
                 
 
   
                      
 
λ
θ
θ
θ
θ
λ
θ
θ
θ
θ
λ

















=
















∑
⋮
⋮
⋮
⋮
⋮
T



  
 
 
(3.4.12) 
 
Where: 
 
168 
 
 
 
(
)
(
)
2
2
2
2
2
2
2
2
1
1
2
1
2
1
2
2
2
2
2 2
2
2
2
1
1
1
1
1
(
)
4(1
)
2
1
(
)
4(1
)
2
n
n
n
n
n
n
n
λ
σ
σ
σ
σ
ρ σ σ
λ
σ
σ
σ
σ
ρ σ
σ
−
−
−
−
=
+
+
+
−
−
=
+
+
+
−
−
⋮
  
 
 
 
(3.4.13) 
 
 
 
2
1
1
1
2
arctan λ
σ
θ
ρσ σ


−
=



  
 
 
 
 
 
 
 
(3.4.14) 
 
We can now write our vector of Brownian motions as: 
 
 
1
1
2
2
1
1
1
1
cos
( )
sin
( )
( )
( )
cos
( )
sin
( )
n
n
n
n
n
n
B t
B t
W t
W t
B
t
B t
θ
λ
θ
λ
σ
σ
θ
λ
θ
λ
−
−


−





= 









−




⋮
⋮
   
 
 
 
(3.4.15) 
 
This is the basis for simulating the correlated commodity processes; where the (B1,…,Bn) is a 
seven-dimensional standard Brownian motion.  The discretisation of the diffusion is then equivalent 
to the discretisation of seven independent standard Brownian motions.  Let X1,…,X7 be seven 
independent and identically distributed random variables taking three values (-h, 0, h) with 
probabilities (pu, pm, pd) respectively.  The idea is to use (X1,…,X7) to approximate the increments 
(B1(t+dt) - B1(t),…, B7(t+dt) - B7(t)).  Due to the independence of (X1,…,X7) the probabilities of 
getting to each one of the 21 new nodes follow.  From here, the discretisation of the price process is 
completed in the same manner as in a one dimensional tree.  We simulate the diffusion up to the 
terminal date and we optimise for the refinery owner at this point.  By backward induction we 
compute the price at the root of the tree and also optimise to find the value of the refinery and the 
optimal decision variables. 
 
169 
 
 
3.4.2. The discrete-time spot price process 
 
Often closed-form mathematical solutions are unavailable when a contingent claim or valuation is 
subject to several sources of uncertainty – this is when discrete dynamic programming can provide 
the solution.  Using stochastic variables that take on finitely many variables enables us to avoid 
measure theory.  Lattices are too complex when dealing with multiple uncertainties, path-
dependence or complex options, see Clewlow and Strickland (1998) for examples.  These problems 
can be managed more effectively with scenario trees than lattices.  Another example by authors 
pricing real options using trees, but with off the shelf software, is Brandao and Dyer (2005); the 
authors instead use a binomial tree with risk neutral probabilities to price a project using NPV and 
they apply a set of GBM processes – they leave for further research the solutions using trinomial 
trees and due to the third party software do not analyse the structure of the implementation program 
which certainly renders our current problem intractable.  Tree data structures and industrial solvers 
liked IBM's CONOPT3 are not available in DPL, a specialist decision tree software package; 
restricting the range of problems that can be solved.  Another example would be the pricing of 
compound options, which can be modelled by adding an additional decision node to a binomial tree.  
If the primary uncertainty associated with an asset is thought to be mean reverting, as in the case of 
petroleum related product prices, then Hahn and Dyer (2008) show how a binomial tree may be 
used to approximate such mean-reverting models as the one factor Ornstein-Uhlenbeck process or 
the two-factor Schwartz and Smith (2000) process; see Schwartz and Smith (2000) for a detailed 
explanation of the calibration.  However, the extra degree of freedom provided by a trinomial tree is 
more realistic for mean reversion.  We build the tree as seven-dimensional with the dimensions 
spanning the spot and forward prices, and the time to maturity, with the trinomial branches for each 
dimension.  Thus, each node leads directly into 21 successor nodes.  The discretisation of the 
continuous process is described formally by use of the Arrow-Debreu prices (see Hull and White 
1996), where we assume that as ∆t → 0, we obtain δt.   
170 
 
 
          Figure 3.16 below, illustrates the above concepts utilised to construct the seven correlated 
prices onto the trinomial tree; this time increment had to be at least one month over six years, 
otherwise valuation would be impossible. 
 
  
 
 
 
171 
 
 
 
(Figure 3.16 Node transitioning on the trinomial tree with probability inputs) 
 
172 
 
 
3.4.2.1. Trinomial tree building procedure: Stage one 
 
This procedure exactly follows Hull and White (1996), the number of branches at each period is 
variable dependent upon the number of time slots but is a function of the commodity price; it is 
important to note that we alter the branching whenever the probabilities would otherwise be 
negative, this is implemented within the code.  If the simulated process becomes negative the 
branching is altered this is also true if the price becomes too large.  The stochastic process in 
equation (4.3) is discretised onto a trinomial tree formally shown in section 5.3.2.; we now discuss 
the general procedure.  The choice of the InSt as the underlying is more computationally efficient 
than using just St; it is also convenient to use a trinomial instead of a binomial tree as it provides an 
extra degree of freedom, incorporating mean reversion as a feature on the tree.  This framework is 
equivalent to using the explicit finite difference method, for a thorough description, see Munk 
(2011).  We use alternative branching based on the whether the forward price is high, average or 
low.  Alternative branching therefore also proves practical for incorporating mean reversion: 
 
 
 
 
 
 
 
 
 
 
 
 
173 
 
 
 
 
(a)                                 Average price branching    
 
 
 
 
(b)                              Lower price branching 
 
(c)                              Higher price branching   
 
 
(Figure 3.17: Branching for the stochastic process along the trinomial tree) 
 
We assume that, on the discretised tree, a function of St, f(St), can be represented by the stochastic 
process shown below: 
 
 
 
 
df(St) = [θ – a f(St)]dt + σdWt  
 
             
 
(3.4.16) 
 
This originates from the form of the spot price process chosen for the commodities; where we have 
chosen the function f, to be a logarithm function.  Hence, we start by setting Zt = In(St), (see Ross 
(1995) for an alternative to the logarithm function), so that we now have: 
 
 
 
 
dZt = [θ – aZt]dt + σdWt 
 
 
              
 
(3.4.17) 
 
The tree building process starts by constructing a tree for Z* where the process is symmetrical about 
174 
 
 
Z*=0.  We define: 
 
dZt = [θ – aZt]dt + σdWt 
and  
dZt* = – aZt*dt + σdWt 
  
 
(3.4.32) 
 
The first stage is to build a tree for Zt*, that follows the same process as, Zt, except that θt=0 and the 
initial value is zero. 
   
For convenience in stability and convergence, the spacing between spot prices on the tree, ∆S, is set 
as: 
 
 
 
 
3
S
t
σ
∆
=
∆   
 
 
 
               
 
(3.4.18) 
 
See Hull & White (1996) for proof of uniqueness and convergence.  This choice for spacing also 
turns out to be good for error minimisation.  The branching method at each node is then applied.  
After the geometry is constructed the transition probabilities are calculated. 
 
Define (i,j) as the node where t=i ∆t and S*=j ∆S.  (The i variable is a positive integer representing 
a time increment and the j is a positive or negative integer representing a space increment).  The 
branching method used at each node must lead to positive probabilities.  We define jmax as the value 
of j where we switch from branching figure (a) to branching figure (c); and jmin as the value of j 
where we switch from branching figure (a) to branching figure (b).  Hull and White (1994) show 
that the probabilities are always positive if jmax is set equal to the smallest integer greater than 
0.184/(a ∆t) and jmin is set equal to - jmax.  We define pu, pm, and pd as the risk-neutral probabilities of 
the highest, middle and lowest branches emanating from the node.  The risk-neutral probabilities are 
chosen to match the expected mean and variance of the change in S* over the next time interval ∆t.  
 
175 
 
 
The mean change in S* in time ∆t is: –aS*∆t, and the variance change is, σ2∆t.  At node (i,j), S* = 
j∆S.  If the branching has the form in branching figure (a), the pu, pm, and pd must satisfy the 
following three equations to match the mean, the standard deviation and sum to one: 
 
 
pu ∆S - pd ∆S = -a j ∆S ∆t 
 
 
 
 
     
 
(3.4.19) 
 
 
pu ∆S2 + pd ∆S2 = σ2 ∆t + a2 j2 ∆S2 ∆t2 
 
 
      
 
(3.4.20) 
 
 
pu + pm + pd = 1 
 
 
 
 
 
      
 
(3.4.21) 
 
Applying,
3
S
t
σ
∆
=
∆ the solution to these is: 
 
2
2
2
1
1 (
)
6
2
u
p
a j
t
aj t
=
+
∆
−
∆
   
 
 
                  
 
(3.4.22) 
2
2
2
2
3
m
p
a j
t
=
−
∆
  
 
 
 
 
 
      
 
(3.4.23) 
2
2
2
1
1 (
)
6
2
d
p
a j
t
aj t
=
+
∆
+
∆
   
 
 
 
      
 
(3.4.24) 
 
If the branching is as in figure (b), the solution becomes: 
 
2
2
2
1
1 (
)
6
2
u
p
a j
t
aj t
=
+
∆
+
∆
  
 
 
 
      
 
(3.4.25) 
2
2
2
1
2
3
m
p
a j
t
aj t
= −
−
∆
−
∆  
 
 
 
      
 
(3.4.26) 
2
2
2
7
1 (
3
)
6
2
d
p
a j
t
aj t
=
+
∆
+
∆
  
 
 
                 
 
(3.4.27) 
 
176 
 
 
If the branching is as in figure (c), the solution becomes: 
 
2
2
2
7
1 (
3
)
6
2
u
p
a j
t
aj t
=
+
∆
−
∆
  
 
 
 
      
 
(3.4.28) 
2
2
2
1
2
3
m
p
a j
t
aj t
= −
−
∆
+
∆  
 
 
 
      
 
(3.4.29) 
2
2
2
1
1 (
)
6
2
d
p
a j
t
aj t
=
+
∆
−
∆
  
 
 
 
      
 
(3.4.30) 
 
We now describe the next stage of the discretisation. 
 
 
3.4.2.2. Trinomial tree building procedure: Stage two 
 
The second stage is to consists of displacing the nodes in the simplified tree to add the proper drift 
and to be consistent with the observed forward prices.   
As shown in equation 3.3.10. the forward prices are described by the following equation: 
 
F(t,T) = exp{e-a(T-t)InSt + (1-e-a(T-t))( θ -σ2/2a) + σ2/4a(1- e2a(T-t))} 
 
 
(3.3.10) 
 
With the calibrated parameters and the spot price at a point in time it is trivial to build the forward 
curve -  see figure A.3 for a comparison of calibration results of the forward curve on WTI oil in 
2014 using one and two factor models. 
 
We can introduce the correct time varying drift by displacing the nodes at time i∆t by an 
amount a.  The a's are selected to ensure that the tree correctly returns the observed forward price 
curve.  The value of Z at node (i,j) in the new tree equals the value of Z* at the corresponding node 
in the original tree plus ai; the probabilities remain unchanged.  The vital part here is to use forward 
177 
 
 
induction and the state prices to ensure that the tree returns the current prices.   
 
This is all accomplished by displacing the nodes on the S*-tree so we define: 
 
 
γ(t) = S(t) – S*(t) 
 
 
 
 
 
      
 
(3.4.31) 
 
It follows that: 
 
 
 
dγt = [θ – aγ t]dt 
 
 
 
 
                  
 
(3.4.32) 
 
The solution to this is: 
 
 
 
γ (t) = F(0,t) + σ2/2a2(1-e-at)2  
 
 
 
     
 
(3.4.33) 
 
The F(0,t) is the initial forward market curve; that can easily be fit with the Hull & White tree.  The 
γ(t)’s are calculated recursively and we define γi(t) as γ (i∆t), the value of S at time i ∆t on the S-tree 
minus the corresponding value of S* at time i ∆t on the S*-tree.  We define Si,j as the present value 
of a commodity if node (i,j) is reached and zero otherwise.  If the underlying stochastic process 
were to include two factors or include a time varying mean or volatility, this tree building procedure 
can be altered to accommodate these features - in chapter four we do just this.  This does  however, 
introduce non-stationarity into the stochastic model.  We now discuss the procedure for calibrating 
the model to market price data. 
 
 
3.4.3. Forward curves calibration procedure using Levenberg-Marquardt (1944) 
 
Often when calibrating parameters academics are using a likelihood function or the Kalman filter; 
here we introduce a more efficient calibration method designed during the second world war 
178 
 
 
(1944).  A convenient tool suited to dealing with markets where the state variables are 
unobservable, but are generated in a Markov manner, is known as the state space form.  
Reformulating the model into this form means the Kalman filter can be used to find the parameters 
that best estimate the model, hence the time series of the unobservable state variables.  Frequently, 
the spot price of a commodity is so volatile that a near futures contract is used as a proxy instead, 
see Harvey (1989) for a survey of state space form algorithms.  The problem with using the Kalman 
filter is that it assumes a linear dynamical system, and that measurements have a multivariate 
Gaussian distribution.  Although, there are applicable extensions that could be applied to this 
refinery model set-up; we chose an algorithm that is simplistic, static and required less 
computational effort.  
 
For a description of an application in simulation using trees see Hoyland and Wallace, 
(2001).  The authors construct a three period tree using volatility clumping and mean reversion; 
however, the tree quickly becomes “over specified” when the number of periods is increased over 
three.  The calibration of the parameters goes hand in hand with the generation of the scenarios; 
hence, the Hull and White trinomial tree (1994) is utilised.  To calibrate the initial stochastic 
forward curve model to the tree, and therefore to the real world, we find the parameters a, θ and σ, 
using the Levenberg-Marquardt (LM) method fitting the first two moments of the Forward curve 
model to the NYMEX future prices data.  This algorithm optimises a least squares error and is 
effective even if an initial set of starting parameters are omitted.  It does however, only find a local 
and not a global minimum; see Levenberg (1944) for the foundations.  The mean and variance were 
equally weighted, and the parameters chosen to enable the model to fit the data with minimum error.  
In general, LM fitting significantly outperforms gradient descent and conjugate gradient methods 
when used on non linear least squares applications.  The formula below minimises a measure of 
distance between the mean and variance of the constructed model, defined in equations, (3.3.13) 
and (3.3.14), and the specified values from the historical future price data: 
 
 
 
 
 
 
 
 
 
 
 
 
 
179 
 
 
 
2
^
, ,
min
(
( , , , )
)
a
i
i
VALi
T
i S
w f
a
T
S
σ θ
σ
θ
∈
−
∑∑
 
 
 
 
 
 
(3.4.34) 
 
Where fi(σ,a,θ) is the mathematical formula for the statistical property being minimised, here i=1 
for the mean, or i=2 for variance, T are the maturity of the future price contracts, σ is the volatility 
in the model, wi is the weight for statistical moment, SVALi is the specified value of the statistical 
property i of S, and a is the mean reversion speed in the forward curve model.  Once the “a”, “θ” 
and the “σ” are obtained from the stochastic process calibration, all the equations needed to create 
the trinomial tree can be calculated, equations (3.4.25 – 3.4.34).  In reality on NYMEX only the 
first five contracts are very liquid and so we use these within the least squares above.  The 
parameters are those that generate the lowest squared sum difference.  GAMS is used for parameter 
calibration and after 14 iterations it takes five minutes to converge, here the tolerance was 10e-4, at 
which point the results are obtained.  There is no guarantee here that the parameters are a unique 
and global solution; therefore we alter upper and lower bounds for the initial values and use 
different sets, the values are insensitive to these tests and so we conclude that the parameters are 
locally optimal.  The results of the above calibration for crude are shown in the following section 
and appendix A.4.; standard errors are in brackets: 
 
 
 
 
 
 
 
 
180 
 
 
3.4.3.1. Results of forward curves calibration 
 
One Factor Stochastic Model: Crude Oil 
Period : 
1/2/2002 to 01/06/2013 
Contracts : 
F1, F3, F5, F7, F9 
NOBS : 
120 
a1   : 
0.561   (0.08) 
θ1   : 
3.343   (0.346) 
σ1   : 
0.2955 (0.03) 
RMSE1  :   0.9993 
 
(Table 3.3: The calibration results of the stochastic one factor model over ten years using 
crude future contract prices from NYMEX.) 
 
The forward contracts in the above table from months one to nine are utilised again in the next 
chapter for calibrating a two factor model.  We could have chosen a daily granularity as the data 
was available but one should model at the same detail as price estimations are required - due to the 
state space being ten years and decisions being made monthly we choose monthly contracts.  It is 
useful to note the term structure of the forward curve under the different models which is shown in 
appendix A 3.0. 
 
An example fitted forward curve for the stochastic crude process is shown below: 
 
181 
 
 
0
50
100
150
200
250
300
350
400
450
0
20
40
60
80
100
120
$/Barrel Futures Crude Oil
Implied Model Prices versus Future Prices on Crude Oil (Jan 2002 - June 2013)
 
 
Estimated Price
Observed Price
  
 
 
(Figure 3.18: Crude oil model prices versus futures market prices, NYMEX) 
 
Crude oil is calibrated using historical data, and the correlated prices are generated for all the 
refined product prices; see the appendix for the results.  By taking into account the historical 
correlations, a multidimensional trinomial tree is constructed.  Firstly, all the SDEs are calibrated 
and discretised onto the scenario tree, next the decision set for commodity volumes is optimised 
over the entire horizon in one sequence; further, the entire process is repeated 1,000 times and 
finally, averages of the optimal values recorded.  For example, we show prices for four periods; spot 
and futures prices for petroleum products are given below for each node on the tree: 
           
 
 
 
182 
 
 
 
 
(Figure 3.19: Four period (months) trinomial tree of spot crude oil market prices.) *72 
periods were calculated in practice. 
 
The values for four months of all oil related products are shown in table 3.4:  
 
(Table 3.4: The prices of crude and refined products at each node in $ per barrel)  
 
NODE 
F1 
F2 
F3 
F4 
F5 
F6 
F7 
A  
80 
185 
80 
125 
145 
60 
15 
B 
117 
296 
117 
200 
232 
96 
24 
C 
83 
236 
83 
155 
185 
76 
19 
D 
58 
176 
58 
114 
138 
57 
14 
E 
163 
386 
163 
260 
301 
128 
35 
F 
115 
322 
115 
222 
253 
106 
28 
G 
82 
264 
82 
179 
207 
86 
25 
H 
57 
201 
57 
138 
161 
68 
23 
I 
41 
145 
41 
96 
114 
48 
17 
J 
160 
411 
160 
278 
324 
135 
13 
K 
113 
350 
113 
238 
277 
116 
35 
L 
80 
291 
80 
197 
230 
95 
30 
M 
56 
231 
56 
157 
183 
78 
24 
N 
40 
170 
40 
116 
136 
55 
19 
O 
158 
475 
158 
319 
269 
152 
15 
P 
112 
395 
112 
265 
304 
127 
40 
Q 
79 
330 
79 
223 
259 
107 
33 
R 
56 
269 
56 
183 
211 
88 
26 
S 
40 
211 
40 
142 
168 
69 
22 
 
Table 3.5 below is constructed using the equations (3.4.25) to (3.4.34) to show the calculated 
183 
 
 
probabilities of the forward curve dynamics over two years; in the final results up to at least six 
years are required.  It is important to note that in the C program used to carry out the real option 
valuation, all $/barrel values were converted to $/ton as the rest of the program is defined in these 
units, and as carried out by refinery practitioners. 
 
In practise we simulate the spot and forward prices up to 72 months forwards.  We calculate 
optimal decisions at each node, along each path by using specialist software package, GAMS. 
 
3.4.3.2. Delta of the oil refinery 
 
One factor stochastic models are applied on trading desks, giving realistic pricing features; 
however, when considering the hedging of an option or a real asset there are serious issues.  In 
practice, pricing can be carried out with a one factor model but hedging requires multifactor models 
– this is also known as outside model hedging.  For example, calculating how sensitive the value of 
the refinery is to a movement in the set of prices, a type of multidimensional delta, should allow for 
many movements in the term structure.  This is however outside the scope of our work and we leave 
it for further research - a Monte Carlo simulation can be investigated for this purpose.  In the next 
section we discuss how to solve the Bellman equation on the discretised trinomial tree along with a 
relevant approximation. 
 
 
 
 
 
 
 
 
184 
 
 
 
(Table 3.5: Transition trinomial probabilities for 2 years is shown below; going from a starting node 
to the next node, with probability up, pu, probability middle pm, and probability down pd.) 
 
185 
 
 
 
3.5. NUMERICAL PROCEDURE FOR THE VALUATION 
 
Our model is related to the literature on the valuation of commodity and energy real options, for 
instance, see a natural gas storage valuation in Thompson et al (2002).  Often the valuations are 
based on low-dimensional representations of the evolution of the spot price.  In contrast, we create a 
discrete-time stochastic dynamic programming model, using a high-dimensional representation of 
the evolution of the underling spot prices.  Jaillet et al. value natural gas storage as an extended 
swing option.  The author’s method implements a low-dimensional model of the gas spot price 
evolution, and restricts the policy space.  Our model uses a high-dimensional approach of the spot 
prices processes, and does not apply constraints on trading decisions.  Haugh and Kogan generalise 
the work of pricing American options.  The refinery is much more difficult than this valuation 
because it features an inventory of hydrocarbon liquid that is absent in American option valuation.  
Secomandi et al. (2008) compare optimal and heuristic approaches for natural gas storage valuation.  
The authors apply a re-optimised deterministic model, restricting the spot price evolution to a one-
factor mean-reverting spot price model, which we have also applied but on seven state variables 
rather than one. 
The quantity of interest in this model is the value of a given oil refinery portfolio at the time 
of inception.  This value depends on how the petroleum product prices change over time as a 
refinery owner acts dependent on these changes as follows: buying crude and refining it at a given 
point in time, storing and doing nothing if prices are unfavourable, selling refined products at a later 
point in time.  Such a portfolio can be valued as the discounted risk-neutral expected value of the 
cash flows from optimally operating the refinery during its tenure, whilst respecting its operational 
constraints. 
The optimisation and valuation take place under the volumetric risk: the volumes of crude 
186 
 
 
and refined products; these are related to the exogenous non-traded variables such as weather and 
price risk.  Decision theory has paid attention to the problem of dynamic choice under uncertainty.  
The notation applied to the refinery optimisation problem is given below: 
 
i – time period 
 
qij – volume of oil product, j, purchased or sold (decision variables) at period i 
 
ξi -  a vector of spot and forward prices of the petroleum products 
 
f(qij, ξi) – a function relating the dependent variables to the cash flow 
 
G = (Gi)i=1,…..,T – sequence of the discrete time cash flows, 
 
(θi)i=1,…..,T – decisions are occurring at these times 
 
ω ε Ω – states of nature 
 
Vi(G, ω) – the value measure (represents the value of the sequence of cash flows) 
 
NB: Cumulative cash flows, as considered in the coherent risk measure literature, can be viewed as 
value processes.   
 
In this framework, the date θi value is assessed recursively by aggregation of the current cash flow, 
Gi, and the discounted value measure, Vi+1, seen from date θi.  It is important to state that here, Vi, is 
a 
i
θ
ℑ
adapted process. 
187 
 
 
Where q1,…,T are the decision variables, ξi the vector of random prices, f(qij, ξi) is a function 
relating the dependent variables to the cash flows, i is the time period, Q is a set of feasible 
volumetric decisions ensuring non-negativity and capacity constraints, it also captures the state 
across periods.   
A set of production decisions at each date θi will result from the optimisation of the chosen 
value measure.  This optimisation not only yields the first set of decisions, q10,….q70 at date θ0, but a 
whole set of decision planning for all subsequent dates up until date θT.   
 
Consider the cash flow sequence on these dates depending on decisions and a multi-
dimensional process: 
1
(
)
:
:
(
,
)
ji
i T
i
ji
ij
G
f q
ξ
ξ
≤≤
=
, here ξij is a vector of spot prices for the oil 
products, qij, is the set of all production variables, and f is a reasonably behaved function relating 
these two variables to the cash flow at each date i. 
            The state is xi, on which decisions depend at time θi.  It is supposed that, after a set of 
decisions, qij, is made at time θi, the state xi, leads to another state: 
1
1
( ,
,
)
i
i
i j
i
x
h x q
+
+
=
∈
, where h is a 
deterministic function, and ε is a normally distributed random variable.  We define the optimisation 
problem related to a dynamic value measure, Vi, at each time period i as: 
 
(
)
(
)
( )
(
)
k
k t
i
i
i
i
i
q
x
J x
Max
V G
≥∈Α
=
  
 
 
                      
 
 
(3.5.1) 
 
Here, Ji, is the optimal value of maximising the value function, Vi, dependent on cash flows Gi, the, 
A(xi) are the set of admissible strategies; the set of qs are calculated to maximise the, Ji(xi) over the 
finite time horizon.  The discrete time model has a finite time horizon, i=1,……,T (months) and a 
set of decision dates (θi)i=1,…..,T.  In addition the commodity is supposed to be traded, stored and 
consumed in the same location.  Refining product j, decision variables qij, corresponding to period i, 
are subject to the standard physical refinery constraints defined in section 3.2.5, in algebraic 
modelling language (AML) described as: 
188 
 
 
 
0 ≤ qi1
buy ≤ Qi1
buy,   0 ≤ qij
sell ≤ Qij
sell,       i ≥ 1,    
2 ≤ j ≤ 7 
  
 
(3.5.2) 
L0 =Linit,   Li= Li-1 + qi1
buy - 
7
,
2
j sell
i
j
q
=
∑
,   1 ≤ i ≤ T,    
2 ≤ j ≤ 7             
 
(3.5.3) 
Lmin ≤ Lij ≤ Lmax             
1,..., ,   
T
end
i
T
L
L
∀=
≥
 ,    1 ≤ j ≤ 7             
 
(3.5.4) 
 
Where Lij is the liquid volume of hydrocarbon for product j at time period i, other notation utilised 
is defined as follows: 
 
L0 – Overall refinery complex liquid volume at date 0 
Linit – initial capacity level defined 
Lmin – minimum refinery complex capacity 
Lmax – maximum refinery complex capacity 
Lend – boundary requirement for optimisation of the refinery 
Qbuy – The maximum volume of crude that can be bought in any period  
Qsell – The maximum volume of particular refined oil that can be produced in any period 
 
Each ω ε Ω represents a realisation of the process ξi = (Fi,i,1, Fi,k,j, k>i),  i=1,….,T – here the index k 
must be greater than i as products are sold forwards, and it takes a minimum of one month to refine 
and deliver to a client.  Only forward contracts are considered where we denote by Fi,k
48 the forward 
price of the commodity end product quoted during period i for delivery in period k (k ≥ i) and Si the 
post price of a commodity, where Si = Fi,i.  All processes will be a discrete time-finite number of 
states of the world.  The assumption that the state space is discrete ensures the convexity of any 
optimal result.  The total cash flow during period, i, is denoted as Gi: 
 
                                                 
48  Here, Fi,j can be considered as the average price over all the quotation dates in the period i on forward contracts for 
delivery in period k 
189 
 
 
  
7
(
)
1
,
1
2
k
i
r
j
j
i
i
i k
i
i
j
G
e
q F
q S
θ
θ
−
−
=
=
−
∑
 ,  
 
 
 
 
     
 
(3.5.5) 
Where j, is refined product index, 1 is the crude oil index and other symbols are as defined 
previously.  Note that it was assumed that there is an absence of credit risk, hence equality between 
forward and futures prices. 
We denote, A(xi) as the set of (Fk) measurable admissible strategies (qk)k>i = (qik
j, qi
1) from 
state: xi = g(Li-1, ξi, ε i).  
 
The fundamental Bellman equation is here: 
 
                   
1
1
[1, ]
0
1
[1,
1]
0
(
,
)
(
,
|
)
max
[
( ,
)
( ,
) |
]
i
i
i
i
i
i
i
i
i
i
i
i
i
q
A q
V q
f q
V
q
ξ
ξ
ζ
ξ
ξ
ζ
−
−
+
+
∈
=
+
 
 
 
(3.5.6) 
 
Assuming between period independence holds for the stochastic processes that generate the cash 
flow, Gi, and at every period, i, we maximise, Gi, then at the last period above, the value function, 
VT(GT-1), is the optimal value of the above problem.  The optimal value calculated recursively until 
the present day is myopic. 
If both the decision set and the objective function are convex, and also closed, then any local 
maximum is a global maximum. 
 
The optimal value Ji(xi) of the portfolio satisfies the dynamic programming equation: 
 
1
1
(
)
(
)
( )
{
(
)
(
(
(
)))}
k
k t
i
i
i
i
i
i
i
q
x
J x
Max
E J
x
β
≥
+
+
∈Α
=
+
i
G
i
q
  
 
 
      
 
(3.5.7) 
 
The state xi+1, is given by the state transition equation: 
xi+1 = (Li-1 -
7
2
j
i
j
q
=
∑
 + qi
1, g(ξi, i+1)) 
 
 
 
 
     
 
(3.5.8) 
 
Trinomial trees are often applied in real option valuation, they are simple for management to 
190 
 
 
understand and robust, see Hahn (2008).  A tree like this determines the refinery prices and 
associated probabilities over each period we select until the lifetime of the refinery.  American 
options can be priced in this way once the state space is judicious.  To understand the valuation of 
the refinery we emphasise the primary differences.  Like a standard approach our tree is 
recombining; all transitional probabilities are positive and add to one; our choice of steps is 
tractable; the mean of the trinomial distribution is equal to the mean of the stochastic process we 
have chosen; the variance also and finally, it spans enough time periods to value the asset.  
American options are often priced using lattice methods, but the number of periods is usually much 
less than six years and there is only one underlying, additionally there are very different constraints 
in place for the refinery. 
In the next section we discuss the properties that will ensure the optimisation is realistic, and 
we will describe how the above formulation is solved computationally; introducing our 
approximation and reason for faster than standard solving time. 
 
3.5.1. Numerical Approximation using stages to obtain a valuation 
 
We can scale down our problem using a parameter, k, within the GAMS code.  The number of 
periods is too high for computation; hence we start with k=1 and increase until our solution takes 
over two hours, which is at k = 11.  This period of time was chosen as the maximum that a user 
would be willing to wait for valuation purposes.  This staging shortcut was only possible due to the 
mapping shortcut described in section 3.6.5. 
 
3.5.2. Methods available to solve the dynamic program 
 
To computationally solve the problem, we use a slightly alternative stochastic programming 
191 
 
 
technique.  The set of realisations of commodity spot prices and the forward curve is represented on 
an event tree with nodes n ε Ν, the decisions, q(t, ω), are indexed on the nodes of the tree, and the 
terminal-date objective maximised numerically with respect to all decisions (qn)n ε Ν  using a large 
scale non-linear solver.  It is important to describe the problem in AML as many linear programs 
cannot in fact be solved or tested due to specific properties of the problem in question. 
The payoff to the refinery owner is defined as the value of refining today, plus the 
discounted expectation of refining over the life of the oil refinery, except at maturity.  The option to 
choose the optimal refining times have no analytical solution in this setting as there are too many 
state variables.  Having discretised the stochastic differential equations onto a trinomial tree, we 
have the equivalent of an explicit finite difference grid. 
 
One way to think of this problem is to assume that we are again in continuous time, as if we 
could refine continuously, and to approximate all the derivatives by finite differences.  The space 
domain can be partitioned using a mesh of x0,…,xj and the time domain using a mesh t0,….,tN.  
Assuming a uniform partition in both space and time, the difference between two consecutive space 
points is h and between two consecutive time points is k.  The points become: 
 
(
,
)
n
j
n
j
V x t
v
=
 , this represents the numerical approximation of Vj,n = f(xj, tn).    
 
Maximum points are:  T = N ∆t, and X = M ∆x 
 
The value of the refinery written in continuous time is the following: 
 
1
1
1
[ ,
1]
(
)
(
)
( )
{
(
, )
(
,
)}
k
k t
i
t
t
t
t
t
t t
q
x
t
V x
Max
x dt
V
x
q
≥
+
+
+
+
∈Α
=
+
∫
t
G
t
q
 
 
 
      
 
(3.5.9) 
 
After some manipulation we have: 
192 
 
 
 
 
[ ]
(
)
(
)
( )
inf
(
,
,
)
t
k
k t
i
dV
t
t
t
t
dx
q
x
V x
H
x q
≥∈Α
=
−
 
 
 
          
      
 
(3.5.10) 
 
Following arguments from (Dixit and Pindyck and Willmott) we can form the dynamic 
programming equation as a HJB equation (3.5.10) with the one factor SDE for each commodity as a 
vector.  To solve the set-up that would follow, one would have to construct a finite difference grid 
with appropriate boundary conditions; we leave this approach for others. 
 
Due to the non-analytical solution of the above HJB equation, we develop a model of dynamic 
programming in a setting where uncertainty is modelled using discrete-time Markov processes.  
Time plays a very important role for investment decisions.  Dynamic programming is a tool that is 
particularly applicable when considering uncertainty.  It fragments a sequence of decisions into two 
parts: the initial decision, and a value function that encapsulates the consequences of all preceding 
decisions, starting with the position that results from the current decision.  In a finite horizon 
problem as we have here, the last decision at its end has nothing afterwards, and can be found using 
a deterministic calculation.  Obtaining this solution then provides the valuation function appropriate 
to the penultimate decision.  This enables a decision two periods from the end, and this continues 
until today’s decisions can be found. 
               The key problem with the oil refinery and the reason why this optimisation has been 
avoided up until now is the huge dimensional space that is present; solving over this number of 
periods for this size state space is rare in engineering applications and even more so in the finance 
literature.  The mathematical complications connected to the real option theory originates from the 
idea that the general issue cannot be solved without a probabilistic solution to a firm’s optimal 
investment decision policy, not only at date 0 but for all periods in time until the end of the real 
asset’s horizon.  For example, Brandao (2002) provides an example of the real option valuation 
application using a binomial tree; hence one less degree of freedom than ours, valuing a highway 
193 
 
 
project in Brazil that includes 20 time periods.  The author’s decision tree has 5.2e5 scenarios, in 
this refinery valuation there are 72 time periods; meaning we have in total 7.5e33 scenarios.  
Solving times and computational analysis are not described by the authors.  As described in Hull 
(2003) in detail, solving a real option problem on a binomial tree with periods n = 30, means 
computation takes approximately 1000 minutes to solve; we find a stochastic value utilising a 
trinomial tree with 72 time periods in just over 140 minutes.  
 
3.5.3. Mathematical issues with the valuation 
 
Many authors have successfully described the convergence properties of discrete tree applications 
of their stochastic processes.  In Heston and Zhou (2000) table 3.1 provides the order of the local 
error and the rate of convergence of multinomial models when the payoff functions are smooth 
enough.  The step size is ∆, which is proportional to 1/n in an n-period multinomial period.  The 
refinery portfolio problem is 72-periods; a step size is approximately 1/72; the trinomial tree, 
assuming payoff functions are smooth enough, converges in at least O((1/72)2).  However, the 
authors Albanese and Mijatovic (2006) go further showing the rate of convergence if the PDF of a 
continuous-time Markov chain to a Brownian motion with drift, is of the same order O(∆2).  
Convergence is assured here as shown by Hull & White (1997) that when the step size is as shown 
in section 3.4, convergence is assured and is optimal.  In the next section we provide the pseudo 
code for the solution of the valuation.  
 
3.5.4. Pseudo Code for the static refinery valuation 
 
A given refinery simulation consists of the following steps: 
• Set up the number of stages; k = 1 to 72 (The maximum possible input is the lifetime of 
194 
 
 
the refinery) 
• Repeat the below steps 1000 times; for j = 1 to 1000 (1000 iterations is enough for 
convergence for valuation of a real option, but usually not for a financial option)49 
• At date 0, build an event tree incorporating each commodity price in time, with 72 
decision steps and 21 sequential branches at each node; representing the possible 
evolutions of the forward curve; (The model can be rerun each day) 
call buildTrinomialTree(j) 
• Starting at Maturity, using the terminal boundary condition, optimise the criterion - 
by deciding on optimal production volumes; (At the terminal date the optimisation is 
static) 
for t=T to t=0 step -1,  
call maxObjective(j, t, k);  
 (The above function considers all possible paths at each node) 
• Move backwards recursively, deciding the previous iteration’s decision variables, 
until date 0 is reached (The problem is path dependent) 
• Record all optimal decision variables for the optimal path, and the valuation on each 
iteration of j; (Optimal values of: qji, V(i), will be obtained) 
Next j 
• Plot numerical results;   
end; 
call plotGraphs(); 
 
                                                 
49 See Albanese (2006) for convergence of a random walk with drift to its continuous-time counterpart 
195 
 
 
The issue with the above steps is that the valuation is carried out on today’s information only and is 
therefore a deterministic valuation.  In the following section we discuss how to convert this to a 
dynamic valuation. 
 
3.5.5. Pseudo Code for the dynamic refinery valuation 
 
Following Geman and Ohana (2008), we construct the following method to value a dynamic paper 
refinery: the realised one factor spot process of the refined products differs from the paths described 
in the event tree as we have to update the optimal strategy at each time step, i.e. we have to re-
optimise the decision variables taking into account the new state of the portfolio and information 
that has become available.  To analyse the robustness of our model with respect to this new 
information we create a simulation which dynamically reproduces strategies under price scenarios 
which are independent of the ones used for the optimisation.  Extending the steps explained in 
section 3.3.4., under a dynamic setting, the model is re-optimised at each new date k, based on new 
information, 
k
ζ  , equation (3.5.9) becomes: 
 
                   
1
1
[1, ]
1
[1,
1]
(
,
)
(
,
|
)
max
[
( ,
)
( ,
) |
]
i
i
i
i
i
i
i
k
i
i
i
i
i
i
k
q
A q
V q
f q
V
q
ξ
ξ
ζ
ξ
ξ
ζ
−
−
+
+
∈
=
+
  
 
 
(3.5.11) 
 
A given dynamic optimisation has the following stages: 
 
• At time 1, build an event tree with 72 decision steps and 21 sequential branching at each 
node representing the possible evolution of the one factor spot price process. 
• Optimise the criterion from the terminal date and implement the date 1 optimal decision. 
196 
 
 
• Simulate the new spot price process at date 2 from equations (ones that have S(t-1)) using 
the Gaussian vector with zero mean and covariance matrix Σ. 
• At time 2, build an event tree whose first node is the information of date 2; this tree has 71 
decision steps. 
• Optimise the criterion on the remaining horizon and implement the time 2 optimal decision. 
• And so on until date 72. 
 
The above procedure is performed 1000 times, leading to 1000 simulations of a six-year refinery 
management optimisation. 
   
In the table below the data shows the leaves at the end of the trinomial tree and their 
corresponding wealth and probability; there is uncertainty associated however with these cash flows 
hence we next show how the risk is considered in this calculation. 
 
The previous studies that consider valuing a refinery consider at most two refined products 
due to the colossal state space and the issues with optimising over this number of variables.  The 
embedded optionality means the owner can choose an alternative product if another is not 
performing as expected within the physical constraints.  This increases the value of the refinery as 
losses can be minimised and profits maximised more efficiently.  The problem has been avoided up 
until now due to the curse of dimensionality present; we alleviate this issue by using a solver in 
GAMS that can optimise a dual or quad core computer whilst leveraging off IBM's CONOPT 
algorithm.  This state space means closed form solutions are rarely available and efficient numerical 
methods must be applied.  The choice of a trinomial tree enables dynamic programming to be 
implemented ensuring that the owner's alternative decision choices are represented accurately and 
realistically.  A limitation here is that the correlation is not investigated in a stochastic or more 
complex manner and we leave as an extension the effects on the refinery valuation due to a change 
in the correlated commodities to others. 
197 
 
 
 
(Table 3.5.1: Refinery valuation across a two year period on the trinomial tree final leaves; along 
with the associated probabilities. 
 
 
 
3.5.1. Risk 
 
There are many issues with value at risk but our aim here is to capture some level of risk for 
investing in the alternative refined products in the volumes claimed maximising by the optimisation.   
198 
 
 
  In table 3.6 where we examine the valuation and computation time for a different number of paths 
for the refinery simulation we also include a calculation of the value at risk to the owner of the 
refinery.  This is how much value we expect to lose in a  worst case scenario over one day with 90% 
confidence - this is calculated using a Monte Carlo simulation and finding the 90th percentile of 
final wealth of the refinery.  Since we are in an incomplete market and portfolio replication is 
impossible our valuation is simply a range of values over the leaves of our tree; implying a risk-
neutral assumption on the stochastic equations that are representing the returns series of the 
commodities.  Since we are not attempting to predict values this is considered a reasonable 
approach; dynamic programming along a trinomial tree is considered a realistic approach to obtain a 
valuation for an illiquid real asset.  This is because of the state space that can be captured with 
seven correlated trees and the embedded optionality calculated using dynamic programming.  There 
are many examples of real option valuations in an incomplete market evident within the literature 
but not for a single oil refinery; see Rollins and Insely (2003) for an optimal harvest comparison.  
Here the authors construct a Markov decision process over a two factor simulation.  They claim 
ignoring the optionality present to management will result in incorrect forest valuation as we find 
with the refinery.  The option to stop, change or increase production is missed by static valuation 
methods.    The refinery is a similar problem in that the exercise price is the operational and 
management costs of the refinery; embedded in the refinery is the opportunity to refine at the 
optimum time based on refined product volumes and prices (despite the returns being modelled), as 
well as the option to abandon production or delay if the refined product prices are too low.  Our 
problem is comparable in that it is essentially an explicit finite difference approach.   
 
Despite the limitation of using a constant correlation matrix to generate the trinomial trees 
the fact that correlation would fluctuate giving higher and lower valuations over time; the valuation 
is capturing the alternative choices available to owner realistically and enabling the numerical 
method to be solved in a logical manner.  If the correlation of the refined products were to increase 
the valuation would decrease and vice versa but we leave as an extension a correlation study.   
199 
 
 
3.6. DYNAMMIC PROGRAMMING USING NODES  
 
In terms of valuing an asset, a multi-period solution is required.  Multi-period valuation for an oil 
refinery has not been accomplished within the literature.  For the optimisation to be classified as 
dynamic, the decisions at each point in time must have an effect on the next time period’s available 
decision set, and a solution must contain a set for the entire planning time horizon.  This is captured 
by the equation (3.5.4) in nodal form: 
 
L0 =Linit,      Ln= Lparent(n) + qn1
buy - 
7
,
2
j sell
n
j
q
=
∑
    
n
N
∀∈
 ,      2 ≤ j ≤ 7 
 
       
 (3.5.4) 
 
This equation ensures that the volume of liquid hydrocarbons held at the refinery complex across 
time is managed within physical limits.  This can be regarded as the state equation for the refinery.  
 
The refiner is assumed to be a price taker, i.e., not able to influence the crude or refined products 
market price, which is exogenous to the model.  We do not consider hedging the producer's 
portfolio by utilising Futures contracts.  We now define the nodal formulation, essential to solving 
the problem with a relevant non linear solver. 
 
3.6.1.0. Set notation for optimisation  
3.6.1.1. Decision Variables 
 
• qn1 : The amount of crude purchased at decision node n 
• qnj : The amount of refined product j sold at decision node n 
200 
 
 
• CWn : The cumulative wealth at node n 
• E[π] : Expected profit for the entire planning period 
• πn : profit for a particular node 
3.6.1.2. Sets and Indices 
 
• N: Set containing indexed nodes n of the event tree 
• Parent(n): Index of predecessor node to n 
• t(n) : Index of time period that corresponds to node n 
• Nend : Set containing all nodes that correspond to the last period 
• k(t, n): Set containing all the stages corresponding to a node and a time period 
 
3.6.1.3. Parameters 
 
• pn : probability of being at node n 
• Lmax : Maximum refinery hydrocarbon capacity in tons 
• Lmin : Minimum refinery hydrocarbon capacity in tons 
• Lend : hydrocarbon capacity level required at the end horizon 
• Lstart : hydrocarbon capacity level at the beginning of the time horizon 
• Tt : Years from now until period t 
• Fnj : The forward price of the refined product j sold at decision node n 
• Sn1 : The price of crude purchased at decision node n 
• prn : The production rate per month in days (refinery run rate) 
201 
 
 
3.6.2. Expected profit maximisation 
 
The nodal mathematical formulation to maximise expected profit is given by: 
q
Maximise   
 
 
( )
7
, 
,
1,
1,
1
[ ]
(1
)
[
]
t n
T
n
j n
j n
n
n
n N
j
E
p
r
F
q
S q
π
−
∈
>




=
+
−




∑
∑
 , 
 
  
 
(3.6.1) 
 
3.6.3. Maximisation of dynamic objective function 
q
Maximise  
 
 
( )
7
, 
,
1,
1,
1
(1
)
[
]
t n
END
T
n
j n
j n
n
n
n N
j
p
r
F
q
S q
−
∈
>




+
−




∑
∑
 
 
 
  
 
(3.6.2) 
Subject to 
 
 
Ln = Lparent(n) + qn1
buy - ∑qnj
sell   
n
N
∀∈
        2 ≤ j ≤ 7     n ≠ n1  
 
(3.6.3) 
 
Ln = Lend, n     
   
  
end
n
N
∀∈
 
 
 
  
 
(3.6.4) 
 
Ln1 = Lstart    
 
 
 
n
N
∀∈
 
 
 
  
 
(3.6.5) 
 
Lmin ≤ Ln ≤ Lmax   
  
  
n
N
∀∈
 
 
 
  
 
(3.6.6) 
            0 ≤ Sn  
 
    
  
n
N
∀∈
 
 
 
  
 
(3.6.7) 
            0 ≤ Fnj  
 
 
  
n
N
∀∈
 
2 ≤ j ≤ 7 
  
 
(3.6.8) 
 
(All other required constraints for each product are given in sections 3.2.4.1 - 3.2.4.4) 
 
 
202 
 
 
3.6.4. Solving the multi-period refinery portfolio model 
 
We adopt a discrete time setting, with a finite horizon.  Decision variables corresponding to period, 
θi, are subject to constraints based on mass balances and capacity constraints, see section 3.2.4. for 
details.  We consider on the tree, spot and future prices of crude to buy and refined products to sell, 
where St = F(t,t).  The crude price is known today and the saleable product prices are not – hence 
obtained from the simulated forward curve.   
 
 
Remark:  Cash-flows due to forward trading are in this paper registered at transaction date and 
discounted from delivery date at the risk free rate r.  We adopt this idea because we want cash-flows 
at dates θi, to only depend on date θi decisions and not on previous ones, as would be the case if 
cash-flows from forward transactions were registered at delivery date.  This only has an effect on 
the final wealth situation.  We also assume that we have liquid spot and one month ahead forward 
markets; prices then remain the same for refining over the month in question. 
 
The decision maker’s optimisation problem as given in equation (3.5.9) was: 
 
1| [1, ]
1
1
(
)
(
)
( )
{
[
(
)
(
(
(
)))]}
k
k t
i
i
i
i
i
i
i
i
i
q
x
J x
Max
E J
x
ξ
ρ
β
≥
+
+
+
∈Α
=
+
i
G
i
q
 
 
 
(3.5.9) 
 
To solve the problem above, the literature commonly uses a procedural function based Bellman 
approach - even if recursion is applied, procedural optimisation is much slower than the 
computation applied here.  We use a node based approximation along with a Bellman recursive 
formulation starting at the end of the scenario tree.  The set of prices on the scenario tree represent 
the information on the monthly commodity spot and forward prices.  For multi-period programming 
this is the gruelling part of the calculation.  Obtaining a realistic discretised process on a tree is a 
wily task.  Each path from the root to a leaf of the tree corresponds to one scenario, over which the 
203 
 
 
multi-period optimisation can be calculated.  The stochastic model is constructed in terms of the 
nodes {1,..., n, …., N} on the scenario tree and the tree structure is described by giving each node 
the probability pn, 1≤ n ≤ N.  The planning horizon is divided into k stages, to relax the 
computational onus (a number of periods can be aggregated into a stage), where each stage is k, 1 ≤ 
k ≤ K, and is associated with the time horizon of the real asset, Tk and to the associated set of nodes 
Nk.  Note that the model can be extended to varying time lengths and is optimised over nodes per 
period.  It is assumed to begin with, that we have a few stages and at maximum, that there are as 
many stages as there are months, as it takes approximately one month for a barrel of crude to arrive, 
be refined and ready to ship to the client.  We assume that the initial storage of hydrocarbon liquid 
is the same at the end of the horizon.   
 
The decision variables qi,n denote the net positions.  The spot price of crude is denoted by 
S1,n, and the forward prices of the refined products are Fi,n, these are the stochastic parameters 
within the formulation.  The objective function representing the producer's profit and financial risk 
consisted of product sales minus crude oil costs and a term representing a composite risk measure 
on profits.  Assuming that the working and operational costs of the refinery were not significantly 
impacted by the stochastic parameters. 
   
The nodal model was constructed with r, the risk-free interest rate, CW(n), the cumulative 
wealth at node n, and we also run the model with, U(CW) an increasing concave utility function of 
wealth describing the producer's risk aversion.  Due to the tree having a trinomial structure, it can 
be imagined that there are three realisations or states of the economy at each node.  The model 
constructed finds values of the decision variables qi,n, using: the stages set - k ε K, the products set - 
j є J, the time period set - θi є Tk, the node set - n є Nk, and finally, pn ε [0,1] – the probability set.  
 
 
 
204 
 
 
3.6.5. Maximising of final wealth  
 
One can also maximise the utility of final wealth, instead of expected profit: 
 
 
 
  NEND
n
n
n
Max
p CW
∈∑
 
 
 
 
 
 
 
(3.6.10) 
Subject to: 
 
 
 
( )
( )
, 
,
1,
1,
1
(1
)
[
]
t n
T
n
Parent n
i n
i n
n
n
i
CW
CW
r
F q
S q
−
>
=
+
+
−
∑
  
 
 
(3.6.11) 
(And all constraints as those described in equations 3.6.3 – 3.6.8, and sections 3.2.4.1 - 3.2.4.4) 
 
 
 
CWn represents the discounted present value (PV) of cumulative wealth at each node n, given in the 
set of end nodes.  The cumulative wealth is used at the leaf nodes; hence a simple sum is required 
over the nodes that belong to the last stage (n ε Nend).  To some this will seem unorthodox but it is 
equivalent to the calculation being carried out along all scenarios from root to leaf.   
The final wealth of the refinery CWT, represents the expected profit by summing up the 
nodal profits, weighted by their nodal probabilities pn.  Each cash flow is discounted by the interest 
rate r, obtained from data on the current Indian yield curve50; we choose an end of horizon of six 
years for comparison reasons but also value up to ten years to find the computational time at this 
horizon - the term structure enables us to discount the relevant cash flows with a realistic discount 
factor.  Additionally, each node has a particular conditional probability, an assigned crude price and 
an assigned set of forward product prices for its particular period θi(n). 
The initial wealth at the start of the tree is zero: 
  
CW0, 0 = 0; 
 
 
 
 
 
 
 
 
(3.6.12) 
 
                                                 
50  Bloomberg: India Govt Bond Generic Bid Yield 10 Year, GIND10YR:IND, 7.25 0.06 0.82% 
205 
 
 
And non-negativity constraints are present: 
 
 
 
qi,n ≥ 0,  
Si,n ≥ 0,  
t ≥ 0,          Fi,n ≥ 0, 
 
 
 
(3.6.13) 
 
Apart from the first node, each node has a parent function pointing to its predecessor node.  It is 
assumed that the operator can sell as much as produced of each refined product onto the oil market 
at each time period or each node as can physically be stored.  Moreover, the other physical 
constraints of the refinery system are respected at each node (please see section 3.2.4. for details). 
The objective function in code can be set to represent the expected utility of the final wealth 
or just the expected profit with composite risk as shown in equation (3.5.9) over the planning 
horizon.   
To solve the original problem given in equation (3.5.9) we apply backward induction.  
Analytical backwards induction is applicable to a wide variety of situations, but can be infeasible if 
there are too many state variables or too many time periods.  Therefore, we solve by a recursive 
calculation along the set of nodes, which are mapped to time periods starting at the end horizon; this 
is visually evident from the trinomial tree.  There is also a stage mapping; these mapping constructs 
are the main contribution of the numerical approach designed for the refinery.  From Moro & Pinto 
(2000) to Grossman (1998) to many other refinery optimisations; most computational or numerical 
procedures either do not state how they solve their objectives in enough computational detail within 
the state space or simply make approximations and solve using procedural programs; this is very 
inefficient.  The approach utilised in this chapter is named dynamic set assignment, enabling the 
problem to be solved, when mapping with the stages in O(n) time as opposed to O(2n), and it will 
replace many procedural based optimisers in the future; enabling larger and more complex state 
space optimisations to be solved. 
206 
 
 
3.7. Numerical Results 
 
In this section we discuss the numerical results obtained by solving the multi-period stochastic 
model.  Technically, the optimisation of this decision process contingent on the future events is 
more parsimonious when “reasoning under uncertainty” can be decoupled from the optimisation 
process itself.  This occurs when the probability distributions describing future events are not 
influenced by the decisions selected by the agent; uncertainty is exogenous to the decision process.  
The idea is to exploit the finite scenario tree approximations in order to extract a good decision 
policy for the correlated continuous commodity distributions. 
The simulation framework is based on GAMS release 23.9.1 x86, for modelling and solving 
the optimisation problem by the non-linear optimisation package, CONOPT3.  This optimisation 
problem is solved using mapping objects or in dynamic set assignment.  Suppose for example, that 
there is a simple map object, m, which maps each value of a function that has already been 
calculated to its own result, and then the function is modified to use it and correspondingly updated.  
The resulting function requires only O(n) time instead of exponential time – in recursive operations 
without this map, exponential time is standard.  This technique of storing values already found is 
called memoization (1968).  The bottom-up approach was applied where smaller values of the 
problem were found first and the larger values built from them.  It takes constant O(1) space, in 
contrast to a Bellman equation solved from the top-down approach which requires O(n) space to 
store the map.  We have used this methodology to obtain solutions to the numerical problem 
presented – this enabled a valuation in reasonable time.  
In the optimisation procedure the tree of prices must firstly be constructed; generating the 
nodes and their values takes the longest fraction of solution time.  C++ carries out all set 
assignments: time set, realisations set and probabilities; generating the files containing a seven-
dimensional correlated scenario tree of discretised prices for input into GAMS.  All trees also utilise 
a common computational data structure named simply as a tree structure.  This enables the 
207 
 
 
computation to gain more time as memory is utilised more efficiently.  Due to the dynamic set 
assignment the problem becomes very similar to matrix multiplication in terms calculating numbers 
per map - essentially the algorithm is closer to O(log(n)) when summing the values along the tree.   
For example, due to the way the problem is constructed in GAMS, one year can be initially 
represented by four stages, which is equivalent to 360 days.  This ability to defragment the problem 
into stages is another uniqueness to the numerical procedure; due to the mapping the program 
knows which stage belongs to which time, product price and all other set variables at any point in 
time.  For instance, with four stages the time sets entered into the code can be represented by: T1 = 
{t: 1 ≤ t ≤ 90}, T2 = {t: 91 ≤ t ≤180}, T3 = {t: 181 ≤ t ≤ 270}, and T4 = {t: 271 ≤ t ≤ 360}.  For four 
stages the nodes entered into code are the following sets: N1 = 1, N2 = {2,..., 4}, N3 = {5 ,..., 9}, N4 
= {15,..., 19}.  In the four stage one year tree, there are 19 nodes and 27 scenarios.  In the final 
model implemented to value the refinery effectively, an 11 stage tree was constructed and 
calculated; in the library of solutions the maximum k used was 72 - taking days, an extremely long 
time to compute.  With a reasonable maximum of k =11, it is possible to have a comparison to the 
static version.  For 11 stages there were 59,049 scenarios, taking four hours and 22 minutes to solve 
on an Intel(R) Pentium M 2.00 GHz processor.  It took approximately 73 minutes to solve the same 
problem on an Intel(R) Core(TM) i5-2320 CPU@3.00 GHz dual core.  The structure of the tree 
reduces the states space and ensures the solution is tractable in both cases.  This is evident from the 
number of scenarios and time taken to solve the model.  All models contain prices that were 
simulated 1000 times to obtain an average representation of the uncertain commodity price set. 
The model was solved by considering the sources of variability of the spot and refined 
product prices.  The price dynamics were captured using the mean-reverting spot price processes.  
In terms of value over and above the intrinsic value we notice an increase in the total expected value 
of the refinery at time horizon T.  Overall, the increase in the value of the objective function went 
from, $560 million to, $1131 million with optionality, over a six year period.  Results are shown in 
Table 3.6 below: 
208 
 
 
 
 Number 
of Stages 
(k) 
 Number of 
Scenarios  
(root to leaf) 
 Refinery 
Value 
$ millions 
 Time to Solve on an 
Intel(R) i5-2320 dual 
core Processor 
(seconds) 
 Risk  
(VaR for 
whole period ) 
Approx. 
Operational 
Years 
Represented 
by tree (T) 
 6  
243 
 556 
0.616 
78.45 
1 
7 
729 
 590 
1.257 
83.20 
2 
8 
2,187 
 743 
4.119 
99.08 
3 
9 
6,561 
999 
34.761 
109.12 
4 
10 
19,683 
1,091 
267.793 
124.66 
5 
11 
59,049 
1,131 
4,375.453 
136.80 
6 
 
(Table 3.6:  Summary of stochastic trinomial tree optimisation of a Topping refinery with risk 
values, including the number of scenarios, and the CPU solution times) *All values were 
calculated 1000 times and then averaged. 
 
3.8. Graphical results 
 
(Figure  3.20: As the number of stages input into the program is increased the solution time 
increases in a non linear way) 
 
209 
 
 
 
(Figure  3.21: As the number of years increases towards six the value begins to stabilise) 
 
210 
 
 
 
(Figure  3.22:  The refinery owner chooses an amount of crude to refine at each monthly period 
over the lifetime of the refinery) 
 
211 
 
 
 
(Figure  3.23: At different periods the valuation increases more than others as the refinery 
manager optimises the decisions along the trinomial tree) 
 
 
The applicability of a portfolio optimisation approach to a real world problem does not only rely 
upon numerical tractability, but crucially also on the robustness of the model.  Deviations in input 
data should not have major consequences on optimisation results; see Zhu and Fukushima (2006) 
for an analysis.  We ran the optimisation at 100 to 100,000 times; the averages and standard 
deviations of the decision variable results of 1,000 simulations are shown in table 3.7 below:  
 
 
 
 
212 
 
 
3.9. Out of sample stability analysis 
 
 
Crude 
Gasoline 
Naphtha 
Fuel Oil 
Jet Fuel 
Heating 
Oil 
Feed 
Average 
optimal 
portfolio 
1350 
2700 
900 
4210 
2300 
1650 
135 
Standard 
deviation in 
optimal 
allocation 
76 
161 
88 
191 
206 
113 
9 
 
(Table 3.7: Stability associated with scenario generation procedure decision variables.  *Trees 
were generated and optimised 1000 times) 
 
 
In general, finite difference schemes offer more flexibility than say binomial trees when handling 
complicated discretisation problems.  We use a trinomial tree, however, as with other explicit 
methods the approach suffers from stability constraints that restrict the time step size used in the 
numerical solution, and can in general extend the solution time.  We have, however, shown that our 
method solves in a reasonable amount of time despite its explicit grid nature, this is due to the 
solving technique of memoization and the other computational constructs described in the previous 
section.  
 
3.10 Analysis of the Valuation Model 
 
The main problem with the set up is the fact that we are using a one factor model – this is not 
representative of the behaviour of refined product series.  For example Koekebakker and Ollmar 
(2005) use ten factors to capture Electricity prices on Nordpool.  However, the stability and speed 
213 
 
 
with which the results are obtained supports the choice of a one factor model.  In practice if the 11 
stage optimisation was considered as too laborious the user can choose to drop down to a suitable 
time – they would miss some intrinsic value but there may be valid reasons for this shortcut.  An 
interesting extension would be to use different stochastic processes and investigate how this would 
affect the valuation of the refinery.  A two factor model would capture more realistic behaviour in 
the price series but strain the computational side of the solution.  The separation of the simulated 
paths and the dynamic recursion is the advantage of this approach; hence mean reversion is 
captured and value extracted from the spot price processes.   
Another piece that has not been addressed is the hedging calculation; this would entail 
creating a representative portfolio of the refinery form market products that are indicative of its 
value.  This is difficult in our case as there is no accessible liquid market for financial contracts 
within India that correlates with our simulated prices.  An approximation could be to use those 
contracts on CBOT that are representative of the required product – we discuss this and the other 
extensions in Chapter four further. 
 
 
3.11. CONCLUSION 
 
In this chapter we have introduced a one factor stochastic model for monthly midterm production 
planning of an oil refinery – enabling a valuation of the real asset to be obtained.  We have shown a 
unique approach for solving a real-option valuation for an oil refinery that is consistent with the 
assumptions of the risk-neutral pricing of options.  The approach is a straightforward and flexible 
model that can be implemented for other real-option valuations where correlated stochastic 
variables provide the uncertainty for the real asset.  The embedded optionality represents a much 
higher price than DCF reveals which is inline with many other real option valuations within the 
214 
 
 
literature.  A limitation of the approach is that we are in an incomplete markets setting hence a 
single no arbitrage price is unavailable due to replicable contracts being unavailable.  The 
embedded optionality is however captured by the correlated trinomial trees built so that dynamic 
programming can be applied.  This usually means there is no closed form setting and the numerical 
approach considered provides a range of values with their associated probabilities.  The model is a 
consistent multi-period optimisation, using dynamic programming to solve from maturity 
backwards until today.  The profit comes from the direct production of the refined products being 
sold instantly onto the spot market, where the risk is the volatility of the commodity market prices 
but can also be set in code to be a composite risk function.  We considered as the stochastic 
variables in the model, the spot prices of crude oil and the forward refined products prices.  The 
method used to generate a finite horizon multi-period value for the refinery is unique; stochastic 
prices are modelled using a trinomial tree, which captures the idiosyncratic features of petroleum 
related products.  The contribution in this chapter has been to obtain a real option valuation of a 
topping oil refinery; enabling a comparison to a static value to be made.  To our knowledge there 
has yet to have been a dynamic programming valuation on an oil refinery in the literature.  Another 
limitation is however that a constant correlation matrix is utilised to obtain the correlated tree of 
prices - this unrealistic but we leave for others the extension of investigating a stochastic correlation 
on the refinery valuation.  After the scenarios are generated on a trinomial tree, the decision 
problem is solved using non linear optimisation within GAMS to obtain a finite horizon value of 
$1,131 million.  The model considered 72 monthly periods, whilst adhering to financial and 
physical constraints at each point in time, combined with a simple intuitive decision rule by 
repeatedly maximising the intrinsic value of the refinery.  Our model captures more of the true 
management flexibility than DCF analysis, which gives a value of $556 million, and to our 
knowledge is the only dynamic real-option valuation of an oil refinery within the literature using 
computational advantages.  The contribution is three-fold; firstly, we have used a nodal 
representation of the tree structure within code – meaning that the data structures enable 
215 
 
 
memorization, and a very quick solution time for this state space.  Secondly, we have used a one 
factor SDE from Schwartz (1997) that is extremely tractable and can manage shifts of the forward 
curve.  Thirdly, to our knowledge this is the only asset valuation approach for the pricing of an 
entrepreneurial refinery using a Bellman equation, with all seven refined product stochastic 
processes being represented along the nodes of the tree, that applies dynamic set assignment 
ensuring that the mapping of the stages is completed in O(n) time and not exponential time.   
  
An interesting extension would be to run the optimisation with a different set of generation 
processes for the stochastic variables along the tree; an alternative formulation for uncertainty, and 
analysing how this impacted the valuation.   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
216 
 
 
 
 
 
Chapter 4 
 
 
 
A CRACK SPREAD OPTION REFINERY VALUATION 
 
 
“We are all in the gutter, but some of us are looking at the stars.” 
Oscar Wilde 
 
In this chapter we develop a valuation methodology for an oil refinery that is tractable, solves in reasonable time and 
captures intrinsic and extrinsic value.  We make standard no arbitrage assumptions, and use European call options to 
suggest that a refinery asset is equivalent to a strip of daily options over its time horizon subject to physical and flow 
constraints.  Using linear program techniques and GAMS we incorporate stochastic equations on the main commodity 
prices and apply dynamic programming to obtain a value for today that is unique locally and globally.  We produce a 
static and dynamic optimisation that can be altered to incorporate additions to the stochastic model or applied to other 
real assets that are reliant upon a spread based valuation.  There are two major benefits of our calculations: one, using 
option valuation techniques for the commodity price we have provided a long term valuation that solves in seconds and 
is realistic in comparison to actual option prices, secondly we have developed an optimisation decision volume set, that 
is more beneficial than a ‘go with the flow’ refinery decision set resulting in economically improved decisions for a 
refinery owner. 
 
 
 
 
 
217 
 
 
 
INTRODUCTION  
 
Auditors, Investment bankers, and entrepreneurial investors all need to value oil refineries 
and we have created an extensive numerical approach in the previous chapter.  This calculation is 
however expensive in terms of time, CPU and memory usage.  The motivation for this chapter is 
that a closed form solution for pricing a real asset where optionality exists, with the assumption that 
the underlying prices follow correlated geometric Brownian motions, does not exist, see Eydeland 
and Geman (1998) for a thorough description – additionally, the valuation is required in a timely 
manner.  Many practitioners use closed form approximations as they are fast and accurate enough 
for estimating values for spreads whether within trading institutions or hedging at a refinery itself, 
but in terms of a valuation, we require an accurate calculation that captures as much of the crack 
spread value as possible.  Thus we want to produce a numerical approach that will provide a 
financial valuation of the refinery complex quickly enough for the various relevant professionals.  
We repeat here for clarity that an average refinery owner will have experience of the oil market in 
his or her's region and usually make decisions based on their experience in addition to  information 
generated from a computer model.  Many of these simulations produced by professional refinery 
software companies such as ASPEN TECH or Honeywell, take many hours to solve and do not 
consider the full dynamics of the underlyings over the complete time horizon due to the 
computational and dimensional hurdles.  Instead the LP program managers are concentrating on 
optimising the mass balance flows or a different problem altogether, for example, supply chain 
optimisation.  If the oil market is in a steady and low volatility state, a refiner owner can “go with 
the flow” and use momentum indications of which product to sell more of – this does not consider 
the knock on effects to the other refined products and a full linear program solution allows a more 
statistically and consistently correct decision set to be made.  Over time, a computerised decision 
218 
 
 
set will outperform the owners and one that solves in a timely manner is more likely to be applied in 
practise; see chapter three for a detailed analysis. 
 
In this chapter we propose a tractable method based on the crack spread and compare it to 
existing spread valuation methods.  We make standard no arbitrage assumptions, and use European 
call options to suggest that a refinery asset is a strip of daily options over its time horizon subject to 
the same physical and flow constraints described in the previous chapter.    We are assuming here 
that owning the refinery complex is equivalent to the value that can be extracted from the 
volumetric comparison in crack spread option contracts.  We repeat here for clarity that the owner 
has a decision set to execute on each day whether to refine or not and how much of each refined 
product to produce at the end of an assay run.  This chapter attempts to achieve what chapter three 
does in a faster time by not building a simulation with trinomial trees and by not using recursion to 
capture the optionality.  Instead we replace this huge state space with option contracts - a simplified 
approach that will reduce the realism, but also simplify the computation.  The choices of which 
methods to use to price spread option contracts follows logically from the literature where the 
following methods are either practically used more frequently or are regarded in the literature as the 
go to method for spread option pricing.  Bachelier, Kirk and Alexander and Venkatramanan are 
described extensively in most spread option analyses included in the excellent and complete 
literature review of spread option pricing in Carmona and Durrelman.  The method used in this 
chapter is trivial but very effective; the refinery optimisation framework from chapter three remains 
but the simulation and trees are removed and replaced with option contracts. 
 
We first use Bachelier’s method assuming an arithmetic Brownian Motion as it allows a 
closed form formulae, on which we can base our numerical method.  It does not include mean 
reversion and has other practical issues, but is still used by traders to this day on the commodity 
trading floor, next to Kirk’s as estimators of options on commodity spreads.  Hence, we include and 
compare it to Kirk's method, the very efficient Alexander and Vankatramanan approximation 
procedure and the famous two factor Schwartz and Smith (2000) commodity based model used to 
219 
 
 
calculate option prices.  We compare our method to these four methods, in addition to actual option 
prices within a linear program to determine the effect on the refinery valuation.  We compare our 
procedure, which we call the CSORV or crack spread oil refinery valuation method, to the four other 
valuations by solving the existing Linear Program and replacing the crack spread option contract in 
the objective function with the relevant methods in the literature, including: Bachelier (1900), BA, 
Kirk (1995), KI, Alexander and Venkatramanan (2011), AV, and Schwartz and Smith (2000), SS.  
The first three methods: BA, KI and AV are considered closed form results, SS and CSORV are 
numerical models.  Kirk’s approximation is ubiquitous on trading floors throughout the commodity 
sector, but it is restricted to low strikes.  Bachalier’s, BA, is a useful approximation for many spread 
options being trivial to calculate and assuming a log normal distribution for the underlying spread – 
this is also a disadvantage.  Both BA and KI suffer from assuming that the underlying prices that 
make up the spread are bivariate lognormal distributions, which is quite unrealistic for most 
commodity price series.  AV's is essentially closed form after the optimisation and calibration of the 
strike convention, whereas SS is used frequently on commodities but requires calibration and then 
simulation.  The CSORV is numerical but still solves in rapid time and is more accurate than the 
above four approaches – this is due to the stochastic equation capturing more of the mean reversion 
over the time period, and using the same strike convention as in AV's paper, enhancing its accuracy.  
A drawback is that stochastic volatility and jumps are not considered.  Correlation frowns rather 
than smiles are a constant feature of the crack spread option market; our method's aim is to value 
real assets, and our focus is not to capture this feature as AV11’s and SS's both do.   
For our purposes of valuation constant correlation is a necessary simplification due to the 
computation and enables a comparison to be made to closed form and numerical spread methods.  
To our knowledge the method in this chapter is the first attempt at valuing an oil refinery using an 
optimisation augmented with a discretised and simplified stochastic price set.  In this chapter we 
have a valuation methodology for an oil refinery that is tractable, solves in reasonable time and 
captures intrinsic and extrinsic value.  Using linear program techniques and GAMS, we incorporate 
220 
 
 
stochastic equations for the main commodity prices and apply dynamic programming to obtain a 
value for today that is unique locally and globally and solves in a timely manner.   
The formulation and modelling of real options that accurately incorporate managerial 
flexibility are unlikely to result in closed form solutions; hence the vast number of complex 
numerical algorithms available in the refinery literature, see Elkamel et al (2011) for an in-depth 
summary.  In the previous chapter we have constructed a numerical method that approximates the 
cash flows generated by the refinery asset under relevant constraints.  The initial choice of selecting 
all seven dimensions to represent the commodity prices captured all state variable value; we now 
choose to reduce this to three state variables.  The most significant reasons for investigating more 
efficient solutions are that practitioners are unlikely to apply a method that is not nearly 
instantaneous – especially on the commodity trade floor.  This would be relevant if for example a 
commodity desk wished to hedge or speculate on oil refinery asset outcomes.  In terms of financial 
valuation; a timely manner is not as high a priority.  However, obtaining a fair valuation is fraught 
with issues; the three main difficulties are:  
 
1 - Which stochastic equations truly represent the commodity price series  realistically?  
2 - How to reduce the dimensionality of the problem to make the calculation  
solvable? 
3 - What is the most efficient way to depict the refinery asset problem within a 
 
mathematical construct?  
 
The method constructed in this chapter is reducing the number of dimensions, this tremendously 
speeds up the valuation and enables other avenues of interest to be pursued more easily, for 
instance, the associated risk measures can be investigated, see appendix for a publication including 
a risk optimised objective function.  Opportunities to the owner of the refinery can be seen as 
embedded optionality, but capturing and simplifying these decisions enables the valuation to be 
tractable and realistic.  The core underlying value of the refinery is the crack spread.  The crack 
221 
 
 
spread as defined in the previous chapter is the primary difference between the purchase and sale of 
refinery products; the main cost being crude oil and the two most significant selling products are the 
heating oil and the gasoline.  The crack spread is the primary risk for the refiner, and at the time of 
deciding which products to refine, the prices of the outputs are unknown.  The production is not 
taking place instantaneously; the time at which purchasing and selling decisions are made cannot be 
successfully made without being computer aided due to the sheer number of factors involved. 
 Once committed to refining the recently purchased crude, the owner is locked in to 
producing a set volume of hydrocarbon outputs one month from the cracking initiation.  The next 
day represents a new decision timeline, regardless of the fact that the process began for a batch the 
day before.  In trading petroleum related contracts listed on an exchange that manage the 
differential risk between crude and its products, the refiner can lock in the margin to ensure that 
either long term contractual obligations are met, or that the crack risk is hedged effectively along 
the horizon.  Decisions made in a timely manner make or break the refinery asset, if a hedge is not 
pursued before cracking and the spread heads southwards – profits for that assay run will be a 
disaster.  The New York Mercantile Exchange, NYMEX, offer a number of contracts on refinery 
spreads that are purchased frequently by refineries throughout the US; we use these despite this 
being less realistic for a refinery based in India.  In making these complex nested decisions the 
owner must be well versed in the fundamental and technical effects that impact the values of the 
various commodity prices.  Table 4.1 below, describes some of the factors that practitioners 
consider when deciding how to manage a hedge or a speculative trade on the crack spread’s value 
along their decision timeline.  We organise the chapter as follows: firstly we introduce crack spread 
options and discuss their nuances.  In section 2.1 we introduce the various methods implemented to 
value the 3-2-1 gulf coast contract.  In section 2.2 we discuss the data, and in section 2.3 explain the 
static valuation.  In 2.4 we present the various methods chosen to price a crack spread option; we 
discuss the stochastic processes choices in section 2.5, whilst in section 3 we analyse the results.  In 
section 4 we compare all four methods to ours, and in section 5 we conclude. 
222 
 
 
 
4.1.1 Crack spread effects 
 
There are many different contracts that refinery owners can utilise for hedging and speculation 
purposes – capturing the spread’s value.  This value is often termed the “paper refinery” by 
petroleum specialists: it is of interest when marking to market or when implementing investment 
decisions. 
 
(Table 4.1: Factors affecting the crack spread value, assuming the other refined products 
remain the same value) 
Concern 
Refinery Effects 
Crack Spread Reaction 
 
1. External geopolitical issues — politics, 
geography, demography, economics and foreign 
policy 
 
Crude oil supply, Crack Spread decreases initially 
— higher crude oil prices relative to refined 
products 
Crack increases later, as refineries respond to 
tighter crude oil supply and reduce product 
outputs 
2. Slower economic growth GDP measures etc. 
Decline in refined products demand 
Crack weakness (value decreases) 
 
3. Strong sustained product demand 
High refinery utilisation 
Crack strength (value increases) 
 
4. Environmental regulation on tighter product 
specifications 
Tightening of product supply 
Crack strength (value increases) 
 
 
5. Expiration of trading month 
Cash market realities — long or short products 
Cracks values can vary due to closing of positions 
 
6. Tax increases after certain date 
Increased sales in front of tax deadlines 
Crack weakens (decreases) in front of tax deadline 
and strengthens post deadline 
 
7. Summer seasonality 
Increase in gasoline demand 
Crack strength (value increases) 
 
8. Winter seasonality 
Increase in distillate demand 
Crack strength (value increases) 
 
9. Refinery maintenance 
Decline in product production 
Crack strength (value increases) 
 
10. Currency weakness (In $) 
Crude oil price strength  (price increases) 
Crack weakness (value decreases) 
 
 
 
223 
 
 
The next stage in assessing the financial worth of the oil refinery was to develop a more tractable 
real option valuation; chapter three’s algorithm cut previous calculation time down to under two 
hours.  In some professional circumstances this would be impractical; interested parties would wish 
to calculate hedging formulas and the valuation itself, in minutes rather than hours.  Valuing assets 
that rely upon commodities is an actively researched and practicable arena for many reasons, for 
example, investment, divestment, hedging and speculation considerations.  An oil refinery is similar 
to a chemical plant - a producer with exposure to a differential spread, subject to a set of physical 
and flow constraints.  The real complexity in an option approach is capturing the value the refinery 
owner has embedded within its complex decision process.  The choice value associated with real 
assets fall into one of the following categories: the option to expand, the option to abandon and the 
option to defer.  The oil refinery owner has the choice to vary intertemporal purchasing and 
production decisions of the crude and refined products, i.e. the option to defer production.  The risk-
neutral valuation approach, ubiquitous within the financial mathematics literature, can be extended 
to elicit a real option valuation of land, plant, equipment and assets.  This approach does not require 
risk adjusted discount rates; yet a set of market price parameters must be calibrated.  Many 
investments are underpinned by the uncertainty connected to future prices.  Throughout the 
programming routines developed in this thesis, the commodity state variables or inputs, were 
defined as sets, see the appendix for an example - these state prices can be extracted explicitly to 
estimate the risk-neutral stochastic processes involved in the refinery calculation, and avoid the 
requirement to obtain a market price of risk parameter for the random variables exemplar set.  
These sets are the standard data structure for computer programs named algebraic modelling 
systems (e.g. GAMS).  The advantages in using GAMS and constructing the problem in this 
manner is that computational data structures do not require development as GAMS has the required 
constructs already available in its library -  dependent on the problem be solved.  We design a 
program in C++ to generate the prices and probabilities on the a trinomial tree for all methods 
requiring discretisation, which is then injected into GAMS as a table data structure (essentially a 
224 
 
 
computational trinomial tree structure).  GAMS can utilise this as input in set notation.  These sets 
enable group/set mathematics to solve calculations instead of procedural calculations - a much 
faster and efficient method than common in the literature; it is standard to do this in a procedural 
way without data structures or applying memoization. 
After the refinery manager’s initial purchase decisions, the knock on effects alter the 
remaining decision set due to the limitations on the particular crude assay being refined; this is 
indicative of the path dependency present in the calculation.  For example, producing more gasoline 
means less heating oil, and yet the CDU must maintain the mass balances at all times, therefore 
certain decisions are not physically possible, e.g. producing all kerosene or no naphtha at all.  If for 
instance, the refiner knew that residual fuel oil would be the highest valued product in one months’ 
time, then as much of this fuel oil as could be physically stored would be processed – this decision 
can be implemented, but only as far as the capacity and flow constraints allow; a difficult number to 
generate without inputting this into the LP.  The refiner has flexibility available - an embedded 
option, which is strictly constrained due to the chemical processes that are viable under the complex 
path dependency present in the refinery.  Capturing this mesh of decisions is the aim of any model 
solution and a numerical approximation should be tractable and remain financially accurate.   
 
4.1.2. Calculating the crack spread value with traded contracts 
 
The dependency of a set of decisions on the next time slot is investigated in this chapter, where we 
use a real data set of commodity future prices and correlations with a simplification where we 
reduce the number of free parameters.  In other words, the 3-2-1 Gulf Coast crack spread contract is 
represented on three trinomial trees consolidated into one; instead of requiring seven.  This is the 
amount of value in a barrel of WTI crude, refined into its two most valuable products: gasoline and 
heating oil.  We firstly calculate a very basic option value, attempting to value the refinery with data 
225 
 
 
from NYMEX51 – the refinery is considered as a strip of 3-2-1 crack spread options over a finite 
horizon; Geman and Eydeland (1999) apply this concept of a daily strip of options on the spread, 
but apply it to a virtual power plant, where the output is electricity and the fuel is natural gas.  At 
first the authors apply a very different production approach in a two parameter family to price 
power options using a power stack function.  Our approach is closer to the daily option spread 
valuation approach used later in their article, where they select a stochastic jump diffusion equation 
to represent electricity by leveraging Merton’s 1976 formula – the authors present the framework, 
but omit the calculation, which is fraught with practical difficulties for a refinery complex over this 
length of duration.  On each day of activity, if the refining cash margin is positive, then we should 
refine, else, we should switch the refinery off.  We will discuss this method’s limitations in the 
following analysis.   
 
The calculation that underpins the optimisation described later in the chapter is to take the 
value on an option on gasoline, and the value of an option on heating oil, consolidate them in the 
correct multiples and thus obtain the value of the crack spread option.   
 
Fully integrated oil majors like Exxon Mobil and Chevron have a natural hedge against the 
refining margins as they own commodity focussed assets and financial contracts in large capacities, 
but independents like Valero and Tesoro do not.  The corresponding trading of financial contracts at 
these firms follow alternate strategies.  The payoff profile of a refinery spread call contract on a 
crude oil future, (Fcrude) and one refined product, (Fprod), with exercise price, K, is given by:  
 
max {(0.42.Fprod – Fcrude) – K, 0}   
 
 
 
 
 
 
(1) 
 
(The 0.42 is due to there being 42 gallons in one barrel of crude)  
 
 
max{(crack-spread value - refining costs), 0} 
 
 
 
 
(2) 
                                                 
51 NYMEX purchased call contracts over ten years from 2000 Dec to Dec 2010 
226 
 
 
 
This type of contract is often utilised by the vertically integrated oil majors to speculate, whereas 
for the independents, it is a hedging instrument.  Applying basic microeconomic arguments, the 
crack spread that is underlying the above contract should be a mean reverting process, despite the 
numerous studies arguing the opposite, in recent months it is clear that crude has again reverted to 
an average or lower.  The spread itself is usually captured using an American style options contract 
when listed on a financial exchange, see Carmona and Durrelman (2003) for an extended discussion 
and summary of spread options related literature.  In the next section we obtain descriptive statistics 
for the underlying value to support the choice of a mean reverting process, and describe the 
characteristics of the crack spread data to build the argument for simplifying the discretised 
stochastic process in the way chosen in this chapter.  
 
4.1.3. Descriptive statistics for commodity future price returns  
  
Statistics for daily crude oil, heating oil, and gasoline futures return data are shown below, where 
we label CR as crude oil, HO as heating oil, and GA as gasoline; each number in the brackets is the 
maturity month of the contract.  There are a total of 3,914 observations from January 2000 to 
December 2012, and the data is collected whenever a price is available (refineries continue cracking 
during weekends).  
 
 
 
 
 
 
 
227 
 
 
(Table 4.2: Descriptive statistics for future price returns) 
The Jarque-Bera test of normality. * indicates significance at the 1% level.  
Contract Drift (µ) Volatility (σ) σ (annual) Skewness Kurtosis Jarque-Bera 
CR(1) 
0.0015 
0.0240 
0.2387 
-0.16 
5.15 
782.09* 
CR(2) 
0.0015 
0.0259 
0.2679 
-0.27 
5.81 
1337.08* 
CR(4) 
0.0014 
0.0276 
0.2855 
-0.29 
6.48 
2114.90* 
HO(1) 
0.0015 
0.0245 
0.2466 
-0.02 
4.97 
652.33* 
HO(2) 
0.0014 
0.0247 
0.2804 
-0.04 
4.71 
485.89* 
HO(4) 
0.0013 
0.0281 
0.3037 
-0.02 
4.51 
383.76* 
GA(1) 
0.0016 
0.0254 
0.2599 
0.29 
8.51 
4872.93* 
GA(2) 
0.0016 
0.0270 
0.2860 
0.15 
7.08 
2830.33* 
GA(4) 
0.0016 
0.0287 
0.3028 
0.08 
6.00 
1369.97* 
 
 
We generate a crack-spread price for the ten-year period with the commodity future price data for 
the prompt month contract collected above. 
 
The most significant and tricky feature to capture in this modelling process is the correlation 
of the saleable hydrocarbon prices as this tremendously affects the profit differential; missing from 
the above table.  GARCH can represent the volatility clustering present in financial returns, but 
analytical results in continuous time for spread valuation capturing this behaviour, without large 
approximations, remain elusive, see Borovkova (2007).  Another reason for the issues in calculating 
spread option values is that we have a weighted sum of variables.  In Borovkova (2007) the state 
variables are assumed log normal, but the sum of log normal variables is not log normal.  It is well 
known that spread option prices calculated via the Bachelier method52 applied by Shimko (1994) for 
instance are also inaccurate compared to those from a Monte Carlo simulation, which can capture 
different stochastic underlying processes.  To alleviate this issue, Borovkova et al (2007) apply 
many  log-normal distributions for the underlying spread value.  This is beneficial as it ensures that 
the state prices in the calculation do not become negative yet they exhibit negative skewness.  In 
reality modelling the spread or the difference between the two commodities with a single Brownian 
motion misses a huge chunk of structure that can be represented with alternative multi-factor 
                                                 
52 The distribution of the spread is assumed normal but gives vastly different numbers for real options 
228 
 
 
stochastic equations; we pursue an approach to represent the structure of the underlying prices in 
our method. 
 
4.1.4. Assumptions underlying the crack spread valuation 
 
At any period in time, without purchasing additional financial contracts, the refiner is short crude 
oil, as it needs buying, and is long the refined products, as the owner has the ability to produce 
saleable hydrocarbons.  Therefore, the refiner is long the crack spread; in practice, traders 
associated with the refinery will construct trades on the constituents of the crack spread based upon 
their estimation of the spread being under or overvalued.  For example, if a dispersion trader feels 
the crack spread is overvalued, then it will be sold short and vice versa.  The hedging replication 
argument enables the refinery to be valued as a strip of crack spread options; a comparison can then 
be made using various existing option calculations. 
 
We create a model in which the set of underlying commodity price dynamics are realistic; 
the stochastic processes should be mean reverting, and for an in depth modelling approach to be 
valid, the following important conditions should additionally be met, see Geman (2005): 
 
1. The crack spread option must have a starting date and a maturity T.  
2. The underlying prices must be clearly identified: S1, S2, S3 or F1, F2, F3, if using futures. 
3. The crude, S1, and refined products S2 to S3, must be traded in continuous time in liquid 
markets, to allow for dynamic hedging, the cornerstone of valuation by arbitrage.  (This is 
not present in our case of the Vadinar refinery, Gujurat India, but we will assume for the 
modelling case here that it is) 
229 
 
 
4. We should be able to exhibit appropriate stochastic processes for the evolution of S1 – S3, in 
particular, because data series of past values are available.  (This includes in the case of the 
refinery the correlation between the saleable product prices) 
5. The type of option must be recognised: European versus American versus compound; since 
it will obviously impact the price obtained for the physical asset.  In practice, this choice is 
rarely unique, and one can make a compromise between the tractability and accuracy of the 
representation. 
6. The stochastic processes for S1-S3 should lead to market completeness; otherwise there will 
not be uniqueness of the valuation, which is a problem if one is paying hundreds of millions 
of dollars to acquire a physical asset, or marking to market a set of complex derivatives.  
 
In figures 2 and 3 the crack spread calculation over ten years is depicted; mean reverting behaviour 
is conspicuous, as are the dips below the $0 profit level, occurring three times within this period; 
despite becoming less so in the latter parts of the graph; on the right hand side it can be seen that the 
price deviates greatly from its mean; this trend has recently reversed and again the case for 
reversion has strengthened.  
 
 
 
 
 
 
 
 
 
 
230 
 
 
 
 
 
(Figure 4.2: Daily calculated crack spread prices, Gulf Coast 3-2-1 underlying in 
$/barrel)*Prices for the crack spread are calculated as standard with the 3-2-1 contract; 1 heating oil barrel spot price 
+ 2 gasoline barrels - 3 crude barrels, all divided by 3.(remembering to multiply gallon prices by 42). 
The calculation for the above figure is standard for the Gulf Coast contract as done in the literature; 
negative values for the crack spread are possible - reasons for this are major source of research with 
some academics claiming the spot price of crude has lag effects and when the value for the refined 
products is low this can cause a negative crack spread. 
 
 
 
 
 
 
 
Log returns (Pt/Pt-1) Gulf Coast 3-2-1 Crack SPread
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
1
10
19
28
37
46
55
64
73
82
91 100 109 118 127 136 145 154 163 172 181 190
Month
Log Returns
Series2
$ per 
barrel 
Days 
231 
 
 
(Figure 4.3: Daily log return crack spread prices, Gulf Coast 3-2-1 underlying) 
 
We calculate the log returns on the crack spread price tomorrow divided by today's.  The scale of 
spikes below varies between twice the original price and the negative of this value, but on average it 
is moving by approximately 30%. 
 
4.1.5. Crack spread modelling 
 
 
 
Some authors suggest modelling the two separate prices (bivariate models) that constitute 
the spread, others consider the difference, hence a univariate model.  Dempster, Medova and Tang 
(2008) suggest that the one factor Schwartz is unlikely to capture the mean reversion on multi asset 
models as well as single asset modelling of the spread.  They provide Engle-Granger and ADF tests 
to support their claims; despite this, the structure of the commodity series will be less accurate than 
modelling each commodity with a univariate model.  The authors provide an in-depth study of long 
term crack spreads and the possible valuation models.  Arithmetic and geometric Brownian motions 
are popular for these purposes; in terms of testing the spread’s behaviour a Jarque-Bera test quickly 
reveals that the returns of gasoline, heating oil and crude oil are not normally distributed; there is 
also weak skewness to the right, and there is excess kurtosis: more evidence for non normality.  A 
Dickey Fuller test on each of these returns shows a unit root, but after first differences all become 
stationary series.  Duan and Pliska (2004) find a negative vega (where the value of the option 
decreases with an increase in volatility) evident in crack spread options, a peculiarity 
underdeveloped at this time, and this cannot be retrieved by univariate modelling – the authors use a 
Taylor expansion, and determine the vegas and deltas, by considering two commodity prices and 
their corresponding standard deviations.  By considering the co-integration of the commodity prices 
they find a negative vega, where the partial derivatives of the spread option value to each one of 
232 
 
 
these four variables is calculated.  In practise this is a serious drawback for univariate methods as 
many hedging and speculation strategies use negative vega as a signal for executing a trade; as 
mentioned the vega is the sensitivity of the option price with respect to a change in the underlying 
volatility.  Another strong incentive for models with the information from all contributing 
commodities, a multivariate model, is that without them, the Greeks are calculated with a huge loss 
in accuracy; see Carmona and Durrleman (2003) for an example.  Additionally, if volatility is to be 
implemented within the stochastic equation the numerical procedure becomes much less tractable - 
in terms of pricing and hedging implications, the accuracy is however a priority.  Authors often 
capture the volatility structure using a GARCH feature if constant volatility is considered too 
unrealistic.  Mahringer and Prokopczuk (2010) describe the issues with the crack spread options 
being of American type and they capture this behaviour by generating a simulation feeding a 
calculation of the optimal stopping time.  The authors simulate 50,000 paths to calculate crack 
spread option prices in a multivariate setting, and it takes in total 2-3 hours for the calculation to 
complete in the bivariate case.  The pricing model has a particular GARCH structure on the 
volatility, and all option prices are estimated using Longstaff and Schwartz (2001).  These studies 
support the case for a multivariate, numerical and tractable model that  not only calculates crack 
spread contract values, but also outputs the Greeks in reasonable time - we leave the Greeks 
calculation for future research.  Dempster and Hong (2000) developed a Fast Fourier transform 
approach that captures stochastic volatility and the jumps for spread options; a very computationally 
intensive calculation.  More recent studies have captured the time varying volatility and co-
integration behaviours of the spread; see Duan and Pliska (2004).  Maturity effects are emphasised 
in Theriault (2007); the Samuelson effect and the volatility effects are important features that any 
model of crack spreads should exhibit.  If we are to include these effects the simulation of futures 
prices for crude oil, heating oil and gasoline should be considered only, as the other prices will 
introduce intractability.  The pricing framework that we are in is one of multiple state factors, 
definite mean reversion, path dependency, and American option features.  Simulation using a 
233 
 
 
multivariate model is the natural method to pursue.  We start by comparing market option prices to 
Kirk’s approximation as it is the most widely applied analytical solution for spread contracts in the 
energy markets, see C. F. Lo (2013); the authors provide a concise derivation of the Kirk formula.  
Li et al. (2008) provide a number of  approximations for the case of multiple prices where 
underlying commodities are distributed jointly normal.  They provide analytical formulas for the 
case of a Geometric Brownian Motion and a log Ornstein-Uhlenbeck process – these formulas are 
fast to calculate and consequently enable the authors to produce the Greeks.  Lower and upper 
bounds are shown in Carmona and Durrleman (2005); there is an assumption of Geometric 
Brownian Motion, but the lower and upper bounds calculated are useful, and as described by the 
authors, studies including baskets of underlyings are underdeveloped generally.  Trading a spread 
option is said to be equivalent to trading the correlation between the two prices.  Kirk (1995), 
Mbanefo (1997) and Alexander and Scourse (2004), all describe the correlation of the prices in a 
spread option calculation as being highly volatile, hence a constant correlation would seem 
inappropriate; we use a constant correlation despite the issues as the options calculated are for no 
longer than monthly periods.  In the next section we introduce the available contracts for futures 
trading on the crack spread.   
 
4.1.6. Types of crack spread options listed on the NYMEX exchange 
 
In table 4.3 below, common crack spread related contracts on the NYMEX exchange are detailed; 
contracts for spreads on location, calendar and production are common.  There is a tremendous 
selection of products on NYMEX; most are heavily traded with volumes in the 100s and 1000s. 
 
 
 
 
234 
 
 
(Table 4.3: Crack spread options listed on the NYMEX exchange*There are days when open 
interest and volumes on all of these products is extremely high)(Data accessed on 
05/03/2013) 
Clearing Globex Floor ClearPort Product 
Name 
Product 
Group 
Subgroup Category 
Sub-
Category 
Cleared 
As 
Exchange Volume Open 
Interest 
RM 
ARE 
RM 
RM 
RBOB 
Gasoline 
Crack Spread 
Futures 
Energy 
Refined 
Products 
North 
American 
Crack 
Spreads 
Futures NYMEX 
0 
2,259 
HK 
AHL 
HK 
HK 
Heating 
Oil 
Crack Spread 
Futures 
Energy 
Refined 
Products 
North 
American 
Crack 
Spreads 
Futures NYMEX 
210 
10,226 
CHY 
CHY 
CH 
CH 
Heating 
Oil 
Crack Spread 
Options 
Energy 
Refined 
Products 
North 
American 
Crack 
Spreads 
Options NYMEX 
0 
0 
3U 
A3U 
3U 
3U 
European 
Gasoil Brent 
Crack Spread 
Average 
Price Option 
Energy 
Refined 
Products 
European Crack 
Spreads 
Options NYMEX 
0 
350 
1ND 
1NA 
1ND 
1ND 
Singapore 
Mogas 
92 
Unleaded 
(Platts) 
Dubai 
(Platts) Crack 
Spread 
Futures 
Energy 
Refined 
Products 
Asian 
Crack 
Spreads 
Futures NYMEX 
0 
0 
SFC 
SFC 
SFC 
SFC 
Singapore 
Fuel Oil 180  Energy 
Refined 
Products 
Asian 
Crack 
Spreads 
Futures NYMEX 
0 
591 
FO 
FO 
FO 
FO 
3.5% 
Fuel 
Oil 
Barges 
FOB 
Rdam 
(Platts) Crack 
Spread 
Futures 
Energy 
Refined 
Products 
European Crack 
Spreads 
Futures NYMEX 
608 
41,960 
3Y 
A3Y 
3Y 
3Y 
RBOB 
Gasoline 
Crack Spread 
Average 
Price Options 
Energy 
Refined 
Products 
North 
American Spreads 
Options NYMEX 
0 
0 
3W 
A3W 
3W 
3W 
Heating 
Oil 
Crack Spread 
Average 
Price Options 
Energy 
Refined 
Products 
North 
American Spreads 
Options NYMEX 
0 
216 
 
 
The contract choice above will be significant in representing the valuation as a set of underlying 
values, but even more vital to the consolidated refinery value process is the correlation.  Ideally, due 
to the commodity prices affecting the refinery’s profits in a dynamic and complex way, the 
correlation structure should be integrated into the model; we omit a time varying correlation 
component for the sake of computational ease.  The option to adjust production over very short time 
235 
 
 
periods is captured by the real option approach, and the grid of the time horizon should fully reflect 
the corresponding coarseness – here we assume decision variables cover monthly periods, but the 
optionality available to the refinery owner is of daily granularity.  In practise when long term 
contracts have to be met the refiner does not always have this level of flexibility as a certain level of 
production will have already been committed.  In that case, the long term contract can be thought of 
as a hedging instrument,  we leave this further analyses for others.   
 
 
Valuing the refinery 
 
4.2.1. Data  
 
The NYMEX heating oil and gasoline crack spread options are the most liquid exchange traded 
crack spread options on the market.  We collect end of day prices for gasoline and heating oil crack 
spread contracts directly from NYMEX; prices for these options are recorded at the end of the open-
outcry session of each trading day.  Whereas, trading of the underlying futures contracts is generally 
on an electronic platform.  The sample of gasoline and heating oil crack spread call options used in 
our valuation comprises price observations covering the period from December 2000 to December 
2012. 
 
We apply one main exclusion criteria to the data where we considered option contracts 
which were exactly one month in maturity length.  We also excluded all options with prices below 
$0.375 to reduce the price discreteness related biases.  In total we ended up with 10,500 gasoline 
crack spread options and 11,600 heating oil crack spread option prices.  The average price of all 
gasoline crack spread options was $2.37 and $2.41 for heating oil crack spread options.   
 
 
 
 
236 
 
 
(Table 4.4: Sample crack spread option prices) 
This table provides the summary statistics of NYMEX crack spread option prices over the period Dec 2000 to Dec 2012. 
 
 
No. of OTM 
price 
observations 
Average 
price 
of 
end 
of 
day OTM 
option 
prices 
No. of ATM 
price 
observations 
Average 
price of 
end 
of 
day 
ATM 
option 
prices 
No. of ITM 
price 
observations 
Average 
price 
of 
end 
of 
day ITM 
option 
prices 
HOO(1) 
2285 
2.16 
660 
2.39 
1460 
2.87 
GAO(1) 
1975 
2.03 
672 
2.21 
1290 
2.38 
 
 
To obtain the correct option price representing the refiner’s optionality we use a calculation to 
replicate the 3-2-1 contract by always aggregating two gasoline crack spread options and one 
heating oil crack spread option; this addition represents an approximation of the total crack spread 
option value for the refinery.  In table 4.5 below are the results of the pricing of an RBOB gasoline 
option from NYMEX across all methods: 
 
(Table 4.5: Gasoline Crack Spread Option prices) 
Underlying: Gasoline RBOB Crack Spread Futures, Date: 29/05/2014, NYMEX quoted price = 
$3.946 
 
Strikes K 
NYMEX 
KI 
BA 
AV 
HW (N=40) 
S.E. 0.04 
SS (N=40) 
S.E. 0.03 
15.50 
3.94 
7.89 
7.06 
5.18 
5.12 
5.16 
15.75 
3.69 
7.67 
7.99 
3.93 
3.87 
3.81 
16.00 
3.44 
7.56 
7.76 
3.79 
3.66 
3.62 
16.25 
3.19 
7.43 
7.57 
3.49 
3.53 
3.59 
16.50 
2.69 
6.94 
6.33 
2.92 
2.82 
2.87 
16.75 
2.44 
6.42 
6.97 
2.79 
2.66 
2.77 
17.00 
2.19 
6.19 
6.64 
2.48 
2.36 
2.43 
17.25 
1.94 
5.35 
5.87 
2.29 
2.09 
2.14 
17.50 
1.69 
5.06 
5.46 
1.96 
1.88 
1.97 
237 
 
 
 
We now describe the model to value the refinery with crack spread option price data. 
 
 
4.2.2. A strip of European crack spread options 
 
Shown in the below formula is the value of the refinery at time 0: the discounted sum of the crack 
spread options over the lifetime of the oil refinery complex (the discount factors are contained 
within the value C of the option itself).  The Linear Program (LP) valuation of the refinery complex 
at period zero is given by, V0
LP, based on the information we have at date zero (ζ0).  It is equal to 
the maximum profit, revenue minus costs, or saleable products, heating oil volume (q2) and gasoline 
volume (q3) minus crude volume (q1) – all in units of tons, over the time horizon (T) in years.  The 
crack spread option Ci,j represents this profit, where the, i, is the  start date of the option and the, j, 
is the maturity of the option.  Each option is assumed European and has a maturity of one month; 
we collect heating oil and gasoline real option prices from NYMEX to represent this value.  The 
total hydrocarbon liquid within the refinery complex at any time is L, and this acts as the state 
variable in an equivalent final Bellman equation.  As explained in detail in chapter three, we 
represent the refinery as a strip of daily crack spread options, where the decision variables are the 
inputs and outputs of the refinery: q1, q2, q3, and L, and the operational costs are assumed constant 
as found in chapter two at $2.29 per barrel.  The valuation formula is: 
 
  
, ,1
1
(0)
(0)
N
N
j
i
i j
i
j i
V
C
q
=
>
= ∑∑
  
 
 
 
 
 
 
 
(3) 
 
 
 
The V(0) is the value of the refinery asset at date zero, where, the sum over i=1 to N periods, where 
the qi,j is the notional amount of crude refined per period in tons, i is the starting period, j is the sale 
date of the refined products (assuming instant sales after being refined), and C(0) is the option value 
238 
 
 
at the date of the valuation today.  We include the above equation (3) in the Linear Program to value 
the refinery by replacing the option price, C(0), with different approximations where the goal is to 
compare existing analytical and numerical approaches to our numerical simulation.  If the variable 
costs of refining were zero, and the refined product set considered as one price, we would have an 
option to exchange one commodity for another – for which there is a closed form formula derived 
by Margrabe (1978), unfortunately with a spread option and a non-zero strike or variable costs, no 
such analytical shortcut exists.  The oil refinery i-j crack spread, crude purchased on date i and 
refined outputs sold on date j, has the following payoff for injecting one unit of crude oil at time Ti, 
using the interest rate curve of the local region for δ, extracting refined products at time Tj, and the ζ 
is the information available at date 0: 
 
3
, ,
1, ,
0
2
(0)
[max(0,
&
) |
]
j
i
j i
i
z i j
i i
i
z
C
E
F
F
O
M
δ
δ
ζ
−
=
=
−
−
∑
  
 
 
 
(4) 
 
 
 
The time, T0, value of such an option is the difference between the sum of refined product futures 
prices, Fi,j and the crude spot price, Si = Fi,i, adjusted for the costs of refining associated with one 
barrel of crude, operational and management costs, O & M, – in this implementation all units are 
converted to tons, with information at date zero, ζ0.   
The model we introduce in this chapter, works with a set of crack spread options,  the model 
assumes the production choice volumes from a ton of crude as defined in section 2.4 – more simply, 
we no longer have seven volume parameters.  Purchasing of crude and sales of refining products are 
associated with maturities 0, 1,…., N-2, and 1,2,…., N-1, respectively.  The aim in this model is to, 
at time t = 0, build a set of refinery spread options, and calculate the decision variables within each 
period; giving an optimal solution to a linear program, and consequently the output value.  The 
decision variables in this linear program are the notional amounts of crude, and the inventory level 
associated with refining over a given period; which indirectly describes the volumes of gasoline and 
heating oil.  The linear program we investigate is subject to the same physical and flow constraints 
239 
 
 
defined in the previous chapter; the value at date 0 of the set of crack spread options is: 
0
0
(
)
LP
V
ζ
, 
where as in the previous chapter, L is the total hydrocarbon liquid held at the refinery – the 
inventory level, q is the volume of the product in question here for products 1-3 and, C is the option 
price on each day.  We now define the mathematical requirements. 
 
4.2.3. The Linear program valuation construction 
 
In equation (5) the Linear program (LP) is defined at date 0 with information, ζ, where the refinery 
owner wants to maximise over the decision variable, q, volume of crude oil and the total petroleum 
liquid L, until maturity of the refinery T - as already explained, throughout this chapter we replace 
the option price C in equation (5) below with a value from one of the five methods: BA, KI, AV, 
SS, CSORV or the NYMEX option real prices: 
 
 
,
0
0
0
0
, ,1
,
[0,..., ]
[1,..., ],
(
) : max
(
)
                 
LP
i j
i j
q L
i
T
j
T i
j
V
C
q
ζ
ζ
∈
∈
<
=
∑
∑
   
 
 
 
  
(5)   
This objective function is subject to the same physical and mass balance constraints defined in the 
previous chapter.  These are not repeated as they are not the important focus of this chapter.  In 
equation (6) below, we define the non-negativity constraints for crude qi,j,1 and heating oil qi,j,2 and 
gasoline qi,j,3, including the upper volumetric limitation at the refinery, defined as Q, where the 
amount of liquid bought Qbuy or sold Qsell, has an upper limit at the refinery complex: 
 
s.t. 
0 ≤ qi,j,1 ≤ Qi
buy,    
0 ≤ qi,j,z ≤ Qij
sell,       i ≥ 1,    
i < j, 
2 ≤ z ≤ 3  
(6)  
 
In chapter three seven variables represented the saleable products, here we use three, and they all 
aggregate at any one time to equal the total hydrocarbon liquid present at the refinery as Li, where 
L0 is on date zero and Linit is a parameter entered into our program to allow a minimum level of 
240 
 
 
hydrocarbon liquid on the start date: 
 
L0 =Linit,    
Li= Li-1 + qi,j,1 - 
3
, ,
2
i j z
z
q
=
∑
,    
1 ≤ i ≤ T,    
 
2 ≤ z ≤ 3 
(7) 
 
In equation (8) below we state the lower and upper bounds on the hydrocarbon liquid at any  time in 
the refinery, this is input into the LP, it is equal to the current total liquid present plus the crude 
bought minus the gasoline and heating oil sold on each day, i, and the end of horizon liquid can also 
be set in the program – analyses of changing this parameter is not central to our problem: 
 
Lmin ≤ Li ≤ Lmax             
1,..., ,                         
T
end
i
T
L
L
∀=
≥
 ,    
 
 
(8) 
 
 [All refinery plant constraints defined as in chapter 3 section 3.2.4.] 
           (9) 
 
The objective function in (5) is the value of the portfolio of spread options over the refinery’s 
lifetime, in contrast to the previous chapter the objective function is altered, although the remaining 
linear program remains unedited.  The profit is not explicitly defined in the objective function as 
seven outputs minus crude as the input – value is instead emulated within the option contract 
approximation; by crude oil, heating oil and RBOB Gasoline, hence the inventory balance has only 
three liquid commodities instead of seven - representative of the Gulf Coast contract.  The 
constraints sets (7) and (8) express inventory balance and boundary conditions, respectively.  
Constraint sets in (6) and (9) enforce capacity and mass balance constraints respectively.  Constraint 
(8) also poses non-negativity conditions on the decision variables, and the end of horizon inventory 
level.  There are no closed form formulae for the spread option values within the objective function 
shown in (5), they can however be computed numerically, or they can be calculated using 
approximate closed form formulae.  We compare five different methods to obtain the solution to 
(5)-(9) above - all approaches approximate the crack spread option values before optimising the LP.  
We assume that these are European call options on the historical crack spread value minus a strike 
241 
 
 
for refining costs – the five methods considered are: (a) Bachelier’s method (BA) (b) Kirk’s 
approximation (1995) (KI) (c) Alexander and Vankatramanan’s (2011) approach (AV), (d) 
Schwartz and Smith (2000) two factor model on the commodity price simulation, and finally (e) our 
reduced trinomial tree method for just three commodity products: WTI crude, RBOB gasoline and 
heating oil, required in formula (1) – we call this the CSORV method.  This tree valuation converges 
to the true solution as is shown in Hull (2003) for option valuation under assumptions of risk 
neutrality and mean reversion; acceptable levels of convergence require at least 100 simulations – 
we apply 10,000.  Real options should be approximated using European options as we can only 
exercise the physical option when we physically hold the underlying commodities53 - despite this, if 
there were American option features present, trinomial trees would provide a framework in which 
to capture them. 
When modelling spreads, the majority of the literature regards the underlying prices as log-
normal in distribution; this is a result of most studies being applied to equity prices.  The positivity 
restriction on equity indices does not apply to the commodity spreads themselves, and as one can 
see in figure 2 of the crack spread calculation, both positive and negative values can and do occur.  
A number of articles propose arithmetic Brownian motions for the dynamics, this permits closed 
form formulae for options on the spread option contract, see Shimko (1991) for the details.  We 
begin with the classical setting - there is a riskless bank account with constant interest rate r, the 
arbitrage-free model contains two commodity prices at time t denoted by, S1(t) and S2(t).  We 
assume that under the risk neutral measure the joint dynamics represent any stochastic differential 
equations (SDE) that are Gaussian in distribution, where their SDEs are driven by volatilities σ1 and 
σ2 respectively, with W1 and W2 as two Brownian motions and constant correlation ρ.  In the case 
of a spread option on two commodity spot prices, the instantaneous convenience yields do not 
appear in the equations.  To reiterate, we know that a European spread option value on two 
underlying prices, with spot prices S1 and S2 and strike price K, has a payoff at maturity of: 
                                                 
53 Raymond Cheng and Walt Tyrrell, “Using options theory for commodity spreads”, EnergyRisk, October, 2006. 
242 
 
 
 
CT = max[S2T - S1T – K, 0] 
 
 
 
 
 
 
(10) 
 
We use the following notation for the formulae required to value crack spreads, where the 
parameters in the model: α, β, γ, δ and κ are defined as follows: 
 
2
2
1
1
(0)
,      =
,      =S (0)
,      =
     =Ke
rT
rT
rT
S
e
T
e
T
and
α
β
σ
γ
δ
σ
κ
−
−
−
=
   
 
 
(11) 
 
Where, r is a constant interest rate, T is the time to maturity, S1(0) and S2(0) the spot prices of two 
commodities at date 0, σ1 and σ2 their volatilities and, K the strike price, here representing the costs 
of refining a barrel of crude.  We next introduce the basic methodology created by Bachelier in his 
PhD thesis from 1900 and apply it to obtain crack spread option prices; see Wilcox (1990) for a 
thorough description. 
 
4.2.4. Crack spread option pricing methods  
 
Poitras (1998) discusses arithmetic Brownian motions when pricing spread options - simplifying to this level enables 
analytical pricing but numerical approaches are found to be more accurate.  An arithmetic Brownian motion approach 
follows. 
 
4.2.4.1 The Bachelier method applied to the crack spread option 
 
According to Bachelier we can approximate the spread, S, by the following, where the other 
parameters are as above: 
 
2
2
1
2
/2
/2
S
S
S
e
e
β
β
δ
δ
α
γ
−
−
=
−
  
 
 
 
 
 
 
(12) 
243 
 
 
 
For the underlying to match the first two moments, the mean and variance, we need: 
 
 
( { },var{ })
S
N E S
S
∼
  
 
 
 
 
 
 
 
(13) 
 
Standard computations give these moments as: 
 
 
{ }
E S
α
γ
=
−
  
 
 
 
 
 
 
 
(14)  
 
2
2
2
2
var{ }
(
1)
2
(
1)
(
1)
S
e
e
e
β
ρβδ
δ
α
αγ
γ
=
−
−
−
+
−
  
 
 
 
(15) 
 
If the value of the crack spread at the terminal date is assumed to have a Gaussian distribution, then 
Bachelier’s European call option price approximation is the following, assuming continuous 
trading, perfect markets, and absence of arbitrage we have, where all symbols are defined above and 
the cumulative and density functions of a normal distribution are utilised: 
 
 
(
)
(
)
(
)
B
B
B
B
C
α
γ
κ
α
γ
κ
α
γ
κ
σ ϕ
σ
σ
−
−
−
−
=
−
−
Φ
+
  
 
 
 
(16) 
 
Where the Bachelier volatility is: 
 
 
2
2
2
2
(
1)
2
(
1)
(
1)
B
e
e
e
β
ρβδ
δ
σ
α
αγ
γ
=
−
−
−
+
−
  
 
 
 
(17) 
 
It is well known that the Bachelier method gives poor results for spread options when either 
correlation is close to 1 or -1, or the strike is near 0.   
The correlation for most of the refined products is very high as already discussed, to 
improve upon this, we also carry out the optimisation using the Kirk approximation of the spread, 
244 
 
 
see Lo (2013) for an analysis of equation (17) shown below, and an analysis of why it is still very 
relevant within energy trading today. 
 
 
4.2.4.2. Kirk’s Approximation applied to the crack spread option 
 
The Kirk method is the market standard for commodity and energy related spread option contracts; 
under particular circumstances it is accurate and remains efficient; for a European call option price 
we have the following: 
 
(
)
(
)
(
)
(
)
(
)
2
2
K
K
K
K
K
In
In
C
α
α
σ
σ
γ
κ
γ
κ
α
γ
κ
σ
σ
+
+
= Φ
+
−
+
Φ
−
  
 
 
(18) 
 
Where the Kirk volatility is: 
 
 
2
2
2
2
K
γ
γ
σ
β
ρβδ
δ
γ
κ
γ
κ


=
−
+


+
+


  
 
 
 
 
(19) 
 
See Kirk (1995) for the derivation - it has been found in numerous studies that the Kirk call option 
approximation price on a spread is very accurate in respect to pricing, but it suffers when 
calculating the Greeks required for hedging purposes or when the volatility is high enough – in this 
valuation model we are more interested in the pricing capabilities, see Carmona and Durrelman 
(2003) for an alternative method when hedging is required.  It also suffers when the strike is high, 
even when the option is at the money (option underlying price equals the strike price); a much more 
consistent price under these circumstances is given by AV, which works well across all strikes and 
is shows more stability even under various maturity contracts.  The authors sum the prices of two 
245 
 
 
compound exchange options, and then apply Margrabe (1978) producing an analytical solution to 
the exchange of two products.  In terms of considering an accurate representation of the spread over 
many significant considerations, the model of Alexander and Scourse (2004) captures observed 
implied volatility skews, market correlation frowns and is consistent and accurate across all strikes; 
however AV11's model is more tractable to implement. 
 
In the figure 4.4 below we compare the calculated crack-spread over the relevant period 
using equation (1), and the option price using the Kirk approximation in equation (17), to show the 
option price relationship with its underlying - the crack spread is the underlying of this option price, 
but this shows the leverage available to a refinery owner who wishes to manage the crack spread 
risk:   
 
 
 
(Figure 4.4: Calculated crack spread prices, versus Kirk approx. of the option value $/barrel, 
strike at $2.44 per barrel) 
 
Although Kirk’s model is applied on most commodity trade floors, in figure 4.4 above it 
emphasises the information contained in the option price - it is highly correlated to the underlying.  
In figure 4.5 the difference between the BA and KI methods in calculating the value of the crack 
246 
 
 
spread option is portrayed.  This difference would be more pronounced if say, the maturity of the 
option were over a longer period of time; we are however, always managing a daily option with one 
month to maturity.  This is an absolute error; it is significant when trading in huge volumes as is 
practised on commodity floors – for a single refinery valuation this error is less evident. 
 
 
 
 
 
 
(Figure 4.5: Calculated option price difference between Bachelier and Kirk $/barrel) 
 
4.2.4.3. Alexander and Venkatramanan’s approximation  
 
The authors discuss the pricing and hedging of European spread options on the spread of two 
underlying log normal prices using a new analytical approximation.  They present the prices and the 
behaviour of the Greeks of the options with formulae that utilise compounded exchange options; a 
unique approach.  They show that market implied volatility frowns are captured more accurately 
than Kirk’s, and option values are consistent across all strikes.  According to the authors, the risk 
247 
 
 
neutral price of a European spread option may be expressed as the sum of risk neutral prices of two 
compounded exchange options: 
 
(
)
1
2
1
2
e
(
{[ [
]] }
{[ [
]] }
t
r T t
T
T
T
T
C
E
w U
U
E
w V
V
−
−
+
+
=
−
+
−
ℚ
ℚ
 
 
 
(20) 
 
Where U1T and V1T are pay-offs to European call and put options on commodity price number 1; 
and U2T and V2T are pay-offs to European call and put options on commodity price number 2, 
respectively.  The general framework, where the above call options follow Black Scholes is applied, 
see the author’s derivation on page 9.  We apply these formulas to compare our numerical method; 
we leave the Greek risk numbers analysis for further work. 
 
We compare the five approaches chosen by altering the objective function within the LP 
described in (5) – (9); only the value of the crack spread option price is altered within each 
simulation, C(0) - the rest of the LP is identical.  For the CSORV approach, the linear program 
developed above, is solved by representing the option values on a discretised trinomial tree, but 
with a reduced dimensional set.  Instead of calculating each commodity price, we only simulate 
those that are required to calculate the 3-2-1 Gulf Coast crack spread, i.e. gasoline, heating oil and 
crude oil – whilst considering the correlation structure and all remaining constraints.  At each node 
on the tree, there are three random prices; the gasoline, heating oil and crude spot prices at each 
time period i.  We obtain the forward prices ahead, by simply using the corresponding period’s 
vector of prices over the corresponding probabilities.  The values, of the crack spread options in the 
above objective function, are elicited through evaluating equation (1) at each time period on the 
tree, and the intrinsic value of the option on the same node using, the difference in the maximum of 
the spread and the discounted value of the next periods potential spread.  We describe this 
calculation in more detail in the next section.     
 
Both the SS and CSORV calculate daily crack spread option prices via trinomial trees; the 
underlying future prices are first calibrated and then consolidated to obtain a final daily crack 
248 
 
 
spread daily price.  Finally, for each day a forty step trinomial tree is simulated forwards and then 
dynamic programming is applied to find the crack spread option price, a minimum of 40 steps is 
required to converge on actual NYMEX prices.  We name these daily forty step trees, sub-trees and 
we describe these calibrations in the next section and in the appendix. 
 
 
 
4.2.4.4. Schwartz and Smith(2000) estimation applied to the crack spread 
option 
 
We build and calibrate the Schwartz and Smith two factor model on the crack spread by calibrating 
it to the commodities that constitute the 3-2-1 Gulf Coast contract.  This is calculated using EIA 
future price data over the ten years studied in this thesis; this is similar to the calibration we carry 
out in the next section with the Hull & White two factor model.  We also use the crack spread 
option data to imply the volatility over the time horizon; the results of this are displayed in the 
appendix.  
 
Allow St to be the commodity price at time t: 
 
 
In(St) = ξt + χt  
 
 
 
 
 
 
 
(21) 
 
Where ξt is the long term equilibrium level and χt is the short term movement from the equilibrium 
price.  The long term variable is modelled as a Brownian motion with a drift of µξ and volatility σξ : 
 
 
dξt = µξ dt + σξ dzξ  
 
 
 
 
 
 
 
(22) 
 
249 
 
 
In the short term the model applies a mean reversion variable, where the mean-reversion coefficient 
is κ and the volatility is σχ : 
 
 
dχt = -κ χt dt + σχ dzχ   
 
 
 
 
 
 
(23) 
 
where the dzχ and the dzξ are correlated standard Brownian motion movements; where dzξ dzχ= 
ρξχdt.  Due to the valuation of the refinery being a risk-neutral one the dynamics of the commodities 
must also be.  Hence, the equations for the two factor price processes become: 
 
 
dχt = (-κ χt - λ χ)dt + σχ dz*χ   
 
 
 
 
            (24) 
 
 
dξt = (µξ - λ ξ )dt + σξ dz*ξ  
 
 
 
 
 
 
(25) 
 
Where here the λ ξ and λ χ are risk premiums; here the short term process reverts to -λχ/ κ instead of 
zero; it now represents the real commodity process.   
 
We discretise the stochastic processes onto a trinomial tree and calculate the crack spread at 
each node by applying a forty step simulation.  Having the final consolidated crack spread on one 
tree means we can calculate the option price using dynamic programming.  This method is mirrored 
in the next section with the Hull & White two factor model once the prices for the crack spread are 
calculated. 
 
4.2.5. Hull and White Two factor model 
 
Extending our one factor model we describe the behaviour of the three underlying spot price 
processes by a new two factor model – with the goal of discretising them onto trinomial trees.  The 
derivation and calibration can be found in the appendix; the two-factor Hull & White model is: 
250 
 
 
 
 
 
dSt = (θt + Ut – aSt)dt + σ1dW1  
 
 
 
 
(26) 
 
 
dUt = -bUtdt + σ2dW2 
 
 
 
 
 
 
(27) 
 
 
ρdt = dW1dW2  
 
 
 
 
 
 
(28) 
 
Despite the individual processes being able to go negative in this model, we regard this as irrelevant in our 
calculation as we wish to value the spread which can become negative.  The spot price process of the 
commodity reverts to a long-term rate that includes the stochastic process, Ut.  The volatility of the spot price 
process and the mean reversion component, Ut, are σ1 and σ2, respectively.  There are two mean reversion 
coefficients: a is the short-term mean reversion speed and b is the long-term mean reversion speed.  When a 
is high and b is low, this model produces a forward curve where short term prices are more volatile than 
long-term prices, and vice versa.  Despite the additional computational complexity, the empirical evidence 
does support the need for additional dimensions of the forward curve shifts represented by two factor 
models.  Principal components analysis shows that 90% of the price changes can be explained by parallel 
shifts, yet changes in the slope of forward curve are also significant.  Chantziara et al. (2008) describe the 
difficulties of forecasting in the oil market applying a PCA analysis to crude oil, gasoline and heating oil 
futures.  Their first three component factors capture 93% of the crude oil structure supporting the need for 
not only parallel but slope shifts.  Below we show a sample of prices for each commodity with the two factor 
model.  The final step to obtain the option prices is to consolidate the commodities onto one final trinomial 
tree by calculating the crack spread and dynamic programming is then applied to obtain the option prices. 
 
 
 
 
 
 
 
 
 
251 
 
 
(Figure 4.6: Crude oil trinomial price series $/barrel) 
 
 
 
 
 
 
 
 
(Figure 4.7: Gasoline trinomial price series $/Gallon) 
 
 
252 
 
 
(Figure 4.8: Heating oil trinomial price series $/Gallon) 
 
In appendix 3.0 we depict the different forward curves in time showing the one factor model 
from chapter three, and the both of the two factor models calibrated here: SS and H&W.  It is clear 
that the one factor model cannot capture the term structure as accurately as the two factor models 
can, which are both very close to each other.  Practitioners of pricing are unlikely to use a model 
that does not fit the initial forward curve, yet for valuation purposes this is less significant. 
Table 4.6 below depicts all option model price errors against the actual NYMEX option 
prices.  Our CSORV model does very well in competing with the more established spread formulae 
and is very effective across all error measures. 
(Table 4.6: European call crack spread option price errors) 
 
Model  
MPE 
MAPE 
RRMSE 
 
Heating Oil crack spread options 
 
Kirk 
-25.75% 
59.64% 
51.13% 
Bachelier 
-25.46% 
58.06% 
50.45% 
Alexander 
-4.32% 
18.84% 
19.91% 
Schwartz 
-4.66% 
19.12% 
21.34% 
CSORV 
-4.26% 
18.63% 
19.91% 
 
Gasoline Oil crack spread options 
 
Kirk 
-26.74% 
52.89% 
52.27% 
Bachelier 
-26.06% 
52.05% 
52.43% 
Alexander 
-4.48% 
19.30% 
21.10% 
Schwartz 
-5.20% 
19.94% 
21.38% 
CSORV 
-4.41% 
19.09% 
21.08% 
253 
 
 
 
In section 3., we state the solving time for each methodology and the final static refinery valuation 
at date 0. 
4.3. The refinery valuation results54 
 
 
 
Static linear 
program valuation 
approach 
Time to solve on a 
dual core i5 
processor (seconds) 
Oil Refinery 
Valuation 
(million $) 
Kirk’s Approx. (KI) 
0.606 
634 
Bachelier’s method 
(BA) 
0.651 
761 
 Alexander and 
Venkatramanan  
(AV) 
 
0.764 
 
753 
 
Schwartz & Smith 
(SS) 
 
3.39 
 
758 
(2.96) 
 
CSORV. (100,000 
simulations) 
 
3.29 
 
 729 
(1.53) 
Actual NYMEX Call 
option Spread 
Values* 
 
0.469 
 
739 
 
(Table 4.7: Oil refinery valuation methods with 100,000 simulations, values in million $.  Standard error 
in valuation in brackets.* NYMEX call crack spread option prices over ten years) 
 
 
After collecting over ten years worth of data, we assume that the 3-2-1 crack spread option price is 
represented by equation (4); equivalent to approximating the refinery option spread value as: Ci,j.  
Solving the above LP using GAMS, choosing ILOG CPLEX optimiser, we obtain a value of $729 
million for the refinery.  It is slower than the analytical methods, but closer to the real option price 
data, yet it remains fast in comparison to a complete numerical solution as described in chapter 
three.  Shown in table 4.7 is a comparison of the various valuations – Bachelier’s method is higher 
                                                 
54 The details of the GAMS Linear programming including analysis are given in chapter three 
254 
 
 
than Kirk’s as it is essentially a one factor model trying to directly estimate the difference,  F2(T) – 
F1(T), as such it cannot capture the structure of the actual spread’s movement completely.  
Alexander’s is higher than Kirk’s and this is due to the volatility structure being more accurately 
depicted.  Our numerical approximation is higher than Kirk’s, but lower than AV's, SS's and BA's 
as it captures more mean reversion than the other approaches possibly can with the assumptions of 
the underlying’s behaviour – assuming normality or another process that is without mean reversion 
will have drawbacks on accuracy.   
The above numerical approach, is however static: we utilise the information available today, 
0
ζ , to construct a solution for the entire lifetime of the oil refinery.  Consequently, leveraging the 
approach described in chapter three, section 5.5., we next re-optimise the LP model, and solve 
daily, with the new information that becomes available – until maturity.   
 
 4.3.1. The Updated linear program valuation results applied to the 
refinery55 
 
As in the updated optimisation procedure described in chapter three, section 5.5., a linear program 
with new information, 
k
ζ ,  becoming available at each new date: k, equations (5)-(9) now becomes: 
 
,
, ,1
,
[ ,..., ]
[ ,..., ],
(
) : max
(
)
                 
LP
i j
k
k
k
k
i j
q L
i
k
T
j
k
T i
j
V
C
q
ζ
ζ
∈
∈
<
=
∑
∑
  
 
 
 
  
(29) 
s.t. 
0 ≤ qi,j,1 ≤ Qi
buy,      0 ≤ qi,j
z ≤ Qij
sell,       i ≥ 1,   i < j,   
2 ≤ z ≤ 3  
(30) 
  
       
L0 =Linit,   Li= Li-1 + qi,j,1 - 
7
,
2
j z
i
z
q
=
∑
,     
k ≤ i ≤ T,    
2 ≤ z ≤ 3 
(31) 
 
Lmin ≤ Li ≤ Lmax             
,..., ,                         
T
end
i
k
T
L
L
∀=
≥
 ,    
 
(32) 
                                                 
55 The details of the GAMS Linear programming, including analysis, are given in chapter three 
255 
 
 
 
 [All refinery plant constraints defined in chapter 3 section 3.2.4.]    
(33) 
The LP approach in this section can only be valid if the refinery is in a market of liquid crack spread 
contracts, which is certainly not the case for the Vadinar refinery in Gujarat. 
 
 
 
 
 
Updated linear 
program valuation 
approach 
Time to solve on a 
dual core i5 
processor (seconds) 
Oil Refinery 
Valuation 
(million $) 
Kirk’s Approx. (KI) 
256 
764 
Bachelier’s method 
(BA) 
302 
863 
Alexander and 
Venkatramanan (AV) 
461 
853 
Schwartz & Smith 
(SS) 
(100,000 simulations) 
1,397 
859 
(7.94) 
CSORV 
 (100,000 simulations) 
1,432 
787 
(4.67) 
Actual NYMEX Call 
option spread values*   
270 
817 
 
(Table 4.8: All methods with an updated oil refinery valuation, values in million $ Standard errors of MC 
in brackets.   *NYMEX call option crack spread prices) 
 
Below in figure 4.9 are the resultant crude volumes in tons over the lifetime of the refinery that 
should be selected to maximise the profit. 
 
 
 
 
 
 
 
 
 
 
256 
 
 
(Figure 4.9: Daily crack spread value ($ per barrel) versus the crude oil purchase decisions 
(tons); (the left y axis is $ per barrel, the right y axis is tons/100).   
 
 
 
 
 
 
The blue line is the left axis, with the crack spread valued in $ per barrel, and the red line is the 
crude volume in tons/100 – the right axis.  The decision process is highly volatile and detailed; 
despite this we can see that when the spread value is high we have a maximum volume decision 
choice, and vice versa, when the spread value is low the volume choice set is low.  It is averaging 
approximately 1000 tons of crude per day, the owner would not in practice follow the optimisation 
program exactly, but would let it inform the general decision process. 
 
 
4.3.2. Analysis  
 
 
In practice, the valuation of the refinery will involve a mixture of positions in each saleable 
commodity using forward/futures contracts to secure future revenues, while allowing for 
maximisation in the next period, and always taking into account the physical and flow constraints.  
$ per barrel 
tons/100 
257 
 
 
The above formulation in (29)-(33) does not specifically take into account the alternative 
commodity production set prices the refinery owner is exposed to per period, but assumes that the 
simplified Gulf Coast crack spread calculation represents all of the optionality.  The value of trading 
the mean reversion of each commodity price adds to the extrinsic component of the valuation – with 
a more simplified price representation, there is less extrinsic trading value opportunities.  It is 
however very close to the linear program with actual option prices and AV11's analytical 
approximation.  This approach in the full state space defined in chapter three is defined as: 
 
7
1
1
1
1
(0)
max
[
(0,
( )
( )
&
)]
T
rt
j
j
i
Q
j
i
V
e
E
Q F i
Q S i
O
M
−
=
=
=
−
−
∑∑
 
 
 
(34) 
 
 
 
To solve the incipient optimisation problem, several methods were available: 
 
1. The most elementary consists of using the current forward curve to find the optimal 
portfolio of long and short forward contracts attached to the refining period of one 
month for up to 10 years in a legitimate financial valuation.  The corresponding values 
represent the intrinsic value of the refinery facility – this is similar to our static 
approach. 
2. More complex, would be to consider a stochastic optimisation on a tree or through 
Monte Carlo simulations: the quantity of cumulative production denoted by Qt, working 
backwards in time to solve the optimisation problem – enabling the extrinsic value to be 
captured accurately - this is in fact our dynamic approach. 
 
The latter is precisely the method that was developed in chapter 3 to calculate the value of the 
refinery complex; this is an accurate representation of intrinsic and extrinsic value, and the 
uncertainty in the stochastic prices facing the refinery owner.  In table 4.9 we compare all 
methodologies applied to the refinery in this thesis. 
258 
 
 
4.3.3. Comparison of all oil refinery valuation methods  
 
 
Valuation approach 
Refinery Value 
($ millions) 
DCF Analysis (Static, Chp. 
II) 
570 
Stochastic Dynamic 
Program (Chp. III) 
1001 
Strip of Crack Spread 
Options (Static, Chp. IV) 
(CSORV) 
729 
(Chp. IV) 
Kirk (1995) 
 
764 
(Chp. IV) , Wilcox et al 
(1990) 
Bachelier (1900) 
 
863 
(Chp. IV) 
Alexander and 
Venkatramanan (2011)  
 
853 
(Chp. III) Out of Sample 
Re-optimised Stochastic 
Program (Chp. III) 
1,206 
Re-optimised strip of crack 
spread options 
(CSORV) 
( Chp. IV) 
790 
Re-optimised SDP 
(Chp. III) 
1,131 
 
(Table 4.9:  Alternative valuation approaches to the oil refinery: five methods from chapter 
4, three from chapter 3, one from chapter I and one from chapter II) 
 
 
4.3.4. Refinery valuation research extensions 
  
The issues from this chapter’s valuation arise due to the underlying processes that we have selected; 
in effect to simplify the main difficulty in valuing the refinery, which is the spread option contract 
value itself.  The three commodity prices that are simulated are done so with a two-factor factor 
259 
 
 
model; this could be enhanced by choosing a more complex continuous stochastic equation to 
capture the characteristics that the two-factor model cannot.  For example, it is well known that 
stochastic volatility enables a more accurate price process as does a stochastic interest rate; this is 
due to smiles and skews being more accurately depicted.  Dempster and Hong (2000) use a Fast 
Fourier Transform method to provide very accurate spread option prices, values and computational 
times are given and compared to a Monte Carlo simulation; yet the authors assume that prices are 
GBM, and it is computationally intensive.  The authors also assume that the characteristic function 
of the multiple assets is known, which is feasible in a setting of normal distributions; varied or 
realistic distributions cannot be assessed in this way.  However, a study into the joint characteristic 
function of the three refinery assets in this chapter could be carried out with the goal of finding an 
analytical approximation that is realistic, or consequently where a Fast Fourier Transform or similar 
numerical algorithm could be applied.  Including an extension to capture accurately transaction 
costs would also improve the model for practitioners - where the correlation could be modelled so 
could the transaction costs behaviour whilst the refinery is in operation.  The approximations for the 
crack spread options that represent the refinery optionality are accurate even for a simplified 
valuation as shown by the resulting error calculations versus NYMEX contracts.  We have shown 
that a numerical approximation using a trinomial tree, CSORV, converges, incorporates mean 
reversion, and captures more optionality than Kirk’s and Bachelier’s whilst being within ‘shooting 
distance’ of an explicit method like Alexander’s model, and close to another two factor model 
Schwartz and Smith.  Finally, due to it being closer to actual NYMEX option values it is 
comparable to the more complex model created in chapter three providing a more accurate value for 
the refinery asset than current available approaches.  However, there are a number of avenues that 
have either been left for further research or not considered in this chapter:   
 
1 - Neither financial risk, for instance a constraint on variance, nor robustness measures, for 
example conditional value at risk (CVAR) were included, as the aim was to obtain a tractable and 
260 
 
 
efficient value - including the refinery owner’s decision set at each period in time.  This extension 
would not be an extensive undertaking as only the objective function would need to be 
reconstructed with an additional term inserted for risk.  After re-optimising a valuation would be 
viable and include a consideration for a level of risk aversion in the strip of decisions made.  The 
overall analysis shows that this simplification, despite taking longer than the other approaches, is 
more realistic and indicates that there are further directions that can be investigated in terms of 
spread options. 
 
2 - Another extension to this chapter could include a calculation for the Greeks and other risk 
sensitivities associated with the oil refinery; either inserted into the objective function or as a set of 
new constraints - this would interest practitioners as the refining climate has morphed dramatically 
in recent years.  
 
3 - Another angle that could be investigated would be alternative operational research techniques to 
either enhance the computation or enable more dimensions for prices to be captured whilst 
remaining practically viable; interrogating the duals for these optimisations would be an interesting 
avenue, as they provide information about shadow costs and can further the economic 
understanding of the resultant refinery decision set. 
 
4 - Further research may consider the modelling of the dynamics of the entire forward curve for 
valuation purposes, further increasing the state space means this is a tremendously difficult 
optimisation.  An alternative would be to include stochastic volatility into the numerical approach to 
capture the smiles and skews apparent in the market structure.  Other methods like stochastic 
optimal control could be implemented with a similar appreciation for the complexity of decisions 
that need to be executed at each refining period. 
 
 
261 
 
 
5 - An approximation with stochastic optimal control, where the problem is perceived from the 
Hamilton Jacobi Bellman point of view would be interesting.  Developing the complexity of the 
model would enhance the accuracy, but would contribute to the computational difficulty; this trade-
off is of the utmost importance when concerning practitioners.   
 
6 - Gibson and Schwartz’s two factor model would be of interest in this framework as would other 
stochastic volatility models.  Computational shortcuts would also provide enhancements, where the 
trinomial tree could be reduced or the recursive function made more efficient.  If a further general 
enhancement were to include regulatory considerations; these could be inserted into the linear 
program constraint equations.  As more regulatory actions are imposed on refiners, the developed 
world refineries will need to simulate the possibilities in methods to enhance profits whilst 
remaining within the environmental constraints – the strategies to maximise profit may well behave 
in a more complex way that only a computer program can replicate.   
 
7 - Another extension could apply to the pricing algorithm of the related 1:1:1 soybean crush spread 
which is one part soybean meal minus one part soybeans or the 2:1:1 cattle crush spread which is 
one part feeder cattle plus one part corn minus two parts live cattle – obtaining data for the options 
would be half the battle; the price series continuous stochastic equations would have to be selected 
and calibrated accordingly. 
 
In chapter three we built the entire state space, and found a numerical structure upon which to make 
the solution tractable, whereas, chapter four has simplified that structure, and focussed in on the 
daily spread value by estimating the option contract’s value within the rigidities already defined.  
This allows a comparison to existing methods for valuing spread options, and gives a resultant 
value that is more accurate than standard spread contract prices within the linear program 
developed. 
262 
 
 
 
4.4. Conclusion 
 
Valuation of oil refineries has been given extra impetus by the recent glut of crude oil not seen in 30 
years.  There has been a glut since OPEC began an oil price war with the US shale industry in 2014.  
It is in both sides benefit to keep oil pumping despite the political conflicts.  Estimates are common 
in the market of oil reaching prices as low as $30 per barrel in January 2016 – with Iran supporting 
production at this price.  US shale production owners have reduced output to cope with falling 
profits and the IMF has halved 2016 estimates of Saudi Arabian GDP.  Measures will be taken by 
some to stem the fall, but a set price is difficult to forecast.  Global demand from the biggest 
consumers: US, China and Japan has stagnated; prices could be skewed by longer term investment 
funds in commodities – yet we will likely see the federal reserve increasing interest rates in the US, 
the Bank of England with an smaller interest rate increase or the European Central Bank 
introducing a quantitative easing program.  Prices for WTI crude oil have moved dramatically in 
recent years, the closing low of $33.87/bbl on December 18th 2015, and the intraday extreme for 
2008 of $32.40/bbl before the price tripled into 2011 with a high of $114.83/bbl.  Refineries are the 
tool by which these countries control the price of the refined products on the markets - 
understanding their value will enable a clearer picture of how to untangle and decipher the 
stranglehold countries and in turn companies have over crude oil.  Obtaining fair value of these vital 
real assets is a hugely under researched area requiring defragmenting and analyses.  
 
In this chapter we have constructed a much simplified linear program which replaces 
chapter three's extensive simulation using trinomial trees with option contract prices.  The choices 
of which methods to use for the spread option prices was evident from the literature as they are the 
de facto methods used in practice and within academia.  The trivial optimisation solves in under 
five minutes and represents a realistic indication of the refinery's financial worth.  The limitations of 
the approach are that a huge amount of the state space has been removed and we are again as in 
263 
 
 
chapter three working within incomplete markets with no single no arbitrage price available.  Yet 
we produce a range of values utilising actual market data and leveraging off current spread option 
pricing methods.  The Linear Program is solved by replacing the crack spread value with a 
European Call Option - as we view the refinery as a strip of options.  We next replaced this option 
value with different values from five industry standard calculations to compare the results.  We find 
that BA, KI and AV are all extremely quick and give solid results but that when compared to actual 
NYMEX option values our CSORV, which is very similar to the SS model, is more accurate - 
despite being much slower. 
 
In comparing the oil refinery valuations a number of interesting avenues to examine are 
revealed.  Chapter three provides the most accurate value but takes the longest; hence chapter four's 
CSORV being faster but less accurate.  The stochastic dynamic program in chapter three is 
computationally intensive, but allows an accurate representation of the profit available by altering 
the production set at each period in time.  For example, a financial dispute on fair value could be 
investigated using this method – with accurate data the algorithm can be altered to suit the auditor’s 
needs - alternative refinery types could be examined with our method, risks can be easily 
incorporated and different time horizons considered.  In contrast, the static strip of crack spread 
options valuation in chapter four is computed very quickly, and provides some of the intrinsic value, 
enabling it to be more suitable for a financial trade on the crack spread. 
   
Thesis Conclusion 
 
This thesis is an investigation into the financial value of an illiquid real asset - a topping oil 
refinery.  In the first chapter we provide a background and history of the oil industry; including the 
refineries distinct characteristics and their marketable products.  A statistical analysis, including the 
first four moments and a time series analysis of all seven refinery based petroleum products: crude 
264 
 
 
oil, naphtha, gasoline, heating oil, kerosene, cracker feed and fuel oil, is constructed.  The technical 
and fundamental drivers of the oil refinery downstream market are described and the purpose of the 
research justified - a better statistical knowledge of these refined products enables an owner to make 
decisions that are more accurate and have computer aided support.  Although we collect data for the 
Indian Gujurat refinery, we  also examine refineries like those at Fawley owned by ExxonMobil - 
the complexity of the refinery has an impact on the products it can supply and hence how far afield 
it can sell its outputs.  The history we describe implies that not only is the oil industry a majorly 
significant economic creator, but it will remain so for the foreseeable future.   
 
In the second chapter, we create a discounted cash flow valuation for a typical topping oil refinery.  
Growth rates in the calculation are assumed constant, as are interest rates.  Sales and costs project 
forwards at a rate researched by oil refinery consultants KBH, at this time and accountancy 
calculations for revenue, and EBITDA is verified by an ACA qualified accountant.  By analysing 
this standard and trivial calculation we find that the outputs under-value the refinery massively by 
ignoring the 'optionality' the owner has - for example the refinery can be switched off if required, 
expanded or even production deferred until a later date.  The time value of money means that all 
future revenues and costs can be discounted back to today - capturing the behaviour of the 
petroleum products is vitally important in financially valuing an asset that is underpinned by their 
performance.  To ignore the dynamics or complexities of the time series misses a huge chunk of 
information that a refinery owner of experience will price into his decision making from the very 
beginning of the refining assay.  The value obtained in chapter two assuming very standard taxation 
and accountancy rules is £570 million.  We investigate refineries being bought and sold from the 
financial press to find if there is a relevant comparable number.  Due to so few refineries being 
bought and sold, and the time between when there were transactions being so many years; there was 
no value that could be used as an indication, hence the programs developed in later chapters of the 
thesis. 
265 
 
 
 
In chapter three, a dynamic programming approach to valuation is sought; where the optimisation is 
carried out on a no arbitrage trinomial tree, that encapsulates the crack spread option value over a 
period of ten years.  This is done by choosing a mean reverting, forward curve capturing, and 
Markov stochastic process for each commodity relevant to the refinery.  We choose to represent the 
seven commodities with Hull & White (1990) single factor processes, this includes the very high 
correlation between each time series, hence are able to be discretised onto a tree that matches the 
means, variances and the constant volatility.  The optimisation along the tree, finds at each node the 
optimal volume of product to buy or sell along with the relevant pricing on that node; the valuation 
is then calculated from the option value at the terminal node, discounted and compared to the 
intrinsic value, recursively backward to the current time period.  The speed of calculation is 
extremely fast due to the program GAMS already having trinomial tree structures built into its 
library; a C++ program can then be used to input all the tree pricing data into the linear program 
required for valuation - the nodal formulation applied in GAMS is unique and enables memoization 
to be utilised in a unique way.  In this LP, we define all relevant capacity and flow constraints, and 
all positivity and hydrocarbon liquid state equations; boundary conditions on the liquid flow are 
necessary to obtain a convergence.  Convergence is achieved in approximately two hours,  despite 
calculating over the huge dimensional state space.  An average valuation of roughly £1131 million 
is found. Because the constraints are all being linear, and the objective function is piecewise and 
convex, GAMS can utilises the CONOPT3 IBM solver to obtain a feasible and unique solution.   
 
Both static and dynamic valuations are provided in chapter three. These rely upon: a risk free curve 
for discounting, the NYMEX data for future commodity prices, and the topping oil refinery linear 
program construction introduced at the beginning of the chapter.  The reasons for choosing this 
construction are described along with the benefits of such a simple linear program that can be 
manipulated in code to support our choice of stochastic equations.  The valuation is carried out 
266 
 
 
under the risk neutral measure, is optimal and unique locally and globally, is carried out under all 
relevant physical and flow constraints, and finally, converges in under two hours for a huge state 
space due to the computational shortcuts and the choices made to construct the model using a nodal 
formulation. 
 
In chapter four, a simplified version of chapter three's LP is created that enables the optionality to 
be captured, not by the simulation of calibrated stochastic equations, but instead relevant crack 
spread option contract prices are injected into the objective function and the speed of valuation is 
cut down to five minutes – enabling a tractable and still realistic calculation to be obtained.  Three 
spread option pricing methods common in practise are utilised within the LP to compare the 
methods to our choice of using the Hull & White two factor model. There is also the advantage that 
there is a large amount of code available for Hull & White methods. We justify the use of the two 
factor model as having a representation of the forward curve, enabling a mean reversion to be 
captured and a more complicated volatility term structure than possible with a one factor model.  
One can also prevent negative values of the commodity prices to occur individually by changing the 
branch structure each time the probability of a negative price may occur.  We find that our method, 
the CSORV method, is extremely accurate and very fast, providing a valuation that is very close to 
actual spread options data from NYMEX – giving a final dynamic valuation of £787 million, 
whereas the actual options within the LP give £817 million. 
 
The difference in the valuation of the refinery compared to the DCF value seem large but this is also 
found by Brandao (2002) and Copeland (2001/2004), where real option approaches dramatically 
increase the potential value of an asset in comparison to static calculations. 
 
The overall analysis and calculations provide an estimation of a refinery's value based on the 
probable prices of its constituent petroleum products over time.  A real option valuation is presented 
267 
 
 
in both chapters three and four, which could be useful to investors, entrepreneurs, speculators and 
refinery hedge traders.  It is in fact very difficult to find a methodology that enables the huge state 
space to be managed -  there are 10 years of monthly periods, along with seven refined products, 
which can move in three different directions.  A stochastic equation that fits appropriately into a 
Hamilton Jacobi Bellman equation and solves analytically could have been selected, yet 
unfortunately there are no relevant equations to capture the complexities required for all seven 
refined products in such a formula.  There are many numerical approaches that can be considered 
on which to build the valuation; from Gaussian quadrature, to binomial trees for the price series. A 
central issue in the thesis is the balance between accuracy and tractability.  For instance, increasing 
the number of stochastic factors in the mean reverting equation in the chapter three program, where 
all seven prices were simulated,  would massively increase the computing time. Hence a one factor 
was chosen in this chapter.  Whereas, in chapter four, using just the crack spread, Gulf Coast 
Contract commodity prices two factor stochastic processes can be included in the valuation, while 
obtaining a value very quickly.  A risk neutral, optimal value is found that can be extended in a 
number of directions in an easy way due to the LP's construction.   
 
The analysis in this thesis opens various interesting research question that may be worth 
investigating.  
 
In this thesis the stochastic processes for prices are given by Hull & White. These have the 
advantage that they are Markov and fit the forward curve. These have the disadvantage that prices 
can go negative, though the code prevents that. There many other possible types of stochastic 
equations for the commodity price series – such as those suggested by Gibson and Schwartz (1990).  
These equations would need to allow for stochastic volatility or included jumps, as the forward 
curve must be included and maintain the Markov property. 
 
268 
 
 
A different numerical construction could be used to simulate the processes; a  high dimensional 
tree, or another method for dealing with the high-dimensional property of the problem. For instance, 
a manipulation of quadrature that could incorporate complex commodity price dynamics or a multi-
dimensional integration incorporating the relevant dynamics.  There are a variety of additional 
methods that can be used if one assumes the process is Gaussian. 
There may be alternative optimisation procedures than those available in GAMS that are more 
accurate and realistic. They would still need to implement the relevant parts of the solution; the 
commodity prices and the physical/mass balance constraints of the refinery.  
 
The valuation procedures developed in chapters 3 and 4 do not generate the Greeks that would be 
used by the refinery owner who wishes to hedge his/her position. It may be possible using Monte 
Carlo methods to extend the procedure to generate the Greeks.  
 
Other possible extensions could include, transactions costs, taxation and regulatory restrictions (e.g. 
limitations on sulphur emissions) within the LP formulation. The analysis in the thesis has assumed 
risk neutrality in the calculation of the objective function - this could be relaxed. The thesis has 
considered the case of a topping refinery, where the LPs were available, but it could be extended to 
a cracking refinery, were the LPs to be available.  
 
 
 
 
 
 
 
 
 
269 
 
 
APPENDIX 
A.1.1. Calibration of gasoline 
 
One Factor Stochastic Model: Gasoline 
Period : 
1/2/2002 to 01/06/2013 
Contracts : 
F1, F3, F5, F7, F9 
NOBS : 
136 
a2   : 
0.451   (0.095) 
θ2   : 
1.003   (0.046) 
σ2   : 
0.4048 (0.105) 
RMSE   :   1.13 
 
(Table A1: The calibration results of the stochastic gasoline one factor model over ten years using 
market prices from NYMEX.  
           
 
A.1.2. Calibration of naphtha 
 
One Factor Stochastic Model: Naphtha 
Period : 
1/2/2002 to 01/06/2013 
Contracts : 
F1, F3, F5, F7, F9 
NOBS : 
136 
a3   : 
0.0722 (0.055) 
θ3   : 
0.8617 (0.046) 
σ3   : 
0.4069 (0.025) 
RMSE   :   2.06 
 
(Table A2: The calibration results of the stochastic naphtha one factor model over ten years using 
market prices from NYMEX.  
270 
 
 
  
A.1.3. Calibration of heating oil 
 
One Factor Stochastic Model: Heating Oil 
Period : 
1/2/2002 to 01/06/2013 
Contracts : 
F1, F3, F5, F7, F9 
NOBS : 
136 
a4   : 
0.5200 (0.049) 
θ4   : 
0.8417 (0.062) 
σ4   : 
0.3775 (0.078) 
RMSE   :   1.87 
 
(Table A3: The calibration results of the stochastic heating oil one factor model over ten years using 
market prices from NYMEX.  
 
A.1.4. Calibration of kerosene 
 
One Factor Stochastic Model: Jet Fuel 
Period : 
1/2/2002 to 01/06/2013 
Contracts : 
F1, F3, F5, F7, F9 
NOBS : 
136 
a5   : 
0.5968 (0.054) 
θ5   : 
0.8183 (0.323) 
σ5   : 
0.4408 (0.184) 
RMSE  :   1.64 
 
(Table A4: The calibration results of the stochastic kerosene one factor model over ten years using 
market prices from NYMEX.  
 
271 
 
 
A.1.5. Calibration of cracker feed 
 
One Factor Stochastic Model: Cracker 
Feed 
Period : 
1/2/2002 to 01/06/2013 
Contracts : 
F1, F3, F5, F7, F9 
NOBS : 
136 
a6   : 
0.301 (0.135) 
θ6   : 
3.093 (0.306) 
σ6   : 
0.334 (0.095) 
RMSE   :   2.54 
 
(Table A5: The calibration results of the stochastic cracker feed one factor model over ten years 
using market prices from NYMEX.  
A.1.6. Calibration of diesel 
 
One Factor Stochastic Model: Diesel 
Period : 
1/2/2002 to 01/06/2013 
Contracts : 
F1, F3, F5, F7, F9 
NOBS : 
136 
a7  : 
0.2095 (0.042) 
θ7   : 
0.3270 (0.071) 
σ7   :     0.4069 (0.016) 
RMSE  :   3.64 
 
(Table A6: The calibration results of the Diesel stochastic one factor model over ten years using 
market prices from NYMEX.  
 
     
272 
 
 
0
50
100
150
200
250
300
350
400
450
0
5
10
15
20
25
30
35
40
$/Gallon rebased Futures Prices Gasoline
Implied Model Prices versus Gasoline Futures (Jan 2002 - Jun 2013)
 
 
Estimated Price
Observed Price
 
(Figure A.1. Gasoline Model prices versus Market Futures prices, NYMEX) 
 
0
50
100
150
200
250
300
350
400
450
1
2
3
4
5
6
7
8
$/Gallon rebased Naptha Residential Prices
Implied model prices versus Naphtha Residential Prices (Jan 2002 - June 2013)
 
 
Estimated Price
Observed Price
 
273 
 
 
(Figure A.2 Naphtha model prices versus market residential prices, EIA) 
 
 
0
50
100
150
200
250
300
350
400
450
0
10
20
30
40
50
60
$/Gallon rebased Future Prices HO
Implied model prices versus Heating Oil Futures Prices (Jan 2002 - June 2013)
 
 
Estimated Price
Observed Price
 
(Figure A.3 Heating oil model prices versus market futures prices, NYMEX) 
 
 
 
 
 
 
 
 
 
274 
 
 
0
50
100
150
200
250
300
350
400
450
0
20
40
60
80
100
120
$/Gallon rebased Diesel retail prices
Implied model prices versus Diesel retail Prices (Jan 2002 - June 2013)
 
 
Estimated Price
Observed Price
 
(Figure A.4. Diesel model prices versus futures market prices, NYMEX) 
 
 
A.2.0. Calibration of Alexander and Venkatramanan (2011) 
We construct and calibrate this model exactly as described by the authors but with different dates on 
their data set.  In the below figure we show the resultant m factor used in the model as a function of 
strike of the crack spread options: 
275 
 
 
 
(Figure: A.5. m as a function of strike K on the 12/02/2014, for option data from NYMEX) 
 
A.2.1. Calibration of Hull and White two factor stochastic process 
 
After the tree has been constructed for the process x, the spot process S can be defined at S(t) = x(t) 
+ α(t), where α is a deterministic function.  It is calculated by applying the Arrow-Debreu node 
prices and the market price of future prices. 
 
We denote by Qi+1, j, k the present value of a commodity that pays 1 if the node (i+1, j , k) is 
reached and zero otherwise.  These are all calculated recursively, knowing αi and Qi, h, 1 for all (h, 
1), by: 
 
 
 
Qi+1, j, k = Σh,l  Qi, h, l qi(h, l, j, k) exp{-( αi + xi, h, l) ∆ti} 
 
 
(1) 
 
276 
 
 
Where qi(h, l, j, k) is the probability of moving from (i, h, l) to (i +1, j, k). 
 
The αi+1 is found by solving: 
 
 
 
SM (0, ti+2) = Σi,j Qi+1, j,k exp{-( αi+1 + xi+1, j, k) ∆ti+1}  
 
 
(2) 
i.e. 
 
 
αi+1 = (1/ ∆ti+1)ln[(Σi,j Qi+1, j,k exp{  xi+1, j, k ∆ti+1})/ SM (0, ti+2)] 
 
 
 
The initial values for α and Q are: Q0,0,0 = 1 and α0 = -ln(SM(0, t1))/t1. 
 
A.2.2. Trinomial tree construction for Hull and White two factor process 
 
We have already seen how to construct a trinomial tree for a single factor process of the type dx(t) = 
-a x(t)dt + σ(t)dW(t), we now use this technique for the two-factor process that represent the spread. 
 
Firstly, we consider the process x verifying the same equation as S, with θ = 0: 
 
 
 
dx(t) = [U(t) – a x(t)]dt + σ1dW1(t),  x(0) = 0 
 
 
 
(3) 
 
 
 
 
 
dU(t) = -bU(t)dt + σ2dW2(t),   
U(0) = 0 
 
 
 
(4) 
 
If we assume that a ≠ b, then the dependence of x on U can be removed by: 
 
 
 
Y = x + (U / b – a ) 
 
 
 
 
 
 
 
(5) 
Hence, 
 
 
dY(t) = -aY(t)dt + σ3dW3(t),   
Y(0) = 0 
 
 
 
(6) 
277 
 
 
 
 
 
dU(t) = -bU(t)dt + σ2W2(t), 
 
U(0) = 0 
 
 
 
(7) 
 
Where 
 
 
σ3
2 = σ1
2 + σ2
2/(b – a)2 + 2ρσ1σ2/(b – a) 
 
 
 
 
(8) 
 
And W3 is a Brownian motion.  The correlation between W2 and W3 is: 
 
 
 
 ρuy = [ρσ1+σ2/(b – a)]/ σ3 
 
 
 
 
 
 
(9) 
 
The first step is to construct a tree for x with two trinomial trees, for both processes U and Y as 
above.  Next we use the formula: 
 
 
 
x = Y – U / (b – a) 
 
 
 
 
 
 
 
(10) 
 
The tree obtained for x will be a two-dimensional trinomial tree, where every node will have nine 
branches, a combination of branches U and Y.  
 
At time ti, we have nodes Y(i, h) and U(i,1), hence the node for x is x(i,h,1).  We define j the 
index of the middle branch in the tree for Y, emanating from y(i,h) with corresponding probabilities 
pu, pm, pd, and define k the index of the middle branch in the tree of U, emanating from U(i, 1), 
with probabilities qu, qm, qd.  Next starting from x(i, h,1), the process moves to nine branches x(i, j 
+ ϵ1, k + ϵ2), where ϵ1 and ϵ2 take values 0, 1 or -1. Finally, the probabilities associated with each 
node of the nine branches is required.  When there is zero correlation between U and Y (i.e. ρuy = 
0), the matrix of probabilities for the nine branches is simply: 
 
 
278 
 
 
 
U-Move 
 
Down 
Middle 
Up 
Down 
pd qd 
pd qm 
pd qu 
Middle 
pm qd 
pm qm 
pm qu 
 
Y-Move 
Up 
pu qd 
pu qm 
pu qu 
 
In correlated processes such as ours, the elements above are shifted so that the sum of the shifts in 
each row and column is zero. 
 
A.2.3. Calibration results for Hull and White two factor process 
We estimate the five parameters of the model (a,σ1,b,σ2,ρ) fitting given observed market data 
(NYMEX crack-spread futures volatility and the European Call option implied volatility surface on 
the crack-spread).  Both sets of values are found by aggregating existing contracts; for the spread 
itself we use three futures, and for the option we combine two option contracts on RBOB and 
Heating oil.   
 
In this example we use the NYMEX crack-spread futures (calculated using RBOB, Heating 
oil and WTI futures contracts) volatility.  The calibration is performed by minimizing the sum of 
the squares of the percentage differences between model and market future prices.  For this purpose, 
we used an optimization algorithm, within GAMS that combines interior point methods and quasi-
Newton techniques.  We give an example of our results in the below table: 
 
Table A.2.3.1: The results of the calibration to crack-spread futures volatility on 12/02/2014 
Maturity 
(months) 
Implied 
Volatility 
Our Volatility 
1 
0.1637 
0.1629 
2 
0.1614 
0.1618 
3 
0.1594 
0.1613 
4 
0.1530 
0.1546 
5 
0.1465 
0.1477 
7 
0.1373 
0.1354 
10 
0.1475 
0.1436 
15 
0.1528 
0.1539 
24 
0.1594 
0.1582 
 
279 
 
 
These results are obtained with the following Hull/White parameters: 
 
 
a = 0.54937 
 
σ1 = 0.004453 
 
b = 0.073944 
 
σ2 = 0.00499 
 
ρ =-0.97311 
 
Table A.2.3.2: At-the-money European crack spread options-volatility quotes on 12/02/2014 
 
1M 
2M 
3M 
4M 
5M 
6M 
7M 
8M 
9M 
10M 
1M 
0.1834 
0.1645 
0.1630 
0.1510 
0.1540 
0.1590 
0.1499 
0.1424 
0.1373 
0.1470 
2M 
0.1813 
0.1604 
0.1690 
0.1594 
0.1520 
0.1592 
0.1465 
0.1430 
0.1300 
0.1485 
3M 
0.1664 
0.1550 
0.1640 
0.1540 
0.1590 
0.1454 
0.1433 
0.1400 
0.1288 
0.1360 
4M 
0.1637 
0.1560 
0.1560 
0.1494 
0.1540 
0.1420 
0.1495 
0.1370 
0.1250 
0.1335 
5M 
0.1485 
0.1482 
0.1460 
0.1440 
0.1403 
0.1478 
0.1455 
0.1330 
0.1122 
0.1201 
7M 
0.1373 
0.1490 
0.1435 
0.1450 
0.1412 
0.1390 
0.1378 
0.1360 
0.1150 
0.1230 
10M 
0.1475 
0.1409 
0.1460 
0.1430 
0.1403 
0.1360 
0.1361 
0.1360 
0.1150 
0.1241 
 
We report the calibration results in the table below that depicts the fitted crack spread option 
volatilities as implied by Hull/White two factor model crack spread prices backed out of the AV 
crack spread option formula. 
 
Table A.2.3.3: At-the-money Hull & White two factor implied volatilities. 
 
 
1M 
2M 
3M 
4M 
5M 
6M 
7M 
8M 
9M 
10M 
1M 
0.1937 
0.1647 
0.1632 
0.1514 
0.1555 
0.1594 
0.1509 
0.1504 
0.1383 
0.1491 
2M 
0.1914 
0.1604 
0.1690 
0.1594 
0.1520 
0.1592 
0.1465 
0.1438 
0.1310 
0.1487 
3M 
0.1794 
0.1569 
0.1640 
0.1540 
0.1590 
0.1454 
0.1433 
0.1423 
0.1242 
0.1312 
4M 
0.1730 
0.1549 
0.1562 
0.1494 
0.1540 
0.1420 
0.1495 
0.1386 
0.1262 
0.1394 
5M 
0.1565 
0.1494 
0.1468 
0.1440 
0.1403 
0.1478 
0.1455 
0.1391 
0.1131 
0.1234 
7M 
0.1373 
0.1483 
0.1435 
0.1459 
0.1412 
0.1390 
0.1378 
0.1366 
0.1163 
0.1234 
10M 
0.1475 
0.1449 
0.1460 
0.1433 
0.1426 
0.1373 
0.1359 
0.1342 
0.1201 
0.1202 
 
 
a = 0.83372, σ1 = 0.017353,  b = 0.090124,  σ2 = 0.00845, ρ =-0.39876 
280 
 
 
A.2.4. Sub-tree calibration for Schwartz and Smith (2000) two-factor process 
 
The model has seven parameters (κ, σχ, µξ, σξ, ρξχ, λ ξ , λ χ ) that need to be estimated for crude oil, 
heating oil and gasoline futures listed on NYMEX; we follow the approach in Jafarizadeh and 
Bratvold (2012), enabling the program to avoid implementing the Kalman filter.  We can define the 
hidden factors in terms of the model parameters whilst fitting the futures curve and implied 
volatility curves on futures along the required tenors.  This will allow NYMEX market data and 
enable a risk neutral approach.  As described in Schwartz and Smith (2000) if ϕ = In(FT,t) and the 
volatility of In(FT,t) is σϕ(t, T), the value of a European call option on a futures contract maturing at 
time T with exercise price K, and time t until the option expires is: 
 
 
c = e-rt { FT,tN(d) - KN[d - σϕ(t, T)]}  
 
 
 
 
(1) 
Where 
 
d = {In(F/K)/ σϕ(t, T)} 1/2 σϕ(t, T) 
 
 
 
 
 
(2) 
 
Here the N(d) is a cumulative probability distribution. 
 
We have collected data for options on crude, heating oil and gasoline futures, Ct, for the 
underlying futures price FT,0, time to maturity T, strike price K; the options were reported on 
NYMEX on 12/02/2014.  The options contracts approximately matched the maturity of the futures 
contracts (t = T); it is then easy to use the inverse problem to find the volatility σϕ(t, T) associated 
with each option.  The σϕ(t, T) is described in terms of the parameters of the Schwartz and Smith 
model: 
    
σϕ(t, T) =  e-2 κ (T-t) (1 - e-2 κ (t))( σ2
χ / 2 κ ) + σ2
ξ + 2 e-2κ(T-t)(1 - e- κt)( ρξχσχσξ/ κ) 
(3) 
 
 
281 
 
 
 
Assuming that the options expire at the same time as the futures then e-2κ(T-t) = e-κ(T-t) = 1.  As 
T approaches infinity, the implied annualised volatility of the futures contracts will approximately 
be equal to the long term factor volatility: 
 
 
σϕ(T, T)/ √(T) ≈ √ (e-2 κ (t) σ2
χ + σ2
ξT + 2e- κT ρξχσχσξ) 
 
 
(4) 
 
However, for near maturity contracts it can be shown that the volatility becomes: 
 
 
σϕ(T, T)/ √(T) ≈ √ (σ2
ξ ) 
 
 
 
 
 
 
(5) 
The deviation from the equilibrium at time 0 is χ0 and the risk premium for the short term λχ.  The 
log of the current spot calculation of the commodity price is the sum of ξ0 and χ0; hence we can 
write: 
 
 
χ0 = In(S0) - ξ0  
 
 
 
 
 
 
 
(6) 
 Note that the risk premiums for the short and long terms cannot be estimated as they are not 
observed.  In this risk neutral approach only the drift factor for the long term need be estimated 
eliminating the need for the market price of risk in this case.  We calibrate all three underlying 
commodities separately and consolidate them to build the final trinomial tree for the crack-spread. 
 
(Table A.2.4.1: Resultant Calibration results for Schwartz and Smith (2000) on WTI crude oil for 
Dec 2000 - Dec 2010) 
Parameter 
Description 
Value 
χ0 
Short term deviation of the log spot price 
0.22 
ξ0 
Long term deviation of the log spot price 
4.87 
σξ 
Volatility of the long term 
14% 
σχ 
Volatility of the short term 
29% 
µξ 
Risk Neutral drift rate for the long term 
-7.27% 
λχ 
Risk premium for the short term 
0 
κ 
Mean reversion coefficient 
0.87 
ρξχ 
Correlation coefficient 
0.276 
282 
 
 
 
(Table A.2.4.2: Resultant Calibration results for Schwartz and Smith (2000) on No.2 heating oil for 
Dec 2000 - Dec 2010) 
Parameter 
Description 
Value 
χ0 
Short term deviation of the log spot price 
0.6462 
ξ0 
Long term deviation of the log spot price 
3.476 
σξ 
Volatility of the long term 
30.75% 
σχ 
Volatility of the short term 
84% 
µξ 
Risk Neutral drift rate for the long term 
-11.21% 
λχ 
Risk premium for the short term 
0 
κ 
Mean reversion coefficient 
3.334 
ρξχ 
Correlation coefficient 
-0.5966 
 
 
(Table A.2.4.3: Resultant Calibration results for Schwartz and Smith (2000) on RBOB NYH 
Gasoline for Dec 2000 - Dec 2010) 
Parameter 
Description 
Value 
χ0 
Short term deviation of the log spot price 
0.16 
ξ0 
Long term deviation of the log spot price 
3.84 
σξ 
Volatility of the long term 
30.74% 
σχ 
Volatility of the short term 
30.61% 
µξ 
Risk Neutral drift rate for the long term 
-15.5% 
λχ 
Risk premium for the short term 
0 
κ 
Mean reversion coefficient 
0.21 
ρξχ 
Correlation coefficient 
-0.84 
 
There are four steps for calculating the parameters in the above process: 
 
Step 1 - Estimating σξ 
 
Calculate the implied volatilities for a long maturity futures contract on the commodity 
using equation (1); then use equation (3) to calculate σξ. 
 
Step 2 - Estimating µξ and κ  
283 
 
 
 
Construct the log of the futures curve from the observed futures price contracts; next we 
calculate the gradient of the log of the futures curve, and finally estimate the µξ by subtracting (1/2 
σ2
ξ) from the gradient. 
 
By calculating the half life from the futures curve = In(2) / κ; we can use it estimate κ. 
 
Step 3 - Estimating σχ and ρξχ 
 
Using equation (1) we calculate the implied volatilities of two near maturity futures 
contracts on the spread and build a set of equations by inserting different implied volatilities into 
equation (4).  Next we insert the estimated parameters from steps 1 and 2 into the set of equations 
and solve for σχ and ρξχ. 
 
Step 4 - Estimating ξ0, χ0, and λχ 
 
We set λχ= 0; using equation (5) and (6) we build a set of equations and solve them to find 
ξ0 and χ0. 
 
Consolidating the results for all three commodities and applying the above processes in 
equations (2) and (3), we next construct a trinomial tree of the crack spread stochastic price process.    
We do this by following Hull & White's trinomial tree building process as already described in 
appendix 6.3; we do this for the long and short term processes and sum the results; we now have a 
crack spread price over 72 monthly periods; simulating 40 steps out from each node then enables 
dynamic programming to be applied resulting in the crack spread option prices.  We name this 
approach a sub-tree simulation.  The results of the individual commodity calibrations are shown 
below; alongside a GBM and Ornstein-Uhlenbeck process to emphasise the accuracy of the model. 
 
 
 
284 
 
 
(Figure A.2.4.1  WTI Crude Oil calibration results using Schwartz and Smith (2000)): 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
285 
 
 
(Figure A.2.4.2  RBOB Gasoline calibration results using Schwartz and Smith (2000)): 
 
$ per Gallon
$ per Gallon
$ per Gallon
 
 
 
 
 
 
 
 
 
 
 
286 
 
 
(Figure A.2.4.3  No.2 Heating Oil NYMEX calibration results using Schwartz and Smith (2000)):): 
 
 
0
50
100
150
200
250
300
Date
0
5
10
Schwartz-Smith 2-factor model on Heating Oil 01/12/2000 - 30/12/2010
Observed Price
Estimated Price
0
50
100
150
200
250
300
Date
0
5
10 geometric Brownian motion on Heating Oil 01/12/2000 - 30/12/2010
Observed Price
Estimated Price
0
50
100
150
200
250
300
Date
0
5
10
Ornstein-Uhlenbeck on Heating Oil 01/12/2000 - 30/12/2010
Observed Price
Estimated Price
 
 
 
 
 
 
287 
 
 
A 3.0 Comparison of forward curves for the one factor and two factor 
models 
 
WTI Crude Oil Forward Curves 25/04/2014
90
92
94
96
98
100
102
104
1
2
3
4
5
6
7
8
9
10
11
Maturity in Months
$ per Barrel
Market Forward prices
One factor
2 Factor H&W
2 Factor Schwartz and Smith
 
 
Figure A.3: Comparison of calibration results of the forward curve on WTI oil using one and two 
factor models. 
 
 
 
 
 
 
 
 
288 
 
 
Published Paper 
A modern two-stage stochastic programming portfolio 
model for an oil refinery with financial risk management 
 
 
Patrick Johnson O'Driscoll 
Department of Economics, Mathematics and Statistics, Birkbeck, 
 University of London, Malet Street, London, UK, WC1E 7HX 
podris01@mail.bbk.ac.uk 
 
 
 
 
 
 
Abstract  
 
The proposal which we wish to make is a two-stage stochastic programming model for a 
competitive oil refinery with stochastic crude and fuel prices.  Most models for refineries are 
deterministic, and those considering the stochastic problem do so by utilising a Gaussian 
assumption on profits - implementing variance as the risk measure.  Our model falls into the 
category of optimisation with coherent risk measures where robustness, rather than ambiguity, is the 
focus.  The objective is to maximise the refiner’s profit under raw material, product inventory 
constraints and a financial risk constraint.  The two-stage model leverages off a unique discrete 
scenario generation technique alongside an admissible and computational tractable drawdown risk 
measure.  The expected value of perfect information calculation of each model gives a value for the 
additional benefit, which the decision maker receives in considering the uncertainty inherent in the 
problem.   
 
Keywords: Stochastic programming; Refinery planning; Optimization under uncertainty; Probability 
fitting; Mean-variance; Conditional Drawdown-At-Risk (CDaR) 
 
1. Introduction 
 
Refiners are exposed to high uncertainties due to the nature of the oil markets.  Recently, they have 
had to contend with much lower refining margins.  This is due to the crude oil market’s lack of 
289 
 
 
spare capacity at the refinery level; hence risk management then becomes a more pressing issue for 
refiner consumers and producers - knowledge of the product prices can help to reduce this risk.  The 
refiner's portfolio includes his own production and a set of sales and costs over one period of time.  
In this paper we develop a stochastic portfolio model with the aim of maximising the refiner’s gross 
refining margin (GRM). 
The goal of such a model is to reduce the economic risk; this is connected to the fact that the oil 
spot price may be highly volatile due to various unpredictable causes.  The basis risk factors include 
the wholesale spot crude and refined products prices.  Optimisation models generally assume that 
market prices are unaffected by the decision of the utility manager, and by the uncertain yields 
within the units of the refinery.  The model we propose differs from those typically discussed in the 
literature, since we want to concentrate on the financial risks; an example is shown in Xidonas, P. et 
al (2010).  We ignore the daily detailed operational refinery flows as analysed for instance by Ribas 
et al (2012). 
 
In the two-stage stochastic programming approach the first stage production variables are selected; 
being unrelated and independent to the uncertain events.  The second term manages the expectation 
of the uncertain events.  The stochastic literature on this subject differs in the way the expectation 
and risk are considered.  In practice, the two stages are usually considered to be over one month, 
with the model being rerun after the uncertain events materialise and the second stage recourse 
decisions have been made.  The objective function can minimise costs or maximise profits, we 
optimise profit. 
 
In section 1, we introduce the current refinery planning and financial risk literature.  In section 2 we 
formulate our two stage stochastic program, creating the foundation for the rest of the paper.  In 
section 3 we implement the measures for financial risk management of the refinery.  In section 4 we 
state and analyse the results for each model implemented.  We conclude in section 5 and propose 
extensions.   
 
1.1. 
Refinery Planning 
 
Benyoucef (2010) presents a non-linear stochastic program representing a network of refineries in 
Algeria.  Scenario generation is utilised to depict the demands of the refined products and 
uncertainties are represented by normal distributions.  To ensure feasibility and maintain the 
model’s realism, a penalty term is included in the objective function, where the aim is to minimise 
costs in the presence of necessary constraints.  We focus on a similar objective as the author, but for 
290 
 
 
a single entrepreneurial refinery where we explicitly capture the stochasticity of the oil prices.  The 
author does however introduce a scenario tree attempting to solve the problem over a number of 
time periods, yet suffers the negative implications of using standard deviation as the risk measure.  
We model the prices rather than the demands, and fit distributions to capture more accurately the 
behavioural aspects of the time series’ characteristics.  Uncertainty is usually represented by the 
variation in the prices inherent in the problem.  Leiras, A. et al. (2011) summarise the numerous 
categories of refinery planning tackled in the literature.  They show that fewer authors formulate a 
model with stochastic variables to capture uncertainty. 
 
Most models of petroleum refineries are linear and avoid the corporate planning requirement.  Neiro 
and Pinto (2003) present the problem of managing multiple refinery operations using a mixed 
integer non linear program (MINLP).  Their objective function maximises the net present value 
under raw material and product inventory constraints.  These include mass balance and operating 
constraints for each refinery in their network.  Instead of using the market prices for assets, demand 
scenarios are generated.  The authors consider the refinery supply chain in their optimisation, 
however, financial risk measures are ignored, and scenarios given are drawn from normal 
distributions.  The disaggregation techniques used here are useful; enabling the large scale MINLP 
to be solved.  Overall there are 383 variables and 349 equations with the algorithm DICOPT used to 
solve the planning problem.  Another example of applying a MINLP is constructed in Aldaihani, 
and Al-Deehani (2010); the efficiency of the Kuwait Stock exchange is examined in an optimisation 
run, where the scenarios of an equally weighted basket of stocks are simulated.  The authors coerce 
mathematical programming with financial strategy, aiming to lower the risks for investors whilst 
reaching a particular level of financial return. 
 
Ribas et al (2012) present a risk averse and risk neutral approach to maximising the profit of a 
refinery; uncertainty is introduced in the product prices, the oil supply and the capacity process.  
The authors test their model using data from the Brazilian refinery system, and measure robustness 
as the objective of the model rather than the profit.  The scenarios are generated by requesting an 
expert’s opinion on oil price uncertainty inputs, which gives an expected value of perfect 
information (EVPI) of 2.54%.  An example of scenario generation using sample average 
approximation (SAA) is shown in Elkamel, A. and Al-Qahtani, K. (2011), the model emphasises the 
added value of a petrochemical network over the deterministic approaches previously defined by 
the authors – the model is tested on an industrial case of multiple refineries and a PVC complex.  
Tahar, R.B.M. and Abduljabbar, W.K. (2010) apply a new genetic based scenario simulation 
algorithm to short term scheduling - maximising the profit whilst considering the transportation and 
291 
 
 
operational costs of the oil refinery which we omit.  
   
1.2. 
Financial Risk 
 
If we wish to measure the risks that the refiner faces, which measure function should we consider?  
In practice models that do not consider risk will not be used; Uryasev and Rockafellar (2002) 
present a number of optimising problems in linear and non linear settings considering different risk 
measures, which still remain relevant today.  They show that by using Conditional Value at Risk, 
(CVaR), global optimums can be found, and tractability will be hugely improved over typical 
measures.  Therefore, CVaR is noted as more conservative, yet more realistic.  There are many 
different methodologies for modelling uncertainty within optimisation, Bertsimas et al (2010) show 
that if the underlyings have a known probability distribution, the ideas of ambiguity and robustness 
can be combined to provide a favourable optimisation.  Our problem is similar in that, the data is 
assumed to have a probability distribution; the difference is that the authors approach the problem 
with a soft robust optimisation – whereas we model with the closely related convex set of risk 
measures.  We then fit the data and generate scenarios using a unique approach focussing on 
uncertainty and not on ambiguity. 
 
Our optimisation model includes, in the final case, the drawdown function as specified in Cheklov 
et al (2003), where distributions can be considered for the underlyings.  The model is 
multidimensional, and the optimisation is carried out with discrete scenario values – which are 
conducive to our scenario generation.  The drawdown function utilised satisfies the axioms of a 
deviation measure: non-negativity, insensitivity to constant shift, positive homogeneity and 
convexity. 
 
 
2. The Two-Stage Stochastic Program 
 
In the current competitive economic climate, global businesses are constantly confronted with 
making decisions in an uncertain environment.  One motivation for refinery stochastic programs is 
to be able to quantify such decisions in terms of profit.  For example, Mulvey’s Towers Perrin-
Tillinghast asset liability model (ALM) saved US West $450 million in opportunity costs in its 
pension plan.  The two-stage stochastic program discussed thus far, aims to aid refineries in this 
process of dealing with uncertainty in a similar way.  During each time period the decision maker in 
292 
 
 
the refinery needs to decide how much crude to purchase, and how much of each refined product to 
release onto the market.  The prices and decisions made today are assumed known, the “here and 
now” problem.  In the next time period the prices will change, and second stage production 
variables will have already been made.  The “wait and see” decisions are made after knowledge of 
the second stage prices has become known.  The “stochasticity” appears in this second term in the 
form of uncertain exogenous variables.  The aim in midterm planning is, to choose the first stage 
decisions in such a way that the second stage variables can be added to the first to give a higher 
expected profit.  Gupta and Maranas (2000) were the first to use the scenario analysis approach; it 
has become a significant method in practice.  For an example applied to real electricity options with 
stochastic simulation, see Culik, M. (2010).  The author considers four different options available to 
the project manager, which are priced into the contracts and ultimately the net present value 
obtained for the project.  One contribution of our work is to provide a unique way in which to 
generate the prices using probability distributions.  Our key assumption here is that the price series 
can indeed be represented by a set of probability distributions.  The representative scenarios are 
required to capture the uncertainty in the random price variables within the stochastic programming 
framework.  These two-stage models are implemented at refineries as they give a detailed view of 
decisions in the short/mid-term.   
 
 
2.1. The producer midterm planning model 
 
The tactical midterm refining planning problem is very complex; hence the decision based systems 
used at refineries to aid owners, e.g. PIMS (AspenTech), which utilises mathematical programming, 
is present at most refineries in the world, see (Mann 2003).  In this paper refining is modelled at the 
level of detail common in the literature - with a granularity of one day at its finest, with start-up and 
shut-down costs considered insignificant, whilst Wang (2013) manages this issue by utilising a 
finite planning horizon model with periodic preventative maintenance, and random failure.  We start 
by introducing the model of the refinery with daily periods, although decisions are considered in 
monthly stages.  The refinery consists of a number of units, the crude distillation unit (CDU), the 
cracker and the vacuum distillation unit (VDU).  We model the refinery complex by considering the 
volumes bought and sold to maximise the decision maker's profit within each period.  Few decide to 
approach this problem stochastically due to its complexity, and even fewer integrate the financial 
planning problem – operational planning is much more common.  
 
The refinery producer must schedule the production of each refined product.  This is expressed as 
293 
 
 
the price of the particular product multiplied by the volume.  As practised in the literature there is 
no restriction on the amount of crude oil that can be purchased except the capacity constraint, and 
no minimum amount that needs to be produced of any refined product.  The decision variables of 
the refinery scheduling problem are stated below: 
 
Xi,t [tonnes/day] : volume of product i bought or sold on this particular day 
7. X1,t: Crude Oil 
8. X2,t: Gasoline  
9. X3,t: Naphtha (after the splitter) 
10. X4,t: Jet Fuel 
11. X5,t: Heating Oil 
12. X6,t: Fuel Oil 
13. X7,t: Naphtha stream exiting the PDU 
14. X8,t: Gas Oil 
15. X9,t: Cracker Feed 
16. X10,t: Residuum 
17. X11,t: Gasoline after splitting of Naphtha exiting the PDU 
18. X12,t: Gas Oil after the splitter 
19. X13,t: Gas Oil stream entering the fuel oil blending facility 
20. X14,t: Cracker Feed after the Splitter 
21. X15,t: Cracker Feed stream entering the fuel oil blending facility 
22. X16,t: Gasoline stream exiting the cracker unit 
23. X17,t: Stream exiting the cracker unit into the splitter 
24. X18,t: Heating oil stream after splitting of cracker output 
25. X19,t: Cracker output stream 
 
Only the decision variables where i=1 to 7, and i=14, are required within the objective function. 
They represent the crude being bought and refined products being sold; the other variables are 
present in the constraints.  The values assigned to the decision variables must satisfy the following 
constraints, which represent the physical restrictions on this particular refinery, (see Appendix A for 
details): 
 
The capacity constraints  
The mass balance constraints for the following units: 
294 
 
 
 
1. Primary Unit 
2. Cracker 
3. Fixed Blends 
4. Unrestricted Balances 
5. Raw Material Availability  
The following general model is used for the purpose of monthly scheduling; it is the deterministic 
objective function of Ravi and Reddy (1996)): 
 
Maximise 
 
Profit = - 8.0 X1 + 18.5 X2 + 8.0 X3 + 12.5 X4 + 14.5 X5 + 6.0 X6 – 1.5 X14  
 
(1) 
 
In equation (1) the authors replace the stochastic commodity prices with their mean prices from a 
set of normal distributions - in fact, it is well known that commodity price series are very far from 
Gaussian.   
 
2.2. From deterministic to stochastic  
 
Updating the above deterministic objective model to today’s mean prices of crude and refined 
products gives Model 1:  
 
Maximise  
 
Profit = -385X1 + 726X2 + 385 X3 + 471X4 + 520X5 +251X6 -72X14  
(2) 
 
The negative terms are the purchasing and operational costs.  The positive values are the saleable 
product prices.  This is a linear optimisation problem; hence the CPLEX algorithm is applied within 
the optimisation program, General Algebraic Modelling System (GAMS).  The solution to equation 
(2), the deterministic objective function, is given below: 
 
Objective Value = $285, 418 per day 
 
This approach does not take into account the stochasticity of the prices; therefore it ignores the 
decision maker's choice to switch to selling alternative amounts of each product.   
295 
 
 
 
When including random prices in an optimisation model, one of the following three methods is 
implemented: 
  
1. Mean value prices replace the stochastic variables  
2. Continuous distributions are manipulated  
3. Scenario generation (using discrete distributions)  
 
Next, the prices of refined products and the crude oil are made stochastic and the problem solved 
using scenario generation (type 3 above).  The expectation of the product prices in the next stage is 
the uncertain component of the profit.  Evaluating uncertain functions and their expectations is the 
core involvement within stochastic programming.  Clay et al (1998) used the certainty equivalent 
transformation, and Ierapetritou et al (2007) used quadrature in order to solve the expectation 
function.  The expectation function can be made more complicated if the distribution of the profit is 
considered continuous.  In which case, a more complex method is required to deal with the multi-
dimensional integral.  Our aim is to “discretise” the objective function as follows:  
                                      
                                       
∑
=
=
NS
S
s
s
X
g
p
Z
1
)
(
 
 
(3) 
 
Where Xs denotes the random parameter vector under scenario S, g(Xs) denotes the objective 
function, and ps, denotes the probability of scenario S.  In a Monte Carlo approach, Z is estimated by 
random sampling.  For our stochastic program (SP) the distribution of the profits are not considered 
directly, but instead historical data is analysed to generate possible scenarios for the uncertain 
parameters in the future.  Prices are examined to determine a distribution of closest fit; rather than 
assuming normality.  There is however no perfect fit, yet to model the expectation, a distribution is 
required to generate the scenarios.  The “bracket-median” approximation was implemented to 
obtain scenario based values, and their corresponding probabilities. 
 
 
2.3. Multivariate Fitting and Scenario Generation 
  
We model the key uncertainties by cumulative distribution functions (CDFs) defined over uncertain 
quantities - these are the prices of the refined products and crude oil.  In practise the CDF is 
obtained in one of two ways: judgemental assessments from an “expert”, or from historical data.  
296 
 
 
This results in several points from each of the CDFs being calculated.  Wettergren, T.A. and 
Baylog, J.G. (2014) confront a discrete search planning problem where the object that is being 
searched for determines the cumulative distribution functions.  They find an optimal solution over a 
finite horizon using a greedy algorithm, with the uncertainty captured by perturbations on the 
known prior probabilities.  In risk analysis, Monte Carlo simulations or triangular function fitting 
for three points are frequently applied.56  In decision analysis for uncertain outcomes, the discrete 
distribution serves as a substitute for the entire continuous distribution.  Without this simplification 
the computational difficulties can increase dramatically and solutions can be impossible to 
calculate.   
 
Scenario generation is accounted for in SPs by using a scenario tree: internal, stratified or even 
random sampling can be used to improve computation.  Scenario generation, by the very fact it has 
far fewer outcomes, means a computational advantage.  The Basel Committee, for example, 
proposes using scenario based risk management for financial companies like Investment Banks.  
The four leading approaches within the literature for scenario generation are: 
 
• Sampling 
• Statistical Approaches 
• Simulation 
• Hybrids 
 
Decision models like SPs are useful because of their ex ante and ex post properties.  With sampling 
approaches, we are extracting numbers from a pdf, so that for a given X value we have the 
associated probability that this is the scenario value and its corresponding branch probability.  
However, as noted in Birge and Louveaux (1997), the scenarios are particularly useful when the 
optimal solution to a stochastic program varies considerably with changes in the value of the 
stochastic variables.  The scenarios must comply with the non-“anticipativity” concept: we do not 
know the future and so decisions for the future can only be based on the past behaviour of the asset 
prices.  These concepts are implemented in our models.  
 
A scenario tree is illustrated in the figure below.  At a point in time the nodes represent the state in 
the world - choices or decisions will be made at each node.  The tree has branches for each value of 
                                                 
56Three-Point Approximations for Continuous Random Variables 
 
Author(s): Donald L. Keefer and Samuel E. Bodily, 1983 
297 
 
 
the random vector representing refinery product prices, ξt = {ξ1t, ξ2t, ... ξnt}, in each time stage t = 
1,.....,T. 
 
 
 
 
 
 
 
t = 0 
 
 
 
 
 
 
 
t = 1 
 
(Figure 1: A Scenario Tree example for product one) 
 
It is known within the oil industry that the market structure has shifted during the last ten years.  
Questions about the properties of the price series being mean-reverting are constantly reoccurring 
see H. Geman (2005).  In the past, fundamental supply and demand factors affected the crude oil 
price predictably.  In the last decade any shock to the refining system has had a resonating impact 
on the price of crude than observed historically – this is due to refinery spare capacity reduction.  In 
contrast to the previous decades and particularly in the US, many refineries have closed and there 
are now many financial markets trading paper or derivative contracts with crude oil as the 
underlying.  Sources state that only 20% of the value in crude is through trading the raw physical 
product itself.  Due to these intense refining markets and financial influences there is a strong 
motivation for refinery models with the necessary components.  To capture the relevant crude 
dynamics that are now present; ten years of historical price data is chosen as input for the scenario 
generation. 
 
The scenario’s prices and probabilities are generated, whilst taking into account historical 
correlations, using the method described by Iman and Conover (1982), where the target correlation 
matrix is shown below in Table 2.  We consider a monthly time period, from t=0 to t=1; the CDFs 
are fitted using historical data.  Next, discrete probabilities are derived to calculate the expectation 
term.  Often practitioners use a standard non-parametric bootstrap; however the problem of how to 
generate a correlation structure in the child nodes is not alleviated.  We define five states of the 
world, where state one is the set of prices regarded as in a low economic state, and state five as the 
state where the set of prices are regarded as in a high economic state.   
ξ㻝㻜 
ξ㻝㻝u 
ξ㻝㻝d 
298 
 
 
 
The selected CDF distributions are significant when fitted to the series data, and the correlations 
shown below are calculated from the same sample data.  Copulas are used extensively within 
Finance to generate multivariate random variable scenarios.  One of the problems with copulas is 
that they still require a rank correlation definition.  We choose the Spearman’s rank correlation 
method using a ranking procedure to generate a set of correlated commodity price series. 
 
  
 WTI  
 Gasoline  
 Naphtha  
 Jet Fuel  
 Heating    
  Oil  
 Fuel Oil  
 Cracker  
  Feed 
 WTI  
1 
  
  
  
  
  
  
 Gasoline  
0.9862 
1 
  
  
  
  
  
 Naphtha  
1 
0.9862 
1 
  
  
  
  
 Jet Fuel  
0.967 
0.985 
0.967 
1 
  
  
  
 Heating Oil  
0.9723 
0.985 
0.9723 
0.743 
1 
  
  
 Fuel Oil  
0.937 
0.943 
0.937 
0.984 
0.9901 
1 
  
 Cracker Feed 
1 
0.9862 
1 
0.967 
0.9723 
0.937 
1 
(Table 2:   Historical Correlations) 
 
 
Asset                     Expected               Standard                          Skewness                   Kurtosis 
                              Value                     Deviation                                
Resultant Fitted 
Distributions 
WTI 
249.15 
242.68 
1.76 
6.89 
Exponential (249.15)*** 
Gasoline 
471.46 
199.32 
1.12 
4.49 
Gamma (2.6302, 123.27) 
*** 
Naphtha 
520.97 
483.91 
1.689 
6.70 
Exponential (513.06) *** 
Jet Fuel 
366.25 
324.19 
3.13 
15.95 
Pearson 5 (2.84, 677.86) 
*** 
Heating Oil 
251.31 
270.20 
3.74 
23.644 
Pearson 5 (3.1008, 677.85) 
*** 
Fuel Oil 
726.41 
198.22 
0.622 
3.36 
Gamma (14.112, 52.301) 
*** 
299 
 
 
Cracker Feed 
72.09 
34.17 
1.25 
4.57 
Gamma (2.4438, 20.947) 
*** 
(Table 3:  Statistical Properties derived from the fitted CDF distributions in units of tons per day.   
***Significance at the 99% level. 
 
2.4. Objective function for the refinery including stochastic prices 
 
The profit function can be split into each state, with its corresponding probability, and the 
expectation term can be made discrete by expanding the expectation as follows: 
       
 
=
−
= ∑
∑
products scenarios
i
s
X
Costs
Sales
p
profit
E
)
(
]
[
 
 
 
   0.2 (-271X1 + 616X2 + 271X3 + 309X4 + 407X5 +182X6 - 51X14) 
 
 
+ 0.2 (-327X1 + 652X2 + 327X3 + 387X4 + 504X5 + 195X6 - 61X14) 
   
 
+ 0.2 (-385X1 + 726X2 + 385X3 + 471X4 + 520X5 + 251X6 - 72X14) 
 
 
  + 0.2 (-446X1 + 866X2 + 446X3 + 573X4 + 5279X5 + 310X6 - 84X14)   
 
 
  + 0.2 (-500X1 + 1036X2 + 500X3 + 939X4 + 586X5 + 328X6 - 94X14)   
(5) 
 
Where, Xi is the decision variable representing the volume of products to be bought and sold. 
 
 
i = 1, 2, 3, 4, 5, 6, 14 ϵ (Products Index) 
 
 
s = 1, 2, 3, 4, 5 ϵ (Scenarios or States of the world) 
 
 
           
In the following approaches, different risk measures are considered to find the most effective 
construction.  The literature varies on the choice of risk measure, and how the decision maker’s 
preferences are taken into account.   
 
 
The prices in each scenario are independent by definition, but within each scenario the product 
prices are conspicuously correlated with each other, and not independent as stated for example in 
300 
 
 
Bernardo et al (1999).  Therefore, the prices are considered constant, and the amounts that go into 
each product as the random variables.  The constraints for the models are defined in the Appendix, 
which describes the mass balances for the refinery units and physical limitations held onsite at the 
refinery complex.  By changing the risk measure considered, RM, and the aversion to risk, α, and 
maximising the profit, Π, a Mean-Risk model is constructed as shown in equations 9-11 below. 
In terms of mean variance, we now write the stochastic model as: 
 
                                      Maximise  
)
(
]
[
0
0
1
π
α
π
π
Var
E
−
=
 
(9) 
                                      Where constraints of the refinery are shown in the Appendix 
 
Due to rules of Programming this can also be reformulated to the following: 
 
                                       Maximise  
)
(
0
1
π
α
π
Var
−
=
 
(10) 
                                       Such that    E[π0] ≥  A Target Objective function value 
                                       Where constraints of the refinery are shown in the Appendix 
 
In this paper the construction modelled enabled us to define a target profit threshold, and solve for 
the decision variables: 
 
                                       Maximise  
)
(
0
1
π
α
π
RM
−
=
 
(11) 
                                       Such that    E[π0] ≥  A Target Objective function value 
                                       Where Constraints of the refinery are shown in the Appendix 
 
The result of the construction is shown in five models detailed in section 4.  
 
Equation (11) is clearly a non-linear and piecewise construction.  In fact, if it is non-convex, a 
convex approximation is required, whereas the convex formulation is trivial to optimise. 
 
Gupta and Maranas (2003) explain why any risk measure implemented should not be symmetric.  
If the investor or decision maker is considering minimising risk, it should be the downside only.  
Therefore, various risk measures are investigated.  The following sections detail the alternative risk 
measure refinery models, which are generated on a sample of 1,000 draws from the fitted 
distributions.  Shown in the figure below is the result of Model 2.   
301 
 
 
 
(Figure  2:  The efficient frontier for the profit function in $ per day for the GRM (Model 2)) 
 
A maximum of $20,000 with a risk of $12,000 per day is achieved.  By applying a utility parameter 
the profit and risk can be adjusted higher, however their underlying relationship does not change.  
 
3. Risk Management
 
Risk management is key for oil refineries – market conditions are tougher than ever and managing 
the volatility of commodity related prices is an intricate job. 
 
3.1. Value-At-Risk (VaR) 
 
''A risk-taking institution that does not compute VaR might escape disaster, but an institution 
that cannot compute VaR will not.'' 57  
 
The value at risk measure was introduced by JP Morgan (RiskMetricsTM 1995) to measure market 
risk, although it can be used for credit and/or operational risk for example.  It was invented as a 
predictive (ex ante) tool to prevent fund managers from exceeding specified portfolio policies.  It 
relies upon three parameters: the time horizon; the confidence interval; and the highest amount of 
value that can be lost in the given time horizon, under normal market conditions.  For the two-stage 
                                                 
57Aaron Brown (June/July 2008). '' Private Profits and Socialized Risk ''. GARP Risk Review.  
302 
 
 
stochastic problem with a finite number of scenarios as in this paper, VaR is calculated by sorting 
the scenarios in ascending profit order, and then taking the profit value of the scenario for which the 
cumulative probability equals the specified confidence level.  The definition follows:    
 
Definition 1 (Value at Risk (VaR)).   
 
For any confidence level α є (0,1], the value at risk, denoted by VaR(α), for a random variable with 
payoff h, is defined as the 'critical value' at which the probability incurring a loss of no less than 
VaR(α) is at least α.  That is, 
 
 
]
1
,
0
(
},
}
Pr{
:
sup{
)
(
0
∈
∀
≥
−
≤
=
α
α
α
u
h
h
u
VaR
 
(12) 
 
Where h0 is the initial payoff amount.  The different levels of α can be considered as differing levels 
of investor utility: 
 
 
(Figure 3: The efficient frontier for the profit function, versus the value at risk (Model 3))  
 
As can be seen from the results, optimising with value at risk produces a non-convex shape, which 
exhibits local minima and is of combinatorial character.  Computationally it is considered an 
unreliable risk measure and thus difficult to optimise.  Basak and Shapiro (1999) show theoretically, 
303 
 
 
that optimal decisions based on VaR, result in higher risk exposure than when decisions are based 
on expected losses.   
 
3.2. Conditional Value-At-Risk (CVaR) 
 
If for example, a portfolio is in a state of negative P&L; the magnitude of the losses is not captured 
accurately by VaR.  Additionally, asset prices are rarely distributed normally; therefore, measures 
other than VaR should be applied. Common alternatives to VaR as a risk metric are expected 
shortfall, variance, and mean absolute deviation (MAD).  Variance for example, penalises up 
movements of variance as well as downward movements, MAD does not penalise outliers, and VaR 
ignores large outliers in the distribution.  For discrete distributions, CVaR is defined as the weighted 
average of those losses exceeding VaR.  However, within the last decade, as stated earlier, the crude 
oil price series is no longer regarded as a mean reverting asset, and therefore, CVaR is the 
immediate progression over the models considering VaR within an optimisation.  Furthermore, 
CVaR within the optimisation literature has not been applied to a single entrepreneurial oil refinery.   
 
If returns are discrete and/or non-normal then sub-additivity is not necessarily present, and 
diversification of a portfolio may increase VaR.  CVaR as a risk measure is coherent, as defined 
below, ''measuring risk without sub-additivity is like measuring the distance between two points 
using a rubber band instead of a ruler ''58: 
 
Definition 2 (Coherence).  A Functional p(x), is a coherent risk measure if it has the following 
properties:   
 
• Sub-additivity:  
 
 
R
y
x
y
p
x
p
y
x
p
∈
∀
+
≤
+
,
),
(
)
(
)
(
 
(13) 
• Positive Homogeneity: 
 
 
R
x
x
p
x
p
∈
∀
=
λ
λ
λ
,
),
(
)
(
                                      
(14)  
• Monotonicity:  
 
R
y
x
y
p
x
p
then
y
x
if
∈
∀
≤
≤
,
),
(
)
(
…
 
           (15) 
• Translational Invariance: 
                                            
R
r
x
x
p
r
x
p
∈
∀
=
+
α
α
α
,
,
),
(
)
(
                                       (16) 
 
                                                 
58Szego 2002 
304 
 
 
 
Definition 3 (Conditional Value-At-Risk) CVaR is the average of the losses greater than the 
VaR, stated with a particular confidence level, here α.   
 
 
]
1
,
0
(
)],
(
|
[
)
(
0
0
∈
∀
≥
−
−
=
α
α
α
VaR
h
h
h
h
E
CVaR
 
(17) 
 
Where h is the random variable, which can for example represent the payoff of a portfolio.  The α, 
is the confidence level, and h0 the initial value for h.   
 
Rockafellar and Uryasev (2002) showed that, CVaR is superior to VaR in all optimisation 
applications.  Computationally, within an optimisation procedure due to the theory of linear 
programming, CVaR can enter into the objective function or into the constraints of the problem, 
producing an equivalent solution.  We implement CVaR within the objective function as shown for 
example in (11).  Furthermore, after implementing CVaR as the risk measure, the increased 
conservativeness emerges: 
 
 
(Figure 4:  The efficient frontier for the profit function versus the conditional value at risk of the refinery’s 
GRM (Model 4))  
 
3.3. Conditional Drawdown-At-Risk (CDaR) 
305 
 
 
 
CDaR, Uryasev (2010), in an aggregated format, is the number and magnitude of the portfolio 
drawdowns over a period of time.  A drawdown is the drop in portfolio value, compared to the 
maximum achieved in the past.  For a particular threshold α, the α – CDaR, is defined as the mean 
of the worst (1- α)*100% draw-downs experienced over a period of time.  The definition of CDaR 
follows.   
 
Definition 4 (CDaR) Conditional DrawDown at Risk is a measure of risk at a specific level of 
confidence α, it is the average of a set of worst drawdowns at a specific threshold. 
 
 
∫
Ω
→
→
−
=
∆
dt
t
x
f
T
x
)
,
(
)
1
(
1
)
(
α
α
 
(18) 
Where, 
 
)}
,
(
)
,
(
:
]
,
0
[
{
t
x
t
x
f
T
t
α
≥
∈
=
Ω
 
 (19) 
 
Where ∆ α is the CDaR, α is the confidence level, f(x, t) is the drawdown function, which is the  
difference between the maximum of the profit function over history preceding the point t, and the 
value of this function at time t.  The CDaR - optimisation problem is non-linear, convex and 
piecewise in structure, hence it can be reduced to a linear programming problem by using auxiliary 
variables. 
 
306 
 
 
(Figure 5: The efficient frontier for the profit function versus the conditional drawdown at risk (Model 5))59  
 
Figures two-five, show that the CDaR model was the best performer from a risk/reward perspective.  
We have chosen α as 90%, 95% and 99% to compare the results across the models.  Another 
important indicator for these types of models is defined below in section.   
 
3.4. Expected Value of Perfect Information (EVPI)  
 
The EVPI is an important measure for stochastic programming, Raiffa and Schlaifer (1961).  Given 
an uncertain situation that is “on the cards”; EVPI provides an example of what value is possible to 
extract practically. 
 
To calculate this figure we need the “here and now” value of the objective function, JHN.  Whereas 
the “wait and see” value is JWS, is an expectation based on the outcome of the uncertain random 
variables.   
 
Definition 5 (Expected Value of perfect information) 
 
For a particular realization ξ = ξ(w), w ϵ I, we consider the objective functional: 
 
J(x, ξ) := CTx + max{qTy | Wy = h – Tx, y ≥ 0 },  
 
 
 
(20) 
 
The associated maximisation problem is: 
 
 
maxx Eξ J(x, ξ) 
 
 
 
 
 
 
 
 
(21) 
 
The optimal solution of the above is sometimes referred to as the here-and-now solution. 
 
We can denote the optimal value of our recourse problem by: 
 
 
RP := maxx J(x, ξ) 
 
 
 
 
 
 
 
 
(22) 
 
                                                 
59Drawdowns for a set of chosen weights, are assumed static over the historical period in question 
307 
 
 
Another related maximisation problem is to find the optimal solution for all possible scenarios and 
to consider the expected value of the associated optimal value: 
 
 
WS := Eξ  maxx J(x, ξ) 
 
 
 
 
 
 
 
(23) 
 
This is called the wait-and-see solution.  The difference between the optimal values of the here-and-
now solution and the wait-and-see solution is the expected value of perfect information: 
 
 
EVPI := RP – WS 
 
 
 
 
 
 
 
 
(24) 
 
 
 
 
4.  Results 
 
The constrained optimisation results are shown in Table 4, see Appendix A for model constraints. 
 
 
Model 
Name 
GAMS File Name 
Profit 
($/day) 
Crude Oil 
Purchased 
Risk 
($/day) 
Stochastic 
Robustness 
(EVPI/WS) 
1 
DETERM_Today_LP 
235,418 
5,795 
- 
- 
2 
STOCH_VAR/COVAR_LP 
329,000 
5,781 
81,195 
15.64% 
3 
STOCH_COV_A_VaR_NLP 
125,000 
2,196 
7,187 
13.86% 
4 
STOCH_COV_A_CVaR_NLP 
125,000 
2,196 
8,197 
4.27% 
5 
STOCH_COV_A_CDaR_NLP 
125,000 
2,196 
5,532 
5.89% 
 
(Table  4:  Summary of all five Models using two-stage stochastic programming.  
 NB:  All models are optimised on prices from the scenario generation averaged from 1,000 
samples.)
 
The results using the different risk measures show that there are large decision discrepancies for the 
refiner based upon which measure is selected.  Models three to five reach a maximum of £125,000 
per day and are solved instantly.  Dependent upon the α, in front of the risk term, the model can also 
be considered at different utility levels – a risk averse investor would select the 10% significance 
308 
 
 
model.  These results show a huge gain in using alternative risk measures over variance.  The lower 
the robustness measure, the more reliable the model; it is clear that the most appropriate model is 
either models four or five as one and two contain an unacceptable level of risk.  If risk is the 
priority, five is the model to implement at the oil refinery, albeit three and four are not impractical.  
These models have a particular structure due to the constraints and optimisation utilised to construct 
them.  Shown in Figure 6 below is a plot of Model 2, where a utility parameter has been used within 
the risk measure; all stochastic models follow this same behaviour.  This illustrates the investor’s 
level of risk aversion, hence the model structure.   
   
 
 
 
 
(Figure 6:  Comparing the efficient frontier for Model 2 and the deterministic profit function using 
a utility parameter from θ=1 to θ=0.00000001)  
 
 
The y-axis represents the objective function of profit with a level aversion to risk; whereas the x-
axis is the profit risk for the refiner.  We can optimise for risk from risk seeking to complete 
aversion.  The red line above represents the deterministic result, and the blue line is the stochastic 
model.  The diagram illustrates that at a particular level of risk there is no more profit to be attained, 
yet with too little risk seeking, a minimal profit level is reachable.  This suggests that there is an 
optimum level of risk to be sought. 
 
309 
 
 
Analysing the model's robustness we calculate the EVPI as described in section three.  For model 
five, we find the following: 
   
WS = $381,972 per day 
 
RP = $367,114 per day 
 
Therefore, the average EVPI has an additional benefit of $14, 858 per day to the owner of the oil 
refinery – this result shows that models considering uncertainty have additional value to the refinery 
owner.  
 
5.  CONCLUSION 
 
In this paper we have introduced a two stage stochastic model with various risk measures for 
monthly midterm production planning of a typical Topping oil refinery.  The output product prices 
are seen as exogenous to the refiner; meaning that there are enough refineries producing the same 
product that the failure of one has minimal impact on the market, i.e. the refiner is a price taker.  
The crude oil and refined product prices are considered stochastic by using a discrete scenario 
generation for five possible economic states – the last being the lowest set of commodity prices and 
the first the highest.  To generate these five states a probability distribution is “discretised” for each 
series and the corresponding probability obtained.  The risk measures are then assessed, and 
optimisation with a variance-covariance matrix is used.  CVaR and CDaR are then introduced into 
the formulation and provide better computational properties than standard attempts using variance 
or VaR.  We found that the quantile functions: CVaR and CDaR, were very efficient and simple to 
implement using GAMS, and the scenario generation method was efficacious.  The expected value 
of perfect information indicates that there is an additional benefit of $14,858 per day to the refiner, 
if the uncertain events are included in the calculation using CDaR.  It is clear how the uncertainty 
can be managed by the refiner, and the tractability of these models would be appealing in practice.  
This work could be extended by considering the operational flow planning problem at a more 
granular level, or integrating the other issues at a refinery complex, e.g. the transportation costs or 
short term scheduling.  Integrated together with the above stochastic formulation, the refinery unit 
yield uncertainties would also provide deeper insights.  Another extension could be achieved by 
considering, the multi-stage version of this problem, which is yet to be formulated successfully.  
Multiple stages introduces computational difficulties, for example the “curse of dimensionality” - if 
successfully formulated, it would possibly leading to a financial value for the refinery.  There is 
310 
 
 
space for investigating optimisations with quantile functions; currently only the financial examples 
are persistent.  
 
 
ACKNOWLEDGEMENTS 
 
The author would like to thank Prof. Ron Smith and Dr. Alexander Karalis Isaac for helpful 
comments, and Ying Wu for her help and support. 
 
 
 
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Xidonas, P., Mavrotas, G. and Psarras, J. (2010). Portfolio management within the frame of 
multiobjective mathematical programming: a categorised bibliographic study.  Int. J. Operational 
Research, 8(1): 21 – 41. 
 
Zhang, Xu and Chen (2009).  Technical Note — A Risk-Averse Newsvendor Model Under the 
CVaR Criterion.  OPERATIONS RESEARCH  57(4): 1040-1044.  
 
 
 
 
 
313 
 
 
6.  APPENDIX 
 
A.1   Mass Balance Constraints  
 
[A Fixed Yields] 
 
Primary Unit:  
 
 -0.13 X1 + X7  = 0 
 
 
 
 -0.15 X1 + X4 = 0 
 
 
 
 -0.22X1 + X8 = 0 
 
 
 
 -0.20X1 + X9 = 0 
 
 
 
 -0.30 X1 + X10 = 0 
 
 
Cracker:  
 
 -0.05X14 + X20 = 0 
 
 
 
 -0.40X14 + X16 = 0 
 
 
 
 -0.55X14 + X17 = 0 
 
 
[Fixed Blends] 
 
Gasoline blending:  
 
 0.5X2 - X11 = 0 
 
 
 
 0.5X2 - X16 = 0 
 
 
Heating Oil Blending:  
  
0.75X5 - X12 = 0 
 
 
  
0.25X5 - X18 = 0 
 
314 
 
 
[Unrestricted Balances]  
 
Naphtha:   
 -X7 + X3 + X11 = 0 
 
 
Gas Oil:     
-X8  + X12  + X13 = 0 
 
 
Cracker Feed:  
  -X9 + X14 + X15 = 0 
 
 
Cracked Oil:   
 -X17 +X18 + X19  = 0 
 
 
Fuel Oil:       
  -X10 + X13 + X15 + X19 + X6  = 0 
 
 
A.2 Raw material availability constraints 
 
Crude Oil:   
 
 
 
 X1 ≤ 15000 
 
Gasoline:   
X2 ≤ 2700 
 
 
Naphtha:   
X3 ≤ 1100 
 
 
Jet Fuel:   
X4 ≤ 2300 
 
 
Heating Oil:   
X5 ≤ 1700 
 
 
 
Fuel Oil:   
 
 
             X6 ≤ 9500 
 
 
 
 
315 
 
 
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Green Energy and Technology
Fethi Aloui
Ibrahim Dincer    
Editors 
Exergy for A Better 
Environment 
and Improved 
Sustainability 2
Applications
Green Energy and Technology
More information about this series at http://www.springer.com/series/8059
Fethi Aloui • Ibrahim Dincer
Editors
Exergy for A Better
Environment and Improved
Sustainability 2
Applications
Editors
Fethi Aloui
LAMIH UMR CNRS 8201
Department of Mechanical Engineering
University of Valenciennes (UVHC)
High Engineering School (ENSIAME)
Valenciennes Cedex, France
Ibrahim Dincer
Faculty of Engineering and Applied Science
University of Ontario Institute of Technology
Oshawa, ON, Canada
ISSN 1865-3529
ISSN 1865-3537
(electronic)
Green Energy and Technology
ISBN 978-3-319-62574-4
ISBN 978-3-319-62575-1
(eBook)
https://doi.org/10.1007/978-3-319-62575-1
Library of Congress Control Number: 2017955058
© Springer International Publishing AG, part of Springer Nature 2018
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of
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The use of general descriptive names, registered names, trademarks, service marks, etc. in this
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Printed on acid-free paper
This Springer imprint is published by the registered company Springer Nature Switzerland AG
The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
During the past two decades, we have witnessed many drastic changes in science,
technology and engineering. One of these drastic changes is the revitalization of
exergy concepts, methods, approaches, etc. Exergy now appears to be a well-
established and distinct discipline which has come out of the second law of
thermodynamics and gone beyond thermodynamics to be used in many other
disciplines, such as chemical, biotechnology, civil, environmental, architectural,
industrial, system, electrical, geology, topography, etc. in engineering and chemis-
try, biology, physics, mathematics, business, informational technology, economy,
medicine, etc. in non-engineering areas.
As a result of these recent changes and advances, exergy has gone beyond
thermodynamics and become a new distinct discipline because of its interdisciplin-
ary character as the conﬂuence of energy and environment. It was a prime motive to
initiate a conference series on Exergy, Energy and Environment in 2003. The
conference, since then, has been running successfully under the title of “Interna-
tional Exergy, Energy and Environment Symposium (IEEES)”. The conference has
a multidisciplinary nature, covering three main areas of exergy, energy and envi-
ronment, and aims to provide a forum for researchers, scientists, engineers and
practitioners from all over the world to exchange information; to present high-
quality research results and new developments in the wide domain covered by
exergy, energy and the environment; and to discuss the future direction and
priorities in the ﬁeld.
This edited book, which is a multidisciplinary reference, will serve as a com-
prehensive source for researchers, scientists, engineers, undergraduate students and
professionals on the recent advances in exergy, energy and environmental issues. It
discusses current problems, future needs and prospects in the area of energy and
environment. This unique book contains, in addition to some invited contributions,
the selected papers from the latest Seventh International Exergy, Energy and
Environment Symposium (IEEES-7) which was held in the University of Valenci-
ennes in Valenciennes, France. It covers a broad range of topics on energy conser-
vation and analysis; entropy and exergy analyses; entropy generation minimization;
v
exergy,
energy
and
environmental
modelling;
exoeconomics
and
thermoeconomics; hydrogen generation and technology; fuels and alternatives;
heat and mass transfer; renewable energy; new and clean energy technologies;
refrigeration and heat pump systems; combustion technology; thermal systems
and applications; air-conditioning systems; thermodynamics optimization; model-
ling of energy systems; combustion/gasiﬁcation; process optimization; sectoral
energy and exergy utilization; waste exergy emissions; etc.
In closing, the editors gratefully acknowledge the assistance provided by numer-
ous individuals, especially for reviewing and revising several chapters, checking for
consistency and ﬁnalizing them for publication. The editors also register their
sincere appreciation to the authors for their contributions which have made this
unique edited book possible.
Valenciennes Cedex 9, France
Fethi Aloui
Oshawa, ON, Canada
Ibrahim Dincer
vi
Preface
Contents
Volume I
Part I
Environment Impact Assessment and Potential Solutions
Wastes of Oil Drilling: Treatment Techniques and Their
Effectiveness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3
Abbas Hadj Abbas, Hacini Messaoud, and Aiad Lahcen
Comparative Study of the Adsorption of Nickel on Natural
Bentonite and on Streptomyces rimosus Dead Biomass . . . . . . . . . . . . .
13
Faroudja Mohellebi and Radia Yous
Process Simulation and Energy Consumption Analysis for CO2
Capture with Different Solvents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
25
Boyang Xue, Yanmei Yu, and Jian Chen
Experimental Investigation of n-Butanol/Diesel Fuel Blends
and n-Butanol Fumigation: Evaluation of Engine Performance,
Exhaust Emissions, and Heat Release . . . . . . . . . . . . . . . . . . . . . . . . . .
47
Zehra S
¸ahin, Orhan Durgun, and Orhan N. Aksu
Effects of Temperature and Biodiesel Fraction on Densities
of Commercially Available Diesel Fuel and Its Blends
with the Highest Methyl Ester Yield Corn Oil Biodiesel
Produced by Using NaOH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
67
Atilla Bilgin and Mert Gülüm
Effects of Temperature and Biodiesel Fraction on Dynamic
Viscosities of Commercially Available Diesel Fuels and Its
Blends with the Highest Methyl Ester Yield Corn Oil
Biodisel Produced by Using KOH . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
83
Gülüm Mert and Bilgin Atilla
vii
Sankey and Grassmann Diagrams for Mineral Trade
in the EU-28 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
103
Guiomar Calvo, Alicia Valero, and Antonio Valero
Development of Solid Waste Management System for Adana
Metropolitan Municipality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
115
Kadir Aydin and C
¸ a
grı U
¨ n
Regeneration of Peel of Peas (Pisum sativum) After
Zinc Adsorption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
133
Sabah Menia, Amina Abbaci, and Noureddine Azzouz
Diesel Engine Performance and Emission Study Using Soybean
Biodiesel Blends with Fossil Diesel . . . . . . . . . . . . . . . . . . . . . . . . . . . .
137
A.K. Azad, M.G. Rasul, B. Giannangelo, and S.F. Ahmed
The Valorization of the Green Alga Spirogyra’s Biomass
in the Region of Ouargla-Algeria into Renewable Biofuel . . . . . . . . . . .
157
Souad Zighmi, Mohamed Bilal Goudjil, Salah Eddine Bencheikh,
and Segni Ladjel
Plasma Technologies for Water Electrolyzers . . . . . . . . . . . . . . . . . . . .
165
V. Fateev, V. Kulygin, S. Nikitin, V. Porembskiy, S. Ostrovskiy,
A. Glukhov, and A. Pushkarev
Determination of Metals in Water and Sediment Samples
of the S€
urmene River, Turkey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
175
Nigar Alkan, Ali Alkan, and Cos
¸kun Erüz
Biodiesel Production by Transesteriﬁcation of Recycled
Vegetable Oils . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
183
Souad Zighmi, Mohamed Bilal Goudjil, Salah Eddine Bencheikh,
and Segni Ladjel
Membrane Desalination Technology in Algeria: Reverse
Osmosis for Coastal Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
197
Z. Tigrine, H. Aburideh, M. Abbas, S. Hout, N. Kasbadji Merzouk,
D. Zioui, and M. Khateb
A Study on Energy and Environmental Management
Techniques Used in Petroleum Process Industries . . . . . . . . . . . . . . . . .
219
A.K. Azad, M.G. Rasul, Rubayat Islam, and S.F. Ahmed
Generating Temperature Maps of a Solar Receiver
for a Domestic Parabolic Concentrator for Cooking Purposes
Under Algerian Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
231
Fatiha Yettou, Boubekeur Azoui, Ali Malek, Narayan Lal Panwar,
and Amor Gama
viii
Contents
Experimental Investigations on the Effects of Low Compression
Ratio in a Direct Injection Diesel Engine . . . . . . . . . . . . . . . . . . . . . . . .
259
M. Vivegananth, K. Ashwin Kanna, and A. Ramesh
Control of Cement Slurry Formulation for an Oil Well
in a Critical Geological Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
273
Soumia Bechar and Djamal Zerrouki
Recovery of Farm Waste After Methanization by Evaporation
on Inclined Plate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
283
Hiba Zouaghi, Souad Harmand, and Sadok Ben Jabrallah
Optimal Operation of MEA-Based Post-combustion Carbon
Capture Process for Natural Gas Combined Cycle Power Plants . . . . .
299
Xiaobo Luo and Meihong Wang
Experimental and Numerical Investigations of “Fabrication
of TiO2 Compact Layer by the Spray Pyrolysis Deposition
System for Dye-Sensitized Solar Cells” . . . . . . . . . . . . . . . . . . . . . . . . .
315
Pernebayeva Damira, Upadhyaya Hari, and Prabhakara Bobbili
Experimental Investigation on Citrullus colocynthis Oil
as Alternative Fuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
333
Aida Cherifa AHMIA, Fetta DANANE, Rhiad ALLOUNE,
and Rahma BESSAH
A Novel Approach to Local-Level Design of Bioenergy Supply
Chains Integrated with District Heating Systems . . . . . . . . . . . . . . . . .
341
Yılmaz Balaman S
¸ebnem and Selim Hasan
Kinetic Study of Plastic Wastes with and Without Catalysts . . . . . . . . .
357
Emna Berrich Betouche and Mohand Tazerout
Effect of Ballast Water on Marine Ecosystem . . . . . . . . . . . . . . . . . . . .
373
Hacer Saglam and Ertug Duzgunes
Regeneration of Waste Frying Oil for Biodiesel Production . . . . . . . . .
383
Fetta Danane, Aida Cherifa Ahmia, Rhiad Alloune, and Rahma Bessah
The Effect of Air with Supplementary Oxygen on Power and Fuel
Consumption of Spark-Ignition Engine . . . . . . . . . . . . . . . . . . . . . . . . .
391
Mojtab Tahani, Mohammadhossein Ahmadi, and Keayvan Keramati
A Comparative Study on Some Methods to Use Tyre Pyrolysis
Oil as an Alternative Fuel in a DI Diesel Engine . . . . . . . . . . . . . . . . . .
405
Sivalingam Murugan, Hariharan Sundaramoorthi,
Govindan Nagarajan, and Bohumil Horak
Contents
ix
Investigation of Effects of Natural Gas Composition
on One-Dimensional Comprehensive Engine Model Calibration . . . . . .
421
Seyed Vahid Ghavami, Ali Salavati-Zadeh, Ahmad Javaheri,
Bahram Bahri, Vahid Esfahanian, and Masoud Masih Tehrani
Experimental Results of Split-Flow Modiﬁcation
for Post-combustion CO2 Capture Process . . . . . . . . . . . . . . . . . . . . . .
441
Marcin Stec, Adam Tatarczuk, Lucyna Wie
˛cław-Solny,
Aleksander Kro
´tki, Tomasz Spietz, Andrzej Wilk, and Dariusz S
´piewak
Hydrogen Production from Methanol Electrolysis . . . . . . . . . . . . . . . .
455
Sabah Menia, Fatiha Lassouane, Hamou Tebibel,
and Abdallah Khellaf
Experimental Investigation of Polypropylene Pyrolysis
for Fuel Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
461
Emna Berrich Betouche, Asma Dhahak, Abdel Aziz Touati,
and Fethi Aloui
Experimental Analysis of Hydrogen-Fueled Homogeneous
Charge Compression Ignition (HCCI) Engine . . . . . . . . . . . . . . . . . . . .
471
M. Mohamed Ibrahim and A. Ramesh
Part II
Sustainable Buildings
Investigations of Thermal Comfort of Building Integrated
Phase Change Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
491
Mustapha Faraji
Determining Optimum Insulation Thickness of a Building
Wall Using an Environmental Impact Approach . . . . . . . . . . . . . . . . . .
501
O
¨ zel Gülcan, Ac
¸ıkkalp Emin, Karakoc T. Hikmet, Hepbasli Arif,
and Aydın Ahmet
Energetic and Exergetic Design Evaluations of a Building
Block Based on a Hybrid Solar Envelope Method . . . . . . . . . . . . . . . . .
515
Mert Yelda and Saygın Nicel
Natural Ventilation Around and Through Building:
A Numerical Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
533
A. Kaddour and S.M.A. Bekkouche
Mathematical Filtering Analysis of Infrared Images
in Integrated-Circuit Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
547
Imre Benk€
o
Performance Analysis of Ceramic Composite Thermal
Protection System Tiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
557
Arjunan Pradeep, Suryan Abhilash, and Kurian Sunish
x
Contents
Developing High-Resolution Remote Sensing Technology
into an Advanced Knowledge Management System to Assess
Small-Scale Hydropower Potential in Kazakhstan . . . . . . . . . . . . . . . .
581
Kabiyeva Marzhan, Kaskina Dina, and Bradshaw Roland
Investigation of Thermal Characteristic of Eutectic Fatty
Acid/Damar Gum as a Composite Phase Change
Material (CPCM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
607
Hadi Fauzi, Hendrik S.C. Metselaar, T.M.I Mahlia,
Mahyar Silakhori, and Hwai Chyuan Ong
Improving of the Angstr€
om-Prescott Model Using
Harmonic Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
617
Yavuz Selim Güc
¸lü, I
˙smail Dabanlı, Eyüp S
¸is
¸man, and Zekai S
¸en
EEG Analysis Using a Wavelet Packet Transforms Mean
Energy and Mean Teager Energy with an Artiﬁcial
Neuro-Fuzzy System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
627
K.S. Biju, M.G. Jibukumar, and C. Rajasekharan
Optical Simulation of Different Cavity Receivers Shape Used
in Solar Tower Power Plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
637
Touﬁk Arrif, Adel Benchabane, Amor Gama, Hakim Merarda,
and Abdelfateh Belaid
Improved Wind Speed Prediction Results by Artiﬁcial
Neural Network Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
651
Asilhan Sevinc Sirdas, Akatas Nilcan, and Izgi Ercan
Technical and Economical Prefeasibility Study of a Solar
Water Heating (SWH) System in an Apartment Building
in Cape Town . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
667
Olugbeminiyi Idowu, Toluwalope Ige, Nicole Lacouve,
Amin A. Mustafa, and Luis Rojas-Solorzano
Use of Straw Bundles in Buildings for a Lower Environmental
Footprint of Insulated Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
681
Jean-Luc Menet
Volume II
Part III
Energy Strategies and Policies
Experimental Performance Analysis of an Integrated
Air Conditioning Split Heat Pump System for Application
in a Mediterranean Climate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
703
Nižetic
´ Sandro, Kizilkan O
¨ nder, and C
ˇ oko Duje
Contents
xi
Technical and Economic Prefeasibility Study of Mini-Hydro
Power Plants in Venezuela Case Study: El Valle River . . . . . . . . . . . . .
715
Victor Trejo, Gabriela Diaz, and Luis Rojas-Solorzano
A Study of the Effects of the External Environment and Driving
Modes on Electric Automotive Air-Conditioning Load . . . . . . . . . . . . .
725
Yew Khoy Chuah and Yu-Tsuen Chen
Optimization of Energy Cost Seawater Desalinization
by Reverse Osmosis: Case of Bousmail Station in Algeria . . . . . . . . . . .
739
Souad Bouzid-Lagha and Yacine Matrouh
Multi-objective Optimization of Distillation Sequences Using
a Genetic-Based Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
751
Mert Suha Orcun and O
¨ zc
¸elik Yavuz
PV Generator Connected to Domestic Three-Phase
Electrical Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
767
Arrouf Mohamed and Almi Med Fayc
¸al
Technical and Economic Prefeasibility Analysis of Residential
Solar PV System in South Kazakhstan . . . . . . . . . . . . . . . . . . . . . . . . .
783
Anuar Assamidanov, Nurbol Nogerbek, and Luis Rojas-Solorzano
Contribution of the Cogeneration Systems to Environment
and Sustainability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
793
C
¸ omakli Kemal, C
¸ akir U
gur, C
¸ okgez Kus
¸ Ays
¸egül, and S
¸ahin Erol
Solar Calculations of Modiﬁed Arch (Semi-spherical)-Type
Greenhouse System for Bayburt City . . . . . . . . . . . . . . . . . . . . . . . . . .
803
C
¸ akir U
gur, S
¸ahin Erol, C
¸ omakli Kemal, and C
¸ okgez Kus
¸ Ays
¸egül
Estimation of Global Solar Radiation in Arid Climates
in Algeria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
817
Malika Fekih and Mohamed Saighi
Technical-Economic Assessment of Energy Efﬁciency
Measures in a Midsize Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
825
Sara Benavides, Maria Bitosova, Javier De Gregorio, Aubin Welschbillig,
and Luis Rojas-Solorzano
Smart Simulator for Tracking the Global Maximum Power
Peak of Photovoltaic Arrays Under Partial-Shaded Conditions . . . . . .
841
Saad Saoud Merwan, Abbassi Hadj Ahmed, Kermiche Saleh,
and Ouada Mahdi
Study and Analysis on Lighting Energy Management
for Highway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
859
Yoomak Suntiti and Ngaopitakkul Atthapol
xii
Contents
Inﬂuence of Wind Farm on Distribution System: Current
Characteristics During Fault Occurrence . . . . . . . . . . . . . . . . . . . . . . .
881
Santipont Ananwattanaporn, Atthapol Ngaopitakkul, Chaiyan Jettanasen,
Chaichan Pothisarn, and Monthon Leelajindakrairerk
Operating Oil Reﬁnery Units Under Uncertainty: Thermodynamic
and Economic Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
907
Eid Al-Mutairi
Ecological Analysis of a Wind-Diesel Hybrid Power System
in the South Algeria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
925
Khaireddine Allali and El Bahi Azzag
Comparative Study of Two Integrated Solar Collectors
with Symmetric and Asymmetric CPC Reﬂectors Based
on a Ray Trace Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
935
Olfa Helal, Raouf Benrejeb, and Be
´chir Chaouachi
Thermoeconomic Optimization of Hydrogen Production
and Liquefaction by Geothermal Power . . . . . . . . . . . . . . . . . . . . . . . .
951
Ceyhun Yilmaz, Mehmet Kanoglu, and Aysegul Abusoglu
A Case Study of Energy Modeling of a School Building
in Astana City (Kazakhstan) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
967
Uyzbayeva Aigerim, Tyo Valeriya, and Sedov Artem
Exergoeconomic and Exergoenvironmental Analysis
and Optimization of the Cogeneration Cycle Under Dynamic
Solar Radiation Model Using Two Renewable Sources . . . . . . . . . . . . .
985
Kaveh Haniﬁ, Kourosh Javaherdeh, and Mortaza Yari
Indicators of Sustainability Energy Management Based
on Energy Audit for Hotels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1013
M.E.U. Oz, M.Z. Sogut, and T.H. Karakoc
¸
Using a Porous Environment In Catalytic Gas Heaters
to Optimize Energy Consumption and Reduce Pollution
in Heating the Furnaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1033
Hossein Afshar, Esmaeil Khosroabadi, and Mehdi Tajdari
Sn/Graphene Binary Nanocomposite Anode Electrodes
for High-Performance Li-Ion Battery Applications . . . . . . . . . . . . . . . . 1045
Guler Mehmet Oguz, Erdas Aslihan, Nalci Deniz, Ozcan Seyma,
and Akbulut Hatem
Data-Driven Modeling for Energy Consumption Estimation . . . . . . . . . 1057
Chunsheng Yang, Qiangqiang Cheng, Pinhua Lai, Jie Liu,
and Hongyu Guo
Contents
xiii
A Simple Model of Finite Resource Exploitation: Application
to the Case of Oil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1069
A. Heitor Reis
Development and Application of a Simple and Reliable Power
Regulator for a Small-Scale Island Wind Turbine . . . . . . . . . . . . . . . . 1081
Yongjun Dong, Yang Zhao, Jianmei Chen, Mingqi Xu, Xueming Zhang,
and Jingfu Guo
Design and Economic Analysis of Photovoltaic Systems
in Different Cities of Turkey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1101
Suphi Anıl Sekuc
¸o
glu and Tülin Bali
Contribution to the Control Power of a Wind System
with a Storage System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1111
Ihssen Hamzaoui, Farid Bouchafaa, and Abdel Aziz Talha
Performance Evaluation of SWRO Desalination Plant
at Skikda (Algeria) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1131
F. Ammour, R. Chekroud, S. Houli, and A. Kettab
Study of a PV-Electrolyzer-Fuel Cell Hybrid System . . . . . . . . . . . . . . 1139
Amina Gueridi, Abdallah Khellaf, Djaffar Semmar, and Larbi Loukarﬁ
Experimental and Numerical Investigations of a Compressed
Air Energy Storage (CAES) System as a Wind Energy
Storage Option . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1147
Abdul Hai Alami, Camilia Aokal, and Monadhel Jabar Alchadirchy
Feasibility Study of a Novel One-Axis Sun Tracking
System with Reﬂector Displacement in a Parabolic
Trough Concentrator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1155
A. Gama, C. Larbes, A. Malek, and F. Yettou
Crystal Growth Analysis of R134a Clathrate with Additives
for Cooling Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1165
Sayem Zafar, Ibrahim Dincer, and Mohamed Gadalla
Modeling, Simulation, and Optimization of an Irrerversible
Solar Absorption Cooling Plant in Transient Regime . . . . . . . . . . . . . . 1177
Boukhchana Yasmina, Fellah Ali, and Ben Brahim Ammar
Technical-Economic Prefeasibility Assessment
of an Off-Grid Mini-hydropower Plant for an Agribusiness
Resort in Kaduna Nigeria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1193
Victor H. ADAMU, Ampofo Nana, Ario Panggi Pramono Jati,
Ryan Tulabing, and Rojas-Solo
´rzano Luis
xiv
Contents
About the Editors
Fethi Aloui was appointed as Professor of Mechanical
Engineering and Energy at the University of Valenci-
ennes (UVHC), France (at the Engineering School
ENSIAME), Laboratory LAMIH (UMR CNRS 8201),
in September 2011 after working more than 14 years as
Associate Professor in Process Engineering, Energy
and Fluid Mechanics at the University of Nantes,
France. His speciality ﬁeld is transfer and transport
phenomena in ﬂuid mechanics and engineering pro-
cesses. He has published over 200 journal and confer-
ence papers so far. He serves as Associate Editor of the
Journal of Applied Fluid Mechanics and the Interna-
tional Journal of Energy Research. He is also the co-organizer (each year since
2010) of the ASME-FEDSM “Symposium on Transport Phenomena in Energy
Conversion from Clean and Sustainable Resources”. He regularly reviews for
Journal of Applied Fluid Mechanics, Applied Energy, International Journal of
Energy Research, International Journal of Heat and Fluid Flow, International
Journal of Coal Science and Technology, Fuel and many other journals.
xv
Ibrahim Dincer is a full Professor of Mechanical
Engineering in the Faculty of Engineering and Applied
Science at UOIT. He is Vice President for Strategy in
International Association for Hydrogen Energy (IAHE)
and Vice-President for World Society of Sustainable
Energy Technologies (WSSET). Renowned for his
pioneering works in the area of sustainable energy
technologies, he has authored and co-authored numer-
ous books and book chapters, more than 1000 refereed
journal and conference papers, and many technical
reports. He has chaired many national and international
conferences, symposia, workshops and technical meet-
ings. He has delivered more than 250 keynote and invited lectures. He is an active
member of various international scientiﬁc organizations and societies and serves as
editor-in-chief, associate editor, regional editor and editorial board member on
various prestigious international journals. He is a recipient of several research,
teaching and service awards, including the Premier’s Research Excellence Award
in Ontario, Canada, in 2004. He has made innovative contributions to the under-
standing and development of sustainable energy technologies and their implemen-
tation. He has actively been working in the areas of hydrogen and fuel cell
technologies, and his group has developed various novel technologies/methods/etc.
xvi
About the Editors
Part I
Environment Impact Assessment and
Potential Solutions
Wastes of Oil Drilling: Treatment Techniques
and Their Effectiveness
Abbas Hadj Abbas, Hacini Messaoud, and Aiad Lahcen
1
Introduction
The development of the oil industry has created many environmental problems that
contribute in the degradation of some natural ecosystems, especially sheets of
groundwater. The environmental laws require adequate treatment of these wastes
in order to avoid any degradation. The oil industry in the region of Hassi Messaoud
is very developed which produces industrial waste with dangerous elements.
During the drilling a large quantity of solid and liquid industrial wastes are
generated. These releases contain toxic chemicals such as heavy metals and organic
pollutants (Lefebrre 1978). These elements cause problems for the humans, animals
(Cranford et al. 1999), and plants (Atlas 1984). Additional information can be given
by Scriban (1999). So, what are the treatment methods and their effectiveness?
The study area is in the ﬁeld of Hassi Messaoud, which is about 850 km in the
south of Algiers and 350 km from the Tunisian border (Askri et al. 2003).
The aim of this work is to ﬁnd solutions that can reduce or stop the inﬂuence of
these toxic elements. For this purpose we adopted a method of work, beginning with
a literature search for oil drilling, drilling ﬂuids, and waste products used (Yaiche
2006). We made site visits to recognize nearby the identity of these wastes, their
A.H. Abbas (*)
Department de forage et MCP, Laboratoire de ge
´ologie de Sahara, Universite
´ de Kasdi Merbah
Ouargla, Rouissat Ouargla, Ouargla 30130, Algeria
e-mail: abbashadjabbas@gmail.com
H. Messaoud
Department de forage et MCP, Laboratoire de ge
´ologie de Sahara, de Kasdi Merbah Ouargla,
Rouissat Ouargla, Ouargla 30130, Algeria
A. Lahcen
la socie
´te
´ de National Oil Well Varco, Hassi Messaoud, Rouissat Ouargla, Ouargla 30130,
Algeria
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_1
3
chemical components, and their inﬂuence on the environment, and we have seen the
appropriate means to treat these wastes and to a make limit concerning their
negative inﬂuence. It was found that there are two methods of treatment which
are chemical and thermal (Khodja 2008). These methods are based on the utiliza-
tion of cement inerting and utilization of high temperatures to trap toxic elements
(Damou 2007). More details on this process can be given by Cheriﬁ(2006).
This ﬁeldwork is made to diagnose the treatment methods of these wastes and
make the neutralizing of these toxic products effective. Sampling of waste is done
according to conventional methods. Samples before and after treatment during
several phases of drilling to make a homogeneous mixture, the latter are of the oil
drilling waste of Hassi Messaoud area before and after each method of treatment
and then transported these samples to the laboratory for analysis and retrieve results
that can improve the use of these methods (use of equipment catalog of heavy
metals, CRD (2010)).
2
Results and Discussion
2.1
Results of Analysis of the Crude Wastes (Before
Treatment)
The results of analysis of nine samples showed that the toxic components are the
hydrocarbon and heavy metals (Pb, Cu, Cr, Cd, Zn, Mn). The concentrations of
hydrocarbon exceed the conventional norms in Algeria or the variation of between
1.98% and 9.61%; the maximum value was recorded in the sample 5 (9.61%) and
the minimum value in the sample 4 (1.98). The norms of the concentrations of
heavy metals are from 14.1 mg/l (sample 4) and 46.5 mg/l (sample 2) for the lead,
between 0.1 mg/l (sample1) and 0.2 mg/l (sample 4) for cadmium, from 5.3 mg/l
(sample 4) and 12.6 mg/l (sample 2) for the zinc, and between 1.2 mg/l (sample 4)
and 1.8 mg/l (sample 1) for manganese, while the norms of lead, copper, chromium,
cadmium, zinc, and manganese are 01, 03, 0.1, 0.2, 05, and 01 in succession. These
norms are indicated in the Ofﬁcial Gazette of the Republic of Algeria (1993) and
some environmental laws in the Ofﬁcial Gazette 2005 version (2005) and 2007
version (2007).
After comparing the results of the previous elements, their norms and standards,
which are indicated in the reference (Ofﬁcial Gazette of the Republic of Algeria
1993), we can say that there are values that exceed the maximum limits, for
example, in (sample 1) the value of lead is equal to 32.6 mg/l, so it exceeds the
norm of lead that is equal to 0.1 mg/l, and the same value for the zinc in the (sample
2) is equal to 12.6 which exceeds the maximum value (0.5 mg/l) and the same for
manganese.
4
A.H. Abbas et al.
For oil tenures there are two values that exceed the norms in the 5 and 6 samples
(9.61% > 5%). According to the above table, the hydrocarbon and heavy metals are
harmful elements conventionally (Table 1).
2.2
Results of Analyses Before and After Solidiﬁcation/
Stabilization
According to the results shown in Table 2 (last page), it was found that the
concentrations are generally less than the maximum limit indicated by the Algerian
state and Sonatrach. But we record a value that exceeds the maximum values, for
example, the value of lead in (sample 1) is equal to 1.8 mg/l > 1 mg/l; to justify this
result, we suppose that the amount of cement added is not sufﬁcient to trap the
metal.
On the other hand, we record the content of chromium in the treated samples
which is equal to 0.23 mg/l which is higher than those of untreated samples 00 mg/l;
thus the presence of this metal in the cement used for solidiﬁcation is the origin of
this augmentation.
2.2.1
Optimization of the Method of Solidiﬁcation/Stabilization
(Chemical Method)
Optimization of Cement
From the ﬁgure, it was found that the content of hydrocarbons in the presence of a
ﬁxed concentration of sodium silicate is inversely proportional to the concentration
of added cement, but it can also be noticed that between the values of the
Table 1 Results of the test of hydrocarbon and heavy metals for the crude wastes (before
treatment)
Samples
Heavy metals concentration in mg/l
Content of
oils (%)
Lead
(pb)
Copper
(Cu)
Chromium
(Cr)
Cadmium
(Cd)
Zinc
(Zn)
Manganese
(Mn)
1
32.6
00
00
0.1
8.5
1.8
4.40
2
46.5
00
00
0.1
12.6
2.1
4.00
3
15.8
00
00
00
7.4
1.7
4.65
4
14.1
00
00
0.2
5.3
1.2
1.98
5
/
/
/
/
/
/
9.61
6
/
/
/
/
/
/
6.56
7
/
/
/
/
/
/
9.40
8
/
/
/
/
/
/
3.2
9
/
/
/
/
/
/
2.2
Wastes of Oil Drilling: Treatment Techniques and Their Effectiveness
5
concentration of cement between (200 and 300 kg/m3), the reduction is not very
important (from 5.08 to 4.28% for almost double the amount of cement added). This
can be justiﬁed by the fact that cement creates a solid matrix which protects the ﬁlm
formed by the sodium silicate and enhances encapsulation. The content of
hydrocarbons decreases as we add cement, but by reaching a certain value of the
quantity of cement added, the content of hydrocarbons continues to decrease but at
a slower manner. The effect of the cement is more interesting at high hydrocarbon
levels.
For economic reasons (the cost of cement), we have an optimal concentration of
cement equal to 200 kg/m3 and try to improve the solidiﬁcation with sand (Fig. 1).
Optimization of Sand (Fig. 2)
According to the shape of the curve, we can say that the result obtained above is
improving as we add sand. The hydrocarbon content is decreased by 6.56% (the
Table 2 Concentrations of heavy metals
Samples
Heavy metals concentration in mg/l
Hydrocarbon
concentration
(%)
Lead
(Pb)
Copper
(Cu)
Chromium
(Cr)
Cadmium
(Cd)
Zinc
(Zn)
Manganese
(Mn)
Sample 1
before
treatment
32.6
00
00
0.1
8.5
1.8
4.40
Sample 1
after
treatment
8.1
00
00
0.1
0.56
0.1
1.40
Sample 2
before
treatment
46.5
00
00
0.1
12.6
2.1
4.00
Sample 2
after
treatment
00
00
00
0.1
00
00
1.30
Sample 3
before
treatment
15.8
00
00
00
7.4
1.7
/
Sample 3
after
treatment
00
00
0.23
00
00
00
0.53
Sample 4
before
treatment
14.1
00
00
0.2
5.3
1.2
1.98
Sample 4
after
treatment
00
00
0.34
0.1
00
00
1.51
6
A.H. Abbas et al.
sand concentration is zero) to 5.21% after addition of 100 kg of sand; this is logical
because the latter strengthens the matrix formed by the cement (solidifying).
We stopped at a concentration of sand equal to 100 kg/m3 (we will consider the
following as optimal) for two reasons:
•
The decrease in the hydrocarbon content is very low, almost 1–100 kg/m3
of sand.
•
The particle size of the sand can increase the permeability of our treated waste,
thus making it less resistant to the inﬁltration liquids inside the matrix.
Optimization of Sodium Silicates
On this curve, it is found that the percentage of hydrocarbons decreases to a value
equal to 6.25%, which corresponds to a concentration of sodium silicate of 10 l/m3.
0
50
100
150
200
250
300
4
4.5
5
5.5
6
6.5
7
cement (kg/m3)
oil(%)
Fig. 1 Changes in the content of hydrocarbons according to the concentration of CPA425 cement
0
20
40
60
80
100
5
5.5
6
6.5
7
sand (kg/m3)
oil (%)
Fig. 2 Changes in the content of hydrocarbons according to the concentration of sand
Wastes of Oil Drilling: Treatment Techniques and Their Effectiveness
7
After this value, it is obtained practically a bearing (or a very small decrease) in
silicate form a covering around the contaminant and attracts Ca++ (from the
cement). The latter in turn attracts other molecule silicates. This force of attraction
becomes weak gradually as the silicate covering is superimposed on each other, and
this is what explains the shape of the graph. The retention of hydrocarbons is
stagnant, once when it exceeded a certain concentration of sodium silicate; in our
case, the concentration is 10 l/m3 (when it is considered as optimal) (Fig. 3).
Optimization of Activated Carbon
From the presented curve, it is found that the content of hydrocarbons decreases to a
value equal to 5.88% which corresponds to a concentration of the activated carbon
equal to 30 kg/m3; after this value the hydrocarbon content rises.
We notice again that our graph has an optimum, which is moving toward a
concentration not to exceed, to avoid the contaminant immigration (Fig. 4).
Test Result of Solidiﬁcation of the Matrix: Resistance of Free Compression
According to the graph above, we see that the resistance of free compression for our
sample treated increases as we add cement.
We have two things: ﬁrstly making concrete from the treated waste requires the
addition of large quantities of cement; secondly, the pretreatment sample was very
brittle (Fig. 5).
0
5
10
15
20
25
6
6.5
7
7.5
8
8.5
9
9.5
silicate (l/m3)
oil (%)
Fig. 3 Changes in the content of hydrocarbons according to the concentration of sodium silicates
8
A.H. Abbas et al.
2.2.2
The Content of the Oil Before and After the Heat Treatment
(TDU or TPS)
From the results recorded in the previous table, it was found that our treatment
(thermal treatment) is very effective for the hydrocarbon; besides the results of the
analysis of the sample after treatment respond fully to the norms (lower to the
maximum-tolerated values). But heavy metals retain the same concentration before
and after treatment. So, this method is effective for hydrocarbon and its inverse for
heavy metals.
0
10
20
30
40
50
60
5
6
7
8
9
10
Activated carbon (kg/m3)
oil (%)
Fig. 4 Changes in the content of hydrocarbons according to the concentration of the activated
carbon
150
200
250
300
14
16
18
20
22
24
26
28
30
32
34
cement (kg/m3)
Resistance (kg/cm2)
Fig. 5 Variation of the mechanical resistance as a function of the concentration of CPJ425 cement
Wastes of Oil Drilling: Treatment Techniques and Their Effectiveness
9
3
Conclusion
Based on the results obtained at the end of this study, we argue that oil discharges
present risks to the environment because of their compositions which exceed the
maximum limits conventional by the Algerian state (degree of contamination of
discharges of approximately 9.61% of the oil, so this value exceeded the 5% crud
waste).
According to the application of the methods of treatment, it is found that the
method of solidiﬁcation is effective for the trap of heavy metals, so the optimum
concentrations are 200 kg/m3 for the cement, 100 kg/m3 for the sand, 10 l/m3 for the
silicate, and 30 kg/m3 for the activated carbon.
On the other side, the thermal treatment is effective for the pollution of hydro-
carbons (some traces).
From the results shown in Table 3, it is found that the method of solidiﬁcation is
not a ﬁnal solution (according to the forces of free compression and moisture that
can liberate of toxic elements), so this is the ﬁltration of these toxic elements into
the water table.
For a good result of treatment, it is necessary to apply a combination of
solidiﬁcation and heat treatment.
References
Askri, H., Belmecheri, A., Benrabah, B., Boudjema, A., Boumendjel, K., Daoudi, M., Drid, M.,
Ghalem, T., Docca, A. M., Ghandriche, H., Ghomari, A., Guellati, N., Khennous, M., Lounici,
R., Naili, H., Takherist, D. et Terkmani, M. Geology of Algeria. WEC Algeries Algeria (2003)
Atlas, R.M.: Petroleum Microbiology, p. 685. Macmillan Publishing Company, New York (1984)
Cheriﬁ, M.: Drilling waste management for environmental protection in Hassi Messaoud Field,
Faculty Design and Technology, School Engineering, the Robert Gordon University, Aber-
deen, June 2006.
Cranford, P.J., Gordon Jr, D.C., Lee, K., Armsworthy, S.L., Tremblay, G.-H.: Chronic toxicity and
physical disturbance effects of water and oil-based drilling ﬂuids and some constituents on
adult sea Scallops (Placopecten magellaniccus). Mar. Environ. Res. 48, 225–256 (1999)
CRD: (Use of equipment catalog of heavy metals). Hassi Messaoud (2010)
Table 3 Results before and after treatment
Samples
Mass of oil expressed
in (g)
Test sample expressed
in (g)
Hydrocarbon
concentration (%)
Before
treatment
3.20
100
3.20
After
treatment
Traces
100
Traces
10
A.H. Abbas et al.
Damou, F.: The adaptability study for the use of thermal desorption and solidiﬁcation/stabilization
processes for the treatment of drill cuttings in HASSI MESSAOUD Field, Faculty of Design
and Technology, School of Engineering, The Robert Gordon University, Aberdeen, September
2007
Khodja, M.: Study of the Performance and Environmental Consideration. University Louis
Pasteur, Strasburg (2008)
Lefebrre, G.: Chemistry of Petroleum Corporation Editions Technip. Publication I.F.P (1978)
Ofﬁcial Gazette of the Republic of Algeria (10/07/1993)
Ofﬁcial Gazette of the Republic of Algeria (11/09/2005)
Ofﬁcial Gazette of the Republic of Algeria (22/05/2007)
Scriban, R.: Biotechnologie, 5e
`me e
´dition edn. Tec. et Doc. Lavoisier, Paris (1999)
Yacine, A.: Environmental impact assesment of the drilling activities in the Hassi Messaoud Field,
Faculty of Design and Technology, School of Engineering, The Robert Gordon University,
Aberdeen, June 2006
Wastes of Oil Drilling: Treatment Techniques and Their Effectiveness
11
Comparative Study of the Adsorption of Nickel
on Natural Bentonite and on Streptomyces
rimosus Dead Biomass
Faroudja Mohellebi and Radia Yous
1
Introduction
Pollution from heavy metals has become a serious problem for human health and
for the environment. Heavy metals are not biodegradable and tend to accumulate in
organisms, causing various diseases (Inglezakis et al. 2003). The existence of heavy
metals, such as copper (Cu), nickel (Ni), zinc (Zn), lead (Pb), mercury (Hg),
chromium (Cr), and cadmium (Cd) in wastewater, is the consequence of several
activities like chemical manufacturing, paint pigments, plastics, metallurgy, and
nuclear industry (Quintelas et al. 2009). Among the various diseases associated
with the presence of these toxic elements in the human body are neurotoxicity,
severe gastrointestinal irritation, and lung cancer (Jiang et al. 2009, Agouborde and
Navia 2009).
For the removal of these metals from wastewater, there are a series of processes
currently used for this object: chemical precipitation (Matlock et al. 2001), mem-
brane ﬁltration (Molinari et al. 2004), electrolytic reduction (Beauchesne et al.
2005), solvent extraction (Silva et al. 2005), ionic exchange (Pehlivan and Altun
2007), and adsorption (Ajmal et al. 1998). Most of these methods may not be
suitable at industrial scale, due to low efﬁciency or expensive applicability to a
wide range of pollutants, generation of residues, and difﬁculty in locating optimal
operating conditions when different heavy metals are present in a solution, and need
a pretreatment. Adsorption of various materials, such as activated carbon (Chen and
F. Mohellebi (*)
Ecole Nationale Polytechnique, De
´partement Ge
´nie Chimique, 10,
Avenue Hassen BADI, El-Harrach, Alger 16200, Algeria
e-mail: ferroudja.iddir@g.enp.edu.dz
R. Yous
Universite
´ Hassiba Ben Bouali, De
´partement de Technologie, BP 151, Chlef, Algeria
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_2
13
Wu 2004), biomaterials (Han et al. 2006), and clay minerals (Sharma 2008), is now
recognized as an efﬁcient and economic method to remove metal ions from aqueous
solutions.
In recent years, many porous materials found increasing interest as adsorbents
due to their abundance in nature, low cost, good cation adsorptive properties, and
large surface area.
Mineral materials used to remove heavy metals include bentonite, zeolite, and
montmorillonite. Low-cost biosorbents such as the dead biomass wastes from the
pharmaceutical industry have been intensively studied for the removal of metallic
ions from industrial efﬂuents.
The aim of the present work is to examine the possibility of using natural
bentonite and dead biomass of Streptomyces rimosus to remove nickel from aque-
ous solutions. The bentonite was obtained from SIG (western Algeria). The Strep-
tomyces rimosus biomass was obtained from the pharmaceutical industry.
The applicability of theoretical models, such as Langmuir and Freundlich, for
the equilibrium data ﬁtting was tested. The values of global diffusion coefﬁcients
and rate constants were calculated.
Nomenclature
B
Adsorption equilibrium constant (L.mg1)
Ce
Equilibrium concentration of metal ion in solution (mg.L1)
C0
Initial concentration of metal ion in solution (mg.L1)
Cs
Concentration of the metal ion at the particle surface (mg.L1)
Ct
Concentration of metal ion in solution at t time (mg.L1)
CB
Adsorbent concentration in the solution (g.m3)
CEC
Cation exchange capacity (meq/100 g)
Du
Diffusion coefﬁcient in the solid (m2.min1) according to Urano and Tachikawa
model
Dw
Diffusion coefﬁcient in the solid (m2.min1) according to Weber and Morris
model
dP
Particle size diameter (m)
K1
Adsorption rate constant for the ﬁrst-order adsorption (min1)
K2
Adsorption rate constant for the second-order adsorption (min1)
kf, nf
Freundlich’s adsorption constants
KW
Diffusion coefﬁcient in the solid (mg.g1.min1/2)
m
Mass of adsorbent (g)
qe
Amount of adsorbed heavy metal per unit adsorbent mass at equilibrium (mg.g1)
qm
Maximum adsorption capacity (mg.g1)
qt
Amount of adsorbed heavy metal per unit adsorbent mass at t time (mg.g1)
S
Surface area of the clay per unit solution volume (m1)
v
Volume of the metal solution (mL)
Greek
letters
β
External mass transfer coefﬁcient (m.min1)
ρapp
Apparent volume mass of the clay (g.m3)
14
F. Mohellebi and R. Yous
2
Diffusion Models
Various models of diffusion have been examined, including single steps of diffu-
sion in external or intraparticle or combined phenomena (Van Vliet et al. 1980;
Mathews and Weber 1984).
2.1
External Mass Transfer Diffusion Model
This model, which is an application of Fick’s law, describes the evolution of the
solute concentration in the solution Ct (mg.L1), as a function of the difference in
the concentrations of the metal ion in the solution, Ct, and at the particle surface CS
(mg.L1) according to Eq. (1)
∂Ct
∂t ¼ βS Ct  CS
ð
Þ
ð1Þ
The coefﬁcient is determined after making some assumptions such as a surface
concentration CS negligible at t ¼ 0, a concentration in solution tending to the initial
concentration C0, and also negligible intraparticle diffusion. So, the previous
equation can be simpliﬁed to
dCt
dt


t!0
¼ β S C0
ð2Þ
The initial rate of sorption, β S (min1), is obtained by polynomial lineariza-
tion of Ct/C0 and subsequent derivation at t ¼ 0.
The surface area is approximated as the external surface area. Moreover, the
particles are supposedly spherical and S is calculated as the external surface
compared to the solid/liquid ratio in the solution; thus,
S ¼ 6CB
dPρapp
ð3Þ
2.2
Intraparticle Mass Transfer Diffusion Model
The models chosen refer to theories developed by Weber and Morris (1962) and
Urano and Tachikawa (1991).
According to the intraparticle diffusion model proposed by Weber and Morris
(1962), the initial rate of intraparticle diffusion was calculated by linearization of
the curve qt ¼ f (t 0.5) and using Eq. (4)
Comparative Study of the Adsorption of Nickel on Natural Bentonite. . .
15
qt ¼ Kwt0:5
ð4Þ
where Kw is the diffusion coefﬁcient in the solid (mg.g1.min1/2) (Eq. (5)).
Kw ¼
12qe
dp

 Dw
π

0,5
ð5Þ
Another kind of intraparticle diffusion model was proposed by Urano and
Tachikawa (1991). The sorption kinetic data were modeled by the following
equation:
Log10 1 
qt
qe

2
"
#
¼ 4π2Dut
2:3 d2
p
ð6Þ
3
Kinetics of Adsorption
Adsorption kinetics is used in order to explain the adsorption mechanism and
adsorption characteristics.
3.1
Pseudo-First-Order Reaction Kinetics
The adsorption rate constant proposed by Lagergren (1898) and Ho (2004) using
ﬁrst-order reaction kinetics is shown below:
dqt
dt ¼ k1 qe  qt
ð
Þ
ð7Þ
The integration of Eq. (7) gives the following expression:
log qe  qt
ð
Þ ¼ logqe 
k1
2:303 t
ð8Þ
3.2
Pseudo-Second-Order Reaction Kinetics
Adsorption data were also evaluated according to the pseudo-second-order reaction
kinetics proposed by Ho and McKay (1998).
16
F. Mohellebi and R. Yous
dqt
dt ¼ k2 qe  qt
ð
Þ2
ð9Þ
If Eq. (9) is integrated, the following expression is obtained:
t
qt
¼
1
k2q2
e
þ t
qe
ð10Þ
4
Adsorption Isotherm Models
In this work, adsorption isotherms of bentonite and treated biomass for nickel ion
were expressed mathematically in terms of the Langmuir and Freundlich models.
The Langmuir equation, in the linear form, is written as:
Ce
qe
¼
1
qm:b þ Ce
qm
ð11Þ
For the Freundlich equation, the linear form is written as:
Logqe ¼ Log Kf þ 1
nf
Log Ce
ð12Þ
5
Materials and Methods
5.1
Adsorbent Characterization
The clay used in this study was collected from Maghnia bentonite deposit, 500 km
northwest of Algiers.
This bentonite sample, white in color, was cleaned, dried, and sieved into sizes
of 100 μm. Chemical and physical properties of the bentonite used for adsorption
experiments are presented in Tables 1 and 2, respectively. This adsorbent was used
directly for the experiments without any treatment.
For the divalent cations, magnesium is the element most dominating (46.0
meq/100 g), that is, to about four times the quantity of calcium (11.0 meq/100 g).
For monovalents cations, the most dominating element is sodium (36.7 meq/100 g).
S. rimosus biomass produced during oxytetracyclin antibiotic production was
collected after fermentation. The biomass was washed with distilled water and dried
at 50 C for 24 h. It was then crushed and sieved in order to select a fraction with
particle diameters ranging between 50 and 160 μm. The biomass was then treated
with 0.1 M NaOH for 30 min and, once again, washed, dried at 50 C for 24 h, and
sieved to retain the particle size fraction between 50 and 160 μm. In these condi-
tions, the solid particles can be considered as spheres with an average diameter of
Comparative Study of the Adsorption of Nickel on Natural Bentonite. . .
17
105 μm, which is the average of the 50–160-μm fraction. The obtained physical and
chemical characteristics of the biomass are reproduced in Table 3.
All experimental results were generally expressed as adsorption capacity “q”
which we deﬁne as follows:
q ¼ C0  Ct
ð
Þ
m
 V
ð13Þ
6
Results and Discussion
6.1
Adsorption Kinetics
Adsorption kinetics is used in order to explain the adsorption mechanism and
adsorption characteristics.
Table 1 Chemical
composition of bentonite (%)
SiO2
66.00
Al2O3
14.20
CaO
3.86
MgO
3.00
Fe2O3
2.42
Na2O
1.42
K2O
1.30
TiO2
0.34
MnO
0.03
P2O5
0.07
S
<0.1
L.O.I*
7.01
*Loss on ignition
Table 2 Physical
composition of bentonite
Color
White
pH
8
SG (g.cm3)
1.90
Size <103 mm
49%
Table 3 Physical and chemical characteristics of the biomass
Properties
Untreated biomass
NaOH-treated biomass
Particle size (μm)
50–160
50–160
Humidity (%)
3.2
4.4
Density (g/cm3)
0.43
0.41
Speciﬁc area (m2/g)
0.13
0.14
Zeta potential (V)
0.06
0.07
18
F. Mohellebi and R. Yous
The kinetics of removing nickel by dead biomass and untreated bentonite clay is
presented in Fig. 1.
The treated biomass adsorbent gave better adsorption capacity than the bentonite
as the contact time increased. It was observed that the bentonite consistently had a
constant adsorption capacity (18 mg.g1) for nickel all throughout the contact times
used which meant equilibrium was rapidly reached, and as quickly as 15 min, the
adsorption sites were already saturated to maximum uptake capacity. For biomass,
there was an initial increase in adsorption capacity, but after 60-min contact time,
the adsorption capacity remained constant (30 mg.g1).
The kinetic studies of adsorption of Ni (II) onto treated biomass and bentonite
were carried out using the ﬁrst-order and second-order models on experimental
data, and the values obtained are given in Table 4.
The regression coefﬁcients obtained from the pseudo-ﬁrst-order kinetic graph
were low. The second-order kinetic model kinetics gave high values of regression
correlation coefﬁcient as seen in Table 4. This implied that the mechanism of
adsorption of nickel on bentonite and treated biomass followed second-order
kinetics.
35
Qt(mg.g-1)
30
25
20
15
10
5
0
0
15 30 45 60 75 90 105 120 135 150 165
Time (min)
Bentonite
NaOH treated Biomass
180 195 210 225 240 255 270 285 300
Fig. 1 Adsorption kinetics of nickel on bentonite clay and on NaOH-treated biomass. Initial metal
concentration 100 mg.L1, adsorbent dosage 1 g/200 mL and pH ¼ 7
Table 4 Values of Ni2+ biosorption and adsorption constant rates
First-order reaction
Second-order reaction
K1 (min1)
R2
K2 (g. mg1.min1)
R2
Bentonite
0.026
0.677
0.07
0.999
Treated biomass
0.028
0.629
0.05
0.966
Comparative Study of the Adsorption of Nickel on Natural Bentonite. . .
19
6.2
Diffusion Models
Table 5 shows the values of the internal diffusion coefﬁcients Dw and Du, and of the
external mass transfer coefﬁcients β.
The above values show that resistance to external mass transfer is negligible
compared to internal distribution. Nonetheless, we must compare the experimental
results with those calculated by the kinetic and diffusional models. This comparison
is illustrated in Figs. 2 and 3.
From these ﬁgures, we ﬁnd that the kinetic model is closer to experimental
results.
Table 5 Parameters of diffusion models
β (m.min1)
external mass transfer
coefﬁcient
Dw (m2.min1)
according to Weber and
Morris model
Du (m2.min1)
according to Urano and
Tachikawa model
Bentonite
3.54. 105
1.5. 1010
0.8. 1010
Treated
biomass
1.2. 104
7.12. 1012
3.43. 1012
400
350
Cs(mg.L-1)
300
250
200
150
100
50
0
0
20
40
60
80
100
120
140
t(min)
Urano and Tachikawa model
Kinetic model
Weber and Morris model
Experimental data
Fig. 2 Comparison of the experimental results with the diffusion models (adsorbent: bentonite)
20
F. Mohellebi and R. Yous
6.3
Adsorption Isotherms
The best estimated values of all the equation parameters are summarized in Table 6.
The adsorption isotherm data well ﬁtted with the linearized Langmuir equation
and provided R2 ¼ 1 for bentonite and R2 ¼ 0.998 for biomass.
7
Conclusion
A comparison of two low-cost adsorbents (bentonite and NaOH-treated biomass)
showed that NaOH-treated biomass as an adsorbent was more efﬁcient in metal ion
removal from solution when compared with bentonite.
These results show that bentonite and biomass can be used effectively for the
removal of Ni(II).
350
adsorption by Biomass
Experimental data
Kinetic model
Weber and Morris model
Urano and Tachikawa model
300
250
200
150
100
50
0
0
20
40
60
80
Time (min)
qt(mg.g-1)
Fig. 3 Comparison of the experimental results with the diffusion models (adsorbent: treated
biomass)
Table 6 The parameters for Langmuir and Freundlich isotherms
Langmuir
Freundlich
b
qm(mg.g1)
R2
Kf
nf
R2
Bentonite
0.45
20
1
46.55
3.62
0.900
Treated biomass
0.08
32.5
0.998
7.84
4.44
0.885
Comparative Study of the Adsorption of Nickel on Natural Bentonite. . .
21
Considering the abundance and low price of these adsorbents and their physical
and chemical characteristics, they are materials to be efﬁciently applied in the
environmental industry. They can be used in the treatment of mining and/or
industrial efﬂuents with metallic contents above the standard values considered
by the corresponding legislation.
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oped from a mixture of industrial and agricultural wastes. J. Hazard. Mater. 167, 536–544
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on natural clinoptilolite: selectivity determination and inﬂuence of acidity on metal uptake.
J. Colloid. Interface. Sci. 261, 49–54 (2003)
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bagasse. J. Hazard. Mater. 164, 1–9 (2009)
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oster stoffe. K. Sven. Vetenskapsakad.
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Mathews, A.R., Weber Jr., A.W.J.: Modeling and parameter evaluation for adsorption in slurry
reactors. Chem. Eng. Commun. 25(1–6), 157–171 (1984)
Matlock, M.M., Howerton, B.S., Atwood, D.A.: Irreversible precipitation of mercury and lead.
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solution by Lewatit CNP 80. J. Hazard. Mater. 140, 299–307 (2007)
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Comparative Study of the Adsorption of Nickel on Natural Bentonite. . .
23
Process Simulation and Energy Consumption
Analysis for CO2 Capture with Different
Solvents
Boyang Xue, Yanmei Yu, and Jian Chen
1
Introduction
Carbon dioxide capture and storage (CCS) technology is considered to be the most
effective technology to reduce greenhouse gas emissions in the future (Jones and
Wigley 1990). The cost of CCS technology applied in fossil fuel power plant is
about 40–60 $/t CO2 (Fei et al. 2005). And the electricity price will increase by 45%
when coupled with CCS (Le Moullec and Kanniche 2011b). Among all the parts of
the cost, the CO2 capture process plays a major role (Fei et al. 2005). Thus, the most
crucial problem CCS faces now is that the energy consumption of capture and
separation process is tremendous. The absorption with amine solutions is the most
reliable and efﬁcient method of CO2 capture, which is widely applied in fossil fuel
power plants at present. Several studies are found in the literature that discuss the
two main paths to reduce energy consumption in CO2 capture process, developing
new solvents and optimization of the process conﬁgurations (Oyenekan and
Rochelle 2007; Aroonwilas and Veawab 2007; Le Moullec and Kanniche 2011a;
Cousins et al. 2011).
Many kinds of amine have been studied in CO2 capture process, such as
monoethanolamine (primary amine, MEA), diethanoamine (secondary amine,
DEA) (Diab et al. 2013), methyldiethanoamine (tertiary amine, MDEA) (Zhang
and Chen 2010) and aminomethylpropanol (sterically hindered primary amine,
AMP) (Li et al. 2013), piperazine (heterocyclic amine, PZ) (Li et al. 2014), and
so on.
But at present MEA is still considered to be the main solvent in aqueous
alkanolamine-based capture processes because of its high absorption rate and low
B. Xue • Y. Yu • J. Chen (*)
State Key Laboratory of Chemical Engineering, Tsinghua University, Beijing 100084, China
e-mail: cj-dce@tsinghua.edu.cn
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_3
25
solvent cost, as well as the fact that MEA is easy to regenerate (Aaron and Tsouris
2005).
As the heat of reaction with CO2 in MEA is quite high (around 85 kJ/mol CO2), it
leads to a high energy requirement for stripping. DEA is also suitable for
low-pressure operations and has a lower heat of reaction with CO2 (around 70 kJ/
mol CO2). Secondary amines, like DEA, are much less reactive to sulfur compo-
nents and their reaction products are not particularly corrosive. All these factors
make DEA an attractive option for CO2 capture. However, a disadvantage of DEA
is that it exhibits slow kinetics (Kohl and Nielsen 1997; Carson et al. 2000;
Gabrielsen et al. 2005; Galindo et al. 2012; Warudkar et al. 2013).
Le Moullec et al. (2014) reviewed 20 different process modiﬁcations, which are
almost exhaustive, for numerous publications so far. However, most studies eval-
uate process modiﬁcation for MEA solvent only and the interaction between
solvent and process is ignored. Therefore, it is worth investigating the energy
consumption of different amine solvents in different process.
As a result, this work proposes a comparative study on CO2 capture process ﬂow
sheet modiﬁcations between MEA and DEA to decrease their energy consumptions.
Including the conventional capture process, 10 process ﬂow sheet modiﬁcations are
evaluated at ﬁrst step, which are inspired by the work of Le Moullec et al. (2014).
The simulations are carried out with commercial software to calculate energy
consumption for different process ﬂow sheets for a power plant and with a CO2
compression process. For further study and discussion, a detailed analysis is
presented to study the effect of some signiﬁcant parameters in capture process
and the total energy consumptions of each condition are evaluated and compared.
2
Simulation Hypothesis
As many commercial simulation softwares perform well in process simulation such
as Aspen Plus, ProMax, PRO/II, CO2SIM, and so on, PRO/II (version 9.0) is
selected to be the simulation software in this work due to the simplicity and
usability that fulﬁll the purpose of this study. With the amine packages in PRO/II,
results obtained for MEA and DEA are accurate enough for use in ﬁnal design
work, because the parameters have been regressed from a large number of sources
for MEA and DEA systems, resulting in good prediction of phase equilibrium. The
accuracy of the simulations using PRO/II can also be validated in the following
part. In the simulation, the property system uses the amine packages, which were
already implemented in PRO/II, and electrolyte algorithm is calculated in both
vapor and liquid phases. An equilibrium stage is assumed in absorber and stripper
of all the processes. Although equilibrium models are known to give qualitatively
different results from rate-based models, equilibrium models are less complex to
solve than rate-based models. On the other hand, the reaction rates of amines like
MEA and DEA are fast enough when large theoretical stages or packed height were
implemented. Kinetics has little effect and the deviations between simulation and
26
B. Xue et al.
experiment are acceptable. Thus, the ﬁrst step of approximation to study the
optimization strategy in capture process is equilibrium models and proposed meth-
odology will be then extended to rate-based models (Rodriguez et al. 2011).
2.1
Chemical Equilibrium of Amine System
The chemistry of aqueous primary and secondary amines scrubbing CO2, like MEA
and DEA, behave similarly in thermodynamics. In aqueous solutions, CO2 reacts in
an acid–base buffer mechanism with alkanolamines. The acid–base equilibrium
reactions in PRO/II are written as chemical dissociations following the approach
taken by Kent and Eisenberg (1976):
H2O $ Hþ þ OH
ð1Þ
CO2 þ H2O $ HCO
3 þ Hþ
ð2Þ
HCO
3 $ CO2
3
þ Hþ
ð3Þ
REACOO þ H2O $ REAH þ HCO
3
ð4Þ
REAH þ Hþ $ REAHþ
2
ð5Þ
where R represents an alkyl group, and here REA equals to MEA, DEA. The
chemical equilibrium constants for the dissociation reactions are represented by
polynomials in temperature as follows:
ln Ki ¼ A þ B
T þ C
T2 þ D
T3
ð6Þ
2.2
Conventional CO2 Capture Process Simulation
Validation
A typical CO2 capture process, shown in Fig. 1, mainly consists of absorber,
stripper, and heat exchanger. As the ﬁgure shows, the ﬂue gas enters into the bottom
of absorber and contacts with the countercurrent lean CO2 loading solvent ﬂow
introduced from the top of the column. After CO2 absorption, amine solvent
becomes a rich CO2 loading ﬂow, which then exits absorber from the bottom and
is pumped to stripper to desorb CO2. Before being injected into stripper, the cold
rich solvent will be preheated by hot, lean solvent exiting from the bottom of
stripper. Heated rich solvent enters into stripper to release CO2 and then becomes
lean solvent again. Pure CO2 ﬂow can be collected from the top of stripper for
further processing and the amine solvent is cycled in two columns to capture CO2
continuously.
Process Simulation and Energy Consumption Analysis. . .
27
The work of Cousins et al. (2012) presented a lot of experimental data on CO2
capture pilot plant. This work selected the pilot plant data of 1/02/2011, 24/03/
2011, 31/03/2011 to validate the simulation process, and the comparison of simu-
lation and experiment is shown in Table 1. As all the parameters were kept the same
as in the literature, good agreements on rich loading and reboiler temperature are
obtained. Because of the neglect of kinetics, around 10% of deviation on CO2
capture ratio and reboiler duty is acceptable. These results can validate the accuracy
of process simulation to some extent.
In process simulation of this work, the ﬂue gas is made up of 10% CO2, 6% H2O,
and 84% N2 in volume, and the ﬂue gas enters at 40 C, 1.2 bar. As mentioned
before, ideal equilibrium stages are used in simulating both absorber and stripper,
and the stage number is 10, which is a feasible amount of stages proved in previous
work (Warudkar et al. 2013). The operating pressure of absorber is 1 bar, stripper is
1.5 bar, and 0.1 bar pressure drops in the two column. The temperature pinch of heat
exchanger is 10 C. For reference simulation, 30 wt% MEA and 40 wt% DEA
aqueous amine are used to capture 90% CO2 of ﬂue gas, and the CO2 lean loading is
set at 0.25 mol CO2/mol MEA and 0.1 mol CO2/mol DEA, which are all at their
typical concentrations in various literatures and practice. All of these parameters
are kept constant in the simulation of following process modiﬁcations in order to
make effective comparison in energy consumption and optimization strategy. For
further study and discussion in this work, parameter changes will be highlighted
individually.
Fig. 1 Conventional CO2 capture process
28
B. Xue et al.
3
Process Modiﬁcations Description
Some studies and reviews of process modiﬁcation have already been published in
open literatures (Oyenekan and Rochelle 2007; Le Moullec and Kanniche 2011a;
Cousins et al. 2011; Ahn et al. 2013; Le Moullec et al. 2014), which contain a
variety of amine-based capture process modiﬁcations for the purpose of energy
consumption reduction. Le Moullec et al. (2014) reviewed 20 different process
modiﬁcations, which are almost exhaustive, for numerous publications so far.
However, most studies evaluate process modiﬁcation for MEA solvent only and
the interaction between solvent and process needs to be considered. Therefore, it is
worth investigating the energy consumption of different amine solvents in different
processes. In this work, nine different process modiﬁcations are simulated using
MEA and DEA solvent to study the energy consumption, and comparing them with
the reference process.
3.1
Intercooled Absorber (ICA)
Intercooled absorber is a widely studied and used modiﬁcation (Aroonwilas and
Veawab 2007; Karimi et al. 2011). Absorption of CO2 is an exothermic process that
will lead to the temperature rise in the absorber. This has a negative effect on
thermodynamic driving force for absorption and it results in lowering the solvent
absorption capacity. Figure 2 illustrates that this modiﬁcation is to remove a part or
all of the liquid ﬂow from the absorber at one of its stages, cooling it, and then
injecting it back at the same part. Intercooled absorber is efﬁcient in control of the
temperature in the absorber column, which can increase the carrying capacity of the
solvent and hence reduce the required amount of recycling solvent as well as the
Table 1 Comparison of pilot plant results with the simulation results for MEA
Date
Rich loading (mol CO2/mol
MEA)
Treboiler (GJ/t
CO2)
CO2 capture
(%)
Qreb (GJ/t
CO2)
1/02/
2011
Literature
0.466
116.9
75.5
4.0
Pro/ii
0.477
116.1
80.1
3.6
24/03/
2011
Literature
0.486
117.1
77.7
4.2
Pro/ii
0.481
116.2
79.7
3.6
31/03/
2011
Literature
0.472
116.5
72.2
3.9
Pro/ii
0.475
115.8
75.1
3.4
Process Simulation and Energy Consumption Analysis. . .
29
size of equipment. In simulation work, the ﬁfth stageis cooled to the temperature of
45 C for MEA and DEA. As a result, the rich CO2 loading reaches 0.492 mol CO2/
mol MEA, which is 0.465 mol CO2/mol MEA in conventional process. For DEA,
0.468 mol CO2/mol DEA obtained as only 0.447 mol CO2/mol DEA in reference. It
is found that the recycled lean amine solvent is reduced by 11.5% for MEA and
4.7% for DEA. Thus, 7.1% of reboiler duty is saved by MEA, and DEA gains 2.8%.
ICA is more efﬁcient for MEA than DEA because the heat of reaction with CO2 is
higher for MEA. In such favorable process in thermodynamics, MEA gains more
beneﬁts by cooling in absorber.
3.2
Flue Gas Precooling (FGP)
Flue gas precooling is a simple modiﬁcation discussed in the work of Tobiesen et al.
(2007) and Le Moullec and Kanniche (2011a). As Fig. 3 shows, ﬂue gas is cooled to
a lower temperature before being introduced to absorber. The principle of ﬂue gas
precooled is similar to that of the intercooled absorber to some extent, which also
lowers the temperature of vapor–liquid mixture in absorber and enhances CO2
absorption in thermodynamic aspect. Thus, higher rich loading solvent and less
reboiler duty are foreseeable. Flue gas is cooled to 30 C in our simulation, and
around 5% reduction in reboiler duty is achieved with MEA, compared with a 2%
saving with the DEA case.
Fig. 2 Intercooled absorber (ICA)
30
B. Xue et al.
3.3
Rich Solvent Split (RSS)
This process modiﬁcation was suggested way back by Eisenberg and Johnson
(1979). In Fig. 4, it splits the cold rich loaded solvent into two ﬂows, and the split
one remains unheated when it enters the top of stripper, while the other one is
heated in the lean/rich heat exchanger and it is injected at lower stage. With the rich
split modiﬁcation, the heated rich solvent can reach a higher temperature at which
CO2 can desorb more easily. Meanwhile, the vapor released from the rich solvent
meets with the cold solvent injected above, which is able to strip a little CO2 from
it. Thus, there is a reduction in reboiler duty. At ﬁrst, 10% of the rich solvent
unheated is split to the top of stripper in our study. There is a saving in reboiler duty
of 7.7% from reference in MEA and 7% in DEA. RSS has neutral effect on rich
loading and solvent required as the absorption process remains the same.
In published literatures, rich solvent split often combines with other modiﬁca-
tions such as rich solvent preheating and split ﬂow, which is discussed in the
following. All these process modiﬁcation combinations have similar principle and
reduce energy consumption in the same way.
3.4
Rich Solvent Pre-heating (RSP)
As Herrin (1989) proposed, the cold rich solvent can be heated by the hot vapor
exiting the stripper, as Fig. 5 shows, which can make use of the latent heat and
reduce the cooling water required in stripper condenser. It seems to be efﬁcient
because the rich solvent can be heated twice. However, due to the temperature of
the hot vapor is exactly similar with the rich solvent temperature after heated by hot
lean solvent, even a little lower, the heat transfer cannot exist if all rich solvent is
Fig. 3 Flue gas precooling (FGP)
Process Simulation and Energy Consumption Analysis. . .
31
heated. No energy reduction is obtained in the simulation of MEA or DEA. But
obvious beneﬁts are gained if combining rich solvent preheating with rich solvent
split (Ahn et al. 2013); contact with a fraction of cold rich solvent can break the heat
transfer limit. Then the wasted heat can be used and other principles of energy
saving are the same with rich solvent split – no more tautology or simulation here.
Fig. 4 Rich solvent split (RSS)
Fig. 5 Rich solvent preheating (RSP)
32
B. Xue et al.
3.5
Solvent Split Flow (SSF)
The modiﬁcation of split ﬂow was ﬁrst proposed by Shoeld (1934), consisting of a
partial regeneration cycle of lean solvent. A ﬂow of semi-lean solvent is taken from
the middle of the stripper, having heat exchange with the cold rich solvent and is
injected to the middle of absorber. Among all the variants of split ﬂow modiﬁca-
tions, the most common one is described by Leites et al. (2003) and Aroonwilas and
Veawab (2007), as shown in Fig. 6. It is a combination of two modiﬁcations: simple
split ﬂow and rich solvent split. Furthermore, as the semi-lean solvent is cooled
down before entering absorber, it also takes a little bit advantage of ICA. Many
parameters need to be taken into account to reach a minimal energy consumption;
for example, the stages to draw off semi-lean solvent from stripper and inject into
absorber, the ﬂow rate of cold rich solvent split fraction and semi-lean solvent, and
the introduced stage of hot rich solvent. In principle and simulation, the semi-lean
stream is drawn off from the middle of stripper to provide the cold rich solvent split
with more heat. Since less rich solvent contacts with the hot lean solvent leaving
stripper, hot inlet stream reaches higher temperature, and then if it is injected at
lower part of stripper, energy saving is further allowed. Optimal energy savings are
found in simulation when taking all these factors into account. As a result, simu-
lation shows that SSF can lead to a 7.6% cut in reboiler duty in MEA case,
correspondingly 7.8% in DEA case.
It is worth mentioning that the required amount of circulating solvent becomes
larger in the solvent split ﬂow modiﬁcation than in the conventional process
because the average solvent working capacity is lowered. Bigger equipments
such as columns and pumps are required to match with the ﬂow rate.
Fig. 6 Solvent split ﬂow (SSF)
Process Simulation and Energy Consumption Analysis. . .
33
3.6
Rich Solvent Flashing (RSF)
The principle of the modiﬁcation of rich solvent ﬂashing is to ﬂash the inlet stream
of stripper before entering, as Fig. 7 illustrates. By ﬂashing the hot rich solvent, a
little more CO2 is gained, whereas vaporization lowers the temperature of liquid
phase. In fact, this ﬂashing process is just like completing separation process once
at an ideal stage, so the phenomenon in which occurs happens in the top stage in
stripper. As a result, this modiﬁcation does not obviously reduce energy consump-
tion except providing one more stripping stage. Simulation result in this work is the
same as what Le Moullec and Kanniche (2011a) claimed.
3.7
Stripper Condensate Bypass (SCB)
In the modiﬁcation of stripper condensate bypass, the condensate liquid is not fed
back to the top of stripper. Instead, this stream is directly injected to the absorber.
This modiﬁcation is used in the work of Oexmann and Kaher (2009) as Fig. 8. The
simulation of this work provide a 0.6% reboiler duty saving with MEA and 0.4%
with DEA, that is, stripper condensate bypass almost makes no difference in
limiting energy consumption. Because of the small ﬂow rate of condensate, the
duty saving for heating it in stripper is restricted.
Fig. 7 Rich solvent ﬂashing (RSF)
34
B. Xue et al.
3.8
Stripper Condensate Heating (SCH)
The modiﬁcation of stripper condensate heating is proposed and studied in
Aroonwilas and Veawab (2007) and Ahn et al. (2013) as Fig. 9. As vapor temper-
ature in the top of the stripper is high, stripper condensate heating is to make use of
this to heat the stripper condensate, and then feeding the hot condensate back to the
bottom of stripper to provide a little heat recovery. Nevertheless, it has been proved
by theoretical analysis and simulation in this work that there is insigniﬁcant gain in
energy consumption. Only 1% of reboiler duty is reduced both for MEA and DEA.
3.9
Lean Vapor Compression (LVC)
Lean vapor compression is one of the most widely suggested modiﬁcations in a
variety of literatures and patents, such as Batteux and Godard (1983), Reddy et al.
(2007), and Woodhouse and Rushfeldt (2008). As Fig. 10 shows, the principle is to
ﬂash the hot lean solvent at a lower pressure, then compress the hot vapor generated
and reinject it into the bottom of stripper. As the vapor beneﬁts from the sensible
heat of hot lean solvent as well as recompression, it can reach a very high pressure
and temperature, which can provide additional steam and heat in the column for
stripping. In the simulation, the hot lean solvent is ﬂashed to the atmospheric
pressure and this modiﬁcation shows signiﬁcant savings in reboiler duty. With
MEA, a 12.8% of reduction is obtained, and as for DEA, LVC allows a gain of
11.9% of reduction in reboiler duty. However, it should be noted that as a com-
pressor is introduced here, it leads to the additional electricity consumption that
cannot be neglected. The adiabatic efﬁciency of the compressor is 80% in
Fig. 8 Stripper condensate bypass (SCB)
Process Simulation and Energy Consumption Analysis. . .
35
simulation, and the performance of total energy saving compared with the conven-
tional process will be discussed in detain in the following.
4
Results and Discussions
Preliminary simulation results have been presented in the previous part; process
modiﬁcations description, and detailed simulation results and further discussion
will be demonstrated in the following paragraphs, including process operating
parameter adjustments, and total energy consumption is calculated for comparison.
Fig. 9 Stripper condensate heating (SCH)
Fig. 10 Lean vapor compression (LVC)
36
B. Xue et al.
4.1
Total Work Calculation
As mentioned before, the process modiﬁcation of LVC introduces a compressor to
generate vapor with high pressure and temperature, and the electricity consumption
should not be neglected. Therefore, it is essential to investigate the total energy
consumption to make a global comparison with the conventional process.
The equivalent work (Weq) is commonly used to evaluate the process conﬁgu-
ration performance to unify the thermal and electrical energy consumptions. As
there are a variety of expressions in calculating the total equivalent work, such as Le
Moullec and Kanniche (2011a), Ahn et al. (2013), and Van Wagener et al. (2013),
we ﬁnally calculate the total equivalent work for this work by the following
equation from Van Wagener and Rochelle (2011) and Liang et al. (2015):
Weq ¼ 0:75  Qreb
Ti þ 10K  Tsink
Ti þ 10K


þ Wcomp þ Wadd
ð7Þ
It uses a Carnot efﬁciency term that accounts for the increasing value of steam at
high temperature. Additionally, 75% efﬁciency is applied to account for nonideal
expansion in the steam turbines. Ti is the reboiler temperature (K); 10 K means the
temperature of steam in the reboiler is 10 K higher than Ti; Qreb is the reboiler duty
(GJ/t CO2); Tsink is the cold end temperature of Carnot engine, and set at 313 K
here; Wcomp is the compression work (GJ/t CO2); Wadd is the additional equipment
work such as the compressor in LVC (GJ/t CO2).
As for calculating the compression work, the simple following correlation can be
used:
Wcomp ¼ 8:3673 þ 22:216 ln PF  27:118 þ 0:0256PF
ð
Þ ln PS
ð8Þ
where Wcomp is the compression work (kWh/t CO2); PF is the ﬁnal delivery
pressure, and PF is set here as 110 bar; PS is the initial pressure of compression,
which equals to the stripper pressure.
The total equivalent work of each of the process modiﬁcations described previ-
ously is shown in Table 2 for MEA and Table 3 for DEA. All the process
modiﬁcations apart from RSF and RSP exhibit lower energy consumption for
MEA. As for DEA, only RSF has negative effect.
4.2
Effect of Amine Concentration and Lean Solvent
Loading
The loading of the lean amine solution is a signiﬁcant factor in reducing the energy
consumption. More solvent is required to be circulated when the lean loading is
high in order to capture the same amount of CO2. The reboiler heat duty is rather
sensitive to the solvent ﬂow rate as the vaporization of water for CO2 stripping
Process Simulation and Energy Consumption Analysis. . .
37
contributes most to the reboiler duty at low solvent ﬂow rate values. If lean loading
is extremely low, more heat is provided by the reboiler duty as the heat of reaction
between amines and CO2 accounts for the majority. As for amine concentration, it
will affect the solvent capture capacity because low rich loading will be obtained if
a more concentrated solution is used. And the proportion of water increases when
diluted solution is implemented. These will all lead to a further reduction of reboiler
duty. It can be observed in Figs. 11 and 12 that the optimal lean loading increases
with MEA concentration rising. The minimum of reboiler duty occurs at approx-
imately 0.17 mol CO2/mol MEA in 30 wt% MEA. When DEA was used, it was
noticed that irrespective of the concentration used, the optimal lean loading is
obtained around 0.05 mol CO2/mol DEA. It also can be concluded that the reboiler
duty is more sensitive to lean loading in process using MEA. And these curves
reveal furthermore that at higher amine concentrations, the ﬂexibility of process
increases because change in the lean loading will have a minor effect. Galindo et al.
(2012) and Dinca (2013) also claimed the same point of view.
Table 2 Total equivalent work of process using MEA
Modiﬁcations
Rich Loading (mol
CO2/mol MEA)
Qreb (GJ/t
CO2)
Wadd
(GJ/t
CO2)
Weq (GJ/t
CO2)
Total Energy
Savings (%)
Conventional
0.465
3.460
0
0.911
ICA
0.492
3.216
0
0.873
4.20
FGP
0.485
3.278
0
0.883
3.17
RSS
0.465
3.192
0
0.869
4.61
RSP
0.465
3.461
0
0.912
0.02
SSF
0.463
3.196
0
0.870
4.54
RSF
0.465
3.634
0
0.939
3.03
SCB
0.465
3.432
0
0.908
0.38
SCH
0.465
3.411
0
0.904
0.80
LVC
0.465
3.018
0.039
0.876
3.87
Table 3 Total equivalent work of process using MEA
Modiﬁcations
Rich Loading (mol
CO2/mol DEA)
Qreb (GJ/t
CO2)
Wadd
(GJ/t
CO2)
Weq (GJ/t
CO2)
Total Energy
Savings (%)
Conventional
0.447
3.168
0
0.856
ICA
0.468
3.078
0
0.842
1.64
FGP
0.468
3.080
0
0.842
1.60
RSS
0.447
2.945
0
0.821
4.06
RSP
0.447
3.153
0
0.854
0.27
SSF
0.440
2.921
0
0.818
4.50
RSF
0.447
3.302
0
0.877
2.44
SCB
0.447
3.131
0
0.850
0.73
SCH
0.447
3.136
0
0.851
0.58
LVC
0.447
2.791
0.0368
0.833
2.70
38
B. Xue et al.
4.3
Effect of CO2 Concentration in the Flue Gas
The CO2 content in the ﬂue gas of typical coal-ﬁred power plants lies in the range of
12–15 vol% (wet), and in natural gas combined cycle power plant, the CO2
concentration will drop to below 5 vol%. It will be of value to explore how CO2
concentration affects the energy consumption in two different amines. Figure 13
3.2
3.3
3.4
3.5
3.6
3.7
3.8
3.9
4
4.1
0.05
0.1
0.15
0.2
0.25
0.3
Reboiler duty (GJ/t CO2) 
Lean loading (mol CO2/mol MEA)
20wt% MEA
30wt% MEA
40wt% MEA
Fig. 11 Effect of MEA concentration and CO2 loading of lean solvent on reboiler duty
2.8
2.9
3
3.1
3.2
3.3
3.4
3.5
3.6
3.7
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
Reboiler duty (GJ/t CO2)
Lean loading (mol CO2/mol DEA)
30wt% DEA
40wt% DEA
50wt% DEA
Fig. 12 Effect of DEA concentration and CO2 loading of lean solvent on reboiler duty
Process Simulation and Energy Consumption Analysis. . .
39
illustrates the simulation results in conventional process when varying CO2 con-
centration from 5 to 20 vol%. As expected, the reboiler duty decreases with CO2
concentration rising up correspondingly both for MEA and DEA. A noticeable
change in reboiler duty is observed when using MEA. In contrast, there is no
signiﬁcant difference for DEA when CO2 is more than 10 vol%. These results are
caused by the difference from the heat of reaction.
4.4
Effect of Stripper Pressure
It is a common view that the operating pressure of the stripper is a key parameter of
reboiler duty reduction, which has been reported in many publications such as
Oyenekan and Rochelle (2007). There is also a process modiﬁcation proposed by
Oyenekan and Rochelle (2006) and Le Moullec and Kanniche (2011a), which is to
operate the stripper at vacuum/subambient pressure. CO2 desorption becomes
easier as stripper pressure is high. From another perspective, if stripper pressure
is lower, lower pressure steam is required for solvent regeneration because the
reboiler temperature goes down. Therefore, the inﬂuence of stripper pressure
should be evaluated in total equivalent work to search for the optimal strategy.
Figure 14 illustrates the results of simulation for conventional process, and it
indicates that both for MEA and DEA, higher pressure is beneﬁcial to reducing
total energy consumption.
3.1
3.15
3.2
3.25
3.3
3.35
3.4
3.45
3.5
3.55
3.6
0%
5%
10%
15%
20%
25%
Reboiler duty (GJ/t CO2)
CO2 concentration in flue gas (vol)
MEA
DEA
Fig. 13 Effect of CO2 concentration in the ﬂue gas on reboiler duty
40
B. Xue et al.
4.5
Effect of Lean Solvent Loading for Process Modiﬁcations
As the principle and simulation results mentioned previously, the loading of the
lean amine solution is of great signiﬁcance in the reducing of energy consumption.
And the simulation results indicated that the reboiler heat duty is rather sensitive to
the lean loading. Thus, it is essential to simulate all the processes to come up with
the optimal energy saving strategies. The process modiﬁcation of ICA, RSS, SCH,
LVC, SSF are selected to make comparison with the conventional process
according to previous simulation results and discussion, as these conﬁgurations
present better performance in terms of reducing energy consumption. The result
shows in Fig. 15 for MEA and Fig. 16 for DEA. As for MEA, all the total equivalent
work of these processes has a minimum point as the lean loading is increasing. In
conventional process, ICA, and SCH, the minimums occur at approximately
0.18 mol CO2/mol MEA, and it rises to 0.22 mol CO2/mol MEA for RSS and
SSF. In contrast, minimum of LVC appears at around 0.16 mol CO2/mol MEA
because the heat provided by compressed vapor is quite effective. At lower lean
CO2 loading, the total equivalent work of RSS, SCH, and SSF is higher than
conventional process due to a larger amount of circulating solution. As a whole,
the total equivalent works of these conﬁgurations for MEA are in the following
order: LVC < ICA < RSS < SSF < SCH < conventional process.
On the other hand, the results of processes using DEA appear somewhat differ-
ent from MEA. The trends of conventional process, SCH, LVC are quite the same,
as all of them have a minimum point at the lean CO2 loading of about 0.15 mol CO2/
mol DEA. ICA raises this point to 0.2 mol CO2/mol DEA while SSF lowers it to
0.1 mol CO2/mol DEA. The total equivalent work of RSS has an obvious change as
the minimum point occurs at 0.15 mol CO2/mol DEA. The energy consumption of
0.85
0.86
0.87
0.88
0.89
0.9
0.91
0.92
0.2
0.7
1.2
1.7
2.2
2.7
Total equivalent work (GJ/t CO2)
Stripper pressure (bar)
MEA
DEA
Fig. 14 Effect of stripper pressure on total equivalent work in the conventional process
Process Simulation and Energy Consumption Analysis. . .
41
SSF is higher than the conventional process due to lower working capacity of amine
and a larger amount of circulating solution. In general, the total equivalent works of
these
conﬁgurations
for
DEA
are
in
the
following
order:
RSS ~ SSF < LVC < ICA < SCH < conventional process. Compared with
MEA, it can be concluded that RSS or the variant of RSS is more efﬁcient than
DEA, because CO2 is easier released from DEA solution in thermodynamics. And
ICA is more favorable to MEA, which is also the reason from the difference of
absorption heat. LVC gains beneﬁts both for MEA and DEA.
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98
1
0.1
0.12
0.14
0.16
0.18
0.2
0.22
0.24
0.26
Total equivalent work (GJ/t CO2)
Lean loading (mol CO2/mol MEA)
Conventional
ICA
RSS
SCH
LVC
SSF
Fig. 15 Compare total equivalent work between different process conﬁgurations at different lean
loading of MEA
0.81
0.82
0.83
0.84
0.85
0.86
0.87
0.88
0.89
0.9
0.91
0
0.05
0.1
0.15
0.2
0.25
0.3
Total equivalent work (GJ/t CO2)
Lean loading (mol CO2/mol DEA)
Conventional
ICA
RSS
SCH
LVC
SSF
Fig. 16 Compare total equivalent work between different process conﬁgurations at different lean
loading of DEA
42
B. Xue et al.
5
Conclusions
In this work, including the conventional process, ten different process conﬁgura-
tions have been simulated both for MEA and DEA on the same operating condi-
tions, such as ﬂue gas composition, CO2 loading of lean amine solution, amine
concentration, CO2 capture ratio, and so on. To make a more valuable and com-
prehensive evaluation on energy consumption reduction, the performance is
presented in terms of the total equivalent work as well as reboiler duty. It is
worth mentioning that all simulations are restrained to maintain the temperature
of amine solution below 120 C, to avoid the degradation of MEA and DEA. As a
result, process modiﬁcations are proved to be an efﬁcient way to optimize the
energy consumption in CO2 capture process using MEA and DEA. It has been
shown that ICA, RSS, SSF, LVC are favorable in MEA, as from 3.87% to 4.61% of
total equivalent work is reduced, respectively, in preliminary simulation; and for
DEA, RSS, SSF, LVC have better performance, as from 2.70% to 4.50% is reduced.
Meanwhile, this work presents the inﬂuence of four operating parameters in
energy savings, namely, amine concentration, loading of lean amine solvent, CO2
concentration in the ﬂue gas, and stripper pressure. All these factors vary in both
conventional process and process modiﬁcations with MEA and DEA. The study of
amine concentration and lean loading shows that the optimal lean loading increases
with MEA concentration rising, but it basically keeps constant in DEA. Moreover,
reboiler duty is more sensitive to lean loading in process using MEA than DEA.
When changing the CO2 concentration in the ﬂue gas, a more signiﬁcant change in
reboiler duty is observed when using MEA and less for DEA when CO2 is more
than 10 vol%. The effect of lean solvent loading on process modiﬁcations for MEA
and DEA is quite different, and the minimum point of total equivalent work also
depends on amine type and process. But, for both MEA and DEA, higher pressure is
beneﬁcial to reducing total energy consumption in all of the processes. Approxi-
mately, 10% reduction can be obtained in process modiﬁcations using MEA and 8%
in DEA. LVC has the best performance when implement higher stripper pressure is
used, and 9% of reboiler duty reduction is obtained in MEA and 8% in DEA.
The comparative study and evaluation of process modiﬁcations between MEA
and DEA are proposed in this work, which present the inﬂuence of the interaction
between solvent and process. It is essential in post-combustion process design to
make optimization strategy. Further work will be continued with different solvents,
such as MDEA, AMP, PZ, or amine blends in different processes.
Acknowledgments This work was supported by China Natural Science Foundation (key project
No.51134017), EU FP7 Marie Curie International Research Staff Exchange Scheme (Ref:
PIRSES-GA-2013-612230), and China State Key Laboratory of Chemical Engineering (key
project No. SKL-ChE-12Z01).
Process Simulation and Energy Consumption Analysis. . .
43
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Process Simulation and Energy Consumption Analysis. . .
45
Experimental Investigation of n-Butanol/Diesel
Fuel Blends and n-Butanol Fumigation:
Evaluation of Engine Performance, Exhaust
Emissions, and Heat Release
Zehra S
¸ahin, Orhan Durgun, and Orhan N. Aksu
1
Introduction
In diesel engine, various alternative fuels and fuel additives such as alcohols (Chen
et al. 2012a; Goldsworthy 2013), biodiesel (Mohsin et al. 2014), dimethyl ether
(Park and Lee 2014), and natural gas (Lounici et al. 2014) can be easily used.
Among them, oxygenated fuels have drawn more attentions as they have the
capability to dramatically reduce particulate matter emissions, without causing
serious penalties on unburned hydrocarbon (HC), NOx, and engine performance
parameters (Chen et al. 2013; Rakopoulos et al. 2014). Also, many oxygenated
fuels, such as biodiesel, ethanol, and n-butanol, can be produced from plants. Since
plants absorb CO2 during growth (Tutak 2014), the combustion of those fuels does
not lead to additional carbon dioxide emission, and this feature consists one of the
key solutions of the global warming gas emissions. Moreover, oxygenated fuels
being biologically renewable, using them as alternative fuels or fuel additives, can
reduce the dependence on un-renewable fossil fuels (Chen et al. 2012a), support
local agricultural industries, and enhance farming incomes (Tutak 2014). Because
of these advantages, much effort has been devoted to investigate the effects of
Z. S
¸ahin (*)
Karadeniz Technical University, Faculty of Engineering, Mechanical Engineering
Department, Trabzon, Turkey
e-mail: zsahin@ktu.edu.tr
O. Durgun
Avrasya University, Faculty of Engineering and Archithecture, Mechanical Engineering
Department, Trabzon, Turkey
e-mail: odurgun@ktu.edu.tr
O.N. Aksu
Karadeniz Technical University, Sürmene Abdullah Kanca High School, Trabzon, Turkey
e-mail: onaksu@ktu.edu.tr
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_4
47
various oxygenated fuels especially alcohols on the performance and emissions of
diesel engines by applying different techniques (Chen et al. 2012a, 2013; Tutak
2014).
Blending and fumigation techniques are the most preferred methods for using
different alcohols in diesel engines (Tutak 2014).The ﬁrst technique is the simplest
method and here any suitable alcohol is mixed with diesel fuel. This mixture is used
by typical fuel supply system and the engine mainly operates due to diesel princi-
ple. However, it is well known that limited amount of alcohol (up to 10% v/v) can
be used in the blends because of the miscibility problems of alcohol in diesel fuel.
For this reason solubility additives are also required (Tutak 2014; Ferreira et al.
2013). The second technique is various alcohol fumigation methods. In this tech-
nique various alcohols are introduced into intake air by using a simple carburetor or
a low-pressure injection system. Here, minor modiﬁcations are required for intake
system.
In the relevant literature, alcohol, especially ethanol and methanol, blends and
fumigation applications can be found (Goldsworthy 2013; Tutak 2014; Abu-Qudais
et al. 2000; Zhang et al. 2011; Chauhan et al. 2011; Sahin et al. 2015). All those
studies revealed promising results for exhaust emissions and engine performance
parameters.
From this brief evaluation of literature, it can be said that n-butanol is a very
competitive biomass-based renewable fuel, and it has more advantages as automo-
tive fuel compared to methanol and ethanol. The main advantages of n-butanol as
engine fuel can be summarized as follows: n-butanol has larger lower heating value,
a higher cetane number, lower volatility, larger viscosity, better lubricity, and a
higher ﬂashpoint. In addition, butanol can be mixed with diesel fuel without serious
phase separation. Owing to these advantages, n-butanol/diesel fuel blend studies
began to increase in the recent years (Chen et al. 2012a, 2014; Yao et al. 2010;
Dogan 2011; Siwale et al. 2013). Even so, n-butanol fumigation studies are very
limited (Chen et al. 2013). For this reason, in the present study, both using of
n-butanol/diesel fuel blends and applying of n-butanol fumigation have been
investigated experimentally, and the obtained results for two methods are compared
to neat diesel fuel (NDF).
2
Experimental System and Test Procedure
2.1
Engine and Experimental Setup
Experiments for NDF, nBDFBs, and nBF were conducted in a DI automotive diesel
engine. Main technical speciﬁcations of the engine are given in Table 1, and
schematic diagram of the test system used was presented in Fig. 1. The test bed
was produced by Cussons. Here, loading was done by a water brake and the brake
moment was measured electronically.
48
Z. S
¸ahin et al.
In-cylinder gas pressure was measured by using of an air-cooled quartz pressure
sensor (type GH13P, AVL). This sensor has a measuring range of 0–250 bar and
linearity of 0.3% for full-scale output, and it was mounted on the head of the ﬁrst
cylinder of the engine in place of the hot plug. The signal outputs of the pressure
sensor were ampliﬁed by an electronic indicating system (type P4411, Cussons).
TDC signal of the engine which was used for injection timing was also used to
determine TDC position. The signals of pressure and crank angle (CA) were
synchronized and recorded by a data acquisition system (NI PCI-6221 type,
National Instruments). The average in-cylinder pressure proﬁle over 100 complete
cycles was used to calculate the rate of heat release.
NOx emission was measured by using a NOx gas analyzer (MEXA-720, Horiba).
Accuracy of NOx is within  3–5% ppm. Smoke was determined by a smoke
opacimeter (MGA-1500, Sun). The readings values are provided as smoke
opacity in % Hartridge units and the accuracy of smoke measurement is
within 0.1%.
2.2
Operating Conditions
In this study, the effects of nBDFBs and nBF on engine performance, combustion,
and exhaust emissions were experimentally studied and compared for two different
loads and speeds. Here, experiments were conducted at two different engine speeds
of 2000 rpm (i.e., the max-torque condition) and 4000 rpm (i.e., the rated-power
condition) for three different low n-butanol ratios (2, 4, and 6%, by vol.). Here,
engine loads of 145 and 132 Nm at 2000 rpm and 110 and 96 Nm at 4000 rpm were
selected. Tests were ﬁrstly carried out for NDF to obtain a database for comparison
of the results of three n-butanol ratios. Then, during nBDFB tests, blends of nB2
(e.g., nB2 contains 2% n-butanol and 98% diesel fuel in volume basis), nB4, and
nB6 were prepared and used in the tests under the same conditions.
Table 1 Main technical speciﬁcations of the test engine
Engine
Renault K9K 700 turbocharged automotive diesel engine
Displacement
1.461 l
Number of cylinder
4
Bore and stroke
76 & 80.5 mm
Compression ratio
18.25:1
Maximum power
48 kW at 4000 rpm
Maximum torque
160 Nm at 1750 rpm
Connecting rot length
Injection system
Number of nozzle holes
Nozzle hole diameter
130 mm
Common rail injection systema
5
0.12 mm
aHigh pressures up to 2000 bar
Experimental Investigation of n-Butanol/Diesel Fuel Blends and n-Butanol. . .
49
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
18
19
20
21
23
22
17
17
17
17
26
27
28
29
24
25
n-butanol
screw
needle
venturi
pipe
(diffuser)
main jet
n-butanol
air
intake channel (air/n-butanol mixture)
air
Fig. 1 Schematic view of the test system. (1) Fuel measurement unit; (2) digital display for
temperatures; (3) speed; (4) force; (5, 6) loading unit; (7) start switch; (8) inclined manometer; (9)
coolant ﬂow meter; (10) oil temperature; (11) inlet manifold pressure; (12) gas throttle; (13)
hydraulic dynamometer; (14) engine; (15) coaling package; (16) inference unit for gas pressure,
fuel line pressure, and crank angle pickup sensors; (17) thermocouples; (18) exhaust gas calorim-
eter; (19) gas analyzer (NOx analyzer); (20) oscilloscope; (21) electronic indicating system; (22)
data acquisition card; (23) computer; (24) smoke analyzer; (25) gas analyzer (HC and CO2
emissions); (26) n-butanol tank; (27) scaled glass bulb; (28) ﬂexible hose; (29) carburetor
50
Z. S
¸ahin et al.
After completed n-butanol blend experiments, n-butanol fumigation tests were
performed. In the fumigation method, n-butanol was introduced into intake air by
using a simple carburetor. This carburetor was mounted on the inlet manifold, and
gas and air throttles and the other auxiliary equipment of the carburetor are
dismantled, and oriﬁce diameter is chosen sufﬁciently large. By this way, it was
aimed to eliminate probable effects of carburetor oriﬁce restriction on the intake air
ﬂow and volumetric efﬁciency. As usual, air inlet was connected to the air con-
sumption measuring box by a ﬂexible hose. On the other hand, n-butanol ﬂow rate
is controlled by a ﬁne threaded adjustment screw which can change the carburetor
main fuel jet section. Technical view of this adapted carburetor was presented in
Fig. 1. Here, for fumigation tests, to obtain three different n-butanol ratios of ~2
(nBF2), 4 (nBF4), and 6 (nBF6)%, by vol., carburetor main jet opening was
adjusted at three different positions.
At the beginning of the experiments, the engine was run for approximately
30 min, and at the end by reaching its steady-state conditions, cooling water
temperature becomes 70  5 oC. For example, at 2000 rpm for NDF tests, ﬁrstly
the load of the engine was adjusted as 145 Nm (506 N loading force). Then, tests
were performed for loading moments 145 and 132 Nm. Here, constant 2000 rpm
speed is retained by adjusting fuel delivery rate suitable. Thus, tests for NDF at
2000 rpm were carried out at two different engine loads. Similar experiments were
repeated at engine speeds of 4000 rpms for NDF. After completing of the NDF
tests, nBDFB tests have been carried out. Here, nBDFBs have been prepared just
the before beginning of blend experiments to obtain as possible as homogeneous
mixtures for tests. However, it was observed that n-butanol blended easily with
reference diesel fuel and any homogeneity problem did not occur for selected fuel
blends. Then, ﬁrstly tests for nB2 have been performed under two different loads at
2000 and 4000 rpm. Similar tests have been performed for nB4 and nB6 blends.
In the fumigation tests, the adapted carburetor was mounted on to intake
manifold of the engine. Also, as shown in Fig. 1, a small n-butanol tank, a scaled
glass bulb, and suitable pipe connections were added to intake system for injecting
and measuring n-butanol ﬂow rate. Any other modiﬁcation on the engine and
experimental system was not done and the engine mainly operates due to diesel
principle. At 2000 rpm, ﬁrstly the load of the engine was adjusted as 149 Nm. Then,
carburetor main jet opening was adjusted to the ﬁrst opening and it was ﬁxed. This
opening gives approximately 2% n-butanol ratio. Thus, tests for nBF2% at
2000 rpm were carried out at 145 and 132 Nm engine loads. The engine speed
retains at 2000 rpm by adjusting suitable fuel delivery rate. After that, carburetor
main jet opening was adjusted to the second opening, and it was again retained ﬁxed
at the same 2000 rpm engine speed. This opening gives approximately nBF4 and
tests for this n-butanol ratio were performed at 145 and 132 Nm engine loads.
Similar experiments for nBF6 were carried out. At 4000 rpm, the same test
procedure was applied for nBF2, nBF4, and BF6. Actual n-butanol ratios for
various carburetor main jet openings were computed by using test measurement
values related to fuel consumption.
Experimental Investigation of n-Butanol/Diesel Fuel Blends and n-Butanol. . .
51
2.3
Calculation of Engine Characteristics
In this section, the principles of the calculation of engine performance parameters
and determination of blended fuel properties for NDF, nBDFB, and nBF are
summarized. The details of the calculation process can be found in various refer-
ences such as Durgun (1990) and Durgun and Ayvaz (1996). Here, fuel consump-
tion of the engine was determined by using a calibrated glass burette, and
consumption duration of 100 mL of diesel fuel or n-butanol/diesel fuel mixture
was measured. By this way, effective power output, total fuel consumption, brake
speciﬁc fuel consumption (bsfc), and effective efﬁciency have been calculated by
using the following relations:
Ne kW
ð
Þ ¼ 0, 1013 Tbω
p0
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
T0=293
p
Xhum
ð1Þ
B kg=h
½
 ¼ Δmf
Δt ¼ ΔV ρf3600
Δt106
, nBDFB
ð2aÞ
B kg=h
½
 ¼ Δmf
Δt ¼ 100 ρd þ VnBρnB
ð
Þ3600
Δt106
, nBF
ð2bÞ
be kg=kWh
½
 ¼ B
Ne
, ηe ¼
3600
QLHVbe
ð3a; bÞ
In Eq. (1), Tb (Nm) is brake torque, ω is angular velocity of the crankshaft, and p0
(MPa) and T0 (K) are pressure and temperatures of ambient air, respectively. Xhum is
the humidity correction factor and it is determined depending on dry and wet
thermometer temperatures. In Eqs. (2a) and (3a), ΔV is the volume of consumed
fuel, Δt (s) is the duration of consumption of ΔV volume (100 mL) of fuel, ρf is the
density of diesel fuel or blended fuel, and QLHV is the lower heating value of diesel
fuel or blended fuel. Here, lower heating values of diesel fuel and n-butanol have
been calculated by using well-known Mendeleyev formula (Kolchin and Demidov
1984).
In the present study, to see clearly the effects of nBDFBs and nBF on engine
performance and exhaust emissions, variation ratios of engine characteristics and
exhaust emissions in respect of NDF were calculated. For example, variation ratio
of bsfc was computed as follows:
Δbe
be
 100 %
½
 ¼
be,bf  be,d
ð
Þ=be,d
ð
Þ100
ð4Þ
where be,bf and be,d are bsfc for blended fuel (or fumigated fuel) and diesel fuel,
respectively.
52
Z. S
¸ahin et al.
2.4
Cost Analysis
Cost analysis has also been done by using the practical relationship, which was
proposed originally by Durgun (Durgun 1990; Durgun and Ayvaz 1996).
2.5
Estimation of Heat Release Rate (HRR)
HRR was calculated by applying the method given by Heywood (1988). Here, in
the heat release analysis, HRR was computed by using cylinder pressure data,
sampled at a resolution of 0.4 crank angle degrees. By applying the ﬁrst law of
thermodynamics, the HRR can be modeled as follows given by Heywood (1988):
dQ
dθ ¼
γ
γ  1 p dV
dθ þ
1
γ  1 V dp
dθ
ð5Þ
where dQ/dθ is the rate of the heat release (J/deg.), γ is the ratio of speciﬁc heats,
p is the in-cylinder gas pressure, and V is in-cylinder volume. Here to calculate
HRR, it is assumed that the cylinder gas content consists of a homogeneous mixture
of air and combustion products. It is further assumed that pressure waves, large
temperature gradients, fuel vaporization, and leakage through the piston rings do
not occur. Thus, HRR analysis calculations have been performed along the crank
angles during the interval of inlet valve closure and exhaust valve opening.
2.6
Error Analysis and Uncertainties
Error analysis was also applied to the measured values and uncertainties were
determined by using Kline and McClintock’s method (Holman 2001). Here, each
value has been measured three times and for this reason Student’s t-distribution has
been applied to the experimental data. By the evaluation of measured data, the
determined uncertainty intervals of torque, effective power, and bsfc values are
found at the levels of 0.1–0.5%, 0.04–0.5%, and 0.1–6.5%, respectively. From these
results, it can be stated that the probable uncertainties in the measuring of the
principle values and in the derived values would not affect signiﬁcantly the
uncertainties of the numerical results.
Experimental Investigation of n-Butanol/Diesel Fuel Blends and n-Butanol. . .
53
3
Results and Discussion
3.1
HRR, In-cylinder Pressure and Temperature
The effects of nBDFB and nBF on HRR, in-cylinder pressure, and temperature are
evaluated separately in the following paragraph. As can be seen in Figs. 2, 3, and 4,
the applied two methods inﬂuence combustion and mixing process in different
ways, for example, HRR diagram shapes and in-cylinder gas pressure curves are
slightly different and systematically changed by n-butanol percentages. For this
reason, here the effects of two methods on HHR, in-cylinder pressure, and temper-
ature are presented separately.
n-Butanol/diesel fuel blends: HRR and crank angle for different nBDFB at 2000
and 4000 rpms are given in Figs. 2a and 3a, respectively. As can be seen from
Figs. 2a and 3a, the tendencies of HRR variations are fairly similar and follow the
same typical characteristics for different nBDFBs and NDFs. It can be also
Fig. 2 (a, b) HRR,
in-cylinder pressure and
temperature versus CA for
NDF and different nBDFBs
at 2000 rpm
54
Z. S
¸ahin et al.
observed in these ﬁgures that the effect of nBDFB on the maximum value of HRR is
small. For nB2, HRR values during diffusion-controlled combustion phase and also
the maximum value of HRR are slightly higher than that of NDF at 2000 and
4000 rpms. This is also conﬁrmed by the bsfc and effective efﬁciency trends as will
be inspected in Figs. 8a, b and 9a, b. These ﬁgures show that effective efﬁciency
increases and bsfc decreases at two engine speeds for nB2. At 2000 rpm, for nB4,
the maximum HRR is also higher than that of NDF, but it is lower than that of nB2.
For this reason, effective efﬁciency and bsfc are also improved slightly. At
4000 rpm, for nB4, values of HRR are generally lower than that of NDF and
therefore bsfc and effective efﬁciency are deteriorated. For nB6, it can be seen in
Figs. 2a and 3a that the values of HRR are generally lower than that of NDF at these
engine speeds; thus, effective efﬁciency and bsfc worsen.
Figures 2b and 3b represent the variations of in-cylinder pressure versus crank
angle for different n-butanol percentages at 2000 and 4000 rpm, respectively. For
2000 rpm, the peak pressure values for nB2 and nB4 are slightly higher than that of
NDF. This can be attributed to the higher heat release ratios for nB2 and nB4
Fig. 3 (a, b) HRR,
in-cylinder pressure and
temperature versus CA for
NDF and different nBDFBs
at 4000 rpm
Experimental Investigation of n-Butanol/Diesel Fuel Blends and n-Butanol. . .
55
compared to NDF at that period, revealing some improvement of combustion for
these n-butanol ratios. However, the peak pressure values for nB6 are slightly lower
than that of NDF. As can be seen in Fig. 2b, for NDF the peak pressure is 152.69 bar
and it occurs at 10.59 CA, while for nB2, nB4, and nB6, the peak pressure values
become 153.38, 153.16, and 151.67 bar and they occur at 11.07, 10.75, and
10.52CA, respectively. For 4000 rpm, similar in-cylinder pressure behavior can
also be observed in Fig. 3b. These results indicate that the cylinder peak pressure
values exhibit only minor differences in magnitude for different n-butanol ratios.
Similar results have also been reported in literature (Chen et al. 2012a, b).
Figures 2b and 3b also display the cylinder mean temperature values for differ-
ent n-butanol percentages at 2000 and 4000 rpms. It can be seen from these ﬁgures
that the cylinder mean temperature reduces generally with the addition of
n-butanol. Since n-butanol has a higher latent heat of evaporation, a larger amount
of heat is required to evaporate the blended fuel. Besides, the lower heating value of
n-butanol is smaller than that of NDF. Both of these factors result in a reduction
tendency of the cylinder mean temperature (Chen et al. 2012a, b).
Fig. 4 (a, b) HRR,
in-cylinder pressure and
temperature versus CA for
NDF and different nBF
ratios at 2000 rpm
56
Z. S
¸ahin et al.
n-Butanol fumigation: In this study the engine performance parameter and
exhaust emission results for nBF10 were not presented. However, HRR,
in-cylinder pressure, and temperature variations for this percentage were given to
see the ﬂammability and combustion features of n-butanol fumigation. HRR,
in-cylinder pressure, and temperature versus crank angle for different nBF ratios
at 2000 and 4000 rpms were shown in Figs. 4a, b and 5a, b. As can be seen in these
ﬁgures, maximum HRR values are lower than that of NDF for all of the selected
n-butanol percentages. For nBF, HRR exhibits slightly different pattern compared
to NDF and there is a double peak in each of the HRR diagrams. The ﬁrst peak
occurs earlier than the baseline maximum and the second peak occurs later. In
addition, this diagram shows that the ﬁrst peak becomes larger and the second peak
diminishes as n-butanol ratio is increased. This feature can also be clearly observed
for nBF10. Possible causes of the occurrence of two peaks in the HRR diagram can
be explained as follows.
It is thought that prepared n-butanol-air mixture during intake period is ready to
burn at the end of the compression stroke, and after a small amount of diesel fuel is
Fig. 5 (a, b) HRR,
in-cylinder pressure and
temperature versus CA for
NDF and different nBF
ratios at 4000 rpm
Experimental Investigation of n-Butanol/Diesel Fuel Blends and n-Butanol. . .
57
injected, the evaporated n-butanol/diesel fuel and air mixture burn instantaneously
when this mixture exceeds lean ﬂammability limit. This instantaneous burning of
the n-butanol-air mixture ahead of the diesel fuel jet could lead to local depletion of
oxygen, resulting in reducing HRR after the ﬁrst peak, even though diesel fuel is
still being injected. Actually, the instantaneous burning of n-butanol-air mixtures
only takes minor role in the HRR, but the burning of these mixtures creates
additional gas motions, and this enhances the mixing of diesel fuel, injected after
this instant, with air more fastly and homogenously. Improving fuel-air mixing by
this additional gas motions means that combustion process would get better and
engine performance could be improved and exhaust pollution would be reduced
(Goldsworthy 2013; Sahin et al. 2015; Chen et al. 2014).
Figures 4b and 5b indicate that in-cylinder pressure rises with the increase of
butanol percentages at 2000 and 4000 rpms. This is a consequence of a higher heat
release ratio for nBF compared to NDF at premixed combustion period. As can be
seen in Fig. 4b, for NDF the peak pressure is 149.23 bar and it occurs at 11 CA,
while for nBF2, nBF4, nBF6, and nBF10, the peak pressure values are 150.71,
150.67, 151.97, and 154.67 bar and they take place at 10.69, 10.58, 10.37, and 10.13
CA, respectively. It can be seen in Fig. 4b that the peak pressures occur at the
angles of 0.61, 0.42, 0.63, and 0.87 CA earlier than that of NDF for 2, 4, 6, and
10% n-butanol at 2000 rpm, respectively. It can be said that n-butanol ignites in
premixed combustion mode and combustion starts slightly earlier than that of NDF
(Chen et al. 2014). These observations are also supported by the apparent HRR
variations presented in Fig. 4a. Similar behaviors are also observed at 4000 rpm as
can be seen in Fig. 5a, b.
The cylinder mean temperature for NDF and different nBF ratios at 2000 and
4000 rpms are presented in Figs. 4b and 5b. It can be seen in these ﬁgures that
maximum in-cylinder mean temperature increases with increasing nBF ratios.
However, for nBF10 approximately 10 oCA after TDC, values of temperature
start to decrease at selected engine speeds. For nBF, the premixed combustion
interval tends to enlarge, so to cause higher combustion temperature.
3.2
Smoke
The effect of nBDFBs and nBF on the smoke emission is shown in Fig. 6a, b.
Smoke decreases signiﬁcantly for both n-butanol blending and fumigation and
decrement ratios of smoke for fumigation method are higher than that of blending
method. Smoke production involves a number of conﬂicting factors. Generally
speaking, enhancing of the premixed combustion would reduce smoke and vice
versa (Giakoumis et al. 2013). It can be seen clearly from HRR ﬁgures that, in the
fumigation method, premixed combustion occurs before TDC and amount of
burned fuel increases in the premixed combustion period with increasing
n-butanol percentages. Also, additional gas motions occur by the effect of instan-
taneously burning of butanol-air mixture surrounding diesel fuel spray, and this
58
Z. S
¸ahin et al.
could improve air utilization and combustion process. Thus, fuel-rich regions could
diminish and this results in soot reduction (Yao et al. 2010).
Ignition delay generally increases for n-butanol blending because of lower
cetane number of n-butanol. In this case mixing of n-butanol/diesel fuel-air before
combustion could be enhanced, and this contributes to the reduction of smoke and
NOx emission simultaneously. But it can be seen in the HRR ﬁgures that premixed
combustion for nBDFB is considerable smaller than that of n-butanol fumigation.
For this reason decrement ratio of smoke is lower than that of fumigation. Also, the
volatility of n-butanol is higher than diesel fuel and therefore n-butanol breaks up
easier and evaporates more effectively compared to diesel fuel. Thus, the spray
penetration length becomes shorter and this could enhance the mixing process,
which reduce soot formation for nBDFBs (Yao et al. 2010).
Moreover, diesel fuel has higher tendency to soot formation due to its lower H/C
ratio and its combustion process nature. For n-butanol blends and fumigation, the
Smoke opacity [ % Hartgidge]
-1
0
1
2
3
4
5
6
7
n-butanol [%],     (a)
0
10
20
30
40
50
60
70
 nBF, 2000 rpm, 148 Nm
 nBF, 2500 rpm, 147 Nm
 nBF, 3000 rpm, 134 Nm
nBDFB, 2000 rpm, 148 Nm
nBDFB, 2500 rpm, 147 Nm
nBDFB, 3000 rpm, 134 Nm
n-butanol [%],     (b)
0
2
4
6
8
DSmoke/Smoke [%]
-100
-80
-60
-40
-20
0
nBDFB, 2000 rpm,  148 Nm 
nBDFB, 2500 rpm,  147 Nm 
nBDFB, 3000 rpm,  134 Nm 
nBF, 2000 rpm, 148 Nm 
nBF, 2500 rpm, 147 Nm 
nBF, 3000 rpm, 134 Nm 
Fig. 6 (a, b) Variations of
smoke and variations of the
variation ratios of smoke at
different engine speeds for
different n-butanol ratios
Experimental Investigation of n-Butanol/Diesel Fuel Blends and n-Butanol. . .
59
hydrogen content of the mixture increases and eventually engine smoke formation
would reduce and this results in reducing of soot formation (Chen et al. 2012a;
Rakopoulos et al. 2014; Sahin et al. 2015). On the other hand, the use of n-butanol
provides leaner running of the engine than that of NDF and thus combustion
assisted by the presence of the fuel-bound oxygen of the n-butanol even in locally
rich zones. This characteristic seems to result in the dominant effect for decreasing
smoke (Rakopoulos et al. 2014; Giakoumis et al. 2013).
3.3
NOx Emission
The variations of NOx emission and the variation of the variation ratios of NOx
emission for various n-butanol blends and fumigation for two different loads at
2000 and 4000 rpms are given in Fig. 7a, d, respectively. As can be observed in
these ﬁgures, for n-butanol blends, NOx emission decreases for nB2, but it increases
for nB4 and nB6 at selected engine speeds and loads. It is well known that NOx
emission mainly depends on the combustion temperature and the presence of excess
NOx [ppm]
-1
0
1
2
3
4
5
6
7
n-butanol [%],     (a)
1000
1100
1200
1300
1400
1500
1600
 145, nBDFB
 132, nBDFB
 145, nBF
 132, nBF
2000 rpm
NOx [ppm]
-1
0
1
2
3
4
5
6
7
n-butanol [%],     (b)
1000
1100
1200
1300
1400
1500
 110, nBDFB
 96, nBDFB
 110, nBF
 96, nBF
4000 rpm
n-butanol [%],     (c)
0
2
4
6
8
DNOx/ NOx [%]
-15
-10
-5
0
5
10
15
20
25
145 Nm, nBDFB
132 Nm, nBDFB
145 Nm, nBF
132 Nm, nBF
2000 rpm
n-butanol [%],     (d)
0
2
4
6
8
DNOx/NOx [%]
-10
-5
0
5
10
15
20
25
110 Nm, nBDFB
96 Nm, nBDFB
110 Nm, nBF
96 Nm, nBF
4000 rpm
Fig. 7 (a, b) Variation of NOx emission, (c, d) the variation ratios of NOx emission versus
different n-butanol ratios for two different loads at 2000 and 4000 rpms, respectively
60
Z. S
¸ahin et al.
oxygen (Heywood 1988). As observed in Figs. 3b and 4b, in-cylinder temperature
decreases generally for selected n-butanol percentages. However, excess air coef-
ﬁcient increases for ethanol percentages. As a result, decreasing of the combustion
temperatures becomes more dominant than that of increasing of the excess air
coefﬁcients and at the end NOx emission decreases for nB2. However, for nB4
and nB6, increasing of excess air coefﬁcient would become more effective than that
of decreasing temperature, which results in the increase of NOx emission ratio.
For nBF, NOx emission decreases for selected n-butanol percentages, and
decrement ratios of NOx emission are higher than that of nBDFB at 2000 rpm. At
4000 rpm NOx emission increases slightly for high load but it decreases at low load.
As explained in the above paragraph, instantaneous burning of n-butanol-air mix-
ture surrounding diesel fuel spray would create some additional gas motions, and
main diesel fuel injected after this period could mix with air homogenously and
fastly. By this way, combustion process could be improved and NOx formation
would decrease. Besides, instantaneous burning of n-butanol-air mixture could lead
to depletion of oxygen around the diesel fuel spray. Thus, as the combustion of
diesel fuel could occur in the leaner oxygen region, formation of NOx emission
would decrease. In the relevant literature, it has also been stated that NOx and soot
formation in homogeneous charge compression ignition engine is lower than that of
conventional diesel engine (Chen et al. 2013). Also, it is well known that combus-
tion process of fumigation method is similar to homogeneous charge compression
ignition engine combustion. Thus, both NOx and smoke emissions decrease in the
fumigated engines. Similar results were reported for diesel fuel, methanol, ethanol,
and dimethyl ether fumigations in the literature (Chapman and Boehman 2008;
Sahin et al. 2015; Tutak 2014; Zhang et al. 2011).
3.4
bsfc and Brake Effective Efﬁciency
The variation and variation ratios of bsfc and effective efﬁciency versus n-butanol
ratios for blending and fumigation methods at 2000 and 4000 rpms are presented in
Figs. 8a–d and 9a–d, respectively. As can be seen in Fig. 9a, c, bsfc slightly
decreases for nB2, but it generally increases for nB4 and nB6 at selected engine
loads and speeds. As lower heating value of n-butanol is smaller than that of diesel
fuel, naturally bsfc takes higher values as n-butanol ratio increases. That is, the
engine consumes more fuel to produce the same effective power and consequently
bsfc increases. However, in the present study, considerably increments in bsfc have
not been observed for nBDFBs. Similar behavior can be observed for brake
effective efﬁciency in Fig. 9b, d.
For nBF, bsfc increases for selected engine loads and speeds. As can be seen in
Eq. 2b, in the fumigation method, the amount of diesel fuel has not been changed
and n-butanol has been introduced to intake air in the intake channel as additional
fuel. As n-butanol is added to diesel fuel, bsfc naturally decreases. Improvement
effect of nBF could not result in considerable enhancement in bsfc because
Experimental Investigation of n-Butanol/Diesel Fuel Blends and n-Butanol. . .
61
n-butanol at small percentages is used and, therefore, bsfc slightly increases for
nBF. It can be seen from HRR ﬁgures that incomplete combustion occurs for small
n-butanol percentages. However, premixed combustion becomes more efﬁcient and
the amount of burned fuel is higher for nBF10 than that of lower n-butanol
percentages.
3.5
Fuel Economy
Variations of variation ratios of total fuel cost for the nBDFBs and nBF compared to
NDF for different loads at 2000 and 4000 rpms were presented in Fig. 10a, b,
respectively. As can be seen from these ﬁgures, the total cost of fuel takes higher
values than that of NDF for nBDFBs and nBF. Total fuel cost becomes higher than
that of diesel fuel, because the price of n-butanol is approximately nine times of
diesel fuel in Turkey. Also, combined cost of fuel for nBF is higher than that of
nBDFBs because of rising bsfc for fumigation method.
bsfc [kg/kWh] (%)
-1
0
1
2
3
4
5
6
7
n-butanol [%],     (a)
21.0
21.5
22.0
22.5
23.0
23.5
24.0
 145 Nm, nBDFB
 132 Nm, nBDFB
 145 Nm, nBF
 132 Nm, nBF
2000 rpm
Effective efficieny [%]
-1
0
1
2
3
4
5
6
7
n-butanol [%],   (b)
35
36
37
38
39
40
41
 145 Nm, nBDFB
 132 Nm, nBDFB
 145 Nm, nBF
 132 Nm, nBF
2000 rpm
bsfc [kg/kWh] (%)
-1
0
1
2
3
4
5
6
7
n-butanol [%],     (c)
23
24
25
26
27
28
 110, nBDFB
 96, nBDFB
 110, nBF
 96, nBF
4000 rpm
Effective efficieny [%]
-1
0
1
2
3
4
5
6
7
n-butanol [%],     (d)
30
31
32
33
34
35
36
37
 110, nBDFB
 96, nBDFB
 110, nBF
 96, nBF
4000 rpm
Fig. 8 (a–d) Variations of bsfc and effective efﬁciency versus different n-butanol ratios for two
different loads at 2000 and 4000 rpms, respectively
62
Z. S
¸ahin et al.
n-butanol [%],     (a)
0
2
4
6
8
Dbe/be [%]
-6
-4
-2
0
2
4
6
8
2000 rpm
145 Nm, nBDFB
132 Nm, nBDFB
145 Nm, nBF
132 Nm, nBF
n-butanol [%],     (b)
0
2
4
6
8
Dhe/he [%]
-10
-8
-6
-4
-2
0
2
4
6
2000 rpm
145 Nm, nBDFB
132 Nm, nBDFB
145 Nm, nBF
132 Nm, nBF
n-butanol [%],     (c)
0
2
4
6
8
Dbe/be [%]
-4
-2
0
2
4
6
8
10
12
14
4000 rpm
110 Nm, nBDFB
96 Nm, nBDFB
110 Nm, nBF
96 Nm, nBF
n-butanol [%],     (d)
0
2
4
6
8
Dhe/he [%]
-14
-12
-10
-8
-6
-4
-2
0
2
4
4000 rpm
110 Nm, nBDFB
96 Nm, nBDFB
110 Nm, nBF
96 Nm, nBF
Fig. 9 (a–d) Variations of the variation ratios of bsfc and effective efﬁciency versus different
n-butanol ratios for two different loads at 2000 and 4000 rpms, respectively
n-butanol [%],     (a)
0
2
4
6
8
DC/C [%]
0
10
20
30
40
50
60
145 Nm, nBDFB
132 Nm, nBDFB
145 Nm, nBF
132 Nm, nBF
2000 rpm
n-butanol [%],     (b)
0
2
4
6
8
DC/C [%]
0
10
20
30
40
50
60
70
4000 rpm
110 Nm, nBDFB
96 Nm, nBDFB
110 Nm, nBF
96 Nm, nBF
Fig. 10 (a, b) Variations of the variation ratios of cost versus different n-butanol ratios for two
different loads at 2000 and 4000 rpms, respectively
Experimental Investigation of n-Butanol/Diesel Fuel Blends and n-Butanol. . .
63
4
Conclusions
From the experimental results and their discussions, the following conclusions
could be drawn:
1. n-Butanol/diesel fuel blend and n-butanol fumigation methods reduce signiﬁ-
cantly smoke. Moreover, improvement effect of n-butanol fumigation on smoke
is higher than that of n-butanol/diesel fuel blends.
2. NOx emission decreases slightly for nB2, but it increases for nB4 and nB6 at
selected loads and speeds. For n-butanol fumigation, NOx emission decreases for
all of the operating conditions at 2000 rpm. However, it remains almost
unchanged at 4000 rpm. The NOx reduction effect of n-butanol fumigation is
better than n-butanol/diesel fuel blends.
3. For nB2 and nB4, effective efﬁciency improves; however, it deteriorates for
nB6. However, bsfc increases; also, effective efﬁciency decreases slightly for
n-butanol fumigation at selected loads and speeds.
4. Generally improvement effects of n-butanol/diesel fuel blends, especially nB2,
on engine performances parameters are slightly better than that of n-butanol
fumigation. Being the cost of n-butanol approximately nine times of diesel fuel,
both two applications become more expensive.
5. For nBDFBs, heat release rate (HRR) diagrams follow similar typical charac-
teristic of NDF, and the effect of nBDFB on the maximum value of HRR is
small. However, for nBF, HRR exhibits slightly a different pattern in respect of
NDF and there are double peaks in the HRR diagram. The ﬁrst peak occurs
earlier than that of NDF and the second peak occurs later. In addition, these
diagrams show that the ﬁrst peak becomes larger and the second peak diminishes
as n-butanol percentage is increased.
6. As an overall conclusion, it may be afﬁrmed that n-butanol can be used safely in
the turbocharged automotive diesel engine used in the experiments by applying
blending and fumigation methods. n-Butanol/diesel fuel blends reduce soot
formation without any signiﬁcant effect on performance characteristics and
NOx emission of used engine. However, nBF results for exhaust emissions are
better than that of NBDFs. Instead of this, engine characteristic results for 2%
n-butanol/diesel fuel blend are more promising than n-butanol fumigation.
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Experimental Investigation of n-Butanol/Diesel Fuel Blends and n-Butanol. . .
65
Effects of Temperature and Biodiesel Fraction
on Densities of Commercially Available Diesel
Fuel and Its Blends with the Highest Methyl
Ester Yield Corn Oil Biodiesel Produced by
Using NaOH
Atilla Bilgin and Mert G€
ul€
um
1
Introduction
Diesel engines are being extensively utilized in a number of sectors such as road
and train transport, agriculture, military, construction, mining, and stationary elec-
tricity production in the world (Esteban et al. 2012). They have appealing features
including robustness, higher torque, and lower fuel consumption under certain
conditions (Esteban et al. 2012). Diesel engines can use many fuels such as light
and heavy diesel fuels, straight vegetable oils (SVO), kerosene, gas fuels, short-
chain alcohols, and biodiesel (Esteban et al. 2012; Iwasaki et al. 1995). Biodiesel is
described as a fuel comprising mono-alkyl esters of long-chain fatty acids
(FA) derived from vegetable oils or animal fats (Yuan et al. 2005). It is usually
produced through transesteriﬁcation reaction, either under low-temperature hetero-
geneous conditions using alkaline, acid, enzyme, or heterogeneous solid catalysts or
under high-temperature (usually > 250
C) homogeneous conditions without using
any catalyst (Lin et al. 2014). Biodiesel is receiving increasing attention day by day
(Canakci 2007) because of its many great beneﬁts over diesel fuel as following:
(1) it is renewable (Yuan et al. 2009), biodegradable (Mejia et al. 2013), and a
non-toxic fuel (Ozcanli et al. 2012); (2) it has a higher cetane number than diesel
fuel and contains about 10–11% oxygen by mass in the molecular structure, thus
improving combustion efﬁciency and reducing the emission of carbon monoxide
(CO), un-burnt hydrocarbons (HCs), and particulate matter (PM) in exhaust emis-
sions (Canakci 2007); (3) it has a higher ﬂash point temperature, making its
handling, use, and transport safer than diesel fuel (Gaurav et al. 2013); (4) it
A. Bilgin (*) • M. Gülüm
Karadeniz Technical University, Faculty of Engineering, Mechanical Engineering
Department, Trabzon 61080, Turkey
e-mail: bilgin@ktu.edu.tr
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_5
67
improves lubricity and reduces premature wearing of fuel pumps (Stalin and Prabhu
2007); (5) the use of biodiesel can help reduce the world’s dependence on fossil
fuels because biodiesel can be produced by using domestic renewable feedstock
(Basha and Gopal 2012); and (6) it can be completely miscible with diesel fuel in
any proportion because of the similar chemical structures of these two fuels.
Although these properties make it an ideal fuel for diesel engines, it also has
some disadvantages such as higher feedstock cost and NOx exhaust emissions in
some cases, inferior storage and oxidative stability, and lower energy content
(Rahimi et al. 2014; Sivanathan and Chandran 2014; Moser 2012).
As the use of biodiesel has become more widespread, researchers have shown a
strong interest in modeling the combustion process in order to understand the
fundamental characteristics of biodiesel combustion (Yuan et al. 2003). They
often use the physical properties of biodiesel as input data in their combustion
models for the computational softwares (KIVA, Fluent, and AVL Fire). However, it
may not be practical at every turn to make measurements of physical properties of
biodiesel or biodiesel–diesel fuel blends for each blending ratio or temperature in
any study. Regression models as a function of temperature, percentage of blend,
and chemical structure have been generally used to calculate these properties
without measurements. Some studies reporting these models are summarized as
follows.
Sivaramakrishnan
and
Ravikumar
(2011)
developed
an
equation
depending on kinematic viscosity, density, and ﬂash point temperature for estimat-
ing higher heating values (HHV) of methyl esters of various vegetable oils. The
equation was able to predict HHV with 0.949 accuracy. Pratas et al. (2011)
measured densities of various biodiesels in the temperature range of 273–363 K
at atmospheric pressure. Three versions of Kay’s mixing rules and two versions of
the group contribution method for predicting saturated liquid (GCVOL) models
were derived by using experimental data in this study. Tong et al. (2011) presented
the relationship between cetane number of pure biodiesel and FAME composition
(carbon number of fatty acid chain) by developing a linear regression. According to
results, the linear equation showed excellent correlation with R2 ¼ 0.9904 and a
maximum average absolute error of 0.49.
The present chapter deals with the investigation of the effects of biodiesel
fraction in blend (X) and temperature (T) on densities of the highest methyl ester
content corn oil biodiesel (B100) and its blends (B5, B15, B20, and B25) with
commercially available diesel fuel (D). Some new one- and two-dimensional
models were also derived for predicting the densities of biodiesel–diesel fuel
blends, and these models were compared with other equations published in the
literature.
Nomenclature
a , b , c , d . . . , g
Regression constants
B5,B10,B15,B20
Biodiesel–diesel fuel blends
B100
Pure corn oil biodiesel
D
Pure diesel fuel
HHV
Higher heating value (kJ/kg)
(continued)
68
A. Bilgin and M. Gülüm
Kball
Coefﬁcient of the viscometer ball (mPascm3/g/s)
mtotal
Mass of the pycnometer ﬁlled with biodiesel (g)
R
Correlation coefﬁcient
t
Falling time of the viscometer ball (s)
T
Temperature (
C)
w1 , w2 , w3 , . . . , wn
Uncertainties of independent variables
x1, x2, x3, ..., xn
Independent variables
X
Biodiesel fraction in blend (%)
Greek letters
μ
Dynamic viscosity (cP  mPa.s)
ν
Kinematic viscosity (cSt  mm2/s)
ρ
Density (kg/m3), (g/cm3)
2
Experimental Methods
2.1
Biodiesel Production
In this study, commercially available reﬁned corn oil was used for biodiesel
production. There was no need to perform a pretreatment to the oil because the
oil was reﬁned. Methanol (CH3OH) of 99.8% purity as alcohol and pure-grade
sodium hydroxide (NaOH) as a catalyst were used in the transesteriﬁcation reac-
tion. To produce corn oil biodiesel having the highest methyl ester yield, optimum
reaction parameters were 0.90% catalyst concentration (mass of NaOH/mass of
corn oil), 50
C reaction temperature, 60 min reaction time, and 6:1 alcohol/oil
molar ratio, as given by Gülüm (2014). The transesteriﬁcation reaction was carried
out in a 1-L ﬂat-bottomed ﬂask, equipped with a magnetic stirrer heater, thermom-
eter, and spiral reﬂux condenser. Haake falling ball viscometer, Isolab pycnometer,
top loading balance with an accuracy of 0.01 g, Haake water bath, and a stopwatch
with an accuracy of 0.01 s were used to measure dynamic viscosity and density.
Before starting the reaction, the catalyst was dissolved in methanol to make an
alcoholic solution of the catalyst in a narrow-neck ﬂask. In the ﬂat-bottomed ﬂask,
the alcoholic solution was added to the 200 g of corn oil that was formerly warmed
to about 80
C in a beaker. These reactants were mixed with a stirring speed of
500 rpm using the magnetic stirrer heater. The transesteriﬁcation reaction was
carried out with the spiral reﬂux condenser for avoiding loss of alcohol. Also,
reaction temperature was controlled using a thermometer to remain constant during
the reaction. At the end of reaction, the resulting products mixture was transferred
to a separating funnel. After a day, two phases formed in the separating funnel. The
upper phase consisted of methyl esters (biodiesel), while the lower one consisted of
glycerol, excess methanol, and the remaining catalyst together with soap. After
separation of the two layers by gravity, the biodiesel phase was washed with warm
Effects of Temperature and Biodiesel Fraction on Densities of Commercially. . .
69
distilled water until the water became clear. The washed biodiesel was heated up to
about 100
C to remove methyl alcohol and water residuals.
2.2
Density Measurements
The densities of the produced biodiesel and its blends were determined by means of
Eq. (1) and measurements in accordance with ISO 4787 standard:
ρblend ¼ mtotal  mpycnometer
mwater
ρwater
ð1Þ
where ρ and m represent density and mass, respectively. In order to minimize
measurement errors, all measurements were conducted three times for each sample
and the results were averaged. Also, an uncertainty analysis was carried out,
depending on the sensitivities of measurement devices.
2.3
Dynamic Viscosity Measurement
The dynamic viscosities were determined in accordance with DIN 53015 standard
using Eq. (2) and making measurements by means of the Haake falling ball
viscometer, Haake water bath, and stopwatch:
μblends ¼ Kball ρball  ρblends
ð
Þt
ð2Þ
where μ is dynamic viscosity, Kball is coefﬁcient of the viscometer ball, and t is
falling time of the ball moving between two horizontal lines marked on the
viscometer tube at limit velocity. Kball and ρball are 0.057 mPascm3/g/s and
2.2 g/cm3, respectively.
The kinematic viscosities were determined from Eq. (3) by dividing dynamic
viscosity to density at the same temperature:
νblend ¼ μblend
ρblend
ð3Þ
In Eq. (3), if μbiodiesel and ρbiodisel are in the unit of (cP) and (kg/L), respectively,
then νbiodiesel is obtained in the unit of cSt.
In this study, dynamic and kinematic viscosities and densities were measured in
the Internal Combustion Engines Laboratory in the Mechanical Engineering
Department at Karadeniz Technical University. The fatty acid methyl esters of
the produced corn oil biodiesel were qualitatively and quantitatively analyzed by
gas chromatography using a Hewlett-Packard HP-6890 Series GC system ﬁtting
70
A. Bilgin and M. Gülüm
with a HP-6890 mass selective detector (1909N-133 innowax capillary column of
30 m length, 0.25 mm I.D, and 0.25 μm ﬁlm thickness) in the Science Research and
Application Center at Mustafa Kemal University. The other properties of the pure
fuels and fuel blends such as ﬂash point temperature (EN ISO 3679) and higher
heating value (DIN 51900-2) were measured at the Prof. Dr. Saadettin GU
¨ NER
Fuel Research and Application Center at Karadeniz Technical University. These
properties and EN 14214 and ASTM D 6751 standard values are given in Table 1.
Also, the fatty acid compositions of the produced corn oil biodiesel and its calcu-
lated average molecular mass and typical formulae are given in Table 2.
2.4
Uncertainty Analysis
The results obtained from experimental studies are generally calculated from
measured physical quantities. These quantities have some uncertainties due to
uncertainties of measuring tools and measurement systems. Therefore, uncertainty
Table 1 Some fuel properties of diesel fuel, produced biodiesel and their blends, and
corresponding standard values for biodiesel
Properties
Unit
D
B5
B10
Viscosity at 40
C
cSt
2.700
3.154
3.332
Density at 15
C
kg/m3
832.62
835.47
838.11
Flash point
C
63
70
76
HHV
kJ/kg
45950
45632
45359
B15
B20
B100
EN14214
ASTM-D6751
3.658
3.865
4.137
3.50–5.00
1.90–6.00
839.13
842.18
882.07
860–900
a
80
88
173
101
130
45051
44758
39981
a
a
aNot speciﬁed
Table 2 Fatty acid methyl
ester composition of the
produced biodiesel
Fatty acid
Mass, %
Palmitic (C16:0)
15.776
Oleic (C18:1)
47.703
Linoleic (C18:2)
33.415
α-Linolenic acid (C18:3)
1.101
Arachidic (C20:0)
0.805
Gadoleic acid (C20:1)
0.493
Behenic (C22:0)
0.347
Lignoceric (C24:0)
0.359
Average molecular mass
292.561 g/mola
Typical formula
C18.74H35.12O2
a
aCalculated from fatty acid distribution
Effects of Temperature and Biodiesel Fraction on Densities of Commercially. . .
71
analysis should be applied for proving reliability of the calculated results. In this
study, uncertainties of the measured and calculated physical quantities such as
dynamic and kinematic viscosities and density values were determined by the
method proposed by Holman (2001). According to this method, if the result R is
a given function of the independent variables x1, x2, x3, ..., xn and w1 , w2 , w3 , . . . ,
wn are the uncertainties of each independent variable, then the uncertainty of the
result wR is calculated by using the equation:
wR ¼
∂R
∂x1
∙w1

2
þ
∂R
∂x2
∙w2

2
þ . . . þ
∂R
∂xn
∙wn

2
"
#1=2
ð4Þ
According to Eq. (4), the highest uncertainty was determined as 0.0364%.
Therefore, it can be said that the results have fairly high reliability.
3
Results and Discussions
3.1
One-Dimensional Linear Models
3.1.1
Effects of Biodiesel Fraction on Density
The variations of densities of fuel blends (B5, B10, B15, and B20) with respect to
biodiesel fractions (X) for different temperatures (T) are shown in Fig. 1. In this
Fig. 1 Changes of density values of fuel blends with respect to biodiesel fraction for various
temperatures
72
A. Bilgin and M. Gülüm
ﬁgure, the points correspond to measured density values at studied temperatures
and biodiesel fractions, while the lines are plots of a curve-ﬁt equation. As well-
known, densities increase with increase in biodiesel fraction for a speciﬁc temper-
ature, and these are directly proportional to biodiesel content. For these reasons, the
linear model, given in Eq. (5), is ﬁtted to the measured data:
ρ ¼ ρ X
ð Þ ¼ a þ bX
ð5Þ
where ρ is density of the blends in kg/m3 and a and b are regression constants.
The measured and calculated density values from Eq. (5), error rates between
measured and calculated values, regression constants, and correlation coefﬁcients
(R) are given in Table 3. The correlation coefﬁcient is a quantitative measure of
goodness of ﬁt of the regression equation to the measured data. For a perfect ﬁt, for
example, R becomes 1, which means that the equation explains 100% of the
Table 3 The measured densities, calculated densities from Eq. (5), error rates between measured
and calculated densities, regression constants, and correlation coefﬁcients for different
temperatures
Temp. T(
C)
Measured, ρ(kg/m3)
Blend, X(%)
0
5
10
15
20
100
10
833.12
835.97
838.62
839.63
842.69
882.60
20
831.87
834.71
837.36
838.37
841.42
881.28
30
829.74
832.58
835.22
836.23
839.27
879.03
40
826.95
829.78
832.41
833.42
836.45
876.07
Regression constants
R
a
b
833.1000
0.4941
0.9996
831.8000
0.4934
0.9996
829.7000
0.4922
0.9996
826.9000
0.4905
0.9996
Calculated, ρ(kg/m3)
Blend, X(%)
0
5
10
15
20
100
833.1000
835.5705
838.0410
840.5115
842.9820
882.5100
831.8000
834.2670
836.7340
839.2010
841.6680
881.1400
829.7000
832.1610
834.6220
837.0830
839.5440
878.9200
826.9000
829.3525
831.8050
834.2575
836.7100
875.9500
Relative error rates (%)
Blend, X(%)
0
5
10
15
20
100
0.0024
0.0478
0.0690
0.1050
0.0347
0.0102
0.0084
0.0531
0.0748
0.0991
0.0295
0.0159
0.0048
0.0503
0.0716
0.1020
0.0326
0.0125
0.0060
0.0515
0.0727
0.1005
0.0311
0.0137
Effects of Temperature and Biodiesel Fraction on Densities of Commercially. . .
73
variability of the measured data (Chapra and Canale 1998). All correlation coefﬁ-
cients and the maximum relative error rate for B15 blend were obtained as 0.9996
and 0.1050%, respectively. These results and Fig. 1 show that the linear model
yields the excellent agreement between measured and calculated density values, as
expected.
3.1.2
Effects of Temperature on Density
Figure 2 presents the effects of temperature on densities of pure fuels and biodiesel–
diesel fuel blends. As shown in the ﬁgure, the densities, as expected, decrease with
increasing temperature and there are similar trends for all fuels and blends in the
studied temperature range. The distributions of densities with temperature were
correlated with the following linear and power models:
Fig. 2 Variations of density values of pure fuels and fuel blends with respect to temperature for
different regression models
74
A. Bilgin and M. Gülüm
The linear model:
ρ ¼ ρ T
ð Þ ¼ a þ bT
ð6Þ
The power model:
ρ ¼ ρ T
ð Þ ¼ aTb þ c
ð7Þ
where T is temperature in
C and a, b, and c are regression constants.
Tables 4 and 5 list the measured and calculated (from Eqs. (6) and (7)) densities
of the blends and pure fuels, error rates between them, regression constants, and
correlation coefﬁcients. For linear and power models, the maximum relative error
rates were computed as 0.0539% and 0.0002%, respectively. The R values are
between 0.9862 and 0.9865 for the linear model, while they all have a value of
0.9999 for the power model. According to these results, the power model as a
function of T has higher accuracy in calculating densities of fuels and blends.
3.2
Two-Dimensional Surface Models
In this study, two-dimensional surface models were also improved to make quick
estimates of densities for a given X and a speciﬁc T simultaneously. As mentioned
previously, there was a linear relationship between density and biodiesel fraction,
while linear and power models were tried to represent changes of densities with
temperature, which may have non-linear characteristics. In the light of this knowl-
edge, the experimental density values were correlated using new two-dimensional
surface models represented as following:
The linear surface model:
ρ ¼ ρ T; X
ð
Þ ¼ a þ bT þ cX
ð8Þ
The model linear with respect to X and power with respect to T:
ρ ¼ ρ T; X
ð
Þ ¼ aTb þ cX
ð9Þ
where ρ is density in (kg/m3) and a, b, and c are regression constants.
Tables 6 and 7 show the regression constants, measured densities, calculated
densities from Eqs. (8) and (9), relative error rates between them, and correlation
coefﬁcients. The maximum relative error rates and R values from Eqs. (8) and (9)
are 0.1506%, 0.1867% and 0.9993, 0.9983, respectively. These results indicate that
variations of densities with X and T simultaneously are observed to be well
correlated by the linear surface model.
Figures 3 and 4 depict plots of changes of constant density lines for fuel blends
as functions of T and X calculated from these models. According to linear surface
model by which changes of densities are well correlated, because the change of
Effects of Temperature and Biodiesel Fraction on Densities of Commercially. . .
75
density with respect to both temperature and biodiesel fraction is linear, the
constant density lines become linear in characteristic and have constant gradients,
as shown in Fig. 3. Therefore, if temperature is changed in a unit amount, in order to
keep the density of the fuel blend constant, the temperature change should be
multiplied by a factor corresponding to the slope of the constant density line, i.e.,
to change the biodiesel fraction in the blend.
Table 4 The measured densities, calculated densities from Eq. (6), error rates between measured
and calculated densities, regression constants, and correlation coefﬁcients for different biodiesel
fractions
Blend
X(%)
Measured, ρ(kg/m3)
Temp., T(
C)
10
20
30
40
0
833.12
831.87
829.74
826.95
5
835.97
834.71
832.58
829.78
10
838.62
837.36
835.22
832.41
15
839.63
838.37
836.23
833.42
20
842.69
841.42
839.27
836.45
100
882.60
881.28
879.03
876.07
Regression constants
R
a
B
835.6000
0.2064
0.9863
838.4000
0.2070
0.9864
841.1000
0.2077
0.9863
842.1000
0.2077
0.9863
845.2000
0.2087
0.9865
885.2000
0.2184
0.9862
Calculated, ρ(kg/m3)
Temp., T(
C)
10
20
30
40
833.5360
831.4720
829.4080
827.3440
836.3300
834.2600
832.1900
830.1200
839.0230
836.9460
834.8690
832.7920
840.0230
837.9460
835.8690
833.7920
843.1130
841.0260
838.9390
836.8520
883.0160
880.8320
878.6480
876.4640
Relative error rates (%)
Temp., T(
C)
10
20
30
40
0.0499
0.0478
0.0400
0.0476
0.0431
0.0539
0.0468
0.0410
0.0481
0.0494
0.0420
0.0459
0.0468
0.0506
0.0432
0.0446
0.0502
0.0468
0.0394
0.0481
0.0471
0.0508
0.0435
0.0450
76
A. Bilgin and M. Gülüm
Table 5 The measured densities, calculated densities from Eq. (7), error rates between measured
and calculated densities, regression constants, and correlation coefﬁcients for different biodiesel
fractions
Blend
X(%)
Measured, ρ(kg/m3)
Temp., T(
C)
10
20
30
40
0
833.12
831.87
829.74
826.95
5
835.97
834.71
832.58
829.78
10
838.62
837.36
835.22
832.41
15
839.63
838.37
836.23
833.42
20
842.69
841.42
839.27
836.45
100
882.60
881.28
879.03
876.07
Regression constants
R
a (103)
B
c
5.5610
1.9210
833.6000
0.9999
5.6270
1.9190
836.4000
0.9999
5.5860
1.9220
839.1000
0.9999
5.5860
1.9220
840.1000
0.9999
5.7140
1.9170
843.2000
0.9999
5.7370
1.9280
883.1000
0.9999
Calculated, ρ(kg/m3)
Temp., T(
C)
10
20
30
40
833.1364
831.8444
829.7744
826.9517
835.9330
834.6342
832.5552
829.7223
838.6332
837.3312
835.2441
832.3971
839.6332
838.3312
836.2441
833.3971
842.7280
841.4176
839.3222
836.4689
882.6139
881.2504
879.0582
876.0619
Relative error rates (%)
Temp., T(
C)
10
20
30
40
0.0020
0.0031
0.0041
0.0002
0.0044
0.0091
0.0030
0.0070
0.0016
0.0034
0.0029
0.0015
0.0004
0.0046
0.0017
0.0027
0.0045
0.0003
0.0062
0.0023
0.0016
0.0034
0.0032
0.0009
Effects of Temperature and Biodiesel Fraction on Densities of Commercially. . .
77
4
Conclusions
In this chapter, the effects of biodiesel fraction and temperature on the densities of
the highest methyl ester content corn oil biodiesel and its blends with commercially
available diesel fuel were investigated. One- and two-dimensional regression
models were also developed to predict the densities of the pure fuels and blends
at different temperatures. The following conclusions can be drawn from this study:
Table 6 The measured densities, calculated densities from Eq. (8), error rates between measured
and calculated densities, regression constants, and correlation coefﬁcient for different biodiesel
fractions and temperatures
Temp.
T(
C)
Blend X(%)
Measured
ρ(kg/m3)
Calculated ρ(kg/m3)
Relative error rates (%)
10
0
833.12
833.5070
0.0465
5
835.97
835.9695
0.0001
10
838.62
838.4320
0.0224
15
839.63
840.8945
0.1506
20
842.69
843.3570
0.0792
100
882.60
882.7570
0.0178
20
0
831.87
831.4140
0.0548
5
834.71
833.8765
0.0999
10
837.36
836.3390
0.1219
15
838.37
838.8015
0.0515
20
841.42
841.2640
0.0185
100
881.28
880.6640
0.0699
30
0
829.74
829.3210
0.0505
5
832.58
831.7835
0.0957
10
835.22
834.2460
0.1166
15
836.23
836.7085
0.0572
20
839.27
839.1710
0.0118
100
879.03
878.5710
0.0522
40
0
826.95
827.2280
0.0336
5
829.78
829.6905
0.0108
10
832.41
832.1530
0.0309
15
833.42
834.6155
0.1434
20
836.45
837.0780
0.0751
100
876.07
876.4780
0.0466
Regression constants
Correlation coefﬁcient
a ¼ 835.6000
R ¼ 0.9993
b ¼ 0.2093
c ¼ 0.4925
78
A. Bilgin and M. Gülüm
•
In linear models, the linear and power ones were quite suitable to represent
density–biodiesel fraction and density–temperature variations, respectively.
Correlation coefﬁcients (R) and maximum relative error rates were determined
as 0.9996, 0.1050% and 0.9999, 0.0002% for the linear and power models,
respectively.
•
Two-dimensional linear surface model with the correlation coefﬁcient of 0.9993
showed the higher degree of accuracy for representing the change in density with
temperature and biodiesel fraction in the blend simultaneously. Maximum
Table 7 The measured densities, calculated densities from Eq. (9), error rates between measured
and calculated densities, regression constants, and correlation coefﬁcient for different biodiesel
fractions and temperatures
Temp.
T(
C)
Blend
X(%)
Measured
ρ(kg/m3)
Calculated
ρ(kg/m3)
Relative error rates (%)
10
0
833.12
833.8099
0.0828
5
835.97
836.2724
0.0362
10
838.62
838.7349
0.0137
15
839.63
841.1974
0.1867
20
842.69
843.6599
0.1151
100
882.60
883.0599
0.0521
20
0
831.87
830.8555
0.1220
5
834.71
833.3180
0.1668
10
837.36
835.7805
0.1886
15
838.37
838.2430
0.0152
20
841.42
840.7055
0.0849
100
881.28
880.1055
0.1333
30
0
829.74
829.1321
0.0733
5
832.58
831.5946
0.1184
10
835.22
834.0571
0.1392
15
836.23
836.5196
0.0346
20
839.27
838.9821
0.0343
100
879.03
878.3821
0.0737
40
0
826.95
827.9115
0.1163
5
829.78
830.3740
0.0716
10
832.41
832.8365
0.0512
15
833.42
835.2990
0.2255
20
836.45
837.7615
0.1568
100
876.07
877.1615
0.1246
Regression constants
Correlation coefﬁcient
a ¼ 843.7000
R ¼ 0.9983
b ¼ 0.005121
c ¼ 0.4925
Effects of Temperature and Biodiesel Fraction on Densities of Commercially. . .
79
relative error rate between the measured and calculated density values were
computed as 0.1506% for this model.
•
The two-dimensional constant density line plot obtained by the linear surface
model has constant gradients, as shown in Fig. 3. This means that when tem-
perature is changed in a unit amount, the temperature change should be multi-
plied by a factor corresponding to the slope of the constant density line for
keeping the density of the fuel blend constant.
Fig. 3 Variations of constant density lines of fuel blends as functions of temperature and biodiesel
fraction simultaneously calculated from Eq. (8)
80
A. Bilgin and M. Gülüm
Acknowledgments The authors express their gratitude to Karadeniz Technical University Sci-
entiﬁc Research Projects Fund for ﬁnancial support received (Project No: 9745).
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82
A. Bilgin and M. Gülüm
Effects of Temperature and Biodiesel Fraction
on Dynamic Viscosities of Commercially
Available Diesel Fuels and Its Blends
with the Highest Methyl Ester Yield Corn Oil
Biodisel Produced by Using KOH
G€
ul€
um Mert and Bilgin Atilla
1
Introduction
Biodiesel, deﬁned as the mono-alkyl esters of long-chain fatty acids derived from a
renewable lipid feedstock, has received considerable attention worldwide as a
medium-term alternative to diesel fuel (Apostolakoua et al. 2009) because of its
many advantages as follows:
1. It does not produce greenhouse effects because the balance between the amount
of carbon dioxide (CO2) emission and the amount of CO2 absorbed by the plants
producing vegetable oil is about equal (Baraba
´s et al. 2010).
2. It has similar fuel characteristics to diesel fuel; therefore, it can be used either as
blends or in pure form without major modiﬁcations of the diesel engine
(Mittelbach and Enzelsberger 1999).
3. It is nontoxic, contains no aromatics and sulfur, degrades about four times
compared to diesel fuel, and pollutes water and soil to a lesser extent (Serdari
et al. 2000; Yusuf et al. 2011).
4. It improves lubricity, which results in longer engine component life (Ferr~
ao et al.
2011).
5. When biodiesel and its blends are used, carbon monoxide (CO), smoke opacity,
and unburnt hydrocarbon (UBHC) emissions are less than those with diesel fuel
because biodiesel has oxygen in its molecular structure, leading to better com-
bustion (Kumar et al. 2012; Swaminathan and Sarangan 2009).
6. It can be produced by using domestic renewable feedstock, thus reducing the
dependence on imported petroleum.
G. Mert (*) • B. Atilla
Karadeniz Technical University, Faculty of Engineering, Mechanical Engineering
Department, Trabzon 61080, Turkey
e-mail: gulum@ktu.edu.tr
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_6
83
7. Flash point of biodiesel (>130
C) is higher than that of diesel fuel, which makes
biodiesel safer from handling and storage point of view (Rao et al. 2010).
8. Depending on used biomass or plant and type of production method, it generally
has a higher cetane number than diesel fuel, which causes a shorter ignition
delay period and increases autoignition capability (Karabektas 2009).
Nevertheless, biodiesel also has some disadvantages, such as lower caloriﬁc
value and volatility; higher viscosity, cloud point temperature, and (generally)
nitrogen oxide (NOx) emissions; and corrosive nature against copper and brass
(Jain and Sharma 2010).
Viscosity, deﬁned as a measure of the internal friction or resistance of a
substance to ﬂow, is one of the most important rheological property regarding
fuel atomization and distribution as well as lubrication (Verduzco et al. 2011).
Fuel with high viscosity has poor atomization and penetration and leads to more
problems in cold weather (Al-Hamamre and Al-Salaymeh 2014). Poor atomization
increases exhaust emissions and decreases engine performance. On the other hand,
fuel with low viscosity may not provide sufﬁcient lubrication for fuel injection
pumps, resulting in leakage and increased wear (Al-Hamamre and Yamin 2014).
As the use of biodiesel has become more widespread, researchers have shown a
strong interest in modeling the combustion process in order to understand the
fundamental characteristics of biodiesel combustion (Yuan et al. 2003). They
often use physical properties of biodiesel as input data in their combustion models
for the most computational software (KIVA, Fluent, AVL Fire). However, it may
not be practical at every turn to make measurements of physical properties of
biodiesel or biodiesel–diesel fuel blends for each blending ratio or temperature in
any study. Regression models as a function of temperature, percentage of blend,
and chemical structure have been generally used to calculate these properties
without measurements. Some studies reporting these models were summarized as
follows. Krisnangkura et al. (2006) proposed an empirical model for the determi-
nation of kinematic viscosities of saturated fatty acid methyl esters (FAMEs)
having various chain lengths (C12:0-C18:0) at different temperatures (20–80
C).
The suggested linearity of natural logarithm model as a function of viscosity-carbon
number matched very well with the experimental values. In the study by Do Carmo
et al. (2012), a new model based on one and two reference ﬂuid corresponding
states was evaluated for the prediction of the dynamic viscosities of pure biodiesels
and their mixture, which takes into account the effects of temperature and compo-
sitional change. The model in this study presented the best results when compared
with Ceriani’s, Yuan’s, and revised Yuan’s models. Benjumea et al. (2008) mea-
sured some basic properties (viscosity; density; heating value; cloud point; calcu-
lated cetane index; and T10, T50, and T90 distillation temperatures) of several palm
oil biodiesel–diesel fuel blends. Arrhenius-type equation and Kay’s mixing rules
were used in order to predict kinematic viscosity and the other properties,
respectively.
In this chapter, the effects of biodiesel fraction in blend (X) and temperature (T)
on dynamic viscosities of the highest methyl ester yield corn oil biodiesel (B100)
84
G. Mert and B. Atilla
and its blends (B5, B15, B20, and B25) with commercially available diesel fuel
(D) were investigated. Some new one- and two-dimensional models were also ﬁtted
to the measurements for predicting dynamic viscosities of the biodiesel–diesel fuel
blends, and these models were compared to previously published models and
measurements to show their validities.
Nomenclature
a , b , c , d , . . . , g
Regression constants
B5 , B10 , B15 , B20
Biodiesel–diesel fuel blends
B100
Pure biodiesel
D
Pure diesel fuel
HHV
Higher heating value (kJ/kg)
Kball
Coefﬁcient of the viscometer ball (mPa ∙s ∙cm3/g/s)
mtotal
Mass of the pycnometer ﬁlled with biodiesel (g)
mpycnometer
Mass of pycnometer (g)
mwater
Mass of pycnometer ﬁlled with pure water (g)
R
Correlation coefﬁcient
t
Falling time of the viscometer ball (s)
T
Temperature (
C)
w1, w2, w3,...,wn
Uncertainties of independent variables
~
w
Dimensionless uncertainty
x1 , x2 , x3 , . . . , xn
Independent variables
X
Biodiesel fraction in blend (%)
Greek letters
μblend
Dynamic viscosity of blend (cP  mPa . s)
νblend
Kinematic viscosity of blend (cSt  mm2/s)
ρball
Density of viscometer ball (g/cm3)
ρblend
Density of blend (kg/m3)
ρwater
Density of pure water (kg/m3)
2
Experimental Methods
2.1
Biodiesel Production
In this study, commercially available reﬁned corn oil was used in biodiesel pro-
duction. Pretreatment to the oil was not required as the oil was reﬁned. Thus,
methanol (CH3OH) of 99.8% purity as alcohol and pure-grade potassium hydroxide
(KOH) as catalyst were used in the transesteriﬁcation reaction. To produce corn oil
biodiesel having the highest methyl ester yield, optimum reaction parameters were
1.10% catalyst concentration (mass of KOH/mass of corn oil), 60
C reaction
temperature, 60 min reaction time, and 6:1 alcohol/oil molar ratio, as given by
Gülüm (2014). The transesteriﬁcation reaction was carried out in a 1-L ﬂat-
Effects of Temperature and Biodiesel Fraction on Dynamic Viscosities. . .
85
bottomed ﬂask, equipped with a magnetic stirrer heater, thermometer, and spiral
reﬂux condenser. Haake falling ball viscometer, Isolab pycnometer, top loading
balance with an accuracy of 0.01 g, Haake water bath, and a stopwatch with an
accuracy of 0.01 s were used to measure dynamic viscosity and density. Before
starting the reaction, the catalyst was dissolved in methanol to make an alcoholic
solution of the catalyst in a narrow-neck ﬂask. In the ﬂat-bottomed ﬂask, the
alcoholic solution was added to the 200 g of corn oil that was formerly warmed
to about 80
C in a beaker. These reactants were mixed with a stirring speed of
500 rpm using the magnetic stirrer heater. The transesteriﬁcation reaction was
carried out with the spiral reﬂux condenser for avoiding loss of alcohol. Also,
reaction temperature was controlled using a thermometer to remain constant during
the reaction. At the end of the reaction, the resulting products mixture was trans-
ferred to a separating funnel. After a day, two phases formed in the separating
funnel. The upper phase consisted of methyl esters (biodiesel), while the lower one
consisted of glycerol, excess methanol, and remaining catalyst together with soap.
After the separation of the two layers by gravity, the biodiesel phase was washed
with warm distilled water until the water became clear. The washed biodiesel was
heated up to about 100
C to remove methyl alcohol and water residuals.
2.2
Density Measurement
The densities of the produced biodiesel and its blends were determined by means of
Eq. (1) and measurements in accordance with ISO 4787 standard:
ρblend ¼ mtotal  mpycnometer
mwater
ρwater
ð1Þ
where ρ and m represent density and mass, respectively. In order to minimize
measurement errors, all the measurements were conducted three times for each
sample and the results were averaged. Also, an uncertainty analysis was carried out,
depending on the sensitivities of measurement devices.
2.3
Dynamic Viscosity Measurement
The dynamic viscosities were determined in accordance with DIN 53015 standard
by using Eq. (2) and making measurements by means of the Haake falling ball
viscometer, Haake water bath, and stopwatch:
μblend ¼ Kball ρball  ρblend
ð
Þt
ð2Þ
86
G. Mert and B. Atilla
where μ is dynamic viscosity, Kball is coefﬁcient of the viscometer ball, and t is
falling time of the ball moving between two horizontal lines marked on the
viscometer tube at limit velocity. Kball and ρball are 0.057 mPa ∙s ∙cm3/g/s and 2.2
g/cm3, respectively.
The kinematic viscosities were determined from Eq. (3) by dividing dynamic
viscosity to density at the same temperature:
νblend ¼ μblend
ρblend
ð3Þ
In Eq. (3), if μblend and ρblend are in unit of (mPa . s) and (kg/L), respectively, then
νblend is obtained in the unit of mm2/s.
In this study, dynamic viscosities and densities were measured in the Internal
Combustion Engines Laboratory in the Mechanical Engineering Department at
Karadeniz Technical University. The fatty acid methyl esters of the produced
corn oil biodiesel were qualitatively and quantitatively analyzed by gas chroma-
tography using a Hewlett-Packard HP-6890 Series GC system ﬁtted with a
HP-6890 mass selective detector (1909 N-133 innowax capillary column of 30 m
length, 0.25 mm I.D, and 0.25 μm ﬁlm thickness) in the Science Research and
Application Center at Mustafa Kemal University. The other properties of the pure
fuels and fuel blends, such as ﬂash point temperature (EN ISO 3679) and higher
heating value (DIN 51900-2), were also measured at the Prof. Dr. Saadettin
GU
¨ NER Fuel Research and Application Center at Karadeniz Technical University.
These properties and EN 14214 and ASTM D 6751 standard values are given in
Table 1. Moreover, the fatty acid compositions of the produced corn oil biodiesel
and its calculated average molecular mass and typical formula are given in Table 2.
Table 1 Some fuel properties of diesel fuel, produced corn oil biodiesel and their blends, and
corresponding standard values for biodiesel
Properties
Units
B100
D
B5
B10
Viscosity
at 40
C
mm2/s
4.094
2.700
2.944
3.240
Density
at 15
C
kg/m3
882.68
832.62
835.67
837.91
Flash point
C
178
63
70
74
HHV
kJ/kg
39,959
45,950
40,263
40,574
B15
B20
EN14214
ASTM-D6751
3.470
3.671
3.50–5.00
1.90–6.00
839.94
842.59
860–900
a
83
90
101
130
40,867
41,169
a
a
aNot speciﬁed
Effects of Temperature and Biodiesel Fraction on Dynamic Viscosities. . .
87
2.4
Uncertainty Analysis
The results obtained from experimental studies are generally calculated from
measured physical quantities. These quantities have some uncertainties due to
uncertainties of measuring tools and measurement systems. Therefore, uncertainty
analysis should be applied for proving reliability of the calculated results. In this
study, uncertainties of the measured and calculated physical quantities, such as
dynamic and kinematic viscosities and density values, were determined by the
method proposed by Holman (2001). According to this method, if the result R is
a given function of the independent variables x1, x2, x3, ..., xn and are the uncer-
tainties of each independent variable, then the uncertainty of the result wR is
calculated by using the equation:
wR ¼
∂R
∂x1
∙w1

2
þ
∂R
∂x2
∙w2

2
þ . . . þ
∂R
∂xn
∙wn

2
"
#1=2
ð4Þ
For example, by using Eqs. (1) and (4), the uncertainty of density of B10 at 15
C
(wρB10,15C) was calculated as:
ρB10,15C ¼ mtotal  mpycnometer
mwater
ρwater,15C
x1  mtotal ¼ 83:91 g
x2  mpycnometer ¼ 42:74 g
mwater ¼ 49:09 g
ρB10,15C ¼ 83:91 g  42:74 g
49:09 g
999:10 kg=m3
Table 2 Fatty acid methyl
ester composition of the
produced biodiesel
Fatty acid
Mass, %
Palmitic (C16:0)
15.343
Oleic (C18:1)
47.305
Linoleic (C18:2)
34.122
α-Linolenic acid (C18:3)
1.183
Arachidic (C20:0)
0.811
Gadoleic acid (C20:1)
0.506
Behenic (C22:0)
0.362
Lignoceric (C24:0)
0.368
Average molecular mass
292.681 g/mol1
Typical formula
C18.72H35.25O2
1
1Calculated from fatty acid distribution
88
G. Mert and B. Atilla
ρB10,15C ¼ 837:91 kg=m3
ρB10,15C ¼ ρB10,15C mtotal; mpycnometer


R  ρB10,15C
∂ρB10,15C
∂mtotal
¼
1
mwater
∙ρwater,15C
∂ρB10,15C
∂mpycnometer
¼
1
mwater
∙ρwater,15C
wρB10,15C ¼
1
mwater
∙ρwater,15C ∙wmtotal

2
þ

1
mwater
∙ρwater,15C ∙wmpycnometer

2
"
#1=2
wρB10,15C
¼
1
49:09 g ∙999:10kg=m3 ∙0:01g

2
þ

1
49:09 g ∙999:10kg=m3 ∙0:01g

2

	1=2
wρB10,15C ¼ 0:2878 kg=m3
Since the density of the B10 at 15
C was determined as 837.91 kg/m3, dimen-
sionless uncertainty of density becomes:
~
w ρB10,15C ¼ 0:2878 kg=m3
837:91 kg=m3 ∙100 ¼ 0:0343%
Similarly, the highest uncertainty was determined as 0.0354%. Therefore, it can
be said that the results have fairly high reliability.
3
Results and Discussions
3.1
One-Dimensional Linear Models
3.1.1
Effects of Biodiesel Fraction on Viscosity
Figure 1 shows the variations of dynamic viscosities of fuel blends (B5, B10, B15,
and B20) with respect to biodiesel fractions (X) for different temperatures (T). In
this ﬁgure, the points correspond to the measured viscosity values at studied
temperatures and biodiesel fractions, while the lines are plots of curve ﬁt equations.
Viscosities increase with increase in biodiesel fraction for a speciﬁc temperature, as
expected, and the change of them tends to be about linear with increase in biodiesel
fraction as temperature is decreasing. Exponential and rational models were
suggested and compared to the well-known Arrhenius model (Joshi and Pegg
2007) for characterizing these changes as:
Effects of Temperature and Biodiesel Fraction on Dynamic Viscosities. . .
89
μ ¼ μ X
ð Þ ¼ μ0 þ a ∙ebX
ð5Þ
μ ¼ μ X
ð Þ ¼ aX þ 1
ð
Þ= b þ c
ð
Þ
ð6Þ
Inμ ¼ Xdiesel ln μdiesel þ XbiodieselInμbiodiesel
ð7Þ
where μ is dynamic viscosity in cP, and μ0, a, b, and c are regression constants.
Tables 3, 4, and 5 list the calculated (from Eqs. (5), (6), and (7)) and measured
viscosities of the blends and pure fuels (D and B100), error rates between measured
and calculated values, regression constants, and correlation coefﬁcients (R). As
known, the correlation coefﬁcient is a quantitative measure of goodness of ﬁt of the
regression equation to the measured data. For a perfect ﬁt, for example, R becomes
1 and the equation explains 100% of the variability of the measured data (Chapra
and Canale 1998). The maximum relative error rates between the measured and
calculated viscosity values were computed as 4.7059%, 4.5976%, and 20.0944%
for Eqs. (5), (6), and (7), respectively. Also, the lowest R values are 0.9941, 0.9942,
and 0.5738 for these equations, respectively. According to these results, the power
model seems the best ﬁt to the data. Moreover, as seen in Fig. 1, Arrhenius model
does not fairly reﬂect the changes of viscosities with respect to biodiesel content for
all studied temperatures, and the relation between dynamic viscosity and biodiesel
fraction in blend was found to be better expressed by the rational model than the
exponential model.
Fig. 1 Changes in viscosity values of fuel blends with respect to biodiesel fraction for different
models
90
G. Mert and B. Atilla
3.1.2
Effects of Temperature on Viscosity
The effects of temperature on the viscosities of pure fuels and biodiesel–diesel fuel
blends are given in Fig. 2. The viscosities of all the blends and fuels follow a similar
trend: they decrease with increase in temperature, as expected. In this ﬁgure, the
experimental data matched predicted values computed by exponential and power
models:
μ ¼ μ T
ð Þ ¼ μ0 þ aebT
ð8Þ
μ ¼ μ T
ð Þ ¼ aTb þ c
ð9Þ
where T is temperature in
C and μ0, a, b, and c are regression constants.
Tables 6 and 7 also present the measured viscosity data, the calculated values
from Eq. (8) and (9), relative error rates between them, regression constants, and
correlation coefﬁcients. The maximum relative error rates and the lowest R values
Table 3 Measured
viscosities, calculated
viscosities from Eq. (5), error
rates between measured and
calculated viscosities,
regression constants, and
correlation coefﬁcients for
different temperatures
Temp.
T (
C)
Measured, μ (cP)
Blend, X (%)
0
5
10
15
20
100
10
3.514
3.850
4.068
4.204
4.409
7.094
20
2.743
2.912
3.119
3.221
3.563
5.346
30
2.376
2.771
2.791
2.923
3.229
4.520
40
2.233
2.443
2.697
2.894
3.072
3.589
Regression constants
R
μ0
a
b
11.9200
8.3490
0.005481
0.9994
6.7470
4.0300
0.010580
0.9984
4.9700
2.5400
0.017310
0.9941
3.6110
1.4080
0.044990
0.9982
Calculated, μ (cP)
Blend, X (%)
0
5
10
15
20
100
3.5710
3.7967
4.0163
4.2300
4.4378
7.0939
2.7170
2.9246
3.1216
3.3084
3.4856
5.3480
2.4300
2.6406
2.8337
3.0108
3.1733
4.5201
2.2030
2.4866
2.7131
2.8940
3.0384
3.5953
Relative error rates (%)
Blend, X (%)
0
5
10
15
20
100
1.6221
1.3844
1.2709
0.6185
0.6532
0.0014
0.9479
0.4327
0.0834
2.7134
2.1723
0.0374
2.2727
4.7059
1.5299
3.0038
1.7250
0.0022
1.3435
1.7847
0.5970
0.0000
1.0937
0.1755
Effects of Temperature and Biodiesel Fraction on Dynamic Viscosities. . .
91
Table 4 Calculated
viscosities from Eq. (6), error
rates between measured and
calculated viscosities,
regression constants, and
correlation coefﬁcients for
different temperatures
Regression constants
R
a
b
c (104)
0.015990
0.2802
8.6220
0.9994
0.022310
0.3681
23.6100
0.9983
0.031040
0.4122
49.5700
0.9942
0.079000
0.4554
201.3000
0.9952
Calculated, μ (cP)
Blend, X (%)
0
5
10
15
20
100
3.5689
3.7958
4.0160
4.2297
4.4371
7.0930
2.7167
2.9259
3.1225
3.3076
3.4821
5.3476
2.4260
2.6436
2.8378
3.0122
3.1697
4.5203
2.1959
2.5088
2.7257
2.8851
3.0070
3.6056
Relative error rates (%)
Blend, X (%)
0
5
10
15
20
100
1.5623
1.4078
1.2783
0.6113
0.6373
0.0141
0.9588
0.4773
0.1122
2.6886
2.2706
0.0299
2.1044
4.5976
1.6768
3.0517
1.8365
0.0066
1.6614
2.6934
1.0641
0.3075
2.1159
0.4625
Table 5 Calculated
viscosities from Eq. (7), error
rates between measured and
calculated viscosities,
regression constants, and
correlation coefﬁcients for
different temperatures
R
Calculated, μ (cP)
Blend, X (%)
0
5
10
15
20
100
0.9787
3.5140
3.6404
3.7705
3.9052
4.0447
7.0940
0.9710
2.7430
2.8359
2.9320
3.0315
3.1343
5.3460
0.8980
2.3760
2.4527
2.5330
2.6159
2.7015
4.5200
0.5738
2.2330
2.2859
2.3408
2.3971
2.4547
3.5890
Relative error rates (%)
Blend, X (%)
0
5
10
15
20
100
0.0000
5.4442
7.3132
7.1075
8.2626
0.0000
0.0000
2.6133
5.9955
5.8833
12.0320
0.0000
0.0000
11.4868
9.2440
10.5063
16.3363
0.0000
0.0000
6.4306
13.2073
17.1700
20.0944
0.0000
92
G. Mert and B. Atilla
were computed as 4.5868%, 0.9881 and 3.9733%, 0.9914 for Eqs. (8) and (9),
respectively. When relative error rates and maximum correlation coefﬁcients in
Tables 6 and 7 are analyzed and Fig. 2 is observed, it can be said that power model
matches better with the experimental data of pure fuels and fuel blends throughout
the studied temperature ranges.
On the other hand, in order to test their validities, these models were also ﬁtted to
the dynamic viscosities of the coconut, colza, and soybean oil biodiesels measured
by Feitosa et al. (2010), as shown in Fig. 3. Tables 8 and 9 show the dynamic
viscosities measured by Feitosa et al., calculated viscosities from Eqs. (8) and (9),
% errors between measured and calculated values, regression constants, and corre-
lation coefﬁcients. The maximum errors computed from Eqs. (8) and (9) are
2.3935% and 2.1726%, while the minimum R values are determined as 0.9996
and 0.9998, respectively. These results and Fig. 3 show that the changes of
kinematic viscosity measurements given by Feitosa et al. are also better demon-
strated by the power model.
3.2
Two-Dimensional Surface Models
In this study, two-dimensional polynomial and combination of exponential and
linear terms surface models were also derived for changes of dynamic viscosities
with respect to T and X simultaneously:
Fig. 2 Changes in viscosity values of pure fuels and fuel blends with respect to temperature for
different models
Effects of Temperature and Biodiesel Fraction on Dynamic Viscosities. . .
93
μ ¼ μ T; X
ð
Þ ¼ a þ bT þ cX þ dT2 þ eTX þ fT3 þ gT2
ð10Þ
μ ¼ μ T; X
ð
Þ ¼ a ∙ebT þ c ∙edX þ eX
ð11Þ
where μ is dynamic viscosity in cP, and a, b, c, d, e, f, and g are regression constants.
Tables 10 and 11 list the measured viscosities, calculated viscosities from
Eqs. (10) and (11), regression constants, R values, and relative error rates. The
Table 6 Measured
viscosities, calculated
viscosities from Eq. (8), error
rates between measured and
calculated viscosities,
regression constants, and
correlation coefﬁcients for
different biodiesel fractions
Blend X (%)
Measured, μ (cP)
Temp., T (C)
10
20
30
40
0
3.514
2.743
2.376
2.233
5
3.850
2.912
2.771
2.443
10
4.068
3.119
2.791
2.697
15
4.204
3.221
2.923
2.894
20
4.409
3.563
3.229
3.072
100
7.094
5.346
4.520
3.589
Regression constants
R
μ0
a
b
2.0970
3.1420
0.079580
0.9999
2.4320
3.6260
0.094660
0.9881
2.6400
4.2900
0.110000
0.9999
2.8530
5.1000
0.132700
0.9995
2.9760
3.4660
0.088350
0.9999
2.1700
7.2950
0.039870
0.9972
Calculated, μ (cP)
Temp., T (C)
10
20
30
40
3.5147
2.7367
2.3856
2.2272
3.8391
2.9780
2.6439
2.5142
4.0680
3.1153
2.7982
2.6927
4.2059
3.2119
2.9482
2.8783
4.4086
3.5681
3.2208
3.0772
7.0663
5.4564
4.3758
3.6505
Relative error rates (%)
Temp., T(
C)
10
20
30
40
0.0199
0.2297
0.4040
0.2597
0.2831
2.2665
4.5868
2.9144
0.0000
0.1186
0.2580
0.1594
0.0452
0.2825
0.8621
0.5425
0.0091
0.1431
0.2539
0.1693
0.3905
2.0651
3.1903
1.7136
94
G. Mert and B. Atilla
maximum relative error rates and R values were computed as 5.1606%, 0.9958 and
6.5139%, 0.9952 for Eqs. (10) and (11), respectively.
Figures 4 and 5 present plots of the changes of constant dynamic viscosity curves
for fuel blends as functions of T and X calculated from Eqs. (10) and (11),
respectively. These plots can be used to make quick estimates of viscosities for a
given blending ratio at a speciﬁc temperature.
Increasing characteristic with decreasing rate of constant viscosity curves (con-
cave characteristic) converts to increasing behavior with increasing rate (convex
characteristic) because of T3 term of the polynomial surface model at the higher
temperature and biodiesel fraction regions, as shown in Fig. 4. However, this
change is not physically meaningful. Accordingly, the combination surface model
including exponential and linear terms can be recommended within 0–20% blend-
ing ratio range for predicting dynamic viscosity values in spite of higher correlation
coefﬁcient and lower maximum relative error rate of the polynomial surface model.
Table 7 Calculated
viscosities from Eq. (9), error
rates between measured and
calculated viscosities,
regression constants, and
correlation coefﬁcients for
different biodiesel fractions
Regression constants
R
a
b
c
9.5140
0.6310
1.2910
0.9994
15.1800
0.9093
1.9720
0.9914
26.1800
1.1820
2.3480
0.9995
57.7400
1.5840
2.7010
0.9989
12.5300
0.7946
2.3980
0.9999
15.9000
0.1115
27.630
0.9997
Calculated, μ(cP)
Temp., T(
C)
10
20
30
40
3.5162
2.7279
2.4035
2.2188
3.8426
2.9680
2.6609
2.5023
4.0697
3.1068
2.8179
2.6825
4.2058
3.2029
2.9651
2.8684
4.4087
3.5572
3.2379
3.0663
7.0760
5.4244
4.3975
3.6402
Relative error rates (%)
Temp., T(
C)
10
20
30
40
0.0626
0.5505
1.1574
0.6359
0.1922
1.9231
3.9733
2.4273
0.0418
0.3912
0.9638
0.5376
0.0428
0.5619
1.4403
0.8846
0.0068
0.1628
0.2756
0.1855
0.2537
1.4665
2.7102
1.4266
Effects of Temperature and Biodiesel Fraction on Dynamic Viscosities. . .
95
There are two different characteristic regions in Fig. 5. Constant viscosity curves
are sparser and closer to horizontal in the ﬁrst region where temperature is higher
and biodiesel fraction is lower. On the other hand, constant viscosity curves are
more frequent and closer to vertical in the second region where temperature is lower
and biodiesel fraction is higher. Consequently, less temperature changes at lower
temperatures or more temperature changes at higher temperatures are needed for a
unit change of viscosity for a given blending ratio. On the other hand, less biodiesel
fraction changes at lower temperatures or more biodiesel fraction changes at higher
temperatures are needed for a unit change of viscosity at a speciﬁc temperature.
4
Conclusions
In this chapter, the dynamic viscosities of corn oil biodiesel–diesel fuel blends were
measured at different temperatures. Measurements were correlated by one- and
two-dimensional models through regression analysis, and the compatibilities of the
models have been investigated by comparing each other. The following conclusions
can be drawn from the study:
Fig. 3 Changes in viscosity values of coconut, colza, and soybean biodiesels measured by Feitosa
et al. (2010) with respect to temperature for different models
96
G. Mert and B. Atilla
Table 8 Viscosities measured by Feitosa et al. (2010), calculated viscosities from Eq. (8), error
rates between measured and calculated viscosities, regression constants, and correlation coefﬁ-
cients for different biodiesel fractions
Biodiesel
Measured, μ (cP)
Temp., T (C)
20
40
60
80
100
Coconut
3.8417
2.4574
1.7226
1.2816
0.9880
Colza
7.4296
4.4727
2.9959
2.1577
1.6294
Soybean
6.4440
3.9640
2.6979
1.9659
1.4959
Regression constants
R
μ0
a
b
0.6650
5.5310
0.02783
0.9997
1.1410
11.6600
0.03096
0.9998
1.0340
9.8130
0.02987
0.9996
Calculated, μ (cP)
Temp., T (C)
20
40
60
80
100
3.8351
2.4820
1.7064
1.2619
1.0071
7.4185
4.5206
2.9605
2.1206
1.6684
6.4335
4.0050
2.6688
1.9335
1.5290
Relative error rates (%)
Temp., T (C)
20
40
60
80
100
0.1718
1.0011
0.9404
1.5371
1.9332
0.1494
1.0709
1.1816
1.7194
2.3935
0.1629
1.0343
1.0786
1.6481
2.2127
Table 9 Calculated viscosities from Eq. (9), error rates between measured and calculated
viscosities, regression constants, and correlation coefﬁcients for different biodiesel fractions
Regression constants
R
a
b
c
18.4200
0.2997
3.6590
0.9998
42.1400
0.4252
4.3530
0.9999
34.0700
0.3819
4.4020
0.9998
Calculated, μ (cP)
Temp., T (C)
20
40
60
80
100
3.8463
2.4385
1.7408
1.2947
0.9743
7.4365
4.4271
3.0367
2.1858
1.5940
6.4500
3.9261
2.7314
1.9892
1.4671
Relative error rates (%)
Temp., T (C)
20
40
60
80
100
0.1197
0.7691
1.0565
1.0222
1.3866
0.0929
1.0195
1.3619
1.3023
2.1726
0.0931
0.9561
1.2417
1.1852
1.9253
Effects of Temperature and Biodiesel Fraction on Dynamic Viscosities. . .
97
•
The relation between dynamic viscosity and biodiesel fraction for the biodiesel–
diesel fuel blends was found to be better expressed by the one-dimensional
rational model.
•
The one-dimensional power model as a function of temperature better predicted
the dynamic viscosities of pure fuels and fuel blends than the one-dimensional
exponential one.
Table 10 Measured viscosities, calculated viscosities from Eq. (10), error rates between mea-
sured and calculated viscosities, regression constants, and correlation coefﬁcient for different
biodiesel fractions and temperatures
Temp.
T (
C)
Blend X(%)
Measured μ (cP)
Calculated μ (cP)
Relative error rates
(%)
10
0
3.514
3.5773
1.8014
5
3.850
3.7930
1.4805
10
4.068
4.0087
1.4577
15
4.204
4.2244
0.4853
20
4.409
4.4401
0.7054
20
0
2.743
2.7292
0.5031
5
2.912
2.9202
0.2816
10
3.119
3.1113
0.2469
15
3.221
3.3023
2.5241
20
3.563
3.4934
1.9534
30
0
2.376
2.4383
2.6221
5
2.771
2.6280
5.1606
10
2.791
2.8177
0.9566
15
2.923
3.0074
2.8874
20
3.229
3.1971
0.9879
40
0
2.233
2.2443
0.5060
5
2.443
2.4559
0.5280
10
2.697
2.6676
1.0901
15
2.894
2.8792
0.5114
20
3.072
3.0909
0.6152
Regression constants
Correlation coefﬁcient
a ¼ 5.4430
R ¼ 0.9958
b ¼ 0.2528
c ¼ 5.2730  102
d ¼ 7.3900  103
e ¼ 1.1920  103
f ¼ 7.6730  105
g ¼ 2.3300  105
98
G. Mert and B. Atilla
•
The two-dimensional combination surface model with higher correlation coef-
ﬁcient of 0.9952 matched the change of dynamic viscosities with biodiesel
fraction and temperature at the same time within 0–20% blending ratio range.
According to the plot of change of constant dynamic viscosity curves calculated
by the surface model, less temperature changes at lower temperatures or more
temperature changes at higher temperatures are requisites for a unit change of
viscosity for a given blending ratio. On the other hand, less biodiesel fraction
changes at lower temperatures or more biodiesel fraction changes at higher
temperatures are required for a unit change of viscosity at a speciﬁc temperature.
Table 11 Calculated viscosities from Eq. (11), error rates between measured and calculated
viscosities, regression constants, and correlation coefﬁcient for different biodiesel fractions and
temperatures
Calculated μ (cP)
Relative error rates
(%)
Regression constants
Correlation coefﬁcient
3.6025
2.5185
a ¼ 3.8190
b ¼ 9.9890  102
c ¼ 2.1960
d ¼ 8.9800  106
e ¼ 4.0760  102
R ¼ 0.9952
3.8062
1.1377
4.0099
1.4282
4.2136
0.2284
4.4173
0.1883
2.7140
1.0572
2.9177
0.1957
3.1214
0.0769
3.3251
3.2319
3.5288
0.9599
2.3868
0.4545
2.5905
6.5139
2.7942
0.1147
2.9979
2.5624
3.2016
0.8486
2.2663
1.4913
2.4700
1.1052
2.6737
0.8639
2.8774
0.5736
3.0811
0.2962
Effects of Temperature and Biodiesel Fraction on Dynamic Viscosities. . .
99
Fig. 4 Changes of constant viscosity curves of fuel blends as functions of temperature and
biodiesel fraction calculated from Eq. (10)
100
G. Mert and B. Atilla
Acknowledgments The authors express their gratitude to Karadeniz Technical University Sci-
entiﬁc Research Projects Fund for the ﬁnancial support received (Project No: 9745).
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102
G. Mert and B. Atilla
Sankey and Grassmann Diagrams for Mineral
Trade in the EU-28
Guiomar Calvo, Alicia Valero, and Antonio Valero
1
Introduction
Raw materials, particularly fossil fuels and non-fuel minerals, are crucial to main-
tain the European Union’s economy. Despite having a rich endowment of mineral
deposits, producing 1.2% and 1.4% of the world level needs of iron and aluminum
respectively (British Geological Survey 2011), European countries depend on raw
material secure supply. As this is a critical issue, in November 2008 the European
Commission published the Raw Materials Initiative to establish the raw material
strategy along with a list of actions that the member states should carry out
(European Commission 2008).
While historically the importance of fossil fuels has been regarded as a priority
issue, non-fuel minerals, which are essential both for electronic equipment and for
the development of renewable energies, have only gained importance in the recent
years. Since minerals are a non-renewable resource that is linked to geological
features of the ground, it is important to analyze its use and the mineral trade
between the different countries.
Material ﬂow analysis has demonstrated to be a key tool to monitor and quantify
the use of natural resources. Usually this analysis of material use, consumption and
trade is carried out through aggregated indicators that take into account minerals as
a whole, sometimes differentiating at most industrial minerals, construction min-
erals and fossil fuels (Weisz et al. 2006; Schandl and Eisenmenger 2006; Steinberg
et al. 2010; Bruckner et al. 2012; Kovanda et al. 2012). Nevertheless it is important
to have disaggregated studies to observe the impact and supply risk of the different
G. Calvo (*) • A. Valero • A. Valero
Research Centre for Energy Resources and Consumption (CIRCE), Campus Rı
´o Ebro,
Mariano Esquillor Go
´mez, 15, 50018 Zaragoza, Spain
e-mail: gcalvose@unizar.es
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_7
103
materials (Achzet and Helbig 2013) and so provide valuable input for decision-
making processes aiming at improving the sustainable use of raw materials (Tiess
2010; Tiess and Kriz 2011; Marinescu et al. 2013).
This chapter undertakes an analysis of the mineral trade in the European Union
(EU-28) from 1995 to 2012. This analysis is done ﬁrstly using tonnage as a
yardstick, accounting for the tons of input (production and imports) and output
materials (recycling, exports and consumption). Then, the same analysis is carried
out using exergy, particularly the so-called exergy replacement costs, which is
explained in the next section. This allows us to compare both methodologies and
bring out the signiﬁcant differences regarding reliability and representativeness.
The ﬁnal aim is to observe the trend and evolution of the mineral trade in Europe
and highlight which of the analyzed minerals can be considered critical due to
external dependency.
2
Methodology
The analysis of the mineral trade of Europe is going to be ﬁrstly undertaken in
tonnes. Domestic extraction, export and import data for the 1995–2012 period have
been obtained from the British Geological Survey European Mineral Statistics
(2014), completed with data from United States Geological Survey yearbooks of
mineral statistics and national services from some European countries. As individ-
ual data for recycling rates of each of the member states of the European Union are
not available, average recycling rates for several metallic minerals have been
obtained from the Recycling Rates of Metals report (UNEP 2011).
In order to assess the mineral depletion more comprehensively, we are going to
apply the exergoecology method initially proposed by Valero (1998) to analyze the
mineral trade in Europe (EU-28) for both fossil fuels and non-fuel minerals. With
this methodology we can evaluate the loss of natural resources through exergy, a
property that is based on the second law of thermodynamics and that can be used to
measure the quality of a system with respect to a given reference. This methodology
is based on calculating the exergy that would be needed to replace a mineral deposit
starting from an environment where all the minerals are dispersed in the crust into
the initial conditions of composition and concentration found in the mine where it
was originally extracted. To perform these calculations we need a model of average
dispersed crust, Thanatia, a planet that represents a possible state of the Earth where
all minerals have been dispersed, all fossil fuels have been consumed and which has
speciﬁc atmospheric conditions (Valero et al. 2011a, b). This Thanatia model
includes a list of minerals with their respective concentration in the crust which
delimits the lowest ore grades of the minerals and that can serve as a boundary to
our calculations. Therefore, the exergy replacement costs of a mineral can be
calculated as the exergy required to restore the minerals from Thanatia into the
conditions found in nature with the current available technology. As quality is being
taken into account in those calculations, scarcer and difﬁcult-to-extract minerals
104
G. Calvo et al.
(in terms of energy expended) will have a higher weight in the ﬁnal accounting
process as the exergy needed to recover a mineral that is dispersed increases
exponentially with scarcity. Accordingly for instance, in the case of limestone, a
material that can be easily extracted and that is very abundant in the crust, its exergy
replacement costs are 2.6 GJ/ton. If we look at scarcer minerals, such as gold or
mercury, these values go up to 583668.4 and 28298.0 GJ/ton, respectively. These
numbers provide hints of which minerals would be the most complicated to replace
hence also giving information about their quality. Thus, carrying out the analysis
using only tonnage can result in biased information since it seems logical that
1 tonne of limestone should not have the same weight in the calculations as 1 tonne
of gold in quality terms.
Since reliable and comparable data are not always easy to ﬁnd, the substances
that are included in this study are the following: aluminum, antimony, arsenic,
barite, bismuth, boron, cadmium, chromium, cobalt, copper, feldspar, ﬂuorspar,
gold, graphite, gypsum, indium, iron ore, lead, limestone, lithium, magnesium,
manganese, mercury, molybdenum, nickel, phosphate rock, potassium, selenium,
silicon, silver, sodium, tantalum, tin, titanium, uranium, vanadium, wolfram, zinc
and zirconium. The exergy replacement costs of these minerals have already been
calculated in previous studies (Valero and Valero 2014). As for fossil fuels, their
exergy replacement costs can be approximated to their high heating values as once
they are consumed and burned they cannot be recovered (Valero and Valero 2012).
To better depict mineral trade, the analysis of EU-28 as a general system is
conducted using Sankey and Grassmann diagrams. The main difference between
both types of graphic representations is that the ﬁrst usually depicts energy or
material ﬂows of a system with the width of the arrows being proportional to the
ﬂow quantity. Grassmann diagrams in turn are essentially the same but represent
such ﬂows in exergy units. These types of representations are very visual and can be
used to evaluate the evolution of the mineral trade of a country or several countries
and also the self-sufﬁciency and external dependency. Additionally, several depen-
dency indicators based on domestic material consumption will be calculated for the
year 2011 in order to evaluate the self-sufﬁciency and foreign dependency and have
a complete picture of mineral trade in the European Union.
3
Mineral Trade in the EU-28
Over the last decades there has been a decreasing tendency in the domestic
extraction, especially notable in the case of fossil fuels. In Fig. 1 we have the
total mineral extraction of the EU-28 from 1995 to 2012, separated by countries. As
it can be seen, the United Kingdom, Spain, Poland, Italy and Germany are histor-
ically the main producers of minerals in EU-28.
Regarding non-fuel minerals, Spain, Italy, the United Kingdom and Germany
are the main extractors. Limestone accounted for an average of 85.3% of the yearly
total non-fuel mineral production, followed by gypsum (8.7%) and salt (4.4%).
Sankey and Grassmann Diagrams for Mineral Trade in the EU-28
105
From 2007 onwards the non-fuel mineral domestic production has been decreasing,
a change that can be attributed, among other factors, to a combination of resource
management improvements and resource efﬁciency policies but also to the ﬁnancial
crisis which has been affecting the member states.
As for fossil fuels, during the period under consideration, the United Kingdom,
France, Germany and Poland were the main European extractor countries. Coal
remained the principal fossil fuel extracted, accounting as an average for 66.7% of
the total EU-28 fossil fuel yearly production. Still, between 1995 and 2012 the total
fossil fuel EU-28 domestic production decreased approximately 28%.
Figure 2 shows a general overview of the imported minerals. Although the total
amount of imported materials is increasing, the ﬂuctuations caused by the ﬁnancial
crisis can also be appreciated. Between 2003 and 2004 the total amount of imported
fossil fuels increased 27%, but from 2007 onwards there has been a sharp decrease.
Between the years 2001 and 2011, the amount of minerals imported in Europe
increased around 35% while the domestic extraction decreased almost 6% during
that same period. The substances mainly imported were metallic minerals and fossil
fuels, the latter mainly coming from Russia, Norway and North Africa. Even if
Europe is rich in natural resources, both domestic production and import values
approximately move within the same range, which highlights the importance of
imports for the states belonging to the European Community.
Material trade deﬁcit (exports minus imports) was analyzed for the 1995–2012
period. Imports exceed exports during the whole time period, generating a substan-
tial trade deﬁcit. The maximum amount of imported non-fuel minerals was
Fig. 1 Tonnes of fossil fuels and non-fuel minerals extracted in Europe from 1995 to 2012
disaggregated by countries
106
G. Calvo et al.
257 million tonnes in 2007 and the maximum exports were 63.3 million tonnes in
2001. On average, exports were equivalent to 23.7% of the imports, and the
maximum trade deﬁcit, 210 million tonnes, occurred in 1998.
4
Using Year 2011 as a Case Study
A detailed analysis of the mineral trade in the EU-28 for the year 2011 was made in
order to better observe the weight of the different substances. In Fig. 3 we can see
the European mineral balance for 2011 expressed in tonnes for the 40 minerals and
the three main fossil fuels that were selected in this study.
The general behavior that can be inferred is that European member states mainly
imported oil, natural gas and iron. The domestic production consisted of coal and
limestone in large quantities and oil, natural gas, iron and potash in lower quanti-
ties. Still scarcer minerals, which are usually critical and more important from an
economic point of view, here remain hidden because in mass terms they have a
considerably lower weight.
As stated before, import dependency for European countries is very high, and we
can see that in 2011 approximately 45.8% of the input materials came from other
countries. It is also noteworthy that all of the imported and produced minerals ended
up being consumed within the European member states’ borders, stressing that, at
least regarding these substances, Europe is an extremely dependent economy.
An alternative to reducing imported materials lies in recycling, saving both
energy consumption and natural resources. The low weight of recycling in Europe
is striking: less than 3.2% of the total inputs (domestic production plus imports)
were recycled in 2011. Although some countries have higher recycling rates, such
Fig. 2 Total tonnes of fossil fuels and non-fuel minerals imported in Europe from 1995 to 2012
Sankey and Grassmann Diagrams for Mineral Trade in the EU-28
107
as Austria, Germany or Belgium, Europe is still wasting vast quantities of valuable
resources and sending them to landﬁlls. Due to low efﬁciencies in the processing
and collection of metal-bearing products that are discarded and because primary
materials are often abundant, the end-of-life recycling rates are very low. At world
level, of the 60 metals analyzed by the United Nations Environment Programme
(UNEP 2011), only 18 had above 50% end-of-life recycling ratios while more than
34 had lower than 1% ratios.
Exports represented 10.7% of the total output and they mainly consist of oil,
natural gas and iron. On the other hand, internal consumption in the EU-28
accounted for 86.2%. These data can help emphasize that Europe is a region mainly
based on domestic consumption.
In Fig. 4 fossil fuel trade data have been removed from the scenario, so we can
speciﬁcally focus on non-fuel mineral trade.
If we compare the mineral trade in mass terms and in exergy replacement costs,
we can clearly see that minerals have different weight. When expressed in mass
terms, limestone and iron are the most traded minerals, accounting for 77.1% of the
total input materials. If we express the same data in exergy replacement costs,
limestone and iron only represent 10.8%, as they are abundant minerals in the crust
and easier to extract. A counter example of limestone is gold, which seemed
negligible in mass units but that in exergy replacement costs represents almost
12% of the total imports.
Fig. 3 Sankey diagram for the European mineral balance for 2011(data in tonnes) (Source: British
Geological Survey (2014))
108
G. Calvo et al.
Therefore, with data in tonnes we can display quantities, which can give us a
general idea of the mineral trade, but at the same time it also gives biased
information as many minerals are not extracted in sufﬁcient quantity to be
represented in the graphics. With exergy replacement costs we can evaluate the
quality of the minerals, bringing out those that are scarcer or less concentrated in the
crust.
In 2014 the European Commission updated the list of critical minerals for the
European Union (European Commission 2014), which now includes antimony,
beryllium, borates, chromium, cobalt, ﬂuorspar, gallium, germanium, indium,
magnesite, magnesium, natural graphite, niobium, PGM, phosphate rock, REE,
Fig. 4 Sankey and Grassmann diagrams for the European mineral balance for 2011 in tonnes
(t) and in exergy replacement costs (Mtoe) for non-fuel minerals
Sankey and Grassmann Diagrams for Mineral Trade in the EU-28
109
silicon and tungsten, of which the vast majority has been taken into account in this
study. For this reason, comparing the results in mass terms and in exergy replace-
ment costs becomes fundamental to analyze the mineral trade as scarcer minerals
are better represented with the latter analysis.
When evaluating the mineral depletion caused by trade in 2011 in the EU-28, we
can see for instance that the internal production of 10 of the 20 minerals considered
critical by the EC in its 2014 report accounted for 0.88% of the total production
expressed in tonnes and 3.19% when expressed in exergy replacement costs. The
critical minerals that were imported from other countries accounted for 5.01% of
the total imports expressed in tonnes and 6.74% when expressed in exergy replace-
ment costs.
In 2011 consumption played an important role both in mass terms and in exergy
terms, representing respectively 86.2% and 74.5% of the total outputs. What draws
our attention is that in tonnes, the percentage corresponding to production (54.2%)
is higher than the one corresponding to imports (45.8%), but in the case of exergy
replacement costs we have the opposite situation (34.1% and 65.9%). The fact that
consumption is always very important is not surprising; what is noteworthy is the
reversed importance of production and imports depending on how the resources are
being evaluated. If we only add tonnes of minerals the production is higher than
imports, but if we take into account the quality of those minerals it is the imports
that become more relevant since as stated before, Europe imports scarcer and more
valuable minerals (from a physical point of view) than those that are domestically
extracted.
5
Mineral Dependency in the EU-28 in 2011
With domestic extraction, imports, exports, consumption and recycling data we can
calculate a number of ratios to evaluate several factors of dependency.
The indicator Domestic Material Consumption (DMC) is calculated as follows:
DMC ¼ extraction + imports  exports. Proceeding from this information, we can
also obtain the ratio of Domestic Extraction to Domestic Material Consumption
(DE/DMC), that is, the self-sufﬁciency ratio. If this value is 1 or more, it means the
self-sufﬁciency ratio is high and thus the country does not need to rely on mineral
trade.
We can also obtain the import-to-DMC (I/DMC) and export-to-DMC (E/DMC)
ratios, used to evaluate the foreign dependency and trade intensity. These ratios will
be calculated using initial data expressed both in tonnes and exergy replacement
costs.
In Table 1 we can see the ratios obtained for the case of non-fuel minerals. In this
ﬁrst case the DE/DMC ratio is 0.79 with data expressed in tones and 0.45 with data
expressed in exergy replacement costs. If we used only the ﬁrst value to evaluate
external dependency on non-fuel minerals, we could conclude that EU-28 is not
very dependent on external supply since it is relatively close to 1. However, there is
110
G. Calvo et al.
a 34% difference between these two DE/DMC values. Again, this is mainly due to
the relevance in mass terms of the limestone extracted in the EU-28, which was
295 million tonnes or 74.5% of the total mineral extraction in 2011. As construction
materials are hardly traded due to their lower price and abundance, putting them at
the same level as scarcer minerals can mask the real situation. Therefore, the ratio
with data expressed in tonnes does not truly reﬂect the situation of external
dependency, which is expected to be higher if those materials were removed from
the calculations. Using exergy replacement costs we have a better approximation of
DE/DMC (0.45) which shows a more accurate value of the EU-28 self-sufﬁciency.
As for the import and export ratios, I/DMC and E/DMC, we have the opposite
situation. These two ratios expressed in exergy replacement costs are higher than
when data are expressed in tonnes, which indicates that EU-28 is extremely
dependent on foreign trade for non-fuel mineral supply (0.94 in the case of imports
and 0.40 for exports).
In Table 2 we can see the ratios obtained for the case of fossil fuels, using data
from natural gas, oil and several types of coal. In this case the ratios are not so
distant from each other when expressed in tonnes or in exergy replacement costs.
The dependency on imports becomes clear when observing the I/DMC ratio,
0.62 when using data in tonnes and 0.76 when using exergy replacement costs.
As it happens with non-fuel minerals, Europe is also very dependent on fossil
fuel supply.
The EU-28 average ratios for DMC, DE/DMC, I/DMC and E/DMC are
represented in Table 3.
In Europe there is a large variation between each of the member states regarding
size, GDP, economic growth, geology, characteristics of the mining industry, etc.
This is why the average ratios obtained must be taken only as a reference. The
average self-sufﬁciency (0.54) and the elevated import dependency (0.77) make
clear that Europe must rely on other regions to cover its own needs.
6
Conclusions
In this chapter we have analyzed mineral trade in Europe from 1995 to 2012, using
the year 2011 as a case study to obtain several ratios to evaluate self-sufﬁciency and
external dependency.
For the period under consideration, domestic extraction has been decreasing
continuously. Especially notable is the case of fossil fuels, which decreased 28%
Table 1 Ratio between domestic extraction and domestic material consumption (DE/DMC),
imports/DMC and exports/DMC for EU-28 for the year 2011 for non-fuel minerals
DE/DMC
I/DMC
E/DMC
Mass terms
0.79
0.30
0.09
Exergy terms
0.45
0.94
0.40
Sankey and Grassmann Diagrams for Mineral Trade in the EU-28
111
between 1995 and 2012. This general decrease can be attributed to emphasis on
resource efﬁciency policies as well as to the ﬁnancial crisis. With the recent
initiatives promoting raw material strategies, increasing recycling rates and creat-
ing synergies between industries, these results are expected to improve in the
following years.
As shown by the results, Europe mainly extracts construction and bulk materials
(limestone, gypsum and salt) and depends on other regions for scarcer minerals and
fossil fuel supply. Considering the elevated trade deﬁcit, the average self-
sufﬁciency ratio of the EU-28 (0.54) and the import dependency (0.77), it can be
stated that the European Union heavily relies on imports and consumption rather
than on domestic production or exports.
As demonstrated by the data presented in this chapter, a conventional Multiple
Factor Analysis (MFA) analysis does not truly reﬂect the real situation of mineral
dispersion, as all the minerals, regardless of their quality, are considered at the same
level. Thus, applying the exergoecology methodology can be useful for policy
makers to obtain more realistic data for the loss of mineral natural capital since it
allows for more robust and reliable analysis. It also avoids subjectivity issues
associated with monetary assessments and places the focus on scarcer resources.
Accordingly, complementing the data in mass terms with exergy replacement costs
we can have a better picture about the current situation with the help of Grassmann
diagrams. Representing the data with both Sankey and Grassmann diagrams has
demonstrated to be a practical way to better differentiate material ﬂows separated
by minerals and to assess external dependency.
Acknowledgments We would like to thank Teresa Brown from the British Geological Survey for
her assistance for obtaining the historical data.
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Mass terms
0.52
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0.76
0.17
Table 3 Average EU-28 ratios for the year 2011. Data are expressed in exergy replacement costs
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113
Development of Solid Waste Management
System for Adana Metropolitan Municipality
Kadir Aydin and C
¸ a
grı U
¨ n
1
Introduction
Several categories of waste are discussed in terms of their arisings, treatment, and
disposal options. The wastes described in detail are municipal solid waste, hazard-
ous including clinical waste, household hazardous waste, and sewage sludge. Other
wastes described are agricultural waste, industrial, construction, and demolition
wastes, mines and quarry wastes, power station ash, scrap tires, and end of life
vehicles (Williams 2005).
Municipal solid waste is a term usually applied to a heterogeneous collection of
wastes produced in urban areas, the nature of which varies from region to region.
The characteristics and quantity of the solid waste generated in a region are not only
a function of the living standard and lifestyle of the region’s inhabitants but also of
the abundance and types of the region’s natural resources. Urban wastes can be
subdivided into two major components; organic and inorganic. In general, the
organic components of urban solid waste can be classiﬁed into three broad catego-
ries: putrescible, fermentable, and nonfermentable. Putrescible wastes tend to
decompose rapidly and, unless carefully controlled, decompose with the production
of objectionable odors and visual unpleasantness. Fermentable wastes tend to
decompose rapidly, but without the unpleasant accompaniments of putrefaction.
Nonfermentable wastes tend to resist decomposition and therefore break down very
slowly. A major source of putrescible waste is food preparation and consumption.
K. Aydin (*)
Department of Mechanical Engineering, Cukurova University, Adana, Turkey
e-mail: kdraydin@cu.edu.tr
C
¸ . U
¨ n
Department of Special Waste Management, Adana Metropolitan Municipality, Adana, Turkey
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_8
115
As such, its nature varies with lifestyle, standard of living, and seasonality of foods.
Fermentable wastes are typiﬁed by crop and market debris.
Wastes generated in countries located in humid, tropical, and semitropical areas
usually are characterized by a high concentration of plant debris, whereas those
generated in areas subject to seasonal changes in temperature or those in which coal
or wood is used for cooking and heating may contain an abundance of ash. The
concentration of ash may be substantially higher during winter. Regardless of
climatic differences, the wastes usually are more or less contaminated with night
soil. These differences prevail even in wastes generated in large metropolitan areas
of a developing country.
The primary difference between wastes generated in developing nations and
those generated in industrialized countries is the higher organic content character-
istic of the former. The extent of the difference is indicated by the data in Table 1, in
which is presented information relative to the quantity and composition of munic-
ipal solid wastes generated in several countries (UNEP 2005).
In the municipal solid waste stream, waste is broadly classiﬁed into organic and
inorganic. Waste composition is categorized as organic, paper, plastic, glass,
metals, and “others.” These categories can be further reﬁned; however, these six
categories are usually sufﬁcient for general solid waste planning purposes. Table 2
describes the different types of waste and their sources.
An important component that needs to be considered is “construction and
demolition waste” (C&D), such as building rubble, concrete, and masonry. In
some cities this can represent as much as 40% of the total waste stream.
Figure 1 shows the MSW composition for the entire world in 2009. Organic
waste comprises the majority of MSW, followed by paper, metal, other wastes,
plastic, and glass. These are only approximate values, given that the data sets are
from various years (Hoornweg and Bhada-Tata 2012).
Nomenclature
Subscripts
MSW
Municipal solid waste
EU
European Union
MOEF
Ministry of Environment and Forestry
MEDWP
Medical waste plastic fuel
2
Waste Generation and Management in Turkey
Waste generation and management have been recognized as a priority for Turkey
and policies are being developed to overcome existing obstacles. Furthermore,
MSW management has been a pressure point for Turkey while being a candidate
country for EU accession.
116
K. Aydin and C
¸ . U
¨ n
Table 1 Comparison of solid waste characterization worldwide (% wet wt)
Location
Putrescibles
Paper
Metals
Glass
Plastics, rubber, leather
Textiles
Ceramics, dust, stones
Wt (g)/cap/day
Bangalore, India
75.2
1.5
0.1
0.2
0.9
3.1
19.0
400
Manila, Philippines
45.5
14.5
4.9
2.7
8.6
1.3
27.5
400
Asuncio
´n, Paraguay
60.8
12.2
2.3
4.6
4.4
2.5
13.2
460
Seoul, Korea
22.3
16.2
4.1
10.6
9.6
3.8
33.4a
2000a
Vienna, Austria
23.3
33.6
3.7
10.4
7.0
3.1
18.9b
1180
Mexico City, Mexico
59.8c
11.9
1.1
3.3
3.5
0.4
20.0
680
Paris, France
16.3
40.9
3.2
9.4
8.4
4.4
17.4
1430
Australia
23.6
39.1
6.6
10.2
9.9
–
9.0
1870
Sunnyvale, California, USA
39.4d
40.8
3.5
4.4
9.6
1.0
1.3
2000
aIncludes briquette ash (average)
bIncludes “all others”
cIncludes small amounts of wood, hay, and straw
dIncludes garden waste
Development of Solid Waste Management System for Adana. . .
117
Environment Law No 2872 was rectiﬁed on 9 August 1983 and was amended in
1988 and 2001 and modiﬁed by Law No: 5491 on 26.04.2006. This Framework law
aimed protection and improvement of environment in line with the sustainable
protection and development principles; it puts forward the rules and principles for
environmental protection, deﬁnes the responsible and authorized institutions and
organizations, determines the processes for the implementation, and establishes
punishments for improper acts and liabilities of the concerned within the frame-
work of the principle “polluter pays.” It also emphasized the efﬁcient use of natural
resources. Environmental Protection Institutes were established later by the year
1989 to ensure the environment law is implemented successfully. The law also
paved the way for the establishment of provincial environment boards to oversee
environmental protection in their respective regions.
In its 3rd section, the law prohibits disposal of all types of waste and scraps into
recipient environment unless necessary precautions have been taken, determined by
speciﬁc regulations. Environment-friendly technologies must be used during all
kinds of activities so as to efﬁciently use natural resources and energy, which also
means reduction of waste production at source and recycling of waste. The law also
determined the status and procedure for protected areas and deﬁnes penalty mech-
anism for those who do not comply with the standards.
Table 2 Types of waste and their sources
Type
Sources
Organic
Food scraps, yard (leaves, grass, brush) waste, wood, process residues
Paper
Paper scraps, cardboard, newspapers, magazines, bags, boxes, wrapping paper, tele-
phone books, shredded paper, and paper beverage cups. Strictly speaking paper is
organic, but unless it is contaminated by food residue, paper is not classiﬁed as
organic
Plastic
Bottles, packaging, containers, bags, lids, cups
Glass
Bottles, broken glassware, light bulbs, colored glass
Metal
Cans, foil, tins, nonhazardous aerosol cans, appliances (white goods), railings,
bicycles
Other
Textiles, leather, rubber, multi-laminates, e-waste, appliances, ash, other inert
materials
Organic
46%
Other
18%
Paper
17%
Plastic
10%
Glass
5%
Metal
4%
Fig. 1 Global solid waste
composition (2009)
118
K. Aydin and C
¸ . U
¨ n
Regulation on Solid Waste Control was approved on 14 March 1991 (No 20814)
and was later amended in 1991, 1992, 1994, 1998, 1999, 2000, 2002, and ﬁnally on
5 April 2005 (No 25777). The general framework of waste management system
requires the reduction of waste generation as far as possible, separation of recov-
erable waste at source and recycling the valuable wastes and disposal of
nonrecyclable wastes by means of environment-friendly methods. The purpose of
this regulation is to forbid all waste management activities posing a threat to the
environment. It also aimed at the protection of natural ﬂora and fauna.
This regulation provides general information about different types of wastes like
packaging waste, construction and demolition waste, waste sorting, waste transport
and disposal, as well as information about landﬁlls and incineration. However, each
of these components is governed by a speciﬁc regulation for itself.
Solid waste collection and management is one of the most signiﬁcant local
public services. The manner in which such services are delivered is of utmost
importance for public health as well as for the protection of the environment. As
per the below laws and regulations, liabilities for the collection, transportation,
recycling, and disposal of solid wastes are entrusted to the municipalities and
metropolitan municipalities.
The By-Law on Solid Waste Control is the ﬁrst important step toward successful
waste management in Turkey. Although it is shown to have some shortcomings in
its implementation, the MSW management system has been improved by new
studies and new regulations. The main reasons for the shortcomings can be iden-
tiﬁed as:
•
Waste management systems development was not a priority policy area.
•
Duties and powers are distributed among many institutions and organizations,
with inadequate coordination and cooperation among them.
•
The fees and taxes collected in return for services were inadequate.
•
The infrastructure (facilities and the existing technical capacity) was limited and
the majority of facilities were in need of modernization.
According to the Metropolitan Municipality Law (10.7.2004, 5216) and the
Municipality Law (3.7.2005, 5393), sole responsibility for the management of
municipal waste falls on the municipalities. They are responsible for providing all
services regarding collection, transportation, separation, recycling, disposal, and
storage of solid wastes, or to appoint others to provide these services. Nevertheless,
while fulﬁlling their duties in collecting and transporting the solid waste to a great
extent, they do not show the required level of activity and attention in solid
municipal waste management. The great majority of solid waste in the country is
still not being disposed in accordance with the legislation. This situation has been
improving by newly adopted management perspectives.
It has been reported that 54% of household waste is disposed in sanitary landﬁll
sites, while the remaining 44% is dumped into dumpsites, according to the Turkish
Statistical Institute. 2% was reported as either undergoing biological treatment or
disposed of by other methods. The number of sanitary landﬁlls is increasing rapidly
in Turkey, as in 2003 there were 15 sanitary landﬁlls, whereas in the 3rd quarter of
Development of Solid Waste Management System for Adana. . .
119
2012 this number has increased to 68. There are references in the literature to an
informal recycling sector which could be responsible for up to 30% of MSW
material recycling. But there is no information on the current situation concerning
this informal recycling practice. Regarding the situation around packaging waste,
an important part of MSW, the Turkish Ministry of Environment and Urbanization
has provided the following information.
The ﬁrst particular regulation on packaging waste control came into force in
2004 with the “By-Law on Control of Packaging Waste” and was revised in August
2011. The aim of the by-law is to minimize the generation of packaging waste and
to also increase the rate of recycled packaging waste which cannot be avoided
within the method of production. The regulation also includes principles and
standards for packaging waste to be collected separately at its source, then sorted
and transported within a certain system. Institutions and suppliers who are not
members of authorized organizations are obliged to recover packaging waste.
Recycling targets are given to authorized institutions and suppliers with this
by-law. The number of economic operators registered to the system is increasing
rapidly in Turkey, from 350 in 2003 to 15,192 in 2012.
The Turkish Ministry of Environment and Urbanization gives licenses to col-
lection, separation, and recycling facilities. Whereas there were only 28 licensed
facilities in 2003, this number increased to 562 in 2012. Development plans are the
main tools for the coordination of public policy in Turkey and they form the basis of
policy documents on solid waste. There have been a number of National Waste
Management Plans covering the period 2009–2013. The main aim of the Plan is to
determine national policies and the decision-making structure for the preparation of
detailed waste management plans for separate waste streams. The latest plan was
made with the aim of fulﬁlling criteria according to the EU harmonization process.
Finally, in 2008, the “By-law on General Principles of Waste Management”
(05.07.2008, 2697) set the framework for waste management in Turkey from waste
generation to disposal so that the procedures are followed in an environmentally
sound way (Bakas and Milios 2013).
The Turkish Ministry of Environment and Urbanization has carried out various
regional ﬁeldworks in order to determine the domestic waste composition of
households in Turkey in addition to solid waste surveys sent to cities that are
representative of Turkey. The outcome of these studies, municipal waste composi-
tion in Turkey, is shown in Fig. 2 (Anonymous 2014).
3
Waste Management of Adana Municipalities
Adana Province is located at the Mediterranean Sea Region among the 35–38
northern latitude and 34–36 eastern longitude. The surface area is 14,030 km2 and
the altitude is approximately 23 m. The province has a 160 km coastline on the
Mediterranean Sea. It comprises 15 districts, 55 municipalities, and 517 villages.
The forest land in the province forms approximately 39% of the total area. Total
120
K. Aydin and C
¸ . U
¨ n
meadow land in the region is equal to 3% of the total area. Various types of
agricultural practices are performed in the region, total agricultural land is equal
to 38% of the total area, and 19% of the total area is used for other purposes.
With the construction of Seyhan Dam and improvements in agricultural tech-
niques, there was an explosive growth in agricultural production during the 1950s.
Large-scale industries were built along D-400 state road and Karatas
¸ road. Service
industry, especially banking, also developed during this period.
Adana is the marketing and distribution center for C
¸ ukurova agricultural region,
where cotton, wheat, corn, soy bean, barley, grapes, and citrus fruits are produced in
great quantities. Farmers of Adana produce half of the corn and soy bean in Turkey.
34% of Turkey’s peanuts and 29% of Turkey’s oranges are harvested in Adana.
Most of the farming and agricultural-based companies of the region have their
ofﬁces in Adana.
Adana has been the agricultural and economical center of attention. As one of
the most fertile plains in Turkey, C
¸ ukurova produces sunﬂower, olive, corn, citrus
(orange, mandarin, and lemon), kiwi, sugarcane, rice, soy bean, cotton, grape,
peanut, almond, melon, loquat, etc. The economy of the city is generally based
on agriculture and stockbreeding. Since land and climate structures are convenient,
all kinds of agricultural produce are grown. Irrigated farming is done in a 49,330 ha
section of total agricultural land. Olive, grain, tomatoes, pepper, tobacco, corn,
sugar beet, cotton, melon, watermelon, and peach are the main produce that are
grown.
According to the new Metropolitan Municipalities Law No. 6360, after the
March 2014 elections, the service area of Adana Metropolitan Municipality has
been expanded to cover the entire geographical area of Adana. Adana Metropolitan
Municipality has to collect, transport, and dispose of solid wastes produced by its
2,149,260 people. Adana Metropolitan Municipality collects on an average
554,139.06 tons per year of solid wastes and 2,831.79 tons per year of medical
wastes, excluding Yedig€
oze Union. The seven municipalities and their villages of
Yedig€
oze Union also collect 94,285 tons/year of solid wastes. About 42.5% of this
PaperiCardboard
19,7%
C&D Waste
10,5%
WEEE
0,5%
Hazardous Waste
0,3%
Other Nonincinerables
12,5%
Other Incinerables
7,0%
Plastic
8,4%
Metal
2,7%
Glass
2,1%
Garden Waste
2,0%
Kitchen Waste
34,4%
Fig. 2 Composition of domestic wastes in Turkey
Development of Solid Waste Management System for Adana. . .
121
waste was collected from Center of Ceyhan municipality, 40.5% from Center of
Kozan municipality, 7.5% from the Center of I
˙mamo
glu municipality, 4.5% from
Center of Yumurtalık municipality, 1.9% from Center of Feke municipality, and
1.6% from Center of Saimbeyli and Alada
g municipalities (Turkstat 2013). All
solid wastes collected from Adana Metropolitan Municipality, except Yedig€
oze
Union, were transported to Adana Metropolitan Municipality Landﬁll Site for
recycling and disposal. Solid wastes collected from Yedig€
oze Union are disposed
of in uncontrolled dumping areas, except Ceyhan that transfers its waste to Adana
Landﬁll site using the transfer station in Ceyhan, situated in each district. All
medical wastes collected from Adana Metropolitan Municipality including
Yedig€
oze Union are transferred to the Adana Metropolitan Medical Waste Steril-
ization Unit, which is located in Adana Metropolitan Municipality landﬁll. After
sterilization, all medical waste is disposed of at the sanitary landﬁll of Adana as
nonhazardous waste (Draft Master Plan for Yedig€
oze Union 2014) (Fig. 3)
(Tables 3, 4, 5, and 6).
The average compositions of solid wastes in Adana Metropolitan Municipality
are 60–64.5% organic wastes, 8.07% plastic materials, 2.42% papers, 0.25 metals,
1.87% glasses, and 22–25% others (tire, leather, textile wastes, ash, stone, and soil).
All organic wastes are transferred to the dry plug ﬂow anaerobic digestion with gas
exploitation, followed by composting after separation process. 22,604,832 Nm3 per
year landﬁll gas is produced from the organic wastes and the landﬁll gas is
converted to 2,712,580 kW of electricity per year by gas engines. 249,318 tons
per year of composts is also produced from the organic wastes. 68,928,799 kg
medical wastes were collected from 1449 health institutions in Turkey in 2012.
46% of medical wastes were sterilized and disposed of at a controlled landﬁll site,
28% of medical wastes were not sterilized and were disposed of at a controlled
landﬁll site, 16% of medical wastes were sterilized and disposed of at municipal
dumping site, 1% of medical wastes were not sterilized and were disposed of at
municipal dumping site, and 8% of medical wastes were disposed of in an incin-
erator. 2,959,837 kg medical wastes were collected from 26 health institutions in
Adana in 2012 and nearly all medical wastes in Adana were sterilized and disposed
of at Adana Metropolitan Municipality dumping site.
The Yedig€
oze Union currently (2014) has a population of 386,848 inhabitants
including seven districts: Ceyhan, Kozan, I
˙mamo
glu, Yumurtalık, Saimbeyli, Feke,
and Alada
g.
The 7 municipalities and their villages in Yedig€
oze Union also collect 94,285
tons/year of solid wastes. About 42.5% of this waste was collected from Center of
Ceyhan municipality, 40.5% from Center of Kozan municipality, 7.5% from the
Center of I
˙mamo
glu municipality, 4.5% from Center of Yumurtalık municipality,
1.9% from Center of Feke municipality and 1.6% from Center of Saimbeyli and
Alada
g municipalities. All solid wastes collected from Adana Metropolitan Munic-
ipality, except Yedig€
oze Union, were transported to Adana Metropolitan Munici-
pality Landﬁll Site for recycling and disposal. Solid wastes collected from
Yedig€
oze Union are disposed to uncontrolled dumping areas, except Ceyhan that
122
K. Aydin and C
¸ . U
¨ n
transfers its waste to Adana Landﬁll site using the transfer station in Ceyhan,
situated in each district.
The 7 municipalities and their villages in Yedig€
oze generated 156,031 tons/year
of waste in 2012. About 60.4% of this waste was generated in urban locations and
39.6% was generated in rural locations. About 36.4% of this waste is generated
from middle income, 12.4% from high income, 39.6% from rural locations, 7.2%
from low income, 2.9% from the commercial strata, and 1.5% from the tourist
strata. The population of Yedig€
oze is affected during the summer season; therefore,
waste generation is affected as well. The average per capita waste production in
Yedig€
oze Union was 0.98 kg/inhabitant*day in 2012 (Draft Master Plan for
Yedig€
oze Union 2014).
Fig. 3 Location of Yedig€
oze Union cities in Adana
Development of Solid Waste Management System for Adana. . .
123
4
Experimental Facility
4.1
Setup
In this study, a pilot cracking reactor was designed and used for thermal cracking.
The reactor consists of a heat exchanger, a PT 100 type thermocouple in order to
measure the variation of temperature inside the reactor, a digital temperature
indicator, a ﬁller cap, a drain cover, and a manometer. Stainless steel number
316 L is used as the main material for the reactor manufacture. Figure 4 shows
the technical drawing of the reactor.
Hospital disposals, like PP and PVC syringes, infusion sets, latex medical
gloves, blood, and diffusion bags, were collected from Adana State Hospitals
(Fig. 5).
Firstly, medical waste plastics were dried, triturated, and loaded into the thermal
cracking reactor. The reactor was heated up to the starting temperature of reaction;
Table 3 Waste composition for Yedig€
oze Union in 2012, (tonnes)
Material
category
High
income
Middle
income
Low
income
Commercial
Rural
Tourism
Total
(ton)
Organic
11,439
32,566
6523
2046
35,396
1245
89,214
Kitchen/
Cant. Waste
11,041
30,999
5208
1510
28,189
1145
78,092
Garden/
Park Waste
398
1567
1315
536
7207
100
11,123
Wood
32
247
26
10
137
6
458
Paper/
Cardboard
1689
4564
440
776
2520
272
10,261
Paper
1037
2626
301
479
1716
178
6338
Cardboard
651
1938
139
296
804
94
3922
Glass
488
1534
148
350
816
90
3426
Plastics
1929
5254
613
569
3428
257
12,051
Textiles
297
2273
391
90
2157
60
5267
Metals
63
274
23
30
127
7
525
Hazardous
Waste
158
248
101
25
555
18
1105
Composites
184
410
73
33
396
23
1119
WEEE
4
18
6
7
34
0
69
Other
Composites
180
392
67
26
362
23
1050
Inert Materials
170
373
1324
94
7535
26
9521
Other
Categories
2502
4798
685
193
3779
307
12,264
Fine < 10 mm
462
4178
911
280
4899
89
10,820
Total
19,411
56,719
11,259
4497
61,745
2400
156,031
124
K. Aydin and C
¸ . U
¨ n
Table 4 Generation rate forecast for Yedig€
oze Union 2012–2045
Material category/year
2012
2015
2025
2045
Organic waste
199.1
207.1
211.0
220
Biodegradable kitchen/Cant. waste
174.3
181.8
186.7
198.4
Biodegradable garden/Park waste
24.8
25.4
24.3
21.6
Wood
1.0
1.1
1.1
1.2
Paper/cardboard
22.9
24.9
29.3
41.9
Paper
14.1
15.5
19.0
28.7
Cardboard
8.8
9.3
10.3
13.2
Glass
7.6
8.0
8.5
9.6
Plastics
26.9
29.9
38.1
62.5
Textiles
11.8
12.2
12.4
12.8
Metals
1.2
1.2
1.4
1.7
Hazardous waste
2.5
2.6
2.7
2.9
Composites
2.5
2.6
2.7
3.0
WEEE
0.2
0.2
0.2
0.3
Other composites
2.3
2.4
2.5
2.7
Inert materials
21.3
21.4
19.5
15.2
Other categories
27.4
29.9
36.0
53.3
Fine < 10 mm
24.2
23.0
17.6
10.3
Total
348.3
364.1
380.4
405.6
Table 5 Medical waste generation and management in the Yedig€
oze Union, 2012
District
Medical waste (tonnes)
Medical waste collection, transport and disposal
Ceyhan
146
Collected by ITC and, after sterilization,
disposed of in Adana Metropolitan
Municipality Sanitary Landﬁll
Kozan
109
I
˙mamo
glu
28
Yumurtalık
7
Saimbeyli
5
Feke
6
Alada
g
6
Total
307
Table 6 Existing dumpsites in Yedig€
oze Union
No. district ownership
No. district ownership
No. district ownership
1
Ceyhan
Transfer Station, No Dumpsite
2
Yumurtalık
Treasury
3
Kozan
Treasury
4
I
˙mamo
glu
Treasury
5
Alada
g
Forest
6
Feke
Treasury
7
Saimbeyli
Treasury
Development of Solid Waste Management System for Adana. . .
125
Fig. 4 Technical drawing of reactor
Fig. 5 Medical waste
plastic samples
126
K. Aydin and C
¸ . U
¨ n
subsequently the reaction was started and continued between 450 C and 475 C.
The reaction was carried out for 1.5 h. During the thermal cracking reaction, the
gaseous phase formed, and then the gaseous phase was transformed to the liquid
form by using plate type heat exchangers. The product was distilled into a drain cab
and the ﬁnal product was taken from the cab. Distilled medical waste plastic fuels
are shown in Fig. 6.
In this study, three different medical waste plastic fuel (MEDWP) blends were
prepared (10% Medical Waste Plastic Fuel + 90% Diesel Fuel, 20% Medical Waste
Plastic Fuel + 80% Diesel Fuel, and 50% Medical Waste Plastic Fuel + 50% Diesel
Fuel). The blends have been analyzed by the standards of ASTM test methods. At
the engine experiments, Mitsubishi 4D34-2A type four stroke-four cylinder diesel
engine, which has a 3907 cc engine volume, 89 kW maximum power at 3200 rpm
and 295 Nm at 1800 rpm, was used. Torque and brake power of the engine were
measured with a dynamometer. The exhaust emissions were measured by a Testo
350XL gas analyzer (Fig. 7). All experiments were repeated three times.
Fig. 6 Distilled medical
waste plastic fuel samples
Exhaust
Gas
Analyzer
Control Panel
Computer
rpm Decoder
Dynamometer
Engine
Fig. 7 Experimental setup
Development of Solid Waste Management System for Adana. . .
127
5
Results and Discussions
5.1
Fuel Properties
Measured fuel properties of MEDWP and its blends are shown in Table 7.
5.2
Engine Performance
Brake power and torque output values of diesel fuel + 10%, 20%, and 50%
MEDWP blends are shown in Figs. 8 and 9.
Table 7 MEDWP fuel properties
Properties
Diesel fuel
MEDWP 10
MEDWP 20
MEDWP 50
MEDWP 100
Density (kg/l)
0.830
0.845
0.858
0.894
0.940
Cetane number
55.57
54.52
53.20
51.48
46.58
Pour point (C)
16.0
13
10.5
6.0
3.0
Viscosity (cSt)
2.45
2.42
2.41
2.39
2.30
Caloriﬁc value
(kcal/kg)
11,320
11,105
10,980
10,650
9850
Flash point (C)
70.5
65.5
65.5
65.5
65.5
Only MEDWP 10 blends met the EN 590 diesel fuel standards
0
10
20
30
40
50
60
1000
1500
2000
2500
3000
Brake Power (kW)
Engine Speed (rpm)
Diesel
MEDWP10
MEDWP20
MEDWP50
Fig. 8 Brake power outputs of diesel and MEDWP blends
128
K. Aydin and C
¸ . U
¨ n
5.3
Exhaust Emissions
CO, CO2, and NOx values of diesel fuel+ 10%, 20%, and 50% MEDWP blends are
shown in Figs. 10, 11, and 12.
CO values of MEDWP blends are lower than diesel fuel because of the lower
viscosity of MEDWP (Fig. 10). Lower viscosity increases atomization of fuel
injection during injection. CO2 values of MEDWP blends are also lower because
100
120
140
160
180
200
220
240
260
280
300
1000
1500
2000
2500
3000
Torque (Nm)
Engine Speed (rpm)
Diesel
MEDWP10
MEDWP20
MEDWP50
Fig. 9 Torque outputs of
diesel fuel and MEDWP
blends
100
150
200
250
300
350
400
1000
1500
2000
2500
3000
CO (ppm)
Engine Speed (rpm)
Diesel
MEDWP10
MEDWP20
MEDWP50
Fig. 10 CO values of diesel fuel and MEDWP blends
Development of Solid Waste Management System for Adana. . .
129
of lower ratio of Carbon atoms in MEDWP fuels than diesel fuel (Fig. 11). NOx
values of MEDWP blends are higher than diesel fuel because better atomization
creates higher combustion temperature in the engine cylinder (Fig. 12). NOx
emissions are only dependent on in-cylinder combustion temperature.
Fig. 11 CO2 values of diesel fuel and MEDWP blends
500
600
700
800
900
1000
1100
1200
1300
1400
1500
1000
1500
2000
2500
3000
NOx (ppm)
Engine Speed (rpm)
Diesel
MEDWP10
MEDWP20
MEDWP50
Fig. 12 NOx values of
diesel fuel and MEDWP
blends
130
K. Aydin and C
¸ . U
¨ n
6
Conclusions
The main aim of this study is to develop a new infrastructure for integrated solid
waste management for Adana Metropolitan Municipality including Yedig€
oze
Union. New transfer stations have to be located in Yedig€
oze Union to transfer all
collected solid wastes to Adana Metropolitan Municipality Landﬁll Site for
recycling and disposal. All plastic materials including medical wastes collected
from Adana Metropolitan Municipality including Yedig€
oze Union have to be
transferred to the thermal and catalytic cracking unit for producing plastic fuel.
All medical wastes will be sterilized and converted to plastic fuel without any
Medical Waste Sterilization Unit. This solution reduces the medical waste disposal
without spending any energy for sterilization. Experiments showed that medical
waste plastic fuel can be blended with diesel fuel by the ratio of 10% and can be
used for waste collection trucks without any modiﬁcation. 10% MEDWP fuel
addition to diesel fuel also reduces CO and CO2 emissions (Aydın and U
¨ n 2014).
MEDWP fuel addition to diesel fuel results in better environmental impact. Creat-
ing an all type waste plastic (municipal and medical) collection infrastructure,
producing and using waste plastic fuel are one of the innovative approaches for
waste management.
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Regeneration of Peel of Peas (Pisum sativum)
After Zinc Adsorption
Sabah Menia, Amina Abbaci, and Noureddine Azzouz
1
Introduction
Heavy metal ions have become an ecotoxicological hazard because of their accu-
mulation in living organisms. Wastewater polluted with heavy metals requires a
system of treatment that can eliminate these pollutants efﬁciently (Rao et al. 2006).
There are many methods to remove metal ion solutions including chemical
precipitation, ion exchange, adsorption, and membrane ﬁltration. However, it is
necessary to have cheap materials to treat large volumes of wastewater
(Mazzi 2002).
Natural materials or waste products from industrial or agricultural processes
with large adsorptive capacities can be ideal sorbents, since they are abundant in
nature and require little processing (Mazzi 2002).
In our study, we have regenerated peels of peas after zinc adsorption; we have
used demineralized water, hydrochloric acid, and sodium hydroxide in our process.
Nomenclature
Co
Initial concentration (mg.L1)
Ce
Equilibrium concentration (mg.L1)
Cf
Final concentration after adsorption (mg.L1)
V
Volume of solution (L)
m
Mass of adsorbent (g)
qa
Quantity of adsorbed metal on the solid phase (mg.g1)
qd
Quantity of desorbed metal (mg.g1)
S. Menia (*) • A. Abbaci • N. Azzouz
University of Jijel, Laboratoire des Interactions Mate
´riaux Energie Environnement,
B.P. 98, 18000 Jijel, Algeria
e-mail: sabah.menia@yahoo.fr
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_9
133
2
Material and Method
2.1
Adsorbent Preparation
Preparation of adsorbents is as follows:
•
Cut vegetables into small pieces.
•
Put vegetables (10 g.L1) into a beaker and wash them with distilled water for
2 h at a speed of 500 revolutions per minute.
•
Dry them in an oven at 80 C for 24 h.
•
Sift with a sieve of 0.05 mm.
2.2
Desorption Procedure
In order to determine the capacity of regeneration of our substrate, we have done
tests
of
adsorption/desorption.
We
have
used
the
following
substances,
demineralized water, HCl (0.5 M), and NaOH (0.5 M), by performing ﬁve consec-
utive cycles. For all tests, initial concentration of zinc is equal to 100 mg.L1, mass
of substrate is equal to 0.4 g, temperature is of 293 K, and stirring speed is of
500 RPM.
3
Results and Discussions
The quantities of adsorbed and desorbed metals are calculated as follows:
qa ¼ Co  Ce
ð
Þ  V
m
ð1Þ
qd ¼ Cf  V
m
ð2Þ
with
Co: Initial concentration (mg.L1)
Ce: Equilibrium concentration (mg.L1)
Cf: Final concentration after desorption (mg.L1)
V: Volume of solution (L)
m: Mass of adsorbent (g)
qa: Quantity of adsorbed metal on the solid phase (mg.g1)
qd: Quantity of desorbed metal (mg.g1)
Results of our study indicate that hydrochloric acid is the most effective sub-
stance for desorption. The maximum amount of zinc desorbed by HCl is equal to
134
S. Menia et al.
10 mg.g1 (Fig. 1), whereas it is 5 mg.g1 with NaOH and demineralized water
(Figs. 2 and 3). We can deduce that elimination of zinc by peels of peas is done by
ion exchange.
According to literature, sulfuric acid is the most effective substance for regen-
eration of moringa (Moringa oleifera) after adsorption of zinc (Kalavathy and
Miranda 2010).
4
Conclusions
The objective of our study was to regenerate an adsorbent after elimination of heavy
metals; we deduct:
•
Hydrochloric acid is the most effective substance for regeneration of our
substrate.
•
The regeneration does not reach 100%.
•
According to the above results, we can conﬁrm that adsorption of zinc on peels
of peas is done by ion exchange.
Fig. 1 Regeneration of
adsorbent by hydrochloric
acid
Regeneration of Peel of Peas (Pisum sativum) After Zinc Adsorption
135
Acknowledgments The laboratory of interactions materials-energy-environment of the Univer-
sity of Jijel, Algeria, supported this research.
References
Kalavathy, M.H., Miranda, L.R.: Moringa oleifera-a solid phase extractant for the removal of
copper, nickel and zinc from aqueous solutions. Chem. Eng. J. 158, 188–199 (2010)
Mazzi, E.: Effectiveness of some low-cost sorbents for treating mixtures of heavy metals in runoff
from the ﬁrst major storm even after the extended dry period. Aquat. Des. Rehabil. CHBE. 465,
1–75 (2002)
Rao, M.M., Ramesh, A., Rao, G.P.C., Seshaiah, K.: Removal of copper and cadmium from the
aqueous solutions by activated carbon derived from Ceiba pentandra hulls. J. Hazard. Mater.
B129, 123–129 (2006)
Fig. 2 Regeneration of
adsorbent by sodium
hydroxide
Fig. 3 Regeneration of
adsorbent by demineralized
water
136
S. Menia et al.
Diesel Engine Performance and Emission
Study Using Soybean Biodiesel Blends
with Fossil Diesel
A.K. Azad, M.G. Rasul, B. Giannangelo, and S.F. Ahmed
1
Introduction
Energy demand is increasing gradually throughout the world. More consumption of
energy leads to increased environmental pollution (Grossman 2015). The increas-
ing energy demand should be met by eco-friendly, low-emission and renewable
sources of energy. The total energy is consumed by different sectors like electricity
generation, transport, manufacturing, industrial, commercial, residential sector, etc.
The transport sector is one of the faster growing, energy and emission intensive
sectors in the world (Azad et al. 2014a). This sector mainly consumes liquid fuels
like diesel, gasoline, octane, etc. Biodiesel is one of the sustainable, alternative
transport fuels which emit low greenhouse gases (Azad et al. 2015b). It can be
produced from different sources and wide arrays of feedstock are available in the
world. Biodiesels are produced from biological resources. It can be used directly or
by blending in different proportions with fossil diesel. The inventions of different
resources of biodiesel are ongoing. New feedstock is being discovered day-by-day
to meet the increasing energy demand. One of the main sources of biodiesel
production is edible oil such as soybean oil, sunﬂower oil, mustard oil, canola oil,
coconut oil, linseed oil, etc. (Azad et al. 2012; Ameer Uddin et al. 2013; Azad and
Uddin 2013). The high oil yield of these edible sources makes it more sustainable
and a prospective source of biodiesel. Soybean oil has been selected as a potential
source of biodiesel in this study. At present, it is one of the major feedstocks for
biodiesel production (Xie and Li 2006). The widely used method for biodiesel
conversion is transesteriﬁcation reaction (Azad et al. 2015a). By this reaction,
A.K. Azad (*) • M.G. Rasul • B. Giannangelo • S.F. Ahmed
Central Queensland University, School of Engineering and Technology,
Rockhampton, QLD 4702, Australia
e-mail: azad.cqu@gmail.com; a.k.azad@cqu.edu.au
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_10
137
yielded methyl esters as biodiesel and glycerine as byproducts is produced (Du et al.
2004). After conversion of biodiesel, it can be blended with diesel and used in
compression ignition (CI) engine in transport and mining. The study presents the
prospect of soybean biodiesel as a sustainable transport fuel in Australia. The
physiochemical fuel properties of the biodiesel were determined by ASTM and
EN standards. Biodiesel blends like B5, B10, B20 and B50 were used for this
experiment. The performance and emission of these blends were tested in a
4-stroke, 4-cylinder diesel test bed engine. ISO 8178–4 international standard was
maintained during the experimental study.
Nomenclature
B5
5% biodiesel and 95% diesel (volume %)
B10
10% biodiesel and 90% diesel (volume %)
B20
20% biodiesel and 80% diesel (volume %)
B50
50% biodiesel and 50% diesel (volume %)
BSFC
Brake-speciﬁc fuel consumption (kg/h)
BP
Brake power (kw)
CO
Carbon monoxide emission (%)
CO2
Carbon-di-oxide emission (%)
NOx
Nitrogen oxides emission (ppm)
HC
Hydrocarbon emission (ppm)
ASTM
American Society for Testing and Materials
2
Soybean Biodiesel
Soybean is one of the widely used biodiesel feedstock in the world. This oil is
edible, biodegradable and can be used as biodiesel feedstock. It is also called soy
biodiesel. The production of biodiesel involves several steps, namely, seed collec-
tion, feedstock preparation, oil extraction, transesteriﬁcation, blending and is ﬁnally
used in transport vehicles (Milazzo et al. 2013b). The production of soybean is
increasing day by day. World total production rose to 243.9 metric tons in 2010 and
is predicted to be 311.1 metric tons in 2020 (Milazzo et al. 2013a; Masuda and
Goldsmith 2009). Soybean biodiesel production steps are presented in Fig. 1. There
are several methods available for oil extraction. Generally, the mechanical extrac-
tion method is used for crude oil extraction due to the simplicity, low cost and quick
method. For large-scale production, the n-hexane method is widely used. In
transesteriﬁcation reaction, crude or reﬁned vegetable oil is used to convert biodie-
sel by alkaline catalyst, heating and pressurizing condition as reaction agents.
Glycerine is removed after conversion as the byproduct. The produced methyl
ester requires washing, fractionation and drying. This methyl ester is called pure
biodiesel which is denoted as B100. It is then ready to blend and use in CI engine.
138
A.K. Azad et al.
3
Material and Methods
3.1
Materials
Soybean biodiesel was supplied by National Biodiesel Limited (NBL), Australia.
NBL is one of the leading suppliers of premium quality soy biodiesel in Australia.
They produce and distribute B100 and biodiesel blends, namely, B5 and B20 in the
Australian biofuel market.
Soybean cultivation
Seed collection and
transport
Crushing and
degumming
Soy meal
Crude soy oil extraction
Refining of the crude oil
Methanol
Diesel
Biodiesel conversion by
transesterification
Glycerine
Biodiesel transport
Blending with diesel
Biofuel distribution
Combustion or uses of
biofuel
Feedstock 
production
Oil extraction
Fuel production
Uses in vehicle
Fig. 1 Biodiesel production steps from soybean oil (Milazzo et al. 2013b)
Diesel Engine Performance and Emission Study Using Soybean Biodiesel Blends. . .
139
3.2
Production of Biodiesel
Virgin oil for CI engine as a substitute of fossil diesel is not practical due to its high
viscosity, low volatility, free fatty acid content and polyunsaturated characteristics
(Ma and Hanna 1999; Nigam and Singh 2011). To resolve these problems, lots of
efforts have been made globally to develop and improve vegetable oil properties as
a substitute of fossil fuel. Many scientist and engineers developed many technol-
ogies and methods to overcome the problems for biodiesel conversion from differ-
ent feedstocks (Atabani et al. 2013). There are four methods, namely, thermal
cracking, chemical cracking, dilution and transesteriﬁcation available in the liter-
atures for biodiesel conversion (Atabani et al. 2012, 2013; Balat and Balat 2010;
Demirbas 2008; Fukuda et al. 2001; Demirbas
¸ 2002; Lin et al. 2011; Srivastava and
Prasad 2000; Canakci and Sanli 2008; Moser 2011; Karmakar et al. 2010; Demirbas
and Demirbas 2007; Chauhan et al. 2010). Transesteriﬁcation is one of the most
common and economic techniques for biodiesel conversion where the chemical
reaction of alcohol with vegetable oil was used due to its high conversion efﬁciency
with minimum time (Meher et al. 2006; Leung et al. 2010). This technique has been
widely used to reduce the viscosity of the vegetable oil and conversion of the
triglycerides into ester. The transesteriﬁcation reaction is presented in Fig. 2.
There are two available ways for transesteriﬁcation reaction, namely, catalytic
transesteriﬁcation and non-catalytic transesteriﬁcation (Atabani et al. 2012, 2013;
Balat and Balat 2010; Salvi and Panwar 2012; Meher et al. 2006; Agarwal 2007;
Balat and Balat 2008; Jain and Sharma 2010; Koh and Mohd. Ghazi 2011; Mahanta
and Shrivastava 2004; Murugesan et al. 2009; Singh and Singh 2010; Yusuf et al.
2011; Dias et al. 2012; Berrios and Skelton 2008). In the catalytic reaction, a
catalyst is used to commence the reaction. The catalyst enhances the solubility of
alcohol and thus increases the reaction rate. The most frequently used process is the
catalytic transesteriﬁcation reaction. Catalytic transesteriﬁcation method includes
acid and base catalyst as well as enzyme catalyst (Royon et al. 2007). The alkaline
catalysts include catalysts such as NaOH, NaOCH3, KOCH3, KOH, NaMeO and
K2CO3 (Atabani et al. 2012; Narasimharao et al. 2007; Kim et al. 2004; Rashid and
Anwar 2008; Liu et al. 2008; Dorado et al. 2004; Demirbas 2009a; Suehara et al.
2005). Acid catalyst includes sulfuric, hydrochloride, ferric sulfate, phosphoric and
Fig. 2 General equation for transesteriﬁcation reaction (Demirbas 2009b; West et al. 2008;
Hincapie
´ et al. 2011)
140
A.K. Azad et al.
organic sulfonic acid (Canakci and Van Gerpen 1999; Lotero et al. 2005).
Non-catalytic
transesteriﬁcation
includes
supercritical,
alcohol
and
BIOX
co-solvent (Kusdiana and Saka 2004; Demirbas
¸ 2002; Minami and Saka 2006;
He et al. 2007).
3.3
Fatty Acid Composition
The fatty acid composition is very important to analyze characteristics of soybean
biodiesel. This analysis can be performed by using gas chromatography. Literatures
reported that there are six main fatty acid compositions in soybean oil. The fatty
acid composition, molecular weight, structure and percent of contents are presented
in Table 1.
3.4
Biodiesel Properties
The physiochemical fuel properties of the biodiesel are very important to analyze
before using it in the IC engine. The engine performance and exhaust gas emission
depends on the properties of fuel used in the engine. The fuel properties, namely,
density, viscosity, caloriﬁc value, cetane number, ﬂash point, poor point, etc., were
determined using ASTM D6751 and EN 14214 standards and compared with
standard biodiesel and petroleum diesel. The properties of the fuels are presented
in Table 2. The table shows that almost every property of the fuels is within the
acceptable range. The biodiesel has higher density compared with petroleum diesel
but it has some good fuel properties like higher ﬂash point, pour point and cloud
point.
Table 1 Fatty acid composition of soybean oil (Milazzo et al. 2013a; Rahman et al. 2014)
Fatty acid
Formula
Molecular weight
Structure
Content (%)
Palmitic
C16H32O2
256
16:0
10
Palmitoleic
C16H30O2
254
16:1
1
Stearic
C18H36O2
284
18:0
4
Oleic
C18H34O2
282
18:1
18
Linoleic
C18H32O2
280
18:2
55
Linolenic
C18H30O2
278
18:3
10
Stearidonic
C18H28O2
276
18:4
2
Saturated
0
Monounsaturated
0
Polyunsaturated
63
Total
100
Diesel Engine Performance and Emission Study Using Soybean Biodiesel Blends. . .
141
3.5
Biodiesel Blending with Diesel
The soybean biodiesel was blended with ultra-low sulfur diesel in different pro-
portions. The blend samples were prepared by blending 5% biodiesel and 95%
diesel denoted as B5, 10% biodiesel with 90% diesel refried as B10, 20% biodiesel
and 80% diesel presented as B20, and 50% biodiesel with 50% diesel denoted as
B50 for soybean biodiesel.
3.6
Test Bed Engine Setup
The Kubota 4 stroke diesel engine was used as test engine for this study. The
speciﬁcation of the engine is Kubota V3300 diesel engine with a bore of 98 mm and
a stroke of 110 mm. The rated power output of the engine is 50.7 kW at 2600 rpm
and the rated torque is 230 Nm at 1400 rpm. The dynamometer that was used for
testing is a Dyno Dynamics engine dynamometer. It works by placing a load on the
engine and then measuring the amount of power the engine produces against the
load. The dynamometer is coupled with test bed engine controlled by a computer.
The schematic diagram of the test bed engine is presented in Fig. 3. Table 3 presents
the detail speciﬁcation of the test engine and its dynamometer.
The exhaust gas analyzer (EGA) that was used during testing is an Andros
6241A, 5 gas analyzer. This EGA takes instantaneous readings of the exhaust gas
and can measure carbon monoxide, carbon dioxide and hydrocarbons using a
non-dispersive infrared (NDIR) sensor. The EGA can also measure oxygen and
Table 2 The fuel properties of the soybean biodiesels (Ramadhas et al. 2005; Moﬁjur et al. 2013)
Properties
Unit
Diesel
Soybean biodiesel
Standard biodiesel
Density at 15 C
kg/m3
827.2
885
880
Viscosity
mm2/s
3.23
4.08
1.9–6.0
Caloriﬁc value
MJ/kg
47.5
39.76
–
Cetane number
–
58
47–52
47
Flash point
C
68.5
69
130
Pour point
C
0
3
– 16
Cloud point
C
5
4
3 to 12
Saturated fatty acid
% mass
–
14.0
–
Monoun-SFA
% mass
–
19.0
–
Polyun-SFA
% mass
–
67.0
1.0
Degree of unsaturation
–
–
153
–
Iodine value
g12/100 g
–
151.53
120
Oxidation stability
hr
–
4.40
–
Allylic position equivalent
–
–
152
–
Bis-allylic position equivalent
–
–
75
–
Saponiﬁcation value
–
–
201.59
–
142
A.K. Azad et al.
NOx
CO2
HC
CO
Exhaust gas
Filter
Fuel
measurement
Air measurement
4 cylinder diesel
engine
Smoke
Amplifier
Electric
Dynamometer
Control
Console
Cooling module
Oil
Air cooling unit
Radiator
Cooling
Fan
Throttle position
Ignition timing
Fuel quantity
3 phase main
power supply
Converter
1 phase power supply
Control
Control
Coupling
Water
Data acquisition
card
PC
Fig. 3 Schematic diagram of the test bed engine setup
Table 3 The detail speciﬁcation of the test bed engine
Items
Unit
Speciﬁcations
Type
–
Vertical, 4 stroke, liquid cooled
No. of cylinders
–
4
Bore
mm
98
Stroke
mm
110
Total displacement
L
3.318
Combustion type
–
Spherical type (E-TVCS)
Rated speed
rpm
2800
Compression ratio
–
22.6:1
Rated power
kw
53.9
Fuel injection timing
–
16 before TDC
Injection pressure
MPa
13.73
Diesel Engine Performance and Emission Study Using Soybean Biodiesel Blends. . .
143
nitrogen oxides using an electrochemical sensor. More information like measure-
ment range, resolution and accuracy of the EGA is presented in Table 4.
3.7
Engine Test Method
The international standard ISO 8178–4 C1 testing method was used in this experi-
mental study. This standard is widely used to measure the exhaust emissions of
non-road internal combustion engines. Table 5 shows the testing procedure for the
C1 test cycle where the mode is deﬁned as engine operating point by speed and torque,
rated speed. The standard also deﬁnes that the intermediate speed shall be the declared
maximum torque speed if it occurs between 60% and 75% of the rated speed. If the
maximum torque speed is less than 60% of the rated speed,then the intermediate speed
shall be 60% of the rated speed. Likewise, if the maximum torque speed is greater than
75% of the rated speed the intermediate speed shall then be 75% of the rated speed.
The measurements were only taken in the last 3 min as it allows the system to stabilize
allowing for more accurate results. At the start of each test, the engine must also be
preconditioned by running the engine at its rated power for 40 min.
3.8
Neural Network for the Test
Different thermodynamic system neural network has been used to predict the
performances of that particular system from some decades (Ghobadian et al.
2009). Recently, this technique is used for modeling the operation in internal
Table 4 The detail speciﬁcation of the EGA
Measured gas
Measurement
Range
Resolution
Accuracy
HC
0–30,000 ppm
(n-Hexane)
1 ppm
4 ppm abs.
CO
0–15%
0.001%
0.02% abs.
CO2
0–20%
0.01%
0.3% abs.
O2
0–25%
0.01%
0.1% abs.
NOx
0–5000 ppm
1 ppm
20 ppm abs.
Table 5 The ISO 8178–4 test procedure with speed and load
Mode
1
2
3
4
5
6
7
8
Speed (rpm)
Rated speed
2400
Intermediate
speed, 1440
Idle speed, 800
Torque (Nm)
180
135
90
18
210
158
105
0
Weight factor
0.15
0.15
0.1
0.1
0.1
0.1
0.1
0.15
144
A.K. Azad et al.
combustion engine. Specially, this approach is well efﬁcient to predict the perfor-
mance and emission characteristics of diesel engine, speciﬁc fuel consumption and
air fuel ration in in-cylinder combustion. The neural network for this experimental
work is presented is Fig. 4.
Table 5 shows the speeds and torques that were tested. The dynamometer ran a
performance curve of the diesel engine and it was found that the engine was not able
to reach its rated power of 50.7 kW. From the performance curve it was determined
that the maximum torque of the engine occurred at 2300 rpm and as this is less than
60% of the rated speed the intermediate test speed used was instead 60% of the
rated speed, 1440 rpm. The idle speed for the dynamometer is 800 rpm which was
chosen as engine idle speed.
4
Results and Discussions
The key parameters for diesel engine performance are brake power, brake torque
and brake-speciﬁc fuel consumption. In emission study, exhaust gases like, CO,
CO2, HC and NOx were measured in the experiment. The diesel engine perfor-
mance and emission study using soybean biodiesel and fossil diesel is brieﬂy
discussed below.
Fig. 4 Neural network for the experimental test bed engine
Diesel Engine Performance and Emission Study Using Soybean Biodiesel Blends. . .
145
4.1
Brake Power (BP)
Figure 5 shows the variation of BP with engine speeds for diesel and biodiesel
blends. The trend of the curves is similar to the diesel engine performance curves as
mentioned in previous studies. The power production of the engine decreases
compared with diesel fuel with the increase of biodiesel percentage. This trend is
expected as biodiesel has less energy content (Table 2) than fossil diesel. The ﬁgure
clearly shows the comparison between diesel and soybean biodiesel blends. Over
the entire range of engine speed, biodiesel blends B5, B10, B20 and B50 produces
2.28%, 5.10%, 9.72% and 17.24% less power compared to diesel fuel, respectively.
The B5 produced maximum output power (average 26.90kw) compared to other
blends (average BP values are 26.13kw (B10), 24.86kw (B20) and 22.78kw (B50)
under ISO 8178 test procedure). However, B5 and B10 produced very close BP
over the entire range of engine speed. The result also shows that output BP
decreases with the increase of biodiesel percentage in the fuel blends. The differ-
ence between the BP values is not a signiﬁcant amount. For example, maximum BP
drops occurred from B5 to B50 is only 0.77kw, 2.05kw and 4.12kw, respectively. It
can be noted that biodiesel blends except B50 produced very close BP to diesel fuel
at higher engine speed at 2400 rpm. B5 and B10 biodiesel blends can be considered
as sustainable alternative fuels in terms of maximum power output in diesel engine.
4.2
Brake Torque (BT)
Figure 6 illustrates another important performance curve like total output BT
variation with engine speed. The trends for each blend can be analyzed and
compared with each other. The biodiesel blends appear to have the expected effect
on torque as higher blends content results in a lower BT in a consistent manner.
By comparing Figs. 5 and 6, it is evident that maximum torque and power
reﬂect each other which are to be expected, as torque and power are directly
proportional. For B5 and B10 biodiesel blends, total torque output is slightly
lower than the diesel fuel; however, lower torque production for B50 is expected
because the property of the biodiesel blends inﬂuenced the in-cylinder combustion
in diesel engine. One of the main reasons is that soybean oil contains more than
55% of linoleic acid which may cause higher viscous fuel by mixing 50% with
fossil diesel. The decrease of BP and BT can be attributed to the biodiesel blends
due to the lower energy content (Table 1) and higher viscosity. Considerable
differences of BT between B5 and B10 biodiesel have been found in this study.
According to these two performance curves (Figs. 5 and 6), B5 and B10 blends have
better performance than other blends.
146
A.K. Azad et al.
Fig. 6 Relationship between engine speed and torque for different biodiesel blends
Fig. 5 Variation of total power output with engine speed for different fuel blends
Diesel Engine Performance and Emission Study Using Soybean Biodiesel Blends. . .
147
4.3
Brake-Speciﬁc Fuel Consumption (BSFC)
BSFC is one of the important factors for engine performance of the fuels. Figure 7
shows the fuel consumption of the different fuel blends over the ISO 8178 test
procedure. As shown from the ﬁgure, biodiesels have higher fuel consumption than
fossil diesel. The BSFC for B5 biodiesel is closer with diesel. As compared with
other blends, B10 has lower BSFC in a consistent way. The trends of this curve are
acceptable according to the literature as biodiesel has greater fuel consumption due
to its lower energy content (Table 1). The average BSFC increases by 15.87%,
28.24% and 36.42% for B10, B20 and B50 blends compared to diesel fuel, respec-
tively. It has been evidenced that BSFC increases with the increase of biodiesel
blends (Moﬁjur et al. 2014). So, the experimental results are acceptable according to
the literatures (Wang et al. 2013; Shahabuddin et al. 2012). More fuel consumption
can be attributed to the lower energy content (Table 1) of biodiesel blends compared
with fossil diesel. So, B10 soybean biodiesel blends showed better performance
considering total output power, torque and BSFC compared with other blends.
4.4
Engine Emission Analysis
4.4.1
Carbon Dioxide (CO2) Emission
Emission analysis is another part of fuel testing in CI engine. In this experiment,
using exhaust gas analyzer, CO, CO2, HC and NOx emissions have been measured
Fig. 7 Variation of BSFC with engine speed for different fuel blends
148
A.K. Azad et al.
as emission parameters for diesel and biodiesel blends. Figure 8 illustrates the total
CO2 emissions for diesel and biodiesel blends over the engine speed in ISO 8178 test
procedure. It has been found that CO2 emission decreases with increase of biodiesel
percentage. The literature reported that biodiesel is carbon neutral fuel and the
combustion of biodiesel in CI engine emits lower greenhouse gases than fossil diesel
(Azad et al. 2014b; Usta et al. 2005). The trend shows the decrease in CO2 emission
compared with diesel which is expected from the experiment. The average CO2
emission decreases by 6.73%, 8.96%, 8.99% and 11.04% for B5, B10, B20 and B50
blends compared with fossil diesel, respectively. It can be noted that the decrease of
CO2 emission for B10 and B20 blends are very close; however, for B20 other
performances like BP, BT and BSFC are lower than B10 biodiesel blend. But for
each blend CO2 emission has a more distinct trend as the more biodiesel is added to
the fuel the less CO2 emission is given off. As the drop in emission does continue
between B5 to B50, it may therefore be assumed that the relationship between CO2
emissions is linear. The highest CO2 reduction occurred using B50 blend.
4.4.2
Carbon Monoxide (CO) Emission
CO emission is another important emission parameter for diesel engine. Literature
reported that CO emission occurred when excess oxygen is not present in the fuel
(Rahman et al. 2014). Some other factors, namely, air/fuel ratio, injection timing,
engine speed, injection pressure, fuel characteristics, etc., are also related to CO
Fig. 8 Variation of CO2 emission with engine speed for different fuel blends
Diesel Engine Performance and Emission Study Using Soybean Biodiesel Blends. . .
149
emission by combustion of fuel in CI engine (Gumus et al. 2012). Figure 9 presents
the variation of CO emissions for diesel and biodiesel blends where most of the
blends emit lower CO except B5 compared with diesel fuel. The CO emission
reduction of about 33.12%, 28.01% and 62.83% for B10, B20 and B50 compared to
diesel fuel is recorded from this study. There is no major trend of CO reduction for
biodiesel blends. The experimental data shows that the amount of biodiesel in the
fuel blend has positive impact on CO emission. Apart from the B5 blend, higher CO
emissions were identiﬁed than that of diesel. The lower CO reading for different
biodiesels could be attributed to biodiesel having higher oxygen content. A higher
oxygen content results in a more complete combustion and less CO emission in the
exhaust. Due to the instrumental error, every fuel has almost the same CO emission
at higher speed at 2400 rpm.
4.4.3
Hydrocarbon (HC) Emission
Incomplete combustion of fuel and ﬂame quenching results in the unburned HC
emission in CI engine. Figure 10 shows the HC emission in exhaust stream for
diesel and biodiesel blends under the ISO 8178 test procedure. As seen from the
ﬁgure, HC emission has not been consistent with biodiesel blends. The United
States Environmental Protection Agency (USEPA) determined that an increase in
biodiesel percentage should decrease the amount of HC in the exhaust stream. The
results obtained from the test for HC emissions roughly follow the USEPA state-
ment. It could be mentioned here that some more error happened during the
Fig. 9 Variation of CO emission with engine speed for different fuel blends
150
A.K. Azad et al.
experiment at 2000 rpm for diesel fuel. A reason for these results varying so much is
because hydrocarbons make up a very small amount of the exhaust stream. The
maximum HC reading was 13 ppm which equates to 0.0013% of the exhaust gas.
As the amount of HC being measured is really small, the error in HC in exhaust gas
will not have signiﬁcant effect on the results.
4.4.4
Nitrogen Oxide (NOx) Emission
Figure 11 illustrates NOx emission by combustion of biodiesels in CI engine, over
the speed range in ISO 8178 test procedure. It has been evidenced from previous
studies that NOx emission is one of the most important problems of biodiesel
combustion in CI engine (Moﬁjur et al. 2014). From the graph, it is clearly seen
that biodiesels have positive impact on NOx emission, that is, a greater percentage
of biodiesel leads to increase NOx emission in exhaust gas. The experimental results
show that NOx emission increases by 24.51%, 30.56%, 41.74% and 56.16% for B5,
B10, B20 and B50 biodiesel blends compared with diesel, respectively. The
comparison between biodiesel blends shows that B5 and B10 emit relatively
lower NOx emission than B20 and B50. The increasing trends of NOx emission
have been identiﬁed in this experiment. The USEPA report shows that an increase
in biodiesel percentage leads to increase of NOx emissions. The maximum amount
of NOx measure for the B50 blend was 2.25 ppm and this equates to 0.000225% of
the total exhaust gas. As such a small amount of NOx was measured, it is
Fig. 10 Variation of HC emission with engine speed for different fuel blends
Diesel Engine Performance and Emission Study Using Soybean Biodiesel Blends. . .
151
understandable that some of the results may be slightly off due to the accuracy of
the exhaust gas analyzer. As discussed earlier, NOx has a global warming potential
of nearly 300, meaning that it is 300 times worse for the atmosphere than CO2
emission. So, the small amounts of NOx emission have greater importance than that
of the other exhaust emissions.
5
Conclusions
The study investigated the use of soybean biodiesel as a sustainable alternative fuel
for diesel engine because it has excellent capability to decrease the environmental
impact, it can potentially reduce the dependency on fossil fuel and it can be used in
different proportion without modiﬁcation of diesel engine. The following conclu-
sions can be drawn from the experimental study. The characteristics of these
biodiesels and their blends meet the requirements of ASTM D6751 and EN
14214 standards. The ISO 8178 test procedure was followed during the engine
performance test and emission study and found that more biodiesel blends produce
less BP, BT and higher BSFC compared to diesel fuel; however, it gives off fewer
emissions than fossil diesel. The overall performance of B5 and B10 soybean
biodiesel blends has been found better than other biodiesel blends. They produce
an average 26.90 kw and 145.22 N.m BP and BT which is only 2.28% and 5.10%
lower BP compared to diesel fuel. The BSFC for B10 blend shows relatively lower
value compared to other blends but a bit higher than diesel. With the various
Fig. 11 Variation of NOx emission with engine speed for different fuel blends
152
A.K. Azad et al.
emission gasses such as CO, CO2, HC and NOx there was decreasing linear trend
found for CO, CO2 and HC emission but increasing trend for NOx emission with the
increase in biodiesel blends. Finally, it can be concluded that both B5 and B10 are
the optimum soybean biodiesel blends that produce more consistent and expected
results than other biodiesel blends.
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155
The Valorization of the Green Alga Spirogyra’s
Biomass in the Region of Ouargla-Algeria into
Renewable Biofuel
Souad Zighmi, Mohamed Bilal Goudjil, Salah Eddine Bencheikh,
and Segni Ladjel
1
Introduction
Biofuels are an effective substrate for fossil energy’s existing sources; they offer the
prospect of ecological sustainability and reduce greenhouse gases’ emissions
(Saharan et al. 2013). Indeed, the algae are presented as a potential source of
biofuel; with their various important advantages (Ramachandra 2013), they got a
great deal of attention as an alternative and renewable source of biomass for
bioethanol production (John et al. 2011; Wibowo et al. 2013).
Bioethanol is deﬁned as an ethyl alcohol produced by various biological pro-
cesses that convert biomass into ethanol (Deenanath et al. 2012). It is a liquid
transport fuel with a high octane number; it is generally mixed with gasoline in
order to contribute to the reduction of vehicles’ carbon monoxide emissions (Tucho
2013).
Fermentation is the well-known process used for alcohol production from a
variety of biomass sources containing sugar, starch, or cellulose. This process
involves the action of yeast, which decompose and convert the sugar into ethanol
(Tucho 2013).
S. Zighmi (*)
University of Ouargla, Faculty of Sciences of the nature and life, Laboratory of Engineering
Laboratory of Water and Environment in Middle Saharian, Ouargla 30000, Algeria
University of Ouargla, Faculty of Applied Sciences, Department of Process Engineering,
Ouargla 30000, Algeria
e-mail: zighmi.so@univ-ouargla.dz; souad.zighmi@gmail.com
M.B. Goudjil • S.E. Bencheikh • S. Ladjel
University of Ouargla, Faculty of Applied Sciences, Laboratory of Process Engineering,
Ouargla 30000, Algeria
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_11
157
Simultaneous sacchariﬁcation and fermentation (SSF) is an important strategy of
the bioethanol production process where the enzyme hydrolysis and the fermenta-
tion are performed in the same container; the inhibition of the ﬁnal product and of
the glucose in the hydrolysis is gradually assimilated by the yeast during the
fermentation process. Therefore, in SSF, the requirement of enzymes is lesser
while the bioethanol yield is higher (Balat et al. 2008).
At present, recent attempts to produce ethanol are focused on the use of algal
biomass as a raw material for the fermentation process, for microalgae are rich in
carbohydrates and proteins that can be used as carbon sources for the fermentation
and do not compete with the food chain – in addition to its none requirement for the
use of large areas (Singh and Gu 2010).
The alga with the potential to be developed as a raw material for bioethanol
production is Spirogyra (Sulfahri and Nurhidayati 2011). It has been demonstrated
that Spirogyra species are able to accumulate high levels of polysaccharides and
starch in their complex cells walls, which allows using it in bioethanol production
(Nguyen and Vu 2012).
In this study, we present a research that addresses the valorization of the algal
biomass in the energy ﬁeld. An experimental effort has been expanded by
converting Spirogyra biomass into ethanol through fermentation. The alga Spiro-
gyra was collected from a natural lake in the region of Touggourt, a part of the city
of Ouargla-Algeria. This study was conducted to determine the feasibility of using
algae to produce ethanol in Algeria.
2
Materials and Methods
2.1
Sampling Site
The sampling of stem was done in the region Touggourt-Ouargla, from a natural
lake (Fig. 1).This region is located in the southeast of Algeria. It corresponds to the
upper part of Oued (river) Righ. It is bordered on the southeast by the Grand Erg
Oriental, on the north by Megarine’s palm groves, and on the west by sand dunes
(Hadjoudj et al. 2011).
2.2
Sample Preparation
The strains are put into sterile glass vials and then transferred to the laboratory. The
alga sample was identiﬁed by microscopic examination, as being part of the
Spirogyra species (Fig. 2).
158
S. Zighmi et al.
Strains were washed with distilled water and subjected to drying away from the
sun. The ﬁne powder of Spirogyra’s biomass, obtained after being dried, crushed,
and then ﬁltered through a sieve of 1 mm, is used for all experiments (Fig. 3).
2.3
Extraction of Spirogyra’s Oil
A step of oil extraction was performed on Spirogyra, with hexane as the solvent
used for the extraction with Soxhlet; this operation aims to reduce the fermentation
time and to increase the yield of bioethanol.
2.4
Depigmentation
The extracted residue of oil subsequently undergoes a step of depigmentation. The
dry material is placed for 12 h under stirring in acetone in a proportion of 150 ml of
Fig. 1 Stem sampling site
Fig. 2 Photo of Spirogyra
species
The Valorization of the Green Alga Spirogyra’s Biomass. . .
159
solvent for 5 g of the used material. Afterward, the mixture is ﬁltered on sintered
glass of porosity 3 and rinsed with absolute ethanol until disappearance of the green
color that reﬂects the elimination of chlorophylls. The residue is then depigmented
with 200 ml of absolute ethanol (for 5 g of material) and boiled for 1 h. This step is
repeated two times, and between each depigmentation step, the mixture is ﬁltered
and washed with absolute ethanol until the color disappears. The obtained residue is
dried in an oven (50 C) for 12 h and then weighed.
2.5
Simultaneous Sacchariﬁcation and Fermentation
Experimentally, for the sacchariﬁcation of the developed algal biomass, we used a
commercial yeast "Saf-Instant" consisting mainly of the Saccharomyces cerevisiae
that produces enzymes, hydrolyzes the cellulose and starch present in the
studied algae, and releases simple sugars that are subjected to fermentation, carried
out for 24 h.
Fig. 3 Set-up for
experiments
160
S. Zighmi et al.
In addition, we used 3 g of yeast for 5 g of depigmentated algae’s residue, heated
at a temperature of 30 C. After 24 h of experimentation, it is estimated that the
fermentation is complete and that all of the glucose contained in the alga is
converted into ethanol. To conﬁrm this, we performed the Fehling’s solution test.
2.6
Fehling Solution Tests
We know that the blue Fehling’s solution has a property to become orange red in the
presence of glucose. We put in the ﬁrst test tube our solution before fermentation
and, into a second tube, the solution obtained after fermentation. We add to each
tube a few drops of Fehling’s solution, heating them in a water bath for a few
seconds with gentle agitation.
2.7
Puriﬁcation
We obtained a fermented solution containing ethanol; however, the product still
contains a large part of water. In order to purify the obtained ethanol, fractional
distillation is carried out.
2.8
The Infrared Spectrophotometer Analysis
To conﬁrm that the obtained product is of ethanol, an infrared spectrophotometry
analysis was carried out, which allows characterizing the major functional groups in
one molecule.
3
Results and Discussion
3.1
Oil Yield
The yield of the extracted oil from the Spirogyra algae is equal to 0.589%, which is
a small quantity; therefore, the use of Spirogyra algae for bioethanol production is a
wise choice.
The Valorization of the Green Alga Spirogyra’s Biomass. . .
161
3.2
Fehling’s Solution Test
It was observed that the ﬁrst tube containing the solution before fermentation
became orange, while the second containing the fermented solution does not change
color. It is deduced that the solution not fermented contains glucose, which by
fermentation gives another product. Reminding that this test does not indicate if the
obtained product is ethanol, this requires further experiments to conﬁrm the result.
3.3
Puriﬁcation
3.3.1
Bioethanol Yield
After completing the experiment, we obtain a clear liquid, of which the recorded
yield is 16.83% of ethanol after only 24 h of fermentation. The obtained result from
the analysis shows that the bioethanol yield is 28.57 times higher than the one
obtained from the extracted oil, which conﬁrms the choice of these algae for the
production of bioethanol.
The characteristics of the product obtained are summarized in the following
table (Table 1):
3.3.2
Checking of Bioethanol Quality
In order to see if the obtained product is consistent with the international standards,
a comparison between the obtained bioethanol and gasoline is essential. Table 2
includes some parameters used for the comparison.
The comparison concerns certain properties, from the analysis results, shown in
Table 2, and their comparison with gasoline can draw the following conclusions:
•
The present bioethanol is renewable with a simple structure.
•
Bioethanol’s molecular weight is low compared to gasoline.
•
Bioethanol’s density is higher than that of gasoline
Table 1 Properties of the
product obtained
Properties
Obtained product
Physical state
Liquid at room temperature
Color
Transparent
Odor
Characteristic
Refractive index
1.3714
Density (g/cm3)
0.787
162
S. Zighmi et al.
3.4
Infrared Spectrum Analysis
The analysis of the infrared spectrum of the studied samples (Fig. 4) shows that the
product has a broad band around 3400 cm1 which conﬁrms the presence of an
alcohol and a stretching vibration at 2900 cm1 that corresponds to a group of
CH. These data conﬁrm the presence of an alcohol, which is none other than ethanol.
4
Conclusion
Bioethanol has become one of the most important biofuels on a worldwide level. In
this study, the production of bioethanol is done by a fermentation process of the
algae Spirogyra biomass, collected from a natural lake in the region of Touggourt, a
part of the city of Ouargla-Algeria.
Table 2 Checking of
bioethanol quality
Parameters
Obtained product
Gasoline
Physical state
Liquid
Liquid
Density (g/cm3)
0.787
0.719
Source
Algae (renewable)
Crude oil
Chemical formula
C2H5OH
C4 to C12
Molecular weight
46
100–105
Fig. 4 IR spectrum of the obtained product
The Valorization of the Green Alga Spirogyra’s Biomass. . .
163
The results show that the hydrolysis and simultaneous fermentation are an
important strategy for bioethanol production, which are carried out in the same
place since the yield recorded is 16.83% of ethanol after only 24 h of fermentation.
Following this study, the analysis of the experimental data and of the obtained
results shows that the Spirogyra algae is one of the green photosynthetic algae that
are ideal candidates for bioethanol production. It has the potential to be an alterna-
tive solution for the production of clean and renewable energy, for it is rich in
polysaccharides that can be extracted to produce fermentable sugars. In this study,
we showed that algal biomass is an effective raw material for the production of
bioethanol. Indeed, the experience made it clear that algae are a promising solution
to the energy crisis with a very low cost and modest technological exploitation
means and much more with no negative impact on the environment.
References
Balat, M., Balat, H., Oz, C.: Progress in bioethanol processing. Prog. Energy Combust. Sci. 34(5),
551–573., ISSN 03601285 (2008)
Deenanath, E.D., Iyuke, S., Rumbold, K.: The bioethanol industry in Sub-Saharan Africa: history,
challenges, and prospects. J. Biomed. Biotechnol. 2012, 416491, 11 pages (2012)
Hadjoudj, M., Manaa, A., Derdoukh, W., Guerzou, A., Souttou, K., Sekour, M. et Doumandji, S.:
LES RONGEURS DE LA REGION DE TOUGGOURT Actes du Se
´minaire International sur
la Biodiversite
´ Faunistique en Zones Arides et Semi-arides p. 244 (2011)
John, R.P., Anisha, G.S., Madhavan Nampoothiri, K., Pandey, A.: Micro and macroalgal biomass:
a renewable source for bioethanol. Bioresour. Technol. 102, 186–193 (2011)
Nguyen, T.H.M., Vu, V.H.: Bioethanol production from marine algae biomass: prospect and
troubles. J. Viet. Env. 3(1), 25–29 (2012)
Ramachandra, T.V., Madhab, M.D., Shilpi, S., Joshi, N.V.: Algal biofuel from urban wastewater in
India: scope and challenges. Renew. Sust. Energ. Rev. 21, 767–777 (2013)
Saharan, B.S., Sharma, D., Sahu, R., Sahin, O., Warren, A.: Towards algal biofuel production: a
concept of greenbio energy development. Innov Roman Food Biotechnol. 12, 1–21 (2013)
Singh, J., Gu, S.: Commercialization potential of microalgae for biofuels production. Renew. Sust.
Energ. Rev. 14, 2596–2610 (2010)
Sulfahri, S.N., Nurhidayati, T.: Aerobic and anaerobic processes of spirogyra extractusing differ-
ent doses of Zymomonas mobilis. J. Appl. Environ. Biol. Sci. 1(10), 420–425 (2011)
Tucho, G.T.: Feasible biomass energy conversion technologies in developing countries. Int.
J. Eng. Res. Technol. (IJERT), ISSN: 2278-0181. 2(6), 2720–2728 (2013)
Wibowo, A.H., Mubarokah, L., Suratman, A.: The fermentation of green algae (Spirogyra
majuscule Kuetz) using immobilization technique of Ca-alginate for Saccharomyces cerevisiae
entrapment. Indo. J. Chem. 13(1), 7–13 (2013)
164
S. Zighmi et al.
Plasma Technologies for Water Electrolyzers
V. Fateev, V. Kulygin, S. Nikitin, V. Porembskiy, S. Ostrovskiy,
A. Glukhov, and A. Pushkarev
1
Introduction
Water electrolyzer development with polymer electrolyte membranes (PEM) was
started in the 1950s with the ﬁrst pilot electrolyzers being produced by General
Electric for space projects 20 years later (Porter 1972). But commercial application
of such electrolyzers is very limited up to now though it is rather intensively
developed by different companies (i.e., Proton Energy Systems, Hydrogenics,
Norsk Hydro Electrolysers AS, CETH2, and others) and research centers and
universities (CNRS Institute of Advanced Technology for Energy Nicola Giordano
(Italy), Laboratory of Electrocatalysis, CNRS UMR, Universite Paris SUD
(France), NRC Kurchatov Institute (Russia), Fraunhofer Institute for Solar Energy
Systems ISE Freiburg (Germany), Institute of Applied Chemistry CAS (China), and
many others). Such electrolyzers considered being most safe and very efﬁcient
when high purity hydrogen production is needed, for example, for PEM fuel cells.
They demonstrate high efﬁciency (about 4.0–4.2 kWh per m3 of hydrogen), high
speciﬁc productivity (current density up to 2–3 A/cm2), and very high gas purity
(H2 > 99.99%, O2 > 99.98% in dry gas) (Carmo et al. 2013; Grigoriev et al. 2006).
Possibility of hydrogen production at an increased pressure in a stack (up to 30 bars)
without use of additional compressors makes them very attractive for renewable
energy systems as it permits to solve the hydrogen storage problem rather efﬁ-
ciently and cheap due to use of traditional hydrogen tanks.
One of the most important limitations for their commercialization is the
electrolyzer’s high price which is tightly connected with large precious metal
V. Fateev (*) • V. Kulygin • S. Nikitin • V. Porembskiy • S. Ostrovskiy
A. Glukhov • A. Pushkarev
NRC “Kurchatov Institute”, Kurchatov sq., 1, Moscow 123182, Russia
e-mail: fateev_vn@nrcki.ru
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_12
165
(mainly platinum metal) loading as these metals are used as electrocatalysts and for
stack components’ protection from corrosion and oxidation. Due to acidic mem-
brane properties, corrosion is very intensive and other catalytic active metals as Ni,
Co, Fe, and so on are not stable at electrolysis conditions. Different methods are
used for stack component protection including CVD, PVD, magnetron sputtering,
and even protective foil application to the surface of bipolar plates or current
collectors (Carmo et al. 2013; Kozlov and Fateev 2009).Different methods were
developed for the synthesis of the electrocatalyst (see, e.g., Carmo et al. 2013;
Kozlov and Fateev 2009). The main group of such methods is a liquid phase
catalyst synthesis by reduction of some catalyst precursors by different reducing
agents. The second group is a “high temperature” synthesis (thermal decomposition
or gas phase reduction). The ﬁrst group of methods is widely used and permits to
obtain nanostructured catalysts with very small particle size (up to 1–2 nm), but it is
characterized by multistage procedure and requires scrupulous catalyst puriﬁcation
after synthesis. It also has some limitations with alloy synthesis and crystalline
structure of catalyst particles. The second group often has problems of small
particle synthesis due to particle agglomeration and limitations in catalyst crystal-
line structure. It is also worth to stress that multistage catalyst synthesis procedure
results in a very high catalyst price. Due to all last decades’ developments, platinum
metal loading was decreased from the level of about 10 to 2–3 mg/cm2 used now
(Carmo et al. 2013). But it is still one of the main problems of PEM electrolyzers.
Certainly a lifetime of PEM electrolyzer is strongly connected with the amount of
used precious metals. It is well known that magnetron sputtering provides a method
of protective ﬁlm or thin catalyst layer (onto either the membrane or the gas
diffusion layer) depositing (Arnell and Kelly 2004; Litster and McLean 2004).
Earlier we established that magnetron sputtering could be rather efﬁcient for
catalyst on carbon carrier synthesis (Fedotov et al. 2013; Grigoriev et al. 2014),
and in our research we investigated the possibility of ion implantation method and
its combination with magnetron sputtering.
2
Experimental Facility
An ion implantation process is illustrated with Fig. 1. Ion implantation experiments
were carried out on the experimental installation “Kremen 1” with implanter
“Sokol-30/100” (vacuum 104 Pa, ﬂow of ions up to 5  1014 ion/cm2 per second,
diameter of Pt/Pd ion source 6 mm, distance between ion source and implantable
target 200 mm, time of implantation up to 24 minutes). The energy of implantable
ions was 1–50 keV. An ion implantation was carried out in an impulse mode due to
technical speciﬁcation of the installation (25–35 impulse/s) and to provide a more
uniform implanted atoms distribution and to avoid a large target heating. Amount of
implanted ions was controlled by weight of a test sample and data of electron
microscopy. Numerical model for estimation of the depth of implanted ion pene-
tration and their concentration distribution was developed using “one particle
166
V. Fateev et al.
implantation” approach for an isotropic and homogeneous medium. The calcula-
tions were realized with using the TRIM program. Magnetron sputtering was
carried out at a modiﬁed laboratory magnetron installation (MIR 1) with a negative
pulse potential bias applied to the implantable sample. Such a bias reached 200 V
and permitted to combine sputtering with additional low-energy ion implantation to
provide surface cleaning and ﬁxation of the sputtered metal particles on the surface
of stack components (and catalyst carrier). At the same time, such an additional
implantation created sputtered atoms of protective ﬁlm material and provided
creation of a more uniform protective coating (or new catalytic centers). Pulse
bias mode (up to 60 impulse/s) was used to avoid electric arc formation and to
provide more uniform catalyst distribution on a catalyst carrier surface. The base
pressure in the chamber was set to 103 Pa and the working pressure of argon was
set to 4  101 Pa. Sputtering of Pt (Pd) was carried out at a current 0.1 A, distance
to the target was 85 mm, and speed of metal atom deposition was about 1013 atoms/
s*cm2. Metal loadings deposited onto the targets were controlled by weight mea-
surements of “sample witness”. For mixing of catalyst carrier particles, a special
device was used (Grigoriev et al. 2014). Electrochemical measurements were
carried out in a standard three-electrode glass cell (saturated silver chloride refer-
ence electrode) in 1 M solution of H2SO4 using a Solartron 1285 potentiostat/
galvanostat (potentiodynamic measurements in a potential range of 0.2 to +1.1 V
vs. Ag/AgCl electrode with 20 mVs1 sweep rate) and in laboratory electrolysis
cells with a round shape catalyst layer area 7 cm2. In PEM water electrolysis cells,
catalyst layers are prepared by spraying of iridium black (anode) or platinum black
on carbon (cathode) dispersion in Naﬁon solution on the cathode side. Porous
titanium sheets (thickness 1 mm, porosity 37%) were used as current collectors.
Material composition and structures were tested using transmission electron
microscopy (TEM) analysis (microscope CM30 Philips) and X-ray diffraction
(XRD) analysis (Bruker D8 Advance). As experimental results were rather similar
for Pt and Pd, we’ll concentrate on the discussion of results obtained with Pt.
Fig. 1 Schematic illustration of an ion implantation process
Plasma Technologies for Water Electrolyzers
167
3
Results and Discussions
Numerical estimations of the depth of implanted ion penetration and their concen-
tration distribution (see Fig. 2) showed that at energies about 10–50 keV at low
doses (less than 1015 ion/cm2), the maximum of Pt ion distribution is relatively deep
(about 30 nm at 50 keV) and surface concentration of implanted ions (atoms) is less
than 1%. With increase of the value of the dose, the surface concentration is
increasing mainly due to sputtering of the surface layer which at the same time
results in precise metal losses. At energies about 1–2 keV, sputtering is practically
absent and surface concentration could reach 40–50% (if a diffusion of implanted
atoms is excluded) though the depth of implanted layer is relatively small (about
1–2 nm). Experimental data of implanted ion distribution did not differ too much
from numerical estimations (see Fig. 2) though in case of Pt and Pd, ions with
different charges are produced and at a constant accelerating voltage they have
different energies. Most of the Pt implanted ions are in a one- and two-charge state
(the last one is dominating) according to Yushkov et al. (2014), and it was taken
into account for further estimations. On potentiodynamic curves, we did not
observe any typical peaks of hydrogen adsorption/desorption for Ti implanted by
Pt ions at energies more than 5 keV even at doses 5  1017. Only at energies about
1 keV and the same or even higher doses, small and deformed peaks were observed
but not regularly. At the same time, one could observe a signiﬁcant increase of
implanted Ti electrode activity in reactions of oxygen evolution (Fig. 3) even at
relatively high energies of implanted atoms (same for hydrogen evolution).
Platinum
concentration
at %
0A
depth
200A
0
2.4
4.8
7.2
9.6
12
14.4
16.8
Fig. 2 Distribution of Pt ions (atoms) in Ti at E ¼ 20 keV and dose 1015 ion/cm2. Triangles are
experimental data
168
V. Fateev et al.
So during implantation of Pt, a solid solution of Pt in Ti was produced with a rather
high electrochemical activity and stability in spite of a low surface Pt concentration.
For accelerated tests of Ti electrode stability at anode polarization, the electrodes
were polarized with current density 80 mA/cm2, and the time during which the
electrode potential was reaching 2.6 V (SCE) was taken as a reference value. Our
experiments (see Table 1 and Fig. 3) showed that at large energies, surface stability
for oxidation does not increase signiﬁcantly though a produced oxide ﬁlm has lower
resistivity as it is doped by precise metal atoms. The surface layer obtained at low
energies is signiﬁcantly more stable against oxidation even at a relatively low
amount of implanted precise metal. One can suppose that here additional effects
also play rather signiﬁcant role – increase of the surface roughness and creation of
radiation defects and partial amorphization of the surface layer during implantation.
2
E, V
1,5
1
-1
0
1
2
1
2
3
4
5
6
7
lg i, mA/cm2
Fig. 3 Quasi stationary polarization curves (30 s at each potential) in 1 N H2SO4 at 20 C for Ti
foil with (1) Pt coating (23 nm) implanted with Ar+ Е ¼ 10 keV, 1016 ion/cm2; (2) Pt coating
(42 nm) implanted with Ar+ Е ¼ 15 keV, 1016 ion/cm2; (3) Pt coating (23 nm) not implanted; (4) Pt
foil, (5) Ti implanted with Pt Е ¼ 10 keV, D ¼ 1017 ion/cm2; (6) Ti implanted with Pt Е ¼ 5 keV,
1017 ion/cm2; (7) Ti implanted with Pt Е ¼ 1 keV, 1017 ion/cm2
Table 1 Dependence of the time when electrode reached potential 2.6 V (SCE) at 80 mA/cm2
upon Ti foil electrode treatment conditions. Pt foil thickness about 10–13 nm
Electrode treatment
Time
Not treated
<1 s
Implanted with Ar ions (2  1017, 40 keV)
~1 s
Implanted with Pt ions (2  1017, 40 keV)
47 min
Implanted with Pt ions (2  1017, 2 keV)
61 min
Implanted with Pt ions (1017, 40 keV, and then 1017, 2 keV)
87 min
Sputtered Pt ﬁlm
184 min
Sputtered Pt ﬁlm treated with Ar+ (5  1014, 20 keV)
248 min
Plasma Technologies for Water Electrolyzers
169
For example, when Ti foil was implanted by Ar ions, only one could observe
signiﬁcantly larger current at potentiodynamic polarization curves in comparison
with not treated Ti. But these effects were not dominating at precise metal implan-
tation, and it is possible to suppose that radiation defects are additionally decreasing
the oxide ﬁlm resistance. Combination of large- and low-energy implantation
modes permitted to reach rather high surface stability with a rather high lifetime
at Pt (Pd) concentrations less than 0.02 mg/cm2. For bipolar plates and current
collectors from porous Ti, a combined technology based on magnetron sputtering of
Pt (Pd) assisted by ion (Ar, O) implantation demonstrated even more efﬁcient
results due to additional chemical and radiation surface modiﬁcation but at slightly
larger platinum metal loadings.
Implantation of C and N ions (1018 ion/cm2, E ¼ 50 keV) resulted in a signiﬁcant
decrease (3–4 times) of hydrogen penetration in Ti.
To obtain thick ﬁlms, magnetron sputtering is signiﬁcantly more attractive. But
titanium plates with mechanically polished surface after Pt deposition by magne-
tron sputtering mainly demonstrated relatively low stability during potential
cycling. We observe anode current decrease with each next cycle and sometimes
even exfoliation of the Pt ﬁlm. When Pt was sputtered onto porous (sintered) Ti, the
stability was better but still not sufﬁcient for long-term use. One can see (Fig. 4) that
there was a strong dependence of the current on potentiodynamic curves (and
obviously Pt-speciﬁc surface) upon Pt ﬁlm thickness. Roughness factor (ratio or
real to visible surface) was continuously increasing from 3.72 for Pt ﬁlm 11 nm to
29.5 for Pt ﬁlm 147 nm.
1
2
3
4
5
-0,2
-0,1
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1,0
1,1
1,2
-0,0020
-0,0015
-0,0010
-0,0005
0
0,0005
0,0010
0,0015
E, V
I, A
Fig. 4 Potentiodynamic polarization curves (20 mV/s in 1М Н2SO4) for 1-Pt foil and porous Ti
with sputtered Pt ﬁlms with different thickness: 2–11 nm, 3–26 nm, 4–147 nm, and Ti implanted
with Pt (E ¼ 5 keV, 2  1017 ion/cm2)
170
V. Fateev et al.
It was possible to suppose that Pt ﬁlm is rather porous and did not protect Ti
surface efﬁciently from oxidation. Data of electron microscopy (Fig. 5) conﬁrmed
such an assumption as Pt ﬁlm had a columnar structure.
A preliminary Ar ion implantation (1015 ions/cm2, E ¼ 10 keV) provided a
signiﬁcant improvement due to increase in surface roughness and purity in such
electrode stability. In such a case, Pt nuclei are more uniformly distributed on the Ti
surface at the initial stage, and ﬁlm structure is also becoming more uniform. But
the most efﬁcient appeared to be simultaneous or consecutive ion implantation
(Ar or same metal ions) together or after sputtering (see Fig. 3). When implantation
was carried out by Ar ions during magnetron sputtering, energies up to 5 keV were
used and such an implantation was most important at initial stage. It provided some
increase of Pt atom energy due to Ar+ ﬂow directed to Ti surface and some
sputtering of the Pt particles created on Ti surface. The accelerated by Ar+ ﬂux
and sputtered atoms were introduced in the Ti surface layer providing some
intermediate layer (Pt solid solution in Ti) and more uniform surface coverage by
Pt. After magnetron sputtering for about 10 nm, ﬁlm intensity of ion implantation
could be reduced. Implantation after magnetron sputtering has one important
disadvantage – one needs relatively high ion energies to reach Pt-Ti boarder
through magnetron-sputtered Pt ﬁlm (about 10 keV for Ar + ions for 10 nm ﬁlm)
and sputtering of Pt atoms (~5.8 atoms per ion) results in signiﬁcant Pt losses. In
case of Pd, sputtering is only slightly lower – 4 atoms/ion. So such a treatment could
be efﬁcient for thin (less than 10 nm) ﬁlms. From data in Table 1 and Fig. 3, one can
see that Pt ﬁlms obtained by using such technique are signiﬁcantly more efﬁcient
for Ti protection. Further improvement of oxidation resistance could be obtained by
Fig. 5 SEM data on
sputtered Pt ﬁlm
Plasma Technologies for Water Electrolyzers
171
additional chemical modiﬁcation of Ti surface by implantation of O and N ions. It is
necessary to mention that combination of magnetron sputtering with ion implanta-
tion could be a very efﬁcient technology for catalyst layers and catalyst synthesis.
Plasma-assisted deposition of platinum and Ir nanoparticles onto carbon carriers
was carried out using the standard DC magnetron sputtering system, but with pulsed
system of potential bias, application to carbon carrier was investigated. Size of Pt
catalyst particles (3% weight) obtained on Vulcan and reduced graphene oxide was
about 1–3 nm (Fig. 6) and speciﬁc surface about 110 m2/g. For Ir 6–8 nm, Volt-
ampere curves of PEM electrolysis cell with such catalysts were practically same as
with catalysts obtained by “polyol” method (Tunold et al. 2010) but with lower
(about 10–20%) precise metal loading. Certainly such catalyst synthesis technique
cannot signiﬁcantly decrease precise metal catalysts use but can signiﬁcantly
decrease catalyst price as it is practically one-stage process.
It is necessary to mention that in case of cathode materials based on titanium
(bipolar plates and current collectors), implantation of carbon and nitrogen permit-
ted to increase the stability of the construction material for about 3–4 times for
hydrogen penetration. Tests of PEM electrolysis stack (5 cells) based on materials
described above at operating pressure 30 bar, 80 C, and current density 1.4 A/cm2
for 400 h did not show any degradation of the stack parameters.
Ion implantation method was also tested for modiﬁcation of alkaline electrolyzer
electrode (Ni) surface. Implantation of Сo, Pd, and Pt ions resulted in an increase of
current density at a constant voltage of about 20–30%, but optimum parameters of
implantation must be chosen for proper electrode lifetime.
Fig. 6 SEM photo (Philips
CM30) of Pt catalyst
particles obtained by
magnetron sputtering on
Vulcan
172
V. Fateev et al.
4
Conclusions
Ion implantation and magnetron sputtering assisted by ion implantation were
studied. It was shown that ion implantation of Pt and Pd in Ti results in solid
solution production and signiﬁcantly increases electrochemical stability (and even
catalytic activity) for hydrogen and oxygen evolution at a rather low loading of
precise metals (about less than 0.02 mg/cm2). Combination of low- and high-energy
ion implantation appeared to be most efﬁcient for lifetime increase as it provided
rather deep surface layer modiﬁcation and high surface concentration of Pt (Pd).
Magnetron sputtering resulted in a surface ﬁlm with a columnar structure produc-
tion which did not protect Ti surface efﬁciently from oxidation. Additional ion
implantation with Ar+ or O+ ions of such ﬁlms signiﬁcantly improved electrode
stability. Implantation of Ni by Co, Pt, or Pd ions increases electrode activity up to
30% in water alkaline electrolysis. Ion implantation of C and N ions provided
decrease of hydrogen penetration in Ti in 3–4 times. Magnetron sputtering assisted
by ion implantation gives a good possibility for efﬁcient and relatively cheap
catalysts synthesis for PEM electrolyzers. Pt particles (diameter 1–3 nm, speciﬁc
surface area about 110 m2/g) on carbon carrier were synthesized and successfully
tested. Ir on carbon carrier also demonstrated high activity. The developed tech-
niques give possibility for precise metal loading decrease in PEM electrolyzers and
decrease the price of protecting coatings and catalysts themselves due to more
cheap production technology – decrease of amount of synthesis stages.
This research was supported by the Russian Scientiﬁc Foundation Grant
№14–29-00111.
References
Arnell, R.D., Kelly, P.J., Bradley, J.W.: Recent developments in pulsed magnetron sputtering.
Surf. Coat. Technol. 188–189, 158–163 (2004)
Carmo, M., Fritz, D.L., Mergel, J., Stolten, D.: A comprehensive review on PEM water electrol-
ysis. Int. J. Hydrog. Energy. 38, 4901–4934 (2013)
Fedotov, A.A., Grigoriev, S.A., Lyutikova, E.K., Millet, P., Fateev, V.N.: Characterization of
carbon-supported platinum nano-particles synthesized using magnetron sputtering for appli-
cation in PEM electrochemical systems. Int. J. Hydrog. Energy. 38, 426–430 (2013)
Grigoriev, S.A., Porembskiy, V.I., Fateev, V.N.: Pure hydrogen production by PEM electrolysis
for hydrogen energy. Int. J. Hydrog. Energy. 31, 171–175 (2006)
Grigoriev,
S.A.,
Fedotov,
A.A.,
Martemianov,
S.A.,
Fateev,
V.N.:
Synthesis
of
NanostructuralElectrocatalytic materials on various carbon substrates by ion plasma sputtering
of platinum metals. Russ. J. Electrochem. 50(7), 638–646 (2014)
Kozlov, S.I., Fateev, V.N.: Hydrogen Energy, p. 520. Gazprom VNIIGAZ, Mascow (2009)
(in Russian)
Litster, S., McLean, G.: PEM fuel cell electrodes. J. Power Sources. 130, 61–76 (2004)
Porter, Jr. F.J.: Development of a solid polymer electrolyte electrolysis cell module and ancillary
components for a breadboard water electrolysis system, Technical Report NASA-CR-112183,
(1972), U.S. NASA
Plasma Technologies for Water Electrolyzers
173
Tunold, R., Marshall, A., Rasten, E., Tsypkin, M., Owe, L.-E., Sunde, S.: Materials for
Electrocatalysis of oxygen evolution process in PEM water electrolysis cells. ECS Trans. 25,
103 (2010)
Yushkov, G.Y., Vodopyanov, A.V., Nikolaev, A.G., Izotov, I.V., Savkin, K.P., Golubev, S.V.,
Oks, E.M.: Generation of high charge state platinum ions on vacuum arc plasma heated by
gyrotron radiation. Rev. Sci. Instrum. 85, 02B902 (2014)
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V. Fateev et al.
Determination of Metals in Water
and Sediment Samples of the S€
urmene
River, Turkey
Nigar Alkan, Ali Alkan, and Cos
¸kun Er€
uz
1
Introduction
River water is used for many purposes such as drinking; irrigation; hydroelectric
power plant; industrial and municipal facilities’ discharge area for uncontrolled
industrial, agricultural, and domestic wastes; and ﬁshing (Camelo et al. 1997; Guieu
et al. 1998; Mendiguchı
´a et al. 2007). The quality of water is determined by
physical, chemical, and microbiological properties (Alkan et al. 2013). Anthropo-
genic inputs are major source of metals and affected the quality of waters. Con-
centration of most metals in river is very low and produced from rock and soil. The
main anthropogenic sources of heavy metal in rivers are mining, fertilizer, and
pesticides in agricultural ﬁelds. Pollutants that come from domestic, industrial, and
agricultural activities are ﬁrst released into rivers and reach the sea and lakes
through rivers. Between heavy metals and organic substances in the aquatic envi-
ronment is a strong interest and this proﬁle can change water quality. Metals, unlike
other pollutants, can accumulate in sediments, uptake to toxic levels, and affect
river organisms over time (Gedik and Boran 2012). Therefore, the determination of
physicochemical proﬁle, heavy metals, and organic substance for the water quality
in coastal areas is important for the future estimation of the pollutant load of the
rivers (Alkan et al. 2014). The aim of this study is to understand temporal and
spatial changes of the land-based metal pollutions in the Sürmene stream.
N. Alkan (*) • C. Erüz
Karadeniz Technical University, Faculty of Marine Sciences, Sürmene, Trabzon, Turkey
e-mail: nalemdag@ktu.edu.tr; anigar@gmail.com
A. Alkan
Karadeniz Technical University, Institute of Marine Sciences
and Technology, Trabzon, Turkey
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_13
175
2
Material and Methods
2.1
Study Area
Water and sediment samples were collected from three stations located between
Sürmene and K€
oprübas
¸ı cities on Sürmene river (Station 1: 40540 32.6800N, 40 060
22.6600E, Station 2: 40520 06.7900N, 40 060 20.6000E, Station 3: 40500 04.2800N,
40 060 42.1400E) in Trabzon between 2010 and 2011 during the autumn, winter,
spring, and summer seasons.
Sürmene river ﬂows into the Black Sea basin starting from the coast and up to
2700 m altitude rough land. The average ﬂow of the river is 5.40 m3/s, precipitation
basin is 227.2 km2, and river length is 41.3 km. The average annual rainfall varies
between 1300 and 2400 mm. There are two towns and 52 neighborhood villages in
the basin that generally carry on agricultural activities. There are no industrial
plants in the basin except six hydroelectric power plants and a tea factory (Boran
and Sivri 2001).
2.2
In Situ Measurements
Temperature (C), pH, electrical conductivity (μs/cm), dissolved oxygen (mg/L),
oxygen saturation (%), salinity (‰S), and total dissolved solid (mg/L) were mea-
sured seasonally in situ by Hach Lange HQ40D multimeter.
2.3
Water Sampling
Water samples were collected in acid-washed bottles from three stations and
ﬁltered through 0.45 μm pore-sized ﬁlters for dissolved metals analysis. After
adding the acid, the samples were stored at 20 C until analysis.
2.4
Sediment Sampling
Sediment samples were collected from 3 stations and they were preserved in acid-
washed bags and placed in ice bags in the ﬁeld. They were stored in deep freeze at
20C in the laboratory until analysis.
176
N. Alkan et al.
2.5
Laboratory Analyses
Dissolved metal (μg/L) concentrations in water samplings were measured
according to the method of EPA (1994). Sediment grain sizes were classiﬁed
according to Folk (1954). Total organic carbon (TOC) was determined by using
modiﬁed Walkley-Black titration method (Gaudette 1974). Carbonate levels were
measured by Piper (1974) method. Metal analyses (Cr, Mn, Al, Co, Ni, Cu, Zn, As,
Mo, Cd, Sb, and Pb) were performed using sediment passed through a sieve with
63 micron. All data in sediment were given mg/kg dry weight. After sediment
samples digested in the closed microwave digestion system, dissolved and partic-
ulate trace metals were determined using ICP-MS (inductively coupled plasma
mass spectrometry). The collision reaction interface (CRI) was used during the
determination of As. Both Sc and In (50 ppb) were added to all standards, blanks,
and samples and acted as internal standards.
The results obtained were classiﬁed regarding the criterias of European Com-
mission Directive (EC 1998), National Recommended Water Quality Criteria
(US EPA 2009), and World Health Organization (WHO 2004) for drinking water.
3
Results and Discussions
Seasonal water and sediment samples were collected from three stations located on
Sürmene stream in Trabzon between 2010 and 2011.
Average seasonal in situ measurment results in the Sürmene river stations were
given in Table 1. The pH of water is indication of its quality and dependent on
carbon dioxide and carbonate-bicarbonate equilibrium. Temperature, dissolved
oxygen, and total dissolved solid of three stations in the river were directly related
with seasons.
As a result of grain size analysis in the sediment samples that the sand-medium
grain sized fraction (32–36%) was generally dominant in the all stations. The sand-
coarse grain sized fraction was varing from 12% to 33% in all stations which was
noticed maximum in the Station 3. The maximum silt fraction was found as 10% in
Station 2 (Fig. 1).
Organic matter is one of the most important factors in metal mobility in
sediment. Minimum and maximum values were determined for pH as 8.18–8.59,
for organic matter as 1.64–2.09%, for carbonate as 7.60–12.45%, and for total
organic carbon as 0.08–0.13% in sediment samples of the three stations (Table 2).
As a result of dissolved metal in the Sürmene stream water, there were statistical
differences ( p < 0.05) and higher than stations 2 and 3 for Zn concentration in the
station 1. There are no statistical differences for other metals ( p < 0.05) among the
stations (Fig. 2).
According to seasonal distributions, there are no statistical differences for Pb,
Cd, Mo, As, Zn, Cu, and Co concentrations. Al concentration in winter period was
Determination of Metals in Water and Sediment Samples of the Sürmene. . .
177
statistically different and lower than other seasons. Similar situation persisted
during the spring for Cr and Ni concentrations. Sb concentration was statistically
different ( p < 0.05) and higher in autumn than second and third stations in other
seasons. There is similar distribution for Mn concentration in spring and autumn.
Table 1 Seasonal average of some physico-chemical parameters in Sürmene Stream
Parameters
Station no
1
2
3
Temperature (C)
12.26  4.13
14.20  4.48
16.66  3.72
pH
7.51  0.27
7.62  0.49
7.94  0.43
Dissolved oxygen (mg/L)
9.98  0.73
9.70  0.72
9.11  0.4
Oxygen saturation (%)
92.73  3.96
93.75  3.52
71.8  34.27
Conductivity (μs/cm)
0.11  0.03
0.10  0.03
0.11  0.02
Salinity (‰ S)
0.06  0.01
0.05  0.01
0.06  0.02
TDS (mg/L)
0.08  0.01
0.06  0.02
0.08  0.01
Fig. 1 Grain size distributions of sediment samples
178
N. Alkan et al.
However, there were statistical differences ( p < 0.05) and higher level in winter and
summer (Fig. 3, Table 3).
4
Conclusions
Physicochemical quality parameters are required for ecological status of river
waters and sediments, but dynamic systems of river may change during the times.
The surface water chemistry of river can inﬂuence the atmospheric inputs, climatic
Table 2 Average pH, organic matter, carbonate, and total organic carbon concentrations in
sediment samples
St. no
pH
Organic matter (%)
Carbonate (%)
Total organic carbon (%)
1
8.59
1.64
12.05
0.08
2
8.32
2.09
7.60
0.13
3
8.18
1.66
10.32
0.11
Average
8.36
1.80
9.99
0.11
Std. Dev.
0.21
0.25
2.25
0.02
 Co    
 Mo    
 Sb
SU1
SU2
SU3
STATION NO
0,0
0,1
0,2
0,3
0,4
0,5
0,6
CONCENTRATION (µg/L) 
 Al    
 Mn    
 Zn
SU1
SU2
SU3
STATION NO
0
10
20
30
40
50
60
70
CONCENTRATION (µg/L) 
 Cr    
 Ni    
 Cu
SU1
SU2
SU3
STATION NO
0,0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
CONCENTRATION (µg/L) 
 Pb    
 As    
 Cd
SU1
SU2
SU3
STATION NO
0,00
0,05
0,10
0,15
0,20
0,25
0,30
0,35
0,40
0,45
CONCENTRATION (µg/L) 
Fig. 2 Average dissolved metal concentrations of stations in the Sürmene Stream
Determination of Metals in Water and Sediment Samples of the Sürmene. . .
179
conditions, and anthropogenic inputs. Metal concentrations in fresh water resources
are important for human health and aquatic ecosystem.
All results obtained were classiﬁed regarding the criteria of European Commis-
sion Directive (1998/83/EC), National Recommended Water Quality Criteria
(US EPA 2009), and World Health Organization (WHO 2004). Results obtained
 Cr   
 Cu   
 Ni    
WINTER
SUMMER
SPRING
AUTUMN
SEASON
0,0
0,2
0,4
0,6
0,8
1,0
CONCENTRATION (µg/L) 
 Co   
 Mo   
 Sb    
WINTER
SUMMER
SPRING
AUTUMN
SEASON
0,0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
CONCENTRATION (µg/L) 
 Al   
 Mn   
 Zn    
WINTER
SUMMER
SPRING
AUTUMN
SEASON
0
10
20
30
40
50
60
70
CONCENTRATION (µg/L) 
 Pb   
 As   
 Cd    
WINTER
SUMMER
SPRING
AUTUMN
SEASON
0,0
0,1
0,2
0,3
0,4
0,5
CONCENTRATION (µg/L) 
Fig. 3 Seasonal dissolved metal distrubitions
Table 3 Average metal concentrations in sediment samples of the Sürmene stations
Metals (mg/kg)
Stations
1
2
3
Average
SD
Al
34,237
33,183
33,083
33,501
639
Fe
31,759.8
30,525.0
30,637.7
30,974
682
Mn
1086.6
840.7
882.4
936.6
131.6
Cr
39.0
33.0
32.5
34.8
3.6
Co
13.5
12.5
13.5
13.2
0.6
Ni
18.5
15.5
19.0
17.7
1.9
Cu
39.5
37.0
32.5
36.3
3.5
Zn
68.0
72.5
62.5
67.7
5.0
As
11.0
12.0
7.5
10.2
2.4
Pb
27.0
23.0
17.5
22.5
4.8
180
N. Alkan et al.
from Sürmene river were found to be lower than legal limits proposed by EC L
330/05 (1998), US EPA (2009), and WHO (2004).
Hydroelectric power plant in water environment can affect living sources and
impair the quality for use of river water. The usage of these natural resources must
be optimum. Protection of ecological status of rivers and supporting local people
sustainable development (irrigation, controlled ﬂooding, hydroelectric power plant,
waste discharges, etc.) must be balanced.
Acknowledgments This research was supported by the Scientiﬁc Research Project of Karadeniz
Technical University (Project code: 890).
References
Alkan, A., Serdar, S., Fidan, D., Akbas
¸, U., Zengin, B., Kılıc
¸, M.B.: Physico-chemical character-
istics and nutrient levels of the eastern Black Sea rivers. Turk. J. Fish. Aquat. Sci. 13, 847–859
(2013)
Alkan, N., Alkan, A., Akbas
¸, U., Fisher, A.: Metal pollution assessment in sediments of the
southeastern Black Sea coast of Turkey. Soil Sediment Contam. 24, 290–305 (2014)
Boran, M., Sivri, N.: Trabzon (Türkiye) I
˙l Sınırları I
˙c
¸erisinde Bulunan Solaklı ve Sürmene
Derelerinde Nütrient ve Askıda Katı Madde Yüklerinin Belirlenmesi, E.U. J Fish Aquat Sci.
18, 343–348 (2001)
Camelo, L.G.L., Miguez, S., Marba
´n Rato de, L.: Heavy metals input with phosphate fertilizers
used in Argentina. Total Environ. 204, 245–250 (1997)
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sion 5.4, EMMC Version. (1994)
European Community Council Directive 98/83/EC (1998). Council Directive 98/83/EC of
3 November 1998 on the quality of water intended for human consumption. (1998)
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nomenclature. J. Geol. 62, 344–359 (1954)
Gaudette, H.E., Flight, W.R., Toner, L., Folger, D.W.: An inexpensive titration method for the
determination of organic carbon in recent sediments. J. Sedimant Petrol. 44, 249–253 (1974)
Gedik, K., Boran, M.: Assesment of metal accumulation and ecological risk around Rize Harbor,
Turkey (southeast Black Sea) affected by copper ore loading operations by using different
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Guieu, C., Martin, J.M., Tankere, S.P.C., Mousty, F., Trincherini, P., Bazot, M., Dai, M.H.: On
trace metal geochemistry in the Danube River and western Black Sea. Estuar. Coast. Shelf Sci.
47, 471–485 (1998)
Mendiguchı
´a, C., Moreno, C., Garcı
´a-Vargas, M.: Evaluation of natural and anthropogenic
inﬂuences on the Guadalquivir River (Spain) by dissolved< heavy metals and nutrients.
Chemosphere. 69, 1509–1517 (2007)
Piper, D.J.W.: Manuel of Sedimentological Techniques. Dalhousie Univ. Publ. Romankevich, E.
A., 1984, Geochemistry of Organic Matter in the Ocean. Springer and Verlag, Berlin, Heidel-
berg, New York (1974)
U.S. Environmental Protection Agency, United States Environmental Protection Agency National
Recommended Water Quality Criteria, (2009)
WHO: Chap. 12, Annex 4. In: Guidelines for Drinking-Water Quality, 3rd edn. World Health
Organization, Geneva (2004)
Determination of Metals in Water and Sediment Samples of the Sürmene. . .
181
Biodiesel Production by Transesteriﬁcation
of Recycled Vegetable Oils
Souad Zighmi, Mohamed Bilal Goudjil, Salah Eddine Bencheikh,
and Segni Ladjel
1
Introduction
The orientation of researches toward renewable energy has become an important
element of energy policy worldwide. Indeed, the development of renewable energy
has become a necessity especially for the environment protection from the various
problems caused by the use of fossils, especially global warming.
In a search for new energy sources, our attention is mainly focused on biomass as
a reliable and renewable source that can satisfy the demand in energy. Currently,
the methyl esters of vegetable oils and animal fats are considered as a real
alternative to liquid fossil fuels (Cvengro and Cvengros
ˇova 2004). However,
biofuels constitute also an effective alternative to existing fossil fuels, for they
offer the prospect of ecological sustainability and reduce greenhouse gas emissions
(Saharan et al. 2013). Among the current biofuels, biodiesel represents an alterna-
tive to petroleum-based fuels (Balat and Balat 2008; Azcan and Yilmaz 2013).
Biodiesel is produced from vegetable oils, animal fats, frying oils, and used wastes
(Mustafa Balat). Biodiesels have many advantages compared with those derived
from petroleum such as producing less smoke, having a higher cetane number, and
releasing small amounts of carbon monoxide; they are renewable and specially
nontoxic (Stavarache et al. 2005). The FAME (Fatty Acid Methyl Ester) produces
fewer greenhouse gas emissions than petrodiesel; CO2 emissions in the engine
S. Zighmi (*)
University of Ouargla, Faculty Sciences of the Nature and Life, Ouargla 30000, Algeria
University of Ouargla, Faculty of Applied Sciences, Department of Process Engineering,
Ouargla 30000, Algeria
e-mail: zighmi.so@univ-ouargla.dz
M.B. Goudjil • S.E. Bencheikh • S. Ladjel
University of Ouargla, Faculty of Applied Sciences, Ouargla 30000, Algeria
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_14
183
output is reduced slightly (1%) with biodiesel if the equipment (tanks and vehicles)
are compatible with the use of biodiesel.Biodiesel is often used after being mixed at
different percentages with conventional diesel oil. It can be used in compression-
ignition diesel engines with little or no modiﬁcation (Kim et al. 2004). Currently,
most biodiesel factories rely on the use of reﬁned vegetable oils as their main raw
materials. However, these types of oil contribute to the increase of the overall
production cost of biodiesel to about 80%; while the use of frying oil for biodiesel
production reduces raw materials cost to about half the price of the ordinary oil
(Azcan and Yilmaz 2013). Note that the edible oils and fats used are considered as a
waste contributing to the environmental pollution and thus their reinjection into
sewers and the underground will cause degradation of the environment. Therefore,
any attempt to use the frying oil as feed for biodiesel synthesis offers a dual beneﬁt
of preserving nature and of producing energy (Nair et al. 2012). The objective of
this contribution is to highlight frying oil as a source of raw material for biodiesel
synthesis.
Nomenclature
t
Time per minute
μ
Viscosity in centipoise (cp)
ρh
The oil density (g/cm3)
ρb
: Ball density (8.03 g/cm3)
k
Viscometer constant 3.3 for size 2
MKOH
Molar mass of KOH (56 g.mol 1)
VKOH
KOH solution volume used (mL)
CKOH
KOH titrated solution exact concentration (mol.L 1)
Moil
Mass of the test sample (g)
v0
Volume in ml of the hydrochloric acid solution (HCl) at 1 mol/l for the blank titration
V
Volume in ml of the hydrochloric acid solution (HCl) at 1 mol/l for the sample titration
N
Exact normality of the hydrochloric acid solution
m
Mass of the sample
Subscripts
NO
Normal oil
RO
Recycled oil
BNO
Biodiesel for normal oil
BRO
Biodiesel for recycled oil
2
Materials and Methods
Today, rapeseed and soybean are the two most commonly used raw materials for
biodiesel production. Rapeseed produces about 435 l to 550 l of biodiesel per
hectare, while soybean produces about 160 l per hectare; also, sunﬂower can
184
S. Zighmi et al.
produce 280 l of biodiesel per hectare (Balat and Balat 2008), (Kojima and Johnson
2005). Note that the majority of biodiesel in the USA is produced from soybean oil,
estimated at more than 90% (Collins 2006). In the present work, we have chosen an
edible oil widely used in Algeria, sold under the name “ELIO.” Thus, two varieties
of this oil are used in this study: the ﬁrst is the unused ELIO oil and the second one
is the same oil, used at most three times for frying.
The ELIO oil is edible vegetable oil composed of 20% sunﬂower and 80%
soybean. The formulas of the corresponding triglycerides are as follows:
Sunflower oil High linoleic
ð
Þ : C57H98O6
Soybean oil : C57H106O10
ELIO Oil : 20% sunflower and 80% soybean
The molecular weight of this oil is given by
M ¼ 0:8  Msoybean þ 0:2  Msunflower
ð1Þ
M ¼ 943:6g=mol
2.1
Characterization of the Studied Oils
Some parameters characterizing these oils were determined as follows: the refrac-
tive index, the cetane number, the acid number, and the saponiﬁcation number, in
addition to the evaluation of density and viscosity variation at different
temperatures.
2.1.1
Variation in the Density of Oils
The oils’ density variation based on temperature is accomplished by means of a
standard densitometer of type DMA 35N (Density Meter, Anton Paar, Serial:
80,306,269, from 0 to 2 g/cm3).
2.1.2
Variation in Viscosity of Oils
The viscosity of the oils based on the temperature was monitored using a falling ball
viscometer (from stainless steel ball). The dynamic viscosity was determined
through the following formula:
μ ¼ k ρb  ρh
ð
Þt
ð2Þ
Biodiesel Production by Transesteriﬁcation of Recycled Vegetable Oils
185
The unit of viscosity obtained is centipoise (1 cp ¼ mPa/s), the kinematic viscosity
is calculated by the following formula: υ ¼ μ/ρh.
2.1.3
The Acid Number
In our work it is believed that the free acids are represented by only linoleic acid;
the IA is given by the following formula:
IA ¼ MKOH  VKOH  CKOH
moil
ð3Þ
The IA in this work is determined by titration.
2.1.4
Saponiﬁcation Number
S ¼ V0  V
ð
Þ  56:1  N
m
ð4Þ
The saponiﬁcation value is given by the following formula (4):The saponiﬁcation
number in this work is determined by titration.
2.2
Biodiesel Synthesis in Laboratory
Methyl esters are obtained by reaction of triglycerides transesteriﬁcation by meth-
anol using KOH as catalyst; our choice of catalyst is based on a previous study by
May (2004), which showed that Na(s), NaOH and KOH are effective catalysts for
the transesteriﬁcation reaction.
For all the tests made we used a heating reﬂux apparatus that consists of a
1000 ml ﬂask equipped with a condenser, the stirring system was provided by a
magnetic bar. The ﬂask was immersed into a water bath; the reaction was carried
out for 2 transesteriﬁcation at a temperature of 50 C. The quantities of materials
used are shown in Table 1.
The molecular weight of methanol is: M (methanol) ¼ 32.04 g/mol; and the
molecular weight of oil is: 943 g/mol.
Table 1 Quantities of
reagents used
Oil
Methanol
Catalyst
250 g
85 g (report 1: 10)
2.5 g (1% weight of oil)
250 g
72 g (report 1: 8)
2.5 g (1% weight of oil)
186
S. Zighmi et al.
2.2.1
Characterization of Biodiesel
Physicochemical characterization touches the following parameters: the acid num-
ber and the ﬂash point with the evaluation of density and viscosity variation at
different temperatures.
2.2.2
Characterization by IR Spectroscopy
We used as a method of analysis, infrared rays (IR) by means of Infrared Spectro-
photometer type IR-408 (SHIMADZU corporation, Serial N. A200129,031,261).
3
Results and Discussion
3.1
Characterization of Oils Used
The characteristics of the two types of oil used in this study, namely: ordinary oil
(NO) and recycled oil (RO) (used for frying) are summarized in Table 2.
The ﬁre point and the ﬂash point were monitored using a semi-Automatic Tag
open cup ﬂash point tester (Stanhope-Stea (mindex) serial 1,027,348, ios
9001:2008), and the index of refraction was measured by ABBE Refractometer
(147434).
From Table 2 we note that the acidity and saponiﬁcation values for RO are
higher than those of NO, caused by the presence of an important gap between the
two types of oil, due, on one hand, to the fact that the edible reﬁned oil (ELIO) was
treated to reduce its acidity and to eliminate the undesirable components (AGL,
Phospholipids, etc.), and, on the other hand, the frying or storage may have caused
the formation of free fatty acids inside the HF, which proves that a good quality oil
possesses a low acid ratio and that a low saponiﬁcation value corresponds to fatty
acids containing a longer chain of carbon. Concerning the other properties men-
tioned in the table (index of refraction, ﬂash point, cetane number), we notice that,
overall, the values recorded for both types of oil are close, with only a thin
difference.
Table 2 Oils
physicochemical
characteristics and properties
The characteristics
(NO)
(RO)
The acid number
0.5
1.83
Saponiﬁcation number
74.66
253.86
Index of refraction
1.4756
1.4762
Fire point (C)
346
340
Cetane number
48
47
Flash point (C)
312
305
Biodiesel Production by Transesteriﬁcation of Recycled Vegetable Oils
187
3.2
Oils Density Variation
Based on the recorded values in Fig. 1, it is noted very clearly that the density of the
two oils decreases with increasing temperature. Moreover, this decrease is similar
for both types of oil between 20 and 50 C; however, from 50 C we notice that the
frying oil has lower densities compared with those recorded for the unused oil.
3.3
Oils Viscosity Variation
From the curves (Fig. 2) it is noted that the oils viscosity has decreased when the oil
temperature increased. Moreover, it is noted that there is a more or less signiﬁcant
difference between the values of the two types of oil. The measured viscosity values
for both oils are very high, which constitutes a handicap for their direct use as
biofuels. These results are consistent with those obtained by Kerschbaum and Rinke
(2004), who have demonstrated that vegetable oils can be used as fuel for diesel
engines, but with a viscosity much higher than that of regular diesel, their use
requires engine modiﬁcations (Kerschbaum and Rinke 2004). Indeed, the high
viscosity seems to be the principal cause of many problems associated with the
direct use of vegetable oils as biofuel (Ryan et al. 1984); knowing that vegetable
oils are extremely viscous with viscosities 10–20 times higher than that of diesel
(C
¸ etin and Yuksel 2007).
0.93
0.92
0.91
0.9
0.89
0.88
0.87
0.86
20
35
40
50
60
70
Normal Oil
Recycled Oil
Poly. (Normal Oil)
Poly. (Recycled Oil)
Temperature (°C)
y = -0.0003x2 - 0.0048x + 0.9225
R2 = 0.9926
y = -0.0003x2 - 0.0051x + 0.9232
R2 = 0.9796
Density (g / cm3)
Fig. 1 NO and RO densities variation based on temperature
188
S. Zighmi et al.
3.4
Yield of Biodiesel
The yield of the synthesized biodiesels from the two types of oil and with the two
molar ratios (1/10 and 1/8) is calculated by the following formula:
η ¼ experimental mass of biodiesel
theoritical mass of biodiesel
ð5Þ
Theoretically, 1 kg of oil gives 1 kg of biodiesel by transesteriﬁcation reaction
while the theoretical mass of biodiesel is the mass of oil that we used in the reaction.
The obtained results are shown schematically in Fig. 3.
From Fig. 3, ﬁrstly, we notice that the yield achieved with the unused ELIO oil is
higher than that achieved with the used oil for both molar ratios. Secondly, the yield
of the ratio (1/8) is greater than that obtained with the ratio (1/10) in this case. In
addition, the analysis of the results presented in Fig. 3 indicates that the difference
in yield between the two types of oil is 07.58% and 20.4% with the ratios (1:8) and
(1:10), respectively, after being exposed at the same temperature, reaction time, and
for the same amount of catalyst. Following these results, it can be said that the use
of frying oil with a molar ratio of 1:8, in 2 h only of reaction and under a
temperature of 50 C, improves the yield of oil conversion by transesteriﬁcation.
Thus, it is a wise choice, for it helps to contribute to environmental remediation,
with costs more or less acceptable. The yield results also conﬁrm that the molar
ratio is an important factor and that our results are consistent with those obtained by
Tomasevic and Siler-Marinkovic (2002), who have found that the molar ratio is far
more effective than the catalyst of the transesteriﬁcation reaction. We notice that
the superior molar ratios are used to improve the solubility and to increase the
contact between the triglyceride molecules and the alcohol (Noureddini et al. 1998).
70
60
50
40
30
20
10
0
20
30
40
50
60
70
80
Normal Oil
Recycled Oil
Poly. (Normal Oil)
Poly. (Recycled Oil)
Temperature (°C)
y = 1.4043x2 - 18.554x + 79.023
R2 = 0.9844
y = 1.1085x2 - 16.436x + 76.763
R2 = 0.9922
Viscosity (mm2.s-1)
Fig. 2 NO and RO viscosity variation based on temperature
Biodiesel Production by Transesteriﬁcation of Recycled Vegetable Oils
189
3.5
Biodiesels Characterization
The biodiesel characteristics obtained by transesteriﬁcation from the two types of
oil are summarized in Table 3.
The measured values for the acid number and the ﬂash point show that there is a
signiﬁcant difference between the two types of biodiesel. Indeed, the frying oil
biodiesel possesses excessive values in comparison with those of the ordinary oil
biodiesel.
3.6
Biodiesel Density Variation
The biodiesel density variations are presented in Fig. 4.
From the results shown in Fig. 4, we notice that the density values for both
biodiesels are less than those of oils, and also the fact that they are decreasing with
an increasing temperature.
3.7
Biodiesel Viscosity Variation
Viscosity is another important property of biodiesel since it affects the functioning
of the injection system. The variation of viscosity based on two biodiesels temper-
ature is presented in Fig. 5.
100.00%
91.04%
70.64%
93.05%
85.47%
80.00%
60.00%
40.00%
20.00%
0.00%
Report 1/10
Report 1/8
Normal Oil
Recycled Oil
Fig. 3 The yield of biodiesel from regular oil ELIO and from frying oil with the two ratios (1/8
and 1/10)
Table 3 Biodiesel
characteristics
Characteristics
BNO
BRO
The acid number
0.56
1.12
Flash point (C)
198
249
190
S. Zighmi et al.
From Fig. 5 it is noted that the biodiesels viscosity decreases with an increasing
temperature. Also the viscosity of the biodiesel synthesized from unused oil (BNO)
is considerably lower than that obtained by the use of frying oils (BRO) and this
while using, for all values, the same temperatures from 20 to 80 C. The most
interesting thing is that the gap between the two is thin, which allows us to say that
the use of frying oil for biodiesel production is better.
Furthermore, comparing the viscosity values of the unused (NO) and the used
oils (RO) with those measured for the synthesized biodiesels (BNO and BRO) at
temperatures of 20 and 80 C, shows that there is a signiﬁcant decrease in viscosity
depending on the temperature increase for the oils and biodiesels, and adding to
that, the values of the biodiesels viscosities are signiﬁcantly lower than those of oils
0.89
0.88
0.87
0.86
0.85
0.84
0.83
0.82
0.81
20
30
40
55
60
70
80
Biodiesel for Normal Oil
Biodiesel for Recycled Oil
Poly. (Biodiesel for
Normal Oil)
Poly. (Biodiesel for
Recycled Oil)
Temperature (°C)
y = 0.0002x3 - 0.0028x2 + 0.001x + 0.881
R2 = 0.9747
y = 0.0003x3 - 0.0025x2 - 0.001x + 0.8836
R2 = 0.9861
Density (g/cm3)
Fig. 4 BNO and BRO density variation based on temperature
8
7
6
5
4
3
2
1
0
20
30
40
50
60
70
80
Biodiesel for Normal Oil
Biodiesel for Recycled Oil
Poly. (Biodiesel for
Normal Oil)
Poly. (Biodiesel for
Recycled Oil)
Temperature (°C)
y = 0.0851x2 - 1.257x + 6.9114
R2 = 0.9895
y = 0.1117x2 - 1.509x + 7.9
R2 = 0.9819
Viscosity (mm2. S-1)
Fig. 5 BNO and BRO viscosity variation based on temperature
Biodiesel Production by Transesteriﬁcation of Recycled Vegetable Oils
191
for both temperatures of 20 and 80 C. The comparison of the viscosity values for
biodiesel and oils was also done; the obtained results are summarized in Table 4.
The analysis results of Table 4 show that the viscosity variation gap during the
transition from oil to biodiesel is signiﬁcant for both oils. However, this gap is much
wider for the unused oil compared to that of the frying oil, for both temperatures
values. It is also observed that this gap is doubled to 3.28% for the unused oil and to
6.56% for the frying oil, when the temperature increases from 20 to 80 C.
Following these results, we can conclude that the use of frying oil for biodiesel
production offers the advantage of a signiﬁcant decrease in viscosity, which pre-
sents the major obstacle for the direct use of vegetable oils as diesel engines fuel
Figs. 6 and 7.
Table 4 Percentage of oils and biodiesel viscosity reduction based on temperature
Oil to Biodiesel
20 C
80 C
Difference
NO to BNO
90.32%
87.04%
3.28%
RO to BRO
89.24%
82.68%
6.56%
Fig. 6 IR spectrum of
produced biodiesel from
unused oil
Fig. 7 IR spectrum of
produced biodiesel from
used frying oil
192
S. Zighmi et al.
3.8
Biodiesels Characterization by IR Spectroscopy
The main functional groups of biodiesels are summarized in Table 5.
The analysis of the Table 5 and spectrum samples allows us to draw the
following information:
•
A stretching vibration between 2900 and 3000 cm1 corresponds to an ester
grouping C-H.
•
A stretching vibration at 1100 cm1 for (BNO) and 1155 cm1 for (BRO)
corresponds to a grouping of C-O.
•
A stretching vibration at 1740 cm1 corresponds to a grouping of C ¼ O.
These data conﬁrm the presence of an ester.
3.9
Checking of Biodiesels Quality
In order to see if the synthesized biodiesels are consistent with the international
standards, a comparison between the two types of synthesized biodiesels and those
standards is essential. Table 6 includes some parameters used for the comparison.
The comparison concerns certain properties, namely, density, viscosity, and the
acid number. Firstly, our present biodiesels are characterized by densities and
viscosities, which are consistent with the standards. Secondly, the ordinary oil
has an acid number conforming to the standards, while the frying oil possesses a
noncompliant acid number. From the analysis results, shown in Table 5, concerning
the ﬂashpoint, we can say that the synthesized biodiesels have, for the high values
⁣recorded, low ﬂammability, as follows: 198 and 248, respectively, for the unused
oil biodiesel and for the frying oil biodiesel. These results conﬁrm that the biodiesel
provides advantages in terms of safety compared to petroleum diesel, for it is much
less ﬂammable compared to petroleum diesel, which is characterized by a 77 C
according to Balat (2005).
Table 5 Biodiesel functional
groups IR spectrum
Functional group
C-H
C ¼ O
C ¼ C
C-O
cm1
BBNO
3000
1750
1640
1100
BBRO
29003000
1740
1645
1155
Table 6 Checking of biodiesels quality
Parameters
BNO
BRO
Standard
Diesel
Density at 15 C
0.884
0.883
0.86–0.9
0.820–0.850
Kinematic viscosity at 40 C
4.1
4.54
3.5–5
2–4.5
Acid number
0.56
1.12
0.8 max
–
Flash point
–
–
101
55 min
Cetane number
–
–
51
49–53
Fire point (C)
198
249
–
–
Biodiesel Production by Transesteriﬁcation of Recycled Vegetable Oils
193
4
Conclusion
Biodiesel is one of the most important renewable energy sources due to its various
advantages. It is synthesized by transesteriﬁcation, which is the most recommended
technique. In this recent study, we applied this technique to synthesize biodiesels
from two types of oil, ﬁrst the ordinary oil ELIO, the second one is the frying oil.
We also used methanol for the reaction and KOH as catalyst. The analysis results
showed that a molar ratio of oil/alcohol of 1:8 is better in our experience, for it
provides a yield of approximately 93.05% of ordinary oil and 85.07% of frying oil.
The characterization of the synthesized biodiesels was initiated thereafter; we used
infrared spectroscopy to identify the functional group of the formed biodiesel,
which conﬁrms that the synthesized biodiesels are methyl esters of fatty acids.
Other parameters were also checked for the oils and biodiesels, namely, acid
number, saponiﬁcation value, refractive index, cetane number (the latter for oil
only), and also the variation of density and viscosity based on temperature. The
analysis of these parameters and their comparison with the standards can draw the
following conclusions:
•
Biodiesels densities are low compared to those of oils.
•
Biodiesels ﬂash point and cetane number are higher than those of diesel.
•
The frying oil relatively high acidity is a priori due to the changes that it has
undergone due to the high temperatures and impurities during its use.
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Biodiesel Production by Transesteriﬁcation of Recycled Vegetable Oils
195
Membrane Desalination Technology
in Algeria: Reverse Osmosis for Coastal Areas
Z. Tigrine, H. Aburideh, M. Abbas, S. Hout, N. Kasbadji Merzouk,
D. Zioui, and M. Khateb
1
Introduction
Seawater desalination is necessary in a number of countries that witness water
shortage, as an option for securing the supply of drinking water to the population,
given the rapid increase in water demand in the sectors of agriculture and industry.
The biggest constraints of the desalination system are its energy consumption per
cubic meters product and environmental impacts due to discharges of brine in the
natural environment. Despite these constraints, desalination plants grow around the
world, including desalination processes to deal with the increasing water demands.
Resources are limited in quality and quantity, resulting in the establishment of
treatment solutions of brackish water and seawater.
Currently, on the industrial scale more than 15,000 desalination plants have been
installed worldwide with a production capacity of about 56 million m3/day with
64% seawater while the global capacity of drinking water production is about
500 million m3/day. In the Mediterranean, the production of desalination plants is
10 million m3/day. The total production capacity is estimated to be more than 8.5
billion gallons/day (Quteishat and Abu-Arabi 2006).
Z. Tigrine (*)
Unite
´ de De
´veloppement des Equipements Solaires, UDES/Centre de De
´veloppement des,
Energies Renouvelables, CDER, Bou Ismail 42415, W. Tipaza, Algeria
USTHB, BP32 El Alia, Bab-Ezzouar, 16111, Algiers, Algeria
e-mail: phyzahia@yahoo.fr
H. Aburideh • M. Abbas • S. Hout • N.K. Merzouk • D. Zioui
Unite
´ de De
´veloppement des Equipements Solaires, UDES/Centre de De
´veloppement des,
Energies Renouvelables, CDER, Bou Ismail 42415, W. Tipaza, Algeria
M. Khateb
Myah Tipaza, Usine de dessalement d’eau de mer Fouka Marine, W. Tipaza, Algeria
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_15
197
In remote regions, particularly in the Middle East and North African countries
MENA, freshwater scarcity is a huge problem. The exploitation of groundwater
aquifers and surface water has contributed to the decrease in quantity and quality of
existing natural water resources (Mohamed et al. 2008).
Availability of electricity networks in these regions is frequently limited; tech-
nologies that are able to remove pathogens and dissolved contaminants require
large amounts of energy (Eltawil et al. 2009; Schafer et al. 2007). Different
economic constraints encouraged the spread of desalination technologies in the
Arab countries where potable water is an essential resource as electricity
(Al-Karaghouli et al. 2009). The total population is around 325 million with a
very high growth rate of 2.7%; the per capita share of the total annual renewable
water resources (TARWR) has dropped well below the UN threshold for water
poverty (1000 m3/year) with most of the Gulf Arab countries reaching per capita
TARWR below 200 m3/year (El-Nashar et al. 2007).
One of the most common membrane desalination methods is reverse osmosis; it
has emerged as the dominating technology to produce freshwater from seawater for
many industrial and domestic applications (Greenlee et al 2009). Several technol-
ogies allow the production of freshwater from seawater and brackish water. In
general, desalination technologies can be classiﬁed into two different processes of
separation, namely, thermal and membrane-based desalination. Among the known
thermal processes, multiple effect distillation (MED), multistage ﬂash (MSF), and
vapor compression distillation (VCD) can be cited while the membrane processes
include reverse osmosis (RO), electrodialysis (ED), and nanoﬁltration (NF).
Processes based on separations’ membranes know in this context a great interest.
They seem to be very powerful tools for desalination, puriﬁcation, and recycling of
ﬂuids for a “zero waste” goal.
The reverse osmosis technique is the most commonly used technology for
desalination where it comprises up to 45% of the total world desalination capacity.
The development of desalination applications and positioning of membrane pro-
cesses compared to thermal processes can boost technological and process innova-
tions in this ﬁeld. The changes aim to reduce energy consumption, investment, and
operating costs.
Furthermore, the reverse osmosis facilities are mainly concentrated in the
regions situated around the Mediterranean Sea where energy consumption is
lower compared to the other water desalination technologies. The energy consump-
tion of seawater reverse osmosis plants decreased in time from 20 kWh/m3 in the
1970s to 3.5 kWh/m3 in the 1990s due to energy recovery, pumping systems, and
the development of efﬁcient membranes destined and characterized for seawater
(MacHarg and Truby 2004).
However, the costs of water desalination are very high for its intensive use of
energy. As seawater has a higher osmotic pressure than brackish water, the energy
requirement to desalinate brackish water was estimated below 1.5 kWh/m3, and it
remains lower than that for seawater (Kehal 1991). As the cost to desalinate
brackish water is less than other existing alternatives, desalination in many arid
and semiarid regions can be the best way to obtain clean and freshwater.
198
Z. Tigrine et al.
More recently, membrane technology has witnessed an enhancement, resulting
in a signiﬁcant increase in water production with high quality and cost saving.
More improvements of membrane desalination efﬁciency, namely, the develop-
ment of new fouling resistant membranes, use of appropriate pretreatment, optimi-
zation of reverse osmosis operating factors, brine management, as well as
renewable energy coupling to desalination technologies, can contribute to reducing
water production cost. Due to rising environmental concerns, renewable energy
technologies are most interesting for powering water desalination facilities.
For well over a decade, there have been a large number of experimental and
theoretical works on characters that have been carried out in order to make changes
and improve the performance of this type of desalination plant. However, in some
cases, for example in small rural sites or during disasters where potable water is not
available, small RO systems operating using photovoltaic (PV) systems could also
be used to obtain drinking water, to help people survive.
Many attempts and experiments have been conducted to ﬁnd appropriate cou-
pling processes between the RO desalination processes and PV systems as renew-
able energy resources. This area has continued to attract the interest of many
researchers. Several studies focused on this type of conﬁguration by carrying out
experimental small plants. With support from the Canadian government, Keefer
et al. (1985) developed two small reverse osmosis systems powered by photovoltaic
energy in Vancouver, British Columbia, to demonstrate the use and optimization of
solar energy consumed with storage batteries (panels have a power of 480 W). To
produce demineralized water 0.5–1 m3/day, they examined the differences between
the direct connections of PV reverse osmosis system, the maximum power tracking,
and included storage battery. Using a positive displacement pump with variable
speed, with energy recovery of the rejected brine, they claim to be able to reduce
life cycle costs by 50% compared to conventional systems OI/PV. Other investi-
gations illustrated that power consumption can be reduced to 0.89 kWh/m3 (Maurel
1991; Kehal 1991), and an attempt was made to model this type of coupling without
using batteries as given by Thomson (Fritzmann et al. 2007).
Hanaﬁ(1994) studied the different desalination technologies associated with
renewable energy, mainly solar, wind, tidal, and geothermal. He presented some
control limits for the use of energy sources including wind, which are more
recommended than RO/PV. A systematic approach to renewable energy-powered
desalination considering all the alternatives was presented by Rodriguez et al.
(1996). Of all the combinations studied, they concluded that RO powered by PV
is interesting in very speciﬁc cases, such as in sunny remote sites.
A small reverse osmosis system (RO) powered by photovoltaic (PV) systems has
been installed and tested at the island of Gran Canada by Herold et al. (1998). A
feasibility study of this small PV system RO of 1 m3/day was presented. The pilot
plant, with an average production capacity of 3.2 m3/day of freshwater, is coupled
to a stand-alone PV system and storage batteries. The rated power consumption is
2.35 kW. They described in detail the technical characteristics of the installation
(RO) as regards its operating constraints and energy consumption.
Membrane Desalination Technology in Algeria: Reverse Osmosis for Coastal Areas
199
Antho Joyce et al. (2001) described a small pilot reverse osmosis unit powered
by a PV system that was implemented in the Department of Renewable Energy,
INETI. This small compact unit with a daily production of 100–500 L operating at
low pressure (<5 bar) with a PV module 3  50Wp can produce drinking water
from brackish water containing salt concentrations of about 5000 ppm. They
presented the preliminary results of laboratory tests carried out in the summer of
2000 in Lisbon. A spiral membrane-type MP-TA50 with a ﬁltration mode coupled
with the low pressure operation (<5 bar) enables low power consumption, around
100 kJ/kg impregnation. However, these results related to daily production have
been conﬁrmed by other operating conditions by comparing the quality of impreg-
nation and the energy consumed according to pressure. Nevertheless, they may
conclude that the energy consumption of the small pilot of reverse osmosis system
decreases as the feed water recovery and supply pressure increase. The results of
these experiments are used to validate a mathematical model of a system based on
the I-V characteristics of PV modules. This model predicts the annual production of
drinking water of this unit and the cost of the water produced by this type of system.
In Algeria, Sadi and Kehal (2002) and Kehal (2003) conducted experiments on a
reverse osmosis desalination plant installed in Hassi Khebi (south-eastern Algeria)
with a capacity of 0.85/h driven by a photovoltaic generator. This unit was acquired
as part of the collaboration between the research center CDER (Algeria) and the
Commissariat 
a l’Energie Atomique (CEA France). They presented the evolution of
power and pressure versus time; the results were encouraging during the experi-
mentation period, giving a conversion rate of 40.7%. Subsequently, the rate
dropped to 24% due to neglect and lack of skilled technicians. Unfortunately, the
membranes were clogged, causing a loss of production. They also presented the
perspectives and desalination opportunities along the Algerian coast with 1200 km
and within the country where several aquifers are characterized by high salinity,
2–5 g/l salts dissolved.
Badreddine et al. (2004) were able to build a prototype of a reverse osmosis
desalination unit with 100 L/h at the laboratory scale powered by a photovoltaic
energy source (550 W–4.2 A). This innovative concept was installed at AAST in
Egypt in the framework of a cooperation project with PROAUT UASZ and
supported by SwissContact. The plant is intended for educational and research
purposes. This preliminary operation experience shows that skilled personnel is
required for operating and maintaining these kinds of desalination systems. They
brieﬂy discussed some of the issues of research that can be studied at laboratory
scale, by modeling the system to optimize energy consumption, system availability,
and production of water under variable weather conditions.
In Jordan S. Abdallah et al. (2005) have presented an experimental study which
aims to investigate the potential for water desalination development using a solar-
powered system. The results have shown that the reverse osmosis system powered
by photovoltaic energy can be easily applied. They may conclude that a gain of
25 and 15% of electrical energy and the ﬂow of desalinated water, respectively, is
possible using the tracking system on east-west axis with ﬂat ﬁxed plate. The results
are presented in curve form, namely, the electric current, voltage, electric power,
200
Z. Tigrine et al.
and the production rate for a ﬁxed surface and a tracking PV system versus time.
Furthermore, the production rate for a ﬁxed surface and a tracking PV system
according to electric current was introduced. They reported that more experimental
work must be done to study the continuous performance of the system, and further
investigation should be directed to the membrane fouling and system recovery.
Different possibilities of coupling RO with the most appropriate sources of
renewable energy as hybrid systems (photovoltaic and wind) are presented by
Bourouni et al. (2011) using a new model based on the genetic algorithms to
minimize the total water cost. Village of Ksar Ghile
`ne located in the south of
Tunisia was chosen for this study that presents the case study of PV/RO unit
installed since 2007. A comparative analysis with reference software (ROSA for
the RO unit and HOMER for the PV modules) was validated.
Numerical simulations on a small-scale, stand-alone, solar-PV-powered
(RO) system, with or without battery storage, were reported by Daniel et al.
(2013). They note that the system scalability inﬂuences the sensitivity of simula-
tions and the type of I-V characteristics used. The results conﬁrm that including
batteries to store excess renewable energy has a signiﬁcant impact on the perfor-
mance of smaller systems compared to larger ones. Various alternative systems can
be used for desalination technology integrating the RO desalination process. Ibarra
et al. (2014) analyzed the performance of a speciﬁc solar desalination organic
Rankine cycle (ORC) system at part load operation. They tried to understand its
behavior from a thermodynamic perspective and were able to predict the total water
production with changing operation conditions. It is seen that the water production
is stable during day and night through the thermal storage where the rate ﬂow was
around 1.2 m3/h.
Current desalination technologies were described by Youssef et al. (2014) by
comparing their performance in terms of input and output water quality, amount of
energy required, and environmental impact. They suggest that adsorption desalina-
tion technology is a suitable technology for seawater desalination with its low
running cost and low environmental impact as it uses waste energy resources.
To meet the demand for potable water in areas where reserves are insufﬁcient,
the recourse to desalination remains the best solution.
The Algerian government has launched several large-scale programs to eradicate
the problem of drinking and irrigation water shortage. Among them are the con-
struction of new dams, water transfer, the implementation of desalination plants,
and the development of new treatment plants and wastewater treatment. The aim of
this chapter is to review the water shortage problem in Algeria and to present
several plants of desalination that can be implemented on the Algerian Mediterra-
nean coastal areas using reverse osmosis technology which are very effective for
solving water scarcity problem from economic and environmental viewpoints. As
the Algerian population is growing, the need for drinking water is increasing; water
desalination is a promising means for producing clean water from saline water
abundant in sea and also in the large Saharan region. We choose a case study: Fouka
seawater desalination plant by providing its monthly rate production and energy
consumption for 1 year.
Membrane Desalination Technology in Algeria: Reverse Osmosis for Coastal Areas
201
Water and energy are two main topics for any country’s development plan. A
small reverse osmosis equipment coupled with a solar energy system such as PV,
wind, and CSP (Concentrating Solar Power) could be useful to produce electricity
which is able to generate the energy required by membrane desalination reverse
osmosis applications. It seems that according to bibliographic research, photovol-
taic energy system is an alternative to provide electricity and clean water. The most
promising PV energy conversion technologies are available for remote and arid
areas. This technology is in development in many regions where various technol-
ogies used to desalinate saline water have different performance and characteristics.
We conclude that this type of technology is destined to remote areas where the
access to electricity and water is a challenge.
2
Problematic and Hydric Resource in Algeria
Algeria is an important Mediterranean country; it faces increasing water shortages
due to climate change (low rainfall, desertiﬁcation, etc.). It stretches from east to
west on a long coastline of 1200 km in length. The continual population growth, of
which 80% is concentrated in the coastal cities, and business growth in socioeco-
nomic sectors such as agriculture, industry, and tourism have led to increased
consumption of water. Desalination and wastewater reuse solutions are imperative
for Algeria to overcome water shortage.
Algeria still faces drinking water supply problem; ﬁrst, mechanisms must be
employed to reduce wastage and water leaks, as water remains insufﬁcient in
semiarid and arid regions. One solution is the production of freshwater from
brackish water and/or seawater. With its ideal location, Algeria has the largest
solar resource in the Mediterranean basin. The average duration of the Algerian
territory sunshine exceeds 2000 h annually, reaching nearly 3500 h of sunshine in
the Sahara desert. The total received power is estimated at 169,400 TWh/year, that
is to say 5000 times the annual electricity consumption of the country. Algeria has
limited water resources that are unevenly distributed in time and space, and the
imbalance between supply and demand of water becomes a major constraint.
Algeria has very limited water resources, largely insufﬁcient to cover domestic,
agricultural, and industrial needs. A population of 90% is concentrated in the
coastal strip where all economic activities of the country are concentrated, and it
requires a considerable water supply. Its climate is diverse according to its geo-
graphical diversity and interannual rainfall variability. A variability in rainfall
between West (350 mm average rainfall), East (1000 mm) and high relief
(2000 mm), which becomes almost absent from the Sahara (average below
100 mm) and a concentration of precipitation over time, is noticed. The evolution
of rainfall during 1922–2005 is presented in Fig. 1 for three regions, namely,
Algiers, Constantine, and Oran. The ﬁgures show strong decrease in rainfall,
especially in the Oran region. This situation causes drought cycles. Practically,
202
Z. Tigrine et al.
1400
1200
1000
Pluie en (mm
Pluie en (mm
Pluie en (mm
800
600
400
200
0
Annual rainfall (mm)
Arithmetic average (mm)
Evolution and trends in rainfull
Algiers
Evolution and trends in rainfull
Constantine
Evolution and trends in rainfull
Region of Oran
Running average 5 years (mm)
1922
1927
1932
1937
1942
1947
1952
1957
1970
1975
1980
1985
1990
1995
2000
2005
1000
800
600
400
200
0
Annual rainfall (mm)
Arithmetic average (mm)
Running average 5 years (mm)
1922
1927
1932
1937
1942
1947
1952
1957
1970
1975
1980
1985
1990
1995
2000
2005
800
600
400
200
0
Annual rainfall (mm)
Arithmetic average (mm)
Running average 5 years (mm)
1922
1927
1932
1937
1942
1947
1952
1957
1970
1975
1980
1985
1990
1995
2000
2005
Fig. 1 Evolution of rainfall in three regions (Algiers, Constantine, and Oran) in 1922–2005
(Services de l’eau en Algerie Faire 2011)
Membrane Desalination Technology in Algeria: Reverse Osmosis for Coastal Areas
203
the North African countries have a low precipitation rate as shown in Table 1 that
depicts water availability in the North African countries in 2005 according to FAO.
The hydrological context of Algeria is summarized within exorheic watersheds
whose wadis have the outﬂow to the Mediterranean Sea, and the endorheic water-
sheds are formed of the chotts and sebkhas (saline lakes). The Sahara regions have
an important subsoil rich in water and oil slicks (fossil fuel) according to Remini
(2010).
Among the main technical problems encountered in the dams that affect the
quantity and quality of water resources in the north of the country are the evapo-
ration of dam lakes, the leaks in dams, the eutrophication of dam reservoirs, and the
intrusion of seawater into coastal aquifers.
The country has ﬁve hydrography networks, by grouping 17 watersheds as
shown in Fig. 2. Water resources are not equally distributed, in terms of either
their geographical distribution, quantity, or nature (surface or groundwater). Water
potential is estimated at 19.4 billion m3/year among them; 12.5 billion m3/year is
distributed in the northern regions (superﬁcial and underground resources) and 5.5
billion m3/year in the Saharan regions (Proble
´matique du secteur de l’eau 2009).
The country currently has 66 dams with a storage capacity of nearly 7 billion m3.
This number is expected to increase as 19 dams are under construction at present to
allow regularize half of the total contribution of the wadis, or 5 billion m3/year for
an installed capacity of around 10 billion, and 20 new dams are planned for 2015. It
is estimated that 1.6 billion is their average annual volume. Figure 3 represents the
total surface water and groundwater resources and their distribution. In 1962, the
annual water availability per capita was recorded about 1500 m3.
This value was rapidly lowered over time; it recorded 720 m3, 680 m3, and
630 m3 in 1990, 1995, and 1998, respectively. Today, owing to population pressure
the annual water availability per capita is 500 m3; this availability will be only
430 m3 per capita by 2020. In terms of water potential, Algeria is below the
theoretical scarcity threshold set by the World Bank (1000 m3 per capita per
year). Table 2 shows the water availability per capita in Algeria by 2020
(Proble
´matique du secteur de l’eau 2009).
According to the Ministry of Water Resources, Algeria has 50 dams in operation,
11 are under construction, and 50 other dams are being studied; they should be
Table 1 Total actual renewable water resources in the North African countries in 2005 according
to AQUASTAT, FAO
Country
Population
(1000 s)
Precip rate
(mm/year)
TARWR
volume (km3/
year)
TARWR per
capita (m3/year)
TARWR per
capita (m3/
year)
Algeria
32,339
100
14
478
440
Tunisia
9937
300
4.6
482
460
Egypt
73,390
100
58
859
790
Morocco
31,064
300
29
971
930
Mauritania
2980
100
11
4278
3830
Libya
5659
100
1
113
106
Sudan
34,333
400
65
2074
1880
204
Z. Tigrine et al.
completed before 2020 in order to catch up a delay found because of water losses
estimated at 50%. Despite the construction of new dams and the use of desalination,
Algeria will record a water deﬁcit of 1 billion m3 by 2025. The lack of resources is
compounded by the poor spatial and temporal distribution of these resources, the
soil erosion and siltation of dams, and the losses due to the obsolescence of
Fig. 2 Map of ﬁve geographic planning areas (Proble
´matique du secteur de l’eau 2009)
-100
0
100
200
300
400
500
600
700
800
Surface water  (hm3)
0
100
200
300
400
Groundwater ( (hm3)
SAHARA
SUD- ATLAS
AURES-NEMEMCHAS
MEDJERDA -MELLEGUE
CHOTT-HODNA
ZAHREZ-SERSOU
CHOTT CHERGUI
COTIERS-ANNABA
SOUMMAM
ALGEROIS
CHELIFF
ORANAIS
Fig. 3 Potential distribution per basin
Membrane Desalination Technology in Algeria: Reverse Osmosis for Coastal Areas
205
distribution networks and insufﬁcient management. In particular, the desalination
of seawater and brackish water is one of the promising techniques for some regions
of the country. It responds primarily to drinking water supply and irrigation of
agricultural land. For this purpose, desalination is encouraged by the state whose
government has installed several desalination plants in Algeria. Several major
centers, such as Arzew which provides 90,000 m3 or center of Beni Saf, have
solved the problem of water scarcity in some cities.
3
Seawater Desalination Technology in Algeria
Water and energy are two main topics for any country’s development plan. Algeria
is the second largest country in Africa and the Arab world after Sudan in terms of
surface area, and the largest around the Mediterranean whose southern areas
include a signiﬁcant part of the Sahara (80%). To meet the demand for freshwater
in areas where reserves are insufﬁcient, several countries have called on water
desalination. The Algerian government has launched several national large-scale
programs to eradicate the problem of drinking and irrigation water shortage.
Among them are the construction of new dams, water transfer, the implementation
of desalination plants, and the development of new treatment plants and wastewater
treatment.
Desalination is a process that removes salts from water so that it may be used in
municipal and industrial applications. With advanced techniques, desalination
processes are becoming cost competitive with other methods of producing fresh-
water. Two main categories of technologies used for desalination can be classiﬁed
as thermal and membrane. These technologies need energy to operate and produce
clean water. Desalination processes using different techniques are determined by
category as shown in Fig. 4.
Due to a severe continuous drought, the Algerian government has adopted a
huge desalination program to overcome the water deﬁcit. Thirteen seawater
Table 2 Water availability per capita in Algeria by 2020 (Proble
´matique du secteur de l’eau
2009)
Basin
hydrographic
Resources (hm)
Population
(106 capita)
Availability
(m3/capita)
Oranie chott
Chergui
1400
6.3
220
Che
´lifer
2072
7.0
300
Alge
´rois
Soumma-Hadna
5125
15.8
320
Const-mejd
Mellegue
5048
10.0
500
Sud
5436
4.9
1120
Total
64,518
44.0
430
206
Z. Tigrine et al.
desalination projects are operational with a total capacity of 2260 million cubic
meters, that is, 2.26 billion liters per day. A determined program launched by the
government in the last decade aims to deal with the lack of conventional drinking
water resources and meet the domestic needs of more than 20 million Algerians.
Moreover, 75 dams in construction will bring the overall volume of 6 billion m3;
they will be operational by 2025. Seawater desalination capacity in Algeria
increased from 152,500 m3/day in 2006 to 1. 2 Mm3/day in 2011. At the end of
2012, the production capacity was 1.3 Mm3/day, and the total capacity in 2014
reached 2.1 Mm3/day. In Algeria, reverse osmosis technology is the most used for
desalination that represents more than 95% as reported by Fig. 5. The general
location and distribution of water desalination plants in Algeria is provided in
Fig. 6.
Desalination of seawater in Algeria is an ambitious program; it is implemented
through the installation of large seawater desalination plants as El-Magta^
a me
´ga
plant near Oran, which is operational since the ﬁrst semester 2014. It is one of the
largest in the world; it uses reverse osmosis process with a capacity of 500,000 m3/
day for the long-term coverage of needs for drinking water of 5 million people. The
station of Hamma, called to ensure drinking water supply to Algiers, was inaugu-
rated in February 2008, with a production capacity of 200,000 m3/day. Figure 6
shows the different desalination plants implemented in Algeria with their capacity
in cubic meters per day. We observe that the west region has beneﬁted from a strong
desalination capacity in comparison to the center region. The progress of these
plants is presented in Fig. 7. This program concerned several regions, namely,
Sahara, east, and west cities (Table 3 and Fig. 8).
Now, let us present the evolution of the installed capacity of water desalination
in Algeria between 2006 and 2014 as given in Fig. 4 (MacHarg and Truby 2004). It
can be seen that the total production capacity of drinking water increases along
Fig. 4 The main desalination technologies and processes
Membrane Desalination Technology in Algeria: Reverse Osmosis for Coastal Areas
207
time. The following table provides estimates of the capacity that was installed
between 2005 and 2010 and which will be installed during 2020–2030. Near-future
water needs are estimated according to regions. We note that 16 projects of large
units bring the desalted water volume to 942 m3H for the horizon 2025. The
achievement of these 13 seawater desalination plants with a total capacity of
about 2260,000 m3/day made up for the actual need for water despite different
technical problems encountered at the station due to electricity cuts (Table 4 and
Fig. 9).
In addition to these great stations, the desalination program reveals the presence
of some monobloc stations of small capacity (between 2500 and 7000 m3/day),
some of which have been relocated to provide water to the most affected areas (see
table 4).
The various stations that exist in Algeria are managed by production companies
piloted by the Algerian Energy Company (AEC) which is created by Sonatrach and
Sonelgaz groups. The production of freshwater from desalinated water is sold to the
Algerienne Des Eaux (ADE) that is a public body of industrial and commercial
character. This public body is under the authority of the minister responsible for
water resources that was created in April 2001 year by executive Decree
No. 01-101. We emphasize that desalinated water prices remained constant for
consumers despite the development of desalination plants (Table 5).
Fig. 5 Energy recovery in
reverse osmosis sea water
desalination (Ibarra et al.
2014)
Fig. 6 Distribution of water desalination plants in Algeria (Remini 2010)
208
Z. Tigrine et al.
The cost of desalinated water is estimated according to three important elements,
namely, ﬁnancial expenses, energy cost, driving operation and maintenance costs.
Over the past decade, the cost of desalination declined by half as the raw materials
cost has increased and will continue to increase in the near future. Investment costs
are estimated at 900–1000 €/m3/h for reverse osmosis. The cost of desalinating
brackish water is signiﬁcantly low (0.2–0.3 €/m3) in comparison to seawater which
varies in the range of 0.4–0.6 €/m3. The desalinated water for large units costs about
two times more than conventional water (Ibarra et al. 2014).
4
Case Study: Seawater Desalination Plant of Fouka
The realization of the seawater desalination plant of Fouka is a part of the national
program of realization of 13 plants. It is the third station established in the wilaya of
Tipaza along with the Bou Ismail station which delivers 5000 m3/day and Oued
Sebt station Gouraya which also delivers 100,000 m3/day. The seawater desalina-
tion plant of Fouka is intended to cover the drinking water needs of the eastern part
of the province of Tipaza and part of the western region of Algiers. It is functional
since 2011, located in the town of Fouka, Douaouda wilaya of Tipaza and
implemented on a surface of 10 ha. This station uses the membrane separation
reverse osmosis technique to desalinate seawater; its daily desalination capacity is
120,000 m3/day.
Fig. 7 Capacity of the seawater desalination plants in Algeria
Membrane Desalination Technology in Algeria: Reverse Osmosis for Coastal Areas
209
Water produced by the desalination plant will be acquired by Sonatrach, the
Algerian waters (ADE) for 25 years. It is equipped with two pumping stations and
seven tanks with a capacity of 14,000 m3 for a total volume of 60,000 m3 affected
for each wilaya. This hydraulic project will cover the needs of 17 municipalities
with a total population estimated at 476,372 inhabitants distributed between the two
wilayas. The communes supplied with drinking water by this station are Douaouda,
Fouka, Bou Ismail, Ain Tagourait, Hattatba, and Chaiba and also the adjoining
communes of the province of Tipaza, namely, Ze
´ralda, Mahelma, and Ain Benian
Staoueli. This station was carried out by the company “Myah Tipasa” which
represents a consortium consisting of AEC (Algerian Electrical Energy) and the
Canadian “SNC Lavalin.”
Table 3 Progress and localization of seawater desalination stations in Algeria (http://www.mre.
dz/baoff/ﬁchiers/PROGRAMME_DESSALEMENT.pdf)
Desalination stations of seawater
Region
Project location
DIM (km)
. . . < Ф
mm  . . .
Progress
Investors (SDEM)
West
Ouest Arzew/Oran
37 km
Ф ¼ 1250
Operating
(August 2005)
Black-Veach
(South Africa)
Souk Tleta/
Tlemcen
157 km
250 < Ф  1400
Operating (May
2011)
Hyﬂux-Malakoff
(Singapore)
Honaine/Tlemcen
160 km
500 < Ф  1200
Operating (July
2012
Geida
(Spain)
Mostaganem
117 km
200 < Ф  1400
Operating
(September 2011)
Inima-Aqualia
(Spain)
Sidi Djelloul/Ain
Temouchent
160 km
250 < Ф  1400
Operating
(December 2009)
Geida
(Spain e)
Mactaa/Oran
21 km
700 < Ф  1800
Works in
progress
Hyﬂux-Malakoff
(Singapore)
Center
Hamma/Alger
12 km
700 < Ф 900
Operating
(February 2008)
Ge.Ionix (USA)
Cap Djinet/
Boumerdes
30 km
900 < Ф  1000
Operating
(August 2012)
Inima-Aqualia (Spain)
Fouka/Tipaza
15 km
350 < Ф  900
Operating (July
2011)
Snc Lavalin-Predisa
(Canada- Spain)
Oued Sebt/Tipaza
127 km
200  Ф  1000
SDEM
not launched
Biwater
(Angleterre)
Tenes/Chlef
254 km
200 < Ф  1400
Travaux en cours
Befesa Agua
(Spain)
East
Echatt/Tarf
20 km Ф ¼ 800
SDEM
not launched
–
Skikda
54 km
400 < Ф  1000
Operating
(March 2009)
Geida
(Spain)
Total
1164 km
210
Z. Tigrine et al.
4.1
Basic Diagram of RO
By deﬁnition, reverse osmosis is a membrane separation process in which pure
water passes from the high pressure seawater side of a semipermeable membrane to
the low pressure side of the membrane. It is the most important part of the seawater
desalination system. Following the pretreatment (ﬂocculation, chemical treatment,
and ﬁltering) and supercharging using the high pressure pump, the seawater enters
the membrane and is separated into freshwater that is the permeated water and
concentrated brine under the high pressure effect. After further treatment, the clean
water is pumped to the terminal users from the storage tanks. The clean water goes
to the storage tanks, and after further treatment, water becomes potable. The brine is
evacuated into the sea, then further treatment is provided using an energy recovery
device (Fig. 10).
Fig. 8 Capacity of the brackish water demineralization plants in Algeria
Table 4 Seawater desalination program (Services de l’eau en Algerie Faire 2011)
Desalination plants’ proposed capacity, m3/day
Region
Number of plants
2005–2010
2020–2030
Nord Ouest
6
1,090,000
1,090,000
Nord Center
6
650,000
740,000
Nord Est
4
150,000
380,000
Total
program
16
1,890,000
2,210,000
Total desalination program in million m3/year
690
807
Membrane Desalination Technology in Algeria: Reverse Osmosis for Coastal Areas
211
4.2
Desalination Production
Now let us present some characteristics and data of Fouka seawater plant that has a
capacity of 120,000 m3/day. It is a private BOO (Build, Own, Operate) contract.
The seawater of Fouka is characterized by an electrical conductivity of 56.6 ms/cm
and a pH ¼ 8.01 at a temperature T ¼ 23.3 C.
This plant is directly related to the water distribution network of the town. The
aim of the Fouka plant is durable supply of freshwater with high quality to several
Fig. 9 Evolution of the installed capacity of water desalination in Algeria between 2006 and 2014
(Recuperation D’energie 2014)
Table 5 Monobloc stations of small capacity (Alge
´rienne des eaux. http://www.ade.dz/index.
php/projets/dessalement)
Province (Wilaya)
Town
Capacity m3/day
Population to
be served
Alger
Ze
´ralda
5000
33,330
Alger
Staoueli
2500
16,660
Alger
Ain Benian
5000
33,330
Tlemcen
Ghazaouet
5000
33,330
Tipasa
Bou Ismail
5000
33,330
Skikda
L.BenMhidi
7000
47,000
Tizi Ouzou
Tigzirt
2500
16,660
Oran
Bou Sfer
5000
33,330
Oran
Ain Turk
2  2500
33,330
Ain Temouchent
Bou Zdjer
5000
33,330
Ain Temouchent
Bou Zdjer
5000
33,330
Boumerdes
Corso
5000
33,330
212
Z. Tigrine et al.
populations. The table shows the variation of total dissolved solids (TDS) with
temperature of the raw water (seawater of Fouka) during 1 year (Table 6).
Figure 11 shows monthly production of freshwater for 2013 and 2014. The
monthly production increases when the temperature of water to be desalinated is
increased. Indeed, temperature has a signiﬁcant impact on the production rate. It is
seen that the production achieved the optimal range between July and October for
the 2 years (2013–2014) when the recorded temperature of the raw water varied in
the range 23–24 C. We note that the minimum production is obtained in December
in the case of 2014. As Fouka is a Mediterranean area, the total dissolved solids
(TDS) in this region is relatively greater within the range 38–39.5 g/l as illustrated
above in Table 6. The variation values of some characteristics such as TDS are very
important. From these parameters the quality of freshwater production and the
desalination cost can be determined. However, the increase of TDS allows operat-
ing the pump for reaching the pressure of 65–70 bar in which this pump HPP
(Italian compagny for pumps’ manufacturing) consumes more energy. Regarding
parameters that can contribute to deﬁning the recovery ratio, seawater has to be
analyzed regularly. The evaluation of monthly production of freshwater with
energy for 2014 is shown in Fig. 12.
Figure 13 shows the obtained data in which the membrane production of Fouka
plant increases and where conversion rate reaches up to a maximum of 46%. The
data relates to a plant that is supplied with an average of 38,000 TDS water. We
note that, when the conversion rate increases the energy consumption rate increases
strongly. It is seen that in the range where the conversion rate of membrane varies
between 44.6 and 45.5% the energy consumption does not exceed the value 4 kWh/
m3, while if the conversion rate exceeds this value a rise of 5% of the energy can be
achieved (from 44.6 to 46%); it corresponds to an increase of energy of about 0.215
kWh/m3. As a result, the reverse osmosis desalination technology requires high
rates of energy and generates brackish water discharges.
In fact, the existence of energy recovery systems decreases power consumption,
and these are efﬁcient for the production of potable water. Actually, renewable
energy can provide a sustainable and alternative solution for reverse osmosis
Fig. 10 General diagram of the seawater desalination plant
Membrane Desalination Technology in Algeria: Reverse Osmosis for Coastal Areas
213
systems that are driven by solar energy. Water desalination technologies operating
with renewable energy for the production of drinking water are considered to be a
sustainable solution to address the deﬁcit of water in rural areas that have no access
to safe drinking water and electrical energy. This principle is currently developed
industrially for water puriﬁcation and seawater desalination (Fig. 14).
Table 6 Monthly
characteristics of the raw
water during 2014
Month
2014
TDS (mg/l) of raw water
T (C)
of raw water
January
38.50
16.00
February
38.90
15.60
March
39.10
15.60
April
39.40
16.40
May
39.50
17.90
June
38.85
21.39
July
38.88
23.99
August
38.68
24.04
September
38.61
24.93
October
38.95
23.20
November
38.38
19.51
December
38.37
17.17
Jan
Feb March Apr
May Jun
Jul Augst Sept Oct
Nov Dec
2400000
2600000
2800000
3000000
3200000
3400000
3600000
3800000
Production (m3)Fouka plant 
Time (month)
2013
2014
Fig. 11 Monthly production of freshwater for 2013 and 2014
214
Z. Tigrine et al.
5
Conclusions
Seawater desalination technology is used in many regions of North Africa due to
population growth, drought, and water scarcity. Different technologies are devel-
oped, and they demand high power consumption. Reverse osmosis is the most
Jan
Feb March Apr
May Jun
Jul Augst Sept Oct
Nov Dec
3,95
4,00
4,05
4,10
4,15
4,20
4,25
Time (month)
Year 2014
Energy consomption KWH/M3
2400000
2600000
2800000
3000000
3200000
3400000
3600000
3800000
Production (m3)Fouka plant 
Fig. 12 Variation of monthly production of freshwater with energy consumption versus time
for 2014
44,6
44,8
45,0
45,2
45,4
45,6
45,8
46,0
3,90
3,95
4,00
4,05
4,10
4,15
4,20
4,25
4,30
Jan-Dec 2014
Energy consomption KWH/M3
Conversion rate %
Fig. 13 Plant energy consumption as a function of conversion rate for 2014
Membrane Desalination Technology in Algeria: Reverse Osmosis for Coastal Areas
215
suitable process for extracting salt from seawater to meet the increasing water
demand.
In this present work, we assessed the currently available seawater desalination
plants which are implanted in Algeria. We reviewed the water shortage problem in
our country by presenting the main problematic and hydric resources in Algeria.
Several plants of desalination located on Algerian Mediterranean coastal areas
using reverse osmosis technology and their capacity in cubic meters per day are
presented. Case study of seawater desalination plant of Fouka is discussed. Mainly,
we show the monthly production of potable water for 2013 and 2014 and their
variation of energy consumption with time. Seawater desalination requires much
energy and as water demand increases, desalinated water cost increases.
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Z. Tigrine et al.
A Study on Energy and Environmental
Management Techniques Used in Petroleum
Process Industries
A.K. Azad, M.G. Rasul, Rubayat Islam, and S.F. Ahmed
1
Introduction
The energy and environment is one of the major concerns in the present world
nowadays (DOE). The environment is a combination of matters and energies
around us. The coordination among the resources of an organization is called
management. So, environmental management is a broad area of research which is
important for the global environment. Environmental pollution can be deﬁned as
the unfavourable alteration of our surroundings by human actions, through direct or
indirect effects of changes in energy patterns, radiation levels, chemical and
physical constitution of organisms, etc. (Hossain 2009; Azad and Alam 2011).
These changes may affect directly or indirectly the environment (MEF 2007;
Cholakov 2010). The ﬁrst environmental activities in Bangladesh were taken
soon after the Stockholm conference on human environment. As a follow-up action
to the Stockholm conference, the Bangladesh Government funded, under the aegis
of the department of public health engineering and with a staff level of 27 and after
promulgating the water pollution control ordinance in 1973, a project primarily
aimed at water pollution control. In the subsequent years, various events took place
(Hossain 2009). In 1977, the Environment Pollution Control Board (EPCB) with
16 members ruled by a member of the planning commission and environment
pollution control cell was formed and renamed as Department of Environment in
A.K. Azad (*) • M.G. Rasul • S.F. Ahmed
Central Queensland University, School of Engineering and Technology,
Rockhampton, QLD 4702, Australia
e-mail: azad.cqu@gmail.com; a.k.azad@cqu.edu.au
R. Islam
Department of Mechanical Engineering, Bangladesh University of Engineering
and Technology, Dhaka 1000, Bangladesh
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_16
219
1985. The department discharges its responsibilities through a head ofﬁce and six
divisional ofﬁces located in Dhaka, Chittagong, Khulna, Rajshahi (Bogra) and Sylhet
in Bangladesh (DoE 2007, Hossain 2009, DoE 2008). The following policy, acts and
rules facilitate the activities of DoE (Department of Environment) in Bangladesh:
Environment Policy, 1992; Environment Conservation Act, 1995, and subsequent
amendments; Environment Conservation Rules, 1997, with amendments; Environ-
ment Court Act, 2000, and subsequent amendments; Ozone Depleting Substances
Control Rules, 2004; and Noise Control Rules in 2006 (Hossain 2009).
The environment can be polluted in many ways. One of the major sources of
environment pollution is industrial waste water which can be deﬁned as any
physical, biological or chemical change in water quality that adversely affects
living organisms or makes water unsuitable for desired uses. The polluted water
has some signs like bad taste, offensive odour, oil and grease ﬂoating on water
surface, etc. (Abdel-Gawad and Abdel-Shafy 2002; El-Gohary et al. 1987; Ramalho
1977). The waste water sources can be categorized into, namely, point source and
non-point source. The factories, power plant, sewage treatment plants, underground
coal mines, gas processing plants and oil well are classiﬁed as point sources.
Non-point sources include runoff from farm ﬁelds, feedlots, lawns and gardens,
construction sites, logging areas, roads, streets, parking lots, etc. (Correia et al.
1994). Waste water treatment is very important to save the environment. This
pollution can be managed by applying some treatment techniques such as efﬂuent
treatment plant (ETP). ETP is the most commonly and widely used technique for
industrial waste water treatment. The treatment techniques mainly depend on the
quality of untreated waste water.
This study summarizes the energy and environmental management practices in
natural gas process industries in Bangladesh. Management and treatment system of
the hazardous pollutant like waste water which is more intensive for environmental
pollution is developed in this study. The rational use of energy and its management
system are also reported, and further recommendation is made for energy-efﬁcient
process industries in Bangladesh.
2
Environmental Management
2.1
Pollution Abatement Technique
Waste water treatment system can be categorized as preliminary treatment, primary
or physical treatment, secondary or biological treatment and tertiary or advanced
treatment (Fakhru’l-Razi et al. 2009; Brindle and Stephenson 1996; Knoblock et al.
1994; Kirk et al. 2002; Cummings 1991; Van Loosdrecht et al. 1998; Glaze et al.
1987; Kuba et al. 1997). The primary treatment involves screening; grit removal
and settling give about 30–35% reduction in biological oxygen demand
(Fakhru’l-Razi et al. 2009; Brindle and Stephenson 1996; Parinos et al. 2007;
220
A.K. Azad et al.
Carballa et al. 2005). It can be performed by (a) sedimentation tank, (b) septic tank,
(c) Imhoff tank and (d) dissolved air ﬂoatation (DAF). Secondary treatment gener-
ally consists of biological aeration steps in which the dissolved organic matter is
converted into an able settled form and removed as sludge by settling in a secondary
settling tank. This sludge having been previously aerated is referred to as activated
sludge, a part of it is recycled back to the aeration tank and the remaining part is
withdrawn from the system as excess sludge (Brindle and Stephenson 1996;
Kornaros and Lyberatos 2006; Lin et al. 2001). The excess sludge and primary
settled sludge are mixed, thickened and sent to a sludge digester for further
stabilization followed by de-watering. Sometimes primary and secondary treatment
can be accomplished together. Treatment in lagoons and ponds is the best example
of this type of treatment (Svenson et al. 1992; Gurbuz et al. 2009; Legrini et al.
1993; Henze 2002; Scott and Ollis 1995; Fuhs and Chen 1975). The tertiary
treatment is also called as advanced treatment. If more treatment is needed to
achieve the standard of the efﬂuent water, then advanced treatment is required.
The actual steps needed for this treatment depend on the purpose for which the
efﬂuent is to be used for. Tertiary treatment consists of air stripping, the step which
removes ammonia, nitrogen or other gases (Kornaros and Lyberatos 2006;
Knoblock et al. 1994; Owen et al. 1995; Oliveros et al. 1997), nitriﬁcation process
(Focrrr and Chang 1975), denitriﬁcation process (Focrrr and Chang 1975), chlori-
nation process (Takht Ravanchi et al. 2009), dechlorination process (Focrrr and
Chang 1975), chemical precipitation (Dean et al. 1972), reverse osmosis (Wang
et al. 2005) and ion exchange process (Namasivayam and Ranganathan 1995). A
typical ﬂow diagram for waste water treatment plant is shown in Fig. 1. This
abatement technique can be used in petroleum process industries for their waste
water treatment.
2.2
Oil and Water Separation Technique Used in Petroleum
Industries
Bangladesh is blessed with natural gas and black coal. Raw natural gas contains
about 0.5–2.0% water (Mondal et al. 2013b). This raw gas is processed in gas
processing plant. In this plant, one of the main challenges is to remove water and
higher hydrocarbons from the raw gas. The water which comes out with the raw gas
is called produced or underground waste water (Barbosa et al. 2007; Lettinga 1995).
At the initial stage, a two- or three-phase knockout separator can separate the
produced water from the gas but has some oil component mixed with it (Mondal
et al. 2013a). API separator is one of the most important devices to remove oil from
the waste water (Dobson and Burgess 2007). It is mainly a couple chambered vessel
containing trash trap (including rods), oil retention bafﬂes, ﬂow distributers (verti-
cal rods), oil layer, slotted pipe skimmer, adjustable overﬂow wire and sludge
sump, chain and ﬂight scraper. The waste water samples were tested for the
A Study on Energy and Environmental Management Techniques Used in Petroleum. . .
221
following
14
parameters:
dissolved
oxygen,
biochemical
oxygen
demand
(at 20 C), chemical oxygen demand, chloride, ammonia, ammonium, nitrate,
chromium, cadmium, lead, total suspended solid, total dissolved solid, phosphate
and sulphate (Fuhs and Chen 1975; Kornaros and Lyberatos 2006; Lund and Lund
1971). Table 1 shows some standard parameters for industrial efﬂuent and their
discharge limit in three discharge points.
2.3
Study on Industrial Efﬂuents
Produced water or waste water is the largest waste stream generated in oil and gas
industries. It is a mixture of different organic and inorganic compounds, minerals
and hydrocarbons. Due to the increasing volume of waste water all over the world
in the current decade, the outcome and effect of discharging produced water on the
environment has lately become a signiﬁcant issue of environmental concern. The
study was made on waste water management and treatment for three natural gas
processing industries in Bangladesh. Due to conﬁdentiality, the names of the
industries have been removed and indicated as Industry A, B and C, respectively.
For the study, the efﬂuent compositions were tested which are presented in Table 2.
From the Table, it seems that the waste water contains a signiﬁcant amount of oil
and grease in petroleum industries. Figure 2 shows the designated process ﬂow
diagram for efﬂuent treatment in petrochemical industries. Produced water is
conventionally treated through different physical, chemical and biological methods.
However, current technologies cannot remove small suspended oil particles and
dissolved elements. Besides, many chemical treatments require high initial running
cost. In onshore facilities, biological pretreatment of oily waste water can be a cost-
effective and environment-friendly method. Table 3 shows the quality of efﬂuent
after treatment.
Fig. 1 Typical ﬂow diagram for waste water treatment plant
222
A.K. Azad et al.
3
Energy Management
Rationalization of an industrial operation is quite complex for an existing industrial
system. However, in order to save energy, the following time frame measures can
be implemented depending on the size of the investment and their cost-
effectiveness as short-term measure, medium-term measure and long-term mea-
sure. In the case study, processing plants used short-term and medium-term mea-
sures to efﬁciently run process using existing process facilities. These three terms
are brieﬂy discussed below.
Table 1 Standard parameters for industrial efﬂuent
Parameters
Unit
Discharge to
Inland surface
water
Secondary treatment
plant
Irrigable
land
BOD at 20 C
mg/L
50
250
100
COD
mg/L
200
400
400
Dissolved oxygen,
DO
mg/L
4.5–8
4.5–8
4.5–8
Electric conductivity
μohm/
cm
1200
1200
1200
Total dissolved solid
mg/L
2100
2100
2100
Oil and grease
mg/L
10
20
10
pH
mg/L
6–9
6–9
6–9
Suspended solid
mg/L
150
500
200
Table 2 Waste water composition in studied industries
Items
Unit
Name of the industry
A
B
C
pH
–
6.5–8.0
8.5–10.0
8.73–11.5
BOD
mg/L
37
26
19
COD
mg/L
400
424
378
Electric conductivity
μohm/cm
–
3.74
2.98
Oil and grease
mg/L
20
263
428
Suspended solid (SS)
mg/L
180
215
231
NH3 (as N)
mg/L
150
–
130
Total Kjeldahl nitrogen
mg/L
200
–
–
Nitrate (NO3-N)
mg/L
50
38
46
Phosphate (PO4-P)
mg/L
30
45
63
Cyanide, CN
mg/L
–
0.1
0.3
Colour
Pt-Co unit
298
303
305
Turbidity
NTU
5.6
6.3
8.6
A Study on Energy and Environmental Management Techniques Used in Petroleum. . .
223
3.1
Short-Term Measures
In the short-term measure, schedule maintenance for energy conservation is needed.
Under this term, no new investment is required except for increased maintenance
task. It only leads to increase labour cost for maintenance. The main objective is to
improve the energy efﬁciency of the equipment by enforcing better schedule for the
maintenance programme. Using exhaust gas analyser for improving combustion
efﬁciency in the furnace and power generator can save more than 5% fuel con-
sumption. The associated work is the better adjustment of the inlet air quality to
improve combustion efﬁciency. Regular cleaning and reduction of pressure drop
Fig. 2 Designated process ﬂow diagram for efﬂuent treatment plant in petroleum industries
Table 3 Efﬂuent quality
after treatment
Items
Unit
Name of the industry
A
B
C
pH
–
6.5–8.0
7.5
9.2
DO
mg/L
–
5.2
2.5
BOD
mg/L
30
14
11
COD
mg/L
192
101
137
Electric conductivity
μohm/cm
–
1.32
1.75
Oil and grease
mg/L
8.9
9.3
10.2
Suspended solid
mg/L
97.3
82.1
98.7
NH3 (as N)
mg/L
48.2
–
52
Colour
Pt-Co
–
219
136
Turbidity
NTU
3.4
1.8
0.79
224
A.K. Azad et al.
can improve the heat transfer efﬁciency of the heat exchangers. Proper cooling
system can help to increase the lifetime of the equipment and enable it for proper
functioning (Azad et al. 2015b).
3.2
Medium-Term Measures
Small investment is required in medium-term measures on the energy consumption
network. It’s neither change principal of operation nor general engineering aspect.
The objective is to reduce the consumption of high-quality energy and use
low-quality energy; some changes are done on the existing network with additional
investment. For example, installation of heat recovery unit is done to reheat or
preheat the feedstock. It will reduce heat loss and save fuel consumption of the main
heating source. Inefﬁcient equipment can be replaced by more efﬁcient one with the
small investment. Repair of leakages and proper insulation or re-insulation could
save energy as well as money. Waste reduction and waste recovery for reuse can
increase productivity and save energy.
3.3
Long-Term Measures
Long-term measures require large investments on the interconnections of the
processes. This can be a combination of various measures. Petroleum process
industry requires both heat and power for continuing the process run. By-products
reprocessing unit installation with the large investment will increase multi-products
and will make healthy gross proﬁt as well. Installation of power line to supply
excess power to grid or other associated organization will save excess produced
power for process run. Very large investments on the principle of the process are
also included in this term. Though the ﬁnished product is not changed, process itself
is modiﬁed or redesigned. Older technologies can be replaced by new and advanced
technologies which are the major contributing factor for lower speciﬁc energy
consumption, especially in the case of energy-intensive sector. The case study
plants are not using long-term measure for energy management due to very big
investment and sophisticated process equipment required. This term of measure is
not suitable for the present condition. Energy-efﬁcient and upgrading process
principals will get property for any new installation in the future.
3.4
Rational Use of Energy
The process industries are the more energy- and pollution-intensive sector through-
out the world. To understand the energy scenario of petrochemical process
A Study on Energy and Environmental Management Techniques Used in Petroleum. . .
225
industries, energy auditing is needed. The main energy consumption is in the form
of heat and power (electricity). The types of energy used by the industry are
presented in Table 4. For petroleum process industries, about 80–85% energy is
consumed by furnace for heating and 15–19% energy consumed by power gener-
ator for process run. The rest of the 1% energy is used in laboratory for property
testing of the ﬁnished products. So, energy conservation measures should be
implemented for more energy-intensive processes such as heating furnace and
power generation (Azad et al. 2015a).
The rational use of energy considering energy utilization through the most
suitable and economically viable methods can save energy and environment con-
currently. The better energy management proved to be a better form of energy
conservation, saving as much as 10–30% without capital investments (Rasul 1994;
Mondal et al. 2014). Worrell et al. (Worrell et al. 1994) investigated the energy
consumption by industrial processes and suggested that by applying best practice
technology, potential improvement in energy efﬁciency on an average 15  4% for
oil petrochemical industries, 21  2% for ammonia, 25  5% for paper, 13  1%
for cement and 27  3% for steel can be achieved (Worrell et al. 1994). Rasul et al.
reviewed the rational use of energy in process industries like textile, steel and
alumina reﬁning, respectively (Rasul 1994; Rasul et al. 2004; Rasheed et al. 2003).
So, energy conservation is important for long-term economic well-being and
security. Utlu and Hepbasli (Utlu and Hepbasli 2007) reviewed the energy efﬁ-
ciency in Turkish industrial sector and reported 90% efﬁciency increase in energy
use in petroleum reﬁning due to its energy recovery system. The chemical and
petrochemical industries account for 30% of industrial energy use globally (Gielen
and Taylor 2007). A generalized energy distribution of the petroleum process
industries is presented in Fig. 3, and the energy ﬂow diagram of petrochemical
industries is presented in Fig. 4.
Table 4 Types of energy used in case studied petroleum process industries
Process
Type of fuel
Raw materials pumping and storing
Electricity
Feedstock preheating
Heat energy
Heating/furnace running
Natural gas
Gas generator used for power generation for process run
Natural gas
Product pumping
Electricity
Product cooling
Electricity
Product testing in laboratory
Natural gas and electricity
Others (AC for equipment, fan, lighting, etc.)
Electricity
Ofﬁce, administration and security light
Electricity
226
A.K. Azad et al.
4
Conclusions and Recommendations
The study reviewed the energy and environmental management for petroleum
process industries in Bangladesh. Pollution abatement techniques, main pollutants
and the problems associated with waste water treatment are identiﬁed in this study.
Oil-water separator used in petroleum process industries and the standard of
industrial efﬂuents are also outlined. It has been found that high salinity and oil
and grease contents of the inﬂuent characteristics have direct inﬂuence on the
turbidity of the efﬂuent of petroleum waste water. The efﬂuent characteristics
before and after treatment and the process ﬂow diagram have been analysed. The
rational use of energy with energy ﬂow diagram has been developed and brieﬂy
Fig. 3 Energy distribution of petroleum industries
Fig. 4 Energy ﬂow diagram for the petroleum industries with liquid and gaseous efﬂuent handling
unit
A Study on Energy and Environmental Management Techniques Used in Petroleum. . .
227
discussed. The time frame energy management process is also presented in this
study for petroleum industries in order to save energy. The study found from the
literature that it is possible to save about 15% of energy uses in petroleum process
industries by implementing the proper energy management system. The study
recommended the proper selection of treatment technique which can be used to
remove the oil, grease and salinity from the waste water by secondary treatment. It
is also important to eliminate the heavy metals dissolved as divalent metal oxide as
Fe, Mn, Zn, Ni or Cr in produced water. By considering the above-mentioned
parameters, an efﬂuent treatment plant can be designed to maintain environmental
quality standards (EQS) in waste water treatment for environment-friendly efﬂuent.
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A.K. Azad et al.
Generating Temperature Maps of a Solar
Receiver for a Domestic Parabolic
Concentrator for Cooking Purposes Under
Algerian Environment
Fatiha Yettou, Boubekeur Azoui, Ali Malek, Narayan Lal Panwar,
and Amor Gama
1
Introduction
When considering thermal applications of solar energy, solar cooking is the best
option and the solar cooker is the most promising appliance. The use of these
cookers provides many advantages, such as fuel economy, greenhouse gases emis-
sion reduction, ﬁrewood utilization saving, lower cost, and high durability (Hager
and Morawicki 2013). However, in many parts of the world, especially in devel-
oping countries, wood and fossil fuel-based cooking still predominate with the
highest share in the global energy consumption of the residential sector. This
situation poses some serious ecological problems such as deforestation (Toonen
2009); economical and health problems are also among the consequences of
ﬁrewood use.
Algeria lies in the sunny belt of the world (Fig. 1). The insulation time over the
quasi-totality of the national territory exceeds 2000 h annually and can reach 3900 h
in the high plains and Sahara. The daily solar energy obtained on a horizontal
F. Yettou (*) • A. Gama
Unite
´ de Recherche Applique
´e en Energies Renouvelables, URAER, Centre de
De
´veloppement des Energies Renouvelables, CDER, 47133 Ghardaı
¨a, Algeria
e-mail: yettou.t@gmail.com
B. Azoui
Laboratoire de Recherche LEB, De
´partement d’Electrotechnique, Universite
´ Hadj Lakhdar,
Boukhlouf Med ElHadi, Batna, Alge
´ria
A. Malek
Centre de De
´veloppement des Energies Renouvelables, CDER, 16340 Algiers, Algeria
N.L. Panwar
Department of Renewable Energy Engineering, Maharana Pratap University of Agriculture
and Technology, Udaipur, Rajasthan 313001, India
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_17
231
surface is 5 kWh/m2 over the major part of the national territory, or about
1700 kWh/m2/year for the North and 2263 kWh/m2/year for the South of the
country (Boudghene Stambouli 2011). These are very favorable climatic conditions
for all solar energy applications, especially for residential cooking, considering that
the global Algerian demand for cooking energy is expected to increase greatly with
the increasing population over the coming years and that actual demand is currently
met through the use of natural gas in cities and through forest wood in rural and
isolated areas.
The amount of power produced by solar cookers depends on the amount of
sunlight to which it is exposed. As the sun’s position changes throughout the day,
the solar cookers must be adjusted several times during cooking. For this purpose,
booster mirrors are usually added to box-type solar cookers (BSCs), and single- or
two-axis tracking systems are used by parabolic solar cookers (PSCs). For both
types of cookers, the tracking is difﬁcult, especially when the cooker is loaded (case
of BSCs) and when the manual device is used to rotate the assembly (case of PSCs).
The performance of solar cookers can be optimized when the cookers are oriented
in such a way that the incident sun lights fall onto solar cookers with an incident
angle equal to zero and therefore the total losses in the absorber/focal point are the
least, so as to reduce the high accuracy requirement for tracking and to overcome
the need of standing in the sun, which are the main drawbacks of most solar cookers
(Yettou et al. 2014).
In our recent work (Yettou et al. 2014), the absorber temperature maps of a
box-type solar cooker with inclined aperture area were established. In this work, the
authors attempted to evaluate the thermal efﬁciency of a paraboloid concentrator
solar cooker in Algerian climatic conditions using a new approach based on optical
simulation of concentrated solar radiation. The estimated temperature maps of the
Fig. 1 Priority areas of the world for the development of solar cooking
232
F. Yettou et al.
concentrator receiver were generated by this study for all Algerian cities and for
several cases. In order to validate the results of simulation experimentally, the
parabolic solar cooker was designed and realized by the authors at Applied
Research Unit on Renewable Energies of Ghardaı
¨a (Algeria) for domestic cooking
applications.
2
Solar Cookers: Deﬁnition and Types
Solar cookers absorb solar energy and convert it into heat, which is utilized to cook
food. Solar cookers also enable some signiﬁcant processes, such as pasteurization
and sterilization (Cuce and Cuce 2013). Several types of solar cookers have been
developed and are still being modiﬁed by researchers and scientists worldwide. The
available solar cookers can be classiﬁed according to different manners, the main
categories are: box type, concentrating type, and indirect type. The most recent
classiﬁcation was proposed by Yettou et al. in their review (Yettou et al. 2014).
2.1
Box Solar Cookers
A solar box cooker (SBC) consists of an insulated box with a transparent glass
cover and a plate absorber painted black in order to absorb a maximum amount of
sunlight (Cuce and Cuce 2013; Saxena et al. 2011). The box is usually equipped
with a mirror booster to reﬂect solar radiation into the box. A description of solar
box cooker is shown in Fig. 2. A maximum of four cooking vessels can be placed
inside the box cooker (Khan 2008; Kothari et al. 2008). Using the box type, a
temperature around 100 C is achieved; this temperature is suitable for cooking by
boiling (Lahkar and Samdarshi 2010). Box-type solar cookers are slow to heat up
but work satisfactorily under conditions in which there is diffuse radiation, con-
vection heat loss caused by wind, intermittent cloud cover, and low ambient
temperature (Funk and Larson 1998; Panwar et al. 2012).
Fig. 2 Components of a
box solar cooker
Generating Temperature Maps of a Solar Receiver for a Domestic Parabolic. . .
233
Many scientists and manufacturers over the world are interested in box solar
cookers (Mirdha and Dhariwal 2008; Anderson et al. 2009; Nahar et al. 1994;
Mullick et al. 1997; Srinivasan Rao 2007). In recent years, researchers highly
focused on developing novel designs of solar cookers. In 2012, Mahavar et al.
(2012) presented the design development and thermal and cooking performance
studies of a novel Single Family Solar Cooker (SFSC) in early 2013, and they
fabricated a Solar Rice Cooker (SRC) (Mahavar et al. 2013). Kumar et al. (2008)
fabricated and tested a multipurpose domestic solar cooker-cum-dryer based on
truncated pyramid geometry, at the Sardar Patel Renewable Energy Research
Institute of India. They also designed and constructed a truncated pyramid
geometry-based multipurpose solar device, which could be used for domestic
cooking as well as for water heating (Kumar et al. 2010). In early 2013, Farooqui
presented an innovative work (Farooqui 2013), which consists of a novel mecha-
nism for one-dimensional tracking of box-type solar cookers.
2.2
Concentrating Solar Cookers
Concentrating-type cookers utilize multifaceted mirrors, Fresnel lenses, or para-
bolic concentrators to attain higher temperatures (up to 200 C) (Lahkar and
Samdarshi 2010). The most popular is the parabolic solar cooker (Fig. 3), which
consists of a parabolic reﬂector supported by a stand with a cooking pot placed at
the focal point of the cooker. Concentrating-type cookers are suitable for frying and
food cooking but need frequent adjustment to track the sun’s position. Therefore,
these cookers are usually equipped with sun-following devices. The most recent
work done in this ﬁeld is the sun tracking system with absorber displacement of
(Gama et al. 2013).
Concentrating-type cookers have attracted more attention, and several concepts
are being brought into reality around the world (Arenas 2007; Sharaf 2002; Sonune
and Philip 2003). Recently, more advanced concentrating-type designs have been
developed, such as the parabolic solar cooker constructed by Al-Soud et al. (2010),
the spherical-type solar cooker with automatic two-axis sun tracking system real-
ized by Abu-Malouh et al. (2011), and the solar cooking stove, which uses a Fresnel
lens for concentration of sunlight, designed and tested in 2011 by (Valmiki et al.
2011). A solar coffee maker was also realized and operated by Sosa-Montemayor
et al. (2009), a solar fryer was designed and developed by Gallagher (2011), a solar
cooker and a water heater were designed and built in 2010 by Badran et al. (2010),
and mostly recently, a new portable solar cooker with PCM-based heat storage was
created by Lecuona et al. (2013).
234
F. Yettou et al.
2.3
Indirect Solar Cookers
The indirect-type solar cookers use a heat-transfer ﬂuid to carry thermal energy
from the point of collection to the cooking vessel(s) (Lahkar and Samdarshi 2010).
This mode of energy collection is useful for indoor cooking applications. One of
such types is the cooker realized by Esen (2004), which uses a vacuum-tube
collector with heat pipes containing different refrigerants, and the cooker
employing ﬂat-plate collectors with the possibility of indoor cooking experimented
by Hussein et al. (2008).
3
Theoretical Study
3.1
Solar Radiation Model
The position of the sun with respect to a horizontal surface is given by the two
coordinates: solar altitude γs and solar azimuth χ, and is calculated as follows
(Yettou et al. 2009; Hoﬁerka and Su
´ri 2002):
Sin γs
ð Þ ¼ Cos ϕ
ð Þ  Cos δ
ð Þ  Cos ω
ð Þ þ Sin ϕ
ð Þ  Sin δ
ð Þ
ð1Þ
Cos χ
ð Þ ¼ Cos δ
ð Þ  Cos ω
ð Þ  Sin ϕ
ð Þ  Sin δ
ð Þ  Cos ϕ
ð Þ
½

Cos γs
ð Þ
ð2Þ
where δ is the sun declination, ω is the hour angle, and φ is the geographical latitude
of the location.
The clear-sky normal beam irradiance IN [W/m2] has been calculated using the
module r.sun of the GRASS GIS platform (S
uri and Hoﬁerka 2004; GRASS
Development Team 2009). It computes the solar radiation using the model of the
European Solar Radiation Atlas (Rigollier et al. 2000):
Fig. 3 Components of a
parabolic solar cooker
Generating Temperature Maps of a Solar Receiver for a Domestic Parabolic. . .
235
IN ¼ G0  exp 0:8662 TLK mA δR mA
ð
Þ
f
g
ð3Þ
in which G0 is the extraterrestrial solar radiation, δR is the spectrally integrated
optical thickness of the clean dry atmosphere, and mA is the relative optical air
mass. The term 0.8662 TLK is the air mass 2 Linke atmospheric turbidity factor
corrected by Kasten (1996). The web-based solar radiation resource SoDa (SoDa
Service e Knowledge in Solar Radiation, www.soda-is.com/) can be used to
calculate monthly values of TL2 for any location in the world by entering geograph-
ical coordinates and elevation data (Gama et al. 2010).
For an assessment of normal beam irradiances for overcast conditions, a sun-
shine fraction factor σo is used. A sunshine fraction data for Algerian cities is
available in Capderou’s books (1987).
3.2
Description of the Parabolic Solar Cooker
The realized parabolic solar cooker (SPC) consists of a parabolic reﬂector
supported by a stand with a cooking pot placed at the focal point of the cooker.
The shape of the cooker is paraboloidal type (Fig. 4) having 0.9 m aperture
diameter. This solar cooker has a steel structure and uses small mirror facets as the
reﬂector. The reﬂective area of the solar cooker is 0.72 m2. The focal length of the
cooker is 0.5 m while the focal area of the cooker is 0.10 m2. The concentration
ratio of this cooker is calculated about 24. The reﬂectivity of the mirror facets is
0.80. The aluminum cooking pot (20 cm in diameter and 10 cm in height) ﬁlled with
water and equipped with a black cover was placed at the focus area of the cooker.
Parabolic reﬂector was assigned a reﬂectivity of 100%, and its receiver is consid-
ered as a perfect absorber. The solar tracking in this cooker is done manually.
Fig. 4 Schematic of a
parabolic solar cooker with
tracking system
236
F. Yettou et al.
3.3
Mathematical Equations
According to Dufﬁe and Beckman (1991), the optimal PSC positions can be deﬁned
by two angles: λ (surface slope) and χ (the surface azimuth angle). For two-axes
tracking, the cooker positions are determined as follows:
λ ¼ θz
ð4Þ
χ ¼ θa
ð5Þ
where θz is the zenith angle of the sun and θa is the solar azimuth angle.
It is a great challenge to maintain the position of the SPC focused image on the
focal point at all times; it results in a lower concentration factor and thus needs more
frequent adjustments of the concentrator. Therefore, it is necessary to move the
cooking pot by a distance f from the focal point (Fig. 4) to increase the focused area
and reduce rays path corrections and then to improve the concentration factor.
The mathematical formulas for calculating the distance f are presented below:
tan ψ ¼ dv=2
f
¼ D=2
F  d
ð6Þ
The parametric equation of the parabola is as follows:
d ¼ 1
4F  D2
2
ð7Þ
By inserting Eq. (7) into Eq. (6) and solving the equation, the new focal point
distance from the parabolic vertex is known by calculating the value of f as follows:
f ¼ F  d
ð
Þ  dv
D
ð8Þ
3.4
Adjustment Tracking Time
It is important to choose the correct tilt angles (γ,χ) of the parabolic cooker, a minor
error will result in a reduced number of incident rays on the receiver. Figure 5 is a
simpliﬁed graphic representation showing the incident rays with a reduced number
of rays to improve readability. The ﬁgure demonstrates a simulation of the ray path
of the parabolic solar cooker for all incident rays and reduced number of incident
rays due to a non-normal incidence.
To deduce the optimum adjustment time, the receiver losses were evaluated for
parabolic solar cookers every 5 min. As a starting time, we chose to simulate the
Generating Temperature Maps of a Solar Receiver for a Domestic Parabolic. . .
237
solar cooker for 1 day at winter solstice from 12:00 (angle of solar incidence equal
to 0) to 12:45 and reported the results every 5 min. The solar ﬂux distribution graphs
are plotted in terms of solar concentrated irradiance (in W/m2).
The parabolic reﬂector focuses the incident radiation to a point, and the proﬁle of
the concentrated spot for the PSC at noon on 23 December is shown in Fig. 6.
Figure7 reveals the simulated ﬂux distribution results of focused image on the focal
point on 23 December at 12:30.
The concentrated irradiance depends on the losses in the receiver of the solar
cooker. The losses in the receiver change with the sun’s position. Therefore, the
concentrated irradiance changes with the sun’s position. Facing south, the receiver
achieved highest concentrations at noon. However, it offered poor efﬁciency after
30 min of operation without adjusting as seen in Fig. 7. Therefore, parabolic solar
cookers need adjustments so as to always face the sun, and the titled angles must be
corrected from the initial values.
In our recent paper (Yettou et al. 2013), we explained a new method based on
optical losses to determine the time adjustment for PSC. According to the results, a
time adjustment of 8 min is required for the parabolic solar cooker.
4
Methodology
In order to draw different cooker maps, several steps are necessary (Fig. 8):
•
Modeling normal beam solar irradiances for clear and cloudy skies based on sun
position parameters, data for Linke turbidity and sunshine fraction factors using
Matlab language (Matlab/Simulink Tutorial (2010)).
Fig. 5 Simulation showing the ray path for parabolic solar cooker: (a) when the collector is
oriented directly to sun; (b) when the collector is tilted at an incorrect angle
238
F. Yettou et al.
•
By importing the conception design of parabolic solar cooker from Solid Works
software (SolidWorks Corporation), a simulation of concentrated solar irradi-
ance on a concentrator receiver was done for 48 cities in Algeria by inserting
modeling results as inputs into TracePro software (Lambda Research Corpora-
tion USA (2010)).
•
Solar cookers are a direct application of the laws of heat transfer by radiation
(Stephan-Boltzmann law), which states that the ﬂux density emitted or received
by a body is proportional to the fourth power of its temperature. Thus, the next
step is the conversion of the obtained optical results for concentrated irradiance
to thermal values using Stephan-Boltzmann law as follows (Chong et al. 2011):
CmaxR1R2IS ¼ σsT4
ð9Þ
where Cmax is the concentration ratio, it is equal to the concentrated radiation/
incident radiation, R1R2 ¼ R is the reﬂectivity of the glasses, I is the incident solar
radiation in W/m2, σs is the Stefan-Boltzmann constant (5.67  108 W/m2 K4), T4
is the temperature in K.
39900
37800
35700
33600
31500
29400
27300
25200
23100
21000
18900
16800
Y (millimeters)
X (millimeters)
14700
12800
10500
8400
6300
4200
2100
100
-100
-80
-60
-40
-20
0
20
40
60
80
100
W/m2
80
60
40
20
20
40
60
80
100
-100
-80
-60
-40
-20
0
20
40
60
80
100
0
0
-20
-20
-40
-40
-60
-60
-80
-80
-100
-100
0
Fig. 6 The proﬁle of the concentrated spot for the parabolic solar cooker on 23 December at 12:00
Generating Temperature Maps of a Solar Receiver for a Domestic Parabolic. . .
239
•
The above steps are applied to 62 points (including the 48 cities) for several
geographical locations of Algeria to create a compatible matrix format for Surfer
Golden Software (Surfer User’s guide). The Golden Software Surfer Inc. soft-
ware is a universal tool path contours, surfaces, and 3D cartographic represen-
tations. It also allows to interpolate between two adjacent points with high
accuracy. Reading the matrix ﬁle XYZ by the Surfer software offers the possi-
bility to project the obtained results on illustrative and analyzable maps. So, maps
of solar irradiance, concentrated solar radiation, and temperatures are obtained.
234000
221000
208000
195000
182000
169000
156000
143000
130000
117000
104000
91000
Y (millimeters)
X (millimeters)
78000
65000
52000
39000
26000
13000
100
-100
-80
-60
-40
-20
0
20
40
60
80
100
W/m2
80
60
40
20
20
40
60
80
100
-100
-80
-60
-40
-20
0
20
40
60
80
100
0
0
-20
-20
-40
-40
-60
-60
-80
-80
-100
-100
0
Fig. 7 The simulated ﬂux distribution results of the parabolic concentrator on the target on
23 December at 12:30
Fig. 8 Steps to obtain parabolic solar cooker maps based on optical approach
240
F. Yettou et al.
5
Optical Simulation, Experimental Work, and Validation
For each city (Lat, Long, Alt) in Algeria, an optical simulation of concentrated solar
irradiance on a concentrator receiver was done. Figures 9a–d represent the results of
simulation, by TracePro software, of concentrated irradiance on the absorber area
for clear and cloudy days in December and June months, respectively.
By applying the Stefan-Boltzmann formula for thermal conversion of optical
values, the following results were obtained for pot water temperatures in December
month at Ghardaı
¨a city: 92.6 C for clear sky and 84.3 C for cloudy sky. By
comparing the theoretical results with experimental data measured at Ghardaı
¨a
Fig. 9 Results of the simulation for the concentrated irradiance on the focal area of the PSC in
Ghardaı
¨a sites. (a) PSC in December, clear day, (b) PSC in December, cloudy day (c) PSC in June,
clear day (d) PSC in June, cloudy day
Generating Temperature Maps of a Solar Receiver for a Domestic Parabolic. . .
241
(Fig. 10), it was found that the values are in good agreement with an acceptable
average error of 3 C. Under direct sun exposition, the cooker water temperatures
achieved 95 C for clear sky and 82 C for overcast sky conditions, just afternoon,
the ambient temperatures were 19 C and 17 C, respectively.
6
Mapping Results
To generate various maps (direct normal radiation, concentrated radiation, temper-
ature receivers), we applied the approach detailed above, namely mathematical
modeling, optical simulation, and data conversion, for the entire Algerian territory.
For this, and in order to cover most of the country’s area, we chose 62 points
including 48 cities with different geographic coordinates (Lat, Lon, Alt) original of
Google Earth Service.
6.1
Solar Radiation and Concentrated Radiation Maps
Figures 11 and 12 show the map of mean values of Linke turbidity factor used for
calculating direct normal irradiance on the parabolic receiver, for clear sky in
winter and summer seasons, respectively. Mapping in Figs. 13 and 14 represents
160
140
120
100
80
60
40
20
0
9:00
10:00
11:00
12:00
13:00
14:00
Ta
TW
Is
Tw (exp) = 95 °C
15:00
16:00
17:00
0
100
200
300
400
500
600
700
800
900
1000
1100
1200
Temps (h)
Températures(°C)
Eclairement solaire (W/m2)
Fig. 10 Practical results of the temperature proﬁle for the PSC’s focal area with solar radiation,
measured on the Ghardaı
¨a site for one of the tests carried out in December
242
F. Yettou et al.
the instantaneous incident irradiances for December and June months obtained
from our Matlab program. We also present, in Figs. 15 and 16, the maps of sunshine
fraction for all Algerian cities used for calculating the solar irradiance incident on
the cooker receiver in cloudy skies.
Figures 17, 18, 19, and 20 represent the mapping of the obtained results for
concentrated irradiances on the receiver for clear skies in winter and summer
months and for cloudy skies in winter and summer months, respectively.
We can easily notice, from these cards, the important quantity of concentrated
solar radiation incident on the cooker receiver in summer season compared to
winter. This remark is also valuable for cloudy days, especially in the south of
the country. The amount of average concentrated irradiance at the receiver of the
cooker is estimated as 2676 W/m2 for a typical day of June month at noon (Fig. 20)
and as 1393 W/m2 for a typical day of December month (Fig. 19). These quantities
are signiﬁcantly increased for clear skies with 4104 W/m2 average value in summer
(Fig. 18) and 2240 W/m2 in winter (Fig. 17), and this is mainly due to the signiﬁcant
amount of direct normal solar radiation received throughout the Algerian territory
during the year. The average value of direct normal irradiance in June month at
noon is estimated at 877 W/m2 (Fig. 14), and an average value in the month of
December is estimated as 867 W/m2 (Fig. 13).
Fig. 11 Mapping of Linke turbidity factor mean values for December month in Algeria
Generating Temperature Maps of a Solar Receiver for a Domestic Parabolic. . .
243
Fig. 12 Mapping of Linke turbidity factor mean values for June month in Algeria
Fig. 13 Mapping of normal beam solar irradiances for December month in Algeria
Fig. 14 Mapping of normal beam solar irradiances for June month in Algeria
Fig. 15 Mapping of sunshine fraction mean values for December month in Algeria
6.2
Cooker Receiver Temperature Maps
Figures 21 and 22 show the mapping of temperatures attained by the cooker
receiver obtained for winter and summer months under clear sky in Algeria,
respectively. The receiver temperatures for winter and summer months under
overcast conditions are also presented in Figs. 23 and 24.
According to the iso-temperature map distributions, it is clear that the solar
cooker can be used in all Algerian territories in clear sky summer season (Fig. 22)
with temperatures exceeding 110 C. For the winter season, the use duration of the
cooker is reduced from the southern to the northern cities (Fig. 21) depending on the
amount of solar radiations. The recorded temperatures are between 62.7 and
68.4 C for sites with latitude greater than 36 N and between 70.2 and 86.2 C
for sites with latitude 34 < φ < 36 N, the estimated temperatures are above 93 C
for South of the country.
The use of the solar cooker under overcast conditions became inefﬁcient in
North and height plains regions (Fig. 23) due to low temperatures (below 80 C).
However, the cooker is exploitable in most of the areas of the country during
summer season almost under cloudy sky; temperatures are estimated between
81.3 and 90.7 C in the north and between 94.6 and 167.7 C in the south (Fig. 24).
Fig. 16 Mapping of sunshine fraction mean values for June month in Algeria
246
F. Yettou et al.
7
Conclusion
A new approach was employed to generate temperature maps of a solar receiver for
a domestic parabolic concentrator used for cooking purposes. A model was devel-
oped to calculate solar irradiance for 48 cities in Algeria; an optical simulation of
concentrated solar radiation was applied to each location. The simulation results are
converted to temperature values based on Stefan-Boltzmann law. The hourly
temperature maps produced can predict the cooker efﬁciency under Algerian
climatic conditions for clear and cloudy skies. Nevertheless, solar cooking remains
a reality that allows healthy cooking of food with energy savings and respect for the
environment.
Fig. 17 Obtained map for concentrated solar irradiance on PSC’s receiver for typical winter
season clear days
Generating Temperature Maps of a Solar Receiver for a Domestic Parabolic. . .
247
Fig. 18 Obtained map for concentrated solar irradiance on PSC’s receiver for typical summer
season clear days
248
F. Yettou et al.
Fig. 19 Obtained map for concentrated solar irradiance on PSC’s receiver for typical winter
season cloud days
Generating Temperature Maps of a Solar Receiver for a Domestic Parabolic. . .
249
Fig. 20 Obtained map for concentrated solar irradiance on PSC’s receiver for typical summer
season cloud days
250
F. Yettou et al.
Fig. 21 Receiver temperature maps of parabolic solar cooker for Algerian clear sky as obtained
by the proposed approach for typical winter season
Generating Temperature Maps of a Solar Receiver for a Domestic Parabolic. . .
251
Fig. 22 Receiver temperature maps of parabolic solar cooker for Algerian clear sky for typical
summer season
252
F. Yettou et al.
Fig. 23 Receiver temperature maps of parabolic solar cooker for Algerian cloudy sky as obtained
by the proposed approach for typical winter season
Generating Temperature Maps of a Solar Receiver for a Domestic Parabolic. . .
253
Fig. 24 Receiver temperature maps of parabolic solar cooker for Algerian cloud sky for typical
summer season
254
F. Yettou et al.
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Generating Temperature Maps of a Solar Receiver for a Domestic Parabolic. . .
257
Experimental Investigations on the Effects
of Low Compression Ratio in a Direct
Injection Diesel Engine
M. Vivegananth, K. Ashwin Kanna, and A. Ramesh
1
Introduction
Diesel engines are based on the concept of compression ignition. They rely mainly
on the high temperature achieved during the compression stroke for autoignition of
the injected diesel. So, higher the compression ratio, better is the cold starting
ability. However, high compression ratios lead to many disadvantages like bulky
and heavy engine components, high friction, low rated engine speed, and high NOx
and soot emission levels. On account of these conﬂicting requirements, optimiza-
tion of the compression ratio is one of the major challenges often faced by
designers, Gardner and Henein (1988). In order to achieve maximum beneﬁt at
all engine operating conditions, variable compression ratio operation in diesel
engines has been explored. However, the complexity of this method makes it
impractical for production engines.
At present, considerable research is being carried out in order to achieve very
low NOx and soot emissions. In diesel engines, reducing the compression ratio is
one of the promising ways to meet these stringent demands. While reducing the
compression ratio, the in-cylinder gas temperature decreases, which in turn reduces
the thermal nitric oxide (NO) formation. It also increases the ignition delay, hence
leading to better fuel-air mixing and thus more fuel burns in the premixed phase of
combustion which leads to low smoke. Beatrice et al. (2008) and MacMillan et al.
(2012) showed that soot/NOx trade-off was improved in low compression ratio
diesel engines. On the other hand, due to incomplete combustion, a signiﬁcant
increase in CO, HC emission, and fuel consumption was also observed. The
injection timing can be advanced in a low compression ratio engine, because of
M. Vivegananth (*) • K.A. Kanna • A. Ramesh
Mechanical Engineering, Indian Institute of Technology Madras, Chennai 600036, India
e-mail: vivegananth@gmail.com
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_18
259
its low peak cylinder pressure when compared to the conventional engines. Hence,
the drop in thermal efﬁciency in a low compression ratio engine can be addressed.
Cursente et al. (2008) reported a 12% increase in brake power at 4000 rpm by
changing the compression ratio from 18:1 to 14:1, because of advanced fuel
injection timing in which the combustion center occurred close to TDC.
Another major issue in operating a low compression ratio diesel engine at high
loads is combustion noise. At high loads the drop in cylinder air temperature during
fuel evaporation was observed to be considerable, and it increased the ignition
delay period which led to high combustion rates. Suh (2011) showed that for the
same peak pressure, the heat release rate could be reduced by about 47% with two
pilot injections, when compared to the single injection at a compression ratio of
15.3:1.
Though researchers are trying to lower compression ratios to about 14:1, poor cold
starting ability and warm-up stability are issues that are to be solved. Diesel engine
cold start problems include long cranking time, combustion instabilities, and high
emissions. Figure 1 shows the various parameters that can affect the starting of a
diesel engine. One of the main challenges in cold starting is to understand the various
thermodynamic processes during engine cranking. Liu et al. (2003) used a thermo-
dynamic simulation model to study the key parameters that affect the cranking time
and combustion instability during idling. They found that accumulated fuel in the
combustion chamber during misﬁring cycles has a major impact on engine cold
starting. Further, cranking speed should be at an optimum level for effective cold
starting, because lower speed causes high heat transfer and blowby losses, whereas
time available for evaporation is reduced in case of higher cranking speeds.
Henein et al. (1992) and Han et al. (2001) investigated combustion instabilities
during cold starting and found that misﬁring is not random but is repeatable. The
engine may often skip one or two cycles during starting because the vaporized fuel
quantity is not sufﬁcient, due to the slow evaporation rate and the net energy
produced from combustion in one cycle not being capable of overcoming the
frictional/inertial losses.
Fig. 1 List of critical parameters affecting the engine starting
260
M. Vivegananth et al.
Zahdeh (1990) found that the peak compression temperature was decreased by
254 C, when the ambient temperature was reduced from þ20 to 20 C. So, cold
starting becomes much more challenging in a low compression ratio diesel engine
at very low ambient conditions. Pacaud et al. (2008), improved the cold starting
ability of a low compression ratio diesel engine with the aid of a glow plug and by
the use of multiple pulse fuel injection techniques. They found that the pilot
injection of diesel promoted the cold ﬂame combustion reaction which in turn
reduces the ignition delay. However, the pre-glowing duration (time required for
the glow plug tip to reach 800 C) will increase drastically at low ambient
temperatures and, hence, leads to long cranking time. In order to improve warm-
up stability, additional methods to trap hot exhaust gases were needed. Peng et al.
(2008) found that recirculating fuel-rich exhaust gases back into the cylinder
through the intake manifold reduced ignition delay and improved combustion
stability. Added advantage of recirculating exhaust gases is the reduction of HC
emission (white smoke) during engine warm-up.
In order to implement suitable cold starting strategies, a proper understanding of
the transient behavior of an engine during starting is essential. Thus, this work is
aimed at understanding the advantages in performance and emissions and also the
challenges in cold starting a low compression ratio diesel engine.
Nomenclature
CR
Compression ratio ()
NO
Nitric oxide (ppm)
HC
Hydrocarbon (ppm)
CO
Carbon monoxide (% volume)
BMEP
Brake mean effective pressure (bar)
IMEP
Indicated mean effective pressure (bar)
TDC
Top dead center (TDC)
degCA
Degree crank angle (degree)
P
Cylinder pressure (bar)
V
Displacement volume (m3)
n
Polytropic constant ()
Q
Heat release rate (J/degCA)
SOI
Start of injection
2
Experimental Facility
The schematic arrangement of the experimental setup is shown in Fig. 2. A 0.55 L
single cylinder diesel engine was used for this experimental study. Detailed spec-
iﬁcations of the engine are provided in Table 1. The geometric compression ratio
was modiﬁed from 16.5:1 (as in the production engine) to 15:1 and 14:1 progres-
sively by increasing the volume of the piston bowl and also maintaining its shape
similar to the original as shown in Fig. 3. Hereafter, these compression ratios will be
referred to as CR16.5, CR15, and CR14, respectively. The engine was coupled to an
Experimental Investigations on the Effects of Low Compression Ratio. . .
261
eddy current dynamometer for loading and to maintain its speed. The airﬂow rate to
the engine was measured using a positive displacement-type airﬂow meter (make,
Dresser, model, Roots Series B3). Fuel consumed by the engine was measured
directly on the mass basis. Exhaust gas emissions (HC, CO, and NO) were mea-
sured using a NDIR-based (AVL Di-gas 444) portable analyzer, while an AVL
415S smoke meter was used for smoke measurements. K-type thermocouples were
Fig. 2 Schematic layout for the single cylinder diesel engine experimental setup
Table 1 Engine speciﬁcations
Engine type
Single cylinder, water cooled, diesel engine
Bore  stroke
80  110 mm
Displacement volume
553 CC
Compression ratio
16.5, 15 and 14
Connecting rod length
231 mm
Max. torque and power
23.5 Nm at 1500 rpm
3.7 kW at 1500 rpm
Fuel injection system
Mechanical (NOP ¼ 220 bar)
Fig. 3 Piston bowl proﬁle
for three compression ratio
conﬁguration
262
M. Vivegananth et al.
used to measure the intake air and exhaust gas temperatures. A resistance temper-
ature detector was used to measure the outlet temperature of the coolant.
In-cylinder pressure was measured using a ﬂush-mounted piezoelectric pressure
transducer (make, Kistler, model, 6043A60) along with a charge ampliﬁer. An
optical encoder was used to determine the position of the crankshaft. A high-speed
data acquisition system (NI data acquisition card 6070E) with in-house developed
software was used to record the in-cylinder pressure on the crank angle basis. An
average of 100 consecutive cycles of cylinder pressure data was used for the
calculation of heat release rate. The heat release rate was determined through a
ﬁrst law analysis of the cylinder pressure data as given below:
dQn
dθ ¼
n
n  1 PdV
dθ þ
1
n  1 VdP
dθ
ð1Þ
Engine was maintained in the required ambient temperature through cold air and
coolant conditioning systems. Engine cooling was achieved by circulating chilled
water in to the engine coolant jacket. Cold air was supplied through a refrigeration
system mounted on the engine intake. Intake air temperature was controlled by
adjusting the refrigerant temperature in the evaporator coil of the air conditioner.
The engine was also equipped with a starter motor and a 12 V cranker (rectiﬁer
which converts 220 V AC to 12 V DC) for starting. To maintain constant cranking
condition and to ensure repeatability during starting, the cranker was used instead
of a battery. Another in-house developed data acquisition software was used for
acquiring the instantaneous cylinder pressure and engine speed data of ﬁrst
100 cycles at 1 intervals during engine starting. Starting was considered to be
successful if the engine ﬁred and accelerated to the idle speed. As mentioned
earlier, engine cranking time will vary for different compression ratios and different
ambient temperatures, so it was necessary to control the starter motor in order to
disengage it once the engine operation became stable. A control system was
developed using a microcontroller for this application. This system measures the
engine speed and disengages the starter motor when it crosses the set threshold; at
this condition it was considered that the engine attains stability and has started
accelerating steadily toward the idling speed. The start ability experiments were
performed in the following order: First, the data acquisition was started; then it
triggers the starter motor control system to crank the engine; this control system
disengages the starter motor if the engine crosses the threshold speed of 700 rpm.
3
Results and Discussions
Experiments were initilally conducted at different compression ratios under differ-
ent constant injection timings, while the load (brake mean effective pressure –
BMEP) was varied. Parameters like brake thermal efﬁciency, cylinder pressure,
heat release rate, and emissions were obtained under steady operating conditions.
These are reported and discussed to evaluate the inﬂuence of compression ratio.
Experimental Investigations on the Effects of Low Compression Ratio. . .
263
Subsequently experiments were conducted to evaluate the startability under differ-
ent ambient temperatures that were simulated as explained earlier.
3.1
NO and Smoke Emissions
Figure 4 indicates the variation of NO emissions under different BMEPs at three
compression ratios with a static injection timing of 23bTDC. The nitric oxide
(NO) emission decreased with a reduction in the compression ratio. This was
mainly due to the low peak in-cylinder temperatures reached after combustion
which were inﬂuenced by the lower charge temperatures at the end of compression
stroke with reduced compression ratios. At BMEPs lower than 3 bar, reduction in
NO levels was signiﬁcant with respect to reduction in compression ratios. However,
at higher BMEPs, NO levels were higher in case of CR14 than CR15 due to the
higher rate of combustion. This is explained in detail later with heat release rate
data. Figure 5 shows that smoke levels get reduced with a reduction in the
Fig. 4 Variation of nitric
oxide emissions with load
Fig. 5 Variation of smoke
emissions with load
264
M. Vivegananth et al.
compression ratio. This is due to increase in the premixed phase of diesel combus-
tion, i.e., with a reduction in the compression ratio, the ignition delay is longer and
majority of injected diesel is burnt in the premixed combustion phase. The higher
ignition delay in the case of low compression ratios thus leads to improved fuel-air
mixing. However, the lower charge temperatures at low compression ratios will
also affect fuel vaporization and mixing under these conditions.
3.2
Combustion Characteristics
Brake thermal efﬁciencies at the three compression ratios were almost similar as
shown in Fig. 6. At higher BMEPs (85% and 100% load), a marginal drop in
efﬁciency of about 1% was observed with CR14. Figure 7 shows the variation in
peak cylinder pressure with load for various compression ratios. It was observed
that for same BMEP the peak cylinder pressure in low compression ratio engines is
Fig. 6 Variation of brake
thermal efﬁciency with load
Fig. 7 Variation of peak
cylinder pressure with load
Experimental Investigations on the Effects of Low Compression Ratio. . .
265
reduced. This reduced in-cylinder pressure leaves scope for improving the brake
thermal efﬁciency by advancing the fuel injection timing and also by reducing
engine friction through reduction in the size and weight of other engine
components.
The variation of heat release rate at ﬁxed BMEPs of 5.3 bar and 2.4 bar under a
constant static injection timing of 23bTDC is depicted in Figs. 8 and 9. It is
observed that combustion gets retarded as the compression ratio is reduced. This
is because of the low cylinder pressure and temperature during the compression
stroke. At high BMEPs (>4 bar), the combustion rate is high in CR15 and CR14
because of the high ignition delay caused due to the drop in cylinder temperature
during fuel evaporation. This leads to high local temperatures inside the cylinder
during combustion which in turn results in high NO emissions and high combustion
noise. At low BMEPs (refer to Fig. 9), the start of combustion gets retarded
Fig. 8 Effect of reducing
compression ratio on heat
release rate (HRR) at 5.3 bar
BMEP
Fig. 9 Effect of reducing
compression ratio on heat
release rate at 2.4 bar BMEP
266
M. Vivegananth et al.
(ignition delay is increased) in CR14 and CR15 because of low in-cylinder pressure
and temperature. However, the peak heat release rate is lower at lower compression
ratios because of the low temperatures in the cylinder that affect mixture prepara-
tion during the ignition delay period. This leads to low NO and smoke but affects
effective expansion of the combusted gases.
3.3
Cold Starting
The effect of lowering the ambient temperature on engine startability was evaluated
in subsequent experiments. Startability of the engine at two compression ratios,
namely, CR14 and CR16.5, was evaluated at 10 C, 15 C, 20 C, and 28 C.
Figures 10 and 11 show the instantaneous engine speed and cylinder pressure plots
for the ﬁrst 30 cycles during engine cranking and starting. At the highest intake
temperature of 28 C, there was no difference in startability between the two
compression ratios as seen in Fig. 10. Combustion occurred right from the ﬁrst
cycle which is inferred from the cylinder pressure plot. The engine started accel-
erating right from the ﬁrst cycle and reached the governor-controlled idle speed
after 25 cycles. The initial peak pressures were higher with CR16.5 as compared to
CR14. Cylinder pressure plot shown in Fig. 10 is at the starting condition.During
starting, the fuel injection pump of this engine injects more fuel than at full load.
Hence, this leads to high cylinder pressures (Pmax) during starting.
At the intake temperature of 10 C (Fig. 11), the engine with compression ratio
of 16.5 (CR16.5) ﬁred in all cycles from the beginning, and this shows that the
engine startability did not deteriorate while reducing the ambient temperature from
28 to 10 C. However, in the case of CR14, the engine misﬁred in the ﬁrst 6 cycles,
and the ﬁrst ﬁring occurred only at the seventh cycle because of the fuel accumu-
lated during the previous cycles.
Combustion instabilities were there till the ﬁrst 20 cycles. The engine ﬁred in
every other cycle or every third cycle till it attained stability. After the twentieth
cycle, the engine was stable and accelerated steadily because of the reduced heat
transfer and blowby losses at high engine speeds and slightly warm engine walls.
For CR14 at ambient temperature of 10 C, the engine took 36 cycles to reach the
governor-controlled idle speed of 1500 rpm.
In order to determine whether the misﬁring cycles that occurred in between the
ﬁring cycles are due to irregularities in injection or actual lack of combustion, heat
release rates were obtained for the ﬁred and misﬁred cycles and also for a cycle
where the fuel injection was completely absent. This is seen in Fig. 12. We see that
in the ﬁred cycle, the heat release rate (HRR) shows a sharp positive peak. It also
shows a negative portion after fuel injection as indicated in Fig. 12. This is due to
vaporization of the fuel accumulated before ignition. This negative portion is not
seen in the cycle where fuel injection is not present. In the misﬁring cycle, we see
that the negative HRR portion is present, but the positive HRR portion is absent
indicating that fuel was injected but combustion was not initiated in the misﬁred
Experimental Investigations on the Effects of Low Compression Ratio. . .
267
Fig. 10 Engine startability
trials at 28 C ambient
temperature
268
M. Vivegananth et al.
Fig. 11 Engine startability
trials at 10 C ambient
temperature
Experimental Investigations on the Effects of Low Compression Ratio. . .
269
cycle. Hence, some of the cycles even after a ﬁring cycle misﬁre under cold start
conditions in the case of CR 14. This could be because during cold start the
concentration of fuel vapor is insufﬁcient for ignition. Hence, repeated injection
of fuel in consecutive cycles that misﬁre raises the vapor concentration and aids
ignition in a following cycle. It is seen that the sequence of ﬁring and misﬁring
cycles is not entirely random. Such observations have also been reported in
literature [Henein (1992)]. It may also be noted that the heat release shows only
premixed combustion. This also indicates that only the fuel that is vaporized
participates in combustion during the initial cycles of cold starting.
The severe IMEP ﬂuctuations seen at starting in CR14 at 10 C are indicated in
Fig. 13.
Figure 14 shows how the starting delay (number of cycles to reach idling speed)
of the engine increases with reduced ambient temperatures. It is evident that even
below ambient temperatures of 15 C, reduction in the compression ratio signiﬁ-
cantly affects startability. This will also have a signiﬁcant inﬂuence on emissions
during starting.
Fig. 12 Rate of heat
release plot during cranking
with CR14 at 10 C
Fig. 13 Indicated mean
effective pressure (IMEP)
plot during cranking at
different ambient
temperatures
270
M. Vivegananth et al.
4
Conclusions
Based on the experimental investigations on a direct injection diesel engine with
three different compression ratios (CR14, CR15, and CR16.5), the following
conclusions were made.
(a) Reducing the compression ratio in the diesel engine reduces the nitric oxide
(NO) emissions at all load conditions by reducing the peak in-cylinder temper-
ature. Particularly signiﬁcant reductions are seen at lower loads (BMEP < 3 bar)
where the combustion rate is minimum. At a BMEP of 1.6 bar, the reduction in
NO with CR14 and CR15 was 49% and 30%, respectively, as compared to
CR16.5. Smoke at all load conditions is also reduced at low compression ratios
due to longer ignition delay. At a BMEP of 1.6 bar, the reduction in smoke with
CR14 and CR15 was 62% and 55%, respectively, as compared to CR16.5.
(b) Brake thermal efﬁciency was not signiﬁcantly affected with reduction in com-
pression ratio.
(c) Starting delay of the engine with CR14 was increased at reduced ambient
temperatures, because of misﬁring (combustion failure) during the intial cycles
when the engine is cold.
(d) During cold starting only the fuel that is vaporized participates in combustion
and the remaining gets accumulated in the bowl. Repeated injection of fuel in
consecutive misﬁring cycles raises the fuel vapor concentration and aids com-
bustion in the following cycle.
References
Beatrice, C., Avolio, G., Del Giacomo, N., Guido, C.: Compression Ratio Inﬂuence on the
Performance of an Advanced Single-Cylinder Diesel Engine Operating in Conventional and
Low Temperature Combustion Mode, SAE Technical Paper 2008-01-1678 (2008)
Fig. 14 Start delay for
CR16.5 and CR14 at
different ambient
temperatures
Experimental Investigations on the Effects of Low Compression Ratio. . .
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Cursente, V., Pacaud, P., Gatellier, B.: Reduction of the Compression Ratio on a HSDI Diesel
Engine: Combustion Design Evolution for Compliance the Future Emission Standards, SAE
Technical Paper 2008-01-0839 (2008)
Gardner, T., Henein, N.: Compression Ratio Optimization in a Direct-Injection Diesel Engine: A
Mathematical Model, SAE Technical Paper 880427 (1988)
Henein, N.A., Zahdeh, A.R., and Yassine, M.K., Bryzik, W.: Diesel Engine Cold Starting:
Combustion Instability, SAE Technical Paper 920005 (1992)
Liu, H., Henein, N.A., Bryzik W.: Simulation of Diesel Engines Cold Start, SAE International,
2003-01-0080 (2003)
MacMillan, D.J., Law, T., Shayler, P.J., Pegg, I.: The Inﬂuence of Compression Ratio on Indicated
Emissions and Fuel Economy Responses to Input Variables for a D.I Diesel Engine
CombustionSystem, SAE Technical Paper 2012-01-0697 (2012)
Pacaud, P., Perrin H., Laget O.: Cold Start on Diesel Engine: Is Low Compression Ratio
Compatible with Cold Start Requirements? SAE Technical Paper 2008-01-1310 (2008)
Peng, H., Cui, Y., Shi, L., Deng, K.: Improve Combustion during Cold-Start of DI Diesel Engine
by EGR under normal ambient temperature, SAE 2008-01-1084 (2008)
Suh, H.K.: Investigations of multiple injection strategies for the improvement of combustion and
exhaust emissions characteristics in a low compressionratio (CR) engine. Appl. Energy. 88,
5013–5019 (2011)
Zahdeh, A., Henein, N., Bryzik, W.: Diesel Cold Starting: Actual Cycle Analysis Under Border-
Line Conditions, SAE Technical Paper 900441 (1990)
Zhiping Han, Naeim Henein, Bogdan Nitu and Walter Bryzik.: Diesel engine cold start combus-
tion instability and control strategy. SAE 2001-01-1237 (2001)
272
M. Vivegananth et al.
Control of Cement Slurry Formulation
for an Oil Well in a Critical Geological Layer
Soumia Bechar and Djamal Zerrouki
1
Introduction
Cementing is an essential operation during construction of an oil or gas well
(Choolaei et al. 2012). The quality of cement in a casing plays a vital role during
drilling and has a serious impact on secondary cementing, stimulation operation,
and protection of casing against corrosion (Backe et al. 1997; Ershadi et al. 2011).
The oil and gas industry has worked for a long time to respond to the challenge of
ensuring the protection of the environment. However, the exploitation of oil and gas
reserves has not always been without secondary ecological effects. However, in all
cases, environmental impacts can be avoided, minimized, or mitigated (Ershadi
et al. 2011). During construction of an oil or gas well, oil well cementing is the
process of placing cement slurry in the annulus space between the well casing and
the geological formations surrounding the well bore, from the injection horizon to
the surface. This procedure is used for providing zonal isolation of different
subterranean formations in order to prevent the exchange of gas or ﬂuids among
different geological formations, as well as for protecting oil-producing zones from
corrosion and collapse. Oil well cementing is less tolerant of errors than conven-
tional cementing works, and long-term performance of the oil well cement slurries
is of great concern (Le Saout et al. 2006). In this way, cementing of oil wells
requires new materials that provide long-term stability in critical conditions.
S. Bechar (*) • D. Zerrouki
Ouargla University, Applied Science, Process Engineering, Dynamic Interactions
and Reactivity of Systems Laboratory, Ouargla 30000, Algeria
e-mail: soumiabechar1@gmail.com
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_19
273
The protective characteristics of oil well cements can be controlled by the addition
of a speciﬁc additive (Bensted 1996). The main challenge in the Hassi Hessaoud
ﬁeld is zonal isolation for the Lias Dolomitic (LD2), called Horizon B. This high
pressure zone is located at a depth that varies from 2400 to 2700 m with a thickness
of about 30–60 m (Figs. 1, 2, and 3). The zone is characterized by a high pore
pressure gradient up to 2.25SG (Bouras et al. 2007). The difﬁculty is in keeping a
hydrostatic column to check pore pressure, but this hydrostatic pressure must be
below the pressure of the fracturing formation. Poor quality and bed placing of the
cement slurry in the annulus space lead to the need for remedial cementing and will
increase the time and cost of the cement job (Nelson 1990). The cost for workover
operations on wells in production can easily reach US$40,000 /day; for example,
for the MD-174 well, 24 days were spent on repairing casing corrosion, as casing
corroded at 2614 m in front of the LD2 formation (Bouras et al. 2007). As provided
in the severe downhole severities, the control of formulation properties is crucial for
obtaining a good cement bond to protect the casing from corrosive ﬂuids.
Fig. 1 A typical well
proﬁle in the Hassi
Messaoud ﬁeld
274
S. Bechar and D. Zerrouki
2
Background Information
The collection of temperature data takes account of the recording at the bottom. The
bottom hole static temperature (BHST) of this well has been estimated to be 120 C,
and the bottom hole circulating (BHCT) temperature is predicted to be 88 C.
The temperature gradient is 2.73 C/100 m.
Fig. 2 Bottom hole static temperature (BHST) simulation
Fig. 3 Lithology example of a typical case (Hassi Messaoud ﬁeld)
Control of Cement Slurry Formulation for an Oil Well in a Critical. . .
275
The water in the geological formation that causes the dilution of cement slurry
during its introduction was sampled and analyzed and the results are shown in
Table 1.
3
Experiments
3.1
Reagents
Portland cement is used a lot in oil well cementing operations, and is a very
important link in the process of oil well construction (Garnier et al. 2007; Neuville
2008; Shenglai et al. 2014). The cement powder used in this study is class “G” HSR
cement according to the American Petroleum Institute (API) standards (API 2002).
It was obtained from Dyckerhoff and the phase composition of class G cement is
presented in Table 2. The cement powder was blended in dry with silica fume (35 %
by weight of cement). The particle size (100-mesh) and crystalline nature of the
silica (98 % of SiO2 or greater), as well as the addition of the mineral pozzolanic
admixture can affect the properties of cement slurry both in a fresh and in a
hardened state (Lohtia and Joshi 1996; Plassais 2003). It extends the mechanical
integrity of the cement over the life of the well and prevents cement strength
retrogression, which would otherwise lead to severe well integrity problems in
abnormal conditions. We used hematite powder to provide stable rheology values
due to the kit’s better suspending properties; it increases cement slurry density to
maximize well control under high well pressures (Johnston et al. 1992). Sodium
chloride (NaCl) 37.2 % by weight of water (bwow) was dissolved to obtain a salt-
saturated water for mixing in order to minimize the deference potential of the
interstitial solution and the exterior environment to avoid the dissolution of the
salt form forming a slurry and thus to avoid a resulting hydrostatic imbalance. Other
Table 1 Analysis of the
water taken from the
geological layer Horizon B
(LD2)
Ions
Chloride calcic waters
HCO3
 (g/L)
1.37
Cl (g/L)
2520
So4
  (g/L)
0
Ca++ (g/L)
1160
Mg++ (g/L)
10
Ba++ (g/L)
0
Na+
63.3
K+ (g/L)
9.24
PH
6
Density 
a 25 C
1.28
Depth (m)
2400–2800
276
S. Bechar and D. Zerrouki
classic additives such as antifoam agents, dispersants, retarders, anti-gel agents, and
ﬂuid loss control are also used in the admixture of cement slurry.
3.2
Material
The cement slurries were prepared in the laboratory according to the API guidelines
with the following standard mixing procedures described in API RP 10B-2: First,
NaCl was dissolved in water for 15 min, after that an anti-gel agent was added to the
water mix with an antifoam agent, dispersant, ﬂuid loss control, retarder, and
hematite powder. Thereafter it was transferred into the cup of the warring blender.
Then, the cement was added within 15 s to the aqueous solution and mixed using
two speeds, 4000 and 1200 RPM (API 2002).
At ﬁrst the cement slurry was poured into a slurry container for the
preconditioning process. After achieving the desired conditions, the slurry was
stirred for about 20 min, and then it was immediately poured into a viscometer
cup. Throughout this time the slurry was stirred in order to prevent it from
remaining static. The ﬁrst reading was taken 10 s after continuous rotation at the
lowest rotating speed. After that, the remaining readings were taken in ascending
order, following continuous rotation of 10 s at each speed. After reading each speed,
in order to continue the reading process, the speed was immediately shifted to the
next considered speed. The rheological measurements were reported (R3, R6,
R100, R200, and R300), and at the end gel at 10 s and gel at 10 min were also
measured. These tests were carried out according to API speciﬁcation 10A standard
(API 2002) using the Fann 35 device.
3.2.1
Thickening Time
The apparatus for measuring the thickening time consists of a pressurized
consistometer, which has a rotating cylindrical slurry container, equipped with a
stationary paddle assembly. At ﬁrst the slurry was loaded into a slurry container and
was placed in a pressure vessel. The temperature and pressure of the cement slurry
were increased according to the appropriate speciﬁcation schedule. The thickening
Table 2 Composition of
class G cement
C3S (wt. %)
53.7
C2S (wt. %)
26.46
C3A (wt. %)
2.8
C4AF (wt. %)
12
SO3
2.0
MgO
0.7
Free lime
0.4
Control of Cement Slurry Formulation for an Oil Well in a Critical. . .
277
time was recorded as the passed time between the ﬁrst application of temperature
and pressure to the pressurized consistometer and the time at which a consistency of
100 Bc was reached (the Bearden unit of consistency (Bc) is the measure of the
consistency of a cement slurry). Finally, the maximum consistency between 15 and
30 min of the stirring period was reported (API 2002).
3.2.2
Compressive Strength
By measuring the variation of the speed of an acoustic signal, the ultrasonic cement
analyzer provides a continuous method for determining the compressive strength as
a function of time (API 2002). See Table 3.
4
Results and Discussion
4.1
Rheological Parameters
The density of the slurry was calculated at 2.24 SG and checked after mixing using
the Halliburton densimeter. We were obliged to readjust the concentration of
dispersant and anti-gel agent to acquire normative values.
•
Viscosity and yield value:
Vp ¼ (R300)  (R100t) *1.5 (Centipoise)
Pv ¼ 67.5 Cp
Yv ¼ lecture 300 t/minVp (en lbf/100ft2)
Yv ¼ 27.5 lbf/100ft2
•
Gel strength
10 s: 10 s: 20 (lbf/100 ft2)
10 min: 10 min: 50 (lbf/100ft2)
Table 3 Fann 35 data
RMP
300
92
200
75
100
50
6
22
3
20
278
S. Bechar and D. Zerrouki
4.2
Fluid Loss and Free Water
FL: 66 cc/30 min @ 1000 psi
FW: 0% cc
4.3
Thickening Time
The pumpability of the cement slurry is the crucial parameter for concretization of
the cementing operation. A multitude of tests have been carried out in order to
obtain a suitable thickening time (pumpabilty time), which was at 05 h but it is
strongly recommended to have enough thickening time for a safety margin (Fig. 4).
4.4
Compressive Strength
•
Ultrasonic cement analyser: The transit time from the slurry is inversely pro-
portional to the compressive strength. This formulation has developed good
mechanical resistance (1500 PSI) (Fig. 5).
Fig. 4 Variation in consistency according to time
Control of Cement Slurry Formulation for an Oil Well in a Critical. . .
279
•
By Crushing Cube after curing for 36 h in a curing chamber at BSHT (Fig. 6)
Compressive Strength ¼ force/Area
5447/4(inch) ¼ 1361 (Psi)
The hardened cement has developed an adequate compressive strength.
5
Conclusion
The correct introduction of the cement slurry between the drilled hole and the
casing in place is important to provide good control of rheological properties. The
important in this is the correct use of additives and the precise prediction of
pumpability time, which are crucial factors for the success of the cementation job
and sustainability of the hardened sheath, as well as the longevity of the well and
reliability of production. This paper presents an initiative study for understanding
the behavior of weighting cement slurry under several constraints in downhole
conditions. Many perspectives are shown in this study to begin to work on improv-
ing the sustainability and strength of the cement-hardened face in the complexity of
the geology in the Hassi Messaoud ﬁeld.
Fig. 5 Compressive strength and transition time (recorded by UCA)
280
S. Bechar and D. Zerrouki
Acknowledgments This research was supported by the Laboratory Dynamic Interactions and
Reactivity of Systems, Ouargla University and Bjsp Laboratory Hassi Messaoud.
References
American Petroleum Institute: Recommended Practice for Testing Well Cements, 23rd edn, http://
www.api.org/, (2002)
Backe, K.R., Lile, O.B., Lyomove, S.K., Elvebakk, H., Skalle, P.: Characterising curing cement
slurries by permeability, tensile strength, and shrinkage. Soc. Pet. Eng. SPE38267, p. 159
(1997)
Bensted, J.: Concrete Admixtures Handbook. Properties, Science, and Technology, 2nd edn.
pp. 1077–1111. Noyes publications (1996). Available online 23 Oct 2008
Bouras, H. Sonatrach, Toukam, E., Martin, F.V., Bedel, J.-P.: Successful application of novel
cementing technology in Hassi-Messaoud, Algeria SPE/IADC 108249 (2007)
Choolaei, M., Rashidi, A.M., Ardjmand, M., Yadegari, A., Soltanian, H.: The effect of nanosilica
on the physical properties of oil well cement. Mater. Sci. Eng. A. 538, 288–294 (2012)
Fig. 6 Chart showing the destructive test of compressive strength
Control of Cement Slurry Formulation for an Oil Well in a Critical. . .
281
Ershadi, V., Ebadi, T., Rabani, A.R., Ershadi, L., Soltanian, H.: The effect of nanosilica on cement
matrix permeability in oil well to decrease the pollution of receptive environment. Int.
J. Environ. Sci. Develop. 2, 128–132 (2011)
Garnier, A., Frauoulet, B., Saint-Marc, J.: Characterization of cement systems to ensure cement
sheath integrity. In: Offshore Technology Conference, Houston (2007)
Guo, S., Bu, Y., Liu, H., Guo, X.: The abnormal phenomenon of class G oil well cement
endangering the cementing security in the presence of retarder. Constr. Build. Mater. 54,
118–122 (2014)
Johnston, N.C., Senese, M.: New approach to high density cement slurries for cementing high-
pressure/high-temperature wells. Paper SPE 16011 presented at the European Petroleum
Conference, Cannes, 16–18 November 1992
Le Saout, G., Lecolier, E., Rivereau, A., Zanni, H.: Chemical structure of cement aged at normal
and elevated temperatures and pressures. Part II. Low permeability class G oil well cement.
Cem. Concr. Res. 36, 428–433 (2006)
Neuville, N.: Etude et modelisation de l’alteration physico-chimique de materiaux de cimentation
des puits pe
´troliers, 25–26 (2008)
Nelson, E.B.: Well Cementing, Chap. 11. In: Rae, P. (ed.) Cement Job Design, pp. 301–335.
Schlumberger Educational Services, Houston (1990)
Plassais, A.: Nanoporosite
´, texture et proprie
´te
´s me
´caniques de p^
ates de ciments. (2003)
Paul Lohtia, R., Joshi, R.C.: Concrete Admixtures Handbook. Properties, Science, and Technol-
ogy, 2nd edn, pp. 657–739. Noyes publications (1996). Available online 23 October 2008
282
S. Bechar and D. Zerrouki
Recovery of Farm Waste After Methanization
by Evaporation on Inclined Plate
Hiba Zouaghi, Souad Harmand, and Sadok Ben Jabrallah
1
Introduction
Wastes present problem to the entire population because of the nuisance it causes.
In rural communities, it is rather animal waste, which causes a big problem
(pollution of soil, rivers, water table, etc.). However, these releases have a product
rich in nutrients and fertilizers (Granier and Texier 1993). On this basis, several
farmers choose recovery of such wastes by anaerobic digestion. The development
of biogas plants not only reduces agricultural emissions by converting biomass but
also produces thermal energy. This thermal energy can have other uses.
After anaerobic digestion, it leads to the production of two types of energy. One
is related to the production of electricity and the other is reused in methanation
process. However, the disadvantage is that the digestate, after anaerobic digestion,
causes problems on many levels. It has high water content (up to 97%), particularly
pig manure. That is why storage or reuse is required. This product is the material
remaining residue at the end of the process. It has excellent agronomic value and
can be used as a fertilizer (Latimier et al., 1996; Levasseur 1998).
The ﬁeld of waste recovery appeared since a long time, especially in regard to
farm waste, because of organic matter and fertilizers. Drying presents an important
process for the management of farm waste, as it reduces the mass and volume of the
H. Zouaghi (*)
National Engineering School of Monastir, Avenue Ibn El Jazzar, Monastir 5019, Tunisia
e-mail: hibazouaghi@yahoo.fr
S. Harmand
University of Lille Nord, UVHC, LAMIH UMR CNRS 8201, Mont Houy, Valenciennes
Cedex 09, 59300, France
S.B. Jabrallah
Sciences Faculty of Bizerte, Zarzouna, Bizerte 7021, Tunisia
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_20
283
product and therefore cost of storage, workforce, and transportation. It is a method
based on evaporation. This process, which is a change of vapor–liquid phase, can be
achieved by exposure to air. But, it can be intensiﬁed by the use of solar energy.
This is one of the most practical methods of preserving the quality of agricultural
products and of recycling waste and efﬂuents. Studies on drying after anaerobic
digestate are rare. However, other waste streams drying exist.
Drying process can reduce the weight and volume of the product and therefore
cost of storage, handling, and transportation. Solar drying plant of sewage sludge
treatment was built as a tunnel-type greenhouse with a ceiling height of 2.5 m in
Turkey (Salihoglu et al., 2006). It has been completely covered by two walls, with a
thickness of 10 mm of transparent polycarbonate with light transmitting sheet of
80%. Solar dryer was constructed with the principle of increasing the difference
between vapor pressure of sludge compared to interior and relative vapor pressure
to obtain an effective drying. Lei et al., (2009) developed a greenhouse solar dryer
in China for drying sludge from wastewater treatment. The greenhouse is made of
glass. Its inner lower surface is painted black in order to increase the absorption of
solar radiation. A high stack of 180 cm was placed on top of the greenhouse.
Samples of fresh sludge were obtained from wastewater treatment plant located in
Shanghai. Before drying, the humidity is 5.16 kg/kg dry and dried at 0.78 kg/kg dry.
It was rolled to 25 mm in thickness in a plate made from 0.1 mm steel mesh and a
surface area of 0.22 m  0.36 m. The drying process lasts 125 h in summer
and about 550 h in winter with a nonregular decrease in moisture content
(Bennamoun, 2011).
Tannery efﬂuent, ejected into the environment, pollutes terrestrial and aquatic
organisms in and around tanneries and evaporates over long periods. Drying may be
accomplished by allowing the efﬂuent to ﬂow on an inclined plane solar sensor.
While the liquid ﬂows over the collector, it is heated by solar energy that helps
increase the evaporation rate. In tannery, water efﬂuent evaporation and recovery of
salt is a method that uses solar energy available in abundance (Mani et al., 1993,
1994; Srithar et al., 2003a, 2003b, 2006), driers having sensor solar plates, and a
spray system to increase evaporation ﬂow. In the case of plate sensor, efﬂuent ﬂows
over the manifold. Therefore, the efﬂuent temperature at the exposure area with air
increases, which increases the rate of evaporation.
This work is about drying of pig digestate after undergoing anaerobic digestion
and phase separation by centrifugation. The treated efﬂuent has a solid content of
2.3%. In this context, an inclined stainless steel plate, 2 m long, 1 m wide, and
inclined by 30, has been put in place. The device is without a glass cover, and tests
were carried out in a laboratory using a 6000-W solar simulator. This device is
designed to be placed on barn roofs.
This study is divided into two parts. In the ﬁrst part, evaporation tests were
conducted by varying the inlet temperature of the efﬂuent. The second part
focuses on the comparison between the experimental and calculated results by
solving equations of heat and mass balances on plate and ﬁlm.
284
H. Zouaghi et al.
Nomenclature
Cp
Heat capacity (J/kg.C)
e
Thickness (mm)
h
Convection coefﬁcient (W/m2.C)
I
Radiated ﬂux (W/m2)
L
Plate length (m)
Lv
Latent heat of vaporization (J/kg)
m
Flow (m3/s)
Pr
Prandtl number
S
Area (m2)
T
Temperature (C)
X
Horizontal axis (m)
Y
Vertical axis (m)
Greek letters
α
Absorptivity μ: viscosity (Pa.s)
ε
Emissivity υ kinematic viscosity (m2/s)
ζ
Transmissivity σ: Boltzmann constant
λ
Thermal conductivity (W/m.C)
Superscripts
*
par unit width
Subscripts
ev
Evaporated in: initial
ext,out
Outside int: inside
f
Fluid
p
Plate
pair
Between plate and air
pf
Between plate and ﬁlm
2
Efﬂuent Characteristics
A thermo-physical characterization of the efﬂuent is performed to determine
thermal conductivity “λ,” heat capacity “Cp,” and dynamic viscosity “μ”; the
efﬂuent is the liquid phase of pig manure that has already undergone methanation
and phase separation by centrifugation.
Analyses were performed on three samples (E0, E1, and E2) having different
concentrations. The sample E0 is the liquid efﬂuent having a concentration of C0
and a dry matter content of 2.3%. The sample E1 was prepared by evaporation of E0
so as to lose one third of its initial volume. As for E2, evaporated volume is about
two thirds of E0. The characteristics of each sample are shown in Table 1.
The thermal conductivity measurements use a device called FP2C. This is a
thermal conductivity meter which is based on hot wire method for measuring the
overall thermal conductivity of a material from the evolution of temperature
Recovery of Farm Waste After Methanization by Evaporation on Inclined Plate
285
measured by a thermocouple placed near a resistive wire. The values of thermal
conductivity λ are expressed in W/m.K. We were able to determine thermal
conductivities of samples for 30 and 60  C (Table 2).
Knowing that the measurement accuracy is 5%, and by referring to Table 2, we
see that thermal conductivity does not depend on the concentration of the samples
but depends on its temperature. When temperature is about 30 C, it is 0.584 W/m.
K almost equal to that of the water of 0.598 W/m.K. At 60  C, λ ¼ 0.739 W/m.K,
whereas λwater ¼ 0.651 W/m.K (Brau 2006).
Measurements of dynamic viscosity use a rheometer DHR III having TA
Instrument mark. The measurements are performed at atmospheric pressure for
three temperatures (20 C, 45 C, and 70 C). Each test was performed twice on
different samples to assess the reproducibility.
Before starting measurements, each sample is deposited on the bottom plate and
spread with a top plate. Excess product is removed with a spatula. The gap is then
adjusted and the temperature of the sample conditioned to be measured with an
accuracy of 0.5 C.
The ﬂowing curve of different samples is obtained using a measuring method
based on a constant shear rate. The values of shear stress and viscosity are noted as
soon as the ﬂow is established. Results are given with an accuracy of 10%,
considering geometric imperfections and rheometer accuracy (Fig. 1).
Table 1 Characteristics of
samples to analyze
Sample name
E0
E1
E2
Concentration
C0
C1 ¼ 1.5C0
C2 ¼ 3C0
Density (g/l)
960
900
850
Table 2 Thermal
conductivity of samples
Sample
E0
E1
E2
Water
T ¼ 30 C
0.593
0.560
0.598
0598
T ¼ 60 C
0.700
0.805
0.713
0.651
Fig. 1 Graph of dynamic
viscosity as function of
temperature for shear rate of
100 s1
286
H. Zouaghi et al.
The inﬂuence of temperature on viscosity of each sample is shown. If temper-
ature increases, viscosity decreases for different shear rates. However, this inﬂu-
ence is less depending on sample, well-marked for E0 and E1 (especially between
20 C and 45 C), and less important for E2. Viscosity of sample E0 is the closest to
that of water (Brau 2006).
As for the measures of the heat capacity, they are made with the DSC Mettler
Toledo DSC1. This device uses the differential scanning calorimetry (DSC), which
measures the change in enthalpy in sample using a standard sapphire.
This method uses the following experimental conditions:
•
Temperature stabilizing at 20 C for 5 min, heating from 20 to 70 C (20 C/min)
•
Stabilization at 70 C for 5 min
•
Continuous wipe neutral gas (nitrogen) to 50 ml/min
The analysis results show that for samples E0 and E2, heat capacity is constant
over the temperature range of 20–67.5 C. It is an average of 0.166 J/g.C for E0 and
0.16 Jg.C for E2. For E1, the heat capacity varies depending on the temperature.
It is from 3.23 J/g.C to T ¼ 20 C. It comes down to 2.91 J/g.C to 22.5 C
and gradually increases. A slight increase is observed for temperatures between
30 and 67.5 C, and it is on average 4.08 J/g.C. Results are shown in Fig. 2.
3
Experimental Facility
This process consists of ﬂowing the aqueous liquid efﬂuent on an inclined plate
subject to solar ﬂux. The purpose is to produce solid fertilizer from aqueous liquid
waste, such as anaerobic digestate. The system should be inexpensive to purchase
and should consume as little energy as possible as it is for farmers who make liquid
methane.
Fig. 2 Heat capacity
function of temperature for
each sample
Recovery of Farm Waste After Methanization by Evaporation on Inclined Plate
287
This device is an evaporator composed of a stainless steel plate with a tilt angle
of 30. This plate is 2 m long and 1 m wide. The liquid is injected from the top of the
plate with an injection nozzle. Fluid ﬂows along the plate and is recovered in a
gutter (Fig. 3).
The steel plate is not covered with glass wool. It is heated using a 6000-W solar
simulator. The solar simulator is placed parallel to the evaporator at a distance of
2 m such that the ﬂow will be focused on the upper part of the plate and there will be
a ﬂux density similar to that of the sun.
Thermocouples are ﬁxed to the stainless steel plate, as shown in Fig. 4. The
thermocouples are positioned into three rows of nine lines. The ﬁrst line of
thermocouples is approximately 20 cm from the top and 15 cm from the edge.
Thermocouples are spaced at a distance of 20 cm. K-type thermocouples were used
and had a margin of error of 2.5  C.
Once steady state was achieved, the liquid was injected from the top of the plate
using a pump that feeds the dispense manifold. The input ﬂow measurement was
performed by weighing the liquid before and after injection for a predetermined
time before the start of the solar simulator.
Fig. 3 Experimental installation
Fig. 4 Layout of
thermocouples on the plate
288
H. Zouaghi et al.
The evaporated ﬂow was measured when the simulator was powered and after
stabilization of the temperature. The measurement was achieved by weighing the
liquid at the output of the plate over a given time.
4
Numerical Scheme
Before circulating the liquid ﬁlm, the plate was exposed to radiation simulator and
its back was isolated (no heat loss). Based on the sum of the incoming ﬂux that
equals the amount of outgoing ﬂux, the heat balance on the plate can then be
written:
αpI0  εpσ T4
p  T4
air


 hext,pa Tp  Tair


¼ 0
ð1Þ
The natural convection coefﬁcient in steady state is a function of temperature of
the plate and that of air (Chen et al. 1986; Huetz et al., 1981):
hext,pa ¼ 0:225 Tp  Tair

1
=
5
ð2Þ
Heat balance becomes
αpI0  εpσ T4
p  T4
air


 0:225 Tp  Tair

6
=
5 ¼ 0
ð3Þ
Once the plate temperature is stabilized, the liquid is circulated (Fig. 5).
Heat balance on ﬁlm can be written:
αpτf I0 þ εpαpσT4
f  εpσT4
p  hint,pf Tp  Tf


¼ 0
ð4Þ
Fig. 5 Balance on liquid
ﬁlm
Recovery of Farm Waste After Methanization by Evaporation on Inclined Plate
289
The forced convection coefﬁcient is a function of the thickness of the water ﬁlm.
In steady state, for an inclined plate of 30, the forced convection coefﬁcient
between the efﬂuent and the plate takes the following form:
hint,pf ¼
0:332:λ
LPr
1
=
3
ﬃﬃﬃﬃﬃﬃﬃ
min
•
υ
s
0
@
1
A 1
ﬃﬃﬃﬃ
ef
p
ð5Þ
As regards the liquid ﬁlm, the mass balance is (Bouchekima et al. 2001)
m
•
x
ð ÞCp Tf x
ð Þ
dx
þ αf I0 þ αf τf ρpI0 þ hint,pf Tp x
ð Þ  Tf x
ð Þ


þ αpεpσT4
p
¼
m
• x
ð Þ  m
•
x þ dx
ð
Þ


Lv þ hint,fair Tf  Tair


þ εf σT4
f
þ m
•
x þ dx
ð
ÞCp Tf x þ dx
ð
Þ
dx
ð6Þ
where Sp ¼ dx . L and
m
•
ev ¼ m
•
in  m
•
out
ð7Þ
The ﬁlm thickness along the plate is variable and depends on the evaporated
ﬂow. Assuming the volume is constant, the ﬁlm thickness is deﬁned by:
ef x
ð Þ ¼ ef 0
ð Þ: m
•
x
ð Þ
min
ð8Þ
The equations are solved using the programming language MATLAB. Solving
equations is performed one by one, as the number of equations is less than the
number of unknowns. Once the temperature along the plate is determined from
eq. (3), it is known and entered as data in (4). Knowing Tp(0) and Tf(0), we can
determine the convection coefﬁcient. Thus, eq. (6) is solved at initial status. Using
the equation (7) the ﬁlm thickness can be derived to estimate x, and the same
approach can be followed to solve (4) and (6).
5
Results and Discussions
The cartography of the ﬂux on plate is shown in Fig. 6. We chose the same points as
thermocouples (three columns of nine lines). The solar ﬂux is measured by a
pyranometer with accuracy 4–10 μV/W/m2.
The maximum ﬂux is at Y ¼ 0.45 m according to the different positions X1, X2,
and X3. It is 805 W/m2 at Y ¼ 0.45 m for position X2. The minimum ﬂux is 111 W/m2
for Y ¼ 1.85 m for position X1. The mean ﬂux along the plate is approximately
424 W/m2.
290
H. Zouaghi et al.
The shape of this curve can be explained by the position of simulator in which
the plate is exposed. Therefore, the ﬂux is not distributed evenly; only the upper part
of the plate is exposed to radiation. Simulator radiates this partly explains the fall of
the stream up to 116 W/m2.
By solving eq. (1), we were able to compare the temperature of plate in different
experimental and calculated positions. Results are shown in Fig. 7.
Results indicate that following columns X1, X2 and X3, curves representing
temperature of plate after have the same shape. It is maximum at Y ¼ 0.45 m and
decreases along plate. This can be explained by the solar ﬂux which is focused on
the top plate when it reaches 805 W/m2.
From eq. (1), the temperature of plate depends on radiation ﬂux and temperature
of the air. The ﬂux is not uniformly distributed on the plate, whose upper portion
only is exposed to radiation. This explains the drop in temperature of the plate from
Y ¼ 1.45 m. This curve has the same shape as the distribution curve of ﬂux along the
plate. Then, the temperature of the plate is strongly dependent of radiated ﬂux.
The slight variation of the calculated and measured plate temperature may
depend on several factors such as laboratory temperature and temperature of the
air between the plate and the simulator.
The liquid ﬂows over the plate. By varying the inlet temperature of the efﬂuent,
the temperature of the efﬂuent on the plate varies as shown in Fig. 8, according
to eq. (4).
The temperature of the efﬂuent is important on top of the plate, at Y ¼ 0.45 m; it
starts to decrease until stable at the bottom of the plate. When the liquid inlet
temperature is 40 C, the ﬂuid reaches a maximum temperature of 69 C at
Y ¼ 0.45 m and for X2 ¼ 0.475 m. Then its temperature decreases to 38 C at
Y ¼ 1.85 m and for X2.
In each case, the ﬁlm temperature is maximal for X2. This can be explained by
the distribution of ﬂux, which is maximum at the top of the plate. Moreover, from
Fig. 6 Cartography of the
ﬂuxes on the plate
Recovery of Farm Waste After Methanization by Evaporation on Inclined Plate
291
eq. (4), the variation in Tp causes the variation of Tf. This implies that ﬂuid
temperature curve along the plate has the same shape as that of the plate. The fall
of ﬂuid temperature is due to the low temperature of the plate as it is not exposed to
the rays of the simulator.
On the top, the temperature of the plate is higher than that of the ﬂuid. The ﬂuid
is heated by the plate. Its temperature increases and becomes close to that of the
plate. In the bottom of the plate, the efﬂuent cools because its temperature is higher
than the plate temperature.
We compared the inlet and outlet liquid temperatures, results are shown in
Fig. 9:
When the liquid is preheated at 20 and 30 C, its outlet temperature is higher
than that of the input. It is respectively 24 and 34 C experimentally and 23 and
31 C calculated. Hot liquid having a temperature around 35 C is cooled on the
bottom of the plate. The difference between the plate and ﬂuid temperatures is low.
However if the liquid is preheated at 40 and 50 C, the difference of the temperature
between the plate and the ﬂuid is large, which explains the temperature drop of the
latter in the outlet. As for the maximum difference between the measured and the
calculated results, it is 10%.
Fig. 7 Evolution of plate temperature
292
H. Zouaghi et al.
Fig. 9 Outlet temperatures
of ﬂuid
Fig. 8 Film temperature along the plate
Recovery of Farm Waste After Methanization by Evaporation on Inclined Plate
293
Evaporated local ﬂow is maximum when the liquid temperature is high. It
gradually decreases. It is low in the bottom of the plate because the ﬂuid temper-
ature is low (Fig. 10).
Experimentally, we measured the total throughput evaporated. Theoretically, it
is determined by integrating the local ﬂow evaporated. We obtained results
presented in Fig. 11.
Fig. 10 Variation of local evaporated ﬂow along the plate
Fig. 11 Comparison
between calculated and
experimental
evaporated ﬂow
294
H. Zouaghi et al.
When the liquid is preheated at temperatures between 19 and 40 C, the total
ﬂow evaporated increases if the inlet temperature is increased. At an inlet temper-
ature of 50 C, the liquid becomes warmer than the plate, producing heat, thus
explaining the decrease in total evaporated ﬂow at this temperature. The maximum
difference between the measured and calculated results is 10%.
The ﬁlm thickness is variable; it decreases along the plate until it stabilizes when
the evaporated ﬂow becomes weak. The curves in Fig. 12 present the result in
solving eq. (7).
Film thickness is the lowest for X2 for each case where the evaporated ﬂow is
maximum. The more the evaporated ﬂow increases, the lower the ﬁlm thickness,
and vice versa. The signiﬁcant decrease in ﬁlm thickness is due to the high
evaporated ﬂow. In the bottom of the plate, the evaporated ﬂow is low, and the
ﬁlm thickness is almost constant.
The volume concentration of the efﬂuent increases following Y. Flowing liquid
evaporates and loses its water content. It becomes more concentrated. Note that the
initial volume concentration of the liquid is 0.0229%. At its release, its maximum
concentration reached 0.0274% (Fig. 13).
At the bottom of the plate, the liquid concentration stabilizes due to low
evaporated ﬂow. The volume concentration is maximum following X2, the maxi-
mum evaporated ﬂow along this axis. The concentration of liquid at output is
maximum (reaching 0.0274) when the liquid is preheated to 40 C.
0,0
0,2
0,4
0,6
0,8
1,0
1,2
1,4
1,6
1,8
2,0
1,60
1,65
1,70
1,75
1,80
1,85
1,90
1,95
2,00
Tinlet=19°C
Film thickness (mm)
Y(m)
 X1
 X2
 X3
0,2
0,4
0,6
0,8
1,0
1,2
1,4
1,6
1,8
2,0
1,60
1,65
1,70
1,75
1,80
1,85
1,90
1,95
2,00
Tinlet=30°C
Film thickness (mm)
Y(m)
 X1
 X2
 X3
0,0
0,2
0,4
0,6
0,8
1,0
1,2
1,4
1,6
1,8
2,0
1,60
1,65
1,70
1,75
1,80
1,85
1,90
1,95
2,00
Tinlet=40°C
Film thickness (mm)
Y(m)
 X1
 X2
 X3
0,0
0,2
0,4
0,6
0,8
1,0
1,2
1,4
1,6
1,8
2,0
1,60
1,65
1,70
1,75
1,80
1,85
1,90
1,95
2,00
Tinlet=50°C
Film thickness (mm)
Y(m)
 X1
 X2
 X3
Fig. 12 Variation of ﬁlm thickness along the plate
Recovery of Farm Waste After Methanization by Evaporation on Inclined Plate
295
6
Conclusion
In a study on the evaporation of pig digestate after centrifugation, the efﬂuent
having 2.3% of dry matter ﬂows as ﬁlm on a stainless steel plate inclined by 30. A
thermophysical characterization of three samples of different concentrations shows
that heat capacity and viscosity depends on the concentration of the liquid. On the
contrary, thermal conductivity does not depend on liquid concentration but on its
temperature.
A comparison between the experimental and calculated results by determining
heat and mass balance on plate and ﬁlm was completed. The difference between the
measured and experimental results did not exceed 7%. We found that since heating
ﬂow is not constant over the plate, the temperature of the plate depends on ﬂux
distribution. If it is maximum, then the plate temperature is maximum, and vice
versa. Once the ﬁlm circulates, its temperature is affected by the temperature of the
plate. Therefore, evaporated ﬂow is proportional to ﬁlm temperature.
Evaporated ﬂow is maximum on the central part of the plate, where ﬂux is
maximum. The ﬁnal concentration of product reached 0.0274%. The liquid lost
nearly 20% of its volume when it was introduced at 40 C. Therefore, when we
preheat liquid between 19 and 40 C, evaporated ﬂow improves. So, liquid tem-
perature becomes higher than that of the plate. In addition, the liquid transfers its
heat to the plate that blocks evaporation phenomenon.
0,0
0,2
0,4
0,6
0,8
1,0
1,2
1,4
1,6
1,8
2,0
0,023
0,024
0,025
0,026
0,027
0,028
0,029
Tinlet=19°C
Volume concentration (%)
Y(m)
 X1
 X2
 X3
0,0
0,2
0,4
0,6
0,8
1,0
1,2
1,4
1,6
1,8
2,0
0,023
0,024
0,025
0,026
0,027
0,028
0,029
Tinlet=30°C
Volume Concentration (%)
Y(m)
 X1
 X2
 X3
0,0
0,2
0,4
0,6
0,8
1,0
1,2
1,4
1,6
1,8
2,0
0,023
0,024
0,025
0,026
0,027
0,028
0,029
Tinlet=40°C
Volume concentration (%)
Y(m)
 X1
 X2
 X3
0,0
0,5
1,0
1,5
2,0
0,023
0,024
0,025
0,026
0,027
0,028
0,029
Tinlet=50°C
Volume concentration (%)
Y(m)
 X1
 X2
 X3
Fig. 13 Variation of efﬂuent concentration along the plate
296
H. Zouaghi et al.
Acknowledgments This research is part of a PhD study in National Engineering School of
Monastir, Tunisia. It is supported by the Laboratory TEMPO of the University of Valenciennes
and Hainaut Cambresis.
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Recovery of Farm Waste After Methanization by Evaporation on Inclined Plate
297
Optimal Operation of MEA-Based Post-
combustion Carbon Capture Process
for Natural Gas Combined Cycle Power Plants
Xiaobo Luo and Meihong Wang
1
Introduction
Using carbon capture and sequence (CCS) technology to control CO2 emissions
from fossil fuel (e.g. coal and natural gas), ﬁred power plants play an important role
to achieve the target of limiting average global temperature increase to 2 C in 2050
(IEA 2012). MEA-based post-combustion absorption is considered as the most
matured technology for carbon capture from fossil fuel-ﬁred power plants (Wang
et al. 2011). However, when power plants are integrated with capture plants, the
two big barriers observed for CCS deployment are: (1) its massive capital cost and
(2) high thermal energy penalty (Luo et al. 2015).
Currently, 20% of global electricity production capacity is supplied from
gas-ﬁred power generation (British Petroleum 2014). This number is expected to
become larger in the next several decades because of the advent of cheap natural
gas and carbon emission mitigation policy. However, the cost of electricity will
increase from £66 to £144.1 per MWh for NGCC power plant integrated with PCC
plant (DECC 2013). Except for an enormous capital investment, the parasitic
energy penalty is also signiﬁcant for NGCC power plant integrated with PCC
process. Therefore, research efforts are required for potential improvements to
reduce both the capital cost and the energy penalty to gain a better economic proﬁle
of commercial deployment of carbon capture.
One of the most important engineering tools for addressing these cost issues is
optimization (Edgar et al. 2001). Optimization of a large scale of conﬁguration
plant, such as NGCC power plant integrated with PCC process in this study, can
involve several levels such as process conﬁgurations, features of equipment designs
X. Luo • M. Wang (*)
School of Engineering, University of Hull, Cottingham Road, Hull HU6 7RX, UK
e-mail: Meihong.Wang@hull.ac.uk
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_21
299
and control variables of plant operations. Most early studies are carried out for the
parametric studies for coal-ﬁred power plants. Key variables such as lean solvent
loading, solvent/gas ratio (L/G ratio), MEA concentration in solvent and stripper
operating pressure have been investigated. The results shows they have big impacts
on the energy consumptions (Abu-Zahra et al. 2007b; Canepa and Wang 2015) and
are highly sensitive to the economic performance of the whole plant (Abu-Zahra
et al. 2007a). However, their optimal values exhibit a large range in different
studies. For example, the optimal value of lean loading is in a big range of
0.18–0.32 mol CO2/mol MEA with corresponding special duty at a range of
4.5–3.8 GJ/ton CO2 (Abu-Zahra et al. 2007b; Mac Dowell and Shah 2013). For
the design parameters, the diameter and packing height of the absorber and the
stripper have large impacts on the capital cost. The optimal values of these design
parameters are coupled with other process variables. Their interactions are highly
nonlinear based on chemical principles. Then it is hard to make fast cost estimation
by simply formatting cost calculation equations.
Another approach to minimize the total cost is optimal operation towards the
changes of marketing, such as volatile electricity demand and pricing, fuel price as
well as carbon price. Mores et al. (2012) found that the total annual cost of carbon
capture plant varies linearly for carbon capture level within a range of 70–80% but
it rises exponentially when carbon capture level increases from 80–95%. Cohen
et al. (2012) investigated the economic beneﬁts of a 500-MW coal-ﬁred power plant
with CO2 capture for a carbon pricing from 0-200USD/ton CO2 and concluded CO2
capture investment is unjustiﬁable at low CO2 prices. In the study by Mac Dowell
and Shah (2013), optimal CO2 capture level is 95% for £30/ton CO2 and £90/MWh
scenario and is around 70% for £8/ton CO2 and £55/MWh scenario for a 660-MWe
coal-ﬁred power plant integrated with a capture plant. The study concludes carbon
price should be more than 40£/ton CO2 to justify the total cost of carbon capture for
an objective of capture level greater than 90% without considering the costs of CO2
compression, transport and storage.
Compared with coal-ﬁred power plant, the CO2 concentration in ﬂue gas is much
lower for a gas-ﬁred power plant which causes some major different features in
terms of the economic performance, such as bigger equipment size and lower L/G
ratio. Thus the optimization results of carbon capture for a coal-ﬁred power plant
may not be transferred to NGCC power plant directly. This paper aims to explore
the optimal operation of MEA-based post-combustion carbon capture plant for
gas-ﬁred NGCC power plant. The novelties of this study are claimed as follows:
(1) whole chain economic estimate was conducted for NGCC power plant inte-
grated with carbon capture, transport and storage; (2) the optimizations were carried
out for the carbon capture level under different carbon price, natural gas (NG) price
and CO2 T&S price; (3) this study pointed out the coactions of carbon price, NG
price and CO2 T&S cost will affect the decision-making about optimal carbon
capture level for operating PPC process for a NGCC power plant.
300
X. Luo and M. Wang
2
Process Model Development
For this large-scale plant optimization, process model is the core part of this
nonlinear programming. Process model contains the bulk of parameters, variables
and constraints in an optimization problem (Edgar et al. 2001). Accurate process
model offer better predictions of process variables in terms of both technical and
economic performance. In this study, the process model includes NGCC power
plant, PCC process and CO2 compression train. In this section, the sub-models were
developed using Aspen Plus®, based on our previous studies (Luo et al. 2015;
Canepa et al. 2013).
2.1
NGCC Power Plant Model
A 453-WMe NGCC reference model with a GE 9351FB gas turbine and a triple-
level pressures reheat HRSG was developed using Aspen Plus®. Peng-Robinson
(Peng and Robinson 1976) with Boston Mathias modiﬁcations (Neau et al. 2009a,
b) (PR-BM), equation of state (EOS) is used for the gas cycle and STEAMNBS
(Aspen-Tech 2012), and EOS is used for the steam cycle for the calculation of
thermodynamic properties.
At the ambient conditions (ambient temperature is assumed to be 9 C and
ambient pressure is assumed to be 1.01 bar in this study), fresh air is compressed to
mix with natural gas to enter the combustion chamber (see in Fig. 1). The hot gas
leaves the combustion chamber and enters the gas turbine to expand to generate a
part of electricity. Exhaust gas from gas turbine, through HRSG, provides the
heating to the steam cycle to generate three kinds of steams at different pressures
of 170 bar, 40 bar and 5 bar, which go to the high pressure steam turbine (HP-ST),
the intermediate pressure steam turbine (IP-ST) and the low pressure steam turbine
(LP-ST), respectively, to generate another part of electricity.
The model parameters are presented in Table 1, referring to IEAGHG report
(IEAGHG 2012). The simulation results using this model were compared with the
simulation results using another software package, GT Pro® (IEAGHG 2012), in
order to make a brief validation. The comparison results of Aspen Plus® and GT
Pro® appear to be in good agreement (Luo et al. 2015).
2.2
PCC and Compression Model
For the reactive absorption process using MEA solvent to absorb CO2, Aspen Plus®
rate-based approach was proven to be capable to provide an acceptable accuracy for
the performance prediction of PCC process (Zhang et al. 2009). The model of PCC
process used in this study is developed with Aspen Plus® based on the previous
Optimal Operation of MEA-Based Post-combustion Carbon Capture Process. . .
301
researches (Canepa et al. 2013; Luo et al. 2015). Electrolyte NRTL (Chen and Song
2004) property method is used to describe the thermodynamic and physical prop-
erties. The simulation results from this model were compared with the experimental
data and also Zhang’s study for a validation purpose. The validation shows a good
agreement of several key design parameters and operational variables, such as lean
solvent loading, rich solvent loading, capture level and the temperature proﬁles of
both the absorber and the stripper (Canepa et al. 2013; Canepa and Wang 2015).
Table 2 shows the model parameters of the PCC process after scaling up to match
the NGCC power plant.
A compression train is needed to pressurize the captured CO2 to reach a high
entry pressure, as high as 110–150 bar for pipeline transport and geologic seques-
tration. By our previous study (Luo et al. 2014), an optimal option was selected to
get a minimum annual cost including annualized capital cost, operating and main-
tenance cost and energy cost. The optimal conﬁguration compromises six stages
integrally geared compressor following a pump with intercoolers at an exit tem-
perature of 20 C, which was also adopted in this study Table 3.
Fig. 1 The ﬂowsheet of NGCC power plant integrated with PCC process and compression
with EGR
302
X. Luo and M. Wang
Table 1 Model parameters of NGCC power plant
Parameters
Value
Natural gas composition (vol %)
CH4
89
C2H6
7
C3-C5
1.11
CO2
2
N2
0.89
Gas turbine
Type
GE 9371FB
Pressure ratio
18.2
Steam turbine
Steam inlet of HP turbine (bar/C)
172.6/601.7
Steam inlet of IP turbine (bar/C)
41.5/601
Steam inlet of LP turbine (bar/C)
5.8/293.1
HP/IP/LP turbine efﬁciencies (%)
92/94/90
Minimum temperature approach of HRSG
Steam and gas (C)
25
Gas and boiling water (C)
10
Water liquid and gas (C)
10
Approach of Economizer (C)
5
Condenser pressure and temperature (bar/C)
0.039/29.0
Table 2 Model parameters
of PCC process with EGR
Parameter
Value
CO2 concentration in ﬂue gas (mol%)
7.32
CO2 capture level (%)
90
CO2 captured (kg/s)
40.92
Columns ﬂooding (%)
65
Lean loading (mol CO2/mol MEA)
0.32
L/G (mol/mol)
2.71
Reboiler duty (MW)
176.227
Reboiler duty (GJ/tonne CO2)
4.31
Lean solvent MEA concentration (wt%)
30
Lean solvent temperature (C)
30
Absorber column numbers
1
Absorber column diameter (m)
16.2
Absorber column pressure (bar)
1.0
Absorber column packing
Mellapack 250Y
Absorber column packing height (m)
20
Stripper column numbers
1
Stripper column diameter (m)
8.6
Stripper column pressure (bar)
2.1
Stripper column packing
Mellapack 250Y
Stripper column packing height (m)
20
Optimal Operation of MEA-Based Post-combustion Carbon Capture Process. . .
303
2.3
NGCC Integrated with PCC and Compression
When the NGCC power plant is integrated with PCC process and compression,
there are several basic interfaces (see Fig. 1), including: (1) Flue gas is lined from
HRSG to the capture process after gas processing. (2) Low pressure steam is
extracted for solvent regeneration. (3) Steam condensate returns to the steam
cycle of NGCC power plant. (4) NGCC power plant provides electrical power
supply for PCC process and CO2 compression. Compared with NGCC stand-alone,
carbon capture case has a total 9.58% net power efﬁciency decrease according to
the previous study. The power plant output is reduced by three main factors:
(1) steam extraction causing a reduction in steam ﬂow rate through the LP-ST
and therefore in its power output; (2) the power consumption of CO2 compression;
and (3) auxiliary power consumption, including the blower and solvent circulation
powers. Out of the three factors, the power reduction due to steam extraction is the
main one.
Another process modiﬁcation for NGCC power plant when integrated with PCC
process is to recirculate a fraction of ﬂue gas (the ratio is 0.38) leaving from the
HRSG back to the compressor inlet where it is mixed with fresh air (see Fig. 1). The
CO2 content in the ﬂue gas from NGCC power plant is as low as 3–4 mol%, whilst it
is 11–13 mol% for a coal-ﬁred power plant, which causes bigger equipment size
requirement and lower absorption efﬁciency of the absorber in PCC capture plant
(Jonshagen et al. 2011). Exhaust gas recirculation (EGR) is an effective solution for
smaller capital cost and better thermal performance for a NGCC power plant
integrated with a PCC process (Luo et al. 2015; Canepa and Wang 2015).
Table 3 Parameters of
compression train
Parameter
Unit
Value
Suction pressure
bar
1.8
Suction temp.
C
20
Pumping suction pressure
bar
80.0
Pumping suction temp.
C
20
Exit pressure
bar
136.0
Stage
–
6
Isentropic efﬁciency
%
75
Intercooler exit temperature
C
20
Last stage exit temp.
C
20
304
X. Luo and M. Wang
3
Cost Model Formulation
3.1
Cost Breakdown
For operating an industrial process plant, the total cost includes capital expenditure
(CAPEX) and operational expenditure (OPEX). OPEX can be split into ﬁxed OPEX
(operating and maintenance (O&M) cost) and variable OPEX (mainly the energy
and utilities cost) (IEA-GHG 2002). CAPEX includes equipment material and
installation, labour cost, engineering and management EPC cost and other costs
happened during the project contracture and commissioning. Fixed OPEX includes
overhead cost, operating and maintenance (O&M) cost and other ﬁxed costs
regardless of the plant running at partial or full loading or shutdown. Variable
OPEX mainly includes fuel cost, energy and utilities costs and solvent make-up
cost. For a NGCC power plant integrated with PCC process, it is noticed that the
variable cost should also include the emission penalty cost of CO2 discharged into
the atmosphere and T&S cost of the CO2 captured.
3.2
Objective Function
For techno-economic evaluation or cost optimization of a power plant integrated
with carbon capture process, different economic indexes have been used in different
studies, including (1) total annual operating proﬁts, (2) total annualized cost,
(3) levelized cost of electricity (LCOE), and (4) cost of CO2 avoided. In this
study, LCOE was formulated to be the objective function of the optimization.
LCOE was calculated by dividing total annual cost by annual net power output.
The total annual cost is the sum of annualized CAPEX, ﬁxed OPEX and variable
OPEX.
LCOE ¼ Total annual cost
Net power output
Total annual cost ¼ Annualized CAPEX þ Fixed OPEX þ Variable OPEX
The annualized CAPEX is the total CAPEX multiplied by capital return factor
(McCollum and Ogden 2006). It would be noticed that this study focuses on the
optimal operation of NGCC power plant with PCC process. Its annualized CPAEX
and ﬁxed OPEX are supposed to be ﬁxed negating the tax and labour cost changes.
Only the variable OPEX was considered to change in response to different market
situations. In this study, the variable OPEX includes fuel cost, cooling utilities cost,
solvent make-up cost, carbon emission cost and CO2 T&S cost.
Optimal Operation of MEA-Based Post-combustion Carbon Capture Process. . .
305
4
Techno-Economic Evaluation of Base Case
In this section, the technical performance was evaluated according to the process
simulation results. Then the cost of whole chain for capturing carbon from NGCC
power plant was evaluated for the base case by combining simulation results and
the literature data, in order to give a basis for later optimal operation study.
4.1
Technical Performance
The base case was set up based on the PCC process described in Sect. 2.2 with 90%
carbon capture level and with EGR for NGCC power plant. The key technical
performance parameters of the base case were compared with the reference case of
NGCC stand-alone and are summarized in Table 4.
4.2
LCOE of the Base Case
For the economic evaluation, CAPEX and ﬁxed OPEX referred to the benchmark
report (IEAGHG 2012). The variable OPEX was summarized from each sub-cost
calculated based on the simulation results from the process model. To harmonize
results for comparison with other studies, the following assumptions are made:
(1) all costs are corrected to €2015 using the harmonized consumer price index
(HICP) in Europe zone; (2) the captured CO2 mixture has no economic value;
(3) cooling water is sourced from a nearby body of water at the cost of pumping and
operation of a cooling tower. Other important cost inputs are provided in Table 5,
with the costs given in Euro.
Table 4 Process performance of the base case
Description
Reference
Base case
Gas turbine power output (MWe)
295.03
294.64
Steam turbine power output (MWe)
170.71
117.69
Power island auxiliary power consumption (MWe)
11.69
9.7
CO2 compression power consumption (MWe)
–
14.8
Mechanical power consumption in capture process (MWe)
–
2.035
Stripper reboiler duty (MWth)
–
176.2
Steam extracted for reboiler (kg/s)
–
71.06
CO2 captured (kg/s)
–
40.92
CO2 emission (kg/MWh)
348.3
40.98
Speciﬁc duty (MJth/kg CO2)
–
4.31
Net plant power output (MWe)
453.872
385.795
Net plant efﬁciency (%, LHV)
58.74
49.93
306
X. Luo and M. Wang
Table 6 shows the comparison results of reference case of NGCC stand-alone
and the base case of carbon capturing. In the base case, the annualized CAPEX of
PCC process is close to the annualized CAPEX of NGCC power plant, and the
variable OPEX accounts for 65% of the total annual cost. For the variable OPEX of
NGCC stand-alone, the fuel cost is the biggest part and carbon emission cost is the
second largest part. However, when NGCC is integrated with PCC process, the
ﬁxed OPEX increases obviously because of new expense items such as CO2 T&S
cost and MEA solvent make-up cost. According to this result, the scenarios of
different fuel price and CO2 T&S price will be analysed to see their impacts on the
optimal study on carbon capture level in response to carbon price.
5
Optimal Operation
The economic evaluation of the base case in Sect. 4.2 shows the high capital cost as
well as great variable operating cost occurs for capturing carbon from NGCC power
plant. For the optimal operation of an assumed existing NGCC power integrated
Table 5 Key economic evaluation cost inputs
Description
Unit
Value
Reference
Refrigerant price
€/t
0.17
Aspen Tech (2012a)
Carbon price
€/kg
7.0
FML (2015)
NG price
€/GJ
6.58
Ycharts (2015)
MEA solvent price
€/ton
1452
Alibaba.com (2015)
CO2 T&S cost
€/ton
39.54
DECC (2013)
Project economic life
year
25
IEAGHG (2012)
Table 6 Model parameters of PCC process with EGR
Description
Unit
Reference
Base case
Annualized CAPEX of NGCCa
M€/year
44.226
41.257
Annualized CAPEX of PCCa
M€/year
–
39.158
Fixed OPEX of NGCCa
M€/year
8.344
7.784
Fixed OPEX of PCCa
M€/year
–
9.078
Variable OPEX
Fuel
M€/year
160.424
160.424
Carbon emission
M€/year
9.695
0.970
T&S
M€/year
–
51.025
Solvent make-up
M€/year
–
2.999
Refrigerant
M€/year
–
0.673
Total annual cost
M€/year
231.034
330.230
LCOE
€/MWh
58.10
97.70
aThe cost refers to a benchmark report from IEAGHG (2012)
Optimal Operation of MEA-Based Post-combustion Carbon Capture Process. . .
307
with PCC process, two major questions are concerned: (1) What is the optimal
carbon capture degree under different market conditions? (2) What are the optimal
values of key operational variables for an optimal capture level?
To answer these questions, in this section, the studies were carried out for the
optimal carbon capture level in response to carbon price ﬁrst. And then the impacts
of fuel price and CO2 T&S price were analysed. Lean loading was chosen to be the
key operational feature variable for optimization.
5.1
LCOE Under Carbon Price
In order to achieve the target of global climate control, CO2 allowance price was set
to drive the actions of reducing CO2 emission. Current carbon price in EU is around
€7/ton CO2 (FML 2015), but future carbon price is highly uncertain with different
paths predicted by DOE of US (see in Fig. 2). The economic performances with a
representative of LCOE were examined under different carbon price conditions of
€7, €50, €100 and €150 per tonne of CO2 in this study.
The results are summarized in Fig. 3. Under low carbon price of €7/ton CO2,
LCOE gets the minimum value of €83.4/MWh with 60% CL at an optimal lean
loading of 0.26 mol CO2/mol MEA. Figure 3 (a) also shows LCOE increases
obviously with higher CL no matter what the lean loading would be. That trend
may indicate that under low carbon price, the carbon emission penalty cost cannot
justify the higher operating cost of PCC process. The optimal operation in terms of
minimum LCOE is discharging the ﬂue gas to the atmosphere by bypassing PCC
process. With higher carbon price of €50/ton CO2, the differences of LCOE of
different CL become smaller. For the scenario of carbon price of €100/ton CO2, the
Fig. 2 Carbon price paths (USDOE 2010)
308
X. Luo and M. Wang
values of LCOE distribute in a very narrow range which means the carbon emission
penalty cost can just justify the extra variable OPEX cost for carbon capture. With
high carbon price of €150/ton CO2, the optimal value of LCOE of 90% CL and 95%
is very close at a lean loading of 0.26–0.28 mol CO2/mol MEA.
This optimization study indicates that the minimum value of carbon price may
be around €100/ton CO2 to justify the investment of CCS deployment for NGCC
power plant. And the carbon price needs to rise up to around €150/ton CO2 to drive
the optimal operation strategy to meet 90% and 95% carbon capture target.
5.2
The Effect of NG Price
In Sect. 4, the economic evaluation results show fuel cost is the largest part of the
variable OPEX and is a huge expense even compared with the annualized CAPEX. It
is realized that the uncertain NG price would have a big impact on deciding the
Fig. 3 LCOE in response to different carbon prices: (a) carbon price is €7/ton CO2, (b) carbon
price is €50/ton CO2, (c) carbon price is €100/ton CO2, (d) carbon price is €150/ton CO2
Optimal Operation of MEA-Based Post-combustion Carbon Capture Process. . .
309
optimal operation strategy. Figure 4 shows the results of the optimal capture level
under different fuel prices assuming a scenario with carbon price at €100/ton CO2. At
the low NG price scenario (see in Fig. 4a), the higher capture level shows a low LCOE
because the CO2 emission penalty can more easily justify the fuel cost. However, the
situation reverses when NG price rises up to €12/GJ. Then a carbon price higher than
€100/ton CO2 is needed to draw the balance back for carbon capture.
5.3
The Effect of CO2 T&S Price
The CO2 transport and storage cost is a signiﬁcant part of variable OPEX of running
a PCC process for the power plant. DECC of UK (2013) issued a report, in which
the transport and storage cost accounts for a big part of the increment of LCOE.
Under FID 2013, FID 2020 and FID 2028 CCS technology scenarios, the CO2 T&S
cost is £40.7, £15.7 and £3.7 per MWh of electricity. The change of the CO2 T&S
Fig. 4 LCOE in response to different fuel price with carbon price of €100/ton CO2: (a) NG price
is €2/GJ, (b) NG price is €6.58/GJ, (c) NG price is €12/GJ
310
X. Luo and M. Wang
price may affect the optimal operation decision largely. In this section, the optimi-
zations were carried out on three different CO2 T&S equivalent prices of €102.5,
€39.54 and €9.32 per tonne of CO2 assuming a scenario with carbon price at €100/
ton CO2. The results are displayed in Fig. 5. With low CO2 T&S price of €9.32/ton
CO2, the optimal capture level is 90–95% compared with 80–90% at the interme-
diate price of €39.54/ton CO2. At the high CO2 T&S price of €102.5/ton CO2, the
high cost of carbon capture would not be justiﬁed (see in Fig. 5a) and a carbon price
higher than €100/ton CO2 is needed to provide driving force for carbon capture.
Otherwise, bypassing PCC process is the optimal choice.
6
Conclusions
In this paper, the optimal operation of NGCC power plant integrated with PCC
process was investigated under different market conditions such as carbon price,
fuel price and CO2 T&S cost. To be a part of the optimization program, a rate-based
Fig. 5 LCOE in response to different CO2 T&S price with carbon price of €100/ton CO2: (a) CO2
T&S price is €9.32/ton CO2, (b) CO2 T&S price is €39.54/ton CO2, (c) CO2 T&S price is €102.5/
ton CO2
Optimal Operation of MEA-Based Post-combustion Carbon Capture Process. . .
311
steady-state process model including NGCC, PCC and compression train was
developed using Aspen Plus®. The objective function to be minimized in the
optimization is formulated for LCOE with dividing total annual cost, a sum of
annualized CAPAX, ﬁxed OPEX and variable OPEX, by annual net power output.
The economic evaluation was carried out for the reference case and the base case
for whole chain of NGCC integrated with PCC, CO2 T&S. The base case was set up
based on a PCC process with 90% carbon capture level and with EGR for NGCC
power plant. The optimal operations were carried out for the carbon capture level
under different carbon price, fuel price and CO2 T&S price by minimizing LCOE.
Some conclusions from this study were summarized for the optimal operation of a
453-WMe NGCC power plant integrated with PCC process, as follows:
•
Fuel cost, carbon emission cost and CO2 T&S cost are major parts of the variable
OPEX cost.
•
Carbon price needs to be more than €100/ton CO2 to justify the cost of carbon
capture.
•
Carbon price needs to be around €150/ton CO2 to drive optimal carbon capture
level to 90–95%.
•
Higher carbon price is required to get same optimal carbon capture level when
NG price rises up.
•
Higher carbon price is required to get same optimal carbon capture level when
CO2 T&S cost rises up.
•
The optimal range of lean loading is 0.26–0.3 mol CO2/mol MEA.
Acknowledgements The authors would like to acknowledge the ﬁnancial support from EU FP7
Marie Curie International Research Staff Exchange Scheme (Ref: PIRSES-GA-2013-612230) and
2013 China–Europe small- and medium-sized enterprises energy saving and carbon reduction
research project (No.SQ2013ZOA100002).
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Experimental and Numerical Investigations
of “Fabrication of TiO2 Compact Layer by
the Spray Pyrolysis Deposition System
for Dye-Sensitized Solar Cells”
Pernebayeva Damira, Upadhyaya Hari, and Prabhakara Bobbili
1
Introduction
Environmental concerns and the escalating demand for power generation are key
drivers for the research and development of environmentally friendly and cost-
effective technologies and products. As fossil fuels diminish and the world becomes
increasingly reliant on the generation of electric power, the use of renewable energy
is being sought as an alternative way for power supply. Among other renewable
energy technologies, photovoltaic (PV) devices could be recognized as the most
promising technology due to the abundance and inexhaustibility of solar radiation
(1361 W/m2 by Kopp et al. 2005).
Generally, solar cell technologies can be divided into the ﬁrst, second, and third
generations. The ﬁrst generation, such as crystalline silicon PV panels, is a more
mature technology, whereas the second includes the emerging thin-ﬁlm technolo-
gies that have just entered the market, and the third covers future technologies
(Hagfeldt et al. 2012). Although silicon wafer PV technology that relies on expen-
sive bulk multi-crystalline or single-crystal semiconductors has been considered as
a dominant in the solar industry, new generation solar cells such as dye-sensitized
or “Gratzel” cell can be considered as a competitor device. The dye-sensitized solar
cell (DSC) is an emerging device that is based on molecular and nanometer-scale
components and could have many commercial applications owed to a large
P. Damira (*)
Nazarbayev University, School of Engineering, Mechanical Engineering Department,
53 Kabanbay Batyr Ave, Astana, Republic of Kazakhstan, 010000
e-mail: damira.pernebayeva@nu.edu.kz
U. Hari • P. Bobbili
Heriot-Watt University, Engineering and Physical Science, Mechanical Engineering
Department, Edinburgh EH14 4AS, UK
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_22
315
ﬂexibility in shape, light weight, color, and transparency (Hagfeldt et al. 2012).
Moreover, DSCs could eclipse traditional PV cells due to their simple fabrication
process at relatively low cost (40% lower compared with silicon PV) and at reduced
material consumption (Hashmi 2011).
Basically, DSC is a photoelectrochemical solar cell device that converts sunlight
into electricity with efﬁciency above 12% for small cells and about 9% for mini
modules (Hagfeldt et al. 2012). Initially, O’regan and Gratzel innovated nanocrys-
talline dye-sensitized solar cells (Oregan and Gratzel 1991). Common DSCs con-
tain the following structure: a mesoporous TiO2 working electrode, a Ru-complex
sensitizer, electrolyte containing I¯/I3¯ redox couple, and transparent conductive
oxides (TCO) counter electrode coated with a thin layer of platinum (Hagfeldt et al.
2012). A mesoporous and nanostructured TiO2 ﬁlm is one of the key components of
the DSCs. It provides a large surface area for photon absorption and electron
transport to the substrate (Hagfeldt et al. 2012). Generally, the mesoporous TiO2
electrode is composed of the following structures: TiO2 compact layer (with a
thickness of ~50 nm), a light-absorbing layer with a thickness of ~10-μm (with
~20 nm particle size), a light-scattering layer with a thickness of ~3 μm porous layer
(with ~400 nanometer-sized TiO2 particles), and an overcoating of TiO2 throughout
the whole structure, formed by aqueous TiCl4 with a further annealing treatment
(Hagfeldt et al. 2012).
Basically, the working principle of the DSC is the following: the energy of an
incident photon is absorbed by a dye molecule and the electrons with sufﬁcient
energy are excited from the valence band to the conduction band of each dye. Then
the electron is injected from the photoexcited dye to the lower energy conduction
band of the semiconductor. Following this, the electrons diffuse through TiO2
semiconductor toward the transparent conductive oxides substrate, and simulta-
neously the oxidized dye molecules are regenerated by oxidizing I to I3
 ions
(Li 2012). The electrons of the I3
 ions are replaced by the ones arriving from the
external circuit toward the counter electrode. This cycle repeats its process and
electricity is produced.
As it has been previously said, the electrons diffuse through the semiconductor
and therefore its properties are worth mentioning. Titanium dioxide, an n-type
wide-bandgap semiconductor, is an efﬁcient photocatalyst and has been considered
as one of the most proposed semiconductor materials for DSCs. It takes the leading
position among other materials due to its abundance and unique photoelectric and
photocatalytic properties such as high refractive index, high dielectric constant,
wide energy bandgap, and transparency. TiO2 photocatalytic activity was ﬁrst
explored by Fujishima and Honda (1972).
Despite all the advantages of DSCs, its commercialization is a major challenge
due to technical constraints such as low light absorption and instability. The factor
causing cell instability is referred to the liquid electrolyte which limits the shape of
the device and introduces risks of evaporation and leakage of the solvent
(De Freitas et al. 2008). Another issue relates to the charge recombination processes
that occur due to the physical contact between the electrolyte and the FTO substrate
surface. Backside electron transfers can occur if the electrons of the semiconductor
316
P. Damira et al.
recombine with the oxidized electrolyte species before diffusing to the TCO. If this
undesired process takes place, it will lead to reduced cell efﬁciency.
However, this process can be overcome by introducing a compact layer (CL) or
blocking layer (BL) between the interfaces of FTO and porous TiO2 that can
effectively prevent the electron recombination due to its high density structure
(Yu et al. 2009). Furthermore, the thickness, doping density, and band alignment of
titanium dioxide blocking layer on FTO are critical parameters (Cameron et al.
2003). Due to the compact layer properties, many research papers have been
focused on compact layer development and fabrication methods, and a variety of
techniques have been already established. Seo et al. investigated the fabrication of
TiO2 blocking layer using Ti sputter deposition and acid-treatment methods (Seo
et al. 2011). As a result, researchers obtained improvements in DSC performance as
the efﬁciency increased by 1.3% compared to DSC without a compact layer.
However, Yu et al. have introduced a better result with a dip-coating technique
(Yu et al. 2009). Blocking layer was formed from TiO2 organic sol and the out-
comes showed that the efﬁciency, short-circuit current, and open-circuit potential of
DSCs with the compact layer were improved by 33.3%, 20.3%, and 10.2%,
respectively. Another work by Patrocı
´nio et al. applied the blocking layer TiO2/
PSS (sodium sulfonated polystyrene) using layer-by-layer method (Patrocionio
et al. 2012). This research presented an increase in the cell efﬁciency from 5.7%
to 7.3%. A different method of the formation of blocking layer was proposed by Li
et al. (2012). BL was fabricated by slow intermolecular electrostatic action between
the
4-tert-butylpyridine
and
the
1,2-dimethyl-3-propylimidazolium
ions.
Researchers highlighted the inﬂuence of the posttreatment effect as aging in the
dark on DSC performance. It was reported that improvements were achieved due to
delayed interaction between electrons and I3
 ions in the electrolyte.
However, among the methods described above, there is another technique called
the spray pyrolysis deposition which is a simple and versatile method of making
thin ﬁlms. This method is considered as the most promising due to its simple work
principle and low operational cost. Moreover, due to its viability, it has been
already applied to the deposition of uniform thin ﬁlms for photovoltaic solar
cells, sensors, powder, and glass production. One of the beneﬁts of spray pyrolysis
deposition system is the deposition on large surface areas. In addition, thin ﬁlm
fabrication process is carried out in the open atmosphere and does not require
expensive vacuum equipment. It also enables the use of a variety of chemicals in
liquid form instead of powders and minimizes the impact on the working environ-
ment and reduces the risks associated with handling toxic solvents (Weber et al.,
2012). Thus, SPD technique could be easily scalable to industrial level based on
these advantages.
The simple apparatus of spray pyrolysis deposition system typically consists of
an atomizer, precursor solution, substrate heater, and temperature controller
(Fig. 1). Basically, SPD technique operates three process stages: the atomization
of precursor solution, spraying the liquid source onto the heated substrate surface,
and the decomposition of the precursor (Perednis et al. 2003).
Experimental and Numerical Investigations. . .
317
Therefore, SPD is of great practical interest in processing dense and porous ﬁlms
for dye-sensitized solar cells by optimizing deposition variables such as the sub-
strate temperature, the composition of precursor solution, the nozzle-to-substrate
distance, the deposition time, the ﬂow rate, the amount of the spread precursor
solution, the droplet size, and the cooling rate after deposition. Since a high
homogeneous structure is needed to produce good-quality thin ﬁlms, the optimiza-
tion and control of the system parameters are essential factors.
Many research papers have focused on the development of thin ﬁlms processing
with an employed SPD technique. Table 1 shows the recent research papers that
focused on the SPD system development and optimized the spraying process.
Initially, the system was pioneered by Viguie and Spitz (1975) and the importance
of substrate temperature was highlighted. Later Perednis and Gauckler (2003) as
well as Duta et al. (2006) carried out further research, reviewed SPD parameters,
and developed the Viguie
´ and Spritz model. It was observed that qualitative and
quantitative precursor compounds, substrate temperature, carrier gas, and droplet
size affect the ﬁlm quality. Another work by Nakaruk and Sorrel investigated the
spray pyrolysis mechanism and they have found that the droplet size depends on
atomization method and ﬁlm thickness that can be controlled by air induction
(Nakaruk and Sorrell 2010). Atomization process refers to the production of
droplets and their scattering into the gas. Ultraﬁne particles can be produced and
determined by the concentration and velocity of the droplet generated by atomizers
(Miki-Yoshida et al. 2006). Frequently, three different atomization methods have
been used in spray pyrolysis including electrostatic, ultrasonic, and pressurized
methods.
Fig. 1 Schematic representation of the typical SPD setup
Table 1 Properties of TiO2
Properties
Anatase phase
Rutile phase
Energy bandgap (eV)
3.26
3.05
Density (g/cm3)
3.90
4.27
Dielectric constant
55
170
Refractive index
2.49–2.55
2.61–2.90
318
P. Damira et al.
Electrostatic spray deposition (ESD) method produces precursor spraying by an
electrohydrodynamic (EHD) force (Zaouk et al. 2000). Basically, a spray is created
by DC voltage applied between the nozzle and the substrate by an electrostatic
force. Regarding ultrasonic spray deposition (USD), ultrasonic atomization relies
on an electromechanical device that vibrates at a very high frequency.
Pressurized spray deposition (PSD) employs a high-speed air to generate an
aerosol. Air under high pressure generates spray toward the substrate; the quality of
spray and droplet size distribution are inﬂuenced by pressure variation (Perednis
2003). Moreover, the aerosol transport to the substrate is also affected by the
chemistry of the precursor solution, the type of the solvent, and the nozzle-to-
substrate distance (Weber et al., 2012).
Eventually, several papers have focused on the system parameters that inﬂuence
ﬁlm properties. Among the large number of processing parameters, substrate
temperature and precursor solution are determined as the most inﬂuencing factors
on ﬁlm quality.
Precursor solution can considerably change the ﬁlm structure, the morphology,
and the surface roughness. The most used precursor materials are titanium, nitrate,
chloride, acetate, etc. Furthermore, different quantity of additives, salt concentra-
tions, and solvents can be added to the precursor solution to modify the ﬁlm
morphology (Jiang et al. 2011). The additives within the solvents enable to develop
a ﬁlm surface structure, adhesion on a substrate surface, improved evaporation rate
(Duta, 2006). TiCl3, HCl, ethyl alcohol, and TiCl4 vapors, titanium(IV)-oxy-
acetylacetonate (TiO(acac)), and Ti powder and H2O concentration were investi-
gated as a precursor solution for TiO2 thin ﬁlm (Jiang et al. 2011). In the paper of
Perednis et al., it was concluded that due to the variation in the precursor solution
with the addition of acetic acid, TiO2 ﬁlm morphology has changed from cracked to
crack-free (Perednis 2003). This was a great achievement in the use of this chemical
composition.
The substrate temperature is another critical parameter. The research article by
Patil investigated the inﬂuence of substrate temperature on ﬁlm porosity and
crystallinity (Jiang et al. 2011). An increase in substrate temperature led to subse-
quent ﬁlm porosity. Obtained tests in thin ﬁlm fabrication showed that the anatase
phase at 325 C has less optical transmittance than at 450 C. In another experiment
by Yanagi et al. (1998), the surface morphology differed from inconstant particles
at 200 C to uniform with 400 C. It was indicated that a homogeneous particle
formation was achieved at higher temperature. Similarly, Masayuki et al. (2003)
attained the anatase type TiO2 ﬁlm by enhancing the substrate temperature from
450 to 500 C.
Research conducted by Perednis et al. (2003) highlighted that the morphology
and the porosity of ﬁlms highly depend on the substrate temperature. For that
reason, the ﬁlm structures were modiﬁed from a cracked form into microporous
due to the temperature increase from 420 to 490 C and above. Although there are
other factors such as deposition time, type of precursor solvents, and salts that can
affect the ﬁlm, the conducted research proves the signiﬁcant role of substrate
temperature.
Experimental and Numerical Investigations. . .
319
Nomenclature
T
Substrate temperature (C),
Q
Flow rate ()
Ø
D
T
σ
R
P
T%
Nozzle diameter
Nozzle-to-substrate distance
Spraying time
Surface roughness
Resistance
Pressure
Transmittance
Subscripts
XRD
TEM
EDS
AFM
SEM
X-ray diffraction
Transmission electron microscopy
Energy dispersive spectroscopy
Atomic force microscopy
Scanning electron microscopy
Current research aims to enhance the DSCs’ performance via introducing the
deposited TiO2 compact layer (CL) between FTO and TiO2 mesoporous ﬁlm
interface. Research focuses on the fabrication of a high quality TiO2 CL by SPD
technique. This work consists of two main objectives: the ﬁrst is to design and
develop the experimental aerosol-assisted SPD setup and optimize the process
parameters; and the second is to fabricate the TiO2 compact layer and assemble
the dye-sensitized solar cell based on it. The ﬁnal part is to investigate the optical
and structural properties of the compact layer and photovoltaic performance of
DSCs by instruments such as HITACHI (S-2700) scanning electron microscopy
(SEM), the BRUKER (D500) X-ray diffractometer (XRD), JENWAY 7310
UV-Vis spectrophotometer, and Newport 29,950–1000 solar simulator.
Eventually, several papers have focused on the system parameters that inﬂuence
ﬁlm properties. Among the large number of processing parameters, substrate
temperature and precursor solution are determined as the most inﬂuencing factors
on ﬁlm quality.
Precursor solution can considerably change the ﬁlm structure, morphology, and
the surface roughness. The most used precursor materials are titanium, nitrate,
chloride, acetate, etc. Furthermore, different quantity of additives, salt concentra-
tions and solvents can be added to the precursor solution to modify the ﬁlm
morphology (Jiang et al. 2011). The additives within the solvents enable to develop
a ﬁlm surface structure, adhesion on a substrate surface, improved evaporation rate
(Duta, 2006).TiCl3, HCl, ethyl alcohol and TiCl4 vapors, titanium(IV)-oxy-
acetylacetonate (TiO(acac)), and Ti powder and H2O concentration were investi-
gated as a precursor solution for TiO2 thin ﬁlm (Jiang et al. 2011). In the paper of
Perednis et al., it was concluded that the variation in the precursor solution with an
addition of acetic acid, TiO2 ﬁlm morphology has changed from cracked to
cracked-free (Perednis 2003). This was a great achievement in the use of this
chemical composition.
320
P. Damira et al.
The substrate temperature is another critical parameter. The research article by
Patil investigated the inﬂuence of substrate temperature on ﬁlm porosity and
crystallinity (Jiang et al. 2011). An increase in substrate temperature leaded to a
subsequent ﬁlm porosity. Obtained tests in thin ﬁlm fabrication showed that the
anatase phase at 325 C has less optical transmittance than at 450 C. In another
experiment by Yanagi et al. (1998), the surface morphology differed from incon-
stant particles at 200 C to uniform with 400 C. It was indicated that a homoge-
neous particle formation was achieved at higher temperature. Similarly, Masayuki
et al. (2003) attained the anatase type TiO2 ﬁlm by enhancing the substrate
temperature from 450 to 500 C.
Research conducted by Perednis et al. (2003) highlighted that the morphology
and the porosity of ﬁlms highly depend on the substrate temperature. For that
reason, the ﬁlm structures were modiﬁed from a cracked form into micro porous
due to the temperature increase from 420 to 490 C and above. Although there are
other factors such as deposition time, type of precursor solvents and salts that can
effect on ﬁlm, the conducted research proves the signiﬁcant role of substrate
temperature.
Current research aims to enhance the DSCs performance via introducing the
deposited TiO2 compact layer (CL) between FTO and TiO2 mesoporous ﬁlm
interface. Research focuses on the fabrication of a high quality TiO2 CL by SPD
technique. This work consists of two main objectives: the ﬁrst is to design and
develop the experimental aerosol assisted SPD set up and optimize the process
parameters; and the second is to fabricate the TiO2 compact layer and assemble the
dye-sensitized solar cell based on it. The ﬁnal part is to investigate the optical and
structural properties of the compact layer and photovoltaic performance of DSCs by
instruments such as HITACHI (S-2700) scanning electron microscopy (SEM), the
BRUKER (D500) X-ray diffractometer (XRD), JENWAY 7310 UV-Vis spectro-
photometer spectrophotometer, and Newport 29,950–1000 solar simulator.
2
Experimental Facility
2.1
Experimental Aerosol-Assisted SPD Setup
The apparatus used for spray pyrolysis was designed and assembled for laboratory
spraying tests. Generally, the system consisted of a spray bottle with a nozzle
diameter of 2 mm, a hot plate, and carrier gas provided by the silicon tube.
Pressurized air was used as an atomizer. The system was constructed in the
laboratory fume board. The image of SPD system is presented in Fig. 2. Through
designed set of experiments, the range of initial spray conditions, such as deposition
temperature, the concentration of precursor solution, the nozzle-to-substrate dis-
tance, the ﬂow rate of carrier gas, and the spraying time over which the ﬁlm could
be reproducibly deposited, were deﬁned. Based on the literature review, it was
found that the substrate temperature has a major impact on the ﬁlm quality.
Experimental and Numerical Investigations. . .
321
Therefore, this parameter was chosen to be investigated while others were kept
constant. The temperature range was set between 300 and 500C, since the quality
of the ﬁlms gradually decreased with lower temperature and at temperature above
500C the substrate glass tended to crack.
2.2
Fabrication of TiO2 Compact Layer by the SPD Setup
The TiO2 suspension was prepared by mixing 0.5 ml of Ti(IV) isopropoxide and
100 ml of ethanol solution. The precursor solution was atomized by compressed air
(0.1–0.2 bar) through the nozzle and toward the preheated substrate glass on the hot
plate.
Fluorine-doped
tin
oxide
(FTO)
glass
substrates
with
a
size
of
20  20  2 mm were used. Before spraying, the FTO glass was cleaned in a
soap solution using an ultrasonic bath for 15 min, and then rinsed with DI water and
isopropyl alcohol (IPA) followed by drying with nitrogen stream. The temperature
range of the hot plate was set between 300 and 500 C with an increasing rate of
100 C. After spraying, the samples were sintered at 500 C for 30 min and then
cooled to room temperature. The distance between the nozzle and the substrate was
about 20–30 cm. The following table presents the range of chosen working param-
eters for spraying process (Table 2).
2.3
The Working Electrode Fabrication by Screen-Printing
Technique
The screen-printing process includes several procedures: coating, storing, and
heating. The working electrode was formed from ﬁve layers: three layers of a
transparent and a double layer of scattering paste resulting in the thickness of
Fig. 2 Image of
experimental aerosol-
assisted SPD setup
322
P. Damira et al.
15 μm. For the transparent and scattering layer, the TiO2 (DSL 18 NR-T) and TiO2
paste (DSL 18NR-AO) were used, respectively. Transparent paste was applied on
the top of the TiO2 compact layer by screen printing and kept in a clean box for 45 s
with acetone so that the paste can relax to minimize the surface irregularity and then
dried for 3 min at 120 C. This procedure was repeated for each layer. Finally,
electrodes coated with the TiO2 paste were subjected to gradual heating at 325 C
for 5 min, at 375 C for 5 min, at 450 C for 15 min, and at 500 C for 15 min.
2.4
Dye and Electrolyte Preparation
The TiO2 photoelectrode was dipped into N-719 dye solution in a mixture of
absolute ethanol and stored at room temperature for 16–20 h. As regards the
electrolyte, the same preparation suggested by Seigo et al. was used. This method
utilizes 0.6 M BMII, 0.03 M I2, 0.10 M guanidinium thiocyanate, and 0.5 M 4-tert-
butylpyridine in a mixture of acetonitrile and valeronitrile (volume ratio,
85:15) (Yu et al. 2009).
2.5
Counter-Electrode Preparation
Counter electrode was prepared by the following procedure. A hole was drilled in
the FTO glass by sand blasting for 50 seconds, which was then washed with DI
water and cleaned with acetone in a sonicated bath for 15 min. Thereafter, the
Platisol T/SP catalyst (purchased from Solaronix) was printed on the FTO glass by
coating which was then heated at 400 C for 15 min (Fig. 3).
Table 2 Working parameters of SPD system
Temperature
300–500 C with 100 C step
Air pressure
0.1 bar
Nozzle-to-substrate distance
20–30 cm
Spraying time
5–10 s
Nozzle diameter
2 mm
Substrate glass
FTO (20  20  2 mm3)
Precursor
0.5 wt% of the (IV)-isopropoxide
Fig. 3 Flow chart of the
counter-electrode
preparation
Experimental and Numerical Investigations. . .
323
2.6
Dye-Sensitized Solar Cell Assembling
Dye-sensitized solar cells were formed based on differences on the compact layer.
The ﬁrst group of cells was assembled on the bare FTO substrate. The second group
was fabricated on TiO2 compact layer deposited by the SDP system at different
temperatures, and the third group was based on a standard TiCl4 treated. DSCs were
assembled into a sandwich-type cell composed of the Pt-counter electrode, the
electrolyte, and the dye-sensitized electrode. A drop of the electrolyte was injected
by the syringe into the hole in the back of the counter electrode and introduced into
the cell via the vacuum machine. The cell was placed in a small chamber to remove
inside air. Finally, the hole was sealed using a hot-smelt ionomer ﬁlm (Surlyn). The
fabrication process of the cell is illustrated in Figs. 4 and 5.
Working electode
Counter electrode
Pt paste coating
& Firing
Hole drilling
Tio2 coating
Dye adsorbing
Sealing & Injecting
Liquid electrolyte
Sealing holes
&
Cell test
Fig. 4 Schematic
representation of the DSC
fabrication procedure
Fig. 5 Dye-sensitized solar
cell assembled
324
P. Damira et al.
2.7
Characterization Instruments
The surface morphology of the transparent TiO2 compact ﬁlm was studied by using
HITACHI (S-2700) scanning electron microscopy (SEM). The crystal structure of
the ﬁlm structure was studied by the BRUKER (D500) X-ray diffractometer
(XRD). The optical transmittance was measured by the JENWAY 7310 spectro-
photometer in the wavelength range of 320–900 nm, and the J–V characteristics of
the DSCs were measured using a Newport 29,950–1000 solar simulator.
3
Results and Discussions
3.1
Transmittance of the TiO2 Compact Layer
The transmittance spectra of the bare FTO, the TiCl4-treated compact layer, and the
TiO2 blocking layer fabricated by the SPD at 300C, 400 C, and 500C are shown
in Fig. 6. It can be clearly observed that the transmissivity of FTO treated with TiO2
and TiCl4 decreased by 10%. The bare FTO glass presented about ~85% of
transmittance spectra in the visible range, whereas with a TiO2 compact layer it
300
400
500
600
700
800
900
20
30
40
50
60
70
80
90
100
Transmittance (%)
Wavelength (nm)
300oC
400oC
500oC
Without CL
TiCl4treated CL
Fig. 6 Transmittance spectra of the compact layers of TiO2 deposited at a temperature of 300 C,
400C, and 500C and TiCl4 treated. The data of a bare FTO glass substrate are presented as a
reference
Experimental and Numerical Investigations. . .
325
was less. At 500C, the compact ﬁlm showed a slightly better transmittance (~70%)
than those deposited at 300 and 400C (>70%). However, the transmittance of the
FTO treated with TiCl4 (~82%) was not inﬂuenced signiﬁcantly. Similar results of
the transmittance of TiO2 ﬁlms were obtained in the literature.
3.2
Structure and Surface Morphology of the TiO2
Compact Film
The SEM study of the TiO2 compact ﬁlms at 300C, 400C, and 500C is illustrated
in Fig. 7. From the images, the uniformity and the roughness of the surface
morphology of TiO2 compact layer can be observed as well as the crack-free
ﬁlms that were obtained. This achieved surface morphology is beneﬁcial for the
overall cell performance.
The X-ray diffraction spectra of the deposited TiO2 ﬁlms are shown in Fig. 8.
SnO2 and TiO2 peaks are visible. In very thin layer deposited at 300C, 400C and
500C, three peaks are found for TiO2 anatase form. Anatase phase, with an energy
bandgap of 3.2 eV, is preferred than the rutile phase, which has an energy bandgap
of 3.0 eV, due to its morphology. The small and spherical grains of anatase phase
allow for higher dye absorption and enhanced electron transport according to Zaouk
et al. (2000) (Figs. 9 and 10).
Fig. 7 SEM images of the
surface morphology of the
TiO2 compact layer
deposited at 300 C
326
P. Damira et al.
Fig. 8 SEM images of the
surface morphology of the
TiO2 compact layer
deposited at 400 C
Fig. 9 SEM images of the
surface morphology of the
TiO2 compact layer
deposited at 500 C
Experimental and Numerical Investigations. . .
327
3.3
Electrical Conductivity Measurement of DSCs
A Newport 29950-1000 solar simulator was used to measure current–voltage values
to determine the power conversion efﬁciencies of solar cells. Measurements were
carried out in the dark and under illumination. The cells were covered with a black
mask with a hole (2 mm in diameter) around the active area (1 cm2) of the cells. The
energy conversion efﬁciency, η, was calculated by the J–V characteristic curve and
following equations:
η ¼
JSC  VOC  ff
Pin
 100%
ff
¼ Pmax  Jsc  Voc
where JSC is the short-circuit density, VOC is the open circuit voltage, ff is the ﬁll
factor, Pin is the illumination intensity, and Pmax is the maximum power output of
the solar cell (Hagfeldt et al. 2012).
DSC based on the TiO2 blocking layer showed better energy conversion efﬁ-
ciency, higher current density, and ﬁll factor than the cell without the blocking
layer. At 300C, the compact layer has improved the DSC’s overall energy efﬁ-
ciency from 3.8% to 6.4%, the current density increased from 11.31 to 12.8 mA/cm
2, and the ﬁll factor enhanced from 0.51 to 0.76. However, the efﬁciency of the cells
declined for the layers that were prepared at 400 and 500C. The best cell perfor-
mance was achieved by traditional TiCl4 treatment that gave efﬁciency of 7.4%, the
short-circuit current density of 13.08 mA/cm2, the open circuit voltage of 0.68 V,
and ﬁll factor of 0.82. The open circuit voltage remained low for all cells which
might be due to the lower shunt resistance and an internal electron leakage from the
FTO (Fig. 11 and Table 3).
10
20
30
40
50
60
70
80
*
(310)
*
(200)
*
(110)
Intensity (a.u.)
2q (degrees)
T=300
oC
T=400
oC
T=500
oC
Fig. 10 XRD patterns for the TiO2 compact ﬁlms deposited at different substrate temperatures
indicate peaks of the anatase phase based on JCPDS no 88–1175 for TiO2, (*) refers to the (SnO2:
F) substrate based on JCPDS card: 00–035-0907 for FTO
328
P. Damira et al.
4
Conclusions
The TiO2 compact ﬁlm as a means of blocking layer has been introduced between
the FTO and nanoporous TiO2 ﬁlm in DSC. The spray pyrolysis deposition tech-
nique was employed to fabricate the compact ﬁlms. The process parameters of the
SPD system were optimized through the number of tests to obtain high-quality
ﬁlms. The results based on the DSC performance revealed that a good quality of
TiO2 compact layer can be obtained at 300C substrate temperature. By X-ray
diffraction, the anatase phase of the TiO2 layer that was deposited at 300C, 400C,
and 500C was found. In addition, the transmittance spectra of the compact layers
were around 70% which is 10% lower than that of the bare FTO transmittance.
Overall, owing to the introduction of the compact layer, the direct contact between
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
 Current density (mA/cm2) 
Voltage (V)
 300
oC
 400
oC
 500
oC
 Without CL
 TiCl4 CL
Fig. 11 J–V curve of DSCs with CL deposited at different substrate temperatures by the SPD
system, CL treated with TiCl4, and DSC without CL
Table 3 Photovoltaic parameters of DSCs without CL, with TiO2 CL deposited at different
temperatures and TiCl4 treated
Sample
Voltage, V oc (V)
Current density,
J sc (mA/cm2)
Fill factor,
ff
Efﬁciency ŋ, (%)
Without CL
0.65
11.31
0.51
3.76
300 C
0.65
12.80
0.76
6.40
400 C
0.67
13.00
0.66
5.77
500 C
0.63
12.19
0.73
5.61
TiCL4-treated CL
0.68
13.08
0.82
7.35
Experimental and Numerical Investigations. . .
329
FTO and electrolyte interfaces was avoided and therefore the electron recombina-
tion was prevented. As a result, Jsc value increased from 11.31 to 12.8 mA/cm2, and
the overall cell efﬁciency was improved almost two times from 3.8% to 6.4%. Thus,
these results indicate the signiﬁcant role of the compact oxide blocking layer in
DSC. Although the cell based on the compact layer treated with TiCl4 remains at
higher performance (the efﬁciency is 7.35%, current density is 13.08 mA/cm2, the
open circuit voltage is 0.68 V, and ﬁll factor is 0.82), the compact layer prepared by
the SPD technique has a great potential. Moreover, the SPD system can be applied
for the fabrication of photoelectrodes and dyes and sequentially reduces the cost
of DSCs.
Acknowledgments This research was supported by Professor Hari Upadhyaya, Dr. Prabhakara
Bobbili, and Dr. Senthilarasu Sundaram, the Laboratory of Energy of Heriot-Watt University.
References
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Experimental and Numerical Investigations. . .
331
Experimental Investigation on Citrullus
colocynthis Oil as Alternative Fuel
Aida Cherifa AHMIA, Fetta DANANE, Rhiad ALLOUNE,
and Rahma BESSAH
1
Introduction
The increase in energy demand and pollution problems caused by industrialization
has urged researchers and economists to ﬁnd new sources of energy. One of the
feasible energy sources is the use of plant oils, which is readily available and
environmentally acceptable (Meher et al. 2006).
The concept using vegetable oil as a fuel dates back to 1895 when Dr. Rudolf
Diesel developed the ﬁrst diesel engine to run on vegetable oil.
Biodiesel is an alternative diesel fuel that consists of alkyl monoesters of fatty
acids from vegetable oils or animal fats. Its acceptance as a substitute for fossil-
derived diesel has grown the world over.
Pure vegetable oils have been used in the past in diesel engine. However, there
have been many problems linked with the direct use of vegetable oils in diesel
engine, such as high viscosities and lower volatilities (Kansedo et al. 2008).
Transesteriﬁcation is one of the accepted processes for the production of bio-
diesel from oils and fats with alcohols in the presence of homogeneous catalysts
(Zhang et al. 2003) or heterogeneous catalysts (Tauﬁq-Yap et al. 2011; Siddiquee
et al. 2011). The homogeneous catalysts have been proven to be more practical in
application (Liu et al. 2008). The alkaline catalyst is capable of producing higher
yield and purity of biodiesel with a reaction time of between 30 and 60 min
A.C. AHMIA (*)
Centre de De
´veloppement des Energies Renouvelables, CDER, 16340 Algiers, Algeria
Universite
´ des Sciences et de la Technologie Houari Boumediene, USTHB,
16111 Algiers, Algeria
e-mail: ahmia.a.c@gmail.com
F. DANANE • R. ALLOUNE • R. BESSAH
Centre de De
´veloppement des Energies Renouvelables, CDER, 16340 Algiers, Algeria
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_23
333
(Liu 1994) and limits the free fatty acids (FFA) content not more than 1.0% (Leung
and Guo 2006).
Owing to their availability, various oils have been in use in different countries as
feedstocks for biodiesel production. Rapeseed and sunﬂower oils in Europe, soy-
bean oil in the U.S., palm oil in Malaysia and Indonesia, and coconut oil in the
Philippines are being used for biodiesel production. Also, the Jatropha tree
(Jatropha curcas), karanja (Pongamia pinnata) and mahua (Mangifera indica)
are used as major biodiesel fuel sources in India (Demirbas 2009).
However, the food-fuel debate over conventional vegetable oils has rekindled
research interest in exploring non-edible and lesser-known oil crops. Among
oleaginous and non-edible plants is Citrullus colocynthis, commonly known as
the colocynth, bitter apple, bitter cucumber or egusi. It is a creeper, short period
crop grown naturally wild in arid zones. It is a viny plant with butter pulp native to
the Mediterranean Basin, tropical Africa and Asia. C. colocynthis is among the
300 species of melon found in Africa and it is cultivated for its seeds, which are rich
in oil (53%) and protein (28%) (Ntui et al. 2009).
The main objective of the present study is to investigate the use of C. colocynthis
as a potential feedstock for biodiesel production by its seed oil transesteriﬁcation, to
measure the physicochemical properties of the methyl ester produced (refractive
index, density) and to compare them with the standards limits. The great potential
of C. colocynthis oil (CCO) in biodiesel production will be highlighted.
Nomenclature
v
Kinematic viscosity (mm2/s)
Yd
nR
gp
St
Cp
Yield of reaction (%)
Refractive index
Grams of pycnometer (g)
Surface tension (mNm1)
Cloud point (C)
Greek letters
ρ
Density (gcm3)
Subscripts
CCO
C. colocynthis oil
CCOME
C. colocynthis oil methyl ester
dw
Distilled water
2
Materials and Methods
2.1
CCO Extraction
Crude C. colocynthis seed oil was purchased from Bou Saada (239 km South
Algiers). Crushed seeds were introduced to a soxhlet extraction system ﬁtted with
a 500 mL three-necked round bottom ﬂask and a condenser. The extraction was
334
A.C. AHMIA et al.
employed, according to the AOAC ofﬁcial method of 963.15 (Association of
Ofﬁcial Analytical Chemist 1976).
Prior to transesteriﬁcation, oil quality properties of C. colocynthis were deter-
mined. The density was evaluated with a pycnometer and using empirical formula
reported in the literature. The refractive index was determined using a
SCT-105.001.26 refractometer. The kinematic viscosity was determined using a
Cannon-Fenske viscometer.
2.2
Transesteriﬁcation Process and CCOME Analysis
The reversible transesteriﬁcation reactions are the most common method of
converting triglycerides from oils into biodiesel, as shown in Fig. 1, and the most
promising solution of the high viscosity oil problem.
The transesteriﬁcation reaction can be non-catalyzed or catalyzed by an acid, a
base or an enzyme. Depending on the solubility of the chemical catalyst in the
reaction mixture, transesteriﬁcation reaction can be homogeneously or heteroge-
neously catalyzed. These reactions can be accomplished as one-step (base or acid)
or two-step (acid/base) processes, depending on the content of free fatty acids.
The reaction was carried out at 60 C for 1 h with 1 wt% of NaOH and methanol-
to-oil molar ratio of 6:1 (Rashid et al. 2008). In order to maintain the catalytic
activity, a mixture of NaOOH and methanol was freshly prepared to avoid methanol
losses and prevent moisture build-up. This was mixed until the complete dissolution
of the catalyst. The solution was added into the reactor and stirred at 600 rpm. The
reaction time started as soon as the catalyst/methanol solution was added to the
reactor. On completion of the reaction, the resulting product was cooled to room
temperature without any agitation and transferred to a separatory funnel for glyc-
erol and methyl ester separation. It was left overnight to allow separation by
gravity. After the two phases have separated, the upper phase was collected and
the excess alcohol was removed.
The resulting C. colocynthis oil methyl ester (CCOME) obtained was puriﬁed by
successive washing with distilled water (50 ml, 30% of the mass of the mixture) to
remove residual catalyst, glycerol, methanol and soap. A small quantity (0.5 ml) of
sulfuric acid was used in the second washing to neutralize the remaining soaps and
catalyst. Drying was carried out by heating and stirring the CCOME at 100
(Boiling temperature of water).
Fig. 1 Transesteriﬁcation
reaction of triglycerides
with alcohol
Experimental Investigation on Citrullus colocynthis Oil as Alternative Fuel
335
The CCOME yield (Yd) was calculated using empirical formula reported in the
literature (Rashid et al. 2010). The density was evaluated with a pycnometer and
using empirical formula reported in the literature.
The choice of the viscometer depends on the nature of the liquid. Like ours is
transparent, we chose a type Cannon-Fenske viscometer size 150, Type 464.
The apparatus used for the calculation of the surface tension is a tensiometer
TD 2000 Prolabo provided with a very thin platinum blade and perfectly wettable.
3
Results and Discussion
3.1
CCO Characterization
The empirical formula (1) was reported from the literature to determine the density
of CCO:
ρ ¼ gp filled with CCO - gp empty
gp filled with dw - gp empty
ð1Þ
The density was determined to be ρ ¼0.9046 g/cm3. This result is well within the
range reported for conventional vegetable oils.
The kinematic viscosity at 40 C is v ¼ 31.52 mm2/s. The result is also well
within the range reported for conventional vegetable oils in the literature
(Srivastava and Prasad 2000).
The SCT-105.001.26 refractometer gave that the refractive index of CCO is
nR ¼ 1.473. The result ﬁts into the limits given by the literature.
3.2
CCOME Characterization
3.2.1
Reaction Yield
Biodiesel (CCOME) yield estimation was done after the separation and puriﬁcation
of the transesteriﬁed product. The 82% yield (Yd ¼ 82%) of CCOME synthesized
was calculated according to Eq. (2) (Rashid et al. 2010):
Yd %
ð
Þ ¼ grams of CCOME produced  100
grams of CCO used
ð2Þ
3.2.2
Density
Density is an important parameter for diesel fuel injection systems. It is the weight
of a unit volume of ﬂuid. A higher density for biodiesel results in the delivery of a
336
A.C. AHMIA et al.
slightly greater mass of fuel since fuel injection equipment operates on a volume
metering system.
The density of CCOME calculated using the empirical formula (1) was found to
be ρ ¼0.8731 g/cm3.
3.2.3
Kinematic Viscosity
Kinematic viscosity is a very important fuel property and it represents the ﬂow
characteristics of fuel. One of the reasons why biodiesel is used as an alternative
fuel instead of pure vegetable oils or animal fats is as a result of its reduced
viscosity which enhances fuel ﬂow characteristics.
The kinematic viscosity of CCOME obtained from Crude C. colocynthis seed oil
used in our work, measured at 40 C was v ¼ 3.98 mm2/s. The result conforms with
the kinematic viscosity of CCOME obtained from Crude C. colocynthis seed oil
found in North and tropical Africa measured at 40 C which was about 3.83 mm2/s
(Giwa et al. 2010) and 4.486 mm2/s (Elsheikh and Akhtar 2014).
3.2.4
Surface Tension
The surface tension is an important property of the fuel, it inﬂuences the spray
characteristics of the fuel droplets, as well as the combustion efﬁciency (De-gang
et al. 2005). It is deﬁned as a force per unit length resulting from the surface free
energy, energy that is manifested in the work required to raise a unit surface area of
a liquid, isothermally and reversibly.
The tensiometer displays the value St ¼ 34.3 mN/m at 25 C.
3.2.5
Cloud Point
The injection system of an engine cannot function properly if the biofuel is
perfectly smooth. Low temperatures lead to the formation of solid crystals invisible
to the naked eye in the ﬂuid, so that the presence of these crystals in biofuels can
edit blocking the motor power by partially or totally the ﬁlters. This situation affects
engine performance, especially when starting vehicles (Owen and Coly 1990).
The cloud point of oil is the temperature at which it begins to solidify when
cooled under standard conditions. The more the temperature decreases, the more
the crystals form. When the crystals become visible to the naked eye (diameter  5 .
mu.m) we see the emergence of a cloudy suspension and the cloud point is deﬁned
at this temperature (Coley 1989).
The temperature representing the cloud point at 25  C according to standard
ASTM D 2500 is Cp ¼ 0.9  C.
Experimental Investigation on Citrullus colocynthis Oil as Alternative Fuel
337
3.3
Properties Comparison
For comparison purpose, the properties of CCOME, biodiesel and diesel fuels are
listed in Table 1.
The density found in this work ﬁts into the limits speciﬁed by the EN 14214
(0.860–0.900 g/cm3 at 15 C) standard.
The kinematic viscosity also conformed to both biodiesel standards EN 14214
(3.5–5 mm2/s at 40 C) and ASTM D 6751-02 (1.9–6.0 mm2/s at 40 C). The
cloud point of CCOME conforms with the value of cloud point of biodiesel from
soybean oil (1 C).
4
Conclusions
An increasing demand of fossil fuels has being a critical problem for us. The natural
resources of fossil fuel are dwindling day by day. Biodiesel that may called natural
fuel may be a good source or substitute for fossil fuel in the future. In this paper,
CCO was transesteriﬁed using methanol in the presence of sodium hydroxide to
produce CCOME as biodiesel. The yield is satisfactory and the determined biodie-
sel fuel properties of CCOME conformed to EN 14214 and ASTM D 6751-02
standards. The potential of C. colocynthis oil as biodiesel feedstocks was clearly
presented in this study.
Acknowledgements The authors gratefully acknowledge “University of Valenciennes” for the
organization of this important conference and “Centre de Developpement des Energies
Renouvelables” to support this investigation. Our sincerest and deep gratitude goes to Doctor
A.M. Aziza and Doctor R. Bessah for their valuable advice.
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Table 1 Properties of CCOME, biodiesel and diesel fuel
Properties
Unit
CCOME
Biodiesel
standard values
Diesel standard values
Density
gcm3
0.8731
0.86–0.9
0.82–0.85
Serdari and Lois (1999)
Kinematic viscosity
mm2s1
3.98
1.9–6.0
2.0–4.5
Surface tension
mNm1
34.3
–
–
Cloud point
C
0.9
–
16
(Chavan et al., 2014)
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Experimental Investigation on Citrullus colocynthis Oil as Alternative Fuel
339
A Novel Approach to Local-Level Design
of Bioenergy Supply Chains Integrated
with District Heating Systems
Yılmaz Balaman S
¸ebnem and Selim Hasan
1
Introduction
Decision models for biomass to energy supply chain network design of increasing
scope and sophistication with an emphasis on environmental issues have been
devised recently. Among them, Ayoub et al. (2009) develop a methodology and
use a multiobjective MILP model to design and evaluate biomass utilization
networks in local areas. Eks
¸io
glu et al. (2009) develop a mathematical model that
integrates long-term biomass to bioreﬁnery supply chain design and midterm
logistics decisions. Giarola et al. (2013) present a multiperiod, multiechelon
MILP framework to optimize the environmental and ﬁnancial performances of
corn
grain
and
stover-based
bioethanol
supply
chains
simultaneously.
Marufuzzaman et al. (2014), Kanzian et al. (2013), Osmani and Zhang (2014),
and Akgül et al. (2012) are among the other studies that propose bioenergy supply
chain design models.
However, it is necessary to develop models that incorporate inherent uncer-
tainties. In addition, these models should incorporate multiple objectives consider-
ing economic, technical, environmental, and social aspects to provide more
effective solutions to real-life problems. This study aims to develop a comprehen-
sive DSS for design and management of local biomass to energy supply chain
networks integrated with district heating systems considering inherent uncer-
tainties. The proposed DSS integrates MODM and fuzzy modeling approaches to
consider multiple objectives simultaneously and deal with the uncertainties in
biomass to energy supply chains effectively. To treat uncertainties in aspiration
Y.B. S
¸ebnem (*) • S. Hasan
Dokuz Eylül University, Faculty of Engineering, Department of Industrial Engineering,
Tınaztepe Campus, I
˙zmir 35397, Turkey
e-mail: s.yilmaz@deu.edu.tr
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_24
341
levels for the goals and obtain the preferred compromise solution, fuzzy goal
programming (FGP) approach is employed.
The decisions that can be made by the proposed DSS include strategic and
tactical decisions about locations of the energy production facilities and biomass
storages, logistics, and transportation, and the optimal heat distribution system. The
aim is to minimize the costs of district heating system and local transportation while
maximizing the service level, which is the level of meeting heat demand of the
residential area.
Once the decisions for all localities in the regional supply chain corresponding to
procurement and allocation of the resources, conﬁguration of the supply chain
network with related connections and capacities and inventory, production and
logistics management are made, the local-level supply chain design model provide
deals with the comprehensive planning of local supply chain network along with a
district heating system. In this study, we use the ﬁndings of Yılmaz Balaman and
Selim (2014) which present a regional design model and its application.
Biomass to energy conversion plants with CHP generators offer the possibility
of installing district heating systems that are used for distributing heat generated in
a centralized location for residential and commercial heating requirements such as
space heating and water heating. The heat is often obtained from a cogeneration
plant burning fossil fuels. After generation, the heat is distributed from the energy
station to the heat consumers through a district heating network that consists of a
network of insulated pipes. Cost of heat distribution system depends on the length
of the network and thermal energy ﬂow in unit time where investment and opera-
tional costs of the main transfer station depend on the installed capacity of the
system.
2
Formulation of the Proposed Model
Notations and related descriptions for model indices, input parameters, and decision
variables are presented in Nomenclature.
Nomenclature
Indices
I
Bioenergy plant sites
l
j
Plant capacity levels for heat production
Heat demand nodes
K
Waste storage sites
M
Energy crop storage sites
P
Crop storage capacity levels
E
The sites including arable out of use area for energy crop cultivation
F
The sites including cultivated agricultural area
T
Time periods
P
Crop storage capacity levels
E
The sites including arable out of use area for energy crop cultivation
(continued)
342
Y.B. S
¸ebnem and S. Hasan
Parameters
Demjt
Heat demand of demand node j at time period t (kWh)
PHCc
Heat production capacity of the plant with capacity level c (kW)
PCc
Biomass capacity of the plant with capacity level c (ton)
Nc
Number of plants in the selected county with capacity level c
Cp
Energy crop capacity of the energy crop storage with capacity level p (ton)
H1W
Biomass to heat conversion rate of waste biomass W (kWh/ton)
H2E
Biomass to heat conversion rate of energy crop E (kWh/ton)
R1
The remaining of the produced heat in plant after internal consumption and
heat losses (%)
R2
The remaining of the stored heat in heat storage after heat losses (%)
EAet
Arable empty area in site e at time period t (da)
TEAt
Total arable empty area in the selected locality at time period t (da)
CAft
Cultivated area in site f at time period t (da)
F
Fertilizer requirement in the cultivated agricultural area (ton/da)
CHNij
Length dependent cost of heat distribution network per kWh of distributed
energy between plant at site i and heat demand node j (€/kWh-km)
CHI
Investment cost of DHS (€/kW)
CHO
Operational cost per kWh of distributed energy of DHS (€/kWh)
CHS
Cost per kW of installed capacity of thermal storage (€/kWth)
TCkitW
Unit transportation cost of waste biomass W from storage k to the bioenergy
plant at site i at time period t (€/ton)
TCmitE
Unit transportation cost of energy crop E from storage site k to the bioenergy
plant at site i at time period t (€/ton)
TCemtE
Unit transportation cost of energy crop E from arable out of use area e to the
energy crop storage at site m at time period t (€/ton)
TCFift
Unit transportation cost of organic fertilizer from the bioenergy plant at site
i to cultivated area f at time period t (€/ton)
MAXSC
Capacity limit for thermal storage (kW)
DECt
Amount of energy crop stored in the energy crop storages at the selected
locality at time period t (ton)
EAPt
Proportion of energy crop cultivation area to total arable empty area in the
selected locality at time period t (%)
TWPImt /
TWPIntW
Total imported / local waste biomass W amount distributed to the selected
locality’s plants at time period t (ton)
Demjt
Heat demand of demand node j at time period t (kWh)
PHCc
Heat production capacity of the plant with capacity level c (kW)
PCc
Biomass capacity of the plant with capacity level c (ton)
Nc
Number of plants in the selected county with capacity level c
Cp
Energy crop capacity of the energy crop storage with capacity level p (ton)
H1W
Biomass to heat conversion rate of waste biomass W (kWh/ton)
H2E
Biomass to heat conversion rate of energy crop E (kWh/ton)
R1
The remaining of the produced heat in plant after internal consumption and
heat losses (%)
R2
The remaining of the stored heat in heat storage after heat losses (%)
EAet
Arable empty area in site e at time period t (da)
(continued)
A Novel Approach to Local-Level Design of Bioenergy Supply Chains Integrated. . .
343
TEAt
Total arable empty area in the selected locality at time period t (da)
CAft
Cultivated area in site f at time period t (da)
F
Fertilizer requirement in the cultivated agricultural area (ton/da)
CHNij
Length dependent cost of heat distribution network per kWh of distributed
energy between plant at site i and heat demand node j (€/kWh-km)
CHI
Investment cost of DHS (€/kW)
CHO
Operational cost per kWh of distributed energy of DHS (€/kWh)
CHS
Cost per kW of installed capacity of thermal storage (€/kWth)
TCkitW
Unit transportation cost of waste biomass W from storage k to the bioenergy
plant at site i at time period t (€/ton)
TCmitE
Unit transportation cost of energy crop E from storage site k to the bioenergy
plant at site i at time period t (€/ton)
TCemtE
Unit transportation cost of energy crop E from arable out of use area e to the
energy crop storage at site m at time period t (€/ton)
TCFift
Unit transportation cost of organic fertilizer from the bioenergy plant at site
i to cultivated area f at time period t (€/ton)
MAXSC
Capacity limit for thermal storage (kW)
DECt
Amount of energy crop stored in the energy crop storages at the selected
locality at time period t (ton)
EAPt
Proportion of energy crop cultivation area to total arable empty area in the
selected locality at time period t (%)
TWPImt /
TWPIntW
Total imported / local waste biomass W amount distributed to the selected
locality’s plants at time period t (ton)
TEPImtE /
TECPIntE
Total imported / local energy crop E amount distributed to the selected
locality’s plants at time period t (ton)
TWSImtW /
TWSIntW
Total imported / local waste biomass W amount distributed to the selected
locality’s waste storages at time period t (ton)
TESImtE /
TESIntE
Total imported / local energy crop E amount distributed to the selected
locality’s energy crop storages at time period t (ton)
TWPExtW /
TEPExtE
Total exported waste biomass W / energy crop E amount that goes to other
localities’ plants from the selected locality’s waste storages / energy crop
storages at time
period t (ton)
TESExtE
Total exported energy crop E amount that goes to other localities’ energy
crop storages from the selected locality’s energy crop ﬁelds at time period
t (ton)
TFInt
Total local fertilizer amount that goes to the selected locality’s cultivated
areas from the selected locality’s plants at time period t (ton)
Decision Variables
WPImitW /
WPInitW
Imported / local waste biomass W amount distributed to plant in site i at time
period t (ton)
EPImitE /EPInitE
Imported / local energy crop E amount distributed to plant in site i at time
period t (ton)
WSImktW /
WSInktW
Imported / local waste biomass W amount distributed to waste storage in site
k at time period t (ton)
ESImmtE /
ESInmtE
Imported / local energy crop E amount distributed to energy crop storage in
site m at time period t (ton)
(continued)
344
Y.B. S
¸ebnem and S. Hasan
ESPInmitE /
WSPInkitW
Local energy crop E / waste biomass W amount distributed to plant in site
i from energy crop storage in site m / waste storage in site k at time period
t (ton)
EAInemtE
Local energy crop amount distributed to energy crop storage in site m from
energy crop ﬁeld in site e at time period t (ton)
EPExmtE /
WPExktW
Exported energy crop E / waste biomass W amount that goes to other
localities’ plants from the selected locality’s energy crop storage in site m /
waste biomass storage in site k at time period t (ton)
ESExetE
Exported energy crop E amount that goes to other localities’ energy crop
storages from the selected locality’s energy crop ﬁeld in site e at time period
t (ton)
FInit
Local fertilizer amount produced at plant in site i at time period t (ton)
FPInift
Local fertilizer amount distributed from selected locality’s plant in site i to
the selected locality’s cultivated area in site f at time period t (ton)
DSEmt
Amount of energy crop stored in the energy crop storage in site m at time
period t (ton)
OHit
Heat production amount of plant in site i at time period t (kWh)
Qijt
Heat distribution amount from plant in site i to demand node j at time period
t (kWh)
SHit
Stored heat amount of plant in site i at time period t (kWh)
SCi
Thermal storage capacity of plant in site i (kW)
Aet
Size of area for energy crop cultivation in arable empty area in site e at time
period t (da)
Wit
Amount of water used in plant at site i (ton)
Yic
Binary variable that indicates whether or not a plant with capacity level c is
constructed in site i
Dk
Binary variable that indicates whether or not a waste storage is constructed in
site k
Smp
Binary variable that indicates whether or not an energy crop storage with
capacity level p is constructed in site m
WPImitW /
WPInitW
Imported / local waste biomass W amount distributed to plant in site i at time
period t (ton)
2.1
The Objective Function
The proposed model includes the following economic and service-level related
objectives:
•
Minimization of district heating system cost and local transportation cost
•
Minimization of the unmet heat demand which leads to maximization of total
service level
First objective: Minimization of district heating system and local transportation
costs.
A Novel Approach to Local-Level Design of Bioenergy Supply Chains Integrated. . .
345
1. Total cost of district heating system
Total cost of district heating system consists of the following cost components:
•
Investment cost of district heating system,
•
Operational cost of district heating system,
•
Cost of heat distribution network,
•
Cost of thermal storage.
Eq. 1 represents the total cost of the district heating system.
Total district heating system cost ¼
CHI∗X
i
X
j
X
t
Qijt
 
!
þ CHO∗X
i
X
j
X
t
Qijt
 
!
þ
X
i
X
j
X
t
CHNij∗Qijt
 
!
þ CHS∗X
i
SCi
 
!
ð1Þ
2. Local transportation costs
Local transportation costs include the following cost components:
•
Cost of waste biomass transportation from waste storages to plants,
•
Cost of energy crop transportation from crop storages to plants,
•
Cost of energy crop transportation from energy crop ﬁelds to crop
storages,
•
Cost of fertilizer transportation from plants to cultivated areas in the
selected locality.
The following equation calculates the local transportation costs:
Local Transportation Cost ¼
X
k
X
i
X
t
X
W
TCkitW∗WSPInkitW
 
!
þ
X
m
X
i
X
t
X
E
TCmitE∗ECSTPInmitE
 
!
þ
X
e
X
m
X
t
X
E
TCemtE
∗ECAInemtE
 
!
þ
X
i
X
f
X
t
TCFift
∗FERPInift
 
!
ð2Þ
346
Y.B. S
¸ebnem and S. Hasan
Second objective: Minimization of the unmet heat demand. This objective
function ensures maximization of the total service level. The second objective
function is represented by Eq. (3):
Unmet Heat Demand ¼
X
j
X
t
Demjt-
X
i
Qijt
 
!
ð3Þ
2.2
The Constraints
Constraints of the local-level supply chain design model and their deﬁnitions are
presented in this section.
Heat ﬂow conservation and heat storage capacity constraints: Heat ﬂow conser-
vation constraints are represented by Eqs. (4) and (5):
OHit
∗R1
ð
Þ þ SHi t1
ð
Þ
∗R2


¼
X
j
Qijt þ SHit
8i, 8t
ð4Þ
X
i
Qijt  Demjt
8j, 8t
ð5Þ
Eq. (6) presents the constraint that ensures the amount of heat stored in a
conversion plant at each time period must not exceed the thermal storage capacity
in that plant. Eq. (7) limits the thermal storage capacity in each plant to the capacity
limit:
SHit  SCi
8i, 8t
ð6Þ
SCi  MAXSC∗X
c
Yic
8i
ð7Þ
Plant number and heat production capacity constraints: Eq. (8) ensures that the
heat production amount of each conversion plant must not exceed the heat produc-
tion capacity of that plant. Eqs. (9) and (10) limit the plant number in the selected
locality to the value determined in the regional level and ensure that only one plant
must be constructed in a plant location:
OHit 
X
c
PHCc
∗Yic
8i, 8t
ð8Þ
X
i
Yic ¼ Nc
8c
ð9Þ
X
c
Yic  1
8i
ð10Þ
A Novel Approach to Local-Level Design of Bioenergy Supply Chains Integrated. . .
347
Biomass and fertilizer ﬂow conservation constraints: Biomass and fertilizer that
are stored, processed, and produced in the selected locality are subdivided into three
categories, namely imported, local, and exported biomass. Equations that represent
the biomass and fertilizer ﬂow conservation constraints are given in the following
along with the related explanations.
1. Eqs. (11, 12, 13, and 14) present the constraints that calculate the amounts of
“imported biomass” that is distributed:
•
To the selected locality’s conversion plants from other localities’ biomass
storages,
•
To the selected locality’s biomass storages from other localities’ biomass
source sites.
TWPImtW ¼
X
i
WPImitW
8t, 8W
ð11Þ
TEPImtE ¼
X
i
EPImitE
8t, 8E
ð12Þ
TWSImtW ¼
X
k
WSImktW
8t, 8W
ð13Þ
TESImtE ¼
X
m
ESImmtE
8t, 8E
ð14Þ
2. Eqs. (15, 16, 17, 18, 19, 20, and 21) present the constraints that calculate the
amounts of “local biomass” that is distributed:
•
To the selected locality’s conversion plants from that locality’s biomass
storages,
•
To the selected locality’s biomass storages from that locality’s biomass
source sites.
TWPIntW ¼
X
i
WPInitW
8t, 8W
ð15Þ
WPInitW ¼
X
k
WSPInkitW
8i, 8t, 8W
ð16Þ
TEPIntE ¼
X
i
EPInitE
8t, 8E
ð17Þ
EPInitE ¼
X
m
ESPInmitE
8i, 8t, 8E
ð18Þ
TWSIntW ¼
X
k
WSInktW
8t, 8W
ð19Þ
348
Y.B. S
¸ebnem and S. Hasan
TESIntE ¼
X
m
ESInmtE
8t, 8E
ð20Þ
ESInmtE ¼
X
e
EAInemtE
8m, 8t, 8E
ð21Þ
3. Eqs. ((22, 23, and 24) present the constraints that calculate the amounts of
“exported biomass” that is distributed
•
To the other localities’ conversion plants from the selected locality’s biomass
storages,
•
To the other localities’ biomass storages from the selected locality’s biomass
source sites.
TWPExtW ¼
X
k
WPExktW
8t, 8W
ð22Þ
TEPExtE ¼
X
m
EPExmtE
8t, 8E
ð23Þ
TESExtE ¼
X
e
ESExetE
8t, 8E
ð24Þ
4. Eqs. (25) and (26) present the constraints that calculate the amount of “local
fertilizer” that is distributed to the selected locality’s cultivated areas from the
selected locality’s conversion plants:
TFInt ¼
X
i
FInit
8t
ð25Þ
FInit ¼
X
f
FPInift
8i, 8t
ð26Þ
Eqs. (27) and (28) ensure that no more biomass is transported from a storage than
what is actually available in that storage at the time of shipment:
X
i
X
E
ESPInmitE þ EPExmtE
 
!
þ DSEmt
¼
X
E
ESInmtE þ ESImmtE
 
!
þ DSEm t1
ð
Þ
8m, 8t
ð27Þ
X
i
WSPInkitW þ WPExktW ¼ WSImktW þ WSInktW
8k, 8t, 8W
ð28Þ
A Novel Approach to Local-Level Design of Bioenergy Supply Chains Integrated. . .
349
Eq. (29) limits the fertilizer distribution amount to fertilizer requirement of the
areas:
X
i
FPInift  CAft
∗V
8f, 8t
ð29Þ
Production and capacity constraints: Eq. (30) calculates the heat production
amount in each plant for each time period:
X
W
WPImitW
∗H1W
 
!
þ
X
W
WPInitW
∗H1W
 
!
þ
X
E
EPImitE
∗H2E
 
!
þ
X
E
EPInitE
∗H2E
 
!
¼ OHit8i, 8t
ð30Þ
Eq. (31) ensures that the total solid content of the biomass slurry in each plant
must be between MinTS and MaxTS. This constraint also determines the amount of
water required to satisfy the total solid content limits:
MinTS 
X
W
TSW
∗WPImitW þWPInitW
ð
Þ
 
!
þ
X
E
TSE
∗EPImitE þEPInitE
ð
Þ
 
!
X
W
WPImitW þWPInitW
 
!
þ
X
E
EPImitE þEPInitE
 
!
þWit
 MaxTS8i,
8t
ð31Þ
Eq. (32) limits the biomass handling of the plants to the maximum biomass
handling capacity of the plant:
X
W
WPImitW þ WPInitW
 
!
þ
X
E
EPImitE þ ECPInitE
 
!

X
c
PCc
∗Yic
8i, 8t
ð32Þ
Storage number and capacity constraints: Eqs. (33) and (34) limit the waste
storage number in the selected locality to the value determined by the regional-level
model and ensure that only one energy crop storage is constructed in a crop storage
location. Eqs. (35, 36, and 37) calculate the capacities of energy crop and waste
storages and also limit the total biomass capacity of the storages:
X
p
Smp  1
8m
ð33Þ
350
Y.B. S
¸ebnem and S. Hasan
X
k
Dk ¼ XW
ð34Þ
DECt ¼
X
m
DSEmt
8t
ð35Þ
X
E
ESInmtE þ ESImmtE
 
!
þ DSEmt 
X
p
Cp
∗Smp
8m, 8t
ð36Þ
X
W
WSInktW þ WSImktW
 
!
 MAXDC∗Dk
8k, 8t
ð37Þ
Energy crop ﬁeld constraints: This constraint set calculates the size of area to be
used for energy crop cultivation in each arable empty area location e at time period t
and limits this value to the total size of arable empty area in that location:
X
e
Aet  EAPt
∗TEAt
8t
ð38Þ
X
m
X
E
EAInemtE þ ESExetE ¼ Aet
∗VEt
8e, 8t
ð39Þ
Aet  EAet
8e, 8t
ð40Þ
3
Case Study
As stated before, we use the application results of Yılmaz Balaman and Selim
(2014) regional-level model.
The regional-level model was applied to all 20 counties of I
˙zmir. Among these
counties, we selected Kiraz as the local application area which means the model in
the local design phase is applied to Kiraz, and the optimal conﬁguration of the
supply chain network with district heating system is obtained.
The number of candidate sites for anaerobic digestion plants, waste storage, and
energy crop storage locations are 15, 10, and 10, respectively. There exist ﬁve
locations including arable empty area for energy crop cultivation and ﬁve locations
for cultivated agricultural area. Total sizes of agricultural and surplus arable areas
in Kiraz are 26,000 da and 185,565 da, respectively. The length of the time period
used in our computational experiments is 1 month, and the planning horizon is
1 year. The thermal energy produced by plants is assumed to be consumed in
residential area in the centre of Kiraz, which is divided into ten heat demand nodes.
The thermal energy is distributed from the energy station to the heat customers via
insulated pipes. We obtain the data on average heat requirement of a household per
m2, which depend on the climatic conditions of the region and the season, by a
literature survey on district heating system installations and by utilizing expert
A Novel Approach to Local-Level Design of Bioenergy Supply Chains Integrated. . .
351
opinion. For more detailed information about heat demand in different countries,
Dong et al. (2012), Marinova et al. (2008), and Uhlemair et al. (2014) can be
referred. Figure 1 presents the results of our computational experiments obtained by
“modiﬁed fuzzy and” operator using six different γ values corresponding to six
solution alternatives. At this stage, a sensitivity analysis is conducted to explore the
impact of “coefﬁcient of compensation” on the results.
In real-life decision problems, relative importance of the objectives assigned by
the decision-makers may change according to decision-maker or over time. To
provide a broader decision spectrum to decision-makers, the solutions are obtained
by using three different relative weight combinations for the objectives (WCost and
WUnmet Heat Demand). Any solution alternative can be selected as the best one
concerning the priorities of the decision-maker on different supply chain perfor-
mance indicators. In this regard, trade-offs among the alternative solutions need to
be considered. If meeting heat demand of the demand nodes is more important than
the cost objective for the decision-maker, then the weight structure of WCost
¼ 0.25 and WUnmet Heat Demand ¼ 0.75 can be considered.
In this case, one of the alternatives of third, fourth, ﬁfth, and sixth (γ ¼ 0.6,
γ ¼ 0.4, γ ¼ 0.2, γ ¼ 0) can be chosen where the unmet heat demand is 114,547
kWh/year. Among these alternatives, the sixth one (γ ¼ 0) also satisﬁes the cost
objective (€ 306,099). Among the solution alternatives for this weight structure, the
second solution alternative γ ¼ 0.8 offers the minimum cost but offers a high value
for unmet heat demand (7,515,843kWh/year). If the decision-maker prefers the
weight structure of WCost ¼ 0.25 and WUnmet Heat Demand ¼ 0.75, comparison
of the results with γ ¼ 0 and γ ¼ 0.8 reveals that a 98.47% decrease in unmet heat
demand can be attained with an 18% increase in cost.
If the objectives are equally important for the decision-maker, then the weight
structure of WCost ¼ 0.5 and WUnmet Heat Demand ¼ 0.5 can be preferred. In this
case, the second alternative with γ ¼ 0.8 outperforms the other alternatives in terms
of the cost value (€ 259,526), but it offers a high value for unmet heat demand
(7,515,883 kWh/year). The fourth, ﬁfth, and sixth alternatives (γ ¼ 0.4, γ ¼ 0.2,
γ ¼ 0) offer the minimum unmet heat demand (114,547 kWh/year) with the costs of
€ 306,442, € 305,929, and € 306,099, respectively. Among these alternatives, the
sixth one (γ ¼ 0) also satisﬁes the cost objective (€ 306,099). If the decision-maker
prefers the weight structure of WCost ¼ 0.5 and WUnmet Heat Demand ¼ 0.5,
comparison of the results with γ ¼ 0 and γ ¼ 0.8 reveals that a 98.47% decrease in
unmet heat demand can be attained with an 18% increase in cost.
If minimization of the cost is more important than minimization of unmet heat
demand, the decision-maker may chose the weight structure of WCost ¼ 0.75 and
WUnmet Heat Demand ¼ 0.25. Hence, the fourth alternative (γ ¼ 0.4) with the
smallest value for WCost can be treated as the best solution. In this case, the cost,
which is sum of district heating system and local transportation costs, is € 259,526.
In addition, the unmet heat demand is 7,515,849 kWh/year in this alternative. The
sixth alternative (γ ¼ 0) offers the minimum unmet heat demand (114,547
kWh/year), whereas the cost is € 305,922. If the decision-maker prefers the weight
structure of WCost ¼ 0.75 and WUnmet Heat Demand ¼ 0.25, comparison of the
352
Y.B. S
¸ebnem and S. Hasan
Fig. 1 The results of computational experiments
A Novel Approach to Local-Level Design of Bioenergy Supply Chains Integrated. . .
353
results with γ ¼ 0 and γ ¼ 0.4 reveals that a 98.47% decrease in unmet heat demand
can be attained with a 17.8% increase in cost.
Let us suppose that the decision-makers accept the results of the model with
γ ¼ 0.6. The changes in the cost and unmet demand values corresponding to γ ¼ 0.6
with three different weight structures can be observed in Fig. 2.
4
Summary and Conclusions
This study deals with effective design and management of comprehensive local
biomass to energy supply chain networks integrated with district heating systems by
considering inherent uncertainties. In this regard, a DSS is developed to make
strategic and tactical decisions for design and management of sustainable and
efﬁcient biomass to energy supply chains, under the condition of limited and
seasonally variable resources and ﬂuctuations in the system parameters caused by
307,973
302,539
259,657
250,000
260,000
270,000
280,000
290,000
300,000
310,000
0.25
0.5
0.75
Weight
Cost (€) 
114,547
1,022,457
7,520,671
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
7,000,000
8,000,000
0.25
0.5
0.75
Weight
Unmet Heat Demad (kWh/year)
Fig. 2 Changes in the cost and unmet demand values corresponding to γ ¼ 0.6 with three different
weight structures
354
Y.B. S
¸ebnem and S. Hasan
unstable economic, environmental, and social policy actions and natural conditions.
The proposed DSS employs FGP as the solution approach.
Once the numbers and capacities of the plants and storages are obtained and
logistics decisions are made in the regional-level bioenergy supply chain design, the
proposed local-level model is utilized to obtain comprehensive design of the local
biomass to energy supply chain network along with district heating system for the
selected locality. Besides the decisions related to the spatial and logistical structures
of the local supply chain network, allocation of heat demand nodes to plants in
terms of heat transmission and heat production/storage decisions is made for each
plant in the selected locality through the proposed model. The aim of the proposed
local-level design model is to minimize local transportation costs for local
bioenergy supply chains, which constitute the regional supply chain, and maximize
the service level which is the level of meeting heat demand of the residential area in
the handled local part of the regional supply chain.
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A Novel Approach to Local-Level Design of Bioenergy Supply Chains Integrated. . .
355
Kinetic Study of Plastic Wastes
with and Without Catalysts
Emna Berrich Betouche and Mohand Tazerout
1
Introduction
Energy valorization from thermoplastic wastes to produce “considerable quanti-
ties” of a “good-quality” fuel is still an actual aim. In 2009, France produced 1.65
million tons of plastic wastes which take one to four centuries to be decomposed in
nature. France recycled 18% and processed about 37% by energy valorization. The
remaining 45% is landﬁlled. “Recovery” in the ﬁeld of plastic production in France
is only 20% against 44% for glass or 60% for paper and cardboard. One ton of
energetic valorized plastic could yet “save” 830 l of oil. Compliance with environ-
mental standards and minimizing energy costs of fuel production processes require
the development of an optimal energy process for plastic wastes recycling to
produce fuels. This process should be reliable and least expensive (in raw material,
energy consumption, and production time, etc.).
Pyrolysis process is based on the thermal degradation of an organic compound to
obtain (gas, liquid, solid). It is performed in the absence of oxygen or oxygen-
deﬁcient atmosphere to prevent oxidation and combustion. It enables us to obtain a
E.B. Betouche (*)
Department Energetic systems and Environment, Laboratory CNRS, GEPEA,UMR6144
Engineering school E
´cole des Mines de Nantes, 4 rue Alfred KASTLER, BP20722, 44307
Nantes, France
Department of Physique, LUNAM Universite
´, Universite
´ de Nantes, Faculte
´ des Sciences et
des Techniques, 2, rue de la Houssinie
`re BP, 92208-44322 Nantes, France
e-mail: emna.berrich@univ-nantes.fr
M. Tazerout
Department Energetic systems and Environment, Laboratory CNRS, GEPEA,UMR6144
Engineering school E
´cole des Mines de Nantes, 4 rue Alfred KASTLER, BP20722, 44307
Nantes, France
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_25
357
carbonaceous solid, liquid (fuel), and gas. It begins at a relatively low level of
temperature and can be continued up to approximately 800 C depending on the
primary material and the operating conditions. Different transformations occur
simultaneously depending on different parameters.
The most widely used catalysts for catalytic conversion of plastic polymers to
chemical products and fuels are zeolite ZSM-5 (Miskolczi et al. 2004), ZSM-2
(Covarrubiasa et al. 2010), BaCO3 (Jan et al. 2010), bimetallic catalyst Al-Zn
composite (Tang et al. 2003), commercial FCC (Lin et al. 2010; Miskolczi et al.
2006), SO3 (Almustapha and Andresen 2011), and ZnO, MgO, CaC2, SiO2, Al2O,
and mixture of SiO2 and Al2O3 (Shah et al. 2010). However, most of these catalysts
need either high cost of production or an operating mode which may not be
industrially feasible due to high degradation temperature and/or high residence
time with low conversion rate.
“Free” catalysts are obtained from shells because they generally contain CaCO3
as a main Ca-based component. The high temperature range above 700 C is
required to transform CaCO3 to CaO catalyst. The temperature of 800 C was
thus selected as a suitable condition to produce CaO catalyst from shells according
to preparation time and energy consumption (Viriya-empikul et al. 2010).
Nomenclature
A
: Pre-exponential factor (s1)
T
: Temperature (C)
m
: Weight (kg)
x
: Conversion rate
Subscripts
Ea
: Activation energy (kJ/mol)
TGA
: Thermogravimetric analysis
2
Experimental Facility and Thermogravimetric Analysis
Thermogravimetry is a technique for measuring the mass variation of a sample
when it is subjected to a temperature program in a controlled atmosphere. This
variation can be a loss of mass (vapor transmission) or weight gain (ﬁxing gas
oxidation), the measures used to monitor the decomposition of the substance and
evaluate their thermal stability.
This analysis provided critical thresholds during the thermal decomposition of
the plastic and the typical range for thermal mass loss. Furthermore, the
thermogravimetric analysis (TGA) provides information according to the following
reaction conditions: the inert gas ﬂow rate, heating rate, and the ﬁnal reaction
temperature set initially.
358
E.B. Betouche and M. Tazerout
The essential elements to be deﬁned for the study of plastic are optimal temper-
ature
plastic
decomposition,
the
initial
temperature
of
decomposition,
pic-temperature corresponding to the maximum degradation temperature, the con-
version rate, the variation of the proportions, or nature of catalysts.
The equipment used for the analysis (Fig. 1) is the SETSYS Evolution. This is
composed of an integrated structure: the controller, the furnace, gas and piloting
systems, the security features, a measuring head, and thermogravimetry (TG). The
structure can accommodate various measurement heads and a multitasking
SETSOFT software controlling one or more appliances.
Catalysts used are the commercial zeolite (as a reference catalyst): zeolite
ZSM-5 (10%), ZSM-5 (20%), oysters’ shell (10%), oysters’ shell (20%), eggs’
shell (10%), eggs’ shell (20%), and blends of oysters’ shell (10%) with eggs’ shell
(10%). Experiments were conducted under N2 atmosphere at heating rate of 5 C/
min from room temperature to 500–550 C (Table 1) and then a heating for 30 min
or 90 min at constant temperature 500 C, 525 C, or 550 C. A ﬁrst-order
decomposition reaction was assumed (Khaghanikavkani and Farid 2011; Zhou
et al. 2009).
Fig. 1 Experimental
installation
Table 1 Operating conditions
Period
Initial T(C)
Final T (C)
Residence time (s)
Heat rate (C/mn)
1
25
25
300
2
25
550
6300
5
3
550
550
5400
Kinetic Study of Plastic Wastes with and Without Catalysts
359
3
Kinetic Analysis
The kinetic study of the decomposition of the plastic was determined using data
from the thermogravimetric analysis. The parameters of the kinetic analysis are the
activation energy and pre-exponential factors. These parameters were determined
by the fact that the thermal decomposition of the polymers usually takes this form:
A(solid) ! B(volatil) þ C(solid)
The polymer degradation rate is generally proportional to the concentration of
reactants; there was thus eq. (1) (Almustapha and Andresen 2011):
r ¼ dx
dt ¼ kf x
ð Þ
ð1Þ
where x is the conversion rate:
x ¼ mi  m0
mi  mf
ð2Þ
and m0, mi, and mf are, respectively, the actual, initial, and ﬁnal weights:
f α
ð Þ ¼ 1  α
ð
Þn
ð3Þ
Using Arrhenius law:
k ¼ AeEa
RT ! dx
dT ¼ kf x
ð Þ ¼ AeEa
RT 1  x
ð
Þn
ð4Þ
For a ﬁxed heat rate β:
β ¼ dT
dt ! dx
dt ¼ A
β AeEa
RT 1  x
ð
Þn
ð5Þ
The integration of the equations gives:
1  1  x
ð
Þ1n
1  n
¼ A
β
Z
T
0
AeEa
RTdT
ð6Þ
However, as
Z
T
0
AeEa
RTdT is not an exact primitive, eEa
RTcan be assumed to an
asymptotic series and integrated:
1  1  x
ð
Þ1n
T2 1  n
ð
Þ
¼ ART2
βE
1  2RT
E


eEa
RT
ð7Þ
360
E.B. Betouche and M. Tazerout
Thus:
ln 1  1  x
ð
Þ1n
T2 1  n
ð
Þ
"
#
¼ ln ART2
βE
1  2RT
E




 Ea
RT
ð8Þ
As RT/E  1 and 1  2RT
E  1, we can write:
ln 1  1  x
ð
Þ1n
T2 1  n
ð
Þ
"
#
¼ ln AR
βE


 Ea
RT n 6¼ 1
ð
Þ
ð9Þ
ln  ln 1  x
ð
Þ
T2


¼ ln AR
βE


 Ea
RT n ¼ 1
ð
Þ
ð10Þ
The energy activation and the pre-exponential factor can thus be determined.
4
Results and Discussions
4.1
Effect of Zeolite Catalyst on Kinetic Parameters of PE
Degradation
The activation energy corresponds to the minimum necessary energy needed so that
the reaction takes place, while the pre-exponential factor corresponds to the colli-
sion frequency between molecules. The calculated overall activation energy and the
pre-exponential factor of HDPE are 241.6 kJ/mol and 3 106 (1/s) for the operation
conditions of 20 ml/min of N2, heat rate of 5 C/mn from 25 to 525 C, and then
heating at constant temperature of 525 C for 30 min. Comparing our results to
Khaghanikavkani and Farid (2011), for 15.5 mg of HDPE, under isothermal
conditions at three temperatures (477 C, 466 C, and 455 C), in the presence of
0.7 l/min of N2, they found an activation energy and a pre-exponential factor of
113.17 kJ/mol and 4.85 105 (s1). Almustapha and Andresen (2011) conducted TG
analysis to study the catalytic degradation of HDPE. They found that HDPE
degradation begins at about 420 C with only 3% at 460 C for a heating rate of
10 C/min and a nitrogen ﬂow rate of 20 ml/min. The sample is weighed between
20 and 30 mg. The activation energy and the frequency factor are, respectively,
equal to 342.95 kJ/ mol and 5.22 1019 s1. Their low conversion rate and their
relatively high-frequency factor are maybe caused by the plastic particle size which
was between 125 and 150 micron. Co-pyrolysis behaviors of HDPE and LDPE
were investigated by Zhou et al. (2009) using TGA. They found that, for a nitrogen
ﬂow rate of 30 ml/min and a heating rate of 20 C/min from room temperature to
750
C, the degradation temperature range is 439–523
C for HDPE and
Kinetic Study of Plastic Wastes with and Without Catalysts
361
426–526 C for LDPE with a conversion rate, respectively, of 97% and 99%. The
activation energy and the pre-exponential factor are equal to 457.2 kJ /mol and 3.5
1030 s1 for HDPE and 300.4 kJ /mol and 2.2 1020 s1 for LDPE. The sample is
weighed 5 mg. The particle size was lesser than 500 μm. Thus, the distribution
particle size has an important effect on the activation energy and the
pre-exponential factor even for approximately the same operating conditions.
Figure 2 presents the evolution of Ln(Ln(1x)/T2) vs. 1/T of PE 90% þ ZSM-
5 10%). It can be seen that the pyrolysis process can be described by one ﬁrst-order
reaction. The activation energy and the pre-exponential factor for HDPE with
zeolite as catalyst (PE 90% þ ZSM-5 10%) are 216.6 kJ/mol and 3 106 (1/sec),
while they are equal to 84.7 kJ/mol and 3.47 104 (1/s) for PE 80% þ ZSM-5 20% if
we consider that the global pyrolysis process can be described by one ﬁrst-order
reaction (Fig. 3). However, the evolution of Ln(Ln(1-x)/T2) vs. 1/T is a quadratic
one. Thus, we opted for Zhou et al. (2009) who described the pyrolysis processes of
LVC-LDPE and LVC-HDPE blends as a series of consecutive ﬁrst-order reactions.
So we tried one and then two independent ﬁrst-order reactions to describe the
plastic pyrolysis catalyzed by ZSM-5. The (PE 80% þ ZSM-5 20%) pyrolysis
can be described by two consecutive ﬁrst-order reactions (Fig. 4). The activation
energy decreases while using catalysts. Twenty percent of zeolite as catalyst can
reduce the activation energy more than the half. The pre-exponential factor char-
acterizes the “disorder” generated by the thermal decomposition, i.e., it is propor-
tional to the frequency of collisions between molecules. This factor can be reduced
if the catalyst proportion is considerable.
1.3
1.35
1.4
1.45
1.5
1.55
1.6
x 10
-3
-19
-18
-17
-16
-15
-14
-13
-12
-11
-10
1/T (K-1)
Ln(-Ln(1-x)/T2)
Experiments
First order reaction profile
Fig. 2 Plots of Ln(Ln(1x)/T2) vs. 1/T of PE 90% þ ZSM-5 10%
362
E.B. Betouche and M. Tazerout
This interception point between the two ﬁrst-order reaction proﬁles corresponds
to a temperature T ¼ 395.3C5C which is the maximum pyrolysis temperature.
For the ﬁrst reaction which takes place from T ¼ 253
C to Tpic, the
Ea1 ¼ 47.40 kJ/mol and A1 ¼ 19.95 104 (1/s), while for the second reaction
1.4
1.5
1.6
1.7
1.8
1.9
2
x 10-3
-20
-19
-18
-17
-16
-15
-14
-13
-12
-11
1/T (K-1)
Ln(-Ln(1-x)/T2)
Experiments           
First order reaction
quadratic evolution
Fig. 3 Plots of Ln(Ln(1x)/T2) vs. 1/T of PE 80% þ ZSM-5 20%
1.4
1.5
1.6
1.7
1.8
1.9
2
x 10-3
-18
-17
-16
-15
-14
-13
-12
-11
1/T (K-1)
Ln(-Ln(1-x)/T2)
 Reaction 1 
Reaction 2 
Fig. 4 Plots of Ln(Ln(1x)/T2) vs. 1/T of PE 80% þ ZSM-5 20%: consecutive ﬁrst-order
reactions
Kinetic Study of Plastic Wastes with and Without Catalysts
363
which takes place form T peak to the temperature at the end of the thermal
degradation ¼ 494 C, Ea2 ¼ 166.37 kJ/mol and A2 ¼ 140 104 (1/s).
4.2
Effect of Oysters’ Shell on Kinetic Parameters of PE
Degradation
Oyster shell is mainly composed of calcium carbonate CaCO3 and a conchyoline
matrix. It was tested as a catalyst on the pyrolysis process of PE. The thermal
degradation range of (PE 90% þ oysters’ shell 10%) is 394–512 C and of
(PE 80% þ oysters’ shell 20%) is 378–503 C. The maximum degradation tem-
perature is, respectively, 484 C for PE 90% þ oysters’ shell 10% and 480 C for
PE 80% þ oysters’ shell 20%.
The activation energy and the pre-exponential factor are 202.4 kJ/mol and 2.26
106 (1/s) for PE 90% þ oysters’ shell 10% for 20 ml/min of N2, at a heat rate of
5 C/mn from 25 to 550 C and then heating at constant temperature, if the pyrolysis
process is described as a ﬁrst-order reaction (Fig. 5). However, it can be described
by two successive ﬁrst-order reactions.
The intersection point between the two proﬁles corresponds to T ¼ 436 C which
is lesser than the Tpeak. For the ﬁrst reaction which takes place from T ¼ 394 C to
436, the Ea1 ¼ 58.5 kJ/mol and A1 ¼ 24.18 104 (1/s), while the second reaction
takes place from 436 to 512 C with Ea2 ¼ 355.85 kJ/mol and A2 ¼ 9.28 106 (1/s)
(Fig. 6).
1.25
1.3
1.35
1.4
1.45
1.5
1.55
x 10-3
-20
-18
-16
-14
-12
-10
-8
1/T (K-1)
Ln(-Ln(1-x)/T2)
Experiments
 First order reaction
 quadratic evolution
Fig. 5 Plots of Ln(Ln(1x)/T2) vs. 1/T of PE 90% þ oysters’ shell 10%
364
E.B. Betouche and M. Tazerout
The intersection point between the two proﬁles of the two successive ﬁrst-order
reactions describing the pyrolysis process of (PE 80% þ oysters’ shell 20%)
(Figs. 7 and 8) corresponds to T ¼ 436 C which is the same intersection
1.25
1.3
1.35
1.4
1.45
1.5
1.55
x 10-3
-18
-17
-16
-15
-14
-13
-12
-11
1/T (K-1)
Ln(-Ln(1-x)/T2)
Reaction 2 
Reaction 1 
Fig. 6 Plots of Ln(Ln(1x)/T2) vs. 1/T of PE 90% þ oysters’ shell 10%: consecutive ﬁrst-order
reactions
1.25
1.3
1.35
1.4
1.45
1.5
1.55
x 10-3
-19
-18
-17
-16
-15
-14
-13
-12
-11
-10
-9
1/T (K-1)
Ln(-Ln(1-x)/T2)
Experiments
 First order equation profile
 quadratic evolution
Fig. 7 Plots of Ln(Ln(1x)/T2) vs. 1/T of PE 80% þ oysters’ shell 20%
Kinetic Study of Plastic Wastes with and Without Catalysts
365
temperature point of the blend (PE 90% þ oysters’ shell 10%). This temperature is
lesser than the maximum temperature degradation of (PE 80% þ oysters’ shell
20%) which is equal to 480 C.
4.3
Effect of Eggs’ Shell on Pyrolysis Process
The evolution of weight losses (TG) and its derivative (dTG) vs. the temperature for
HDPE (pellet) with eggs’ shell is, respectively, shown in Figs. 9 and 10
(PE 90% þ eggs’ shell 10%) and in Figs. 11 and 12 (PE 80% þ eggs’ shell 20%).
The results proved that the thermal degradation temperature range (PE 90% þ eggs’
shell 10%) is 387–512 C. For the blend (PE 80% þ eggs’ shell 20%), TG proﬁle
illustrates that the pyrolysis reaction starts at 248 C for a heat rate of 5 C/min and
that the reaction is almost complete at 530 C. The maximum degradation temper-
ature corresponds to the peak of the dTG proﬁle function of the temperature, i.e., the
red point on the curves of Figs. 10 and 12. It is equal to 481 C for PE 90% þ eggs’
shell 10% and 479 C for PE 80% þ eggs’ shell 20%. The eggs’ shell decreases the
maximum temperature needed to have a good conversion rate. The conversion rates
are more than 99%.
The plots of Ln(Ln(1x)/T2) vs. 1/T of PE 90% þ eggs’ shell 10% and PE
80% þ eggs’ shell 20% are shown in Figs. 13 and 14. They prove that the pyrolysis
process does correspond neither to one ﬁrst-order reaction equation nor to a
1.25
1.3
1.35
1.4
1.45
1.5
1.55
x 10-3
-18
-17
-16
-15
-14
-13
-12
-11
1/T (K-1)
Ln(-Ln(1-x)/T2)
Reaction 2 
Reaction 1 
Fig. 8 Plots of Ln(Ln(1x)/T2) vs. 1/T of PE 80% þ oysters’ shell 20%: consecutive ﬁrst-order
reactions
366
E.B. Betouche and M. Tazerout
quadratic evolution. The pyrolysis process can be described by a three consecutive
ﬁrst-order reactions for PE 90% þ eggs’ shell 10% (Fig. 15) and four consecutive
ﬁrst-order reactions for PE 80% þ eggs’ shell 20% (Fig. 16).
150
200
250
300
350
400
450
500
550
-9
-8
-7
-6
-5
-4
-3
-2
-1
0
1
X: 387.7
Y: -0.0303
T (°C)
dTG (mg/s)
Fig. 10 Weight losses and derivative dTG function of temperature for PE 90% þ eggs’ shell 10%
150
200
250
300
350
400
450
500
550
0
10
20
30
40
50
60
T (°C)
weight loss (mg)
Fig. 9 Plots of weight losses (TG) and its derivative dTG function of temperature for PE
90% þ eggs’ shell 10%
Kinetic Study of Plastic Wastes with and Without Catalysts
367
50
100
150
200
250
300
350
400
450
500
550
-6
-5
-4
-3
-2
-1
0
1
T (°C)
dTG (mg/s)
Fig. 12 Weight losses and derivative dTG function of temperature for PE 80% þ eggs’ shell 20%
50
100
150
200
250
300
350
400
450
500
550
10
20
30
40
50
60
70
T (°C)
weight loss (mg)
Fig. 11 Plots of weight losses (TG) and its derivative dTG function of temperature for PE
80% þ eggs’ shell 20%
368
E.B. Betouche and M. Tazerout
1.3
1.4
1.5
1.6
1.7
1.8
1.9
x 10-3
-18
-17
-16
-15
-14
-13
-12
1/T (K-1)
Ln(-Ln(1-x)/T2)
y = - 8.2e+003*x - 2.8
y = 1.2e+007*x2 - 4.6e+004*x + 27
y = - 6.9e+010*x3 + 3.4e+008*x2 - 5.7e+005*x + 3e+002
y = 1.2e+014*x4 - 8.4e+011*x3 + 2.2e+009*x2 - 2.5e+006*x + 1.1e+003
Experiments
First order reaction
quadratic evolution
cubic evolution
4th degree evolution
Fig. 14 Plots of Ln(Ln(1x)/T2) vs. 1/T of PE 80% þ eggs’ shell 20%
1.25
1.3
1.35
1.4
1.45
1.5
1.55
x 10-3
-20
-18
-16
-14
-12
-10
-8
1/T (K-1)
Ln(-Ln(1-x)/T2)
y = - 2.8e+004*x + 24
y = 1.1e+008*x2 - 3.5e+005*x + 2.4e+002
y = 3.4e+011*x3 - 1.3e+009*x2 + 1.6e+006*x - 6.7e+002
y = - 6.4e+015*x4 + 3.6e+013*x3 - 7.6e+010*x2 + 7.1e+007*x -
      2.5e+004
Exepriments
First order reaction
quadratic evolution
cubic evolution
4th degree evolution
Fig. 13 Plots of Ln(Ln(1x)/T2) vs. 1/T of PE 90% þ eggs’ shell 10%
Kinetic Study of Plastic Wastes with and Without Catalysts
369
1.25
1.3
1.35
1.4
1.45
1.5
1.55
x 10-3
-18
-17
-16
-15
-14
-13
-12
-11
1/T (K-1)
Ln(-Ln(1-x)/T2)
Reaction 2
Reaction 1 
Reaction 3
Fig. 15 Plots of Ln(Ln(1x)/T2) vs. 1/T of PE 90% þ eggs’ shell 10%: consecutive ﬁrst-order
reactions
1.3
1.4
1.5
1.6
1.7
1.8
1.9
x 10-3
-18
-17
-16
-15
-14
-13
-12
1/T (K-1)
Ln(-Ln(1-x)/T2)
Reaction 3
Reaction 2
Reaction 1
Reaction 4
Fig. 16 Plots of Ln(Ln(1x)/T2) vs. 1/T of PE 80% þ eggs’ shell 20%: consecutive ﬁrst-order
reactions
370
E.B. Betouche and M. Tazerout
5
Conclusions
The kinetic parameters strongly depend on operating conditions, especially on the
catalyst nature and quantities. According to these two parameters and under the
same operating parameters (temperature range of heat, heat rate, nitrogen rate), the
pyrolysis process can be described by one ﬁrst-order reaction or consecutive two,
three, or four ﬁrst-order reactions. The activation energy and the pre-exponential
factor were determined for every case. The activation energy generally decreases
while using catalysts. For example, 20% of zeolite as catalyst can reduce the
activation energy more than the half. The pre-exponential factor characterizes the
“disorder” generated by the thermal decomposition due to the frequency of colli-
sions between molecules. This factor can be reduced if the catalyst proportion is
considerable.
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Kinetic Study of Plastic Wastes with and Without Catalysts
371
Effect of Ballast Water on Marine Ecosystem
Hacer Saglam and Ertug Duzgunes
1
Introduction
Modern shipping cannot operate without ballast water, which provides balance and
stability to unladen ships. Shipping has a share about 90% of world trade in volume
and a mere 10 billion tons of ballast water globally transferred annually from one
place to another. However, it may also cause serious ecological, economic, and
health threats (IMO 2010).
Introduction of invasive aquatic species to the new environment by ballast water,
attaching to hulls and via other vectors has been identiﬁed as one of the four greatest
threats to the world’s oceans. The other three are land-based sources of marine
pollution, overexploitation of marine living resources, and physical alteration/
destruction of marine habitat (Raj 2014).
In major oil spills, the ecological impacts are the most likely to occur very fast,
be catastrophic and acute, and highly visible. However, impacts will decrease over
time as the oil degrades and cleanup and rehabilitation activities are undertaken.
In aquatic bio-invasion, the initial impacts may be nonexistent to minor and
invisible. However, as the population increases, the impacts will increase over
time, in an insidious, chronic, and irreversible manner (Fig. 1) (Raaymakers 2002).
Shipping is one of the sources of transfer of non-native aquatic organisms.
Ballast water acts as a vector for such organisms while at the same time being
absolutely essential to the safe and efﬁcient operation of ships at present
(IMO 2013).
H. Saglam (*) • E. Duzgunes
Karadeniz Technical University Faculty of Marine Science, Camburnu,
Trabzon 61530, Turkiye
e-mail: hacersaglam@yahoo.com
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_26
373
Turkey has a competitive advantage in maritime transport since it is surrounded
by the Mediterranean, the Aegean, and the Black Sea, together with the straits of the
Dardanelles and the Bosporus. The length of coasts of Turkey is 8333 kilometers
and has 295 port facilities. According to the recent reports, Turkey has one of the
most vulnerable coasts due to intensive maritime trafﬁc in surrounding seas.
Currently, 23 million tons of ballast water was discharged to Turkish coastal waters.
Most of the ballast is discharged to four hot spots in Izmit, Iskenderun, Izmir, and
Istanbul bays (Fig. 2) (Korcak 2011). Ballast water discharged to Turkish seas
comes from the Mediterranean (54%), Black Sea (27%), Northeastern Atlantic
(6%), Northwestern Atlantic (3%), Indian Paciﬁc (3%), and other countries (Red
Sea, Persian Gulf, etc.) (Tıktık 2010).
This review will focus on impacts, treatments, and management of the ballast
water with its regulations through the IMO to protect ecosystem.
Impacts
Time
Oil Pollution
Aquatic Bio-invasions
Fig. 1 Impacts over time, major oil spill versus aquatic bio-invasions (Raaymakers 2002)
Fig. 2 Ballast water
discharged in Turkey coasts
(Korcak 2010)
374
H. Saglam and E. Duzgunes
Nomenclature
IMO
International Maritime Organization
BWM
Ballast water management
BWE
Ballast water exchange
D1
Ballast water exchange standard
D2
Ballast water treatment standards
μm
Micrometer (1 μm ¼ 103 mm)
Cfu
Colony forming unit
Nm
Nautical mile
2
Ballast Water and Invasive Species
Ballast is any material used to weight and/or balance an object. Ballast water is the
water carried by ships to ensure stability, trim, and structural integrity. Ships have
carried solid ballast, in the form of rocks, sand, or metal, for thousands of years.
Since modern times, mainly water has been used as ballast. It is much easier to load
on and off and is therefore more efﬁcient and economical than solid ballasts. When
a ship is empty of cargo, it ﬁlls ballast water. When it loads cargo, the ballast water
is discharged (Fig. 3).
Ballast water capacities depend on ship type. Large tankers and bulk carriers
carry the highest ballast water volumes (Table 1) (Hewitt et al. 2009).
Ballast water usually contains a variety of biological organisms including
animals, plants, and pathogens. If the organisms survive in the new environment,
they can cause major ecological and economic damage to the ecosystem (MIT Sea
Grant 2002). The rate of ship types arriving to national ports is given in Fig. 4.
According to the surveys, there are 66 different invasive species carried by ships
to Turkish coasts, of which 19 of them can be categorized as harmful organisms
(Fig. 5). In particular, three major alien and invasive species – namely, Mnemiopsis
leidyi from the North Atlantic, Rapana venosa from the Japan Sea, and Beroe ovata
from the North Atlantic – were transferred to the Black Sea, where they collapsed
the entire ﬁsh stock and caused signiﬁcant economic losses in the region.
3
Ecological and Economic Impacts of Ballast Waters
The success of introduced species can depend on several factors including lack of
natural predators, abundance of food sources, better tolerance of pollution, disease
and other stressors, and out-competing a less aggressive species that currently
occupies a biological niche that suits the introduced species (Deacutis and
Richard 2002).
Effect of Ballast Water on Marine Ecosystem
375
A successful invader in its new environment can cause a range of ecological
impacts. These include competing with native species for space and food; preying
upon native species; altering habitat, environmental conditions (e.g., increased
water clarity due to mass ﬁlter feeding), food web, and the overall ecosystem;
and displacing native species by reducing native biodiversity and even causing
local extinctions (Raj 2014).
Table 1 Ballast water capacities for different types of ships (IMO GloBallast)
Vessel type
DWT
Normal (tons)
% of DWT
Heavy (tons)
% of DWT
Bulk carrier
250,000
75,000
30
113,000
45
Bulk carrier
150,000
45,000
30
67,000
45
Bulk carrier
70,000
25,000
36
40,000
57
Bulk carrier
35,000
10,000
30
17,000
49
Tanker
100,000
40,000
40
45,000
45
Tanker
40,000
12,000
30
15,000
38
Container
40,000
12,000
30
15,000
38
Container
15,000
5000
30
–
–
G. Cargo
17,000
6000
35
–
–
G. Cargo
8000
3000
38
–
–
Passenger/RORO
3000
1000
33
–
–
discharging
cargo
Cargo hold
empty
Cargo hold
full
loading
cargo
Loading ballast water
1
4
2
3 At destination port
During voyage
Ballast tanks empty
Discharging ballast water
At source port
During voyage
Ballast tanks full
Fig. 3 Loading and discharging ballast water exchange between ports (IMO GloBallast)
376
H. Saglam and E. Duzgunes
In the past, when the Black Sea was in a healthy ecological state, all the niches
were occupied, and the chances for newcomers to develop in the Black Sea
sustainable populations were at minimum. When the ecological conditions were
drastically damaged as a consequence of pollution, eutrophication process, the
empty niches were ready to absorb new populations. Now the Black Sea environ-
mental resistance is very fragile (Gomoiu 2001).
Food web structure may change completely after the introduction of a single
species. An example of food web destabilization due to the introduction of alien
species is the invasion of the Black Sea by the Asian carnivorous whelk Rapana
venosa. This gastropod, a voracious consumer of bivalves, has been responsible of a
drastic reduction in the local oyster and mussel stocks, limiting also their larval
recruitment. The scarcity of bivalve larvae in the water column led to a decline of
the plankton-eating ﬁsh populations (Drapkin 1963).
Another one is the comb jelly Mnemiopsis leidyi, which had been affecting the
Black Sea food web. This ctenophore eats large quantities of zooplankton and is the
most important reason for the sharp decline of anchovy and other pelagic ﬁsh stocks
in the Black Sea (Kideys 1994).
Fig. 4 The rate of ship
types arriving to national
ports (Tıktık 2010)
176
66
6
3
12
Lessepsian
Ship-
transferred
Gibraltar
Aquaculture
Unknown
Fig. 5 The number of invasive species in the Turkish coast according to introduction pathways
(Tıktık 2010)
Effect of Ballast Water on Marine Ecosystem
377
The major economic impact is the reduction in ﬁsheries production (including
collapse of the ﬁshery) due to competition and predation and impacts on aquacul-
ture (including closure of ﬁsh farms), especially from introduced harmful algal
blooms which was not the case for Turkey.
The economic impacts of invasive alien species can be very high. By 1989, the
biomass of Mnemiopsis leidyi was estimated as 1 billion tons consumed vast
quantities of ﬁsh eggs and larvae, as well as the zooplankton that commercially
important ﬁsh feed on. In 1992, the annual losses caused due to decline of
commercial catches were estimated to be at least 240 million US$ (IMO 2010).
4
Ballast Water Legislation and Regulations
In 2004, IMO adopted “The International Convention for the Control and Manage-
ment of Ships’ Ballast Water and Sediments” (BWMC) with the aim of protecting
the marine environment from the transfer of the harmful aquatic organisms in
ballast water carried by ships.
The convention will enter into force 12 months after it has been ratiﬁed by
30 states representing 35% of the world’s merchant shipping tonnage. The conven-
tion will apply to all ships and offshore structures that carry ballast water and are
engaged in international voyages. Turkey joined other 43 states which had ratiﬁed
the BWM Convention. BWM Convention applies to all ships greater than 400 gross
tons. The convention will come into force at different times depending on ballast
tank capacity and date of vessel construction (IMO 2014). The BWM Convention
was ratiﬁed by a sufﬁcient number of states on 8 September 2016, bringing the total
gross tonnage to over 35% from the signatory states. This means the convention
will enter into force 12 months later, on 8 September 2017 (GloBallast Monograph
25, 2017). The BWM Convention is a key international measure for environmental
protection that aims to stop the spread of potentially invasive aquatic species
through ships’ ballast water.
4.1
Ballast Water Management (BWM) Convention
The convention stipulates two standards for discharged ballast water. The D1
standard covers ballast water exchange, while the D2 standard covers ballast
water treatment systems and speciﬁes levels of viable organisms left in water
after treatment.
The criteria for selecting a treatment method can be summarized as follows:
•
Safety of the crew and passengers
•
Ease of operating treatment equipment
•
Amount of interference with normal ship operations and travel times
378
H. Saglam and E. Duzgunes
•
Structural integrity of the ship
•
Size and effectiveness of treatment equipment
•
Amount of potential damage to the environment
•
Ease for port authorities to monitor for compliance with regulations (IMO 2013)
The convention requires either D1 or D2 standards after entry into force.
4.2
Ballast Water Exchange (BWE) (Regulation D1)
Ballast water exchange is the oldest method and usually recommends minimizing
the risk of introducing non-native species in the open ocean. This method is
effective because organisms from coastal waters do not survive in the open
ocean. But some of the disadvantages are difﬁculty to completely remove sediments
and residual water from the bottom of ballast tanks, organisms being stuck to the
sides of the tank and structural supports within the tank which will not be readily
removed, and during stormy or rough seas unsafe exchange of ballast water for
a ship.
Mid-ocean ballast exchange that occurs at least 200 nm from the shore and in
water at least 200 m in depth currently provides the best available option to reduce
the risk of alien species introduction and transfer; however, it is subject to serious
ship safety limits. In cases where the distance requirement cannot be met, it is
permissible to perform BWE at a distance of 50 nm from the shore. The convention
speciﬁes at least 95% volumetric exchange of ballast water. The two most common
approaches to ballast water exchange are sequential exchange and ﬂow-through
exchange (IMO 2013; ABS 2014).
Sequential exchange: A process by which a ballast tank is ﬁrst emptied and then
reﬁlled with replacement ballast water.
Flow-through exchange: A process by which replacement ballast water is
pumped into a ballast tank, allowing water to ﬂow through overﬂow or other
arrangements. At least three times the tank volume is to be pumped through the
tank (ABS 2014).
4.3
Ballast Water Treatment (Regulation D2)
D2 standard speciﬁes that treated and discharged ballast water must be in line with
the criteria shown in Table 2.
In general, ballast water treatment technologies are divided into two groups:
separation technologies or disinfection technologies. Separation technologies
remove organisms from ballast water upon intake or prior to discharge. Disinfection
technologies kill or render organisms incapable of reproducing (ABS 2014).
Effect of Ballast Water on Marine Ecosystem
379
4.3.1
Separation Technologies
The most predominant type of separation technology in BWM system is ﬁltration
systems. Filtration is the passage of a ﬂuid through a porous medium to remove
suspended matter, such as sediment, organisms, and silt. BWM system ﬁlters are
reported to remove organisms from 10 to 200 μm in diameter. Various other types
of separation technologies are being used. A hydrocyclone uses centrifugal force to
separate items of different densities for removal of organisms.
4.3.2
Disinfection Technologies
Several disinfection technologies are used in BWM system, including chlorination,
ozone, deoxygenation, ultraviolet (UV), and heat treatments. The ability for tech-
nologies to be effective disinfectants is impacted by the salinity and turbidity of the
seawater.
•
Chlorination is a traditional technique for waste water disinfection and can be
accomplished through conversion of naturally occurring chlorine in seawater or
direct injection of chlorine containing compounds.
•
Ozone treatment is an effective disinfectant. In seawater, ozone treatment
initiates chemical reactions similar to chlorination that result in the formation
of the highly effective biocide/germicide of hypobromous acid.
•
UV treatment is used to break down cell membranes killing organisms outright
or destroying its ability to reproduce. The effectiveness depends on the turbidity
of the ballast water (i.e., the concentration of sediments) as this could limit the
transmission of the UV treatment (ABS 2014).
•
Heat treatment involves heating the ballast water to reach a temperature that will
kill the organisms between 35 and 45 C.
All treatment options are under research for improving because no one method
has yet been proven to remove all organisms from ballast water. Scientists improve
existing treatment methods (Alkan and Satır, 2005). There are currently 23 treat-
ment systems homologated by the IMO to meet IMO-D2 standards.
Table 2 IMO D2 standards
for discharged ballast water
(ABS 2014)
Organism category
Regulation
Organisms >50 μm
<10 cells/m3
50 μm > organisms  10 μm
<10 cells/ml
Toxicogenic Vibrio cholera
<1 cfu/100 ml
Escherichia coli
<250 cfu/100 ml
Intestinal enterococci
<100 cfu/100 ml
380
H. Saglam and E. Duzgunes
5
Conclusions
Shipping is one source of unwanted aquatic organisms. The introduction of invasive
aquatic species into new environments by ballast water, attached to ship’s hulls and
via other vectors, has been identiﬁed.
Ballast water discharge typically contains a variety of biological materials,
including plants, animals, viruses, and bacteria. These materials often include
non-native, nuisance, exotic species that can cause extensive ecological and eco-
nomic damage to marine ecosystems. Various studies have shown that thousands of
different species are carried in ballast tanks, which signiﬁcantly threaten the
biodiversity in the seas around the globe.
The International Maritime Organization (IMO) aims to assist developing coun-
tries to reduce the transfer of harmful organisms in ships. In order to the overcome
ballast water problem, the International Convention for the Control and Manage-
ment of Ship’s Ballast Water and Sediments was adopted in 2004 to prevent,
minimize, and eliminate the risk of introduction of harmful aquatic organisms
through ships.
References
American Bureau of Shipping (ABS).: Ballast Water Treatment Advisory. ABS, Houston (2014)
Alkan, G. B., Satır, T.: Ballast Water Problem in the Black Sea and Turkish Straits, October 8–12,
2005, 13th International Symposium on Environmental Pollution Pollution and its impact Life
in the Mediterranean Region, MESAEP, Thessaloniki, Greece (2005)
Deacutis, C.F., Richard,C.R.: Ballast water and introduce species: Management options for
Narragansett Bay and Rhode Island. A report prepared to fulﬁll the requirements of
Chapter 46–17.3 of the Rhode Island general laws related to ballast water. Narragansett Bay
Estuary Program, R.I. Department of environmental management (2002)
Drapkin, E.: Effect of Rapana bezoar Linne
´ (Mollusca, Muricidae) on the Black Sea fauna.
Doklady Akademii Nauk SRR. 151, 700–703 (1963)
GloBallast Monograph 25: The GloBallast Story: Reﬂections from a Global Family GEF-UNDP-
IMO GloBallast Partnerships (2017)
Gomoiu, M.T.: Impacts of naval transport development on marine ecosystems and invasive
species. J. Environ. Prot. Ecol. 2, 475–481 (2001)
Hewitt, C.L., Gollasch, S., Minchin, D.: The vessel as a vector-Biofouling, ballast water and
sediments. In: Rilov, G., Crooks, J.A. (eds.) Biological Invasions in Marine Ecosystems,
pp. 117–132. Springer, Berlin/Heidelberg (2009)
IMO GloBallast: http://globallast.imo.org/
IMO.: Economic assesments for ballast water management: A guideline. GloBallast Monography
Series No. 19 (2010)
IMO.: Identifying and managing risks from organisms carried in ships’ballast water. Globallast
Monography Series No. 21 (2013)
IMO.: Ballast water treatment advisory 2014 (Retrieved 4 Dec 2008) (2014)
Kideys, A.E.: Recent dramatic changes in the Black Sea ecosystem: The reason for the sharp
decrease in Turkish anchovy ﬁsheries. J. Mar. Syst. 5, 171–181 (1994)
Korcak, M.: Türkiye ic
¸in Balast Suyu De
gerlendirme Raporu. Denizcilik Müstes
¸arlı
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Korcak, M.: Ballast water management in Turkey-an overview. In: Telli, O.A., Karakoc
¸, F., Haag, F.
(eds.) Ballast Water Management Systems 26–28 October 2011. I
˙stanbul, Turkey (2011)
MIT Sea Grant.: Marine bioinvasions fact sheet: Ballast water (2002)
Raaymakers, S.: The ballast water problem: Global ecological, economic and human health
impacts. RECSO / IMO Joint Seminar on Tanker Ballast Water Management & Technologies
Dubai, UAE, 16–18 Dec 2002
Raj, C.Y.: Ballast water its impact on ecology and human health. Int. J. Appl. Eng. Res. 15,
2881–2892 (2014)
Tıktık, O.: Ballast Water Management in Turkey. International Black Sea Day. 31 Oct 2010.
Trabzon Turkey (2010)
382
H. Saglam and E. Duzgunes
Regeneration of Waste Frying Oil for Biodiesel
Production
Fetta Danane, Aida Cherifa Ahmia, Rhiad Alloune, and Rahma Bessah
1
Introduction
The increasing energy demand, together with the depletion of fossil fuels and
environmental issues, is posing great challenges for researchers and the scientiﬁc
community worldwide today. The present energy scenario has stimulated very
active research interest in the production of renewable biofuels. Biodiesel is
considered as a viable alternative to petroleum-derived diesel in the near future
due to its interesting characteristics. Chemically, biodiesel is composed of
monoalkyl esters of long chain fatty acids derived from renewable lipid feedstocks
such as vegetable oils or animal fats (Leung et al. 2010). Biodiesel offers several
advantages, including renewability, biodegradability, negligible toxicity, environ-
mentally friendly emission proﬁle, higher combustion efﬁciency, higher cetane
number, higher ﬂash point, and contains 10–11% oxygen by weight and better
lubrication (Nair et al. 2012). The energy content and the physicochemical proper-
ties of biodiesel are almost similar to conventional diesel fuel; therefore, it can be
used on its own or mixed with conventional diesel in the existing conventional
compression-ignition engines without any major modiﬁcations (Leung et al. 2010).
However, the high cost of biodiesel production is the major obstacle to its com-
mercialization. It has been reported that approximately 70–95% of the total biodie-
sel production cost is related to the cost of the raw materials (Azo
´car et al. 2010).
F. Danane (*) • A.C. Ahmia
Centre de De
´veloppement des Energies Renouvelables, CDER, 16340 Algiers, Algeria
Universite
´ des Sciences et de la Technologie Houari Boumediene, USTHB,
16111 Algiers, Algeria
e-mail: f.danane@cder.dz
R. Alloune • R. Bessah
Centre de De
´veloppement des Energies Renouvelables, CDER, 16340 Algiers, Algeria
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_27
383
In this context, waste cooking oil (WCO) is considered a promising feedstock
where the biodiesel production cost could be effectively reduced by 60–70% when
using this low cost raw material (Math et al. 2010).
Moreover, the production of biodiesel from WCO will not only avoid competi-
tion for the same oil resources for food and fuel but will also solve the problems
associated with WCO disposal.
Usually, biodiesel is produced by the transesteriﬁcation reaction (Fig. 1).
Frequently, the actual amount of methanol or ethanol used for transesteriﬁcation
is higher than the stoichiometric amount in order to achieve acceptable conversions.
For instance, a methanol or ethanol to oil molar ratio of 6:1 is a good choice for
achieving 95% conversion at 60 C with one (1) gr of NaOH as catalyst. Obtaining
such good results, however, requires certain precautions. First, triglyceride should
be nearly pure. Studies show that the presence of water or Free Fatty Acids (FFA)
acts as poison for the catalyst. Second, the price of pure triglyceride does not allow
biodiesel to compete with diesel fuel in cost (Klass 1998) and (Kemp 2006). These
disadvantages are the main reasons why researchers have recently focused on other
feedstock for biodiesel production. Acid oils, which have high FFA content, are
well known as a potential alternative raw material. The presence of FFA requires
the addition of a pretreatment step before transesteriﬁcation, in which FFAs react
with methanol or ethanol in the presence of acidic catalyst reacts, as shown in
Fig. 2.
In this context, the main objective of our study is to eliminate the fatty acids
before starting transesteriﬁcation and testing the effectiveness of ethanol and
methanol to ensure a better yield in the production of biodiesel from used frying oil.
Fig. 1 Transesteriﬁcation reaction of triglycerides
Fig. 2 Esteriﬁcation reaction
384
F. Danane et al.
2
Basic Chemical Aspects of Cooking
Cooking is a dehydration process; it means that water and soluble compounds are
transferred from fried food to oil. At the same time, fried products absorb a part of
the surrounding oil. Edible oils are constantly exposed to chemical reaction during
cooking due to their composition and external inﬂuences. The reactions are led by
oxygen, light, and heat. Three main reactions take place: oxidation, polymerization,
and hydrolysis (Berrios et al. 2010).
(a) Oxidation produces oil aging due to contact with atmospheric oxygen. Oxida-
tion is accelerated because of temperature and light. Triglycerides, which are
present in oil, are oxidizable organic compounds. In fact, when the number of
double bonds rises, oxidation takes place easily. As well as degraded products,
oxidation produces hydroperoxides, aldehydes, and ketones.
(b) Polymerization is a chemical reaction where unsaturated fatty acids due to the
inﬂuence of heat, heavy metals (Cu, Fe), or light, and by means of the breakage
of the double bond, react to form dimers (molecule composed of two identical
subunits or monomers linked together) and polymers of triglycerides. As a
consequence of polymerization, the oil’s molecular weight rises and the oil
becomes more viscous.
(c) Hydrolysis is led by water acquisition in the fried product and is supported by
certain products deep-fried in batter. Free fatty acids (FFAs) are generated
because triglycerides are broken down. Moreover, when the water evaporates
through the oil, monoglycerides, diglycerides, and FFAs are created.
Thus, the new products formed during frying are polymers, dimers, oxidized
triglycerides (hydroperoxides, aldehydes and ketones), as well as diglycerides and
fatty acids (Ruiz et al. 2008). Therefore, used frying oils are heterogeneous as
compared to crude or reﬁned oils. All these groups possess higher polarity than the
initial triglycerides and can be easily quantiﬁed by means of adsorption chroma-
tography (Ruiz et al. 2008) and (IUPAC 1992) to ﬁnd out the total degradation of
frying oils.
Oil, containing a large quantity of free fatty acids, cannot be directly used for
biodiesel fuel production applying the commonly used alkaline transesteriﬁcation
procedure; free fatty acids have to be esteriﬁed before the transesteriﬁcation of
triglycerides can take place (Berrios et al. 2010). Canakci and Van Gerpen (Canakci
and Van Gerpen 2001) recommend this pretreatment since, if the FFA level exceeds
5%, saponiﬁcation will hinder the separation of the ester from glycerin and reduce
the yield and formation rate of FAME due to alkaline catalyst consumption. This is
the case of a few used frying oils. Then, an esteriﬁcation step is necessary before
transesteriﬁcation.
Regeneration of Waste Frying Oil for Biodiesel Production
385
3
Experimental Setup
3.1
Pretreatment of Waste Cooking Oil
After collecting the waste frying oil (used sunﬂower oil), it was ﬁltered to remove
any inorganic residues and suspended matters and heated at 110 C in order to get
rid of water.
The acid value was determined by means of the titration with KOH solution in
accordance with the EN 14104 Standard (EN 14104 2003). The result shows that the
FFA level exceeds 5 wt. %. Therefore, an esteriﬁcation reaction to eliminate the FFAs
is required. The esteriﬁcation reaction is catalyzed by acid, a sulfuric acid (H2SO4),
which is the most commonly used because of its low cost and ready availability. The
dosage of sulfuric acid taken is 0.5 wt.% and agitated at 600 rpm during 1 h.
3.2
Transesteriﬁcation of Waste Cooking Oil
Transesteriﬁcation of vegetable oils with alcohol is the best method for biodiesel
production and reducing viscosity.
Many different alcohols can be used in this reaction, such as, ethanol, methanol,
propanol, and butanol. The alkali catalysts often used are NaOH, KOH, and
CH3─ONa.
After pretreatment, the transesteriﬁcation process was carried out. The different
steps are illustrated in the diagram below (Fig. 3) (Awad et al. 2013).
Fig. 3 Transesteriﬁcation process
386
F. Danane et al.
4
Results and Discussions
The main factors affecting the transesteriﬁcation reaction are reaction temperature,
reaction time, alcohol type, quantity, and catalyst concentration.
In this work, for the transesteriﬁcation reaction, we used NaOH (2%) mixed with
alcohol; 1:6 molar ratio methanol or ethanol to oil was taken. Then, the mixture was
stirred by means of a magnetic stirrer under constant temperature (60 C) and at
600 rpm until no traces of solid NaOH are observed, which means that the reaction
is accomplished. The time varies between 30 and 120 min.
At the end of the reaction, the products were poured through a separating funnel
in order to separate the glycerol phase from biodiesel. As both phases contain
alcohol and catalyst, after the separation step, the biodiesel was washed several
times with water until the aqueous phase became clear and neutral. Finally, the
biodiesel is ﬁltered and weighed. The yield of biodiesel was calculated.
In this work, we have chosen to test the effectiveness of methanol and ethanol in
the transesteriﬁcation process. For this, we calculated the yields (Yd) of the
transesteriﬁcation of used sunﬂower oil according to reaction time. The results
are grouped in Fig. 4.
Results show that methanol gives a higher yield compared to ethanol, and this
whatever the reaction time. These results are consistent with the work of Ramadhas
et al. (2005), indicating that the production of biodiesel using ethanol is more
complicated than that using methanol. This is explained by the fact that the use of
ethanol leads to the formation of a stable emulsion. Indeed, during the
transesteriﬁcation reaction, there is always emulsion formation.
In the case of methanol, emulsions are easily decomposed to form an upper layer
rich in methyl esters and a lower layer rich in glycerol. For ethanol, emulsions are
more stable and complicate the separation of the two layers because of the physical
structure of ethanol, which has a larger nonpolar group than that of methanol.
In literature, we also found that the most commonly used alcohols are ethanol
and methanol, especially the latter, given its low cost and its physical beneﬁts
(chains shorter and more polar alcohol) (Kansedo et al. 2009). However, ethanol
has the advantage of coming from a renewable source through fermentation of
sugar derived from sugarcane or beet. Biodiesel thus obtained is 100% renewable.
5
Conclusions
In this work, biodiesel was obtained from used sunﬂower oils with a high percent-
age of free fatty acids (>0.5%). It was necessary to carry it out in two steps:
esteriﬁcation and transesteriﬁcation, testing two types of alcohol—methanol and
ethanol.
It is concluded that the FFA content has a negative effect on the whole process.
The FFAs form soap with the catalyst, which reduce considerably its efﬁciency for
Regeneration of Waste Frying Oil for Biodiesel Production
387
transesteriﬁcation. Thus, higher amounts of catalyst are required. The formation of
soap during alkali-catalyzed reaction creates proper conditions for the emulsion’s
appearance so that the esters are captured into a stable emulsion with glycerol and
excess alcohol, and at the end of the process the phase’s separation will not be
possible.
Based on this work, it is recommended to use methanol in alkali-catalyzed
transesteriﬁcation of waste oils with relatively high FFA contents. To avoid their
negative effects, FFAs in the base oil are both converted to soap and removed from
the process, or they may be esteriﬁed (yielding more biodiesel) using an acidic
catalyst.
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´n, M.A., Chica, A.F., Martı
´n, A.: Study of esteriﬁcation and transesteriﬁcation in
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(2003)
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Press, Oxford (1992)
Kansedo, J., Lee, K.T., Bhatia, S.: Cerbera odollam (sea mango) oil as a promising non-edible
feedstock for biodiesel production. Fuel. 88, 1148–1150 (2009)
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home and farm. Aztext Press, Ontario (2006)
Fig. 4 Effect of alcohol
type on the yield of the used
sunﬂower oil
transesteriﬁcation
388
F. Danane et al.
Klass, L.D.: Biomass for renewable energy, fuels and chemicals. Academic Press, New York
(1998)
Leung, D.Y.C., Wu, X., Leung, M.K.H.: A review on biodiesel production using catalyzed
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Regeneration of Waste Frying Oil for Biodiesel Production
389
The Effect of Air with Supplementary Oxygen
on Power and Fuel Consumption
of Spark-Ignition Engine
Mojtab Tahani, Mohammadhossein Ahmadi, and Keayvan Keramati
1
Introduction
With the fast growth of society and industry, the requirement of fossil fuels is rising
higher and higher; thus, there is great concern about the dearth of energy due to
ﬁnite reserves or for additional political reasons (such as the petroleum crisis).
Furthermore, environmental protection problems have been emphasized upon all
over the world in recent years; therefore, it is critical to reduce fuel consumption to
meet environmental needs. Besides, the torque of a spark-ignition engine varies
with air/fuel ratio (AFR). This means the engine torque will change with change in
fuel consumption.
Motor vehicle energy demand through motion depends mostly on rolling, aero-
dynamic drag, acceleration, and gravitational losses. Depending on the engine
efﬁciency of a vehicle and the energy required by vehicle accessories, a deﬁnite
amount of the fuel energy is spent to overcome forces resisting the motor vehicle’s
motion through a driving cycle (Tolouei and Titheridge 2009). Delayed injection
produces a lower temperature and pressure during most of the combustion. Advanc-
ing the injection leads to an input of fuel at the time that the temperature and
pressure in the combustion chamber are lower, which retards the start of the
M. Tahani
University of Tehran, Faculty of New Sciences and Technologies,
North Kargar St., Tehran 1439955941, Iran
M. Ahmadi (*)
Razi University, Department of Mechanical Engineering of Agricultural Machinery,
Kermanshah, Iran
e-mail: mmhahmadi@gmail.com
K. Keramati
Iran University of Science and Technology, Department of Automotive
Engineering, Tehran, Iran
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_28
391
combustion and growths the amount of fuel that enters throughout the ignition delay
(Duran et al. 2012). Oxygen percentage in the air is an important variable having
the potential to noticeably affect the fuel consumption.
Numerous researchers have been aggressively investigating the special effects of
oxygenates on diesel engine combustion, and reviews of several relevant papers
have been published by Litzinger (Litzinger et al. 2000) and Natarajan (Natarajan
et al. 2001). Boehman and coworkers have considered oxygenation of diesel engine
by alternative fuels, additives, and oxygen enrichment of intake air (Boehman et al.
2005; Litzinger et al. 2000; Song et al. 2002, 2004, 2006). Most of the literature on
oxygen-enriched combustion reports signiﬁcant decrease in smoke emissions,
upper peak cylinder pressure, improved power output, and considerably reduced
ignition delay by increasing oxygen in the intake air (Desai et al. 1993; Ghojel and
Hilliard 1983; Karim 1968). However, the effect of oxygen percentage in the air on
fuel consumption remains unclear in their study.
Reduction in fuel consumption would be very important to energy saving and
environment protection. Numerous scientists have conveyed that high energy
efﬁciency and low emission could be achieved if air with supplementary oxygen
was used so it induced complete combustion (Arre
`gle et al. 2003; Song et al. 2003).
There have been several reports regarding the result of oxygen-enriched air on the
emission gas and efﬁciency of a blast furnace (Kondo 1992; Petela et al. 2002).
There have been little studies on the presentation of gas membranes to the spark-
ignition engine in order to ﬁgure out the special effects of air with supplementary
oxygen on the emission gases (Saito et al. 1991; Rigby and Watson 1994; Byun
et al. 2006).
The features of oxygen-enriched combustion would moreover be of advantage to
decrease the necessities of compression, which would cause to lower manufacturing
costs, improved fuel conversion efﬁciency, and increased durability (Perez and
Boehman 2010). Therefore, oxygenating the air causes wide ﬂammability and
improvement in power output.
So, in this study, the effects of air with supplementary oxygen on fuel consump-
tion and torque of a spark-ignition engine in new way were investigated. Here, we
can ﬁnd out the research innovations and suggestions in the ﬁeld of the effect of air
with supplementary oxygen on power and fuel consumption of spark-ignition
engine:
•
As usual, for increasing the amount of oxygen in combustion reaction to perform
complete combustion, premixed reactant of oxygen like hydrogen-oxygen-air,
methane-air, etc., are used as supplementary oxygen feeding. But in this study,
the supplementary oxygen is fed into the engine directly and without any
mediated mixture.
•
The survey results of the study among the performed researches have shown that
the best Method to increasing power of spark-ignition engine is increasing of
oxygen percentage by oxygenating inlet air.
•
In this study a new method to oxygenating inlet air directly has been found by
using a gas mixing chamber.
392
M. Tahani et al.
•
The structure of testing instruments used in this study could be an invention to
tune cars by decrement and increment of fuel consumption and power of the
spark-ignition engine, respectively, in the future.
•
The results of this study could be a way for other studies in the ﬁeld of CFD to
simulate the in-cylinder combustion and the reaction products, in future.
•
From an economic perspective, adding pure oxygen instead of premixed reactant
to air has very little cost to increase oxygen content of air in the engine.
From an industrial perspective, availability and safety of pure oxygen instead of
premixed reactant are the beneﬁts of using pure oxygen to increase oxygen content
of air in the engine.
Nomenclature
Tw
: Torque obtained from wheel (N.m)
Te
: Torque of the engine (N.m)
ne
: Rotational speed of the engine (rpm)
nw
: Rotational speed of the wheels (rpm)
hp
: Horsepower (hp)
AFRstoich
: Stoichiometric air–fuel ratio
b
: Hydrogen/carbon ratio
MWO2
: Molecular weight of oxygen (kg/kmole)
MWN2
: Molecular weight of nitrogen (kg/kmole)
XO2
: Oxygen molar fraction
2
Experimental Facility
2.1
Setup
The components of the system to produce air with different percentage of oxygen
include an air compressor, an oxygen tank, and a gas mixing chamber.
An electric motor air compressor was used to supply air. Speciﬁcs: 8.7 cfm air
displacement, 50 L air receiver, 2.5 hp, 230 V, 8 bar maximum in work pressure,
equipped with thermal overload protection, controlled by a completely automatic
pressure switch.
An up to 8495 L of oxygen Steel “T” medical oxygen cylinder with a CGA540
valve and STANDM60T01-A adjustable cylinder stand was employed to supply
pure oxygen.
An oxygen pressure regulator (speciﬁcs: 20,700 KPa Maximum inlet pressure,
Includes a Swagelok™1/4 in and NPT to 1/8 in) was installed to regulate and adjust
desired pressure of oxygen ﬂow. Oxygen was carried to the gas-mixing chamber at
an ultimate pressure of 40 KPa as measured on output gauge indicator of pressure
regulator. Then the ﬂow rate of oxygen gas was regulated to chosen rate by passing
The Effect of Air with Supplementary Oxygen on Power and Fuel Consumption. . .
393
it over rotameter (speciﬁcs: Tube-cube model, Azmoon Motamem Instruments
Tehran Iran, 0–500 cc/min was used to measure O2 ﬂow rate). The rotameter was
calibrated by using water displacement method and veriﬁed by a digital ﬂow meter
(Jour Research Company, Sweden). Directed whole ﬂow rate of gas mixture was
calculated on the basis of the some of the ﬂow rates necessary for each mixture,
ﬂow rate of every gas (air and pure oxygen) being proportional to its concentration
in the mixture.
A gas-mixing chamber was used to mix the compressor air and pure oxygen to
produce air with different percentage of oxygen (speciﬁcs: 6750 cc capacity,
developed by Rameshbabu et al. (1991) but with different conﬁguration and
optimization by Momen (2002)). The unit consisted of 26 pieces of (5 and 3 mm
thickness) Perspex plates and two threaded rods (5 mm diameter) for holding the
entire mixer assembly together (Fig. 1c).
A 100  120 cm bates cargo pack was used as an air bag to store air with
different percentage of oxygen (Fig. 1d).
After preparing the air with different percentage of oxygen, the valve of the air
bag was attached to the air manifold before the air ﬁlter of the engine.
Tests were performed in a spark-ignition engine Pride (Saba) M13NI (speciﬁcs:
1323 cm3, 4 cylinder arranged in-line, four-stroke, direct-injection, water-cooled,
9.7:1 compression ratio) (Fig. 1f). The maximum torque was 103.3 N.m at
2800 rpm and the maximum engine power was 62.5 hp at 5500 rpm. There were
three engines for tests. The engines were not new but were reconditioned to usual
speciﬁcations.
Fuel consumption tests were performed with fuel ﬂow meter DFM 50CK. Fuel
ﬂow meter DFM has 3D ring type measuring cavity. DFM produces the impulse,
while the volume of fuel (which is equivalent to the volume of measuring cavity)
Fig. 1 A schematic diagram of the experimental setup: (a) air compressor, (b) oxygen tank, (c)
gas mixing chamber, (d) voluminous air bag (bates cargo pack), (e) air ﬁlter, (f) spark-ignition
engine, (g) ﬂywheel, (h) clutch, (i) gearbox, (j) differential, (k) Datum Electronics Series 420 PTO
(Power Take Off) and tire, (l) fuel ﬂow meter DFM 50CK
394
M. Tahani et al.
passes over it. For 1 L of spent fuel, DFM produces the number of impulses which is
indicated on the meter harness. DFM allows solving the following jobs: fuel
consumption rationing, fuel monitoring, sensing and avoiding theft of fuel, optimi-
zation of fuel consumption and real-time monitoring, and tests of engine fuel
consumption. The inaccuracy of the fuel ﬂow meter DFM 50CK was 1% (Fig. 1l).
A gear shift involves three stages: (1) torque regulator phase, (2) speed rate
synchronization stage, and (3) torque tracking stage (Fig. 2) (Pettersson and Nielsen
2000).
2.2
Measurements
Through the torque control stage, the engine is controlled till zero brake torque is
reached. Once reached, neutral gear is engaged decoupling the engine from the
transmission. Through the decoupled phase, the engine speed is synchronized to the
transmission speed by the correct conversion ratio of the new gear. Once synchro-
nization is achieved, the new gear is engaged and the engine torque is regulated to
track the required torque till ﬁnally giving complete control to the driver. The most
inﬂuential stage in a gear shift is the torque control stage. During this stage, it is
important to precisely estimate brake torque. For instance, if a big torque mismatch
happens when the neutral gear is engaged, the driveline will resonate, which trans-
lates to engine and transmission wear, driver discomfort, and noise (Pettersson and
Nielsen 2000). In this study, the torque obtained after control was given to driver
phase was measured.
Torque measurement tests were carried out with Datum Electronics Series
420 PTO (Power Take Off). Datum Electronics Series 420 PTO shaft torque and
power monitoring system will display and log transmitted power, shaft speed and
the torque accurately while testing new systems driven from entirely standard PTO
Shafts. The Series 420 PTO System has a noncontact transmissions system that runs
a digital output rightly proportional to torque. Provided as a wide-ranging
Fig. 2 Gear shifting stages for an automatic manual transmission (Pettersson and Nielsen 2000)
The Effect of Air with Supplementary Oxygen on Power and Fuel Consumption. . .
395
transducer with bearings to support the stator component, this strong design pro-
vides performance data through real measurement on the revolving drive shaft. The
PTO structure has a female coupling on one end and a male ﬁtting on the other. The
male and female ends were coupled to the female and male end of the Power
transducer linkage designed by author, respectively. The PTO system turns like an
extension device, with the male end copying the male end of the application. The
speed and torque signals are transferred from the shaft to a static cover assemblage.
Datum Electronics Series 420 PTO operating speed and torque ratings are up to
3000 rpm and up to 2500 N.m, respectively (Fig. 1k).
By torque transducer (Datum Electronics Series 420 PTO) we measured the
torque of the wheels, but we needed the torque of the engine, then we needed the
rotational speed of the wheels (rpm). By stopping one wheel all of the speed goes to
the other wheel.
The rotational speed of the wheel measurement was carried out with Lutron
Photo/Contact Tachometer – DT-2230. The accuracy, resolution, and amplitude of
this device were (0.1 % þ 1digit)@ reading, 0.1 RPM (&lt;1000 RPM), 1 RPM
(1000 RPM) and Photo Tachometer (5–99,999 RPM), Contact Tachometer
(0.5–19,999 RPM), respectively.
The fuel used in this study was Euro 5 type.
After setting up the experimental instrument, the engine was started and the air
contained in the air bag was sucked in by the engine automatically and without any
loading on it.
Each running phase was held for 10 min till engine performance was stabilized
and continued while every parameter was measured and logged in three engine
speeds through the last 3 min of every running step. A ﬁrst test was performed with
natural atmospheric air content at the beginning, in order to compare engine
performance. Engine tests were performed on the same three engines the same
day, so that they have almost the same environmental conditions within 3 repetitions
of each test.
Fuel consumption and torque from three engines in seven different oxygen
percentages of air were measured. The different percentages of oxygen contained
in the air were 20.8%, 21.6%, 22.6%, 23.6%, 24.8%, and 27%. The experiments
were performed at three different engine speeds ranging from 1000 rpm to
3000 rpm, at 1000 rpm increments.
3
Numerical Scheme
Assuming that all parts of the driving line are spur gears and regardless of the route
of transmission with torque losses, we could have written
396
M. Tahani et al.
Tw
Te
¼ ne
nw
ð1Þ
where Tw ¼ the torque obtained from wheel (N.m), Te¼ the torque of the engine (N.
m), ne¼ the rotational speed of the engine (rpm), and nw ¼ the rotational speed of
the wheels (rpm).
The power of the engine is calculated by
hp ¼ 0:737561
5252
 Te  ne
ð2Þ
where hp ¼ horsepower, Te ¼ torque of the engine (N.m), and ne ¼ the rotational
speed of the engine (rpm).
4
Results and Discussions
4.1
Effect of Percentage of Oxygen in the Air and Engine
Speed on Brake Engine Torque
Determination of brake engine torque isn’t an unimportant task. Parameters such as
the pressure and temperature of the intake manifold, pressure and temperature of
the exhaust manifold, engine speed, fuel quantity, and engine geometry all have a
considerable effect on engine brake torque. There are also additional complicating
factors that affect the determination of brake torque such as accessory parasitic
loads. Accessory parasitic loads are modules such as cooling fans, alternators, A/C
compressors, oil pumps, air compressors, power steering pumps, and water pumps
(Franco et al. 2008).
Figure 3 displays the increasing trend for the engine brake torque when engine
speed was increased from 1000 to 3000 rpm with different percentage of oxygen
content in the air.
When the oxygen percentage in the intake rises from 20.8% to 27%, engine
brake torque is increased from 65.48 N.m to 78.36 N.m in 1000 rpm engine speed,
76.56 N.m to 85.96 N.m in 2000 rpm engine speed, and 80.51 N.m to 102.16 N.m in
3000 rpm engine speed. According to the results, it was found that engine brake
torque increased with the increase in oxygen percentage at the intake.
Oxygen addition to the air increases gasoline’s heating value, and increase in
power and torque were obtained. This is described with some causes. Beneﬁcial
effect of oxygenating the air is a potential reason for more complete combustion,
thus increasing the torque. Furthermore, a bigger fuel for the similar volume is
injected to the cylinder because of greater concentration of oxygen. This results in
an increase in power and torque.
The Effect of Air with Supplementary Oxygen on Power and Fuel Consumption. . .
397
While the engine rotational speed increased from 1000 to 3000 rpm, the intake of
air quantity also increased. At higher engine speed, the combustion and exhaust
temperature increased, which can promote higher efﬁciency. Additionally, the air–
fuel mixing procedure improved because the turbulence intensity increased at the
upper engine rotational speeds, thus providing more complete combustion. This
was primarily because of an increase in both the volumetric efﬁciency and ﬂow rate
of the reactant mixture at upper engine speeds.
4.2
Effect of Percentage of Oxygen in the Air and Engine
Speed on Brake Engine Power
One of the theories in this Investigation was to prove the capability of oxygen-
enriched gasoline combustion to increase the power output in a spark-ignition
engine because of the potential to burn extra fuel at a certain stoichiometry
(oxygen-to-fuel ratio). Related to this theory Assanis and coworkers found that an
increase in oxygen volume fraction at the intake air from 21% to 35% leads to
increase in brake power by up to 90%.
Figure 4 displays the increasing trend in engine power when engine speed is
increased from 1000 to 3000 rpm at different percentages of oxygen content in the air.
When the oxygen percentage at the intake rises from 20.8% to 27%, the engine
torque increases from 9.19 hp to 11.00 hp in 1000 rpm engine speed, 21.50 hp to
24.14 hp in 2000 rpm engine speed, and 33.91 hp to 43.04 hp in 3000 rpm engine
speed.
Fig. 3 Effect of percentage of oxygen in the air and engine speed on brake engine torque
398
M. Tahani et al.
According to the results, it was found that the engine power increased by the
increase in oxygen percentage at the intake. The air with supplementary oxygen
causes the complete combustion of a spark-ignition engine. This means that almost
all of the fuel carbons reacted with oxygen and produced more power. Conse-
quently, it can be seen from the graph that the engine brake power were increased
by increasing the oxygen percentage inside the intake. Therefore, Addition of
oxygen increases the thermal efﬁciency resulting in engine power enhancement.
Assanis et al. (2001) obtained similar results.
This oxygen enrichment stratagem, mentioned as rich oxygen-to-fuel ratio
(Poola and Sekar 2003), has been the most used in the oxygen enrichment inves-
tigations reported in the literature. According to the literature, brake power output is
not affected a great deal by oxygen enrichment unless the fuel amount injected is
also increased, keeping a constant oxygen-to-fuel ratio (Assanis et al. 2001; Poola
and Sekar 2003), or by using greater fuel ﬂow rates (Rakopoulos et al. 2004).
4.3
Effect of Percent of Oxygen in the Air and Engine Speed
on Fuel Consumption of the Engine
Also, the results indicated that the higher the engine speed, the higher the engine
brake power. This was primarily because of an increase in both the ﬂow velocity
and volumetric efﬁciency of the reactant mixture at greater engine speeds.
Fig. 4 Effect of percentage of oxygen in the air and engine speed on brake power of the engine
The Effect of Air with Supplementary Oxygen on Power and Fuel Consumption. . .
399
To determine fuel consumption at stable working situations (constant speeds,
constant torques), consumption of a certain quantity of fuel in 100 km was mea-
sured. It is well known that the amount of heating value of fuel affects the fuel
consumption of a spark-ignition engine.
Figure 5 displays the increasing trend for engine fuel consumption when the
engine speed is increased from 1000 to 3000 rpm at different percentages of oxygen
content in the air.
Fuel consumption is affected through oxygen enrichment because of the varia-
tion in the equivalence ratio as oxygen concentration at the intake air manifold
changes. The relationship below is described as the stoichiometric air–fuel ratio
(Heywood 1988), where it is simply seen that, for a constant hydrogen to carbon
molar ratio (H=C), the stoichiometric air–fuel ratio (AFRstoich) decreases while the
oxygen concentration in air increases. This means that lesser air is required for
complete combustion of gasoline fuel. When air mass ﬂow is constant, as in these
experiments, the extra oxygen could be used to burn extra fuel to improve fuel
consumption:
AFRstoich ¼
1 þ b
4


 MWO2 þ
1XO2
XO2 MWN2


h
i
12:011 þ b
ð
Þ
ð3Þ
where b ¼ H=C molar ratio, MWO2 and MWN2 are molecular weight of oxygen and
nitrogen, respectively, and XO2 is the oxygen molar fraction (or volume fraction) in
the combustion air.
Fig. 5 Effect of percentage of oxygen in the air and engine speed on fuel consumption of the
engine
400
M. Tahani et al.
When the oxygen percentage at the intake rises from 20.8% to 27%, fuel
consumption of the engine is increased from 5.46
1 100
km
=
to 10.35
1 100
km
=
in
1000 rpm engine speed, 7.41
1 100
km
=
to 11.78
1 100
km
=
in 2000 rpm engine speed,
and 9.93 1 100
km
=
to 14.65 1 100
km
=
in 3000 rpm engine speed.
According to the results, we found that the presence of greater oxygen concen-
tration resulted in a slight increase in the fuel consumption rate.
In the graph, it is shown that the fuel consumption rate increased approximately
in proportion with engine rotational speed under constant engine torque conditions.
This is attributed to the fact that when engine speed increased, the friction horse-
power increased according to the drop in the mechanical efﬁciency to maintain a
constant torque output (Lin and Wang 2004), leading to an increase in the fuel
consumption rate.
5
Conclusions
The use of oxygen enrichment on a spark-ignition engine was studied, and some
variables in engine performance, such as brake torque output, brake power output,
and fuel consumption, were examined. The main conclusions can be summarized as
follows:
1. The brake torque output and the brake power output increased with the increase
in oxygen concentration at the air inlet manifold, representing that air with
supplementary oxygen induced the complete combustion of engine. Also,
when the engine speed was increased from 1000 to 3000 rpm, the brake torque
output and the brake power output were increased. At higher engine speed, the
combustion and exhaust temperature increased, which can promote higher
efﬁciency.
2. The fuel consumption increased with the increase in oxygen concentration at the
air inlet. When air mass ﬂow is constant, the extra oxygen could be used to burn
extra fuel to improve fuel consumption, leading to decrease air–fuel ratio. When
engine speed increases, the friction horsepower rises according to the drop in the
mechanical efﬁciency to maintain a constant torque output, leading to an
increase in the fuel consumption amount.
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The Effect of Air with Supplementary Oxygen on Power and Fuel Consumption. . .
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A Comparative Study on Some Methods to Use
Tyre Pyrolysis Oil as an Alternative Fuel
in a DI Diesel Engine
Sivalingam Murugan, Hariharan Sundaramoorthi, Govindan Nagarajan,
and Bohumil Horak
1
Introduction
Increase in automobile vehicles is a growing concern, and in the coming years, the
disposal of waste tyres will become complex. Issues associated with waste tyres
have exercised the minds of both government policymakers and sections of the tyre
industry around the world for many years. Yet there remains a strong perception
that waste tyres are not being managed as well as they might. It is a fact that waste
tyre management techniques adopted in India are very less when compared to the
other developed countries. A majority of waste tyres are disposed through landﬁll,
or there are reportedly signiﬁcant numbers that are dumped illegally or disposed off
in other inappropriate ways as reported by Christian Roy et al. (1999). The current
disposal methods of waste automobile tyres are (i) landﬁll, (ii) crumbing, (iii)
remould, (iv) incineration, (v) tyre-derived fuel and (vi) energy recovery through
pyrolysis and gasiﬁcation as reported by Cunliffe and Williams (1998). Effective
waste minimisation beneﬁts industry, community and environment. At present,
about 50% of the waste automobile tyres are used for landﬁll. Some tyres are also
used for engineering purposes in landﬁll sites. If disposed off in large volumes,
tyres in landﬁll sites can lead to ﬁres and instability by rising to the surface. This
S. Murugan (*) • B. Horak
Department of Mechanical Engineering, National Institute of Technology, Rourkela, India
Department of Biomedical and Cybernatics, VSB Technical University, Ostrava, Czech
Republic
e-mail: s.murugan@nitrkl.ac.in; muruganresearch@yahoo.com
H. Sundaramoorthi
Development Engineer, AVL MTC AB, Haninge, Sweden
G. Nagarajan
Department of Mechanical Engineering, Anna University, Chennai, India
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_29
405
affects long-term settlement and may cause problems for future use and land
reclamation.
The current disposal method of waste tyres by landﬁlling causes a loss of
valuable resource. Other methods merely delay the inevitable need for disposal.
Pyrolysis is a better method of disposing waste automobile tyres as reported by
Isabel de Marco Rodriguez (2001). Another method of disposal is crumbing. This
method involves cutting of tyres at several stages until rubber attains crumb form.
These are several possible outlets for tyre crumb. But the current use is only around
25% of the total waste. Remould of waste tyres requires a lot of work on the part of
the manufacturers, as many designs of tyres are not suitable for remould. About
20% of total waste automobile tyres are remouldable, and in turn this will increase
by 5% more in the future. An alternative method for disposal of waste automobile
tyres is incineration. By incineration of waste automobile tyres, electricity can be
generated. However, high investment cost and high pollution are the two major
problems associated with incineration of waste automobile tyres. Tyre has a high
energy value and can be utilised for generation of heat and electrical power. The
substance obtained from tyre for such purposes is called tyre-derived fuel (TDF).
Instead of coal, TDF is burnt in cement kilns for heating purpose. However, because
of chemicals in the tyres, the manufacturing ability of cement kiln decreases. There
is also a potential problem of atmospheric pollution. Pyrolysis is a better method of
disposing waste automobile tyres as reported by Yu Min Chang (1996).
Pyrolysis is the thermal cracking of organic substance in a theoretically oxygen-
free environment (Zabaniotou and Stavropoulos 2003). Several researchers have
studied on pyrolysis of biomass, waste automobile tyres, plastics and other organic
materials. Pyrolysis of waste automobile tyres yields three principal products,
namely, tyre pyrolysis oil (TPO), pyrogas and carbon black. Pyrolysis oil obtained
from the organic substances can be used as alternative fuels for internal and external
combustion engines (Bertoli et al. 2000; Farid Nasir and Kawser Jamil 1999). For
instance, Yrjo Solantausta et al. (1993) studied the ﬂash wood pyrolysis oil as an
alternative fuel for diesel power plants. In this study, engine tests were carried out
on a single-cylinder, diesel engine in three steps: (i) only with wood pyrolysis oil,
(ii) ignition improver-enhanced pyrolysis oil and ethanol (iii) and poor-quality
reference fuel. Engine test was also conducted on a multi-cylinder high-speed
diesel engine with pilot injection. It was reported that NOx emissions were higher
by about 28%, and smoke was lesser by about 23% for wood pyrolysis oil compared
to diesel. It was also reported that both NOx and smoke have reduced by about 80%
when ignition improver was added to pyrolysis oil. The ignition delay was found to
be 6 CA for diesel fuel, and with poor-ignition-quality reference fuel, it was 8 CA.
It was observed that the ignition improver was not as effective with pyrolysis oil as
with ethanol. The minimum concentration of additive used was 3% and the ignition
delay was 15 CA and the engine operation was unstable. Only a small difference
was noticed in ignition delay (i.e. 1 CA), when improver concentration was
increased from 5% to 9% in pyrolysis oil, and ignition delay was still longer than
that of poor- quality reference fuel. Combustion started late with ethanol, pyrolysis
oil containing 3% additive and poor-quality reference fuel (10% heat released at
406
S. Murugan et al.
5–16 ATDC). Pyrolysis oil with 5% and 9% improver and conventional diesel
were considerably faster (10% heat release at 3 ATDC). Combustion duration for
50% heat release was almost the same for pyrolysis oil (5% and 9% additive) and
diesel. The time taken for 90% heat release was the shortest with pyrolysis oil,
approximately 15 CA for pyrolysis oil when compared to that of 25 CA for diesel
operation. The time taken between 10% and 90% heat release was roughly 22 CA
for diesel and 13–17 CA for pyrolysis oil. Frigo et al. (1996) investigated cylinder
high-speed diesel engine with pilot injection. It was reported that NOx was higher
by about 28%, and smoke was lesser by about 23% for wood pyrolysis oil compared
to diesel. It was also reported that both NOx and smoke have reduced by about 80%
when ignition improver was added to pyrolysis oil. The ignition delay for diesel fuel
was 6 CA, and with poor-ignition-quality reference fuel, it was about 8 CA. It was
observed that the ignition improver was not as effective with pyrolysis oil as with
ethanol.
In the present investigation samples of waste truck tyres were subjected to
vacuum pyrolysis process to obtain the TPO. Then the physico-chemical properties
of the derived TPO were determined and compared with those of diesel fuel
(DF) properties. Then, attempts were to explore the possible methods to use TPO
as an alternative fuel in a compression ignition (CI) engine by adopting few fuel and
engine modiﬁcations. The combustion, performance and emission parameters of the
engine run on crude TPO and modiﬁed fuels were tested in a single-cylinder, four-
stroke, air-cooled, direct injection (DI) engine. The experimental results were
compared with those of diesel fuel operation in the same engine and presented in
this paper.
2
Materials and Methods
2.1
Tyre Pyrolysis Oil (TPO)
In the present investigation, waste tyres originated from trucks were collected from
disposal areas and shredded into smaller pieces. The shredded tyre chips were
washed and then dried for removal of moisture. The elemental composition of
tyre sample (tread rubber) is given in Table 1.
The detailed description of the vacuum pyrolysis carried out in this study is
described in (Murugan et al. 2008a, b). Nitrogen was used to purge the oxygen from
the reactor. The process was carried out between 450 and 650 C in the reactor for
4 h and 30 min. The fuel properties are important in predicting the engine behaviour
(Watanabe et al. 1998). The heat energy required to convert the waste tyres into the
products was around 7.8 MJ/kg. The properties of TPO obtained in the pyrolysis
process are compared and given in Table 2.
A Comparative Study on Some Methods to Use Tyre Pyrolysis Oil. . .
407
2.2
Experimental Setup
The schematic layout of the experimental setup is shown in Fig. 1. The speciﬁca-
tions of the engine are given in Table 3. An electrical dynamometer was used to
provide the engine load. An air-box was ﬁtted to the engine for airﬂow measure-
ments. The fuel ﬂow rate was measured on volumetric basis using a burette and a
stopwatch. Chromel-alumel thermocouple in conjunction with a digital temperature
indicator was used to measure the exhaust gas temperature. A pressure transducer in
conjunction with Kistler makes charge ampliﬁer, and a cathode ray oscilloscope
(CRO) was used to measure the cylinder pressure. The pressure pickup was
mounted on the cylinder head and before mounting it was calibrated with a dead
weight tester. A TDC optical sensor with a signal conditioner was used to detect the
engine crank angle.
A gas analyser was used to measure NOx/HC/CO emissions in the exhaust. The
accuracy of the instrument is 1 ppm. Smoke was measured by a Bosch smoke
metre. The speciﬁcations of the exhaust gas analyser and smoke metre are given in
Table 1 Elemental composition of waste tyres (Murugan 2008)
Elemental composition (wt %)
Proximate analysis (Dry air wt %)
Carbon: 88.87
Volatile matter: 67.06
Hydrogen: 7.09
Fixed carbon: 28.13
Oxygen: 2.17
Moisture content: Nil
Nitrogen: 0.24
Ash content: 4.81
Sulphur: 1.63
Total: 100.00
100.00
Table 2 Comparison of properties of TPO and DTPO with petroleum fuels (Murugan et al.
2008a, b)
Property
Diesel
Gasoline
TPO
DTPO
Density @ 15 C, kg/m3
830
740
923.9
771
Kinematic Viscosity, cSt @ 40 C
2.58
–
3.77
1.7
Net Caloriﬁc Value, MJ / kg
43.8
45
38
39
Flash Point, C
50
42.78
43
36
Fire Point, C
56
48.89
50
48
Sulphur Content, %
0.29
_
0.72
0.26
Ash content, %
0.01
_
0.31
–
Carbon residue, %
0.35
0.7
0.02
Aromatic content, %
26
60
Distillation temperature, C
Boiling Point
198.5
70
10%
240.5
114.5
50%
278.5
296.1
90%
330.5
386.4
EP
344
388.7
408
S. Murugan et al.
Appendix 3. Initially experiments were carried out using base diesel fuel (DF). All
the experiments were conducted at the rated engine speed of 1500 rpm. A series of
performance and exhaust emission tests were carried out on the engine using TPO-/
DTPO-based fuels and DF. All the tests were conducted by starting the engine with
DF only. After the engine was warmed up, it was then switched to TPO-DF run for
sometime with DF to ﬂush out the TPO-DF from the fuel line and the injection
system.
Fig. 1 Experimental setup
Table 3 Engine details
Name of the engine
Kirloskar
General details
Four stroke, CI, air cooled, single cylinder
Bore (mm)
87.5
Stroke (mm)
110
Compression ratio
17.5:1
Rated output at 1500 rpm (kW)
4.4
Fuel injection pressure (bar)
210
Injection timing (CA)
23 BTDC
A Comparative Study on Some Methods to Use Tyre Pyrolysis Oil. . .
409
2.3
TPO-Diesel Fuel (DF) Blends
Initially, 10–70% of TPO on volume basis was blended with DF and was under
observation for 15 days to check for the miscibility. It was observed that no
separation took place in the blend of TPO and DF. TPO blended with DF is
indicated as TPO xx, like TPO 10 denotes 10% TPO blended with 90% DF.
2.4
Engine Modiﬁcation: Higher Injection Pressure
Increasing the injection pressure improves the atomisation of fuel (Mbarawa et al.
1999; Yoshiyuki Kidoguchi et al. 2000). Normal injection pressure of engine in
which the tests were conducted was 210 bar. In this study, various nozzle opening
pressures, viz., 220 bar, 230 bar and 250 bar, with the same fuel injection pump and
injectors were used.
2.5
Distilled Tyre Pyrolysis Oil-DF Blends
As the crude TPO contained moisture, foreign particles and impurities, the perfor-
mance and other factors would be affected. The desulphurisation and distillation of
TPO improved a few physico-chemical properties. Hence, DTPO was blended with
DF from 10% to 40% (in steps of 10%) and used as a partial fuel in the same engine.
The injection pressure was kept at original injection pressure of 210 bar. The
performance, emission and combustion characteristics of the single-cylinder,
four-stroke, air-cooled, DI diesel engine were studied.
2.6
TPO-DEE Dual Fuel Mode
Initially the engine was able to run up to 70% of TPO blended with 30% DF. After
distillation of TPO, the DTPO was blended with DF up to 90 percent (90% of DTPO
and 10% of DF) and used as a fuel in the engine. But the engine was not able to run
with 100% TPO or DTPO as fuel. The probable reason was due to lower cetane
number of the fuel that resulted in a longer ignition delay, thereby delaying the start
of combustion. Fuels with very high cetane numbers can reduce the ignition delay
to a greater extent (Nagarajan and Prabhu Kumar 2004). Low cetane fuels can be
solely used in a diesel engine on dual fuel mode (Eugene Ecklund et al. 1984).
There are a number of such fuels such as dimethyl ether, diethyl ether and diglyme
[Anand and Mahalakshmi (2006) and Nicos Ladommatos et al. (1996)]. Out of
these fuels, diethyl ether was found to be the most potential fuel. Diethyl ether
410
S. Murugan et al.
(DEE) has a cetane number greater than 125 as reported by Subramanian and
Ramesh (2002). Dimethyl ether is more volatile than diethyl ether and is prone to
create vapour lock problems in the fuel lines. Diglyme is also a high cetane fuel that
can be used as an ignition enhancer but it is costly. Normally IC engines can be
operated with two fuels such as gaseous or high volatile fuel and another liquid fuel.
Such an engine is called a dual fuel engine. The two fuels can be admitted in
varying proportions. In this investigation, TPO was injected into the cylinder as
main fuel, and DEE was inducted into the cylinder along with air. The properties of
DEE are given in Table 4.
A plastic container storing DEE was connected to a burette through a stopcock.
From the burette, the DEE was allowed to ﬂow through an inlet valve (I.V) needle
of 2 mm diameter directly into the intake pipe located at 5 cm before the inlet
manifold. The DEE was admitted into the intake pipe in the form of droplets. As
DEE is highly volatile, it readily mixes with the air drawn and enters into the
cylinder. The engine started easily with TPO. The time taken for DEE and TPO was
recorded to determine the ﬂow rates, respectively. Experiments were conducted at
various ﬂow rates of DEE.
DEE aspirated into the intake air was gradually varied to achieve the constant
speed of 1500 rpm at various loads. At each load, three ﬂow rates of DEE, viz., 65 g/
h, 130 g/h and 170 g/h, were maintained in such a way that the onset of unstable
operation or misﬁring and knocking was observed from the pressure crank angle
trace. The quantities of DEE required for starting the engine and for idling was
16.5% by mass. The energy share of DEE at different loads on mass basis is given in
Table 5.
During the engine operation on dual fuel mode, there was no knocking noticed at
low loads. This was ensured through the pressure crank angle diagram recorded in
an oscilloscope.
3
Results and Discussions
Based on the fuel economy and emission results, the following were considered in
each mode of test conducted:
Table 4 Properties of DEE
(Hariharan et al. 2013)
Property
DEE
Viscosity at 20 C, centipoise
0.23
Speciﬁc gravity
0.714
Density, kg/m
3
713
Heating value, MX kg
33.89
Cetane number
>125
Autoigniton temperature, C
160
Boiling point, C
34.4
A Comparative Study on Some Methods to Use Tyre Pyrolysis Oil. . .
411
(i) TPO30 (30% TPO in crude form + 70% DF on volume basis) subjected to fuel
injection pressure of 220 bar was chosen as optimum in the case of TPO-DF
blend operation.
(ii) DTPO40 (40% distilled tyre pyrolysis oil + 60% DF on volume basis) was
chosen as optimum when the engine was run on DTPO-DF fuel blends.
(iii) When TPO was injected as a pilot fuel and DEE was inducted at three different
ﬂow rates on the dual fuel mode, TPO + 130 g/h was chosen as an optimum
condition.
3.1
Pressure Crank Angle Diagram
Figure 2 shows the pressure crank angle diagram for TPO operation for different
techniques and DF. The peak cylinder pressure in a CI engine is inﬂuenced by the
ignition delay and mixture formation (Heywood 1998). It can be observed from the
ﬁgure that the cylinder peak pressure for DF is 71 bar. Fuels with a high cetane
number and better mixture formation result in higher cylinder pressure (Nicos
Ladommatos et al. 1996). In the case of TPO 30 at 220 bar, DTPO 40 and
TPO-DEE at 130 g/h, the cylinder peak pressures at full load are 73 bar and
68 bar and 74 bar, respectively. Longer ignition delay of TPO 30 at 220 bar may
be the reason for increase in cylinder peak pressure. In the case of DTPO 40, the
peak pressure is lesser by about 3 bar than DF.
Higher latent heat of evaporation may be the reason for the decrease in the peak
pressure. For TPO-DEE at 130 g/h, the peak pressure increases by 3 bar compared
to DF as a result of longer ignition delay. Longer ignition delay may be attributed to
the interaction of DEE with the aromatics in the TPO, delaying the onset of
combustion.
3.2
Ignition Delay
Ignition delay is the time difference in crank angle between the start of injection and
start of combustion. Figure 3 shows the variation of ignition delay with brake power
while using TPO and DF.
Table 5 Energy share of
DEE at different loads on
mass basis (in percentage)
DEE inducted at different ﬂow rates (g/h)
Load (%)
65
130
170
No load
1.6
16.5
21.1
25
7.2
15.2
20.4
50
6.5
12.8
16.9
75
5.3
9.9
13.1
90
4.7
8.7
11.7
100
4.0
7.4
10.8
412
S. Murugan et al.
It may be seen from the ﬁgure that the ignition delay for DF varies from 7.7 CA
at low load to 6.5 CA at full load. For TPO 30 with fuel injection pressure of
220 bar, it varies from 8.4 CA at low load to 7.1 CA. In the case of DTPO 40, it
varies from 8.9 CA at low load to 7.5 CA at full load. For TPO-DEE operation, it
varies from 10.7 CA at low load to 9.3 CA at full load. Longer ignition delay is
noticed in TPO-DEE operation since TPO is a low-quality fuel. And also, the
quality of TPO is inferior compared to that of DTPO 40 and TPO 30 with fuel
injection pressure of 220 bar.
3.3
Cylinder Peak Pressure
It can be noticed that the cylinder peak pressure is higher for TPO 30 and TPO-DEE
and lower for DTPO 40 operation compared to DF. The cylinder peak pressure for
0
20
40
60
80
-30
0
30
Pressure, bar
Crank angle, deg 
DF
TPO 30:Inj.pr. 220 bar
DTPO 40: Inj.Pr.210 bar
TPO-DEE 130 g/h
Fig. 2 Variation of
cylinder pressure with crank
angle
6
8
10
12
14
16
0
1
2
3
4
5
Ignition
delay,
oCA 
Brake power, kW 
DF
TPO 30:Inj.pr.220 bar
DTPO 40: Inj.Pr.210 bar
TPO-DEE 130 g/h
Fig. 3 Ignition delay with brake power
A Comparative Study on Some Methods to Use Tyre Pyrolysis Oil. . .
413
DF increases from 58 bar at low load to 71 bar at full load, and for the fuel injection
pressure of 220 bar for TPO 30 operation, it varies from 59 bar at low load to 73 bar
at full load. For DTPO 40, the peak pressure varies from 57 bar at low load to
68 bar, and in the case of TPO-DEE operation, it ranges from 57 bar at low load to
73 bar at full load. Higher viscosity and lower volatility of the TPO 30 blend may be
the reason for higher peak pressure. The variation of cylinder peak pressure with
brake power is shown in Fig. 4.
The increase in the peak pressure in the case of TPO-DEE operation may be due
to longer ignition delay, and the increase in heat release rate occurs before TDC by
the early combustion of DEE (Nagarajan et al. 2002). Higher latent heat of
evaporation of DTPO 40 may be the reason for lower cylinder peak pressure.
3.4
Rate of Heat Release
The heat release rate in a CI engine depends on the ignition delay and initial stages
of combustion (Huang et al. 2004; Jamil Ghojel and Damon Honnery 2005). The
heat release pattern of each technique of TPO operation and DF at full load is shown
in Fig. 5.
DF shows lesser heat release rate at the initial stage and longer combustion
duration at full load. For DF the maximum heat release rate is 57.4 J/CA which
occurs at 6.7  before TDC. The maximum rate of heat release for TPO 30 with fuel
injection pressure of 220 bar is 60 J/CA which occurs at 6.5  before TDC. In the
case of DTPO 40 operation, the maximum heat release rate is 56.3 J/CA which
occurs at 5.9 CA before TDC. For TPO-DEE operation it is 59.5 J/CA occurring
at 6.4 CA. This may be due to the higher vaporising tendency of DEE even though
the ignition delay is longer when compared to that of DF. Longer ignition delay is
the reason for higher peak pressures and heat release for TPO 30 and DTPO40. The
heat of evaporation of DEE is comparatively higher than that of DF. In the case of
Fig. 4 Maximum cylinder
pressure with brake power
414
S. Murugan et al.
TPO-DEE operation, the diffusion combustion is longer though the cetane number
of DEE is higher. This may be due to lower volatility of TPO compared to that
of DF.
3.5
Brake Speciﬁc Energy Consumption (BSEC)
Figure 6 shows the comparison of BSEC with brake power for different TPO
blended fuels and DF.
It can be noticed from the ﬁgure that the BSEC for DF varies from 24.4 MJ/kWh
at low load to 12.2 MJ/kW h at full load. At the fuel injection pressure of 220 bar for
TPO 30 operation, the BSEC varies from 22.2 MJ/kW h at low load to 12.3 MJ/kW h
at full load. Low viscous and higher caloriﬁc value fuels consume less fuel
(Prabhakar Reddy 1999; Pradeep and Sharma 2005). The BSEC for TPO at
220 bar is marginally lesser compared to that of DF, due to better spray formation.
In the case of DTPO 40, the BSEC varies from 25.2 MJ/kW h at low load to 12.4 MJ/
-20
0
20
40
60
-30
0
30
Heat release rate, J/oCA 
Crank angle, deg 
DF
TPO 30:Inj.pr:220 bar
DTPO 40: Inj.Pr:210 bar
TPO-DEE 130 g/h
Fig. 5 Heat release rate
with crank angle at full load
0
20
40
60
0
1
2
3
4
5
Brake power, kW
DF
TPO 30: 220 bar Inj.pr
DTPO 40: Inj.Pr.210 bar
TPO-DEE 130 g/h
BSEC,
MJ/k
Wh
Fig. 6 BSEC with brake
power
A Comparative Study on Some Methods to Use Tyre Pyrolysis Oil. . .
415
kW h at full load. The BSEC for DTPO 40 is the same as that of DF operation. It
varies from 28.03 MJ/kW h at low load to 12.7 MJ/kW h at full load for TPO-DEE
operation. It is observed that the energy required for TPO-DEE operation, to develop
the same power output, is higher than DF, and hence, more amount of fuel is
consumed. This is due to the lower heating value of TPO and DEE than DF.
3.6
Exhaust Gas Temperature
The comparison of exhaust gas temperature with brake power is shown in Fig. 7.
Exhaust gas temperature is used to predict the useful work in an internal combus-
tion (IC) engine (Ganesan 2003). The exhaust gas temperature for DF varies from
190 C at no load to 424 C at full load. At the fuel injection pressure of 220 bar for
TPO 30 operation, the exhaust gas temperature varies from 223 C at no load to
485 C at full load. In the case of DTPO 40, the exhaust gas temperature varies from
171 C at no load to 426 C at full load. Longer ignition delay is the reason for
higher exhaust gas temperatures in the TPO-DF and DTPO-DF operation. It varies
from 238 C at low load to 414 C at full load for TPO-DEE operation.
Higher latent heat evaporation of DEE is the reason for the lower exhaust gas
temperature for TPO-DEE operation.
3.7
Oxides of Nitrogen (NOx) Emission
Figure 8 depicts the variation of NOx emissions with brake power for TPO 30 with
fuel injection pressure of 220 bar, DTPO 40, TPO-DEE and DF. The NOx emission
in a compression ignition (CI) engine depends on the oxygen availability and the
cylinder temperature (Yakup Icingur and Duran Altiparmak 2003). NOx for DF
varies from 16.9 g/kW h at low load to 14.9 g/kW h at full load, whereas it varies
150
300
450
600
750
0
1
2
3
4
5
Exhaust gas temperature, oC 
Brake power,kW
TPO-DEE 130 g/h
TPO 30: Inj.220 bar
DTPO 40: Inj.Pr.210 bar
DEE 130 g/h
Fig. 7 Exhaust gas
temperature with brake
power
416
S. Murugan et al.
from 18.2 g/kW h at low load to 15.7 g/kW h at full load for TPO 30 at 220 bar. In
the case of DTPO 40, NOx varies from 14.2 g/kW h at low load to 10.7 g/kW h at
full load and from 10.4 g/kW h at low load to 14.2 g/kW h at full load with the
induction of DEE at 130 g/h. In comparison, TPO 30 shows the highest NOx values
compared to DF and the other best of techniques. Increasing fuel injection pressure
to certain pressure increases the mixture formation (Reddy et al. 2000).
This may be attributed to lower volatility, higher viscosity of TPO 30. DTPO-DF
operation and TPO-DEE operation produce lower NOx levels compared to that of
DF and TPO-DF operation. Higher latent heat of a fuel reduces the intensity of the
premixed phase combustion due to lower cylinder temperature (Lu Xing-Cai et al.
2004). The reason for lesser NOx may be due to the higher latent heat of evaporation
of DTPO. In the case of TPO-DEE operation, lower combustion temperature caused
by DEE is the reason for lower NOx formation.
3.8
Smoke Emission
Aromatic content and fuel properties play important role in smoke emission of a CI
engine (Kent et al. 1994, 1995). The variation of smoke emission with brake power
for the tested fuels is shown in Fig. 9. The smoke density for DF varies between
0 BSU at no load and 1.45 BSU at full load. Smoke reduces marginally for TPO
30 operation with fuel injection pressure of 220 bar at full load, and the value lies
between 0.1 BSU at no load and 1 BSU at full load. Diesel has a high cetane number
and less aromatic content than the TPO and DTPO; hence, the smoke is lower than
TPO-based operation except when it is subjected to higher injection pressure.
For DTPO 40, it varies from 0.2 BSU at no load to 1.65 BSU at full load, while
for TPO-DEE operation, it varies from 1.8 BSU at low load to 3.2 BSU at full load.
In comparison, TPO 30 with fuel injection pressure is found to emit lesser smoke
value compared to that of DF. But DTPO-DF operation and TPO-DEE operation
give higher smoke compared to that of TPO 30 and DF. Higher injection pressure of
5
10
15
20
25
30
0
1
2
3
4
5
NOx emission,   g/kW h
Brake power,kW
DF
TPO 30: Inj.220 bar
DTPO 40: Inj.Pr.210 bar
TPO-DEE 130 g/h
Fig. 8 NOx emissions with
brake power
A Comparative Study on Some Methods to Use Tyre Pyrolysis Oil. . .
417
TPO 30 would improve the atomisation of fuel results in a slightly lower smoke
emission. But in the case of DTPO-DF operation, lower cylinder temperature is
caused due to higher latent heat of evaporation, in addition to PAH present in the
DTPO which may be the reasons for higher smoke. Smoke emission at full load in
each of the techniques is given in Table 5.10.
4
Conclusion
Based on the investigations carried out on a single-cylinder, four-stroke, air-cooled,
DI diesel engine fuelled with diesel and tyre pyrolysis oil (TPO) using different
techniques, the following conclusions are drawn:
•
Increasing injection pressure of fuel nozzle reduced the ignition delay with
improvement in the brake thermal efﬁciency. But the exhaust gas temperature
increased. The NOx emission increased, while the smoke emission reduced. The
rate of pressure rise was higher than that of diesel at full load. Brake thermal
efﬁciency for DF at full load is 29.5%, whereas it is 28.9% for the engine fuelled
with TPO 30.NOx for TPO 30 is 4.5% higher compared to DF at full load.
Smoke decreases by about 1 BSU when the nozzle opening is increased to
220 bar from 210 bar.
•
Although the ignition delay was marginally higher for DTPO40 than that of
TPO30 at 220 bar, the NOx emission was reduced, but with marginal increase in
the smoke emission at full load. NOx reduces by about 29% in DTPO 40 oper-
ation compared to DF operation. Smoke density is 14% higher in DTPO 40 com-
pared to DF.
•
By running the engine TPO-DEE on the dual fuel mode, the ignition delay was
higher than the other operations, at full load. However, the results prove that
utilisation of TPO as a sole fuel in a diesel engine is possible. Thermal efﬁciency
0
1
2
3
4
0
1
2
3
4
5
Smoke, BSU
Brake Power, kW
DF
TPO 30:Inj.pr.220 bar
DTPO 40: Inj.Pr.201 bar
TPO-DEE 130 g/h
Fig. 9 Smoke with brake
power
418
S. Murugan et al.
reduced by about 2.5% with TPO-DEE operation at full load compared to
DF. NOx is 5% lesser for TPO-DEE operation than DF at full load. Thirty-
eight percentage increase in smoke emission is observed for TPO-DEE com-
pared to DF at full load.
•
Overall, the DTPO 40 blend shows a better performance with reduced emissions
compared to that of TPO 30, TPO-DEE and DF operations.
Acknowledgements This paper has been elaborated in the framework of the project new creative
teams in priorities of scientiﬁc research, reg. no. CZ.1.07/2.3.00/30.0055, supported by Opera-
tional Programme Education for Competitiveness and coﬁnanced by the European Social Fund
and the state budget of the Czech Republic.
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Investigation of Effects of Natural Gas
Composition on One-Dimensional
Comprehensive Engine Model Calibration
Seyed Vahid Ghavami, Ali Salavati-Zadeh, Ahmad Javaheri,
Bahram Bahri, Vahid Esfahanian, and Masoud Masih Tehrani
1
Introduction
Natural gas as a potential clean alternative fuel is playing a growing role in
stationary and transportation industries. Large reservoirs have made this fuel to
be even more promising. On the other hand, employment of natural gas comes with
an inevitable shortcoming of considerable deviation in composition of gases
extracted from different resources, which causes change in these gases’ combustion
features.
Concerns over the energy crisis and rising environmental issues have made
combustion system designers to face challenges in the design of modern systems.
The development of numerous prototypes is not feasible or even possible anymore.
Therefore, computer simulation can be an efﬁcient tool. In spite of this, three-
dimensional high-resolution modelling strategies are very time-consuming and
won’t be suitable tools for providing the rapid responses obliged by market
demands or regulatory agenda. Reduced-order modelling strategies would be a
S.V. Ghavami • A. Salavati-Zadeh (*) • A. Javaheri
Vehicle, fuel and Environment Research Institute, University of Tehran,
North Kargar Ave, Tehran, Iran
e-mail: alisalavati@ut.ac.ir
B. Bahri
Automotive Engineering Department, Shahreza Branch,
Islamic Azad University, Shahreza, Iran
V. Esfahanian
School of Mechanical Engineering, Engineering College,
University of Tehran, Tehran, Iran
M. Masih Tehrani
School of Automotive Engineering, Iran University of Science
and Technology, Tehran, Iran
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_30
421
favourable option in analysis and optimization of combustion systems, i.e. internal
combustion engines. An example of these simulation strategies is comprehensive
one-dimensional engine cycle modelling, which since its development in the late
1970s has undergone many developments and plays a pivotal role in the design and
optimization processes of internal combustion engines. The main assumption in this
modelling strategy is that the engine pipes and runners’ length to diameter ratios are
large enough, so that the ﬂow could be considered to be one dimensional. It is also
assumed that the ﬂow rate is high enough to neglect the inﬂuences of viscosity
(Benson 1982; Ramos 1989). It is therefore convenient to employ inviscid
one-dimensional Euler system of equations known as gas dynamic system of
equations (White 2003) to simulate the ﬂow ﬁeld inside the engine pipes. Other
elements such as cylinder, air cleaner, plenums, junctions, injectors, catalytic
converters, throttle, etc. are simulated using the thermodynamic zero-dimensional
models. Meanwhile, another methodology for in-cylinder ﬂow ﬁeld simulation with
more accuracy follows the general lines of multi-zone modelling technique, which
considers the cylinder to be divided to different thermodynamic zero-dimensional
zones, i.e. burned and unburned. The results of ﬂow simulation in different ele-
ments are implemented as boundary conditions to gas dynamic system of equations.
On the other hand, this approach suffers a great drawback. It has a very low
accuracy where the three-dimensional effects take part in ﬂow features,
homentropic assumption does not apply or the ﬂow bending radius is very small.
Similar to all other reduced-order simulation procedures, this technique also
involves employment of empirical correction factors which should be calibrated
against experimental observations. Examples of these factors can be ﬂow or
discharge coefﬁcients at the connection keypoints of elements and runners, in
which the gas dynamic system of equations is solved. Besides these coefﬁcients,
several factors are required and should be calibrated for adjustment of results
obtained from simulation of ﬂow inside other elements. Examples of these coefﬁ-
cients would be factors considered in combustion thermodynamic function,
i.e. Wiebe function (Ghojel 2010), or in-cylinder wall heat transfer coefﬁcients
and temperatures.
In this framework, the position of calibration process is of crucial necessity when
employing reduced-order modelling techniques. A one-dimensional model may end
in several results, one of which correctly predicts the engine intake and exhaust
masses and reconstructs the in-cylinder pressure and heat release proﬁle with high
accuracy (Winterbone and Pearson 2000; Caton 2016). When the engine cycle 1D
comprehensive model is fully calibrated, it could be employed for predicting of
engine performance in several conditions (Benson 1982; Ramos 1989; Winterbone
and Pearson 2000; Caton 2016), whereas in case of charge in many other perfor-
mance conditions, such as valve lift timing and proﬁle, it is mandatory to recalibrate
the model with experimental observations and/or high-ﬁdelity multidimensional
simulation results.
It should be noted that by change in the fuel of an engine, the calibration of the
thermodynamic cycle won’t be reliable anymore. This fact follows the high depen-
dency of burn rate function, Wiebe function as an example, on fuel bond types and
422
S.V. Ghavami et al.
energies, transport factors, etc. (Ghojel 2010). In addition, the change in the density
of fuel and air mixture may inﬂuence the ﬂow coefﬁcients at the boundaries and
may therefore harm the model correctness (Medina et al. 2014).
The natural gas consists of several small normal or branched aliphatic C1 to C5
hydrocarbons along with inert species, mainly nitrogen (N2) and carbon dioxide
(CO2) molecules. On the other hand, its composition differs among various wells.
Additionally, it may also change based on the petrochemical process. In most of the
simulation tools, pure methane is considered as the main surrogate candidate for
natural gas, whose composition may differ and alter signiﬁcantly from one area to
another, and this matter has caused concerns about the correctness of reduced
models of CNG-fuelled combustion systems.
Considering the above-mentioned facts, and bearing in mind that natural gas
shares about 70% of the Iran total energy consumption basket, along with the fact
that approximately 3 million vehicles consume compressed natural gas (CNG) in
Iran, the most in the world, this research aims to investigate the effects of deviations
in composition of natural gas within Iran’s range on the calibration of engine
one-dimensional cycle model. To accomplish this, a gas-fuelled spark ignition
engine is simulated using AVL BOOST v2013 software. The model results are
calibrated for three different engine loads at three different compression ratios
versus experimental observations for engine working with pure methane as fuel.
The goal functions during the calibration process are air and fuel mass ﬂow rates
and in-cylinder open cycle pressure proﬁle. After ﬁnalizing the model calibration,
the models are used for predicting the performance of the same engine without
change in the model-calibrated parameters when working with natural gases dis-
tributed in Mashhad and Tehran. The simulation results are compared with exper-
imental observations on the same engine working with Mashhad and Tehran natural
gases, which the latter is proved to have the maximum deviation from pure methane
in composition. The ﬁndings indicate that the results of the model remain valid for
air ﬂow rate, fuel consumption and in-cylinder open cycle pressure proﬁle, in spite
of change in the composition of the fuel. This proves that the range of deviation in
composition of natural gases distributed in Iran does not affect the calibration
correctness of the engine comprehensive thermodynamic cycle model.
2
Studied Engine
The test engine employed in this study is a spark ignition single-cylinder natural
gas-fuelled engine. The engine is developed by AVL GmbH and is currently
installed in Vehicle, Fuel and Environment Research Institute, University of Teh-
ran. The engine compression ratio can be varied between 6 and 16, by altering the
shims between the cylinder head and engine block. General speciﬁcations of the
engine are shown in Table 1. In addition, the test bed is also illustrated in Fig. 1.
More information on the engine and test bed speciﬁcations is brought by
Javaheri et al. (2014).
Investigation of Effects of Natural Gas Composition on One-Dimensional. . .
423
The experiments are carried out at three different compression ratios of 12, 14
and 16. Three different engine loads are also considered. For part-load conditions,
the break mean effective pressure is set to be equal to 2 and 4 bar. The full load
conditions are also investigated.
3
Simulation and Calibration Approach
In this research, it is aimed to create a baseline model with high accuracy based on
experimental observations for a single-cylinder spark ignition natural gas-fuelled
test engine at three different compression ratios, i.e. 12, 14 and 16. In all of the tests,
Table 1 Main engine
information (Javaheri et al.
2014)
Engine type
4 Stroke
Cylinder
1
Valve
4
Bore
86 mm
Course
86 mm
Connecting rod
143 mm
Compression ratio
6–16
Injection
Intake port injection
Fig. 1 Illustration of the test bed (Javaheri et al. 2014)
424
S.V. Ghavami et al.
the engine speed is held constant at 2000 rpm. All of the tests are repeated for three
different throttle angles:
1. Wide open throttle (WOT) state, i.e. full load conditions
2. Partially open throttle state, so that the brake mean effective pressure equals to
4 bar
3. Partially open throttle state, so that the brake mean effective pressure equals to
2 bar
As described earlier, the tests are repeated for three different natural gas com-
positions. These compositions are shown in Table 2.
Fuel 1 is pure methane, fuel 2 is the natural gas distributed in Mashhad and fuel
3 represents the natural gas typically used in Tehran.
The engine one-dimensional comprehensive model is developed using AVL
BOOST v2013 software, schematic of which is shown in Fig. 2. The general
strategy of the modelling and calibration methodology will be described here
brieﬂy.
An internal combustion engine could be considered as a system of pipes
connecting different elements. The important point which should be considered
Table 2 Studied fuel
compositions (mass
fraction based)
Fuel 1
Fuel 2
Fuel 3
Methane
100
94.778
77.696
Ethane
0
1.221
6.621
Propane
0
0.694
6.498
Carbone dioxide
0
2.052
2.689
Nitrogen
0
1.255
6.496
Fig. 2 One-dimensional
comprehensive model
developed in AVL BOOST
v2013
Investigation of Effects of Natural Gas Composition on One-Dimensional. . .
425
here is that each pipe’s length to diameter ratio is large enough, so that the ﬂow
could be considered one dimensional (Benson 1982; Winterbone and Pearson
2000). On the other hand, with the relatively high ﬂow speed, it would be conve-
nient to employ the inviscid assumption. Hence, the one-dimensional Euler system
of inviscid equations could be used for simulating the ﬂuid ﬂow characteristics
inside the pipes. After creating the overall model topology, geometrical parameters,
such as pipe lengths, elements, volumes, bore, stroke, connecting rod length,
compression ratio, etc., are set. The simulation process could be started after setting
up the operating parameters and required factors for numerical simulation process.
The calibration will be started after the model setup is complete.
The air ﬂow rate at each node of the pipe is calculated under the light of solving
system
of
one-dimensional
gas
dynamics
equations
using
fourth-order
MacCormack (Tannehill et al. 1997) scheme. All the boundary conditions related
to different elements are implemented using the non-homentropic approach
(Benson 1982).
The fuel ﬂow rate is found by setting the equivalence ratio in the injector element
based on the results of gas dynamic system of equations. It is worth mentioning that
the same applies for experimental results. During the test procedure, only the air
ﬂow rate is observed directly as raw data from the test cell and the fuel ﬂow rate is
calculated using the injector control instruments based on the input air-fuel ratio
and is then reported to the user.
Combustion thermodynamic simulation is carried out using Wiebe two-zone
algorithm, following the general lines of multi-zone modelling strategy (Heywood
1988; Medina et al. 2014). To simulate the heat transfer phenomenon, the AVL
2000 model is used (Schwarz 2012).
The calibration process is started for motoring conditions and the main focus is
on the in-cylinder pressure proﬁle. To ﬁnalize the calibration process at this step,
various model parameters and coefﬁcients are set based on trial and error strategy.
These parameters include 24 ﬂow coefﬁcients, 7 heat transfer coefﬁcients and
temperatures for cylinder components, i.e. piston, cylinder head, liner at both
conditions of piston at bottom dead centre and top dead centre. In addition, due
to non-homentropic treatment of boundary conditions, and the necessity for correct
positioning of interaction point of the characteristic curve with energy ellipse, the
temperatures and heat transfer coefﬁcients at the intake and exhaust ports are also
set. After ﬁnalizing the calibration process for motoring condition, fuel injection
and combustion-related parameters are added to begin the second step of calibration
process. Therefore, four factors are added to calibrate the two-zone Wiebe function.
It should be considered that, after adding fuel injection and combustion to the
model developed initially for motoring conditions, all the parameters including
ﬂow coefﬁcients, heat transfer coefﬁcients and temperatures should be readjusted.
As it was mentioned earlier, one of the main goal functions during the calibration
process is the minimum error between the experimentally achieved in-cylinder
pressure proﬁle and the numerical simulation results. To this end, the trial and
error procedure was designed so that minimum error exists during combustion,
expansion, exhaust and intake periods of the cycle. In addition, during the
426
S.V. Ghavami et al.
compression stroke, the numerically and experimentally obtained pressure proﬁle
should be completely identical, so that one can assure the agreement between the
in-cylinder-trapped mass obtained from experiment and numerical simulation. The
calibrated results for motoring condition are shown in Figs. 3, 4 and 5 for the
compression ratios 12, 14 and 16, respectively.
From Fig. 3, it is evident that the root mean square of errors between experi-
mental and numerical in-cylinder proﬁle during the compression stroke does not
exceed 0.8%. On the other hand, the overall root mean square of experiment and
simulation results on in-cylinder pressure proﬁle remains less than 1.5%. For
compression ratio of 14, as depicted in Fig. 4, the situation is the same. The root
mean square of errors during the compression stroke remains less than 0.65%, while
the overall error does not exceed 1.2%. For compression ratio of 16, as illustrated in
Fig. 5, the root mean square of errors during the compression stroke remains less
than 0.9%, while the overall error does not exceed 1.5%.
The air and fuel mass ﬂow rates obtained from experimental data and numerical
simulation results for engine working with stoichiometric mixture of air and pure
methane (fuel 1) at constant engine speed of 2000 rpm in three different compres-
sion ratios of 12, 14 and 16 are shown in Tables 3, 4 and 5 for engine working at
part-load condition of brake mean effective pressure (BMEP) of 2 and 4 bar and full
load conditions, respectively. From the tables, it is obvious that the errors always
stay below 1%, which proves acceptable consistency of the numerical simulation
results and experimental ﬁndings.
Fig. 3 In-cylinder pressure proﬁle for motoring condition at CR ¼ 12
Investigation of Effects of Natural Gas Composition on One-Dimensional. . .
427
Fig. 4 In-cylinder pressure proﬁle for motoring condition at CR ¼ 14
Fig. 5 In-cylinder pressure proﬁle for motoring condition at CR ¼ 16
428
S.V. Ghavami et al.
The in-cylinder pressure proﬁle for three different engine loads and compression
ratios of 12, 14 and 16 are depicted in Figs. 6, 7 and 8, respectively. In addition, the
root mean squares of the errors between numerical and experimental results are
shown in Table 6. These results indicate that in spite of negligible discrepancy of
simulation results from experimental ﬁndings, the model calibration for engine
working on pure methane (fuel 1) is promising.
During the calibration, the following results are also obtained for tuning the
temperatures of different combustion chamber parts at different engine loads and
compression ratios.
As the compression ratio rises, the gas temperature at the exhaust will decrease,
due to higher work obtained during combustion period (Ferguson and Kirkpatrick
2000; Heywood 1988). Experimental data on exhaust gas temperatures at 5 cm after
cylinder for pure methane is shown in Table 7. Therefore, the following results are
obtained during the calibration of cylinder part temperatures:
1. All the temperatures are lowered about 2.5 C when the compression ratio is
reduced from 16 to 14 and from 14 to 12.
Table 3 Experimental and
simulation results of air and
fuel mass ﬂow rates for fuel
1 in BMEP ¼ 2 bar
CR
Exp. (gr/s)
Sim. (gr/s)
Error (%)
12
Air
2.860
2.866
0.209
Fuel
0.166
0.167
0.602
14
Air
2.764
2.779
0.542
Fuel
0.161
0.162
0.621
16
Air
2.907
2.889
0.619
Fuel
0.169
0.168
0.591
Table 4 Experimental and
simulation results of air and
fuel mass ﬂow rates for fuel
1 in BMEP ¼ 4 bar
CR
Exp. (gr/s)
Sim. (gr/s)
Error (%)
12
Air
4.004
4.034
0.749
Fuel
0.233
0.235
0.858
14
Air
3.956
3.971
0.379
Fuel
0.230
0.231
0.434
16
Air
4.051
4.018
0.814
Fuel
0.236
0.234
0.847
Table 5 Experimental and
simulation results of air and
fuel mass ﬂow rates for fuel
1 in full load
CR
Exp. (gr/s)
Sim. (gr/s)
Error (%)
12
Air
5.434
5.407
0.501
Fuel
0.316
0.315
0.316
14
Air
5.196
5.252
1.082
Fuel
0.302
0.305
0.993
16
Air
5.106
5.093
0.244
Fuel
0.297
0.296
0.336
Investigation of Effects of Natural Gas Composition on One-Dimensional. . .
429
2. The engine part temperatures don’t change when the engine load is lowered from
full load to brake mean effective pressure of 4 bar.
3. With decrease in load at part-load condition from 4 to 2 bar, the engine part
temperatures are reduced about 4 C at all the compression ratios.
4
Results and Discussion
In the next step, the model parameters are held ﬁxed, and the fuel composition is
changed. Figure 9 illustrates the in-cylinder pressure proﬁle obtained from simula-
tion and experiment at three different engine compression ratios of 12, 14 and
16, respectively. The natural gas is gathered from Mashhad (fuel 2) and the same
composition is considered for the simulation. As it is evident from the ﬁgures, the
Fig. 6 In-cylinder pressure proﬁle for fuel 1 at compression ratio of 12 for (a) BMEP ¼ 2 bar, (b)
BMEP ¼ 4 bar and (c) full load conditions. Solid lines denote simulation and symbols denote
experimental results
430
S.V. Ghavami et al.
root mean squares of the errors don’t exceed 1.1%, proving proper agreement of the
simulation results and experimental data.
Once again, it is important to note that all of the experiments and simulations are
carried out at a constant engine speed of 2000 rpm and stoichiometric mixture
conditions at the injector. Meanwhile, the mass ﬂow rates of air and fuel are shown
in Table 8. The numerical and experimental results in the table are at the full load
condition (throttle at wide open state).
It is evident from the table that the errors between numerical and experimental
results for natural gas distributed in Mashhad (fuel 2) always stay below 4.5%. This
small and negligible discrepancy indicates good agreement of the gas dynamic
system of equation results with experimental data. These numerical results are
calibrated using ﬂow coefﬁcients at the boundaries of each pipe, i.e. engine ele-
ments. Therefore, one can conclude that in addition to the thermodynamic com-
bustion model (Fig. 9), the Euler equation calibration remains also valid.
Fig. 7 In-cylinder pressure proﬁle for fuel 1 at compression ratio of 14 for (a) BMEP ¼ 2 bar, (b)
BMEP ¼ 4 bar and (c) full load conditions. Solid lines denote simulation and symbols denote
experimental results
Investigation of Effects of Natural Gas Composition on One-Dimensional. . .
431
Fig. 8 In-cylinder pressure proﬁle for fuel 1 at compression ratio of 16 for (a) BMEP ¼ 2 bar, (b)
BMEP ¼ 4 bar and (c) full load conditions. Solid lines denote simulation and symbols denote
experimental results
Table 6 Exhaust gas temperatures at different engine loads and compression ratios
CR ¼ 16
CR ¼ 14
CR ¼ 12
Full load
458
467
481
BMEP ¼ 4 bar
457
467
479
BMEP ¼ 2 bar
442
453
460
Table 7 Root mean squares of errors for numerical and experimental pressure proﬁles for fuel 1
CR ¼ 16 (%)
CR ¼ 14 (%)
CR ¼ 12 (%)
Full load
0.93
0.69
0.73
BMEP ¼ 4 bar
0.64
0.77
0.82
BMEP ¼ 2 bar
0.88
0.79
0.77
432
S.V. Ghavami et al.
The simulation and experiment procedures are then repeated for the natural gas
distributed in Tehran. It was mentioned earlier that Tehran natural gas has the
maximum deviation from pure methane in composition. Hence, if the model
remains calibrated for this composition of natural gas, it will be valid for all types
of natural gas found in Iran.
Fig. 9 In-cylinder pressure proﬁle for fuel 2 at (a) CR ¼ 12 bar, (b) CR ¼ 14 bar and (c) CR ¼ 16.
Solid lines denote simulation and symbols denote experimental results
Table 8 Experimental and
simulation results of air and
fuel mass ﬂow rates for fuel
2 in full load
CR
Exp. (gr/s)
Sim. (gr/s)
Error (%)
12
Air
5.527
5.534
0.132
Fuel
0.331
0.332
0.591
14
Air
5.573
5.374
3.563
Fuel
0.333
0.324
2.664
16
Air
5.480
5.234
4.485
Fuel
0.328
0.317
3.228
Investigation of Effects of Natural Gas Composition on One-Dimensional. . .
433
As for fuels 1 and 2, the veriﬁcation of the model’s calibrated results is
investigated at two different steps.
First, it is investigated that the cylinder-related part temperatures and heat
transfer coefﬁcients are still correct or not. In addition, the calibration of the
combustion zero-dimensional thermodynamic model, i.e. Wiebe function, is also
controlled. To accomplish this step, the in-cylinder pressure proﬁles obtained from
simulation and experiment should be compared. This is done in Figs. 10, 11 and 12.
As can be seen in the ﬁgures, for all the case studies, the pressure curves are still in
good agreement. The root mean squares of experimental and simulation data
discrepancies are shown in Table 9. The root mean square of errors does not exceed
1.9%, which could be regarded as a very good agreement.
For the second step, the performance of the gas dynamic system of equations
with calibrated ﬂow coefﬁcients is qualiﬁed. To this end, the experimental
and simulation results for the mass ﬂow rates of air and fuel are shown in
Fig. 10 In-cylinder pressure proﬁle for fuel 3 at compression ratio of 12 for (a) BMEP ¼ 2 bar, (b)
BMEP ¼ 4 bar and (c) full load conditions. Solid lines denote simulation and symbols denote
experimental results
434
S.V. Ghavami et al.
Tables 10, 11 and 12. The data in the table indicate that errors are larger than those
for Mashhad gas, which was also predictable due to larger deviation of Tehran gas
composition. Nevertheless, these errors do not exceed 4.5%, proving that the results
are still in good agreement and the model results are still reliable.
5
Concluding Remarks
Natural gas burns through a clean combustion process and has large resources
throughout the world. These key features let natural gas be considered as a
promising alternative fuel. In spite of all these, it faces severe drawback of
deviation in composition between different reservoirs, which affects its combustion
characteristics.
Fig. 11 In-cylinder pressure proﬁle for fuel 3 at compression ratio of 14 for (a) BMEP ¼ 2 bar, (b)
BMEP ¼ 4 bar and (c) full load conditions. Solid lines denote simulation and symbols denote
experimental results
Investigation of Effects of Natural Gas Composition on One-Dimensional. . .
435
Fig. 12 In-cylinder pressure proﬁle for fuel 3 at compression ratio of 16 for (a) BMEP ¼ 2 bar, (b)
BMEP ¼ 4 bar and (c) full load conditions. Solid lines denote simulation and symbols denote
experimental results
Table 9 Root mean squares of errors for numerical and experimental pressure proﬁles for fuel 3
CR ¼ 16 (%)
CR ¼ 14 (%)
CR ¼ 12 (%)
Full load
0.87
1.82
0.93
BMEP ¼ 4 bar
1.84
0.89
1.80
BMEP ¼ 2 bar
1.31
1.10
1.89
Table 10 Experimental and simulation results of air and fuel mass ﬂow rates for fuel 3 in
BMEP ¼ 2 bar
CR
Exp. (gr/s)
Sim. (gr/s)
Error (%)
12
Air
2.728
2.827
3.641
Fuel
0.197
0.203
2.861
14
Air
2.775
2.840
2.332
Fuel
0.180
0.184
2.377
16
Air
2.842
2.930
3.071
Fuel
0.184
0.190
3.008
436
S.V. Ghavami et al.
One-dimensional comprehensive engine models have key importance during the
engine design or optimization processes. This modelling technique consists of
solving one-dimensional Euler system of inviscid ﬂow equations inside the engine
pipes and embedding them with zero-dimensional thermodynamic models for other
engine elements such as cylinders. This methodology engages employment of
several empirical factors which have to be calibrated and may differ as the working
ﬂuid composition changes. These parameters can be divided into three main
categories:
1. Flow coefﬁcients: The one-dimensional system of gas dynamic equations con-
siders the ﬂow to be homentropic (Benson 1982; Winterbone and Pearson 2000),
i.e. having constant entropy over the ﬂow domain. Therefore, the mass ﬂow rates
of working ﬂuid into and out from the elements should be adjusted with ﬂow
coefﬁcients, which are computed as the ratio of actual to isentropic ﬂow rates.
2. Zero-dimensional combustion thermodynamic function is Wiebe function in this
work. For Wiebe function, there are four calibration parameters.
3. Cylinder parts, i.e. liner, cylinder head, piston, valve and port temperatures and
heat transfer coefﬁcients which inﬂuence the in-cylinder thermal losses during
the cycle.
Hereby, it is worth mentioning again that the present work’s aim is not to
investigate the effects of natural gas composition on the performance of the gas
engine, but it aims to answer the question that does the change in composition of
natural gas affect the calibration of the engine comprehensive gas dynamics model?
To answer this question, a one-dimensional comprehensive gas dynamics model
is developed and calibrated for a single-cylinder spark ignition gas engine working
with pure methane as fuel for three different engine compression ratios and three
different engine loads. All the tests are carried out at constant engine speed of
Table 11 Experimental and
simulation results of air and
fuel mass ﬂow rates for fuel
1 in BMEP ¼ 4 bar
CR
Exp. (gr/s)
Sim. (gr/s)
Error (%)
12
Air
3.970
3.955
0.377
Fuel
0.258
0.257
0.256
14
Air
3.800
3.967
4.392
Fuel
0.247
0.257
4.269
16
Air
3.872
4.047
4.505
Fuel
0.252
0.263
4.420
Table 12 Experimental and
simulation results of air and
fuel mass ﬂow rates for fuel
3 in full load
CR
Exp. (gr/s)
Sim. (gr/s)
Error (%)
12
Air
5.379
5.518
2.583
Fuel
0.350
0.359
2.662
14
Air
5.294
5.316
0.414
Fuel
0.344
0.346
0.486
16
Air
5.150
5.187
0.727
Fuel
0.335
0.337
0.687
Investigation of Effects of Natural Gas Composition on One-Dimensional. . .
437
2000 rpm at stoichiometric condition. The engine used in this research is designed
and manufactured by AVL List Gmbh. Details on the test cell and engine are
provided by Javaheri et al. (2014). In addition, the AVL BOOST v2013 software
is employed for the simulations. The following results are achieved during the
calibration process:
1. As the compression ratio rises, the engine part temperatures are reduced. This
was predictable, because with increase in compression ratio, the temperature of
cylinder exhaust gas will reduce (Heywood 1988; Ferguson and Kirkpatrick
2001; Pulkrabek 1997). In this work, as the compression ratio is increased from
12 to 14 and from 14 to 16, the temperature of piston, cylinder head, liner, ports
and valves is considered to be 2.5 C lower. This consideration yielded to the
numerical predictions with most agreement with experimental ﬁndings.
2. In relatively high engine loads, the engine part temperature can be considered
equal to those at engine full loads. In the present work, as the engine brake mean
effective pressure is reduced from 6.2 at full load condition to 4, the engine part
temperature remains constant in the model. Nevertheless, with further reduction
of engine load to 2, the engine part temperatures should be reduced by 4 C to
achieve the best possible agreement.
To study the effects of changes in gas composition, the range of natural gas
compositions in Iran is brought under consideration. Therefore, the natural gas
distributed in Tehran which has the maximum deviation from pure methane is
chosen. Another natural gas is also chosen from Mashhad, the second great city in
Iran. Nine tests (three engine loads at three different compression ratios of 12, 14
and 16) are repeated for the engine working with each of the fuels. It is worth
mentioning that in these tests, the engine speed is held constant at 2000 rpm, and the
mixture is stoichiometric. The calibrated model is employed to simulate the engine
cycle, without any change in parameters. The following results are obtained:
1. Despite the fact that the discrepancies between numerical predictions and
experimental observations for in-cylinder pressure proﬁle and mass ﬂow rates
of air and fuel increase as the deviation in the composition rises (the discrepan-
cies of the simulation results from experimental data are higher for Tehran gas
compared to Mashhad); the simulation results and experimental data remain in
good agreement for fuels 2 and 3. Therefore, one can conclude that when
studying an engine working the natural gases distributed in Iran, it would be
sufﬁcient to calibrate the engine comprehensive one-dimensional model with
pure methane or each of the gas compositions that exist in Iran. The model
predictions will be accurate enough for other gases.
2. The model validity with change in composition of the natural gases in the range
that exist in Iran is regardless of engine compression ratio and load.
3. Considering the above-mentioned facts, it seems that the inﬂuence of natural gas
composition inside Iran, on the gas-fuelled internal combustion engine working
parameters, such as engine temperature, is very negligible.
438
S.V. Ghavami et al.
Acknowledgements This research was supported by the Vehicle, Fuel and Environment
Research Institute (VFERI), University of Tehran. The authors gratefully acknowledge this
support.
References
Benson, R.S.: The Thermodynamics and Gas Dynamics of Internal Combustion Engines. Oxford
University Press, London (1982)
Caton, J.A.: An Introduction to Thermodynamic Cycle Simulations for Internal Combustion
Engines. John Wiley and Sons, West Sussex (2016)
Ferguson C.R., Kirkpatrick A.T., Internal Combustion Engines, Applied Thermosciences. 2nd
edn, Wiley (2000)
Ferguson, C.R., Kirkpatrick, A.T.: Internal Combustion Engines. Applied Thermosciences,
New York, John Wiley and Sons (2001)
Ghojel, J.I.: Review of the development and applications of the Wiebe function: a tribute to the
contribution of Ivan Wiebe to engine research. International Journal of Engine Research. 11,
297–312 (2010)
Heywood, J.B.: Internal Combustion Engine Fundamentals. McGrawHill, New York (1988)
Javaheri, A., Esfahanian, V., Salavati-Zadeh, A., Darzi, M.: Energetic and exergetic analyses of a
variable compression ratio spark ignition gas engine. Energy Convers. Manag. 88, 739–748
(2014)
Medina, A., Curto-Risso, P.L., Hernandez, A.C., Guzman-Vargas, L., Angulo-Brown, F., Sen, A.
K.: Quasi-Dimensional Simulation of Spark Ignition Engines from Thermodynamic Optimi-
zation to Cyclic Variability. Springer, London (2014)
Pulkrabek, W.W.: Engineering Fundamentals of the Internal Combustion Engine. Prentice Hall,
Upper Saddle River (1997)
Ramos, J.I.: Internal Combustion Engine Modelling. Taylor and Francis, London (1989)
Schwarz, C.: Calculation of real working process. In: Merker, G.P., Schwarz, C., Teichmann,
R. (eds.) Combustion Engines Development: Mixture Formation, Combustion Emissions and
Simulation. Springer, Berlin (2012)
Tannehill, J.C., Anderson, D.A., Pletcher, R.H.: Computational Fluid Mechanics and Heat Trans-
fer. Taylor and Francis, Washington DC (1997)
White, F.M.: Fluid Mechanics. McGrawHill, Boston (2003)
Winterbone D.E., Pearson R.J., Theory of engine manifold design: wave action methods for IC
engines. Professional Engineering Pub (2000)
Investigation of Effects of Natural Gas Composition on One-Dimensional. . .
439
Experimental Results of Split-Flow
Modiﬁcation for Post-combustion CO2
Capture Process
Marcin Stec, Adam Tatarczuk, Lucyna Wie
˛cław-Solny, Aleksander Kro
´tki,
Tomasz Spietz, Andrzej Wilk, and Dariusz S
´piewak
1
Introduction
Carbon capture from ﬂue gases is gaining interest in the European Union due to
current council obligations (Wie
˛cław-Solny et al. 2012) concerning reduction of
greenhouse gas emissions. To fulﬁl council liabilities, it is necessary to develop
technically feasible CO2 separation processes allowing the reduction of greenhouse
gases from fossil fuel power plants. Amine-based ﬂue gas scrubbing is the most
promising technology which may be used in CO2 separation processes. The main
advantage of this process is simplicity of incorporation into existing power plants
(Skorek-Osikowska et al. 2012). However, amine-based CO2 separation processes
add serious energy penalty, reducing the efﬁciency of the power plant (Chen and
Rochelle 2011). Therefore, current research concentrates on examination of
energy-saving design approaches (Spietz et al. 2014) and on solvent developments
(Wilk et al. 2013).
This paper deals with the results of tests of the split-ﬂow modiﬁcation of amine
scrubbing ﬂow sheet. Splitting the ﬂow of the solvent is advantageous and can
reduce energy consumption of the process (Thompson and King 1987) and
increases CO2 recovery (Polasek et al. 1983). Description and modelling of the
split-ﬂow process have been well established. The numerous papers deal with
energy considerations, capital costs (Karimi et al. 2011), evaluation of different
split-ﬂow conﬁgurations (Ahn et al. 2013) or thermodynamic aspects of the ﬂow
splitting (Leites et al. 2003). In contrast, studies describing experimental
M. Stec (*) • A. Tatarczuk • L. Wie
˛cław-Solny • A. Kro
´tki • T. Spietz • A. Wilk • D. S
´piewak
Institute for Chemical Processing of Coal, Zamkowa 1, 41-803 Zabrze, Poland
e-mail: mstec@ichpw.pl
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_31
441
implementation of the split-ﬂow process are scarce (Cousins et al. 2012) or out of
date (Polasek et al. 1983). This conjuncture encouraged the authors to present the
results of tests carried out at process development unit (PDU) for amine-based post-
combustion carbon capture located at Clean Coal Technologies Centre in Zabrze,
Poland.
Process development unit having capacity up to 100 m3
n/h was designed to test
the amine scrubbing carbon capture process from ﬂue gases or mixtures of technical
gases. PDU (Fig. 1) incorporates novel process ﬂow sheet introducing both con-
cepts: split-ﬂow process and rich split; therefore, the results are a valuable material
for model validation. Flexible conﬁguration of the PDU allows straightforward
changes in process ﬂow sheet; therefore, tests of conventional as well as novel ﬂow
sheets are possible. This feature of the PDU makes comparisons between conﬁgu-
rations effortless.
This paper deals with the detailed description of four tests: two tests for standard
process ﬂow sheet and two tests carried out with split-ﬂow arrangement. Process
conditions were carefully selected to make the comparison between standard and
split-ﬂow ﬂow sheets possible. Vast number of process parameters are included,
making this paper a valuable source of data for model validation.
The tests presented in this paper were carried out using 30 wt% MEA
(monoethanolamine) aqueous solution considered baseline solvent for pilot plant
studies of post-combustion carbon dioxide capture by reactive absorption
(Mangalapally et al. 2009).
Fig. 1 Overview of the PDU for amine-based post-combustion carbon capture at Clean Coal
Technologies Centre in Zabrze, Poland
442
M. Stec et al.
2
Experimental
2.1
Chemicals
Concentrated ethanolamine (MEA, CAS: 141-43-5, technical grade) was obtained
from Brenntag NV. Aqueous solution of ethanolamine was prepared on site using
mains water.
The following additives were used in minor quantities: Silpian W-3 purchased
from Silikony Polskie sp. z o.o. as antifoaming agent, potassium metavanadate
(KVO3, CAS: 13769-43-2) as corrosion inhibitor and hydrazine hydrate solution
(H4N2 H2O, CAS: 7803-57-8) as antioxidant.
2.2
Process Development Unit Description
The overview of the process development unit for amine-based post-combustion
carbon capture is shown in Fig. 1.
In Fig. 2 process ﬂow sheet of the PDU is introduced. The PDU allows CO2
separation from gas streams. Either ﬂue gas fed by blower or mixture of technical
gases can be treated. The CO2-rich gas (volumetric ﬂow up to 100 m3
n/h) is fed into
the pretreatment scrubber where the temperature of the gas is set, and gas is
saturated with water. Pretreatment scrubber suits therefore as direct contact cooler
using water as cooling medium. To avoid excessive amine degradation while
testing the process on ﬂue gases, the activated coal SOx adsorber is located
downstream of the scrubber. The CO2-rich gas enters the absorber at the bottom.
The absorber is built of three sections. Middle section, where gas contacts counter
currently with semi-lean amine, top section where lean amine is fed and water wash
Flue gas
Pre-treatment
scrubber
Absorber
Purified gas
Stripper
Condensate
Separator
Electrical
reboiler
Lean amine
Semi-lean amine
Rich amine
Water
CO2
SOx
absorber
Fig. 2 Flow sheet of the process development unit
Experimental Results of Split-Flow Modiﬁcation for Post-combustion. . .
443
section above the lean solvent inlet. The water wash section, where make-up water
is added, acts as cooler and prohibits the increase of amine concentration in the
solvent. Packing parameters and dimensions of the absorber are given in Table 1.
Carbon dioxide from CO2-rich gas is absorbed into the liquid phase. The rich
solvent is pumped into the stripper through rich lean and rich semi-lean heat
exchangers. The rich solvent is heated to higher temperatures through hot solvents
leaving the stripper. Such split-ﬂow conﬁguration is based on the invention pro-
posed by Shoeld (1934). In Fig. 2 additional line of rich solvent, bypassing heat
exchangers, can be noticed. Using this line, small portion of rich solvent remains
unheated and enters the top of the stripper. This modiﬁcation known as ‘rich split’
was suggested by Eisenberg and Johnson (1979). Concluding, the rich amine can be
fed to the stripper by means of three feed points: as unheated or heated either with
semi-lean or lean solvent.
The solvent in the bottom of the stripper is heated using electrical heating
element. Energy delivered to the rich solvent is spent on its regeneration. A portion
of the solvent is drawn from the intermediate section (semi-lean solvent) of the
stripper and fed to the absorber at some mid-column feed point. Because of the side
draw, remaining amine ﬂow to the reboiler is lower, resulting lower lean amine
loading. Lean amine is pumped back to the absorber and enters the top of the
column, as for conventional process ﬂow sheet. Further details regarding construc-
tion, packing and dimensions of the stripper are given in Table 1.
Product CO2 saturated with water vapours is collected from the top of the
stripper. Remaining part of water is removed in condenser installed downstream
of the column and almost pure CO2 is obtained.
Effects of foregoing modiﬁcations will be described in detail in consecutive
sections.
The PDU uses 30 wt% MEA (monoethanolamine) aqueous solution as solvent.
Table 1 Column size, packing heights and packing materials at the process development unit for
amine-based post-combustion carbon capture
Column
Diameter (mm)
Packing height (mm)
Packing material
Absorber
273
1400
Cylindrical ring 5 mm
VFF GmbH
1200
Berl saddles 10 mm
VFF GmbH
2000
Novalox saddles 13
VFF GmbH
Stripper
273
320
Sulzer CY
320
Sulzer CY
480
Sulzer CY
1600
Interpack #2
VFF GmbH
1000
Interpack #1
VFF GmbH
444
M. Stec et al.
For additional details concerning the PDU as well as other facilities located at
Clean Coal Technologies Centre in Zabrze, Poland, see (Lajnert and Latkowska
2013).
2.3
Gas and Liquid Analysis
Gas and liquid analysis are the most important and sophisticated measurements and
therefore will be described in detail.
Gas analysis is conducted online by using a ULTRAMAT 23 gas analyser. The
measuring principle of the instrument is based on the molecule-speciﬁc absorption
of bands of infrared radiation. Prior to feeding the gas to analyser, it is dedusted and
cooled to separate water vapours. The CO2 concentration from the instrument is
given directly as volumetric percent.
A liquid samples of rich, semi-lean and lean amine are collected during the
steady state, before the trial is stopped. The liquid samples are further analysed to
determine the amine concentration and CO2 loading.
The concentration of the solvent is checked by titration, and CO2 loading is
estimated based on the density of the solvent using correlations given by Hartono
et al. (2014).
3
Results and Discussions
3.1
Split-Flow Process Analysis
A literature review on description of the split-ﬂow process is outlined below.
The concept of the ﬂow splitting was ﬁrst suggested by Shoeld (1934) in patent
aiming to remove H2S from fuel gases using sodium phenolate. Shoeld suggested
splitting the streams of both lean and rich amine and claims that such modiﬁcation
reduces steam usage by 50% comparing to conventional single ﬂow process.
Shoeld’s idea has been improved by several authors (Condorelli et al. 2001;
Freguia et al. 2004; Towler et al. 1997). Despite the differences in various split-ﬂow
modiﬁcations, there is one common feature present in every split-ﬂow conﬁgura-
tion. Because of semi-lean amine drawn off the middle of the stripper, the amount
of the solvent remaining in the stripper for further regeneration is lower; therefore,
it can be regenerated to a higher extent than for conventional process. Resulting
lean amine is very clean and can be fed to the top of the absorber to ‘polish’ the gas
(Polasek et al. 1983). Semi-lean amine recycled to an intermediate stage of the
absorber is used to absorb the bulk of CO2. Additionally semi-lean amine, which is
cooled before being fed to the column, suits as interstage absorber cooling. More
Experimental Results of Split-Flow Modiﬁcation for Post-combustion. . .
445
optimal temperature proﬁle obtained makes better absorption of CO2 possible
(Leites et al. 2003).
In split-ﬂow designs lean amine is fed to the stripper at various heights (Fig. 2).
Forcing the lean solvent at different column heights changes temperature and
concentration in the stripper, bringing together the operating and the equilibrium
line (Thompson and King 1987). According to the second law of thermodynamics,
in order to reduce heat consumption of the process, it is necessary to reduce driving
force (Leites et al. 2003). Therefore, split-ﬂow designs are advantageous in terms of
the reduction of the heat consumption.
Comprehensive analysis of the heat reduction potential of the split-ﬂow designs,
based on exergy losses, was presented by Amrollahi et al. (2011).
Flow sheet of the PDU shown in Fig. 2 contains also rich-split modiﬁcation
suggested by Eisenberg and Johnson (1979). One of the streams of the lean amine is
routed directly to the amine stripper bypassing heat exchangers. This stream is
heated by condensing steam in the column which would normally be lost from the
stripper. Reducing the losses of the steam and heating of a portion of amine reduce
the overall energy requirements of the process.
Simulations of CO2 removal in split-ﬂow processes conﬁrm beneﬁcial character
of split-ﬂow modiﬁcations. The reduction of the reboiler heat duty by 5–18% than
for conventional process was claimed by Hyung Kun Bae (2011). In (Cousins et al.
2011) authors presented simulations of rich-split and split-ﬂow modiﬁcations where
the reduction of the reboiler heat duty over standard process reached 10.3% and
11.6%, respectively.
3.2
Experimental Data
In this section, the measurement results of the test cases are presented. Cases 1 and
2 should be analysed together as presenting the comparison of standard and ﬂow
sheet process, for reboiler heating element power set to 33.0 kW. Similarly, cases
3 and 4 are the same comparison for the reboiler heating element power set to
29.7 kW.
The gas for the separation process using PDU was prepared as mixture of carbon
dioxide (concentration: 12.30 vol %) and nitrogen (concentration: 87.7 vol %).
Such CO2 concentration is typical for ﬂue gases from coal-ﬁred power plant.
Detailed speciﬁcation of the gas at the absorber inlet is shown in Table 2.
Prior to feeding the gas into the absorber, it is saturated with water in
pretreatment scrubber using water wash. Generally, the pretreatment scrubber is
intended to dedust and cool ﬂue gases; however, presented experiments were
conducted using technical gases; therefore, such operations were not required.
The gas is treated in the absorber, contacting amine solvent counter currently.
The solvent absorbs CO2, treated gas (CO2-lean gas) is vented to the atmosphere,
and CO2-rich solvent is pumped to the stripper. The operation conditions of the
experiments, including solvent ﬂows, temperatures, solvent loadings, etc., are
presented in Table 4.
446
M. Stec et al.
The conditions and composition of CO2-lean gas collected from the absorber,
together with conditions of CO2 leaving the stripper, are summarised in Table 3.
In every case, carbon dioxide leaving the stripper is cooled to 25 C, and water is
removed, in condenser installed downstream of the stripper. Water collected in the
separator is afterwards pumped back to the absorber to avoid excess water losses
from the solvent.
Contrary, water vapours leaving the absorber with CO2-lean gas are not returned
to the process; therefore,
water balance is disturbed. Fortunately partial pressure of water vapours at the
outlet of the absorber is not high, thanks to moderate temperatures of CO2-lean gas
stream.
However, to maintain the concentration of the solvent constant, the levels in the
bottoms of absorber and stripper are carefully controlled, and water is added in case
of level deviations.
The reboiler heat duties shown in Table 4 are gross values, therefore include heat
losses to the ambient. The losses depend on many parameters, and its estimation is
not straightforward; however, for comparable pilot plant, authors claim than an
average value of 10% of the reboiler heat duty could be an acceptable simpliﬁcation
for the heat losses (Stec et al. 2015).
Table 2 Gas conditions and
composition at the absorber
inlet
Process variable
Case
1
2
3
4
Conditions
Volumetric ﬂow (m3/h)
94.4
94.4
92.9
92.9
Mass ﬂow (kg/h)
126.3
126.3
124.2
124.2
Pressure (kPa)
34.8
38.6
38.3
38.1
Temperature (C)
17.3
15.4
15.2
15.2
Composition (vol % – dry)
Nitrogen
87.7
Carbon dioxide
12.3
Table 3 Gas conditions and
composition at the absorber
inlet
Process variable
Case
1
2
3
4
CO2-lean gas leaving absorber
Pressure (kPa)
29.99
29.99
29.98
30.00
Temperature (C)
37.9
41.1
36.2
38.8
CO2 concentration
(vol% – dry)
1.19
1.00
1.11
0.95
CO2 leaving stripper
Pressure (kPa)
45.02
45.01
44.99
44.98
Temperature (C)
97.9
99.2
95.3
94.9
Experimental Results of Split-Flow Modiﬁcation for Post-combustion. . .
447
3.3
Absorber Operating Lines
Figures 3 and 4 show a comparison between absorber operating lines for standard
and split-ﬂow process ﬂow sheets and equilibrium curves. The equilibrium curves
are plotted based on experimental data taken from (Jou et al. 1995). Temperature in
the absorber varies along the columns’ height from 40 to 60 C for a typical test, and
the equilibrium data for this temperature range are presented in Figs. 3 and 4.
The experimental data for the CO2 partial pressure is available at three points of
the absorber: at the inlet, in the middle section and at the outlet; however, straight
lines were used to connect experimental data, but these lines serve only to join the
data points.
Despite the semi-lean amine loading is higher than the lean amine loading for
standard case, the slope of the operating lines for both process ﬂow sheets remains
similar for lower section of the absorber. This fact is clearly visible in Fig. 4, where
the operating lines almost overlap for higher partial pressures of CO2 (lower section
of the absorber), which in terms of driving force means the same CO2 absorption
capabilities.
Table 4 Operation conditions of the experiments
Process variable
Case
1
2
3
4
CO2 recovery (%)
91.1
92.5
91.8
93.0
Reboiler heat duty (MJ/kgCO2)
5.25
5.05
4.74
4.46
Absorber pressure (kPa)
29.99
29.99
29.98
30.00
Stripper pressure (kPa)
45.02
45.01
44.99
44.98
L/G (kg/kg)
5.20
5.22
5.31
5.32
Overall rich amine mass ﬂow (kg/h)
694.9
685.3
694.3
693.3
Rich amine mass ﬂow – top stripper inlet (kg/h)
60.5
60.5
60.6
60.6
Rich amine mass ﬂow – middle stripper inlet (kg/h)
19.5
267.0
16.7
274.8
Rich amine mass ﬂow – bottom stripper inlet (kg/h)
614.9
357.7
617.0
357.9
Lean amine mass ﬂow (kg/h)
656.7
323.8
659.7
324.4
Semi-lean amine mass ﬂow (kg/h)
–
335.5
–
336.3
Rich amine mass loading (molCO2/molMEA)
0.411
0.420
0.427
0.423
Lean amine mass loading (molCO2/molMEA)
0.273
0.234
0.296
0.240
Semi-lean amine loading (molCO2/molMEA)
–
0.329
–
0.336
Rich amine temperature – at absorber outlet (C)
54.6
52.5
53.0
51.9
Rich amine temperature – top stripper inlet (C)
54.6
52.5
53.0
51.9
Rich amine temperature – middle stripper inlet (C)
–
97.0
–
95.9
Rich amine temperature – bottom stripper inlet (C)
102.0
105.5
101.0
103.3
Lean amine temperature (C)
110.5
111.5
109.8
110.7
Semi-lean amine temperature (C)
–
107.6
–
106.4
Reboiler heating element power (kW)
33.0
33.0
29.7
29.7
The temperature at the absorber inlet is always 40 C for the solvent
448
M. Stec et al.
0
5
10
15
20
25
0,2
0,3
0,4
0,5
0,6
pCO2 (kPa)
α (molCO2/molMEA)
Standard
(case 3)
Split-flow
(case 4)
Equ., 40°C
Equ., 60°C
Fig. 4 Comparison of
absorber operating lines for
cases 3 and 4 to equilibrium
curve
0
5
10
15
20
25
0,2
0,3
0,4
0,5
0,6
pCO2 (kPa)
α (molCO2/molMEA)
Standard
(case 1)
Split-flow
(case 2)
Equ., 40°C
Equ., 60°C
Fig. 3 Comparison of
absorber operating lines for
cases 1 and 2 to equilibrium
curve
Experimental Results of Split-Flow Modiﬁcation for Post-combustion. . .
449
Contrary, the driving force for top section of the absorber in split-ﬂow process is
much higher than for standard process ﬂow sheet. This is expected because the lean
solvent loading for split-ﬂow process is lower than for standard process ﬂow sheet.
Summarising, the CO2 recovery in lower part of the absorber remains similar for
both process ﬂow sheets, as the driving force is also at a similar level. However,
split-ﬂow process becomes beneﬁcial in top part of the absorber where the gas is
contacting the solvent having very low loading. Thanks to increased driving force
in upper part of the column, overall CO2 recovery is higher for split-ﬂow process by
1.4% when comparing case 1 and case 2, and by 1.2% for cases 3 and 4. It should be
noted here that comparisons between standard and split-ﬂow process were carried
out for constant power delivered to the process.
As shown above the split-ﬂow process increases CO2 recovery comparing to
standard process. The advantage of split-ﬂow designs would reveal more signiﬁ-
cantly for systems where the lean solvent loading is very low. The split-ﬂow
designs are particularly preferred when high-quality CO2 lean gas is required
(Polasek et al. 1983).
3.4
Absorber Temperature Proﬁles
Figures 5 and 6 show absorber temperature proﬁles for standard and split-ﬂow
processes. The presence of the pronounced temperature increase is common for
every case. This temperature bulge (Kvamsdal and Rochelle 2008) can be explained
0
0,5
1
1,5
2
2,5
3
3,5
20,0
35,0
50,0
65,0
80,0
h (m)
tabs (°C)
Standard (case 1)
Split-flow (case 2)
Fig. 5 Absorber
temperature proﬁles for
cases 1 (standard) and
2 (split-ﬂow process)
450
M. Stec et al.
by the fact that during absorption, the heat is released and ﬂows towards the top of
the column causing the temperature increase.
It can be noticed that the temperature bulge for split-ﬂow process is less
prominent (Fig. 5). This is intercooling effect caused by the injection of cool
semi-lean solvent. The semi-lean solvent is fed into the absorber at 40 C and
cools interior of the absorber. Additionally the heat released in lower part of the
absorber with split-ﬂow designs is slightly lower comparing to standard process due
to lower loading of the semi-lean amine. Both reasons together cause temperature
decrease below semi-lean solvent inlet for split-ﬂow process.
The opposite effect occurs in top section of the absorber where the temperature
increase can be noticed for split-ﬂow process (Figs. 5 and 6). The lean solvent fed
into the top inlet of the absorber has the same temperature either for standard or
split-ﬂow process; therefore, the intercooling effect is equal for both conﬁgurations.
However, the loading of the solvent is lower for split-ﬂow designs; therefore, the
amount of CO2 absorbed in upper part of the column increases. Higher CO2
absorption causes higher heat release and the increase of the temperature.
Feeding the solvent, having different loadings, at various heights of the absorber
causes that the temperature proﬁle of the column for split-ﬂow process is uniform
and slightly lower on average than for standard process ﬂow sheet. Lower average
temperature of the absorber is in favour of higher driving forces for the
absorption process and also increases the absorption capacity of the solvent
(Amrollahi et al. 2012).
0
0,5
1
1,5
2
2,5
3
3,5
20,0
35,0
50,0
65,0
80,0
h (m)
tabs (°C)
Standard (case 3)
Split-flow (case 4)
Fig. 6 Absorber
temperature proﬁles for
cases 3 (standard) and
4 (split-ﬂow process)
Experimental Results of Split-Flow Modiﬁcation for Post-combustion. . .
451
4
Conclusions
The effects of splitting the ﬂow of the solvent being injected into the absorber have
been described in detail. It must be mentioned that split-ﬂow modiﬁcations in
absorber are intended to increase CO2 absorption driving force and according to
the second law of thermodynamics cause the increase in energy demand of the
process. However, introduction of colder rich solvent to the top of the stripper,
hotter rich streams below, taking the semi-lean amine from the stripper, changes its
temperature proﬁle. Changed temperature proﬁle causes the operating line of the
stripper parallel more closely the equilibrium curve thereby the condition necessary
to reduce the energy consumption in the stripper is fulﬁlled.
Analysing energy demand of the split-ﬂow process modiﬁcation, we are facing
contradictory effects: the increase of energy demand in the absorber and decrease in
the stripper. However, the total energy consumption of the process is reduced
comparing to the standard ﬂow sheet, making this modiﬁcation proﬁtable.
The reduction in the reboiler heat duty for split-ﬂow process during trials
presented in this paper ranges from 4 to 6%. Apart from the reboiler heat duty
reduction, the increase in CO2 recovery is also observed with split-ﬂow designs.
Split-ﬂow process improvement proved its value during experimental tests
because with minor increase in process complexity, noticeable increase in process
efﬁciency was perceived.
It can be expected that split-ﬂow modiﬁcation coupled with new solvent would
drastically decrease the energy demand and increase the CO2 recovery of the
amine-based post-combustion CO2 capture process.
Acknowledgements The results presented in this paper were obtained during research
coﬁnanced by the National Centre of Research and Development in the framework of Contract
SP/E/1/67484/10 – Strategic Research Programme – Advanced technologies for energy genera-
tion: Development of a technology for highly efﬁcient zero-emission coal-ﬁred power units
integrated with CO2 capture.
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453
Hydrogen Production from Methanol
Electrolysis
Sabah Menia, Fatiha Lassouane, Hamou Tebibel, and Abdallah Khellaf
1
Introduction
Various methods are available for hydrogen production and each of these methods
has its own advantages and disadvantages. Currently, steam reforming of natural
gas is considered as the most cost-effective route for large-scale hydrogen produc-
tion. Due to various issues related to the storage and transport of hydrogen, there is
an immediate requirement of small-scale methods for on-site hydrogen production
and for applications such as for fuel cells. Though compact steam reformers using
natural gas and methanol are under development, there are many issues associated
with them, such as carbon monoxide removal, long start-up time, and poor transient
response (Sasikumar et al. 2008).
Electrolysis is the best option for producing hydrogen very quickly and conve-
niently. Electrolysis of water can generate very high pure hydrogen and is consid-
ered as the most promising technology for small-scale production of hydrogen. The
disadvantage of this process is that the energy requirement is very high. Theoret-
ically, the energy consumption is 39.4 kWh/kg or 3.54 kWh/Nm3 of hydrogen,
while for a commercial water electrolyser, it is about 50–55 kWh/kg or 4.5–5 kWh/
Nm3 of hydrogen. Though various methods have been reported for reducing energy
consumption of water electrolysis, theoretical requirement itself is very high. This
method will become more attractive if low-cost electricity or electricity from
renewable sources is available (Sasikumar et al. 2008).
Electrolysis of aqueous methanol is another promising method for on-site
hydrogen production, and it has been reported that hydrogen can be generated by
S. Menia (*) • F. Lassouane • H. Tebibel • A. Khellaf
Centre de De
´veloppement des Energies Renouvelables, CDER, B.P. 62, Route de
l’Observatoire, 16340 Bouzareah, Algiers, Algeria
e-mail: s.menia@cder.dz
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_32
455
electrolysis of methanol–water mixture, at a very low operating voltage, compared
to water electrolysis. Though carbon dioxide, a greenhouse gas, is produced during
this process, the net carbon dioxide emission will be negligible, since methanol can
be produced from biomass, a renewable source. When aqueous methanol is elec-
trolyzed, hydrogen and carbon dioxide are produced, at ambient temperature and
pressure (Sasikumar et al. 2008).
The reaction at the anode and cathode and overall reaction are represented as
follows:
Anode : CH3OH þ 6OH ! 5H2O þ CO2 þ 6e
ð1Þ
Cathode : 2H2O þ 2 e ! 2OH þ 3H2
ð2Þ
Overall reaction : CH3OH þ H2O ! 3H2 þ CO2
ð3Þ
The theoretical voltage required for the above methanol–water solution electrol-
ysis is 0.02 V only, which is very much lower compared to 1.23 V required for
water electrolysis. Hence, the corresponding theoretical energy consumption for
aqueous methanol electrolysis is very low (Sasikumar et al. 2008).
In this paper, the basic concept of methanol–water solution electrolysis with an
alkaline membrane is introduced.
2
Methanol Electrolysis
Liquid alcohols could serve as a bridge between gasoline and gaseous H2. They
possess high energy densities and can be easily stored and distributed through the
existing gasoline infrastructure. It has also been argued that alcohols are the next
liquid fuels to use after the depletion of petroleum resources. Methanol can be
economically mass-produced from nonrenewable resources, such as natural gas and
coal, or from renewable resources such as biomass. Alcohols can be used to produce
electricity in direct fuel cells or mechanical energy in internal combustion engines.
However, an alcohol electrochemical reformer or electrolyser can be used to
produce clean H2, which can be used in other systems, resulting in an improvement
in the overall system performance. Electrochemical reforming or electrolysis is a
process in which an organic or alcohol fuel is electro-oxidized to form H2. Electri-
cal power is required to split the chemically bonded species (Cloutier and Wilkin-
son 2010).
The aqueous solution of CH3OH fed to the anode is electro-oxidized via a
dehydrogenation process to produce carbon dioxide (CO2), hydrogen ions (H+),
and electrons (e-) (Cloutier and Wilkinson 2010).
456
S. Menia et al.
Most H2 is currently produced by the well-established catalytic steam reforming
(SR) process for hydrocarbon-based fuels, which requires higher temperatures in
the range of 250–1100 C. It can be carried out externally, integrated with a fuel
cell, or directly inside a fuel cell such as a molten-carbonate fuel cell (MCFC,
600–700 C) or a solid oxide fuel cell (SOFC, 600–1000 C). On-board gasoline
reforming was originally believed to be the best way of generating H2 for trans-
portation, but it was determined that it did not offer clear advantages over other
available technologies, such as gasoline ICE and battery hybrids. The practical
issues of durability, size and weight, resistance to vibration, cold start, transient
response, and H2 purity concerns complicate the application of SR for H2 genera-
tion in transportation. An important approach to achieve the ultimate performance
targets, including start-up time and start-up energy, could be to efﬁciently carry out
on-board reforming at temperatures lower than that of a conventional SR. It was
reported that methanol catalytic reforming can be carried out in the aqueous phase
at lower temperatures (80–200 C). It seems that no studies have veriﬁed if
low-temperature methanol reforming could meet the on-board fuel processing
targets and if its adoption could serve as a bridge toward the implementation of a
H2 economy. Under certain operating conditions, the alcohol electrochemical
reformer or electrolyser may result in reduced energy consumption over compara-
ble existing technologies. First, the single-step electrochemical reforming process
could be conducted in the liquid phase at a lower operating temperature and has the
potential to have a higher overall system efﬁciency compared to conventional high-
temperature multi-reactor SR systems. Secondly, H2 production from methanol
electrolysis should result in lower power consumption than H2 production from
water electrolysis, the large-scale commercialization of which is restrained by its
signiﬁcant electricity requirements. In fact, the standard potential for the oxidation
of CH3OH in the liquid phase is only 0.016 V versus the standard H2 electrode
(SHE) compared to -1.23 V versus standard H2 electrode for the oxidation of water.
It has been estimated that H2 production from the electrolysis of CH3OH costs
about 50% less compared to that of water, even when the cost of CH3OH is taken
into account. The main disadvantages of alcohol electrolysis are issues also faced
by direct alcohol fuel cells: the slow kinetics of the anode alcohol electrooxidation
and the loss of fuel resulting from the alcohol and water crossover from the anode to
the cathode. Although CO2, a greenhouse gas, is produced whether CH3OH is
thermally or electrochemically reformed, it is more localized and concentrated in
an electrochemical reforming process. This has an advantage for effective capture
for sequestration and reduces the cost for CO2 disposal in many applications. The
cost of CO2 capture and disposal for various coal-ﬁred generation technologies
ranges from 1.1 to 4.9 cent/ kWhe and 0.6 to 6.7 cent/kWhe, respectively (Cloutier
and Wilkinson 2010).
Reichman and William demonstrated the electrochemical production of H2 gas
from CH3OH in aqueous NaOH or KOH media at temperatures between 23 and
60 C. The inclusion of a base permits the generation of H2 without CO2 emission.
As H2 would be the only gas produced, it would not require separation from other
gaseous by-products, and single-chamber conﬁgurations might be possible.
Hydrogen Production from Methanol Electrolysis
457
However, in a dual-chamber conﬁguration, the bicarbonate ions and/or carbonate
ions generated as soluble by-products might degrade the performance of the
electrolyser. Teruo et al. efﬁciently produced H2 by the electrochemical decompo-
sition of an aqueous solution of liquid fuel via electrolysis in a two-chamber
electrolytic cell using a diaphragm and metals dispersed on carbon as the anode
and cathode catalysts. Woods et al. developed an electrochemical reformer and fuel
cell system, which uses a solid-state or liquid state acidic or basic electrolyte and
relies on electricity and/or thermal energy to supply the necessary reaction energy
to reform organic fuels into H2 and CO2, while the H2 generated produces electric-
ity in a fuel cell (Cloutier and Wilkinson 2010).
To date, all literature published studies pertaining to the performance of alcohol
electrolysers have been carried out in a recycle operation mode or ﬂowing mode. In
these studies, the anode is fed with unacidiﬁed CH3OH aqueous solution, which is
fed using a pump, while the cathode compartment containing H2 is purged with
argon. In the recycle operation studies, e.g., carbon paper, typical anode catalysts
used are Pt/C or Pt–Ru/C, and although Pt/C is typically the most studied cathode
catalyst, cathode catalysts such as Pt–WC/C have also been investigated (Cloutier
and Wilkinson 2010) (Fig. 1).
3
Comparison with Ethanol Electrolysis
Electrolysis of aqueous alcohols has been proposed as a promising method for
on-site hydrogen production with lower power demands, since part of the energy
required for the electrolysis is provided by the organic molecule. For instance, it has
been reported that hydrogen can be produced by the electrolysis of methanol–water
mixtures, at a very low operating voltage compared with water electrolysis. This
process of water–alcohol mixture electrolysis has also been named as electrochem-
ical reforming or electro-reforming and is based on the use of electrical power to
split the chemically bonded species by the electrooxidation of the alcohol fuel.
Although CO2 is produced, this process is considered as an environment-friendly
one, if a bio-alcohol is used as the fuel. Then, the produced CO2 can be recaptured
by living plants to regenerate the required biomass. In addition, a clear advantage of
this method compared to the catalytic steam reforming of alcohols is the production
of pure hydrogen in the cathode compartment of the cell, which is automatically
separated from the other reaction products (Caravaca et al. 2012).
Hence, the electrochemical reforming of methanol and glycerol has been studied
in the literature showing very promising results for pure H2 production. However, to
the best of our knowledge, there are no previous works related to the electrochem-
ical reforming of ethanol. This latter is expected to be the most widely used biofuel
around the globe since it can be produced from abundant supplies of starch/
cellulose biomass. In addition, ethanol is harmless to humans and can be easily
transported and stored (Caravaca et al. 2012) (Fig. 2).
458
S. Menia et al.
4
Conclusions
The concept of methanol–water solution electrolysis in a fuel cell equipped with an
alkaline membrane for hydrogen production is introduced, offering the possibility
for lower power consumption in hydrogen production when compared to traditional
water electrolysis.
Fig. 1 Schematic of
methanol–water solution
electrolysis with an alkaline
membrane (Tuomi et al.
2013)
Fig. 2 Representation of the setup used for the ethanol electro-reforming process (Caravaca et al.
2012)
Hydrogen Production from Methanol Electrolysis
459
The alkaline membrane electrolyser is an attractive alternative to the conven-
tional electrolysers with proton exchange membranes enabling the utilization of
low-cost membrane materials offering savings with material expenses.
This novel method opens a new area for investigation in hydrogen production
and enables alternative material choices to produce commercially viable methanol
electrolysers although further development is required.
Acknowledgments Development center of renewable energy in Algeria supported this research.
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the anode gas diffusion layer. Int. J. Hydrog. Energy. 38(1), 73–81 (2013)
Sasikumar, G., Muthumeenal, A., Pethaiah, S.S., Nachiappan, N., Balaji, R.: Aqueous methanol
electrolysis using proton-conducting membrane for hydrogen production. Int. J. Hydrog.
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Tuomi, S., Santasalo-Aarnio, A., Petri, K.P., Kallio, T.: Hydrogen production by methanol water
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460
S. Menia et al.
Experimental Investigation of Polypropylene
Pyrolysis for Fuel Production
Emna Berrich Betouche, Asma Dhahak, Abdel Aziz Touati, and Fethi Aloui
1
Introduction
Waste plastics have a long biodegradability lifetime of between 100 and 1000 years
(Dussud and Ghiglione 2014). The treatment of such waste is carried out by several
methods (Panda 2011). The thermo-chemical processes seek waste valorization
through their high caloriﬁc value. Indeed, this power is equivalent to that of coal or
oil, and is 3.5 times higher than that of paper or cardboard (Carrega and Verney 2012).
The pyrolysis of waste plastics is very interesting. It is based on an endothermic
reaction that takes place in a low-oxygen atmosphere (less than 2% of O2). The
waste is decomposed under three phases: a solid residue (pyrolysis coke or coal),
liquid (mixture of condensable heavy and light condensable), and gas (CO, H2,
E.B. Betouche (*)
LUNAM University, Nantes University, UFR Sciences et Technology, 2 Chemin de la
Houssinie
`re, 44300 Nantes, France
CNRS, GEPEA, UMR6144, IMT Atlantique E
´cole des Mines de Nantes, 4 Rue Alfred Kastler,
44300 Nantes, France
e-mail: Emna.berrich@univ-nantes.fr
A. Dhahak
Ecole Nationale d’Inge
´nieurs de Gabes, Chemical Engineering-Processes Department, Avenue
Omar Ibn El Khattab, Zrig Eddakhlania, GABES 6072, Tunisia
A.A. Touati
LAMIH CNRS UMR 8201, Department of Mechanics, University of Valenciennes (UVHC),
Campus Mont Houy, F-59313 Valenciennes Cedex 9, France
F. Aloui
LAMIH CNRS UMR 8201, Department of Mechanics, University of Valenciennes (UVHC),
High Engineering School (ENSIAME), Campus Mont Houy, F-59313 Valenciennes Cedex 9,
France
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_33
461
CO2, CH4, C2H4, and C2H6) (Le
´ger and Lafortune 2001). This process is efﬁcient.
Its advantages are (Patni et al. 2013):
•
The ability to develop all the products obtained: pyrolysis oil that can be used as
fuel for engines, gas can be used for heating or electricity generation, and coke
can later be converted into products such coal assets or material used in the
building industry.
•
Reducing waste volume (<50–90%)
•
The use of low oxygen provides the reduced emission of pollutants such as
dioxins.
•
The ease of storage and transportation fuel.
Polypropylene (PP) was discovered in 1955 by Prof. Natta. It is generally used in
automobiles, packaging, bags, bottle caps, and clingﬁlm. The commercial symbol of
polypropylene (PP) is shown is Fig.1. Its chemical structure is shown in Fig. 2. It is
formed from naphtha, which is produced between the kerosene and gasoline produc-
tion steps. It then undergoes steam cracking to produce the monomers (ethylene and
propylene). The reaction mechanism (series of transformations) involved in produc-
ing PP is produced by a low-pressure propene polymerization reaction (Mc et al.
2010). It has an asymmetrical chemical structure (Karger-Kocsis 1994).
2
Experimental Facility
2.1
Setup
The experiments were conducted in a semi-batch reactor in an inert atmosphere.
The experimental installation is shown in Fig. 3. It comprises a batch reactor, a gas
analyser, a pressure sensor, two temperature sensors, a heater, a condenser, a
nitrogen tank, a ﬂowmeter, a thermometer, a liquid bottle, and acquisition software.
Initially, the sample is collected, cut, weighed, and then loaded into the reactor.
The latter has to be tightly closed, purged with a stream of nitrogen to remove
oxygen from the reactor, covered by insulation to limit heat loss, and then heated
through a heater. The sample weights were 100 g. The nitrogen ﬂow is ﬁxed to
Fig. 1 The commercial
symbol of polypropylene
(PP)
Fig. 2 Chemical structure
of polypropylene (PP)
462
E.B. Betouche et al.
20 ml/min. A heating rate equal to 10 C/min was maintained until a well-ﬁxed
temperature depending on the functional parameters and the thermo-gravimetric
analysis resulted. Then the heating was decreased to 2 C/min. During experiments,
there are two types of products: non-condensable gases and condensable gases that
became liquid using the condenser.
3
Results and Discussion
The heat ﬂow rate was initially ﬁxed to 10 C/min up to a temperature of 325 C. It
was then reduced to 2 C/min. Based on the thermo-gravimetric analysis, before a
temperature of 325 C, no reaction was detected, thus the heat rate can be increased
in order to reduce residence time on the reactor. The set-point temperature was
ﬁxed to 550 C because after this temperature, no important reaction was generated
under the heat rate given. Thus, heating after reaching this temperature should be
considered as heat losses, which reduce the process’s efﬁciency. The water ﬂow
was ﬁxed to 6.84  0.18 L/min. Development of the quantity and quality of liquid
of the pyrolysis product during one of the experiments carried out is shown in
Fig. 4.
Condensation begins after 43 min of heating, and is very slow. The ﬁrst gas
condensation temperature is equal to 385.8 C. At the beginning of the condensa-
tion, the liquid has a yellowish color, and then it becomes reddish.
Fig. 3 Experimental installation
Experimental Investigation of Polypropylene Pyrolysis for Fuel Production
463
Fig. 4 Development of the quantity and quality of the PP pyrolysis liquid
464
E.B. Betouche et al.
3.1
Temporal Development of the Reactor Temperature
The time-evolution of the temperature at the bottom of the reactor is illustrated in
Fig. 5.
The reactor residence time was 6 h 8 min, and the ﬁnal temperature was
458.2 C. The condensation temperature was more or less stabilized and the
condensation became very slow.
Figure 6 shows the temporal development of the temperature differences
between the bottom Tf and the middle height Tm of the reactor ΔT:
ΔT ¼ Tf  Tm
ð1Þ
The solid and liquid products obtained are shown in Fig. 7. ΔT decreases when
Tf reaches 330.9 C after 32 min (1920 s) of heating. Degradation begins at this
temperature. There are three peaks in the temperature proﬁle (Fig. 6). The ﬁrst peak
appears at 2342 s, and corresponds to a production of propane. More details are
given below and in Fig. 8 which illustrates the time-evolution of volumetric
percentage of PP pyrolysis gases.
510
480
450
420
390
360
330
300
270
240
210
180
150
120
90
60
30
0
0
2000
4000
6000
8000
10000
12000
t (s)
Tf (°C)
14000
16000
18000
20000
22000
β = 10 °C/mn
β = 2 °C/mn
Fig. 5 Time-evolution of the temperature at the bottom of the reactor
Experimental Investigation of Polypropylene Pyrolysis for Fuel Production
465
The second peak appears at 3551 s. Figure 8 shows that between 3448 and
3751 s, the volatile gas volumetric percentage decreases. Therefore, it corresponds
to the condensation phase of gases having a carbon number greater than 6.
The broad peak between 4250 and 15672 s is due to the release of
non-condensable gases (at approximately 6498 s) and condensable gases
(at approximately 8456 s). Condensation was rapid in the following situations:
•
After 2 h 22 min (8520 s), at a temperature equal to 417.9 C.
•
After 2 h 39 min (9540 s), at a temperature equal to 420 C.
•
After 3 h 05 min (11,100 s), at a temperature equal to 432.5 C.
The degradation is almost complete at 454 C, after 4 h 21 min of PP pyrolysis.
Fig. 6 Time-evolution of PP pyrolysis temperature difference between the bottom and the middle
of the reactor
Fig. 7 PP pyrolysis
products
466
E.B. Betouche et al.
3.2
PP Pyrolysis Product Analysis
Under the ﬁxed operational parameters, the pyrolysis of 100 g of PP enables 63.13 g
of liquid to be obtained (a volume of 84 ml) and 0.36 g of solid. Thus, the liquid,
solid, and gas products mass yields are, respectively, 63.13%, 0.36%, and 36.51%.
The performance of the gas obtained is very important so that good condensation is
achieved for cooling the gas released.
3.2.1
Gas Analysis
The time-evolution of the volumetric percentages of gases obtained by PP pyrolysis
is presented on Fig. 8.
In the alkene family, propene has a higher percentage of approximately 17.91%
at 401.3  C. This has been conﬁrmed in the literature (Wong and Broadbelt 2001).
For alkanes, ethane and propane are predominantly formed.
3.2.2
Liquid Analysis
The liquid chromatograph of PP pyrolysis is represented in Fig. 9. The compounds
identiﬁed in this liquid are listed in Table 1. The liquid is made up essentially with
Fig. 8 Time-evolution of volumetric percentage of PP pyrolysis gases
Experimental Investigation of Polypropylene Pyrolysis for Fuel Production
467
Fig. 9 Liquid chromatogram of PP pyrolysis
Table 1 List of the major components in the PP pyrolysis liquid
Retention time
min
Component
Molar mass
(g/mol)
Chemical
formula
2.586
2,3-dimethyl-Hexane
114
C8H18
2.782
Octane
114
C8H18
3.121
2,4-dimethyl-1-heptene
126
C9H18
3.563
(S)-3-ethyl-4-methylpentanol
130
C8H18O
3.602
Oxalate d’isobutyle et de nonyle
272
C15H28O4
4.589
1-decene
140
C10H20
4.827
2,6-dimethyl-Nonane
156
C11H24
5.615
isotridecanol
200
C13H28O
5. 768
(Z)-3-tetradecene
196
C14H28
6.459
(2,4,6-trimethylcyclohexyl) methanol
156
C10H20O
6.979
1-dodecene
168
C12H24
8.158
1-tridecene
182
C13H26
8.272
2-isopropyl-5-methyl-1-heptanol
172
C11H24O
9.031
Acetate de 11,13-dimethyl-12-
tetradecen-1-yle
282
C18H34O2
9.262
(E)- 9-octadecene
252
C18H36
10.306
(E)-5-eicosene
280
C20H40
10.566
1-tricosene
322
C23H46
10.855
2-hexyl- 1-decanol
242
C16H34O
12.461
(E)-9-eicosene
280
C20H40
11.364
cyclohexadecane
224
C16H32
13.206
1-hentetracontanol
592,592
C41H84O
468
E.B. Betouche et al.
42.86% alkenes and 19.04% alkanes whose carbon number is greater than
8. Among the constituents, there are also oxygenates (38.1%), which are mainly
composed of alcohols. This is may be due to the oxidation of polypropylene in the
presence of the limited oxygen quantity inside the reactor. The polypropylene
contains tertiary carbons, which promote oxidation compared to other types of
heat treatments. In the presence of oxygen, tertiary radicals form the peroxide
chemical formula-C-O-O-H even at 150 C. The peroxides are decomposed later
in more stable oxygenated groups such as hydroxyl and carbonyl groups (Panda and
Singh 2013).
The liquid characteristics are presented in Table 2. The liquid obtained has
almost the density of gasoline, while its higher heating value (HHV) and its ﬂash
point are closer to those of kerosene.
The PP liquid dynamic viscosity obtained is higher than that of gasoline and
lower than that of kerosene.
The liquid is a mixture of 47.63% of heavy hydrocarbons (>C15) and 52.37% of
light hydrocarbons (C8–C15). Thus PP pyrolysis promotes the production of
unsaturated light hydrocarbons.
4
Conclusions
The pyrolysis of polypropylene is realised to produce fuel. Under the operational
parameters, the obtained liquid mass yield is 63.13%. It is made up of 47.63%
heavy hydrocarbons and 52.37% light hydrocarbons. It has approximately the same
gasoline density but a higher heating value (HHV). Its ﬂash point is closer to that of
kerosene.
Acknowledgements This work was supported by LUNAM University, Nantes University, UFR
Sciences et Technology, the “Processes engineering, Environment and Agri-food” laboratory
(GEPEA), Energetic Systems and Environment (DSEE) Department, IMT Atlantique, Mines
Table 2 Comparison of PP liquid to other fuels
Properties
Gasoline
Kerosene
Diesel
PP pyrolysis liquid
obtained
Density (kg/m3)
743–751
(20–30 C)
760–767
(20–30 C)
870–1000
751.644
Flash point (C)
46
38
38–58
31
HHV (MJ/kg)
47
46
45
46.151  1.33
Viscosity (cP) at
40 C
0.41
1.7
3.35
0.67
Composition on
carbon
C7–C11
C12–C15
C16–C18
C8–C41
(C8–C11: 38.09%
C12–C15: 14.28%
>C15: 47.63%)
Experimental Investigation of Polypropylene Pyrolysis for Fuel Production
469
Engineering school of Nantes in FRANCE and the Engineering School of Gabes, Chemical
Engineering-Processes of Gabes, Tunisia. This support is gratefully acknowledged.
References
Carrega, M., Verney, V.: Matie
`res plastiques, 3e
`me e
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Inge
´nierie”, 680 (2012)
Dussud, C., Ghiglione, J. F.: La de
´gradation des plastiques en mer, SFE report, december. http://
www.sfecologie.org/regards/2014/12/26/r63-plastiques-en-mer-dussud-et-ghiglione/#com
ment-259688 (2014)
Karger-Kocsis, J.: Polypropylene Structure, Blends and Composites, Structure and Morphology,
p. 169. Chapman & Hall/Springer, London (1994)
Le
´ger, S., Lafortune, F.: Pyrolyse et gazeﬁcation des pneus hors d’usage. http://www.recyc-
quebec.gouv.qc.ca/upload/publications/pneus/pyrolyse.pdf (2001)
Mc Guigan, J., Moyer, R., Harris, F.: Managerial Economics: Applications, Strategy and Tactics,
12e
`me e
´dition, USA, Cengage Learning, 403 (2010)
Panda, A. K.: Studies on process optimization for production of liquid fuels from waste plastics,
Doctorat en ge
´nie chimique: De
´partement de ge
´nie chimique Institut national de technologie
(2011)
Panda, A. K., Singh, R. K.: Experimental Optimization of Process for the Thermo-catalytic
Degradation of Waste Polypropylene to Liquid Fuel, Advances in Energy Engineering
(AEE), 1 (2013)
Patni, N., Shah, P., Agarwal, S., Singhal, P.: Alternate Strategies for Conversion of Waste Plastic
to Fuels, ISRN Renewable Energy, ID 902053. http://www.hindawi.com/journals/isrn/2013/
902053/ [page consulte
´e le 13.04.2015] (2013)
Wong, H.-W., Broadbelt, L.J.: Tertiary resource recovery from waste polymers via pyrolysis: neat
and binary mixture reactions of polypropylene and polystyrene. Ind. Eng. Chem. Res. 40,
22 (2001)
470
E.B. Betouche et al.
Experimental Analysis of Hydrogen-Fueled
Homogeneous Charge Compression Ignition
(HCCI) Engine
M. Mohamed Ibrahim and A. Ramesh
Nomenclature
HCCI
Homogeneous charge compression ignition
HHCCI
Hydrogen homogeneous charge compression ignition
HDHCCI
Hydrogen diesel homogeneous charge compression ignition
HER
Hydrogen energy ratio
BMEP
Brake mean effective pressure
CI
Compression ignition
SI
Spark ignition
EGR
Exhaust gas recirculation
CO2
Carbon dioxide
CO
Carbon monoxide
HC
Hydrocarbon
NOX
Oxides of nitrogen
NO
Nitric oxide
PM
Particulate matter
TDC
Top dead center
IMEP
Indicated mean effective pressure
C
Degree Celsius
CA
Degree crank angle
RTD
Resistance temperature detector
CRI
Common rail injection
ICT
Intake charge temperature
rpm
Revolution per minute
(continued)
M.M. Ibrahim (*) • A. Ramesh
Department of Mechanical Engineering, Indian Institute of Technology Madras,
Tamilnadu, Chennai 600 036, India
e-mail: ibrahimengine@gmail.com
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_34
471
ppm
Parts per million
J/CA
Joule per degree crank angle
BTDC
Degree crank angle before top dead center
ϕ
Equivalence ratio
BTE
Brake thermal efﬁciency
ITE
Indicated thermal efﬁciency
IT
Injection timing of diesel
COV
Coefﬁcient of variation
LTR
Low-temperature reaction
HTR
High-temperature reaction
SOC
Start of combustion
SOI
Start of injection
FSN
Filter smoke number
1
Introduction
Research in internal combustion engines is currently focused on decreasing the
level of emissions without sacriﬁcing fuel economy. A relatively new alternative
combustion strategy known as homogeneous charge compression ignition (HCCI)
can
lead
to
ultralow
oxides
of
nitrogen
(NOX)
and
particulate
matter
(PM) emissions. It also has the potential to operate at high thermal efﬁciency. In
this combustion mode, a lean and nearly homogeneous mixture of air and fuel is
ignited by compression. Combustion of this mixture occurs at multiple ignition
locations in the combustion chamber almost simultaneously (Yao et al. 2009; Zhao
2007; Jittu et al. 2007). HCCI combustion was studied initially by Onishi et al.
(1979) in a two-stroke engine. One of the earliest experiments on a four-stroke
HCCI engine fueled with a mixture of isooctane and n-heptane in the HCCI
combustion by Najt and Foster (1983) reported that in these engines, combustion
is controlled by chemical kinetics. Thring (1989) studied the four-stroke diesel
HCCI mode with control by changing the intake temperature and exhaust gas
recirculation (EGR) over a range of equivalence ratios and was the ﬁrst to name
this kind of combustion mode as homogeneous charge compression ignition. The
HCCI combustion can be experimented in both modiﬁed spark ignition (SI) and
compression ignition (CI) engines with different types of fuels like gasoline, diesel,
alcohols, natural gas, liqueﬁed petroleum gas (LPG), biogas, and hydrogen (Yao
et al. 2009; Zhao 2007). However, most of the research work focuses mainly on
diesel and natural gas.
Investigations on diesel-fueled HCCI operation with intake manifold injection
indicated the requirement of high intake temperature for effective mixture forma-
tion (Ryan and Callahan 1996). The problems faced were very high HC emissions,
advanced combustion phasing, and low thermal efﬁciency. However, the levels of
NOX and PM emissions were signiﬁcantly reduced. In-cylinder injection of diesel
472
M.M. Ibrahim and A. Ramesh
was also found to be advantageous as compared to manifold injection of diesel as it
could eliminate intake heating and minimize the level of lubricating oil dilution
(Nathan et al. 2007; Walter and Gatellier 2002; Ibrahim and Ramesh 2013). In order
to overcome the problems such as poor vaporization of diesel, lubricating oil
dilution, and high HC emissions, multi-pulse injection method (up to eight injec-
tions per cycle) with a common rail system at high injection pressure was
employed. On modifying the injection strategy, HC emission was reduced when
the injection timing was moved toward top dead center (TDC) to minimize wall
impingement of diesel. A reduction of CO emissions was obtained by increasing the
boost pressure or by late-injection strategy. EGR (about 61%) could extend the
operating range of IMEPs to 9 bar (Zheng and Kumar 2009). Gaseous fuels are
suitable for HCCI operation as this readily form a homogeneous mixture. Neat
natural gas-fueled HCCI operation needs relatively too high intake temperatures as
methane has a high self-ignition temperature (Kobayashi et al. 2011; Yap et al.
2004). Most researchers have experimented with the natural gas in the HCCI mode
with inlet heating, use of fuel additives like dimethyl ether (DME) or hydrogen, and
addition of pilot diesel to aid the ignition (Kobayashi et al. 2011; Yap et al. 2004;
Srinivasan et al. 2007; Din et al. 2010; Zhao 2007). Hydrogen has also been used to
improve natural gas HCCI operation with residual gas trapping, and it was shown
that it reduces the intake temperatures required for autoignition (Yap et al. 2004).
Biogas has also been used in the HCCI mode. It requires relatively too high
intake charge temperature of about 200 C to sustain the HCCI combustion because
of its high autoignition temperature (Bedoya et al. 2012). A signiﬁcant reduction in
the intake temperature needed for sustaining combustion was observed when diesel
was injected into the manifold of a biogas-fueled HCCI engine. The intake charge
also had to be heated, and a temperature of 135 C was found to be optimal. The
presence of CO2 in biogas was found to lower the high heat release rates that are
normally encountered in diesel HCCI operation (Nathan et al. 2010).
Limited work has been reported on the use of hydrogen in the HCCI mode. Neat
hydrogen HCCI combustion needs relatively high intake temperatures in the range
of 80–100 C. Though the load range is limited, the thermal efﬁciency is high.
Emissions are very low as compared to the diesel-fueled CI mode (Caton and Pruitt
2009; Gomes et al. 2008). The maximum IMEP that can be reached is about 3.5 bar
which is restricted by knocking mode (Stenlaas et al. 2004). Addition of CO2
extended the load range of operation in the hydrogen HCCI mode to an IMEP to
4.5 bar (Ibrahim and Ramesh 2014). Studies have been conducted with diesel being
injected into the manifold along with hydrogen in order to extend the load range.
However, the intake had to be heated to about 75 C to vaporize the diesel (Guo
et al. 2011). In-cylinder injection of diesel along with manifold induction of
hydrogen eliminated the need to heat the intake charge. Hydrogen retards the
combustion of diesel and results in improvements in performance and emissions
in the HCCI mode (Ibrahim and Ramesh 2013).
In this work a single-cylinder diesel engine was modiﬁed to run in the HCCI
mode with hydrogen as the fuel. Inﬂuences of intake charge temperature and
equivalence ratio were studied. Further, the same engine was equipped with a
Experimental Analysis of Hydrogen-Fueled Homogeneous Charge Compression. . .
473
common rail diesel injection system, and experiments were done with manifold
injection of hydrogen and in-cylinder injection of diesel. The injection timing of
diesel, exhaust gas recirculation ratio, and hydrogen-to-diesel-energy ratio were
optimized. The results of combustion and emissions in the above two modes of
operation are presented. Further, the different modes have been compared at the
best operating conditions with the neat diesel CI mode to bring out the relative
performance and emission beneﬁts of using hydrogen in the HCCI mode.
2
Engine Setup and Experiments
The engine whose speciﬁcations are given in Table 1 was coupled to an eddy
current dynamometer with closed loop control. The schematic diagram of engine
setup and the accessories used to operate the engine in the hydrogen-fueled HCCI
mode are depicted in Fig. 1. An air preheater with a controller was installed in the
intake manifold to set the required intake charge temperature. Hydrogen was
inducted into the engine through the intake manifold after this heater. A thermal
mass ﬂow meter was used to measure the mass ﬂow rate of hydrogen. The air ﬂow
rate was measured using a positive displacement volumetric ﬂow meter.
A high-pressure common rail diesel system with completely ﬂexible fuel injec-
tion controls was adopted on the single-cylinder diesel engine for the hydrogen
diesel HCCI mode. The ﬂow rate of diesel fuel was measured directly on the mass
basis. The intake charge temperature was measured using the K-type thermocouple.
The exhaust system was also provided with an arrangement to extract exhaust gases
for recirculation, i.e., EGR. The level of NO emission was measured using chemi-
luminescence analyzer. AVL DiGas analyzer was used for the measurement of CO
and HC emissions. Smoke emission was measured using an AVL smoke meter.
A piezo electric cylinder pressure transducer was ﬂush mounted on the cylinder
head for the measurement of cylinder pressure on the basis of crank position. An
optical encoder (Make: Kistler, Switzerland) was mounted on the crank shaft to
provide crank angle information to the data acquisition system. The pressure signals
Table 1 Speciﬁcations of the engine
Engine type
Single-cylinder, four-stroke, naturally aspirated,
water-cooled, direct injection, diesel engine
Bore
80 mm
Stroke
110 mm
Connecting rod length
231 mm
Compression ratio
16:01
Rated power
3.7 kW @1500 rpm
Injection system
Modiﬁed- to high-pressure common rail system
474
M.M. Ibrahim and A. Ramesh
were then recorded by an in-house developed high-speed data acquisition system
(National Instruments, 6070E data acquisition card). The heat release rate was
determined using an average of cylinder pressure data for 100 consecutive cycles.
In this case, the cyclic dispersion was adequately low. This was checked. The
coefﬁcient of variation (COV) of IMEP was 3–4%, and hence, this method was
used. The coolant temperature was measured using a resistance temperature detec-
tor (RTD). To check the leakage of hydrogen, a leak detector was used.
In order to perform experiments in the HHCCI mode, the engine was motored
initially without introduction of hydrogen. Intake charge temperature was raised up
to a level where combustion could be sustained with little amounts of hydrogen
being admitted. This temperature was around 130 C. Thereafter the intake charge
temperature was set at the required value, while the quantity of hydrogen was varied
at the same instant to achieve stable HCCI combustion. Subsequently the intake
charge temperature was maintained at different values, and the hydrogen ﬂow rate
was also varied to perform experiments at different equivalence ratios. The range of
equivalence ratios was limited by knock on the higher hydrogen ﬂow rates and
misﬁre on the lower hydrogen ﬂow rates. These experiments were repeated at
various constant intake charge temperatures ranging from 80 to 130 C. In all
cases, the coolant outlet temperature was maintained at 50 C by adjusting its
Fig. 1 The schematic diagram of engine setup and the accessories used to operate the engine in
the hydrogen-fueled HCCI mode
Experimental Analysis of Hydrogen-Fueled Homogeneous Charge Compression. . .
475
ﬂow rate. During the HDHCCI mode of operation, the engine was initially motored
and started in the CI mode. Once the coolant temperature reached a ﬁxed value of
50 C, a mode switch over from CI to HCCI was feasible by advancing the injection
timing of diesel using the common rail injection (CRI) system. The intake charge
temperature was maintained at 30 C for all operating range of BMEPs. Using the
electronic controller of the CRI system, the injection timing of diesel was always
set at the best value for thermal efﬁciency. Additionally the inﬂuence of different
levels of EGR was studied. The intake charge temperature was maintained by the
heater described earlier. All these experiments were conducted at a constant speed
of 1500 rpm.
3
Results and Discussion
Experiments were performed on the single cylinder stationary diesel engine in three
different modes of operation, namely, hydrogen-fueled HCCI (HHCCI), hydrogen
diesel HCCI (HDHCCI), and neat diesel compression ignition (CI). Results of
combustion, emissions, and performance in these different modes and also a
comparison to quantify the potential beneﬁts of switching between the modes are
presented.
3.1
Combustion Characteristics of the Hydrogen-Fueled
HCCI (HHCCI) Engine
It is seen from Fig. 2 that the start of combustion was signiﬁcantly retarded when
the intake charge temperature (ICT) was decreased from 90 to 80 C at a constant
hydrogen ﬂow rate of 0.18 kg/h. The highest brake thermal efﬁciency was obtained
at the lowest possible ICT of 80 C with hydrogen. Therefore, retarded combustion
phasing led to better thermal efﬁciency, and further reduction in the ICT resulted in
misﬁre. Thermal efﬁciency values in the HHCCI mode were higher than in the
conventional CI mode of operation because of shorter combustion duration. In
HHCCI mode, ignition starts due to the temperature reached during compression.
Ignition is the point where the heat release rate curve becomes positive. The range
of intake charge temperatures (80–130 C) was adequate to sustain hydrogen-fueled
HCCI combustion at a compression ratio of 16:1.
A drastic reduction of NO emission as indicated in Fig. 2 was observed as
compared to the CI mode. The concentration of smoke, HC, and CO emissions as
expected were negligible.
Increase in the equivalence ratio (ϕ) also raised the combustion rate and also
advanced it as shown in Fig. 3. Though the combustion gets advanced to crank
angles much before TDC, we see that the brake thermal efﬁciency has also
476
M.M. Ibrahim and A. Ramesh
increased. This is because higher equivalence ratios lead to higher torque (BMEP
output) and thus the mechanical efﬁciency increases.
However, we see that the indicated thermal efﬁciency (ITE) actually decreases
with increase in equivalence ratio because of advanced combustion. With increase
in equivalence ratio, the level of NO emission increases slightly only. On the whole,
extremely low NO emission was seen, and high intake charge temperatures were
needed to sustain neat hydrogen-fueled HCCI combustion.
3.2
Effect of Hydrogen Energy Ratio in the HDHCCI Mode
In order to eliminate intake charge heating, in-cylinder injection of diesel was
employed long with hydrogen. Initially these experiments were conducted at a
BMEP of 2 bar where the engine could be operated in the HDHCCI mode and also
in the diesel-fueled HCCI (DHCCI) modes and also with EGR. The variables whose
effect was studied are hydrogen energy ratio, injection timing, and EGR level. The
effect of varying the hydrogen energy ratio (HER) which is deﬁned as the energy
derived from hydrogen to the total energy derived from diesel and hydrogen while
maintaining the injection timing (at 93BTDC) without EGR was initially studied.
Fig. 2 Effect of intake charge temperature on HHCCI operation and its comparison with CI mode
Experimental Analysis of Hydrogen-Fueled Homogeneous Charge Compression. . .
477
Figure 4 shows that the addition of hydrogen increased the thermal efﬁciency. This
is because in the diesel-fueled HCCI operation (HER ¼ 0), combustion occurs too
early due to the low self-ignition temperature of diesel. Hydrogen delays the
combustion to crank angles where energy conversion to work is effective. The
combustion process gets retarded with hydrogen because of dilution and chemical
effects as reported in literature (Guo et al. 2011). However, after a particular
amount of hydrogen (33.8% energy ratio), the engine began to misﬁre because of
too retarded combustion on account of the small amount of diesel that was used.
The level of NO emissions depicted in Fig. 4 decreases with introduction of
hydrogen due to lowered combustion rates and retarded combustion which lead to
lower temperatures.
In the HDHCCI mode, smoke emission is mainly due to the injected diesel.
Hence, increase in the fraction of hydrogen lowers smoke concentrations as seen.
HC and CO are also only contributed by the injected diesel, and hence, similar
trends are seen for these pollutants as well (Fig. 4). In the case of 0% energy ratio,
the combustion is too advanced, and this is the reason for the low thermal efﬁ-
ciency. Even with the highest hydrogen ﬂow rate, most of the combustion gets
completed before TDC. Though still retarded, combustion could have yielded better
thermal efﬁciencies, and it was not possible due to misﬁre.
Fig. 3 Effect of equivalence ration on HHCCI mode at a constant intake charge temperature of
110 C
478
M.M. Ibrahim and A. Ramesh
3.3
Effect of Injection Timing of Diesel with HDHCCI Mode
of Operation
Experiments were done with ﬁxed hydrogen and diesel ﬂow rates (ﬁxed energy
ratio of hydrogen) and no EGR. The highest thermal efﬁciency was obtained at an
injection timing of 80BTDC. The heat release rate versus crank angle curves
depicted in Fig. 5 show that as the injection timing was advanced, the combustion
was retarded. This is because earlier injection timings of diesel give it increased
residence time to form a more homogeneous mixture which leads to retarded
combustion. Such an occurrence has been indicated by other researchers also in
diesel-fueled HCCI engines. The best injection timing is a compromise between
combustion rate and combustion phasing (combustion occurring close to TDC).
Operation in the HDHCCI mode was restricted by severe knocking below an
injection advance of 60BTDC, and the engine started to misﬁre above an injection
timing of 120BTDC at this condition. Advancing the injection timing signiﬁcantly
lowers the NO levels (Fig. 5) due to reduced temperatures as a result of retarded and
slower combustion. Smoke emissions are due to the injected diesel. It may be
noted that in these results, the amounts of diesel and hydrogen are ﬁxed. Smoke
formed in the combustion chamber can be oxidized if there is sufﬁcient oxygen and
Fig. 4 Inﬂuence of adding hydrogen on diesel HCCI mode at a constant BMEP of 2 bar
Experimental Analysis of Hydrogen-Fueled Homogeneous Charge Compression. . .
479
the temperature is also high. In all these cases, the overall equivalence ratios are
very low.
The impingement of diesel on the bowl of the combustion chamber is high when
the injection timing is retarded (piston is near top dead center). It is to be noted that
the injector in this case is with a single hole along the axis of the cylinder. Advanced
injection timings led to more homogeneous mixtures and thus could result in lower
smoke levels. A combination of these factors inﬂuences the variation of smoke
indicated in Fig. 5. Smoke peaked at a particular injection timing. The concentra-
tion of HC and CO emissions increased as the injection timing of diesel was
advanced, and this is because of lower in-cylinder gas temperatures which affect
oxidation of these components.
3.4
Effect of EGR Ratio in the HDHCCI Mode
The following results are at a constant hydrogen energy ratio and injection timing
and indicate the inﬂuence of EGR level. Adding EGR increases the thermal
efﬁciency as seen in Fig. 6 due to proper combustion. Introduction of EGR lowers
the level of NO emissions because of lower combustion rates as seen in Fig. 6. The
Fig. 5 Effect of injection timing of diesel on HDHCCI mode of operation
480
M.M. Ibrahim and A. Ramesh
concentration of smoke, HC, and CO emissions are increased with EGR as depicted
due to lower oxygen availability. On the whole, it is seen that EGR, injection
timing, and hydrogen energy ratio affect performance and emissions signiﬁcantly.
Further studies were done at ﬁxed BMEPs under variable hydrogen energy ratio,
while the injection timing of diesel was always adjusted for best thermal efﬁciency.
3.5
Comparison of HHCCI, HDHCCI, and CI Modes
of Operation
In this section the different modes of operation are compared at the respective best
operating conditions at every BMEP. It may be noted that in the HHCCI mode, only
BMEPs in the range 0.5 to 2.2 bar were possible and that too with intake charge
heating, whereas in the HDHCCI mode, best results were obtained only without
intake charge heating. In some cases in the HDHCCI mode, EGR was needed to
properly phase the combustion process. Hence, the results in the HDHCCI mode are
with the optimum levels of EGR and injection timings. BMEPs in the range 2 to
4 bar were only possible in the HDHCCI mode of operation.
Fig. 6 Effect of EGR on HDHCCI mode
Experimental Analysis of Hydrogen-Fueled Homogeneous Charge Compression. . .
481
HHCCI mode was only possible in the low BMEP range (0.5–2.2 bar) as seen in
Fig. 7. However, at these conditions, the brake thermal efﬁciency was better than
the CI mode due to better combustion phasing and faster combustion. However,
high intake charge temperatures (ICT) were needed. At a BMEP of 2.2 bar, i.e., as
the equivalence ratio went up, HHCCI operation is not possible due to too rapid
combustion that results in knock. Thereafter, only HDHCCI was possible. In this
mode, no heating of the intake charge was needed. As the BMEP went up, it was
required to lower the hydrogen energy ratio. In fact at the highest possible BMEP of
4 bar, the HER was only 7.4%, and the operation resembled the neat diesel HCCI
mode. High EGR levels were needed to control the combustion rate, and this led to
the use of low amounts of hydrogen in order to avoid misﬁre. In the entire range, the
operation in both the HCCI modes was higher in terms of thermal efﬁciency than
the CI mode. Thus, the use of hydrogen in the HCCI mode along with diesel or in
the single fuel mode is advantageous. However, the range of operation is still
restricted.
Fig. 7 Comparison of brake thermal efﬁciency with BMEP among HHCCI, HDHCCI, and CI
modes
482
M.M. Ibrahim and A. Ramesh
An additional consideration is the impingement of diesel on the cylinder walls
when the hydrogen energy ratio is low which leads to dilution of the lubricating oil,
HC, CO, and smoke emissions. This will mean that operation in the HDHCCI mode
has to be limited to high HERs, i.e., low BMEPs. The NO emissions in the HHCCI
and HDHCCI modes (Fig. 8) were extremely lower than in the CI mode as expected
to low combustion temperatures.
Smoke emission in the HDHCCI mode was higher than in the CI mode as seen in
Fig. 9. This is because of fuel impingement on the bowl of the combustion chamber.
We see that from Figs. 10 and 11, the HC and CO emissions are also inﬂuenced
by this effect in the case of the HDHCCI mode. Of particular importance is the rise
in CO, HC, and smoke with BMEP in the HDHCCI mode. This is due to the
increase in the amount of diesel that was injected because the hydrogen energy ratio
had to be reduced with increase in BMEP.
The start of injection and start of combustion (low-temperature combustion and
main combustion) have been indicated in Table 2 for the HDHCCI mode.
Fig. 8 Comparison of NO emissions with BMEP among HHCCI, HDHCCI, and CI modes
Experimental Analysis of Hydrogen-Fueled Homogeneous Charge Compression. . .
483
4
Conclusions
Based on the experiments conducted in the neat hydrogen HCCI (HHCCI) mode
and the hydrogen diesel HCCI (HDHCCI) mode, the following conclusions are
drawn:
•
The load range in the HHCCI mode is restricted by knocking at about 2 bar, and
intake charge heating is essential. However, the thermal efﬁciency is higher, and
the NO emission is signiﬁcantly lower than normal diesel operation.
•
Hydrogen can be used to phase the combustion in diesel-fueled HCCI engines.
However, even in the HDHCCI mode, only BMEPs of about 4 bar can be
reached when EGR is also employed. HDHCCI operation is also better than
conventional diesel operation in terms of efﬁciency and NO emissions. No
heating of the intake charge is needed, and the upper limit of operation is
restricted by knock.
Fig. 9 Comparison of smoke emissions with BMEP among HHCCI, HDHCCI, and CI modes
484
M.M. Ibrahim and A. Ramesh
•
HDHCCI results in higher levels of smoke and HC emissions particularly at the
higher end of BMEPs as EGR levels are high and hydrogen amount is low at
these conditions. The high amount of diesel and advanced injection timing result
in impingement of the diesel on the bowl of the combustion chamber in
this mode.
On the whole, by shifting from the HHCCI mode at low BMEPs to the HDHCCI
mode at medium BMEPs and to the CI mode at high BMEPs, operation with high
thermal efﬁciency and low NO emission is possible. However, control of intake
charge temperature in the HHCCI mode and elevated smoke and HC levels in the
HDHCCI mode are issues to be further tackled.
Fig. 10 Comparison of HC emissions with BMEP among HHCCI, HDHCCI, and CI modes
Experimental Analysis of Hydrogen-Fueled Homogeneous Charge Compression. . .
485
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Table 2 The SOI and SOC
(LTR and HTR) for the
HDHCCI mode at best
operating condition
BMEP [bar]
2
3
4
Start of injection of diesel [BTDC]
75
86
108
Start of combustion (LTR) [CA]
334
333
340
Start of combustion (HTR) [CA]
348
348
350
486
M.M. Ibrahim and A. Ramesh
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Experimental Analysis of Hydrogen-Fueled Homogeneous Charge Compression. . .
487
Part II
Sustainable Buildings
Investigations of Thermal Comfort of Building
Integrated Phase Change Materials
Mustapha Faraji
1
Introduction
The overheating/subcooling is the major problem of the most modern constructions
because of the use of lightweight materials with low thermal inertia. As a possible
solution to this problem, we propose the use of phase change materials (PCM) that
allow changing the thermal behavior of the building by improving the thermal
inertia. The wallboards are cheap and widely used in a variety of applications,
making them very suitable for PCM encapsulation. On the other hand, the princi-
ples of latent heat storage can be applied to any appropriate building materials. The
latent heat of fusion increases the thermal mass of the building. Ahmed et al. (2006)
presented a study of different types of walls containing PCM. The partitions consist
of a locally marketed panel. Three types of panels are tested: (1) polycarbonates
with granulated parafﬁn, (2) polycarbonate ﬁlled with polyethylene glycol (PEG
600 PCM), and (3) polyvinyl chlorite (PVC) ﬁlled with PEG 600. The experimental
setup makes possible to measure the thermal response of these different types of
walls. The experimental results obtained are compared with those of the numerical
simulations based on the apparent capacity method. The results showed that type
(3) is the best appropriate panel. Lightweight structures are frequently used in
modern buildings; they are characterized by low thermal inertia. The possible
solution is the incorporation of PCMs in the envelope of these structures. In this
sense, Potvin and Gosselin (2009) studied the thermal performance of a cell made
of lightweight materials with PCM. The walls of the cell are subjected to local
climatic conditions. The obtained results are compared with those of a reference
M. Faraji (*)
Hassan II University, Faculty of Sciences Ain Chock, Physics Department,
LPMMAT Laboratory, Casablanca, Morocco
e-mail: farajimustapha@yahoo.fr
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_35
491
one without PCM. The study revealed that the apparent capacity of the PCM cell
was found to be higher than that of the reference cell. The solar energy transmitted
inside the PCM cell via a glass window is stored in the walls. The thickness of the
walls with PCM is reduced in comparison with that of the cell without PCM.
Several measurements of the temperatures and the heat ﬂow are taken for the
different walls. Experimental results and numerical simulation are confronted,
and they showed good agreement. Stetiu and Feustel (1998) used a thermal building
simulation program based on the ﬁnite difference approach to numerically evaluate
the latent heat storage performance of PCM wallboard in a building environment.
The capability of PCMs to reduce peak loads is well documented. Kissock et al.
(1998) measured peak temperature reductions of up to 10 C in side-by-side testing
of unconditioned experimental houses with and without parafﬁnic PCM wallboard.
Kosny et al. (2006) reported that PCM-enhanced cellulose insulation can reduce
wall-generated peak-hour cooling loads by about 40%.
2
Physical Model
Figure 1 shows the physical model proposed as a heating system. It is a composite
wall with three layers. Their thicknesses are respectively e1, em, and e2. The internal
layers e1 and the external layers e2 are made of concrete, and the layer em consists of
a capsulated PCM, which is stratiﬁed in the direction of the width of the wall. The
PCM is located in the position xm. The wall is located in Casablanca, Morocco
(33360N, 07360W). Temperature of the ﬁrst external node is denoted T2, out and
that of the ﬁrst internal node is denoted T1, in. The radiative heat ﬂux density γQext is
absorbed by the outer surface of the wall. The radiative heat ﬂux exchanged
between the wall and the sky is Qw , rad. The sky is considered as a black body.
The absorptivity and the emissivity of the wall are respectively γ and ε. During the
day, solar energy is stored in the concrete and the PCM as a sensible and latent heat.
Fig. 1 Concrete/phase
change material wall
492
M. Faraji
The melted PCM accumulates a signiﬁcant amount of heat during the period of
charge. During the night, room temperature decreases to relatively low levels; the
PCM solidiﬁes and discharges from heat stored during the day. This heat is used for
the night heating purposes.
Indoor air of local is heated by an active system regulated in the temperature of
comfort: Tc ¼ 22 C. All the borders of the local are thermally isolated with the
exception of the concrete/PCM left fac
¸ade. This wall is facing south and is subject
to boundary conditions generally encountered in buildings. The objective of this
study is to reduce uptime of active heating system (air conditioner, radiator,
convector, etc.).
To simplify the mathematical formulation of the problem, the following assump-
tions were made (ASHRAE n.d.):
•
The interface resistances between the different layers of the wall are neglected.
•
The PCM is considered homogeneous and isotropic.
•
Heat transfer conduction in the composite wall is monodimensional; the end
effects are neglected; and vertical surfaces of the wall are isotherms.
•
Thermophysical properties of concrete and PCM are supposed constants; the
PCM liqueﬁed at Tm.
•
The internal convective heat coefﬁcient hi is assumed to be 10 W/m2 K, and the
external convective heat coefﬁcient ho is assumed to be 21 W/m2 K.
•
The chosen encapsulated PCM are rectangular panels with respectively a thick-
ness and a length of 0.5 cm and 1 m. These panels are vertically arranged
between the lining up of bricks as it is used in buildings. Natural convection in
melted PCM is neglected (encapsulated PCM).
The energy transport in PCM/wall may be written using the enthalpy formula-
tion (Voller and Peng 1994) as follows:
∂H
∂t ¼ ∇k∇T
ð
Þ
ð1Þ
where
H T
ð Þ ¼ h T
ð Þ þ ρλfΔHf
ð2Þ
and
h T
ð Þ ¼ h Tm
ð
Þ þ
ZT
Tm
ρcpdT
ð3Þ
λ ¼ 1 in the PCM and λ ¼ 0 in the concrete.
Using sensible enthalpy h, Eq. (2) is rewritten as
Investigations of Thermal Comfort of Building Integrated Phase Change Materials
493
∂h
∂t ¼ α ∂2h
∂x2 ρλΔHf
∂f
∂t
ð4Þ
The liquid fraction f in the PCM layer is estimated as
f ¼
1
if
T > Tm
0
if
Tm < T
0 < f < 1
if
T ¼ Tm
8
<
:
ð5Þ
The following relations provide the thermal properties at interfaces:
ki ¼ kþk δ þ δþ
ð
Þ
kþδ þ kδþ
, km ¼ fk1 þ 1  f
ð
Þks
ð6Þ
ρcp ¼ f ρcp


l þ 1  f
ð
Þ ρcp


s
where δ+ is the distance between the interface and the ﬁrst node material ‘+’, and δ
is the distance between the interface and the ﬁrst node on the inside of the material
‘’. The boundary conditions are as follows:
•
The continuity of ﬂow and temperatures in interfaces PCM / concrete:
kþ
∂T
∂x




xþ
¼ k
∂T
∂x




x
, Tþ ¼ T
ð7Þ
•
At x ¼ 0,
k∂T
∂x




x¼0
¼ hi Tc ¼ T
ð
Þ
ð8Þ
•
At x ¼ L,
k∂T
∂x




x¼L
¼ ho Ta  T
ð
Þ þ Qw,rad þ γQext
ð9Þ
with
Qw,rad ¼ σεF T4
sky  T4


¼ hr
sky Ta  T
ð
Þ
ð10Þ
The sky is considered as a black body. The sky temperature Tsky is given by
(Swinbank 1963)
Tsky ¼ 0:0552  Ta þ 273:15
ð
Þ1:5  273:15
ð11Þ
The coefﬁcient of radiative transfer hr
sky is given as follows:
494
M. Faraji
hr
sky ¼
σε T4
sky  T4


Ta  T
ð12Þ
The heat ﬂux at interfaces x ¼ xm and x ¼ xm + em between the PCM and the
concrete is evaluated by
q00
i 
kþk
kþδ þ kδþ
Tþ  T
ð
Þ
ð13Þ
3
Results and Discussion
Thermal properties of the concrete wall and the RT 22 PCM (Rubitherm Technol-
ogies GmbH 2013) are summarized in Table 1. Figure 2 shows the timewise
variations of the ambient temperature and total solar radiations received by wall
faces during the typical days of the cold period of the year (winter – January,
Casablanca – Morocco, 33360 N, 07360 W).
Figure 2 shows that minimum temperatures are obtained during the night. On
average, minima and maxima temperatures range between 3 C and 15 C, respec-
tively, and the ambient temperature swings between these extremes. Radiations
remain zero during the night and increase in the following day. Solar radiations, Q,
received by a wall are zero during the ﬁrst 7 h every day and increase with sunrise,
and the ambient temperature increases. Solar radiation reaches a maximum value
(720 W/m2) between solar noon and 15 h 00 and falls to 0 W/m2 at 18 h 00 (sunset
and cancellation of solar radiations). Radiations remain zero during the night. The
climate of Casablanca is characterized by signiﬁcant temperature ﬂuctuations with
lower nocturnal values of temperature.
Table 1 Thermal properties of the RT 22 PCM
Parameter
Value
Melting area
20–23 C
Congealing area
23–20 C
Combined heat storage capacity (latent and sensible
heat in a temperature range of 14–29 C)
200 kJ/kg
Speciﬁc heat capacity
2 kJ/kg K
Density
0.73 kg/l
Heat conductivity
Concrete wall thermal conductivity
0.2 W/m K
1.04 W/m K
Rubitherm Technologies GmbH (2013)
Investigations of Thermal Comfort of Building Integrated Phase Change Materials
495
Figure 3 compares the thermal behavior of the ordinary and the PCM walls. Data
analysis shows that even though both walls undergo the same climatic conditions,
during night, the internal temperature (at a height of 1.5 m) decreases depending on
the heat dissipation capacity of each constructive system. The thermal reduction is
slower in the PCM wall case since it has stored more heat (latent) during the day.
On average, inside temperature varies from 16 to 22 C for PCM wall and from
14 to 26 C for an ordinary wall without PCM. Remember that ambient temperature
varies between 3 and 15 C (Fig. 2), but the building walls receive also a radiative
heat ﬂux due to the solar radiations absorbed by the concrete (mortar solar absorp-
tion, α ¼ 0.8) combined with convective heat ﬂux, and leads to the increase of the
PCM temperature to its melting point. When the PCM undergoes phase change, the
slope of T curve weakens because the melting and solidiﬁcation of PCM occur at a
nearly constant temperature; as a consequence, sensible heat dissipation weakens
and the decrease of the PCM wall’s nocturnal temperature is shifted. During the
day, the rise of solar radiations accumulates sensible heat in concrete and stores
latent heat in the PCM layer. The results showed, for the wall with PCM envelope, a
signiﬁcant reduction of indoor temperature due to absorption of solar gains in the
composite walls in conjunction with melting of the PCM. In fact, composite PCM–
0
12
24
36
48
60
72
84
96
108
2
4
6
8
10
12
14
16
18
20
22
24
26
28
100
200
300
400
500
600
700
800
T ambient
Q
Q (W/m²)
Time (hr)
T (°C)
Fig. 2 Timewise variations of the ambient temperature, Ta, and the global solar radiations, Q,
received by walls (January – Casablanca, Morocco 33360N, 07360W)
496
M. Faraji
concrete walls can be considered as important heat storage devices. During the day,
an important amount of the solar heat is stored in the PCM wall structure with less
temperature ﬂuctuations until the full melting of PCM. The stored heat during a day
is naturally released for heating needs in the following cold night.
Figure 4 represents the total heat ﬂux, q, measured at walls for the two test cases.
Negative values correspond to the indoor thermal gain and positive values quantify
the thermal loss. For the case without PCM, heat ﬂux penetrates and leaves the wall
easily and, therefore, important temperature ﬂuctuations take place. The heating of
the wall is due to the rise of the sensible heat storage during the day. The ordinary
wall loses heat from 21 00 h to 12 00 h, and inner air temperature falls after a certain
delay due to the thermal inertia and to the relatively weak value of thermal
conductivity of concrete. Note that the temperature minima in case of the wall
with PCM, as shown by Fig. 3, is clearly greater than that achieved without PCM,
because the nocturnal heat lost to the ambient by the walls is shifted by the release
of the important diurnal latent heat stored in PCM. Day/night cycles allow for
charging/discharging of the PCM panels. Figure 4 shows also that the wall with
PCM is more insulated and that PCM is melted and solidiﬁed periodically. The
ﬂuctuation amplitude of the inner temperature (wall with PCM case) decreases
because the accumulated heat during the melting processes added to the sensible
12
24
36
48
60
72
84
96
108
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
T, With PCM
T, NO PCM
Time, hr
T (°C)
Fig. 3 Variations of the wall’s inner temperature
Investigations of Thermal Comfort of Building Integrated Phase Change Materials
497
heat stored in the wall/PCM structure brake the fast decrease of the temperature
during the night. Notice that, for walls without PCM case, the heat ﬂux increases to
a high value at night, and that explains the lower temperatures achieved with more
pronounced ﬂuctuations.
The performance of the wall with PCM (RT 22) as a heating system for the
building has been evaluated in terms of thermal load leveling, TLL, using the
following equation:
TLL ¼ Tmax  Tmin
Tmax þ Tmin
ð14Þ
Thermal load leveling, TLL, puts a ﬁgure on the ﬂuctuations of temperature
inside the room. The less the ﬂuctuations the better is the environment for the
occupants. In winter, without heating arrangement for thermal comfort require-
ments, thermal load leveling should have lower value by incorporating heating
method due to the increase of ((Ti , in)max + (Ti , in)min) and the decrease of ((Ti ,
in)max  (Ti , in)min).
The results for daily variation of thermal load leveling, TLL, for the walls with
and without PCM are summarized in Table 2. It can be seen that the value of TLL is
maximum for the wall without PCM, and it is reduced to about 28% for the wall
with PCM. The lower values of thermal load leveling indicate that, due to the PCM
0
12
24
36
48
60
72
84
96
108
-4000
-2000
0
2000
4000
6000
8000
With PCM
NO PCM
Time, hr
q (J)
Cooling
Heating
Phase change period
Fig. 4 Heat ﬂuxes
498
M. Faraji
layer, less ﬂuctuations of indoor temperature are obtained and, thereby, there occurs
an improvement of thermal comfort environment in the building.
4
Conclusion
Investigation of the thermal performance of composite concrete/PCM wall was
performed. The results showed a signiﬁcant reduction of indoor temperature ﬂuc-
tuations due to absorption and release of solar gains in the composite wall in
conjunction with the melting of the PCM. The results showed that thermal load
leveling of the wall containing PCM was reduced compared to the case without
PCM with more constant conditions, It emerged that the wall with PCM/concrete
wall is able to provide good performance. The thermal conditions of the indoor
environment achieved with the presence of PCM panels were considerably
improved compared to the wall without PCM.
Nomenclature
cp
Speciﬁc heat (kJ/kg K)
f
Liquid fraction
em
PCM layer thickness (m)
h
Enthalpy (J)
hi
Inner wall convective heat transfer coefﬁcient (W/m2 K)
ho
Outside wall convective heat transfer coefﬁcient (W/m2 K)
hr
sky
Irradiative heat transfer coefﬁcient (W/m2 K)
k
Thermal conductivity (W/m K)
L
Total wall thickness (m)
Qext
Solar radiation (W/m2)
Qw,rad
Heat lost to the ambient by radiations (J)
x
Cartesian coordinate (m)
t
Time (s)
T
Temperature (C)
Indices
i
Interface
m
Melting, PCM
c
Comfort
In
Inner wall face
(continued)
Table 2 Thermal load
leveling
Day
TLL (with PCM)
TLL (no PCM)
1
0.20
0.28
2
0.16
0.27
3
0.17
0.28
4
0.15
0.26
5
0.17
0.27
Investigations of Thermal Comfort of Building Integrated Phase Change Materials
499
Out
Outer wall face
+,
Right, left nodes
f
Fusion
Greeks
ρ
Density (kg/m3)
γ
Absorptivity
α
Thermal diffusivity (m/s2)
σ
Stefan-Boltzmann constant
ε
Concrete emissivity
ΔHf
Latent heat of PCM (kJ/kg)
References
Ahmad, M., Bontemps, A., Salle
´e, H., Quenard, D.: Experimental investigation and computer
simulation of thermal behaviour of wallboards containing a phase change material. Energ.
Buildings. 38(4), 357–366 (2006)
ASHRAE: Handbook – fundamental(SI). http://www.techstreet.com/ashrae /products/ 1858361?
ashrae_auth_token¼
Kelly Kissock, J., Michael Hannig, J., Thomas I.: Testing and simulation of phase change
wallboard for thermal storage in buildings. In: Morehouse, J.M., Hogan, R.E. (eds.) Pro-
ceedings of 1998 International Solar Energy Conference, Albuquerque, June 14–17. American
Society of Mechanical Engineers, New York (1998)
Kosny, J., Yarbrough, D., Wilkes, K., Leuthold, D., Syad, A.: “PCM-Enhanced Cellulose Insula-
tion –Thermal Mass in Lightweight Natural Fibers” 2006 ECOSTOCK Conference, IEA,
DOE, Richard Stockton College of New Jersey, June 2006 (2006)
Potvin, F.M., Gosselin, L.: Thermal shielding of multilayer walls with phase change materials
under different transient boundary conditions. Int. J. Therm. Sci. 48, 1707–1717 (2009)
Rubitherm Technologies GmbH: Internet: www.rubitherm.com, Data sheet, Date: 23.07.2013
Stetiu, C., Feustel, H.E.: Phase change wallboard and mechanical night ventilation in commercial
buildings. Lawrence Berkeley National Laboratory Berkeley(1998)
Swinbank, W.C.: Long-wave radiation from clearskies. Q. J. R. Meteorol. Soc. 381(89), 339–348
(1963)
Voller, V.R., Peng, S.: An enthalpy formulation based on an arbitrarily mesh for solution of the
stefan problem. Comput. Mech. 14, 492–502 (1994)
500
M. Faraji
Determining Optimum Insulation Thickness
of a Building Wall Using an Environmental
Impact Approach
€
Ozel G€
ulcan, Ac
¸ıkkalp Emin, Karakoc T. Hikmet, Hepbasli Arif,
and Aydın Ahmet
1
Introduction
In the world, environmental problems have become widespread along with popu-
lation growth, energy consumption, and industrialization (Dinc
¸er 1999). At the
present time, the most important indicator of environmental problems is global
warming. Gases (carbon dioxide, methane, nitrous oxide, and ﬂuorinated gases)
that trap heat in the atmosphere are called greenhouse gases (United States EPA
2014) Carbon dioxide which is the output of the energy conversion process has the
largest effect on global warming by a rate of 81% (Karakoc et al. 2011). The effects
of global warming have reached sensible levels, and environmental impact analysis
has gained greater importance in the energy policies of the countries.
In Turkey, buildings are responsible for 30% of the total green gas emissions
(Karakoc et al. 2011). To decrease CO2 emissions and fuel consumption, energy
losses from the building must be minimized. Thermal insulation is an important,
applicable, and rational solution to achieve this aim by altering the properties of
O
¨ . Gülcan (*) • A. Emin
Bilecik S.E. University, Engineering Faculty, Department of Mechanical
and Manufacturing Engineering, Bilecik, Turkey
e-mail: gulcan.ozel@bilecik.edu.tr
K.T. Hikmet
Anadolu University, Faculty of Aeronautics and Astronautics, Department of Airframe
and Powerplant Maintenance, Eskisehir, Turkey
H. Arif
Yasar University, Faculty of Engineering, Department of Energy
Systems Engineering, Izmir, Turkey
A. Ahmet
Sakarya University, Faculty of Engineering, Department of Mechanical
Engineering, Sakarya, Turkey
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_36
501
building envelopes. In addition to this, thermal insulation provides cost savings and
thermal comfort (Ekici and Aksoy 2011).
Many studies have been performed on the optimum insulation thickness in the
literature (Hasan 1999; Bolattürk 2006; Sisman et al. 2007; Yıldız et al. 2008; Yu
et al. 2009; Kaynakli 2008). These studies determine optimum thickness using
energy, exergy, economic, and emissions methods (Balli et al. 2008). Ucar and Balo
(2010) and Ucar et al. (2011) determined optimum insulation thickness by using
exergy-based analysis methods for the different climatic regions and fuels. A
parametric study that investigated all the parameters affecting the optimum thermal
insulation thickness for building walls was carried out by Kaynakli (2011). He used
an economic model based on the life cycle cost analysis to determine the optimum
insulation thicknesses. Bolattürk (2008) investigated the optimum insulation thick-
ness for the external walls of buildings with respect to cooling and heating degree-
hours in the warmest zone of Turkey. He calculated that the optimum insulation
thickness varies between 3.2 and 3.8 cm depending on the cooling degree-hours and
1.6 and 2.7 cm for the heating loads. Effect of the fuel type, wall conﬁguration, and
combustion parameters on optimum insulation thickness was determined by Arslan
et al. (2010). He calculated optimum insulation thicknesses between 0.291 and
0.1352 m depending on the fuel types, wall types, and combustion parameters.
Environmental impact obtained from the LCA is combined with exergy analysis,
and it is called as exergoenvironmental analysis. This new method is used for
calculating environmental impact of the energy conversion systems (Meyer et al.
2009; Tsatsaronis 2008). Environmental impacts of several materials are associated
with points and listed in the ECO indicator 95 and ECO indicator 99 (Goedkoop and
Spriensma 2000; Goedkoop 1995).
In this study, to determine the optimum insulation thickness, a novel method
based on the environmental impact analysis is performed to an external building
wall. In our analysis, the rockwool and polystyrene are used as the insulation
materials. LCC analysis is commonly used in studies mentioned above for evalu-
ating the optimum insulation thickness. LCC analysis is applied for the investigated
wall, and the results of LLC and environmental analyses are compared.
b
Environmental impact point (mPts/kg)
B
Total environmental impact of the system (mPts/m2-year)
E
Annual energy (J/ m2-year)
h
Convection heat transfer coefﬁcient (W/m2.K)
Hu
Heating value (J/kg)
HDD
Annual heating degree day (C.day)
k
Thermal conductivity (W/m.K)
L
Thickness of the wall component (m)
m
Annual fuel mass (kg/m2-year)
q
The annual heating loss (J/m2-year)
R
Thermal resistance (m2.K/W)
S
Annual net saving of environmental impact (mPts/ m2-year)
(continued)
502
O
¨ . Gülcan et al.
SE
Annual net saving of energy loss (J/ m2-year)
T
Temperature (C or K)
U
Heat transfer coefﬁcient (W/ m2.K)
V
Volume (m3)
x
Insulation thickness (m)
Greek letters
η
Efﬁciency of the heating system (%)
φ
Chemical exergy factor of the fuel
ρ
Density (kg/m3)
Subscripts
br
Brick
CO2
Carbon dioxide
c
Cost
en
Environmental
F
Fuel
i
Inside air
ip
Inside plaster
ener
Inside air
ins
Insulation
Loss
Loss
o
Outside air
op
Outside plaster
nins
No-insulation
T
Total
2
Modeling and Analysis
2.1
Environmental Impact Analysis
A composite wall investigated in this study is presented schematically in Fig. 1. The
building wall consists of parallel layers of inside plaster, brick, insulation material,
and outside plaster. Rockwool and polystyrene are chosen as the insulation mate-
rials in the calculations. Some properties of the building wall components are given
in Table 1. Temperatures of the outside and inside air are assumed at constant To
and Ti. Natural gas is used as fuel for the heating system operated in 90% efﬁciency.
Calculations are made annually for unit area of an external building wall.
The annual heating loss (J/m2-year) from the unit area of the wall is determined
from Eq. (1) by the way of heating degree-day (Bas
¸o
gul and Kec
¸ebas
¸ 2011):
q ¼ 86400HDDU
ð1Þ
Determining Optimum Insulation Thickness of a Building Wall Using. . .
503
where HDD is the heating degree-days (C.day) and U is the heat transfer coefﬁ-
cient (W/m2K). Heat transfer coefﬁcients (W/m2K) for no-insulation and the
insulated wall conditions can be calculated using Eqs. (2) and (3), respectively:
Unins ¼
1
Ri þ Rip þ Rbr þ Rop þ Ro
¼
1
RT,nins
ð2Þ
Uins ¼
1
Ri þ Rip þ Rbr þ Rins þ Rop þ Ro
¼
1
RT,ins
ð3Þ
Inside plaster
Brick
Insulation material
Outside plaster
x
Lop
Lbr
Lip
Fig. 1 Investigated building wall system
Table 1 Some properties of the building wall materials
Layer
Thickness (m)
Conductivity (W/mK)
Inside plaster
0.02
0.87
Brick
0.19
0.45
I
˙nsulation material
Rockwool
0–0.4
0.04
Polystyrene
0–0.4
0.032
Outside plaster
0.03
0.87
Bolattürk (2006) and Izocam (2014)
504
O
¨ . Gülcan et al.
RT , nins is the total thermal resistance of the wall without the insulation material,
which is
RT,nins ¼ 1
hi
þ Lip
kip
þ Lbr
kbr
þ Lop
kop
þ 1
ho
ð4Þ
where hi and ho are the convection heat transfer coefﬁcients on the inside and
outside of the wall. L and k are the thickness and the thermal conductivity of the
wall components, respectively. Subscripts ip, br, and op denote the inside plaster,
brick, and outside plaster, respectively.
Also, RT , ins is the total thermal resistance of the insulated wall, and it is deﬁned
as follows:
RT,nins ¼ RT,ins þ x
kins
ð5Þ
where x and kins are the thickness and the thermal conductivity of the insulation
material, respectively. Annual energy (J/m2-year) need from the unit area of the
wall is calculated by using efﬁciency of the heating system (η) and the annual
heating loss (q):
E ¼ 86400HDDU
η
ð6Þ
Annual fuel consumption (kg/m2-year), depending on the annual energy need, is
determined using Eq. (7):.
mF ¼ 86400HDDU
Huη
ð7Þ
where Hu is the lower heating value of the fuel (J/kg). More than 90% of natural gas
is composed of methane (CH4); so, methane can be used in the combustion
equation, and the combustion process is assumed as complete to facilitate
calculations.
Combustion equation can be written as follows:
CH4 þ 2 O2 þ 3:76N2
ð
Þ ! CO2 þ 2H2O þ 7:52N2
From this equation, CO2 emission (kg/m2-year) can be given as in Eq. (8):
mCO2 ¼ 2:75 86400HDDU
Huη


ð8Þ
Total environmental impact function of the system (O
¨ zel et al. 2014), BT (mPts/
m2-year), is calculated from Eq. (9):
Determining Optimum Insulation Thickness of a Building Wall Using. . .
505
BT ¼ bFmF þ bCO2mCO2 þ binsρinsxins
ð9Þ
Here, bF is the environmental impact of the fuel (mPts/kg), bCO2is the environ-
mental impact of CO2 (mPts/kg), and bins is the environmental impact of the
insulation material (mPts/kg). Also, ρins (kg/m3) is the density of the insulation
material. The optimum insulation thickness is obtained by getting the derivative of
BT with respect to x and set equal to zero as follows:
xen ¼ kinsRT,nins þ 487:44
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
bCO2 þ 0:36bF
ð
ÞHDDkins
binsHuηρins
s
ð10Þ
BT will receive the minimum value at the optimum insulation thickness. The net
saving of the environmental impact (mPts/m2-year) is
S ¼ bFmF þ bCO2mCO2
ð
Þnins
 bFmF þ bCO2mCO2 þ binsρinsxins
ð
Þins
ð11Þ
The net saving of energy loss from the pipe surface (J.K/m) is
SE ¼ Enins  Eins
ð12Þ
2.2
Life Cycle Cost Analysis
Life cycle cost analysis is an economic evaluation method that determines the total
cost of system components over a period of time. For a building wall, LCC analysis
is used to determine the optimum insulation thickness to take into account the
change in interest and inﬂation (Ucar 2010).
The annual fuel cost per unit area is
cF ¼ cfmF
ð13Þ
where cf is the cost of fuel. The fuel cost over a lifetime is calculated using the
present worth factor (PWF) in the life cycle cost. The PWF depends on the inﬂation
rate, g, and interest rate, i, and is adjusted for inﬂation as (Ekici et al. 2012)
i∗¼
i  g
1 þ g
; i > g
g  i
1 þ i
;
i < g
8
>
<
>
:
ð14Þ
and then PWF is deﬁned as follows (Arslan and Kose 2006):
506
O
¨ . Gülcan et al.
PWF ¼
1  1 þ i∗
ð
ÞN
i∗
1 þ i
ð
Þ1
;
;
i 6¼ g
i ¼ g
8
<
:
ð15Þ
where i* is the interest rate adjusted for the inﬂation rate and N is the lifetime of the
insulation material.
Finally, the annual fuel cost can be shown as
CF ¼ cfPWFmF
ð16Þ
The annual cost of the insulation material per unit volume can be calculated as
Cins ¼ ciVins
ð17Þ
where ci is the cost of insulation material per m3 and Vins is the volume of the
insulation material.
The annual total cost of the building wall is
CT ¼ cfPWFmF þ ciVins
ð18Þ
The annual cost saving per unit area of the wall is
SC ¼ CT,nins  CT,ins
ð19Þ
The optimum insulation thickness is obtained by getting the derivative of SC
with respect to x and set equal to zero. SC will receive maximum value at the
optimum insulation thickness. Data used in the calculations can be seen in Table 2.
xC ¼ 293:94
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
CFHDDkinsPWF
CinsHuη

s
kinsRT,ins
ð20Þ
3
Results and Discussion
In the present study, the environmental analysis and life cycle cost analysis are
applied to an external building wall to determine the optimum insulation thickness.
Rockwool and polystyrene are chosen as the insulation materials for building walls
in the city of Bilecik, Turkey.
It is seen from Fig. 2 that the total environmental impact of the system decreases
with the insulation thickness until a certain point called as optimum point. Envi-
ronmental impact of the system gets the minimum value at this point named
optimum insulation thickness. The optimum insulation thicknesses for the
rockwool and polystyrene are calculated as 0.232 and 0.219 m, respectively by
using Eq. (10). At optimum points, environmental impacts of the system are
Determining Optimum Insulation Thickness of a Building Wall Using. . .
507
Table 2 Parameters used in calculations
Parameter
Unit
Value
Environmental impact point
mPts/kg
Rockwool (Goedkoop and Spriensma 2000)
4.2
Polystyrene (Goedkoop and Spriensma 2000)
8.3
Fuel (Goedkoop 1995)
114
CO2 (Goedkoop 1995)
5.45
Mean temperature for heating period (Turkish Meteorological
Ofﬁce 2014)
C
9
Heating degree-days (Dombaycı 2009)
C-days
2966
Boiler efﬁciency
0.9
Density of insulation material
kg/m3
Rockwool (Izocam 2014)
105
Polystyrene (Kaya and Aydin 2006)
45
Lower heating value of the fuel (Arin and Akdemir 2002)
kJ/kgK
50  103
Fuel cost (Aksa Natural Gas 2014)
$/kg
0.53
Inﬂation rate (Central Bank of the Republıc of Turkey 2014)
8.39
Interest rate (Central Bank of the Republıc of Turkey 2014)
9.65
Lifetime (N)
year
10
Glass wool cost (Kaynakli 2008)
$/m3
75
Rockwool cost (Izocam 2014)
$/m3
132
0.0
0.2
0.4
0
500
1000
BT(Rockwool) 
BT(Polystyrene)
S (Rockwool) 
 S (Polystyrene) 
insulation thickness (m)
Environmental Impact Saving (mPts/m
2-year)
Total Environmental Impact (mPts/m
2-year)
0
500
1000
Fig. 2 Changes of the total environmental impact and the net environmental impact saving
according to the insulation thickness
508
O
¨ . Gülcan et al.
determined as 216.12 and 171.304 mPts/m2-year. The net environmental impact
saving of the system tends to increase logarithmically up to the optimum insulation
thickness while the total environmental impact decreases.
As it appears in Fig. 3, application of the insulation material decreases the
energy loss from the building wall. Until the environmental optimum insulation
thickness, energy loss decreases signiﬁcantly, and after the optimum point, its
decrease continues negligibly. According to these results, when rockwool and
polystyrene are used at the optimum thickness, energy loss from the building wall
decreases by 89% and 92%.
Variations of the fuel consumption and the CO2 emission versus the insulation
thickness are presented in Fig. 4. As insulation thickness increases, the fuel
consumption and CO2 emission decrease. Similar to the energy loss, fuel consump-
tion and CO2 emission decrease logarithmically. Up to the environmental optimum
point, a rapid decrease in fuel consumption and CO2 emission is observed. The
results show that for rockwool, decreasing of the no insulation condition up to 0.4 m
insulation thickness is 94% for fuel consumption and CO2 emission. Eighty-nine
percent of these are obtained up to the optimum insulation thickness. For polysty-
rene, decrease in fuel consumption and CO2 emission is 95% between no insulation
condition and 0.4 m insulation thickness. Ninety-two percent of these are obtained
up to the optimum insulation thickness.
As a part of the LCC analysis, insulation and fuel costs are evaluated, and results
are presented in Fig. 5. Investigating the graphics, it can be seen that initially fuel
0.0
0.2
0.4
0.0
2.0x10
5
4.0x10
5
insulation thickness (m)
 E (Rockwoll) 
 SE (Rockwoll) 
 E (Polystyrene)
 E (Polystyrene) 
0.0
2.0x10
5
4.0x10
5
 Energy Loss Saving (kJ/m
2-year) 
Energy Loss (kJ/m
2-year) 
Fig. 3 Effect of the insulation thickness on energy loss and energy loss saving
Determining Optimum Insulation Thickness of a Building Wall Using. . .
509
cost decreases in larger steps up to a certain point, and then decreasing steps get
smaller. Insulation cost increases linearly because it only depends on the insulation
thickness. Total cost of the system decreases logarithmically up to the minimum
point, called the economical optimum point, after which it starts to increase (Fig. 6).
For rockwool and polystyrene, economical optimum points are calculated as
0.065 and 0.085 m, respectively. The net cost saving of the system tends to increase
logarithmically up to the optimum insulation thickness while the total cost
decreases.
4
Conclusions
In this study, optimum insulation thickness of a building wall is determined by
using two different analysis methods. First, the environmental impact analysis
based on the life cycle assessment is performed for the wall system. And then
results are compared with the LCC analysis which is most commonly used for
determining the optimum insulation thickness of a building wall. Some concluding
remarks can be summarized as follows:
•
For rockwool, environmental and economical optimum insulation thicknesses
are calculated as 0.232 and 0.065 m, respectively.
•
For polystyrene, environmental and economical optimum insulation thicknesses
are calculated as 0.219 and 0.085 m, respectively.
0.0
0.1
0.2
0.3
0.4
0
5
10
15
20
25
insulation thickness (m)
Fuel Consumption & CO2 emission (kg/m2-year) 
 mF (Rockwoll)
mCO2 (Rockwoll)
mF (Polystyrene)
mCO2 (Polystyrene)
Fig. 4 Fuel consumption and CO2 emission versus insulation thickness
510
O
¨ . Gülcan et al.
0.0
0.1
0.2
0.3
0
10
20
30
40
50
60
70
80
insulation thickness (m)
Cost ($/m2-year) 
 Cins (Rockwool)
 CF (Rockwool)
 Cins (Polystyrene)
 CF (Polystyrene) 
Fig. 5 Variations of insulation and fuel cost with insulation thickness
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0
10
20
30
40
50
60
70
insulation thickness (m)
Cins (Rockwool)
SC (Rockwool) 
Cins (Polystyrene)
SC (Polystyrene) 
Cost ($/m2-year) 
Fig. 6 Variations of total cost and cost saving with insulation thickness
Determining Optimum Insulation Thickness of a Building Wall Using. . .
511
•
For different insulation materials and wall components, optimum insulation
thicknesses based on the environmental impact and LCC analysis can be calcu-
lated by using Eqs. (10, 20)
•
In comparing the two methods, the optimum insulation thickness with the life
cycle cost analysis is lower than that with the environmental impact analysis.
References
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Determining Optimum Insulation Thickness of a Building Wall Using. . .
513
Energetic and Exergetic Design Evaluations
of a Building Block Based on a Hybrid Solar
Envelope Method
Mert Yelda and Saygın Nicel
1
Introduction
Nowadays, buildings are usually designed with an abstract and single perspective.
This study shows that the context in which it will be located and the climate in
which it is built also need to be considered.
At all levels of land-use planning decisions, the use of energy has to be taken into
account and urban planners have to develop solutions for efﬁcient use of energy.
Land-use patterns directly affect energy consumption and inﬂuence energy sys-
tems. This is seen, for example, from the small scale of “a house” to the large scale
of “a country.” No matter what the scale of land investigated, it is crucial to
understand the signiﬁcance of efﬁcient energy planning in the contribution to global
energy conservation.
When looking from an energy-efﬁciency point of view, the different properties
of the spatial structure are important. The fundamentals exercised when planning
and decision making for local energy-efﬁciency planning are as effective for
decisions on a regional scale (Owens 1990). Beside properties like orientation
and microclimate on the smaller scale, wider spatial properties are also important
on a larger scale. On a small scale, direct forward changes bring considerable
improvements, for instance, adjusting the orientation of the building for the sake
of energy saving and not adding extra cost to the construction. For comprehensive
M. Yelda (*)
Yüzüncü Yil University, Faculty of Engineering and Architecture, Department of City
and Regional Planning, Kampüs, Van 65080, Turkey
e-mail: mertyelda@gmail.com
S. Nicel
I
˙zmir Institute of Technology, Faculty of Architecture, Department of City and Regional
Planning, Gülbahc
¸e, I
˙zmir 35100, Turkey
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_37
515
energy effectiveness on a larger scale, climatic and microclimatic properties of the
urban area have to be considered with great care because of the loads arising from
the heating and cooling needs.
It was concluded by Ovalı (2009) in her study that 50 % of energy consumed in
buildings can be conserved when a climate-friendly building and built-environment
design is applied. The situation in Turkey, where there is only one Building Act,
underestimates the effects of different climate regions in the country. According to
Act 3030, a building is designed and placed 5 m from the frontage and 3 m from
ﬂank front. These standards were taken from German standards during the planning
of Ankara. However, this plan was for a building in Germany with 2–3 stories,
while in Turkey other standards have been implemented so that the distance
between the buildings increases 0.5 m for every storey in the building (Tokuc
¸
2005). Planning, design, and implementation regulations do not take local and
regional differences into account. As a result the urban environment is not in
harmony with the local properties (Aydemir et al. 2004). Similarly, on a building
scale, implementation for the regulation of energy performance in a building only
focuses on decreasing the energy demand instead of applying the tools for increas-
ing energy efﬁciency (C
¸ akmanus 2010), although it deals with issues like the
importance of orientation, passive solar gain, and microclimatic effects.
Morello and Ratti (2009) applied a solar envelope method to extensive urban
areas and have made this method simple to apply to urban planning. Digital
elevation models are used with a computerized method for dealing with this
massive model.
Knowles (2003a, b) analyzed the solar enveloped method in detail. Its impor-
tance is pointed out in the context of sustainable development and improved
esthetic possibilities for architecture and urban design.
When energy-efﬁcient planning and design are taken into consideration, a
relationship between land use and building design comes to the mind (Mangan
and Oral 2013; Ovalı 2009; Owens 1990) . Energy-efﬁcient planning principles
systematically investigate the city on four scales, namely the building (small scale),
the neighborhood (building block), the settlement (city), and the region (large
scale). Figure 1 shows the basics of this framework and the relationship between
energy and spatial properties. The relationship between the dominant properties in
energy consumption and scales of investigation is identiﬁed and given considering
intrinsic energy need and efﬁcient use of energy in the framework. Land-use
decisions should be made taking into consideration how to reduce the effects on
climate change, ensuring efﬁcient and effective use of energy, and providing
sustainable urban policies (Ayan 1985). Moreover, the aim of energy-efﬁcient
planning is to help people carry out their daily activities in the most efﬁcient way
from an energy point of view, and to minimize energy usage (Owens 1990).
This study aimed to state the amount of energy conservation in a building block
when hybrid solar envelope design strategies are applied. In other words, how
energy and exergy can be saved when hybrid solar envelope design strategies are
applied and what differences that they will bring, compared to the existing design of
the building block.
516
M. Yelda and S. Nicel
2
Hybrid Solar Envelope Method
The solar envelop method depends on the arrangement of the height of the
building(s) in accordance with the sun’s path in the effective hours of radiation.
The effective sun is the time between 10:00 am and 02:00 pm in which the sun
has a greater heating capacity than the other hours of the day. The design is
developed according to the angle of sunlight during this period (Canan 2008;
Knowles 2003a, b).
The method aims at a scale impact and the height is not limited, only adjusted,
depending on the solar angle and the topography. High-rises can also be considered
Building
Neighbourhood
Settlement
Region
Built form
Orientation
Siting
Layout
Density
Clustering
(employment,
services)  
Land use
Shape
Size
Regional Structure
(settlement pattern) 
INTRINSIC
ENERGY
REQUIREMENTS  
Within place
between place
EFFICIENT USE
OF ENERGY   
Fig. 1 Framework for analysis of energy/spatial structure relationship (Source: Owens 1990,
p. 60)
Energetic and Exergetic Design Evaluations of a Building Block Based. . .
517
with this method (Canan 2008; Knowles, 2003a, b). Generally, a terraced structure
is the result of this method being applied to building heights or to a building’s
singular design.
Various parameters such as physical environmental parameters, which include
temperature, wind, sun path, climate, orientation, building form, distance between
buildings, building organization, building envelope, and materials and landscaping
are taken into account in designing energy-efﬁcient alternatives.
The angle 12 from south towards east is chosen as the main orientation for a
case area (Tokuc
¸ 2005) depending on the latitude. Based on the ﬁndings in the
literature, for a hot-humid climate region optimum building form is taken as 1:1.7 in
this study (Olgyay 1973). In the design, the width of the building is chosen as 30 m
and the length as 18 m. Moreover, the long side of the building faces southward to
increase the solar gain.
The spacing allowed between the buildings is of particular importance when the
shadow effect is taken into account. The building’s shadow must not block another
building’s solar input. The calculation used with this in mind is based on the sun’s
radiation angle to the earth. In this calculation, the angular value for 21 December is
used, as the solar radiation’s angle is at its lowest in the year on this date. An
angular value of 29 is used in calculations at 12:00 pm, which is selected as the
maximum radiation occurs at noon. Based on the calculations, the x/y ratio must be
0.55 and the maximum shadow length should be 1.96 times the height of the
building. In this study the ratio of the spacing between the buildings is thus
determined to be a rounded “2” in order to prevent a shadow effect.
In addition to the energy-efﬁcient design parameters, a criterion for the solar
envelop method is integrated in a single method. This hybrid method includes the
requirements of orientation, spacing, landscaping, and building form as well as the
building height properties as proposed in the solar envelop method.
Figure 2 shows a calculated solar enveloped for an area. Using these calculated
angles of the solar path the building’s height is adjusted for achieving maximum use
of passive solar energy.
Fig. 2 Solar envelope
method (Source: Knowles
and Koenig 2002, p. 3)
518
M. Yelda and S. Nicel
The solar envelop method depends on understanding the changing position of
the sun throughout the day and year. If this dynamic behavior is a factor in the
design of the urban area environmental friendliness, sustainability and reduced
energy consumption in cities can be achieved (Canan 2008; Knowles 2003a, b;
Topalo
glu 2003). Figure 3 shows an example of an application of a solar enveloped-
based design to an urban area. The terraced structure depending on the peak of the
solar envelope and the angles of facades can be clearly seen in this design
alternative.
3
Exergy Analysis of a Residential Area
The calculations for the exergy load of residential building blocks are complicated.
This process starts with the data-handling, which is composed of some major design
parameters of buildings and building blocks, as listed below (Mert 2014):
•
Building scale: location of the building, orientation of the building, building
form, area/volume ratio of the building, openings to building ratio, size of the
building, design of the building, insulation of the building, resident information,
heating and cooling system properties.
•
Building block scale: building block form, size of building block, perimeter-to-
area ratio, landscaping and planting of building block, microclimatic properties
(wind, average temperature, etc.), shadowing due to the conﬁguration of build-
ings, topography of the building block.
After completion of processing and analyzing data, exergy calculations take
place:
•
Calculation of the Shadow Effect Factor (SEF).
•
Calculation of the exergy load of each building for heating and cooling.
•
Calculation of the exergy loads for the building blocks.
Fig. 3 Solar envelope method in high-rise buildings (Source: Knowles 2003a, p. 13)
Energetic and Exergetic Design Evaluations of a Building Block Based. . .
519
This procedure calculates
_
E xdemand , which shows us the exergy need of the
building. For the calculation of exergy demand, the fundamental procedure pro-
posed by LowEx (Hepbas
¸lı 2012) is used:
_
E xdemand ¼ _
E xloss  _
E xgain
ð1Þ
The transmission losses through the doors, walls, windows, and roofs cause
exergy loss _
E xloss;
_
E xloss ¼ _
E xloss,transmission þ _
E xloss,ventillation
ð2Þ
_
E xloss,transmisson ¼
X
Ui:Ai: Ti  To
ð
Þ
ð3Þ
The transmission losses are calculated by taking the heat transfer coefﬁcient of
walls, doors, roofs, and ceilings (Ui) as well as the areas (Ai) and the indoor (Ti) and
exterior (To) air temperature differences into consideration.
_
E xloss,ventillation ¼ Cpρ:V:nd: 1  nv
ð
Þ: Ti  To
ð
Þ
ð4Þ
where ρ and Cp are the density [kg/m3] and speciﬁc heat [kJ/kgK] of air, respec-
tively. nd and nv are the air exchange rate [m3/h] and the efﬁciency constants,
respectively. V represents the volume [m3].
Solar gains through the openings in the buildings are the source of exergy gain
 _
E xgain

that is a function of SEF. Other gains, such as lighting (2 W/m2), that arise
from the auxiliary equipment in the settlements are also taken into account. In order
to calculate the gain, the determinations of the facades of the buildings are neces-
sary, since the effect of the orientation of the buildings on the energy and exergy
performance of the building depends on it. Each separate building is taken into
account in this perspective. Facades that face from southwest to southeast and from
northwest
to
northeast
are
also
calculated
and
taken
into
consideration
correspondingly.
_
E xgain ¼ _
E xgain,solar þ _
E xgain,internal
ð5Þ
_
E xgain,solar ¼ Is: 100  SEF
100


: 1  Ff
ð
Þ:Aw g
ð6Þ
Here, solar radiation is shown by Is [W/m2], Ff is the window frame fraction that
is taken as 0.3, Aw is the window area [m2], and g is the total transmittance.
SEF ¼
tshadow
tdaytime
100


ð7Þ
The SEF is an indication of a building’s blockage by the shadow of other objects
and buildings. This is the result of the overlapping shadow on the buildings standing
behind another one. The 3D models are used for computing the SEF for both
520
M. Yelda and S. Nicel
summer and winter periods. In SEF, calculation models of the existing situation and
the proposed plan are developed separately. These models allow us to simulate the
time-dependent effect of the sunlight with regard to the attitude of the case are. SEF
is evaluated by determining the ratio of the time under shadow (tshadow, [min]) of the
building to the daytime (tdaytime, [min]) with direct sunlight access in an approxi-
mate manner by using the 3D model of the area.
_
E xgain ,in ¼ no:φi,o þ AN:φi,e
ð8Þ
where no is the number of occupants, AN is the ﬂoor area of the building [m2], ΦI,o
and ΦI,e are speciﬁc internal gains of occupants [W/occupant] and speciﬁc internal
gains of equipment [W/m2], respectively.
_
E xinput is calculated by taking _
E xdemand into consideration in addition to the
efﬁciency of the heat production and heat distribution systems, which are used as
0.95 and 0.93, respectively.
The exergy ﬂexibility factor shows the possibility of replacing a thermal system
with another system, especially renewable, to meet the exergy demand (Hepbas
¸lı
2012).
EFF ¼
_
E xdemand
_
E xinput
ð9Þ
_
E xinput ¼
_
E xdemand
ηheat sys
þ _
E xloss þ _
E xaxu
ð10Þ
The heat gains of the buildings are also effective with regard to the values of the
cooling exergy load of the building. The average temperature value is taken as
26.8 C (MGM 2013) for the summer season.
_
E xcooling ¼ Exaxu þ _
E xgain,solar þ _
E xgain,transmisson
ð11Þ
4
Case Study: Mavis
¸ehir Mass Housing Area
The case area is located in Izmir, which is in the western part of Turkey (Fig. 4).
Izmir has a hot-humid climate; the summers are hot and dry while the winters are
mild and rainy. According to the climactic and geographic characteristics, both the
solar potential and the wind potential of Izmir are noticeably high. July and August
are the hottest months in the city, with temperatures of 27.3 C and 27.6 C,
respectively; the coldest months are January and February, with temperatures of
8.6 C and 9.6 C, respectively (MGM 2013).
Energetic and Exergetic Design Evaluations of a Building Block Based. . .
521
Moreover, the prevailing winds in Izmir come from the southeast and the west
(MGM 2013; Serin 2011). The prevailing wind direction of the Izmir–Ci
gli station
was selected for the study because the station is located very close to the case area
(Fig. 5).
The sun path diagram of Izmir is given in Fig. 6. The green curve on the upper
side of the diagram represents the path of the sun from east to west during the
daytime and indicates the angles on 21 June. The blue curve represents the path of
the sun on 21 December. As seen in Fig. 6, the angle of sunlight reaching Izmir
varies between the maximum angles of 72–29 at 12 pm during the year.
The existing situation of the selected case area is a mass housing area and
consists of high-rise (from 12-storey to 23-storey) buildings (Fig. 7). In addition
to the housing units, the area also includes social and leisure facilities such as sports
areas, green areas, parking areas, playgrounds, and education and commercial
areas. Having a central gas heating system, double-glazed windows, sun blinds,
decorative coated doors, and double bathrooms, each type of accommodation in
Mavis
¸ehir is a luxury residential high-rise apartment or villa (Fig. 8) (Aydo
gan
2005; Koc
¸ 2001; O
¨ zc
¸elik 1998).
Fig. 4 Location of Mavis
¸ehir in Izmir Bay (Source: Google 2013)
522
M. Yelda and S. Nicel
Fig. 5 The prevailing wind
direction in Izmir–Ci
gli
Station (Source: Windﬁnder
2013)
Fig. 6 Sun path diagram of Izmir (Source: Gaisma 2013)
Energetic and Exergetic Design Evaluations of a Building Block Based. . .
523
5
Redesign of a Building Block: Hybrid Solar Envelope
Method
The structure of a building block is designed as a terraced structure in accordance
with the solar envelope values developed for Izmir (Fig. 6) and the structure of this
model. The number of storeys changes within the range of 2 to 12 (Fig. 9) and the
area of each housing unit is about 110 m2. This selection is based on the peak point
of the solar envelope and the prevailing angles of the solar envelope for east, west,
Fig. 7 View of the case area (Source: Google 2013)
Fig. 8 Site model of the existing building block
524
M. Yelda and S. Nicel
north, and south. Green areas, playgrounds, sports area, parking area, and social,
cultural, and educational areas are also considered, and areas are demarcated for
these needs. The number of storeys of the buildings and height of the buildings
change according to the solar envelope calculation. As a result, the distances
between the buildings are also affected and changing.
Figures 10, 11, 12, and 13 show the developed site plan in detail for building
blocks with regard to the hybrid solar enveloped-based energy-efﬁcient design
method. The effect of the solar envelop can be seen in Fig. 12. In Table 1 the
number of buildings and housing units in the proposed solar enveloped-based
design is shown.
6
Results and Discussion
In this study, exergy analysis is used to investigate a building block located in I
˙zmir
Mavis
¸ehir. Exergy concepts in relation to the environment ease the quantiﬁcation of
economic and ecological problems by underlining the potential of a successful
20°
29°
65°
22°
Fig. 9 Developed solar
envelope of the case area
Fig. 10 Site plan of solar envelope-based alternative
Energetic and Exergetic Design Evaluations of a Building Block Based. . .
525
exergy analysis (Mert 2006). The important effects of the relationship between
exergy and the environment are seen in the reduction of waste energy emissions, a
decrease in resources of energy-related sectors, and more efﬁcient use of energy
(Dinc
¸er 2002).
Some assumptions made in the analysis are listed below:
•
The reference state for exergy analysis is selected as 298 K temperature and
1 atm pressure in atmospheric concentrations.
•
Indoor air temperature is 21 C.
•
Outdoor temperature is 10.9 C, the average winter temperature of Izmir
(between the years of 1960–2012) (MGM 2013).
Fig. 11 Site plan of solar envelope-based alternative in detail
Fig. 12 Site model of solar envelope-based alternative
526
M. Yelda and S. Nicel
•
Outdoor temperature is 28.6 C, the average summer temperature of Izmir
(MGM 2013).
•
The heat transfer coefﬁcients of the walls and doors are gathered from real
values depending on the plan details of the buildings, which are obtained from
the Kars
¸ıyaka Municipality, and on the reported data of the construction com-
pany (Kars
¸ıyaka Municipality 2012).
•
An average value of four residents is assumed during the calculations.
•
70/50 heating system (inlet/outlet temperature of the radiators) is used in
calculations in accordance with the reality and the condensing boiler systems.
In Table 2 results for exergy analysis of a residential area for solar envelope-
based design is given. It must be mentioned that the summer exergy load and winter
exergy load is in accordance with the expectation that cooling load is higher than
the heating load and the exergy efﬁciency is in the range of 7–11, depending on the
Fig. 13 Site model of solar envelope-based alternative
Table 1 Details for the solar
envelop-based design
Storey
No. of buildings
No. of housing units
12
4
192
10
9
360
8
11
352
6
13
312
4
12
192
2
7
56
Total
56
1464
Energetic and Exergetic Design Evaluations of a Building Block Based. . .
527
local climate properties. The higher the building height the larger the exergy loads,
whereas lower exergy loads are also seen in the investigation.
The exergy efﬁciency values are in the range of 10.8–11.2 % and the exergy by
fuel value changes from 2,845.7 to 5,424.9 kW. These results are in accordance
with the literature, as Hepbas
¸lı (2012) pointed out that the efﬁciency of buildings
with a LowEx design reaches 7–8 %. When the existing plan and the proposed plan
alternative are compared it is seen that the exergy load value for the existing plan is
ten times higher than the new design. It can be seen that the hybrid solar envelope
design can lead to high ratios of energy conservation. In Fig. 14, the exergy per
separate housing unit is shown. It is seen that a solar enveloped-type housing has
better energy conservation with lower than 184 W, while in the existing plan this
value lies in the range of 1,800 W. The proposed design’s exergy load values are
lower than the others mainly because of the general high performance of the heating
system in a great number of housing units. This investigation is carried out per
housing unit since the housing unit numbers are different in the proposed design
alternative and to the existing situation. So in order to achieve a logical discussion,
comparison per housing unit is applied.
From a cooling load point of view in the summer period, it is seen that the saving
is much higher since the exergy load value decreases from 3,181 to 346. This is a
result of proper airing and particularly use of excellent construction materials
(Table 3). When the values from the literature (Hepbas
¸lı 2012) of cooling loads
are investigated it is seen that cooling loads for hot-humid zones are higher than
heating loads and it is seen that the results of this study are in accordance with the
literature.
Table 2 Results for the solar envelop-based design
Storey
Exergy load
summer [W]
Exergy load
winter [W]
Exergy by
fuel [W]
Exergy
efﬁciency
[%]
Exergy ﬂexibility
factor [%]
12
16,753
8,330
6,200
10.1
39
10
14,048
7,078
5,183
10.5
38
8
9,848
5,425
3,438
10.9
36
6
8,638
4,575
3,148
11.0
33
4
5,932
3,323
2,131
11.2
29
2
3,227
2,071
1,113
7.4
19
0
1000
2000
3000
4000
Solar Envelope Based Design
Existing Plan
Exergy [W] 
Exergy Load Winter
Exergy by Fuel
Cooling Load
Fig. 14 Exergy load and
exergy according to fuel
values of proposed
alternative and existing
plans
528
M. Yelda and S. Nicel
The amount of money and greenhouse emissions saved is shown in Table 4 per
housing unit in the building block. It is seen that every housing unit saves 1,072.48
TL (nearly US$500) in a year according to the effect of the energy-efﬁcient design.
This value is very important for most of the families in Turkey. Of more importance
than the monetary savings, nearly 1.79 tons of greenhouse gas emissions per
housing unit have not been emitted into the atmosphere, which is a magniﬁcent
contribution to the environment for the sake of a sustainable and green future. These
values are calculated using the energy price of US$0.08 /kW for cost and using
reaction stoichiometry calculations with natural gas as the fuel in the case area.
The exergy calculations are concluded with determinations of exergy efﬁciency
and exergy loads in addition to the annual conservation of exergy, money, and
greenhouse gases.
In Table 5, general features of the existing building block and the proposed
design alternative are summarized. The design alternative has less housing units
than the existing building block. As seen in Table 3, the housing unit number is
decreased in the alternative proposed design. This result mostly arises from the fact
that the spacing between the buildings is increased in the energy-efﬁcient design,
which brings a low density to the built area.
Table 3 The savings by energy-efﬁcient design per housing unit
Solar envelope based design
Existing plan
Exergy load winter [W]
184.80
1798.0
Exergy by fuel [W]
125.37
1173.0
Exergy load summer [W]
346.89
3181
Table 4 The annual saving
using an energy-efﬁcient
design
Conservation type
Conservation amount (per year)
Exergy winter [W]
2,188.67
Exergy summer [W]
1,583.68
Money [TL]
1,072.48
CO2 emission [kg]
1,787.46
Table 5 Design values of existing and proposed plan alternatives
Existing building block
Solar envelop-based building block
Number of buildings
155
56
Housing unit (number)
5320
1464
Floor area of housing unit (m2)
56 m2–150 m2
117 m2
Total housing area (m2)
54,135
30,240
Energetic and Exergetic Design Evaluations of a Building Block Based. . .
529
From an energy-efﬁciency point of view it is seen from this study that when the
planning and design are carried out taking into consideration the energy-efﬁcient
design parameters, the efﬁciencies increase considerably. That brings us to a
position to propose considering these parameters in every planning and design
study as well as implementing these in building and planning acts.
7
Conclusions
The building block is redesigned in order to increase energy. The results of the
exergy analysis of the proposed design alternative show that the exergy efﬁciency
values have increased to 11 % from 1.5 % in the existing design. Since the
efﬁciency value is the representation of the performance of using the energy in an
effective manner, it is free of the magnitude (size of the area) and can be evaluated
for understanding the energetic behavior of the existing and proposed design
alternative. The exergy efﬁciency was increased considerably, and a large amount
of energy and money can be conserved through the application of the proposed
design. The results also show that the annual exergy load of a single housing unit
was decreased from 1,800 to 184.8 W for winter and from 3,180 to 346 W in
summer.
It is seen that when energy-efﬁcient design parameters are taken into account,
such as proper distance allocation among the buildings for sunlight purposes, a
lower density building block is needed. In the existing plan the parking area is also
larger than the proposed building block because of the high number of housing
units. Moreover, the high housing unit number creates a decrease in the green open
spaces, sports areas, and playground areas. It was also found out that the alternative
design proposed larger green areas, open spaces, and social areas with respect to the
existing situation in the area. That also mostly arose from the spacing between the
buildings.
As a result of this study, it was found that by applying a solar envelope design to
the selected building block, 1,613 and 2,834 W of energy could be conserved during
the winter and the summer periods, respectively, for a housing unit. With this
energy conservation, 1,072 TL (nearly US$500) per housing unit was saved annu-
ally. Of more importance, 1,787 kg of emission gases (CO2, etc.) per housing is not
released into the atmosphere. This results in the formation of a more sustainable
neighborhood. When the total area is taken into account, the exergy efﬁciency
reaches up to 11 %. The Exergy by Fuel Value is 125.37 kW, with a Summer
Exergy Load of 346.89 kW and a Winter Exergy Load of 184.80 kW. These are the
main indications of the importance of the exergetic analysis in a building block.
In this study, it is revealed that according to energy efﬁciency, efﬁciencies
increase considerably when a hybrid solar envelope design is considered during
the planning and design periods. Furthermore, the results show that the concept of
energy efﬁciency should be taken seriously from every urban planning and design
stage to implementation.
530
M. Yelda and S. Nicel
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Energetic and Exergetic Design Evaluations of a Building Block Based. . .
531
Natural Ventilation Around and Through
Building: A Numerical Study
A. Kaddour and S.M.A. Bekkouche
1
Introduction
In the planning process of a building, the heating and cooling concepts are devel-
oped, and both have the ventilation concept as an integrated part. Mechanical,
natural, and even hybrid ways of ventilation are nowadays in use. Natural ventila-
tion of buildings, relying on pressure or on difference in temperature, is the most
common form of ventilation. The air is exchanged through doors and open or tilted
windows. For layout purposes, it is important to estimate the magnitude of the air
change rates correctly. Ventilation through tilted windows is sparsely represented in
the literature to date Teppner (2014).
The 1970s housing crisis had inspired the interest in bioclimatic architecture, as
the most nowadays built houses are intact and combustible energy reserves are
exhausted. The case study of this research is the agglomeration of Ghardaia situated
in M’zab valley in the northern part of Algeria Bekkouche (2011).
Ghardaia region is situated at the south of the capital (600 km) between 32 and
3320 northern latitude and 230 longitude east at an average altitude of 500 m.
The climate is hot and dry in summer, with large temperature swings, intense solar
irradiation, and strong winds. The winter is cold and moderately wet, characterized
by very low precipitations (160 mm/year), very high temperatures in summer, and
low temperatures in winter (frosty from December to mid-February). The temper-
atures vary between a maximum of around 50 C and a minimum of 20 C, thus
A. Kaddour (*) • S.M.A. Bekkouche
Unite
´ de Recherche Applique
´e en Energies Renouvelables URAER, Centre de De
´veloppement
des Energies Renouvelables (CDER), 47133 Ghardaia, Algeria
e-mail: kaddour.majid@gmail.com
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_38
533
giving a large diurnal temperature swing. Winter temperatures vary between a
maximum of 24 C and a minimum of 0 C. Its normal temperature in January is
10.4 C; it is 36.3 C in July. And the average annual range is about 12.2 
amplitudes of monthly average temperatures. They are more moderate in winter
than in summer (average 11 in winter cons 13.5 in summer). The monthly
maximum amplitudes are larger in summer than in winter, which ﬂuctuates around
20 C. Solar irradiation is intense throughout the year with a maximum of
700 Wm-2 in winter and 1000 Wm-2 in summer, measured on the horizontal
surface Mingozzi (2009).
In Ghardaia region, stone is the most used material for construction because of
its widespread availability. In carrying out the current work, our inspiration stems
from Ghardaia traditional housing architecture. It is adapted to contemporary life
convenience, where local materials such as stone, plaster, and mortar cement are
used. Figure 1 represents a schematic view of typical housing of 88 m2 areas, and
walls’ height is equal to 2.8 m.
Temperatures of windows and doors, made of ordinary wood, depend on several
factors including local climate, orientation, frame (frame + insert), relative area
(window, ﬂooring), and solar and nocturnal occultation performances. When exter-
nal temperatures are moderate, these surfaces would yield a higher gain.
Nomenclature
g
Gravitational constant (m.s1)
A
Area (m2)
Ai
Area of the zone surface I, (m2)
AL
Cp
Speciﬁc heat of the air (J/(kg.K)
Cw
Coefﬁcient for wind-induced inﬁltration ((L/s)2/(cm4 (m/s)2)
Cz
Air capacitance (J/K)
(continued)
Fig. 1 Descriptive plan of studied housing
534
A. Kaddour and S.M.A. Bekkouche
Fschedule
Value from a user-deﬁned schedule [0,.. .,1]
hi
Heat transfer coefﬁcient (W/(m2. K))

Mass airﬂow due to inﬁltration (kg/s)
_
m i
Mass airﬂow due to interzone air mixing (kg/s)
_
m sys
Mass airﬂow of the systems (kg/s)
_
Q i
Convective internal loads (J/s)
_
Q sys
Heat power of the air systems (W)
swind
Local wind speed (m/s)
t
Time (s)
Tz
Indoor air temperature (K)
T t
z
Indoor air temperature at the time b step t
Ttδt
z
Indoor air temperature at the previous time step t (K)
Tsi
Temperature in the surface I (K)
T1
Outdoor air temperature (K)
Tsup
Temperature of the supply of air (K)
X
Nsl
i¼1
_
Q i
Sum of the convective internal loads (J/s)
X
Nsurfaces
i¼1
hi Ai
Tsi  Tz
ð
Þ
Convective heat transfer from the zone surfaces (J/s)
X
N zones
i¼1
_
m i Cp
Tzi  Tz
ð
Þ
Heat transfer due to interzone air mixing (J/s)
_
m inf Cp T1  Tz
ð
Þ
Heat transfer due to inﬁltration of outsider air (J/s)
_
Q sys
Air system output (J/s)
Cz
dTz
dt
Power stored in the zone air (J/s)
2
Energetic Balance
If we seek to optimize comfort by minimizing bought consumed energy, thus it is
vital to understand the importance and behavior of heat ﬂow. Otherwise, it would be
difﬁcult and costly to measure all heat ﬂuxes passing through the building and
practically impossible in case of a building under construction. Therefore, the
energy balance determination is of paramount importance Fanger (1982).
Building energy balance is based upon the fact that practically all energy ﬂuxes
penetrating a given building end up transformed to heat. Given that on average basis
the building’s inside temperature remains constant, thus all energy ﬂuxes
would exit.
Energy balance penetrating and exiting a building is compatible for a given
period of time. It is evident that this balance must be equilibrated by energy
conservation.
Natural Ventilation Around and Through Building: A Numerical Study
535
Energy balance includes all gains and losses and the consumption (see Table 1)
period is equal and is relatively high (a year as a typical example, even a month if no
high storage capacity exists).
The housing receives energy of different forms:
•
Fuels: diesel, gas, and woods
•
Electricity
Solar radiation and external thermal radiation
3
Instantaneous Thermal Balance
Heat transfers may be carried out in several processes: conduction in stationary
material, convection in case poof ﬂuid, radiation in transparent medium, and
vaporization-condensation of water vapor.
It is evident that a relatively precise resolution, of all dynamical equations that
model heat transfer, coupled with limit conditions (between different materials and
meteorological data) and subject to a given initial condition, in real housing
comprising hundreds of construction elements and occupied by a resident with
varying living conditions, is only achieved using powerful computers equipped
with a dedicated software.
If by means of dynamical method, one knew all ﬂuxes and temperatures present
in housing at each time instant, one can integrate these values over a given time
period (month, season, and year as example) and determine consumed thermal
energy by housing and comfort conditions over that period.
Thermal instantaneous balance may be written in the following manner: required
power to heating Pc is equal to the sum of losses (transmission and ventilation)
minus solar gains and internal gains plus accumulated heat inside housing.
Air thermal balance of a given zone of housing is given by Perez (2012):
Table 1 Housing energy balance
Gradual losses
Gains
Heat transmission through envelope
Solar radiation penetrating through windows and
other passive collectors
Heat transmission through ﬂoor
Inhabitants’ metabolic heat
Heat losses in stale air
Solar collectors
Heat loss in sewage (hot water) and
structure-accumulated heat
Heat returned by the structure
Technical installation gradual loss
Fuel electrical energy supply
Total losses
Total gains
536
A. Kaddour and S.M.A. Bekkouche
Cz
dTz
dt ¼
X
Nsl
i¼1
_
Q i þ
X
Nsurfaces
i¼1
hiAi Tsi  Tz
ð
Þ þ
X
Nzones
i¼1
_
m iCp Tzi  Tz
ð
Þ
þ _
m inf Cp T1  Tz
ð
Þ þ _
Q sys
ð1Þ
This balance depends on internal convection loads, heat transfer by convection
with different zone surfaces, heat transfer due to air inﬁltration, and any existing
aeration systems.
In order to solve Eq. 1, an algorithm is designed to obtain internal temperature of
the studied zone. The solution is given by:
T t
z
¼
Ttδt
z

PNsl
i¼1 _
Q i þPNsurfaces
i¼1
hiAiTsi þPNzones
i¼1
_
m iCpTzi þ _
m infCpT1 þ _
m sysCpTsup
PNsurfaces
i¼1
hiAi þPNzones
i¼1
_
m iCp þ _
m infCp þ _
m sysCp
 
!
exp 
PNsurfaces
i¼1
hiAi þPNzones
i¼1
_
m iCp þ _
m infCp þ _
m sysCp
Cz
δt
 
!
þ
PNsl
i¼1 _
Q i þPNsurfaces
i¼1
hiAiTsi þPNzones
i¼1
_
m iCpTzi þ _
m infCpT1 þ _
m sysCpTsup
PNsurfaces
i¼1
hiAi þPNzones
i¼1
_
m iCp þ _
m infCp þ _
m sysCp
ð2Þ
4
Gradual Losses Through Ventilation
Housing ventilation may engender energy consumption for the following:
1. To heat, cool, and condition external air in order to bring its temperature and its
moisture to comfortable values Fanger (1988)
2. To move the cold air and the stale air.
Energy consumption to conditioning air is done in such way that housing is
whether equipped or not with a dedicated ventilation system or air conditioning
facility. This consumption is basically given by
QV ¼ m ΔH 1  ηr
ð
Þ ¼ ρ _
V t ΔH 1  ηr
ð
Þ
ð3Þ
QV is energy consumption over a considered time period [J].
m is air mass passing through housing [kg].
ΔH is enthalpy difference between internal and external air, including the amount
of energy required for varying temperature and humidity [J/kg] (not to be
confused with between H, enthalpy in J/kg, and coefﬁcient of gradual losses in
W/K).
ρ is air volumic mass [kg/m3].
_
V is air rate [m3/h].
t is considered the time period.
Natural Ventilation Around and Through Building: A Numerical Study
537
ηris heat recovered from fouled air efﬁciency.
In housing without an air conditioning system, in winter, moisture contribution
is largely ensured by human activities and moistener is often unnecessary. Besides
that, ventilation is often natural.
In this case, the demand of energy is restricted to that required for heating:
QV ¼ m CpΔT 1  ηr
ð
Þ ¼ ρ _
V t CpΔT 1  ηr
ð
Þ
ð4Þ
where ΔT is the difference in temperature between inside and outside and Cp is
air-speciﬁc heat (1000 J/kgK).
For coefﬁcient of gradual loss caused by ventilation, HV is thus
HV ¼ _
m Cp 1  ηr
ð
Þ ¼ ρ _
V t Cp 1  ηr
ð
Þ
ð5Þ
At ambient temperature, ρCp is equal to 1200 J/(m3 K) or 0.33 Wh/(m3 K).
5
Heat Recovery
In case of building equipped with heat return system between fresh and extracted
air, gradual losses caused by mechanical ventilation are reduced by a factor (1  ηr),
where ηr is global efﬁciency of the heat return system.
For system with heat recovery capacity from fouled air toward water heating
system or local heater using heat pump, the rate of air renewal is calculated without
reduction. The lowering of energy need due to heat recovery is thus taken into
account when calculating the considered energy consumption system.
It is important to note here that ηr is the global efﬁciency of recovered heat which
is always less than the exchanger recovery capacity efﬁcacy, determined in the
production site. According to measurement done by the author, the global real
efﬁciency is at best near 85% of the theoretic efﬁcacy and be equal to nil in most
extreme critical cases (see Fig. 2). Only heat of fouled air that passes through the
exchanger is recovered. Indeed, heat within the air is lost through exﬁltration along
the envelope of the housing or in air circulating through opening located between
extraction canal and pulse canal, is not recovered. In addition to that, the
recirculation increases pulsed air rate without improving air quality. All carried
out calculation is done to obtain a relationship between exchanger recovery efﬁ-
cacy, εHR, and global heat recovery and is given by:
ηr ﬃ1  γexf
ð
Þ 1  Re
ð
Þ
1  Re γexf
εHR
ð6Þ
γexf is the exﬁltration ratio, including fresh air rate and the amount of fresh air
that penetrates the housing and escapes through envelope openings.
538
A. Kaddour and S.M.A. Bekkouche
Re is the recirculation rate including extracted air rate that recirculate.
6
Internal Gains
These heat gains “free of charge” emanate from metabolic heat of inhabitants and
heat radiated by appliances, lighting, etc. that are not speciﬁcally devoted to heating
but resulting from other energy consumption processes inside the heated volume.
Thermal power evacuated by inhabitants present during a time period h may be
calculated using Eq. (5):
ϕh ¼ NPh
24 ¼ A Ph
24D
ð7Þ
N Number on inhabitants present in heated zone
P Evacuated power by inhabitant
h Presence time in hour per day
A Heated ﬂooring crude surface occupied by inhabitants
D Available surface to inhabitants
0
20
40
60
80
100
0
20
40
60
80
100
 Recovery global efficiency (%)
Exfiltration rate (%)
Recirculation 
 20%
 40%
 60%
 80%
 100%
Fig. 2 Recovery global efﬁciency in function of internal recirculating and exﬁltration rate,
respectively
Natural Ventilation Around and Through Building: A Numerical Study
539
The presence rate depends on inhabitant type that can be classiﬁed into two
categories: housing or establishment such as school. The evacuated power depends
on activities and inhabitants’ size. We can however admit that there is an average
activity and classify inhabitants into two categories: adults and infants. SIA 380/1
proposes ﬁgures given in Table 2.
The power supplied by appliances is generally calculated from electrical power
Pel consumed by appliances:
ϕa ¼ Pel f e
ð8Þ
where fe is a correction factor taking into account the fact that electrical apparatus
are not present in heated space together (e.g., public lighting, freezer in the
basement, etc.).
[SIA, 2001 #328] proposed ﬁgures given in Table 3 for average consumed
electrical power for heated ﬂoor surface Pel/SPC and for factor fe.
Thus, the internal energy is given in period t by
Qi ¼ t ϕh þ ϕa
ð
Þ
ð9Þ
At the ﬁrst approximation, internal gain rates for a given family are in the order
of 1300 MJ for a month period, having an average power of 500 W. Another method
consists in counting an average power of 5 W/m2 of ﬂooring in the housings.
Table 2 Thermal power evacuated by inhabitants, according to SIA (2001)
Housing type
Occupation (m2/pers)
Presence (h/j)
Power (W)
Collective housing
40
12
70
Individual
housing
60
12
70
Ofﬁce
20
6
80
School
10
4
70
Restaurant
5
3
100
Table 3 Thermal power dissipated by appliances, associated with heated crude ﬂoor surface,
according to SIA (2001)
Housing
type
Annual consumption (MJ/m2)
Correction factor
Collective housing
100
0.7
Individual
housing
80
0.7
Ofﬁce
80
0.9
School
40
0.9
Restaurant
120
0.7
540
A. Kaddour and S.M.A. Bekkouche
7
Heating Needs
The gradual loss, Ql, and heat supplies, Qg, are calculated for each step of period
calculation. The need of heating of locals for each period is summarized as follows:
Qh ¼ Ql  ηQg
ð10Þ
where:
Ql Heat gradual losses
Qg Heat gains
η Utilization rate
8
Results and Discussions
The rate of utilization takes into account the fact that we have from time to time
whether to reject or not to collect a part from internal gains and/or solar in order to
avoid overheating. According to Fig. 3, the utilization rate depends on the ratio of
gain to losses γ, of housing thermal inertia expressed by a time constant τ, and
amplitude of admitted temperature variation in case of internal temperature.
We see in Fig. 4 the regulation system of the heater has also inﬂuence on
utilization of gains, but in practice, we prefer to take into account this fact
separately. Housing thermal balance is calculated as possible independently of
heating system, and imperfect control effects are then included in global efﬁciency
of the heating system.
The heating system is supposed to be perfectly regulated; parameters presenting
a major inﬂuence on utilization rate are the following:
The ratio of gradual loss, γ, is deﬁned as
γ ¼ Qg
Ql
ð11Þ
The variation of utilization rate with its variables is well described by empirical
relationship:
η ¼
1  γa
1  γaþ1
si γ 6¼ 1
ð12Þ
η ¼
a
a þ 1
si γ ¼ 1
where a is numerical parameter depending on time constant τ.
Figure 3 gives utilization rate for period of calculation on a monthly basis for
several time constants.
Natural Ventilation Around and Through Building: A Numerical Study
541
According to Fig. 5, an important difference between external and internal air
temperature is spotted.
Furthermore, semi-open windows contribute to reduce internal air temperature;
we can notice that the opening effect represents the important factor of internal
temperature variation.
0,0
0,5
1,0
1,5
2,0
2,5
3,0
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1,0
1,1
Utilization rate
Ratio: gains/losses
 infinite
 168 hours
 48 hours
 24 hours
 8 hours
Fig. 3 Utilization rate in function of gains/gradual loss ratio for constant times 8 h, 1 day, 2 days,
1 week, and inﬁnite, valid for calculating period of 1 month
0,0
0,5
1,0
1,5
2,0
2,5
3,0
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1,0
1,1
Utilization rate
Ratio: gains/losses
 infinite
 168 hours
 48 hours
 24 hours
 8 hours
Fig. 4 Utilization rate in function of gains/gradual loss ratio for housing lightly permanently
heated and housing heated during day only
542
A. Kaddour and S.M.A. Bekkouche
The building forms a single entity including the structure, installations, and
occupants. An action on one of its components may have secondary effects on
other components. In this section, we examine some of these interactions.
Strong moisture favors the growth of moisture agents and other microorganisms
that can demolish building structure and its occupant. In order to limit the risk, it is
most recommended to avoid the following:
Leaks in envelop (mainly ceiling) and in water pipes or sewer
Any condensation over internal surfaces of housing’s outer walls during the most
coldest periods
That relative local moisture exceeds 80% on surfaces susceptible to favor the
growth of moisture (plasters, woods, wall paper, paintings, etc.)
Water vapor can be condensed in sufﬁcient quantity inside walls.
It is generally also admitted that the risk of growth of moisture on walls increases
strongly when local humidity relatively exceeds 80%. This humidity depends on
several factors:
Wall surfaces’ temperature
Ventilation rate
Vapor production in air
The simple developed model given below allows estimating this risk. Temper-
ature of internal surface of the element is given by
Ts,i ¼ Te þ f Rsi Ti  Te
ð
Þ
ð13Þ
where T represents temperatures, index i designs inner and e outer, and fRsi is
peripheral temperature coefﬁcient that can be computed in all places at all times
by solving heat equation.
Fig. 5 Comparison between internal and external temperature variation (21–23 July 2013)
Natural Ventilation Around and Through Building: A Numerical Study
543
Partial pressure of water vapor of external air depends also of its external
temperature. The following mixture ratio is deduced:
xe ¼ Mw
Ma
Pe
Pa  Pe
¼ 0:62198
Pe
Pa  Pe
kg=kg
½

ð14Þ
where:
M is molar mass (index e for water, a for air)
Pa is atmospheric pressure (101,300 Pa in normal conditions at sea level)
Minimum air rate _
V min is allowed to avoid moisture growth risk and is given by
_
V min ¼
Se,i
ρa xi  xe
ð
Þ
m3=h


ð15Þ
where:
Se , i is rate production of water vapor inside [kg/h]
ρa is volumic mass of internal air [kg/m2]
By solving this equation, we obtain mixture ratio of internal air for a given air
rate and rate of production and a given humidity rate production:
xi ¼ Se,i
ρa _
V þ xe
kg=kg
½

ð16Þ
Hence, pressure vapor inside is
pi ¼
MaPaxi
Maxi þ Me
¼
Paxi
xi þ 0:62198
Pa
½

ð17Þ
This model allowed to plot the Fig. 6. We show minimum air rate necessary to
evacuate water vapor produced by a person and avoid the risk of moisture. Air rate
can be reduced because of cold when external air is particularly dry but may
signiﬁcantly be increased at midseason.
Note that temperature factor (see Fig. 7) in insulated walls according to modern
norms (U < 0.3 W/m2 K) is at least 0.85. It is with thermal bridges that this factor
may be reduced and achieves its critical value.
At the time of appearance of moisture in the housing, it often happens that the
landlord blamed the occupant of producing too much humidity or insufﬁcient
ventilation. However, occupant claims that the insulation is absent.
544
A. Kaddour and S.M.A. Bekkouche
2
4
6
8
10
12
14
16
18
20
22
5
10
15
20
25
30
35
40
Temperature factor
 100%
 75%
 50%
Mnimum air flow rate per person [m3/h]
External temperature [°C]
Fig. 6 Minimum air ﬂow rate per person, at 20 C internal temperature
2
4
6
8
10
12
14
16
18
20
0,45
0,50
0,55
0,60
0,65
0,70
0,75
0,80
0,85
0,90
0,95
1,00
1,05
Minimal temperature factor
External temperature [°C]
Internal temperature
 24°C
 20°C
 16°C
Fig. 7 Minimal temperature factor fRsi minimal at 15 m3/h per person
Natural Ventilation Around and Through Building: A Numerical Study
545
9
Conclusions
This leads to the circulation of large quantities of air in the ventilated space, without
producing any real dilution effect, which in turn leads to great heat losses. The right
solution is the natural ventilation, which makes it possible to minimize heat losses
because the air mixing occurs during the ventilation stop period.
To demonstrate the inﬂuence of the internal and external temperatures, this study
also conducted housing energy simulations associated with air change rates
deduced by the energy simulation program and calculations based on Matlab
software.
The natural ventilation of building that successively open and close at regular
time intervals can limit the excessive circulation of air masses, which in turn limits
heat losses.
The ratio of gain to losses, γ, of housing thermal inertia has a big effect on the
utilization rate. The natural ventilation concepts are evaluated.
References
Bekkouche, S.M.A., Benouaz, T., Yaiche, M.R., Cherier, M.K., Hamdani, M., Chellali, F.:
Introduction to control of solar gain and internal temperatures by thermal insulation, proper
orientation and eaves. Energ. Buildings. 43(9), 2414–2421 (2011)
Fanger, P.O.: Thermal Comfort. R. E Krieger, Florida (1982)
Fanger, P.O., Melikov, A.K., Hansawa, H., Ring, J.: Air turbulence and the sensation of draught.
Energ. Buildings. 12, 21–39 (1988)
Mingozzi, A., Bottiglioni, S., Medola, M.: Passive cooling of a bioclimatic building in the
continental climate of the padan plain: analyzing the role of thermal mass with dynamic
simulations. Int J Sustainable Energy. 28, 141–156 (2009)
Perez, I.V., Østergaarfb, P.A., Remmenb, A.: Model of natural ventilation by using a coupled
thermal-airﬂow simulation program. Energ. Buildings. 49, 388–393 (2012)
Teppner, R., Langensteiner, B., Meile, W., Brenn, G., Kerschbaumer, S.: Air change rates driven
by the ﬂow around and through a building storey with fully open or tilted windows: An
experimental and numerical study. Energ. Buildings. 80, 570–583 (2014)
546
A. Kaddour and S.M.A. Bekkouche
Mathematical Filtering Analysis of Infrared
Images in Integrated-Circuit Techniques
Imre Benk€
o
1
Introduction
This paper covers only the data obtained through the computer processing of
infrared (IR) thermograms which had to be evaluated because the IR images were
gained from various measurements and because of the nature of the objectives
(Benko
˝ 1992, 1997). The methods of ﬁltering can be grouped as follows: smoothing
ﬁlters, sharpening ﬁlters, band ﬁlters and gradient ﬁlters. It is important to empha-
size that, after the ﬁlters are applied, the colours and temperatures indicated in the
resultant image have no relation to the actual temperatures but to the physical
essence of the ﬁltering method selected. The illustrations for IR image analysis are
taken from the electronics industry (see Fig. 1a, b) (Benk€
o 1998).
2
Generalities
The principal objective of image enhancement techniques is to process an image so
that results are more suitable for a speciﬁc application than the original image. In
the IR thermogrammetry the goal of the image processing is to obtain a new IR
image showing some thermal faults or singularities in the temperature ﬁeld. It is to
be noted that the image enhancement techniques are very much problem oriented.
I. Benk€
o (*)
Budapest University of Technology and Economics, Budapest, Hungary
Faculty of Mechanical Engineering, Institute of Thermal Engineering,
Cirmos u.1, H-1112 Budapest, Hungary
e-mail: ibenko@freestart.hu
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_39
547
Generally, image enhancement methods are based on either spatial or frequency
domain techniques. The spatial domain refers to the image plane itself, and
approaches in this category are based on direct manipulation of pixels in an
image. Frequency domain processing techniques are based on modifying the
Fourier transform of an image (see Sect. 6).
3
Spatial Domain Methods of Image Filtering
The term spatial domain refers to the aggregate of pixels composing an image, and
spatial domain methods are procedures that operate directly on these pixels (Gon-
zales and Woods 1977). Image processing functions in the spatial domain may be
expressed as
Fig. 1 (a) The component side and (b) the chip side of an integrated circuit
548
I. Benk€
o
g x; y
ð
Þ ¼ T f x; y
ð
Þ
½

ð1Þ
where f(x,y) is the input image, g(x,y) is the processed image and T is an operator
on f, deﬁned over some neighbourhood of (x,y).
One of the principal approaches in eq. (1) is based on the use of so-called masks
(also referred to as templates, windows or ﬁlters). Basically, a mask is a small
two-dimensional array, in which the values of the coefﬁcients determine the nature
of the process, such as sharpening. Enhancement techniques based on this type of
approach often are referred to as mask processing or ﬁltering.
4
Smoothing Filters
Smoothing ﬁlters are used for blurring and for noise reduction. Blurring is used in
preprocessing steps, such as removal of small details from an image prior to (large)
object extraction, and bridging of very small gaps in line or curves. Noise reduction
can be accomplished by blurring with a linear ﬁlter and also by non-linear ﬁltering.
4.1
Neighbouring Filter
In this ﬁltering function the value of the pixel is calculated by averaging the values
of the neighbouring pixels. Either four or eight neighbouring pixels can be included.
For four pixels the equation g(x,y) is deﬁned as follows:
g x; y
ð
Þ ¼ 1
4
X
n;m
ð
Þf n; m
ð
Þ
ð2Þ
In the above equation, g(x,y) is the modiﬁed pixel and f(n,m) is the neighbouring
pixels.
4.2
Sharpening Filters
The principal objective of sharpening is to highlight ﬁne details in an image or to
enhance detail that has been blurred, either in error or as a natural effect of a
particular method of image acquisition. Uses of image sharpening vary and include
applications ranging from electronic printing and medical imaging to industrial
inspection and detection of some thermal faults in a printed circuit in electronic
technology.
Mathematical Filtering Analysis of Infrared Images in Integrated-Circuit. . .
549
4.3
Derivative Filters
Averaging of pixels over a region tends to blur detail in an image. As averaging is
analogous to integration, differentiation can be expected to have opposite effect and
thus to sharpen an image. The most common method of differentiation in image
processing applications is the gradient.
4.4
Gradient Filter
The gradient ﬁlter assesses a pixel with regard to the numerical difference to its
neighbourhood pixels. Either of the two gradient equations can be selected:
(a) Common (or normal) gradient equation
(b) The so-called Roberts gradient equation
They differ in how the neighbouring pixels are viewed.
The normal gradient. The gradient (difference) equation is deﬁned as follows:
g x; y
ð
Þ ¼ f x; y
ð
Þ  f x þ 1; y
ð
Þ
½
 þ f x; y
ð
Þ  f x; y þ 1
ð
Þ
½

ð3Þ
The result g(x,y) replaces f(x,y).
The Roberts gradient. The gradient equation is deﬁned as follows:
g x; y
ð
Þ ¼ f x; y
ð
Þ  f x þ 1; y þ 1
ð
Þ
½
 þ f x þ 1; y
ð
Þ  f x; y þ 1
ð
Þ
½

ð4Þ
The result g(x,y) replaces f(x,y).
In both gradient ﬁlter functions, the calculated value of the pixel can be modiﬁed
by further parameter options.
5
Matrix Filter
Within the function, three ﬁlter sub-functions are available:
(a) Low pass
(b) High pass
(c) User
The matrix ﬁlter works according to the following mathematical operation. The
pixel value of a 3x3 matrix is replaced by the relation
g m1; m2
ð
Þ ¼
X
n1
X
n2 F n1; n2
ð
Þ  H m1  n1  1; m2  n2 þ 1
ð
Þ
ð5Þ
where F and H represent the image matrix and the convolution matrix, respectively.
550
I. Benk€
o
5.1
Sobel Filter
The Sobel ﬁlter is a special matrix ﬁlter which is particularly suitable for recogniz-
ing contours. The results are similar to those achieved with the gradient ﬁlter.
In the Sobel ﬁlter, the gradient equation is as follows:
g ¼ w1x þ w1
tx
ð6Þ
where w1 is the 3  3 weight matrix that the user has to deﬁne, and w1
t is the weight
matrix transposed from w1 and x is the 3  3 image section matrix. For pixel
modiﬁcation, the centre pixel of the image section matrix x is replaced. This is
always taken from the original image.
6
Analysis of Thermal Singularities of an Integrated
Circuit
The applicability (advantages and limitation) of IR images and their scientiﬁc value
are determined in the technical practice by the following main features:
(a) The type of the technical phenomenon investigated
(b) Consideration of rules of IR optics during the IR image taking
(c) Proper selection of ambient parameters
Under optimum conditions, the IR image will contain important information on
the thermal character of the temperature ﬁeld of the examined object or phenom-
enon. As a consequence, the evaluation of IR images, the thermal physical charac-
teristics and the interpretation will contribute to the scientiﬁc value of IR images.
In the actual practice of IR image analysis, the following general methods may
be chosen, while their relative advantages and disadvantages must be decided in the
light of the test being done. The ﬁrst is the traditional phenomenological analysis,
e.g. determination of the temperature at speciﬁed points (e.g. the centre point of the
cross hairs) (see Fig. 3a), a comparison between temperature distribution along the
horizontal, vertical or optional lines (see Fig. 3a) and relief representation of the
temperature ﬁeld (see Fig. 3c). Two new methods serve for mathematical evalua-
tion of temperature ﬁelds:
(a) Histographic analysis, i.e. the application of the distribution curve of temper-
ature histogram for process monitoring (see Fig. 3b) (Benk€
o et al 1998)
(b) Mathematical ﬁltering of IR images to reveal the sites of highest temperature or
of the largest temperature alteration (see Fig. 2)
Mathematical Filtering Analysis of Infrared Images in Integrated-Circuit. . .
551
Fig. 2 Series of IR images concerning the ﬁltering of (a) original IR image, (b) ﬁrst step (the
Sobel ﬁlter) of ﬁltering and (c) second step (the Roberts gradient) of ﬁltering
552
I. Benk€
o
Fig. 3 Common evaluation of IR images on the chip side (a) line thermogram, (b) histogram and
(c) reli
Mathematical Filtering Analysis of Infrared Images in Integrated-Circuit. . .
553
(c) In the section, several techniques are from the ﬁeld of integrated-circuit tech-
niques (Fig. 1). An example for the ﬁltering of the component side is shown in
Fig. 2b (Sobel) and the applied Roberts gradient in Fig. 2c, where the thermal
singularities can be seen. Therefore the recommendable process of mathemat-
ical ﬁltering applying to the original IR images in similar technical cases is as
follows. The ﬁrst step is the Sobel ﬁlter which is a sharpening step, equalizing
the smaller temperature differences so that the contours of the high-temperature
ﬁeld can be achieved (Fig. 2b). The second step is subsequent ﬁltering of
Fig. 2b by the Roberts gradient. The Roberts gradient ﬁltering ensures that all
the pixels have a gradient value larger than a threshold and the other ﬁltering
provides a background level value. Therefore Fig. 2 has a threshold of 80 and a
background of 15 and shows the ‘hot ﬁeld’ of the component side of the
integrated circuit.
A common evaluation for analysis of the chip side (Fig. 1b) is presented in
Fig. 3. For comparison of ‘classical’ and ﬁltering evaluation of IR images, Fig. 3
presents some possibilities for chip side of the integrated circuit (Fig. 1b). Figure 3a
shows the temperature distribution along lines created horizontally and vertically,
similar to Fig. 2a. Figure 3b is the histographically processed result of Fig. 3a,
representing the more signiﬁcant characteristics of the histogram in the speciﬁed
area in Fig. 3b: the highest (maximum 65.4 C), the lowest (minimum 28.6 C) and
the middle (average 37.7 C) temperature, etc. Figure 3c is a three-dimensional
relief display of Figs. 3a and 4.
When the spots with the highest temperatures on both the chip and the compo-
nent sides are known, the faulty or improperly sized elements can be replaced. Thus
unexpected later failures due to the high local thermal load can be prevented.
7
Conclusions
IR image ﬁltering is a practical, quick and normalizable evaluation method with the
aim of analysing the temperature ﬁeld initially. The topic presented in this paper is
representative of techniques that are commonly used in practice for digital IR image
enhancement. However, this area of image processing is basically problem
oriented, and a dynamic ﬁeld in the literature, too. For this reason, the
methods included in this paper have to select both the topic and the goal of thermal
analysis.
554
I. Benk€
o
References
Benko
˝, I.: Applications of infrared thermogrammetry in thermal engineering. In: Proceeding of the
Conference on Quantitative Infrared Thermography (QIRT 92). Eurotherm Se
´ries 27, pp.
343–349. Edition Europe
´ennes Thermique et Industrie, Paris (1992)
Benk€
o, I.: Infra-red picture analysis in thermal engineering. In: Benko
˝, I. (ed.) Proceedings of the
Tenth International Conference on Thermal Engineering and Thermogrammetry, pp. 46–52.
MATE TE and TGM, Budapest (1997)
Fig. 4 Infrared thermogram (above) of a sample (Pace-04) and picture of coated slate roof
Mathematical Filtering Analysis of Infrared Images in Integrated-Circuit. . .
555
Benk€
o, I.: Applications of infrared ﬁltering in electronic technology. In: Wiecek, B. (ed.) Book of
Abstracts of Quantitative Infrared Thermography (QIRT 98), pp. 86–87. PKOptoSEP, Lodz
(1998)
Benk€
o, I., K€
oteles, G.J., Ne
´meth, G.: New infrared histographic investigation of the effect of beta-
irradiation in medical ﬁeld. In: Balageas, D., Busse, G., Carlomagno, C.M., Wiecek, B. (eds.)
Proceedings of the Conference on Medical Infrared Thermography (MIRT 98), pp. 40–45.
PKOptoSEP, Lodz (1998)
Gonzales, R.G., Woods, R.E.: Digital image processing. Addison-Wesley, Wokingham (1977)
556
I. Benk€
o
Performance Analysis of Ceramic Composite
Thermal Protection System Tiles
Arjunan Pradeep, Suryan Abhilash, and Kurian Sunish
1
Introduction
When a space craft enters a planetary atmosphere from space, the surface exposes to
high heat ﬂuxes generated by dissipation of kinetic energy due to aerobraking and
friction with atmospheric gases, which depends on many parameters including
entry velocity, entry angle, ballistic coefﬁcient, vehicle bluntness, enthalpy char-
acteristics, and density and temperature of atmospheric gases. The design of a
successful thermal protection system (TPS) is a signiﬁcant engineering challenge
as its failure will ultimately breakup the entire craft including its payload, structure,
and crew. Reusable TPSs are primarily developed for extended ﬂight durations with
much better insulating capacity than ablators and to maximize reradiation of the
incident aerothermodynamic heat to atmospheric environment (Shukla et al. 2006).
Reentry vehicles with sharp leading edges imply lower aerodynamic drag,
improved performance, safety, and maneuverability but results higher surface
temperature than blunt vehicles. As the leading edge radius decreases, the surface
temperature increases (Savino et al. 2010). Therefore, in order to achieve maximum
performance, materials are needed, which are both capable of withstanding the
reentry environment at temperatures greater than 2000 K and have a high thermal
conductivity that will direct more energy away from the tip of the leading edge
allowing for even further improvements in vehicle performance. It leads to the
demand of development of high-temperature materials which can ﬁnd applications
in hypersonic ﬂight vehicles with sharp leading edges. From the family of ceramic
materials known as ultrahigh-temperature ceramics (UHTCs), refractory metal
A. Pradeep (*) • S. Abhilash • K. Sunish
University of Kerala, Department of Mechanical Engineering, College of Engineering
Trivandrum, Thiruvananthapuram 695016, India
e-mail: pradeeparch@gmail.com
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_40
557
diborides with some additives such as SiC (e.g.: HfB2-SiC) and/or refractory metal
carbides (HfC), can be identiﬁed as most promising candidates as an effective
thermal protection system for nose cap and other sharp leading edges. These
materials are characterized by improved mechanical and thermal properties, excel-
lent chemical stability like good oxidation resistance, and high melting point
(>3000 K) (Savino et al. 2008).
Figure.1 shows how the surface temperature for the sharp UHTC leading edge is
determined by an energy balance of incident heat ﬂux, reradiated energy, and
energy pulled away from the leading edge tip and reradiated out the sides of the
component in which the incident heat ﬂux is lower (Kontinos et al. 2001).
When a steady state is achieved, global radiative equilibrium is established, in
the sense that the overall convective heat ﬂux is perfectly balanced by the overall
surface radiative ﬂux, and, if the material thermal conductivity is high, a relatively
low equilibrium temperature is achieved. Thus, the need for highly conducting yet
refractory materials is essential in the design of sharp vehicles (Gasch et al. 2005).
Nomenclature
1-D,2-D,3-D
One, two, three dimensional
TPS
Thermal protection system
UHTC
Ultrahigh temperature ceramics
SiC
Silicon carbide
HfB2
Hafnium diboride
ZrB2
Zirconium diboride
HfC
Hafnium carbide
ZrC
Zirconium carbide
HfN
Hafnium nitride
MoSi2
Molybdenum disilicide
Si3N4
Silicon nitride
(continued)
Fig. 1 Effect of conductivity for a blunt and sharp leading edge of a reentry vehicle
558
A. Pradeep et al.
q
Heat ﬂux
RCC
Reinforced carbon/carbon
MFI
Multifoil insulation
RSI
Reusable surface insulation
H
Speciﬁc total enthalpy
PWT
Plasma wind tunnel
SHS
Sharp hot structures
FEM
Finite element method
k
Conductivity
T
Temperature
Cp
Speciﬁc heat at constant pressure
Cv
Speciﬁc Heat at constant volume
Kn
Knudsen number
E
Extinction coefﬁcient
Greek letters
τ
Time in seconds
μ
Dynamic viscosity
γ
Ratio of speciﬁc heats
α
Accommodation coefﬁcient
σ
Stefan-Boltzmann constant
ρ
Density
Subscripts
rad
Radiative
cond
Conductive
conv
Convective
s
Solid
g
Gas
1.1
Problem Deﬁnition
The main objective of this work is to investigate the temperature distribution by
conduction, convection, and radiation through ceramic composite porous tile.
Currently, studies on thermal response of nonablating pure ceramic tiles are avail-
able as a TPS material applying on surface other than leading edges of wings and
nose cone of a space vehicle, and in this study, performance analysis of TPS Tile
made of silica-based ceramic composite is considered instead of a single-
component silica tile.
Performance Analysis of Ceramic Composite Thermal Protection System Tiles
559
2
Review of Literature
Comparison
of
measured
and
predicted
temperature
proﬁles
of
selected
multicomponent TPS to an aerodynamic arc-jet environment was studied at surface
heating rates high enough to include radiation heat transfer by Stewart and
Leiser (1985).
Stauffer et al. (1992) developed a model for evaluating the steady-state heat
transfer through multifoil insulation (MFI). The model enables the calculation of an
effective thermal conductivity as a function of temperature and pressure. The heat
transfer through MFI, a composite insulation system consisting of thin metallic foils
held apart by tiny ceramic particles, consists of three components: foil to foil
radiation heat transfer, gas conduction via interstitial gas molecules, and solid
conduction through ceramic spacer particles. The radiation heat transfer and gas-
eous conduction models are validated by comparison with test data reported in the
literature. Empirical correlations are developed to describe the nondimensional
contact conductance, and these show that contact conductance is a function of
conﬁguration and operating conditions. The model can provide important informa-
tion to designer of thermal protection system (TPS) of advanced hypersonic
vehicles.
Chiu (1992) developed a simulation model and illustrates the thermal response
of reusable surface insulation (RSI) tiles and blankets during aeropass. For some
cases, experimental measurements were available from arc-jet testing, and com-
parison between calculated and measured temperature is also carried out. Thermal
analyses of both tiles and blankets have been conducted in this study. Comparison
between the in-depth temperatures of an advanced tile calculated using transient
thermal conductivities and those measured under stagnation heating during arc-jet
test show good agreement, which indicates the accuracy of the transient thermal
conductivities. The analysis in this paper was carried out to predict the temperature
performance of a spectrally reﬂective coating on a tile, to access the temperature
errors in a tile due to thermo couple lead wires installations, to determine the extent
of thermal distortion in a blanket due to adjacent tiles, and to correlate the
temperature measurements of a thermocouple probe in a blanket.
The need of research and development of UHTC materials for low thrust rocket
propulsion and hypersonic spacecraft applications are emphasized by Upadhya
et al. (1997). They observes that by developing an ultrahigh-temperature material
with temperature capabilities in the range of 2200–3000 C, the fuel-ﬁlm and
regenerative cooling can be signiﬁcantly reduced and/or eliminated resulting in
cleaner burning of rocket engine. Thus, fuel utilization can be vastly improved,
more payloads can be sent to space, higher speciﬁc impulse can be achieved, and
ﬁnally, the cost of the rocket engine could be reduced. Mechanical, thermal, and
oxidation behavior of refractory metals and alloys, refractory carbides, refractory
borides, and carbon–carbon composites are summarized in the paper.
Opeka et al. (1999) reported mechanical, thermal, and oxidation properties of
HfB2, HfC, HfN, ZrB2, ZrC, and SiC ceramics. It was found that HfB2 had a much
560
A. Pradeep et al.
lower ductile-to-brittle transition temperature than HfN or HfC. The effect of
lowering the carbon stoichiometry was also to decrease the transition temperature.
The thermal conductivity of HfB2 was much greater than the carbides or nitride.
The coefﬁcient of thermal expansion of all materials tested was approximately the
same up to 1500 C, with HfN exhibiting a higher expansion than the others up to
2500 C. The HfB2 ceramics had the highest modulus of the materials tested,
whereas HfC had the lowest. The oxidation behavior of the ceramics was charac-
terized as a function of phase composition. The SiC-containing ZrB2 ceramics had
high oxidation resistance up to 1500 C compared with pure ZrB2 and ZrC
ceramics. The ZrB2/SiC ratio of about 2 (25 vol% SiC) is necessary for the best
oxidation protection. The presence of ZrC in ZrB2 ceramics negatively affects their
oxidation resistance. A hypothesis describing oxidation behavior of the ZrB2/ZrC/
SiC ceramics is proposed.
Daryabeigi (2002) investigated the use and optimization of high-temperature
insulation for metallic thermal protection systems. The multilayer insulation con-
sidered consists of ceramic foils with high-reﬂectance gold coatings. Here, the
author’s main aim was to model the combined radiation/conduction heat transfer
through multilayer insulations with a numerical model validated by experimental
lists and to use the numerical model to design optimum multilayer conﬁgurations.
The effective thermal conductivity of a multilayer insulation was measured over an
extended temperature of 373–1273 K and a pressure range of 1.33105 –
101.32 kPa. A numerical model was developed for modeling combined radiation/
conduction heat transfer in high-temperature multilayer insulations. The numerical
model was validated by comparison with steady-state effective thermal conductiv-
ity measurements and by transient thermal tests simulating reentry aerodynamic
heating conditions. A design of experimental approach was used to determine the
optimum design for multilayer insulations subjected to reentry aerodynamic
heating condition.
The need for materials development, ground testing, and sophisticated modeling
techniques for the development of new TPS material for future space missions are
emphasized by Laub and Venkatapathy (2003). As a base information, they
describe about two classes namely ablative and reusable TPS materials and a
brief history of ablative TPS so far. They also describes the lessons learned and
TPS challenges for future missions based on the Jupiter, Venus, Titan, Mars,
Neptune, and other sample return missions. The authors pointed out that TPS
innovations are required because above missions were done with materials devel-
oped over many decades ago. The authors emphasize on the establishment of a
cross-cutting TPS Technology program with elements focused on sustaining cur-
rent technologies and elements focused on enabling future higher speed return
missions.
Matthew et al. (2005) summarizes experimental results on HfB2- and ZrB2-based
compositions. They focused on identifying additives like SiC to improve mechan-
ical and thermal properties and to improve oxidation resistance. These are charac-
terized by high melting points, chemical inertness, and good oxidation resistance in
Performance Analysis of Ceramic Composite Thermal Protection System Tiles
561
extreme environments at temperatures greater than 2000 C as experienced during
reentry. They are providing variation of thermal properties with rise in temperature.
Shukla et al. (2006) studied thermal response of the nonablating ceramic tiles by
ﬁnite element method. A continuum model for the porous materials is used for the
determination of thermal conductivity. The temperature distribution for the
one-dimensional model is compared with the available arc-jet result. The 1-D and
2-D temperature contours and the heat ﬂux distributions for the silica tiles are also
presented. The expression for the pressure distribution in a silica tile is derived.
They used pure silica material and developed a methodology to analyze the
performance of ablative thermal protection system and proved that, by using
temperature limits provided from the materials used for the structure of the space-
craft, a TPS can be designed to prevent the structure from overheating. The models
used were validated with experimental arc-jet data. The assumptions used in the
computations were an adiabatic back wall, low surface catalysis, and no convective
cooling during soak out.
Upon reviewing this literature, it was realized that performance analysis of TPS
tile made of silica-based ceramic composite can also be considered by a similar
methodology done by them. The application of these tile materials can be found in
nose cap and wing tips of the reentry vehicle or locations where the vehicle
structure interacts with the heat ﬂux to cause the temperature to rise to its peak
values.
Scatteia et al. (2006) reported results of an experimental investigation into the
efﬁciency of sintered ZrB2-SiC compounds and of plasma-sprayed ZrB2-SiC coat-
ing for heat radiation and for the recombination of atomic oxygen. Experiments on
emissivity measurements of ZrB2-SiC ceramic composite at 200 Pa and 0.001 Pa
pressure conditions are performed. The results under vacuum conditions are lower
than the ones obtained at high pressure. High emissivity values and low recombi-
nation coefﬁcients were found in agreement with previous experimental studies
performed on similar ceramic compounds but at lower temperatures using a differ-
ent measurement technique. According to the surface analysis, the oxide scale is a
silica or borosilicate glassy layer. This represents a rather promising result, because
the radiative efﬁciency of silica-based glassy compounds is reportedly higher than
that of pristine UHTCs.
Loehman et al. (2006) reported results of thermal diffusivity, thermal conduc-
tivity, and speciﬁc heat measurements of ZrB2 and HfB2. Thermal diffusivities
were measured to 2000 C for ZrB2 and HfB2 ceramics with SiC contents from 2 to
20%. Thermal conductivities were calculated from thermal diffusivities and mea-
sured heat capacities. Thermal diffusivities were modeled using different two-phase
composite models. They prove that these materials exhibit excellent high-
temperature properties and are attractive for further development for thermal
protection systems.
Zhang et al. (2008) conducted ablation tests of the ﬂat-face models for ZrB2–
20vol%SiC ultrahigh-temperature ceramic (UHTC) which was prepared by hot
pressing. The tests conducted under ground simulated atmospheric reentry condi-
tions using arc-jet testing with heat ﬂuxes of 1.7 and 5.4 MW/m2 under subsonic
562
A. Pradeep et al.
conditions, respectively. For temperatures in the order of 1600–1700 C, the
material was able to endure the heating conditions; however, for temperatures in
the order of 2300 C, evident oxidation and ablation occurred, and the material was
unable to offer a valuable resistance to the applied aerothermal load. Results
indicate that ZrB2–SiC ultrahigh-temperature ceramics are the potential candidates
for leading edges. Results indicated that ZrB2–SiC can maintain the high-oxidation
resistance coupled with conﬁgurational stability at temperatures lower than that
point which results in signiﬁcant softening and degradation of the oxide scale, and
that point will be the temperature limit for UHTC.
Savino et al. (2008) investigated the behavior of pressure less sintered two
different ultrahigh-temperature ceramics, HfB2 + 5% MoSi2 and HfC +5%MoSi2,
which were exposed to an average speciﬁc total enthalpy of the ﬂow around the
body of the order of 5–10 MJ/kg and at atmospheric pressure typical of atmospheric
reentry environment, with an arc-jet facility at temperatures exceeding 2000 C.
The surface temperature and emissivity of the materials were evaluated during the
test. The microstructure modiﬁcations were analyzed after exposure. The
HfB2 + 5%MoSi2 model surface reached a peak value of 1950 C for speciﬁc
total enthalpy (H) approaching 8 MJ/kg. The cross section after exposure showed
the formation of a compact silica oxide (about 15 μm) which sealed the underlying
HfO2 scale. The HfC + 5% MoSi2 model surface reached peak values of 2100 and
2400 C. Cross-sectional analysis showed a layered structure, constituted of an
outer layer of porous HfO2 and an inner layer mainly constituted of HfO2 and silica.
Borrellia et al. (2009) tested a nose cap demonstrator in the plasma wind tunnel
(PWT) facility to focus on the assessment of the applicability of UHTCs to the
fabrication of high performance and sharp hot structures (SHS) for reusable launch
vehicles. In this paper, the FEM-based thermo-structural analyses are presented.
Comparisons with experimental data measured in the PWT have been introduced to
validate the FEM model and to help in interpreting the experimental test itself.
Synergies between numerical and experimental activities have been ﬁnalized to the
improvement of knowledge on the physical phenomenon under investigation. The
effects on the thermal response due to the assumption of the catalytic condition of
the wall, due to the uncertainties related to heat ﬂux and pressure measurements on
the probe (which inﬂuence the heat ﬂux computation), and due to uncertainties in
the determination of some UHTC thermal properties, have been investigated.
Discrepancies between the numerical results and experimental ones in terms of
wall temperature distribution on the massive UHTC nose tip were found, and
possible sources of error have been analyzed. The experimental temperatures
curves fall very close to the numerical envelope (taking in account several sources
of error) for all the test duration and the noncatalytic wall model was found more
reliable in reproducing thermal behavior of the nose cap.
Savino et al. (2010) deal with arc-jet experiments on different UHTC models
which have been carried out in two different facilities, to analyze the aerothermal
environment and to characterize the material behavior in high enthalpy hypersonic
nonequilibrium ﬂow. Typical geometries of interest for nose tip or wing leading
edges of hypersonic vehicles, as rounded wedge, hemisphere, and cone are
Performance Analysis of Ceramic Composite Thermal Protection System Tiles
563
considered. The ZrB2-based UHTC material sample tested for several minutes to
temperatures up to 2050 K showing a good oxidation resistance in extreme condi-
tions. The ﬂow conditions and the sharpness of the models are similar in both
facilities, but the larger model (rounded wedge) is characterized by a heat ﬂux
distributions (peaking at the leading edge and strongly decreases downstream)
resulting in a lower average surface heat ﬂux and therefore (also due to the
relatively high thermal conductivity) in a smaller equilibrium temperature in
comparison with the smaller specimens. Numerical-experimental correlations
show a good agreement with proper modeling of the surface catalytic behavior.
As expected, the higher temperature achieved in the small-sized specimens, sub-
mitted to hypersonic arc-jet conditions than in the rounded wedge tests and the
lower pressure in comparison with the subsonic arc-jet tests increase the oxidation
phenomena. The change in surface composition can justify the lower value
(0.6–0.65) of the surface emissivity estimated in their work in comparison with
the subsonic experiments (0.9) where poor oxidation phenomena were observed.
Levinskas et al. (2011) report the experimental investigations of the new com-
posite material based on light silicate frame impregnated by polymer composite
tested in high-temperature air jet and generated by means of plasma torch (temper-
ature – (1320–2420) C, velocity – (40–50) m/s). Data of composite material
ablation rate and temperature of protective sample set surface during experiment
are presented. Recent advances in polymer-layered silicate nanocomposites, espe-
cially with the improved thermal stability, ﬂame retardancy, and enhanced barrier
properties promote the investigation of these materials as potential ablatives.
Introduction of the layered nanosilicates (montmorillonite, tobermorite) into poly-
mer matrix results in the increase of thermal stability of polymer nanocomposites
and ablation resistance, which are not observed in each component. Experimental
investigations of the ablation resistance of the set with protective shell were
provided in two different plasma ﬂows–air plasma jet (for ﬁrst set of samples in
which ablation resistance is found remarkably high in air gas ﬂow environment) and
combustion gases plasma jet with reduced oxygen content (for second set of
samples in which existence of the reinforcement coating remarkably decreased
the ablation rate initially). The light silicate shell has demonstrated good resistance
to the impact of high-temperature gas ﬂow initiated by plasma jet. The additional
impregnation of light silicate shell with epoxy nanocomposite reinforcement coat-
ing increased the temperature on the shell surface due to exothermic reactions but
decreased the ablation rate accordingly. The experiments in reduced oxygen ﬂow
have shown good thermal stability of the protective shell. The structure imparts
high thermal shock resistance and dimensional stability.
Justin and Jankowiak (2011) present ZrB2-SiC and some other composites
developed for leading edges or air intakes of future hypersonic civilian aircrafts
ﬂying up to Mach 6. Addition of 20 vol% of SiC is found optimal for good oxidation
resistance. They observe that these composites possess high hardness, high ﬂexural
stress, good machinability, high emissivity, good thermal conductivity, and thermal
shock resistance.
564
A. Pradeep et al.
Mallik et al. (2012) investigated thermal properties of ZrB2–SiC, HfB2–SiC,
ZrB2–SiC–Si3N4, and ZrB2–ZrC–SiC–Si3N4 composites within temperature range
between 25 and 1300 C. Thermal conductivity increases with addition of SiC,
while it decreases on ZrC addition. Variations of thermal conductivity, speciﬁc
heat, and thermal diffusivity with temperature are plotted. Gregory et al. (2012)
reviewed results on thermal conductivity of HfB2 and ZrB2. Pure HfB2, pure ZrB2,
and composites of HfB2 and ZrB2 with various vol% of SiC are reviewed in detail.
Can and Yue (2013) are developing a numerical model combining radiation and
conduction for porous materials based on the ﬁnite volume method. The model can
be used to investigate high-temperature thermal insulations that are widely used in
metallic thermal protection systems on reusable launch vehicles and high-
temperature fuel cells. The effective thermal conductivities which are measured
experimentally can hardly be used separately to analyze the heat transfer behaviors
of conduction and radiation for high-temperature insulation. By ﬁtting the effective
thermal conductivities with experimental data, the equivalent radiation transmit-
tance, absorptivity and reﬂectivity, as well as a linear function to describe the
relationship between temperature and conductivity can be estimated by an inverse
problems method.
Yang et al. (2013) compared and investigated the effect of high-temperature
oxidation on mechanical properties and anti-ablation property of ZrB2/SiC as a
protective coating that was obtained on the surface of C/SiC composites. The
following are the observations done by the authors: C/SiC composites are good
thermal shielding for aerospace applications, provided that they are protected from
oxidation by suitable coatings. UHTC, and in particular ZrB2, is among the best
oxidation resistant materials as known. Mechanical tests were conducted before and
after oxidation test. Anti-ablation property was tested under oxy-acetylene torch.
Compared with the uncoated composites, the linear and mass ablation rates of the
coated composites decreased by 62.1% and 46.1%, respectively, after ablation for
30s. The formation of zirconia and silicon dioxide from the oxidation of ZrB2/SiC
improved the ablation resistance of the composites because of the evaporation at
elevated temperature, which absorbed heat from the ﬂame and reduced the erosive
attack to carbon ﬁbers and SiC matrix. They tried to prove that ZrB2/SiC coating for
C/SiC composites could fully fulﬁll the advantages of refractory compounds.
3
Heat Transfer Through Porous Ceramic/Ceramic
Composite Tile
3.1
Heat Transfer by Gas Conduction in Pores
Conduction is one of the main modes of heat transfer in tiles. The heat is transmitted
along the solid skeleton of the tile and through the gas ﬁlling the space in the
insulation. With increasing porosity of the insulator, the second way becomes
Performance Analysis of Ceramic Composite Thermal Protection System Tiles
565
dominant. Accordingly, the thermal conductivity of high-quality insulators comes
close to that of the contained gas, which is usually considered as air/inert gas.
According to the fundamental equation of the theory of heat conduction, the steady-
state (stationary) thermal ﬂuxes across an isothermal surface in a body are
q ¼ k∇T
ð1Þ
In general form, the transient equation of heat conduction is
ρ Cp
∂T
∂τ ¼ k ∇2T
ð2Þ
The kinetic theory of gas usually consider two extreme cases of heat transfer by
gas conduction: L < <δ and L> > δ where L is the mean free path of the gas
molecules and δ is the distance between the heat exchanging surfaces (/character-
istic length). The atmospheric pressure under reentry condition at high altitude is
taken as 0.01 atm. The value of L at this pressure and altitude is very high as
compared with δ (In this case, δ is. pore diameter, which is in the range of
micrometers). So, Kn> > 1. According to the kinetic theory of gas, when Knudsen
number, Kn < <1, the conductivity is independent of the gas pressure. If the
pressure is sufﬁciently low and Kn> > 1, the gas molecules bounce from wall to
wall without colliding with each other. The amount of heat transferred is then
proportional to the number of molecules participating in the transfer and thus to the
gas pressure. When the distance between the walls is larger, the path of the
molecule becomes longer, but their number per unit surface also increases. As a
result, the rate of heat transfer is independent of the separation of the walls. For a
monatomic gas, the thermal conductivity of gas can be expressed as
kg ¼
k0
1 þ 2ac  Kn
ð
Þ
ð3Þ
where k0 ¼ thermal conductivity of the gas at atmosphere pressure and can be
obtained by k0 ¼ e μ CV where e ¼ (9γ  5)/4
ac ¼
2e
1 þ γ

 2  α
α


α ¼ accommodation coefﬁcient that allows for incomplete energy exchange
between the gas molecules and surface and is deﬁned by
α ¼ T0
2  T1
T2  T1
566
A. Pradeep et al.
where T1 is the temperature corresponding to the energy of the molecule colliding
with the wall, the temperature of which is T2, and T02 is the temperature
corresponding to the energy of the reﬂected molecules.
3.2
Transient Heat Transfer by Radiation
The mode of heat transfer across TPS materials is by solid conduction, gaseous
conduction through gases trapped in the pores, free and forced convection, as well
as radiation in pores. The convection is generally neglected and will not be
considered here. However, it should be noted that especially when considering
the case of an applied rigid ﬁbrous refractory insulation for a space vehicle during
reentry, forced convection may affect the overall heat transfer. The mechanism of
conduction by residual gas and solid conduction, if not similar physically, is similar
mathematically in the sense that the heat ﬂux is proportional to the thermal
conductivity and local heat gradient. Radiation, on the other hand, is a complex
process and has to be treated separately. The local inhomogenities in the material
affect the transmission of radiant energy. For example, radiation leaving a surface,
on passing through the material may (i) pass through voids in the ﬁbrous material,
(ii) be absorbed by the residual gas and subsequently reemitted, (iii) be absorbed by
the particle and subsequently reemitted, (iv) be scattered by particles, and (v) be
scattered by ﬁbers. The TPS material will be considered as homogeneous and
continuous. These assumptions may be justiﬁed if the gas voids and the particles
of the insulation are essentially in the equilibrium with the surrounding gas, and if
the particle spacing is sufﬁciently small, so that the temperature difference between
the adjacent solid particles is small compared with the total temperature. For a
radiative ﬂux,
qrad ¼ 16
3E σ n2 T3
rad
∂T
∂x ¼ krad
∂T
∂x
ð4Þ
where E is extinction coefﬁcient, n is refractive index (taken as 1), and σ is Stefan-
Boltzmann constant.
T3
rad ¼ T2 þ T2
a


T þ Ta
ð
Þ
4
where T is temperature of the body, and Ta is temperature of the atmosphere.
The energy ﬂux vector by combined radiation and conduction at any position in
the medium can be expressed as
Performance Analysis of Ceramic Composite Thermal Protection System Tiles
567
q ¼ qrad þ qg ¼  krad þ kg

 ∂T
∂x
ð5Þ
This can be used as heat conduction equation, to obtain an energy balance on a
differential volume element within an absorbing–emitting medium. The medium
behaves like a conductor with thermal conductivity dependent on temperature.
3.3
Effective Thermal Conductivity
Effective thermal conductivity of the bulk material is as follows:
k ¼ ηA1 θ
ð Þks þ 1  θ
ð
Þkg þ βA2 θ
ð Þkrad
ð6Þ
where η ¼ 1.93θ (η is called bonding efﬁciency factor), θ ¼ volume faction,
β ¼  8.571(1.0  θ) + 0.84 for porosity >0.84, and β ¼ 1.2 for porosity <0.84 (β
is called density scale factor on the change in emittance of composite insulation).
The two adjustment parameters η and β which relate to solid conduction and
radiation heat transfer are found to be independent of composition and ﬁber
structure and to depend only on the solid volume fraction and porosity.
One-dimensional transient energy equation without internal heat source can be
now expressed as
ρ Cp
∂T
∂τ ¼ k ∂2T
∂x2
ð7Þ
4
Modeling Details
The tile gets heat ﬂux due to air friction. It emits a part of this heat as reradiated
energy. Remaining part is conducting through porous material. Effective conduc-
tivity value, k is applied in governing equation. The thermal analysis is done across
the cross section of tile, and temperature proﬁle is observed on selected nodes in the
line through the center of tile. Upper and bottom surfaces of the tile are considered
as insulated to examine the maximum heat penetration across the tile. A
one-dimensional thermal analysis is sufﬁcient for design purpose to determine the
transient temperature response near the center of the tiles for stagnation heating.
Aerothermal heat ﬂux of 400,000 W/m2 is applied at the surface of the tile (x ¼ a).
Heat is reradiated from the surface (at x ¼ a) to deep space as well as conducted
through TPS component. The emittance and speciﬁc heat are functions of temper-
ature and pressure. The normal density, thermal conductivity, heat capacity, and
emittance of various RSI are listed by Chiu and Pitts (1991) and applied for analysis
of pure ceramic TPS tile in phase I. A numerical model built by COMSOL
568
A. Pradeep et al.
Multiphysics is used in the present study. Added physics is heat transfer in porous
media with time-dependent study. Materials listed in COMSOL material library
and material properties collected from the literature reviews are applied for analysis
of composite ceramic tile in phase II. The details of geometry, governing equation,
and boundary condition are described below.
4.1
Geometry and Meshing
The geometry of the present computational model is based on Shukla et al. (2006).
The silica tile is considered as one-dimensional rod element having length of its
cross section and is divided into 20 nodes of equal size. Figure 2 shows the
geometry marked with ﬁrst seven nodes from which variation of temperature with
respect to time is observed during analysis.
The mesh sequence type is selected as physics-controlled mesh having
extremely ﬁne element size.
4.2
Governing Equation
The main objective of this work is to investigate the temperature distribution
through ceramic composite porous tile. As phase I, the analysis with numerical
model built by COMSOL Multiphysics is validated by predicting the thermal
response of the reusable surface insulations made of porous pure ceramic material
and compared with the results of Shukla et al. (2006). In the phase II, tile with
material selected from UHTCs are analyzed. For both cases, the governing equation
can be written as
ρcp
∂T
∂t ¼ ∇k∇T
ð
Þ
ð8Þ
Fig. 2 1-Dimensional Analysis rod element of blunt/sharp leading edge of a reentry vehicle
Performance Analysis of Ceramic Composite Thermal Protection System Tiles
569
4.3
Initial and Boundary Conditions
To obtain the temperature distribution in the medium, Eq. (8) will be solved
subjected to initial and boundary conditions. The boundary conditions would
often be speciﬁed temperatures of the enclosure surfaces. However, near a bound-
ary, the diffusion approximation is not valid; consequently, the solution is incorrect
near the wall and cannot be matched directly to the boundary conditions. To
overcome this difﬁculty, the boundary conditions at the edge of the absorbing–
emitting medium are modiﬁed, so the resulting solution to the diffusion equation
with this effective boundary condition will be correct in the region away from the
boundaries where the diffusion approximation is valid. In the pure radiation case, a
temperature slip was introduced to overcome difﬁculty of matching diffusion
solution in the medium to the wall temperature. For combined conduction–radia-
tion, a similar concept was introduced by Howell and Seigel (1981). With the
diffusion approximation, results for combined radiation and conduction can be
obtained for both energy transfer and temperature proﬁle.
Governing Eq. (8) is subjected to following initial and boundary conditions:
T x; 0
ð
Þ ¼ T0
ð9Þ
k ∂T
∂x


x¼a
¼ q  εσT4
ð10Þ
k ∂T
∂x


x¼b
¼ 0
ð11Þ
5
Identiﬁcation of Ceramic Composites
The temperature at the tip of the leading edge is inversely proportional to the square
root of the leading edge nose radius, and the reduced curvature radius results in
higher surface temperature than that of the actual blunt vehicles that could not be
withstood by the conventional thermal protection system materials (Savino et al.
2010). As per the data collected from review of literature, the family of ceramic
matrix composites named as UHTCs are identiﬁed as promising candidates of for
such structure materials, because they posses high melting point, dimensional
stability, high hardness, good chemical inertness, and oxidation resistance at ele-
vated temperatures. However, the good thermal conductivity and high melting point
values of UHTCs bound its application in sharp nose cones and leading edges of
space vehicles than in other structures of vehicle. Other TPS materials having low
thermal conductivity like pure ceramic are having comparatively low melting point
and very high value of heat ﬂux developing due to aerobraking of vehicle with sharp
nose cone can melt them. As an RSI system, such materials are not preferable for
sharp nose cone and leading edges.
570
A. Pradeep et al.
The incident convective heat ﬂux is balanced by reradiated energy and energy
conducted away from the leading edge tip to other surface of tile near to another
layer of low thermal conductivity TPS material/substructure of the vehicle. When a
steady state is achieved, global radiative equilibrium is established, and because the
material thermal conductivity is high, a relatively low equilibrium temperature is
achieved. Thus, the need for highly conducting refractory materials can be met with
UHTCs in the design of sharp vehicles (Gasch et al. 2005).
Paul et al. (2012) report that Carbides and borides of transition metal elements
such as Hf and Zr are widely studied due to their desirable combinations of
mechanical and physical properties, including high melting points (>3000 C),
high thermal and electrical conductivities, and chemical inertness against molten
metals. Even though carbides have higher melting points than borides, the latter
have much higher thermal conductivities and thus good thermal shock resistance
making HfB2 and ZrB2 more attractive for ultrahigh-temperature applications.
The following observations contribute to the selection of HfB2-20vol%SiC and
ZrB2-20vol%SiC as the tile materials. Reviews by Upadhya et al. (1997) explore
that addition of SiC can improve oxidation resistance of both HfB2 and ZrB2. They
pointed out the experimental results in the temperature range of 1300–1500 C with
SiC addition to ZrB2. Inner layer of Zirconium Oxide and outermost rich glassy
layer of Silicon oxide is forming. The formation of this glass provides oxidation
resistance at high temperature due to good wettability and good surface coverage.
As reported by Scatteia et al. (2006), these layers of oxides which partially cover the
pores are characterized by higher emissivity, and that causes an increase in emis-
sivity as the temperature rises. This behavior will yield to increase in reradiated ﬂux
and hence attains steady state at much lower equilibrium temperature.
Addition of 20 vol% SiC to either ZrB2 or HfB2 matrix composites is an
optimum composition which leads to signiﬁcant increase in their thermal conduc-
tivities as well as effective densiﬁcation during its production by sintering
(Loehman et al. 2006).
Hereafter, HfB2-20vol%SiC and ZrB2-20vol%SiC are given sample names
HB20S and ZB20S.
6
Results and Discussions
6.1
Heat Transfer Analysis on Pure Ceramic Tile
The primary objective of this phase-I analysis is to predict the temperature distri-
bution through TPS tile of pure ceramic porous material and to compare with the
results of Shukla et al. (2006) as part of the validation of analysis with numerical
model built by COMSOL Multiphysics. The material properties used are listed in
Table 1.
Performance Analysis of Ceramic Composite Thermal Protection System Tiles
571
The pressure considered here is 0.01 atm. The heat ﬂux value of 400,000 W/m2
is taken as a time-dependent value, which means after 400 s the heat ﬂux is assumed
as zero.
The thermal conductivity plays a major role in determining accuracy of calcu-
lated temperature response. The silica tile is considered as one-dimensional rod
element and is divided into 20 nodes. Figure 3 shows the in-depth temperature
response of ﬁrst 7 nodes of the TPS tile. The rod will absorb a part of the heat ﬂux
which is conducted through it, and the remainder is reﬂected as reradiated heat
energy to the outer space. The rod attains a maximum temperature of 1720 K in the
outer surface. The values of the thermal conductivity, speciﬁc heat at constant
pressure, and emissivity were used from listed tables/graphs showing variation of
Table 1 Properties of pure
silica TPS tile
Property
Value
Material
Silica
Density, ρ (kg/m3)
352
Volume Fraction, θ
0.8
Pore Diameter, δ (μm)
0.8
Extinction Coefﬁcient, E (m-1)
14900
Fig. 3 1-D transient temperature distribution in silica tile
572
A. Pradeep et al.
respective property with temperature as given by Chiu (1992), and Touloukian and
Buyco (1970).
These inputs were used in COMSOL Multiphysics model builder. This analysis
helps to ﬁnd how a tile responds to a high heat load, from one end to another end.
Due to the low value of effective thermal conductivity, each node shows a ﬁnite
amount of decrease in temperature as proceeding from outer surface to inner
surface. The nodes beyond 7th node is showing temperature readings converging
to a constant safe value, which is an important observation for designers to ﬁx the
thickness required for the ceramic tile as a TPS.
Figure 4 compares the present temperature proﬁle of nodes 1 and 2 with the
temperature proﬁles of same nodes reported by Shukla et al. (2006).
The present software gives same values for maximum temperature attained by
these nodes, but current data show the extreme temperature values up to 400 s, and
thereafter, the heat ﬂux is withdrawn suddenly. As per reference data, a gradual
withdrawal of heat ﬂux initiated just before reaching 400 s. Otherwise, the two
graphs hardly show any difference in the temperature distribution. Based on these
satisfactory observations, the phase II of the analysis is done with ceramic com-
posite tiles.
Fig. 4 Comparison of temperature distribution
Performance Analysis of Ceramic Composite Thermal Protection System Tiles
573
6.2
Heat Transfer Analysis on Ceramic Composite Tile
The primary objective of this phase-II analysis is to predict the temperature
distribution through TPS tile of composite ceramic porous material and to identify
its ability as TPS. It is reported by Parthasarathy et al. (2012) that UHTC-based
leading-edge samples proved to withstand the simulated hypersonic conditions up
to Mach 7. At this speed, heat ﬂux in the range of 2 MW/m2 can be evolved. So, by
following the same procedure of phase-I analysis, this heat ﬂux value is applied,
and after 400 sec, the heat ﬂux is assumed as zero. However, during reentry
conditions, the heat ﬂux will be vanished before 400 s due to the increased ﬂight
velocity and lesser time to descent to an altitude of “cooler” conditions. The
pressure applied is 0.01 atm. Volume fraction of 0.8 is applied for selected
HB20S and ZB20S.
The material used for HB20S is chosen from COMSOL Multiphysics material
library, and HfB2-20SiC [solid, 99% dense] is selected. Input parameters such as
thermal conductivity, emissivity, speciﬁc heat, and density are obtained from
material library. The density observed from COMSOL material library is ranging
from 9338 to 8962 kg/m3 with respect to temperature ranging from 300 to 2500 K.
Figure 5 shows 1-D transient temperature distribution of all node points in HB20S
tile. The rod attains a maximum temperature of 2408 K in the outer surface. All
Fig. 5 1-D Transient Temperature distribution in HB20S tile
574
A. Pradeep et al.
node points reaches a ﬁnal temperature of 1816 K. Due to high conductivity, the
equilibrium temperature is reached at a faster rate.
Figure 6 shows 2-D transient temperature distribution of all node points in
HB20S tile. A maximum temperature of 2452 K is observed, which reaches to
1862 K after withdrawing heat ﬂux. All input parameters are kept same as that
applied in 1-D analysis. The temperature distribution shows 2–3% increase in
temperature than 1-D analysis, and in a broad sense, both temperature distribution
curves can be considered as same.
The material used for ZB20S is chosen from COMSOL Multiphysics material
library, and ZrB2-20SiC (solid, 99% dense) is selected. Input parameters such as
thermal conductivity, emissivity, speciﬁc heat at constant pressure, and density are
obtained from COMSOL Multiphysics material library. Figure 7 shows 1-D tran-
sient temperature distribution of all node points in ZB20S tile.
The rod attains a maximum temperature of 2446 K in the outer surface. All node
points reach a ﬁnal temperature of 1886 K. The temperature distribution shows 2%
increase in maximum value of temperature in outer surface than maximum value of
temperature obtained in 1-D analysis of HB20S sample whereas an increase of 4%
is observed for steady-state equilibrium temperature of both cases.
The density observed from COMSOL material library is 5560 kg/m3 at 300 K,
which is applied.
Fig. 6 2-D Transient Temperature distribution in HB20S tile
Performance Analysis of Ceramic Composite Thermal Protection System Tiles
575
Figure 8 shows 2-D transient temperature distribution of all node points in
ZB20S tile. A maximum temperature of 2501 K is observed, which reaches to
1936 K after withdrawing heat ﬂux. The temperature distribution of ZB20S sample
shows 2–3% increase in temperature than 1-D analysis of the same.
When comparing the temperature distributions of both HB20S and ZB20S
samples during 2-D analysis, the rise in maximum temperature of outside surface
is 2%, and the rise in steady-state equilibrium temperature is 4%.
Figure 9 shows 3-D transient temperature distribution of all node points in
ZB20S tile. A maximum temperature of 2495 K is observed, which reaches to
1944 K after withdrawing heat ﬂux. The temperature distribution shows 2–3%
increase in temperature than 1-D analysis of ZB20S sample, whereas the percentage
difference with temperature of 2-D analysis is negligible.
7
Conclusions
7.1
Summary
The transient temperature distribution of HB20S and ZB20S samples are almost
same. The density of HB20S sample is 68% greater than that of ZB20S. In other
Fig. 7 1-D Transient Temperature distribution in ZB20S tile
576
A. Pradeep et al.
Fig. 8 2-D Transient Temperature distribution in ZB20S tile
Fig. 9 3-D Transient Temperature distribution in ZB20S tile
Performance Analysis of Ceramic Composite Thermal Protection System Tiles
577
words, weight of ZB20S TPS tile having same dimensions of HB20S TPS tile is
40% less than weight of HB20S tile. This is an important contribution to gain in
weight of the vehicle, if TPS of ZB20S samples are selected. The melting points of
both samples are above 3000 K with a difference less than 4%. The evaluation of
these properties and along with other fantastic observations in terms of chemical
and mechanical properties available from the literature survey, even under limited
research facilities, shows that ceramics based on ZrB2, especially ZrB2-20vol%SiC,
offer promise for use as structural materials in extreme environments.
The HB20S and ZB20S sample materials can ﬁnd potential applications in
microelectronics, molten metal containments, nuclear reactors, high-temperature
electrodes, wear-resistant surfaces, heat shield structures under extreme environ-
ments, etc. For aerospace vehicles, the leading edges under elevated temperatures
can ﬁnd its proper candidate from this sample. In rocket propulsion system and
reentry vehicles, extreme heat ﬂux is experienced in sharp structures like leading
edges, nose cones, and nozzles. The proposed type of materials can improve the
ability of these types of vehicle structures in terms of mechanical, thermal, and
chemical perspectives. Sharp leading edges would imply lower aerodynamic drag,
improved ﬂight performances and crew safety, due to the larger cross range and
maneuverability along with more gentle reentry trajectories.
7.2
Scope for Future Work
In this paper, heat penetration across the cross section of a pure ceramic and two
samples of ceramic composites are evaluated. If the incident heat ﬂux value is again
increased beyond Mach 7, phase changes will occur due to melting, which require
another detailed study. Different combinations of UHTCs with low-conductivity
TPS materials like pure ceramic tiles can be examined. It can be in the form of a
UHTC-coated or UHTC–ceramic multilayer TPS tile keeping an eye on the beneﬁt
of weight reduction when the secondary material is having low density. 2-D or 3-D
thermal analysis of a UHTC nose cone model with maximum heat ﬂux at nose cone
tip, which is gradually decreasing in the downstream surface of cone, can be done.
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Developing High-Resolution Remote Sensing
Technology into an Advanced Knowledge
Management System to Assess Small-Scale
Hydropower Potential in Kazakhstan
Kabiyeva Marzhan, Kaskina Dina, and Bradshaw Roland
1
Introduction
For the last several decades, remote sensing technology and image processing have
made a signiﬁcant inﬂuence on the ﬁeld of environmental protection and water
management. This project aims to advance the application of remote sensing in the
context of managing water resource by integrating high-resolution remote sensing
technology into a knowledge management system for the management of water
resources and the infrastructure to control them. Remote sensing technology is used
to complement the development of a knowledge management system by advancing
the remote sensing capability and image processing to near real time. This capa-
bility can be used to monitor and assess a river basin to develop ﬂood extent and
predictions but also to assess hydrological capacities for hydropower generation.
The application of high-resolution remote sensing technology and advanced
image processing using advanced algorithms and statistical pattern recognition
will considerably advance the ability to assess water resources. Remote sensing
technology is used to complement the development of a knowledge management
system by advancing the remote sensing capability and image processing to near
real time.
In this research, we address how the integration of remote monitoring technol-
ogy into a knowledge management framework provides water resource manage-
ment organizations with a model for continuous improvement in making credible
and defensible decisions for managing water resources and their associated
infrastructure.
K. Marzhan • K. Dina • B. Roland (*)
Nazarbayev University, School of Engineering, Civil Engineering Department, 53 Kabanbay
Batyr Av, Astana 010000, Republic of Kazakhstan
e-mail: roland.bradshaw@nu.edu.kz
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_41
581
The project corresponds to the Supreme Science and Technology Commission
Protocol (№20–55/372) to advance the science in the ﬁeld of energy research, deep
processing of raw materials and products, information and communication tech-
nology, life sciences, and the continued development of intellectual potential of the
country by aligning water resource technology and management issues to these
priority areas.
2
Background
Based on its historic legacy from economic policies in the Soviet Union, Kazakh-
stan faces a real environmental threat from intensively polluted water bodies arising
from its mining, metallurgical, and chemical industries as well as untreated waste-
water discharges to the environment from urban and rural settlements (Nazarbayev
2013).
Over the last two decades, economic development policies marginalized many
of the existing problems in the water sector of which one of the main areas of
concern is the natural imbalance between available water resources and a growing
water demand for consumption. This water scarcity in many of Kazakhstan’s
catchments has a negative impact on river water qualities. With the amount of
river water in steadily decline, increases in the level of accumulated contaminants
in the ﬂood plains of rivers become increasingly evident. Thus, it is negatively
affecting the quality of agricultural products and the health of the human population
(Z
auı
`rbek 2013).
Another major issue in Kazakhstan is that only half of the water set aside for
irrigation reaches the ﬁelds due to unrecoverable losses of water occurring within
the irrigation networks and ﬁelds. In the period 1990–2012, this has led to an
alarming reduction in irrigated land from 2.5 million hectares to 2.1 million
hectares, of which no more than 1.4 million hectares is used today. This is estimated
to cost the country 700 billion tenge to compensate for the shortfalls in agricultural
production (Nazarbayev 2013).
Another concern is the increase in water salinity. Continuous monitoring studies
have shown that currently more than 50% of irrigated lands have increased levels of
salinity, while—at the same time—large amounts of land drainage pollute natural
water resources. As a result, the productivity of agricultural land decreases, and
their products do not meet environmental requirements (Z
auı
`rbek 2013).
The acute water resource deﬁcit for the Ishim, Nura-Sarysu, and Tobol-Torgai
river basins in Kazakhstan is considered an obstacle to the development of the
mining industry in these locations, which constitutes a signiﬁcant opportunity cost
for the country. Besides coal, copper, and iron ore, the region also possesses large
reserves of manganese and lead-zinc, ore, tungsten, molybdenum, bauxite, and
asbestos raw materials for the development of the chemical industry and others.
With 90% of the drainage for the Ishim, Nura-Sarysu, and Tobol-Torgai rivers
occurring during 1–2 months in the spring and the potential to continue developing
582
K. Marzhan et al.
the raw material sector, a water resource strategy is required to retain and possibly
divert water resources from neighboring river basins as a solution to the local deﬁcit
(UNDP 2004). Beyond its vast amount of oil, gas, and coal, the Republic of
Kazakhstan has a hydraulic potential of about 170 billion kWh per year, with the
technological potential being 62 billion kWh per year. Despite this enormous
potential, only 27 billion kWh is being generated with hydroelectric power systems.
The economic potential of small hydropower generating units alone is estimated at
7.5–11.0 terawatt-hours per year, of which only 5% is produced (UNDP 2004).
These requirements demand a thorough restructuring of the existing system of
natural water resource allocation and use as suggested by Z
auı
`rbek (2013) which is
largely in line with the literature on Integrated Water Resource Management
(IWRM) (Al Radif 1999; Biswas 2004; Jewitt 2002; Jonch-Clausen and Fugl
2001; Mitchell 2005; Thomas, Durham 2003; van der Brugge and Rotmans
2007). The major list of suggestions includes (1) developing and implementing
international principles of water allocation and management in trans-boundary river
basins, (2) developing and implementing mechanisms for the rational and efﬁcient
use of water resources, (3) developing and implementing the water-saving technol-
ogies that will transform industries into nonconsuming or low water-consuming
industries, (4) developing and implementing the water pollution control technolo-
gies that will transform industries into nonpolluting or low-pollution industries,
(5) developing and implementing action plans to improve water quality in the water
bodies, (6) developing water rates that promote water-saving and water pollution
control objectives, and (7) establishing a network monitoring service for accounting
and allocation of water resources.
With respect to the latter, the current draft state program on water management
of the Republic of Kazakhstan for 2014 to 2040 anticipates a signiﬁcant investment
in hydrological observation stations which—upon completion—will monitor and
control 75% of the irrigation and drainage systems using modern gauging stations,
water metering instruments, and automated process control system and will bring
the number of state gauging stations to 500 in total by 2020. The coverage of
hydrological observation network primarily concentrates on medium to large sys-
tems as well as small systems with signiﬁcant impact on the economy (Nazarbayev
2013). In addition to ﬁeld observation station, remote sensing technology is
increasingly used to monitor and assess water resources.
3
Literature Review
3.1
Remote Sensing Technology
Increasingly, water resource management studies are conducted about the use of
remote sensing techniques to understand the extent of water surfaces and their
volumetric characteristics. This study will investigate how remote sensing
Developing High-Resolution Remote Sensing Technology into an Advanced. . .
583
technology can be applied and integrated in the management of water resources and
the infrastructure that control them.
Real-time remote monitoring (RTRM) systems combine a wide range of data
modalities and technologies to enable high-speed visualization and analytics. In the
water quality assessments, the monitoring platform usually consists of data
processing device, automatic water sampler, hydrological proﬁler, rainfall sensor,
enclosures for experimental equipment, humidity and temperature sensors, sunlight
intensity sensor, lighting protector, wind speed sensor, and energy source such as
solar. CAAE RTRM is an example of such a platform that can be made use in
automatic data processing and alert response system making extensive use of
remote monitoring techniques. There exist several RTRM programs; they are:
1. Baltic Sea Portal (http://www2.ﬁmr.ﬁ/en/itamerikanta.html)
2. Carolinas Coastal Ocean Observing and Prediction System (Caro-COOPS)
(http://carocoops.org/carocoops_website/index.php)
3. Chesapeake Bay Program (http://www.chesapeakebay.net/overview.htm)
4. Chesapeake
Bay
“Eyes
on
the
Bay”
(http://mddnr.chesapeakebay.net/
eyesonthebay/index.cfm)
5. MYSound monitoring-real-time observations, University of Connecticut
(http://www.mysound.uconn.edu/)
6. Upper Susquehanna/Lackawanna River (http://wilkes.edu/gisriver/)
7. US EPA Environmental Monitoring and Assessment Program (EMAP) (http://
www.epa.gov/emap/)
8. Horn Point Choptank River Index Site—CISNet program, University of Mary-
land—(http://www.hpl.umces.edu/)
9. Hydrometeorological Automated Data System (HADS)—(http://www.nws.
noaa.gov/oh/hads/)
10. Louisiana Universities Marine Consortium (LUMCON) current weather—
(http://weather.lumcon.edu)
11. National Data Buoy Center (NDBC)—(http://www.ndbc.noaa.gov/)
12. National Estuarine Research Reserve System (NERRS)—(http://nerrs.noaa.
gov/)
13. Rutgers University Coastal Ocean Observation Laboratory (RU-COOL) pro-
gram—(http://marine.rutgers.edu/cool/)
14. San Francisco Bay, USGS RTRM SFPORTS—(http://sfports.wr.usgs.gov/)
15. South Carolina Algal Ecology Laboratory (SCAEL), South Carolina Depart-
ment of Natural Resources/University of South Carolina—(http://links.baruch.
sc.edu/scael/)
16. Woods Hole Oceanographic Institution (WHOI)—(http://www.whgrp.com/
semb.htm)
RTRM platforms help to host the sensors and would enable the water engineers
to monitor the quality and environmental conditions on a regular basis. The
incorporation of imaging technologies into RTRM systems would enable better
visualization and monitoring using modern satellite imaging. The high-resolution
spatial data can be coupled with GPS and ﬁeld computing devices. Though this is an
584
K. Marzhan et al.
attractive scheme for real-time monitoring, they often suffer from feature
misclassiﬁcation such as resulting from shadows. In the last decade, this ﬁeld has
further extended to exploit the possibility to utilize very-high-resolution (VHR)
multimodal/multi-temporal image fusion for different remote sensing applications.
The state of the art suffers from the effects of layover, shadow, and foreshortening.
These are largely open problems that affect the full utilization of VHR images for
important problems of feature extraction and classiﬁcation. We are interested in
using VHR images to understand and study the water resources.
Detecting water bodies using remote sensing data has been described by a
number of researchers who use digital imaging processing techniques to map the
surface extent of water bodies. With increasing technological advances in remote
sensing technology, this emphasis has shifted toward developing technologies that
are capable of studying underwater depth of lake or ocean ﬂoors, and it is antici-
pated that remote sensing has potential to make signiﬁcant contributions to river
research by providing extensive, quantitative data that could yield insights on the
changing nature of river dynamics at catchment scale level (Marcus and Fonstad
2008). Remote sensing of water depth in shallow marine settings has a long history
(Lyzenga 1978; Philpot 1989; Maritorena et al. 1994), and the literature suggests
increasing applications of remote sensing in the study of riverbed characteristics
(Marcus and Fonstad 2008) using passive optical remote sensing involving the
measurement of visible and near-infrared reﬂected solar energy that has interacted
with the atmosphere, the water column, and the streambed. The relevant processes
were summarized by Legleiter et al. (2009), based on previous studies by Philpot
(1989), and Maritorena et al. (1994).
There are different methods for retrieving water depth from remote sensing data.
Lyzenga (1979) proposed a method to retrieve water depth by removing water
column effects to obtain bottom reﬂectance parameters from remote sensing imag-
ery. This approach to river depth measurement is based on the transmission
equation of electromagnetic radiation in water, and by measuring the optical
parameters within the water body, water depth can be computed by neglecting the
attenuation effect of a water body to obtain the relationship between water-leaving
radiant energy and water depth (Lyzenga 1979). Alternatively, a linear relationship
between water depth and a linear combination of the logarithmic radiant intensity
can be assumed (Lyzenga 1978, 1981). Other methods include bottom albedo-based
single-band models and multiband ratio models (Wang et al. 2007) which assume
an ideal situation with vertical homogeneity for water body’s photochemistry, high
and invariable bottom albedo, and shallow water (Polcyn and Sattinger 1969;
Polcyn and Lyzenga 1973). Under the assumption of no variability of bottom
albedo in relation to bottom composition, Brown (1971) was able to increase the
accuracy of water depth mapping by taking the ratio between two bands to
minimize the bottom effects. Holding water quality and atmospheric conditions
constant, Philpot (1989) discussed the effects of incrementing the number of
inﬂuencing factors for water depth mapping, including bottom composition and
water types.
Developing High-Resolution Remote Sensing Technology into an Advanced. . .
585
Efforts to minimize the use of in situ data for light attenuation coefﬁcients in
water columns have been demonstrated by Stumpf et al. (2003) who were able to
reduce ﬁve standard coefﬁcients for bathymetry mapping down to two by develop-
ing a reﬂectance ratio model based on the variable radiant absorptivity among
spectral bands requiring only a few water depth points from nautical charts for
the calibration of the model. The estimation of the attenuation coefﬁcients using
only water levels present on multi-temporal images in reference to simultaneous
sea level observations, thus obtaining absolute water depth directly from remote
sensing images, was explored.
Increasingly, the ability of multispectral satellites to detect light in the blue
(450–510 nm), green (510–580 nm), and red bands (630–690 nm) is leveraged to
achieve good water depth estimates up to 15 meters in depth. It is anticipated that
newer airborne, high-resolution, multispectral platforms with the ability to detect
light between 400 and 450 may show that accurate bathymetric measurements can
be achieved up to 20 meters and deeper.
The other main techniques for calculating bathymetry based on multispectral
satellite imagery use a photogrammetric approach. In this method, stereoscopic
images of an area are used to develop a digital elevation model (DEM) of a
riverbed.
The application of a photogrammetric approach using radiometric data may be a
promising, new application to develop accurate bathymetric models of shallow
environments without ground truth. However, the technique has not been widely
studied due to current limitations in the capabilities of remote sensing equipment
and current sensors as well as image classiﬁcation and analysis.
Identifying vegetations, waterways, and man-made structures from remote sens-
ing images involves large amount of data processing and makes remote sensing data
uniquely suitable for statistical pattern recognition methods. With respect to the
latter, classifying objects and patterns is one of the major tasks for unsupervised
classiﬁcation of remote sensing imagery which are emerging to interpret the cluster
centers of an image and to reveal a suitable number of classes to overcome the
disadvantage of unsupervised classiﬁcation (Burman 1999; Chen and Ho 2008;
Pasolli et al. 2009; Schwarz and Datcu 1997; Stathakis and Vasilakos 2006; Yang
and Yang 2004).
In line with current research in this ﬁeld (Feng and Tian 2013; Hou et al. 2014;
Li et al. 2008; Merabtene et al. 2002; Wang et al. 2009), the application of high-
resolution remote sensing technology and advanced image processing using
advanced algorithms and statistical pattern recognition will considerably advance
the ability to monitor and assess water resources.
3.2
Knowledge Management
The exhaustive, unsustainable, and inefﬁcient use of water resources along with the
real environmental threat from intensively polluted water bodies arising from
586
K. Marzhan et al.
mining, metallurgical, and chemical industries as well as untreated wastewater
discharges to the environment from urban and rural settlements requires a new
model for water resource allocation, consumption, use, and disposal.
Water resources and infrastructure management models are increasingly devel-
oped that promote sustainable use of water resources and infrastructures (Cardoso
et al. 2012; Marlow et al. 2010; Marlow et al. 2011; Michele and Daniela 2011;
Zhu et al. 2010; Zhu et al. 2011; Burns 2002), but integral to a water resource
program are comprehensive knowledge management systems to support decision-
making processes for water resource allocation, consumption, use, and disposal as
well as infrastructure management.
Over the past few years, there has been a growing interest in treating knowledge
as a signiﬁcant organizational resource or asset (Alavi and Leidner 2001). Along-
side, researchers have begun promoting a class of information systems, referred to
as knowledge management systems (KMS), and a vast body of literature has
emerged to address this interest (Alavi and Leidner 2001; Argote et al. 2003;
Becerra-Fernandez and Sabherwal 2001; Earl 2001; Gold et al. 2001; Hedlund
1994; Liao 2003; McDermott 1999; Nickerson and Zenger 2004; Schultze and
Leidner 2002; Spender 1996; Studer et al. 1998; Tanriverdi 2005; Tsoukas and
Vladimirou 2001; Zack 1999). According to Alavi and Leidner (2001); the objec-
tive of KMS is to support creation, transfer, and application of knowledge in
organizations. Asset-centric data management or dynamic asset registries depend
upon the identiﬁcation of signiﬁcant systems, assets, or equipment via a unique
identiﬁer or spatial location in each of the supporting information systems thereby
providing prospective user a comprehensive view of all relevant information. Such
an aggregate upper tier system forms the initial building block for a new dynamic
register that binds crucial data and information into one clear and cohesive asset-
centric knowledge database for any management decision-making purposes. With
the onset of the ITIS revolution and ever-increasing capability to produce, analyze,
and store data, a research interest is emerging that demands a new generation of
data integration forming a system that brings together disparate information
irrespective of source system, location, or format type for the purpose of knowledge
or intelligence building (Bradshaw et al. 2011d). Yet, knowledge and knowledge
management are complex and multifaceted concepts that are developed in context.
Speciﬁc to water resource management, infrastructure management, and asset
management, we ﬁnd an increasing body of literature on strategies to support
organizations and governments to manage resources using information and knowl-
edge management systems. Early models and uses of decision support tools for
collaborative planning in integrated water resource management have been
described by Ubbels and Verhallen (2001), Boddy et al. (2007), Chen et al.
(2010), Dalcanale et al. (2011), de Rezende et al. (2008), Grigg (2006), Karki
et al. (2011), Jianxin et al. (2015), McNamara et al. (2009), Mikulecky et al. (2003),
Quinn et al. (2010), Siontorou and Efthymiadou (2008), Toman (2007), Dewi et al.
(2008), and Zhang et al. (2015).
Considering the multidimensional character of (water resource) management
practices, different frameworks have been developed to encapsulate the main
Developing High-Resolution Remote Sensing Technology into an Advanced. . .
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aspects and requirements for knowledge-based asset management into a cohesive
idea (Dalcanale et al. 2011; Champion and Leon Patterson 2012; Illaszewicz et al.
2013). The challenge is in designing a framework that is comprehensive enough to
capture the key issues, but that continues to be manageable (UNDP 2009:32). As a
result, one goal of the framework is to present the collective information as simply
as possible in order to avoid dissuading its adoption by smaller organizations while
still retaining applicability for advanced users. Alongside those goals is the need to
retain ﬂexibility for a wide range of potential situations. For this project, a concep-
tual water resource knowledge management framework has been developed to
enquire and collect relevant information needs for water resource management
and decision-making. The framework corresponds with the requirements for
IWRM (Al Radif 1999; Biswas 2004; Jewitt 2002; Jonch-Clausen and Fugl 2001;
Mitchell 2005; Thomas and Durham 2003; van der Brugge and Rotmans 2007;
Sandhawalia and Dalcher 2008).
Recently frameworks have appeared that are designed to measure and develop
organizational capability with respect to asset management in organizations
(Illaszewicz et al. 2013). In the context of this review, the term “capability” is
considered to represent the degree to which an organization can identify needs,
plan, and implement decisions to achieve desired outcomes based on the knowledge
stakeholders have. Capability development, therefore, represents “transformations
that empower individuals, leaders, organizations and societies” (UNDP 2009:6).
The outcomes are largely unique to the organization but are typically oriented
around delivering the expected levels of service from infrastructure assets for the
system users. To enable capacity development, a framework must exist to provide
the frame of reference. A capability assessment model should allow an organization
to locate itself on the framework, understand the opportunities that may be available
to it (e.g., either for increasing or decreasing organizational capabilities), and make
informed decisions and corresponding changes (e.g., competency training, capabil-
ity development) as need be. To achieve process improvements, a system must be
available to measure the current and target capabilities for which a variety of
systems are available, such as benchmarking, total quality management, the
Deming Cycle, or Capability Maturity Models.
A structured capability building framework spanning the breadth of knowledge
management may offer a powerful tool for clarifying these relationships while
returning the emphasis to development or improvement of organizational manage-
ment capacity.
The knowledge management system has the aim to support the user to develop
credible and defensible water resource management plans in line with IWRM
objectives and in particular:
(a) Describe water resources and identify actual and emerging problems of water
pollution and water use inefﬁciencies as well as infrastructure needs.
(b) Formulate plans and set priorities for water quality, water use management and
infrastructure needs.
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(c) Develop and implement water quality management programs, water allocation
strategies and river basin asset management plans.
Such applications with the integration of geo-information systems have signif-
icant potential as a platform for managing environmental, water resource, and
infrastructure data (Chen et al. 2010), and the mapping and geo-spatial analysis
of information will see further advancements by integrating models and real-time
monitoring thus providing comprehensive knowledge management systems used by
regulators and public agencies responsible for water resource management and
environmental engineering and management decision-making.
So far, the academic literature does not identify any signiﬁcant maturity building
knowledge management framework in the context of delivering water resource
objectives and associated infrastructure. This type of knowledge management
framework is considered novel and original in its conception. Developing a solution
for integrating high-resolution remote sensing information into a comprehensive
knowledge management system is considered original and novel.
4
Methodology
Considering the breadth of this research project and the need for ensuring compli-
ance with the principles of scientiﬁc ethics and ethical management procedures,
maintaining high standards of intellectual honesty, and avoiding the fabrication of
scientiﬁc data, a clear perspective on the applied research strategy and methodology
is required. A range of research strategies were considered and used in the design of
this project, in particular relating to the design and information needs of a knowl-
edge management system. Ethnography is concerned with speciﬁc people or cul-
tural groups to describe a way of life or, in this project’s context, “the way we do
things here”. As part of the sample group, the researcher interacts with the group.
This poses a serious disadvantage since the researcher inﬂuences the information
obtained in the studies. However, this may serve as an advantage from an action
research perspective.
Action research has been promoted by practitioners as a moral responsibility to
work socially meaningful in changing a situation for the better by the researchers’
involvement. It is “research becoming praxis – practical, reﬂective, pragmatic
action – directed towards solving the problems in the world” and has a deliberate
interaction with the subject areas of study.
This project is considered to identifying an area of study and allows ﬁndings to
emerge from systematically collected data. It is data driven with developed
methods of collection and analysis that can stand up to rigor, reliability, and
validity. It is approached by broad and exploratory search before focusing on
emerging ﬁndings. This type of grounded theory allows the researcher to cover
more territory while remaining relevant within the real world. It is a constant
Developing High-Resolution Remote Sensing Technology into an Advanced. . .
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comparative approach building on a continual review of new data against previ-
ously collected data that help to reﬁne the development of a theory or hypothesis.
The project execution in this project follows Lee (1999) who identiﬁes eight
steps for grounded theory research (Fig.1). In this context, data acquisition strate-
gies have to be considered for this project, and it was decided that the research
scope of this project was best studied with qualitative research methods such as
participant observation, interviews, surveys, document reviews, and keeping per-
sonal learning logs.
Qualitative data collection uses language, description, and expression (Trochim
2000) and emphasizes the human element in a “real” perspective. Using observa-
tional methods enables the collection of qualitative data by observing what groups
or individuals do. Recording their actions and describing their activities in “real-
world research” offer good advantages. Interviews provide a source of data from
interacting in a conversation. The spectrum of interviews ranges from unstructured
Fig. 1 Grounded theory research (According to Lee 1999)
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via semi-structured to structured interviews. Whereas the former can provide very
rich and detailed data with expressive and enlightening information, it lacks
standardization in its results which is a deﬁnitive advantage in structured inter-
views. Yet, structured interviews lack in the inability to react to emergent topics
raised by the interviewee. Surveys and questionnaires are an extension to interviews
(Trochim 2000) and can be designed for quantitative analysis and even for
self-administration. They offer a time-effective means of data acquisition, but
questions arise over the quality of data obtained, e.g., unanswered questions and
misinterpretation.
5
Results and Discussions
5.1
Remote Sensing Methodology Development
The scope of the work package included the following:
1. Creating and updating the inventory of water resources
2. Creating and updating the inventory of water bodies
3. River basin mapping and waterline identiﬁcation
4. Detecting intermittent streams (streamﬂow and hollows)
5. Mapping of lakes and reservoirs and their major components (edge, coastal
slope, coast, coastal shallows)
6. Determining the type of lake water cycle in nature (waste, closed drainage,
ﬂow, temporary waste, etc.)
7. Separation Lake mineralization at relatively fresh and salty
8. Identifying the elements of the structure of ﬂoodplains of major rivers (bends,
meanders, furca, oxbow lakes, etc.)
9. Detecting and typing of large ﬂuvial forms and their elements (ridge, rolls,
reaches, backwaters, etc.)
10. Identifying the structure of young ﬂoodplain formations (islands, shoals,
braids, blind area, bichevniki, etc.)
11. Identifying area of river ﬂooding during ﬂooding and ﬂood forecasting
12. Delimiting the ﬂooded areas during ﬂoods and a preliminary assessment of the
impact of ﬂoods
13. Detecting unauthorized building in ﬂoodplains
In the initial pilot study, a methodology and algorithms were developed that
were tested on imagery obtained for the Borovoy lake system (located in North
Kazakhstan). The study sought to identify the structure of lake system including the
bathymetric model of the lakes. The study will be complemented by ﬁeld data from
existing monitoring control station to determine the correlation between imagery
and ﬁeld data.
Developing High-Resolution Remote Sensing Technology into an Advanced. . .
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These image processing algorithms utilize very-high-resolution images and will
synthesize any monitoring data for their presentation in tables, plots, or other
graphical displays for standard computer as well as mobile phones and tablets.
We would develop customized methods for segmentation, extraction of features,
and learning methods to estimate the water volume and capacity within a predeﬁned
geographical region across the Ishim River basin. The identiﬁcation of the water
volume from the high-resolution images to be integrated to the knowledge man-
agement and data analysis framework involves the study of:
1. Imagery acquisition parameters: Identify appropriate acquisition windows and
address the challenges of working with large geographic extents of high-
resolution satellite data.
2. Field data sampling from multiple sensors: The reference data and recalibration
of the point and polygon ﬁled data to use with high-resolution images.
3. Spectral confusion: Solve the possible issues of spectral confusion associated
with cover-type classiﬁcations.
4. Shadows: The issues of shadows on image classiﬁcation and ways these errors
can be accounted for or avoided.
5. Accuracy assessments: Develop techniques to model and assess the accuracy of
the output maps outlining the water resources and its availability and accuracy of
estimation in real-time setup. This work package will procure high-resolution
remote sensing data to develop advanced algorithms to assess and monitor water
resources for river basin water resource management. Data shall be provided by
KGS from its own remote sensing satellites KazEOsat-1 and KazEOsat-2 or
KGS partner’s satellites. KGS performs distribution activities of the satellites
within Astrium Geoinformation constellation and additionally RapidEye con-
stellation. KazEOsat-1 and KazEOsat-2 operation is expected to start from the
middle of 2014. The project will use remote sensing data procured from a high-
resolution (1 m panchromatic and 4 m multispectral) remote sensing satellite
with the following speciﬁcations: PAN, 600 nm; blue, 455–520 nm; green,
525–595 nm; red, 630–695 nm; and NIR, 775–850 nm. Middle-resolution
satellite has a 6.5 m multispectral resolution with the following bandwidths:
blue, 430–500 nm; green, 520–590 nm; red, 630–685 nm; red edge,
790–730 nm; and NIR, 760–850 nm.
The scope of this work package includes the development and application of
algorithms to assess and monitor the hydrology of lakes, rivers, and reservoirs.
Four main groups of work with application of KazEOsat-1 and KazEOsat-2
datasets will be applied:
(A) Surface water coverage area (SWCA) extraction from the multispectral bands
KazEOsat-1 and KazEOsat-2
(B) Digital elevation model (DEM) preparation from the stereo datasets KazEOsat-
1 and KazEOsat-2
(C) Digital terrain model (DTM) preparation from DEM with bathymetry, water
depth, and subtraction
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(D) Water volume ﬂuctuation computation and modeling with DTM applications
(A) SWCA extraction from the multispectral bands KazEOsat-1 and KazEOsat-2
From the various surveyed methods for extracting SWCA, normalized spectral
indices, manual translation, and parametric classiﬁcation of images are the most
widely used. In comparison with other methods, the use of spectral indices has
many advantages: this method relies primarily on the transformation of numerical
values, which allows a decrease in background effects and reduced data dimen-
sionality, providing a level of standardization for comparative purposes and
enhancing the required signal for speciﬁc land cover and land use areas. Thus,
normalized indices increase the separation ability of information extracted from
remote sensing data. Because of spectral differences among diverse land cover and
land use areas, surface areas can be calculated from different combinations of
remotely sensed image bands depending on the type of surface analyzed, e.g.,
water, vegetation, or urban areas. KazEOsat-1 and KazEOsat-2 datasets are iden-
tical to RapidEye and SPOT satellites but have high pixel resolution. We developed
(Sagin et al. 2015) a methodology to track contemporary water coverage changes
using remote sensing. We prepared a GIS automated routine based on the modiﬁed
normalized difference water index (mNDWI) to extract the surface water coverage
area (SWCA) from optical satellite datasets using the surface water extraction
coverage area tool (SWECAT). It was applied to measure SWCA during drought
and ﬂood peaks in the Saskatchewan River delta, Canada, from Landsat, SPOT, and
RapidEye images. Landsat results were compared favorably with Canadian
National Hydro Network (CNHN) GeoBase data, with deviations between SWCA
classiﬁcations, and the base CNHN GeoBase shapeﬁle of ~2%. Difference levels
between the extracted SWCA layer from Landsat and the higher-resolution com-
mercial satellites (SPOT and RapidEye) are also less than 2%. SWCA was tightly
linked to discharge and level measurements from in-channel gauges (r2 > 0.70).
We targeted to test and to adapt the SWECAT for KazEOsat-1 and KazEOsat-2
datasets. The current research adapted the approach of normalized spectral differ-
ence indices for the identiﬁcation of water areas. Here, the normalized difference
water index (NDWI) in the form of the modiﬁed NDWI (mNDWI) was used for the
delineation of open surface water areas within the studied region:
mNDWI ¼ Bgreen  BSWIR NIR
ð
Þ
Bgreen þ BSWIR NIR
ð
Þ
ð1Þ
where Bgreen and BSWIR(NIR) are sensor spectral and Bgreen, BSWIR short-wave
infrared, and B(NIR) near-infrared band values, respectively, for Landsat MMS,
SPOT2 HRV, and RapidEye JSS56 that used the BNIR and for Landsat TM,
SPOT4 HRVIR, and SPOT5 HRG that used the BSWIR sensors. The mNDWI
index varies from 1 to 1, depending on the proportions of subpixel water or
non-water components (e.g., soil and/or vegetation). An application of a threshold
can control the analysis output. Zero was set as the mNDWI threshold for open
Developing High-Resolution Remote Sensing Technology into an Advanced. . .
593
surface water areas. In addition to the water index threshold, the BSWIR(NIR)
thresholds were used for a better open surface water area identiﬁcation and delin-
eation. The BSWIR(NIR) thresholds were estimated for the satellite datasets using a
visual assessment of the preliminary identiﬁcation results and histograms of the
image analysis. The different mNDWI thresholds and limits of the BSWIR(NIR)
thresholds used for open surface water areas identiﬁcation and delineation are:
(a) Landsat MSS (NIR), 30; (b) Landsat TM (SWIR), 35; (c) Landsat ETM +
(SWIR), 35; (d) SPOT2 HRV (NIR), 40; (e) SPOT4 HRVIR (SWIR), 40; (f) SPOT5
HRG (SWIR), 60; and (g) RapidEye JSS56 (NIR), 2500. RapidEye’s threshold
is higher because it has a higher radiometric resolution (16 bit data), compared
to Landsat and SPOT (8 bit data). The general spatial resolution rule states
that for the successful identiﬁcation of the object of interest, there is a need for
at least four spatial observations—pixels—in the case of remote sensing raster
data. For example, the identiﬁcation of a water object with a 60 m diameter
would require four pixels with at least 30 m by 30 m spatial resolution. Therefore,
the identiﬁed open surface areas were ﬁltered: the objects less or equal to the
area of three pixels were removed to avoid misinterpretation. After ﬁltering, the
identiﬁed open surface areas that intersect the study region were extracted as a
ﬁnal result.
KazEOsat-1 and KazEOsat-2 datasets have higher spectral resolution. In our
search works, we will need to test for KazEOsat-1 and KazEOsat-2 datasets and
ﬁnd out the optimal threshold values to extract the SWCA.
(B) DEM preparation from the stereo datasets KazEOsat-1 and KazEOsat-2
DEM is one of the most important input data for any kind of hydrological, water
management studies and modeling. Most of the hydrologists desire to apply the
DEM from Light Detection and Ranging (LIDAR). However LIDAR is very
expensive and still will be complicated to cover the big Kazakhstan territory. The
other main techniques for DEM preparation is to use multispectral satellite imagery
with a photogrammetric approach. In this method, stereoscopic images of an area
are used to develop a DEM. Stereo SPOT, Landsat, ASTER, and RapidEye are used
widely to extract a DEM. The high-resolution stereo KazEOsat-1 and KazEOsat-2
datasets are our target to extract the high-resolution DEM. The KazEOsat stereo-
extracted DEM will be calibrated and veriﬁed with new German TanDEM-X
(TerraSAR-X add-on for digital elevation measurement) spaceborne radar remote
sensing datasets, which will be available from October 2014. Satellites were
launched successfully from Kazakhstan Baikonur Cosmodrome recently and
DEM data under processing by the German Space Agency, Microwave and Radar
Institute. The DEMs with a spatial resolution of 12 m will be generated for the
global TanDEM-X DEM as the primary mission goal. Moreover, local DEMs of
higher accuracy level (spatial resolution of 6 m and relative vertical accuracy of
0.8 m) and applications based on along-track interferometry (ATI) will be avail-
able. We have submitted a science grant proposal to TanDEM-X Science Service
System (https://tandemx-science.dlr.de/) to test the new high-resolution DEM. This
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is one of the main research targets of the high-resolution DEM preparation for
Kazakhstan’s territory.
Mainly, in this project, identiﬁcation of structure of lakes will be based on
imagery data, and digital elevation model (DEM) of bathymetry will be created
from high-resolution imagery using PCI Geomatica software. Particularly, image
correlation is used to extract matching pixels in two overlapping images and then
uses the sensor geometry from a computed match model to calculate x, y, and
z positions.
Collecting stereoscopic imagery of the shallow ocean ﬂoor is in how light
interacts with the air/water interface. At high angles of incidence, light is
completely reﬂected off the water surface thus preventing any subaquatic proﬁles
from being observed. In this application, a sensor is required to collect enough high-
resolution stereoscopic imagery within the narrow angle to penetrate the surface of
a water body. Thus, measured depth points from satellites and bathymetric data will
be used to extract sensor reﬂectance values from each image, and then the points are
divided for testing and calibrating.
(C) Digital terrain model (DTM) preparation from DEM with bathymetry, water
depth, and subtraction
DTM is a bare-earth model that contains elevations of natural terrain features
such as barren ridge tops and river valleys. Elevations of vegetation and cultural
features, such as buildings and roads, and water parts are digitally removed. We
targeted to collect the river and lakes’ bathymetry, water depth, and subtract from
the DEM the bathymetry data. This methodology has been used widely, including
Canada, by the water resource companies and agencies. Saginatyev’s research
group applied the methodology for Saskatchewan and Slave River basins in
Canada.
(D) Water volume ﬂuctuation computation and modeling with DTM applications
The SWCA group of data will be prepared in the research work (A) from the
multispectral bands KazEOsat-1 and KazEOsat-2 in combination with DTM data.
Furthermore, using the GIS platform and the ﬁeld data provided by Ministry of
Environment and Water Resources in Kazakhstan, modeling of water volume
ﬂuctuation will be created by comparing water volume data in the 1980s with
recent years. Further, a change in water volume comparison will be computed and
shown statistically.
(C) Will be used for water volume ﬂuctuation, computation and modeling
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5.2
Methodology for Development of Water Resource
Knowledge
Moreover, by now, the methodology for developing water resource knowledge has
been developed. It includes implementation of the following steps:
1. Find old historical maps covering the lake research area that were
50–100 years ago.
2. Georeference all these maps in GIS and extract surface water coverage of all
lakes.
3. Download all Landsat data covering the research area from 1972 till the present
day from http://glovis.usgs.gov/.
4. Make all atmospheric corrections of all Landsat data, preprocess, and extract
surface water coverage of all lakes.
5. Compute surface water coverage changes by using your processed data, similar
to the paper in (4).
6. Prepare high-resolution digital terrain model (DTM) from stereo KazEOSat
dataset, by using similar tools such as OrthoEngine and stereo DEM extraction
tool from PCI Geomatica, ENVI, or ERDAS.
7. Compute water volume by compiling the surface water coverage from
processed data and DTM by using GIS tools.
8. Compute water volume changes by using your processed data, and show
statistics in GIS, Excel, or MATLAB.
9. Track algae pollution by using the multispectral satellite bands from Landsat or
KazEOSat-1.
10. Compare the extracted water pollution data from satellites and the ﬁeld
hydrochemical analysis, and show statistics.
11. Apply ArcSWAT for modeling to predict the future changes, including water
quantity and water quality.
By means of this scheme, it’s planned to focus ﬁrst of all on lakes located in
Borovoy area and then on water bodies of East Kazakhstan.
Historical maps 50–100 years ago will allow conducting comparative analysis
with the recent years (1); these maps will be georeferenced to existing GIS data so
that it would be possible to digitize the information from them. The historical maps
and satellite images, in turn, will provide us a change through time (2).Regarding
monitoring change of surface water coverage, it is quite important for the manage-
ment of water, biological components (Sagin et.al. 2014), and hydraulic alterations.
This extraction of surface water coverage area will be possible using GIS platform
and PCI Geomatica from Landsat images (3, 4, 5). Performing the water manage-
ment tasks is not possible without simulation of the natural terrain elevation (6);
particularly it will enable us to compute water volumes using bathymetry data
(7, 8).
Further it is planning to monitor water pollution, speciﬁcally caused by algae
pollution. Based on high-resolution satellite data, it is possible to track algae
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pollution in terms of color of a water body; the color occurs usually because of the
presence of some organic pollutants in a water body, so that it affects water quality
and living organisms (9).Furthermore, once we extract water pollution data, ﬁeld
hydrochemical analysis will be compared statistically (10).Based on all research,
prediction of future changes will be conducted using ArcSWAT platform.
5.3
Knowledge Management System
The objective of this work package was to identify information requirements for
managing water resource and developing a functional description of a software
platform that contains all relevant information required for water resource decision-
making and management.
Alongside the requirements for IWRM, a conceptual water resource knowledge
management framework has been developed to inquire and collect relevant infor-
mation needs for water resource management and decision-making. The framework
is an extension to the one designed by Dalcanale, Fontane, and Csapo (2011).The
conceptual water resource knowledge management framework is currently in
development as part of the Nazarbayev University seed grant program and was
developed to include nine distinct but interrelated components (Table 1).
The testing and further reﬁnement of knowledge management platform were
conducted by inviting water resource management experts, professionals, and
professional organizations and institutions to participate in workshops, interviews,
and surveys. These were carried out to develop a comprehensive understanding of
management decision-making and management processes as well as information
and data needs, in particular relating to:
Water Resource Strategy and Policy
•
Formulate strategies, policies, and plans and setting priorities for water quality,
water use management, and infrastructure needs.
•
Develop water quality management program, water allocation strategies, and
river basin asset management plans.
Table 1 Water resource knowledge management framework
Water resource strategy and policy
Stakeholder management
Ownership, responsibility, authority, and resources
Inventory of the water resource and its infrastructure
Condition and performance of a water resource and its infrastructure
Valuations and ﬁnancial review
Monitoring, control, and knowledge management
Internal process development and review
Risk assessment, management, and emergency response
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Stakeholder Management
•
Develop and maintain a stakeholder network with relevant government organi-
zations involved in water resource management.
Ownership, Responsibility, Authority, and Resources
•
Develop an understanding of the governance structure for a water resource river
basin including ownership, responsibility, authority for decision-making, and
decision-making resources.
Inventory
of
the
Water
Resources
and
Its
Monitoring
and
Control
Infrastructure
•
Inventory key attributes of water resources and their monitoring and control
infrastructure
Condition and Performance of a Water Resource and Its Infrastructure
•
Assess and monitor the condition of water resource and their infrastructure
assets including physical structures, monitoring and control stations, and
equipment.
•
Identify actual and emerging problems of water pollution, quantitative water use
restrictions, and infrastructure needs.
•
Obtain, analyze, and prepare monitoring data from existing ﬂow monitoring
stations.
•
Specify and develop and/or assess a rainfall-surface water runoff model for the
water resource.
•
Valuations and ﬁnancial review.
•
Evaluate water resource infrastructure assets.
•
Evaluate water resource as an economic resource for domestic, industrial, and
commercial use.
•
Evaluate the economic, commercial, and ﬁnancial implications for inter-basin
water conveyance systems.
Internal Process Development and Review
•
Document the governing processes for water abstraction and pollution control
for a water resource river basin based on the review of ownership, responsibility,
authority for decision-making, and decision-making resources.
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K. Marzhan et al.
Risk Assessment, Management, and Emergency Response
•
Document risk assessments and risk management systems as well as processes
and procedures for responses to emergencies (ﬂoods, draughts, structural fail-
ures, etc.).
It is anticipated to further engage with experts, professionals, and professional
institutions with relevant experience in water resource management and the man-
agement of water resource assets and infrastructure to further reﬁne the framework.
5.4
Technical Knowledge Management Architecture
The knowledge management platform requires integration of many different tech-
nologies and methods including network technology, sensor technology, database
technology, telecommunication technology, and information management method.
In our proposal, the system consists of four parts: wireless sensor network, local
server, monitoring service platform, and application server which are connected
and operated coordinately. They form an integral part of real-time data collecting,
management of information, comprehensive analysis and support planning, and
real-time control. The algorithms developed in WP3 will be embedded into the
software script to integrate (near) real-time monitoring and control into a software
platform used to manage water resources and their controlling infrastructure.
The software and hardware design principle of (near) real-time monitoring and
management system in water resources will be developed as shown in Fig. 2.
The main structure of the system in Fig. 1 can be divided into two parts: central
management system and communication system.
The central management system includes a monitoring service platform and
application server. The monitoring service platform provides real-time data collec-
tion of various parameters related to water resource management, such as temper-
ature, moisture content (air humidity), water ﬂow rate, water volume, water quality,
Fig. 2 Architecture of the real-time monitoring system for water resources
Developing High-Resolution Remote Sensing Technology into an Advanced. . .
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and rainfall. The system will analyze the validity of the data before saving it to a
central database. It can output the information in a variety of charts or simulation
graphs to show a trend including short-term forecasting or prediction. That involves
mathematical modeling using artiﬁcial intelligence and context awareness, based
on data that have been collected. Apart from that, the system can have built-in
decision-making, for example, activate certain control or send alert messages, when
there is a sudden change in temperature. The interface to users will be done through
the application server in the form of graphical user interface (GUI).
The communication system includes both wired and wireless communications.
Various sensors will be communicating wirelessly with a control terminal (local
server that serves as a coordinating focal point) that is located within the coverage
area using various standards, for example, ZigBee, based on IEEE 802.15.4. The
control terminal will be connected to the central management system (see item A
above) using high-speed cable communication link.
6
Conclusions
In this project, it is aimed to make two distinct contributions to knowledge:
1. High-resolution remote sensing: The development of a real-time online moni-
toring and assessment capability based on genetic algorithms and statistical
pattern recognition has potential to ﬁnd signiﬁcant, customized application to
advance the management of water resources and environmental systems by
enabling real-time and remote monitoring of physical and hydraulic attributes
of water resources. The integration of advanced algorithms to assess and monitor
water resources for river basin water resource management into a prototype for a
water resource monitoring capability that is complementary on the ground ﬁeld
observation stations is novel.
2. Knowledge management: The management of water resources and its infrastruc-
ture requires evidence-based decisions that are based on capabilities to monitor,
evaluate, and optimize water resource management activities. Tools and pro-
cesses at strategic, policy, and tactical level form an integrated monitoring and
control framework to proactively and consistently identify and assess water
resources. This places the identiﬁcation, assessment, and control of water
resources at the center of water resource management and therefore the avail-
ability and quality of information at the center of decision-making.
The application developed in this project will form a prototype for water
resource and infrastructure knowledge management system. With the mapping
and geo-spatial analysis of information as a basis, it is anticipated that the integra-
tion of real-time monitoring provides a comprehensive knowledge management
systems to support water resource management and environmental engineering
decision-making processes in regulatory and public agency organizations.
600
K. Marzhan et al.
In the continuation of this project, we will develop a methodology for hydro-
potential assessment using remote sensing data. The objective is to develop a water
resource development strategy with speciﬁc emphasis on developing hydroelectric
power generation capacity in Kazakhstan. Therefore, the project includes:
•
Studying practice of water resource management and information sources as
they relate to the East Kazakhstan river basin
•
Obtaining remote sensing data and algorithm development to assess hydropower
generation potential
•
Developing an understanding of hydroelectric power generation potential on
rivers in East Kazakhstan
•
Developing a methodology for prioritizing more detailed investigations into the
feasibility of small power stations based on parameters speciﬁed in the RFP and
this pre-proposal
•
Identifying three sites for a detailed feasibility analysis
•
Conducting three detailed feasibility analyses
The project will result in a comprehensive technical and economic feasibility
study for investing in hydropower generation by quantifying ﬂow volumes, identify
potential sites for water storage, and assess geographic elevation gradients that are
suitable to produce electricity with small hydropower stations. The study includes
the technical speciﬁcations and risk assessment for design and construction of three
hydroelectric power generating plants and is presented with comprehensive cost
estimates and revenue projections. Given the local potentialities of each particular
location (ﬂow duration curve and available head), a reservoir or a run-of-river unit
will be recommended up to basic engineering level, according to local and world-
wide providers. Additionally, an assessment of the economic and technical viability
of each unit will be performed based on CapEx, OpEx, and general ﬁnancial
parameters (discount rate, inﬂation, energy escalation, etc.). The study will include
a sensitivity and risk analysis to determine the best and worst conditions as well as
to guide public policies to stimulate private or public investments in the sector with
a fair proﬁt or to value strategic decision-making.
Acknowledgments This research was supported by NURIS of Nazarbayev University. These
supports are gratefully acknowledged.
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Developing High-Resolution Remote Sensing Technology into an Advanced. . .
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Investigation of Thermal Characteristic
of Eutectic Fatty Acid/Damar Gum
as a Composite Phase Change Material
(CPCM)
Hadi Fauzi, Hendrik S.C. Metselaar, T.M.I Mahlia, Mahyar Silakhori,
and Hwai Chyuan Ong
1
Introduction
Thermal energy storage (TES) plays an important role in generating solar energy to
be used in a wide range of applications such as heating and cooling, solar power,
and industrial waste heat recovery (Al-Abidi et al. 2012; Cabeza et al. 2011; Kuravi
et al. 2013; Nomura et al. 2010). Latent heat thermal energy storage (LHTES)
materials known as phase change materials (PCMs) offer more advantage over
sensible heat thermal energy storage (SHTES) materials, particularly the small
temperature difference between melting and solidifying point, small volume, and
low weight per unit of storage capacity. PCMs absorb heat as storage energy during
the heating process and release it during cooling (Pielichowska and Pielichowski
2014). Phase change materials (PCM) used in thermal energy storage (TES)
application have been classiﬁed into two categories: inorganic and organic phase
change materials (PCMs). Although the inorganic PCMs have a great heat storage
capacity and a wide range of phase transition temperature, they have a number of
H. Fauzi (*)
Department of Mechanical Engineering, University of Malaya, Kuala Lumpur 50603,
Malaysia
Department of Chemical Engineering, Syiah Kuala University, Banda Aceh 23111, Indonesia
e-mail: hadidoank@gmail.com
H.S.C. Metselaar (*) • M. Silakhori • H.C. Ong
Department of Mechanical Engineering, University of Malaya, Kuala Lumpur 50603,
Malaysia
e-mail: h.metselaar@um.edu.my
T.M.I. Mahlia
Department of Mechanical Engineering, Universiti Tenaga Nasional, Kajang 43000, Selangor,
Malaysia
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_42
607
disadvantages such as subcooling, corrosion, phase separation, phase segregation,
and lack of thermal stability, compared with organic PCMs which only have a
problem with low heat transfer rate due to low thermal conductivity (Fauzi et al.
2014; Zalba et al. 2003; Zhou et al. 2014). Therefore, enhancement of thermal
conductivity of organic phase change materials (PCMs) has become one of the main
issues in a wide application of thermal energy storage (TES).
A few different methods have been proposed to enhance the thermal conductiv-
ity of PCMs. Encapsulation was one of the methods proposed to improve the
thermal conductivity of PCMs. On the other hand, this method has handled the
leakage problem properly and brings a high cost in production (Zhou et al. 2014). In
addition, thermal conductivity of PCM can be improved by adding high conductive
porous material using simple mixing method, solution casting method, or impreg-
nation method (Alkan and Sari 2008; Kim and Drza 2009; Nomura et al. 2009;
Zhang and Fang 2006).
In this work, we prepared the composite phase change material (CPCM) by
adding Shorea javanica obtained from puriﬁed damar gum as a porous material to
improve the thermal conductivity of myristic acid/palmitic acid/sodium laurat
(MA/PA/SM) eutectic mixture, combining simple mixing and impregnation
methods.
Nomenclature
ΔHf
Latent heat of fusion (J.g1)
ΔHf,m
Latent heat of fusion at melting (J.g1)
ΔHf,s
Latent heat of fusion on solidifying (J.g1)
Tm
Melting temperature (C)
Ts
Solidiﬁcation temperature (C)
2
Material and Methods
2.1
Materials
The myristic acid/palmitic acid with the addition of 5 wt.% sodium myristate,
known as MA/PA/SM eutectic mixture, proposed by Fauzi et al. (2013) was used
as a base phase-change material (PCM). Natural damar gum or Shorea javanica
collected from conventional plantation in Indonesia has been proposed for use as
porous material. An additional puriﬁcation process is required to obtain impurities-
free Shorea javanica (SJ).
Puriﬁcation method by organic solvent has been used to purify Shorea javanica
(SJ) (Setianingsih 1992). Toluene MW. 92.14 (Fisher Scientiﬁc) and SJ in compo-
sition ratio of 1:8 wt.% were stirred at 2000 rpm for 20 min under ambient
temperature to obtain a completely dissolved SJ solution. Furthermore, 1 wt.% of
608
H. Fauzi et al.
activated charcoal (AC) supplied from Acros Organic was added for decolorizing
the SJ solution, and stirred for 15 min at 45 C. Sedimentation and ﬁltration
processes were performed to separate the impurities from the solution. Then, the
pure solution of Shorea javanica (SJ) was placed in the evaporator to remove the
solvent, and ﬁnally, the button product of SJ was dried in the oven at 80 C for 8 h to
obtain pure Shorea javanica powder. Furthermore, the powder of Shorea javanica
(SJ) was ground in a rotary ball mill and further put into a sieve shaker separator to
obtain particles of Shorea javanica (SJ) of the same size (100 μm).
2.2
Preparation and Analysis Methods
A combination of simple mixing (Kim and Drza 2009) and impregnation (Nomura
et al. 2009) methods known as mixing–impregnation method has been used to
prepare a novel eutectic composite phase change material (CPCM) of MA/PA/SM/
SJ. The MA/PA/SM and SJ in various composition ratios of 1, 2, 3, 4, and 5 wt.%
were placed together in a jacketed ﬂask reactor at different times under vacuum
pressure, and the heat absorbed from the heat transfer ﬂuid (HTF) at 70 C.
Furthermore, stirrer mixing was applied for homogenous distribution of SJ in
MA/PA/SM eutectic mixture. These preparation processes were performed for
2 h and closed by drying under ambient temperature.
The prepared eutectic composite of MA/PA/SM/SJ in different composition
ratios of SJ have been separately evaluated for the thermal properties, thermal
conductivity and thermal stability, using the thermal differential scanning calori-
metric (DSC) thermal analysis (Metler Toledo, DSC1 Stare system), the thermal
conductivity analyzer (Hot Disc, TPS 2500 S), and Simultaneous Thermal Analyzer
(STA 6000, Perkin Elmer). The Fourier transform infrared spectroscopy (FT-IR,
Bruker Tensor 27) was used to identify the chemical reaction that occurred between
the MA/PA/SM eutectic mixture and Shorea javanica. The analysis was performed
using ATR sample compartment with MIR spectra in the wave number range of
4000–400 cm1.
Moreover, the thermal performance of the MA/PA/SM/SJ eutectic composite
was evaluated using customized thermal performance test setup as shown in Fig. 1.
The setup consisted of two ﬂuid circulation baths for hot and cold ﬂuids to circulate
the heat transfer ﬂuid (HTF) to the chamber. Two cylindrical glass tubes were
attached inside the chamber and contacted with HTF. Furthermore, 6 g of the two
samples – MA/PA/SM eutectic mixture and MA/PA/SM/SJ eutectic composite –
were placed in the glass tubes. The alteration of the temperature of these samples
against endothermic and exothermic time was detected by a thermocouple (J-type,
Omega) on DAQ system. The temperature of hot and cold HTF was set above and
below the melting temperatures of the samples, which were 65 C and 30 C,
respectively.
Investigation of Thermal Characteristic of Eutectic Fatty Acid/Damar Gum. . .
609
3
Results and Discussion
3.1
Thermal Characteristic of MA/PA/SM/SJ
Fauzi et al. (2013) previously have done work on the improvement the thermal
properties of myristic acid/palmitic acid (MA/PA) eutectic mixture by adding 5 wt.
% sodium myristate (SM) to reduce the phase transition temperature (considering to
tropical weather applications) and increase the latent heat of fusion of eutectic
PCM. The studies reported that the addition of sodium myristate (SM) was able to
reduce the melting temperature (Tm) and improve the latent heat of fusion (ΔHf) of
MA/PA eutectic mixture, as 11.5% and 15.3%, respectively. But it does not show a
signiﬁcant improvement on thermal conductivity. Therefore, this current study has
been designed to achieve a good improvement on thermal conductivity of prepared
PCM without much effect on reducing the thermal properties.
The DSC curve in Fig. 2 presents the thermal properties of CPCM in different
compositions of SJ. The melting temperature (Tm) and solidiﬁcation temperature
(Ts) of MA/PA/SM/SJ were interpreted as onset points, whereas the latent heat of
fusion during melting (ΔHf,m) and solidiﬁcation phase (ΔHfs) were obtained from
interpolating of the chart’s peak area. The results, as shown in Table 1, indicated
that the phase transition temperature of MA/PA/SM/SJ eutectic composite rises up
against increasing percentage composition of SJ, and at the same time, its latent
heat of fusion tends to decrease with increasing SJ composition.
The thermal conductivity of MA/PA/SM/SJ eutectic composite in different
compositions of SJ which is tabulated in Table 2 shows that the thermal conduc-
tivity increased simultaneously with increasing percentage composition of SJ. From
these result obtained, the highest thermal conductivity value was belong to eutectic
composite mixture of MA/PA/SM with 5 wt. % SJ as 0.492 Wm1 K1. However,
the thermal properties of MA/PA/SM + 5 wt.% SJ eutectic composite show an
Fig. 1 Thermal
performance test setup
610
H. Fauzi et al.
unexpected value on latent heat of fusion that shows a signiﬁcant drop to 167.38 Jg
1 compared with the latent heat of fusion of initial MA/PA/SM eutectic mixture as
179.92 Jg1. However, an improvement of thermal conductivity without signiﬁcant
drop of latent heat of fusion is shown by MA/PA/SM + 3 wt.% SJ eutectic
composite. The thermal conductivity of MA/PA/SM/SJ showed a very signiﬁcant
improvement of 101.65% by adding 3 wt.% SJ compared with the thermal
Fig. 2 DSC thermal properties curve of MA/PA/SM/SJ composite mixture
Table 1 Thermal properties of MA/PA/SM eutectic mixture with Shorea javanica
Composite phase change materials (CPCMs)
Tm (C)
ΔHf,m (J/g)
Ts (C)
ΔHf,s (J/g)
MA/PA/SM + 1% SJ
40.54
179.92
41.46
183.55
MA/PA/SM + 2% SJ
43.20
176.39
41.95
179.95
MA/PA/SM + 3% SJ
43.96
177.45
41.73
180.85
MA/PA/SM + 4% SJ
43.89
169.35
41.75
172.29
MA/PA/SM + 5% SJ
43.75
167.38
41.67
175.63
Table 2 Thermal conductivity of composite phase change materials (CPCMs)
Composite phase change materials (CPCMs)
Thermal conductivity, Wm1 K1
MA/PA/SM + 1% SJ
0.463
MA/PA/SM + 2% SJ
0.475
MA/PA/SM + 3% SJ
0.488
MA/PA/SM + 4% SJ
0.489
MA/PA/SM + 5% SJ
0.492
Investigation of Thermal Characteristic of Eutectic Fatty Acid/Damar Gum. . .
611
conductivity value of MA/PA/SM eutectic mixture (Fauzi et al. 2013). Meanwhile,
the latent heat of fusion only presents a small decrease of 0.93% from 179.12 to
177.45 Jg1.
Different kinds of porous materials have been used in a number of studies to
improve the thermal conductivity of PCMs. Karaipekli et al. (2007) proposed a
different mass fraction of expanded graphite (EG) and carbon ﬁber (CF) to enhance
thermal conductivity of stearic acid (SA). The result of this study reported that
thermal conductivity of SA increased with increasing mass fraction of EG and CF
and indicated an insigniﬁcant decrease on its latent heat of fusion at the same time.
Sari and Karaipekli (2007) studied the effect of addition of expanded graphite
(EG) into parafﬁn in improving the thermal conductivity of parafﬁn. The results
indicated that the thermal conductivity of parafﬁn/EG composite mixture increased
with increasing mass fraction of EG. The decrease of latent heat of fusion also
occurred by increasing the composition of EG in parafﬁn in this study. In the
extended work, these authors evaluated the thermal characteristic and thermal
conductivity improvement of capric acid–myristic acid/expanded perlite and
some fatty acid compounds with expanded graphite. They also proved that these
porous materials were able to improve the thermal conductivity of PCMs and also
reduce its latent heat of fusion insigniﬁcantly (Karaipekli and Sari 2008; Sari et al.
2008).
Moreover, the improvement of thermal conductivity of MA/PA/SM + 3 wt.% SJ
composite mixture was proved by the improvement of its heat transfer rate as seen
in Fig. 3, which shows the heat storage/release duration time needed for the
prepared MA/PA/SM and MA/PA/SM + 3 wt.% SJ to change the phase from
solid to liquid and vice versa. These curves show that the MA/PA/SM + 3 wt.%
SJ eutectic composite needs the shortest time to reach the melting and solidiﬁcation
points compared with the MA/PA/SM eutectic mixture. The heat storage/release
curve of the prepared MA/PA/SM and MA/PA/SM + 3 wt.% SJ also indicates that
the heat transfer rate during heat release process increases faster than theheat
storage process. It is because of the fact thatthe heat transfer process during the
heat storage was controlled by natural convection, and during the heat release, the
heat transfer was controlled by thermal conduction. The increase of thermal con-
ductivity of CPCM had a signiﬁcant enhancement effect on the heat transfer during
heat release than heat storage processes (Zhang and Fang 2006).
3.2
Compatibility of SJ with MA/PA/SM
The FT-IR spectra in Fig. 4 show the absorbance peak for each functional group of
chemical structures of MA/PA/SM, MA/PA/SM + 3 wt.% SJ, and SJ. The spectra
show the same peak area in every range of wave number between MA/PA/SM
eutectic mixture and MA/PA/SM + 3 wt.% SJ eutectic composite. It means that the
improvement of the thermal conductivity of CPCM by the addition of 3 wt. % of
Shorea javanica (SJ) as a porous material does not occur in the chemical reaction in
612
H. Fauzi et al.
the mixture. Thus, it can be noted that the change of thermal properties of MA/PA/
SM/SJ eutectic CPCM was not caused by chemical reaction but due to the physical
properties of Shorea javanica (SJ), namely high melting temperature and low latent
Fig. 3 Heat storage and release curves of MA/PA/SM and MA/AP/SM/SJ
Fig. 4 FT-IR curve of MA/PA/SM, SJ, and MA/PA/SM + 3 wt.% SJ
Investigation of Thermal Characteristic of Eutectic Fatty Acid/Damar Gum. . .
613
heat of fusion, and thereby contributing to the increase in melting temperature and
drop in latent heat of fusion of MA/PA/SM/SJ eutectic CPCM.
3.3
Thermal Stability of MA/PA/SM/SJ
A thermal stability curve of MA/PA/SM + 3 wt.% SJ eutectic composite demon-
strated in Fig. 5 shows that the prepared MA/PA/SM + 3 wt.% SJ eutectic com-
posite does not show any mass degradation within the work temperature 30–160 C.
The weight degradation of MA/PA/SM + 3 wt.% SJ begins to appear once the work
temperature is 168.7 C and reaches an optimum weight degradation at the work
temperature of 289.39 C. Thus, these results indicate that the MA/PA/SM + 3 wt.%
SJ eutectic composite presented good stability to apply as a CPCM in thermal
energy storage application (TES) with the operation temperature under 168 C.
Thermal stability of composite phase change materials (CPCMs) have also been
studied by other researchers. Kim and Drza (2009) measured thermal stability of
parafﬁn/xGnP composite PCM at temperature range of 30–600 C. The mass of
CPCMs started decomposition at 200 C and reached total decomposition at
280 C. In the other work, Jeong et al. (2013) analyzed thermal decomposition of
n-octadecane/diatomite CPCMs at a temperature range of 30–400 C. In this study,
we have showed that the weight decomposition of n-hexadecane/diatomite com-
posite was 50% lower than that of pure n-hexadecane PCM at operation
Fig. 5 TGA curve of MA/PA/SM + 3 wt.% SJ
614
H. Fauzi et al.
temperature 200 C. According to these studies, it can be noted that CPCMs have a
higher thermal stability compared with pure PCMs.
4
Conclusions
In this current study, the preparation and thermal characteristics of a novel eutectic
composite phase change material (CPCM) which involves the myristic acid/
palmitic acid/sodium lautare (MA/PA/SM) and Shorea javanica (SJ) have been
evaluated. The thermal conductivity of this CPCM was simultaneously increased
with increasing the composition of SJ as 1, 2, 3, 4, and 5 wt. %, respectively. But
CPCM with composition 3 wt. % SJ shows a good improvement on thermal
conductivity without signiﬁcant impact in the decreasing latent heat of fusion of
CPCM. The eutectic composite of MA/PA/SM + 3 wt.% SJ also indicates a good
thermal performance, no chemical reaction between each component in the mix-
ture, and has a good thermal stability without any weight degradation within the
work temperature of 30–168 C. Therefore, it can be concluded that Shorea
javanica (SJ) is acceptable to be used as a novel porous material to improve the
thermal conductivity of composite phase change materials (CPCM).
Acknowledgments The authors acknowledge the Minister of Higher Education and Faculty of
Engineering, University of Malaya, through High Impact Research grant (UM.R/HIR/MOHE/
ENG/21-D000021-16001).
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energy storage for air conditioning systems. Renew. Sust. Energ. Rev. 16(8), 5802–5819
(2012) http://dx.doi.org/10.1016/j.rser.2012.05.030
Alkan, C., Sari, A.: Fatty acid/poly(methyl methacrylate) (PMMA) blends as form-stable phase
change materials for latent heat thermal energy storage. Sol. Energy. 82(2), 118–124 (2008)
http://dx.doi.org/10.1016/j.solener.2007.07.001
Cabeza, L. F., Castell, A., Barreneche, C., de Gracia, A., Ferna
´ndez, A. I.: Materials used as PCM
in thermal energy storage in buildings: a review. Renew. Sust. Energ. Rev. 15(3), 1675–1695
(2011) http://dx.doi.org/10.1016/j.rser.2010.11.018
Fauzi, H., Metselaar, H. S. C., Mahlia, T. M. I., Silakhori, M., Nur, H.: Phase change material:
optimizing the thermal properties and thermal conductivity of myristic acid/palmitic acid
eutectic mixture with acid-based surfactants. Appl. Therm. Eng. 60(1–2), 261–265 (2013)
http://dx.doi.org/10.1016/j.applthermaleng.2013.06.050
Fauzi, H., Metselaar, H. S. C., Mahlia, T. M. I., Silakhori, M.: Thermo-physical stability of fatty
acid eutectic mixtures subjected to accelerated aging for thermal energy storage (TES)
application. Appl. Therm. Eng. 66(1–2), 328–334 (2014) http://dx.doi.org/10.1016/j.
applthermaleng.2014.02.014
Investigation of Thermal Characteristic of Eutectic Fatty Acid/Damar Gum. . .
615
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form-stable phase change material for latent heat thermal energy storage. Renew. Energy. 33
(12), 2599–2605 (2008) http://dx.doi.org/10.1016/j.renene.2008.02.024
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(2008) http://dx.doi.org/10.1016/j.renene.2008.02.024
Karaipekli, A., Sarı, A., Kaygusuz, K.: Thermal conductivity improvement of stearic acid using
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616
H. Fauzi et al.
Improving of the Angstr€
om-Prescott Model
Using Harmonic Analysis
Yavuz Selim G€
uc
¸l€
u, _
Ismail Dabanlı, Ey€
up S
¸is
¸man, and Zekai S
¸en
1
Introduction
The fossil fuels source is expected to run out in the next 50 years. This is convenient
in a way because the atmosphere will not be loaded with greenhouse gases, but
instead new renewable energy sources with low carbon emissions should enter the
scene. In the meantime, there are numerous climate change-based researches
focusing on clean and renewable energy ﬁelds (S
¸en 2009). The sun as the mother
of almost all energy sources provides opportunities for solar energy research at
different parts of the world theoretically and through technological investigations
and applications. Solar irradiation calculations and predictions are among the major
ﬁelds of research.
The ﬁrst study in this ﬁeld was presented by Angstr€
om (1924), who suggested
the linear equation between solar irradiation and sunshine duration. Later, his
equation was reﬁned by Prescott (1940) in terms of extraterrestrial solar radiation.
The same formulation was developed by Page et al. (1964) through changing open
day solar irradiation term with lateral solar irradiation that comes outside of
atmosphere. Later on, linear relation between sunshine duration and solar irradia-
tion was studied by many researchers (Swartman and Ogunlade 1967; Sabbagh
Y.S. Güc
¸lü
Istanbul Technical University, Faculty of Civil Engineering, Department of Hydraulics,
Maslak, Istanbul 34469, Turkey
I
˙. Dabanlı, Assist. Prof. Dr. (*) • Z. S
¸en
Istanbul Medipol University, Faculty of Engineering and Natural Sciences, Department of
Civil Engineering, Beykoz, Istanbul 34810, Turkey
e-mail: idabanli@medipol.edu.tr
E. S
¸is
¸man
Yildiz Technical University, Faculty of Civil Engineering, Department of Hydraulics,
Davutpas
¸a, Istanbul 34220, Turkey
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_43
617
et al. 1977; Rietveld 1978; Gopinathan 1988; Soler 1991; Wahab 1993; S
¸en 2001;
Almorox et al. 2013).
S
¸ahin and S
¸en (1998) contributed dynamic terms to linear relation for solar
irradiations. On the other hand, a second degree relation was given by O
¨ gelman
et al. (1984), and they claimed that their method better describes the relationship
between sunshine duration and solar irradiation. Similar research was examined by
Akıno
glu and Ecevit (1990). Furthermore, Samuel (1991) developed a third degree
relationship for the same purpose. S
¸en (2007) also provided non-linear relationship
in the form of power function between sunshine duration and solar irradiation.
Fuzzy logic approaches and neural network modelling techniques were also
suggested for solar energy calculations by S
¸en (1998) and Benghanem et al.
(2009). Neither the linear nor the non-linear modelling techniques include depen-
dency for each term to the previous data. Along this line of research, Güc
¸lü et al.
(2014a, b) gave place to such a dependency between sunshine duration and solar
irradiation in their model by using current and previous data. However, all accom-
plished models do not include periodicity of sunshine data.
The periodicity of meteorological data is generated from the earth’s rotation
around its axes and movement around the sun. These rotation and movements are
formed daily, monthly, seasonally and annually in the form of solar irradiation
periodicities. Some researchers used sine and cosine or harmonic analysis tech-
niques in order not to predict sunshine duration versus solar irradiation (Balling
1983; Amato et al. 1986; Baldasano and Berna 1988; S
¸ahin et al. 2001).
The aim of this study is to combine harmonic analysis and the Angstr€
om-
Prescott model to obtain better solar irradiation predictions with their comparisons
with the classical Angstr€
om-Prescott model.
2
Methodology
The harmonic and the Angstr€
om-Prescott models are used for the prediction of solar
irradiation on the basis of sunshine duration data.
2.1
Angstr€
om-Prescott Model
The original Angstr€
om-Prescott (1940) equation relates to the global irradiation
amount, H, to sunshine duration, S, after their division to respective astronomical
(extraterrestrial) counterparts, H0 and S0, and also to dimensionless coefﬁcients,
a and b, and it is expressed simply as:
H
H0


¼ a þ b
S
S0


ð1Þ
618
Y.S. Güc
¸lü et al.
2.2
Improved Model
The periodicity on sunshine duration and solar irradiation occurs from hour to
annual periods naturally (S
¸en 2002). Periodicity can be recognized visually when
the data set variation scatter diagrams are generated. The initial step of model is to
get arithmetic average for each day on the basis of daily time interval for sunshine
duration and solar irradiation. Hence, there are 1st, 2nd, 3rd, . . ., 365th daily
averages for the harmonic analysis.
Harmonic analysis mathematical expression can be formulized in two different
ways according to Eqs. 2 and 3 as follows:
Yi ¼ a sin
2π i
T þ φsin


þ 
Y
ð2Þ
Yi ¼ a cos
2π i
T þ φcos


þ 
Y
ð3Þ
where a is a coefﬁcient and i refers to the count from 1 to 365, whereas T is the
whole duration as 365 days, sin and cos are phase angles, Ῡis the average of the
data and Yi is the ith day measurement. By using trigonometric substituents, Eq. 2
can be converted to Eq. 4 as:
Yi ¼ a cos φ
ð Þ sin
2π i
T


þ a sin φ
ð Þ cos
2π i
T


þ 
Y
ð4Þ
In this equation, A and B constant coefﬁcients are named succinctly as in the
following equation:
Yi ¼ A sin
2π i
T


þ B cos
2π i
T


þ 
Y
ð5Þ
After calculating the numerical values of A, B, then Ῡand Yi can be solved
easily. The numerical solution needs ﬁrst to integrate Eq. 4 from 0 to T as in the
following expression:
ZT
0
Yidi ¼ A
ZT
0
sin
2π i
T


di þ B
ZT
0
cos
2π i
T


di þ 
Y
ZT
0
di
ð6Þ
The mathematical integration results for the ﬁrst and second terms in Eq. 6 go to
0 (see Eqs. 7 and 8):
ZT
0
sin
2π i
T


di ¼  cos
2π i
T





T
0
¼ 0
ð7Þ
Improving of the Angstr€
om-Prescott Model Using Harmonic Analysis
619
ZT
0
cos
2π i
T


di ¼ sin
2π i
T





T
0
¼ 0
ð8Þ
When this mathematical substition is applied in Eq. 6, then the following new
formula is generated:
ZT
0
Yidi ¼ 
Y
ZT
0
di ¼ 
YijT
0 ¼ 
YT
ð9Þ
The ﬁrst term in this equation expresses in serial by considering Y0 ¼ 0, then Ῡ
can be calculated as constant through the following formula:

Y ¼ 1
T
X
T
i¼1
Yi ¼ constant
ð10Þ
Numerical solution of A and B coefﬁcients can be calculated from Eqs. 11, 12
and 13. Firstly, Eq. 5 is multiplied by and then calculation is achieved through the
integration as:
ZT
0
Yi sin
2π i
T


di ¼
A
ZT
0
sin 2 2π i
T


di þ B
ZT
0
cos
2π i
T


sin
2π i
T


di þ 
Y
ZT
0
sin
2π i
T


di
ð11Þ
ZT
0
cos
2π i
T


sin
2π i
T


di ¼ 1
2cos 2 2π i
T





T
0
¼ 1
2 þ 1
2 ¼ 0
ð12Þ
ZT
0
sin 2 2π i
T


di ¼ 1
8π

4π4  T sin
4π i
T





T
0
¼ T
2
ð13Þ
If the results of the above equations are combined into Eq. 14 and then if the ﬁrst
term is expanded into a series, then Eq. 15 can be generated as a constant:
ZT
0
Yi sin
2π i
T


di ¼ AT
2
ð14Þ
620
Y.S. Güc
¸lü et al.
A ¼ 2
T
X
T
i¼1
Yi sin
2π i
T


¼ constant
ð15Þ
The same calculation steps for determining B coefﬁcient follows A coefﬁcient
determination procedure, and after serial expression, the following expression can
be produced:
B ¼ 2
T
X
T
i¼1
Yi cos
2π i
T


¼ constant
ð16Þ
Daily average of sunshine duration and solar irradiation data are suitable for
harmonic analysis. The new sequence is generated according to harmonic analyses
by subtracting the daily averages, (Yi_mean), from the available data for S and H.
These sequences are divided by S0 and H0, respectively, to obtain the dimensionless
sequences. These new sequences are necessary for the Angstr€
om-Prescott model
production in a better way. The prediction of daily irradiation values is then
calculated by use of improved model. For this purpose, Hp is predicted as solar
irradiation with Hh, as harmonic model result and HA as Angstr€
om-Prescott model
results. Mathematical expression for solar irradiation prediction results as:
Hp ¼ Hh þ HA
ð17Þ
3
Application
Data obtained from the Turkish State Meteorological Service, for Diyarbakır City
of Turkey, are used for the application. This location lies at latitude 40 13.50 E and
longitude 37 54.50 N. The data set includes sunshine duration and solar irradiation
daily measurements from 2000 to 2008. Data set is split into two groups as for
testing and validation, where test data consist of 2000–2005 years and remaining
data are for validation.
3.1
The Angstr€
om-Prescott Model
The classical Angstr€
om-Prescott model scatter diagram provides results as seen in
Fig. 1, where a and b coefﬁcients are calculated as 0.2893 and 0.4871, respectively.
After this stage, the predictions are achieved through the Angstr€
om-Prescott
model. The estimated solar radiation values are compared with current values to
determine the amount of errors.
Improving of the Angstr€
om-Prescott Model Using Harmonic Analysis
621
3.2
Improved Model
Measured time series between 2000 to 2005 for sunshine duration and solar
irradiation are illustrated in Fig. 2. As seen in this ﬁgure, periodicity of solar
irradiation and sunshine duration is very obvious. Harmonic analyses results are
plotted for sunshine duration and solar irradiation in Figs. 3 and 4.
On the other hand, A, B and Ῡcoefﬁcients in Eq. 5 are calculated after harmonic
analyses end up, and the results are tabulated in Table 1.
The new Angstr€
om-Prescott equation for improved model is generated by using
the difference of the harmonic analysis values (Yi) from daily averages, (Yi_mean)
and normalized by S0 and H0. The model results are illustrated in Fig. 5. Angstr€
om-
Prescott equation coefﬁcients a and b are calculated as 0.0101 and 0.5294, respec-
tively, for this model.
The errors of predictions are calculated according to the following well-known
expression as the mean absolute error (MAE), which can be written for N data
values:
MAE ¼ 1
N
X
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
Measured Data  Predicted Data
ð
Þ2
q
ð18Þ
Fig. 1 Scatter diagram for the classical Angstr€
om-Prescott model
622
Y.S. Güc
¸lü et al.
Fig. 2 Time series for measured sunshine duration and solar irradiation
Fig. 3 Sunshine duration harmonic analysis diagram for improved model
Improving of the Angstr€
om-Prescott Model Using Harmonic Analysis
623
The improved model’s error summation is equal to 40,817.5 cal/cm2 for solar
irradiation. Besides this, the Angstr€
om-Prescott model’s sum of errors equals to
43,996.2 cal/cm2. These numerical values prove that improved model predictions
are more successful than Angstr€
om-Prescott model at approximately 7.2%.
4
Conclusion
Recently clean energy resources are under intensive research all over the world due
to atmospheric pollution caused by fossil fuels through global warming and green-
house effects, which trigger climate change and variation. Consequently, clean
energy sources, such as the wind, solar, solar-hydrogen and geothermal energies,
are becoming increasingly under focus for technological and scientiﬁc develop-
ments. In this paper a new approach is suggested for the estimation of solar energy
Fig. 4 Solar irradiation harmonic analysis diagram for improved model
Table 1 A, B and Ῡ
coefﬁcients
Sunshine duration
Solar irradiation
Ῡ
7,790,594
427,5009
A
1,080406
10,5402
B
4,276,403
248,7756
624
Y.S. Güc
¸lü et al.
by considering the combined classical Angstr€
om-Prescott equation and the har-
monic analyses model. In the literature, a variety of models are suggested for solar
irradiation estimation, but none of them include harmonic analyses. The suggested
method is a simple and new approach for solar energy calculations. The improved
model outperforms the classical linear model at approximately 7%.
Acknowledgments This paper consists same methodology but different application of Güc
¸lü
et al. (2015).
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glu, B.G., Ecevit, A.: Construction of a quadratic model using modiﬁed Angstr€
om coefﬁ-
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Energy. 37(3), 179–194 (1986)
Angstr€
om, A.: Solar terrestrial radiation. Q. J. R. Meteorol. Soc. 50, 121–126 (1924)
Fig. 5 Scatter diagram for the classical Angstr€
om-Prescott model for different values
Improving of the Angstr€
om-Prescott Model Using Harmonic Analysis
625
Baldasano, J.M., Clar, J., Berna, A.: Fourier analysis of daily solar radiation data in Spain. Sol.
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Y.S. Güc
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EEG Analysis Using a Wavelet Packet
Transforms Mean Energy and Mean Teager
Energy with an Artiﬁcial Neuro-Fuzzy System
K.S. Biju, M.G. Jibukumar, and C. Rajasekharan
1
Introduction
The human body can achieve many functions. It is one of the most perfect creations
imaginable. The human body is controlled by the brain, and the efﬁciency of the
human body in achieving assigned functions is directly related to the capability of
the human brain .The brain is considered a system, which is made up of the central
nervous system, which is the master controller, and the peripheral nervous system,
which interconnects the brain to everything (Fauci et al. 2011). Different parts of
brain have different functionality. These functions are indicated as electrical activ-
ity in the brain, which is generated from brain neurons. This electrical potential is
effectively recorded using an electroencephalography (EEG).
The EEG contains different frequency bands that show the different types of
brain activity. The main challenge is to develop a suitable method to detect
unknown patterns that are an indication of abnormal brain function (Gotman
1999). The time-frequency analysis of EEG data has been much improved by
feature extraction (Tzallas et al. 2009). However, time and frequency information
K.S. Biju (*)
Electronics and Communication Engineering Department, Government Engineering College,
Bartonhill, Thiruvananthapuram 695035, India
Electronics Engineering Division, School of Engineering, Cochin University of Science &
Technology, Kochi 682022, India
e-mail: bijukarunnya@gmail.com
M.G. Jibukumar
Electronics Engineering Division, School of Engineering, Cochin University of Science &
Technology, Kochi 682022, India
C. Rajasekharan
Medical College Hospital, Thiruvananthapuram 695011, India
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_44
627
are both trade-offs for each other, depending on the duration of the window. The
wavelet transform decomposes the signal for a ﬁxed basis of functions to avoid
constraints associated with non-stationary signals consisting of both time and
frequency information (Khan and Gotman 2003). The EEG power spectrum is
used as a computer diagnostic tool for epilepsy, an abnormal state of the brain
(Kerr et al. 2012). It reduces the diagnosis time compared with conventional long-
term video monitoring. EEG raw data are classiﬁed into different frequency bands
of brain waves using a wavelet packet transform (Sinno and Kifah 2008; Zandi et al.
2010), which are then compared with different statistical parameters. The Teager
energy measurement of electocorticography is used for functional mapping of the
brain during a controlled 50-Hz cortical stimulation (Gwinn et al. 2008). It has been
observed that the post-signal power occurs in the beta and gamma frequency bands.
The artiﬁcial neural network is a popular computational model for multi-input
multi-output systems using back-propagation tuning (Narendra and Parthasarathy
1990). The artiﬁcial neural network is the leading performance predictor of the
proton member fuel cell (Bhagavatula et al. 2012). Here four leading parameters
are input, with four hidden layers, and the back-propagation method is adopted for
interpreting the data. Neural networks are used for feature selection methods for EEG
signals (Garrett et al. 2003).The ANFIS (adaptive neuro-fuzzy inference system)
hybrid learning procedure is used for human experience with if-then rules and
prescribed input-output data (Jang 1993). ANFIS is also used in the energy research
ﬁeld. ANFIS is effectively used for modelling proton member fuel cell-based
nanocomposites and recast naﬁon membranes (Amirinejad et al. 2013). It compares
the different neural network models. It is also used for evaluation of human emotions
(Malkawi and Murad 2013). Each measured human variable has a separate input-
output membership function, and a set of fuzzy rules for the different emotions can be
studied. Using a combination of different classiﬁcation methods improves the rate of
prediction. The hybrid wavelet and neural network methods show improved fore-
casting of the energy usage (Voronin and Partanen 2014).
In this study the EEG is decomposed into four levels of wavelet packet transform.
Theselectedpacketscontainthe frequency rhythmsofthe EEG,i.e., delta,theta,alpha,
beta, and gamma. Figure 44.1 shows the signal ﬂow diagram of the system. They are
then compared using different statistical analyses of each packet. The classiﬁcation
into normal and abnormal EEG is performed using ANFIS, as described above.
2
Methodology
2.1
Data Collection
The EEG data were obtained from the Department of Epileptology, University of
Bonn, Bonn, Germany (Andrzejak et al. 2001). There are ﬁve sets of normal and
abnormal EEG data with 500 single-channel EEG segments. The data set A and
628
K.S. Biju et al.
data set B are the healthy EEG sets in open-eye and closed-eye conditions. The data
set C and data set D are the unhealthy EEG sets, with an interictal condition. The
data set E is an abnormal (seizure) EEG. The data are sampled at 173.61 Hz with
each segment being 23.3 s in duration and having 4,096 data points (Subramaniyam
and Hyttinen 2013). In this study set B and set E are only used for analysis. The raw
EEG signal is fed through a low-pass ﬁlter of 0.5 Hz and also through a 64-Hz high-
pass ﬁlter to eliminate high-frequency noise before it is loaded to the system.
2.2
Wavelet Packet Transform
The wavelet packet transform (WPT) is an extension of the wavelet transform. It is
a collection of the linear combination multiple bases. In WPT decomposition the
ﬁnite energy signal is down-sampled by a factor of two and split into a detail
approximation signal. The approximation contains low-frequency information and
the detail contains high-frequency information (Wu et al. 2008). Figure 44.2 shows
the one-leg sub-spaces of the four-level wavelet packet decomposition. The original
signal is decomposed into different sub-bands, known as packets. At each level in
the decomposition, numbers of packets are 2l, where l is the number of the level.
During the four-level wavelet packet decomposition, the selected packets are
assigned to an EEG frequency rhythm, the sub-band A(4,1) as a delta band, A(4,2)
as a theta band, A(4,3) as an alpha band, summation of A(4,4) and A(2,2) as the beta
band, and A(1,2) as the gamma band, which are equivalent to frequency bands of
the EEG. The wavelet packet energy spectrum provides time-frequency informa-
tion of a time series EEG signal (Dianguo et al. 2010).
EEG signal
WPT
decomposition
Statistical
comparison
Classification
using ANFIS
Classified Output
Fig. 44.1 Signal ﬂow of
the proposed system
EEG Analysis Using a Wavelet Packet Transforms Mean Energy and Mean Teager. . .
629
3
Feature Vector Formation
The method involved in this study was ﬁrst to load the EEG signal of the system.
Taking the wavelet packet decomposition of each channel, the assigned packets for
the frequency rhythms of the EEG signal, the coefﬁcients of energy, entropy, mean
energy, and mean Teager energy are calculated and the coefﬁcients are used for
training data using ANFIS (Sinno and Kifah 2008). Average errors of the epoch for
a healthy EEG and an abnormal EEG are determined.
To form the feature vectors, statistical parameters like energy, entropy mean
energy, and mean Teager energy are calculated by
(i) Energy(E)
The energy of a signal shows the quantum of activity. The energy of a ﬁnite
signal is deﬁned as follows (Artameeyanant et al. 2012):
EnergyðlÞ ¼
Xn
i¼1 xðiÞ2
ð44:1Þ
where x(i) is the instant quantized value of the brain wave, n is the total number of
samples, and l represents the decomposed level. The energy of the abnormal EEG
and normal EEG are compared, with the abnormal EEG containing maximum
energy.
(ii) Entropy
Entropy is the measure of the unpredictability of a signal (Krstacic et al. 2002),
which is represented as:
EEG
A (1, 1)
A (3, 1)
A (2, 1)
A (1, 2)
2
2
2
2
2
2
2
2
A (2, 2)
A (3, 2)
A (4, 2)
A (4, 1)
Fig. 44.2 Wavelet packet
decomposition
630
K.S. Biju et al.
EntropyðlÞ ¼
XN
j¼1 D2
i,jlogðD2
i,jÞi ¼ 1, 2, . . . ::l
ð44:2Þ
(iii) Mean energy
The mean energy is the amount of energy over a speciﬁc period of time. When
mean energy is increased in a signal associated with an abnormality of the brain, the
duration of the period is given as:
Mean EnergyðlÞ ¼ 1
N
X l
i¼lNþ1 xðiÞ2
ð44:3Þ
where N is the length of segment in the decomposed wavelet packet transform.
(iv) Mean Teager energy (MTE)
The mean Teager energy is highly efﬁcient for spike detection, which is char-
acterized by localized high frequencies and an increase in instantaneous energy
(Gopan et al. 2013). It can be deﬁned as:
MTEðlÞ ¼ log 1
N
Xl
i¼lNþ3 xði  1Þ2  xðiÞxði  2Þ

ð44:4Þ
where N is the length of the corresponding wavelet decomposed segment. The
feature normalization is accomplished by logarithmic scaling. Mean Teager energy
gives the maximum value for abnormal EEG.
4
Results and Discussion
The EEG data used in this study contained ﬁve sets of 100 single channels of 23.3 s
duration sampled at 173.6 Hz. A total of 4,096 samples were present in each
segment of the dataset. The frequency band of the EEG is ﬁxed as 0.5 Hz–64 Hz.
Frequencies greater than 64 Hz are considered to be noise and can be eliminated.
The signal is ﬁrst decomposed into four levels of WPT. The selected decomposed
packets are the sub-bands of EEG rhythm. The sub-band frequencies of 0.5–4 Hz,
4–8 Hz, 8–12 Hz, 12–32 Hz, and >32 Hz are available in A(4,1), A(4,2), A(4,3),
combination of A(4,4) and A(2,2), and A(1,2) packets, respectively. Table 44.1
shows the different values of the statistical parameters.
The feature vectors of each proposed sub-band in abnormal and normal signals
are calculated with the energy, entropy mean energy, and mean Teager energy
parameters. The mean energy and mean Teager energy are the low values in the
normal and abnormal signals. The normal EEG in all parameters has less magnitude
EEG Analysis Using a Wavelet Packet Transforms Mean Energy and Mean Teager. . .
631
in abnormal EEG signals. In an abnormal EEG the delta band has the highest mean
energy of all the sub-bands. Next highest mean energy is in theta sub-band. This
indicates that the abnormal state (epilepsy) of the brain requires a high level of
energy to demodulate to normal and pathological neuronal activities (Cloix and
He
´vor 2009; Wu et al. 2015).The feature vector is input into the ANFIS. Using
ANFIS the given input/output data are constructed on a fuzzy inference system
(FIS), the membership function parameters of which are tuned using a back-
propagation algorithm. This allows fuzzy systems to learn from the input/output
data and model an expert system. Table 44.2 shows the average error of FIS training
for 1,000 epochs.
The mean energy and the mean Teager energy provide the maximum efﬁciency
with minimum error compared to other features. Figure 44.3 shows the training
error of mean Teager energy on the delta band in the ANFIS editor.
It was observed that in the ANFIS editor mean Teager energy has a minimum
training error of 0.0325 in the delta sub-band of the EEG.
The training error in all sub-bands of the EEG was much lower for mean Teager
energy compared to other parameters. This suggests that mean Teager energy is
more suitable for further classiﬁcation of EEG signals.
By observing the wavelet packet spectrum of normal and abnormal EEG signals,
the abnormal EEG spectrum is denser than the spectrum of the normal EEG signals.
Table 44.1 Tabulation of
statistical parameters
Delta
Theta
Alpha
Beta
Gamma
En (107)
N
2
0.3
2
2
0.2
A
62
55
53
45
23
MEn
N
9.1
5.0
9.2
5.6
5.5
A
10.2
8.4
7.5
7.2
5.1
MTE
N
2.5
3.5
4
6
5
A
10.1
9.5
9.5
7.1
7.4
E (108)
N
4.5
4.5
4.5
3
0.3
A
93
11
150
150
100
En energy, MTE mean Teager energy, MEn mean energy,
E entropy, A abnormal, N normal
Table 44.2 Average error for 1,000-epoch fuzzy inference system (FIS) training
Frequency band
Delta
Theta
Alpha
Beta
Gamma
En (105)
5.24
1.24
6.02
3.26
7.37
MEn (107)
0.039
0.232
0.267
0.299
0.157
MTE
0.032
0.231
0.245
0.186
0.034
E (106)
10.396
2.261
11.964
0.616
14.385
En energy, MTE mean Teager energy, MEn mean energy, E entropy, A abnormal, N normal
632
K.S. Biju et al.
It indicates that during the abnormal state (epilepsy) of the brain more energy is
delivered to the lower frequency regions (Fig. 44.4).
5
Conclusion
In this proposed method, the EEG signal is analyzed using wavelet packet trans-
form for feature extraction and a hybrid neuro-fuzzy system for classiﬁcation. Here
the signal is ﬁltered by a band-pass ﬁlter to suppress the noise. Then the wavelet
packet transform decomposes into four levels to obtain the frequency rhythm of
delta, theta, alpha, beta, and gamma bands. Features of the sub-band coefﬁcients are
analysed by different statistical parameters like entropy, energy, mean Teager
energy, and mean energy. If the brain produces high energy in the alpha band it
reﬂects a normal brain state. If the brain produces high energy in the delta and theta
bands, it seems to reﬂect an abnormal brain state (epilepsy). Finally, a constructed
feature vector, the mean Teager energy, is used as an input to the artiﬁcial neuro
fuzzy system to classify abnormal and normal EEG signals. Out of four parameters
Fig. 44.3 Training error of mean Teager energy on delta band
EEG Analysis Using a Wavelet Packet Transforms Mean Energy and Mean Teager. . .
633
it was shown that the mean energy and mean Teager energy coefﬁcient of features
have a minimum training error of 1,000 epochs. The mean Teager energy coefﬁ-
cient vector has a lower error than mean energy. Therefore mean Teager energy is
used for constructing the feature vector for classiﬁcation of normal and abnormal
EEG signals.
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635
Optical Simulation of Different Cavity
Receivers Shape Used in Solar Tower
Power Plant
Touﬁk Arrif, Adel Benchabane, Amor Gama, Hakim Merarda,
and Abdelfateh Belaid
1
Introduction
In a CRS, the solar receiver is the heat exchanger where the solar radiation is
absorbed and transformed into thermal energy useful in power conversion systems.
There are different classiﬁcation criteria for solar receivers, depending on the
geometrical conﬁguration and the absorber materials used to transfer the energy
to the working ﬂuid. In this survey, receivers are classiﬁed into three groups widely
employed in central receiver system, volumetric receivers, cavity receivers and
particle receivers (Omar et al. 2013). This paper is interested in cavity receivers
group.
In a cavity receiver, the radiation reﬂected from the heliostats passes through an
aperture into a boxlike structure before impinging on the heat transfer surface.
James and Terry (1985) have investigated the thermal performance of ﬁve cavity
receivers of different geometries comprising spherical, hetero-conical, conical,
T. Arrif (*)
Unite
´ de Recherche Applique
´e en Energies Renouvelables, URAER, Centre de
De
´veloppement des EnergiesRenouvelables, CDER, 47133 Ghardaı
¨a, Algeria
University Mohamed KhiderBiskra, Mechanical Engineering Department, Laboratory
of Mechanical Engineering, PO Box145, RP07000 Biskra, Algeria
e-mail: arriftou@yahoo.fr
A. Benchabane
University Mohamed KhiderBiskra, Mechanical Engineering Department, Laboratory
of Mechanical Engineering, PO Box145, RP07000 Biskra, Algeria
A. Gama • H. Merarda • A. Belaid
Unite
´ de Recherche Applique
´e en Energies Renouvelables, URAER, Centre de
De
´veloppement des EnergiesRenouvelables, CDER, 47133 Ghardaı
¨a, Algeria
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_45
637
cylindrical, and elliptical. They have found that the rim angle and cavity geometry
have a strong effect on the energy absorption efﬁciency.
Fang et al. (2011) have described a methodology for evaluating thermal perfor-
mance of saturated steam solar cavity receiver under windy environment. To this
end, the Monte Carlo method, the correlations of the ﬂow boiling heat transfer, and
the calculation of air ﬂow ﬁeld were coupled to assess absorbed solar energy.
They have concluded that the air velocity attained the maximum value when the
wind came from the side of the receiver and the thermal loss of receiver also
reached the highest value due to the side-on wind.
Yang et al. (2012) have used computational ﬂuid dynamics to look into the
distributions of temperature, heat ﬂux, and the heat transfer characteristics of a
molten salt tube receiver of a central receiver system. They have concluded that
temperature distribution of the tube wall and HTF (Heat Transfer Fluid) is irregular
and the heat ﬂux of the exposed surface rise with the rise of molten salt velocity.
Paitoonsurikarn and Lovegrove (2006) have numerically examined three differ-
ent cavity geometries. They have then established a correlation of the Nusselt
number for natural convection.
Fang et al. (2011) have proposed an iterative method, based on Monte Carlo ray
tracing (MCRT) technique, to determine surface temperature and to investigate the
performance of cavity receiver under windy conditions.
Wu et al. (2011) have conducted a 3-D numerical study to examine the effect of
geometric design of heat pipe receiver on natural convection characteristics. They
have then proposed a new accurate correlation of Nusselt number that can estimate
natural convection heat losses.
Garcia et al. (2008) have compared six simulation environments for solar tower
power investigation: UHC, DELSOL, HFLCAL, MIRVAL, FIAT LUX, and
SOLTRACE. They have then classiﬁed them into two groups. The ﬁrst one
comprises optimization codes HFLCAL, UHC-RCELL, and DELSOL. The second
group includes performance analysis codes such as FIAT LUX, MIRVAL,
UHC-NS, and SOLTRACE.
Lina et al. (2014) have investigated experimentally as well as numerically the
optical and thermal performance of a linear Fresnel lens solar collector using
different shapes of cavity receiver. The optical performance was analyzed by
using TracePro software which uses Monte Carlo ray tracing (MCRT) method.
They have found that triangular cavity receiver is suitable for solar thermal utili-
zation in the medium temperature range (100–150 C) without using the conven-
tional glass-metal tube absorber.
In this paper, a new study of solar power tower systems using TracePro software
to obtain a concentrated ray distribution as uniform as possible on the inner walls of
the cavity receiver of different shape was carried out. It will allow us to deﬁne a
reference incident ﬂux distribution that will be used to predict thermal performance
of the cavity receiver.TracePro software uses Monte Carlo ray tracing which is a
technique that computes the outcome of random processes.
In Monte Carlo ray tracing method, scattering and diffraction are treated as
random processes. That is, instead of propagating a distribution of light, discrete
638
T. Arrif et al.
samples of the distribution (rays) are propagated. The samples are randomly
chosen, using the scattering distribution as a probability density which in turn
facilitates the use of the well-developed techniques of ray tracing to model scatter-
ing (Lambda Research Corporation 2009).
Nomenclature
δ
Solar declination (radians)
φ
Latitude (radians)
nd
Day number
ωsunrise
Hour angle of sunrise
ωsunset
Hour angle of sunset
h
Solar altitude (radians)
~
S
a
Solar azimuth (radians)
HT
Tower height (m)
(XM, YM, ZM)
Heliostat Cartesian coordinate
system (m)
r
Ring radius (m)
H
Heliostat
R
Receiver
Dap
Aperture diameter (m)
Lcav
Cavity length (m)
Aw
Cavity inner wall area (m2)
Greek letters
ψ
Heliostat relative angle referenced
to the base of the tower (radians)
β
Angle of beam rays (radians)
θ
Angle of incidence (radians)
γ
Heliostat azimuth angle (radians)
α
Heliostat inclination angle (radians)
λ
Cavity inclination angle (degree)
2
Numerical Procedure
2.1
Selecting a Reference Case
The ﬁrst step in the optical study is to deﬁne a reference case to complete the
following survey. In our case, a mini solar power plant was chosen to study in turn
12 heliostats on a 15  14 m area (each one 1 m2) (Figs. 1 and 2), for the winter
solstice (December 21) at 12 GMT. Based on the work of Siala and Elayeb (2001),
Belhomme et al. (2009), and Lundy (2003) and using the DELPHI environment, we
Optical Simulation of Different Cavity Receivers Shape Used in Solar Tower. . .
639
have calculated the ﬁeld layout by considering shading and blockage effects.
The cavity receiver is placed at 6.5 m at the top of the tower.
Direct solar radiation incident on a horizontal plane in Ghardaia (clear day) for
the selected dates (21 December) is 525 W/m2. It was obtained using a known
radiation model R_SUN which is developed in MATLAB.
Parameters relating to the heliostat ﬁeld, position of the cavity receiver, sun
position, angle of incidence, the reﬂected beam position, and orientation of
the heliostat are calculated (MATLAB) to ﬁnd the angles to be followed by the
heliostat to reﬂect the incident ray of the sun toward a ﬁxed target at the top of the
tower.
Fig. 1 The shape and
dimensions of the heliostat
Fig. 2 Radial stagger
arrangement of 12 heliostats
in TracePro environment
(December 21 at 12 pm)
640
T. Arrif et al.
2.2
Solar Position
The solar declination δ and hour angle of sunrise and sunset ω as a function of day
number nd and latitude φ (all angles in radians) are calculated as (Dufﬁe and
Beckman 2006), (Capderou 1986):
δ ¼ 23:45π
180
sin
2π 284 þ nd
365


ð1Þ
ωsunrise ¼ cos 1 tan φ tan δ
ð
Þ  π ¼ ωsunset
ð2Þ
The sun’s position relative to an observer on the ground is described by two
angles, the solar altitude h and azimuth a:
sin a ¼ cos δ sin ω
cos h
ð3Þ
sin h ¼ sin φ sin δ þ cos φ cos δ cos ω
ð4Þ
2.3
Heliostat-Tour Relations
We consider the heliostat ﬁeld, which is deﬁned in an orthonormal reference, whose
center is at the base of the tower, and the three directions are, respectively, the
north, west, and the zenith. Figure 3 shows the heliostat tower together in a
Fig. 3 Description of angles of a heliostat-tour system in Cartesian coordinates
Optical Simulation of Different Cavity Receivers Shape Used in Solar Tower. . .
641
Cartesian coordinate system associated to the tower base(0, 0, HT) with HT ¼ 6.5 m,
the coordinates of heliostat (XM, YM, ZM) are deﬁned in this orthonormal
reference by:
XM ¼ r sin ψ
YM ¼ r cos ψ
ZM ¼ ZM
8
>
>
<
>
>
:
ð5Þ
and
r ¼
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
ﬃ
X2
M þ Y2
M
q
ð6Þ
From the coordinates of the heliostat H and cavity R, we deﬁne a positioning
relative angle ψ referenced to the base of the tower in a horizontal plane
(by convention, the heliostats to the west of the tower have a positive angle ψ)
and a target angle of β in a vertical plane (D Claude 1983), and according to
previous relations, we obtain the following relations for ψ and β:
ψ ¼ tan 1 XM
YM


β ¼ tan 1 HT  ZM
r


ð7Þ
The angle of incidence θ of the rays on the reﬂective surface is calculated with
the following equation (Chong and Tan 2012):
θ ¼ 1
2 cos 1 sin h sin β þ cos h cos β sin a sin ψ þ cos a cos ψ
ð
Þ
ð
Þ
ð8Þ
2.4
Normal Heliostat Orientation
Each heliostat is individually adjusted according to two angles, heliostat azimuth
angle γ and heliostat inclination angle α (the normal/horizontal). To reﬂect the rays
to the center of the cavity aperture R, the position of the heliostat is then given by
the following relations (Chong and Tan 2012):
642
T. Arrif et al.
α ¼ sin 1
sin h þ sin β
2 cos θ


ð9Þ
γ ¼ sin 1
sin a cos h þ cos β sin ψ
2 cos θ cos α


ð10Þ
2.5
Size of the Cavity aperture
The cavity receiver must be inclined in such a way that the normal vector to the
cavity aperture is directed toward the center of the heliostat ﬁeld and such that the
incident ﬂux will be intercepted in the middle area of the vertical wall opposing the
cavity aperture (Benjamin n.d.), the angle was calculated as λ ¼ 32 . 02
,
In order to ﬁnd the size of the cavity receiver aperture, the heliostat ﬁeld reﬂects
the rays on a 2  2 m rectangular plate target as shown in Fig. 4, and the results are
shown in Fig. 5.
It is observed that the angle of inclination of a rectangular plate made the
difference in the maximum value of the concentrated ray ﬂux and in the size of
the sunspot; thus, the size of the cavity receiver aperture will be Dap ¼ 1.2 m.
3
Types of Cavities Investigated
The current work deals with cavity receiver which has been reported in the
literature (Pavlovic and Penot 1991, Fang et al. 2013, Robert n.d., Benjamin n.d.)
for central receiver power plant. These are cylindrical, cubical, and trapezoidal
prism shape; the 3-D cavity models were developed using GAMBIT 2.4.6 and
imported in TracePro and are shown in Fig. 6. The optical properties of heliostat
Fig. 4 Size of the
rectangular ﬂat plate
Optical Simulation of Different Cavity Receivers Shape Used in Solar Tower. . .
643
Fig. 5 Incident concentrated rays on the rectangular ﬂat plate (2  2 m): (a) λ ¼ 00 (b) λ ¼ 32 . 020
644
T. Arrif et al.
mirrors, cavity wall, and rectangular ﬂat plate used in this simulation are listed in
Table 1. While comparing these different shapes, their aperture diameter is taken to
be same Dap ¼ 1.2 m.
The effect of shape on the concentrated ray distribution is studied with the
aperture diameter ﬁxed (Dap ¼ 1.2 m); the length of cavity is varied to get constant
inner wall area Aw equal to 7.2 m2 for all shapes. This is due to the fact that for a
given central receiver power plant, the size of the cavity is ﬁxed and represents
(an almost) 3–4 m placed on an 80–120 m high tower. In the present study, a mini
solar central receiver power plant of 20 (12) heliostats (1 m2 each) is considered, the
size of the cavity can be rather small compared to a real central receiver power
plant.
While comparing these different cavities, cubical cavity is taken as the base
cavity with diameter of aperture Dap ¼ 1.2 m, and the length of the cavity
Lcav ¼ 1.2 m and cavity inner wall area Aw ¼ 7.2 m2.
4
Results and Discussions
4.1
Flux Distribution
In the present simulation, for Ghardaia region in Algeria, the solar radiation value
of 525 W/m2 was chosen as input source, which represents the maximum average
radiation received over a typical day December 21 in Ghardaia (south Algeria). The
simulated ray tracing results for 562,500 sunrays with heat ﬂux of 525 W/m2 are
shown in Fig. 7. As seen, most of the rays traced by the simulation could be
concentrated on the cavity receiver.
Fig. 6 Types of cavities
investigated
Table 1 Optical parameters
of heliostat and cavity
Device
Reﬂectance
Absorptance
Rectangular ﬂat plate
0.1
0.9
Heliostat mirror
0.95
0.05
Cavity wall
0.1
0.9
Optical Simulation of Different Cavity Receivers Shape Used in Solar Tower. . .
645
Optical simulation was performed employing TracePro ray tracing software
using MCRT method. The incident solar ﬂux distribution on absorbing surface of
three different types of cavity receiver involved was obtained and is shown in
Figs. 8 and 9.
Based on incident solar ﬂux distribution maps shown in Figs. 8 and 9 and ﬂux
reported in Table 2, it is seen that the cavity receiver in trapezoidal prism shape
experiences more uniform distribution on absorbing surface, what means minimum
peak ﬂux density with minimum lost ﬂux.
Fig. 8 Distribution of
incident solar ﬂux in
internal wall of the three
cavity receivers
Fig. 7 Radial stagger arrangement of heliostats in TracePro with incident rays in red and reﬂected
rays in blue
646
T. Arrif et al.
5
Conclusions
This paper presents a numerical investigation on ﬂux distribution on a solar cavity
receiver using three different shapes: cubical, cylindrical, and trapezoidal prism.
The heat ﬂux distribution for three types of cavity receivers used in solar tower
power plant was carried out by employing MCRT method. Ray tracing results show
that the cavity with trapezoidal prism shape receives the best uniform ﬂux
distribution.
Optical efﬁciency is not shown in this paper and will be shown in our future
work with thermal efﬁciency to better analyze the performance of cavity receiver;
for this reason, numerical as well as experimental methods must be employed.
Fig. 9 Distribution of incident solar ﬂux in the internal opposite aperture wall: (a) cubical (b)
cylindrical (c) trapezoidal prism
Optical Simulation of Different Cavity Receivers Shape Used in Solar Tower. . .
647
References
Belhomme, B., Pitz-Paal, R., Schwarzboezl, P., Ulmer, S.: A new fast ray tracing tool for high-
precision simulation of heliostat ﬁelds [Journal]. J. Sol. Energy Eng. Trans. ASME. 131,
3 (2009)
Benjamin, G.: Mode
´lisation et dimensionnement d’un re
´cepteur solaire 
a air pressurise
´ pour le
projet PEGASE [Journal]. The
`se univ- Perpignan [s.n.]
Capderou.: Atlas solaire de l’Alge
´rie [Journal]. OPU Tome 2 (1986)
Chong, K.K., Tan, M.H.: Comparison study of two different sun-tracking methods in optical
efﬁciency of heliostat ﬁeld [Journal]. Hindawi Publ. Corp. Int. J. Photoenergy. 2012, 10 (2012)
D Claude.: Performance et limites de chaudie
`re 
a gaz de centrales solaires [Journal]. The
`se N 474.
Vol. The
`se N 474 (1983)
Dufﬁe, J.A., Beckman, W.A.: Solar Engineering of Thermal Processes [Journal]. Wiley (2006)
Fang, J.B., Tu, N., Wei, J.J.: Numerical investigation of start-up performance of a solar cavity
receiver [Journal]. Renew. Energy. 53, 35–42 (2013)
Fang, J.B., Wei, J.J., Dong, X.W., Wang, Y.S.: Thermal performance simulation of a solar cavity
receiver under windy conditions [Journal]. Sol. Energy. 85, 126–138 (2011)
Garcia, P., Ferriere, A., Bezian, J.J.: Codes for solar ﬂux calculation dedicated to central receiver
system applications: a comparative review [Journal]. Sol. Energy. 82, 189–197 (2008)
James, A., Terry, G.: Thermal performance of solar concentrator cavity receiver systems [Journal].
Sol. Energy. 34, 135–142 (1985)
Lambda Research Corporation User’s Manual [Journal]. Lambda Research Corporation, Littleton
[s.n.], Release 5.0 : Vol. Release 5.0 (2009)
Lina, M., Sumathy, K., Dai, Y.J., Zhao, X.K.: Performance investigation on a linear Fresnel lens
solar collector using cavity receiver [Journal]. Sol. Energy. 107, 50–62 (2014)
Table 2 Flux report of different cavities
Cavities
Incident ﬂux (Watt)
Absorbed ﬂux (Watt)
Lost ﬂux (Watt)
Cylindrical
Opp_aperture
7699.11
6929.20
41.98
Surf_int
3464.64
3118.18
62.64
Sum
11,163.75
10,047.38
104.62
Cubical
Opp_aperture
9908.37
8917.53
71.23
Surf_int_H
231.59
208.43
18.06
Surf_int_B
271.96
244.76
23.22
Surf_int_D
751.41
676.27
20.01
Surf_int_G
1133.56
1020.21
21.45
Sum
12,296.89
11,067.20
153.98
Trapezoidal prism
Opp_aperture
5182.66
4664.39
3.14
Surf_H
502.49
452.24
14.20
Surf_B
1310.46
1179.41
14.70
Surf_D
1502.66
1352.40
13.80
Surf_G
1505.63
1355.06
15.66
Sum
10,003.90
9003.51
61.50
648
T. Arrif et al.
Lundy: Sargent & Assessment of Parabolic Trough and Power Tower Solar Technology Cost and
Performance Forecasts [Journal]. National Renewable Energy Laboratory NREL. (NREL/SR-
550-34440) : Vols. (NREL/SR-550-34440) (2003)
Omar, B., Abdallah, K., Kamal, M.: A review of studies on central receiver solar thermal power
plants [Journal]. Renew. Sust. Energ. Rev. 23, 12–39 (2013)
Paitoonsurikarn, S., Lovegrove, K.: A new correlation for predicting the free convection loss from
solar dish concentrating receivers [Journal]. Proceedings of 44th ANZSES Conference.
Australia : [s.n.], (2006)
Pavlovic, M., Penot, F.: Experiments in the mixed convection regime in an isothermal open cubic
cavity [Journal]. Exp. Thermal Fluid Sci. 4, 648–655 (1991)
Robert, Y.Ma.: Wind Effects on Convective Heat Loss from a Cavity Receiver for a Parabolic
Concentrating Solar Collector [Journal]. Sandia National Laboratories Technical Library,
Vols. SAND92–7293
Siala, F.M.F., Elayeb, M.E.: Mathematical formulation of a graphical method for a no-blocking
heliostat ﬁeld layout [Journal]. Renew. Energy. 23, 77–92 (2001)
Wu, S.Y., Xiao, L., Li, Y.R.: Effect of aperture position and size on natural convection heat loss of
a solar heat-pipe receiver [Journal]. Appl. Therm. Eng. 31, 2787–2796 (2011)
Yang, X., Yang, X., Ding, J., Shao, Y., Fan, H.: Numerical simulation study on the heat transfer
characteristics of the tube receiver of the solar thermal power tower [Journal]. Appl. Energy.
90, 142–147 (2012)
Optical Simulation of Different Cavity Receivers Shape Used in Solar Tower. . .
649
Improved Wind Speed Prediction Results
by Artiﬁcial Neural Network Method
Asilhan Sevinc Sirdas, Akatas Nilcan, and Izgi Ercan
1
Introduction
Recently, increase in industrialization and urbanization has brought about a rise in
energy demand. Orientation to renewable energy sources is inevitable because
resources used in energy production have been running out, and they cause irre-
versible damages to the environment. Wind is one of these renewable energy
resources. The most positive impact of wind energy is to not cause the release of
greenhouse gases that are formed as a result of the combustion of fossil fuels.
Besides, the widespread use of wind energy will also reduce pollutant emissions as
a result of reduction in fossil fuel consumption (Fig. 1 and Table 1).
Turkey is under the inﬂuence of the northern wind caused by the general
circulation of the atmosphere. It is also surrounded by seas on three sides and has
high valleys, especially in the eastern regions. All these lead to high wind energy
potential for Turkey. Turkey’s gross wind potential is thought to be 400 billion kWh
per year, while technical potential is thought to be 120 billion kWh per year
(Genc
¸o
glu 2002). According to the Global Wind Report published by Global
Wind Energy Council, the total installed capacity of Turkey was 2312 MW at the
end of 2012; then 646 MW were added in 2013, and it increased to 2959 MW at the
end of 2013 (Url-1 2014).
A.S. Sirdas (*) • A. Nilcan
Istanbul Technical University, Faculty of Aeronautics and Astronautics, Department of
Meteorological Engineering, Maslak, 34469 Istanbul, Turkey
e-mail: sirdas@itu.edu.tr; ssirdas@gmail.com
I. Ercan
Yildiz Technical University, Faculty of Electrical Engineering,
Davutpasa Campus, Istanbul, Turkey
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_46
651
Turkey has continued to make new breakthroughs related to wind energy. In this
respect, this work aimed to form a preliminary study for a wind turbine planned to
take place in Terkos, Istanbul, as the ﬁrst national turbine of Turkey. For this
reason, short-term analysis and predictions of wind for Terkos region were handled
in this study.
Nomenclature
V1
The observed wind speed (m.s1)
V2
The calculated wind speed (m.s1)
Z2
The height level 2 (m)
Z1
The height level 1 (m)
Greek letters
α
An empirically derived coefﬁcient that varies depending upon the stability of the
atmosphere
(continued)
4580000
4578000
4576000
4574000
4572000
4570000
636000
638000
640000
642000
644000
Fig. 1 Digital map of study area by WaSPV3
652
A.S. Sirdas et al.
Superscripts
α
0.169
Subscripts
i
Time step
n
Number of time steps
2
Data and Methodology
The study area, Terkos, is in the northwest of Istanbul, Turkey, with 41 180 N
latitude and 28 390 E longitude (Fig. 2). A measurement mast with measuring
instruments at 20, 40, 65, 80, and 81 m is located in the area, which is at 51 m from
sea level. Temperature, pressure, wind speed, and wind direction data can be
obtained on these levels at 10 min intervals. The measurement mast is shown in
Fig. 3. In this study, wind speed data from August 1, 2012, to August 1, 2013, were
Table 1 Roughness lengths speciﬁed in WAsP1
Physical z0
[m]
Terrain surface characteristics
Roughness class
z0
Speciﬁed in WAsP
[m]
1.5
4 (1.5 m)
1.5
>1
Tall forest
>1
1.00
City
1.00
0.80
Forest
0.80
0.50
Suburbs
0.50
0.40
3 (0.40 m)
0.40
0.30
Shelter belts
0.30
0.20
Many trees and/or bushes
0.20
0.10
Farmland with closed appearance
2 (0.10 m)
0.10
0.05
Farmland with open appearance
0.05
0.03
Farmland with very few buildings/
trees
1 (0.03 m)
0.03
0.02
Airport areas with buildings and
trees
0.02
0.01
Airport runway areas
0.01
0.008
Mown grass
0.008
0.005
Bare soil (smooth)
0.005
0.001
Snow surfaces (smooth)
0.003
0.0003
Sand surfaces (smooth)
0.003
0.0002
(Used for water surfaces in the
Atlas)
0 (0.0002 m)
0.0
0.0001
Water areas (lakes, fjords, open sea)
0.0
Improved Wind Speed Prediction Results by Artiﬁcial Neural Network Method
653
measured at all levels. Wind direction data were obtained from 20 and 65 m of the
mast for the same time period.
Due to the absence of data measured at 10 m, it was obtained from the other
levels by using power law (Eq. 1):
Fig. 2 Study area
654
A.S. Sirdas et al.
V2 ¼ V1
Z2
Z1

α
ð1Þ
where V2 (m/s) is the calculated wind speed at height z2 (m), V1 (m/s) is the
observed wind speed at z1 (m), and α is the power law exponent, which is affected
by the roughness of the location. For the study area, α was found as 0.169.
The daily and monthly averages of wind speeds were shown by time series
graphs. Daily mean wind speed time series derived from 10 min interval observa-
tion data showed that wind speeds were higher in October, November, and February
(Fig. 4). Especially in the earliest days of February, wind speeds reached maximum
values.
Monthly mean wind speed graphs show that wind speed values decrease in
summer season of the region (May–June–July) and increase in autumn season
(August–September–October) (Fig. 4). Determining the wind directions is crucial
Fig. 3 Measurement mast
Improved Wind Speed Prediction Results by Artiﬁcial Neural Network Method
655
for wind energy studies. Wind roses derived from wind direction data from 20 to
65 m of measurement mast are demonstrated in Fig. 5. Accordingly, the most
windward directions are northwest and southeast.
2.1
Short-Term Wind Prediction with WRF/ARW
The Weather Research and Forecasting (WRF) model has two dynamical core
variants named nonhydrostatic mesoscale (NMM) and advance research (ARW).
NMM is used for making operational forecasts, while ARW is used for both
meteorological research and numerical weather prediction. In this study,
WRF/ARW version 3.2.1 was used.
2.1.1
Initial and Boundary Conditions
The initial and boundary conditions supplied to the WRF/ARW model were
provided by the National Centers for Environmental Prediction (NCEP) Final
20
15
10
Windspeed (m/s)
Windspeed (m/s)
5
0
0
2
4
6
8
Aug
Aug
Sep
Sep
2012
Oct
Oct
Nov
Nov
Dec
Dec
Jan
Jan
Feb
Feb
Mar
Mar
Apr
Apr
May
May
Jun
Jun
Jul
Jul
81 m
80 m
65 m
40 m
20 m
81 m
80 m
65 m
40 m
20 m
10 m
2013
Fig. 4 Time series of daily mean wind speed
656
A.S. Sirdas et al.
337.5°
315°
292.5°
270°
247.5°
225°
202.5°
180°
157.5°
135°
112.5°
90°
67.5°
45°
22.5°
0°
6%
12%
18%
0% calm
337.5°
315°
292.5°
270°
247.5°
225°
202.5°
180°
157.5°
135°
112.5°
90°
67.5°
45°
22.5°
0°
6%
12%
18%
0% calm
a
b
Fig. 5 Wind rose for (a) 65 m (b) 20 m
Improved Wind Speed Prediction Results by Artiﬁcial Neural Network Method
657
Operational Model Global Tropospheric Analyses, with 1 of spatial and 6 h of
temporal resolution.
2.1.2
Design of the Simulations
The model was built over three nested domains shown in Fig. 6. The coarser domain
(d01) with 30 km spatial resolution covers eastern Europe and Turkey between
33–49 N latitudes and 19–39 E longitudes. The middle domain (d02) with 10 km
spatial resolution covers Marmara Region located in the northwest of Turkey. The
inner domain (d03) with 3 km spatial resolution covers Thrace region and Terkos.
All domains are cantered to the same point where measurement mast locates with
latitude 41 180 N and longitude 28 390 E. The vertical structure of the model
contains 28 layers.
There are several physical options for the WRF model predictions. These
physical options consist of the combination of microphysics, cumulus parameter-
izations, surface physics, planetary boundary layer (PBL) physics, and atmospheric
radiation physics. In this study, six different WRF/ARW simulations obtained with
different physical options are listed in Table 2. It was aimed to test mainly the PBL
parameterizations. In the simulations, Asymmetrical Convective Model version
2 (ACM2), Medium Range Forecast Model (MRF), Mellor–Yamada–Janjic
48°N
46°N
44°N
42°N
40°N
38°N
36°N
34°N
20°E
22°E
24°E
26°E
28°E
d03
d02
30°E
32°E
34°E
36°E
38°E
Fig. 6 WRF/ARW domain conﬁguration
658
A.S. Sirdas et al.
(MYJ), Mellor–Yamada Nakanishi and Niino Level 2 (MYNN2), Yonsei Univer-
sity (YSU), and Quasi-Normal Scale Elimination (QNSE) PBL options were used
(Table 2). The differences between the PBL parameterizations are indicated in
Table 3. They can differ from each other by the prognostic variables TKE_PBL
(turbulent kinetic energy from PBL) and QKE (turbulent heat ﬂux) and diagnostic
variables EL_PBL (length scale from PBL), exch_h (scalar exchange coefﬁcient),
exch_m (exchange coefﬁcient), Tsq (liquid water potential temperature variance),
Qsq (liquid water variance), and Cov (liquid water-liquid water potential
temperature covariance) (Url-3 2014).
According to the National Center for Atmospheric Research (NCAR) Technical
Note, microphysics schemes have a wide range of options from basic physics for
idealized studies to complicated mixed-phase physics for process studies and
numerical weather prediction (Skamarock et al. 2008). In this study, Thompson
option including both ice-phase and mixed-phase processes were chosen for all
simulations as the microphysics scheme.
Another parameterization option is cumulus physics scheme. The cumulus
physics schemes are responsible for the subgrid-scale effects of convective and/or
shallow clouds (Skamarock et al. 2008). The Kain–Fritsch scheme including
updraft and downdraft changes was used for this study. This scheme makes the
calculations by using a basic cloud model bearing updrafts and downdrafts with
dragging effects (Skamarock et al. 2008).
Using these parameterizations, 3-day and 10-day predictions were performed by
WRF/ARW. Simulation period covered 1–4 February and 1–4 March for 3-day
Table 2 WRF/ARW physics options
Parameterization
PBL
Land surface model
Surface layer physics
1
ACM2
Pleim-Xiu
Pleim-Xiu
2
MRF
Noah LSM
Monin-Obukhov
3
MYJ
Noah LSM
Eta similarity
4
MYNN2
Noah LSM
MYNN
5
YSU
Noah LSM
Monin-Obukhov
6
QNSE
Noah LSM
QNSE
Table 3 WRF/ARW PBL schemes
Scheme
WRF dynamic
core
Prognostic
variables
Diagnostic variables
Cloud mixing
ratio
ACM2
ARW
–
–
QC, QI
MRF
ARW NMM
–
–
QC, QI
MYJ
ARW NMM
TKE_PBL
EL_PBL, exch_h
QC, QI
MYNN2
ARW
QKE
Tsq, Qsq, Cov, exch_h,
exch_m
QC
YSU
ARW NMM
–
exch_h
QC, QI
QNSE
ARW NMM
TKE_PBL
EL_PBL, exch_h, exch_m
QC, QI
Url-2 (2014)
Improved Wind Speed Prediction Results by Artiﬁcial Neural Network Method
659
runs. In the 10-day predictions, 1–11 February and 1–11 March periods were
chosen. The results were derived as 1 h outputs.
Because the WRF/ARW gave results only for the grid points, the data on the
grids were moved to Terkos where the measurement mast locates by two down-
scaling methods: weighted average method and nearest neighbor method.
2.2
Artiﬁcial Neural Networks (ANN)
The artiﬁcial neural network (ANN) method was used to try and reduce the errors of
WRF/ARW results that were derived from different parameterizations. The ANN
method is the study of cellular networks with storage of the experimental knowl-
edge feature (Aleksander 1989). The development of ANN is known to be inspired
by the neurons in the brain. The functioning of the artiﬁcial neuron is shown in
Fig. 7.
An ANN model is trained using the available data and then tested with the rest of
the data. The purpose of the training is to calculate the weights and minimize the
errors (As
¸kın et al. 2011). In this study, 70% of the WRF/ARW prediction results
were used as training data and the remaining 30% data were tested.
In the ANN model, the Levenberg–Marquardt algorithm was performed. It is a
least squares calculation method mainly based on the maximum neighborhood and
consists of the best features of Gauss–Newton and gradient descent algorithms
(As
¸kın et al. 2011).
3
Applications
WRF/ARW was run with six different initial conditions, and the results were
obtained. First, February 1–4 and March 1–4 results were derived. Then they
were downscaled to the point where the observation data exist. Model results
were achieved for the selected nesting area separately.
Hourly wind speed data (measured) were compared to the hourly model results
(predicted) at 10 m. Results are shown in Figs. 8 and 9.
From a coarser domain to the inner domain, the model results were closer to the
observations (Fig. 7). A bigger domain and lower resolution made predictions that
Fig. 7 An artiﬁcial neuron
(Gershenson 2001)
660
A.S. Sirdas et al.
Fig. 8 1–4 February model results (downscaled by nearest neighbor method) and observations for
(a) coarser domain (d01), (b) middle domain (d02), (c) inner domain (d03)
Fig. 9 March 1–4 model results (downscaled by nearest neighbor method) and observations for
(a) coarser domain (d01), (b) middle domain (d02), (c) inner domain (d03)
Improved Wind Speed Prediction Results by Artiﬁcial Neural Network Method
661
were far from the observed data. The model results were seen to be close to each
other, and WRF-3 results were closer to the observations.
4
Results and Discussions
4.1
WRF/ARW Predictions
Model performances were established by the root mean square error (RMSE)
(Eq. 2) compared to the measured data in Terkos:
RMSE ¼
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
1
n
X
n
i¼1
Fi  Oi
ð
Þ
s
ð2Þ
where n is number of data, Fi is forecast values, and Oi is observed values at time i.
The RMSE calculations are given in Tables 4 and 5.
The model results belonging to March 1–4 were more successful than February
1–4 results. Where the wind speeds are high, especially in March 3, observations
and predictions overlapped well (Fig. 9). Although observations had more ﬂuctu-
ations than the predictions, general oscillation could be followed by the
simulations.
4.2
ANN Predictions
The different model predictions were used in a two-layer ANN model to get more
correlated results with the observations. The ﬁrst 70% of hourly March 1–11 results
were inserted in the ANN as the training data. Then the remaining data were used as
the test data. The predictions were attained hourly for the ﬁrst 6 h. Table 6 shows the
correlations and RMSE between predictions derived by using WRF/ARW simula-
tions and observations. “1” refers to training and “2” refers to test data.
In order to make a comparison, six different WRF/ARW simulation results and
ANN results using these simulations are indicated in Table 7.
Correlations between the observations and the forecasts used as the training data
began to decrease at the third hour. Generated test data were more correlated to the
observations when compared to the training data. Temporal variation of the corre-
lation is noticeable in Table 5. For all WRF/ARW simulations, correlations of the
ﬁrst hour were very low, whereas the ANN correlations were considerably higher.
The general view was that ANN increased the correlations substantially.
662
A.S. Sirdas et al.
5
Conclusions
WRF/ARW simulation results showed that inner domain results are closer to the
observations than the other domains due to higher spatial resolution. In addition, the
nearest neighbor downscaling method generally worked better than the weighted
average method. When the wind speeds are higher than 12–13 m/s, model results
were much more underestimated while comparing the rest. Because WRF/ARW is
a mesoscale model, it was unable to predict the short-time variation of the winds in
microscales and follow the general oscillation on time.
February results had less accuracy because of relatively high wind speed values
when compared to March results. Predictions were accurate for the wind speeds less
Table 4 RMSE results for 1–4 February
1–4 February RMSE (m/s)
WRF-1
WRF-2
WRF-3
WRF-4
WRF-5
WRF-6
D01
Nearest neighbor
1.32
1.36
1.20
1.47
1.44
1.29
Weighted ave.
1.38
1.38
1.21
1.51
1.50
1.31
D02
Nearest neighbor
1.16
1.17
1.09
1.20
1.17
1.12
Weighted ave.
1.35
1.38
1.20
1.44
1.39
1.27
D03
Nearest neighbor
1.19
1.44
1.31
1.38
1.38
1.30
Weighted ave.
1.21
1.45
1.29
1.37
1.36
1.30
Table 5 RMSE results for 1–4 March
1–4 March RMSE (m/s)
WRF-1
WRF-2
WRF-3
WRF-4
WRF-5
WRF-6
D01
Nearest neighbor
0.89
0.91
0.91
0.89
0.87
0.95
Weighted ave.
0.87
0.90
0.89
0.91
0.85
0.92
D02
Nearest neighbor
0.70
0.71
0.80
0.86
0.70
0.87
Weighted ave.
0.78
0.89
0.84
0.86
0.70
0.83
D03
Nearest neighbor
0.79
0.88
0.88
0.85
0.76
1.01
Weighted ave.
0.78
0.96
0.85
0.85
0.74
0.96
Table 6 ANN results
1 h
2 h
3 h
4 h
5 h
6 h
Correlation 1
0.77
0.66
0.61
0.50
0.40
0.25
Correlation 2
0.58
0.61
0.60
0.65
0.57
0.57
RMSE 1
1.54
1.80
1.90
2.06
2.18
2.28
RMSE 2
2.23
2.17
2.34
2.29
2.46
3.05
Improved Wind Speed Prediction Results by Artiﬁcial Neural Network Method
663
than 10 m/s, especially in March results. On the contrary, the predictions were
underestimated for the peak values in February.
Different parameterizations showed slightly different results. While WRF-3 and
WRF-6 parameterizations had fewer errors in February predictions, WRF-1 and
WRF-5 parameterizations were more successful than the others in March results.
Consequently, it was observed that different initial conditions, such as physics
options or resolution, gave different results. If different scheme results are com-
bined in ANN, much more accurate results can be obtained.
Acknowledgments This research was supported by The Scientiﬁc and Technological Research
Council of Turkey (TUBITAK) 1007-KAMAG; MI
˙LRES (National Wind Power System). We
would like to acknowledge the support of TUBITAK who encouraged our research.
References
Aleksander, I.: Neural Computing Architectures: The Design of Brain-Like Machines. North
Oxford Academic Publ, London (1989)
As
¸kın, D., I
˙skender, I
˙., Mamızadeh, A.: Farklı Yapay Sinir A
gları Y€
ontemlerini Kullanarak Kuru
Tip Transformat€
or Sargısının Termal Analizi. Gazi U
¨ niversitesi Mühendislik Mimarlık
Fakültesi Dergisi Cilt. 26(4), 9005–9913 (2011)
Genc
¸o
glu, M.T.: Yenilenebilir Enerji Kaynaklarının Türkiye Ac
¸ısından O
¨ nemi. Fırat U
¨ niversitesi
Fen ve Mühendislik Bilimleri Dergisi. 14(2), 57–64 (2002)
Gershenson, C.: Artiﬁcial Neural Networks for Beginners, 9 pages. University of Sussex, Cogni-
tive and computing sciences (2001)
Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, M., Duda, K.G., Huang, X.Y.,
Wang, W., Powers, J.G.: A description of the advanced research WRF version 3. NCAR/TN–
Table 7 ANN results
1 h
2 h
3 h
4 h
5 h
6 h
WRF-1
Correlation
0.001
0.28
0.28
0.31
0.35
0.25
WRF-2
Correlation
0.07
0.03
0.013
0.0008
0.22
0.44
WRF-3
Correlation
0.013
0.17
0.19
0.25
0.21
0.17
WRF-4
Correlation
0.016
0.18
0.24
0.28
0.25
0.23
WRF-5
Correlation
0.046
0.011
0.001
0.034
0.039
0.06
WRF-6
Correlation
0.02
0.27
0.29
0.35
0.34
0.22
YSA (training)
Correlation
0.77
0.66
0.61
0.50
0.40
0.25
YSA (test)
Correlation
0.58
0.61
0.60
0.65
0.57
0.57
664
A.S. Sirdas et al.
475+STR. National Center For Atmospheric Research Boulder Co Mesoscale and Microscale
Meteorology Div (2008)
Url-1.: http://www.gwec.net/wp-content/uploads/2014/04/GWEC-Global-Wind-Report_9-April-
2014.pdf . Retrieved time: 01.10.2014
Url-2.:
http://www2.mmm.ucar.edu/wrf/users/docs/user_guide_V3/ARWUsersGuideV3.pdf.
Retrieved time: 22.09.2014
Url-3.: http://www2.mmm.ucar.edu/wrf/users/wrfv3.5/Registry.EM_COMMON. Retrieved time:
22.09.2014
Improved Wind Speed Prediction Results by Artiﬁcial Neural Network Method
665
Technical and Economical Prefeasibility Study
of a Solar Water Heating (SWH) System
in an Apartment Building in Cape Town
Olugbeminiyi Idowu, Toluwalope Ige, Nicole Lacouve, Amin A. Mustafa,
and Luis Rojas-Solorzano
1
Introduction
The centrally generated electrical power in South Africa consists of 92.6% coal-
fuelled power—the aging coal-fuelled South African plants have the lowest oper-
ating efﬁciency in the world (de Groot and Sebitosi 2013). Moreover, water heating
represents up to 48% of total electricity consumption in South African homes
(Geldenhuys 1998). Although the country has a fairly high annual average solar
irradiation levels of 5.4 kWh/m2/day measured on a horizontal plane that could
make solar energy recovery a favorable alternative (Boxwell 2015), only about 1%
of households utilize solar water heaters (DME 2003). Rising electricity rates,
capital investments in electricity production, and distribution, as well as needs to
reduce CO2 emissions, have all led the government to start promoting alternative,
renewable energy solutions to meet growing energy demands (Donev et al. 2012).
Promoting solar water heating (SWH) has been at the forefront of this initiative,
with signiﬁcant grants being offered by Eskom, South Africa’s public electricity
utility. Between the years 2008 and 2011 alone, Eskom has incentivized 156,000
installations with its Solar Water Heating Rebate Programme and has partnered
with the Department of Energy to reduce the demand on the public grid by 2300
GWh through the use of SWH (ESKOM 2012). The legislative capital of the
country, Cape Town, has launched its own initiative in the form of the Residential
O. Idowu • T. Ige (*) • N. Lacouve • A.A. Mustafa
Institut Mines-Te
´le
´com Atlantique, Department of Energy and Environment Systems,
Nantes 44300, France
e-mail: olugbeminiyi@gmail.com; fetoige@yahoo.com; nicole.legenski@gmail.com;
amin.moniem@gmail.com
L. Rojas-Solorzano
Nazarbayev University, Department of Mechanical Engineering, Astana 010000, Kazakhstan
e-mail: luis.rojas@nu.edu.kz
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_47
667
Solar Water Heater Programme, which has encouraged residents through ﬁnancial
services and technical support to “invest to save” in SWH (City of Cape Town
2011). With temperatures in Cape Town ranging from 2 C to 37 C, an annual
average of 17 C (The Weather Channel LLC 2014), and an average of 2993 h of
sunshine per year (Climatemps 2014), SWH is an attractive clean energy alternative
to electric water heaters. According to a recent survey conducted by the City of
Cape Town, nearly 70% of residents want a solar water heater (with energy cost
savings cited as the primary reason), and half of the respondents replied that it is
likely they would install one within the next 3 years (City of Cape Town and Du
Toit 2013).
Given that energy cost savings are an important motivating factor for consumers
who plan to install a solar water heating system (SWHS), economic feasibility
studies of these types of systems could be useful decision-making tools. However,
accurately predicting the long-term proﬁtability of such investments is difﬁcult due
to the project’s dependence on multiple external factors and thus requires the use of
a robust scientiﬁc model and careful precision of climatic and economic parameters
to achieve an accurate result. Therefore, the purpose of this study is to perform a
prefeasibility study of a possible SWHS in the Cape Town area and to evaluate the
sensitivity of various parameters to the long-term ability of the project to produce
energy cost savings. In this study, the technical and economic prefeasibility of
installing a collective domestic SWH system in an apartment building is evaluated
using the RETScreen Clean Energy Project Analysis Software, an advanced model
equipped to analyze feasibility and energy performance of clean energy projects. A
prefeasibility study of this nature is not currently available for South Africa in the
literature, although there are similar types of feasibility studies for other locations
throughout the world including Taiwan (Lin et al. 2015), Morocco (Allouhi et al.
2015), Jordan (Kablam 2004), Oman (Gastli and Charabi 2011), and Serbia
(Stevanovic and Pucar 2012).
In this project, the RETScreen software was used to perform energy and eco-
nomic feasibility analyses on a glazed ﬂat-plate SWHS with an electrical coil for
auxiliary heating. The SWHS is designed for a new ﬂat roof apartment building
with nine domicile units, located approximately 20 km southeast of the city center
and near the Cape Town International Airport. Hardware coefﬁcients of perfor-
mance are obtained for SWH units that are available for purchase in the Cape Town
region, and pricing for these units and installations are provided by actual suppliers
servicing the region.
The results of interest from this study include energy produced by the SWHS,
energy costs avoided by using the SWHS, greenhouse gas (GHG) emissions
avoided by using the SWHS, net present value (NPV), and internal rate of return
(IRR) of the investment, as well as sensitivity of these results to parameters of the
project such as changing electricity costs, loan interest rates, or government subsidy
amount.
668
O. Idowu et al.
Nomenclature
SWH
Solar water heating
SWHS
Solar water heating system
NPV
Net present value
IRR
Rate of return
GHG
Greenhouse gas
f
Solar fraction
FR (τα)
Collector heat removal factor
FRUL
Collector heat loss coefﬁcient [W/(m2 K)]
HT
Monthly average daily radiation incident on the collector plane
Ta
Monthly average ambient temperature
Tw
Hot water temperature
Tm
Monthly average water supply temperature
Ca
Actual storage capacity
Cs
Standard storage capacity
2
Literature Review
The presence of similar feasibility studies for SWHS in the literature can be noted
as early as 2002, when Kablam (2004) performed a technoeconomic analysis for a
SWHS in Jordan. In this study, a model was developed to determine the economic
feasibility of a SWHS with an electric coil as an auxiliary fuel as compared to the
base case of a conventional gas-powered water heater. It was determined that the
SWHS remained economically preferable if the auxiliary electric coil was used for
less than 120 days out of the year.
A study that is very similar in goal and scope to the current project was done by
Gastli and Charabi (2011), who performed a full RETScreen analysis on a SWHS in
Oman. In this study, the SWHS was compared to the base case of a conventional
electric-powered water heater. The project for a four-person household was
assumed to be ﬁnanced 50% by government subsidies and 50% by the household.
The pre-tax IRR for assets was calculated to be 12.2%, and the equity payback
period was found to be 8.5 years. In addition, the net annual GHG emission was
reduced by 3.6 tCO2 equivalents.
There is also another study based on RETScreen aimed at determining the
ﬁnancial feasibility of a SWHS in Serbia (Stevanovic and Pucar 2012). This
study performed a RETScreen analysis in six Serbian cities for a SHWS for a
household of four people. For a government subsidy of 50% of initial costs, equity
payback period ranged from 4.7 to 6 years depending on the location. In addition,
this study also made a ﬁnancial analysis to determine the most appropriate level of
government subsidies for the project.
Technical and Economical Prefeasibility Study of a Solar Water Heating (SWH). . .
669
3
SWHS Prefeasibility Study in Cape Town
3.1
SWHS Design
The purpose of this project is to determine the feasibility of a typical SHWS in the
Cape Town area. Since South Africa’s public utility ESKOM has implemented
grants of 40% of initial costs, it is in the public interest to demonstrate that these
types of projects can be proﬁtable and to determine ﬁnancial indicators, such as
equity payback period, IRR, and NPV. These results are here calculated using the
support tool RETScreen, which comprises several types of analyses: energy model,
GHG emission, reduction, cost, ﬁnancial, and risk analyses.
In order to accomplish these objectives, it is necessary to design a SWHS with
components that can be obtained in the region. For this project, a SWHS is
conceived for the collective water heating of an apartment building. The area
chosen for the placement of this system is near the Cape Town International
Airport, as shown in Fig. 1. This location was chosen due to the abundance of
meteorological solar irradiance data available for this area. Table 1, shows mete-
orological data for this area used by the model.
The apartment building is chosen to be a new ﬂat-roofed structure with adequate
space to accommodate the SWHS collectors and storage tanks. The SHWS com-
prises 20 glazed ﬂat-plate solar panels, each with a gross area of 2.14 m2, a 150-L
storage tank, and a thermosyphon passive heat exchanger from the Jiangsu Sunrain
Solar Energy Company. A thermosyphon heat exchanger uses the natural
Fig. 1 Geographic location of SWH project (Google Earth 2015)
670
O. Idowu et al.
circulation of warm and cool water to direct ﬂow through the solar collector and to
the hot water output of the unit. Figure 2 shows the general principle of such a unit.
The apartment building has nine domicile units, with four occupants each. It is
assumed that each household member consumes an estimated 60 L of hot water per
day (Donev et al. 2012).
An important parameter in the feasibility of a SWHS project is the electricity
rate. South Africa has historically had low electricity tariffs due to abundance of
coal reserves, consistent government subsidies, and centralized control of both coal
supply and electricity production (de Groot and Sebitosi 2013). The electricity
tariffs for domestic households of the City of Cape Town are indicated in Table 3.
Table 1 Meteorological data for Cape Town project area provided by RETScreen
Month
Air
temperature
Relative
humidity
Daily solar radiation –
horizontal
Heatingdegree-
days
C
%
KWh/m2/d
C-d
January
20.4
68.0
7.72
0
February
20.4
69.9
7.05
0
March
19.2
72.6
5.86
0
April
16.9
76.6
4.17
33
May
14.4
79.6
2.97
112
June
12.5
79.9
2.45
165
July
11.9
78.9
2.62
189
August
12.4
78.6
3.40
174
September
13.7
76.6
4.75
129
October
15.6
71.6
6.09
74
November
17.9
68.9
7.48
3
December
19.5
68.4
7.85
0
Annual
16.2
74.2
5.19
879
Fig. 2 Thermosyphon passive heat exchange, glazed ﬂat-plate SWH
Technical and Economical Prefeasibility Study of a Solar Water Heating (SWH). . .
671
3.2
Energy Model
The RETScreen energy model calculates the solar fraction f in order to determine
the amount of energy produced by the SWHS. The solar fraction refers to the
amount of heating demand that is met by the SWHS. The solar fraction is calculated
in the following manner (Stevanovic and Pucar 2012):
f ¼ 1:029Y  0:065X  0:245Y2 þ 0:0018X2 þ 0:0215Y3
ð1Þ
where X and Y are determined as follows:
X ¼ ACFRUL Tref  Ta
ð
Þ
L
∗Ca
Cs

0:25∗11:6 þ 1:18Tw þ 3:86Tm  2:32Ta
ð
Þ
Tref  Ta
ð
Þ
Y ¼ ACFR τα
ð
ÞHTN
L
ð2Þ
where Tref is 100  C, L is the total monthly heating load, Ca is actual storage
capacity, Cs is standard storage capacity, N is the number of days in the month, Ac is
the collector area in m2, FR (τα) is the collector heat removal factor, FRUL is the
collector heat loss coefﬁcient in Wm2 K1, HT is the monthly average daily
radiation incident on the collector plane, Ta is the monthly average ambient
temperature, Tw is the hot water temperature, and Tm is the monthly average
water supply temperature.
Table 2 gives the necessary input parameters used for this SWHS.
Table 2 Parameters used in
energy model of SWHS
Parameter
Value
Unit
Number of units
9
Occupancy rate
100
%
Daily hot water use
2160
L/d
Temperature of heated water
60
C
Operating days per week
7
Slope of collectors
30
Degrees
Aperture area per solar collector
1.86
m2
Gross area per solar collector
2.14
m2
Fr (tau alpha) coefﬁcient
0.67
FrUL coefﬁcient
4.62
W/m2/C
Number of collectors
20
Storage capacity/solar collector area
70
L/m2
Electricity rate
0.11
€/kWh
672
O. Idowu et al.
3.3
Electricity Pricing
In studying this feasibility for SWHS, electric heaters are considered as the base
case with the cost of electricity being the fuel in comparison with the cost of solar
radiation that is free. Electricity tariff in Cape Town is set by the City of Cape Town
Electricity Services, with different prices being set depending on the expected
consumption of the residence (City of Cape Town 2014a, b). Table 3 shows the
residential electricity pricing in place; however, the tariff used in modeling this case
is that for a monthly consumption of 0–600 kWh as set in July 2014. This is after
taking into account the consumption needs of the apartment model, especially
regarding hot water consumption of 240 L/day. When using a rate of 5.1 kWh/
100 L to increase water temperature from 16 to 60 C (Thomson 2013), energy use
for water heating could be up to 367 kWh/month. According to a survey by the
(City of Cape Town and Du Toit 2013), electric water heater accounts for 30–50%
of the domestic electricity bill of a household in Cape Town. Furthermore, this
same survey presents that each household spends on average R764.66 (537 kWh)
on electricity monthly.
According to the electricity services board of the city, the tariff is expected to
increase by 9.92% in 2015 and 9.26% in 2016 (Rencontre 2013). However,
although future tariff changes are expected to occur in a manner that cannot be
readily modeled for the lifetime of the project, an escalation rate of 10.0% is
factored in by assuming that the annual increase in electricity price during the
lifetime of the project will remain at about the same rate for the 2015 and 2016
projections. Additionally, a trend analysis of the rate of price increase from 2006 to
2014 was made to deﬁne a cap of 15.34% while evaluating the sensitivity of the
project to electricity price escalation.
3.4
System Cost
The selection of the system and its cost plays a fundamental role in the feasibility of
the project. In deﬁning the cost of the selected system, estimates were obtained
Table 3 Residential
electricity pricing in
2014/2015
Units received (kWh/month)
Tariff (cents/kWh)
Rand
Euros
Lifeline (<450)
First 50 kWh
Free
Block 1
0–350
96.12
7.1
Block 2
350–450
233.30
17.3
Domestic (> 450)
Block 1
0–600
153.63
11.4
Block 2
600þ
186.81
13.8
City of Cape Town (2014a, b)
Technical and Economical Prefeasibility Study of a Solar Water Heating (SWH). . .
673
from a Chinese supplier, two Eskom-approved suppliers in Cape Town, and an
agent with the SWH division of Sustainable Energy Society of Southern Africa
(SESSA). However, the estimate presented by one of the suppliers in Cape Town
was used since they provided a breakdown of the individual components in the
overall cost, as detailed in Table 4. Furthermore, the difference in the cost estimates
from these four sources was small, and a contingency of 10% was factored into
calculations. Apart from the system cost, it was also important to factor in the
installation and maintenance costs as well as the costs of auxiliary systems such as
pipes and pumps.
3.5
Financing
As with most clean energy projects, the initial costs are often a barrier. According to a
market research conducted by the City of Cape Town in 2013, 67.9% of respondents
are desirous of SWHs; however, the SWH unit installation and upfront costs are given
as the main hindrances to installing one. Duly noting that 67.2% of interviewed
persons consider upfront cost as a major drawback (City of Cape Town and Du Toit
2013), in coping with this, Eskom offers a SWH rebate program to cover the initial
costs. This rebate is about 40% of the cost of the solar collector unit and ranges from
€243 to €663 (R3280 to R8964) for each unit installed that meets certain speciﬁed
conditions. The calculation of the exact amount depends on the type of system
installed (ESKOM 2012). The system considered for this study meets the criteria
for beneﬁting from the rebate and is estimated as €7640 (€382 per unit).
However, considering that 54.2% of persons will be motivated to obtain a system
only if there are no upfront costs and that 62.5% would like to pay less than €148
(R2000) for the initial cost of the system (City of Cape Town and Du Toit 2013),
ﬁnancial calculations of the viability of the project are made with the assumption
that the remainder of costs not covered by the rebate is taken as a bank loan to be
paid over a 5-year term. The complete ﬁnancial parameters for the project are
speciﬁed in Table 5.
Table 4 Cost estimates for
the selected SWHS
Item
Cost
Rand
Euros
Feasibility study
8605
559
SWHS (20)
249,517
18,464
Engineering and Installation
146,356
10,830
Training and Commissioning
770
50
Unskilled Labor
847
55
Total
406,096
29,958
1 rand ¼ 0:074 euros; Costs are inclusive of 14% VAT
674
O. Idowu et al.
4
Results and Discussions
Following the simulation of these design parameters as described in the preceding
sections using RETScreen, the results obtained are as follows.
4.1
Energy Savings
The designed SWHS provided 17 MWh of heating per year, which is equivalent to a
solar fraction of 42%. The use of the system resulted in an electricity consumption
of 23.3 MWh, compared to the base case consumption of 40.3 MWh. This repre-
sents an electricity savings of 17 MWh per annum, which is equivalent to €1934.
4.2
Emissions Reduction
The amount of emissions (normalized to tons of CO2) estimated from the use of the
SWHS is 24 tCO2 equivalents, while with the use of electricity for water heating, it
was 41.5 tCO2 equivalents. This results in a saving of 17.5 tCO2 equivalents, which
is equivalent to 3.2 cars taken off the road in a year.
4.3
Financial Analysis
The results obtained from the simulation of the ﬁnancial parameters for an invest-
ment in the SWHS taking into consideration the present situation in South Africa
and the projections described above in the Financial discussion section (and Table 5
Table 5 Financial
parameters for the feasibility
study simulation
Financial parameter
Value
Unit
Inﬂation ratea
5.3
%
Fuel escalation rateb
10.0
%
Debt ratio
60.0
%
Discount rate
9.0
%
Debt term
5
Years
Debt interest ratec
11.0
%
System lifetime
25
Years
Government rebate for 20 unitsd
7640
€
aTriami Media BV (2014)
bRencontre (2013)
cTrading Economics (2014)
dCity of Cape Town (2011)
Technical and Economical Prefeasibility Study of a Solar Water Heating (SWH). . .
675
above) show that the net present value (NPV) on the investment is €27,028 with an
internal rate of return (IRR) of 17.3% and an equity payback time of 9.9 years. The
beneﬁt–cost ratio of the investment is 3.05. Figure 3 below shows the progression of
the cumulative cash ﬂow from the investment over time.
With all the parameters employed for this simulation, it can be seen that the
parameter with the highest inﬂuence on the proﬁtability of this investment is the
cost of the electricity, as seen from the relative impact graph shown in Fig. 4, based
on a Monte Carlo analysis of 500 combinations of possible scenarios with an
uncertainty of 10%.
From the relative impact shown in Fig. 4, it can be seen that the cost of fuel
(which is the local cost of electricity) has a high impact on the viability of this
project. This parameter was analyzed by seeing how the variation of the escalation
rate of electricity will affect the NPV, the payback time, and the IRR. Table 6 shows
how these values vary with the different escalation rates of electricity.
Another important parameter is the availability of rebate. Presently, the rebate is
40% of the cost of the equipment, which amounts to €7386. Figure 5 shows the
effect a reduction or removal of this rebate will have on the after-tax IRR of the
investment. The removal of the rebate will give an after-tax IRR of 13.7%, a
payback period of 11.2 years, an NPV of €19,642, and a beneﬁt–cost ratio of 2.49.
Fig. 3 RETScreen cumulative cash ﬂow graph
676
O. Idowu et al.
5
Conclusions
With 42% of energy savings and a matching percentage in emissions reduction, it is
very reasonable to say that the justiﬁcation behind the technical beneﬁts of SWH
have been validated in the case of an apartment building similar to the one deﬁned
in this work in the City of Cape Town, South Africa.
The designed SWHS yielded a yearly 17 MWh in energy savings, 17.5 tCO2
equivalents emissions reduction, along with a net present value (NPV) on the
investment of €27,028, with an internal rate of return (IRR) of 17.3%, and an equity
payback period of 9.9 years.
Nevertheless, the current ESKOM rebate scheme plays a pivotal role in the
attractiveness of investments in such SWH systems. The 40% rebate scheme
Fig. 4 RETScreen tornado diagram of sensitivity analysis on after-tax IRR
Table 6 Effects of changes in fuel escalation rates on ﬁnancial returns
Fuel escalation
rate
After-tax IRR asset
(%)
Beneﬁt–cost
ratio
Equity payback
(years)
NPV
(€)
5.0
10.5
1.25
12.3
3,294
7.5
13.9
1.99
10.9
13,046
10.0
17.3
3.05
9.9
27,028
12.5
20.7
4.59
9.1
47,292
15.0
24.2
6.85
8.4
76,909
Technical and Economical Prefeasibility Study of a Solar Water Heating (SWH). . .
677
(on initial investment) is responsible for a 1.3-year reduction of the payback period
and a 5% reduction in the after-tax IRR.
Although the rebate scheme was signiﬁcant as 67% of the residents of Cape
Town indicated concerns regarding the initial investment, the outcomes of the study
highlight a greater ﬁnancial sensitivity to the fuel escalation rate. Generally, the
application and adoption of SWHS in Cape Town has yielded positive overall
outcomes.
Acknowledgments The team wishes to acknowledge and thank the Sustainable Energy Society
of Southern Africa (SESSA), Prof. Bernard Bourges, and Prof. Luiz Crespo for their contributions
toward the successful ﬁnalization of this study. Additionally, the team would also like to extend its
sincere appreciation to the EU’s Education, Audiovisual and Culture Executive Agency (EACEA),
speciﬁcally the Erasmus Mundus program for making the project possible.
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679
Use of Straw Bundles in Buildings for a Lower
Environmental Footprint of Insulated Systems
Jean-Luc Menet
1
Introduction
Population on earth is supposed to reach around ten billion people in 2050 (O’Neill
et al. 2010). Consequently, building market is an industrial sector in permanent
development all around the world because of two major necessities:
•
More people mean more houses and buildings.
•
Environmental impacts must be reduced.
This second point should lead companies to use high-performance materials, to
reduce energy consumptions on the whole life of the buildings, and to limit raw
materials
quantities
that
are
employed
to
ensure
the
required
technical
performances.
Among the objectives aimed by environmental politics and regulations all over
the world, the passive buildings seem to be an appropriate solution permitting a
decrease or at least a stabilization of climate change indicators in order to limit the
greenhouse effect. To reach this goal, buildings must be correctly insulated; it is the
reason why, regardless of the country, thermal regulations are developed and
require high insulating performances of houses and buildings.
The regulation relative to the building’s envelope performance greatly varies
from one country to the other. Besides, the building systems must be adapted to
each speciﬁc decision or to the design context. For example, in France, a recent
thermal regulation imposes for every new construction a given insulating perfor-
mance depending on the region as shown in the RT 2012 regulation (2012).
J.-L. Menet (*)
ENSIAME, Universite
´ de Valenciennes et du Hainaut-Cambre
´sis,
Le Mont Houy –, 59313 Valenciennes Cedex, France
e-mail: jean-luc.menet@univ-valenciennes.fr
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_48
681
But another problem appears: if a correct insulating system should save energy
for heating and air conditioning, the real question is to evaluate its contribution to
the total energy consumption, i.e., on the whole life of the buildings. Indeed, the
more the buildings are isolated, the less the energy consumption will be; then the
choice of the materials becomes preponderant. Consequently, in building projects a
“life cycle thinking” should be systematically conducted.
More precisely, considering the energy consumption, if a conventional insulat-
ing material, such as glass wool or rock wool, is used, it is interesting to analyze if
the total energy consumption “contained” in the product, namely, the grey energy,
is not greater than the one which could be saved by using this material in the
building during all its life. The grey energy is the energy used during the different
steps of the life of the product: the extraction of the raw materials, the fabrication,
the distribution, and the transportation of the different elements used for the
product, and the end of life (e.g., incineration). Consequently, a study dealing
with energy consumption of buildings must consider the different steps of the
life cycle.
Regarding the energy consumption, this question has been studied by different
authors, and fortunately the use of insulating material generally seems to be
beneﬁcial for the environment (Schmidt et al. 2004). Some of these results are
available on www.inies.fr.
Now, let us presume that the indicator which should be considered is not the
energy consumption but the Non-Renewable Energy Consumption (NREC),
because it creates greenhouse effect.
Moreover, considering insulating materials, this question must be answered not
only considering the energy consumption or the greenhouse gases emissions but
also using different other environmental indicators. This is mainly due to the fact
that the reduction of one environmental impact could lead to an increase of another;
this phenomenon is called the pollution displacement.
But an insulating material is rarely used alone; it is generally associated to other
products constituting the ﬁnal element. Then, to study the environmental impact of
insulating systems, it is necessary to evaluate the contribution of the different
elements, i.e., the whole wall.
In other words, to evaluate the environmental effect of an insulating system, the
recommended methodology must be:
•
A multistep study, which is conducted on the whole life cycle of the system
•
A multicriteria analysis considering few environmental impacts
•
A multielement consideration
Such a methodology does exist; it is called the Life Cycle Assessment (LCA),
which is described in the international standards ISO 14040 (2006) and ISO 14044
(2006).
Conventional insulating materials have been studied separately using the LCA
methodology (Schmidt et al. 2004; inies.fr n.d.), i.e., independent of the existence
of the wall. This can be useful for the choice of environmentally friendly materials
for a given wall. This approach can be at the same time pertinent and sufﬁcient if
682
J.-L. Menet
only the insulating materials have to be chosen, which is generally the case when
building conventional walls. But if another insulating material, such straw bundles
is used, all the wall must probably be adapted and a speciﬁc study is necessary,
which must includes all the elements constituting the wall.
On the other side, different authors have studied the environmental impacts of
wall assemblies (Frenette et al. 2010) to evaluate few environmental impacts, for
example, the global climate change indicator. But in such studies, to the authors’
knowledge, natural materials, such as straw bundles, are generally not considered.
Besides, comparative studies are very rare, while a pertinent choice should be made
using comparisons.
Now, our question is to know if natural materials, such as the straw bundles, are
better for the environment than the conventional ones, e.g., rock wool or glass wool
with common breeze blocks. The LCA methodology permits this comparison and
the analysis of keys parameters. This paper, following other studies on this theme
(Menet and Gruescu 2013; Menet 2014), tries to answer this question; it deals with
an LCA study on different insulating exterior walls in order to evaluate the
environmental interest of the use of natural insulating materials, such as straw
bundles, for buildings..
2
Use of Straw Bundles in Buildings
2.1
Building with Straw
Building using natural materials is not a new concept. On the contrary, it has been
used for centuries all over the world and is always used here or there nowadays.
Compressed straw coming from wheat cultivation is a high-performance thermal
and phonic material. It is said to regulate the humidity in the different rooms of
buildings. It has a remarkable ﬁre behavior.
Many papers have been published dealing with straw buildings (see, e.g., Minke
and Mahlke 2005; King 2006; Ashour et al. 2011a, b; Chaussinand et al. 2015). It is
attested that straw bundles can be used either for insulating systems or for the
structural elements.
The ﬁrst use of straw bundles for “modern” houses has been identiﬁed in
Nebraska, United States (Marks 2005). This house was built in 1896 and still
stands. This information will be useful for estimating the life duration of such a
construction.
2.2
Physical Properties of Straw Bundles
Straw is the stalk of grasses, such as the wheat. It is generally conditioned as bales
or bundles.
Use of Straw Bundles in Buildings for a Lower Environmental Footprint. . .
683
As it will be explained in the following, straw is so plentiful in the considered
country (France) that it can be considered as a waste.
Straw buildings use straw bundles, which have the following dimensions
(Table 1).
Let us write that these quantities can be exchanged according to the building
choices. For example, either the width or the height of the bundle can constitute the
wall thickness.
The previous data conduct to a mass of about 17.5 kg for each bundle. Two
bundles are necessary to build a square meter of wall of 35 cm thickness.
The physical properties of straw bundles are well known. They are listed in
Table 2. Let us notice that the ﬁre behavior is M1, i.e., the material is incombustible
(stones are classiﬁed as M0, absolutely incombustible; mineral wools are classiﬁed
as M1).
A particular attention is needed; particularly, the presence of pests or rodents,
and also the moisture resistance must be considered. These problems that have been
integrated in the building procedures are widely studied (Goodhew et al. 2004).
2.3
Procedures, Regulation, and Building Modes
Professional procedures and regulations have been developed in 2012 in France
(Floissac 2012). In France, these procedures, namely, CP2012, indicate four
requirements to be met:
•
Respect for the regulation (required technical speciﬁcations)
•
Qualiﬁed material relative to the considered use
•
Respect for the design and implementation rules
•
Staff trained to the building mode
Table 1 Dimensions of the
considered straw bundles
Length (cm)
100
Width (cm)
35
Height (cm)
50
Table 2 Main properties of compressed straw
Thermal conductivity (W/mK)
0.06 < λ < 0.075a
Thermal resistance (m2.K/W)
5 < R < 7
Fire behavior (class)
M1b
Humidity potential storage (% of mass)
30
Acoustic absorption (dBA)
48 < a < 57.3
Water vapor diffusion
1 < μ < 2
aIn the following the mean value of λ has been chosen, i.e., λ ¼ 0.0675 W/mK
bFrance
684
J.-L. Menet
Such requirements exist in different countries. France has been chosen because
the present study is supposed to be applied to a wall built in France.
There are many ways to use straw for building. Bales or straw can be used. In this
paper, the chosen technique was proposed by the GREB (Groupe de Recherches
Ecologiques de la Batture – http://www.greb.ca) born in Canada, where the climate
leaves no place to mediocre quality houses. The wall system is composed of the
following elements (see Fig. 2): the wood (brackets and beams) used for the load-
bearing structure, the straw bundles, the nails or screws, and the mortar.
The needed quantities of these elements have been established by Menet 2014
and have been partially adapted for this study (see below): 30 straw bundles;
4 beams, 5 m long and 16 beams, 3 m long; 40 brackets, 35 cm long; about 1m3
mortar; 240 nails or screws; and 273 m string for the straw bundles. Only the
exterior side of the wall assembly is considered, because the interior side generally
depends on the consumer.
3
The Life Cycle Assessment Methodology
The Life Cycle Assessment (LCA) methodology leads to the quantiﬁcation of the
environmental footprint for goods, services, and processes, called products in the
following. One of the attended objectives is to identify some main points allowing
to do design choices permitting the diminution of the environmental impacts
concerning the different life cycle steps. This approach is also called life cycle
analysis, ecobalance, or cradle-to-grave analysis.
A Life Cycle Assessment is the investigation and valuation of the potential
environmental impacts of a given product. It is a variant of an input-output analysis,
but it focuses on physical rather than monetary ﬂows. LCA is both a multicriteria
and a multistep study, and it has the particularity of being “goal dependent”; this
means that the goal and scope deﬁnition of the study are not only important, but
they can be “redeﬁned” during the entire study if necessary. In the same way, every
phase of an LCA (see Fig. 1) is linked with at least two others.
A framework for LCA has been standardized in the ISO 14040-44 series by the
International Organization for Standardization (ISO). As shown in Fig. 1, it consists
of the following phases:
•
Goal and scope deﬁnition. This phase deﬁnes the goal and the intended use of the
LCA; it scopes to clarify the system boundaries, the function and the considered
ﬂows, the required data quality, and the technology and assessment parameters.
•
Life Cycle Inventory (LCI). This “activity” consists of collecting data on inputs
(resources, such as the energy or the raw materials consumptions, and interme-
diate products) and outputs (emissions, wastes) for all the processes in the
considered product system.
•
Life Cycle Impact Assessment (LCIA). It is the phase of the LCA where inventory
data on inputs and outputs are translated into indicators about the product
Use of Straw Bundles in Buildings for a Lower Environmental Footprint. . .
685
system’s potential impacts on the environment, on human health, and on the
availability of natural resources.
•
Interpretation. This is the crucial phase where the results of the LCI and the
LCIA are interpreted according to the goal of the study and where sensitivity and
uncertainty analysis can also be performed to qualify the results and the conclu-
sions. This phase permits the analysis of the results relative to the
abovementioned ﬁrst phase.
Some of these phases are divided into several steps, particularly the ﬁrst phase of
the methodology (goal and scope deﬁnition) which must be made very precisely.
In particular, the LCA methodology needs to precisely deﬁne a functional unit,
which permits to compare one system with another.
These characteristics of the studied wall are described in Sect. 4.1.
In this paper, the study takes into account the following ﬁve classical steps of the
life of a product:
•
Manufacturing
•
Distribution
•
Installation
•
Use
•
End of life
4
Results and Discussion According to the LCA
Methodology
In the following, the LCA methodology is applied to evaluate the potential envi-
ronmental impacts of a wall made of straw bundles.
Fig. 1 Scheme of the LCA
methodology
686
J.-L. Menet
4.1
Goal and Scope Deﬁnition
In the following, the customers are deﬁned, the main objective is described, the
function and the functional unit are clariﬁed, the keys parameters are highlighted,
and the system boundaries are ﬁxed.
The study is intended for every person (private person or builder) expecting to
insulate a building or a house using straw bundles.
Insulations made from straw bundles are supposed to be ecological. The main
objective of this study is to quantify the environmental impacts of these building
modes on a given wall and to compare them to the ones obtained on a conventional
wall made of breeze blocks and glass wool. The studied “straw wall” is made of
wood structure and has a 15 m2 area (3 m high and 5 m long).
According to the LCA methodology, the function of the products must be
correctly and precisely deﬁned. Concerning insulation, it is supposed to reach at
least a thermal resistance of 5 m2.K/W, which corresponds to the thermal resistance
of a straw bundle. The use of additive wood panels will increase this thermal
resistance, so that it is higher than the recommended value of 4 m2.K/W for an
insulated wall according to the RT2012 regulation.
The chosen functional unit is to build an exterior wall assembly of 15 m2 which
has the following characteristics:
•
The technical building objectives are reached.
•
The thermal resistance of the ﬁnal assembly is at least 5 m2.K/W (which
corresponds to the thermal resistance of a straw bundle).
•
The lifetime of the assembly is 100 years.
This last consideration is validated by the real lifetime of the ﬁrst straw houses,
and this corresponds to the mean rate of renewal of the house park which is around
1% in France.
Concerning the end of life phase, the conventional wall is landﬁll (dumped
waste). The straw wall is treated as follows:
•
The concrete is landﬁll (dumped waste).
•
The wooden panels, the wooden structure, and the wooden brackets are incin-
erated with an energetic valorization.
•
The straw bundles are recycled (applied in the neighborhood).
The key parameters are the straw thicknesses leading to the wished thermal
resistance, as well as the life expectancy of the various materials. The thicknesses
are easily determined from the thermal conductivity of both types of insulating
materials. Straw reaches 100 years without any problem if correctly implemented.
Concerning the structure, it will be necessary to choose a wood which can resist
100 years.
Concerning the boundaries of the study, let us remind that only the exterior side
of the wall assembly is designed because a comparative study is projected.
Use of Straw Bundles in Buildings for a Lower Environmental Footprint. . .
687
The ﬁnal casing of the exterior is not taken into account, just as the inside casing
(e.g., plaster plates and painting). The LCA of the different machines used to
cultivate the cereals (for straw) are not considered in this study, because the straw
is in fact considered as a waste of the cultivation. Tools (saws, hammers, screw-
drivers, etc.) and natural resources (e.g., water) used for the building have not been
taken into account. The different elements constituting the “straw wall” are
presented in Sect. 4.2.
The different steps and elements of the considered products must be determined
precisely. This phase of the LCA methodology must be made very carefully. Some
hypothesis can be made, for example, by neglecting some of the life cycle steps or
some of the evaluated impacts.
4.2
Life Cycle Inventory (LCI)
The LCI must precisely take into account the different elements constituting the life
cycle of the wall, for example, the location of the manufacturing or distribution (life
cycle tree). These elements are not provided in this paper; let us notice that the
straw bundles are made near the wall installation and that every manufacturing or
distribution has been located in the neighborhood. The main information
concerning the conventional wall and the “straw wall” is respectively reported in
Tables 3 and 4.
The previous tables are linked with the considered walls represented in Figs. 2
(conventional wall) and 3 (“straw wall”).
The data corresponding to the LCI are associated to the following ﬁve steps of
the LCA methodology:
Table 3 Main information for the material constituting the conventional wall (reference)
Materials
Quantity
Mass (kg)
Life duration
Number of use
kms
Breeze blocks
99
1500
100 y
1
75
Mortar
x
1300
100 y
1
21
Glass wool
4
52
50 y
2
550
Table 4 Main information for the material constituting the “straw wall”
Materials
Quantity
Mass (kg)
Life duration
Number of use
kms
Wooden panels
7
160
100 y
1
65
Straw bundles
30
525
100 y
1
3
Concrete
X
700
100 y
1
21
Wooden structure
X
135
100 y
1
160
Wooden brackets
40
7
100 y
1
6
688
J.-L. Menet
•
Step 1
The manufacturing takes into account the pollutions created by the use of raw
materials to build the product.
•
Step 2
The distribution phase takes into account the impacts generated during the
transportation of the product toward the destination where it will be used.
wood panel
wooden structure
wooden brackets
concrete base
straw bundle
OUTSIDE
INSIDE
a
b
Fig. 3 (a) Scheme of the
“straw wall” (front view).
(b) Scheme of the “straw
wall” (side view)
OUTSIDE
INSIDE
breeze block
glass wool
air gap
Fig. 2 Scheme of the
conventional wall (side
view)
Use of Straw Bundles in Buildings for a Lower Environmental Footprint. . .
689
•
Step 3
The installation phase deals with the implementation of the wall.
•
Step 4
In the present case, the consumer use phase is not really considered because
the maintenance (e.g., the wall painting or cleaning) is not included in the study.
•
Step 5
The end of life phase (post-consumer use) depends on the wall (conventional or
natural); it has been presented in Sect. 4.1.
4.3
Life Cycle Impact Assessment (LCIA)
The environmental impacts are calculated using endpoint and midpoint indicators
associated with the well-known CML 2001 evaluation method (http://www.
leidenuniv.nl/interfac/cml/ssp/projects/lca2/index.html).
The used data are coming from the famous Ecoinvent dataset (http://www.
ecoinvent.ch/), from the INIES database (www.inies.fr), and also from the
European
Life
Cycle
Database
(ELCD)
in
adequacy
with
the
ILCD
handbook (2012).
To evaluate the environmental impacts, the EIME TM v5.5.0.11 software (http://
www.codde.fr/logiciel-acv.com) is used with a set of indicators coming from PEP
ecopassport® (http://www.pep-ecopassport.org).
The chosen impact indicators are referenced in Table 5 and are described below
(let us remind that the indicators deal with potential impacts):
•
The Air Acidiﬁcation (AA) indicator presents the air acidiﬁcation by gases
released to the atmosphere. It is expressed in grams of H+, as if all gases were H
+, using equivalency in their acidiﬁcation. The AA indicator is expressed in
kilograms of H+ equivalent (kg H+ eq.).
Table 5 Used indicators (midpoint indicators for PEP)
Code
Name
Unit
AA
Air acidiﬁcation
kg H+ eq.
AT
Air toxicity
m3
ED
Energy depletion
MJ
GWP
Global warning potential (100Y)
kg CO2 eq.
HWP
Hazardous waste potential
kg
ODP
Stratospheric ozone depletion potential
CFC-11 eq.
POCP
Photochemical ozone creation potential
C2H4 eq.
RMD
Natural resources indicator
Y-1
WD
Water depletion
dm3
WE
Water eutrophication
PO4
3 eq.
WT
Water toxicity
dm3
690
J.-L. Menet
•
The Air Toxicity (AT) indicator represents the air toxicity in a human environ-
ment, taking into account the usually accepted concentrations tolerated for
several gases and the quantity released. The given indication corresponds to
the air volume necessary to dilute “contaminated air.” The limit values are
expressed in g/l; consequently, the AT indicator is expressed in volume m3.
•
The Energy Depletion (ED) indicator accounts for energy consumption (or use),
either derived from the combustion of fuels or from other sources (fossil,
renewable, or nuclear for electricity production). This indicator also considers
the grey energy in materials. The ED indicator is expressed in J or MJ.
•
The Global Warning Potential (GWP) indicator represents the contribution to
global warming due to speciﬁc gas emissions in the atmosphere during the
product life cycle. It is expressed in grams of carbon dioxide equivalent (CO2
eq.). The indicator implemented in the EIME TM software is the index called
IPCC-Greenhouse effect (100 years). This indicator considers the potential
direct effects on the Greenhouse Effect of the emission of 64 greenhouse gases
over 100 years.
•
The Hazardous Waste Potential (HWP) indicator calculates the quantity of
hazardous waste produced for a given product. It is added to the ﬂows of the
LCA inventory and is expressed in kg.
•
The Stratospheric Ozone Depletion (OD) indicator represents the contribution to
the depletion of the stratospheric ozone layer by the emission of speciﬁc gases
and is expressed in grams of CFC-11 equivalent (CFC-11 eq.).
•
The Photochemical Ozone Creation Potential (POCP) indicator calculates the
potential creation of ozone in troposphere (e.g., leading to “smog”) by the release
of speciﬁc gases which will become oxidants in the low atmosphere because of the
solar radiation. It is expressed in grams of ethylene equivalent (C2H4 eq.).
•
The Natural Resources indicator (RMD) calculates the depletion of natural
resources, taking into account the size of the resource reserve in ground and
the consumption rate of today’s economy. It is expressed in fraction of reserve
disappearing per year (Y-1).
•
The Water Depletion (WD) indicator represents the water consumption during
the whole life cycle of the considered product, i.e., the sum of consumptions
from any kind of water source or quality. It is expressed in dm3.
NB.: The water used for cooling or used in a closed loop process is not taken
into account in the calculation.
•
The Water Eutrophication (WE) indicator represents the water enrichment in
nutritive elements of lakes and marine waters by the release of speciﬁc sub-
stances in the efﬂuents leading to eutrophication. The nitriﬁcation potential is
evaluated. The WE indicator is expressed in grams or kilograms of PO4
3
equivalent (PO4
3 eq.).
•
The Water Toxicity (WT) indicator represents the water toxicity. This indicator
takes into consideration the usually accepted concentrations tolerated for several
substances and the quantity released. The given indication corresponds to the
water volume necessary to dilute the “contaminated water.” The limit values are
expressed in g/l; consequently, the WT indicator is expressed in dm3.
Use of Straw Bundles in Buildings for a Lower Environmental Footprint. . .
691
In the following, the environmental impacts of the two studied walls are
presented using the abovementioned indicators.
The results dealing with the environmental impacts are presented in Table 6 and
Fig. 4 concerning the conventional wall schemed in Fig. 2 and in Table 7 and Fig. 5
for the “straw wall.”
In fact, each indicator has a different unit, so that the impacts must be normal-
ized, according to the speciﬁcations of the normalized ISO procedures 14040
(2006) and 14044 (2006). Consequently, Figs. 4 and 5 represent nondimensional
values of each indicator for the different steps of life.
4.4
Interpretation
This fourth step of the LCA methodology does not really consist of a results
analysis; it permits to assess the results obtained from the LCIA phase, relatively
to the aim of the study described in part IV.1.
Let us specify that there is no environmental impact for the installation step and
for the use of each studied product. This fact is directly linked to the aim of the
study: the installation phase has not been taken into consideration and the use phase
does not impact the system:
•
In a whole system including the inside ﬁnished surface of the wall (e.g., gypsum
boards, painting, etc.), the environmental impacts could be important. In the
present study, a comparative calculation is conducted, so that the revetment is
supposed to be the same whatever the wall; consequently it has not been taken
into account in the calculation.
•
The use step does not create any impact for a similar reason (the painting, for
example, is considered out of the boundaries of the system).
Concerning the three other steps, the ﬁrst results depend on the studied wall:
•
For the conventional wall (see Table 5 and Fig. 4), the main contribution of the
environmental impacts is essentially found either in the manufacturing step or at
the end of life, whatever the considered indicator. This means that for the
conventional wall, it is not necessary to focus on the distribution step: the
manufacturing step, due to the fabrication of concrete for the breeze blocks,
for example, generates high environmental impacts. On the same way, the end of
life generates high impacts because of the complexity or impossibility to recycle
most of the materials used in the conventional wall.
•
For the “straw wall” (see Table 6 and Fig. 5) the previous results are globally
similar considering the repartition of the main step generating the higher envi-
ronmental impacts, but we can notice that the manufacturing step globally
generates lower impacts compared with the “straw wall.” It can be deduced
that the natural material used (the straw) is effectively better for the environ-
mental than the conventional wall for most of the indicators.
692
J.-L. Menet
Table 6 Environmental footprint of the conventional wall
Use of Straw Bundles in Buildings for a Lower Environmental Footprint. . .
693
For the two studied walls, the end of life step is very important for the following
environmental impacts: the Global Warning Potential (WWP), the Photochemical
Ozone Creation Potential (POCP), the Water Eutrophication (WE), and the Water
Toxicity (WT). These two last elements concerning water should have probably
been more important if the water used for the installation step had been considered.
Considering the global contribution, the main impacts depend on the wall:
•
For the conventional wall (see Table 5), the Air Toxicity is about 2E8 m3, the
Energy Depletion reaches 10,000 MJ, and the Global Warning Potential is just
under 3000 kg CO2 eq., which globally represents the greenhouse gases rejected
by a “modern” car on around 30,000 km.
•
For the “straw wall” (see Table 6), the Air Toxicity remains important and of the
same order, essentially because of the manufacturing step. This is mainly due to
the wood chemical treatment which should be improved if we want to reduce the
environmental footprint. The Energy Depletion is about 6000 MJ and the Global
Warning Potential reaches 1400 kg CO2 eq., which globally represents the
greenhouse gases rejected by a “modern” car on a distance of around
14,000 km (a mean year of use in France).
Now we aim to compare the environmental impacts of the two considered walls.
The results for the manufacturing step, the distribution, and the end of life are
respectively represented in Tables 8, 9, and 10.
Concerning the manufacturing step, the environmental footprint of the “straw
wall” is always inferior to the one of the conventional wall, except for Air Toxicity
indicator (chemical treatment of the wood). The results concerning the distribution
step are clear: the environmental impacts of the straw wall are lower; this can be
explained by the fact that for the “straw wall,” local production has been preferred.
The end of life step leads to lower environmental impacts for the “straw wall”
regardless of the indicator.
If the global environmental footprint is considered (see Table 11 and Fig. 6), it
can be concluded that in comparison with the conventional wall, the “straw wall”:
100
75
Values (%)
50
25
0
AA for PEP
AT for PEP
ED for PEP
GWP for PEP
1. Manufacturing
2. Distribution
3. Installation
4. Use
5. End of life
HWP for PEP
ODP for PEP
POCP for PEP
RMD for PEP
WD for PEP
WE for PEP
WT for PEP
© EIME Chart
Fig. 4 Normalized impacts for the conventional wall
694
J.-L. Menet
Table 7 Environmental footprint of the “straw wall”
Use of Straw Bundles in Buildings for a Lower Environmental Footprint. . .
695
100
75
50
Values (%)
25
0
AA for PEP
AT for PEP
ED for PEP
GWP for PEP
HWP for PEP
ODP for PEP
POCP for PEP
RMD for PEP
WD for PEP
WE for PEP
WT for PEP
© EIME Chart
5. End of life
4. Use
3. Installation
2. Distribution
1. Manufacturing
Fig. 5 Normalized impacts for the “straw wall”
Table 8 Comparison of the
environmental footprint for
the manufacturing step
Indicator
Conv. wall
Straw wall
“Straw/conv” (%)
AA
1.04E  01
1.10E  02
11
AT
1.33E + 08
1.46E + 08
110
AD
7.91E + 03
4.11E + 03
52
GWP
8.08E + 02
1.85E + 02
23
HWP
4.94E  02
4.07E  02
82
ODP
4.09E  05
6.29E  06
15
POCP
1.05E  01
2.65E  02
25
RMD
1.37E  14
3.14E  15
23
WD
1.32E + 03
3.69E + 02
28
WE
1.30E  02
4.07E  04
3
WT
2.23E + 02
2.13E + 01
10
Table 9 Comparison of the
environmental footprint for
the distribution step
Indicator
Conv. wall
Straw wall
“Straw/conv” (%)
AA
3.99E  03
1.04E  03
26
AT
5.90E + 06
1.54E + 06
26
AD
3.79E + 02
8.50E + 01
22
GWP
2.96E + 01
6.31E + 00
21
HWP
9.75E  03
9.43E  04
10
ODP
1.85E  05
1.79E  06
10
POCP
2.37E  02
3.31E  03
14
RMD
5.21E  16
1.20E  16
23
WD
3.18E + 01
3.42E + 00
11
WE
4.42E  04
4.90E  05
11
WT
5.11E + 00
1.96E + 00
38
696
J.-L. Menet
•
Generates around ﬁve times less kg H+ eq.
•
Depletes twice less energy
•
Generates twice less CO2 eq.
•
Produces one and a half time less dangerous wastes
•
Destroys ﬁve times less ozone in the troposphere
•
Generates twice times less photochemical pollution
•
Depletes three times less natural resources and water
•
Contributes twice less to the water eutrophication and the water toxicity
•
Generates toxic pollutants in the air with the same magnitude
In other words, the environmental footprint of the “straw wall” is at least twice
lower regardless of the indicator, except concerning the air toxicity essentially
because of the chemical treatment of the wood.
Table 10 Comparison of the
environmental footprint for
the end of life step
Indicator
Conv. wall
Straw wall
“Straw/conv” (%)
AA
3.17E  02
1.91E  02
60
AT
5.95E + 07
3.62E + 07
61
AD
2.35E + 03
1.56E + 03
66
GWP
2.04E + 03
1.23E + 03
60
HWP
6.96E  03
4.94E  03
71
ODP
5.71E  06
3.79E  06
66
POCP
4.14E  01
2.53E  01
61
RMD
4.14E  01
2.92E  15
0
WD
6.85E + 02
4.11E + 02
60
WE
8.65E  01
4.70E  01
54
WT
1.05E + 03
5.83E + 02
56
Table 11 Comparison of the
global environmental
footprint of the two studied
walls
Indicator
Conv. wall
Straw wall
“Straw/conv” (%)
AA
1.39E  01
3.12E  02
22
AT
1.98E + 08
1.84E + 08
93
AD
1.06E + 04
5.76E + 03
54
GWP
2.88E + 03
1.42E + 03
49
HWP
6.62E  02
4.66E  02
70
ODP
6.51E  05
1.19E  05
18
POCP
5.43E  01
2.83E  01
52
RMD
1.86E  14
6.18E  15
33
WD
2.03E + 03
7.83E + 02
39
WE
8.78E  01
4.71E  01
54
WT
1.28E + 03
6.06E + 02
47
Use of Straw Bundles in Buildings for a Lower Environmental Footprint. . .
697
5
Conclusion
This study deals with the use of the Life Cycle Assessment (LCA) methodology and
tools to quantify the environmental impacts of two wall assemblies: a conventional
wall and a “straw wall” using wood and straw bundles. The considered wall
assemblies were ﬁrst described from a physical and functional point of view. The
LCA methodology has next been presented and applied to the previous introduced
insulating systems assemblies. The goal of this study was to compare two walls for
a 100-year life duration, a thermal resistance reaching 5 m2.K/W, and a 15 m2 area.
The obtained results show that the wall using straw bundles has a lower
environmental footprint than the studied conventional wall, regardless of the
indicator. Of course, this study used some restrictive hypothesis which must be
veriﬁed and tested in future works. Besides, the study has been simpliﬁed to allow a
ﬁrst comparison, and it must be continued, notably to quantify the inﬂuence of the
Fig. 6 Comparative environmental footprint of the two studied walls
698
J.-L. Menet
different elements or steps in the life cycle of a wall assembly. However, the present
results give encouraging elements for future studies concerning the use of local
and/or natural material for housing as it has been shown that straw bundles
associated to wood structure and panel is an interesting alternative not only for
local economy but also for waste treatment (straw bundles are considered as wastes)
and environmental footprint.
These three elements are linked to the three pillars of sustainable development:
planet, people, and proﬁt. A “straw house” is made of natural and renewable
materials (planet), is said to be pleasant and comfortable for the inhabitants
(people), and permits the development of a local and non-“relocated” economy
(proﬁt).
Acknowledgments The author wants to acknowledge the different students who have collabo-
rated on this project, particularly Thomas Alglaeve and Amandine Degallaix who have especially
participated in the LCIA study of this work.
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Part III
Energy Strategies and Policies
Experimental Performance Analysis of an
Integrated Air Conditioning Split Heat Pump
System for Application in a Mediterranean
Climate
Nižetić Sandro, Kizilkan €
Onder, and Čoko Duje
1
Introduction
A hybrid energy system presents energy solutions where the advantage of each
individual energy technology can be used and different forms of energy demands
can be ensured for occupants. For example, in majority of residential facilities,
there is a demand for simultaneous space heating/cooling and hot water preparation.
Furthermore, hybrid energy systems can be assembled from developed market
energy technologies such as heat pump technology, photovoltaic technology,
solar thermal systems, and fuel cell systems. Hence, the objective is to ﬁnd an
appropriate hybrid energy solution that is acceptable from a techno-economic
aspect and also from an aspect of environmental suitability through the implemen-
tation of renewable energy technologies. There are different studies that focus on
hybrid energy system research, for example, Fong et al. (2010), Herrando et al.
(2014), Hongbing et al. (2011), Huang et al. (2010), Klein et al. (2014), Ozgener
(2010), Rezaie et al. (2011), Tyagi et al. (2012), Xingxing et al. (2013, 2014),
Yamada et al. (2012), and Zafar and Dincer (2014).
N. Sandro (*)
University of Split, Faculty of Electrical and Mechanical Engineering and Naval Architecture,
LTEF-Laboratory for Thermodynamics and Energy Efﬁciency, R. Boskovica 32, Split, HR
21000, Croatia
e-mail: snizetic@fesb.hr
K. O
¨ nder
Süleyman Demirel University, Faculty of Technology, Department of Energy Systems
Engineering, Isparta 32260, Turkey
C
ˇ . Duje
University of Split, Faculty of Electrical and Mechanical Engineering and Naval Architecture,
Department of Electronics, R. Boskovica 32, Split, HR 21000, Croatia
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_49
703
The proposed hybrid energy system by Nizetic et al. (2014) was tested at a
geographical location in Croatia, in the city of Split situated on the Adriatic coast,
which is a typical coast-side city with a Mediterranean climate. The advantage of a
Mediterranean climate lies in the fact that heat pump systems can be used through-
out the year in an efﬁcient way, and in general, the potential for implementation of
renewable energy sources (Nizetic et al. 2008) (Nizetic 2011) in a Mediterranean
region is great. On the other side, the majority of building facilities in Croatia use
standard heat pump systems (split-type air conditioning units) and standard boilers
for preparation of hot water (with directly installed electric heaters) to cover space
heating/cooling demands and hot water demands, respectively. The energy market
offers a variety of available solutions but not in the form of hybrid energy systems,
so consumers are more oriented on the usage of individual technologies instead of
using the more efﬁcient hybrid energy systems. Hence, our idea was to use existing
market-available technologies that are installed in majority of building facilities,
basically modify them, and establish a unique, more efﬁcient energy solution.
Regarding the previously proposed hybrid energy solution, it should provide
space cooling/heating capacity and simultaneously ensure hot water preparation.
Additionally, the sole requirement is that the system should be a renewable one and
assembled from market-available energy technologies (the base concept is shown in
Fig. 1).
The objective of this chapter is to present further analysis results of the hybrid
energy system proposed by Nizetic et al. (2014), where a speciﬁc design and
performance parameters for cooling mode were elaborated. Additionally, in this
study, performance parameters for heating mode are presented, and a comparison
between summer and winter hybrid energy system operation is discussed.
2
A Speciﬁc Experimental Setup
In ensuring the previously deﬁned goals, the hybrid energy system that was
proposed by Nizetic et al. (2014) and the basic concept of the proposed hybrid
energy system are shown in Fig. 2. According to Fig. 2, the hybrid energy system
Fig. 1 General
requirements for renewable
hybrid energy concept
704
N. Sandro et al.
was assembled from a standard split heat pump air conditioning unit with a heating/
cooling capacity of 3.7 kW/3.5 kW, and the refrigerant used was R410A. Further-
more, the air conditioning unit was connected to a standard boiler for hot water
preparation in the same way that a spiral copper heat exchanger is installed into the
boiler (water tank with a capacity of 80 L). Additionally, the whole system was
supplied with electricity from a small PV plant (off-grid system, 1.8 kW installed
electric power, four batteries, each of 330 Ah, and monocrystalline PV technol-
ogy—195 W of nominal power in each panel). It is noticeable that the previously
speciﬁed individual components that form the proposed hybrid energy system can
be found in majority of building facilities (touristic, residential, etc.). The proposed
hybrid energy system (Fig. 2) is able to produce space cooling/heating capacity and
hot water and is driven by a small photovoltaic (PV) plant; it basically represents a
renewable energy system that ﬁts the requirements deﬁned in the previous section
of the manuscript.
Regarding the speciﬁcity of the Mediterranean coast, the proposed energy
system is especially useful for implementation in touristic facilities (small or
medium ones) as it represents an energy-efﬁcient solution that is renewable and
can cover the required energy demands (it could also be suitable for remote
locations with electricity supply issues).
Fig. 2 Simpliﬁed scheme of the proposed hybrid energy system (Nizetic et al. 2014)
Experimental Performance Analysis of an Integrated Air Conditioning Split. . .
705
3
Measurement Rig
An indoor unit and water tank were installed on a test stand as shown in Fig. 3a. PV
panels were mounted on the south terrace of the Faculty building (Fig. 4.), and the
rest of the electronics were installed inside the laboratory of Thermodynamics at the
Faculty of FESB in Split.
A simpliﬁed measurement rig is shown in Fig. 5. So, in essence, it is a conven-
tional split heat pump system scheme but additionally expanded with an accumu-
lation boiler (water tank). Electricity consumption was measured with an energy
logger (current, voltage, and engaged electric power). A pyrometer was mounted on
the PV system to measure insolation, to detect the average electrical efﬁciency of
the PV system, and, ﬁnally, to estimate battery capacity (autonomy for speciﬁc
working regimes). The PV panels were inclined at 25, which is in accordance with
the speciﬁc geographical location. As mentioned earlier, a heat exchanger (con-
denser 1, Fig. 5) was added to utilize waste heat and ensure heating capacity for hot
water preparation. The installed heat exchanger in the boiler always acts as a
condenser both in heating and cooling mode. Besides the previous modiﬁcation,
the electronics of a standard air conditioning unit was also modiﬁed to ensure easy
handling for consumers. Namely, the device has a heating and cooling mode (where
the water is also heated simultaneously in the boiler) and a water heating mode
(without heating/cooling capacity).
The surrounding air temperatures, ambient air temperature inside the cooled/
heated space, and the air temperature at the outlet of the indoor unit were measured
with temperature sensors. Air mass ﬂow was calculated according to the measured
air velocity and temperature at the outlet of the indoor unit. The temperature of the
water inside the boiler was also measured, so more than 15 parameters were
measured to evaluate performance parameters for summer and winter conditions.
Simulation of water consumption was also performed to investigate the impact of
water consumption on the performance parameters, and a more realistic situation
was analyzed, where simultaneous heating/cooling capacity was provided together
with hot water preparation. In the next section, performance analysis results are
presented for summer and winter operation together with a discussion of the gained
experimental results.
4
Performance Analysis: An Analytical Approach
According to the measured parameters, a performance analysis was conducted for a
typical winter and summer day in the geographical location of Split (Croatia),
which typically represents a Mediterranean climate. An energy balance equation
can be written for the proposed hybrid energy system for heating and cooling mode,
respectively, to determine the COP value:
706
N. Sandro et al.
Fig. 3 (a) Indoor unit and water tank (boiler) on a test stand. (b) Adaptation of a standard air
conditioning unit
Experimental Performance Analysis of an Integrated Air Conditioning Split. . .
707
COPH ¼
_
Q Con
PComp
¼
_
Q Con1 þ _
Q Con2
_
E El=τ
ð1Þ
COPC ¼
_
Q Ev þ _
Q Con1
_
E el=τ
ð2Þ
The heat rejected from the refrigerant to the water (condenser 1, Fig. 5) can be
calculated from the measured water temperatures and the overall quantity of water
Fig. 4 PV system components
Fig. 5 Simpliﬁed measurement test rig (Nizetic et al. 2014)
708
N. Sandro et al.
wasted in a certain period of time, respectively (water was wasted a few times
during the experimental testing):
_
Q Con1 ¼
X
_
m wcpw Tw2  Tw1
ð
Þ
ð3Þ
The rejected and taken heat from the space, depending on the working mode, can
be calculated from the measured air ﬂow average (via air velocity) and air temper-
ature at the outlet of the indoor unit, respectively:
_
Q Con2 ¼ _
m acpa

Th  
Tsh

ð4Þ
Finally, the performance coefﬁcient for the hybrid energy system can be deﬁned
for heating and cooling modes as follows:
COPH ¼
P mwcpw Tw2  Tw1
ð
Þ þ _
m acpa

Th  
Tsh

τ
_
E el=τ
ð5Þ
COPc ¼
P mwcpw Tw2  Tw1
ð
Þ þ _
m acpa

Tsc  
Tc

τ
_
E El=τ
ð6Þ
The above elaborated calculation framework was used to determine the hybrid
energy system performance parameters based on measured parameters under real-
istic conditions.
4.1
Summer Period
The hybrid energy system was tested during the summer period in a daily working
regime and night working regime. The daily mean air temperatures ranged from
28 C to 34 C and from 27 C to 30 C during night regime. In both analyzed cases,
the set cooled space temperature was 24 C. During the experimental measure-
ments, solar irradiation ranged between 600 and 800 W/m2, and the mean achieved
PV electrical efﬁciency was around 15%. The total measurement time ranged from
3 to 4 h. Refrigerant pressure in a steady regime was around 10.0 bars on the
evaporator side and around 24.0 bars on the condenser side. Furthermore, the
quantity of simulated water consumption ranged between 80 and 90 L for
showering and hand washing purposes. General circumstances and achieved per-
formance parameters are summarized in Table 1.
According to the recorded electricity consumption during steady-state night
operation, we calculated that the PV system autonomy is around 12 h. We also
examined the inﬂuence of water consumption on the outlet temperature of the air
conditioning unit and discovered that the effect was minor. Namely, the variation in
air temperature at the outlet of the indoor unit was around 1 C; therefore, the
Experimental Performance Analysis of an Integrated Air Conditioning Split. . .
709
system performance is not affected at all. Regarding hot water preparation, the
hybrid energy system was able to heat the water in the boiler from 30 C to 45 C in
approximately 20–25 min, depending on the working circumstances. However, if
the water is not released from the boiler during system operation, it was possible to
heat the water in the boiler up to 60 C. The COP value during daytime operation
ranged on average from 4.6 to 6.1 and nighttime operation ranged from 4.0 to 6.7. In
both the previous working circumstances, the mean COP number achieved was
around 5.4, which proves the good efﬁciency of the proposed hybrid energy system
as we are dealing with a small energy system. Engaged compressor power ranged
from 500 to 700 W in steady-state operation, and hourly electricity consumption
was around 0.66 kWh in daytime operation and around 20% less in nighttime
operation. Finally, it can be concluded that the achieved performance parameters
of the proposed hybrid energy system in cooling mode are excellent as a relatively
high COP value was achieved, the system was able to heat water to an appropriate
temperature, and autonomy was provided during nighttime operation.
4.2
Winter Period
Regarding winter period, the hybrid energy system was tested only in daytime
operation. The surrounding air temperature ranged from 8 to 13 C, and solar
irradiation ranged from 300 to 500 W/m2. In all cases, the heating space temper-
ature was 24 C. Water consumption was also simulated, and it ranged from 30 to
80 L. The average measurement time varied from 2 to 4 h. The mean performance
results for steady-state operation are presented in Table 2 for a heating regime.
The system was able to heat water from 20 C to 50 C for approximately half an
hour, and the average air temperature at the indoor outlet unit ranged from 41 C to
44 C. However, when water was wasted, we noticed a signiﬁcant decrease in air
temperature at the outlet of the indoor unit. To be speciﬁc, immediately after water
was released, the temperature at the indoor outlet unit decreased from 6 to 8 C,
which caused insufﬁcient temperature level at the indoor outlet unit. An example of
water waste effect is shown in Fig. 6. In this case, approximately 20 min was needed
Table 1 Performance
parameters and general
working circumstances for the
summer period
Performance parameters (daytime and nighttime operation)
Surrounding air temperature
27–34 C
Indoor air temperature
24 C
Total measurement time
3–4 h
Water consumption
80–90 l
Mean refrigerant pressure (condenser)
24 bars
Mean refrigerant pressure (evaporator)
10 bars
Mean water temperature
46–51 C
Mean hourly electricity consumption
0.55–0.66 kWh
Mean COP number
5.3–5.4
710
N. Sandro et al.
to achieve proper air temperature at the indoor outlet unit (around 40 C). However,
the system was able to recover the temperature at the indoor outlet unit in reason-
able time.
An example of COP value variation (heating mode) as the function of surround-
ing air temperature is shown in Fig. 7. According to the provided measurements, it
is noticeable that if the average temperature of the surrounding air is above 10 C,
the mean COP value will be above 5.5, but if the temperature of the surrounding air
is below 10 C, the mean COP value will be around 20% lower. However, the
proposed hybrid energy system is assumed to operate in mild climates where the
mean surrounding air temperatures in winter period are around 10 C.
Table 2 Performance
parameters and general
working circumstances for
winter period
Winter performance parameters (daytime operation)
Surrounding air temperature
8–13 C
Indoor air temperature
24 C
Total measurement time
2–4 h
Water consumption
30–80 L
Mean refrigerant pressure (condenser)
28 bars
Mean refrigerant pressure (evaporator)
8.5 bars
Mean water temperature
44 C
Mean hourly electricity consumption
1.14 kWh
Mean COP number
5.3
Fig. 6 Effect of water consumption on air temperature at the outlet of the indoor unit
Experimental Performance Analysis of an Integrated Air Conditioning Split. . .
711
Engaged compressor power in heating mode was around 1.0 kW in steady-state
operation, and mean current was around 5 A. An example of engaged compressor
power variation is shown in Fig. 8. The mean hourly electricity consumption was
around 1.14 kWh, and in comparison with summer operation, it is a doubled
amount.
Regarding the preparation of hot water in the summer period, the hybrid energy
system was able to heat the water in the boiler to 56 C, and the average temperature
of hot water was 44 C. Water consumption was also simulated, and one boiler
capacity (80 L) was wasted in 4 h. The effect of water consumption on water
temperature is shown in Fig. 8. According to Fig. 9, the sudden drops in water
temperature correspond with the simulated water consumption. After water con-
sumption, the water temperature in the boiler reduced, ranging from 30 to 35 C,
and the system needed around 20 min to ensure proper water temperature at around
45 C.
Fig. 7 COP value as the
function of surrounding air
temperature
Fig. 8 Variation of compressor power in steady-state operation
712
N. Sandro et al.
Performance parameters of the hybrid energy system are also satisfactory in the
heating mode, although there is an issue regarding the impact of water release on air
temperature at the outlet of the indoor unit. Electricity consumption is also doubled
in comparison with cooling mode, and PV system autonomy is signiﬁcantly
reduced. Therefore, the system has got lower overall performance in heating
mode in comparison to cooling mode but is still a good and applicable energy
solution for building applications.
5
Conclusions
In this chapter, the speciﬁc experimental setup and design of the hybrid energy
system was elaborated in detail. The proposed energy system is suitable for
operation in mild climates, such as a Mediterranean climate, where a heat pump
can work efﬁciently throughout the year. The main characteristic of the proposed
system is that it has been assembled from existing market-available energy tech-
nologies to ensure fast implementation into the market through simple adjustments
of existing energy systems that are already used in building applications. Moreover,
the system is totally renewable, which is an important advantage in these times
when climate issues are our main concern. According to experimental measure-
ments, the system has shown promising performance parameters. The mean COP
value was both 5.4 in summer and winter periods, but the mean hourly electricity
consumption was around 0.5 kWh in the summer period and around 1.1 kWh in the
Fig. 9 Effect of water consumption on mean temperature in water tank
Experimental Performance Analysis of an Integrated Air Conditioning Split. . .
713
winter period. The PV system autonomy was up to 12 h in the summer period and
6 h in the winter period. Furthermore, the system was able to heat up water to a
mean temperature of 45 C, and the maximal achieved water temperature was
56 C. Hence, it can be concluded that the achieved water temperature level is
suitable for domestic or other needs. If we compare the summer and winter
performance parameters of the hybrid energy system, it can be concluded that
summer performance parameters are better. In other words, in cooling mode,
electricity consumption will be halved, the COP number will be higher, water
temperature in the boiler will also be higher, and PV system autonomy will be
doubled; therefore, these advantages are important in the potential application of
the proposed energy system. Finally, it can be concluded that the proposed hybrid
energy system is efﬁcient for summer and winter operations and has potential
applications in touristic building facilities and residential facilities in mild climates
as well.
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Technical and Economic Prefeasibility Study
of Mini-Hydro Power Plants in Venezuela Case
Study: El Valle River
Victor Trejo, Gabriela Diaz, and Luis Rojas-Solorzano
1
Introduction
Venezuela’s income derives almost exclusively from oil exports, and it is one of the
few countries favored with both large amounts of fossil fuels and important
renewable resources. According to statistics from the International Energy Agency
(2013), 68.5% of the country’s internal electricity consumption is produced by
hydroelectric power plants, and the rest is covered by thermoelectric generation
(Ministerio del Poder Popular para la Energı
´a Ele
´ctrica 2013). However, over 87%
of the hydroelectric production comes from a single source, the Simo
´n Bolı
´var
Hydroelectric Central, which ranks as the fourth largest in the world, and no
investments in small hydroelectric power are scheduled. Investments in thermo-
electric power generation are instead currently being carried out. At this moment,
Venezuela is also the country with the largest CO2 generation per capita in Latin
America (IEA 2013). This situation, along with typical highly competitive interna-
tional fuel prices that otherwise would increase internal revenues received from oil
exports, extremely needed to fund education and social programs, makes Venezuela
the perfect example in the study of renewable energies. Recent changes in oil prices
introduced uncertainty in the described scenario.
The RETScreen® V.4 software (RETScreen® International 2004) is a wide-
spread clean energy project analysis tool that facilitates the execution of
prefeasibility and feasibility analyses. Several different types of projects have
V. Trejo (*) • G. Diaz
Simo
´n Bolı
´var University, Department of Energy Conversion and Transport, Sartenejas,
Caracas 1080, Venezuela
e-mail: victormanueltn@gmail.com
L. Rojas-Solorzano
Nazarbayev University, Faculty, Department, Institute, Astana 010000, Kazakhstan
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_50
715
been studied with this tool, a few examples of which are the comparison between
landﬁll gas and waste incineration for power generation in Ghana (Anaglate et al.
2012), the evaluation of prospects of wind farm development in Algeria (Himri
et al. 2009) and an environmental, technical, and ﬁnancial feasibility study of solar
power plants in Iran (Hajiseyed Mirzahosseini and Taheri 2012) and the assessment
of the prediction capacity of wind-electric generation models (Romero and Rojas-
Solo
´rzano 2014) to just name a few.
In this work, the technical and economic feasibility of the installation of a mini-
hydroelectric power plant on El Valle River in Venezuela is assessed, which is
supported by RETScreen® software. This chapter is organized as follows: In the ﬁrst
two sections, technical and economic feasibility studies and their results are described
in detail. Special attention is paid to modeling Venezuela’s energy and economic
scenario to assess the viability of the project under these circumstances. Sensitivity
and risk analysis complement the prefeasibility study and are presented in Sect. 3.
In Sect. 4, the CO2 emission analysis is described. Finally, conclusions are drawn.
2
Technical Analysis
2.1
Installation Site and Type of Power Plant
The El Valle River in the Los Salias Municipality of the Miranda State, Venezuela,
was chosen for this study. The river, along with the streams Cujı
´ and El Indio, feed
the La Mariposa reservoir. The water from the reservoir ﬂows by gravity into the La
Mariposa water puriﬁcation facility, which, according to Hidrocapital (2013), the
company in charge of its operation, has a maximum capacity of 4.3 m3/s and
provides drinking water to 80% of Los Altos’ Mirandinos community and 15% of
Caracas, Venezuela’s capital city. In order to minimize investment costs and the
effect on the environment, the installation of a run-of-the-river hydroelectric power
plant was chosen.
2.2
Evaluation of Hydric Resource
The estimate of usable ﬂow was based on hydrological data provided by
Venezuela’s National Institute of Meteorology and Hydrology (INAMEH). The
data comprised monthly average, minimum, and maximum ﬂows in the El Valle
River during the period 1978–1991 measured at El Valle in la Mariposa hydrolog-
ical station. With this information, the ﬂow-duration curve in Fig. 1 was prepared.
In accordance with the type of power plant (run-of-the-river) and turbine selection,
a ﬂow of 1.25 m3/s exceeding 97.7% was set as design ﬂow for the power plant.
For estimating the available hydraulic head, the computational tool Google
Earth was employed. This tool is capable of providing the elevation proﬁle along
716
V. Trejo et al.
a trajectory drawn by the user on a satellite view of the terrain. A trajectory along
the El Valle River was traced in order to estimate the hydraulic head. The resulting
elevation proﬁle is shown in Fig. 2 with two arrows indicating possible positions for
the ﬂow intake and the turbine. The section limited by these two positions has a
horizontal length of 0.55 km and a hydraulic head of 40 m.
2.3
Turbine Selection
Based on the available ﬂow and head, a Francis turbine emerged as the most
appropriate unit (Minicentrales hidroele
´ctricas - Manual de energı
´as renovables
2006). A 405-kW turbine along with its accessories (generator, governor, excitation
0
2
4
6
8
10
0
20
40
60
80
100
Flow [m3/s]
Flow exceedance percentile [%]
Fig. 1 Flow-duration curve
of El Valle River
Fig. 2 Elevation curve along the El Valle River (Google Earth 2012)
Technical and Economic Prefeasibility Study of Mini-Hydro Power Plants. . .
717
system, main inlet valve, spare parts, and special tools) was chosen from the
catalogue of a manufacturer, and its characteristics are summarized in Table 1.
The annual production of energy was estimated taking into consideration the
operating conditions of the turbine, its type and ﬂow-duration curve, a typical
power curve, and the generator–turbine efﬁciencies. The obtained projected annual
energy is 3121 MWh, which shows that the El Valle River is adequate for the
installation of a mini-hydroelectric power plant.
3
Economic Analysis
The economic prefeasibility analysis was carried out in the RETScreen® V.4
software. Electricity prices in Venezuela are very low since they are subsidized
by the government, which means that investing in electricity generation is not
proﬁtable for any private entity unless some sort or arrangement is agreed upon
with the authorities. Therefore, the study was carried out from the perspective that
the only possible investor is the Venezuelan Government, for whom the investment
may be beneﬁcial in the sense that it opens up the opportunity for substituting
thermoelectric generation and exporting the fuels that would have been otherwise
consumed. The modeling of the project is described in detail in this section.
3.1
Value of the Produced kWh
Electricity prices in Venezuela (Ministerios de la Produccio
´n y del Comercio y de la
Energı
´a y Minas 2013) has been subsidized under regulation since 2002 as indi-
cated by the ofﬁcial gazette from April 3 (2002) and do not reﬂect inﬂation rates
(Banco Central de Venezuela 2012). For this reason, using the commercialization
price of electricity as the value of the produced kWh underestimates greatly the
economic impact of any generation project in the country. Since Venezuela is an oil
producer and exporter, each kWh produced via renewable energies translates into a
Table 1 Key characteristics
of selected turbine
Turbine features
Max head
42 m
Design head
40 m
Runner diameter
0.5 m
Design ﬂow
1.25m3/s
Unit output
405 kW
Turbine efﬁciency
89%
Generator efﬁciency
92.5%
Frequency
60 Hz
718
V. Trejo et al.
kWh that is not produced in thermoelectric power plants and can thus be exported at
considerably higher international rates.
The investment in renewable sources of energy competes directly with the
ongoing expansion of Venezuela’s thermoelectric installed capacity. More specif-
ically, hydroelectric power currently competes with power generation with gasoil
(diesel); according to Venezuela’s Centro Nacional de Despacho (2010), generation
with fueloil and natural gas decreased by 0.57% and 6.77%, respectively, in 2010
with respect to 2009, while generation with gasoil increased by 25.4%. Therefore,
the value of the saved kWh was set to $242/MWh according to gasoil prices
reported by the IEA (2012) for the date of the modeling and after considering the
efﬁciency of a typical diesel power plant according to the US Energy Information
Administration (2013).
3.2
Financial Parameters
A project life of 50 years was considered since hydroelectric power plants can
operate for over 50 years without major overhauls. A yearly average escalation rate
for gasoil was calculated with available data corresponding to the period
2007–2014 and an average US inﬂation rate was calculated with data corresponding
to the period 1980–2010 (Index Mundi 2014). The obtained value of the former is
1.1% and for the latter 2.6%. For the discount rate, a value of 10% was chosen,
which corresponds to a typical return value of Venezuelan US$ bonds.
3.3
Costs
The costs of the selected turbine and related equipment are detailed in Table 2 and
add up to a total of US$104,000, which represents the highest portion of the initial
costs.
The costs of the feasibility study, development, and engineering were estimated
as 10% of the equipment and civil work costs, and equal to 8.4% of the initial costs.
As reported by the Instituto para la Diversiﬁcacio
´n y Ahorro de Energı
´a (2006),
civil works for a hydroelectric power plant typically represent 40% of the initial
Table 2 Costs of turbine
and related equipment
Equipment
Cost [US $]
Turbine
38,000
Generator
25,000
Governor
17,000
Excitation system
12,000
Main inlet valve
6000
Spare parts and special tools
6000
Technical and Economic Prefeasibility Study of Mini-Hydro Power Plants. . .
719
costs. This is a conservative value for a run-of-the-river installation, and it is meant
to account for any contingencies. Transportation fees for the equipment were
calculated as $10,000, near 10% of the equipment costs.
Three months was estimated as the time necessary for the construction and start-
up of the power plant. It was estimated that during this period, two engineers, three
technicians, and three manual workers would be required. Additionally, the turbine
provider demands a supervisor, and the future operator of the power plant would
require training during this period. The costs associated to the described personnel
are detailed in Table 3. The salary of the supervisor is ﬁxed by the provider, while
the rest of the salaries were calculated based on Venezuela’s labor market
(Gonzalez 2012).
3.4
Results of the Prefeasibility Study
The economic indicators resulting from the prefeasibility analysis carried out are
summarized in Table 4.
As the resulting economic indicators show, the herein described project would
be highly proﬁtable, the investment of which would be recovered rather quickly
(1.1 years) and that would produce beneﬁts during its complete lifetime. The study
shows a very high internal rate of return (280.30%), which is quite superior to the
discount rate (10%). Figure 3 shows the growth of the net present value in the
cumulative cash ﬂow during the project’s lifetime. The beneﬁt–cost ratio indicates
that for each dollar invested, $30.77 will be gained including all expenditures,
which equates to $804,050 as annual savings.
Table 3 Salaries of staff
during construction
and start-up
Quantity
Position
$/month
Total
1
Supervisor
2400
2400
2
Engineers
1860
3720
3
Technicians
1290
3870
3
Manual workers
1000
3000
1
Operator
1290
1290
Complete staff
(3 months)
$40,260
Table 4 Economic
indicators of the project’s
proﬁtability
Indicator
Value
Internal rate of return
280.30%
Payback period
0.4 years
Net present value
US$7992008
Equivalent annual savings
US$804050
Beneﬁt-cost ratio
30.77
720
V. Trejo et al.
3.5
Risk Analysis
The economic gains of the project are achieved through gasoil savings and sales.
Fuel prices are difﬁcult to predict since they depend heavily on international
politics. For these reasons, it is especially important to assess the possible impact
of a decrease in the price of gasoil on the proﬁtability of the project. Sensitivity and
risk analyses were carried out.
In the sensitivity analysis, the effects of possible variations of initial costs,
maintenance costs, and the price of gasoil (modeled as the electricity export rate)
on the internal rate of return, the net present value, and the payback period were
considered. Results are summarized in Table 5 and show that the most inﬂuential
factor is the price of gasoil followed by the initial costs. The price of gasoil has a
strong inﬂuence on all three economic indicators, while the initial costs impact the
internal rate of return and the payback period mostly. The operation and mainte-
nance costs do not show an important relative impact.
60,000,000
50,000,000
40,000,000
30,000,000
20,000,000
Cumulative cash flows ($)
10,000,000
-10,000,000
0
0
2
4
6
8
10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48
50
Fig. 3 Cumulative cash ﬂow
Table 5 Results of sensitivity analysis
Parameter
Relative impact ()
Internal rate of return
Payback period
Net present value
Gasoil price
0.8
0.8
1
Initial costs
0.55
0.6
>0.1
Operation and maintenance costs
>0.1
<0.1
>0.1
Technical and Economic Prefeasibility Study of Mini-Hydro Power Plants. . .
721
Following the results of the sensitivity analysis, the inﬂuence of the price of
gasoil and the initial costs on the proﬁtability of the project were quantiﬁed with the
risk analysis. A wide sensitivity range of 50% was considered for both factors, that
is, scenarios that combined variations of each factor ranging from 50% to 150% of
the original values were considered. The results of the risk analysis are shown in
Tables 5 and 6 for the internal rate of return and payback period, respectively.
An increase of 50% in the initial costs and a decrease of 50% in the price of
gasoil result in the worst-case scenario: an internal rate of return of 92% and a
payback period of 1.1 years. These are less attractive but still very proﬁtable
indicators, which shows that, even though the project is very sensitive to variations
in initial costs and the price of gasoil, it can still be proﬁtable when facing very
unfavorable scenarios such as recent changes in oil price, as long as the gap
between gasoil production costs and its sale price remains wide enough (Table 7).
4
Emission Analysis
Due to the high demand that the Venezuelan electrical network has been experienc-
ing since the energy crisis of 2009, new thermoelectric plants have been installed
along the Venezuelan territory in the recent years.
With the installation of the mini-hydropower plant in the El Valle River, it is
possible to contribute to the relief of the already existing network.
Since the electric grid is overloaded, the proposed mini-hydropower plant would
displace the thermoelectric power plants that are to be installed likewise
Table 6 Risk analysis for the internal rate of return
Price of gasoil variation [%]
Initial costs variation [%]
50
25
0
25
50
50
274%
183%
138%
110%
92%
25
417%
278%
209%
167%
140%
0
559%
373%
280%
224%
187%
25
702%
468%
352%
282%
235%
50
845%
564%
423%
339%
282%
Table 7 Risk analysis for payback period
Price of gasoil variation [%]
Initial costs variation [%]
50
25
0
25
50
50
0.4
0.5
0.7
0.9
1.1
25
0.2
0.4
0.5
0.6
0.7
0
0.2
0.3
0.4
0.4
0.5
25
0.1
0.2
0.3
0.4
0.4
50
0.1
0.2
0.2
0.3
0.4
722
V. Trejo et al.
contributing to reduce the fuel consumption required for the operation of these new
thermoelectric plants, and thus the greenhouse gases emissions. To determine the
amount of emissions that would be reduced with the proposed project, an analysis
of emissions was performed.
The resulting savings in greenhouse gases emission resulting from this study is
3079 t of equivalent CO2 per year. Some equivalent measures to better visualize this
beneﬁt may be considered: 3079 t CO2 are equivalent to removing 564 automobiles
from circulation, to avoid consuming 7160 barrels of gasoline, or 1,322,961 l of
gasoline per year.
5
Conclusions
A technical and economic prefeasibility study of the installation of a mini-
hydroelectric power plant on the El Valle River in Venezuela was carried out.
The technical prefeasibility study showed that the El Valle River reunites the
necessary characteristics for the installation of a run-of-the-river hydroelectric
power plant.
The economic prefeasibility study was carried out from the perspective that,
taking Venezuela’s very particular energy and economic characteristics into con-
sideration, the national government is the only possible investor. The results of the
study show very attractive economic indicators, such as a 280.3% internal rate of
return, a 0.4 years payback period, and a 30.77 beneﬁt–cost ratio.
Sensitivity and risk analyses demonstrated that, even when facing highly unfa-
vorable variations in gasoil prices and initial costs, the project can still be proﬁtable.
The emission analysis’ results indicate possible annual reductions of greenhouse
gases emissions equivalent to 3079 t CO2.
The proﬁtability of the project, as well as its environmental advantages, is
enhanced by the fuel savings and possible export. Both economic and environmen-
tal attractiveness of the installation of the mini-hydroelectric power plant are
strongly reinforced by Venezuela’s current energy and economic characteristics.
In this sense, the positive results of the study are an indication that clean energy
could ﬁnd very fertile scenarios in countries such as Venezuela, where the advan-
tages of their application may not be evident.
References
Anaglate, S.A., Rahmaputro, S., Ruiz, C., Rojas-Solo
´rzano, L.R.: Comparison between landﬁll gas
and waste incineration for power generation in Accra, Ghana. Int. J. Environ. Sci. Eng. Res. 3,
1–7 (2012)
Banco Central de Venezuela: Banco Central de Venezuela – Indicadores. [Online]. Available at:
http://www.Bcv.Org.Ve/c2/indicadores.Asp (2012). Accessed 10 Nov 2012
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Centro Nacional de Despacho: Scribd. [Online]. Available at: http://es.Scribd.Com/doc/58926153/
Comentarios-Centro-Nacional-de-Despacho-Informe-2010 (2010). Accessed 5 Nov 2013
Google Earth. El Valle River satellite view 1023057.95N 6655036.56O, elevation 1024M.
[Online]. Available: http://www.Google.Com/earth/index.Html (2012). Accessed Nov 2013
Gonza
´lez,
D.:
Revista
econo
´mica
de
Venezuela.
[Online].
Available
at:
http://
revistaeconomicadevenezuela.Blogspot.Com/2011/04/salario-nominal-en-venezuela-1999-
2011.Html (2012). Accessed 10 Nov 2012
Hajiseyed Mirzahosseini, A., Taheri, T.: Environmental, technical and ﬁnancial feasibility study
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Hidrocapital. Hidrocapital. [Online]. Available at: http://www.Hidrocapital.Com.Ve/internet/
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nuestras-plantas-de-tratamiento&Itemid¼45 (2013). Accessed 13 Nov 2013
Himri, Y., Boundghene Stambouli, A., Draoui, B.: Prospects of wind farm development in Algeria.
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´n y Ahorro de Energı
´a: Minicentrales hidroele
´ctricas – manual de
energı
´as renovables. In: Madrid: s.N., p. 53 (2006)
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Eia.Gov/petroleum/gasdiesel/ (2012). Accessed 2 July 2012
International
Energy
Agency:
[Online].
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at:
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statisticssearch/report/?&country¼VENEZUELA&year¼2011&product¼ElectricityandHeat
(2013). Accessed 5 Nov 2013
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´a Ele
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Energı
´a Ele
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(2013).
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´n y del Comercio y de la Energı
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servicios/documentos/normas/gacetatarifaselectricas.Pdf (2002). Accessed 5 Nov 2013
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Textbook. Canada: s.n. 9 (2004)
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724
V. Trejo et al.
A Study of the Effects of the External
Environment and Driving Modes on Electric
Automotive Air-Conditioning Load
Yew Khoy Chuah and Yu-Tsuen Chen
1
Introduction
According to the International Energy Agency (IEA) (2013) the oil consumption of
OECD countries in the ﬁrst quarter of 2013 amounts to 46 million barrels per day, a
reduction of 200 thousand barrels with respect to the same period in 2012. How-
ever, there was an increase of 1.3 million barrels per day for non-OECD countries,
up to about 44 million barrels per day.
The above ﬁgures clearly state the problem of our ever-increasing need for oil.
Material needs in all aspects of comfortable modern life that are a consequence of
economic growth will further aggravate our ever-growing need for energy. Inevi-
tably the global warming problem resulting from the increasing atmospheric con-
centration of CO2 is becoming worse each year.
Of all sectors of our lives, the internal combustion engine automobile is one of
the largest sources of CO2 emission. Hence electric-powered or hybrid-powered
vehicles are seen to be a solution to reducing the need for oil. Conventional
automobile air conditioning is powered by coupling to the engine. The
air-conditioning system for hybrid- or electric-powered vehicles has to be electric
powered. In addition, intelligent control adapted to driving modes is needed to
reduce the power consumption and prolong the driving range. Therefore optimiza-
tion of energy efﬁciency by control strategies is imperative. However, energy
saving that does not affect driving thermal comfort has to be achieved.
Nakane et al. (2010) found that when all driving modes are considered, an
air-conditioning system may reduce the driving range by one-half. Factors
Y.K. Chuah (*) • Y.-T. Chen
Department of Energy and Refrigerating Air-Conditioning Engineering, National Taipei
University of Technology, 1, Sec. 3, Zhongxiao E. Road, Taipei 10608, Taiwan
e-mail: yhtsai@ntut.edu.tw
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_51
725
considered in their research included the external environment, driving mode such
as idling or moving, solar radiation angles, etc., that would greatly affect the
cooling load. They also suggested that intelligent energy saving control would
elevate energy management and greatly prolong the driving time without compris-
ing thermal comfort.
Makino et al. (2003) suggested that a commercial compact scroll compressor can
be used in electric vehicles. In comparison, it is smaller in size and has higher
energy efﬁciency. Hosoz and Direk (2006) evaluated the use of a four-way valve so
to provide heating in winter. They found that the heat exchanger has to be
redesigned to achieve effective heating.
Brown (2009) compared the use of a new low GWP HFO refrigerant to the
current R134a applied to automobiles. Although HFO refrigerant has thermal
properties close to those of R134a, he suggested that more information is needed
for further evaluation. Lee and Jung (2012) compared R1234yf to R134a under the
same operating experimental conditions and found that R1234yf had a lower
cooling capacity.
Alkan and Hosoz (2010) found that when the condenser fan of an automobile’s
air conditioner has variable speed control, variable speed compressor will perform
better than that of a constant speed compressor.
2
Cooling Load Computation
Air-conditioning load is fundamental to the design and selection of equipment. A
cooling computation model is proposed in this paper. In the proposed model,
vehicle cooling load calculation is based on inputs of ambient air temperature,
wind speed, solar radiation, driving mode, and passenger metabolism heat, among
other things. The calculation also considers the vehicle’s shell structure and mate-
rials, radiation angle at the glass, low-e glass application, occupancy, and outdoor
air. Heat conduction, convection, and radiation are all considered in the model.
Radiation transmitted through the large glass area with respect to the different
incidence angles was considered in the model. The model allows for part of the
glass having low emissivity coating as for most vehicles sold in Taiwan. For heat
conduction through the body of the vehicle, an effective temperature was used
instead of outdoor air temperature to account for solar radiation effects. A predic-
tion of the external car body temperature was also made.
The total cooling load of an automobile can be calculated using Eq. (1), which is
also shown schematically in Fig. 1.
Qtotal ¼ Qsolar þ Qcond þ Qfresh þ Qinside
ð1Þ
Qtotal: heat removed from inside an automobile (W)
Qsolar: Solar radiation through glass (W)
Qcond: Heat conduction into automobile(W)
726
Y.K. Chuah and Y.-T. Chen
Qfresh: Treating outdoor air to inside conditions (W)
Qinside: Internal heat due to passengers and others (W)
2.1
External Convective Transfer Coefﬁcient ho
Heat convection between the external automobile surface and ambient air was
considered in the analysis. In this study natural convection due to temperature
difference was also considered. Heat transfer coefﬁcients were calculated by
referring to chapter 4 of the ASHRAE Handbook-Fundamentals (2009a).
2.2
Transmitted Solar Radiation QSolar
Automobiles with a large glass area transmitting solar radiation have a major source
of air-conditioner load. The direct solar heat is affected by the transmittance,
reﬂectivity, and absorptivity of the glass material or coating on it. Moreover, the
glass thickness and solar incidence angle are also important factors to consider, as
stated in chapter 14 of the ASHRAE Handbook-Fundamentals (2009b). The trans-
mitted solar radiation can be calculated as shown in Eq. (2).
QSolar ¼
X
TbEt,b þ Td Et,d þ Et,r
ð
Þ
½
Ag
ð2Þ
Tb: Solar direct transmittance, fraction
Td: Solar diffuse transmittance, fraction
Et,b: Solar direct intensity (W.m2)
Et,d:Solar diffuse intensity (W.m2)
QFresh
QSolar
QSolar
QInside
QCond./v.
QCond./v.
Fig. 1 Cooling load considered in the analysis
A Study of the Effects of the External Environment and Driving Modes. . .
727
Et,r: Surface reﬂectance (W.m2)
Ag: Glass area (m2)
There are some cases where the glass area has a coating to isolate some solar
radiation. Normally the isolation effect can be as high as 65%. Therefore for a glass
area with isolation coating Qsolar has to be revised, as in Eq. (3).
Q
0
solar ¼ Qsolar 1  isolation
ð
Þ
ð3Þ
2.3
Heat Conduction QCond./v.
When the shell structure of an automobile is exposed to outside conditions, the
external surface temperature is substantially higher than that of the internal surface.
The external surface temperature is affected by solar radiation, outside air temper-
ature, and driving speed. The glass area can be assumed to have a low thermal mass.
However, for an opaque shell, heat conductivity has to be considered in the
calculation. Moreover, the inside and outside convective heat resistance also has
to be considered.
To simplify the computation the internal convective resistance is taken to be a
constant value. The conduction calculation begins with the external surface tem-
perature, therefore only internal convective resistance is considered. The calcula-
tion of heat conduction and the overall heat transfer coefﬁcient U are calculated by
Eqs. (4) and (5). In Eq. (5) ra is the resistance of the air gap in the automobile shell
structure and tset is the air-conditioning temperature set.
Qcond ¼
X
UA tes  tset
ð
Þ
ð4Þ
U ¼
1
P dj=kj
þ ra þ 1=hi
ð5Þ
2.3.1
External Surface Temperature Tes
As shown in Fig. 2, the solar heat absorbed would partly be transmitted into the
inside, but also partly reradiated or convected to the outside air. As can be seen in
Eq. (6), heat conduction is balanced by three heat transfer mechanisms.
q
00
conduction ¼ q
00
solar þ q
00
convection þ q
00
radiation
ð6Þ
q00conduction: Heat conduction ﬂux (W.m2)
q00solar: Absorbed solar ﬂux (W.m2)
q00convection: Heat convection ﬂux (W.m2)
q00radiation: Radiation ﬂux (W.m2)
728
Y.K. Chuah and Y.-T. Chen
A. Absorbed solar ﬂux q00solar.
(a) Opaque part calculated using Eq. (7):
q00
solar ¼ ε Et,b þ Et,d þ Et,r
ð
Þ
ð7Þ
(b) Clear part:
For part with no isolation coating using Eq. (8):
q00
solar ¼ εf
1,bEt,b þ εf
1,d Et,d þ Et,r
ð
Þ
ð8Þ
Without isolation coating using Eq. (9):
q00
solar ¼ εf
1,bEt,b þ εf
1,d Et,d þ Et,r
ð
Þ


1  εf
2


þ Etεf
2
ð9Þ
ε: Radiation absorptivity
εf
1,b: Direct radiation absorptivity
εf
1,d: Diffuse radiation absorptivity
εf
2: Isolation coating effect, 0.65 for absorption type, 0.35 for reﬂective type
B. External convection q
00
convection using Eq. (10):
q00
convection ¼ ho to  tes
ð
Þ
ð10Þ
ho: External convection coefﬁcient (W.m2.K1)
to: External temperature (K)
C. The solar radiative heat gain q00radiationcan be calculated using Eqs. (11, 12, 13,
14, 15, and 16):
Fig. 2 Heat balance
calculation Scheme
A Study of the Effects of the External Environment and Driving Modes. . .
729
q00
radiation ¼ hr,g tg  tes


þ hr,sky tsky  tes


ð11Þ
hr,g ¼ εσ Fsg tg4  tes4


tg  tes


ð12Þ
hr,sky ¼ εσ Fssky tsky4  tes4


tsky  tes


ð13Þ
Fsg ¼ 1  cos α
2
ð14Þ
Fssky ¼ 1 þ cos α
2
ð15Þ
tsky ¼ to  6 cos α
2
ð16Þ
Hr,g: Surface to ground radiation transfer coefﬁcient (Wm2 K1)
Hr,sky: Surface to sky radiation transfer coefﬁcient (Wm2 K1)
tg: Ground surface temperature (K)
tsky: Effective sky temperature (K)
ɛ: Surface long wave emissivity
σ: Stefan-Boltzmann constant, 5.67  108 W.m2.K4
Fs  g: Surface to ground view angle factor
Fs  sky: Surface to sky view angle factor
α: Surface inclination angle relative to the normal
tes can be calculated by substituting Eqs. (7), (10) and (11) into (6) to obtain
Eq. (17).
tes ¼ Utset þ aGt þ hoto þ hr,gtg þ hr,skytsky
U þ ho þ hr,g þ hr,sky
ð17Þ
Iteration is required in the computation as hr,g, hr,sky and ho (for natural convec-
tion) are determined based on the external surface temperature.
2.4
Outdoor Air-Cooling Load, QFresh
Outdoor air ventilation is required to maintain the air quality inside an automobile.
The outdoor air has to be treated to meet thermal comfort conditions. The cooling
load due to fresh air ventilation can be calculated using Eq. (18).
730
Y.K. Chuah and Y.-T. Chen
Qfresh ¼ Qρairnp hout  hin
ð
Þ
ð18Þ
Q: Minimum air volume per person as given in ASHRAE standard 62.1 (2004) and
can be set at 0.15 m3min1person1.
ρair: Air density, taken as 1.2 kg/m2
Np: Number of passengers
h: Air enthalpy (kJ.kg1)
2.5
Heat Gain Due to Passengers, Equipment, and Lighting,
Qinside
QInside ¼ Qhuman þ Qequ: þ Qlight
ð19Þ
Components of Eq. (19) are described below:
Qhuman: 115 W  number of person
Qequ: Include fan motor, GPS, stereo, electric equipment, estimated at 638 W
Qlight: Taken to zero at day time
3
Computation Software Development
Based on the cooling load computation model described above, an Excel-VBA
based air-conditioning load calculation tool was developed. The cooling load
calculation needs only conditions of ambient environment and the driving mode
parameters such as speed and GPS information. With the highest temperature of the
day known or predicted, the possible maximum load can be calculated. The design
of automotive air conditioning and the control strategy can be performed with the
calculation tool to save time and reduce costs. Prediction of the surface temperature
of the shell structure can be calculated using the Excel-VBA based tool.
3.1
Experimental Measurements of Automobile Surface
Temperature
A comparison of the prediction model with actual measurement of the outside shell
temperature of a car was conducted. Figure 3 shows a sedan placed outside exposed
to the sun. The comparison was made under the actual weather conditions such as
temperature, humidity, and wind speed, car heading direction, sky clearness, and
ground surface temperature.
A Study of the Effects of the External Environment and Driving Modes. . .
731
The measurements were carried out on a sunny day in 2013 at noon. Other
measured data are described below:
1. Ground surface temperature was 60 C.
2. Ambient air temperature was 32 C.
3. Ambient air relative humidity was 49%.
4. Ambient air velocity was 1.3 m/s.
In the measurement, the automobile was headed towards south.
3.2
Veriﬁcation of External Surface Temperature
The comparison between the prediction and the measurement of surface tempera-
ture and heat transfer are shown in Fig. 4. Comparison is made between the
different glass and opaque areas. It can be seen that the errors for the opaque and
glass areas are, respectively, 2.2 C and 1 C. The largest errors for temperature
Fig. 3 An automobile
under test
250
70
60
50
40
30
20
10
0
200
150
100
50
0
Top
Shell
Measurement of heat transfer
Calculation of heat transfer
Measuring of external temperature
Calculation of external temperature
Right
Shell
Left
Shell
Front
Glass
Right
Glass
Left
Glass
Back
Glass
W
°C
Fig. 4 Comparison of prediction and measurement of surface temperature and heat transfer
732
Y.K. Chuah and Y.-T. Chen
and heat transfer are, respectively, 4.5% and 12%. The average errors for surface
temperature and heat transfer are, respectively, 0.34% and 1.07%. As a whole the
inner and outside car body temperatures were predicted to an accuracy of 1 C.
4
Energy-Saving Potential Analysis
Energy saving has to ﬁrst satisfy the thermal comfort of the passengers. However,
energy saving is critical to the driving range. Air conditioning of electric vehicles is
powered by the refrigerant compressor and the fans. This study focuses on the
compressor power and the effects of driving modes and the ambient environment.
Therefore the analysis presented in this paper reﬂects an energy saving potential
that is closely related to the actual driving modes of electric vehicles.
4.1
Compressor Performance Curves
Figure 5 presents the performance curves of an electric vehicle refrigerant com-
pressor as presented by Hsiao (2011). The compressor curves were used in the
analysis of the compressor according to the needs of the air-conditioning capacity.
It can be seen that the refrigerating capacity changes with the compressor speed,
and compressor power changes with compressor speed. Note also the coefﬁcient of
performance (COP) for compressor peak at lower speed. COP peaks at about 4.6 at
around 1300 rpm; however, at 3500 rpm COP reduces to about 1.5. Therefore it can
Fig. 5 The performance curves of a compressor
A Study of the Effects of the External Environment and Driving Modes. . .
733
be seen that the control strategy is key to energy saving for an air-conditioning
system for electric vehicles. It is also obvious that the compressor must be kept at a
lower speed whenever possible.
4.2
Control Strategy for Passenger Numbers
For vehicles with front and back seat air outlets, air-conditioning can be controlled
to meet the actual need. When only the front seats are occupied, only front air
outlets are turned on. Moreover, outdoor air supply volume can be controlled
according to number of passengers in the car. Figure 6 shows the results of analysis.
It can be seen that relative to ﬁve passengers, the cooling load can be reduced by
about 11.2%. Also compressor power can be reduced by 28.9–52.8% when only one
passenger is in the automobile. The high rate of energy saving is feasible, as
discussed above, with compressor power being much lower at lower speed and at
lower capacity.
4.3
Control Strategy to Meet the Driving Modes
In the course of a day a vehicle will be exposed to different temperature, humidity,
and solar radiation conditions. Therefore the air-conditioning load will differ at
4.5
Cooling Load (kW)
Compressor Power Consumption (kW)
Reduce Cooling Load (%)
Reduce Compressor Power Consumption (%)
3
2
1
0
5
4
3
2
1
0
10
20
30
40
50
60
%
kW
Occupant
3.5
2.5
1.5
0.5
4
Fig. 6 Compressor energy saving for different numbers of passengers
734
Y.K. Chuah and Y.-T. Chen
different times. Figure 7 shows the analysis of the performance at different times of
the day. Relative to the peak load that occurs at about 1:00 pm, the compressor
power can be reduced by about 3–39% at other times when the compressor speed is
controlled according to needs.
4.4
Control Strategy at Different Driving Speeds
The results of the analysis are shown in Fig. 8. It can be seen that cooling is highest
when the vehicle is still at 0 km/h. This is due to a higher shell temperature when the
car is not moving. A higher driving speed, however, was found to have a smaller
cooling load. This is due to convective heat transfer that maintains the shell at a
temperature approaching the ambient temperature. The cooling load reduces with
speed up to 50 km/h, after that the effects of a lower cooling load are only marginal.
It can be seen that the control strategy of compressor speed to meet the cooling load
at different car speeds can reduce the compressor power by 44–62% with respect to
a speed of 0.0 km/h.
3.5
Cooling Load (kW)
Compressor Power Consumption (kW)
Reduce Cooling Load (%)
Reduce Compressor Power Consumption (%)
3
2
1
0
13
8
9
10
11
12
14
15
17
16
0
5
10
20
25
15
30
40
35
45
%
kW
Clock
2.5
1.5
0.5
Fig. 7 Compressor power at different times of the day
A Study of the Effects of the External Environment and Driving Modes. . .
735
4.5
Energy-Saving Analysis for a 1-h Drive
A small size black sedan with ﬁve passengers on board was simulated for a ride
from 13:00 pm to 14:00 pm in the month of July and with clear skies. The results are
shown in Fig. 9. The 1-h drive consists of 10 min of engine idling, 30 min at 30 km/
h, and 20 min at 50 km/h. The solar effects were also modelled for each 10-min
period. The vehicle was assumed to drive south with an outdoor air temperature and
humidity of 33.9 C and 58% RH, respectively. The temperature and humidity
inside the car were set at 23 C and 55% RH, respectively.
The results of the analysis are also presented in Table 1. It can be seen that for the
1-h driving schedule mentioned above, when the compressor can be controlled to
adapt to the air-conditioning needs, energy can be saved by as much as 50.4%.
5
Discussion on Energy Management Adapted to Driving
Modes
Energy saving measures for air conditioning are very important for electric- or
hybrid-powered vehicles. The driving range of a charged battery can be affected
greatly by the air-conditioning needs. In particular, it is necessary to allow
air-conditioning control to adapt to different driving modes under different ambient
conditions.
4.5
4
3.5
3
2.5
Cooling Load (kW)
Compressor Power Consumption (kW)
Reduce Cooling Load (%)
Reduce Compressor Power Consumption (%)
2
1.5
1
0.5
0
0
10
20
30
50
40
60
70
80
Speed km/hr
90
100 110 120
0
10
20
30
%
kW
40
50
60
70
Fig. 8 Compressor at different speeds
736
Y.K. Chuah and Y.-T. Chen
A DC variable speed compressor, electronic expansion valve, and other controls
can be used to regulate the power use of automotive air conditioning. The occu-
pants’ comfort can be satisﬁed by multi-air outlets for different seating in a vehicle.
It has been shown that compressor power is strongly dependent on compressor
speed. Further optimization of the compressor operation matching the cooling
capacity could be a control strategy in future research.
Speed
Cooling Load
Compressor Power Consumption
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
60
55
50
45
40
35
30
25
20
15
10
5
0
10
20
30
40
50
km/hr
kW
60
0
mins
Fig. 9 Analysis of cooling load and compressor power for a 1-h drive
Table 1 Air-conditioning energy analysis for 1 h of driving
Item
Operate at highest
load
Control to the cooling
need
Driving time
(min)
60
60
Cooling need
(W)
3887
3243 ~ 3887
Compressor power (kW)
1.76
0.69 ~ 1.76
Energy used
(kWh)
1.76
0.87
Energy saving rate relative to operating at highest
load (%)
–
50.4
A Study of the Effects of the External Environment and Driving Modes. . .
737
6
Conclusion
A cooling-load computation model is proposed in this paper. A comparison of the
prediction with the actual measurement has shown the car body’s temperatures
were predicted to an accuracy of 1 C. Optimization of the compressor operation
matching the cooling capacity can save compressor power by as much as 52.8%. It
was also found that a lighter color car has a lower cooling load. At higher driving
speeds convective heat transfer would maintain the car shell at temperatures
approaching the ambient temperature and result in lower energy being used. A
single drive consists of different driving modes, and when adapting to the
air-conditioning needs, energy-saving control can reduce the energy use by 50.4%.
Acknowledgment The support of the National Science Council of Taiwan with project NSC
101-2221-E-027 -047 is gratefully acknowledged.
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ﬁxed and variable capacity compressors. Int. J. Refrig. 33, 487–495 (2010)
ANSI/ASHRAE Standard 62.1-2004. Ventilation for Acceptable Indoor Air Quality. ASHRAE,
Atlanta, USA (2004)
ASHRAE Handbook, Fundamentals, Chapter 4. ASHRAE, Atlanta, USA (2009a)
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Hosoz, M., Direk, M.: Performance evaluation of an integrated automotive air conditioning and
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Hsiao, P.: A study on refrigerant volume control and energy parameters for electric powered
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738
Y.K. Chuah and Y.-T. Chen
Optimization of Energy Cost Seawater
Desalinization by Reverse Osmosis: Case
of Bousmail Station in Algeria
Souad Bouzid-Lagha and Yacine Matrouh
1
Introduction
The limitation of water resources caused by precipitation shortage and irregularity,
to which is added the rapid increase in population’s need to drinking water,
motivated the Algerian residents to choose seawater desalinization.
The low costs of desalinization, specially reverse osmosis technology, motivated
this choice. This decrease is mainly due to progress in energy consumption, which
is considered as the main component in the desalinization cost structure.
The aim of this work is the study of a new energy recovery technology in
desalinization plants. It relates to a device for transferring the pressure energy of
a ﬂuid ﬂow at relatively high pressure to another ﬂuid ﬂow at relatively low
pressure, in this case the pressure exchanger (PX).
The main purpose of the study is to explain the method and conditions of the
commissioning of this device, the evaluation of its performance in energy gain, and
its application in the case of Bousmail desalinization plant using the technology as
such as that of El-HAMMA, the ﬁrst plant used in Algeria.
S. Bouzid-Lagha (*) • Y. Matrouh
Laboratory of Environment, Water, Geomechanics and Structures (LEEGO), Faculty of Civil
Engineering (FGC), University of Sciences and Technologies Houari Boumediene (USTHB)
BP. 32, El-Allia, Bab-Ezzouar, 16111 Algiers, Algeria
e-mail: bouzidsouad@yahoo.fr
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_52
739
2
Seawater Desalination Perspectives
2.1
Water in the World
The saltwater in the world represents 97.5% of the total water volume. This leaves
more than 2.5% of freshwater (ice, groundwater, streams, lakes, etc.). Seventy
percent of the freshwater glaciers is concentrated in the ice of the polar regions.
It is estimated that 1.4 billion people lack access to safe drinking water and 2.5
billion Others may suffer from lack of water in 2050, caused by the changing
demographics and the increase in water consumption in the world .
2.2
Desalination in Algeria
2.2.1
Desalination Advantages in Algeria
Several factors militate in favor of the implementation of this technology in our
country, namely:
•
A long coastline of 1200 km and a proven availability inexhaustible seawater
resource;
•
Proximity to the sea of Agricultural and industrial areas large consumer of water.
•
A Rapid population growth leading to increased constantly water needs, and
along the coast.
2.2.2
Desalination Strategy
To overcome the lack of conventional drinking water resources and meet the
domestic needs of over 15 million inhabitants of coastal regions, 43 desalination
plants are being ﬁnalized. It is expected to reach a total capacity of 2260 million m3/
day, 2.26 billion l/day of desalinated water, from the year 2015 .
3
Desalinization Techniques
All curent installations in use are working with two technologies:
•
The e
´vaporation-condensation process based on phase change.
•
Membrane processes based on water transport through semi-permeable
membranes.
Among the above methods, distillation and reverse osmosis are the most
marketed processes in the global market for desalinization. Other techniques have
740
S. Bouzid-Lagha and Y. Matrouh
not experienced signiﬁcant growth in the area because of the problems usually
related energy consumption and/or the amount of investment they require.
Currently, the reverse osmosis (RO) is the process that develops more as
compared to other techniques. Its rapid expansion during the past decade is due to:
•
The advancement of membrane technology (lower costs, better quality, simple
maintenance...).
•
The simpliﬁed process
•
At the Lower energy costs (less than 3 KWh/m3) which leads to lower cost per m
3 of desalinated water.
4
The Process of Reverse Osmosis
4.1
Deﬁnition of the Membrane
The membrane acts as a very speciﬁc ﬁlter that allows water to pass while retaining
suspended solids and other dissolved substances.
4.2
Reverse Osmosis Membrane
In the reverse osmosis membranes are used (Fig. 1):
•
Composite polyamide (CAP) spiral (most common)
•
Membrane-hollow ﬁber (triacetate cellulose)
They are characterized by
•
Salt rejection: 99.5–99.8%
•
The Production by module: 17–32 m3/day.
4.3
Process of Osmosis
Osmosis is a natural phenomenon that manifests through a semipermeable mem-
brane. Freshwater migrates to the salt one, which is more concentrated. The
phenomenon stops upon reaching a pressure (Π), called osmotic pressure (Fig. 2).
Optimization of Energy Cost Seawater Desalinization by Reverse Osmosis. . .
741
4.4
Reverse Osmosis
It is possible to reverse the operation (Fig. 2) by exerting a pressure (P >> Π) on
the saltwater to migrate the water molecules outside from the concentrated envi-
ronment; this process is known as reverse osmosis (RO).
Permeate Tube
Interconnector
Mesh Spacer
O-Ring
Membrane
Mesh Spacer
Permeate
Carrier
Adhesive
Bond
(at edges of Membrane Envelope)
Feed Flow
Feed Flow
Permeate 
Flow
Concentrate
Flow
Concentrate
Flow
Fig. 1 Spiral membrane module
Fig. 2 Process of osmosis and reverse osmosis
742
S. Bouzid-Lagha and Y. Matrouh
5
Design of a Desalination Plant by Reverse Osmosis
The pressurizing part is the most important part of the process. It consists of the
following items:
•
Reverse osmosis block (membranes);
•
Pressurizing system (high-pressure (HP) pump group);
•
Energy recovery system.
It is noteworthy that 55–60% of the energy injected into the system is in the
rejection as high-pressure brine. So, how to recover this energy with maximum
efﬁciency to reinject it into the system?
This energy could be recovered by
•
Turbo pumps placed in series with the HP pumps;
•
Pelton turbines coupled directly to the HP pump to relieve the electric motors;
•
Pressure exchangers (PX).
The method of the pressure exchanger is a system which guarantees a yield
exceeding 95% and an energy recovery from 50% to 60%. So, it achieves signif-
icantly lower energy consumption of 3 kWh/m3 of permeate, for the sole purpose of
pressurizing the RO.
6
Description of the Pressure Exchanger, PX
The device relates to a pressure exchanger for transferring pressure energy from a
relatively high-pressure ﬂuid ﬂow to another ﬂuid ﬂow at relatively low pressure.
It consists of a ceramic rotor positioned on a central axis between two end caps
within a vessel under pressure, with a pair of inlet and outlet coaxial in communi-
cation with a pair of oriﬁces:
•
A low-pressure,
•
A pair of high-pressure oriﬁces.
The rotor rotates in the ceramic liner; it is the only moving part during operation
(Fig. 3).
7
Introducing the El-Hamma Plant
The desalinization plant EL-HAMMA covers an area of 50,000 m2. It is the largest
resort in Africa. With the daily production capacity of 200,000 m3/day, it serves a
population of 2.7 million for the capital Algiers. Thus, it would cover the needs of
the capital’s drinking water for 25 years.
Optimization of Energy Cost Seawater Desalinization by Reverse Osmosis. . .
743
El-Hamma plant consists of: taking seawater, clariﬁers, multilayer ﬁlters, cartridge
ﬁlters, reverse osmosis station, remineralization, reservoir, and production pumps.
8
Introducing the Bousmail Plant
The Bousmail desalinization plant was designed for drinking water production of
5000 m3/day by the reverse osmosis technology. It is characterized by
•
Localization: Bousmail Wilaya of Tipaza
•
Flow rate of raw water inlet: 520 m3/h
•
Desalinated water production ﬂow rate: 208 m3/h
•
Total production of desalinated water: 5000 m3/day.
9
Comparative Study of the Energy Cost of Bousmail Plant
The cost structure based on the various positions is given in Table 1.
Fig. 3 The pressure exchanger, PX
744
S. Bouzid-Lagha and Y. Matrouh
9.1
Energy Cost with the Pelton Turbine (Indirect Recovery)
Recovery Pelton turbines are used for more than 20 years. The system recovers
energy indirectly from concentrates. The driving of the high-pressure pump is done
by the electric motor and the recovery turbine (Fig. 4).
Calculation method
QA: ﬂow rate of seawater
QC: rejected concentrate ﬂow
Qp: permeate ﬂow
The conversion rate:
Y ¼ Qp=QA  100
The pressure loss in the membranes is
ΔP ¼ PB  PC
The power absorbed by the pump in kW:
Pp ¼ Q:d  P=36:7  ηp:ηm
with
d: density of seawater
QB: ﬂow rate delivered by the pump
ηp: pump efﬁciency
ηm: motor pump efﬁciency
Pressure across the pump motor is
P ¼ PB  PA
The energy consumption per m3 of water permeate:
Table 1 Summary of the
energy cost of desalinization
by reverse osmosis
Item
Share in %
Amortization
33–43%
Energy
37–43%, of which 70%
consumed by the HP pump
Labor
4–11%
Maintenance
3.5–4.5%
Membrane
2–5%
Reactive
2–6%
Optimization of Energy Cost Seawater Desalinization by Reverse Osmosis. . .
745
WkWh=m3 ¼ Pp=Qp
The energy recovered by the turbine per m3 of water produced, WkWh/m3:
WkWh=m3 ¼ PC  QC  ηtr
ð
Þ=36:7  Qp
So, the total energy consumed by the system is
WkWh=m3 ¼ WPOMPE  WTURBINE
Results of the calculation are given in the Table 2:
9.2
Energy Cost with Pressure Exchanger (Direct Recovery)
The pressure exchanger directly transfers the concentrate hydraulic power in the
feed circuit. This is the pressure exchanger, PX, which is the most recent technique.
The presence of a booster pump to compensate the pressure losses induced in the
pressure exchanger should be noted (Fig. 5).
Calculation method
Power absorbed by the HP pump:
Pp
¼ QC PC  PA
ð
Þ=ηp  ηm  36:7
PP (kW): is the power absorbed across the pump motor.
Power absorbed by the booster pump:
Pps
¼ QD PE  PA
ð
Þ=ηps  ηmps  36:7
where
Pps (kW) is the power absorbed by the pump from overpressure; ηps and ηmps are
the performances of the booster pump and its motor.
Fig. 4 Indirect recovery energy with the Pelton turbine
746
S. Bouzid-Lagha and Y. Matrouh
The osmoser energy consumption reduced to kWh/m3 product:
WKWh=m
3 ¼ P=QF ¼ Pp þ PPS


=QF
QF: permeate ﬂow
QC ¼ QF + QL
QL: lubrication ﬂow
PD ¼ PG  ΔP
hp
ð
Þ
PH ¼ PB  ΔP
lp
ð Þ
ΔP(hp): pressure difference in high-pressure PX.
ΔP(lp): pressure difference in low-pressure PX.
Calculation results are given in the Table 3.
Table 2 Pelton turbine
energy balance (Bousmail)
Pump HP consumption
Flow rate of the HP pump
231.5 m3/h
Efﬁciency of the HP pump
76%
Motor efﬁciency
96%
Power consumption
520.5 kW
Consumed energy
5 kWh/m3
Recovered energy by turbine Pelton
turbine yield
75%
Recovered energy
1.53 kWh/m3
Consumed energy per m3 product
3.47 kWh/m3
Energy gain of the turbine
30.6%
Fig. 5 Schematic of a reverse osmosis desalination system using a pressure exchanger: (A)
Seawater inﬂow, (F) Freshwater ﬂow, (G) Concentrate ﬂow, (D) Seawater ﬂow, (H) Concentrate
(drain), (C) HP pump ﬂow, and (B) Circulation ﬂow
Optimization of Energy Cost Seawater Desalinization by Reverse Osmosis. . .
747
10
Results Comparison and Conclusion
•
The pressure exchanger system (PX) allows an efﬁciency of 94.7%, while the
turbine recovers only 75% of hydraulic energy of the concentrate which reﬂects
the net gain of 17.4% energy compared to the Pelton turbine.
•
Using the pressure exchanger system decreases the size of the high-pressure
pump at a rate of 108 m3/h instead of 231.5 m3/h, and reduces power
consumption.
•
The pressure exchanger directly recovering the energy of the concentrate
operates as an independent HP pump of the primary pump, while the turbine
with indirect recovery causes changes in ﬂow and pressure in the reverse
osmosis system.
•
Note the low efﬁciency of the HP pump of Bousmail compared to the previous
example. Today, there are pumps with only 0.86% yield. So, the choice of the
HP pump is important to optimize the energy costs of a reverse osmosis system .
•
The electrical energy needs for desalinization of seawater by reverse osmosis are
such that they constitute the largest share of operating costs. Of this energy, 70%
is absorbed only by the pressurizing device membranes, hence its importance in
the price of per m3 of freshwater produced.
•
Therefore, researches in the ﬁeld of energy recovery from the concentrate were a
very important factor in lowering desalination cost.
•
From the results obtained by applying the technique of the pressure exchanger
on Bousmail station, it was found that with an efﬁciency of 95%, the pressure
exchanger provides an energy gain of 18% relative to the Pelton turbine
actually used.
•
The pressure exchanger recovers energy directly by reducing the size of the
high-pressure pump from the rate of 231.5 m3/h to a rate of 108 m3/h. It
functions as an independent pump from the main pump, which gives a greater
Table 3 Energy balance of
the osmoser
Consumption of HP pump
Flow rate of HP pump
41.3 m3/h
Efﬁciency of HP pump
0.86%
Motor efﬁciency
0.96%
Consumed electric energy
79 kW
Consumption of booster pump
Flow rate
58.7 m3/h
Efﬁciency of booster pump
0.85%
Motor efﬁciency
0.94%
Consumed electric energy
4 kW
Total consumed energy
83 kW
Consumed energy per m3 product
2.07 kW/m3
Efﬁciency of PX
94.7%
Energy gain of PX
56.8%
748
S. Bouzid-Lagha and Y. Matrouh
stability of the system, the fact that working at reduced pressures and higher
ﬂows, avoiding the precipitation of salts and reducing, by consequence, the
clogging and membrane fouling .
•
It is also noted that the compact and small size of PX modules allows its location
in smaller premises, thus minimizing the visual impact of the installations,
thanks to its optimal integration into its environment.
•
The establishment of solutions, more and more innovative to the new seawater
desalinization plants as is the case of the pressure exchanger, contributed to the
decline in energy consumption, increasing capacity of stations, and reducing the
cost of desalinated water.
•
All energy balance made in the design of desalinization plants must take account
of the energy recovery sector. Components with the best mechanical, hydraulic,
and electrical efﬁciencies and also offering all the guarantees of operation will
be called on. We must integrate new technologies addressing the factors that
favor the reduction of the cost of desalinization which are as follows:
– Improving the energy efﬁciency of the system and the energy recovery
system
– Maintenance of membranes
– Choice of intake
– Improved pretreatments
– Optimization of the size of the installation
To this end, it is important to incorporate desalinization techniques as an option as
part of integrated planning and management of water resources and to develop
national expertise in this area, with the objective of sustainable development and
decision support
References
Agashicheva, S.P., Lootahb, K.N.: Inﬂuence of temperature and permeate recovery on energy
consumption of a reverse osmosis system. Desalination. 154(3), 253–266 (2003)
Audinos, R..: Se
´parations e
´lectrochimiques: E
´lectrodialyse, Technique de l’Inge
´nieur (1997)
Ballif, J.L.P.: L’eau, ressource vitale. Editions JOHANET, Paris (2002)
Barlow, M., Clarke, T.: L’or bleu : l’eau, le grand enjeu du XXIe
`me sie
`cle, Hachette Litteratures,
Paris (2002)
Cerci, Y.: Exergy analysis of a reverse osmosis desalination plant in California. Desalination. 142,
257–266 (2002)
Danis, P.: Techniques de l’Inge
´nieur, et dessalement d’eau de mer. Paris (2007)
Maurel, A.: Techniques se
´paratives 
a membranes – conside
´rations the
´oriques. In: Techniques de
l’Inge
´nieur. Paris (2003)
Sharif, A.O., Merdaw, A.A., Al Bahadili, H., Al Taee, A., Al Aibi, A., Rahal, Z., Derwish, G.A.
W.: A new theoretical approach to estimate the speciﬁc energy consumption of reverse osmosis
and other pressure driven liquid phase membrane processes. Desalination Water Treat. 3,
111–119 (2009)
Optimization of Energy Cost Seawater Desalinization by Reverse Osmosis. . .
749
Multi-objective Optimization of Distillation
Sequences Using a Genetic-Based Algorithm
Mert Suha Orcun and €
Ozc
¸elik Yavuz
1
Introduction
Distillation is a widely used separation system in chemical processes. Being
popular in use but an extensive energy-dependent process, distillation systems
have to be carefully handled in the design phase. Not only is the energy usage
critical to the system but the initial capital investment and efﬁcient design is also
very crucial. Handling the separation process of hydrocarbon mixtures is the main
issue in terms of the chemical processes adopted throughout the world. Properly
sequencing and deciding the conﬁgurations of the columns are common problems
during the analysis of topics of relevant studies.
On the other hand, design of this separation system including detailed column
conﬁguration and deciding the sequencing of the columns is a complex problem. By
nature, it has a complex nonlinear mixed-integer superstructure depending on the
component number, type and compositions.
There are various studies presented in the literature about distillation systems
and its optimization, where some of them also search a sequencing and multi-
objective approach.
The sequencing of a separation system, including a special hierarchical struc-
ture, is proposed in the study done by Wang et al. (2008) which is modelled as a
multi-hierarchy combinatorial optimization. Moreover, in an earlier work,
M.S. Orcun (*)
Yuzuncu Yıl University, Faculty of Engineering and Architecture, Department of Chemical
Engineering, Kampüs, Van 65080, Turkiye
e-mail: yavuz.ozcelik60@gmail.com
O
¨ . Yavuz
Ege University, Faculty of Engineering, Department of Chemical Engineering, Bornova, I
˙zmir
35100, Turkiye
e-mail: orcunmert@gmail.com
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_53
751
sequencing and heat integration were mixed and optimization was based on min-
imizing the cost of the column (Wang et al. 1998). In the study done by Jain et al.
(2012), energetic-efﬁcient of distillation systems was attempted by optimization of
distillation sequencing, in which heat integration was included. The solution com-
plex superstructure of sequencing columns for separating azeotropic mixtures was
also achieved in the literature (Bauer and Stichlmair 1998; Caballero and
Grossmann 2004). Also, in one study, a multi-objective approach was handled for
optimization of a compressor-aided distillation system sequencing (Alca
´ntara-
Avila et al. 2012).
Various distillation systems including batch columns, solar-driven membrane
systems and multi-effect columns are evaluated in the literature from an optimiza-
tion point of view and most of them use meta-heuristic methods such as simulated
annealing and genetic algorithm because of low risk of local optima problem, easy
development or commercial availability (Cardoso et al. 2000; Hanke and Li 2000;
Burri and Manousiouthakis 2004; Chang et al. 2010; Sayyaadi and Saffari 2010). In
some studies, simulation programs like Aspen and MATLAB are used for evaluat-
ing the ﬂowchart and physical data for the sake of decreasing the code development
(More et al. 2010). In a previous study, exergetic single-objective optimization is
applied to a distillation system (O
¨ zc
¸elik 2007) considering the sequencing.
Moreover, the design of distillation columns and separation systems are also
handled in studies considering reactive distillation (Burri and Manousiouthakis
2004; Amte et al. 2013) and heat integration systems (Gadalla et al. 2003).
Differing from these studies in this chapter, a multi-objective optimization of
distillation
sequencing for
two hydrocarbon mixtures
is completed
from
exergoeconomic proﬁt and exergy destruction points of view. A computer program
is developed to solve this complex problem based on a hybrid genetic algorithm that
designs each column in detail, including costing, and then tries to optimize the
sequencing considering objective functions. Being a unique study with in-house
program development and uniting exergy concept with multi-objective optimiza-
tion, the manuscript greatly contributes to the researchers focused on separation
systems, especially distillation trains.
Nomenclature
C
Column
Ccolumn
Installed capital cost for a distillation column ($)
CCond
Capital cost for a condenser ($)
Ccw
Annual cost of cooling medium
Cp,W
Heat capacity of water (kJ/kg C)
CReb
Capital cost for a reboiler ($)
Ctray
Installed capital cost for trays ($)
cw
Unit cost of coolant ($/kg)
Dc
The column diameter (m)
DF
The ﬂow rate of the distillate
Exdest
Exergy destruction [kW]
Hbottom
Enthalpy of bottoms (kJ/kmol)
(continued)
752
M.S. Orcun and O
¨ . Yavuz
Hc
Height of distillation column (m)
Hfeed
Enthalpy of feed, (kJ/kmol)
Htop
Enthalpy of distillate (kJ/kmol)
N
Molar ﬂow rate
N
Tray number
NC
Number of components
Nmin
Minimum number of tray
NS
Number of distillation sequence
P
Exergoeconomic proﬁt [$/kW]
P
Pressure (bar or atm)
QC
Rate of heat ﬂow for condenser
QH
Rate of heat ﬂow for reboiler (kJ/hr)
R
Reﬂux ratio
TAC
Total annual cost of a distillation sequence
TCC
Total annual cost of a distillation sequence
TD
Distillate temperature
Z
Objective function
Subscript
B
Bottom product
D
Distillate
Dest
destruction
HK
Heavy Key
in
inlet
LK
Light Key
out
Outlet
Greek
α
Relative volatility
η
Efﬁciency
η
Tray efﬁciency
2
Distillation Sequencing and Design
Hydrocarbon mixtures are widely handled in the industry considering the broad
usage areas and energy needs globally. Proper separations of these components are
mainly accomplished using distillation trains. In this phase, evaluating and deciding
the sequences of a distillation of a multi-component mixture is a complex problem
from both mathematical and economic points of view.
The selection of the proper sequencing of a sharp split distillation of multi-
component mixtures is a mixed-integer nonlinear programming (MINLP) problem
(Andrecovich and Westerberg 1985). Since existence of the columns are present in
Multi-objective Optimization of Distillation Sequences Using a Genetic-Based. . .
753
the model, as well as the detailed design considering reﬂux rations, vapour/liquid
rations and plate design are considered.
The number of possible sharp distillation sequences increases markedly with the
number of feed components and can be calculated using the following equation
(Wang et al. 1998):
NS ¼
2 NC  1
ð
Þ
½
!
NC!
NC  1
ð
Þ!
ð1Þ
Any alternative distillation sequence for a separation of a mixture can be
represented (O
¨ zc
¸elik 2011) by a binary vector with 2NC1 elements (continuous
variable vector with NC1 elements). For example, if a mixture involves ﬁve
components, such as A, B, C, D and E, it can be represented as given in the
Fig. 1 depending on where the determined separation points are, and a sequence
number is given according to the developed binary sequencing.
NS and NC are the number of possible distillation sequences and number of
compounds, respectively. In a computer-based optimization, many alternatives of
sharp distillation sequences may be examined to determine the best sequence,
according to a given criterion.
The decision on the sequence of the distillation train completely affects the
design of each column in the system and eventually has a considerable effect on the
investment and operational cost of the process. As the separation characteristics of
the fed mixtures in the columns of the distillation train change, exergetic efﬁciency
BV = 4, 1, 0, 0, 3, 2, 0, 0, 0     (0 represents end products)
(0)
(4)
(1)
(2)
(3)
(0)
(0)
(0)
(0)
E
A B C D/ E
A /B C D
B C /D
D
B /C
A
B
C
A B C D E
1   2   3   4 
Separation points
B, C
A,B,C,D,E
E
A,B,C,D
A
B,C,D
D
B
C
Fig. 1 Binary variable
presentation and
visualization of an
alternative distillation
sequence
754
M.S. Orcun and O
¨ . Yavuz
is altered depending on the exergy destruction. Laying onto these phenomena, the
exergoeconomic cost of the separation operation changes.
The operating variables in each column such as reﬂux ratio, ‘feed vapour/liquid
ratio’ and the column pressure are dominant parameters that affect the character-
istics of the design. If the feed conditions, design pressures, reﬂux ratio and the
quality of the products are determined, the capital and operating costs of the
columns in the sequences can be calculated using sharp distillation column design
technique. To calculate the cost of the column, it is necessary to calculate the
diameter, height and minimum number of trays in the columns (Pibouleau et al.
1983). The diameter (Dc), minimum number of trays (Nmin) and the height of the
column (Hc) are calculated as follows:
Dc ¼
4
πDF R þ 1
ð
Þ
22:2TD
0:761 1=P
ð
Þ0:5273
1
3600P
(
)0:5
ð2Þ
Nmin ¼
ln
nLK,DnHK,B
nHK,DnLK,B
l
m
ln /LKHK
ð3Þ
Hc ¼ 0:61N
η þ 4:27
ð4Þ
The condenser and reboiler duty of each column is calculated and necessary
utilities are evaluated for exergy analysis:
Qc ¼ R þ 1
ð
ÞDFλtop
ð5Þ
QH ¼ Hbot þ Htop þ Qc  Hfeed
ð6Þ
Costs of the columns are evaluated depending on the calculated parameters, such
as the height, diameter and heat duties. The correlations proposed by Guthrie are
used for the evaluation. The general vessels are designed in accordance with
American Society of Mechanical Engineering (ASME) codes and thicknesses of
the equipment are calculated for resisting 4.5 atm as internal pressure. Trays, tray
assemblies, packed beds, lining and other internals are priced and added to the
general cost:
Ccolumn ¼
M&S
280


101:9 Dc=0:3048
ð
Þ1:066
Hc=0:3048
ð
Þ0:802 2:18 þ Fc
ð
Þ
ð7Þ
Ctray ¼
M&S
280


4:7 Dc=0:3048
ð
Þ1:55 1:64NT
ð
ÞFc
ð8Þ
The total annual cost of a distillation column in the distillation sequence is
calculated in terms of the column, tray and utility costs:
Multi-objective Optimization of Distillation Sequences Using a Genetic-Based. . .
755
TCC ¼ CColumn þ CTray þ CCond þ CReb


=tL
ð9Þ
Depending on the cost of each column, the annual cost of the distillation
sequence is calculated:
TAC ¼
X
NC1
i¼1
CCi
ð10Þ
3
Exergoeconomic Multi-objective Optimization
(ExMOO)
Multi-objective optimization is a mathematical programming technique that con-
siders multiple objectives explicitly and simultaneously in a multi-objective opti-
mization framework. In fact, most of the physical and chemical phenomena are
multi-objective in its nature and a complex superstructure. There are many methods
available to tackle these kinds of problems.
In this study, a comprehensive exergoeconomic multi-objective optimization
(ExMOO) is applied to ﬁnd out the optimum sequence for distillation of predeﬁned
hydrocarbon mixtures from maximization of exergoeconomic proﬁt and minimiza-
tion of exergy destruction points of view:
Max PEx ¼ ExTop
∗CTop þ ExBot
∗CBot


 ExF
∗CF þ ExCU
∗CCUþExHU
∗CHU þ Z
ð
Þ
ð11Þ
Min ExD ¼ ExF þ ExCUþExHU  ExOut
ð12Þ
The weighing sum of objectives method is used for dealing the complex super-
structure of this multi-objective problem. A weighing factor is given to the objec-
tive function parameters which are between 0 and 1 in this method. So the decision
of the factors is crucial for the solution. This study offers to the users a chance to
select the weighing factors, and besides, the study itself covers a parametric study
for different weighing factors with a user-friendly program. The multi-objective
problem is separately solved for each case. The results are discussed depending on
these broad Pareto solution set:
Min Z ¼
w PEx  PEx,max
ð
Þ2 þ 1  w
ð
Þ ExDest  ExDest,Min
ð
Þ2

1=2
ð13Þ
Also, in addition to the multi-objective optimization, the parametric investiga-
tion of the weighing factors is also implemented to DISMO as well as the
exergoeconomic multi-objective optimization, and this behaviour is evaluated.
756
M.S. Orcun and O
¨ . Yavuz
Exergoeconomic analysis covers the economic concerns with exergy analysis,
equipment costs and related thermodynamic irreversibilities through the system
(Mert et al. 2007a). Exergy and costs are closely in contact since the exergy analysis
seeks for the efﬁcient and effective use of energy through the system. In addition,
this situation automatically brings a cost-effective operation.
The exergetic efﬁciencies of each column and the sequence besides the exergy
destruction are calculated according to the exergy balance (Mert et al. 2007b;
Dincer and Rosen 2012):
Exdest ¼ Exin  Exout
ð14Þ
For the calculation of the exergy efﬁciency, exergy recovered (exergy output) to
the exergy input is taken into account. Exergy output can be deﬁned as the desired
exergy output or useful exergy output (Mert et al. 2012):
η ¼ Exout
Exin
ð15Þ
Exergetic cost for separation operation is calculated by exergoeconomic analy-
sis. These calculated costs are generally used for feasibility studies and investment
decisions and also for comparing alternative techniques. Besides the choice for the
operating conditions and sustaining a cost-effective operation, exergoeconomic
analysis should be used (Modesto and Nebra 2009; Mert et al. 2014b).
The aim of the exergoeconomic analysis is deﬁned as follows (Tsatsaronis and
Winhold 1985; Mert and O
¨ zc
¸elik 2013):
•
To identify the location, magnitude and sources of thermodynamic losses
•
To calculate the cost associated to exergetic losses and destroyed exergy in any
system component
•
To analyse the cost formation of each subsystem and product separately
The overall exergetic cost balance is used as follows:
X  _
E xin,i  Cin,i

þ _
Z tot ¼
X  _
E xout,i  Cout,i

þ Pnet  CW
ð16Þ
where the _
E xin,i, _
E xout,i, Cin,i, and Cout,i are the exergies and exergy costs. Ztot is the
annualized cost of the total system inside the control volume. CW is the cost of the
work or the power of the equipment. Pnet is the net power produced from separation
system. The cost balance is applied to the overall system to calculate the cost of the
separation system, and depending on these, the exergetic proﬁt is calculated. In
order to calculate Zequipment, the annualized (or levelized) cost method (Tsatsaronis
and Moran 1997) is used.
Multi-objective Optimization of Distillation Sequences Using a Genetic-Based. . .
757
4
DISMO Computer Program
This study covers production of a hybrid genetic algorithm-based solver
implemented Multi-Objective Optimizer (MOO) program (DISMO) (Fig. 2) in
order to solve the complex structure of distillation of the mixtures.
DISMO has a running sub-program (CRANE) which is also developed by our
group that governs a database of 650 components for evaluation with all physical
and thermodynamic properties deﬁned in the system, including a detailed steam
table, and each of these components is suitable for implanting a case study.
The feeds of thermal properties and compositions are dynamically taken by
DISMO and related thermodynamic and physical data are calculated by a selection
of subroutine depending of CRANE.
In the present study, an algorithm that is a hybrid of Nonlinear Simplex and a
Genetic Algorithm (O
¨ zc
¸elik 2011) based on the stochastic generation of solution
vectors was used to minimize the following multiple objective functions that united
with the weighing sum of objectives method (Mert et al. 2014a).
The algorithm of the DISMO is represented in Fig. 3. The calculations begin
with the entry of the data and consequently followed by estimation of the physical
and thermodynamic properties and initialization of the genetic algorithm. Evalua-
tion of the objective function is dependent on the weighing factors and ﬁtness
function produced by the genetic algorithm. The program simultaneously tries to
optimize both minimization of the exergy destruction and maximization of the
exergetic proﬁt. Termination criteria changes depending on the structure of the
solution, that in small number of alternatives, program calculates every alternative
solution and decides depending on these latters, where (as in large number of
Fig. 2 A sample results screen of the DISMO computer program
758
M.S. Orcun and O
¨ . Yavuz
alternatives) genetic algorithm is strictly applied and convergence of the ﬁtness
function is required.
The basic steps of the genetic-based algorithm are given as follows:
•
Encoding and generation of initial population depending on the number of
variables
•
The generation of a new population
– Reproduction
– Crossover
– Mutation
•
Generation of new random vectors
•
Termination criteria
abs FAvgi  FAvgi25



2
ð17Þ
Selection of the chemical 
compounds in mixture and 
Input the required data.
The generation of a new 
population of the distillation 
parameters and alternatives 
Design of the each 
Column in the 
configuration
Estimation of 
physical 
properties 
The physical and 
thermodynamic 
properties constants 
from database
Termination
Termination 
criteria
The calculation of 
Exergoeconomic Profit and 
Exergetic destruction for each 
alternative sequence in population
Calculation of the values of the 
objective function for each 
alternative sequence 
Fig. 3 The algorithm of the DISMO computer program
Multi-objective Optimization of Distillation Sequences Using a Genetic-Based. . .
759
FAvgi and FAvgi25 are the average objective function values for 25 consecutive
generations.
5
Case Studies
Two hydrocarbon mixtures are selected as case studies for revealing the perfor-
mance of the derived program and investigated on a complex separation system.
Table 1 shows the molar compositions of the cases as well as the temperature,
pressure and ﬂow rate of feed to the ﬁrst column of the system.
The cases are separately investigated covering a range of weighing factors
selected depending on the behaviour of the distillation system in a great accordance.
Each case has slightly different range of weighing factors depending on the
convergence of one of the objective functions to its maximum value which is
found by single optimization.
6
Results and Discussion
Each case of the parametric investigation has a different trend depending on the
detailed design of each column and costing with an exergetic perspective.
The variation of the exergoeconomic proﬁt is represented in Fig. 4 considering
the variation of the weighing factors to the proﬁt objective for Case 1. Considering
that Case 1 has three components, the necessary distillation column number is
2, and there are only two alternatives for the separation operation of this mixture. As
shown in the ﬁgure, increasing the factor of proﬁt objective, the systems stand on a
ﬁnal value with a conﬁguration of 2–1. Any further increase does not change the
result as there is no alternative sequencing left. Also Fig. 5 shows the investigation
using the other perspective as the importance for the proﬁt increases, the exergetic
Table 1 Feed compositions
and properties of cases
Component/property
Case 1
Case 2
n-Pentane [mol %]
–
20
n-Hexane [mol %]
30
20
n-Heptane [mol %]
–
20
n-Octane [mol %]
28
20
n-Decane [mol %]
42
20
n-Nonane [mol %]
–
–
Pressure [atm]
1
1
Temperature [C]
40
40
Flow Rate [kmol/h]
600
600
760
M.S. Orcun and O
¨ . Yavuz
destruction also increases which proves the conﬂicting situation between the
objectives. The numerical results for the details of the columns of the separation
system are tabulated in Table 2. The maximum proﬁt reaches 358707.5 $/kW in the
inspection where minimum exergy destruction is 403.84 kW.
0
100000
200000
300000
400000
Profit [$/kW]
Multi-Objective Weight
Maximum Proﬁt
1,2
2,1
1,2
2,1
2,1
2,1
2,1
2,1
Fig. 4 The variation of
exergoeconomic proﬁt
depending on weighing
factor for Case 1
0
500
1000
1500
2000
2500
3000
Exergy  Destruction [kW]
Multi-Objective Weight
Minimum Exergy 
Destruction
1,2
2,1
1,2
2,1
2,1
2,1
Fig. 5 The variation of
exergy destruction
depending on weighing
factor for Case 1
Table 2 Design results of
Case 1
Maximum proﬁt
358708.3 [$/kW]
Column
Dc
[m]
H[m] [m]
NT
[]
R
[]
1
2.41
23.72
26
0.15
2
1.74
50
60
0.58
Minimum exergy destruction
403.84 [kW]
Column
Dc
[m]
H[m] [m]
NT
[]
R
[]
1
1.70
15.52
15
0.24
2
1.93
18.70
19
0.77
Multi-objective Optimization of Distillation Sequences Using a Genetic-Based. . .
761
The situation is quite different when the number of components increases as
Fig. 6 shows the variation of the exergoeconomic proﬁt changes to 30% with
respect to weight given to it. The maximum value of proﬁt reaches 116,826$/kW
with a sequencing of 4-3-2-1. We can see there are only ﬁve sequencing schemes
present having better cost and efﬁciency values, and the difference in the objective
functions results mainly from the variation in the reﬂux besides the other
parameters.
The detailed results of each column can be seen in Table 3 where both the limit
cases of maximum proﬁt and minimum exergy destruction are given. As it is seen,
each column has a different structure depending on the sequence selected since the
mixture properties and compositions vary. This complex structure is evaluated from
an exergy destruction point of view (Fig. 7). Therefore, the ﬁrst sequences of 2-3-4-
1 have better results when compared with better proﬁt sequences such as 4-3-2-1.
The minimum destruction that can be reached is 5132.42 kW which is approxi-
mately 10% higher than the global minimum.
0
20000
40000
60000
80000
100000
120000
140000
Profit [$/kW]
Multi-Objective Weight
Maximum Profit
2,3,4,1
2,3,4,1
1,3,2,4
1,3,2,4
1,3,2,4
1,3,2,4
1,3,2,4
1,3,2,4
1,3,2,4
1,4,3,2
1,4,3,2
1,4,3,2
4,3,2,1
1,4,3,2
1,4,3,2
4,3,2,1
Fig. 6 The variation of
exergoeconomic proﬁt
depending on weighing
factor for Case 2
Table 3 Design results of
Case 2
Maximum proﬁt 116826.9 [$/kW]
Column
Dc
[m]
H[m] [m]
NT
[]
R
[]
1
2.70
20.28
21
0.08
2
2.77
19.43
20
0.96
3
2.14
18.64
19
1.03
4
1.66
16.32
16
1.67
Minimum exergy destruction 4704.1 [kW]
Column
Dc
[m]
H[m] [m]
NT
[]
R
[]
1
1.85
13.96
13
1.43
2
1.81
20.07
21
1.58
3
1.88
29.55
33
1.76
4
1.53
16.63
16
0.78
762
M.S. Orcun and O
¨ . Yavuz
7
Conclusion
A comprehensive model development and multi-objective optimization have been
applied for determining the proper distillation sequences for two hydrocarbon
mixtures in an exergoeconomic perspective using a hybrid genetic algorithm-
based solver. The in-house developed computer program DISMO is used for
achieving this goal. DISMO has a wide chemical database CRANE with the
capability of calculating thermophysical properties of materials.
Distillation sequencing is a crucial step of chemical process modelling and
optimization as being an energy and cost-intensive process. In order to reveal the
true characteristics of this complex structure, exergoeconomic perspective is used
laying on the unavoidable performance of exergy analysis on thermal systems. The
multi-objective optimization depending on proﬁt maximization and exergy destruc-
tion minimization led us to operate the system using optimum conditions. The
weighing sum of objectives method eases the investigation if the decision-maker’s
choices change from best proﬁt to best exergy destruction.
As a result of the study, a broad PARETO range has been gathered for each
weighing factor of each case. Every solution is an optimum one and correct and
selected depending on the ﬁtness function of a family of evaluated results in genetic
algorithm. The selection of the best optimum is the decision-maker’s choice that
this study reveals the tendencies of the systems underlines the system dynamics.
This study brings a novel and innovative perspective to the decision-making
process in the sequencing of a distillation-based separation system. The results
reveal that the best proﬁt is 116826.3 $/kW for ﬁve sequences in Case 2 with
4704.1 kW exergy destruction in a sequencing of 4-3-2-1 and 1-2-3-4. On the other
hand, for Case 1 where there are only three components and two distillation
columns because of low number of alternatives, the model results in a similar
0
2000
4000
6000
8000
10000
12000
0.0005
0.001
0.002
0.003
0.004
0.005
0.01
0.02
0.03
0.04
0.05
0.1
0.2
0.3
0.4
0.5
Multi-Objective Weight
Minimum Exergy 
Destruction
2,3,4,1
2,3,4,1
1,3,2,4
1,3,2,4
1,3,2,4
1,3,2,4
1,3,2,4
1,3,2,4
1,3,2,4
1,4,3,2
1,4,3,2
1,4,3,2
1,4,3,2
4,3,2,1
4,3,2,1
1,4,3,2
Fig. 7 The variation of
Exergy destruction
depending on weighing
factor for Case 2
Multi-objective Optimization of Distillation Sequences Using a Genetic-Based. . .
763
structured results that maximum proﬁt is converged to 358708.3 $/kW with
403.84 kW exergy destruction in a 2-1 sequence.
The small changes in the optimum values are generally the result of reﬂux ratio
and other parameters’ effect on the model, whereas the big changes in the results are
the consequence of changing in the distillation conﬁguration.
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Multi-objective Optimization of Distillation Sequences Using a Genetic-Based. . .
765
PV Generator Connected to Domestic
Three-Phase Electrical Network
Arrouf Mohamed and Almi Med Fayc
¸al
1
Introduction
The most important photovoltaic (PV) application in this century is the PV-grid-
connected installations that are proposed to supply the public electrical network.
There are various topologies of management of the PVG connected to a three-
phase electrical supply network. Nevertheless, all these approaches based on a PVG
connected to electrical network using inverters, which are not only any more
limited to convert DC power to AC power but they also ensure a reliable monitoring
of the electrical network, to protect this last against the breakdowns and interrupt
the connection in the event of problems occurring on either, the electrical network
or the installation.
The ﬁrst difﬁculty generated by the use of a photovoltaic chain conversion is the
coupling between photovoltaic generator and load, either continuous or alternative.
Since there are many applications, these problems remain. One of the existing
technological barriers in this type of connection is the incorrect dimensioning of the
system, because it affects the production and the transfer of power from photovol-
taic generator and makes it operate far from its maximum capability. The second
difﬁculty resides at the level of losses generated by the adaptation system during the
transfer of the maximum power to the load, as the conversion system output is not
always ideally adapted to the application.
A. Mohamed (*) • A.M. Fayc
¸al
University of Batna, Faculty of Technology, Department of Electrical Engineering, Avenue
chahid boukhlouf med El hadi, Batna 05000, Algeria
e-mail: karrouf@hotmail.com
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_54
767
2
System Layout
The proposed system layout is shown in Fig. 1. This basic structure requires only
one inverter. Nevertheless, a number of adaptations are necessary to ensure the right
operation of the system.
3
Modeling of Photovoltaic Generator
The modeling of a photovoltaic cell can be carried out according to various levels of
complexity of the model. The simpliﬁed electrical model of photovoltaic cell is
shown in Fig. 2.
This model translates a reference point (Iref, Vref) to a new point (I, V) according
to equations below (Hoque and Wahid 2000):
Iref ¼ Isc 1  C1 exp
Vref
C2Vco


 1




ð1Þ
C1 ¼
1  Imp
Isc


exp Vmp
C2Vco


ð2Þ
C2 ¼
Vmp
Vco  1


ln 1  Imp
Isc


ð3Þ
ΔT ¼ T  Tref
ð4Þ
ΔI ¼ α
E
Eref


ΔT þ
E
Eref
 1


Isc
ð5Þ
ΔV ¼ βΔT  RsΔI
ð6Þ
V ¼ Vref þ ΔV
Fig. 1 General scheme of photovoltaic system connected to three-phase electrical network
768
A. Mohamed and A.M. Fayc
¸al
I ¼ Iref þ ΔI
ð7Þ
α: Coefﬁcient of current variation according to the temperature.
β: Coefﬁcient of voltage variation according to the temperature.
4
Maximum Power Point Tracking Model
The incremental conductance algorithm (INC) is chosen for its good ratio quality
price and ease of implementation (Sera et al. 2000).
The efﬁciency of maximum power point tracking (MPPT) module is deﬁned as
ηMPPT ¼
R
t
0
Pm t
ð Þdt
R
t
0
PMAX t
ð Þdt
ð8Þ
where Pm is the measured output power of panel with MPPT and PMAX is the
maximum power that can be delivered by the panel.
The efﬁciency of the operating point which results from this relation makes it
possible to check the validity of the control technique. In fact, it can also be called
the control technique efﬁciency.
Table 1 below shows the efﬁciency of ﬁve MPPT algorithms (Faranda and Leva
2008):
Figures 3 and 4 show the algorithm of incremental conductance technique and its
implementation with MATLAB/Simulink.
Fig. 2 Simpliﬁed electrical
model of photovoltaic cell
PV Generator Connected to Domestic Three-Phase Electrical Network
769
5
Inverter Model
The three-phase voltage source inverter is used in the system in order to convert DC
power to AC power and ensure a reliable monitoring of the electrical network.
Figure 5 shows the inverter circuit.
The ratio between the commutation variable vector [a b c]T and the phase
voltage vector Vab
Vbc
Vca
½
T is given by
Table 1 MPPT’s efﬁciency
in (%)
MPPT
Efﬁciency
CV
73%
P&O
81% 
a 85%
HC
95,52 
a 99,13%
INC
88% 
a89,9%
Fig. 3 Algorithm of incremental conductance technique
770
A. Mohamed and A.M. Fayc
¸al
Vab
Vbc
Vca
2
4
3
5 ¼ VDC
1
1
0
0
1
1
1
0
1
2
4
3
5
a
b
c
2
4
3
5
ð10Þ
Nevertheless, the ratio between the commutation variable vector a
b
c
½
T and
the line voltage vector Va
Vb
Vc
½
T can be written as
Fig. 4 Implementation of I&C algorithm
Fig. 5 Three-phase voltage source inverter
PV Generator Connected to Domestic Three-Phase Electrical Network
771
Van
Vbn
Vcn
2
4
3
5 ¼ VDC
3
2
1
1
1
2
1
1
1
2
2
4
3
5
a
b
c
2
4
3
5
ð11Þ
6
Dimensioning of Photovoltaic System
In the case of photovoltaic generator connected to electrical network, dimensioning
of the system is based on the amounts of power to be developed not according to the
need that has to cover, but according to the producible one that a conﬁguration can
offer (ﬁeld/inverter).
6.1
Determination of Continuous Line Voltage VBus
The inverter connected to three-phase electrical network with line voltage can reach
220 RMS, and the continuous line voltage has to pursue the following relationship
(website):
VBus ¼ 220
ﬃﬃﬃ
2
p
ﬃﬃﬃ
3
p
þ δV ¼ 538:89 þ δV
ð12Þ
δV: Voltage drops in the inverter semiconductors and the output ﬁlter, which can
be approximated as
δV ¼ 2  VCE IGBT
ð
Þ þ 2  Lfiltre  ^
I þ 2  Rfiltre  ^
I
ð13Þ
with
VCE IGBT
ð
Þ ¼ 3 V
Lfiltre ¼ 360 μH
Rfiltre ¼ 0:5 Ω
^
I ¼ Ineff 
ﬃﬃﬃ
2
p
ð14Þ
Ineff ¼
Pn
2  Vneff
ð15Þ
Ineff ¼ 3000
660 ¼ 4:545 A
^
I ¼ 6:43 A
772
A. Mohamed and A.M. Fayc
¸al
δV  12:44 V
Therefore, continuous line voltage is greater than 550 V.
VBus  580 V is considered in order to obtain the operation in all conditions.
6.2
Continuous Line Voltage Regulation
Continuous line voltage shown in Fig. 6 is regulated with PI corrector parameter-
ized according to the value of the capacitance and the dynamics of the loop as
shown in Fig. 7.
IPV ¼ IC þ ION
ð16Þ
IC ¼ CdVDC
dt
ð17Þ
IPV ¼ CdVDC
dt
þ ION ) CdVDC
dt
¼ IPV  ION
C  S ¼ IPV  ION
VDC ¼ IPV  ION
C  S
Fig. 6 Continuous line
Fig. 7 Continuous line voltage regulation loop
PV Generator Connected to Domestic Three-Phase Electrical Network
773
PDC ¼ PR
ð18Þ
PDC ¼ ION  VDC
ð19Þ
PR ¼ 3VS  IS ¼ 2
3 V d
R  I d
R
ð20Þ
ION ¼
2
3VDC
 V d
R  I d
R
ð21Þ
VDC ¼
1
C  S
IPV  I d
R  2  V d
R
3  VDC


ð22Þ
Pdc: Continuous line power of PVG
PR: Power injected into grid
Vs: Inverter RMS output voltage
Is: Inverter RMS output current
V d
R: Projection of Vs voltage in Park frame
I d
R: Projection of Is current in Park frame
6.3
Evaluation of the Capacitance C at the Inverter Input
The purpose of dimensioning the continuous line-side capacity is to limit the ripple
of the continuous voltage on the basis of single-phase analysis. The ripple of the
voltage is given by the following relationship (Abdeddaim and Betka 2007):
ΔV ¼ 1
C
Z
P0 t
ð Þ
VDCref
dt ¼
V  I
2  C  VDCref
sin 2ωt
ð23Þ
Maximal ripple ΔVm gives the capacitance value
C ¼
P0
ω  VDCref  ΔVm
ð24Þ
For standards conditions (E ¼ 1000 W/m2, T ¼ 25)
P0 ¼ 3 KW
VDCref ¼ 693 V
ΔVm ¼ 2 V
C ¼ 6:95  103F
774
A. Mohamed and A.M. Fayc
¸al
6.4
LC Filter Circuit
A system with forced commutation like PWM (Pulse Midth Modulator) or other
control techniques of voltage source inverter generates chopping harmonics. The
effect of such harmonics is masked in many works for the fact that the grid to which
the inverter is connected is supposed to be ideal. It is obvious that in reality this is
not the case, since a direct connection can lead to major dysfunctions. In order to
eliminate this harmonics, an LC ﬁlter circuit shown in Fig. 8 is inserted between the
inverter and the three-phase grid.
6.4.1
Calculation of L and C Low Band Filter (Qin et al. 2002)
Without load I2 ¼ 0, if the internal resistance of the inductor is neglect (R ¼ 0) the
ﬁlter transfer function becomes
FT s
ð Þ ¼ Vc s
ð Þ
V s
ð Þ ¼
1
LCs2 þ 1
ð25Þ
s ¼ jω
ð26Þ
FT jω
ð
Þ ¼ Vc jω
ð
Þ
V jω
ð
Þ ¼
1
LC jω
ð
Þ2  1
ð27Þ
FT jω
ð
Þ ¼
1
1  LCω2
ð28Þ
In order that the ﬁlter operates without diminution of output signal magnitude, it
must be that
FT jω
ð
Þ
j
j ¼ 1
ð29Þ
1 ¼ LCω2
c
ωc ¼ 2  πf c
ð30Þ
where fc is the cutoff frequency (resonance) of LC ﬁlter
Fig. 8 LC ﬁlter circuit
PV Generator Connected to Domestic Three-Phase Electrical Network
775
L ¼
1
4π2f 2
c  C
ð31Þ
The choice of the inductance and the capacity values can be obtained by laying
down the simple condition, which consists in eliminating the harmonics of a nature
higher than two, this being checked by the fact that they have a frequency equal to
twice or higher than that of the fundamental.
In our case, we have chosen
f c ¼ f d
10
f d ¼ 10khz
f c ¼ 1khz
LC ¼
1
4π2  f 2
c
¼
1
4π2  106
LC ¼ 0:0253  106
If we consider C ¼ 70 μF, subsequently L ¼ 0:0253106
70106
¼ 360 μH.
6.5
The Three-Phase Electrical Network
The considered electrical network is shown in Fig. 9 with several signiﬁcant aspects
such as source power short circuit, various loads consumption, and voltage drop due
to lines impedances. Thus, its behavior can approach the reality under normal
operation, and this furthermore offers the possibility of carrying out certain defects.
6.5.1
Three-Phase PLL (Phase Looked Loop) in Park Domain
The PLL is a system that is intended to control the instantaneous output signal phase
φs(t) on the instantaneous input signal phase φe(t). Such a system is the base of
numerous electronic circuits: synchronous detection and magnitude demodulation
Fig. 9 Three-phase
electrical grid 380 V/50 Hz
776
A. Mohamed and A.M. Fayc
¸al
of frequency (FM and FSK), frequency synthesis, and digital telecommunications
(Best 1987).
In electrical network supply applications, the PLL is primarily used for estimat-
ing and ﬁltering the phase and the instantaneous magnitude of the equivalent phasor
of a three-phase system. Figure 10 shows the classic structure of three-phase PLL.
6.6
Current-Controlled Voltage Source Inverter
The output array of a voltage source inverter is a voltage. However, this device
correctly ﬁltered at the output and with an adequate control can be used for the
injection of a controlled current in the grid. The purpose of this control is to obtain
an output current which follows its reference with lag and a possible small error,
and a sufﬁcient high dynamics.
The simple solution to control the inverter is by hysteresis which gives rapid
dynamics, good accuracy, and high rigidity, and there is no compensation in short
circuit. However, the major problem of hysteresis control is that the average
commutation frequency varies with the load current and therefore irregular and
hazardous behavior of switches. The consequence of such behavior is a comple-
mentary fatigue of switches, and the dimensioning of the ﬁlter becomes difﬁcult.
The control strategy is based on the regulation of current (voltage) in continuous
coordinate which is widely used. The closed loops’ regulation used in such control
provides a rapid response in transient regime and a high performance in a stationary
regime. This control strategy allows the inverter to control the voltage in an
autonomous way in frequency and magnitude across the load. This strategy con-
tains two regulation loops: internal loop for current regulation and external loop for
voltage regulation. The two arrays are controlled in a rotating framework dq. The
regulation of id current component makes it possible to act on the active power ﬂux,
whereas the regulation of iq current component acts on the reactive power ﬂux.
There are many types of current correctors, and the choice of the corrector type
depends again on the characteristics of applications.
Fig. 10 Principle of PLL in
Park domain
PV Generator Connected to Domestic Three-Phase Electrical Network
777
In this case, the PI regulator has been chosen for its rigidity and good accuracy
stability. Figure 11 shows the scheme of current-controlled PV system (Raou and
Lamchich 2004).
7
Model of PVG Connected to Three-Phase Electrical
Network
Figure 12 shows the photovoltaic system modeled in MATLAB/Simulink
environment.
Fig. 11 Scheme of current-controlled photovoltaic system
Fig. 12 Scheme of PVG connection to a three-phase electrical network
778
A. Mohamed and A.M. Fayc
¸al
8
Simulation Results
Figure 13 shows the shape of continuous line voltage, which follows its reference
without overshooting and static error nearly null in permanent regime. Figure 14
shows the phase voltage between a and b before ﬁltering. Figures 15 and 16 show
the line voltage and phase voltage which approximately equals to 220 V and 380 V,
respectively, after response time equal to 0.008 s.
Figure 17 shows phase voltage and line current in phase which mean that unit
output power factor, whereas Fig. 18 shows the shape of the modulation ratio which
stabilizes around 0.86 after a response time of 0.008 s
800
600
400
200
0
0.005
0.01
0.015
Vdc
Vdcref
Temps t(s)
Tension V(V)
0.02
0
Fig. 13 Regulation
characteristics of
continuous line voltage
800
600
400
200
0
-200
-400
-600
-800
0
0.01
0.02
0.03
0.04
temps t(s)
Tension Vab(V)
0.05
0.06
Fig. 14 Inverter output phase voltage Vab
PV Generator Connected to Domestic Three-Phase Electrical Network
779
9
Conclusions
The system study is focused on the optimization of photovoltaic energy as well as
its injection in three-phase electrical network through an inverter with minimum
possible losses.
The adopted approach is to improve the operation of different parts of the chain
beginning with the selection of photovoltaic panel, the choice of maximum power
point (MPPT) strategy, as well as the techniques of connection to the grid.
In order to conceive and make an application, a photovoltaic system connected
to three-phase electrical network, a mathematical model of each subsystem is
presented.
We have observed a good dynamic behavior of the system. This is due to the
control strategy chosen (MLI).
400
300
200
100
0
Tensions simples V(V)
-100
-200
-300
-400
0
0.02
0.04
0.06
Temps t(s)
0.08
0.1
Fig. 15 Line voltage across
the load
0
0.02
0.04
0.06
0.08
Temps t(s)
Tension composé Vab(V)
0.1
600
400
200
0
-200
-400
-600
Fig. 16 Phase voltage Vab
across the load
780
A. Mohamed and A.M. Fayc
¸al
15
10
5
0
-5
-10
-150
0.02
0.04
0.06
0.08
0.1
Van
la
Temps t(s)
Courant la(A) et Tension Van(10.PU)
Fig. 17 Line current and
voltage across the load
1
0.8
0.6
rapport de modulation m
0.4
0.2
0
0
0.02
0.04
0.06
Temps t(s)
0.08
0.1
Fig. 18 Modulation ratio m
PV Generator Connected to Domestic Three-Phase Electrical Network
781
Appendix
Shell SP150-PC panel Characteristics
Isc ¼ 4:8 A, Iop ¼ 4:4 A, Vco ¼ 43:4 V, Vop ¼ 34 V
RS ¼ 0:529 Ω, α ¼ 2 mA=℃, β ¼ 152 mV=℃
Ns ¼ 20, Np ¼ 1
Regulator PI parameters: KP1 ¼ 2 , Ki1 ¼ 107.48 , KP2 ¼ 0.4 , Ki2 ¼ 100
Inverter input ﬁlter parameters:
ΔVm ¼ 2V, C ¼ 6:95  102F
Inverter output ﬁlter parameters:
C ¼ 70 μF, L ¼ 360 μH
References
Abdeddaim, S., Betka, A.: Connexion au re
´seau d’une source photovoltaı
¨que 
a facteur de
puissance unitaire. ICRE’07 Universite
´ Bejaia, Algeria (2007)
Best, R.E.: Phase locked loups design, simulation, and applications. Best Engineering, Oberwil
(1987)
Faranda, R., Leva, S.: Energy comparison of MPPT techniques for PV systems. WSEAS TRANS-
ACTIONS on POWER SYSTEMS, 3(6), (2008). ISSN: 1790-5060
Hoque, A.K., Wahid, A.: New mathematical model of a photovoltaic generator (PVG).
J. Electrical Eng. The Institute of Engineers, Bangladesh. EE 28(1), (2000)
Qin, Y.C., Mohan, N., West, R., Bonn, R.: Status and needs of power electronics for photovoltaic
inverters. Sandia National Laboratories, Albuquerque (2002)
Raou, M., Lamchich, M.T.: Average current mode control of a voltage source inverter connected
to the grid: application to different ﬁlter cells. J. Electrical Eng. 55(3–4), 77–82 (2004)
Sera, D., Teodorescu, R., Kerekes, T.: Teaching maximum power point trackers using a photo-
voltaic Array model with graphical user Interface. Photovoltaic specialists conference record
of the 28th IEEE 15–22, 1699–1702 (2000). Website: Differentes Archituctures. http://Hmf.
Enseeiht.Fr/Travaux/CD0102/Travaux/Optemf/bei_eol/Ge/Archi.Htm
782
A. Mohamed and A.M. Fayc
¸al
Technical and Economic Prefeasibility
Analysis of Residential Solar PV System
in South Kazakhstan
Anuar Assamidanov, Nurbol Nogerbek, and Luis Rojas-Solorzano
1
Introduction
Kazakhstan has substantial inﬂuence over energy supply of the world since it owns
large natural resources (oil, gas, coal, uranium, and other commodities) and a 3% of
the raw material available in the planet. According to statistics (EIA 2013), in 2012,
the total power generation capacity of Kazakhstan was approximately 19.5 GW,
85% of which was coal-ﬁred power and the remaining 15% was hydropower. Since
2010, the country decided to import electricity from Kyrgyzstan and Uzbekistan to
supply its southern regions, since the installed power plants do not reach estimated
load and, therefore, the consumption of electricity overcomes its production in the
country.
Thus, within this framework, the country is currently devoted to the develop-
ment of its renewable energy sector. Kazakhstan has important potential in power
generation from wind, solar, hydrothermal, and small hydro sources. Indeed, the
potential energy generation of Kazakhstan exceeds one trillion kWh per year which
is about 10 times the annual energy consumption in the country to date (EIA 2013).
By the year 2050, the milestone for renewable energy sources is targeted to reach
50% of total energy consumption (Kazenergy 2014).
At this moment, apart from partial use of hydropower, the potential for renew-
able energy is not used sufﬁciently, and a signiﬁcant generation of electricity may
be added by solar power resources according to the economic development targets
of the country. For example, 2200–3000 sunny hours per year and 1300–1800
kWh/m2/year are available due to solar radiation in the country (see Fig. 1 as a
reference of the potential in the country) (Kazenergy 2014).
A. Assamidanov (*) • N. Nogerbek • L. Rojas-Solorzano
Nazarbayev University, Department of Mechanical Engineering, Astana 010000, Kazakhstan
e-mail: aassamidanov@nu.edu.kz
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_55
783
The potential of solar energy in Kazakhstan is estimated at 340 billion tonnes of
oil equivalent (toe) annually. However, despite this very attractive scenario for
solar power generation this resource is still scarcely used in the country (Energy
Charter Secretariat 2013).
Within these favorable conditions, photovoltaics (PV) today attract considerable
attention, but on-grid PV market is not a proﬁtable sector by itself (Dornfeldt 2014).
It is dependent on the governmental support, which still must stimulate investment
with subsidies. In recent years, rapid development in grid-connected building-
integrated PV systems around the world, mainly in developed countries, is due to
the government-initiated renewable energy programs aiming at the development of
renewable energy applications and reduction of greenhouse gas emissions (Erge
et al. 2001). For example, in 1990, Germany introduced a “100,000-roof program”
(Ramana 2005). Japan came with a 70,000-roof program that started in 1994 and
lasted for the rest of that decade (Yang et al. 2004). A PV system dissemination
program has been very successful in the USA, and its 1 million solar-roof initiative
is gradually advancing (SSE 2009). Grid-connected PV systems, thus, took off in
the mid- to late 1990s, and since then, it has been the dominant application in the
PV sector.
South Kazakhstan is situated between 42.3 and 44.9 north latitude and 65.5
and 71.4 east longitude with an area of 726 km2, which is an ideal location for solar
energy utilization, as previously shown in Fig. 1. Daily solar radiation varies in the
range of 4–4.45 kWh/m2. Thus, densely populated cities like Shymkent, Taraz, and
Fig. 1 Horizontal solar radiation on the territory of Kazakhstan (source indicated on the map)
784
A. Assamidanov et al.
Kyzylorda could be electriﬁed by PV on-grid systems using the inexhaustible and
pollution-free solar energy with locally available technologies. Extra beneﬁts, such
as supporting a weak grid and reducing CO2 emissions, could be also accounted as
potential incomes once the program is in mass scale.
Additionally, Kazakhstan has also established at the end of May 2014 a new
feed-in tariff (FIT) law – “On Supporting the Use of Renewable Energy Sources.”
This policy is expected to further increase the installation of grid-connected pho-
tovoltaic (PV) systems. Such policy has been implemented and also has been
studied in a number of countries such as the UK, Ukraine, Australia, Spain, Taiwan,
Germany, Tanzania, and other countries, with very positive results (Erge et al.
2001). As city-level FIT laws for promoting solar photovoltaic panels are very
recent and its suitability has not been thoroughly examined yet (Renewable Market
Watch 2014), the fundamental objective of this investigation is to analyze technical
and economic prefeasibility of implementing residential photovoltaic system in
South Kazakhstan, taking into account the diversity in the southern region and new
regulations, as no systematic study has been done to justify the viability of solar
power generation in this region to date. As an alternative solution, ﬁnancial
incentives could be obtained if the project is included in the list of priority sectors
of the “Business Road Map – 2020” that is one of the “DAMU Entrepreneurship
Development Fund” projects. This initiative has as its main goal to provide gov-
ernmental support for projects of non-primary sectors of the economy (Halyk
bank 2013).
2
Solar Energy Resource Potential
2.1
Theoretical Potential
The average sunny hours per day and monthly solar radiation were found, based on
an average solar radiation data taken from NASA, for three widespread locations in
South Kazakhstan available through RETScreen workbench tool (SSE 2009).
According to the climate database from NASA, South Kazakhstan receives approx-
imately 1185 GWh of solar energy every year, which is more than 30 times higher
than the current electricity generation in those cities. However, in the course of
exploitation, constraints such as land use, geographical area, and climate are
encountered. Theoretically, a great potential for developing solar power system is
considered when the average daily radiation is above 4 Wh/m2/day on an average
per year (Energy Charter Secretariat 2013).
Technical and Economic Prefeasibility Analysis of Residential Solar PV. . .
785
2.2
Technical Potential
There is a clear market potential for grid-connected PV systems in the densely
populated urban and electriﬁed areas, i.e., for solar home systems are central-grid
households. Locally available PV system speciﬁcations and ﬁnancial assumptions,
according to current conditions and trends in Kazakhstan, were entered in the
RETScreen workbench. Then, the expected generation of electricity by the solar
power home system was calculated on a yearly basis under different scenarios of
feed-in tariff (FIT) in place.
Local company Astana Solar is currently producing Poly-Si photovoltaic mod-
ules using Kazakhstani silicon (Astana Solar 2012). The efﬁciency of the PV array
is about 16% and Astana Solar guarantees that its products have a lifetime of at least
25 years. Table 2 shows the detailed technical speciﬁcations of the solar panel that
was used in this study.
3
Economic Viability of Grid-Connected Solar PV System
3.1
Global Solar Radiation
Due to limited availability of solar radiation data in Kazakhstan, a NASA data set
for the period from 1985 to 1995 was used. Ten-year averaged NASA global solar
radiation data from three widespread South Kazakhstan locations (Table 1) were
used for the technical-economic analysis of grid-connected solar PV systems
(SS 2009).
3.2
Proposed 6.6 kW Solar PV System and Financial
Assumption
The proposed solar PV grid-connected system is an array with a total power
capacity of 6.6 kWp consisting of 30 ﬁxed panels with a total area of 49.2 m2
(Table 2). The size of the array was chosen such that it ﬁts the estimated roof area of
a South Kazakhstani residence. The solar array system costs 2354 US$/kWp
(Astana Solar 2012). The PV array is faced toward south and is inclined at a
Table 1 Average daily solar radiation in South Kazakhstan
City name
Elevation (m)
Latitude
Longitude
Radiation (NASA) (kWh/m2/day)
Kyzylorda
130
44.9
65.5
4.21
Taraz
655
42.9
71.4
4
Shymkent
604
42.3
69.7
4.45
786
A. Assamidanov et al.
42angle, equal to the site latitude. Zero azimuth angles were taken for all the
studied locations. DC-into-AC string inverters were utilized in the proposed system
with a total capacity of 5.94 kW, with an efﬁciency of 90%. The cost of the inverter
is 220 US$/kWp (Astana Solar 2012). The economic feasibility analysis was
performed using data on initial costs associated with the implementation of the
proposed system, and prevailing loan interest, inﬂation, and energy escalation rates
were prevalent in the country.
In this study, project life is assumed to be 25 years. As this project is in
prefeasibility level, calculation of the total cost has been simpliﬁed. With respect
to total expenses, development represents 5%, and engineering 15% of the total
cost. Annual O&M is about 1% of total initial cost. The cost of the construction
phase would be 75% of the total initial expenses. Table 3 provides some details and
assumptions considered for the entire ﬁnancial analysis, based on the current
market and ﬁnancial situation of the country (The National Bank of Kazakhstan
2015). Feed-in tariff (FIT) is specially included according to new government
Table 2 Technical
speciﬁcation of solar PV
panels used in the analysis
Item
Speciﬁcation
Manufacturer
Astana solar LLP
PV module type
Multi-Si
Module number
KZ PV 230 M60
Efﬁciency
16%
Rated power (Pmax)
220 Wp/module
Number of modules
30 (6.6 kWp array)
Voltage at Pmax
29.4 V
Current at Pmax
7.5 A
Short circuit current
8.3 A
Open circuit voltage
36.5
Frame area
1.64
Dimension
1.649m  0.992m  0.40 m
Weight
19.5 kg
Table 3 Financial
assumption and data for the
study
Parameter
Value (currency at year 2014 value)
Solar panel
2354 US$/kWp
Inverter cost
220 US$/kWp
Annual O&M
US$168
Inﬂation rate
5.4%
FIT
191.4 US$/MWh
FIT escalation rate
8%
Debt ratio
80%
Debt interest rate
17%
Debt term
10 years
Project life
25 years
Technical and Economic Prefeasibility Analysis of Residential Solar PV. . .
787
policies starting on the generation of power from renewable sources, in place since
2014 (Ministry of Energy of Kazakhstan 2014). Therefore in this analysis, the FIT
will be assumed as 191.4 $/MWh.
3.3
Results
3.3.1
Electricity Generation
The amount of equivalent DC electrical energy actually generated by the proposed
6.6 kWp solar grid-connected system to the utility was calculated for all three
locations in the annual electricity production. The highest electricity annual pro-
duction was obtained in Shymkent with about 8.9 MWh. The lowest production was
obtained in Taraz with an annual electricity generation of 8.1 MWh. For an average
city in South Kazakhstan, an estimate of 6.1 MWh/year of electricity can be
delivered using the proposed PV system.
3.3.2
Economic Feasibility Indicators
The decision making indicators from the ﬁnancial analysis throughout the PV
system lifetime are presented as follows (see also Table 4):
Internal rate of return (IRR): The maximum IRR of 17.9% was observed in
Shymkent, while the minimum IRR of 9.9% was observed in Taraz. Therefore, an
IRR of 16% can be obtained from an average city in South Kazakhstan.
Net present value (NPV): The highest NPV was about $14,523 for Shymkent,
and the lowest was about $11,366 for Taraz.
Beneﬁt-cost (B-C) ratio: The B-C ratio was found to be highest (9.65) in
Shymkent, while the lowest (7.84) was found in Kyzylorda.
Simple payback (SP): It was found that on an average, an SP of about 9.9 years
can be obtained from any location of South Kazakhstan.
Table 4 Economic
indicators for 6.6 – kWp solar
PV system for three locations,
South Kazakhstan
Financial results
Shymkent
Kyzylorda
Taraz
IRR on equity
17.9%
17.3%
16%
Net annual income
$1191
$1078
$973
Net present value
$14,523
$13,741
$11,366
Payback period
9.9
10.2
10.8
Beneﬁt-cost ratio
9.65
9.03
7.84
788
A. Assamidanov et al.
3.4
Sensitivity Analysis
A Monte Carlo analysis, based on a sample of 500 scenarios, allowed ﬁnding the
probable outcomes under known uncertainty of input parameters. In this study,
a  10% uncertainty is assumed for the initial cost, O&M, FIT, and debt ratio, while
an uncertainty of 20% was assumed for debt interest rate and debt term, as debt
parameters vary widely with current Kazakhstani bank services that depend on
project scope.
The impact tornado graph, presented in Fig. 2, shows how much of the variation
in the ﬁnancial parameter can be explained in each input parameter. The impact
Fig. 2 Impact results of NPV and IRR for Shymkent
Technical and Economic Prefeasibility Analysis of Residential Solar PV. . .
789
analysis demonstrates that the changes in NPV and IRR are largely due to variation
of FIT, followed by initial cost and debt interest rate in about the same level of
importance, as shown in Fig. 2.
Since residential solar PV has no analogies within the country, this analysis
should be used as a support mechanism to stimulate implementation of these types
of projects in the country.
According to these results, an even better solution for Shymkent could be
obtained if the project is included in the list of priority sectors of the “Business
Road Map – 2020” that is one of the “DAMU Entrepreneurship Development
Fund” projects. This initiative has, as its main goal, to provide governmental
support for projects of non-primary sectors of the economy. In this particular
case, if implemented, the government may subsidize 50% of the initial cost of
project, and the remainder is taken completely as loan (100% debt ratio, i.e., no
equity needed) from the bank by the owner at 7% of debt interest rate (Halyk bank
2013). As it can be observed in Table 5, there are signiﬁcant differences in ﬁnancial
results when government provides subsidies. All ﬁnancial parameters indicate that
government subsidy is a very beneﬁcial approach to promoting residential solar PV
(Fig. 3).
4
Conclusions
This study analyzes the technical and economic potential of solar photovoltaic-grid
connected system in South Kazakhstan. The technical assessment considers several
locally manufactured PV systems. The study focuses on polycrystalline solar cells
(poly-Si) due to its optimal ﬁnancial and technical speciﬁcations for South Kazakh-
stan climatic conditions.
The analysis determined that with a 6.6 kWp PV array it is possible to generate
and export to the grid a minimum of 8.9 MWh in Taraz, and a maximum of 9.1
MWh in Shymkent. Financial indicators from the life cycle cost analysis for all sites
showed favorable conditions for the development of the proposed residential solar
Table 5 Comparison of ﬁnancial viability
Financial results
Residential PV in
Shymkent
Residential PV in Shymkent with government
subsidy
IRR on equity
17.9
18.1
Net annual
income
$1191
$2523
Net present
value
$14,523
$26,220
Payback period
9.9
5.5
Beneﬁt-cost
ratio
9.65
4.12
790
A. Assamidanov et al.
PV system in South Kazakhstan, proving that on a lifetime frame of 25 years, the
application of solar PV for residential grid-connected systems is quite feasible
ﬁnancially.
A new policy in conjunction with the feed-in tariff (FIT) is envisioned from the
study. The economic analysis suggests that a subsidy of 50% of total initial cost
should be considered to maximize the advantage for the residential owner of the
PV-system in southern regions. This will also help to support the weak grid in the
region, under constant threat of breakdown during peak load hours.
Fig. 3 Impact results of NPV and IRR for Shymkent with government support
Technical and Economic Prefeasibility Analysis of Residential Solar PV. . .
791
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Contribution of the Cogeneration Systems
to Environment and Sustainability
C
¸ omakli Kemal, C
¸ akir U
gur, C
¸ okgez Kus
¸ Ays
¸eg€
ul, and S
¸ahin Erol
1
Introduction
The reserve of energy resources in the world is tending to decrease, while the
amount of energy needed by humanity is tending to increase. In addition, the
dependence of the humanity on using energy is increasing day by day due to the
improving technology and the rise in the life standards of people in the world. This
situation is becoming the most important and essential issue of the world. In
general, there are two ways to overcome this problem. One of them is to bring
out and improve new and renewable energy sources such as solar or wind energy
systems. The other way is to improve conventional energy converting systems for
using existing energy source more efﬁciently and for longer time, such as cogen-
eration systems. In other words, people have to do their best to improve the
sustainability of the energy resources. Cogeneration can be explained as the simul-
taneous production of power and usable heat by using one type of energy source
such as oil, coal, natural gas, liqueﬁed gas, biomass, or the sun. This system affords
remarkable energy savings and frequently makes it possible to operate with greater
efﬁciency when compared to a system producing heat or power separately. In
conventional power plants, a large amount of heat is produced but not used.
C
¸ . Kemal (*) • C
¸ .K. Ays
¸egül
Atatürk University, Faculty of Engineering Department of Mechanical Engineering, 25000
Erzurum, Turkey
e-mail: ucakir@bayburt.edu.tr
C
¸ . U
gur
Bayburt University, Faculty of Engineering Department of Mechanical Engineering, 69000
Bayburt, Turkey
S
¸. Erol
Ordu University, Technical Vocational High School Machinery and Metal Technology
Department, 52000 Ordu, Turkey
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_56
793
These systems utilize the waste heat produced during electricity generation and
allow more efﬁcient fuel consumption. Along with the saving of fossil fuels,
cogeneration also allows to reduce the emission of greenhouse gases (GHG) like
CO2 per unit useful energy output. By designing systems that can use the waste
heat, the efﬁciency of energy production can be increased from current levels that
range from 35% to 55% in conventional plants to over 90% in cogeneration
systems.
Using conventional energy conversion systems and fossil fuels to run them has
very negative effect on nature, environment, and ecology. This is known for many
years and this issue is on top of the energy politics of the countries. Air pollution
and greenhouse problem are the issues that are studied by the scientists to overcome
for many years. There is a general thought by nonspecialists in environmental
issues renewable energy sources can be used to overcome the negative effects of
fossil energy sources and conventional energy conversion systems. But we must
analyze this situation as in this millennium, energy is a necessity for us to continue
our lives, just like water and air and it is impossible to live without it. In addition to
this, in the current situation, all of the existing usable potential of the renewable
energy sources is not sufﬁcient to meet the energy demand of humanity. In light of
these points, it is apparent we are obliged to use conventional energy sources and
fossil fuels; otherwise life will be very hard for us. This means that air pollution and
greenhouse effect will keep increasing day by day. Even though it seems impossible
to stop air pollution and greenhouse effect, they can be reduced by using more
efﬁcient energy conversion systems by using less fuel. One of the energy conver-
sion systems which produce more energy by using less fuel is cogeneration systems.
As mentioned before, cogeneration systems produce two or more kinds of energy
by using one type of fuel. Generally, heat energy and electrical energy are produced
in cogeneration systems. This means that only one type of fuel is required to
produce two types of energy. This way, less harmful gases and substances would
be emitted to the atmosphere. In this study, environmental beneﬁts and necessity of
cogeneration systems are put forward. To achieve this aim, some scientiﬁc studies
previously made on cogeneration systems are referred and analyzed.
2
Environmental Impact Evaluation of Cogeneration
Although cogeneration is an old and proven energy conversion system, in recent
years, a resurgence of interest has come into the world of energetic issue because of
the energy crisis that has taken part in dockets of the countries in the world.
Cogeneration systems and district heating/cooling applications offer us some
proven, reliable, applicable, and cost-effective solutions which make very impor-
tant contributions to meet global heat and electricity demand. Energy supply
efﬁciency, the use of waste heat, and low carbon renewable energy resources of
those systems, they are an important part of greenhouse gas emission reduction
strategies of the countries in the world. Cogeneration is the simultaneous
794
C
¸ . Kemal et al.
production of power and usable heat by using one type of energy source such as oil,
coal, natural gas, liqueﬁed gas, biomass, or the sun. In most of the cogeneration
applications, the energy types produced simultaneously are electric and heat
energy. Generally, those systems utilize the waste heat energy produced during
electricity generation (C
¸ akır et al. 2012; Can et al. 2009; Abus
¸o
glu and Kano
glu
2009).
2.1
Emission Balances for Cogeneration and Separate Heat
and Electricity
Computing the air quality effects of any technological change is always made
difﬁcult by the complexity, expense, and inaccuracy of air quality modeling. In
the case of cogeneration, this computation is further complicated by difﬁculties in
determining the emission changes occurring in the central utility system and by
substantial variability in the emission factors to be applied to the cogeneration
systems. The response of the utility system to an increase in cogenerated power, a
critical parameter in determining not only emission impact but also oil savings
(or loss), is difﬁcult to predict. The addition of signiﬁcant levels of cogenerated
power to a utility’s service area will affect both its current operations and future
expansion. If the cogenerated power represents a displacement of current electricity
demand in the service area (i.e., with retroﬁt of an existing facility for cogenera-
tion), the utility will either reduce its own electricity production or power imports,
with its decision based on costs, contractual obligations, or, perhaps, politics. It may
also move up the retirement date for an older power plant or cancel planned
capacity additions in response to cogeneration systems’ displacement of either
current or anticipated future demand. Because most utility grids draw on a mix of
nuclear, coal, and oil-ﬁred steam electric generators for base and intermediate
loads, these plants may be scattered over a wide area; and because control systems
for the fossil plants may vary drastically in effectiveness, the pollution implications
of the response of the utility system to cogeneration are highly variable (Gibbons
1983).
3
Cogeneration Systems’ Beneﬁts
Generally, environment-friendly energy sources are thought as a kind of energy
which is renewable, meaning that the use of such energy never damages the
environmental stability and does not harm nature. Some types of energy can be
thought of as renewable and friendly to the environment, and many governments
promote the use of such energy and the development of new types of energy
generating technology which ﬁts this explanation. Corresponding to the rising
Contribution of the Cogeneration Systems to Environment and Sustainability
795
energy demand of people, there is an increase in concerns about that at which the
ratio on greenhouse gas emissions and its damages will be realized. There are a lot
of factors effective on decreasing the harmful emissions along with some choices
that may exist. In connection to this point, energy efﬁciency is another important
consideration. Increasing the energy efﬁciency is a vital way to make sustainable
energy stretch further (Pehnt 2008).
From this point, cogeneration systems may make positive effect to protect the
nature in two ways. One of them is to use cogeneration systems together with some
renewable energy conversion systems like solar system or heat pumps. This way
can be thought as a ﬁrst-hand application for saving nature. The second one is fuel
saving aspect of cogeneration. Less fuel usage brings about less harmful gas
emissions.
There are many studies on using cogeneration systems with renewable systems
in the open literature. One of the studies conducted was by Martin Pehnt, namely,
Environmental impacts of distributed energy systems: the case of micro cogenera-
tion. This study investigates environmental impacts of micro cogeneration by
carrying out a detailed life cycle assessment and an analysis of local air quality
impacts of micro cogeneration systems. Most micro cogeneration systems are
superior, as far as the reduction of GHG emissions is concerned, not only to average
electricity and heat supply but also to state-of-the art separate production of
electricity in gas power plants and heat in condensing boilers. The GHG advantages
of micro cogeneration plants are comparable to district heating with combined heat
and power (CHP). Under the assumption that gas condensing boilers are the
competing heat-supply technology, all technologies are within a very narrow
range. Looking at the GHG reduction potential on the level of a supply object
(e.g., a single-family house) by modeling the operation with a CHP optimization
tool, the achievable mitigation potential is somewhat lower, because the micro
cogeneration systems do not supply the whole energy demand. Here, fuel cells offer
the advantage of a higher power-to-heat ratio. Environmental impacts other than
those related to climate and resource protection relate more speciﬁcally to technol-
ogy. In addition to investigating the emission side, analysis of the air quality
situation of a residential area supplied by reciprocating engines was carried out.
The analysis shows that for the selected conditions, the additional emission of NOx
due to the engines does not create severe additional environmental impacts.
Another study was conducted by Thilak Raj et al. as a review study on renewable
energy based on cogeneration technologies. That paper reviews the present-day
cogeneration technologies based on renewable sources of energy. In addition, the
study of novel methods, existing designs, theoretical and experimental analyses,
modeling and simulation, environmental issues, and economic and related energy
policies have been discussed in this study. One of the energy conversion applica-
tions which is suitable for being used together or as a cogeneration system is solar
energy system. Solar energy can be importantly utilized for cogeneration systems
and various such technologies have been proposed. Using focusing collectors, solar
energy can be converted in a central power plant to electrical energy which can then
be utilized to operate a vapor compression refrigerator to produce cooling. At the
796
C
¸ . Kemal et al.
same time, the waste heat rejected by the heat engine can be used to drive an
absorption refrigerator. This system is simply called a solar powered cogeneration
system for air conditioning and refrigeration and can play a dual role by saving
energy and reducing the environmental pollution (Thilak Raj et al. 2011). Y. June
Wu and Marc A. Rosen conducted a study on assessing and optimizing the
economic
and
environmental
impacts
of
cogeneration/district
energy
(DE) systems using an energy equilibrium model. In this study, energy equilibrium
models can be valuable aids in energy planning and decision making. In such
models, supply is represented by a cost-minimizing linear submodel and demand
by a smooth vector-valued function of prices. In this paper, we use the energy
equilibrium model to study conventional systems and cogeneration-based district
energy (DE) systems for providing heating, cooling, and electrical services, not
only to assess the potential economic and environmental beneﬁts of cogeneration-
based DE systems but also to develop optimal conﬁgurations while accounting for
such factors as economics and environmental impact. The energy equilibrium
model is formulated and solved with the software called WATEMS, which uses
sequential nonlinear programming to calculate intertemporal equilibrium of energy
supplies and demands. The methods of analysis and evaluation for the economic
and environmental impacts are carefully explored. An illustrative energy equilib-
rium model of conventional and cogeneration-based DE systems is developed
within WATEMS to compare quantitatively the economic and environmental
impacts of those systems for various scenarios (June Wu and Rosen 1999). Goktun
studied the solar power cogeneration system for air conditioning and refrigeration.
By employing the energetic optimization technique, the optimal performance of a
focusing collector-driven, irreversible Carnot cogeneration system for air condi-
tioning and refrigeration is investigated. A minimum value for the total solar
insolation needed to overcome internal irreversibilities for start-up of the system
is deﬁned, and the effect of the collector design parameters on this value is
investigated (G€
oktun 1999). Photovoltaic cogeneration in the built environment
was investigated and studied by Morgan D Bazilian et al. In this study, it is said that
building integrated photovoltaic (BiPV) systems can form a cohesive design,
construction, and energy solution for the built environment. The beneﬁts of build-
ing integration are well documented and are gaining signiﬁcant public recognition
and government support. PV cells, however, convert only a small portion of the
incoming insolation into electricity. The rest is either reﬂected or lost in the form of
sensible heat and light. Various research projects have been conducted on the forms
these by-products can take as cogeneration. The term cogeneration is usually
associated with utility-scale fossil-fuel electrical generation using combined heat
and power production. It is used here in the same sense as in the evaluation of waste
heat and by-products in the production of PV electricity. It is important to have a
proper synthesis between BiPV cogeneration products, building design, and other
HVAC systems in order to avoid overheating or redundancy. Thus, this paper looks
at the state of the art in PV cogeneration from a whole building perspective. Both
built examples and research will be reviewed. By taking a holistic approach to the
research and products already available, the tools for a more effective building
Contribution of the Cogeneration Systems to Environment and Sustainability
797
integrated system can be devised. This should increase net system efﬁciency and
lower installed cost per unit area. An evaluation method is also presented that
examines the energy and economic performances of PV/T systems. The performed
evaluation shows that applications that most efﬁciently use the low-quality thermal
energy produced will be the most suitable niche markets in the short- and mid-term
(Bazilian Frederik Leenders et al. 2001). Experimental activity on two tubular solid
oxide fuel cell cogeneration plants in a real industrial environment was investigated
by M. Gariglio et al. The aim of the mentioned study is the comparison of two
similar experimental campaigns performed on the two prototypes with different
nominal power, in order to investigate the performance of the two generator
designs. The factorial analysis has been applied considering two factors: setup
temperature of the generator and fuel utilization factor. First, the obtained data
have been analyzed by using the ANOVA of the experimental data of some
dependent variables. Then, the regression models have been obtained for every
dependent variable considered, and an optimization analysis has been performed.
The analysis shows that the stack voltage sensitivity to the fuel utilization of the two
systems has nearly the same value; and the stack voltage sensitivity to the generator
setup temperature is different for the two systems (Gariglio et al. 2009). In a study
on solar cogeneration panels, which investigated the method of combining photo-
voltaic cells with the transpired solar air heater, constructed prototypes measured
the combined electrical and thermal energies produced and compared the results
with single function reference panels. The results showed that combining the PV
cells with the transpired solar wall panels can produce higher total combined solar
efﬁciencies than either of the PV or thermal panels on their own (Hollick 1998).
Lindenberger et al. presented an article on optimization of solar district heating
systems, seasonal storage, heat pumps, and cogeneration which focused on to
demonstrate the working of deco in a pilot housing project of the Bayerische
Forschungsstiftung (Bavarian Research Foundation). The quantitative results, i.e.,
the percentages of fossil fuels saved and emissions reduced with the help of
different technology combinations at different costs, are speciﬁc to the pilot project.
On the other hand, the qualitative interdependencies between energy conservation,
emission mitigation, and cost increases revealed by deco are likely to be the same in
all regional energy systems in moderate climates at the present level of energy
prices. The use of heat pumps (especially electric driven) has formed a new area of
research. Heat pumps can be used together with cogeneration systems in some ways
(Lindenberger et al. 2000). Marc A. Rosen conducted a study which is Allocating
carbon dioxide emissions from cogeneration systems: descriptions of selected
output-based methods. In this article, selected methods for allocating emissions
for cogeneration systems are described and compared. In addition, exergy values
for typical commodities encountered in cogeneration are presented. The reasoning
behind the author’s view is that the exergy-based method is the most meaningful
and accurate of all the methods. Mancarella (2009) proposed a novel approach to
energy and CO2 emission modeling of cogeneration systems coupled to electric
heat pumps. The speciﬁc objectives were to identify the relevant parameters and
variables involved in the analysis of such composite systems and to provide a
798
C
¸ . Kemal et al.
synthetic and indicative assessment of the energy and environmental beneﬁts
potentially brought with respect to conventional energy systems. The conditions
at which energy and emission beneﬁts occur, and their extent with respect to
classical generation means, are illustrated through various numerical examples,
highlighting the generality and effectiveness of the models introduced (Rosen
2008). A comparative parametric analysis was carried out on a small-scale com-
bined heat and power plant incorporating a heat pump and the conventional system
in which heat is produced in a hot water boiler and electrical energy is drawn from
the power grid (Soltani et al. 2014).
A study was conducted on multi-objective optimization of a solar-hybrid cogen-
eration cycle application to CGAM problem. In their study, an exergo-economic
multi-objective optimization is reported here of a solar-hybrid cogeneration cycle.
Modiﬁcations are applied to the well-known CGAM problem through hybridization
by appropriate heliostat ﬁeld design around the power tower to meet the plant’s
annual demand. The new cycle is optimized via a multi-objective genetic algorithm
in Matlab optimization toolbox. Considering exergy efﬁciency and product cost as
objective functions, and principal variables as decision variables, the optimum
point is determined according to Pareto frontier graphs. The corresponding opti-
mum decision variables are set as inputs of the system and the technical results are a
48% reduction in fuel consumption which leads to a corresponding decrease in CO2
emissions and a considerable decrease in chemical exergy destruction as the main
source of irreversibility. In the analyses, the net power generated is ﬁxed at 30 MW
with a marginal deviation in order to compare the results with the conventional
cycle. Despite the technical advantages of this scheme, the total product cost rises
signiﬁcantly (by about 87%), which is an expected economic outcome (Malinowska
and Malinowski 2003). The researchers conducted a study called cogeneration solar
system using thermoelectric module and Fresnel lens. The main purpose of their
paper is the experimental investigation of an electricity and preheated water
cogeneration system by thermoelectric. In the presented design, Fresnel lens and
thermoelectric (TE) module were utilized in order to concentrate solar beam and
generate electrical power, respectively. The energy of concentrated sunlight on the
heat absorber of TE module is transferred to cold water reservoir. Heat transfer in
TE module leads to temperature difference in its both sides and ﬁnally electrical
power is generated. The main components of this system consist of a mono-axial
adjustable structure, a thermoelectric generator (TEG), and a Fresnel lens with an
area of 0.09 m2. Results revealed that matched load output power is 1.08 W with
51.33% efﬁciency under radiation intensity of 705.9 W/m2. In order to apply TE
module capacity optimally for electrical generation, it is recommended to employ
an array of Fresnel lenses which transfer heat to TE module by an intermediate ﬂuid
(Hasan Nia et al. 2014).
Rafael Galvao et al. presented the development of an energy model based on a
mixed system of renewable energy, with primary energy sources as solar and
biomass. It was a hybrid and autonomous system with solar PV panels and gasiﬁ-
cation cogeneration technology. Also it was an environment-friendly process
aiming to reduce the energy demand, costs, and emissions. This energy model is
Contribution of the Cogeneration Systems to Environment and Sustainability
799
a new sustainable standard on energy consumption efﬁciency (electrical and ther-
mal demands) of a small hotel building and a relevant contribution to certify the
building in compliance with the laws of the country on the thermal performance of
buildings (Rafael Galv~
ao et al. 2011). Some other researchers investigated the
socioeconomic drivers of large urban biomass cogeneration sustainable energy
supply for Austria’s capital, Vienna. They provided a detailed case study on
Austria’s by far largest biomass cogeneration plant. They described and analyzed
the history of the project, putting particular emphasis on the main driving forces and
actors behind the entire project development process.
There are some other works in the literature on using cogeneration with different
systems to save more energy (Madlener and Bachhiesl 2007).
In the study conducted by Burer et al., Multi-criteria optimization of a district
cogeneration plant integrating a solid oxide fuel cell–gas turbine combined cycle,
heat pumps and chillers, a simultaneous optimization of the design and operation of
a district heating, cooling, and power generation plant supplying a small stock of
residential buildings has been undertaken with regard to cost and O2 emissions. The
simulation of the plant considers a super structure including a solid oxide fuel cell–
gas turbine combined cycle, a compression heat pump, a compression chiller and/or
an absorption chiller, and an additional gas boiler. The Pareto-frontier obtained as
the global solution of the optimization problem delivers the minimal CO2 emission
rates, achievable with the technology considered for a given accepted investment,
or respectively the minimal cost associated with a given emission abatement
commitment (Burer et al. 2003).
A study was carried out by Jeff Smithers on the review of sugar cane trash
recovery systems for energy cogeneration in South Africa. He says that biomass is a
potential sustainable source of energy. Approximately one third of the energy
available from sugar cane is contained in the top sand leaves trash, which are
generally either burnt prior to harvesting or are not recovered from the ﬁeld. Based
on results reported in the literature and assuming a 50% trash recovery efﬁciency, it
is estimated that 1.353 million ton of soft trash is available annually for cogenera-
tion in South Africa, which could potentially produce 180.1 MW over a 200-day
milling season. Studies in Brazil and Australia have shown that, the most efﬁcient
way of recovering the top sand leaves for cogeneration of power at sugar mills, is to
use a chopper harvester with the separation of canes talk sand trash on the harvester
either fully or partially turned off. In South Africa, more than 90% of the sugar cane
crop is burnt and manually harvested, and hence new systems are proposed to
recover the trash and transport the material to the mill (Smithers 2014). Dario Buoro
and his friends conducted a study on Optimization of a Distributed Cogeneration
System with solar district heating. The aim of the related study is to identify the
optimal energy production system and its optimal operation strategy required to
satisfy the energy demand of a set of users in an industrial area. A distributed energy
supply system is made up of a district heating network, a solar thermal plant with
long-term heat storage, a set of combined heat and power units, and conventional
components also, such as boilers and compression chillers. In this way, the required
heat can be produced by solar thermal modules, by natural gas cogenerators, or by
800
C
¸ . Kemal et al.
conventional boilers. The decision variable set of the optimization procedure
includes the sizes of various components, the solar ﬁeld extension, and the thermal
energy recovered in the heat storage, while additional binary decision variables
describe the existence/absence of each considered component and its on/off oper-
ation status. The optimization algorithm is based on a mixed integer linear pro-
gramming (MILP) model that minimizes the total annual cost for owning,
maintaining, and operating the whole energy supply system. It allows calculating
both the economic and the environmental beneﬁts of the solar thermal plant,
cooperating with the cogeneration units, as well as the share of the thermal demand
covered by renewable energy, in the optimal solutions. The results obtained by
analyzing different system conﬁgurations show that the minimum value of the
average useful heat costs is achieved when cogenerators, district heating network,
solar ﬁeld, and heat storage are all included in the energy supply system and
optimized consistently. Thus, the integrated solution turns out to be the best from
both the economic and environmental points of view (Buoro et al. 2014). The aim of
the study by Marta Serrano Delgado and his friend is to model and simulate the
thermal and electrical efﬁciencies of the cogeneration plant of a paper mill. The
ﬁnal purpose is the beneﬁts optimization by adjusting production to the amount of
energy to be sold. It is necessary to know it because the sale price goes down when
the actual production of electrical energy does not match the scheduled power
(Delgado et al. 2013).
4
Conclusion
Importance of cogeneration systems is well known for many years by the people
who are interested in energy conservation. But environmental and ecological
aspects of cogeneration are very important too. The systems’ less fuel-using
speciﬁcation and efﬁciency level make them very important to the environment.
Due to the primary energy savings with high efﬁciency levels and decrease in
greenhouse gas emissions, these systems make important contributions to the
environment and nature. This efﬁciency also reduces air pollution and greenhouse
gas emissions, increases power reliability and quality, reduces grid congestion, and
avoids distribution losses. All in all, cogeneration systems make very important
contributions to nature in different ways.
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802
C
¸ . Kemal et al.
Solar Calculations of Modiﬁed Arch
(Semi-spherical)-Type Greenhouse System
for Bayburt City
C
¸ akir U
gur, S
¸ahin Erol, C
¸ omakli Kemal, and C
¸ okgez Kus
¸ Ays
¸eg€
ul
1
Introduction
Passive heating and cooling processes can be applied in any object around the world
using solar energy. It is necessary to determine primarily the utilisable solar
radiation in these structures and the design of the systems where solar energy is
used. Parameters such as the design of the collectors, the lighting load in the
buildings, calculation of the heating power gained from the sun, the value of the
solar ovens and the insolation rate of the greenhouses are established in accordance
with the amount of radiation reaching the earth. The oblique rays of the winter sun
and the steep-angled rays of the summer sun make the southern face of the northern
hemisphere receive more solar radiation in winter and be protected easily in
summer. That is why the south-facing fronts are more valued fronts in architecture.
Unfortunately, the heat energy gained from the sun is not taken into consideration in
the design and construction of many buildings in our country. Especially, this is
completely ignored in the buildings designed by small-scale companies or
constructed by individuals. As mentioned in the previous section, the south-facing
fronts in such buildings are assessed separately from the other parts in quoting and
solicitation planning (Kilic and Ozturk 1983).
C
¸ . U
gur (*)
Bayburt University, Faculty of Engineering Department of Mechanical
Engineering, 69000 Bayburt, Turkey
e-mail: ucakir@bayburt.edu.tr
S
¸. Erol
Ordu University, Technical Vocational High School Machinery and Metal Technology
Department, 52000 Ordu, Turkey
C
¸ . Kemal • C
¸ .K. Ays
¸egül
Atatürk University, Faculty of Engineering Department of Mechanical Engineering, 25000
Erzurum, Turkey
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_57
803
Greenhouse is deﬁned as a highly systematic plant-growing process done in
various ways in a structural building covered with different types of transparent
materials such as glass or plastic to grow various plants, their seeds, seedlings and
saplings, and protect or exhibit them throughout the year by controlling the factors
such as heat, light, moisture and ventilation without completely or partially
depending on the climate-related environmental circumstances (MEGEP 2012).
Among the countries around the world, the USA, Japan and the Netherlands do the
highest amount of greenhouse plant production. In the USA, California and Florida
have the highest amount of production, where 39% of the greenhouses are glass
covered. Seventy-eight per cent of the greenhouses produce ﬂowers. In Europe, the
Netherlands is the leading greenhouse producer. Bulbed ﬂowers are the main
products. Plastic-covered greenhouses are used in countries such as Spain, France
and Italy (Anonymous 2012a). There is vast literature on solar energy and green-
houses, some of which are presented in this section. Gupta et al. (2012) demon-
strated solar radiations and greenhouses in a virtual environment on AutoCAD.
They used the lighting system on AutoCAD as the solar radiation. In the 3D
environment they deﬁned, they made comparisons by changing the location and
design of the greenhouses. Kilic and Ozturk (1983) analysed the impact of the solar
radiations on earth. They determined the angle of the solar radiations and analysed
the intensity of the solar radiation reaching the earth at different hours of the day
and on different days of the year. Their study of solar energy has become an
inspiration for many other forthcoming studies. The terms regarding solar angles
have been accepted as in their study. Pucar (2001) studied the calculation of the
greenhouse wall and roof angles to receive the highest amount of solar radiation.
Pucar (2001) calculated the most ideal roof and wall angles, where she studied how
to increase the heat effect of the solar radiation getting in through the roof and walls
of the greenhouse and reﬂecting on the opposite wall. Sethi (2009) worked on the
determination of the angle and design of greenhouses that make the highest use of
solar radiation. In the study, the walls of various greenhouses were studied sepa-
rately in terms of their receiving solar radiation. Five types of generally accepted
greenhouses were investigated in terms of the solar energy they received at every
hour of the day.
In this study, a numerical model was developed that estimates the dimensions
and directions for receiving the highest and most direct amount of solar radiation in
semi-hemispheric greenhouses suitable for the conditions in the city of Bayburt in
the periods when greenhouses were actively used in the city. The estimation process
was considered as an optimisation problem, and the numerical model was created
on MATLAB accordingly.
804
C
¸ . U
gur et al.
2
The City of Bayburt
The city of Bayburt is located on the coordinates of 401503500N 401304000E.
Bayburt has a transition climate dominated by continental climate between East
Black Sea climate and East Anatolia climate. That is why, it is hot and dry in
summer, while it is cold and rainy in winter. However, the climate is milder than
that of East Anatolia due to low altitude and the microclimate created by the system
of valleys (Anonymous 2012b).
3
Solar Energy Calculations
In order to make good use of solar energy, it is required to know the characteristics
and amount of all the solar radiations in the respective area and the time period. To
do that, it is necessary to determine the incidence angle of the solar radiation and the
sunshine duration of the respective area in accordance with the location and earth’s
movement around itself and around the sun. Majority of the solar energy calcula-
tions used in this study are based on the book Solar Energy (Kilic and Ozturk 1983).
3.1
Real Solar Angles
The direction of the straight solar radiation reaching any point on earth can be
calculated if the latitude, hour angle and the declination angle of that particular
point is known. These angles are known as real solar angles. Latitude angle: It is the
angle of a point on the Earth’s surface from the Equatorial plane and the radius to
that point. Hour angle: The hour angle of a point on the Earth’s surface is the angle
through which the earth would turn to bring the meridian of the point directly under
the sun. Declination angle: The declination of the sun is the angle between the
equator and a line drawn from the centre of the Earth to the centre of the sun. It is
calculated as follows:
d ¼ 23:45 sin
360 n þ 284
ð
Þ
365


ð1Þ
Solar Calculations of Modiﬁed Arch (Semi-spherical)-Type Greenhouse System. . .
805
3.2
Derived Solar Angles
Zenith Angle: It is the angle between the sun and a line that goes straight up (to the
zenith). In other words, it is the angle of the solar radiation to the horizontal plane. It
is calculated as follows:
cos z
ð Þ ¼ cos d
ð Þ: cos e
ð Þ: cos h
ð Þ þ sin d
ð Þ: sin e
ð Þ
ð2Þ
At times of sunrise and sunset, the solar radiations are parallel to the horizontal
plane. By using these moments, the sunrise and sunset angles and day length are
calculated as follows:
cos H
ð Þ ¼  sin d
ð Þ: sin e
ð Þ
cos d
ð Þ: cos e
ð Þ ¼  tan d
ð Þ: tan e
ð Þ
ð3Þ
3.3
Inclined Plane Angles
The solar azimuth angle deﬁnes the angle of the inclined plane to a horizontal plane
and projection of the normal of the plane to the horizontal plane due West. The
projection of the normal inclined plane to the normal of the horizontal plane is cos
(s) and to the horizontal plane is sin (s). Solar incidence angle (g) is the angle of the
solar radiation to normal of any inclined plane, and it is calculated as follows:
cos g
ð Þ ¼ cos d
ð Þ: cos e
ð Þ∗cos h
ð Þ: cos s
ð Þ


þ cos a
ð Þ: cos d
ð Þ: sin e
ð Þ: cos h
ð Þ: sin s
ð Þ
½

þ sin a
ð Þ: cos d
ð Þ: sin h
ð Þ: sin s
ð Þ
½
 þ sin d
ð Þ: sin e
ð Þ: cos s
ð Þ
½

 cos a
ð Þ: sin d
ð Þ: cos e
ð Þ: sin s
ð Þ
½

ð4Þ
To calculate the amount of solar energy of a plane at a given time, it is necessary
to accumulate all the energy the solar radiations give from the very ﬁrst moment
when the solar radiation reaches the plane to the last radiation reaching the plane.
For the calculation of the whole day solar radiation, one should do the following:
When the solar radiation is parallel to the plane, when g ¼ 90
C1 ¼ sin a
ð Þ: cos d
ð Þ:

sin s
ð Þ
ð5Þ
C2 ¼ cos d
ð Þ:
cos e
ð Þ: cos s
ð Þ
þ cos a
ð Þ: sin e
ð Þ: sin s
ð Þ


ð6Þ
C2 ¼ sin d
ð Þ:
sin e
ð Þ: cos s
ð Þ
 cos a
ð Þ: cos e
ð Þ: sin s
ð Þ


ð7Þ
D2 ¼ C2
1 þ C2
2  C2
3
ð8Þ
806
C
¸ . U
gur et al.
This way, for D2 > 0 the hour angles, where solar radiations are parallel to plane
are calculated as in Eqs. 9 and 10:
H1p ¼ 2 arctan
C1  D
C2  C3


ð9Þ
H2p ¼ 2 arctan
C1 þ D
C2  C3


ð10Þ
The moment the solar radiation is parallel can be before the sunrise or after the
sunset. That is why, if the hour angle of the solar radiation’s parallel incidence to
the inclined plane is bigger than sunrise hour angle in terms of absolute value, then
the ﬁrst incidence hour angle is at sunrise. At solar noon (h ¼ 0) the cosine of solar
incidence angle (go) can be calculated with the Eq. 11. Then, by using the algorithm
in Fig. 1, the sun’s ﬁrst incidence and last declination angles to the inclined plane
are determined (Table 1):
cos go
ð
Þ ¼ C2 þ C3
ð11Þ
How many hours the solar radiation reaches the inclined plane is also another
important factor. The duration of solar radiation onto the inclined plane is calcu-
lated according to the equations presented below:
d > 0ie
c in : teg ¼ 2
15 arccos  tan d
ð Þ: tan e  s
ð
Þ
½

ð12Þ
d < 0 ie
c in : teg ¼ 2
15 arccos  tan e
ð Þ: tan d
ð Þ
½

ð13Þ
The intensity of the solar radiation reaching inclined planes outside atmosphere
changes in accordance with the angle of incidence of solar radiation and angle of
inclined plane. The amount of radiation reaching the inclined planes outside the
atmosphere in a day is calculated with the Eq. 14.
Qoe ¼ 12
π :Igs:f:
π
180 H2  H1
ð
Þ: sin d
: sin e: cos s  cos e: sin s: cos a
ð
Þ
þ sin H2  sin H1
ð
Þ: cos d
: cos e: cos s þ sin e: sin s: cos a
ð
Þ
 cos H2  cos H1
ð
Þ: cos d: sin s: sin a
2
6
6
6
6
6
4
3
7
7
7
7
7
5
ð14Þ
Solar Calculations of Modiﬁed Arch (Semi-spherical)-Type Greenhouse System. . .
807
3.4
Calculations for Arch Greenhouse
A sample design is given in Fig. 1 to visually describe the greenhouse under
scrutiny. As a result of this study, it is understood that there are two different
seasons to use greenhouses in the city of Bayburt: the optimum greenhouse size and
direction to gather the required amount of solar radiation in these seasons (1 April–
15 June and 15 August–10 November). For instance, for someone who plans to
build a greenhouse of a certain size, the optimum size of that greenhouse and the
optimum angle of direction for that size can be determined. Figure 2 shows the
schematic review of greenhouses according to different directions and angles.
The accepted dimensions for arch greenhouses are given below. The height of
the sidewalls is 2-m ﬂat. The distance between these walls and the highest point of
the rooftop of the greenhouse is 1 m ﬂat too (Fig. 3). All the other necessary
dimensions are calculated in accordance with the area of the roof. The length of
the arch is calculated which is divided into ﬁve equal sections for solar energy
calculations. The circular roof of the arch greenhouse can be analysed as small
rectangular surfaces by dividing it into small linear sections. Figure 4 shows that the
roof of the arch greenhouse is formed into ﬁve different surfaces with equal
dimensions but different inclination angles by dividing it into ﬁve equal sections.
Fig. 1 Hemispherical
greenhouse type
Table 1 First and last angles
of incidence of solar radiation
to inclined plane
(H1)
(H2)
cos(go) > 0
(go > 90)
D2 > 0
D2 < 0
max (H1p, H)
H
min (H2p, H)
H
cos(go) < 0
(go > 90)
D2 > 0
D2 < 0
max (H2p, H)
min (H1p, H)
No solar radiation
808
C
¸ . U
gur et al.
4
Assumptions
The required longitudinal solar measurement for the study in Bayburt does not
exist. The study uses direct radiation only. That is why all of the conditions such as
distributed radiation, wind, reﬂection, uncleanliness and datedness of the covering
material, cloudiness and the shadowing impact of the greenhouse materials were
regarded as the same for all greenhouses. Moreover, the maximum height inside the
greenhouses of all types and sizes was regarded as limited to 3 m. Since the study
SOUTH
The wall 
faced to 
west
EAST
The roof 
C1
The roof faced to 
north  
C2
The wall 
faced
to east
D1
The wall faced to north
D4
D2
The wall faced to 
south
D3
NORTH
Fig. 2 Schematic review of greenhouses according to various angles
W/2
W/2
W
L
k=L/W
GREENHOUSE AREA
A=L∗W
H1=1m
H=2m
Fig. 3 Dimensions for arch
greenhouses
Solar Calculations of Modiﬁed Arch (Semi-spherical)-Type Greenhouse System. . .
809
aims to compare different types of greenhouses to each other, these assumptions
will not affect the results of the study.
5
Results
5.1
Arch Greenhouses with Surface Area of 400 m2
Table 2 shows the dimensions of length and width and surface area of a greenhouse
with a surface area of 400 m2. Similar tables for greenhouses of various dimensions
were created; however, only 400 m2 greenhouse is presented.
Figure 5 shows the change in radiation levels received from the sun according to
different days of the year and different azimuth angles for different k values. As
seen in the ﬁgure, the higher the k value (L/W) for a 400-m2 arch greenhouse, the
higher the amount of radiation it receives. This situation changes only when it is
around the middle of summer, and the value of azimuth angle is lower than
40 degrees. Figure 6 presents the changes in total solar radiation received in the
periods when the greenhouse can be used in Bayburt according to different L/W
values for an arch greenhouse of 400 m2. As seen, for a greenhouse of this type, the
optimum azimuth angle is 90 and the optimum k value is 10.
Fig. 4 Sectioning of the circular roof of arch greenhouses
810
C
¸ . U
gur et al.
5.2
300-m2 Arch Greenhouse
Figure 7 shows the changes in total solar radiation received in the periods when
greenhouse can be used in Bayburt according to different L/W values for an arch
greenhouse of 300 m2. As seen, for a greenhouse of this type, the optimum azimuth
angle is 90 and the optimum k value is 10. The azimuth angle can also be a value
between 30 and 40.
Table 2 400-m2 arch greenhouse dimension
k
L (m)
W (m)
D1, D3 (m2)
D2, D4 (m2)
Roof Part. (m2)
1
20.00
20.00
53.36
40.00
40.26
2
28.28
14.14
37.75
56.57
40.53
3
34.64
11.55
30.84
69.28
40.79
4
40.00
10.00
26.72
80.00
41.05
5
44.72
8.94
23.91
89.44
41.31
6
49.00
8.166
21.84
97.98
41.57
7
52.92
7.56
20.23
105.83
41.83
8
56.57
7.07
18.93
113.14
42.08
9
60.00
6.67
17.86
120.00
42.33
10
63.25
6.33
16.95
126.49
42.59
1.4
× 10
4
1.2
1
0.8
0.6
400
300
200
DAYS OF THE
YEAR
THE TOTAL RADIATION THAT THE GREENHOUSE
COLLECTED FROM THE SUN (MJ/DAY)
100
0
0
10
20
30
40
50
GREENHOUSE AZIMUTH ANGLES
60
70
80
90
k=10
k=9
k=8
k=7
k=6
k=5
k=4
k=3
k=2
k=1
Fig. 5 Changes in solar radiation for 400-m2 arch greenhouse
Solar Calculations of Modiﬁed Arch (Semi-spherical)-Type Greenhouse System. . .
811
5.3
250-m2 Arch Greenhouse
Figure 8 shows changes in radiation levels for a 250-m2 arch greenhouse received
from the sun according to different days of the year and different azimuth angles for
different k values. As seen in the ﬁgure, the higher the k value (L/W) for a 250-m2
arch greenhouse, the higher the amount of radiation it receives. Thus, for a
greenhouse of this type, the optimum azimuth angle is 35, and the optimum
k value is 10.
1.8
1.75
1.65
1.55
1.45
1.35
1.4
0
10
20
30
GREENHOUSE AZIMUTH ANGLE (a2)
THE TOTAL RADIATION THAT THE GREENHOUSE
COLLECTED FROM THE SUN (MJ/PERIOD)
40
50
60
70
80
90
k=9
k=8
k=7
k=6
k=5
k=4
k=3
k=2
k=1
k=10
1.7
1.6
1.5
× 10
6
Fig. 6 Changes in solar radiation for 400-m2 arch greenhouse
1.4 × 10
6
1.3
1.2
1.1
0
10
20
30
40
50
GREENHOUSE AZIMUTH ANGLE (a2)
THE TOTAL RADIATION THAT THE GREENHOUSE
COLLECTED FROM THE SUN (MJ/PERIOD)
60
70
80
k=10
k=1
k=2
k=3
k=4
k=5
k=6
k=7
k=8
k=9
90
1.35
1.25
1.15
1.05
Fig. 7 Changes in solar radiation for 300-m2 arch greenhouse
812
C
¸ . U
gur et al.
5.4
200-m2 Arch Greenhouse
Figure 9 shows changes in total solar radiation received in the periods when
greenhouse can be used according to different L/W values for an arch greenhouse
of 200 m2. As understood from the ﬁgure, the optimum k value for this greenhouse
is 10. However, at this point, one should pay attention to the existing dimensions of
the greenhouse and how ergonomically it has been built. It is understood that if
k value is 10, then the greenhouse becomes too narrow to be used. Thus, it is
considered that under these conditions, the width of the greenhouse should be at
least 5 m and k value 8. As seen, for this type of greenhouse, the optimum azimuth
angle is 35, and the optimum k value is 8.
5.5
150-m2 Arch Greenhouse
Due to the condition mentioned above, the width of this greenhouse should be at
least 5 m and k value 6. Figure 11 presents changes in total solar radiation received
in the periods when greenhouse can be used according to different L/W values for
an arch greenhouse of 200 m2 with a highest k value of 6. As can be seen, the
optimum azimuth angle for this type of greenhouse is 34, and the optimum k value
is 6 (Fig. 10).
1.2 × 10
6
1.1
1
0
10
20
k=10
k=1
k=2
k=3
X: 35
Y: 1.172e+006
k=4
k=5
k=6
k=7
k=9
k=8
30
40
GREENHOUSE AZIMUTH ANGLE (MJ/DAY)
THE TOTAL RADIATION THAT THE GREENHOUSE
COLLECTED FROM THE SUN (MJ/PERIOD)
50
60
70
80
90
1.15
0.95
1.05
0.9
Fig. 8 Changes in solar radiation for 250-m2 arch greenhouse
Solar Calculations of Modiﬁed Arch (Semi-spherical)-Type Greenhouse System. . .
813
5.6
100-m2 Arch Greenhouse
Figure 11 presents changes in total solar radiation received in the periods when
greenhouse can be used according to different L/W values for an arch greenhouse of
100 m2 with a highest k value of 4. As can be seen, the optimum azimuth angle for
this type of greenhouse is 33 and the optimum k value is 4.
9.6 × 10
5
9.2
9
8.4
0
10
20
k=1
k=2
k=3
X: 35
Y: 9.487e+005
k=4
k=5
k=6
k=7
k=8
30
40
GREENHOUSE AZIMUTH ANGLE (a2)
COLLECTED FROM THE SUN (MJ/PERIOD)
50
60
70
80
90
9.4
8.2
8
8.8
8.6
7.8
7.6
Fig. 9 Changes in solar radiation for 200-m2 arch greenhouse
5.1
5
4.9
4.8
THE TOTAL RADIATION THAT THE GREENHOUSE
COLLECTED FROM THE SUN (MJ/PERIOD)
4.7
4.6
4.5
4.40
10
20
30
40
GREENHOUSE AZIMUTH ANGLE (a2)
50
60
70
80
90
k=4
k=3
k=2
k=1
X: 33
Y: 5.022e+005
× 10
5
Fig. 11 Changes in solar radiation for 100-m2 arch greenhouse
814
C
¸ . U
gur et al.
5.7
50-m2 Arch Greenhouse
For these conditions, it is considered that the width of the greenhouse should be at
least 5 m and the highest k value 5. As can be seen in Fig. 12, the optimum azimuth
angle for this type of greenhouse is 22 and the optimum k value is 4.
As understood above, positioning of the greenhouses varies according to differ-
ent sizes of greenhouses. This analysis is based only on the months when agricul-
tural activities can be done. Winter season was not taken into account as farming is
not possible.
6
Conclusion
Greenhouses are used in temperate climate regions, to make agricultural applica-
tions, especially in winter. As it is known, solar radiation energy is captured and
kept in greenhouses by the help of greenhouse effect. In this study, we analysed one
of the mostly used greenhouse types. Building the best and most useful greenhouses
in cold climate regions mean extending the greenhouse season and increasing the
productivity.
This script is applicable and suitable for other buildings such as residents and
business centres in order to evaluate the incoming solar energy and provide
economic beneﬁt by saving energy. Positioning the buildings in order to get more
solar heat energy in winter and less in summer brings out energy and money savings
and increases the comfort.
7.2
6.8
6.6
6.4
6.2
6
0
10
20
30
40
X: 34
Y: 7.254e+005
THE TOTAL RADIATION THAT THE GREENHOUSE
COLLECTED FROM THE SUN (MJ/PERIOD)
50
60
70
80
k=6
k=5
k=4
k=3
k=2
k=1
GREENHOUSE AZIMUTH ANGLE (a2)
7
× 10
5
Fig. 10 Changes in solar radiation for 150-m2 arch greenhouse
Solar Calculations of Modiﬁed Arch (Semi-spherical)-Type Greenhouse System. . .
815
References
Anonymous.: www.serabirlik.com (2012a)
Anonymous.: Ministry of Cultur and tourism. Turkey (2012b)
Gupta, R., Tiwari, G.N., Kumar, A., Gupta, Y.: Calculation of total solar fraction for different
orientation of greenhouse using 3D-shadow analysis in AutoCAD. Energy Build. 47, 27–34
(2012)
Kilic
¸ A., O
¨ ztürk A.: Günes
¸ Enerjisi, Kipas
¸ Da
gıtım ve Yayıncılık (book in Turkish) (1983)
MEGEP.: Bahc
¸ecilik ve Sera Yapım Teknikleri, (book in Turkish) (2012)
Pucar, M.D.: Enhancement of ground radiation in greenhouses by reﬂection of direct unlight.
Renew. Energy. 26(2002), 561–586 (2001)
Sethi, V.P.: On the selection of shape and orientation of a greenhouse, ermal modeling and
experimental validation. Sol. Energy. 83(1), 21–38 (2009)
3
X: 22
k=1
k=2
k=3
k=4
Y: 2.976e+005
2.95
2.9
2.8
THE TOTAL RADIATION THAT THE GREENHOUSE
COLLECTED FROM THE SUN (MJ/PERIOD)
2.7
2.6
0
10
20
30
40
GREENHOUSE AZIMUTH ANGLE (a2)
50
60
70
80
90
2.85
2.75
2.65
× 10
5
Fig. 12 Changes in solar radiation for 50-m2 arch greenhouse
816
C
¸ . U
gur et al.
Estimation of Global Solar Radiation in Arid
Climates in Algeria
Malika Fekih and Mohamed Saighi
1
Introduction
In any solar energy conversion system, the knowledge of global solar radiation is
extremely important for the optimal design and the prediction of the system
performance. The best way of knowing the amount of global solar radiation at a
site is to install pyranometers at many locations in the given region and look after
their day-to-day maintenance and recording, which is a very costly exercise. The
alternative approach is to correlate the global solar radiation with the meteorolog-
ical parameters at the place where the data are collected. The resultant correlation
may then be used for locations of similar meteorological and geographical charac-
teristics at which solar data are not available.
Over the years, many models have been proposed to predict the amount of solar
radiation using various parameters Canada (1988a, b). Some works used the
sunshine duration (Gueymard 1993). Others used mean daytime cloud cover or
relative humidity and maximum and minimum temperature (Supit and Van Kappel
1998), while others used the number of rainy days, sunshine hours and a factor that
depends on latitude and altitude (Canada 1988a, b).
Algeria is a high insolation country. The number of sunshine hours amounts
almost 3300 h/year. The climate is most favorable for solar energy utilization, but
the distribution of the solar radiation is not well known. The importance of this
work lies on the fundamental need of knowledge of the global solar radiation data in
the country.
M. Fekih (*) • M. Saighi
Laboratory of Thermodynamics and Energetically Systems/LTSE, Faculty of Physics,
University of Science and Technology Houari Boumediene, BP 32 El Alia Bab Ezzouar, 16111
Algiers, Algeria
e-mail: fekih.mali2005@yahoo.fr
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_58
817
Solar radiation measurements are not easily available for many developing
countries for not being able to afford the measurement equipment and techniques
involved. Therefore, it is rather important to elaborate methods to estimate the solar
radiation on the basis of more readily meteorological data.
Several empirical formulae have been developed to calculate the solar radiation
using various parameters.
In the present work, an empirical method originally formulated by Saighi (2002)
has been modiﬁed to make it ﬁt some Algerian meteorological stations. The model
only requires the duration of sunshine and minimum air mass.
2
Calculation Procedure
In the present work, data of monthly mean of daily global solar radiation and
sunshine duration from two Algerian meteorological stations (Be
´char and Taman-
rasset) are used by Capderou (1988). The geographical location of stations are
presented in Table 1. The duration of records of sunshine duration is 25 years and of
global solar radiation is approximately 10 years. Measurements of global solar
radiation were performed with Robitzsh and Kipp-Zonen pyranometers. For the
recording of sunshine duration, Campbell-Stokes heliographs are used.
The two meteorological stations are divided into two zones according to the
relative duration of sunshine
•
Sahara climate for Be
´char
•
Tamanrasset which is inﬂuenced by the African tropical climate
3
Estimation Methods
To estimate the global solar radiation Rg, data consisting of monthly mean tem-
perature, relative humidity, soil temperature, ambient temperature and sunshine
duration were taken from the State Meteorology Ofﬁce of Algiers between 2010
and 2012. Monthly mean daily extraterrestrial radiations Rg, day length N for using
the average day of the month, were calculated from Eqs. (1, 2 and 3), respectively
(Dufﬁe and Beckman 1991). The clear sky radiation was determined by using
methodology related in section estimation of clear sky radiation.
Table 1 Geographical location of stations
Station
Latitude (deg.)(N)
Altitude (m)
Longitude (degree)
Be
´char
31.63
806
2.40 W
Tamanrasset
22.47
1378
5.31 E
818
M. Fekih and M. Saighi
3.1
Model for Computing Radiation on the Horizontal
Surface
The total radiation received on the horizontal is a summation of the direct and
diffuse radiation (Parker 1991; Dufﬁe and Beckman 2006).
We estimate the amount of global solar radiation on horizontal surfaces using
various climatic parameters, such as sunshine duration, cloud cover, humidity,
maximum and minimum ambient temperatures, wind speed, etc. (Chegaar and
Chibani 2001; El-Sebaii and Trabea 2005; Gopinathan 1988; Halouani et al.
1993; Jacovides et al. 2006).
In the present work, data of monthly mean of daily global solar radiation from
two Algerian meteorological stations (Be
´char and Tamanrasset) are used.
Measurements of global solar radiation were performed with Kipp and Zonen
pyranometers (Figs. 1 and 2). Fekih (2013).
Fig. 1 Kipp & Zonen
pyranometers used
Fig. 2 The solar
integrator used
Estimation of Global Solar Radiation in Arid Climates in Algeria
819
3.2
Radiative Flows (Rn)
The net radiation corresponds to the general balance sheet of the exchanges of
radiations of short and big wavelengths on the level of the surface of the ground, it
can be written:
Rn ¼ 1  α
ð
ÞRg þ εσ Ta  6
ð
Þ4  εσT 4
S
3.2.1
Direct Flow
The direct radiation on the presumably horizontal surface of the ground exclaims in
the following way and can be written:
RD ¼ I0λi A1exp
A2
sin h
ð Þ


sin h
ð Þ
ð1Þ
with
I0 ¼ constant solar ¼ 1360 W/m2
λi ¼ coefﬁcient of distance ground sun (Saighi 2002)
h ¼ height of the sun on the horizon (Saighi 2002)
A1, A2 ¼ coefﬁcients of disorder
3.2.2
Diffuse Flow
Correlations showed that in clear weather, diffuse ﬂow was a function of the height
h of the sun. According to Saighi (2002) and Chouard et al. (1977), suppose that at
ﬁrst approximation diffuse ﬂow is proportional to sin (h) and, basing itself on
American statistical studies carried out from raised experimental, propose the
following relation for the calculation of diffuse ﬂow:
Rd ¼ I0λi 0:271  0:2939A1exp
A2
sin h
ð Þ




sin h
ð Þ
ð2Þ
3.2.3
Global Radiation
The total radiation is composed of the direct radiation and the diffuse radiation. Its
expression is given by:
Rg ¼ 0:271 I0 λi A1 sin h
ð Þ þ 0:706 I0 λi A1 sin h
ð Þ  exp
A2
sin h
ð Þ


ð3Þ
820
M. Fekih and M. Saighi
This formulation of the irradiation is relatively precise and offers the advantage
of a great ease of use.
The coefﬁcients A1 and A2 depend on the clearness of the sky to the days and
place considered; they were identiﬁed numerically starting from weather data.
They were identiﬁed numerically starting from weather data. The values which
we used in this study result from “the solar Atlas of Algeria” of Capderou (1985),
the only reference which gives these coefﬁcients in Algeria.
4
Results and Discussions
The Algerian meteorological stations are divided into two zones according to the
characteristics of their climate, Mediterranean climate for Algiers and Oran, Sahara
climate for Be
´char and Tamanrasset which is inﬂuenced by the African tropical
climate.
Figures 3 and 4 represent an example of curves relating to the global irradiation
of the town of Tamanrasset and Be
´char calculated starting from the formula
established previously.
The global solar radiations are then calculated using Eq. (3).
A comparison with measurements and calculations made by Capderou is also
given. The variation of the monthly global irradiation measured and computed is
represented in Fig. 3 and in Fig. 4. The best estimates of global irradiation were
calculated for Be
´char and Tamanrasset.
Fig. 3 Monthly average of daily global radiance in Be
´char
Estimation of Global Solar Radiation in Arid Climates in Algeria
821
5
Conclusions
The objective of this study was to evaluate a model for the estimation of the
monthly average daily global solar radiation on a horizontal surface for arid climate
in Algeria.
The model is tested for two provinces (Be
´char and Tamanrasset) of Algeria.
Meteorological data were used as the input of the radiation models. Model valida-
tion was employed by means of daily solar measurements.
Comparison of the model with the measured data revealed that the model pro-
vides predictions in good agreement with the measured data.
References
Canada, J.: Global solar radiation in Pais Valenciano using sunshine hours. International Journal of
Ambient Energy. 4, 197–201 (1988a)
Canada, J.: Global solar radiation in Valencia using sunshine hours and meteorological data’. Solar
& Wind Technology. 5, 597–599 (1988b)
Capderou, M.: Atals Solaire de l’Alge
´rie Tome 2. Edition OPU, Alger (1985)
Capderou, M.: Atlas Solaire de l’Alge
´rie. Ofﬁce des Publications Universitaires, ’T’. 1–3 (1988)
Chegaar, M., Chibani, A.: Global solar radiation estimation in Algeria. Energy Convers. Manag.
42, 967–973 (2001)
Chouard, P. et al. - Bilan thermique d’une maison solaire, Ed. Eyrolles Paris, (1977)
Dufﬂe, J.A., Beckman, W.R.: Solar Engineering for Thermal Processes, 3rd edn. Wiley, New York
(2006)
Fig. 4 Monthly average of daily global radiance in Tamanrasset
822
M. Fekih and M. Saighi
Dufﬁe, John.A. and. Beckman, William A : “Solar engineering of thermal processes”, Madison:
John Wiley & Sons, Inc., pp. 10–23, (1991)
El-Sebaii, A.A., Trabea, A.A.: Estimating global solar radiation on horizontal surfaces over Egypt.
Egyptian Journal of Solids. 28, 163–175 (2005)
Fekih, M.: Etude ae
´rodynamique et ae
´rothermique d’un e
´coulement de ventautour d’un bac de
mesure d’e
´vaporation d’eau. PhD thesis, Universite
´ des Sciences et de la Technologie
HouariBoumediene USTHB, Alger, Alge
´rie (2013)
Gopinathan, K.K.: A general formula for computing the coefﬁcients of the correlation connecting
global solar radiation to sunshine duration. Sol. Energy. 41, 499–502 (1988)
Gueymard, C.: Analysis of monthly average solar radiation and bright sunshine for different
thresholds at Cape Canaveral, Florida. Sol. Energy. 51, 139145 (1993)
Halouani, N., Nguyen, C.T., Vo-Ngoc, D.: Calculation of monthly average global solar radiation
on horizontal surfaces using daily hours of bright sunshine. Sol. Energy. 50, 247–258 (1993)
Jacovides, C.P., Tymvios, F.S., Assimakopoulos, V.D., Kaltsounides, N.A.: Comparative study of
various correlations in estimating hourly diffuse fraction of global solar radiation. Renew.
Energy. 31, 2492–2504 (2006)
Parker, B.F.: Energy in world agriculture, solar energy in agriculture, pp. 1–66. Elsevier Science
Publishers, New York (1991)
Saı
¨ghi, M.: Nouveau mode
`le de transfert hydrique dans le syste
`me solplante-atmosphe
`re, PhD
thesis, Universite
´ des Sciences et de laTechnologie Houari Boumediene USTHB, Alger,
Alge
´rie (2002)
Supit, I., Van Kappel, R.R.: A simple method to estimate global radiation. Sol. Energy. 63,
147–160 (1998)
Estimation of Global Solar Radiation in Arid Climates in Algeria
823
Technical-Economic Assessment of Energy
Efﬁciency Measures in a Midsize Industry
Sara Benavides, Maria Bitosova, Javier De Gregorio, Aubin Welschbillig,
and Luis Rojas-Solorzano
1
Introduction
For the realization of this project, the real case of a midsize dairy product
manufacturing industry called “CILAM” located in Saint Pierre, South of Reunion
Island, was studied. CILAM produces various dairy products such as milk, bever-
ages, desserts, cheese, and ice creams and accounts for about 60% of the dairy
market on the island (Agro-oi.com 2015).
The aim is to develop a prefeasibility study in order to evaluate the impact of
implementing energy efﬁciency measures at CILAM. Thus, the outcome will be
energy and money savings within a short payback ratio.
Reunion Island is located in the Indian Ocean, east of Madagascar, about 200 km
southwest of Mauritius (nearest island). It is an integral part of the French Republic
with the same status as those situated on the European mainland. The import of oil
products at Reunion Island is primarily intended to cover the energy demand of the
transportation and electricity generation sector, as well as industrial and agricultural
activities. In 2000, the fuel supply was 886.9 ktoe. Between 2000 and 2011, the
fossil fuel supply had increased by 6.1% making the island very dependent on
exterior sources. Reunion Island imports as much as 7/8 of its ﬁnal energy con-
sumption corresponding to fossil fuels, while the remaining 1/8 comes from local
resources.
S. Benavides (*) • M. Bitosova • J. De Gregorio • A. Welschbillig
Ecole des Mines de Nantes, Department of Energy
and Environment Systems, Nantes 44300, France
e-mail: sarabenc@live.com; bitosova@hotmail.com; javierdgpm3e@gmail.com;
aubin.welschbillig@gmail.com
L. Rojas-Solorzano
Nazarbayev University, Department of Mechanical Engineering, Astana 010000, Kazakhstan
e-mail: luis.rojas@nu.edu.kz
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_59
825
Thus, as part of the effort to accomplish the stipulations and guidelines portrayed
in Agenda 21 (Agenda21france.org 2015), the Reunion Island government has
adopted a strategy of energy autonomy based on energy efﬁciency and renewable
energy alternatives. One of the main objectives of Agenda 21 is that Energy Self-
Sufﬁciency must be achieved by 2030 (Ricci et al. 2014). In consequence, by the
end of 2010, Reunion Island had PV installations which represent approximately an
installed capacity of 80 MWp for an electricity production of 60 GWh/year, solar
thermal installations that produce near 120 kWh/day, which corresponds to a total
of 20.16 MWh of electricity saved during the summer; additionally, the cooling
plants have saved 16.5 tons of CO2 yearly. Wind energy installations produce
15.5 MWh of electricity per year, taking advantage of trade winds on the island’s
eastern side, while hydropower installations produce 632 GWh and biomass
300 kWh, both on a yearly basis (Praene et al. 2012).
Moreover, the industrial sector is facing many challenges, such as global com-
petition, energy pricing, and environmental impact among others. Consequently,
the necessity for energy efﬁciency measures has become evident, framing the
objective of this project so as to assess the technical and economic prefeasibility
of implementing energy efﬁciency measures in a dairy products manufacturing
company located at the south of Reunion Island. The scope of the project is focused
on one of the nine buildings where the company accomplishes different production
processes, speciﬁcally in the ultra-high temperature pasteurization facility building
(UHT).
Nowadays, dairy industries as well as the majority of other economic sectors are
moving toward sustainable production, minimization of energy consumption, and
impact on the environment. Application of best energy practices is a ﬁeld of interest
not only to the government and environmental regulators but also to companies’
senior management and stakeholders due to their interest in the proﬁtability of the
business. In this study, two main sectors of energy use at the UHT building of the
dairy production plant are targeted: savings on electrical energy and savings on
thermal energy. Savings on electrical energy can be achieved by redesigning the
lighting system and improving the processes involved on the cooling system
performance. On the other hand, additional savings are foreseen for thermal energy
by the reduction of steam losses and the installation of a heat recovery unit for the
boiler, which represents economic advantages regarding the consumption of elec-
tricity and fossil fuel.
The
technical
and
economic
prefeasibility
study
was
carried
out
in
RETScreen®, a Clean Energy Management Software system for energy efﬁciency,
renewable energy and cogeneration project feasibility analysis as well as ongoing
energy performance analysis. This Excel-based software is a tool that helps deci-
sion makers to determine the technical and ﬁnancial viability of potential clean-
energy projects in a quick and inexpensive way.
The performed study showed that the proposed energy efﬁciency actions repre-
sent beneﬁts for CILAM and are qualiﬁed to be implemented in order to reduce
operational costs and carbon footprint and augment the quality of the goods
produced. The analysis was done from the point of view that the industrial parks
826
S. Benavides et al.
at the Reunion Island are committed to support the strategy conducted by the
regional government, with the purpose of achieving energy autonomy based on
greater energy efﬁciency and renewable energy alternatives, as stated in the island’s
2030 goals and Reunion Agenda 21.
Nomenclature
CILAM
Compagnie Laitie
`re des Mascareignes
UHT
Ultra High Temperature
HVAC
Heating, Ventilation and Air-Conditioning.
Charcs.
Characteristics
Op. hrs.
Operating Hours
Psteam
Steam Pressure
Tcond.
Condensation Temperature
TWmakeup
Temperature Water Makeup
Tsh
Super-Heated Temperature
IRR
Internal Rate of Return
NPV
Net Present Value
LED
Light Emitting Diode
kWh
Kilowatt-hour
INRS
Institut National de Recherche et de Securite
´
GHG
Green House Gases
2
Methodology
The prefeasibility analysis, is done using RETScreen. The inputs concerned or
researched for the project were those associated with pasteurization at ultra-high
temperature. The technical data are obtained through an energy management data
collection system owned by CILAM. Input data implemented in RETscreen were
considered for 1 year consumption based on 2013. The tasks to reach the results
were carried out by the researchers with the support of two more parties: the
academic tutor and the energy manager of the company on site. To manage
communication and adjust the schedules, helpful tools such as Dropbox, Google
Docs, and Skype calls for online meetings with the international partners were
utilized.
3
Prefeasibility Study
3.1
General Description of the Facility
The company consists of nine buildings where different processes are performed;
however, for this study, only the UHT Process Building will be considered.
Technical-Economic Assessment of Energy Efﬁciency Measures in a Midsize. . .
827
This paper considers the most energy intense processes:
•
Steam production and leakages for pasteurization and sterilization
•
General refrigeration system for cooling of products and storage purposes
•
Lighting in the building
3.2
Data Collection
The consumption for different energy sources was obtained from the annual energy
bill of the company in 2013. This data was proportioned by the Energy Manager of
CILAM industry. Moreover, reliable and high-quality data was placed as an input in
RETScreen, thanks to his direct involvement on the different processes performed
in the company.1
AutoCAD drawings of the industry were used to determine the area of the UHT
building, which has a surface of 2688 m2. In addition, according to the Energy
Manager, the industry uses air conditioning systems only for ofﬁces in the social/
commercial building. Production buildings such as the UHT building do not have
any HVAC system installed and use natural ventilation due to the stable weather of
Reunion Island. Consequently, space heating and cooling are not carried into
account for this prefeasibility analysis.
3.3
General Considerations
Some assumptions were made to complete the prefeasibility analysis and are
presented as follows:
•
Steam leakages were estimated at around 20% of the total steam production by
the energy manager during an audit in 2013 locating 12 visible leakages in the
network. From that audit on, the leakages have been reduced to 5% of the total
production. Even though the steam leakage problem has been already solved in
the company, the proﬁtability of such energy efﬁciency improvement will be
analyzed in this report.
•
Actual lighting data information was not available; hence, the lighting informa-
tion was estimated with standard values for industries with no energy efﬁciency
measures.
For this study the ﬁnancial parameters presented in Table 1 will be used for
ﬁnancial analysis.
1Energy consumptions of processes, operational parameters, technical drawings, pictures, etc.
828
S. Benavides et al.
Since the analysis is based on the 2013 energy consumption data of the com-
pany, fuel prices considered for this prefeasibility analysis are based on the price of
energy in Reunion Island during the same year:
•
Electricity: 0.098 €/kWh
•
Diesel: 0.784 €/L
At this moment, there is no deﬁned incentive or subsidy exclusively for the
implementation of Energy Efﬁciency measures in Reunion Island industry.
3.4
Base Case
In the ﬁrst place, an assessment of the actual conditions of the industry was
addressed (base case). Table 2 shows the discrepancy between the simulation in
RETscreen and the historical consumption from energy bills of 2013.
The base case energy distribution scheme during 2013 is shown in Fig. 1
(Appendix). The values calculated by RETScreen account for the energy consump-
tion related to the systems and equipment used by UHT building processes,
represented in a Sankey diagram.
3.5
Energy Efﬁciency Improvements
After gathering the data and analyzing the impact of the energy consumption of this
industry, four main aspects were identiﬁed for improvement: (a) lack of heat
recovery from ﬂue gas, (b) sources of signiﬁcant steam leakages, (c) lighting
system, and (d) system refrigeration unit performance.
Table 1 Financial
parameters
Parameter
Value
Inﬂation rate
1.0%a
Discount rate
9.0%b
Project lifetime
10 years
Fuel cost escalation rate
5.5%c
aINSEE (2014)
bRETScreen Data Base
cCNR (2014)
Table 2 Comparison between RETScreen simulation and real consumptions of the industry
Fuel type
Fuel consumption
– unit
Fuel consumption –
historical
Fuel
consumption
Fuel consumption –
variance
Base case
Electricity
MWh
5345
5381
1%
Diesel (#2
oil)
L
736,000
726,890
1%
Technical-Economic Assessment of Energy Efﬁciency Measures in a Midsize. . .
829
Diesel
Electricity
supplier
UHT
building
5382 MWh
Boiler
n–85%
Steam
20% lost
5280 MWh
736 m3
7749 MWh
6587 MWh
boiler losses: 1162 MWh
steam losses: 1307 MWh
Customers
incomes
Cilam
Total
energy
13131 MWh
Losses
5671 MWh
5671 MWh
heal: 2821 MWh
heat + electricity: 4991 MWh
Cooling
unit
COP-1.3
Compressors
Compressed air: 988 MWh
electricity and lighting: 2224 MWh
988 MWh
electricity: 2170 MWh
Products
Dairy
processes
40773 tons
Fig. 1 Energy distribution for base case
830
S. Benavides et al.
3.5.1
Boiler–Steam Production
Table 3 shows the characteristics and operating parameters of the boiler related to
the systems and equipment used by the UHT building processes.
In order to improve the current energy consumption of the boiler, it was agreed
that an economizer, to recover part of the heat contained in the ﬂue gas, needed to
be installed. According to the manufacturer LOOS, now part of BOSH Company,
“The heat contained in the ﬂue gases is recovered and the efﬁciency increased in
this way by up to 7% in dry running operation” (Efﬁciency on a large scale Steam
boilers n.d.) for the integration of an economizer in the boiler, but due to possible
insulation problems, we account it as 5%. Table 4 shows the ﬁnancial data for this
energy-saving technology.
3.5.2
Steam Leakages
An energy audit was performed in 2013, and it was determined that about 20% of
the total steam produced in the boiler was lost due to leakages or to steam traps.
During further inspections, twelve steam plumes of about 1 m were identiﬁed.
Table 5 summarizes characteristics of the leakages identiﬁed.
It has been ﬁnally considered that the 75% of the leakages (9 units) can be ﬁxed
with an immediate action based on professional plumbing service which would cost
approximately 5500 € per leakage according to another RETscreen case study
(RETScreen International 2012). Financial parameters of this improvement are
indicated in Table 6.
Table 7 provides an estimation of diesel and cost reduction after economizer
implementation and steam leakage reduction.
Table 3 Boiler characteristic and operating parameters
Charcs.
Operating parameters
Energy consumption: useful steam
465 KW
η ¼ 0.85
Uses diesel
Steam ﬂow: 877 kg/h
Op.hrs: 24 h/day
Psteam: 12 bars
Tsh: 170 C
Cond. return: 60%
Tcond.: 85 C
TWmakeup: 25 C
From energy bills and audit: 5473 MWh/year
Estimated by RETscreen: 5280 MWh/year
Discrepancy: 3.5%
Share of total energy consumption: 40.2%
Table 4 Financial impacts of economizer implementation
Costs
Savings
Simple payback time
Investment:
35,000 € the 1st year
Maintenance:
2000 €/year
384 MWh/year of diesel
(42,393 L of diesel/year)
33,236 €/year
35,000 €/ 33,236 €/year ¼ 1.1 year
Technical-Economic Assessment of Energy Efﬁciency Measures in a Midsize. . .
831
3.5.3
Lighting
Since the available information provided by CILAM’s energy manager did not
include the conditions and the actual electricity consumption of lighting, the
estimation of the consumption was made according to the National Institute of
Safety and Research of France (INRS), taking into the consideration that currently
ﬂuorescent light bulbs are installed in the building.
Table 8 contains data about estimated beneﬁts of replacing old light bulbs with
LED, based on the lightning requirements of INRS (120 lum/m2) representing an
investment cost of 3456 € (GE Lighting LED 2012/2013) and savings of 22.318 €/
year and simple payback time in 0.15year, according to RETSCreen simulation.
Table 5 Leakage characteristics and operational parameters
Characteristics
Operating parameters
Energy consumption: steam leakages
12 Units
1 Leakage/unit 1 m
plume
Steam ﬂow/units: 16 kg/h
Steam pressure: 12 bar
Superheated temperature:
170 C
Operating hours: 24 h/day
1370 MWh/year
Estimated by RETscreen:
1307 MWh/year
Discrepancy: 4.6%
Share of total consumption: 9.9%
Table 6 Financial impacts of steam leakage reduction
Costs
Savings
Simple payback time
Investment:
50,000 €
1st year Maintenance: 4000 €/
year
981 MWh/year of diesel
(108,302 L of diesel/
year)
84,824 €/year
50,000 €/ 84,824 €/
year ¼ 0.6 year
Table 7 Estimated diesel and cost reduction after economizer implementation and steam leakage
reduction
Diesel consumption
Costs
Before improvements (based on 2013 data)
726 m3/year ¼ 7749 MWh/year
569,882 €/year
After improvements
576 m3/year ¼ 6144 MWh/year
451,822 €/year
Estimated savings
150 m3/year ¼ 1605 MWh/year
20% reduction
118,060 €/year
Table 8 Estimated electricity and cost reduction after lighting changes
Lighting power load
Electricity consumption
Costs
Before improvements
10.80 W/m2
253 MWh/year
24.657 €/year
After improvements
1.07 W/m2
24 MWh/year
2.339 €/year
Estimated savings
9.73 W/m2
229 MWh/year
90% reduction
22.318 €/year
832
S. Benavides et al.
3.5.4
Refrigeration System
Data for CILAM’s current refrigeration system used for cooling and storage
purposes are indicated in Table 9.
Energy savings for refrigeration system were proposed by replacing the old
cooling system. The aim is to install a new one with higher coefﬁcient of perfor-
mance (COP ¼ 5) to reduce the electricity consumption from 2170 MWh to
564 MWh per year, according to RETScreen simulation. Financial impacts of this
measure are listed in Table 10.
3.6
Proposed Case
After applying the identiﬁed possible improvement of energy efﬁciency, the energy
savings are estimated at 3440 MWh/year in total (26.2% of savings). The energy
distribution of industrial site after applying the measures proposed would be as
shown in Fig. 2.
4
Results and Discussions
From the analysis made using RETScreen software, the results obtained are as
follows.
Table 9 Refrigeration system characteristics and operating parameters
Charcs.
Operating parameters
Energy consumption: cooling products and storage
450 kW
COP ¼ 1.3
Duty cycle: 70%
Operating hours: 24 h/day
Drive by electricity
From energy bills: 2209 MWh/year
Estimated by RETscreen: 2170 MWh/year
Discrepancy: 1.7%
Share of total consumption: 16.5%
Table 10 Financial impacts of a new PAC chiller
Costs
Savings
Simple payback time
Investment: 370,000 € the ﬁrst yeara
1606 MWh/year
156,711 €/year
(Electricity)
370,000 €
156,711 €/year ¼ 2.4 year
aSabroe PAC Chillers (2014)
Technical-Economic Assessment of Energy Efﬁciency Measures in a Midsize. . .
833
Electricity
supplier
3547 MWh
UHT
building
988 MWh
5222 MWh
Boiler
n=85%
Diesel
576 m3
6144 MWh
Steam
6% lost
Economizer
(5% recovery)
boiler losses: 922 MWh
Heat recovery: 384 MWh
economizer losses: 538 MWh
steam losses: 326 MWh
5280 MWh
Compressors
heat: 2821 MWh
Cooling
unit
COP-5
Dairy
processes
5442 MWh
heat + electricity: 3385 MWh
Products
40773 tons
Customers
incomes
Cilam
Total
energy
Losses
9691 MWh
5442 MWh
Compressed air: 988 MWh
electricity and lighting: 1995 MWh
electricity: 564 MWh
Fig. 2 Energy distribution for proposed case
834
S. Benavides et al.
4.1
Emission Analysis
Reunion Island electricity mix and its associated Green House Gases (GHG)
emissions are deﬁned based on data from the annual report of energy production
in Reunion Island (Arer.org 2015). The improvement proposed in the industry
should lead to a reduction of GHG emission in 1876 tons of CO2 equivalent per
year (equivalent to 800 m3 of gasoline not consumed).
4.2
Financial Analysis
After implementing the proposed energy efﬁciency measures, and according to the
ﬁnancial parameter presented above in Table 1, the prefeasibility shows positive
NPV of 2,052,109 € with an Annual Life Cycle Savings of 319,760 €/year. The
main saving is observed due to the reduction in the fuel consumption, considering
Diesel Fuel and Electricity as the inputs to run the UHT process shown in Table 11.
The total savings achieved with the energy efﬁciency measures proposed is
297,089 € at year 0 value, as it can be also observed in Table 11, regarding the
savings in electricity and diesel consumption.
In addition, to complete the ﬁnancial analysis, it is required to take in consider-
ation the cumulative cash ﬂows graph, shown in appendix Fig. 3; the short payback
period of 1.5 years is remarkable. Such a short period of time may encourage rapid
implementation of the improvements. Finally, the net beneﬁt–cost (B-C) ratio is
5.5, which indicates the high proﬁtability in this project.
4.3
Risk Analysis
In order to calculate the risks in implementing the proposed efﬁciency measures in
this case study, a set of uncertainties are prescribed, as shown in Table 12.
The range of uncertainty for the fuel cost on the base case is 0% because the
current fuel cost value was used throughout the study. For the fuel cost of the
Table 11 Ultra high temperature process energy costs
UHT process energy costs
Base
Proposed
Savings
Electricity
(+cooling)
kWh Consumed
5,381,600
3,547,200
1,834,400
Cost (€)
525,240 €
346,211 €
179,029 €
Diesel
Diesel Consumed (L)
726,890
576,304
150,586
kWh Consumed
7,749,400
6,143,800
1,605,600
Cost (€)
569,882 €
451,822 €
118,060 €
Technical-Economic Assessment of Energy Efﬁciency Measures in a Midsize. . .
835
proposed case, an uncertainty value of 5% was selected since it is unlikely that the
price of the fuel will drastically change within the following year.
As is observed in Fig. 4, RETScreen displays a “Tornado Chart” identifying
which parameters have the most inﬂuence on the variability of a selected ﬁnancial
parameter, such as the after-tax IRR as it is analyzed in this case. The impact graph
shows how much of the variation in the ﬁnancial parameter can be explained by
variation in each input parameter in the risk analysis. This is expressed in relative
terms how future changes in fuel cost will impact the feasibility of the proposed
energy efﬁciency measures, as well as any ﬂuctuation on the initial cost will
inﬂuence the viability of the project. The fuel and initial costs are the most critical
input parameters; an increase of one standard deviation in the fuel cost leads to a
decrease in the IRR of nearly 0.8 standard deviations, also an increase of one
standard deviation in the initial cost leads to a decrease in the IRR of nearly 0.6
standard deviations.
Fig. 3 Cash ﬂow beneﬁts of proposed case over base case
Table 12 The uncertainty range for deﬁned input parameters
Parameter
Unit
Value
Range (+/)
Minimum
Maximum
Initial costs
€
455,001
10%
409,501
500,501
O&M
€
1999
10%
1799
2199
Fuel cost – proposed
€
798,033
5%
758,131
837,935
Fuel cost – base case
€
1,095,122
0%
1,095,122
1,095,122
836
S. Benavides et al.
The Monte Carlo analysis, shown in Fig. 5, generates a probability distribution
for the ﬁnancial parameter based on the assumed variations for the input parame-
ters. Specifying a desired level of risk at 5%, RETScreen indicates the range of
outcomes for which the after-tax IRR will fall outside the range of 67.1–82.5%. The
model calculates the after-tax internal rate of return (IRR) on equity (%), which
represents the true interest yield provided by the project equity over its life, after
income tax. The yields returned for the project, regarding the proposed case
implementation, are gains in the order of 2% and 15% positive values, which
measure how much cash ﬂow the company will get for each dollar invested in an
equity position.
Impact-After-tax IRR - equity
Relative impact (standard deviation) of parameter
-0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0
Sorted by the impact
Fuel cost - base case
Fuel cost - proposed case
Initial costs
O&M
Fig. 4 Tornado chart analysis from RETscreen simulation
Fig. 5 Monte Carlo analysis from RETscreen simulation
Technical-Economic Assessment of Energy Efﬁciency Measures in a Midsize. . .
837
5
Conclusions
In Reunion Island, as in many other islands around the globe, power generation
relies mainly on expensive and high-polluting technologies and methods, like diesel
generators (Blechinger et al. 2013). Nowadays, during permanent increase of
energy prices and production growth, it is highly important to implement energy-
saving technologies as much as possible. However, due to the economic situation
worldwide, stakeholders of industrial assets would like to be aware about different
aspects of technologies that would allow them to choose the best option for the
investment. RETScreen software provides the possibility to perform the analysis of
energy-saving procedure and evaluate its attractiveness not only from an environ-
mental but also a ﬁnancial point of view.
In this paper, an analysis of four different technologies was performed for
boosting the energy efﬁciency at a dairy plant: installation of an economizer for
boiler, steam leakage decreasing, improvements in lighting, and the installation of
more efﬁcient refrigeration system. It was estimated that the biggest savings of
energy were made with improvements in the refrigeration system; however, it got
the biggest investment costs and the longest payback. Installation of an economizer
and steam leakages made a total diesel savings of 20%, which make these measures
ﬁnancially attractive.
Thanks to the energy-saving technologies that could be implemented, the total
savings forecasted are 26.2% of total energy use, 319,760 €/year of life cycle, and
1876 tons of CO2 in annual savings. These numbers combined with a short payback
period of 1.5 years show that the proposed improvements are reasonable and
proﬁtable.
In addition, the results here presented are aligned with the initiatives of diesel-
free power generation. By using fewer diesels, the power systems are less depen-
dent on the diesel’s volatility price which at the same time decreases the risk of the
Island’s energy supply (Blechinger et al. 2013). For further studies, research about
possible introduction of renewable energy sources and the optimization of produc-
tion equipment can be performed. Moreover, an opportunity of governmental
ﬁnancial support should be taken into account during the decision-making process
due to its ability to increase total project proﬁtability.
Acknowledgments We would like to thank Compagnie Laitie
`re des Mascareignes (CILAM) for
providing us with the necessary data. Especially, we would like to address our gratitude to CILAM
energy manager Mr. Marc Bourhis for his assistance and time. We also would like to thank our
academic tutor, Pr. Luis R. Rojas S., for introducing us to RETScreen software and his kind
guidance during all stages of the project. Finally, we would like to thank Mrs. Fe
´licie The
´ron for
her administration of the course.
838
S. Benavides et al.
Appendix
References
Agenda21france.org: Agenda 21 de Territoire. [online] Available at: http://www.agenda21france.
org/agenda-21-de-territoire/index.html (2015). Retrieved 19 Dec 2014
Agro-oi.com: Agro-oi | CILAM – Compagnie Laitiere Des Mascareignes. [online] Available at:
http://www.agro-oi.com/fr/7/1330/cilam-compagnie-laitiere-des-mascareignes.html#.
VLzQxkfF-AU (2015). Accessed 1 Oct 2014
Arer.org: Bilan e
´nerge
´tique 2012 de La Re
´union. [online] Available at: http://www.arer.org/Bilan-
energetique-2012-de-La,736.html?espace¼Education (2015). Accessed 1 Nov 2014
Blechinger, P., Howe, E., Cader, C., Pleßmann, G., Hlusiak, M., Seguin, R., Breyer, C.: Assess-
ment of the Global Potential of Renewable Energy Storage Systems on Small Islands in 8th
IRES, pp. 294–300, no. (2013)
Efﬁciency on a large scale Steam boilers. 1st ed. [ebook] Bosch Group, p. 4. Available at: http://
www.bosch-industrial.com/ﬁles/BR_SteamBoilers_en.pdf (n.d.). Accessed 10 Oct 2014
GE
Lighting
LED:
LED
Catalogue.
[online]
Available
at:
http://www.gelighting.com/
LightingWeb/apac/images/LED-LED-ctlg-EN-052012-PG47-APAC_tcm281-33329.pdf
(2012/2013). Accessed 15 Nov 2014
Institut national de la statistique et des e
´tudes e
´conomiques (INSEE): Inﬂation Rate Database.
http://www.insee.fr/fr/. Accessed 3 Nov 2014
Praene, J., David, M., Sinama, F., Morau, D., Marc, O.: Renewable energy: progressing towards a
net zero energy island, the case of Reunion Island. Renew. Sust. Energ. Rev. 16(1), 426–442
(2012)
RETScreen International: Clean Energy Project Analysis: RETScreen Engineering and Cases
Textbook (2005)
RETScreen International: Energy efﬁciency measures – Industrial – Steam losses (2012). [online]
Available at: http://www.retscreen.net/ang/templates_steam_losses.php. Accessed 10 Nov
2014
Ricci, O., Selosse, S., Garabedian, S. and Maizi, N.: Re
´union Island’s energy autonomy objective
by 2030. [online] Econpapers.repec.org (2014). Available at: http://econpapers.repec.org/
paper/ekd006356/7069.htm. Accessed 11 Nov 2014
Sabroe PAC Chillers: Catalogue. [online] Available at: http://www.sabroe.com/ﬁleadmin/user_
upload/Marketing/Brochures/Chillers/PAC_recip_SB-3571_Jan_2015_GB120dpi.pdf (2014).
Accessed 20 Oct 2014
Technical-Economic Assessment of Energy Efﬁciency Measures in a Midsize. . .
839
Smart Simulator for Tracking the Global
Maximum Power Peak of Photovoltaic
Arrays Under Partial-Shaded Conditions
Saad Saoud Merwan, Abbassi Hadj Ahmed, Kermiche Saleh,
and Ouada Mahdi
1
Introduction
The functioning of a photovoltaic (P-V) array is affected by temperature, solar
irradiance, shading, and array conﬁguration. Frequently, the P-V arrays become
shadowed, either completely or partially. When the array is operated under partially
shaded conditions, the PV characteristics become more complex and the multiple
maximum power points tracking (MPPT) can fail to track the absolute MPP. To
overcome this problem, complex algorithms have been developed (Kawamura et al.
2003; Nguyen and Low 2010). The effect of shading on the output of the P-V
modules and the changes in their I–V characteristics were investigated by
Kobayashi et al. (2003).
Because partial shading conditions (PSCs) occur quite commonly due to clouds,
trees, or nearby buildings, it is necessary to develop special MPPT schemes that can
track the real MPP under PSCs. Although some researchers have worked on real
MPP tracking under partial shading conditions, (Carannante et al. 2009; Miyatake
et al. 2007, their methods have some drawbacks because of their complexity,
tracking failure of the real MPP position, and difﬁculties involved in their applica-
tion to the installed power conditioning system.
S.S. Merwan (*) • A. Hadj Ahmed • K. Saleh
Annaba University, Faculty of Engineering Science,
Electronic Department, Sidi amar, Annaba 23000, Algeria
e-mail: maro34ss@gmail.com; hadj-Ahmed.abbassi@univ-annaba.dz;
salah.kermiche@univ-annaba.dz
O. Mahdi
Annaba University, Faculty of Science of Engineer, Electromechanical
Department, Sidi amar, Annaba 23000, Algeria
e-mail: ouadamehdi@gmail.com
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_60
841
In general, most of the conventional MPPT algorithms do not have the capability
to detect the global peak (the true maximum of the multiple local peaks) effectively,
which will lead to considerable power loss (Ramaprabha and Mathur 2009).
Some researchers (Kashif and Salam 2003) have worked on GP-tracking
schemes for P-V arrays operating under non-uniform irradiation conditions.
Solodovnik et al. (2004) have reported an MPPT scheme that uses a Fibonacci
sequence to track the GP under partially shaded conditions. In Bidram et al. (2012),
a review on the state-of-the-art MPPT techniques available is presented by Kashif
et al. (2011). However, Patel and Agarwal (2008) stated that the “Power Curve
Slope” MPPT method is efﬁcient under shading conditions. In this technique, the
symbol ∂P/∂V at different points is used to track the global maximum, and the
change in ∂P/∂V sign from negative to positive indicates the existence of another
maximum on the right side of the existing one (Yousra Shaiek et al. 2013). If a local
maximum is found, as indicated by a change in ∂P/∂V, it is compared with the
stored maximum. If the detected local maximum is greater than the stored maxi-
mum, the stored maximum will be updated. Alonso-Gracia et al. (2006) present a
comparison between conventional methods and the GA approach for MPPT of
shaded solar P-V generators. An experimental study of the effect of partial shading
on I–V characteristics of the P-V module and the constituent cells was carried out by
Hsu (2010).
Recently, one of the best methods for MPPT under partial shade, Particle Swarm
Optimization (PSO), has come to light. This is a technique based on the stochastic
optimization population developed by Eberhart and Kennedy (1995), inspired by
the social behavior of bird ﬂocking or ﬁsh schooling. PSO is based on search
optimization where the system is initialized with a population of random solutions
and searches for optima by updating generations. A special property of PSO is that
it can be operated directly in continuous real number space (Ishaque et al. 2012). A
signiﬁcant number of researchers have shown interest in this approach (e.g., Chao
et al. 2013). The key advantage of the proposed technique in Miyatake et al. (2011)
is the elimination of PI control loops using the direct duty cycle control method.
Kashif and Salam (2013) have successfully studied a modiﬁed PSO-based MPPT
method, and have presented a novel algorithm based on conventional PSO.
Mermoud et al. (1998) developed a comprehensive Matlab/Simulink P-V system
simulator with partial shading capability based on a two-diode model.
The presence of multiple peaks reduces the effectiveness of the existing MPPT
schemes. There is a need to develop a special simulator that can track the GP under
these conditions.
The possibility for evaluating the behavior of a system in the presence of shading
conditions is to include some commercial programs, P-VSyst, for example [25].
However, these programs give only approximate data, in very limited positions, and
are costly.
It is very important to model a simulator for techno-economic purposes having a
design and concept of photovoltaic systems, because this will provide a predictive
image before the installation, which can considerably reduce the cost of installation
and increase the efﬁciency of P-V generators.
842
S.S. Merwan et al.
For this reason, the present paper proposes a simpliﬁed soft simulator that can
achieve this operation. A Matlab/Simulink P-V simulator is used to track the global
power peak under partially shaded conditions, using a model of two diodes, based
on an extensive study of partially shaded P-V arrays. The simulation results are
presented to tested the accuracy and consistency of this simulator; to justify the
performance of the proposed model, the results are tested against a comparison
method of the simulation and experimental results of PV characteristics under the
partially shaded conditions of PSO MPPT proposed in Miyatake et al. (2011).
The paper is organized as follows:
In Section 2 P-V array characteristics under uniform conditions with the impact
of various temperatures and irradiations are presented. In Section 3 the study of P-V
array characteristics under partially shadied conditions is shown. Finally, the
simulation of the outputs for the proposed application is given. The proposed
work can be very useful for simulator developers and it is able to validate the
effectiveness of existing and new MPPT techniques.
2
P-V Array Characteristics under Uniform Conditions
In the literature there are two possible approaches for extracting solar module
parameters: using analytical methods according to Gottschalg et al. (1999), where
several parameters of the datasheet are required. However, module datasheets only
provide information on standard test conditions (irradiation ¼ 1000 w/m2 and
temperature ¼ 25). It is known that P-V arrays are impacted by temperature and
solar irradiance variations, and numerical methods that are based on a mathematical
algorithm to adjust all the points on the I–V curve will provide more accurate
results.
The solar cell can be represented by the electrical model with two diodes, as
shown in Fig. 1. Its current voltage characteristic is expressed by the following
Eq. (1):
Id1
I
V
D1
Rs
Rsh
Ish
Id2
D2
Fig. 1 Equivalent circuit of
a solar cell
Smart Simulator for Tracking the Global Maximum Power Peak of Photovoltaic. . .
843
I ¼ IPh  Id1  Id2  Ish
ð1Þ
where I and V are the solar cell output current and voltage respectively, Iph
represents the photovoltaic current, q is the charge of an electron, N is the diode
quality (ideality) factor, k is the Boltzmann constant, and Rs and Rsh are the series
and shunt resistors of the solar cell. Id1, Id2, are the currents of diode 1 and diode
2, Ish is the shunt resistor current. When the photocurrent equal, the following
equation is derived:
IPh ¼ IPh,n þ KIΔT
ð
Þ G
Gn
ð2Þ
ΔT ¼ T  Tn (being T and Tn the actual and nominal temperatures [K]), G [W/m2] is
the irradiation on the device surface, and Gn is the nominal irradiation. The current
diodes are:
Id1 ¼ I01 exp V þ IRs
a1VT1


 1


ð3Þ
Id2 ¼ I02 exp V þ IRs
a2VT2


 1


ð4Þ
where I01 and I02 are the reverse saturation currents of diode 1 and diode 2, VT1 and
VT2 are the thermal voltages of respective diodes, and a1 and a2 represent the diode
ideality constants.
I01 ¼ I02 ¼
Isc n þ KIΔT
ð
Þ
exp Voc,n þ KVΔT
ð
Þ=VT
½
  1
ð5Þ
Isc: short circuit current [A].
Ki: cell’s short-circuit current temperature coefﬁcient.
Voc: open circuit voltage.
Kv: cell’s open circuit voltage temperature coefﬁcient.
A: ideal factor.Shunt current equal:
Ish ¼ V þ RsI
Rsh
ð6Þ
Vm ¼ Ns  V
ð7Þ
with Vm the module voltage and Ns number of cell in series.
The P-V array used in this paper is a combination of three series of the Kyocera
KC200GT modules. A series combination of modules was chosen to obtain higher
output voltage of P-V module. If this chain is under uniform insolation, the MPP
value is p ¼ 600 W.
844
S.S. Merwan et al.
The KC200GT module itself is composed of 54 silicon cells connected in a
series. Each module can generate current up to 8.21 A and a voltage of 32.9 volts.
Figure 2 shows the standard condition, and the rise in the maximum power to a
200-W peak at standard testing conditions (25 C, 1000 W/m2 and AM ¼ 1.5)
(Figs. 3, 4, and 5).
3
Inﬂuence of Partial Shading in P-V Array
Characteristics
The considerable advantage of the modeling and simulation method in this research
is to cover different scales of a P-V system under both normal and partial shading
conditions, without analyzing the in-depth semiconductor physics deﬁnitions
(Carannante et al. 2009).
There are times where some part of the P-V arrays might be shaded (Silvestre
et al. 2009).The P-V characteristic of the P-V array exhibits multiple local maxima
and only one of them corresponds to the global MPP.
If there is one shaded panel in a series of connected arrays, it can then act as a
load on the array. The shaded P-V cells absorb a large amount of the electric power
that is generated by other P-V cells that receive high illumination and convert it into
heat. This situation is called the hot-spot problem. This is often solved with the
inclusion of a bypass diode to a speciﬁc number of cells in the series circuit.
5
10
15
20
25
30
35
40
1
2
3
4
5
6
7
8
9
Voltage (V)
Current (A)
00C
250C
750C
500C
Fig. 2 KC200GT module I-V curve at various temperatures
Smart Simulator for Tracking the Global Maximum Power Peak of Photovoltaic. . .
845
The bypass diodes are connected in an anti-parallel way with each panel, and
where the panel is shaded current ﬂows through the bypass diode rather than
through the panel (Hsu et al. 2010).
0
5
10
15
20
25
30
35
0
2
4
6
8
10
voltage(v)
current(A)
I-V charactéristics
1000 W/m2
800 W/m2
600 W/m2
400 W/m2
200 W/m2
Fig. 4 KC200GT module I-V curve at various insolations
0
5
10
15
20
25
30
35
40
0
50
100
150
200
250
Voltage (V)
Power (W)
00C
250C
500C
750C
Fig. 3 KC200GT module P-V curve at various temperatures
846
S.S. Merwan et al.
For this study, three panels connected in a series with non-uniform insulation
have been considered. The same concept can be extended to a number of panels
connected in series. Figure 6 shows the series connection of three panels with three
bypasses diodes.
The inserted bypass diodes that may cause multiple peaks are established in the
I-V and P-V characteristic curves under partial-shaded conditions (Villalva et al.
2009), as shown in Figs. 7, 8, and 9
4
The User Interface Simulator
A user can communicate with the proposed simulator through a graphic interface
that allows the possibility of loading data sheet parameters of selected P-V modules
directly (Voc, Isc, Vmp, Imp, Kv, Ki, and Ns). This step is achieved by calculating
Rs and Rsh, a method presented in Villalva et al. (2009), which is used to obtain this
operation, as shown in Fig. 10a. In this case the test parameters of irradiation must
be equal to modulate these parameters (under uniform conditions),and the user
pushes the button “ran” to simulate the adjusting power peak, following which the
user pushes the button “next” to then input the different irradiations proposed. For
example, authors chose the three panels KC200GT connected in a series under 1000
w/m2, shown in Fig. 10b.
0
5
10
15
20
25
30
35
0
50
100
150
200
250
voltage(v)
power(w)
P-V charactéristics
1000 W/m2
800 W/m2
600 W/m2
400 W/m2
200 W/m2
Fig. 5 KC200GT module P-V curve at various irradiations
Smart Simulator for Tracking the Global Maximum Power Peak of Photovoltaic. . .
847
5
Results and Discussion
This section presents the simulation results with the proposed simulator; the ﬁrst
aim of this study was to simulate the characteristics of three panels KC200GT
connected in a series response during mismatching conditions, where the author test
parameters chosen are:
For ﬁrst group: The ﬁrst panel is under uniform irradiation G1 ¼ 1000 w/m2, the
second panel is shaded by G2 ¼ 250 w/m2, and the third panel is shaded by
G3 ¼ 500 w/m2. When the ﬁrst panel’s current becomes greater than that of the
second and third panels, the shaded modules absorb energy. To solve this problem,
a bypass diode is connected in parallel to each group.
For the second group all panels are under the same irradiation G ¼ 500 w/m2,
and the purpose of this choice is to validate that the algorithm is able to track the
MPP under uniform irradiation too, as shown in Fig. 11.
The second step is tracking the maximum global power where it is based on two
phases:
The ﬁrst is to ﬁnd the locals peaks: the system scans the power values period-
ically to ﬁnd all peaks, than the algorithm searches the maximum power point from
Rs
I
Rsh
Ish
Id
G1
IPh
Rs
I
Rsh
Ish
Id
G3
IPh
+
-
Module 1 
Module 2 
Rs
I
Rsh
Ish
Id
IPh
G2
Module 3 
bypass diode 2
bypass diode 1
bypass diode 3
Fig. 6 Schematic representation of three series of panels with bypass diodes
848
S.S. Merwan et al.
the maximum local peaks. The obtained parameter will be called the global peak;
the simulation result is shown in Fig. 12.
The biggest advantage of the proposed simulator is that there is the possibility to
execute a sequential simulation with only initialization parameters, which allows
giving the opportunity to see the different energy levels produced in the function of
level shading panels. This clears the impact of partial shading and provides a
predictive study before doing the practical installation of solar panels. Figures 13
and 14 explain this advantage, where the following test parameters successively
used are:
G1
¼
[1000
500,250],
G2
¼
[750,600
1000],
G3
¼
[750300800],
G4 ¼ [100300800] (w/m2).
To conﬁrm the efﬁcacy of the simulator we tried using it with mono-crystalline
panels e19/425 sunpower, and the results are as follows:
9
8
7
6
5
4
Voltage(V)
Current(A)
3
2
1
00
10
20
30
40
50
60
70
Uniform
Shading
80
90
100
Fig. 7 The resulting simulation I–V curve
Smart Simulator for Tracking the Global Maximum Power Peak of Photovoltaic. . .
849
6
Comparative Search for the Maximum Global Peak
Power Simulation
To prove that the program is efﬁcient and is accurate in the calculating GP, we
compared its responses with the simulation and experimental results of P–V
characteristics presented in [20], where the same initialization parameters of P-V
systems is carried out.
Here, eight panels KC200GT are connected by the connection method shown in
Fig. 15, and the obtained results are shown in Fig. 16.
G1 ¼ 500 w/m2.
G2 ¼ 800 w/m2.
The obtained results indicated that the GP is tracked successfully. The results are
shown in Fig. 16.
The proposed method does not present any complexity and will be a develop-
ment platform for researchers in this ﬁeld. The following conclusions emerge from
this study:
250
KC200GT P-V Curve for non uniform insolation at T=25°C 
200
150
100
50
0
0
10
20
30
40
50
60
Module Voltage (V)
Module power (w)
70
80
90
100
Fig. 8 The simulation results of the P–V curve under shaded conditions
850
S.S. Merwan et al.
The hot-spot is a problem that occures where the shaded P-V cells absorb the
electric power generated by other P-V cells under uniform irradiation and convert it
into heat.
The shaded panels should be bypassed by a diode when they are connected in a
series.
The presence of multiple peaks is needed to develop a special algorithm that can
track the GP under shaded conditions.
Power provided by P-V cells under partial shading conditions is very low
compared to that obtained with P-V cells under standard conditions.
Start
Calculate p=v*i ,Adjusting model of Rs , Rsh and Find peaks
Return
Shading conditions
Uniform conditions
Compare all Pi to get GP
Store the global maxima GP
Pi=1
Pi>1
Conventional MPPT Algorithms
Input STC Manufacturer parameters Voc, Isc, Vmp, Imp, Kv,Ki
and Ns
Switch1 ON
S1=1
Calculate number of peaks
Pi=Nb peaks
Switch2 ON
S2=1
a
Fig. 9 Flowchart for the proposed algorithm
Smart Simulator for Tracking the Global Maximum Power Peak of Photovoltaic. . .
851
Fig. 10 Adjusting parameter values
852
S.S. Merwan et al.
Fig. 11 Searching for the maximum power peak under irradiation G ¼ 500 w/m2
Fig. 12 Searching for the maximum global peak power
Smart Simulator for Tracking the Global Maximum Power Peak of Photovoltaic. . .
853
Fig. 13 Searching for the maximum global power peak
Fig. 14 Search of maximum global power peak of E19/425 panels
854
S.S. Merwan et al.
7
Conclusions
Adaptive and ﬂexible simulators with a simple technique for tracking a global
maximum power under mismatching conditions based on MATLAB Simulink were
developed in this study. The mathematical model and output characteristics of the
P1
P2
P3
P4
P5
P7
P6
P8
G1
G2
G1
G1
G1
G2
G2
G2
+
-
Fig. 15 Schematic
representation of eight
series of panels with bypass
diodes in mismatching
conditions
0
20
40
60
80
100
120
140
160
0
100
200
300
400
500
600
700
800
900
1000
Our Simulation
Liturature Simulation
Fig. 16 Simulation results of a P-V curve under shaded conditions
Smart Simulator for Tracking the Global Maximum Power Peak of Photovoltaic. . .
855
P-V panels were analyzed, and the effects of partially shaded phenomena in P-V
arrays were examined in detail. The simulation results verify the feasibility and
effectiveness of the proposed simulator under the inﬂuence of these conditions and
proved that the algorithm is capable of tracking the MPP with simplicity, a fast
response, and a high level of precision in most cases.
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Smart Simulator for Tracking the Global Maximum Power Peak of Photovoltaic. . .
857
Study and Analysis on Lighting Energy
Management for Highway
Yoomak Suntiti and Ngaopitakkul Atthapol
1
Introduction
In general, most of accidents on the roadway occur at night due to the decreased
visual ability with illuminance and age. The principal purpose of roadway and
motorway lighting is to improve the visibility for the driver, including accident
reduction. Some lighting from roadway lighting will emit onto footpath, which is
advantageous for providing sufﬁcient light for pedestrians as well as for drivers. In
the past, the high-pressure sodium (HPS) was one of the popular luminaires used in
roadway and highway lighting systems. However, the traditional HPS luminaire
usage has caused high energy consumption. Nowadays, the trend of employing the
light-emitting diode (LED) luminaire for street and roadway lighting has been
increased in several capital cities as well as small towns throughout the world
owing to its attractive advantages such as long lifetime, low energy consumption,
quick turn on, etc. However, the replacement of conventional road lighting, like
high-pressure sodium (HPS) luminaire and metal halide (MH) luminaire with LED
luminaire, has some disadvantages, for example, high initial cost of LED installa-
tion, lighting pollution, and change in the color of the urban sky glow; thus, there
are several research papers investigating on the LED component lighting system.
The street lighting regulations and requirements, the thermal management, and
the buck-boost power factor control (PFC) stage were considered to the design of
LED street lighting system by Nuttall et al. (2008), and Luo et al. (2007) presented
that the thermal had an effect to lifetime and efﬁciency of LED luminaire. Long
et al. (2009) proposed the development of 9-LED module (9LEDM) that had been
Y. Suntiti (*) • N. Atthapol
Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Chalongkrung
Road, Bangkok 10520, Thailand
e-mail: knatthap@kmitl.ac.th
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_61
859
applied to a street lighting system and supported by efﬁcient, intelligent, and
reliable electronic drivers. The signiﬁcant achievement of the 9LEDM was a
dramatic reduction in package thermal resistance from the PN junction of the
LED chip to ﬁxtures. Moreover, experimental results based on the suggested
methodology have been obtained from laboratory measurements and a demonstra-
tion project. An intelligent driver based on a quasi-resonant operation ﬂyback
topology has been developed by Long and Zhou (2008) for a high performance in
terms of the photometric, thermal, and electrical issues. Camponogara et al. (2012)
presented a prototype based on a buck-boost discontinuous conduction mode
(DCM) and a buck-boost continuous conduction mode (CCM), which was built to
increase the efﬁciency such as high power factor, low total harmonic distortion, and
long life span. The experimental results showed a good agreement with the project
and a high overall efﬁciency. A sensor-integrated ultra-long span LED street
luminaire was studied by Wang and Liu (2007). The LED street luminaires will
automatically adjust their working condition according to the change of
environments.
In terms of energy, the comparison of energy efﬁciency, color of light, control
dimmer, and mesopic vision during LED luminaire with HPS luminaire for street
lighting system was presented by Fusheng et al. (2009) and Rodrigues et al. (2011).
Next paper showed the design of light distribution for LED luminaire, which is a
factor for illuminance on road surface, and was proposed by Bender et al. (2013).
The results of simulation could calculate the property of lens that gave optimal
illuminance and uniformity. Luo et al. (2008) and Yang et al. (2011) designed light
distribution lens with optimal performance for street light; their best characteristic
was being rectangular. In Taiwan from 2009 to 2011, Huang et al. (2012) proposed
the replacement of the mercury luminaires with LED luminaires. The result was
shown that LED luminaires could save energy up to 50%; this was beneﬁcial to
support the use of LED luminaires. In addition, nowadays, LED luminaires are
applied for the solar system that was presented by Ali et al. (2011). The obtained
results from LED luminaires applied to the solar system have a high efﬁciency than
the other types and are installed for street lighting to save energy.
As a result of all of the above papers, researchers have mostly focused on
developing and designing the driver, PFC, and sensor for LED lamp, in order to
attain the low energy consumption, high power factor, low total harmonic distor-
tion, and reduced thermal in ﬁxtures, but not for comparison between LED lumi-
naire and HPS luminaire in terms of the illumination change.
In Thailand, replacement of 250 W HPS with 120 W LED has been studied in
roadway lighting system in Bangkok in which MEA (Metropolitan Electricity
Authority) is responsible. LED luminaire can save 60% of energy consumption
more than traditional luminaire because 250 W HPS luminaire consumes a total
energy of 299 W, but 120 W LED luminaire only consumes 130 W. This paper
focuses on the energy consumption of 120 W LED luminaire which will replace the
250 W HPS luminaire for roadway lighting system in Thailand. The comparison
between HPS luminaire and LED luminaire in terms of illuminance and uniformity
is presented with DIALux program in this paper. The three different luminous
860
Y. Suntiti and N. Atthapol
intensity distribution curves (or polar curve) of LED luminaire are also considered
in order to compare among the characteristics of arrangements of roadway lighting
with various polar curves. In addition, this paper proposes the improvement of LED
luminaire for roadway lighting system in terms of illuminance and uniformity using
the mounting height adjustment method and the pole spacing adjustment method.
2
Illuminance Simulation and Results Between HPS
and LED Luminaires
In the case being studied revealed that roadway is simulated using DIALux
program. The 250 W HPS luminaire is compared with the 120 W LED luminaire
to analyze the illuminance and uniformity with roadway lighting standard of
Thailand (by considering illuminance and uniformity). The luminous intensity for
both luminaires is illustrated in Fig. 1. By considering Fig. 1, it can be observed that
the similarity between both polar curves can be seen. In addition, the luminous ﬂux
of HPS luminaire is 31,100 lumens while LED luminaire is 9035 lumens.
The layout or arrangement for the roadway luminaires is one factor to calculate
the illuminance; its drawing for DIALux program is illustrated in Fig. 2. By
considering Fig. 2a, the width of the road (Y) is 10 m, while the street isle (Z) is
0 m (without street isle). Likewise, in Fig. 2b, the width of the road is also 10 m,
while the street isle is 1.5 m. The characteristics of roadway lighting pole param-
eters such as (1) mounting height of 9 m, (2) overhang of 1.8 m, (3) boom angle of
15, and (4) boom length of 2.5 m are determined for simulation with DIALux
program as shown in Fig. 3. Moreover, the length of roadway is equal to that of the
distances between luminaires. Quality or type of road reﬂective properties on the
road surface R3 (u0Q0¼ 0.07) is asphalt road surface.
Before simulation, the mounting height, overhang, road width, and spacing
between lighting poles are determined as 9, 1.8, 10, and 40 m, respectively. The
maintenance factor (MF) is also determined as 0.8. In addition, there are several
arrangements of roadway lighting that can be used such as without isle (single row,
double row with opposing, and double row with offset) and with isle (double row
with opposing, double row with offset, and twin central), so the arrangement is
varied and compared in terms of illuminance with the other arrangements.
After applying the various arrangements and luminaires, the obtained average
illuminance on road surface in each case study is illustrated in Figs. 4 and 5,
Table 1, and Table 2. By observing Fig. 4, the difference between the illuminance
distribution (or it is called as false color rendering) with various arrangements
without street isle and luminaire can be clearly seen. By considering the single row
arrangement as shown in Fig. 4a, b, the HPS luminaire and LED luminaire are
considered as an example; the obtained results can be seen that the difference
between the two false color rendering can be visually observed.
Study and Analysis on Lighting Energy Management for Highway
861
Fig. 1 Luminous intensity distribution curve or polar curve. (a) 250 W HPS luminaire. (b) 120 W
LED luminaire
862
Y. Suntiti and N. Atthapol
Based on a further analysis of Table 1, by considering the average illuminance
(Eav), it can be seen that the average illuminance and uniformity (u0) that are
obtained from both cases (HPS luminaire and LED luminaire) are less than the
roadway lighting standard of Thailand (standard case). For the next case study, by
changing the arrangement of roadway as double row with opposing, the difference
between the two false color renderings can be also observed as shown in Fig. 4c, d;
it can be seen that the average illuminance of both cases are more than the standard
case. By considering the uniformity (u0), it can be seen that the obtained uniformity
(u0) in case of HPS luminaire is higher than the standard case, while the case of the
Y
X
(a) Roadway lighting without street isle
Z
Y
Y
X
(b) Roadway lighting with street isle
Fig. 2 The layout of roadway for simulation. (a) Roadway lighting without street isle. (b)
Roadway lighting with street isle
Fig. 3 Characteristics of
street lighting pole used in
simulation
Study and Analysis on Lighting Energy Management for Highway
863
LED luminaire is less than that of the standard case; this indicates that the
arrangement of roadway luminaires has impact for decision of the luminaire
installation in roadway lighting system as shown in Table 1. To support this
assertion, by considering the roadway arrangement constructed as double row
with offset, which is shown in Fig. 4e, f, it can be seen that the obtained average
illuminance and uniformity (u0) which are obtained from both cases (HPS luminaire
and LED luminaire) have the same characteristics as in the case of the arrangement
of roadway as double row with opposing; this indicates that the LED luminaire
should be improved.
0
6.25
12.50
18.75
25
31.25
37.50
43.75
50
1× 
HPS luminaire for single row arrangment
LED luminaire for single row arrangment
a
b
0
6.25
12.50
18.75
25
31.25
37.50
43.75
50
1× 
HPS luminaire for double row with opposing
LED luminaire for double row with opposing
c
d
0
6.25
12.50
18.75
25
31.25
37.50
43.75
50
1× 
HPS luminaire for double row with offset
LED luminaire for double row with offset
e
f
Fig. 4 Simulation results of illuminance distribution (false color rendering) for roadway without
isle. (a) HPS luminaire for single row arrangement. (b) LED luminaire for single row arrangement.
(c) HPS luminaire for double row with opposing. (d) LED luminaire for double row with opposing.
(e) HPS luminaire for double row with offset. (f) LED luminaire for double row with offset
864
Y. Suntiti and N. Atthapol
To evaluate the illuminance and uniformity (u0) in the case of street isle as
shown in Fig. 5 and Table 2, the roadway lighting standard of Thailand (standard
case) will be compared with HPS luminaire case and LED luminaire case. Consid-
ering the overall results of illuminance for various arrangements of lighting pole,
the average illuminance of HPS luminaire is higher than the standard case for all
arrangements of lighting pole, while the average illuminance of LED luminaire is
less than that of both the standard case and HPS luminaire case for all arrangements
of lighting pole. Likewise, by considering the overall results of uniformity for
0
6.25
12.50
18.75
25
31.25
37.50
43.75
50
1× 
HPS luminaire for double row with opposing
LED luminaire for double row with opposing
a
b
0
6.25
12.50
18.75
25
31.25
37.50
43.75
50
1× 
LED luminaire for twin central arrangement
HPS luminaire for twin central arrangement
c
d
0
6.25
12.50
18.75
25
31.25
37.50
43.75
50
1× 
HPS luminaire for double row with offset
LED luminaire for double row with offset
e
f
Fig. 5 Simulation results of illuminance distribution (false color rendering) for roadway with
1.5 m width of isle. (a) HPS luminaire for double row with opposing. (b) LED luminaire for double
row with opposing. (c) HPS luminaire for twin central arrangement. (d) LED luminaire for twin
central arrangement. (e) HPS luminaire for double row with offset. (f) LED luminaire for double
row with offset
Study and Analysis on Lighting Energy Management for Highway
865
various arrangements of lighting pole, the uniformity of HPS luminaire is slightly
less than the standard case, except for the arrangement of roadway as double row
with offset, whereas the uniformity of LED luminaire is signiﬁcantly less than the
standard case for all arrangements of lighting pole; this also indicates that the
installation of LED luminaire should be improved to increase the average illumi-
nance and uniformity (u0) for roadway lighting.
As a result, the type of luminaire and the arrangement of lighting pole including
the luminous ﬂux of luminaire are key factors for the decision of the installation of
LED luminaire for roadway lighting.
3
Illuminance Simulation and Results for Polar Curves
of LED Luminaire
In the previous section, the simulated result shows that HPS luminaire is replaced
with LED luminaire in the case of the nearby polar distribution diagram; in terms of
energy usage, the energy consumption of LED luminaire is less than HPS luminaire
Table 1 Comparison between HPS and LED luminaires for various arrangements without street
isle
Data
Standard
HPS luminaire
LED luminaire
Single
row
Double row
with
opposing
Double
row with
offset
Single
row
Double row
with
opposing
Double
row with
offset
Eav
(lux)
21.5
19
37
37
11
22
22
Emin
(lux)
–
4.89
15
28
1.69
5.52
4.16
Emax
(lux)
–
39
77
50
34
38
39
u0
0.4
0.262
0.414
0.761
0.188
0.239
0.242
Table 2 Comparison between HPS and LED luminaires for various arrangements with 1.5 m
width of street isle
Data
Standard
HPS luminaire
LED luminaire
Double row
with
opposing
Twin
central
Double
row with
offset
Double row
with
opposing
Twin
central
Double
row with
offset
Eav
(lux)
21.5
28
24
28
12
14
12
Emin
(lux)
–
10
7.93
12
3.84
3.91
3.1
Emax
(lux)
–
57
56
50
36
44
36
u0
0.4
0.331
0.337
0.417
0.308
0.273
0.249
866
Y. Suntiti and N. Atthapol
which is the advantage. However, in terms of the average illuminance and unifor-
mity, the LED luminaire is less efﬁcient than HPS luminaire, which is the disad-
vantage. To overcome this disadvantage, the polar distribution curve of LED
luminaire is varied in order to improve the average illuminance and uniformity as
shown in Fig. 6. By considering Fig. 6, it can be seen that there are three types of
luminous intensity distribution curves which are in comparison with all arrange-
ments of lighting pole, and the results of simulation are shown in Table 3 and
Table 4.
By considering the data in Table 3, the arrangements of lighting pole in the case
of the roadway without isle (such as single row, double row with offset, and double
row with opposing arrangement) are considered as the case study; it can be seen that
105°
105°
90°
90°
75°
75°
60°
60°
45°
45°
30°
15°
0°
15°
30°
11467 lm
C0 - C180
C90 - C270
cd
3200
4800
6400
105°
105°
90°
90°
75°
75°
60°
60°
45°
45°
30°
15°
0°
15°
30°
η = 86%
η = 100%
C90 - C270
C0 - C180
cd/klm
cd/klm
200
300
400
600
500
105°
105°
90°
90°
75°
75°
60°
60°
45°
45°
30°
15°
0°
15°
30°
C0 - C180
C90 - C270
600
400
800
1000
Polar curve A
Polar curve C
Polar curve B
a
b
c
Fig. 6 Luminous intensity distribution curve or polar curve of various LED luminaires. (a) Polar
curve A. (b) Polar curve B. (c) Polar curve C
Study and Analysis on Lighting Energy Management for Highway
867
Table 3 Comparison among various polar distribution curves of LED luminaire in case of roadway without isle
Data
Standard
Polar curve A
Polar curve B
Polar curve C
Single
row
Double row
with opposing
Double row
with offset
Single
row
Double row
with opposing
Double row
with offset
Single
row
Double row
with opposing
Double row
with offset
Eav
(lux)
21.5
11
22
22
9.16
18
18
12
23
23
Emin
(lux)
–
1.69
5.52
4.16
2.87
6.83
12
3.55
9.1
13
Emax
(lux)
–
34
38
39
24
37
28
23
44
31
u0
0.4
0.188
0.239
0.242
0.313
0.373
0.656
0.305
0.391
0.545
868
Y. Suntiti and N. Atthapol
Table 4 Comparison among various polar distribution curves of LED luminaire in case of roadway with 1.5 m width of isle
Data
Standard
Polar curve A
Polar curve B
Polar curve C
Double row
with offset
Twin
central
Double row
with opposing
Double row
with offset
Twin
central
Double row
with opposing
Double row
with offset
Twin
central
Double row
with opposing
Eav
(lux)
21.5
12
14
12
10
11
10
14
13
14
Emin
(lux)
–
3.84
3.91
3.1
4.45
3.23
4.54
4.37
5.13
4.41
Emax
(lux)
–
36
44
36
24
30
24
25
26
24
u0
0.4
0.308
0.273
0.249
0.425
0.294
0.434
0.321
0.385
0.324
Study and Analysis on Lighting Energy Management for Highway
869
the obtained result of polar distribution curve of type A is equal to that of the
previous section so that it is decided as the base case. Considering the average
illuminance in Table 3, it can be observed that the polar distribution curve of type C
is slightly more than both the standard case and the base case, except for the
arrangement of roadway as single row, while the polar distribution curve of type
B is fairly less than those of all arrangement of roadway.
For the uniformity in Table 3 that is taken into consideration, it can be observed
that the polar distribution curve of type C is signiﬁcantly increased but less than the
standard case, except for arrangement of roadway as double row with offset.
Likewise, the polar distribution curve of type B is similar to that of type C. This
indicates that the luminous intensity distribution curve plays an important role for
the uniformity.
To support this assertion, the arrangements of lighting pole in case of the
roadway with isle are considered as the case study as shown in Table 4. Considering
the average illuminance in Table 4, it can be observed that both polar distribution
curves of type B and type C are fairly less than the standard case for the all
arrangement of roadway. Considering the uniformity in Table 4, it can be observed
that the polar distribution curve of type C is slightly less than that of the all
arrangement of roadway in comparison with the standard case, but the uniformity
is more than the base case. Considering the polar distribution curve of type B, it can
be seen that the obtained uniformity is more than the standard case, except for the
arrangement of roadway in Twin central. As a result, although the polar distribution
curve of type C gives better results than those of the luminous intensity distribution
curve, in terms of the uniformity, it cannot give satisfactory results, especially for
the arrangements of lighting pole in case of the roadway with isle. Due to the
average illuminance and uniformity which are obtained from the standard of
Thailand, it was only speciﬁed for HPS luminaire (150–400 W) so that the LED
luminaire should be improved by changing the installation of roadway lighting in
order for the driver and the pedestrian to beneﬁt.
4
Improvement for LED Luminaire
As in the previous section, the LED luminaire should be improved by changing the
installation of roadway lighting. Generally, the installation of roadway luminaire
under the responsibility of Department of Highways within the geographical
boundary of Thailand is particularly designed for HPS luminance so that the
replacement with LED luminaire may be advantageous in terms of energy saving,
but in terms of illuminance, it will be disadvantageous for the roadway lighting.
This section proposes the methods to improve roadway lighting using mounting
height adjustment method and pole spacing adjustment method.
870
Y. Suntiti and N. Atthapol
Table 5 The comparison result of various mounting heights for the roadway without isle
Roadway without isle
Mounting height (m)
LED
Standard
Single row
Double row with opposing
Double row with offset
6
7
8
9
6
7
8
9
6
7
8
9
Eav Eav[lux]
21.5
14
13
12
12
28
26
25
23
28
26
25
23
u0
0.400
0.110
0.156
0.211
0.280
0.136
0.204
0.287
0.384
0.344
0.420
0.339
0.567
Study and Analysis on Lighting Energy Management for Highway
871
4.1
Mounting Height Adjustment Method
Before the simulation process, the 120 W LED luminaire with polar distribution
curve of type C is employed in this section because it gives better results than the
other polar distribution curves. The arrangements of lighting pole are also consid-
ered such as the roadway without isle and roadway with isle 1.5 m. The mounting
heights of lighting pole are designated as mounting height range of 6–9 m while the
spacing between lighting poles is 40 m.
After adjusting the mounting height, the obtained results for the roadway
without isle are shown in Table 5 and Fig. 7, while the results for the roadway
with isle are shown in Table 6 and Fig. 8.
By considering the data in Table 5, the arrangements of lighting pole in the case
of the street without isle (such as single row, double row with offset, and double row
with opposing arrangement) are considered as the case study; it can be seen that,
when the mounting height is decreased from 9 to 6 m, the average illuminance tends
to increase but the uniformity tends to decrease; this indicates that the reduction of
mounting height can slightly improve the average illuminance but not impact on the
increase of the uniformity. Considering the false color rendering in Fig. 7, it can be
observed that the average illuminance in the case of the arrangement of roadway
such as double row with opposing is equal to that of the double row with offset case,
but the obtained uniformity has a value of approximately two times smaller.
Similarity, for the arrangements of lighting pole in the case of the street with isle
in Table 6 that are taken in consideration, it can be observed that the average
illuminance tends to increase with the decrease in the mounting height, while the
uniformity tends to decrease; this is the same characteristic as the arrangements of
lighting pole in the case of the street without isle. Considering the false color
rendering in Fig. 8, it can be observed that both the average illuminance and the
uniformity for the all arrangement of roadway with isle have a similar value in
comparison with the same mounting height.
0
6.25
12.50
18.75
25
31.25
37.50
43.75
50
1× 
Double row with opposing
Double row with offset
Single row
a
b
c
Fig. 7 The false color rendering in case the mounting height is 6 m for the roadway without isle.
(a) Single row. (b) Double row with opposing. (c) Double row with offset
872
Y. Suntiti and N. Atthapol
Table 6 The comparison result of various mounting heights for the roadway with 1.5 m width of isle
Roadway with isle
Mounting height)m)
LED
Standard
Double row with opposing
Double row with offset
Twin central
6
7
8
9
6
7
8
9
6
7
8
9
Eav [lux]
21.5
15
15
14
14
15
15
14
14
15
14
14
13
u0
0.400
0.129
0.177
0.233
0.299
0.138
0.186
0.239
0.303
0.130
0.188
0.259
0.342
Study and Analysis on Lighting Energy Management for Highway
873
As a result, the adjustment of mounting height has slightly improve the average
illuminance but not for the uniformity.
4.2
Pole Spacing Adjustment Method
Before the simulation process, the 120 W LED luminaire with polar distribution
curve of type C and the arrangements of lighting pole are also used as same as the
previous method. Meanwhile, while the spacing between lighting pole is designated
as pole spacing range of 32–40 m, the mounting height of lighting pole is deter-
mined as 9 m.
After applying the pole spacing method, the obtained results for the roadway
without isle are shown in Table 7 and Fig. 9, while the results for the roadway with
isle are shown in Table 8 and Fig. 10.
By considering the data in Table 7, the arrangements of lighting pole in the case
of the street without isle (such as single row, double row with offset, and double row
with opposing arrangement) are considered as the case study; it can be seen that,
when the pole spacing is decreased from 40 to 32 m for all the arrangement of
lighting pole without isle, the average illuminance and the uniformity tend to
increase; this indicates that the reduction of pole spacing can signiﬁcantly improve
the installation effectiveness of LED luminaire for roadway lighting. Considering
the false color rendering in Fig. 9, it can be observed that the average illuminance in
the case of the arrangement of roadway as double row with opposing is equal to that
of the double row with offset case, but the obtained uniformity has almost the same
value in comparison with the same pole spacing.
Similarly, for the arrangements of lighting pole in the case of the street with isle
in Table 8 that are taken into consideration, it can be observed that the average
0
6.25
12.50
18.75
25
31.25
37.50
43.75
50
1× 
Double row with opposing
Double row with offset
Twin central
a
b
c
Fig. 8 The false color rendering in case the mounting height is 6 m for the roadway with isle. (a)
Double row with opposing. (b) Double row with offset. (c) Twin central
874
Y. Suntiti and N. Atthapol
Table 7 The comparison result of various pole spacings for the roadway without isle
Roadway without isle
Pole distance (m)
LED
Standard
Single row
Double row with opposing
Double row with offset
32
34
36
38
40
32
34
36
38
40
32
34
36
38
40
Eav Eav[lux]
21.5
15
14
13
12
12
29
27
26
25
23
29
27
26
25
23
u0
0.400
0.422
0.394
0.355
0.311
0.280
0.546
0.514
0.475
0.424
0.384
0.569
0.571
0.569
0.567
0.567
Study and Analysis on Lighting Energy Management for Highway
875
illuminance and the uniformity tend to increase with decreasing pole spacing; this is
the same characteristic as the arrangements of lighting pole in the case of the street
without isle. Considering the false color rendering in Fig. 10, it can be observed that
both the average illuminance and the uniformity for the all arrangement of roadway
with isle have almost the same value in comparison with the same pole spacing. As
a result, the adjustment of pole spacing of lighting pole can signiﬁcantly improve
the installation effectiveness of LED luminaire for roadway lighting.
5
Conclusion
This paper presented the impact of installation of LED luminaire roadway lighting
system in terms of energy consumption and illumination, based on the arrangement
of roadway with isle and without isle. In order to evaluate the impact of LED
luminaire, the average illuminance and the uniformity were considered and com-
pared with the roadway lighting standard of Thailand and the installation of
roadway lighting with HPS luminaire and with LED luminaire. The results can be
summarized as follows:
•
In terms of energy consumption, LED luminaire uses less energy than the HPS
luminaire.
•
In terms of illumination, the average illuminance and uniformity of LED lumi-
naire are less than the standard of Thailand as well as the HPS luminaire,
especially for the arrangement of roadway with isle.
•
Three types of luminous intensity distribution curve is considered and compared
in order to improve the average illuminance and uniformity. The obtained results
can be concluded that the polar distribution curve can give more illuminance and
uniformity.
0
6.25
12.50
18.75
25
31.25
37.50
43.75
50
1× 
Single row
Double row with opposing
Double row with offset
a
b
c
Fig. 9 The false color rendering in case the pole spacing is 32 m for the roadway without isle. (a)
Single row. (b) Double row with opposing. (c) Double row with offset
876
Y. Suntiti and N. Atthapol
Table 8 The comparison result of various pole spacings for the roadway with isle
Roadway with isle
Pole distance)m)
LED
Standard
Double row with opposing
Double row with offset
Twin central
32
34
36
38
40
32
34
36
38
40
32
34
36
38
40
Eav Eav[lux]
21.5
17
16
15
14
14
17
16
15
14
14
16
15
15
14
13
u0
0.400
0.423
0.399
0.365
0.327
0.299
0.417
0.397
0.365
0.332
0.303
0.526
0.488
0.438
0.380
0.342
Study and Analysis on Lighting Energy Management for Highway
877
•
The adjustment of mounting height for lighting pole slightly improves the
average illuminance but not for the uniformity.
•
The adjustment of lighting pole spacing can signiﬁcantly improve the installa-
tion effectiveness of LED luminaire for roadway lighting.
Finally, by considering the overall results, the results can be summarized by
focusing on the illumination that is an important factor apart from the energy usage.
Further work will be the consideration of the optimal mounting height and pole
spacing of lighting pole to attain optimum result for both the energy usage and
illumination. In addition, the evaluation of break-even point will be considered to
decide worthily the economic investment for LED luminaire, and it will be useful in
the installation of LED luminaire for roadway lighting in the future.
Acknowledgments The authors wish to gratefully acknowledge ﬁnancial support for this
research (No. KREF045611) from King Mongkut’s Institute of Technology Ladkrabang Research
fund, Thailand.
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Bender, V.C., Mendes, F.B., Maggi, T., Dalla Costa, M.A., Marchesan, T.B.: Design methodology
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Camponogara, D., Ferreira, G.F., Campos, A., Dalla Costa, M.A.: Off-line LED driver for street
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0
6.25
12.50
18.75
25
31.25
37.50
43.75
50
1× 
Double row with opposing
Double row with offset
Twin central
a
b
c
Fig. 10 The false color rendering in case the pole spacing is 32 m for the roadway with isle. (a)
Double row with opposing. (b) Double row with offset. (c) Twin central
878
Y. Suntiti and N. Atthapol
Huang, S., Lee, L., Jeng, M., Hsieh, Y.: Assessment of energy-efﬁcient LED street lighting
through large-scale demonstration, International Conference on Renewable Energy Research
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Li, F., Chen, D., Song, X., Chen, Y.: LEDs: A promising energy-saving light source for road
lighting, Power and Energy Engineering Conference, 1–3, (2009)
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Congress, 1–5 (2008)
Long, X., Liao, R., Zhou, J.: Development of street lighting system-based novel high-brightness
LED modules. IET Optoelectron. 3, 40–46 (2009)
Luo, X., Cheng, T., Xiong, W., Gan, Z., Liu, S.: Thermal analysis of an 80 W light-emitting diode
street lamp. IET Optoelectron. 1, 191–196 (2007)
Luo, Y., Zhang, X., Liu, J., Zhou, C., Qian, K., Han, Y.: LED street lighting technologies with high
human-eye comfortability, International Nano-Optoelectronics Workshop, 84–85 (2008)
Nuttall, D.R., Shuttleworth, R., Routledge, G.: Design of a LED street lighting system, Proc. 4th
IET Conference on Power Electronics, Machines and Drives (PEMD 2008), 436–440 (2008)
Rodrigues, C., Almeida, P.S., Soares, G.M., Jorge, J.M., Pinto, D.P., A C Braga, H.: An experi-
mental comparison between different technologies arising for public lighting: LED luminaires
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Study and Analysis on Lighting Energy Management for Highway
879
Inﬂuence of Wind Farm on Distribution
System: Current Characteristics During
Fault Occurrence
Santipont Ananwattanaporn, Atthapol Ngaopitakkul, Chaiyan Jettanasen,
Chaichan Pothisarn, and Monthon Leelajindakrairerk
1
Introduction
Energy and environmental issue has been giving a huge attention in this past
decade. Energy consumption rate has been rapidly increasing proportion to eco-
nomic growth. Energy from fossil fuel causes an environmental issue due to
emission of greenhouse gas causing greenhouse effect. Many countries have been
investing alternative energy that has more efﬁciency and is environmentally
friendly. Distributed generator (DG) from renewable energy source has become
topic of interest with the increase of environmental issue and cost of transmission.
Wind power generation is one of the renewable energy sources that attracts
attention in many countries and rapidly increases in their capacity (Mozina 2013).
This is because the wind power technology has been currently developed in many
aspects, thus providing reduction in investment costs and increasing performance
and reliability (IEA 2013). Many utilities are connecting DG to distribution system,
and this number is going to get higher in the near future. Several advantages such as
improvement in power reliability, reduction of loss in transmission system, and
improved efﬁciency in network can be achieved. However, signiﬁcant amounts of
DG cause several effects on distribution system that need to be considered and
solved, such as power ﬂow, grid losses, voltage control, protection scheme, and
fault level. Effect of DG strongly depends on type of DG, penetration level, and
location of DG (Coster et al. 2011; Sarabia 2011).
S. Ananwattanaporn • A. Ngaopitakkul (*) • C. Jettanasen • C. Pothisarn
M. Leelajindakrairerk
Department of Electrical Engineering, Faculty of Engineering, King Mongkut’s
Institute of Technology Ladkrabang, Bangkok 10520, Thailand
e-mail: atthapol.ng@kmitl.ac.th
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_62
881
From past research, it can be seen that installation of DG has affected many
aspects of distribution system. Coster et al. (2010) presented effect of distributed
generation on the protection scheme. Another research by Nimpitiwan et al. (2006)
also presented that fault current was increased in the presence of DG and proposed
optimization method to solve protection device coordination. Ciric et al. (2011)
studied and revealed that fault current increased in every type of fault in distribution
with the presence of DG.
In the case of wind power generation, many researches have investigated on the
effect on distribution system. Research of Mozina (2013) has highlighted technical
problem of wind power generation installed into power system that has never been
designed for interconnection of DG. Research on different factors that have an
effect on short circuit behavior in wind power plant has been done by Muljadi et al.
(2013). Many case studies of wind power generation negatively affecting on system
performance have been reviewed (Su et al. 2011; Elansari et al. 2012; Kim et al.
2012; Ouyang et al. 2012; Scarlatache and Grigoras 2014).
It can be seen that wind power generation causes negative effect on distribution
system when disturbance occurs, but many current researches do not clearly
evaluate relation among fault level, fault type, fault location, and wind power
generation location. This paper aims to study an effect of wind power generation
on distribution system characteristics such as current and voltage during fault
occurrence. Many factors able to change system characteristics have been taken
into account such as distribution system consisting of multi-wind power generation,
size of wind power generation, fault type, and location of fault. The system
consisting of single- and multi-wind generation units has been used. Wind power
generation has a capacity of 2 MW. Two types of fault have been used for
simulation: single line-to-ground fault and three-phase fault. This case selection
is based on the fact that about 80% of fault occurrence is single phase-to-ground
fault and three-phase fault, which has the highest fault current in the power system
(The Electricity Training Association 1995). The analysis on system that consists of
wind power generation must be done to ensure safety and reliability of the system
when disturbance occurs.
2
Simulation
The simulation is performed using PSCAD/EMTP. Topology of system under this
study is from the Provincial Electricity Authority (PEA) part of Thailand’s distri-
bution system. Single-line diagram is shown in Fig. 1, and single-line diagram in
PSCAD is shown in Fig. 2. From Fig. 1, it can be seen that the substation bus and
load bus are connected with 30 km transmission line, and wind power generation
bus is connected between the distances of 15 km away from substation. Distribution
model used in this simulation consists of three-phase voltage source with base
voltage and power, which are 22 kV and 100 MVA, respectively. Overhead line
type is space aerial cable (SAC) under PEA’s regulation. Load proﬁle is obtained
882
S. Ananwattanaporn et al.
from PEA’s Nan province substation. The real power and reactive power of total
connected load are 5.2 MW and 2.3 MVar, respectively. Diagram of wind power
generation in detail is shown in Fig. 2. From PSCAD model, wind power generation
consists of wind source that controls wind speed and is located at point 1, wind
turbine governor that controls pitch angle of wind turbine by using speed and real
power and is located at point 2, wind turbine module that generates mechanical
torque and is located at point 3, and synchronous machine with AC exciters that
generates voltage and current to power transformer and is located at point 4. Power
generated from wind power generation ﬂows to grid from point A as shown in
Figs. 2 and 3. The parameter setting for each device is presented in Table 1. To
evaluate the impact of wind power generation, various fault types are used. Data are
measured at different fault points: substation bus, wind power generation bus, fault
location, and load bus.
Sending end
Receiving end
22 kV 50Hz
100MVA
Wind Power
Generation No.1
22 kV 50Hz
2 MW  
Load
5.2 MW 0.91p.f   
Wind Power
Generation No.2 
22 kV 50Hz
2 MW   
 
Fig. 1 Single-line diagram of 22 kv distribution network with integration of 2 MW wind
generation
V
V
A
TLine1
TLine2
Timed
Fault
Logic
A->G
TLine3
TLine4
P+jQ
A
V
Wind Generation No. 1
Wind Generation No. 2
Ph
22.0
0.0
Fig. 2 Single-line diagram of distribution system with installation of two wind generation units
using PSCAD
Inﬂuence of Wind Farm on Distribution System. . .
883
3
Single-Wind Generation Simulation
To evaluate effect of wind generation location on system parameters when fault
occurs, distribution system consisting of single-wind power generation has been
used for simulation. In case studies, parameters taken into account are location of
wind power generation, fault location, and fault type as follows:
– Two MW wind power generation units are installed at 3, 15, and 27 km away
from substation.
– Location of fault is varied along transmission line at 3, 9, 15, 21, and 27 km away
from substation.
TIME
TIME
S2M
L2N
VT
IT
3
If
Ef
Ef0
Vref
VS
Exciter_(AC1A)
Vref0
G
sT
1 + sT
w
w
G1 + sT1
1 + sT2
BETA
ES
Vw
Tm
Vw
Beta
W
P
Wind Turbine
MOD 2 Type
GR
TIME
Wind Turbine
Governor
Wm
Beta
Pg
MOD 2 Type
P
A
B
Ctrl
Ctrl =
1
1.0
A
B
Ctrl
Ctrl =
1
Vw
ES
Wind Source
Mean
w
1.0
A
B
Ctrl
Ctrl =
1
w
N
D
N/D
3.0
Pole pairs
Pref
2 Pi
*
50.0
*
*
Tm0
CNT
CNT
1.0
Controls
30
0
Es
7
200
0
GR
55
3
1
Pref
2
#1
#2
2 [MVA]
0.69 [kV] / 22 [kV]
S
Te
3
A
V
Tm
Tm0
Ef0
Tm
w
Ef
If
1
2
3
4
A
Fig. 3 Diagram of 2 MW wind power generation
884
S. Ananwattanaporn et al.
– Type of fault used in simulation is single line-to-ground and three-phase fault.
– System data obtained from measurement point located at substation bus
(source), wind power generation bus, fault location, and load bus.
Results from the simulation are shown in Tables 2, 3, 4, 5, 6, and 7. The current
waveform when fault occurs in some cases is shown in Fig. 4. For the current
waveform when fault occurs in the case of distribution system with wind power
generation installed at 15 km away from substation, it can be seen from waveform
that in the case of single line-to-ground fault occurrence in Fig. 4a, fault phase in
substation bus and wind power generation has the highest fault current level. In load
bus, fault phase current level drops down due to current from substation and wind
power generation ﬂowing to fault location. The reason is that the impedance in the
system is reduced when fault occurs. In the case of three-phase fault in Fig. 4b,
current in substation bus is increased about 4–5 times that of normal condition in
every phase. Current at wind generation bus also increases, while current at load
bus is much lower than single line-to-ground fault case study. Obtained data used
for analysis by relation between current at different measured points and fault
location are plotted in Figs. 5, 6, 7, 8, 9, and 10.
In Table 2, it shows data obtained from simulation in the case of single line-to-
ground fault occurrence and wind power generation installed at 3 km away from
substation. Obtained results can be summarized as follows:
– In the case without wind power generation, current in substation bus increases
signiﬁcantly when fault occurs and steadily decreases when fault is located
further away from substation bus. On the other hand, voltage is decreased
when fault occurs and steadily increased when fault is located further away
from substation bus. In load bus, current and voltage are increased when fault
moves toward the bus.
Table 1 Parameters of wind power generation unit used in PSCAD simulation
Component
Parameters
Data
Synchronous machine
Rate voltage per phase (kV)
0.398
Rate current (kA)
1.840
Frequency (Hz)
50
Wind turbine
Generator rated (MVA)
2
Rotor radius (m)
43.5
Rotor area (m2)
5944
Air density (kg/m3)
1.225
Wind source
Average wind speed (m/s)
13
Three-phase transformer
Frequency (Hz)
50
Apparent power (MVA)
2
Primary voltage (kV)
0.690
Secondary voltage (kV)
22
Inﬂuence of Wind Farm on Distribution System. . .
885
Table 2 System voltage and current when single phase-to-ground fault occurs (AG) in the case of distribution system with 2 MW wind generation installed at
3 km away from substation
Data
Without wind power generation
With wind power generation
Normal condition
Fault location
Normal condition
Fault location
3
15
27
3
15
27
Source
Voltage rms (kV)
A
21.182
16.654
18.361
19.310
21.976
17.351
19.098
20.069
B
21.182
19.766
20.592
20.762
21.976
20.433
21.334
21.525
C
21.182
21.104
20.931
21.022
21.977
21.840
21.706
21.806
Current rms (kA)
A
0.184
1.661
0.852
0.590
0.126
1.616
0.744
0.469
B
0.184
0.182
0.207
0.218
0.126
0.153
0.130
0.129
C
0.184
0.189
0.203
0.210
0.126
0.126
0.156
0.173
Wind
generation no. 1
Voltage rms (kV)
A
0.000
0.000
0.000
0.000
22.235
14.612
16.913
18.803
B
0.000
0.000
0.000
0.000
22.236
23.791
22.584
22.167
C
0.000
0.000
0.000
0.000
22.235
21.307
22.181
22.359
Current rms (kA)
A
0.000
0.000
0.000
0.000
0.152
0.188
0.172
0.166
B
0.000
0.000
0.000
0.000
0.152
0.148
0.149
0.150
C
0.000
0.000
0.000
0.000
0.151
0.142
0.151
0.151
Load
Voltage rms (kV)
A
18.467
11.394
6.087
4.533
19.401
12.123
6.461
4.792
B
18.467
18.270
20.773
21.916
19.401
19.137
21.767
23.004
C
18.466
18.948
20.368
21.101
19.401
19.852
21.458
22.203
Current rms (kA)
A
0.184
0.114
0.061
0.045
0.194
0.121
0.064
0.048
B
0.184
0.182
0.207
0.219
0.194
0.191
0.217
0.230
C
0.184
0.189
0.203
0.211
0.194
0.198
0.214
0.222
Fault current rms (kA)
A
0.000
1.555
0.793
0.547
0.000
1.652
0.838
0.578
B
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
C
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
886
S. Ananwattanaporn et al.
Table 3 System voltage and current when three-phase fault occurs (ABC) in the case of distribution system with 2 MW wind generation installed at 3 km
away from substation
Data
Without wind power generation
With wind power generation
Normal condition
Fault location
Normal condition
Fault location
3
15
27
3
15
27
Source
Voltage rms (kV)
A
21.182
10.093
14.772
16.921
21.976
10.356
15.319
17.569
B
21.182
10.093
14.772
16.921
21.976
10.355
15.319
17.569
C
21.182
10.093
14.772
16.921
21.977
10.355
15.319
17.569
Current rms (kA)
A
0.184
2.520
1.379
0.948
0.126
2.520
1.288
0.835
B
0.184
2.520
1.379
0.948
0.126
2.520
1.288
0.835
C
0.184
2.520
1.379
0.948
0.126
2.520
1.288
0.835
Wind
generation no. 1
Voltage rms (kV)
A
0.000
0.000
0.000
0.000
22.235
7.875
13.410
16.393
B
0.000
0.000
0.000
0.000
22.236
7.875
13.410
16.393
C
0.000
0.000
0.000
0.000
22.235
7.875
13.410
16.393
Current rms (kA)
A
0.000
0.000
0.000
0.000
0.152
0.196
0.179
0.171
B
0.000
0.000
0.000
0.000
0.152
0.196
0.179
0.171
C
0.000
0.000
0.000
0.000
0.151
0.196
0.179
0.171
Load
Voltage rms (kV)
A
18.467
6.285
3.625
2.631
19.401
6.670
3.842
2.780
B
18.467
6.285
3.625
2.631
19.401
6.670
3.842
2.780
C
18.466
6.285
3.625
2.630
19.401
6.670
3.842
2.780
Current rms (kA)
A
0.184
0.063
0.036
0.026
0.194
0.067
0.038
0.028
B
0.184
0.063
0.036
0.026
0.194
0.067
0.038
0.028
C
0.184
0.063
0.036
0.026
0.194
0.067
0.038
0.028
Fault current rms (kA)
A
0.000
2.465
1.347
0.924
0.000
2.616
1.427
0.977
B
0.000
2.464
1.347
0.924
0.000
2.616
1.427
0.977
C
0.000
2.464
1.347
0.924
0.000
2.616
1.427
0.977
Inﬂuence of Wind Farm on Distribution System. . .
887
Table 4 System voltage and current when single phase-to-ground fault occurs (AG) in the case of distribution system with 2 MW wind generation installed at
15 km away from substation
Data
Without wind power generation
With wind power generation
Normal condition
Fault location
Normal condition
Fault location
3
15
27
3
15
27
Source
Voltage rms (kV)
A
21.182
16.654
18.361
19.310
21.933
17.294
18.879
19.943
B
21.182
19.766
20.592
20.762
21.933
20.415
21.157
21.419
C
21.182
21.104
20.931
21.022
21.933
21.802
21.616
21.736
Current rms (kA)
A
0.184
1.661
0.852
0.590
0.138
1.628
0.793
0.501
B
0.184
0.182
0.207
0.218
0.138
0.164
0.147
0.145
C
0.184
0.189
0.203
0.210
0.138
0.138
0.157
0.178
Wind generation no. 1
Voltage rms (kV)
A
0.000
0.000
0.000
0.000
22.058
14.841
8.074
12.185
B
0.000
0.000
0.000
0.000
22.055
22.730
26.748
23.849
C
0.000
0.000
0.000
0.000
22.057
21.628
23.483
23.389
Current rms (kA)
A
0.000
0.000
0.000
0.000
0.152
0.185
0.192
0.179
B
0.000
0.000
0.000
0.000
0.152
0.154
0.127
0.143
C
0.000
0.000
0.000
0.000
0.152
0.143
0.144
0.147
Load
Voltage rms (kV)
A
18.467
11.394
6.087
4.533
20.312
13.291
6.950
5.116
B
18.467
18.270
20.773
21.916
20.312
20.020
22.535
23.905
C
18.466
18.948
20.368
21.101
20.313
20.668
22.374
23.279
Current rms (kA)
A
0.184
0.114
0.061
0.045
0.203
0.133
0.069
0.051
B
0.184
0.182
0.207
0.219
0.203
0.200
0.225
0.239
C
0.184
0.189
0.203
0.211
0.203
0.206
0.223
0.232
Fault current rms (kA)
A
0.000
1.555
0.793
0.547
0.000
1.645
0.901
0.616
B
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
C
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
888
S. Ananwattanaporn et al.
Table. 5 System voltage and current when three-phase fault occurs (ABC) in the case of distribution system with 2 MW wind generation installed at 15 km
away from substation
Data
Without wind power generation
With wind power generation
Normal condition
Fault location
Normal condition
Fault location
3
15
27
3
15
27
Source
Voltage rms (kV)
A
21.182
10.093
14.772
16.921
21.933
10.309
14.788
17.322
B
21.182
10.093
14.772
16.921
21.933
10.309
14.790
17.323
C
21.182
10.093
14.772
16.921
21.933
10.309
14.789
17.322
Current rms (kA)
A
0.184
2.520
1.379
0.948
0.138
2.528
1.375
0.877
B
0.184
2.520
1.379
0.948
0.138
2.528
1.375
0.877
C
0.184
2.520
1.379
0.948
0.138
2.528
1.375
0.877
Wind generation no. 1
Voltage rms (kV)
A
0.000
0.000
0.000
0.000
22.058
8.702
4.011
9.881
B
0.000
0.000
0.000
0.000
22.055
8.702
4.011
9.881
C
0.000
0.000
0.000
0.000
22.057
8.702
4.012
9.881
Current rms (kA)
A
0.000
0.000
0.000
0.000
0.152
0.193
0.211
0.189
B
0.000
0.000
0.000
0.000
0.152
0.193
0.211
0.189
C
0.000
0.000
0.000
0.000
0.152
0.193
0.211
0.189
Load
Voltage rms (kV)
A
18.467
6.285
3.625
2.631
20.312
7.805
3.654
2.960
B
18.467
6.285
3.625
2.631
20.312
7.805
3.654
2.960
C
18.466
6.285
3.625
2.630
20.313
7.805
3.654
2.960
Current rms (kA)
A
0.184
0.063
0.036
0.026
0.203
0.078
0.036
0.030
B
0.184
0.063
0.036
0.026
0.203
0.078
0.036
0.030
C
0.184
0.063
0.036
0.026
0.203
0.078
0.036
0.030
Fault current rms (kA)
A
0.000
2.465
1.347
0.924
0.000
2.598
1.358
1.040
B
0.000
2.464
1.347
0.924
0.000
2.598
1.358
1.040
C
0.000
2.464
1.347
0.924
0.000
2.598
1.358
1.040
Inﬂuence of Wind Farm on Distribution System. . .
889
Table 6 System voltage and current when single phase-to-ground fault occurs (AG) in the case of distribution system with 2 MW wind generation installed at
27 km away from substation
Data
Without wind power generation
With wind power generation
Normal condition
Fault location
Normal condition
Fault location
3
15
27
3
15
27
Source
Voltage rms (kV)
A
21.182
16.654
18.361
19.310
21.888
17.236
18.852
19.769
B
21.182
19.766
20.592
20.762
21.888
20.379
21.179
21.311
C
21.182
21.104
20.931
21.022
21.888
21.756
21.573
21.649
Current rms (kA)
A
0.184
1.661
0.852
0.590
0.150
1.640
0.800
0.535
B
0.184
0.182
0.207
0.218
0.150
0.174
0.161
0.162
C
0.184
0.189
0.203
0.210
0.150
0.152
0.170
0.184
Wind generation no. 1
Voltage rms (kV)
A
0.000
0.000
0.000
0.000
21.799
14.937
8.629
6.009
B
0.000
0.000
0.000
0.000
21.798
21.690
24.536
26.011
C
0.000
0.000
0.000
0.000
21.801
21.951
23.566
24.572
Current rms (kA)
A
0.000
0.000
0.000
0.000
0.153
0.183
0.189
0.193
B
0.000
0.000
0.000
0.000
0.153
0.159
0.141
0.132
C
0.000
0.000
0.000
0.000
0.153
0.144
0.145
0.140
Load
Voltage rms (kV)
A
18.467
11.394
6.087
4.533
21.223
14.361
8.167
5.468
B
18.467
18.270
20.773
21.916
21.223
20.929
23.507
24.864
C
18.466
18.948
20.368
21.101
21.224
21.551
23.171
24.187
Current rms (kA)
A
0.184
0.114
0.061
0.045
0.212
0.143
0.081
0.054
B
0.184
0.182
0.207
0.219
0.212
0.209
0.235
0.248
C
0.184
0.189
0.203
0.211
0.212
0.215
0.231
0.242
Fault current rms (kA)
A
0.000
1.555
0.793
0.000
1.637
0.143
0.887
0.659
B
0.000
0.000
0.000
0.000
0.000
0.209
0.000
0.000
C
0.000
0.000
0.000
0.000
0.000
0.215
0.000
0.000
890
S. Ananwattanaporn et al.
Table 7 System voltage and current when three-phase fault occurs (ABC) in the case of distribution system with 2 MW wind generation installed at 27 km
away from substation
Data
Without wind power generation
With wind power generation
Normal condition
Fault location
Normal condition
Fault location
3
15
27
3
15
27
Source
Voltage rms (kV)
A
21.182
10.093
14.772
16.921
21.888
10.260
14.788
16.930
B
21.182
10.093
14.772
16.921
21.888
10.259
14.790
16.930
C
21.182
10.093
14.772
16.921
21.888
10.259
14.789
16.931
Current rms (kA)
A
0.184
2.520
1.379
0.948
0.150
2.535
1.375
0.946
B
0.184
2.520
1.379
0.948
0.150
2.535
1.375
0.946
C
0.184
2.520
1.379
0.948
0.150
2.535
1.375
0.946
Wind generation no. 1
Voltage rms (kV)
A
0.000
0.000
0.000
0.000
21.799
9.435
5.352
2.882
B
0.000
0.000
0.000
0.000
21.798
9.435
5.350
2.882
C
0.000
0.000
0.000
0.000
21.801
9.435
5.351
2.882
Current rms
(kA)
A
0.000
0.000
0.000
0.000
0.153
0.191
0.208
0.210
B
0.000
0.000
0.000
0.000
0.153
0.191
0.208
0.210
C
0.000
0.000
0.000
0.000
0.153
0.191
0.208
0.210
Load
Voltage rms (kV)
A
18.467
6.285
3.625
2.631
21.223
8.974
4.717
2.687
B
18.467
6.285
3.625
2.631
21.223
8.974
4.719
2.687
C
18.466
6.285
3.625
2.630
21.224
8.974
4.717
2.687
Current rms (kA)
A
0.184
0.063
0.036
0.026
0.212
0.090
0.046
0.027
B
0.184
0.063
0.036
0.026
0.212
0.090
0.046
0.027
C
0.184
0.063
0.036
0.026
0.212
0.090
0.046
0.027
Fault current rms (kA)
A
0.000
2.465
1.347
0.924
0.000
2.579
1.357
0.944
B
0.000
2.464
1.347
0.924
0.000
2.579
1.357
0.944
C
0.000
2.464
1.347
0.924
0.000
2.579
1.358
0.944
Inﬂuence of Wind Farm on Distribution System. . .
891
Fig. 4 Current waveform when various fault types occur in distribution system with wind power
generation installed at 15 km away from substation. (a) Single line-to-ground fault. (b) Three-
phase fault
892
S. Ananwattanaporn et al.
– In the case with wind power generation, current and voltage are increased in
substation bus, fault bus, and load bus because wind power generation generates
power to fault location, while wind power generation injects constant current.
– The relation between current and location of fault is shown in Fig. 5. It can be
seen that without installation of wind power generation, current measured from
fault location is slightly lower than that measured from substation bus. When
No Fault
3
6
9
12
15
18
21
24
27
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
Fault Location (km)
Current rms (kA)
Source Current
Load Current
Fault Current
Fault Location (km)
No Fault
3
6
9
12
15
18
21
24
27
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
Current rms (kA)
Source Current
Wind Power Current
Load Current
Fault Current
a
b
Fig. 5 System current measured from key bus when single phase-to-ground fault occurs in
distribution system with 2 MW wind generation installed at 3 km away from substation. (a)
Without installation of wind power generation unit. (b) With installation of wind power generation
unit
Inﬂuence of Wind Farm on Distribution System. . .
893
installing wind power generation, current at fault bus is higher than that at
substation bus; this is because wind power generation contributes a signiﬁcant
current to fault location.
In Table 3, it shows data obtained from simulation in the case of three-phase
fault occurrence and wind power generation installed at 3 km away from substation.
The obtained data can be summarized as follows:
No Fault
3
6
9
12
15
18
21
24
27
0
0.5
1
1.5
2
2.5
3
Fault Location (km)
Current rms (kA)
Source Current
Load Current
Fault Current
No Fault
3
6
9
12
15
18
21
24
27
0
0.5
1
1.5
2
2.5
3
Current rms (kA)
Fault Location (km)
Source Current
Wind Power Current
Load Current
Fault Current
a
b
Fig. 6 System current measured from key bus when three-phase fault occurs in distribution
system with 2 MW wind generation installed at 3 km away from substation. (a) Without
installation of wind power generation unit. (b) With installation of wind power generation unit
894
S. Ananwattanaporn et al.
– In the case without wind power generation, current and voltage behave similarly
to the case of single line-to-ground fault, but magnitude of increased current and
decreased voltage is much larger owing to the nature of three-phase fault.
– In the case with wind power generation, voltage at substation bus is increased
but current is decreased owing to current injection of wind power generation into
No Fault
3
6
9
12
15
18
21
24
27
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
Fault Location (km)
Current rms (kA)
Source Current
Load Current
Fault Current
No Fault
3
6
9
12
15
18
21
24
27
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
Current rms (kA)
Fault Location (km)
Source Current
Wind Power Current
Load Current
Fault Current
a
b
Fig. 7 System current measured from key bus when single phase-to-ground fault occurs in
distribution system with 2 MW wind generation installed at 15 km away from substation. (a)
Without installation of wind power generation unit. (b) With installation of wind power generation
unit
Inﬂuence of Wind Farm on Distribution System. . .
895
fault location. Voltage and current at load bus are increased as well as current
injected to fault location.
– The relation between current and location of fault is shown in Fig. 6. It also
shows that installation of wind power generation makes current higher at fault
bus and higher than current at substation bus.
No Fault
3
6
9
12
15
18
21
24
27
0
0.5
1
1.5
2
2.5
3
Fault Location (km)
Current rms (kA)
Source Current
Load Current
Fault Current
No Fault
3
6
9
12
15
18
21
24
27
0
0.5
1
1.5
2
2.5
3
Current rms (kA)
Fault Location (km)
Source Current
Wind Power Current
Load Current
Fault Current
a
b
Fig. 8 System current measured from key bus when three-phase fault occurs in distribution
system with 2 MW wind generation installed at 15 km away from substation. (a) Without
installation of wind power generation unit. (b) With installation of wind power generation unit
896
S. Ananwattanaporn et al.
In Table 4, it shows data obtained from simulation in the case of single line-to-
ground fault occurrence and wind power generation installed at 15 km away from
substation, and Table 5 shows data in the case of three-phase fault. In these two
cases, load current and voltage are higher than the previous case due to the location
of wind power generation that can generate more power to load bus. The relation
between current and fault location behaves like the previous case as shown in Figs.7
and 8.
No Fault
3
6
9
12
15
18
21
24
27
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
Fault Location (km)
Current rms (kA)
Source Current
Load Current
Fault Current
No Fault
3
6
9
12
15
18
21
24
27
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
Current rms (kA)
Fault Location (km)
Source Current
Wind Power Current
Load Current
Fault Current
a
b
Fig. 9 System current measured from key bus when single phase-to-ground fault occurs in
distribution system with 2 MW wind generation installed at 27 km away from substation. (a)
Without installation of wind power generation unit. (b) With installation of wind power generation
unit
Inﬂuence of Wind Farm on Distribution System. . .
897
In Table 6, it shows data obtained from simulation in the case of single line-to-
ground fault occurrence and wind power generation installed at 27 km away from
substation, and in Table 7, it shows data in the case of three-phase fault, In these two
cases, load current and voltage are also higher than the previous case, but fault
current is equal to substation bus current in three-phase fault because wind power
generation generates power to load bus, not to fault location.
No Fault
3
6
9
12
15
18
21
24
27
0
0.5
1
1.5
2
2.5
3
Fault Location (km)
Current rms (kA)
Source Current
Load Current
Fault Current
No Fault
3
6
9
12
15
18
21
24
27
0
0.5
1
1.5
2
2.5
3
Current rms (kA)
Fault Location (km)
Source Current
Wind Power Current
Load Current
Fault Current
a
b
Fig. 10 System current measured from key bus when three-phase fault occurs in distribution
system with 2 MW wind generation installed at 27 km away from substation. (a) Without
installation of wind power generation unit. (b) With installation of wind power generation unit
898
S. Ananwattanaporn et al.
4
Multi-wind Generation Simulation
To evaluate the effect of multi-wind generation, distribution system consisting of
two wind power generations with capacity of 2 MW has been used. Simulation in
these case studies is done by ﬁxing wind generation No. 1 at 3 km away from
substation, and wind generation No. 2 is varied along distribution line at 6, 9,
12, 15, 18, 21, 24, and 27 km, respectively. Parameters measured from simulation
model are three-phase voltage and three-phase current. Measurement points are
located at substation bus (source), wind generation No. 1 bus, wind generation
No. 2 bus, fault location, and load bus. Results from the simulation are shown in
Tables 8, 9, and 10.
Table 8 shows data in the case of distribution system without fault. In normal
condition, current from substation increases when wind power generation unit
2 moves toward load bus. This increases load current and voltage.
In Table 9, it shows data in the case of distribution system when single line-to-
ground fault occurs at 15 km away from substation. Current from substation is
increased since wind generation unit 2 moves toward load. Substation and wind
power generation unit 1 current in phase A are increased signiﬁcantly.
In Table 10, it shows data in the case of distribution system when three-phase
fault occurs at 15 km away from substation. Voltage and current are dropped down
near zero because all sources are located before fault location; power cannot ﬂow to
load bus until wind power generation unit 2 is located between fault location and
load bus.
5
Conclusion
This paper has studied the effect of wind power generation-integrated distribution
system in the case of single- and multi-wind power generation units, in terms of
voltage and current characteristics. The results indicated that wind power genera-
tion injected signiﬁcant amount of current when fault occurred in distribution
system. When fault occurred between substation bus and wind power generation
bus, current at load bus was increased when compared to the system without wind
power generation due to current from wind power generation unit. Current from
substation bus was decreased when fault occurred after wind power generation bus
and load bus because wind power injected current to fault location.
Location of DG has impact on source and load current when wind power
generation is installed near substation; current at fault location is higher than the
case of installing near load. The reason is that current from wind power generation
installed near load ﬂows into load side. Current from wind power generation when
installing near substation is contributed to fault location, thus, increasing fault
current.
Inﬂuence of Wind Farm on Distribution System. . .
899
Table 8 System current and voltage in the case of distribution system with 3 MW multi-wind generation installation ﬁxed at 3 km and another generation
varied along transmission line
Data
Location of wind generation no. 2 (km)
6
9
12
15
18
21
24
27
Source
Voltage rms (kV)
A
23.093
23.093
23.093
23.093
23.093
23.093
23.093
23.093
B
23.093
23.093
23.093
23.093
23.093
23.093
23.093
23.093
C
23.093
23.093
23.093
23.093
23.093
23.093
23.093
23.093
Current rms (kA)
A
0.066
0.068
0.071
0.074
0.077
0.080
0.083
0.087
B
0.066
0.068
0.071
0.074
0.077
0.080
0.083
0.087
C
0.066
0.068
0.071
0.074
0.077
0.080
0.083
0.087
Wind generation no. 1
Voltage rms (kV)
A
22.878
22.693
22.521
22.359
22.200
22.055
21.911
21.779
B
22.878
22.693
22.521
22.521
22.200
22.055
21.911
21.779
C
22.878
22.693
22.521
22.521
22.200
22.055
21.911
21.779
Current rms (kA)
A
0.100
0.100
0.100
0.1000
0.100
0.100
0.100
0.100
B
0.100
0.100
0.100
0.1000
0.100
0.100
0.100
0.100
C
0.100
0.100
0.100
0.1000
0.100
0.100
0.100
0.100
Wind generation no. 2
Voltage rms (kV)
A
23.081
23.087
23.093
23.098
23.103
23.107
23.111
23.114
B
23.081
23.087
23.093
23.098
23.103
23.107
23.111
23.114
C
23.081
23.087
23.093
23.098
23.103
23.107
23.111
23.114
Current rms (kA)
A
0.063
0.066
0.068
0.071
0.073
0.076
0.078
0.080
B
0.063
0.066
0.068
0.071
0.073
0.076
0.078
0.080
C
0.063
0.066
0.068
0.071
0.073
0.076
0.078
0.080
Load
Voltage rms (kV)
A
20.465
20.592
20.717
20.850
20.995
21.147
21.308
21.475
B
20.465
20.592
20.717
20.850
20.995
21.147
21.308
21.475
C
20.465
20.592
20.717
20.850
20.995
21.147
21.308
21.475
Current rms (kA)
A
0.194
0.196
0.197
0.198
0.199
0.201
0.202
0.203
B
0.194
0.196
0.197
0.198
0.199
0.201
0.202
0.203
C
0.194
0.196
0.197
0.198
0.199
0.201
0.202
0.203
900
S. Ananwattanaporn et al.
Table 9 System current and voltage when single phase-to-ground fault occurs (AG) in the case of distribution system with 3 MW multi-wind generation
installation ﬁxed at 3 km and another generation varied along transmission line
Data
Location of wind generation no. 2 (km)
6
9
12
15
18
21
24
27
Source
Voltage rms
(kV)
A
23.094
23.094
23.094
23.094
23.094
23.094
23.094
23.094
B
23,094
23,094
23,094
23,094
23,094
23,094
23,094
23,094
C
23.094
23.094
23.094
23.094
23.094
23.094
23.094
23.094
Current rms
(kA)
A
1.099
1.092
1.100
1.115
1.112
1.108
1.106
1.102
B
0.074
0.074
0.072
0.071
0.069
0.066
0.073
0.062
C
0.121
0.121
0.119
0.123
0.121
0.126
0.135
0.126
Wind generation
no. 1
Voltage rms
(kV)
A
14.170
9.546
4.837
0.024
0.932
1.846
2.714
3.391
B
25.401
26.864
28.394
29.910
28.941
28.030
27.116
26.518
C
25.195
26.597
27.568
28.978
28.664
28.442
28.201
27.819
Current rms
(kA)
A
0.075
0.086
0.102
0.123
0.118
0.117
0.119
0.121
B
0.059
0.060
0.064
0.069
0.069
0.071
0.077
0.079
C
0.075
0.082
0.089
0.094
0.095
0.096
0.101
0.103
Wind generation
no. 2
Voltage rms
(kV)
A
18.738
18.743
18.704
18.64
18.642
18.641
18.641
18.641
B
24.268
24.265
24.306
24.373
24.367
24.392
24.396
24.429
C
24.183
24.245
24.216
24.201
24.187
24.212
24.229
24.232
Current rms
(kA)
A
0.105
0.105
0.105
0.105
0.105
0.105
0.106
0.105
B
0.099
0.100
0.100
0.100
0.100
0.100
0.100
0.100
C
0.110
0.110
0.110
0.110
0.110
0.110
0.110
0.110
(continued)
Inﬂuence of Wind Farm on Distribution System. . .
901
Table 9 (continued)
Data
Location of wind generation no. 2 (km)
6
9
12
15
18
21
24
27
Load
Voltage rms
(kV)
A
3.387
3.427
3.446
3.473
3.640
3.901
4.086
4.648
B
24.105
24.325
24.382
24.468
24.611
24.713
24.953
24.381
C
26.219
26.390
26.625
26.844
26.962
27.259
27.491
27.083
Current rms
(kA)
A
0.032
0.032
0.033
0.033
0.035
0.037
0.039
0.044
B
0.229
0.231
0.232
0.237
0.234
0.235
0.237
0.231
C
0.249
0.251
0.253
0.255
0.256
0.259
0.261
0.257
Fault current (kA)
1.269
1.277
1.302
1.338
1.329
1.326
1.324
1.316
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
902
S. Ananwattanaporn et al.
Table 10 System current and voltage when three-phase fault occurs (ABC) in the case of distribution system with 3 MW multi-wind generation installation
ﬁxed at 3 km and another generation varied along transmission line
Data
Location of wind generation no. 2 (km)
6
9
12
15
18
21
24
27
Source
Voltage rms (kV)
A
23.094
23.094
23.094
23.094
23.094
23.094
23.094
23.094
B
23.094
23.094
23.094
23.094
23.094
23.094
23.094
23.094
C
23.094
23.094
23.094
23.094
23.094
23.094
23.094
23.094
Current rms (kV)
A
2.262
2.262
2.297
2.319
2.318
2.319
2.319
2.318
B
2.262
2.261
2.304
2.319
2.318
2.319
2.319
2.318
C
2.262
2.262
2.300
2.319
2.318
2.319
2.319
2.318
Wind generation no. 1
Voltage rms (kV)
A
14.216
9.584
10.278
10.190
10.236
10.248
10.232
10.271
B
14.218
9.584
10.278
10.190
10.236
10.248
10.232
10.271
C
14.217
9.584
10.278
10.190
10.236
10.248
10.232
10.271
Current rms (kV)
A
0.104
0.103
0.170
0.180
0.173
0.173
0.171
0.172
B
0.103
0.102
0.172
0.177
0.172
0.170
0.171
0.170
C
0.103
0.102
0.159
0.165
0.173
0.172
0.176
0.171
Wind generation no. 2
Voltage rms (kV)
A
18.781
18.779
18.710
18.673
18.674
18.674
18.675
18.673
B
18.780
18.780
18.697
18.673
18.674
18.674
18.675
18.673
C
18.781
18.779
18.697
18.673
18.674
18.674
18.675
18.673
Current rms (kV)
A
0.132
0.129
0.131
0.131
0.132
0.130
0.132
0.132
B
0.132
0.130
0.131
0.131
0.132
0.130
0.132
0.132
C
0.132
0.130
0.131
0.131
0.132
0.130
0.132
0.132
Load
Voltage rms (kV)
A
0.017
0.017
0.014
0.014
0.418
0.888
1.301
1.745
B
0.015
0.015
0.014
0.014
0.421
0.909
1.299
1.745
C
0.015
0.015
0.013
0.014
0.412
0.894
1.288
1.745
Current rms (kV)
A
0.001
0.001
0.000
0.000
0.005
0.008
0.012
0.016
B
0.001
0.001
0.000
0.000
0.004
0.008
0.012
0.016
C
0.001
0.001
0.000
0.000
0.004
0.008
0.012
0.016
(continued)
Inﬂuence of Wind Farm on Distribution System. . .
903
Table 10 (continued)
Data
Location of wind generation no. 2 (km)
6
9
12
15
18
21
24
27
Fault current (kA)
A
2.484
2.484
2.500
2.566
2.371
2.423
2.529
2.565
B
2.484
2.484
2.500
2.596
2.397
2.400
2.520
2.557
C
2.484
2.484
2.526
2.564
2.407
2.437
2.554
2.531
904
S. Ananwattanaporn et al.
In the case of multi-wind power generation unit located near the fault location, it
contributed signiﬁcant amount of current to fault location. When unit was located
near load, it generated power to load instead of source or other wind power
generation unit. These two case studies revealed that wind power generation
injected current to fault location. This has changed current characteristics in
distribution system and caused a false operation for protective device. A design
for new protection scheme when installing wind power generation to distribution
system must be carefully done in order to ensure reliability of the system.
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Inﬂuence of Wind Farm on Distribution System. . .
905
Operating Oil Reﬁnery Units Under
Uncertainty: Thermodynamic
and Economic Implications
Eid Al-Mutairi
1
Introduction
Energy conservation is of prime importance in the process industries because of the
cost and environmental impact of energy production. The pinch design method
(PDM) has been extensively used to achieve energy savings (Linnhoff and
Hindmarsh 1983; Linnhoff et al. 1982; Smith 2005). This technique has been
studied at length using different approaches, such as genetic algorithms (Allen
et al. 2009; Dipama et al. 2008), tree searching methods (Pho and Lapidus 1973),
and neural networks (Bittanti and Piroddi 1997). The assumption of constant
process stream parameters within the supply and target temperature bounds is
common in most of the methodologies proposed for analyzing and solving heat
exchanger network (HEN) synthesis problems (Ponce-Ortega et al. 2008).
The physical properties of the process streams depend on the temperature
variation along the process streams in the HEN. This temperature dependency
could have a signiﬁcant effect on the design and retroﬁt of the HEN. Using the
concept of the PDM and an evolutionary design, the synthesis of a HEN for
maximum energy recovery is guided by the determination of (i) the target utilities
and (ii) the pinch point (PP). The outcomes of these design procedures are highly
dependent on the number of process streams, their supplied and targeted tempera-
tures, and the heat capacity ﬂow rates.
The assumption of a constant speciﬁc heat capacity (Cp) would only be a valid
approximation for process streams existing within the temperature bounds in which
no phase change occurred. However, for phase-changing process streams, the
E. Al-Mutairi (*)
King Fahd Universiry of Petroleum & Minerals, Department of Chemical Engineering,
Dhahran 31261, Saudi Arabia
e-mail: mutairi@kfupm.edu.sa
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_63
907
assumption of a constant Cp at the targeting and synthesis stages is no longer valid.
Hence, any conclusion drawn based on this assumption may not be accurate. Castier
and Queiroz (2002) reported a methodology based on the PP method to solve the
energy targeting problem with phase-changing streams. Liporace et al. (2004) and
Nejad et al. (2012) presented alternative simpliﬁed procedures in which the streams
with phase changes were divided into sub-streams. However, they did not provide
detailed reports on how the stream properties responded to variations of the process
stream temperature.
In the present study, various process stream parameters sensitive to temperature
variations were estimated to provide insight on how they can affect the analysis of a
HEN. The problem table algorithm (PTA) approach for utility targeting in the PDM
was used to compare the energy targets and PPs for (1) the base case, when the
process stream properties are constant within temperature intervals deﬁned by the
supply and target temperatures, and (2) the phase change case, when the process
stream properties are constant within the temperature interval deﬁned by the dew
and bubble points.
2
Data Extraction and Process Description
The examined process streams and the data used for this analysis were extracted
from simulated data from an existing and functional crude distillation unit. Crude
distillation units are mostly similar, with various degrees of design complexity.
Generally, the crude feed passes through a group of exchangers, a desalter, followed
by another group of exchangers, a preﬂash column, a heater, and a main column.
From the main column, a stream leads to an overhead system with a condenser, a
compressor (for gas recovery, stabilization, and liqueﬁed petroleum gas recovery),
and a gasoline splitter. Additionally, side streams enter columns that strip side
products. From the bottom of the main column, the topping residue passes through a
series of exchangers and is sent to storage.
3
Methodology
The simulation model of the process was developed using the data obtained from
the plant. The parameters for the process ﬂow diagram were input in the HYSYS
(2003) process simulator software. This enabled the validation of simulation data
through the comparison with operation data. The simulation was adjusted until a
close representation of the real process was obtained. From this model, the stream
data were extracted. This data set represented the base case process stream data. To
account for the variations of the process stream parameters with the temperature,
the supply and target temperature bounds were segmented into new temperature
intervals deﬁned by the dew and bubble points. The thermodynamic properties of
908
E. Al-Mutairi
the process stream mixture were estimated using an Excel macro-enabled software
(ChemSOF), and the analysis was performed using Microsoft Excel spreadsheets.
The PPs and the utility demands were estimated using a heat-integration software
(Heat-Int 2012).
3.1
Estimation of Heat Load in the Existing Stream
A commonly used heat exchanger in this type of operation is the U-tube shell and
tube heat exchanger. The heat capacity ﬂow rate for each stream, which is the
product of the mass ﬂow rate (M) and the speciﬁc heat capacity (Cp), was estimated
from the heat load of the heat exchanger and the supply and target temperatures
using Eq. (1) (Sinnott 2005):
MCp ¼ Q
ΔT :
ð1Þ
3.2
Determination of Bubble Point, Dew Point, Vaporized
Fraction, and Enthalpy
To calculate appropriate temperature intervals with constant process stream prop-
erties for a reasonable approximation, the dew and bubble point temperatures were
estimated. Subsequently, the existing supply and target temperature bounds in each
process stream were segmented into temperature sub-intervals using the estimated
bubble and dew points to deﬁne the new temperature bounds. Because the compo-
nents of the mixture were mostly organic compounds (see Table 2), the Peng–
Robinson equation of state was used for the estimation of the bubble and dew point
temperatures, and the total pressure for the process stream components was
assumed constant. The online chemical engineering software ChemSOF was used
for the estimation of multicomponent bubble points and dew points, and for ﬂash
calculations. The vaporized fractions of the mixture at different temperatures were
also generated by the software. The enthalpy of the mixture was estimated by the
weighted average of the enthalpy of the individual components at the speciﬁed
temperature using Eq. (2) (Levine 1997; Singh and Heldman 1993):
Hm ¼ Hiwi þ . . . þ Hnwn,
ð2Þ
where Hm is the enthalpy of the mixture, Hi is the enthalpy of component i, wi is the
mass fraction of component i, and n is the number of components in the mixture.
Operating Oil Reﬁnery Units Under Uncertainty: Thermodynamic and Economic. . .
909
3.3
Estimation of Mass Flow Rate and Effective Mean
Speciﬁc Heat Capacity
For the purpose of energy targeting, the mass ﬂow rate and the effective mean
speciﬁc heat capacity Cpeff

for each stream were estimated using Eqs. (3) and (4),
respectively:
Mass flow rate M
ð
Þ
kg
s


¼ Heat load ΔH
ð
Þ
kJ
s
 
Enthalpy H
ð Þ
kJ
kg
  ,
ð3Þ
Cpeff

¼
kJ
kg
C


¼
Heat capacity flow rate MCp


kJ
sC


Mass flow rate M
ð
Þ
kg
s
 
:
ð4Þ
The enthalpy change ΔH0 was estimated using Eq. (5):
The enthalpy change ΔH0
ð
Þ kJ
kg


¼ H0
S  H0
T,
ð5Þ
where H0
S and H0
T are the enthalpy values at the supply and target temperatures,
respectively.
Depending on the supply and target temperatures of the base case process
stream, the interval bounds in the phase change process stream may exist under
superheating, phase change, and subcooling conditions. Hence, to account for phase
changes in the process stream, the base case process streams were segmented using
the temperature interval bounds deﬁned as follows:
For the superheating condition:
TS  T  TDP,
ð6Þ
for the changing phase condition:
TDP  T  TBP,
ð7Þ
and for the subcooling condition:
TS  T  TBP:
ð8Þ
The consideration of the phase change was important because the assumption of
a linear distribution of energy during sensible heat transfer due to the superheating,
phase change, and subcooling conditions was a more realistic and reasonable
approximation than the consideration of sensible heat transfer based on the process
stream supply and target temperatures.
910
E. Al-Mutairi
3.4
Determination of Thermodynamic Properties
of the Process Stream Mixture
The equation of state (EOS) property calculation spreadsheet in ChemSOF was
used for the estimation of the enthalpy (H) and density (ρ) of each component of the
mixture at speciﬁed temperatures. Subsequently, these thermodynamic properties
were used with the appropriate mixing rules to obtain the properties of the mixture
of the process streams. The following properties of the mixture were estimated: the
speciﬁc heat capacity of the mixture, the viscosity of the mixture, the thermal
conductivity of the mixture, the Nusselt number, the Reynolds number, the Prandtl
number, and the heat transfer coefﬁcient. A tube internal diameter (De) of
0.01905 m was assumed for the heat exchanger and used in the estimations (Coletti
and Macchietto 2011).
3.4.1
Estimation of Prandtl Number
The Prandtl number (Pr) is a function of the speciﬁc heat capacity, ﬂuid viscosity,
and thermal conductivity. The Pr for the hot stream, z, and the cold stream, j, were
estimated using Eqs. (9a) and (9b), respectively (Wilkes et al. 2010):
Pr,z ¼ Cpm,zμm,z
km,z
ð9aÞ
Pr,j ¼ Cpm,jμm,j
km,j
ð9bÞ
where Cpm , z and km , z are the respective speciﬁc heat capacity and thermal con-
ductivity of the process stream mixture for the hot stream and Cpm , j and km , j are the
respective speciﬁc heat capacity and thermal conductivity of the process stream
mixture for the cold stream.
The speciﬁc heat capacities of the individual components of the mixture at each
speciﬁed temperature were obtained from the literature (Levine 1997). The speciﬁc
heat capacity of the mixture was estimated by the weighted average of the speciﬁc
heat capacities of the individual components at the speciﬁed temperature using
Eq. (10) (Singh and Heldman 1993):
Cp,m ¼ Cp,iwi þ . . . þ Cp,nwn
ð10Þ
where Cp , m is the speciﬁc heat capacity of the mixture and Cp , i is the speciﬁc heat
capacity of the individual component i in the mixture.
For the estimation of the thermal conductivity (ki) of each component of the
mixture, the Weber equation (Weber 1980), given as Eq. (11), was used:
Operating Oil Reﬁnery Units Under Uncertainty: Thermodynamic and Economic. . .
911
ki ¼ 3:56  105Cp
ρ4
i
Mwt

1
=
3:
ð11Þ
Weighted averages are sufﬁciently accurate for the estimation of the thermal
conductivity of the mixtures (km). Hence, Eq. (12) was used:
km ¼
X
n
i¼1
kiwi:
ð12Þ
The heat transfer coefﬁcient is a function of the Nusselt number (Nu), the thermal
conductivity, and the inner tube diameter of the heat exchanger. It is expressed by
Eqs. (13a) and (13b) for the hot stream, z, and the cold stream, j, respectively:
hz ¼ Nuzkz
De
ð13aÞ
hj ¼ Nujkj
De
,
ð13bÞ
whereas the Nusselt number is computed as follows.
For gases and for liquids at high or moderate Reynolds numbers, the Nusselt
number can be estimated for the hot and cold streams using Eqs. (14a) and (14b),
respectively (Donohue 1949):
Nuz ¼ 0:023 NRez
ð
Þ0:8 Prz
ð
Þ0:4
ð14aÞ
Nuj ¼ 0:023 NRej

0:8 Prj

0:3:
ð14bÞ
4
Results and Discussion
4.1
Results of Process Stream Data Extraction
The results of the process stream identiﬁcation and the speciﬁcation of the base case
indicated 24 process streams, of which 16 were hot and eight were cold, as shown in
Table 1. The two utility sources were steam and cooling water. Using Eq. (1), the
heat capacity ﬂow rates for each process stream were estimated and are presented in
Table 1.
912
E. Al-Mutairi
4.2
Results of Bubble Point, Dew Point, Vaporized Fraction,
and Enthalpy Calculations
The bubble and dew point temperatures for the mixture were 105.37 C and
159.07 C, respectively. A comparison of the bubble and dew point temperatures
with the supply and target temperatures in Table 1 revealed the possibility of phase
changes in the process. This thermodynamic observation showed the true trend of
the heat distribution in the shell and tube sides of the heat exchanger. Table 2
presents the condition of the process stream mixture, the vaporized fraction, and
their enthalpy at speciﬁed temperatures.
The examination of the enthalpy values provided a clear picture of the temper-
ature dependence of the enthalpy. As expected, the enthalpy increased as the
Table 1 Base case process streams data
Streams
TS (C)
TT (C)
Heat capacity ﬂow rate (MCp; kW/C)
Heat load (ΔH; kW)
Hot streams
1
366
188.6
37
6563.8
2
340.6
60
29
8137.4
3
328.8
318
172
1857.6
4
292.2
222.6
324
22,550.4
5
272.9
55
142
30,941.8
6
237
157.7
336
26,644.8
7
222.6
131.1
77
7045.5
8
188.5
40
51
7573.5
9
172.7
145.7
107
2889
10
165.7
75
230
20,861
11
146.1
55
225
20,497.5
12
137.7
40
60
5862
13
135.9
40
33
3164.7
14
118.1
45
291
21,272.1
15
93
40
107
5671
16
56.4
38
207
3808.8
Cold streams
17
35
223.9
459
86,705.1
18
223.9
239
1257
18,980.7
19
45
118
62
4526
20
55
120.8
72
4737.6
21
127.6
135.9
263
2182.9
22
137.7
145
573
4182.9
23
232.9
264.6
441
13,979.7
24
264.6
365.5
679
68,511.1
Utilities streams
CU
25
30
–
–
HU
450
440
–
–
Operating Oil Reﬁnery Units Under Uncertainty: Thermodynamic and Economic. . .
913
Table 2 Vaporized mass fraction, condition, and calculated enthalpy for the process stream
mixture as a function of temperature
Temperature of mixture
(C)
Condition
Vaporized mass
fraction
Enthalpy (H0)
(kJ/kg)
366
Vapor
1.0000
657.42
365.5
Vapor
1.0000
658.39
340.6
Vapor
1.0000
709.32
328.8
Vapor
1.0000
735.48
318
Vapor
1.0000
760.7
292.2
Vapor
1.0000
826.57
272.9
Vapor
1.0000
881.84
264.6
Vapor
1.0000
907.45
239
Vapor
1.0000
994.66
237
Vapor
1.0000
1002.06
232.9
Vapor
1.0000
1017.53
223.9
Vapor
1.0000
1052.97
222.6
Vapor
1.0000
1058.41
188.6
Vapor
1.0000
7169.49
188.5
Vapor
1.0000
7171.01
172.7
Vapor
1.0000
7407.42
165.7
Vapor
1.0000
13,932.5
159.07
Vapor
1.0000
14,101.7
157.7
Liquid + vapor
0.9437
14,136.2
146.1
Liquid + vapor
0.5805
14423.3
145.7
Liquid + vapor
0.5709
14,433
145
Liquid + vapor
0.5545
14,450.1
137.7
Liquid + vapor
0.4065
14,626.4
135.9
Liquid + vapor
0.3754
14,669.5
131.1
Liquid + vapor
0.3
14,783.9
127.6
Liquid + vapor
0.2507
14,875.4
120.8
Liquid + vapor
0.165
15,026.9
118.1
Liquid + vapor
0.1337
15,090.2
118
Liquid + vapor
0.1326
15,092.5
105.37
Liquid
0.0000
18,899.1
93
Liquid
0.0000
19,280.4
75
Liquid
0.0000
19,817.2
60
Liquid
0.0000
22,758
56.4
Liquid
0.0000
25,176.8
55
Liquid
0.0000
25,233.1
45
Liquid
0.0000
25,627.5
40
Liquid
0.0000
25,820.2
38
Liquid
0.0000
25,896.5
35
Liquid
0.0000
26,010.3
914
E. Al-Mutairi
temperature of the process stream mixture increased as shown in Fig. 1. The
dependence of the density of the process stream mixture on the temperature is
also shown in Fig. 2. The density of the mixture increased as the temperature of the
mixture decreased. At high temperatures, the volume of the unit mass of the
mixtures increased and thereby produced a decrease in the density.
4.3
Results of Mass Flow Rate, Enthalpy, and Effective
Mean Speciﬁc Heat Capacity Calculations
The effective mass ﬂow rate, speciﬁc heat capacity, and enthalpy of the base case
process streams are presented in Table 3, as estimated using the data from Tables 1
and 3 in Eqs. (3), (4), and (5), respectively. As observed from the heat load and
enthalpy change columns in the table, the heat distribution was not a linear function.
It depended on the process stream supply and target temperatures and the heat
capacity ﬂow rate. Consequently, the wider the difference between the supply and
target temperature ranges, the wider the deviation from the exact representation of
Fig. 1 Dependence of enthalpy on temperature
Operating Oil Reﬁnery Units Under Uncertainty: Thermodynamic and Economic. . .
915
Table 3 Process stream properties for the base case
Streams
TS (C)
TT (C)
Heat capacity ﬂow rate (MCp) (kW/C)
Heat load (ΔH) (kW)
Hot stream
1
366
188.6
37
6563.8
2
340.6
60
29
8137.4
3
328.8
318
172
1857.6
4
292.2
222.6
324
22,550.4
5
272.9
55
142
30,941.8
6
237
157.7
336
26,644.8
7
222.6
131.1
77
7045.5
8
188.5
40
51
7573.5
9
172.7
145.7
107
2889
10
165.7
75
230
20,861
11
146.1
55
225
20,497.5
12
137.7
40
60
5862
13
135.9
40
33
3164.7
14
118.1
45
291
21,272.1
15
93
40
107
5671
16
56.4
38
207
3808.8
Cold stream
17
35
223.9
459
86,705.1
18
223.9
239
1257
18,980.7
19
45
118
62
4526
20
55
120.8
72
4737.6
21
127.6
135.9
263
2182.9
22
137.7
145
573
4182.9
23
232.9
264.6
441
13,979.7
24
264.6
365.5
679
68511.1
Enthalpy change (ΔH0)
(kJ/kg)
Mass ﬂow rate (M)
(kg/s)
Effective mean heat capacity (Cp)
(kJ/kgC)
Hot stream
6512.07
1.0
36.70
22048.68
0.36
78.57
25.22
73.65
2.33
231.84
97.26
3.33
24351.26
1.27
111.75
13134.14
2.02
165.62
13725.49
0.51
150.0
18649.19
0.40
125.58
7025.58
0.41
260.20
5884.7
3.54
64.88
10809.8
1.894
118.65
11193.8
0.52
114.57
11150.7
0.28
116.27
(continued)
916
E. Al-Mutairi
the process when the assumption of a constant process parameter was made in the
process analysis.
The results for the phase change case are presented in Table 4. The values ofCpeff

in both Tables 4 and 5 further revealed their dependency on the supply and
temperature bounds. The new phase change process streams presented in Table 4
gave a better and more realistic approximation for the heat distribution in the
process streams (Figs. 3 and 4).
4.4
Energy Targeting Using Problem Table Algorithms
Energy targeting was performed by inputting the data presented in Table 4 into
heat-integration software (Heat-Int 2012). The results for the base and phase change
cases and for different values of ΔTmin are presented in Table 5. The phase changes
affected the PPs and utility demands. This inﬂuence was more pronounced as the
ΔTmin increased. The crude distillation unit retroﬁt is often based on a ΔTmin of
30–40 C (Linnhoff 1998). The hot and cold pinch temperatures at a ΔTmin of 30 C
were 253.9 C and 223.9 C, respectively, for the constant Cp case, whereas the hot
and cold pinch temperatures were 237.0 C and 207.0 C, respectively, for the
phase change case. The reduction in the pinch points in the phase change case
compared with the constant Cp scenario was an indication of a better heat distribu-
tion for the former. In the base case, the heating utility demand in the phase change
case at a ΔTmin of 30 C increased to 80655.1 kW from 77844.7 kW. The cooling
utility demand in the phase change case at the same ΔTmin also increased to
72190.0 kW from 69379.6 kW. This trend was the same for the other values of
ΔTmin except that the percent increase in utility demands increased with increasing
ΔTmin.
Table 3 (continued)
Enthalpy change (ΔH0)
(kJ/kg)
Mass ﬂow rate (M)
(kg/s)
Effective mean heat capacity (Cp)
(kJ/kgC)
10537.3
2.01
144.14
6539.8
0.86
123.39
719.7
5.295
39.11
Cold stream
24957.33
3.47
132.11
58.31
325.51
3.86
10,535
0.429
144.31
10206.2
0.46
155.10
205.9
10.60
24.80
176.3
23.72
24.15
110.08
126.99
3.47
249.06
275.07
2.46
Operating Oil Reﬁnery Units Under Uncertainty: Thermodynamic and Economic. . .
917
Table 4 Process stream properties for the phase change original process streams (base case) to
generate the case. The ﬁrst two columns show the segmentation of the new process streams (phase
change case)
Original streams
New streams
TS (C)
TT (C)
ΔH0 (kJ/kg)
1
1
366
188.6
6512.07
2
2
340.6
159.07
13,392.38
3
159.07
105.37
4797.4
4
105.37
60
3858.9
3
5
328.8
318
25.22
4
6
292.2
222.6
231.84
5
7
272.9
159.07
13,219.86
8
159.07
105.37
4797.4
9
105.37
55
6334
6
10
237
159.07
13,099.64
11
159.07
157.7
34.5
7
12
222.6
159.07
13,043.29
13
159.07
131.1
682.2
8
14
188.5
159.07
6930.69
15
159.07
105.37
4797.4
16
105.37
40
6921.1
9
17
172.7
159.07
6694.28
18
159.07
145.7
331.3
10
19
165.7
159.07
169.2
20
159.07
105.37
4797.4
21
105.37
75
918.1
11
22
146.1
105.37
4475.8
23
105.37
55
6334
12
24
137.7
105.37
4272.7
25
105.37
40
6921.1
13
26
135.9
105.37
4229.6
27
105.37
40
6921.1
14
28
118.1
105.37
3808.9
29
105.37
45
6728.4
15
30
93
40
6539.8
16
31
56.4
38
719.7
17
32
35
105.37
7111.2
33
105.37
159.07
4797.4
34
159.07
223.9
13,048.73
18
35
223.9
239
58.31
19
36
45
105.37
6728.4
37
105.37
118
3806.6
20
38
55
105.37
6334
39
105.37
120.8
3872.2
21
40
127.6
135.9
205.9
22
41
137.7
145
176.3
23
42
232.9
264.6
110.08
24
43
264.6
365.5
249.06
(continued)
918
E. Al-Mutairi
Table 4 (continued)
Mass ﬂow rate
(M) (kg/s)
Heat duty
(ΔH) (kW)
Heat capacity ﬂow rate
(MCp) (kW/C)
Speciﬁc heat capacity
(Cp) (kJ/kgC)
1.0079
6563.80
37.00
36.71
0.3691
4942.66
27.23
73.78
1770.55
32.97
89.34
1424.19
31.39
85.05
73.6558
1857.60
172.00
2.34
97.2671
22,550.40
324.00
3.33
1.2706
16,797.75
147.57
116.14
6095.79
113.52
89.34
8048.26
159.78
125.75
26574.81
341.01
168.09
69.99
51.09
25.18
6695.32
105.39
205.31
350.18
12.52
24.39
2814.58
95.64
235.50
1948.24
36.28
89.34
2810.68
43.00
105.88
2752.77
201.96
491.14
136.23
10.19
24.78
599.81
90.47
25.52
17006.57
316.70
89.34
3254.62
107.17
30.23
8486.99
208.37
109.89
12010.51
238.45
125.75
2237.54
69.21
132.16
3624.46
55.45
105.88
1200.41
39.32
138.54
1964.29
30.05
105.88
2.0187
7689.19
604.02
299.21
13,582.91
224.99
111.45
0.8672
5671.00
107.00
123.39
5.2922
3808.80
207.00
39.11
3.4741
24,705.26
351.08
101.05
16,666.81
310.37
89.34
45,333.03
699.26
201.28
325.5136
18,980.70
1257.00
3.86
0.4296
2890.63
47.88
111.45
1635.37
129.48
301.39
0.4642
2940.17
58.37
125.75
1797.43
116.49
250.95
10.6017
2182.90
263.00
24.81
23.7260
4182.90
573.00
24.15
126.9958
13,979.70
441.00
3.47
275.0787
68,511.10
679.00
2.47
Operating Oil Reﬁnery Units Under Uncertainty: Thermodynamic and Economic. . .
919
Table 5 Results of energy targeting
ΔTmin ¼ 20 (C)
QH, min (kW)
QC, min (kW)
72,524.7
64,059.6
73,662.5
65,197.4
1.6
1.8
ΔTmin ¼ 20 (C)
QH, min (kW)
QC, min (kW)
75,184.7
66,719.6
77,158.8
68,693.7
2.6
3.0
ΔTmin ¼ 20 (C)
QH, min (kW)
QC, min (kW)
77,844.7
69,379.6
80,655.1
72,190
3.6
4.1
ΔTmin ¼ 35 (C)
QH, min (kW)
QC, min (kW)
80,504.7
72,039.6
84,151.4
75,686.3
4.5
5.1
ΔTmin ¼ 40 (C)
QH, min (kW)
QC, min (kW)
83,164.7
74,699.6
87,647.7
79,182.6
5.4
6.0
ΔTmin ¼ 35 (C)
Case
Pinch temperature (C)
Original set of process streams
258.9–223.9
New set of process streams
237.0–202.0
Difference in energy target (%)
ΔTmin ¼ 40 (C)
Case
Pinch temperature (oC)
Original set of process streams
263.9–223.9
New set of process streams
237.0–197.0
Difference in energy target (%)
ΔTmin ¼ 20 (C)
Case
Pinch temperature (C)
Original set of process streams
243.9–223.9
New set of process streams
237.0–217.0
Difference in energy target (%)
ΔTmin ¼ 25 (C)
Case
Pinch temperature (oC)
Original set of process streams
248.9–223.9
(continued)
920
E. Al-Mutairi
Table 5 (continued)
New set of process streams
236.9–211.9
Difference in energy target (%)
ΔTmin ¼ 30 (C)
Case
Pinch temperature (C)
Original set of process streams
253.9–223.9
New set of process streams
237.0–207.0
Difference in energy target (%)
Fig. 2 Dependence of density of process stream mixture on temperature
Fig. 3 Composite curves for the phase change process streams at 30 C
Operating Oil Reﬁnery Units Under Uncertainty: Thermodynamic and Economic. . .
921
5
Conclusions
The dependency of the thermodynamic properties of the process streams on the
temperature of a mixture was observed, and the results revealed an inﬂuence of
phase changes on the process, including a reduced pinch point and increased utility
demands and heat transfer area requirements. This could be due to a better heat
distribution obtained when the temperature interval was deﬁned by the bubble and
dew points rather than the target and supply temperatures. The study concluded that
the design of a HEN without a consideration for the phase changes would not
provide an accurate representation of the thermal behavior of the heat exchanger.
Acknowledgment The author acknowledges the support provided by King Abdulaziz City for
Science and Technology (KACST) through the Science & Technology Unit at King Fahd
University of Petroleum & Minerals (KFUPM) for funding this work through project
No. 13-ENE-59-04 as part of the National Science, Technology and Innovation Plan.
References
Allen, B., Savard-Goguen, M., Gosselin, L.: Optimizing heat exchanger networks with genetic
algorithms for designing each heat exchanger including condensers. Appl. Therm. Eng. 29(16),
3437–3444 (2009)
Biegler, L., Grossmann, I., Westerberg, A.: Systematic Methods of Chemical Process Design.
Prentice-Hall, Upper Saddle River (1997)
Fig. 4 Grand composite curves for the phase change process streams at 30 C
922
E. Al-Mutairi
Bittanti, S., Piroddi, L.: Nonlinear identiﬁcation and control of a heat exchanger: A neural network
approach. J. Franklin Inst. 334, 135–153 (1997)
Brooke, A., Kendrick, D., Meeruas, A., Raman, R.: GAMS-Language Guide. GAMS Develop-
ment Corporation, Washington, D.C. (2006)
Castier, M., Queiroz, E.M.: Energy targeting in heat exchanger network synthesis using rigorous
physical property calculations. Ind. Eng. Chem. Res. 41, 1511–1515 (2002)
ChemSOF.: www.chemsof.com
Coletti, F., Macchietto, S.: A dynamic, distribution model of shell and tube heat exchangers
undergoing crude oil fouling. J. Ind. Eng. Chem. Res. 50, 4515–4533 (2011)
Dipama, J., Teyssedou, A., Mikhaı
¨l, S.: Synthesis of heat exchanger networks using genetic
algorithms. Appl. Therm. Eng. 28(14), 1763–1773 (2008)
Donohue, A.: Ind. Eng. Chem. 41(11), 2499 (1949)
Heat-Int: Version 1.4.1.335, Built by Process Integration Limited (PIL) (2012)
HYSYS: Integrated simulation environment features advances in steady state and dynamic
capabilities critical for optimal design of upstream and reﬁning processes, Aspen Technology
Inc. (2003)
Levine, L.: Estimating moisture ﬂash upon discharge from an extrusion die. Cereal Food World.
42, 144 (1997)
Linnhoff, B.: Introduction to Pinch Technology, pp. 1–63 (1998)
Linnhoff, B., Hindmarsh, E.: The pinch design method for heat exchanger networks. Chem. Eng.
Sci. 38(5), 745 (1983)
Linnhoff, B., Townsend, D.W., Boland, D., Hewitt, G.F., Thomas, B.E.A., Guy, A.R., Marsland,
R.H.: A User Guide on Process Integration for Efﬁcient Use of Energy. Warwick Printing
Company Ltd, Warks (1982)
Liporace, F.S., Pessoa, F.L.P., Queiroz, E.M.: Heat exchanger network synthesis considering
change phase streams. Therm. Eng. 3(2), 87–95 (2004)
Nejad, S.H.A., Shahraki, F., Birjandi, M.R.S., Kovac Kralj, A., Fazlollahi, F.: Modiﬁcation of heat
exchanger network design by considering physical properties variation. Chem. Biochem. Eng.
26(2), 79–87 (2012)
Pho, T.K., Lapidus, L.: Topics in computer-aided design. Part II. Synthesis of optimal heat
exchanger networks by tree searching algorithms. AICHE J. 19, 1182–1189 (1973)
Ponce-Ortega, J.M., Jime
´nez-Gutie
´rrez, A., Grossmann, I.E.: Optimal synthesis of heat exchanger
networks involving isothermal process streams. Comp. and Chem. Engng. 32, 1918–1942
(2008)
Singh, R.P., Heldman, D.R.: Introduction to Food Engineering. Academic Press Inc., San Diego
(1993)
Sinnott, R.K.: Chemical Engineering Design, vol. 6, 4th edn. Elsevier Butter-Worth Heinemann,
Oxford (2005)
Smith, R.: Chemical Process Design and Integration. John Wiley and Sons Ltd (2005). isbn:0-471-
48680-9
Weber, H.F.: Ann. Phy. Chem. 10(103) (1980)
Wilkes, J.O., Birmingham, S.G., Kirby, B.J., Cheng, C.: Fluid Mechanics for Chemical Engineers,
2nd edn. Prentice Hall International Series in the Physical and Chemical Engineering Science,
Upper Saddle River (2010)
Operating Oil Reﬁnery Units Under Uncertainty: Thermodynamic and Economic. . .
923
Ecological Analysis of a Wind-Diesel Hybrid
Power System in the South Algeria
Khaireddine Allali and El Bahi Azzag
1
Introduction
Energy is one of the major inputs for the economic development of any country. In
the case of the developing countries, the energy sector assumes a critical impor-
tance in view of the ever-increasing energy needs requiring huge investments to
meet them. The growth of the world’s human population has created several
problems. One of them is global warming caused by the abundance of CO2 in the
atmosphere. Many of these gases are produced from electrical plants burning fossil
fuel all over the world. To reduce these emanations out into the atmosphere,
alternative sources of energy must be used. In the last two decades, solar energy
and wind energy have become an alternative to traditional energy sources. These
alternative energy sources are nonpolluting, free in their availability, and renewable
De Souza Ribeiro et al. (2011).
In isolated areas such as the Algerian Sahara (Adrar, Bechar, In Salah,
Timimoun, Tindouf, Amenas, etc.), electrical energy is often produced with the
help of diesel generators. Moreover, the electricity production by the diesel is
ineffective, presents signiﬁcant environmental risks (spilling), contaminates the
local air, and largely contributes to GHG emission. In all, we estimate at
16,086 kg/year GHG emission resulting from the use of diesel generators for the
subscribers of the autonomous networks in Algeria Basbous et al. (2012), Saheb-
Koussa et al. (2010). But numerous isolated areas have signiﬁcant wind energy
potential. It is then interesting to associate diesel with some wind generators as
diesel electricity is generally more expensive than wind electricity. To reduce fuel
K. Allali (*) • E.B. Azzag
Badji Mokhtar University-Annaba, Faculty of Engineering Science, Electrical Engineering
Department, P.O. Box 12, Annaba 23000, Algeria
e-mail: allali23000@gmail.com
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_64
925
consumption and power variations of the diesel, an energy storage system can be
associated with the wind-diesel system Leclercq et al. (2003).
The subject of this work is to combine these diesel generators (DG) with wind
turbine generators (WTG) plant sized to the needs of consumption, while providing
continuous high-quality electric power. The main goal with wind-diesel hybrid
system (WDHS) is to reduce fuel consumption and in this way to reduce system
operating costs for economic purpose and environmental impact. This system
equipped with a control system; a proper control strategy has to be developed to
take full advantage of the wind energy during the periods of time.
Nomenclature
WDHS
Wind-diesel hybrid system
DG
Diesel generator
WTG
Wind turbine generator
GHG
Greenhouse gas
DCS
Distributed control system
Ρ
Air density (kg/m3)
A
Area swept by turbine blades (m2)
Cp
Rotor power coefﬁcient
vω
Wind speed (m/s)
2
Geographical and Meteorological Data
of the Studied Site
2.1
Geographical Situation
The studied site is located in Adrar region with geographical coordinates (27590N,
0110W, 263 m). Adrar state situated in the extreme Algerian South-West extends
about over 427,968 km2 (1/5) of the country.
2.2
Meteorological Data
The climate data of wind speed recorded at Adrar region (Algeria) for the year 2012
were measured at the weather station of the Renewable Energy Research Unit in
Saharan Medium (URER-MS) Adrar (see Table 1 below), URER-MS (2012).
926
K. Allali and E.B. Azzag
Table 1 Wind speed data for a studied site
Months
January
February
March
April
May
June
July
August
September
October
November
December
v (m/s)
6.44
6.22
6.05
5.33
6.27
5.27
7.78
6.41
4.94
4.8
4.86
5.58
Ecological Analysis of a Wind-Diesel Hybrid Power System in the South Algeria
927
3
Hybrid System Overview
In this study, Fig. 1 shows the conﬁguration considered in this paper. This conﬁg-
uration consists of wind turbine generator (WTG), diesel generator (DG), battery
bank, consumer load, power electronic converters (AC/DC rectiﬁer, DC/AC
inverter), monitoring system, distributed control system (DCS), switches and
relays, controller and other accessory devices, and cables Kamal et al. (2010),
Ibrahim et al. (2011).
4
Operation Modes of Wind-Diesel Hybrid System
The WDHS is classiﬁed as being high penetration (HP), Ibrahim et al. (2011).
HP-WDHS have three modes as follows:
1. Weak winds (vω  3 m/s): diesel only (DO)
2. Moderate winds (3 m/s < vω  10 m/s): wind and diesel (WD) in service
3. Strong winds (vω > 10 m/s): wind only (WO)
Fig. 1 Schematic diagram of the hybrid wind-diesel generation power system
928
K. Allali and E.B. Azzag
5
The Proposed Control System Strategy
For a multisource energy system, a power ﬂow management strategy is needed
according to wind speed values and the power demanded by the consumer load. The
power management strategy used in this study is according to the ﬂow chart shown
in Fig. 2 (Sedaghat et al. 2012).
 
Vw<= 3
Start
End
Read inputs data: (wind speed, load
demand, ρ, A, Cp)
Calculate generated power by WTG (PT)
Vw<= 10
No
DG or WTG
stop
Yes
DG & WTG
inservice
Yes
Run DG
PT > PL
No
Charge battery bank
Discharge battery
Yes
No
No
Supply load
Yes
Calculate power stored
∆PB= PT-PL
Supply load
Run WTG
Fig. 2 Main ﬂow chart
Ecological Analysis of a Wind-Diesel Hybrid Power System in the South Algeria
929
6
Simulation Results and Discussions
From Table 1, the highest wind speed was in July (7.78 m/s). Figure 3 shows the
average daily wind speed for the considered site in July.
The load detail for the hybrid system shown in Fig. 4 shows the load proﬁle
adopted in this study. This proﬁle is considered to be the same for all the days of the
year with peak load as 98 kW.
Figure 5 shows electricity produced by wind turbine generator and diesel
generator. The wind turbine generator only supplies energy during 12th days; in
these cases, the generated power (PT) is greater than the required power by the
consumer load (PL). So, the surplus wind energy will be stored in a battery bank.
But on July 7 and 22, the supply power is ensured by diesel generator only because
the wind speed is less than 3 m/s. The battery bank is used only when the renewable
source and/or the conventional diesel power system is not able to satisfy the load
demand and also when the DG or WTG broke down.
12
11
10
9
8
7
6
5
Wind speed (m/s)
4
3
2
1
0
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
Days
Fig. 3 The average daily wind speed in July
930
K. Allali and E.B. Azzag
The summary of various costs related to the hybrid wind-diesel power system is
summarized in Table 2: the energy cost of WTG is greater than DG; this is due to
the number of operation of each system, while the latter has the effect of fuel
consumption and its impact on CO2 emissions, moreover cost of fuel consumption.
7
Conclusions
This paper presents a techno-economic analysis and the design of a complete hybrid
system, consisting of a wind turbine generator, a diesel generator, and a battery
system as a backup power source for a typical isolated area situated in Adrar region.
We have demonstrated that the electricity produced with the help of diesel gener-
ators is relatively inefﬁcient, very expensive, and responsible for the emission of
100
90
80
70
60
50
40
Electric load (kW)
Hours
30
20
10
0
0
5
10
15
20
25
Fig. 4 The daily proﬁle load
Ecological Analysis of a Wind-Diesel Hybrid Power System in the South Algeria
931
greenhouse gas (GHG). The WDHS is a great potential, technical, economical, and
ecological promoter and very cost-effective compared with the traditional diesel
system. This system has a good control strategy for the management of different
power sources (wind, diesel, battery) that allows to optimize the operation of the
hybrid system, to take full advantage of the wind energy during the periods of time,
and to minimize diesel fuel consumption, in this way to reduce system operating
costs and environmental beneﬁts. Therefore, the wind-diesel power system is
widely recommended especially for isolated sites that have signiﬁcant wind energy
potential.
160
140
120
100
80
60
40
20
0
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
Days
July
Surpus of wind energy
WTG
DG
Generated power (kW)
Fig. 5 The daily energy production by wind-diesel hybrid system
932
K. Allali and E.B. Azzag
Table 2 Summary of various costs related to the hybrid wind-diesel power system
Days
Energy cost WTG
(DA) (5.958
DA/kWh)
Energy cost DG
(DA) (5.958
DA/kWh)
Fuel
consumption
(l/day)
Fuel cost
(DA) (13.7
DA/l)
CO2 emission
(kg/day)
(2.6 kg/l)
1
180.42
411.102
25.119
344.1303
65.3094
2
791.52
0
0
0
0
3
791.52
0
0
0
0
4
483.06
101.286
12.327
168.8799
32.0502
5
180.42
411.102
25.119
344.1303
65.3094
6
110.58
482.598
28.071
384.5727
72.9846
7
0
595.8
32.745
448.6065
85.137
8
110.58
482.598
28.071
384.5727
72.9846
9
180.42
411.102
25.119
344.1303
65.3094
10
320.1
268.11
19.215
263.2455
49.959
11
931.2
0
0
0
0
12
791.52
0
0
0
0
13
791.52
0
0
0
0
14
483.06
101.286
12.327
168.8799
32.0502
15
180.42
411.102
25.119
344.1303
65.3094
16
791.52
0
0
0
0
17
791.52
0
0
0
0
18
180.42
411.102
25.119
344.1303
65.3094
19
320.1
268.11
19.215
263.2455
49.959
20
483.06
101.286
12.327
168.8799
32.0502
21
110.58
482.598
28.071
384.5727
72.9846
22
0
595.8
32.745
448.6065
85.137
23
483.06
101.286
12.327
168.8799
32.0502
24
320.1
268.11
19.215
263.2455
49.959
25
791.52
0
8.145
111.5865
21.177
26
110.58
482.598
28.071
384.5727
72.9846
27
320.1
268.11
19.215
263.2455
49.959
28
791.52
0
0
0
0
29
791.52
0
0
0
0
30
791.52
0
0
0
0
31
791.52
0
0
0
0
Total
14194.98
6655.086
429.537
5884.6569
1116.7962
Ecological Analysis of a Wind-Diesel Hybrid Power System in the South Algeria
933
References
Basbous, T., Younes, R., Ilinca, A., Perron, J.: A new hybrid pneumatic combustion engine to
improve fuel consumption of wind-diesel power system for non-interconnected areas. Appl.
Energy. 96, 459–476 (2012)
De Souza Ribeiro, L.A., Saavedra, O.R., De Lima, S.L., De Matos, J.G.: Isolated micro-grids with
renewable hybrid generation: the case of Lenc
¸o
´is Island. IEEE Transactions on Sustainable
Energy. 2(1), 1–11 (2011)
Ibrahim, H., Youne
`s, R., Basbous, T., Ilinca, A., Dimitrova, M.: Optimization of diesel engine
performances for a hybrid wind-diesel system with compressed air energy storage. Energy. 36,
3079–3091 (2011)
Kamal, E., Koutb, M., Sobaih, A.A., Abozalam, B.: An intelligent maximum power extraction
algorithm for hybrid wind diesel-storage system. Electrical Power and Energy Systems. 32,
170–177 (2010)
Leclercq, L., Robyns, B., Grave, J.-M.: Control based on fuzzy logic of a ﬂywheel energy storage
system associated with wind and diesel generators. Math. Comput. Simul. 63, 271–280 (2003)
Saheb-Koussa, D., Haddadi, M., Belhamel, M.: Etude de faisabilite
´ et optimisation d’un syste
`me
hybride
(Eolien-PhotovoltaЇque-Diesel)

a
fourniture
d’e
´nergie
e
´lectrique
totalement
autonome. Rev. Sci. Fond. App. 2(1), 84–95 (2010)
Sedaghat, B., Jalilvand, A., Noroozian, R.: Design of a multilevel control strategy for integration
of stand-alone wind/diesel system. Electrical Power and Energy Systems. 35, 123–137 (2012)
Weather station of the Renewable Energy Research Unit in Saharian Medium (URER-MS) Adrar,
Algeria. http://www.urerms.dz/ (2012)
934
K. Allali and E.B. Azzag
Comparative Study of Two Integrated Solar
Collectors with Symmetric and Asymmetric
CPC Reﬂectors Based on a Ray Trace Analysis
Olfa Helal, Raouf Benrejeb, and Be
´chir Chaouachi
1
Introduction
In order to achieve temperatures in excess of approximately 70 C from a solar
collector, the weak solar radiation has to be concentrated (Helal et al. 2011a). By
using internal reﬂectors, the hot absorber area can be reduced and this minimizes
the heat losses. CPCs (Compound parabolic Concentrators) are the optimal choice,
since they have the capability of reﬂecting to the absorber the maximum possible
incident radiation (Helal et al. 2011a). The design and performance of different
CPC conﬁgurations are described in numerous references (Rabl 1976; Souliotis and
Tripanagnostopoulos 2008; Colina-Marquez et al. 2010; Helal et al. 2011a;
Benrejeb et al. 2015).
Integrated Collector Storage (ICS) systems can satisfactorily cover the need of
about 100–200 l per day of hot water in the low temperature range of 40–70 C
(Helal et al. 2011a). These systems consist of one device with dual operation, to
collect solar radiation and to preserve the heat of the water storage tank during the
night. The main advantages of ICS systems are their lower cost and simpler
construction compared to the Flat Plate Thermosiphonic Units (FPTU). On the
other hand, their main problem is the greater thermal losses of the stored water tank
compared to the corresponding thermal losses of the water tank in the FPTU (Helal
et al. 2010). Many researchers have studied the design of several types of ICS
systems (Tripanagnostopoulos and Yianoulis 1992; Kessentini and Bouden 2013;
O. Helal (*) • R. Benrejeb • B. Chaouachi
University of Gabes, National High Engineering School, Research Unit: Environment,
Catalyzes and Process Analysis, Omar Ibn Khettab Street, 6029 Gabes, Tunisia
e-mail: helal_olfa@yahoo.fr
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_65
935
Benrejeb et al. 2015), suggesting improvements for their operation which aim to
achieve low cost systems with considerable thermal performance by maximizing
solar radiation collection whilst minimizing thermal losses. The modeling of the
solar radiation on a CPC can be made by using the ray-tracing technique based on
computer programs (Souliotis and Tripanagnostopoulos 2008; Colina-Marquez
et al. 2010; Helal et al. 2011b; Benrejeb et al. 2015).
In the present work, a ray trace analysis simulating the reﬂection of the solar
radiation was done on two ICS solar water heater based on asymmetric and
symmetric CPC reﬂectors: ICS_SCPC and ICS_ACPC. The ﬁrst system was
designed, realized, and experimented by (Helal et al. 2011a). It consisted of a
compound parabolic concentrating (CPC) reﬂector composed of two concentrating
stages: the ﬁrst one contains two symmetric parabolic sections and the second one
comprises one parabolic part. A cylindrical storage tank of volume equal to 100 l
was placed on its focal plane. However, the second model was only designed for
comparison. In this new design (ICS_ACPC), which is presented by (Benrejeb et al.
2015), the concentration system is also composed of two concentrating stages. The
upper one comprises two symmetric parabolic branches with focal axes tilted by
48 from the vertical plane. The lower one is constituted of three involute
reﬂectors. Its cylindrical storage tank covers the triangle formed by the three
involute part centers.
The two studied models differ only in the design of the lower stage concentrator
and also the placement of their cylindrical storage tanks of same volume. This
choice was done for comparison.
Truncation of the upper part of the reﬂectors was carried out to 50% in order to
reduce their sizes, minimize their manufacturing cost, and facilitate their
installation.
For this target, a theoretical study based on the ray trace analysis was done on
both systems. This technique allows plotting and simulating the reﬂected rays on
the CPC reﬂectors at any instant using mathematical equations written on a Matlab
code. Then, this ray-tracing method was used to produce diagrams corresponding to
the spatial distribution of the incoming solar radiation on the absorber surface.
The paper is organized as follows: Section 1 introduces an overview of the ICS
systems and the different research studies done in this ﬁeld. Section 2 provides the
design of each studied system. In Section 3, the ray trace analysis is given. Section 4
deals with the energy ﬂux distribution. Section 5 concludes the study.
Nomenclature
Aapp
Aperture surface area (m2)
Aab
Absorber (receiver) surface area (m2)
Al
Aluminum
C
Concentration ratio
Dab
Cylindrical storage tank diameter (m)
Ds
Depth of the system (m)
f
Focal distance of the parabola (m)
F
Focus of the parabola
(continued)
936
O. Helal et al.
Ib
Beam radiation (kJ ¼ m2 h)
Id
Diffuse radiation (kJ ¼ m2 h)
IT
Flux on a tilted surface (kJ ¼ m2 h)
Ri
For (i ¼ 1, 2 and 3) is the radius of the ith involute reactor (m)
T
Translated vector of parameter a and b
Vab
Cylindrical storage tank volume (l)
W
Half system entrance aperture width (m)
W0
Half exit aperture of the system (m)
(x; y)
Cartesian coordinate system
(xf1; yf1)
The coordinates of focus F1 (m)
(u; v)
Cartesian coordinate system rotated counter clockwise from vertical axis
Greek symbols
αr
Absorptivity of the absorbing surface
ηo
Optical efﬁciency
τs
Transmissivity of the cover
θa
Half acceptance angle of the CPC concentrator (rad)
ρr
Reﬂectivity of the reﬂector
ρe
Effective reﬂectivity of the concentrator surface for all radiation
2
Design of the ICS Models
2.1
Systems with Full CPC
Two integrated solar collectors with symmetric and asymmetric CPC reﬂectors
(ICS_FSCPC and ICS_FACPC) of half acceptance angles of θa ¼ 48 were
designed.
Side section views of the full collectors with their respective cylindrical storage
tanks are illustrated in Figs. 1 and 2 and the speciﬁcations (physical and geometric
characteristics) for each of the collectors are detailed in Table 1.
Both systems are characterized by:
(a) The upper stage concentrator is a symmetrical CPC reﬂector comprising para-
bolic reﬂectors AF1 and BF2 between entry aperture AB of width 2W and exit
aperture F1F2 of width 2W’. It is common to both systems ICS_FACPC and
ICS_FSCPC, and it consists of an untruncated CPC of concentration ratio
C1 ¼ 2 W/2 W’ ¼ 1.34. Both sections AF1, BF2 are parts of parabolas of focuses
F2, F1 and focal axis parallel to BF1, AF2, which are tilted by 48 from the
vertical plane.
Considering that the two branches are symmetrical relative to the y-axis thus it is
enough to determine the equation of one branch and the other is deduced.
(b) The lower stage consists of:
Comparative Study of Two Integrated Solar Collectors with Symmetric. . .
937
•
A parabolic reﬂector positioned in the exit aperture F1F2 of the ﬁrst stage
designed to reﬂect radiation on a cylindrical absorber of diameter Dab set in
the cavity of the system. The focal point of the parabolic reﬂector, F3, is at
the intersection of QF1 and PF2.
•
An asymmetric CPC reﬂector with three involute parts (F1C), (CD) and
(DF2) of radius R1 ¼ W0, R2 ¼ 0.5R1 and R3 ¼ R2.
-60
-40
-20
0
20
40
60
0
20
40
60
80
100
x(cm)
y(cm)
Fig. 1 Side section view of
the ICS_FSCPC
-60
-40
-20
0
20
40
60
-50
0
50
100
x(cm)
y(cm)
Fig. 2 Side section of the
ICS_FACPC
938
O. Helal et al.
(c) The cylindrical storage tank (absorber) has a volume Vab ¼ 100 l.
Figures 3 and 4 represent the different geometrical parameters of both models.
Figure 5 gives a photo of the realized ICS_FSCPC.
Table 1 Geometric
characteristics of the
ICS_FSCPC and
ICS_FACPC collectors
Characterization
ICS_SFCPC
ICS_AFCPC
Half acceptance angle
48
48
1st stage Concentration ratio
1.34
1.34
System concentration ratio
1.05
1.21
Aperture width(mm)
1180
1180
Aperture length (mm)
1270
1114
Reﬂector height (mm)
1084
1369
Absorber diameter(mm)
360
311
Absorber length (mm)
990
1114
Reﬂector material
Aluminum
Aluminum
Reﬂectivity of reﬂector
0.85
0.85
Total reﬂector length (mm)
3430
3282
Fig. 3 Geometrical parameters of the ICS_FSCPC
Comparative Study of Two Integrated Solar Collectors with Symmetric. . .
939
2.2
Systems with Truncated CPC
In a second step, truncation of the upper part of the reﬂectors was carried out to 50%
for both collectors (ICS_TSCPC and ICS_TACPC) (Figs. 6 and 7).
Figures 3 and 4 give the side section views of both systems and the speciﬁcations
(physical and geometric characteristics) are given in Table 2.
3
Ray Trace Analysis
Ray trace technique was employed to evaluate full and truncated CPC designs.
Direct solar radiation was only considered.
In Figs. 8, 9, 10, and 11, 50 equally spaced rays across the collector aperture for
the incident angles 0 and 30 were traced. It can be seen that:
•
At 0 incident angle (case of Figs. 8b, 9, 10, and 11b) it can be observed that
almost all the rays perpendicular to the aperture of the ICS systems reach the
absorber directly or after being reﬂected by the reﬂector surfaces as it was
expected and that even the absorber zones which were not exposed to direct
Fig. 4 Geometrical parameters of the ICS_FACPC
940
O. Helal et al.
Fig. 5 Photo of the
ICS_FSCPC
-60
-40
-20
0
20
40
60
-50
-40
-30
-20
-10
0
10
20
30
40
50
x(cm)
y(cm)
Fig. 6 Side section of the
ICS_TACPC
Comparative Study of Two Integrated Solar Collectors with Symmetric. . .
941
-60
-40
-20
0
20
40
60
-10
0
10
20
30
40
50
x(cm)
y(cm)
Fig. 7 Side sections of the
ICS_TSCPC
Table 2 Geometric
characteristics of the
ICS_TSCPC and
ICS_TACPC collectors
Characterization
ICS_TSCPC
ICS_TACPC
Half acceptance angle
65
65
1st stage Concentration ratio
1.27
1.27
System concentration ratio
0.98
1.14
Aperture width (mm)
559
559
Aperture length (mm)
1270
1114
Reﬂector height (mm)
563
905
Absorber diameter (mm)
360
311
Absorber length (mm)
1114
1114
Reﬂector material
Aluminium
Aluminium
Reﬂectivity of reﬂector
0.85
0.85
(b)
(a)
-60
-40
-20
0
20
40
60
-50
0
50
100
x(cm)
y (cm)
-60
-40
-20
0
20
40
60
-50
0
50
100
x(cm)
y (cm)
Fig. 8 Ray trace diagram for the ICS_FACPC proﬁle with direct solar radiation at 30 (a) and
perpendicular (b)
942
O. Helal et al.
radiation received some energy after successive reﬂections. This results in
creating a mirror symmetry between the left and the right sides of the reﬂector
and the absorber for the case of ICS_FSCPC and ICS_TSCPC. However, it does
not create a symmetry for the ICS_FACPC and ICS_TACPC because of the
asymmetrical shape of their corresponding reﬂectors.
•
The ray trace diagrams at 30 incident angle (Figs. 8a and 9a) showed that rays
are spread across the absorber sections, but not covering the full absorber and are
more concentrated to the right side of both the full and truncated CPC collectors.
The lower left of the absorber section in the truncated CPC collectors has more
rays than that in the full collector. The concentration of the incident rays on the
absorber becomes higher with truncation compared to that of full collectors.
(a)
-60
-40
-20
0
20
40
60
-50
-40
-30
-20
-10
0
10
20
30
40
50
x(cm)
y (cm)
(b)
-60
-40
-20
0
20
40
60
-50
-40
-30
-20
-10
0
10
20
30
40
50
x(cm)
y (cm)
Fig. 9 Ray trace diagram for the ICS_TACPC proﬁle with direct solar radiation at 30 (a) and
perpendicular (b)
(a)
-60
-40
-20
0
20
40
60
-20
0
20
40
60
80
100
x(cm)
y(cm)
(b)
-60
-40
-20
0
20
40
60
-20
0
20
40
60
80
100
x(cm)
y(cm)
Fig. 10 Ray trace diagram for the ICS_FSCPC proﬁle with direct solar radiation at 30 (a) and
perpendicular (b)
Comparative Study of Two Integrated Solar Collectors with Symmetric. . .
943
•
In Figs. 10a and 11a, it can be seen at 30 incident angle that at least an important
proportion of solar rays which strike one or the two branches of the ICS_FSCPC
and ICS_TSCPC designs are considered towards outside. This causes a reduc-
tion in absorbed energy ﬂux. However, ICS_FACPC and ICS_TACPC designs
make it possible to reﬂect all the rays towards the absorber, even having carried
out some reﬂections.
•
The truncation allows a fraction of solar radiation reaching the absorber directly
or after being reﬂected. This fraction increases with the decrease of the height of
the upper stage concentrator which improves the system’s optical performances.
4
Energy Flux Distribution
The energy ﬂux distribution diagrams showing the absorbed solar radiation distri-
bution on the absorber of the ICS systems were generated and plotted by using the
results of the developed ray trace technique. Figures 13, 14, and 15 illustrate at noon
(12 h) the distribution patterns of the energy ﬂux on the absorber of each ICS
system. For all predictions, the solar radiation intensity assumed was 1000 W/m2.
Figures 12 and 14 illustrate the distribution patterns of the energy ﬂux distribu-
tion on the absorber of each of the full ICS_FSCPC and ICS_FACPC collectors at
0 and 30 incident angles. A variation in energy concentration was realized across
the absorber resulting from variation of incidence angles of insolation on the
aperture of the collectors. Different distribution patterns resulted from the reﬂection
and refraction of incoming rays at different surfaces. Low energy peaks on the
absorber resulted from low ﬂux intensities emerging from either multiple
intersecting of the rays with the reﬂector or rays missing the absorber and exiting
to the external environment.
In Tables 1 and 2, several optical and thermal performances are given to
compare between the two full ICS systems and then to show the effect of truncation
(a)
-60
-40
-20
0
20
40
60
-10
0
10
20
30
40
50
x(cm)
y(cm)
(b)
-60
-40
-20
0
20
40
60
-10
0
10
20
30
40
50
x(cm)
y(cm)
Fig. 11 Ray trace diagram for the ICS_TSCPC proﬁle with direct solar radiation at 30 (a) and
perpendicular (b)
944
O. Helal et al.
70
60
50
40
30
20
10
0
45
40
35
30
25
20
15
10
5
00
50
100
150
200
250
300
350
400
0
50
100
150
200
Absorber angle degree (°)
Absorber angle degree (°)
Energy flux (W/m2)
Energy flux (W/m2)
250
300
350
400
a
b
Fig. 12 Energy ﬂux distribution on the absorber of ICS_FACPC at 12 h for incident angles 30 (a)
and 0 (b)
Comparative Study of Two Integrated Solar Collectors with Symmetric. . .
945
20
a
b
18
16
14
12
10
8
6
4
2
0
40
35
30
25
20
15
10
5
00
50
100
150
200
250
300
350
400
0
50
100
150
200
250
300
350
400
Absorber angle degree (°)
Absorber angle degree (°)
Energy flux (W/m2)
Energy flux (W/m2)
Fig. 13 Energy ﬂux distribution on the absorber of ICS_TACPC at 12 h for incident angles 30 (a)
and 0 (b)
946
O. Helal et al.
20
a
b
18
16
14
12
10
8
6
4
2
0
40
35
30
25
20
15
10
5
0
0
50
100
150
200
250
300
350
400
0
50
100
150
200
250
300
350
400
Absorber angle degree (°)
Absorber angle degree (°)
Energy flux (W/m2)
Energy flux (W/m2)
Fig. 14 Energy ﬂux distribution on the absorber of ICS_FSCPC at 12 h for incident angles 30 (a)
and 0 (b)
Comparative Study of Two Integrated Solar Collectors with Symmetric. . .
947
25
a
b
20
15
10
5
0
25
20
15
10
5
00
50
100
150
200
250
300
350
400
50
100
150
200
250
300
350
Absorber angle degree (°)
Absorber angle degree (°)
Energy flux (W/m2)
Energy flux (W/m2)
Fig. 15 Energy ﬂux distribution on the absorber of ICS_TSCPC at 12 h for incident angles 30 (a)
and 0 (b)
948
O. Helal et al.
on each system. The average number of reﬂections, the optical efﬁciency, and the
total energy ﬂux are given at 0 and 30 incident angles.
From Tables 3 and 4, we can see:
•
At 0 incident angle, the cylindrical absorber of the ICS_FSCPC receives about
690 W/m2, and that of the ICS_TCPC obtains the value of 719 W/m2. However,
both absorbers of ICS_FACPC and ICS_TACPC receive only 599 W/m2 and
659 W/m2. At this incident angle, the full and truncated ICS_SCPC absorbed
more energy ﬂux than the ICS_ACPC collector. The gain is about 13.18% for the
full design and 8.34% for the truncated one. If we compare the full with the
truncated for both models, we can deduce that truncation has a positive effect on
the received energy ﬂux. A gain of 4% is reached for the symmetric design and
9.1% for the asymmetric model.
•
An increase in incidence angle from 0 to 30 resulted in a reduction of
ICS_SCPC (full and truncated) system total received energy ﬂux. The
ICS_FACPC achieves a gain of 38.44% and the ICS_TACPC wins 41.88%.
5
Conclusions
The ICS_SFCPC and ICS_AFCPC systems were modeled, analyzed, and compared
using a ray trace technique based on a mathematical code written in Matlab. The
investigation studied the behavior of the reﬂected radiation and the energy ﬂux
distribution on the absorber of each system at the incidence angles 0 and 30 for
Table 3 Optical and thermal performances of the ICS_FSCPC and ICS_TSCPC collectors
ICS_FSCPC
ICS_TSCPC
Average number of reﬂections
0
0.94
0.92
30
0.38
0.36
Optical efﬁciency
0
0.69
0.71
30
0.46
0.43
Total energy ﬂux
0
690
719
30
397
376
Table 4 Optical and thermal performances of the ICS_FACPC and ICS_TACPC collectors
ICS_FACPC
ICS_TACPC
Average number of reﬂections
0
1.26
1.22
30
0.72
0.7
Optical efﬁciency
0
0.6
0.65
30
0.74
0.75
Total energy ﬂux
0
599
659
30
645
647
Comparative Study of Two Integrated Solar Collectors with Symmetric. . .
949
the full and truncated designs. From the simulation analysis, a number of conclu-
sions were determined:
•
With incident solar radiation perpendicular to the aperture of the collector, the
full and truncated ICS_SCPC perform better than the ICS_ACPC model (full
and truncated).
•
At 30 incident angle, the full and truncated ICS_ACPC perform better than the
symmetric model.
•
Truncation has a positive effect on the optical and thermal performances,
especially at 0 incident angle for both designs.
References
Benrejeb, R., Helal, O., Chaouachi, B.: Optical and thermal performances improvement of an ICS
solar water heater system. Sol. Energy. 112, 108–119 (2015)
Colina-Marquez, J.A., Lopez-Vasquez, A.F., Machuca-Martinez, F.: Modeling of direct solar
radiation in a compound parabolic collector (CPC) with the ray tracing technique. Dyna. 77
(163), 132–140. Medellin (2010)
Helal, O., Chaouachi, B., Gabsi, S., Bouden, S.: Design modeling and simulation of an integrated
collector storage solar water heater. In: Proceedings of the International Conference CICME
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Helal, O., Chaouachi, B., Gabsi, S.: Design and thermal performance on an ICS solar water heater
based on three parabolic sections. Sol. Energy. 85, 2421–2432 (2011a)
Helal, O., Chaouachi, B., Gabsi, S., Colina-Marquez, J.: Ray tracing technique analysis of an
integrated collector storage solar water heater. In: The International Conference on Energy
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Kessentini, H., Bouden, C.: Numerical and experimental study of an integrated solar collector with
CPC reectors. Renew. Energy. 57, 577–586 (2013)
Rabl, A.: Solar concentrators with maximal concentration for cylindrical absorbers. Appl. Opt. 15,
1871–1873 (1976)
Souliotis, S., Tripanagnostopoulos, Y.: Study of the distribution of the absorbed solar radiation on
the performance of a CPC-type ICS water. Renew. Energy. 33, 846–858 (2008)
Tripanagnostopoulos, Y., Yianoulis, P.: Integrated collector/storage systems with suppressed
thermal losses. Sol. Energy. 48, 31–43 (1992)
950
O. Helal et al.
Thermoeconomic Optimization of Hydrogen
Production and Liquefaction by Geothermal
Power
Ceyhun Yilmaz, Mehmet Kanoglu, and Aysegul Abusoglu
1
Introduction
Sustainable energy economy requires sustainable production of energy from renew-
able energy sources. Hydrogen is a clean energy carrier for renewable energies.
Geothermal-based hydrogen production is a potential pathway for a future hydro-
gen economy. Hydrogen has high energy content by mass but low energy content
by volume in gas state. Storage of hydrogen is a challenging task. Hydrogen can be
stored as a compressed gas at high pressures, as a liquid which requires a cryogenic
temperature of 253 C, or combined with other compounds in a solid form like
being absorbed in a metal hydride. Storage in gas state requires very large tanks;
liquefaction requires large work input and super insulated storage tanks; and a metal
hydride can only absorb a small amount of hydrogen. Also, hydrogen liquefaction is
a low-efﬁciency process (Scott 2007).
In the last 30 years the development of thermoeconomics has been impressive in
more than one direction. The recent developments by Tsatsaronis and Pisa (1994),
Lozano and Valero (1993), Frangopoulos (1994), von Spakovsky (1994), and
d’Accadia and Rossi (1998) adequately represent the different directions of devel-
opment. With regard to thermoeconomic optimization methodologies, Tsatsaronis
et al. (2002) use an iterative technique of thermoeconomic performance improve-
ment where the analyzer can take part in decision making in the optimization
process. Valero et al. (1994) have used the concept of assigning appropriate cost
to each and every exergy ﬂow, and thermoeconomic performance improvement of
the system is done through local optimization of the subsystems. Frangopoulos
(1994) and von Spakovsky (1994) have used the functional decomposition of the
C. Yilmaz (*) • M. Kanoglu • A. Abusoglu
Department of Mechanical Engineering, University of Gaziantep, 27310 Gaziantep, Turkey
e-mail: ceyhunyilmaz16@gmail.com
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_66
951
system in the thermoeconomic optimization of the systems. The major ﬁelds of
application of these developments are the large cogeneration and combined power
plants
and
chemical
plants,
whereas
the
domain
of
refrigeration
and
air-conditioning is limited. It is because the industrial utilities are probably consid-
ered with great interest, as they are capital intensive. However, the performances of
refrigeration and air-conditioning have even higher rates of energy consumption
and poor performance. These, therefore, deserve greater attention both in the design
phase and in everyday handling.
In this study, the thermoeconomic optimization of a hydrogen production and
liquefaction by geothermal power system using the thermoeconomic evaluation and
optimization method, based on the works of Tsatsaronis et al. (2002), is presented.
The exergoeconomic optimization approach uses an iterative design improve-
ment procedure that does not aim at calculating the global optimum of a
predetermined objective function, as the conventional optimization methods do,
but tries to ﬁnd a “good” solution for the overall system design. The basic idea lies
in a commonly accepted concept from the cost viewpoint: At constant capacity for a
well-designed component, group of components, or total system, a higher invest-
ment cost should correspond to a more efﬁcient component and vice versa. With
this approach, the cost-optimal exergetic efﬁciency is obtained for a component
isolated from the remaining system components. The iterative exergoeconomic
optimization technique consists of the following steps (Abusoglu and Kanoglu,
2009): (i) evaluation of the detailed schematics and inputs of the existing system
(including the actual plant data), (ii) a detailed thermoeconomic analysis and
evaluations of the system and obtaining the decision variables that affect both the
exergetic efﬁciency and the investment costs, (iii) modiﬁcation of the cost rates of
the components having signiﬁcantly higher cost rates than the remaining compo-
nents, to their corresponding cost-optimal exergetic efﬁciency, and (iv) calculation
of the relative deviations of the actual values from the cost-optimal values for the
exergetic efﬁciency and relative cost difference.
In this study, the iterative exergoeconomic optimization method integrated from
thermoeconomic isolation method and game theory is applied for the optimization
of a hydrogen production and liquefaction by geothermal power system. The use of
this optimization approach requires exergetic and exergoeconomic analysis results
of the plant, and the exergoeconomic optimization procedure of the system is
described. The procedure is used for obtaining cost-optimal exergetic efﬁciencies
and related performance parameters for a component isolated from the remaining
system components. The objective functions of the hydrogen production and
liquefaction system components are expressed for the optimization criterion as a
function of dependent and independent variables.
Nomenclature
C
Cost associated with an exergy stream ($/h)
c
Speciﬁc cost of exergy, cost per unit exergy ($/kJ)
Ex
Exergy (kW)
ex
Speciﬁc exergy (kJ/kg)
(continued)
952
C. Yilmaz et al.
f
Exergoeconomic factor
LHV
Lower heating value (kJ/kg)
LC
Levelized cost ($/yr)
PEC
Purchased equipment cost ($)
r
Relative cost difference
W
Power production or power consumption (kW)
y
Exergy destruction ratio over total exergy input
y*
Component exergy destruction over total exergy destruction
Z
Cost associated with investment expenditure ($/h)
Greek symbols
ε
Exergetic efﬁciency
τ
Capacity factor of plant operation
Subscripts
CC
Carrying charge (for economic calculation)
F
Exergy of fuel
i
ith stream
k
kth component
D
Exergy destruction
OMC
Operation and maintenance
P
Exergy of product
tot
Overall system
Z
Investment costs
2
System Deﬁnition and the Assumptions Used
in the System
Figure 1 shows the schematic diagram of a hydrogen production and liquefaction by
geothermal power system, which uses geothermal water as power source for
electrolysis and liquefaction units. The following assumptions are made and
expressed below to analyze the system.
The system works in steady state. The temperatures of the condensers, heat
exchangers, and electrolysis are constant and uniform throughout the components.
The pressure losses in pipes between the components are neglected. The reference
environmental state for the system is T0 ¼ 25 C (environment temperature) and
P0 ¼ 1 atm (atmospheric pressure). The geothermal water temperature at the
production well is 200 C and 100 kg/s (liquid dominated). The cooling water
ﬁrst enters the condenser at 25 C, then passes through the condenser, and ﬁnally is
rejected by the system at 35 C. The air to be cooled enters the evaporator at 25 C
and leaves at 35 C. The ﬁrst ﬂash process pressure assumption is 600 kPa at state
2. Binary cycle low and high pressures are between 400 and 2100 kPa, respectively.
The ﬂash cycle steam turbine exit proper pressure is 10 kPa for water-cooled
Thermoeconomic Optimization of Hydrogen Production and Liquefaction. . .
953
condenser. The isotropic efﬁciencies of all turbines and pumps are assumed to be
85%. The electrolysis operation pressure and the temperature are assumed to be
1 atm and 25 C, respectively. The liquefaction cycle compressor pressure is
assumed to be 4000 kPa at state 13. The cryogenic turbine gas fraction is assumed
to be 0.6.
The combined ﬂash binary geothermal power plants incorporate both a binary
unit and a ﬂashing unit to exploit the advantages associated with both the systems.
The liquid portion of geothermal ﬂuid serves as the heat input for the binary cycle
and the steam portion of geothermal ﬂuid drives a steam turbine to produce
electrical power. The combined ﬂash binary geothermal power plant operates in a
closed loop with no environmental discharge and totally reinjection of geothermal
ﬂuid. The geothermal water supplies the energy input for the plant. As the result of
the ﬂashes process, the geothermal water properties are 158.8 C and 600 kPa at the
inlet state of the plant. The liquid part of the geothermal water for the binary cycle
and the steam portion of geothermal water drives a steam turbine to produce power.
In this ﬁrst cycle, the extracted steam ﬂows through the steam turbine to produce
electricity while the geothermal water is reinjected to ground and condensed by
water-cooled condenser. Then in the binary cycle of the plant, isobutene working
ﬂuid is heated and vaporized in the heat exchanger by the geothermal water. Then
the isobutane ﬂows through the turbine to produce electricity, and it is condensed by
air-cooled condenser and pumped back to the heat exchanger, completing the
binary cycle. Finally, the geothermal water collected from these two cycles is
elec
W
×
comp
W
×
turb
W
×
elec
W
×
R
Q
×
Fig. 1 An integrated system of hydrogen production and liquefaction by geothermal power
954
C. Yilmaz et al.
reinjected back to the ground. The temperature of the isobutane at the inlet state of
turbine (or at the exit state of the binary hehat exchanger) is taken to be 150 C,
which is lower than the separated liquid dominated geothermal water temperature at
the heat exchanger inlet. About 20% of the power output is used for internal
demands of the binary cycle and 5% of the power output is used for internal
demand of steam cycle of geothermal plant, such as powering fans in the water
and air-cooled condensers. These values closely correspond to those of an actual
geothermal power plant.
The electrical power for electrolysis and liquefaction processes is supplied from
the geothermal power plant. The geothermal power is used for the electrolysis to
produce hydrogen gas and the remaining part of the power is used for the liquefac-
tion of hydrogen gas in the Claude cycle. Alkaline water electrolysis process occurs
in the environmental conditions. Hydrogen and oxygen are produced at the envi-
ronmental conditions. The Claude cycle is shown on the right side of the Fig. 1.
Hydrogen gas enters the compressor where its pressure is raised to a high value. The
compressed hydrogen gas ﬂows through a series of heat exchangers and eventually
through a Joule-Thompson valve. Hydrogen to be liquﬁed continues through the
process and is ﬁnally expanded through the expansion valve to the liquid recevier.
The cold gas from the liquid recevier is returned through the heat exchangers to
cool incoming gas.
A turbine extracts work from high-pressure hydrogen gas in order to reduce
work requirement in the cycle.
3
Economic Modeling
The economic analysis takes into account the cost of each component, including
operation and maintenance, and the cost of fuel consumption. To deﬁne a cost
function, which depends on the optimization parameters of interest, component
costs have to be expressed as functions of thermodynamic variables. Through an
economic analysis, the levelized values of capital investment, fuel costs, and O&M
cost for the entire economic life of the analyzed plant are calculated. The total
revenue requirement (TRR) method was applied in this paper (Bejan et al. 1996).
Table 1 summarizes the main assumptions and parameters used in the economic
analysis. The economic life for all components and for the overall system was
assumed to be 20 years except for the alkaline water electrolysis unit. The elec-
trolysis unit lifetime was assumed to be approximately 40,000 h, so the stack should
be replaced every 5 years (40,000 h with 85% of capacity factor cover nearly
5 years). The future value of the purchased equipment cost of the electrolysis unit is
predicted using the nominal escalation rate (e.g., 5.0%) and is discounted to the
present value, in 2014, using the average interest rate of return (e.g., 15%).
The purchase and equipment costs (PEC) of the components are estimated to the
program of Aspen Plus economic analysis library (Aspen PlusV8.4 2014) and
updated to the values for 1 January 2014. Details of the cost model of each
component are summarized in Table 2.
Thermoeconomic Optimization of Hydrogen Production and Liquefaction. . .
955
Table 1 Aspen Plus assumption for the economic analysis of system (Aspen PlusV8.4 2014)
Parameter
Value
IF (ROR interest factor)
1.2
Nominal escalation rate (%)
5
Construction period (year) (1 January 2014–1 January 2015)
1
Start of commercial operation
January 2016
Economic lifetime for the plant (year)
20
Tax-related plant lifetime (year)
15
Average annual capacity factor (%)
85
Average labor cost ($/yr./person)
482,130
Fixed O&M cost (%)
161,075
Unit cost of isobutane [25]
15.6 $/GJ
Salvage value (percent of initial capital cost, %)
20
Depreciation method
Straight line
Tax rate (%)
40
AF (ROR annuity factor, %)
5
ROR (desired rate of return/interest rate, %)
15
Working capital percentage (%)
5
Table 2 Purchase equipment cost system of each component (Aspen PlusV8.4 2014)
Components
PEC (103$)
Zk
CI ($/h)
Zk
OM ($/h)
Zk
T ($/h)
Flash valve
20.500
0.5289
0.1063
0.6352
Separator
65.200
1.682
0.3381
2.02
Steam turbine
92.600
24.32
4.889
29.21
Water-cooled condenser
242.900
6.266
1.26
7.526
Binary heat exchanger
348.600
8.993
1.808
10.8
Isobutane turbine
973.400
25.11
5.048
30.16
Air-cooled condenser
229.900
5.931
1.192
7.123
Pump
153.400
3.957
0.7956
4.753
Compressor
7620.000
196.6
39.52
236.1
Heat exchanger 1
69.300
1.788
0.3594
2.147
Heat exchanger 2
21.600
0.5572
0.112
0.6693
Heat exchanger 3
11.800
0.3044
0.0612
0.3656
Turbine
1228.000
3.168
0.6369
3.805
J-T valve
20.500
0.5289
0.1063
0.6352
Receiver
22.700
0.5856
0.1177
0.7033
Electrolysis unit
6.531.300
168.5
33.87
202.4
Other system components
500.00
12.9
2.593
15.49
Purchase and equipment costs (PEC)
17,886.800
Operating and maintenance costs (OMC)
2962.878
956
C. Yilmaz et al.
Through the economic analysis, the levelized cost of carrying charges and the
levelized cost of OMC were calculated and the values were distributed to each
component, proportionally to the purchased equipment cost. Finally, the values
were converted considering the capacity factor of the whole plant operation, as
expressed:
_
Z k ¼
CCL þ OMCL
τ

 PECk
P
k
PECk
ð1Þ
All these levelized capital costs of each component, calculated in economic
analysis, were used as inputs for the exergoeconomic analysis.
4
Thermoeconomic Modeling
In an exergoeconomic analysis the exergy costing principle is applied, in which a
speciﬁc cost ci is assigned to each exergy stream including material streams and
energy streams (Bejan et al. 1996). Through this principle, all cost ﬂows within a
system can be analyzed comprehensively in a quantitative manner.
The cost rate associated with the ith stream is calculated by multiplying the
speciﬁc cost of the ith stream ci to the exergy rate of the same stream i as shown:
_
C i ¼ ci _
E xi ¼ ci

_
m iexi

ð2Þ
To calculate the speciﬁc cost of each stream, a cost balance for each component
should be stated as shown in Eq. (3), and it can be restated as Eq. (4) based on the
concept of fuel exergy and product exergy for the component. Equation (3) implies
that the cost of the exergy of fuel and the cost of capital investment for each
component are charged to the exergy of product of the same component:
X
m
1
_
C i þ _
Z k ¼
X
n
1
_
C e
ð3Þ
_
C F þ _
Z k ¼ _
C P
ð4Þ
The thermoeconomic costs of all the ﬂows that appear in the system
“Fuel×Product” deﬁnition are obtained through exergy costing principles. Exergy
costing involves formulation of cost balances for each component, which are
discussed in the following paragraphs. All cost balance equations and auxiliary
equations applied to the system are summarized in Table 3.
When the abovementioned balance equations and auxiliary equations are stated
and mathematically solved, the parameters for the exergoeconomic analysis can be
calculated (Bejan et al. 1996). The cost of exergy destruction is calculated using
Eq. (5):
Thermoeconomic Optimization of Hydrogen Production and Liquefaction. . .
957
_
C D,k ¼ cF,k _
E xD,k
ð5Þ
where cF , k is the speciﬁc cost of the fuel exergy for the kth component, and it can be
calculated using Eq. (6)
cF,k ¼
_
C F,k
_
E xF,k
ð6Þ
Similar to the deﬁnition of speciﬁc cost of the fuel exergy, the speciﬁc cost of the
exergy of product for the kth component can be calculated using Eq. (7):
Table 3 Cost balance equations and auxiliary equations for the exergy costing of the system
Components
Exergetic cost rate balance equations for the
components of system
Auxiliary
equations
Expansion valve
_
C 1 þ _
Z EV ¼ _
C 2
c1 (known)
c2 (variable)
Separator
_
C 2 þ _
Z SEP ¼ _
C 3 þ _
C 6
c3 ¼ c6
Steam turbine
_
C 3 þ _
Z ST ¼ _
C WST þ _
C 4
cST (variable)
c4 ¼ c3
Water-cooled
condenser
 _
C 4  _
C 5

þ _
Z WCC ¼ _
C b  _
C a
c5 ¼ c4
ca ¼ 0
cb (variable)
Isobutane turbine
_
C 8 þ _
Z IT ¼ _
C WIT þ _
C 9
cIT (variable)
c9 ¼ c8
Binary heat
exchanger
 _
C 6  _
C 7

þ _
Z HE ¼
 _
C 8  _
C 11

c7 ¼ c6
c8 (variable)
Air-cooled
condenser
 _
C 9  _
C 10

þ _
Z ACC ¼
 _
C d  _
C c

c10 ¼ c9
cc ¼ 0
cd (variable)
Pomp
_
C 10 þ _
Z P þ _
W P ¼ _
C 11
c5 (known)
c6 (variable)
Compressor
_
C 12 þ _
Z COMP þ _
W COMP ¼ _
C 13
c12 (known)
c13 (variable)
Heat exchanger 1
 _
C 12  _
C 20

þ _
Z HE1 ¼
 _
C 13  _
C 14

c20 ¼ c12
c14 (variable)
Heat exchanger 2
 _
C 20  _
C 19

þ _
Z HE2 ¼
 _
C 14  _
C 15

c19 ¼ c20
c15 (variable)
Heat exchanger 3
 _
C 18  _
C gas

þ _
Z HE3 ¼
 _
C 15  _
C 16

c18 ¼ cgas
c16 (variable)
Rectiﬁer
_
C 17 þ _
Z REC ¼ _
C gas þ _
C liquid
cliquid ¼ cgas
J-T valve
_
C 16 þ _
Z EV3 ¼ _
C 17
Cryogenic turbine
_
C 140 þ _
Z TURB ¼ _
C WTURB þ _
C e
ce ¼ c14’
cTURB
(variable)
Electrolysis unit
_
C 22 þ _
Z Electrolysis þ _
W Elecricity ¼ _
C 12 þ _
C 23
c22 ¼ 0
c12 (variable)
958
C. Yilmaz et al.
cP,k ¼
_
C P,k
_
E xP,k
ð7Þ
The exergoeconomic factor, which indicates how much the capital investment
contributes to the total cost, is calculated using Eq. (8). In components showing a
high value of fk the capital investment has a dominant effect on the total cost for the
component, so reducing investment cost at the expense of thermodynamic efﬁ-
ciency can be considered for reducing the cost of the product from the overall
system. A relatively low value of fk implies that the efﬁciency of the kth component
could be improved by increasing the capital investment cost to reduce the exergy
destruction, and the consequent exergy destruction cost of the component.
f k ¼
_
Z k
_
Z k þ _
C D,k
ð8Þ
Another important variable for the exergoeconomic evaluation is the relative
cost difference, rk, which is deﬁned by Eq. (9).
rk ¼ cP,k  cF,k
cF,k
¼
_
C D,k þ _
Z k
cF,k _
E xP,k
ð9Þ
5
Thermoeconomic Optimization Modeling
The deﬁnition of the two objective functions of the multiobjective optimization
problem, which are the exergetic efﬁciency of the plant (to be maximized) and the
total cost rate of operation (to be minimized), are deﬁned as follows (Baghernejad
and Yaghoubi 2010):
εoverall ¼
_
E xP,total
_
E xF,total
ð10Þ
_
C overall ¼
X
k
_
Z k
ð11Þ
The thermodynamic decision variables should be chosen for the system optimi-
zation. The following decision variables are selected: the ﬂash pressure of the
geothermal water P2, the compressor pressure ratio of liquefaction compressor Pr,
the isentropic efﬁciency of the compressor ηC, the temperature of the binary
isobutane entering the turbine T8, the isentropic efﬁciency of the pump ηP, the
isobutane inlet pressure of the heat exchanger P11, and the cryogenic turbine mass
fraction of liquefaction cycle x. The system is treated as the base case, and the
Thermoeconomic Optimization of Hydrogen Production and Liquefaction. . .
959
following nominal values of the decision variables are selected based on the
operation program of the constructed site: P2 ¼ 600 kPa, Pr ¼ 40, ηC ¼ 0.70,
T8 ¼ 148.8 C, ηp ¼ 0.85, P11 ¼ 2100 kPa, and x ¼ 0.6.
Although the decision variables may be varied in optimization procedure, each
decision variable is normally required to be within a given practical range of
operation as follows [29]: 100  P2  1000 kPa, 10  Pr  50 kPa,
0.70  ηC  0.90, 130  T8  190 C, 0.70  ηP  0.90, 1000  P11  3000 kPa
and 0.5  x  0.8.
6
Results and Discussion
For an objective system such as the geothermal hydrogen production and liquefac-
tion system of the study, performance evaluation and optimization procedure are
directly proportional to what may be considered as performance improvement and
searching a good solution for the overall system rather than to ﬁnd a global
optimum results by EES subprogram of genetic optimization (application with
exergoeconomic analysis) (Klein 2015). In Table 4, the system plant-based assump-
tion results of energy and exergy analysis in the system with respect to Fig. 1 are
given. Exergy ﬂow rates, cost ﬂow rates, and unit exergy costs associated with each
stream of the system with 7572 kW electricity of geothermal plant, 126.2 kg/h
hydrogen production, and 21.62 kg/h liquid hydrogen are given in Table 5.
In Table 6, unit exergetic costs of fuels and products, relative exergetic cost
differences, exergy economic factors, cost rate of exergy destructions, and exergy
efﬁciencies for the system components are shown.
The minimum value of the objective function of ﬂash pressure against the
number of generations, the ﬁtness curve, is shown in Figs. 2 and 3. It is seen that
after about 1000 iterations the objective function stabilizes with EES genetic
optimization tool part. The corresponding plots of product cost, fuel cost, exergetic
efﬁciency, cost of exergy destruction, and capital investment cost against the
number of generations are shown in Table 7, respectively. The decision variables
for the base case and optimum case are given in Table 7.
The comparative results of the base case and the optimum case are given in
Tables 7 and 8. The results clearly show decrease in the product cost and the
objective function. The exergy destruction, however, is increased by 16.2% with a
4.38% decrease in the exergetic efﬁciency of the system.
Table 8 compares the fuel exergy, total exergy destruction, fuel cost rate, and the
cost rate of exergy destruction in the base case and optimal solution. According to
Table 8, the optimization leads to 5.40% reduction in the fuel exergy, 16.2%
increase in the total exergy destruction, and also 5.93 and 36.5% reduction in the
unit exergetic cost rate of hydrogen production and liquefaction, respectively.
960
C. Yilmaz et al.
7
Conclusions
Exergoeconomic and thermoeconomic evaluations are conducted for a geothermal-
based hydrogen production and liquefaction system. The system and its compo-
nents are analyzed using the ﬁrst and second laws of thermodynamics. By combin-
ing exergetic analysis and economic calculations, the cost structure of each
component and of the entire system are obtained. The electrolysis unit, liquefaction
unit, heat exchangers, and turbines appear to be responsible for most of the
exergetic costs. By using a geothermal water source at 200 C at a rate of 100 kg/
s, hydrogen gas can be produced to 0.038 kg/s and can be liqueﬁed to 253 C at a
Table 4 System data, thermodynamic properties, mass ﬂow rates, and exergy data in the system
with respect to the state points of the ammonia-water absorption refrigeration cycle in Fig. 1
State
Pressure
P (kPa)
Temperature
T
Mass ﬂow
rate _
m (kg/s)
Enthalpy
h (kJ/kg)
Entropy
s (kJ/kg K)
Speciﬁc
exergy ex
(kJ/kg)
0
100
25
100
104.8
0.3672
0
00
100
25
60.8
599
2.515
0
000
100
25
2793
293.6
5.699
0
0000
100
25
0.03486
3929
53.37
0
1
1555
200
100
352.3
2.331
162.3
2
600
159
100
352.3
2.352
153.6
3
600
159
8.72
2756
6.759
720.1
4
10
45
8.72
2233
7.049
138
5
10
45
8.72
191.8
0.6492
2.351
6
600
159
91.28
670.40
1.931
90.2
7
600
76
91.28
313.6
1.029
17
8
2100
149
60.8
302.7
2.689
145.5
9
400
100
60.8
732.5
2.722
72.99
10
400
30
60.8
270.8
1.245
50.03
11
2100
31
60.8
274.5
1.246
52.97
12
100
25
0.030
3929
53.37
117,113
13
5000
25
0.030
3951
37.24
4562
14
5000
113
0.030
2015
28.55
5211
14’
5000
113
0.180
2015
28.55
5211
15
5000
210
0.012
732.4
16.07
10,128
16
5000
226
0.012
450.7
10.85
12,889
17
100
253
0.012
450.7
22.17
12,019
18
100
208
0.00708
926
34.61
2596
19
100
201
0.02508
999.7
35.69
3312
20
100
143
0.02503
1613
25.76
5653
21
100
25
0.3115
104.8
0.3672
900
22
100
25
0.2767
0
0.89
3970
23
36
74
100
307.6
0.9971
15.1
Thermoeconomic Optimization of Hydrogen Production and Liquefaction. . .
961
rate of 0.008 kg/s in the liquefaction cycle. Exergoeconomically optimal values for
total product cost ﬂow rate, total cost fuel ﬂow rate, cost of electricity, cost of
hydrogen production, and cost of hydrogen liquefaction are calculated to be 1820
$/h, 274.2 $/h, 0.01908 $/kWh, 1.967 $/kg, and 1.095 $/kg, respectively, whereas
the corresponding actual base case values are 3031 $/h, 290 $/h, 34.34 $/h, 0.02076
$/kWh, 2.091 $/kg, and 1.725 $/kg, respectively. The use of geothermal energy in
Table 5 The exergy ﬂow rates, unit exergy costs and the cost ﬂow rates associated with each
stream of the plant
State
_
E x (kW)
C ($/GJ)
_
C ($/h)
1
16,227
1.373
80.21
2
15,359
1.459
80.65
3
7247
1.484
38.73
4
1389
1.484
7.421
5
28.69
1.484
0.1533
6
8112
1.484
43.35
7
1529
1.484
8.171
8
8109
2.614
76.32
9
3844
2.614
36.18
10
2789
2.614
26.25
11
2953
3.157
33.56
12
8268
8.577
255.3
13
159.8
4.821
2.773
14
72.99
4.821
1.267
15
180.5
4.821
2.462
16
168.4
4.821
3.133
17
20.78
5.906
3.58
18
96.1
4.821
0.3607
19
164.2
4.821
1.668
20
281.6
0.003916
0.002315
21
1103
0
0
22
1510
0
0
23
16,227
1.531
8.324
e
51.47
4.241
0.7858
g
44.28
11.58
1.847
f
62.96
11.58
2.626
_
W P
193
11.11
8.928
_
W IT
2828
19.78
304
_
W ST
4778
2.14
35.14
_
W COMP
19
10.83
8.0315
_
W TURB
666.5
4.241
10.175
Produced hydrogen
8268
17.4
255.6
Liquid hydrogen
72.13
14.4
3.71
962
C. Yilmaz et al.
Table 6 Energy, exergy, and exergoeconomic performance results for the subsystems in geothermal hydrogen production and liquefaction cycle
Component
_
E xF kW
ð
Þ
_
E xP kW
ð
Þ
_
E xD kW
ð
Þ
ε (%)
r (%)
f (%)
CF,k ($/GJ)
CP,k ($/GJ)
($/h)
Steam turbine
5859
5029
829.2
85.8
92.8
82.24
1.462
2.95
3.96
Isobutane turbine
4877
4270
606.1
87.6
69.4
79.54
2.497
4.23
5.45
Pomp
223.5
190.2
33.36
85.1
47.4
82.82
5.766
3.036
0.693
Heat exchanger
7575
5978
1598
78.9
70.8
47.42
1.462
2.49
8.41
Air-cooled condenser
1291
433.8
857.6
33.6
69.4
39.36
2.497
4.23
7.71
Water-cooled condenser
1169
330.8
868.7
25.7
45.1
53.62
1.462
0.804
4.57
Separator
15,591
15,591
0
100
103.5
100
1.437
1.46
0
Expansion valve
16,227
15,591
636.6
96.1
4.66
12.42
1.373
1.44
3.15
Electrolysis unit
5484
4117
1367
75.1
201.7
83.36
5.766
17.4
28.4
Compressor
260.3
159
101.3
61.1
72.2
96.32
17.39
4.84
6.34
Heat exchanger 1
163.5
86.37
77.08
52.8
259.2
917.6
4.843
17.4
1.34
Heat exchanger 2
68.57
67.79
0.776
98.9
0
97.2
4.843
0.004
0.014
Heat exchanger 3
75.1
38.5
36.6
51.3
29.6
67.36
4.843
4.84
0.638
J-T valve
179.7
167.6
12.13
93.3
22.5
67.83
4.843
5.93
0.212
Rectiﬁer
167.6
167.6
0
100
141.9
100
5.933
18.09
0
System
58,668
51,721
6947
88.2
945.2
91.85
1.373
14.4
66.9
Thermoeconomic Optimization of Hydrogen Production and Liquefaction. . .
963
100
150
200
250
300
350
400
450
500
550
600
650
700
750
800
0.90
0.95
1.00
1.05
1.10
1.15
1.20
1.25
1.30
1.35
1.40
0.0185
0.0190
0.0195
0.0200
0.0205
0.0210
0.0215
0.0220
Hydrogen cost ($/kg)
Electricity cost ($/kWh)
Flash pressure (P2 kPa)
Optimum flash pressure
Liquid H2
Production H2
Electricity
Fig. 2 Optimum unit exergetic cost rate of hydrogen production and liquefaction with respect to
the geothermal water ﬂash pressure
1000
1200
1400
1600
1800
2000
2200
2400
2600
0.90
0.95
1.00
1.05
1.10
1.15
0.0186
0.0188
0.0190
0.0192
0.0194
0.0196
Heat exchanger pressure (P8 kPa)
Hydrogen cost ($/kg)
Electricity cost ($/kWh)
Optimum exchanger pressure
Liquid H2
Production H2
Electricity
Fig. 3 Optimum unit exergetic cost rate of hydrogen production and liquefaction with respect to
the geothermal water ﬂash pressure
964
C. Yilmaz et al.
hydrogen production and liquefaction can be considered to be a viable pathway in
the sustainable hydrogen technology, and the proposed system is an alternative of
such an application.
Acknowledgments This study is sponsored by the Scientiﬁc and Technological Research Coun-
cil of Turkey (TUBITAK) with project number 113 M207. This support is greatly appreciated.
References
Abusoglu, A., Kanoglu, M.: Exergoeconomic analysis and optimization of combined heat and
power production: a review. Renew. Sustain. Energy Rev. 13(9), 2295–2308 (2009)
Aspen PlusV8.4. Engineering Economic Analysis Library. Based on 01 January (2014)
Baghernejad, A., Yaghoubi, M.: Genetic algorithm for multi-objective exergetic and economic
optimization of parabolic trough collectors integration into combined cycle system (ISCCS).
Accepted for presentation in the ASME 2010 Conference (ESDA2010), Istanbul, 12–14 July
(2010)
Table 7 Comparison of values in base cases design with those obtained at optimum solution of
EES genetic optimization (1000 iterations) (Klein 2015)
Properties
Base case
Optimum case
Flash pressure
600 kPa
324.8 kPa
Binary heat exchanger pressure
2100 kPa
1020 kPa
Binary heat exchanger pinch point
5 C
5 C
Liquefaction compressor pressure
5000 kPa
4009 kPa
Liquefaction cycle gas fraction (x)
0.6
0.75
Binary heat exchanger temperature
148.8 C
126.3 C
Binary heat exchanger pinch point temperature
108.1 C
72.2 C
Geothermal electricity production
7572 kW
7611 kW
Electrolysis unit hydrogen production
125.5 kg/h
126.1 kg/h
Table 8 Comparative results between the base case and optimum case of the geothermal
hydrogen production and liquefaction system
Properties
Base case
Optimum case
% Variation
Fuel exergy
58,668 kW
55,475 kW
5.40
Product exergy
51,721 kW
47,188 kW
8.76
Exergy destruction
6947 kW
8287 kW
þ16.2
Fuel cost ($/h)
290 $/h
274.2 $/h
5.44
Product cost ($/h)
3031 $/h
1820 $/h
39.9
Exergy destruction cost ($/h)
34.34 $/h
40.96 $/h
þ16.2
Exergetic unit cost of electricity
0.02076 $/kWh
0.01908 $/kWh
8.10
Exergetic cost of hydrogen production
2.091 $/kg
1.967 $/kg
5.93
Exergetic cost of hydrogen liquefaction
1.725 $/kg
1.095 $/kg
36.5
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C. Yilmaz et al.
A Case Study of Energy Modeling of a School
Building in Astana City (Kazakhstan)
Uyzbayeva Aigerim, Tyo Valeriya, and Sedov Artem
1
Introduction
The growing concern of international community about the rise of energy con-
sumption and production of CO2 emissions in the construction sector has led to the
increased application of energy-efﬁcient measures at the early design stage. The
analysis of the State Energy Register of Kazakhstan showed that secondary schools
and nurseries are responsible for 36.5% (EPESDI 2014) of buildings consuming
more than 500 tons of oil equivalents (Adilet 2017) and are eligible for mandatory
energy auditing. This fact directly points at the existing problems in energy balance
of these types of buildings. The major concerns are related to high cost of heating
and cooling of schools due to possible heat loss through building envelope, use of
inefﬁcient technologies, and high energy waste. It should be noted that Astana city
is the second coldest capital in the world and energy conservation is an important
factor in the city’s climatic conditions. According to the Statistics Agency of
Kazakhstan, there are around three million students in schools across the country
(Statistic Agency of Kazakhstan 2014), and they spend almost 30% of their lives at
school and about 60% of their time indoors during the day (Bako-Biro et al. 2012).
Consequently, in addition to energy saving, adequate indoor comfort in school
buildings must be ensured. Currently, there are no regulations in Kazakhstan’s
building codes regarding the compulsory tentative energy analysis in computational
U. Aigerim (*) • T. Valeriya
Nazarbayev University Research and Innovation System, Laboratory of Intelligent Systems
and Energy Efﬁciency, 53, Kabanbay batyr Ave., 9302B, Astana 010000, Kazakhstan
e-mail: aigerim.uyzbayeva@nu.edu.kz; valeriya.tyo@nu.edu.kz
S. Artem
Moscow State University of Civil Engineering (Smart City), Laboratory of intelligent systems
in automation, 26, Yaroslavskoye shosse, Moscow 129337, Russia
e-mail: sedov.eit@gmail.com
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_67
967
programs. Preliminary computer modeling and designing energy-efﬁcient solutions
of school buildings lead not only to rational power consumption and development
of ecological environment in classrooms but also to improvement of students’
academic performance.
There have been many studies carried out toward improving energy performance
of buildings (Sartori and Hestnes 2007) including those using computer simulation
programs. Most of them are targeted on modeling and assessing residential
(Sadeghifam et al. 2015) and ofﬁce buildings (Andarini 2014). Educational insti-
tutions have been evaluated in terms of life cycle cost analysis (Bull et al. 2014),
environmental performance (Scheyer et al. 2003), energy consumption (Pereira
et al. 2014), and indoor air quality (Elbayoumi et al. 2014). Some research has been
done on energy simulation of school buildings (Katafygiotou and Serghides 2014).
This study presents the results of energy modeling of a school building located in
Astana city considering its extreme weather conditions and current costs of energy
sources. It adopts a holistic approach to evaluate energy consumption, potential of
renewable sources, and ﬁnancial feasibility as well as to make a set of recommen-
dations toward improving energy efﬁciency and design solutions.
2
Methodology and Approach: Energy Modeling
The approach of estimating the thermal behavior of the school building is given in
this section.
2.1
Choice of Materials
The building materials for the envelope is chosen in accordance with local building
code – SN RK “2.04.01-2004 Thermal Performance of The Buildings” and are
based on the estimation of thermophysical properties of building materials. The
building envelope should satisfy the minimum requirements. The normalized heat
transfer resistance (minimum required thermal transmittance) of the envelope for
the given location is calculated as
Ro
min ¼ a  Dd þ b
ð1Þ
a – coefﬁcients (SNRK 2008)
Dd – degree-days of heating period, C, determined by the formula
Dd ¼ tint  tht
ð
Þzht
ð2Þ
where
968
U. Aigerim et al.
tint – design temperature of indoor air, C, taken according to GOST 30494
depending on the building type (between 16 and 21C)
tht – average temperature of outdoor air, C
zht – duration of heating period, days (SNRK 2008)
While designing the building according to the complex index of thermal energy
needs, the conventional value of the thermal resistance of each individual element
Ro
con should be set. In the design of the buildings the condition Ro
min  Ro
con
should be satisﬁed.
For a composite building element made up of a number of layers of different
materials, its total resistance without thermally conductive inclusions is condition-
ally determined by the following formula:
Ro
con ¼ 1=αint þ R1 þ R2 þ    þ Rn þ 1=αext, м2 
С=W,
ð3Þ
where
R1, R2, Rn – thermal resistance of ﬁrst, second, and other layers of the envelope, м2
С/W
αint – heat transfer coefﬁcient of the inner wall surface, W/(м2С)
αext – heat transfer coefﬁcient of external wall surface, W/(м2С)
Reduced total thermal resistance with heat-conducting inclusions is given as
Ro
r ¼ Ro
con  r, м2
С=W
ð4Þ
where r – heat transfer performance uniformity factor of building envelope that
considers heat transfer inclusions (jamb, jamb joints, end lap, fastening elements,
concrete studs, etc.).
The materials for the school building envelope including roofs and ﬂooring
are chosen according to the results of the abovementioned calculations and to
SNRK (2008).
2.2
Selecting a Modeling Tool: Energy Modeling
It is known that the parameters affecting the energy performance of a building
should be addressed during early design stages. Energy modeling is a widely used
tool for predicting energy use of a building and applying various energy-saving
measures. It creates prerequisites for designers, owners, and other stakeholders to
implement the design decisions related to the reduction of greenhouse emissions
and the overall impact on the environment, as well as to minimize the energy needs
for buildings, along with the use of energy-efﬁcient technologies and sources of
low-carbon emissions.
A Case Study of Energy Modeling of a School Building in Astana City (Kazakhstan)
969
Simulation model of the proposed building is consistent with the provision of
documentation including the proper accounting of windows and opaque walls,
interior lighting, type of control, ventilation and air-conditioning, size, maintenance
and management of water supply systems, as well as premises occupation. Analysis
of energy consumption includes all energy costs associated with the maintenance of
the school building including PC, cooking and other equipment, energy, lightings,
hot water supply, and HVAC. Construction materials’ thermophysical properties,
building orientation, climatic conditions, R value, conductivity, glazing, analysis of
thermal
bridges,
and
inﬁltration/exﬁltration
coefﬁcients
are
taken
into
consideration.
For the energy model development, dynamic simulation software TAS,
engineered by EDSL, is chosen. The program has been approved by CIBSE
ASHRAE as an instrument for thermodynamic analysis and is approved by inter-
national standards on ecological certiﬁcation including LEED and BREAM.
2.3
Systems
For the preliminary analysis of energy consumption, the school building was
investigated with three different systems:
1. Heating/ventilation technology-based fan coil units (FCU)
2. Heating/ventilation system on the basis of constant air supply (CAV)
3. Heating/ventilation on the basis of mechanical ventilation system with air
preheating and central heating system (water)
It is assumed that the envelope remains the same in all the cases. The parameters
of the ventilation system and the efﬁciency of the main elements are set the same for
three systems. All systems contain a heat exchanger with the efﬁciency of 60%. In
addition, the possibilities of introducing renewable energy technologies are
observed.
3
Input Data
3.1
Building Characteristics
The selected building represents a traditional standardized design of a two-story
secondary school with the capacity of 300 students. The building is assumed to be
located in Astana city, Kazakhstan. All design and technical solutions meet the
requirements of current building codes and safety standards adopted by the country.
The virtual model is drawn in AutoCad software before being transferred into TAS.
970
U. Aigerim et al.
The school operates in two shifts with the ﬁrst group of students starting from
08:00 am to 01:30 pm and the second group of students from 01:30 pm to 07:00 pm.
Floor heights of the ﬁrst and the second ﬂoors are 3 m each, and the height of the
basement is 2.7 m.
Building constructions and materials of the proposed model are selected in
compliance with the local building codes. The composition of the constructions is
given below.
The sloping tiled roof is composed of 220 mm reinforced concrete, 180 mm rock
wool insulation, 100 mm keramzit expanded clay, and 5 mm Ruberoid rooﬁng
material. In cold season the internal R value is 5503 m2C/W and the external
R value is 5443 m2C/W for upward heat direction.
The external wall consists of the following layers: 20 mm render, 380 mm
brickwork, 132 mm rock wool insulation, and 7 mm of protecting render. When
the heat direction is horizontal, R value is 4051 m2C/W for internal and 3961 m2
C/W for external parameters.
The composition of underground wall is 20 mm render, 300 mm concrete block,
117 mm rock wool insulation, 20 mm render, and 30 mm splitter tile. Internal and
external R values are 3501 m2C/W and 3411 m2C/W respectively for horizontal
heat direction.
For the slab above semiground spaces, we selected 5 mm plastic, 50 mm
concrete screed, 125 mm concrete, 175 mm rock wool insulation, and 0.1 mm
aluminum. For downward heat direction in cold season, the R values are
4801 m2C/W for internal and 4671 m2C/W for external parameters.
The slab on the ground is composed of 5 mm plastic, 50 mm concrete screed,
125 mm concrete, 150 mm crushed brick, and 1000 mm dry sand. This resulted in
3845 m2C/W and 3715 m2C/W of internal and external R values, respectively,
for downward heat direction in cold season.
Similarly, window parameters meet the requirement of the local building codes.
The parameters are represented in Table 1.
The selection of frame and glazing materials is based on the condition that the
building is not operating during the summer period.
3.2
Weather Data
The weather data ﬁle for energy modeling has been generated from Meteonorm
7.0.20 and Climate Consultant databases to determine average weather parameters
of Astana city for 2000–2014 years.
Generally, Astana has a humid continental climate with warm summers and no
dry season. As shown in Fig. 1, the temperature typically varies from 22 to 26 C
and is rarely below 32 C or above 32 C over the course of a year. The warm
season lasts from May to mid-September with an average daily high temperature
above 19 C. The hottest month of the year is July, with an average high of 26 C
and low of 12 C. The cold season lasts from the end of November to March with an
A Case Study of Energy Modeling of a School Building in Astana City (Kazakhstan)
971
average daily high temperature below 5 C. The coldest day of the year is in
January, with an average low of 22 C and high of 13 C.
The length of the day varies signiﬁcantly over the course of the year. The
shortest days are in December with less than 8 h of daylight; the longest days are
in July with 16.5 h of daylight (Fig. 2).
The precipitation in the region varies throughout the year. The most common
forms of precipitation are moderate snow, light rain, light snow, and thunderstorms.
The most intensive period of precipitation is summer with peak in July, while in
February there is less than 30 mm of precipitation observed (Fig. 3).
Table 1 Windows parameters
Elements
Parameters
Baseline model
Proposed model
Glazing
U-value
0.735 W/m2C
0.736 W/m2C
g-value
0.39
0.39
Shading devices
Not applied
Not applied
VLT
59.5
59.5
Frame
U-value
1.3 W/m2C
1.3 W/m2C
Fig. 1 Temperature, Astana
972
U. Aigerim et al.
Fig. 2 Sunshine duration and radiation, Astana
A Case Study of Energy Modeling of a School Building in Astana City (Kazakhstan)
973
Figure 4 shows that the wind most often blows from the southwest and south,
while less often is out of north and northwest.
Over the course of the year, typical wind speeds vary from light air and moderate
breeze with 1 to 7 m/s to strong breeze rarely exceeding 11 m/s (Fig. 5).
Fig. 3 Precipitation, Astana
Fig. 4 Wind directions over the year, Astana
974
U. Aigerim et al.
3.3
Energy Sources Rate
According to the tariffs set by the provider of electricity and heat in Astana
“Astanaenergosbyt” LLP in 2014, the average rate for electricity is 0.076 USD
per 1 kW/h and 0.011 USD per 1 kW/h for heat energy (Astanaenergosbyt 2015). In
this context, considering the given costs integration of any electric heating systems
including heat pumps in the building is less preferable compared to the central
heating system.
4
Results and Discussion
Based on the energy model, several zones with similar thermal requirements
serviced by the same mechanical equipment and controls are identiﬁed (Fig. 6) in
order to calculate loads for each zone to be able to select optimal building
orientation, assess thermal comfort, determine the energy consumption, and con-
duct ﬁnancial and life cycle analyses.
4.1
Optimal Building Orientation and Daylight Potential
The loads analysis based on comparison of annual changes in key parameters
(heating, cooling, humidity, dehumidiﬁcation, solar lighting, occupancy, equip-
ment, and internal loads) calculated in four different cardinal directions has been
12 m/s
10 m/s
8 m/s
6 m/s
4 m/s
2 m/s
Jan
Feb
Mar
Apr
May
Jun
Jul
3 m/s
daily mean
daily max
Aug 4
5 m/s
daily max
Mar 18
6 m/s
4 m/s
daily mean
Aug
Sep
Oct
Nov
Dec
Fig. 5 Wind velocity range, Astana
A Case Study of Energy Modeling of a School Building in Astana City (Kazakhstan)
975
carried out to identify the most optimal orientation of the building. Figure 7 shows
that small distinction of the same parameters in different directions is observed. The
minimum load on heating and dehumidiﬁcation is achieved if the building is
oriented to the west (270 C).
To identify daylight potential, two types of glazing are compared. The types are
glazing with shading (VLT 0.59) and glazing transparent (VLT 0.80). The results are
shown in KEO and Lux units in Fig. 8a for VLT 0.59 and in Fig. 8b for VLT 0.80.
Fig. 6 Thermal zoning of energy model
Fig. 7 Annual loads comparison
976
U. Aigerim et al.
Obviously, daylight potential is higher with VLT 0.80. Given the fact that the
school building is not fully operating in summer period when the sun is strong, it is
recommended to select multichamber glazing with no shading or sputtering. In
order to reduce energy losses, automatic or manual lighting dimmers should be
installed in the classrooms.
Daylight Factors (<)
a
b
0,200
0,400
0,600
0,800
1,000
1,200
1,400
1,500
1,600
1,800
Lux levels(<)
50,0
100,0
150,0
200,0
250,0
300,0
350,0
400,0
450,0
500,0
Lux levels(<)
50,0
100,0
150,0
200,0
250,0
300,0
350,0
400,0
450,0
500,0
Daylight Factors (<)
0,200
0,400
0,600
0,800
1,000
1,200
1,400
1,500
1,600
1,800
Fig. 8 (a) Daylight factor and lux level for VLT 0.59. (b) Daylight factor and lux level for VLT
0.80
A Case Study of Energy Modeling of a School Building in Astana City (Kazakhstan)
977
4.2
Thermal Comfort
The analysis of thermal comfort is based on the simulation of air temperature and
air velocity in the classroom (Fig. 9) and the dining room (Fig. 10), where students
spend most of their time at school.
As a result, the values of air temperature, predicted mean vote (PMV), and
predicted percentage dissatisﬁed (PPD) are identiﬁed as optimal, while air velocity
value is found to be lower than required. More speciﬁcally, the average air
temperature including mean radiant temperature in classroom and dining room
are 23.7 and 22.5 C, respectively. The index of PMV is 0.02 in the classroom
Fig. 9 Temperature range and air velocity in classroom
978
U. Aigerim et al.
and 0.22 in the dining room, which is in line with the recommended limits: 0.5
to 0.5 by ASHRAE (2017). The values of PPD are 5.5% in the classroom and 6.2%
in the dining room, which is conforming to acceptable PPD range in ASHRAE 55.
The largest concern relates to air velocity with only 0.0023–0.042 m/s values,
which is not sufﬁcient compared to 0.1 m/s required by the international standards.
The low air movement in the room points to inadequate engineering systems in the
building.
Fig. 10 Temperature range and air velocity in dining room
A Case Study of Energy Modeling of a School Building in Astana City (Kazakhstan)
979
4.3
Systems and Energy Consumption
The parameters of the ventilation system and the efﬁciency of the main elements are
taken the same as for three systems. All systems contain a heat exchanger with the
efﬁciency of 60%. The water part for all three systems represents a common tank
with the capacity of 1000 l (thermoaccumulator) connected with the heat source.
The storage tank is also connected with the solar thermal system and DHW
consumers. The model accounts for the uniform supply of hot water during working
hours (the rate of 2500 l of hot water at a temperature of 55 C per day). For the
purposes of this study, it is assumed that 2000 l of hot water should be consumed by
students and school staff. The rest 500 l are used for the needs of the building. Hot
water is only consumed during working days of the school.
The graphical representation of the systems is given below (Figs. 11, 12, and 13):
The systems consist of 122 m2 PV panels with the total installed capacity of
25 kW and 10 small-scale wind turbines (Fig. 14).
Fig. 11 Schematic representation of the heating/ventilation system based on FCU
Fig. 12 Schematic representation of the heating/ventilation system based on CAV
980
U. Aigerim et al.
Solar thermal and PV collectors more intensively produce thermal and electrical
energies in summer season, when the school is not being exploited in normal
regime. Taking into account the high cost of RET, it is necessary to conduct a
comprehensive analysis of the feasibility of installing such systems in schools. The
following recommendations are given based on the analysis of the energy modeling
of the school.
The results of the estimated energy consumption for the assumed three types of
HVAC systems are given in Table 2. The values of power consumption and cost are
provisional and conditional and may not correspond to the true values.
The calculated values given in Table 2 show the distribution of power consump-
tion parameters depending on the system used. The last column of the table
indicates a power value in accordance with tariff and rates in local currency
(1 USD ¼ 184 KZT). Energy assessment is based on a simpliﬁed and idealized
model of the building and may not fully reﬂect all the details and complexities of
the operation of the building as well as all the features of system maintenance and
Fig. 13 Schematic representation of the heating/ventilation system based on mechanical
ventilation
Fig. 14 Dynamics of total generation of electricity from RET
A Case Study of Energy Modeling of a School Building in Astana City (Kazakhstan)
981
management. The results are interpreting the potential energy efﬁciency of the
building in the simulation of energy consumption using standard operating ﬁle
listed in ASHRAE 90.1-2007.
The preliminary life cycle analysis for the three systems has been done in IES
software, and 60 years of operation are used in calculations. The inﬂation rate for
the entire period is not used, and the analysis is carried out in the forecast prices
without deﬂation. Initial data in the structure of life cycle cost is the modeled energy
consumption of the building, as well as structural features of the building.
After analyzing the energy consumption of three different systems, it is con-
cluded that the use of system No.3 – the system with central heating and ventilation
based on mechanical ventilation with the air preheating – for an energy-efﬁcient
school in Astana is most optimal and economically feasible. It should be noted that
the Government of Kazakhstan has recently approved ﬁxed tariffs for electricity
supply from RES. However, the tariffs are not applicable in our case. Therefore, the
rates are not taken into account in economic calculations.
5
Recommendations and Conclusion
The following recommendations are given based on the analysis of the energy
modeling of the school.
Site selection: in order to reduce environmental impact on the site, it is necessary
to conduct environmental site assessment, to develop an action plan on pollution
prevention from construction activity and automobile use, as well as to implement a
storm water management plan to eliminate pollution from storm water runoff and
soil contamination.
Water management: limited or no potable water should be used for landscape
irrigation; storm drains water system and water consumption monitoring and rain
water harvesting systems should be integrated to ensure efﬁcient water use.
Optimization of energy performance: to be able to reduce economic and envi-
ronmental impacts associated with excessive energy use, there should be different
technologies employed like renewable energy sources such as passive solar heating,
geothermal, PV and groundwater cooling as well as optimizing system control
strategies by using occupancy sensors CO2 and air quality monitoring systems.
Table 2 Systems energy consumption and total cost
Analyzed systems
Energy consumption,
kWh
Heat,
kWh
Electricity,
kWh
Cost, KZT
FCU
738,200
423,400
314,800
4,596,780.5
CAV
765,300
394,600
370,700
5,292,402
Mechanical vent/
radiators
811,200
549,300
261,900
4,155,805.8
982
U. Aigerim et al.
Building envelope: to ensure durability and high performance of walls, roofs,
and other assemblies, the materials should be selected based on long-term insula-
tion and air barrier requirements. It is preferable to use recycled and locally
produced materials.
Generally, the proposed model of school building is used to simulate traditional
solutions used in older schools previously. From the results of simulation, it could
be seen that the air velocity in classrooms is not sufﬁcient. Hence, the design,
engineering solutions, and perhaps the legislative base regarding the microclimate
in school buildings should also be revised in the future. As the economic feasibility
analysis shows, while the cost of electricity and RET is high, the only viable option
in Astana weather conditions is central heating system and mechanical ventilation
with air preheating.
For the future research, this model could be used and optimized according to
different climatic conditions of different regions of Kazakhstan. The life cost and
life cycle analysis should also be done.
Acknowledgments This research was supported by the Intelligent Systems and Energy Efﬁ-
ciency Laboratory of the “Nazarbayev University Research and Innovation system” Private
Institution.
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Exergoeconomic and Exergoenvironmental
Analysis and Optimization of the Cogeneration
Cycle Under Dynamic Solar Radiation Model
Using Two Renewable Sources
Kaveh Haniﬁ, Kourosh Javaherdeh, and Mortaza Yari
1
Introduction
In recent years, availability of new sources of energy is a vital factor for economic
development of different countries. According to recent researches, the develop-
ment of a country is in a direct relationship with the energy consumptions. Soaring
regime for energy consumption and access limitation to conventional energy
sources such as coal, petroleum, and natural gas, in addition to polluting effects
of such energies, have urged the utilization of sustainable and renewable sources
such as solar and biomass. Rankine and organic Rankine power cycle, applying
solar and biomass energy, are one of the most efﬁcient approaches to produce
electricity and hydrogen. In order to make a better use of thermal energy, organic
substances have been utilized in power cycle instead of water. Many studies are
carried out on selecting the working ﬂuids (Bejan et al. 1996; Delgado-Torres and
Garcia-Rodriguez 2010; Chen et al. 2006). Among these working ﬂuids, carbon
dioxide has absorbed much attention recently since it has more favorable thermo-
dynamic properties, and it reaches to supercritical state (Beckman 2004). In recent
years, Sun et al. (2012), Wang et al. (2013), and Song et al. (2012) introduced the
novel power systems driven by solar energy and LNG as their heat sinks. In their
K. Haniﬁ(*)
Department of Mechanical Engineering, Lashtenesha-Zibakenar Branch,
Islamic Azad University, Lashtenesha, Iran
e-mail: k.haniﬁ@lziau.ac.ir
K. Javaherdeh
Departments of Mechanical Engineering, Mechanical Engineering
Faculty, Guilan University, Rasht, Iran
M. Yari
Department of Mechanical Engineering, Mechanical Engineering
Faculty, Tabriz University, Tabriz, Iran
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_68
985
investigation, they carried out thermodynamic analysis on polygeneration systems
with various working ﬂuids.
If thermal systems are optimized by only considering the thermodynamic factor,
the total cost of the system increases dramatically. Therefore, a thermal system
should be evaluated economically and thermodynamically (Tsatsaronis et al. 1993).
Sahoo (2008), by using the exergoeconomic analysis, produced 50 MW of elec-
tricity and 15 kg/s of saturated steam at 2.5 bar in the cogeneration system by using
evolutionary program optimization. He showed that the cost of electricity and
production cost are 9.9% lower in comparison with the base case. Sayyaadi and
Sabzaligol (2009) performed an exergoeconomic optimization by using genetic
algorithm and demonstrated the fuel cost of the optimized system is greater than the
base case. He demonstrated by saving a greater monetary in other economic parts
that these lacks are made up. Ahmadi et al. (2001a, b) with using multi-objective
optimization presented the cost of the exergy destruction decreases if the gas
turbine inlet temperature increases. Carvalho et al. (2011) have considered the
trigeneration system to be used in hospital in Spain. The aim of their investigation
was to minimize the cost and carbon dioxide emission. Bagdanavicius et al. (2012)
took notice to analysis of the four different thermal systems functioned by biomass.
However, energy and exergy modeling of a biomass along with gasiﬁcation com-
bined cycle plant were observed by Bhattacharya et al. (2011). Zare et al. (2012)
carried out the exergoeconomic analysis of ammonia-water cycle, by using genetic
algorithm optimization; they indicated the total cost of the cycle products is less
than by around 18.6% and 25.9% compared to the base case. The thermoeconomic
modeling of trigeneration system with steam turbine prime mover using biomass
was illustrated by Lian and Chou. (2010). In their analysis, they studied four
different conﬁgurations through exergy destruction and overall production cost.
Ahmadi and Dincer (2010, 2011) considered the primary aim function adding with
the cost rate of each equipment and environmental blow. They showed the increas-
ing net power output, the pressure ratio, the compressor and turbine isentropic
efﬁciency,
and
turbine
inlet
temperature
would
preferably
be
increased.
Al-sulaiman et al. (2013) examined three systems: SOFC-trigeneration, biomass-
trigeneration, and solar-trigeneration. Their investigation revealed that solar-
trigeneration is the best exergoeconomic performance since the cost per exergy
unit is the lowest among the three conﬁgurations. Sanaye and Shirazi (2013) carried
out exergoeconomic optimization of an ice thermal energy storage system. The
results of their genetic algorithm optimization indicated that the capital and oper-
ational costs in electricity consumption and CO2 emission were 9% and 9.8% lower
than the base case, respectively. Li et al. (2014) practiced the exergoeconomic
performance of CO2 transcritical power cycle and organic Rankine cycle. The
results showed the ORC working with R600a presented the highest net power
output, while the highest thermal and exergy efﬁciencies are gained by regenerative
ORC working with R601. They showed, with increasing the turbine inlet pressure,
the cost per net power output was ﬁrst reduced to the minimum and then increased
ﬁnally. Mokheimer et al. (2014) investigated the trigeneration, power/cooling/
heating, and system with biomass energy. Their results showed that the optimum
986
K. Haniﬁet al.
operation of the trigeneration system can be achieved with the lowest ORC evap-
orator pinch point and the lowest ORC minimum temperatures 20 K and 345 K,
respectively. However, they demonstrated that the fuel efﬁciency from the electri-
cal power to trigeneration increases from 12% to 88% and in trigeneration system,
ORC maximum exergy efﬁciency increases from 13% up to 28%.
As mentioned above, it seems, there is little data on thermoeconomic analysis of
CO2 transcritical cogeneration system. To overcome this shortage, this paper
applies the exergoeconomic and exergoenvironmental model to scrutinize hydro-
gen/refrigeration cogeneration cycle by using two sources of energy. In this study,
three conﬁgurations of cogeneration system are considered. The cycle is then
optimized by means of genetic algorithm from the viewpoints of both thermody-
namics and economics by using the EES (Engineering Equation Solver) software.
The objectives of this paper are to maximize the hydrogen production rate and
refrigeration power and to minimize the sum of the unit costs of the system
products, respectively. The objectives of this paper from thermodynamic and
exergoeconomic perspective are to maximize the hydrogen production rate and
the refrigeration power and to minimize the cost of the system products,
respectively.
Nomenclature
_
C
Cost rate ($/h)
c
Cost per exergy unit ($/MJ)
e
Speciﬁc exergy (kJ/kg)
_
E x
Exergy rate (kW)
h
Enthalpy (kJ/Kg)
GA
Genetic algorithm
ir
Interest rate
K
Constant
_
m
Mass ﬂow rate (kg/s)
P
Pressure (bar)
_
Q
Heat transfer rate (kW)
s
Speciﬁc entropy (kJ/kgK)
T
Temperature (C or K)
V
Volume (m3); volumetric ﬂow rate (lit/s)
Z
Investment cost of components. ($)
_
Z
Investment cost rate of components. ($/h)
_
Q
Heat transfer rate (kW)
s
Speciﬁc entropy (kJ/kg.K)
Greek letters
α
Absorbpivity
β
Collector tilt factor ()
δ
Declination ()
θ
Angle of incidence ()
φ
Latitude ()
(continued)
Exergoeconomic and Exergoenvironmental Analysis and Optimization. . .
987
ω
Hour angle ()
ε
Emissivity
η
Exergy efﬁciency
τ
Annual plant operation hours
ηp
Isentropic efﬁciency
ρ
Reﬂectivity
Subscripts and abbreviations
0
Ambient
boil
Boiler
cond
Condenser
HPROD
Hydrogen production rate optimal design
i
Inlet
e
Exit
CI
Capital investment
0
Ambient
P
Product, pump
PHE
Plate heat exchanger
ref
Refrigeration
RPOD
Refrigeration power optimal design
s
Solar
ST
Storage tank
SUCP
Sum of the unit costs of the products
tur
Turbine
2
System Description
The schematic diagrams of three different cogeneration systems consist of SCTCS,
BCTCS, and SBCTCS and are shown in Fig. 1a–c. These systems are composed of
three subsystems as the energy source subsystem, the CO2 transcritical power
subsystem, and the hydrogen production subsystem. The liquid ﬂat-plate collector
is employed as the source of energy in the system due to its low cost and wide
applications, and the thermal storage tank is to bring heat to the power cycles. These
cycles are designed to produce hydrogen and refrigeration as the main and the side
products, respectively. All of them are composed of three subsystems as the energy
source subsystem, the CO2 transcritical power subsystem, and the hydrogen pro-
duction subsystem. To simplify the systems, the following assumptions are made
Sun et al. (2012):
•
The system reaches a steady state; the kinetic and potential energies of ﬂuids are
neglected.
•
The condenser outlet state is saturated liquid.
•
The pressure drops of CO2 in refrigeration and boiler are assumed to be 2%.
988
K. Haniﬁet al.
Fig. 1 The schematic of three different conﬁgurations of cogeneration system. SCTCS (a),
BCTCS (b), and SBCTCS (c)
Exergoeconomic and Exergoenvironmental Analysis and Optimization. . .
989
•
The LNG is vaporized at a constant pressure of 0.6 MPa, which equals to the
liqueﬁed in LNG ﬁlling station.
•
All the calculations are based on high heating value (HHV) of hydrogen rather
than LHV. Since, HHV accounts for the total amount of energy in the electrol-
ysis process.
•
An alkaline electrolyzer with 77% efﬁciency is chosen to simulation.
•
The pressure drops in biomass burner are assumed to be 10% (Al-sulaiman
et al. 2012).
2.1
Solar Radiation Dynamic Modeling
The liquid ﬂat-plate collector is employed as the source of energy in the system due
to its low cost and wide applications, and the thermal storage tank is to bring heat to
the power cycles. The hourly radiation falling on tilted surface is given by:
IT ¼ IbRb þ IdRd þ Ib þ Id
ð
ÞRr
ð1Þ
where Ib and Id are the beam and diffuse radiation, respectively, and Rb, Rd, and Rr
as tilt factors for beam, diffuse, and reﬂected radiations, respectively, are deﬁned
by:
Rb ¼ Cos θ
ð Þ
Cos θz
ð
Þ ¼ Sin δSin φ  β
ð
Þ þ Cos δCos ωCos φ  β
ð
Þ
Sin φSin δ þ Cos φCos δCos ω
ð2Þ
δ ¼ 23:45Sin 360
365 284 þ n
ð
Þ


ð3Þ
Rr ¼ 1 þ Cos β
2
ð4Þ
Rr ¼ ρ 1  Cos β
2


ð5Þ
The incident solar ﬂux absorbed in the absorber plate is given by:
S ¼ IbRb τα
ð
Þb þ IdRd þ Ib þ Id
ð
ÞRr
½
 τα
ð
Þd
ð6Þ
where (τα)b and (τα)d are transmissivity-absorptivity product for beam and diffuse
radiation falling on the collector, respectively. The overall loss coefﬁcient UL is
introduced to express the heat lost from the collector
990
K. Haniﬁet al.
qL ¼ ULAP Tpm  Ta


ð7Þ
where AP represents the area of the absorber plate, Tpm is the average temperature of
the absorber plate, and Ta is the environment temperature. Since the heat lost from
the collector consists of three parts, namely, top loss, side loss, and bottom loss, the
overall loss coefﬁcient is a sum of the three components:
UL ¼ Ub þ Ut þ Us
ð8Þ
Ub ¼ Ki
δb
and Us ¼ L1 þ L2
ð
ÞL3Ki
L1L2δs
ð9Þ
Where Ki is the thermal conductivity of the insulation and δb the thickness of the
insulation.
Ut ¼
M
C
Tpm

	
TpmTa
Mþf
ð
Þ
0:33 þ 1
hw
2
4
3
5
1
þ
σ T2
pm þ T2
a

	
Tpm þ Ta


1
ε þ 0:05M 1  εp

 þ 2M þ f  1
ð
Þ
εc
 M
2
6
6
6
4
3
7
7
7
5
ð10Þ
where
f ¼ 1  0:04hw þ 0:0005h2
w


1 þ 0:091M
ð
Þ
ð11Þ
C ¼ 365:9 1  0:00883β þ 0:0001298β2


ð12Þ
The collector efﬁciency factor, F
0, which represents the ratio of actual useful
gain rate to the gain which would occur when the collector absorber plate is at the
temperature Tﬁ,is deﬁned as:
F0 ¼
1
WUL
1
UL
WDo
ð
Þ ϕþDo
ð
Þ þ
1
πDihf

	
ð13Þ
Qu ¼ FRAP S  UL Tfi  Ta
ð
Þ
½

ð14Þ
where
FR ¼ mwaterCP
ULAP
1  exp  F0ULAP
mwaterCP




ð15Þ
Figure 2 shows that the incident solar ﬂux, useful and load heat gain, and water
temperature at storage tank outlet change deeply with the radiation intensity, rise to
Exergoeconomic and Exergoenvironmental Analysis and Optimization. . .
991
its peak in midday, and dramatically drop approaching zero when the sun falls
(Dufﬁe and Beckman 2006). The system could be operated normally after sunset
thanks to the thermal storage tank.
2.2
Mathematical Model and Thermodynamic Analysis
Each term of exergy involves four distinctive components. They are physical,
chemical, kinetic, and potential exergies. In this research, two of the items, the
kinetic and potential, are counted as negligible as the elevation and speed have so
little changes (Bejan et al. 1996; Ameri and Ahmadi 2007; Dincer and Al-Muslim
2001). The chemical exergy is an important part of exergy in combustion process.
Therefore, the chemical exergy used in two systems consists of biomass burner. The
physical exergy is described as a maximum theoretical work gained of a system in a
reversible procedure to reach to a dead state (Dincer and Rosen 2007). The required
equations (energy and exergy) for modeling of the SCTCS, thermal storage tank,
and the CO2 transcritical system are shown by Sun et al. (2012), Wang et al. (2013),
and Song et al. (2012). However, the biomass burner is one of the main components
in the BCTCS considered the required details modeling. Further details of the
biomass combustion, factors like moisture content factor, low heating value, and
the relation governments, are discussed by Al-sulaiman et al. (2012). The exergy
destruction rate and the exergy efﬁciency for each component for whole system in
the cycle are shown in Table 1.
For the validity of thermodynamic purpose, the mass ﬂow rate of the CO2, net
power output, the turbine power, and S-H2 exergy efﬁciency of the cycle are
compared with the available data in the literatures reported by Song et al. (2012)
600
500
400
300
200
100
0
6
8
10
12
14
16
18
0
20
40
60
80
Time (h)
Incident solar flux
Useful heat gain
load heat gain
Water temp.in storage tank
Power (KW)
Temperature (°C)
Fig. 2 Variations of S, Qu, Qload, and Tst with time over a day
992
K. Haniﬁet al.
and Sun et al. (2012), respectively. Figures 3 and 4 indicate the comparison between
the results. However, Table 2 shows the comparison of the values of modeling in
the base and optimum cases in present work via Sun et al. (2012) reference.
These differences are due to the different sources of thermodynamic properties
for CO2 and LNG used in two works. The thermodynamic properties in reference
are calculated using the REFPROP, while the properties of the present work are
taken from EES software (Sun et al. 2012).
3
Exergoeconomic Analysis
Numerical correlations are applied to evaluate the heat transfer coefﬁcients in the
heat exchangers. To evaluate the heat transfer coefﬁcients, the types of exchangers
are determined. As for its high efﬁciency and intensive form, the plate heat
exchanger (PHE) type is used in this system (Li et al. 2014). It is supposed in
boiler and refrigeration; the single-phase ﬂow regimes and condenser are assumed
to have two-phase ﬂow regime. The governing equations for calculation of con-
vection heat transfer coefﬁcient and heat transfer area are explained in Reference Li
et al. (2014).
The exergoeconomic model is the latest method to evaluate the cost of system
processes based on exergy and economic analysis. By using this method, the cost
per exergy unit of the product can be calculated (Bejan et al. 1996; Zare et al. 2012).
As in order to deﬁne a cost function, component cost can obligatory be expressed as
a function of thermodynamic design parameters. The cost balance equations with
the required auxiliary equations are applied to estimate each component of the
cycle. The cost balance equations may be written as (Bejan et al. 1996):
Table 1 The exergy destruction rate and exergy efﬁciency equation for cycle components
Components
Exergy destruction
Exergy efﬁciency
Boiler
_
E xD,B ¼
 _
E x3 þ _
E x8


 _
E x4 þ _
E x9

ηEx,B ¼
_
E x4  _
E x3
_
E x8  _
E x9
Turbine
_
E xD,TUR ¼ _
E x4  _
E x5  _
W TUR
ηEx,TUR ¼
_
W TUR
_
E x4  _
E x5
Condenser
_
E xD,CON ¼
 _
E x5 þ _
E x6


 _
E x1 þ _
E x7

ηEx,CON ¼
1 
_
E xD,CON
_
E x5 þ _
E x6


Pump
_
E xD,P ¼ _
E x1  _
E x2 þ _
W P
ηEx,PUMP ¼
_
E x2  _
E x1
_
W P
Refrigeration
_
E xD,REF ¼
 _
E x2 þ _
E x12


 _
E x3 þ _
E x13

ηEx,REF ¼
1 
_
E xD,REF
_
E x2 þ _
E x12


Storage tank
_
E xD,TSt ¼
 _
E x9 þ _
E x10


 _
E x8 þ _
E x11

ηEx,B ¼
_
E x8  _
E x9
_
E x11  _
E x10
Burner
_
E xD,bb ¼ _
E x8 þ _
E xcc  _
E x9
ηEx,bb ¼
_
E x9
_
E x8 þ _
E xcc
Exergoeconomic and Exergoenvironmental Analysis and Optimization. . .
993
X
e
_
C e,k þ _
C w,k ¼
X
i
_
C i,k þ _
C q,k þ _
Z k
ð16Þ
_
C j ¼ cj _
E xj
ð17Þ
The term _
C is the cost rate, and w and q are the subscripts associated with the
output power from the component and input thermal energy to the component,
respectively. The balance equations for all components of the system involve a set
of nonlinear equations, which were solved for
_
C j and cj. In the cost balance
formulation Eq. (16), the cost of the exergy destruction rate is unclear. Thus, if
one combines the exergy balance and exergoeconomic balance together, one can
obtain the following equations (Bejan et al. 1996; Ahmadi et al. 2001a, b):
5.2
5.1
5
4.9
4.8
4.7
4.6
9.5
28
26
24
22
20
18
17.5
18.5
19.5
20.5
21.5
Boiler inlet temperature (°C)
Turbine power (KW)
10
10.5
Sun et al.
Present work
Hydrogen efficincy
11
TIT=65(°C)
CT=-20(°C)
LNG-IT=-161.48(°C)
BIT=20(°C)
TIT=65(°C)
CT=-20(°C)
LNG-IT=-161.48(°C)
TIP=10(MPa)
11.5
Turbine inlet pressure (MPa)
Sun et al.
Present work
12
12.5
a
b
Fig. 3 Validation of hydrogen efﬁciency (a) and turbine power (b) of the model developed in the
present work by that of the Sun et al. (2012)
994
K. Haniﬁet al.
_
E xF,K ¼ _
E xP,K þ _
E xD,K
ð18Þ
_
C F,K ¼ cF,K _
E xD,K
ð19Þ
_
C P,K ¼ cP,K _
E xD,K
ð20Þ
Further details on exergoeconomic analysis, cost balance equation, and
exergoeconomic factor are discussed in Refs. Tsatsaronis (1987), Balli and Aras
(2007), and Rosen and Dincer (2003). However, the cost functions as suggested by
Ahmadi and Sanaye (2008) and Roosen et al. (2003) are referred. The term _
Z k in
Eq. (16) is the total cost rate which is related with capital investment, operation, and
maintenance for kth component:
0.6
0.5
0.4
Mass flow rate of CO2 (kg/s)
Net power output (KW)
0.3
0.2
14
12
10
8
6
4
2
6
8
10
Song et al.
Present Work
12
14
16
18
20
22
TIME (h)
8
9
10
11
Turbine inlet pressure (MPa)
12
LNG-IT=-161.48(°C)
CT=-10(°C)
TIT=65(°C)
TIT=65(°C)
CT=-10(°C)
LNG-IT=-161.48(°C)
present work
Song et al.
13
14
15
a
b
Fig. 4 Validation of mass ﬂow rate (a) and net power output (b) of the model developed in the
present work by that of the Song et al. (2012)
Exergoeconomic and Exergoenvironmental Analysis and Optimization. . .
995
_
Z k ¼ _
Z CI
k þ _
Z OM
k
ð21Þ
The annual levelized capital investment for the kth component can be calculated
as (Misra et al. 2006):
_
Z CI
k
¼
CRF  φ
τ


Zk
ð22Þ
where CRF, τ, and φ are the capital recovery factor, the annual plant operation
hours, and maintenance factor, which is often 1.06, respectively. The required
equations for achieving the investment cost of the system equipment ( _
Z k) such as
heat exchangers, pump, and turbine are explained in Ref. Li and Dai (2014), and
thermal storage tank is explained in Ref. Bejan (1997). The capital recovery factor
is a function of the interest rate, ir, and the number of useful years of the plant
operation, n:
CRF ¼
ir 1 þ ir
ð
Þn
1 þ ir
ð
Þn  1
ð23Þ
_
Z OM
k
¼ γkZk þ ωk _
E xp,k þ Rk
ð24Þ
where γk, ωk, and Rk are the ﬁxed and variable operation and maintenance costs and
all the other operation and maintenance costs, respectively. The last two terms on
the right side of the equation are smaller compared with the ﬁrst one and may also
be ignored (Vieira et al. 2009; Misra et al. 2003).
For thermoeconomic validation, the simple cycle of CO2 transcritical system is
considered. The thermal efﬁciency, the net power output, the total areas of the heat
exchangers, and the cost per net power output (CPP) of this cycle are simulated. The
results are compared with the available data in the literature reported by Li et al.
(2012). According to Figs. 5 and 6, this comparison shows a descent ﬂow within the
results. However, Table 3 indicates the comparison between the modeling and
optimum values in the present work and the reference given above.
Table 2 Base and optimum values of the present work and Sun et al. (2012)
Terms
Base values
Optimum values
Sun et al.
Present work
Sun et al.
Present work
Collector exergy gain (kW)
145.6
146.1
-------
------
Turbine work (kW)
24.22
24.28
41.66
42.56
Pump work (kW)
5.77
5.782
7.16
7.898
Net power output (kW)
18.45
18.5
34.50
34.67
Refrigeration output (kW)
3.66
3.691
11.52
11.498
Hydrogen production rate (L/s)
1.12
1.236
2.1
2.315
S-H2 exergy efﬁciency (%)
4.98
4.333
12.38
10.95
CO2- H2 exergy efﬁciency (%)
9.26
9.27
32.05
34.67
Exergy destruction rate (kW)
115.27
115.6
22.25
19.543
996
K. Haniﬁet al.
3.1
Cost Balance Equations
There are two parts in three conﬁgurations which are similar: the CO2 transcritical
power and the hydrogen production subsystems. The major difference of these
systems is in their sources of energy. Therefore, the formulation of cost balance and
required auxiliary equations for main parts conform to one another. These equations
are as follows:
8
6
4
Thermal efficiency (%)
Network output (KW)
2
0
6
200
160
120
80
40
0
6
8
10
12
14
16
18
8
10
Turbine inlet pressure (MPa)
Turbine inlet pressure (MPa)
12
Present work
Li et al.
Present work
Li et al.
TIT=371 (K)
TIT=371 (K)
14
16
18
a
b
Fig. 5 Validation of thermal efﬁciency (a) and net power output (b) of the model developed in the
present work by that of the Li et al. (2012)
Exergoeconomic and Exergoenvironmental Analysis and Optimization. . .
997
180
160
TIT=371 (K)
140
120
Area (m2)
100
80
60
6
50000000
40000000
30000000
Cost per net power ($/W)
20000000
10000000
0
6
8
10
12
14
16
18
TIT=383(K)
8
10
Present work
Present work
Li et al.
Li et al.
Turbine inlet pressure (MPa)
Turbine inlet pressure (MPa)
12
14
a
b
Fig. 6 Validation of area (a) and cost per net power (b) of the model developed in the present
work by that of the Li et al. (2012)
Table 3 Base and optimum values of the present work and Li et al. (2014)
Terms
Base values
Optimum values
Li et al.
Present work
Li et al.
Present work
CPP ($/W)
1.88E7
1.568E7
1.750E7
1.349E7
Net power output (KW)
153.635
153.72
173.57
187.4
Thermal efﬁciency (%)
4.7368
4.846
5.50
5.707
Exergy efﬁciency (%)
47.235
48.026
54.40
56.451
998
K. Haniﬁet al.
Pump:
_
C 2 ¼ _
C 1 þ _
C WP þ _
Z P
ð25Þ
Refrigeration:
_
C 3 þ _
C 13 ¼ _
C 2 þ _
C 12 þ _
Z ref
ð26Þ
_
C 2
_
E x2
¼
_
C 3
_
E x3
or
c2 ¼ c3
ð27Þ
80
60
40
20
Cp total ($/MJ)
0
-30
-25
BIT=20°C
TIT=65°C
LNG-IT=-161.48°C
TIP=10MPa
-20
-15
SCTCS
BCTCS
SBCTCS
-10
-5
0
Condensation Temperature (°C)
Fig. 6 Effect of condensation temperature on the SUCP of three conﬁgurations
80
60
40
20
Cp total ($/MJ)
0
8
9
BIT=20°C
TIT=65°C
LNG-IT=-161.48°C
CT=-20°C
10
11
SCTCS
BCTCS
SBCTCS
12
13
14
Turbine inlet pressure (MPa)
Fig. 7 Effect of turbine inlet pressure on the SUCP of the three conﬁgurations
Exergoeconomic and Exergoenvironmental Analysis and Optimization. . .
999
Turbine:
_
C 5 þ _
C WT ¼ _
C 4 þ _
Z tur
ð28Þ
_
C 4
_
E x4
¼
_
C 5
_
E x5
or
c4 ¼ c5
ð29Þ
Condenser:
_
C 1 þ _
C 7 ¼ _
C 5 þ _
C 6 þ _
Z Cond
ð30Þ
80
60
40
20
Cp total ($/MJ)
0
50
54
BIT=20°C
TIP=10MPa
LNG-IT=-161.48°C
CT=-20°C
58
62
SCTCS
BCTCS
SBCTCS
66
70
74
Turbine inlet temperature (°C)
Fig. 8 Effect of vapor supercritical temperature of CO2 on the SUCP in three conﬁgurations
35
Exergy destruction (KW)
Exergy destruction (%)
30
25
20
15
10
5
0
Refrigeration
Exergy destruction
Boiler
Turbine
Condensor
Pump
Storage tank
Heat
exchanger
Biomass
burner
Fig. 9 Exergy destruction of each component of the systems
1000
K. Haniﬁet al.
_
C 6
_
E x6
¼
_
C 7
_
E x7
or
c6 ¼ c7
ð31Þ
Electrolyzer:
_
C H2 ¼ _
C WT þ _
Z elec
ð32Þ
On the contrary, the energy source of three conﬁgurations differs from each
other. Thus, the cost balance and required auxiliary equations are formulated
separately.
3.1.1
Solar Energy Source
Boiler:
_
C 4 þ _
C 9 ¼ _
C 3 þ _
C 8 þ _
Z boil
ð33Þ
_
C 8
_
E x8
¼
_
C 9
_
E x9
or
c8 ¼ c9
ð34Þ
Thermal storage tank:
_
C 8 þ _
C 11 ¼ _
C 9 þ _
C 10  _
C Qlhst þ _
Z st
ð35Þ
_
C 10
_
E x10
¼
_
C 11
_
E x11
or
c10 ¼ c11
ð36Þ
Flat-plate solar collector:
_
C 10 ¼ _
C 11 þ _
C fpsc þ _
Z fpsc
ð37Þ
3.1.2
Biomass Energy Source
Biomass burner:
_
C 10 ¼ _
C 8 þ _
C 9 þ _
C env þ _
Z bb
ð38Þ
Boiler:
_
C 4 þ _
C 11 ¼ _
C 3 þ _
C 10 þ _
Z evap
ð35Þ
Exergoeconomic and Exergoenvironmental Analysis and Optimization. . .
1001
_
C 10
_
E x10
¼
_
C 11
_
E x11
or
c10 ¼ c11
ð36Þ
3.1.3
Solar-Biomass Energy Source
Biomass burner:
_
C 10 ¼ _
C 8 þ _
C 9 þ _
C env þ _
Z bb
ð38Þ
Heat exchanger:
_
C 11 þ _
C 14 ¼ _
C 10 þ _
C 16 þ _
Z hex
ð39Þ
_
C 10
_
E x10
¼
_
C 11
_
E x11
or
c10 ¼ c11
ð40Þ
Boiler:
_
C 4 þ _
C 15 ¼ _
C 4 þ _
C 14 þ _
Z boil
ð41Þ
_
C 14
_
E x14
¼
_
C 15
_
E x15
or
c14 ¼ c15
ð42Þ
Thermal storage tank:
_
C 16 þ _
C 18 ¼ _
C 15 þ _
C 17  _
C Qlhst þ _
Z st
ð43Þ
_
C 17
_
E x17
¼
_
C 18
_
E x18
or
c17 ¼ c18
ð44Þ
Flat-plate collector:
_
C 17 ¼ _
C 18 þ _
C fpsc þ _
Z fpsc
ð45Þ
The following supplementary equation:
_
C WT
_
W T
¼
_
C WP
_
W P
cWT ¼ cWP
ð46Þ
1002
K. Haniﬁet al.
4
Exergoenvironmental Analysis
Recently, a decline of carbon dioxide emission such as main greenhouse gas and
optimization of cogeneration systems based on environmental blows is paid much
attention to. At present work, special attention on emissions of pollutants (e.g., CO
and NOx) is considered, and environmental blows are ignored just as much research
reported. Adiabatic ﬂame temperature in primary zone of the biomass burner can be
expressed as follows (Ahmadi et al. 2001a, b; Toffolo and Lazzaretto 2004):
TPZ ¼ Aσαexp
β σ þ λ
ð
Þ2

	
πx∗θy∗ψz∗
ð47Þ
where π is dimensionless pressure (P/Pref), θ is dimensionless temperature (T/Tref),
ψ is the H/C atomic ratio, σ ¼ φ for φ  1 (where φ is mass or molar ratio), and
σ ¼ φ  0.7 for φ  1. Values for these parameters and further details on the
methodology are presented by Ahmadi et al. (2001a, b) and Toffolo and Lazzaretto
(2004). The pollutant emissions (in gram per kilogram of fuel) can be determined as
follows:
mCO ¼ 0:179 E 9exp 7800=TPZ
ð
Þ
P2
bb τ ΔPbb=Pbb
ð
Þ0:5
ð48Þ
mNOx ¼ 0:15 E 16 τexp 71100=TPZ
ð
Þ
P0:05
bb
ΔPbb=Pbb
ð
Þ0:5
ð49Þ
where τ is the residence time in the combustion zone (assumed constant and equal
to 0.002 s) (Rizk and Mongia 1993). TPZ is the primary zone combustion temper-
ature, Pbb is the biomass burner inlet pressure, and ΔPbb/Pbb is the non-dimensional
pressure drop in the biomass burner.
5
Result and Discussion
In this simulation, the boiler inlet temperature (BIT), turbine inlet temperature
(TIT), turbine inlet pressure (TIP), LNG inlet temperature (LNG-IT), and conden-
sation temperature (CT), such as decision variables, affect on the value of the
objective, the sum of the unit cost of the products (SUCP), which is carried out.
The basic assumption and input parameters are used in simulation and are given in
Table 4. The pine trees is found broadly throughout the world and support different
climates. One of the common waste wood products is pine sawdust (Al-Sulaiman
et al. 2013). Therefore, in this study, the pine sawdust is selected as a biomass type
in biomass burner, and its characteristics are listed in Table 5.
An important factor in determining the cost of the products, the unit cost of
refrigeration may vary in an extended range. However, the value of 0.09 US$/kWh
Exergoeconomic and Exergoenvironmental Analysis and Optimization. . .
1003
is considered for the base case and parametric study (Sanaye and Shirazi 2013).
Here, _
C f ¼ cf:
_
m
: LHVf. Also cf is biomass wood cost, which is taken to be 0.01$/
kWh (LHV) [18]. The cost of the pollutant emission is considered such as term in
biomass burner
_
C env ¼
_
mCO CCO þ
_
mNOx CNOx where
_
m CO and
_
m NOx are the
pollutant emissions and CCO ¼ 0.02086 $/kg and CNOx ¼ 6:853 $/kg are unit
damage cost of CO and NOx, respectively. Considering a known value for the
unit exergetic cost of cooling (c6), the unit exergetic cost of the cooling water can be
neglected c12 ¼ 0 (Gebreslassie et al. 2009a, b), and the unit exergetic cost of the
Table 4 The input data
assumed in the simulation
Parameters
Value
Ambient temperature (C)
25
Ambient pressure (MPa)
0.1
Time
14:00
Collector tilt angle
8
Inner diameter of the absorber tube (m)
0.014
Outer diameter of the absorber tube (m)
0.018
Total surface area of absorber plate (m2)
450
Volume of the storage tank (m3)
86
Boiler inlet temperature (C)
20
Turbine inlet temperature (C)
65
Condensation inlet temperature (C)
20
LNG inlet temperature (C)
161.48
Turbine inlet pressure (MPa)
10
LNG inlet pressure (MPa)
0.6
Declination
20.4415
Area of the collector (m2)
1.6 * 1.1
Emissivity of plate
0.92
Emissivity of cover
0.88
Incident ﬂux absorbed by the plate
547.799
Turbine efﬁciency (%)
80
Pump efﬁciency (%)
80
Number of collector
256
τ (h/year)
7000
ir (%)
12
n (year)
20
Table 5 Characteristics of
pine sawdust (Al-Sulaiman
et al. (2013))
Biomass type
Pine sawdust
Moisture content in the fuel (% wt)
10%
Ultimate analysis (% wt dry basis)
wc
50.54%
wh
7.08%
wo
41.11%
ws
0.57%
1004
K. Haniﬁet al.
heat loss is assumed zero cQhlst ¼ 0 (Bejan 1997). The linear system of equations for
SCTCS, BCTCS, and SBCTCS is included with 18, 17, and 24 unknown variables,
respectively. Tables 6, 7, and 8 show the calculated thermoeconomic properties
along with the cost ﬂow rates and unit costs at different state points of the SCTCS,
BCTCS, and SBCTCS for the base case operating condition, respectively.
Figure 6 shows the variation of sum of the unit cost of products with condensa-
tion temperature. The ﬁgure illustrates a decrease in condensation temperature
leading to an increase in the Cp total. This is due to the decrease in condensation
temperature which causes to increase the pump power and heat transfer in con-
denser; as a result, the SUCP increases.
The effect of turbine inlet pressure on the Cp total of cycle is presented in Fig. 7.
The ﬁgure demonstrates the SUCP of the three systems which can be minimized at
speciﬁc value of turbine inlet pressure.
The effect of turbine inlet temperature on SUCP of cycle is shown in Fig. 8. The
ﬁgure shows with an increase in turbine temperature, the SUCP decreases. In fact,
by increasing the turbine temperature, the net power output increases. When the
turbine temperature increases by 22, the Cp of three conﬁgurations is decreased by
29.6%, 9%, and 14.7%, respectively.
6
Exergoeconomic Optimization
There are four optimization methods, direct search, variable metric, and genetic
algorithm, which are available in EES software. Also, unlike the direct search and
variable metric methods, the genetic method is not affected by the assumed values
Table 6 Thermodynamic properties and costs of the SCTCS for the base case
Streams
Temp.
(C)
Pressure
(MPa)
Mass ﬂow
(kg/s)
Exergy rate
(kW)
Costs
_
C ($/h)
c
($/MJ)
1
20
1.97
0.5696
123.6
853.3
1.918
2
14.69
10.41
0.5696
128.1
917.6
1.99
3
20
10.2
0.5696
124.4
891.3
1.99
4
65
10
0.5696
130.6
1091
2.322
5
20
1.97
0.5696
99.13
828.6
2.322
6
161.5
0.6
0.176
190.8
17.17
0.025
7
30
0.6
0.176
50.74
4.567
0.025
8
83.41
0.11
0.8
16.99
232.4
3.8
9
50
0.11
0.8
3.33
45.56
3.8
10
89.83
0.11
5.799
149.9
1009
1.869
11
83.41
0.11
5.799
123.1
828.4
1.869
12
25
0.1
1.059
0
0
0
13
15
0.1
1.059
0.7599
39.16
14.32
Exergoeconomic and Exergoenvironmental Analysis and Optimization. . .
1005
Table 7 Thermodynamic properties and costs of the BCTCS for the base case
Streams
Temp.
(C)
Pressure
(MPa)
Mass ﬂow
(kg/s)
Exergy rate
(kW)
Costs
_
C ($/h)
c
($/MJ)
1
20
1.97
0.5696
123.6
529.8
1.191
2
14.69
10.41
0.5696
128.1
569.6
1.236
3
20
10.2
0.5696
124.4
553.3
1.236
4
65
10
0.5696
130.6
663.2
1.411
5
20
1.97
0.5696
99.13
503.5
1.411
6
161.5
0.6
0.176
190.8
17.17
0.025
7
30
0.6
0.176
50.74
4.567
0.025
8
25
0.1
0.1798
39.7
71.84
0.5026
9
25
0.09
0.62
1.26
0
0
10
1809
0.09
0.7998
15.07
88.99
1.641
11
60
0.09
0.7998
0.7729
4.565
1.641
12
25
0.1
1.059
0
0
0
13
15
0.1
1.059
0.7599
28.71
10.49
Table 8 Thermodynamic properties and costs of the SBCTCS for the base case
Streams
Temp.
(C)
Pressure
(MPa)
Mass ﬂow
(kg/s)
Exergy rate
(kW)
Costs
_
C ($/h)
c
($/MJ)
1
20
1.97
0.5696
123.6
985.1
2.214
2
14.69
10.41
0.5696
128.1
1059
2.298
3
20
10.2
0.5696
124.4
1029
2.298
4
65
10
0.5696
130.6
1263
2.687
5
20
1.97
0.5696
99.13
958.8
2.687
6
161.5
0.6
0.176
190.8
17.17
0.025
7
30
0.6
0.176
50.74
4.563
0.025
8
25
0.1
0.1093
24.14
43.68
0.5026
9
25
0.09
0.377
0.7663
0
0
10
1809
0.09
0.4863
9.161
60.84
1.845
11
103.5
0.09
0.4863
0.4444
2.951
1.845
12
25
0.1
1.059
0
0
0
13
15
0.1
1.059
0.7599
42.73
15.62
14
83.41
0.11
0.8
16.98
275.9
4.512
15
50
0.11
0.8
3.33
54.09
4.512
16
63.53
0.11
0.8
7.683
199.8
7.223
17
69.09
0.11
5.799
72.1
619.3
2.386
18
63.53
0.11
5.799
55.69
478.4
2.386
1006
K. Haniﬁet al.
of the independent variables. The genetic algorithm is one of the best stochastic
global search method based on the Darwinian survival of natural principle.
In present work, the genetic algorithm and direct search method are applied for
optimization aim. Both of them yield the same optimization results. The parameter
optimization conditions are indicated in Table 9. The optimum value of the decision
variables in three cases of hydrogen production rate optimal design (HPROD),
refrigeration power optimal design (RPOD), and cost optimal design (COD) is
given in Tables 10, 11, and 12. To compare, the value of the base case is also
presented in these tables. The ﬁrst three parameters in Table 9 can be illustrated by
the EES user. Other parameters of the genetic algorithm are set to default values
suggested in the PIKAIA program and are unchangeable within the EES. In this
simulation, the number of generation is considered to be 72 after some test.
Tables 10 and 11 show that, in both conﬁgurations, the hydrogen production rate
in HPROD rises from 1.235 up to 1.81 and refrigeration power in PROD rises
4.429 kW up to 6.515 kW.
In the same condition, the SUCP of the BCTCS is lower than the SCTCS (about
9 $/MJ). The comparison in results shows that more percentage of reduction in costs
has happened in SCTCS (about 32.8%). It’s about 19% and 23% in BCTCS and
SBCTCS, respectively.
Table 12 demonstrates the hydrogen production rate in HPROD and COD cases
rises from 1.235 up to 1.656 and 1.533, respectively; however, the SUCP for those
cases is 1.92% and 23.6% lower than base case, respectively.
The results indicate the maximum cost of products happened in PROD case. In
addition, in three conﬁgurations, the reduction of SUCP is more when the optimi-
zation is based on refrigeration power instead of hydrogen production rate.
It can be seen from comparisons between three tables that the refrigeration
power and hydrogen production rate are equal in SCTCS and BCTCS and they
are more than the SBCTCS. However, the SUCP of BCTCS is considerably lower
than two other systems. The COD case is the most important reference in designing
energy conversion systems, as economic effective system. The results of Table 11
indicated the considered cycle is a cost-effective option, if the required hydrogen
Table 9 GA parameters and
the range of decision
variables for optimization
Parameters
Value
Number of individual in the population
34
Number of generation
72
Maximum mutation rate
0.25
Initial mutation rate
0.005
Minimum mutation rate
0.0005
Crossover probability
0.85
The range of BIT (C)
18–23
The range of CT (C)
50 to 0
The range of TIT (C)
50–75
The range of TIP (C)
8–14
The range of LNG-IT (C)
161.48 to 70
Exergoeconomic and Exergoenvironmental Analysis and Optimization. . .
1007
Table 10 SCTCS optimum values of decision variables and objective functions
Decision variable/objective function
Base case
Optimal case
HPROD 1
RPOD
COD
Boiler inlet temp. (C)
20
22.64
23
23
Turbine inlet temp. (C)
65
75
75
75
Turbine inlet pres (MPa)
10
8
8
9.152
Condensation temp. (C)
20
30
30
30
LNG inlet temp. (C)
161.48
70
134.5
70
Net power output (kW)
18.5
27.12
27
26.32
CO2-H2 exergy efﬁciency (%)
9.27
28.33
27.68
28.84
H2 production rate (L/s)
1.235
1.811
1.71
1.757
Ref. output (KW)
4.429
6.36
6.515
6.164
Sum of unit cost of product ($/MJ)
43.54
33.01
35.69
29.23
Table 11 BCTCS optimum values of decision variables and objective functions
Decision variable/objective function
Base case
Optimal case
HPROD
RPOD
COD
Boiler inlet temp. (C)
20
23
23
23
Turbine inlet temp. (C)
65
75
72.76
75
Turbine inlet pres (MPa)
10
8
8
9.395
Condensation temp. (C)
20
30
30
30
LNG inlet temp. (C)
161.48
78.71
72.39
70
Net power output (kW)
18.5
27.2
26.7
26.09
CO2-H2 exergy efﬁciency (%)
9.27
26.02
27.05
28.16
H2 production rate (L/s)
1.235
1.817
1.78
1.754
Ref. output (KW)
4.429
6.425
6.515
6.103
Sum of unit cost of product ($/MJ)
24.92
22.24
22.68
20.16
Table 12 SBCTCS optimum values of decision variables and objective functions
Decision variable/objective function
Base case
Optimal case
HPROD
RPOD
COD
Boiler inlet temp. (C)
20
22
23
23
Turbine inlet temp. (C)
65
75
50
75
Turbine inlet pres (MPa)
10
9.104
8.693
10.2
Condensation temp. (C)
20
22.54
30
30
LNG inlet temp. (C)
161.48
161.48
161.48
161.48
Net power output (kW)
18.5
21.81
19.91
22.96
CO2-H2 exergy efﬁciency (%)
9.27
10.98
10.15
11.57
H2 production rate (L/s)
1.235
1.656
1.329
1.533
Ref. output (KW)
4.429
4.987
6.284
5.387
Sum of unit cost of product ($/MJ)
58.3
57.18
72.73
44.51
1008
K. Haniﬁet al.
production power and refrigeration output are about 1.754 lit/s and 6.103 kW,
respectively.
The exergoeconomic items of each component of the system are shown in
Tables 13, 14, and 15. The results shown in tables indicate that for all three optimal
cases, the biomass burner and condenser have the larger contribution on exergy
destruction, respectively. For all three optimal cases, in the three conﬁgurations, the
investment cost related with the pump is the lowest.
For the overall system, results indicate the lowest total exergy destruction and
investment cost rates are associated with HPROD and COD cases, respectively, as
expected. For all three optimal cases, the total exergy rate and investment cost of
BCTSC are the lowest among three conﬁgurations. These parameters are the
highest in the SBCTCS as there are many components in this conﬁguration.
Figure 9 shows that the condenser has the largest exergy destruction rate. The
second source of the exergy destruction rate is storage tank. This is due to a large
temperature difference in these components. On the other hand, the pump has the
lowest exergy destruction rate.
Table 13 SCTCS exergoeconomic items of components for optimal cases
Components
HPROD case
RPOD case
COD case
_
E xD,K (kW)
_
Z K ($/h)
_
E xD,K (kW)
_
Z K ($/h)
_
E xD,K (kW)
_
Z K ($/h)
Refrigeration
5.113
12.44
5.124
12.48
4.779
12.41
Boiler
10.5
12.12
10.46
12.12
8.347
11.53
Turbine
9.7
2.754
9.667
2.745
9.671
2.751
Condenser
19.02
22.158
23.9
22.33
18.55
21.73
Pump
1.094
1.066
1.097
1.067
1.276
1.148
Storage tank
11.16
6.52
11.25
6.555
11.81
6.555
Electrolyzer
6.238
2.145
6.21
2.13
6.053
2.1
Overall system
62.825
59.203
67.708
59.427
60.486
58.224
Table 14 BCTCS exergoeconomic items of components for optimal cases
Components
HPROD case
RPOD case
COD case
_
E xD,K (kW)
_
Z K ($/h)
_
E xD,K (kW)
_
Z K ($/h)
_
E xD,K (kW)
_
Z K ($/h)
Refrigeration
5.055
12.32
4.665
12.4
4.715
12.46
Boiler
11.69
25.15
6.176
28.04
9.906
25.42
Turbine
9.782
2.775
9.633
2.722
9.641
2.746
Condenser
24.54
33.54
16.99
32.38
18.62
22.16
Pump
1.183
1.105
1.536
1.068
1.319
1.166
Biomass burner
30.29
13.07
12.78
13.28
28.62
12.84
Electrolyzer
6.213
2.832
5.746
2.832
5.995
2.832
Overall system
88.753
90.792
57.526
92.722
78.816
79.624
Exergoeconomic and Exergoenvironmental Analysis and Optimization. . .
1009
7
Conclusions
Exergoeconomic and exergoenvironmental analysis and optimization of the three
conﬁgurations for production of hydrogen and refrigeration are carried out in this
paper. In SCTCS and SBCTCS, the dynamic model would be registered to search
the system behavior during a day (Fig. 2). The effect of key parameters on the
hydrogen production, refrigeration power, and costs of products is examined. The
optimal values of parameters are examined utilizing genetic algorithm optimiza-
tion. The achieved results are as follows:
1. In all three cycles, the biomass burner and condenser have the highest exergy
destruction rate. Their destruction rate in the RPOD case is the lowest among
three cases.
2. The results of the exergoeconomic items of components indicate that the total
exergy destruction rate of the overall system, in the HPROD case, is the highest;
however, the total investment cost rate of the overall system, in the COD case, is
lower than two other cases.
3. As result cogeneration using biomass is the most economic effective system
among the three alternative processes.
4. The simulation results show there is an optimum turbine inlet pressure for
minimum SUCP. The SUCP decreases as turbine inlet temperature increases
and condensation temperature decreases.
5. As the obtained results, in the SCTCS, BCTCS, and SBCTCS, the SUCP in COD
case decreases by 32.8%, 19.1%, and 23.6%, regarding the base case, respec-
tively. However, the SUCP value of base case in BCTCS is the lowest among the
three cycles.
6. The results of optimization in cogeneration cycle using biomass show that the
SUCP is reduced by 9% when the hydrogen production rate and refrigeration
power are decreased from 1.811 to 1.754 lit/s and 6.425 to 6.103 kW from
HPROD case to COD case, respectively.
Table 15 SCBTCS exergoeconomic items of components for optimal cases
Components
HPROD case
RPOD case
COD case
_
E xD,K (kW)
_
Z K ($/h)
_
E xD,K (kW)
_
Z K ($/h)
_
E xD,K (kW)
_
Z K ($/h)
Refrigeration
5.182
12.43
5.203
12.51
4.779
12.862
Boiler
10.49
12.12
10.57
12.16
8.448
12.620
Turbine
9.7
2.756
9.663
2.736
9.706
2.823
Condenser
19.02
37.21
25.012
29.8
19.04
21.036
Pump
1.094
1.067
1.536
1.256
1.076
1.147
Storage tank
11.17
6.55
12.78
6.645
10.759
6.55
Heat exchanger
10.98
25.264
6.052
72.394
10.884
25.13
Biomass burner
29.68
14.23
11.98
55.239
30.247
13.052
Electrolyzer
6.235
2.13
5.746
2.13
6.053
2.13
Overall system
103.569
113.757
88.542
194.87
100.991
97.35
1010
K. Haniﬁet al.
7. For all three optimal cases, the total exergy rate and investment cost of BCTCS
are the lowest among three cases; however, these parameters are the highest in
the SBCTCS as there are many components in this system.
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Indicators of Sustainability Energy
Management Based on Energy Audit for Hotels
M.E.U. Oz, M.Z. Sogut, and T.H. Karakoc
¸
1
Introduction
Hotels within the building sector are commercial structures that consume intensive
energy with around 6% of the total energy consumption of the sector. It is predicted
that the tourism sector, which has got the capacity exceeding 980 million people in
2011, will exceed about 1.5 million people in 2020 with an increase reaching 55%.
This situation, which directly affects the lodging sector, has rendered energy
efﬁciency obligatory especially depending on the development of environmental
consciousness of people in the last years together with the cost effects created by the
competition among hotels (Karatas and Babur 2013).
In the study made for 610 hotels in Europe including Turkey, energy consump-
tion varies between 200–400 kWh/m2 per year depending on their dimensions. This
is an important consumption potential depending on the occupancy rate of hotels
and deﬁned as one of high emission resources in the building sector. So the
determination of energy efﬁciency and energy-saving potential is important, espe-
cially for the reduction of the waste and lost energy sources and accordingly for the
reduction of CO2 emissions (Unwto 2011).
M.E.U. Oz (*)
Vocational School of Technical Sciences, Energy Department, Uludag University,
Bursa, Turkey
e-mail: meuguroz@uludag.edu.tr
M.Z. Sogut
Mechanical Engineering, Engineering Faculty, Orhangazi University, Bursa, Turkey
e-mail: mzsogut@gmail.com
T.H. Karakoc
¸
School of Graduate Sciences, Anadolu University, Airframe and Powerplant Maintenance
Department, Eskisehir, Turkey
e-mail: hikmetkarakoc@gmail.com
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_69
1013
All these evaluations render the development of sustainable energy management
and management strategies obligatory. Hotels have deﬁned the customer-centred
comfort conditions as a primary goal. In this process energy consumption and
effects of consumption cost are deﬁned as important items in the customer costs.
However, energy management in structural analyses is generally evaluated for m2
areas. When considered as an enterprise, cost loads according to the occupancy rate
must be taken into account in terms of management strategies.
The effective use of energy resources and energy efﬁciency in structures essen-
tially depend on the management of energy and the effectiveness of the manage-
ment chains. The use of energy directly concerns with the management ability of
the system. That an energy management can develop and manage strategies is
possible especially when they know the consumption structure, efﬁciency potential
and saving rates. Pre-studies essentially based on energy audits must be made for
energy efﬁciency and management in the whole structure. This study comprises the
pre-studies made by taking the occupancy rate and m2 loads of a sample hotel into
account. In the study, two separate studies were made for the hotel’s insulated and
uninsulated state, and in both states changes, the saving loads were evaluated in
terms of management.
2
Energy Management and Energy Audit in Hotels
Hotels having about 360 MToe energy consumption in the world energy consump-
tion have also important effect on the environment due to their activities and
energy-based substructures. In the hotel sector having about 7% share within the
building sector, European hotels cover about 42% rate with about 5.45 million bed
capacity. According to a study including these hotels, mean use of energy is
between 305 and 330 kWh/m2/yr. This value suggests around 160–200 kgCO2/m2
emission per unit energy as CO2 emission potential for a typical hotel. Energy
consumption in hotels is essentially fossil fuels. While about 39% of the total
energy for a typical hotel is natural gas and fossil fuels, about 61% is electricity.
When the activity structures of hotels are examined, it is seen that energy is used to
lighten as well as to heat, cool, air condition and ventilate. The use of the heating
requirements comes to the forefront in operations like the swimming pool, cooking
and preparing hot meal as well as heating the building (Unwto 2011). Energy use
distribution in a typical hotel is seen in Fig. 1 according to the intended use.
According to the energy-saving studies, when the capacity and use rates in hotels
are taken into account, it is seen that the energy-saving potentials reach 30%. In
European hotels, energy-saving potentials based on used areas are between 15%
and 20% in heating, 5% and 30% in cooling, 40% and 70% in preparing hot water,
and 7% and 60% in lighting (Unwto 2011). Depending on all these evaluations,
establishing an effective and sustainable energy management gains importance
when attention is paid to the operating cost effects of yearly energy consumption
and energy costs in hotels. For hotels, energy management, with its shortest state, is
1014
M.E.U. Oz et al.
a disciplined study that has been structured and organized without making conces-
sions from the service quality, comfort, security or all environmental conditions and
to use energy effectively. Energy management is an important acquirement for
structures having multipurpose use and wide-ranging energy consumption like
hotels. A short information concerning the energy management concept and orga-
nization is given below.
2.1
Energy Management
From the nineteenth century to the present, the developing market economies have
planned their social development strategies by paying attention to their abilities in
meeting the future generations’ requirements. This structure has incited the mass
consumption goal in social development perspective depending on economic
expectations. This impact has ﬁrstly created economic imbalances in societies
and also many problems such as uncontrolled consumptions of fossil fuels, water
resources, and raw materials which directly destroy ecological balance. However,
at the beginning of the twentieth century, the sustainable development concept has
changed depending on the development in environmental consciousness in socie-
ties, and this concept has been developed as providing development by protecting
the quality of life and the natural systems supporting life in the environment (Harris
2000). By this aim, healthy operating of the economy, society, and environment
components being elements of sustainable development ﬁrst depends on true and
effective use or management of energy.
Today, it is seen that actualizing a sustainable environment concept seems quite
hard especially due to socioeconomic reasons. Therefore, many scenarios have
been developed with international associations with respect to limiting CO2
Fig. 1 Distributions of energy consumption rates in a typical hotel
Indicators of Sustainability Energy Management Based on Energy Audit for Hotels
1015
emission, being one of the most important reasons of global temperature increase.
Among these scenarios, this value has been limited as 450 ppm in the fourth
evaluation report of the Intergovernmental Panel on Climate Change (IPCC)
established by the United Nations Environment Programme (UNEP) and World
Meteorology Organization (WMO), and it is predicted that this will increase the
world temperature (280 ppm) 2C compared to the value before industrialization.
However, scientiﬁc studies of the last years have revealed that the safety limit must
be withdrawn to 350 ppm in terms of important ecological transformations like not
living climatic ruin this value will create. In spite of all these goals, depending on
increased demand of energy, the fossil fuel consumption leads to uncontrolled
increases in CO2 emission (Algedik 2013; IEA 2012).
In the light of all these evaluations, energy is the most important element of
social development, and the energy audit comes across us as a primary topic for
sustainable development. By this aim, energy should be managed accurately in a
sustainable structure from the resource to the use and to environmental effects it
leads. However, that energy becomes sustainable in every point from international
share and supply security to its management in national base, and its consumption
in sectoral points gains value if energy policies, strategies, and formed energy
managements happen in a holistic understanding. In this state, a sustainable energy
depends on developing national politics and strategies by taking basic components
of management into account. By this aim, a model has ﬁrstly been developed to
deﬁne sustainable energy management components, and the distribution scheme of
this model has been given in Fig. 2.
The success of energy managements in a country primarily depends on the road
maps and strategies developed connected to national goals. And then, it takes shape
with an effective management system of energy which each consumer in each
sub-sector forms depending on these concepts. In Turkey, this effect has been
shaped with the ISO 50001 Energy Management System. The ISO 50001 takes
efﬁciency and productivity of energy as basis in each energy consumption point
from the raw material and service to the product or service output for an enterprise.
Energy management system is a system which plans commercial and industrial
buildings or standard organizations to continuously develop energy performances,
optimize consumptions and so reduce operational costs. This system also indirectly
contributes to reduction of greenhouse gas emissions (Pekacar 2014; ISO 2011).
Energy management system has a ﬂow plan given in Fig. 3.
This circle deﬁned as the circle Plan—Implement—Check—Correct actually
shows parallelism with the ISO 14001 environment management system. Effective
management of energy is a structure accommodating many concepts from individ-
uals to system, investment costs to sourcing. However, before forming strategies
and policies in this topic, if effects of these concepts on each other are not deﬁned
and national strategies are not internalized by the society individuals, the success
effect of managements becomes low. Therefore, holistic management of energy and
deﬁning what kind of interaction the directly related concepts have are ﬁrstly
important. In Turkey this structure has been shaped with the ISO 50001 Energy
Management System. Energy management system is a system which plans
1016
M.E.U. Oz et al.
commercial and industrial buildings or standard organizations to continuously
develop energy performances, optimize consumptions and so reduce operational
costs. This system also indirectly contributes to reduce greenhouse gas emissions
(Pekacar 2014; ISO 2011). In this scope, an effective energy management is a
dynamic structure that acts within the circle of planning, implementation, control,
and taking measure. The primary duty of energy management based on an effective
Fig. 2 Sustainable energy management
Energy policy
Planning
İmplementation and
operation 
Checking and 
corrective action
Monitor and measure
Internal audit
Management review
Continual
improvement 
Fig. 3 Cycle of energy management system
Indicators of Sustainability Energy Management Based on Energy Audit for Hotels
1017
organization is to determine all energy processes and determine the saving poten-
tials with energy audits.
2.2
Energy Audit
Energy audit is the most important phase in determining energy-saving potential
and developing productivity. Depending on properties of energy management
processes, in the industry sector, the studies for determining the energy-saving
potential are widely used. However, in commercial building applications like
hotels, the energy management and applications haven’t got corporate structure.
So there isn’t any standard method that has been developed for hotels. Energy
consumption in hotels is based on fossil fuel or electricity for needs of electricity,
heating, and cooling. In service buildings like hotels, additional energy types used
by service sections depending on the type of the enterprise must be taken into
account as well as the use of the electricity, heating, and cooling energies in daily
needs. Depending on these evaluations, energy audit has been formed depending on
an algorithm formed in the study. In Fig. 4, energy audit ﬂow scheme is given.
The ﬁrst and signiﬁcant phase of energy audit that will be made in hotels is the
data collection process depending on energy consumption types. The data collec-
tion process necessitates a strategic approach for energy managements. The type
and structure of the data collection have a different structure for every hotel, where
the data are collected. According to this, the data collection strategy for a hotel
comprises the detection of the data of the cost concerning energy consumption
types, measurement types and periods, measurement points, measurement times,
data collection places and energy types. In healthy being of the data collection
during energy audit, accurate working of the energy measurement devices, with
which measurement and taking data are realized, making calibrations and accurate
setting of the data records are obligatory. Although measurement periods are
important in determining the saving potential based on energy audit, measurement
periods are made according to the specialties of the structure and the type of energy
will provide the results to be realistic. At least 10–20 sets of data must be taken in
periods which will be chosen in energy audit. One of the most important processes
in the data collection process is the determination of the data collection type and
measurement times. It will be suitable that for collection time, at least 10 weeks
interval should be in weekly measurements and at least 1 year interval in monthly
measurements.
An approach must be formed by energy managements for the collected data in
energy audit to be processed regularly. By this aim, the collected data ﬁrst must be
transformed into a standard structure, and their unit transformations must be
provided by doing unit analyses. In this study, for the data to be processed, the
energy-saving potential determination was realized with the two-method energy
consumption standard and cumulative sum values (CUSUM) approach. Both
methods are actively used in the determination of energy-saving potential in the
1018
M.E.U. Oz et al.
industry sector. In this study, the applicability of these methods in hotel applications
has been examined.
Energy consumption standard is essentially composed of phases of energy
consumptions, target energy consumptions, and energy-saving potential determi-
nation. Energy consumption in buildings, depending on many factors, can vary day
to day, week to week or month to month. These factors are separated into the two as
speciﬁc variables and controllable variables (Kedici 1993). Speciﬁc variables are
variables that determine energy demand according to the need demanded by the
structure. These variables are used in the standard equations used in order to
calculate energy demand. The controllable variables are variables such as the
energy systems operating applications, system control and maintenance standard
that are planned by the management in order to lower energy consumption to the
least. In general, standard equation is a linear equation showing that energy
requirement depends on speciﬁc variables. This equation
E ¼ a þ b P
ð Þ
ð1Þ
Here, a and b are constants, and P is a speciﬁc variable. Even if three separate linear
equations are generally used in applications, in the industrial applications the above
Start 
Determination of Buildings 
Architectural features
Energy characteristics
Energy Audit
Envorimental parameters
Energy consumption data
Specific target consumption
Unit energy consumption
Energy saving potentials
Energy and Exergy analyses 
Energy analyses
Exergy analyses (fuel and
exergy demand)  
Entropy analyses 
Improvement potential
Environmental analyses 
CO2 emission parameters
CO2 emission potentials
Improvement rate of CO2
emission
Life cycle analyses
Reporting 
Fig. 4 Flow scheme of energy audit
Indicators of Sustainability Energy Management Based on Energy Audit for Hotels
1019
linear equation showing energy requirement depends on speciﬁc variables is pre-
ferred (S€
o
güt and Oktay 2006).
After the standard equation is formed depending on energy consumption data,
goals are determined paying attention to consumption processes. According to the
specialty of the structure, this process can be calculated separately as partial or
within the whole of the structure. And the target equation calculated based on
energy consumption processes is a linear equation being in the same form with the
standard. The data formed for the target equation are classiﬁed as the data
remaining under the standard line, and a target line is formed again. The equation
of this line is the target equation deﬁning the target consumption. For performance
to be evaluated after determining the target in the structure, regular comparison of
the expected energy use with real energy consumption values must be made.
Speciﬁc energy consumption values are used to do this. It is possible to deﬁne
speciﬁc energy consumption as the value of energy, which is used depending on the
unit need value, in the unit time. For example, speciﬁc variable of a building is
deﬁned with energy needed in the unit of time. Speciﬁc Energy Consumption is
stated as
SEC ¼ Ec=Hour
ð2Þ
Here, Ec is energy consumption. SEC value is important especially for monitoring
the effect of the enterprise’s operating conditions on energy consumption perfor-
mance. The rise in the SEC value refers to poor performance and unnecessary
increase of energy consumption (Kedici 1993).
Another method in determining energy-saving potential is the cumulative sum
values (CUSUM) method. This method is essentially the cumulative sum of
potentials of energy data with the least squares method. This sum value provides
a structure to be seen by the help of a graphic. In the CUSUM method, the saving
potentials based on the target consumptions are determined in the system examined
with graphical study. In order to draw the CUSUM chart, an evaluation is made
between the energy data based on the established power and the real energy data
(Kedici 1993). The target consumptions based on the data obtained are calculated
and energy saving in cumulative sum is calculated. The CUSUM chart is drawn in
accordance with the data. When this chart is examined, it is seen that values whose
slope is negative and areas staying in the negative region show times when the
enterprise has had a good performance, and positive areas show times when
deterioration occurs (S€
o
güt 2005). In order to form the CUSUM chart in the
building sector, ﬁrst, the difference between energy consumption costs and target
energy consumption costs is calculated with the cumulative sum. The sum-saving
potential depending on the data collection method is
1020
M.E.U. Oz et al.
X
i
i¼n
Ctotal ¼
X
i
i¼n
Ccons: 
X
i
i¼n
Ctarget
ð3Þ
Here, ∑Csum is cumulative sum-saving potential, ∑Ccon is the sum energy
consumption cost, and ∑Ctarget is the target energy consumption cost. In determin-
ing cumulative sum-saving potential, the determination of the target consumptions
and costs is very important. The target energy consumption potential is
X
i
i¼n
Etarget ¼
X
i
i¼n
Econs: 

1  αs

ð4Þ
Here, ∑Etarget is the target energy consumption, ∑Econ is the consumed energy, and
αs is energy-saving rate. Here, αs is the proportion of energy-saving rate to the unit
energy consumption. The unit energy-saving rate is the difference between the unit
energy consumption and the unit target energy consumption. According to this, the
target energy cost being important for CUSUM is
X
i
i¼n
Ctarget ¼
X
i
i¼n
Ccons 

1  αs

ð5Þ
Here, ∑Ctarget is the target energy consumption cost. The CUSUM chart is an
important chart which can provide each collected datum to be examined in terms of
both the target energy saving and also energy-saving costs.
3
Result and Discussions
Insulation applications cover signiﬁcant place in efﬁcient use of energy and energy-
saving studies in buildings. A hotel, where pre-study has been made, has realized its
external wall insulation in 2011 and formed a saving potential of almost 43% in the
building load values. However, it is seen that this hasn’t been so useful when energy
consumptions have been evaluated for insulated and uninsulated conditions in
terms of unit consumptions. In the study, pre-study analyses have been handled
two sided as insulated and uninsulated and the effects of the insulation application
on consumption have also been questioned. The study, from this aspect, comprises
two different pre-study analyses by taking consumption loads of the years 2008 and
2013 as references.
The structure components for the building energy need values have been eval-
uated separately. The ﬂoor areas are 2152 m2 for the earth touch, balcony area is
32.29 m2, the total window area is 1442.42 m2, and the total door area is 13.57 m2.
In the project, the total ﬁll and concrete wall area is 6693.45 m2, and the total
Indicators of Sustainability Energy Management Based on Energy Audit for Hotels
1021
penthouse and roof area is 2092.97 m2. The hotel’s closed area’s total m2 measure-
ment is 13,253.04 m2.
In the pre-studies, energy consumption and cost distributions of both two 2 years
have ﬁrstly been examined, and the unit consumption and costs and distribution
rates have been calculated. In Table 1, energy consumption distributions of the
years 2008 and 2013 are seen.
In these analyses, it is seen that 987.40 TEP/year energy consumption comes
true for 2008, while the unit energy yearly cost is 0.398 TL/kWh for 2008 in the
hotel consuming 875.12 TEP/year energy, in 2013; it is 0.578 TL/kWh in 2013. It
can be seen that the investment hasn’t got the expected effect even in these two
parameters. However, the evaluation should not be made with such simple criteria
in commercial enterprises where priority is comfort as in hotels. Pre-study energy
evaluations are generally made taking m2 as a reference in the pre-studies like
hotels. This structure is important for energy consumption identity. However,
occupancy rate in hotels having commercial identity affects directly the cost-
effectiveness. In this study, the pre-audit of energy and, depending on this,
energy-saving potentials in hotels have been made taking both m2 and occupancy
rate as references. In the pre-study, the data evaluations have been dealt monthly.
The hotel’s 2008 and 2003 electricity and natural gas consumptions are given in
Fig. 5.
The hotel’s increased costs of electricity in the summer months are conspicuous.
Especially, the effect of central air-conditioning application is seen. However, even
if a parallelism is seen between both these data, the increase rate in electricity
consumption between the 9th and 12th months for 2013 disturbs the general ﬂow.
That the building has insulation for 2013 hasn’t created positivity especially in
electricity consumptions; in contrast, that consumption has increased for the 9th and
12th months is seen. Especially in the winter months in natural gas consumption, a
26.51% decrease is seen in the 2008 consumption. This positive effect can be fully
evaluated as the effect of insulation if the operating cost is ignored. However,
energy consumption has a decreasing graphic also for the next months (this need is
for hot water and kitchen use). The unit consumption analyses of the hotel for both
2 months have been made, and ﬁrstly its standard and then target consumptions
have been found and its saving potentials have been examined. This examination
Table 1 Distribution of energy consumptions 2008 and 2013
Years
Energy
Distribution rate
Unit cost
Total cost
Distribution rate
2008
(kWh)
%
TL
TL
%
Electricity
1,389,970,4
56
0.2
277,994.07
28
Natural gas
1,092,611,5
44
0.65
710,197.45
72
Total
2,482,581,8
988,191.52
2013
Electricity
1,406,277,3
60
0.36
506,259.84
37
Natural gas
949,498,5
40
0.9
854,548.65
63
Total
2,355,775,8
1,360,808.5
1022
M.E.U. Oz et al.
has been made taking both occupancy rate and also unit m2 consumptions as
references, ﬁrst in Fig. 6. The hotel’s 2008 and 2013 years occupancy rates are
given.
While the hotel’s 2008 year occupancy mean is 74.64%; this rate has become
51.77% in 2013. This value is important in terms of energy consumption and the
hotel’s operating efﬁciency. So while 30.64% decrease has occurred in the occu-
pancy rate, the decrease in energy consumption has been 11.92%. The hotel’s unit
standard energy consumptions together with the target consumptions and energy
potentials have been examined separately, and the data for the unit m2 are given in
Fig. 7.
Fig. 5 Distributions of energy consumption of 2008 and 2013. (a) Electricity, (b) distributions
Indicators of Sustainability Energy Management Based on Energy Audit for Hotels
1023
Fig. 6 Occupancy rate of hotel for 2008 and 2013
30
25
20
15
10
5
0
0
20
40
60
b
a
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
Months
Months
Saving rate %
Standard
Target
Saving
kWh/m2
Fig. 7 Speciﬁc energy consumption analyses of 2008 for m2. (a) Amounts of standard, (b)
Distributions of saving rate for m2 target and saving potential for m2
1024
M.E.U. Oz et al.
While the hotel’s unit m2 standard consumption has turned out to be 16.83
kWh/m2 for 2008, the peak values for these consumptions has been at a minimum
of 11.64 kWh/m2 and maximum of 28.27 kWh/m2. The hotel’s consumption
potential has been found at 12.43 kWh/m2 in the target consumption analyses
made based on Eq. (1), and the peak values for this potential have been calculated
at minimum of 7.96 kWh/m2 and maximum of 17.34 kWh/m2. In all these analyses
for the year 2008, the yearly unit-saving quantity has been found at 4.4 kWh/m2 and
the saving rate at 36.6% in yearly mean. The saving rates according to months have
been calculated at a maximum of 42.5% and 5.2%, respectively. Similar analyses
have been realized for 2013, and the results are given in Fig. 8.
The 2013 data have given higher results for the unit m2. While standard unit
consumption has turned out to be 14.81 kWh/m2 yearly mean of 2013, the target
consumption potential in yearly mean has been found at 7.64 kWh/m2, and this
shows a 59.7% saving potential in yearly mean. This consumption refers to negative
effects of speciﬁc variables on consumption when Eq. (1) is paid attention. The m2
consumption in hotels should also be taken into account in terms of the building
components and seasonal effects. However, in enterprises there are controllable
25
20
15
10
5
0
0
20
40
60
b
a
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
Months
Months
Saving rate %
Standard
Target
Saving
kWh/m2
Fig. 8 Speciﬁc energy consumption analyses of 2013 for m2. (a) Amounts of standard, (b)
Distributions of saving rate for m2 target and saving potential for m2
Indicators of Sustainability Energy Management Based on Energy Audit for Hotels
1025
effects that become effective on consumptions based on the comfort conditions of
energy resources.
Hotels are structures having commercial aspects, and operational costs or con-
sumption costs per bed are parameters they pay attention in the pricing processes. In
pricing this is identiﬁed depending on experience or may not be paid attention much
in the market competition. However, this is directly related to the occupancy rate of
the hotel, and the consumption costs lived in the name of not making concessions
from the comfort conditions can lead to important impacts. In this study, the
pre-studies based on occupancy rate have been made for each year separately,
and the saving potentials have been identiﬁed separately. In Fig. 9, the speciﬁc
energy consumption analyses according to the year 2008 occupancy rate have been
given.
For 74.64% occupancy rate of the year 2008, the hotel’s yearly mean standard
consumption per unit bed has turned out to be 1.56 kWh/per. The consumption
distribution per year is between 1 kWh/per and 2.73 kWh/per. To these values, the
Fig. 9 Speciﬁc energy consumption analyses of 2008 for per. (a) Amounts of standard, (b)
Distributions of saving rate for per target and saving potential for person
1026
M.E.U. Oz et al.
hotel’s target consumption potential is 1.13 kWh/per in yearly mean, and this value
states a 27.3% saving in yearly mean. It is seen that consumption surpluses
especially in the winter months are signiﬁcant potential for energy management
in the hotel. In this state, how thermal controls and occupancy management are
performed in the enterprise must be questioned. Similar analyses have been handled
for the year 2013, and speciﬁc consumption data is given in Fig. 10.
The yearly mean standard consumption has turned out to be 1.95 kWh/per bed in
the hotel having an occupancy rate of 30.64% lower than the year 2008. It’s seen
that the peak standard consumption interval in the year is maximum at 2.99
kWh/per and minimum of 1.42 kWh/per. About 25% increase is observed in
standard mean consumption when compared to the year 2008. According to these
consumptions, while yearly mean target consumption potential is found at 1.1
kWh/per, this value identiﬁes a 43.8% saving in yearly mean for 2013. Hotels
must drive the energy management organization via the target consumptions,
especially in terms of occupancy rates.
Fig. 10 Speciﬁc energy consumption analyses of 2013 for per. (a) Amounts of standard, (b)
Distributions of saving rate for per target and saving potential for person
Indicators of Sustainability Energy Management Based on Energy Audit for Hotels
1027
Another approach different from the saving approach provided with speciﬁc
analyses made in pre-studies is the formation of cumulative sum values chart
(CUSUM). This approach identifying the total saving per the referenced year is
an important indication for the hotel. The cumulative sum values chart (CUSUM)
depending on the building’s pre-audits of energy has only been made for 2013. By
this aim, the CUSUM chart formed with the least squares’ method depending on the
sum consumption data of the year 2013 is given in Fig. 11.
While approximate saving potential for the unit area in cumulative sum is
26.11% for 2008, this value has been found 48.4 % approximately when paid
attention to consumptions in 2013. For the hotel the 2013 year energy consumption
data have given results which have to be scrutinized greatly. Results of similar
analyses that have been made according to the occupancy rate are given in Fig. 12.
Fig. 11 CUSUM charts for unit area
1028
M.E.U. Oz et al.
While cumulative sum-saving potential depending on speciﬁc energy consump-
tion analyses according to occupancy rate has turned out to be 27.28% for 2008, this
value for 2013 has been found to be at 43.82%. Values for both criteria have given
quite close results to each other. This state emphasizes signiﬁcant problems in terms
of energy-saving potential in the hotel.
4
Conclusions
This study covers pre-studies executed by considering occupancy rate and loads per
m2 of a hotel taken as a sample. Pre-studies should be treated as an engineering
study leading to energy management for strategy and applications. As a matter of
Fig. 12 Cumulative saving potential (kWh/per)
Indicators of Sustainability Energy Management Based on Energy Audit for Hotels
1029
fact, a hotel-unemployed energy manager must establish ﬁrst energy management
system for ISO-50001 utility works. All facilities consuming 500 TEP/year energy
in Turkey must certainly establish statutory energy management system. Some
results obtained from the study are submitted below:
(a) For uninsulated situation according to scanning in year 2008, yearly mean
standard consumption per m2 is 16.83 kWh/m2, target consumption 12.43
kWh/m2 with a decreasing rate of 36.6%.
(b) For insulated hotel building (in 2013), yearly mean standard consumption per m
2 is 14.81 kWh/m2, target consumption 7.64 kWh/m2 decreasing rate 59.7%.
(c) Occupancy rate in year 2013 of an insulated hotel building is 51.77% and
74.64% in year 2008. Yearly mean standard consumption in year 2008 is 1.56
kWh/person and 1.95 kWh/person in year 2013.
(d) Target consumption by considering capacity is 1.13 kWh/person in year 2008
and 1.10 kWh/person in 2013 respectively.
(e) Yearly mean energy saving in cumulative sum for year 2013 is 48.4% per m2,
43.8% per person, 26.11%, and 27.3%, respectively, for 2008.
According to this pre-study, advantage of building insulation in energy con-
sumption has not been utilized sufﬁciently, any structure on energy requirement
control is not present and applications based on efﬁciency in system choice have not
been chosen. It is a big defectiveness to not use renewable energy resource like
solar energy system in spite of sunny local of hotel. Real potential in the projects to
increase energy efﬁciency is needed ﬁrst in detailed studies for energy and exergy
analysis.
References
Algedik, O
¨ .: Role of local government in combating climate change, civil climate summit report,
civil climate summit project, Turkey, October (TR). www.iklimzirvesi.org/wp.../11/Yerel-
Yonetimlerin-ID-Mucadelede-Rolu.pdf (2013)
Harris, J.M.: Basic principles of sustainable development. Global Development and Environment
Institute Working Paper:00–04, Tufts University, Medford. http://www.sdergi.hacettepe.edu.
tr/makaleler/EmineOzmet2eviri.pdf (2000)
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ISO (International Organization for Standardization): ISO 50001:2011(en). https://www.iso.org/
obp/ui/#iso:std:iso:50001:ed-1:v1:en (2011)
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¸, M., Babür, S.: Place of tourism sector in the developing world. KMU J. Soc. Econ. Res.
15 (25), 15–24 (2013) (TR)
Kedici O
¨ .: Energy management, general directorate of electrical power resources survey and
development administration energy resources studies department, Ankara (TR) (1993)
Pekacar M.: ISO 50001 energy management system, EVD energy management system, EVD
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(19.02.2014)
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1e9a35c8a1d9357_ek.pdf (2014)
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güt, Z., Oktay, Z.: Effect energy efﬁciency of energy audit in industrial sector and an applica-
tion, dumlupınar university. J. Sci. Inst. 10, 151–162 (2006) (TR)
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¨ T, Z.: “Energy audit and energy and exergy analyses in production line of heat processes in
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(TR) (2005)
Unwto: Hotel energy solutions, analysis on energy use by European hotels: online survey and desk
research, hotel energy solutions project publications, Revised version, Madrid, July 2011.
www.unwto.org (2011)
Indicators of Sustainability Energy Management Based on Energy Audit for Hotels
1031
Using a Porous Environment In Catalytic Gas
Heaters to Optimize Energy Consumption
and Reduce Pollution in Heating the Furnaces
Hossein Afshar, Esmaeil Khosroabadi, and Mehdi Tajdari
1
Introduction
Furnaces are the main energy consumption sources in petrochemical and reﬁning
processes in most of which the consumption share of furnaces is about 25%
(Berman 1978).
Studying ﬂame quality and optimum consumption systems, analyzing the prob-
lems of industrial furnaces and boilers, providing appropriate laboratory facilities to
evaluate the performance of industrial torches, and carrying out researches on
related subjects are of crucial importance which directly lead to optimization of
fuel consumption, decrement of emitting gases from chimneys, and increment of
efﬁciency of torches (Jamali 2004).
There are various methods to optimize energy consumption in furnaces from
which the method of decreasing the excess air and preheating of combustion air has
absorbed more attention. There are different techniques for execution of energy
optimization methods in furnaces. These methods can be classiﬁed into two groups.
The ﬁrst group acts upon Pinch technology, and the second group was obtained by
experimental methods (Jegla et al. 2000).
H. Afshar
Faculty of Mechanical Engineering, East Tehran Branch, Islamic Azad University,
Tehran, Iran
E. Khosroabadi (*)
Faculty of Engineering and New Technologies, Iran Industrial and Mining University,
Pakdasht 3397149346, Iran
e-mail: ho_afshar@yahoo.com; afshar@iauet.ac.ir
M. Tajdari
Faculty of Mechanical Engineering, Arak Branch, Islamic Azad University,
Tehran 1483796111, Iran
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_70
1033
The emitted gases due to the combustion of fuel in furnaces are often regarded as
environmental pollutants. Although in a simple combustion process H2O, CO, and
CO2 are usually observed, in relation to combustion analysis and the interactions
with the air in the furnaces, SOx and NOx are the two main pollutants emitted in the
furnaces which are also among the main causes of acid rain. This, however, is
noteworthy that other polluting gases with a very low percentage are also emitted in
this process.
There are generally three main processes in the production of NOx. The main
mechanism of its production in natural gas is Thermal NOx which occurs as a result
of molecule separation of O2 and N2 due to heat and their joining together. The most
amount of NOx are released somewhere near the ﬂame which has the highest level
of heat. The inﬂuential parameters in heating NOx include the amount of oxygen,
high temperature, and length of time spent in high temperature. Any increment in
these factors will increase the NOx production. The second mechanism is Prompt
NOx which is produced through the interaction of nitrogen molecules in the air with
free hydrocarbon radicals of fuel. The amount of the produced Prompt NOx in the
ﬂame is low compared to Thermal NOx. The third mechanism is fuel NOx which is
the result of the interaction between the nitrogen present in fuel with oxygen.
Basically, if gases with low nitrogen were used, the amount of released NOx
would be low (Kaviani and Mesgarpor 2009).
Figure 1 indicates the amount of gases, especially NOx, emitted in combustion
spaces with different ranges of temperature.
NOx in combustion spaces is produced through fuel NOx and heating NOx.
Heating NOx is emitted when the nitrogen present in fuel interacts with oxygen.
Fuel NOx, however, can be controlled by decreasing the amount of nitrogen in fuel
(by using low-nitrogen fuels such as natural gas) or by lowering the oxygen
available around the combustion zone. Heating NOx is also produced when the
nitrogen in the air interacts with oxygen (in high temperature) which can be reduced
Fig. 1 Showing percent
conversion of gases
produced in and the range of
temperatures in the
combustion chamber
1034
H. Afshar et al.
by decreasing the available oxygen around the combustion zone or lowering the
temperature of the combustion zone. If water or water steam is poured into the
combustion space, it can act like a heating well and decrease the temperature by
absorbing the heat in the interaction zone. The decrement in temperature can lead
into the decrement in NOx emission.
The conducted researches to improve the current technology have led into the
creation of a new combustion system called non-ﬂame combustion. And in creating
a uniform combustion throughout the furnace and improving the efﬁciency of
combustion, the new system signiﬁcantly decreases the emission of the pollutants.
Two main conditions which must be met for non-ﬂame combustion are too much
thinning and increasing the temperature of the interactors. If the emitting gases
from the interaction zone are reversed, both of these conditions will be met.
Today, many efforts have been made to decrease the nitrogen pollutant gases.
The volume of nitrogen pollutant gases depends on the ﬂame temperature. Injection
of water vapor or water decreases the peak temperature and, in turn, decreases the
production of nitrogen pollutant gases. There are many papers, including Liever
(Mehdizadeh et al. 2005), about decrement of NOx by CFD using spray of water.
He showed that by increment of water or steam spray, NOx is decreased by 94%,
but this increases the emission of CO signiﬁcantly (Kaviani and Mesgarpor 2009).
It must be noticed that the way of injection of fuel at the entrance of combustion
chamber is very important for complete combustion and emissions (Study of
hydraulic behavior 2001).
A study by Jodeiri et al., investigating the effect of debit changes of natural gas
on the efﬁciency of the interaction, indicated that any increment in feeding rate, due
to the lack of the required oxygen, causes a decrement in the rate of interaction.
Moreover, by increasing the density of other hydrocarbons along with natural gas in
the process of feeding fuel, the efﬁciency of the interaction decreases, and if the
required oxygen is added to the surface, the level of efﬁciency can be controlled
(Jodeiri et al. 2010).
The present study has used the porous environment to produce non-ﬂame heat
for the heating process of the furnaces which eventually will lead into the simul-
taneous optimization of energy and environmental factors in furnaces. Decrement
in fuel consumption, increment in optimization, complete removal of NOx, and
lowering of other harmful gases are among other advantages of using this heat-
supplying system. A catalytic pad with distinctive porous surface to supply heat is
used for this purpose.
De Soete (1966) was one of the pioneers to work on the ﬁeld of combustion in a
porous environment. He analyzed the resistance and dispersal of the ﬂame in a
sandy environment with different measures and offered a semi-experimental model
to measure the speed of ﬂame and the effect of preheating through solid heat
direction.
In 2007, Lari (Hosseinpour and Haddadi 2008) conducted a research on thermal
analysis of porous torches in transient state by exploiting two ﬂux models and
investigated the effects of various parameters on the performance of the system.
The results suggested that the torches with more optical depth have lower
Using a Porous Environment In Catalytic Gas Heaters to Optimize Energy. . .
1035
temperature peak. An increment in emissions can be observed, and as a result, the
efﬁciency of the torch is increased as well.
Hosseinpour and Moallemi (2009) investigated the effects of some different
multilevel chemical kinetics on temperature graphs, mini bodies, and emission of
pollutants and found concordance between the results obtained from different
kinetics (Hussein et al. n.d.). A ﬂat radiant porous torch is shown below (Fig. 2).
Gas torches, according to the type of mixing fuel and air, can be divided into two
types: (1) premixed torches and (2) torches with crude gas or without premixing. In
torches with no premixing, fuel and oxidant are kept away from each other till the
condition is ready for the combustion. This is usually applied in torches in which
the possibility of ﬂame reversing and explosion is an imminent danger (such as
oxygen and fuel torches). Generally, however, radiant torches are of the ﬁrst type
(Baukal 2003).
Nowadays, considering the necessity of designing and building heat-generating
systems with high efﬁciency and eco-friendliness, porous radiating heaters, in
which noble metals such as platinum and palladium are used to decrease the
activation energy of interaction, have absorbed a lot of attention. Therefore, due
to applying radiation panels to generate heat, as well as elimination of ﬂame and its
associated dangers, fewer pollutants are released into the environment. Moreover,
by using radiation heat in low temperature, the heating of nitrogen of the air which
is common in most systems as they produce a great deal of heat and, therefore,
cause a decrease in efﬁciency is lowered, and a higher level of efﬁciency can be
expected.
Combustion in catalytic porous environment was ﬁrst introduced by Davy. His
experiments showed that platinum threads can act as catalyst and ignite the mixture
of fuel air without any ﬂame and only by a radiant ﬂux (Pfefferle and Pfefferle
1987).
The process of combustion in this method starts with a very short preheating
period to heat the porous environment in order to prepare the conditions for the
interaction. In temperature about 100 degrees celsius, (ﬂammable) gas ﬂows
through the preheated porous environment and passing through the preheated
catalytic pad, oxidation starts (fuel enters from the back of the panel, and the
required oxygen comes from the front surface, and the combustion happens on
the surface of the panel in the presence of catalytic pad and keeps on spontane-
ously). The catalytic pad used in these panels is made of noble metals such as
platinum and palladium on a base of alumina ﬁber.
Fig. 2 An example of a
porous radiant burner bed
1036
H. Afshar et al.
A porous catalytic torch with 300 mm and stable temperature of 800 K was
modeled in a study. According to the momentum conservation principle, and
considering the fuel requirements of the torch, the stable velocity condition is
taken as the boundary condition for the entering nozzle, and all other boundaries
are assumed to be faultless except the external boundary (catalyst surface) for
which the stable pressure (101,325 Pascal) is assumed. The geometry and network
of this torch are shown in Fig. 3. To analyze the effects of exiting combustion
products from the porous surface of torch on the Nusselt number on the partition,
the modeling is made in two forms of with and without considering the feeding
speed behind the porous environment. Figure 4 shows the speed proﬁle in 250 mm
height, with and without considering the speed of feeding gas in the temperature of
800 K. As is shown, the two speed proﬁles are very akin to each other; therefore, the
feeding speed in porous environment, due to its low debit, has no signiﬁcant effect
on speed distribution in front part of the panel. Figure 5 shows the temperature
proﬁles for two states of with and without considering the feeding speed. As it is
shown, since no signiﬁcant modiﬁcation can be observed in speed proﬁles (it can be
concluded that) after applying the feeding with speed and if other equations and
physical properties are kept the same, no signiﬁcant change can be observed (Nield
and Bejan 2006, Kreith and Bohn 1993, Incropera and DeWitt 2002).
If all the combustible elements in the fuel are burnt or reach the last level of
oxidation, the combustion is completed; if, however, the combustion product
contains carbon, the combustion is not completed which results in energy loss
and the emission of pollutant gases. Using the proposed system, a complete
combustion can be guaranteed.
Fig. 3 Geometry and
network model
Using a Porous Environment In Catalytic Gas Heaters to Optimize Energy. . .
1037
2
The Procedures of the Manufactured Porous Pad
Operation
The proposed system is proportional to industrial application. In this system,
instead of using high-temperature furnaces (with torches) or using electric furnaces,
which are not usually economical, a porous environment can be used to provide
non-ﬂame radiant heat.
In these panels, a porous environment with a special porous surface is exploited.
The results of BET1 test for this pad is shown in Fig. 6. The ﬁrst step in order to
produce heat is a very short preheating period to prepare the catalyst for the
interaction. As soon as it reaches the proper temperature, the condition is ready to
feed the gas in order to activate the catalytic combustion. The combustible gas is
ﬂown in the preheated porous environment and passes through the preheated
catalytic pad; it causes the oxidation of gas and releases heating energy.
Fig. 4 The speed for two states of with and without considering the feeding speed
1Brunauer Emmett Teller
1038
H. Afshar et al.
Fig. 5 The temperature proﬁles for two states of with and without considering the feeding speed
Fig. 6 The graph of porous surface of the catalyst
Using a Porous Environment In Catalytic Gas Heaters to Optimize Energy. . .
1039
Due to the complete combustion of gas in this system of providing heat, the
pollutant gases such as NOx and CO are decreased to the lowest possible level
which is a vitally important environmental factor.
To analyze the performance of the proposed system, a machine equipped with
measuring tools was designed (Fig. 7). The foregoing machines are capable of
measuring parameters such as the temperature of panel surface, debit of consumed
fuel, pollutant gases, and wetness.
To measure the temperature of panel surface, a thermometer with the exactitude
of 0.1 c was applied that measured the temperature by using infrared rays. To
register the consumption debit, a G4 counter was exploited. To measure the
combustion output, an analyzing machine (Fig. 8) made in AiRREX was used
which was capable of measuring the released CO, HC, and CO2 using infrared
spectrometry method with exactitude of %0.001 ppm and %0.01, respectively, as
well as NOx and O2 using electrochemical method with the exactitude of %
Fig. 7 Schematic of the test collection of the operation of catalytic porous pad
Fig. 8 The gas-analyzing machine implemented in the test
1040
H. Afshar et al.
0.01 ppm1. This machine is equipped with some ﬁlters to absorb the dust and moist
as well as an automatic calibration.
3
Experimental Facility
In an experimental study, electrostatic powder paint dryer furnace was studied with
two different methods of providing heat, and its parameters were analyzed via the
aforementioned machine manufactured in the previous stage which was equipped
with measuring devices. In the ﬁrst method, direct ﬂame was used to provide heat,
and the furnace needed 1 h of combustion and consumed 0.9 m3 of natural gas in
order to dry the powder paint. Considering the 20-min time of baking paint (time of
baking paint: the time in which the painted pieces are placed in preheated furnace to
dry their paint during which the ﬂame of the furnace is turned off to avoid burning
the paint), the whole process takes 80 min. In the second test, the new proposed
system was used. This system has a high starting speed of heating, and considering
the radiant mechanism of heat transfer, the whole process takes less than 25 min,
and gas consumption can be economized up to 22.23%, and the required time is also
optimized as much as 68.75%.
Figure 9 indicates the gas consumption over time for the furnaces with two
foregoing methods to provide heat for the same heating power, which conﬁrms the
optimized gas consumption in non-ﬂame combustion and waste of energy in the
other method.
Table 1 indicates the released gases after the combustion in two systems of
providing heat over the same conditions (same time and heating power) which
conﬁrms the eco-friendlier conditions in non-ﬂame combustion.
3
2.5
2
1.5
1
0.5
0
0
2
4
6
8
Time in hours
Gas consumption per cubic meter
Combustion flame
Non-flame combustion
Fig. 9 Gas consumption
diagram
m3
h


Using a Porous Environment In Catalytic Gas Heaters to Optimize Energy. . .
1041
4
Conclusions
In the present study, a catalytic porous pad is constructed to produce radiant heat.
Related laboratory equipment was designed and manufactured to test and analyze
the heat transfer in a powder paint dryer furnace in two different methods: (1) using
direct torch and (2) applying porous system.
Using the proposed system of providing heat results in the complete removal of
NOx release and a signiﬁcant decrease in CO which are among the most dangerous
environmental pollutants. Moreover, the procedure of heat transfer in this method is
radiation which offers the possibility of concentrating the heat on the surface of the
intended product. Therefore, through this concentration, the surrounding space is
not affected by heat, and it takes less time for the product to cool, when the system
is turned off. Exploiting this method of heating due to radiant heat transfer, high
speed in reaching the intended temperature and requiring less space for installation
in furnaces (radiant systems take one third space compared to traditional systems
with the same power) results in higher efﬁciency in furnaces and economizing the
fuel consumption.
Acknowledgments We feel obliged to thank the chairman of Tehran Pooshesh Company (Alborz
province, Iran) for his cordial support to the research team.
References
Baukal Jr., C.E.: Industrial Burners Handbook. CRC Press, Boca Raton, London, New York,
Washington, DC (2003)
Berman, H.L.: Fired heater. Chem. Eng. 85, 129–140 (1978.) MC Graw-Hill
Hosseinpour, S., Haddadi, B.: Numerical study of the effects of porous burner parameters on
combustion and pollutants formation. pp. 1505–1509, WCE, (2008)
Hosseinpour, S., Moallemi, N.: 2D Numerical simulation of combustion in porous media using
four different multilevel chemical processes, Iranian Combustion Association, Persian, (2009)
Incropera, F.P., DeWitt, D.P.: Fundamentals of Heat and Mass Transfer, 5th edn. Wiley (2002)
Jamali, A.: Experimental and numerical studies in preheating of heat transformers in oil industry
and present a procedure for improving their efﬁciency, Monthly magazine of oil and energy,
1st year of publish, March (2004)
Table 1 Gas emissions from combustion
Emitted gas
Initial
Non-ﬂame combustion
Flame combustion
HC (ppm)
0
282
40
CO (%)
0
.0022
3.67
CO2 (%)
0
1.43
5.88
NOX (ppm)
0
0
30
O2 (%)
20.79
20.59
20.69
1042
H. Afshar et al.
Jegla, Z., Kohoutek, J., Zachoval, J., Stehlık, P.: Software supporting efﬁcient retroﬁt design of
furnaces and connected pipes as one integrated system, 14th International Congress of Chem-
ical and Process Engineering CHISA (2000)
Jodeiri, N., Wu, L., Mmbaga, J., Hayes, R.E., Wanke, S.E.: Catalytic combustion of VOC in a
counter-diffusive reactor. Catal. Today. 155(1–2), 147–153 (2010)
Kaviani, H., Mesgarpor.: Optimized decrement of Nox pollution caused by gas combustion by
adding catalysts. The Third Conference of Fuel and Combustion in Iran. Isfand 1388 (2009)
Kreith, F., Manglik, R.M., Bohn, M.S.: Principles of heat transfer. 5th edn. pp. 80–121. Paciﬁc
grove Brooks, USA. http://bcs.wiley.com/he-bcs/Books?action=index&itemId=0471386502&
itemTypeId=BKS&bcsId=1737.
https://www.amazon.com/Principles-Heat-Transfer-Frank-
Kreith/dp/0495667706 (1993)
Mehdizadeh N.S. Tabejamaat, S. Yashini, M.: NOx reduction analysis in fuel gas combustion
chamber using Water Injection,Aerospace Eng. Department, Amirkabir Univ. of Technology,
MAEJ (2005)
Nield, D.A., Bejan, A.: Convection in Porous Media, 3rd edn, pp. 110–154. Springer, New York
(2006)
Pfefferle, L.D., Pfefferle, W.C.: Catalysis in combustion. Catal. Rev. Sci. Eng. 29, 219–267 (1987)
Soete G.D.: “Stability and Propagation of Combustion Wave in Porous Media” On combustion,
pp. 959–966 (1966)
Study of hydraulic behavior of gas in liquid basement reactors, Governmental investigation plan,
oil industry investigation center, years of performance (2001–2002)
Using a Porous Environment In Catalytic Gas Heaters to Optimize Energy. . .
1043
Sn/Graphene Binary Nanocomposite Anode
Electrodes for High-Performance Li-Ion
Battery Applications
Guler Mehmet Oguz, Erdas Aslihan, Nalci Deniz, Ozcan Seyma,
and Akbulut Hatem
1
Introduction
Li-ion batteries are the most suitable power supplies for many portable electronic
devices, such as cellular phones, digital cameras, and notebooks, because of their
high energy and power density. The ﬁrst commercial Li-ion battery was developed
by Sony in 1991, and since then a variety of efforts have been undertaken to
improve the electrochemical performance of battery materials. Current Li-ion
batteries use graphite anodes in which one unit (six carbon atoms for graphite)
can store one or less lithium ion, leading to a limited theoretical capacity of
372 mAh g1. To circumvent the low energy and power density of graphite,
alternative materials are highly desired.
Tin anodes have attracted much attention because it delivers a capacity up to
three times higher than that of graphite. Theoretically, one tin atom can maximally
react with 4.4 lithium atoms to form Li4.4Sn alloy, reaching a capacity of 993 mAh
g1. However, the large amount of lithium insertion/extraction into/from Sn causes
a large volume change (about 300%), which causes pulverization of tin particles
and loss of contact with current collector, resulting in poor electrochemical perfor-
mance. Various approaches have been carried out to overcome this issue, including
the use of nanosized active materials by Adpakpang et al. (2014) and Tang et al.
(2014), active/inactive composite materials by Yi et al. (2014) and Wang et al.
(2008), and tin–carbon composites by Park et al. (2007) and Zhang et al. (2005,
2009). These studies have resulted in improvements of the electrochemical perfor-
mance of Sn-based anodes but only to a limited extent. Recently it has been
G.M. Oguz (*) • E. Aslihan • N. Deniz • O. Seyma • A. Hatem
Sakarya University, Department of Metallurgical & Materials Engineering,
Esentepe Campus, Adapazarı, Sakarya 54187, Turkey
e-mail: guler@sakarya.edu.tr
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_71
1045
reported by Zhang et al. (2004) and Liu et al. (2005) that silicon nanowires directly
grown on a current collector can greatly improve the performance of the Si anode
due to the excellent electrical connection between Si nanowires and the current
collector and the nature of one-dimensionality to effectively release the strain.
Zhang et al. (2007) and Mao et al. (1999) also reported great anode performance
using carbon-coated, very small (10 nm) silicon nanoparticles (SiNPs) or silicon
nanotubes. However, in these nanosilicon electrodes, the heavy current collector is
larger in weight than Si-active material. In a commercial lithium-ion cell, the anode
material is usually coated on a copper foil current collector to form an anode
electrode in thin sheet form. The metal current collector on the anode side is usually
a 10 μm thick copper sheet with an areal density 10 mg/cm2. This copper sheet is a
relatively heavy component in a lithium-ion cell, which is comparable in weight to
the anode-active material and accounts for 10% of the total weight of the cell.
Graphene nanosheets could be a promising candidate to serve as both the
conductive network and the buffer to alleviate the stress from volume expansion,
owing to its advantages of a unique network structure with low electrical resistivity
and good mechanical properties, such as strength, stiffness, and resilience. All of
the previous reports used different dedicated chemical or physical methods to
prepare composite materials, which would also increase the cost of the materials.
In this study, ﬁrstly Sn nanoparticles were synthesized by chemical reduction
method. This method is more suitable for the Sn nanoparticle synthesis because
the chemical reduction can use a low temperature, resulting in a better control of
thermal oxidation of Sn nanoparticles. During the synthesis process, surfactants
were used to protect the Sn nanoparticle from oxidation. Multilayer graphene was
obtained from graphite ﬂakes using the method described by Hummers. Free-
standing and ﬂexible tin/graphene nanocomposite paper was produced by a vacuum
ﬁltration technique for use as an anode electrode without using any binder or
additives. The unique feature of the sheet-to-sheet assembly (2D–2D) is that each
tin and graphene nanosheet will have maximum electrical contact with graphene,
which could result in high conductivity of the hybrids. Moreover, strong interfacial
interactions between tin and graphene contribute to a robust linking between the
two components, which further promoted interfacial electron and lithium-ion trans-
port. Beneﬁtting from the morphological compatibility and intimate integration
between tin and graphene, the binder-free and free-standing hybrid electrode
exhibited signiﬁcantly enhanced lithium storage properties in terms of higher
speciﬁc capacities, better cyclic stability, and rate capability compared to tradi-
tional binder-containing electrodes and pure tin electrodes. To the best of our
knowledge, this is the ﬁrst demonstration of a self-supporting binder-free anode
prototype with a lamellar hierarchical structure and strong interfacial interaction,
which is totally different from previously reported graphene-based hybrid ﬁlms.
1046
G.M. Oguz et al.
2
Experimental Details
2.1
Synthesis of Sn Nanoparticles
A 4.76 g of Tin(II) chloride dehydrate, SnCl2.H2O (Sigma-Aldrich, 98%), was
added to a suspension of 2.5 g of polyethylene glycol, PEG 6000 (Sigma-Aldrich),
5 ml of acetic acid, CH3COOH (Merck), and 100 ml bidistilled water. A clear
solution was obtained after mechanically stirring for a few minutes. A 2.5 g of
sodium borohydride, NaBH4 (Merck), was dissolved in 100 ml bidistilled water
solution that was then introduced dropwise. The mixture turned to red, indicating
the formation of a sol of PEG-capped Sn nanoparticles. The hydrosol was further
stirred for 2 h before the Sn nanoparticles were spun down in an ultracentrifuge
(15,000 rpm for 1 h). The solid product recovered as such was washed with water
and methanol, and vacuum dried at 50 C for 12h.
2.2
Microwave-Assisted Hydrothermal Carbonization of Sn
Nanoparticles
Glucose (99.5%, Sigma-Aldrich) was used as the starting precursors for the
microwave-assisted
hydrothermal
carbonization
of
as-synthesized
Sn
nanoparticles. Aqueous suspensions of the Sn nanoparticles were prepared using
glucose as a catalyst reagent. A 1.5 mol L1 catalyst stock solution was prepared in
bidistilled water. About 500 mg of Sn nanoparticles were poured in a reaction
container ﬁlled with 50 mL of stock solution. The suspensions were placed in a
100 mL polytetraﬂuoroethylene (PTFE)-sealed reactor for microwave processing.
The materials were hydrothermally carbonized using a microwave lab station
(Milestone RotoSYNTH) with a magnetron frequency of 2.45 GHz, 1000 W at
maximum power and 10 W pulse-controlled power fractions. The system was
heated from 20 C to 85 C at 22 C min1, then from 85 C to 145 C at 7 C min
1, and from 145 C to 180 C at 14 C min1; ﬁnally an isotherm was held at
180 C for 5 min. The temperature during microwave irradiation was controlled by
a thermocouple installed in a reference container. After the carbonization process,
the reactor was cooled at room temperature, and the carbonized materials were
ﬁltered with PVDF ﬁlter (0.45 μm, Millipore) using a mechanical vacuum pump
and subsequently washed with bidistilled water until reaching a neutral pH. The
solid products were dried under vaccum at 40 C for 12 h.
Sn/Graphene Binary Nanocomposite Anode Electrodes for High-Performance. . .
1047
2.3
Synthesis of Graphene Oxide
Graphite oxide was synthesized from natural ﬂake graphite by using modiﬁed
Hummers method. The exfoliation of graphite oxide into graphene oxide was
obtained by ultrasonication. For preparation of graphene oxide paper, 30 mg of
as-synthesized graphitic oxide was dispersed in 100 mL of distilled water and
sonicated for 2 h, and then the solution was ﬁltered on polyvinylidene ﬂuoride,
PVDF (0.45 μm, Millipore), membrane by vacuum ﬁltration technique.
2.4
Synthesis of Sn/Graphene Free-Standing Electrodes
A schematic illustration for the preparation process of Sn/graphene nanocomposite
is given in Fig. 1. A 30 mg as-synthesized graphene oxide and carbon-coated Sn
was dispersed in 50 mL bidistilled water by the aid of 80 mg of SDS (sodium
dodecyl sulfate, Merck, Calbiochem, >97%) surfactant and sonicated to form a
well-dispersed
suspension.
In
order
to
produce
Sn/graphene
paper,
the
as-synthesized graphene oxide paper was chemically reduced immediately after
ﬁltration by hydrazine solution. A 2.0 M, 50 mL hydrazine (anhydrous, 98%,
Sigma-Aldrich) solution was slowly poured onto the membrane-supported
graphene oxide paper and ﬁltered via vacuum technique. All production steps
were performed under open atmospheric conditions. Then the resulting solid was
washed up, and the sample dried in vacuum at 40 C overnight, and the Sn–C/
Graphene ﬁlms were peeled off from the membrane.
Fig. 1 A schematic illustration for the preparation process of Sn/graphene nanocomposites
1048
G.M. Oguz et al.
2.5
Characterization
The surface and cross-sectional morphologies of the produced sample electrodes
were observed by scanning electron microscopy (SEM, Jeol 6060 LV). The phase
structures of the samples were investigated by X-ray diffraction (XRD) (Rigaku
D/MAX 2000 with thin ﬁlm attachment) with CuKα radiation.
2.6
Electrochemical Characterization
Coin-type CR2016 cells were assembled in an argon-ﬁlled glove box. The electrolyte
solution was 1 M LiPF6 (Sigma-Aldrich, 99.99%) in EC/DMC (Sigma-Aldrich,
99%/Sigma-Aldrich, 99%) (1/1 by volume). The electrochemical performance of
the Sn/graphene nanocomposites was evaluated by galvanostatic discharge–charge
measurement using a computer-controlled battery tester between 0.02 and 2.5 V using
metallic lithium as the counter electrode. The cells were cyclically tested on a MTI
Model BST8-MA electrochemical analyzer using 1C (18 mA/dm2) current density
over a voltage range of 0.02–2.5 V. After being cycled for 50 cycles, electrochemical
impedance spectroscopy (EIS) was conducted on coin cells using an electrochemical
workstation (Gamry Instruments Reference 3000) over a frequency range from
100 kHz to 0.001 Hz with an AC amplitude of 5 mV. The measured voltage was
about 0.2 V after the cells were relaxed for 1 h. The data has been normalized and
referred per unit of mass for the purpose of comparison. Cyclic voltammograms (CVs)
were recorded on an electrochemical workstation (Gamry Instruments Reference
3000) at a scan rate of 0.5mVs1 between 0.02 and 2.5 V. All the potentials indicated
here were referred to the Li/Li+ electrode potential. All electrochemistry tests were
carried out at room temperature (25 C).
3
Results and Discussions
Figure 2 shows the XRD patterns of the as-synthesized Sn nanoparticles. XRD
patterns could be readily indexed to the tetragonal phase of Sn (space group I41/
and (141)), lattice constants a ¼ 0.583 1 nm and c ¼ 0.318 2 nm (JCPDS 04–0673)).
The relative intensity of the peaks was consistent with that of the Sn nanoparticles
reported by Jiang et al. (2006). From the XRD patterns, no obvious oxidation or
impurity peaks were found. The XRD pattern of the samples produced by
microwave-assisted hydrothermal process; carbon with characteristic (002) peak
is also displayed in Fig. 2.
The characteristic (002) plane of the carbon also demonstrate that the crystalline
nature of carbon. The characteristic peak at 24.5 corresponds to the planes of
graphene, indicating that the interplanar spacing of d002 had been expanded to
Sn/Graphene Binary Nanocomposite Anode Electrodes for High-Performance. . .
1049
0.769 nm after reduction. This is ascribed to the oxygen-containing functional
groups that were attached, which has been previously conﬁrmed by Zhang
et al. (2007).
The SEM results examined indicate that the sizes and size distributions of Sn
nanoparticles synthesized via chemical reduction differ between 10 and 50 nm in
size. The extremely polydisperse nanoparticles obtained using tin (II) chloride
precursors can be interpreted as the results of multiple nucleation events during
extended nucleation periods. The reduction process of Sn ions supplied from the
precursor could be represented by the reaction:
BH4

þ 8OH
þ
4Snþ2 ! B OH
ð
Þ4

þ
4H2O g
ð Þ þ 4Sn
ð1Þ
Reaction (1) will be divided into next two reactions:
BH4

þ 8OH
! B OH
ð
Þ4

þ
4H2O g
ð Þ þ 8e
ð2Þ
4Snþ2 þ 8e-
! 4Sn
ð3Þ
The SEM images of the samples produced by microwave-assisted hydrothermal
carbonization of Sn nanoparticles are also shown in Fig. 3.
As can be concluded from the ﬁgure, the surfaces of Sn nanoparticles are
surrounded with carbon as a conductive network which will buffer to alleviate the
stress from volume expansion, owing to its advantages of a unique network
structure with low electrical resistivity and good mechanical properties, such as
strength, stiffness, and resilience as also reported by Hsu et al. (2006) and Yang
et al. (2000).
The morphology of the Sn/graphene nanocomposite was observed by SEM.
Figure 4 shows a SEM image of the Sn/graphene nanocomposite. In general as
Fig. 2 XRD patterns of Sn,
Sn–C, and Sn–C/graphene
nanoparticles
1050
G.M. Oguz et al.
shown in Fig. 4a, graphene nanosheets were crumpled to a curly and wavy shape,
resembling ﬂower pedals. The energy-dispersive X-ray spectroscopy (EDS) anal-
ysis in Fig. 4a also shows that tiny Sn nanoparticles are homogeneously distributed
on the curly graphene nanosheets. Due to the corrugated nature of the graphene
nanosheets, substantial voids exist between individual nanosheets. The anchored Sn
nanoparticles could act as a spacer to prevent the re-stacking of individual graphene
nanosheets. Figure 4b also shows that the graphene paper appears as a porous
entangled mat; the ﬁlm is self-standing due to inter-bundle van der Waals forces
and mechanical interlocking within the sheet. Tin nanoparticles are also tiny Sn
nanoparticles that are homogeneously distributed over the ﬁlm as shown in the
energy-dispersive X-ray spectroscopy (EDS) analysis.
Cyclic voltammetry (CV) is initially carried out to investigate the electrochem-
ical reactivity of Sn–C/graphene, with the results shown in Fig. 5. Two cathodic
current peaks for the Sn–C/graphene electrodes could be observed in the ﬁrst cycle.
The peak around 1.0 V could be assigned to the formation of a solid electrolyte
interface on Sn–C/graphene composites, while the peak between 0.01 and 0.5 V is
related to the alloying of lithium ions with Sn and the intercalation of lithium ions
into graphene as also stated by Duan et al. (2012) and Huang et al. (2010) In the
Fig. 3 XRD patterns of Sn
and Sn–C nanoparticles
Sn/Graphene Binary Nanocomposite Anode Electrodes for High-Performance. . .
1051
subsequent anodic polarization process of the ﬁrst cycle, ﬁve peaks could be
observed at 0.15 V, 0.52 V, 0.62 V, 0.72 V, and 0.79 V, respectively, and are
associated with lithium ion extraction from graphene and de-alloying from Li–Sn
alloys. In the following second to ﬁfth cycles, the CV curves are nearly overlapped,
which suggests good stability of the Sn–C/graphene electrode from the second
cycle.
The galvanostatic discharge–charge curves at current density 18 mA dm2
(1C) of Sn–C/graphene anode electrodes between 0.2 and 2.5 V vs. Li+/Li is
presented in Fig. 6. The discharge capacity of the Sn–C/graphene decreases to
818 mAh g1 in the second cycle and 812 mAh g1, respectively. The capacity fade
is slower in subsequent cycles and a stable capacity of 670 mAh g1 after
100 cycles. It is known that cycling performance of metal oxide-based anodes is
signiﬁcantly affected by the volume change of active particles during lithium
insertion–extraction process. If active particles could not tolerate the volume
Fig. 4 SEM images of (a) surface and (b) cross-sectional area of the Sn–C/graphene electrodes
Fig. 5 SEM images of (a)
surface and (b) cross-
sectional area of the Sn–C/
graphene electrodes
1052
G.M. Oguz et al.
change, they will pulverize into smaller particles, and electrode is strongly polar-
ized as a result. The nanocomposite electrode’s strong performance was due to its
unique structure, the synergy of combining Sn and graphene nanosheets, the
suppression of side reactions between the nanocomposite, and the electrolyte and
an optimization of the SEI ﬁlm. It is also reasonable to suggest that the smaller
crystalline size of Sn in the nanocomposite than when as pure nanoparticles also
likely improved capacity because of shorter conduction paths for electrons and
lithium ions.
The lithium insertion capacity (discharge) of Sn/graphene nanocomposite elec-
trodes vs. cycle number is shown in Fig. 7. In the ﬁrst cycle, the Sn/graphene
electrode delivered a discharge capacity of 976 mAh g1 and a reversible charging
capacity of 960 mAh g1. The irreversible capacity could be mainly ascribed to the
formation of the solid electrolyte interphase (SEI) layers on the surface of the
Fig. 6 Galvanostatic
charge/discharge potential
proﬁles of Sn–C/graphene
electrodes
Fig. 7 Cyclic
performances of bare Sn,
graphene, and Sn–C/
graphene electrodes
Sn/Graphene Binary Nanocomposite Anode Electrodes for High-Performance. . .
1053
electrode. From the second cycle, the reversibility of the electrode was gradually
improved on cycling, with an average columbic efﬁciency of 98.4% up to
100 cycles. The electrode maintained a capacity of 670 mAh g1 after 100 cycles.
As a comparison, the cycling data of bare graphene electrode and microcrystalline
Sn powder electrode are also presented in Fig. 7. The bare graphene electrode
delivered a lithium storage capacity of 255 mAh g1 after 100 cycles. The cycling
performance of the bare Sn electrode is very poor. After 10 cycles, the bare Sn
electrode failed. Therefore, Sn/graphene nanocomposite exhibited an optimized
electrochemical performance compared to bare graphene and bare Sn powders.
4
Conclusions
In this study, Sn nanoparticles were synthesized via chemical reduction techniques
using tin (II) chloride as the precursor. The as-synthesized Sn nanoparticles were
subjected to microwave-assisted hydrothermal carbonization process in order to
increase the electrochemical efﬁciencies during the lithiuation and delithiuation
processes. Sn–C/graphene free-standing electrodes were then prepared by vacuum
ﬁltration techniques. The resulting Sn–C/graphene hybrid anode thus exhibited
superior Li-ion performance with high reversible capacity, excellent cycle ability,
and good rate capability. This improved performance could be attributed to the
formation of 2D graphene framework decorated with well-dispersed Sn–C
nanocrystals, thus inducing fast diffusion of Li ions.
Acknowledgments This research was supported by the Scientiﬁc and Technological Research
Council of Turkey (TUBITAK), under the contract number 214 M125. These supports are
gratefully acknowledged by authors.
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Sn/Graphene Binary Nanocomposite Anode Electrodes for High-Performance. . .
1055
Data-Driven Modeling for Energy
Consumption Estimation
Chunsheng Yang, Qiangqiang Cheng, Pinhua Lai, Jie Liu, and Hongyu Guo
1
Introduction
Today it is estimated that building industry contributes over 40% of total energy
consumption. The owners of building aim at reducing the cost of energy consump-
tion by developing efﬁcient technologies to optimize or reduce the energy con-
sumptions. With the quick increase of the energy price, developing new technology
for building energy management systems (BEMS) or applying new philosophy of
operating the systems is becoming more and more important and necessary. In
utility industry, one of philosophy to change the existing operation policy is called
demand response (D/R) program. It is deﬁned as the incentive program to promote
the lower electricity use at times of high wholesale market prices (Albadi and
EI-Saadany 2008). To avoid the high prices or the peak time of electricity con-
sumption, BEMS must be able to adjust the use of electricity by changing the usage
of energy from their normal consumption pattern or routine operation temporally.
To achieve the response, the operators can just reduce the electricity usage during
peak hours when the price is high without changing their consumption pattern
during other periods. However, this will temporally lose the comfort. It is not
expected from the customers. It is desirable that the BEMS can change the energy
consumption pattern to avoid the peak hour without any loss of the comfort. For
instance, the BEMS can use ice bank for air conditioning (A/C) during peak hours
C. Yang (*) • H. Guo
National Research Council Canada, 1200 Montreal Road, Ottawa, ON K1A 0R6, Canada
e-mail: Chunsheng.Yang@nrc.gc.ca
Q. Cheng • P. Lai
Nanchang University, 999 Xuefu Dadao, New Honggutan District, Nanchang, China
J. Liu
Careleton University, 1125 Colonel By Drive, Ottawa, ON, Canada, K1S 5B6
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_72
1057
and make and store ice during the idle hours such as midnight. To address this issue,
it is necessary and urgent to develop a novel technology which allows BEMS
automatically to modify the building operation pattern/condition or change control
policy to respond to D/R signal (Online 2000). To this end, one of the fundamental
issues is how we can estimate the short-term /long-term energy consumption and
the energy consumption of each subsystem to provide the solution for changing the
policy or control the energy usage of BEMS.
Recently, many research efforts have been invested on the development of data-
driven models for long-/short-term energy consumption prediction by applying
machine learning algorithms such as neural network, decision tree, and regression
analysis (He et al. 2005; Yalcintas and Akkurt 2005; Catalina et al. 2008; Olofsson
et al. 1998; Tso and Yau 2007). There have been some research focusing on
development of advanced AHU control methods (Bi et al. 2000) or accessing the
building occupancy for energy load prediction (Kwok et al. 2011). There are,
however, few works in developing the models for estimating energy consumption
of each subsystem or optimizing the usage of energy consumption using data-driven
approach rather than traditional modeling which faces a challenge to model energy
consumption. To address the issues in developing predictive models for energy
consumption estimation and to provide an alternative for modeling energy con-
sumption, we propose a data mining-based modeling technique for developing data-
driven predictive models from historic building operation data. This work focused
on the development of energy consumption estimation for BEMS subsystems. In
this paper we ﬁrst introduce the data mining-based methodology. Then we present
the development of data-driven models for estimating energy consumption of sub-
systems. In particular, the modeling results for a chiller subsystem and a supply fan
in AHU will be presented and discussed as a case study.
The rest of this paper is organized as follows. Section 2 brieﬂy introduces the
BEMS and its modeling requirement in general. Section 3 brieﬂy describes the
methodology. Section 4 presents the development of data-driven models to estimate
energy consumption for a chiller and a supply fan subsystem. Section 5 provides
some preliminary experimental results. Section 6 discusses the results and limita-
tion. The ﬁnal Section concludes the paper.
2
Building Energy Management Systems and Modeling
BEMS mainly consists of heating, ventilation, and air-conditioning (HVAC) sys-
tems and lighting systems. HVAC contains many subsystems such as AHU (air
handling units), chiller, ice bank, etc. To automatically respond to the D/R signal
from smart grid, these subsystems must be integrated as a smart energy consump-
tion system (Online 2000) to perform optimized operation by changing control
policies or energy usage pattern. Such a smart system is capable of making decision
on when to change the operation pattern, where to optimize the usage of energy for
each subsystem, and how to set up the control policy without losing the comfort
1058
C. Yang et al.
based on D/R signal and weather forecast. To this end, one of the fundamental
issues is how we can predict the peak usage of energy consumption, the short-term /
long-term energy consumption, and the energy consumption of each subsystem. In
general, four kinds of models are required:
1. To predict the expected total building energy consumption
2. To estimate energy consumption for each subsystems such as chiller, AHU, ice
bank, lighting etc.
3. To express control policy by exploiting the relationship between consumption of
a given subsystem and its variables such as speed, water temperature, and valves
4. To optimize the energy usage to respond to D/R single from grid or to avoid the
peak hours based on the prediction results from former models, weather forecast
information, and constrains of comfort settings
Shown in Fig. 1 is an example of BEMS that we have to model the energy
consumption for these subsystems. Here two types of models are requested to
perform D/R optimization. One is the model for estimating energy consumption
given the energy demanding from D/R single; another one is for energy estimation
given the comfort setting or health requirement from control policy. In case that
energy demanding comes from D/R smart grid, the system decides energy con-
sumption for each subsystem (Echi, Esf, Erf, and Eice). So we have to build a model
to decide the control policy or control variables for each subsystem. With the help
of energy consumption models in the mid-layer, each model corresponding the
subsystem will determine the control policy or variables (CVchi, CVsf, CVrf, and
CVice) to control the system to meet the comfort and health requirement in the
building.
Unfortunately due to time-varying characteristic and nonlinearity in existing
BEMS, it is difﬁcult to build traditional mathematical/physical models to meet
those needs. To our best knowledge, there do not exist publicly available models
that could predict the complete performance and energy consumption for subsys-
tems (Bendapudi et al. 2002). To address this issue, we proposed to develop energy
Consumption
Models
Controls
CVchi
CVsf
CVrf
Return Fun
D/R model
IceBank
D/R model
Energy
demand
DABO (data)
weather data
new features
...
CVice
Comfort/productivity/health
Supply Fun
D/R model
Chiller
D/R Model
Echi
Esf
Erf
Eice
Fig. 1 An example of energy consumption modeling for BEMS subsystems
Data-Driven Modeling for Energy Consumption Estimation
1059
consumption models for these subsystems using data mining-based approach. The
models are able to be built from historic database using techniques from machine
learning. As an example, Fig. 1 depicts the so-called DABO (Roche n.d.) database,
which is often used to collect building operation data by domain experts. In our
case, this database contains over 1600 variables which represent the data collection
points for building control, room temperature setting, responding variables, and so
on. In this work, we use this DABO database to conduct modeling and model
evaluation.
3
Data Mining-Based Methodology
Modern building operation has generated massive data from BEMS, including
energy consumption history, building characteristics, operating condition, occu-
pancy, and control records for subsystems, among others. These data are a valuable
resource for developing data-driven predictive models. To build the models from
these historic operation data, we develop a data-driven methodology by using
machine learning and data mining techniques. Figure 2 illustrates the methodology
which consists of four steps: data gathering, data transformation, modeling, and
model evaluation. The following is a brief description of each step.
3.1
Data Gathering
Most data mining algorithms require as input a dataset, which contains instances
consisting of vectors of attribute values. Modern building operation often generates
many such datasets. The ﬁrst problem is to select the best dataset(s) to use to build
models for a particular subsystem. Advice from subject matter experts and reliable
Data
Gathering 
Transfor
mation
Modeling
Evaluation
Building
operation
data
Weather
forecast
data 
Predictive
Models 
Fig. 2 The data-driven methodology for modeling energy consumption
1060
C. Yang et al.
documentation can simplify this choice and help avoid a lengthy trial and error
process. Not only must a dataset be selected, but a subset of instances must be
selected to use in the analysis. The datasets are typically very large so it is
inefﬁcient to build models using all instances. Simple solutions, such as random
sampling, are also inappropriate. To build the desired predictive models, a much
more focused approach was required. In this work, four types of data are used,
including energy consumption history, control variables, weather forecast, building
parameters, and ambient conditions. Data gathering is to obtain a dataset for sub-
systems from a big building operation database. This dataset will combine all types
of operation data into a set of vectors.
3.2
Data Transformation
In modeling energy consumption, the target variable is numeric. Therefore, this
methodology focuses on numeric modeling by applying machine algorithms. In
other words, the main goal is to develop repressors by using regression algorithms.
To improve the initial, as measured, representation and remove outliers from the
original data, data transformation is necessary. The main task is to generate some
new features such as moving average, standard deviation, pattern expressions by
using methods from process physics, signal processing, time series analysis, etc.
The generated new features will enhance model performance signiﬁcantly.
3.3
Modeling
After updating the initial dataset incorporating data representation enhancements,
machine learning algorithms were used to build the predictive models. Dataset is
separated into training and testing datasets. The training dataset was used for
developing the models, and the remaining data were kept for testing to evaluate
the built model. Any regression learning algorithm can be used. In early experi-
ments, simple algorithms such as regression decision trees (Quinlan 1993; Mitchell
1996) and support vector machine were preferred over more complex ones such as
ANN (Dzeroski and Zenko 2002) because of their efﬁciency and comprehensibility.
The same algorithm was applied several times with varying attribute subsets and a
range of cost functions. Therefore, feature selection was also applied on the
augmented data representation to automatically remove redundant or irrelevant
features.
In order to build high-performance energy prediction models, another method is
to perform the model fusion. Model fusion can be used for two reasons. First, when
more than one dataset is relevant for a given component, we can build a model for
each dataset and then use model fusion to combine predictions from the various
models. Second, we can apply model fusion for performance optimization
Data-Driven Modeling for Energy Consumption Estimation
1061
regardless of the number of datasets selected. In this case, we learn various models
using various techniques or parameter settings and combine them to obtain better
performance than using any single model. Bagging and boosting (Dietterich 2000)
are two popular techniques to combine models, but they are only applicable when
there is a single dataset and one kind of model (a single learning algorithm). For
heterogeneous models or multiple datasets, we apply methods based on voting or
stacking strategy (Tsoumakas and Blahavas 2004; Zhang et al. 1998). These
techniques are globally referred to as multiple model systems (Opitz and Maclin
1999).
3.4
Model Evaluation
To evaluate the performance of the data-driven models, the most important measure
of performance is the estimation accuracy achieved by the models after develop-
ment. The accuracy is often deﬁned using the forecast error which is the difference
between the actual and predicted values (Dietterich 2000). Several criteria are
available from statistics. The most widely used ones are the mean absolute error
(MAE), the sum of squared error (SSE), the mean squared error (MSE), the root
mean squared error (RMSE), and the mean absolute percentage error (MAPE).
They are deﬁned as follows:
MAE ¼ 1
N
X
N
i¼1
j ei j
ð1Þ
SSE ¼
X
N
i¼1
e2
i
ð2Þ
MSE ¼
X
N
i¼1
ﬃﬃﬃﬃ
ei
p
ð3Þ
RMSE ¼
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
MSE
p
ð4Þ
MAPE ¼ 1
N
X
N
i¼1
j ei
yi
i j 100
ð5Þ
where ei is the individual prediction error, yi is the actual value, and N is the number
of examples in the test data.
The general method is to apply the testing dataset (unseen data) to the models by
computing the error terms mentioned above. Each measure metric has its advan-
tages and limitations. It is not necessary to compute all accuracy measures. In this
work we focused on three widely used error criteria, namely, MAE, MSE,
and MAPE.
1062
C. Yang et al.
4
Energy Consumption Modeling for BEMS
In this section, we demonstrate the development of models for predicting the energy
consumption of BEMS subsystems by using the data-driven methodology. We
focus on two main subsystems: the chiller and supply fun in an HVAC system.
Chiller is a key subsystem in the HAVC system, which provides the chilled water or
cooled air to the coil of the AHU for air-conditioning the building. For this work,
the chiller is used together with ice bank to provide A/C for the building. It is
possible to increase the water temperature of chiller to reduce energy consumption
by using ice bank to provide the cooled air for substituting chiller function during
the peak hour. On the other hand, chiller can be used to charge ice bank during
valley time such as midnight.
Another main subsystem is supply fan which provide fan for ventilation of
heating and cooling air. It is also one of the main sources of energy consumer in
HAVC. There is a great potential for optimizing the energy consumption to avoid
peak time or to respond the D/R signal if we can accurately predict the energy
consumption of chiller and set up an optimized control policy.
4.1
Model Development for Chiller and Supply Fan
In the development of chiller and supply fan energy consumption models, we use
building operation data from a modern building which uses chiller and ice bank for
A/C in the summer. The data were collected from 2009 to 2010. The database
contains over 1000 variables for all sensors or control points on the building. There
is only one chiller in the building and one supply fan in the AHU in the selected
building zone. The ﬁrst task is to get data related to the chiller and supply fan from
this database over thousand of variables. After analyzing the signals of chiller
control system and consulting with building operator, the four groups of data are
identiﬁed, and their variables are extracted from the database. These data are energy
consumption (output of chiller), building occupancy, water temperatures at differ-
ent points, and variables related to ice bank. In order to build high-performance
models, we also collected weather forecast data from a local weather station. The
weather data consists of temperature, wind speed, wind direction, and humidity. In
the end, we can describe the chiller diagram as shown in Fig. 3, and supply fan
diagram as shown in Fig. 4. Supply fan provides cooled air to 37 ofﬁces on a given
building zone, each having their own temperature set point and duct outlet with a
programmable damper position. All temperature settings will be input in the
models.
After gathering data relevant to chiller and supply fan from the database and
combining with weather forecast data, data transformation was performed to
generate some new features following the developed methodology. The new fea-
tures mainly include time series features such as weekday, seasons, moving
Data-Driven Modeling for Energy Consumption Estimation
1063
averages, and the energy consumption for selected past days. These new features
enhance the prediction accuracy for the models.
Using dataset with the new features, the modeling experiments were conducted
by carefully choosing machine learning algorithms. As mentioned in (Mitchell
1996), many regression-based algorithms were ﬁrst evaluated by trial–error
approach. Then we decide to use two algorithms, i.e., DecisionRegTree and
SVM, to model energy consumption for chiller and supply fan. The next subsection
presents the experimental results for these selected algorithms.
4.2
Experiment Results
Using the dataset with new features, we conducted modeling experiments. The
dataset contains 12,734 instances. We separated the dataset into training and testing
sets. Training dataset contains 8000 instances and testing dataset has 4734
instances. Against the training dataset, the predicted models are built with two
selected learning algorithms. Then we run the trained models on the testing dataset.
Using evaluation criteria, we computed the performance for each model. In build-
ing operation, the chill has two working modes: chilling only mode and chill and
charging mode. Therefore, we conducted two kinds of modeling experiments
corresponding two chiller working modes. Figure 5 shows the modeling result of
chilling only mode. The x-axis is the estimate and y-axis is the actual value of
energy consumption. The performance of models, as measured by the MAE, MSE,
and MAPE, are shown in Table 1.
Chiller
Variables related to AHU   
Variables related to ice bank
Energy Consumption
Water
temperature
Building
characteristics
Weather
forecast data
Fig. 3 The diagram of
chiller energy consumption
Supply Fan
in AHU
Variables related to AHU  
Weather forecast data
Energy Consumption
Supply fan
Speed (%)
Zone temperatures
Occupancy rate
/CO2
Fig. 4 The diagram of
supply fan energy
consumption
1064
C. Yang et al.
Figure 6 shows modeling results of chilling and charging mode, and the perfor-
mance of the model is shown in Table 2.
Figure 7 shows modeling results of supply fan, and the performance of the model
is shown in Table 3.
5
Discussion
The experimental results above demonstrated that the data-driven modeling is
useful and effective for developing predictive models to estimate the energy
consumption for BEMS subsystems such as chiller and AHU. From Tables 1 and
2, it is obvious that nonlinear models show high accuracy results for estimating
energy consumptions. In many building operations, it is difﬁcult to build traditional
math models to estimate energy consumption because of operation complexity and
mutual interaction and constrains among subsystems. The proposed data-driven
modeling method provides a feasible alternative for modeling BEMS energy
consumption.
Fig. 5 The results of estimated values vs. actual values (chilling only mode)
Table 1 The performance of
the built models (chilling only
mode)
SVM
DecisionRegTree
MAE
1.72
1.28
MSE
13.25
8.15
MAPE (%)
5
4
Data-Driven Modeling for Energy Consumption Estimation
1065
Fig. 6 The results of estimated values vs. actual values (chilling and charging mode)
Table 2 The performance of
the built models (chilling and
charging mode)
SVM
DecisionRegTree
MAE
1.40
1.36
MSE
9.64
6.63
MAPE (%)
3
3
Fig. 7 The results of estimated values vs. actual values for supply fan subsystem
1066
C. Yang et al.
It is worth to noting that the energy estimations for two chiller modes are very
similar. This suggested that the developed models are very robust and transferable
for different chiller working modes.
It is also worth to pointing out that the estimate accuracy for low energy
consumption is not as good as that for high energy consumption. The reasons
need to be further investigated. But it also suggested that there is a possibility to
improve the model performance by focusing on low energy consumption of the
BEMS subsystems.
One limitation for the proposed method is the lack of ability of dealing with
noisy sensor data. In this work, we found that some data is not accurate under some
operation condition. For example, when chiller is operated at low energy consump-
tion, the sensor readings are incorrect; we removed those data based on domain
experts’ input. Another problem, some data were missing in the database. There-
fore, it is better to investigate an effective mechanism to deal with noisy and
missing values in the data.
The proposed modeling techniques mainly focused on energy consumption
estimation for reactive building operation. For proactive or long-term building
operation, it is more important to predict energy consumption ahead in a period
of time, say 24 h beyond. To meet such a requirement, we have to develop a
predictive model which is able to predict energy load for the whole building or all
BEMS 24 h ahead or longer period of time. This will be more challenging. We are
working on this issue. The results will be reported in other papers.
6
Conclusion and Future Work
In this paper, we proposed a data-driven methodology for developing data-driven
models to estimate energy consumption for BEMS subsystems such as chiller and
supply fan in AHU. To attain this goal, we deployed some state-of-the-art machine
learning and data mining techniques. We applied the proposed method to build
predictive models for chiller in a given commercial building and supply fan in an
AHU. The developed models were evaluated using real data from the building
owner. The experimental results show that the data-driven method is a useful and
effective alternative for modeling BEMS energy consumptions.
As presented in this paper, we have only developed the data-driven models for
chiller and one supply fan subsystem. For our future work, we will continue
working on modeling other subsystems such as return fan in AHU. In order to
perform optimized energy consumption management, we have to also develop data-
Table 3 The performance of
supply fan energy
consumption models
SVM
DecisionRegTree
MAE
7.58
2.39
MSE
108.74
21.61
MAPE (%)
0.28
0.10
Data-Driven Modeling for Energy Consumption Estimation
1067
driven models for tailoring control policies based on the energy consumption
information for speciﬁed subsystems and for projecting short-term energy loads
for the whole building. Consequently, such predictive models can be used to make
decision on energy consumption for each subsystem, resulting in reaching the ﬁnal
goal of energy saving for building operations.
Acknowledgment Many people at the National Research Council Canada have contributed to
this work. Special thanks go to Elizabeth Scarlett for the technical support. Thanks are also given
to the National Resource of Canada for providing the building data to conduct the modeling
experiments and domain knowledge to exploit the database.
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C. Yang et al.
A Simple Model of Finite Resource
Exploitation: Application to the Case of Oil
A. Heitor Reis
1
Introduction
Most resources of economic value are ﬁnite, and the “window of opportunity” for
its exploitation is also a ﬁnite period in history. Though they cannot fully predict the
future, models of resource exploitation can help to put up prospective scenarios of
resource availability. The most paradigmatic of such models is due to the
pioneering work of M. K. Hubert in the 1950s (see Hubbert 1962) who proposed
that oil production would follow a “bell-shaped” symmetric curve. Based on oil
data exploitation, Hubbert was able to successfully predict 1970 as the peak year for
oil production in the USA. Hubbert’s curve is now applied to prospective studies on
world’s oil production (e.g., Campbell and Laherre
`re 1998; Rui 2003, 2006; Bardi
and Lavacchi 2009), as well as to exploitation of coal and minerals (Bardi 2007;
Bardi and Lavacchi 2009). Attempts have been made to establish the theoretical
grounds of Hubert’s curve by using either system dynamics (Naill 1973), stochastic
modeling (Bardi 2005), or else economics (Holland 2008). In a recent paper, Bardi
and Lavacchi (2009) develop an explanation of Hubert’s curve based on a “predator
and prey” model.
In this paper we try a different approach based on two main drivers of the rate at
which resources are exploited: the level of economic demand and the level of the
technology available for resource exploitation.
A.H. Reis (*)
Physics Department, and Institute of Earth Sciences, University of E
´vora,
R. Rom~
ao Ramalho, 59, 7000-671 E
´vora, Portugal
e-mail: ahr@uevora.pt
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_73
1069
Nomenclature
m
Technologic and management index(nondimensional)
N
Number of activities that use oil
t
Time (years)
X
Resource amount (kg)
A, B, D, b, x
Ratio X/Xo
Constants
Greek letters
ϕ
Cost ratio (exergy lost in crudeexploitation/exergy content of crude)
ε
Avidity of market (nondimensional)
Subscripts
0
Global reserve
u
Useful (commodity)
i, a
Ordinals
2
The Model
Differently from renewable resources, the global amount of ﬁnite resources is either
constant or continuous; it decreases due to irreversible degradation as they are used
in human and economic activities. Common examples of the ﬁrst case are minerals,
while oil, gas, and coal stand for the second one. In this way, a characteristic
common to every ﬁnite resource is that historically there is a window of opportunity
to explore them. Some of them, such as minerals, remain relatively stable as they
are used, while others suffer irreversible transformations (e.g., coal, gas, oil) and
once they become depleted will never be used in the global economy.
No resource is exploited without a cost associated to its exploitation. The
simplest measure of this cost is the fraction of a resource unit whose market
value equals the cost of exploitation of a unit of resource. We will call this
nondimensional measure, the cost ratio. If we speak of oil, the cost ratio is the
ratio of the exergy spent in the extraction of some oil amount to the useful exergy
that it is able to deliver in the average conditions of its use in society. With respect
to measuring exploitation cost of oil, gas, and coal, a measure commonly used is
EROI which means energy return on investment (Hall and Cleveland 1981).
Though EROI has various deﬁnitions (Bardi and Lavacchi 2009), the most suitable
to the present case is societal EROI, which is deﬁned as the ratio of the energy
content of some amount of fuel to the energy lost in its exploitation. EROI does not
take into account the quality of energy, i.e., it does not differentiate between heat
and power, a fact that assigns it some ambiguity as a measure of exploitation cost.
Such ambiguity would vanish if EROI was deﬁned as the ratio of the exergy content
of some amount of fuel to the exergy lost in its exploitation. In this case, EROI
would come close to the inverse of cost ratio, because it may be shown that exergy
can be taken as an appropriate measure of market value (Reis 2006).
1070
A.H. Reis
2.1
Cost Ratio
Let us analyze ﬁrst the case of fossil fuels and consider that at a time t, some fossil
fuel (ﬁnite), whose known global (planetary) magnitude amounts to X0, is exploited
at the rate _
X with a cost ratio ϕ. Therefore, the “net fuel” (i.e., the part that is
available for the other economic sectors) is put into the market at a rate _
X u that is
given by:
_
X u ¼ _
X 1  ϕ
ð
Þ
ð1Þ
We assume that the cost ratio increases in time as the reservoirs become depleted
of the resource, or new reservoirs are discovered at a greater depth. In this way, we
also assume that such increase may be modeled as a power law of the degree of
global shortage of that fossil fuel in the natural reservoirs, i.e.:
ϕ ¼ A 1  X=X0
ð
Þm
ð2Þ
where X is total amount exploited up to time t, A stands for the cost ratio at the
beginning of the exploitation (X~0), and m is an exponent accounting for the degree
of efﬁciency of the exploitation process. When m > 1, relatively high m means low
efﬁciency, while low m means high efﬁciency (high developed exploitation tech-
nologies together with good management of exploitation). If m < 1 the cost ratio
decreases in time, which means that the degree of exploitation efﬁciency is high
enough to overshadow the negative effect of reservoir shortage.
By integrating Eq. (1) with the help of Eq. (2), one obtains:
xu ¼ x þ
A
1  m 1  x
ð
Þ1m
ð3Þ
where xu and x stand for Xu/X0 and X/X0, respectively. Equation (3) describes the
total (historic) amount of “net fuel” put into the market in relation to total fuel
extracted from the natural reservoirs.
2.2
Market Demand
The exploitation of ﬁnite natural resources is driven by the market demand.
Demand in the case of fossil fuels is driven by all sectors in society that use energy,
the most of it with origin in fossil fuel consumption. In a mental picture, we can
imagine the growth of the use of fossil fuels in economy and in society as a diffusive
process in which energy of fossil origin is used in an increasingly higher number of
activities. Every simple diffusive process scales with t1/2(where t stands for time),
A Simple Model of Finite Resource Exploitation: Application to the Case of Oil
1071
and therefore the number N of activities that use fossil fuel energy scales accord-
ingly with
N  Dt1=2
ð4Þ
where D is a constant that accounts for the diffusibility of fossil fuel energy in
society. Additionally, we assume that the number n of units in each activity a
(industrial unit, house, vehicle, etc.) grows in time according to a power law
X
i, a
ni  bitε
ð5Þ
where bi is a constant, and the exponent ε accounts for the growth of sector
i powered by the economic development, and namely, by the availability of energy
of fossil fuel origin. Therefore, the total fossil fuel energy consumption rate in each
activity (sector) is given by:
_
X a ¼
X
i
ni _
X i,a 
X
i
bi _
X i,atε ¼ Batε
ð6Þ
0
0,05
0,1
0,15
0,2
0,25
0,3
0,35
0,4
0,45
0,5
0
0,2
0,4
0,6
0,8
1
f
x
m=0.8
m=1,7
m=3
Fig. 1 Cost ratio as a
function of fraction of
resource extracted in
various technologic
scenarios
1072
A.H. Reis
where _
X i,a is the consumption rate of unit i in sector a. In this way, by using Eq. (6)
and summing for all activities (sectors), we ﬁnd an estimate of the rate of global
energy demand as:
_
X u  Bt1=2þε
ð7Þ
In Eq. (7) ε ¼ 0 corresponds to pure physical driven diffusion of resource X and
means indifference of the market with respect to resource X, while ε > 0 represents
avidity for that resource, and ε < 0 stands for the cases when X is combated by the
society (e.g., pollution, hallucinogenic drugs).
2.3
Exploitation Curve
By combining Eqs. (1) and (7) and integrating the resulting equation, one obtains:
x þ
A
1  m 1  x
ð
Þ1m  ^
t3=2þε ¼ 0
ð8Þ
In Eq. (8) the constant B has been eliminated through an appropriate choice of
the time scale. Therefore, here ^
t represents a time scale in which ^
t ¼ 1 corresponds
to the period at the end of which resource X is completely depleted (x ¼ 1).
The exploitation curve represented by eq. (8) is parameterized by:
(i) A, the cost ratio at the beginning of the exploitation (X~0)
(ii) m, the exponent accounting for the degree of efﬁciency of the exploitation
process (technology and management)
(iii) ε, the exponent that accounts for the market avidity for energy of fossil fuel
origin
These parameters may be estimated through the data available from resource
exploitation, therefore enabling us to construct prospective scenarios of resource
availability.
3
Analysis of Finite Resource Exploitation Curve
3.1
The Case of Oil Production
For the case of world oil exploitation, the relation of _
x  tα with the exponent
α ¼ 3.1 ﬁts pretty well the curve of annual production in the period 1930–1980 (see
Fig. 2). On the other hand, the average value of A would be close to 0.01 (Gagnon
et al. 2009). These basic data enable us to draw some future scenarios for oil
production. These scenarios will not include production from shale oil reservoirs
A Simple Model of Finite Resource Exploitation: Application to the Case of Oil
1073
because the parameters were estimated from data of oil production from normal
crude oil reservoirs. With this purpose, we assume that α must be close to 1/2 þ ε
(see Eq. 7), because for low values of x, both the curves _
x t
ð Þ and _
x u t
ð Þ practically
coincide (see Figs. 3, 4, and 5). Therefore, by taking ε ¼ 2.6 and A ¼ 0.01, we use
Eq. (8) to ﬁnd out the production curve that in its ﬁrst part is described by the same
exponent α ¼ 3.1. Such curve, which is represented in Fig. 3, is parameterized by
m ¼ 1.7. In this way, the curve in Fig. 3 is likely to stand for a liable scenario of
future oil production. On the other hand, as discussed above, the exponent m ¼ 1.7
indicates
a
moderate
level
of
exploitation
technology
and
exploitation
70
60
50
40
30
20
10
0
1920 1940 1960 1980 2000
Time (year)
Production rate (MMSTB/D)
Fig. 2 Evolution of crude
oil production rate
0
0,01
0,02
0,03
0,04
0,05
0,06
0,07
0,08
0
0,2
0,4
0,6
0,8
1
% of total resource/year;   cost ratio
Time
Production
Net Production
Cost ratio
m = 1.7
e =  2.6
Fig. 3 Scenario for
evolution crude oil
production rate together
with associated cost ratio
with parameters estimated
from actual data
1074
A.H. Reis
management. Several interesting comments can be drawn from Fig. 3. One ﬁrst
comment respects to cost ratio. Though it is not usual to ﬁnd estimates of this
variable in the literature, it can be estimated indirectly because its value is very
close to the inverse of the EROI. In a recent paper, Gagnon et al. (2009) have
0
0,01
0,02
0,03
0,04
0,05
0,06
0,07
0,08
0
0,2
0,4
0,6
0,8
1
% of total resource/year;   cost ratio
Time
Production
Net Production
Cost ratio
m = 0.5
e =  2.6
Fig. 4 Scenario for
evolution of crude oil
production rate together
with associated cost ratio
with parameters
corresponding to extremely
developed exploitation
technology
0
0,01
0,02
0,03
0,04
0,05
0,06
0,07
0,08
0
0,2
0,4
0,6
0,8
1
% of total resource/year;   cost ratio
Time
Production
Net Production
Cost ratio
m = 3.0
e =  2.6
Fig. 5 Scenario for
evolution of crude oil
production rate together
with associated cost ratio
with parameters
corresponding to
underdeveloped
exploitation technology
A Simple Model of Finite Resource Exploitation: Application to the Case of Oil
1075
published recent estimates of EROI for crude oil in the period 1950–2005, which
indicate that EROI in the period 1992–1999 was relatively stable close to 38 and has
decreased to about 20 from 1999 to 2005. A very recent estimate indicates that
EROI might be close to 11 in 2009 (Hall et al. 2009). By coming back to Fig. 3, this
kind of evolution ﬁts the steepest part of the cost ratio curve, while the cost
ratio ¼ 1/EROI ¼ 0.05 roughly corresponds to the peak of net production. Beyond
this point, the rate at which oil is delivered to the market decreases sharply.
If the curve somehow represents a realistic scenario for world oil production, we
must conclude that the peak of global net oil production is occurring at the present
time. The claim that global oil production is reaching its peak is assumed by many
people and international groups, namely, the Association for the Study of Peak Oil
(ASPO).
In the scenario depicted on Fig. 3, the peak would occur late in the period
corresponding to the “window of opportunity” for oil exploitation, more precisely
at time ^
t  0:91. Based on Eq. (8), we conclude that at ^
t  0:91 about 79% of the
initial oil amount should have been extracted from reservoirs.
The remaining 21% would not be extracted due either to technologic reasons or
to lack of economic interest. Moreover, Fig. 3 indicates that the period mediating
between the peak of production and the end of economic interest of oil exploitation
is of order ^
t  0:09.
Considering 200 years as the “window of opportunity” for oil extraction, it
means that between the peak and the end of exploitation, oil extraction will mediate
a period of 18 years. This result must be viewed with some caution because the
exponent for oil demand α ¼ 3.1 in the period of globalization of the use of oil
(1930–1980) was also used for establishing the scenario for the period after the peak
production has occurred. Modeling of oil demand in this period must not be
described by a single exponent only due to the fact that energy demand will
move toward other energy sources, namely, coal and the renewables.
By contrast with Hubert’s, the present model does not predict a symmetric curve
for oil production.
The reason is that not only oil extraction technology is much more developed but
also demand is global, and therefore huge tensions must be at stage by the end of oil
production. A scenario in which oil extraction technology is pushed to its limits by
achieving reduction of cost ratio (m ¼ 0.5) in the context of increasing complexity
of oil production is represented in Fig. 4. Here we can see that more oil would be
extracted: however, the post-peak period would be shorter than in the more realistic
case of Fig. 3.
The scenario in which technology and extraction management would perform
worse than that of Fig. 3 is represented in Fig. 5. In this case, not only much more oil
would remain unexplored but also the cost ratio would start to increase earlier.
1076
A.H. Reis
3.2
Some Limiting Cases for Generic Finite Resources
The previous analysis may be extended to other ﬁnite resources with the appropriate
adaptations.
The scenario represented in Fig. 6 corresponds to both poor demand (ε ¼ 0.5)
and poor exploitation technology (m ¼ 3.0). In this scenario the cost ratio stands
high and rises signiﬁcantly since the beginning of the exploitation. Only about 59%
of the resource would be extracted at the end of exploitation (see Eq. 8).
The scenario in Fig. 7 is intended to represent average conditions: moderate
demand (ε ¼ 1) together with average technology development (m ¼ 2.0). The
resource will be exploited up to 72%, while the cost ratio will rise moderately.
Finally, the scenario in Fig. 8 stands for a ﬁnite resource whose trade is
combated by the society (e.g., uranium), the case in which ε < 0.
For the case of uranium, the exponent m must be high due to the many
technologic problems with its exploitation. In such a case, the resource would be
explored for economic reasons only up to 66% of the global reservoir, while the
cost ratio would rise signiﬁcantly since the beginning of the exploration.
As a general comment, one must stress that the model presented above is very
simple and may be improved to better describe real cases, namely, by allowing the
exponents m and ε to be corrected for accommodating either the technologic
breakthroughs or sudden discovery of new reservoirs, or else unexpected changes
in global policies.
0
0,01
0,02
0,03
0,04
0,05
0,06
0,07
0,08
0
0,2
0,4
0,6
0,8
1
% of total resource/year;   cost ratio
Time
Production
Net Production
Cost ratio
m = 3.0
e =  0.5
Fig. 6 Scenario for
evolution of generic
resource exploitation rate
together with associated
cost ratio with parameters
corresponding to low
market demand together
with underdeveloped
exploitation technology
A Simple Model of Finite Resource Exploitation: Application to the Case of Oil
1077
4
Conclusions
Despite its simplicity, the model developed in this paper enables capture of basic
features of resource exploitation. The inputs to the model, namely, resource market
demand index and initial cost ratio of exploitation, may be estimated from historical
0
0,01
0,02
0,03
0,04
0,05
0,06
0,07
0,08
0
0,2
0,4
0,6
0,8
1
% of total resource/year;   cost ratio
Time
Production
Net Production
Cost ratio
m = 2.0
e =  1.0
Fig. 7 Scenario for
evolution of generic
resource exploitation rate
together with associated
cost ratio with parameters
corresponding to average
market demand together
with average exploitation
technology
0
0,01
0,02
0,03
0,04
0,05
0,06
0,07
0,08
0
0,2
0,4
0,6
0,8
1
% of total resource/year;   cost ratio
Time
Production
Net Production
Cost ratio
m = 3
e = -0.2  
Fig. 8 Scenario for
evolution of resource
exploitation rate together
with associated cost ratio
and A ¼ 0.05 in negative
market demand conditions
(restrained exploitation)
1078
A.H. Reis
data, while the technologic index, which is a parameter associated to the level of
exploitation technology, may be also inferred from the application of the model to
historical data. The case of oil production was considered in the analysis, and a
possible future scenario of oil production rate was put up on the basis of the
historical production rate. Some scenarios for generic resource exploitation were
also considered and analyzed. The model allows future improvements, namely, by
considering either unexpected discovery of new reservoirs or market demand
transition for new energy sources.
Acknowledgments This research was supported by the Institute of Earth Sciences (ICT), under
contract with FCT (Portugal). This support is gratefully acknowledged by the author.
References
Bardi, U.: The mineral economy: a model for the shape of oil production curves. Energ Policy. 33,
53–61 (2005)
Bardi, U.: Peak oil’s Ancestor: The Peak of British Coal Production in the 1920s. ASPO, Uppsala
(2007)
Bardi, U., Lavacchi, A.: A simple interpretation of Hubbert’s model of resource exploitation.
Energies. 2, 646–661 (2009)
Campbell, C.J., Laherre
`re, J.H.: The end of cheap oil. Sci. Am. 278, 78–83 (1998)
Gagnon, N., Hall, C.A.S., Brinker, L.: A preliminary investigation of energy return on energy
investment for global oil and gas production. Energies. 2, 490–503 (2009)
Hall, C.A.S., Cleveland, C.J.: Petroleum drilling and production in the U.S.: yield per effort and
net energy analysis. Science. 211, 576–579 (1981)
Hall, C.A.S., Balogh, S., Murphy, D.J.R.: What is the minimum EROI that a sustainable society
must have? Energies. 2, 25–47 (2009)
Holland, S.P.: Modeling peak oil. Energy J. 29, 61–80 (2008)
Hubbert, M.K.: Energy Resources. A Report to the Committee on Natural Resources, p. 54.
National Academy of Sciences, National Research Council, Washington, DC (1962)
Naill, R.F.: The discovery life cycle of a ﬁnite resource. In: Meadows, D.H. (ed.) Towards Global
Equilibrium: Collected Papers. Business & Economics, Geneva (1973)
Reis, A.H.: Exergy based analysis of economic sustainability in Perspectives on Econophysics,
147–159, Ed. Un. of Evora. (2006)
Rosa, R.N.: Economic growth in a closed ﬁnite world. In Perspectives on Econophysics, 127–146,
Ed. Un. of Evora (2006)
Rui R.: Climate Change and Oil Depletion. Energy Exploration & Exploitation 21(1), 11–28
(2003)
A Simple Model of Finite Resource Exploitation: Application to the Case of Oil
1079
Development and Application of a Simple
and Reliable Power Regulator
for a Small-Scale Island Wind Turbine
Yongjun Dong, Yang Zhao, Jianmei Chen, Mingqi Xu,
Xueming Zhang, and Jingfu Guo
1
Introduction
Islands, which occupy an important place in marine economics, are increasingly
concerned. Due to lack of electricity and fresh water, it is very inconvenient for the
daily life of the people living on islands. Even if they can depend on the diesel
generation, during the onset of severe weather, it is impossible to supply fuel oil,
especially to the remote islands. In addition, diesel generation not only has high cost
but also easily causes environmental pollution. Thanks to rich wind resources in the
islands, electricity generation from wind power is considered as one of the most
effective ways to solve the electric power supply on islands.
For wind power generation on islands, because of its high maintenance costs, the
reliability of the machine has a higher priority than its efﬁciency (Whale 2009).
Therefore, the most worrisome property in the application of island wind power
machine is its operational reliability, especially in strong winds or gusts. For a
small-scale horizontal-axis wind power machine, its protection in high wind speeds
usually depends on some version of yawing mechanism which tends to turn the
rotor out of the wind direction so as to limit the captured energy of the rotor. Some
of these yawing devices adopt the method of furling a tail vane to control the rotor
speed and output power of the WTG (Bialasiewicz 2003; Bowen et al. 2003; Wright
and Wood 2007; Arifujjaman et al. 2008; Audierne et al. 2010). Others achieve the
function of yawing by using the combination of a DC motor, a reducer, roller
Y. Dong • Y. Zhao • J. Chen • M. Xu • X. Zhang • J. Guo (*)
School of Physics, Northeast Normal University, 5268 Renmin Street, Changchun 130024,
Jilin Province, China
Key Laboratory of Advanced Energy Development and Application Innovation under Jilin
province, 5268 Renmin Street, Changchun 130024, Jilin Province, China
e-mail: renewableerlab@163.com
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_74
1081
bearings, a worm, and so on, which act on the yaw axis (Wu and Wang 2012).
Besides, some small-scale horizontal-axis wind turbines have a variable pitch
control system (Nagai et al. 2009; Whale 2009; Narayana et al. 2012), just like
large wind turbines that limit the captured energy of the rotor by feathering the
blades, and some wind turbines adopt passive stall blades. The mechanical furling
principle easily leads to frequent swing of the tail vane when the wind changes
rapidly, resulting in the vibration of the wind turbine. Meanwhile, because the hinge
structures are more fragile under the effect of corrosion in oceanic environments,
this impact on the hinge is worse for the frequent swing of the tail vane. Power
control by the yawing mechanism acting on the yaw axis or variable pitch mech-
anism will increase the complexity of wind turbine structure and lead to a cost
increase of wind turbines. Controlling the increase of power by passive stall blades
mainly depends on the airfoil proﬁle of the blade. It is hard to control the dynamic
behavior of stall airfoil blade, especially in continuously increasing wind speed.
Because of wrongly estimating for the power grade and blade loads, the captured
energy of the turbine will increase uncontrollably with increasing wind speed
(Munteanu et al. 2008).
To solve overspeed protection and output power limitation of small-scale wind
turbine in strong winds, it is very necessary to develop a simple, reliable, and
effective power regulating method. In this paper, we design a power regulator based
on electric linear actuator (ELA) and aerodynamic vane. The regulator consists of a
rigid tail with an electrical control rotating aerodynamic vane (it is used to yaw the
rotor edgewise to the wind direction in high wind speeds), an electric linear
actuator, and a controller. It has several advantages including a simple structure
that is easy to be manufactured and installed, high controllability, and increased
reliability compared to the conventional mechanical vane applied in small-scale
wind turbines. The implementation of rotating the vane actively only depends on
the generator speed by measuring AC electrical frequency and the output electric
power by measuring two DC electrical quantities (i.e., voltage and current), and no
mechanical sensors are needed. The regulator has been applied to a 15 kW wind
turbine prototype operating on an island for more than 3 years. Field test results as
well as simulations will be provided and discussed.
Nomenclature
A
Area of the wind rotor
B
Intensity of magnetic ﬁeld
CE
Electromotive force constant
CP
Power coefﬁcient of the wind rotor
CT
Torque constant
D
Equivalent viscous damping coefﬁcient of the ELA
Dm
Viscous damping coefﬁcient of the DC motor
D2
Equivalent viscous damping coefﬁcient of the transmission
Ea(t)
Induced electromotive force of the DC motor
fe
AC frequency of the PMSG
Ia(t)
Armature current of the DC motor
(continued)
1082
Y. Dong et al.
Idc
DC bus current of the WTG after rectiﬁcation
J
Equivalent moment of inertia of the ELA
Jm
Moment of inertia of the DC motor
J2
Equivalent moment of inertia of the transmission
KE
Proportional constant of the back electromotive force
KT
Proportional constant of the torque
La
Armature inductance of the DC motor
p
Pole pairs of the PMSG
Pa
Aerodynamic power captured by the wind rotor
Pc
Reference power given by the controller PI1
Pm
Measured power of the WTG
Prated
Rated power of the WTG
R
Rotor radius
Ra
Armature resistance of the DC motor
Tem(t)
Electromagnetic torque of the DC motor
Ua(t)
Power supply voltage of the DC motor
Udc
DC bus voltage of the WTG after rectiﬁcation
v
Speed of the incident wind
vc-in
Cut-in wind speed of the wind turbine
vc-out
Cut-out wind speed of the wind turbine
vω-rated
Wind speed of the turbine speed achieving its rated value
vrated
Wind speed of the generation power achieving its rated value
Greek letters
ρ
Air density
λ
Tip speed ratio
α
Furling angle of the tail vane
β
Blade pitch angle
γ
Yawing angle
θm(t)
Rotation angle of the DC motor rotor
ω
Angular speed of the generator rotor
ωm(t)
Angular speed of the DC motor rotor
ωm
Measured rotational speed of the turbine
ωrated
Rated rotational speed of the turbine
Φ
Main magnetic ﬂux of the DC motor
Abbreviation
AC
Alternating current
DC
Direct current
ELA
Electric linear actuator
HCS
Hill-climb searching
MPPT
Maximum power point track
PMSG
Permanent magnet synchronous generator
WTG
Wind turbine generation
Development and Application of a Simple and Reliable Power Regulator. . .
1083
2
Power Control by Electric-Driven Furling
2.1
Principle of Power Control by Yawing
For direct-driven wind energy generation system, the wind turbine captures the
power from the kinetic energy contained in the wind and transfers it directly to the
rotor shaft of the electric generator. The mechanical energy captured by the wind
rotor is estimated by (Burton et al. 2001)
Pa ¼ 1
2 ρAv3CP
ð1Þ
where, ρ is the air density (kg/m3), A is the area of the rotor (m2) and v is the speed
of the incident wind (m/s) that ﬂows through the turbine. The power coefﬁcient CP
is usually given as a function of the tip speed ratio and the blade pitch angle. The tip
speed ratio λ of a turbine is deﬁned as
λ ¼ ωR
v
ð2Þ
where ω is the angular speed of the rotor and R is the rotor radius. For the ﬁxed-
pitch wind turbine, CP only depends on the tip speed ratio λ. In fact, when the
turbine is yawed, the value of yawing angle γ, shown in Fig. 1, has a signiﬁcant
effect on the power coefﬁcient. It is veriﬁed that the peak power output in yawing
condition closely follows the theoretical value given as (Adaramola and Krogstad
2011)
CP γ
ð Þ ¼ CP γ ¼ 0
ð
Þ cos 3 γ
ð Þ
ð3Þ
According to Eq. (3), the relation between the power coefﬁcient and the yawing
angle is illustrated in Fig. 2. By adjusting the yawing angle of the wind turbine, the
angle of attack on rotor blades would also change, leading to variation in the
aerodynamic characteristics of the blade. In addition, when the turbine is in a
v
O´
Rotor
ω
O
γ
α
Tail vane
Fig. 1 Horizontal-axis wind turbine in yawing condition by furling the tail vane
1084
Y. Dong et al.
yawing position, it can affect both the effective wind speed and the rotor swept area.
The blades operate in a deep stall regime at very low tip speed ratios, therefore the
power curves are not signiﬁcantly affected by the yawing angle. Figure 2 shows that
the output power of the turbine will be controlled by adjusting the yawing angle
above the rated wind velocity.
For a small-scale horizontal-axis wind turbine, it usually has a tail vane (shown
in Fig. 1.) that tends to stay aligned with the wind direction so as to regulate the
turbine to properly orient wind direction in low and medium wind speeds. At higher
wind speeds, in order to yaw the rotor for overspeed protection and output power
limitation, it is an attractive method to furl the tail vane, as shown in Fig. 1. When
the tail vane folds at a certain angle α, the wind acting on the tail vane will cause a
yawing moment that tends to turn the rotor out of the wind, and the tail vane
remains approximately aligned with the wind direction. At a constant wind speed, a
new stop position is reached and a yawing angle γ of the rotor is generated. Then,
the captured energy of the rotor will be reduced because of the yaw angle. Once the
wind speed has fallen below a critical value, recovery from this position and
therefore realignment with the wind direction is enabled by a restoring moment
that is generally provided by an external force.
2.2
Electric-Driven Furling
Compared to the conventional mechanical furling structure by simply depending on
the aerodynamic principles, the method of utilizing electric driver to furl the tail
vane actively has high controllability in yawing the rotor edgewise to the wind
direction. Such a novel device consists of a rigid tail with an electrical control
rotating aerodynamic vane, an electric linear actuator, and a controller, as shown in
Fig. 3a.
Fig. 2 Power coefﬁcient
with the change of the yaw
angle
Development and Application of a Simple and Reliable Power Regulator. . .
1085
The ELA is an actuator that creates linear motion by converting rotational
movement. It depends on the motor’s bidirectional rotation to complete the motion
of the extension tube along a straight line, and it has a lot of advantages compared to
traditional mechanical and hydraulic systems. Once the tube suffers overloads or
moves to the end of the stroke, the motion will be forced to stop by an overload
clutch or electronic load monitoring. Besides, the actuator will hold its loads with
power removed and no maintenance is required. The rotating tail vane adopts the
airfoil structures, which has excellent aerodynamic characteristics compared to the
traditional ﬂat tail. By applying the airfoil tail vane, the steering mechanism of
small-scale WTG could yaw the turbine more easily.
When the rotation speed of the rotor or the output power is below the
corresponding rated value, the tube of the actuator will be in its initial position
and the aerodynamic force on the tail of the wind machine keeps it aligned with the
wind direction, shown in Fig. 3a, as a common vane does. Once the measured value
reaches its rated value, the tube of the electric linear actuator will be stretched out to
push the vane to rotate through an angle α around the furl axis O0, shown in Fig. 3b.
Then, the drag force on the vane makes the rotor rotate through an angle γ around its
v
O
O´
ω
Tail vane
Rotor
Electric linear actuator
a
v
O
O´
ω
γ
α
Yaw axis
Furl axis
Extension tube
b
Fig. 3 (a) Structure and operation principle (b) of electric-driven furling
1086
Y. Dong et al.
hinge axis O, causing the whole machine to yaw gradually through γ as the wind
speed increases, shown in Fig. 3b. Referring to Fig. 2, the captured power will be
dramatically reduced by increasing the yawing angle γ.
3
Model of the Electric Furling System
3.1
Structure of the ELA
Figure 4 shows the typical structure of a DC ELA based on the worm gear and the
ball screw. It mainly consists of a DC motor, a worm-gear reducer, a ball screw, an
extension tube, and a control unit. When powering the electric linear actuator, the
DC motor rotor rotates and then drives the lead screw to rotate through the worm-
gear reducer. The lead nut, which is connected to the extension tube, is driven by
the lead screw to move along the screw. Thus, the rotation of the motor is converted
to the linear movement of the extension tube.
3.2
Model of the ELA
As an electromechanical system, the ELA can be modeled by combining several
subsystem models, such as the model of the DC motor, the model of transmission,
and the model of control unit, as shown in Fig. 5. Figures 6, 7, and 8 show the
typical equivalent circuit of the DC motor, the kinetic model of the DC motor, and
the model of the transmission system, respectively. By mathematical modeling, the
parameters of the electrical drive system can be shown as the linear motion
parameters of the mechanical load.
Based on the principles of electrical machinery and mechanical transmission, the
transfer function of the electric linear actuator by Laplace transform can be
represented as (Ruiz-Rojas et al. 2008)
DC motor
Worm reducer
Controller
Lead screw
Lead nut
Extension
tube
Electric
cable
Fig. 4 Typical structure of a DC ELA based on the worm gear and the ball screw
Development and Application of a Simple and Reliable Power Regulator. . .
1087
X s
ð Þ
Ua s
ð Þ ¼
KT
P Js2 þ Ds
ð
Þ Ra þ Las
ð
Þ þ KTKEs
ð4Þ
where, X(s) is the Laplace transform of the linear advance x(t) of the ball-screw
lead; Ua(s) is the Laplace transform of the DC power supply voltage Ua(t) of the
motor; J and D are the equivalent moment of inertia and the equivalent viscous
damping coefﬁcient including the motor and the transmission, respectively; Ra and
Model
of DC motor
Model
of control unit
Model
of reducer
Control
signals
Electrical
load
Model
of transmission
Model
of ball screw
Motor
torque
Rotational angle
of worm gear
Model
of mechanism
Linear movement
distance of
extension tube
Feedback
signals
Mechanical
loads
Fig. 5 Diagram of mathematical modeling of linear actuator powered by electricity
Rotor
M
Magnetic
field
Ra
La
Ia(t)
Ea(t)
Ua(t)
θm(t)
Tem(t)
TL(t)
Fig. 6 Equivalent circuit of
the DC motor
fm
Jm
θm(t)
Tem(t)
TL(t)
Fig. 7 The kinetic model
of the motor and the
transmission
1088
Y. Dong et al.
La are the armature resistance and inductance of the motor, respectively; KT and KE
denote the proportional constant of the torque and the back electromotive force in a
constant magnetic ﬁeld, respectively; P is a constant deﬁned as the ratio of 2π and
the screw pitch l.
Corresponding state space equations of Eq. (4) can be expressed as follows
(Ruiz-Rojas et al. 2008)
_
x 1 t
ð Þ
_
x 2 t
ð Þ
_
x 3 t
ð Þ
2
4
3
5 ¼
0
1
0
0
D
J
PKT
J
0
 KE
PLa
Ra
La
2
6
6
6
6
6
4
3
7
7
7
7
7
5
x1 t
ð Þ
x2 t
ð Þ
x3 t
ð Þ
2
4
3
5 þ
0
0
1
La
2
6
4
3
7
5Ua t
ð Þ
y ¼ 1 0 0
½

x1 t
ð Þ
x2 t
ð Þ
x3 t
ð Þ
2
4
3
5
ð5Þ
3.3
Model of the Electric Furling Device
Figure 9 shows the structural diagram of the furling device when the measured
rotational speed ωm of the rotor is larger than the rated value ωrated. Based on the
output signal that compared ωm with ωrated, the extension tube of the ELA is
elongated to push the tail vane to rotate around the furl axis O0, shown in Fig. 9.
For a certain furl angle α, it can be addressed as
α ¼ β0  β
ð6Þ
where,
Worm-gear
θm(t)
T2(t), θ2(t)
TL(t)
f2
J2
T3(t)
x(t)
Ball-screw
Z2
Z1
p
Fig. 8 The model of the transmission
Development and Application of a Simple and Reliable Power Regulator. . .
1089
β0 ¼ cos 1 A2 þ B2  L02
2AB
¼ cos 1 A2 þ B2  L þ x t
ð Þ
½
2
2AB
ð7Þ
β ¼ cos 1 A2 þ B2  L2
2AB
ð8Þ
Thus, according to Eqs. (5) and (6), we can obtain the furling angle of this
electric furling device under steady wind velocity. By adjusting the furling angle,
the power captured by the turbine would be limited effectively.
4
Control Method
4.1
Standalone WTG Conﬁguration
Figure 10 shows the block diagram of the standalone WTG with electric furling
device. In our design of the WTG prototype, the mechanical torque to the multi-
polar three-phase PMSG is directly provided by a horizontal-axis, ﬁxed-pitch, four-
blade wind turbine, which is suitable for the application in low wind speeds. A
three-phase diode rectiﬁer bridge is used for rectifying the alternating current of the
generator to direct current. The high-capacity capacitor C1 ﬁlters out the output
voltage oscillations of the rectiﬁer and creates a stable DC-link for the following
subsystem. The DC-DC converter converts the output voltage Udc of the rectiﬁer to
the voltage Ubat for the charge of the batteries under the control of the power
regulator. At high wind speeds, according to the measured output electric power
and AC frequency, the furling controller provides control instructions to the electric
furling device to furl the tail vane. Under the effect of aerodynamic force, the
turbine will yaw and then its captured power will be reduced. The MPPT control of
the wind turbine depends on the variable-step hill-climb searching (HCS) method
v
O´
γ
α
B
L´=L+x(t)
A
H
L
β
β'
Fig. 9 The structural diagram of the furling device when furling behavior occurs
1090
Y. Dong et al.
(Kesraoui et al. 2011; Abdullah et al. 2012). The furling controller aims to limit the
rotational speed and the captured power of the turbine, and to protect the WTG
device against damage during strong winds, and this is also the main issue of
this part.
4.2
Control Strategy Analysis
For variable-speed ﬁxed-pitch wind turbine in its operating regimes of the entire
wind speed domain from cut-in vc-in to cut-out vc-out, as depicted in Fig. 11, its ideal
operation could be divided into three regimes (S
¸erban and Marinescu 2012). In the
regime between vc-in and vω-rated, the control aims to maintain an optimal power-
speed characteristic of the wind turbine, which is also called the MPPT control. In
the regime between vω-rated and vrated, the wind turbine operates in a quasi-constant
rotor speed regime. In the regime between vrated and vc-out, the wind turbine is
controlled to maintain the constant output power no higher than the rated power so
that the turbine and generator are not overloaded and dynamic loads do not result in
mechanical failure (Bianchi et al. 2007; Behjat and Hamrahi 2014). When the wind
speed is above vc-out, the wind turbine must be shut down to avoid being damaged
(Abdullah et al. 2012).
Considering the reliable operation of small-scale horizontal-axis ﬁxed-pitch
WTG, enough importance should be attached not only during strong wind and
gusts but also at low wind speed. When the wind speed exceeds the rated threshold
(usually around 9–12 m/s) (S
¸erban and Marinescu 2012), the tail vane must be
furled timely so as to limit the rotational speed and the captured power. When the
PMSG
Udc
three-phase
Rectifier
＋
－
Ubat
C1
C2
Rbl
Wind turbine
Loads
T1
MPPT
controller
Furling
controller
PWM
Signals
Furling
control
Signal
Controller
Idc
Ibat
Udc and Idc
fe
Ibl
DC-DC
converter
＋
－
＋
－
Furling
device
Yawing
Fig. 10 Standalone WTG conﬁguration with electric furling device
Development and Application of a Simple and Reliable Power Regulator. . .
1091
wind speed is below its rated value, although the output power couldn’t exceed its
rated value, if partial loads or full loads are removed unexpectedly, the rotational
speed of the rotor would increase remarkably and would exceed the rated rotor
speed. Thus, continuous running under the condition of a rotor speed much higher
than the rated value is very harmful to the structure of the WTG. It is necessary to
limit the rotational speed of the rotor when the loads are removed during low wind
speed.
Based on the above analysis, the electric furling control strategy is proposed, as
illustrated in Fig. 12. In this ﬁgure, Pc is a reference power, Pm denotes the
measured output electric power which is the product of the measured DC voltage
Udc and the measured DC current Idc, and Prated is the corresponding rated value.
For the direct-driven permanent magnet synchronous generator that is generally
adopted in small-scale wind turbine, the rotational speed ωm of the turbine can be
obtained by
ωm ¼ 2πf e
p
ð9Þ
where, fe and p denotes the AC frequency and the pole pairs of the PMSG,
respectively. The control method aims to maintain one of the output power and
the rotor speed to be quasi-constant, while the other is no larger than the
corresponding rated value. As one can see in Fig. 12, the regulator PI1 acts on the
vc-in
vc-out
vw
0
Pe
Prated
vω-rated vrated
Fig. 11 The ideal operation
curve of variable-speed
ﬁxed-pitch wind turbine
PI1
ωm
ωrated
＋
－
0
Pc
Prated
Pm
＋
－
×
Udc
Idc
PI2
Control
signal
Actuator
driver
＋
0
Fig. 12 The control block diagram of the electric furling device
1092
Y. Dong et al.
error between the measured rotor speed ωm and its rated value ωrated, providing a
reference value Pc. Then, the difference value obtained by the rated power Prated
subtracting the measured output power Pm and the reference power Pc is provided
for the regulator PI2 to generate the control signal of the furling system.
When the wind speed varies between vc-in and vrated, Pm is less than Prated, while
ωm may exceed ωrated. If ωm  ωrated, the regulator PI1 is in a state of reverse
saturation and its output is identically equal to zero by limiting. Meanwhile,
because Pm is less than Prated, the regulator PI2 is also in reverse saturation and
its output is also equal to zero by limiting. Thus, the actuator of the furling system
stays in its initial state. In this regime, if ωm > ωrated for some reason, the regulator
PI1 outputs the reference power Pc. Although Pm is still less than Prated, the sum of
Pc and Pm will be larger than Prated, and the regulator PI2 acts on the error between
the sum and Prated, providing the control signal for the furling system. When the
wind speed is between vrated and vc-out, the output power will exceed its rated value,
while ωm may be below ωrated because of the increase in the loads. Regardless of
whether ωm exceeds ωrated, the sum of Pc and Pm will be greater than Prated. Then
the regulator PI2 acts on the error between the sum and Prated, providing the control
signal for the furling system.
5
Simulation Results
In this section, the performance of the proposed furling control method is evaluated
by means of simulation. All the simulation models are constructed and
implemented in MATLAB/Simulink. According to different furling conditions,
such as suffering high wind speed and removing loads in low wind speed, the
simulation results are divided into two parts for discussion.
5.1
Suffering High Wind Speed
For a step in wind speed between 8 and 12 m/s illustrated in Fig. 13a, the
corresponding characteristics of the different parameters based on simulation are
shown in Fig. 13b–f. In the Fig. 13b–d, the dotted red lines denote the output
characteristic curves of the WTG without any measures of overspeed protection and
power limitation, such as furling the tail vane. After the wind speed steps to 12 m/s,
the corresponding rotational speed and output power of the generator begin to
increase. Supposing that the WTG is still controlled by MPPT, its rotational
speed would almost achieve 18 rad/s and corresponding output power will achieve
26 kW, as shown in Fig. 13b, c, respectively. They have far exceeded their rated
value for rated speed 13.6 rad/s and rated power 15 kW. Continuous overspeed
operation will intensify the structural vibrations of the wind turbine (Guimar~
aes
et al. 2015), which is harmful to the stability of the wind turbine. For overload
Development and Application of a Simple and Reliable Power Regulator. . .
1093
protection of electric power, it makes more rigorous demands for the selection of
the generator and the power electronic devices, which would cause signiﬁcant
increase in the cost of the wind turbine. The solid blue lines, illustrated in
Fig. 13b–f, represent the characteristics by the furling system proposed in this
paper. According to the furling model stated in Section 3 and the control algorithm
proposed in Section 4, the controller outputs the furling control signal Ua and then
the furling angle α of the vane gradually increases to about 33, as shown in
Fig. 13f, e, respectively. Under the ideal condition, the turning of the small-scale
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
6
8
10
12
14
v(m/s)
a
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0
10
20
wm(rad/s)
b
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0
1
2
3
x 10
4
Pm(W)
c
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0
0.2
0.4
Cp
d
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0
10
20
Time(s)
Ua(V)
f
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0
20
40
a(°)
e
Fig. 13 Output characteristics function of the WTG when suffering high wind speed
1094
Y. Dong et al.
wind turbine completely depends on its tail vane. Finally, the rotational speed and
the output electric power are limited to the corresponding rated value, as illustrated
in Fig. 13b, c, respectively. Thus, the furling system realizes the overspeed protec-
tion and the output power limitation of the WTG at high wind speed.
5.2
Removing Loads
Figure 14 illustrates the characteristics of the different parameters under the
condition of partial loads removing when the wind speed is 8 m/s (rated value
10 m/s).
The dotted red lines, as shown in Fig. 14b–d, denote the output characteristics of
the WTG without any protections, and the solid blue lines in Fig. 14b–f represent
the characteristics after furling the tail vane. When partial loads are removed for
some reasons, the rotational speed of the rotor gradually increases. Although the
output power becomes less than before (shown in Fig. 14c) and still below the rated
power, the ﬁnal rotor speed will achieve 15.5 rad/s (shown in Fig. 14b), which is
higher than the rated rotor speed 13.6 rad/s. As is mentioned above, continuous high
rotor speed will bring unforeseen dangers to the wind turbine structures. By taking
the measure of actively furling the tail vane, the vane rotates nearly 26, as shown in
Fig. 14e. Finally, the rotor speed is limited in its safe range (below 13.6 rad/s here),
as illustrated in Fig. 14b.
6
Field Test
To assess the performance and to verify the functionality of the electric furling
system, a device prototype was manufactured and installed on a specially designed
wind turbine prototype, which was funded by State Oceanic Administration of
China and installed in Dachen Island, Zhejiang Province, as shown in Fig. 15.
The structure of the electric furling actuator and the airfoil tail are illustrated in
Fig. 16. The aerodynamic vane consists of an aluminum alloy skeleton and alumi-
num skin ﬁlled with polyurethane foam, and uses the symmetric airfoil of
NACA0012 to get better aerodynamic performance. The actuator is installed on
the rigid tail stock next to the generator, and its extension tube is connected to the
leading edge of the airfoil vane (shown in Fig. 16). The rotation axis of the vane lies
on the focus of the airfoil (28% of the chord length), which will make the turbine
yaw smoothly out of the wind during high wind speed depending on the aerody-
namic compensation ability of the airfoil tail.
The reducer of the ELA is based on worm and gear, which have several
advantages, including high reduction ratio, large output torque, low noise level,
and self-locking. The ELA could output maximum push force by 7500 N, and its
speed control depends on a PWM signal.
Development and Application of a Simple and Reliable Power Regulator. . .
1095
Figure 17 shows the furling controller of the wind turbine prototype, which is
separate with the generator controller. The DC output parameters of the generator
are obtained from the generator controller through the 485 bus, and the AC
frequency is collected from the AC voltage signal by the AD converter. The DC
power of the controller is supplied by 24 V lead-acid batteries which are charged
directly from the electricity of the generator controlled by a transformer and a
DC-DC converter. The control algorithm is realized by a microprocessor.
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
7
8
9
v(m/s)
a
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0
6
12
18
wm(rad/s)
b
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0
5000
10000
Pm(W)
c
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0.2
0.3
0.4
Cp
d
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0
10
20
Time(s)
Ua(V)
f
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0
20
40
a(°)
e
Fig. 14 Output characteristics function of the WTG when removing partial loads at low wind
speed
1096
Y. Dong et al.
By monitoring the operation of the WTG, the output characteristics of the WTG
are analyzed based on the method of bins, illustrated in Fig. 18. As one can see in
Fig. 18a, when the output electric power exceeds its rated value, it will be limited
around the rated value under the control of the furling system. Figure 18b shows the
power coefﬁcient of the prototype.
Fig. 15 Operating wind
turbine equipped with
electric furling system
Fig. 16 Structure of the
electric furling actuator and
the airfoil tail
Fig. 17 Furling controller of the wind turbine prototype
Development and Application of a Simple and Reliable Power Regulator. . .
1097
Obviously, when the wind speed is below its rated value, the prototype almost
maintains its maximum power coefﬁcient. In the high wind region, the coefﬁcient
decreases because of the limitation of the output power.
Fig. 18 Characteristics of the wind turbine prototype: the output electric power (a); the power
coefﬁcient (b)
1098
Y. Dong et al.
7
Conclusion
In this paper, a new method for overspeed protection and power limitation of small-
scale, horizontal-axis, ﬁxed-pitch wind turbine generation system has been
described. The electric furling system based on an electric linear actuator and an
airfoil tail was developed and a corresponding structure was modeled. The simu-
lation results of the regulator indicated that the control method can easily prevent
the turbine from exceeding the rated values of rotational speed and output power.
Meanwhile, the ﬁeld test of a wind turbine prototype equipped with a furling device
veriﬁed that this electric furling method could effectively limit the rotational speed
and the output power of the turbine. For further optimization, this method can be
widely applied for overspeed protection and power limitation of standalone wind
energy conversion system.
Acknowledgments This work is sponsored by Science Foundation for Young Teachers of
Northeast Normal University (No.14QNJJ009) and Special funds of Marine Renewable Energy
of State Oceanic Administration of China (No.GHME2011CL02, No.ZJME2013ZB02).
Appendix
WTG parameters: radius of turbine r ¼ 4.9 m, stator resistance Ra ¼ 0.55Ω, stator
inductances Ld ¼ Lq ¼ 0.15 mH, moment of inertia J ¼ 19.2 kg m, pole pairs
p ¼ 22.
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Design and Economic Analysis of Photovoltaic
Systems in Different Cities of Turkey
Suphi Anıl Sekuc
¸o
glu and T€
ulin Bali
1
Introduction
The need for energy is increasing day by day. The conventional fossil fuels are
limited. Therefore, research is focused on renewable energy sources. The share of
use of renewable energy sources in electricity generation is expected to rise to 22%
in 2030.
Turkey is very rich in renewables potential. But few of these potentials have
been utilized so far. Renewable energy has become one of the increasingly impor-
tant topics in Turkey since renewables are considered not only as a way of
mitigating import dependency in energy resources but also as a part of ﬁnding
solutions to environmental problems.
Biomass and hydropower are the two major types of renewable sources com-
monly used in Turkey, in addition to the rarer uses of other types, such as
geothermal, wind, and solar. The expected electric power capacity development
in Turkey is summarized in Table 1 for 2010 and 2020. The total installed
photovoltaic power capacity in Turkey is estimated at around 300 kW, which
should be increased in the near future, together with other renewable energy
systems. The currently installed photovoltaic power is relatively small considering
the high solar energy resource in Turkey, as summarized in Table 2, regionally.
Photovoltaic conversion is the direct conversion of sunlight into electricity with
no intervening heat engine. Photovoltaic devices are simple in design and require
very little maintenance. Solar photovoltaic devices can be constructed as stand-
alone systems to give outputs from microwatts to megawatts. Therefore, they can be
S.A. Sekuc
¸o
glu • T. Bali (*)
Karadeniz Technical University, Engineering Faculty, Mechanical Engineering Department,
Karadeniz Technical University, Mechanical Engineering Department, Trabzon 61080, Turkey
e-mail: bali@ktu.edu.tr
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_75
1101
used as power sources for calculators, watches, water pumps, remote buildings,
communications, satellites, and space vehicles, and even multimegawatt scale
power plants. With such a vast array of applications, the demand for photovoltaics
is increasing every year. In 2012, over 31,000 MWp of photovoltaic panels were
sold for terrestrial use and the worldwide market has been growing at a phenomenal
rate since 2000.
Photovoltaics have been one of the fastest growing energy technologies in the
world. The countries with most installed photovoltaic power currently are Ger-
many, Japan, Italy, and the USA, which are the biggest photovoltaic module-
producing countries as well. As a result of the high growth rate of stand-alone
photovoltaic applications throughout the world, many articles have been published
to calculate their performance by Siegel et al.(1981), Bucciarelli (1984), Klein and
Beckman (1987), and Clark et al.(1984).
The grid-connected photovoltaic systems have experienced a rapid growth in
recent years. This was achieved mostly through the government-induced energy
policies of especially the developed countries. Nowadays, more and more articles
appear in the literature reporting on the grid-connected photovoltaic systems. These
articles include as ﬁnancial incentives, a reduction in the investment costs, increase
in reliability, and distribution of information and enhancement of environmental
awareness. Life cycle techno-economic analysis of a grid-connected photovoltaic
house in Turkey was investigated by Celik (2006).
Table 1 Renewable energy
potential of Turkey
Type
Potential
In operation
Hydro
45.000 MW
17,359.3 MW
Wind
48.000 MW
1,792.7 MW
Solar
300 TWh/year
–
Geothermal
600 MW
114.2 MW
Biomass
17 Mtoe
117.4 MW
Source: Turkish Energy Market: An Investor’s Guide 2012,
Energy Market Regulatory Authority (EMRA)
Table 2 Annual average
solar energy potential by
regions of Turkey
Solar radiation
(kWh/m2)
Sunshine duration
(h)
Marmara
1168
2409
Southeast
Anatolia
1460
2993
Aegean
1304
2738
Mediterranean
1390
2956
Black Sea
1120
1971
Central Anatolia
1314
2628
East Anatolia
1365
2664
Average
1303
2623
1102
S.A. Sekuc
¸o
glu and T. Bali
Solar power purchase price in Europe and state grants are as Table 3.
This paper presents a parametric study examining PV systems that can meet the
average energy needs of a single house in Turkey. This study was carried out for six
cities that came on more or less solar radiation according to the average annual solar
radiation of Turkey. There isn’t a comprehensive parametric study about off-grid
and on-grid PV systems in selected cities. At the same time, investigation with PV
system types, on-grid, off-grid, and on-grid feed-in tariff systems, allows for
comparative cost analysis. This study also investigates the effects of a capacity
shortage fraction on cost-sizing.
In this paper, the current status of photovoltaic energy in Turkey was studied and
the lifetime techno-economic analysis of stand-alone and grid-connected photovol-
taic systems was carried out.
This paper presents a comparative study about on-grid (grid-connected) and
off-grid (stand-alone) photovoltaic (PV) systems for a single house (2500 kWh
annually) in different cities of Turkey.
Nomenclature
i
Annual real interest rest
N
Project lifetime
Abbreviations
COE
Levelized cost of energy
CRF
Capital recovery factor
CSF
Capacity shortage fraction
NPC
Net present cost
PV
Photovoltaic
PVS
Photovoltaic system
REF
Renewable energy fraction
TAC
Total annualized cost of the system
Belen (36290N, 36130E), Gelibolu (40280N, 26430E), Konya (31.42N,
37.08E), Sinop (42 01N, 35 09E), Kırklareli (41.44N, 27.12E), and Karaman
(37.11N, 33.14E) cities were selected as the design areas in Turkey. The objective
of this study is to predict the system costs, the cost of energy generated by the PVS,
and to compare costs of stand-alone and grid-connected systems. In order to carry
Table 3 Solar energy
purchase prices (state
support)
Austria
0.30–0.46 €/kWh þ investment support
Belgium
0.45 €/kWh þ for 20 yearsa
France
0.30–0.40 € /kWh þ for 20 yearsa
Germany
0.3769–0.4921€/kWh þ for 20 yearsa
Italy
0.36–0.49 € /kWh þ for 20 yearsa
Portugal
0.28–0.45 € /kWh þ for 15 yearsa
Spain
0.23–0.44 € /kWh þ for 25 yearsa
Turkey
0.133 $/kWh þ for 10 yearsa
aGuaranteed government purchases
Design and Economic Analysis of Photovoltaic Systems in Different Cities. . .
1103
out system designs and determine the technical and economical parameters of PVS,
the HOMER program developed by the National Renewable Energy Laboratory
(NREL) was used.
2
HOMER Analysis
In this study, to carry out system designs and determine the technical and econom-
ical parameters of each system, the HOMER program was used. HOMER “Hybrid
Optimization Model for Electric Renewables” ensures the following (NREL 2012):
•
A computer software is developed by United States National Renewable Energy
Laboratory (NREL).
•
It makes the simulation of the work of the systems throughout the year using the
data of hourly time.
•
Total net present cost is used for the calculation of the life cycle cost of the
system.
•
HOMER searches the conﬁguration of the system with the lowest total net
present cost and sorts appropriate systems according to this value.
•
Assumes that all price increases will take effect at the same rate throughout the
life of the project.
The load proﬁle of the system is expressed as the amount of energy per hour
arising from the energy requirement of the system. Installation status was deter-
mined based on an average annual energy need of a house in Turkey, which was
2500 kWh. In this study, it was assumed that the same daily program is
implemented all year round but randomness factors were also taken into consider-
ation in order to ensure ﬂexibility. Randomness values from day to day and hourly
time steps were taken at 5%. At the same time, it is assumed that the amount of
daylight is enough and is not done lighting during the day. As can be seen in
Table 4, the schedule of a daily electricity usage of a house is prepared under these
assumptions by Sekuco
glu (2012).
The daily load proﬁle can be seen in Fig. 1. These load proﬁles were obtained
with HOMER program according to the time intervals and the default operation of
devices.
PV module and battery characteristics are shown in Table 5. The cost of module
is taken as $ 2.84 (2.69 €/Wp) per Watt. To take into account the setup fee and the
cost of other system components, total investment cost is considered to be twice the
price of the module. The PV life is taken as 25 years. PV panels are placed at an
angle of about 30 to the horizontal direction of the south. Tracking system is not
used in this study. The cost of battery is taken as $ 0.213 per 1 Wh. Of the total
capital cost and cost of replacement, it is assumed the same. Maintenance and repair
costs are considered to be $ 0.
Inverter costs for stand-alone systems and grid-connected systems were $ 1 and
$ 0.714 per 1 Watt, respectively. The unit energy cost of electricity purchased from
the grid was $ 0.12/kWh.
1104
S.A. Sekuc
¸o
glu and T. Bali
The software HOMER ﬁnds the optimum system based on the net present cost,
renewable factor, and payback period. The net present cost is the present value of
all setup and maintenance costs over the life of the project. It is calculated by using
the following equation:
NPC $
ð Þ ¼ TAC
CRF
Table 4 Electricity usage chart for the load proﬁle of a house (2500 kWh)
Device
Power (kW)
Daily working time (h)
Daily consumption (kWh)
Refrigerator
–
24
1.54
Washing machine
1.03
1
1.03
Dishwasher
0.90
1
0.9
Vacuum cleaner
0.60
1
0.6
Iron
0.60
1
0.6
Lamp (40 W) 3
0.12
8
0.96
Kitchen devices
0.075
2
0.15
Television
0.075
6
0.45
Satellite receiver
0.036
6
0.216
Computer
0.065
6
0.39
1.2
Load (kW)
1.0
0.8
0.6
0.4
0.2
0.0
0
6
12
18
Hour
Daily Profile
24
Fig. 1 Daily load proﬁle ( for annually 2500 kWh energy-consuming house)
Table 5 PV module and
battery characteristics (stand-
alone system)
PV module
Battery
BP Solar BP4175T
Vision 6FM200D
1.587 m * 0.790 m
V, 12 Volt
Voc 43.6 V
200 Ah
I 5.45 A
η(STC) % 14
Cost per Watt, $ 2.84 (2.69 €/Wp)
$ 0.213
Design and Economic Analysis of Photovoltaic Systems in Different Cities. . .
1105
In this equation, TAC and CRF are the total annualized cost of the system and
the capital recovery factor, respectively. CRF is deﬁned as follows:
CRF ¼
i 1 þ i
ð
ÞN
1 þ i
ð
ÞN  1
Here, N is the number of years that the project is expected to last and i is the
annual real interest rate. The annual real interest rate is taken as 6%. The project
lifetime is taken as 20 years.
The renewable factor is the amount of energy consumed that comes from a
renewable source. It is calculated simply by dividing the amount of electricity
produced by renewable sources by the total energy consumption of the property.
The value of renewable factor is 1 for stand-alone systems.
Capacity shortage is simply a measure of how much of the load is not met.
HOMER will add up all of the load that is not supplied in a year to determine the
unmet load. The capacity shortage is the ratio between the unmet load and the total
load (whether supplied or not). The capacity shortage fraction in the system design
was less than 0.5%. In addition, the effects of 10% reduction in the capacity
shortage fraction on the system costs are investigated for stand-alone systems.
3
Results and Discussions
The system and levelized costs of energy were investigated for a load of 2500 kWh
case annually and the effect of capacity shortage fraction on the system was
examined. The systems were designed as grid-connected and stand-alone systems. A
stand-alone PVS system and a grid-connected PVS system can be seen in Fig. 2a,
b. Batteries were not needed in the grid-connected system because power could be sold
back onto the grid at a better rate than it would cost to store it for later use in a battery.
Fig. 2 (a) Stand-alone PVS model. (b) Grid-connected PVS model
1106
S.A. Sekuc
¸o
glu and T. Bali
Stand-alone system optimization results are shown in Table 6. PV module
numbers in Marmara and Black Sea coastal cities (Gelibolu, Kırklareli, and
Sinop) are higher than inner Anatolian cities (Konya, Karaman). The net present
cost and the levelized cost of energy are obtained at least in Karaman, 35,942 $ and
1.263 $/kWh, respectively.
The network cost element of prices also increased, due to both rising mainte-
nance and grid expansion costs, as well as other costs sometimes incorporated into
network costs and tariffs. The cost of energy is high for stand-alone systems under
today’s conditions. The support provided by the state based on solar energy for
electricity generation is 13.3 cents per 1 kWh.
In this study, the effects on the optimum system design and the energy produc-
tion costs of CSF were investigated. Grid-connected system optimization results for
CSF 10% are shown in Table 7. The levelized costs of energy vary between 0.283
and 0.361 $/kWh for PVS in the case of CSF 10%. Program didn’t simulate
according to peak load in the case of CSF 10%. In this result, the system doesn’t
include expensive equipment. CSF can be selected between 5% and 10% for
optimum system. With a decrease of 10% in the CSF, there exist a decrease between
35% and 41% in the total net present costs and a decrease between 29% and 36% in
the levelized costs.
Table 6 Stand-alone PVS optimization results
City
Belen
Gelibolu
Konya
Sinop
Kırklareli
Karaman
PV array
[kW]
3.850
5.250
3.675
5.425
5.600
3.675
Capacity of
battery [Ah]
Number
200
200
200
200
200
200
20
22
18
22
22
16
Inverter
[kW]
3.1
4.2
3.0
4.4
4.5
3.0
NPC [$]
40,180
50,559
37,535
51,733
52,786
35,942
COE
[$/kWh]
1.412
1.778
1.318
1.819
1.856
1.263
Table 7 Optimization results for CSFmax ¼ 10
City
Belen
Gelibolu
Konya
Sinop
Kırklareli
Karaman
PV array
[kW]
2.625
3.150
2.450
3.150
3.325
2.275
Capacity of
battery [Ah]
Number
200
200
200
200
200
200
10
12
8
14
12
8
Inverter
[kW]
2.1
2.6
2.0
2.6
2.7
1.9
NPC [$]
26,276
30,013
24,538
31,069
31,165
23,513
COE
[$/kWh]
1.004
1.153
0.937
1.188
1.192
0.897
CSF [%]
10
10
10
10
10
10
Design and Economic Analysis of Photovoltaic Systems in Different Cities. . .
1107
Total net present cost values are shown in Fig. 3. With a decrease of 10% in the
CSF, more decreases in the total net present costs and in the levelized costs existed
in Gelibolu, Sinop, and Kırklareli than other cities examined. Thus, the system costs
are said to have approached each other in all the regions.
The effect of feed-in tariff on the system and levelized costs of energy is
investigated for grid-connected PVS. Simulation results of grid-connected PVS
are shown in Table 8. The levelized costs of energy vary between 0.283 and 0.361
$/kWh for PVS in the case of feed-in tariff. According to the obtained results, it is
determined that the grid-connected systems are more cost effective than stand-alone
systems.
4
Conclusions
In this study, stand-alone and grid-connected PV systems are investigated for six
cities in coastal and inner regions of Turkey. According to the results, while the
levelized costs of energy vary between 0.283 and 0.361 $/kWh for the case of feed-
in tariff, they vary between 0.897 and 1.192 $/kWh for the stand-alone systems. The
least levelized cost of energy values is observed in Karaman for all situations. The
Fig. 3 Total net present cost values for PVS2500 according to CSF
Table 8 Simulation results of grid-connected PVS
City
PVS2500
NPC [$]
COE [$/kWh]
REF
Belen
19,953
0.313
0.81
Gelibolu
26,678
0.350
0.84
Konya
18,661
0.285
0.82
Sinop
27,611
0.355
0.84
Kırklareli
28,441
0.361
0.85
Karaman
18,965
0.283
0.83
1108
S.A. Sekuc
¸o
glu and T. Bali
utilization rate of renewable energy also varies between 83% and 85% in the case of
feed-in tariff. The grid-connected systems are found to be the most suitable
solutions for present conditions.
Nowadays, despite the PV systems being expensive, their prices are decreasing
with advancing technology. While a PV module price was $ 3.38 per watt in
January 2011, it decreased to $ 2.42 per watt in January 2012 (World Energy
Resources 2013). Furthermore, these systems are expected to be widely used in
the near future due to their characteristics of being maintenance-free, environmen-
tally friendly, noiseless operation, and long life.
References
Celik, A.N.: Present status of photovoltaic energy in Turkey and life cycle techno-economic
analysis of a grid-connected photovoltaic-house. Renew. Sust. Energ. Rev. 10(4), 370–387
(2006)
Bucciarelli, L.L.: Estimating loss-of-power probabilities of stand-alone photovoltaic solar energy
systems. Solar Energy. 32, 205–209 (1984)
Clark, D.R., Klein, S.A., Beckman, W.A.: A method for estimating the performance of photovol-
taic system. Solar Energy. 33, 551–555 (1984)
Klein, S.A., Beckman, W.A.: Loss of load probabilities for stand-alone photovoltaic systems.
Solar Energy. 39, 499–512 (1987)
National Renewable Energy Laboratory (NREL).: http://www.nrel.gov (2012)
Sekuc
¸o
glu, S.A.: Design and economic analysis of PV, Wind and PV-Wind Hybrid systems. MS
Thesis, Karadeniz Technical University (2012)
Siegel, M.D., Klein, S.A., Beckman, W.A.: A simpliﬁed method for estimating the monthly-
average performance of photovoltaic systems. Solar Energy. 26, 413–418 (1981)
Turkish Energy Market: An Investor’s Guide: Energy Market Regulatory Authority (EMRA)
(2012)
World Energy Resources: Survey, World Energy Council, ISBN: 978 0 946121 29 8 (2013)
Design and Economic Analysis of Photovoltaic Systems in Different Cities. . .
1109
Contribution to the Control Power of a Wind
System with a Storage System
Ihssen Hamzaoui, Farid Bouchafaa, and Abdel Aziz Talha
1
Introduction
The development and use of renewable energy have experienced strong growth in
recent years (Fernandez et al. 2008). Wind turbines that have their operation based
on a double-fed induction generator are widely recognized in the industry as one of
the most promising wind turbine conﬁgurations (Zhi and Xu 2007). Indeed, this
action allows the functioning of a variable-speed wind turbine, which enables
production of the maximum possible power with a wide range of variation in
wind speed. Furthermore, static converters used to control the machine can be
designed to pass only a fraction of the total power (which represents the power of
sliding) (Yong et al. 2011). The penetration rate of wind power is limited to less
than 30%.
To overcome this drawback, distributed generation must contribute to system
services such as that of frequency adjustment, voltage, reactive power, the ability to
self-start, and islanding operation. Due to the highly ﬂuctuating and unpredictable
nature of the wind, wind alone cannot be relied on in services systems (Robyns et al.
2006). It is necessary to add generation systems or energy storage allowing the
possibility of having reserves. To ensure the generation/consumption balance, an
inertial storage system has been considered. There are several reasons for this
I. Hamzaoui (*)
University of Khemis Miliana, Faculty of Sciences and Technology, Ain Deﬂa, Algeria
e-mail: hamzaoui_ihssen2000@yahoo.fr
F. Bouchafaa • A.A. Talha
Laboratory of Instrumentation, Faculty of Electronics and Computer, University of Sciences
and Technology Houari Boumediene, BP 32 El- Alia, 16111 Bab-Ezzouar, Algeria
e-mail: fbouchafa@gmail.com; abtalha@gmail.com
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_76
1111
choice, for example: good momentum, good performance, and long lasting, similar
to wind (Cimuca et al. 2006).
However, the ongoing development in the ﬁeld of power electronics allows us
the possibility of developing different systems to improve power quality and reduce
harmonics generated by the power electronics converters.
Today, in addition to their main function, existing regulations impose an addi-
tional condition of good ‘quality’ power. Several techniques have been employed in
the control of these converters (Belfedal et al. 2008; Casadei et al. 2002). In this
paper, we present two different command techniques, DTC (direct torque control)
and DPC (direct power control), that are applied to the system for converting wind
energy with storage. Figure 1 shows the conﬁguration of the generation system
retained. The wind turbine is coupled directly to the double-fed induction generator
(DFIG) with the inverter connected to the rotor controlled by DTC. The Flywheel
Energy Storage System (FESS) is linked with the wind generator via a DC bus
controlled by the DTC. The network side converter has three levels that feed and
provide constant DC bus voltage, and is controlled by the DPC. The conversion
system is connected to the grid.
2
Wind Tubine Model
The aerodynamic torque produced by the wind turbine is given as:
Taer ¼ Paero
Ωt
¼ 1
2Ωt
Cp λ; β
ð
Þ:ρπR2
t v3
ð1Þ
Uc1
i ond
Converter 1
ı
Converter 2
DFIG
Uc2
Uc
Pr,Qr
R-L
Ps,Qs
β
v
Converter 3
Conv
n e
v rt
r er 3
r
FESS
Pst
Flywheel
Induction
Machine
Grid 
 DTC
 DTC
 DPC
i red
Fig. 1 The system under study
1112
I. Hamzaoui et al.
Where ρ is the air density, v is the wind speed, Rt is the turbine radius, and Ωt is
the angular velocity of the wind turbine. The power coefﬁcient Cp is a function of
the speed ratio λ and the pitch angle of the blades β (Fig. 2). Here, it is given by the
expression:
Cp λ; β
ð
Þ ¼ 0:5176 116
λi
 0:4  5


:exp 21
λi


þ 0:0068λ
ð2Þ
with: 1
λi ¼
1
λþ0:08:β  0:035
β3þ1.
The speed ratio λ is expressed by the following relationship:
λ ¼ ΩtRt
v
ð3Þ
To maximize the power converted, the turbine speed must be adjusted in relation
to the wind speed. This is obtained if the relative velocity λ is equal to its optimal
value (λopt ¼ 8.1, Cpmax ¼ 0.48, β ¼0), as shown in Fig. 2.
The purpose of this control is the ongoing search for the Maximum Power Point
Tracking (MPPT). To achieve this, the value of the electromagnetic torque refer-
ence is set at the maximum value given (Hamzaoui et al. 2011):
Temref ¼ 1
2
ρπRt5Cpmax
λ3
optG3
Ω2
mec
ð4Þ
0
5
10
15
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Cp
0
5
10
15
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
beta=20
beta=15
beta=10
beta=5
beta=2
beta=1
beta=0
Cpmax
landa-opt
Fig. 2 Representation of Cp as a function of λ for different values of β
Contribution to the Control Power of a Wind System with a Storage System
1113
3
Direct Torque Control of the Double-fed Induction
Generator (DFIG)
A three-phase inverter at two levels of voltage has six switching cells giving eight
possible switching states. Among these eight vectors that can be applied to the
bounds of the DFIG, the vector ν0 and ν7 drive at zero voltage, others give, in the
reference α–β, the six directions that the voltage vector can take (Hamzaoui et al.
2013). The direct torque control (DTC) aims to direct torque control of the DFIG,
by applying different voltages to vectors of the inverter.
The controlled variables are the rotor ﬂux and the electromagnetic torque, which
are controlled by regulator hysteresis. This is to maintain both the instantaneous
magnitudes within a band around the desired value. The output of these regulators
determines the optimal voltage vector of the inverter to be applied to each switching
time (Casadei et al. 2002).
The structure of the direct control of torque is illustrated in Fig. 3.
In the αβ reference, the rotor ﬂux components of the DFIG are determined as
follows (Ca
´rdenas et al. 2013):
Φrα,β ¼
Zt
0
vrα,β  Rrirα,β


dt
Φr ¼
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
Φ2
rα þ Φ2
rβ
q
θr ¼ arctg Φrβ
Φrα


8
>
>
>
>
>
>
>
<
>
>
>
>
>
>
>
:
ð5Þ
The electromagnetic torque is estimated as:
Φr-ref
Φr-ref
Sector
Arctang
+
Reference flux
Tem-ref
Torque and Flux 
estimator
+–
–
+
+
Er_Flux
Er_Torque
Tem-est
a
r
F
b
r
F
Program 
optimization
and limitation 
0
50
100
150
200
250
300
0
1000
2000
3000
4000
5000
6000
7000
8000
Vitesse mécanique (rad/s)
Puissance mécanique (W)
9.8 m/s
7  m/s
5 m/s
9  m/s
Φr-est
Converter 1
DFIG
Uc2
i rond
β
v
DTC
S a S b S c
Fig. 3 Structure of the direct torque control applied to a double-fed induction generator (DFIG)
1114
I. Hamzaoui et al.
TemDFIG ¼ p Φrαirβ  Φrβirα


ð6Þ
The reference torque is obtained from the wind MPPT technique (see Eq. (5)).
The reference ﬂow is obtained by blocking deﬂuxing (Idjdarene et al. 2011).
Φref ¼
Φn if
Ω
j j  Ωn
j
j
Φn
Ωn
Ω
j j
if
Ω
j j > Ωn
j
j
8
<
:
ð7Þ
A decision table (Table 1) allows determination of the switching states in the
function of the output of each hysteresis controller and the number of the sector θi in
which the rotor ﬂux vector is found.
Figure 4 shows an example of a rotor ﬂux vector that is located in sector θ1.
4
Flywheel Energy Storage System (FESS)
With the aim of involving a variable wind speed services system, energy storage of
the inertial type is considered with a ﬂywheel mechanically coupled to an asyn-
chronous machine and driven by a power converter, as shown in Fig. 1. Ev energy
stored in the ﬂywheel Jv is shown by the expression:
Table 1 Table switching
1
2
3
4
5
6
Comparator
CFlu ¼ 1
CT ¼ 1
V2
V3
V4
V5
V6
V1
2 level
CT ¼ 1
V0
V7
V0
V7
V0
V7
CT ¼ 1
V6
V1
V2
V3
V4
V5
3 level
CFlu ¼ 1
CT ¼ 1
V3
V4
V5
V6
V1
V2
2 level
CT ¼ 1
V0
V7
V0
V7
V0
V7
CT ¼ 1
V5
V6
V1
V2
V3
V4
3 level
b
a
V0
Vi+3
Vi-2
Vi+2
Vi+1
Vi
Vi-1
r
F
Tem
r
F
Tem
r
F
Tem
r
F
Tem
2
q
3
q
4
q
5
q
6
q
Fig. 4 Selection voltage
vector
Contribution to the Control Power of a Wind System with a Storage System
1115
Ev ¼ 1
2 JvΩv
2
ð8Þ
To calculate the inertia of the wheel, it is based on a power supply for a time Δt:
we want the storage to provide the inertial rated PIMn during a time Δt when energy
is needed Ev ¼ PIMn. Δt is given by: ΔEv ¼ 1/2Jv  ΔΩv
2 and ΔΩ2
v ¼ ΔΩ2
vMAX  Δ
Ω2
vMIN , so we get:
Jv ¼
2PIMnΔt
Ω2
vMAX  Ω2
vMIN


ð9Þ
Two areas of operation for the electrical machine are shown in Fig. 5 (Kairous
et al. 2010).
For 0  Ωv  ΩvN, the nominal torque of the machine is available, but the
maximum power is variable, depending on the speed (PIM ¼ k Ωv), and smaller than
the nominal power. This area does not have much interest for FESS.
For Ωv > ΩvN the power is at a maximum and corresponds to the nominal power
of the machine; the electromagnetic torque is inversely proportional to the speed of
rotation (Tem ¼ k/Ωv). This is the area of operation used in FESS because here the
power of the machine is available for any speed.
The induction machine with inertial storage will be used in the speed range
below ΩvN  Ωv  2ΩvN, thus allowing operation at a rated power constant.
From a reference power, Pv-ref., one can deduce the torque electromagnetic
reference of the machine, Tem-ref, causing the ﬂywheel to measure the speed by
the number of rotations, Ωv-mes.
Temref ¼ Pvref
Ωvmes
ð10Þ
The electromagnetic torque reference is limited to nominal torque for the speed
range including between 0 and the nominal speed; beyond the rated speed, the
Fig. 5 Allure power and
torque versus speed
1116
I. Hamzaoui et al.
torque will decrease in order to keep the product Tem-ref  Ωv constant. The torque
reduction is carried out by deﬂuxing of the machine beyond the synchronous speed;
see Eq. (7).
4.1
Direct Torque Control of the FESS
This functions on the same principle of DTC applied to the rotor of the DFIG. The
estimated torque values Tem and stator ﬂux Φs are compared, respectively, at their
reference values Tem-ref and Φs-ref, using two non-linear elements of a kind of
hysteresis in order to ascertain information of the trends in the ﬂux and torque. A
decision table (Table 1) allows determination of the switching states in the function
of the output of each hysteresis controller and the number of sector θi wherein the
stator ﬂux vector is found (Idjdarene et al. 2011).
Figure 6 shows the general diagram of the DTC applied to a FESS. Figures 7 and
8 llustrate the operation of the inertial storage system with direct torque control.
The value of the inertia coefﬁcient was calculated for a speed range of between
157 and 314 rad/s, and a rated power of 1.5 kW during a time corresponding to
2.5 s.
The initial velocity of the steering wheel is ﬁxed at 157 rad/s. When the storage
reference power, Pv-ref, is set at 1.5 kW, the speed increases to 157–314 rad/s, and
the system stores energy. When the power is ﬁxed to 1.5 kW, the speed decreases
to 314–157 rad/s, and the system provides energy.
The main function of the FESS is to smooth the power output of the wind
generator, which can cause several problems in the network. To reduce to a
Arctang
Reference speed 
w
computer 
Péol
+
Uc1
Converter 3
ı
Induction
 machine
Uc2
Flywheel
FESS
Pst
Pgref
PV-ref
+–
Reference flux
T em-ref
Φr-ref
Torque and Flux 
estimator
+–
––
+
+
Sector
Er_Flux
Er_T orque
T em-est
a
r
F
b
r
F
Sec
S
tor
Fig. 6 Block diagram of direct torque control of inertial storage
Contribution to the Control Power of a Wind System with a Storage System
1117
minimum the ﬂuctuations of this power, the FESS must ensure compensation for
variations in wind power.
The reference power of the FESS is determined by the difference between the
power generated by the wind generator, Pwind , and the power it takes to deliver to a
network or on isolated loads, Pregl, according to the principle illustrated in Fig. 9.
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
-2500
-2000
-1500
-1000
-500
0
500
1000
1500
2000
Time (s)
Power (W)
Fig. 7 The power delivered or absorbed by the IM
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
1400
1600
1800
2000
2200
2400
2600
2800
3000
3200
Time (s)
Speed (tr/min)
Fig. 8 Speed of the ﬂywheel
1118
I. Hamzaoui et al.
4.2
Direct Control of Power for the Grid Side Converter
The three-level NPC converter to which a connected source of wind energy is
associated with the ﬂywheel energy storage system can be represented by 27 pos-
sible states of switching, 18 active vectors, and three inactive vectors (see Fig. 10).
These vectors are expressed by the following equation (Chen et al. 2008;
Kulikowskli and Sikorski, 2010):
Vm ¼ 1
3
Uc:ej m1
ð
Þπ
3, for
m ¼ 1; 2; . . . 6
f
g
ﬃﬃﬃ
3
p
:Uc:ej m8
ð
Þπ
3, for
m ¼ 8; 9; . . . 13
f
g
2:Uc:ej m15
ð
Þ, for
m ¼ 15; 16; . . . 20
f
g
8
>
<
>
:
ð11Þ
Where: Uc is DC link voltage, m 2 0,1,2..,“0” is azero vector, and m is the vector
number.
The DPC consists of a selective control vector, using a switching table. This is
based on the digital error, Sp, Sq of the instantaneous active and reactive power, as
well as on the estimated angular position of the voltage determined by:
Wind
System
Distribution
Network
Flywheel Energy
Storage System
Fig. 9 Smoothing power using the Flywheel Energy Storage System (FESS)
V1
V2
V3
V4
V5
V6
V8
V9
V10
V11
V12
V13
V15
V16
V17
V18
V19
V20
n1
n2
n3
n4
n5
n6
n7
n8
n9
n11
n10
n12
V0 V7 V14
Fig. 10 Space vector
diagram of a three-level
inverter
Contribution to the Control Power of a Wind System with a Storage System
1119
θ ¼ Arctg vα
vβ


ð12Þ
With Vα-β being the grid voltage in the stationary (α, β) coordinate.
The stationary coordinates (α, β) are divided into 12 sectors expressed as
(Bouaﬁa et al. 2009):
n  2
ð
Þπ
6 < sec tn < n  1
ð
Þπ
6 n 2 1; ::; 12
f
g
ð13Þ
The active power reference is given by the regulation of the DC bus, and the
reactive power reference depends on the reactive power desired by the grid. The
reactive power reference is set to zero to control the unity power factor (Hamzaoui
et al. 2011).
Figure 11 shows the overall conﬁguration of the direct power control of three-
level converter.
Direct power control (DPC) is developed by analogy with the direct torque
control (DTC) of electrical machines, instead of the torque and ﬂux, the instanta-
neous active and reactive power are control variables. This ensures the PWM
rectiﬁer has a sinusoidal current absorption with a decoupled control of active
and reactive power.
P
^ ¼ L dia
dt ia
dib
dt ib
dic
dt ic


þ
Vdc1 Sa1ia þ Sb1ib þ Sc1ic
ð
Þþ
⋮
Vdc2 Sa2ia þ Sb2ib þ Sc2ic
ð
Þ
ð14Þ
DDi1
K
IK
Si2
DDi0
Si1
Si3
Si4
+
Urect
_
R-L
N
ec
eb
SP
+ -
n
 Sector
ea
+ -
qref
Pref
Sq
power and voltage
Estimator bloc 
Pest
qest
Arctang
ib
ia
Va
Vb
UC1
UC2
a
b
V1
V2 V9
V3
V10
V4
V11
V5 V12
V13
V6
V21
V22
V23
V24
V25
V26
V15
V16
V17
V18
V19
V20
V8
+
+
U
cref
-
+
PI
icref
DDi1
K
IK
Si2
DDi0
Si1
Si3
Si4
+
Urec
ec
e t
_
UC1
UC2
ia
ib
ic
DC Bus control 
voltage corrector 
iond
Sa1 Sb1 Sc1 Sa2 Sb2 Sc2
Fig. 11 Diagram of extended direct power control system
1120
I. Hamzaoui et al.
q
^ ¼ 1ﬃﬃﬃ
3
p 
3L dia
dt ic  dic
dt ia


 vdc1

Sa1 ib  ic
ð
Þ þ Sb1 ic  ia
ð
Þ
þ Sc1 ia  ib
ð
Þ

 vdc2

Sa2 ib  ic
ð
Þ þ Sb2 ic  ia
ð
Þ þ Sc2 ia  ib
ð
Þ

ð15Þ
With: ea, eb, ec three-phase power-source voltages; VA, VB, and VC are terminal
voltages of the PWM rectiﬁer; ia, ib, and ic are the line currents of three-phase grid.
L and R represent inductance and resistance of interconnecting reactors, respec-
tively. Sa1, Sb1, Sc1, Sa2, Sb2, and Sc2 are switching states of the converter.
The value of capacitors connected in series in the DC side is C, the voltages of
which are Uc1 and Uc2.
The active and reactive powers are estimated using the switching state of the
converter, the three-phase line currents, the DC bus voltage, and the inductance of
the reactors. This can be derived as:
Errors between the commands and the estimated feedback power are input to the
hysteresis comparators and digitized to the signals Sp and Sq, where:
Sp ¼ 1
if
Pref Pest > hp
Sp ¼ 0
if
hp < Pref P est < hp
Sp ¼ 1
if
Pref Pest < hp
8
>
<
>
:
Sq ¼ 1
if
qref qest > hq
Sq ¼ 0
if
hq < qref qest < hq
Sq ¼ 1
if
qref qest < hq
8
<
:
ð16Þ
Once the logic outputs of the hysteresis comparators are established, and fol-
lowing the sector number of the estimated voltage vector, the vector of voltages to
be applied to the input of the rectiﬁer is selected from the switching table, as shown
in Table 2.
The synthesis of the switching table (Table 2) based on the signs is derived from
active and reactive power in each sector, given by the Eq. (17), and presented in
Figs. 12 and 13.
Table 2 Table switching
Sectn
1
2
3
4
5
6
7
8
9
10
11
12
1
1
V8
V15
V9
V16
V10
V17
V11
V18
V12
V19
V13
V20
0
V15
V9
V16
V10
V11
V17
V12
V18
V13
V19
V8
V20
1
V5
V5
V6
V6
V1
V1
V2
V2
V3
V3
V4
V4
0
1
V3
V3
V4
V4
V5
V5
V6
V6
V1
V1
V2
V2
0
V25
V26
V26
V21
V21
V22
V22
V23
V23
V24
V24
V25
1
V23
V24
V24
V25
V25
V26
V26
V21
V21
V22
V22
V23
Contribution to the Control Power of a Wind System with a Storage System
1121
ΔPi ¼
ΔPi
1
LE:
ﬃﬃﬃ
2
3
r
UC
¼
E
ﬃﬃﬃ
2
3
r
UC

cos θ
ð Þ:
vαi þ sin θ
ð Þ:
vβi


Δqi
¼
Δqi
1
LE:
ﬃﬃﬃ
2
3
r
UC
¼
cos θ
ð Þ:
vβi  sin θ
ð Þ:
vαi


ð17Þ
with 
vαi ¼
vαi
ﬃﬃﬃﬃﬃﬃﬃﬃ
2
3UC
q
 
vβi ¼
vβi
ﬃﬃﬃﬃﬃﬃ
2
3UC
p
-30
0
30
60
90
120
150
180
210
240
270
300
330
-1
-0.5
0
0.5
1
1.5
V3
V5
V6
V10
V11
V13
V12
V8
V4
V17
V18
V19
V20
V9V2
V0,V7,V14
V16
V15
V1
Sect 1
Sect 2
Sect 3
Sect 4
Sect 5
Sect 6
Sect 7
Sect 8
Sect 9
Sect 10
Sect 11
Sect 12
Fig. 12 Change in active power
-30
0
30
60
90
120
150
180
210
240
270
300
330
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
V1
V3
V4
V5
V6
V2
V9
V8
V12
V13
V10
V15
V11
V16
V17
V20
V18
V19
V0,V7,V14
Sect 2
Sect 3
Sect 4
Sect 5
Sect 6
Sect 7
Sect 8
Sect 9
Sect 10
Sect 11
Sect 12
Sect 1
Fig. 13 Change in reactive power
1122
I. Hamzaoui et al.
1
2
3
4
5
6
7
8
9
10
-60
-40
-20
0
20
40
60
80
Time (s)
Torque (N.m)
0
2
4
6
8
10
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
Time (s)
Flux (wb)
0
2
4
6
8
10
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6
Time (s)
Sector
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
Flux-alpha
Flux-beta
Fig. 14 Torque and rotor
ﬂux of the double-fed
induction generator (DFIG)
Contribution to the Control Power of a Wind System with a Storage System
1123
0
2
4
6
8
10
-15
-10
-5
0
5
10
15
Time (s)
Torque (N.m)
0
2
4
6
8
10
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Time (s)
Flux (N.m)
0
2
4
6
8
10
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6
Time (s)
Sector
-1.5
-1
-0.5
0
0.5
1
1.5
-1.5
-1
-0.5
0
0.5
1
1.5
Flux-alpha
Flux-beta
Fig. 15 Torque and stotor
ﬂux of the induction
machine (IM)
1124
I. Hamzaoui et al.
We have shown the simulation results with the two control techniques, DTC
(direct torque control) and DPC (direct power control), applied to the system for
converting wind energy with storage, as presented in Fig. 1. FESS with an IM of
1.5 kW is associated with a wind generator of 7.5 kW.
A good dynamic torque has an estimated value that is acceptable for the setpoint
value, for DFIG and IM (Figs. 14a and 15a). The module rotor ﬂux of DFIG, and the
module of the stator ﬂux IM are shown in Figs. 14b and 15b, following their
reference values determined by a deﬂuxing block. The evolution of these ﬂows
(α,β), present a circular trajectory with an amplitude ﬁxed at its nominal value
(Figs. 14d and 15d). The power delivered by wind as present in Figs. 16. and 17
shows the active and reactive power passed to the network, and their references.
0
2
4
6
8
10
-8000
-6000
-4000
-2000
0
2000
4000
6000
8000
Time (s)
Power (w)
Fig. 16 Power delivered by
the wind
1
2
3
4
5
6
7
8
9
10
-10000
-8000
-6000
-4000
-2000
0
2000
4000
Time (s)
P(w) , Q (var)
Q grid
Pgrid
Fig. 17 Power delivered to the network
Contribution to the Control Power of a Wind System with a Storage System
1125
Figure 18 shows the power storage unit. This power can be positive or negative
depending on wind conditions that allow either charging or discharging, and is
limited to1.5 kW. When the reference power is positive, FESS stores electric
energy as the speed of the ﬂywheel increases. FESS captures the electrical energy,
and when the reference power becomes negative the ﬂywheel speed decreases
(Fig. 19). The setting of the DC bus is ensured by the three-level converter side
grid, which is kept constant (Fig. 20), and the trajectory of the end of the voltage
vector (estimated) is circular (Fig. 21).
0
2
4
6
8
10
-2000
-1500
-1000
-500
0
500
1000
1500
2000
Time (s)
power (w)
Fig. 18 Electric power of the Flywheel Energy Storage System (FESS)
0
2
4
6
8
10
140
150
160
170
180
190
200
210
Time (s)
Speed (rad/s)
Fig. 19 Speed of the ﬂywheel
1126
I. Hamzaoui et al.
5
Conclusion
This article presents two control techniques: DTC (direct torque control) and DPC
(direct power control), which are dedicated to a wind turbine with a variable speed
based in DFIG with storage, in order to achieve better performance. Mathematical
models of the studied system are presented. DTC applied in DFIG control and IM
used in FESS present a torque control of high performance and dynamics of high
importance while keeping good accuracy in control. This precision is based on the
right choice of the voltage vector, which plays a primordial role in the regulation of
the ﬂux vector, and therefore torque. The IM used in FESS very frequently changes
its operating mode (motor or generator).The FESS is controlled by a reference
power calculated according to the generated power and the power we want to send
to the network. The NPC three-level converter control by the DPC is only used to
0
1
2
3
4
5
6
7
8
9
10
0
100
200
300
400
500
600
700
Time (s)
Uc1+Uc2 (v)
0
1
2
3
4
5
6
7
8
9
10
0
50
100
150
200
250
300
350
Time (s)
Uc1 and Uc2 (v)
Fig. 20 DC bus voltage
Contribution to the Control Power of a Wind System with a Storage System
1127
implant control strategies, allowing the wind generator + FESS to provide system
services (control the mains voltage, control the frequency, and control the reactive
power). The effectiveness of these techniques is veriﬁed by simulation with Matlab/
Simulink.
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4
4.05
4.1
4.15
4.2
0
2
4
6
8
10
12
Time (s)
Sector
-300
-200
-100
0
100
200
300
-300
-200
-100
0
100
200
300
V-alpha
V-beta
1.22
1.24
1.26
1.28
1.3
1.32
1.34
-400
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-100
0
100
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Contribution to the Control Power of a Wind System with a Storage System
1129
Performance Evaluation of SWRO
Desalination Plant at Skikda (Algeria)
F. Ammour, R. Chekroud, S. Houli, and A. Kettab
1
Introduction
During the last decades, water resources have been intensively used in Algeria,
added to a period of intense drought occurring in the zone, leading to water scarcity
(Metiche et al. 2010).
Seawater and brackish water desalination is a strategic option for Algeria which
has implemented an ambitious program of desalination of seawater with a produc-
tion capacity of 2260 million M3 (Metiche et al. 2010; Kehal 2001).
Due to recent developments in membrane technology, the trend in the desalina-
tion industry is to use reverse osmosis (RO) for desalting seawater.
One of these plants is investigated in this study. It is an SWRO desalination plant
of Skikda, located in the east of Algeria, commissioned in 2009, with a capacity of
100,000 m3/day.
The most serious problem in BWRO and SWRO plants’ operation is the
complexity to control membrane fouling and scaling mainly due to frequent
variation of quantity/quality of raw water (Arras et al. 2009).
The objective of this paper is to study the performance of the plant and identify
the different shortcomings. The plant performance was established from the oper-
ation and maintenance data as well as from visits made to the plant.
F. Ammour (*) • R. Chekroud • S. Houli
Laboratoire de Mobilisation et Valorisation des Ressources en Eau (M.V.R.E.), Ecole
Nationale Supe
´rieure de l’Hydraulique (ENSH), Souma^
a, Algeria
e-mail: fasonidz@yahoo.fr
A. Kettab
Laboratoire des Sciences de l’eau, Ecole Nationale polytechnique d’Alger, El Harrach, Algeria
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_77
1131
2
Plant Description
2.1
Study Site
Skikda SWRO plant is located in the Sonatrach company industrial complex in the
east of Algeria (Fig. 1).
2.2
Seawater Intake
The choice of the point of collection was decided after various studies to obtain a
good quality of water all year round.
An SWRO desalination plant studied includes an open seawater intake. The
water captured at a distance of 1431 m is conveyed gravitationally by canal of 2 m
of diameter and collected in a tank.
The intake inlet point is located at a depth of 9 m.
2.3
Raw Water Characteristics
Raw water analysis is presented in Table 1:
Fig. 1 Location of Skikda SWRO plant
1132
F. Ammour et al.
2.4
Operating Conditions
Operating conditions at Skikda SWRO desalination plant are shown in Table 2.
2.5
Pretreatment and Posttreatment
The pretreatment has a prevalent importance as well on physicochemical and
microbiological qualities of permeate as on the longevity of the membrane.
On arrival at the station, the raw water must undergo disinfection. NaOCl is used
at the concentration of 5 ppm. The residual chlorine is then removed by dosing of an
average of 1.8 ppm of sodium bisulﬁte.
Sulfuric acid is dosed as carbonate antiscalant to destroy carbonate/bicarbonate
in the feed at the concentration of 37.7 ppm.
Ferric chloride is employed in the coagulation step at the concentration of 6 ppm.
The seawater is ﬁltered through 25 pressure sand ﬁlters (the ﬁrst ﬁltration) to
remove suspended solids. Then it passes through 15 anthracite ﬁlters (second
ﬁltration).
Ten cartridge ﬁlters of 5 micron are installed just before the high pressure pumps
to protect RO membranes from fouling. This pretreated seawater feed has an
SD1 < 3.
The quality of the produced permeate does not meet all end-use requirements,
hence the need for its additional treatments. The water stabilization and alkalinity
Table 1 Raw water
composition
Physicochemical analysis
Ca2+ (mg/l)
464.21
Mg2+ (mg/l)
1278.94
Na+ (mg/l)
12301.65
K + (mg/l)
487.89
Al3+ (mg/l)
1
Sr2+ (mg/l)
5.12
SiO2 (mg/l)
25.42
Cl (mg/l)
21611.96
SO4
2 (mg/l)
3087.03
HCO3
 (mg/l)
133.45
F (mg/l)
1.37
CO3
2 (mg/l)
0.39
Alkalinity (mg/l CaCO3)
110.08
Turbidity (NTU)
1.3
T (C)
27
PH
6.49
Conductivity (μS/cm)
54463.21
STD (mg/l)
39377.53
Performance Evaluation of SWRO Desalination Plant at Skikda (Algeria)
1133
adjustment is done by the passage of permeate on a bed of dolomite, to increase the
pH from 5.03 to 8.08.
Product chlorination is done as a ﬁnal step to prevent growth of microorganism
during water distribution or storage.
2.6
RO Units
Filtered water is aspired by high pressure pumps and is driven back with a pressure
of 67.8 bars, before being sent toward the tubes of pressure for the separation of salt
and water. Each module contains seven spiral wound polyamide membranes. The
minimum salt rejection is 99.45%.
The brine is discharged into the sea, at a distance of 714 m, with insufﬁcient
dilution.
The technical characteristics of the high pressure system are shown in Table 3.
3
Plant Performance Evaluation and Improvements
Quality parameters of permeate are grouped in the following table. The product
water from this plant is within Algerian potable water standards (Table 4).
As mentioned earlier, the Skikda plant is built with polyamide membranes and
utilizes seven elements, per pressure vessel.
Membrane cleaning was carried out four times per year. Chemicals used in
cleaning membrane are limited to three: citric acid, soda, and biocide.
Cleaning products must be neutralized before their discharge at sea.
Since Skikda plant commissioning, the membranes have been changed only
once. Indeed, the chlorine and bisulﬁte dosing errors caused serious membrane
damages during the ﬁrst year of the Skikda plant operation. Membranes used in the
Skikda plant are very sensitive to chlorine, which should be completely removed, to
zero chlorine level, from the feed; otherwise the membranes will be damaged.
Table 2 Operating
conditions
Design capacity (m3/day)
100,000
Hours of operation per day (h)
24
Hours of operation per year (day)
355
Seawater intake capacity (m3/h)
8.998
Seawater salinity (ppm)
39.381
Seawater temperature (C)
24
Volume of rejected water (m3/h)
4831.56
Recovery (%)
47
The quality of the permeate (ppm)
<400
Pressure OI (bars)
67.8
1134
F. Ammour et al.
The Major problem at the Skikda plant is biological fouling of the cartridge ﬁlter
and the desalination unit. One of these reasons is the reduction of the chlorine
dosing due to the incident mentioned above.
The second possible reason for the biological fouling is that the chlorine
residence time is insufﬁcient to cause 100% kill of microorganisms, allowing for
the passage of some live microorganisms to the cartridge ﬁlter and the desalination
part of the plant (Ata et al. 1989; Fernandez-Torquemada 2005; Kamel and Chheibi
2001).
This problem can be solved by optimizing the chlorination-dechlorination stage
or by replacing chlorine with copper sulphate (CuSO4).
Scaling phenomena, fouling, and chemical degradation affect the performance
of the desalination System (Maurel 2001; Ammour 2014):
•
Decrease in ﬂow.
•
Increase in salinity.
•
Increase in pressure losses.
•
Reduction of the lifetime of membranes.
The second major problem at the Skikda plant is in the treatment plant, which, in
times of ﬂooding, failed to produce the proper SDI, i.e., SD1<3. This deterioration
of water quality is due to an increase in turbidity and to the haulage of suspended
Table 3 Technical characteristics of high pressure system
Total pumped water ﬂow
4.317 m3/h
Number of pumps in operation
5
Minimum temperature of raw water
18 C
maximum temperature of raw water
27 C
Seawater density
1.0257
Seawater viscosity
0.9266
Pressure losses in pipings and valves
1.4
Pressure at the inlet of the membranes
66.1 bars
Pressure of aspiration of the pump
2.00 bars
Total pump output pressure
67.8 bars
Pump efﬁciency
84.5%
Number of membranes by module
7
Type of membrane
Arrollamiento en espirale
Material of membrane
Polyamide aromatique
Maximum speciﬁc feed
13.96 l/m2/h
Table 4 Quality parameters
of permeate
TDS (mg/L)
<400
Alcalinity (mg/l CaCO3)
65
Hardness (mg/l CaCO3)
50–65
pH
8–8.5
LSI
0–0.4
Performance Evaluation of SWRO Desalination Plant at Skikda (Algeria)
1135
sediment and the colloidal matters by the Wadi Safsaf, whose mouth is located near
the station. The SDI at the entrance of the membrane increases up to 5, which
requires the complete stop of the station in winter.
This problem can be solved by increasing the dose of coagulant and injecting a
ﬂocculant.
Another problem encountered in the Skikda plant is material corrosion in
pretreatment, especially in the cartridge ﬁlters and sand ﬁlters, as shown in
Figs. 2 and 3.
This corrosion is due to the washing of sand and anthracite ﬁlters by the brine
and the quality of the material chosen for the joints, ﬁttings, and valves.
4
Conclusions
The pretreatment has a prevalent importance as well on physicochemical and
microbiological qualities of permeate as on the longevity of the membrane and
equipment. This needs a good operation and maintenance program.
In this paper the performance evaluation of SWRO desalination plant was
carried out, and recommendations are suggested to resolve these problems. The
plant performance was established from the operation and maintenance data as well
as from visits made to the plant.
Fig. 2 Corrosion in the
cartridge ﬁlters
1136
F. Ammour et al.
The SWRO plant supplies a good quality drinking water to Skikda town and
Sonatrach company industrial complex. The product water is within Algerian
potable water standards.
The ﬁrst major problem at the Skikda plant is biological fouling of the cartridge
ﬁlter and the desalination unit. This problem can be solved by optimizing the
chlorination-dechlorination stage or by replacing chlorine with copper sulphate
(CuSO4).
The second major problem is the deterioration of water quality and the SDI
increase up to 5 at the entrance of the membrane, which requires the complete
stoppage of the station in winter.
This problem can be solved by increasing the dose of coagulant and injecting a
ﬂocculant.
Another problem encountered at the Skikda plant is material corrosion in
pretreatment, especially in the cartridge ﬁlters and sand ﬁlters. This corrosion is
due to washing of sand and anthracite ﬁlters by the brine and the quality of the
material chosen for the joints, ﬁttings, and valves. Also, the plant performance is
rated well without serious consequences on equipment and material over the 5 years
of operation.
Acknowledgments This research was supported by this project and has been made possible
through the cooperation of the National High School for Hydraulic (ENSH) and ADE-ALGERIA,
which is gratefully acknowledged.
The authors wish to thank also the industrial complex of Sonatrach Company, for providing
their laboratory facilities.
Fig. 3 Corrosion in sand
ﬁlters
Performance Evaluation of SWRO Desalination Plant at Skikda (Algeria)
1137
References
Ammour, F., Lounes, S., Houli, S., Kettab, A.: Environmental impact of desalination plant of Bou
Smail (Algeria). 13th International Conference on Clean Energy, Istanbul – Turkey (2014)
Arras, W., Ghaffour, N., Hamou, A.: Performance evaluation of BWRO desalination plant-a case
study. Desalination. 235, 170–178 (2009)
Ata, H., Al jarah, S., Al -lohib, T.: Performance evaluation of SWCC and SWRO plants.
Desalination. 74, 37–50 (1989)
Fernandez-Torquemada, Y., Sanchez-Lizaso, J.L., Gonzalez-Correa, J.M.: Preliminary results of
the monitoring of the brine discharge produced by the SWRO desalination plant of Alicante
(SE Spain). Desalination. 182, 395–402 (2005)
Kamel, F., Chheibi, H.: Performances de la Station de Dessalement de Gabes (22,500 mVj) apres
cinq ans de fonctionnement. Desalination. 136, 263–272 (2001)
Kehal, S.: Re
´trospective et perspectives du dessalement en Alge
´Ørie. Desalination. 42, 135–136
(2001)
Maurel, A.: Dessalement de l’eau de mer et des eaux saum^
atres, e
´ditions Technique & Documen-
tation Lavoisier, France (2001)
Mitiche, R., Metaiche, M., Kettab, A., Ammour, F.: Desalination in Algeria: current situation and
development programs. Desalin. Water Treat. 14, 259–264 (2010)
1138
F. Ammour et al.
Study of a PV-Electrolyzer-Fuel Cell Hybrid
System
Amina Gueridi, Abdallah Khellaf, Djaffar Semmar, and Larbi Loukarﬁ
1
Introduction
The use of renewable energies, with hydrogen as a means of storage, offers
autonomy of electric power production (Ipsakisa et al. 2008).
There are several types of hybrid electric systems of autonomous productions
like the photovoltaic-fuel cells (PV-FC) (Ganguly et al. 2010), wind energy-fuel
cells (W-FC) (Khan et al. 2005), or photovoltaic-wind energy-fuel cells (PV-W-FC)
(Hatti et al. 2011).
In the PV-FC hybrid systems, the electrical energy produced by the photovoltaic
generators is used directly to feed the load, whereas the excess energy is converted
by electrolysis system to hydrogen. The produced hydrogen can be converted into
electricity via fuel cells during the period when electrical energy produced from
solar PV is not sufﬁcient.
Several softwares are used to size and model the hybrid systems (Vechiu 2005).
Among these softwares, we can name HOMER, SOMES, RAPSIM, SOLSIM, and
INSEL. All these softwares are used to optimize the hybrid systems. However, the
optimization method differs from one software to the other.
A. Gueridi (*)
University of Blida, Faculty of Science, Department of Physics, Blida, Algeria
e-mail: amina.gueridi@yahoo.com
A. Khellaf
Center of Development of Renewable Energies, Algiers, Algiers, Algeria
D. Semmar
University of Blida, Faculty of Technologie, Department of Mechanics, Blida, Algeria
L. Loukarﬁ
University of Chlef, Faculty of Technologie, Department of Mechanics, Chlef, Algeria
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_78
1139
The object of this work concerns the sizing of an autonomous PV-FC energy
system capable of meeting the electric needs of the University of Chlef using the
software of sizing HOMER (Givler and Lilienthal 2005).
2
PV-FC System
A schematic representation of the system under consideration is shown in Fig. 1. In
this system, the photovoltaic ﬁeld feeds directly the user. The excess of energy is
stored under chemical form and used when needed. An electrolyzer is used to split
water into hydrogen and oxygen. The hydrogen is stored. When the photovoltaic
ﬁeld cannot supply the totality of the requisite electricity, the fuel cell is connected.
It generates the needed electricity by recombining the hydrogen and the oxygen
directly from the air. The fuel cell produces some pure water, which is stored to
furnish the electrolyzer.
3
Characteristics of the Region of Chlef
For this study, we used the ONM data. These data are measured at Chlef airport,
situated a few kilometers northeast from downtown Chlef. These data cover a
period of 10 years, extending from 1999 to 2008. Table 1 shows the various
geographical characteristics of Chlef measurement station.
Fig. 1 PV-FC hybrid
system model used in the
study
1140
A. Gueridi et al.
Concerning sunshine duration, the data have also been acquired from ONM.
These data under the form of monthly average daily sunshine duration cover a
10-year period from 1999 to 2008. By means of HOMER, the values of the
clearness index, and the characteristics of the measurement site, as reported in
Table 1, the daily average irradiation for each month of the year is determined. In
Fig. 2, the monthly evaluation of the irradiation (in yellow) and that of the clearness
index (in red) are represented.
For the temperature of the region of Chlef, as for the cases of meteorological
data and the sunshine duration data, the average monthly values of the measured
temperature for 15 years (from 1993 till 2007) are acquired from ONM. Figure 3
represents the variation in the average monthly temperature over a 15-year period
(from 1993 till 2007).
4
Electrical Load Variation of the University
For the proﬁle of consumption, we used data supplied by the company
SONELGAZ. They correspond to the monthly electric power consumed by the
University of Chlef during the year 2008. We can see, in Fig. 4, the monthly
evolution of the power over 1 year.
Table 1 Geographical characteristics of the meteorological station of Chlef
Latitude
Longitude
Roughness
Albedo
36120 N
1200 E
0.05
20%
0.0
0.2
0.4
0.6
0.8
1.0
Jan
Feb Mar Apr May Jun
Jul
Aug Sep Oct
Nov Dec
0
1
2
3
4
5
6
7
Daily Radiation (kWh/m²/d)
Global Horizontal Radiation
Clearness Index
Daily Radiation
Clearness Index
Fig. 2 Chlef solar resource graph
Study of a PV-Electrolyzer-Fuel Cell Hybrid System
1141
5
Technical Characteristics of Components
The position of the photovoltaic modules in relation to the sun inﬂuences directly
their energy production. It is very important to optimize their orientation (Labouret
and Villoz 2005). Two parameters deﬁne the position of the photovoltaic modules:
•
The orientation (azimuth): the cardinal point to which the active face of the
panel is turned (the South, the North, Southwest)
•
The tilt: the angle between the photovoltaic panel and the horizontal plan
For our system, we have considered several values of tilt. However, we have
chosen only the orientation due south, that is in an azimuth of 0.
The temperature inﬂuences directly the PV panels.
The characteristics of PV panels are presented in Table 2.
Electrolyzers are used to produce the necessary hydrogen to feed the fuel cells
for the generation of electricity required to meet the maximum loads of the
University of Chlef. Table 3 shows the technical characteristics of the used
electrolyzer.
Jan
Feb
Mar
Apr
May Jun
Jul
Aug Sep
Oct
Nov Dec Ann
10
15
20
25
30
35
Temperature (°C)
Ambient Temperature
max
daily high
mean
daily low
min
Fig. 3 Monthly evolution of the temperature
Jan Feb Mar
Apr May Jun
Jul
Aug Sep Oct Nov Dec Ann
0
100
200
300
400
500
600
Load (kW)
Seasonal Profile
max
daily high
mean
daily low
min
Fig. 4 Monthly average load variation at the university for the year 2008
1142
A. Gueridi et al.
In the PV-FC systems, converters are used to transform DC into AC. Table 4
shows the technical characteristics of the used converter.
The stored hydrogen must be sufﬁcient to meet the needs of fuel cells. Tank must
be sufﬁcient to store the hydrogen by matching the characteristics quoted in
Table 5.
Fuel cells have to be of a sufﬁcient power to feed the load’s maximum energy
demand by matching the characteristics of Table 6.
6
Economic Parameters
In order to carry out an economic study of our system, we have reported the most
important parameters we have used in Table 7. Among these parameters, we have
the average of the costs of initial investment, replacement, and operation and
maintenance of the various components of the PV-FC system. All the costs are
expressed in US dollars. We have also included in this table the technical charac-
teristics such as the cycle lifespan, the return, and the availability of these
components.
7
Results and Discussion
The simulation by HOMER software gives a list of the various possibilities of
installation of the system cross PV-FC, with the optimum possibilities sorted out
according to economic criteria. All these systems are capable of meeting the
University of Chlef’s needs in a continuous way.
Table 2 PV panel’s technical data (Accord Eco vert)
Technology
Thermal coefﬁcient of the
cell
NOCT
Return in the standard
conditions
Multicrystalline
silicon
0.5%/C
47 C
13%
Table 3 Electrolyzer
technical data
Characteristic
Type
Return
Cycle
Value
Alkaline/PEM
75%
25 years
Table 4 Converters technical data
Characteristic
Cycle
Return (DC/AC)
Return (AC/DC)
Value
15 ans
90%
90%
Study of a PV-Electrolyzer-Fuel Cell Hybrid System
1143
The optimal position of PV panels is found to be at a tilt of 45, as we reported in
Fig. 5. This unit consists of a solar PV ﬁeld, an electrolyzer, and a fuel cell system.
Table 8 shows the characteristics of the different components of this unit.
Table 5 Hydrogen tank
technical data
Characteristic
Value
Cycle
20 years
Reserved volume
95%
Table 6 Fuel cells
technical data
Characteristic
Value
Cycle
43,800 h
Speciﬁc consumption
0.03 kgH2/kWh
Way of functioning
DC
Table 7 Components cost data (KasheﬁKaviani et al. 2009)
Component
PV
panel
Electrolyzer
Hydrogen
tank
Fuel
cells
Converters
Initial capital cost
[US$ / unit]
6500
2000
1300
3000
800
Cost of replacement [US$ /
unit]
5500
1500
1200
2500
750
O&M [US$/unit-year]
65
25
15
175
8
Cycle [years]
20
20
20
5
15
Return
[%]
13
75
95
50
90
Unit
1 kW
1 kW
1 kg
1 kW
1 kW
Fig. 5 The optimization results with the optimum systems ranked
Table 8 Composition of the PV-FC system
Power crest of
the PV ﬁeld
[kW]
Rated output of
the converter [kW]
Rated output of the
electrolyzer [kW]
Size of the
reservoir of H2
[kg]
Rated output
of the FC [kW]
4500
600
2900
5700
600
1144
A. Gueridi et al.
The results of the optimization of the system are reported in Fig. 6. This ﬁgure
shows the energy production of the PV-FC hybrid system.
We can see that 92% of the annual energy produced by the system is photovol-
taic energy. Only 8% of this energy is produced by fuel cells. The load consumes
59% of the total consumed energy and 41% is consumed by the electrolyzer to
produce the necessary hydrogen to feed the fuel cell during the night and in case of
overcast conditions. Table 9 shows the energy balance of the autonomous PV-FC
system.
The results of the economic study of this system are detailed in Table 10. The
costs of every element of the system as well as the cost of kWh are reported.
Emissions
Greenhouse gas emissions by our hybrid PV-FC system is very low and can be said
to be negligible. This is because of the used fuel, which is hydrogen. This hydrogen
is produced through water electrolysis and using solar energy. The greenhouse
gases produced by the PV-FC system under consideration are reported in Table 11.
8
Conclusion
In this study, we have investigated the use of renewable energy to meet the energy
needs of the University of Chlef. We have determined the most suitable system
using HOMER software and the costs of the actual system components. The results
of the dimensioning models were used to determine the optimal sizing of the
conﬁgurations. PV panels and the electrolyzer are the major cost factors of the
proposed system after optimization.
Moreover, we have found an important excess in energy. This energy could be
used to produce more hydrogen. This hydrogen can be used at another energy
requirement, such as fuel for transport.
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
0
200
400
600
800
1,000
Power (kW)
Monthly Average Electric Production
PV
Fuel Cell
Fig. 6 Monthly energy yield from PV-FC system
Study of a PV-Electrolyzer-Fuel Cell Hybrid System
1145
References
Accord Eco Vert., www.accord-ev.com
Ganguly, A., Misra, D., Ghosh, S.: Modeling and analysis of solar photovoltaic-electrolyzer-fuel
cell hybrid power system integrated with a ﬂoriculture greenhouse. Energ. Buildings. 42,
2036–2043 (2010)
Givler, T., Lilienthal, P.: Using HOMER Software, NREL’s Micro Power optimization model, to
Explore the role of gen-Sets in small solar power systems; case study: Sri Lanka technical
report. National Renewable Energy Laboratory, USA (2005)
Hatti, M., Meharrar, A., Tioursi, M.: Power management strategy in the alternative energy
photovoltaic/PEM fuel cell hybrid system. Renew. Sust. Energ. Rev. 15, 5104–5110 (2011)
Ipsakisa, D., Voutetakisa, S., Seferlisa, P., Stergiopoulos, F., Papadopoulou, S., Elmasides, C.,
Keivanidis, C.: Energy management in a stand-alone power system for the production of
electrical energy with long term hydrogen storage. Comput. Aided Chemical Eng. 25,
1125–1130 (2008)
KasheﬁKaviani, A., Riahy, G.H., Kouhsari, S.H.M.: Optimal design of a reliable hydrogen-based
stand-alone wind/PV generating system, considering component outages. Renew. Energy. 34,
2380–2390 (2009)
Khan, F.I., Hawboldt, K., Iqbal, M.T.: Life cycle analysis of wind–fuel cell integrated system.
Renew. Energy. 30, 157–177 (2005)
Labouret, A., Villoz, M.: Energie solaire photovoltaı
¨que, Dunod Edit., 2nd Edition, Collection
“Technique et Inge
´nierie”, Paris, France (2005)
Vechiu, I.: Mode
´lisation et analyse de l’integration des energies renouvelables dans un re
´seau
autonome. Doctorat thesis, University of HAVER (2005)
Table 9 Energy contribution of different energy sources
Component
Production
consumption
Excess
PV
FC
Elec
Load
Energy [kWh/year]
7,303,934
674,588
1,064,448
1,508,294
5,238,289
[%]
92
8
41
59
65.7
Table 10 Energy contribution of different energy sources
Component
PV
Conv
Hydrogen
FC
System
Elec
Res H2
Cost [$]
35,100,000
726,000
7,250,000
9,120,000
14,932,030
67,127,992
Elementary
cost
0.240
$/kWh
–
166 $/kg H2
139 $/h
2.225
$/kWh
Table 11 Greenhouse gases emitted by PV-FC hybrid system
Emissions [kg/year]
CO2
CO
SO2
NOx
PV-FC
0
0
0
243
1146
A. Gueridi et al.
Experimental and Numerical Investigations
of a Compressed Air Energy Storage (CAES)
System as a Wind Energy Storage Option
Abdul Hai Alami, Camilia Aokal, and Monadhel Jabar Alchadirchy
1
Introduction
Energy supply has always been plagued with demand inconsistencies that ﬂuctuate
around an ever-growing base load. Energy storage became a necessity to normalize
the supply/demand deﬁcit, but the problem recently has been more pronounced with
the increased dependence on renewable resources that has an inherent unpredictable
supply (Huggins 2010). The challenge remains in capturing sufﬁcient amounts of
energy at peak times for later use and pass them through as little conversion steps as
possible from their intended end use. This involves the invention of new technol-
ogies that would maintain the energy quality and provide power with high
efﬁciencies.
To classify energy storage options, power density and energy density are two
key concepts to take into consideration (Abbaspour et al. 2013). The latter is used
when a moderate supply of energy needs to be consumed over a long period of time,
whereas the former is used when very high amounts of energy is needed for a short
period of time. An ideal method of storage would incorporate high amounts of both
energy densities and power densities. This, however, may not always be possible.
Figure 1 shows the attractive position where compressed air energy storage systems
rank, since discharge rates can be mitigated to supply a certain level of energy over
an extended period of time depending on air containers which can range from small
air bottles capable of holding a few liters under 2–3 bar up to underground caverns
occupying volumes of up to 150,000 m3 with storage pressures reaching up to
70 bar (Brown et al. 2014; Xia et al. 2015; Marano et al. 2012; Safaei et al. 2013).
A.H. Alami (*) • C. Aokal • M.J. Alchadirchy
Sustainable and Renewable Energy Department, University of Sharjah, PO Box 27272,
Sharjah, United Arab Emirates
e-mail: aalalami@sharjah.ac.ae
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_79
1147
The main challenge of CAES is the signiﬁcant temperature rise in the air as
compression work is supplied to the system (Xia et al. 2015; Madlener and Latz
2013), requiring cooling before proceeding with the discharge part of the cycle as
investigated by many authors (Safaei et al. 2013; Fertig and Apt 2011).
Modeling of CAES systems involved many assumptions depending on the size
and discharge rate (power density) of the setup. Smaller systems suffer insigniﬁcant
temperature change and thus can be modeled as isothermal (Sun et al. 2015) large
insulated systems can be modeled as adiabatic (Robb 2011; Wolf and Budt 2014).
Other systems utilize intercoolers to approach isothermal compression (Kushnir
et al. 2012) and such a system is called diabatic (Zhang et al. 2014a).
Modern CAES systems overlap with the energy density of batteries, as seen in
Fig. 1, but offer a higher power density depending on storage tank material and
capacity and on discharge rate from the nozzle. Such systems have advantages over
batteries by being nontoxic as they utilize no dangerous chemicals and have longer
lives (Zhang et al. 2014b).
The main objective of this work is to investigate and identify the important
parameters pertinent to compressed air energy storage (CAES) systems. A simpli-
ﬁed approach to theoretical modeling based on the ideal gas law is used with the
small-scale experimental setup that helps verify the model and provide a quantita-
tive system characterization. The system is upscalable and best suited for coupling
with existing wind energy installations as it is economical and easy to implement.
Fig. 1 Energy storage options classiﬁcation
1148
A.H. Alami et al.
2
Experimental Facility
2.1
Setup
The experimental setup consists of a steel thin-walled pressure vessel with a rating
of 8 bar (gauge), ﬁtted with a one-way valve for pressurization and a two-position
(close/open) nozzle for discharge. A pressure gauge reads the internal pressure of
the cylinder, while a manual reciprocating pump provides the required pressuriza-
tion. The nozzle discharges into a 12-bucket, 10-cm diameter Pelton wheel
weighing 300 g. The wheel is mechanically coupled to a 3 W generator (Vmax ¼
6 V) with lead wires connected to an NI USB-6009 data acquisition (DAQ) device
set at 1 kHz for 5000 samples, since initial tests on the system revealed that the
discharge is complete in t  4.5 s. The pressure within the cylinder is varied at two
values, 2 and 3 bar (gauge), while the DAQ system measures the voltage and
current output (through a 0.01 Ω current conditioner) from the generator with the
goal of approximating the efﬁciency of energy conversion compared with theoret-
ical calculations. A tachometer is also used to measure the rotational velocity of the
Pelton wheel during discharge.
A schematic of the experimental setup is shown in Fig. 2.
3
Numerical Scheme
There are three main techniques found in literature used to model CAES systems,
based on how to treat the signiﬁcant amount of heat energy generated during air
compression. First there is the isothermal process approximation, second the
Fig. 2 (a) Experimental setup and (b) close up to nozzle section
Experimental and Numerical Investigations of a Compressed Air Energy Storage. . .
1149
adiabatic (isentropic) process, and ﬁnally the large system thermodynamics approx-
imation for large storage systems in underground caverns. For the current analysis
and considering the small size of the experimental setup that will be introduced in
the next section, the system can be considered to be isothermal, as the pressures
involved are relatively small and will not exceed 3 bar. This assumption allows the
utilization of the ideal gas law (PV ¼ nRT) to represent the air compression process.
Since the absolute temperature between initial state A and ﬁnal state B is assumed
to be constant (TA ¼ TB ¼ T), the work done on a control volume of air taken within
the compression cylinder between these two states is given to be:
WA!B ¼
Z VB
VA
PdV ¼
Z VB
VA
nRT
V dV ¼ nRT
Z VB
VA
1
V dV
ð1Þ
This can be expanded by carrying out the integration to be:
WA!B ¼ nRT ln VB  ln VA
ð
Þ ¼ nRT ln VB
VA
ð2Þ
which can also be written more conveniently in terms of pressures PA and PB:
WA!B ¼ PAVA ln PA
PB
ð3Þ
Since isothermal conditions are assumed to hold, a simpliﬁed form of the energy
equations is applied between points A and B to estimate the maximum exit velocity
of air from the nozzle to be:
vmax ¼
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
2 PA
ρave
s
ð4Þ
where ρave is the average air density between states A and B, assuming a linear
pressure drop during system discharge (Cengel and Boles 2001). The angular
velocity, ω in rad/s, can be estimated from the basic torque eq. T ¼ I  α, where
I is the moment of inertia of the Pelton wheel and α is the uniform angular
acceleration in rad/s2. Starting from rest, the angular acceleration of the wheel is
estimated to be the rate of change of the angular velocity over rotation time. The
torque that the air jet exerts on the wheel is the thrust force, dm/dt.vmax, times the
radius of the wheel.
For pressure values of 2 and 3 bar, the air density at 303 K is found to be 3.46 and
4.61 kg/m3, respectively; the theoretical values of jet velocity, thrust, torque and
angular velocity are given in Table 1.
1150
A.H. Alami et al.
4
Results and Discussions
Assuming that charging the system with air (compression) will come externally
from available excess energy at periods of low demand, only the expansion portion
will be of interest for system analysis. Thus, the pressure drop within the cylinder at
the onset of discharge (nozzle on) is assumed to drop linearly, converting the stored
potential energy of air as static pressure into kinetic energy manifesting as air
velocity at the nozzle and angular rotation in the Pelton wheel.
The output voltage, Vout, with time is shown in Fig. 3 for the two pressure
settings of 2 and 3 bar within the cylinder. Figure 3a shows the time needed to reach
a steady-state voltage at around 2 s compared to the case of 3 bar cylinder pressure
that required 2.7 s. This result is expected due to the higher potential energy stored
as static pressure in the latter case. The ﬂuctuation seen is also due to the binary
options for the discharge nozzle (either on or off) and the transient instability
caused by the sudden valve opening. It can also be seen from the ﬁgure that the
average output voltage in the 3 bar setting is around 1.55 V, which is 13% higher
than that at 2 bar as is expected given the higher stored potential.
From Table 1, the theoretical exit velocities from the nozzle are 385.6 m/s for the
2 bar case, while it is calculated to be 445.3 m/s for the 3 bar case. From these
values, the corresponding expected rotational velocity, ω, for the Pelton wheel can
be calculated to be 78.2 rad/s and 90.3 rad/s, respectively. The value at low pressure
can be compared with the tachometer reading of 741 rpm (77.6 rad/s), which is
lower than predicted most probably due to ﬂuidic losses (expansion, skin friction)
and mechanical (coupling friction, windage). At the higher pressures of 3 bar,
however, the theoretical model underestimates the angular velocity by around
15% which is measured by the tachometer to be around 1000 rpm (104.7 rad/s).
The discrepancy between the 2 and 3 bar settings can be attributed to the air jet
convergence into the Pelton wheel blades. The nonlinear response of the jet is better
accounted for if an isentropic model of air compression is used instead of the
simpliﬁed ideal gas law that is used for the theoretical calculation, the latter
being a more convenient and straightforward approach (Zukcer and Biblarz 2002).
The experimental power density is shown in Fig. 4 for the two pressure settings,
represented by the area under the generated IV curve. It can be seen that the
ﬂuctuations in the curve for the 3 bar setting is less than the one for 2 bar, indicating
a smoother supply of current as the voltage varies. The area under the curve is
around 2.31 W, which is within the rating of the generator. Higher values of
pressure were not attempted, as this may cause higher electrical power that can
damage the generator, along with being a major safety concern.
Table 1 Theoretical
estimation of kinetic
and kinematic quantities
vmax m/s
Fthrust, N
T, N.m
ω, rad/s
2 bar
385.7
0.47
0.047
78.2
3 bar
445.3
0.54
0.054
90.3
Experimental and Numerical Investigations of a Compressed Air Energy Storage. . .
1151
0
0.5
1
1.5
2
2.5
0
1
2
3
4
5
0.5
1
1.5
2
2.5
time / s
Vout / V
(a)
(b)
2 bar
3 bar
Fig. 3 Voltage variation vs. discharge duration at (a) 2 bar and (b) 3 bar cylinder pressure
0
0.5
1
1.5
2
2.5
0.6
0.8
1
1.2
1.4
1.6
1.8
2
2.2
Iout / A
Vout / V
2 bar
3 bar
Fig. 4 Voltage change with output current for 2 and 3 bar cylinder pressure
1152
A.H. Alami et al.
The efﬁciency of the system is a function of the pressure and can be calculated
by considering the potential energy stored within the cylinder at the two values of
cylinder pressure (2 and 3 bar) as has been reported in literature for an isothermal
process (Zhang et al. 2014a, b). Performing the simple calculation at respective
pressure value, the system efﬁciency is calculated to be 64.8% and 84.8% for 2 and
3 bar internal pressure cases, respectively.
Finally, to help with the varying discharge demand while keeping the isothermal
assumption valid, a modular array of multiple pressure cylinders can be connected
through valves, as shown in Fig. 5, with the valve operation being controlled via a
programmable logic controller (PLC).
This setup helps to control the discharge from the cylinders by either opening all
the valves at the same time for high power density requirements or opening them
sequentially if longer periods of times are needed, hence fulﬁlling the overlap with
the battery storage that is seen in Fig. 1.
5
Conclusions
Energy storage by virtue of a compressed air system was evaluated. An isothermal
compression of air was assumed to accurately model the system since negligible
temperature rise is expected in the small cylinder used for the experiment. A fast
data acquisition system monitored the voltage generated during discharge and
found that the selected electric generator matched the rate of energy generation.
The overall system efﬁciency is a function of cylinder pressure and had a maximum
value of 84.8%. The system is amenable for energy storage in wind energy farms
and can easily be upscalable.
Fig. 5 Modular system
depiction as applied
in (Alami et al. 2017)
Experimental and Numerical Investigations of a Compressed Air Energy Storage. . .
1153
References
Abbaspour, M., Satkin, M., Mohammadi-Ivatloo, B., Hoseinzadeh, L.F., Noorollahi, Y.: Optimal
operation scheduling of wind power integrated with compressed air energy storage (CAES).
Renew. Energy. 51, 53–59 (2013)
Alami, A.H., Aokal, K., Abed, J., Alhemyari, M.: Low pressure, modular compressed air energy
storage (CAES) system for wind energy storage applications. Renew. Energy. 106, 201–211
(2017)
Brown, T.L., Atluri, V.P., Schmiedeler, J.P.: A low-cost hybrid drivetrain concept based on
compressed air energy storage. Appl. Energy. 134(1), 477–489 (2014)
Cengel, Y., Boles, M.: Thermodynamics: An Engineering Approach, 4th edn. Mcgraw-Hill
College, London (2001)
Fertig, E., Apt, J.: Economics of compressed air energy storage to integrate wind power: A case
study in ERCOT. Energy Policy. 39(5), 2330–2342 (2011)
Huggins, R.A.: Energy Storage, 2nd edn. Springer, New York (2010)
Kushnir, R., Dayan, A., Ullmann, A.: Temperature and pressure variations within compressed air
energy storage caverns. Int. J. Heat Mass Transfer. 55(21–22), 5616–5630 (2012)
Madlener, R., Latz, J.: Economics of centralized and decentralized compressed air energy storage
for enhanced grid integration of wind power. Appl. Energy. 101, 299–309 (2013)
Marano, V., Rizzo, G., Tiano, F.A.: Application of dynamic programming to the optimal man-
agement of a hybrid power plant with wind turbines, photovoltaic panels and compressed air
energy storage. Appl. Energy. 97, 849–859 (2012)
Robb, D.: The CAES for wind. Renew. Energy Focus. 12(1), 18–19 (2011)
Safaei, H., Keith, D.W., Hugo, R.J.: Compressed air energy storage (CAES) with compressors
distributed at heat loads to enable waste heat utilization. Appl. Energy. 103, 165–179 (2013)
Sun, H., Luo, X., Wang, J.: Feasibility study of a hybrid wind turbine system – Integration with
compressed air energy storage. Appl. Energy. 137, 617–628 (2015.) http://dx.doi.org/10.
1016/j.apenergy.2014.06.083
Wolf, D., Budt, M.: LTA-CAES – A low-temperature approach to Adiabatic Compressed Air
Energy Storage. Appl. Energy. 125(15), 158–164 (2014)
Xia, C., Yu, Z., Zhou, S., Zhang, P., Wang, F.: A simpliﬁed and uniﬁed analytical solution for
temperature and pressure variations in compressed air energy storage caverns. Renew. Energy.
74, 718–726 (2015)
Zhang C., Yan B., Wieberdink J., Li P.Y., Van de Ven J.D., Loth E., Simon T.W.: Thermal
analysis of a compressor for application to Compressed Air Energy Storage, Appl. Therm. Eng.
(2014a) http://dx.doi.org/10.1016/j.applthermaleng.2014.08.014
Zhang Y., Yang K., Li X., Xu J.: Thermodynamic analysis of energy conversion and transfer in
hybrid system consisting of wind turbine and advanced adiabatic compressed air energy
storage. Energy. (2014b) http://dx.doi.org/10.1016/j.energy.2014.09.030
Zukcer, R., Biblarz, O.: Fundamentals of gas dynamics, 3rd edn. Wiley, New Jersey (2002)
1154
A.H. Alami et al.
Feasibility Study of a Novel One-Axis Sun
Tracking System with Reﬂector Displacement
in a Parabolic Trough Concentrator
A. Gama, C. Larbes, A. Malek, and F. Yettou
1
Introduction
After the growth of oil prices at the time of the petroleum crisis in 1974, and the
facing of disastrous effects caused by hydrocarbon combustion without forgetting
the exhaustion of fossils fuels, there is a conscious effort engaged in favoring the
renewable energies. South Algeria has an important amount of solar energy of more
than 2200 kWh/m2/year (Boudghene Stambouli 2010), and also a big surface which
favors investment in this ﬁeld. Concentration systems were the best in solar energy
conversion, especially the parabolic trough concentrator (PTC). The PTC systems
have an important impact in solar energy ﬁeld, this ﬁeld motivate researchers to
work on, in order to improve its performances, especially sun tracking systems, this
system which is essential for the best performance and best PTC efﬁciency during
the day and the year.
The size and the placement of concentrators limit the choices of a sun tracking
system; generally, the PTC uses one rotation axis sun tracking system. The sun
elevation causes many optical losses, named the cosine effect. It’s clear that the
cosine effect is negligible in PTC power plants, but in a medium or small installa-
tion of parabolic trough concentrator (Fernandez-Garcıa et al. 2010), these losses
A. Gama (*) • F. Yettou
Unite
´ de Recherche Applique
´e en Energies Renouvelables, URAER, Centre de
De
´veloppement des Energies Renouvelables, CDER, 47133 Ghardaı
¨a, Algeria
e-mail: gama_amor@yahoo.fr
C. Larbes
National Polytechnic School, 10 Hassen Badi Avenue, P.O. Box 182, El Harrach Algiers,
Algeria
A. Malek
Centre de De
´veloppement des Energies Renouvelables, CDER, 16340 Algiers, Algeria
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_80
1155
are very important and a solution must be found. In this work, we choose to move
the reﬂector in order to recover these losses. In this study, we analyze this idea
where we propose a mechanical system as a solution. To verify the efﬁciency of the
proposed system, we have simulated the functionality of two identical parabolic
trough concentrators where the ﬁrst has a regular sun tracking system and the
second is equipped with the new proposed system. The optical behavior was
simulated by calculating the displacement distance and the concentrated irradiance;
for the thermal behavior we use the TRNSYS software in order to calculate the
useful energies and the efﬁciency of the two concentrators.
2
Cosine Effect
If the incident direct irradiance (Ed) forms an angle (θ) with the normal of the
aperture plane (Fig. 1), and if concentration ratio is (C), then the irradiance in the
absorber is (Chausseriau 1984)
Ec ¼ Ed  Cos θ
ð Þ  C
ð1Þ
To have the best concentration efﬁciency in parabolic trough concentrators, the
solar ray must be perpendicular to the aperture area; the cosine effect has a relation
with the focal distance:
L ¼ F  Cos θ
ð Þ
ð2Þ
For different focal distances, we have calculated the distance L of the cosine
effect in South Algeria, precisely in the Ghardaı
¨a region (Fig. 2).
Fig. 1 Cosine effect in the parabolic trough concentrator
1156
A. Gama et al.
3
Mechanical Solution Proposed
The tracking mechanism must be reliable and able to follow the sun with accuracy
whatever the weather (Kalogirou 1996). We ﬁnd two types of sun tracking mech-
anisms with one-axis and two-axis.
Generally, the two-axis tracker follows the sun’s movement from the East to the
West and changes the incline angle according to the sun’s elevation angle, while the
one-axis tracking follows the sun’s movement only from the East to the West, and
in this case, the energy collected by the solar collector during winter is less due to
the sun’s changing elevation (Bakos 2006). In PTC, we use the second type of sun
tracking system. To minimize the optical losses due to the sun’s changing elevation
(cosine effect), we propose two solutions (Gama et al. 2013). The ﬁrst consists of
the displacement of the absorber, and the second the displacement of the reﬂector.
We prefer to adopt the last solution for technical reasons.
In this study, we consider a parabolic trough concentrator that is “14.4 m” long,
“4 m” wide, and “1.8 m” of focal distance; the concentrator is equipped with
one-axis sun tracking oriented N-S.
The mechanical solution that we propose allows us to make sure that the rotation
and the displacement of the reﬂector occur at the same time. For the rotation of the
reﬂector we use a mechanical jack, and for the translation movement we use a CC
Fig. 2 The distance “L” of cosine effect (in millimeters) as a function of the day of year for
different focal distances in Ghardaı
¨a (arid region south of Algeria)
Feasibility Study of a Novel One-Axis Sun Tracking System with Reﬂector. . .
1157
motor (3) connected to a speed reducer (4), which rotates a threaded rod (1) that
translates the reﬂector (see Fig. 3). The displacement distance (L) is calculated at
any time by the conversion of solar elevation angle, this parameter must be added to
the one-axis sun tracking strategy, and we deduce that the displacement distance for
South Algeria is between “2.82 m” in winter and “0.41 m” in summer.
4
Thermal Simulation
In order to calculate the thermal performances, we use the TRNSYS software,
where the Linear Parabolic Concentrator model is based on equations taken from
Dufﬁe and Beckman’s “Solar Engineering of Thermal Processes” (Dufﬁe and
Beckman 1991). We compare two parabolic trough concentrators with regular
sun tracking system and with displacement reﬂector.
The TRNSYS model of installation (Fig. 4) contains the parabolic trough
concentrator linked to a pump of “20 kg/h” ﬂow rate and a storage tank of “1 m3”
equipped with a heat exchanger; the concentrator is simulated for a region in the
south of Algeria (GHARDAI
¨A, Lat ¼ 32.23, long ¼ 3.66, Alt ¼ 450 m) for a
short and a long day of the year. First, we calculate the useful energy gain and the
efﬁciency of the ﬁrst concentrator; then for the second concentrator, we use the
same installation by adding to the length of the concentrator the distance of the
displacement (L) for any day without changing the concentration ratio.
The results obtained after the TRNSYS simulation prove that the new proposed
system is efﬁcient, the useful energy gain in the case of concentrator with reﬂector
Fig. 3 The proposed parabolic trough concentrator with reﬂector displacement
1158
A. Gama et al.
displacement has increased considerably compared with the case of ordinary sun
tracking systems (Fig. 5), and the efﬁciency of the system increases from “31.8%”
to “37.98%” during the shortest day of the year and from “62.57%” to “64.3%”
during the longest day of the year (Fig. 6).
Type109
Pump
Type536
Tank
Type24
Plotter 1
System_Printer
System_Printer-2
Fig. 4 TRNSYS model of simulation facility
Fig. 5 Irradiation and useful energy gain of a PTC with regular sun tracking system and with
reﬂector displacement
Feasibility Study of a Novel One-Axis Sun Tracking System with Reﬂector. . .
1159
5
Optical Simulation
According to optical simulation, we can observe that the displacement of the
reﬂector induces the displacement of the concentration line, and therefore we
have eliminated the optical losses (cosine effect). In South Algeria, for the shortest
day of the year, the cosine effect is very high. We have displaced the reﬂector by a
distance of “2.8 m” (Fig. 7), and the efﬁciency of the system increases considerably
by the recovery of these optical losses.
In South Algeria, the cosine effect is very small in the summer because the solar
elevation was at the maximum (Fig. 8), we have displaced the reﬂector by “0.41 m”
(Fig. 9), and the efﬁciency of the system increases by a small value because the
optical losses are very small.
6
Conclusions
Optical loss is the primary disadvantage in the parabolic trough concentrator. The
minimization of optical losses increase the optical efﬁciency of this systems. In this
work, we proposed a new sun tracking system based on reﬂector displacement
Fig. 6 Efﬁciency of system in two simulation cases
1160
A. Gama et al.
intended to be used in small and medium installations where the optical losses are
minimized. To prove this idea we have simulated two systems: the ﬁrst system with
a displaced reﬂector and the second with an ordinary sun tracking system. The
results were very interesting: the efﬁciency increased considerably with a simple
Fig. 7 Presentation of concentrated irradiance beam for the shortest day of the year, system with
regular sun tracking
Feasibility Study of a Novel One-Axis Sun Tracking System with Reﬂector. . .
1161
mechanical solution and a few modiﬁcations in the sun tracking strategy. Currently,
we are working to prove the new sun tracking system experimentally through a new
prototype installed in the south of Algeria.
Fig. 8 Presentation of concentrated irradiance beam for the shortest day of the year, system with
reﬂector translation
1162
A. Gama et al.
Fig. 9 Presentation of concentrated irradiance beam for the longest day of the year, system with
regular sun tracking
Feasibility Study of a Novel One-Axis Sun Tracking System with Reﬂector. . .
1163
References
Bakos, G.C.: Design and construction of a two-axis sun tracking system for parabolic trough
collector (PTC) efﬁciency improvement [Revue]. Renew. Energy. 31, 2411–2421 (2006)
Boudghene Stambouli, A.: Algerian renewable energy assessment: the challenge of sustainability
[Revue]. Energy Policy. (2010). doi:10.1016/j.enpol.2010.10.005
Chausseriau, J.-M.: Conversion thermique du rayonnement solaire [Ouvrage]. Dunod, Paris (1984)
Dufﬁe, J., Beckman, W.: Solar Engineering of Thermal Processes [Ouvrage]. Wiley, Hoboken
(1991)
Fernandez-Garcıa, A., Zarza, E., Valenzuela, L., Perez, M.: Parabolic-trough solar collectors and
their applications [Revue]. Renew. Sust. Energ. Rev. 14, 1695–1721 (2010)
Gama, A., Larbes, C., Malek, A., Yettou, F., Adouane, B.: Design and realization of a novel sun
tracking system with absorber displacement for parabolic trough collectors [Revue].
J. Renewable Sustainable Energy. 5, 033108 (2013). doi:10.1063/1.4807476
Kalogirou, S.A.: Design and construction of a one-axis sun-tracking system [Revue]. Sol. Energy.
57, 465–469 (1996)
1164
A. Gama et al.
Crystal Growth Analysis of R134a Clathrate
with Additives for Cooling Applications
Sayem Zafar, Ibrahim Dincer, and Mohamed Gadalla
1
Introduction
The recent global trend has changed from rudimentary energy production or
rejection to precisely manage and absorb energy. The energy management is a
challenge that needs to be dealt with in order to achieve the goal of sustainable
growth. In order to improve the performance of energy systems, more effective
tools need to be utilized (Rosen and Dincer 1997). Thermal energy storage (TES) is
one such tool that can be applied to manage the energy. TES refers to the storage of
heat by increasing or decreasing the temperature of a substance or by changing its
phase (Dincer and Rosen 2002).
The most common phase change material in use today for cooling is water.
Water changes its phase at 273 K at atmospheric pressure, which means that water
has to be taken to 273 K, from ambient temperature, to make it change its phase.
However, it would be beneﬁcial to have a material that can change its phase above
that temperature so the amount of work to be done gets reduced. Research has
shown that refrigerant clathrates can be used for cooling applications where phase
change is desired above freezing (Mori and Isobe 1991). Clathrates tend to form
when gas molecules get trapped in the water molecule cage under low temperature
and high pressure (George 1989; Sloan 1990). Refrigerant clathrates can be used for
active as well as passive cooling applications and hence are considered more
S. Zafar (*) • I. Dincer
Faculty of Engineering and Applied Science, University of Ontario Institute of Technology,
2000 Simcoe Street North, Oshawa, ON L1H 7K4, Canada
e-mail: sayem.zafar@uoit.ca; ibrahim.dincer@uoit.ca
M. Gadalla
Department of Mechanical Engineering, American University of Sharjah, Sharjah, UAE
e-mail: mgadalla@aus.edu
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_81
1165
effective compared with other types of PCMs (Bi et al. 2004; Inaba 2000). Refrig-
erant clathrates have high heat of fusion and high density which allows them to
store more energy per unit volume. Refrigerant clathrates are no more toxic than the
base refrigerant, so the existing systems can be utilized to contain them.
Many refrigerants form clathrates, but only a handful are in commercial use.
Several chloroﬂuorocarbons (CFCs), hydrochloroﬂuorocarbons (HCFCs), and
hydroﬂuorocarbons (HFCs) can form clathrates of refrigerant (Eslamimanesh
et al. 2011). For use in cold thermal energy storage system, the refrigerant clathrate
should form between temperature ranges from 278 to 285 K (Guo et al. 1996).
CFCs are forbidden due to stratospheric ozone layer depletion concerns which
leave the hydrochloroﬂuorocarbon and hydroﬂuorocarbons to be used for PCM.
Refrigerant clathrates of R134a show they can be effective in their role as cold
thermal energy storage through phase change (Guo et al. 1996).
PCMs based on refrigerant clathrates have poor thermal transport properties. To
make refrigerant clathrates as effective PCMs, additives of different materials have
been studied. For instance, adding calcium hypochlorite or benzenesulfonic acid
sodium salt improved the cold energy storage capacity and the cold energy transfer
rate of R141b based clathrate (Bi et al. 2006). Adding alcohol in R134a-based
clathrate has also been studied, which shows it accelerates the cool storage rate and
eliminates the ﬂoating clathrate during the hydration process (Wua and Wangb
2012).
Metallic nanoparticles have also been added to study the improvement in
thermal transport properties. Studies show that even a small fraction of
nanoparticles of low thermal conductivity metallic oxides can favorably increase
the thermal conductivity of pure a substances, such as water (Murshed et al. 2009;
Duangthongsuk and Wongwises 2009). Even for organic compounds such as
monoethylene glycol and parafﬁn ﬂuids, copper oxide nanoparticles can improve
the thermal conductivity (Moghadassi et al. 2010). Addition of pure copper
nanoparticles in ethylene glycol increases the thermal conductivity by 40% (East-
man et al. 2001). For the refrigerant hydrate, nanoparticles of copper are studied
which shows that the heat transfer increases with the addition of nanoparticles of
copper (Lia et al. 2006).
This paper describes the experimental investigation that is conducted to test the
thermal behavior of R134a clathrates with and without additives. The refrigerant
clathrate and additives are studied as phase change materials (PCMs) for cooling
applications. The study is focused on the charging capabilities of the PCM while its
behavior is analyzed and evaluated. The paper discusses the crystal growth time of
R134a clathrate with different proportions of refrigerant. It also studies the effect on
crystal growth time when selected additives are added in the R134a clathrate.
1166
S. Zafar et al.
2
Analysis
The general energy and exergy balance equations for the PCM charging is
described in this section. Charging is described as the process of solidiﬁcation
where the PCM gives away the heat during this process.
For charging the PCM, mass balance for the charging ﬂuid can be described as
follows:
mPCM
ð
Þi ¼ mPCM
ð
Þf
ð1Þ
where subscripts “i” for initial and “f” for ﬁnal.
For water, the mass balance becomes
mwater
ð
Þi ¼ mwater
ð
Þf
ð2Þ
Energy balance can be described as follows:
mΔh
ð
Þbath ¼ Qsur þ Qheater  Qcoil
ð3Þ
mΔh
ð
ÞPCM ¼ Qbath
where “sur” is referring to heat loss to the surrounding and “coil” for
refrigeration coil.
The entropy balance is written as follows:
mΔs
ð
Þbath ¼ Qsur
T0
þ Qheater
T0
 Qcoil
T0
ð4Þ
mΔs
ð
ÞPCM ¼ Qbath
T0
ð5Þ
Also, the exergy balance can be written as
mΔex
ð
Þbath ¼ mΔh
ð
Þbath  T0 mΔs
ð
Þbath
ð6Þ
mΔex
ð
ÞPCM ¼ mΔh
ð
ÞPCM  T0 mΔs
ð
ÞPCM
ð7Þ
3
Experimental Setup and Procedure
For the experiments, a cold constant temperature bath is used as a constant
temperature source. Refrigerant, water, and the desired additive are mixed in a
pressurized glass tube. The glass tube, that can sustain high pressure, is used with a
mounted pressure gauge and an access valve to ﬁll the refrigerant. The tube is
comprehensively tested for leaks and measures are taken to make sure there are no
Crystal Growth Analysis of R134a Clathrate with Additives for Cooling. . .
1167
leaks. It is important to use a glass tube since the onset of phase change needs to be
observed visually. The refrigerant clathrate with additive, named PCM, are formed
in glass tubes. The tubes are submerged in the constant temperature water bath for
which the temperature is set at 3. The schematic diagram of the experimental
layout is shown in Fig. 1.
The constant temperature bath works by providing cold energy and heat simul-
taneously to the distilled water to maintain its temperature at 3. A refrigeration
system with cooling coils around the water bath pumps out the heat. A controller
constantly monitors the water temperature in the bath while it continues to provide
the desired heat to maintain the temperature at 3. A stirrer is also used which
circulates the water in the bath to keep a homogeneous temperature distribution
within the water bath. With it, the water near the hot or cold source would change its
temperature, while the water away from the source would see its effect later.
4
Results and Discussion
Experiments are conducted to measure the onset and end-set time for R134a
clathrate at different proportions. Crystal growth time is important to determine
as it yields the total energy used to form the PCM. Low crystal growth time means
low energy while increased time means large amount of energy required to form the
PCM. Experiments are conducted to determine the onset and end-set time when
additives are used with the R134a clathrate. Refrigerant R134a is mixed with
distilled water at 25%, 30%, 35%, and 40%. After ﬁguring out the most appropriate
percent composition for refrigerant clathrate, additives are included to see the
improvement in the crystal growth time. For the additives, ethanol, sodium
Fig. 1 Schematic diagram of the proposed PCM testing system
1168
S. Zafar et al.
chloride, magnesium nitrate hexahydrate, copper, and aluminum are used. Addi-
tives are added from 1% by mass to 5% by mass to see their effect on crystal
growth time.
Figure 2 shows the R134a clathrate crystal growth time formation for different
refrigerant mass proportions. Onset time is the time clathrate takes to start freezing,
while end set is when the process of freezing is complete. It is to be noted that
complete freezing does not necessarily mean everything in the tube is frozen. For
some percentages, either the water or the refrigerant remains liquid and does not
freeze at 3 C. Refrigerant proportions of 25–40% are shown in the ﬁgure. Below
25%, a large proportion of the water remains unmixed while above 40%, the
refrigerant does not have enough water to mix with. The graph shows that the
crystal growth time reduces until 35% refrigerant mass ratio and then it starts to
increase. From the tests, it is concluded that 35% is the most optimal mass
percentage for refrigerant since it takes the least amount of time.
Figure 3 shows the results of the tests conducted on R134a clathrate with copper
particles as additives. Copper mass percentage is varied from 1% to 5% while their
onset and end-set time of the crystal growth is recorded. The onset time with copper
particles is found to be the same for all ﬁve cases at 10 min. The end-set time varied
from 60 min for 1% additive to 90 min for 5% additive. The onset time remains the
same since the same amount to additive interacts to initiate the solidiﬁcation
process. The end-set time increases with the increase in additive percentage.
Copper particles settle at the bottom of the tube and the ones that are present in
the clathrate are not evenly distributed. This causes clathrate to remain liquid in
some sections while frozen in the other. The heat transfer process is slow in the
frozen region; hence the time to completely solidify the mixture increases. It is
important to note that end-set time, unlike onset time where the freezing is obvious,
is difﬁcult to note as the change may not be visible.
Figure 4 shows the test results for R134a clathrate crystal growth formation time
with magnesium nitrate hexahydrate as additive. The average onset time with
0
20
40
60
80
100
120
140
25%
30%
35%
40%
Time (minutes)
Refrigerant Percentage in Clathrate
End
Set
Time
Fig. 2 R134a clathrate crystal growth time for onset and end set at different refrigerant mass
proportions
Crystal Growth Analysis of R134a Clathrate with Additives for Cooling. . .
1169
magnesium nitrate hexahydrate is found to be 20 min. The end-set time varied from
30 min for 1% additive to 50 min for 5% additive. The onset and end-set times
increase slightly with the increase in additives. At low additive ratios and for onset,
magnesium nitrate hexahydrate improves the clathrate formation time because of
its slightly better thermal conductivity compared to water. However, end-set time
for high additive proportions tends to increase due to the nature of salts to resist
clathrate formation.
0
10
20
30
40
50
60
70
80
90
100
1%
2%
3%
4%
5%
Time (minutes)
Additive Percentage by Mass
Onset
End Set
Fig. 3 Crystal growth time for clathrate formation at 3 C with copper additive at different
additive proportions
0
10
20
30
40
50
60
1%
2%
3%
4%
5%
Time (minutes)
Additive Percentage by Mass
Onset
End Set
Fig. 4 Crystal growth time for clathrate formation at 3 C with MgNO3 at different mass
proportions
1170
S. Zafar et al.
Figure 5 shows results for R134a clathrate-crystal growth time with ethanol. The
onset time with ethanol increased from 25 to 60 min. The end-set time varied from
65 min for 1% additive to 85 min for 5% additive. The onset and end-set time
increases with the increase in additives. Although the onset time for ethanol is high,
the end set is achieved rather fast once the solidiﬁcation begins. Since ethanol is
liquid, it mixes well with the clathrate; hence the heat transfer is fairly uniform.
Figure 6 shows the results of the tests conducted on R134a clathrate with
aluminum particles as additives. The onset time with aluminum particles is found
to be 11 min for all ﬁve cases. The end-set time varied from 65 min for 1% additive
to 90 min for 5% additive. Aluminum, like copper particles, settles at the bottom of
the tube and the ones that are present in the clathrate are not evenly distributed. The
clathrate remains liquid in some sections while frozen in the other. In the frozen
section, the heat transfer process is slow which increases the solidiﬁcation time.
Figure 7 shows the test results of the crystal growth formation time for sodium
chloride as additive in R134a clathrate. The average onset time varied at 40 min for
1% additive to 65 min for 3% additive. The end-set time varied from 80 min for 1%
additive to 110 min for 3% additive. For sodium chloride, the R134a clathrate did
not form for higher additive percentages. The onset and end-set times increase with
the increase in additive proportion. Sodium chloride increased the onset and end-set
time for all the tested proportions. Sodium chloride has lower thermal conductivity
than the water. It also slows the clathrate formation since salt particles interact with
the water molecules preventing it to form the inclusion compound with the refrig-
erant (clathrate). Sodium chloride at 4% and 5% as additive does not form R134a
clathrate.
Figure 8 shows the average onset time of the crystal growth for the ﬁve tested
additives. The graph shows that copper and aluminum have the lowest onset time
0
10
20
30
40
50
60
70
80
90
100
1%
2%
3%
4%
5%
Time (minutes)
Additive Percentage by Mass
Onset
End Set
Fig. 5 Crystal growth time for clathrate formation at 3 C with ethanol for different mass
proportions
Crystal Growth Analysis of R134a Clathrate with Additives for Cooling. . .
1171
followed by magnesium nitrate hexahydrate, ethanol, and then sodium chloride. For
1% additive by mass, the onset time decreased by 25 min for copper and aluminum
when compared to refrigerant clathrate without additive. Magnesium nitrate hexa-
hydrate decreased the time by 20 min, while ethanol reduced it by 10 min. Sodium
chloride maintained the onset time. At high additive concentrations, onset time
decreased by 25 min for copper and aluminum. For magnesium nitrate hexahydrate,
the improvement is of 15 min. For ethanol, the clathrate formation time increased to
above 25 min, while for sodium chloride, the increase is of 30 min.
0
10
20
30
40
50
60
70
80
90
1%
2%
3%
4%
5%
Time (minutes)
Additive Percentage by Mass
Onset
End Set
Fig. 6 Crystal growth time for clathrate formation at 3 C with aluminum for different mass
proportions
0
20
40
60
80
100
120
1%
2%
3%
Time (minutes)
Additive Percentage by Mass
Onset
End Set
Fig. 7 Crystal growth time clathrate formation at 3 C with NaCl for different mass proportions
1172
S. Zafar et al.
Figure 9 shows the average end-set time for R134a clathrate crystal growth for
ﬁve tested additives. The graph shows that magnesium nitrate hexahydrate has the
lowest end-set time followed by copper, ethanol, aluminum, and then sodium
chloride. For 1% additive by mass, the end-set time decreased by 35 min for
magnesium nitrate hexahydrate and 15 min for copper. Ethanol and aluminum
maintained the end-set time relatively the same as the base R134a clathrate. Sodium
chloride increased the end-set time by 10 min. At high additive concentrations, the
end-set time decreased by 20 min for magnesium nitrate hexahydrate. All the other
additives either maintained the end-set time or increased it.
0
10
20
30
40
50
60
70
0%
1%
2%
3%
4%
5%
Time (minutes)
Additive Percentage by Mass
Copper
MgNO3
Ethanol
Aluminum
NaCl
Fig. 8 Clathrate crystal growth onset time comparison for different used additives
0
20
40
60
80
100
120
0%
1%
2%
3%
4%
5%
Time (minutes)
Additive Percentage by Mass
Copper
MgNO3
Ethanol
Aluminum
NaCl
Fig. 9 Clathrate crystal growth end-set time comparison for different used additives
Crystal Growth Analysis of R134a Clathrate with Additives for Cooling. . .
1173
5
Conclusions
The paper looks at the crystal growth time for R134a clathrate at different mass
proportions and with ﬁve different additives. Formation of R134a clathrate is tested
due to their possible use in active as well as passive cooling applications. First set of
tests are conducted to ﬁgure out the best refrigerant proportion in the R134a and
water mixture. Further tests are conducted with mixing ﬁve different additives in
the base R134a clathrate. The additive mass proportion is varied from 1% to 5% at
1% interval. Test results are analyzed for onset and end-set time of the crystal
growth durations to determine when the solidiﬁcation starts and when it ends at the
prescribed water bath temperatures. The current experimental results for chosen
sets of additives and operating conditions conﬁrm the following closing remarks:
•
A 35% refrigerant requires the lowest time to form the clathrate.
•
Copper, aluminum, magnesium nitrate hexahydrate, and ethanol decrease the
onset time. Sodium chloride increases the onset time when used as an additive.
•
Magnesium nitrate hexahydrate forms the clathrate fastest, followed by copper,
ethanol, aluminum, and then sodium chloride.
•
Magnesium nitrate hexahydrate and copper accelerate the clathrate formation,
while aluminum and ethanol do not affect the crystal growth time much.
•
Sodium chloride delays the clathrate formation time while at 4% and 5% sodium
chloride mass percentage, it does not allow clathrate formation.
•
Increasing the additive proportion does not help accelerate the process, while in
some cases, it decelerates the crystal growth.
Nomenclature
CFC
Chloroﬂuorocarbons
ex
Speciﬁc exergy (J/kg)
HCFC
Hydrochloroﬂuorocarbons
HFC
Hydroﬂuorocarbons
m
Mass (kg)
PCM
Phase change material
Q
Heat (J)
s
Speciﬁc entropy (J/kg)
T
Temperature (K)
t
Time (s)
TES
Thermal energy storage
Subscripts
0
Ambient
c
Charging
f
Final
i
Initial
sur
Surrounding
1174
S. Zafar et al.
References
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Bi, Y.H., Guo, T.W., Zhu, T.Y., Zhang, L., Chen, L.: Inﬂuences of additives on the gas hydrate
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thermal conductivities of ethylene glycol-based nanoﬂuids containing copper nanoparticles.
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Eslamimanesh, A., Mohammadi, A.H., Richon, D.: Thermodynamic model for predicting phase
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Thermal Design. McGraw Hill Book Co., US (1989)
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phase change in mixed gas-hydrate formation. In: Wang, B.X. (ed.) Heat Transfer Science and
Technology, pp. 728–733. Higher Education Press, Beijing (1996)
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Moghadassi, A.R., Hosseini, S.M., Henneke, D.E.: Effect of CuO nanoparticles in enhancing the
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of nanoﬂuids. Appl. Therm. Eng. 29, 2477–2483 (2009)
Rosen, M.A., Dincer, I.: On exergy and environmental impact. Int. J. Energy Res. 21, 643–654
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Sloan, E.D.: Clathrate Hydrates of Natural Gases. Marcel, New York (1990)
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Crystal Growth Analysis of R134a Clathrate with Additives for Cooling. . .
1175
Modeling, Simulation, and Optimization of an
Irrerversible Solar Absorption Cooling Plant
in Transient Regime
Boukhchana Yasmina, Fellah Ali, and Ben Brahim Ammar
1
Introduction
Heat-absorption cooling systems have been the subject of considerable research
during the last two decades (Stolk 1980; Suri and Ayyash 1982; Wijeysundera
1996; Ayyash et al. 1985; Charia et al. 1991; Gordon and Choon 1995; Bejan et al.
1995; Vargas et al. 1996). The new interest in this class of refrigeration systems is
due to the development of new technologies in renewable energy sources such as
solar and waste heat. On the other hand, the improvement and optimization in the
design of heat-driven refrigerators has become crucial. They need to compete with
conventional refrigeration systems in modern cooling installations. Additionally,
the utilization of low-grade heat sources is stressed by economic considerations and
by the need for low-environmental-impact refrigeration systems.
From the thermodynamics point of view, a large amount of fundamental
research has been carried out (Bejan et al. 1995; Vargas et al. 1996; Bejan 1988a,
b; 1989) to optimize thermal systems. In these studies, the method of entropy-
generation minimization, also known as the method of ﬁnite-time thermodynamics,
was used. The method consists of the simultaneous application of heat transfer and
thermodynamic principles in order to pursue realistic models that take into account
the inherent irreversibility of heat, mass, and ﬂuid ﬂow. The method has been
B. Yasmina (*) • B.B. Ammar
Research Unit of Applied Thermodynamics, Department of Chemical and Processes
Engineering, National School of Engineers of Gabes, University of Gabes, St Omar Ibn
El-Khattab, 6029 Gabes, Tunisia
e-mail: boukhchana.yasmina@gmail.com
F. Ali
Research Unit of Applied Thermodynamics, Technology Department, High Institute
of Applied Sciences and Technology, University of Gabes, 6029 Gabes, Tunisia
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_82
1177
gaining more importance in the context of shape and structure in engineering and
nature.
The objective functions in ﬁnite time thermodynamics are often pure thermody-
namic parameters including efﬁciency, entropy production, cooling load, heating
load, and coefﬁcient of performance. The results obtained for various thermody-
namic cycle analyses using FTT are closer to real device performance than those
using classical thermodynamics (Van and Chen 1989; Chen and Yan 1989; Denton
2002; Feidt 2010). Many studies for the performance optimization of refrigerators
based on endoreversible and irreversible models have been performed by consid-
ering various objective functions.
An extensive study on the theory of irreversible heat transfer refrigeration
systems has been done recently (Bejan 1989). Other works give formulae for the
coefﬁcient of performance (COP) and cooling rate of an endoreversible refrigera-
tion machine (Agrawal and Menon 1993) and also when maximum speciﬁc cooling
load is required (Wu 1995). An analytical model for predicting general performance
characteristics of an irreversible Carnot cycle machine has been achieved recently
(Feidt et al. 2005, 2007). It may be applied to direct or reverse cycle machines. The
optimization procedure focused on several objectives, namely, maximum useful
effect, minimum consumption, or minimum total dissipation. Several operational
and dimensional constraints were introduced in the model. Internal and external
irreversibilities of the cycle are also taken into account by two terms that corre-
spond to internal and, respectively, total entropy generation. The internal entropy
generation term has been introduced as a parameter in the model so that a more
general approach than the irreversibility ratio used in other papers (Sieniutycz and
Salamon 1990; Calvo Herna
´ndez et al. 2004; El Haj Assad 2000) has been accom-
plished. The approach concerning the entropic analysis of machines and processes
(Bejan 1988a, b; Petrescu et al. 2002; Feidt 1996) becomes an important tool for the
design of real-operating machines.
Nevertheless, all those studies focus on the systems’ steady-state properties and
ignore completely their dynamic behavior.
Steady-state models are useful under many conditions although under strongly
dynamic conditions that are often seen in real-life operation, these models can
become unacceptably inaccurate (Browne and Bansal 2002). However, steady-state
models do not provide time-dependent information on the thermal behavior of
absorption refrigerators and are therefore not suitable for transient system
simulations.
Research on dynamic system behavior was carried out for absorption refriger-
ators. Although, Vargas et al. (1998, 2000) studied a transient endoreversible model
of a heat-driven refrigeration plant driven by a fuel burning heater. Their optimi-
zation is done from the point of view of the heat that drives the cycle. Recently,
Hamed et al. (2012) conducted a thermodynamic transient regime simulation of an
endoreversible solar-driven absorption refrigerator to ﬁnd the optimal conditions of
a solar-driven absorption refrigerator. In contrast, the model presented in this work
allows the simulation of the dynamic absorption refrigerator behavior. It extends
the range of applicable models for transient system simulations, where the time
1178
B. Yasmina et al.
constants of the refrigerator signiﬁcantly inﬂuence the system performance. The
dynamic model of an irreversible absorption refrigerator allows the simulations of
its transient behavior for changing input conditions or design parameters. This is
important because absorption refrigerators usually have a high thermal mass,
consisting of their internal heat exchangers, the absorbing solution and the exter-
nally supplied heat transfer media.
In the present article, we propose to carry out the optimization of an irreversible
absorption refrigeration system that uses a solar collector as the high-temperature
heat source. The optimization is done from the point of view of the heat that drives
the cycle – in other words, the heat transfer rate received from the solar collector to
the generator. The challenge is to minimize the time required to reach a certain
operation temperature in the refrigerated space, ﬁnding an optimum heat rate and
investigating the effect of time in solar collector stagnation temperature and
collector temperature. This issue becomes more important in large-scale cooling
applications, in which the thermal inertia of the refrigerated space becomes very
large.
Nomenclature
A
Area, (m2)
a, b
Constant in Eq. (5)
B
Dimensionless collector size parameter, Eq. (17)
C
Speciﬁc heat, (kJ/kg K)
G
Irradiance on collector surface, (W/m2)
M
Mass of air in the refrigerated space, (kg)
Q
Heat transfer rate, (W)
S
Entropy generation rate, (kJ/K)
t
Time, (s)
T
Temperature, K
U
Global heat transfer coefﬁcient, (W/m2 K)
Greek letters
τ
Dimensionless temperature
θ
Dimensionless time
γ
Dimensionless thermal conductance
η
Efﬁciency of a ﬂat-plate collector
Subscripts
0
Ambient
air
Air
C
Reversible compartment
H
Heat source
L
Refrigerated space
load
Cold space thermal load
opt
Optimum
SC
Solar collector
set
Set point
st
Collector stagnation temperature
Modeling, Simulation, and Optimization of an Irrerversible Solar Absorption. . .
1179
2
Solar System and Mathematical Model
2.1
Solar System
A solar-operated absorption refrigeration system is made of a solar collector and a
refrigeration cycle as shown in Fig. 1. The system differs from a customary fossil-
fuel-ﬁred unit in that the energy supplied to the generator is directly from the solar
collector. This arrangement has the advantage of making high-temperature thermal
energy available to the generator.
In this analysis, the collector adopted is a ﬂat-plate collector. Flat-plate solar
collectors are commonly used in solar space heating. It is economically adopted
when the same collector is used for both heating and cooling spaces. However, and
regarding the relatively low temperatures attainable, only a few practical applica-
tions are available in ﬂat-plate solar-operated cooling processes. One of the most
promising schemes is the utilization of an absorption refrigeration cycle with solar
energy serving as the source of heat to operate the generator.
The primary components of an absorption refrigeration system are a generator,
an absorber, a condenser, and an evaporator. The cycle has negligible work input.
The cycle is driven by the heat transfer rate QH received from the source temper-
ature TH, which is determined by the operation temperature of the generator. The
refrigeration load QL is removed from the refrigerated space, at TL, and the heat
transfer rate Q0 is rejected to the ambient, T0.
Fig. 1 The heat transfer
irreversible model of a
solar-driven absorption
refrigeration system
1180
B. Yasmina et al.
In this analysis, it is assumed that there is no heat loss between the solar collector
and the generator and no work exchange occurs between the refrigerator and its
environment. The work input required by the solution pump also is negligible
relative to the energy input to the generator and, consequently, it is often neglected
for the purposes of analysis.
2.2
The Transient Model
A mathematical model is developed that combines empirical correlations, classical
thermodynamics principles, and heat transfer law. The proposed model is then
utilized to simulate numerically the system transient and steady-state responses
under different operating conditions. The irreversible model takes into account the
external and internal irreversibilities, which are fundamental features that will be
present in the design of actual absorption refrigerators, no matter how complicated
these designs may be.
The instantaneous heat transfer interactions obey the linear heat-transfer law,
“Newton’s heat transfer law,” and are given by
QH ¼ UA
ð
ÞH TH  THC
ð
Þ
ð1Þ
QL ¼ UA
ð
ÞL TL  TLC
ð
Þ
ð2Þ
Q0 ¼ UA
ð
Þ0 T0C  T0
ð
Þ
ð3Þ
The generator heat input QH could be estimated by the following expression:
QH ¼ ηSCASCG
ð4Þ
where AS.C represents the collector area, G is the irradiance at the collector surface,
and ηSC stands for the collector efﬁciency.
The efﬁciency of a ﬂat-plate collector can be calculated as (Bejan et al. 1995;
Sokolov and Hershgal 1993a, b)
ηSC ¼ a  b TH  T0
ð
Þ
ð5Þ
where a and b are two constants that can be calculated, as discussed by Sokolov and
Hershgal (Bejan et al. 1995; Sokolov and Hershgal 1993). Equation (5) can be
rewritten by introducing the collector stagnation temperature Tst as follows:
ηSC ¼ b TSt  TH
ð
Þ
ð6Þ
where Tst (for which ηS . C ¼ 0) is given by
Modeling, Simulation, and Optimization of an Irrerversible Solar Absorption. . .
1181
TSt ¼ T0 þ a=b
ð7Þ
The equation for heat input QH can be rewritten by combining Eqs. (4) and (6) as
follows:
QH ¼ ASCGb Tst  TH
ð
Þ
ð8Þ
From the ﬁrst law of thermodynamics
QH þ QL  Q0 ¼ 0
ð9Þ
The refrigerator is considered to be irreversible (include external and internal
irreversibilities). Internal irreversibilities due to heat transfer, throttling, mixing,
and internal dissipation of the working ﬂuid, which are responsible for the entropy
generation, are always present in a real heat-driven refrigerator. According to the
second law of thermodynamic and the irreversible property of the cycle, we have
dSin
dt ¼ Q0
T0C
 QH
THC
 QL
TLC
ð10Þ
Generally, it is difﬁcult to model all internal entropy generation sources in order
to get an analytical variation law. We have chosen to consider the following
approaches (Wijeysundera 1997; Gordon and Ng 2000).
The entropy of the working ﬂuid is represented by using linear variation law with
temperature:
dSin
dt ¼ β1 THC  T0C
ð
Þ þ β2 T0C  TLC
ð
Þ
ð11Þ
where the parameters β1 , β2 are to be estimated by ﬁtting detailed simulation data to
predictions.To obtain the best estimates of the parameters β1 and β2 from simulated
performance data (Boukhchana et al. 2013, 2014), the following least-square
procedure is used.
We account for the transient cooling of the refrigerated space by writing the ﬁrst
law:
MairCv:air
dTL
dt ¼ UA
ð
ÞW T0  TL
ð
Þ þ Qload  QL
ð12Þ
where (UA)W(T0  TL) accounts for the rate of heat gain from the ambient through
the walls of the refrigerated space and Qload is the thermal load or rate of heat
generated inside the refrigerated space. By writing the set of Eqs. (1), (2), (3), (4),
(5), (6), (7), (8), (9), and (10) for the absorption refrigerator and (12) for the
refrigerated room, we take into account the fact that the thermal inertia of the
refrigerated space is large enough such that the transient operation of the
1182
B. Yasmina et al.
refrigerator can be neglected when compared to the time evolution of the temper-
ature inside the refrigerated space.
According to the cycle model mentioned earlier, the rate of entropy generated by
the cycle is described quantitatively by the second law as
dSTot
dt
¼ Q0
T0
 QH
TH
 QL
TL
ð13Þ
In order to present general results for the system conﬁguration proposed in
Fig. 1, dimensionless variables are needed. First, since the thermal conductances
(UA)i present in all subsystems i shown in Fig. 1 are commodities in short supply, it
makes sense to recognize the thermal conductance inventory (hardware) as a
constraint. For example, by selecting the absorption refrigerator system, one may
deﬁne the total external conductance inventory, UA (hardware), as a design
constraint:
UA ¼ UA
ð
ÞH þ UA
ð
ÞL þ UA
ð
Þ0
ð14Þ
As a result, for the whole system, dimensionless thermal conductances are
deﬁned by
γi ¼ UA
ð
Þi
UA
ð15Þ
Dimensionless heat transfer and work rates, temperatures, time, and mass are
deﬁned by

Qi ¼
Qi
UAT0
, τi ¼ Ti
T0
, θ ¼
tUA
MairCv,air
ð16Þ
Dimensionless collector size parameter and the rate of entropy generated are
deﬁned by
B ¼ ASCGb
UA
, 
S ¼
S
MairCv,air
ð17Þ
In Eqs. (15), (16), and (17), subscripts i refers to a particular location or
subsystem in the system shown in Fig. 1.
In a dimensionless model, all variables are directly proportional to the actual
dimensional ones, as Eqs. (15), (16), and (17) demonstrate. Therefore, this allows
for scaling up or down any system with similar characteristics to the system
analyzed by the model. Another important aspect is that any dimensionless variable
value used in the simulations could represent an entire and numerous set of
dimensional values by varying appropriately the parameters in the dimensionless
variables deﬁnition, which by itself stresses the generality of the dimensionless
Modeling, Simulation, and Optimization of an Irrerversible Solar Absorption. . .
1183
model. Physically, the set of results of a dimensionless model actually represent the
expected system response to numerous combinations of system parameters (geom-
etry, architecture) and operating conditions (e.g., ambient conditions, mass ﬂow
rates), without having to simulate each of them individually, as a dimensional
model would require.
The complete set of nondimensional equations for the model, including the
absorption refrigerator steady-state equations is as follows:

QH ¼ γH τH  τHC
ð
Þ
ð18Þ

QL ¼ γL τL  τLC
ð
Þ
ð19Þ

Q0 ¼ 1  γH  γL
ð
Þ τ0C  1
ð
Þ
ð20Þ

QH ¼ B τst  τH
ð
Þ
ð21Þ

QH þ 
QL  
Q0 ¼ 0
ð22Þ
d
Sin
dθ ¼

Q0
τ0C


QH
τHC


QL
τLC
ð23Þ
d
Sin
dθ ¼ β1 τHC  τ0C
ð
Þ þ β2 τ0C  τLC
ð
Þ
ð24Þ
d
Stot
dθ ¼ 
Q0 

QH
τH


QL
τL
ð25Þ
dτL
dθ ¼ γw 1  τL
ð
Þ þ 
Qload  
QL
ð26Þ
3
Thermodynamic Optimization
The problem consists of integrating Eqs. (25) and (26) in time and solving the
nonlinear system (18), (19), (20), (21), (22), (23), and (24) at each time step. The
objective is to minimize the time θset to reach a speciﬁed refrigerated space
temperature, τLset, in transient operation. To generate the results shown in Figs. 2,
3, 4, 5, 6, and 7, some selected parameters were held constant and others were
varied. The numerical method calculates the transient behavior of the system,
starting from a set of initial conditions, then the solution is marched in time and
checked for accuracy until a desired condition is achieved (temperature set points or
steady state). The equations are integrated in time explicitly using an adaptive time
step, 4th–5th order Runge–Kutta method (Yang et al. 2005). Newton–Raphson’s
method with appropriate initial guesses was employed for solving the above set of
nonlinear equations.
During the integration of the ordinary differential equations, once the set of ﬁxed
parameters τH , τst, B, γH , γL , γW and 
Qload is deﬁned Eqs. (18) and (18) give τHC.
The system of Eqs. (18), (19), (20), (21), (22), (23), and (24), at each time step of
integration of Eqs. (25) and (26), delivers 
Q0, 
QL, τ0C, and τLC.
1184
B. Yasmina et al.
To test the model and for conducting the analysis presented in this section,
assuming a small absorption refrigeration unit with a low total thermal conductance
(UA ¼ 400 W/K), we considered a total heat exchanger area A ¼ 4 m2 and an
average global heat transfer coefﬁcient U ¼ 0.1 kW/m2 K in the heat exchangers
and Uw ¼ 1.472 kW/m2 K across the walls, which have a total surface area
0
5
10
15
20
25
30
0.965
0.97
0.975
0.98
0.985
0.99
0.995
1
1.005
Q
tsf
B=0.1
B=0.04
B=0.03
Fig. 2 The behavior refrigeration space temperature, τL in time (τH ¼ 1.3, τst ¼ 1.6)
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
7
7.5
8
8.5
9
9.5
10
B
Qset
tst =1.6
tst =1.5
tst =1.4
Fig. 3 The effect of dimensionless collector size, B on time set point temperature
Modeling, Simulation, and Optimization of an Irrerversible Solar Absorption. . .
1185
Aw ¼ 54 m2, T0 ¼ 25
C, and Qload ¼ 0.8 kW. Considering a typical
air-conditioning application, the refrigerated space temperature to be achieved
was established at TL,set ¼ 16 C.
0
5
10
15
20
25
30
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
Q
Stot
B=0.1
B=0.04
B=0.03
Fig. 4 Transient behavior of total entropy generated during the time (τH ¼ 1.3, τst ¼ 1.6)
0
5
10
15
20
25
30
0
1
2
3
4
5
6
7
8
x 10
-4
Q
Sin
B=0.1
B=0.04
B=0.03
Fig. 5 Transient behavior of internal entropy generated during the time (τH ¼ 1.3, τst ¼ 1.6)
1186
B. Yasmina et al.
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.025
0.03
0.035
0.04
0.045
0.05
0.055
0.06
B
Stot
tst =1.6
tst =1.5
tst =1.4
Fig. 6 Total entropy generated to reach a refrigerated space temperature set point temperature
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
1.4
1.6
1.8
2
2.2
2.4
2.6 x 10-4
B
Sin
tst =1.6
tst =1.5
tst =1.3
Fig. 7 Internal Total entropy generated to reach a refrigerated space temperature set point
temperature
Modeling, Simulation, and Optimization of an Irrerversible Solar Absorption. . .
1187
4
Results and Discussion
The search for system thermodynamic optimization opportunities starts by moni-
toring the behavior of refrigeration space temperature τL in time, for three dimen-
sionless collector size parameter B, while holding the other constants, that is,
dimensionless collector temperature τH ¼ 1.5 and dimensionless collector stagna-
tion temperature τst ¼ 1.8.
Figure 2 shows that during the heat-up period, the temperature of the evaporator
starts to decrease linearly, and then it decreases very slowly. Here, the reaction of
the evaporator is seen strongly affected by the generator behavior. This temperature
starts rising linearly and then it becomes stable. As the temperature of the generator
is higher, more heat is absorbed in the evaporator. While, the temperature of the
evaporator is decreasing very slowly the temperature of the generator still
maintained quite constantly, indicating that the equilibrium state has reached
(Abdullah and Hien 2010). Also, there is an intermediate value of the collector
size parameter B, between 0.063 and 0.175, such that the temporal temperature
gradient is maximum, minimizing the time to achieve prescribed set point temper-
ature (τL , set ¼ 0.97).
The optimization with respect to the collector size B is pursued in Fig. 3 for time
set point temperature for three different values of the collector stagnation temper-
ature τst and heat source temperatures τH ¼ 1.3. The time θset decreases gradually
according to the collector size parameter M until reaching a minimum time θset,min
and then it increases. This conﬁrms the results found previously (Fig. 2). The
existence of an optimum for the thermal energy input

QH is not due to the
irreversible model aspects. However, an optimal thermal energy input 
QH results
when the irreversible equations are constrained by the recognized total external
conductance inventory, UA in Eq. (14), which is ﬁnite, and the generator operating
temperature TH. These constraints are the physical reasons for the existence of the
optimum point. The minimum time to achieve prescribed temperature is the same
for different values of the stagnation temperature τst.
During the transient operation and to reach the desired set point temperature,
there are an internal generated by the working ﬂuid and a total entropy generated by
the cycle, which is obtained by integrating Eqs. (23) and (25) in time. Figures 4 and
5 show the behavior of internal and total entropy generated during the simulation
time for three different collector size parameters (B ¼ 0.03, 0.04, 0.1), holding τH,
and τst constant (τH ¼ 1.3 and τst ¼ 1.6). From these ﬁgures, it is observed that the
entropy rises with the increase of time and it is clear on the basis of the second law
of thermodynamics that the entropy production is always positive for an irreversible
cycle.
Figures 6 and 7 display the effect of the collector size on the internal and total
entropy up to θset. It can be inferred that there is minimum entropy generated for a
certain collector size B.
The variation of evaporator heat transfer with size collector parameters is
predicted and shown in Fig. 8. It can be identiﬁed from the simulation results that
1188
B. Yasmina et al.
the cooling capacity increases from the beginning of the refrigeration system start-
up. This phenomenon is typical of the system start-up working regime when there is
a refrigerant hot ﬂow inside the evaporator that would heat the interior of the
refrigerator while the refrigeration system cooling capacity increases. Afterward,
evaporator heat transfer reaches a maximum when the steady-state condition is
attained in the refrigerator interior. The maximum cooling capacity obtained for the
tested refrigerator was 0.0143, for the different values of the stagnation tempera-
ture. Maximum cooling capacity does not mean minimum entropy generation by
the cycle.
5
Conclusions
A thermodynamic transient regime simulation of a solar-driven absorption refrig-
erator has been presented in this study. An irreversible model has been analyzed
numerically to ﬁnd the optimal conditions of a solar-driven absorption refrigerator.
The existence of an optimal size collector for minimum time to reach a speciﬁed
temperature in the refrigerated space and minimum entropy generation inside the
cycle is demonstrated. The model accounts for the irreversibilities of the three heat
exchangers and the ﬁniteness of the heat exchanger inventory (total thermal con-
ductance). Appropriate dimensionless groups were identiﬁed and the generalized
results reported in charts using dimensionless variables.
From the experiment analysis that had been carried out, the following conclu-
sions can be drawn:
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.0134
0.0136
0.0138
0.014
0.0142
0.0144
B
QC
tst =1.4
tst =1.5
tst =1.6
Fig. 8 The effect of dimensionless collector size, B on heat absorbed by the evaporator
Modeling, Simulation, and Optimization of an Irrerversible Solar Absorption. . .
1189
1. The optimum collector size Bopt and minimum time set point temperature θset ,
min depend strongly on the collector temperature τH.
2. Optimal size collector identiﬁed for minimum time to reach set point tempera-
ture in the refrigerated space does not coincide with Bopt, where minimum
internal and total entropy occurs.
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Modeling, Simulation, and Optimization of an Irrerversible Solar Absorption. . .
1191
Technical-Economic Prefeasibility Assessment
of an Off-Grid Mini-hydropower Plant for an
Agribusiness Resort in Kaduna Nigeria
Victor H. ADAMU, Ampofo Nana, Ario Panggi Pramono Jati,
Ryan Tulabing, and Rojas-Solo
´rzano Luis
1
Introduction
Per capita consumption of electricity in Nigeria is currently estimated to be
149 kWh/year, which is considered very low compared to 13,246 kWh/year per
capita electricity consumption in the USA (World Bank Data 2015). This demon-
strates low access to electricity, which is a consequence of poor electricity gener-
ation and supply. While a large hydropower potential exists in Nigeria, even above
the total electricity demand of the country, by 2013 this technology only accounted
for about 32% of the total installed generation capacity connected to the grid. This
has led to an extensive use of diesel- or gasoline-operated generating sets for
electricity generation for industrial and commercial activities. These emissions
are associated to the wide use of small-scale fossil fuel-powered generators by
about 55% of its 180 million people, yet to be connected to the grid (Nnaji 2011)
(BPE 2011) (Energy Commission 2013) (SEC, 2014). Tuwan Agribusiness Resort
(TAR) in Kaduna State, Nigeria, developed by Premier Agricultural Development
(PAD) Ltd. is an agricultural service provision centre offering agricultural value
chain development services to rural farmers. TAR has demonstration facilities that
show various technologies in production and processing of crops and ﬁsh. The
demonstration facility sits on a 15 hectare plot of land with a stream of water
ﬂowing across it. Requirements for electricity are lighting and powering equipment
in ofﬁces, animal feed production and storage warehouse, recreation spots, lodging
V.H. ADAMU • A. Nana • A.P.P. Jati • R. Tulabing
Ecole des Mines de Nantes, Graduate School of Engineering, PM3E/ME3,
La Chantrerie - 4, rue Alfred Kastler – BP 20722, Nantes 44307, France
R.-S. Luis (*)
Nazarbayev University, Department of Mechanical Engineering, Astana 010000, Kazakhstan
e-mail: luis.rojas@nu.edu.kz
© Springer International Publishing AG, part of Springer Nature 2018
F. Aloui, I. Dincer (eds.), Exergy for A Better Environment and Improved
Sustainability 2, Green Energy and Technology,
https://doi.org/10.1007/978-3-319-62575-1_83
1193
facility and the ﬁsh ﬁngerling production hatchery. This is currently being met by
two 25 kW diesel generators, which increases the operational cost of running TAR
beyond what is expected when electricity is accessed from the grid and makes the
venture unproﬁtable in the long run in addition to the considerable negative
environmental effects of associated high greenhouse gas (GHG) emissions.
Initiatives similar to TAR are fundamental in Nigeria, where there is a need to
support food production activities to enable reduction of hunger and poverty in the
country, with about 70% of the population depending on agriculture for income
(Osugiri et al. 2012) Fig. 1.
With booming energy demands from a growing population, one major house-
hold or business decision is the source of their energy supply, by either renewable
sources, fossil fuels or a combination of both.
This study analyses, at prefeasibility level, the technical, economic and envi-
ronmental advantages of a proposed run-off river, mini-hydro power plant on the
Tuwan River supplying part of the energy demand of TAR in comparison with
supplying the full demand with two 25 kW diesel generators. This is a relevant
project to the community since TAR, an agribusiness service provision outﬁt, is a
social enterprise with a potential to stimulate the creation of over 1000 jobs when
fully operational. The expected medium-term outcome is the provision of a viable
sustainable alternative to electricity supply for TAR, development of local capacity
and community awareness on clean energy and sustainable development, while
motivating research by local communities and tertiary institutions. In addition, it
will serve as guide for other development agencies and small business owners
investing in local communities on meeting their electricity needs beyond the classic
choice of purchasing a diesel generating set (The Economist 2010).
Fig. 1 Tuwan Agribusiness Resort conceptual design Source: Premier Agricultural Development
(Nig.) Limited, 2012
1194
V.H. ADAMU et al.
2
Methodology
The technical and ﬁnancial analysis is mainly performed using the RETScreen
software supported by extensive bibliographic review of relevant technology and
ﬁnancial information. Required technical input data are obtained through direct
ﬁeld measurements and from existing environmental and technical information
databases online, as well as previously developed papers on the subject. Google
Earth was also used for the topography study of the target site and to determine the
gross head of the river with respect to the position of the hydropower turbine. It was
fundamental to maintain close communication between the authors and interna-
tional partners in Nigeria to obtain reliable onsite data.
3
Results and Discussions
3.1
Field Assessment
The dimensioning of the hydropower plant is based on the net head and the water
ﬂow duration curve (Nasir 2014). These are determined speciﬁcally from the
physiographical and hydrological analysis, respectively, of data measured for at
least 1 year. This is usually available from databases of the government or other
private agencies.
Climate and geological conditions are also needed to have a comprehensive
perspective of the location. In the absence of data, in situ measurements can be
made as in this case; for which ﬂow measurements were made in collaboration with
workers at the project site. The ﬂoat method was used to measure the average
volumetric ﬂow rate of the river (Froend 2012) as shown in Fig. 2. This measure-
ment was made in November 2014, during the dry season in Nigeria.
Hence, the ﬂow rate measured can safely be taken as the ﬁrm ﬂow available at
95–100% of the time since ﬂow is expected to increase during the rainy season. A
20 m length of relatively uniform stretch of the river was marked with three points:
start, middle and end points. A ﬂoat was made to travel between the two extreme
points, and the travel time was measured in order to determine the surface velocity
(Vsurface). To estimate the average cross-sectional area (Aave) of the river, the depth
was measured at sections 0.5 m apart along the width of the river at the points
marked as shown in Fig. 3.
The results of the measurement and the data used for the calculations are shown
in Table 1.
From this method, Aave and Vsurface were determined to be 2.81 m2 and 0.62 m/s,
respectively, and used in Eq. (1):
Q ¼ Aave  Vsurface  Correction factor
ð1Þ
Technical-Economic Prefeasibility Assessment of an Off-Grid Mini-hydropower. . .
1195
Fig. 2 Flow measurement by ﬂoat method (Joy et al. 2005)
Fig. 3 Cross-sectional area measurement (Reckhow et al. 2010)
Table 1 River ﬂow measurement results
Point
Start
Mid
End
Width (m)
4.0
3.9
4.6
Depth across stream width (m)
y1
0.58
0.63
0.25
y2
0.67
0.68
0.25
y3
0.74
0.74
0.50
y4
0.80
0.72
0.63
y5
0.83
0.62
0.59
y6
0.83
0.48
0.45
y7
0.55
0.76
0.50
y8
0.76
0.34
0.23
Time of travel (s)
Track 1
33.10
Track 2
30.98
Track 3
32.15
1196
V.H. ADAMU et al.
The cross-sectional area was calculated as shown in Fig. 4 above using measured
data from the ﬁeld.
Subsequently, the average ﬂow (Q) of the river has been calculated as 1.2 m3 /s.
The correction factor of 0.85 was used based on the characteristics of the river
channel and bed (Hydromatch 2014). This ﬂow is considerably high in comparison
to other similar small streams with ﬂow rates in the range of 0.1–1 m3/s for widths
in the range of 1–8 m (WHO 1996). The ﬂow rate was measured in November, a
month characterized by draught based on the climatic and rainfall patterns of
Nigeria. It is therefore sufﬁciently accurate at pre-feasibility phase to use this
measured ﬂow as the ﬁrm ﬂow available at 95–100% of the time in order to evaluate
the power potential of the river. The residual ﬂow has been assumed to be nil at this
phase.
The land formation of the site is generally of gentle slopes. The approximate
head available was determined via topographical applications of Google Earth to be
3 m over a distance of 321 m. Although this value will generally be considered low
head (Adhau et al. 2012), the relatively high ﬂow compensates it in order to
improve the general potential of the site.
The environmental impact of the project has been considered in terms of civil
works. As a run-off river of low-power capacity, there is a need for the construction
of a shallow reservoir, for water accumulation and channelling through a penstock.
This will have minimum ecological modiﬁcation impacts to the natural ambience.
Nevertheless, an in-depth and compressive environmental impact assessment is
mandatory as part of the feasibility study in the advanced stages of such a project.
3.2
Technical Assessment
With a 50 kW peak load from the Tuwan agribusiness off grid system, this
hydropower plant is designed to supply half of the load that is currently fully
supplied with two 25-kW diesel generators. It is assumed that the small hydropower
plant runs all year round alongside one of the diesel generators to accommodate the
entire electrical load at TAR, while the second diesel generator is dropped as
standby and only activated to take care of peak load conditions or emergencies.
Fig. 4 Cross-sectional area
calculation (Joy et al. 2005)
Technical-Economic Prefeasibility Assessment of an Off-Grid Mini-hydropower. . .
1197
This project is a run-off river type considering a year-round ﬂow and the low
initial cost since there is no need to construct a dam. With a low head, a Kaplan type
turbine is selected for this system with the assumption that the ﬂow will be at least
1.2 m3/s all year round. Power generated from this system is predicted based on
Eq. 2 (RETScreen 2004):
Power kW
ð
Þ  7  Head m
ð Þ  Flow m3=s


ð2Þ
Power kW
ð
Þ  7  3m  1:2m3=s  25:2kW
ð3Þ
By conducting a detailed Retscreen simulation, considering the hydraulic losses
and generator efﬁciency, the power output from the generator is about 21 kW. An
AC direct electricity system is chosen for this project. An asynchronous generator is
used due to the consideration that this type of generator is suitable for isolated small
hydropower of less than 100 kW installed capacity. It has several advantages, such
as cheaper price compared to synchronous generators and ease of maintenance
(Azhumakan et al. 2013). The electrical diagram for the system layout is presented
in Fig. 5.
As shown in the system layout, the output generator is connected to a rectiﬁer
and diversion load. This diversion load is used to consume any excess energy
generated. It also protects the generator and inverter from over speed and overvolt-
age, respectively. The DC system is then connected to the Inverter to provide the
energy to the load with 220 VAC, 50 Hz.
3.3
Cost and Financial Analysis
The entire 20-year lifespan of the project is considered in the cost analysis. This
comprises the initial cost, annual cost and periodic cost. Table 2 provides the cost
breakdown of the project.
The total initial cost prior to the operation phase of the project amounts to
132,887 USD which is based on the plant capacity and pricing of materials and
labour in local and international standards. This breaks down to feasibility study
Fig. 5 Typical arrangement of electrical system in mini hydropower plant (Home power 2008)
1198
V.H. ADAMU et al.
(3.8%), development (5.6%), engineering (11.3%), power system (53%) and bal-
ance of system and miscellaneous costs (26.3%). The bulk of the initial cost is taken
by the civil works, turbine, generator and electrical system.
On the other hand, the annual cost in terms of operation, maintenance and
payment of debt terms (up to 5 years) sums up to 36,451 USD per year. Meanwhile,
a periodic cost of 2500 USD every 5 years is allotted for the replacement of
inverters and other parts.
Fortunately, a projected grant of 50,000 USD that can be accessed from devel-
opment funding partners of PAD Ltd. should reduce the share of loan required to
fund the project. Thus, the debt ratio is only 20%, which means only 26,577 USD is
to be borrowed from the bank to be paid in 5 years with 11% interest rate. Inﬂation
is assumed to be 8.5%, while the fuel escalation is 10% from its present value of
0.54 USD per litre at year 2014. This project has a positive cash ﬂow of 65,723 USD
per year and a steadily increasing positive cash ﬂows in the long run as seen in
Fig. 6.
The income elements are mainly the amount of fuel savings and 15,000 USD
salvage value of the system at the end of the project lifespan as shown in Table 3.
Aside from the grant, another positive factor is the tax holiday of 5 years
(i.e. exception from 15% tax on income during the ﬁrst 5 years of the project)
granted by the government to agricultural organizations and rural infrastructural
development (KPMG 2012).
In summary, as indicated in Table 4, the project is viable with net present value
(NPV) of 568,178 USD (11% discount rate), internal rate of return (IRR) of 68.1%
and beneﬁt-cost ratio of 6.34.
Table 2 Project cost breakdown
Project cost summary
Initial cost
100%
Feasibility study
5049.71 USD
4%
Development
7441.67 USD
6%
Engineering
15,016.23 USD
11%
Power systems
70,430.11 USD
53%
Balance of systems & misc
34,949.28 USD
26%
Annual cost
Operation & maintenance
29,260.00 USD
Debt payment
7191.00 USD
Periodic cost
Inverter and parts replacement every 5 years
25,000.00 USD
Technical-Economic Prefeasibility Assessment of an Off-Grid Mini-hydropower. . .
1199
3.4
Green House Gas (GHG) Emission
According to UNIPCC (United Nations Intergovernmental Panel on Climate
Change) compounds considered as GHG are carbon di-oxide (CO2), methane
Fig. 6 Cumulative cash ﬂow of the project in 20-year lifespan (RETScreen 2004)
Table 3 Summary of project
income and savings
Project income summary
Annual income
Fuel savings
65,723.00 USD
Periodic income
System salvage value after 20 years
15,000.00 USD
Table 4 Summary of
ﬁnancial viability of the
project
Financial viability
Pre-tax IRR – equity
72.7%
Pre-tax IRR – assets
53.6%
After-tax IRR – equity
68.1%
After-tax IRR – assets
54.1%
Simple payback
2.3 yr
Equity payback
1.7 yr
Net present value (NPV)
568,178 USD
Annual life cycle savings
71,349 USD
Beneﬁt-cost (B-C) ratio
6.34
Debt service coverage
5.64
1200
V.H. ADAMU et al.
(CH4), nitrous oxide (N2O), ozone (O3) and water vapour measured in units of tCO2
equivalent. The Kyoto protocol deﬁnes additional compounds of sulphur
hexaﬂuoride (SF6), hydroﬂourocarbons (HFCs) and perﬂourocarbons (PFCs) as
greenhouse gases. Diesel generators emit a number of these gases, mainly CO2
and water vapour. Considering the emission factor of diesel fuel of 0.2520 tCO2 per
MWh and estimated energy of 1221 MWh/year produced by the hydro power plant
in place of the second diesel generator, the GHG emission avoided is a total of
307.4 tCO2 equivalent per year. The hydropower plant has no GHG emission as it
does not make use of fossil fuel. This GHG emission savings translate to 56 cars and
light trucks off the road per year.
3.5
Sensitivity and Risk Analysis
The sensitivity analysis was performed on the net present value (NPV), which is
favourable to such a capital-intensive project with long-term proﬁtability. A thresh-
old of 200,000 USD and a sensitivity range of 20% show that varying the fuel cost
and the debt interest rate against the initial project cost results in values all above
the threshold. This means that the sensitive factors of fuel cost, initial cost,
operation and maintenance cost and debt interest rate cannot make the project
unviable in the case that they change over the provided range. An increase in the
fuel price and a reduction of initial cost, operating and maintenance costs and debt
interest rate are favourable to the project analysis. The tornado diagram in Fig. 7
shows that an increase in the fuel cost (base case) and debt ratio will affect the
project positively. On the other hand, an increase in the operating and maintenance
cost of the mini-hydropower plant, the initial costs of the project, debt term and debt
interest rate will affect the project negatively.
At a conservative risk level of 10% (90% of conﬁdence range), the project has a
median NPV of 569,451 USD. The minimum and maximum conﬁdence NPV
values of 516,324 USD and 625,649 USD, respectively, conﬁrm that the project
has 90% of likelihood to succeed in that range, which are indeed still very attractive
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
Relative impact (standard deviation) of parameter
Impact - Net Present Value (NPV)
0.8
1
1.2
Debt ratio
Sorted by the impact
Debt interest rate
Debt term
Initial costs
O&M
Fuel cost - base case
Fig. 7 Project sensitivity analysis (RETScreen 2004)
Technical-Economic Prefeasibility Assessment of an Off-Grid Mini-hydropower. . .
1201
values for the NPV. The histogram in Fig. 8 shows that the 90% conﬁdence region
is much higher than the 10% risk region.
4
Conclusions
The potential of electricity generation by a small hydropower plant on Tuwan
River, which ﬂows across TAR (Tuwan Agribusiness Resort) is presented by this
project. In particular, the ﬁnancial and GHG savings of the proposed case are
compared to the full power supply using diesel generator sets. The following
conclusions are drawn from the analysis:
•
According to local measurements, Tuwan River may provide to this project an
estimate of at least 21 kW power generation potential with a Kaplan turbine
throughout the year.
•
The hydropower project provides GHG emissions reduction of 307.4 tCO2
equivalent.
•
A net present value (NPV of 568,178 USD (11% discount rate), internal rate of
return (IRR) of 68.1% and beneﬁt-cost ratio of 6.34 with incentives of a 50,000
USD grant and a tax holiday are obtained in favour of the mini-hydropower
plant.
The analysis shows that it is economically and environmentally attractive to
pursue a mini-hydro project in this location. Thus, it is safe to invest more funds to
conduct an extensive feasibility study with the aim of convincing fund (grant and
loan) providers to invest in the project.
Acknowledgements This research was made possible with the support of THERON Felicie of
Ecole Nationale Superieure des Mines de Nantes. Premier Agricultural Development Ltd. pro-
vided the site and corresponding data and their staff carried out required ﬁeld measurements.
14%
12%
10%
8%
6%
4%
2%
0%
474,371
494,781
515,192
535,603
556,013
Distribution - Net Present Value (NPV)
Frequency
576,424
596,834
617,245
637,656
658,066
Fig. 8 Project risk analysis (RETScreen 2004)
1202
V.H. ADAMU et al.
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